Miracle-dz/UniFAR-FTUs huggingface dataset:
MimicBot: Combining Imitation and Reinforcement Learning to win in Bot Bowl

This paper describe an hybrid agent trained to play in Fantasy Football AI which participated in the Bot Bowl III competition. The agent, MimicBot, is implemented using a specifically designed deep policy network and trained using a combination of imitation and reinforcement learning. Previous attempts in using a reinforcement learning approach in such context failed for a number of reasons, e.g. due to the intrinsic randomness in the environment and the large and uneven number of actions available, with a curriculum learning approach failing to consistently beat a randomly paying agent. Currently no machine learning approach can beat a scripted bot which makes use of the domain knowledge on the game. Our solution, thanks to an imitation learning and a hybrid decision-making process, consistently beat such scripted agents. Moreover we shed lights on how to more efficiently train in a reinforcement learning setting while drastically increasing sample efficiency. MimicBot is the winner of the Bot Bowl III competition, and it is currently the state-of-the-art solution.
Effects of Small-scale Absorption Systems on Neutral Islands during the Late Epoch of Reionization

The reionization process is expected to be prolonged by the small-scale absorbers (SSAs) of ionizing photons, which have been seen as Lyman-limit systems in quasar absorption line observations. We use a set of semi-numerical simulations to investigate the effects of absorption systems on the reionization process, especially their impacts on the neutral islands during the late epoch of reionization (EoR). Three models are studied, i.e., the extreme case of no-SSA model with a high level of ionizing background, the moderate-SSA model with a relatively high level of ionizing background, and the dense-SSA model with a low level of ionizing background. We find that while the characteristic scale of neutral regions decreases during the early and middle stages of reionization, it stays nearly unchanged at about 10 co-moving Mpc during the late stage for the no-SSA and moderate-SSA models. However, in the case of weak ionizing background in the dense-SSA model, the characteristic island scale shows obvious evolution, as large islands break into many small ones that are slowly ionized. The evolutionary behavior of neutral islands during the late EoR thus provides us with a novel way to constrain the abundance of SSAs. We discuss the 21 cm observation with the upcoming Square Kilometre Array. The different models can be distinguished by either the 21 cm imaging or the 21 cm power spectrum measurements.
QoS-Oriented Sensing–Communication–Control Co-Design for UAV-Enabled Positioning

Unmanned aerial vehicle (UAV)-enabled positioning that uses UAVs as aerial anchor nodes has been envisioned as a promising solution for providing positioning services in harsh environments. In previous research, state sensing and control of UAVs was either ignored or assumed to be performed continuously, resulting in system instability or a waste of wireless resources. Therefore, in this paper, we propose a quality-of-service (QoS)-oriented sensing-communication-control (SCC) co-design scheme for UAV-enabled positioning systems. We first establish mathematical models of UAV state sensing and control. Then, we analyze the influence of sensing scheduling and transmission failure on the stability of UAV, as well as the performance of positioning services in the presence of UAV control error. Based on these models and analysis results, we further study the problem of minimizing the amount of data transmitted by optimizing the sensing scheduling and blocklength allocation under the condition of meeting each user’s requirement on position accuracy. Finally, a heuristic algorithm is developed to solve this mixed-integer nonlinear problem. Numerical results demonstrate the validity and superiority of the proposed scheme. Compared with two benchmark schemes, our scheme reduces the failure rate or resource consumption of positioning services by more than 75% or 80%.
On the abundance of supersymmetric strings in AdS3 × S × S × S describing BPS line operators

We study supersymmetric open strings in type IIB AdS3 × S × S × S with mixed R-R and NS-NS fields. We focus on strings ending along a straight line at the boundary of AdS3, which can be interpreted as line operators in a dual CFT2. We study both classical configurations and quadratic fluctuations around them. We find that strings sitting at a fixed point in S × S × S, i.e. satisfying Dirichlet boundary conditions, are 1/2 BPS. We also show that strings sitting at different points of certain submanifolds of S × S × S can still share some fraction of the supersymmetry. This allows to define supersymmetric smeared configurations by the superposition of them, which range from 1/2 BPS to 1/8 BPS. In addition to the smeared configurations, there are as well 1/4 BPS and 1/8 BPS strings satisfying Neumann boundary conditions. All these supersymmetric strings are shown to be connected by a network of interpolating BPS boundary conditions. Our study reveals the existence of a rich moduli of supersymmetric open string configurations, for which the appearance of massless fermionic fields in the spectrum of quadratic fluctuations is crucial. ar X iv :2 10 8. 09 38 0v 2 [ he pth ] 3 S ep 2 02 1
The Physics of Neutrinoless Double Beta Decay: A Primer

Neutrinoless double beta decay is a hypothetical radioactive process which, if observed, would prove the neutrino to be a Majorana fermion: a particle that is its own antiparticle. In this lecture mini-series I discuss the physics of Majorana fermions and the connection between the nature of neutrino mass and neutrinoless double beta decay. We review Dirac and Majorana spinors, discuss methods of distinguishing between Majorana and Dirac fermions, and derive in outline the connection between neutrino mass and double beta decay rates. We conclude by briefly summarizing the experimental landscape and the challenges associated with searches for this elusive process.
Discovery of galaxy clusters and a head–tail radio galaxy in the direction of globular cluster NGC 6752

We report on the discovery of CXOU J191100–595621 and CXOU J191012–595619, two galaxy clusters serendipitously detected in the direction of globular cluster NGC 6752, based on archival Chandra observations with a total exposure time of ∼344 ks. The deep Chandra X-ray data enabled us to measure the properties of both systems. We found a redshift of z = 0.239 ± 0.013 and z = 0.375 ± 0.016, a temperature of $kT=3.32^{+0.57}_{-0.46}$ keV and $kT=3.71^{+1.18}_{-0.86}$ keV, an iron abundance of $Z_{\rm Fe}=0.64^{+0.34}_{-0.29}Z_{\rm Fe\odot }$ and $Z_{\rm Fe}=1.29^{+0.97}_{-0.65}Z_{\rm Fe\odot }$ and a rest-frame full band (0.5–7 keV) luminosity of $L_{\rm X}=9.2^{+1.2}_{-1.1}\times 10^{43} {\rm \, erg\, s^{-1}}$ and $L_{\rm X}=9.9^{+2.7}_{-2.2}\times 10^{43} {\rm \, erg\, s^{-1}}$ for CXOU J191100–595621 and CXOU J191012–595619, respectively. The temperature profile of CXOU J191100–595621 is found to decrease with decreasing radius, indicating a cool core in this cluster. The hydrostatic equilibrium estimation suggests that the clusters are moderately weighted, with $M_{500}=(1.3\pm 0.4)\times 10^{14}\, \mathrm{M}_{\odot }$ and $M_{500}=(2.0\pm 1.5)\times 10^{14}\, \mathrm{M}_{\odot }$, respectively. We search for optical and radio counterparts of X-ray point sources in the clusters. Three active galactic nuclei are found, among which one is identified with a narrow-angle-tail radio galaxy, and one is found to be associated with the brightest central galaxy of CXOU J191100–595621.
A Learning-Based Fast Uplink Grant for Massive IoT via Support Vector Machines and Long Short-Term Memory

The current random access (RA) allocation techniques suffer from congestion and high signaling overhead while serving massive machine-type communication (mMTC) applications. To this end, third-generation partnership project introduced the need to use fast uplink grant (FUG) allocation in order to reduce latency and increase reliability for smart Internet of Things (IoT) applications with strict Quality-of-Service constraints. We propose a novel FUG allocation based on support vector machine (SVM). First, machine-type communication (MTC) devices are prioritized using an SVM classifier. Second, a long short-term memory architecture is used for traffic prediction and correction techniques to overcome prediction errors. Both results are used to achieve an efficient resource scheduler in terms of the average latency and total throughput. A coupled Markov modulated Poisson process (CMMPP) traffic model with mixed alarm and regular traffic is applied to compare the proposed FUG allocation to other existing allocation techniques. In addition, an extended traffic model-based CMMPP is used to evaluate the proposed algorithm in a more dense network. We test the proposed scheme using real-time measurement data collected from the Numenta anomaly benchmark (NAB) database. Our simulation results show the proposed model outperforms the existing RA allocation schemes by achieving the highest throughput and the lowest access delay of the order of 1 ms by achieving prediction accuracy of 98 % when serving the target massive and critical MTC applications with a limited number of resources.
A Multiple-View Geometric Model for Specularity Prediction on Non-Uniformly Curved Surfaces

—Specularity prediction is essential to many computer vision applications. In Augmented Reality (AR), it improves the realism of virtual objects inserted in a live video stream by providing a coherent shape, appearance and motion to specularities. Specularity prediction also gives important visual cues that could be used in Simultaneous Localisation and Mapping (SLAM), 3D reconstruction and material modeling, thus improving scene understanding. However, specularity prediction is a challenging task requiring numerous information from the scene including the camera pose, the geometry of the scene, the light sources and the material properties. Our previous work have addressed this task by creating an explicit model using an ellipsoid whose projection ﬁts the specularity image contours for a given camera pose. These ellipsoid-based approaches belong to a family of models called JOint-LIght MAterial Specularity (JOLIMAS), where we have attempted to gradually remove assumptions on the scene such as the geometry of the specular surfaces to provide real-time specularity prediction. However, our most recent approach is still limited to uniformly curved surfaces. This paper builds upon these methods by generalising JOLIMAS to any surface geometry while improving the quality of specularity prediction, without sacriﬁcing computation performances. The proposed method establishes a link between surface curvature and specularity shape in order to lift the geometric assumptions from previous work. Contrary to previous work, our new model is built from a physics-based local illumination model namely Torrance-Sparrow, providing a better model reconstruction. Specularity prediction using our new model is tested against the most recent JOLIMAS version on both synthetic and real sequences with objects of varying shape curvatures, reconstructed using a CAD model or the Kinect v2 depth camera. Our method outperforms previous approaches in specularity prediction, including the real-time setup, as shown in the supplementary material using videos.
A Systematic Approach to Cleaning Routine Health Surveillance Datasets: An Illustration Using National Vector Borne Disease Control Programme Data of Punjab, India

Advances in ICT4D and data science facilitate systematic, reproducible, and scalable data cleaning for strengthening routine health information systems. A logic model for data cleaning was used and it included an algorithm for screening, diagnosis, and editing datasets in a rule-based, interactive, and semi-automated manner. Apriori computational workflows and operational definitions were prepared. Model performance was illustrated using the dengue line-list of the National Vector Borne Disease Control Programme, Punjab, India from 01 January 2015 to 31 December 2019. Cleaning and imputation for an estimated date were successful for 96.1% and 98.9% records for the year 2015 and 2016 respectively, and for all cases in the year 2017, 2018, and 2019. Information for age and sex was cleaned and extracted for more than 98.4% and 99.4% records. The logic model application resulted in the development of an analysis-ready dataset that can be used to understand spatiotemporal epidemiology and facilitate data-based public health decision making.
Marginal donors and organ shortness: concomitant surgical procedures during heart transplantation: a literature review

Heart transplantation represents the gold standard for end-stage heart failure. However, due to the increasing demand and the shortage of available organs, donor supply remains the main limitation. Marginal donor hearts in high-risk candidates who do not meet standard listing criteria are the only alternative when life expectancy is limited, but their use is still debated. Surgical correction of detected coronary lesions or valvular heart defects allows further enlargement of the number of available organs. In this article, we offer a literature review on this topic and report two marginal donor hearts with angiography evidence of coronary stenosis and preserved ventricular function, which underwent concomitant myocardial revascularization during heart implantation.
Flourishing or Languishing? The Relationship Between Mental Health, Health Locus of Control and Generalised Self-Efficacy

Mental health is a core component of overall wellbeing and can contribute to positive functioning and purpose in life. Mental health is purportedly related to the constructs of generalised self-efficacy (GSE) and health locus of control (LOC). Most research, however, has explored GSE and LOC and their relationship with psychopathology, or defined mental health as a lack of psychopathology indicators. This contradicts the two continua model of mental health, as it does not incorporate positive mental functioning. The current study sought to re-examine the relationship between health LOC, GSE, and mental health, whilst controlling for potential confounding factors. It was anticipated that health LOC and GSE would predict mental health. A sample of 224 adults completed an online survey comprised of validated psychometric tools scales. Regression analyses were conducted to determine whether health LOC and GSE predicted mental health, after controlling for self-esteem, social support, and coping. Results indicated that GSE was a significant unique positive predictor of mental health. Health locus of control was not a significant predictor of mental health. Limitations were present, including a cross-sectional research design and use of convenience sampling. This study builds upon existing research in providing a better understanding of contributors to mental health, which may assist in the development of evidence-based mental health promotion interventions.
Severe Acute Respiratory Syndrome Coronavirus 2 in Farmed Mink (Neovison vison), Poland

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the etiologic agent of coronavirus disease and has been spreading worldwide since December 2019. The virus can infect different animal species under experimental conditions, and mink on fur farms in Europe and other areas are susceptible to SARS-CoV-2 infection. We investigated SARS-CoV-2 infection in 91 mink from a farm in northern Poland. Using reverse transcription PCR, antigen detection, and next-generation sequencing, we confirmed that 15 animals were positive for SARS-CoV-2. We verified this finding by sequencing full viral genomes and confirmed a virus variant that has sporadic mutations through the full genome sequence in the spike protein (G75V and C1247F). We were unable to find other SARS-CoV-2 sequences simultaneously containing these 2 mutations. Country-scale monitoring by veterinary inspection should be implemented to detect SARS-CoV-2 in other mink farms.
Aryl hydrocarbon receptor controls skin homeostasis, regeneration, and hair follicle cycling by adjusting epidermal stem cell function

Skin integrity requires constant maintenance of a quiescent, yet responsive, population of stem cells. While interfollicular epidermal progenitors control normal homeostasis, hair follicle stem cells residing within the bulge provide regenerative potential during hair cycle and in response to wounding. The aryl hydrocarbon receptor (AhR) modulates cell plasticity and differentiation and its overactivation results in severe skin lesions in humans. However, its physiological role in skin homeostasis and hair growth is unknown. Reconstitution assays grafting primary keratinocytes and dermal fibroblasts into nude mice and 3‐D epidermal equivalents revealed a positive role for AhR in skin regeneration, epidermal differentiation, and stem cell maintenance. Furthermore, lack of receptor expression in AhR−/− mice delayed morphogenesis and impaired hair regrowth with a phenotype closely correlating with a reduction in suprabasal bulge stem cells (α6lowCD34+). Moreover, RNA‐microarray and RT‐qPCR analyses of fluorescence‐activated cell sorting (FACS)‐isolated bulge stem cells revealed that AhR depletion impaired transcriptional signatures typical of both epidermal progenitors and bulge stem cells but upregulated differentiation markers likely compromising their undifferentiated phenotype. Altogether, our findings support that AhR controls skin regeneration and homeostasis by ensuring epidermal stem cell identity and highlights this receptor as potential target for the treatment of cutaneous pathologies.
Nursing students' experiences with simulation-based education as a pedagogic method in low-resource settings: A mixed-method study.

AIMS AND OBJECTIVES This study introduced simulation-based education in nurse education programs in Tanzania and Madagascar and explored nursing students' experiences with this pedagogic method as a mode of learning. BACKGROUND Simulation-based education has barely been introduced to education programs in resource-constrained settings. The study was conducted in two nurse education programs: one in rural Tanzania and the other in the mid-land of Madagascar. Both institutions offer diploma programs in nursing. Simulation-based education has not been included in the teaching methods used in these nursing programs. DESIGN A descriptive and convergent mixed method design was employed. METHODS Ninety-nine nursing students were included in the study. Simulation sessions followed by data collection took place once in 2017 and twice in 2018. Data were collected by means of several questionnaires and six focus groups. The data were analyzed using descriptive statistics and qualitative content analysis. The Standards for Reporting Qualitative Research (SRQR) was used to report the results. RESULTS The quantitative data revealed that the students rated all the questions related to the simulation design elements, educational practices, and students' satisfaction and self-confidence in learning with scores of above four on a 5-point Likert scale. The qualitative data from the first theme, building competence and confidence, further emphasized and outlined the quantitative results. Additionally, the qualitative data revealed a second theme, improving through encouragement and corrections. The students clearly expressed that they wanted to be aware of their weaknesses to be able to improve; however, the provision of feedback should be carried out in an encouraging way. CONCLUSION The findings indicated that the nursing students were satisfied with simulation as a pedagogic method, as it improved their competence and prepared them for professional practice. Further research is necessary to explore whether the students are able to transfer their knowledge into clinical practice. RELEVANCE TO CLINICAL PRACTICE Simulation as a pedagogic method is valuable for the learning of clinical skills and preparation for clinical practice.
Reconceptualization of borderline conditions through the lens of the alternative model of personality disorders.

Borderline (BPD) and schizotypal personality disorder (SPD) were introduced in the Diagnostic and Statistical Manual of Mental Disorders, Third Edition (DSM-III). However, the clinical differentiation of the 2 diagnoses (e.g., psychotic-like features) was challenging for diagnostic classification and clinical management. With the introduction of the alternative model for personality disorders (AMPD) in DSM-5 Section III, a dimensional approach was proposed, which potentially holds promise for better future differentiation between BPD and SPD. The present study sought to examine the psychopathology using the AMPD model. A total of 105 patients were interviewed, 25 were excluded according to exclusion criteria, and the final sample comprised 80 patients who fulfilled the DSM-5 criteria for BPD (n = 35), SPD (n = 25), and comorbid BPD + SPD (n = 20), respectively. All patients were administered The Structured Clinical Interview for DSM-5 alternative model for personality disorders Modules I and II. One-way analysis of variance tests with planned contrasts were used. Results showed that for AMPD Criterion A, the BPD + SPD group had the most severe impairment of personality functioning, except for Identity, where the SPD group showed the most severe impairment. For AMPD Criterion B, the domain of Detachment and the facet of Eccentricity from the Psychoticism domain were most prominent for the SPD group relative to the 2 other groups. The differentiating between BPD and SPD manifestations of cognitive/perceptual disturbances does not seem resolved by the Psychoticism domain, which covers broader aspects of psychopathology. Future research should further investigate the construct of Psychoticism, especially to differentiate nonpsychotic symptoms (e.g., dissociation) and address thought disorder. (PsycInfo Database Record (c) 2021 APA, all rights reserved).