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11th China Conference on Command and Control, C2 2023

作者： Wan, Liangtian He, Jifeng Liu, Kaihui Sun, Lu Wang, Xianpeng Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Dalian116620 China School of Electrical Engineering and Intelligentization Dongguan University of Technology Dongguan523820 China Department of Communication Engineering Institute of Information Science Technology Dalian Maritime University Dalian116026 China State Key Laboratory of Marine Resource Utilization in South China Sea and School of Information and Communication Engineering Hainan University Haikou570228 China

ISBN: (纸本)9789819990207

Previous studies often address channel estimation and channel state information (CSI) implicit feedback issues in 5G new radio (NR) system independently, posing a challenge to achieve effective coordination between the two processes in a shared environment. Consequently, we propose a deep learning-based scheme that jointly addresses channel estimation and implicit CSI feedback. In this scheme, the received pilot information from user equipment (UE) is regarded as a two-dimensional image. Channel estimation is performed employing the ResMLP architecture, whereas the Transformer architecture is utilized for implicit CSI feedback. Furthermore, a novel training approach is introduced to further enhance the performance of the proposed scheme. Simulation results illustrate a substantial improvement compared to the reference scheme employed in the 3rd Wireless Communication Artificial Intelligence (AI) Competition (WAIC). © Chinese Institute of Command and Control 2024.

关键词： Channel estimation

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Truly Bi-stable Ferroelectric Liquid Crystal Based Modulators

Truly Bi-stable Ferroelectric Liquid Crystal Based Modulator...

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International Symposium, Seminar, and Exhibition, Display Week 2024

作者： Swaminathan, Vigneshwaran Vashchenko, Valerii Vashchenko, Olena Bhadra, Debjyoti Mathew, Chris Kwok, Hoi-Sing Srivastava, Abhishek. K. State Key Laboratory of Advanced Displays and Optoelectronics Technologies and Center for Display Research Department of Electronics and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Road Kowloon Hong Kong

We developed a novel method for achieving long-term (>24 hours) true bi-stability in helix-free Ferroelectric Liquid Crystals (FLCs). Instead of employing asymmetric boundary conditions, traditionally used for achieving bi-stability, we utilized symmetric boundary conditions and carefully optimized the anchoring conditions on both the top and bottom substrates using the photoalignment technology. The key to the newly observed long term bi-stability is the twisted alignment structure. Surface roughness was found to play a crucial role in achieving sustained bi-stability. This breakthrough holds promise for developing truly bi-stable, fast modulators for photonic applications. © 2024, John Wiley and Sons Inc. All rights reserved.

关键词： Liquid crystals

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VSM: A Versatile Semi-supervised Model for Multi-modal Cell Instance Segmentation 36

VSM: A Versatile Semi-supervised Model for Multi-modal Cell ...

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Cell Segmentation Challenge in Multi-Modality High-Resolution Microscopy Images - 36th Conference on Neural Information Processing Systems, NeurIPS 2022

作者： Cai, Xiaochen Cai, Hengxing Tu, Weiwei Xu, Kele Li, Wu-Jun National Key Laboratory for Novel Software Technology National Institute of Healthcare Data Science Nanjing University Nanjing China 4 Paradigm Inc. Beijing China School of Computer National University of Defense Technology Changsha China Center for Medical Big Data Nanjing Drum Tower Hospital Nanjing China

Cell instance segmentation is a fundamental task in analyzing microscopy images, with applications in computer-aided biomedical research. In recent years, deep learning techniques have been widely used in this field. However, existing methods exhibit inadequate generalization ability towards multi-modal cellular images and require a considerable amount of manually labeled data for training. To overcome these limitations, we present VSM, a versatile semi-supervised model for multi-modal cell instance segmentation. Our method delivers high accuracy and efficiency, as verified through comprehensive experiments. Additionally, VSM achieved a top-five ranking in the Weakly Supervised Cell Segmentation category of the multi-modal High-Resolution Microscopy competition. © 2023 SPIE.

关键词： Cells

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A Robust Semantic Communication System for Image Transmission

A Robust Semantic Communication System for Image Transmissio...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Peng, Xiang Qin, Zhijin Tao, Xiaoming Lu, Jianhua Letaief, Khaled B. Tsinghua University Department of Electronic Engineering Beijing China Beijing China State Key Laboratory of Space Network and Communications Beijing China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong

ISBN: (纸本)9798350351255

Semantic communications have gained significant attention as a promising approach to address the transmission bottleneck, especially with the continuous development of 6G techniques. Distinct from the well investigated physical channel impairments, this paper focuses on semantic impairments in images, particularly those arising from adversarial perturbations. Specifically, we propose a novel metric for quantifying the intensity of semantic impairment and develop a semantic impairment dataset. Furthermore, we introduce a deep learning enabled semantic communication system, termed as DeepSC-RI, to enhance the robustness of image transmission, which incorporates a multi-scale semantic extractor with a dual-branch architecture for extracting semantics with varying granularity, thereby improving the robustness of the system. The fine-grained branch incorporates a semantic importance evaluation module to identify and prioritize crucial semantics, while the coarse-grained branch adopts a hierarchical approach for capturing the robust semantics. These two streams of semantics are seamlessly integrated via an advanced cross-attention-based semantic fusion module. Experimental results demonstrate the superior performance of DeepSC-RI under various levels of semantic impairment intensity. © 2024 IEEE.

关键词： Image communication systems

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PRIVACY-PRESERVING ONLINE SHARING CHARGING PILE SCHEME WITH DIFFERENT NEEDS MATCHING

arXiv

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arXiv 2023年

作者： Huang, Zhiyu School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China

With the development of electric vehicles, more and more electric vehicles have difficulties in parking and charging. One of the reasons is that the number of charging piles is difficult to support the energy supply of electric vehicles, and a large number of private charging piles have a long idle time, so the energy supply problem of electric vehicles can be solved by sharing charging piles. The shared charging pile scheme uses Paillier encryption scheme and improved scheme to effectively protect user data. The scheme has homomorphism of addition and subtraction, and can process information without decryption. However, considering that different users have different needs, the matching is carried out after calculating the needs put forward by users. This scheme can effectively protect users' privacy and provide matching mechanisms with different requirements, so that users can better match the appropriate charging piles. The final result shows that its efficiency is better than the original Paillier scheme, and it can also meet the security requirements. © 2023, CC BY.

关键词： Piles

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Gaussian-Flow: 4D Reconstruction with Dynamic 3D Gaussian Particle

Gaussian-Flow: 4D Reconstruction with Dynamic 3D Gaussian Pa...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Youtian Lin Zuozhuo Dai Siyu Zhu Yao Yao State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Intelligence Science and Technology Nanjing University Suzhou China Alibaba Group Fudan University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We introduce Gaussian-Flow, a novel point-based approach for fast dynamic scene reconstruction and real-time rendering from both multi-view and monocular videos. In contrast to the prevalent NeRF-based approaches hampered by slow training and rendering speeds, our approach harnesses recent advancements in point-based 3D Gaussian Splatting (3DGS). Specifically, a novel Dual-Domain Deformation Model (DDDM) is proposed to explicitly model attribute deformations of each Gaussian point, where the time-dependent residual of each attribute is captured by a polynomial fitting in the time domain, and a Fourier series fitting in the frequency domain. The proposed DDDM is capable of modeling complex scene deformations across long video footage, eliminating the need for training separate 3DGS for each frame or introducing an additional implicit neural field to model 3D dynamics. Moreover, the explicit deformation modeling for discretized Gaussian points ensures ultra-fast training and rendering of a 4D scene, which is comparable to the original 3DGS designed for static 3D reconstruction. Our proposed approach showcases a substantial efficiency improvement, achieving a 5 x faster training speed compared to the per-frame 3DGS modeling. In addition, quantitative results demonstrate that the proposed Gaussian-Flow significantly outperforms previous leading methods in novel view rendering quality. Project page: https://***/projects/Gaussian-Flow.

关键词： Training Deformable models Solid modeling Three-dimensional displays Deformation Frequency-domain analysis Fitting

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Evaluating Learning-to-Rank Models for Prioritizing Code Review Requests using Process Simulation

Evaluating Learning-to-Rank Models for Prioritizing Code Rev...

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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Lanxin Yang Bohan Liu Junyu Jia Junming Xue Jinwei Xu Alberto Bacchelli He Zhang State Key Laboratory of Novel Software Technology Software Institute Nanjing University China Department of Informatics University of Zurich Switzerland

In large-scale, active software projects, one of the main challenges with code review is prioritizing the many Code Review Requests (CRRs) these projects receive. Prior studies have developed many Learning-to-Rank (LtR) models in support of prioritizing CRRs and adopted rich evaluation metrics to compare their performances. However, the evaluation was performed before observing the complex interactions between CRRs and reviewers, activities and activities in real-world code reviews. Such a pre-review evaluation provides few indications about how effective LtR models contribute to code reviews. This study aims to perform a post-review evaluation on LtR models for prioritizing CRRs. To establish the evaluation environment, we employ Discrete-Event Simulation (DES) paradigm-based software Process Simulation Modeling (SPSM) to simulate real-world code review processes, together with three customized evaluation metrics. We develop seven LtR models and use the historical review orders of CRRs as baselines for evaluation. The results indicate that employing LtR can effectively help to accelerate the completion of reviewing CRRs and the delivery of qualified code changes. Among the seven LtR models, LambdaMART and AdaRank are particularly beneficial for accelerating completion and delivery, respectively. This study empirically demonstrates the effectiveness of using DES-based SPSM for simulating code review processes, the benefits of using LtR for prioritizing CRRs, and the specific advantages of several LtR models. This study provides new ideas for software organizations that seek to evaluate LtR models and other artificial intelligence-powered software ***&materials: https://***/s/a033e99cd2a61e64c8bc.

关键词： Measurement Analytical models Codes Organizations software

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Multi-View Representation Learning for Multi-Instance Learning with Applications to Medical Image Classification

Multi-View Representation Learning for Multi-Instance Learni...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Zhao, Lu Yuan, Liming Li, Zhenliang Wen, Xianbin School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin300384 China

ISBN: (纸本)9781665468190

Multi-Instance Learning (MIL) is a weakly supervised learning paradigm, in which every training example is a labeled bag of unlabeled instances. In typical MIL applications, instances are often used for describing the features of regions/parts in a whole object, e.g., regional patches/lesions in an eye-fundus image. However, for a (semantically) complex part the standard MIL formulation puts a heavy burden on the representation ability of the corresponding instance. To alleviate this pressure, we still adopt a bag-of-instances as an example in this paper, but extract from each instance a set of representations using 1 times1 convolutions. The advantages of this tactic are two-fold: i) This set of representations can be regarded as multi-view representations for an instance;ii) Compared to building multi-view representations directly from scratch, extracting them automatically using 1 times1 convolutions is more economical, and may be more effective since 1 times1 convolutions can be embedded into the whole network. Furthermore, we apply two consecutive multi-instance pooling operations on the reconstituted bag that has actually become a bag of sets of multi-view representations. We have conducted extensive experiments on several canonical MIL data sets from different application domains. The experimental results show that the proposed framework outperforms the standard MIL formulation in terms of classification performance and has good interpretability. © 2022 IEEE.

关键词： Convolution

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Time-aware Medication Recommendation via Intervention of Dynamic Treatment Regimes 25

Time-aware Medication Recommendation via Intervention of Dyn...

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34th ACM Web Conference, WWW 2025

作者： Li, Yishuo Zhang, Qi Lu, Wenpeng Peng, Xueping Zhang, Weiyu Si, Jiasheng Gong, Yongshun Hu, Liang Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Department of Computer Science and Technology Tongji University Shanghai China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia School of Software Shandong University Jinan China

ISBN: (纸本)9798400712746

Medication recommendation aims to suggest personalized drug combinations to patients based on their longitudinal medical histories stored in electronic health record (EHR) datasets. Patients’ Dynamic Treatment Regimes (DTRs) determine how patients’ drug combinations change along with the evolution of disease treatment. DTRs are effective for comprehending disease-treatment dynamics and for recommending a timely and personalized combination of medications for patients. However, existing medication recommender systems (MRSs) overlook the multiple treatment pathways generated by the intervention of DTRs and can only recommend a single treatment paradigm, ignoring the fact that patients may be at different treatment stages and thus require different treatment regime. Such disregard leads to a significant limitation in recommending personalized medication combinations tailored to different treatment stages, yielding greatly compromised accuracy and applicability of MRSs. Moreover, existing methods often overlook the time interval information over patients’ successive visits, which is critical to indicate patients’ treatment evolution. To address these significant gaps, we propose a Time-aware Medication Recommendation Framework via Intervention of Dynamic Treatment Regimes, called MR-DTR. To explicitly illustrate the intervention processes of DTRs on similar patients, we employ a co-guided graph to connect various patient sequences. In addition, to fully utilize the time interval information, we design a time-aware guidance mechanism dedicated to the co-guided graph to efficiently learn medication representation using the patient’s guidance information. We also introduce relative time intervals in the encoder to act as positional information. Extensive experiments on two real-world datasets demonstrate that MR-DTR surpasses state-of-the-art models in terms of recommendation performance. © 2025 Copyright held by the owner/author(s).

关键词： Electronic health record

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Deep Multi-Instance Learning with Adaptive Recurrent Pooling for Medical Image Classification

Deep Multi-Instance Learning with Adaptive Recurrent Pooling...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Ding, Yi Zhao, Lu Yuan, Liming Wen, Xianbin School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin300384 China

ISBN: (纸本)9781665468190

Recently, deep multi-instance neural networks have been successfully applied for medical image classification, where only image-level labels rather than fine-grained patch-level labels are available for use. One key issue for these multi-instance neural networks is how to aggregate all patch (instance) features into an entire image (bag) representation, referred to as multi-instance pooling, e.g., max, mean, and attention based pooling. Nevertheless, these multi-instance pooling operations do not take the structural information within an image into account. This is obviously inappropriate for medical image classification since there often exist certain dependencies among regional patches/lesions. We propose an adaptive recurrent pooling based deep multi-instance neural network in this paper. In this network, we first extract multi-view global structural features from every bag using the self-attention mechanism, and then aggregate these multi-view features into a whole bag representation based on the adaptive recurrent pooling operation in order to further capture the contextual information within the bag. Moreover, we introduce the cross-normalization operation used in the Unit Force Operated Vision Transformer into the self-attention module to reduce its computational complexity. We have experimentally evaluated the performance of the proposed network on three medical image datasets, namely UCSB breast, Messidor, and Colon cancer. The results demonstrate the advantage of our network over current state-of-the-art deep multi-instance networks in terms of classification accuracy and interpretability. © 2022 IEEE.

关键词： Aggregates

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