Miracle-dz/UniFAR-FTUs huggingface dataset:
Venture Capital, Stock Price Performance, and Downward-sloping Demand Curves Theory

We have found that the post-IPO underperformance took place in China's A-share market between 2013 and 2018 in the prior research. To understand it better, we study the role of VC in the post-IPO performance. The results show that VC begins to influence the stock price performance after a year and a half. At that time, VC backed companies start to underperform more seriously than non-VC backed group. Moreover, the stock price performance of portfolio companies after a year and a half is positively correlated with the reputation of VC, which is measured by conducting PCA. We exclude some possible explanations such as the monitoring role and information asymmetry theory, and then conclude that the downward-sloping demand curves theory based on grandstanding theory and one-year lock-up period in China can explain this phenomenon.
Research on the Impact of Covid-19 to the Clothing Industry based on Fama-French Model

During the Covid-19 pandemic period, the U.S. government and the Federal Reserve started a radical monetary simulation plan to save the economy. The current capital market experiences more fluctuation and are more unpredictable. Capital asset pricing tools are more important at such a special time. This article uses the Fama-French five-factor Model and analyzes the clothing industry in the United States from July 2019 to November 2020, separating into pre and post pandemic periods. The regression analysis performed contains five coefficient factors, and each corresponds to a specific property of the clothing industry after Covid-19 emerged. The market factor indicates that the clothing industry is negatively affected by the pandemic. SMB shows that small businesses are more favorable to investors. HML indicates that value stocks are more preferred. RMW shows that investors tend to look for companies with higher profitability, whereas CMA indicates a minimal change to the clothing industry. It is recommended that more attention should be paid to the companies with small-cap, high BM ratio and stable profitability.
Research on Influencing Factors of Job Satisfaction of College Graduate Village Officials: -Analysis of Empirical Investigation Data from W City

Since 1995, the implementation of the policy of college-graduate village officials, relevant work has been carried out in various parts of China. In order to understand the current work status of college-graduate village officials and their job satisfaction level. At the same time, the influencing factors of job satisfaction are deeply explored. Therefore, a deep and systematic study is carried out. This paper takes the empirical investigation of job satisfaction with college-graduate village officials in W city as the data source, applying SPSS20.0 statistical analysis tool, descriptive statistical analysis factor analysis and regression analysis so as to conduct an empirical study on the influencing factors of job satisfaction with college-graduate village officials. The results show that the main influencing factors of job satisfaction of college-graduate village officials include management system, career development, interpersonal relationship, workload and working environment, among which the influence of management system and career development is slightly higher, and the influence of interpersonal relationship is relatively lower; At present, the job satisfaction of college-graduate village officials is on the upper-middle level, among which the satisfaction level of interpersonal relationship is relatively high, while the overall satisfaction level of management system, working environment and career development is not high. Therefore, it is necessary to clarify the job orientation, promote the career planning management, establish and improve the work incentive system, and strengthen the work performance management of college-graduate village officials, so as to further enhance the job satisfaction level of college-graduate village officials.
Kinetic-energy-flux-constrained model using an artificial neural network for large-eddy simulation of compressible wall-bounded turbulence

Abstract Kinetic energy flux (KEF) is an important physical quantity that characterizes cascades of kinetic energy in turbulent flows. In large-eddy simulation (LES), it is crucial for the subgrid-scale (SGS) model to accurately predict the KEF in turbulence. In this paper, we propose a new eddy-viscosity SGS model constrained by the properly modelled KEF for LES of compressible wall-bounded turbulence. The new methodology has the advantages of both accurate prediction of the KEF and strong numerical stability in LES. We can obtain an approximate KEF by the tensor-diffusivity model, which has a high correlation with the real value. Then, using the artificial neural network method, the local ratios between the real KEF and the approximate KEF are accurately modelled. Consequently, the SGS model can be improved by the product of that ratio and the approximate KEF. In LES of compressible turbulent channel flow, the new model can accurately predict mean velocity profile, turbulence intensities, Reynolds stress, temperature–velocity correlation, etc. Additionally, for the case of a compressible flat-plate boundary layer, the new model can accurately predict some key quantities, including the onset of transitions and transition peaks, the skin-friction coefficient, the mean velocity in the turbulence region, etc., and it can also predict the energy backscatters in turbulence. Furthermore, the proposed model also shows more advantages for coarser grids.
Config 2.0: towards reinforcement learning based configuration of stream processing systems

Distributed stream processing systems (DSPSs) have enabled us to build scalable, fast, and reactive applications. As faults are common in any distributed system, DSPSs allow for recovering from faults using various techniques, for example, snapshotting their state on a key-value (KV) store and then replaying the events in case of a failure. The performance of the recovery process is dependent on the workload, configuration of the DSPS and performance of the KV store. Performance tuning of a KV store is a notoriously difficult job. In this thesis proposal, we propose a reinforcement learning (RL) based agent which configures the DSPS and the KV store under varying workloads to minimize the impact of a failure on the performance.
The Effect of CEO Power on Corporate Debt Financing Costs: Integrating of Agency Theory and Stewardship Theory

This paper assesses the impact of Chief Executive Officer (CEO) power on corporate debt financing cost. Thus far, most studies have addressed the relationship between CEO power and debt financing from the agency theory, while fewer have applied the stewardship theory. In this paper, we apply both theories to analyze CEO power in terms of the debt financing cost data from a sample of A-share listed companies in China from 2008 to 2019. I found a significantly negative correlation between CEO power and corporate debt financing cost. This was mainly attributed to the structure power of CEO affected by the board, to the ownership power created by shareholding, and to the prestige power produced by higher education. I also found that either CEOs’ financial background or the companies’ high-quality audits could strengthen this negative correlation. These research findings provide some support for stewardship theory and agency theory.
Does Digital Employee Experience Matters for Employee Service Orientation, Ambidexterity, and Agility?

∗ Competition among industries in this era is getting tougher. Many companies will eventually focus on their service to be able to compete with others. For service industries, who have service as their main product, need to enhance its quality even further. As service become the focus, it is important to measure it through employee agility. The industries’ quality could be reflected through the extent that the employee could adapt and respond to customers’ need. The study aims to examine the effect of employee’s service orientation and ambidexterity on employee agility which moderated by digital employee experience. This research used a quantitative and cross-sectional or one-shot method. The sample size consisted of 48 respondents by using purposive sampling. In the study used variance-based structural equation modeling. With employees in dry port industry as the unit analysis, this research proposed that employee service orientation and employee ambidexterity positively affected employee agility. The effects were strengthened by digital employee experience as moderator.
Gastrointestinal Image Classification based on Convolutional Neural Network

The intelligent gastrointestinal image classification based on computer-aided diagnosis not only alleviates the shortage of endoscopist's missed diagnosis and misdiagnosis but also reduces the heavy diagnostic tasks to help prevent the deterioration of gastric diseases into gastric cancer. In our research work, we propose to use the Inception-Resnet-v2 with attention mechanism based on transfer learning to predict three-class anomalies of the gastrointestinal endoscopic imagery. Our model achieves a promising classification performance with 92.5% accuracy, 98.46% precision and 99.89% recall.
FlexOS: easy specialization of OS safety properties

Modern operating systems are tightly coupled to a specific isolation approach and safety mechanism. At design time, the isolation strategy is set in stone and rarely revisited later, due to prohibitive costs. This lack of flexibility hurts specialization, makes it hard to leverage new software/hardware isolation technologies, and makes the OS less resilient to attacks targeting the isolation mechanism. To address these issues we have developed FlexOS, a novel libOS approach that decouples isolation properties from the OS design. Depending on the configuration, the same FlexOS code can mimic a microkernel with multiple address-spaces, a single-address-space OS with Intel MPK compartments, or many other OS isolation approaches. In this paper, we summarize the current state of FlexOS and present two main research avenues that we aim to explore next: automated porting to make OS safety property specialization really easy, and support for CHERI hardware capabilities to better showcase FlexOS' potential.
Analyze the Value of European Options and Power Options Based on Black-Scholes Model

Options are crucial to global financial markets. However, there is rarely existing literature combining a specific option and a certain company, with simultaneous analysis and conclusions of the option. Based on the ordinary least square (OLS) regression and Black-Scholes (BS) model, we design two simulated options for Morgan Stanley, which are European options and power options. Then the option's value is identified in this article. Besides, through the sensitivity analysis of the two options and their related parameters, we get some results and conclusions about the characteristics of the two options. In general, the value of options is affected by independent variables and parameters. Compared with a European option, a power option has a higher risk and higher future expected return. This paper provides better advice for investors, on the premise of fully considering various possible situations, compare the advantages and disadvantages of the two options, and make their own investment choices.
Thermal analysis of multilevel grid side converters for 10 MW wind turbines under Low Voltage Ride Through

As the power level of a single wind turbine is continuously pushed up even to 7 MW, the wind power generation system are required to be more reliable, and able to withstand extreme grid disturbances. Moreover, it is becoming a need that the wind power generation system should be more active, and able to contribute to the grid recovery by injecting reactive current during grid faults. Consequently, the full-scale power converter solutions are becoming more and more popular to fulfill the growing challenges in the wind power application. Nevertheless, the loading of the power devices in full-scale power converters, especially during grid faults may compromise the reliability performance and further increase the cost of the system. In this paper, three promising grid side multilevel converter topologies for the next generation 10 MW wind turbines are proposed and basically designed as case study. The operation status, as well as loss and thermal distributions of power devices are investigated, simulated and compared aimed at various Low Voltage Ride Through (LVRT) conditions. It is found that the all of the proposed converter topologies will suffer from higher junction temperature in some heavy loaded power devices especially the diodes under LVRT, and both of the three-level and five-level H-bridge topologies show more potential to reduce the device stress than the well-known three-level Neutral Point Clamped topology.
Imputation in Scrna-seq Data Using Supervised Deep Generative Networks

The main challenge of single-cell RNA sequencing (scRNA-seq) studies arises from the large data sizes and various technical noises such as the excess zero counts within individual cells (called dropout events). This inaccurate measurement of gene expressions may introduce bias in downstream analyses of scRNA-seq data, so it is necessary to correct the false zero expression by computational imputation methods. Most current imputation pipelines typically use unsupervised modeling approaches that expect to recover biologically meaningful gene expression without known cell labels. However, unsupervised imputation often yields only limited gains since very little cell identity information is retained in the observed expression data. And for datasets with known cell labels, the use of cell label-guided imputation is expected to recover more accurate gene expression dynamics in different cell populations. In this work, we developed a supervised deep generative imputation model called scIDG to recover the biologically meaningful gene expression in scRNA-seq data with known cell type information. scIDG can learn both intrinsic features of the observed gene expression data and the interpretable representations of cell types, generating biologically meaningful imputation values under specified cell types and recovering gene expression dynamics. We tested scIDG on simulated and real scRNA-seq datasets and compared it with several state-of-the-art methods for imputation using cell type information. Experiments showed that scIDG can more accurately recover the heterogeneity of different cell types, significantly improving downstream differential expression analysis and temporal trajectory inference analysis.
Modeling of Fish Adaptive Behaviors in Unsteady Flows

Fish can swim swiftly in complicated flow environments, which conceives inspirations for man-made underwater vehicles. This paper concentrates on observation and modeling of fish adaptive behaviors in unsteady flows. A good representative of bony fish, crucian, is taken as the experimental specimen for investigating biological adaptation with response to alteration of surrounding flow patterns. Difference of swimming parameters is confirmed by recorded samples within several flow patterns. Furthermore, a bio-inspired gait model is constructed to stimulate fish adaptive behaviors, since the traditional model is hardly suitable. The model is inspired and supported by biological neural oscillators. By using the developed neural oscillator model, not only certain rhythmic motions under a steady flow pattern can be generated, but also behavioral transitions between multiple different patterns within unsteady flows come true. Experimental results validate the effectiveness of the developed neural model in continuously and smoothly regulating fish propulsive patterns within unsteady flows.
Development of PRPK Directed Phthalimides

Immunomodulatory drugs (IMiDs) thalidomide, lenalidomide, and pomalidomide (Pom) bind to cereblon (CRBN) and trigger proteasomal degradation of neo-substrates IKZF1/3 leading to multiple myeloma (MM) cell apoptosis. Pomalidomide (Pom) also binds albeit weakly to p53-related protein kinase (PRPK, aka TP53RK), an understudied kinase reported to be associated with poor prognosis in MM patients. Here, we developed a series of IMiDs based on Pom and conducted a structure-activity relationship (SAR) study to identify a potent and selective PRPK binder. Structural analysis showed that IMiDs bind PRPK in a fundamentally different way from CRBN, and suggested specific derivatization to improve affinity. We employed a structure-guided strategy to develop compound TXM-02-118, which exhibited nanomolar affinityfor PRPK in binding assays, and showed high selectivity for PRPK over CRBN. Overall, the work represents an initial effort to develop tool compounds for studying PRPK. Moreover, it illustrates how a single class of molecules can use different recognition elements to bind diverse targets using fundamentally different binding poses. This has broad implications for chemical probe and lead compound selectivity profiling, and argues for more wide-spread use of global proteomics or similar methodologies.
DeepSNEM: Deep Signaling Network Embeddings for compound mechanism of action identification

Motivation The analysis and comparison of compounds’ transcriptomic signatures can help elucidate a compound’s Mechanism of Action (MoA) in a biological system. In order to take into account the complexity of the biological system, several computational methods have been developed that utilize prior knowledge of molecular interactions to create a signaling network representation that best explains the compound’s effect. However, due to their complex structure, large scale datasets of compound-induced signaling networks and methods specifically tailored to their analysis and comparison are very limited. Our goal is to develop graph deep learning models that are optimized to transform compound-induced signaling networks into high-dimensional representations and investigate their relationship with their respective MoAs. Results We created a new dataset of compound-induced signaling networks by applying the CARNIVAL network creation pipeline on the gene expression profiles of the CMap dataset. Furthermore, we developed a novel unsupervised graph deep learning pipeline, called deepSNEM, to encode the information in the compound-induced signaling networks in fixed-length high-dimensional representations. The core of deepSNEM is a graph transformer network, trained to maximize the mutual information between whole-graph and sub-graph representations that belong to similar perturbations. By clustering the deepSNEM embeddings, using the k-means algorithm, we were able to identify distinct clusters that are significantly enriched for mTOR, topoisomerase, HDAC and protein synthesis inhibitors respectively. Additionally, we developed a subgraph importance pipeline and identified important nodes and subgraphs that were found to be directly related to the most prevalent MoA of the assigned cluster. As a use case, deepSNEM was applied on compounds’ gene expression profiles from various experimental platforms (MicroArrays and RNA sequencing) and the results indicate that correct hypotheses can be generated regarding their MoA. Availability and Implementation The source code and pre-trained deepSNEM models are available at https://github.com/BioSysLab/deepSNEM. Contact Email for correspondence: leo@mail.ntua. gr. Supplementary information Accompanying supplementary material are available online.
Representation of Drosophila larval behaviors by muscle activity patterns

How muscle actions are coordinated to realize animal movement is a fundamental question in behavioral study. To obtain the overall muscular activity patterns accompanying behaviors at high spatiotemporal resolution is technically difficult. In this work, we used light sheet microscopy to simultaneously image and analyze the activity, length and orientation of Drosophila larval muscles across body segments at single muscle resolution in nearly free behaviors. For typical behavioral modes such as peristalsis, head cast and turning, larval muscles showed behavioral mode specific activity patterns. Unexpectedly, reorientation of larval head involves muscle tone in the apparently motionless posterior segments. With a STGCN(spatial temporal graph convolution neural network)-Generator model, sequence of larval behavioral poses outlined by morphological patterns of muscles could be accurately predicted based on the time series of ventral but not dorsal muscle activities, and vice versa. Laser ablation of ventral but not dorsal muscles interrupted peristaltic wave and undermined head cast in both frequency and amplitude. Our results provide a simplified muscle activity representation of soft body motion that can be used for probing the key components of animal motor control.
Safety, Immunogenicity and Efficacy of a Recombinant Vesicular Stomatitis Virus Vectored Vaccine Against Severe Fever with Thrombocytopenia Syndrome Virus and Heartland Bandaviruses

Severe fever with thrombocytopenia syndrome virus (SFTSV) is a recently emerged tickborne virus in east Asia with over 8,000 confirmed cases. With a high case fatality ratio, SFTSV has been designated a high priority pathogen by the WHO and the NIAID. Despite this, there are currently no approved therapies or vaccines to treat or prevent SFTS. Vesicular stomatitis virus (VSV) represents an FDA-approved vaccine platform that has been considered for numerous viruses due to its low sero-prevalence in humans, ease in genetic manipulation and promiscuity in incorporating foreign glycoproteins into its virions. In this study, we developed a recombinant VSV (rVSV) expressing the SFTSV glycoproteins Gn/Gc (rVSV-SFTSV) and assessed its safety, immunogenicity and efficacy in mice. We demonstrate that rVSV-SFTSV is safe when given to immunocompromised animals and is not neuropathogenic when injected intracranially into young immunocompetent mice. Immunization of Ifnar-/- mice with rVSV-SFTSV resulted in high levels of neutralizing antibodies and protection against lethal SFTSV challenge. Additionally, passive transfer of sera from immunized Ifnar-/- mice into naïve animals was protective when given pre- or post-exposure. Finally, we demonstrate that immunization with rVSV-SFTSV cross protects mice against challenge with the closely related Heartland virus despite low neutralizing titers to the virus. Taken together, these data suggest that rVSV-SFTSV is a promising vaccine candidate. Importance Tick borne diseases are a growing threat to human health. Severe fever with thrombocytopenia syndrome (SFTS) and Heartland viruses are recently recognized, highly-pathogenic, tick-transmitted viruses. The fatality rates for individuals infected with SFTSV or HRTV are high and there are no therapeutics or vaccines available. The recent introduction of the tick vector for SFTSV (Haemaphysalis longicornis) to the eastern half of the United States and Austrailia raises concerns for SFTSV outbreaks outside East Asia. Here we report the development of a potential vaccine for SFTSV and HRTV based on the viral vector platform that has been successfully used for an Ebola vaccine. We demonstrate that the rVSV-SFTSV protects from lethal SFTSV or HRTV challenge when given as a single dose. We evaluated possible pathogenic effects of the vaccine and show that it is safe in immune compromised animlas and when introduced into the central nervous system.