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Towards human-leveled vision systems

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science China Technological sciences 2024年第8期67卷 2331-2349页

作者： DING JianHao HUANG TieJun State key laboratory of Multimedia information Processing School of Computer Science Peking University

The human visual system is a complex and interconnected network comprising billions of neurons. It plays an essential role in translating environmental light stimuli into information that guides and shapes human perception and action. Research on the visual system aims to uncover the underlying neural structure principles of human visual perception and their possible ***, there are two main approaches: biological system analysis and simulation, artificial intelligence models based on deep learning. Here we aim to discuss the two approaches to human-level vision systems. Deep learning has significantly impacted the field of vision with achievements in representation, modeling, and hardware design. However, there is still a significant gap between deep learning models and the human visual system in terms of scalability, transferability, and sustainability. The progress of the biological visual system can help fill the gap by further understanding the properties and functions of different components of the system. We take the efforts of reconstructing the retina as an example to illustrate that even if we are unable to replicate the visual system on a computer right now, we can still learn a lot by combining existing research outcomes in neuroscience. At the end of the paper, we suggest tracing back to gradually build visual systems from the computational counterpart of biological structures to achieve a human-level vision system in the future.

关键词： human-leveled vision system deep learning biological visual system

L_(1)-Smooth SVM with Distributed Adaptive Proximal Stochastic Gradient Descent with Momentum for Fast Brain Tumor Detection

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Computers, Materials & Continua 2024年第5期79卷 1975-1994页

作者： Chuandong Qin Yu Cao Liqun Meng school of mathematics and information science North Minzu UniversityYinchuan750021China Ningxia key laboratory of Intelligent information and Big Data Processing Yinchuan750021China

Brain tumors come in various types,each with distinct characteristics and treatment approaches,making manual detection a time-consuming and potentially ambiguous *** tumor detection is a valuable tool for gaining a deeper understanding of tumors and improving treatment *** learning models have become key players in automating brain tumor *** descent methods are the mainstream algorithms for solving machine learning *** this paper,we propose a novel distributed proximal stochastic gradient descent approach to solve the L_(1)-Smooth Support Vector Machine(SVM)classifier for brain tumor ***,the smooth hinge loss is introduced to be used as the loss function of *** avoids the issue of nondifferentiability at the zero point encountered by the traditional hinge loss function during gradient descent ***,the L_(1) regularization method is employed to sparsify features and enhance the robustness of the ***,adaptive proximal stochastic gradient descent(PGD)with momentum,and distributed adaptive PGDwithmomentum(DPGD)are proposed and applied to the L_(1)-Smooth *** computing is crucial in large-scale data analysis,with its value manifested in extending algorithms to distributed clusters,thus enabling more efficient processing ofmassive amounts of *** DPGD algorithm leverages Spark,enabling full utilization of the computer’s multi-core *** to its sparsity induced by L_(1) regularization on parameters,it exhibits significantly accelerated convergence *** the perspective of loss reduction,DPGD converges faster than *** experimental results show that adaptive PGD withmomentumand its variants have achieved cutting-edge accuracy and efficiency in brain tumor ***-trained models,both the PGD andDPGD outperform other models,boasting an accuracy of 95.21%.

关键词： Support vector machine proximal stochastic gradient descent brain tumor detection distributed computing

A deep learning model enabled multi-event recognition for distributed optical fiber sensing

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science China(information sciences) 2024年第3期67卷 291-307页

作者： Yujiao LI Xiaomin CAO Wenhao NI Kuanglu YU Institute of information science School of Computer and Information TechnologyBeijing Jiaotong University Beijing key laboratory of Advanced information science and Network Technology Beijing Jiaotong University

Fiber optic sensors that utilize backscattered light offer distributed real-time measurements and have been seen tremendous improvements in sensing distance and spatial resolution over the last ***, these improvements in sensor capabilities lead to a significant increase in the amount of data that needs to be processed. Traditional processing schemes are no longer adequate, so the development of novel signal processing methods is critical. Phase-sensitive optical time domain reflectometry(Φ-OTDR) is now applied in various applications for multi-event recognition, and it would usually be difficult, sometimes even unrealistic to label all the acquired samples due to its real-time and seamless monitoring nature. To fully take advantage of the information contained within the large number of unlabeled samples, which were formerly not utilized and hence wasted, we propose a semi-supervised model to boost the event classification performance of Φ-OTDR. The model extracts respectively the temporal features and the spatial bidirectional features together with a dual attention mechanism. Its classification accuracy has been improved up to 96.9%with only 1230 labeled samples. In addition, our model shows significant advantages when the number of labeled samples is reduced. Importantly, our method improves the accuracy of multi-event classification without any modification to the optical setup.

关键词： $\Phi$-OTDR event recognition semi-supervised learning mean teacher MT-ACNN-SA-BiLSTM

On Leray's problem in an infinitely long pipe with the Navier-slip boundary condition

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science China mathematics 2024年第4期67卷 819-854页

作者： Zijin Li Xinghong Pan Jiaqi Yang school of mathematics and Statistics Nanjing University of Information Science and TechnologyNanjing 210044China school of mathematics and key laboratory of Ministry of Industry and information Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China school of mathematics and Statistics Northwestern Polytechnical UniversityXi'an 710129China

The original Leray’s problem concerns the well-posedness of weak solutions to the steady incompressible Navier-Stokes equations in a distorted pipe,which approach the Poiseuille flow subject to the no-slip boundary condition at spatial *** this paper,the same problem with the Navier-slip boundary condition instead of the no-slip boundary condition,is *** to the complexity of the boundary condition,some new ideas,presented as follows,are introduced to handle the extra difficulties caused by boundary ***,the Poiseuille flow in the semi-infinite straight pipe with the Navier-slip boundary condition will be introduced,which will serve as the asymptotic profile of the solution to the generalized Leray’s problem at spatial ***,a solenoidal vector function defined in the whole pipe,satisfying the Navierslip boundary condition,having the designated flux and equalling the Poiseuille flow at a large distance,will be carefully *** plays an important role in reformulating our ***,the energy estimates depend on a combined L2-estimate of the gradient and the stress tensor of the velocity.

关键词： stationary Navier-Stokes system Navier-slip boundary condition Leray's problem

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