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Pisces: In-Path Distributed Denial-of-Service Defense via Efficient Authentication Code Embedded in IP Address

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Zhao, Yi Liu, Bingyang Jiang, Weiyu Xu, Ke Li, Qi Wang, Chuang Dou, Zongxin Liu, Yanjun Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Huawei Beijing100095 China Tsinghua University Department of Computer Science and Technology Beijing100084 China Zhongguancun Laboratory Beijing100094 China

High-volume brute-force distributed denial-of-service (DDoS) attack is among the top threats on the Internet. Existing widely deployed methods (e.g., BGP blackhole and scrubbing center) have difficulty achieving legitimate traffic friendliness, low cost, low latency, and high accuracy. We present an in-path DDoS defense mechanism, namely Pisces. Without requiring modifications to existing IP protocols, Pisces embeds authentication information into the IP address. Simultaneously, we design a QUIC-based extension to distribute authentication information. Pisces incorporates a translator module and a filter module, which accurately identifies malicious and legitimate traffic. These multi-dimensional compatibility advantages make it easy to deploy in the real world. We implement Pisces on a high-end commercial router with service processing units. Even without hardware acceleration, a single CPU can achieve 20Gbps throughput and the performance can scale linearly with the number of CPUs. The additional latency for the victim-related traffic and other traffic is around 27us and 0.5us, respectively, whose cost is far less than the scrubbing center. Remarkably, Pisces without false positives can provide high-quality datasets for intelligent approaches and form a prominent complementary effect. © 2004-2012 IEEE.

关键词： Denial-of-service attack

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Transfer learning enhanced graph neural network for aldehyde oxidase metabolism prediction and its experimental application

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Acta Pharmaceutica Sinica B 2024年第2期14卷 623-634页

作者： Jiacheng Xiong Rongrong Cui Zhaojun Li Wei Zhang Runze Zhang Zunyun Fu Xiaohong Liu Zhenghao Li Kaixian Chen Mingyue Zheng Drug Discovery and Design Center State Key Laboratory of Drug ResearchShanghai Institute of Materia MedicaChinese Academy of SciencesShanghai 201203China University of Chinese Academy of Sciences Beijing 100049China College of Computer and Information Engineering Dezhou UniversityDezhou 253023China AI Department Suzhou Alphama Biotechnology Co.Ltd.Suzhou 215000China Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and TechnologyShanghaiTech UniversityShanghai 200031China State Key Laboratory of Pharmaceutical Biotechnology Nanjing UniversityNanjing 210023China

Aldehyde oxidase(AOX)is a molybdoenzyme that is primarily expressed in the liver and is involved in the metabolism of drugs and other ***-mediated metabolism can result in unexpected outcomes,such as the production of toxic metabolites and high metabolic clearance,which can lead to the clinical failure of novel therapeutic *** models can assist medicinal chemists in rapidly evaluating the AOX metabolic risk of compounds during the early phases of drug discovery and provide valuable clues for manipulating AOX-mediated metabolism *** this study,we developed a novel graph neural network called AOMP for predicting AOX-mediated *** integrated the tasks of metabolic substrate/non-substrate classification and metabolic site prediction,while utilizing transfer learning from 13C nuclear magnetic resonance data to enhance its performance on both *** significantly outperformed the benchmark methods in both cross-validation and external *** AOMP,we systematically assessed the AOX-mediated metabolism of common fragments in kinase inhibitors and successfully identified four new scaffolds with AOX metabolism liability,which were validated through in vitro ***,for the convenience of the community,we established the first online service for AOX metabolism prediction based on AOMP,which is freely available at https://***.

关键词： Drugmetabolism Aldehyde oxidase Transferlearning Graph neural network Kinase inhibitor

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BENCHMARKING COMMUNITY DRUG RESPONSE PREDICTION MODELS: DATASETS, MODELS, TOOLS, AND METRICS FOR CROSS-DATASET GENERALIZATION ANALYSIS

arXiv

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arXiv 2025年

作者： Partin, Alexander Vasanthakumari, Priyanka Narykov, Oleksandr Wilke, Andreas Koussa, Natasha Jones, Sara E. Zhu, Yitan Overbeek, Jamie C. Jain, Rajeev Fernando, Gayara Demini Sanchez-Villalobos, Cesar Garcia-Cardona, Cristina Mohd-Yusof, Jamaludin Chia, Nicholas Wozniak, Justin M. Ghosh, Souparno Pal, Ranadip Brettin, Thomas S. Weil, M. Ryan Stevens, Rick L. Division of Data Science and Learning Argonne National Laboratory LemontIL United States Frederick National Laboratory for Cancer Research Cancer Data Science Initiatives Cancer Research Technology Program FrederickMD United States Department of Statistics University of Nebraska–Lincoln LincolnNE United States Department of Electrical & Computer Engineering Texas Tech University LubbockTX United States Division of Computer Computational and Statistical Sciences Los Alamos National Laboratory Los AlamosNM United States Department of Computer Science The University of Chicago ChicagoIL United States

Deep learning (DL) and machine learning (ML) models have shown promise in drug response prediction (DRP), yet their ability to generalize across datasets remains an open question, raising concerns about their real-world applicability. Due to the lack of standardized benchmarking approaches, model evaluations and comparisons often rely on inconsistent datasets and evaluation criteria, making it difficult to assess true predictive capabilities. In this work, we introduce a benchmarking framework for evaluating cross-dataset prediction generalization in DRP models. Our framework incorporates five publicly available drug screening datasets, six standardized DRP models, and a scalable workflow for systematic evaluation. To assess model generalization, we introduce a set of evaluation metrics that quantify both absolute performance (e.g., predictive accuracy across datasets) and relative performance (e.g., performance drop compared to within-dataset results), enabling a more comprehensive assessment of model transferability. Our results reveal substantial performance drops when models are tested on unseen datasets, underscoring the importance of rigorous generalization assessments. While several models demonstrate relatively strong cross-dataset generalization, no single model consistently outperforms across all datasets. Furthermore, we identify CTRPv2 as the most effective source dataset for training, yielding higher generalization scores across target datasets. By sharing this standardized evaluation framework with the community, our study aims to establish a rigorous foundation for model comparison, and accelerate the development of robust DRP models for real-world applications. © 2025, CC BY.

关键词： Prediction models

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Triple Point Masking

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Liu, Jiaming Kong, Linghe Wu, Yue Gong, Maoguo Li, Hao Miao, Qiguang Ma, Wenping Qin, Can The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The School of Computer Science and Technology Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an710071 China The School of Electronic Engineering Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an710071 China The College of Mathematics Science Inner Mongolia Normal University Hohhot010022 China The School of Artificial Intelligence Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education Xidian University Xi’an710071 China The Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States

Existing 3D mask learning methods encounter performance bottlenecks under limited data, and our objective is to overcome this limitation. In this paper, we introduce a triple point masking scheme, named TPM, which serves as a scalable plug- and-play framework for MAE pre-training to achieve multi-mask learning for 3D point clouds. Specifically, we augment the baseline methods with two additional mask choices (i.e., medium mask and low mask) as our core insight is that the recovery process of an object can manifest in diverse ways. Previous high-masking schemes focus on capturing the global representation information but lack fine-grained recovery capabilities, so that the generated pre-training weights tend to play a limited role in the fine-tuning process. With the support of the proposed TPM, current methods can exhibit more flexible and accurate completion capabilities, enabling the potential autoencoder in the pre-training stage to consider multiple representations of a single 3D point cloud object. In addition, during the fine-tuning stage, an SVM-guided weight selection module is proposed to fill the encoder parameters for downstream networks with the optimal weight, maximizing linear accuracy and facilitating the acquisition of intricate representations for new objects. Extensive experimental results and theoretical analysis show that five baselines equipped with the proposed TPM achieve comprehensive performance improvements on various downstream tasks. © 2025 IEEE. All rights reserved.

关键词： 3D modeling

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Point spread function-inspired deformable convolutional network for holographic displays

Point spread function-inspired deformable convolutional netw...

引用

2023 Advanced Fiber Laser Conference, AFL 2023

作者： Zhou, Mi Jiao, Shuming Chakravarthula, Praneeth Yue, Yang Su, Ping Kuruoglu, Ercan Engin Geng, Zihan Institute of Data and Information Tsinghua Shenzhen International School Tsinghua University Shenzhen China Peng Cheng Laboratory Shenzhen China Department of Computer Science University of North Carolina at Chapel Hill Chapel Hill United States School of Information and Communications Engineering Xi an Jiaotong University Xi an China

ISBN: (纸本)9781510677661

Convolutional neural networks (CNNs) have found extensive application in computer-generated holography (CGH). Nonetheless, CNNs possess limited capability to effectively model intricate geometric transformations between object points and their corresponding point spread functions due to the constrained structures of fixed convolutional kernels. In order to address this issue, we propose deformable holography (DeH) algorithm for CGH. We demonstrate that utilizing deformable convolutions enable adaptive modeling of geometric transformations. The proposed DeH algorithm generates high-quality 1080P 3D holograms in real-Time, consistently outperforming existing approaches. We also validate our approach on an experimental prototype holographic display, and demonstrate DeH algorithm's ability to accurately reconstruct 3D scenes. Overall, our work introduces new possibilities of utilizing deformable convolutions for deep learning in the realm of holographic displays. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Optical transfer function

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Simulated Annealing for Optimal Path Selection and Scheduling in Energy Routing for Smart Grids 5

Simulated Annealing for Optimal Path Selection and Schedulin...

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5th International Conference on computer and Applications, ICCA 2023

作者： Nacef, Assala Louail, Lemia Mechta, Djamila Benchikh, Lina Harous, Saad University Ferhat Abbas setif 1 LRSD laboratory Faculty of Sciences Setif Algeria Université de Lorraine CRAN NancyF-54000 France University of Sharjah College of Computing and Informatics Department of Computer Science Sharjah United Arab Emirates

ISBN: (纸本)9798350303254

Technological advancements in electronics and power generation devices have led to the development of smart grids and the energy internet. Energy routing is a critical step in creating a reliable and functional energy system capable of harnessing the full potential of renewable energy. One of the major challenges in energy routing is the optimal path determination problem, which involves finding the path with the least power loss. An optimal path can alleviate network congestion and minimize energy costs for grid users. In this paper, we present an innovative path discovery protocol based on simulated annealing. We evaluate the efficiency of our protocol by comparing it to two other existing methods in the literature using simulation. The findings highlight the effectiveness of our approach in achieving optimal results for both large and small networks. © 2023 IEEE.

关键词： Simulated annealing

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Minimising makespan in embedded real-time systems with failure rate requirements

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International Journal of Industrial and Systems Engineering 2020年第4期35卷 451-462页

作者： Kalla, Salim Bendib, Sonia Sabrina Riadh, Hocine Kalla, Hamoudi Department of Computer Science LaSTIC Laboratory University of Batna 2 Batna Algeria

Scheduling of real-time tasks in embedded real-time systems with quantitative reliability requirements, such as failure rate, is one of the important issues in system design. In this paper, we present a real-time scheduling heuristic that minimise makespan and satisfy reliability requirements for systems that are subject to processor and communication faults. The heuristic is based on a cost function to minimise makespan and on task redundancy to meet reliability requirements. Our approach is dedicated to heterogeneous architectures with multiple processors linked by several shared buses. It is based on active redundancy and data fragmentation strategies, which allow fast error detection and error handling. Thanks to the above-mentioned strategies, we are able to show with simulation results that our approach can generally reduce the run-time overhead. Copyright © 2020 Inderscience Enterprises Ltd.

关键词： Redundancy

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Privacy-Preserving Load Forecasting via Personalized Model Obfuscation

arXiv

引用

arXiv 2023年

作者： Bose, Shourya Zhang, Yu Kim, Kibaek Department of Electrical & Computer Engineering University of California Santa CruzCA United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States

The widespread adoption of smart meters provides access to detailed and localized load consumption data, suitable for training building-level load forecasting models. To mitigate privacy concerns stemming from model-induced data leakage, federated learning (FL) has been proposed. This paper addresses the performance challenges of short-term load forecasting models trained with FL on heterogeneous data, emphasizing privacy preservation through model obfuscation. Our proposed algorithm, Privacy Preserving Federated Learning (PPFL), incorporates personalization layers for localized training at each smart meter. Additionally, we employ a differentially private mechanism to safeguard against data leakage from shared layers. Simulations on the NREL ComStock dataset corroborate the effectiveness of our approach. Copyright © 2023, The Authors. All rights reserved.

关键词： Smart meters

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Optimal entanglement-assisted electromagnetic sensing and communication in the presence of noise

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Physical Review Applied 2024年第3期21卷 034004-034004页

作者： Haowei Shi Bingzhi Zhang Jeffrey H. Shapiro Zheshen Zhang Quntao Zhuang Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles California 90089 USA Department of Physics and Astronomy University of Southern California Los Angeles California 90089 USA Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor Michigan 48109 USA

High time-bandwidth product signal and idler pulses comprised of independent identically distributed two-mode squeezed vacuum (TMSV) states are readily produced by spontaneous parametric down-conversion. These pulses are virtually unique among entangled states in that they offer quantum performance advantages—over their best classical-state competitors—in scenarios whose loss and noise break their initial entanglement. Broadband TMSV states’ quantum advantage derives from its signal and idler having a strongly nonclassical phase-sensitive cross correlation, which leads to information-bearing signatures in lossy, noisy scenarios stronger than what can be obtained from classical-state systems of the same transmitted energy. Previous broadband TMSV receiver architectures focused on converting phase-sensitive cross correlation into phase-insensitive cross correlation, which can be measured in second-order interference. In general, however, these receivers fail to deliver broadband TMSV states’ full quantum advantage, even if they are implemented with ideal equipment. This paper introduces the correlation-to-displacement receiver—an alternative architecture comprised of a correlation-to-displacement converter, a programmable mode selector, and a coherent-state information extractor—that can be configured to achieve quantum optimal performance in known sensing and communication protocols for which broadband TMSV provides quantum advantage that is robust against entanglement-breaking loss and noise.

关键词： Optical & microwave phenomena Photon pairs & parametric down-conversion Quantum communication, protocols & technology Quantum entanglement Quantum sensing Squeezing of quantum noise Devices Optical parametric oscillators & amplifiers

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CIXG: A Comprehensive Approach to Driver Gene Identification and Causal Interpretation

CIXG: A Comprehensive Approach to Driver Gene Identification...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yawen Liu Yiwen Chen Shanling Nie Hai Yang Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Center for Continuing and Lifelong Education National University of Singapore Faculty of Engineering The University of Sydney Australia

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

With the ongoing advancements in science and technology and the increasing research focus on cancer-related issues, there has been a proliferation of omics-related resources for in-depth analysis and exploration. This burgeoning volume and complexity of biological data have fostered the integration of machine-learning techniques into biology. As a result, numerous machine-learning strategies have been established to identify driver mutations. Yet, many of these strategies produce complex models, complicating comprehension and thereby clouding the impact of input features on the resulting predictions. Our analysis presented the CIXG framework, which integrates a driver gene prediction module using XGBoost with a causality interpretation module anchored on CXPlain. This architecture enables quantifying each input feature’s contribution to the prediction outcome and ensures precise predictions of driver genes. When benchmarked against the state-of-the-art (SOTA) method, CIXG demonstrated superior accuracy in pinpointing driver genes across pan-cancer studies and within the 32 specific cancer types. Importantly, our results underscored that mutation features chiefly influence CIXG’s predictive prowess, with additional support from other omics features.

关键词： Accuracy Biological system modeling Cause effect analysis Machine learning Predictive models Benchmark testing Complexity theory Bioinformatics Cancer

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