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Empirical investigation of stochastic local search for maximum satisfiability

Frontiers of computer science 2019年第1期13卷 86-98页

作者： Yi CHU Chuan LUO Shaowei CAI Haihang YOU State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190China University of Chinese Academy of Sciences Beijing 100049China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China

The maximum satisfiability (MAX-SAT)problem is an important NP-hard problem in theory,and has a broad range of applications in *** local search (SLS)is becoming an increasingly popular method for solving ***,a powerful SLS algorithm called CCLS shows efficiency on solving random and crafted MAX-SAT ***,the performance of CCLS on solving industrial MAX-SAT instances lags far *** this paper,we focus on experimentally analyzing the performance of SLS algorithms for solving industrial MAXSAT ***,we conduct experiments to analyze why CCLS performs poor on industrial *** we propose a new strategy called additive BMS (Best from Multiple Selections)to ease the serious *** integrating CCLS and additive BMS,we develop a new SLS algorithm for MAXSAT called CCABMS,and related experiments indicate the efficiency of ***,we experimentally analyze the effectiveness of initialization methods on SLS algorithms for MAX-SAT,and combine an effective initialization method with CCABMS,resulting in an enhanced *** results show that our enhanced algorithm performs better than its state-of-the-art SLS competitors on a large number of industrial MAX-SAT instances.

关键词： empirical investigation stochastic local search maximum satisfiability industrial instances additive BMS

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Slice Management in Radio Access Network via Deep Reinforcement Learning 91

Slice Management in Radio Access Network via Deep Reinforcem...

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91st IEEE Vehicular Technology Conference, VTC Spring 2020

作者： Khodapanah, Behnam Awada, Ahmad Viering, Ingo Barreto, Andre Noll Simsek, Meryem Fettweis, Gerhard Technische Universität Dresden Vodafone Chair Mobile Communications Systems Germany Nokia Bell Labs Munich Germany Nomor Research GmbH Munich Germany Barkhausen Institut Dresden Germany International Computer Science Institute Berkeley United States

ISBN: (纸本)9781728152073

In future 5G systems, it is envisioned that the physical resources of a single network will be dynamically shared between the virtual end-to-end networks called 'slices' and the network is 'sliced'. The dynamic sharing of resources can bring about pooling gains, but different slices can easily influence each other. Focusing on slicing the radio access network, a slice management entity is required to steer the radio resource management (RRM) so that all of the slices are satisfied and negative inter-slice influences are minimized. The steering of RRM can be done by adjusting slice-specific control parameters in scheduler and admission controller mechanisms. We use a model-free reinforcement learning (RL) framework and train an agent as a slice manager. Simulation results show that such agents are capable of relatively quickly learning how to steer the RRM. Furthermore, a hybrid method of Jacobian-matrix approximation with RL approach has been devised and shown to be a practical and efficient solution. © 2020 IEEE.

关键词： Reinforcement learning

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UnPaSt: unsupervised patient stratification by differentially expressed biclusters in omics data

arXiv

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arXiv 2024年

作者： Hartung, Michael Maier, Andreas Delgado-Chaves, Fernando Burankova, Yuliya Isaeva, Olga I. de Sá Patroni, Fábio Malta He, Daniel Shannon, Casey Kaufmann, Katharina Lohmann, Jens Savchik, Alexey Hartebrodt, Anne Chervontseva, Zoe Firoozbakht, Farzaneh Probul, Niklas Zotova, Evgenia Tsoy, Olga Blumenthal, David B. Ester, Martin Laske, Tanja Baumbach, Jan Zolotareva, Olga Institute for Computational Systems Biology University of Hamburg Albert-Einstein-Ring 10 Hamburg Germany Chair of Proteomics and Bioanalytics TUM School of Life Sciences Technical University of Munich Freising Germany Division of Tumor Biology & Immunology Netherlands Cancer Institute Plesmanlaan 121 Amsterdam Netherlands University of Campinas Campinas Brazil University of British Columbia Vancouver Canada ACMetric Amsterdam Netherlands Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Altius Institute for Biomedical Sciences Seattle United States Simon Fraser University Burnaby Canada Vancouver Prostate Centre Vancouver Canada Department of Mathematics and Computer Science University of Southern Denmark Odense Denmark Data Science in Systems Biology TUM School of Life Sciences Technical University of Munich Freising Germany

Most complex diseases, including cancer and non-malignant diseases like asthma, have distinct molecular subtypes that require distinct clinical approaches. However, existing computational patient stratification methods have been benchmarked almost exclusively on cancer omics data and only perform well when mutually exclusive subtypes can be characterized by many biomarkers. Here, we contribute with a massive evaluation attempt, quantitatively exploring the power of 22 unsupervised patient stratification methods using both, simulated and real transcriptome data. From this experience, we developed UnPaSt (https://***/unpast/) optimizing unsupervised patient stratification, working even with only a limited number of subtype-predictive biomarkers. We evaluated all 23 methods on real-world breast cancer and asthma transcriptomics data. Although many methods reliably detected major breast cancer subtypes, only few identified Th2-high asthma, and UnPaSt significantly outperformed its closest competitors in both test datasets. Essentially, we showed that UnPaSt can detect many biologically insightful and reproducible patterns in omic datasets. © 2024, CC BY.

关键词： Lung cancer

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Cerebral micro-bleeding identification based on a nine-layer convolutional neural network with stochastic pooling

Cerebral micro-bleeding identification based on a nine-layer...

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作者： Wang, Shuihua Sun, Junding Mehmood, Irfan Pan, Chichun Chen, Yi Zhang, Yu-Dong School of Computer Science and Technology Henan Polytechnic University Jiaozuo China School of Architecture Building and Civil Engineering Loughborough University Loughborough United Kingdom Department of Informatics University of Leicester Leicester United Kingdom Department of Computer Science and Engineering College of Electronics and Information Engineering Sejong University Seoul Korea Republic of School of Computer Science and Technology Nanjing Normal University Nanjing China School of Business Nanjing Normal University Nanjing China Jiangsu Laboratory of Lake Environment Remote Sensing Technologies Huaiyin Institute of Technology Huaian China

Cerebral micro-bleedings are small chronic brain hemorrhages caused by structural abnormalities of the small vessels. CMBs can be found from individuals with stroke at memory clinics and even healthy elderly people. CMBs indicate hemorrhage-prone pathological states. Research shows that CMBs are associated with an increased risk of future ischemic stroke, intra-cerebral hemorrhage (ICH), dementia, and death. Considering that CMBs severely influence people's life, it is necessary to identify the CMBs in an early stage to prevent from further deterioration and to help people live a healthy life. In this paper, we proposed using CNN with stochastic pooling for the CMB detection. CNN has good performance in image and video recognition, recommender system, and nature language processing. Based on the collected subject, the experiment result shows that the six-convolution layer and three fully-connected layer CNN, nine-layers in total, achieved sensitivity, specificity, accuracy, and precision as 97.22%, and 97.35%, 97.28%, and 97.35% in average of ten runs, which shows better performance than five state-of-the-art methods. © 2019 John Wiley & Sons, Ltd.

关键词： Convolution

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Semantic segmentation of longitudinal thermal images for identification of hot and cool spots in urban areas

arXiv

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arXiv 2023年

作者： Ramani, Vasantha Arjunan, Pandarasamy Poolla, Kameshwar Miller, Clayton Berkeley Education Alliance for Research in Singapore CREATE Tower 1 Create Way 138602 Singapore Robert Bosch Centre for Cyber-physical Systems Indian Institute of Science Karnataka Bengaluru560012 India Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720-1770 United States Department of the Built Environment College of Design and Engineering National University of Singapore 4 Architecture Drive 117566 Singapore

This work presents the analysis of semantically segmented, longitudinally, and spatially rich thermal images collected at the neighborhood scale to identify hot and cool spots in urban areas. An infrared observatory was operated over a few months to collect thermal images of different types of buildings on the educational campus of the National University of Singapore. A subset of the thermal image dataset was used to train state-of-the-art deep learning models to segment various urban features such as buildings, vegetation, sky, and roads. It was observed that the U-Net segmentation model with ‘resnet34’ CNN backbone has the highest mIoU score of 0.99 on the test dataset, compared to other models such as DeepLabV3, DeeplabV3+, FPN, and PSPnet. The masks generated using the segmentation models were then used to extract the temperature from thermal images and correct for differences in the emissivity of various urban features. Further, various statistical measures of the temperature extracted using the predicted segmentation masks are shown to closely match the temperature extracted using the ground truth masks. Finally, the masks were used to identify hot and cool spots in the urban feature at various instances of time. This forms one of the very few studies demonstrating the automated analysis of thermal images, which can be of potential use to urban planners for devising mitigation strategies for reducing the urban heat island (UHI) effect, improving building energy efficiency, and maximizing outdoor thermal comfort. © 2023, CC BY-NC-SA.

关键词： Observatories

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City Models: Past, Present and Future Prospects

arXiv

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arXiv 2025年

作者： Ritter, Helge Herzog, Otthein Rothermel, Kurt Cohn, Anthony G. Wu, Zhiqiang College of Architecture and Urban Planning Tongji University 1239 Siping Road Shanghai200092 China Faculty of Technology Bielefeld University Universitätsstr.25 Bielefeld33615 Germany Institute of Parallel and Distributed Systems University of Stuttgart Universitätsstr. 38 Stuttgart70569 Germany FB 03 University of Bremen Bibliotheksstr. 5 Bremen28539 Germany School of Computer Science University of Leeds LeedsLS2 9JT United Kingdom The Alan Turing Institute LondonNW1 2DB United Kingdom

This paper attempts to take a comprehensive look at the challenges of representing the spatio-temporal structures and dynamic processes that define a city’s overall characteristics. For the task of urban planning and urban operation, we take the stance that even if the necessary representations of these structures and processes can be achieved, the most important representation of the relevant mindsets of the citizens are, unfortunately, mostly neglected. After a review of major "traditional" urban models of structures behind urban scale, form, and dynamics, we turn to major recent modeling approaches triggered by recent advances in AI that enable multimodal generative models. Some of these models can create representations of geometries, networks and images, and reason flexibly at a human-compatible semantic level. They provide huge amounts of knowledge extracted from huge collections of text and image documents and cover the required rich representation spectrum including geographic knowledge by different knowledge sources, degrees of granularity and scales. We then discuss what these new opportunities mean for coping with the modeling challenges posed by cities, in particular with regard to the role and impact of citizens and their interactions within the city infrastructure. We propose to integrate these possibilities with existing approaches, such as agent-based models, which opens up new modeling spaces including rich citizen models which are able to also represent social interactions. Finally, we put forward some thoughts about a vision of a "social AI in a city ecosystem" that adds relevant citizen models to state-of-the-art structural and process models. This extended city representation will enable urban planners to establish citizen-oriented planning of city infrastructures, to make them into inviting environments that reconcile and foster human culture, city resilience and sustainability. © 2025, CC BY-NC-ND.

关键词： Digital elevation model

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Effects of Target Displacement on Single-Snapshot DOA Estimation in Automotive Radar

Effects of Target Displacement on Single-Snapshot DOA Estima...

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IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNet)

作者： Haoqing Liu Jonas Fuchs Thomas Horn Markus Gardill Institute for Electronics Engineering Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Germany InnoSenT GmbH 97499 Donnersdorf Germany Chair of Computer Science VII Julius-Maximilians-Universität Würzburg Germany

This paper focuses on the effect of range velocity displacement of two targets on the accuracy of direction-of-arrival estimation in an automotive radar system. A simulative analysis is performed to evaluate the effect on the received signal phases for two targets with different positions, i.e. range and velocity displacement, within a range velocity cell. Experiments show an increasing error of the maximum likelihood estimation for two targets azimuth angle, as their spacing within the range velocity cell is increased. Finally the root cause and the impact of this behavior on real-world scenarios is analyzed.

关键词： Wireless communication Wireless sensor networks Maximum likelihood estimation Radar cross-sections Direction-of-arrival estimation Systematics Conferences

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Interface Responsibility Patterns: Processing Resources and Operation Responsibilities 20

Interface Responsibility Patterns: Processing Resources and ...

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2020 European Conference on Pattern Languages of Programs, EuroPLoP 2020

作者： Zimmermann, Olaf Lübke, Daniel Zdun, Uwe Pautasso, Cesare Stocker, Mirko University of Applied Sciences of Eastern Switzerland Rapperswil Switzerland IQuest GmbH Han over Germany University of Vienna Faculty of Computer Science Software Architecture Research Group Vienna Austria Software Institute Faculty of Informatics Usi Lugano Switzerland

ISBN: (纸本)9781450377690

Remote Application Programming Interfaces (APIs), as for instance offered in microservices architectures, are used in almost any distributed system today and are thus enablers for many digitalization efforts. It is hard to design such APIs so that they are easy and effective to use;maintaining their runtime qualities while preserving backward compatibility is equally challenging. Finding well suited granularities in terms of the architectural capabilities of endpoints and the read-write semantics of their operations are particularly important design concerns. Existing pattern languages have dealt with local APIs in object-oriented programming, with remote objects, with queue-based messaging and with service-oriented computing platforms. However, patterns or equivalent guidances for the architectural design of API endpoints, operations and their request and response message structures are still missing. In this paper, we extend our microservice API pattern language (MAP) and introduce endpoint role and operation responsibility patterns, namely Processing Resource, Computation Function, State Creation Operation, Retrieval Operation, and State Transition Operation. Known uses and examples of the patterns are drawn from public Web APIs, as well as application development and system integration projects the authors have been involved in. © 2020 ACM.

关键词： Object oriented programming

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Estimating motor symptom presence and severity in Parkinson's disease from wrist accelerometer time series using ROCKET and InceptionTime

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Scientific reports 2025年第1期15卷 19140页

作者： Cedric Donié Neha Das Satoshi Endo Sandra Hirche TUM School of Computation Information and Technology Department of Computer Engineering Chair of Information-oriented Control Technical University of Munich Munich Germany. cedric.donie@tum.de. TUM School of Computation Information and Technology Department of Computer Engineering Chair of Information-oriented Control Technical University of Munich Munich Germany. Munich Institute of Robotics and Machine Intelligence (MIRMI) Munich Germany. Munich Data Science Institute (MDSI) Munich Germany.

Parkinson's disease (PD) is a neurodegenerative condition characterized by frequently changing motor symptoms, necessitating continuous symptom monitoring for more targeted treatment. Classical time series classification and deep learning techniques have demonstrated limited efficacy in monitoring PD symptoms using wearable accelerometer data due to complex PD movement patterns and the small size of available datasets. We investigate InceptionTime and RandOm Convolutional KErnel Transform (ROCKET) as they are promising for PD symptom monitoring. InceptionTime's high learning capacity is well-suited to modeling complex movement patterns, while ROCKET is suited to small datasets. With random search methodology, we identify the highest-scoring InceptionTime architecture and compare its performance to ROCKET with a ridge classifier and a multi-layer perceptron on wrist motion data from PD patients. Our findings indicate that all approaches can learn to estimate tremor severity and bradykinesia presence with moderate performance but encounter challenges in detecting dyskinesia. Among the presented approaches, ROCKET demonstrates higher scores in identifying dyskinesia, whereas InceptionTime exhibits slightly better performance in tremor and bradykinesia estimation. Notably, both methods outperform the multi-layer perceptron. In conclusion, InceptionTime can classify complex wrist motion time series and holds potential for continuous symptom monitoring in PD with further development.

关键词： Accelerometer Deep learning IMU InceptionTime Machine learning Parkinson’s disease ROCKET Time series classification

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HydraMini: An FPGA-based Affordable Research and Education Platform for Autonomous Driving

HydraMini: An FPGA-based Affordable Research and Education P...

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International Conference on Connected and Autonomous Driving

作者： Tianze Wu Yifan Wang Weisong Shi Joshua Lu SKL of Computer Architecture Institute of Computing Technology CAS Beijing China Department of Computer Science Wayne State University Michigan USA Xilinx Shanghai China

ISBN: (数字)9781728160597

ISBN: (纸本)9781728160603

Autonomous driving has been a hot topic recently, so many industrial and academic groups are putting much engineering and research efforts into this topic. However, it is difficult for most researchers or students to afford a car as a research platform to conduct experiments for autonomous driving. Further, we believe that only when more people have the chance to make contributions will this area be more prosperous. Therefore, in this paper, we present HydraMini, an affordable experimental research and education platform supporting the experiments from hardware systems to vision algorithms, and its high flexibility makes it easily extended and modified. It is equipped with the Xilinx PYNQ-Z2 board as the computing platform, which deploys the Deep Learning Processing Unit (DPU) in FPGA to accelerate the deep learning inference. It also provides useful tools like a simulator for model training and testing in a virtual environment to facilitate the use of HydraMini. Our platform will help researchers and students build and test their own solutions for autonomous driving algorithms and systems easily and efficiently.

关键词： Platform Hardware system Automobile driving Virtual environments Field programmable gate arrays Cars simulator Students Chance Education Model trains motivation

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