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arXiv 2023年

作者： Goldstein, Ariel Ham, Eric Schain, Mariano Nastase, Samuel A. Zada, Zaid Grinstein-Dabus, Avigail Aubrey, Bobbi Gazula, Harshvardhan Feder, Amir Doyle, Werner Devore, Sasha Dugan, Patricia Friedman, Daniel Reichart, Roi Brenner, Michael Hassidim, Avinatan Devinsky, Orrin Flinker, Adeen Levy, Omer Hasson, Uri Department of Psychology The Neuroscience Institute Princeton University PrincetonNJ United States Google Research United States New York University Grossman School of Medicine New YorkNY United States School of Engineering and Applied Science Harvard University CambridgeMA United States New York University Tandon School of Engineering BrooklynNY United States Blavatnik School of Computer Science Tel-Aviv University Israel Department of Cognitive and Brain Sciences Business School Hebrew University of Jerusalem Israel Bioinformatics Interdepartmental Program University of California Los Angeles Los AngelesCA United States Technion - Israel Institute of Technology Haifa Israel

Deep Language Models (DLMs) provide a novel computational paradigm for understanding the mechanisms of natural language processing in the human brain. Unlike traditional psycholinguistic models, DLMs use layered sequences of continuous numerical vectors to represent words and context, allowing a plethora of emerging applications such as human-like text generation. In this paper we show evidence that the layered hierarchy of DLMs may be used to model the temporal dynamics of language comprehension in the brain by demonstrating a strong correlation between DLM layer depth and the time at which layers are most predictive of the human brain. Our ability to temporally resolve individual layers benefits from our use of electrocorticography (ECoG) data, which has a much higher temporal resolution than noninvasive methods like fMRI. Using ECoG, we record neural activity from participants listening to a 30-minute narrative while also feeding the same narrative to a high-performing DLM (GPT2-XL). We then extract contextual embeddings from the different layers of the DLM and use linear encoding models to predict neural activity. We first focus on the Inferior Frontal Gyrus (IFG, or Broca’s area) and then extend our model to track the increasing temporal receptive window along the linguistic processing hierarchy from auditory to syntactic and semantic areas. Our results reveal a connection between human language processing and DLMs, with the DLM’s layer-by-layer accumulation of contextual information mirroring the timing of neural activity in high-order language areas. © 2023, CC BY-NC-ND.

关键词： Brain

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Polarization entanglement enabled by orthogonally stacked van der Waals NbOCl2 crystals

arXiv

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arXiv 2024年

作者： Guo, Qiangbing Wu, Yun-Kun Zhang, Di Zhang, Qiuhong Guo, Guang-Can Alù, Andrea Ren, Xi-Feng Qiu, Cheng-Wei Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Centre for Advanced 2D Materials Graphene Research Centre National University of Singapore Singapore Singapore CAS Key Laboratory of Quantum Information CAS Synergetic Innovation Center of Quantum Information & Quantum Physics University of Science and Technology of China Hefei China Hefei National Laboratory University of Science and Technology of China Hefei China Photonics Initiative Advanced Science Research Center City University of New York New York United States Physics Program Graduate Center City University of New York New York United States The Key Laboratory of Weak-Light Nonlinear Photonics Ministry of Education School of Physics TEDA Applied Physics Institute Nankai University Tianjin China School of Chemistry and Materials Science Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou China

Polarization entanglement holds significant importance for photonic quantum technologies. Recently emerging subwavelength nonlinear quantum light sources, e.g., GaP and LiNbO3 thin films, benefiting from the relaxed phase-matching constraints and volume confinement, has shown intriguing properties, such as high-dimensional hyperentanglement and robust entanglement anti-degradation. Van der Waals (vdW) NbOCl2 crystal, renowned for its superior optical nonlinearities, has emerged as one of ideal candidates for ultrathin quantum light sources [Nature 613, 53 (2023)]. However, polarization-entanglement is inaccessible in NbOCl2 crystal due to its unfavorable nonlinear susceptibility tensor. Here, by leveraging the twist-stacking degree of freedom inherently in vdW systems, we showcase the preparation of tunable polarization entanglement and quantum Bell states. Our work not only provides a new and tunable polarization-entangled vdW photon-pair source, but also introduces a new knob in engineering the entanglement state of quantum light at the nanoscale. © 2024, CC BY-NC-ND.

关键词： Van der Waals forces

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Splicing Image Forgery Detection by Deploying Deep Learning Model

Splicing Image Forgery Detection by Deploying Deep Learning ...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： N Krishnamoorthy C. Amuthadevi M. K. Geedtha Poli Lokeshwara Reddy Anitha Rani K S R. Gopinathan Department of Masters in Computer Application Ramapuram Campus SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Information Technology Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Anurag University Hyderabad Telangana India School of CSA REVA University Bangalore Karnataka India Department of Mechatronics Engineering Sri Krishna College of Engineering and Technology Coimbatore Tamil Nadu India

ISBN: (纸本)9781665460859

The use of digital images has become more important in numerous industries, including journalism, medical diagnosis, law enforcement, and forensics. Images could be easily modified due to the availability of photo-altering tools and software, which can distort the original information. The availability of low-cost and simple image-editing software has also drastically decreased the time and money needed to engage in image tampering. This destroys the purity of the image, making it vulnerable to misuse by anyone. Copy-move, splicing, and enhancement falsification are prominent methods used to create fake images. Using a combination of two or more images, a new image is created called splicing, which may be shared across many online channels and used to spread information and impact target audiences. The discovery could have beneficial as well as negative results. Consequently, it is important to provide a method that can identify the splicing forgery in an image. The study used a Deep Learning (DL) model named mobileNet to distinguish between authentic and manipulated images. Both the training and testing of the DL model make use of the CASIA data. Resizing and enhancing methods are used to process the collected data. Accuracy, F1 score, and recall are employed to test the reliability of the created DL model.

关键词： Deep learning Training Renewable energy sources Splicing Computational modeling Forgery Data models

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ASEAN entrepreneurship profiling: A new model for mobile-based application development

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AIP Conference Proceedings 2023年第1期2938卷

作者： Reinhart Previano Koentjoro Sasmoko Sonya Rapinta Manalu Yasinta Indrianti Jurike Moniaga School of Computer Science Bina Nusantara University Jakarta Indonesia 11470 Primary Teacher Education Department Faculty of Humanities Bina Nusantara University Jakarta Indonesia 11470 Research Interest Group in Educational Technology Bina Nusantara University Jakarta Indonesia 11470 Mobile Application & Technology School of Computer Science Bina Nusantara University Jakarta Indonesia 11470 Entrepreneurship Department Universitas Agung Podomoro Jakarta Indonesia 11480

The ASEAN Entrepreneurship Profiling (AEP) mobile application is an application developed to measure the profile of entrepreneurs in ASEAN. By utilizing this application, it is hoped that entrepreneurs can conduct self-assessments independently and obtain a self-profile picture that can be used to see the gaps in indicators of successful entrepreneurs. The purpose of this research is to develop AEP mobile application with Flutter and Laravel based application development. The application can be used for Android and iOS considering the variety of mobile phones used by entrepreneurs. The AEP mobile application backend uses the Laravel framework and the PHP programming language. The research method used is the waterfall method as a simple and systematic development model that divides the entire work into several stages, namely communication, planning, modeling, construction, and deployment. The results of the research are in the form of AEP mobile application that are ready to be implemented by entrepreneurs so that they can quickly find out their profile as entrepreneurs. This application can assist entrepreneurs in setting self-development strategies based on a priority scale according to the conditions experienced.

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Understanding the predictability of path flow distribution in urban road networks using an information entropy approach

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Multimodal Transportation 2024年第2期3卷

作者： Guo, Bao Huang, Zhiren Zheng, Zhihao Zhang, Fan Wang, Pu School of Traffic and Transportation Engineering Rail Data Research and Application Key Laboratory of Hunan Province Central South University Changsha China Shandong Provincial Communications Planning and Design Institute Group Co. Ltd Jinan China Department of Computer Science Aalto University Helsinki Finland Department of Civil Engineering McGill University Montreal H3A 0C3 QC Canada Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

Predicting the distributions of path flow between origin-destination (OD) pairs in an urban road network is crucial for developing efficient traffic control and management strategies. Here, we use the large-scale taxi GPS trajectory data of San Francisco and Shenzhen to study the predictability of path flow distribution in urban road networks. We develop an approach to project the time-varying path flow distributions into a high-dimensional space. In the high-dimensional space, information entropy is used to measure the predictability of path flow distribution. We find that the distributions of path flow between OD pairs are in general characterized with a high predictability. In addition, we analyze the factors affecting the predictability of path flow distribution. Finally, an n-gram model incorporating high-order gram and low-order gram is proposed to predict the distribution of path flow. A relatively high prediction accuracy is achieved. © 2024

关键词： Human mobility Information entropy Path flow distribution Predictability

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Data-Efficient Federated Semi-Supervised Learning Framework Via Pseudo Supervision Refinement Strategy for Lung Tumor Segmentation

SSRN

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SSRN 2024年

作者： Shi, Zhenwei Li, Weixing Pan, Xipeng Zhang, Zhen Wu, Guangyao Ye, Guanchao Wee, Leonard Dekker, Andre Han, Chu Shi, Lei Liu, Zaiyi Liu, Zhenbing School of Computer and Information Security Guilin University of Electronic Technology Guilin541004 China Department of Radiology Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Southern Medical University Guangzhou510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou510080 China Chinese Academy of Sciences Zhejiang Hangzhou310022 China Chinese Academy of Sciences Zhejiang Hangzhou310022 China Department of Radiology Huazhong University of Science and Technology Tongji Medical College Union Hospital Hubei Wuhan430021 China Clinical Data Science Faculty of Health Medicine Life Sciences Maastricht University Maastricht6229 ET Netherlands GROW School of Oncology and Reproduction Maastricht University Medical Centre+ Maastricht6229 ET Netherlands Guangdong Provincial People's Hospital China

Background and Objective: Accurate lung tumor segmentation is crucial for early clinical diagnosis and subsequent treatment planning for patients. Conducting centralized training to enhance model performance is impractical in real-world scenarios. Although federated learning offers a promising solution to this issue, effectively and efficiently utilizing unlabeled data remains a challenge. Methods: In this work, we propose a novel federated semi-supervised learning framework (FSSL) for complex and realistic scenario, which can efficiently leverage the limited labeled data and abundant unlabeled data from multiple clinical sites. Specifically, we initially conduct federated self-supervised pre-training at completely unlabeled sites using masked image modeling strategy. Subsequently, in the fine-tuning stage, we propose a pseudo supervision refinement strategy to leverage the unlabeled data from partially labeled sites, which not only reduces pseudo label noise but also helps stabilize the training process. Moreover, we propose a dynamic model aggregation strategy to assist the server in dynamically combining all local models during communication round. Results: We conducted extensive comparative and ablation experiments to validate the effectiveness of our proposed method. When only a limited amount of labeled data is available in the partially labeled sites, even without loading the pre-trained model, our approach consistently achieves the highest average Dice scores of 0.7383, 0.4768, and 0.6629 on the internal test sets, external unseen test sets, and pre-training test sets, respectively. Upon loading the pre-trained model, the scores on the three test sets further improve to 0.8205, 0.6185, and 0.7334, respectively. Conclusion: Our proposed method is capable of collaboratively training a global model with robust segmentation performance and generalizability by efficiently utilizing unlabeled data from different sites, even when only a limited amount of labeled da

关键词： Federated learning

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Spatial atlas of the mouse central nervous system at molecular resolution (vol 622, pg 552, 2023)

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NATURE 2024年第7993期625卷 E6-E6页

作者： Shi, Hailing He, Yichun Zhou, Yiming Huang, Jiahao Maher, Kamal Wang, Brandon Tang, Zefang Luo, Shuchen Tan, Peng Wu, Morgan Lin, Zuwan Ren, Jingyi Thapa, Yaman Tang, Xin Chan, Ken Y. Deverman, Benjamin E. Shen, Hao Liu, Albert Liu, Jia Wang, Xiao Broad Institute of MIT and Harvard Cambridge MA USA Department of Chemistry Massachusetts Institute of Technology Cambridge MA USA John A. Paulson School of Engineering and Applied Sciences Harvard University Boston MA USA Computational and Systems Biology PhD Program Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Biology Massachusetts Institute of Technology Cambridge MA USA Klarman Cell Observatory Broad Institute of MIT and Harvard Cambridge MA USA Department of Chemistry and Chemical Biology Harvard University Cambridge MA USA Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA USA

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Learning High-Resolution and Efficient Non-local Features for Brain Glioma Segmentation in MR Images 23rd

Learning High-Resolution and Efficient Non-local Features fo...

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23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Jia, Haozhe Xia, Yong Cai, Weidong Huang, Heng Research and Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen518057 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xian710072 China School of Computer Science University of Sydney SydneyNSW2006 Australia Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15261 United States JD Finance America Corporation Mountain ViewCA94043 United States

ISBN: (纸本)9783030597184

Brain glioma segmentation using multi-parametric magnetic resonance (MR) imaging has significant clinical value. Although 3D convolutional neural networks (CNNs) have become increasingly prevalent in delivering this segmentation task, these models still suffer from an insufficient ability to high-resolution feature representation for small and irregular regions, limited local receptive fields, and poor long-range dependencies. In this paper, we propose a 3D High-resolution and Non-local Feature Network (HNF-Net) for brain glioma segmentation using multi-parametric MR imaging. We construct HNF-Net based mainly on the parallel multi-scale fusion (PMF) module, which helps produce strong high-resolution feature representation and aggregate multi-scale contextual information. We also introduce the expectation-maximization attention (EMA) module to HNF-Net, aiming to capture the long-range dependent contextual information and reduce the feature redundancy in a lightweight fashion. We evaluated our HNF-Net on the BraTS 2019 Challenge dataset against eight top-ranking methods listed on the challenge leaderboard. Our results suggest that the proposed HNF-Net achieves improved overall performance over these methods, and our ablation study demonstrates the effectiveness of the PMF module and EMA module. © 2020, Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

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Data-Efficient Federated Semi-Supervised Learning Framework Via Pseudo Supervision Refinement Strategy for Lung Tumor Segmentation

SSRN

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SSRN 2024年

作者： Shi, Zhenwei Li, Weixing Pan, Xipeng Zhang, Zhen Wu, Guangyao Ye, Guanchao Wee, Leonard Andre, Dekker Han, Chu Shi, Lei Liu, Zaiyi Liu, Zhenbing School of Computer and Information Security Guilin University of Electronic Technology Guilin541004 China Department of Radiology Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Southern Medical University Guangzhou510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou510080 China Chinese Academy of Sciences Zhejiang Hangzhou310022 China Chinese Academy of Sciences Zhejiang Hangzhou310022 China Department of Radiology Huazhong University of Science and Technology Tongji Medical College Union Hospital Hubei Wuhan430021 China Clinical Data Science Faculty of Health Medicine Life Sciences Maastricht University Maastricht6229 ET Netherlands GROW School of Oncology and Reproduction Maastricht University Medical Centre+ Maastricht6229 ET Netherlands Guangdong Provincial People's Hospital China

Accurate lung tumor segmentation is crucial for early clinical diagnosis and subsequent treatment planning for patients. Centralized model training faces privacy constraints, although federated learning offers a promising solution to this issue, effectively and efficiently utilizing unlabeled data remains a challenge. In this work, we propose a novel federated semi-supervised learning framework for complex and realistic scenario, which can efficiently leverage the limited labeled data and more homogeneous unlabeled data from multiple clinical sites for collaborative model training. Specifically, we initially conduct federated self-supervised pre-training at completely unlabeled sites using masked image modeling strategy. Subsequently, in the fine-tuning stage, we propose a pseudo supervision refinement strategy to leverage the unlabeled data from partially labeled sites, which not only reduces pseudo label noise but also helps stabilize the training process. Moreover, we propose a dynamic model aggregation strategy to assist the server in dynamically combining local models during each communication round to enhance the robustness and generalizability of the global model. Extensive comparative and ablation experiments confirm the efficacy of our proposed methodology, demonstrating improvement over recent state-of-the-art federated semi-supervised segmentation methods in scenarios with only a small amount of labeled data. This approach highlights the significant potential of multi-source and heterogeneous unlabeled data in facilitating the training of federated deep learning models and promotes clinical applications, assists radiologists in diagnosis and improves patient outcomes. The source code will be released on https://***/zhenweishi/FedPSR © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Grand Challenge on Respiratory Sound Classification for SPRSound Dataset

Grand Challenge on Respiratory Sound Classification for SPRS...

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2022 IEEE Biomedical Circuits and Systems Conference, BioCAS 2022

作者： Zhang, Qing Zhang, Jing Yuan, Jiajun Huang, Huajie Zhang, Yuhang Zhang, Baoqin Lv, Gaomei Lin, Shuzhu Wang, Na Liu, Xin Tang, Mingyu Wang, Yahua Ma, Hui Liu, Lu Yuan, Shuhua Zhou, Hongyuan Zhao, Jian Li, Yongfu Yin, Yong Zhao, Liebin Wang, Guoxing Lian, Yong Shanghai Jiao Tong University Department of Micro-Nano Electronics MoE Key Lab of Artificial Intelligence China Shanghai Jiao Tong University School of Medicine Pediatric Ai Clinical Application and Research Center Shanghai Children's Medical Center China Shanghai Jiao Tong University School of Medicine Shanghai Children's Medical Center Department of Respiratory Medicine China China Shanghai University School of Computer Engineering and Science China Sanya Maternity and Child Care Hospital China Taicang Affiliated Hospital of Soochow University The First People's Hospital of Taicang Department of Paediatrics China Linyi City People Hospital China Fengcheng Hospital Department of Paediatrics China Linyi City Maternal Child Health Hospital China Fifth Affiliated Hospital of Harbin Medical University Department of Pediatrics China Shanghai Tuoxiao Intelligent Technology Co. Ltd China

ISBN: (数字)9781665469173

ISBN: (纸本)9781665469173

It is important to continuously monitor our respiratory system to prevent us from suffering respiratory-releated diseases. This demands for an automatic respiratory sounds software to speed up diagnosis and to reduce the workload of physicians. In the IEEE BioCAS 2022 conference, we have organized the first grand challenge on respiratory sound classification using the paediatric respiratory sound (SPRSound). This event has invited 45 teams with more than 100 open source entries and the top 5 teams are invited to present their works in the IEEE BioCAS 2022. © 2022 IEEE.

关键词： Open source software

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