Miracle-dz/UniFAR-FTUs huggingface dataset:
Supported Policy Optimization for Offline Reinforcement Learning

Policy constraint methods to offline reinforcement learning (RL) typically utilize parameterization or regularization that constrains the policy to perform actions within the support set of the behavior policy. The elaborative designs of parameterization methods usually intrude into the policy networks, which may bring extra inference cost and cannot take full advantage of well-established online methods. Regularization methods reduce the divergence between the learned policy and the behavior policy, which may mismatch the inherent density-based definition of support set thereby failing to avoid the out-of-distribution actions effectively. This paper presents Supported Policy OpTimization (SPOT), which is directly derived from the theoretical formalization of the density-based support constraint. SPOT adopts a VAE-based density estimator to explicitly model the support set of behavior policy and presents a simple but effective density-based regularization term, which can be plugged non-intrusively into off-the-shelf off-policy RL algorithms. SPOT achieves the state-of-the-art performance on standard benchmarks for offline RL. Benefiting from the pluggable design, offline pretrained models from SPOT can also be applied to perform online fine-tuning seamlessly.
Online Approval Committee Elections

Assume k candidates need to be selected. The candidates appear over time. Each time one appears, it must be immediately selected or rejected---a decision that is made by a group of individuals through voting. Assume the voters use approval ballots, i.e., for each candidate they only specify whether they consider it acceptable or not. This setting can be seen as a voting variant of choosing k secretaries. Our contribution is twofold. (1) We assess to what extent the committees that are computed online can proportionally represent the voters. (2) If a prior probability over candidate approvals is available, we show how to compute committees with maximal expected score.
CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning

Modern deep neural networks can easily overfit to biased training data containing corrupted labels or class imbalance. Sample re-weighting methods are popularly used to alleviate this data bias issue. Most current methods, however, require to manually pre-specify the weighting schemes relying on the characteristics of the investigated problem and training data. This makes them fairly hard to be generally applied in practical scenarios, due to their significant complexities and inter-class variations of data bias. To address this issue, we propose a meta-model capable of adaptively learning an explicit weighting scheme directly from data. Specifically, by seeing each training class as a separate learning task, our method aims to extract an explicit weighting function with sample loss and task/class feature as input, and sample weight as output, expecting to impose adaptively varying weighting schemes to different sample classes based on their own intrinsic bias characteristics. Extensive experiments substantiate the capability of our method on achieving proper weighting schemes in various data bias cases, like class imbalance, feature-independent and dependent label noises, and more complicated bias scenarios beyond conventional cases. Besides, the task-transferability of the learned weighting scheme is also substantiated, by readily deploying the weighting function learned on relatively smaller-scale CIFAR-10 dataset on much larger-scale full WebVision dataset. The general availability of our method for multiple robust deep learning issues, including partial-label learning, semi-supervised learning and selective classification, has also been validated. Code for reproducing our experiments is available at https://github.com/xjtushujun/CMW-Net.
Asymmetric Nuclear Matter in Relativistic Mean-field Models with Isoscalar- and Isovector-meson Mixing

Using the relativistic mean-field model with nonlinear couplings between the isoscalar and isovector mesons, we study the properties of isospin-asymmetric nuclear matter. Not only the vector mixing, ω μ ω μ ρ ν ρ ν , but also the quartic interaction due to the scalar mesons, σ 2 δ 2, is taken into account to investigate the density dependence of nuclear symmetry energy, E sym, and the neutron star properties. It is found that the δ meson increases E sym at high densities, whereas the σ–δ mixing makes E sym soft above the saturation density. Furthermore, the δ meson and its mixing have a large influence on the radius and tidal deformability of a neutron star. In particular, the σ–δ mixing reduces the neutron star radius; thus, the present calculation can simultaneously reproduce the dimensionless tidal deformabilities of a canonical 1.4 M ⊙ neutron star observed from the binary neutron star merger GW170817 and the compact binary coalescence GW190814.
Slicing Aided Hyper Inference and Fine-Tuning for Small Object Detection

Detection of small objects and objects far away in the scene is a major challenge in surveillance applications. Such objects are represented by small number of pixels in the image and lack sufficient details, making them difficult to detect using conventional detectors. In this work, an open-source framework called Slicing Aided Hyper Inference (SAHI) is proposed that provides a generic slicing aided inference and fine-tuning pipeline for small object detection. The proposed technique is generic in the sense that it can be applied on top of any available object detector without any fine-tuning. Experimental evaluations, using object detection baselines on the Visdrone and xView aerial object detection datasets show that the proposed inference method can increase object detection AP by 6.8%, 5.1% and 5.3% for FCOS, VFNet and TOOD detectors, respectively. Moreover, the detection accuracy can be further increased with a slicing aided fine-tuning, resulting in a cumulative increase of 12.7%, 13.4% and 14.5% AP in the same order. Proposed technique has been integrated with Detectron2, MMDetection and YOLOv5 models and it is publicly available at https://github.com/obss/sahi.git
Galactic Positrons from Thermonuclear Supernovae

Type Ia supernovae (SNe Ia) may originate from a wide variety of explosion scenarios and progenitor channels. They exhibit a factor of ≈10 difference in brightness and thus a differentiation in the mass of 56Ni → 56Co → 56Fe. We present a study on the fate of positrons within SNe Ia in order to evaluate their escape fractions and energy spectra. Our detailed Monte Carlo transport simulations for positrons and γ-rays include both β + decay of 56Co and pair production. We simulate a wide variety of explosion scenarios, including the explosion of white dwarfs (WDs) close to the Chandrasekhar mass (M Ch), He-triggered explosions of sub-M Ch WDs, and dynamical mergers of two WDs. For each model, we study the influence of the size and morphology of the progenitor magnetic field between 1 and 1013 G. Population synthesis based on the observed brightness distribution of SNe Ia was used to estimate the overall contributions to Galactic positrons due to escape from SNe Ia. We find that this is dominated by SNe Ia of normal brightness, where variations in the distribution of emitted positrons are small. We estimate a total SNe Ia contribution to Galactic positrons of <2% and, depending on the magnetic field morphology, <6–20% for M Ch and sub-M Ch, respectively.
Exploring the 4D scales of eco-geomorphic interactions along a river corridor using repeat UAV Laser Scanning (UAV-LS), multispectral imagery, and a functional traits framework

Vegetation plays a critical role in the modulation of fluvial process and morphological evolution. However, adequately capturing the spatial variability and complexity of vegetation characteristics remains a challenge. Currently, most of the research seeking to address these issues takes place at either the individual plant scale or via larger scale bulk 10 classifications, with the former seeking to characterise vegetation-flow interactions and the latter identifying spatial variation in vegetation types. Herein, we devise a method which extracts functional vegetation traits using UAV laser scanning and multispectral imagery, and upscale these to reach scale guild classifications. Simultaneous monitoring of morphological change is undertaken to identify eco-geomorphic links between different guilds and the geomorphic response of the system in the context of long-term decadal changes. Identification of four guilds from quantitative structural modelling based on analysis of 15 terrestrial and UAV based laser scanning and two further guilds from image analysis was achieved. These were upscaled to reach-scale guild classifications with an overall accuracy of 80% and links to magnitudes of geomorphic activity explored. We show that different vegetation guilds have a role in influencing morphological change through the stabilisation of banks, but that limits on this influence are evident in the prior long-term analysis. This research reveals that remote sensing offers a solution to the difficulty of scaling traits-based approaches for eco-geomorphic research, and that these methods may be applied 20 to larger areas using airborne laser scanning and satellite imagery datasets.
Pretraining for Language-Conditioned Imitation with Transformers

We study reinforcement learning (RL) agents which can utilize language inputs and efficiently learn on downstream tasks. To investigate this, we propose a new multimodal benchmark – Text-Conditioned Frostbite – in which an agent must complete tasks specified by text instructions in the Atari Frostbite environment. We curate and release a dataset of 5M text-labelled transitions for training, and to encourage further research in this direction. On this benchmark, we evaluate Text Decision Transformer (TDT), a transformer directly operating on text, state, and action tokens, and find it improves upon baseline architectures. Furthermore, we evaluate the effect of pretraining, finding unsupervised pretraining can yield improved results in low-data settings.
The Effects of Online Review Platforms on Restaurant Revenue, Survival Rate, Consumer Learning and Welfare

This paper quantifies the effects of online review platforms on restaurants and consumer welfare. Using a novel dataset containing restaurant revenues and information from major online review platforms, I show that online review platforms help consumers learn faster about restaurant quality. The effects on learning show up in restaurant revenues and survival rates. Specifically, doubling consumers’ exposure to Yelp, the dominant platform, increases the revenue of a high-quality new independent restaurant by 8-20% and decreases that of a low-quality restaurant by a similar amount. Doubling Yelp exposure also raises the survival rate of a young high-quality independent restaurant by 7-19 basis points and reduces that of a low-quality restaurant by about the same degree. Other platforms have similar effects but in smaller magnitude. In contrast, online review platforms do not significantly affect the revenues or survival rates of chains and old independent restaurants. Building on this evidence, I develop a structural demand model with social learning. Counterfactual analyses indicate that online review platforms improve the welfare for restaurant goers by $2.5 per person per meal, equivalent to a 12.6% discount on the average meal price. Despite large effects on individual restaurants, online review platforms have little impact on the total industry revenue.
Radioprotective effects of ethanolic extracts of Gongronema latifolium leaf against radiation induced oxidative stress in Wistar albino Rats

Background : The effects of low radiation exposures are naturally harmful to the humans and in this study we aimed to investigate the radioprotective effects of Gongronema latifolium extracts (GLE) against radiation induced oxidative stress. Materials and Methods : Forty eight rats were divided into control and experimental groups. The control group had two sub-groups; (i) normal control (NC) was neither irradiated nor treated with GLE and (ii) irradiated control (IC) was irradiated (with 4 and 6 Gy) but no GLE treatment. The experimental group also had two sub-groups; (i) pre-treated (PRT) was treated with GLE before irradiation and (ii) post-treated (PST) was irradiated before treatment with GLE. Radioprotective effects were measured by noting changes in biochemical parameters of liver, lipid peroxidation, antioxidants enzymes and changes in body weight that occurred in the sub-groups. ANOVA with Tukey’s test were used to determine significance differences in the sub-groups. Results : There were significant changes in all biochemical enzyme markers between the NC and IC with values of % inhibition of lipid peroxidation 33±1.5 and 71 ± 5.5 at 6 Gy dose of radiation exposure. The values of lipid peroxidation in PRT and PST differed significantly from the IC with values 35 ± 5.0 and 57 ± 1.0 at 6 Gy respectively; thus significant difference exists between PRT and PST. The IC sub-group recorded the highest radiation induced weight loss (56%). Conclusion : Preliminary result indicates that GLE ameliorated oxidative stress in pretreated sub-groups than in post-treated sub-groups, suggesting GLE as a potential prophylaxis.
Revisiting the Government Purchase Multiplier at the Zero Lower Bound

This paper revisits the magnitude of government purchase multiplier at the zero lower bound on nominal interest rates (ZLB). The novelty of this paper is that I compute the multiplier in a more realistic environment while keeping the model simple enough to identify mechanisms driving the result. In particular, I build on a standard New Keynesian model with occasionally binding ZLB and Rotemberg pricing with rebate, where the probability of hitting the ZLB and the government purchase shock are in line with the the US data. Moreover, I compute the multiplier in a state that mimics the Great Recession. The main findings of the paper include: (1) The multiplier is around 1.25; (2) Without occasionally binding ZLB or with transient government purchase or without rebate, the multiplier is around 1 or less; and (3) The multiplier is non-monotonic in the persistence of government purchase. JEL classification: C61, C63, E52, E62.
The Impact of Influenza Vaccine Uptake on COVID-19 Infection amongst a Family Medicine Population with Multiple Morbidities in Saudi Arabia: An observational study

Background: The COVID 19 Pandemic, which appeared in 2019 and is caused by SARS Coronavirus 2, has had a huge impact on the health of millions of people worldwide, with some patients having no symptoms or mild to moderate symptoms and others having prolonged, complicated courses, with some succumbing to the disease. The presence of co-morbidities puts the patient at a greater risk of acquiring a more severe infection. This population are also at a greater risk for more severe influenza infection and Influenza vaccination is recommended for this group on an annual basis. Objectives: To assess whether influenza vaccination may protect against COVID 19 infection, in a Family Medicine population with co-morbidities. The study will shed light on the influence of influenza vaccination on acquiring COVID 19 infection. Design: This is an observational, retrospective study. Setting: Family Medicine Clinic in Saudi Arabia. Materials and Methods: Charts from family medicine patients with one or more co-morbidities who received the influenza vaccine during the 2019/2020 Influenza season, were reviewed. Patients aged between 25 to 75 with comorbidities were included and had received the influenza vaccine from July 2019 to March 2020. Children under 18 and pregnant patients were excluded from the study. Sample size: 250 patients. Conclusions: There may be beneficial effects of using the Influenza vaccine in a high-risk community population, with co-morbidities, during the COVID-19 Pandemic. *Correspondence: Patricia McWalter, Consultant Family Physician, King Faisal Specialist Hospital and Research Center, PO Box 3354, Riyadh 11211, Saudi Arabia. Received: 15 June 2021; Accepted: 18 July 2021 Microbiology & Infectious Diseases ISSN 2639-9458
Steroids, steroid associated substances and gestational diabetes mellitus.

As gestational diabetes mellitus (GDM) is both a frequent and serious complication, steroid levels in pregnancy are extremely elevated and their role in pregnancy is crucial, this review focuses on the role of steroids and related substances in the GDM pathophysiology. Low SHBG levels are associated with insulin resistance and hyperinsulinemia, while also predicting a predisposition to GDM. Other relevant agents are placental hormones such as kisspeptin and CRH, playing also an important role beyond pregnancy, but which are synthesized here in smaller amounts in the hypothalamus. These hormones affect both the course of pregnancy as well as the synthesis of pregnancy steroids and may also be involved in the GDM pathophysiology. Steroids, whose biosynthesis is mainly provided by the fetal adrenal glands, placenta, maternal adrenal glands, and both maternal and fetal livers, are also synthesized in limited amounts directly in the pancreas and may influence the development of GDM. These substances involve the sulfated ? 5 steroids primarily acting via modulating different ion channels and influencing the development of GDM in different directions, mostly diabetogenic progesterone and predominantly anti-diabetic estradiol acting both in genomic and non-genomic way, androgens associated with IR and hyperinsulinemia, neuroactive steroids affecting the pituitary functioning, and cortisol whose production is stimulated by CRH but which suppresses its pro-inflammatory effects. Due to the complex actions of steroids, studies assessing their predominant effect and studies assessing their predictive values for estimating predisposition to GDM are needed.
A Comparison between Aggression and Self-Esteem among Universiti Teknologi Mara (Uitm) Shah Alam Athletes

Human aggression is a major worldwide health issue that brings negative effects to victims, perpetrators, and society. One of the areas that may be potentially variables for understanding aggression is self-esteem that can influence whether someone will display aggressive behaviour. Therefore, the aim of this study was to determine the relationship between the level of aggression and level of self-esteem among Universiti Teknologi MARA (UiTM) Shah Alam student-athletes. This study is very important to determine the athlete’s aggression and self-esteem level based on the differences of genders and types of sport. Furthermore, the findings of this study may help in promoting a beneficial knowledge among coaches and athletes about aggression level and also self-esteem level toward maintaining and improving sports performance. A correlational study was selected in order to conduct this study. This correlational study was selected because this study was closely related to both descriptive and causal-comparative research. The instrument used for this study comprised of a 10-item Rosenberg SelfEsteem Scale (RSES) and 19-item of Aggression Questionnaire (AQ), which had been distributed in UiTM Shah Alam. The samples consisted of 260 UiTM Shah Alam athletes, included male and female, individual and team sports, age ranged between 18 until 26 years old. Independent t-test showed there was no significant difference between genders and types of sport on aggression level, p > 0.05. Result also showed that there was no significant difference on self-esteem between types of sport, p > 0.05. Meanwhile, a result found that there was a significant difference on self-esteem between genders, p < 0.05. Result also showed that there was a positive correlation between aggression and self-esteem among UiTM Shah Alam athletes, (r = 0.20, p < 0.05). Present findings indicated that athletes who had higher aggression level tend to have a higher level of self-esteem. Therefore, psychological skill training (PST) recommended as a better coping technique in order to promote better adaptation to the constraints of sports involvement.
The propensity to adaptation under the new era of climate changes

Decision utility or experienced utility: which one of them helps us better understand how it will occur the process to the adaptation of behavior’s consumer regarding the impact of climate change? This paper argues how each one of the aforementioned concepts most may affect the consumer’s routine as “decision-makers”, within the context that disturbances and scarcity must narrow their available options. For this, we use the individual’s choice reported in the 8 wave of European Social Survey, based on the choice-oriented perspective, which establishes a link between well-being and the propensity to adapt within this scenario. For this, an ordered logit model is applied upon environmental and socio-economic variables. In the end, our findings are consistent with a strong presence of rationality in the decision process towards adaptation.
LARVICIDAL EFFECT OF Jatropha curcas (TUBA-TUBA) BARK ETHANOLIC EXTRACT AGAINST Aedes aegypti LARVAE

This study aimed at determining the larvicidal activity of Jatropha curcas (Tuba-tuba) bark ethanolic extract against Aedes aegypti larvae. Aedes aegypti is a widely known vector of dengue that claims much death annually. Thus the objective of this study is to determine the physiocochemical properties of Jatropha curcas extract of its larvicidal effect against Aedes aegypti larvae. The study utilizes an experimental design where a comparison of the number of larvae exposed to the different concentrations of the extract to the number of larvae exposed to the negative and positive controls is done. Preliminary tests such as Physicochemical tests, Iodoform test, Thin Layer Chromatography (TLC), and Mosquito Larvae Bioassay were done. The TLC procedure showed positive levels of alkaloid. After the performance of the Bioassay, the alternative hypothesis was accepted since the statistical data confirmed that there is a significant difference between J. curcas bark ethanol extract against Aedes aegypti larvae when compared to the positive control. At 24 hours of contact exposure, 100 ppm, 200 ppm, and 400 ppm significantly differed with the positive control. At 48 hours, only 100 ppm and 200 ppm have displayed a significant difference. Lastly, there was no significant difference between the mean mortality rates of the mosquito larvae across concentrations after 24 hours against 48 hours. The researchers further recommended for further study the identification and quantification of an alkaloid fraction of the extract using High-Performance Liquid Chromatography (HPLC) and to determine its effectiveness as an isolate.
University graduates’ satisfaction with the quality of education: specifics of assessment methods

The purpose of the article is to determine the satisfaction of university graduates’ with the quality of training in educational programs. The research methodology is based on the characteristics of the quality of education, which can be expressed in certain quantitative indicators. Such measurable characteristics include traditional quantitative assessments of the level of knowledge gained, quantitative indicators of the demand for graduated specialists in the labor market and the degree of satisfaction with the training of the students themselves. The study included 120 graduates of the enlarged group of “Cultural studies and socio-cultural design” of the years 2018-2020. The following methods were used: survey methodsquantitative (questionnaire survey “Factors affecting the satisfaction of graduates quality of higher education”), and qualitative (interviewing). The proposed and tested methodology for measuring the satisfaction of university graduates with educational programs can become the basis for the development of a university system for monitoring the measurement of satisfaction of university graduates with educational programs and a university quality management system.