Miracle-dz/UniFAR-FTUs huggingface dataset:
Stapled peptides as potential inhibitors of SARS‐CoV‐2 binding to the hACE2 receptor

Stapled peptides are synthetic peptidomimetics of bioactive sites in folded proteins which carry chemical links, introduced during peptide synthesis, designed to retain the secondary structure in the native protein molecule. Stapled peptides have been investigated as potential modulators of protein–protein interactions for over two decades. The potential use of stapled peptides as inhibitors of viral entry, and therefore as antiviral therapeutics, has been established for several important viruses causing disease in humans, such as the human immunodeficiency virus type 1 (HIV‐1), respiratory syncytial virus (RSV), and Middle East Respiratory Syndrome (MERS) coronavirus. Several independent research initiatives have investigated the inhibitory effect of stapled peptides on binding of the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), the causative agent of COVID‐19, to its receptor, angiotensin‐converting‐enzyme 2 (ACE2). These stapled peptides, which mimic Helix 1 of the human ACE2 receptor, have demonstrated mixed ability to prevent infection with SARS‐CoV‐2 in cell‐based studies.
Primate as a model for crash injury

A number of anesthetized rhesus monkeys, baboons, and chimpanzees were subjected to a plus G//z rectangular deceleration-time history. The parameters of deceleration versus average time duration were plotted as a function of spinal trauma to come up with a series of sensitivity curves based on spinal injury for three families of sub-human primates. The locus, distribution, type, and severity of vertebral body fracture was distinguished on post-impact roentgenographs. The primates were euthanized and the process of documenting and interpreting spinal trauma was repeated following necropsy and water maceration of the soft tissue. The mechanics of vertebral injury were identified. Normal skeletal geometry and proportionate torso kinesiology of the rhesus monkey, baboon, and chimpanzee and man were compared. The peculiarities and consistencies in injury distribution and their variations were interpreted in terms of distinctive vertebral morphology and functional kinesiology of each animal model. The distinguishing variations in injury patterns and their distribution was related to the scatter of human vertebral injury as reported in aircraft escape system accelerations, crash decelerations, and clinical statistics. Language: en
Is this Herpes or Syphilis?: Latent Dirichlet Allocation Analysis of Sexually Transmitted Disease-Related Reddit Posts During the COVID-19 Pandemic

Background: Sexually Transmitted Diseases (STDs) are common and costly, impacting approximately one in five people annually. Reddit, the sixth most used internet site in the world, is a user-generated social media discussion platform that may be useful in monitoring discussion about STD symptoms and exposure. Objective: This study sought to define and identify patterns and insights into STD related discussions on Reddit over the course of the COVID-19 pandemic. Methods: We extracted posts from Reddit from March 2019 through July 2021. We used a machine learning text mining method, Latent Dirichlet Allocation (LDA), to conduct a text analysis to identify the most common topics discussed in the Reddit posts. We then used word clouds, qualitative topic labelling, and spline regression to characterize the content and distribution of topics observed. Results: Our extraction resulted in 24,311 total posts. LDA Coding showed that with 8 topics for each time period we achieved high coherence values (pre-COVID=0.41, pre-vaccine=0.42; post-vaccine=0.44). While most topic categories remained the same over time, the relative proportion of topics changed and new topics emerged. Spline regression revealed some key terms had variability in the percentage of posts that coincided with COVID-19 pre- and post- periods, while others were uniform across the study periods. Conclusions: Our studys use of Reddit is a novel way to gain insights into STD symptoms experienced, potential exposures, testing decisions, common questions, and behavior patterns (e.g., during lock down periods). For example, reduction in STD screening may result in observed negative health outcomes due to missed cases, which also impacts onward transmission. As Reddit use is anonymous, users may discuss sensitive topics with greater detail, and more freely than in clinical encounters. Data from anonymous Reddit posts may be leveraged to enhance understanding of the distribution of disease and need for targeted outreach/screening programs. This study demonstrates Reddit has feasibility and utility to enhance understanding of sexual behaviors, STD experiences, and needed health engagement with the public.
Using LASSO Regression to Estimate the Population-Level Impact of Pneumococcal Conjugate Vaccines

The pneumococcal conjugate vaccines (PCVs) protect against diseases caused by Streptococcus pneumoniae, such as meningitis, bacteremia, and pneumonia. It is challenging to estimate their population-level impact due to the lack of a perfect control population and the subtleness of signals when the endpoint - like all-cause pneumonia - is non-specific. Here we present a new approach to estimate PCVs' impact - using LASSO regression to predict the counterfactual outcome for vaccine impact inference. We first used a simulation study to test the performance of LASSO regression and established methods including the synthetic control (SC) approach. We found that LASSO achieved accurate and precise estimation, even in complex simulation scenarios where the association between outcome and all control variables was non-causal. We then applied LASSO to real-world data and found that it yielded estimates of vaccine impact similar to SC. The LASSO method is accurate, easily implementable, and can be applied to study the impact of PCVs and of other vaccines.
On Stokes wave solutions

Using a perturbation method with the aid of MATLAB® Symbolic Toolbox, a new set of Stokes wave solutions, up to the fifth order, are derived. The new solutions are expressed in terms of free surface profile, velocity potential and wave celerity (or frequency dispersion relation). The solutions in the deep water limit are also deduced and discussed. The new solutions are compared with the existing solutions up to the fifth order. Differences appear among the existing and the present solutions, starting at the third order. The causes for the differences are identified. The characteristic differences were highlighted in the figures for high order solutions of Stokes waves. Numerical examples are provided to quantify the differences between the new fifth-order solutions and Fenton’s, and Skjelbreia and Hendrickson’s solutions.
Impact of bonus shares on eps and mps an empirical analysis

When additional shares are allotted to existing shareholders without being paid any additional payment for them, it is known as bonus shares. Bonus shares are issued by a company when it intends to pay dividend by issuing shares. Companies giving bonus shares are regarded very highly by investor fraternity. This study scrutinize the impact of bonus issue on earning per share and market price per share after record date during 2009–10 of 15 public limited companies listed on Bombay Stock Exchange. The result divulges that EPS of 9 companies out of 15 companies descent but not in that proportion in which bonus share has been issued. MPS of all the companies decay as a result of bonus shares approximately in the same proportion of bonus shares. Correlation between EPS and bonus share is -0.42 and between MPS and bonus share is -0.77. Thus MPS is much negatively correlated than EPS with bonus shares.
Regression function model in risk management of bank resource allocation

Abstract Commercial bank asset-liability management is a method of overall risk control and resource allocation. It has an essential impact on commercial banks’ operational safety and affects the stability of a country's overall financial system due to the domino effect. The article chooses the regression function model to measure the expected default frequency (EDF) in the resource allocation risk of Chinese banks. It analyses the transmission process of the impact of the main macroeconomic variables on the EDF of the resource allocation risk of Chinese commercial banks. The study found that under the significance level of 5%, standardised industrial output growth rate, money supply growth rate, and real estate price index are the main reasons for the changes in bank resource allocation risk EDF.
Simulation of Enhancement Mode GaN HEMTs with Threshold > 5 V using P-type Buffer

A high threshold voltage enhancement-mode GaN HEMT with p-type doped buffer is discussed and simulated. Analytical expressions are derived to explain the role of buffer capacitance in designing and enhancing threshold voltage. Simulations of the proposed device with p-type buffer show threshold voltages above 5 V, and a positive shift in threshold voltage as the oxide capacitance is reduced, thus enabling threshold voltage tunability over an unprecedented range for GaN-based HEMTs. The electric field profiles, breakdown performance, on-resistance and delay tradeoffs in the proposed pGaN back HEMT device are also discussed.
Using Information-Theoretic Co-clustering for Power System Black-Start Decision Making with Incomplete Information

Abstract Black-start decision making plays an important role in power system restoration after a large-area blackout. In this article, information-theoretic co-clustering is employed to deal with the black-start decision-making problem with incomplete information. A novel black-start decision making method based on information-theoretic co-clustering, called ITCC-BSDM, is proposed. In the proposed method ITCC-BSDM, information-theoretic co-clustering is used to simultaneously cluster the black-start schemes and indexes. Based on the scheme clusters and index clusters, the missing values in the decision matrix are predicted, and a complete decision matrix is constructed. Finally, the standard deviation method and the simple additive weighting method are used to determine the best black-start scheme. Based on the data of Guangdong power system of China, experiments were carried out to investigate the effectiveness of the proposed method. The results show that the proposed method even performs better than some black-start decision making methods considering complete information.
Effect of Cu content on hot-crack resistance of Al-Cu-Mg alloys produced by laser powder bed fusion

ABSTRACT The lack of high-strength Al alloys that can be processed by laser powder bed fusion (LPBF) without formation of cracks is considered as one of the main issues for a wider diffusion of this technology in many fields. In this study, the effect of Cu content on the hot-crack resistance of Al-Cu-Mg alloys produced by LPBF was investigated. Powder batches with different compositions were produced by mixing pre-alloyed gas-atomised 2024 alloy powder with increasing amounts (4, 6, 8, 10, and 12 wt.%) of Cu particles and then processed by LPBF. Thermodynamic simulation and microstructure investigations showed that the addition of Cu promotes the formation of abundant eutectic phase mixture, refines the grain structure and reduces the solidification temperature range, thus decreasing the solidification cracking susceptibility of the material. Samples produced with more than 10 wt. % of Cu featured relative density higher than 99.5% and no solidification cracks.
Application of Social Media for Tracking Knowledge in Agile Software Projects

Delivering software solutions in faster, better and cheaper way remains to be one of the challenges in the software industry. The task of software developer is greatly simplified if any approach is available to them that will ensure better interactivity, collaboration, sharing and an optimal exploitation of collaborative intelligence. In the recent past more and more software enterprises shift towards light weight and agile methods to achieve the goal of rapid, cost effective and quality software. We propose an application of the social media services to keep track of tacit knowledge that is frequently required to solve recurrent/demanding software tasks in agile projects. This is win-win approach that is focused towards sharing of intelligence and keeping it ready, Just-in-Case (JIC) it is required and/or creating solution, Just-in-Time (JIT) as and when needed. The anecdotal evidence suggest that any such approach where intelligence is shared collaboratively leads to solving problem quickly and comprehensively with less pressure on human resources and better productivity.
Knowledge is Power: Symbolic Knowledge Distillation, Commonsense Morality, & Multimodal Script Knowledge

Scale appears to be the winning recipe in today's AI leaderboards. And yet, extreme-scale neural models are still brittle to make errors that are often nonsensical and even counterintuitive. In this talk, I will argue for the importance of knowledge, especially commonsense knowledge, and demonstrate how smaller models developed in academia can still have an edge over larger industry-scale models, if powered with knowledge. First, I will introduce "symbolic knowledge distillation", a new framework to distill larger neural language models into smaller commonsense models, which leads to a machine-authored KB that wins, for the first time, over a human-authored KB in all criteria: scale, accuracy, and diversity. Next, I will present an experimental conceptual framework toward computational social norms and commonsense morality, so that neural language models can learn to reason that "helping a friend" is generally a good thing to do, but "helping a friend spread fake news" is not. Finally, I will discuss an approach to multimodal script knowledge demonstrating the power of complex raw data, which leads to new SOTA performances on a dozen leaderboards that require grounded, temporal, and causal commonsense reasoning.
Community Trend Prediction on Heterogeneous Graph in E-commerce

In online shopping, ever-changing fashion trends make merchants need to prepare more differentiated products to meet the diversified demands, and e-commerce platforms need to capture the market trend with a prophetic vision. For the trend prediction, the attribute tags, as the essential description of items, can genuinely reflect the decision basis of consumers. However, few existing works explore the attribute trend in the specific community for e-commerce. In this paper, we focus on the community trend prediction on the item attribute and propose a unified framework that combines the dynamic evolution of two graph patterns to predict the attribute trend in a specific community. Specifically, we first design a community-attribute bipartite graph at each time step to learn the collaboration of different communities. Next, we transform the bipartite graph into a hypergraph to exploit the associations of different attribute tags in one community. Lastly, we introduce a dynamic evolution component based on the recurrent neural networks to capture the fashion trend of attribute tags. Extensive experiments on three real-world datasets in a large e-commerce platform show the superiority of the proposed approach over several strong alternatives and demonstrate the ability to discover the community trend in advance.
Robust Graph Learning for Misbehavior Detection

Recent years have witnessed the thriving of online services like social media, e-commerce, and e-finance. Those services facilitate our daily lives while breeding malicious actors like fraudsters and spammers to promote misinformation, gain monetary rewards, or reap end users' privacy. Graph-based machine learning models have been playing a critical and irreplaceable role in modeling and detecting online misbehavior. With the observation that misbehaviors are different from massive regular behaviors, the graph models can leverage the relationship between data entities from a holistic view and reveal suspicious behaviors as anomalous nodes/edges/subgraphs on the graph. In this proposal, we investigate the graph-based misbehavior detection models from an adversarial perspective, considering the adversarial nature of malicious actors and real-world factors that impair graph models' robustness. We first introduce two published works enhancing the robustness of several graph-based misbehavior detectors using reinforcement learning. Then, we propose to explore: 1) the robustness of graph neural networks for misinformation detection on social media; and 2) the general robustness of graph neural networks towards unknown perturbations.
A Sequence-to-Sequence Model for Large-scale Chinese Abbreviation Database Construction

Abbreviations often used in our daily communication play an important role in natural language processing. Most of the existing studies regard the Chinese abbreviation prediction as a sequence labeling problem. However, sequence labeling models usually ignore label dependencies in the process of abbreviation prediction, and the label prediction of each character should be conditioned on its previous labels. In this paper, we propose to formalize the Chinese abbreviation prediction task as a sequence generation problem, and a novel sequence-to-sequence model is designed. To boost the performance of our deep model, we further propose a multi-level pre-trained model that incorporates character, word, and concept-level embeddings. To evaluate our methods, a new dataset for Chinese abbreviation prediction is automatically built, which contains 81,351 pairs of full forms and abbreviations. Finally, we conduct extensive experiments on a public dataset and the built dataset, and the experimental results on both datasets show that our model outperforms the state-of-the-art methods. More importantly, we build a large-scale database for a specific domain, i.e., life services in Meituan Inc., with high accuracy of about 82.7%, which contains 4,134,142 pairs of full forms and abbreviations. The online A/B testing on Meituan APP and Dianping APP suggests that Click-Through Rate increases by 0.59% and 0.86% respectively when the built database is used in the searching system. We have released our API on http://kw.fudan. edu.cn/ddemos/abbr/ with over 87k API calls in 9 months.
Enhancing Configurable Limitless Paths in Virtual Reality Environments

Locomotion in a virtual environment within a limited physical space is a complex activity. There exist established techniques to support limitless natural walking in virtual environments. These include Redirected walking, Dynamic path generation, and Walk-In-place technique, etc. PragPal is one such limitless path generation technique that supports natural walking in virtual environments. It is a novel software-based non-haptic locomotion technique. In this paper, we detail the enhancements to the existing PragPal path generation technique that addresses underlying issues in the technique like (1) path collision at angular turns, (2) effective usage of the physical play area, and (3) the ability to set path-width during path turns.
Friend Story Ranking with Edge-Contextual Local Graph Convolutions

Social platforms have paved the way in creating new, modern ways for users to communicate with each other. In recent years, multiple platforms have introduced ''Stories'' features, which enable broadcasting of ephemeral multimedia content. Specifically, ''Friend Stories,'' or Stories meant to be consumed by one's close friends, are a popular feature, promoting significant user-user interactions by allowing people to see (visually) what their friends and family are up to. A key challenge in surfacing Friend Stories for a given user, is in ranking over each viewing user's friends to efficiently prioritize and route limited user attention. In this work, we explore the novel problem of Friend Story Ranking from a graph representation learning perspective. More generally, our problem is a link ranking task, where inferences are made over existing links (relations), unlike common node or graph-based tasks, or link prediction tasks, where the goal is to make inferences about non-existing links. We propose ELR, an edge-contextual approach which carefully considers local graph structure, differences between local edge types and directionality, and rich edge attributes, building on the backbone of graph convolutions. ELR handles social sparsity challenges by considering and attending over neighboring nodes, and incorporating multiple edge types in local surrounding egonet structures. We validate ELR on two large country-level datasets with millions of users and tens of millions of links from Snapchat. ELR shows superior performance over alternatives by 8% and 5% error reduction measured by MSE and MAE correspondingly. Further generality, data efficiency and ablation experiments confirm the advantages of ELR.
How Members of Covert Networks Conceal the Identities of Their Leaders

Centrality measures are the most commonly advocated social network analysis tools for identifying leaders of covert organizations. While the literature has predominantly focused on studying the effectiveness of existing centrality measures or developing new ones, we study the problem from the opposite perspective, by focusing on how a group of leaders can avoid being identified by centrality measures as key members of a covert network. More specifically, we analyze the problem of choosing a set of edges to be added to a network to decrease the leaders’ ranking according to three fundamental centrality measures, namely, degree, closeness, and betweenness. We prove that this problem is NP-complete for each measure. Moreover, we study how the leaders can construct a network from scratch, designed specifically to keep them hidden from centrality measures. We identify a network structure that not only guarantees to hide the leaders to a certain extent but also allows them to spread their influence across the network.
How Do You Test a Test?: A Multifaceted Examination of Significance Tests

We examine three statistical significance tests -- a recently proposed ANOVA model and two baseline tests -- using a suite of measures to determine which is better suited for offline evaluation. We apply our analysis to both the runs of a whole TREC track and also to the runs submitted by six participant groups. The former reveals test behavior in the heterogeneous settings of a large-scale offline evaluation initiative; the latter, almost overlooked in past work (to the best of our knowledge), reveals what happens in the much more restricted case of variants of a single system, i.e. the typical context in which companies and research groups operate. We find the ANOVA test strikingly consistent in large-scale settings, but worryingly inconsistent in some participant experiments. Of greater concern, the participant only experiments show one of our baseline tests (a test widely used in research) can produce a substantial number of inconsistent results. We discuss the implications of this inconsistency for possible publication bias.