Miracle-dz/UniFAR-FTUs huggingface dataset:
Emotion recognition using spatial-temporal EEG features through convolutional graph attention network

Objective. Constructing an efficient human emotion recognition model based on electroencephalogram (EEG) signals is significant for realizing emotional brain–computer interaction and improving machine intelligence. Approach. In this paper, we present a spatial-temporal feature fused convolutional graph attention network (STFCGAT) model based on multi-channel EEG signals for human emotion recognition. First, we combined the single-channel differential entropy (DE) feature with the cross-channel functional connectivity (FC) feature to extract both the temporal variation and spatial topological information of EEG. After that, a novel convolutional graph attention network was used to fuse the DE and FC features and further extract higher-level graph structural information with sufficient expressive power for emotion recognition. Furthermore, we introduced a multi-headed attention mechanism in graph neural networks to improve the generalization ability of the model. Main results. We evaluated the emotion recognition performance of our proposed model on the public SEED and DEAP datasets, which achieved a classification accuracy of 99.11% ± 0.83% and 94.83% ± 3.41% in the subject-dependent and subject-independent experiments on the SEED dataset, and achieved an accuracy of 91.19% ± 1.24% and 92.03% ± 4.57% for discrimination of arousal and valence in subject-independent experiments on DEAP dataset. Notably, our model achieved state-of-the-art performance on cross-subject emotion recognition tasks for both datasets. In addition, we gained insight into the proposed frame through both the ablation experiments and the analysis of spatial patterns of FC and DE features. Significance. All these results prove the effectiveness of the STFCGAT architecture for emotion recognition and also indicate that there are significant differences in the spatial-temporal characteristics of the brain under different emotional states.
A Step Beyond Kneser's Theorem for Abelian Finite Groups

A precise description of a subset A of Z / n Z satisfying | A + A | ⩽ 2.04 | A | is given. Basically, there exists a subgroup H of Z / n Z such that A is included in an arithmetic progression of l cosets modulo H and (l − 1) | H | ⩽ | A + A | − | A |. 2000 Mathematical Subject Classification: 11B50, 11B83, 20E34.
Effectiveness of acupuncture for fatigue in patients with relapsing-remitting multiple sclerosis: a randomized controlled trial

Background: Fatigue is experienced by more than 65% of individuals with multiple sclerosis (MS). Some studies have supported the effectiveness of acupuncture in improving the symptoms of MS. Objective: The present research was intended to investigate the effectiveness of acupuncture plus amantadine compared with amantadine alone on fatigue in patients with relapsing-remitting MS (RRMS) in the remission stage of the disease. Methods: In this randomized controlled trial, 60 participants with RRMS suffering from fatigue were recruited and randomized equally to acupuncture (n = 30) and control (n = 30) groups. The acupuncture group received treatment 2 to 3 times per week for 10 sessions over 4 weeks. Both the acupuncture and control groups received amantadine 100 mg daily and routine treatment with immuno-modulators. The primary outcome was the fatigue severity scale (FSS) score, which was evaluated at baseline, and after 2 and 4 weeks. The secondary outcome was the Multiple Sclerosis Quality of Life 54 (MSQOL-54) questionnaire score, measured at baseline and the end of the 4-week treatment period. Results: The severity of fatigue was reduced in both groups. However, after 4 weeks of treatment, the reduction of fatigue in the acupuncture group was more significant than in the control group (P < 0.01, mean difference (MD) = −1.14, 95% confidence interval (CI): −1.83 to −0.45). Quality of life, including mental and physical status, was significantly improved in the acupuncture group compared with the control group (P < 0.05, MD = 9.09, 95% CI: 0.46 to 17.73). No adverse events occurred in any of the participants. Conclusions: Acupuncture combined with amantadine and routine care compared with amantadine and routine care alone appears to be an effective short-term treatment for reducing fatigue and enhancing quality of life, including physical function and mental status, in patients with RRMS.
Relationship Between Pitch Counts and Shoulder Strength in Collegiate Baseball Players

Abstract Gagnon, A, Payette, J, Park, M, Juras, K, and Slater, L. Relationship between pitch counts and shoulder strength in collegiate baseball players. J Strength Cond Res 37(9): 1860–1864, 2023—Large volumes of throwing leads to changes in shoulder strength that have been associated with increased risk of injury. The purpose of this study was to investigate the relationship between throwing volume and eccentric shoulder strength. Twelve NCAA Division I baseball pitchers volunteered to participate. Prepractice and postpractice eccentric shoulder strength was measured using a handheld dynamometer at 30°/second. Change in strength was calculated as the prepractice strength measure subtracted from the postpractice strength measure. After initial testing, subjects self-reported pitch counts before completing postpractice strength testing. Subjects were separated into high-strength and low-strength groups based on the median external rotation strength. Paired t-tests were used to compare change in shoulder strength and Pearson's r correlations were used to quantify the association between pitches and change in strength. There were no changes between prestrength and poststrength assessments in external rotation strength or internal rotation strength for the entire group. When divided into strength groups, correlations between strength and total pitches were nonsignificant for the high-strength group but were strong for both external rotation (r = −0.87, p = 0.025) and internal rotation (r = −0.91, p = 0.013) in the low-strength group. Pitchers with lower shoulder eccentric strength at baseline demonstrated greater declines in strength, whereas pitchers with higher shoulder eccentric strength did not demonstrate changes in strength. Changes in strength during practice could lead to compensatory movement that may predispose athletes to injury. Sport performance professionals may benefit from obtaining eccentric shoulder strength to assist with performance periodization and rehabilitation after injury.
A patient-centered comparative effectiveness research study of culturally appropriate options for diabetes self-management

This project compared the effectiveness of two evidence-based models of culturally competent diabetes health promotion: The Diabetes Self-Management Support Empowerment Model (DSMS), and The Chronic Care Model (CCM). Our primary outcome was improvement in patient capacity for diabetes self-management as measured by the Diabetes Knowledge Questionnaire (DKQ) and the Patient Activation Measure (PAM). Our secondary outcome was patient success at diabetes self-management as measured by improvement in A1c, depression sores using the PHQ-9, and Body Mass Index (BMI). We also gathered data on the cultural competence of the program using the Consumer Assessment of Healthcare Providers and Systems Cultural Competence Set (CAHPS-CC). We compared patient outcomes in two existing sites in Albuquerque, New Mexico that serve a large population of Latino diabetes patients from low-income households. Participants were enrolled as dyads--a patient participant (n=226) and a social support participant (n=226). Outcomes over time and by program were analyzed using longitudinal linear mixed modeling, adjusted for patient participant demographic characteristics and other potential confounding covariates. Secondary outcomes were also adjusted for potential confounders. Interactions with both time and program helped to assess outcomes. This study did not find a difference between the two sites with respect to the primary outcome measures and only one of the three secondary outcomes showed differential results. The main difference between programs was that depression decreased more for CCM than for DSMS. An exploratory, subgroup analysis revealed that at CCM, patient participants with a very high A1c (>10) demonstrated a clinically meaningful decrease. However, given the higher cultural competence rating for the CCM, statistically significant improvement in depression, and the importance of social support to the patients, results suggest that a culturally and contextually situated diabetes self-management and education program design may deliver benefit for patients, especially for patients with higher A1c levels.
An Expert-guided Hierarchical Graph Attention Network for Post-traumatic Stress Disorder Highlyassociative Genetic Biomarkers Identification

Post-traumatic Stress Disorder (PTSD) is a common debilitating mental disorder, that occurs in some individuals following extremely traumatic events. Traditional identification of Genetic Markers (GM) for PTSD is mainly based on a statistical clinical approach by comparing PTSD patients with normal controls. However, these statistical studies present limitations, often generating inconsistent results. Few studies have yet examined thoroughly the role of somatic mutations, PTSD disease pathways and their relationships. Capitalizing on deep learning techniques, we have developed a novel hierarchical graph attention network to identify highly correlational GM (HGMs) of PTSD. The network presents the following novelties: First, both a hierarchical graph structure and a graph attention mechanism have been integrated into a model to develop a graph attention network (GAtN) model. Second, domain-specific knowledge, including somatic mutations, genes, PTSD pathways and their correlations have been incorporated into the graph structures. Third, 12 somatic mutations having high or moderate impacts on proteins or genes have been identified as the potential HGMs for PTSD. Fourth, our study is carefully guided by prominent PTSD literature or clinical experts of the field; any high saliency HGMs generated from our model are further verified by existing PTSD-related authoritative medical journals. Our study illustrates the utility and significance of a hybrid approach, integrating both AI and expert-guided/domain-specific knowledge for thorough identification of biomarkers of PTSD, while building on the nature of convergence and divergence of PTSD pathways. Our expert-guided AI-driven methodology can be extended to other pathological-based HGM identification studies; it will transform the methodology of biomarker identification for different life-threatening diseases to speed up the complex lengthy procedures of new biomarker identification.
End-to-End Full-Atom Antibody Design

Antibody design is an essential yet challenging task in various domains like therapeutics and biology. There are two major defects in current learning-based methods: 1) tackling only a certain subtask of the whole antibody design pipeline, making them suboptimal or resource-intensive. 2) omitting either the framework regions or side chains, thus incapable of capturing the full-atom geometry. To address these pitfalls, we propose dynamic Multi-channel Equivariant grAph Network (dyMEAN), an end-to-end full-atom model for E(3)-equivariant antibody design given the epitope and the incomplete sequence of the antibody. Specifically, we first explore structural initialization as a knowledgeable guess of the antibody structure and then propose shadow paratope to bridge the epitope-antibody connections. Both 1D sequences and 3D structures are updated via an adaptive multi-channel equivariant encoder that is able to process protein residues of variable sizes when considering full atoms. Finally, the updated antibody is docked to the epitope via the alignment of the shadow paratope. Experiments on epitope-binding CDR-H3 design, complex structure prediction, and affinity optimization demonstrate the superiority of our end-to-end framework and full-atom modeling.
Ignore, Trust, or Negotiate: Understanding Clinician Acceptance of AI-Based Treatment Recommendations in Health Care

Artificial intelligence (AI) in healthcare has the potential to improve patient outcomes, but clinician acceptance remains a critical barrier. We developed a novel decision support interface that provides interpretable treatment recommendations for sepsis, a life-threatening condition in which decisional uncertainty is common, treatment practices vary widely, and poor outcomes can occur even with optimal decisions. This system formed the basis of a mixed-methods study in which 24 intensive care clinicians made AI-assisted decisions on real patient cases. We found that explanations generally increased confidence in the AI, but concordance with specific recommendations varied beyond the binary acceptance or rejection described in prior work. Although clinicians sometimes ignored or trusted the AI, they also often prioritized aspects of the recommendations to follow, reject, or delay in a process we term “negotiation. ” These results reveal novel barriers to adoption of treatment-focused AI tools and suggest ways to better support differing clinician perspectives.
You are a Bot! - Studying the Development of Bot Accusations on Twitter

The characterization and detection of bots with their presumed ability to manipulate society on social media platforms have been subject to many research endeavors over the last decade. In the absence of ground truth data (i.e., accounts that are labeled as bots by experts or self-declare their automated nature), researchers interested in the characterization and detection of bots may want to tap into the wisdom of the crowd. But how many people need to accuse another user as a bot before we can assume that the account is most likely automated? And more importantly, are bot accusations on social media at all a valid signal for the detection of bots? Our research presents the first large-scale study of bot accusations on Twitter and shows how the term bot became an instrument of dehumanization in social media conversations since it is predominantly used to deny the humanness of conversation partners. Consequently, bot accusations on social media should not be naively used as a signal to train or test bot detection models.
Free Lunch for Domain Adversarial Training: Environment Label Smoothing

A fundamental challenge for machine learning models is how to generalize learned models for out-of-distribution (OOD) data. Among various approaches, exploiting invariant features by Domain Adversarial Training (DAT) received widespread attention. Despite its success, we observe training instability from DAT, mostly due to over-confident domain discriminator and environment label noise. To address this issue, we proposed Environment Label Smoothing (ELS), which encourages the discriminator to output soft probability, which thus reduces the confidence of the discriminator and alleviates the impact of noisy environment labels. We demonstrate, both experimentally and theoretically, that ELS can improve training stability, local convergence, and robustness to noisy environment labels. By incorporating ELS with DAT methods, we are able to yield state-of-art results on a wide range of domain generalization/adaptation tasks, particularly when the environment labels are highly noisy.
Contribution to the Theory of Sampling Human Populations

AT A CONFERENCE on Sampling Human Populations held last April A at the Department of Agriculture Graduate School in Washington, a problem was presented by Mr. Milton Friedman and Dr. Sidney Wilcox for which I could not offer a solution at the time. Since it seemed to be important and of general interest, I have considered it in some detail. The purpose of this paper is to present the results I have obtained. 2. STATEMENT OF THE PROBLEM
Ionic liquid-electrode interface: classification of ions, saturation of layers, and structure-determined potentials

With a single-electrode model, we investigated surface charge screening at an ionic liquid–electrode interface. Using the obtained geometric packing densities, we estimated structure-determined potentials and capacitance values for 40 studied ions. By comparing the predictions with the empirical data, we show that exact surface charge screening by a monolayer of counter-ions (as well as crowding of ions) is improbable for common ions within their electrochemical stability window. We also predict that theoretically such a screening state is possible for frisbee-shape ions.
On the Role of Morphological Information for Contextual Lemmatization

Lemmatization is a natural language processing (NLP) task which consists of producing, from a given inflected word, its canonical form or lemma. Lemmatization is one of the basic tasks that facilitate downstream NLP applications, and is of particular importance for high-inflected languages. Given that the process to obtain a lemma from an inflected word can be explained by looking at its morphosyntactic category, including fine-grained morphosyntactic information to train contextual lemmatizers has become common practice, without considering whether that is the optimum in terms of downstream performance. In order to address this issue, in this paper we empirically investigate the role of morphological information to develop contextual lemmatizers in six languages within a varied spectrum of morphological complexity: Basque, Turkish, Russian, Czech, Spanish and English. Furthermore, and unlike the vast majority of previous work, we also evaluate lemmatizers in out-of-domain settings, which constitutes, after all, their most common application use. The results of our study are rather surprising. It turns out that providing lemmatizers with fine-grained morphological features during training is not that beneficial, not even for agglutinative languages. In fact, modern contextual word representations seem to implicitly encode enough morphological information to obtain competitive contextual lemmatizers without seeing any explicit morphological signal. Moreover, our experiments suggest that the best lemmatizers out-of-domain are those using simple UPOS tags or those trained without morphology and, finally, that current evaluation practices for lemmatization are not adequate to clearly discriminate between models.
Gravitational-wave template banks for novel compact binaries

We introduce a novel method to generate a bank of gravitational-waveform templates of binary black hole (BBH) mergers for matched-filter searches in LIGO, Virgo and Kagra data.We derive a novel expression for the metric approximation to the distance between templates, which is suitable for precessing BBHs and/or systems with higher-order modes (HM) imprints and we use it to meaningfully define a template probability density across the parameter space. We employ a masked autoregressive normalizing flow model which can be conveniently trained to quickly reproduce the target probability distribution and sample templates from it. Thanks to the normalizing flow, our code takes a few {\it hours} to produce random template banks with millions of templates, making it particularly suitable for high-dimensional spaces, such as those associated to precession, eccentricity and/or HM. After validating the performance of our method, we generate a bank for precessing black holes and a bank for aligned-spin binaries with HMs: with only 5% of the injections with fitting factor below the target of 0.97, we show that both banks cover satisfactorily the space. Our publicly released code mbank will enable searches of high-dimensional regions of BBH signal space, hitherto unfeasible due to the prohibitive cost of bank generation.
A Comprehensive Survey of Continual Learning: Theory, Method and Application

To cope with real-world dynamics, an intelligent system needs to incrementally acquire, update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as continual learning, provides a foundation for AI systems to develop themselves adaptively. In a general sense, continual learning is explicitly limited by catastrophic forgetting, where learning a new task usually results in a dramatic performance drop of the old tasks. Beyond this, increasingly numerous advances have emerged in recent years that largely extend the understanding and application of continual learning. The growing and widespread interest in this direction demonstrates its realistic significance as well as complexity. In this work, we present a comprehensive survey of continual learning, seeking to bridge the basic settings, theoretical foundations, representative methods, and practical applications. Based on existing theoretical and empirical results, we summarize the general objectives of continual learning as ensuring a proper stability-plasticity trade-off and an adequate intra/inter-task generalizability in the context of resource efficiency. Then we provide a state-of-the-art and elaborated taxonomy, extensively analyzing how representative strategies address continual learning, and how they are adapted to particular challenges in various applications. Through an in-depth discussion of promising directions, we believe that such a holistic perspective can greatly facilitate subsequent exploration in this field and beyond.
On Memory Codelets: Prefetching, Recoding, Moving and Streaming Data

For decades, memory capabilities have scaled up much slower than compute capabilities, leaving memory utilization as a major bottleneck. Prefetching and cache hierarchies mitigate this in applications with easily predictable memory accesses or those with high locality. In other applications like sparse linear algebra or graph-based applications, these strategies do not achieve effective utilization of memory. This is the case for the von Neumann model of computation, but other program execution models (PXM) provide different opportunities. Furthermore, the problem is complicated by increasing levels of heterogeneity and devices' varying memory subsystems. The Codelet PXM presented in this paper provides a program structure that allows for well-defined prefetching, streaming, and recoding operations to improve memory utilization and efficiently coordinate data movement with respect to computation. We propose the Memory Codelet, an extension to the original Codelet Model, to provide users these functionalities in a well-defined manner within the Codelet PXM.
Open-VCLIP: Transforming CLIP to an Open-vocabulary Video Model via Interpolated Weight Optimization

Contrastive Language-Image Pretraining (CLIP) has demonstrated impressive zero-shot learning abilities for image understanding, yet limited effort has been made to investigate CLIP for zero-shot video recognition. We introduce Open-VCLIP, a simple yet effective approach that transforms CLIP into a strong zero-shot video classifier that can recognize unseen actions and events at test time. Our framework extends CLIP with minimal modifications to model spatial-temporal relationships in videos, making it a specialized video classifier, while striving for generalization. We formally show that training an Open-VCLIP is equivalent to continual learning with zero historical data. To address this problem, we propose Interpolated Weight Optimization, which utilizes the benefit of weight interpolation in both training and test time. We evaluate our method on three popular and challenging action recognition datasets following various zero-shot evaluation protocols and we demonstrate our approach outperforms state-of-the-art methods by clear margins. In particular, we achieve 87.9%, 58.3%, 81.1% zero-shot accuracy on UCF, HMDB and Kinetics-600 respectively, outperforming state-of-the-art methods by 8.3%, 7.8% and 12.2%. Code is released at https://github.com/wengzejia1/Open-VCLIP.