Miracle-dz/UniFAR-FTUs huggingface dataset:
Firefighters’ Health Knowledge, Cardiovascular Disease Risk Factors, and Sociodemographic Characteristics as Predictors of Firefighters Attitudes Toward Health

This paper adds new knowledge on firefighters’ attitudes toward physical activity habits, diet, cardiovascular disease risk factors in firefighters. The study noted significant predictors of firefighters’ attitudes toward healthier lifestyles, according to sociodemographic characteristics and cardiovascular disease risk status, which may prove beneficial in reducing cardiovascular risk in this population. Objective The aim of the study is to determine the predictors of firefighters’ attitudes toward health based on firefighters’ knowledge cardiovascular disease (CVD) risk factors and sociodemographic characteristics. Methods The study used a web-based cross-sectional, descriptive, and correlational research design. Convenience sampling was used to recruit 110 firefighters, males, and females, aged 18 to 65 years. Results Firefighters’ health knowledge, age group 30 to 39 years, and having CVD risk factors present were significant predictors of firefighters’ attitudes toward regular aerobic exercise (P < 0.05). Firefighters’ health knowledge was a significant predictor of firefighters liking exercise to look physically fit, and health knowledge and the age group 20 to 29 and 30 to 39 years were significant predictors of firefighters liking exercise to be fit for firefighting (P < 0.05). Conclusions Health knowledge, age group, years of experience, and having CVD risk factors present were significant predictors of firefighters’ attitudes toward health.
A mindfulness-based cognitive therapy informed virtual psychiatry trainee wellbeing programme: Development and preliminary feedback

Objective To report the development and feedback on a newly created mindfulness-based cognitive therapy (MBCT) informed virtual wellbeing programme for psychiatry trainees. Methods Thirteen of the 14 trainees participated in the programme provided feedback via an online questionnaire. Qualitative data was analysed using thematic analysis. Results Thematic analysis revealed three main themes: timing of the intervention in relation to the COVID-19 pandemic; trainees were connected to the facilitator, their peers and within oneself; and trainees were going through a transformative experience. Discussion Our findings support including an optional MBCT informed wellbeing programme in psychiatry training programmes. Future research could measure efficacy of this online programme by utilising pre- and post-outcome measures of dispositional mindfulness and stress.
Important roles of transporters in the pharmacokinetics of anti-viral nucleoside/nucleotide analogs

ABSTRACT Introduction Nucleoside analogs are an important class of antiviral agents. Due to the high hydrophilicity and limited membrane permeability of antiviral nucleoside/nucleotide analogs (AVNAs), transporters play critical roles in AVNA pharmacokinetics. Understanding the properties of these transporters is important to accelerate translational research for AVNAs. Areas covered The roles of key transporters in the pharmacokinetics of 25 approved AVNAs were reviewed. Clinically relevant information that can be explained by the modulation of transporter functions is also highlighted. Expert opinion Although the roles of transporters in the intestinal absorption and renal excretion of AVNAs have been well identified, more research is warranted to understand their roles in the distribution of AVNAs, especially to immune privileged compartments where treatment of viral infection is challenging. P-gp, MRP4, BCRP, and nucleoside transporters have shown extensive impacts in the disposition of AVNAs. It is highly recommended that the role of transporters should be investigated during the development of novel AVNAs. Clinically, co-administered inhibitors and genetic polymorphism of transporters are the two most frequently reported factors altering AVNA pharmacokinetics. Physiopathology conditions also regulate transporter activities, while their effects on pharmacokinetics need further exploration. Pharmacokinetic models could be useful for elucidating these complicated factors in clinical settings.
Development of cerebral microbleeds and its impact on cognitive function in adult patients receiving medical management alone for ischemic moyamoya disease: supplementary analysis of a 5-year prospective cohort

ABSTRACT Objective De novo cerebral microbleeds (CMBs) on T2*-weighted magnetic resonance imaging (MRI) develop over time in adult moyamoya disease (MMD) and are generally associated with a decline in global cognitive function. The present supplementary analysis of a 5-year prospective cohort aimed to elucidate the incidence of an interval increase in CMBs in adult patients receiving medical management alone for ischemic MMD and its impact on cognitive function. Methods Sixty-four patients without misery perfusion in the symptomatic cerebral hemispheres at inclusion who did not experience any further ischemic symptoms or new hemorrhagic events during a 5-year follow-up period underwent T2*-weighted MRI and five kinds of neuropsychologic tests at inclusion and the end of the 5-year follow-up. Results When T2*-weighted MRI was compared between inclusion and the end of the 5-year follow-up, 10 patients (15%) had an interval increase in CMBs in the symptomatic cerebral hemisphere at inclusion. The scores from two kinds of neuropsychologic tests significantly deteriorated at the end of the 5-year follow-up compared with those at inclusion in patients with an interval increase in CMBs, whereas the scores of four kinds of neuropsychologic tests significantly improved at the end of the 5-year follow-up compared with those at inclusion in patients without interval increases in CMBs, asymptomatic ischemic lesions, or angiographic disease progression. Conclusions The incidence of an interval increase in CMBs was 15% per 5 years in adult patients receiving medical management alone for ischemic MMD, and this increase was associated with a decline in cognitive decline.
DeepSportradar-v1: Computer Vision Dataset for Sports Understanding with High Quality Annotations

With the recent development of Deep Learning applied to Computer Vision, sport video understanding has gained a lot of attention, providing much richer information for both sport consumers and leagues. This paper introduces DeepSportradar-v1, a suite of computer vision tasks, datasets and benchmarks for automated sport understanding. The main purpose of this framework is to close the gap between academic research and real world settings. To this end, the datasets provide high-resolution raw images, camera parameters and high quality annotations. DeepSportradar currently supports four challenging tasks related to basketball: ball 3D localization, camera calibration, player instance segmentation and player re-identification. For each of the four tasks, a detailed description of the dataset, objective, performance metrics, and the proposed baseline method are provided. To encourage further research on advanced methods for sport understanding, a competition is organized as part of the MMSports workshop from the ACM Multimedia 2022 conference, where participants have to develop state-of-the-art methods to solve the above tasks. The four datasets, development kits and baselines are publicly available.
An Efficient Multi-Step Framework for Malware Packing Identification

Malware developers use combinations of techniques such as compression, encryption, and obfuscation to bypass anti-virus software. Malware with anti-analysis technologies can bypass AI-based anti-virus software and malware analysis tools. Therefore, classifying pack files is one of the big challenges. Problems arise if the malware classifiers learn packers' features, not those of malware. Training the models with unintended erroneous data turn into poisoning attacks, adversarial attacks, and evasion attacks. Therefore, researchers should consider packing to build appropriate malware classifier models. In this paper, we propose a multi-step framework for classifying and identifying packed samples which is consists of pseudo-optimal feature selection, machine learning-based classifiers, and packer identification steps. In the first step, we use the CART algorithm and the permutation importance to preselect important 20 features. In the second step, each model learns 20 preselected features for classifying the packed files with the highest performance. As a result, the XGBoost, which learned the features preselected by XGBoost with the permutation importance, showed the highest performance than any other experiment scenarios with an accuracy of 99.67%, an F1-Score of 99.46%, and an area under the curve (AUC) of 99.98%. In the third step, we propose a new approach that can identify packers only for samples classified as Well-Known Packed.
Robust and scalable uncertainty estimation with conformal prediction for machine-learned interatomic potentials

Uncertainty quantification (UQ) is important to machine learning (ML) force fields to assess the level of confidence during prediction, as ML models are not inherently physical and can therefore yield catastrophically incorrect predictions. Established a-posteriori UQ methods, including ensemble methods, the dropout method, the delta method, and various heuristic distance metrics, have limitations such as being computationally challenging for large models due to model re-training. In addition, the uncertainty estimates are often not rigorously calibrated. In this work, we propose combining the distribution-free UQ method, known as conformal prediction (CP), with the distances in the neural network’s latent space to estimate the uncertainty of energies predicted by neural network force fields. We evaluate this method (CP+latent) along with other UQ methods on two essential aspects, calibration, and sharpness, and find this method to be both calibrated and sharp under the assumption of independent and identically-distributed (i.i.d.) data. We show that the method is relatively insensitive to hyperparameters selected, and test the limitations of the method when the i.i. d. assumption is violated. Finally, we demonstrate that this method can be readily applied to trained neural network force fields with traditional and graph neural network architectures to obtain estimates of uncertainty with low computational costs on a training dataset of 1 million images to showcase its scalability and portability. Incorporating the CP method with latent distances offers a calibrated, sharp and efficient strategy to estimate the uncertainty of neural network force fields. In addition, the CP approach can also function as a promising strategy for calibrating uncertainty estimated by other approaches.
Efficient dynamic point cloud coding using Slice-Wise Segmentation

— With the fast growth of immersive video sequences, achieving seamless and high-quality compressed 3D content is even more critical. MPEG recently developed a video-based point cloud compression (V-PCC) standard for dynamic point cloud coding. However, reconstructed point clouds using V-PCC suffer from different artifacts, including losing data during pre-processing before applying existing video coding techniques, e.g., High-Efficiency Video Coding (HEVC). Patch generations and self-occluded points in the 3D to the 2D projection are the main reasons for missing data using V-PCC. This paper proposes a new method that introduces overlapping slicing as an alternative to patch generation to decrease the number of patches generated and the amount of data lost. In the proposed method, the entire point cloud has been cross-sectioned into variable-sized slices based on the number of self-occluded points so that data loss can be minimized in the patch generation process and projection. For this, a variable number of layers are considered, partially overlapped to retain the self-occluded points. The proposed method's added advantage is to reduce the bits requirement and to encode geometric data using the slicing base position. The experimental results show that the proposed method is much more flexible than the standard V-PCC method, improves the rate-distortion performance, and decreases the data loss significantly compared to the standard V-PCC method.
Constant-Depth Sorting Networks

In this paper, we address sorting networks that are constructed from comparators of arity $k>2$. That is, in our setting the arity of the comparators -- or, in other words, the number of inputs that can be sorted at the unit cost -- is a parameter. We study its relationship with two other parameters -- $n$, the number of inputs, and $d$, the depth. This model received considerable attention. Partly, its motivation is to better understand the structure of sorting networks. In particular, sorting networks with large arity are related to recursive constructions of ordinary sorting networks. Additionally, studies of this model have natural correspondence with a recent line of work on constructing circuits for majority functions from majority gates of lower fan-in. Motivated by these questions, we obtain the first lower bounds on the arity of constant-depth sorting networks. More precisely, we consider sorting networks of depth $d$ up to 4, and determine the minimal $k$ for which there is such a network with comparators of arity $k$. For depths $d=1,2$ we observe that $k=n$. For $d=3$ we show that $k = \lceil \frac n2 \rceil$. For $d=4$ the minimal arity becomes sublinear: $k = \Theta(n^{2/3})$. This contrasts with the case of majority circuits, in which $k = O(n^{2/3})$ is achievable already for depth $d=3$.
Toroﬂux: A counterpart of the Chandrasekhar-Kendall state in noncentrosymmetric superconductors

We demonstrate that superconductors with broken inversion symmetry support a family of stable, spatially localized conﬁgurations of the self-knotted magnetic ﬁeld. These solutions, that we term “toroﬂux”, are the superconducting counterparts of the Chandrasekhar-Kendall states (spheromaks) that appear in highly conducting, force-free astrophysical and nuclear-fusion plasmas. The superconducting toroﬂuxes are solutions of superconducting models, in the presence of a parity breaking Lifshitz invariant associated with the O point group symmetry. We demonstrate that a magnetic dipole or a ferromagnetic inclusion in the bulk of a noncentrosymmetric superconductor source ﬁnite-energy toroﬂux solutions.
A Simplified Model Predictive Control for T-Type Inverter with Output LC Filter

In this paper, a model predictive control scheme for the T-type inverter with an output LC filter is presented. A simplified dynamics model is proposed to reduce the number of the measurement and control variables, resulting in a decrease in the cost and complexity of the system. Furthermore, the main contribution of the paper is the approach to evaluate the cost function. By employing the selection of sector information distribution in the reference inverter voltage and capacitor voltage balancing, the execution time of the proposed algorithm is significantly reduced by 36% compared with conventional model predictive control without too much impact on control performance. Simulation and experimental results are studied and compared with conventional finite control set model predictive control to validate the effectiveness of the proposed method.
An optimal sensors-based simulation method for spatiotemporal event detection

Human movements in urban areas are essential for understanding the human-environment interactions. However, activities and associated movements are full of uncertainties due to the complexity of a city. In this paper, we propose an optimal sensors-based simulation method for spatiotemporal event detection using human activity signals derived from taxi trip data. A sensor here is an abstract concept such that only the true observation data at the sensor location will be treated as known data for the simulation. Specifically, we first identify the optimal number of sensors and their locations that have the strongest correlation with the whole dataset. The observation data points from these sensors are then used to simulate a regular, uneventful scenario using the Discrete Empirical Interpolation Method. By comparing the simulated and observation scenarios, events are extracted both spatially and temporally. We apply this method in New York City with taxi trip records data. Results show that this method is effective in detecting when and where events occur.
How does the degree of novelty impacts semi-supervised representation learning for novel class retrieval?

Supervised representation learning with deep networks tends to overfit the training classes and the generalization to novel classes is a challenging question. It is common to evaluate a learned embedding on held-out images of the same training classes. In real applications however, data comes from new sources and novel classes are likely to arise. We hypothesize that incorporating unlabelled images of novel classes in the training set in a semi-supervised fashion would be beneficial for the efficient retrieval of novel-class images compared to a vanilla supervised representation. To verify this hypothesis in a comprehensive way, we propose an original evaluation methodology that varies the degree of novelty of novel classes by partitioning the dataset category-wise either randomly, or semantically, i.e. by minimizing the shared semantics between base and novel classes. This evaluation procedure allows to train a representation blindly to any novel-class labels and evaluate the frozen representation on the retrieval of base or novel classes. We find that a vanilla supervised representation falls short on the retrieval of novel classes even more so when the semantics gap is higher. Semi-supervised algorithms allow to partially bridge this performance gap but there is still much room for improvement.
TRoVE: Transforming Road Scene Datasets into Photorealistic Virtual Environments

High-quality structured data with rich annotations are critical components in intelligent vehicle systems dealing with road scenes. However, data curation and annotation require intensive investments and yield low-diversity scenarios. The recently growing interest in synthetic data raises questions about the scope of improvement in such systems and the amount of manual work still required to produce high volumes and variations of simulated data. This work proposes a synthetic data generation pipeline that utilizes existing datasets, like nuScenes, to address the difficulties and domain-gaps present in simulated datasets. We show that using annotations and visual cues from existing datasets, we can facilitate automated multi-modal data generation, mimicking real scene properties with high-fidelity, along with mechanisms to diversify samples in a physically meaningful way. We demonstrate improvements in mIoU metrics by presenting qualitative and quantitative experiments with real and synthetic data for semantic segmentation on the Cityscapes and KITTI-STEP datasets. All relevant code and data is released on github (https://github.com/shubham1810/trove_toolkit).
Ex-Ante Assessment of Discrimination in Dataset

Data owners face increasing liability for how the use of their data could harm under-priviliged communities. Stakeholders would like to identify the characteristics of data that lead to algorithms being biased against any particular demographic groups, for example, defined by their race, gender, age, and/or religion. Specifically, we are interested in identifying subsets of the feature space where the ground truth response function from features to observed outcomes differs across demographic groups. To this end, we propose FORESEE, a FORESt of decision trEEs algorithm, which generates a score that captures how likely an individual's response varies with sensitive attributes. Empirically, we find that our approach allows us to identify the individuals who are most likely to be misclassified by several classifiers, including Random Forest, Logistic Regression, Support Vector Machine, and k-Nearest Neighbors. The advantage of our approach is that it allows stakeholders to characterize risky samples that may contribute to discrimination, as well as, use the FORESEE to estimate the risk of upcoming samples.
Increasing numbers and improved overall survival of patients on kidney replacement therapy over the last decade in Europe: an ERA Registry study

ABSTRACT Background The aim of this study was to describe the trends in the incidence, prevalence and survival of patients on kidney replacement therapy (KRT) for end-stage kidney disease (ESKD) across Europe from 2008 to 2017. Methods Data from renal registries in 9 countries and 16 regions that provided individual patient data to the ERA Registry from 2008 to 2017 were included. These registries cover 34% of the general population in Europe. Crude and standardized incidence and prevalence per million population (pmp) were determined. Trends over time were studied using Joinpoint regression. Survival probabilities were estimated using Kaplan–Meier analysis and hazard ratios (HRs) using Cox regression analysis. Results The standardized incidence of KRT was stable [annual percentage change (APC): −1.48 (−3.15; 0.21)] from 2008 (146.0 pmp) to 2011 (141.6 pmp), followed by a slight increase [APC: 1.01 (0.43; 1.60)] to 148.0 pmp in 2017, although trends in incidence varied across countries. This increase was primarily due to a rise in the incidence of KRT in men older than 65 years. Moreover, as a cause of kidney failure, diabetes mellitus is increasing. The standardized prevalence increased from 2008 (990.0 pmp) to 2017 (1166.8 pmp) [APC: 1.82 (1.75; 1.89)]. Patient survival on KRT improved in the time period 2011–13 compared with 2008–[adjusted HR: 0.94 (0.93; 0.95)]. Conclusion This study showed an overall increase in the incidence and prevalence of KRT for ESKD as well as an increase in the KRT patient survival over the last decade in Europe.
Comparison of salivary interleukin-6, interleukin-8, C - reactive protein levels and total antioxidants capacity of obese individuals with normal-weight ones

Abstract Objective: Obesity is a worldwide concern that may lead to type 2 diabetes, cardiovascular diseases, etc. Several serum biomarkers have been identified in the saliva of obese individuals, including inflammatory cytokines, adipokines, insulin, and cortisol. The present study aimed to compare salivary interleukin-6 (IL-6), interleukin-8 (IL-8), C-reactive protein (CRP) levels and total antioxidants capacity (TAC) of obese individuals with normal-weighted ones. Methods: In this case–control study, 92 participants matched in terms of age and gender were placed into two groups according to the body mass index (BMI); case group: BMI>30 and control group: 18.5<BMI<24.99. Unstimulated saliva was collected. ELISA and FRAP method were used to determine IL-6, IL-8, CRP and TAC. Data was analyzed using SPSS 24, at the significant level of 0.05. Results: Mean salivary levels of IL-6 and IL-8 in the normal individuals were 53.36 and 421.25ng/mL, with 86.09 ng/mL and 510.19 ng/mL in obese individuals, respectively. There were significant differences in the mean salivary levels of IL-6 and IL-8 between two experimental groups. The mean salivary levels of CRP of control and case group was 2.84 and 2.63 ng/mL and the total salivary antioxidant levels in the normal and obese individuals were 0.29 and 0.36, respectively which had no significant different. Conclusion: According to the results of the present study, salivary levels IL-8 and IL-6 was significantly higher in obese individuals than in those with normal weight. However, the mean salivary CRP and TAC were not significantly different between the obese individuals and normal-weighted ones.