Miracle-dz/UniFAR-FTUs huggingface dataset:
Your blush gives you away: detecting hidden mental states with remote photoplethysmography and thermal imaging

Multimodal emotion recognition techniques are increasingly essential for assessing mental states. Image-based methods, however, tend to focus predominantly on overt visual cues and often overlook subtler mental state changes. Psychophysiological research has demonstrated that heart rate (HR) and skin temperature are effective in detecting autonomic nervous system (ANS) activities, thereby revealing these subtle changes. However, traditional HR tools are generally more costly and less portable, while skin temperature analysis usually necessitates extensive manual processing. Advances in remote photoplethysmography (r-PPG) and automatic thermal region of interest (ROI) detection algorithms have been developed to address these issues, yet their accuracy in practical applications remains limited. This study aims to bridge this gap by integrating r-PPG with thermal imaging to enhance prediction performance. Ninety participants completed a 20-min questionnaire to induce cognitive stress, followed by watching a film aimed at eliciting moral elevation. The results demonstrate that the combination of r-PPG and thermal imaging effectively detects emotional shifts. Using r-PPG alone, the prediction accuracy was 77% for cognitive stress and 61% for moral elevation, as determined by a support vector machine (SVM). Thermal imaging alone achieved 79% accuracy for cognitive stress and 78% for moral elevation, utilizing a random forest (RF) algorithm. An early fusion strategy of these modalities significantly improved accuracies, achieving 87% for cognitive stress and 83% for moral elevation using RF. Further analysis, which utilized statistical metrics and explainable machine learning methods including SHapley Additive exPlanations (SHAP), highlighted key features and clarified the relationship between cardiac responses and facial temperature variations. Notably, it was observed that cardiovascular features derived from r-PPG models had a more pronounced influence in data fusion, despite thermal imaging’s higher predictive accuracy in unimodal analysis.
Switching plane: A novel crystalline interface in orthorhombic perovskite films

The atomic configuration of phases and their interfaces is fundamental to materials design and engineering. Here, we unveil a novel crystalline interface, whose formation is driven by energetic influences that compete in determining the orientation of an orthorhombic perovskite film. Observed within LaVO$_3$ grown on $(101)_{\text{orth}}$ DyScO$_3$, the film resolves this competition by switching its orientation at an atomically-flat plane within its volume, not at the film-substrate interface. Remarkably, at either side of this switching plane, characteristic orthorhombic distortions tend to zero to couple mismatched atomic structures. The resulting boundary is highly energetic; by using second-principles modeling to map the phase space of film growth, we show how its formation requires structural relaxation of an entire film grown beyond a significant critical thickness. This structural-energetic phenomenon opens avenues towards engineering new functional interfaces, offering the opportunity of contacting distinct phases of one compound that would never otherwise coexist.
A comparative study of the seasonal trend of Biological Oxygen Demand, Chemical Oxygen Demand and Dissolved Organic Matter in two fresh water aquaculture ponds of Assam

Seasonal variation of BOD, COD and DOM were studied in two perennial aquaculture ponds located at Gauhati University Campus, Guwahati, Assam India. One of the experimental pond is manually managed with the application of lime and inorganic and organic fertilizer. The other is a naturally maintained pen culture pond recovered from a part of swamp without addition of lime and fertilizer. The reclaimed zone of natural pond is separated from the perennial swamp by bamboo screen. All the three organic parameters of water BOD, COD and DOM maintain higher profile in manually managed pond with high degree of fluctuation. Results of all three parameters verify that both the ponds are free from pollution.
Reinforcement Learning for Conversational Question Answering over Knowledge Graph

Conversational question answering (ConvQA) over law knowledge bases (KBs) involves answering multi-turn natural language questions about law and hope to find answers in the law knowledge base. Despite many methods have been proposed. Existing law knowledge base ConvQA model assume that the input question is clear and can perfectly reflect user's intention. However, in real world, the input questions are noisy and inexplict. This makes the model hard to find the correct answer in the law knowledge bases. In this paper, we try to use reinforcement learning to solve this problem. The reinforcement learning agent can automatically learn how to find the answer based on the input question and the conversation history, even when the input question is inexplicit. We test the proposed method on several real world datasets and the results show the effectivenss of the proposed model.
Leveraging Gradients for Unsupervised Accuracy Estimation under Distribution Shift

Estimating test accuracy without access to the ground-truth test labels under varying test environments is a challenging, yet extremely important problem in the safe deployment of machine learning algorithms. Existing works rely on the information from either the outputs or the extracted features of neural networks to formulate an estimation score correlating with the ground-truth test accuracy. In this paper, we investigate--both empirically and theoretically--how the information provided by the gradients can be predictive of the ground-truth test accuracy even under a distribution shift. Specifically, we use the norm of classification-layer gradients, backpropagated from the cross-entropy loss after only one gradient step over test data. Our key idea is that the model should be adjusted with a higher magnitude of gradients when it does not generalize to the test dataset with a distribution shift. We provide theoretical insights highlighting the main ingredients of such an approach ensuring its empirical success. Extensive experiments conducted on diverse distribution shifts and model structures demonstrate that our method significantly outperforms state-of-the-art algorithms.
Augmenting Math Word Problems via Iterative Question Composing

Despite the advancements in large language models (LLMs) for mathematical reasoning, solving competition-level math problems remains a significant challenge, especially for open-source LLMs without external tools. We introduce the MMIQC dataset, comprising a mixture of processed web data and synthetic question-response pairs, aimed at enhancing the mathematical reasoning capabilities of base language models. Models fine-tuned on MMIQC consistently surpass their counterparts in performance on the MATH benchmark across various model sizes. Notably, Qwen-72B-MMIQC achieves a 45.0% accuracy, exceeding the previous open-source state-of-the-art by 8.2% and outperforming the initial version GPT-4 released in 2023. Extensive evaluation results on Hungarian high school finals suggest that such improvement can generalize to unseen data. Our ablation study on MMIQC reveals that a large part of the improvement can be attributed to our novel augmentation method, Iterative Question Composing (IQC), which involves iteratively composing new questions from seed problems using an LLM and applying rejection sampling through another LLM. The MMIQC dataset is available on the HuggingFace hub at https://huggingface.co/datasets/Vivacem/MMIQC. Our code is available at https://github.com/iiis-ai/IterativeQuestionComposing.
Dynamic Brain Behaviours in Stroke: A Longitudinal Investigation Based on fMRI Analysis

Background: The brain's functional network constantly adapts to external changes. However, the mechanisms underlying this dynamic adaptive behavior in stroke patients with motor injuries and its role in post-stroke motor recovery remain poorly understood. Method: This study conducted a long-term investigation involving 15 first-stroke patients. Each participant underwent five fMRI scans distributed equally over a six-month period. Using functional neuroimaging data, time-varying functional modularity in post-stroke patients was detected, and subsequently, the dynamic brain behaviors, including recruitment, integration, and flexibility, along with their longitudinal changes, were assessed. Results: Our findings reveal that stroke lesions lead to significant and enduring alterations in all three dynamic behaviors within functional brain networks. Furthermore, during the six-month recovery period, patients who exhibited good and poor recovery showed notable differences in recruitment and flexibility, indicating distinct recovery trajectories for these groups. Notably, when predicting post-stroke recovery status, whole-brain recruitment emerged as a robust and reliable feature, achieving an AUC of 85.93 Significance: Our study offers a comprehensive depiction of dynamic brain behavior in the post-ischemic-stroke brain, with a focus on longitudinal changes concurrent with functional recovery. These dynamic patterns hold promise as valuable tools for evaluating and predicting motor recovery following stroke.
TACO: Benchmarking Generalizable Bimanual Tool-ACtion-Object Understanding

Humans commonly work with multiple objects in daily life and can intuitively transfer manipulation skills to novel objects by understanding object functional regularities. However, existing technical approaches for analyzing and synthesizing hand-object manipulation are mostly limited to handling a single hand and object due to the lack of data support. To address this, we construct TACO, an extensive bimanual hand-object-interaction dataset spanning a large variety of tool-action-object compositions for daily human activities. TACO contains 2.5K motion sequences paired with third-person and egocentric views, precise hand-object 3D meshes, and action labels. To rapidly expand the data scale, we present a fully automatic data acquisition pipeline combining multi-view sensing with an optical motion capture system. With the vast research fields provided by TACO, we benchmark three generalizable hand-object-interaction tasks: compositional action recognition, generalizable hand-object motion forecasting, and cooperative grasp synthesis. Extensive experiments reveal new insights, challenges, and opportunities for advancing the studies of generalizable hand-object motion analysis and synthesis. Our data and code are available at https://taco2024.github. io.
Scalable Pre-training of Large Autoregressive Image Models

This paper introduces AIM, a collection of vision models pre-trained with an autoregressive objective. These models are inspired by their textual counterparts, i.e., Large Language Models (LLMs), and exhibit similar scaling properties. Specifically, we highlight two key findings: (1) the performance of the visual features scale with both the model capacity and the quantity of data, (2) the value of the objective function correlates with the performance of the model on downstream tasks. We illustrate the practical implication of these findings by pre-training a 7 billion parameter AIM on 2 billion images, that achieves 84.0% on ImageNet-1k with a frozen trunk. Interestingly, even at this scale, we observe no sign of saturation in performance, suggesting that AIM potentially represents a new frontier for training large-scale vision models. The pre-training of AIM is similar to the pre-training of LLMs, and does not require any image-specific strategy to stabilize the training at scale.
Similar but Faster: Manipulation of Tempo in Music Audio Embeddings for Tempo Prediction and Search

Audio embeddings enable large scale comparisons of the similarity of audio files for applications such as search and recommendation. Due to the subjectivity of audio similarity, it can be desirable to design systems that answer not only whether audio is similar, but similar in what way (e.g., wrt. tempo, mood or genre). Previous works have proposed disentangled embedding spaces where subspaces representing specific, yet possibly correlated, attributes can be weighted to emphasize those attributes in downstream tasks. However, no research has been conducted into the independence of these subspaces, nor their manipulation, in order to retrieve tracks that are similar but different in a specific way. Here, we explore the manipulation of tempo in embedding spaces as a case-study towards this goal. We propose tempo translation functions that allow for efficient manipulation of tempo within a pre-existing embedding space whilst maintaining other properties such as genre. As this translation is specific to tempo it enables retrieval of tracks that are similar but have specifically different tempi. We show that such a function can be used as an efficient data augmentation strategy for both training of downstream tempo predictors, and improved nearest neighbor retrieval of properties largely independent of tempo.
Decoupled Prototype Learning for Reliable Test-Time Adaptation

Test-time adaptation (TTA) is a task that continually adapts a pre-trained source model to the target domain during inference. One popular approach involves fine-tuning model with cross-entropy loss according to estimated pseudo-labels. However, its performance is significantly affected by noisy pseudo-labels. This study reveals that minimizing the classification error of each sample causes the cross-entropy loss's vulnerability to label noise. To address this issue, we propose a novel Decoupled Prototype Learning (DPL) method that features prototype-centric loss computation. First, we decouple the optimization of class prototypes. For each class prototype, we reduce its distance with positive samples and enlarge its distance with negative samples in a contrastive manner. This strategy prevents the model from overfitting to noisy pseudo-labels. Second, we propose a memory-based strategy to enhance DPL's robustness for the small batch sizes often encountered in TTA. We update each class's pseudo-feature from a memory in a momentum manner and insert an additional DPL loss. Finally, we introduce a consistency regularization-based approach to leverage samples with unconfident pseudo-labels. This approach transfers feature styles of samples with unconfident pseudo-labels to those with confident pseudo-labels. Thus, more reliable samples for TTA are created. The experimental results demonstrate that our methods achieve state-of-the-art performance on domain generalization benchmarks, and reliably improve the performance of self-training-based methods on image corruption benchmarks. The code will be released.
Minimal Representations of Tropical Rational Functions

This paper studies the following question: given a piecewise-linear function, find its minimal algebraic representation as a tropical rational signomial. We put forward two different notions of minimality, one based on monomial length, the other based on factorization length. We show that in dimension one, both notions coincide, but this is not true in dimensions two or more. We prove uniqueness of the minimal representation for dimension one and certain subclasses of piecewise-linear functions in dimension two. As a proof step, we obtain counting formulas and lower bounds for the number of regions in an arrangement of tropical hypersurfaces, giving a small extension for a result by Mont\'{u}far, Ren and Zhang. As an equivalent formulation, it gives a lower bound on the number of vertices in a regular mixed subdivision of a Minkowski sum, giving a small extension for Adiprasito's Lower Bound Theorem for Minkowski sums.
A pathogenic variant in RAB32 causes autosomal dominant Parkinson’s disease and activates LRRK2 kinase

Background Parkinson disease (PD) is a progressive neurodegenerative disorder. Mendelian forms have revealed multiple genes, with a notable emphasis on membrane trafficking; RAB GTPases play an important role in PD as a subset are both regulators and substrates of LRRK2 protein kinase. To explore the role of RAB GTPases in PD, we undertook a comprehensive examination of their genetic variability in familial PD. Methods Affected probands from 130 multi-incident PD families underwent whole-exome sequencing and genotyping, Potential pathogenic variants in 61 RAB GTPases were genotyped in relatives to assess disease segregation. These variants were also genotyped in a larger case-control series, totaling 3,078 individuals (2,734 with PD). The single most significant finding was subsequently validated within genetic data (6,043 with PD). Clinical and pathologic findings were summarized for gene-identified patients, and haplotypes were constructed. In parallel, wild-type and mutant RAB GTPase structural variation, protein interactions, and resultant enzyme activities were assessed. Findings We found RAB32 c.213C>G (Ser71Arg) to co-segregate with autosomal dominant parkinsonism in three multi-incident families. RAB32 Ser71Arg was also significantly associated with PD in case-control samples: genotyping and database searches identified thirteen more patients with the same variant that was absent in unaffected controls. Notably, RAB32 Ser71Arg heterozygotes share a common haplotype. At autopsy, one patient had sparse neurofibrillary tangle pathology in the midbrain and thalamus, without Lewy body pathology. In transfected cells the RAB32 Arg71 was twice as potent as Ser71 wild type to activate LRRK2 kinase. Interpretation Our study provides unequivocal evidence to implicate RAB32 Ser71Arg in PD. Functional analysis demonstrates LRRK2 kinase activation. We provide a mechanistic explanation to expand and unify the etiopathogenesis of monogenic PD. Funding National Institutes of Health, the Canada Excellence Research Chairs program, Aligning Science Across Parkinson s, the Michael J. Fox Foundation for Parkinsons Research, and the UK Medical Research Council.
Region-based analysis with functional annotation identifies genes associated with cognitive function in South Asians from India

The prevalence of dementia among South Asians across India is approximately 7.4% in those 60 years and older, yet little is known about genetic risk factors for dementia in this population. Most known risk loci for Alzheimer's disease (AD) have been identified from studies conducted in European Ancestry (EA) but are unknown in South Asians. Using whole-genome sequence data from 2680 participants from the Diagnostic Assessment of Dementia for the Longitudinal Aging Study of India (LASI-DAD), we performed a gene-based analysis of 84 genes previously associated with AD in EA. We investigated associations with the Hindi Mental State Examination (HMSE) score and factor scores for general cognitive function and five cognitive domains. For each gene, we examined missense/loss-of-function (LoF) variants and brain-specific promoter/enhancer variants, separately, both with and without incorporating additional annotation weights (e.g., deleteriousness, conservation scores) using the variant-Set Test for Association using Annotation infoRmation (STAAR). In the missense/LoF analysis without annotation weights and controlling for age, sex, state/territory, and genetic ancestry, three genes had an association with at least one measure of cognitive function (FDR q<0.1). APOE was associated with four measures of cognitive function, PICALM was associated with HMSE score, and TSPOAP1 was associated with executive function. The most strongly associated variants in each gene were rs429358 (APOE e4), rs779406084 (PICALM), and rs9913145 (TSPOAP1). rs779406084 is a rare missense mutation that is more prevalent in LASI-DAD than in EA (minor allele frequency=0.075% vs. 0.0015%); the other two are common variants. No genes in the brain-specific promoter/enhancer analysis met criteria for significance. Results with and without annotation weights were similar. Missense/LoF variants in some genes previously associated with AD in EA are associated with measures of cognitive function in South Asians from India. Analyzing genome sequence data allows identification of potential novel causal variants enriched in South Asians.
The impact of visit‐to‐visit heart rate variability on all‐cause mortality in atrial fibrillation

Abstract Objective We aimed to investigate the association between visit‐to‐visit heart rate variability (VVHRV) and all‐cause mortality in patients diagnosed with atrial fibrillation (AF). Previous studies have shown a positive correlation between VVHRV and several adverse outcomes. However, the relationship between VVHRV and the prognosis of AF remains uncertain. Methods In our study, we aimed to examine the relationship between VVHRV and mortality rates among 3983 participants with AF, who were part of the AFFIRM study (Atrial Fibrillation Follow‐Up Investigation of Rhythm Management). We used the standard deviation of heart rate (HRSD) to measure VVHRV and divided the patients into four groups based on quartiles of HRSD (1st, <5.69; 2nd, 5.69–8.00; 3rd, 8.01–11.01; and 4th, ≥11.02). Our primary endpoint was all‐cause death, and we estimated the hazard ratios for mortality using the Cox proportional hazard regressions. Results Our analysis included 3983 participants from the AFFIRM study and followed for an average of 3.5 years. During this period, 621 participants died from all causes. In multiple‐adjustment models, we found that the lowest and highest quartiles of HRSD independently predicted an increased risk of all‐cause mortality compared to the other two quartiles, presenting a U‐shaped relationship (1st vs 2nd, hazard ratio = 2.28, 95% CI = 1.63–3.20, p < .01; 1st vs. 3rd, hazard ratio = 2.23, 95% CI = 1.60–3.11, p < .01; 4th vs. 2nd, hazard ratio = 1.82, 95% CI = 1.26–2.61, p < .01; and 4th vs. 3rd, hazard ratio = 1.78, 95% CI = 1.25–2.52, p < .01). Conclusion In patients with AF, we found that both lower VVHRV and higher VVHRV increased the risk of all‐cause mortality, indicating a U‐shaped curve relationship.
Gluten-free diet on video platforms: Retrospective infodemiology study

Background Video platform is an important approach for individuals to access and adopt health information. Online information on gluten-free diet (GFD) videos remains underinvestigated. Methods GFD videos were identified by hashtag-based searching strategy. Videos' basic information, engagement metrics, and content were recorded. Mann-Kendall test was performed to examine time trends of submitting videos and engagement metrics. Video quality was evaluated by the DISCERN instrument and the HONcode. Results A total of 822 videos were included in the analysis, with the majority focusing on gluten-free food recipes. The number of videos related to GFD was showing an upward trend. Engagement metrics varied between platforms and video types, with non-recipe videos receiving higher user engagement. The average DISCERN score was 50.20 out of 80 and the average HONcode score was 1.93 out of 8. Videos submitted by health professionals demonstrated better quality compared to those submitted by patients or general users. Conclusion There was a rise in the number of videos related to GFD on Chinese video platforms. The overall quality of these videos was poor, most of them were not rigorous enough. Highlighting using social media as a health information source has the potential risk of disseminating one-sided messages and misleading. Efforts should be made to enhance the transparency of advertisements and establish clear guidelines for information sharing on social media platforms.