Miracle-dz/UniFAR-FTUs huggingface dataset:
A simulation study of galactic proton modulation from solar minimum to maximum conditions

The observation of various cosmic ray particles at the Earth had been donewith the PAMELA space detector for almost 10 years, from June 2006 to January 2016. The AMS-02 space experiment provides similar cosmic ray data. The purpose of this work is to utilize the available state-of-theart numerical modulation model for the transport of cosmic rays in the heliosphere to compute the modulation of galactic protons from minimum to maximum solar activity. These modeling results, which simulate realistic heliospheric conditions, are compared to proton observations from PAMELA taken between 2006 and 2010 and to similar AMS-02 observations after 2011. It will be shown how differently modulation mechanisms influence the time-evolution of the proton spectra when modulation conditions change from minimum to maximum, including a reversal of the polarity of the heliospheric magnetic field.
THE EFFECT OF CORPORATE SOCIAL RESPONSIBILITY DISCLOSURE, LEVERAGE, AND FREE CASH FLOW ON EARNINGS MANAGEMENT

This study aims to determine the effect of CSR Disclosure, Leverage, and Free Cash Flow on profit management. The population in this study was manufacturing companies in the food and beverage sector which were listed on the Indonesia Stock Exchange (BEI) in the 2016-2018 period. Sampling was carried out using a purposive sampling method, and the number of samples used in the study was 26 data of food companies and drinks. Based on the results of research and data analysis using SPSS shows that: (1) CSR disclosure does not affect earnings management, (2) Leverage does not affect earnings management, and (3) Free cash flow does not affect earnings management. The results of this study imply that CSR disclosure is not the only indicator that is considered by investors. Investors may pay more attention to the company's prospects in the future along with the performance of the company. It is expected that companies can pay more attention to social responsibility as an effort to establish policies in promoting a balance between company profits and the benefits of society as a whole. Besides, in conditions where leverage and free cash flow are high, managers do not always think to undertake earning management to overcome the problems. Managers may consider the reversal impact after the earning management which may give a more negative impact on the company.
Improved Techniques for Quantizing Deep Networks with Adaptive Bit-Widths

Quantizing deep networks with adaptive bit-widths is a promising technique for efficient inference across many devices and resource constraints. In contrast to static methods that repeat the quantization process and train different models for different constraints, adaptive quantization enables us to flexibly adjust the bit-widths of a single deep network during inference for instant adaptation in different scenarios. While existing research shows encouraging results on common image classification benchmarks, this paper investigates how to train such adaptive networks more effectively. Specifically, we present two novel techniques for quantizing deep neural networks with adaptive bit-widths of weights and activations. First, we propose a collaborative strategy to choose a high-precision teacher for transferring knowledge to the low-precision student while jointly optimizing the model with all bit-widths. Second, to effectively transfer knowledge, we develop a dynamic block swapping method by randomly replacing the blocks in the lower-precision student network with the corresponding blocks in the higher-precision teacher network. Extensive experiments on multiple image classification datasets including video classification benchmarks for the first time, well demonstrate the efficacy of our approach over state-of-the-art methods.
Fine-Grained Style Modeling, Transfer and Prediction in Text-to-Speech Synthesis via Phone-Level Content-Style Disentanglement

This paper presents a novel design of neural network system for fine-grained style modeling, transfer and prediction in expressive text-to-speech (TTS) synthesis. Fine-grained modeling is realized by extracting style embeddings from the mel-spectrograms of phone-level speech segments. Collaborative learning and adversarial learning strategies are applied in order to achieve effective disentanglement of content and style factors in speech and alleviate the"content leakage"problem in style modeling. The proposed system can be used for varying-content speech style transfer in the single-speaker scenario. The results of objective and subjective evaluation show that our system performs better than other fine-grained speech style transfer models, especially in the aspect of content preservation. By incorporating a style predictor, the proposed system can also be used for text-to-speech synthesis. Audio samples are provided for system demonstration https://daxintan-cuhk.github. io/pl-csd-speech .
Locally Valid and Discriminative Prediction Intervals for Deep Learning Models

Crucial for building trust in deep learning models for critical real-world applications is efficient and theoretically sound uncertainty quantification, a task that continues to be challenging. Useful uncertainty information is expected to have two key properties: It should be valid (guaranteeing coverage) and discriminative (more uncertain when the expected risk is high). Moreover, when combined with deep learning (DL) methods, it should be scalable and affect the DL model performance minimally. Most existing Bayesian methods lack frequentist coverage guarantees and usually affect model performance. The few available frequentist methods are rarely discriminative and/or violate coverage guarantees due to unrealistic assumptions. Moreover, many methods are expensive or require substantial modifications to the base neural network. Building upon recent advances in conformal prediction [13, 33] and leveraging the classical idea of kernel regression, we propose Locally Valid and Discriminative prediction intervals (LVD), a simple, efficient and lightweight method to construct discriminative prediction intervals (PIs) for almost any DL model. With no assumptions on the data distribution, such PIs also offer finite-sample local coverage guarantees (contrasted to the simpler marginal coverage). We empirically verify, using diverse datasets, that besides being the only locally valid method for DL, LVD also exceeds or matches the performance (including coverage rate and prediction accuracy) of existing uncertainty quantification methods, while offering additional benefits in scalability and flexibility.
Robust Fixed-Order Controller Design for Uncertain Systems With Generalized Common Lyapunov Strictly Positive Realness Characterization

This article investigates the design of a robust fixed-order controller for single-input–single-output (SISO) polytopic systems with interval uncertainties, with the aim that the closed-loop stability is appropriately ensured and the performance specifications on sensitivity shaping are conformed in a specific finite frequency range. Utilizing the notion of generalized common Lyapunov strictly positive realness (CL-SPRness), the equivalence between strictly positive realness (SPRness) and strictly bounded realness (SBRness) is established; and then, the specifications on robust stability and performance are transformed into the SPRness of newly constructed systems and further characterized in the framework of linear matrix inequality (LMI) conditions. The proposed methodology avoids the tedious yet mandatory evaluations of the specifications on all vertices of the uncertain polytopic system in an explicit form. Instead, solving five LMIs exclusively suffices for ensuring the robust stability and performance regardless of the number of vertices, and thus, the typically heavy computational burden is considerably alleviated. It is also noteworthy that the proposed methodology additionally provides the necessary and sufficient conditions for this robust controller design with the consideration of a prescribed finite frequency range, and therefore, significantly less conservatism is attained in the system performance.
Existence of partially hyperbolic motions in the N-body problem

In the context of the Newtonian N-body problem, we prove the existence of a partially hyperbolic motion with prescribed positive energy and any initial collisionless configuration. Moreover, it is a free time minimizer of the respective supercritical Newtonian action or equivalently a geodesic ray for the respective Jacobi-Maupertuis metric.
Valid two‐sample graph testing via optimal transport Procrustes and multiscale graph correlation with applications in connectomics

Testing whether two graphs come from the same distribution is of interest in many real‐world scenarios, including brain network analysis. Under the random dot product graph model, the nonparametric hypothesis testing framework consists of embedding the graphs using the adjacency spectral embedding (ASE), followed by aligning the embeddings using the median flip heuristic and finally applying the nonparametric maximum mean discrepancy (MMD) test to obtain a p value. Using synthetic data generated from Drosophila brain networks, we show that the median flip heuristic results in an invalid test and demonstrate that optimal transport Procrustes (OTP) for alignment resolves the invalidity. We further demonstrate that substituting the MMD test with the multiscale graph correlation (MGC) test leads to a more powerful test both in synthetic and in simulated data. Lastly, we apply this powerful test to the right and left hemispheres of the larval Drosophila mushroom body brain networks and conclude that there is not sufficient evidence to reject the null hypothesis that the two hemispheres are equally distributed.
Riemannian Stochastic Hybrid Gradient Algorithm for Nonconvex Optimization

In recent years, Riemannian stochastic gradient descent (R-SGD), Riemannian stochastic variance reduction (R-SVRG) and Riemannian stochastic recursive gradient (R-SRG) have attracted considerable attention on Riemannian optimization. Under normal circumstances, it is impossible to analyze the convergence of R-SRG algorithm alone. The main reason is that the conditional expectation of the descending direction is a biased estimation. However, in this paper, we consider linear combination of three descent directions on Riemannian manifolds as the new descent direction (i.e., R-SRG, R-SVRG and R-SGD) and the parameters are time-varying. At first, we propose a Riemannian stochastic hybrid gradient(R-SHG) algorithm with adaptive parameters. The algorithm gets a global convergence analysis with a decaying step size. For the case of step-size is fixed, we consider two cases with the inner loop fixed and time-varying. Meanwhile, we quantitatively research the convergence speed of the algorithm. Since the global convergence of the R-SHG algorithm with adaptive parameters requires higher functional differentiability, we propose a R-SHG algorithm with time-varying parameters. And we obtain similar conclusions under weaker conditions.
The effects of reciprocal teaching on the perceived reading comprehension self-efficacy of 2nd-grade pupils: Reflections of the pupils and their teacher

This study examined the effects of reciprocal teaching on the perceived reading comprehension self-efficacy of 2nd-grade pupils. The study was designed in pre-post test experimental design with a control group. Qualitative data was also collected from the intervention group students and the classroom teacher who carried out the intervention lessons. Participants consist of typically developing 2nd-grade pupils from a state primary school (n = 34) in Turkey. While the intervention group used a total of 36 lesson hours of reciprocal teaching techniques for 6 lessons per week for 6 weeks, the lesson was taught in traditional ways in the control group. According to the research findings, there was no statistically significant difference between the post-test scores of the intervention and control groups. Students stated that the most difficult reciprocal teaching strategy was summarizing, while the easiest was the prediction. As a result of the observations of the classroom teacher who conducted the intervention lessons, the positive effects of reciprocal teaching on the pupils' reading comprehension were emphasized. For this reason, it is suggested that the use of reciprocal teaching in Literacy lessons should be widespread in Turkey.
A large-sample crisis? Exaggerated false positives by popular differential expression methods

We report a surprising phenomenon about identifying differentially expressed genes (DEGs) from population-level RNA-seq data: two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates (FDRs). Via permutation analysis on an immunotherapy RNA-seq dataset, we observed that DESeq2 and edgeR identified even more DEGs after samples’ condition labels were randomly permuted. Motivated by this, we evaluated five DEG identification methods (DESeq2, edgeR, limma-voom, NOISeq, and the Wilcoxon rank-sum test) on population-level RNA-seq datasets. We found that the three popular parametric methods---DESeq2, edgeR, and limma-voom---failed to control the FDR. In particular, the actual FDRs of DESeq2 and edgeR sometimes exceeded 20% when the target FDR threshold was only 5%. Although NOISeq, a non-parametric method used by GTEx, controlled the FDR better than the three parametric methods did, it had low power. Based on these results, for population-level RNA-seq studies, we recommend the classic non-parametric test: the Wilcoxon rank-sum test. One-Sentence Summary Popular differential expression methods have high false-positive risks on population-level RNA-seq data with large sample sizes.
Reinforcement Learning and Bayesian Inference Provide Complementary Models for the Unique Advantage of Adolescents in Stochastic Reversal

During adolescence, youth venture out, explore the wider world, and are challenged to learn how to navigate novel and uncertain environments. We investigated whether adolescents are uniquely adapted to this transition, compared to younger children and adults. In a stochastic, volatile reversal-learning task with a sample of 291 participants aged 8-30, we found that adolescents outperformed both younger and older participants. We developed two independent cognitive models, based on Reinforcement learning (RL) and Bayesian inference (BI). The RL parameter for learning from negative outcomes and the BI parameters specifying participants’ mental models peaked closest to optimal in adolescents, suggesting a central role in adolescent cognitive processing. By contrast, persistence and noise parameters improved monotonously with age. We distilled the insights of RL and BI using principal component analysis and found that three shared components interacted to form the adolescent performance peak: adult-like behavioral quality, child-like time scales, and developmentally-unique processing of positive feedback. This research highlights adolescence as a neurodevelopmental window that may be specifically adapted for volatile and uncertain environments. It also shows how detailed insights can be gleaned by using cognitive models in new ways.
Surface Albedo Prediction Using Random Forests

With rising concerns over climate change, there is an increasing need for renewable energy sources. Photovoltaic(PV) systems are one of the most environmentally friendly ways of producing energy. However, the fluctuations in power outputs from utility scale PV arrays makes it difficult to incorporate them into electric grids. The power output is directly related to the irradiance and the irradiance is related to the surface albedo, which is the fraction of sunlight reflected by a surface. If we can predict the surface albedo, we can predict the power output. Using random forest regression, we can make predictions of the power output based on various features. In response to this prediction, the topology of the system may be reconfigured.
Meteorological variables associations and the occurrence of covid-19 in the city of São Paulo, Brazil

Several studies evaluate the association between meteorological variables and cases of respiratory diseases, such as acute respiratory syndrome (SARS) and influenza. Since the emergence of the new coronavirus (SARS-CoV-2), researchers from all over the world have joined forces to study the influence of demographic, anthropogenic and climatic aspects on the incidence of new cases of the disease. According to this, the objective of this study is to present a preliminary assessment of the associations between confirmed cases and deaths caused by the disease COVID-19, in the first days of the contamination in the city of São Paulo-Brazil, with the meteorological variables temperature, relative humidity, atmospheric pressure, and wind speed. Data on the daily numbers of contamination cases and deaths from COVID-19 in addition to meteorological variables were collected from February 26, 2020, to April 6, 2020. Associations were measured using Spearman's correlation coefficient and daily incidence rates (IRR), with their 95% confidence intervals (CI), for each meteorological factor was estimated using the generalized linear regression model with a negative binomial distribution. It was observed temperature and relative humidity are important variables to identify meteorological conditions associated with the number of deaths and contamination by the COVID-19. Extreme events may be related to an increase in the daily numbers of new cases and deaths at the beginning of contamination in the city of São Paulo.
Analysis of the Relationship Between IT and Industry 4.0 Technologies with Internationalization and Business Performance

The impact of information technologies (IT) on organizations has attracted the attention of professionals and academics in recent years. In this article, since Industry 4.0 technologies have not yet been implemented at the same level as general purpose information technologies, they are analyzed separately to understand their relationship with internationalization and business performance. Through a survey of 168 companies in the city of Bogotá, Colombia, different regression analyses were performed according to a research model proposed to estimate the effects of the determined variables. It was evident that the companies with a greater use of IT present a higher degree of internationalization. Likewise, the proposed model demonstrates the direct relationship between the use of IT and Industry 4.0 technologies and business performance. The software R studio was used for statistical analysis
Data Augmentation in Deep Learning for Hate Speech Detection in Lower Resource Settings

With the increase in online communication within the last decade, the need to automatize the detection and removal of hateful content has become ever more present. However, because of data scarcity especially in languages other than English, current state of the art neural approaches are limited in their ability to predict hate speech efficiently. To address this problem, this project focuses on applying several data augmentation techniques on German and Italian data, for both multi-class and binary classification. We experiment with a hybrid CNN/RNN architecture, as well as two classical, statistical machine learning models. Our findings show that augmentation can improve hate speech detection to varying degrees. For German multi-class classification, our models could achieve an absolute increase in overall macro F1-score of up to 8.5% and ∼78% in recall of the least represented class. For Italian binary classification, we observe an absolute improvement of up to ∼14% in precision for detecting the hate class and 6.8% in macro F1-score.
Bovine Mastitis in Fiji: Economic Implications and Management—A Review

Mastitis is a devastating disease condition in the dairy industry throughout the world and is caused due to the inflammation of the mammary gland. The etiological agents causing mastitis varies from one place to another depending on the animal breed, climate, and husbandry practices. However, the etiological agents causing mastitis include an extensive variety of gram-negative and gram-positive bacteria and fungi. Furthermore, the most common bacterial species responsible for causing mastitis include Staphylococcus aureus, Streptococcus dysgalactiae, Streptococcus (Strep.) agalactiae, Strep. Uberis and various Gram-negative bacteria. This review highlights the type of bacteriological etiology causing intramammary infection (IMI) is an essential part of effective mastitis control, prevention, and treatment. It also discusses the diagnostic tests used to test for mastitis in Fiji include Somatic cell count, California Mastitis Test (CMT), and bacteriological culturing. The development of Polymerase Chain Reaction (PCR) technology along with the version of real-time and multiplex PCR has improved the sensitivity and rapidity of mastitis diagnosis. The subclinical and clinical forms of mastitis can be treated with early detection of the signs of mastitis infection. Moreover, it is also essential to create awareness to the farmers about the cost, knowledge about mastitis and the loss it can cause.
Quinazoline derivatives as an multiplex inhibitor and method for the preparation thereof

The present invention relates to pharmaceutical compositions and therapeutic composition containing the quinazoline derivatives and pharmaceutically acceptable salts thereof, their preparation, the active ingredient it as a multi-inhibitors, the quinazoline derivatives of the present invention are tyrosine kinase as a multi-inhibitors selecting a disease caused by excessive activity and can be effectively suppressed.
SPEECH EMOTION DETECTION USING MACHINE LEARNING TECHNIQUES

Communication is the key to express one’s thoughts and ideas clearly. Amongst all forms of communication, speech is the most preferred and powerful form of communications in human. The era of the Internet of Things (IoT) is rapidly advancing in bringing more intelligent systems available for everyday use. These applications range from simple wearables and widgets to complex self-driving vehicles and automated systems employed in various fields. Intelligent applications are interactive and require minimum user effort to function, and mostly function on voice-based input. This creates the necessity for these computer applications to completely comprehend human speech. A speech percept can reveal information about the speaker including gender, age, language, and emotion. Several existing speech recognition systems used in IoT applications are integrated with an emotion detection system in order to analyze the emotional state of the speaker. The performance of the emotion detection system can greatly influence the overall performance of the IoT application in many ways and can provide many advantages over the functionalities of these applications. This research presents a speech emotion detection system with improvements over an existing system in terms of data, feature selection, and methodology that aims at classifying speech percepts based on emotions, more accurately. SPEECH EMOTION DETECTION USING MACHINE LEARNING TECHNIQUES