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Transcriptomic and Macroscopic Architectures of Multimodal Covariance Network Reveal Molecular–Structural–Functional Co-alterations

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research 2024年第1期2023卷 471-487页

作者： Lin Jiang Yueheng Peng Runyang He Qingqing Yang Chanlin Yi Yuqin Li Bin Zhu Yajing Si Tao Zhang Bharat B.Biswal Dezhong Yao Lan Xiong Fali Li Peng Xu The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengdu 611731China School of Life Science and Technology Center for Information in BioMedicineUniversity of Electronic Science and Technology of ChinaChengdu 611731China School of Psychology Xinxiang Medical UniversityXinxiang 453003China School of Science Xihua UniversityChengdu 610039China Department of Biomedical Engineering New Jersey Institute of TechnologyNewarkNJUSA School of Electrical Engineering Zhengzhou UniversityZhengzhou 450001China Research Unit of NeuroInformation Chinese Academy of Medical Sciences2019RU035ChengduChina Montreal Neurological Institute and Hospital McGill UniversityMontrealQCCanada Department of Electrical and Computer Engineering Faculty of Science and TechnologyUniversity of MacaoMacaoChina Radiation Oncology Key Laboratory of Sichuan Province 610041 ChengduChina Rehabilitation Center Qilu Hospital of Shandong UniversityJinan 250012China

Human cognition is usually underpinned by intrinsic structure and functional neural co-activation in spatially distributed brain *** to lacking an effective approach to quantifying the covarying of structure and functional responses,how the structural–functional circuits interact and how genes encode the relationships,to deepen our knowledge of human cognition and disease,are still ***,we propose a multimodal covariance network(MCN)construction approach to capture interregional covarying of the structural skeleton and transient functional activities for a single *** further explored the potential association between brain-wide gene expression patterns and structural–functional covarying in individuals involved in a gambling task and individuals with major depression disorder(MDD),adopting multimodal data from a publicly available human brain transcriptomic atlas and 2 independent *** analysis showed a replicable cortical structural–functional fine map in healthy individuals,and the expression of cognition-and disease phenotype-related genes was found to be spatially correlated with the corresponding MCN *** analysis of cell type-specific signature genes suggests that the excitatory and inhibitory neuron transcriptomic changes could account for most of the observed correlation with task-evoked MCN differences.

关键词： alterations evoked spatially

The Need and Status of Sea Turtle Conservation and Survey of Associated computer Vision Advances 8

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The Need and Status of Sea Turtle Conservation and Survey of...

8th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2021

作者： Paul, Aditya Jyoti Cognitive Applications Research Lab India Department of Computer Science and Engineering Srm Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9781665409629

For over hundreds of millions of years, sea turtles and their ancestors have swum in the vast expanses of the ocean. They have undergone a number of evolutionary changes, leading to speciation and sub-speciation. However, in the past few decades, some of the most notable forces driving the genetic variance and population decline have been global warming and anthropogenic impact ranging from large-scale poaching, collecting turtle eggs for food, besides dumping trash including plastic waste into the ocean. This leads to severe detrimental effects in the sea turtle population, driving them to extinction. This research focusses on the forces causing the decline in sea turtle population, the necessity for the global conservation efforts along with its successes and failures, followed by an in-depth analysis of the modern advances in detection and recognition of sea turtles, involving Machine Learning and computer Vision systems, aiding the conservation efforts. © 2021 IEEE.

关键词： Object detection

Collaborative Knowledge Fusion: A Novel Approach for Multi-task Recommender Systems via LLMs

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arXiv 2024年

作者： Zhao, Chuang Su, Xing He, Ming Zhao, Hongke Fan, Jianping Li, Xiaomeng Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong AI Lab at Lenovo Research Beijing China College of Management and Economics Tianjin University Tianjin China

Owing to the impressive general intelligence of large language models (LLMs), there has been a growing trend to integrate them into recommender systems to gain a more profound insight into human interests and intentions. Existing LLMs-based recommender systems primarily leverage item attributes and user interaction histories in textual format, improving the single task like rating prediction or explainable recommendation. Nevertheless, these approaches overlook the crucial contribution of traditional collaborative signals in discerning users’ profound intentions and disregard the interrelatedness among tasks. To address these limitations, we introduce a novel framework known as CKF, specifically developed to boost multi-task recommendations via personalized collaborative knowledge fusion into LLMs. Specifically, our method synergizes traditional collaborative filtering models to produce collaborative embeddings, subsequently employing the meta-network to construct personalized mapping bridges tailored for each user. Upon mapped, the embeddings are incorporated into meticulously designed prompt templates and then fed into an advanced LLM to represent user interests. To investigate the intrinsic relationship among diverse recommendation tasks, we develop Multi-Lora, a new parameter-efficient approach for multi-task optimization, adept at distinctly segregating task-shared and task-specific information. This method forges a connection between LLMs and recommendation scenarios, while simultaneously enriching the supervisory signal through mutual knowledge transfer among various tasks. Extensive experiments and in-depth robustness analyses across four common recommendation tasks on four large public data sets substantiate the effectiveness and superiority of our framework. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge transfer

Advances in Classifying the Stages of Diabetic Retinopathy Using Convolutional Neural Networks in Low Memory Edge Devices

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Advances in Classifying the Stages of Diabetic Retinopathy U...

2021 IEEE Madras Section International Conference, MASCON 2021

作者： Paul, Aditya Jyoti Cognitive Applications Research Lab India Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9781665404051

Diabetic Retinopathy (DR) is a severe complication that may lead to retinal vascular damage and is one of the leading causes of vision impairment and blindness. DR broadly is classified into two stages – non-proliferative (NPDR), where there are almost no symptoms, except a few microaneurysms, and proliferative (PDR) involving a huge number of microaneurysms and hemorrhages, soft and hard exudates, neo-vascularization, macular ischemia or a combination of these, making it easier to detect. More specifically, DR is usually classified into five levels, labeled 0-4, from 0 indicating no DR to 4 which is most severe. This paper firstly presents a discussion on the risk factors of the disease, then surveys the recent literature on the topic followed by examining certain techniques which were found to be highly effective in improving the prognosis accuracy. Finally, a convolutional neural network model is proposed to detect all the stages of DR on a low-memory edge microcontroller. The model has a size of just 5.9 MB, accuracy and F1 score both of 94% and an inference speed of about 20 frames per second. © 2021 IEEE.

关键词： Neural network models

Implementation of Internet of Things (IoT) framework for Governing Modern Cyber Attacks in computer Network

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Implementation of Internet of Things (IoT) framework for Gov...

International Conference on ICT in Business Industry & Government (ICTBIG)

作者： S. Surya Dipen Bhuva Abhishek Bhuva Sunil S. Chavan D. Khalandar Basha Saumitra Chattopadhyay Sri Ramachandra Faculty of Engineering & Technology (SRET) Sri Ramachandra Institute of Higher Education & Research Porur Chennai Department of EECS Cleveland State University Cleveland Ohio USA Department of Computer Science University of Massachusetts Boston Massachusetts Boston USA Smt. Indira Gandhi College of Engineering Ghansoli Navi Mumbai Maharashtra India ECE INSTITUTE OF AERONAUTICAL ENGINEERING DUNDIGAL HYDERABAD Department of Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand

The Internet of Things (IoT) is the most reliable structure for improve the effectiveness and convenience of human existence on the planet. The Internet of Things has made important contributions to a wide range of application fields. With the rapid proliferation of smart gadgets, as well as their reliance on wireless technologies for information transmission, they have become more vulnerable to hackers. As a result of the increasing danger of cyber attacks, cyber security has emerged as among the most essential aspects of the Internet of Things, ranking second only to data privacy (IoT). The goal of IoT cyber security is to decrease cyber security risk for companies & consumers by ensuring the security of IoT resources and the safeguarding of personal information. Various methodologies and frameworks for assessing cyber security risks have been developed and are now being used in various governmental & commercial enterprises.

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GANPrompt: Enhancing Robustness in LLM-Based Recommendations with GAN-Enhanced Diversity Prompts

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arXiv 2024年

作者： Li, Xinyu Zhao, Chuang Zhao, Hongke Wu, Likang He, Ming College of Management and Economics Tianjin University Tianjin China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong AI Lab Lenovo Research Beijing China

In recent years, Large Language Models (LLMs) have demonstrated remarkable proficiency in comprehending and generating natural language, with a growing prevalence in the domain of recommendation systems. However, LLMs still face a significant challenge called prompt sensitivity, which refers to that it is highly susceptible to the influence of prompt words. This inconsistency in response to minor alterations in prompt input may compromise the accuracy and resilience of recommendation models. To address this issue, this paper proposes GANPrompt, a multi-dimensional LLMs prompt diversity framework based on Generative Adversarial Networks (GANs). The framework enhances the model’s adaptability and stability to diverse prompts by integrating GANs generation techniques with the deep semantic understanding capabilities of LLMs. GANPrompt first trains a generator capable of producing diverse prompts by analysing multidimensional user behavioural data. These diverse prompts are then used to train the LLMs to improve its performance in the face of unseen prompts. Furthermore, to ensure a high degree of diversity and relevance of the prompts, this study introduces a mathematical theory-based diversity constraint mechanism that optimises the generated prompts to ensure that they are not only superficially distinct, but also semantically cover a wide range of user intentions. Through extensive experiments on multiple datasets, we demonstrate the effectiveness of the proposed framework, especially in improving the adaptability and robustness of recommendation systems in complex and dynamic environments. The experimental results demonstrate that GANPrompt yields substantial enhancements in accuracy and robustness relative to existing state-of-the-art methodologies. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

OTT Service Copyright Management Framework Using ODRL Based on Hyperledger Fabric 23

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OTT Service Copyright Management Framework Using ODRL Based ...

2023 International Conference on Intelligent Computing and Its Emerging Applications, ICEA 2023

作者： Son, Wooyoung Kwon, Soonhong Oh, Sungheun Lee, Jong-Hyouk Protocol Engineering Lab Sejong University Seoul South Korea Department of Computer and Information Security Sejong University Seoul South Korea Technical Research Center DigiCAP Seoul South Korea

ISBN: (纸本)9798400709050

As the demand and diversity of digital content continue to grow, so does its accessibility and utilization. With this shift, Over-The-Top (OTT) services have emerged, allowing users to conveniently access various digital content. However, due to the ongoing issues of illegal copying and distribution of digital content, this paper proposes a framework for managing copyrights in OTT services by using Open Digital Rights Language (ODRL) within the Hyperledger Fabric environment. This system utilizes ODRL and chaincode to automatically verify usage rights of digital content, ensuring that digital content provided aligns with these rights. Moreover, the system aims to construct an OTT service that ensures the rights of copyright holders by allowing transparent verification of copyright royalties settled and distributed based on ODRL. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Hyperledger Fabric ODRL OTT Service

TOWARDS SMART NETWORKING WITH SDN ENABLED IPV6 NETWORK

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arXiv 2022年

作者： Dawadi, Babu R. Joshi, Shashidhar R. Rawat, Danda B. Manzoni, Pietro Department of Electronics and Computer Engineering Institute of Engineering Pulchowk Campus Tribhuvan University Nepal Cyber Security and Wireless Networking Innovations Lab EECS Department Howard University WashingtonDC20059 United States Department of Computer Engineering Universitat Politècnica De València Valencia46022 Spain

This paper presents the features and benefits of legacy IPv4 network migration towards major two latest networking paradigms viz. Internet protocol version 6 (IPv6) and the software-defined networking (SDN). These latest networking paradigms are the enabler of future generation networking so that the standards and requirements of fifth generation (5G) wireless networking can be achieved. Features and migration approaches of IPv6 and SDN will be separately discussed, then a joint migration approach of SDN and IPv6 network termed as SoDIP6 network migration will be presented, and the integration of SoDIP6 network as a backbone of 5G network will be introduced. © 2022, CC BY-NC-SA.

关键词： 5G mobile communication systems

3D Landmark Detection on Human Point Clouds: A Benchmark and A Dual Cascade Point Transformer Framework

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arXiv 2024年

作者： Zhang, Fan Mao, Shuyi Li, Qing Peng, Xiaojiang College of Big Data and Internet Shenzhen Technology University Shenzhen China Department of Electrical and Computer Engineering Georgia Institute of Technology Shenzhen China AI Lab Lenovo Research Shenzhen China

3D landmark detection plays a pivotal role in various applications such as 3D registration, pose estimation, and virtual try-on. While considerable success has been achieved in 2D human landmark detection or pose estimation, there is a notable scarcity of reported works on landmark detection in unordered 3D point clouds. This paper introduces a novel challenge, namely 3D landmark detection on human point clouds, presenting two primary contributions. Firstly, we establish a comprehensive human point cloud dataset, named HPoint103, designed to support the 3D landmark detection community. This dataset comprises 103 human point clouds created with commercial software and actors, each manually annotated with 11 stable landmarks. Secondly, we propose a Dual Cascade Point Transformer (D-CPT) model for precise point-based landmark detection. D-CPT gradually refines the landmarks through cascade Transformer decoder layers across the entire point cloud stream, simultaneously enhancing landmark coordinates with a RefineNet over local regions. Comparative evaluations with popular point-based methods on HPoint103 and the public dataset DHP19 demonstrate the dramatic outperformance of our D-CPT. Additionally, the integration of our RefineNet into existing methods consistently improves performance. Code and data will be released soon. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning