# JournalGate JournalGate is a modern, dynamic platform for discovering and listening to academic research papers podcasts/summaries. It features a curated collection of papers with metadata, abstracts, and multi-lingual audio summaries. ## Q&A about JournalGate **Q: What is JournalGate?** A: JournalGate is an online platform that hosts academic research papers podcasts/summaries, providing an easy-to-use interface for discovering papers, viewing abstracts, and listening to 10-minute audio explanations of the research in multiple languages. **Q: How are papers organized?** A: Papers are categorized by research subjects and tags. They can be filtered by year, access level (free or premium), and whether they have audio capabilities. **Q: What features does JournalGate offer for accessibility?** A: JournalGate explains research papers in 10-minute audio formats available in multiple languages, allowing users to quickly grasp complex research on the go. ## Available Papers ### Integrin Alpha8 Beta1 (81): An In-Depth Review of an Overlooked RGD-Binding Receptor - **Authors:** Iman Ezzat, Marisa Zallocchi - **DOI:** https://doi.org/10.32604/biocell.2025.062325 - **Date:** 2025-05-01 - **Topics:** Medicine **Abstract:** Integrins are heterodimeric transmembrane receptors that mediate bidirectional interactions between the intracellular cytoskeletal array and the extracellular matrix. This review focuses on recent advances regarding the function of integrin alpha 8 beta 1 (α8β1) during organ development, with a particular interest in kidney and inner ear development. The paper discusses α8β1’s role in injury and disease, its importance for mesenchymal to epithelial transition during cancer development, and its potential as a diagnostic and therapeutic tool for hearing function and disease elimination. --- ### Time-based Fairness Improves Performance in Multi-rate WLANs - **Authors:** Godfrey Tan, John V. Guttag - **DOI:** https://doi.org/10.48550/arxiv.2603.08623 - **Date:** 2026-03-09 - **Topics:** Computer Science **Abstract:** The performance seen by individual clients on a wireless local area network (WLAN) is heavily influenced by the manner in which wireless channel capacity is allocated. The popular MAC protocol DCF (Distributed Coordination Function) used in 802.11 networks provides equal long-term transmission opportunities to competing nodes when all nodes experience similar channel conditions. When similar-sized packets are also used, DCF leads to equal achieved throughputs (throughput-based fairness) among contending nodes. Because of varying indoor channel conditions, the 802.11 standard supports multiple data transmission rates to exploit the trade-off between data rate and bit error rate. This leads to considerable rate diversity, particularly when the network is congested. Under such conditions, throughput-based fairness can lead to drastically reduced aggregate throughput. In this paper, we argue the advantages of time-based fairness, in which each competing node receives an equal share of the wireless channel occupancy time. We demonstrate that this notion of fairness can lead to significant improvements in aggregate performance while still guaranteeing that no node receives worse channel access than it would in a single-rate WLAN. We also describe our algorithm, TBR (Time-based Regulator), which runs on the AP and works with any MAC protocol to provide time-based fairness by regulating packets. Through experiments, we show that our practical and backward compatible implementation of TBR in conjunction with an existing implementation of DCF achieves time-based fairness. --- ### Entanglement as Topology: Hopf Linking as the Geometric Origin of Quantum Correlation - **Authors:** Alexander Novickis - **DOI:** https://doi.org/10.5281/zenodo.20549411 - **Date:** 2026-06-05 - **Topics:** Uncategorized **Abstract:** This research proposes that quantum entanglement has a geometric origin rooted in the topological linking of soliton field configurations within a shared Hopf fiber bundle. By framing particles as topological solitons governed by the Hopf fibration, the author suggests that the linking of preimage curves results in topological inseparability which manifests as entanglement. This framework aims to reproduce essential quantum properties—such as nonlocality, the Born rule, and the no-cloning theorem—as consequences of topology, effectively removing 'spooky action at a distance' by showing that distance along the shared Hopf fiber is zero. --- ### Modules for Experiments in Stellar Astrophysics (MESA) (r26.04.1) - **Authors:** Paxton, B. - **DOI:** https://doi.org/10.5281/zenodo.19685576 - **Date:** 2026-04-23 - **Topics:** Uncategorized **Abstract:** A software suite designed for conducting experiments in stellar astrophysics. MESA provides modular open-source tools for simulating stellar evolution and other astrophysical phenomena. This specific record refers to version r26.04.1 of the software. --- ### Deliberative Democracy or Agonistic Pluralism? - **Authors:** Chantal Mouffe - **DOI:** https://doi.org/10.4324/9781003763987-14 - **Date:** 2026-02-02 - **Topics:** Uncategorized **Abstract:** As testified by the increasing success of the extreme right in several countries, western societies are witnessing a growing disaffection with traditional democratic institutions. --- ### Just a moment... - **Authors:** Information not available - **DOI:** https://doi.org/10.1145/3788687 - **Date:** 2026-05-07 - **Topics:** Uncategorized **Abstract:** The provided content is a Cloudflare security challenge page (bot protection) and does not contain scientific research data. Access to the paper associated with DOI 10.1145/3788687 was restricted at the time of the snapshot, preventing the extraction of research-specific metadata such as a summary or author list. --- ### More than 75 percent decline over 27 years in total flying insect biomass in protected areas - **Authors:** Caspar A. Hallmann, Martin Sorg, Eelke Jongejans, H. Siepel, Nick Hofland, Heinz Schwan, Werner Stenmans, Antoine Müller, Hubert Sumser, Thomas Hörren - **DOI:** https://doi.org/10.1371/journal.pone.0185809 - **Date:** 2017-10-18 - **Topics:** Biology **Abstract:** Global declines in insects have sparked wide interest among scientists, politicians, and the general public. Loss of insect diversity and abundance is expected to provoke cascading effects on food webs and to jeopardize ecosystem services. Our understanding of the extent and underlying causes of this decline is based on the abundance of single species or taxonomic groups only, rather than changes in insect biomass which is more relevant for ecological functioning. Here, we used a standardized protocol to measure total insect biomass using Malaise traps, deployed over 27 years in 63 nature protection areas in Germany (96 unique location-year combinations) to infer on the status and trend of local entomofauna. Our analysis estimates a seasonal decline of 76%, and mid-summer decline of 82% in flying insect biomass over the 27 years of study. We show that this decline is apparent regardless of habitat type, while changes in weather, land use, and habitat characteristics cannot explain this overall decline. This yet unrecognized loss of insect biomass must be taken into account in evaluating declines in abundance of species depending on insects as a food source, and ecosystem functioning in the European landscape. --- ### The Fear of COVID-19 Scale: Development and Initial Validation - **Authors:** Daniel Kwasi Ahorsu, Chung‐Ying Lin, Vida Imani, Mohsen Saffari, Mark D. Griffiths, Amir H. Pakpour - **DOI:** https://doi.org/10.1007/s11469-020-00270-8 - **Date:** 2020-03-27 - **Topics:** Psychology **Abstract:** Abstract Background The emergence of the COVID-19 and its consequences has led to fears, worries, and anxiety among individuals worldwide. The present study developed the Fear of COVID-19 Scale (FCV-19S) to complement the clinical efforts in preventing the spread and treating of COVID-19 cases. Methods The sample comprised 717 Iranian participants. The items of the FCV-19S were constructed based on extensive review of existing scales on fears, expert evaluations, and participant interviews. Several psychometric tests were conducted to ascertain its reliability and validity properties. Results After panel review and corrected item-total correlation testing, seven items with acceptable corrected item-total correlation (0.47 to 0.56) were retained and further confirmed by significant and strong factor loadings (0.66 to 0.74). Also, other properties evaluated using both classical test theory and Rasch model were satisfactory on the seven-item scale. More specifically, reliability values such as internal consistency ( α = .82) and test–retest reliability (ICC = .72) were acceptable. Concurrent validity was supported by the Hospital Anxiety and Depression Scale (with depression, r = 0.425 and anxiety, r = 0.511) and the Perceived Vulnerability to Disease Scale (with perceived infectability, r = 0.483 and germ aversion, r = 0.459). Conclusion The Fear of COVID-19 Scale, a seven-item scale, has robust psychometric properties. It is reliable and valid in assessing fear of COVID-19 among the general population and will also be useful in allaying COVID-19 fears among individuals. --- ### Knowledge, attitudes, and practices towards COVID-19 among Chinese residents during the rapid rise period of the COVID-19 outbreak: a quick online cross-sectional survey - **Authors:** Bao‐Liang Zhong, Wei Luo, Hai-Mei Li, Qian-Qian Zhang, Xiao-Ge Liu, Wen-Tian Li, Yi Li - **DOI:** https://doi.org/10.7150/ijbs.45221 - **Date:** 2020-01-01 - **Topics:** Psychology **Abstract:** Unprecedented measures have been adopted to control the rapid spread of the ongoing COVID-19 epidemic in China. People's adherence to control measures is affected by their knowledge, attitudes, and practices (KAP) towards COVID-19. In this study, we investigated Chinese residents' KAP towards COVID-19 during the rapid rise period of the outbreak. An online sample of Chinese residents was successfully recruited via the authors' networks with residents and popular media in Hubei, China. A self-developed online KAP questionnaire was completed by the participants. The knowledge questionnaire consisted of 12 questions regarding the clinical characteristics and prevention of COVID-19. Assessments on residents' attitudes and practices towards COVID-19 included questions on confidence in winning the battle against COVID-19 and wearing masks when going out in recent days. Among the survey completers (n=6910), 65.7% were women, 63.5% held a bachelor degree or above, and 56.2% engaged in mental labor. The overall correct rate of the knowledge questionnaire was 90%. The majority of the respondents (97.1%) had confidence that China can win the battle against COVID-19. Nearly all of the participants (98.0%) wore masks when going out in recent days. In multiple logistic regression analyses, the COVID-19 knowledge score (OR: 0.75-0.90, P<0.001) was significantly associated with a lower likelihood of negative attitudes and preventive practices towards COVID-2019. Most Chinese residents of a relatively high socioeconomic status, in particular women, are knowledgeable about COVID-19, hold optimistic attitudes, and have appropriate practices towards COVID-19. Health education programs aimed at improving COVID-19 knowledge are helpful for Chinese residents to hold optimistic attitudes and maintain appropriate practices. Due to the limited sample representativeness, we must be cautious when generalizing these findings to populations of a low socioeconomic status. --- ### Effects of COVID-19 on College Students’ Mental Health in the United States: Interview Survey Study - **Authors:** Changwon Son, Sudeep Hegde, Alec Smith, Xiaomei Wang, Farzan Sasangohar - **DOI:** https://doi.org/10.2196/21279 - **Date:** 2020-08-15 - **Topics:** Psychology **Abstract:** BACKGROUND: Student mental health in higher education has been an increasing concern. The COVID-19 pandemic situation has brought this vulnerable population into renewed focus. OBJECTIVE: Our study aims to conduct a timely assessment of the effects of the COVID-19 pandemic on the mental health of college students. METHODS: We conducted interview surveys with 195 students at a large public university in the United States to understand the effects of the pandemic on their mental health and well-being. The data were analyzed through quantitative and qualitative methods. RESULTS: Of the 195 students, 138 (71%) indicated increased stress and anxiety due to the COVID-19 outbreak. Multiple stressors were identified that contributed to the increased levels of stress, anxiety, and depressive thoughts among students. These included fear and worry about their own health and of their loved ones (177/195, 91% reported negative impacts of the pandemic), difficulty in concentrating (173/195, 89%), disruptions to sleeping patterns (168/195, 86%), decreased social interactions due to physical distancing (167/195, 86%), and increased concerns on academic performance (159/195, 82%). To cope with stress and anxiety, participants have sought support from others and helped themselves by adopting either negative or positive coping mechanisms. CONCLUSIONS: Due to the long-lasting pandemic situation and onerous measures such as lockdown and stay-at-home orders, the COVID-19 pandemic brings negative impacts on higher education. The findings of our study highlight the urgent need to develop interventions and preventive strategies to address the mental health of college students. --- ### A tutorial on regularized partial correlation networks. - **Authors:** Sacha Epskamp, Eiko I. Fried - **DOI:** https://doi.org/10.1037/met0000167 - **Date:** 2018-03-29 - **Topics:** Psychology **Abstract:** Recent years have seen an emergence of network modeling applied to moods, attitudes, and problems in the realm of psychology. In this framework, psychological variables are understood to directly affect each other rather than being caused by an unobserved latent entity. In this tutorial, we introduce the reader to estimating the most popular network model for psychological data: the partial correlation network. We describe how regularization techniques can be used to efficiently estimate a parsimonious and interpretable network structure in psychological data. We show how to perform these analyses in R and demonstrate the method in an empirical example on posttraumatic stress disorder data. In addition, we discuss the effect of the hyperparameter that needs to be manually set by the researcher, how to handle non-normal data, how to determine the required sample size for a network analysis, and provide a checklist with potential solutions for problems that can arise when estimating regularized partial correlation networks. (PsycINFO Database Record (c) 2018 APA, all rights reserved). --- ### Youth Risk Behavior Surveillance — United States, 2017 - **Authors:** Laura Kann, Tim McManus, William A. Harris, Shari L. Shanklin, Katherine H. Flint, Barbara Queen, Richard Lowry, David Chyen, Lisa Whittle, Jemekia Thornton - **DOI:** https://doi.org/10.15585/mmwr.ss6708a1 - **Date:** 2018-06-14 - **Topics:** Psychology **Abstract:** PROBLEM: Health-risk behaviors contribute to the leading causes of morbidity and mortality among youth and adults in the United States. In addition, significant health disparities exist among demographic subgroups of youth defined by sex, race/ethnicity, and grade in school and between sexual minority and nonsexual minority youth. Population-based data on the most important health-related behaviors at the national, state, and local levels can be used to help monitor the effectiveness of public health interventions designed to protect and promote the health of youth at the national, state, and local levels. REPORTING PERIOD COVERED: September 2016-December 2017. DESCRIPTION OF THE SYSTEM: The Youth Risk Behavior Surveillance System (YRBSS) monitors six categories of priority health-related behaviors among youth and young adults: 1) behaviors that contribute to unintentional injuries and violence; 2) tobacco use; 3) alcohol and other drug use; 4) sexual behaviors related to unintended pregnancy and sexually transmitted infections (STIs), including human immunodeficiency virus (HIV) infection; 5) unhealthy dietary behaviors; and 6) physical inactivity. In addition, YRBSS monitors the prevalence of other health-related behaviors, obesity, and asthma. YRBSS includes a national school-based Youth Risk Behavior Survey (YRBS) conducted by CDC and state and large urban school district school-based YRBSs conducted by state and local education and health agencies. Starting with the 2015 YRBSS cycle, a question to ascertain sexual identity and a question to ascertain sex of sexual contacts were added to the national YRBS questionnaire and to the standard YRBS questionnaire used by the states and large urban school districts as a starting point for their questionnaires. This report summarizes results from the 2017 national YRBS for 121 health-related behaviors and for obesity, overweight, and asthma by demographic subgroups defined by sex, race/ethnicity, and grade in school and by sexual minority status; updates the numbers of sexual minority students nationwide; and describes overall trends in health-related behaviors during 1991-2017. This reports also summarizes results from 39 state and 21 large urban school district surveys with weighted data for the 2017 YRBSS cycle by sex and sexual minority status (where available). RESULTS: Results from the 2017 national YRBS indicated that many high school students are engaged in health-risk behaviors associated with the leading causes of death among persons aged 10-24 years in the United States. During the 30 days before the survey, 39.2% of high school students nationwide (among the 62.8% who drove a car or other vehicle during the 30 days before the survey) had texted or e-mailed while driving, 29.8% reported current alcohol use, and 19.8% reported current marijuana use. In addition, 14.0% of students had taken prescription pain medicine without a doctor's prescription or differently than how a doctor told them to use it one or more times during their life. During the 12 months before the survey, 19.0% had been bullied on school property and 7.4% had attempted suicide. Many high school students are engaged in sexual risk behaviors that relate to unintended pregnancies and STIs, including HIV infection. Nationwide, 39.5% of students had ever had sexual intercourse and 9.7% had had sexual intercourse with four or more persons during their life. Among currently sexually active students, 53.8% reported that either they or their partner had used a condom during their last sexual intercourse. Results from the 2017 national YRBS also indicated many high school students are engaged in behaviors associated with chronic diseases, such as cardiovascular disease, cancer, and diabetes. Nationwide, 8.8% of high school students had smoked cigarettes and 13.2% had used an electronic vapor product on at least 1 day during the 30 days before the survey. Forty-three percent played video or computer games or used a computer for 3 or more hours per day on an average school day for something that was not school work and 15.4% had not been physically active for a total of at least 60 minutes on at least 1 day during the 7 days before the survey. Further, 14.8% had obesity and 15.6% were overweight. The prevalence of most health-related behaviors varies by sex, race/ethnicity, and, particularly, sexual identity and sex of sexual contacts. Specifically, the prevalence of many health-risk behaviors is significantly higher among sexual minority students compared with nonsexual minority students. Nonetheless, analysis of long-term temporal trends indicates that the overall prevalence of most health-risk behaviors has moved in the desired direction. INTERPRETATION: Most high school students cope with the transition from childhood through adolescence to adulthood successfully and become healthy and productive adults. However, this report documents that some subgroups of students defined by sex, race/ethnicity, grade in school, and especially sexual minority status have a higher prevalence of many health-risk behaviors that might place them at risk for unnecessary or premature mortality, morbidity, and social problems (e.g., academic failure, poverty, and crime). PUBLIC HEALTH ACTION: YRBSS data are used widely to compare the prevalence of health-related behaviors among subpopulations of students; assess trends in health-related behaviors over time; monitor progress toward achieving 21 national health objectives; provide comparable state and large urban school district data; and take public health actions to decrease health-risk behaviors and improve health outcomes among youth. Using this and other reports based on scientifically sound data is important for raising awareness about the prevalence of health-related behaviors among students in grades 9-12, especially sexual minority students, among decision makers, the public, and a wide variety of agencies and organizations that work with youth. These agencies and organizations, including schools and youth-friendly health care providers, can help facilitate access to critically important education, health care, and high-impact, evidence-based interventions. --- ### Planck 2018 results - **Authors:** N. Aghanim, Y. Akrami, M. Ashdown, J. Aumont, C. Baccigalupi, M. Ballardini, A. J. Banday, R. B. Barreiro, N. Bartolo, S. Basak - **DOI:** https://doi.org/10.1051/0004-6361/201833910 - **Date:** 2020-04-03 - **Topics:** Physics **Abstract:** We present cosmological parameter results from the final full-mission Planck measurements of the cosmic microwave background (CMB) anisotropies, combining information from the temperature and polarization maps and the lensing reconstruction. Compared to the 2015 results, improved measurements of large-scale polarization allow the reionization optical depth to be measured with higher precision, leading to significant gains in the precision of other correlated parameters. Improved modelling of the small-scale polarization leads to more robust constraints on many parameters, with residual modelling uncertainties estimated to affect them only at the 0.5 σ level. We find good consistency with the standard spatially-flat 6-parameter ΛCDM cosmology having a power-law spectrum of adiabatic scalar perturbations (denoted “base ΛCDM” in this paper), from polarization, temperature, and lensing, separately and in combination. A combined analysis gives dark matter density Ω c h 2 = 0.120 ± 0.001, baryon density Ω b h 2 = 0.0224 ± 0.0001, scalar spectral index n s = 0.965 ± 0.004, and optical depth τ = 0.054 ± 0.007 (in this abstract we quote 68% confidence regions on measured parameters and 95% on upper limits). The angular acoustic scale is measured to 0.03% precision, with 100 θ * = 1.0411 ± 0.0003. These results are only weakly dependent on the cosmological model and remain stable, with somewhat increased errors, in many commonly considered extensions. Assuming the base-ΛCDM cosmology, the inferred (model-dependent) late-Universe parameters are: Hubble constant H 0 = (67.4 ± 0.5) km s −1 Mpc −1 ; matter density parameter Ω m = 0.315 ± 0.007; and matter fluctuation amplitude σ 8 = 0.811 ± 0.006. We find no compelling evidence for extensions to the base-ΛCDM model. Combining with baryon acoustic oscillation (BAO) measurements (and considering single-parameter extensions) we constrain the effective extra relativistic degrees of freedom to be N eff = 2.99 ± 0.17, in agreement with the Standard Model prediction N eff = 3.046, and find that the neutrino mass is tightly constrained to ∑ m ν < 0.12 eV. The CMB spectra continue to prefer higher lensing amplitudes than predicted in base ΛCDM at over 2 σ , which pulls some parameters that affect the lensing amplitude away from the ΛCDM model; however, this is not supported by the lensing reconstruction or (in models that also change the background geometry) BAO data. The joint constraint with BAO measurements on spatial curvature is consistent with a flat universe, Ω K = 0.001 ± 0.002. Also combining with Type Ia supernovae (SNe), the dark-energy equation of state parameter is measured to be w 0 = −1.03 ± 0.03, consistent with a cosmological constant. We find no evidence for deviations from a purely power-law primordial spectrum, and combining with data from BAO, BICEP2, and Keck Array data, we place a limit on the tensor-to-scalar ratio r 0.002 < 0.06. Standard big-bang nucleosynthesis predictions for the helium and deuterium abundances for the base-ΛCDM cosmology are in excellent agreement with observations. The Planck base-ΛCDM results are in good agreement with BAO, SNe, and some galaxy lensing observations, but in slight tension with the Dark Energy Survey’s combined-probe results including galaxy clustering (which prefers lower fluctuation amplitudes or matter density parameters), and in significant, 3.6 σ , tension with local measurements of the Hubble constant (which prefer a higher value). Simple model extensions that can partially resolve these tensions are not favoured by the Planck data. --- ### In situ click chemistry generation of cyclooxygenase-2 inhibitors - **Authors:** Atul Bhardwaj, Jatinder Kaur, Melinda Wuest, Frank Wuest - **DOI:** https://doi.org/10.1038/s41467-016-0009-6 - **Date:** 2017-02-09 - **Topics:** Chemistry **Abstract:** Cyclooxygenase-2 isozyme is a promising anti-inflammatory drug target, and overexpression of this enzyme is also associated with several cancers and neurodegenerative diseases. The amino-acid sequence and structural similarity between inducible cyclooxygenase-2 and housekeeping cyclooxygenase-1 isoforms present a significant challenge to design selective cyclooxygenase-2 inhibitors. Herein, we describe the use of the cyclooxygenase-2 active site as a reaction vessel for the in situ generation of its own highly specific inhibitors. Multi-component competitive-binding studies confirmed that the cyclooxygenase-2 isozyme can judiciously select most appropriate chemical building blocks from a pool of chemicals to build its own highly potent inhibitor. Herein, with the use of kinetic target-guided synthesis, also termed as in situ click chemistry, we describe the discovery of two highly potent and selective cyclooxygenase-2 isozyme inhibitors. The in vivo anti-inflammatory activity of these two novel small molecules is significantly higher than that of widely used selective cyclooxygenase-2 inhibitors.Traditional inflammation and pain relief drugs target both cyclooxygenase 1 and 2 (COX-1 and COX-2), causing severe side effects. Here, the authors use in situ click chemistry to develop COX-2 specific inhibitors with high in vivo anti-inflammatory activity. --- ### A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker) - **Authors:** Thomas Hale, Noam Angrist, Rafael Goldszmidt, Beatriz Kira, Anna Petherick, Toby Phillips, Samuel Webster, Emily Cameron-Blake, Laura Hallas, Saptarshi Majumdar - **DOI:** https://doi.org/10.1038/s41562-021-01079-8 - **Date:** 2021-03-08 - **Topics:** Economics **Abstract:** COVID-19 has prompted unprecedented government action around the world. We introduce the Oxford COVID-19 Government Response Tracker (OxCGRT), a dataset that addresses the need for continuously updated, readily usable and comparable information on policy measures. From 1 January 2020, the data capture government policies related to closure and containment, health and economic policy for more than 180 countries, plus several countries’ subnational jurisdictions. Policy responses are recorded on ordinal or continuous scales for 19 policy areas, capturing variation in degree of response. We present two motivating applications of the data, highlighting patterns in the timing of policy adoption and subsequent policy easing and reimposition, and illustrating how the data can be combined with behavioural and epidemiological indicators. This database enables researchers and policymakers to explore the empirical effects of policy responses on the spread of COVID-19 cases and deaths, as well as on economic and social welfare. The Oxford COVID-19 Government Response Tracker (OxCGRT) records data on 19 different government COVID-19 policy indicators for over 190 countries. Covering closure and containment, health and economics measures, it creates an evidence base for effective responses. --- ### Bartik Instruments: What, When, Why, and How - **Authors:** Paul Goldsmith-Pinkham, Isaac Sorkin, Henry Swift - **DOI:** https://doi.org/10.1257/aer.20181047 - **Date:** 2020-07-28 - **Topics:** Economics **Abstract:** The Bartik instrument is formed by interacting local industry shares and national industry growth rates. We show that the typical use of a Bartik instrument assumes a pooled exposure research design, where the shares measure differential exposure to common shocks, and identification is based on exogeneity of the shares. Next, we show how the Bartik instrument weights each of the exposure designs. Finally, we discuss how to assess the plausibility of the research design. We illustrate our results through two applications: estimating the elasticity of labor supply, and estimating the elasticity of substitution between immigrants and natives. (JEL C51, F14, J15, J22, L60, R23, R32) --- ### The KOF Globalisation Index – revisited - **Authors:** Savina Gygli, Florian Haelg, Niklas Potrafke, Jan‐Egbert Sturm - **DOI:** https://doi.org/10.1007/s11558-019-09344-2 - **Date:** 2019-01-28 - **Topics:** Economics **Abstract:** We introduce the revised version of the KOF Globalisation Index, a composite index measuring globalization for every country in the world along the economic, social and political dimension. The original index was introduced by Dreher (Applied Economics, 38(10):1091–1110, 2006) and updated in Dreher et al. (2008). This second revision of the index distinguishes between de facto and de jure measures along the different dimensions of globalization. We also disentangle trade and financial globalization within the economic dimension of globalization and use time-varying weighting of the variables. The new index is based on 43 instead of 23 variables in the previous version. Following Dreher (Applied Economics, 38(10):1091–1110, 2006), we use the new index to examine the effect of globalization on economic growth. The results suggest that de facto and de jure globalization influence economic growth differently. Future research should use the new KOF Globalisation Index to re-examine other important consequences of globalization and why globalization was proceeding rapidly in some countries, such as South Korea, but less so in others. The KOF Globalisation Index can be downloaded from http://www.kof.ethz.ch/globalisation/ . --- ### Sensitivity, Specificity, and Predictive Values: Foundations, Pliabilities, and Pitfalls in Research and Practice - **Authors:** Robert Trevethan - **DOI:** https://doi.org/10.3389/fpubh.2017.00307 - **Date:** 2017-11-20 - **Topics:** Economics **Abstract:** Within the context of screening tests, it is important to avoid misconceptions about sensitivity, specificity, and predictive values. In this article, therefore, foundations are first established concerning these metrics along with the first of several aspects of pliability that should be recognized in relation to those metrics. Clarification is then provided about the definitions of sensitivity, specificity, and predictive values and why researchers and clinicians can misunderstand and misrepresent them. Arguments are made that sensitivity and specificity should usually be applied only in the context of describing a screening test's attributes relative to a reference standard; that predictive values are more appropriate and informative in actual screening contexts, but that sensitivity and specificity can be used for screening decisions about individual people if they are extremely high; that predictive values need not always be high and might be used to advantage by adjusting the sensitivity and specificity of screening tests; that, in screening contexts, researchers should provide information about all four metrics and how they were derived; and that, where necessary, consumers of health research should have the skills to interpret those metrics effectively for maximum benefit to clients and the healthcare system. --- ### PsychoPy2: Experiments in behavior made easy - **Authors:** Jonathan W. Peirce, Jeremy Gray, Sol Simpson, Michael R. MacAskill, Richard Höchenberger, Hiroyuki Sogo, Erik K. Kastman, Jonas Kristoffer Lindeløv - **DOI:** https://doi.org/10.3758/s13428-018-01193-y - **Date:** 2019-02-01 - **Topics:** Neuroscience **Abstract:** PsychoPy is an application for the creation of experiments in behavioral science (psychology, neuroscience, linguistics, etc.) with precise spatial control and timing of stimuli. It now provides a choice of interface; users can write scripts in Python if they choose, while those who prefer to construct experiments graphically can use the new Builder interface. Here we describe the features that have been added over the last 10 years of its development. The most notable addition has been that Builder interface, allowing users to create studies with minimal or no programming, while also allowing the insertion of Python code for maximal flexibility. We also present some of the other new features, including further stimulus options, asynchronous time-stamped hardware polling, and better support for open science and reproducibility. Tens of thousands of users now launch PsychoPy every month, and more than 90 people have contributed to the code. We discuss the current state of the project, as well as plans for the future. --- ### Semantics derived automatically from language corpora contain human-like biases - **Authors:** Aylin Caliskan, Joanna J. Bryson, Arvind Narayanan - **DOI:** https://doi.org/10.1126/science.aal4230 - **Date:** 2017-04-13 - **Topics:** Neuroscience **Abstract:** Machine learning is a means to derive artificial intelligence by discovering patterns in existing data. Here, we show that applying machine learning to ordinary human language results in human-like semantic biases. We replicated a spectrum of known biases, as measured by the Implicit Association Test, using a widely used, purely statistical machine-learning model trained on a standard corpus of text from the World Wide Web. Our results indicate that text corpora contain recoverable and accurate imprints of our historic biases, whether morally neutral as toward insects or flowers, problematic as toward race or gender, or even simply veridical, reflecting the status quo distribution of gender with respect to careers or first names. Our methods hold promise for identifying and addressing sources of bias in culture, including technology. --- ### Inference and analysis of cell-cell communication using CellChat - **Authors:** Suoqin Jin, Christian F. Guerrero‐Juarez, Lihua Zhang, Ivan Chang, Raúl Ramos, Chen‐Hsiang Kuan, Peggy Myung, Maksim V. Plikus, Qing Nie - **DOI:** https://doi.org/10.1038/s41467-021-21246-9 - **Date:** 2021-02-17 - **Topics:** Engineering **Abstract:** Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links. We construct a database of interactions among ligands, receptors and their cofactors that accurately represent known heteromeric molecular complexes. We then develop CellChat, a tool that is able to quantitatively infer and analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data. CellChat predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Through manifold learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets. Applying CellChat to mouse and human skin datasets shows its ability to extract complex signaling patterns. Our versatile and easy-to-use toolkit CellChat and a web-based Explorer ( http://www.cellchat.org/ ) will help discover novel intercellular communications and build cell-cell communication atlases in diverse tissues. --- ### Cesium-containing triple cation perovskite solar cells: improved stability, reproducibility and high efficiency - **Authors:** Michael Saliba, Taisuke Matsui, Ji-Youn Seo, Konrad Domanski, Juan‐Pablo Correa‐Baena, Mohammad Khaja Nazeeruddin, Shaik M. Zakeeruddin, Wolfgang Tress, Antonio Abate, Anders Hagfeldt - **DOI:** https://doi.org/10.1039/c5ee03874j - **Date:** 2016-01-01 - **Topics:** Engineering **Abstract:** Today's best perovskite solar cells use a mixture of formamidinium and methylammonium as the monovalent cations. With the addition of inorganic cesium, the resulting triple cation perovskite compositions are thermally more stable, contain less phase impurities and are less sensitive to processing conditions. This enables more reproducible device performances to reach a stabilized power output of 21.1% and ∼18% after 250 hours under operational conditions. These properties are key for the industrialization of perovskite photovoltaics. --- ### Wireless communications with unmanned aerial vehicles: opportunities and challenges - **Authors:** Yong Zeng, Rui Zhang, Teng Joon Lim - **DOI:** https://doi.org/10.1109/mcom.2016.7470933 - **Date:** 2016-05-01 - **Topics:** Engineering **Abstract:** Wireless communication systems that include unmanned aerial vehicles promise to provide cost-effective wireless connectivity for devices without infrastructure coverage. Compared to terrestrial communications or those based on high-altitude platforms, on-demand wireless systems with low-altitude UAVs are in general faster to deploy, more flexibly reconfigured, and likely to have better communication channels due to the presence of short-range line-of-sight links. However, the utilization of highly mobile and energy-constrained UAVs for wireless communications also introduces many new challenges. In this article, we provide an overview of UAV-aided wireless communications, by introducing the basic networking architecture and main channel characteristics, highlighting the key design considerations as well as the new opportunities to be exploited. --- ### Carbon capture and storage (CCS): the way forward - **Authors:** Mai Bui, Claire S. Adjiman, André Bardow, Edward J. Anthony, Andy Boston, Solomon Brown, Paul S. Fennell, Sabine Fuss, Amparo Galindo, Leigh A. Hackett - **DOI:** https://doi.org/10.1039/c7ee02342a - **Date:** 2018-01-01 - **Topics:** Engineering **Abstract:** Carbon capture and storage (CCS) is vital to climate change mitigation, and has application across the economy, in addition to facilitating atmospheric carbon dioxide removal resulting in emissions offsets and net negative emissions. This contribution reviews the state-of-the-art and identifies key challenges which must be overcome in order to pave the way for its large-scale deployment. --- ### Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication - **Authors:** Chongwen Huang, Alessio Zappone, George C. Alexandropoulos, Mérouane Debbah, Chau Yuen - **DOI:** https://doi.org/10.1109/twc.2019.2922609 - **Date:** 2018-10-16 - **Topics:** Engineering **Abstract:** The adoption of a Reconfigurable Intelligent Surface (RIS) for downlink multi-user communication from a multi-antenna base station is investigated in this paper. We develop energy-efficient designs for both the transmit power allocation and the phase shifts of the surface reflecting elements, subject to individual link budget guarantees for the mobile users. This leads to non-convex design optimization problems for which to tackle we propose two computationally affordable approaches, capitalizing on alternating maximization, gradient descent search, and sequential fractional programming. Specifically, one algorithm employs gradient descent for obtaining the RIS phase coefficients, and fractional programming for optimal transmit power allocation. Instead, the second algorithm employs sequential fractional programming for the optimization of the RIS phase shifts. In addition, a realistic power consumption model for RIS-based systems is presented, and the performance of the proposed methods is analyzed in a realistic outdoor environment. In particular, our results show that the proposed RIS-based resource allocation methods are able to provide up to $300\%$ higher energy efficiency, in comparison with the use of regular multi-antenna amplify-and-forward relaying. --- ### Ultrastructural Characterization of the Lower Motor System in a Mouse Model of Krabbe Disease - **Authors:** Valentina Cappello, Laura Marchetti, Paola Parlanti, Silvia Landi, Ilaria Tonazzini, Marco Cecchini, Vincenzo Piazza, Mauro Gemmi - **DOI:** https://doi.org/10.1038/s41598-016-0001-8 - **Date:** 2016-11-11 - **Topics:** Medicine **Abstract:** Krabbe disease (KD) is a neurodegenerative disorder caused by the lack of β- galactosylceramidase enzymatic activity and by widespread accumulation of the cytotoxic galactosyl-sphingosine in neuronal, myelinating and endothelial cells. Despite the wide use of Twitcher mice as experimental model for KD, the ultrastructure of this model is partial and mainly addressing peripheral nerves. More details are requested to elucidate the basis of the motor defects, which are the first to appear during KD onset. Here we use transmission electron microscopy (TEM) to focus on the alterations produced by KD in the lower motor system at postnatal day 15 (P15), a nearly asymptomatic stage, and in the juvenile P30 mouse. We find mild effects on motorneuron soma, severe ones on sciatic nerves and very severe effects on nerve terminals and neuromuscular junctions at P30, with peripheral damage being already detectable at P15. Finally, we find that the gastrocnemius muscle undergoes atrophy and structural changes that are independent of denervation at P15. Our data further characterize the ultrastructural analysis of the KD mouse model, and support recent theories of a dying-back mechanism for neuronal degeneration, which is independent of demyelination. --- ### A new coronavirus associated with human respiratory disease in China - **Authors:** Fan Wu, Zhao Su, Bin Yu, Yanmei Chen, Wen Wang, Zhi-Gang Song, Yi Hu, Zhao-Wu Tao, Jun-Hua Tian, Yuan-Yuan Pei - **DOI:** https://doi.org/10.1038/s41586-020-2008-3 - **Date:** 2020-02-03 - **Topics:** Medicine **Abstract:** Abstract Emerging infectious diseases, such as severe acute respiratory syndrome (SARS) and Zika virus disease, present a major threat to public health 1–3 . Despite intense research efforts, how, when and where new diseases appear are still a source of considerable uncertainty. A severe respiratory disease was recently reported in Wuhan, Hubei province, China. As of 25 January 2020, at least 1,975 cases had been reported since the first patient was hospitalized on 12 December 2019. Epidemiological investigations have suggested that the outbreak was associated with a seafood market in Wuhan. Here we study a single patient who was a worker at the market and who was admitted to the Central Hospital of Wuhan on 26 December 2019 while experiencing a severe respiratory syndrome that included fever, dizziness and a cough. Metagenomic RNA sequencing 4 of a sample of bronchoalveolar lavage fluid from the patient identified a new RNA virus strain from the family Coronaviridae , which is designated here ‘WH-Human 1’ coronavirus (and has also been referred to as ‘2019-nCoV’). Phylogenetic analysis of the complete viral genome (29,903 nucleotides) revealed that the virus was most closely related (89.1% nucleotide similarity) to a group of SARS-like coronaviruses (genus Betacoronavirus, subgenus Sarbecovirus) that had previously been found in bats in China 5 . This outbreak highlights the ongoing ability of viral spill-over from animals to cause severe disease in humans. --- ### A pneumonia outbreak associated with a new coronavirus of probable bat origin - **Authors:** Peng Zhou, Xing‐Lou Yang, Xian-Guang Wang, Ben Hu, Lei Zhang, Wei Zhang, Hao-Rui Si, Yan Zhu, Bei Li, Chao-Lin Huang - **DOI:** https://doi.org/10.1038/s41586-020-2012-7 - **Date:** 2020-02-03 - **Topics:** Medicine **Abstract:** Abstract Since the outbreak of severe acute respiratory syndrome (SARS) 18 years ago, a large number of SARS-related coronaviruses (SARSr-CoVs) have been discovered in their natural reservoir host, bats 1–4 . Previous studies have shown that some bat SARSr-CoVs have the potential to infect humans 5–7 . Here we report the identification and characterization of a new coronavirus (2019-nCoV), which caused an epidemic of acute respiratory syndrome in humans in Wuhan, China. The epidemic, which started on 12 December 2019, had caused 2,794 laboratory-confirmed infections including 80 deaths by 26 January 2020. Full-length genome sequences were obtained from five patients at an early stage of the outbreak. The sequences are almost identical and share 79.6% sequence identity to SARS-CoV. Furthermore, we show that 2019-nCoV is 96% identical at the whole-genome level to a bat coronavirus. Pairwise protein sequence analysis of seven conserved non-structural proteins domains show that this virus belongs to the species of . In addition, 2019-nCoV virus isolated from the bronchoalveolar lavage fluid of a critically ill patient could be neutralized by sera from several patients. Notably, we confirmed that 2019-nCoV uses the same cell entry receptor—angiotensin converting enzyme II (ACE2)—as SARS-CoV. --- ### PyTorch: An Imperative Style, High-Performance Deep Learning Library - **Authors:** Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga - **DOI:** https://doi.org/10.48550/arxiv.1912.01703 - **Date:** 2019-12-03 - **Topics:** Computer Science **Abstract:** Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it provides an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several common benchmarks. --- ### A survey on Image Data Augmentation for Deep Learning - **Authors:** Connor Shorten, Taghi M. Khoshgoftaar - **DOI:** https://doi.org/10.1186/s40537-019-0197-0 - **Date:** 2019-07-06 - **Topics:** Computer Science **Abstract:** Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a network learns a function with very high variance such as to perfectly model the training data. Unfortunately, many application domains do not have access to big data, such as medical image analysis. This survey focuses on Data Augmentation, a data-space solution to the problem of limited data. Data Augmentation encompasses a suite of techniques that enhance the size and quality of training datasets such that better Deep Learning models can be built using them. The image augmentation algorithms discussed in this survey include geometric transformations, color space augmentations, kernel filters, mixing images, random erasing, feature space augmentation, adversarial training, generative adversarial networks, neural style transfer, and meta-learning. The application of augmentation methods based on GANs are heavily covered in this survey. In addition to augmentation techniques, this paper will briefly discuss other characteristics of Data Augmentation such as test-time augmentation, resolution impact, final dataset size, and curriculum learning. This survey will present existing methods for Data Augmentation, promising developments, and meta-level decisions for implementing Data Augmentation. Readers will understand how Data Augmentation can improve the performance of their models and expand limited datasets to take advantage of the capabilities of big data. --- ### Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising - **Authors:** Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, Lei Zhang - **DOI:** https://doi.org/10.1109/tip.2017.2662206 - **Date:** 2017-02-01 - **Topics:** Computer Science **Abstract:** The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture, learning algorithm, and regularization method into image denoising. Specifically, residual learning and batch normalization are utilized to speed up the training process as well as boost the denoising performance. Different from the existing discriminative denoising models which usually train a specific model for additive white Gaussian noise at a certain noise level, our DnCNN model is able to handle Gaussian denoising with unknown noise level (i.e., blind Gaussian denoising). With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers. This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks, such as Gaussian denoising, single image super-resolution, and JPEG image deblocking. Our extensive experiments demonstrate that our DnCNN model can not only exhibit high effectiveness in several general image denoising tasks, but also be efficiently implemented by benefiting from GPU computing. --- ### SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules - **Authors:** Antoine Daina, Olivier Michielin, Vincent Zoete - **DOI:** https://doi.org/10.1038/srep42717 - **Date:** 2017-03-03 - **Topics:** Computer Science **Abstract:** To be effective as a drug, a potent molecule must reach its target in the body in sufficient concentration, and stay there in a bioactive form long enough for the expected biologic events to occur. Drug development involves assessment of absorption, distribution, metabolism and excretion (ADME) increasingly earlier in the discovery process, at a stage when considered compounds are numerous but access to the physical samples is limited. In that context, computer models constitute valid alternatives to experiments. Here, we present the new SwissADME web tool that gives free access to a pool of fast yet robust predictive models for physicochemical properties, pharmacokinetics, drug-likeness and medicinal chemistry friendliness, among which in-house proficient methods such as the BOILED-Egg, iLOGP and Bioavailability Radar. Easy efficient input and interpretation are ensured thanks to a user-friendly interface through the login-free website http://www.swissadme.ch. Specialists, but also nonexpert in cheminformatics or computational chemistry can predict rapidly key parameters for a collection of molecules to support their drug discovery endeavours. --- ### The FAIR Guiding Principles for scientific data management and stewardship - **Authors:** Mark D. Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg, Jan‐Willem Boiten, Luiz Olavo Bonino da Silva Santos, Philip E. Bourne - **DOI:** https://doi.org/10.1038/sdata.2016.18 - **Date:** 2016-03-15 - **Topics:** Computer Science **Abstract:** There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders-representing academia, industry, funding agencies, and scholarly publishers-have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community. --- ### Array programming with NumPy - **Authors:** Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith - **DOI:** https://doi.org/10.1038/s41586-020-2649-2 - **Date:** 2020-09-16 - **Topics:** Computer Science **Abstract:** Abstract Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves 1 and in the first imaging of a black hole 2 . Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis. --- ### Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks - **Authors:** Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun - **DOI:** https://doi.org/10.1109/tpami.2016.2577031 - **Date:** 2016-06-06 - **Topics:** Computer Science **Abstract:** State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet [1] and Fast R-CNN [2] have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features-using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model [3] , our detection system has a frame rate of 5 fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available. --- ### SciPy 1.0: fundamental algorithms for scientific computing in Python - **Authors:** Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright - **DOI:** https://doi.org/10.1038/s41592-019-0686-2 - **Date:** 2020-02-03 - **Topics:** Computer Science **Abstract:** SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments. --- ### ImageNet classification with deep convolutional neural networks - **Authors:** Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton - **DOI:** https://doi.org/10.1145/3065386 - **Date:** 2017-05-24 - **Topics:** Computer Science **Abstract:** We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry. ---