Miracle-dz/UniFAR-FTUs huggingface dataset:
Brief report: how implicit attitudes toward emotion regulation influence partner-directed aggression

Intimate Partner Violence (IPV) perpetration involves many risk factors related to cognitions and emotions, such as insufficient emotion regulation. Those who inadequately regulate negative emotions have been shown to be more likely to have a history of partner violence. However, during routine activities, such as dealing with an intimate partner, emotions are regulated without effortful processing or monitoring. Because this unmonitored emotion regulation process takes place, implicit, or indirect measures of emotion regulation may be better suited to assess unconscious attitudes toward regulating negative emotions. We examined if implicit attitudes toward emotion regulation are related to the frequency of aggressive reactions and if the frequency differs based on a previous history of IPV. A history of IPV predicted some aggressive responding, but the implicit measure unexpected predicted aggression negatively. Possible explanations, clinical implications of inadequate emotion regulation, and intervention suggestions are discussed.
Acceptability indices of performance for bounded càdlàg processes

ABSTRACT Indices of acceptability are well suited to frame the axiomatic features of many performance measures, associated to terminal random cash flows. We extend this notion to classes of càdlàg processes modelling cash flows over a fixed investment horizon. We provide a representation result for bounded paths. We suggest an acceptability index based both on the static Average Value-at-Risk functional and the running minimum of the paths, which eventually represents a RAROC-type model. Some numerical comparisons clarify the magnitude of performance evaluation for processes.
A Rational Finance Explanation of the Stock Predictability Puzzle

In this paper, we address one of the main puzzles in finance observed in the stock market by proponents of behavioral finance: the stock predictability puzzle. We offer a statistical model within the context of rational finance which can be used without relying on behavioral finance assumptions to model the predictability of stock returns. We incorporate the predictability of stock returns into the well-known Black-Scholes option pricing formula. Empirically, we analyze the option and spot trader's market predictability of stock prices by defining a forward-looking measure which we call "implied excess predictability". The empirical results indicate the effect of option trader's predictability of stock returns on the price of stock options is an increasing function of moneyness, while this effect is decreasing for spot traders. These empirical results indicate potential asymmetric predictability of stock prices by spot and option traders. We show in pricing options with the strike price significantly higher or lower than the stock price, the predictability of the underlying stock's return should be incorporated into the option pricing formula. In pricing options that have moneyness close to one, stock return predictability is not incorporated into the option pricing model because stock return predictability is the same for both types of traders. In other words, spot traders and option traders are equally informed about the future value of the stock market in this case. Comparing different volatility measures, we find that the difference between implied and realized variances or variance risk premium can potentially be used as a stock return predictor.
Why X rather than Y? Explaining Neural Model' Predictions by Generating Intervention Counterfactual Samples

Even though the topic of explainable AI/ML is very popular in text and computer vision domain, most of the previous literatures are not suitable for explaining black-box models' predictions on general data mining datasets. This is because these datasets are usually in high-dimensional vectored features format that are not as friendly and comprehensible as texts and images to the end users. In this paper, we combine the best of both worlds: "explanations by intervention" from causality and "explanations are contrastive" from philosophy and social science domain to explain neural models' predictions for tabular datasets. Specifically, given a model's prediction as label X, we propose a novel idea to intervene and generate minimally modified contrastive sample to be classified as Y, that then results in a simple natural text giving answer to the question "Why X rather than Y? ". We carry out experiments with several datasets of different scales and compare our approach with other baselines on three different areas: fidelity, reasonableness and explainability.
Interpretable Self-Attention Temporal Reasoning for Driving Behavior Understanding

Performing driving behaviors based on causal reasoning is essential to ensure driving safety. In this work, we investigated how state-of-the-art 3D Convolutional Neural Networks (CNNs) perform on classifying driving behaviors based on causal reasoning. We proposed a perturbation-based visual explanation method to inspect the models’ performance visually. By examining the video attention saliency, we found that existing models could not precisely capture the causes (e.g., traffic light) of the specific action (e.g., stopping). Therefore, the Temporal Reasoning Block (TRB) was proposed and introduced to the models. With the TRB models, we achieved the accuracy of 86.3%, which outperform the state-of-the-art 3D CNNs from previous works. The attention saliency also demonstrated that TRB helped models focus on the causes more precisely. With both numerical and visual evaluations, we concluded that our proposed TRB models were able to provide accurate driving behavior prediction by learning the causal reasoning of the behaviors.
USE OF GEOTHERMAL ENERGY FOR TOMATO DRYING

INTRODUCTION Dehydration (or drying) of fruit or vegetables is one of the oldest forms of food preservation methods known to man. The process involves the slow removal of the majority of water contained in the fruit or vegetable so that the moisture contents of the dried product is below 20%. In the Mediterranean countries the traditional technique of vegetable and fruit drying (including tomatoes) is by using the sun, a technique that has remained largely unchanged from ancient times. However, on an industrial scale, most fruit is dried using sun (or sometimes solar drying), while most vegetable are dried using continuous forced-air processes. Dried fruits and vegetables can be produced by a variety of processes. These processes differ primarily by the type of drying method used, which depends on the type of food and the type of characteristics of the final product (Mujundar, 1988; Nijhuis, et al., 1998):
Monocular 3D Sway Tracking for Assessing Postural Instability in Cerebral Hypoperfusion During Quiet Standing

Postural instability is prevalent in aging and neurodegenerative disease, decreasing quality of life and independence. Quantitatively monitoring balance control is important for assessing treatment efficacy and rehabilitation progress. However, existing technologies for assessing postural sway are complex and expensive, limiting their widespread utility. Here, we propose a monocular imaging system capable of assessing sub-millimeter 3D sway dynamics during quiet standing. Two anatomical targets with known feature geometries were placed on the lumbar and shoulder. Upper and lower trunk 3D kinematic motion were automatically assessed from a set of 2D frames through geometric feature tracking and an inverse motion model. Sway was tracked in 3D and compared between control and hypoperfusion conditions in 14 healthy young adults. The proposed system demonstrated high agreement with a commercial motion capture system (error $1.5 \times 10^{-4}\,\,\text {mm}$ , [−0.52, 0.52]). Between-condition differences in sway dynamics were observed in anterior-posterior sway during early and mid stance, and medial-lateral sway during mid stance commensurate with decreased cerebral perfusion, followed by recovered sway dynamics during late stance with cerebral perfusion recovery. This inexpensive single-camera system enables quantitative 3D sway monitoring for assessing neuromuscular balance control in weakly constrained environments.
Hierarchical Mixtures of Generators for Adversarial Learning

Generative adversarial networks (GANs) are deep neural networks that allow us to sample from an arbitrary probability distribution without explicitly estimating the distribution. There is a generator that takes a latent vector as input and transforms it into a valid sample from the distribution. There is also a discriminator that is trained to discriminate such fake samples from true samples of the distribution; at the same time, the generator is trained to generate fakes that the discriminator cannot tell apart from the true samples. Instead of learning a global generator, a recent approach involves training multiple generators each responsible from one part of the distribution. In this work, we review such approaches and propose the hierarchical mixture of generators, inspired from the hierarchical mixture of experts model, that learns a tree structure implementing a hierarchical clustering with soft splits in the decision nodes and local generators in the leaves. Since the generators are combined softly, the whole model is continuous and can be trained using gradient-based optimization, just like the original GAN model. Our experiments on five image data sets, namely, MNIST, FashionMNIST, UTZap50K, Oxford Flowers, and CelebA, show that our proposed model generates samples of high quality and diversity in terms of popular GAN evaluation metrics. The learned hierarchical structure also leads to knowledge extraction.
Volunteer Models and Management

This chapter introduces and defines the concept of volunteer management. Historical models of volunteer management are described, culminating in an indepth description of the only model of contemporary volunteer management based on empirical data collected from actual volunteer managers, the PEP Model of Volunteer Administration: (Personal) Preparation, (Volunteer) Engagement, and (Program) Perpetuation.
Spatial Attention for Far-Field Speech Recognition with Deep Beamforming Neural Networks

In this paper, we introduce spatial attention for refining the information in multi-direction neural beamformer for far-field automatic speech recognition. Previous approaches of neural beamformers with multiple look directions, such as the factored complex linear projection, have shown promising results. However, the features extracted by such methods contain redundant information, as only the direction of the target speech is relevant. We propose using a spatial attention subnet to weigh the features from different directions, so that the subsequent acoustic model could focus on the most relevant features for the speech recognition. Our experimental results show that spatial attention achieves up to 9% relative word error rate improvement over methods without the attention.
An Affective Situation Labeling System from Psychological Behaviors in Emotion Recognition

This paper presents a computational framework for providing affective labels to real-life situations, called A-Situ. We first define an affective situation, as a specific arrangement of affective entities relevant to emotion elicitation in a situation. Then, the affective situation is represented as a set of labels in the valence-arousal emotion space. Based on physiological behaviors in response to a situation, the proposed framework quantifies the expected emotion evoked by the interaction with a stimulus event. The accumulated result in a spatiotemporal situation is represented as a polynomial curve called the affective curve, which bridges the semantic gap between cognitive and affective perception in real-world situations. We show the efficacy of the curve for reliable emotion labeling in real-world experiments, respectively concerning 1) a comparison between the results from our system and existing explicit assessments for measuring emotion, 2) physiological distinctiveness in emotional states, and 3) physiological characteristics correlated to continuous labels. The efficiency of affective curves to discriminate emotional states is evaluated through subject-dependent classification performance using bicoherence features to represent discrete affective states in the valence-arousal space. Furthermore, electroencephalography-based statistical analysis revealed the physiological correlates of the affective curves.
Finding Strength in Weakness: Learning to Separate Sounds With Weak Supervision

While there has been much recent progress using deep learning techniques to separate speech and music audio signals, these systems typically require large collections of isolated sources during the training process. When extending audio source separation algorithms to more general domains such as environmental monitoring, it may not be possible to obtain isolated signals for training. Here, we propose objective functions and network architectures that enable training a source separation system with weak labels. In this scenario, weak labels are defined in contrast with strong time-frequency (TF) labels such as those obtained from isolated sources, and refer either to frame-level weak labels where one only has access to the time periods when different sources are active in an audio mixture, or to clip-level weak labels that only indicate the presence or absence of sounds in an entire audio clip. We train a separator that estimates a TF mask for each type of sound event, using a sound event classifier as an assessor of the separator's performance to bridge the gap between the TF-level separation and the ground truth weak labels only available at the frame or clip level. Our objective function requires the separator to estimate a source such that the classifier applied to it will assign high probability to the class corresponding to that source and low probability to all other classes. The objective function also enforces that the separated sources sum up to the mixture. We benchmark the performance of our algorithm using synthetic mixtures of overlapping events created from a database of sounds recorded in urban environments, and show that the method can also be applied to other tasks such as music source separation. Compared to training a network using isolated sources, our model achieves somewhat lower but still significant SI-SDR improvement, even in scenarios with significant sound event overlap.
On the Size of a Black Hole: The Schwarzschild is the Biggest

We consider static black holes in Einstein gravity and study parameters characterizing the black hole size, namely the radii of the horizon R+, photon sphere Rph, and black hole shadow Rsh. We find a sequence of inequalities 32R+≤Rph≤13Rsh≤3M, where M is the black hole mass. These are consistent with and beyond the previously known upper bounds in literature. The Schwarzschild black hole saturates all the inequalities, making it the biggest of all for a given mass. The inequalities include an upper bound of entropy for any quantum system with a given energy. We also point out that some black holes satisfying the dominant energy condition can trap photons to form a stable photon shield outside the event horizon, but the shadow hides it from an observer at infinity.
Firm Entry, Endogenous Markups and the Dynamics of the Labor Share of Income

Recent U.S. evidence suggests that the response of labor share to a productivity shock is characterized by countercyclicality and overshooting. These findings cannot be easily reconciled with existing business cycle models. We extend the Diamond-Mortensen-Pissarides model of search in the labor market by considering strategic interactions among an endogenous number of producers, which leads to countercyclical price markups. While Nash bargaining delivers a countercyclical labor share, we show that countercyclical markups are fundamental to address the overshooting. On the contrary, we find that real wage rigidity does not seem to play a crucial role for the dynamics of the labor share of income.
An Empirical Exploration of the Antecedents and Outcomes of NPD Portfolio Success

The manuscript first combines theory and previous empirical findings to build a model of new product development portfolio success. Because relationships between product development portfolio decision-making effectiveness, portfolio success and firm-level success have not previously been investigated, we generate propositions that the relationships in general exist (rather than directional hypotheses) and then test those relationships with empirical data. Data from 378 paired dyads in 189 Dutch firms were used to test the initial general model, and then explored further to produce a final structural equation model of these relationships with good fit statistics. Several unexpected findings arise, with important implications both for theory and practice. For example, portfolio balance, one recommended measure of portfolio success (Cooper et al. 2001) has no direct link to market performance, but operates strictly through the other two dimensions of portfolio success, strategic alignment and maximal value. In addition, we find that all three dimensions of portfolio decision-making effectiveness are associated with achieving portfolio success, which in turn influences market performance. No one dimension of decision-making effectiveness or portfolio success is sufficient to achieve market performance.
Control Strategies for Microgrids with Renewable Energy Generation and Battery Energy Storage Systems

In this report, several control strategies for microgrids with renewable energy resources and battery energy storage systems are proposed. Renewable energy has the potential to reduce global carbon emissions, while higher penetration of renewable energy is hindered by the inherent variability and intermittency of renewable resources. This motivates the development of microgrids supplied by renewable energy resources. With proper control of storage units and communications with the energy market, the non-dispatchable energy can be regulated to reduce the difficulty of power scheduling in the grid operation. Battery energy storage systems (BESS) are widely used to make renewable energy more controllable and usable on demand. With suitable prediction techniques and control algorithms, BESS can store part of the generated renewable energy and supply some stored energy at different periods, so that the actual power dispatched can meet the required operating criteria. The control algorithms introduced in this report are developed to maximize the profit and minimize the energy cost in microgrids, which require predicted data on renewable power, temperature, electricity price, and load demand.
Semiparametric estimation of cross‐covariance functions for multivariate random fields

The prevalence of spatially referenced multivariate data has impelled researchers to develop procedures for joint modeling of multiple spatial processes. This ordinarily involves modeling marginal and cross‐process dependence for any arbitrary pair of locations using a multivariate spatial covariance function. However, building a flexible multivariate spatial covariance function that is nonnegative definite is challenging. Here, we propose a semiparametric approach for multivariate spatial covariance function estimation with approximate Matérn marginals and highly flexible cross‐covariance functions via their spectral representations. The flexibility in our cross‐covariance function arises due to B‐spline–based specification of the underlying coherence functions, which in turn allows us to capture nontrivial cross‐spectral features. We then develop a likelihood‐based estimation procedure and perform multiple simulation studies to demonstrate the performance of our method, especially on the coherence function estimation. Finally, we analyze particulate matter concentrations (PM2.5) and wind speed data over the West‐North‐Central climatic region of the United States, where we illustrate that our proposed method outperforms the commonly used full bivariate Matérn model and the linear model of coregionalization for spatial prediction.
Characterization of geothermal systems in volcano-tectonic depressions : Japan and New Zealand.

Characterization of geothermal systems was investigated in two regional scale volcanotectonic depressions, the Kuju-Beppu Graben (KBG), in Japan, and the young-Taupo Volcanic Zone (TVZ) in New Zealand. The distribution patterns of geothermal fields are different. In the KBG they are restricted to areas on and behind only the youngest volcanic front, but are evenly spaced throughout the whole of the young-TVZ. The youngest volcanism (<0.3 Ma) in the KBG comprises andesitic lava domes and stratovolcanoes, but is dominated by caldera-forming or dome-building rhyolite eruptions in the TVZ. The heat sources of the active geothermal fields are assumed to be andesitic magma chambers and consolidated magma plutons in the KBG, and are associated with high-level rhyolitic magma chambers in the TVZ. The different heat sources and heat flow regimes result in different geothermal field characteristics. In the TVZ shallow (500-1500 m depth), exploitable reservoirs contain boiling or near-boiling fluids (250-300C) stored in Quaternary permeable layered aquifers of volcano-sedimentary origin lying above a poorly permeable Mesozoic basement. In the KBG the reservoirs are deeper (1000-2000 m depth) and lower temperature (ca. 220C) because the Quaternary andesitic magma-ambient zone is believed to be deeper than that of the TVZ, and deep-penetrating groundwater extracts heat from fractured volcanics above a thicker conductive zone with lower geothermal gradient. Thus it is concluded that the heat discharged by geothermal fluids in the two volcano-tectonic depressions is greatly controlled both by the depth of the magma-ambient zones, and by the depth to which the cool groundwater convection cells can penetrate.
uqSim: Scalable and Validated Simulation of Cloud Microservices

Current cloud services are moving away from monolithic designs and towards graphs of many loosely-coupled, single-concerned microservices. Microservices have several advantages, including speeding up development and deployment, allowing specialization of the software infrastructure, and helping with debugging and error isolation. At the same time they introduce several hardware and software challenges. Given that most of the performance and efficiency implications of microservices happen at scales larger than what is available outside production deployments, studying such effects requires designing the right simulation infrastructures. We present uqSim, a scalable and validated queueing network simulator designed specifically for interactive microservices. uqSim provides detailed intra- and inter-microservice models that allow it to faithfully reproduce the behavior of complex, many-tier applications. uqSim is also modular, allowing reuse of individual models across microservices and end-to-end applications. We have validated uqSim both against simple and more complex microservices graphs, and have shown that it accurately captures performance in terms of throughput and tail latency. Finally, we use uqSim to model the tail at scale effects of request fanout, and the performance impact of power management in latency-sensitive microservices.