Miracle-dz/UniFAR-FTUs huggingface dataset:
Burnout and depression: A cross sectional study among health care workers providing HIV care during the COVID-19 pandemic in Malawi

Health care workers (HCWs) in eastern Africa experience high levels of burnout and depression, and this may be exacerbated during the COVID-19 pandemic due to anxiety and increased work pressure. We assessed the prevalence of burnout, depression and associated factors among Malawian HCWs who provided HIV care during the COVID-19 pandemic. From April-May 2021, between the second and third COVID-19 waves in Malawi, we randomly selected HCWs from 32 purposively selected PEPFAR/USAID-supported health facilities for a cross-sectional survey. We screened for depression using the World Health Organization Self Report Questionnaire (positive screen: score≥8) and for burnout using the Maslach Burnout Inventory tool, (positive screen: moderate-high Emotional Exhaustion and/or moderate-high Depersonalization, and/or low-moderate Personal Accomplishment scores). Logistic regression models were used to evaluate factors associated with depression and burnout. We enrolled 435 HCWs, median age 32 years (IQR 28–38), 54% male, 34% were clinical cadres and 66% lay cadres. Of those surveyed, 28% screened positive for depression, 29% for burnout and 13% for both. In analyses that controlled for age, district, and residence (rural/urban), we found that screening positive for depression was associated with expecting to be infected with COVID-19 in the next 12 months (aOR 2.7, 95%CI 1.3–5.5), and previously having a COVID-19 infection (aOR 2.58, 95CI 1.4–5.0). Screening positive for burnout was associated with being in the clinical cadre (aOR 1.86; 95% CI: 1.2–3.0) and having a positive depression screen (aOR 3.2; 95% CI: 1.9–5.4). Reports of symptoms consistent with burnout and depression were common among Malawian HCWs providing HIV care but prevalence was not higher than in surveys before the COVID-19 pandemic. Regular screening for burnout and depression should be encouraged, given the potential for adverse HCW health outcomes and reduced work performance. Feasible interventions for burnout and depression among HCWs in our setting need to be introduced urgently.
Study on the primary production in the Vembanad lake

This paper forms part of a study on the phytoplankton standing crop, primary production and related properties in the Vembanad Lake and connected backwaters extending from -Aleppey in the south to Azhicode in the north. A total area of 300 sq. km. has been covered by 28 stations for hydrographic properties and 7 stations for primary and secondary production. In the composition of phytoplankton, spatial heterogeneity is observed with fresh water, estuarine and marine forms distributed at different regions. The standing crop does not always reflect the photosynthetic production which has regional and spatial variation In the magnitude. The annual grass production ranger from about 150 to 650 gClm2)d.y maximum during pre and post-monsoon period unlike in the adjacent marine environment. Total organic production in the Vembanad Lake has been estimated as 10.00,000 tonne of carbon. The quantum of respiration by zooplankton and the surplus quantity left for alternate pathways in the food chain have been indicated.
Information Technology and Sustainable Development

A recent United Nations (UN) study concludes that degradation of the world's ecosystems over the past century ". could have devastating implications for human development and the welfare of all species. ” (United Nations, et al., 2000, p. 6). The report calls for an "ecosystems" approach to environmental problems that would foster "sustainable development," development capable of meeting the needs of today without sacrificing resources needed by future generations. The ecosystems approach would take a holistic view of problems; include the public in the management of ecosystems; integrate social and economic information with environmental information to address the ecosystems “information gap”; and bring urban planning into ecosystem management, as urbanization places severe pressures on ecosystems today. This paper argues that ecosystems management constitutes a "wicked" problem (Rittel and Webber, 1973) or a "mess" (Ackoff, 1999), and that Singerian inquiring organizations (Churchman, 1971; Courtney, Paradice and Croasdell, 1998; Richardson, Courtney and Paradice, 1999) can provide a framework for ecosystems management. A study of infrastructure decision making in Houston, Texas (Lomax, et al., 1998) is presented as an example of using the Singer model in ecosystem management. The objective of the study is to develop decision support systems for Houston's infrastructure systems, including roads, sewers, water supply, and drainage. A very broad perspective on infrastructure decision making is being taken. The relationships among built, human and natural environments are being considered. Preliminary results show that infrastructure decision environments are indeed wicked, that the Singerian model does provide a useful framework in which to investigate them, and that decision factors tend to cluster into political, need, economic, and environmental categories. The wicked domain presents special problems for DSS development. However, an effective DSS may be help alleviate the present lack of communication among stakeholders, especially if it is integrated with a geographic information system currently under development. Introduction: The Problem The summary of a United Nations–sponsored report on the status of ecosystems worldwide states that “The current rate of decline in the long-term productive capacity of ecosystems could have devastating implications for human development and the welfare of all species. ” (United Nations, et al., 2000, p. 6). The study, conducted by 197 scientists around the globe,
Magnetic order in the computational 2D materials database (C2DB) from high throughput spin spiral calculations

We report a detailed investigation of the magnetic order in 192 stable magnetic two-dimensional materials from the Computational 2D Materials Database having one magnetic atom in the unit cell. The calculations are based on a systematic workflow that employs spin spiral calculations and yields the magnetic order in terms of a two-dimensional ordering vector $\mathbf{Q}$. We then include spin-orbit coupling to extract the easy and hard axes for collinear structures and the orientation of spiral planes in non-collinear structures. Finally, for all predicted ferromagnets we compute the Dzyaloshinskii-Moriya interactions and determine whether or not these are strong enough to overcome the magnetic anisotropy and stabilise a chiral spin spiral ground state. These steps completely determines the ground state order within the spiralling ansatz. We find 58 ferromagnets, 21 collinear anti-ferromagnets, and 85 non-collinear ground states of which 15 are chiral spin spirals driven by Dzyaloshinskii-Moriya interactions. The results show that non-collinear order is in fact as common as collinear order in these materials and emphasise the need for detailed investigation of the magnetic ground state when reporting magnetic properties of new materials. Furthermore, non-collinear order typically breaks symmetries inherent to the lattice and may give rise to emergent properties such as multiferroicity, magnetoelectricity or second order optical effects that would be predicted as absent based on a collinear assumption.
Dimension of Pinned Distance Sets for Semi-Regular Sets

We prove that if $E\subseteq \R^2$ is analytic and $1<d<\dim_H(E)$, there are ``many'' points $x\in E$ such that the Hausdorff dimension of the pinned distance set $\Delta_x E$ is at least $d\left(1 - \frac{\left(D-1\right)\left(D-d\right)}{2D^2+\left(2-4d\right)D+d^2+d-2}\right)$, where $D = \dim_P(E)$. In particular, we prove that $\dim_H(\Delta_x E) \geq \frac{d(d-4)}{d-5}$ for these $x$, which gives the best known lower bound for this problem when $d \in (1, 5-\sqrt{15})$. We also prove that there exists some $x\in E$ such that the packing dimension of $\Delta_x E$ is at least $\frac{12 -\sqrt{2}}{8\sqrt{2}}$. Moreover, whenever the packing dimension of $E$ is sufficiently close to the Hausdorff dimension of $E$, we show the pinned distance set $\Delta_x E$ has full Hausdorff dimension for many points $x\in E$; in particular the condition is that $D<\frac{(3+\sqrt{5})d-1-\sqrt{5}}{2}$. We also consider the pinned distance problem between two sets $X, Y\subseteq \R^2$, both of Hausdorff dimension greater than 1. We show that if either $X$ or $Y$ has equal Hausdorff and packing dimensions, the pinned distance $\Delta_x Y$ has full Hausdorff dimension for many points $x\in X$.
Optimal Non-Gaussian Dvoretzky–Milman Embeddings

We construct the first non-gaussian ensemble that yields the optimal estimate in the Dvoretzky–Milman Theorem: the ensemble exhibits almost Euclidean sections in arbitrary normed spaces of the same dimension as the gaussian embedding—despite being very far from gaussian (in fact, it happens to be heavy-tailed).
CPR-Coach: Recognizing Composite Error Actions based on Single-class Training

The fine-grained medical action analysis task has received considerable attention from pattern recognition communities recently, but it faces the problems of data and algorithm shortage. Cardiopulmonary Resuscitation (CPR) is an essential skill in emergency treatment. Currently, the assessment of CPR skills mainly depends on dummies and trainers, leading to high training costs and low efficiency. For the first time, this paper constructs a vision-based system to complete error action recognition and skill assessment in CPR. Specifically, we define 13 types of single-error actions and 74 types of composite error actions during external cardiac compression and then develop a video dataset named CPR-Coach. By taking the CPR-Coach as a benchmark, this paper thoroughly investigates and compares the performance of existing action recognition models based on different data modalities. To solve the unavoidable Single-class Training&Multi-class Testing problem, we propose a humancognition-inspired framework named ImagineNet to improve the model's multierror recognition performance under restricted supervision. Extensive experiments verify the effectiveness of the framework. We hope this work could advance research toward fine-grained medical action analysis and skill assessment. The CPR-Coach dataset and the code of ImagineNet are publicly available on Github.
SupeRBNN: Randomized Binary Neural Network Using Adiabatic Superconductor Josephson Devices

Adiabatic Quantum-Flux-Parametron (AQFP) is a superconducting logic with extremely high energy efficiency. By employing the distinct polarity of current to denote logic ‘0' and ‘1', AQFP devices serve as excellent carriers for binary neural network (BNN) computations. Although recent research has made initial strides toward developing an AQFP-based BNN accelerator, several critical challenges remain, preventing the design from being a comprehensive solution. In this paper, we propose SupeRBNN, an AQFP-based randomized BNN acceleration framework that leverages softwarehardware co-optimization to eventually make the AQFP devices a feasible solution for BNN acceleration. Specifically, we investigate the randomized behavior of the AQFP devices and analyze the impact of crossbar size on current attenuation, subsequently formulating the current amplitude into the values suitable for use in BNN computation. To tackle the accumulation problem and improve overall hardware performance, we propose a stochastic computing-based accumulation module and a clocking scheme adjustment-based circuit optimization method. To effectively train the BNN models that are compatible with the distinctive characteristics of AQFP devices, we further propose a novel randomized BNN training solution that utilizes algorithm-hardware co-optimization, enabling simultaneous optimization of hardware configurations. In addition, we propose implementing batch normalization matching and the weight rectified clamp method to further improve the overall performance. We validate our SupeRBNN framework across various datasets and network architectures, comparing it with implementations based on different technologies, including CMOS, ReRAM, and superconducting RSFQ/ERSFQ. Experimental results demonstrate that our design achieves an energy efficiency of approximately 7.8 × 104 times higher than that of the ReRAM-based BNN framework while maintaining a similar level of model accuracy. Furthermore, when compared with superconductor-based counterparts, our framework demonstrates at least two orders of magnitude higher energy efficiency.CCS CONCEPTS• Hardware → Emerging technologies; • Computing methodologies → Machine learning; • Computer systems organization → Architectures.
The Metallicity and Distance of Leo A from Blue Supergiants

We have obtained high-quality spectra of blue supergiant candidates in the dwarf irregular galaxy Leo A with the Low Resolution Imaging Spectrometer at the Keck I telescope. From the quantitative analysis of seven B8–A0 stars, we derive a mean metallicity [Z] = −1.35 ± 0.08, in excellent agreement with the gas-phase chemical abundance. From the stellar parameters and the flux-weighted gravity–luminosity relation (FGLR), we derive a spectroscopic distance modulus m − M = 24.77 ± 0.11 mag, significantly larger (∼0.4 mag) than the value indicated by RR Lyrae and other stellar indicators. We explain the bulk of this discrepancy with blue loop stellar evolution at very low metallicity and show that the combination of metallicity effects and blue loop evolution amounts, in the case of Leo A, to an ∼0.35 mag offset of the FGLR to fainter bolometric luminosities. We identify one outlier of low bolometric magnitude as a post-AGB star. Its metallicity is consistent with that of the young population, confirming the slow chemical enrichment of Leo A.
Turbulence d'une interaction onde de choc/couche limite sur une paroi plane adiabatique ou chauffée

La description experimentale des champs moyens et plus particulierement des champs turbulents, a ete entreprise pour quatre configurations d'interaction onde de choc / couche limite. Nous avons considere deux couches limites, l'une adiabatique, l'autre soumise a une apport de chaleur parietal, et deux intensites de choc dont la plus forte provoque un decollement de la couche limite. Nous avons determiner empiriquement une longueur d'interaction qui permet de prendre en compte les effets du chauffage parietal pour les champs moyens de vitesse et de temperature dans la zone de relaxation. Les aspect thermiques et spectraux de la turbulence ont ete etudie par anemometrie fil chaud a courant constant. Un apport d'energie, centre sur la frequence moyenne de battement du choc reflechi, est observe sur les repartition spectrales d'energie. Cet effet basse frequence s'attenue lorsqu'on s'ecarte de la paroi ou si l'on s'eloigne de la zone d'interaction. L'impact de ce phenomene est surtout sensible sur les echelles integrales de la turbulence qui sont d'autant plus augmentees que l'interaction est plus importante. Dans nos cas d'etude, ce phenomene ne semble pas preponderant. Au passage du premier choc, un calcul de distorsion rapide conduit a une estimation satisfaisante de l'amplification de la variance de vitesse, malgre cet apport d'energie aux basses frequences. D'autre part, les liens entre les champs turbulents de vitesse et de temperature restent tres forts, ce qui pourrai resulter d'un mecanisme de diffusion a gradient de la temperature par la vitesse a l'aval des interactions.
Smooth Nash Equilibria: Algorithms and Complexity

A fundamental shortcoming of the concept of Nash equilibrium is its computational intractability: approximating Nash equilibria in normal-form games is PPAD-hard. In this paper, inspired by the ideas of smoothed analysis, we introduce a relaxed variant of Nash equilibrium called $\sigma$-smooth Nash equilibrium, for a smoothness parameter $\sigma$. In a $\sigma$-smooth Nash equilibrium, players only need to achieve utility at least as high as their best deviation to a $\sigma$-smooth strategy, which is a distribution that does not put too much mass (as parametrized by $\sigma$) on any fixed action. We distinguish two variants of $\sigma$-smooth Nash equilibria: strong $\sigma$-smooth Nash equilibria, in which players are required to play $\sigma$-smooth strategies under equilibrium play, and weak $\sigma$-smooth Nash equilibria, where there is no such requirement. We show that both weak and strong $\sigma$-smooth Nash equilibria have superior computational properties to Nash equilibria: when $\sigma$ as well as an approximation parameter $\epsilon$ and the number of players are all constants, there is a constant-time randomized algorithm to find a weak $\epsilon$-approximate $\sigma$-smooth Nash equilibrium in normal-form games. In the same parameter regime, there is a polynomial-time deterministic algorithm to find a strong $\epsilon$-approximate $\sigma$-smooth Nash equilibrium in a normal-form game. These results stand in contrast to the optimal algorithm for computing $\epsilon$-approximate Nash equilibria, which cannot run in faster than quasipolynomial-time. We complement our upper bounds by showing that when either $\sigma$ or $\epsilon$ is an inverse polynomial, finding a weak $\epsilon$-approximate $\sigma$-smooth Nash equilibria becomes computationally intractable.
Examining the Limitations of Computational Rumor Detection Models Trained on Static Datasets

A crucial aspect of a rumor detection model is its ability to generalize, particularly its ability to detect emerging, previously unknown rumors. Past research has indicated that content-based (i.e., using solely source posts as input) rumor detection models tend to perform less effectively on unseen rumors. At the same time, the potential of context-based models remains largely untapped. The main contribution of this paper is in the in-depth evaluation of the performance gap between content and context-based models specifically on detecting new, unseen rumors. Our empirical findings demonstrate that context-based models are still overly dependent on the information derived from the rumors' source post and tend to overlook the significant role that contextual information can play. We also study the effect of data split strategies on classifier performance. Based on our experimental results, the paper also offers practical suggestions on how to minimize the effects of temporal concept drift in static datasets during the training of rumor detection methods.
Predictability and Comprehensibility in Post-Hoc XAI Methods: A User-Centered Analysis

Post-hoc explainability methods aim to clarify predictions of black-box machine learning models. However, it is still largely unclear how well users comprehend the provided explanations and whether these increase the users ability to predict the model behavior. We approach this question by conducting a user study to evaluate comprehensibility and predictability in two widely used tools: LIME and SHAP. Moreover, we investigate the effect of counterfactual explanations and misclassifications on users ability to understand and predict the model behavior. We find that the comprehensibility of SHAP is significantly reduced when explanations are provided for samples near a model's decision boundary. Furthermore, we find that counterfactual explanations and misclassifications can significantly increase the users understanding of how a machine learning model is making decisions. Based on our findings, we also derive design recommendations for future post-hoc explainability methods with increased comprehensibility and predictability.
CASM Monte Carlo: Calculations of the thermodynamic and kinetic properties of complex multicomponent crystals

Monte Carlo techniques play a central role in statistical mechanics approaches for connecting macroscopic thermodynamic and kinetic properties to the electronic structure of a material. This paper describes the implementation of Monte Carlo techniques for the study multicomponent crystalline materials within the Clusters Approach to Statistical Mechanics (CASM) software suite, and demonstrates their use in model systems to calculate free energies and kinetic coefficients, study phase transitions, and construct first-principles based phase diagrams. Many crystal structures are complex, with multiple sublattices occupied by differing sets of chemical species, along with the presence of vacancies or interstitial species. This imposes constraints on concentration variables, the form of thermodynamic potentials, and the values of kinetic transport coefficients. The framework used by CASM to formulate thermodynamic potentials and kinetic transport coefficients accounting for arbitrarily complex crystal structures is presented and demonstrated with examples applying it to crystal systems of increasing complexity. Additionally, a new software package is introduced, casm-flow, which helps automate the setup, submission, management, and analysis of Monte Carlo simulations performed using CASM.
Deep Learning Meets Swarm Intelligence for UAV-Assisted IoT Coverage in Massive MIMO

This study considers an unmanned aerial vehicle (UAV)-assisted multiuser massive multiple-input multiple-output (MU-mMIMO) systems, where a decode-and-forward (DF) relay in the form of an UAV facilitates the transmission of multiple data streams from a base station (BS) to multiple Internet of Things (IoT) users. A joint optimization problem of hybrid beamforming (HBF), UAV relay positioning, and power allocation (PA) to multiple IoT users to maximize the total achievable rate (AR) is investigated. The study adopts a geometry-based millimeter-wave (mmWave) channel model for both links and proposes three different swarm intelligence (SI)-based algorithmic solutions to optimize: 1) UAV location with equal PA; 2) PA with fixed UAV location; and 3) joint PA with UAV deployment. The radio frequency (RF) stages are designed to reduce the number of RF chains based on the slow time-varying angular information, while the baseband (BB) stages are designed using the reduced-dimension effective channel matrices. Then, a novel deep learning (DL)-based low-complexity joint HBF, UAV location, and PA optimization scheme (J-HBF-DLLPA) is proposed via fully connected deep neural network (DNN), consisting of an offline training phase, and an online prediction of UAV location and optimal power values for maximizing the AR. The illustrative results show that the proposed algorithmic solutions can attain higher capacity and reduce average delay for delay-constrained transmissions in a UAV-assisted MU-mMIMO IoT systems. Additionally, the proposed J-HBF-DLLPA can closely approach the optimal capacity while significantly reducing the runtime by 99%, which makes the DL-based solution a promising implementation for real-time online applications in UAV-assisted MU-mMIMO IoT systems.
Muon-induced nuclear magnetic moments in spinless muonic atoms: a simple estimate

In an attempt to address the long-standing fine-structure puzzle in heavy muonic atoms we investigate the magnetic interaction between a nucleus and its bound muon. A simple estimate shows that the effect is only noticeable for unrealistic nuclear parameters. A further investigation as to the relation of this effect to nuclear polarisation (NP) identifies the interaction as the magnetic dipole part of NP. Motivated by the relative closeness of this simple estimate to rigorous evaluations of NP, we extract effective values for the nuclear magnetic polarisability, a quantity otherwise unknown for all but the lightest nuclei.