Miracle-dz/UniFAR-FTUs huggingface dataset:
Association between abdominal muscle stiffness, diaphragm thickness and peak expiratory flow in younger versus older adults

The present study aimed to evaluate forced expiration based on transverse abdominis (TrA) stiffness by identifying the relationship between TrA stiffness and peak expiratory flow (PEF) in both younger and older adults. We also assessed the relationship between diaphragm thickness and PEF. A total of 31 younger (21.24 ± 2.73 years) and 34 older (71.35 ± 5.26 years) adults were included in the present study. TrA muscle stiffness was measured at rest and during abdominal bracing using shear wave elastography. Diaphragm thickness was measured during deep inspiration and expiration using B‐mode ultrasound, and respiratory function was assessed by measuring PEF using a spirometer. We found that TrA stiffness during bracing was significantly lower in older than younger adults (p < 0.05). Similarly, the difference in absolute stiffness of the TrA when bracing versus at rest was significantly lower in older than younger adults (p < 0.05). Additionally, TrA stiffness during bracing was positively associated with PEF in the younger group (r = 0.483), while a very weak correlation was found in the older group (r = 0.172). Similarly, PEF was moderately correlated with diaphragm thickness during expiration as well as during changes between inspiration and expiration in the younger group (r = 0.405 and r = 0.403); however, no significant correlation was found in the older group. These findings of the present study indicate that the variations in PEF between younger and older adults may be due to age‐associated changes in the musculoskeletal structure and muscle fibre type.
MRI findings in movement disorders and associated sleep disturbances.

BACKGROUND One of the most useful tools for identifying sleep disturbances is neuroimaging, especially magnetic resonance imaging (MRI). This review research was to look at the role of MRI findings in movement disorders and sleep disturbances. METHODS This review collects all MRI data on movement disorders and sleep disruptions. Between 2000 and 2022, PubMed and Google Scholar were utilized to find original English publications and reviews. According to the inclusion and exclusion criteria, around 100 publications were included. We only looked at research that explored MRI modality together with movement problems, sleep disorders, and brain area involvement. Most of the information focuses on movement irregularities and sleep interruptions. RESULTS Movement disorders such as Parkinson's disease (PD), Huntington's disease (HD), neuromuscular diseases, rapid eye movement (REM) sleep behavior movement disorder (RBD), cerebellar movement disorders, and brainstem movement disorders are assessed using MRI-based neuroimaging techniques. Some of the brain areas were associated with disorders in movement abnormalities and related sleep disturbances. This review found that many people with mobility disorders also have sleep problems. Some brain areas' malfunctions may cause motor and sleep issues. CONCLUSION Neuroimaging helps us understand the sleep difficulties associated with movement disorders by examining the structural and functional implications of movement disorders and sleep disturbances.
Venous Thromboembolism in Pediatric Inflammatory Bowel Disease: A Scoping Review

Objectives: In addition to gastrointestinal symptoms, pediatric inflammatory bowel disease (pIBD) may present with extra-intestinal manifestations including venous thromboembolism (VTE). Prevention and treatment guidelines for VTE in pediatric patients are needed. In this scoping review, we sought to detail the available data on the prevention and management of VTE in pIBD. Methods: Using PRISMA extension for Scoping Reviews (PRISMA-ScR), we identified, screened, graded quality of, and analyzed, literature on VTE in pediatric IBD, published between 1967 and 2023. Results: Data were extracted from 107 studies (including 216 patients). IBD patients with VTE had a median age of 14 years. Children with VTE more frequently had ulcerative colitis (70%, n = 216), developed their VTE within the first year of IBD diagnosis (52%, n = 97), had recent steroid use (62%, n = 50), and had central venous catheters (38%, n = 42). Cerebral venous sinus thrombus was the most common VTE type (34% of all VTE). Testing for thrombophilia conditions was rarely available but 65% (n = 23) of subjects tested had elevated Factor VIII activity. While most patients made a full recovery, 5% (n = 11) died secondary to their VTE. Conclusions: While randomized clinical trials assessing interventions to prevent and treat VTE in pIBD would be ideal, the feasibility of doing such studies is low. However, there has been an increase in interest in this topic and an increase in literature over the past decade. As such, a consensus statement from a multidisciplinary group of experts based on available literature and clinical experience would be valuable for practicing clinicians.
Tea tree oil, a vibrant source of neuroprotection via neuroinflammation inhibition: a critical insight into repurposing Melaleuca alternifolia by unfolding its characteristics

Over the past few decades, complementary and alternative treatments have become increasingly popular worldwide. The purported therapeutic characteristics of natural products have come under increased scrutiny both in vitro and in vivo as part of efforts to legitimize their usage. One such product is tea tree oil (TTO), a volatile essential oil primarily obtained from the native Australian plant, Melaleuca alternifolia, which has diverse traditional and industrial applications such as topical preparations for the treatment of skin infections. Its anti-inflammatory-linked immunomodulatory actions have also been reported. This systematic review focuses on the anti-inflammatory effects of TTO and its main components that have shown strong immunomodulatory potential. An extensive literature search was performed electronically for data curation on worldwide accepted scientific databases, such as Web of Science, Google Scholar, PubMed, ScienceDirect, Scopus, and esteemed publishers such as Elsevier, Springer, Frontiers, and Taylor & Francis. Considering that the majority of pharmacological studies were conducted on crude oils only, the extracted data were critically analyzed to gain further insight into the prospects of TTO being used as a neuroprotective agent by drug formulation or dietary supplement. In addition, the active constituents contributing to the activity of TTO have not been well justified, and the core mechanisms need to be unveiled especially for anti-inflammatory and immunomodulatory effects leading to neuroprotection. Therefore, this review attempts to correlate the anti-inflammatory and immunomodulatory activity of TTO with its neuroprotective mechanisms.
Preliminary Experience with a Four-Lead Implantable Pulse Generator for Deep Brain Stimulation

Background: Implantable pulse generators (IPGs) store energy and deliver electrical impulses for deep brain stimulation (DBS) to treat neurological and psychiatric disorders. IPGs have evolved over time to meet the demands of expanding clinical indications and more nuanced therapeutic approaches. Objectives: The aim of this study was to examine the workflow of the first 4-lead IPG for DBS in patients with complex disease. Method: The engineering capabilities, clinical use cases, and surgical technique are described in a cohort of 12 patients with epilepsy, essential tremor, Parkinson’s disease, mixed tremor, and Tourette’s syndrome with comorbid obsessive-compulsive disorder between July 2021 and July 2022. Results: This system is a rechargeable 32-channel, 4-port system with independent current control that can be connected to 8 contact linear or directionally segmented leads. The system is ideal for patients with mixed disease or those with multiple severe symptoms amenable to >2 lead implantations. A multidisciplinary team including neurologists, radiologists, and neurosurgeons is necessary to safely plan the procedure. There were no serious intraoperative or postoperative adverse events. One patient required revision surgery for bowstringing. Conclusions: This new 4-lead IPG represents an important new tool for DBS surgery with the ability to expand lead implantation paradigms for patients with complex disease.
Computational ghost imaging of hot objects in long-wave infrared range

Ghost imaging (GI) is an intriguing imaging modality to obtain the object information from the correlation calculations of spatial intensity fluctuations. In this letter, we report the computational GI of hot objects in the long-wave infrared range both in experiment and simulation. Without employing an independent light source, we reconstruct thermal images of objects only based on the intensity correlations of their thermal radiation at room temperature. By comparing different GI reconstruction algorithms, we demonstrate that GI with compressive sensing can efficiently obtain the thermal object information only with a single-pixel infrared camera, which might be applied to night-vision, environmental sensing, military detection, etc.
Description of a new Pavlovskyella species (Acari: Argasidae) from Chile

Soft ticks (Argasidae) of the Pavlovskyella Pospelova-Shtrom subgenus are important vectors of relapsing fever spirochetes, which are agents of disease globally. South American representatives of the Pavlovskyella subgenus include 3 species: Ornithodoros (Pavlovskyella) brasiliensis Aragão, Ornithodoros (Pavlovskyella) furcosus Neumann, and Ornithodoros (Pavlovskyella) rostratus Aragão. Here, we describe a fourth species based on morphological and mitogenomic evidence of ticks collected in burrows of unknown hosts in central Chile. The larva of the new species separates from other South American soft ticks by the following combination of characters: 13 pairs of dorsolateral setae, dorsal plate hexagonal, hypostome blunt with denticles from apex almost to the base. Adults of this new species lack cheeks, possess a dorsoventral groove, and have humps, similar to O. (P.) brasiliensis; however, they lack bulging structures on the flanks of idiosoma. Moreover, females and males differ from O. (P.) rostratus by having 3 humps instead of spurs in tarsi I and from O. (P.) furcosus because of their smaller size and thinner anterior lip of the genital aperture in females. The phylogenetic analysis performed with mitogenomes of the Argasidae family depicts the new Pavlovskyella species from Chile in a monophyletic clade with other South American species in the subgenus, confirming a regional group.
Efficient GPU Implementation of Affine Index Permutations on Arrays

Optimal usage of the memory system is a key element of fast GPU algorithms. Unfortunately many common algorithms fail in this regard despite exhibiting great regularity in memory access patterns. In this paper we propose efficient kernels to permute the elements of an array. We handle a class of permutations known as Bit Matrix Multiply Complement (BMMC) permutations, for which we design kernels of speed comparable to that of a simple array copy. This is a first step towards implementing a set of array combinators based on these permutations.
Generic bidirectional typing for dependent type theories

Bidirectional typing is a discipline in which the typing judgment is decomposed explicitly into inference and checking modes, allowing to control the flow of type information in typing rules and to specify algorithmically how they should be used. Bidirectional typing has been fruitfully studied and bidirectional systems have been developed for many type theories. However, the formal development of bidirectional typing has until now been kept confined to specific theories, with general guidelines remaining informal. In this work, we give a generic account of bidirectional typing for a general class of dependent type theories. This is done by first giving a general definition of type theories (or equivalently, a logical framework), for which we define declarative and bidirectional type systems. We then show, in a theory-independent fashion, that the two systems are equivalent. This equivalence is then explored to establish the decidability of typing for weak normalizing theories, yielding a generic type-checking algorithm that has been implemented in a prototype and used in practice with many theories.
Identifying Direct Causes using Intervened Target Variable

Identifying the direct causes or causal parents of a target variable is crucial for scientific discovery. Focusing on linear models, the invariant prediction framework was built upon the invariance principle, namely, the conditional distribution of the target variable given its causal parents is invariant across multiple environments or experimental conditions. However, their identifiability results for causal parents can be restrictive with respect to the underlying graph structure and the experimental conditions for generating interventional data. Motivated by a recent alternative formulation of invariance, called the invariant matching property, we establish identifiability results under relatively mild assumptions, which leads to a simple yet effective procedure for identifying causal parents. We demonstrate the performance of the proposed method over various synthetic and real datasets.
Reconstruction of 3-Axis Seismocardiogram from Right-to-left and Head-to-foot Components Using A Long Short-Term Memory Network

This pilot study aims to develop a deep learning model for predicting seismocardiogram (SCG) signals in the dorsoventral direction from the SCG signals in the right-to-left and head-to-foot directions (SCGx and SCGy). The dataset used for the training and validation of the model was obtained from 15 healthy adult subjects. The SCG signals were recorded using tri-axial accelerometers placed on the chest of each subject. The signals were then segmented using electrocardiogram R waves, and the segments were downsampled, normalized, and centered around zero. The resulting dataset was used to train and validate a long short-term memory (LSTM) network with two layers and a dropout layer to prevent overfitting. The network took as input 100-time steps of SCGx and SCGy, representing one cardiac cycle, and outputted a vector that mapped to the target variable being predicted. The results showed that the LSTM model had a mean square error of 0.09 between the predicted and actual SCG segments in the dorsoventral direction. The study demonstrates the potential of deep learning models for reconstructing 3-axis SCG signals using the data obtained from dual-axis accelerometers.
SoccerKDNet: A Knowledge Distillation Framework for Action Recognition in Soccer Videos

Classifying player actions from soccer videos is a challenging problem, which has become increasingly important in sports analytics over the years. Most state-of-the-art methods employ highly complex offline networks, which makes it difficult to deploy such models in resource constrained scenarios. Here, in this paper we propose a novel end-to-end knowledge distillation based transfer learning network pre-trained on the Kinetics400 dataset and then perform extensive analysis on the learned framework by introducing a unique loss parameterization. We also introduce a new dataset named SoccerDB1 containing 448 videos and consisting of 4 diverse classes each of players playing soccer. Furthermore, we introduce an unique loss parameter that help us linearly weigh the extent to which the predictions of each network are utilized. Finally, we also perform a thorough performance study using various changed hyperparameters. We also benchmark the first classification results on the new SoccerDB1 dataset obtaining 67.20% validation accuracy. Apart from outperforming prior arts significantly, our model also generalizes to new datasets easily. The dataset has been made publicly available at: https://bit.ly/soccerdb1
Visual Analytics For Machine Learning: A Data Perspective Survey

The past decade has witnessed a plethora of works that leverage the power of visualization (VIS) to interpret machine learning (ML) models. The corresponding research topic, VIS4ML, keeps growing at a fast pace. To better organize the enormous works and shed light on the developing trend of VIS4ML, we provide a systematic review of these works through this survey. Since data quality greatly impacts the performance of ML models, our survey focuses specifically on summarizing VIS4ML works from the data perspective. First, we categorize the common data handled by ML models into five types, explain the unique features of each type, and highlight the corresponding ML models that are good at learning from them. Second, from the large number of VIS4ML works, we tease out six tasks that operate on these types of data (i.e., data-centric tasks) at different stages of the ML pipeline to understand, diagnose, and refine ML models. Lastly, by studying the distribution of 143 surveyed papers across the five data types, six data-centric tasks, and their intersections, we analyze the prospective research directions and envision future research trends.
Modular Denotational Semantics for Effects with Guarded Interaction Trees

We present guarded interaction trees — a structure and a fully formalized framework for representing higher-order computations with higher-order effects in Coq, inspired by domain theory and the recently proposed interaction trees. We also present an accompanying separation logic for reasoning about guarded interaction trees. To demonstrate that guarded interaction trees provide a convenient domain for interpreting higher-order languages with effects, we define an interpretation of a PCF-like language with effects and show that this interpretation is sound and computationally adequate; we prove the latter using a logical relation defined using the separation logic. Guarded interaction trees also allow us to combine different effects and reason about them modularly. To illustrate this point, we give a modular proof of type soundness of cross-language interactions for safe interoperability of different higher-order languages with different effects. All results in the paper are formalized in Coq using the Iris logic over guarded type theory.
For One-Shot Decoding: Self-supervised Deep Learning-Based Polar Decoder

We propose a self-supervised deep learning-based decoding scheme that enables one-shot decoding of polar codes. In the proposed scheme, rather than using the information bit vectors as labels for training the neural network (NN) through supervised learning as the conventional scheme did, the NN is trained to function as a bounded distance decoder by leveraging the generator matrix of polar codes through self-supervised learning. This approach eliminates the reliance on predefined labels, empowering the potential to train directly on the actual data within communication systems and thereby enhancing the applicability. Furthermore, computer simulations demonstrate that (i) the bit error rate (BER) and block error rate (BLER) performances of the proposed scheme can approach those of the maximum a posteriori (MAP) decoder for very short packets and (ii) the proposed NN decoder (NND) exhibits much superior generalization ability compared to the conventional one.
Well-posedness and stability of a stochastic neural field in the form of a partial differential equation

A system of partial differential equations representing stochastic neural fields was recently proposed with the aim of modelling the activity of noisy grid cells when a mammal travels through physical space. The system was rigorously derived from a stochastic particle system and its noise-driven pattern-forming bifurcations have been characterised. However, due to its nonlinear and non-local nature, standard well-posedness theory for smooth time-dependent solutions of parabolic equations does not apply. In this article, we transform the problem through a suitable change of variables into a nonlinear Stefan-like free boundary problem and use its representation formulae to construct local-in-time smooth solutions under mild hypotheses. Then, we prove that fast-decaying initial conditions and globally Lipschitz modulation functions imply that solutions are global-in-time. Last, we improve previous linear stability results by showing nonlinear asymptotic stability of stationary solutions under suitable assumptions.
Fast Algorithms for Energy Games in Special Cases

In this paper, we study algorithms for special cases of energy games, a class of turn-based games on graphs that show up in the quantitative analysis of reactive systems. In an energy game, the vertices of a weighted directed graph belong either to Alice or to Bob. A token is moved to a next vertex by the player controlling its current location, and its energy is changed by the weight of the edge. Given a fixed starting vertex and initial energy, Alice wins the game if the energy of the token remains nonnegative at every moment. If the energy goes below zero at some point, then Bob wins. The problem of determining the winner in an energy game lies in $\mathsf{NP} \cap \mathsf{coNP}$. It is a long standing open problem whether a polynomial time algorithm for this problem exists. We devise new algorithms for three special cases of the problem. The first two results focus on the single-player version, where either Alice or Bob controls the whole game graph. We develop an $\tilde{O}(n^\omega W^\omega)$ time algorithm for a game graph controlled by Alice, by providing a reduction to the All-Pairs Nonnegative Prefix Paths problem (APNP). Thus we study the APNP problem separately, for which we develop an $\tilde{O}(n^\omega W^\omega)$ time algorithm. For both problems, we improve over the state of the art of $\tilde O(mn)$ for small $W$. For the APNP problem, we also provide a conditional lower bound which states that there is no $O(n^{3-\epsilon})$ time algorithm for any $\epsilon>0$, unless the APSP Hypothesis fails. For a game graph controlled by Bob, we obtain a near-linear time algorithm. Regarding our third result, we present a variant of the value iteration algorithm, and we prove that it gives an $O(mn)$ time algorithm for game graphs without negative cycles, which improves a previous upper bound.
Leveraging Recommender Systems to Reduce Content Gaps on Peer Production Platforms

Peer production platforms like Wikipedia commonly suffer from content gaps. Prior research suggests recommender systems can help solve this problem, by guiding editors towards underrepresented topics. However, it remains unclear whether this approach would result in less relevant recommendations, leading to reduced overall engagement with recommended items. To answer this question, we first conducted offline analyses (Study 1) on SuggestBot, a task-routing recommender system for Wikipedia, then did a three-month controlled experiment (Study 2). Our results show that presenting users with articles from underrepresented topics increased the proportion of work done on those articles without significantly reducing overall recommendation uptake. We discuss the implications of our results, including how ignoring the article discovery process can artificially narrow recommendations. We draw parallels between this phenomenon and the common issue of"filter bubbles"to show how any platform that employs recommender systems is susceptible to it.