Miracle-dz/UniFAR-FTUs huggingface dataset:
Daily Mental Health Monitoring from Speech: A Real-World Japanese Dataset and Multitask Learning Analysis

Translating mental health recognition from clinical research into real-world application requires extensive data, yet existing emotion datasets are impoverished in terms of daily mental health monitoring, especially when aiming for self-reported anxiety and depression recognition. We introduce the Japanese Daily Speech Dataset (JDSD), a large in-the-wild daily speech emotion dataset consisting of 20,827 speech samples from 342 speakers and 54 hours of total duration. The data is annotated on the Depression and Anxiety Mood Scale (DAMS) – 9 self-reported emotions to evaluate mood state including "vigorous", "gloomy", "concerned", "happy", "unpleasant", "anxious", "cheerful", "depressed", and "worried". Our dataset possesses emotional states, activity, and time diversity, making it useful for training models to track daily emotional states for healthcare purposes. We partition our corpus and provide a multi-task benchmark across nine emotions, demonstrating that mental health states can be predicted reliably from self-reports with a Concordance Correlation Coefficient value of . 547 on average. We hope that JDSD will become a valuable resource to further the development of daily emotional healthcare tracking.
Progressive Refinement Learning Based on Feature Cross Perception for Residential Areas Semantic Segmentation

Due to the pixel-level accurate annotation of remote sensing images consumes a lot of labor costs, weak annotation semantic segmentation has become a hotspot in recent years. However, due to the lack of label accuracy, these methods often have insufficient expression ability. In this paper, we proposed a semantic segmentation method for residential areas by progressive refinement learning. This method mainly consists of two parts. In the first part, we constructed a classification network and proposed an initial pixel-level label calculation method based on multi-layer category feature awareness. In the second part, we proposed to construct feature cross perceptron module in the structure of the multi-level codec to achieve image pair semantic co-segmentation. In addition, we used confidence maps to modify the loss function to achieve more accurate results. Comprehensive evaluations with GeoEye-1 dataset and the comparison with 7 methods validate the superiority of the proposed model.
Estimation of Cardiac Fibre Direction Based on Activation Maps

Estimating tissue conductivity parameters from electrograms (EGMs) could be an important tool for diagnosing and treating heart rhythm disorders such as atrial fibrillation (AF). One of these parameters is the fibre direction, often assumed to be known in conductivity estimation methods. In this paper, a novel method to estimate the fibre direction from EGMs is presented. This method is based on local conduction slowness vectors of a propagating activation wave. These conduction slowness vectors follow an elliptical pattern that depends on the underlying conductivity parameters. The fibre direction and conductivity anisotropy ratio can therefore be estimated by fitting an ellipse to the conduction slowness vectors. Applying the presented method on simulated data shows that it can estimate the fibre direction more accurately than existing methods, and that its performance depends mostly on the range of wavefront directions present in the measurement area. The main advantage of the presented method is that it still functions relatively well in the presence of conduction blocks, as long as the surrounding tissue is approximately homogeneous.
Road Feature Detection for Advance Driver Assistance System Using Deep Learning

Hundreds of people are injured or killed in road accidents. These accidents are caused by several intrinsic and extrinsic factors, including the attentiveness of the driver towards the road and its associated features. These features include approaching vehicles, pedestrians, and static fixtures, such as road lanes and traffic signs. If a driver is made aware of these features in a timely manner, a huge chunk of these accidents can be avoided. This study proposes a computer vision-based solution for detecting and recognizing traffic types and signs to help drivers pave the door for self-driving cars. A real-world roadside dataset was collected under varying lighting and road conditions, and individual frames were annotated. Two deep learning models, YOLOv7 and Faster RCNN, were trained on this custom-collected dataset to detect the aforementioned road features. The models produced mean Average Precision (mAP) scores of 87.20% and 75.64%, respectively, along with class accuracies of over 98.80%; all of these were state-of-the-art. The proposed model provides an excellent benchmark to build on to help improve traffic situations and enable future technological advances, such as Advance Driver Assistance System (ADAS) and self-driving cars.
Sparse Asynchronous Samples from Networks of Tems for Reconstruction of Classes of Non-Bandlimited Signals

We present a signal driven multi-channel time encoding system for sampling signals with finite rate of innovation (FRI). The system produces samples in the form of finite differences from which the input signal can be exactly reconstructed. The use of finite differences allows diversity of TEM parameters between channels, particularly with regards to resetting characteristics and the multi-channel approach allows for better control and minimisation of the number of samples generated meaning the sampler can be considered more energy efficient than existing methods which inherently produce large numbers of samples. Furthermore, the system only generates samples in response to innovations in the signal. This makes the system well suited to sampling truly asynchronous signals consisting of bursts of activity interspersed with long indeterminate periods of quiescence while also generating sparse sets of samples.
Steerable Simultaneous Multibeam Antenna Based on Reconfigurable Reflectarray

Multibeam system is a core part for wireless concurrent communication and massive multiple-input–mulitple-output (MIMO) applications. Traditional concurrent communication techniques, like phased-array or digital beam-forming system, suffer from massive RF frontends or power-hungry high-speed analog-to-digital converters, which makes them complex and expensive in fabrication. Inheriting the advantages of reflectarray and passive multibeam system, we reported in this letter a novel and cost-effective concept of steerable simultaneous multibeam antenna (SMBA). New features and the practicality of it are fully verified with theoretical analysis and experimental implementation of a prototype working at 7 GHz with 300 MHz bandwidth. With multiple beams steered simultaneously, the SMBA can eliminate the crossover regions between adjacent beams of passive multibeam antenna. Experimental results indicate its potential in the design of diverse concurrent communication systems such as base stations and point-to-point links.
Profiling of antibiotic resistance among uropathogens isolated from patients attending Kericho County Referral Hospital

Introduction urinary tract infection (UTI) comes second after respiratory infections in most communities and hospital settings, affecting people of all ages. Frequent use of antibiotics to manage UTI has resulted in development of resistance, calling upon policymakers to fast-track and enforce policies that guide the use of antibiotics in the country. This study intended to determine the current antibiotic resistance to uropathogens among patients attending Kericho County Referral Hospital. Methods three hundred urine samples from eligible participants were cultured and bacteria colonies identified using biochemical tests. Antibiotic sensitivity was done using Kirby Bauer disk diffusion method on Mueller Hinton Agar. Results: the aetiological agents of UTI were Staphylococcus aureus, Enterococci faecalis, E. coli, Proteus spp and Klebsiella pneumonia. Antibiotic resistance was observed among these uropathogens to commonly used antibiotics namely; ampicillin (84.3%), azithromycin (71.9%) and augmentin (69.8%). However, there were some bacteria that were susceptible to all or some commonly used antibiotics. There was moderate resistance to norfloxacin (43%) except in Staphylococcus aureus which showed 64% resistance. The isolates showed less resistance to cefoxitine (13.2%), gentamycin (11.6%) and ciprofloxacin (10%). While most bacteria showed multiple resistance to 3 drugs, some showed resistance to at most 5 drugs tested in the study. Conclusion: this study found Staphylococcus aureus to be the predominant aetiological agent of UTI. Cefoxitine, gentamycin and ciprofloxacin are good therapeutic choices for confirmed recurrent UTI when culture results are unavailable. There is need to have regular screening of aetiological agents of UTI and their resistance to antimicrobials.
Synergetic Effect of Silver Nanocrystals Applied in PbS Colloidal Quantum Dots for High-Performance Infrared Photodetectors

PbS colloidal quantum dot (CQD) photodetectors hold great potential for near-infrared detection due to their extremely high sensitivity and low-cost solution processing. In this paper we report that incorporation of 0.5% to 1% (by weight) Ag nanocrystals (NCs) into the PbS CQDs film could simultaneously enhance the photocurrent and suppress dark current and hence significantly boost device detectivity. A set of control experiments suggested that Ag NCs, once added to the PbS CQD film, could trap photogenerated electrons from neighboring PbS CQDs, extend carrier lifetime, and increase photocurrent. We further built a sensitive flexible photodetector using the optimized composite on stone paper, achieving an estimated detectivity as high as 1.5 × 1010 Jones. The synergetic effect found in our PbS CQD/Ag NC composite photodetectors is expected to be extendable to other binary NC systems for various applications.
SEPDIFF: Speech Separation Based on Denoising Diffusion Model

Speech separation aims to extract multiple speech sources from mixed signals. In this paper, we propose SepDiff - a monaural speech separation method based on the denoising diffusion model (diffusion model). By modifying the diffusion and reverse process, we show that the diffusion model achieves an impressive performance on speech separation. To generate speech sources, we use mel spectrogram of the mixture as a condition in the training procedure and insert it in every step of the sampling procedure. We propose a novel DNN structure to leverage local and global speech information through successive feature channel attention and dilated 2-D convolution blocks on multi-resolution time-frequency features. We use a neural vocoder to get waveform from the generated mel spectrogram. We evaluate SepDiff on LibriMix datasets. Compared to SepFormer approach, SepDiff yields a higher mean opinion score (MOS) of 0.11.
Statistical Background Modeling for Foreground Detection: A Survey

Background modeling is often used in the context of moving objects detection from static cameras. Numerous methods have been developed over the recent years and the most used are the statistical ones. The purpose of this chapter is to provide a recent survey of these different statistical methods. For this, we have classified them in term of generation following the years of publication and the statistical tools used. We then focus on the first generation methods: Single Gaussian, Mixture of Gaussians, Kernel Density Estimation and Subspace Learning using PCA. These original methods are reminded and then we have classified their different improvements in term of strategies. After analyzing the strategies and identifying their limitations, we conclude with several promising directions for future research.
Investigating snowplow-related injury severity along mountainous roadway in Wyoming

Snow removal and deicing using snowplow trucks assist transportation agencies to enhance roadway safety and mobility. However, due to slower travel speeds during these operations, motorists often end up in crashes for poor visibility and disturbance of the snow. Despite the risk associated with snowplows, no previous study was found that exclusively investigate the factors associated with injury severity in snowplow-involved crashes. Therefore, this paper presents an extensive exploratory analysis and fills this knowledge gap by identifying the significant contributing factors affecting the occupant injury severity from the aspects of crashes with snowplow involvement. The study utilized eleven years (2010-2020) of historical snowplow-related crash data from Wyoming. Both the binary logit model and mixed binary logit model were developed to investigate the impacts of the various occupant, vehicle, crash, roadway, and environmental characteristics on the corresponding occupant injury severity. As one of the important findings from this research concludes that other vehicle drivers are more responsible than snowplow drivers contributing to more severe injuries in crashes involving snowplows. Recommendations suggested based on the modeling results are expected to help transportation agencies and policymakers take necessary actions in reducing snowplow-involved crashes by targeting appropriate strategies and proper resource allocation.
Improving Phase-Vocoder-Based Time Stretching by Time-Directional Spectrogram Squeezing

Time stretching of music signals has a crucial problem, i.e., smearing of percussive sounds. Some time stretching algorithms have addressed this problem by detecting percussive components and manipulating them differently from the other components. However, conventional methods cause artifacts. In this paper, to prevent percussion smearing, we propose a preprocessing for time stretching. The proposed algorithm aims to preserve time scale of percussive components while stretching the rest of components in the ordinary way. To do so, time-frequency bins dominated by percussive components are squeezed in time direction so as to preserve the shape of spectrogram of percussive components. Our experiment showed that our method could improve sound quality for long stretching.
Improvement of the performance of a classical matched filter by an independent component analysis preprocessing

Current gravitational wave searches for compact binaries coalescence are done using a bank of templates (matched filters) on each running detector. Given a network of interferometers, we propose to use a denoising strategy based on an independent component analysis which considers two interferometers at a time and then to use a standard matched filter on the processed data. We show that this method allows to lower the level of noise and increases the signal-to-nose ratio at the output of the matched filter.
Sparse Representations with Cone Atoms

We extend the notion of sparse representation to the case where the atoms are not vectors, but cones, hence infinite sets. The sparse representation is linear, as usual, but the most convenient vector is chosen from each selected cone. We give a cone version of Orthogonal Matching Pursuit (OMP) and show that its complexity is only a few times larger than that of OMP. The new cone OMP can be used for anomaly detection; we apply it with very good results to the detection of abnormal heartbeats.
Neural Network Models with Integrated Training and Adaptation For Nonlinear Acoustic System Identification

System identification is instrumental in various tasks of acoustics, including acoustic measurement and acoustic echo cancellation. Adaptive filtering is proven to be successful for the identification of linear parts with variable impulse responses. Neural network frameworks are recently considered for nonlinear modeling, but the desire for variable system identification on individual sequences may be incompatible with the general concept of training across batch data. This paper therefore pursues architectures mixed from trainable and non-trainable adaptive sections. By seamlessly integrating adaptive FIR filters into end-to-end neural network architectures, the desired flexibility in the network is achieved. In-network adaptivity is here implemented as a recurrent computational layer according to the normalized least mean-square (NLMS) algorithm. Altogether six architectures are explored in order to study what part of a system better relies on training or adaptation. The proposed implementations are thus verified on synthetic data with dedicated variations of speech or noise input, linear or nonlinear system components, and fixed or variant acoustic impulse responses.
Multimodal Emotion Recognition Based on Deep Temporal Features Using Cross-Modal Transformer and Self-Attention

Multimodal speech emotion recognition (MSER) is an emerging and challenging field of research due to its more robust characteristics than unimodal. However, in multimodal approaches, the interactive relations for model building using different modalities of speech representations for emotion recognition have not been well investigated yet. To address this issue, we introduce a new approach to capturing the deep temporal features of audio and text. The audio features are learned with a convolution neural network (CNN) and a Bi-directional Gated Recurrent Unit (Bi-GRU) network. The textual features are represented by GloVe word embedding along with Bi-GRU. A cross-modal transformers block is designed for multimodal learning to capture better inter- and intra-interactions and temporal information between the audio and textual features. Further, a self-attention (SA) network is employed to select more important emotional information from the fused multimodal features. We evaluate the proposed method on the IEMOCAP dataset on four emotion classes (i.e., angry, neutral, sad, and happy). The proposed method performs significantly better than the most recent state-of-the-art MSER methods.
MicroRNA Dysregulation in Early Breast Cancer Diagnosis: A Systematic Review and Meta-Analysis

Breast cancer continues to be the leading cause of death in women worldwide. Mammography, which is the current gold standard technique used to diagnose it, presents strong limitations in early ages where breast cancer is much more aggressive and fatal. MiRNAs present in numerous body fluids might represent a new line of research in breast cancer biomarkers, especially oncomiRNAs, known to play an important role in the suppression and development of neoplasms. The aim of this systematic review and meta-analysis was to evaluate dysregulated miRNA biomarkers and their diagnostic accuracy in breast cancer. Two independent researchers reviewed the included studies according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. A protocol for this review was registered in PROSPERO with the registration number “CRD42021256338”. Observational case-control-based studies analyzing concentrations of microRNAs which have been published within the last 10 years were selected, and the concentrations of miRNAs in women with breast cancer and healthy controls were analyzed. Random-effects meta-analyses of miR-155 were performed on the studies which provided enough data to calculate diagnostic odds ratios. We determined that 34 microRNAs were substantially dysregulated and could be considered biomarkers of breast cancer. Individually, miR-155 provided better diagnostic results than mammography on average. However, when several miRNAs are used to screen, forming a panel, sensitivity and specificity rates improve, and they can be associated with classic biomarkers such us CA-125 or CEA. Based on the results of our meta-analysis, miR-155 might be a promising diagnostic biomarker for this patient population.
Noise-Aware Target Extension with Self-Distillation for Robust Speech Recognition

Data augmentation using additive noise is a framework for robustly training automatic speech recognition models. To utilize noise information efficiently, previous studies used an additional branch to classify noise conditions. This added branch has a limited effect on the ASR because it performs independently of the ASR branch that classifies senones. In this paper, we propose a noise-aware target extension (NATE) that extends the senone target to contain noise awareness by jointly classifying the senone and noise in a single branch. In the inference stage, the output of the model is processed separately by the noise condition and then aggregated to match the senone posterior distribution. In addition, we combine NATE with self-distillation (NATEsd) to reduce the model parameters and avoid discrepancies between the outputs of training and inference. The effectiveness of the NATE method is validated on the two benchmark development and evaluation sets and simulated noisy test sets, resulting in significant improvements over the previous methods.
Steganalysis SVM algorithm based on adjacent pixel and texture correlations

Steganalysis searches to obtain higher detection ratios for image steganography and to extend the range of steganalysis application.A least significant bit(LSB) steganalysis algorithm was developed using statistical features to correlate adjacent pixels and the texture.A support vector machine method is used to classify these statistical adjacent pixels and texture correctations.Tests of 1 600 raw lossless-stored BMP images with various embedded information ratios validate the performance of the method.The results show that this method is a reliable detection method with higher accuracy than the regular singular(RS) and gray-level plane crossing(GPC).
Environmental Factors Affecting Amphibian Communities in River Basins of the Southern Apennines

The study of the environmental features affecting amphibian communities is a priority task for addressing effective conservation initiatives. In the southern edge of the Apennines (Sila Massif, Calabria Region, Italy), we surveyed the distribution of amphibians in lotic freshwater habitats (eight rivers, 17 sampling stations, 87 transects) and recorded nine environmental variables, including the occurrence of potential predators (fish and Eurasian otter Lutra lutra), potentially affecting the distribution and breeding success of amphibian species. A total of seven amphibian species was recorded (75.9% of transects). Fish occurred in all rivers while the otter was found in four rivers (Amato, Lese, Neto, and Savuto). Illuminance and bank heterogeneity were the main factors affecting amphibian reproduction, while neither otter nor fish presence showed significant effects on the amphibian community. Overall, habitat complexity and coevolution history seemed to shape the distribution of amphibians and their breeding sites, while the ongoing recolonization of the study area by the otter is expected to have a negligible impact on the richness of the amphibian community.
Energy flow analysis of plate structures based on the finite element method

As a result of the in-plane waves length are longer than the out-plane waves length in most time, at present the in-plane motions of plate structures are ignored in the energy flow analysis based on finite element,but this approximation is not rational in some particular condition. Based on the traditional theories of lamina, the in-plane and out-plane motions are uncoupled in this paper, then two energy flow models of these two types motion are deduced, and the coupling loss factor of bending waves are given by energy balance equations. The numerical example shows that the method presented by this paper can calculate the in-plane and out-plane energy, then this method can be a more accurate calculation method for the coupling loss factor.