Miracle-dz/UniFAR-FTUs huggingface dataset:
The effect of courage on stress: The mediating mechanism of behavioral inhibition and behavioral activation in high-risk occupations

Employees in high-risk occupations are exposed to tremendous work acute stress or prolonged stress disorders that are likely to undermine the health and organizational effectiveness. Based on positive psychology, courage which refers to behavioral approach despite the experience of fear could buffer the negative effects on stress. However, there is little known about the mechanisms by which courage decreases the risk of stress. Motivational systems may play an underlying role in this process, as behavioral inhibition system (BIS) is inhibited and behavioral activation system (BAS) is evoked by risk or threat. The current study aimed to examine the mediating effects of behavioral inhibition and activation on the relationship between courage and stress in the high-risk occupations. This study recruited 1,761 high-risk employees aged from 18 to 27 (M = 19.32; SD = 4.14) with a cluster sampling method who completed Courage Measure (CM), the BIS/BAS Scales and the Psychological Stress Evaluation Test (PSET). The correlation and mediation analyses examined the inter-variable correlations as well as the underlying mechanism between courage and stress. The results support the hypothesis and reveal that the behavioral inhibition mediates the association between courage and stress (Bindirect = −0.02, p < 0. 01, 95%CI = −0.03 to −0.003). The behavioral activation of fun seeking mediates the association between courage and stress as well (Bindirect = −0.04, p < 0. 01, 95%CI = −0.058 to −0.029). These findings suggest that behavioral inhibition and activation of fun seeking play imperative mechanism underpinning the buffering effect of courage on stress. Other theoretical and applied implications for desensitizing stress in the high-risk occupations are discussed.
Exploring the effect of perceived overqualification on knowledge hiding: The role of psychological capital and person-organization fit

Individuals' knowledge hiding behavior may lead to massive economic losses to organizations, and exploring the antecedents of it has crucial relevance for mitigating its negative influences. This research aims to investigate the impact of perceived overqualification on knowledge hiding by testing the mediating effect of psychological capital and the moderating effect of person-organization fit. Empirical analyses were conducted on 249 employee dataset using versions SPSS 26 and AMOS 26. Results illustrate an inverse correlation between perceived overqualification and knowledge hiding behavior which is partly mediated by psychological capital and moderated by person-organization fit, implying that good organizational atmosphere that builds up individual psychological capital with better person-organization fit will allow employees to work positively to reduce knowledge hiding behavior when perceived overqualified. This study complements a small quantity of discussions on the positive impact of perceived overqualification on knowledge management and fills omissions in previous studies on the negative effect of perceived overqualification on knowledge hiding behavior in changing surroundings.
A comparison of placebo and nocebo effects on objective and subjective postural stability: a double-edged sword?

Background: Positive expectations (i.e., placebo effect) can improve postural control during quiet standing. This raises an important question: if postural control is susceptible to positive expectations, is it possible to elicit the opposite, a decline in postural stability, simply by suggesting a performance impairment (i.e., nocebo) will take place? Yet no studies have examined the nocebo effect on balance performance. To better understand both phenomena, comparative studies, which include both placebo and nocebo conditions, are needed. Method: Forty-two healthy adults were initially assessed for objective (center of pressure movement) and subjective (perceived) postural stability and performance expectations. Participants were then randomly assigned in equal numbers to a placebo (positive expectation), nocebo (negative expectation) or control (no suggestion) group. Participants in the placebo/nocebo groups were deceptively administered an inert capsule described as a potent supplement which would either positively or negatively influence their balance performance. Objective and subjective postural stability, and performance expectations were reassessed 20 min later. Results: The nocebo procedure evoked an increase in COP sway movements and reduced perceived stability compared to a control group. The placebo group presented with reductions COP sway movements and increased perceived stability following expectation manipulation. Compared to the control group, the placebo group showed a significantly higher performance expectation whilst the nocebo group showed a significantly lower performance expectation. Regression analyses also revealed that performance expectations following the placebo/nocebo procedure significantly predicted perceptions of postural instability (i.e., perceived performance), accounting for around 50% of the variance. These results remained even when controlling for actual performance (i.e., objective postural stability). Conclusion: Our findings indicate that positive and negative performance expectations evoked by instructional manipulation can profoundly influence both objective and subjective postural stability. Postural control—and perceptions regarding such—are clearly susceptible to expectation manipulation, which could have important practical implications and repercussions on testing, training interventions and rehabilitation programs. Positive and negative expectancies are a double-edged sword for postural control.
Calvin Calvin Non-thiolate ligation of nickel by nucleotide-free UreG of Non-thiolate ligation of nickel by nucleotide-free UreG of Klebsiella aerogenes Klebsiella aerogenes

Objective. To compare the effects a pharmaceutical industry decision guide and International Patient Decision Aids Standard (IPDAS) compliant patient decision aids (PtDA) on patient medication beliefs and choice to intensify therapy. Methods. Rheumatoid arthritis (RA) patients, who had never taken etanercept (Enbrel), took part in a mail survey. They were presented with a hypothetical decision scenario where they were asked to consider adding etanercept to their current regimen. Each patient was randomized to review 1 of 3 forms of an etanercept-specific decision support: a long PtDA (LONG DA), a short PtDA (SHORT DA), or the manufacturer’s Enbrel decision guide (Pharm Booklet). Results. We had 402 RA patients participate in the study (response rate, 52%). Of the patients randomized to the Pharm Booklet, 30.6% elected to initiate etanercept. Only 14.6% and 14.0% of patients who reviewed the LONG DA or SHORT DA choose to take etanercept ( x 2 = 15.7; P \ 0.001). Patients who reviewed the LONG DA or SHORT DA had a greater increase in knowledge about etanercept than those who reviewed the Pharm Booklet. There was no difference in decisional conflict among the groups. A logistic regression model explained 44.2% (R 2 = 0.442) of patient choice to intensify therapy by initiating etanercept. The strongest predictor of choice to intensify therapy were beliefs about etanercept’s ability to improve symptoms (OR = 2.56, 96%CI [1.71, 3.80]), and its use by others like the respondent (OR = 2.24, 95%CI [1.49, 3.35]). analysis confirmed the presence of a partial mediating effect of decision support on patients’ intent to take etanercept (OR = 0.59, 95%CI [0.39, 0.89]). Conclusions. Patients supported by the Pharm Booklet were twice as likely to choose to intensify therapy. The Pharm Booklet’s effects are partially mediated through persuasive communication techniques that influence patients’ beliefs that symptoms will improve, and increase social normative beliefs, rather than by increasing the relevant knowledge, clarifying patient values about positive or negative treatment outcomes, or increasing their self-efficacy.
Algorithm 977

A new software for computing the singular value decomposition (SVD) of real or complex matrices is proposed. The method implemented in the code xGESVDQ is essentially the QR SVD algorithm available as xGESVD in LAPACK. The novelty is an extra step, the QR factorization with column (or complete row and column) pivoting, also already available in LAPACK as xGEQP3. For experts in matrix computations, the combination of the QR factorization and an SVD computation routine is not new. However, what seems to be new and important for applications is that the resulting procedure is numerically superior to xGESVD and that it is capable of reaching the accuracy of the Jacobi SVD. Further, when combined with pivoted Cholesky factorization, xGESVDQ provides numerically accurate and fast solvers (designated as xPHEVC, xPSEVC) for the Hermitian positive definite eigenvalue problem. For instance, using accurately computed Cholesky factor, xPSEVC computes all eigenvalues of the 200 × 200 Hilbert matrix (whose spectral condition number is greater that 10300) to nearly full machine precision. Furthermore, xGESVDQ can be used for accurate spectral decomposition of general (indefinite) Hermitian matrices.
Genetic Programming Techniques Applied to Measurement Data

ii Abstract In this project, an unknown system structure was identi ed using the Genetic Programming technique. A program was developed that, instead of combining linear systems, evolves nonlinear ordinary di erential equations to describe a system. The solution space was increased by using this approach. Automatically de ned functions were used to represent the ordinary di erential equations. A new way was introduced to speed up the evolution of genetic trees. The genetic trees representing the equations were written in C notation to a le to be compiled by a C compiler and evaluated by the computer. This required modi cations to the Genetic Programming kernel formerly used. The modi cations facilitated evaluation of a complete generation at one time to minimise compiler overhead. In order to build up a C++ class hierarchy, the kernel was completely restructured. A lot of features like shrink mutation, variable tournament size, improved deme handling etc. were also added. A parameter study was carried out to investigate the in uence of important control parameters. The Genetic Programming system was applied to a known, nonlinear system to verify its ability. After this proved to be successful, the input/output response data of a helicopter engine was used to identify that system. Candidate models were derived from that analysis.
Principal Components versus Principle Axis Factors: When Will We Ever Learn?.

Analysts differ quite heatedly over the use of principal components rather than principle axis factor analysis. The difference between the two approaches involves the entries used on the diagonal of the matrix of associations that is analyzed. This paper uses an actual data set (N=539; variables =98) to illustrate that these two methods converge in their results when the number of variables is large, even if score reliabilities are low. (Contains 2 tables and 13 references.) (Author/SLD) Reproductions supplied by EDRS are the best that can be made from the original document. pc_v_paf. wp1 1/24/01 Principal Components versus Principle Axis Factors: When Will We Ever Learn? Bruce Thompson Sherry A. Vidal-Brown Texas A&M University and Baylor College of Medicine PERMISSION TO REPRODUCE AND DISSEMINATE THIS MATERIAL HAS BEEN GRANTED BY
PF-GNN: Differentiable particle filtering based approximation of universal graph representations

Message passing Graph Neural Networks (GNNs) are known to be limited in expressive power by the 1-WL color-refinement test for graph isomorphism. Other more expressive models either are computationally expensive or need preprocessing to extract structural features from the graph. In this work, we propose to make GNNs universal by guiding the learning process with exact isomorphism solver techniques which operate on the paradigm of Individualization and Refinement (IR), a method to artificially introduce asymmetry and further refine the coloring when 1-WL stops. Isomorphism solvers generate a search tree of colorings whose leaves uniquely identify the graph. However, the tree grows exponentially large and needs hand-crafted pruning techniques which are not desirable from a learning perspective. We take a probabilistic view and approximate the search tree of colorings (i.e. embeddings) by sampling multiple paths from root to leaves of the search tree. To learn more discriminative representations, we guide the sampling process with particle filter updates, a principled approach for sequential state estimation. Our algorithm is end-to-end differentiable, can be applied with any GNN as backbone and learns richer graph representations with only linear increase in runtime. Experimental evaluation shows that our approach consistently outperforms leading GNN models on both synthetic benchmarks for isomorphism detection as well as real-world datasets.
Deep Attentive Variational Inference

Stochastic Variational Inference is a powerful framework for learning large-scale probabilistic latent variable models. However, typical assumptions on the factorization or independence of the latent variables can substantially restrict its capacity for inference and generative modeling. A major line of active research aims at building more expressive variational models by designing deep hierarchies of interdependent latent variables. Although these models exhibit superior performance and enable richer latent representations, we show that they incur diminishing returns: adding more stochastic layers to an already very deep model yields small predictive improvement while substantially increasing the inference and training time. Moreover, the architecture for this class of models favors proximate interactions among the latent variables between neighboring layers when designing the conditioning factors of the involved distributions. This is the first work that proposes attention mechanisms to build more expressive variational distributions in deep probabilistic models by explicitly modeling both nearby and distant interactions in the latent space. Specifically, we propose deep attentive variational autoencoder and test it on a variety of established datasets. We show it achieves state-of-the-art log-likelihoods while using fewer latent layers and requiring less training time than existing models. The proposed holistic inference reduces computational footprint by alleviating the need for deep hierarchies. Project code: https://github.com/ifiaposto/Deep_Attentive_VI
Compositional Training for End-to-End Deep AUC Maximization

Recently, deep AUC maximization (DAM) has achieved great success in different domains (e.g., medical image classification). However, the end-to-end training for deep AUC maximization still remains a challenging problem. Previous studies employ an ad-hoc two-stage approach that first trains the network by optimizing a traditional loss (e.g., cross-entropy loss) and then finetunes the network by optimizing an AUC loss. This is because that training a deep neural network from scratch by maximizing an AUC loss usually does not yield a satisfactory performance. This phenomenon can be attributed to the degraded feature representations learned by maximizing the AUC loss from scratch. To address this issue, we propose a novel compositional training framework for end-to-end DAM, namely compositional DAM. The key idea of compositional training is to minimize a compositional objective function, where the outer function corresponds to an AUC loss and the inner function represents a gradient descent step for minimizing a traditional loss, e.g., the cross-entropy (CE) loss. To optimize the non-standard compositional objective, we propose an efficient and provable stochastic optimization algorithm. The proposed algorithm enhances the capabilities of both robust feature learning and robust classifier learning by alternatively taking a gradient descent step for the CE loss and for the AUC loss in a systematic way. We conduct extensive empirical studies on imbalanced benchmark and medical image datasets, which unanimously verify the effectiveness of the proposed method. Our results show that the compositional training approach dramatically improves both the feature representations and the testing AUC score compared with traditional deep learning approaches, and yields better performance than the two-stage approaches for DAM as well. The proposed method is implemented in our open-sourced library LibAUC (www.libauc.org) and code is available at https://github.com/Optimization-AI/LibAUC.
Sample Efficient Stochastic Policy Extragradient Algorithm for Zero-Sum Markov Game

Two-player zero-sum Markov game is a fundamental problem in reinforcement learning and game theory. Although many algorithms have been proposed for solving zero-sum Markov games in the existing literature, many of them either require a full knowledge of the environment or are not sample-efﬁcient. In this paper, we develop a fully decentralized and sample-efﬁcient stochastic policy extragradient algorithm for solving tabular zero-sum Markov games. In particular, our algorithm utilizes multiple stochastic estimators to accurately estimate the value functions involved in the stochastic updates, and leverages entropy regularization to accelerate the convergence. Speciﬁcally, with a proper entropy-regularization parameter, we prove that the stochastic policy extragradient algorithm has a sample complexity of the order (cid:101) O ( A max µ min (cid:15) 5 . 5 (1 − γ ) 13 . 5 ) for ﬁnding a solution that achieves (cid:15) -Nash equilibrium duality gap, where A max is the maximum number of actions between the players, µ min is the lower bound of state stationary distribution, and γ is the discount factor. Such a sample complexity result substantially improves the state-of-the-art complexity result.
Audio Lottery: Speech Recognition Made Ultra-Lightweight, Noise-Robust, and Transferable

Lightweight speech recognition models have seen explosive demands owing to a growing amount of speech-interactive features on mobile devices. Since designing such systems from scratch is non-trivial, practitioners typically choose to compress large (pre-trained) speech models. Recently, lottery ticket hypothesis reveals the existence of highly sparse subnetworks that can be trained in isolation without sacrificing the performance of the full models. In this paper, we investigate the tantalizing possibility of using lottery ticket hypothesis to discover lightweight speech recognition models, that are (1) robust to various noise existing in speech; (2) transferable to fit the open-world personalization; and 3) compatible with structured sparsity. We conducted extensive experiments on CNN-LSTM, RNNTransducer, and Transformer models, and verified the existence of highly sparse “winning tickets” that can match the full model performance across those backbones. We obtained winning tickets that have less than 20% of full model weights on all backbones, while the most lightweight one only keeps 4.4% weights. Those winning tickets generalize to structured sparsity with no performance loss, and transfer exceptionally from large source datasets to various target datasets. Perhaps most surprisingly, when the training utterances have high background noises, the winning tickets even substantially outperform the full models, showing the extra bonus of noise robustness by inducing sparsity. Codes are available at https://github.com/VITA-Group/Audio-Lottery.
Task Relatedness-Based Generalization Bounds for Meta Learning

Supposing the n training tasks and the new task are sampled from the same environment, traditional meta learning theory derives an error bound on the expected loss over the new task in terms of the empirical training loss, uniformly over the set of all hypothesis spaces. However, there is still little research on how the relatedness of these tasks can affect the full utilization of all mn training data (with m examples per task). In this paper, we propose to address this problem by defining a new notion of task relatedness according to the existence of the bijective transformation between two tasks. A novel generalization bound of O( 1 √ mn ) for meta learning is thus derived by exploiting the proposed task relatedness. Moreover, when investigating a special branch of meta learning that involves representation learning with deep neural networks, we establish spectrally-normalized bounds for both classification and regression problems. Finally, we demonstrate that the relatedness requirement between two tasks is satisfied when the sample space possesses the completeness and separability properties, validating the rationality and applicability of our proposed task-relatedness measure.
Anisotropic Random Feature Regression in High Dimensions

In contrast to standard statistical wisdom, modern learning algorithms typically find their best performance in the overparameterized regime in which the model has many more parameters than needed to fit the training data. A growing number of recent works have shown that random feature models can offer a detailed theoretical explanation for this unexpected behavior, but typically these analyses have utilized isotropic distributional assumptions on the underlying data generation process, thereby failing to provide a realistic characterization of real-world models that are designed to identify and harness the structure in natural data. In this work, we examine the high-dimensional asymptotics of random feature regression in the presence of structured data, allowing for arbitrary input correlations and arbitrary alignment between the data and the weights of the target function. We define a partial order on the space of weight-data alignments and prove that generalization performance improves in response to stronger alignment. We also clarify several previous observations in the literature by distinguishing the behavior of the samplewise and parameter-wise learning curves, finding that sample-wise multiple descent can occur at scales dictated by the eigenstructure of the data covariance, but that parameter-wise multiple descent is limited to double descent, although strong anisotropy can induce additional signatures such as wide plateaus and steep cliffs. Finally, these signatures are related to phase transitions in the spectrum of the feature kernel matrix, and unlike the double descent peak, persist even under optimal regularization.
EXACT: Scalable Graph Neural Networks Training via Extreme Activation Compression

Training Graph Neural Networks (GNNs) on large graphs is a fundamental challenge due to the high memory usage, which is mainly occupied by activations (e.g., node embeddings). Previous works usually focus on reducing the number of nodes retained in memory. In parallel, unlike what has been developed for other types of neural networks, training with compressed activation maps is less explored for GNNs. This extension is notoriously difﬁcult to implement due to the lack of necessary tools in common graph learning packages. To un-leash the potential of this direction, we provide an optimized GPU implementation which supports training GNNs with compressed activations. Based on the implementation, we propose a memory-efﬁcient framework called “EXACT”, which for the ﬁrst time demonstrates the potential and evaluates the feasibility of training GNNs with compressed activations. We systematically analyze the trade-off among the memory saving, time overhead, and accuracy drop. In practice, EXACT can reduce the memory footprint of activations by up to 32 × with 0 . 2 - 0 . 5% accuracy drop and 10 - 25% time overhead across different models and datasets. We implement EXACT as an extension for Pytorch Geometric and Pytorch. In practice, for Pytorch Geometric, EXACT can trim down the hardware requirement of training a three-layer full-batch GraphSAGE on ogbn-products from a 48GB GPU to a 12GB GPU. The code is available at https://github.com/warai-0toko