Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Stage Audio-Visual Fusion for Dysarthric Speech Recognition With Pre-Trained Models

Dysarthric speech recognition helps speakers with dysarthria to enjoy better communication. However, collecting dysarthric speech is difficult. The machine learning models cannot be trained sufficiently using dysarthric speech. To further improve the accuracy of dysarthric speech recognition, we proposed a Multi-stage AV-HuBERT (MAV-HuBERT) framework by fusing the visual information and acoustic information of the dysarthric speech. During the first stage, we proposed to use convolutional neural networks model to encode the motor information by incorporating all facial speech function areas. This operation is different from the traditional approach solely based on the movement of lip in audio-visual fusion framework. During the second stage, we proposed to use the AV-HuBERT framework to pre-train the recognition architecture of fusing audio and visual information of the dysarthric speech. The knowledge gained by the pre-trained model is applied to address the overfitting problem of the model. The experiments based on UASpeech are designed to evaluate our proposed method. Compared with the results of the baseline method, the best word error rate (WER) of our proposed method was reduced by 13.5% on moderate dysarthric speech. In addition, for the mild dysarthric speech, our proposed method shows the best result that the WER of our proposed method arrives at 6.05%. Even for the extremely severe dysarthric speech, the WER of our proposed method achieves at 63.98%, which reduces by 2.72% and 4.02% compared with the WERs of wav2vec and HuBERT, respectively. The proposed method can effectively further reduce the WER of the dysarthric speech.
A Comparison of Gulf Stream Sea Surface Height Fields Derived from Geosat Altimeter Data and Those Derived from Sea Surface Temperature Data

Abstract Two types of satellite data, Geosat altimeter data and sea surface temperature data (SST), are compared and evaluated for their usefulness in assimilation into a numerical model of the Gulf Stream region. Synoptic sea surface height (SSH) fields are derived from the SST data in the following way: first three-dimensional temperature and salinity analysis fields are obtained through the Optimum Thermal Interpolation System (OTIS), and then SSH fields are calculated using a primitive equation, free-surface, numerical model running in a diagnostic mode. The aforementioned SSH fields are compared with SSH fields obtained from the Geosat altimeter data. Use of Geosat data requires an estimate of the cream SSH field relative to the earth geoid. Three different methods to obtain the mean SSH field are demonstrated. The first method uses altimetry and SST data, the second uses a diagnostic calculation with climatological data; and the third uses prognostic numerical calculations. The three estimates compa.
Convergence of adaptive FEM for elliptic obstacle problems

We treat the convergence of adaptive lowest‐order FEM for some elliptic obstacle problem with affine obstacle. For error estimation, we use a residual error estimator which is an extended version of the estimator from [2] and additionally controls the data oscillations. The main result states that an appropriately weighted sum of energy error, edge residuals, and data oscillations satisfies a contraction property that leads to convergence. In addition, we discuss the generalization to the case of inhomogeneous Dirichlet data and non‐affine obstacles χ ∈ H2(Ω) for which similar results are obtained. (© 2011 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Uncovering the Role of PdePrx12 Peroxidase in Enhancing Disease Resistance in Poplar Trees

Peroxidase (Prx)-related genes are reported to be involved in the metabolism of hydrogen peroxide (H2O2) in plants. Here, we found that the expression of the PdePrx12 gene was upregulated in wild-type (WT) poplar line NL895 infected with the pathogens Botryosphaeria dothidea strain 3C and Alternaria alternata strain 3E. The PdePrx12 gene was cloned in the poplar line NL895 and its overexpression (OE) and reduced-expression (RE) vectors were constructed. OE and RE transgenic lines were then generated. The H2O2 content in the leaves was measured by DAB staining and spectrophotometric analysis, and the data revealed that the OE line had a reduced H2O2 content, whereas the RE line had an increased H2O2 content. These transgenic and WT plants were also inoculated with the 3C/3E pathogens. The leaf area infected by pathogen 3C/3E was determined and the OE line was found to have a larger area of infection, whereas the RE line was found to have a smaller area of infection. This result suggested PdePRX12 is involved in disease resistance in poplar. Given these results, this study demonstrated that when poplar is infected by pathogens, the expression of PdePrx12 is inhibited, leading to an increase in H2O2 content, thereby enhancing disease resistance.
Tensor Independent Component Analysis and Tensor Non-Negative Factorization

In this chapter, we describe two tensor-based subspace analysis approaches (tensor ICA and tensor NMF) that can be used in many fields like face recognition and other biometric recognition. Section 10.1 gives the background and development of the two tensor-based subspace analysis approaches. Section 10.2 introduces tensor independent component analysis. Section 10.3 presents tensor nonnega-tive factorization. Section 10.4 discusses some potential applications of these two subspace analysis approaches in biometrics. Finally, we summarize this chapter in Section 10.5.
The EOFs of the Geopotential Eddies at 500 mb in Winter and Their Probability Density Distributions

Abstract In this paper we examine the empirical orthogonal functions (EOFs) of the geopotential eddies at 500 mb in the northern extratropical atmosphere during the cold season, and discuss the time variability of the EOF coefficients and their probability density distributions. Our data sample includes 1152 5-day mean fields derived from 32 years of NMC and ECMWF analyses. The first EOF is representative of the wintertime stationary eddies: the second and the fourth show good similarity to the PNA and WA teleconnection patterns, respectively. The pattern of the fifth EOF resembles a zonal wavenumber-3 with a latitudinally confined amplitude. The probability density distributions, estimated by means of nonparametric statistical techniques, have been computed both for the time-dependent amplitudes of individual EOFs and for indices of the spatial variance explained by more than one pattern. The EOF-5 appeared to be particularly relevant in this analysis; its amplitude shows a bimodal distribution, and bimo.
Late Clinical Outcomes of Total Arterial Revascularization or Multiple Arterial Grafting Compared to Conventional Single Arterial with Saphenous Vein Grafting for Coronary Surgery

Coronary surgery provides better long-term outcomes than percutaneous coronary intervention. Conventional practice is to use a single arterial conduit supplemented by saphenous vein grafts. The use of multiple arterial revascularization (MAG), or exclusive arterial revascularization (TAR), however, is reported as having improved late survival. Survival is a surrogate for graft failure that may lead to premature death, and improved survival reflects fewer graft failures in the non-conventional strategy groups. The reasons for not using MAG or TAR may be due to perceived technical difficulties, a lack of definitive large-scale randomized evidence, a lack of confidence in arterial conduits, or resources or time constraints. Most people consider radial artery (RA) grafting to be new, with use representing approximately 2–5% worldwide, despite select centers reporting routine use in most patients for decades with improved results. In conclusion, the current body of evidence supports more extensive use of total and multiple arterial revascularization procedures in the absence of contraindications.
Cache Placement with Virtual Matching in Large-Scale Cache-Enabled Multicasting Wireless Networks

To solve the multi-dimensional resource allocation problem in the large-scale cache-enabled multicasting wireless networks, this paper proposes a multi-stage virtual matching game method. Specifically, the proposed method performs virtual grouping of mobile users (MUs) according to location distribution of MUs, the file request probabilities of MUs, and the coverage area of cache-enabled access points (CAPs). Moreover, by virtual grouping, the huge decision-making space has been significantly reduced. Then, we propose the multi-stage virtual matching game method, which is superior to the better response (BR) algorithm in computation of single iteration, convergence speed, and system utility function.
Boundary element analysis of three‐dimensional mixed‐mode thermoelastic crack problems using the interaction and energy domain integrals

A three‐dimensional boundary element method (BEM) implementation of the interaction integral methodology for the numerical analysis of mixed‐mode three‐dimensional thermoelastic crack problems is presented in this paper. The interaction integral is evaluated from a domain representation naturally compatible with the BEM, since stresses, strains, temperatures and derivatives of displacements and temperatures at internal points can be evaluated using the appropriate boundary integral equations. Several examples are analysed and the results compared with those available in the literature to demonstrate the efficiency and accuracy of the implementation to solve straight and curved crack‐front problems. Copyright © 2007 John Wiley & Sons, Ltd.
Fuzzy Algebras of Concepts

Preconcepts are basic units of knowledge that form the basis of formal concepts in formal concept analysis (FCA). This paper investigates the relations among different kinds of preconcepts, such as protoconcepts, meet and join-semiconcepts and formal concepts. The first contribution of this paper, is to present a fuzzy powerset lattice gradation, that coincides with the preconcept lattice at its 1-cut. The second and more significant contribution, is to introduce a preconcept algebra gradation that yields different algebras for protoconcepts, semiconcepts, and concepts at different cuts. This result reveals new insights into the structure and properties of the different categories of preconcepts.
Highlighting In Vitro the Role of Brain-like Endothelial Cells on the Maturation and Metabolism of Brain Pericytes by SWATH Proteomics

Within the neurovascular unit, brain pericytes (BPs) are of major importance for the induction and maintenance of the properties of the blood-brain barrier (BBB) carried by the brain microvessel endothelial cells (ECs). Throughout barriergenesis, ECs take advantage of soluble elements or contact with BPs to maintain BBB integrity and the regulation of their cellular homeostasis. However, very few studies have focused on the role of ECs in the maturation of BPs. The aim of this study is to shed light on the proteome of BPs solocultured (hBP-solo) or cocultured with ECs (hBP-coc) to model the human BBB in a non-contact manner. We first generated protein libraries for each condition and identified 2233 proteins in hBP-solo versus 2492 in hBP-coc and 2035 common proteins. We performed a quantification of the enriched proteins in each condition by sequential window acquisition of all theoretical mass spectra (SWATH) analysis. We found 51 proteins enriched in hBP-solo related to cell proliferation, contractility, adhesion and extracellular matrix element production, a protein pattern related to an immature cell. In contrast, 90 proteins are enriched in hBP-coc associated with a reduction in contractile activities as observed in vivo in ‘mature’ BPs, and a significant gain in different metabolic functions, particularly related to mitochondrial activities and sterol metabolism. This study highlights that BPs take advantage of ECs during barriergenesis to make a metabolic switch in favor of BBB homeostasis in vitro.
A 32-Channel Sleeve Antenna Receiver Array for Human Head MRI Applications at 10.5 T

For human brain magnetic resonance imaging (MRI), high channel count ( $\ge 32$ ) radiofrequency receiver coil arrays are utilized to achieve maximum signal-to-noise ratio (SNR) and to accelerate parallel imaging techniques. With ultra-high field (UHF) MRI at 7 tesla (T) and higher, dipole antenna arrays have been shown to generate high SNR in the deep regions of the brain, however the array elements exhibit increased electromagnetic coupling with one another, making array construction more difficult with the increasing number of elements. Compared to a classical dipole antenna array, a sleeve antenna array incorporates the coaxial ground into the feed-point, resulting in a modified asymmetric antenna structure with improved intra-element decoupling. Here, we extended our previous 16-channel sleeve transceiver work and developed a 32-channel azimuthally arranged sleeve antenna receive–only array for 10.5 T human brain imaging. We experimentally compared the achievable SNR of the sleeve antenna array at 10.5 T to a more traditional 32-channel loop array bult onto a human head-shaped former. The results obtained with a head shaped phantom clearly demonstrated that peripheral intrinsic SNR can be significantly improved compared to a loop array with the same number of elements– except for the superior part of the phantom where sleeve antenna elements are not located.
Energy Aware Load Balancing Framework for Smart Grid Using Cloud and Fog Computing

Data centers are producing a lot of data as cloud-based smart grids replace traditional grids. The number of automated systems has increased rapidly, which in turn necessitates the rise of cloud computing. Cloud computing helps enterprises offer services cheaply and efficiently. Despite the challenges of managing resources, longer response plus processing time, and higher energy consumption, more people are using cloud computing. Fog computing extends cloud computing. It adds cloud services that minimize traffic, increase security, and speed up processes. Cloud and fog computing help smart grids save energy by aggregating and distributing the submitted requests. The paper discusses a load-balancing approach in Smart Grid using Rock Hyrax Optimization (RHO) to optimize response time and energy consumption. The proposed algorithm assigns tasks to virtual machines for execution and shuts off unused virtual machines, reducing the energy consumed by virtual machines. The proposed model is implemented on the CloudAnalyst simulator, and the results demonstrate that the proposed method has a better and quicker response time with lower energy requirements as compared with both static and dynamic algorithms. The suggested algorithm reduces processing time by 26%, response time by 15%, energy consumption by 29%, cost by 6%, and delay by 14%.
Tendon Driven Dexterous Hand Operation Control Based on Impedance Control and Tension Restraint

Tendon rope drive can greatly improve hand flexibility by setting the driving mechanism of the forearm. It has a relatively large advantage in the service industry, high-end flexible manufacturing and other applications of human-computer interaction and cooperation. To control the contact force within the appropriate limit range in the tendon drive dexterous hand finger operation space, an operating impedance control method of tendon-driven dexterous hand with contact force planning is proposed. First, design a contact force controller in the tendon space. Since the kinematic coupling between the joints of the tendon cord space controller can cause undesirable ascent, a path planning method is further adopted to reduce the upshoot effect. Co-simulation using MATLAB and ADAMS validated the controller. The simulation results show that through impedance control and tension management, the controller can constrain the tendon rope tension within the limit, and the contact force can track the contact force in a very short time. However, due to the joint coupling effect of the tendon cord space, the controller of the tendon space needs to plan a reasonable trajectory to eliminate the interference when the tendon rope is stretched. Therefore, the control effect of the controller is ideal and effective.
Amniotic Membrane Restores Chronic Wound Features to Normal in a Keratinocyte TGF-β-Chronified Cell Model

Unsuccessful wound closure in chronic wounds can be linked to altered keratinocyte activation and their inability to re-epithelize. Suggested mechanisms driving this impairment involve unbalanced cytokine signaling. However, the molecular events leading to these aberrant responses are poorly understood. Among cytokines affecting keratinocyte responses, Transforming Growth Factor-β (TFG-β) is thought to have a great impact. In this study, we have used a previously characterized skin epidermal in vitro model, HaCaT cells continuously exposed to TGF-β1, to study the wound recovery capabilities of chronified/senescent keratinocytes. In this setting, chronified keratinocytes show decreased migration and reduced activation in response to injury. Amniotic membrane (AM) has been used successfully to manage unresponsive complicated wounds. In our in vitro setting, AM treatment of chronified keratinocytes re-enabled migration in the early stages of wound healing, also promoting proliferation at later stages. Interestingly, when checking the gene expression of markers known to be altered in TGF-β chronified cells and involved in cell cycle regulation, early migratory responses, senescence, and chronic inflammation, we discovered that AM treatment seemed to reset back to keratinocyte status. The analysis of the evolution of both the levels of keratinocyte activation marker cytokeratin 17 and the spatial-temporal expression pattern of the proliferation marker Ki-67 in human in vivo biopsy samples suggests that responses to AM recorded in TGF-β chronified HaCaT cells would be homologous to those of resident keratinocytes in chronic wounds. All these results provide further evidence that sustained TGF-β might play a key role in wound chronification and postulate the validity of our TGF-β chronified HaCaT in vitro model for the study of chronic wound physiology.
Optical trapping and manipulation of nanoparticles using a meta plasmonic structure

In this paper, a novel structure of nano optical tweezers using triple-slit plasmonic grating structure is introduced and analyzed. The tweezers have deep potential wells that can trap sub-10 nm dielectric particle stably and efficiently. The resultant 50 KT potential well provides tight 2D trapping to the particle. The plasmonic device allows for steering the particle by simply changing the angle of the incident plane. This simple control allows efficient manipulation of the trapped particle over wide range of angles.
Enhanced Three-Phase AC Common-Mode Filter With Optimized Damping Network for VFDs

This article presents a highly integrated three-phase AC common-mode (CM) filter with optimized damping network for low voltage, high current motor drive applications. Simplified reduced order models and design guidelines for tuning of RLC parameters are proposed. A single turn AC-CM inductor can be realized using the proposed circuit and design technique, due to lower magnetizing inductance requirement. The RC network is found to provide 50% higher attenuation to peak CM currents compared to a conventional AC inductor alone. At the same time, the peak-volt-second across the CM inductor can be reduced, which is critical for weight sensitive applications. A trade-off between AC-CM inductance and damping resistor losses is evaluated to extract the desired optimal RLC parameters of the AC filter. The proposed circuit is designed and verified with high power SiC-based Three Level T-type inverter.
On Machine Learning in Clinical Interpretation of Retinal Diseases Using OCT Images

Optical coherence tomography (OCT) is a noninvasive imaging technique that provides high-resolution cross-sectional retina images, enabling ophthalmologists to gather crucial information for diagnosing various retinal diseases. Despite its benefits, manual analysis of OCT images is time-consuming and heavily dependent on the personal experience of the analyst. This paper focuses on using machine learning to analyse OCT images in the clinical interpretation of retinal diseases. The complexity of understanding the biomarkers present in OCT images has been a challenge for many researchers, particularly those from nonclinical disciplines. This paper aims to provide an overview of the current state-of-the-art OCT image processing techniques, including image denoising and layer segmentation. It also highlights the potential of machine learning algorithms to automate the analysis of OCT images, reducing time consumption and improving diagnostic accuracy. Using machine learning in OCT image analysis can mitigate the limitations of manual analysis methods and provide a more reliable and objective approach to diagnosing retinal diseases. This paper will be of interest to ophthalmologists, researchers, and data scientists working in the field of retinal disease diagnosis and machine learning. By presenting the latest advancements in OCT image analysis using machine learning, this paper will contribute to the ongoing efforts to improve the diagnostic accuracy of retinal diseases.
Quadratic variation of potentials and harmonic functions

We prove the existence of a finite quadratic variation for stochastic processes u( Y), where Y is Brownian motion on a Green doihain of RI, stopped upon reaching the Martin boundary, and u is a positive superharmonic function on the domain. As by-products we have results which are also of interest from a nonprobabilistic point of view.
Comparison of Machine Learning Algorithms for Predicting Patients with Suspected COVID-19

The coronavirus disease (COVID-19) outbreak has infected millions of people, causing a high death rate worldwide. Patients suspected of having COVID-19 are transferred to different health facilities, which has caused a saturation in care, for which it is necessary to have a prediction model to classify patients at high risk of clinical deterioration. The objective of the research was to compare classification algorithms based on automatic learning machines, for the prediction of clinical diagnosis in patients with COVID-19. 1000 records of patients with suspected SARS-CoV-2 infection who were admitted by the emergency service in health establishments in Peru were collected. After pre-processing the data and engineering the attributes, a sample of 700 records was determined. Models were designed and algorithms were compared: Logistic Regression, Support Vector Machine, Nearest Neighbors, Decision Tree, Random Forest, and Navie Bayes. The evaluation of the results of each algorithm was carried out using Accuracy, precision, sensitivity and Chohen's Kappa to know the degree of agreement between the prediction by the learning machine and the results of reality, that is, to what extent both results agree in their measurement. The algorithm that presented the best results was the Support Vector Machine and Random Forest, which predicted the patients with an accuracy of 97%, and Cohen's Kappa of 0.95, with figures higher than the other models evaluated.