Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing speaker identification through reverberation modeling and cancelable techniques using ANNs

This paper introduces a method aiming at enhancing the efficacy of speaker identification systems within challenging acoustic environments characterized by noise and reverberation. The methodology encompasses the utilization of diverse feature extraction techniques, including Mel-Frequency Cepstral Coefficients (MFCCs) and discrete transforms, such as Discrete Cosine Transform (DCT), Discrete Sine Transform (DST), and Discrete Wavelet Transform (DWT). Additionally, an Artificial Neural Network (ANN) serves as the classifier for this method. Reverberation is modeled using varying-length comb filters, and its impact on pitch frequency estimation is explored via the Auto Correlation Function (ACF). This paper also contributes to the field of cancelable speaker identification in both open and reverberation environments. The proposed method depends on comb filtering at the feature level, deliberately distorting MFCCs. This distortion, incorporated within a cancelable framework, serves to obscure speaker identities, rendering the system resilient to potential intruders. Three systems are presented in this work; a reverberation-affected speaker identification system, a system depending on cancelable features through comb filtering, and a novel cancelable speaker identification system within reverbration environments. The findings revealed that, in both scenarios with and without reverberation effects, the DWT-based features exhibited superior performance within the speaker identification system. Conversely, within the cancelable speaker identification system, the DCT-based features represent the top-performing choice.
Association Between Dietary Fiber Intake and Low Muscle Strength Among Korean Adults

The health benefits of dietary fiber are widely recognized, but its impact on muscle health remains unclear. Therefore, this study aimed to elucidate the relationship between dietary fiber intake and muscle strength through a cross-sectional analysis of data from the Korea National Health and Examination Survey (KNHANES). Data from a single 24-h dietary recall and handgrip strength tests of 10,883 younger adults aged 19 to 64 years and 3,961 older adults aged ≥ 65 years were analyzed. Low muscle strength was defined as handgrip strength < 28 kg for men and < 18 kg for women. Multivariable linear and logistic regression analyses were conducted to determine the association of dietary fiber intake with muscle strength. Approximately 43% of Korean adults met the recommended intake of dietary fiber, and those with higher dietary fiber consumption also had higher total energy and protein intake. After adjusting for confounding variables, dietary fiber intake was found to be positively associated with maximal handgrip strength in younger women aged 19 to 64 years (β = 0.015; standard error [SE] = 0.006) and older men aged ≥ 65 years (β = 0.035; SE = 0.014). For older women aged ≥ 65 years, those in the lowest quartile of dietary fiber intake had a higher risk of low muscle strength than those in the highest quartile after adjustment of confounders (odds ratio 1.709; 95% confidence interval 1.130–2.585). These results suggest that adequate dietary fiber intake may reduce the risk of sarcopenia in older Korean women.
Active deformation in the southern Andes from GPS and FEM models

Spatial and temporal variations of surface deformation along subduction zones provide primary information for understanding the process of great earthquakes. This study presents observations of recent crustal deformation and numerical modeling to explore the interplay between earthquake cycle and upper plate fault deformation in the southern Andean forearc (36-46 S). It includes surface displacements derived from a dense array of GPS stations and 3D viscoelastic FEMs that simulate forearc deformation processes. GPS-derived velocities show a heterogeneous surface deformation, which mainly resembles a seismotectonic segmentation defined by the rupture of the two past megathrust earthquakes; the Concepción 1835 (M ∼8.5) and Valdivia 1960 (Mw=9.5). The great 1960 event plays a principal role in the active surface deformation. It has induced a prolonged postseismic viscoelastic deformation and has affected the degree of plate locking. Postseismic viscoelastic deformation produces seaward displacements of up to ∼20 mm/yr and counterclockwise rotation across the northern limit of the rupture. Along the Valdivia segment, the interface appears to be strongly locked in the central part of the rupture, which might fail in the future before the entire megathrust of this segment is fully locked. Conversely, the Concepción segment seems to be strongly locked along the entire segment, suggesting an advanced interseismic phase. This segment has not ruptured in a M>8 earthquake since 1835, and may be considered as a potential area to host a future earthquake. A secondorder deformation signal is associated with forearc sliver motion, which spatially coincides with the Valdivia segment. This motion absorbs ∼25% of the margin-parallel plate convergence and decreases northward. It produces spatial and temporal redistribution of the stress field over a suit of existing crustal-scale faults, revealing a block-like forearc deformation. A buttress effect related to a major lithologic discontinuity across the leading edge of the sliver resists the translation and produces transpressional deformation. The degree of strain partitioning decreases considerably along the Concepción segment. Here, crustal faults may affect the interseismic deformation locally but with significant amplitude. Forearc sliver translation, partitioning gradient and margin parallel shortening are long-term features of this margin. Hence, lateral variations in the upper plate may have controlled the plate interface properties causing long-term seismic slip to vary along the subduction zone. Major geological features of the forearc most probably control the spatial recurrence of megathrust earthquakes in the southern Andes.
University makes me angry: Investigating stimulus-response (S-R) and cognitive-mediation (C-M) emotion beliefs in undergraduate students

Emotion regulation through cognitive reappraisal is well-studied, but less so are the predispositional and superordinate beliefs that influence reappraisal. Recently, researchers developed the cognitive mediation beliefs questionnaire (CMBQ), which measures two emotion beliefs, namely stimulus-response (S-R) generation beliefs and cognitive mediation (C-M) change beliefs. In working populations S-R generation beliefs are inversely related to cognitive reappraisal tendencies and positive mental health, and positively related to emotion reactivity. C-M change beliefs are positively related to cognitive reappraisal tendencies, and inversely related to emotion reactivity and positive mental health. As yet, there is no evidence for the validity of the CMBQ within student samples, or for the associations between its subscales and cognitive reappraisal, emotion reactivity, and positive mental health. Therefore, in the present study the CMBQ is tested for factorial, convergent (associations with cognitive reappraisal), and concurrent (associations with emotion reactivity and positive mental health) validity in a cohort of 621 undergraduate students in the United Kingdom (U.K.). Results indicate support for the factorial and convergent validity of the CMBQ, with mixed evidence for the concurrent validity of the CMBQ. A CM-SR discrepancy score appeared to provide a promising variable when associated with emotion reactivity and positive mental health. The findings are discussed in terms of practical and research implications of the findings.
Feasibility Study of Convolutional Long ShortTerm Memory Network for Pulmonary Movement Prediction in CT Images

Background: During X-ray imaging, pulmonary movements can cause many image artifacts. To tackle this issue, several studies, including mathematical algorithms and 2D-3D image registration methods, have been presented. Recently, the application of deep artificial neural networks has been considered for image generation and prediction. Objective: In this study, a convolutional long short-term memory (ConvLSTM) neural network is used to predict spatiotemporal 4DCT images. Material and Methods: In this analytical analysis study, two ConvLSTM structures, consisting of stacked ConvLSTM models along with the hyperparameter optimizer algorithm and a new design of the ConvLSTM model are proposed. The hyperparameter optimizer algorithm in the conventional ConvLSTM includes the number of layers, number of filters, kernel size, epoch number, optimizer, and learning rate. The two ConvLSTM structures were also evaluated through six experiments based on Root Mean Square Error (RMSE) and structural similarity index (SSIM). Results: Comparing the two networks demonstrates that the new design of the ConvLSTM network is faster, more accurate, and more reliable in comparison to the tuned-stacked ConvLSTM model. For all patients, the estimated RMSE and SSIM were 3.17 and 0.988, respectively, and a significant improvement can be observed in comparison to the previous studies. Conclusion: Overall, the results of the new design of the ConvLSTM network show excellent performances in terms of RMSE and SSIM. Also, the generated CT images with the new design of the ConvLSTM model show a good consistency with the corresponding references regarding registration accuracy and robustness.
Women’s perceptions and attitudes towards the use of AI in mammography in Sweden: a qualitative interview study

Background Understanding women’s perspectives can help to create an effective and acceptable artificial intelligence (AI) implementation for triaging mammograms, ensuring a high proportion of screening-detected cancer. This study aimed to explore Swedish women’s perceptions and attitudes towards the use of AI in mammography. Method Semistructured interviews were conducted with 16 women recruited in the spring of 2023 at Capio S:t Görans Hospital, Sweden, during an ongoing clinical trial of AI in screening (ScreenTrustCAD, NCT 04778670) with Philips equipment. The interview transcripts were analysed using inductive thematic content analysis. Results In general, women viewed AI as an excellent complementary tool to help radiologists in their decision-making, rather than a complete replacement of their expertise. To trust the AI, the women requested a thorough evaluation, transparency about AI usage in healthcare, and the involvement of a radiologist in the assessment. They would rather be more worried because of being called in more often for scans than risk having overlooked a sign of cancer. They expressed substantial trust in the healthcare system if the implementation of AI was to become a standard practice. Conclusion The findings suggest that the interviewed women, in general, hold a positive attitude towards the implementation of AI in mammography; nonetheless, they expect and demand more from an AI than a radiologist. Effective communication regarding the role and limitations of AI is crucial to ensure that patients understand the purpose and potential outcomes of AI-assisted healthcare.
No‐reflow after stroke reperfusion therapy: An emerging phenomenon to be explored

Abstract In the field of stroke thrombectomy, ineffective clinical and angiographic reperfusion after successful recanalization has drawn attention. Partial or complete microcirculatory reperfusion failure after the achievement of full patency of a former obstructed large vessel, known as the “no‐reflow phenomenon” or “microvascular obstruction,” was first reported in the 1960s and was later detected in both experimental models and patients with stroke. The no‐reflow phenomenon (NRP) was reported to result from intraluminal occlusions formed by blood components and extraluminal constriction exerted by the surrounding structures of the vessel wall. More recently, an emerging number of clinical studies have estimated the prevalence of the NRP in stroke patients following reperfusion therapy, ranging from 3.3% to 63% depending on its evaluation methods or study population. Studies also demonstrated its detrimental effects on infarction progress and neurological outcomes. In this review, we discuss the research advances, underlying pathogenesis, diagnostic techniques, and management approaches concerning the no‐reflow phenomenon in the stroke population to provide a comprehensive understanding of this phenomenon and offer references for future investigations.
Machine learning algorithm for predict the in-hospital mortality in critically ill patients with congestive heart failure combined with chronic kidney disease

Abstract Background The objective of this study was to develop and validate a machine learning (ML) model for predict in-hospital mortality among critically ill patients with congestive heart failure (CHF) combined with chronic kidney disease (CKD). Methods After employing least absolute shrinkage and selection operator regression for feature selection, six distinct methodologies were employed in the construction of the model. The selection of the optimal model was based on the area under the curve (AUC). Furthermore, the interpretation of the chosen model was facilitated through the utilization of SHapley Additive exPlanation (SHAP) values and the Local Interpretable Model-Agnostic Explanations (LIME) algorithm. Results This study collected data and enrolled 5041 patients on CHF combined with CKD from 2008 to 2019, utilizing the Medical Information Mart for Intensive Care Unit. After selection, 22 of the 47 variables collected post-intensive care unit admission were identified as mortality-associated and subsequently utilized in the development of ML models. Among the six models generated, the eXtreme Gradient Boosting (XGBoost) model demonstrated the highest AUC at 0.837. Notably, the SHAP values highlighted the sequential organ failure assessment score, age, simplified acute physiology score II, and urine output as the four most influential variables in the XGBoost model. In addition, the LIME algorithm explains the individualized predictions. Conclusions In conclusion, our study accomplished the successful development and validation of ML models for predicting in-hospital mortality in critically ill patients with CHF combined with CKD. Notably, the XGBoost model emerged as the most efficacious among all the ML models employed.
Disentangled Cross-Modal Transformer for RGB-D Salient Object Detection and Beyond

Previous multi-modal transformers for RGB-D salient object detection (SOD) generally directly connect all patches from two modalities to model cross-modal correlation and perform multi-modal combination without differentiation, which can lead to confusing and inefficient fusion. Instead, we disentangle the cross-modal complementarity from two views to reduce cross-modal fusion ambiguity: 1) Context disentanglement. We argue that modeling long-range dependencies across modalities as done before is uninformative due to the severe modality gap. Differently, we propose to disentangle the cross-modal complementary contexts to intra-modal self-attention to explore global complementary understanding, and spatial-aligned inter-modal attention to capture local cross-modal correlations, respectively. 2) Representation disentanglement. Unlike previous undifferentiated combination of cross-modal representations, we find that cross-modal cues complement each other by enhancing common discriminative regions and mutually supplement modal-specific highlights. On top of this, we divide the tokens into consistent and private ones in the channel dimension to disentangle the multi-modal integration path and explicitly boost two complementary ways. By progressively propagate this strategy across layers, the proposed Disentangled Feature Pyramid module (DFP) enables informative cross-modal cross-level integration and better fusion adaptivity. Comprehensive experiments on a large variety of public datasets verify the efficacy of our context and representation disentanglement and the consistent improvement over state-of-the-art models. Additionally, our cross-modal attention hierarchy can be plug-and-play for different backbone architectures (both transformer and CNN) and downstream tasks, and experiments on a CNN-based model and RGB-D semantic segmentation verify this generalization ability.
Hearing Aid Amplification Improves Postural Control for Older Adults With Hearing Loss When Other Sensory Cues Are Impoverished

Recent studies suggest that sound amplification via hearing aids can improve postural control in adults with hearing impairments. Unfortunately, only a few studies used well-defined posturography measures to assess balance in adults with hearing loss with and without their hearing aids. Of these, only two examined postural control specifically in the elderly with hearing loss. The present study examined the impact of hearing aid use on postural control during various sensory perturbations in older adults with age-related hearing loss. Thirty individuals with age-related hearing impairments and using hearing aids bilaterally were tested. Participants were asked to perform a modified clinical sensory integration in balance test on a force platform with and without hearing aids. The experiment was conducted in the presence of a broadband noise ranging from 0.1 to 4 kHz presented through a loudspeaker. As expected, hearing aid use had a beneficial impact on postural control, but only when visual and somatosensory inputs were both reduced. Data also suggest that hearing aid use decreases the dependence on somatosensory input for maintaining postural control. This finding can be of particular importance in older adults considering the reduction of tactile and proprioceptive sensitivity and acuity often associated with aging. These results provide an additional argument for encouraging early hearing aid fitting for people with hearing loss.
Bayesian Inference on Brain-Computer Interfaces via GLASS

Brain-computer interfaces (BCIs), particularly the P300 BCI, facilitate direct communication between the brain and computers. The fundamental statistical problem in P300 BCIs lies in classifying target and non-target stimuli based on electroencephalogram (EEG) signals. However, the low signal-to-noise ratio (SNR) and complex spatial/temporal correlations of EEG signals present challenges in modeling and computation, especially for individuals with severe physical disabilities-BCI's primary users. To address these challenges, we introduce a novel Gaussian Latent channel model with Sparse time-varying effects (GLASS) under a fully Bayesian framework. GLASS is built upon a constrained multinomial logistic regression particularly designed for the imbalanced target and non-target stimuli. The novel latent channel decomposition efficiently alleviates strong spatial correlations between EEG channels, while the soft-thresholded Gaussian process (STGP) prior ensures sparse and smooth time-varying effects. We demonstrate GLASS substantially improves BCI's performance in participants with amyotrophic lateral sclerosis (ALS) and identifies important EEG channels (PO8, Oz, PO7, and Pz) in parietal and occipital regions that align with existing literature. For broader accessibility, we develop an efficient gradient-based variational inference (GBVI) algorithm for posterior computation and provide a user-friendly Python module available at https://github.com/BangyaoZhao/GLASS.
Multimodal Action Quality Assessment

Action quality assessment (AQA) is to assess how well an action is performed. Previous works perform modelling by only the use of visual information, ignoring audio information. We argue that although AQA is highly dependent on visual information, the audio is useful complementary information for improving the score regression accuracy, especially for sports with background music, such as figure skating and rhythmic gymnastics. To leverage multimodal information for AQA, i.e., RGB, optical flow and audio information, we propose a Progressive Adaptive Multimodal Fusion Network (PAMFN) that separately models modality-specific information and mixed-modality information. Our model consists of with three modality-specific branches that independently explore modality-specific information and a mixed-modality branch that progressively aggregates the modality-specific information from the modality-specific branches. To build the bridge between modality-specific branches and the mixed-modality branch, three novel modules are proposed. First, a Modality-specific Feature Decoder module is designed to selectively transfer modality-specific information to the mixed-modality branch. Second, when exploring the interaction between modality-specific information, we argue that using an invariant multimodal fusion policy may lead to suboptimal results, so as to take the potential diversity in different parts of an action into consideration. Therefore, an Adaptive Fusion Module is proposed to learn adaptive multimodal fusion policies in different parts of an action. This module consists of several FusionNets for exploring different multimodal fusion strategies and a PolicyNet for deciding which FusionNets are enabled. Third, a module called Cross-modal Feature Decoder is designed to transfer cross-modal features generated by Adaptive Fusion Module to the mixed-modality branch. Our extensive experiments validate the efficacy of the proposed method, and our method achieves state-of-the-art performance on two public datasets. Code is available at https://github.com/qinghuannn/PAMFN.
Operadic Kazhdan-Lusztig-Stanley theory

We introduce a new type of operad-like structure called a P-operad, which depends on the choice of some collection of posets P, and which is governed by chains in posets of P. We introduce several examples of such structures which are related to classical poset theoretic notions such as poset homology, Cohen--Macaulayness and lexicographic shellability. We then show that P-operads form a satisfactory framework to categorify Kazhdan--Lusztig polynomials of geometric lattices and their kernel. In particular, this leads to a new proof of the positivity of the coefficients of Kazhdan--Lusztig polynomials of geometric lattices.
Quantum algorithm for bioinformatics to compute the similarity between proteins (preprint)

Drug discovery has become a main challenge in our society, following the Covid-19 pandemic. Even pharmaceutical companies are already using computing to accelerate drug discovery. They are increasingly interested in Quantum Computing with a view to improve the speed of research and development process for new drugs. Here, the authors propose a quantum method to generate random sequences based on the occurrence in a protein database and another quantum process to compute a similarity rate between proteins. The aim is to find proteins that are closest to the generated protein and to have an ordering of these proteins. First, the authors will present the construction of a quantum generator of proteins who define a protein, called the test protein. The aim is to have a randomly defined amino-acids sequence according to a proteins database given. The authors will then describe two different methods to compute the similarity's rate between the test protein and each protein of the database and present results obtained for the test protein and for a case study, the elafin.
Mathematical Explanations

A definition of what counts as an explanation of mathematical statement, and when one explanation is better than another, is given. Since all mathematical facts must be true in all causal models, and hence known by an agent, mathematical facts cannot be part of an explanation (under the standard notion of explanation). This problem is solved using impossible possible worlds.