Miracle-dz/UniFAR-FTUs huggingface dataset:
Purpose Claims and Capacity-Based Credibility: Evidence from the Labor Market

Organizational scholars have long recognized the importance of corporate purpose, deﬁned as a goal beyond proﬁt maximization, meant to galvanize workers in the ﬁrm. Increasingly, however, companies are making claims about corporate purpose to external audiences, and we have little understanding how these claims may be perceived. A key question is credibility; under what conditions are these claims believed by audiences? We argue that purpose claims can attract external stakeholders, and that the ambition and future-orientation of these claims makes ﬁrm capacity a key source of credibility. We examine these issues in the labor market context, where employers make claims of corporate purpose in recruitment eﬀorts. In our ﬁrst study, with job posting and application data from an online job board, we develop a novel measure of purpose claim language and examine its eﬀect on application likelihood. We then examine the moderating eﬀect of ﬁrm size, as a proxy for capacity. We ﬁnd that high purpose job posts receive approximately 52% more applications than low purpose job posts when the ﬁrm has more than 1,000 employees, but only receive a 13% increase when the ﬁrm has fewer than 50 employees. In a second study, we use an online experiment to test whether diﬀerences in ﬁrm size are interpreted as diﬀering degrees of capacity to realize purpose claims, and whether these diﬀerent perceptions of capacity impact application likelihood. Our results conﬁrm that perceptions of capacity help to explain the relationship between corporate purpose claims, ﬁrm size, and job attraction.
IMAGE AND VIDEO CAPTIONING USING DEEP LEARNING

: Image and video caption generation has gained significant attention in recent years due to its potential in enhancing content accessibility, search ability, and user experience. Deep learning techniques have shown remarkable success in addressing this task by leveraging the power of convolutional neural networks (CNNs) and recurrent neural networks (RNNs). In this paper, we propose a novel approach for image and video caption generation using deep learning. Our model consists of two key components: an image/video encoder and a caption generator. The image/video encoder utilizes a pre-trained CNN, such as ResNet or VGG, to extract high-level visual features from the input image or video frames. These features are then fed into an RNN-based caption generator, which sequentially generates a caption word-by-word, taking into account both the visual features and the context provided by the previously generated words. To further enhance the caption generation process, we incorporate attention mechanisms that allow the model to focus on relevant regions or frames while generating each word. This attention mechanism helps the model capture fine-grained details and improves the overall quality and coherence of the generated captions. To train our model, we use large-scale image and video caption datasets, such as MSCOCO and MSVD, which provide extensive annotations for training and evaluation. Experimental results demonstrate that our proposed approach achieves state-of-the-art performance on several benchmark datasets
Comparison of Single-Machine and Distributed Calculation of Temporal Degree Metrics

Graph data is not only extremely huge nowadays because of its rapid expansion, but it is also changing over time. Since graph data to be stored and analyzed becomes extremely massive, many graph processing systems are using distributed execution to minimize the runtimes while processing their data in a scalable manner. The question is whether distributed architectures are suitable for all dataset sizes, from small ones with only a few hunred nodes and edges to large ones with billions of edges. This thesis compares the runtime of a distributed implementation to a single machine implementation of a selected graph analysis operation, namely the degree evolution of all vertices of a temporal graph. The distributed implementation in Apache Flink is already finished and executable. Benchmarks of the single machine implementations consist of a self-developed Java implementation as well as an alternative implementation in the graph database management system Neo4j by using the Cypher query language. The results show that distributed computing has a significant runtime advantage for large datasets (several gigabytes), with some smaller datasets slower than the Java single machine implementation. Moreover, the parallelism setting obviously affects the runtime of distributed computing. The runtimes evaluated by the Neo4j implementation consistently lags very behind.
Nonlinear Causal Discovery with Latent Confounders

Causal discovery, the task of discovering the causal graph over a set of observed variables X 1 , . , X m , is a challenging problem. One of the cornerstone assumptions is that of causal sufficiency: that all common causes of all measured variables have been observed. When it does not hold, causal discovery algorithms making this assumption return networks with many spurious edges. In this paper, we propose a nonlinear causal model involving hidden confounders. We show that it is identifiable from only the observed data and propose an efficient method for recovering this causal model. At the heart of our approach is a variational autoencoder which parametrizes both the causal interactions between observed variables as well as the influence of the unobserved confounders. Empirically we show that it outperforms other state-of-the-art methods for causal discovery under latent confounding on synthetic and real-world data.
A Directed Acyclic Graph Neural Network for AC Optimal Power Flow

AC optimal power flow (OPF) is of great significance for power system security, reliability, and economy. As an NP-hard problem, its solution can be time consuming by traditional optimization techniques. For more efficient AC OPF algorithms, a Direct Acyclic Graph Neural Network (DAG-NN) is proposed in this paper, which enables an explicit design of a neural network utilizing the intrinsic structural information of the problem to be solved. The approach first reformulates an iterative Newton-Raphson based AC OPF algorithm as a compositional function, accordingly constructs a DAG, and then designs the neural network by realizing its each node by a shallow neural network. The paper also analyzes errors of the DAG-NN. The proposed approach is tested on a modified PJM 5-bus system.
DNA BARCODING AND PHYLOGENETIC ANALYSIS OF SOME GASTROPOD MOLLUSCS (CLASS-GASTROPODA) FROM THREE ECOLOGICAL HABITATS OF BANGLADESH

An attempt has been made for molecular characterization of gastropods from three different ecological habitats (marine, freshwater and terrestrial) in Bangladesh. A total of eight species of gastropod ( Lissachatina fulica , Pila globosa, Pila virens, Brotia costula, Nerita articulata, Nerita balteata, Macrochlamys indica and Telescopium telescopium ) were identified belongs to seven genera and six families. These species were confirmed through morphological studies and by adopting DNA barcoding using mitochondrial cytochrome c oxidase subunit I (COI) and 16S ribosomal RNA (16S rRNA) gene. The average GC content 39.53% and 33.40% were found for COI and 16S rRNA, respectively. Genetic divergence increased as predicted with higher taxonomic rank, ranging from 0% to 2.84% with an average 1.45% within species and 3.57% to 35.23% between species with a DNA barcoding gap of 0.73 for 16S. This study revealed that gastropod species can be discriminated using both COI and 16S rRNA gene. Phylogenetic relationships among identified species were established where individuals belonging to the same species were grouped under the same clade. Molecular characterization of remaining molluscan species of Bangladesh remains to be completed.
Localization of Cortical Oscillations Induced by SCS Using Coherence

This paper suggests a method based on coherence analysis and scalp mapping of coherence suitable for more accurate localization of cortical oscillations induced by electric stimulation of the dorsal spinal cord (SCS), which were previously detected using spectral analysis. While power spectral density shows the increase of power during SCS only at small number of electrodes, coherence extends this area and sharpens its boundary simultaneously. Parameters of the method were experimentally optimized to maximize its reliability. SCS is applied to suppress chronic, intractable pain by patients, whom pharmacotherapy does not relieve. In our study, the pain developed in lower back and lower extremity as the result of unsuccessful vertebral discotomy, which is called failed-back surgery syndrome (FBSS). Our method replicated the results of previous analysis using PSD and extended them with more accurate localization of the area influenced by SCS.
Introducing the New Generation of Chinese Geostationary Weather Satellites, Fengyun-4

AbstractChina is developing a new generation of geostationary meteorological satellites called Fengyun-4 (FY-4), which is planned for launch beginning in 2016. Following upon the current FY-2 satellite series, FY-4 will carry four new instruments: the Advanced Geosynchronous Radiation Imager (AGRI), the Geosynchronous Interferometric Infrared Sounder (GIIRS), the Lightning Mapping Imager (LMI), and the Space Environment Package (SEP). The first satellite of the FY-4 series launched on 11 December 2016 is experimental, and the following four or more satellites will be operational.The main objectives of the FY-4 series are to monitor rapidly changing weather systems and to improve warning and forecasting capabilities. The FY-4 measurements are aimed at accomplishing 1) high temporal and spatial resolution imaging in 14 spectral bands from the visible, near-infrared, and infrared (IR) spectral regions; 2) lightning imaging; and 3) high-spectral-resolution IR sounding observations over China and adjacent regi.
Adaptive reshaper for high dynamic range and wide color gamut video compression

High Dynamic Range (HDR) and Wider Color Gamut (WCG) content represents a greater range of luminance levels and a more complete reproduction of colors found in real-world scenes. The characteristics of HDR/WCG content are very different from the SDR content. It poses a challenge to the compression system which is originally designed for SDR content. Recently in MPEG/VCEG, two directions have been taken to improve compression performances for HDR/WCG video using HEVC Main10 codec. The first direction is to improve HDR-10 using encoder optimization. The second direction is to modify the video signal in pre/post processing to better fit compression system. The process therefore is out of coding loop and does not involve changes to the HEVC specification. Among many proposals in the second direction, reshaper is identified to be the key component. In this paper, a novel luma reshaper is presented which re-allocates the codewords to help codec improve subjective quality. In addition, encoder optimization can be performed jointly with reshaping. Experiments are conducted with ICtCp color difference signal. Simulation results show that if both joint optimization of reshaper and encoder are carried out, there is evidence that improvement over the HDR-10 anchor can be achieved.
The impact of social network support on opportunity intention among prospective male and female entrepreneurs during 2019-nCov pandemic

PurposeThe novel coronavirus (nCoV) pandemic, and the challenges of social distancing, proffer a unique opportunity to re-explore the role of social network support in entrepreneurship. Applying social support theory and gender schema theory, this study aims to examine the gender-based differences in prospective entrepreneurs' reliance on their social networks in their entrepreneurial journey amid social turmoil.Design/methodology/approachThe authors collected two-stage primary survey data of prospective entrepreneurs within the pandemic's timeframe from Science and Technology Parks in Iran, one of the first countries to deal with the first, second and third waves of the 2019-nCoV virus.FindingsThe findings demonstrate that female entrepreneurs rely more strongly on their social network support for guidance and encouragement, which positively affects their opportunity intention. While this effect is also seen in men, the effect size is smaller. Also, prospective female entrepreneurs were generally more dissuaded from opportunity intention by the severe perceived environmental uncertainty of the crisis than were men.Originality/valuePrior research on the interaction between social network support and opportunity intentions has been examined in the context of socio-economic normalcy. The authors test whether, how and why these interactions hold in times of crisis, with especial attention to the mechanisms of experienced stress, perceived environmental uncertainty and idea innovativeness.
55-Fitness Studio Consumer Behavior Research Summary

: Consumer characteristics and behavior patterns of fitness studios. The consumers of fitness studios are mainly young people, they have high income level and education level, they pay attention to their image and health, and they like to try new and personalized things. The consumer behavior mode of fitness studios mainly includes the following aspects: Their needs and expectations for fitness studios include more professional, personalized, flexible, diversified and convenient services. Specifically, expected performance, effort expectations, social influence, facilitators, hedonistic motivation, price trade-offs, and habits are all factors that influence behavioral intention because their regression coefficient values are all positive and significant.
Estimating school effectiveness with student growth percentile and gain score models

ABSTRACT Measuring school effectiveness using student test scores is controversial and some methods used for this can be inaccurate in some situations. The validity of two statistical models – the Student Growth Percentile (SGP) model and a multilevel gain score model – are evaluated. The SGP model conditions on previous test scores thereby unblocking a backdoor path between true school/teacher effectiveness and student test scores. When the product of the coefficients that make up this unblocked backdoor path is positive, the SGP estimates can be inaccurate. The accuracy of the multilevel gain score model was not associated with the product of this backdoor path. The gain score model appears promising in these situations where the SGP and other covariate adjusted models perform poorly.
Your College Dorm and Dormmates: Fair Resource Sharing with Externalities

We study a fair resource sharing problem, where a set of resources are to be shared among a group of agents. Each agent demands one resource and each resource can serve a limited number of agents. An agent cares about what resource they get as well as the externalities imposed by their mates, who share the same resource with them. Clearly, the strong notion of envy-freeness, where no agent envies another for their resource or mates, cannot always be achieved and we show that even deciding the existence of such a strongly envy-free assignment is an intractable problem. Hence, a more interesting question is whether (and in what situations) a relaxed notion of envy-freeness, the Pareto envyfreeness, can be achieved. Under this relaxed notion, an agent envies another only when they envy both the resource and the mates of the other agent. In particular, we are interested in a dorm assignment problem, where students are to be assigned to dorms with the same capacity and they have dichotomous preference over their dormmates. We show that when the capacity of each dorm is 2, a Pareto envy-free assignment always exists and we present a polynomial-time algorithm to compute such an assignment. Nevertheless, the result breaks immediately when the capacity increases to 3, in which case even Pareto envyfreeness cannot be guaranteed. In addition to the existential results, we also investigate the utility guarantees of (Pareto) envy-free assignments in our model.
Assembling Existing Labels from Public Datasets to Diagnose Novel Diseases: COVID-19 in Late 2019

The success of deep learning relies heavily on the availability of large annotated datasets, but neither sizable data nor annotation is easily accessible for novel diseases. This paper uses the classification of COVID-19 in late 2019 as an example to demonstrate the effectiveness of a novel strategy, named “Label-Assemble”. To facilitate the diagnosis of novel diseases, we propose to assemble existing labels from public datasets. Although novel diseases are not in the existing labels, we discover that learning from alternative labels can dramatically improve the diagnosis of the novel disease as these labels can better define the classification boundary of the novel disease. This discovery has the potential to accelerate the development circle of computer-aided diagnosis of novel diseases, in which positive label is hard to collect, yet negative labels are usually available and relatively easier to assemble. Label-Assemble achieves 99.3% accuracy on the COVIDx-CXR2 dataset, which significantly exceeds the previous state of the art (96.3% accuracy) and only uses 3% of the annotated COVID-19 images. We further investigate the implementation of the assembling strategy, showing that assembling pathologically related labels, supplemented by semi-supervised learning, is preferred.
MEB: an Efficient and Accurate Multicast using Bloom Filter with Customized Hash Function

Multicast is widely used to support a huge range of applications with one-to-many or many-to-many communication patterns. However, multicast systems do not scale due to considerable state and communication overheads. Some stateful multicast approaches require maintaining the state of each multicast session at switches, thus incurring large memory overhead. Some stateless ones utilize Bloom filter (BF) to encode multicast tree into the packet header to minimize communication overhead, but potentially suffer from the substantial false positive due to the probabilistic nature of Bloom filter. In this paper, we propose a stateless multicast scheme MEB, which uses Bloom filter to achieve large-scale multicast communication with low error, small overhead and high scalability. Specifically, to control the rate of false positive, MEB elaborately selects the hash functions for Bloom filters when constructing the packet header at the sender side, and makes forwarding decision according to packet header at the switch with negligible overhead. We compare MEB against the state-of-the-art multicast system in large-scale simulations. The test results show that MEB reduces the traffic overhead by up to 70% with small error rate.
Efﬁcient Dual Output Regulating Rectiﬁer and Adiabatic Charge Pump for Biomedical Applications Employing Wireless Power Transfer †

: A power management unit (PMU) is an essential block for diversiﬁed multi-functional low-power Internet of Things (IoT) and biomedical electronics. This paper includes a theoretical analysis of a high current, single-stage ac-dc, reconﬁgurable, dual output, regulating rectiﬁer consisting of pulse width modulation (PWM) and pulse frequency modulation (PFM). The regulating rectiﬁer provides two independently regulated supply voltages of 1.8 V and 3.3 V from an input ac voltage. The PFM control feedback consists of feedback-driven regulation to adjust the driving frequency of the power transistors through adaptive buffers in the active rectiﬁer. The PWM/PFM mode control provides a feedback loop to adjust the conduction duration accurately and minimize power losses. The design also includes an adiabatic charge pump (CP) to provide a higher voltage level. The adiabatic CP consists of latch-up and power-saving topologies to enhance its power efﬁciency. Simulation results show that the dual regulating rectiﬁer has 94.3% voltage conversion efﬁciency with an ac input magnitude of 3.5 Vp. The power conversion efﬁciency of the regulated 3.3 V output voltage is 82.3%. The adiabatic CP has an overall voltage conversion efﬁciency (VCE) of 92.9% with a total on-chip capacitance of 60 pF. The circuit was designed using 180 nm CMOS technology.
Forced Oscillation Location Estimation with Partial Observation of the Power System

—Forced oscillations are usually excited by external periodical sources, e.g., malfunctions in exciters, power system stabilizers, and governors. Unlike natural oscillations which can be attenuated on their own if the system has sufficient damping, forced oscillations cannot be fully mitigated until the source is identified and removed. Therefore, fast and accurate source location is of importance for forced oscillation mitigation in interconnected power grids. This paper proposes a synthetic approach for forced oscillation source location using synchrophasor measurements. This approach uses three methods, dissipating potential-based, oscillation magnitude-based, and oscillation mode angle-based, to estimate the source location individually, and then provide the final estimation based on the weights of different methods. A tool called Forced Oscillation Localization Tool is developed, which uses voltage synchrophasor measurements for source location without knowing system topology information. The developed tool is tested with the IEEE test case library of power system sustained oscillations, which are simulated forced oscillation events in a reduced 179-bus WECC model with full observation of the system. Also, the tool is tested using an actual forced oscillation event in EI. The test results demonstrate the effectiveness of the tool in forced oscillation source location.
Category-Intent Fusion Graph Neural Network for Session-based Recommendation

: A user's interaction with an item is determined by a combination of intentions, such as a desire to purchase an electronic device while following a trend. However, these intentions are often unobservable, making it difficult to model user intentions and improve session recommendations. To tackle this problem, we propose a novel approach called the Category-Intent Graph Neural Network (CIGNN), which leverages the relationship between item categories and user intentions to provide accurate recommendations. We translate the category information into a compact representation, which represents the user's intent, and construct a category-intent fusion graph with item, category, and intent nodes. This graph connects multiple potential intents for each item in a session to capture user intentions and increase the expressiveness of item representations. The CIGNN model transfers information between intent, item, and category nodes, updating their representations alternately. Our experimental results on three benchmark datasets demonstrate the superiority of the CIGNN model over state-of-the-art (SOTA) methods in session-based recommendation (SBR).