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35th ACM Symposium on Parallelism in algorithms and Architectures (SPAA)

作者： Goodrich, Michael T. Jacob, Riko Univ Calif Irvine Irvine CA 92697 USA IT Univ Copenhagen Copenhagen Denmark

ISBN: (纸本)9781450395458

We present comparison-based parallel algorithms for sorting n comparable items subject to comparison errors. We consider errors to occur according to a well-studied framework, where the comparison of two elements returns the wrong answer with a fixed probability. In the persistent model, the result of the comparison of two given elements, x and y, always has the same result, and is independent of all other pairs of elements. In the non-persistent model, the result of the comparison of each pair of elements, x and y, is independent of all prior comparisons, including for x and y. It is not possible to always correctly sort a given input set in the persistent model, so we study algorithms that achieve a small maximum dislocation and small total dislocation of the elements in the output permutation. In this paper, we provide parallel algorithms for sorting with comparison errors in the persistent and non-persistent models. Our algorithms are asymptotically optimal in terms of their span, work, and, in the case of persistent errors, maximum and total dislocation. The main results are algorithms for the binary-forking parallel model with atomics, but we also provide algorithms for the CREW PRAM model. Our algorithms include a number of novel techniques and analysis tools, including a PRAM-to-binary-forking-model simulation result, and are the first optimal parallel algorithms for the persistent model and the non-persistent model in the binary-forking parallel model with atomics. In particular, our algorithms haveO(log n) span, O(n log n) work, and, in the case of the persistent model, O(log n) maximum dislocation and O(n) total dislocation, with high probability. We achieve similar results for the CREW PRAM model, which are the first optimal methods for the persistent model and the first optimal results for the non-persistent model with reasonable constant factors in the performance bounds.

关键词： sorting noisy searching algorithm analysis randomized algorithms

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Chemical Discrimination of Benzene Series and Molecular Recognition of the Sensing Process over Ti-Doped Co3O4

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ACS SENSORS 2022年第6期7卷 1757-1765页

作者： Cao, Zhengmao Ge, Yingzhu Wang, Wu Sheng, Jianping Zhang, Zijian Li, Jieyuan Sun, Yanjuan Dong, Fan Univ Elect Sci & Technol China Inst Fundamental & Frontier Sci Res Ctr Environm & Energy Catalysis Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Sch Resources & Environm Chengdu 611731 Peoples R China

This work achieved the chemical discrimination of benzene series (toluene, xylene isomers, and ethylbenzene gases) based on the Ti-doped Co3O4 sensor. Benzene series gases presented different gas-response features due to the differences in redox rate on the surface of the Ti-doped Co3O4 sensor, which created an opportunity to discriminate benzene series via the algorithm analysis. Excellent groupings were obtained via the principal component analysis. High prediction accuracies were acquired via k-nearest neighbors, linear discrimination analysis (LDA), and support vector machine classifiers. With the confusion matrix for the data set using the LDA classifier, the benzene series have been well classified with 100% accuracy. Furthermore, in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and density functional theory calculations were conducted to investigate the molecular gas-solid interfacial sensing mechanism. Ti-doped Co3O4 showed strong Lewis acid sites and adsorption capability toward reaction species, which benefited the toluene gas-sensing reaction and resulted in the highly boosted gas-sensing performance. Our research proposed a facile distinction methodology to recognize similar gases and provided new insights into the recognition of gas-solid interfacial sensing mechanisms.

关键词： xylene isomers algorithm analysis Ti-doped Co3O4 in situ DRIFTS sensing mechanism

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Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2022年第6期26卷 1336-1350页

作者： Moosbauer, Julia Binder, Martin Schneider, Lennart Pfisterer, Florian Becker, Marc Lang, Michel Kotthoff, Lars Bischl, Bernd Ludwig Maximilians Univ Munchen Dept Stat D-80539 Munich Germany Res Ctr Trustworthy Data Sci & Secur D-44227 Dortmund Germany Univ Wyoming Dept Comp Sci Laramie WY 82071 USA

Automated hyperparameter optimization (HPO) has gained great popularity and is an important component of most automated machine learning frameworks. However, the process of designing HPO algorithms is still an unsystematic and manual process: new algorithms are often built on top of prior work, where limitations are identified and improvements are proposed. Even though this approach is guided by expert knowledge, it is still somewhat arbitrary. The process rarely allows for gaining a holistic understanding of which algorithmic components drive performance and carries the risk of overlooking good algorithmic design choices. We present a principled approach to automated benchmark-driven algorithm design applied to multifidelity HPO (MF-HPO). First, we formalize a rich space of MF-HPO candidates that includes, but is not limited to, common existing HPO algorithms and then present a configurable framework covering this space. To find the best candidate automatically and systematically, we follow a programming-by-optimization approach and search over the space of algorithm candidates via Bayesian optimization. We challenge whether the found design choices are necessary or could be replaced by more naive and simpler ones by performing an ablation analysis. We observe that using a relatively simple configuration (in some ways, simpler than established methods) performs very well as long as some critical configuration parameters are set to the right value.

关键词： algorithm analysis algorithm design automated machine learning (AutoML) hyperparameter optimization (HPO) multifidelity

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Enhancing Statistical-Based Remote Sensing Image Classification algorithms: An Optimization Study 8th

Enhancing Statistical-Based Remote Sensing Image Classificat...

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8th International Conference on Inventive Communication and Computational Technologies, ICICCT 2024

作者： Yu, Tianyi Liu, L. Faculty of Science and Technology BNU-HKBU United International College Zhuhai519087 China Centre for Evaluation Studies BNU-HKBU United International College Zhuhai519087 China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College Zhuhai519087 China

ISBN: (纸本)9789819777099

This study presents an optimization approach for statistically based remote sensing image classification algorithms. Traditional image classification methods suffer from subjectivity and inefficiency due to manual annotation. Taking advantage of advances in artificial intelligence and computer vision, this study focuses on content-based image classification methods. Specifically, the study focuses on optimizing statistically based algorithms for classifying remote sensing images, which are known for their complexity. The methodology includes efficient image feature extraction using visual saliency features based on regional covariance, followed by an optimized statistical-based classification algorithm using extended sparse coding and an empty spectrum dictionary model. Experimental evaluation conducted on remotely sensed images of Hong Kong demonstrates the superior performance of the proposed method compared to existing approaches, with an average accuracy rate of 99.41%. A comprehensive error analysis highlights the robustness and consistency of the proposed approach, confirming its effectiveness in addressing challenges of remote sensing image classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： algorithm analysis Image classification Optimization Remote sensing Statistical-based

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Multiobjective Evolutionary Component Effect on algorithm Behavior

Multiobjective Evolutionary Component Effect on Algorithm Be...

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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Lavinas, Yuri Ladeira, Marcelo Ochoa, Gabriela Aranha, Claus IRIT - CNRS UMR5505 University of Toulouse Toulouse France University of Brasilia Brasilia Brazil University of Stirling Stirling United Kingdom University of Tsukuba Tsukuba Japan

ISBN: (纸本)9798400704956

There has been an increased interest in the automatic design of multiobjective algorithms from their components to simplify the development and application of new approaches. These machine designed algorithms (auto-MOEA) can outperform their human-designed counterparts, but it is still unknown which components mostly influence their performance. We propose a methodology to investigate the components' effects on the final auto-MOEA configuration. We study the components' impact using Search Trajectory Networks (STNs), diversity of the population, and anytime hyper-volume values. We found that analyzing the objective and decision spaces simultaneously provides complementary information about the algorithms *** paper for the Hot-off-the-Press track at GECCO 2024 summarizes the work Yuri Lavinas, Marcelo Ladeira, Gabriela Ochoa, and Claus Aranha. 2023. Multiobjective Evolutionary Component Effect on algorithm behavior. ACM Trans. Evol. Learn. Optim. Just Accepted (August 2023). https://***/10.1145/3612933 [3]. © 2024 Copyright held by the owner/author(s).

关键词： algorithm analysis

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Exact distributional analysis of online algorithms with lookahead

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4OR-A QUARTERLY JOURNAL OF OPERATIONS RESEARCH 2021年第2期19卷 199-233页

作者： Dunke, Fabian Nickel, Stefan Karlsruhe Inst Technol Inst Operat Res Discrete Optimizat & Logist Kaiserstr 12 D-76131 Karlsruhe Germany

In online optimization, input data is revealed sequentially. Optimization problems in practice often exhibit this type of information disclosure as opposed to standard offline optimization where all information is known in advance. We analyze the performance of algorithms for online optimization with lookahead using a holistic distributional approach. To this end, we first introduce the performance measurement method of counting distribution functions. Then, we derive analytical expressions for the counting distribution functions of the objective value and the performance ratio in elementary cases of the online bin packing and the online traveling salesman problem. For bin packing, we also establish a relation between algorithm processing and the Catalan numbers. The paper shows that an exact analysis is strongly interconnected to the combinatorial structure of the problem and algorithm under consideration. Results further indicate that the value of lookahead heavily relies on the problem itself. The analysis also shows that exact distributional analysis could be used in order to discover key effects and identify related root causes in relatively simple problem settings. These insights can then be transferred to the analysis of more complex settings where the introduced performance measurement approach has to be used on an approximative basis (e.g., in a simulation-based optimization).

关键词： Online optimization Lookahead Distributional analysis algorithm analysis

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A Cost-Aware Logical Framework

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PROCEEDINGS OF THE ACM ON PROGRAMMING LANGUAGES-PACMPL 2022年第POPL期6卷 1–31页

作者： Niu, Yue Sterling, Jonathan Grodin, Harrison Harper, Robert Carnegie Mellon Univ 5000 Forbes Ave Pittsburgh PA 15213 USA Aarhus Univ Aabogade 34 DK-8000 Aarhus C Denmark

We present calf, a cost-aware logical framework for studying quantitative aspects of functional programs. Taking inspiration from recent work that reconstructs traditional aspects of programming languages in terms of a modal account of phase distinctions, we argue that the cost structure of programs motivates a phase distinction between intension and extension. Armed with this technology, we contribute a synthetic account of cost structure as a computational effect in which cost-aware programs enjoy an internal noninterference property: input/output behavior cannot depend on cost. As a full-spectrum dependent type theory, calf presents a unified language for programming and specification of both cost and behavior that can be integrated smoothly with existing mathematical libraries available in type theoretic proof assistants. We evaluate calf as a general framework for cost analysis by implementing two fundamental techniques for algorithm analysis: the method of recurrence relations and physicist's method for amortized analysis. We deploy these techniques on a variety of case studies: we prove a tight, closed bound for Euclid's algorithm, verify the amortized complexity of batched queues, and derive tight, closed bounds for the sequential and parallel complexity of merge sort, all fully mechanized in the Agda proof assistant. Lastly we substantiate the soundness of quantitative reasoning in calf by means of a model construction.

关键词： algorithm analysis cost models phase distinction noninterference intensional property behavioral verification equational reasoning modal type theory mechanized proof proof assistants recurrence relations amortized analysis parallel algorithms

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Decision/Objective Space Trajectory Networks for Multi-objective Combinatorial Optimisation 1

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23rd European Conference on Evolutionary Computation in Combinatorial Optimisation (EvoCOP) Held as Part of EvoStar Conference

作者： Ochoa, Gabriela Liefooghe, Arnaud Lavinas, Yuri Aranha, Claus Univ Stirling Stirling FK9 4LA Scotland Univ Lille CNRS UMR CRIStAL 9189 InriaCent Lille F-59000 Lille France Univ Tsukuba 1-1-1 Tennodai Tsukuba Ibaraki 3058577 Japan

ISBN: (数字)9783031300356

ISBN: (纸本)9783031300349;9783031300356

This paper adapts a graph-based analysis and visualisation tool, search trajectory networks (STNs) to multi-objective combinatorial optimisation. We formally define multi-objective STNs and apply them to study the dynamics of two state-of-the-art multi-objective evolutionary algorithms: MOEA/D and NSGA2. In terms of benchmark, we consider two- and ***-landscapes for constructing multi-objective multi-modal landscapes with objective correlation. We find that STN metrics and visualisation offer valuable insights into both problem structure and algorithm performance. Most previous visual tools in multi-objective optimisation consider the objective space only. Instead, our newly proposed tool asses algorithm behaviour in the decision and objective spaces simultaneously.

关键词： algorithm analysis search trajectory networks STNs combinatorial optimisation visualisation multi-objective optimisation

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Creating Tangible VR Spaces for Exploring algorithm Complexity and Data Structures 53

Creating Tangible VR Spaces for Exploring Algorithm Complexi...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Green, Nathan School of Technology and Innovation Marymount University ArlingtonVA United States

ISBN: (纸本)9798350336429

This research explores the use of virtual reality (VR) in Computer Science education to enhance the visualization and understanding of algorithm complexity and data structures. The proposed VR system aims to provide undergraduate students with a more tangible and interactive learning experience, allowing them to interact with algorithms and data structures in a virtual environment. Through the implementation of immersive techniques and real-time visualization, students can manipulate and observe the behavior of these abstract concepts. The VR application covers topics such as algorithm analysis, queues, stacks, tree structures, and algorithmic complexity. The use of VR in Computer Science education has the potential to transform teaching methods and provide instructors with valuable supplementary materials. Future studies will evaluate the impact of VR on student learning outcomes, and the VR application will be made available for further research and instruction in the field. © 2023 IEEE.

关键词： algorithm analysis Computer Science Data Structures Virtual Reality XR

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The Innovation of Library Service by Artificial Intelligence Robot 3

The Innovation of Library Service by Artificial Intelligence...

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3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC)

作者： Li, Jingjing Heilongjiang Bayi Agr Univ Daqing Peoples R China

ISBN: (数字)9781665459471

ISBN: (纸本)9781665459471

The introduction of Intelligent Robot Service is the direction of library development. The application of intelligent robot technology will greatly improve the library service efficiency and service level. At present, there are still some problems in library service of intelligent robot, such as the core technology needs to be mastered skillfully, the level of intelligent service is not enough, and the construction of Corpus needs to be improved. In the era of library intelligent service, intelligent robot should pay more attention to the application of multipurpose intelligent robot system in library, the fusion of Intelligent Library and robot technology under the background of Internet of things, and the development trend of intelligent robot providing various care services for different readers. The introduction of intelligent robot technology will greatly improve the efficiency of books on and off shelves, and expand the spacetime limit of reference service. In the process of building the intelligent library, the infrastructure and service of the traditional library are reconstructed by using the automatic book access robot and the Consulting robot system, it can be concluded that Book Auto-access Technology and IM consultation system are most suitable for library management system, and Book auto-access robot technology can solve the bottleneck problem of library intelligent robot technology, promoting the transformation and upgrading of libraries to smart libraries.

关键词： path planning self-positioning system intelligent consulting system algorithm analysis intelligent library

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