commit message

.gitattributes

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+* annex.backend=MD5E
+**/.git* annex.largefiles=nothing
+* annex.largefiles=((mimeencoding=binary)and(largerthan=0))

.gitignore

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+# ---> Python
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*,cover
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+

.gitmodules

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+[submodule "inspire-harvest"]
+	path = inspire-harvest
+	url = git@gin.g-node.org:/lucasgautheron/inspire-harvest.git
+	datalad-id = 3f7d09c3-436a-480d-b3b7-7bcebe4cecb4
+	datalad-url = git@gin.g-node.org:/lucasgautheron/inspire-harvest.git

LICENSE

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+MIT License
+Copyright (c) <year> <copyright holders>
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

README.md

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+# detecting-innovations
+

acl/acl.parquet

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+../.git/annex/objects/pW/G3/MD5E-s512856279--693529823b13502e92bc634d21cb71cc/MD5E-s512856279--693529823b13502e92bc634d21cb71cc

acl/articles.parquet

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+../.git/annex/objects/xp/9Q/MD5E-s827613342--4b47f7ada56248546b8f3ea2b5e27844/MD5E-s827613342--4b47f7ada56248546b8f3ea2b5e27844

acl/articles_authors.parquet

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+../.git/annex/objects/V3/p9/MD5E-s3370862--86a68ef8bfcccb50f6759feda90b270d/MD5E-s3370862--86a68ef8bfcccb50f6759feda90b270d

acl/export.py

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+import pandas as pd 
+
+df = pd.read_parquet("acl.parquet")
+
+articles = df
+articles["article_id"] = df.index.astype(int)
+articles["date_created"] = articles["year"]
+articles["categories"] = None
+
+articles_authors = articles[["article_id", "acl_id", "author"]]
+articles_authors["bai"] = articles_authors["author"].str.split("and\n").fillna("")
+articles_authors = articles_authors.explode("bai")
+articles_authors["bai"] = articles_authors["bai"].str.lower().str.strip()
+articles_authors["bai"] = articles_authors["bai"].str.extract(r"^((?:.*), [a-z])", expand=False)
+articles_authors = articles_authors[articles_authors["bai"].fillna("").map(len)>1]
+articles_authors = articles_authors[["article_id", "acl_id", "bai"]]
+articles_authors.to_parquet("articles_authors.parquet")
+
+articles.to_parquet("articles.parquet")

code/authors_sociality.py

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+import pandas as pd
+import numpy as np
+import argparse
+
+import networkx as nx
+
+from os.path import join as opj
+
+from itertools import combinations
+
+import pickle
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--input")
+parser.add_argument("--dataset", default="inspire-harvest/database")
+parser.add_argument("--begin", type=int, default=2000)
+parser.add_argument("--end", type=int, default=2009)
+parser.add_argument("--categories", nargs="+", default=[], required=False)
+args = parser.parse_args()
+
+n_topics = len(pd.read_csv(opj(args.input, "topics.csv")))
+
+articles = pd.read_parquet(opj(args.dataset, "articles.parquet"))[
+    ["date_created", "categories", "article_id"]
+]
+articles = articles[articles["date_created"].str.len() >= 4]
+if "year" not in articles.columns:
+    articles["year"] = articles["date_created"].str[:4].astype(int)
+else:
+    articles["year"] = articles["year"].astype(int)
+
+articles = articles[(articles["year"] >= args.begin) & (articles["year"] <= args.end)]
+
+topic_matrix = np.load(opj(args.input, "topics_counts.npy"))
+_articles = pd.read_csv(opj(args.input, "articles.csv"))
+_articles["topics"] = [topic_matrix[i, :] for i in range(len(_articles))]
+articles["article_id"] = articles.article_id.astype(int)
+articles = _articles.merge(articles, how="inner").set_index("article_id")
+
+if len(args.categories):
+    articles = articles[
+        articles.categories.map(lambda l: any([x in l for x in args.categories]))
+    ]
+
+articles_authors = pd.read_parquet(
+   opj(args.dataset, "articles_authors.parquet")
+)
+articles_authors["article_id"] = articles_authors.article_id.astype(int)
+articles_authors = articles_authors[articles_authors["article_id"].isin(articles.index)]
+
+articles_authors_list = articles_authors.groupby("article_id").agg(
+    authors=("bai", lambda l: "||".join(filter(None, l)))
+)
+
+articles = articles.merge(articles_authors_list, left_index=True, right_index=True)
+articles["authors"] = articles["authors"].map(lambda s: s.split("||"))
+
+G = nx.Graph()
+for article_id, authors in articles_authors.groupby("article_id"):
+    if len(authors) >= 50:
+        continue
+
+    for a, b in combinations(authors["bai"].tolist(), 2):
+        if G.has_edge(a, b):
+            G[a][b]["weight"] = max(G[a][b]["weight"], 1 / (len(authors) - 1))
+        else:
+            G.add_edge(a, b, weight=1 / (len(authors) - 1))
+
+# degree = G.degree(weight="weight")
+# degree = {node: value for node, value in degree}
+
+selected_authors = pd.read_csv(opj(args.input, "aggregate.csv"))
+
+N = len(G.nodes)
+brokerage = np.zeros(N)
+degree = np.zeros(N)
+
+for i, bai in enumerate(G.nodes):    
+    co_authors = list(G.neighbors(bai))
+    degree[i] = np.sum([G[bai][x]["weight"] for x in co_authors])
+
+    for x,y in combinations(co_authors, 2):
+        if not G.has_edge(x,y):
+            common_neighbors = set(G.neighbors(x))&set(G.neighbors(y))
+            b = G[bai][x]["weight"]*G[bai][y]["weight"]
+            if len(common_neighbors)<=1:
+                brokerage[i] += b
+
+pd.DataFrame({
+    "bai": list(G.nodes), "brokerage": brokerage, "degree": degree
+}).to_csv(opj(args.input, "brokerage.csv"))
+
+N = len(selected_authors)
+pooled_resources = np.zeros((N, n_topics))
+
+for i, bai in enumerate(selected_authors["bai"].tolist()):    
+    if bai not in G.nodes:
+        continue
+
+    co_authors = list(G.neighbors(bai))
+        
+    for co_author in co_authors:
+        co_author_own_pubs = articles[
+            articles["authors"].apply(lambda l: co_author in l and bai not in l)
+        ]
+
+        if len(co_author_own_pubs) == 0:
+            continue
+
+        co_author_expertise = np.stack(co_author_own_pubs["topics"].fillna(0).values)
+        weight = np.array(1.0 / co_author_own_pubs.authors.map(len))
+        co_author_expertise = co_author_expertise * weight[:, np.newaxis]
+
+        co_author_expertise = (
+            co_author_expertise.sum(axis=0) / co_author_expertise.sum()
+        )
+
+        co_author_expertise = np.nan_to_num(co_author_expertise)
+
+        print(bai, G[bai][co_author]["weight"], len(co_author_own_pubs), co_author_expertise.argmax(), weight.mean())
+
+        pooled_resources[i, :] += G[bai][co_author]["weight"] * co_author_expertise
+
+bai = selected_authors["bai"]
+
+selected_authors["pooled_resources"] = [
+    pooled_resources[i] for i in range(len(selected_authors))
+]
+selected_authors[["bai", "pooled_resources"]].to_parquet(
+    opj(args.input, "pooled_resources.parquet")
+)

code/change.stan

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+functions {
+    vector z_scale(vector x) {
+        return (x-mean(x))/sd(x);
+    }
+}
+
+data {
+    int<lower=1> N;
+    int<lower=1> K;
+    vector<lower=0>[N] soc_cap;
+    vector<lower=0>[N] soc_div;
+    vector<lower=0>[N] int_div;
+    vector[N] res_soc_div;
+    //vector<lower=0>[N] age;
+    vector<lower=0>[N] m;
+    matrix<lower=0,upper=1>[N,K] x;
+    vector[N] stable;
+    //vector<lower=0>[N] age;
+    array [N] int<lower=0,upper=K-1> primary_research_area;
+}
+
+transformed data {
+    vector[N] z_m = z_scale(m);
+    vector[N] z_soc_cap = z_scale(soc_cap);
+    vector[N] z_soc_div = z_scale(soc_div);
+    vector[N] z_int_div = z_scale(int_div);
+    vector[N] z_res_soc_div = z_scale(res_soc_div);
+}
+
+parameters {
+    real beta_soc_cap;
+    real beta_soc_div;
+    real beta_int_div;
+    real beta_stable;
+    //real beta_age;
+    vector[K] beta_x;
+
+    real mu;
+    real<lower=0> tau;
+    //real<lower=1> sigma;
+    real<lower=0> sigma;
+
+    vector<lower=0,upper=1>[K] mu_x;
+    vector<lower=1>[K] eta;
+    real<lower=0,upper=1> mu_pop;
+    real<lower=1> eta_pop;
+}
+
+model {
+    vector[N] beta_research_area;
+    for (k in 1:N) {
+        beta_research_area[k] = beta_x[primary_research_area[k]+1]*tau;
+    }
+
+    beta_soc_cap ~ normal(0, 1);
+    beta_soc_div ~ normal(0, 1);
+    beta_int_div ~ normal(0, 1);
+    beta_x ~ double_exponential(0, 1);
+    beta_stable ~ normal(0, 1);
+    //beta_age ~ normal(0, 1);
+
+    mu ~ normal(0, 1);
+    tau ~ exponential(1);
+    //sigma ~ pareto(1, 1.5);
+    sigma ~ exponential(1);
+
+    //m ~ beta_proportion(inv_logit(beta_soc_cap*z_soc_cap + beta_soc_div*res_soc_div + beta_int_div*z_int_div + beta_stable*stable + beta_research_area + mu), sigma);
+    z_m ~ normal(beta_soc_cap*z_soc_cap + beta_soc_div*z_res_soc_div + beta_int_div*z_int_div + beta_stable*stable + beta_research_area + mu, sigma);
+
+    eta ~ pareto(1, 1.5);
+    mu_x ~ uniform(0, 1);
+    eta_pop ~ pareto(1, 1.5);
+    mu_pop ~ uniform(0, 1);
+    for (k in 1:N) {
+       m[k] ~ beta_proportion(mu_x[primary_research_area[k]+1], eta[primary_research_area[k]+1]);
+    }
+    m ~ beta_proportion(mu_pop, eta_pop);
+}
+
+generated quantities {
+    real R2 = 0;
+    {
+        vector[N] beta_research_area;
+        for (k in 1:N) {
+            beta_research_area[k] = beta_x[primary_research_area[k]+1]*tau;
+        }
+        //vector[N] pred = inv_logit(beta_soc_cap*z_soc_cap + beta_soc_div*res_soc_div + beta_int_div*z_int_div + beta_stable*stable + beta_research_area + mu);
+        vector[N] pred = beta_soc_cap*z_soc_cap + beta_soc_div*z_res_soc_div + beta_int_div*z_int_div + beta_stable*stable + beta_research_area + mu;
+
+        R2 = mean(square(z_m-pred))/variance(z_m);
+        R2 = 1-R2;
+    }
+    
+}
+

code/comparative_analysis.py

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+from cProfile import label
+import numpy as np
+import pandas as pd
+from scipy.stats import entropy
+import ot
+from sklearn.linear_model import LinearRegression
+
+from matplotlib import pyplot as plt
+import matplotlib
+matplotlib.use("pgf")
+matplotlib.rcParams.update(
+    {
+        "pgf.texsystem": "xelatex",
+        "font.family": "serif",
+        "font.serif": "Times New Roman",
+        "text.usetex": True,
+        "pgf.rcfonts": False,
+    }
+)
+plt.rcParams["text.latex.preamble"].join([
+        r"\usepackage{amsmath}",              
+        r"\setmainfont{amssymb}",
+])
+
+from textwrap import wrap
+
+import argparse
+from os.path import join as opj, exists
+import pickle
+
+from cmdstanpy import CmdStanModel
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--input")
+parser.add_argument("--suffix", default=None)
+parser.add_argument("--metric", default="change", choices=["change", "disruption", "diversification", "diversification_stirling", "entered", "exited"])
+parser.add_argument("--diversity", default="entropy", choices=["entropy", "stirling"])
+parser.add_argument("--power", choices=["magnitude", "brokerage"], default="magnitude")
+parser.add_argument("--model", default="", choices=["", "bare"])
+parser.add_argument("--compact", action="store_true", default=False)
+args = parser.parse_args()
+
+def institution_stability():
+    if exists(opj(args.input, "institutional_stability.csv")):
+        return pd.read_csv(opj(args.input, "institutional_stability.csv"), index_col="bai")
+    
+    affiliations = pd.read_parquet("../semantics/inspire-harvest/database/affiliations.parquet")
+    affiliations["article_id"] = affiliations.article_id.astype(int)
+
+    articles = pd.read_parquet("../semantics/inspire-harvest/database/articles.parquet")[["article_id", "date_created"]]
+
+    articles = articles[articles["date_created"].str.len() >= 4]
+    articles["year"] = articles["date_created"].str[:4].astype(int) - 2000
+
+    articles["article_id"] = articles.article_id.astype(int)
+    articles = articles[articles["year"] <= 2019 - 2000]
+    articles = articles[articles["year"] >= 0]
+
+    affiliations["article_id"] = affiliations.article_id.astype(int)
+    affiliations = affiliations.merge(articles, how="inner", left_on="article_id", right_on="article_id")
+    affiliations = affiliations[affiliations["bai"].isin(df["bai"])]
+
+    authors_last = affiliations.groupby("bai").agg(last_article=("year", "max"))
+
+    hosts = affiliations.sort_values(["bai", "institution_id", "year"]).groupby(["bai", "institution_id"]).agg(
+        first=("year", "min"),
+        last=("year", "max")
+    )
+    hosts["duration"] = hosts["last"]-hosts["first"]
+    stability = hosts.groupby("bai").agg(stability=("duration", "max"), last=("last", "max"), first=("first", "min"))
+    stability = stability.merge(authors_last, left_index=True, right_index=True)
+    stability["stable"] = stability["stability"]>=(stability["last"]-stability["first"]-1)
+    stability.to_csv(opj(args.input, "institutional_stability.csv"))
+    return stability
+
+suffix = f"_{args.suffix}" if args.suffix is not None else ""
+
+topics = pd.read_csv(opj(args.input, "topics.csv"))
+junk = topics["label"].str.contains("Junk")
+topics = topics[~junk]["label"].tolist()
+
+fig, ax = plt.subplots()
+
+n_topics = len(pd.read_csv(opj(args.input, "topics.csv")))
+df = pd.read_csv(opj(args.input, "aggregate.csv"))
+
+resources = pd.read_parquet(opj(args.input, "pooled_resources.parquet"))
+df = df.merge(resources, left_on="bai", right_on="bai")
+
+NR = np.stack(df[[f"start_{k+1}" for k in range(n_topics)]].values).astype(int)
+NC = np.stack(df[[f"end_{k+1}" for k in range(n_topics)]].values).astype(int)
+expertise = np.stack(df[[f"expertise_{k+1}" for k in range(n_topics)]].values)
+S = np.stack(df["pooled_resources"])
+
+brokerage = pd.read_csv("output/authors_brokerage.csv")
+df = df.merge(brokerage, left_on="bai", right_on="bai")
+
+NR = NR[:,~junk]
+NC = NC[:,~junk]
+expertise = expertise[:,~junk]
+S = S[:,~junk]
+
+x = NR/NR.sum(axis=1)[:,np.newaxis]
+y = NC/NC.sum(axis=1)[:,np.newaxis]
+S_distrib = S/S.sum(axis=1)[:,np.newaxis]
+
+
+# R = np.array([
+#     [((expertise[:,i]>expertise[:,i].mean())&(expertise[:,j]>expertise[:,j].mean())).mean()/((expertise[:,i]>expertise[:,i].mean())|(expertise[:,j]>expertise[:,j].mean())).mean() for j in range(len(topics))]
+#     for i in range(len(topics))
+# ])
+
+R = np.array([
+    [((expertise[:,i]>expertise[:,i].mean())&(expertise[:,j]>expertise[:,j].mean())).mean()/(expertise[:,i]>expertise[:,i].mean()).mean() for j in range(len(topics))]
+    for i in range(len(topics))
+])
+
+change = np.abs(y-x).sum(axis=1)/2
+diversification = (np.exp(entropy(y, axis=1))-np.exp(entropy(x, axis=1)))/x.shape[1]
+x_matrix = np.einsum("ki,kj->kij", x, x)
+y_matrix = np.einsum("ki,kj->kij", y, y)
+x_stirling = 1-np.einsum("ij,kij->k", R, x_matrix)
+y_stirling = 1-np.einsum("ij,kij->k", R, y_matrix)
+
+disruption = np.zeros(len(change))
+for a in range(len(change)):
+    disruption[a] = ot.emd2(x[a,:].copy(order='C'), y[a,:].copy(order='C'), 1-R, processes=4)
+
+alpha = 1
+exited = ((x>alpha*x.mean(axis=0))&(y<alpha*y.mean(axis=0))).sum(axis=1)
+entered = ((x<alpha*x.mean(axis=0))&(y>alpha*y.mean(axis=0))).sum(axis=1)
+
+fig, ax = plt.subplots(figsize=[6.4, 3.2])
+ax.hist(change, bins=np.linspace(0,1,50), histtype="step")
+ax.set_xlabel(f"Change score $c_a = \\frac{{1}}{{2}}\\sum_k |y_{{ak}}-x_{{ak}}|$")
+ax.set_ylabel("\\# of scientists")
+fig.savefig(opj(args.input, "change_score.eps"), bbox_inches="tight")
+
+print("change 50%% interval: ", np.quantile(change,q=0.25), np.quantile(change,q=1-0.25))
+
+fig, ax = plt.subplots(figsize=[6.4, 3.2])
+ax.hist(diversification, bins=np.linspace(-0.5,0.5,50), histtype="step")
+ax.set_xlabel(f"Diversification score $\\Delta_a$")
+ax.set_ylabel("\\# of scientists")
+fig.savefig(opj(args.input, "diversification_score.eps"), bbox_inches="tight")
+
+fig, ax = plt.subplots()
+ax.hist(disruption, bins=np.linspace(0,1,50), histtype="step")
+ax.set_xlabel(f"Disruption score $d_a$")
+ax.set_ylabel("\\# of scientists")
+fig.savefig(opj(args.input, "disruption_score.eps"), bbox_inches="tight")
+
+df["change_score"] = change
+df["disruption_score"] = disruption
+df["diversification_score"] = diversification
+df["diversification_stirling_score"] = y_stirling-x_stirling
+df["entered_score"] = (entered>0).astype(int)
+df["exited_score"] = (exited>0).astype(int)
+
+df["origin"] = np.argmax(x, axis=1)
+df["target"] = np.argmax(y, axis=1)
+
+df["origin_value"] = x.max(axis=1)
+df["target_value"] = y.max(axis=1)
+
+df["origin_final_value"] = np.array(y[a,df.loc[a, "origin"]] for a in range(x.shape[0]))
+df["target_initial_value"] = np.array(x[a,df.loc[a, "target"]] for a in range(x.shape[0]))
+
+df["origin_label"] = df["origin"].apply(lambda k: topics[k])
+df["target_label"] = df["target"].apply(lambda k: topics[k])
+
+df["origin_label"] = df.apply(lambda row: row["origin_label"] + (f" ({row['origin_value']:.2f})" if row["origin"]==row["target"] else f" ({row['origin_value']:.2f}$\\to${row['origin_final_value']:.2f})"), axis=1)
+df["target_label"] = df.apply(lambda row: row["target_label"] + (f" ({row['target_value']:.2f})" if row["origin"]==row["target"] else f" ({row['target_initial_value']:.2f}$\\to${row['target_value']:.2f})"), axis=1)
+
+df["social_entropy"] = np.exp(entropy(S,axis=1))
+df["intellectual_entropy"] = np.exp(entropy(expertise,axis=1))
+
+expertise_matrix = np.einsum("ki,kj->kij", expertise, expertise)
+social_expertise_matrix = np.einsum("ki,kj->kij", S_distrib, S_distrib)
+df["intellectual_stirling"] = 1-np.einsum("ij,kij->k", R, expertise_matrix)
+df["social_stirling"] = 1-np.einsum("ij,kij->k", R, social_expertise_matrix)
+
+
+stability = institution_stability()
+df = df.merge(stability, left_on="bai", right_index=True)
+
+age = pd.read_csv(opj(args.input, "outcomes.csv"))[["bai", "age"]].drop_duplicates()
+df = df.merge(age, left_on="bai", right_on="bai")
+
+df["primary_research_area"] = x.argmax(axis=1)
+
+df["social_diversity"] = df[f"social_{args.diversity}"].fillna(0)
+df["intellectual_diversity"] = df[f"intellectual_{args.diversity}"].fillna(0)
+
+df["res_social_diversity"] = df["social_diversity"]-LinearRegression().fit(df[["intellectual_diversity"]], df["social_diversity"]).predict(df[["intellectual_diversity"]])
+
+data = {
+    "N": len(df),
+    "K": x.shape[1],
+    "m": df[f"{args.metric}_score"],
+    # "soc_cap": np.log(1+S.sum(axis=1)),
+    "soc_cap": S.sum(axis=1) if args.power == "magnitude" else df["brokerage"].values,
+    "soc_div": df["social_diversity"],
+    "int_div": df["intellectual_diversity"],
+    "res_soc_div": df["res_social_diversity"],
+    "x": x,
+    "initial_div": np.exp(entropy(x, axis=1)),
+    "primary_research_area": df["primary_research_area"],
+    "stable": df["stable"].astype(float).values,
+    "age": df["age"].values
+}
+
+fig, ax = plt.subplots(figsize=[6.4, 3.2])
+ax.hist(change[df["primary_research_area"] != 4], bins=np.linspace(0,1,25), histtype="step", label=f"Others ($\\mu={change[df['primary_research_area'] != 4].mean():.2f}$)", density=True)
+ax.hist(change[df["primary_research_area"] == 4], bins=np.linspace(0,1,25), histtype="step", label=f"Collider physics ($\\mu={change[df['primary_research_area'] == 4].mean():.2f}$)", density=True)
+ax.set_xlabel(f"Change score $c_a = \\frac{{1}}{{2}}\\sum_k |y_{{ak}}-x_{{ak}}|$")
+ax.set_ylabel("\\# of scientists")
+ax.legend(loc='upper right', bbox_to_anchor=(1, 1.2))
+fig.savefig(opj(args.input, "change_score_collider_physics.eps"), bbox_inches="tight")
+
+fig, ax = plt.subplots(figsize=[6.4, 3.2])
+ax.hist(disruption[df["primary_research_area"] != 4], bins=np.linspace(0,1,25), histtype="step", label=f"Others ($\\mu={disruption[df['primary_research_area'] != 4].mean():.2f}$)", density=True)
+ax.hist(disruption[df["primary_research_area"] == 4], bins=np.linspace(0,1,25), histtype="step", label=f"Collider physics ($\\mu={disruption[df['primary_research_area'] == 4].mean():.2f}$)", density=True)
+ax.set_xlabel(f"Disruption score $d_a$")
+ax.set_ylabel("\\# of scientists")
+ax.legend(loc='upper right', bbox_to_anchor=(1, 1.2))
+fig.savefig(opj(args.input, "disruption_score_collider_physics.eps"), bbox_inches="tight")
+
+if not exists(opj(args.input, f"samples_{args.metric}_{args.diversity}_{args.power}.npz")):
+    model = CmdStanModel(
+        stan_file=f"code/{args.metric}.stan" if args.model=="" else f"code/{args.metric}_{args.model}_{args.power}.stan",
+    )
+
+    fit = model.sample(
+        data=data,
+        chains=4,
+        iter_sampling=10000,
+        iter_warmup=1000,
+        show_console=True
+    )
+
+    vars = fit.stan_variables()
+    samples = {}
+    for (k, v) in vars.items():
+        samples[k] = v
+
+    np.savez_compressed(opj(args.input, f"samples_{args.metric}_{args.diversity}_{args.power}.npz"), **samples)
+
+
+samples = np.load(opj(args.input, f"samples_{args.metric}_{args.diversity}_{args.power}.npz"))
+
+labels = [
+    "Intellectual capital (diversity)",
+    "Social capital (diversity)",
+    "Social capital (power)",
+    "Stable affiliation",
+]
+labels = [f"\\textbf{{{label}}}" for label in labels]
+
+labels += topics
+
+names = [
+    "beta_int_div", "beta_soc_div", "beta_soc_cap", "beta_stable"
+]
+
+if args.metric not in ["entered", "exited"]:
+    mu = np.array([samples[name].mean() for name in names] + [(samples["beta_x"][:,i]*samples["tau"]).mean() for i in range(x.shape[1])])
+    low = np.array([np.quantile(samples[name], q=0.05/2) for name in names] + [np.quantile(samples["beta_x"][:,i]*samples["tau"], q=0.05/2) for i in range(x.shape[1])])
+    up = np.array([np.quantile(samples[name], q=1-0.05/2) for name in names] + [np.quantile(samples["beta_x"][:,i]*samples["tau"], q=1-0.05/2) for i in range(x.shape[1])])
+    sig = up*low>0
+
+    prob = np.array([(samples[name]*np.sign(samples[name].mean())<0).mean() for name in names] + [((samples["beta_x"][:,i]*np.sign(samples["beta_x"][:,i].mean()))<0).mean() for i in range(x.shape[1])])
+
+    keep = sig | (np.arange(len(sig))<len(names))
+    mu = mu[keep]
+    low = low[keep]
+    up = up[keep]
+    prob = prob[keep]
+
+    sign = ["<" if _mu>0 else ">" for i, _mu in enumerate(mu)]
+    labels = [label for i, label in enumerate(labels) if keep[i]]
+    n_vars = len(labels)
+
+
+    # effect of capital and controls
+    fig, ax = plt.subplots(figsize=[6.4, 0.4*(1+n_vars)])
+
+    ax.scatter(mu, np.arange(len(labels))[::-1])
+    ax.errorbar(mu, np.arange(len(labels))[::-1], xerr=(mu-low,up-mu), ls="none", capsize=4, elinewidth=1)
+    ax.set_yticks(np.arange(len(labels))[::-1], labels)
+    for i, p in enumerate(prob):
+        if p>1e-4 and np.abs(p-0.5)>0.4:
+            ax.text(
+                -0.02 if mu[i]>0 else 0.02,
+                np.arange(len(labels))[::-1][i],
+                f"\\scriptsize $\\mu(\\beta)={mu[i]:.2g}, P(\\beta{sign[i]}0)={p:.2g}$",
+                ha="right" if mu[i]>0 else "left",
+                va="center"
+            )
+        elif p<0.05/2 or p>1-0.05/2:
+            ax.text(
+                -0.02 if mu[i]>0 else 0.02,
+                np.arange(len(labels))[::-1][i],
+                f"\\scriptsize $\\mu(\\beta)={mu[i]:.2g}$",
+                ha="right" if mu[i]>0 else "left",
+                va="center"
+            )
+
+    ax.set_xlabel(f"Effect on {args.metric}")
+    ax.axvline(0, color="black")
+    fig.savefig(opj(args.input, f"{args.metric}_score_effects_{args.diversity}_{args.power}.eps"), bbox_inches="tight")
+
+    # average change score per research area
+    ratio = args.metric != "diversification"
+    labels = topics
+    if ratio:
+        mu = np.array([(samples["mu_x"][:,i]/samples["mu_pop"]).mean() for i in range(x.shape[1])])
+        low = np.array([np.quantile(samples["mu_x"][:,i]/samples["mu_pop"], q=0.05/2) for i in range(x.shape[1])])
+        up = np.array([np.quantile(samples["mu_x"][:,i]/samples["mu_pop"], q=1-0.05/2) for i in range(x.shape[1])])
+        sig = (up-1)*(low-1)>0
+    else:
+        mu = np.array([(samples["mu_x"][:,i]-samples["mu_pop"]).mean() for i in range(x.shape[1])])
+        low = np.array([np.quantile(samples["mu_x"][:,i]-samples["mu_pop"], q=0.05/2) for i in range(x.shape[1])])
+        up = np.array([np.quantile(samples["mu_x"][:,i]-samples["mu_pop"], q=1-0.05/2) for i in range(x.shape[1])])
+        sig = (up)*(low)>0
+
+    keep = sig
+    mu = mu[keep]
+    low = low[keep]
+    up = up[keep]
+    labels = [label for i, label in enumerate(labels) if keep[i]]
+
+    fig, ax = plt.subplots(figsize=[6.4, 3.2])
+    ax.scatter(mu, np.arange(len(labels))[::-1])
+    ax.errorbar(mu, np.arange(len(labels))[::-1], xerr=(mu-low,up-mu), ls="none", capsize=4, elinewidth=1)
+    ax.set_yticks(np.arange(len(labels))[::-1], labels)
+
+    fig, ax = plt.subplots(figsize=[6.4, 3.2])
+
+    df["m_ratio"] = df[f"{args.metric}_score"]/df[f"{args.metric}_score"].mean()
+    research_areas = df.groupby("primary_research_area").agg(
+        mu=("m_ratio", "mean"),
+        low=("m_ratio", lambda x: np.quantile(x, q=0.05/2)),
+        up=("m_ratio", lambda x: np.quantile(x, q=1-0.05/2)),
+        label=("origin_label", lambda x: x.iloc[0])
+    ).reset_index()
+
+    ax.scatter(research_areas["mu"], research_areas.index)
+    ax.errorbar(research_areas["mu"], research_areas.index, xerr=(research_areas["mu"]-research_areas["low"],research_areas["up"]-research_areas["low"]), ls="none", capsize=4, elinewidth=1)
+    ax.set_yticks(research_areas.index, research_areas["label"])
+
+    ax.set_xlabel(f"Ratio to average {args.metric} score" if ratio else f"Difference with average {args.metric} score")
+    ax.axvline(1 if ratio else 0, color="black")
+    fig.savefig(opj(args.input, f"{args.metric}_research_area.eps"), bbox_inches="tight")
+else:
+
+    labels = [
+        "Intellectual capital (diversity)",
+        "Social capital (diversity)",
+        "Social capital (power)",
+        "Stable affiliation",
+    ]
+
+    if not args.compact:
+        labels = [f"\\textbf{{{label}}}" for label in labels]
+
+    labels += topics
+
+    samples = [
+        np.load(opj(args.input, f"samples_entered_{args.diversity}_{args.power}.npz")),
+        np.load(opj(args.input, f"samples_exited_{args.diversity}_{args.power}.npz"))
+    ]
+
+    mu = [None, None]
+    low = [None, None]
+    up = [None, None]
+    sig = [None, None]
+    prob = [None, None]
+    for i in range(2):
+        mu[i] = np.array([samples[i][name].mean() for name in names] + [(samples[i]["beta_x"][:,j]*samples[i]["tau"]).mean() for j in range(x.shape[1])])
+        low[i] = np.array([np.quantile(samples[i][name], q=0.05/2) for name in names] + [np.quantile(samples[i]["beta_x"][:,j]*samples[i]["tau"], q=0.05/2) for j in range(x.shape[1])])
+        up[i] = np.array([np.quantile(samples[i][name], q=1-0.05/2) for name in names] + [np.quantile(samples[i]["beta_x"][:,j]*samples[i]["tau"], q=1-0.05/2) for j in range(x.shape[1])])
+        sig[i] = up[i]*low[i]>0
+        prob[i] = np.array([(samples[i][name]*np.sign(samples[i][name].mean())<0).mean() for name in names] + [((samples[i]["beta_x"][:,j]*np.sign(samples[i]["beta_x"][:,j].mean()))<0).mean() for j in range(x.shape[1])])
+
+    if args.compact:
+        keep = (np.arange(len(sig[0]))<len(names))
+    else:
+        keep = sig[0] | sig[1] | (np.arange(len(sig[0]))<len(names))
+
+    for i in range(2):
+        mu[i] = mu[i][keep]
+        low[i] = low[i][keep]
+        up[i] = up[i][keep]
+        prob[i] = prob[i][keep]
+
+    sign = [["<" if _mu>0 else ">" for j, _mu in enumerate(mu[i])] for i in range(2)]
+    labels = [label for i, label in enumerate(labels) if keep[i]]
+    n_vars = len(labels)
+
+    if args.compact:
+        labels = [
+            '\n'.join(map(lambda x: f"\\textbf{{{x}}}", wrap(label, width=15))) if i < 4
+            else
+            '\n'.join(wrap(label, width=15))
+            for i, label in enumerate(labels)
+        ]
+        print(labels)
+
+
+    # effect of capital and controls
+    fig, ax = plt.subplots(figsize=[4.8 if args.compact else 6.4, 0.52*(1+n_vars)])
+    colors = ['#377eb8', '#ff7f00']
+    legend = ["entered new research area", "exited research area"]
+
+    if args.compact:
+        ax.set_xlim(-0.9, 1.25)
+
+    for j in range(2):
+        dy = -0.125 if j else +0.125
+        ax.scatter(mu[j], np.arange(len(labels))[::-1]+dy, color=colors[j])
+        ax.errorbar(mu[j], np.arange(len(labels))[::-1]+dy, xerr=(mu[j]-low[j],up[j]-mu[j]), ls="none", capsize=4, elinewidth=1, color=colors[j], label=legend[j])
+        for i, p in enumerate(prob[j]):
+            significant = p<0.05/2
+            if p>1e-4 and np.abs(p-0.5)>0.4 and significant:
+                ax.text(
+                    -0.02 if mu[j][i]>0 else 0.02,
+                    np.arange(len(labels))[::-1][i]+dy,
+                    f"\\scriptsize $\\mu(\\beta)={mu[j][i]:.2g},P(\\beta{sign[j][i]}0)={p:.2g}$",
+                    ha="right" if mu[j][i]>0 else "left",
+                    va="center"
+                )
+            elif p>1e-4 and np.abs(p-0.5)>0.4 and (not significant):
+                ax.text(
+                    -0.02 if mu[j][i]>0 else 0.02,
+                    np.arange(len(labels))[::-1][i]+dy,
+                    f"\\scriptsize $P(\\beta{sign[j][i]}0)={p:.2g}$",
+                    ha="right" if mu[j][i]>0 else "left",
+                    va="center"
+                )
+            elif significant:
+                ax.text(
+                    -0.02 if mu[j][i]>0 else 0.02,
+                    np.arange(len(labels))[::-1][i]+dy,
+                    f"\\scriptsize $\\mu(\\beta)={mu[j][i]:.2g}$",
+                    ha="right" if mu[j][i]>0 else "left",
+                    va="center"
+                )
+
+    ax.set_yticks(np.arange(len(labels))[::-1], labels)
+    ax.set_xlabel(f"Effect size (log odds ratio)")
+    ax.axvline(0, color="black")
+    if args.compact:
+        ax.legend(loc='upper right', bbox_to_anchor=(1, 1.3))
+    else:
+        ax.legend(loc='upper right', bbox_to_anchor=(1, 1.2))
+    fig.savefig(opj(args.input, f"{args.metric}_score_effects_{args.diversity}_{args.power}{'_compact' if args.compact else ''}.eps"), bbox_inches="tight")    
+
+table = df[["bai", "stable", f"{args.metric}_score", "intellectual_entropy", "social_entropy", "origin_label", "target_label"]].sort_values(f"{args.metric}_score", ascending=False)
+table.to_csv(opj(args.input, f"{args.metric}_scores.csv"))
+
+table["bai"] = table["bai"].str.replace(".1", "")
+table["bai"] = table["bai"].str.replace(r"^([A-Z])\.", r"\1.~")
+table["bai"] = table["bai"].str.replace(r"\.\~([A-Z])\.", r".~\1.~")
+table["bai"] = table["bai"].str.replace(r"([a-zA-Z]{2,})\.", r"\1 ")
+table["bai"] = table.apply(lambda r: r["bai"] if not r["stable"] else f"{r['bai']} ($\\ast$)", axis=1)
+
+table["target_label"] += "EOL"
+
+latex = table.head(20).to_latex(
+    columns=["bai", f"{args.metric}_score", "intellectual_entropy", "social_entropy", "origin_label", "target_label"],
+    header=["Physicist", "$c_a$", "$D(\\bm{I_a})$", "$D(\\bm{S_a})$", "Previous main area", "Current main area"],
+    index=False,    
+    multirow=True,
+    multicolumn=True,
+    column_format='p{0.15\\textwidth}|c|c|c|b{0.25\\textwidth}|b{0.25\\textwidth}',
+    escape=False,
+    float_format=lambda x: f"{x:.2f}",
+    caption="Physicists with the highest change scores $c_a$. $D(\\bm{I_a})$ and $D(\\bm{S_a})$ measure the diversity of intellectual and social capital. Numbers in parentheses indicate the share of attention dedicated to each research area during each time-period. Asterisks ($\\ast$) indicate physicists with a permanent position.",
+    label=f"table:top_{args.metric}",
+    position="H"
+)
+
+latex = latex.replace('EOL \\\\\n', '\\\\ \\hline\n')
+
+with open(opj(args.input, f"top_{args.metric}.tex"), "w+") as fp:
+    fp.write(latex)
+
+
+latex = table.sort_values(f"{args.metric}_score", ascending=True).head(20).to_latex(
+    columns=["bai", f"{args.metric}_score", "intellectual_entropy", "social_entropy", "origin_label", "target_label"],
+    header=["Physicist", "$c_a$", "$D(\\bm{I_a})$", "$D(\\bm{S_a})$", "Previous main area", "Current main area"],
+    index=False,    
+    multirow=True,
+    multicolumn=True,
+    column_format='p{0.15\\textwidth}|c|c|c|b{0.25\\textwidth}|b{0.25\\textwidth}',
+    escape=False,
+    float_format=lambda x: f"{x:.2f}",
+    caption="Physicists with the lowest change scores $c_a$. $D(\\bm{I_a})$ and $D(\\bm{S_a})$ measure the diversity of intellectual and social capital. Numbers in parentheses indicate the share of attention dedicated to each research area. Asterisks ($\\ast$) indicate physicists with a permanent position.",
+    label=f"table:low_{args.metric}",
+    position="H"
+)
+
+latex = latex.replace('EOL \\\\\n', '\\\\ \\hline\n')
+
+with open(opj(args.input, f"low_{args.metric}.tex"), "w+") as fp:
+    fp.write(latex)
+

code/disruption.stan

@@ -0,0 +1,97 @@
+functions {
+    vector z_scale(vector x) {
+        return (x-mean(x))/sd(x);
+    }
+}
+
+data {
+    int<lower=1> N;
+    int<lower=1> K;
+    vector<lower=0>[N] soc_cap;
+    vector<lower=0>[N] soc_div;
+    vector<lower=0>[N] int_div;
+    //vector<lower=0>[N] age;
+    vector<lower=0>[N] m;
+    matrix<lower=0,upper=1>[N,K] x;
+    vector[N] stable;
+    array [N] int<lower=0,upper=K-1> primary_research_area;
+}
+
+transformed data {
+    vector[N] z_m = z_scale(m);
+    vector[N] z_soc_cap = z_scale(soc_cap);
+    vector[N] z_soc_div = z_scale(exp(soc_div));
+    vector[N] z_int_div = z_scale(exp(int_div));
+}
+
+parameters {
+    real beta_soc_cap;
+    real beta_soc_div;
+    real beta_int_div;
+    real beta_stable;
+    vector[K] beta_x;
+
+    real mu;
+    real<lower=0> tau;
+    real<lower=0> sigma;
+
+    real lambda_div;
+    real mu_div;
+    real<lower=0> sigma_div;
+
+    vector<lower=0,upper=1>[K] mu_x;
+    vector<lower=1>[K] eta;
+    real<lower=0,upper=1> mu_pop;
+    real<lower=1> eta_pop;
+}
+
+model {
+    vector[N] beta_research_area;
+    for (k in 1:N) {
+        beta_research_area[k] = beta_x[primary_research_area[k]+1];
+    }
+    vector[N] res_soc_div = z_scale(soc_div - (lambda_div*z_int_div + mu_div));
+
+    beta_soc_cap ~ normal(0, 1);
+    beta_soc_div ~ normal(0, 1);
+    beta_int_div ~ normal(0, 1);
+    beta_x ~ normal(0, tau);
+    beta_stable ~ normal(0, 1);
+
+    z_soc_div ~ normal(lambda_div*z_int_div + mu_div, sigma_div);
+    lambda_div ~ normal(0, 1);
+    mu_div ~ normal(0, 1);
+    sigma_div ~ exponential(1);
+
+    mu ~ normal(0, 1);
+    tau ~ exponential(1);
+    sigma ~ exponential(1);
+
+    z_m ~ normal(beta_soc_cap*z_soc_cap + beta_soc_div*res_soc_div + beta_int_div*z_int_div + beta_stable*stable + beta_research_area + mu, sigma);
+
+    eta ~ pareto(1, 1.5);
+    mu_x ~ uniform(0, 1);
+    eta_pop ~ pareto(1, 1.5);
+    mu_pop ~ uniform(0, 1);
+    for (k in 1:N) {
+       m[k] ~ beta_proportion(mu_x[primary_research_area[k]+1], eta[primary_research_area[k]+1]);
+    }
+    m ~ beta_proportion(mu_pop, eta_pop);
+}
+
+generated quantities {
+    real R2 = 0;
+    {
+        vector[N] beta_research_area;
+        for (k in 1:N) {
+            beta_research_area[k] = beta_x[primary_research_area[k]+1];
+        }
+        vector[N] res_soc_div = z_scale(soc_div - (lambda_div*z_int_div + mu_div));
+        vector[N] pred = beta_soc_cap*z_soc_cap + beta_soc_div*res_soc_div + beta_int_div*z_int_div + beta_stable*stable + beta_research_area + mu;
+
+        R2 = mean(square(z_m-pred))/variance(z_m);
+        R2 = 1-R2;
+    }
+    
+}
+

code/ei_cov_softmax_control_nu.stan

@@ -0,0 +1,160 @@
+functions {
+    real partial_sum_lpdf(array[] vector X,
+        int start, int end,
+        int R,
+        int C,
+        array[,] int indices,
+        array[,] int NR,
+        array[,] int NC,
+        array[] vector cov,
+        array[] vector expertise,
+        matrix mu, vector L_sigma,
+        array[] vector delta,
+        array[] vector gamma,
+        array[] vector beta_vec
+        ) {
+            vector[C] theta;
+            matrix[C,R] beta;
+            vector [C] tmp = rep_vector(0, C);
+
+            real lpmf = 0;
+
+            for (i in start:end) {
+                for(r in 1:R) {
+                    if (NR[i,r] == 0) {
+                        beta[:,r] = rep_vector(0, C); 
+                    }
+                    else {
+                        lpmf += normal_lpdf(beta_vec[indices[i,r]] | 0, 1);
+                        tmp = mu[r,:]';//'
+                        tmp[1:C-1] += beta_vec[indices[i,r]] .* L_sigma;
+                        beta[:,r] = softmax(tmp + delta[r] .* cov[i] + gamma[r] .* expertise[i]); 
+                    }
+                }
+
+                theta = beta*X[i-start+1,:];
+                lpmf += multinomial_lpmf(NC[i,:] | theta);
+            }
+            return lpmf;
+    }
+}
+
+data {
+    int<lower=1> n_units;
+    int<lower=2> R;
+    int<lower=2> C;
+
+    array[n_units,R] int NR;
+    array[n_units,C] int NC;
+    array[n_units] vector[C] cov;
+    array[n_units] vector[C] expertise;
+    matrix<lower=0,upper=1>[R,C] nu;
+
+    int<lower=1> threads;
+}
+
+transformed data {
+    array [n_units] int population;
+    array[n_units] vector<lower=0,upper=1>[R] X;
+    array[n_units,R] int indices;
+    int largest_index = 1;
+    
+    for (i in 1:n_units) {
+        population[i] = sum(NR[i,:]);
+        for (r in 1:R) {
+            X[i,r] = (NR[i,r]*1.0)/(population[i]*1.0);
+
+            if (NR[i,r] > 0) {
+                indices[i,r] = largest_index;
+                largest_index += 1;
+            }
+            else {
+                indices[i,r] = 0;
+            }
+        }
+    }
+
+    print("largest index:", largest_index);
+    print("RxN: ", R*n_units);
+}
+
+parameters {
+    matrix[R, C-1] mu_;
+    array[R] vector[C] delta;
+    array[R] vector[C] gamma;
+    array [largest_index] vector[C-1] beta_vec;
+
+    real delta_0;
+    real delta_nu;
+    //real gamma_nu;
+    real mu_nu;
+
+    //cholesky_factor_corr[C-1] L_Omega;
+    vector<lower=0>[C-1] L_sigma;
+}
+
+model {
+    matrix[R, C] mu;
+    for (r in 1:R) {
+        mu[r,:C-1] = mu_[r];
+        mu[r,C] = 0;
+        mu[r,:] += mu_nu*nu[r,:];
+    }
+
+    //matrix[C-1, C-1] L_Sigma = diag_pre_multiply(L_sigma, L_Omega);
+    //L_Omega ~ lkj_corr_cholesky(10);
+    L_sigma ~ exponential(1);
+    
+    for (r in 1:R) {
+        mu_[r] ~ normal(0, 1);
+        delta[r] ~ normal(delta_0+delta_nu*nu[r,:], 1);
+        gamma[r] ~ normal(0, 1);
+    }
+
+    delta_0 ~ normal(0, 1);
+    delta_nu ~ normal(0, 1);
+    mu_nu ~ normal(0, 1);
+
+    target += reduce_sum(
+        partial_sum_lpdf, X, n_units%/%(threads*40),
+        R, C, indices, NR, NC, cov, expertise,
+        mu, L_sigma,
+        delta, gamma,
+        beta_vec
+    );
+}
+
+generated quantities {
+    array [n_units,R] vector[C] beta;
+    array [R] vector[C] counts;
+
+    for (r in 1:R) {
+        counts[r] = rep_vector(0, C);
+    }
+
+    vector [C] tmp = rep_vector(0, C);
+
+    matrix[R, C] mu;
+    for (r in 1:R) {
+        mu[r,:C-1] = mu_[r];
+        mu[r,C] = 0;
+        mu[r,:] += mu_nu*nu[r,:];
+    }
+
+    
+    for (r in 1:R) {
+        for (i in 1:n_units) {
+            if (NR[i,r] == 0) {
+                beta[i,r] = rep_vector(0, C);
+            }
+            else {
+                tmp = mu[r,:]';//'
+                tmp[1:C-1] += beta_vec[indices[i,r]] .* L_sigma;
+                beta[i,r] = softmax(tmp + delta[r] .* cov[i] + gamma[r] .* expertise[i]);
+                counts[r] += NR[i,r]*beta[i,r,:];
+            }
+            
+        }
+        counts[r] = counts[r]/sum(population);
+    }
+}

code/ei_map.py

@@ -0,0 +1,277 @@
+from socketserver import ThreadingUnixStreamServer
+import pandas as pd
+import numpy as np
+
+import torch
+from sklearn.model_selection import train_test_split, KFold
+
+from scipy.special import softmax
+
+import argparse
+import pickle
+
+from os.path import join as opj, basename
+
+import seaborn as sns
+from matplotlib import pyplot as plt
+import matplotlib
+matplotlib.use("pgf")
+matplotlib.rcParams.update(
+    {
+        "pgf.texsystem": "xelatex",
+        "font.family": "serif",
+        "font.serif": "Times New Roman",
+        "text.usetex": True,
+        "pgf.rcfonts": False,
+    }
+)
+plt.rcParams["text.latex.preamble"].join([
+        r"\usepackage{amsmath}",              
+        r"\setmainfont{amssymb}",
+])
+
+from multiprocessing import Pool
+from functools import partial
+
+
+class TrajectoryModel(torch.nn.Module):
+    def __init__(self, N, R, C, nu):
+        super().__init__()
+
+        self.N = N
+        self.R = R
+        self.C = C
+        self.nu = nu
+
+        self.dtype = torch.float
+
+        self.init_weights()
+
+        torch.autograd.set_detect_anomaly(True)
+
+        if torch.cuda.is_available():
+            self.device = torch.device("cuda")
+            self.to(self.device)
+        else:
+            print("GPU is not available, using CPU instead")
+
+    def init_weights(self):
+        self.beta = torch.nn.Parameter(
+            torch.zeros((self.N, self.R, self.C))
+        )
+        self.mu = torch.nn.Parameter(torch.zeros((self.R, self.C-1)))
+        self.gamma = torch.nn.Parameter(torch.zeros((self.R, self.C)))
+        self.delta = torch.nn.Parameter(torch.zeros((self.R, self.C)))
+        self.eps = torch.nn.Parameter(
+            torch.zeros((self.N, self.R, self.C-1))
+        )
+        self.sigma = torch.nn.Parameter(torch.zeros((R,C-1)))
+        self.mu_nu = torch.nn.Parameter(torch.zeros(1))
+
+    def train(self, train, validation, epoch=50, autostop=False, printouts=1000):
+        optimizer = torch.optim.Adam(self.parameters(), lr=0.01)
+        # clipping_value = 1e6
+
+        epochs = []
+        train_loss = []
+        validation_loss = []
+        reference_loss = []
+
+        for t in range(epoch):
+            # print("mu:", self.mu)
+            
+            loss = self.loss(train)
+            print(t, loss.item())
+
+            optimizer.zero_grad()
+            loss.backward()
+
+            optimizer.step()
+
+            if (t % 2) == 0:
+                # beta = (torch.einsum("ld,lij->dij", self.M, self.lambd)).detach().numpy()
+                y_pred_train = self.predict(train).detach().numpy()
+                y_pred_validation = self.predict(validation).detach().numpy()
+
+                epochs.append(t)
+                train_loss.append((np.abs(train["y"]-y_pred_train).sum(axis=1)/2).mean())
+                validation_loss.append((np.abs(validation["y"]-y_pred_validation).sum(axis=1)/2).mean())
+                reference_loss.append((np.abs(validation["y"]-validation["x"]).sum(axis=1)/2).mean())
+
+                if (t % printouts) == 0:
+                    fig, ax = plt.subplots(figsize=[6.4*0.75, 4.8*0.75])
+                    ax.plot(np.array(epochs), train_loss, label="$d_{\\mathrm{TV}}(\\vec{y}_a,\\vec{y}_a^{\\mathrm{pred}})$ -- training set")
+                    ax.plot(np.array(epochs), validation_loss, label="$d_{\\mathrm{TV}}(\\vec{y}_a,\\vec{y}_a^{\\mathrm{pred}})$ -- test set")
+                    ax.plot(np.array(epochs), reference_loss, label="$d_{\\mathrm{TV}}(\\vec{y}_a,\\vec{x}_a)$ -- test set")
+                    ax.set_xlabel("Epochs")
+                    ax.set_ylabel("Performance (total variation distance)")
+                    ax.legend(loc='upper right', bbox_to_anchor=(1, 1.2))
+                    ax.set_ylim(0.3,0.6)
+                    fig.savefig(f"status_{basename(args.input)}.eps", bbox_inches="tight")
+
+            if autostop and len(validation_loss)>2 and validation_loss[-1]>validation_loss[-2] and validation_loss[-2]>validation_loss[-3]:
+                break
+
+        return train_loss, validation_loss, reference_loss
+
+    def predict(self, data, eps=None):
+        N = data["N"]
+
+        mu = torch.zeros((self.R, self.C))
+        mu[:,:-1] = self.mu
+        mu += self.nu*self.mu_nu
+
+        s = torch.zeros((N, self.R, self.C))
+        s = s+torch.einsum("ij,aj->aij", self.gamma, data["expertise"])
+        s = s+torch.einsum("ij,aj->aij", self.delta, data["cov"])
+        s = s+mu
+
+        if eps is not None:
+            eps_ = torch.zeros((N, self.R, self.C))
+            eps_[:,:,:-1] = eps
+            s += eps_
+
+        b = torch.softmax(s, dim=2)
+        p = torch.einsum("aij,ai->aj", b, data["x"])
+        return p
+
+    def loss(self, data):
+        Y = data["Y"]
+        
+        loss = 0
+
+        p = self.predict(data, self.eps)
+        
+        for a in range(p.shape[0]):
+            multi = torch.distributions.multinomial.Multinomial(
+                total_count=Y[a,:].sum().max().item(),
+                probs=p[a,:]
+            )
+            loss -= multi.log_prob(Y[a,:]).sum()
+
+        print("evidence loss: ", loss/data["N"])
+
+        eps_prior = torch.distributions.normal.Normal(0, self.sigma.exp())
+        sigma_prior = torch.distributions.exponential.Exponential(1)
+        normal_prior = torch.distributions.normal.Normal(0, 1)
+
+        priors_loss = 0
+        priors_loss -= eps_prior.log_prob(self.eps).sum()
+        priors_loss -= sigma_prior.log_prob(self.sigma.exp()).sum()
+        priors_loss -= normal_prior.log_prob(self.mu).sum()
+        priors_loss -= normal_prior.log_prob(self.delta).sum()
+        priors_loss -= normal_prior.log_prob(self.gamma).sum()
+        priors_loss -= normal_prior.log_prob(self.mu_nu).sum()
+        
+        print("priors loss:", priors_loss/data["N"])
+
+        loss += priors_loss
+        loss /= data["N"]
+
+        return loss
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--input")
+parser.add_argument("--folds", default=0, type=int)
+
+args = parser.parse_args()
+
+n_topics = len(pd.read_csv(opj(args.input, "topics.csv")))
+
+df = pd.read_csv(opj(args.input, "aggregate.csv"))
+df = df[df[[f"start_{k+1}" for k in range(n_topics)]].sum(axis=1) >= 100]
+
+resources = pd.read_parquet(opj(args.input, "pooled_resources.parquet"))
+df = df.merge(resources, left_on="bai", right_on="bai")
+
+data = {
+    "NR": np.stack(df[[f"start_{k+1}" for k in range(n_topics)]].values).astype(int),
+    "NC": np.stack(df[[f"end_{k+1}" for k in range(n_topics)]].values).astype(int),
+    "expertise": np.stack(df[[f"expertise_{k+1}" for k in range(n_topics)]].fillna(0).values),
+}
+
+data["cov"] = np.stack(df["pooled_resources"])
+
+junk = np.sum(data["NR"] + data["NC"], axis=0) == 0
+
+for col in ["NR", "NC", "cov", "expertise"]:
+    data[col] = data[col][:, ~junk]
+
+R = n_topics-junk.sum()
+C = n_topics-junk.sum()
+
+data["cov"] = np.nan_to_num(data["cov"])# / np.maximum(data["cov"].sum(axis=1)[:, np.newaxis], 1)
+data["expertise"] = np.nan_to_num(data["expertise"])# / np.maximum(data["cov"].sum(axis=1)[:, np.newaxis], 1)
+
+
+expertise = data["expertise"]
+nu = np.array([
+    [((expertise[:,i]>expertise[:,i].mean())&(expertise[:,j]>expertise[:,j].mean())).mean()/(expertise[:,i]>expertise[:,i].mean()).mean() for j in range(R)]
+    for i in range(R)
+])
+
+data["Y"] = data["NC"]
+data["x"] = data["NR"]/data["NR"].sum(axis=1)[:,np.newaxis]
+data["y"] = data["NC"]/data["NC"].sum(axis=1)[:,np.newaxis]
+
+N = data["x"].shape[0]
+
+def split_train_validation(data, test_size):
+    train_ind, test_ind = train_test_split(np.arange(N), test_size=test_size)
+
+    train, validation = {}, {}
+    for k in data:
+        train[k] = torch.from_numpy(data[k][train_ind])
+        validation[k] = torch.from_numpy(data[k][test_ind])
+
+    return train, validation
+
+def folds(data, folds):
+    f = []
+    kf = KFold(n_splits=folds, shuffle=True)
+
+    for i, (train_ind, test_ind) in enumerate(kf.split(np.arange(N))):
+        fold_train, fold_test = {}, {}
+        for k in data:
+            fold_train[k] = torch.from_numpy(data[k][train_ind])
+            fold_test[k] = torch.from_numpy(data[k][test_ind])
+
+        f.append((fold_train, fold_test))
+
+    return f
+
+def run_model(data):
+    data[0]["N"] = data[0]["x"].shape[0]
+    data[1]["N"] = data[1]["x"].shape[0]
+
+    mdl = TrajectoryModel(data[0]["N"], R, C, torch.from_numpy(nu))
+    train_loss, validation_loss, reference_loss = mdl.train(
+        data[0], data[1],
+        epoch=1000,
+        autostop=True,
+        printouts=50
+    )
+    scores = [
+        train_loss[-1].detach().numpy(), validation_loss[-1].detach().numpy(), reference_loss[-1].detach().numpy()
+    ]
+    print(scores)
+    return scores
+
+if args.folds > 0:
+    f = folds(data, args.folds)
+
+    with Pool(processes=args.folds) as pool:
+        scores = pool.map(run_model, f)
+
+    scores = np.array(scores)
+
+    np.save(opj(args.input, f"scores.npy"), scores)
+
+else:
+    train, validation = split_train_validation(data, test_size=0.2)
+    train["N"] = train["x"].shape[0]
+    validation["N"] = validation["x"].shape[0]
+
+    mdl = TrajectoryModel(train["N"], R, C, torch.from_numpy(nu))
+    mdl.train(train, validation, epoch=800)
+

code/entered.stan

@@ -0,0 +1,89 @@
+functions {
+    vector z_scale(vector x) {
+        return (x-mean(x))/sd(x);
+    }
+}
+
+data {
+    int<lower=1> N;
+    int<lower=1> K;
+    vector<lower=0>[N] soc_cap;
+    vector<lower=0>[N] soc_div;
+    vector<lower=0>[N] int_div;
+    vector[N] res_soc_div;
+    //vector<lower=0>[N] age;
+    array[N] int<lower=0,upper=1> m;
+    matrix<lower=0,upper=1>[N,K] x;
+    vector[N] stable;
+    array [N] int<lower=0,upper=K-1> primary_research_area;
+}
+
+transformed data {
+    //vector[N] z_m = z_scale(m);
+    vector[N] z_soc_cap = z_scale(soc_cap);
+    vector[N] z_soc_div = z_scale(soc_div);
+    vector[N] z_int_div = z_scale(int_div);
+    vector[N] z_res_soc_div = z_scale(res_soc_div);
+
+}
+
+parameters {
+    real beta_soc_cap;
+    real beta_soc_div;
+    real beta_int_div;
+    real beta_stable;
+    vector[K] beta_x;
+
+    real mu;
+    real<lower=0> tau;
+    real<lower=1> sigma;
+
+    // vector[K] mu_x;
+    // vector<lower=0>[K] sigma_x;
+    // real mu_pop;
+    // real<lower=0> sigma_pop;
+}
+
+model {
+    vector[N] beta_research_area;
+    for (k in 1:N) {
+        beta_research_area[k] = tau*beta_x[primary_research_area[k]+1];
+    }
+
+    beta_soc_cap ~ normal(0, 1);
+    beta_soc_div ~ normal(0, 1);
+    beta_int_div ~ normal(0, 1);
+    beta_x ~ double_exponential(0, 1);
+    beta_stable ~ normal(0, 1);
+
+    mu ~ normal(0, 1);
+    tau ~ exponential(1);
+    sigma ~ pareto(1,1.5);
+
+    m ~ bernoulli_logit(beta_soc_cap*z_soc_cap + beta_soc_div*z_res_soc_div + beta_int_div*z_int_div + beta_stable*stable + beta_research_area + mu);
+
+    // m ~ beta(mu_pop, sigma_pop);
+    // for (k in 1:N) {
+    //    m[k] ~ normal(mu_x[primary_research_area[k]+1], sigma_x[primary_research_area[k]+1]);
+    // }
+    // mu_x ~ normal(0, 1);
+    // mu_pop ~ normal(0, 1);
+    // sigma_x ~ exponential(1);
+    // sigma_pop ~ exponential(1);
+}
+
+generated quantities {
+    // real R2 = 0;
+    // {
+    //     vector[N] beta_research_area;
+    //     for (k in 1:N) {
+    //         beta_research_area[k] = beta_x[primary_research_area[k]+1];
+    //     }
+    //     vector[N] pred = inv_logit(beta_soc_cap*z_soc_cap + beta_soc_div*z_res_soc_div + beta_int_div*z_int_div + beta_stable*stable + beta_research_area + mu);
+
+    //     R2 = mean(square(z_m-pred));
+    //     R2 = 1-R2;
+    // }
+    
+}
+

code/etm.py

@@ -0,0 +1,427 @@
+from AbstractSemantics.terms import TermExtractor
+from AbstractSemantics.embeddings import GensimWord2Vec
+from gensim.models import KeyedVectors
+
+import nltk
+
+import pandas as pd
+import numpy as np
+
+from scipy.sparse import csr_matrix
+
+from os.path import join as opj
+from os.path import exists
+
+import itertools
+from functools import partial
+from collections import defaultdict
+
+import re
+
+import multiprocessing as mp
+
+# from matplotlib import pyplot as plt
+
+import argparse
+import yaml
+import sys
+import pickle
+
+from gensim.models.callbacks import CallbackAny2Vec
+
+
+class MonitorCallback(CallbackAny2Vec):
+    def __init__(self, test_words):
+        self._test_words = test_words
+        self.epoch = 0
+
+    def on_epoch_end(self, model):
+        loss = model.get_latest_training_loss()
+        if self.epoch == 0:
+            print("Loss after epoch {}: {}".format(self.epoch, loss))
+        else:
+            print(
+                "Loss after epoch {}: {}".format(
+                    self.epoch, loss - self.loss_previous_step
+                )
+            )
+
+        self.epoch += 1
+        self.loss_previous_step = loss
+
+        for word in self._test_words:  # show wv logic changes
+            print(f"{word}: {model.wv.most_similar(word)}")
+
+
+def filter_ngrams(l, wl):
+    return [ngram for ngram in l if ngram in wl]
+
+
+def construct_bow(l, n):
+    items = list(set(l))
+    return np.array(items), np.array([l.count(i) for i in items])
+
+
+def ngram_inclusion(i, js):
+    return [
+        j.find(i) >= 0  # matching
+        and bool(re.search(f"(^|\_){re.escape(i)}($|\_)", j))
+        and (j.count("_") == i.count("_") + 1)
+        and (
+            (i.count("_") >= 1)
+            or bool(re.search(f"(^|\_){re.escape(i)}$", j))
+            or bool(re.search(f"^{re.escape(i)}($|\_)", j))
+        )
+        for j in js
+    ]
+
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser("CT Model")
+    parser.add_argument("location", help="model directory")
+    parser.add_argument(
+        "filter", choices=["categories", "keywords", "no-filter"], help="filter type"
+    )
+    parser.add_argument("--values", nargs="+", default=[], help="filter allowed values")
+
+    parser.add_argument("--dataset", default="inspire-harvest/database")
+
+
+    # sample size
+    parser.add_argument("--samples", type=int, default=50000)
+    parser.add_argument("--constant-sampling", type=int, default=0)
+
+    # text pre-processing
+    parser.add_argument(
+        "--add-title", default=False, action="store_true", help="include title"
+    )
+    parser.add_argument(
+        "--remove-latex", default=False, action="store_true", help="remove latex"
+    )
+    parser.add_argument(
+        "--lemmatize", default=False, action="store_true", help="lemmatize"
+    )
+    parser.add_argument(
+        "--limit-redundancy",
+        default=False,
+        action="store_true",
+        help="limit redundancy",
+    )
+    parser.add_argument("--blacklist", default=None, help="blacklist")
+
+    # embeddings
+    parser.add_argument("--dimensions", type=int, default=50)
+    parser.add_argument("--pre-trained-embeddings", default=False, action="store_true")
+    parser.add_argument("--use-saved-embeddings", default=False, action="store_true")
+
+    # topic model parameters
+    parser.add_argument("--topics", type=int, default=25)
+    parser.add_argument("--min-df", type=float, default=0.001)
+    parser.add_argument("--max-df", type=float, default=0.15)
+
+    parser.add_argument("--threads", type=int, default=4)
+
+    args = parser.parse_args(
+        [
+            "output/acl_2002_2022",
+            "no-filter",
+            "--dataset",
+            "../acl",
+            "--constant-sampling",
+            "12000",
+            "--samples",
+            "300000",
+            "--threads",
+            "30",
+            "--add-title",
+            "--remove-latex",
+            "--dimensions",
+            "50",
+            "--topics",
+            "20",
+            "--min-df",
+            "0.00075",
+            "--lemmatize",
+            "--pre-trained-embeddings",
+            # "--limit-redundancy"
+            "--use-saved-embeddings"
+            # "--blacklist",
+            # "output/medialab/blacklist",
+        ]
+    )
+
+    with open(opj(args.location, "params.yml"), "w+") as fp:
+        yaml.dump(args, fp)
+
+    articles = pd.read_parquet(
+        opj(args.dataset, "articles.parquet")
+    )[["title", "abstract", "article_id", "date_created", "categories"]]
+
+    if args.add_title:
+        articles["abstract"] = articles["abstract"].str.cat(articles["title"], sep=". ")
+
+    articles.drop(columns=["title"], inplace=True)
+
+    if args.remove_latex:
+        articles["abstract"] = articles["abstract"].apply(
+            lambda s: re.sub("$[^>]+$", "", s)
+        )
+
+    articles["abstract"] = articles["abstract"].apply(
+        lambda s: re.sub(r"\b\\\w+", "", s)
+    )
+    articles["abstract"] = articles["abstract"].apply(
+        lambda s: re.sub("[^0-9a-zA-Z--- -\.]+", "", s)
+    )
+    # articles["abstract"] = articles["abstract"].str.replace("-", " ") # NEW
+
+    articles = articles[articles["abstract"].map(len) >= 100]
+    articles["abstract"] = articles["abstract"].str.lower()
+
+    articles = articles[articles["date_created"].str.len() >= 4]
+
+    if "year" not in articles.columns:
+        articles["year"] = articles["date_created"].str[:4].astype(int) - 2000
+        articles = articles[(articles["year"] >= 0) & (articles["year"] <= 40)]
+    else:
+        articles["year"] = articles["year"].astype(int)
+        articles = articles[articles["year"]>=2002]
+    
+    articles["year_group"] = articles["year"] // 5
+    keep = pd.Series([False] * len(articles), index=articles.index)
+
+    print("Applying filter...")
+    if args.filter == "keywords":
+        for value in args.values:
+            keep |= articles["abstract"].str.contains(value)
+    elif args.filter == "categories":
+        for value in args.values:
+            keep |= articles["categories"].apply(lambda l: value in l)
+    elif args.filter == "no-filter":
+        keep |= True
+
+    articles = articles[keep == True].sample(frac=1)
+
+    if args.constant_sampling > 0:
+        articles = articles.groupby("year").head(args.constant_sampling)
+
+    articles = articles.sample(frac=1).head(args.samples)
+
+    articles.reset_index(inplace=True)
+    articles[["article_id"]].to_csv(opj(args.location, "articles.csv"))
+    print(articles)
+
+    print("Extracting n-grams...")
+    extractor = TermExtractor(
+        articles["abstract"].tolist(),
+        limit_redundancy=args.limit_redundancy,
+        patterns=[
+            ["JJ.*"],
+            ["NN.*"],
+            ["JJ.*", "NN.*"],
+            ["JJ.*", "NN.*", "NN.*"],
+            # ["JJ.*", "NN", "CC", "NN.*"],
+            # ["JJ.*", "NN.*", "JJ.*", "NN.*"],
+            # ["RB.*", "JJ.*", "NN.*", "NN.*"],
+        ],
+    )
+
+    ngrams = extractor.ngrams(
+        threads=args.threads,
+        lemmatize=args.lemmatize,
+        lemmatize_ngrams=args.lemmatize,
+        split_sentences=args.pre_trained_embeddings and not args.use_saved_embeddings,
+    )
+
+    del extractor
+    del articles["abstract"]
+
+    if args.pre_trained_embeddings and not args.use_saved_embeddings:
+        ngrams = map(
+            lambda l: [
+                [
+                    ("_".join(n))
+                    .strip()
+                    .replace("-", "_")
+                    .replace(".._", "")
+                    .replace("_..", "")
+                    for n in sent
+                ]
+                for sent in l
+            ],
+            ngrams,
+        )
+        ngrams = list(ngrams)
+
+        print("Pre-training embeddings...")
+        emb = GensimWord2Vec(
+            [sentence for sentences in ngrams for sentence in sentences]
+        )
+        model = emb.create_model(
+            vector_size=args.dimensions,
+            window=5,
+            workers=args.threads,
+            compute_loss=True,
+            # epochs=90,
+            # min_count=30,
+            epochs=80,
+            min_count=15,
+            sg=1,
+            callbacks=[
+                MonitorCallback(
+                    [
+                        "transformer",
+                        "embedding",
+                        "syntax",
+                        "grammar"
+                    ]
+                )
+            ],
+        )
+        model.wv.save_word2vec_format(opj(args.location, "embeddings.bin"), binary=True)
+        del model
+
+        ngrams = [
+            list(itertools.chain.from_iterable(article_sentences))
+            for article_sentences in ngrams
+        ]
+    else:
+        ngrams = map(
+            lambda l: [
+                "_".join(n)
+                .strip()
+                .replace("-", "_")
+                .replace(".._", "")
+                .replace("_..", "")
+                for n in l
+            ],
+            ngrams,
+        )
+
+    ngrams = list(ngrams)
+
+    print("Deriving vocabulary...")
+    voc = defaultdict(int)
+
+    for article_ngrams in ngrams:
+        _ngrams = set(article_ngrams)
+        for ngram in _ngrams:
+            voc[ngram] += 1
+
+    voc = pd.DataFrame({"ngram": voc.keys(), "count": voc.values()})
+
+    voc["df"] = voc["count"] / len(articles)
+    voc.set_index("ngram", inplace=True)
+
+    if args.min_df < 1:
+        voc = voc[voc["df"] >= args.min_df]
+    else:
+        voc = voc[voc["count"] >= args.min_df]
+
+    if args.max_df < 1:
+        voc = voc[voc["df"] <= args.max_df]
+    else:
+        voc = voc[voc["count"] <= args.max_df]
+
+    voc["len"] = voc.index.map(len)
+    voc = voc[voc["len"] >= 2]
+
+    stop_words = nltk.corpus.stopwords.words("english")
+    voc = voc[~voc.index.isin(stop_words)]
+
+    if args.blacklist is not None:
+        print("Filtering black-listed keywords...")
+        blacklist = pd.read_csv(args.blacklist)["ngram"].tolist()
+        voc = voc[
+            voc.index.map(lambda s: not any([ngram in s for ngram in blacklist]))
+            == True
+        ]
+        print("Filtering completed.")
+
+    voc = voc.sort_values("df", ascending=False)
+    voc.to_csv(opj(args.location, "ngrams.csv"))
+
+    voc = pd.read_csv(opj(args.location, "ngrams.csv"), keep_default_na=False)[
+        "ngram"
+    ].tolist()
+
+    vocabulary = {n: i for i, n in enumerate(voc)}
+
+    print("Filtering n-grams...")
+    with mp.Pool(processes=args.threads) as pool:
+        ngrams = pool.map(partial(filter_ngrams, wl=voc), ngrams)
+
+    print("Constructing bag-of-words...")
+    bow = [[vocabulary[ngram] for ngram in _ngrams] for _ngrams in ngrams]
+
+    # if args.limit_redundancy:
+    #     print("Building 'within' matrix...")
+    #     with mp.Pool(processes=args.threads) as pool:
+    #         within = pool.map(partial(ngram_inclusion, js=voc), voc)
+
+    #     within = np.array(within).astype(int)
+
+    #     print("Removing double-counting...")
+    #     bow = csr_matrix(bow)
+    #     double_counting = bow.dot(csr_matrix(within.T))
+    #     bow = bow - double_counting
+    #     print(double_counting.sum(), "redundant keywords removed")
+    #     del double_counting
+    #     bow = bow.todense()
+    #     print((bow <= -1).sum(), "keywords had negative counts after removal")
+
+    del ngrams
+
+    with mp.Pool(processes=args.threads) as pool:
+        bow = pool.map(partial(construct_bow, n=len(voc)), bow)
+
+    keep = [i for i in range(len(bow)) if len(bow[i][0]) > 0]
+
+    articles = articles.iloc[keep]
+    articles[["article_id"]].to_csv(opj(args.location, "articles.csv"))
+
+    bow = [bow[i] for i in keep]
+
+    dataset = {
+        "tokens": [bow[i][0] for i in range(len(bow))],
+        "counts": [bow[i][1] for i in range(len(bow))],
+        "article_id": articles["article_id"],
+    }
+
+    del bow
+
+    with open(opj(args.location, "dataset.pickle"), "wb") as handle:
+        pickle.dump(dataset, handle, protocol=pickle.HIGHEST_PROTOCOL)
+
+    print("Training...")
+    from embedded_topic_model.models.etm import ETM
+
+    etm_instance = ETM(
+        voc,
+        num_topics=args.topics,
+        rho_size=args.dimensions,
+        emb_size=args.dimensions,
+        epochs=25,
+        debug_mode=True,
+        train_embeddings=not args.pre_trained_embeddings,
+        model_path=opj(args.location, "model"),
+        embeddings=opj(args.location, "embeddings.bin")
+        if args.pre_trained_embeddings
+        else None,
+        use_c_format_w2vec=True,
+    )
+
+    etm_instance.fit(dataset)
+
+    with open(opj(args.location, "etm_instance.pickle"), "wb") as handle:
+        pickle.dump(etm_instance, handle, protocol=pickle.HIGHEST_PROTOCOL)
+
+    topics = etm_instance.get_topics(20)
+    print(topics)
+    topic_coherence = etm_instance.get_topic_coherence()
+    print(topic_coherence)
+    topic_diversity = etm_instance.get_topic_diversity()
+    print(topic_diversity)
+
+

code/etm_compile.py

@@ -0,0 +1,103 @@
+import pandas as pd
+import pickle
+import numpy as np
+from scipy.stats import entropy
+
+from os.path import join as opj
+
+import argparse
+
+from tqdm import trange
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--input")
+parser.add_argument("--dataset", default="inspire-harvest/database")
+
+parser.add_argument("--write-topics", action="store_true", default=False)
+parser.add_argument("--debug", action="store_true", default=False)
+args = parser.parse_args()
+
+ngrams = pd.read_csv(opj(args.input, "ngrams.csv"))
+
+with open(opj(args.input, "dataset.pickle"), "rb") as handle:
+    data = pickle.load(handle)
+
+with open(opj(args.input, "etm_instance.pickle"), "rb") as handle:
+    etm_instance = pickle.load(handle)
+
+articles = pd.read_parquet(opj(args.dataset, "articles.parquet"))[["article_id", "date_created"]]
+articles = articles[articles["date_created"].str.len() >= 4]
+if "year" not in articles.columns:
+    articles["year"] = articles["date_created"].str[:4].astype(int)-2000
+
+articles["article_id"] = articles.article_id.astype(int)
+_articles = pd.read_csv(opj(args.input, "articles.csv"))
+articles = _articles.merge(articles, how="inner")
+years = articles["year"].values
+
+if args.write_topics:
+
+    top_words = [",".join(l) for l in etm_instance.get_topics(20)]
+
+    topics = pd.DataFrame({"top_words": top_words})
+    topics["label"] = ""
+    topics.to_csv(opj(args.input, "topics.csv"))
+
+topics = pd.read_csv(opj(args.input, "topics.csv"))
+theta = etm_instance.get_document_topic_dist()
+
+is_junk_topic = np.array(topics["label"].str.contains("Junk"))
+
+if args.debug:
+    import seaborn as sns
+
+    sns.heatmap(
+        np.corrcoef(theta, theta, rowvar=False), vmin=-0.5, vmax=0.5, cmap="RdBu"
+    )
+    plt.show()
+
+topic_counts = np.zeros((theta.shape[0], theta.shape[1]))
+p_w_z = etm_instance.get_topic_word_dist()
+print("Computing P(w|d) matrix...")
+p_w_d = theta @ p_w_z
+
+keywords = np.zeros((articles.year.max()+1, p_w_z.shape[1], p_w_z.shape[0]))
+
+for d in trange(theta.shape[0]):
+    for i, w in enumerate(data["tokens"][d]):
+        p = p_w_z[:, w] * theta[d, :] / p_w_d[d, w]
+        S = np.exp(entropy(p))
+        if S >= 2:
+            if args.debug:
+                word = ngrams.iloc[w]["ngram"]
+                print(f"{word} is ambiguous, entropy={S:.2f}")
+            continue
+        else:
+            k = np.argmax(p)
+            if is_junk_topic[k]:
+                continue
+
+            topic_counts[d, k] += data["counts"][d][i]
+            if args.debug:
+                word = ngrams.iloc[w]["ngram"]
+                print(word, topics.iloc[k]["label"], data["counts"][d][i])
+
+    n_words = topic_counts[d,:].sum()
+
+    if args.debug:
+        print(n_words)
+    
+    if n_words == 0:
+        continue
+
+    for i, w in enumerate(data["tokens"][d]):
+        keywords[years[d],w,:] += topic_counts[d,:]
+    
+
+print(topic_counts)
+print(topic_counts.mean(axis=0))
+print(topic_counts.sum(axis=0))
+
+np.save(opj(args.input, "keywords.npy"), keywords)
+np.save(opj(args.input, "topics_counts.npy"), topic_counts)
+

code/etm_ei.py

@@ -0,0 +1,85 @@
+from socketserver import ThreadingUnixStreamServer
+import pandas as pd
+import numpy as np
+
+from cmdstanpy import CmdStanModel
+
+import argparse
+import pickle
+
+from os.path import join as opj
+
+import seaborn as sns
+from matplotlib import pyplot as plt
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--input")
+parser.add_argument("--n", type=int, default=200)
+parser.add_argument("--min-pop", type=int, default=100)
+parser.add_argument("--stack-rows", type=int, default=1)
+
+parser.add_argument("--chains", type=int, default=1)
+parser.add_argument("--threads-per-chain", type=int, default=4)
+parser.add_argument("--samples", type=int, default=500)
+parser.add_argument("--warmup", type=int, default=1000)
+
+args = parser.parse_args()
+
+n_topics = len(pd.read_csv(opj(args.input, "topics.csv")))
+
+df = pd.read_csv(opj(args.input, "aggregate.csv"))
+df = df[df[[f"start_{k+1}" for k in range(n_topics)]].sum(axis=1) >= args.min_pop]
+df = df.sample(n=args.n)
+
+resources = pd.read_parquet(opj(args.input, "pooled_resources.parquet"))
+df = df.merge(resources, left_on="bai", right_on="bai")
+
+data = {
+    "NR": np.stack(df[[f"start_{k+1}" for k in range(n_topics)]].values).astype(int),
+    "NC": np.stack(df[[f"end_{k+1}" for k in range(n_topics)]].values).astype(int),
+    "R": n_topics,
+    "C": n_topics,
+    "n_units": len(df),
+    "threads": args.threads_per_chain
+}
+
+data["cov"] = np.stack(df["pooled_resources"])
+
+junk = np.sum(data["NR"] + data["NC"], axis=0) == 0
+
+for col in ["NR", "NC", "cov"]:
+    data[col] = data[col][:, ~junk]
+
+data["R"] -= junk.sum()
+data["C"] -= junk.sum()
+
+data["cov"] = data["cov"] / np.maximum(data["cov"].sum(axis=1)[:, np.newaxis], 1)
+sns.heatmap(
+    np.corrcoef(data["NC"].T, data["cov"].T), vmin=-0.5, vmax=0.5, cmap="RdBu"
+)
+plt.show()
+
+
+print(data["cov"].shape)
+
+
+model = CmdStanModel(
+    stan_file=f"code/ei_cov_softmax_control.stan",
+    cpp_options={"STAN_THREADS": "TRUE"},
+    compile="force",
+)
+
+fit = model.sample(
+    data=data,
+    chains=args.chains,
+    threads_per_chain=args.threads_per_chain,
+    iter_sampling=args.samples,
+    iter_warmup=args.warmup,
+)
+
+vars = fit.stan_variables()
+samples = {}
+for (k, v) in vars.items():
+    samples[k] = v
+
+np.savez_compressed(opj(args.input, "ei_samples.npz"), **samples)

code/etm_transfers.py

@@ -0,0 +1,169 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+import pandas as pd 
+import numpy as np
+
+import networkx as nx 
+from ipysigma import Sigma
+
+from matplotlib import pyplot as plt
+import seaborn as sns
+
+import pickle
+
+from os.path import join as opj
+
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--location")
+parser.add_argument("--dataset", default="inspire-harvest/database")
+parser.add_argument("--keywords-threshold", type=int, default=200)
+parser.add_argument("--articles-threshold", type=int, default=5)
+parser.add_argument("--late-periods", nargs="+", type=int, default=[3]) # [2,3] for ACL, [3] for HEP
+args = parser.parse_args()
+
+topics = pd.read_csv(opj(args.location, "topics.csv"))["label"].tolist()
+topic_matrix = np.load(opj(args.location, "topics_counts.npy"))
+
+articles = pd.read_parquet(opj(args.dataset, "articles.parquet"))[["article_id", "date_created", "title"]]
+articles = articles[articles["date_created"].str.len() >= 4]
+if "years" not in articles.columns:
+    articles["year"] = articles["date_created"].str[:4].astype(int)-2000
+else:
+    articles["year"] = articles["year"].astype(int)-2002
+
+articles = articles[(articles["year"] >= 0) & (articles["year"] <= 40)]
+articles["year_group"] = articles["year"]//5
+_articles = pd.read_csv(opj(args.location,"articles.csv"))
+articles["article_id"] = articles.article_id.astype(int)
+articles = _articles.merge(articles, how="left")
+print(len(_articles))
+print(len(articles))
+
+articles["main_topic"] = topic_matrix.argmax(axis=1)
+articles["main_topic"] = articles["main_topic"].map(lambda k: topics[k])
+
+print(articles[["title", "main_topic"]].sample(frac=1).head(10))
+print(articles[["title", "main_topic"]].sample(frac=1).head(10))
+
+all_authors = pd.read_parquet(opj(args.dataset, "articles_authors.parquet"))
+all_authors["article_id"] = all_authors.article_id.astype(int)
+n_authors = all_authors.groupby("article_id").agg(n_authors=("bai", lambda x: x.nunique())).reset_index()
+
+n_articles = len(articles)
+articles = articles.merge(n_authors, how="left", left_on="article_id", right_on="article_id")
+assert len(articles)==n_articles, "# of articles does not match! cannot continue"
+
+all_authors = all_authors.merge(articles, how="inner", left_on="article_id", right_on="article_id")
+all_authors["year_range"] = all_authors["year"]//5
+
+n_papers = all_authors.groupby(["bai", "year_range"]).agg(n=("article_id", "count")).reset_index()
+filtered_authors = []
+for author, n in n_papers.groupby("bai"):
+    start = n[n["year_range"]<=1]
+    # end = n[n["year_range"]==3]
+    end = n[n["year_range"].isin(args.late_periods)]
+    if len(start) and len(end):
+        filtered_authors.append({
+            "author": author,
+            "n_start": start.iloc[0]["n"],
+            "n_end": end.iloc[0]["n"],
+        })
+
+filtered_authors = pd.DataFrame(filtered_authors)
+filtered_authors = filtered_authors[(filtered_authors["n_start"] >= args.articles_threshold) & (filtered_authors["n_end"] >= args.articles_threshold)]
+authors=all_authors[all_authors["bai"].isin(filtered_authors["author"])]
+start_authors = authors[authors["year_range"]<=1]
+# end_authors = authors[authors["year_range"]==3]
+end_authors = authors[authors["year_range"].isin(args.late_periods)]
+
+authorlist = list(authors["bai"].unique())
+inv_articles = {n: i for i,n in enumerate(articles["article_id"].values)}
+inv_authorlist = {author: i for i, author in enumerate(authorlist)}
+n_authors = len(authorlist)
+
+n_clusters = topic_matrix.shape[1]
+n_years = articles["year"].max()+1
+
+start = np.zeros((n_authors, n_clusters))
+end = np.zeros((n_authors, n_clusters))
+expertise = np.zeros((n_authors, n_clusters))
+
+start_count = np.zeros(n_authors)
+end_count = np.zeros(n_authors)
+expertise_norm = np.zeros(n_authors)
+
+for author, _articles in start_authors.groupby("bai"):
+    for article_id in _articles["article_id"].tolist():
+        start[inv_authorlist[author],:] += topic_matrix[inv_articles[article_id],:].flat
+        start_count[inv_authorlist[author]] += topic_matrix[inv_articles[article_id],:].sum()
+
+        n = articles.iloc[inv_articles[article_id]]["n_authors"]
+        expertise[inv_authorlist[author]] += (1/n)*topic_matrix[inv_articles[article_id],:].flat
+        expertise_norm[inv_authorlist[author]] += (1/n)*topic_matrix[inv_articles[article_id],:].sum()
+        
+for author, _articles in end_authors.groupby("bai"):
+    for article_id in _articles["article_id"].tolist():
+        end[inv_authorlist[author],:] += topic_matrix[inv_articles[article_id],:].flat
+        end_count[inv_authorlist[author]] += topic_matrix[inv_articles[article_id],:].sum()
+
+authors_records = {}
+
+for author, _articles in all_authors.groupby("bai"):
+    record = np.zeros((n_years, n_clusters))
+    record_count = np.zeros((n_years, n_clusters))
+
+    for article in _articles.to_dict(orient="records"):
+        year = article["year"]
+        article_id = article["article_id"]
+        
+        record[year,:] += topic_matrix[inv_articles[article_id],:].flat
+        record_count[year] += topic_matrix[inv_articles[article_id],:].sum()
+
+    authors_records[author] = {
+        "record": record,
+        "record_count": record_count
+    }
+
+with open(opj(args.location, "authors_full_records.pickle"), "wb") as handle:
+    pickle.dump(authors_records, handle, protocol=pickle.HIGHEST_PROTOCOL)
+
+ok = (start_count>=args.keywords_threshold)&(end_count>=args.keywords_threshold)
+
+cluster_names_start = [f"start_{n+1}" for n in range(n_clusters)]
+cluster_names_end = [f"end_{n+1}" for n in range(n_clusters)]
+cluster_names_expertise = [f"expertise_{n+1}" for n in range(n_clusters)]
+
+start = start[ok]
+end = end[ok]
+start_count = start_count[ok]
+end_count = end_count[ok]
+expertise = expertise[ok]/expertise_norm[ok][:,np.newaxis]
+
+start_norm = (start/start_count[:,np.newaxis])
+end_norm = (end/end_count[:,np.newaxis])
+
+
+print(start_norm.shape)
+print(end_norm.shape)
+
+print(start_norm.mean(axis=0))
+print(end_norm.mean(axis=0))
+
+aggregate = {}
+for i in range(n_clusters):
+    aggregate[cluster_names_start[i]] = start[:,i]
+    aggregate[cluster_names_end[i]] = end[:,i]
+    aggregate[cluster_names_expertise[i]] = expertise[:,i]
+
+aggregate = pd.DataFrame(aggregate)
+aggregate["bai"] = [bai for i, bai in enumerate(authorlist) if ok[i]]
+
+aggregate.to_csv(opj(args.location, "aggregate.csv"))
+
+sns.heatmap(np.corrcoef(start_norm.T, end_norm.T), vmin=-0.5, vmax=0.5, cmap="RdBu")
+plt.show()
+
+

code/exited.stan

@@ -0,0 +1,89 @@
+functions {
+    vector z_scale(vector x) {
+        return (x-mean(x))/sd(x);
+    }
+}
+
+data {
+    int<lower=1> N;
+    int<lower=1> K;
+    vector<lower=0>[N] soc_cap;
+    vector<lower=0>[N] soc_div;
+    vector<lower=0>[N] int_div;
+    vector[N] res_soc_div;
+    //vector<lower=0>[N] age;
+    array[N] int<lower=0,upper=1> m;
+    matrix<lower=0,upper=1>[N,K] x;
+    vector[N] stable;
+    array [N] int<lower=0,upper=K-1> primary_research_area;
+}
+
+transformed data {
+    //vector[N] z_m = z_scale(m);
+    vector[N] z_soc_cap = z_scale(soc_cap);
+    vector[N] z_soc_div = z_scale(soc_div);
+    vector[N] z_int_div = z_scale(int_div);
+    vector[N] z_res_soc_div = z_scale(res_soc_div);
+
+}
+
+parameters {
+    real beta_soc_cap;
+    real beta_soc_div;
+    real beta_int_div;
+    real beta_stable;
+    vector[K] beta_x;
+
+    real mu;
+    real<lower=0> tau;
+    real<lower=1> sigma;
+
+    // vector[K] mu_x;
+    // vector<lower=0>[K] sigma_x;
+    // real mu_pop;
+    // real<lower=0> sigma_pop;
+}
+
+model {
+    vector[N] beta_research_area;
+    for (k in 1:N) {
+        beta_research_area[k] = tau*beta_x[primary_research_area[k]+1];
+    }
+
+    beta_soc_cap ~ normal(0, 1);
+    beta_soc_div ~ normal(0, 1);
+    beta_int_div ~ normal(0, 1);
+    beta_x ~ double_exponential(0, 1);
+    beta_stable ~ normal(0, 1);
+
+    mu ~ normal(0, 1);
+    tau ~ exponential(1);
+    sigma ~ pareto(1,1.5);
+
+    m ~ bernoulli_logit(beta_soc_cap*z_soc_cap + beta_soc_div*z_res_soc_div + beta_int_div*z_int_div + beta_stable*stable + beta_research_area + mu);
+
+    // m ~ beta(mu_pop, sigma_pop);
+    // for (k in 1:N) {
+    //    m[k] ~ normal(mu_x[primary_research_area[k]+1], sigma_x[primary_research_area[k]+1]);
+    // }
+    // mu_x ~ normal(0, 1);
+    // mu_pop ~ normal(0, 1);
+    // sigma_x ~ exponential(1);
+    // sigma_pop ~ exponential(1);
+}
+
+generated quantities {
+    // real R2 = 0;
+    // {
+    //     vector[N] beta_research_area;
+    //     for (k in 1:N) {
+    //         beta_research_area[k] = beta_x[primary_research_area[k]+1];
+    //     }
+    //     vector[N] pred = inv_logit(beta_soc_cap*z_soc_cap + beta_soc_div*z_res_soc_div + beta_int_div*z_int_div + beta_stable*stable + beta_research_area + mu);
+
+    //     R2 = mean(square(z_m-pred));
+    //     R2 = 1-R2;
+    // }
+    
+}
+

code/optimal_transport.py

@@ -0,0 +1,421 @@
+from calendar import c
+import numpy as np
+import pandas as pd
+from scipy.stats import norm
+from scipy.special import softmax
+import cvxpy as cp
+import ot
+from sklearn.linear_model import LinearRegression
+from scipy.linalg import logm
+
+from matplotlib import pyplot as plt
+import matplotlib
+matplotlib.use("pgf")
+matplotlib.rcParams.update(
+    {
+        "pgf.texsystem": "xelatex",
+        "font.family": "serif",
+        "font.serif": "Times New Roman",
+        "text.usetex": True,
+        "pgf.rcfonts": False,
+        'mathtext.default': 'regular',
+    }
+)
+plt.rcParams["text.latex.preamble"].join([
+        r"\usepackage{amsmath}",              
+        r"\usepackage{bm}",              
+        r"\setmainfont{amssymb}",
+])
+import seaborn as sns
+
+import argparse
+from os.path import join as opj, exists
+import pickle
+
+from cmdstanpy import CmdStanModel
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--input")
+parser.add_argument("--suffix", default=None)
+parser.add_argument("--model", default="knowledge", choices=["knowledge", "identity", "random", "etm", "linguistic", "linguistic_symmetric"])
+parser.add_argument("--prior", default="bounded", choices=["bounded"])
+parser.add_argument("--steps", default=1000000, type=int)
+parser.add_argument("--burnin", default=50000, type=int)
+parser.add_argument("--thin", default=100, type=int)
+parser.add_argument("--alpha-prior", default=5, type=float)
+args = parser.parse_args()
+
+suffix = f"_{args.suffix}" if args.suffix is not None else ""
+
+samples = np.load(opj(args.input, f"ei_samples{suffix}.npz"))
+topics = pd.read_csv(opj(args.input, "topics.csv"))
+junk = topics["label"].str.contains("Junk")
+topics = topics[~junk]["label"].tolist()
+
+fig, ax = plt.subplots()
+
+n_topics = len(pd.read_csv(opj(args.input, "topics.csv")))
+df = pd.read_csv(opj(args.input, "aggregate.csv"))
+
+resources = pd.read_parquet(opj(args.input, "pooled_resources.parquet"))
+df = df.merge(resources, left_on="bai", right_on="bai")
+
+NR = np.stack(df[[f"start_{k+1}" for k in range(n_topics)]].values).astype(int)
+NC = np.stack(df[[f"end_{k+1}" for k in range(n_topics)]].values).astype(int)
+expertise = np.stack(df[[f"expertise_{k+1}" for k in range(n_topics)]].values)
+S = np.stack(df["pooled_resources"])
+
+# junk = np.sum(NR + NC, axis=0) == 0
+
+N = NR.shape[0]
+NR = NR[:,~junk]
+NC = NC[:,~junk]
+expertise = expertise[:,~junk]
+S = S[:,~junk]
+
+x = NR/NR.sum(axis=1)[:,np.newaxis]
+y = NC/NC.sum(axis=1)[:,np.newaxis]
+S_distrib = S/S.sum(axis=1)[:,np.newaxis]
+print(S_distrib)
+
+
+R = np.array([
+    [((expertise[:,i]>expertise[:,i].mean())&(expertise[:,j]>expertise[:,j].mean())).mean()/(expertise[:,i]>expertise[:,i].mean()).mean() for j in range(len(topics))]
+    for i in range(len(topics))
+])
+
+K = expertise.shape[1]
+
+# observed couplings
+theta = samples["beta"].mean(axis=0)
+theta = np.einsum("ai,aij->ij", x, theta)
+
+order = np.load(opj(args.input, "topics_order.npy"))
+
+def mcmc_bounded(T, x, alpha_prior, sigma, steps=1000):
+    # x = x/x.std()
+    T = T/T.sum()
+    m = T.shape[0]
+    n = T.shape[1]
+
+    K = np.zeros((steps+1, m, n))
+
+    lambd = m*n*3
+    # Transform K in a way that sends C to the prior support
+    # while preserving cross-ratios
+    Dc = cp.Variable(m)
+    prob = cp.Problem(
+        cp.Minimize(cp.sum(cp.abs(Dc))),
+        [
+            m*cp.sum(Dc)==-np.sum(np.log(T))-lambd, # C sums to m*n*lambda
+            Dc <= -np.log(np.max(T, axis=0)) # C is positive
+        ]
+    )
+    prob.solve(verbose=True)
+
+    K[0] = T@np.diag(np.exp(Dc.value))
+    beta = np.random.randn(steps+1)
+
+    C = np.zeros((steps+1, m, n))
+    C[0] = -np.log(K[0])/lambd
+    
+    accepted = np.array([False]*(steps+1))
+    accepted[0] = True
+
+    oob = np.array([False]*(steps+1))
+
+    beta_prior = norm(loc=0,scale=1)
+    
+    for i in range(steps):
+        Dr = np.random.randn(m)*sigma
+        Dr = np.exp(Dr)
+        Dc = 1/Dr # preserve the sum of C
+
+        beta[i+1] = np.random.randn()*sigma+beta[i]
+        K[i+1] = np.diag(Dr)@K[i]@np.diag(Dc)
+        C[i+1] = -np.log(K[i+1])/lambd
+        
+        distrib_prop = softmax(x.flatten()*beta[i+1])
+        distrib_prev = softmax(x.flatten()*beta[i])
+
+        oob[i+1] = np.abs(C[i+1].sum()-1)>1e-6 or np.any(C[i+1]<0)
+
+        if not oob[i+1]:
+            p_prop = beta_prior.logpdf(beta[i+1])
+            p_prev = beta_prior.logpdf(beta[i])
+
+            p_prop += -alpha_prior*(C[i+1].flatten()*np.log(C[i+1].flatten()/distrib_prop)).sum() - 0.5*np.log(C[i+1].flatten()).sum()
+            p_prev += -alpha_prior*(C[i].flatten()*np.log(C[i].flatten()/distrib_prev)).sum() - 0.5*np.log(C[i].flatten()).sum()
+
+            a = p_prop-p_prev
+            u = np.random.uniform(0, 1)
+
+        if oob[i+1] or a <= np.log(u):
+            C[i+1] = C[i]
+            K[i+1] = K[i]
+            beta[i+1] = beta[i]
+            accepted[i+1] = False
+        else:
+            accepted[i+1] = True
+
+        if i % 1000 == 0:
+            print(f"step {i}/{steps}, rate={accepted[:i].mean():.3f}, oob={oob[:i].mean():.3f}, acc={accepted[:i].sum():.0f}")
+            print(f"beta: {beta[:i].mean():.2f}, beta batch: {beta[i-1000:i].mean():.2f}, std batch: {beta[i-1000:i].std():.2f}")
+
+    return C, beta, accepted
+
+
+output = opj(args.input, f"cost_{args.model}_{args.prior}.npz")
+
+if args.model == "knowledge":
+    matrix = 1-np.load(opj(args.input, "nu_expertise.npy"))
+elif args.model == "etm":
+    matrix = 1-np.load(opj(args.input, "nu_etm.npy"))
+elif args.model == "identity":
+    matrix = 1-np.eye(K)
+elif args.model == "random":
+    matrix = np.random.uniform(0, 1, size=(K,K))
+elif args.model == "linguistic":
+    matrix = np.load(opj(args.input, "nu_ling.npy"))
+elif args.model == "linguistic_symmetric":
+    matrix = np.load(opj(args.input, "nu_ling_symmetric.npy"))
+
+    fig, ax = plt.subplots()
+    sns.heatmap(
+        matrix[:, order][order],
+        cmap="Reds",
+        vmin=0,
+        vmax=+np.max(np.abs(matrix)),
+        xticklabels=[topics[i] for i in order],
+        yticklabels=[topics[i] for i in order],
+        ax=ax,
+    )
+    fig.savefig(opj(args.input, f"linguistic_gap_{args.model}_{args.prior}.eps"), bbox_inches="tight")
+
+matrix_sd = 1
+
+if not exists(output):
+    if args.model in ["knowledge", "etm"]:
+        C, beta, accepted = mcmc_bounded(theta, matrix, args.alpha_prior*K*K, 0.1, steps=args.steps)
+    else:
+        C, beta, accepted = mcmc_bounded(theta, matrix/matrix.std(), args.alpha_prior*K*K, 0.1, steps=args.steps)
+
+    C = C[args.burnin::args.thin]
+    beta = beta[args.burnin::args.thin]
+    accepted = accepted[args.burnin::args.thin]
+    np.savez_compressed(output, C=C, beta=beta)
+
+else:
+    samples = np.load(output)
+    C = samples["C"]
+    beta = samples["beta"]
+
+print(beta.mean())
+print(beta.std())
+
+res = C-np.einsum("s,ij->sij", beta, matrix/matrix_sd)
+delta = res.mean(axis=0)
+res = (res**2).mean(axis=(1,2))
+var = np.array([C[s].flatten().var() for s in range(C.shape[0])])
+res = res/var 
+res = 1-res
+print(res.mean())
+
+fig, ax = plt.subplots()
+sns.heatmap(
+    C.mean(axis=0)[:, order][order],
+    xticklabels=[topics[i] for i in order],
+    yticklabels=[topics[i] for i in order],
+    cmap="Reds",
+    vmin=+np.min(C.mean(axis=0)),
+    vmax=+np.max(C.mean(axis=0)),
+    ax=ax,
+)
+fig.savefig(opj(args.input, f"cost_matrix_{args.model}_{args.prior}.eps"), bbox_inches="tight")
+
+pearson = np.corrcoef(C.mean(axis=0).flatten(), matrix.flatten())[0,1]
+print("R:", pearson)
+print("R^2:", pearson**2)
+
+reg = LinearRegression()
+fit = reg.fit(matrix.flatten().reshape(-1, 1),C.mean(axis=0).flatten())
+
+if args.model == "knowledge":
+    fig, ax = plt.subplots(figsize=(0.75*4.8,0.75*3.2))
+    xs = np.linspace(0, 1, 4)
+    ax.plot(1-xs, fit.predict(xs.reshape(-1, 1)), color="black")
+    ax.scatter(1-matrix.flatten(), C.mean(axis=0).flatten(), s=4)
+    
+    # error bars are boring as they only reflect the degeneracy of the cost matrix
+    # low = np.quantile(C, q=0.05/2, axis=0)
+    # up = np.quantile(C, q=1-0.05/2, axis=0)
+    # mean = C.mean(axis=0)
+    # ax.errorbar(
+    #     1-matrix.flatten(),
+    #     mean.flatten(), 
+    #     (np.maximum(mean.flatten()-low.flatten(), 0), np.maximum(up.flatten()-mean.flatten(), 0)),
+    #     ls="none",
+    #     lw=0.5
+    # )
+    ax.set_xlabel("Fraction of physicists with expertise in $k'$\namong those with expertise in $k$ ($\\nu_{k,k'}$)")
+    # pearson = np.corrcoef(softmax(np.einsum("s,i->si", beta, (1-matrix.flatten())/matrix.std()), axis=1).mean(axis=0), C.mean(axis=0).flatten())[0,1]
+    ax.text(0.95, 0.95, f"$R={-pearson:.2f}$", ha="right", va="top", transform=ax.transAxes)
+    ax.set_ylabel("Cost of shifting attention\nfrom $k$ to $k'$ ($C_{k,k'}$)")
+    fig.savefig(opj(args.input, f"cost_vs_nu_{args.model}.eps"), bbox_inches="tight")
+elif args.model == "identity":
+    fig, ax = plt.subplots(figsize=(0.75*4.8,0.75*3.2))
+    ax.axline((0,0), slope=-beta.mean(axis=0)/matrix_sd, color="black")
+    ax.scatter((1-matrix).flatten(), C.mean(axis=0).flatten(), s=4)
+    ax.set_xlabel("1 if $k=k'$, 0 otherwise")
+    ax.text(0.95, 0.95, f"$R={-pearson:.2f}$", ha="right", va="top", transform=ax.transAxes)
+    ax.set_ylabel("Cost of shifting attention\nfrom $k$ to $k'$ ($C_{k,k'}$)")
+    fig.savefig(opj(args.input, f"cost_vs_nu_{args.model}.eps"), bbox_inches="tight")
+elif args.model == "linguistic":
+    fig, ax = plt.subplots(figsize=(0.75*4.8,0.75*3.2))
+    ax.axline((0,0), slope=beta.mean(axis=0)/matrix_sd, color="black")
+    ax.scatter(matrix.flatten(), C.mean(axis=0).flatten(), s=4)
+    ax.set_xlabel("Linguistic gap from $k$ to $k'$\n$\\Delta_{k,k'}=H(\\varphi_{k'}+\\varphi_k)-H(\\varphi_k)$")
+    ax.text(0.05, 0.95, f"$R={pearson:.2f}$", ha="left", va="top", transform=ax.transAxes)
+    ax.set_ylabel("Cost of shifting attention\nfrom $k$ to $k'$ ($C_{k,k'}$)")
+    fig.savefig(opj(args.input, f"cost_vs_nu_{args.model}.eps"), bbox_inches="tight")
+elif args.model == "linguistic_symmetric":
+    fig, ax = plt.subplots(figsize=(0.75*4.8,0.75*3.2))        
+    ax.scatter(matrix.flatten(), C.mean(axis=0).flatten(), s=4)
+    ax.set_xlabel("Linguistic gap from $k$ to $k'$\n$\\Delta_{k,k'}=H(\\varphi_{k'}+\\varphi_k)-H(\\varphi_k)$")
+    pearson = np.corrcoef(softmax(np.einsum("s,i->si", beta, matrix.flatten()/matrix.std()), axis=1).mean(axis=0), C.mean(axis=0).flatten())[0,1]
+    ax.text(0.05, 0.95, f"$R={pearson:.2f}$", ha="left", va="top", transform=ax.transAxes)
+    ax.set_ylabel("Cost of shifting attention\nfrom $k$ to $k'$ ($C_{k,k'}$)")
+    fig.savefig(opj(args.input, f"cost_vs_nu_{args.model}.eps"), bbox_inches="tight")
+
+# predicted transfers
+origin = x.mean(axis=0)
+target = y.mean(axis=0)
+
+fig, ax = plt.subplots()
+shifts = theta[:, order][order]/theta.sum()
+sig = shifts>origin[order]*target[order]
+shifts = shifts/shifts.sum(axis=1)[:,np.newaxis]
+sns.heatmap(
+    shifts,
+    xticklabels=[topics[i] for i in order],
+    yticklabels=[topics[i] for i in order],
+    cmap="Blues",
+    vmin=0,
+    ax=ax,
+    annot=[[f"\\textbf{{{shifts[i,j]:.2f}}}" if sig[i,j] else "" for j in range(len(topics))] for i in range(len(topics))],
+    fmt="",
+    annot_kws={"fontsize": 6},
+)
+fig.savefig(opj(args.input, f"cost_matrix_true_couplings_{args.model}_{args.prior}.eps"), bbox_inches="tight")
+
+T = ot.sinkhorn(
+    origin,
+    target,
+    softmax(np.einsum("s,i->si", beta, matrix.flatten() if args.model in ["knowledge", "etm"] else matrix.flatten()/matrix.std()), axis=1).reshape((len(beta), K, K)).mean(axis=0),
+    1/(3*K*K)
+)
+
+shifts = T[:, order][order]
+sig = shifts>origin[order]*target[order]
+shifts = shifts/shifts.sum(axis=1)[:,np.newaxis]
+
+fig, ax = plt.subplots()
+sns.heatmap(
+    shifts,
+    xticklabels=[topics[i] for i in order],
+    yticklabels=[topics[i] for i in order],
+    cmap="Blues",
+    vmin=0,
+    ax=ax,
+    annot=[[f"\\textbf{{{shifts[i,j]:.2f}}}" if sig[i,j] else "" for j in range(len(topics))] for i in range(len(topics))],
+    fmt="",
+    annot_kws={"fontsize": 6},
+)
+fig.savefig(opj(args.input, f"cost_matrix_predicted_couplings_{args.model}_{args.prior}.eps"), bbox_inches="tight")
+
+
+T_baseline = ot.sinkhorn(
+    origin,
+    target,
+    (1-np.identity(K))/K,
+    50/(10*K*K)
+)
+
+
+fig, ax = plt.subplots()
+shifts = T_baseline[:, order][order]
+sig = shifts>origin[order]*target[order]
+shifts = shifts/shifts.sum(axis=1)[:,np.newaxis]
+
+fig, ax = plt.subplots()
+sns.heatmap(
+    shifts,
+    xticklabels=[topics[i] for i in order],
+    yticklabels=[topics[i] for i in order],
+    cmap="Blues",
+    vmin=0,
+    ax=ax,
+    annot=[[f"\\textbf{{{shifts[i,j]:.2f}}}" if sig[i,j] else "" for j in range(len(topics))] for i in range(len(topics))],
+    fmt="",
+    annot_kws={"fontsize": 6},
+)
+fig.savefig(opj(args.input, f"cost_matrix_predicted_couplings_identity.eps"), bbox_inches="tight")
+
+def tv_dist(x, y):
+    return  np.abs(y/y.sum()-x/x.sum()).sum()/2
+
+lambdas = np.logspace(np.log10(1/(5*10*K*K)), np.log10(100/(K*K)), 200)
+perf = []
+baseline = []
+for l in lambdas:
+    T = ot.sinkhorn(
+        origin,
+        target,
+        softmax(np.einsum("s,i->si", beta, matrix.flatten() if args.model == "knowledge" else matrix.flatten()/matrix.std()), axis=1).reshape((len(beta), K, K)).mean(axis=0),
+        l
+    )
+    
+    T_baseline = ot.sinkhorn(
+        origin,
+        target,
+        (1-np.identity(K))/K,
+        l
+    )
+
+    perf.append(tv_dist(T.flatten(), theta.flatten()))
+    baseline.append(tv_dist(T_baseline.flatten(), theta.flatten()))
+
+fig, ax = plt.subplots()
+ax.plot(lambdas, perf, label=f"{args.model} ({np.min(perf):.3f})")
+ax.plot(lambdas, baseline, label=f"baseline ({np.min(baseline):.3f})")
+ax.set_xscale("log")
+fig.legend()
+fig.savefig(opj(args.input, f"performance_{args.model}_{args.prior}.eps"), bbox_inches="tight")
+
+# counterfactual
+
+
+T = ot.sinkhorn(
+    origin,
+    target,
+    C.mean(axis=0)/C.mean(axis=0).sum(),
+    1/(3*K*K)
+)
+
+shifts = T[:, order][order]
+sig = shifts>origin[order]*target[order]
+shifts = shifts/shifts.sum(axis=1)[:,np.newaxis]
+
+fig, ax = plt.subplots()
+sns.heatmap(
+    shifts,
+    xticklabels=[topics[i] for i in order],
+    yticklabels=[topics[i] for i in order],
+    cmap="Blues",
+    vmin=0,
+    ax=ax,
+    annot=[[f"\\textbf{{{shifts[i,j]:.2f}}}" if sig[i,j] else "" for j in range(len(topics))] for i in range(len(topics))],
+    fmt="",
+    annot_kws={"fontsize": 6},
+)
+fig.savefig(opj(args.input, f"cost_matrix_counterfactual_couplings_{args.model}_{args.prior}.eps"), bbox_inches="tight")

code/topic_distance.py

@@ -0,0 +1,72 @@
+import pandas as pd
+import pickle
+import numpy as np
+from scipy.stats import entropy
+
+from os.path import join as opj
+
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--input")
+args = parser.parse_args()
+
+with open(opj(args.input, "dataset.pickle"), "rb") as handle:
+    data = pickle.load(handle)
+
+with open(opj(args.input, "etm_instance.pickle"), "rb") as handle:
+    etm_instance = pickle.load(handle)
+
+p_w_z = etm_instance.get_topic_word_dist()
+n_topics = p_w_z.shape[0]
+
+topics = pd.read_csv(opj(args.input, "topics.csv"))
+n_topics = len(topics)
+junk = np.array(topics["label"].str.contains("Junk"))
+
+df = pd.read_csv(opj(args.input, "aggregate.csv"))
+expertise = np.stack(df[[f"expertise_{k+1}" for k in range(n_topics)]].values)
+
+expertise = expertise[:,~junk]
+p_w_z = p_w_z[~junk,:]
+theta = etm_instance.get_document_topic_dist()[:,~junk]
+
+n_topics -= junk.sum()
+
+R = np.array([
+    [((expertise[:,i]>expertise[:,i].mean())&(expertise[:,j]>expertise[:,j].mean())).mean()/(expertise[:,i]>expertise[:,i].mean()).mean() for j in range(n_topics)]
+    for i in range(n_topics)
+])
+
+np.save(opj(args.input, "nu_expertise.npy"), R)
+
+R = np.array([
+    [((expertise[:,i]>expertise[:,i].mean())&(expertise[:,j]>expertise[:,j].mean())).mean()/((expertise[:,i]>expertise[:,i].mean())|(expertise[:,j]>expertise[:,j].mean())).mean() for j in range(n_topics)]
+    for i in range(n_topics)
+])
+
+np.save(opj(args.input, "nu_expertise_symmetric.npy"), R)
+
+
+def dist(p,q):
+    return entropy(p+q)-entropy(p)
+
+def dist_symmetric(p,q):
+    return entropy(p+q)-0.5*(entropy(p)+entropy(q))
+
+distance = np.zeros((n_topics, n_topics))
+distance_symmetric = np.zeros((n_topics, n_topics))
+
+for i in range(n_topics):
+    for j in range(n_topics):
+        distance[i,j] = dist(p_w_z[i], p_w_z[j])
+        distance_symmetric[i,j] = dist_symmetric(p_w_z[i], p_w_z[j])
+
+V = len(pd.read_csv(opj(args.input, "ngrams.csv")))
+distance_symmetric /= np.log(V)
+
+np.save(opj(args.input, "nu_ling.npy"), distance)
+np.save(opj(args.input, "nu_ling_symmetric.npy"), distance_symmetric)
+
+np.save(opj(args.input, "nu_etm.npy"), np.corrcoef(theta.T))
+

inspire-harvest

@@ -0,0 +1 @@
+Subproject commit 7c8b25105648538f2d173bb643bfc6c144c3d615

output/acl_2002_2022/aggregate.csv

@@ -0,0 +1,579 @@
+,start_1,end_1,expertise_1,start_2,end_2,expertise_2,start_3,end_3,expertise_3,start_4,end_4,expertise_4,start_5,end_5,expertise_5,start_6,end_6,expertise_6,start_7,end_7,expertise_7,start_8,end_8,expertise_8,start_9,end_9,expertise_9,start_10,end_10,expertise_10,start_11,end_11,expertise_11,start_12,end_12,expertise_12,start_13,end_13,expertise_13,start_14,end_14,expertise_14,start_15,end_15,expertise_15,start_16,end_16,expertise_16,start_17,end_17,expertise_17,start_18,end_18,expertise_18,start_19,end_19,expertise_19,start_20,end_20,expertise_20,bai
+0,0.0,0.0,0.0,12.0,195.0,0.016154204200093093,120.0,37.0,0.16126824192974287,0.0,0.0,0.0,0.0,0.0,0.0,26.0,90.0,0.033129808613750235,0.0,0.0,0.0,15.0,112.0,0.01998740519672535,0.0,76.0,0.0,122.0,75.0,0.1306847739780412,10.0,56.0,0.009583002491580646,0.0,0.0,0.0,109.0,133.0,0.1421022369465816,6.0,134.0,0.009856802562768666,25.0,40.0,0.022314705801823504,175.0,294.0,0.18867562905566357,141.0,117.0,0.18837444897735672,0.0,0.0,0.0,45.0,280.0,0.05076253319825862,18.0,40.0,0.02710620704761383,"chen, k"
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+2,0.0,0.0,0.0,0.0,0.0,0.0,9.0,8.0,0.021669741427274593,0.0,0.0,0.0,0.0,0.0,0.0,13.0,94.0,0.0345837359805558,0.0,0.0,0.0,31.0,3.0,0.09312132126855838,3.0,0.0,0.01229904243169639,5.0,1.0,0.026647925268675512,1.0,51.0,0.001757006061670913,0.0,0.0,0.0,45.0,39.0,0.14524583443146213,45.0,130.0,0.2644294122814724,48.0,23.0,0.11561099885794607,9.0,9.0,0.05534569094263376,2.0,8.0,0.005856686872236376,0.0,0.0,0.0,35.0,56.0,0.2070338809335559,4.0,8.0,0.016398723242261853,"gaizauskas, r"
+3,0.0,0.0,0.0,3.0,12.0,0.005705591479650057,60.0,11.0,0.4351464435146443,0.0,0.0,0.0,0.0,0.0,0.0,0.0,13.0,0.0,0.0,0.0,0.0,0.0,8.0,0.0,0.0,1.0,0.0,7.0,19.0,0.020920502092050212,0.0,5.0,0.0,0.0,0.0,0.0,2.0,6.0,0.003803727653100038,0.0,48.0,0.0,51.0,34.0,0.1692658805629517,1.0,11.0,0.005705591479650057,3.0,6.0,0.051350323316850514,0.0,0.0,0.0,34.0,484.0,0.28623050589577786,6.0,17.0,0.021871434005325215,"forcada, m"
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+5,0.0,0.0,0.0,14.0,25.0,0.05143448782605792,13.0,9.0,0.04776059583848235,0.0,0.0,0.0,0.0,0.0,0.0,38.0,45.0,0.08730503323202299,0.0,0.0,0.0,0.0,1.0,0.0,0.0,23.0,0.0,14.0,3.0,0.049230152633512575,0.0,0.0,0.0,0.0,0.0,0.0,7.0,23.0,0.02204335192545339,4.0,8.0,0.01285862195651448,48.0,10.0,0.17450986940983934,44.0,43.0,0.13309508700444206,0.0,1.0,0.0,0.0,0.0,0.0,111.0,70.0,0.30970909455262013,31.0,9.0,0.11205370562105474,"kirchhoff, k"
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+7,0.0,0.0,0.0,1.0,126.0,0.000259051698083881,14.0,52.0,0.018738072828067394,0.0,0.0,0.0,0.0,0.0,0.0,3.0,28.0,0.009066809432935835,0.0,0.0,0.0,4.0,13.0,0.009066809432935835,3.0,101.0,0.005440085659761501,42.0,72.0,0.06219831270993982,4.0,26.0,0.0060445396219572225,0.0,0.0,0.0,10.0,144.0,0.010577944338425141,62.0,158.0,0.07283670244458454,26.0,44.0,0.05582564093707636,15.0,79.0,0.017226937922578088,0.0,81.0,0.0,0.0,0.0,0.0,448.0,1261.0,0.6976607631663029,33.0,53.0,0.03505832980735189,"koehn, p"
+8,0.0,0.0,0.0,0.0,17.0,0.0,61.0,15.0,0.3799472295514512,0.0,0.0,0.0,0.0,0.0,0.0,3.0,9.0,0.021108179419525065,0.0,0.0,0.0,0.0,12.0,0.0,7.0,19.0,0.03693931398416886,49.0,7.0,0.3007915567282322,0.0,8.0,0.0,0.0,0.0,0.0,0.0,21.0,0.0,0.0,39.0,0.0,11.0,8.0,0.079155672823219,0.0,206.0,0.0,14.0,40.0,0.1108179419525066,0.0,0.0,0.0,10.0,12.0,0.06596306068601582,1.0,35.0,0.005277044854881266,"briscoe, t"
+9,0.0,0.0,0.0,3.0,26.0,0.004807658954524958,5.0,4.0,0.009259195023529548,0.0,0.0,0.0,0.0,0.0,0.0,0.0,97.0,0.0,0.0,0.0,0.0,0.0,7.0,0.0,0.0,0.0,0.0,13.0,10.0,0.07417530958409935,3.0,11.0,0.01923063581809983,0.0,0.0,0.0,5.0,31.0,0.03205105969683305,26.0,43.0,0.06980008556199198,1.0,57.0,0.0016025529848416526,23.0,34.0,0.04807658954524958,0.0,16.0,0.0,0.0,0.0,0.0,235.0,201.0,0.6483540878976032,18.0,21.0,0.09264282493322697,"banchs, r"
+10,0.0,0.0,0.0,0.0,43.0,0.0,25.0,77.0,0.09123667867821818,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,0.012880472283983745,0.0,0.0,0.0,2.0,0.0,0.008586981522655829,12.0,1.0,0.03864141685195123,1.0,11.0,0.004293490761327914,0.0,1.0,0.0,0.0,0.0,0.0,9.0,14.0,0.0354212987809553,8.0,8.0,0.012880472283983745,6.0,0.0,0.01771064939047765,23.0,7.0,0.047765084719773046,0.0,0.0,0.0,0.0,0.0,0.0,228.0,292.0,0.7169362876638808,4.0,18.0,0.0136471670627923,"de gispert, a"
+11,0.0,0.0,0.0,4.0,17.0,0.008665511265164644,4.0,0.0,0.006547275178124398,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,7.0,0.0,12.0,8.0,0.07328546669967814,0.0,54.0,0.0,0.0,0.0,0.0,2.0,2.0,0.01386481802426343,10.0,1.0,0.02002695936838051,1.0,22.0,0.0017331022530329288,22.0,1.0,0.0415944540727903,0.0,0.0,0.0,0.0,0.0,0.0,229.0,139.0,0.7329371956754973,18.0,0.0,0.10134521746306842,"lambert, p"
+12,0.0,0.0,0.0,3.0,0.0,0.005483748953498125,11.0,14.0,0.05808711854446163,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0077991096227528894,0.0,2.0,0.0,3.0,2.0,0.00710276054929281,0.0,0.0,0.0,0.0,0.0,0.0,10.0,0.0,0.05849332217064667,15.0,9.0,0.038183140861394356,2.0,0.0,0.004752582426365042,23.0,7.0,0.048744435142205556,0.0,1.0,0.0,0.0,0.0,0.0,249.0,107.0,0.7692647345090027,1.0,0.0,0.002089047220380238,"mari{\~n}o, j"
+13,0.0,0.0,0.0,0.0,9.0,0.0,76.0,55.0,0.6135593220338984,0.0,0.0,0.0,0.0,0.0,0.0,5.0,26.0,0.04067796610169492,0.0,0.0,0.0,0.0,6.0,0.0,0.0,1.0,0.0,6.0,9.0,0.061016949152542375,0.0,6.0,0.0,0.0,0.0,0.0,0.0,26.0,0.0,1.0,23.0,0.006779661016949152,2.0,6.0,0.02033898305084746,15.0,0.0,0.15254237288135594,0.0,22.0,0.0,0.0,0.0,0.0,0.0,49.0,0.0,11.0,4.0,0.10508474576271186,"menzel, w"
+14,0.0,0.0,0.0,0.0,48.0,0.0,7.0,23.0,0.060034305317324184,0.0,0.0,0.0,0.0,0.0,0.0,0.0,14.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,23.0,0.0,10.0,80.0,0.08404802744425387,0.0,109.0,0.0,0.0,0.0,0.0,18.0,6.0,0.10806174957118353,1.0,1.0,0.006003430531732418,4.0,152.0,0.0330188679245283,0.0,44.0,0.0,0.0,8.0,0.0,0.0,0.0,0.0,108.0,142.0,0.6668096054888508,7.0,38.0,0.04202401372212692,"sadat, f"
+15,0.0,0.0,0.0,2.0,51.0,0.01382852627419992,0.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,5.0,0.020742789411299884,0.0,0.0,0.0,1.0,2.0,0.002765705254839984,0.0,3.0,0.0,6.0,12.0,0.03318846305807981,0.0,1.0,0.0,0.0,0.0,0.0,14.0,31.0,0.07605689450809956,0.0,92.0,0.0,6.0,3.0,0.016594231529039907,5.0,9.0,0.03318846305807981,0.0,8.0,0.0,0.0,0.0,0.0,158.0,394.0,0.7649150533386012,6.0,44.0,0.03871987356775978,"foster, g"
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+17,0.0,0.0,0.0,4.0,40.0,0.013095535046146172,7.0,1.0,0.03863182838613121,0.0,0.0,0.0,0.0,0.0,0.0,7.0,18.0,0.017678972312297335,0.0,0.0,0.0,1.0,67.0,0.006547767523073086,2.0,8.0,0.008730356697430781,8.0,0.0,0.028810177101521582,0.0,22.0,0.0,0.0,0.0,0.0,17.0,2.0,0.10803816413070592,22.0,3.0,0.07137066600149665,9.0,25.0,0.032302319780493895,9.0,2.0,0.03055624844100773,143.0,38.0,0.45878024444998766,0.0,0.0,0.0,46.0,2.0,0.14136941880768272,12.0,0.0,0.04408830132202545,"martin, j"
+18,0.0,0.0,0.0,1.0,283.0,0.0018353113911660345,10.0,39.0,0.018353113911660345,0.0,0.0,0.0,0.0,0.0,0.0,31.0,395.0,0.06411354459806681,0.0,0.0,0.0,36.0,266.0,0.07402422611036338,5.0,188.0,0.014927199314817079,26.0,22.0,0.06080998409396794,27.0,431.0,0.058729964517313105,0.0,0.0,0.0,54.0,101.0,0.12418940413556831,37.0,180.0,0.08895142542518047,5.0,62.0,0.014682491129328276,78.0,70.0,0.1965006729475101,18.0,87.0,0.03854153921448673,0.0,0.0,0.0,12.0,140.0,0.023247277621436437,85.0,151.0,0.22109384558913492,"zhu, x"
+19,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.005876591576885407,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,86.0,34.0,0.6072477962781586,0.0,8.0,0.0,0.0,0.0,0.0,2.0,1.0,0.029382957884427033,3.0,0.0,0.026444662095984332,2.0,12.0,0.029382957884427033,0.0,0.0,0.0,13.0,82.0,0.11802154750244859,0.0,0.0,0.0,25.0,20.0,0.18364348677766895,0.0,0.0,0.0,"takeuchi, k"
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+45,0.0,0.0,0.0,0.0,125.0,0.0,27.0,49.0,0.3230769230769231,0.0,0.0,0.0,0.0,0.0,0.0,11.0,23.0,0.06769230769230769,0.0,0.0,0.0,8.0,33.0,0.07384615384615384,0.0,97.0,0.0,12.0,1.0,0.15384615384615383,8.0,58.0,0.08000000000000002,0.0,0.0,0.0,14.0,30.0,0.08615384615384615,0.0,47.0,0.0,0.0,11.0,0.0,9.0,94.0,0.10153846153846155,5.0,52.0,0.04,0.0,0.0,0.0,0.0,10.0,0.0,6.0,30.0,0.07384615384615385,"zhou, q"
+46,0.0,0.0,0.0,2.0,148.0,0.020689655172413793,23.0,42.0,0.08965517241379309,0.0,0.0,0.0,0.0,0.0,0.0,8.0,29.0,0.08275862068965517,0.0,0.0,0.0,4.0,34.0,0.020689655172413793,0.0,64.0,0.0,1.0,13.0,0.005172413793103448,1.0,140.0,0.010344827586206896,0.0,0.0,0.0,17.0,46.0,0.17586206896551723,0.0,93.0,0.0,26.0,80.0,0.2482758620689655,20.0,32.0,0.1672413793103448,0.0,25.0,0.0,0.0,0.0,0.0,38.0,165.0,0.17931034482758618,0.0,4.0,0.0,"singh, a"
+47,0.0,0.0,0.0,0.0,13.0,0.0,48.0,176.0,0.35342465753424657,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,1.0,0.0,9.0,10.0,0.09315068493150684,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.0,0.0,0.0,7.0,0.0,12.0,17.0,0.08493150684931505,0.0,8.0,0.0,28.0,0.0,0.2273972602739726,0.0,0.0,0.0,15.0,2.0,0.24109589041095889,0.0,1.0,0.0,"husain, s"
+48,0.0,0.0,0.0,4.0,213.0,0.005706134094151212,50.0,134.0,0.06405135520684734,0.0,0.0,0.0,0.0,0.0,0.0,50.0,198.0,0.04778887303851639,0.0,0.0,0.0,8.0,63.0,0.008559201141226817,0.0,358.0,0.0,343.0,264.0,0.4739895387541606,9.0,338.0,0.008559201141226817,0.0,0.0,0.0,58.0,422.0,0.06861626248216832,5.0,378.0,0.008559201141226817,21.0,196.0,0.028245363766048498,13.0,58.0,0.016880646695197333,167.0,129.0,0.2204469805040418,0.0,0.0,0.0,22.0,75.0,0.029101283880171178,13.0,67.0,0.01949595815501664,"baldwin, t"
+49,0.0,0.0,0.0,0.0,0.0,0.0,1.0,15.0,0.004898359049718345,0.0,0.0,0.0,0.0,0.0,0.0,3.0,11.0,0.014695077149155033,0.0,0.0,0.0,1.0,3.0,0.002939015429831007,0.0,0.0,0.0,32.0,14.0,0.16164584864070536,0.0,6.0,0.0,0.0,0.0,0.0,8.0,11.0,0.030124908155767825,28.0,11.0,0.15429831006612785,6.0,30.0,0.023512123438648055,3.0,9.0,0.02204261572373255,4.0,0.0,0.011756061719324027,0.0,0.0,0.0,109.0,67.0,0.57408768062699,0.0,0.0,0.0,"babych, b"
+50,0.0,0.0,0.0,0.0,89.0,0.0,15.0,22.0,0.043868200428933524,0.0,0.0,0.0,0.0,0.0,0.0,0.0,46.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,13.0,12.0,0.03801910703840905,90.0,28.0,0.46597777344511604,0.0,136.0,0.0,0.0,0.0,0.0,33.0,22.0,0.10333398323259897,1.0,73.0,0.005849093390524469,4.0,35.0,0.023396373562097877,8.0,3.0,0.033144862546305325,0.0,12.0,0.0,0.0,0.0,0.0,94.0,580.0,0.2864106063560149,0.0,12.0,0.0,"naskar, s"
+51,0.0,0.0,0.0,0.0,36.0,0.0,48.0,37.0,0.34790089470061936,0.0,0.0,0.0,0.0,0.0,0.0,2.0,69.0,0.004129387474191328,0.0,0.0,0.0,0.0,8.0,0.0,0.0,44.0,0.0,11.0,24.0,0.14693737095664142,0.0,28.0,0.0,0.0,0.0,0.0,18.0,81.0,0.24569855471438404,0.0,10.0,0.0,2.0,26.0,0.004817618719889883,3.0,15.0,0.03131452167928424,27.0,36.0,0.1059876118375774,0.0,0.0,0.0,2.0,53.0,0.021679284239504475,13.0,6.0,0.09153475567790777,"yuret, d"
+52,0.0,0.0,0.0,0.0,119.0,0.0,56.0,66.0,0.17924675730582906,0.0,0.0,0.0,0.0,0.0,0.0,13.0,111.0,0.022190967338646665,0.0,0.0,0.0,1.0,110.0,0.001562744178777934,5.0,145.0,0.02344116268166901,72.0,190.0,0.2212845757149555,1.0,333.0,0.0023441162681669013,0.0,0.0,0.0,29.0,196.0,0.09142053445850915,6.0,87.0,0.005860290670417253,129.0,614.0,0.2554305360212533,23.0,119.0,0.031020471948741996,42.0,183.0,0.0617283950617284,0.0,0.0,0.0,33.0,177.0,0.05055477418346617,17.0,62.0,0.05391467416783873,"diab, m"
+53,0.0,0.0,0.0,4.0,35.0,0.016840332596568783,20.0,15.0,0.06483528049678981,0.0,0.0,0.0,0.0,0.0,0.0,28.0,15.0,0.10367329754762655,0.0,0.0,0.0,7.0,83.0,0.029470582043995365,0.0,86.0,0.0,7.0,6.0,0.03473318598042312,6.0,2.0,0.025260498894853173,0.0,0.0,0.0,13.0,5.0,0.015156299336911904,22.0,16.0,0.07146616145668877,17.0,18.0,0.058941164087990744,23.0,10.0,0.08841174613198612,13.0,50.0,0.0333649089569519,0.0,0.0,0.0,53.0,38.0,0.2662877591832439,41.0,5.0,0.1915587832859699,"roukos, s"
+54,0.0,0.0,0.0,0.0,106.0,0.0,12.0,63.0,0.07598371777476257,0.0,0.0,0.0,0.0,0.0,0.0,15.0,14.0,0.06687342508238031,0.0,0.0,0.0,2.0,91.0,0.009304128707113784,0.0,159.0,0.0,19.0,18.0,0.09885636751308395,1.0,5.0,0.007753440589261485,0.0,0.0,0.0,24.0,14.0,0.14498933901918978,2.0,32.0,0.008722620662919171,14.0,21.0,0.1163016088389223,14.0,10.0,0.07016863733281645,10.0,58.0,0.0610583446404342,0.0,0.0,0.0,22.0,33.0,0.15506881178522972,26.0,14.0,0.18491955805388643,"florian, r"
+55,0.0,0.0,0.0,0.0,21.0,0.0,91.0,0.0,0.33651856959905435,0.0,0.0,0.0,0.0,0.0,0.0,17.0,12.0,0.043443995665451686,0.0,0.0,0.0,0.0,1.0,0.0,26.0,101.0,0.1024529602994779,4.0,6.0,0.009851246182642106,0.0,15.0,0.0,0.0,0.0,0.0,23.0,10.0,0.05713722785932421,3.0,8.0,0.00935868387351,14.0,30.0,0.04236035858536106,30.0,0.0,0.09693626243719831,3.0,0.0,0.004433060782188947,0.0,0.0,0.0,81.0,201.0,0.2541621515121663,11.0,9.0,0.04334548320362527,"hassan, h"
+56,0.0,0.0,0.0,3.0,144.0,0.004117916203673443,112.0,203.0,0.3478266553369501,0.0,0.0,0.0,0.0,0.0,0.0,10.0,84.0,0.013268841100725537,0.0,0.0,0.0,26.0,57.0,0.05216027191319694,0.0,97.0,0.0,57.0,85.0,0.09443754493757761,2.0,91.0,0.0032028237139682327,0.0,0.0,0.0,48.0,99.0,0.06661873325053926,0.0,43.0,0.0,19.0,35.0,0.025348061964834304,55.0,168.0,0.06861886397803778,3.0,101.0,0.008235832407346886,0.0,0.0,0.0,47.0,81.0,0.18988169161383095,63.0,6.0,0.12628276357931892,"lee, y"
+57,0.0,0.0,0.0,0.0,87.0,0.0,81.0,113.0,0.32342657342657344,0.0,0.0,0.0,0.0,0.0,0.0,4.0,115.0,0.007867132867132868,0.0,0.0,0.0,1.0,34.0,0.005244755244755245,1.0,75.0,0.0034965034965034965,1.0,8.0,0.0034965034965034965,1.0,44.0,0.005244755244755245,0.0,0.0,0.0,38.0,27.0,0.13986013986013984,0.0,13.0,0.0,54.0,121.0,0.2701048951048951,1.0,102.0,0.0008741258741258741,4.0,20.0,0.01748251748251748,0.0,0.0,0.0,47.0,20.0,0.22290209790209792,0.0,17.0,0.0,"xia, f"
+58,0.0,0.0,0.0,0.0,0.0,0.0,45.0,3.0,0.15280898876404492,0.0,0.0,0.0,0.0,0.0,0.0,2.0,121.0,0.0067415730337078645,0.0,0.0,0.0,31.0,21.0,0.08112359550561797,22.0,3.0,0.07191011235955055,20.0,14.0,0.0502247191011236,2.0,0.0,0.00449438202247191,0.0,0.0,0.0,60.0,33.0,0.17011235955056178,3.0,1.0,0.007865168539325841,4.0,56.0,0.014831460674157304,4.0,0.0,0.00898876404494382,38.0,3.0,0.10449438202247191,0.0,0.0,0.0,72.0,6.0,0.22752808988764042,41.0,15.0,0.09887640449438201,"lin, d"
+59,0.0,0.0,0.0,0.0,48.0,0.0,80.0,106.0,0.23772915434654052,0.0,0.0,0.0,0.0,0.0,0.0,3.0,34.0,0.007096392667060911,0.0,0.0,0.0,2.0,4.0,0.007096392667060911,3.0,23.0,0.008870490833826138,15.0,12.0,0.03784742755765819,1.0,85.0,0.0035481963335304554,0.0,0.0,0.0,10.0,21.0,0.03075103489059728,7.0,65.0,0.03311649911295091,6.0,32.0,0.016558249556475457,15.0,21.0,0.03784742755765819,4.0,8.0,0.013010053222945003,0.0,0.0,0.0,119.0,314.0,0.4458900059136606,43.0,61.0,0.12063867534003549,"cherry, c"
+60,0.0,0.0,0.0,4.0,220.0,0.01001115277546038,21.0,158.0,0.08592906132270162,0.0,0.0,0.0,0.0,0.0,0.0,13.0,40.0,0.04698216434097635,0.0,0.0,0.0,1.0,274.0,0.00166852546257673,7.0,186.0,0.017519517357055668,1.0,12.0,0.00166852546257673,1.0,11.0,0.002502788193865095,0.0,0.0,0.0,12.0,146.0,0.015016729163190572,7.0,71.0,0.013304295135809191,11.0,60.0,0.032869951612761586,26.0,9.0,0.055061340265032094,73.0,191.0,0.21857683559755164,0.0,0.0,0.0,6.0,43.0,0.02002230555092076,213.0,203.0,0.4788668077595215,"mccallum, a"
+61,0.0,0.0,0.0,0.0,766.0,0.0,19.0,83.0,0.05046071967590753,0.0,0.0,0.0,0.0,0.0,0.0,28.0,714.0,0.03120780046071967,0.0,0.0,0.0,2.0,235.0,0.005560409881642703,8.0,718.0,0.03336245928985622,24.0,31.0,0.07228532846135513,0.0,236.0,0.0,0.0,0.0,0.0,65.0,221.0,0.13344983715942488,30.0,330.0,0.10981809516244338,9.0,97.0,0.007774644530939708,70.0,262.0,0.17167765509571845,41.0,191.0,0.10523075701008816,0.0,0.0,0.0,92.0,872.0,0.2032726983874811,27.0,163.0,0.0758995948844229,"zhang, j"
+62,0.0,0.0,0.0,1.0,445.0,0.0023712915634715712,35.0,157.0,0.0664752068293197,0.0,0.0,0.0,0.0,0.0,0.0,39.0,576.0,0.04729409284923856,0.0,0.0,0.0,16.0,474.0,0.05216841439637457,2.0,628.0,0.002766506824050166,78.0,137.0,0.18298466564788957,26.0,155.0,0.04228803288190968,0.0,0.0,0.0,72.0,148.0,0.14056489434578703,22.0,235.0,0.031748959266480484,11.0,95.0,0.013516361911787957,113.0,311.0,0.20543289244875376,34.0,229.0,0.05991463350371503,0.0,0.0,0.0,16.0,442.0,0.035095115139379254,79.0,157.0,0.11737893239184276,"chen, j"
+63,0.0,0.0,0.0,0.0,19.0,0.0,12.0,7.0,0.1091679915209327,0.0,0.0,0.0,0.0,0.0,0.0,1.0,5.0,0.005564387917329093,0.0,0.0,0.0,32.0,17.0,0.27821939586645467,0.0,10.0,0.0,6.0,13.0,0.06677265500794913,0.0,25.0,0.0,0.0,0.0,0.0,35.0,10.0,0.3404875463698993,4.0,17.0,0.03974562798092209,12.0,13.0,0.09326974032856383,0.0,5.0,0.0,3.0,3.0,0.033386327503974564,0.0,0.0,0.0,2.0,42.0,0.033386327503974564,0.0,0.0,0.0,"vilnat, a"
+64,0.0,0.0,0.0,6.0,70.0,0.011623136879529605,1.0,26.0,0.004102283604539861,0.0,0.0,0.0,0.0,0.0,0.0,23.0,308.0,0.04478326268289348,0.0,0.0,0.0,12.0,86.0,0.016682619991795433,2.0,37.0,0.004102283604539861,103.0,51.0,0.14838871416199462,18.0,393.0,0.0382879803090387,0.0,0.0,0.0,118.0,181.0,0.25946943798714617,69.0,121.0,0.16238205934636946,47.0,83.0,0.0823875290578422,7.0,113.0,0.016409134418159443,70.0,89.0,0.12990564747709557,0.0,0.0,0.0,17.0,12.0,0.02712065271890241,20.0,29.0,0.05435525776015315,"mihalcea, r"
+65,0.0,0.0,0.0,0.0,13.0,0.0,4.0,2.0,0.015457277801631603,0.0,0.0,0.0,0.0,0.0,0.0,1.0,20.0,0.0012881064834693002,0.0,0.0,0.0,1.0,14.0,0.005152425933877201,0.0,5.0,0.0,107.0,39.0,0.37355088020609706,0.0,205.0,0.0,0.0,0.0,0.0,167.0,97.0,0.4413911550021468,4.0,21.0,0.010304851867754402,5.0,29.0,0.012881064834693003,3.0,0.0,0.007728638900815802,17.0,30.0,0.03349076857020181,0.0,0.0,0.0,24.0,3.0,0.05753542292829541,9.0,10.0,0.041219407471017606,"pedersen, t"
+66,0.0,0.0,0.0,4.0,154.0,0.01590372942455006,27.0,74.0,0.0912192480565264,0.0,0.0,0.0,0.0,0.0,0.0,12.0,484.0,0.011624868888897303,0.0,0.0,0.0,20.0,172.0,0.019595666612392034,0.0,182.0,0.0,155.0,53.0,0.3015082036737615,92.0,166.0,0.20277255016301324,0.0,0.0,0.0,36.0,97.0,0.053675086807856445,17.0,128.0,0.03626050308797413,13.0,60.0,0.012741916550859747,28.0,99.0,0.04294006944628516,69.0,74.0,0.13557929334428925,0.0,0.0,0.0,6.0,185.0,0.018686882073846316,42.0,57.0,0.05749198186974845,"kim, s"
+67,0.0,0.0,0.0,4.0,6.0,0.010431219148738008,57.0,32.0,0.2008941044984633,0.0,0.0,0.0,0.0,0.0,0.0,31.0,14.0,0.06091086895780944,0.0,0.0,0.0,0.0,2.0,0.0,0.0,5.0,0.0,14.0,30.0,0.04498463257893266,0.0,2.0,0.0,0.0,0.0,0.0,16.0,42.0,0.04563658377572879,8.0,0.0,0.024122194281456642,14.0,83.0,0.04936201918599236,17.0,5.0,0.03688181056160939,131.0,93.0,0.47853217844835616,0.0,0.0,0.0,5.0,14.0,0.018254633510291517,8.0,1.0,0.029989755052621775,"nugues, p"
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+73,0.0,0.0,0.0,0.0,70.0,0.0,2.0,13.0,0.009189842805320435,0.0,0.0,0.0,0.0,0.0,0.0,30.0,271.0,0.16154776299879078,0.0,0.0,0.0,1.0,52.0,0.004594921402660218,1.0,30.0,0.002297460701330109,0.0,43.0,0.0,0.0,21.0,0.0,0.0,0.0,0.0,4.0,18.0,0.01837968561064087,5.0,31.0,0.01366384522370012,22.0,72.0,0.10108827085852479,8.0,18.0,0.04365175332527207,0.0,92.0,0.0,0.0,0.0,0.0,118.0,19.0,0.6203143893591294,4.0,3.0,0.025272067714631197,"voss, c"
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+87,0.0,0.0,0.0,0.0,4.0,0.0,2.0,8.0,0.01699029126213592,0.0,0.0,0.0,0.0,0.0,0.0,6.0,23.0,0.0412621359223301,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.0,0.0,8.0,23.0,0.05825242718446603,3.0,213.0,0.02184466019417476,0.0,0.0,0.0,8.0,15.0,0.05825242718446603,2.0,24.0,0.00970873786407767,2.0,44.0,0.014563106796116507,3.0,12.0,0.029126213592233014,7.0,1.0,0.06796116504854369,0.0,0.0,0.0,51.0,0.0,0.4563106796116505,16.0,8.0,0.22572815533980586,"aramaki, e"
+88,0.0,0.0,0.0,4.0,174.0,0.004840689452483445,78.0,307.0,0.12283249485676742,0.0,0.0,0.0,0.0,0.0,0.0,47.0,119.0,0.061373026986843694,0.0,0.0,0.0,12.0,132.0,0.015732240720571198,1.0,115.0,0.0012101723631208613,123.0,139.0,0.21474508583579688,4.0,75.0,0.006655947997164738,0.0,0.0,0.0,80.0,99.0,0.12622097747350586,18.0,71.0,0.023027851252528393,29.0,85.0,0.02188683159587158,28.0,171.0,0.026926335079439166,133.0,295.0,0.20542675863976625,0.0,0.0,0.0,102.0,653.0,0.1346316753971958,21.0,74.0,0.03448991234894455,"kurohashi, s"
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+230,0.0,0.0,0.0,2.0,37.0,0.024390243902439025,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,11.0,10.0,0.10162601626016259,0.0,0.0,0.0,3.0,10.0,0.036585365853658534,0.0,0.0,0.0,63.0,58.0,0.5934959349593496,0.0,12.0,0.0,0.0,0.0,0.0,11.0,12.0,0.1138211382113821,2.0,3.0,0.016260162601626015,5.0,27.0,0.05691056910569105,0.0,5.0,0.0,4.0,28.0,0.03252032520325203,0.0,0.0,0.0,0.0,1.0,0.0,3.0,0.0,0.024390243902439025,"lafourcade, m"
+231,0.0,0.0,0.0,0.0,67.0,0.0,7.0,10.0,0.04387504387504388,0.0,0.0,0.0,0.0,0.0,0.0,0.0,41.0,0.0,0.0,0.0,0.0,0.0,65.0,0.0,0.0,85.0,0.0,14.0,16.0,0.06212706212706213,0.0,83.0,0.0,0.0,0.0,0.0,11.0,121.0,0.08845208845208845,7.0,37.0,0.03474903474903475,8.0,6.0,0.08950508950508951,15.0,5.0,0.11232011232011231,26.0,43.0,0.22815022815022812,0.0,0.0,0.0,39.0,60.0,0.19866619866619867,13.0,26.0,0.14215514215514216,"zhao, s"
+232,0.0,0.0,0.0,35.0,542.0,0.04405607800570996,104.0,144.0,0.0789269654209955,0.0,0.0,0.0,0.0,0.0,0.0,44.0,452.0,0.05395273320989118,0.0,0.0,0.0,44.0,381.0,0.03977816898196711,1.0,415.0,0.0007661926609688689,53.0,48.0,0.05165415522698457,24.0,591.0,0.043098337179498875,0.0,0.0,0.0,86.0,274.0,0.09756186549670263,58.0,313.0,0.05382503443306304,12.0,88.0,0.011301341749290816,125.0,463.0,0.18420548557459887,154.0,485.0,0.1213594446927476,0.0,0.0,0.0,188.0,210.0,0.16619995804183046,53.0,370.0,0.053314239325750454,"li, s"
+233,0.0,0.0,0.0,0.0,20.0,0.0,5.0,21.0,0.02840909090909091,0.0,0.0,0.0,0.0,0.0,0.0,0.0,68.0,0.0,0.0,0.0,0.0,50.0,24.0,0.2840909090909091,0.0,1.0,0.0,0.0,31.0,0.0,84.0,197.0,0.4772727272727273,0.0,0.0,0.0,0.0,19.0,0.0,1.0,81.0,0.005681818181818183,12.0,50.0,0.06818181818181819,6.0,40.0,0.034090909090909095,4.0,53.0,0.02272727272727273,0.0,0.0,0.0,0.0,6.0,0.0,14.0,1.0,0.07954545454545454,"ku, l"
+234,0.0,0.0,0.0,0.0,125.0,0.0,22.0,38.0,0.035869565217391305,0.0,0.0,0.0,0.0,0.0,0.0,9.0,39.0,0.017934782608695653,0.0,0.0,0.0,24.0,10.0,0.04184782608695652,7.0,19.0,0.014673913043478263,21.0,10.0,0.04782608695652174,0.0,58.0,0.0,0.0,0.0,0.0,175.0,55.0,0.3554347826086957,39.0,30.0,0.08315217391304348,13.0,9.0,0.04782608695652174,45.0,35.0,0.10597826086956523,59.0,100.0,0.13260869565217392,0.0,0.0,0.0,1.0,5.0,0.002173913043478261,62.0,45.0,0.11467391304347829,"ji, d"
+235,0.0,0.0,0.0,0.0,76.0,0.0,84.0,51.0,0.10909555442003063,0.0,0.0,0.0,0.0,0.0,0.0,5.0,117.0,0.009439050701186622,0.0,0.0,0.0,0.0,84.0,0.0,7.0,80.0,0.013413387838528358,16.0,23.0,0.030304320672230732,4.0,86.0,0.007948674274683471,0.0,0.0,0.0,134.0,51.0,0.2481973542269914,5.0,89.0,0.006756373133480951,1.0,27.0,0.0011923011412025208,18.0,36.0,0.027820359961392146,207.0,157.0,0.3209845000851643,0.0,0.0,0.0,73.0,118.0,0.09111167887355928,84.0,36.0,0.1337364446715494,"tan, c"
+236,0.0,0.0,0.0,5.0,87.0,0.02702702702702703,7.0,2.0,0.036036036036036036,0.0,0.0,0.0,0.0,0.0,0.0,76.0,701.0,0.4558558558558558,0.0,0.0,0.0,4.0,20.0,0.043243243243243246,0.0,9.0,0.0,1.0,30.0,0.010810810810810811,0.0,3.0,0.0,0.0,0.0,0.0,1.0,27.0,0.0036036036036036032,3.0,31.0,0.010810810810810811,20.0,36.0,0.07567567567567568,27.0,57.0,0.13153153153153152,32.0,105.0,0.20180180180180182,0.0,0.0,0.0,0.0,9.0,0.0,1.0,7.0,0.0036036036036036032,"schlangen, d"
+237,0.0,0.0,0.0,2.0,758.0,0.0057821554173289455,76.0,155.0,0.19295878649829162,0.0,0.0,0.0,0.0,0.0,0.0,17.0,726.0,0.08962340896859865,0.0,0.0,0.0,1.0,545.0,0.0028910777086644727,10.0,913.0,0.03392197844832981,18.0,73.0,0.05203939875596051,2.0,444.0,0.0057821554173289455,0.0,0.0,0.0,23.0,215.0,0.08095017584260523,0.0,189.0,0.0,34.0,232.0,0.09829664209459207,73.0,363.0,0.22550406127582887,40.0,386.0,0.09003642006983643,0.0,0.0,0.0,38.0,314.0,0.052827874494687184,22.0,351.0,0.06938586500794733,"li, y"
+238,0.0,0.0,0.0,2.0,33.0,0.009081277433025578,67.0,22.0,0.22703193582563944,0.0,0.0,0.0,0.0,0.0,0.0,20.0,21.0,0.06281216891176024,0.0,0.0,0.0,0.0,5.0,0.0,0.0,3.0,0.0,21.0,37.0,0.056757983956409866,0.0,164.0,0.0,0.0,0.0,0.0,65.0,27.0,0.18692296049644314,21.0,58.0,0.05827153019524745,6.0,53.0,0.022703193582563945,30.0,94.0,0.08551536249432419,43.0,6.0,0.12668382019070681,0.0,0.0,0.0,18.0,88.0,0.04767670652338429,39.0,5.0,0.11654306039049493,"van den bosch, a"
+239,0.0,0.0,0.0,2.0,17.0,0.010573618821041502,0.0,18.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,24.0,59.0,0.08353158868622787,0.0,0.0,0.0,15.0,2.0,0.11419508326724824,1.0,18.0,0.005286809410520751,21.0,11.0,0.11102299762093579,1.0,122.0,0.003965107057890564,0.0,0.0,0.0,2.0,28.0,0.010573618821041502,3.0,9.0,0.01145475372279496,34.0,13.0,0.14670896114195087,35.0,54.0,0.18151378976121244,21.0,29.0,0.13877874702616974,0.0,0.0,0.0,19.0,9.0,0.10044937879989427,16.0,3.0,0.08194554586307164,"narayanan, s"
+240,0.0,0.0,0.0,2.0,216.0,0.003440975538909278,17.0,40.0,0.04301219423636597,0.0,0.0,0.0,0.0,0.0,0.0,24.0,217.0,0.06439539937101649,0.0,0.0,0.0,19.0,56.0,0.05548573056491211,5.0,193.0,0.010753048559091493,27.0,37.0,0.06004762995361759,33.0,157.0,0.11183170501455153,0.0,0.0,0.0,60.0,89.0,0.16416320800213013,20.0,132.0,0.04860377948709355,49.0,92.0,0.06954941468037842,100.0,50.0,0.1987368193692662,27.0,49.0,0.07025325058606442,0.0,0.0,0.0,24.0,292.0,0.04854233349532731,26.0,122.0,0.05118451114127552,"yu, h"
+241,0.0,0.0,0.0,0.0,1.0,0.0,21.0,2.0,0.04570575056011949,0.0,0.0,0.0,0.0,0.0,0.0,8.0,7.0,0.022404779686333084,0.0,0.0,0.0,1.0,3.0,0.004480955937266617,1.0,4.0,0.004480955937266617,99.0,71.0,0.2840926064227035,14.0,20.0,0.02598954443614638,0.0,0.0,0.0,30.0,14.0,0.12322628827483197,20.0,5.0,0.050784167289021666,13.0,7.0,0.04480955937266617,74.0,29.0,0.2025392083644511,20.0,11.0,0.042120985810306204,0.0,0.0,0.0,62.0,69.0,0.14936519790888722,0.0,0.0,0.0,"kwong, o"
+242,0.0,0.0,0.0,0.0,138.0,0.0,2.0,49.0,0.01083130246412131,0.0,0.0,0.0,0.0,0.0,0.0,6.0,64.0,0.018954779312212292,0.0,0.0,0.0,1.0,116.0,0.005415651232060655,0.0,148.0,0.0,22.0,49.0,0.1232060655293799,0.0,99.0,0.0,0.0,0.0,0.0,51.0,178.0,0.18413214189006227,5.0,8.0,0.031139994584348768,9.0,45.0,0.03411860276198213,33.0,65.0,0.16978066612510154,12.0,186.0,0.08394259409694015,0.0,0.0,0.0,45.0,4.0,0.1868399675060926,28.0,69.0,0.15163823449769834,"sun, l"
+243,0.0,0.0,0.0,0.0,143.0,0.0,9.0,73.0,0.04261572373254959,0.0,0.0,0.0,0.0,0.0,0.0,9.0,75.0,0.03379867744305657,0.0,0.0,0.0,2.0,54.0,0.011756061719324026,0.0,531.0,0.0,149.0,385.0,0.639235855988244,0.0,21.0,0.0,0.0,0.0,0.0,4.0,244.0,0.01763409257898604,8.0,60.0,0.03747244673034533,22.0,119.0,0.07200587803085966,8.0,14.0,0.03820720058780308,15.0,163.0,0.05731080088170462,0.0,0.0,0.0,10.0,71.0,0.032329169728141066,4.0,77.0,0.01763409257898604,"korhonen, a"
+244,0.0,0.0,0.0,0.0,38.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,5.0,0.03428571428571429,0.0,0.0,0.0,15.0,59.0,0.11714285714285715,0.0,9.0,0.0,8.0,3.0,0.06857142857142857,0.0,2.0,0.0,0.0,0.0,0.0,36.0,52.0,0.29428571428571426,16.0,171.0,0.11714285714285715,14.0,0.0,0.09714285714285714,0.0,4.0,0.0,14.0,54.0,0.11714285714285715,0.0,0.0,0.0,2.0,1.0,0.017142857142857144,22.0,26.0,0.13714285714285715,"chali, y"
+245,0.0,0.0,0.0,0.0,186.0,0.0,0.0,78.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,205.0,0.020066889632107024,0.0,0.0,0.0,15.0,114.0,0.137123745819398,0.0,419.0,0.0,8.0,23.0,0.0802675585284281,0.0,71.0,0.0,0.0,0.0,0.0,24.0,55.0,0.22408026755852845,16.0,133.0,0.137123745819398,13.0,28.0,0.10367892976588629,0.0,49.0,0.0,14.0,200.0,0.137123745819398,0.0,0.0,0.0,2.0,133.0,0.020066889632107024,20.0,120.0,0.14046822742474918,"joty, s"
+246,0.0,0.0,0.0,2.0,67.0,0.021337126600284494,6.0,3.0,0.04800853485064011,0.0,0.0,0.0,0.0,0.0,0.0,9.0,19.0,0.059032716927453766,0.0,0.0,0.0,20.0,30.0,0.13940256045519203,0.0,11.0,0.0,8.0,35.0,0.03520625889046942,0.0,70.0,0.0,0.0,0.0,0.0,12.0,35.0,0.06721194879089616,14.0,41.0,0.0995732574679943,23.0,26.0,0.18029871977240397,1.0,23.0,0.005334281650071123,42.0,27.0,0.33499288762446655,0.0,0.0,0.0,2.0,99.0,0.009601706970128023,0.0,1.0,0.0,"mitkov, r"
+247,0.0,0.0,0.0,0.0,44.0,0.0,5.0,15.0,0.022798176145908327,0.0,0.0,0.0,0.0,0.0,0.0,11.0,40.0,0.05279577633789297,0.0,0.0,0.0,98.0,215.0,0.4871610271178305,0.0,24.0,0.0,0.0,26.0,0.0,3.0,8.0,0.02159827213822894,0.0,0.0,0.0,18.0,40.0,0.10319174466042716,2.0,77.0,0.010079193664506839,19.0,29.0,0.09719222462203024,20.0,6.0,0.09599232061435084,15.0,32.0,0.04463642908567315,0.0,0.0,0.0,6.0,6.0,0.021358291336693064,9.0,15.0,0.04319654427645788,"nyberg, e"
+248,0.0,0.0,0.0,3.0,611.0,0.009978380176284716,9.0,95.0,0.03858307001496757,0.0,0.0,0.0,0.0,0.0,0.0,2.0,428.0,0.0066522534508564775,0.0,0.0,0.0,12.0,236.0,0.02993514052885415,0.0,439.0,0.0,9.0,54.0,0.061865957092965246,0.0,249.0,0.0,0.0,0.0,0.0,21.0,186.0,0.08115749210044902,0.0,264.0,0.0,5.0,75.0,0.014967570264427075,69.0,155.0,0.1979045401629802,0.0,242.0,0.0,0.0,0.0,0.0,178.0,505.0,0.5140528854149343,11.0,162.0,0.044902710793281224,"xu, j"
+249,0.0,0.0,0.0,0.0,26.0,0.0,34.0,2.0,0.1023391812865497,0.0,0.0,0.0,0.0,0.0,0.0,16.0,39.0,0.04093567251461988,0.0,0.0,0.0,20.0,16.0,0.05360623781676413,6.0,7.0,0.02046783625730994,11.0,8.0,0.029239766081871343,31.0,191.0,0.07992202729044834,0.0,0.0,0.0,18.0,14.0,0.038011695906432746,14.0,14.0,0.020955165692007796,7.0,56.0,0.02046783625730994,7.0,100.0,0.016569200779727095,92.0,351.0,0.3961988304093567,0.0,0.0,0.0,5.0,2.0,0.008771929824561403,57.0,64.0,0.17251461988304093,"ng, v"
+250,0.0,0.0,0.0,0.0,191.0,0.0,29.0,83.0,0.18449197860962568,0.0,0.0,0.0,0.0,0.0,0.0,4.0,78.0,0.0213903743315508,0.0,0.0,0.0,32.0,129.0,0.16844919786096257,28.0,149.0,0.18716577540106952,0.0,6.0,0.0,0.0,40.0,0.0,0.0,0.0,0.0,3.0,70.0,0.016042780748663103,1.0,41.0,0.0053475935828877,6.0,34.0,0.0374331550802139,8.0,52.0,0.06417112299465241,27.0,63.0,0.15106951871657753,0.0,0.0,0.0,0.0,97.0,0.0,23.0,124.0,0.1644385026737968,"huang, z"
+251,0.0,0.0,0.0,0.0,6.0,0.0,0.0,32.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,12.0,29.0,0.04995836802664447,0.0,0.0,0.0,39.0,34.0,0.13322231473771856,0.0,38.0,0.0,82.0,102.0,0.3880099916736053,2.0,69.0,0.009991673605328892,0.0,0.0,0.0,69.0,91.0,0.29059117402164863,2.0,34.0,0.00832639467110741,8.0,25.0,0.03164029975020816,4.0,85.0,0.019983347210657785,15.0,121.0,0.05745212323064113,0.0,0.0,0.0,4.0,7.0,0.010824313072439633,0.0,33.0,0.0,"hirst, g"
+252,0.0,0.0,0.0,4.0,151.0,0.012066365007541475,25.0,95.0,0.08295625942684764,0.0,0.0,0.0,0.0,0.0,0.0,12.0,67.0,0.03619909502262443,0.0,0.0,0.0,18.0,72.0,0.05429864253393664,2.0,57.0,0.006033182503770738,27.0,93.0,0.0965309200603318,22.0,97.0,0.05656108597285067,0.0,0.0,0.0,27.0,62.0,0.10799396681749622,95.0,177.0,0.36108597285067867,9.0,22.0,0.02639517345399698,1.0,43.0,0.004524886877828054,18.0,79.0,0.055806938159879325,0.0,0.0,0.0,2.0,99.0,0.009049773755656108,29.0,34.0,0.09049773755656107,"okumura, m"
+253,0.0,0.0,0.0,0.0,23.0,0.0,1.0,3.0,0.007451564828614008,0.0,0.0,0.0,0.0,0.0,0.0,0.0,17.0,0.0,0.0,0.0,0.0,23.0,28.0,0.1213540557802853,1.0,0.0,0.0031935277836917177,31.0,47.0,0.22301469022780498,53.0,180.0,0.26751117734724295,0.0,0.0,0.0,38.0,42.0,0.24105812220566314,10.0,10.0,0.04657228017883755,9.0,60.0,0.04247391952309985,0.0,1.0,0.0,1.0,60.0,0.007451564828614008,0.0,0.0,0.0,0.0,3.0,0.0,5.0,15.0,0.039919097296146476,"montoyo, a"
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+255,0.0,0.0,0.0,0.0,60.0,0.0,7.0,19.0,0.0516795865633075,0.0,0.0,0.0,0.0,0.0,0.0,2.0,127.0,0.015503875968992248,0.0,0.0,0.0,0.0,108.0,0.0,0.0,57.0,0.0,2.0,25.0,0.015503875968992248,0.0,82.0,0.0,0.0,0.0,0.0,37.0,95.0,0.2868217054263566,27.0,77.0,0.20930232558139536,2.0,53.0,0.010335917312661497,0.0,25.0,0.0,7.0,89.0,0.05426356589147287,0.0,0.0,0.0,33.0,150.0,0.22222222222222224,18.0,68.0,0.1343669250645995,"daum{\'e} iii, h"
+256,0.0,0.0,0.0,0.0,669.0,0.0,7.0,310.0,0.01780264496439471,0.0,0.0,0.0,0.0,0.0,0.0,10.0,634.0,0.057364078218605175,0.0,0.0,0.0,7.0,641.0,0.014581213970837574,1.0,617.0,0.005934214988131571,26.0,38.0,0.06993896236012208,15.0,359.0,0.08307900983384199,0.0,0.0,0.0,5.0,238.0,0.017520063298293205,3.0,278.0,0.011868429976263141,0.0,103.0,0.0,109.0,224.0,0.3958121397083757,15.0,280.0,0.037385554425228895,0.0,0.0,0.0,36.0,314.0,0.13244602690177462,33.0,297.0,0.15626766135413134,"wang, z"
+257,0.0,0.0,0.0,4.0,44.0,0.01664816870144284,5.0,2.0,0.012486126526082131,0.0,0.0,0.0,0.0,0.0,0.0,11.0,61.0,0.04328523862375139,0.0,0.0,0.0,0.0,10.0,0.0,0.0,28.0,0.0,0.0,140.0,0.0,0.0,170.0,0.0,0.0,0.0,0.0,27.0,32.0,0.1838235294117647,59.0,206.0,0.29994450610432855,14.0,82.0,0.05674250832408436,2.0,12.0,0.013873473917869035,34.0,30.0,0.3440621531631521,0.0,0.0,0.0,5.0,36.0,0.029134295227524976,0.0,4.0,0.0,"saggion, h"
+258,0.0,0.0,0.0,4.0,453.0,0.0058606341206118495,21.0,93.0,0.04922932661313954,0.0,0.0,0.0,0.0,0.0,0.0,81.0,524.0,0.06997597140010549,0.0,0.0,0.0,66.0,308.0,0.07736037039207642,4.0,392.0,0.004395475590458888,23.0,119.0,0.032819551075426355,104.0,530.0,0.12952001406552188,0.0,0.0,0.0,145.0,178.0,0.17253706851081288,25.0,142.0,0.03692199495985466,27.0,117.0,0.029889234015120437,128.0,577.0,0.25347242571646256,42.0,297.0,0.043368692492527686,0.0,0.0,0.0,33.0,216.0,0.05040145343726191,43.0,189.0,0.04424778761061947,"chen, h"
+259,0.0,0.0,0.0,6.0,10.0,0.06905812392096682,2.0,0.0,0.007673124880107424,0.0,0.0,0.0,0.0,0.0,0.0,16.0,3.0,0.11241127949357375,0.0,0.0,0.0,1.0,1.0,0.0023019374640322276,0.0,4.0,0.0,3.0,15.0,0.015346249760214849,0.0,0.0,0.0,0.0,0.0,0.0,43.0,18.0,0.4565509303663917,6.0,15.0,0.030692499520429697,5.0,10.0,0.057548436600805684,0.0,22.0,0.0,2.0,2.0,0.007673124880107424,0.0,0.0,0.0,41.0,242.0,0.22923460579320928,1.0,1.0,0.011509687320161137,"max, a"
+260,0.0,0.0,0.0,3.0,93.0,0.016981132075471698,75.0,182.0,0.5207547169811321,0.0,0.0,0.0,0.0,0.0,0.0,10.0,151.0,0.0509433962264151,0.0,0.0,0.0,1.0,75.0,0.0056603773584905665,0.0,20.0,0.0,37.0,34.0,0.1471698113207547,3.0,12.0,0.022641509433962266,0.0,0.0,0.0,0.0,14.0,0.0,1.0,85.0,0.0056603773584905665,1.0,24.0,0.0056603773584905665,0.0,28.0,0.0,40.0,53.0,0.22452830188679246,0.0,0.0,0.0,0.0,26.0,0.0,0.0,4.0,0.0,"gardent, c"
+261,0.0,0.0,0.0,0.0,0.0,0.0,88.0,64.0,0.8176670441676104,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,21.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,3.0,5.0,0.03963759909399773,0.0,0.0,0.0,10.0,6.0,0.13703284258210646,0.0,0.0,0.0,1.0,0.0,0.0056625141562853904,0.0,0.0,0.0,"parmentier, y"
+262,0.0,0.0,0.0,0.0,0.0,0.0,16.0,0.0,0.2080200501253133,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,1.0,17.0,0.015037593984962405,59.0,3.0,0.3558897243107769,0.0,2.0,0.0,0.0,0.0,0.0,23.0,51.0,0.16666666666666666,0.0,2.0,0.0,6.0,8.0,0.09022556390977443,1.0,4.0,0.0037593984962406013,22.0,7.0,0.15037593984962405,0.0,0.0,0.0,1.0,8.0,0.005012531328320802,1.0,5.0,0.005012531328320802,"preiss, j"
+263,0.0,0.0,0.0,10.0,19.0,0.022209234365867917,11.0,104.0,0.04558737580362362,0.0,0.0,0.0,0.0,0.0,0.0,49.0,32.0,0.16656925774400938,0.0,0.0,0.0,5.0,5.0,0.031560490940970194,0.0,40.0,0.0,2.0,21.0,0.0058445353594389245,0.0,5.0,0.0,0.0,0.0,0.0,21.0,46.0,0.06428988895382817,1.0,4.0,0.003506721215663355,181.0,82.0,0.6183518410286383,1.0,99.0,0.003506721215663355,7.0,5.0,0.017533606078316777,0.0,0.0,0.0,4.0,80.0,0.021040327293980133,0.0,7.0,0.0,"bird, s"
+264,0.0,0.0,0.0,7.0,223.0,0.014126289023873428,20.0,29.0,0.1356123746291849,0.0,0.0,0.0,0.0,0.0,0.0,23.0,219.0,0.05452747563215143,0.0,0.0,0.0,15.0,176.0,0.04237886707162029,1.0,338.0,0.004237886707162028,59.0,15.0,0.16386495267693177,0.0,121.0,0.0,0.0,0.0,0.0,59.0,42.0,0.16061590620144087,3.0,121.0,0.005085464048594435,77.0,88.0,0.15877948862833732,32.0,118.0,0.10086170363045628,22.0,59.0,0.14408814804350897,0.0,0.0,0.0,3.0,78.0,0.007345670292414183,2.0,55.0,0.008475773414324057,"lee, h"
+265,0.0,0.0,0.0,0.0,109.0,0.0,1.0,21.0,0.006888633754305395,0.0,0.0,0.0,0.0,0.0,0.0,12.0,84.0,0.09644087256027553,0.0,0.0,0.0,2.0,69.0,0.018369690011481053,2.0,68.0,0.01377726750861079,2.0,12.0,0.011481056257175657,2.0,96.0,0.01377726750861079,0.0,0.0,0.0,2.0,37.0,0.011481056257175657,59.0,53.0,0.3283582089552239,18.0,8.0,0.11940298507462684,50.0,53.0,0.3444316877152698,1.0,71.0,0.0034443168771526975,0.0,0.0,0.0,7.0,184.0,0.025258323765786447,1.0,55.0,0.006888633754305395,"zhou, l"
+266,0.0,0.0,0.0,0.0,0.0,0.0,8.0,0.0,0.05911330049261084,0.0,0.0,0.0,0.0,0.0,0.0,1.0,4.0,0.004547176960970065,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,60.0,42.0,0.31337627889352027,0.0,0.0,0.0,0.0,0.0,0.0,78.0,9.0,0.5456612353164078,1.0,5.0,0.0022735884804850324,12.0,30.0,0.03296703296703297,3.0,1.0,0.0034103827207275484,0.0,4.0,0.0,0.0,0.0,0.0,9.0,4.0,0.03865100416824555,0.0,0.0,0.0,"kilgarriff, a"
+267,0.0,0.0,0.0,3.0,16.0,0.0121654501216545,2.0,1.0,0.024330900243309,0.0,0.0,0.0,0.0,0.0,0.0,6.0,14.0,0.024330900243309,0.0,0.0,0.0,5.0,3.0,0.0608272506082725,0.0,18.0,0.0,0.0,6.0,0.0,0.0,12.0,0.0,0.0,0.0,0.0,2.0,28.0,0.016220600162206,1.0,30.0,0.0121654501216545,15.0,68.0,0.1727493917274939,0.0,25.0,0.0,0.0,14.0,0.0,0.0,0.0,0.0,66.0,220.0,0.6326034063260341,9.0,0.0,0.0446066504460665,"germann, u"
+268,0.0,0.0,0.0,1.0,644.0,0.005628517823639774,7.0,149.0,0.02814258911819887,0.0,0.0,0.0,0.0,0.0,0.0,8.0,380.0,0.030018761726078796,0.0,0.0,0.0,4.0,565.0,0.015009380863039398,0.0,709.0,0.0,40.0,107.0,0.20450281425891176,49.0,333.0,0.1643527204502814,0.0,0.0,0.0,55.0,199.0,0.21651031894934333,4.0,286.0,0.00900562851782364,11.0,40.0,0.04127579737335834,43.0,280.0,0.15309568480300187,9.0,243.0,0.050656660412757966,0.0,0.0,0.0,17.0,226.0,0.06378986866791743,8.0,200.0,0.01801125703564728,"liu, j"
+269,0.0,0.0,0.0,0.0,109.0,0.0,86.0,191.0,0.4328289775332417,0.0,0.0,0.0,0.0,0.0,0.0,23.0,164.0,0.09903713892709766,0.0,0.0,0.0,2.0,18.0,0.011004126547455296,0.0,84.0,0.0,5.0,13.0,0.016047684548372305,0.0,17.0,0.0,0.0,0.0,0.0,7.0,2.0,0.04218248509857864,23.0,89.0,0.0972031178358551,8.0,23.0,0.06602475928473177,6.0,42.0,0.06052269601100413,9.0,92.0,0.058688674919761576,0.0,0.0,0.0,13.0,69.0,0.05593764328289776,11.0,4.0,0.06052269601100413,"white, m"
+270,0.0,0.0,0.0,0.0,21.0,0.0,2.0,16.0,0.006795969881987443,0.0,0.0,0.0,0.0,0.0,0.0,154.0,38.0,0.5629220513041789,0.0,0.0,0.0,0.0,13.0,0.0,0.0,4.0,0.0,17.0,2.0,0.09242519039502922,0.0,20.0,0.0,0.0,0.0,0.0,4.0,5.0,0.010873551811179908,14.0,104.0,0.050484347694763856,1.0,11.0,0.010873551811179908,24.0,14.0,0.0843994735820155,14.0,29.0,0.13229488036935558,0.0,0.0,0.0,9.0,3.0,0.04893098315030959,0.0,0.0,0.0,"stent, a"
+271,0.0,0.0,0.0,0.0,1.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,56.0,60.0,0.5476100151745069,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,8.0,22.0,0.1274658573596358,0.0,0.0,0.0,0.0,26.0,0.0,22.0,18.0,0.17526555386949924,4.0,8.0,0.018209408194233688,4.0,5.0,0.0637329286798179,4.0,8.0,0.03186646433990895,0.0,0.0,0.0,4.0,6.0,0.035849772382397574,0.0,1.0,0.0,"oberlander, j"
+272,0.0,0.0,0.0,1.0,6.0,0.006699846860643184,3.0,1.0,0.046898928024502295,0.0,0.0,0.0,0.0,0.0,0.0,24.0,3.0,0.1609877488514548,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,22.0,0.00861408882082695,0.0,1.0,0.0,0.0,0.0,0.0,16.0,15.0,0.14739663093415004,6.0,11.0,0.043070444104134756,13.0,8.0,0.10384762633996936,22.0,0.0,0.1280149310872894,2.0,1.0,0.010049770290964776,0.0,0.0,0.0,62.0,124.0,0.3444199846860643,0.0,11.0,0.0,"blanchon, h"
+273,0.0,0.0,0.0,0.0,0.0,0.0,19.0,4.0,0.01879480720790545,0.0,0.0,0.0,0.0,0.0,0.0,260.0,132.0,0.5855281368505552,0.0,0.0,0.0,5.0,1.0,0.011722534392559585,0.0,0.0,0.0,4.0,4.0,0.011722534392559585,1.0,29.0,0.002131369889556288,0.0,0.0,0.0,6.0,0.0,0.008719240457275726,63.0,5.0,0.13252553491848204,6.0,2.0,0.006850831787859497,51.0,17.0,0.1167686218064052,25.0,13.0,0.07140089130013565,0.0,0.0,0.0,4.0,3.0,0.008259058322030615,10.0,6.0,0.025576438674675458,"moore, j"
+274,0.0,0.0,0.0,0.0,6.0,0.0,5.0,3.0,0.03804089396100808,0.0,0.0,0.0,0.0,0.0,0.0,42.0,33.0,0.3680456490727531,0.0,0.0,0.0,0.0,6.0,0.0,0.0,10.0,0.0,0.0,9.0,0.0,10.0,43.0,0.09795530194959581,0.0,0.0,0.0,1.0,4.0,0.00951022349025202,0.0,14.0,0.0,4.0,32.0,0.022824536376604847,29.0,10.0,0.27104136947218255,2.0,21.0,0.01426533523537803,0.0,0.0,0.0,15.0,3.0,0.13552068473609127,6.0,0.0,0.04279600570613409,"b{\'e}chet, f"
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+276,0.0,0.0,0.0,0.0,3.0,0.0,9.0,24.0,0.08265392184425076,0.0,0.0,0.0,0.0,0.0,0.0,53.0,8.0,0.4191734607815573,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,1.0,66.0,0.007871802080404834,0.0,14.0,0.0,0.0,0.0,0.0,2.0,1.0,0.011807703120607251,1.0,8.0,0.007871802080404834,7.0,57.0,0.046443632274388524,21.0,23.0,0.15653640708462185,23.0,7.0,0.2322181613719426,0.0,0.0,0.0,3.0,1.0,0.035423109361821754,0.0,2.0,0.0,"antoine, j"
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+357,0.0,0.0,0.0,9.0,807.0,0.024289531212047605,0.0,55.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,31.0,474.0,0.07469030847704639,0.0,0.0,0.0,21.0,296.0,0.043113917901384496,0.0,516.0,0.0,1.0,33.0,0.0024289531212047604,0.0,1058.0,0.0,0.0,0.0,0.0,103.0,203.0,0.22479961136750054,2.0,228.0,0.004857906242409521,23.0,131.0,0.04809327179985425,119.0,421.0,0.2783580276900656,57.0,191.0,0.13845032790867132,0.0,0.0,0.0,42.0,202.0,0.12387660918144279,15.0,184.0,0.037041535098372595,"wang, j"
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+361,0.0,0.0,0.0,0.0,98.0,0.0,32.0,149.0,0.18601583113456469,0.0,0.0,0.0,0.0,0.0,0.0,2.0,30.0,0.010554089709762534,0.0,0.0,0.0,1.0,71.0,0.0079155672823219,0.0,45.0,0.0,0.0,0.0,0.0,0.0,7.0,0.0,0.0,0.0,0.0,0.0,17.0,0.0,1.0,16.0,0.005277044854881267,19.0,23.0,0.11873350923482852,95.0,60.0,0.4551451187335093,0.0,19.0,0.0,0.0,0.0,0.0,39.0,52.0,0.15831134564643798,12.0,9.0,0.05804749340369394,"jiang, w"
+362,0.0,0.0,0.0,4.0,531.0,0.04920049200492005,1.0,140.0,0.012300123001230012,0.0,0.0,0.0,0.0,0.0,0.0,5.0,531.0,0.06150061500615006,0.0,0.0,0.0,0.0,320.0,0.0,9.0,727.0,0.04428044280442805,1.0,48.0,0.012300123001230012,0.0,146.0,0.0,0.0,0.0,0.0,14.0,129.0,0.17220172201722017,6.0,218.0,0.07380073800738007,1.0,48.0,0.006150061500615006,62.0,188.0,0.41943419434194346,3.0,261.0,0.03690036900369004,0.0,0.0,0.0,2.0,419.0,0.024600246002460024,15.0,185.0,0.08733087330873308,"zhou, j"
+363,0.0,0.0,0.0,0.0,73.0,0.0,2.0,23.0,0.009569377990430623,0.0,0.0,0.0,0.0,0.0,0.0,27.0,92.0,0.34928229665071775,0.0,0.0,0.0,0.0,60.0,0.0,0.0,121.0,0.0,0.0,2.0,0.0,1.0,122.0,0.014354066985645935,0.0,0.0,0.0,0.0,56.0,0.0,1.0,120.0,0.004784688995215312,6.0,34.0,0.0861244019138756,60.0,71.0,0.3397129186602871,12.0,62.0,0.0861244019138756,0.0,0.0,0.0,0.0,151.0,0.0,16.0,40.0,0.11004784688995216,"wang, k"
+364,0.0,0.0,0.0,0.0,156.0,0.0,0.0,63.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,12.0,36.0,0.06646971935007386,0.0,0.0,0.0,5.0,45.0,0.029542097488921712,0.0,139.0,0.0,43.0,52.0,0.37518463810930575,1.0,75.0,0.008862629246676515,0.0,0.0,0.0,53.0,34.0,0.2998522895125554,24.0,73.0,0.10782865583456425,2.0,41.0,0.01772525849335303,3.0,105.0,0.019202363367799114,9.0,74.0,0.07533234859675038,0.0,0.0,0.0,0.0,99.0,0.0,0.0,70.0,0.0,"liu, m"
+365,0.0,0.0,0.0,12.0,326.0,0.03432857348911005,134.0,133.0,0.16442377037357564,0.0,0.0,0.0,0.0,0.0,0.0,8.0,163.0,0.024231934227607093,0.0,0.0,0.0,0.0,126.0,0.0,3.0,117.0,0.0022717438338381653,34.0,27.0,0.04205250252415981,2.0,401.0,0.002524159815375739,0.0,0.0,0.0,42.0,173.0,0.10540891389009086,4.0,54.0,0.00242319342276071,1.0,57.0,0.0006057983556901775,72.0,138.0,0.14433145824318475,264.0,687.0,0.22064041540458673,0.0,0.0,0.0,15.0,283.0,0.011358719169190826,141.0,166.0,0.2453988172508293,"zhou, g"
+366,0.0,0.0,0.0,6.0,344.0,0.02457897132453345,0.0,44.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,111.0,179.0,0.2216659080564406,0.0,0.0,0.0,23.0,222.0,0.055985434683659525,16.0,478.0,0.0436959490213928,10.0,44.0,0.023213472917614924,0.0,289.0,0.0,0.0,0.0,0.0,10.0,143.0,0.029358215748748286,5.0,101.0,0.019116977696859352,15.0,53.0,0.051206190259444694,88.0,348.0,0.2744651797906235,45.0,137.0,0.14610832954028216,0.0,0.0,0.0,30.0,249.0,0.0819299044151115,14.0,121.0,0.028675466545289026,"wang, c"
+367,0.0,0.0,0.0,6.0,0.0,0.014073494917904614,2.0,2.0,0.004691164972634871,0.0,0.0,0.0,0.0,0.0,0.0,74.0,98.0,0.5136825645035185,0.0,0.0,0.0,1.0,3.0,0.009382329945269743,0.0,0.0,0.0,0.0,0.0,0.0,8.0,4.0,0.07193119624706802,0.0,0.0,0.0,0.0,2.0,0.0,32.0,12.0,0.1970289288506646,5.0,22.0,0.023455824863174355,6.0,0.0,0.03752931978107897,16.0,12.0,0.12822517591868648,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,"mellish, c"
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+432,0.0,0.0,0.0,0.0,350.0,0.0,0.0,87.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,47.0,224.0,0.16725978647686834,0.0,0.0,0.0,0.0,156.0,0.0,0.0,243.0,0.0,0.0,26.0,0.0,0.0,201.0,0.0,0.0,0.0,0.0,1.0,168.0,0.005338078291814947,0.0,146.0,0.0,0.0,29.0,0.0,68.0,251.0,0.5791814946619217,2.0,70.0,0.010676156583629894,0.0,0.0,0.0,7.0,400.0,0.045373665480427046,24.0,115.0,0.19217081850533807,"liu, s"
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+434,0.0,0.0,0.0,0.0,72.0,0.0,2.0,66.0,0.015238095238095238,0.0,0.0,0.0,0.0,0.0,0.0,2.0,20.0,0.022857142857142857,0.0,0.0,0.0,0.0,17.0,0.0,0.0,39.0,0.0,17.0,6.0,0.17142857142857143,2.0,24.0,0.011428571428571429,0.0,0.0,0.0,9.0,0.0,0.05142857142857143,29.0,27.0,0.23619047619047617,11.0,16.0,0.12571428571428572,6.0,31.0,0.06095238095238095,0.0,42.0,0.0,0.0,0.0,0.0,8.0,15.0,0.07238095238095238,31.0,11.0,0.23238095238095235,"ye, y"
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+436,0.0,0.0,0.0,7.0,18.0,0.013671753624346372,15.0,0.0,0.026189398176507662,0.0,0.0,0.0,0.0,0.0,0.0,14.0,58.0,0.03149386102751218,0.0,0.0,0.0,3.0,4.0,0.004638630693974662,0.0,0.0,0.0,102.0,59.0,0.19482248914693578,8.0,13.0,0.01503892898678101,0.0,0.0,0.0,17.0,2.0,0.032714553315400255,136.0,22.0,0.17694711517120762,34.0,49.0,0.06066174838646673,30.0,11.0,0.049360357241146645,16.0,39.0,0.037109045551797296,0.0,0.0,0.0,178.0,74.0,0.32707894993829956,11.0,7.0,0.030273168739624108,"dorr, b"
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output/acl_2002_2022/authors_full_records.pickle

@@ -0,0 +1 @@
+../../.git/annex/objects/wV/J8/MD5E-s296079362--59b159e18d2ee633e45f54d91dd52455/MD5E-s296079362--59b159e18d2ee633e45f54d91dd52455

output/acl_2002_2022/dataset.pickle

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+../../.git/annex/objects/Z6/21/MD5E-s37618571--7d4899ae27ce960b6c81fe267ee5a736/MD5E-s37618571--7d4899ae27ce960b6c81fe267ee5a736

output/acl_2002_2022/ei_samples_control_nu.npz

@@ -0,0 +1 @@
+../../.git/annex/objects/5v/7G/MD5E-s800809813--6d8467963be8c83f01971467b96708a4.npz/MD5E-s800809813--6d8467963be8c83f01971467b96708a4.npz

output/acl_2002_2022/embeddings.bin

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+../../.git/annex/objects/PV/z3/MD5E-s3699310--c6bbbb6dfdc244d8a91d8ab9f2bc6337.bin/MD5E-s3699310--c6bbbb6dfdc244d8a91d8ab9f2bc6337.bin

output/acl_2002_2022/etm_instance.pickle

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+../../.git/annex/objects/fv/w9/MD5E-s105964159--00635c343eac76e253622afced1ff32f/MD5E-s105964159--00635c343eac76e253622afced1ff32f

output/acl_2002_2022/keywords.npy

@@ -0,0 +1 @@
+../../.git/annex/objects/Jw/pK/MD5E-s21955008--42972d707477818e8876b11b36024c53.npy/MD5E-s21955008--42972d707477818e8876b11b36024c53.npy

output/acl_2002_2022/largest_transfers.tex

@@ -0,0 +1,20 @@
+\begin{table}
+\caption{Largest transfers across research areas.}
+\label{table:largest_transfers}
+\begin{tabular}{b{0.4\textwidth}|b{0.4\textwidth}|c}
+\toprule
+Origin research area & Target research area & Magnitude \\ \hline
+\midrule
+Grammar & Translation & 1.76 \\ \hline
+Translation & Deep learning & 1.57 \\ \hline
+Speech & Deep learning & 1.55 \\ \hline
+Translation & Dialogues, generation, interfaces & 1.29 \\ \hline
+Clustering & Sentiment detection & 1.14 \\ \hline
+Translation & Corpora and pre-trained models & 1.10 \\ \hline
+Speech & Dialogues, generation, interfaces & 1.07 \\ \hline
+Statistical modelling & Corpora and pre-trained models & 0.95 \\ \hline
+Translation & Speech & 0.95 \\ \hline
+Grammar & Deep learning & 0.93 \\ \hline
+\bottomrule
+\end{tabular}
+\end{table}

output/acl_2002_2022/model

@@ -0,0 +1 @@
+../../.git/annex/objects/Qz/ZF/MD5E-s22987863--5b8f9dd52e4d89570ebe23030ac71c16/MD5E-s22987863--5b8f9dd52e4d89570ebe23030ac71c16

output/acl_2002_2022/most_conservative.tex

@@ -0,0 +1,20 @@
+\begin{table}
+\caption{Most conservative research areas.}
+\label{table:most_conservative}
+\begin{tabular}{b{0.4\textwidth}|b{0.4\textwidth}|c}
+\toprule
+Research area & Conservatism \\ \hline
+\midrule
+Translation & 0.43 \\ \hline
+Sentiment detection & 0.40 \\ \hline
+Dialogues, generation, interfaces & 0.36 \\ \hline
+Software & 0.33 \\ \hline
+Grammar & 0.31 \\ \hline
+Deep learning & 0.31 \\ \hline
+Information retrieval & 0.30 \\ \hline
+Semantics & 0.30 \\ \hline
+Languages & 0.26 \\ \hline
+Knowledge & 0.21 \\ \hline
+\bottomrule
+\end{tabular}
+\end{table}

output/acl_2002_2022/nu_expertise.npy

@@ -0,0 +1 @@
+../../.git/annex/objects/m2/K5/MD5E-s1696--865702a6c8bd35eee9503561d731a855.npy/MD5E-s1696--865702a6c8bd35eee9503561d731a855.npy

output/acl_2002_2022/nu_expertise_symmetric.npy

@@ -0,0 +1 @@
+../../.git/annex/objects/8x/vK/MD5E-s1696--6e297e5e7b5f8a94d2a155201e98508d.npy/MD5E-s1696--6e297e5e7b5f8a94d2a155201e98508d.npy

output/acl_2002_2022/nu_ling.npy

@@ -0,0 +1 @@
+../../.git/annex/objects/kv/Pj/MD5E-s1696--1c82dd44bc039d9b6e5d62ed9afc9582.npy/MD5E-s1696--1c82dd44bc039d9b6e5d62ed9afc9582.npy

output/acl_2002_2022/nu_ling_symmetric.npy

@@ -0,0 +1 @@
+../../.git/annex/objects/8V/fp/MD5E-s1696--9fb43db8186a9957c1fbbc42176a05a6.npy/MD5E-s1696--9fb43db8186a9957c1fbbc42176a05a6.npy

output/acl_2002_2022/params.yml

@@ -0,0 +1,19 @@
+!!python/object:argparse.Namespace
+add_title: true
+blacklist: null
+constant_sampling: 12000
+dataset: ../acl
+dimensions: 50
+filter: no-filter
+lemmatize: true
+limit_redundancy: false
+location: output/acl_2002_2022
+max_df: 0.15
+min_df: 0.00075
+pre_trained_embeddings: true
+remove_latex: true
+samples: 300000
+threads: 30
+topics: 20
+use_saved_embeddings: true
+values: []

output/acl_2002_2022/pooled_resources.parquet

@@ -0,0 +1 @@
+../../.git/annex/objects/Kf/VW/MD5E-s86719--b67d8df336c28cf6fb3776645946691e/MD5E-s86719--b67d8df336c28cf6fb3776645946691e

output/acl_2002_2022/sankey_control_nu.pdf

@@ -0,0 +1 @@
+/annex/objects/MD5E-s21783--68f32a04669b598c7250700151452194.pdf

output/acl_2002_2022/sankey_control_nu_acl.pdf

@@ -0,0 +1 @@
+../../.git/annex/objects/59/mK/MD5E-s21783--68f32a04669b598c7250700151452194.pdf/MD5E-s21783--68f32a04669b598c7250700151452194.pdf

output/acl_2002_2022/scores.npy

@@ -0,0 +1 @@
+../../.git/annex/objects/zX/p2/MD5E-s368--ea3a48ae4ddaa1b5e6a4fe45d68ea5ec.npy/MD5E-s368--ea3a48ae4ddaa1b5e6a4fe45d68ea5ec.npy

output/acl_2002_2022/topics.csv

@@ -0,0 +1,21 @@
+,top_words,label
+0,"previous,experimental,experimental_result,novel,state_of_the_art,significant,previous_work,baseline,show,empirical_result,future,conventional,state_of_theart,demonstrate,state_ofthe_art,outperforms,strong,recent,recent_work,improved","Junk"
+1,"network,neural,representation,convolutional,sequence,architecture,neural_network,input,mechanism,graph,rnns,encoders,lstm,memory,layer,recurrent,module,abstraction,encoding,attention","Deep learning"
+2,"dependency,grammar,parser,parsing,tree,treebank,tag,morphological,deterministic,treebanks,rule,derivation,formalism,analyzer,morphology,constraint,parse,universal,turkish,syntactic_dependency","Syntactic Parsing \& Grammar"
+3,"dans,le,nous,un,une,la,article,du,par,de,pour,ca,sur,en,particulier,non,ce,et,que,premier","Junk"
+4,"entity,relation,domain,extraction,event,type,terminology,temporal,concept,term,biomedical,mention,attribute,literature,expression,extract,record,temporal_expression,name,trigger","Junk"
+5,"natural,dialog,natural_language,dialogue,utterance,understanding,user,spoken,interaction,conversation,video,natural_language_generation,multimodal,ubiquitous,intention,response,interface,generation,instruction,simulation","Dialogues \& interfaces"
+6,"set,score,accuracy,test,classifier,f_score,reasonable,best,average,baseline,combination,recall,rank,second,best_result,run,precision,submission,ensemble,rule","Junk"
+7,"question,knowledge,dataset,fact,answer,evidence,datasets,commonsense,reasoning,answering,qa,base,benchmark,challenge,testbed,comprehension,passage,gap,multi_hop,large_scale","Question Answering \& Knowledge Bases"
+8,"embeddings,datasets,pre_trained,training,augmentation,fine_tune,fine_tuned,cross_lingual,http,pretrained,unavailable,sample,dataset,al.,example,transferability,setting,benchmark,counterpart,fine_tuning","Embeddings \& Pre-trained Models"
+9,"english,lexical,verb,entry,lexicon,construction,japanese,english_word,french,thesaurus,meaning,german,compound,spanish,us,expression,synonym,dictionary,noun,collocation","Lexical Resources \& Multilingual Entries"
+10,"sentiment,classification,detection,tweet,comment,twitter,emotion,sentiment_analysis,social_medium,stance,post,medium,social,sentiment_classification,opinion,affect,facebook,mining,feed,identifying","Sentiment Analysis"
+11,"linguistic,scholar,computational,literary,background,body,scientist,philosophy,science,cognitive,linguist,expertise,computational_analysis,linguistic_analysis,extent,linguistics,applied,academic,scientific,technological","Junk"
+12,"document,context,space,similarity,vector,topic,disambiguation,cluster,technique,query,clustering,sense,relatedness,co_occurrence,retrieval,semantic_similarity,search,snippet,window,indexing","Document Similarity \& Semantic Analysis"
+13,"automatic,human,metric,summary,summarization,generation,news,story,quality,headline,human_evaluation,assessment,fluency,content,diversity,coherent,automated,ranking,automatic_summarization,informativeness","Information retrieval"
+14,"annotation,tool,processing,nlp,resource,standard,effort,application,research,toolkit,challenge,arabic,area,project,natural_language_processing,community,researcher,development,api,guideline","NLP Tools \& Resources"
+15,"error,speech,recognition,chinese,character,asr,segmentation,correction,speaker,study,rate,pronunciation,mandarin,native,accent,vietnamese,disfluency,learner,track,transcription","Speech"
+16,"semantic,role,discourse,predicate,argument,relation,semantics,semantic_structure,implicit,syntactic,resolution,co_reference,structure,parsing,structural,anaphora,semantic_role,semantic_task,textual,semantic_parsing","Semantics \& Discourse"
+17,"several,first,time,many,various,multiple,single,number,possible,numerous,first_time,step,range,potential,larger,ten,essential,optimized,crucial,impractical","Junk"
+18,"smt,quality,statistical,estimation,statistical_machine,bilingual,statistical_machine_translation,nmt,syntax_based,bitexts,alignment,pair,source,mt,neural_machine,neural_machine_translation,bleu,parallel,pbsmt,automatic_translation","Statistical \& Neural Machine Translation"
+19,"learning,framework,algorithm,optimization,field,problem,random,decision,function,conditional,estimator,bayesian,sequential,joint,hidden,markov,semi_supervised,active,perceptron,policy","Learning Algorithms \& Optimization Techniques"

output/acl_2002_2022/topics_counts.npy

@@ -0,0 +1 @@
+../../.git/annex/objects/2k/J0/MD5E-s9326368--421a92ecdde721ec04122003728a4d4f.npy/MD5E-s9326368--421a92ecdde721ec04122003728a4d4f.npy

output/acl_2002_2022/topics_order.npy

@@ -0,0 +1 @@
+../../.git/annex/objects/gx/Wv/MD5E-s240--39d8655e7a5cc9a96bf769298882516d.npy/MD5E-s240--39d8655e7a5cc9a96bf769298882516d.npy

output/etm_20_pretrained/.DS_Store

@@ -0,0 +1 @@
+../../.git/annex/objects/kf/M8/MD5E-s6148--194577a7e20bdcc7afbb718f502c134c/MD5E-s6148--194577a7e20bdcc7afbb718f502c134c

output/etm_20_pretrained/age_vs_position.png

@@ -0,0 +1 @@
+../../.git/annex/objects/9x/zv/MD5E-s41783--b19649ca03421c8a76ed60db0516f036.png/MD5E-s41783--b19649ca03421c8a76ed60db0516f036.png

output/etm_20_pretrained/authors_full_records.pickle

@@ -0,0 +1 @@
+../../.git/annex/objects/GV/Vp/MD5E-s655733001--1b1ccf65ca24580f829ae5e57f2c8150/MD5E-s655733001--1b1ccf65ca24580f829ae5e57f2c8150

output/etm_20_pretrained/authors_keywords_record.npz

@@ -0,0 +1 @@
+../../.git/annex/objects/xQ/1w/MD5E-s2030672--af9380409f0b7e3369331c23343f5a44.npz/MD5E-s2030672--af9380409f0b7e3369331c23343f5a44.npz

output/etm_20_pretrained/capital_measures.csv

@@ -0,0 +1,6 @@
+,Unnamed: 0_x,start_1,end_1,expertise_1,start_2,end_2,expertise_2,start_3,end_3,expertise_3,start_4,end_4,expertise_4,start_5,end_5,expertise_5,start_6,end_6,expertise_6,start_7,end_7,expertise_7,start_8,end_8,expertise_8,start_9,end_9,expertise_9,start_10,end_10,expertise_10,start_11,end_11,expertise_11,start_12,end_12,expertise_12,start_13,end_13,expertise_13,start_14,end_14,expertise_14,start_15,end_15,expertise_15,start_16,end_16,expertise_16,start_17,end_17,expertise_17,start_18,end_18,expertise_18,start_19,end_19,expertise_19,start_20,end_20,expertise_20,pooled_resources,research_diversity,social_diversity,intellectual_diversity,social_magnitude,intellectual_stirling,social_stirling,excess_social_diversity,excess_social_stirling,Unnamed: 0_y,brokerage,degree
+mean,1096.8605287146763,62.890610756608936,45.73655423883318,0.07328036555769059,92.29443938012763,35.25159525979945,0.11770560791240284,44.60574293527803,25.21877848678213,0.05968199666033241,0.0,0.0,0.0,26.619416590701913,21.820875113947128,0.035205018322006724,87.72743846855059,59.02917046490428,0.0996793834883834,37.74567000911577,26.12260711030082,0.050015807660719176,0.0,0.0,0.0,79.14448495897904,37.341841385597085,0.08140163571660784,38.16453965360073,34.2538742023701,0.05028589878061855,50.05059252506837,30.307657247037376,0.06221887208283163,0.0,0.0,0.0,0.0,0.0,0.0,33.97948951686418,52.33637192342753,0.03853371119383729,32.275296262534184,23.580674567000912,0.04326812731059411,38.90929808568824,27.57383773928897,0.05011398007260723,104.15041020966272,70.57110300820419,0.10062191114556264,48.71103008204193,34.81084776663628,0.06549772650763844,22.470373746581586,17.240656335460347,0.03673399410033161,25.7616226071103,26.962169553327257,0.03575596348783511,"[0.65109277 1.21143537 0.52102686 0.         0.32169839 0.88587754
+ 0.46152954 0.         0.80231566 0.44165957 0.61516255 0.
+ 0.         0.36094724 0.40677678 0.47347229 1.08248172 0.53431475
+ 0.34403412 0.33269506]",6.170389436645058,8.501217915401353,6.092267370810347,9.446520219981013,0.4730547902969735,0.5508875865939536,0.004368396762728921,0.0002557203383153612,5063.0916134913405,44.80506006366023,10.82377883459128
+std,633.8970340356047,136.19834411816285,113.43601688303553,0.12866953840678674,182.75871453707256,83.45828603661717,0.18928416932501696,79.84806013637288,45.71162501315768,0.09684092204466932,0.0,0.0,0.0,27.900687114735003,27.47390756449949,0.033580014194481665,209.51603735271044,140.50801927527698,0.19833739970770667,40.2681395902263,31.741963078922243,0.04597035361247265,0.0,0.0,0.0,221.66806914065444,101.46645147397231,0.16947450071300124,100.60653631220639,95.07737409250996,0.10783122884388115,55.473229985054836,33.26634855074712,0.05154927606365638,0.0,0.0,0.0,0.0,0.0,0.0,94.74121538138365,128.7281992177133,0.09470060515269516,37.557906172091876,28.523350012619158,0.04500419694403698,98.15314385707796,77.0135944149388,0.10404546283517781,217.52660352169727,133.4096294612293,0.1448320153706696,97.73396237860004,75.83907441622516,0.11154605758400196,48.470771342660306,34.4309014369307,0.08024927582408375,47.7888096492231,57.59183019875305,0.06251771553797464,,2.1678528711447713,2.215671427943636,2.1710788790924274,5.743170151753923,0.09867248851083135,0.07163586080936535,1.4531890586745433,0.05193824206030997,4986.433904385217,66.1629678931609,6.276507540988735

output/etm_20_pretrained/dataset.pickle

@@ -0,0 +1 @@
+../../.git/annex/objects/8M/0g/MD5E-s125920179--bca15cd429ae9491d421666d0fc09a17/MD5E-s125920179--bca15cd429ae9491d421666d0fc09a17

output/etm_20_pretrained/ei_samples_control_nu.npz

@@ -0,0 +1 @@
+../../.git/annex/objects/Vv/vQ/MD5E-s6526947812--36dddef366e30f1e97c60888b6436446.npz/MD5E-s6526947812--36dddef366e30f1e97c60888b6436446.npz

output/etm_20_pretrained/ei_samples_control_nu_crossval.npz

@@ -0,0 +1 @@
+../../.git/annex/objects/61/0F/MD5E-s4145673789--5b4f3c827320ddfc40db4da2d5ae5f35.npz/MD5E-s4145673789--5b4f3c827320ddfc40db4da2d5ae5f35.npz

output/etm_20_pretrained/embeddings.bin

@@ -0,0 +1 @@
+../../.git/annex/objects/97/4V/MD5E-s3381859--2346dc4fcdbdb895df85c1511542c6f6.bin/MD5E-s3381859--2346dc4fcdbdb895df85c1511542c6f6.bin

output/etm_20_pretrained/embeddings.mdl

@@ -0,0 +1 @@
+../../.git/annex/objects/K5/5z/MD5E-s1528639--37b60ab62d269334888d4b1e96666279.mdl/MD5E-s1528639--37b60ab62d269334888d4b1e96666279.mdl

output/etm_20_pretrained/etm_instance.pickle

@@ -0,0 +1 @@
+../../.git/annex/objects/9Q/1F/MD5E-s180102844--ba2b0edda6892bd2f9c85f2022603f33/MD5E-s180102844--ba2b0edda6892bd2f9c85f2022603f33

output/etm_20_pretrained/keywords.npy

@@ -0,0 +1 @@
+../../.git/annex/objects/F1/w5/MD5E-s16723648--c2d89b19ffab8ee82f92571148f4532d.npy/MD5E-s16723648--c2d89b19ffab8ee82f92571148f4532d.npy

output/etm_20_pretrained/keywords_topics.npy

@@ -0,0 +1 @@
+../../.git/annex/objects/55/FP/MD5E-s4468016--41b185c36efc5dd5722039c7b150bc52.npy/MD5E-s4468016--41b185c36efc5dd5722039c7b150bc52.npy

output/etm_20_pretrained/knowledge_transfers_weights.npz

@@ -0,0 +1 @@
+../../.git/annex/objects/6X/85/MD5E-s170535--1dce439699eb3cea9f013fbd96531d9a.npz/MD5E-s170535--1dce439699eb3cea9f013fbd96531d9a.npz

output/etm_20_pretrained/low_change.tex

@@ -0,0 +1,31 @@
+\begin{table}[H]
+\centering
+\caption{Physicists with the lowest change scores $c_a$. $D(\bm{I_a})$ and $D(\bm{S_a})$ measure the diversity of intellectual and social capital. Numbers in parentheses indicate the share of attention dedicated to each research area. Asterisks ($\ast$) indicate physicists with a permanent position.}
+\label{table:low_change}
+\begin{tabular}{p{0.15\textwidth}|c|c|c|b{0.25\textwidth}|b{0.25\textwidth}}
+\toprule
+              Physicist & $c_a$ & $D(\bm{I_a})$ & $D(\bm{S_a})$ &                   Previous main area &                       Current main area \\
+\midrule
+     J.~Huston ($\ast$) &  0.04 &          2.96 &          5.50 &              Collider physics (0.61) &              Collider physics (0.60)\\ \hline
+  U.~D.~Alesio ($\ast$) &  0.05 &          1.68 &          4.50 &              Collider physics (0.87) &              Collider physics (0.88)\\ \hline
+            S.~Schumann &  0.06 &          4.15 &          7.90 &              Collider physics (0.46) &              Collider physics (0.45)\\ \hline
+          A.~V.~Lipatov &  0.06 &          2.88 &          4.28 &              Collider physics (0.68) &              Collider physics (0.68)\\ \hline
+           Andreas Metz &  0.06 &          2.01 &          3.61 &              Collider physics (0.85) &              Collider physics (0.89)\\ \hline
+               I.~Vitev &  0.07 &          2.95 &          5.49 &              Collider physics (0.72) &              Collider physics (0.73)\\ \hline
+ Xin Nian Wang ($\ast$) &  0.07 &          2.61 &          4.06 &              Collider physics (0.77) &              Collider physics (0.75)\\ \hline
+             S.~Goswami &  0.07 &          2.15 &          5.22 &  Neutrinos \& flavour physics (0.83) &  Neutrinos \& flavour physics (0.79)\\ \hline
+         Y.~Mehtar Tani &  0.07 &          2.75 &          5.17 &              Collider physics (0.75) &              Collider physics (0.75)\\ \hline
+           M.~Trigiante &  0.07 &          5.11 &          6.42 & String theory \& supergravity (0.55) & String theory \& supergravity (0.56)\\ \hline
+     F.~Murgia ($\ast$) &  0.07 &          1.84 &          4.57 &              Collider physics (0.86) &              Collider physics (0.87)\\ \hline
+     B.~Kayser ($\ast$) &  0.07 &          2.07 &          4.94 &  Neutrinos \& flavour physics (0.76) &  Neutrinos \& flavour physics (0.77)\\ \hline
+   J.~F.~Owens ($\ast$) &  0.07 &          2.01 &          5.42 &              Collider physics (0.78) &              Collider physics (0.80)\\ \hline
+           A.~Bacchetta &  0.07 &          2.07 &          3.19 &              Collider physics (0.84) &              Collider physics (0.81)\\ \hline
+P.~M.~Nadolsky ($\ast$) &  0.07 &          3.19 &          5.28 &              Collider physics (0.62) &              Collider physics (0.59)\\ \hline
+            D.~Martelli &  0.07 &          4.42 &          5.97 & String theory \& supergravity (0.57) & String theory \& supergravity (0.58)\\ \hline
+              C.~Pisano &  0.08 &          1.77 &          4.53 &              Collider physics (0.87) &              Collider physics (0.88)\\ \hline
+   M.~Strikman ($\ast$) &  0.08 &          2.77 &          4.99 &              Collider physics (0.77) &              Collider physics (0.75)\\ \hline
+    J.~Nemchik ($\ast$) &  0.08 &          2.67 &          4.69 &              Collider physics (0.80) &              Collider physics (0.78)\\ \hline
+Zhi Zhong Xing ($\ast$) &  0.08 &          3.44 &          8.17 &  Neutrinos \& flavour physics (0.71) &  Neutrinos \& flavour physics (0.65)\\ \hline
+\bottomrule
+\end{tabular}
+\end{table}

output/etm_20_pretrained/low_disruption.tex

@@ -0,0 +1,31 @@
+\begin{table}[H]
+\centering
+\caption{Physicists with the lowest change scores $c_a$. $D(\bm{I_a})$ and $D(\bm{S_a})$ measure the diversity of intellectual and social capital. Numbers in parentheses indicate the share of attention dedicated to each research area. Asterisks ($\ast$) indicate physicists with a permanent position.}
+\label{table:low_disruption}
+\begin{tabular}{p{0.15\textwidth}|c|c|c|b{0.25\textwidth}|b{0.25\textwidth}}
+\toprule
+              Physicist & $c_a$ & $D(\bm{I_a})$ & $D(\bm{S_a})$ &                   Previous main area &                       Current main area \\
+\midrule
+     J.~Huston ($\ast$) &  0.03 &          2.96 &          5.50 &              Collider physics (0.61) &              Collider physics (0.60)\\ \hline
+  U.~D.~Alesio ($\ast$) &  0.03 &          1.68 &          4.50 &              Collider physics (0.87) &              Collider physics (0.88)\\ \hline
+          A.~V.~Lipatov &  0.03 &          2.88 &          4.28 &              Collider physics (0.68) &              Collider physics (0.68)\\ \hline
+             S.~Goswami &  0.03 &          2.15 &          5.22 &  Neutrinos \& flavour physics (0.83) &  Neutrinos \& flavour physics (0.79)\\ \hline
+            S.~Schumann &  0.04 &          4.15 &          7.90 &              Collider physics (0.46) &              Collider physics (0.45)\\ \hline
+           M.~Trigiante &  0.04 &          5.11 &          6.42 & String theory \& supergravity (0.55) & String theory \& supergravity (0.56)\\ \hline
+               I.~Vitev &  0.04 &          2.95 &          5.49 &              Collider physics (0.72) &              Collider physics (0.73)\\ \hline
+           Andreas Metz &  0.04 &          2.01 &          3.61 &              Collider physics (0.85) &              Collider physics (0.89)\\ \hline
+           A.~Bacchetta &  0.04 &          2.07 &          3.19 &              Collider physics (0.84) &              Collider physics (0.81)\\ \hline
+            D.~Martelli &  0.04 &          4.42 &          5.97 & String theory \& supergravity (0.57) & String theory \& supergravity (0.58)\\ \hline
+ Xin Nian Wang ($\ast$) &  0.04 &          2.61 &          4.06 &              Collider physics (0.77) &              Collider physics (0.75)\\ \hline
+     F.~Murgia ($\ast$) &  0.04 &          1.84 &          4.57 &              Collider physics (0.86) &              Collider physics (0.87)\\ \hline
+              C.~Pisano &  0.05 &          1.77 &          4.53 &              Collider physics (0.87) &              Collider physics (0.88)\\ \hline
+         Y.~Mehtar Tani &  0.05 &          2.75 &          5.17 &              Collider physics (0.75) &              Collider physics (0.75)\\ \hline
+           E.~R.~Sharpe &  0.05 &          2.96 &          4.05 & String theory \& supergravity (0.75) & String theory \& supergravity (0.74)\\ \hline
+   J.~F.~Owens ($\ast$) &  0.05 &          2.01 &          5.42 &              Collider physics (0.78) &              Collider physics (0.80)\\ \hline
+    Qiang Zhao ($\ast$) &  0.05 &          3.84 &          5.50 &                       Hadrons (0.65) &                       Hadrons (0.69)\\ \hline
+   M.~Strikman ($\ast$) &  0.05 &          2.77 &          4.99 &              Collider physics (0.77) &              Collider physics (0.75)\\ \hline
+P.~M.~Nadolsky ($\ast$) &  0.05 &          3.19 &          5.28 &              Collider physics (0.62) &              Collider physics (0.59)\\ \hline
+    J.~Nemchik ($\ast$) &  0.05 &          2.67 &          4.69 &              Collider physics (0.80) &              Collider physics (0.78)\\ \hline
+\bottomrule
+\end{tabular}
+\end{table}