Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Dataset for Topic-Based Paragraph Classification in Genocide-Related Court Transcripts

Recent progress in natural language processing has been impressive in many different areas with transformer-based approaches setting new benchmarks for a wide range of applications. This development has also lowered the barriers for people outside the NLP community to tap into the tools and resources applied to a variety of domain-specific applications. The bottleneck however still remains the lack of annotated gold-standard collections as soon as one’s research or professional interest falls outside the scope of what is readily available. One such area is genocide-related research (also including the work of experts who have a professional interest in accessing, exploring and searching large-scale document collections on the topic, such as lawyers). We present GTC (Genocide Transcript Corpus), the first annotated corpus of genocide-related court transcripts which serves three purposes: (1) to provide a first reference corpus for the community, (2) to establish benchmark performances (using state-of-the-art transformer-based approaches) for the new classification task of paragraph identification of violence-related witness statements, (3) to explore first steps towards transfer learning within the domain. We consider our contribution to be addressing in particular this year’s hot topic on Language Technology for All.
Eliminating Primary Beam Effect in Foreground Subtraction of Neutral Hydrogen Intensity Mapping Survey with Deep Learning

In neutral hydrogen (H i) intensity mapping (IM) survey, foreground contamination on cosmological signal is extremely severe, and systematic effects caused by radio telescopes further aggravate the difficulties in subtracting foreground. We investigate whether the deep-learning method, the 3D U-Net algorithm, can play a crucial role in foreground subtraction when considering the systematic effect caused by the telescope’s primary beam. We consider two beam models, i.e., the Gaussian beam and Cosine beam models. The traditional principal component analysis (PCA) method is employed as a preprocessing step for the U-Net method to reduce the map dynamic range. We find that in the case of the Gaussian beam, the PCA method can effectively clean the foreground. However, the PCA method cannot handle the systematic effect induced by the Cosine beam, and the additional U-Net method can improve the result significantly. To show how well the PCA and U-Net methods can recover the H i signal, we also derive the H i angular power spectrum and H i 2D power spectrum after performing foreground subtraction. It is found that in the case of Gaussian beam, the concordance with the original H i map using U-Net is better than that using PCA by 27.4%, and in the case of Cosine beam, the concordance using U-Net is better than that using PCA by 144.8%. Therefore, the U-Net–based foreground subtraction can efficiently eliminate the telescope primary beam effect and shed new light on recovering H i power spectrum for future H i IM experiments.
RODD: A Self-Supervised Approach for Robust Out-of-Distribution Detection

Recent studies have started to address the concern of detecting and rejecting the out-of-distribution (OOD) samples as a major challenge in the safe deployment of deep learning (DL) models. It is desired that the DL model should only be confident about the in-distribution (ID) data which reinforces the driving principle of the OOD detection. In this paper, we propose a simple yet effective generalized OOD detection method independent of out-of-distribution datasets. Our approach relies on self-supervised feature learning of the training samples, where the embeddings lie on a compact low-dimensional space. Motivated by the recent studies that show self-supervised adversarial contrastive learning helps robustify the model, we empirically show that a pre-trained model with self-supervised contrastive learning yields a better model for uni-dimensional feature learning in the latent space. The method proposed in this work, referred to as RODD, outperforms SOTA detection performance on extensive suite of benchmark datasets on OOD detection tasks. On the CIFAR-100 benchmarks, RODD achieves a 26.97 % lower false positive rate (FPR@95) compared to SOTA methods. Our code is publicly available.1
Assessment of Women’s Plus-Size Swimwear for Industry Applications

Abstract Using the functional, expressive and aesthetic (FEA) consumer needs model, this research investigated plus-size women’s swimwear needs. Swimming is a popular activity for women, which offers numerous physical and mental health benefits. However, little is known about plus-size women’s design preferences and satisfaction with swimwear. Attributes of swimwear were assessed through a mixed-methods qualitative survey (n = 58) and five follow-up focus groups (n = 18) with plus-size women in the western region of the United States. Participants reported greatest dissatisfaction with the functional and aesthetic attributes of swimwear available. The women in this study suggested incorporating greater bust support, increasing the ease of donning and doffing, and offering a variety of styles with modern aesthetics that visually flatter their figures. Based on the results, design implementation should include functional, expressive and aesthetic needs to develop swimwear that satisfies the plus-size consumer. Industry applications are provided.
Non-parametric inference about mean functionals of non-ignorable non-response data without identifying the joint distribution.

We consider identification and inference about mean functionals of observed covariates and an outcome variable subject to non-ignorable missingness. By leveraging a shadow variable, we establish a necessary and sufficient condition for identification of the mean functional even if the full data distribution is not identified. We further characterize a necessary condition for n-estimability of the mean functional. This condition naturally strengthens the identifying condition, and it requires the existence of a function as a solution to a representer equation that connects the shadow variable to the mean functional. Solutions to the representer equation may not be unique, which presents substantial challenges for non-parametric estimation, and standard theories for non-parametric sieve estimators are not applicable here. We construct a consistent estimator of the solution set and then adapt the theory of extremum estimators to find from the estimated set a consistent estimator of an appropriately chosen solution. The estimator is asymptotically normal, locally efficient and attains the semi-parametric efficiency bound under certain regularity conditions. We illustrate the proposed approach via simulations and a real data application on home pricing.
Monotone improvement of information-geometric optimization algorithms with a surrogate function

A surrogate function is often employed to reduce the number of objective function evaluations for optimization. However, the effect of using a surrogate model in evolutionary approaches has not been theoretically investigated. This paper theoretically analyzes the information-geometric optimization framework using a surrogate function. The value of the expected objective function under the candidate sampling distribution is used as the measure of progress of the algorithm. We assume that the surrogate function is maintained so that the population version of the Kendall's rank correlation coefficient between the surrogate function and the objective function under the candidate sampling distribution is greater than or equal to a predefined threshold. We prove that information-geometric optimization using such a surrogate function leads to a monotonic decrease in the expected objective function value if the threshold is sufficiently close to one. The acceptable threshold value is analyzed for the case of the information-geometric optimization instantiated with Gaussian distributions, i.e., the rank-μ update CMA-ES, on a convex quadratic objective function. As an alternative to the Kendall's rank correlation coefficient, we investigate the use of the Pearson correlation coefficient between the weights assigned to candidate solutions based on the objective function and the surrogate function.
Local connectedness of boundaries for relatively hyperbolic groups

Let (Γ , P ) be a relatively hyperbolic group pair that is relatively one ended. Then the Bowditch boundary of (Γ , P ) is locally connected. Bowditch previously established this conclusion under the additional assumption that all peripheral subgroups are ﬁnitely presented, either one or two ended, and contain no inﬁnite torsion subgroups. We remove these restrictions; we make no restriction on the cardinality of Γ and no restriction on the peripheral subgroups P ∈ P .
Deep Learning-based Small Magnitude Earthquake Detection and Seismic Phase Classification

Reliable earthquake detection and seismic phase classiﬁcation is often challenging especially in the circumstances of low magnitude events or poor signal-to-noise ratio. With improved seismometers and better global coverage, a sharp increase in the volume of recorded seismic data is witnessed. This makes the handling of the seismic data rather daunting based on traditional approaches and therefore fuels the need for a more robust and reliable method. In this study, we investigate two deep learning-basedmodels,termed1DResidualNeuralNetwork(ResNet)andmulti-branchResNet,fortacklingtheproblemofseismicsignaldetectionandphaseidentiﬁcation,especiallythelatercanbeusedinthecasewheremultipleclassesisorganizedinthehierarchicalformat.ThesemethodsaretrainedandtestedonthedatasetoftheSouthernCaliforniaSeismicNetwork. Resultsdemonstratethattheproposedmethodscanachieverobustperformanceforthedetectionofseismicsignals,andtheidentiﬁcationofseismicphases,evenwhentheseismiceventsareofsmallmagnitudeandaremaskedbynoise.Comparedwithpreviouslyproposeddeeplearningmethods,theintroducedframeworksachieve4 % improvement in earthquake monitoring, and a slight enhancement in seismic phase classiﬁcation.
Beyond Separability: Analyzing the Linear Transferability of Contrastive Representations to Related Subpopulations

Contrastive learning is a highly effective method for learning representations from unlabeled data. Recent works show that contrastive representations can transfer across domains, leading to simple state-of-the-art algorithms for unsupervised domain adaptation. In particular, a linear classifier trained to separate the representations on the source domain can also predict classes on the target domain accurately, even though the representations of the two domains are far from each other. We refer to this phenomenon as linear transferability. This paper analyzes when and why contrastive representations exhibit linear transferability in a general unsupervised domain adaptation setting. We prove that linear transferability can occur when data from the same class in different domains (e.g., photo dogs and cartoon dogs) are more related with each other than data from different classes in different domains (e.g., photo dogs and cartoon cats) are. Our analyses are in a realistic regime where the source and target domains can have unbounded density ratios and be weakly related, and they have distant representations across domains.
Scalar Induced Gravitational Waves from Warm Inflation

Stochastic gravitational waves can be induced from the primordial curvature perturbations generated during inflation, through scalar-tensor mode coupling at the second order of cosmological perturbation theory. Here we discuss a model of warm inflation in which large curvature perturbations are generated at the small scales because of inflaton dissipation. These overdense perturbations then collapse at later epoch to form primordial black holes, as was studied in our earlier work (Ref. \cite{Arya:2019wck}), and therefore may also act as a source to the second-order tensor perturbations. In this study, we calculate the spectrum of these secondary gravitational waves from our warm inflationary model. We find that our model leads to a generation of scalar induced gravitational waves (SIGW) over a frequency range ($1-10^6$) Hz. Further, we discuss the detection possibilities of these SIGWs, taking in account the sensitivity of different ongoing and future gravitational wave experiments.
Gait Recognition in the Wild with Dense 3D Representations and A Benchmark

Existing studies for gait recognition are dominated by 2D representations like the silhouette or skeleton of the human body in constrained scenes. However, humans live and walk in the unconstrained 3D space, so projecting the 3D human body onto the 2D plane will discard a lot of crucial information like the viewpoint, shape, and dynamics for gait recognition. Therefore, this paper aims to explore dense 3D representations for gait recognition in the wild, which is a practical yet neglected problem. In particular, we propose a novel framework to explore the 3D Skinned Multi-Person Linear (SMPL) model of the human body for gait recognition, named SMPLGait. Our framework has two elaborately-designed branches of which one extracts appearance features from silhouettes, the other learns knowledge of 3D viewpoints and shapes from the 3D SMPL model. In addition, due to the lack of suitable datasets, we build the first large-scale 3D representation-based gait recognition dataset, named Gait3D. It contains 4,000 subjects and over 25,000 sequences extracted from 39 cameras in an unconstrained indoor scene. More importantly, it provides 3D SMPL models recovered from video frames which can provide dense 3D information of body shape, viewpoint, and dynamics. Based on Gait3D, we comprehensively compare our method with existing gait recognition approaches, which reflects the superior performance of our framework and the potential of 3D representations for gait recognition in the wild. The code and dataset are available at: https://gait3d.github. io.
A Hessian Inversion-Free Exact Second Order Method for Distributed Consensus Optimization

We consider a standard distributed consensus optimization problem where a set of agents connected over an undirected network minimize the sum of their individual (local) strongly convex costs. Alternating Direction Method of Multipliers (ADMM) and Proximal Method of Multipliers (PMM) have been proved to be effective frameworks for design of exact distributed second order methods (involving calculation of local cost Hessians). However, existing methods involve explicit calculation of local Hessian inverses at each iteration that may be very costly when the dimension of the optimization variable is large. In this article, we develop a novel method, termed Inexact Newton method for Distributed Optimization (INDO), that alleviates the need for Hessian inverse calculation. INDO follows the PMM framework but, unlike existing work, approximates the Newton direction through a generic fixed point method (e.g., Jacobi Overrelaxation) that does not involve Hessian inverses. We prove exact global linear convergence of INDO and provide analytical studies on how the degree of inexactness in the Newton direction calculation affects the overall method's convergence factor. Numerical experiments on several real data sets demonstrate that INDO's speed is on par (or better) as state of the art methods iteration-wise, hence having a comparable communication cost. At the same time, for sufficiently large optimization problem dimensions $n$ (even at $n$ on the order of couple of hundreds), INDO achieves savings in computational cost by at least an order of magnitude.
rienting of visual attention in aging

Changes in attention are among the most important cognitive shifts associated with aging, with implications for maintenance of vocational competencies, participation in social interactions, and successful execution of activities of daily living. An important facet of attention is orienting, the ability to selectively attend a location or modality and thereby engender perceptual augmentation. Orienting also involves shifting of the focus of attention in response to unanticipated salient events. Aging may impact orienting through a variety of neurocognitive mechanisms and the interactions between them. We review findings regarding factors that mediate the impact of aging on orienting, including overt vs. covert attending, exogenous vs. endogenous processes, orienting benefits vs. reorienting costs, cue-target onset asynchrony
BANK WHOLESALE FUNDING, MONETARY TRANSMISSION AND SYSTEMIC RISK: EVIDENCE FROM CHINA

We study how interbank wholesale funding in China inﬂuences monetary policy transmission under a dual-track interest-rate system and how it contributes to increasing systemic risks in recent years. By constructing a bank-panel dataset, we ﬁnd that wholesale funding via interbank certiﬁcates of deposit not only facilitates policy interest rates to transmit into loan by non-state banks, but also leads to fast growth in their shadow banking activities as an unintended consequence. Accordingly, non-state banks with a heavier exposure to wholesale funding witness a larger increase in systemic risks in response to negative shocks to the economy since 2018. We advance a theoretical explanation of our empirical ﬁndings and quantify the trade-oﬀ of banking regulation on wholesale funding between the eﬀectiveness of monetary policy transmission and exposure to systemic risks within this framework.
Short UV-C treatments extend the shelf-life of fresh-cut strawberries (Fragaria x ananassa Duch cv. Camarosa)

In recent years there has been a marked increase in the demand for fresh-cut fruit. Although these products have high user convenience, they are extremely perishable. Consequently, they must be marketed within a very short period. A number of studies have shown that brief UV-C irradiation prior to storage may reduce postharvest deterioration in whole fruit. Herein, we evaluated the influence of different UV-C dose and intensity combinations on the quality and shelf-life of fresh-cut strawberries. Fresh berries were cut in wedges and subjected to brief UV-C treatments having different combinations of radiation intensity (0, 9, or 36 W m-2) and dose (0, 2, or 4 kJ m-2). Treatments with a dosage of 4 kJ m-2 at an intensity of 36 W m-2 decreased decay, juice leakage, dehydration softening, and yeasts and mold counts. UV-C treated berries also scored better in freshness color and overall acceptability in consumer tests. The treatments did not affect the acidity, soluble solids, or phenolic compounds. Results suggest that short UV-C treatment could be useful to supplement cold storage, extending the shelf-life of fresh-cut strawberries.
Detection of qnr genes and gyrA mutation to quinolone phenotypic resistance of UTI pathogens in Bangladesh and the implications

Background: Plasmid-mediated quinolone-resistant (PMQR) genes and mutations within the quinolone resistance-determining regions (QRDRs) resulted in the advent of quinolone-resistant pathogenic microbes. This research was designed to assess the roles of three PMQR genes, qnrA , qnrB , and qnrS, and any mutation in the gyrA gene in the QRDR as a process of quinolone/fluoroquinolone resistance to urinary tract infection (UTI) bacteria in Bangladesh to guide future management of UTIs. Methods: Pathogens from UTIs were isolated and identified, and their phenotype antibiotic susceptibilities were tested for lomefloxacin, ofloxacin, ciprofloxacin, and nalidixic acid. Polymerase chain reaction (PCR) detected the qnrA , qnrB , and qnrS genes. PCR and sequencing were performed to evaluate any mutation within the QRDRs of the gyrA gene. Results: Of 100 UTI bacteria, phenotypic resistance was observed in 95.0%, 89.0%, 83.0%, and 71.0% against lomefloxacin, nalidixic acid, ofloxacin, and ciprofloxacin, respectively. PMQR genes qnrS, qnrA , and qnrB genes were found in 54.0%, 1.0%, and 4.0% of isolates, respectively. Sequencing the gyrA gene revealed single mutation (Ser-83 to Leu) and double mutations (Ser-83 to Leu and Asp-87 to Asn). PMQR genes showed a statistically nonsignificant association with phenotypic resistance. Conclusions: This study confirms the presence of QRDR mutations that were independent of PMQR genes. Consequently, high resistance against quinolones among uropathogens is evident, and their future use needs to be moderated.
A Computational Architecture for a Pro-Social Rule Bending Agent

There have been many attempts to implement ethical reasoning in artiﬁcial agents. The principal philosophical approaches attempted have been mainly deontological or utilitarian. Virtue ethics has been discussed but not thoroughly explored in implementations of ethical agents. A particularity of strict implementations of deontological/utilitarian approaches is that the results produced by these do not always conform to human intuitions of “the right thing to do”. Intu-itions of the right thing to do in a particular social context are often independent of which philosophical school of thought human beings relate to. This is partly due to the ability of humans to step outside their particular reasoning framework, and make virtuous decisions based on what would be beneﬁ-cial to society, not just themselves. This behaviour is called pro-social rule bending . This is a work-in-progress paper that details our attempt to implement pro-social rule bending in an artiﬁcial agent.
Online Communication: The Implementation of the Collaborative mLearning Science Module in a Malaysian Secondary School

: Communication skills are required for collaborative discussions among scientists in constructing science knowledge. A collaborative mLearning (CmL) module with online communication tools (discussion forum, wiki, and text messaging) can enable authentic discussions for learning of science outside the classroom. This study seeks to determine whether online communication and collaboration is effective for learning. A developmental research approach with three phases was employed: Needs analysis, design, and implementation. An urban school with a multiracial composition was selected for this study. In the Needs Analysis phase, a survey of 158 learners' background in the use of technology was conducted to determine the types of tools which can be used in the CmL module. The module was implemented with twenty (20) selected Form 2 students to determine the types of interactions in their online discussions. Transcripts of their online communications were analyzed according to the types of interactions and processes, and the learners were interviewed to verify if learning was effective in the CmL module. The findings showed that learners interacted with the content, instructor, and other learners in the discussion forum. However, only learner-content interaction was detected in the wiki, and learner-instructor in the text messaging SMS Quiz. Findings from interviews verified that learning was effective in the CmL module. In addition, the discussion forum allowed for argumentation to challenge the dualism of science knowledge. This study is significant as it shows that online communication tools force collaboration and are effective