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arXiv 2021年

作者： Ozturk, Anil Gunel, Mustafa Burak Dagdanov, Resul Vural, Mirac Ekim Yurdakul, Ferhat Dal, Melih Ure, Nazim Kemal ITU Artificial Intelligence and Data Science Research Center Department of Computer Engineering Istanbul Technical University Turkey Faculty of Computer Engineering Bogazici University Turkey

applications of reinforcement learning (RL) are popular in autonomous driving tasks. That being said, tuning the performance of an RL agent and guaranteeing the generalization performance across variety of different driving scenarios is still largely an open problem. In particular, getting good performance on complex road and weather conditions require exhaustive tuning and computation time. Curriculum RL, which focuses on solving simpler automation tasks in order to transfer knowledge to complex tasks, is attracting attention in RL community. The main contribution of this paper is a systematic study for investigating the value of curriculum reinforcement learning in autonomous driving applications. For this purpose, we setup several different driving scenarios in a realistic driving simulator, with varying road complexity and weather conditions. Next, we train and evaluate performance of RL agents on different sequences of task combinations and curricula. Results show that curriculum RL can yield significant gains in complex driving tasks, both in terms of driving performance and sample complexity. Results also demonstrate that different curricula might enable different benefits, which hints future research directions for automated curriculum training. Copyright © 2021, The Authors. All rights reserved.

关键词： Curricula

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PointCG: Self-supervised Point Cloud Learning via Joint Completion and Generation

arXiv

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arXiv 2024年

作者： Liu, Yun Li, Peng Yan, Xuefeng Nan, Liangliang Wang, Bing Chen, Honghua Gong, Lina Zhao, Wei Wei, Mingqiang The School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China The Shenzhen Institute of Research Nanjing University of Aeronautics and Astronautics Shenzhen China The Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Urban Data Science section Delft University of Technology Delft Netherlands The Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic University Hong Kong

The core of self-supervised point cloud learning lies in setting up appropriate pretext tasks, to construct a pre-training framework that enables the encoder to perceive 3D objects effectively. In this paper, we integrate two prevalent methods, masked point modeling (MPM) and 3D-to-2D generation, as pretext tasks within a pre-training framework. We leverage the spatial awareness and precise supervision offered by these two methods to address their respective limitations: ambiguous supervision signals and insensitivity to geometric information. Specifically, the proposed framework, abbreviated as PointCG, consists of a Hidden Point Completion (HPC) module and an Arbitrary-view Image Generation (aiG) module. We first capture visible points from arbitrary views as inputs by removing hidden points. Then, HPC extracts representations of the inputs with an encoder and completes the entire shape with a decoder, while aiG is used to generate rendered images based on the visible points' representations. Extensive experiments demonstrate the superiority of the proposed method over the baselines in various downstream tasks. Our code will be made available upon acceptance. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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HC-GAE: the hierarchical cluster-based graph auto-encoder for graph representation learning 24

HC-GAE: the hierarchical cluster-based graph auto-encoder fo...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Lu Bai Zhuo Xu Lixin Cui Ming Li Yue Wang Edwin R. Hancock School of Artificial Intelligence Beijing Normal University Beijing China and Engineering Research Center of Intelligent Technology and Educational Application Ministry of Education Beijing Normal University Beijing China School of Artificial Intelligence Beijing Normal University Beijing China School of Information Central University of Finance and Economics Beijing China Zhejiang Institute of Optoelectronicss Jinhua China and Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua China Department of Computer Science University of York York United Kingdom

ISBN: (纸本)9798331314385

Graph Auto-Encoders (GAEs) are powerful tools for graph representation learning. In this paper, we develop a novel Hierarchical Cluster-based GAE (HC-GAE), that can learn effective structural characteristics for graph data analysis. To this end, during the encoding process, we commence by utilizing the hard node assignment to decompose a sample graph into a family of separated subgraphs. We compress each subgraph into a coarsened node, transforming the original graph into a coarsened graph. On the other hand, during the decoding process, we adopt the soft node assignment to reconstruct the original graph structure by expanding the coarsened nodes. By hierarchically performing the above compressing procedure during the decoding process as well as the expanding procedure during the decoding process, the proposed HC-GAE can effectively extract bidirectionally hierarchical structural features of the original sample graph. Furthermore, we re-design the loss function that can integrate the information from either the encoder or the decoder. Since the associated graph convolution operation of the proposed HC-GAE is restricted in each individual separated subgraph and cannot propagate the node information between different subgraphs, the proposed HC-GAE can significantly reduce the over-smoothing problem arising in the classical convolution-based GAEs. The proposed HC-GAE can generate effective representations for either node classification or graph classification, and the experiments demonstrate the effectiveness on real-world datasets.

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CCGRID 2023: A Holistic Approach to Inclusion and Belonging

CCGRID 2023: A Holistic Approach to Inclusion and Belonging

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IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID)

作者： Beth Plale Preeti Malakar Meenakshi D'Souza Hemangee K. Kapoor Yogesh Simmhan Ilkay Altintas Manohar Swaminathan School of Informatics Computing and Engineering Indiana University Bloomington IN USA Department of Computer Science and Engineering Indian Institute of Technology Kanpur Kalyanpur Kanpur India International Institute of Information Technology Bangalore Bengaluru Karnataka India Department of Computer Science and Engineering Indian Institute of Technology Guwahati Guwahati Assam India Department of Computational and Data Sciences Indian Institute of Science Bangalore India San Diego Supercomputer Center University of California San Diego MC USA Microsoft Research Lab - India Bengaluru Karnataka India

“CCGRID will act with responsibility as its primary consideration; with equity, diversity, and inclusion as its central goals.” from the CCGRID 2023 web site [1]

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CATD: Unified Representation Learning for EEG-to-fMRI Cross-Modal Generation

arXiv

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arXiv 2024年

作者： Yao, Weiheng Lyu, Zhihan Mahmud, Mufti Zhong, Ning Lei, Baiying Wang, Shuqiang Southern University of Science and Technology Shenzhen518055 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Department of Game Design Faculty of Arts Uppsala University Sweden Department of Information and Computer Science SDAIA-KFUPM Joint Research Center for AI Interdisciplinary Research Center for Biosystems and Machines King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia The Faculty of Engineering Maebashi Institute of Technology Gunma Japan Chongqing University of Posts and Telecommunications Chongqing China School of Biomedical Engineering Shenzhen University Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging Health Science Center Guangdong Shenzhen518000 China

Multi-modal neuroimaging analysis is crucial for a comprehensive understanding of brain function and pathology, as it allows for the integration of different imaging techniques, thus overcoming the limitations of individual modalities. However, the high costs and limited availability of certain modalities pose significant challenges. To address these issues, this paper proposes the Condition-Aligned Temporal Diffusion (CATD) framework for end-to-end cross-modal synthesis of neuroimaging, enabling the generation of functional magnetic resonance imaging (fMRI)-detected Blood Oxygen Level Dependent (BOLD) signals from more accessible Electroencephalography (EEG) signals. By constructing Conditionally Aligned Block (CAB), heterogeneous neuroimages are aligned into a latent space, achieving a unified representation that provides the foundation for cross-modal transformation in neuroimaging. The combination with the constructed Dynamic Time-Frequency Segmentation (DTFS) module also enables the use of EEG signals to improve the temporal resolution of BOLD signals, thus augmenting the capture of the dynamic details of the brain. Experimental validation demonstrates that the framework improves the accuracy of brain activity state prediction by 9.13% (reaching 69.8%), enhances the diagnostic accuracy of brain disorders by 4.10% (reaching 99.55%), effectively identifies abnormal brain regions, enhancing the temporal resolution of BOLD signals. The proposed framework establishes a new paradigm for cross-modal synthesis of neuroimaging by unifying heterogeneous neuroimaging data into a latent representation space, showing promise in medical applications such as improving Parkinson’s disease prediction and identifying abnormal brain regions. Copyright © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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Advancing Personalized Medicine: A Scalable LLM-based Recommender System for Patient Matching

Advancing Personalized Medicine: A Scalable LLM-based Recomm...

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IEEE International Conference on Big data

作者： Armin Berger David Berghaus Ali Hamza Bashir Lorenz Grigull Lara Fendrich Tom Anglim Lagones Henriette Högl Gundula Ernst Ralf Schmidt David Bascom Tobias Deußer Thiago Bell Max Lübbering Rafet Sifa Department of Media Engineering Fraunhofer IAIS Germany Department of Computer Science University of Bonn Germany West-AI Federal Ministry of Education and Research Germany Lamarr Institute Germany Center for Rare Diseases University Hospital Bonn Germany Department of Health Queensland Australia School of Medicine Griffith University Australia Children’s Network for Chronic Illnesses and Disabilities Germany Department of Medical Psychology Medical School Hannover Germany

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

This study explores efficient algorithms to enhance user matching in ***, a novel social networking platform designed to connect individuals affected by rare diseases. Our primary objective is to develop a recommender system that identifies and suggests users with similar medical conditions, facilitating meaningful connections within these unique communities. Utilizing textual user profile data, we train sentence embedder models to generate similar embeddings for users that have rated each other high. We investigate various fine-tuning strategies, as well as a hybrid approach between a dense embedder and sparse SPLADE embeddings. Furthermore, we investigate the efficacy of various clustering algorithms, such as TopicBERT for thematic analysis, K-Means for centroid-based grouping, and Latent Dirichlet Allocation (LDA) for probabilistic topic modeling, to reduce the matching complexity and enable better scalability of the platform.

关键词： Measurement Sparse approximation Social networking (online) Biological system modeling Computational modeling Clustering algorithms Performance gain data models Recommender systems Diseases

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Optimizing Rare Disease Patient Matching with Large Language Models

Optimizing Rare Disease Patient Matching with Large Language...

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IEEE International Conference on Big data

作者： Armin Berger Ali Hamza Bashir David Berghaus Mowmita Nazia Afsan Lorenz Grigull Lara Fendrich Henriette Högl Gundula Ernst Ralf Schmidt David Bascom Tom Anglim Lagones Tobias Deußer Thiago Bell Max Lübbering Rafet Sifa Department of Media Engineering Fraunhofer IAIS Germany Department of Computer Science University of Bonn Germany West-AI Federal Ministry of Education and Research Germany Lamarr Institute Germany Center for Rare Diseases University Hospital Bonn Germany Children's Network for Chronic Illnesses and Disabilities Germany Department of Medical Psychology Medical School Hannover Germany Department of Health Queensland Australia Griffith University - School of Medicine Australia

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

We present RepLLaMA, a neural ranking model for optimizing patient matching in rare disease communities. Using data from *** consisting of over two thousand profiles and over ten thousand ratings, our bi-encoder architecture maps profiles to 4096-dimensional vectors, enabling efficient similarity computations. The system processes unstructured symptom descriptions and structured responses, incorporating expert-guided LLM enhancements. Results show Top-10 Recall of 49.36% $(\pm 2.03)$ , surpassing baselines while maintaining generalization. The implementation provides a scalable solution for rare disease patient matching, addressing computational complexity challenges.

关键词： Measurement Computational modeling Large language models computer architecture Vectors data models Computational complexity Recommender systems Optimization Diseases

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Towards a multimodal neuroimaging-based risk score for Alzheimer’s disease by combining clinical and large N>37000 population data

Towards a multimodal neuroimaging-based risk score for Alzhe...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Elaheh Zendehrouh Mohammad S. E. Sendi Vince D. Calhoun Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA Tri-Institutional Center for Translational Research in Neuroimaging Data Science (TReNDS): Georgia State University Georgia Institute of Technology Emory University Atlanta Georgia Mclean Hospital and Harvard Medical School Boston MA

Alzheimer’s disease (AD) is the most prevalent age-related dementia and causes memory, reasoning, and social skills to deteriorate. In recent years many studies have explored the genetic risk of AD, but less work has been done to identify a brain imaging-based AD risk measure. The current study proposed a new neuroimaging-based measure of AD risk, called brain-wide risk score or BRS, based on multimodal brain features. Using the proposed AD BRS, we identified four AD biotypes from a large sample of subjects (N>37,000) from the UK Biobank dataset: one with high AD BRS, one with low AD BRS, and two with moderate AD BRS. Next, we further showed that the cognitive scores of the biotype with lower AD BRS are significantly better than those of other biotypes.

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Artificial Intelligence without Restriction Surpassing Human Intelligence with Probability One: Theoretical Insight into Secrets of the Brain with ai Twins of the Brain

arXiv

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arXiv 2024年

作者： Huang, Guang-Bin Westover, M. Brandon Tan, Eng-King Wang, Haibo Cui, Dongshun Ma, Wei-Ying Wang, Tiantong He, Qi Wei, Haikun Wang, Ning Tian, Qiyuan Lam, Kwok-Yan Yao, Xin Wong, Tien Yin School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Beth Israel Deaconess Medical Center Harvard Medical School Boston United States Department of Neurology National Neuroscience Institute Singapore Research Centre of Big Data and Artificial Intelligence for Medicine First Affiliated Hospital of Sun Yat-Sen University Guangzhou China Duke-NUS Medical School National University of Singapore Singapore Mind PointEye Singapore Institute for AI Industry Research Tsinghua University Beijing China College of Computing and Data Science Nanyang Technological University Singapore School of Biomedical Engineering Tsinghua University Beijing China Singapore AI Safety Institute Nanyang Technological University Singapore School of Data Science Lingnan University Hong Kong School of Computer Science University of Birmingham United Kingdom Singapore Eye Research Institute Singapore National Eye Centre Singapore Tsinghua Medicine Tsinghua University Beijing China

Artificial Intelligence (ai) has apparently become one of the most important techniques discovered by humans in history while the human brain is widely recognized as one of the most complex systems in the universe. One fundamental critical question which would affect human sustainability remains open: Will artificial intelligence (ai) evolve to surpass human intelligence in the future? This paper shows that in theory new ai twins with fresh cellular level of ai techniques for neuroscience could approximate the brain and its functioning systems (e.g. perception and cognition functions) with any expected small error and ai without restrictions could surpass human intelligence with probability one in the end. This paper indirectly proves the validity of the conjecture made by Frank Rosenblatt 70 years ago about the potential capabilities of ai, especially in the realm of artificial neural networks. This paper also gives the answer to the two widely discussed fundamental questions: 1) whether ai could have potentials of discovering new principles in nature;2) whether error backpropagation (BP) algorithm commonly and efficiently used in tuning parameters in ai applications is also adopted in the brain. Intelligence is just one of fortuitous but sophisticated creations of the nature which has not been fully discovered. Like mathematics and physics, with no restrictions artificial intelligence would lead to a new subject with its self-contained systems and principles. We anticipate that this paper opens new doors for 1) ai twins and other ai techniques to be used in cellular level of efficient neuroscience dynamic analysis, functioning analysis of the brain and brain illness solutions;2) new worldwide collaborative scheme for interdisciplinary teams concurrently working on and modelling different types of neurons and synapses and different level of functioning subsystems of the brain with ai techniques;3) development of low energy of ai techniques with the aid of fundament

关键词： Neurology

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How to respond to emerging threats of cyberspace security via social engineering? A novel paradigm to solve cyberspace security issues

TechRxiv

引用

TechRxiv 2022年

作者： Lin, Hao Wang, Chun-Dong Gao, Hao-Yu School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing Novel Software Technology Ministry of Education Tianjin China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China College of Data Science and Application Inner Mongolia University of Technology Hohhot China

Cybersecurity researchers always ignore the "people" behind the issues when dealing with cyberspace security issues. The human and technological aspects of cyberspace security must be simultaneously addressed to enable a secure cyber environment. Social engineering is a discipline whose research object is people, and the combination of social engineering and cyberspace security can provide new solution ideas for lots of cyberspace security issues. This paper attempts to answer the question —— "How to solve cyberspace security issues via social engineering?" We briefly review existing frameworks for social engineering attacks and propose a novel, recursive, and iterative framework for social engineering attacks in cyberspace. The existing research achievements of combination of social engineering and cyberspace security were reviewed in the order of cyber personality, hacker psychology, human flesh search, and social engineering defense. To answer this question, we summarized four "guidelines" for using social engineering. Finally, we look ahead, give our thoughts, and discuss answers of the question and future research. It is aimed at getting more researchers of cyberspace security and social science to pay attention to this field. © 2022, CC BY.

关键词： Cybersecurity

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