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Conceptual Method of Temperature Sensation in Bionic Hand by Extraordinary Perceptual Phenomenon

Journal of Bionic engineering 2021年第6期18卷 1344-1357页

作者： Saeed Bahrami Moqadam Ahamd Saleh Asheghabadi Farzaneh Norouzi Hamed Jafarzadeh Ali Khosroabadi AfshinAlagheband Ghazal Bangash Negar Morovatdar Jing Xu State Key Laboratory of Tribology Department of Mechanical EngineeringTsinghua UniversityBeijing100084China Medical University of Mashhad 13131–99137MashhadIran Center for Computational and Data Intensive Science and Engineering(CDISE) Skolkovo Institute of Science and Technology(Skoltech)Moscow121205Russia Department of Mechanical Engineering Ferdowsi UniversityMashhad***Iran Department of Electrical Engineering Ferdowsi UniversityMashhad***Iran Department of Computer Engineering Ferdowsi UniversityMashhad***Iran Clinical Research Unit Imam Reza HospitalMashhad University of Medical Sciences13131–99137MashhadIran

Lack of temperature sensation of myoelectric prosthetic hand limits the daily activities of *** this end,a noninvasive temperature sensation method is proposed to train amputees to sense temperature with psychophysical sensory *** this study,22 healthy participants took part besides 5 amputee *** duration time of the study was 31 days with five test steps according to the Leitner *** adjustable temperature mug and a Peltier were used to change the temperature of the water/phantom digits to induce temperature to ***,to isolate the surroundings and show colors,a Virtual Reality(VR)glass was *** statistical results conducted are based on the response of participants with questionnaire *** Chi-square tests,it is concluded that participants answer the experiment significantly correctly using the Leitner technique(P value<0.05).Also,by applying the“Repeated Measures ANOVA”,it is noticed that the time of numbness felt by participants had significant(P value<0.001)*** could remember lowest and highest temperatures significantly better than other temperatures(P value<0.001);furthermore,the well-trained amputee participant practically using the prosthesis with 72.58%could identify object’s temperature with only once time experimenting the color temperature.

关键词： Temperature perceptions Leitner learning technique Classical conditioning Psychophysical sensory substitution Extraordinary perceptual phenomenon Bionic hand prosthesis

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EDSep: An Effective Diffusion-Based Method for Speech Source Separation

arXiv

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arXiv 2025年

作者： Dong, Jinwei Wang, Xinsheng Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications China Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China Audio Speech and Language Processing Group School of Computer Science Northwestern Polytechnical University Xi’an China

Generative models have attracted considerable attention for speech separation tasks, and among these, diffusion-based methods are being explored. Despite the notable success of diffusion techniques in generation tasks, their adaptation to speech separation has encountered challenges, notably slow convergence and suboptimal separation outcomes. To address these issues and enhance the efficacy of diffusion-based speech separation, we introduce EDSep, a novel single-channel method grounded in score matching via stochastic differential equation (SDE). This method enhances generative modeling for speech source separation by optimizing training and sampling efficiency. Specifically, a novel denoiser function is proposed to approximate data distributions, which obtains ideal denoiser outputs. Additionally, a stochastic sampler is carefully designed to resolve the reverse SDE during the sampling process, gradually separating speech from mixtures. Extensive experiments on databases such as WSJ0-2mix, LRS2-2mix, and VoxCeleb2-2mix demonstrate our proposed method’s superior performance over existing diffusion and discriminative models, validating its efficacy. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic systems

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Unbiased Parameter Estimation for Bayesian Inverse Problems

arXiv

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arXiv 2025年

作者： Chada, Neil K. Jasra, Ajay Maama, Mohamed Tempone, Raul Department of Mathematics City University of Hong Kong China School of Data Science The Chinese University of Hong Kong Shenzhen CN Shenzhen China Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In this paper we consider the estimation of unknown parameters in Bayesian inverse problems. In most cases of practical interest, there are several barriers to performing such estimation, This includes a numerical approximation of a solution of a differential equation and, even if exact solutions are available, an analytical intractability of the marginal likelihood and its associated gradient, which is used for parameter estimation. The focus of this article is to deliver unbiased estimates of the unknown parameters, that is, stochastic estimators that, in expectation, are equal to the maximizer of the marginal likelihood, and possess no numerical approximation error. Based upon the ideas of [4] we develop a new approach for unbiased parameter estimation for Bayesian inverse problems. We prove unbiasedness and establish numerically that the associated estimation procedure is faster than the current state-of-the-art methodology for this problem. We demonstrate the performance of our methodology on a range of problems which include a PDE and ODE. Copyright © 2025, The Authors. All rights reserved.

关键词： Markov processes

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A THEORY OF INITIALISATION’S IMPACT ON SPECIALISATION

arXiv

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arXiv 2025年

作者： Jarvis, Devon Lee, Sebastian Dominé, Clémentine Carla Juliette Saxe, Andrew M. Mannelli, Stefano Sarao School of Computer Science and Applied Mathematics University of the Witwatersrand South Africa Center for Computational Neuroscience Flatiron Institute Simons Foundation Gatsby Computational Neuroscience Unit Sainsbury Wellcome Centre UCL United Kingdom Data Science and AI Computer Science and Engineering Chalmers University of Technology University of Gothenburg Sweden Machine Intelligence and Neural Discovery Institute University of the Witwatersrand South Africa CIFAR Azrieli Global Scholar CIFAR

Prior work has demonstrated a consistent tendency in neural networks engaged in continual learning tasks, wherein intermediate task similarity results in the highest levels of catastrophic interference. This phenomenon is attributed to the network’s tendency to reuse learned features across tasks. However, this explanation heavily relies on the premise that neuron specialisation occurs, i.e. the emergence of localised representations. Our investigation challenges the validity of this assumption. Using theoretical frameworks for the analysis of neural networks, we show a strong dependence of specialisation on the initial condition. More precisely, we show that weight imbalance and high weight entropy can favour specialised solutions. We then apply these insights in the context of continual learning, first showing the emergence of a monotonic relation between task-similarity and forgetting in non-specialised networks. Finally, we show that specialization by weight imbalance is beneficial on the commonly employed elastic weight consolidation regularisation technique. Copyright © 2025, The Authors. All rights reserved.

关键词： Neural networks

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Advancing cancer driver gene detection via Schur complement graph augmentation and independent subspace feature extraction

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computers in Biology and Medicine 2024年 174卷 108484-108484页

作者： Ma, Xinqian Li, Zhen Du, Zhenya Xu, Yan Chen, Yifan Zhuo, Linlin Fu, Xiangzheng Liu, Ruijun School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou325027 China Guangzhou Xinhua University Guangzhou510520 China School of Computer Science of Information Technology Qiannan Normal University for Nationalities Guizhou Duyun558000 China Institute of Computational Science and Technology Guangzhou University Guangzhou510000 China College of Computer and Information Engineering Central South University of Forestry and Technology Hunan Changsha410004 China College of Computer Science and Electronic Engineering Hunan University Changsha410012 China School of Software Beihang University Beijing China

Accurately identifying cancer driver genes (CDGs) is crucial for guiding cancer treatment and has recently received great attention from researchers. However, the high complexity and heterogeneity of cancer gene regulatory networks limit the precition accuracy of existing deep learning models. To address this, we introduce a model called SCIS-CDG that utilizes Schur complement graph augmentation and independent subspace feature extraction techniques to effectively predict potential CDGs. Firstly, a random Schur complement strategy is adopted to generate two augmented views of gene network within a graph contrastive learning framework. Rapid randomization of the random Schur complement strategy enhances the model's generalization and its ability to handle complex networks effectively. Upholding the Schur complement principle in expectations promotes the preservation of the original gene network's vital structure in the augmented views. Subsequently, we employ feature extraction technology using multiple independent subspaces, each trained with independent weights to reduce inter-subspace dependence and improve the model's expressiveness. Concurrently, we introduced a feature expansion component based on the structure of the gene network to address issues arising from the limited dimensionality of node features. Moreover, it can alleviate the challenges posed by the heterogeneity of cancer gene networks to some extent. Finally, we integrate a learnable attention weight mechanism into the graph neural network (GNN) encoder, utilizing feature expansion technology to optimize the significance of various feature levels in the prediction task. Following extensive experimental validation, the SCIS-CDG model has exhibited high efficiency in identifying known CDGs and uncovering potential unknown CDGs in external datasets. Particularly when compared to previous conventional GNN models, its performance has seen significant improved. The code and data are publicly available at:

关键词： Genes

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Meter-scale variation within a single transect demands attention to taxon accumulation curves in riverine microbiome studies

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Frontiers of Environmental science & engineering 2022年第5期16卷 115-125页

作者： Bingdi Liu Lin Zhang Jason H.Knouft Fangqiong Ling Department of Energy Environmental and Chemical EngineeringWashington University in St.LouisSt.LouisMO 63130USA Department of Biology Saint Louis UniversitySt.LouisMO 63103USA National Great Rivers Research and Education Center East AltonMO 62035USA Department of Computer Science and Engineering Washington University in St.LouisSt.LouisMO 63130USA Division of Biological and Biomedical Sciences Washington University in St.LouisSt.LouisMO 63110USA Division of Computational and Data Science Washington University in St.LouisSt.LouisMO 63130USA

Microbial communities inhabiting river ecosystems play crucial roles in global biogeochemical cycling and pollution *** variations in local microbial assemblages are important for detailed understanding of community assembly and developing robust biodiversity sampling ***,we intensely analyzed twenty water samples collected from a one-meter spaced transect from the near-shore to the near-center in the Meramec River in eastern Missouri,USA and examined the microbial community composition with 16S rRNA gene amplicon *** microbiomes across the transect exhibited extremely high similarity,with Pearson’s correlation coefficients above 0.9 for all pairwise community composition ***,despite the high similarity,PERMANOVA revealed significant spatial differences between near-shore and nearcenter communities(p=0.001).Sloan’s neutral model simulations revealed that within-transect community composition variation was largely explained by demographic stochasticity(R^(2)=0.89).Despite being primarily explained by neutral processes,LefSe analyses also revealed taxa from ten families of which relative abundances differed directionally from the bank to the river center,indicating an additional role of environmental ***,the local variations within a river transect can have profound impacts on the documentation of alpha ***-accumulation curves indicated that even twenty samples did not fully saturate the sampling effort at the genus level,yet four,six and seven samples were able to capture 80%of the phylum-level,family-level,and genus-level diversity,*** study for the first time reveals hyperlocal variations in riverine microbiomes and their assembly mechanisms,demanding attention to more robust sampling strategies for documenting microbial diversity in riverine systems.

关键词： Microbiome Freshwater Taxon accumulation curve Community assembly

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Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes

Classification of Breast Thermal Images into Healthy/Cancer ...

引用

2022 International Conference on Machine Learning and data engineering, ICMLDE 2022

作者： Kadry, Seifedine Crespo, Rubén González Herrera-Viedma, Enrique Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja Andalusia26006 Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai602105 India

In the women's community, Breast Cancer (BC) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to BC. BC is curable if detected early. Since thermal imaging is non-invasive and supports disease detection, it is commonly used in clinics. Compared to other methods, it keeps BC early and accurate. The proposed work aims to evaluate the performance of the Pretrained Deep-Learning Methods (PDLM) in detecting BC using the thermal images collected from the benchmark dataset. It includes the following stages: primary image processing, deep feature mining, handcrafted feature mining, feature optimization using Firefly-Algorithm (FA), classification and validation. Visual Lab thermal images were used in the study. The investigational outcome of this study authenticates that the VGG16, along with the DT, provides better detection accuracy (95.5%) compared to other classifiers used in this study. To justify the significance of the implemented technique, the proposed work not only improved accuracy, but also improved precision, sensitivity, specificity, and F1-Scores. © 2023 The Authors. Published by Elsevier B.V.

关键词： Classification (of information)

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Adaptive fast-reaching nonsingular terminal sliding mode tracking control for quadrotor UAVs subject to model uncertainties and external disturbances

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Ain Shams engineering Journal 2023年第8期14卷

作者： Mobayen, Saleh El-Sousy, Fayez F.M. Alattas, Khalid A Mofid, Omid Fekih, Afef Rojsiraphisal, Thaned Graduate School of Intelligent Data Science National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin640301 Taiwan Department of Electrical Engineering Prince Sattam Bin Abdulaziz University Al Kharj Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Department of Electrical and Computer Engineering University of Louisiana at Lafayette Lafayette United States Advanced Research Center for Computational Simulation Department of Mathematics Faculty of Science Chiang Mai University Chiang Mai50200 Thailand Data Science Research Center Department of Mathematics Faculty of Science Chiang Mai University Chiang Mai50200 Thailand

This paper proposes an adaptive fast-reaching nonsingular terminal sliding mode control approach for the position and attitude tracking control of a quadrotor UAV subject to model uncertainty and external disturbances. To this end, a fast-reaching nonsingular terminal sliding surface is defined with the aim of fast convergence of the tracking error. The fast reachability of the nonsingular terminal sliding surface is proven using the Lyapunov stability theory. Since, in practice, the upper bounds of model uncertainties and external disturbances are unknown, an adaptive control scheme is considered to approximate them. The fast-reaching nonsingular terminal sliding mode control approach was augmented with the adaptive scheme to ensure both, the fast convergence of the switching surfaces and the rejection of model uncertainties and external disturbances. The effectiveness of the proposed adaptive fast-reaching nonsingular terminal sliding mode control approach is assessed using simulations of a quadrotor system implemented in MATLAB/Simulink software. The obtained results are further compared to those of an existing approach in terms of tracking performance, robustness, and integrator of absolute-value of error criteria. © 2022 THE AUTHORS

关键词： Unmanned aerial vehicles (UAV)

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Pelvic bone region segmentation (PBRS) from X-ray image using convolutional neural network (CNN)

Pelvic bone region segmentation (PBRS) from X-ray image usin...

引用

International Conference on computer and Information Technology (ICCIT)

作者： Syed Alif Ul Alam Saadia Binte Alam Sourav Saha Mahmudul Haque Rashedur Rahman Syoji Kobashi Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh Center for Computational & Data Sciences Independent University Bangladesh Dhaka Bangladesh Graduate School of Engineering University of Hyogo Shosha Himeji Hyogo Japan

Pelvic and hip fractures offer considerable public health risks with high morbidity and mortality rates. Because of the complicated bone structure of the pelvic bone region, detecting fractures is difficult. Though X-ray imaging is routinely utilised for detecting fractures, manual fracture diagnosis is prone to inaccuracies. This paper proposes the use of deep learning algorithms for automated segmentation of the pelvic bone region in X-ray images. In our work, we have investigated U-Net based pelvic area segmentation models with various convolutional neural network (CNN) backbones. The DenseNet121-based U-Net design emerged as the most optimal model, establishing a compromise between performance and computational efficiency. Although it had a modest loss in IoU and F1 scores when compared to InceptionNetV3, it had a remarkable 59.44% reduction in the number of parameters.

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Development of Phylum Chordata Sound Recognition System using Machine Learning and Deep Learning Techniques: A Case Study of Catfish

Development of Phylum Chordata Sound Recognition System usin...

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Electrical, computer and Energy Technologies (ICECET), International Conference on

作者： Lateef Adesola Akinyemi Olamide Peter Oshinuga Oluwafikunmi Adegbenga Eshilokun Sunday Oladayo Oladejo Stephen Obono Ekwe Departments of Computer Science and Electrical Engineering CSET University of South Africa Johannesburg South Africa Department of Electronic and Computer Engineering Lagos State University Lagos Nigeria School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa Department of Electrical Electronic and Computer Engineering Cape Peninsula University of Technology Cape Town South Africa

Sound recognition refers to the technology or process of identifying and classifying different sounds or audio signals. This study aims to develop a sound recognition system using machine learning (ML) and deep learning (DL) techniques to classify and identify the gender of catfish based on their speech patterns. The study explores a range of supervised ML and DL algorithms, such as the Artificial Neural Network (ANN), Naïve Bayes (NB), and others, to predict the gender of catfish. The extracted features from the audio signals of the catfish serve as inputs to these algorithms, which produce binary predictions classifying the catfish samples as either male or female. This study further explores data analysis of the audio signals from the male and female catfish. The automated sound recognition system proves to be a more efficient and accurate approach compared to traditional methods, which rely on time-consuming visual observations. Evaluation metrics such as Accuracy, Sensitivity, and more, were employed to gauge the effectiveness of the algorithms in predicting the gender of catfish based on speech patterns. Among the employed algorithms, ANN and NB demonstrate the highest accuracy, achieving a score of 89.47%.

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