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arXiv 2022年

作者： Yudistira, Novanto Kavitha, Muthu Subash Rajan, Jeny Kurita, Takio Intelligent System Lab Faculty of Computer Science Brawijaya University Indonesia School of Information and Data Sciences Nagasaki University Nagasaki Japan Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal India Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Japan

The detection of induced pluripotent stem cell (iPSC) colonies often needs the precise extraction of the colony features. However, existing computerized systems relied on segmentation of contours by preprocessing for classifying the colony conditions were task-extensive. To maximize the efficiency in categorizing colony conditions, we propose a multiple instance learning (MIL) in weakly supervised settings. It is designed in a single model to produce weak segmentation and classification of colonies without using finely labeled samples. As a single model, we employ a U-net-like convolution neural network (CNN) to train on binary image-level labels for MIL colonies classification. Furthermore, to specify the object of interest we used a simple post-processing method. The proposed approach is compared over conventional methods using five-fold cross-validation and receiver operating characteristic (ROC) curve. The maximum accuracy of the MIL-net is 95%, which is 15% higher than the conventional methods. Furthermore, the ability to interpret the location of the iPSC colonies based on the image level label without using a pixel-wise ground truth image is more appealing and cost-effective in colony condition recognition. © 2022, CC BY.

关键词： Learning systems

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Mixing Histopathology Prototypes into Robust Slide-Level Representations for Cancer Subtyping

arXiv

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arXiv 2023年

作者： Butke, Joshua Hashimoto, Noriaki Takeuchi, Ichiro Miyoshi, Hiroaki Ohshima, Koichi Sakuma, Jun Machine Learning and Data Mining Lab University of Tsukuba Japan RIKEN Center for Advanced Intelligence Project Japan Department of Mechanical Systems Engineering Nagoya University Japan Department of Pathology Kurume University Japan Department of Computer Science Tokyo Institute of Technology Japan

Whole-slide image analysis via the means of computational pathology often relies on processing tessellated gigapixel images with only slide-level labels available. Applying multiple instance learning-based methods or transformer models is computationally expensive as, for each image, all instances have to be processed simultaneously. The MLP-Mixer is an under-explored alternative model to common vision transformers, especially for large-scale datasets. Due to the lack of a self-attention mechanism, they have linear computational complexity to the number of input patches but achieve comparable performance on natural image datasets. We propose a combination of feature embedding and clustering to preprocess the full whole-slide image into a reduced prototype representation which can then serve as input to a suitable MLP-Mixer architecture. Our experiments on two public benchmarks and one inhouse malignant lymphoma dataset show comparable performance to current state-of-the-art methods, while achieving lower training costs in terms of computational time and memory load. Code is publicly available at https://***/butkej/ProtoMixer. Copyright © 2023, The Authors. All rights reserved.

关键词： Mixers (machinery)

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Synthesized Acoustic Vortex-Frequency Comb via Rotational Doppler Effect

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Physical Review Letters 2025年第13期134卷 137001-137001页

作者： Yurou Jia Xuan Zhang Suying Zhang Guoqiang Xu Taoyu Chen Hongtao Zhou Yechao Bai Ying Cheng Dajian Wu Department of Physics MOE Key Laboratory of Modern Acoustics Collaborative Innovation Center of Advanced Microstructures Jiangsu Physical Science Research Center Nanjing University Nanjing 210093 China Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Mechanics School of Mechanical Engineering Tianjin University Tianjin 300350 China School of Electronic Science and Engineering Nanjing University Nanjing 210023 China Jiangsu Key Lab on Opto-Electronic Technology School of Physics and Technology Nanjing Normal University Nanjing 210023 China

The synergistic control of light’s frequency and orbital angular momentum (OAM) via integrated nonlinear ring microresonators is crucial for creating spatiotemporal optical waveforms and advancing optical metrology. A direct analog of this capability in acoustics is challenging due to the nonlinearity-based mechanisms’ dependence on external driving and limited frequency conversion efficiency. Here, we present an acoustic synthesized comb structure derived from spatiotemporal metasurfaces, concurrently controlling frequency and OAM characteristics with high conversion efficiency and removing the constraints of critical driving frequency and power level in nonlinear mechanisms. By optimizing comb structures, energy distributions among OAM modes are adjusted to different frequency lines. We finally propose spatiotemporal metalens devices based on vortex-frequency combs, enabling synchronously multiple-order spatial differential operations for parallel data processing. This Letter is groundbreaking in integrating spectral- and spatial-domain acoustic waves, promising multifunctional imaging and advantages in spatiotemporal beams.

关键词： Vortex flow

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PPIRB:Achieving an privacy-preserving image retrieval scheme Based on Blockchain 5

PPIRB:Achieving an privacy-preserving image retrieval scheme...

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5th International Conference on Data Science and Information Technology, DSIT 2022

作者： Yuan, Bo Peng, Jiahui Li, Chunpei Qiu, Wangjie School of Computer Science and Engineering State Key Laboratory of Software Development Environment Beihang University Beijing100191 China Guangxi Key Lab of Multi-source Information Mining Security Guangxi Normal University GuiLin China State Key Laboratory of Software Development Environment Beihang University Advanced Innovation Center for Future Blockchain and Privacy Computing Beijing100191 China

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

With the proliferation of cloud services, the work of large-scale image retrieval is carried out on remote cloud become a trend in order to get rid of the storage burden and computation. However, traditional retrieval work based on centralized cloud services is considered to take risk in giving away users' privacy in many researches. We need a secure way for retrieving and storing images by decentralized approach. In this paper, we propose a decentralized scheme named PPIRB(An Privacy-Preserving Image Retrieval Scheme Base on Blockchain) for retrieving images based on blockchain. We used the data structure called amplifying locality sensitive hashing (ALSH) to improve search accuracy. Searchable symmetric en-cryption(SSE) and ALSH used to realize the encryption retrieval of image index on the blockchain. Our scheme also uses IPFS to replace the cloud server to realize distributed storage, and uses secret sharing scheme to realize the access control. We implemented and evaluated the scheme in Ethereum, and the results show that our scheme is efficient and accurate in finding similar images while preserving privacy. The overall overhead is reasonable. © 2022 IEEE.

关键词： Blockchain

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EPRD: Exploiting prior knowledge for evidence-providing automatic rumor detection

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Neurocomputing 2024年 563卷

作者： Li, Jiawen Li, Ronghui Ni, Shiwen Kao, Hung-Yu Naval Architecture and Shipping College Guangdong Ocean University Guangdong Zhanjiang524000 China Technical Research Center for Ship Intelligence and Safety Engineering of Guangdong Province Guangdong Zhanjiang524000 China Intelligent Knowledge Management Lab National Cheng Kung University Tainan Taiwan Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

With the prevalence of social media platforms, rumors have been a serious social problem. Notably, existing rumor detection methods simply provide detection labels while ignoring their explanation. However, illustrating the reasons why a suspicious statement is a rumor is essential. To address this realistic scenario, we propose a novel Evidence-Providing Rumor Detection model called EPRD. EPRD incorporates a wide variety of information from both prior knowledge sources and current comments. It also learns bilaterally friendly representations for interpretable rumor detection. EPRD first retrieves evidence from prior knowledge sources and checks the relationship between the given statement and its evidence. Our model then constructs two heterogeneous graph objects to simulate the propagation layout of the current comments and evidence relationships. Finally, EPRD integrates the GraphSAGE component and attention mechanism to detect rumors. To the best of our knowledge, we propose the first model that incorporates prior knowledge to verify rumors and boost credibility. Experiments on two real-world Twitter datasets demonstrate that EPRD consistently exhibits the best rumor detection performance. Moreover, EPRD outperforms other baselines in the early rumor detection task. © 2023

关键词： Graph neural networks

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Machine Learning-Based Identification Method of New Strengthening Element and the Study on High-Strength Al-Zn-Mg-Cu-Zr-Hf Alloy

SSRN

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SSRN 2023年

作者： Juan, Yongfei Niu, Guoshuai Yang, Yang Dai, Yongbing Yang, Jian Zhang, Jiao Shanghai Key Lab of Advanced High-temperature Materials and Precision Forming State Key Lab of Metal Matrix Composites School of Materials Science and Engineering Shanghai Jiao Tong University Shanghai200240 China State Grid Shanghai Electric Power Research Institute Shanghai200437 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Collaborative Innovation Center for Advanced Ship and Deep-sea Exploration Shanghai Jiao Tong University Shanghai200240 China

We proposed a strengthening element screening method through integrating a machine learning-based property optimization model with strength descriptors of Al-Zn-Mg-Cu alloy. By comparing the predicted properties of alloys with different elements added, we found that the trace element Hf, beyond the dataset, can effectively improve the strength and toughness of Al-Zn-Mg-Cu alloy. According to the alloy optimization result, we fabricated a high-strength Al-Zn-Mg-Cu-Zr-Hf alloy with superior mechanical properties with 816 MPa for tensile strength, 779 MPa for yield strength, and 9.1% for elongation. © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Fitting Solder305 Anand Model Parameters with Artificial Neural Networks

Fitting Solder305 Anand Model Parameters with Artificial Neu...

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International Microsystems, Packaging, Assembly and Circuits Technology (IMPACT)

作者： Qinghua Su Cadmus Yuan K.N. Chiang Advanced Microsystem Packaging and Nano-Mechanics Research Lab Taiwan Dept. of Power Mechanical Engineering National Tsing Hua University Hsinchu 300 Taiwan R.O.C. Dept. of Mechanical and Computer-Aided Engineering Feng Chia University Taichung 407 Taiwan R.O.C.

The Anand constitutive model is frequently utilized to explain the mechanical characteristics of solder materials in electronic components. In the Anand model, plasticity and creep are integrated and governed by a unified set of flow and evolutionary equations. The uniaxial tensile test results of Pb-free SAC305 are applied in this research to obtain the parameters of the Anand model. Due to the complexity of the model equations, it is difficult to determine all the parameters at once. The traditional approach involves using a standard multi-step procedure to estimate the model parameters. The experimental data used in this study were obtained from Motalab et al.[1], and the curve fitting method they employed was the least squares method. An artificial neural network (ANN) based method is applied for the parameter abstraction from the experimental results, with the freedom of initial guessing of these parameters. The framework and the procedure will be described. After completing the initial fitting of the Anand model, we will explore the influence of the specific parameters A, ho, and s 0 on the fitting of the model curve when they are varied across different orders of magnitude. Additionally, we will demonstrate the impact of Anand model with values at different orders of magnitude on the simulation results of finite element models for WLCSP (Wafer Level Chip Scale Packaging).

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Machine Learning-Based Identification Method of New Strengthening Element and the Study on High-Strength Al-Zn-Mg-Cu-Zr-Hf Alloy

SSRN

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SSRN 2023年

关键词： Machine learning

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Acceleration Slip Regulation for Electric Vehicles Based on Fuzzy PID Controller 11

Acceleration Slip Regulation for Electric Vehicles Based on ...

引用

11th International Conference on Control, Automation and Information Sciences, ICCAIS 2022

作者： Nguyen, An-Toan Nguyen, Binh-Minh Trovao, Joao Pedro F. Ta, Minh C. Université de Sherbrooke Dept. of Electrical and Computer Engineering SherbrookeJ1K 2R1 Canada Quy Nhon University Faculty of Engineering and Technology Binh Dinh Quy Nhon Viet Nam University of Tokyo Dept. of Advanced Energy Tokyo Japan Polytechnic of Coimbra IPC-ISEC Inesc Coimbra 3030-199 Portugal Hanoi University of Science and Technology Cti Lab. for EVs Hanoi Viet Nam

ISBN: (数字)9781665452489

ISBN: (纸本)9781665452489

This paper proposes an acceleration slip regulation (ASR) by the fuzzy PID (FPID) controller for electric vehicles with four-wheel independent motor drive. The ASR architecture is hierarchically presented by three layers. The upper layer determines the total traction force based on the control of vehicle velocity. The middle layer determines optimum tire longitudinal force distribution depending on the total tire workload. In case of wheel slip, the optimum longitudinal force will be adjusted by the FPID controller. In the lower layer, the traction forces are converted to reference torque for each powertrain. The modeling and control organization of the studied vehicle are presented by using energetic macroscopic representation (EMR). Simulation tests show that ASR with FPID controller can limit the slip ratio of the wheels and improve the vehicle stability. © 2022 IEEE.

关键词： Traction control

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Frozen-DETR: Enhancing DETR with Image Understanding from Frozen Foundation Models

arXiv

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arXiv 2024年

作者： Fu, Shenghao Yan, Junkai Yang, Qize Wei, Xihan Xie, Xiaohua Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Peng Cheng Laboratory Shenzhen518055 China Tongyi Lab Alibaba Group China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Guangdong Province Key Laboratory of Information Security Technology China Guangdong Guangzhou510555 China

Recent vision foundation models can extract universal representations and show impressive abilities in various tasks. However, their application on object detection is largely overlooked, especially without fine-tuning them. In this work, we show that frozen foundation models can be a versatile feature enhancer, even though they are not pre-trained for object detection. Specifically, we explore directly transferring the high-level image understanding of foundation models to detectors in the following two ways. First, the class token in foundation models provides an in-depth understanding of the complex scene, which facilitates decoding object queries in the detector’s decoder by providing a compact context. Additionally, the patch tokens in foundation models can enrich the features in the detector’s encoder by providing semantic details. Utilizing frozen foundation models as plug-and-play modules rather than the commonly used backbone can significantly enhance the detector’s performance while preventing the problems caused by the architecture discrepancy between the detector’s backbone and the foundation model. With such a novel paradigm, we boost the SOTA query-based detector DINO from 49.0% AP to 51.9% AP (+2.9% AP) and further to 53.8% AP (+4.8% AP) by integrating one or two foundation models respectively, on the COCO validation set after training for 12 epochs with R50 as the detector’s backbone. Copyright © 2024, The Authors. All rights reserved.

关键词： Decoding

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