Imports
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import json
import time
import torch
import re
from sentence_transformers import SentenceTransformer, util
In the context of machine learning, an embedding is a lower-dimensional representation of a high-dimensional data point. It is a vector of numerical values that captures some of the important characteristics of the original data point, while discarding less relevant or redundant information.
Embeddings are commonly used in natural language processing, where they are used to represent words or phrases as numerical vectors. These vectors capture the meaning and context of the words or phrases, and can be used in downstream tasks such as classification or similarity measurement.
Sentence transformers are a type of natural language processing model that is used to generate sentence-level embeddings. These embeddings capture the meaning and context of sentences, and can be used for downstream tasks such as classification or similarity measurement.
The sentence transformer allenai-specter is a conversion of the AllenAI SPECTER model to sentence-transformers. It can be used to map the titles and abstracts of scientific publications to a vector space such that similar papers are close.
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import json
import time
import torch
import re
from sentence_transformers import SentenceTransformer, util
= pd.read_csv(INPUT_FILE)
df = df.drop_duplicates(subset=["doi"])\
df =["doi", "abstract"]).reset_index(drop=True)
.dropna(subset= (df["title"] + " " + df["abstract"]).values sentences
= SentenceTransformer('sentence-transformers/allenai-specter') model
if COMPUTE_EMBEDDINGS:
= model.encode(sentences, normalize_embeddings=True)
embeddings
np.save(OUTPUT_FILE_EMBEDDINGS, embeddings)else:
= np.load(OUTPUT_FILE_EMBEDDINGS) embeddings
Find the most and the least similar publications given the COVID-19 query we used to initially query the Dimensions database using semantic search.
def find_n_similar(query, n=5, most_similar=True):
= model.encode(query, normalize_embeddings=True)
query_emb
= util.dot_score(query_emb, embeddings)[0].cpu().tolist()
scores = list(zip(sentences, scores))
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=most_similar)
doc_score_pairs
for doc, score in doc_score_pairs[:n]:
print(score, doc)
= "2019-nCoV COVID-19 SARS-CoV-2 HCoV-2019 hcov NCOVID-19 severe acute respiratory syndrome coronavirus 2 severe acute respiratory syndrome corona virus 2 “coronavirus disease 2019” coronavirus corona virus Wuhan China novel"
covid_query print("MOST SIMILAR:")
=5, most_similar=True)
find_n_similar(covid_query, nprint("--------------")
print("LEAST SIMILAR:")
=5, most_similar=False) find_n_similar(covid_query, n
MOST SIMILAR:
0.9295981526374817 Brief Evaluation on COVID-19: The 2020 Epidemic Caused by SARS-CoV-2 The virus responsible for the coronavirus disease of 2019 (COVID-19) is Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Initially it is recognized in Wuhan (Hubei, China) in December2019 The beginning of 2020 has seen the rise of COVID-19 outburst caused by a novel coronavirus, it has since been declared an epidemic by the World Health Organization in March of 2020. And there a need to better understand this new virus and to develop ways to control its spread. This study, will provide a brief review of viral origin, identification, symptoms, transmission, diagnosis, and potential treatment strategies for the newly identified SARS-CoV2.
0.92499178647995 Is SARS CoV-2 a new Frankenstein monster virus? (Preprint) <sec>
BACKGROUND
<p>Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel virus of beta coronavirus genus originated in Wuhan, China in December 2019, resulted in the pandemic spread of Corona Virus Disease 2019 (COVID-19) worldwide. This genus also contains SARS-CoV (originated in China 2002-2003) and MERS-CoV (found in Saudi Arabia 2012). The nucleotide sequences of SARS-CoV-2 is closer to SARS-CoV (with approximately 80% identity) than to MERS-CoV. Despite these similarities, SARS-CoV-2 has two main features over the other coronaviruses. The first is the high contagious rate and the second is the immune response evasion. The higher transmission ability makes this virus quickly spread worldwide with a high mortality rate and more economic losses. This review will provide an overview of the current knowledge on the role of some viral and host cell factors in higher transmission and contagious spread of SARS-CoV-2.</p>
</sec>
0.9248875379562378 COVID-19: A Conundrum to Decipher OBJECTIVE: Recent worldwide outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of respiratory coronavirus disease 2019 (COVID-19), is a current, ongoing life-threatening crisis and international public health emergency. The early diagnosis and management of the disease remains a major challenge. In this review, we aim to summarize the updated epidemiology, causes, clinical manifestation and diagnosis, as well as prevention and control of the novel coronavirus SARS-CoV-2.MATERIALS AND METHODS: A broad search of the literature was performed in “PubMed” “Medline” “Web of knowledge”, and “Google Scholar” World Health Organization-WHO” using the keywords “severe acute respiratory syndrome coronavirus”, “2019-nCoV”, “COVID-19, “SARS”, “SARS-CoV-2” “Epidemiology” “Transmission” “Pathogenesis” “Clinical Characteristics”. We reviewed and documented the information obtained from literature on epidemiology, pathogenesis and clinical appearances of SARS-CoV-2 infection.RESULTS: The global cases of COVID-19 as of April 2, 2020 have risen to more than 900,000 and morbidity has reached more than 47,000. The incidence rate for COVID-19 has been predicted to be higher than the previous outbreaks of other coronavirus family members, including those of SARS-CoV and the Middle East Respiratory Syndrome Coronavirus (MERS-CoV). The main clinical presentation of SARS-CoV-2 infection ranges from asymptomatic stages to severe lower respiratory infection in the form of pneumonia. Most of the patients also presented with fever, cough, sore throat, headache, fatigue, myalgia and breathlessness.Individuals at higher risk for severe illness include elderly people and patients with a weakened immune system or that are suffering from a underlying chronic medical condition like hypertension, diabetes, cancer, respiratory illness or cardiovascular diseases.CONCLUSIONS: SARS-Cov-2 has emerged as a worldwide threat, currently affecting 170 countries and territories across the globe. There is still much to be understood regarding SARS-CoV-2 about its virology, epidemiology and clinical management strategies; this knowledge will be essential to both manage the current pandemic and to conceive comprehensive measures to prevent such outbreaks in the future.
0.9225337505340576 Why COVID-19 Transmission is More Efficient and Aggressive than Viral Transmission in Previous Coronavirus Epidemics? Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is causing pandemic of coronavirus disease 2019 (COVID-19). The worldwide transmission of COVID-19 from human to human is spreading like wildfire, affecting almost every country in the world. In the past 100 years, the globe did not face microbial pandemic similar in scale to COVID-19. Taken together, both previous outbreaks of other members of the coronavirus family (SARS-CoV and MERS-CoV) did not produce even 1% of the global harm already inflicted by COVID-19. There are also four other CoVs capable of infecting humans (HCoVs), which circulate continuously in the human population, but their phenotypes are generally mild, and these HCoVs received relatively little attention. These dramatic differences between infection with HCoVs, SARS-CoV, MERS-CoV, and SARS-CoV-2 raise many questions, such as: Why is COVID-19 transmitted so quickly? Is it due to the some specific features of the viral structure? Are there some specific human (host) factors? Are there some environmental factors? The aim of this review is to collect and concisely summaries the possible and logic answers to these questions.
0.9212179183959961 <strong>Update on the </strong><strong>SARS-CoV-2 (COVID-19) Outbreak: A Global Pandemic Challenge</strong> The 2019 novel coronavirus (previously 2019-nCoV) or coronavirus infectious disease 2019 (COVID-19) outbreak has been summarized as on March 29, 2020. COVID-19 is a highly transmittable and pathogenic viral infection caused by severe acute respiratory syndrome coronavirus 2 (SERS-CoV-2). The disease was first seen during an outbreak in Wuhan, China and continuous spreading from human to human around the sphere. The disease is uncontrolled and increasing the death toll through. The world is facing a global challenge to protect human lives caused by coronavirus outbreak. The number of infected patients is increasing day by day due to COVID-19 as a pandemic. The world health organization (WHO) has declared global public health emergency on January 30, 2020. The disease has been spread around 201 countries with total confirmed cases 634835 and death cases 29891 as on March 29, 2020. The goal of this review to summaries and update the clinical/medical features and suggestions for diagnosis of the COVID-19 as a pandemic. The discussion of the various therapeutic algorithms, risk, prevention and control based on the latest reports has been provided.
--------------
LEAST SIMILAR:
0.35479187965393066 Measuring Learning Organization Practice & Its Impact on Employees’ Job Satisfaction: A Study on the Banking Sector of Bangladesh Practice of learning organization (LO) is essential for firms that want to meet global challenges and adapt to rapid changing environment. Firms that do not learn continuously or support their employees to learn, struggle to remain competitive in some circumstances (e.g., Covid 19). Some research showed that LO is more productive than the traditional firm. But there is no study conducted to measure the impact of learning organization practice on Employees’ job satisfaction in Bangladesh. This empirical study will measure the practice of LO and its impact on employees’ job satisfaction in the banking sector. To measure the practice of LO and its impacts on job satisfaction, the indicators are grouped under seven dimensions of LO that identified by Watkins and Marsik (2003). Multiple regression analysis has been conducted to measure the impact of LO dimensions on the job satisfaction of employees. The study reveals that commercial banks in Bangladesh practice moderate level of learning organization although there is a significant impact of learning organization practice on employees’ job satisfaction. Research implies that learning organization practice is one of the important drivers of competitive advantage. Organizations that integrate all the dimensions into their organizational systems, culture and structure can better satisfy their employees than their competitors. The study also suggests for similar studies in other sectors and countries.
0.3753575384616852 E-Payment Services Among Undergraduate Students in Palestine: Survey Study This work aims to explore the extend of knowledge that undergraduate students at local Universities have about electronic payment (e-payment) methods. The population is chosen to be the undergraduate students studying at the faculty of information technology (IT) and engineering faculty at both the Islamic University of Gaza and Gaza University in Gaza, Palestine. To satisfy the aim of this work, electronic questionnaire (e-questionnaire) is built and distributed to the population of this study. The retrieved questionnaires were 10% of the population of the study. The collected data have been analyzed using SPSS program. The results showed that the students trust the e-payment services but they lack the needed knowledge about the system and its usage. To over this deficiency, we recommend that local universities should provide training courses and specialized lectures given by experts in the field to the students. In addition, we recommend that local universities introduce new courses to the students in which they learn about e-commerce, e-banking and e-payment methods.
0.37568485736846924 Employee Retention: A Study of Indian Insurance Industry The aim of this paper is to understand the need of employee retention in insurance industry in India. Employee retention has ever been termed as complex phenomenon and always intrigued HR managers. General Insurance industry in India is sunrise industry. Post-liberalization, the insurance industry in India has recorded significant growth which could not have been possible without efforts of human resources involved in it. The factors which make employees to stay in organization are known as retention factors. An attempt has been made to study the influencing factors like effect of compensation & rewards, Job security, working environment & work life balance, on the employees of insurance companies which they feel are more relevant & realistic to stay in the organization. A study of employees working in various general insurance companies in Ahmedabad region has been carried out to measure the influence of retention factors on employees’ retention. The data collected is analyzed using correlation analysis, hypothesis testing & multiple regression.
0.3824041485786438 E-Payment Services Among Undergraduate Students in Palestine: Survey Study This work aims to explore the extend of knowledge that undergraduate students at local Universities have about electronic payment (e-payment) methods. The population is chosen to be the undergraduate students studying at the faculty of information technology (IT) and engineering faculty at both the Islamic University of Gaza and Gaza University in Gaza, Palestine. To satisfy the aim of this work, an electronic questionnaire (e-questionnaire) is built and distributed to the population of this study. The retrieved questionnaires were 10% of the population of the study. The collected data have been analyzed using SPSS program. The results showed that the students trust the e-payment services but they lack the needed knowledge about the system and its usage. To over this deficiency, we recommend that local universities should provide training courses and specialized lectures given by experts in the field to the students. In addition, we recommend that local universities introduce new courses to the students in which they learn about e-commerce, e-banking and e-payment methods.
0.3857731819152832 Effects of a blend of essential oils in milk replacer on performance, rumen fermentation, blood parameters and health scores of dairy heifers Abstract The objective of this study was to evaluate how the inclusion of a blend of essential oils in milk replacer (MR) affects different outcomes of dairy heifers. The outcomes evaluated: feed intake, performance, body development, blood cells and metabolites, insulin-like growth factor-1 (IGF-1), rumen fermentation, fecal scores and respiratory scores. All outcomes were evaluated during pre-weaning (4 – 60 d of age), and carryover effects on post-weaning (61 – 90 d of age) periods. The experimental units utilized were 29 newborn Holstein × Gyr crossbred dairy heifers, with genetic composition of 5/8 or more Holstein and 3/8 or less Gyr and body weight (BW) at birth of 32.2 ± 5.2 kg. Experimental units were randomly assigned to either a control group (CON, n = 15) or a treatment group, consisting of supplementation of a blend of essential oils (BEO, n = 14) with1 g/d/calf (Apex Calf, Adisseo, China). During the pre-weaning phase, all heifers were fed 5 L of MR/d reconstituted at 15% (dry matter basis), divided into two equal meals. Water and starter were provided ad libitum . During the post-weaning, animals received a maximum 3 kg of starter/d, and ad libitum corn silage, divided into two meals. The outcomes feed intake, fecal and respiratory scores were evaluated daily. BW was measured every three days, while body development was recorded weekly. Blood samples were collected on 0, 30 and 60 d of age for total blood cell count, weekly to determinate ß-hydroxybutyrate, urea and glucose, and biweekly for IGF-1. Ruminal parameters (pH, volatile fatty acids, ammonia-N and acetate:proprionate proportion - C2:C3) were measured each 14 days. A randomized complete block design with an interaction between treatment and week was the experimental method of choice to test the hypothesis of effect of BEO on all outcomes. An ANOVA procedure was used for continuous outcomes and a non-parametric test was used for the ordered categorical outcomes both adopting a C.I. = 95%. Results indicated that there was not enough evidence to accept the alternative hypothesis of effect of BEO in MR on: feed intake, performance, body development and blood metabolites during both pre-weaning and post-weaning periods. However, results indicated that the inclusion of BEO in MR significantly affects the proportion of C2:C3 during pre and post-weaning (P ≤ 0.05). Similarly, the effect is significant for basophil (P ≤ 0.001) and platelet (P ≤ 0.05) counts in pre-weaning. The interaction between week and treatment was also significant for lymphocytes (P ≤ 0.001), revealing a long-term treatment immunological effect. Lastly, the effect on fecal scores was also significant (P ≤ 0.05) during pre-weaning, with lower values for BEO. BEO contributed for ruminal manipulation on pre-weaning and carry over effect on post-weaning; immunity improvement and a decrease morbidity of neonatal diarrhea in pre-weaning phase.
Cf. https://www.sbert.net/docs/usage/semantic_textual_similarity.html
Detects duplicated content (like different versions of preprints) and publications that are similar in some aspect (topic, language, …).
= util.dot_score(embeddings[:100], embeddings[:100])
pairwise_scores
= []
pairs for i in range(len(pairwise_scores)-1):
for j in range(i+1, len(pairwise_scores)):
'index': [i, j], 'score': pairwise_scores[i][j]})
pairs.append({
= sorted(pairs, key=lambda x: x['score'], reverse=True)
pairs
def results(n=50, slen=120):
def shorten(s):
if len(s) < slen:
return s
else:
return s[:slen] + "..."
for pair in pairs[:n]:
= pair['index']
i, j yield {"i":i, "j":j, "s[i]": shorten(sentences[i]), "s[j]": shorten(sentences[j]), "score": float(pair['score'])}
= pd.DataFrame(results())
results_df results_df.to_html()
i | j | s[i] | s[j] | score | |
---|---|---|---|---|---|
0 | 6 | 7 | An Optimization Method Combining RSSI and PDR Data to Estimate Distance Between Smart Devices Distance estimation method… | An Optimization Method Combining RSSI and PDR Data to Estimate Distance Between Smart Devices Distance estimation method… | 1.000000 |
1 | 25 | 26 | Free Energy Landscapes for RBD Opening in SARS-CoV-2 Spike Glycoprotein Simulations Suggest Key Interactions and a Poten… | Free Energy Landscapes for RBD Opening in SARS-CoV-2 Spike Glycoprotein Simulations Suggest Key Interactions and a Poten… | 1.000000 |
2 | 8 | 15 | DAMPAK PANDEMI COVID-19 TERHADAP SEKTOR PENDIDIKAN TERUTAMA BAGI PELAJAR <p>Pandemi covid-19 tentunya bukan hanya mereng… | DAMPAK PANDEMI COVID-19 TERHADAP SEKTOR PENDIDIKAN TERUTAMA BAGI PELAJAR <p>Pandemi covid-19 tentunya bukan hanya mereng… | 0.998892 |
3 | 14 | 24 | MEMOTIVASI DIRI DAN MENGEMBANGKAN POTENSI DIRI DIKALA PANDEMI <p>Tujuan penelitian ini adalah meningkatkan motivasi diri… | MOTIVASI BERPRESTASI MAHASISWA DI MASA PANDEMI <p>Pada masa pandemi Covid-19 ini, pembelajaran dilakukan secara daring d… | 0.910311 |
4 | 15 | 61 | DAMPAK PANDEMI COVID-19 TERHADAP SEKTOR PENDIDIKAN TERUTAMA BAGI PELAJAR <p>Pandemi covid-19 tentunya bukan hanya mereng… | MEMERANGI TITIK JENUH KULIAH ONLINE DI MASA PANDEMI COVID 19 <p>Wabah pandemi Covid-19 ini mengakibatkan setiap orang ha… | 0.900757 |
5 | 5 | 27 | Sars-CoV-2 Vaccines: Time to Consider a Child-Focused Strategy With the arrival of SARS-CoV-2 vaccines, a new stage of t… | Citizen science initiative points at childhood BCG vaccination as a risk factor for COVID-19 Current results do not prov… | 0.899988 |
6 | 5 | 65 | Sars-CoV-2 Vaccines: Time to Consider a Child-Focused Strategy With the arrival of SARS-CoV-2 vaccines, a new stage of t… | External validity of phase III trials on vaccines against SARS-CoV-2 to a middle-aged and elderly Western European popul… | 0.897312 |
7 | 8 | 10 | DAMPAK PANDEMI COVID-19 TERHADAP SEKTOR PENDIDIKAN TERUTAMA BAGI PELAJAR <p>Pandemi covid-19 tentunya bukan hanya mereng… | MUNCULNYA CULTURE SHOCK PADA MAHASISWA BARU DALAM PERKULIAHAN DARING SELAMA PANDEMI COVID-19 <p>Culture Shock adalah kon… | 0.895884 |
8 | 10 | 15 | MUNCULNYA CULTURE SHOCK PADA MAHASISWA BARU DALAM PERKULIAHAN DARING SELAMA PANDEMI COVID-19 <p>Culture Shock adalah kon… | DAMPAK PANDEMI COVID-19 TERHADAP SEKTOR PENDIDIKAN TERUTAMA BAGI PELAJAR <p>Pandemi covid-19 tentunya bukan hanya mereng… | 0.895678 |
9 | 41 | 71 | Sex differences and Psychological Stress: Responses to the COVID-19 epidemic in China <p><strong>Background</strong>Abo… | The effects of the COVID-19 pandemic on mental health: a web-based study among Romanian adults (Preprint) <sec> BACKGROU… | 0.893713 |
10 | 8 | 61 | DAMPAK PANDEMI COVID-19 TERHADAP SEKTOR PENDIDIKAN TERUTAMA BAGI PELAJAR <p>Pandemi covid-19 tentunya bukan hanya mereng… | MEMERANGI TITIK JENUH KULIAH ONLINE DI MASA PANDEMI COVID 19 <p>Wabah pandemi Covid-19 ini mengakibatkan setiap orang ha… | 0.890895 |
11 | 8 | 24 | DAMPAK PANDEMI COVID-19 TERHADAP SEKTOR PENDIDIKAN TERUTAMA BAGI PELAJAR <p>Pandemi covid-19 tentunya bukan hanya mereng… | MOTIVASI BERPRESTASI MAHASISWA DI MASA PANDEMI <p>Pada masa pandemi Covid-19 ini, pembelajaran dilakukan secara daring d… | 0.884889 |
12 | 66 | 75 | SARS-CoV-2-induced impaired immune response by Prostaglandin E2 is accelerated by age, male sex and air pollution <p>The… | SARS-CoV-2 Nsp5 Protein Causes Acute Lung Inflammation: A Mathematical Model In the present work we propose a mathematic… | 0.884857 |
13 | 64 | 87 | Factors that influence the duration of symptom resolution in COVID-19 patients in Ethiopia: A follow up study involving … | Unraveling COVID-19-related hospital costs: The impact of clinical and demographic conditions ABSTRACT Introduction Alt… | 0.884020 |
14 | 15 | 24 | DAMPAK PANDEMI COVID-19 TERHADAP SEKTOR PENDIDIKAN TERUTAMA BAGI PELAJAR <p>Pandemi covid-19 tentunya bukan hanya mereng… | MOTIVASI BERPRESTASI MAHASISWA DI MASA PANDEMI <p>Pada masa pandemi Covid-19 ini, pembelajaran dilakukan secara daring d… | 0.882654 |
15 | 64 | 70 | Factors that influence the duration of symptom resolution in COVID-19 patients in Ethiopia: A follow up study involving … | Mortality Rate Due to Pulmonary Fibrosis Associated with Sars-Cov-2 Infection: Scope of Best Fit Regression <p>In this a… | 0.880081 |
16 | 51 | 78 | Tipping the Scales: A Theoretical Model to Describe the Opposing Effects of the COVID-19 Pandemic on Mortality <p><stron… | Longitudinal variability in mortality predicts Covid-19 deaths Abstract Within Europe, death rates due to covid-19 vary … | 0.875515 |
17 | 14 | 15 | MEMOTIVASI DIRI DAN MENGEMBANGKAN POTENSI DIRI DIKALA PANDEMI <p>Tujuan penelitian ini adalah meningkatkan motivasi diri… | DAMPAK PANDEMI COVID-19 TERHADAP SEKTOR PENDIDIKAN TERUTAMA BAGI PELAJAR <p>Pandemi covid-19 tentunya bukan hanya mereng… | 0.874045 |
18 | 8 | 14 | DAMPAK PANDEMI COVID-19 TERHADAP SEKTOR PENDIDIKAN TERUTAMA BAGI PELAJAR <p>Pandemi covid-19 tentunya bukan hanya mereng… | MEMOTIVASI DIRI DAN MENGEMBANGKAN POTENSI DIRI DIKALA PANDEMI <p>Tujuan penelitian ini adalah meningkatkan motivasi diri… | 0.872154 |
19 | 42 | 89 | Sex differences in viral entry protein expression and host transcript responses to SARS-CoV-2 <p>Epidemiological studies… | Smoking and SARS-CoV-2 Impair Dendritic Cells and Regulate DC-SIGN Expression in Tissues Summary The current spreading n… | 0.870097 |
20 | 16 | 95 | Pregnancy during the pandemic: The impact of COVID-19-related stress on risk for prenatal depression <p>Pregnant women m… | Mechanisms of Parental Stress During and After the COVID-19 Lockdown: A two-wave longitudinal study <p>Background: In th… | 0.868622 |
21 | 63 | 80 | Optimality in COVID-19 vaccination strategies determined by heterogeneity in human-human interaction networks <p>Interac… | The interplay between subcritical fluctuations and import: understanding COVID-19 epidemiology dynamics Abstract The ef… | 0.866891 |
22 | 41 | 95 | Sex differences and Psychological Stress: Responses to the COVID-19 epidemic in China <p><strong>Background</strong>Abo… | Mechanisms of Parental Stress During and After the COVID-19 Lockdown: A two-wave longitudinal study <p>Background: In th… | 0.864205 |
23 | 80 | 83 | The interplay between subcritical fluctuations and import: understanding COVID-19 epidemiology dynamics Abstract The ef… | Simulating Retarded SEIRS model for COVID-19: will the second epidemic happen? Abstract In this paper, we want to simula… | 0.864152 |
24 | 31 | 68 | Public adherence to precautionary measures and preventive guidelines against COVID-19 in Sudan: An application of the He… | Adoption of Preventive Measures During the Very Early Phase of the COVID-19 Outbreak in China: National Cross-sectional … | 0.861506 |
25 | 79 | 84 | Genomic Diversity of the SARS-CoV-2 in Turkey and the Impact of Virus Genome Mutations on Clinical Outcomes Summary COVI… | S gene dropout patterns in SARS-CoV-2 tests suggest spread of the H69del/V70del mutation in the US Recently, multiple no… | 0.861408 |
26 | 2 | 3 | Twitter Users Display Desensitization to Bad Health News: An Observational Study (Preprint) <sec> BACKGROUND <p>Among th… | COVID-19 Vaccine Hesitancy in Canada: Content Analysis of Tweets Using the Theoretical Domains Framework (Preprint) <sec… | 0.858481 |
27 | 41 | 68 | Sex differences and Psychological Stress: Responses to the COVID-19 epidemic in China <p><strong>Background</strong>Abo… | Adoption of Preventive Measures During the Very Early Phase of the COVID-19 Outbreak in China: National Cross-sectional … | 0.857303 |
28 | 42 | 58 | Sex differences in viral entry protein expression and host transcript responses to SARS-CoV-2 <p>Epidemiological studies… | Intersection between ageing-related Genes and SARS-CoV-2 Interactome: Is it higher than expected? <p><strong>Background:… | 0.857153 |
29 | 75 | 89 | SARS-CoV-2 Nsp5 Protein Causes Acute Lung Inflammation: A Mathematical Model In the present work we propose a mathematic… | Smoking and SARS-CoV-2 Impair Dendritic Cells and Regulate DC-SIGN Expression in Tissues Summary The current spreading n… | 0.856863 |
30 | 66 | 89 | SARS-CoV-2-induced impaired immune response by Prostaglandin E2 is accelerated by age, male sex and air pollution <p>The… | Smoking and SARS-CoV-2 Impair Dendritic Cells and Regulate DC-SIGN Expression in Tissues Summary The current spreading n… | 0.852484 |
31 | 40 | 53 | COVID-19 – Protocol for Observational Studies Utilising Near Real-Time Electronic Australian General Practice Data to Pr… | Citizen workshops in public libraries to disseminate and discuss primary care research results: a scaling-up study <h2>B… | 0.852168 |
32 | 31 | 99 | Public adherence to precautionary measures and preventive guidelines against COVID-19 in Sudan: An application of the He… | EVALUATION OF KNOWLEDGE, ATTITUDES AND BEHAVIOURS ABOUT COVID-19 AMONG CANCER PATIENTS Background COVID-19 pandemic is e… | 0.849881 |
33 | 58 | 89 | Intersection between ageing-related Genes and SARS-CoV-2 Interactome: Is it higher than expected? <p><strong>Background:… | Smoking and SARS-CoV-2 Impair Dendritic Cells and Regulate DC-SIGN Expression in Tissues Summary The current spreading n… | 0.849775 |
34 | 17 | 95 | What drives beliefs in COVID-19 conspiracy theories? The role of psychotic-like experiences and confinement-related fact… | Mechanisms of Parental Stress During and After the COVID-19 Lockdown: A two-wave longitudinal study <p>Background: In th… | 0.848155 |
35 | 63 | 82 | Optimality in COVID-19 vaccination strategies determined by heterogeneity in human-human interaction networks <p>Interac… | Contact Tracing of COVID-19 in Karnataka, India: Superspreading and Determinants of Infectiousness and Symptomaticity Br… | 0.847457 |
36 | 81 | 85 | Staff to staff transmission as a driver of healthcare worker infections with COVID-19 ABSTRACT Objectives To investigat… | Acceptable Performance of the Abbott ID NOW Among Symptomatic Individuals with Confirmed COVID-19 INTRODUCTION Point of … | 0.845383 |
37 | 62 | 68 | Study on Awareness of COVID-19 and Compliance with Social Distancing during COVID-19 Pandemic in Indonesia <p>To slow do… | Adoption of Preventive Measures During the Very Early Phase of the COVID-19 Outbreak in China: National Cross-sectional … | 0.845025 |
38 | 27 | 65 | Citizen science initiative points at childhood BCG vaccination as a risk factor for COVID-19 Current results do not prov… | External validity of phase III trials on vaccines against SARS-CoV-2 to a middle-aged and elderly Western European popul… | 0.843634 |
39 | 16 | 41 | Pregnancy during the pandemic: The impact of COVID-19-related stress on risk for prenatal depression <p>Pregnant women m… | Sex differences and Psychological Stress: Responses to the COVID-19 epidemic in China <p><strong>Background</strong>Abo… | 0.843199 |
40 | 39 | 53 | Using Web Conferencing to Engage Aboriginal and Torres Strait Islander Young People in Research: A Feasibility Study <p>… | Citizen workshops in public libraries to disseminate and discuss primary care research results: a scaling-up study <h2>B… | 0.843191 |
41 | 70 | 75 | Mortality Rate Due to Pulmonary Fibrosis Associated with Sars-Cov-2 Infection: Scope of Best Fit Regression <p>In this a… | SARS-CoV-2 Nsp5 Protein Causes Acute Lung Inflammation: A Mathematical Model In the present work we propose a mathematic… | 0.842993 |
42 | 2 | 71 | Twitter Users Display Desensitization to Bad Health News: An Observational Study (Preprint) <sec> BACKGROUND <p>Among th… | The effects of the COVID-19 pandemic on mental health: a web-based study among Romanian adults (Preprint) <sec> BACKGROU… | 0.842328 |
43 | 71 | 73 | The effects of the COVID-19 pandemic on mental health: a web-based study among Romanian adults (Preprint) <sec> BACKGROU… | Youths’ and Parents’ Experiences and Perceived Effects of Internet-Based Cognitive Behavioral Therapy for Anxiety Disord… | 0.841731 |
44 | 63 | 83 | Optimality in COVID-19 vaccination strategies determined by heterogeneity in human-human interaction networks <p>Interac… | Simulating Retarded SEIRS model for COVID-19: will the second epidemic happen? Abstract In this paper, we want to simula… | 0.839534 |
45 | 14 | 19 | MEMOTIVASI DIRI DAN MENGEMBANGKAN POTENSI DIRI DIKALA PANDEMI <p>Tujuan penelitian ini adalah meningkatkan motivasi diri… | Komunikasi Termediasi dan Momentum Revitalisasi Pembelajaran Daring <p>Alkhajar, E. N. S. (2020). Komunikasi Termediasi … | 0.838842 |
46 | 24 | 61 | MOTIVASI BERPRESTASI MAHASISWA DI MASA PANDEMI <p>Pada masa pandemi Covid-19 ini, pembelajaran dilakukan secara daring d… | MEMERANGI TITIK JENUH KULIAH ONLINE DI MASA PANDEMI COVID 19 <p>Wabah pandemi Covid-19 ini mengakibatkan setiap orang ha… | 0.838382 |
47 | 17 | 71 | What drives beliefs in COVID-19 conspiracy theories? The role of psychotic-like experiences and confinement-related fact… | The effects of the COVID-19 pandemic on mental health: a web-based study among Romanian adults (Preprint) <sec> BACKGROU… | 0.837906 |
48 | 0 | 75 | The kidnapping of mitochondrial function associated to the SARS-CoV-2 infection <p>Severe Acute Respiratory Syndrome Cor… | SARS-CoV-2 Nsp5 Protein Causes Acute Lung Inflammation: A Mathematical Model In the present work we propose a mathematic… | 0.837089 |
49 | 34 | 53 | Conceptual Framework for Task Shifting and Task Sharing: An International Delphi Study <p><strong>Background</strong></p… | Citizen workshops in public libraries to disseminate and discuss primary care research results: a scaling-up study <h2>B… | 0.836426 |
# cf. https://github.com/UKPLab/sentence-transformers/blob/master/examples/applications/clustering/fast_clustering.py
if COMPUTE_CLUSTERS:
print("Start clustering")
= time.time()
start_time
#Two parameters to tune:
#min_cluster_size: Only consider cluster that have at least 25 elements
#threshold: Consider sentence pairs with a cosine-similarity larger than threshold as similar
= util.community_detection(torch.Tensor(embeddings), min_community_size=100, threshold=0.85, batch_size=1024)
clusters
print("Clustering done after {:.2f} sec".format(time.time() - start_time))
with open(OUTPUT_FILE_CLUSTERS, "w") as f:
f.write(json.dumps(clusters))else:
with open(OUTPUT_FILE_CLUSTERS, "r") as f:
= json.load(f) clusters
#Print for all clusters the top 3 and bottom 3 elements
def sanitize(s):
= re.sub("<[^>]*>", "", s)
s = s.replace("\n", " ")
s return re.escape(s)
for i, cluster in enumerate(clusters):
print("#### Cluster {} ({} Elements) \n\n".format(i+1, len(cluster)))
for sentence_id in cluster[0:3]:
print("- ", sanitize(sentences[sentence_id][:80]), "...", "\n")
print("...", "\n")
for sentence_id in cluster[-3:]:
print("- ", sanitize(sentences[sentence_id][:80]), "...", "\n")
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The t-SNE algorithm is a way to make complex data easier to understand by simplifying it and showing its most important features. It does this by taking data with many dimensions and turning it into a picture with fewer dimensions that we can see. This makes it easier to see patterns and relationships in the data. The t-SNE algorithm is often used to explore and visualize high-dimensional data.
T-SNE plots are pictures of complex data that can help us understand it better. However, they can be hard to read because they can get crowded, they don’t always show the big picture, and it’s not always clear what the points in the plot mean. So, we need to be careful when we interpret them.
This guide on distill.pub shows very nicely how to use T-SNE effectively.
from sklearn.manifold import TSNE
if COMPUTE_TSNE:
= torch.Tensor(embeddings)
X = TSNE(n_components=2, learning_rate='auto',init='random', perplexity=50, n_iter=1000).fit_transform(X)
X_embedded
np.save(OUTPUT_FILE_TSNE, X_embedded)else:
= np.load(OUTPUT_FILE_TSNE) X_embedded
= pd.read_csv("../data/covid_preprints_dimensions_categories.csv")
df_cats = df_cats[df_cats.category_label == "category_hra"].drop_duplicates(subset=["id"])
df_hra = df_cats[df_cats.category_label == "category_for"].drop_duplicates(subset=["id"])
df_x
= df
df_subset 'tsne-2d-one'] = X_embedded[:,0]
df_subset['tsne-2d-two'] = X_embedded[:,1]
df_subset["has_resulting_publication"] = -pd.isna(df_subset.resulting_publication_doi)
df_subset[= pd.merge(df_subset, df_hra, on="id") df_subset
=(16,10))
plt.figure(figsize
sns.scatterplot(="tsne-2d-one", y="tsne-2d-two",
x=df_subset,
data=0.1,
alpha=sns.color_palette("hls", len(df_subset["category_name"].unique())),
palette="full",
legend="category_name"
hue )
<AxesSubplot:xlabel='tsne-2d-one', ylabel='tsne-2d-two'>