Miracle-dz/UniFAR-FTUs huggingface dataset:
Beyond Mimicking Under-Represented Emotions: Deep Data Augmentation with Emotional Subspace Constraints for EEG-Based Emotion Recognition

In recent years, using Electroencephalography (EEG) to recognize emotions has garnered considerable attention. Despite advancements, limited EEG data restricts its potential. Thus, Generative Adversarial Networks (GANs) are proposed to mimic the observed distributions and generate EEG data. However, for imbalanced datasets, GANs struggle to produce reliable augmentations for under-represented minority emotions by merely mimicking them. Thus, we introduce Emotional Subspace Constrained Generative Adversarial Networks (ESC-GAN) as an alternative to existing frameworks. We first propose the EEG editing paradigm, editing reference EEG signals from well-represented to under-represented emotional subspaces. Then, we introduce diversity-aware and boundary-aware losses to constrain the augmented subspace. Here, the diversity-aware loss encourages a diverse emotional subspace by enlarging the sample difference, while boundary-aware loss constrains the augmented subspace near the decision boundary where recognition models can be vulnerable. Experiments show ESC-GAN boosts emotion recognition performance on benchmark datasets, DEAP, AMIGOS, and SEED, while protecting against potential adversarial attacks. Finally, the proposed method opens new avenues for editing EEG signals under emotional subspace constraints, facilitating unbiased and secure EEG data augmentation.
Severe Thrombocytopenia Associated with Acute Hepatitis E Virus Infection

ABSTRACT We describe here what is, to the best of our knowledge, the third reported case of severe thrombocytopenia associated with acute hepatitis E virus infection. The patient was a 72-year-old French woman. It seems likely that the cause of the thrombocytopenia was acute hepatitis E virus infection, possibly occurring via an immune mechanism. No complications were noted, in contrast to the two previous reports.
Unsupervised speaker diarization using riemannian manifold clustering

We address the problem of speaker clustering for robust unsupervised speaker diarization. We model each speakerhomogeneous segment as one single full multivariate Gaussian probability density function (pdf) and take into consideration the Riemannian property of Gaussian pdfs. By assuming that segments from different speakers lie on different (possibly intersected) sub-manifolds of the manifold of Gaussian pdfs, we formulate the original problem as a Riemannian manifold clustering problem. To apply the computationally simple Riemannian locally linear embedding (LLE) algorithm, we impose a constraint on the length of each segment so as to ensure the fitness of single-Gaussian modeling and to increase the chance that all k-nearest neighbors of a pdf are from the same submanifold (speaker). Experiments on the microphone-recorded conversational interviews from NIST 2010 speaker recognition evaluation set demonstrate promising results of less than 1%
Approximation Scheme for Weighted Metric Clustering via Sherali-Adams

Motivated by applications to classification problems on metric data, we study Weighted Metric Clustering problem: given a metric d over n points and a k x k symmetric matrix A with non-negative entries, the goal is to find a k-partition of these points into clusters C1,. ,Ck, while minimizing the sum of A[i,j] * d(u,v) over all pairs of clusters Ci and Cj and all pairs of points u from Ci and v from Cj. Specific choices of A lead to Weighted Metric Clustering capturing well-studied graph partitioning problems in metric spaces, such as Min-Uncut, Min-k-Sum, Min-k-Cut, and more. Our main result is that Weighted Metric Clustering admits a polynomial-time approximation scheme (PTAS). Our algorithm handles all the above problems using the Sherali-Adams linear programming relaxation. This subsumes several prior works, unifies many of the techniques for various metric clustering objectives, and yields a PTAS for several new problems, including metric clustering on manifolds and a new family of hierarchical clustering objectives. Our experiments on the hierarchical clustering objective show that it better captures the ground-truth structural information compared to the popular Dasgupta's objective.
Is wet season spelling achievable at a whole of property scale

This study examined issues with adoption and implementation of wet season spelling strategies across northern Australia at the whole of property level. Continuous grazing of pastures often leads to increased pressure on desirable species and in some instances a decline in land condition overtime. The use of wet season spelling is a highly recommended best management practice for beef producers across northern Australia. Significant effort has evolved around research to quantify the benefits of wet season spelling. These include increased ground cover, maintenance and/or improvement in land condition, improved pasture and animal production and reduction in loss of sediment. This study reviewed current guidelines and implementation processes for wet season spelling at the whole of property level. The review concludes that insufficient processes are described in detail to facilitate successful beef enterprise adoption of this highly recommended management practice. The review also highlights gaps in research and extension materials available for beef producers, including enterprise case studies that describe sound and successful, proven adoption strategies.
Meta-Analyses of Septal Reduction Therapies for Obstructive Hypertrophic Cardiomyopathy: Comparative Rates of Overall Mortality and Sudden Cardiac Death After Treatment

Background—Septal reduction for obstructive hypertrophic cardiomyopathy may be performed by surgical myectomy or alcohol septal ablation (ASA). Unlike surgical myectomy, ASA creates an intramyocardial scar that may potentiate the risk of ventricular arrhythmias and sudden cardiac death (SCD). Methods and Results—Systematic reviews for ASA and surgical myectomy were performed. Study selection and data extraction were completed independently by 2 investigators. Comparative data analyses were completed using a random effects model and regression analysis. Kappa statistics for agreement on initial study inclusion were high for both ASA (0.78; 95% CI, 0.68 to 0.88) and surgical myectomy studies (0.95; 95% CI, 0.84 to 1.0). Nineteen ASA studies (2207 patients) and 8 surgical myectomy studies (1887 patients) were included. Median follow-up was shorter for ASA than for myectomy studies (51 versus 1266 patient-years; P<0.001). For ASA and surgical myectomy, unadjusted rates (events/patient-years) of all-cause mortality (0.021 versus 0.018, respectively; P=0.37) and SCD (0.004 versus 0.003, respectively; P=0.36) were similar. Patients treated with ASA were older (weighted mean, 55 versus 44 years; P<0.001) and had less septal hypertrophy (weighted mean, 21 versus 23 mm; P<0.001) compared with those treated with myectomy. After adjustment for available baseline characteristics, odds ratios for treatment effect on all-cause mortality and SCD were 0.28 (95% CI, 0.16 to 0.46) and 0.32 (95% CI, 0.11 to 0.97), respectively, favoring ASA. Conclusions—Rates of all-cause mortality and SCD after both ASA and surgical myectomy were similarly low. Adjusted for baseline characteristics, the odds ratios for treatment effect on all-cause mortality and SCD were lower in ASA cohorts compared with surgical myectomy cohorts.
Feature Distribution Matching by Optimal Transport for Effective and Robust Coreset Selection

Training neural networks with good generalization requires large computational costs in many deep learning methods due to large-scale datasets and over-parameterized models. Despite the emergence of a number of coreset selection methods to reduce the computational costs, the problem of coreset distribution bias, i.e., the skewed distribution between the coreset and the entire dataset, has not been well studied. In this paper, we find that the closer the feature distribution of the coreset is to that of the entire dataset, the better the generalization performance of the coreset, particularly under extreme pruning. This motivates us to propose a simple yet effective method for coreset selection to alleviate the distribution bias between the coreset and the entire dataset, called feature distribution matching (FDMat). Unlike gradient-based methods, which selects samples with larger gradient values or approximates gradient values of the entire dataset, FDMat aims to select coreset that is closest to feature distribution of the entire dataset. Specifically, FDMat transfers coreset selection as an optimal transport problem from the coreset to the entire dataset in feature embedding spaces. Moreover, our method shows strong robustness due to the removal of samples far from the distribution, especially for the entire dataset containing noisy and class-imbalanced samples. Extensive experiments on multiple benchmarks show that FDMat can improve the performance of coreset selection than existing coreset methods. The code is available at https://github.com/successhaha/FDMat.
Experiments on the France Telecom 3000 Voice Agency corpus: academic research on an industrial spoken dialog system

The recent advances in speech recognition technologies, and the experience acquired in the development of WEB or Interactive Voice Response interfaces, have facilitated the integration of speech modules in robust Spoken Dialog Systems (SDS), leading to the deployment on a large scale of speech-enabled services. With these services it is possible to obtain very large corpora of human-machine interactions by collecting system logs. This new kinds of systems and dialogue corpora offer new opportunities for academic research while raising two issues: How can academic research take profit of the system logs of deployed SDS in order to build the next generation of SDS, although the dialogues collected have a dialogue flow constrained by the previous SDS generation? On the other side, what immediate benefits can academic research offer for the improvement of deployed system? This paper addresses these aspects in the framework of the deployed France Telecom 3000 Voice Agency service.
Enhancing High-dimensional Bayesian Optimization by Optimizing the Acquisition Function Maximizer Initialization

Bayesian optimization (BO) is widely used to optimize black-box functions. It works by ﬁrst building a surrogate for the objective and quantifying the uncertainty in that surrogate. It then decides where to sample by maximizing an acquisition function deﬁned by the surrogate model. Prior approaches typically use randomly generated raw samples to initialize the acquisition function maximizer. However, this strategy is ill-suited for high-dimensional BO. Given the large regions of high posterior uncertainty in high dimensions, a randomly initialized acquisition function maximizer is likely to focus on areas with high posterior uncertainty, leading to overly exploring areas that offer little gain. This paper provides the ﬁrst comprehensive empirical study to reveal the importance of the initialization phase of acquisition function maximization. It proposes a better initialization approach by employing multiple heuristic optimizers to leverage the knowledge of already evaluated samples to generate initial points to be explored by an acquisition function maximizer. We evaluate our approach on widely used synthetic test functions and real-world applications. Experimental results show that our techniques, while simple, can signiﬁcantly enhance the standard BO and outperforms state-of-the-art high-dimensional BO techniques by a large margin in most test cases.
CI-STHPAN: Pre-trained Attention Network for Stock Selection with Channel-Independent Spatio-Temporal Hypergraph

Quantitative stock selection is one of the most challenging FinTech tasks due to the non-stationary dynamics and complex market dependencies. Existing studies rely on channel mixing methods, exacerbating the issue of distribution shift in financial time series. Additionally, complex model structures they build make it difficult to handle very long sequences. Furthermore, most of them are based on predefined stock relationships thus making it difficult to capture the dynamic and highly volatile stock markets. To address the above issues, in this paper, we propose Channel-Independent based Spatio-Temporal Hypergraph Pre-trained Attention Networks (CI-STHPAN), a two-stage framework for stock selection, involving Transformer and HGAT based stock time series self-supervised pre-training and stock-ranking based downstream task fine-tuning. We calculate the similarity of stock time series of different channel in dynamic intervals based on Dynamic Time Warping (DTW), and further construct channel-independent stock dynamic hypergraph based on the similarity. Experiments with NASDAQ and NYSE markets data over five years show that our framework outperforms SOTA approaches in terms of investment return ratio (IRR) and Sharpe ratio (SR). Additionally, we find that even without introducing graph information, self-supervised learning based on the vanilla Transformer Encoder also surpasses SOTA results. Notable improvements are gained on the NYSE market. It is mainly attributed to the improvement of fine-tuning approach on Information Coefficient (IC) and Information Ratio based IC (ICIR), indicating that the fine-tuning method enhances the accuracy and stability of the model prediction.
Türkiye Orjinli Yulaf Genotiplerinin Basit Dizi Tekrarları (SSR) Markörleriyle Karakterizasyonu

Yulaf bitkisinde molekuler araclar bugday, misir ve arpaya gore daha az olmakla birlikte son zamanlarda, oldukca onemli miktarda basit dizi tekrarlari (SSR) ve tek nukleotid poliformizmi (SNP) markorleri gelistirilmistir. Bu calismada, SSR markorleri kullanilarak farkli gen bankalarindan elde edilen Turkiye orijinli 384 yulaf genotipinin genetik cesitliligi incelenmistir. Yulaf genotipleri saksilara ekilmis ve yapraklarindan DNA izolasyonu yapilmistir. PCR ve elektroforez isleminden sonra DNA bantlari skorlanmis, elde edilen veriler NTSYSpc 2.21q analiz programlari kullanilarak filogenetik agac olusturulmustur. Molekuler karakterizasyon calismalari sonucunda, yulaf genotipleri 40 SSR markoru ile genotiplenmis ve 130 allel elde edilmistir. Elde edilen bantlar kullanilarak olusturulan dendograma bakildiginda Ulkemiz genetik kaynaklarindan olan yerel yulaflarin oldukca genis bir varyasyon gosterdikleri belirlenmistir. Genotipik verilerin analizi sonucunda yerel yulaf cesitlerinin genis bir dagilimla birlikte temelde % 51’lik genetik benzerlik gosterdigi belirlenmistir. Arastirmada kullanilan SSR markorlerinden AB_AM_467, AB_AM_829 ve AB_AM_874 markorleri 5 allel ile en cok bant elde edilen markorler olmustur. Molekuler analizlerin en onemli sonuclarindan bir tanesi de genotiplerin birbirlerine akrabaliklarinin yani sira, duplikasyonlari onlemesi acisindan TL462 ve TL464 genotiplerinin birbirlerine % 100 benzer bulunmasidir. Arastirmada, Turkiye kaynakli yulaf genotiplerinin birbirlerine olan genetik mesafeleri belirlenmis olup, yulaf islah programlarinda ozellikle birbirlerine uzak akrabalarin melezlenmesi ile genetik varyasyonlar artirilabilir. Bunun disinda tek tohum soyundan elde edilen yerel genotipler gen bankasina kazandirilmistir.
Streptococcus sanguis Modulates Type II Collagen-Induced Arthritis in DBA/1J Mice1

Native type II collagen is tolerogenic when given orally or i.p. to DBA/1J mice and induces autoimmune arthritis when given s.c. in CFA. The tolerogenic epitope is contained in cyanogen bromide fragment 11 (CB11) and is structurally mimicked by PGEQGPK within the platelet aggregation-associated protein (PAAP) on Streptococcus sanguis. To learn whether S. sanguis modulates transmucosally the Ag-specific development of autoimmune arthritis, DBA/1J pups were given live S. sanguis, CB11, or type II collagen intragastrically. Feeding S. sanguis at 6 days postpartum delayed the onset of arthritis, and reduced the rate, final severity, and percentage of affected limbs. Next, PAAP+ S. sanguis and type II collagen were tested for T cell cross-reactivity. T cells primed with the tolerogenic epitope of type II collagen proliferated more when incubated with PAAP+ S. sanguis than with PAAP− Streptococcus gordonii or type II collagen, suggesting an Ag-specific transmucosal tolerogenic effect. In neonatal mice, therefore, bacterial surface Ags that mimic self can transmucosally stimulate Ag-specific inhibitory T cells. In adult mice immunized with type II collagen, these Ag-specific inhibitory T cells manifest later as attenuated arthritis. The PAAP+ S. sanguis appear to activate adult memory, rather than naive, type II collagen-specific T cells, suggesting that systemic challenge with commensal self-mimicking microorganisms may perpetuate existing autoimmunity, but not initiate autorecognition.
Complex Control of GABA(A) Receptor Subunit mRNA Expression: Variation, Covariation, and Genetic Regulation

GABA type-A receptors are essential for fast inhibitory neurotransmission and are critical in brain function. Surprisingly, expression of receptor subunits is highly variable among individuals, but the cause and impact of this fluctuation remains unknown. We have studied sources of variation for all 19 receptor subunits using massive expression data sets collected across multiple brain regions and platforms in mice and humans. Expression of Gabra1, Gabra2, Gabrb2, Gabrb3, and Gabrg2 is highly variable and heritable among the large cohort of BXD strains derived from crosses of fully sequenced parents—C57BL/6J and DBA/2J. Genetic control of these subunits is complex and highly dependent on tissue and mRNA region. Remarkably, this high variation is generally not linked to phenotypic differences. The single exception is Gabrb3, a locus that is linked to anxiety. We identified upstream genetic loci that influence subunit expression, including three unlinked regions of chromosome 5 that modulate the expression of nine subunits in hippocampus, and that are also associated with multiple phenotypes. Candidate genes within these loci include, Naaa, Nos1, and Zkscan1. We confirmed a high level of coexpression for subunits comprising the major channel—Gabra1, Gabrb2, and Gabrg2—and identified conserved members of this expression network in mice and humans. Gucy1a3, Gucy1b3, and Lis1 are novel and conserved associates of multiple subunits that are involved in inhibitory signaling. Finally, proximal and distal regions of the 3′ UTRs of single subunits have remarkably independent expression patterns in both species. However, corresponding regions of different subunits often show congruent genetic control and coexpression (proximal-to-proximal or distal-to-distal), even in the absence of sequence homology. Our findings identify novel sources of variation that modulate subunit expression and highlight the extraordinary capacity of biological networks to buffer 4–100 fold differences in mRNA levels.
Efficient Representation Learning of Satellite Image Time Series and Their Fusion for Spatiotemporal Applications

Satellite data bolstered by their increasing accessibility is leading to many endeavors of automated monitoring of the earth's surface for various applications. Such applications demand high spatial resolution images at a temporal resolution of a few days which entails the challenge of processing a huge volume of image time series data. To overcome this computing bottleneck, we present PatchNet, a bespoke adaptation of beam search and attention mechanism. PatchNet is an automated patch selection neural network that requires only a partial spatial traversal of an image time series and yet achieves impressive results. Satellite systems face a trade-off between spatial and temporal resolutions due to budget/technical constraints e.g., Landsat-8/9 or Sentinel-2 have high spatial resolution whereas, MODIS has high temporal resolution. To deal with the limitation of coarse temporal resolution, we propose FuSITSNet, a twofold feature-based generic fusion model with multimodal learning in a contrastive setting. It produces a learned representation after fusion of two satellite image time series leveraging finer spatial resolution of Landsat and finer temporal resolution of MODIS. The patch alignment module of FuSITSNet aligns the PatchNet processed patches of Landsat-8 with the corresponding MODIS regions to incorporate its finer resolution temporal features. The untraversed patches are handled by the cross-modality attention which highlights additional hot spot features from the two modalities. We conduct extensive experiments on more than 2000 counties of US for crop yield, snow cover, and solar energy prediction and show that even one-fourth spatial processing of image time series produces state-of-the-art results. FuSITSNet outperforms the predictions of single modality and data obtained using existing generative fusion models and allows for monitoring of dynamic phenomena using freely accessible images, thereby unlocking new opportunities.
A Local-Ascending-Global Learning Strategy for Brain-Computer Interface

Neuroscience research indicates that the interaction among different functional regions of the brain plays a crucial role in driving various cognitive tasks. Existing studies have primarily focused on constructing either local or global functional connectivity maps within the brain, often lacking an adaptive approach to fuse functional brain regions and explore latent relationships between localization during different cognitive tasks. This paper introduces a novel approach called the Local-Ascending-Global Learning Strategy (LAG) to uncover higher-level latent topological patterns among functional brain regions. The strategy initiates from the local connectivity of individual brain functional regions and develops a K-Level Self-Adaptive Ascending Network (SALK) to dynamically capture strong connectivity patterns among brain regions during different cognitive tasks. Through the step-by-step fusion of brain regions, this approach captures higher-level latent patterns, shedding light on the progressively adaptive fusion of various brain functional regions under different cognitive tasks. Notably, this study represents the first exploration of higher-level latent patterns through progressively adaptive fusion of diverse brain functional regions under different cognitive tasks. The proposed LAG strategy is validated using datasets related to fatigue (SEED-VIG), emotion (SEED-IV), and motor imagery (BCI_C_IV_2a). The results demonstrate the generalizability of LAG, achieving satisfactory outcomes in independent-subject experiments across all three datasets. This suggests that LAG effectively characterizes higher-level latent patterns associated with different cognitive tasks, presenting a novel approach to understanding brain patterns in varying cognitive contexts.
Encoding Constraints as Binary Constraint Networks Satisfying BTP

Recently, the Binary Constraint Tree (BCT), a tree structured Binary Constraint Network (BCN), has been shown to be more succinct than various ad-hoc constraints. In this paper, we investigate the modelling power of a well-known tractable hybrid class generalizing BCT, i.e. the class of BCNs satisfying Broken Triangle Property (BTP) called BTP Networks (BTPNs). We show that the consistency checker of BTPN can be computed by polysize monotone circuit, thus, some global constraints cannot be encoded as polysize BTPN, such as the AllDifferent and Linear constraints. Then our study reveals that BTPN is strictly more succinct than the DNNF constraint and all 14 ad-hoc constraints discussed in (Wang and Yap 2023), such as the context-free grammar, BCT and smart table constraints. Furthermore, we also show that BTPN is as powerful as DNNF in terms of computing various operations and queries. In addition, we prove that it is NP-hard to determine the minimum sized BTPN encoding a constraint.