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31st International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2025, which was held as part of the International Joint Conferences on Theory and Practice of Software, ETAPS 2025

作者： Chen, Yu-Fang Chung, Kai-Min Hsieh, Min-Hsiu Huang, Wei-Jia Lengál, Ondřej Lin, Jyun-Ao Tsai, Wei-Lun Institute of Information Science Academia Sinica Taipei Taiwan Hon Hai Quantum Computing Research Center Taipei Taiwan Faculty of Information Technology Brno University of Technology Brno Czech Republic Innovation Frontier Institute of Research for Science and Technology & Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9783031906596

We present a verifier of quantum programs called AutoQ 2.0. Quantum programs extend quantum circuits (the domain of AutoQ 1.0) by classical control flow constructs, which enable users to describe advanced quantum algorithms in a formal and precise manner. The extension is highly non-trivial, as we needed to tackle both theoretical challenges (such as the treatment of measurement, the normalization problem, and lifting techniques for verification of classical programs with loops to the quantum world), and engineering issues (such as extending the input format with a support for specifying loop invariants). We have successfully used AutoQ 2.0 to verify two types of advanced quantum programs that cannot be expressed using only quantum circuits: the repeat-until-success (RUS) algorithm and the weak-measurement-based version of Grover’s search algorithm. AutoQ 2.0 can efficiently verify all our benchmarks: all RUS algorithms were verified instantly and, for the weak-measurement-based version of Grover’s search, we were able to handle the case of 100 qubits in ∼20 minutes. © The Author(s) 2025.

关键词： Qubits

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Multi-consensus decentralized accelerated gradient descent

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 14474-14523页

作者： Haishan Ye Luo Luo Ziang Zhou Tong Zhang Center for Intelligent Decision-Making and Machine Learning School of Management Xi'an Jiaotong University Xi'an China School of Data Science Fudan University Shanghai China Department of Computing The Hong Kong Polytechnic University Hong Kong China Computer Science & Mathematics The Hong Kong University of Science and Technology Hong Kong China

This paper considers the decentralized convex optimization problem, which has a wide range of applications in large-scale machine learning, sensor networks, and control theory. We propose novel algorithms that achieve optimal computation complexity and near optimal communication complexity. Our theoretical results give affirmative answers to the open problem on whether there exists an algorithm that can achieve a communication complexity (nearly) matching the lower bound depending on the global condition number instead of the local one. Furthermore, the linear convergence of our algorithms only depends on the strong convexity of global objective and it does not require the local functions to be convex. The design of our methods relies on a novel integration of well-known techniques including Nesterov's acceleration, multi-consensus and gradient-tracking. Empirical studies show the outperformance of our methods for machine learning applications.

关键词： consensus optimization decentralized algorithm accelerated gradient descent gradient tracking composite optimization

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Automating Generative Deep Learning for Artistic Purposes: Challenges and Opportunities 12

Automating Generative Deep Learning for Artistic Purposes: C...

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12th International Conference on Computational Creativity, ICCC 2021

作者： Berns, Sebastian Broad, Terence Guckelsberger, Christian Colton, Simon School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Computing Goldsmiths University of London United Kingdom Creative Computing Institute University of the Arts London United Kingdom Department of Computer Science Aalto University Espoo Finland Finnish Center for Artificial Intelligence Finland SensiLab Faculty of IT Monash University Melbourne Australia

ISBN: (纸本)9789895416035

We present a framework for automating generative deep learning with a specific focus on artistic applications. The framework provides opportunities to hand over creative responsibilities to a generative system as targets for automation. For the definition of targets, we adopt core concepts from automated machine learning and an analysis of generative deep learning pipelines, both in standard and artistic settings. To motivate the framework, we argue that automation aligns well with the goal of increasing the creative responsibility of a generative system, a central theme in computational creativity research. We understand automation as the challenge of granting a generative system more creative autonomy, by framing the interaction between the user and the system as a co-creative process. The development of the framework is informed by our analysis of the relationship between automation and creative autonomy. An illustrative example shows how the framework can give inspiration and guidance in the process of handing over creative responsibility. © ICCC *** rights reserved.

关键词： Automation

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visual Support for the Loop Grafting Workflow on Proteins

arXiv

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arXiv 2024年

作者： Opálený, Filip Ulbrich, Pavol Planas-Iglesias, Joan Byška, Jan Štourač, Jan Bednář, David Furmanová, Katarína Kozlíková, Barbora The Department of Visual Computing Faculty of Informatics Masaryk University Brno Czech Republic Department of Informatics University of Bergen Bergen Norway Loschmidt Laboratories Department of Experimental Biology and RECETOX Faculty of Science Masaryk University Brno Czech Republic International Clinical Research Center St. Anne’s University Hospital Brno Czech Republic

In understanding and redesigning the function of proteins in modern biochemistry, protein engineers are increasingly focusing on exploring regions in proteins called loops. Analyzing various characteristics of these regions helps the experts design the transfer of the desired function from one protein to another. This process is denoted as loop grafting. We designed a set of interactive visualizations that provide experts with visual support through all the loop grafting pipeline steps. The workflow is divided into several phases, reflecting the steps of the pipeline. Each phase is supported by a specific set of abstracted 2D visual representations of proteins and their loops that are interactively linked with the 3D View of proteins. By sequentially passing through the individual phases, the user shapes the list of loops that are potential candidates for loop grafting. Finally, the actual in-silico insertion of the loop candidates from one protein to the other is performed, and the results are visually presented to the user. In this way, the fully computational rational design of proteins and their loops results in newly designed protein structures that can be further assembled and tested through in-vitro experiments. We showcase the contribution of our visual support design on a real case scenario changing the enantiomer selectivity of the engineered enzyme. Moreover, we provide the readers with the experts’ feedback. © 2024, CC BY.

关键词： Grafting (chemical)

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A dynamic platform for global pullorum disease and fowl typhoid

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Animal Diseases 2025年第1期5卷 113-123页

作者： Xiamei Kang Canghong Jin Pengkun Gu Song Wang Zhengjie Gao Chenyi Dai Xiao Zhou Abubakar Siddique Haiyang Zhou Linlin Huang Yan Li Min Yue Department of Veterinary Medicine Zhejiang University College of Animal SciencesHangzhou 310058China School of Computer and Computing Science Hangzhou City UniversityHangzhou 310015China Hainan Institute of Zhejiang University Sanya 572000China School of Life Science Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024China State Key Laboratory for Diagnosis and Treatment of Infectious Diseases National Clinical Research Center for Infectious DiseasesNational Medical Center for Infectious DiseasesThe First Affiliated HospitalCollege of MedicineZhejiang UniversityHangzhou 310003China Ningbo Academy of Agricultural Sciences Ningbo 315040China NewFit(Hangzhou)Information Technology Co. LtdHangzhou 310000China

Pullorum disease and fowl typhoid are caused by the Salmonella serovars Gallinarum biovars Pullorum and Gal-linarum,*** prevalence of these diseases varies across regions and is affected by different risk fac-tors that remain insufficiently *** fill this knowledge gap,we have compiled a global dataset for its prevalence,drawing upon a collection of literature from the last seven decades obtained from bilingual ***,a more interactive and dynamic platform is still needed for both academics and policymakers to improve biosecurity measures,limit disease transmission,and prevent future outbreaks at the global and local ***,we developed an advanced visualization platform to depict the prevalence of Salmonella Pullorum and Gallinarum,espe-cially in China,which is categorized by geographical region and temporal *** platform offers a user-friendly,efficient,and visually engaging tool to explore the prevalence of pullorum disease and fowl typhoid between 1945 and 2021 in different ***,this platform allows users to understand the influence of various fac-tors,such as breed,farm mode,economic usage and even the sex of the primary host,chickens,on the prevalence of this *** further provided a detailed overview of individual province within *** particular,by selecting two different provinces on the interface,users can quickly visualize and grasp the disparities in disease prevalence between the chosen *** interactive toolkit enables a dynamic exploration of the patterns and factors con-tributing to the prevalence of Salmonella Pullorum and *** interactive platform is freely available open source at http://139.9.85.208/.

关键词： Pullorum disease Fowl typhoid Interactive platform Prevalence prediction

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Relativistic Digital Twin: Bringing the IoT to the Future

arXiv

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arXiv 2023年

作者： Sciullo, Luca De Marchi, Alberto Trotta, Angelo Montori, Federico Bononi, Luciano Felice, Marco Di Department of Computer Science and Engineering University of Bologna Italy Advanced Research Center on Electronic Systems University of Bologna Italy Institute of Applied Mathematics and Scientific Computing Department of Aerospace Engineering University of the Bundeswehr Munich Germany

Complex IoT ecosystems often require the usage of Digital Twins (DTs) of their physical assets in order to perform predictive analytics and simulate what-if scenarios. DTs are able to replicate IoT devices and adapt over time to their behavioral changes. However, DTs in IoT are typically tailored to a specific use case, without the possibility to seamlessly adapt to different scenarios. Further, the fragmentation of IoT poses additional challenges on how to deploy DTs in heterogeneous scenarios characterized by the usage of multiple data formats and IoT network protocols. In this paper, we propose the Relativistic Digital Twin (RDT) framework, through which we automatically generate general-purpose DTs of IoT entities and tune their behavioral models over time by constantly observing their real counterparts. The framework relies on the object representation via the Web of Things (WoT), to offer a standardized interface to each of the IoT devices as well as to their DTs. To this purpose, we extended the W3C WoT standard in order to encompass the concept of behavioral model and define it in the Thing Description (TD) through a new vocabulary. Finally, we evaluated the RDT framework over two disjoint use cases to assess its correctness and learning performance, i.e., the DT of a simulated smart home scenario with the capability of forecasting the indoor temperature, and the DT of a real-world drone with the capability of forecasting its trajectory in an outdoor scenario. Experiments show that the generated DT can estimate the behavior of its real counterpart after an observation stage, regardless of the considered scenario. © 2023, CC BY.

关键词： Internet of things

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Shape-aware contrastive deep supervision for esophageal tumor segmentation from CT scans

Shape-aware contrastive deep supervision for esophageal tumo...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Jin, Qiangguo Cui, Hui Sun, Changming Huang, Jiapeng Xuan, Ping Xu, Yiyue Wang, Linlin Cao, Leilei Wei, Leyi Su, Ran Northwestern Polytechnical University School of Software Shaanxi China Yangtze River Delta Research Institute of Northwestern Polytechnical University Taicang China La Trobe University Department of Computer Science and Information Technology Melbourne Australia Csiro Data61 Sydney Australia Shantou University School of Engineering Department of Computer Science Guangdong China Shandong First Medical University Shandong Academy of Medical Sciences Shandong Cancer Hospital and Institute Department of Radiation Oncology Shandong China Zhejiang University Innovation Center of Yangtze River Delta Zhejiang China Shandong University School of Software Shandong China Tianjin University School of Computer Software College of Intelligence and Computing Tianjin China

ISBN: (纸本)9798350337488

Accurate tumor segmentation is crucial for esophageal cancer radiotherapy treatment planning. The low contrast among the esophagus, tumors, and surrounding tissues, and irregular tumor shapes limit the performance of automatic segmentation methods. In this paper, we aim to exploit the irregular shapes of tumors to facilitate accurate segmentation. We propose a simple and pluggable shape-aware contrastive deep supervision network (SCDSNet) with shape-aware regularization and voxel-to-voxel contrastive deep supervision. Specifically, the shape-aware regularization with an uncertainty minimization strategy encourages the precise predictions of an additional shape-aware head. The voxel-to-voxel contrastive deep supervision enhances the multi-scale shape-tumor contrast for better voxel-to-voxel prediction of shapes. The proposed method is simple and highly pluggable, which can easily be extended to other frameworks. Further, we establish a large in-house dataset on esophageal cancer to validate the effectiveness of our proposed method. The quantitative and qualitative experimental results demonstrate the effectiveness of SCDSNet on the esophageal cancer dataset. © 2023 IEEE.

关键词： Deep supervision Esophagus tumor segmentation Shape-aware regularization Voxel-to-voxel contrastive learning

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Resilience of Quantum Key Distribution Source against Laser-Damage Attack by a Variety of Lasers

Resilience of Quantum Key Distribution Source against Laser-...

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2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2023

作者： Ruzhitskaya, Daria Zhluktova, Irina Ponosova, Anastasiya Trefilov, Daniil Chaiwongkhot, Poompong Huang, Anqi Kamynin, Vladimir Makarov, Vadim Russian Quantum Center Skolkovo Moscow121205 Russia NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow119049 Russia Prokhorov General Physics Institute of Russian Academy of Sciences Moscow119991 Russia MIREA - Russian Technological University Moscow119454 Russia Vigo Quantum Communication Center University of Vigo VigoE-36310 Spain Department of Physics Faculty of Science Mahidol University Bangkok10400 Thailand Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Shanghai Branch National Laboratory for Physical Sciences at Microscale CAS Center for Excellence in Quantum Information University of Science and Technology of China Shanghai201315 China

ISBN: (纸本)9798350345995

Quantum key distribution (QKD) systems provide quantum-safe key exchange. Therefore, complete security analysis of implementations of QKD protocols is in the focus of interest of a worldwide information-security community. For today, a number of QKD loopholes are closed by countermeasures, which are also considered in emerging QKD security evaluation and certification [1-3]. However, new threats to practical QKD implementations are still found, such as the laser-damage attack, which is a powerful hacking strategy. In investigations, CW laser radiation is most often used, but in contrast to it, the interaction of pulsed laser radiation with optical materials may lead to a wide range of effects, like nonlinear effects, dielectric breakdown, etc. © 2023 IEEE.

关键词： Continuous wave lasers

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Wave-Controlled Metasurface-Based Reconfigurable Intelligent Surfaces

arXiv

引用

arXiv 2022年

作者： Ayanoglu, Ender Capolino, Filippo Lee Swindlehurst, A. Center for Pervasive Communications and Computing Department of Electrical Engineering and Computer Science University of California IrvineCA92697-2625 United States

Reconfigurable Intelligent Surfaces (RISs) are programmable metasurfaces that can adaptively steer received electromagnetic energy in desired directions by employing controllable phase shifting cells. Among other uses, an RIS can modify the propagation environment in order to provide wireless access to user locations that are not otherwise reachable by a base station. Alternatively, an RIS can steer the waves away from particular locations in space, to eliminate interference and allow for co-existence of the wireless network with other types of fixed wireless services (e.g., radars, unlicensed radio bands, etc.). The novel approach in this work is a wave-controlled architecture that properly accounts for the maximum possible change in the local reflection phase that can be achieved by adjacent RIS elements. It obviates the need for dense wiring and signal paths that would be required for individual control of every RIS element, and thus offers a substantial reduction in the required hardware. We specify this wave-controlled RIS architecture in detail and discuss signal processing and machine learning methods that exploit it in both point-to-point and multi-cell MIMO systems. Such implementations can lead to a dramatic improvement in next-generation wireless, radar, and navigation systems where RIS finds wide applications. They have the potential to improve the efficiency of spectrum utilization and coexistence by orders of magnitude. © 2022, CC BY.

关键词： Navigation systems

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Deep Learning Based Multiclass Tumor Identification and Classification Using Fusion of CNN Models

Deep Learning Based Multiclass Tumor Identification and Clas...

引用

International Conference on Signal Processing Proceedings (ICSP)

作者： Zubair Saeed Tarraf Torfeh Souha Aouadi Jim Xiuquan Ji Othmane Bouhali Department of Electrical and Computer Engineering Texas A&M University College Station TX USA Department of Electrical and Computer Engineering Texas A&M University at Qatar Doha Qatar Department of Radiation Oncology National Center for Cancer Care and Research Hamad Medical Corporation Doha Qatar Qatar Center for Quantum Computing College of Science and Engineering Hamad Bin Khalifa University Doha Qatar

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

The rising incidents of brain tumors pose significant challenges in diagnosis and treatment. Tumorous brain cancers present unique difficulties due to their critical locations and functional implications. Magnetic Resonance Imaging (MRI) offers safe and precise imaging, free from the risks associated with radiation-based methods. Deep learning, particularly Convolutional Neural Networks (CNN), has emerged as a valuable tool in identifying brain tumors in MRI scans, enabling more accurate diagnoses by fusing two well-known DL models. This study explores a fusion strategy combining AlexNet and VGG16 models, outperforming individual models trained with different learning rates. While VGG16 and AlexNet achieved accuracies of 79% and 77% respectively. Our fusion approach potentially achieved an accuracy of 81%, underscoring its efficacy in enhancing brain tumor classification accuracy. This study helps medical professionals to accurately and timely classify tumors for better outcomes for patients.

关键词： Deep learning Accuracy Magnetic resonance imaging Computed tomography Computational modeling Brain tumors Brain modeling Transformers Convolutional neural networks Positron emission tomography

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