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arXiv 2023年

作者： Reimann, Marcel Won, Jungeun Takahashi, Hiroyuki Yaghy, Antonio Hwang, Yunchan Ploner, Stefan Lin, Junhong Girgis, Jessica Lam, Kenneth Chen, Siyu Waheed, Nadia K. Maier, Andreas Fujimoto, James G. Department of Electrical Engineering and Computer Science Research Laboratory of Electronics Massachusetts Institute of Technology United States Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Germany New England Eye Center Tufts Medical Center United States

Recent advances in optical coherence tomography such as the development of high speed ultrahigh resolution scanners and corresponding signal processing techniques may reveal new potential biomarkers in retinal diseases. Newly visible features are, for example, small hyperreflective specks in age-related macular degeneration. Identifying these new markers is crucial to investigate potential association with disease progression and treatment outcomes. Therefore, it is necessary to reliably detect these features in 3D volumetric scans. Because manual labeling of entire volumes is infeasible a need for automatic detection arises. Labeled datasets are often not publicly available and there are usually large variations in scan protocols and scanner types. Thus, this work focuses on an unsupervised approach that is based on local peak-detection and random walker segmentation to detect small features on each B-scan of the volume. © 2023, CC BY-NC-SA.

关键词： Optical tomography

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Ferroelectric domain nucleation and switching pathways in hafnium oxide.

arXiv

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arXiv 2023年

作者： Sebastian Calderon, V. Jaszewski, Samantha T. Kelley, Kyle P. Ihlefeld, Jon F. Dickey, Elizabeth Department of Materials Science and Engineering Carnegie Mellon University PittsburghPA15213 United States Department of Materials Science and Engineering University of Virginia CharlottesvilleVA22904 United States Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Charles L. Brown Department of Electrical and Computer Engineering CharlottesvilleVA22904 United States

Nanoscale ferroelectrics that can be integrated into microelectronic fabrication processes are highly desirable for low-power computing and non-volatile memory devices. However, scalable novel ferroelectric materials, such as hafnium oxide (HfO2), remain in a state of development, and a clear understanding of the effects of relevant compositional and processing parameters to control their ferroelectric properties and the actual polarization switching mechanisms are still under investigation. One key fundamental knowledge gap is the polarization switching pathway in ferroelectric hafnia. To further our fundamental understanding of domain nucleation and switching, we have studied polarization switching pathways in HfO2-x thin films in real-time at the atomic scale using transmission electron microscopy. We employed differential phase contrast imaging that allows for the acquisition of both hafnium and oxygen atomic column signals and facilitates the observation of relative movement of atomic columns between both sublattices. Our results demonstrate that the switching pathway involves a transient tetragonal-like local structure, as oxygen ions shift in locations and remain within their parent hafnium polyhedra. © 2023, CC BY.

关键词： Hafnium oxides

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A transfer learning based model for brain tumor detection using magnetic resonance imaging

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AIP Conference Proceedings 2025年第1期3224卷

作者： Saurabh Srivastava Tasneem Ahmed Rajeev Kumar Amit Saxena Department of Computer Science & Engineering Moradabad Institute of Technology Moradabad India Advanced Computing and Research Laboratory Dept. of Computer Application Integral University Lucknow India

Rapid and uncontrolled proliferation of cells is the cause of brain tumors. If treatment is not carried out early, death can occur. A major challenge in detecting a brain tumor is that it originates from variations in size, shape, and location. Since tumor cells are diverse, it might be difficult for radiologists to classify brain tumors, which are malignant diseases. The overall survival of patients, treatment planning, and risk factor identification depend on robust and accurate tumor classification and prediction. In the medical field, deep learning (DL) and machine learning (ML) are often employed to diagnose illnesses. Various tools have been developed that are used to diagnose different diseases such as heart disease, lung cancer, and many more. In this study, a DL-based transfer learning technique has been used to build a model that can easily identify whether a patient is suffering from a brain tumor or not. Brain tumors have been categorized into four types of categories based on the characteristics of the tumor. Patients who are suffering from the first and second categories of tumors can be saved by treatments that include surgery, radiation therapy, and chemotherapy. The third and fourth categories of tumors are more dangerous because these categories of tumors spread very fast, so if it is diagnosed early stage then the patient can be saved or increase the patient’s life span. Glioma, Meningioma, and Pituitary are a dangerous form of tumors, and the survival rate of patients who are suffering from these is very low. The developed system can predict that a patient is suffering from a brain tumor or not using MRI, and also predict the type of tumor such as Glioma, Meningioma, and Pituitary.

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PFS3F: Probabilistic Fusion of Superpixel-wise and Semantic-aware Structural Features for Hyperspectral Image Classification

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Zhang, Ying Duan, Puhong Liang, Lianhui Kang, Xudong Li, Jun Plaza, Antonio Hunan University of Technology College of Railway Transportation Zhuzhou412007 China Hunan University School of Robotics Changsha410082 China Guangxi University School of Electrical Engineering Nanning530004 China China University of Geosciences Hubei Key Laboratory of Intelligent Geo-Information Processing the School of Computer Science Wuhan430078 China Hyperspectral Computing Laboratory Department of Technology of Computers and Communications Escuela Politecnica University of Extremadura CaceresE-10071 Spain

Processing high-dimensional data cubes and developing high-performance classifiers are core objectives in the field of hyperspectral image classification (HSIC). Superpixel-based methods are widely used in HSIC due to their efficacy in reducing redundant information and enhancing local features. However, imprecise segmentation, especially in complex structures and textures of hyperspectral images (HSIs), may lead to inconsistencies in the regions extracted by superpixels and the boundaries between different ground objects. Such inconsistencies significantly degrade the classification performance of HSIs. Alternatively, when parameter settings are inaccurate, edge-aware feature extraction methods often introduce sharpening artifacts at the image boundaries, resulting in a decrease in classification accuracy. To effectively address these challenges, we propose a novel probabilistic fusion method for HSIC. This method consists of the following stages. First, spatial information is extracted by a multiscale superpixel segmentation method and then probabilistically optimized by the extended random walk (ERW) method. Next, semantic-aware structural features (S2Fs) are extracted along with edge information of different objects. Lastly, a probabilistic framework is proposed to fuse the class probabilities of superpixel-based spatial information and semantic-aware structural features. Experimental results on three real datasets show state-of-the-art classification performance, even with limited training sets. ©1991-2012 IEEE.

关键词： Superpixels

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Scalable Multi-Agent Reinforcement Learning for Residential Load Scheduling Under Data Governance

SSRN

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SSRN 2023年

作者： Qin, Zhaoming Dong, Nanqing Liu, Di Cao, Junwei Automatic Control Laboratory EPFL Lausanne1015 Switzerland Department of Computer Science University of Oxford OxfordOX1 3QD United Kingdom Department of Electrical Engineering Tsinghua University Beijing100084 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

As a data-driven approach, multi-agent reinforcement learning (MARL) has made remarkable advances in solving cooperative residential load scheduling problems. In a simplified scenario, centralized training, the most common paradigm for MARL, precludes large-scale deployment of MARL in communication-constrained cloud-edge environments. As a remedy, distributed training shows unparalleled advantages in real-life application scenarios compared with its centralized counterpart. However, distributed training still faces the challenge of system scalability, e.g., the high communication burden during coordinating individual agents, and needs to comply with data governance in terms of privacy. In this work, we propose a novel MARL solution to address these two practical issues. The proposed solution is based on actor-critic methods, where the global critic is a learned function of individual critics computed solely based on local observations of residences. In this way, the private data of residences is preserved completely and the communication cost is reduced significantly. The simulation experiments demonstrate that the proposed framework can achieve comparable performance to the state-of-the-art actor-critic framework without data governance and communication constraints. © 2023, The Authors. All rights reserved.

关键词： Edge computing

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SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data

SynFacePAD 2023: Competition on Face Presentation Attack Det...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Meiling Fang Marco Huber Julian Fierrez Raghavendra Ramachandra Naser Damer Alhasan Alkhaddour Maksim Kasantcev Vasiliy Pryadchenko Ziyuan Yang Huijie Huangfu Yingyu Chen Yi Zhang Yuchen Pan Junjun Jiang Xianming Liu Xianyun Sun Caiyong Wang Xingyu Liu Zhaohua Chang Guangzhe Zhao Juan Tapia Lazaro Gonzalez-Soler Carlos Aravena Daniel Schulz Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain Norwegian University of Science and Technology (NTNU) Norway ID R&D Inc New York US School of Cyber Science and Engineering Sichuan University Chengdu China School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture China Biometrics and Security Research Group Hochschule Darmstadt Darmstadt Germany I+D Vision Center Santiago Chile

This paper presents a summary of the Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data (SynFacePAD 2023) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition attracted a total of 8 participating teams with valid submissions from academia and industry. The competition aimed to motivate and attract solutions that target detecting face presentation attacks while considering synthetic-based training data motivated by privacy, legal and ethical concerns associated with personal data. To achieve that, the training data used by the participants was limited to synthetic data provided by the organizers. The submitted solutions presented innovations and novel approaches that led to outperforming the considered baseline in the investigated benchmarks.

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L-SPEX: LOCALIZED TARGET SPEAKER EXTRACTION

arXiv

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arXiv 2022年

作者： Ge, Meng Xu, Chenglin Wang, Longbiao Chng, Eng Siong Dang, Jianwu Li, Haizhou College of Intelligence and Computing Tianjin University Tianjin China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Kuaishou Technology Beijing China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Japan Advanced Institute of Science and Technology Ishikawa Japan The Chinese University of Hong Kong Shenzhen China

Speaker extraction aims to extract the target speaker’s voice from a multi-talker speech mixture given an auxiliary reference utterance. Recent studies show that speaker extraction benefits from the location or direction of the target speaker. However, these studies assume that the target speaker’s location is known in advance or detected by an extra visual cue, e.g., face image or video. In this paper, we propose an end-to-end localized target speaker extraction on pure speech cues, that is called L-SpEx. Specifically, we design a speaker localizer driven by the target speaker’s embedding to extract the spatial features, including direction-of-arrival (DOA) of the target speaker and beamforming output. Then, the spatial cues and target speaker’s embedding are both used to form a top-down auditory attention to the target speaker. Experiments on the multi-channel reverberant dataset called MCLibri2Mix show that our L-SpEx approach significantly outperforms the baseline system. Copyright © 2022, The Authors. All rights reserved.

关键词： Beamforming

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Leveraging large language models for nano synthesis mechanism explanation: solid foundations or mere conjectures?

arXiv

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arXiv 2024年

作者： Pu, Yingming Huang, Liping Lin, Tao Chen, Hongyu Zhejiang University Zhejiang Hangzhou310030 China Department of Computer Science and Engineering School of Engineering Wetlake University Zhejiang Hangzhou310030 China Department of Chemistry School of Science Key Laboratory for Quantum Materials of Zhejiang Province Wetlake University Zhejiang Hangzhou310030 China Institute of Natural Sciences Westlake Institute for Advanced Study Hangzhou310024 China Research Center for Industries of the Future Wetlake University Zhejiang Hangzhou310030 China

With the rapid development of artificial intelligence (AI), large language models (LLMs) such as GPT-4 have garnered significant attention in the scientific community, demonstrating great potential in advancing scientific discovery. This progress raises a critical question: are these LLMs well-aligned with real-world physicochemical principles? Current evaluation strategies largely emphasize fact-based knowledge, such as material property prediction or name recognition, but they often lack an understanding of fundamental physicochemical mechanisms that require logical reasoning. To bridge this gap, our study developed a benchmark consisting of 775 multiple-choice questions focusing on the mechanisms of gold nanoparticle synthesis. By reflecting on existing evaluation metrics, we question whether a direct true-or-false assessment merely suggests conjecture. Hence, we propose a novel evaluation metric, the confidence-based score (c-score), which probes the output logits to derive the precise probability for the correct answer. Based on extensive experiments, our results show that in the context of gold nanoparticle synthesis, LLMs understand the underlying physicochemical mechanisms rather than relying on conjecture. This study underscores the potential of LLMs to grasp intrinsic scientific mechanisms and sets the stage for developing more reliable and effective AI tools across various scientific domains. Copyright © 2024, The Authors. All rights reserved.

关键词： Gold nanoparticles

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Detection of Veterinary Drug Residues in Raw Milk: Machine Learning Modeling and Federated Learning Optimization

SSRN

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SSRN 2025年

作者： Cheng, Wenxu Song, Xiaodong Ding, Haohan Dong, Guanjun Cui, Xiaohui Yu, Wei Wilson, David I. School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China Key Laboratory of Dairy Quality Digital Intelligence Monitoring Technology State Administration for Market Regulation Hohhot011517 China Science Center for Future Foods Jiangnan University Wuxi214122 China School of Cyber Science and Engineering Wuhan University Wuhan430072 China Department of Chemical & Materials Engineering University of Auckland Auckland1010 New Zealand Electrical and Electronic Engineering Department Auckland University of Technology Auckland1010 New Zealand

Veterinary drug residues in raw milk can lead to serious safety problems, while traditional detection methods are inefficient and costly. Difference in regional agricultural practice and the need for data privacy lend towards a unified detection method. This study developed a detection method for veterinary drug residues in raw milk using machine learning and federated learning. Using raw milk quality monitoring data from 8 Chinese regions (2022-2024), we combined SMOTE with random under-sampling to solve the serious problem of data imbalance. We evaluated several machine learning classifiers with XGBoost showing the best performance, with psychrophilic bacteria count played a crucial role in distinguishing milk quality. The integration of federated learning and the XGBoost model enhanced stability across regions while preserving data privacy. These results demonstrate that the proposed and cost-effective method can help producers identify samples requiring further laboratory confirmation while reducing unnecessary testing. © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Lens-to-Lens Bokeh Effect Transformation. NTIRE 2023 Challenge Report

Lens-to-Lens Bokeh Effect Transformation. NTIRE 2023 Challen...

引用

2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Conde, Marcos V. Kolmet, Manuel Seizinger, Tim Bishop, Tom E. Timofte, Radu Kong, Xiangyu Zhang, Dafeng Wu, Jinlong Wang, Fan Peng, Juewen Pan, Zhiyu Liu, Chengxin Luo, Xianrui Sun, Huiqiang Shen, Liao Cao, Zhiguo Xian, Ke Liu, Chaowei Chen, Zigeng Yang, Xingyi Liu, Songhua Jing, Yongcheng Mi, Michael Bi Wang, Xinchao Yang, Zhihao Lian, Wenyi Lai, Siyuan Zhang, Haichuan Hoang, Trung Yazdani, Amirsaeed Monga, Vishal Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Zhao, Yuxuan Chen, Baoliang Xu, Yiqing Niu, Jixiang Computer Vision Lab CAIDAS IFI University of Würzburg Germany Glass Imaging Inc. China Huazhong University of Science and Technology China Nanyang Technological University Singapore National University of Singapore Singapore University of Sydney Australia Huawei Uppsala University Sweden Department of Electrical Engineering Pennsylvania State University United States Department of Information Technology Uppsala University Sweden Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education Xidian University Xi'an China North China University of Technology China

ISBN: (纸本)9798350302493

We present the new Bokeh Effect Transformation Dataset (BETD), and review the proposed solutions for this novel task at the NTIRE 2023 Bokeh Effect Transformation Challenge. Recent advancements of mobile photography aim to reach the visual quality of full-frame cameras. Now, a goal in computational photography is to optimize the Bokeh effect itself, which is the aesthetic quality of the blur in out-of-focus areas of an image. Photographers create this aesthetic effect by benefiting from the lens optical *** aim of this work is to design a neural network capable of converting the the Bokeh effect of one lens to the effect of another lens without harming the sharp foreground regions in the image. For a given input image, knowing the target lens type, we render or transform the Bokeh effect accordingly to the lens properties. We build the BETD using two full-frame Sony cameras, and diverse lens *** the best of our knowledge, we are the first attempt to solve this novel task, and we provide the first BETD dataset and benchmark for it. The challenge had 99 registered participants. The submitted methods gauge the state-of-the-art in Bokeh effect rendering and transformation. © 2023 IEEE.

关键词： Color photography

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