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International Conference on Network Protocols

作者： Ziyue Jia Lin Chen School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Provincial Key Laboratory of Information Security Technology Guangzhou China

ISBN: (数字)9798350351712

ISBN: (纸本)9798350351729

Recently emerged as a disruptive networking paradigm, quantum networks rely on the mysterious quantum entanglement to teleport qubits without physically transferring quantum particles. However, the state of quantum systems is extremely fragile due to environment noise. A promising technique to combat against quantum decoherence is entanglement purification. To fully exploit its benefit, two fundamental research questions need to be answered: (1) given an entanglement path, what is the optimal entanglement purification schedule? (2) how to compute min-cost end-to-end entanglement paths subject to fidelity constraint? In this paper, we give algorithmic solutions to both questions. For the first question, we develop an optimal entanglement purification scheduling algorithm for the singlehop case and analyze the PURIFY-AND-SWAP strategy in the multi-hop case by establishing the closed-form condition for its optimality. For the second question, we design a polynomialtime algorithm constructing an $\epsilon$ -optimal fidelity-constrained path. The effectiveness of our algorithms are also numerically demonstrated by extensive simulations.

关键词： Schedules Quantum system Protocols Purification Quantum entanglement Scheduling algorithms Qubit Routing Quantum decoherence Quantum networks

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Sample Reduction Using 1 -Norm Twin Bounded Support Vector Machine 2nd

Sample Reduction Using 1 -Norm Twin Bounded Support Vector M...

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2nd International Conference on Neural Computing for Advanced Applications, NCAA 2021

作者： Zheng, Xiaohan Zhang, Li Yan, Leilei School of Computer Science and Technology Joint International Research Laboratory of Machine Learning and Neuromorphic Computing Soochow University Suzhou215006 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China

ISBN: (纸本)9789811651878

Twin support machine (TSVM) has a lower time complexity than support vector machine (SVM), but it has a poor ability to perform sample reduction. In order to improve the ability of TSVM to reduce sample, we propose an 1 -norm twin bounded support machine (1 -TBSVM) inspired by the sparsity of 1 -norm in feature space. The objective function of 1 -TBSVM contains the hinge loss and the 1 -norm terms, both which can induce sparsity. We solve the primal programming problems of 1 -TBSVM to prevent the disappearance of sparsity and avoid the situation that the inverse of matrix does not exist. Thus, 1 -TBSVM has a good sparsity, or a good ability to reduce sample. Experimental results on synthetic and UCI datasets indicate that 1 -TBSVM has a good ability to perform sample reduction and simultaneously enhances the classification performance. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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3D Contour Generation based on Diffusion Probabilistic Models

3D Contour Generation based on Diffusion Probabilistic Model...

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International Conference on computer Supported Cooperative Work in Design

作者： Yiqi Wu Xuan Huang Kelin Song Fazhi He Dejun Zhang The Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences School of Computer Science Wuhan University Information Systems Technology And Design Pillar Singapore University of Technology and Design

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The contours of objects effectively represent essential 3D information such as shapes and boundaries. Existing contour detection methods mostly rely on thresholding or neural networks to classify points as edge or non-edge points. However, these methods often lack generalization ability on datasets with different shapes, leading to issues such as missing contours and discontinuous distribution. To address this, we propose a 3D point cloud contour generation method based on the denoising diffusion probabilistic model (DDPM). Our method treats the complete point cloud as an explicit condition to guide the generation of contour from noise. Specifically, our method trains the DDPM to be a conditional generative network customized for contour generation tasks. In the network, we design the Conditional Feature Extraction (CFE) module that obtains multi-scale feature information, and the Conditional Feature Fusion (CFF) module embeds this information in the generation process to guide contour generation. The experimental results demonstrate the effectiveness of our method. The source code of our method is available at: https://***/djzgroup/ContourGeneration.

关键词： Point cloud compression Three-dimensional displays Shape Source coding Noise reduction Noise Feature extraction

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FreqBlender: Enhancing DeepFake Detection by Blending Frequency Knowledge

arXiv

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arXiv 2024年

作者： Li, Hanzhe Zhou, Jiaran Li, Yuezun Wu, Baoyuan Li, Bin Dong, Junyu School of Computer Science and Technology Ocean University of China China School of Data Science The Chinese University of Hong Kong Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

Generating synthetic fake faces, known as pseudo-fake faces, is an effective way to improve the generalization of DeepFake detection. Existing methods typically generate these faces by blending real or fake faces in spatial domain. While these methods have shown promise, they overlook the simulation of frequency distribution in pseudo-fake faces, limiting the learning of generic forgery traces in-depth. To address this, this paper introduces FreqBlender, a new method that can generate pseudo-fake faces by blending frequency knowledge. Concretely, we investigate the major frequency components and propose a Frequency Parsing Network to adaptively partition frequency components related to forgery traces. Then we blend this frequency knowledge from fake faces into real faces to generate pseudo-fake faces. Since there is no ground truth for frequency components, we describe a dedicated training strategy by leveraging the inner correlations among different frequency knowledge to instruct the learning process. Experimental results demonstrate the effectiveness of our method in enhancing DeepFake detection, making it a potential plug-and-play strategy for other methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Fake detection

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Unveiling a giant electrocaloric effect at low electric fields through continuous phase transition design

Advanced Powder Materials

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Advanced Powder Materials 2024年第5期3卷 33-45页

作者： Yunyao Huang Leiyang Zhang Pingji Ge Ruiyi Jing Wenjing Shi Chao Li Xiang Niu Vladimir Shur Haibo Zhang Shengguo Lu Yintang Yang Dawei Wang Xiaoqin Ke Li Jin Electronic Materials Research Laboratory Key Laboratory of the Ministry of EducationSchool of Electronic Science and EngineeringFaculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an710049China School of Physics MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed MatterXi’an Jiaotong UniversityXi’an710049China Instrument Analysis Center Xi’an Jiaotong UniversityXi’an10049China Guangdong Provincial Research Center on Smart Materials and Energy Conversion Devices School of Materials and EnergyGuangdong University of TechnologyGuangzhou510006China School of Natural Sciences and Mathematics Ural Federal UniversityEkaterinburg20000Russia School of Materials Science and Engineering State Key Laboratory of Material Processing and Die&Mould TechnologyHuazhong University of Science and TechnologyWuhan430074China School of Microelectronics Xidian UniversityXi’an710071China Functional Materials and Acousto-Optic Instruments Institute School of Instrumentation Science and EngineeringHarbin Institute of TechnologyHarbin150080China

The reported electrocaloric(EC)effect in ferroelectrics is poised for application in the next generation of solidstate refrigeration technology,exhibiting substantial developmental *** study introduces a novel and efficient EC effect strategy in(1-x)Pb(Lu_(1/2)Nb_(1/2))O_(3)-xPbTiO_(3)(PLN-xPT)ceramics for low electric-fielddriven ***-field simulations provide fundamental insights into thermally induced continuous phase transitions,guiding subsequent experimental investigations.A comprehensive composition/temperature-driven phase evolution diagram is constructed,elucidating the sequential transformation from ferroelectric(FE)to antiferroelectric(AFE)and finally to paraelectric(PE)phases for x=0.10-0.18 *** measurements of EC performance highlight x=0.16 as an outstanding performer,exhibiting remarkable properties,including an adiabatic temperature change(ΔT)of 3.03 K,EC strength(ΔT/ΔE)of 0.08 K cm kV-1,and a temperature span(Tspan)of 31℃.The superior EC effect performance is attributed to the temperature-induced FE to AFE transition at low electric fields and diffusion phase transition behavior contributing to the wide *** work provides valuable insights into developing high-performance EC effect across broad temperature ranges through the strategic design of continuous phase transitions,offering a simplified and economical approach for advancing ecofriendly and efficient solid-state cooling technologies.

关键词： PbPb(Lu_(1/2)Nb_(1/2))O_(3)-xPbTiO_(3)(PLN-PT) Electrocaloric effect(ECE) Phase transition Low electric field Antiferroelectric

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Leveraging Tumor Heterogeneity: Heterogeneous Graph Representation Learning for Cancer Survival Prediction in Whole Slide Images 38

Leveraging Tumor Heterogeneity: Heterogeneous Graph Represen...

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38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Wu, Junxian Ke, Xinyi Jiang, Xiaoming Wu, Huanwen Kong, Youyong Shao, Lizhi School of Internet Anhui University China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University China Department of Pathology State Key Laboratory of Complex Severe and Rare Disease Molecular Pathology Research Center Peking Union Medical College Hospital Chinese Academy of Medical Sciences Peking Union Medical College Beijing China School of Bioinformatics Chongqing University of Post and Telecommunications China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education China

Survival prediction is a significant challenge in cancer management. Tumor microenvironment is a highly sophisticated ecosystem consisting of cancer cells, immune cells, endothelial cells, fibroblasts, nerves and extracellular matrix. The intratumor heterogeneity and the interaction across multiple tissue types profoundly impact the prognosis. However, current methods often neglect the fact that the contribution to prognosis differs with tissue types. In this paper, we propose ProtoSurv, a novel heterogeneous graph model for WSI survival prediction. The learning process of ProtoSurv is not only driven by data but also incorporates pathological domain knowledge, including the awareness of tissue heterogeneity, the emphasis on prior knowledge of prognostic-related tissues, and the depiction of spatial interaction across multiple tissues. We validate ProtoSurv across five different cancer types from TCGA (i.e., BRCA, LGG, LUAD, COAD and PAAD), and demonstrate the superiority of our method over the state-of-the-art methods. Source code is available at https://***/wjx-error/ProtoSurv. © 2024 Neural information processing systems foundation. All rights reserved.

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Unsupervised Graph-Sequence Anomaly Detection for 5G Core Network Control Plane Traffic 29

Unsupervised Graph-Sequence Anomaly Detection for 5G Core Ne...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Wang, Min Li, Peng Cheng, Zhang Liu, Wenmao Nie, Lei Bao, Haizhou Liu, Qin Zhang, Kai Wuhan University of Science and Technology School of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System China Nsfocus Technologies Group Co. Ltd China Wuhan University School of Cyber Science and Engineering China

ISBN: (纸本)9798350330717

5G Core network (5GC) employs a Service Based Architecture (SBA). This architecture decomposes the control plane into multiple independent Network Functions (NFs). NFs open interfaces to provide services to other NFs, which makes the control plane more susceptible to external malicious attacks. However, existing anomaly detection methods focus more on traffic statistics features and are difficult to apply to the 5GC control plane. In this paper, we proposed GSAD, a Graph-Sequence analysis-based Anomaly Detection method for 5GC control plane traffic. We model control plane traffic as a directed graph to depict topological and NF interaction information. Further, we use the normalizing flows with temporal dependencies to mine the sequential information in the traffic. GSAD combines the topological and sequential information to provide fine-grained detection. We evaluate our proposed framework on the 5GC testbed using Free5GC and UERANSIM in various scenarios. Experimental results demonstrate that our framework outperforms the baselines significantly in terms of detection performance. © 2023 IEEE.

关键词： 5G core network anomaly detection control plane graph neural network normalizing flows

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TEMPLE: Temporal Preference Learning of Video LLMs via Difficulty Scheduling and Pre-SFT Alignment

arXiv

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arXiv 2025年

作者： Li, Shicheng Li, Lei Ouyang, Kun Ren, Shuhuai Liu, Yuanxin Zhang, Yuanxing Zhang, Fuzheng Kong, Lingpeng Liu, Qi Sun, Xu National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China The University of Hong Kong Hong Kong Kuaishou Technology China

Video Large Language Models (Video LLMs) have achieved significant success by leveraging a two-stage paradigm: pretraining on large-scale video-text data for vision-language alignment, followed by supervised fine-tuning (SFT) for task-specific capabilities. However, existing approaches struggle with temporal reasoning due to weak temporal correspondence in the data and reliance on the next-token prediction paradigm during training. To address these limitations, we propose TEMPLE (TEMporal Preference Learning), a systematic framework that enhances Video LLMs’ temporal reasoning capabilities through Direct Preference Optimization (DPO). To facilitate this, we introduce an automated preference data generation pipeline that systematically constructs preference pairs by selecting videos that are rich in temporal information, designing video-specific perturbation strategies, and finally evaluating model responses on clean and perturbed video inputs. Our temporal alignment features two key innovations: curriculum learning which that progressively increases perturbation difficulty to improve model robustness and adaptability;and "Pre-SFT Alignment", applying preference optimization before instruction tuning to prioritize fine-grained temporal comprehension. Extensive experiments demonstrate that our approach consistently improves Video LLM performance across multiple benchmarks with a relatively small set of self-generated DPO data. We further analyze the transferability of DPO data across architectures and the role of difficulty scheduling in optimization. Our findings highlight our TEMPLE as a scalable and efficient complement to SFT-based methods, paving the way for developing reliable Video LLMs. Code is available at https://***/lscpku/TEMPLE. © 2025, CC BY.

关键词： Supervised learning

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Hypernetwork Representation Learning With the Transformation Strategy 22

Hypernetwork Representation Learning With the Transformation...

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6th International Conference on High Performance Compilation, Computing and Communications, HP3C 2022

作者： Zhu, Yu Zhao, Haixing Huang, Jianqiang Wang, Xiaoying Qinghai University Department of Computer Technology and Applications Xining810000 China Qinghai Normal University State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining810000 China

ISBN: (纸本)9781450396295

In real life, there are many cases that cannot be described by the network abstracted as the graph, but can be described perfectly by the hypernetwork abstracted as the hypergraph. Different from the network, the hypernetwork structure is more complex and poses a great challenge to the existing network representation learning methods. Therefore, in order to overcome the challenge of the hypernetwork structure, a hypernetwork representation learning method with the transformation strategy is proposed. Firstly, as three types of transformation strategies from the hypergraph to the graph, line graph, incidence graph and 2-section graph are combined into three types of integral graphs with the hyperedge information, namely incidence graph + 2-section graph, line graph + incidence graph and line graph + incidence graph + 2-section graph. Secondly, a shallow neural network algorithm is trained respectively on five types of networks abstracted as incidence graph, 2-section graph, incidence graph + 2-section graph, line graph + incidence graph and line graph +incidence graph + 2-section graph to obtain node representation vectors. Finally, the evaluation experiment is conducted on four different types of hypernetwork datasets. The experimental results demonstrate that the node classification performance of 2-section graph is better than that of other graphs, and the link prediction performance of incidence graph + 2-section graph is better than that of other graphs. © 2022 ACM.

关键词： Graph theory

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AUCSeg: AUC-oriented pixel-level long-tail semantic segmentation 24

AUCSeg: AUC-oriented pixel-level long-tail semantic segmenta...

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Proceedings of the 38th International Conference on Neural information processing Systems

作者： Boyu Han Qianqian Xu Zhiyong Yang Shilong Bao Peisong Wen Yangbangyan Jiang Qingming Huang Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and School of Computer Science and Tech. University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Peng Cheng Laboratory School of Computer Science and Tech. University of Chinese Academy of Sciences School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Key Laboratory of Big Data Mining and Knowledge Management CAS

ISBN: (纸本)9798331314385

The Area Under the ROC Curve (AUC) is a well-known metric for evaluating instance-level long-tail learning problems. In the past two decades, many AUC optimization methods have been proposed to improve model performance under long-tail distributions. In this paper, we explore AUC optimization methods in the context of pixel-level long-tail semantic segmentation, a much more complicated scenario. This task introduces two major challenges for AUC optimization techniques. On one hand, AUC optimization in a pixel-level task involves complex coupling across loss terms, with structured inner-image and pairwise inter-image dependencies, complicating theoretical analysis. On the other hand, we find that mini-batch estimation of AUC loss in this case requires a larger batch size, resulting in an unaffordable space complexity. To address these issues, we develop a pixel-level AUC loss function and conduct a dependency-graph-based theoretical analysis of the algorithm's generalization ability. Additionally, we design a Tail-Classes Memory Bank (T-Memory Bank) to manage the significant memory demand. Finally, comprehensive experiments across various benchmarks confirm the effectiveness of our proposed AUCSeg method. The code is available at https://***/boyuh/AUCSeg.

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