检索结果-内蒙古大学图书馆

A Comparison of Approximate Analytic and Neural Network Solutions for Effective Mass Schrodinger Equation With Yukawa's Potential 2

学校读者我要写书评

暂无评论

A Comparison of Approximate Analytic and Neural Network Solu...

2nd IEEE International Conference on Artificial Intelligence in Everything, AIE 2022

作者： Fotso Kamsu, Brigitte Vubangsi, Mercel Tchoffo, M. Al-Turjman, Fadi University of Dschang Condensed Matter Electronics and Signal Processing Research Unit Department of Physics Faculty of Science Cameroon Near East University Ai and Robotics Institute Artificial Intelligence Engineering Dept. Nicosia Turkey University of Bamenda Nwr Computational Materials Science Lab Department of Computer Science Htttc Bambili Cameroon University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Cyprus

ISBN: (数字)9781665474009

ISBN: (纸本)9781665474009

We model a compositionally graded heterostructure using the effective mass approach with a screened Coulomb potential. Approximate analytical solutions to the model are compared with a Neural Network output and good agreement is achieved, paving the way for Artificial Intelligence modeling of systems with graded composition. © 2022 IEEE.

关键词： Grading

DRSM: EFFICIENT NEURAL 4D DECOMPOSITION FOR DYNAMIC RECONSTRUCTION IN STATIONARY MONOCULAR CAMERAS

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Xie, Weixing Dong, Xiao Yang, Yong Lin, Qiqin Chen, Jingze Yao, Junfeng Guo, Xiaohu Center for Digital Media Computing School of Film School of Informatics Xiamen University China National Institute for Data Science in Health and Medicine Xiamen University China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Department of Computer Science BNU-HKBU United International College China Department of Computer Science The University of Texas Dallas United States

With the popularity of monocular videos generated by video sharing and live broadcasting applications, reconstructing and editing dynamic scenes in stationary monocular cameras has become a special but anticipated technology. In contrast to scene reconstructions that exploit multi-view observations, the problem of modeling a dynamic scene from a single view is significantly more under-constrained and ill-posed. Inspired by recent progress in neural rendering, we present a novel framework to tackle 4D decomposition problem for dynamic scenes in monocular cameras. Our framework utilizes decomposed static and dynamic feature planes to represent 4D scenes and emphasizes the learning of dynamic regions through dense ray casting. Inadequate 3D clues from a single-view and occlusion are also particular challenges in scene reconstruction. To overcome these difficulties, we propose deep supervised optimization and ray casting strategies. With experiments on various videos, our method generates higher-fidelity results than existing methods for single-view dynamic scene representation. © 2024, CC BY.

关键词： Cameras

Permutation Recovery Problem against Deletion Errors for DNA Data Storage

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Singhvi, Shubhransh Gupta, Charchit Boruchovsky, Avital Goldberg, Yuval Kiah, Han Mao Yaakobi, Eitan Signal Processing & Communications Research Center IIIT Hyderabad India IIIT Hyderabad India Department of Computer Science Technion-Israel Institute of Technology Haifa3200003 Israel School of Physical and Mathematical Sciences Nanyang Technological University Singapore Singapore

Owing to its immense storage density and durability, DNA has emerged as a promising storage medium. However, due to technological constraints, data can only be written onto many short DNA molecules called data blocks that are stored in an unordered way. To handle the unordered nature of DNA data storage systems, a unique address is typically prepended to each data block to form a DNA strand. However, DNA storage systems are prone to errors and generate multiple noisy copies of each strand called DNA reads. Thus, we study the permutation recovery problem against deletions errors for DNA data storage. The permutation recovery problem for DNA data storage requires one to reconstruct the addresses or in other words to uniquely identify the noisy reads. By successfully reconstructing the addresses, one can essentially determine the correct order of the data blocks, effectively solving the clustering problem. We first show that we can almost surely identify all the noisy reads under certain mild assumptions. We then propose a permutation recovery procedure and analyze its complexity. Copyright © 2024, The Authors. All rights reserved.

关键词： DNA

T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Li, Siyang Wang, Ziwei Luo, Hanbin Ding, Lieyun Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China Henan Key Laboratory of Brain Science and Brain Computer Interface Technology School of Electrical and Information Engineering Zhengzhou University China School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China

Objective: An electroencephalogram (EEG)-based brain-computer interface (BCI) enables direct communication between the human brain and a computer. Due to individual differences and non-stationarity of EEG signals, such BCIs usually require a subject-specific calibration session before each use, which is time-consuming and user-unfriendly. Transfer learning (TL) has been proposed to shorten or eliminate this calibration, but existing TL approaches mainly consider offline settings, where all unlabeled EEG trials from the new user are available. Methods: This paper proposes Test-Time Information Maximization Ensemble (T-TIME) to accommodate the most challenging online TL scenario, where unlabeled EEG data from the new user arrive in a stream, and immediate classification is performed. T-TIME initializes multiple classifiers from the aligned source data. When an unlabeled test EEG trial arrives, T-TIME first predicts its labels using ensemble learning, and then updates each classifier by conditional entropy minimization and adaptive marginal distribution regularization. Our code is publicized. Results: Extensive experiments on three public motor imagery based BCI datasets demonstrated that T-TIME outperformed about 20 classical and state-of-the-art TL approaches. Significance: To our knowledge, this is the first work on test time adaptation for calibration-free EEG-based BCIs, making plug-and-play BCIs possible. © 2024, CC BY.

关键词： Calibration

COVIDx CXR-3: A Large-Scale, Open-Source Benchmark Dataset of Chest X-ray images for computer-Aided COVID-19 Diagnostics

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Pavlova, Maya Tuinstra, Tia Aboutalebi, Hossein Zhao, Andy Gunraj, Hayden Wong, Alexander Vision and Image Processing Lab University of Waterloo Canada Department of Systems Design Engineering University of Waterloo Canada Cheriton School of Computer Science University of Waterloo Canada Waterloo AI Institute University of Waterloo Canada DarwinAI Corp Canada

After more than two years since the beginning of the COVID-19 pandemic, the pressure of this crisis continues to devastate globally. The use of chest X-ray (CXR) imaging as a complementary screening strategy to RT-PCR testing is not only prevailing but has greatly increased due to its routine clinical use for respiratory complaints. Thus far, many visual perception models have been proposed for COVID-19 screening based on CXR imaging. Nevertheless, the accuracy and the generalization capacity of these models are very much dependent on the diversity and the size of the dataset they were trained on. Motivated by this, we introduce COVIDx CXR-3, a large-scale benchmark dataset of CXR images for supporting COVID-19 computer vision research. COVIDx CXR-3 is composed of 30,386 CXR images from a multinational cohort of 17,026 patients from at least 51 countries, making it, to the best of our knowledge, the most extensive, most diverse COVID-19 CXR dataset in open access form. Here, we provide comprehensive details on the various aspects of the proposed dataset including patient demographics, imaging views, and infection types. The hope is that COVIDx CXR-3 can assist scientists in advancing machine learning research against both the COVID-19 pandemic and related diseases. Copyright © 2022, The Authors. All rights reserved.

关键词： COVID-19

Predicting the Success of Mediation Requests Using Case Properties and Textual Information for Reducing the Burden on the Court

学校读者我要写书评

暂无评论

Digital Government: Research and Practice 2021年第4期2卷 1–18页

作者： Hsieh, Hsun-Ping Jiang, Jiawei Yang, Tzu-Hsin Hu, Renfen Wu, Cheng-Lin Department of Electrical Engineering National Cheng Kung University Tainan Taiwan Department of Computer Science University of California DavisCA United States Institute of Chinese Information Processing Beijing Normal University Beijing China Department of Electrical Engineering National Cheng Kung University Tainan Taiwan

The success of mediation is affected by many factors, such as the context of the quarrel, the personality of both parties, and the negotiation skill of the mediator, which lead to uncertainty for the work of prediction. This article takes a different approach from that of previous legal prediction research. It analyzes and predicts whether two parties in a dispute can reach an agreement peacefully through the conciliation of mediation. With the inference result, we can know whether mediation is a more practical and time-saving method to solve the dispute. Existing works about legal case prediction mostly focus on prosecution or criminal cases. In this work, we propose a long short-term memory (LSTM)-based framework, called LSTMEnsembler, to predict mediation results by assembling multiple classifiers. Among these classifiers, some are powerful for modeling the numerical and categorical features of case information, for example, XGBoost and LightGBM. Some are effective for dealing with textual data, for example, TextCNN and BERT. The proposed LSTMEnsembler aims to not only combine the effectiveness of different classifiers intelligently but also to capture temporal dependencies from previous cases to boost the performance of mediation prediction. Our experimental results show that our proposed LSTMEnsembler can achieve 85.6% for F-measure on real-world mediation data. © 2022 Association for Computing Machinery.

关键词： Forecasting

Deep Uncertainty-aware Tracking for Maneuvering Targets

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Zhang, Shuyang Gao, Chang Zhang, Qingfu Jia, Tianyi Liu, Hongwei The National Key Laboratory of Radar Signal Processing Xidian University Xi’an710071 China The Department of Computer Science City University of Hong Kong Hong Kong The City University of Hong Kong Shenzhen Research Institute Shenzhen518057 China

When tracking maneuvering targets, model-driven approaches encounter difficulties in comprehensively delineating complex real-world scenarios and are prone to model mismatch when the targets maneuver. Meanwhile, contemporary data-driven methods have overlooked measurements’ confidence, markedly escalating the challenge of fitting a mapping from measurement sequences to target state sequences. To address these issues, this paper presents a deep maneuvering target tracking methodology based on target state space projection. The proposed methodology initially establishes a projection from the target measurement sequence to the target state space by formulating the probability density function of measurement error and samples the distribution information of measurement noise within the target state space as a measurement representation. Under this representation, the sequential regression task of target state estimation can be transmuted into a task of detecting the target location in the state space. Subsequently, a deep detection network is devised to accomplish target location detection in the target state space. Finally, a loss function is designed to facilitate the network’s training for attaining the desired network performance. Simulation experiments suggest that the proposed method can maintain satisfactory tracking performance even when the target maneuvers, and can rapidly converge and achieve higher estimation accuracy compared with existing methods after the target maneuvers. Copyright © 2024, The Authors. All rights reserved.

关键词： Probability density function

BotScan: An Unsupervised Bot Detection Based on Adversarial Learning and Social Perception

学校读者我要写书评

暂无评论

BotScan: An Unsupervised Bot Detection Based on Adversarial ...

Asian Control Conference

作者： Hong Lin Nuo Chen Yang Chen Xiang Li Cong Li Department of Electronic Engineering Adaptive Networks and Control Lab School of Information Science and Technology Fudan University China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

Within online social networks, the rapid expansion of automated accounts, known as social bots, poses a significant challenge. This dynamic has sparked a strategic contest between bots and detectors. Both sides continually learn and adjust their strategies to gain an edge in this silent war. To effectively manage risks from bot evolution, proactive detection methods have garnered widespread attention. In this work, we propose a graph-based and structure-aware adversarial learning framework for multi-category social bot detection. In detail, we adopt a categorical generative adversarial network to create synthetic evolved bot samples for adversarial learning, aiming to boost the detection performance. Besides, we design a user representation learning module, including local and remote feature extractors to mine neighborhood information and achieve multiple network structure representations. Results demonstrate that our model outperforms state-of-the-art approaches and exhibits strong robustness.

关键词： Representation learning Training Technological innovation Social networking (online) image edge detection Detectors Feature extraction

Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation

学校读者我要写书评

暂无评论

Logical Relation Modeling and Mining in Hyperbolic Space for...

International Conference on Data Engineering

作者： Yanchao Tan Hang Lv Zihao Zhou Wenzhong Guo Bo Xiong Weiming Liu Chaochao Chen Shiping Wang Carl Yang College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Institute for Artificial Intelligence University of Stuttgart Stuttgart Germany College of Computer Science Zhejiang University Hangzhou China Department of Computer Science Emory University Atlanta United States

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

The sparse interactions between users and items have aggravated the difficulty of their representations in recommender systems. Existing methods leverage tags to alleviate the sparsity problem but ignore prevalent logical relations among items and tags (e.g., membership, hierarchy, and exclusion), which can be leveraged to enhance the accuracy of modeling user preferences and conducting recommendations. To this end, we propose to extract logical relations among item tags from existing tag taxonomies and exploit the individual strengths of the Poincaré and the Lorentz models in hyperbolic space for logical relation modeling towards enhanced recommendations. Moreover, we find that the logical relations directly extracted from existing tag taxonomies can be inaccurate and coarse. Therefore, we further devise innovative consistency-based and granularity- based weighting mechanisms based on user behavior patterns for data-driven logical relation mining that can be jointly optimized along with recommendations in an end-to-end fashion. Extensive experiments on four real-world benchmark datasets show drastic performance gains brought by our proposed framework, which constantly achieves an average of 8.25% improvement over state-of-the-art competitors regarding both Recall and NDCG metrics. Insightful case studies further demonstrate that our automatically refined logical relations are highly accurate and interpretable.

关键词： Measurement Accuracy Taxonomy Semantics Predictive models Performance gain Linguistics