Miracle-dz/UniFAR-FTUs huggingface dataset:
THE ROLE OF LEARNING READINESS IN MODERATING THE EFFECT OF PARTICIPATION AND MOTIVATION ON THE STUDENTS UNDERSTANDING OF ECONOMICS LEARNING DURING DISTANCE EDUCATION

This study aims to determine the level of understanding of students' economic learning during the implementation of distance education in terms of participation, motivation and readiness to learn. In addition, this study also uses the learning readiness variable not only as an independent variable but also as a moderating variable that will strengthen or weaken the independent variable. This study uses a quantitative approach with explanatory research methods. The number of samples in this study amounted to 104 respondents consisting of students of economic education at the State University of Malang Batch 2020. The data analysis used in the study to test the hypothesis was the Moderated Regression Analysis (MRA) test, and for hypothesis decision making, the coefficient of determination test (R Square) was used. ) and partial test (t-test). The results showed that participation and motivation had a significant positive effect on students' understanding of economic learning during distance education with learning readiness as a moderating variable. The findings in this study also show that the learning readiness variable can not only be a moderating variable, but the learning readiness variable can also be an independent variable that can have a significant effect on the dependent variable. Future researchers are expected to be able to use other variables that are used as moderating variables more specifically and following the character of the research subject and can use a combination of deeper data collection methods such as interviews and observations.
Voting in a Pandemic: Lessons From the 2020 South Korean Legislative Election

In this paper, we examine the extent to which a government's response to a pandemic affects election outcomes. Using detailed data on South Korea's 21st legislative election, held in April 2020 during the COVID-19 pandemic, we find that a candidate of the ruling Democratic Party was less preferred in electoral districts where the COVID-19 infection rate was higher. We also find that the South Korean government's successful control of the disease contributed significantly to the overwhelming victory of the ruling party against the leading opposition party. Specifically, our counterfactual analysis predicts that each party would have taken 129 and 102 of the 231 constituency seats considered in the analysis, respectively, had the COVID infection rate been the same as the OECD average during the election period. Given that the observed result was 147 to 84, this implies that 18 electees of the ruling party would have lost to the candidates of the leading opposition party, which in turn would have granted the opposition party the ability to block any attempt by the ruling party to fast-track debatable bills.
Macro Outsourcing: Evaluating Government Reliance on the Private Sector

Government outsourcing of services to private sector entities is increasingly common. The conventional wisdom ties governments’ outsourcing decisions to either an ideological preference for market-based solutions or fiscal pressures. However, these conjectures have not been systematically subjected to empirical scrutiny. I develop an aggregate annual measure of US state-level outsourcing decisions—macro outsourcing—and explore whether the evidence supports these pathways. I also point to an underappreciated political pathway by which potential losers—bureaucrats organized into public sector unions—affect the decision to outsource. The results offer little support for the received wisdom and instead demonstrate that states with strong unions are less likely to rely on private actors. I bolster this finding with preliminary analyses showing that states with laws that sap union power exhibit higher levels of outsourcing. Overall, these results show that outsourcing is a decidedly political phenomenon, albeit via an unexpected route.
The productivity of bread wheat under different irrigation conditions

Wheat production in Mexico depends on more than 90% of the irrigated areas, which are classified as having a high water shortage, so varieties that are efficient in the use of water must be planted. In such a way that the objective of the investigation was to evaluate the behavior of the yield and its components of genotypes in function of the decrease of irrigation sheets (LR). Eight varieties of bread wheat and four LR were used. Genotypes were established in a split-plot design with large plots arranged as a randomized full-block design, with two replications. LR reductions of 1 to 0.8, 0.6, and 0.4 m decreased grain yield by 14.4, 37.6, and 76.8%, respectively. In limiting humidity (0.4 m) the number of spikes and biological yield decreased 63.7 and 73.3%. Grain yield was correlated with the number of grains and spikes per square meter. When the LR decreased from 1 to 0.8 m, Rebeca F2000 and Temporalera M87 presented grain yields greater than 6.5 t ha associated with high biological yields. While Temporalera M87, in the 0.4 m sheet, was the one with the highest biological yield, number of grains and grain yield with 2.4 t ha. Therefore, genotypes and yield components were identified to help in the selection process with higher yield potential in limiting humidity conditions.
Panoptic SegFormer

We present Panoptic SegFormer, a general framework for end-to-end panoptic segmentation with Transformers. The proposed method extends Deformable DETR with a uniﬁed mask prediction workﬂow for both things and stuff, mak-ing the panoptic segmentation pipeline concise and effective. With a ResNet-50 backbone, our method achieves 50.0% PQ on the COCO test-dev split, surpassing previous state-of-the-art methods by signiﬁcant margins without bells and whistles. Using a more powerful PVTv2-B5 backbone, Panoptic SegFormer achieves a new record of 54.1%PQ and 54.4% PQ on the COCO val and test-dev splits with single scale input.
Constraining beyond ΛCDM models with 21cm intensity mapping forecasted observations combined with latest CMB data

We explore constraints on dark energy and modified gravity with forecasted 21cm intensity mapping measurements using the Effective Field Theory approach. We construct a realistic mock data set forecasting a low redshift 21cm signal power spectrum P 21(z,k) measurement from the MeerKAT radio-telescope. We compute constraints on cosmological and model parameters through Monte-Carlo Markov-Chain techniques, testing both the constraining power of P 21(k) alone and its effect when combined with the latest Planck 2018 CMB data. We complement our analysis by testing the effects of tomography from an ideal mock data set of observations in multiple redshift bins. We conduct our analysis numerically with the codes EFTCAMB/EFTCosmoMC, which we extend by implementing a likelihood module fully integrated with the original codes. We find that adding P 21(k) to CMB data provides significantly tighter constraints on Ωc h 2 and H 0, with a reduction of the error with respect to Planck results at the level of more than 60%. For the parameters describing beyond ΛCDM theories, we observe a reduction in the error with respect to the Planck constraints at the level of ≲ 10%. The improvement increases up to ∼ 35% when we constrain the parameters using ideal, tomographic mock observations. We conclude that the power spectrum of the 21cm signal is sensitive to variations of the parameters describing the examined beyond ΛCDM models and, thus, P 21(k) observations could help to constrain dark energy. The constraining power on such theories is improved significantly by tomography.
Some Applications of Dynamical Belyi Polynomials

We give necessary and sufficient conditions for post-critically finite polynomials to have persistent bad reduction at a given prime. We also answer in the negative a question posed by Silverman about conservative polynomials. Both proofs rely on conservative dynamical Belyi polynomials as exemplars of PCF (resp. conservative) maps.
Parameterizing and Simulating from Causal Models

Many statistical problems in causal inference involve a probability distribution other than the one from which data are actually observed; as an additional complication, the object of interest is often a marginal quantity of this other probability distribution. This creates many practical complications for statistical inference, even where the problem is non-parametrically identified. In particular, it is difficult to perform likelihood-based inference, or even to simulate from the model in a general way. We introduce the ‘frugal parameterization’, which places the causal effect of interest at its centre, and then builds the rest of the model around it. We do this in a way that provides a recipe for constructing a regular, non-redundant parameterization using causal quantities of interest. In the case of discrete variables we can use odds ratios to complete the parameterization, while in the continuous case copulas are the natural choice; other possibilities are also discussed. Our methods allow us to construct and simulate from models with parametrically specified causal distributions, and fit them using likelihood-based methods, including fully Bayesian approaches. Our proposal includes parameterizations for the average causal effect and effect of treatment on the treated, as well as other causal quantities of interest.
Text-Free Prosody-Aware Generative Spoken Language Modeling

Speech pre-training has primarily demonstrated efficacy on classification tasks, while its capability of generating novel speech, similar to how GPT-2 can generate coherent paragraphs, has barely been explored. Generative Spoken Language Modeling (GSLM) (CITATION) is the only prior work addressing the generative aspect of speech pre-training, which builds a text-free language model using discovered units. Unfortunately, because the units used in GSLM discard most prosodic information, GSLM fails to leverage prosody for better comprehension and does not generate expressive speech. In this work, we present a prosody-aware generative spoken language model (pGSLM). It is composed of a multi-stream transformer language model (MS-TLM) of speech, represented as discovered unit and prosodic feature streams, and an adapted HiFi-GAN model converting MS-TLM outputs to waveforms. Experimental results show that the pGSLM can utilize prosody to improve both prosody and content modeling, and also generate natural, meaningful, and coherent speech given a spoken prompt. Audio samples can be found at https://speechbot.github. io/pgslm. Codes and models are available at https://github.com/pytorch/fairseq/tree/main/examples/textless_nlp/pgslm.
A Clustering-aided Ensemble Method for Predicting Ridesourcing Demand in Chicago

Accurately forecasting ridesourcing demand is important for effective transportation planning and policy-making. With the rise of Artificial Intelligence (AI), researchers have started to utilize machine learning models to forecast travel demand, which, in many cases, can produce higher prediction accuracy than statistical models. However, most existing machinelearning studies used a global model to predict the demand and ignored the influence of spatial heterogeneity (i.e., the spatial variations in the impacts of explanatory variables). Spatial heterogeneity can drive the parameter estimations varying over space; failing to consider the spatial variations may limit the model’s prediction performance. To account for spatial heterogeneity, this study proposes a Clustering-aided Ensemble Method (CEM) to forecast the zone-to-zone (census-tract-to-census-tract) travel demand for ridesourcing services. Specifically, we develop a clustering framework to split the origin-destination pairs into different clusters and ensemble the cluster-specific machine learning models for prediction. We implement and test the proposed methodology by using the ridesourcing-trip data in Chicago. The results show that, with a more transparent and flexible model structure, the CEM significantly improves the prediction accuracy than the benchmark models (i.e., global machine-learning and statistical models directly trained on all observations). This study offers transportation researchers and practitioners a new methodology of travel demand forecasting, especially for new travel modes like ridesourcing and micromobility.
CSYseq: The first Y-chromosome sequencing tool typing a large number of Y-SNPs and Y-STRs to unravel worldwide human population genetics

Male-specific Y-chromosome (chrY) polymorphisms are interesting components of the DNA for population genetics. While single nucleotide polymorphisms (Y-SNPs) indicate distant evolutionary ancestry, short tandem repeats (Y-STRs) are able to identify close familial kinships. Detailed chrY analysis provides thus both biogeographical background information as paternal lineage identification. The rapid advancement of high-throughput massive parallel sequencing (MPS) technology in the past decade has revolutionized genetic research. Using MPS, single-base information of both Y-SNPs as Y-STRs can be analyzed in a single assay typing multiple samples at once. In this study, we present the first extensive chrY-specific targeted resequencing panel, the ‘CSYseq’, which simultaneously identifies slow mutating Y-SNPs as evolution markers and rapid mutating Y-STRs as patrilineage markers. The panel was validated by paired-end sequencing of 130 males, distributed over 65 deep-rooted pedigrees covering 1,279 generations. The CSYseq successfully targets 15,611 Y-SNPs including 9,014 phylogenetic informative Y-SNPs to identify 1,443 human evolutionary Y-subhaplogroup lineages worldwide. In addition, the CSYseq properly targets 202 Y-STRs, including 81 slow, 68 moderate, 27 fast and 26 rapid mutating Y-STRs to individualize close paternal relatives. The targeted chrY markers cover a high average number of reads (Y-SNP = 717, Y-STR = 150), easy interpretation, powerful discrimination capacity and chrY specificity. The CSYseq is interesting for research on different time scales: to identify evolutionary ancestry, to find distant family and to discriminate closely related males. Therefore, this panel serves as a unique tool valuable for a wide range of genetic-genealogical applications in interdisciplinary research within evolutionary, population, molecular, medical and forensic genetics.
Examining community participation in crime prevention in rural Peshawar, Pakistan

ABSTRACT Lower crime rates in rural areas of the world are often taken as a sign that crime is not a major concern in these areas. However, rural crime undermines sustainable development to a large extent. This research presents an empirical examination of the prospects of community policing in crime prevention in rural Peshawar, Pakistan as little is known about its effectiveness. A cross-sectional survey is conducted to gather quantitative data by interviewing a sample of the community residents in Palosi and Regi neighbourhoods. Results of logistic regression indicate that overall community participation is an effective strategy in preventing crime in the study area indicating a chi-square significance of <0.05, but police are not successful in developing trust in the communities. It is suggested that police have to render quality services including information and communication technologies that largely contribute to the rapid transfer of information and knowledge and crime control.
Uncovering Learners’ Experiences to New Normal Education: Implications of Asynchronous Instruction in GE 5 Course Teaching

The implementation of online learning modality posed different risks, problems and challenges to students and other educational stakeholders, especially in the higher education institutions. The study presented in this paper aimed to explore students’ attitude towards online-based learning system in the new normal. Specifically, it investigated the factor structure and the level of attitudes of 200 students towards online-based learning system in the new normal. This study utilized a mixed method of research utilizing in-depth interview and a dimension reduction technique through Principal Component Analysis. Results revealed that in the context of the University of Mindanao Digos College, attitudes toward online-based learning system is multidimensional exploring eight dimensions namely: Engagement, Convenience, Satisfaction, Technology Acceptance, Adaptability, Interaction, Self-Regulation and Control. Moreover, the level of attitudes of students revealed a high level of convenience, technology acceptance, adaptability, interaction, assessment satisfaction, self-regulation and control and a moderate level of student engagement. Thus, the researchers recommend a training proposal for teachers as well as recalibrating the result of the study utilizing Confirmatory Factor Analysis.
Ghost-DeblurGAN and Its Application to Fiducial Marker System

—Motion blur can impede marker detection and marker-based pose estimation, which is common in real-world robotic applications involving ﬁducial markers. To solve this problem, we propose a novel lightweight generative adversarial network (GAN), Ghost-DeblurGAN, for real-time motion deblurring. Furthermore, a new large-scale dataset, YorkTag, provides pairs of sharp/blurred images containing ﬁducial markers and is proposed to train and qualitatively and quantitatively evaluate our model. Experimental results demonstrate that when applied along with fudicual marker systems to motion-blurred images, Ghost-DeblurGAN improves the marker detection signiﬁcantly and mitigates the rotational ambiguity problem in marker-based pose estimation.
A Review of Sound Source Localization with Deep Learning Methods

—This article is a review on deep learning methods for single and multiple sound source localization. We are particularly interested in sound source localization in indoor/domestic environment, where reverberation and diffuse noise are present. We provide an exhaustive topography of the neural-based localization literature in this context, organized according to several aspects: the neural network architecture, the type of input features, the output strategy (classiﬁcation or regression), the types of data used for model training and evaluation, and the model training strategy. This way, an interested reader can easily comprehend the vast panorama of the deep learning-based sound source localization methods. Tables summarizing the literature review are provided at the end of the review for a quick search of methods with a given set of target characteristics.
Symmetry breaking patterns for two coupled complex scalar fields at finite temperature and in an external magnetic field

A model of two coupled complex scalar fields is studied at finite temperature and under an external magnetic field. The results are obtained in the context of the nonperturbative method of the optimized perturbation theory and contrasted with those obtained in perturbation theory and in the one-loop approximation. The emergence of phenomena related to inverse symmetry breaking and symmetry nonrestoration are analyzed.
Maximal tori in $HH^1$ and the fundamental group

We investigate maximal tori in the Hochschild cohomology Lie algebra HH(A) of a finite dimensional algebra A, and their connection with the fundamental groups associated to presentations of A. We prove that every maximal torus in HH(A) arises as the dual of some fundamental group of A, extending work of Farkas, Green and Marcos; de la Peña and Saoŕın; and Le Meur. Combining this with known invariance results for Hochschild cohomology, we deduce that (in rough terms) the largest rank of a fundamental group of A is a derived invariant quantity, and among self-injective algebras, an invariant under stable equivalences of Morita type. Using this we prove that there are only finitely many monomial algebras in any derived equivalence class of finite dimensional algebras; hitherto this was known only for very restricted classes of monomial algebras.
Open Aspect Target Sentiment Classification with Natural Language Prompts

For many business applications, we often seek to analyze sentiments associated with any arbitrary aspects of commercial products, despite having a very limited amount of labels or even without any labels at all. However, existing aspect target sentiment classification (ATSC) models are not trainable if annotated datasets are not available. Even with labeled data, they fall short of reaching satisfactory performance. To address this, we propose simple approaches that better solve ATSC with natural language prompts, enabling the task under zero-shot cases and enhancing supervised settings, especially for few-shot cases. Under the few-shot setting for SemEval 2014 Task 4 laptop domain, our method of reformulating ATSC as an NLI task outperforms supervised SOTA approaches by up to 24.13 accuracy points and 33.14 macro F1 points. Moreover, we demonstrate that our prompts could handle implicitly stated aspects as well: our models reach about 77% accuracy on detecting sentiments for aspect categories (e.g., food), which do not necessarily appear within the text, even though we trained the models only with explicitly mentioned aspect terms (e.g., fajitas) from just 16 reviews - while the accuracy of the no-prompt baseline is only around 65%.
Vision Matters When It Should: Sanity Checking Multimodal Machine Translation Models

Multimodal machine translation (MMT) systems have been shown to outperform their text-only neural machine translation (NMT) counterparts when visual context is available. However, recent studies have also shown that the performance of MMT models is only marginally impacted when the associated image is replaced with an unrelated image or noise, which suggests that the visual context might not be exploited by the model at all. We hypothesize that this might be caused by the nature of the commonly used evaluation benchmark, also known as Multi30K, where the translations of image captions were prepared without actually showing the images to human translators. In this paper, we present a qualitative study that examines the role of datasets in stimulating the leverage of visual modality and we propose methods to highlight the importance of visual signals in the datasets which demonstrate improvements in reliance of models on the source images. Our findings suggest the research on effective MMT architectures is currently impaired by the lack of suitable datasets and careful consideration must be taken in creation of future MMT datasets, for which we also provide useful insights.
Corporate governance and the financial performance of commercial banks in Ghana

This study aims to provide further evidence on the effect of corporate governance on the performance of Ghanaian banks. Two performance measures were used in this study, namely: Return on Asset (ROA) and Cost-Income Ratio (CIR). Data for the analysis were sourced from 21 commercial banks from 2005 to 2015. Regression estimation techniques were employed for analysis purposes. The result revealed that large board size reduces banks’ performance. Furthermore, CEO duality and foreign ownership negatively affect the performance of banks. However, while the effect of CEO duality was significant on CIR, it was not significant in the case of ROA. On the contrary, the effect of foreign ownership was only significant on ROA. Moreover, board independence has a significant positive effect on both CIR and ROA, while audit committee independence has no significant effect on CIR and ROA. The paper argues that for a good corporate governance practice, banks should institute a small board with more than half of the members being independent directors. Furthermore, the role of the board chair should be separated from that of the managing director/CEO. The study provides insight and further evidence to stakeholders and regulators to deal with the crisis in the Ghanaian banking sector.