Miracle-dz/UniFAR-FTUs huggingface dataset:
Speaker Counting Based on a Novel Hive Shaped Nested Microphone Array by WPT and 2D Adaptive SRP Algorithms in Near-Field Scenarios

Speech processing algorithms, especially sound source localization (SSL), speech enhancement, and speaker tracking are considered to be the main fields in this application. Most speech processing algorithms require knowing the number of speakers for real implementation. In this article, a novel method for estimating the number of speakers is proposed based on the hive shaped nested microphone array (HNMA) by wavelet packet transform (WPT) and 2D sub-band adaptive steered response power (SB-2DASRP) with phase transform (PHAT) and maximum likelihood (ML) filters, and, finally, the agglomerative classification and elbow criteria for obtaining the number of speakers in near-field scenarios. The proposed HNMA is presented for aliasing and imaging elimination and preparing the proper signals for the speaker counting method. In the following, the Blackman–Tukey spectral estimation method is selected for detecting the proper frequency components of the recorded signal. The WPT is considered for smart sub-band processing by focusing on the frequency bins of the speech signal. In addition, the SRP method is implemented in 2D format and adaptively by ML and PHAT filters on the sub-band signals. The SB-2DASRP peak positions are extracted on various time frames based on the standard deviation (SD) criteria, and the final number of speakers is estimated by unsupervised agglomerative clustering and elbow criteria. The proposed HNMA-SB-2DASRP method is compared with the frequency-domain magnitude squared coherence (FD-MSC), i-vector probabilistic linear discriminant analysis (i-vector PLDA), ambisonics features of the correlational recurrent neural network (AF-CRNN), and speaker counting by density-based classification and clustering decision (SC-DCCD) algorithms on noisy and reverberant environments, which represents the superiority of the proposed method for real implementation.
Does Rumination Cause “Inhibitory” Deficits?

Inhibitory processes have been implicated in depressive rumination. Inhibitory deficits may cause difficulties in disengaging from ruminative content (e.g., Joormann, 2005), or rumination may constitute a working memory load, causing deficits in inhibitory control (e.g., Hertel, 2004). These hypotheses have different implications for the treatment of depression. We conducted a systematic review of existing evidence, and conclude that most studies do not unambiguously measure inhibition. The majority of published evidence is correlational, and thus supports neither causal direction. No published experimental studies have investigated the inhibitory deficit -? rumination causal direction, and only six have investigated the rumination -? inhibitory deficit hypothesis. In two of these studies the dependent variable has low construct validity. One study reported no effect of rumination on interference, and three did not control for mood effects. There is need for carefully designed experimental research that has the potential to investigate these proposed causal mechanisms.
Light rays in the gravitational field of rotating disks of dust in general relativity

The gravitational fields of galaxies are a major influence on light rays reaching the earth from the corners of the universe, and the images obtained by telescopes of the night sky. A complete and consistent study of the effects of the gravitational fields produced by massive astrophysical bodies requires recourse to Einstein's general theory of relativity. To this end, an analysis is presented of the behaviour of light rays in the relativistic gravitational field of rotating, pressure free, ideal fluid disks. These disks are models for rotating galaxies whose fluid particles are the individual stars comprising the galaxy in question. The stars interact with one another solely via the gravitational force. The main results in this thesis include: (1) Visualisation of rotating galaxies in curved spacetime (2) The gravitational deflection of individual light rays (3) Gravitational lensing (multiple images, caustics etc.)
Fast Optical Signals for Real-Time Retinotopy and Brain Computer Interface

A brain–computer interface (BCI) allows users to control external devices through brain activity. Portable neuroimaging techniques, such as near-infrared (NIR) imaging, are suitable for this goal. NIR imaging has been used to measure rapid changes in brain optical properties associated with neuronal activation, namely fast optical signals (FOS) with good spatiotemporal resolution. However, FOS have a low signal-to-noise ratio, limiting their BCI application. Here FOS were acquired with a frequency-domain optical system from the visual cortex during visual stimulation consisting of a rotating checkerboard wedge, flickering at 5 Hz. We used measures of photon count (Direct Current, DC light intensity) and time of flight (phase) at two NIR wavelengths (690 nm and 830 nm) combined with a machine learning approach for fast estimation of visual-field quadrant stimulation. The input features of a cross-validated support vector machine classifier were computed as the average modulus of the wavelet coherence between each channel and the average response among all channels in 512 ms time windows. An above chance performance was obtained when differentiating visual stimulation quadrants (left vs. right or top vs. bottom) with the best classification accuracy of ~63% (information transfer rate of ~6 bits/min) when classifying the superior and inferior stimulation quadrants using DC at 830 nm. The method is the first attempt to provide generalizable retinotopy classification relying on FOS, paving the way for the use of FOS in real-time BCI.
Permutation-based inference for function-on-scalar regression with an application in PET brain imaging

The density of various proteins throughout the human brain can be studied through the use of positron emission tomography (PET) imaging. We report here on data from a study of serotonin transporter (5-HTT) binding. While PET imaging data analysis is most commonly performed on data that are aggregated into several discrete a priori regions of interest, in this study, primary interest is on measures of 5-HTT binding potential that are made at many locations along a continuous anatomically defined tract, one that was chosen to follow serotonergic axons. Our goal is to characterise the binding patterns along this tract and also to determine how such patterns differ between control subjects and depressed patients. Due to the nature of our data, we utilise function-on-scalar regression modelling to make optimal use of our data. Inference on both main effects (position along the tract; diagnostic group) and their interactions are made using permutation testing strategies that do not require distributional assumptions. Also, to investigate the question of homogeneity we implement a permutation testing strategy, which adapts a ‘block bootstrapping’ approach from time series analysis to the functional data setting.
Do line call errors differ between men’s and women’s events in tennis? A case study from the 2021 Wimbledon Tournament

ABSTRACT Line umpires make quick decisions. Ball trajectories and mechanical characteristics (velocity, spin) are different for male and female events, which can affect the decision quality. The aim is to analyse patterns of line umpire errors at the Wimbledon 2021 with a particular focus on differences between men’s and women’s matches. Data were obtained from the official match reports from 290 matches where the Hawk-Eye system was used to determine the umpires’ calls and used to calculate descriptive statistics and to run different linear regression models. Line umpires made 1 error per 17.7 games, with a mean error size of 30 ± 39 mm. Error size on long lines (18 ± 24 mm) was smaller compared to cross lines (40 ± 45 mm), which also served as a significant predictor in all regression models. In women’s matches, we observed a 7 mm bigger mean error size, which was in particular caused by a significantly bigger average error size for erroneous “out” calls (12.2 mm), which could be supported by the regression models. These results help to understand human decision-making performance on the grass. To improve line umpiring, we suggest to put awareness about the nature of line umpire errors, which indeed indicates that the task differs between men’s and women’s matches.
Microalgae Produce Antioxidant Molecules with Potential Preventive Effects on Mitochondrial Functions and Skeletal Muscular Oxidative Stress

In recent years, microalgae have become a source of molecules for a healthy life. Their composition of carbohydrates, peptides, lipids, vitamins and carotenoids makes them a promising new source of antioxidant molecules. Skeletal muscle is a tissue that requires constant remodeling via protein turnover, and its regular functioning consumes energy in the form of adenosine triphosphate (ATP), which is produced by mitochondria. Under conditions of traumatic exercise or muscular diseases, a high production of reactive oxygen species (ROS) at the origin of oxidative stress (OS) will lead to inflammation and muscle atrophy, with life-long consequences. In this review, we describe the potential antioxidant effects of microalgae and their biomolecules on mitochondrial functions and skeletal muscular oxidative stress during exercises or in musculoskeletal diseases, as in sarcopenia, chronic obstructive pulmonary disease (COPD) and Duchenne muscular dystrophy (DMD), through the increase in and regulation of antioxidant pathways and protein synthesis.
A Simple Yet Effective Approach to Structured Knowledge Distillation

Structured prediction models aim at solving tasks where the output is a complex structure, rather than a single variable. Performing knowledge distillation for such problems is non- trivial due to their exponentially large output space. Previous works address this problem by developing particular distillation strategies (e.g., dynamic programming) that are both complicated and of low run-time efficiency. In this work, we propose an approach that is much simpler in its formulation, far more efficient for training than existing methods, and even performs better than our baselines. Specifically, we transfer the knowledge from a teacher model to its student by locally matching their computations on all internal structures rather than the final outputs. In this manner, we avoid time-consuming techniques like Monte Carlo Sampling for decoding output structures, permitting parallel computation and efficient training. Besides, we show that it encourages the student model to better mimic the internal behavior of the teacher model. Experiments on two structured prediction tasks demonstrate that our approach not only halves the time cost, but also outperforms previous methods on two widely adopted benchmark datasets.1 2
A New SVM-Based Handwritten Chinese Character Recognition Method

A new recognition method of handwritten Chinese characters by support vector machine is presented.Support vector machines(SVM) operate on the principle of structure risk minimization which not only keeps the empirical risk minimal but also controls VC confidence of discriminant functions,hence a better generalization ability is guaranteed.In this paper,the problems to be solved while applying SVM in Chinese character recognition are addressed at first,and then a two stage of recognition scheme is suggested.Finally,experimental results on 1034 categories of Chinese character from 120 sets of samples are given.For the Polynomial kernel SVM,a 3 38% average increment of recognition rate is obtained showing the efficiency of the proposed approach.
SUVR: A Search-Based Approach to Unsupervised Visual Representation Learning

Unsupervised learning has grown in popularity because of the difficulty of collecting annotated data and the development of modern frameworks that allow us to learn from unlabeled data. Existing studies, however, either disregard variations at different levels of similarity or only consider negative samples from one batch. We argue that image pairs should have varying degrees of similarity, and the negative samples should be allowed to be drawn from the entire dataset. In this work, we propose Search-based Unsupervised Visual Representation Learning (SUVR) to learn better image representations in an unsupervised manner. We first construct a graph from the image dataset by the similarity between images, and adopt the concept of graph traversal to explore positive samples. In the meantime, we make sure that negative samples can be drawn from the full dataset. Quantitative experiments on five benchmark image classification datasets demonstrate that SUVR can significantly outperform strong competing methods on unsupervised embedding learning. Qualitative experiments also show that SUVR can produce better representations in which similar images are clustered closer together than unrelated images in the latent space.
Robust Adaptive Beamforming with Proximal Method

This work revisits the classic robust adaptive beamforming that is widely adopted for interference suppression. A first-order method is proposed to solve the beamformers for large arrays. The method uses proximal gradient descent along with Nesterov’s acceleration. It has ${\mathcal{O}}\left( {{N^2}} \right)$ computational complexity per iteration where N is the array size. For sparse linearly constrained adaptive beamforming, the proposed method achieves performances comparable to the conjugate gradient method. For sparse robust adaptive beamforming with conic constraints, the proposed method is much more efficient than the standard interior point solver.
Image Fast Reconstruction Method Based on Compressive Sensing

Orthogonal Matching Pursuit(OMP) algorithm is used to reconstruct images based on the combinations of several common sparse transform bases and measurement matrices.This paper analyzes and compares the reconstruction results with above various combinations.And on this basis,a fast image reconstruction method is proposed.A multi-scale wavelet decomposition is used to extract the approximate coefficients from the image.It uses compressed sensing method to recover these approximate coefficients,and the reconstructed images are obtained with inverse wavelet transform based on detail coefficients and recovered approximate coefficients.Experimental results show that with the same number of measurement values,the run time of this method and the quality of the reconstructive images have great improvements.
Resistance to larvicides in mosquito populations and how it could benefit malaria control

We model larviciding of mosquitoes taking into account the evolution of resistance to the larvicides, the evolutionary costs of resistance and the implications for malaria control. There is evidence that resistance comes with various costs, one of which is reduced adult longevity for resistant mutants. The mosquito adult lifespan is one of the most crucial parameters in malaria transmission due to a long developmental time for the malaria parasite in the insect. A possible malaria control strategy is therefore to shorten this adult lifespan by larviciding with a potent larvicide to which mosquitoes become resistant. This novel strategy is studied using a mathematical model for the wild type and resistant mutants and by incorporating the malaria disease dynamics using an SEI type model with standard incidence that incorporates the latency period of the parasite in wild-type and resistant mosquitoes. We consider the linear stability of the malaria-free equilibrium in which the resistant strain is dominant and derive a condition for the global eradication of malaria. Numerical simulations are presented, which offer further insights. The parameter to which the analysis is most sensitive is the per-capita death rate of resistant adult mosquitoes. Increasing this parameter dramatically reduces the basic reproduction number R0. However, increasing it too much causes the wild type to outcompete the resistant mutants, and the control strategy fails. Exploitation of costs of resistance to larvicides thus offers a possible malaria control measure if the larvicide is sufficiently potent and costs of resistance are neither too great nor too small.
Design of 3D reconstruction system based on multi-view stereo vision

To solve the problem of three-dimensional reconstruction of the surface of the product in the process of industrial products quality inspection,a method based on multi-view stereo vision was proposed.A system of three-dimensional reconstruction program with eight linear industrial cameras was designed.Firstly,through the image acquisition module,the images of the target object were acquired in eight different directions.Secondly,those images were used to preprocess,including image background suppression and target object segmentation.Then the internal and external parameters of eight cameras were got by the camera calibration module,and combined with Harris corner detection and Difference of Gaussian detection algorithm,feature point extraction was achieved for the pretreatment pictures.Based on these results,the extractive the feature points were matched and checked by the triangular method.Finally,after accurately obtaining and optimizing the angle points,finding a corner feature matching points,the surface of object was reconstructed accurately by the Poisson surface reconstruction method.The experimental results show that this system can reconstruct the three-dimensional surface of the static object,the surface of the object is integrity and its structure is clear in the reconstruction results.The characters on the surface are reconstructed completely,which can be well used for character recognition.
CNN Filter for Super-Resolution with RPR Functionality in VVC

High resolution (HR) videos are easy to go beyond the band-width limit during transmission after encoding. Downsampling followed by upsampling is a well-known strategy for compressing HR videos with limited bandwidth. Versatile video coding (VVC) provides a reference picture resampling (RPR) functionality to consider the limited bandwidth. In this paper, we propose a convolutional neural network (CNN) filter for super-resolution (SR) with the RPR functionality in VVC. We design a lightweight SR network that combines CNN with the RPR functionality in VVC, called lightweight network of multi-level mixed scale and depth information with attention mechanism (LMSDANet). For LMSDANet, we provide a lightweight block of multi-mixed scale and depth information with attention (LMSDAB) to extract multi-scale and convolutional layer depth information while enhancing the representation of features. Compared with VTM-11. 0_NNVC-2.0 anchor, LMSDANet achieves {-9.16% (Y), 17.03% (U), -7.61% (V)} and {-4.14% (Y), 6.34% (U), -2.25% (V)} BD-rate changes (average on A1 and A2) in AI and RA configurations, respectively.
Angle-Of-Arrival Target Tracking Using A Mobile Uav In External Signal-Denied Environment

This paper focuses on the angle-of-arrival (AOA) target tracking problem using a mobile unmanned aerial vehicle (UAV) equipped with an angle-of-arrival (AOA) sensor to observe targets in an external-denied (no global positioning system, inertial navigation system aid) environment. The mathematical formulations are based on two known anchors. The extended Kalman filter (EKF) is modified to calculate the UAV’s and target’s absolute coordinates. To improve the tracking accuracy, a gradient-based UAV path optimization algorithm using simultaneous perturbation stochastic approximation (SPSA) is developed. The geometry of anchors and UAV impacts the estimation accuracy, especially when the selected anchors are close to the target. The simulations have demonstrated the effectiveness of the proposed scheme.
Sampled‐data‐based event‐triggered adaptive prescribed performance control of uncertain strict‐feedback nonlinear systems

This article studies the problem of global adaptive prescribed performance control (PPC) for a class of uncertain strict‐feedback nonlinear systems based on the sampled‐data‐based event‐triggering mechanism. A novel time‐varying scaling function, whose reciprocal plays the role of a prescribed performance function, is constructed and utilized to remove the range restriction on the initial tracking error and achieve the global PPC. Meanwhile, to reduce the communication frequency and avoid the Zeno behavior in mechanism, a sampled‐data‐based event‐triggering mechanism is introduced, and an event‐triggered global adaptive prescribed performance controller is designed by employing the error transformation technique and the adaptive backstepping method. It is proved that all signals of the closed‐loop system are bounded and the tracking error is confined within the prescribed performance boundary for arbitrary initial values. Since there is no need to continuously monitor whether the triggering condition is satisfied, the obtained result is conducive to physical implementation. Finally, a network‐based robotic manipulator system is employed to verify the effectiveness of the proposed method.
Influence of Sports Participation on the Behaviors of Customers of Sports Services: Linear and Qualitative Comparative Analysis Models

Sports services have become an important enclave for our society. Due to its complete sports offer, customers can perform physical activity that helps them to improve their health and well-being. In fitness centers, managers try to know what their customers are looking for in order to create more personalized experiences, as well as to improve their health. This study seeks to understand the influence that sports frequency has on the future behavior of users of a sports center, using two complementary methodologies. A sample of 383 users of a private sports center was used. The two complementary methodologies used were linear models and comparative qualitative analysis, based on the combination of sets. The results show how sports frequency influences the process of creating users’ future behaviors. Considering that none of the variables are necessary, it is observed that perceived value has a significant influence on users’ future behaviors. The use of two complementary methodologies provides a more complete understanding, which helps sports managers to plan and manage effectively to ensure user satisfaction and loyalty. In addition, facilities can incentivize customers through loyalty programs and promotions to maintain their engagement, as well as healthy styles to encourage service recommendations.
Ambulatory Resistant Hypertension and Risk of Heart Failure in the Elderly

(1) Background: The aim of the study was to assess the risk of heart failure (HF) in elderly treated hypertensive patients with white coat uncontrolled hypertension (WUCH), ambulatory nonresistant hypertension (ANRH) and ambulatory resistant hypertension (ARH), when compared to those with controlled hypertension (CH). (2) We studied 745 treated hypertensive subjects older than 65 years. CH was defined as clinic blood pressure (BP) < 140/90 mmHg and 24-h BP < 130/80 mmHg; WUCH was defined as clinic BP ≥ 140/90 mmHg and 24-h BP < 130/80 mmHg; ANRH was defined as 24-h BP ≥ 130/80 mmHg in patients receiving ≤2 antihypertensive drugs; ARH was defined as 24-h BP ≥ 130/80 mmHg in patients receiving ≥3 antihypertensive drugs. (3) Results: 153 patients had CH, 153 had WUCH, 307 had ANRH and 132 (18%) had ARH. During the follow-up (8.4 ± 4.8 years), 82 HF events occurred. After adjustment for various covariates, when compared to CH, the hazard ratio (95% confidence interval) for HF was 1.30 (0.51–3.32), 2.14 (1.03–4.43) and 3.52 (1.56–7.96) in WUCH, ANRH and ARH, respectively. (4) Conclusions: among elderly treated hypertensive patients, those with ARH are at a considerably higher risk of developing HF when compared to CH.