Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficacy of conventional rehabilitation, transcutaneous electrical nerve stimulation, or early mobilization to reverse acquired weakness in the intensive care unit. Randomized controlled pilot trial.

Objective: To compare the efficacy of conventional rehabilitation, transcutaneous electrical nerve stimulation, and early mobilization in reducing the time needed to reverse intensive care unit-acquired weakness (ICUAW), as assessed by the Medical Research Council muscle strength scale (MRC-MSS) score, in patients clinically diagnosed with ICUAW. Setting: Medical-surgical ICU of a general acute care hospital with 26 beds. Design: Pilot trial with random assignment and a control group. Interventions: Group 1: standard or routine rehabilitation, Group 2: transcutaneous electrical nerve stimulation, and Group 3: early mobilization protocol. Subjects: Individuals over 18 years old admitted to the ICU with invasive mechanical ventilation for more than 24 hours and diagnosed with ICUAW defined as Score MRC-MSS<48 measured over two consecutive days. Results: 18 subjects were included. One-way ANOVA showed differences in the treatment days needed to achieve an MRC-MSS [≥]48 (p = 0.01), as well as in the number of sessions required to reverse ICUAW (p = 0.01) when comparing the different interventions. Through post hoc analysis, the group treated with transcutaneous electrical nerve stimulation was identified as requiring significantly more days and sessions to reverse ICUAW. Differences were observed in the Barthel index at the end and in the variation of the Barthel index as a result of functional capacity among the different treatments. Conclusion: The group treated with transcutaneous electrical nerve stimulation required a significantly greater number of days and sessions to achieve an MRC-SS [≥]48. No differences in functional limitation were observed. These findings should be corroborated in similar studies. (Clinicaltrial.gov NCT04613908)
The psychedelic afterglow phenomenon: a systematic review of subacute effects of classic serotonergic psychedelics

Background: Classic serotonergic psychedelics have anecdotally been reported to show a characteristic pattern of subacute effects that persist after the acute effects of the substance have subsided. These transient effects, sometimes labeled as the ‘psychedelic afterglow’, have been suggested to be associated with enhanced effectiveness of psychotherapeutic interventions in the subacute period. Objectives: This systematic review provides an overview of subacute effects of psychedelics. Methods: Electronic databases (MEDLINE, Web of Science Core Collection) were searched for studies that assessed the effects of psychedelics (LSD, psilocybin, DMT, 5-MeO-DMT, mescaline, or ayahuasca) on psychological outcome measures and subacute adverse effects in human adults between 1950 and August 2021, occurring between 1 day and 1 month after drug use. Results: Forty-eight studies including a total number of 1,774 participants were eligible for review. Taken together, the following subacute effects were observed: reductions in different psychopathological symptoms; increases in wellbeing, mood, mindfulness, social measures, spirituality, and positive behavioral changes; mixed changes in personality/values/attitudes, and creativity/flexibility. Subacute adverse effects comprised a wide range of complaints, including headaches, sleep disturbances, and individual cases of increased psychological distress. Discussion: Results support narrative reports of a subacute psychedelic ‘afterglow’ phenomenon comprising potentially beneficial changes in the perception of self, others, and the environment. Subacute adverse events were mild to severe, and no serious adverse events were reported. Many studies, however, lacked a standardized assessment of adverse effects. Future studies are needed to investigate the role of possible moderator variables and to reveal if and how positive effects from the subacute window may consolidate into long-term mental health benefits.
Graph2topic: an opensource topic modeling framework based on sentence embedding and community detection

It has been reported that clustering-based topic models, which cluster high-quality sentence embeddings with an appropriate word selection method, can generate better topics than generative probabilistic topic models. However, these approaches suffer from the inability to select appropriate parameters and incomplete models that overlook the quantitative relation between words with topics and topics with text. To solve these issues, we propose graph to topic (G2T), a simple but effective framework for topic modelling. The framework is composed of four modules. First, document representation is acquired using pretrained language models. Second, a semantic graph is constructed according to the similarity between document representations. Third, communities in document semantic graphs are identified, and the relationship between topics and documents is quantified accordingly. Fourth, the word--topic distribution is computed based on a variant of TFIDF. Automatic evaluation suggests that G2T achieved state-of-the-art performance on both English and Chinese documents with different lengths.
The Role of an Exoskeleton Simulation of Senescence in Health Sciences Education

Background Education in the formation of human capital in health constantly presents challenges. New tools in the emerging contexts may strengthen empathic attitudes. We developed an educational intervention that included a senescence simulator and assessed its impact on perception and attitudes in healthcare students. Methods A cross-sectional comparative study was conducted that assessed acquired knowledge and self-perception using a semistructured survey administered before and after the demonstration and intervention using the simulator and reported the experience through the role of the patient and caregiver. The data were analyzed statistically to identify the demographic characteristics and differences between the groups of students. The data were analyzed statistically to identify the demographic characteristics and differences between the groups of students in the responses pre-post intervention, using statistical software (IBM SPSS Statistics 26.0). Results Of the 256 participants surveyed before the intervention, 93.8% described cognitive deterioration as a significant disability and 53.1% considered the health system to be inadequate in meeting the needs of older individuals. Only 59.8% stated that the current academic training meets the educational requirements for the care of the elderly. In total, 98.9% of the participants reported that the simulator changed their perception by increasing their empathy. In total, 76.2% showed greater sensitivity to older adults and 79.3% reported that the experiential learning consolidated their professionalizing perspective. Among the younger participants (aged 18–20 years), sensitivity and reorientation toward pursuing an associated graduate degree were higher after the intervention (p=0.01). Conclusions Educational strategies, such as the senescence simulator, offer an experiential intervention that strengthens the knowledge and attitudes toward older individuals. During the pandemic emergency, it proved to be a useful educational strategy in consolidating caring behavior as a hybrid educational tactic. The senescence simulation enabled the participants to widen their educational and professional schemes to encompass the care of the older population.
Clustering Undergraduate Students Based on Academic Burnout and Satisfaction from the Field Using Partitioning around Medoid

Background Academic satisfaction is known as one of the most important factors in increasing students' efficiency, and academic burnout is one of the most significant challenges of the educational system, reducing student motivation and enthusiasm. Clustering methods try to categorize individuals into a number of homogenous groups. Aims To cluster undergraduate students at Shahrekord University of Medical Sciences based on academic burnout and satisfaction with their field of study. Materials and Methods The multistage cluster sampling method was used to select 400 undergraduate students from various fields in 2022. The data collection tool included a 15-item academic burnout questionnaire and a 7-item academic satisfaction questionnaire. The average silhouette index was used to estimate the number of optimal clusters. The NbClust package in R 4.2. 1 software was used for clustering analysis based on the k-medoid approach. Results The mean score of academic satisfaction was 17.70 ± 5.39, while academic burnout averaged 37.90 ± 13.27. The optimal number of clusters was estimated at two based on the average silhouette index. The first cluster included 221 students, and the second cluster included 179 students. Students in the second cluster had higher levels of academic burnout than the first cluster. Conclusion It is suggested that university officials take measures to reduce the level of academic burnout through academic burnout training workshops led by consultants to promote the students' interests.
A Review Paper about Deep Learning for Medical Image Analysis

Medical imaging refers to the process of obtaining images of internal organs for therapeutic purposes such as discovering or studying diseases. The primary objective of medical image analysis is to improve the efficacy of clinical research and treatment options. Deep learning has revamped medical image analysis, yielding excellent results in image processing tasks such as registration, segmentation, feature extraction, and classification. The prime motivations for this are the availability of computational resources and the resurgence of deep convolutional neural networks. Deep learning techniques are good at observing hidden patterns in images and supporting clinicians in achieving diagnostic perfection. It has proven to be the most effective method for organ segmentation, cancer detection, disease categorization, and computer-assisted diagnosis. Many deep learning approaches have been published to analyze medical images for various diagnostic purposes. In this paper, we review the work exploiting current state-of-the-art deep learning approaches in medical image processing. We begin the survey by providing a synopsis of research works in medical imaging based on convolutional neural networks. Second, we discuss popular pretrained models and general adversarial networks that aid in improving convolutional networks' performance. Finally, to ease direct evaluation, we compile the performance metrics of deep learning models focusing on COVID-19 detection and child bone age prediction.
ALGORITHMS FOR BUILDING CONCEPT SETS AND CONCEPT LATTICES

In our days there is an increasing interest on the application of concept lattices for data mining, especially for generating association rules. The building of concept lattice consists of two, usually distinct phases. In the first phase the set of concepts is generated. The lattice is built in the second phase from the generated set. The paper gives an overview of the available methods and presents a proposed method for contexts of large size where the full context can not be stored in the main memory and some objects may be repeated in the context several times. The proposed algorithm for concept set generation is a fine-tuned version of the incremental concept set building method. At the end of the paper, the test results for comparing the new method with some known methods are given. The proposed method yields in a significantly better cost value than the other methods under the assumed conditions.
Microbiological Indoor and Outdoor Air Quality in Chicken Fattening Houses

This study was conducted at one of the largest poultry companies in Kuwait during November and December 2019 to evaluate the microbiological threats of Escherichia coli (APEC), Salmonella spp. , and Aspergillus fumigatus to chickens in fattening houses by counting and identifying the microorganisms by culturing and pyrosequencing analysis. During the fattening cycle, the temperature and humidity ranged between 23.6°C and 29°C and 64.1% and 87.1%, respectively. The total bacterial population and Aspergillus fumigatus measured in the indoor and outdoor air exhibited a linear relationship during the fattening cycle. The total bacterial and Aspergillus concentrations determined during the cycle ranged between 150 and 2000 CFU/m3 and 0 and 1000 CFU/m3, respectively. E. coli and Salmonella spp. concentrations determined during the cycle ranged between 1 and 220 CFU/m3 and 4 and 110 CFU/m3, respectively. Pyrosequencing analysis of the air inside the houses at the end of the cycle revealed extensive biodiversity in the microorganisms, detecting 32 bacterial genera and 14 species. The identified species belonging to the genera Corynebacterium, Haemophilus, Streptococcus, Veillonella, and Aspergillus were identified as potentially affecting human and broiler health. The emission of potentially pathogenic bacteria to the outdoor environment from chicken housing can pose a considerable risk to human health and environmental microbial pollution. This study could guide the development of integrated control devices for monitoring microbes in broiler production facilities during chicken collection for transport to slaughterhouses.
STING is redundant for host defense and pathology of COVID-19-like disease in mice

STING-deficient mice infected with SARS-CoV-2 do not show altered disease development or viral load, nor any major differences in inflammatory responses. Critical COVID-19 is characterized by lack of early type I interferon-mediated host defense and subsequent hyper-inflammation in the lungs. Aberrant activation of macrophages and neutrophils has been reported to lead to excessive activation of innate immunological pathways. It has recently been suggested that the DNA-sensing cGAS–STING pathway drives pathology in the SARS-CoV-2–infected lungs, but mechanistic understanding from in vivo models is needed. Here, we tested whether STING is involved in COVID-19-like disease using the K18-hACE2 mouse model. We report that disease development after SARS-CoV-2 infection is unaltered in STING-deficient K18-hACE2 mice. In agreement with this, STING deficiency did not affect control of viral replication or production of interferons and inflammatory cytokines. This was accompanied by comparable profiles of infiltrating immune cells into the lungs of infected mice. These data do not support a role for STING in COVID-19 pathology and calls for further investigation into the pathogenesis of critical COVID-19.
ESL-SNNs: An Evolutionary Structure Learning Strategy for Spiking Neural Networks

Spiking neural networks (SNNs) have manifested remarkable advantages in power consumption and event-driven property during the inference process. To take full advantage of low power consumption and improve the efficiency of these models further, the pruning methods have been explored to find sparse SNNs without redundancy connections after training. However, parameter redundancy still hinders the efficiency of SNNs during training. In the human brain, the rewiring process of neural networks is highly dynamic, while synaptic connections maintain relatively sparse during brain development. Inspired by this, here we propose an efficient evolutionary structure learning (ESL) framework for SNNs, named ESL-SNNs, to implement the sparse SNN training from scratch. The pruning and regeneration of synaptic connections in SNNs evolve dynamically during learning, yet keep the structural sparsity at a certain level. As a result, the ESL-SNNs can search for optimal sparse connectivity by exploring all possible parameters across time. Our experiments show that the proposed ESL-SNNs framework is able to learn SNNs with sparse structures effectively while reducing the limited accuracy. The ESL-SNNs achieve merely 0.28% accuracy loss with 10% connection density on the DVS-Cifar10 dataset. Our work presents a brand-new approach for sparse training of SNNs from scratch with biologically plausible evolutionary mechanisms, closing the gap in the expressibility between sparse training and dense training. Hence, it has great potential for SNN lightweight training and inference with low power consumption and small memory usage.
AHA!: Facilitating AI Impact Assessment by Generating Examples of Harms

While demands for change and accountability for harmful AI consequences mount, foreseeing the downstream effects of deploying AI systems remains a challenging task. We developed AHA! (Anticipating Harms of AI), a generative framework to assist AI practitioners and decision-makers in anticipating potential harms and unintended consequences of AI systems prior to development or deployment. Given an AI deployment scenario, AHA! generates descriptions of possible harms for different stakeholders. To do so, AHA! systematically considers the interplay between common problematic AI behaviors as well as their potential impacts on different stakeholders, and narrates these conditions through vignettes. These vignettes are then filled in with descriptions of possible harms by prompting crowd workers and large language models. By examining 4113 harms surfaced by AHA! for five different AI deployment scenarios, we found that AHA! generates meaningful examples of harms, with different problematic AI behaviors resulting in different types of harms. Prompting both crowds and a large language model with the vignettes resulted in more diverse examples of harms than those generated by either the crowd or the model alone. To gauge AHA! 's potential practical utility, we also conducted semi-structured interviews with responsible AI professionals (N=9). Participants found AHA! 's systematic approach to surfacing harms important for ethical reflection and discovered meaningful stakeholders and harms they believed they would not have thought of otherwise. Participants, however, differed in their opinions about whether AHA! should be used upfront or as a secondary-check and noted that AHA! may shift harm anticipation from an ideation problem to a potentially demanding review problem. Drawing on our results, we discuss design implications of building tools to help practitioners envision possible harms.
On the complexity of isomorphism problems for tensors, groups, and polynomials III: actions by classical groups

We study the complexity of isomorphism problems for d-way arrays, or tensors, under natural actions by classical groups such as orthogonal, unitary, and symplectic groups. Such problems arise naturally in statistical data analysis and quantum information. We study two types of complexity-theoretic questions. First, for a fixed action type (isomorphism, conjugacy, etc.), we relate the complexity of the isomorphism problem over a classical group to that over the general linear group. Second, for a fixed group type (orthogonal, unitary, or symplectic), we compare the complexity of the decision problems for different actions. Our main results are as follows. First, for orthogonal and symplectic groups acting on 3-way arrays, the isomorphism problems reduce to the corresponding problem over the general linear group. Second, for orthogonal and unitary groups, the isomorphism problems of five natural actions on 3-way arrays are polynomial-time equivalent, and the d-tensor isomorphism problem reduces to the 3-tensor isomorphism problem for any fixed d>3. For unitary groups, the preceding result implies that LOCC classification of tripartite quantum states is at least as difficult as LOCC classification of d-partite quantum states for any d. Lastly, we also show that the graph isomorphism problem reduces to the tensor isomorphism problem over orthogonal and unitary groups.
Single-Shot Global Localization via Graph-Theoretic Correspondence Matching

This paper describes a method of global localization based on graph-theoretic association of instances between a query and the prior map. The proposed framework employs correspondence matching based on the maximum clique problem (MCP). The framework is potentially applicable to other map and/or query modalities thanks to the graph-based abstraction of the problem, while many of existing global localization methods rely on a query and the dataset in the same modality. We implement it with a semantically labeled 3D point cloud map, and a semantic segmentation image as a query. Leveraging the graph-theoretic framework, the proposed method realizes global localization exploiting only the map and the query. The method shows promising results on multiple large-scale simulated maps of urban scenes.
VR.net: A Real-world Dataset for Virtual Reality Motion Sickness Research

Researchers have used machine learning approaches to identify motion sickness in VR experience. These approaches would certainly benefit from an accurately labeled, real-world, diverse dataset that enables the development of generalizable ML models. We introduce 'VR.net', a dataset comprising 165-hour gameplay videos from 100 real-world games spanning ten diverse genres, evaluated by 500 participants. VR.net accurately assigns 24 motion sickness-related labels for each video frame, such as camera/object movement, depth of field, and motion flow. Building such a dataset is challenging since manual labeling would require an infeasible amount of time. Instead, we implement a tool to automatically and precisely extract ground truth data from 3D engines' rendering pipelines without accessing VR games' source code. We illustrate the utility of VR.net through several applications, such as risk factor detection and sickness level prediction. We believe that the scale, accuracy, and diversity of VR.net can offer unparalleled opportunities for VR motion sickness research and beyond.We also provide access to our data collection tool, enabling researchers to contribute to the expansion of VR.net.
Exact controllability of incompressible ideal magnetohydrodynamics in $2$D

This work examines the controllability of planar incompressible ideal magnetohydrodynamics (MHD). Interior controls are obtained for problems posed in doubly-connected regions; simply-connected configurations are driven by boundary controls. Up to now, only straight channels regulated at opposing walls have been studied. Hence, the present program adds to the literature an exploration of interior controllability, extends the known boundary controllability results, and contributes ideas for treating general domains. To transship obstacles stemming from the MHD coupling and the magnetic field topology, a divide-and-control strategy is proposed. This leads to a family of nonlinear velocity-controlled sub-problems which are solved using J.-M. Coron's return method. The latter is here developed based on a reference trajectory in the domain's first cohomology space.