Miracle-dz/UniFAR-FTUs huggingface dataset:
Sim-to-Real Learning of Multi-agent End-to-End Control via Data-driven Simulation

: 1 Deep reinforcement learning (RL) has transformed the ability to learn highly per-2 formant control policies without expert imitation data. However, for learning in 3 highly interactive domains such as multi-agent perception-to-control driving, RL 4 agents typically rely on privileged state information, or are evaluated solely on 5 simulated environments with simpliﬁed perception. In this paper, we present a 6 novel method for efﬁciently training multi-agent visual control policies entirely 7 in data-driven simulation. Our high-ﬁdelity simulation environment is photoreal-8 istic and supports direct sim-to-real transfer of policies onboard a full-scale au-9 tonomous vehicle. We demonstrate that our approach achieves high experimental 10 performance on various multi-agent driving tasks across varying levels of difﬁ-11 culty, ranging from car-following to dynamic obstacle avoidance and overtaking. 12 We believe our ﬁndings open new opportunities to train and deploy rich, sim-13 to-real control policies in multi-agent settings, which demands simulating rich 14 perception as well as interactions with other agents. 15
General Aspects, Islanding Detection, and Energy Management in Microgrids: A Review

Distributed generators (DGs) have emerged as an advanced technology for satisfying growing energy demands and significantly mitigating the pollution caused by emissions. Microgrids (MGs) are attractive energy systems because they offer the reliable integration of DGs into the utility grid. An MG-based approach uses a self-sustained system that can operate in a grid-tied mode under normal conditions, as well as in an islanded mode when grid disturbance occurs. Islanding detection is essential; islanding may injure utility operators and disturb electricity generation and supply because of unsynchronized re-closure. In MGs, an energy management system (EMS) is essential for the optimal use of DGs in intelligent, sustainable, reliable, and integrated ways. In this comprehensive review, the classification of different operating modes of MGs, islanding detection techniques (IDTs), and EMSs are presented and discussed. This review shows that the existing IDTs and EMSs can be used when operating MGs. However, further development of IDTs and EMSs is still required to achieve more reliable operation and cost-effective energy management of MGs in the future. This review also highlights various MG challenges and recommendations for the operation of MGs, which will enhance the cost, efficiency, and reliability of MG operation for next-generation smart grid applications.
PROVISION OF SUPPLEMENTARY FOOD FOR SASIGO AND BISCUIT IN INCREASING THE WEIGHT OF CHILDREN : A COMPARATIVE STUDY IN NAMBO PRIMARY HEALTH CARE IN KENDARI CITY

Background:The prevalence of community nutritional status in the Kendari City area in 2016 was 0.23%. In 2017, the prevalence of nutritional status decreased in 0.13% and then increased in 2018 by 4.67%. in 2019, the prevalence of nutritional status decreased by 2.25% . Thus, children under five have nutritional status did not reached the target This study aimed to understand the effect of giving the sasigo and biscuit on increase the children’s weight in Nambo Primary Health Care in Kendari City. Methods:The type of research in this research is pre-experimental research with a Pre Test Post Test design. This research was conducted in the working area of Nambo Primary Health Care, Kendari City. Population were 227 and a sample was 67 malnourished toddlers in 2019. Result:Based on the independent sample test output in the Equal Variances Assumed section on Local supplementaty food (BageaSasigo), it is known that the sig (2-tailed) value is 0.001 <0.05, so Ha is accepted so that there is a significant difference between the provision of local supplementary food (BageaSasigo). Whereas in the Government PMT (biscuits) it is known that the sig (2-tailed) value is 0.000 <0.05, so Ha is accepted so that there is a significant difference between the provision of government PMT (biscuits) to toddlers in the Work Area of the Nambo Primary Health Care, Kendari City. Conclusion:Provision of Local supplementary food (Bagea Sasigo) and Government SP (Biscuits) for toddlers. Before giving the intervention it was not effective while after giving the intervention it was effective and significant.
An Efficient Dual-reference Training Data Acquisition Method for CNN Image Super-Resolution

—For deep learning methods of image super-resolution, the most critical issue is whether the paired low and high resolution images for training accurately reﬂect the sampling process of real cameras. Low and high resolution (LR ∼ HR) image pairs synthesized by existing degradation models ( e.g ., bicubic downsampling) deviate from those in reality; thus the super-resolution CNN trained by these synthesized LR ∼ HR image pairs does not perform well when being applied to real images. In this paper, we propose a novel method to capture a large set of realistic LR ∼ HR image pairs using real cameras. The data acquisition is carried out under controllable lab conditions with minimum human intervention and at high throughput (about 500 image pairs per hour). The high level of automation makes it easy to produce a set of real LR ∼ HR training image pairs for each camera. Our innovation is to shoot images displayed on an ultra-high quality screen at different resolutions. There are three distinctive advantages of our method for image super-resolution. First, as the LR and HR images are taken of a 3D planar surface (the screen) the registration problem ﬁts exactly to a homography model and we can display specially designed markers on the image to improve the registration precision. Second, the displayed digital image ﬁle can be exploited as a reference to optimize the high frequency content of the restored image. Third, this high-efﬁciency data collection method makes it possible to collect a customized dataset for each camera sensor, for which one can train a speciﬁc model for the intended camera sensor. Experimental results show that training a super-resolution CNN by our LR ∼ HR dataset has superior restoration performance than training it by existing datasets on real world images at the inference stage.
Playing favourites-a review and discussion on the allocation of vertebrate orders and foci in home range and habitat selection studies

Home range and habitat selection are key subjects when studying animal ecology. Defining the space use and resource management of an animal establishes a solid basis for further behavioural and ecological research, as well as conservation management. Studies focusing on determining home range and habitat selection often include further questions regarding for example conservation, animal movement, population dynamics, and interor intraspecific interactions. It is therefore unsurprising that home range and habitat selection have been the focus of numerous studies on different vertebrate taxa over the years. We have reviewed 903 publications, on all extant vertebrate clades focusing on these topics from 1980 to the first quarter of 2018. We have observed that allocation of vertebrate orders are independent of species richness, relatedness, and portion of threatened species within the order. We have highlighted the relation between publication numbers and species richness and offer ideas for future research in proposing possible causes for the observed allocation and in highlighting understudied clades. Furthermore, we have observed that topics often studied in concordance with home range and habitat selection are conservation and human influence, intraspecific differences, and home range shifts/exploratory behaviour. Meanwhile, topics like population density, reproductive behaviour, territoriality/aggressive behaviour, and interspecific interactions seem to be less studied. This review highlights and discusses the current distribution of focal points in studies concerning home range and habitat use while identifying less studied fields and taxa thereby emphasizing potential opportunities for further research.
Package ‘ UBL ’ March 29 , 2021 Type Package

March 29, 2021 Type Package Title An Implementation of Re-Sampling Approaches to Utility-Based Learning for Both Classification and Regression Tasks Description Provides a set of functions that can be used to obtain better predictive performance on costsensitive and cost/benefits tasks (for both regression and classification). This includes resampling approaches that modify the original data set biasing it towards the user preferences. Version 0.0. 7 Depends R(>= 3.0), methods, grDevices, graphics, stats, MBA, gstat, automap, sp, randomForest Suggests MASS, rpart, testthat, DMwR2, ggplot2, e1071 Date 2021-03-29
Complete genome sequence of Bean pod mottle virus identified from common bean (Phaseolus vulgaris)

plant virus infecting common bean. We obtained genomes of two BPMV isolates referred as Tigerskin-A and Tigerskin-B consisting of two RNA segments from common bean plants displaying viral disease symptoms by RNA-sequencing. RNA1 encodes a single polyprotein containing five mature proteins while RNA2 encodes a single polyprotein containing a movement protein and two coat proteins. The two BPMV isolates showed high sequence similarity (99.65% to 99.70%) to two isolates, IA-Di1 and Iowa-Desmodium illinoense 1, from the Desmodium illinoense in USA belonging to a flowering plant in the bean family.
Does TB stigma affect emotion recognition? A study with a Portuguese sample

TB stigma constitutes a major barrier to disease control and social distress. This study aims to understand better the inherent social processes on the perception of emotions. Thus far, the specific role of TB stigma in this respect was not tested. We hypothesize that individuals in TB treatment (vs. a control non-clinical group) would identify more negative emotions in the faces of others, specifically rejecting emotions (e.g., disgust) when preoccupied with TB stigma. Two groups of participants completed a questionnaire with 23 faces, identified the emotions portrayed in the pictures, and reported their level of Stigma Consciousness, Interpersonal-Rejection Related to Stigma, and Rejection Sensitivity. Results show that the two groups significantly differ in their perceptions of negative emotions: participants in treatment identify less disgust and more sadness, fear, and anger versus the control group. Findings are discussed concerning the literature on stigma and its consequences.
Benchmarking Open-World LiDAR Instance Segmentation

: With the advent of deep learning, supervised learning led to increased 1 performance in object detection and instance segmentation. However, existing 2 methods explicitly assume that complete world knowledge is available at training 3 time. However, most classes appear in the unbounded long tail of the object class 4 distribution, for which only a few or even zero training samples are available. In 5 this paper, we propose an extension of the SemanticKITTI dataset for benchmark-6 ing of object instance segmentation evaluation in the open-world setting. It pro-7 vides only partial information about the world at training time. At inference time, 8 methods need to segment both, known objects present in the train set and unknown 9 objects that appear in the long, unbounded tail of the object class distribution. Us-10 ing our test-bed, we perform a thorough evaluation of existing model-based and 11 data-driven LiDAR instance segmentation methods in the open-world setting. 12
Optimal Treatment Assignment Rules on Networked Populations

I study the problem of optimally distributing treatments among individuals on a network in the presence of spillovers in the effect of treatment across linked individuals. I consider the problem of a planner who needs to distribute of a limited number of preventative treatments (e.g., vaccines) for a deadly infectious disease among individuals in a target village in order to maximize population welfare. Since the planner does not know the extent of spillovers or the heterogeneity in treatment effects, she uses data coming from an experiment conducted in a separate pilot village. By placing restrictions on how others’ treatments affect one’s outcome on the contact network, I derive theoretical limits on how the data from the experiment could be used to best allocate the treatments when the planner observes the contact network structure in both the target and pilot village. For this purpose, I extend the empirical welfare maximization (EWM) procedure to derive an optimal statistical treatment rule. Under restrictions on the shape of the contact network, I provide finite sample bounds for the uniform regret (a measure of the effectiveness of a treatment rule). The main takeaway is that the uniform regret associated with EWM, extended to account for spillovers, converges to 0 at the parametric rate as the size of the pilot experiment grows. I also show that no statistical treatment rule admits a faster rate of convergence for the uniform regret, suggesting that the EWM procedure is rate-optimal. Acknowledgements: The author is grateful for all the helpful comments and suggestions from advisor, Francesca Molinari and committee members, Larry Blume, David Easley and Jörg Stoye. All errors that remain are author’s own, likely despite warnings by the aforementioned.
MonteCarlo Tree Search with Goal-Based Heuristic

The use of a domain-driven symbolic planner may provide interesting performances, even with the most challenging planning domain. However, sometimes a domain utility-function to be maximized does not exist: there are cases in which creating such a function is difﬁcult and error-prone. This paper investigates an alternative approach to afford deterministic planning when no utility-functions are available. In cases like these, classical planning may provide bad performances. The use of a MonteCarlo approach, in conjunction with a goal-based heuristic, has given promising results.
AW-Opt: Learning Robotic Skills with Imitation and Reinforcement at Scale

Robotic skills can be learned via imitation learning (IL) using user-provided demonstrations, or via reinforcement learning (RL) using large amountsof autonomously collected experience.Both methods have complementarystrengths and weaknesses: RL can reach a high level of performance, but requiresexploration, which can be very time consuming and unsafe; IL does not requireexploration, but only learns skills that are as good as the provided demonstrations.Can a single method combine the strengths of both approaches? A number ofprior methods have aimed to address this question, proposing a variety of tech-niques that integrate elements of IL and RL. However, scaling up such methodsto complex robotic skills that integrate diverse offline data and generalize mean-ingfully to real-world scenarios still presents a major challenge. In this paper, ouraim is to test the scalability of prior IL + RL algorithms and devise a system basedon detailed empirical experimentation that combines existing components in themost effective and scalable way. To that end, we present a series of experimentsaimed at understanding the implications of each design decision, so as to develop acombined approach that can utilize demonstrations and heterogeneous prior datato attain the best performance on a range of real-world and realistic simulatedrobotic problems. Our complete method, which we call AW-Opt, combines ele-ments of advantage-weighted regression [1, 2] and QT-Opt [3], providing a unifiedapproach for integrating demonstrations and offline data for robotic manipulation.Please see https://awopt.github. io for more details.
An Empirical Analysis of Factors affecting the Loan Repayment Performance of Microfinance Institutions

Andhra Pradesh Microfinance Crisis 2010 triggered by accounts of suicides, rising debt stress among over-indebted clients of some of India's biggest microfinance institutions (MFIs): SKS Microfinance, Spandana, Share, and others. The Non-Performing Assets (NPAs) of Andhra MFIs were at an all-time high over 96 %. Thereby, smaller emerging MFIs shut down their business operations. This is a matter of concern for Microfinance sector and the causes of declining recovery rate to be analyzed. Therefore the important issue of rising NonPerforming Assets (NPAs) has been examined. The study used panel data over the period 2006 to 2013 withdrawn from MIX (Microfinance information Exchange) market of Andhra MFIs. The findings revealed that important determinants of loan repayment performance are yield on gross loan portfolio, average loan balance per borrower adjusted by GNI per capita, labour cost to assets ratio and percentage of female clients. Moreover, the findings revealed that increasing interest rates resulted in increasing loan delinquencies and increasing share of women borrower in loan portfolio reduces loan defaults.
Gamma-Glutamyl Transpeptidase to Platelet Ratio: A New Inflammatory Marker Associated with Outcomes after Cardiac Arrest

Introduction In recent years, gamma-glutamyl transpeptidase to platelet ratio (GPR) has been proposed as a new inflammatory marker. We aimed to evaluate the association between GPR and outcomes after cardiac arrest (CA). Methods A total of 354 consecutive patients with CA were included in this retrospective study. Patients were divided into three groups according to tertiles of GPR (low, n = 119; middle, n = 117; and high, n = 118). To determine the relationship between GPR and prognosis, a logistic regression analysis was performed. The ability of GPR to predict the outcomes was evaluated by receiver operating characteristic (ROC) curve analysis. Two prediction models were established, and the likelihood ratio test (LRT) and the Akaike Information Criterion (AIC) were utilized for model comparison. Results Among the 354 patients (age 62 [52, 74], 254/354 male) who were finally included in the analysis, those in the high GPR group had poor outcomes. Multivariate logistic regression analysis revealed that GPR was independently associated with the three outcomes, for ICU mortality (odds ratios (OR) = 1.738, 95% confidence interval (CI): 1.221-2.474, P = 0.002), hospital mortality (OR = 1.676[1.164 − 2.413], P = 0.005), and unfavorable neurologic outcomes (OR = 1.623[1.121 − 2.351], P = 0.010). The area under the ROC curve was 0.611 (95% Cl: 0.558-0.662) for ICU mortality, 0.600 (95% CI: 0.547-0.651) for hospital mortality, and 0.602 (95% CI: 0.549-0.653) for unfavorable neurologic outcomes. Further, the LRT analysis showed that compared with the model without GPR, the GPR-combined model had a higher likelihood ratio χ2 score and smaller AIC. Conclusion GPR, as an inflammatory indicator, was independently associated with outcomes after CA. GPR is helpful in estimating the clinical outcomes of patients with CA.
Adaptive T cells regulate disease tolerance in human malaria

Immunity to severe malaria is acquired quickly, operates independently of pathogen load and represents a highly effective form of disease tolerance. The mechanism that underpins tolerance in human malaria remains unknown. We developed a re-challenge model of falciparum malaria in which healthy naive adult volunteers were infected three times over a 12-month period to track the development of disease tolerance in real-time. We found that parasitaemia triggered a hardwired emergency myeloid response that led to systemic inflammation, pyrexia and hallmark symptoms of clinical malaria across the first three infections of life. In contrast, CD4+ T cell activation was quickly modified to reduce the number and diversity of effector cells upon re-challenge. Crucially, this did not silence critical helper T cell functions but instead prevented the generation of cytotoxic effectors associated with autoinflammatory disease. Tolerised hosts were thus able to prevent collateral tissue damage and injury. Host control of T cell activation can therefore be established after a single infection and in the absence of anti-parasite immunity. And furthermore, this rapid host adaptation can protect vital organs to minimise the harm caused by systemic inflammation and sequestration.
Cumulative Regret Analysis of the Piyavskii-Shubert Algorithm and Its Variants for Global Optimization

We study the problem of global optimization, where we analyze the performance of the Piyavskii--Shubert algorithm and its variants. For any given time duration T, instead of the extensively studied simple regret (which is the difference of the losses between the best estimate up to T and the global minimum), we study the cumulative regret up to time T. For L-Lipschitz continuous functions, we show that the cumulative regret is O(L logT). For H-Lipschitz smooth functions, we show that the cumulative regret is O(H). We analytically extend our results for functions with Hölder continuous derivatives, which cover both the Lipschitz continuous and the Lipschitz smooth functions, individually. We further show that a simpler variant of the Piyavskii-Shubert algorithm performs just as well as the traditional variants for the Lipschitz continuous or the Lipschitz smooth functions. We further extend our results to broader classes of functions, and show that, our algorithm efficiently determines its queries; and achieves nearly minimax optimal (up to log factors) cumulative regret, for general convex or even concave regularity conditions on the extrema of the objective (which encompasses many preceding regularities). We consider further extensions by investigating the performance of the Piyavskii-Shubert variants in the scenarios with unknown regularity, noisy evaluation and multivariate domain.
On Adaptive Transmission for Distributed Detection in Energy Harvesting Wireless Sensor Networks With Limited Fusion Center Feedback

We consider a wireless sensor network tasked with solving a binary distributed detection problem. Sensors communicate directly with a fusion center (FC) over orthogonal fading channels, with additive Gaussian noise. Each sensor can harvest randomly arriving energy units and store them in a battery. Also, it knows its quantized channel state information (CSI), acquired via a limited feedback channel from the FC. Modeling the randomly arriving energy units during a time slot as a Poisson process and the battery dynamics as a <inline-formula> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula>-state Markov chain (where <inline-formula> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> is the battery size), we propose a channel-dependent transmit power control strategy such that the <inline-formula> <tex-math notation="LaTeX">${J}$ </tex-math></inline-formula>-divergence based detection metric is maximized at the FC, subject to an average transmit power per sensor constraint. The proposed strategy is parametrized in terms of the channel gain quantization thresholds and the scale factors corresponding to the quantization intervals. This strategy allows each sensor to adapt its transmit power based on its battery state and its qunatized CSI. Finding optimal strategy requires solving a non-convex optimization problem that is not differentiable with respect to the optimization variables. We propose near-optimal strategies based on random search methods that have a low-computational complexity and provide a close-to-optimal performance.