Miracle-dz/UniFAR-FTUs huggingface dataset:
Agent-Level Inverse Reinforcement Learning for Mean Field Games through Policy Matching

: Inverse reinforcement learning (IRL) algorithms can be used to recover 1 a reward function from expert demonstrations that can explain expert behaviours. 2 While IRL shows promise in many multi-agent tasks, its application is often hin-3 dered by a large number of agents, due to the explosion of the joint state-action 4 space. Mean ﬁeld games (MFG) are natural models of large-scale multi-agent sys-5 tems, which simpliﬁes the model through reducing the interactions among agents 6 to those between an individual agent and the average effect from the population. 7 This paper thus addresses the task of IRL for MFG, which aims to recover the 8 agent-level (ground-truth) reward function from expert demonstrations sampled 9 from a mean ﬁeld Nash equilibrium (MFNE). We put forward a novel framework 10 called Agent-Level Mean Field IRL (AL-MFIRL). The central idea of AL-MFIRL 11 is to express the policy in terms of rewards and tune a reward function by opti-12 mising this policy so as to match the expert policy. We show that the learned 13 reward function by AL-MFIRL necessarily interprets the expert behaviours as a 14 MFNE under standard assumptions. To derive a practical algorithm, we adopt the 15 smooth Boltzmann policy to approximate the elicited policy and optimises this 16 policy using gradient methods. We evaluate AL-MFIRL on ﬁve simulated envi-17 ronments. Experiment results demonstrate the outperformance of AL-MFIRL on 18 reward recovery and robustness against the changing environment dynamics. 19
Enhancement of COVID-19 symptom-based screening with quality-based classifier optimisation

Efforts of the scientific community led to the development of multiple screening approaches for COVID-19 that rely on machine learning methods. However, there is a lack of works showing how to tune the classification models used for such a task and what the tuning effect is in terms of various classification quality measures. Understanding the impact of classifier tuning on the results obtained will allow the users to apply the provided tools consciously. Therefore, using a given screening test they will be able to choose the threshold value characterising the classifier that gives, for example, an acceptable balance between sensitivity and specificity. The presented work introduces the optimisation approach and the resulting classifiers obtained for various quality threshold assumptions. As a result of the research, an online service was created that makes the obtained models available and enables the verification of various solutions for different threshold values on new data. © 2021 The Author(s).
Risk-Averse Zero-Order Trajectory Optimization

: We introduce a simple but effective method for managing risk in zero-order trajectory optimization that involves probabilistic safety constraints and balancing of optimism in the face of epistemic uncertainty and pessimism in the face of aleatoric uncertainty of an ensemble of stochastic neural networks. We empirically validate that the separation of uncertainties is essential to performing well with data-driven MPC approaches in uncertain and safety-critical control environments.
p WAVE: A Novel Dataset for Emotional Conﬁdence Detection

We present a novel hand-labeled dataset for learning emotional conﬁdence detection on American English speech. Our dataset consists of 9 quarterly earnings calls, each labeled by one to two raters. In this paper, we describe some useful pre-processing on the collected data, develop some metrics of inter-rater reliability, explain a number of modeling features from the raw call audio, and introduce baseline models for the task at hand.
COVID-19 Vaccine Hesitancy in Healthcare Workers Amidst the Second Wave of the Pandemic in India: A Single Centre Study

Introduction Coronavirus disease 2019 (COVID-19) vaccine hesitancy amongst healthcare workers (HCW) has been reported in varying degrees in different parts of the world. In this study, we investigate the degree of vaccine hesitancy amongst HCWs and factors associated with it during the second wave of the pandemic in our centre. Methods We undertook this single-centre, cross-sectional study in an urban tertiary care hospital, using a modified Oxford COVID-19 vaccine hesitancy scale. We performed descriptive and appropriate univariate analysis. We used the Kruskal Wallis test as appropriate, and Spearman rank correlation to evaluate the relation between general attitude to vaccination and COVID vaccine hesitancy score. Results We obtained 223 responses. The majority of HCWs in our sample (n = 201; 90.1%) had received at least one dose of the vaccine. The mean (SD) Oxford vaccine hesitancy score was 28.54 ± 2.05, with no significant difference observed between doctors (28.45 ± 2.26) and nurses (28.68 ± 1.70), or across different specialities. Of the respondents, 92.7% (n = 216) responded positively to taking the vaccine. The lack of dependents at home was the only significant contributor to vaccine hesitancy. Age, gender, marital status, and COVID-19 infection status did not significantly affect vaccine hesitancy. Conclusion We found a significantly lower degree of hesitancy towards COVID-19 amongst HCWs in our centre during the pandemic’s second wave. A more comprehensive and multi-centric study is required to validate this finding.
Placement of electric vehicle charging station and solar distributed generation in distribution system considering uncertainties

The use of Electric Vehicle (EV) to the transport section is increasing and replacing the conventional fossil fuel-based vehicles. Still, EV has not received success due to some limitations such as cost of the vehicle, battery capacity, and availability of charging station. The availability of charging station depends on its geographical location. At the same time, location of the electrical network a(cid:11)ects the energy loss and voltage deviation. Therefore, the test system considered here is a road network of the urban area overlapped with a 33-bus radial network. Allocation of EV charging stations and photovoltaic energy resources as renewable distributed generation was attempted simultaneously using 2-layer optimization. Di(cid:11)erential Evolution and Harris Hawks Optimization (HHO) are the two tools that were used to solve the problem and the (cid:12)nal results were validated using eight other established optimization techniques. 2m point estimation method was employed to deal with uncertainties in EV and PV . Monte-Carlo simulation was applied to cross verify the performance. The land cost and customer accessibility to charging stations were taken into account to allocate it to proper places. The entire study was performed based on the 24-hour dynamically varying EV (cid:13)ows and PV outputs.
Data Protection Mechanisms in IoT: A Vital Challenge

The Internet of Things (IoT) associated with life is a budding real-time environment paradigm. Smart Users can control and analyze Things through Smart gadgets connected through the network. As gazillions of devices are connected and communicated via a complex, distributed network, the number of potential threats and attacks has grown drastically against protection and privacy. Security must be provided continuously for IoT service. Exploring the issues to be improved in IoT setup includes Information Secrecy, Access Control, Authentication, Integrity, Privacy, and Trust. The paper summarizes the issues with IoT device security and the efficiency of the existing security solutions. This paper analyses the schemes to guard IoT communications and the methods adopted by the researchers while providing security of data in IoT.
Comparison of Wall Treatments and Meshes in Large-Eddy Simulations of Mixing Tees

Abstract Large-eddy simulations (LESs) for two different T-junctions are performed for the prediction of thermal mixing loads on piping. In particular, the effects of wall treatment and mesh on temperature and wall heat flux fluctuations are studied. Wall-resolved LES shows good agreement with an experiment having adiabatic walls, but using wall functions shows deviations in root-mean-squared (RMS) temperatures and cross-stream mean velocities. The simulations show increases in peak RMS temperatures with local mesh refinement, and hence, too-low peak values are obtained with wall functions. The highest temperature fluctuations occur locally near the T-junction requiring a dense mesh. Wall functions are unable to capture high wall heat fluxes at a sharp corner, but otherwise, the maximum RMS value is close to a wall-resolved LES. For a T-junction having a round corner, higher RMS heat flux is obtained with wall functions compared to a wall-resolved case. Wall functions show lower instantaneous heat fluxes than wall-resolved LES, but the wall functions nonetheless result in higher pipe wall temperature fluctuations due to lower frequency content.
SARCOPENIA : A REVIEW OF THE VALIDITY AND USEFULNESS OF FIVE COMMONLY EMPLOYED SCREENING TESTS FOR EARLY DETECTION IN CLINICAL PRACTICE

Objective: Sarcopenia is defined as a progressive, generalized loss of skeletal muscle mass and strength. Early detection with screening procedures is thought to be a key to mitigating the damage of sarcopenia. Sarcopenia often goes unrecognized in primary health care settings. Chiropractors are uniquely positioned to play an important role in screening this condition. The purpose of this paper is to review the evidence assessing the validity and usefulness of 5 commonly applied sarcopenia screening tools for use in clinical settings. Methods: MEDLINE, PubMed, Scopus and Google Scholar searches were conducted to identify relevant papers published between 1985-2021. Three questions drove this review: what is the prevalence of sarcopenia; what are the potential health consequences of sarcopenia; and, what screening tools are available for the early detection of sarcopenia. Results: Papers were screened according to the following eligibility criteria. Population: community dwelling adults aged 40 and over; men and women screened for sarcopenia with clear-cut diagnostic criteria. Studies were included in the review if they were RCT’s, reviews or guidelines. Limits: English language, human adults. Of 3,224 records a total of 36 papers were included in this review. Although researchers generally are in agreement of the need for screening tools to identify sarcopenia in clinical settings, there is no consensus about which screening tools are most useful at performing this task. Conclusions: This paper presents evidence on the predictive value of five screening tools for sarcopenia. This data has importance to chiropractors who by their practice privileges are uniquely positioned to detect patients at risk for sarcopenia with use of viable screening tools. Although more research is needed to determine the most effective screening tools for sarcopenia, utilization of these procedures, by chiropractors, may be valuable in helping to mitigate health consequences related to sarcopenia. (J Contemporary Chiropr 2021;4:72-78) Key Indexing Terms: Sarcopenia; Older adults; Physical Function; Screening Procedures
Facial Emotion Recognition Using 3D Face Reconstruction

In recent days, autonomous driving systems (ADS) effectively utilize facial emotion recognition (FER) results for safe driving. In FER, the system provides the user emotions such as happy, sad, anger, surprise, disgust, fear, or neutral. These emotions provide helpful information for safe driving and reduce the chances of road accidents. The conventional FER approaches use 2D images as their inputs and classify the user emotions. However, the 2D face images in the conventional FER approaches have limited features for model training. In addition, the features from the 2D face images themselves are not sufficient for accurate emotion classification. To reduce the feature extraction issues in the conventional FER approaches, we propose a 3D face image-based FER approach that uses the 3D face reconstruction technique for converting the 2D face images into 3D face images. The deep convolutional neural networks (DCNNs) used in the proposed FER approach efficiently use the 3D face images as inputs and classify the user emotions with minimum errors. The experiment results show that the proposed 3D face image-based FER approach achieves 99% classification accuracy which is better than the conventional 2D face image-based FER approach.
Deep Learning-based Colorectal Cancer Detection in Endoscopic Images

Colorectal cancer (CRC) is the most prevalent cancer found in the small bowel of the human gastrointestinal (GI) tract. Polyps are antecedents to CRC and are detected in approximately half of the people at age 50 within the GI. In this paper, an improved version of You Only Live Once (YOLO) is presented for the detection of polyp within the endoscopic images. We have improved the YOLOv3-tiny model by adding more convolutional layers to extract enriched and deeper features. For fair benchmarking, the efficacy of the proposed model is evaluated against the default version of YOLOv3-tiny in terms of recall, precision, F1-score, and F2-score
Bis coumarinyl bis triazolothiadiazinyl ethane derivatives: Synthesis, antiviral activity evaluation, and molecular docking studies

ABSTRACT A series of novel 3,3′-(3,3′-(dihydroxy/hydroxyethane-1,2-diyl)bis(7H-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazine-6,3-diyl))bis(2H-chromen-2-ones) were prepared by the condensation of thiocarbohydrazide with tartaric acid or malic acid followed by various 3-(2-bromoacetyl)-2H-chromen-2-ones in two steps with good yields. All the synthesized compounds were characterized by analytical and spectral (IR, 1H NMR, 13C NMR, and mass) data. These synthesized bis(triazolothiadiazinyl coumarin) compounds were evaluated for broad spectrum of antiviral activity. Among all the tested compounds, compound 5f exhibited antiviral activity against H1N1 virus. The molecular docking studies of these compounds against H1N1 neuraminidase enzyme were performed. The binding affinity and binding values were compared with standard drugs. GRAPHICAL ABSTRACT
Control Yourself! Examining the Dimensions of Eudaimonic Psychological Well-Being as Related to Locus of Control Using Structural Equation Modeling

Current literature on the relationship between locus of control (LOC) and psychological well-being (PWB) consistently demonstrates that individuals with an internal LOC generally report experiencing greater psychological well-being than externally-oriented individuals. However, there is minimal depth of understanding regarding the specific dynamics of this well-established relationship. The aims of the present quantitative study were (a) to replicate and provide support for the existing knowledge on the relationship between these two factors and (b) to use a structural equation model (SEM) to further explore the relationship between LOC and the six dimensions in Ryff’s model of eudaimonic PWB, which are combined to assess one’s psychological welfare. Using cross-sectional survey methodology, 267 adult participants were recruited through convenience and snowball sampling to anonymously complete a series of questionnaires online. A Pearson r bivariate correlation analysis examining the relationship between PWB and LOC revealed a significant negative correlation between the two constructs (r = -.280, p = .001). This finding replicates previous research, where individuals with an internal LOC reported experiencing significantly greater PWB, while individuals with an external LOC reported significantly less PWB. In order to further examine how the six factors of PWB (self-acceptance, positive relationships, autonomy, environmental mastery, purpose in life, and personal growth) relate to LOC, a SEM was performed. Although all six dimensions of PWB show significant negative correlations with LOC, environmental mastery, self-acceptance, and purpose in life were the most impactful on the relationship between these two constructs, as they were shown to have higher significance in the SEM. Considering the SEM suggests that environmental mastery, self-acceptance, and purpose in life are the most weighted factors of PWB’s correlation to LOC, possible interventions on these factors in a clinical or applied setting may see improvement in the relationship between PWB and LOC.
End-to-end Methods for Autonomous Driving in Simulation

Fully autonomous driving is considered one of the grand challenges of modern technology and a variety of approaches have emerged for creating and evaluating autonomous driving agents. The self-driving industry typically adopts a modular software architecture and uses large fleets of autonomous vehicles for data collection and evaluation. Simulation generally occupies a minor role in these workflows as a tool for basic unit tests and debugging. However, recent end-to-end methods in academia have elevated simulation’s role in the training of machine learning models and demonstrate state-of-the-art performance on simulated driving benchmarks as a result. In this thesis, we consider two questions. First, what are the state-ofthe-art approaches for end-to-end driving in simulation and how can we improve them? We use the open-source CARLA simulator as our testbed and work with the recent Leaderboard benchmark for evaluation. In our first project, we give a detailed failure analysis of “Learning by Cheating” (LBC) which has been considered the state-of-the-art in imitation learning. We then propose offline imitative reinforcement learning (OIRL) as an alternative approach and investigate its performance. In our second project, we give a failure analysis of the “World on Rails” (WOR) offline reinforcement learning method and propose “Slightly Online Reinforcement Learning” (SORL) as an improvement. Second, what value does simulation provide for the training and evaluation of driving algorithms? We draw attention to the crucial role that privileged information plays in both LBC and WOR’s algorithms. We emphasize that these methods are impossible to replicate without simulation and advocate for privileged information as an important tool for developing planning algorithms. Finally, we give a critical analysis of CARLA by pointing out issues in its perceptual and behavioral realism. We also critique the Leaderboard benchmark and provide discussion on why its primary metric cannot be reliably used as a proxy for good driving behavior.
Measuring and evaluating visitors' behaviors inside museums: the Co.ME. project

The use of ICT in the field of Cultural Heritage offers unprecedented opportunities to improve cultural sites management (e.g., archaeological areas or museum). Taking advantage of innovative methods of data acquisition through ambient intelligence and space sensing infrastructures, the Co.ME. project has developed a system to turn exhibitions into sensitive spaces, able to quantify and register visitors’ behavior. A solution that provides to museum professionals all the necessary data to understand their public, making possible to structure a cultural offer based on visitors’ needs. After a short theoretical introduction on the project framework, the paper presents the case study of the Civic Museums of Palazzo Buonaccorsi in Macerata (Italy), demonstrating the effectiveness of this approach in terms of both data collection and monitoring system; useful solutions to improve museum installations and communication, as well to optimally plan staff attendance.
Power Focusing with Intelligent Reflective Surfaces for Wireless Indoor Communication Systems

Intelligent reﬂective surfaces (IRS) are envisioned to have a signiﬁcant number of applications in future wireless network systems by reconﬁguring the signal propagation to improve system performance. In particular, the IRS’s cell elements can independently reﬂect the incident signal by tuning its phase and achieving passive beamforming to enhance the received signal of the user equipment. We study the IRS-aided point to point multiple-input single-output (MISO) wireless system where one IRS is deployed to assist the wireless communication network from a multi-antenna base station to a single-antenna user. The user receives the signal transmitted directly from the base station as well as that reﬂected by the IRS. We aim to maximize the total received signal power at the user by jointly optimizing the (active) transmit beamforming at the base station and (passive) reﬂect beamforming by the IRS’s reﬂective cells. First, we propose a signal model for the passive IRS-aided wireless network for empty-room and dead-zone scenarios. Second, we propose a beamforming optimization algorithm to analyze the received signal power’s maximization for the given scenarios. The performance of the proposed methods is analyzed in an ideal indoor environment where all the antennas both in the base station and the IRS are in a perfect line-of-sight propagation environment. Furthermore, numerical results show signiﬁcant performance enhancement with the use of IRS in typical wireless networks compared to benchmark schemes. The numerical results show how the IRS-aided wireless system compensates for the attenuation loss blocking by the obstacle wall with different materials in the dead-zone scenarios. Moreover, we present the relationship between the size of the IRS and the received signal power and the size of the IRS and the IRS deploying positions, respectively. It is veriﬁed that the IRS can drastically enhance the link quality and coverage over the traditional setup without the IRS.
An in-depth statistical analysis of the COVID-19 pandemic's initial spread in the WHO African region

Objective: To quantify the initial spread of COVID-19 in the WHO African region, and to investigate the possible drivers responsible for variation in the epidemic among member states. Design: A cross-sectional study. Setting: COVID-19 daily case and death data from the initial case through 29 November 2020. Participants: 46 countries comprising the WHO African region. Main outcome measures: We used five pandemic response indicators for each country: speed at which the pandemic reached the country, speed at which the first 50 cases accumulated, maximum monthly attack rate, cumulative attack rate, and crude case fatality ratio (CFR). We studied the effect of 13 predictor variables on the country-level variation in them using a principal component analysis, followed by regression. Results: Countries with higher tourism activities, GDP per capita, and proportion of older people had higher monthly (p < 0.001) and cumulative attack rates (p < 0.001) and lower CFRs (p = 0.052). Countries having more stringent early COVID-19 response policies experienced greater delay in arrival of the first case (p < 0.001). The speed at which the first 50 cases occurred was slower in countries whose neighbors had higher cumulative attack rates (p = 0.06). Conclusions: While global connectivity and tourism could facilitate the spread of airborne infectious agents, the observed differences in attack rates between African countries might also be due to differences in testing capacities or age distribution. Wealthy countries managed to minimize adverse outcomes. Further, careful and early implementation of strict government policies, such as restricting tourism, could be pivotal to controlling the COVID-19 pandemic. Evidently, good quality data and sufficient testing capacities are essential to unravel the epidemiology of an outbreak. We thus urge decision-makers to reduce these barriers to ensure rapid responses to future threats to public health and economic stability.
Preclinical evaluation of apoptosis resensitisation with Inhibitor-of-apoptosis-protein (IAP) antagonist in glioblastoma cell lines and in an orthotopic mouse GBM model

Presented Registrar’s Prize in Clinical Neuroscience 2015, RDS Dublin (oral presentation, 13 November 2015) Zakaria Z, O’Brien DF, Byrne AT, Prehn JHM. Patient-derived glioblastoma cells show significant heterogeneity in treatment responses to the inhibitor-of-apoptosis-protein (IAP) antagonist