Miracle-dz/UniFAR-FTUs huggingface dataset:
Changes in Work/Life Situations and Psychological Distress during the Prolonged COVID-19 Pandemic in Japan

This paper discusses the relationship between work/life situations and psychological distress as of June 2021, when the COVID-19 pandemic was having a prolonged impact. Specifically, we focus on psychological distress associated with the living situation under the infectious disease epidemic, such as refraining from going out and other activities, as well as household insecurity associated with job loss or income decline due to the COVID-19 crisis, and changes in work styles such as switching to remote work. In Japan, the first case of COVID-19 infection was confirmed in January 2020, and the first state of emergency was declared in April 2020 following the first wave of infections. Although the first and second rounds of vaccinations progressed through the summer of 2021, daily life and economic activities had not fully recovered, and there was still a strong sense of anxiety about infection. In daily life, citizens continued to be asked to refrain from going out as much as possible or eating in large groups, which was a stress factor for many people. A total of four emergency declarations were issued through 2021, first targeting prefectures with severe infection situations and then expanding nationwide depending on the situation. Business operations were constrained or forced to respond over a long period of time to the government’s requests to restaurants and other businesses to temporarily close or shorten their hours of operation, and companies were asked to reduce the number of commuters. As of June 2021, the time of the fifth wave of the Japan Institute for Labour Policy and Training (JILPT) panel survey (see Section III for details), more than one year had passed since the beginning of the pandemic. However, the pandemic had not yet been controlled since its outbreak, which had a long-term impact on workers’ work situations and their household budgets. With regard to the labor market, while no significant increase in the unemployment rate has been observed in Japan under COVID-19, the impact on employment was not at all small, with a sharp increase in the number of people taking leave in April 2020. During this period, many furloughs for employer reasons were observed due to employment adjustments. Not only furloughs, but various other employment adjustments at companies have had a major impact on workers’ lives, including reductions in overtime and scheduled working hours, which in turn have reduced wages. In the case of those who lost their jobs under the COVID-19 pandemic as well as those who suffered a reduction in income or a reduction in working hours for employer reasons, it is speculated that the wellbeing of workers may have deteriorated significantly due to uncertainty about their livelihoods. Conversely, it can be hypothesized that those who were able to maintain secure work environments through switching to remote work as infections spread have retained their well-being. This study invastingated these research questions.
Implications of the Radiocarbon Timescale for Ice-Sheet Chronology and Sea-Level Change

Abstract A new extended radiocarbon calibration curve has allowed a reexamination of relative sea-level data based on pre-Holocene dates. At sites located far from any Late Pleistocene ice sheet, the effect of employing this new calibration curve is that the calibrated data (with ages up to 17,100 14C yr B.P.) now agree with the corresponding relative sea-level curve predicted by the ICE-3G deglaciation model. An important implication of this new calibration curve is that the last glacial maximum (ca. 18,000 14C yr B.P.) is inferred to have occurred 22,000-21,000 cal yr B.P. This allows for additional ice to be incorporated in a revised deglaciation model than suggested by ICE-3G. The predicted relative sealevel curves of this new model match the relative sea-level data as well as those of ICE-3G. Further, the total sea-level rise of 124 m at Barbados since the last glacial maximum predicted by this new model agrees with the estimated value of 121 ± 5 m obtained from the depths of drowned reef-crest corals.
Effective connectivity analysis: testing commonalities and differences across multisubjects' network by state-space model

In fMRI experiments, data from multiple subjects are usually collected. Combining the information from multiple subjects allows us to look for congruence or divergence of such patterns in the fMRI data across individuals. Inter-subject commonalities and differences in the connectivity pattern are seldom investigated in effective connectivity studies. In this paper, we propose a state-space modeling framework to study the dynamic relationship between multiple brain regions and also account for the individual variability in effective connectivity analysis. Our approach decomposes the observed multiple time series into measurement error and the BOLD signals. The proposed model consists of the activation and connectivity equations. In the activation equation, we model the observed signals at each brain region as a function of the BOLD signal. One special feature of our model for capturing the complexities of the dynamic processes in the brain is that the region-specific time-varying coefficients in the activation equation are subsequently modelled, in the connectivity equation, as a function of the BOLD signals at other brain regions. Because our model has a state-space representation, the parameters are readily estimated by maximum likelihood method.
FIRST RECORDS OF Leptolalax eos OHLER, WOLLENBERG, GROSJEAN, HENDRIX, VENCES, ZIEGLER ET DUBOIS, 2011 AND Hylarana cubitalis (SMITH, 1917) (ANURA: MEGOPHRYIDAE, RANIDAE) FROM VIETNAM

The knowledge about the diversity of amphibians inVietnam has remarkably increased from 82 recorded spe-cies in Nguyen and Ho (1996) to 162 in Nguyen et al.(2005) and up to 177 in Nguyen et al. (2009). Since thennew country records and new species descriptions of am-phibians have been regularly published from the country(e.g., Ziegler and Nguyen, 2010; Nguyen et al., 2012,2013; Luu et al., 2013; Rowley et al., 2013, 2014; Poyar-kov et al., 2014; Vassilieva et al., 2014). However, taxo-nomic, geographical and ecological knowledge of am-phibians in many areas of Vietnam is still limited, partic-ularly in remote evergreen forests of northwestern Viet-nam. A wild range of elevations and the complexity oflandforms have given this region a great diversity of nat-ural habitats and a high level of biodiversity potential, inparticular the Hoang Lien Range, which represents thesouthernmost extension of the Himalayas (Sterling et al.,2006). As a result of additional field surveys conductedin northwestern Vietnam in 2012 and 2013, we hereinreport two new records of anurans for the country, com-prising one species of Megophryidae and another speciesof Ranidae.
HYDROLOGICAL RESEARCH IN SOUTH AFRICAN FORESTRY.

SUMMARY This paper was presented to the Fifth British Empire Forestry Conference in London, 1947, and is reprinted here with the kind permission of the Director of Forestry. Progress made at the Jonkershoek Forest Influences Research Station in the Cape Province, the establishment of which was recommended by the Empire Forestry Conference in 1935, is described. A second research station, being established at Cathedral Peak in Natal, is briefly referred to. Surveys of site factors, technique of observations on rainfall, streamflow, net rainfall under forest canopies, infiltration, evaporation and transpiration, treatments being investigated, and design and interpretation of streamflow experiments, are discussed. The importance of streamflow investigations is specially stressed, and it is recommended that such research should be greatly extended, not only to test the effects of various forms of land management, but also to provide essential data on water supplies.
Optimal selection of artificial neural network parameters for the prediction of a karstic aquifer's response

The simulation of karstic aquifers is difficult using traditional groundwater numerical simulators, as the exact knowledge of the hydraulic characteristics of the physical system in small scale is rarely available and the numerical simulators produce results of limited reliability. In the present work, artificial neural networks (ANNs) are utilized to predict the response of a karstic aquifer, using the hydraulic head change per time step rather than the hydraulic head itself as output parameter of the network. As it will be demonstrated, in the first case a better approximation of the physical system's response is achieved as the change of the hydraulic head is more naturally connected to the input parameters of the network, which model the aquatic equilibrium of the system. The correlation of rainfall and hydraulic head change per time step was initially used to determine the time lag of the rainfall input data, which represents the time needed by the rainfall to percolate and reach the water table. In a second step, a differential evolution (DE) algorithm is utilized for the optimal selection of rainfall time lag as well as ANN's architecture and training parameters. Although a time consuming procedure, the improvement obtained suggests that the empirical determination of the ANN parameters and structure is not always sufficient and an optimization procedure, which minimizes the training and evaluation errors of the ANN, may provide substantially better simulation results. The optimized networks were finally used for midterm predictions (30 to 90 days ahead) of the hydraulic head, showing the ability of the ANN with hydraulic head change as output parameter to provide predictions with high accuracy at the end of the considered time period. Copyright © 2009 John Wiley & Sons, Ltd.
Potential Factors Leading to Popularity Unfairness in Recommender Systems: A User-Centered Analysis

Popularity bias is a well-known issue in recommender systems where few popular items are over-represented in the input data, while majority of other less popular items are under-represented. This disparate representation often leads to bias in exposure given to the items in the recommendation results. Extensive research examined this bias from item perspective and attempted to mitigate it by enhancing the recommendation of less popular items. However, a recent research has revealed the impact of this bias on users. Users with different degree of tolerance toward popular items are not fairly served by the recommendation system: users interested in less popular items receive more popular items in their recommendations, while users interested in popular items are recommended what they want. This is mainly due to the popularity bias that popular items are over-recommended. In this paper, we aim at investigating the factors leading to this user-side unfairness of popularity bias in recommender systems. In particular, we investigate two factors: 1) the relationship between this unfairness and users' interest toward items' categories (e.g., movie genres), 2) the relationship between this unfairness and the diversity of the popularity group in users' profile (the degree to which the user is interested in items with different degree of popularity). Experiments on a movie recommendation dataset using multiple recommendation algorithms show that these two factors are significantly correlated with the degree of popularity unfairness in the recommendation results.
SONA: An Accelerator for Transform-Domain Neural Networks with Sparse-Orthogonal Weights

Recent advances in model pruning have enabled sparsity-aware deep neural network accelerators that improve the energy-efficiency and performance of inference tasks. We introduce SONA, a novel transform-domain neural network accelerator in which convolution operations are replaced by element-wise multiplications with sparse-orthogonal weights. SONA employs an output stationary dataflow coupled with an energy-efficient memory organization to reduce the overhead of sparse-orthogonal transform-domain kernels that are concurrently processed without any conflicts. Weights in SONA are non-uniformly quantized with bit-sparse canonical-signed-digit representations to reduce multiplications to simple additions. Moreover, for sparse fully-connected layers (FCLs), SONA introduces column-based-block structured pruning, which is integrated into the same architecture that maintains full multiply-and-accumulate (MAC) array utilization. Compared to prior dense and sparse neural networks accelerators, SONA can reduce inference energy by $5.1\times$ and $2.4 \times$ and increase performance by $5.2\times$ and $2.1\times$, respectively, for convolution layers. For sparse FCLs, SONA can reduce inference energy by $2.4\times$ and increase performance by $2\times$ compared to prior work.
Enhancing Primary Frequency Control in Microgrids through Self-Smoothing Photovoltaic Systems

The intermittency of solar photovoltaic (PV) generation creates challenges for power systems frequency control which raises the need to employ fast-acting reserves e.g., battery energy storage systems (BESS) to improve the system frequency response. This paper explores the concept of orientational smoothing together with the frequency control of deloaded PV systems to improve self-balancing, reduce reliance on BESS, and ensure more consistent availability of the regulation reserves. The methodology involves EMT-type simulations for seven different cases of distributed PV orientations. These cases are studied for three different frequency control scenarios of a microgrid. The results demonstrate that PV systems with real rooftop and east-west orientations exhibit reduced frequency deviations and reduced rate of change of frequency.
Episodic Memory Theory for the Mechanistic Interpretation of Recurrent Neural Networks

Understanding the intricate operations of Recurrent Neural Networks (RNNs) mechanistically is pivotal for advancing their capabilities and applications. In this pursuit, we propose the Episodic Memory Theory (EMT), illustrating that RNNs can be conceptualized as discrete-time analogs of the recently proposed General Sequential Episodic Memory Model. To substantiate EMT, we introduce a novel set of algorithmic tasks tailored to probe the variable binding behavior in RNNs. Utilizing the EMT, we formulate a mathematically rigorous circuit that facilitates variable binding in these tasks. Our empirical investigations reveal that trained RNNs consistently converge to the variable binding circuit, thus indicating universality in the dynamics of RNNs. Building on these findings, we devise an algorithm to define a privileged basis, which reveals hidden neurons instrumental in the temporal storage and composition of variables, a mechanism vital for the successful generalization in these tasks. We show that the privileged basis enhances the interpretability of the learned parameters and hidden states of RNNs. Our work represents a step toward demystifying the internal mechanisms of RNNs and, for computational neuroscience, serves to bridge the gap between artificial neural networks and neural memory models.
Prompting Audios Using Acoustic Properties For Emotion Representation

Emotions lie on a continuum, but current models treat emotions as a finite valued discrete variable. This representation does not capture the diversity in the expression of emotion. To better represent emotions we propose the use of natural language descriptions (or prompts). In this work, we address the challenge of automatically generating these prompts and training a model to better learn emotion representations from audio and prompt pairs. We use acoustic properties that are correlated to emotion like pitch, intensity, speech rate, and articulation rate to automatically generate prompts i.e. 'acoustic prompts'. We use a contrastive learning objective to map speech to their respective acoustic prompts. We evaluate our model on Emotion Audio Retrieval and Speech Emotion Recognition. Our results show that the acoustic prompts significantly improve the model's performance in EAR, in various Precision@K metrics. In SER, we observe a 3.8% relative accuracy improvement on the Ravdess dataset.
A Swarm Intelligence Algorithm for Global Optimization Based on Neural Network Control System Swarm and Game Decision-Making

This paper uses a new optimization algorithm employing a neural network control system that swarms with game decision-making (NNPS-C) to solve global optimization problems. The transfer function is added to the original feedback loop, it is combined with the PID controller to form a single-neuron controller, and multiple single neurons are connected to form a neural network controller. Concurrently, the fill function allows the control system to jump out of the local optimum. The grouping method is similar to the company’s organizational structure; it divides neural network subsystems into several groups with a pyramidal structure. In the meantime, the subsystems of each layer are connected with other subsystems via game decision-making, including the connection with the farthest layer. Finally, five test functions are used to prove the effectiveness of the proposed algorithm.
Graph Curvature and Local Discrepancy

In recent years, discrete notions of curvature have been defined and exploited to understand various geometric properties of graphs; especially regarding heat flow, and spectral properties. In this paper, we study various combinatorial properties implied by satisfying the Bakry-´Emery curvature dimension inequality CD ( ∞ , K ). In particular we derive a local discrepancy inequality, similar in spirit to the expander mixing lemma from spectral graph theory, which certifies a type of ‘local pseudo-randomness’ of the edge set of the graph, for graphs satisfying a curvature lower bound. In addition, several other consequences are derived regarding graph connectivity and cycle statistics of the graph.
Target visual detection and positioning for autonomous driving

In response to the problems of low accuracy, high computational complexity, and low real-time performance in the binocular visual positioning for autonomous driving, an improved binocular visual positioning algorithm suitable for autonomous driving was proposed by modifying the ORB algorithm based on feature point matching. In the improved positioning algorithm, the YOLOv7 network was used for target detection to obtain the target area, and the Shi-Tomasi algorithm and the GMS algorithm were introduced to improve the S-G-ORB algorithm for stereo matching in the target area to obtain disparity and calculate the three-dimensional coordinates. Experimental results showed that the proposed algorithm has high accuracy and real-time performance, which meets the requirements of target detection and positioning for autonomous driving.
Delta-AI: Local objectives for amortized inference in sparse graphical models

We present a new algorithm for amortized inference in sparse probabilistic graphical models (PGMs), which we call $\Delta$-amortized inference ($\Delta$-AI). Our approach is based on the observation that when the sampling of variables in a PGM is seen as a sequence of actions taken by an agent, sparsity of the PGM enables local credit assignment in the agent's policy learning objective. This yields a local constraint that can be turned into a local loss in the style of generative flow networks (GFlowNets) that enables off-policy training but avoids the need to instantiate all the random variables for each parameter update, thus speeding up training considerably. The $\Delta$-AI objective matches the conditional distribution of a variable given its Markov blanket in a tractable learned sampler, which has the structure of a Bayesian network, with the same conditional distribution under the target PGM. As such, the trained sampler recovers marginals and conditional distributions of interest and enables inference of partial subsets of variables. We illustrate $\Delta$-AI's effectiveness for sampling from synthetic PGMs and training latent variable models with sparse factor structure.
A >130,000-Year-Long Pollen Record from Pittsburg Basin, Illinois

Pittsburg Basin, in south-central Illinois, contains a sediment record extending from the present back to the end of the late Illinoian glaciation, when central Illinois was covered with Picea/Pinus forest. During the last interglaciation, a temperate deciduous forest more diverse than Holocene Quercus/Carya forest replaced the Illinoian late-glacial boreal forest. Prairie pollen types and the charcoal/pollen ratio, indicating fire frequency, temporarily increased. Then forest, with high Juniperus percentages, became dominant once more, as the charcoal/pollen ratio dropped. After the last interglaciation, the charcoal/pollen ratio increased again and prairie and wetland surrounded Pittsburg Basin through the entire Wisconsinan glacial age. The area was still prairie in late Wisconsinan time, but with some Picea and Pinus. During the Holocene, the region has been a mixture of prairie and Quercus/Carya forest. During the last interglaciation, Pittsburg Basin was surrounded by vegetation different from that surrounding it during the present interglaciation. Rather than indicating substantial differences in climate between analogous phases of different glacial/interglacial cycles, this variation may be due to changes in fire frequency, which could be caused by small changes in climate, human activity, or differences in soil.
Development and evaluation of a Point-of-Care Test (POCT) in a low resource setting with high 1 rates of Chlamydia trachomatis urogenital infections in Fiji

Rapid and precise detection of Chlamydia trachomatis —the leading global cause of sexually 23 transmitted infections (STI)—at the point-of-care (POC) is required for treatment decisions to 24 prevent transmission and sequelae including pelvic inflammatory disease, ectopic pregnancy, 25 tubal-factor infertility and preterm birth. We developed a rapid POC test (POCT), termed LH- 26 POCT, which uses Loop-mediated AMPlification (LAMP) of nucleic acids, and performed a 27 head-to-head comparison with the Cepheid Xpert  CT/NG assay using clinician-collected de- 28 identified paired vaginal samples from a parent study that consecutively enrolled symptomatic 29 and asymptomatic females over age 18 years from the Ministry of Health and Medical Services 30 Health Centers in Fiji. Samples were processed by the Xpert  CT/NG assay and LH-POCT, 31 blinded to the comparator. Discrepant samples were resolved by qPCR. De-identified clinical 32 data and tests for Trichomonas vaginalis , Candida and bacterial vaginosis (BV) were provided. 33 There were a total of 353 samples from 327 females. C. trachomatis positivity was 16.7% 34 (59/353) while the prevalence was 16.82% (55/327) after discrepant resolution. Seven discrepant 35 samples resolved to: four false negatives, two false positives and one true positive for the LH- 36 POCT. The sensitivity of the LH-POCT was 93.65% (95% CI: 84.53% to 98.24%) and 37 specificity 99.31% (95% CI: 97.53% to 99.92%). Discrepant samples clustered among women 38 with vaginal discharge and/or BV. The prototype LH-POCT workflow has excellent 39 performance, meeting many World Health Organization ASSURED criteria for POC tests, 40 including a sample-to-result time of 35 minutes. Our LH-POCT holds promise for improving 41 clinical practice to prevent and control C. trachomatis STIs in diverse health care settings