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International Conference on computer and Communications (ICCC)

作者： Manqing Tian Yang Zhao Ni Zeng Xiaomei Kang Jihong Pei Xuan Yang Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Reproductive Maternal and Child Health Hospital of Hubei Province Tongji Medical College Huazhong University of Science and Technology Wuhan China

Semen quality assessment is a very important tool for the timely detection and treatment of infertility disorders. Many artificial intelligence methods for sperm image analysis have emerged in recent years. Sperm detection in sperm microscopy is the first step in semen quality assessment. However, the acquisition of sperm electron microscopy images can produce a variety of different background lighting situations. The performance of established methods is susceptible to changes in background illumination. Therefore, a sperm detection method with heterogeneous Laplacian distribution noise background modeling (HLDNBM) is proposed. In this paper the method uses background illumination surface fitting to eliminate the effect of illumination in a single sperm image, and realizes the segmentation of sperm head and sperm tail with different grayscale by upper and lower segmentation threshold surfaces to achieve complete sperm image segmentation and multi-target detection in a single image. This method effectively avoids the degradation of target detection performance caused by sudden changes in illumination in other background modeling methods. Moreover, this method shows strong adaptability for images with different lighting backgrounds, and is friendly to small datasets as well. The experimental results show that the accuracy and sensitivity of the algorithm for sperm detection in images with different grayscale characteristics are very promising, with high universality and robustness.

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Deep Learning of First-Order Nonlinear Hyperbolic Conservation Law Solvers

SSRN

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SSRN 2023年

作者： Morand, Victor Müller, Nils Weightman, Ryan Keimer, Alexander Piccoli, Benedetto Bayen, Alexandre M. Ecole Polytechnique Palaiseau France Department of Electrical Engineering and Computer Science University of California BerkeleyCA United States Max Planck Institute for Software Systems Saarland Informatics Campus Germany Max Planck Institute for Intelligent Systems Germany Department of Mathematics Rutgers University NJ United States Department of Mathematics FAU Erlangen-Nürnberg Germany

In this contribution, we study the numerical approximation of scalar conservation laws by computational optimization of the numerical flux function in a first-order finite volume method. The cell-averaging inherent to finite volume schemes presents a challenge in the design of such numerical flux functions toward achieving high solution accuracy. Dense neural networks, as well as (piecewise) polynomials, serve as function classes for the numerical flux. Using a parametrization of such a function class, we optimize the numerical flux with respect to its solution accuracy on a set of Riemann problems for which we have closed-form solutions. By design, our resulting first-order finite volume schemes are *** show empirically that the proposed schemes can achieve higher accuracy than the Godunov and EnquistOsher method on a wide range of *** proposed method can be applied to the inverse problem, i.e., the data-driven synthesis of macroscopic physical laws for real-world flux phenomena. © 2023, The Authors. All rights reserved.

关键词： Finite volume method

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Few-shot learning based histopathological image classification of colorectal cancer

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intelligent Medicine 2024年第4期4卷 256-267页

作者： Rui Li Xiaoyan Li Hongzan Sun Jinzhu Yang Md Rahaman Marcin Grzegozek Tao Jiang Xinyu Huang Chen Li Key Laboratory of Intelligent Computing in Medical Image Ministry of EducationNortheastern UniversityShenyangLiaoning 110167China Cancer Hospital China Medical UniversityShenyangLiaoning 110122China Shengjing Hospital China Medical UniversityShenyang.Liaoning 110000China Institute for Medical Informatics University of LuebeckGermany Department of Knowledge Engineering University of Economics in KatowicePoland School of Intelligent Medicine Chengdu University of Traditional Chinese MedicineChengduSichuan 610075China International Joint Institute of Robotics and Intelligent Systems Chengdu University of Information TechnologyChengduSichuan 610225China School of Computer Science and Engineering University of New South WalesSydneyNSW 2052Australia

Background Colorectal cancer is a prevalent and deadly disease worldwide,posing significant diagnostic *** histopathologic image classification is often inefficient and *** some histopathologists use computer-aided diagnosis to improve efficiency,these methods depend heavily on exten-sive data and specific annotations,limiting their *** address these challenges,this paper proposes a method based on few-shot *** This study introduced a few-shot learning approach that combines transfer learning and contrastive learning to classify colorectal cancer histopathology images into benign and malignant *** model comprises modules for feature extraction,dimensionality reduction,and classification,trained using a combi-nation of contrast loss and cross-entropy *** this paper,we detailed the setup of hyperparameters:n-way,κ-shot,β,and the creation of support,query,and test *** Our method achieved over 98% accuracy on a query dataset with 35 samples per category using only 10 training samples per *** documented the model’s loss,accuracy,and the confusion matrix of the ***,we employed the t-SNE algorithm to analyze and assess the model’s classification *** The proposed model may demonstrate significant advantages in accuracy and minimal data depen-dency,performing robustly across all tested n-way,κ-shot *** consistently achieved over 93% accuracy on comprehensive test datasets,including 1916 samples,confirming its high classification accuracy and strong generalization *** research could advance the use of few-shot learning in medical diagnostics and also lays the groundwork for extending it to deal with rare,difficult-to-diagnose cases.

关键词： Colorectal cancer Few-shot learning Transfer learning Contrastive learning Histopathological images Benign and malignant categories

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Detecting Adversarial Data by Probing Multiple Perturbations Using Expected Perturbation Score

arXiv

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arXiv 2023年

作者： Zhang, Shuhai Liu, Feng Yang, Jiahao Yang, Yifan Li, Changsheng Han, Bo Tan, Mingkui School of Software Engineering South China University of Technology China Pazhou Laboratory China The University of Melbourne Australia Beijing Institute of Technology Beijing China Department of Computer Science Hong Kong Baptist University Hong Kong Key Laboratory of Big Data and Intelligent Robot Ministry of Education China

Adversarial detection aims to determine whether a given sample is an adversarial one based on the discrepancy between natural and adversarial distributions. Unfortunately, estimating or comparing two data distributions is extremely difficult, especially in high-dimension spaces. Recently, the gradient of log probability density (a.k.a., score) w.r.t. the sample is used as an alternative statistic to compute. However, we find that the score is sensitive in identifying adversarial samples due to insufficient information with one sample only. In this paper, we propose a new statistic called expected perturbation score (EPS), which is essentially the expected score of a sample after various perturbations. Specifically, to obtain adequate information regarding one sample, we perturb it by adding various noises to capture its multi-view observations. We theoretically prove that EPS is a proper statistic to compute the discrepancy between two samples under mild conditions. In practice, we can use a pre-trained diffusion model to estimate EPS for each sample. Last, we propose an EPS-based adversarial detection (EPSAD) method, in which we develop EPS-based maximum mean discrepancy (MMD) as a metric to measure the discrepancy between the test sample and natural samples. We also prove that the EPS-based MMD between natural and adversarial samples is larger than that among natural samples. Extensive experiments show the superior adversarial detection performance of our EPS-AD. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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SGNet: Salient Geometric Network for Point Cloud Registration

SGNet: Salient Geometric Network for Point Cloud Registratio...

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IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Qianliang Wu Yaqing Ding Lei Luo Haobo Jiang Shuo Gu Chuanwei Zhou Jin Xie Jian Yang PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague Prague Czech Republic State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Intelligence Science and Technology Nanjing University Suzhou China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Point Cloud Registration (PCR) is a critical and challenging task in computer vision and robotics. One of the primary difficulties in PCR is identifying salient and meaningful points that exhibit consistent semantic and geometric properties across different scans. Previous methods have encountered challenges with ambiguous matching due to the similarity among patch blocks throughout the entire point cloud and the lack of consideration for efficient global geometric consistency. To address these issues, we propose a new framework that includes several novel techniques. Firstly, we introduce a semantic-aware geometric encoder that combines object-level and patch-level semantic information. This encoder significantly improves registration recall by reducing ambiguity in patch-level superpoint matching. Additionally, we incorporate a prior knowledge approach that utilizes an intrinsic shape signature to identify salient points. This enables us to extract the most salient super points and meaningful dense points in the scene. Secondly, we introduce an innovative transformer that encodes High-Order (HO) geometric features. These features are crucial for identifying salient points within initial overlap regions while considering global high-order geometric consistency. We introduce an anchor node selection strategy to optimize this high-order transformer further. By encoding inter-frame triangle or polyhedron consistency features based on these anchor nodes, we can effectively learn high-order geometric features of salient super points. These high-order features are then propagated to dense points and utilized by a Sinkhorn matching module to identify critical correspondences for successful registration. The experiments conducted on the 3DMatch/3DLoMatch and KITTI datasets demonstrate the effectiveness of our method.

关键词： Point cloud compression computer vision Accuracy Shape Semantics Transformers Feature extraction Encoding intelligent robots

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Electrochemical Detection of Lactate and Glucose with Enzyme-Free and Dual-Sensing Using Nickel-Oxide Nanoparticles

SSRN

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SSRN 2025年

作者： Nasiri, Rohollah Guagliano, Giuseppe Van Gastel, Dirkje Sanei, Reihaneh Madadelahi, Masoud Tanriverdi, Selim Jain, Saumey Fayazbaksh, Farzaneh Lee, Sang Won Zhu, Yangzhi Joensson, Haakan N. Russom, Aman Herland, Anna Division of Nanobiotechnology Department of Protein Science Science for Life Laboratory KTH Royal Institute of Technology Solna Sweden AIMES Center for the Advancement of Integrated Medical and Engineering Sciences Department of Neuroscience Karolinska Institute Solna Sweden Department of Chemistry Materials and Chemical Engineering "G. Natta" Politecnico di Milano Milan Italy Division of Micro and Nanosystems Department of Intelligent Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Terasaki Institute for Biomedical Innovation Los AngelesCA90064 United States

Detection and monitoring of lactate and glucose levels in biological fluids and cell cultures are critical for understanding metabolic disorders. While electrochemical biosensors are commonly used, traditional enzymatic sensors face stability, reproducibility, and cost challenges. To address these issues, we developed non-enzymatic sensors using nanostructured electrodes, specifically nickel oxide (NiO)-based, for lactate and glucose detection. The sensors were fabricated by drop-casting a NiO/Nafion/ethanol dispersion onto the working electrode, with performance evaluated through cyclic voltammetry and amperometry. Optimal sensitivity and linearity were achieved at a working voltage of ~0.5 V. To address glucose interference, sensors were modified with Prussian Blue, creating a dual sensor for lactate and glucose. The sensors respectively exhibited linear responses for lactate (0.1 – 5 mM) and glucose (0.1 – 5 mM) with detection limits of 0.60 mM and 0.35 mM, and it showed sensitivities of 0.43 ± 0.05 µA/mM (lactate) and 0.82 ± 0.02 µA/mM (glucose) in phosphate buffer solution. Validation against the lactate and glucose assay kits in a cell culture medium showed good agreement, highlighting the potential of these enzyme-free electrochemical sensors for metabolic analysis. Finally, the sensor was integrated with a microfluidic device, demonstrating its potential as a flow-through, enzyme-free metabolic sensor for future organ-on-chip applications. © 2025, The Authors. All rights reserved.

关键词： Cyclic voltammetry

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Hybrid Data-Free Knowledge Distillation

arXiv

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arXiv 2024年

作者： Tang, Jialiang Chen, Shuo Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China Center for Advanced Intelligence Project RIKEN Japan Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

Data-free knowledge distillation aims to learn a compact student network from a pre-trained large teacher network without using the original training data of the teacher network. Existing collection-based and generation-based methods train student networks by collecting massive real examples and generating synthetic examples, respectively. However, they inevitably become weak in practical scenarios due to the difficulties in gathering or emulating sufficient real-world data. To solve this problem, we propose a novel method called Hybrid Data-Free Distillation (HiDFD), which leverages only a small amount of collected data as well as generates sufficient examples for training student networks. Our HiDFD comprises two primary modules, i.e., the teacher-guided generation and student distillation. The teacher-guided generation module guides a Generative Adversarial Network (GAN) by the teacher network to produce high-quality synthetic examples from very few real-world collected examples. Specifically, we design a feature integration mechanism to prevent the GAN from overfitting and facilitate the reliable representation learning from the teacher network. Meanwhile, we drive a category frequency smoothing technique via the teacher network to balance the generative training of each category. In the student distillation module, we explore a data inflation strategy to properly utilize a blend of real and synthetic data to train the student network via a classifier-sharing-based feature alignment technique. Intensive experiments across multiple benchmarks demonstrate that our HiDFD can achieve state-of-the-art performance using 120 times less collected data than existing methods. Code is available at https://***/tangjialiang97/HiDFD. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

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Distributed Flow Shop Scheduling with Sequence-Dependent Setup Times Using an Improved Iterated Greedy Algorithm

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Complex System Modeling and Simulation 2021年第3期1卷 198-217页

作者： Xue Han Yuyan Han Qingda Chen Junqing Li Hongyan Sang Yiping Liu Quanke Pan Yusuke Nojima the School of Computer Science Liaocheng UniversityLiaocheng 252000China the State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China the School of Information Science and Engineering Shandong Normal UniversityJinan 252000China the College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China the School of Mechatronic Engineering and Automation Shanghai UniversityShanghai 200072China the Department of Computer Science and Intelligent Systems Osaka Prefecture UniversityOsaka 599-8531Japan

To meet the multi-cooperation production demand of enterprises,the distributed permutation flow shop scheduling problem(DPFSP)has become the frontier research in the field of manufacturing *** this paper,we investigate the DPFSP by minimizing a makespan criterion under the constraint of sequence-dependent setup *** solve DPFSPs,significant developments of some metaheuristic algorithms are *** this context,a simple and effective improved iterated greedy(NIG)algorithm is proposed to minimize makespan in *** to the features of DPFSPs,a two-stage local search based on single job swapping and job block swapping within the key factory is designed in the proposed *** compare the proposed algorithm with state-of-the-art algorithms,including the iterative greedy algorithm(2019),iterative greedy proposed by Ruiz and Pan(2019),discrete differential evolution algorithm(2018),discrete artificial bee colony(2018),and artificial chemical reaction optimization(2017).Simulation results show that NIG outperforms the compared algorithms.

关键词： distributed permutation flow shop iterated greedy local search swapping strategy

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Performance Analysis of MIMO-HARQ Assisted V2V Communications With Keyhole Effect

arXiv

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arXiv 2022年

作者： Zhang, Huan Liao, Zhengtao Shi, Zheng Yang, Guanghua Dou, Qingping Ma, Shaodan The State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau 999078 China The School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China

Vehicle-to-vehicle (V2V) communications under dense urban environments usually experience severe keyhole fading effect especially for multi-input multi-output (MIMO) channels, which degrades the capacity and outage performance due to the rank deficiency. To avoid these, the integration of MIMO and hybrid automatic repeat request (HARQ) is proposed to assist V2V communications in this paper. By using the methods of integral transforms, the outage probabilities are derived in closed-form for different HARQ-assisted schemes, including Type I-HARQ, HARQ with chase combining (HARQ-CC), and HARQ with incremental redundancy (HARQ-IR). With the results, meaningful insights are gained by conducting the asymptotic outage analysis. Specifically, it is revealed that full time diversity order can be achieved, while full spatial diversity order is unreachable as compared to MIMO-HARQ systems without keyhole effect. Moreover, we prove that the asymptotic outage probability is a monotonically increasing and convex function of the transmission rate. More importantly, although HARQ-IR performs better than HARQ-CC owing to its higher coding complexity, this advantage becomes negligible in the large-scale array regime. Finally, the numerical results are verified by Monte-Carlo simulations along with some in-depth discussions. © 2022, CC BY.

关键词： Functions

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MEGCF: Multimodal Entity Graph Collaborative Filtering for Personalized Recommendation

arXiv

引用

arXiv 2022年

作者： Liu, Kang Xue, Feng Guo, Dan Wu, Le Li, Shujie Hong, Richang Hefei University of Technology School of Computer Science and Information Engineering Key Laboratory of Knowledge Engineering with Big Data Intelligent Interconnected Systems Laboratory of Anhui Province 485 Danxia Road Anhui Province Hefei230601 China Hefei University of Technology School of Software Key Laboratory of Knowledge Engineering with Big Data Intelligent Interconnected Systems Laboratory of Anhui Province 485 Danxia Road Anhui Province Hefei230601 China

In most E-commerce platforms, whether the displayed items trigger the user's interest largely depends on their most eye-catching multimodal content. Consequently, increasing efforts focus on modeling multimodal user preference, and the pressing paradigm is to incorporate complete multimodal deep features of the items into the recommendation module. However, the existing studies ignore the mismatch problem between multimodal feature extraction (MFE) and user interest modeling (UIM). That is, MFE and UIM have different emphases. Specifically, MFE is migrated from and adapted to upstream tasks such as image classification. In addition, it is mainly a content-oriented and non-personalized process, while UIM, with its greater focus on understanding user interaction, is essentially a user-oriented and personalized process. Therefore, the direct incorporation of MFE into UIM for purely user-oriented tasks, tends to introduce a large number of preference-independent multimodal noise and contaminate the embedding representations in UIM. This paper aims at solving the mismatch problem between MFE and UIM, so as to generate high-quality embedding representations and better model multimodal user preferences. Towards this end, we develop a novel model, multimodal entity graph collaborative filtering, short for MEGCF. The UIM of the proposed model captures the semantic correlation between interactions and the features obtained from MFE, thus making a better match between MFE and UIM. More precisely, semantic-rich entities are first extracted from the multimodal data, since they are more relevant to user preferences than other multimodal information. These entities are then integrated into the user-item interaction graph. Afterwards, a symmetric linear Graph Convolution Network (GCN) module is constructed to perform message propagation over the graph, in order to capture both high-order semantic correlation and collaborative filtering signals. Finally, the sentiment information fr

关键词： Sentiment analysis

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