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arXiv 2025年

作者： Stanley, Michael Batlle, Pau Patil, Pratik Owhadi, Houman Kuusela, Mikael Department of Computing and Mathematical Sciences California Institute of Technology CA91125 United States Department of Statistics University of California BerkeleyCA94720 United States Department of Statistics and Data Science Carnegie Mellon University PittsburghPA15213 United States

We address functional uncertainty quantification for ill-posed inverse problems where it is possible to evaluate a possibly rank-deficient forward model, the observation noise distribution is known, and there are known parameter constraints. We present four constraint-aware confidence interval constructions extending the theoretical test inversion framework in Batlle et al. (2023) by making the intervals both computationally feasible and less conservative. Our approach first shrinks the potentially unbounded constraint set compact in a data-adaptive way, obtains samples of the relevant test statistic inside this set to estimate a quantile function, and then uses these computed quantities to produce the constraint-aware confidence intervals. Our approach to bounding the constraint set in a data-adaptive way is based on the approach by Berger and Boos (1994), and involves defining a subset of the constraint set where the true parameter is guaranteed to exist with high probability. The probabilistic guarantee of this compact subset is then incorporated into the final coverage guarantee in the form of an uncertainty budget. We then propose custom sampling algorithms to efficiently sample from this subset, even when the parameter space is high-dimensional. Optimization-based interval methods formulate confidence interval computation as two endpoint optimizations, where the optimization constraints can be set to achieve different types of interval calibration while seamlessly incorporating parameter constraints. However, choosing optimization constraints to obtain coverage for a single functional has been elusive. We show that all four proposed intervals achieve nominal coverage for a particular functional both theoretically and in practice, with several numerical examples demonstrating superior performance of our intervals over the OSB interval in terms of both coverage and expected interval length. In particular, we show the superior performance of our intervals in a re

关键词： Budget control

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Correction: Web service location‑allocation using discrete NSGA‑II with matrix based genetic operations and a repair mechanism

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Journal of Ambient Intelligence and Humanized computing 2023年第1期15卷 797-798页

作者： Verma, Shanu Pant, Millie Snasel, Vaclav Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee Roorkee India Centre for Artificial Intelligence and Data Science Indian Institute of Technology Roorkee Roorkee India Department of Computer Science VSB-Technical University of Ostrava Ostrava Czech Republic

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t-READi: Transformer-Powered Robust and Efficient Multimodal Inference for Autonomous Driving

arXiv

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arXiv 2024年

作者： Hu, Pengfei Qian, Yuhang Zheng, Tianyue Li, Ang Chen, Zhe Gao, Yue Cheng, Xiuzhen Luo, Jun School of Computer Science and Technology Shandong University China Department of Computer Science and Engineering Southern University of Science and Technology China Department of Electrical and Computer Engineering University of Maryland College Park United States Institue of Space Internet School of Computer Science Fudan University China College of Computing and Data Science Nanyang Technological University Singapore

Given the wide adoption of multimodal sensors (e.g., camera, lidar, radar) by autonomous vehicles (AVs), deep analytics to fuse their outputs for a robust perception become imperative. However, existing fusion methods often make two assumptions rarely holding in practice: i) similar data distributions for all inputs and ii) constant availability for all sensors. Because, for example, lidars have various resolutions and failures of radars may occur, such variability often results in significant performance degradation in fusion. To this end, we present t-READi, an adaptive inference system that accommodates the variability of multimodal sensory data and thus enables robust and efficient perception. t-READi identifies variation-sensitive yet structure-specific model parameters;it then adapts only these parameters while keeping the rest intact. t-READi also leverages a cross-modality contrastive learning method to compensate for the loss from missing modalities. Both functions are implemented to maintain compatibility with existing multimodal deep fusion methods. The extensive experiments evidently demonstrate that compared with the status quo approaches, t-READi not only improves the average inference accuracy by more than 6% but also reduces the inference latency by almost 15× with the cost of only 5% extra memory overhead in the worst case under realistic data and modal variations. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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From Missing Pieces to Masterpieces: Image Completion with Context-Adaptive Diffusion

arXiv

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arXiv 2025年

作者： Shamsolmoali, Pourya Zareapoor, Masoumeh Zhou, Huiyu Felsberg, Michael Tao, Dacheng Li, Xuelong Department of Computer Science University of York University of UK SCEE East China Normal University China Department of Electrical Engineering Shanghai Jiao Tong University China School of Computing and Mathematical Sciences University of Leicester United Kingdom Computer Vision Laboratory Linkoping University Sweden College of Computing and Data Science Nanyang Technological University Singapore of China Telecom China

Image completion is a challenging task, particularly when ensuring that generated content seamlessly integrates with existing parts of an image. While recent diffusion models have shown promise, they often struggle with maintaining coherence between known and unknown (missing) regions. This issue arises from the lack of explicit spatial and semantic alignment during the diffusion process, resulting in content that does not smoothly integrate with the original image. Additionally, diffusion models typically rely on global learned distributions rather than localized features, leading to inconsistencies between the generated and existing image parts. In this work, we propose ConFill, a novel framework that introduces a Context-Adaptive Discrepancy (CAD) model to ensure that intermediate distributions of known and unknown regions are closely aligned throughout the diffusion process. By incorporating CAD, our model progressively reduces discrepancies between generated and original images at each diffusion step, leading to contextually aligned completion. Moreover, ConFill uses a new Dynamic Sampling mechanism that adaptively increases the sampling rate in regions with high reconstruction complexity. This approach enables precise adjustments, enhancing detail and integration in restored areas. Extensive experiments demonstrate that ConFill outperforms current methods, setting a new benchmark in image completion. © 2025, CC BY-SA.

关键词： Photointerpretation

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Unsupervised Localization Toward Crowdsourced Trajectory data: A Deep Reinforcement Learning Approach

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IEEE Transactions on Wireless Communications 2025年

作者： Si, Haonan Hou, Xiangwang Wang, Jingjing Boateng, Gordon Owusu Zhang, Zekai Guo, Xiansheng Niyato, Dusit University of Electronic Science and Technology of China Department of Electronic Engineering Chengdu611731 China Tsinghua University Department of Electronic Engineering Beijing100084 China Beihang University School of Cyber Science and Technology Beijing100191 China Tsinghua University Tsinghua Shenzhen International Graduate School Shenzhen518055 China Nanyang Technological University College of Computing and Data Science 117583 Singapore

Crowdsourcing is an effective method to alleviate the burden of conducting a site-survey procedure for localization tasks. However, crowdsourced data is typically inaccurately and scarcely annotated, rendering accurate localization a rather challenging problem. To alleviate this problem, we propose VRLoc, a deep reinforcement learning (DRL)-based unsupervised wireless localization framework using crowdsourced trajectory data. The proposed VRLoc primarily encompasses three components, i.e., a robust K-means (RKM) clustering method for generating a series of virtual reference points (VRPs), DRL for determining the physical layout for VRPs, and online localization based on VRPs. Specifically, the proposed RKM method employs a density-based approach for the initialization of cluster centers, rather than the commonly used random solution, yielding repeatable and reliable VRP generation results. To accurately determine the physical locations for VRPs, we develop a modified soft actor-critic (SAC)- based VRP layout method with multiple objectives, i.e., the connection topology among VRPs, the floor-plan information, and the near-field condition. Then, we effectively predict locations of target users by utilizing classification models to match the online collected samples with the VRPs annotated by physical locations. The proposed framework is advantageous in achieving high-accuracy unsupervised localization, with the VRPs bridging the unlabeled crowdsourced data and physical location space. Both experimental and simulation results demonstrate the effectiveness and superiority of the proposed VRLoc framework as an accurate and practical solution for unsupervised localization. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

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FlexPara: Flexible Neural Surface Parameterization

arXiv

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arXiv 2025年

作者： Zhao, Yuming Zhang, Qijian Hou, Junhui Xia, Jiazhi Wang, Wenping He, Ying Department of Computer Science City University of Hong Kong Hong Kong TiMi L1 Studio Tencent Games China School of Computer Science and Engineering Central South University China Department of Computer Science & Engineering Texas A&M University United States College of Computing and Data Science Nanyang Technological University Singapore

Surface parameterization is a fundamental geometry processing task, laying the foundations for the visual presentation of 3D assets and numerous downstream shape analysis scenarios. Conventional parameterization approaches demand high-quality mesh triangulation and are restricted to certain simple topologies unless additional surface cutting and decomposition are provided. In practice, the optimal configurations (e.g., type of parameterization domains, distribution of cutting seams, number of mapping charts) may vary drastically with different surface structures and task characteristics, thus requiring more flexible and controllable processing pipelines. To this end, this paper introduces FlexPara, an unsupervised neural optimization framework to achieve both global and multi-chart surface parameterizations by establishing point-wise mappings between 3D surface points and adaptively-deformed 2D UV coordinates. We ingeniously design and combine a series of geometrically-interpretable sub-networks, with specific functionalities of cutting, deforming, unwrapping, and wrapping, to construct a bi-directional cycle mapping framework for global parameterization without the need for manually specified cutting seams. Furthermore, we construct a multi-chart parameterization framework with adaptively-learned chart assignment. Extensive experiments demonstrate the universality, superiority, and inspiring potential of our neural surface parameterization paradigm. The code will be publicly available at https://***/AidenZhao/FlexPara. Copyright © 2025, The Authors. All rights reserved.

关键词： Photomapping

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Investigation of Environmental Monitoring Based on Three IoT Wireless Sensors 2

Investigation of Environmental Monitoring Based on Three IoT...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Yamini, B. Arthy, M. Priya, S Geetha Kathiresan, S. Jeneetha Jebanazer, J. Uthayakumar, G.S. Srm Institute of Science and Technology Department of Networking and Communication Engineering Tamil Nadu Kattankulathur Chennai India Srm Institute of Science and Technology School of Computing Department of Data Science& Business Systems Tamil Nadu Kattankulathur Chennai India R. M. D Engineering College Department of Computer Science Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350325799

Environmental monitoring has become more crucial as a result of the recent climatic changes, making it a hot issue and a very active study field. This field uses wireless sensor networks and remote sensing to collect data about the environment. Recent developments, including the Internet of Things (IoT) concept, the cloud computing paradigm, and cyber-physical systems, assist the administration and transmission of enormous volumes of data about changes in environmental parameters. The current work presents three different IoT-based wireless sensors for monitoring the environment and the surrounding area: one uses Wi-Fi User datagram Protocol (UDP)-based communication, one uses Wi-Fi and Hypertext Transfer Protocol (HTTP), and a third one uses Bluetooth Smart. The ability to capture data at distant places and see it from any device with an Internet connection is a feature shared by all of the systems that have been discussed. This makes it possible to monitor vast geographic regions. These systems' specific development details are discussed, along with their main distinctions and affinities. The three created systems were evaluated for their suitability for IoT-based solutions, taking into consideration their energy independence, usability, solution complexity, and Internet connectivity capability. © 2023 IEEE.

关键词： Bluetooth

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Graph Contrastive Learning for Truth Inference

Graph Contrastive Learning for Truth Inference

引用

International Conference on data Engineering

作者： Hao Liu Jiacheng Liu Feilong Tang Peng Li Long Chen Jiadi Yu Yanmin Zhu Min Gao Yanqin Yang Xiaofeng Hou School of Data Science and Engineering East China Normal University Shanghai China Department of Computer Science and Engineering Chinese University of Hong Kong China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China School of Computer Science and Engineering University of Aizu Japan School of Computing Science Simon Fraser University Canada

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Crowdsourcing has become a popular paradigm for collecting large-scale labeled datasets by leveraging numerous annotators. However, these annotators often provide noisy labels due to varying expertise. Truth inference aims to infer accurate consensus labels from noisy crowdsourced annotations. Existing approaches rely heavily on hand-engineered assumptions or ground truth data, limiting their applicability. To address this, we propose GOVERN, a graph contrastive learning framework for truth inference without such external supervision. GOVERN employs a novel graph data augmentation strategy to generate views capturing worker coordination patterns. A contrastive objective then encourages invariant representations across views, enabling the discovery of features related to the hidden consensus. Further, a label correction method based on k-nearest neighbors refines noisy pseudo-labels to supervise model training. Comprehensive experiments on 9 real-world datasets demonstrate that GOVERN outperforms state-of-the-art truth inference techniques.

关键词： Training Crowdsourcing Limiting Accuracy Annotations Contrastive learning Nearest neighbor methods

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ADEdgeDrop: Adversarial Edge Dropping for Robust Graph Neural Networks

arXiv

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arXiv 2024年

作者： Chen, Zhaoliang Wu, Zhihao Sadikaj, Ylli Plant, Claudia Dai, Hong-Ning Wang, Shiping Cheung, Yiu-Ming Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou350116 China Department of Computer Science Hong Kong Baptist University Hong Kong Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China Faculty of Computer Science and with the research network Data Science @ Uni Vienna University of Vienna Vienna1090 Austria

Although Graph Neural Networks (GNNs) have exhibited the powerful ability to gather graph-structured information from neighborhood nodes via various message-passing mechanisms, the performance of GNNs is limited by poor generalization and fragile robustness caused by noisy and redundant graph data. As a prominent solution, Graph Augmentation Learning (GAL) has recently received increasing attention in the literature. Among the existing GAL approaches, edge-dropping methods that randomly remove edges from a graph during training are effective techniques to improve the robustness of GNNs. However, randomly dropping edges often results in bypassing critical edges. Consequently, the effectiveness of message passing is weakened. In this paper, we propose a novel adversarial edgedropping method (ADEdgeDrop) that leverages an adversarial edge predictor guiding the removal of edges, which can be flexibly incorporated into diverse GNN backbones. Employing an adversarial training framework, the edge predictor utilizes the line graph transformed from the original graph to estimate the edges to be dropped, which improves the interpretability of the edge-dropping method. The proposed ADEdgeDrop is optimized alternately by stochastic gradient descent and projected gradient descent. Comprehensive experiments on eight graph benchmark datasets demonstrate that the proposed ADEdgeDrop outperforms state-of-the-art baselines across various GNN backbones, demonstrating improved generalization and robustness. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Hybrid Semantic/Bit Communication Based Networking Problem Optimization

arXiv

引用

arXiv 2024年

作者： Xia, Le Sun, Yao Niyato, Dusit Zhang, Lan Zhang, Lei Imran, Muhammad Ali James Watt School of Engineering University of Glasgow Glasgow United Kingdom College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering Clemson University United States

This paper jointly investigates user association (UA), mode selection (MS), and bandwidth allocation (BA) problems in a novel and practical next-generation cellular network where two modes of semantic communication (SemCom) and conventional bit communication (BitCom) coexist, namely hybrid semantic/bit communication network (HSB-Net). Concretely, we first identify a unified performance metric of message throughput for both SemCom and BitCom links. Next, we comprehensively develop a knowledge matching-aware two-stage tandem packet queuing model and theoretically derive the average packet loss ratio and queuing latency. Combined with several practical constraints, we then formulate a joint optimization problem for UA, MS, and BA to maximize the overall message throughput of HSB-Net. Afterward, we propose an optimal resource management strategy by employing a Lagrange primal-dual method and devising a preference list-based heuristic algorithm. Finally, numerical results validate the performance superiority of our proposed strategy compared with different benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Frequency allocation

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