Tsinghua Science and Technology最新文献_第3页

A DQN-Based Edge Offloading Method for Smart City Pollution Control 基于dqn的智慧城市污染控制边缘卸载方法

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2025-04-29 DOI: 10.26599/TST.2024.9010105

Jiajie Xu;Haolong Xiang;Shaobo Zang;Muhammad Bilal;Maqbool Khan;Guangming Cui

{"title":"A DQN-Based Edge Offloading Method for Smart City Pollution Control","authors":"Jiajie Xu;Haolong Xiang;Shaobo Zang;Muhammad Bilal;Maqbool Khan;Guangming Cui","doi":"10.26599/TST.2024.9010105","DOIUrl":"https://doi.org/10.26599/TST.2024.9010105","url":null,"abstract":"Smart city pollution control is fundamental to urban sustainability, which relies extensively on physical infrastructure such as sensors and cameras for real-time monitoring. Generally, monitoring data needs to be transmitted to centralized servers for pollution control service determination. In order to achieve highly efficient service quality, edge computing is involved in the smart city pollution control system (SCPCS) as it provides computational capabilities near the monitoring devices and low-latency pollution control services. However, considering the diversity of service requests, determination of offloading destination is a crucial challenge for SCPCS. In this paper, A Deep Q-Network (DQN)-based edge offloading method, called N-DEO, is proposed. Initially, N-DEO employs neural hierarchical interpolation for time series forecasting (N-HITS) to forecast pollution control service requests. Afterwards, an epsilon-greedy policy is designed to select actions. Finally, the optimal service offloading strategy is determined by the DQN algorithm. Experimental results demonstrate that N-DEO achieves the higher performance on service latency and system load compared with the current state-of-the-art methods.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 5","pages":"2227-2242"},"PeriodicalIF":6.6,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979785","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Innovative Algorithm for Attacking Symmetric Ciphers Using D-Wave Quantum Annealing 一种利用d波量子退火攻击对称密码的创新算法

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2025-04-29 DOI: 10.26599/TST.2024.9010231

Zhi Pei;Chunlei Hong;Fen Xia;Chao Wang

{"title":"An Innovative Algorithm for Attacking Symmetric Ciphers Using D-Wave Quantum Annealing","authors":"Zhi Pei;Chunlei Hong;Fen Xia;Chao Wang","doi":"10.26599/TST.2024.9010231","DOIUrl":"https://doi.org/10.26599/TST.2024.9010231","url":null,"abstract":"Quantum computing is generally considered non-threatening to symmetric ciphers. Quantum attacks on symmetric ciphers require a thorough analysis of their internal structures, posing considerable difficulties and challenges. As of 2023, Google's quantum supremacy chip, Sycamore, is still incapable of cryptanalysis. Leveraging D-Wave's quantum annealing exploits the unique quantum tunneling effect, providing an edge in solving combinatorial optimization problems. It can be regarded as a class of artificial intelligence algorithm that can achieve global optimization. We propose a quantum heuristic symmetric cipher attack algorithm for substitution-permutation network (SPN) symmetric ciphers, which transforms the plaintext-ciphertext propagation rules within SPN structure into the problem of solving a constrained quadratic model (CQM). A novel reduction algorithm is employed to eliminate redundant constraint conditions. The D-Wave Advantage quantum computer is used to recover the encryption sub-keys. Using the quantum approximate optimization algorithm, IBM Q Experience can only recover two rounds of the Heys Cipher sub-key, whereas D-Wave Advantage achieves complete key recovery, validating its potential in quantum symmetric cipher attacks.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 5","pages":"2184-2194"},"PeriodicalIF":6.6,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979788","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Maximizing Depth of Graph-Structured Convolutional Neural Networks with Efficient Pathway Usage for Remote Sensing 基于有效路径的图结构卷积神经网络深度最大化遥感研究

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2025-04-29 DOI: 10.26599/TST.2024.9010102

Difeng Wang;Liangming Chen;Fang Gong;Qiankun Zhu

{"title":"Maximizing Depth of Graph-Structured Convolutional Neural Networks with Efficient Pathway Usage for Remote Sensing","authors":"Difeng Wang;Liangming Chen;Fang Gong;Qiankun Zhu","doi":"10.26599/TST.2024.9010102","DOIUrl":"https://doi.org/10.26599/TST.2024.9010102","url":null,"abstract":"Recently, Randomly Wired Neural Networks (RWNNs) using random graphs for Convolutional Neural Network (CNN) construction have shown efficient layer connectivity, but may limit depth, affecting approximation, generalization, and robustness. In this work, we increase the depth of graph-structured CNNs while maintaining efficient pathway usage, which is achieved by building a feature-extraction backbone with a depth-first search, employing edges that have not been traversed for parameter-efficient skip connections. The proposed Efficiently Pathed Deep Network (EPDN) reaches maximum graph-based architecture depth without redundant node use, ensuring feature propagation with reduced connectivity. The deep structure of EPDN, coupled with its efficient pathway usage, allows for a nuanced feature extraction. EPDN is highly beneficial for processing remote sensing images, as its performance relies on the ability to resolve intricate spatial details. EPDN facilitates this by preserving low-level details through its deep and efficient skip connections, allowing for enhanced feature extraction. Additionally, the remote-sensing-adapted EPDN variant is akin to a special case of a multistep method for solving an Ordinary Differential Equation (ODE), leveraging historical data for improved prediction. EPDN outperforms existing CNNs in generalization and robustness on image classification benchmarks and remote sensing tasks. The source code is publicly available at https://github.com/AnonymousGithubLink/EPDN.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 5","pages":"1940-1953"},"PeriodicalIF":6.6,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979817","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Very Compact and a Threshold Implementation of uBlock for Internet of Things 物联网uBlock的一种非常紧凑的阈值实现

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2025-04-29 DOI: 10.26599/TST.2024.9010257

Botao Liu;Ming Tang

{"title":"A Very Compact and a Threshold Implementation of uBlock for Internet of Things","authors":"Botao Liu;Ming Tang","doi":"10.26599/TST.2024.9010257","DOIUrl":"https://doi.org/10.26599/TST.2024.9010257","url":null,"abstract":"The rapid proliferation of Internet of Things (IoT) devices necessitates lightweight cryptographic algorithms and their secure physical implementations. Masking, as a provably secure countermeasure against Side-Channel Attacks (SCA), has been extensively studied in the context of lightweight cryptography algorithms. Currently, some cryptographers have proposed a low-cost Threshold Implementation (TI) of the uBlock algorithm. However, their approach suffers from significant area overhead due to the inefficient serial and pipelined implementation of uBlock's Pshufb-Xor (PX) network structure. To address this issue, we develop a new serial and pipelined implementation method that optimizes the area of the uBlock algorithm. Based on this optimization, we implement a 2-share TI scheme for uBlock that requires minimal area resources and does not need fresh randomness. Compared to the state-of-the-art appoach, our method reduces slice area by 63.4% on Field Programmable Gate Arrays (FPGA) platform and Gate Equivalent (GE) area by 17.2% on Application-Specific Integrated Circuit (ASIC) platform for the unprotected implementation. For the protected implementation, our method reduces slice area by 41.5% and GE area by 14.0%. Finally, our protection scheme is validated using the automated tool PROLEAD and evaluated with Test Vector Leakage Assessment (TVLA), achieving first-order glitch-extended probing security.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 5","pages":"2270-2283"},"PeriodicalIF":6.6,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979790","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring Pathogenic Mutation in Allosteric Proteins: The Prediction and Beyond 探索变构蛋白的致病突变：预测和超越

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2025-04-29 DOI: 10.26599/TST.2024.9010226

Huiling Zhang;Zhen Ju;Jingjing Zhang;Xijian Li;Hanyang Xiao;Xiaochuan Chen;Yuetong Li;Xinran Wang;Yanjie Wei

{"title":"Exploring Pathogenic Mutation in Allosteric Proteins: The Prediction and Beyond","authors":"Huiling Zhang;Zhen Ju;Jingjing Zhang;Xijian Li;Hanyang Xiao;Xiaochuan Chen;Yuetong Li;Xinran Wang;Yanjie Wei","doi":"10.26599/TST.2024.9010226","DOIUrl":"https://doi.org/10.26599/TST.2024.9010226","url":null,"abstract":"In the post-genomic era, a central challenge for disease genomes is the identification of the biological effects of specific somatic variants on allosteric proteins and the phenotypes they influence during the initiation and progression of diseases. Here, we analyze more than 38 539 mutations observed in 90 human genes with 740 allosteric protein chains. We find that existing allosteric protein mutations are associated with many diseases, but the clinical significance of most mutations in allosteric proteins remains unclear. Next, we develop an ensemble-learning-based model for pathogenic mutation prediction of allosteric proteins based on the intrinsic characteristics of proteins and the prediction results from existed methods. When tested on the benchmark allosteric protein dataset, the proposed method achieves an AUCs of 0.868 and an AUPR of 0.894 on allosteric proteins. Furthermore, we explore the performance of existing methods in predicting the pathogenicity of mutations at allosteric sites and identify potential significant pathogenic mutations at allosteric sites using the proposed method. In summary, these findings illuminate the significance of allosteric mutation in disease processes, and contribute a valuable tool for the identification of pathogenic mutations as well as previously unknown disease-causing allosteric-protein-encoded genes.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 5","pages":"2284-2299"},"PeriodicalIF":6.6,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979792","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Zeroing Neural Network for Time-Varying Matrix Pseudoinversion in the Presence of Linear Noises 线性噪声下时变矩阵伪反演的归零神经网络

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2025-04-29 DOI: 10.26599/TST.2024.9010120

Jianfeng Li;Linxi Qu;Yueming Zhu;Zhan Li;Bolin Liao

{"title":"A Novel Zeroing Neural Network for Time-Varying Matrix Pseudoinversion in the Presence of Linear Noises","authors":"Jianfeng Li;Linxi Qu;Yueming Zhu;Zhan Li;Bolin Liao","doi":"10.26599/TST.2024.9010120","DOIUrl":"https://doi.org/10.26599/TST.2024.9010120","url":null,"abstract":"The computation of matrix pseudoinverses is a recurrent requirement across various scientific computing and engineering domains. The prevailing models for matrix pseudoinverse typically operate under the assumption of a noise-free solution process or presume that any noise present has been effectively addressed prior to computation. However, the concurrent real-time computation of time-varying matrix pseudoinverses holds significant practical utility, while the preemptive preprocessing for noise elimination or reduction may impose supplementary computational overheads on real-time implementations. Different from previous models for solving the pseudoinverse of time-varying matrices, in this paper, a model for solving the pseudoinverse of time-varying matrices using a double-integral structure, called Double-Integral-Enhanced Zeroing Neural Network (DIEZNN) model, is proposed and investigated, which is capable of solving time-varying matrix pseudoinverse while efficiently eliminating the negative effects of linear noise perturbations. The experimental results show that in the presence of linear noise, the DIEZNN model demonstrates better noise suppression performance compared to both the original zeroing neural network model and the Zeroing Neural Network (ZNN) model enhanced with a Li-type activation function. In addition, these models are applied to the control of chaotic system of controllable permanent magnet synchronous motor, which further verifies the superiority of DIEZNN in engineering application.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 5","pages":"1911-1926"},"PeriodicalIF":6.6,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979798","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

End-to-End Two-Branch Bionic Network for Autonomous Driving 端到端自动驾驶双分支仿生网络

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2025-04-29 DOI: 10.26599/TST.2024.9010170

Guoliang Sun;Sifa Zheng;Xingrui Gong;Yijie Pan;Rui Yang;Yingying Yu;Shanshan Pei

引用次数: 0

Error-Accumulation Improved Newton Algorithm in Model Predictive Control for Novel Compliant Actuator-Driven Upper-Limb Exoskeleton 基于误差积累改进牛顿算法的柔性外骨骼模型预测控制

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2025-04-29 DOI: 10.26599/TST.2024.9010145

Changxian Xu;Jiliang Zhang;Keping Liu;Jian Wang;Zhongbo Sun

引用次数: 0

Feedback Feedforward Iterative Learning Control for Networked Nonlinear System Under Iteratively Variable Trial Lengths and Data Dropouts 网络非线性系统在迭代变试验长度和数据丢失条件下的反馈前馈迭代学习控制

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2025-04-29 DOI: 10.26599/TST.2024.9010130

Yunshan Wei;Sixian Xiong;Wenli Shang

{"title":"Feedback Feedforward Iterative Learning Control for Networked Nonlinear System Under Iteratively Variable Trial Lengths and Data Dropouts","authors":"Yunshan Wei;Sixian Xiong;Wenli Shang","doi":"10.26599/TST.2024.9010130","DOIUrl":"https://doi.org/10.26599/TST.2024.9010130","url":null,"abstract":"This paper proposed a feedback feedforward Iterative Learning Control (ILC) law for nonlinear system with iteratively variable trial lengths under a networked systems structure, where the both sensor and actuator occurs random data lost separately. The feedforward ILC part includes the calculated input signal, actual input signal, and the modified tracking error of last iteration. Some tracking signal would be lost at last iteration because of the iterative varying trial lengths. In order to offset the missing signal of last trial, the tracking error of present trial is adopted by feedback control part. It is established that the convergence relied on the feedforward control gain merely, while the rate of convergence is also expedited by the feedback control component. When the initial state expectation equals to the reference one, it is established that the tracking error expectation can be controlled to zero. With an illustrative simulation, the effectiveness of the developed algorithm can be demonstrated.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 5","pages":"1897-1910"},"PeriodicalIF":6.6,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979793","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Backbone Network Construction in Wireless Artificial Intelligent Computing Systems 无线人工智能计算系统中高效骨干网的构建

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2025-04-29 DOI: 10.26599/TST.2024.9010259

Ming Sun;Xinyu Wu;Yi Zhou;Jin-Kao Hao;Zhang-Hua Fu

{"title":"Efficient Backbone Network Construction in Wireless Artificial Intelligent Computing Systems","authors":"Ming Sun;Xinyu Wu;Yi Zhou;Jin-Kao Hao;Zhang-Hua Fu","doi":"10.26599/TST.2024.9010259","DOIUrl":"https://doi.org/10.26599/TST.2024.9010259","url":null,"abstract":"In wireless artificial intelligent computing systems, the construction of backbone network, which determines the optimum network for a set of given terminal nodes like users, switches, and concentrators, can be naturally formed as the Steiner tree problem. The Steiner tree problem asks for a minimum edge-weighted tree spanning a given set of terminal vertices from a given graph. As a well-known graph problem, many algorithms have been developed for solving this computationally challenging problem in the past decades. However, existing algorithms typically encounter difficulties for solving large instances, i.e., graphs with a high number of vertices and terminals. In this paper, we present a novel partition-and-merge algorithm for effectively handle large-scale graphs. The algorithm breaks the input network into small subgraphs and then merges the subgraphs in a bottom-up manner. In the merging procedure, partial Steiner trees in the subgraphs are also created and optimized by an efficient local optimization. When the merging procedure ends, the algorithm terminates and reports the final solution for the input graph. We evaluated the algorithm on a wide range of benchmark instances, showing that the algorithm outperforms the best-known algorithms on large instances and competes favorably with them on small or middle-sized instances.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 5","pages":"2300-2319"},"PeriodicalIF":6.6,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979815","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0