Miracle-dz/UniFAR-FTUs huggingface dataset:
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Background: Clinical factors associated with exclusion from recombinant tissue plasminogen activator in both men and women are not completely understood. The aim of this study is to determine whether there is a gender difference in clinical risk factors that excluded ischemic stroke patients with a history of smoking from recombinant tissue plasminogen activator. Methods: Retrospective data from a stroke registry were analyzed, and multivariable linear regression models were used to determine gender differences. Logistic regression models determined exclusion clinical risk factors for thrombolysis in male and female acute ischemic stroke patients with a history of smoking, while sequentially adjusting for sociodemographic, clinical, and stroke-related variables. The Kaplan–Meier survival analysis was used to determine the exclusion probabilities of men and women with a history of smoking within the stroke population. Results: Of the 1,446 acute ischemic stroke patients eligible for recombinant tissue plasminogen activator, 379 patients with a history of smoking were examined, of which 181 received recombinant tissue plasminogen activator while 198 were excluded from receiving recombinant tissue plasminogen activator. Of the 198 patients, 75 females and 123 males were excluded from receiving recombinant tissue plasminogen activator. After multivariable adjustment for age, National Institutes of Health scores, and stroke-related factors, females who present with weakness/paresis on initial examination (OR = 0.117, 95% CI, 0.025–0.548) and men who present with a history of previous transient ischemic attack (OR = 0.169, 95% CI, 0.044–0.655), antiplatelet medication use (OR = 0.456, 95% CI, 0.230–0.906), and weakness/ paresis on initial examination (OR = 0.171, 95% CI, 0.056–0.521) were less likely to be excluded from recombinant tissue plasminogen activator (thrombolysis therapy). Conclusions: In an ischemic stroke population with a history of smoking, female smokers are more likely to be excluded from thrombolysis therapy in comparison to men, even after adjustment for confounding variables.
Weight status and psychosomatic complaints in Swedish adolescent boys and girls: examining the buffering role of family support.

Introduction: Psychosomatic complaints have increased among adolescents in recent decades, as well as overweight and obesity rates, which have become a public health issue. The associations between weight status and psychosomatic complaints are not clear, therefore further research is needed. The aim of this study was to explore the associations between weight status and psychosomatic complaints among Swedish adolescents, and the possible buffering effect of family support. Methods: This study was based on cross-sectional data of the Swedish Health Behavior in School-aged Children (HBSC) of 2017/18, that included 3,133 boys and girls aged 11, 13 and 15 years. Weight status was based on self-reported information on weight and height, which was used to calculate body mass index (BMI) and construct the categories “non-overweight”, “overweight” and “obese”. Psychosomatic complaints were based on information on the frequency of eight different complaints, that was summed to an index. Family support was measured by three items that described the received support. An index was constructed which was dichotomized into two categories, low and high family support. Gender, age and family affluence were included as covariates. Linear regression analyses were performed to investigate the associations between weight status and psychosomatic complaints. Interaction analyses were used to see whether family support moderates the main association. Additional family support-and sex-stratified analyses were conducted. Results: The results revealed that obesity is associated with higher levels of psychosomatic complaints. Being non-overweight or overweight did not show any significant associations with the outcome. Girls reported higher levels of psychosomatic complaints compared to boys. Family support could buffer against obese girls’ psychosomatic complaints. Conclusions: The findings of this study highlight the importance of public health actions to prevent childhood obesity and additionally the significant role of family support in decreasing obese adolescents’ psychosomatic complaints. Furthermore, more research is needed for better understanding of these associations.
The evaluation of oxidative stress and inflammation markers in serum and saliva of the patients with temporomandibular disorders

Background/aim Temporomandibular Disorders (TMD), as in the occurrence of many diseases, have been associated with oxidative stress (OS) resulting from the disruption of antioxidant mechanisms and the accumulation of reactive oxygen species in tissues. This study was designed to compare salivary and serum OS and inflammation markers of individuals with TMD and healthy subjects. Materials and methods A prospective cross-sectional study was conducted. Twenty-seven TMD patients diagnosed with disc displacement (DD) according to Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD) and 17 healthy subjects were enrolled in the study. Prior to any treatment, serum, and saliva samples were taken from the patients and centrifuged, and stored at −80 °C until analyzed. All samples were examined for Interleukin-6 (IL-6), Malondialdehyde (MDA), and 8-hydroxydeoxyguanosine (8-OHdG) concentrations. Results There was no significant difference between the groups regarding median values of 8-OHdG, IL-6, and MDA (p > 0.05). When the relationship between serum and salivary 8-OHdG, IL-6, and MDA levels in all subjects was evaluated, there was a strong positive correlation between the levels of 8-OHdG and IL-6 in the serum (r = 0.752, p <0.001). In the study group, when the relationship between pain levels and serum and saliva 8-OHdG, IL-6, and MDA levels was assessed, a positive and strong correlation was found between the levels of 8-OHdG and IL-6 in serum. Conclusion Although the strong correlation between pain scores and serum 8-OHdG and MDA levels supports the hypothesis that inflammation and OS mechanisms may be interrelated, according to the results of the study, inflammatory and OS markers in patients with TMD were not different from healthy individuals.
Organic Farmers Speak on Economics and Beyond: A Nation Wide Survey of Farmers' Experiences in India

The India Organic Pathway - the organic strategy for India prepared by one of us (Tej Partap) and published by International Competence Centre for Organic Agriculture (ICCOA) in 2006, presented an overview of the developing organic movement and the strategy for India to mainstream it. The work aimed at building crucial information in the organic sector. However, the knowledge gap in the economics of organic farming was often quoted in every meeting. Caution was issued to not make big statements about promoting organic farming unless evidence was generated about the comparative economics of organic farming. To convince more people in the mainstreaming process, it was absolutely necessary that such knowledge and information was gathered. Therefore, under this backdrop the two of us met and held a series of discussions about the possibility of conducting a "Nation Wide Survey of Organic Farmers about their Economic, Ecological and other Experiences".
Faktor-faktor yang Mempengaruhi Kepuasan Pelanggan

This study aims to analyze the influence of product quality, price, and service quality on customer satisfaction at PT Kanefusa Indonesia. The population of this research consists of PT Kanefusa Indonesia's customers, with a sample size of 75 individuals. The data analysis method used is Multiple Linear Regression. The results of the study indicate that the simultaneous hypothesis test shows that the variables of product quality, price, and service quality significantly affect customer satisfaction. Additionally, the partial hypothesis test demonstrates that each variable, namely product, price, and service quality, significantly influences customer satisfaction. This research provides valuable insights for PT Kanefusa Indonesia in understanding the factors that affect customer satisfaction. By paying attention to and improving product quality, offering competitive prices, and providing excellent service quality, PT Kanefusa Indonesia can better meet customer expectations and strengthen long-term relationships with customers. The findings of this study can also serve as a basis for designing more effective marketing and management strategies to enhance customer loyalty and achieve a competitive advantage in their industry.
The Data Fusion and Semantic Segmentation Method Based on Multi-Sensor System

The design and implementation of a prototype environmental perception system are introduced, which consists of the multi-camera, LiDAR, and IMU sensor. The processing steps of the environmental data collected by the multi-sensor platform include joint calibration, data fusion, and semantic segmentation. First, the optimization method based on the epipolar constraint is proposed for the joint calibration of the multi-camera and the LiDAR system. In the data fusion section, an improved data association method for the point cloud is proposed where the foreground segmentation method is used to reduce scale estimation error due to the scale-sudden-change. Finally, the semantic segmentation method based on the deep-learning is proposed. The convolutional neural network based on the Squeeze-and-Excitation Net is designed to identify and classify the point cloud data accurately. The physical simulation result shows that the system collects and identifies the environmental information accurately.
Virtual human speech emotion recognition based on multi-channel CNN: MFCC, LPC, and F0 features

Convolutional neural networks (CNNs) have shown promise in virtual human speech emotion expression. Previous studies have utilized CNNs for speech emotion recognition and achieved good results. However, there are research gaps in avatar speech emotion expression, particularly concerning speaker characteristics, and limited available datasets. To address these issues, this paper collects and pre-processes speech data from multiple speakers, using features such as Mel Frequency Cepstral Coefficient (MFCC) and Linear Predictive Coding (LPC). A multi-channel CNN (MUC-CNN) model is designed to fuse different feature information and update model parameters using the Adam optimization algorithm. The model‘s performance is compared with classical methods like Support Vector Machine (SVM), Random Forest (RF), and k-Nearest Neighbors (k-NN) to determine its applicability and optimize its design and training process. Experimental evaluation shows that the MUC-CNN model outperforms classical methods in recognizing and expressing emotions in virtual human speech. By incorporating MFCC, LPC, and F0 features, the model‘s recognition capabilities are improved. The multi-channel architecture allows independent processing of each feature type, enhancing the model‘s discriminative aptitude. The performance of the model is influenced by the quantity of convolutional layers and kernels utilized. The outcomes highlight the effectiveness of the proposed MUC-CNN model for the recognition and expression of speech emotions in virtual human interactions. Future research can explore alternative feature information and refine the model architecture to further optimize performance. This technology has the potential to enhance user experience and interaction in various fields, including speech interaction, virtual reality, games, and education.
Trajectories of self-kindness, common humanity, and mindfulness during the COVID-19 pandemic: A person-oriented multi-trajectory approach

The COVID-19 pandemic has produced unprecedented changes in the lives of many people. Although research has documented associations between concerns related to COVID-19 and poor mental health indicators, fewer studies have focused on positive factors that could help people better cope with this stressful social context. To fill this gap, the present research investigated the trajectories of self-compassion facets in times of dramatic social change. Using a longitudinal research design, we described the trajectories of self-kindness, common humanity, and mindfulness during the first eight months of the COVID-19 pandemic, in a representative sample of Canadian adults (N = 3617). Relying on a multi-trajectory group-based approach, we identified clusters of individuals following persistently low (4.0%), moderate-low (39.3%), moderate-high (46.7%), and high (10.0%) levels of self-kindness, common humanity, and mindfulness. Interestingly, we found that compassionate self-responding trajectories were mainly stable over time with minor fluctuations for some groups of individuals, in line with the epidemiological situation. In terms of covariates, we observed that older women were more likely to follow trajectories of high compassionate self-responding, as compared to the other age and gender groups. In terms of mental health indicators, we demonstrated that trajectory groups with high levels of compassionate self-responding were associated with greater life satisfaction, more happiness, better sleep quality, higher sleep quantity, and fewer negative emotions, as compared to lower trajectory groups. The results supported the idea that self-compassion during the COVID-19 pandemic could have favored better mental health indicators and could possibly be promoted as a psychological intervention in the general population.
Conflict Detection for Temporal Knowledge Graphs: A Fast Constraint Mining Algorithm and New Benchmarks

Temporal facts, which are used to describe events that occur during specific time periods, have become a topic of increased interest in the field of knowledge graph (KG) research. In terms of quality management, the introduction of time restrictions brings new challenges to maintaining the temporal consistency of KGs. Previous studies rely on manually enumerated temporal constraints to detect conflicts, which are labor-intensive and may have granularity issues. To address this problem, we start from the common pattern of temporal facts and propose a pattern-based temporal constraint mining method, PaTeCon. Unlike previous studies, PaTeCon uses graph patterns and statistical information relevant to the given KG to automatically generate temporal constraints, without the need for human experts. In this paper, we illustrate how this method can be optimized to achieve significant speed improvement. We also annotate Wikidata and Freebase to build two new benchmarks for conflict detection. Extensive experiments demonstrate that our pattern-based automatic constraint mining approach is highly effective in generating valuable temporal constraints.
Delving Deeper Into Astromorphic Transformers

Preliminary attempts at incorporating the critical role of astrocytes - cells that constitute more than 50% of human brain cells - in brain-inspired neuromorphic computing remain in infancy. This paper seeks to delve deeper into various key aspects of neuron-synapse-astrocyte interactions to mimic self-attention mechanisms in Transformers. The cross-layer perspective explored in this work involves bio-plausible modeling of Hebbian and pre-synaptic plasticities in neuron-astrocyte networks, incorporating effects of non-linearities and feedback along with algorithmic formulations to map the neuron-astrocyte computations to self-attention mechanism and evaluating the impact of incorporating bio-realistic effects from the machine learning application side. Our analysis on sentiment and image classification tasks on the IMDB and CIFAR10 datasets underscores the importance of constructing Astromorphic Transformers from both accuracy and learning speed improvement perspectives.
Sharable Clothoid-based Continuous Motion Planning for Connected Automated Vehicles

A continuous motion planning method for connected automated vehicles is considered for generating feasible trajectories in real-time using three consecutive clothoids. The proposed method reduces path planning to a small set of nonlinear algebraic equations such that the generated path can be efficiently checked for feasibility and collision. After path planning, velocity planning is executed while maintaining a parallel simple structure. Key strengths of this framework include its interpretability, shareability, and ability to specify boundary conditions. Its interpretability and shareability stem from the succinct representation of the resulting local motion plan using a handful of physically meaningful parameters. Vehicles may share these parameters via V2X communication so that the recipients can precisely reconstruct the planned trajectory of the senders and respond accordingly. The proposed local planner guarantees the satisfaction of boundary conditions, thus ensuring seamless integration with a wide array of higher-level global motion planners. The tunable nature of the method enables tailoring the local plans to specific maneuvers like turns at intersections, lane changes, and U-turns.
Spherical Mask: Coarse-to-Fine 3D Point Cloud Instance Segmentation with Spherical Representation

Coarse-to-fine 3D instance segmentation methods show weak performances compared to recent Grouping-based, Kernel-based and Transformer-based methods. We argue that this is due to two limitations: 1) Instance size overestimation by axis-aligned bounding box(AABB) 2) False negative error accumulation from inaccurate box to the refinement phase. In this work, we introduce Spherical Mask, a novel coarse-to-fine approach based on spherical representation, overcoming those two limitations with several benefits. Specifically, our coarse detection estimates each instance with a 3D polygon using a center and radial distance predictions, which avoids excessive size estimation of AABB. To cut the error propagation in the existing coarse-to-fine approaches, we virtually migrate points based on the polygon, allowing all foreground points, including false negatives, to be refined. During inference, the proposal and point migration modules run in parallel and are assembled to form binary masks of instances. We also introduce two margin-based losses for the point migration to enforce corrections for the false positives/negatives and cohesion of foreground points, significantly improving the performance. Experimental results from three datasets, such as ScanNetV2, S3DIS, and STPLS3D, show that our proposed method outperforms existing works, demonstrating the effectiveness of the new instance representation with spherical coordinates.
GAvatar: Animatable 3D Gaussian Avatars with Implicit Mesh Learning

Gaussian splatting has emerged as a powerful 3D representation that harnesses the advantages of both explicit (mesh) and implicit (NeRF) 3D representations. In this paper, we seek to leverage Gaussian splatting to generate realistic animatable avatars from textual descriptions, addressing the limitations (e.g., flexibility and efficiency) imposed by mesh or NeRF-based representations. However, a naive application of Gaussian splatting cannot generate high-quality animatable avatars and suffers from learning instability; it also cannot capture fine avatar geometries and often leads to degenerate body parts. To tackle these problems, we first propose a primitive-based 3D Gaussian representation where Gaussians are defined inside pose-driven primitives to facilitate animation. Second, to stabilize and amortize the learning of millions of Gaussians, we propose to use neural implicit fields to predict the Gaussian attributes (e.g., colors). Finally, to capture fine avatar geometries and extract detailed meshes, we propose a novel SDF-based implicit mesh learning approach for 3D Gaussians that regularizes the underlying geometries and extracts highly detailed textured meshes. Our proposed method, GAvatar, enables the large-scale generation of diverse animatable avatars using only text prompts. GAvatar significantly surpasses existing methods in terms of both appearance and geometry quality, and achieves extremely fast rendering (100 fps) at 1K resolution.
Deep learning approaches for seizure video analysis: A review

Seizure events can manifest as transient disruptions in the control of movements which may be organized in distinct behavioral sequences, accompanied or not by other observable features such as altered facial expressions. The analysis of these clinical signs, referred to as semiology, is subject to observer variations when specialists evaluate video-recorded events in the clinical setting. To enhance the accuracy and consistency of evaluations, computer-aided video analysis of seizures has emerged as a natural avenue. In the field of medical applications, deep learning and computer vision approaches have driven substantial advancements. Historically, these approaches have been used for disease detection, classification, and prediction using diagnostic data; however, there has been limited exploration of their application in evaluating video-based motion detection in the clinical epileptology setting. While vision-based technologies do not aim to replace clinical expertise, they can significantly contribute to medical decision-making and patient care by providing quantitative evidence and decision support. Behavior monitoring tools offer several advantages such as providing objective information, detecting challenging-to-observe events, reducing documentation efforts, and extending assessment capabilities to areas with limited expertise. The main applications of these could be (1) improved seizure detection methods; (2) refined semiology analysis for predicting seizure type and cerebral localization. In this paper, we detail the foundation technologies used in vision-based systems in the analysis of seizure videos, highlighting their success in semiology detection and analysis, focusing on work published in the last 7 years. We systematically present these methods and indicate how the adoption of deep learning for the analysis of video recordings of seizures could be approached. Additionally, we illustrate how existing technologies can be interconnected through an integrated system for video-based semiology analysis. Each module can be customized and improved by adapting more accurate and robust deep learning approaches as these evolve. Finally, we discuss challenges and research directions for future studies.
Acausality in truncated 'MIS'-type theory

The causal-stable Muller-Israel-Stewart (MIS) theory is known to have finite numbers of out of equilibrium derivative order corrections but includes new degrees of freedom such as shear tensor apart from the fundamental fluid degrees of freedom like velocity and temperature. In this work, it has been argued that it is possible to rewrite the MIS theory only in terms of velocity and temperature, but the resultant constitutive relation for dissipation must include all orders of gradient corrections. It has been further shown that any finitely truncated order of derivative correction, however high it is, leads to a theory that is acausal, unless the corrections are infinitely summed up to all orders.