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arXiv 2024年

作者： Fernando, Duneesha Rodriguez, Maria A. Arroba, Patricia Ismail, Leila Buyya, Rajkumar Laboratory School of Computing and Information Systems The University of Melbourne Parkville Australia The CCS Center for Computational Simulation Universidad Politécnica de Madrid Madrid Spain Lab Department of Computer Science and Software Engineering United Arab Emirates University United Arab Emirates

Microservice architectures are increasingly used to modularize IoT applications and deploy them in distributed and heterogeneous edge computing environments. Over time, these microservice-based IoT applications are susceptible to performance anomalies caused by resource hogging (e.g., CPU or memory), resource contention, etc., which can negatively impact their Quality of Service and violate their Service Level Agreements. Existing research on performance anomaly detection for edge computing environments focuses on model training approaches that either achieve high accuracy at the expense of a time-consuming and resource-intensive training process or prioritize training efficiency at the cost of lower accuracy. To address this gap, while considering the resource constraints and the large number of devices in modern edge platforms, we propose two clustering-based model training approaches: (1) intra-cluster parameter transfer learning-based model training (ICPTL) and (2) cluster-level model training (CM). These approaches aim to find a trade-off between the training efficiency of anomaly detection models and their accuracy. We compared the models trained under ICPTL and CM to models trained for specific devices (most accurate, least efficient) and a single general model trained for all devices (least accurate, most efficient). Our findings show that ICPTL's model accuracy is comparable to that of the model per device approach while requiring only 40% of the training time. In addition, CM further improves training efficiency by requiring 23% less training time and reducing the number of trained models by approximately 66% compared to ICPTL, yet achieving a higher accuracy than a single general model. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Exploring Behaviors of Hybrid systems via the Voronoi Bias over Output Signals

arXiv

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arXiv 2024年

作者： Ernst, Gidon Fejlek, Jiří Software and Computational Systems Lab Department of Computer Science Ludwig-Maximilians-Universität München Oettingenstraße 67 Munich80538 Germany The Czech Academy of Sciences Institute of Computer Science Pod Vodárenskou věží 271/2 Prague182 07 Czech Republic

In this paper, we consider an analysis of temporal properties of hybrid systems based on simulations, so-called falsification of requirements. We present a novel exploration-based algorithm for falsification of black-box models of hybrid systems based on the Voronoi bias in the output space. This approach is inspired by techniques used originally in motion planning: rapidly exploring random trees. Instead of commonly employed exploration that is based on coverage of inputs, the proposed algorithm aims to cover all possible outputs directly. Compared to other state-of-the-art falsification tools, it also does not require robustness or other guidance metrics tied to a specific behavior that is being falsified. This allows our algorithm to falsify specifications for which robustness is not conclusive enough to guide the falsification procedure. Copyright © 2024, The Authors. All rights reserved.

关键词： Decision trees

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A Predator-Prey Population Dynamics Simulation for Biological Control Of Frankliniella Occidentalis (Western Flower Thrips) By Orius Laevigatus In Strawberry Plants

SSRN

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SSRN 2023年

作者： Amarathunga, Don Chathurika Parry, Hazel Grundy, John Dorin, Alan Computational and Collective Intelligence Department Data Science and AI Faculty of Information Technology Monash University Wellington Rd ClaytonVIC3800 Australia Commonwealth Scientific and Industrial Research Organisation EcoScience Precinct 41 Boggo Road Dutton ParkQLD4102 Australia Humanise Lab Department of Software Systems and Cybersecurity Faculty of Information Technology Monash University Wellington Rd ClaytonVIC3800 Australia

The use of predatory insect species to control agricultural pests is a key aspect of Integrated Pest Management (IPM) that can help to reduce the use of chemical pesticides. A simulation model that captures the population dynamics and interactions of this predator-prey system can serve as a valuable IPM decision-support tool. However, such a simulation is challenging to construct due to the interactions between life stages of insect predators, prey and the crop plants. Frankliniella occidentalis (Western Flower Thrip, WFT) is an economically devastating global pest that damages diverse crops. Orius laevigatus is an effective and commercially available predator that controls WFTs. We, therefore, developed a population dynamics simulation that models their interactions to better understand these insects' predator-prey dynamics in a strawberry crop. The model considers critical multi-stage predator-prey interaction, prey consumption reduction due to intraspecific competition, and the relationship between predation and oviposition rates of Orius. The simulation was structured using the boxcar train method, a stage-structured approach allowing insect populations to flow from one stage to the next over time. It was parameterised using data from existing literature. The results indicate the potential of the model to forecast the predator and prey species' population dynamics for different initial populations. A global sensitivity analysis identified that the daily oviposition rate of WFTs and egg survival rate of Orius were the most significant parameters. The prey handling time of Orius on WFTs did not significantly impact population variation. This model provides a useful tool for scenario exploration and assessment of the effects of Orius on WFTs that can aid decision-making in IPM prior to implementation. © 2023, The Authors. All rights reserved.

关键词： Crops

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An Approach to Gamifying Acquisitions for Assessing Impact on Military Strategy of Nation States

An Approach to Gamifying Acquisitions for Assessing Impact o...

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2021 IEEE International Conference on Cognitive and computational Aspects of Situation Management, CogSIMA 2021

作者： Ruvinsky, Alicia Gonzalez, Megan E. Richards, James Salter, Cody Church, Joshua McKee, Cpt Deems Seale, Maria Us Army Engineer Research and Development Center Scientific Software Branch Information Technology Lab VicksburgMS United States Us Army Engineer Research and Development Center Computational Analysis Branch Information Technology Lab VicksburgMS United States Us Army Engineer Research and Development Center Inst. for Systems Eng. Research Information Technology Lab VicksburgMS United States Army Us Army Engineer Research and Development Center VicksburgMS United States

ISBN: (纸本)9781728176987

Complex systems in which humans play a role, namely Human-Integrated Complex systems (HICS), can be difficult to model or simulate due to the uncertainty introduced by the human component. Traditional modeling approaches such as physics-based modeling do not provide predictive insight towards situation awareness and management. War game designers, and game architects are familiar with HICS problem spaces, and use gamification of such complex contexts as a means of modeling human behavior to inform, predict, and manage an HICS style problem. The game play thereby becomes a means of providing situation awareness and management of the HICS by using human action during game play as a heuristic for pruning the intractable possibility space of the problem at large into a likely probability subspace based on the actions players actually take when playing an HICS game simulation. This paper explores the approach of gamification of real-world HICS problem spaces for situation awareness and management. A gamification methodology is introduced and investigated through the use case of military acquisitions. © 2021 IEEE.

关键词： Behavioral research

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LEGION: Best-First Concolic Testing

LEGION: Best-First Concolic Testing

引用

IEEE International Conference on Automated software Engineering (ASE)

作者： Dongge Liu Gidon Ernst Toby Murray Benjamin I.P. Rubinstein The University of Melbourne School of Computing and Information Systems Melbourne Victoria Australia Software and Computational Systems Lab Munich Bavaria Germany

ISBN: (数字)9781450367684

ISBN: (纸本)9781728172811

Concolic execution and fuzzing are two complementary coverage-based testing techniques. How to achieve the best of both remains an open challenge. To address this research problem, we propose and evaluate Legion. Legion re-engineers the Monte Carlo tree search (MCTS) framework from the AI literature to treat automated test generation as a problem of sequential decision-making under uncertainty. Its best-first search strategy provides a principled way to learn the most promising program states to investigate at each search iteration, based on observed rewards from previous iterations. Legion incorporates a form of directed fuzzing that we call approximate path-preserving fuzzing (APPFUZZING) to investigate program states selected by MCTS. APPFUZZING serves as the Monte Carlo simulation technique and is implemented by extending prior work on constrained sampling. We evaluate Legion against competitors on 2531 benchmarks from the coverage category of Test-Comp 2020, as well as measuring its sensitivity to hyperparameters, demonstrating its effectiveness on a wide variety of input programs.

关键词： Monte Carlo methods Sensitivity Uncertainty Fuzzing Benchmark testing Search problems Test pattern generators

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拓扑异质结(PbSe)_(5)(Bi_(2)Se_(3))_(6)高压诱导超导电性研究

引用

Science China Materials 2023年第7期66卷 2822-2828页

作者：裴翠颖朱鹏李炳谈赵毅高玲玲李昌华朱世豪张庆华应天平谷林高波缑慧阳姚延荪孙建刘寒雨陈宇林王秩伟姚裕贵齐彦鹏 School of Physical Science and Technology ShanghaiTech UniversityShanghai 201210China Centre for Quantum Physics Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement(MOE)School of PhysicsBeijing Institute of Technology Beijing 100081China Beijing Key Lab of Nanophotonics and Ultrafine Optoelectronic Systems Beijing Institute of TechnologyBeijing 100081China Material Science Center Yangtze Delta Region Academy of Beijing Institute of TechnologyJiaxing 314011China State Key Laboratory of Superhard Materials and International Center for Computational Method and Software College of PhysicsJilin UniversityChangchun 130012China International Center of Future Science Jilin UniversityChangchun 130012China 7 Beijing National Laboratory for Condensed Matter PhysicsInstitute of PhysicsChinese Academy of SciencesBeijing 100190China Center for High Pressure Science and Technology Advanced Research Beijing 100094China Department of Physics and Engineering Physics University of SaskatchewanSaskatoonSaskatchewan S7N 5E2Canada National Laboratory of Solid State Microstructures School of Physics and Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing 210093China ShanghaiTech Laboratory for Topological Physics ShanghaiTech UniversityShanghai 201210China Department of Physics Clarendon LaboratoryUniversity of OxfordParks RoadOxford OX13PUUK Shanghai Key Laboratory of High-resolution Electron Microscopy ShanghaiTech UniversityShanghai 201210China

最近,天然异质结(PbSe)_(5)(Bi_(2)Se_(3))_(6)在理论上预测并实验证实为拓扑绝缘体.本文通过高压原位量子调控在(PbSe)_(5)(Bi_(2)Se_(3))_(6)中成功引入超导电性.当压力增加到10 GPa时,超导电性突然出现,T_(c)约为4.6 K,随后T_(c)急... 详细信息

最近,天然异质结(PbSe)_(5)(Bi_(2)Se_(3))_(6)在理论上预测并实验证实为拓扑绝缘体.本文通过高压原位量子调控在(PbSe)_(5)(Bi_(2)Se_(3))_(6)中成功引入超导电性.当压力增加到10 GPa时,超导电性突然出现,T_(c)约为4.6 K,随后T_(c)急剧下降.值得注意的是,当进一步施加压力到30 GPa以上时,出现了一种新的超导态,且T_(c)直到本实验最高压力仍未饱和.结合XRD和拉曼光谱,我们认为(PbSe)_(5)(Bi_(2)Se_(3))_(6)中两个超导态的出现与压力诱导的结构相变有关.拓扑异质结压力下出现新的量子态,为进一步研究拓扑和超导的关系提供了一个良好的平台.

关键词： high pressure superconductivity heterostructure topological material

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Explaining predictions of deep neural classifier via activation analysis

arXiv

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arXiv 2020年

作者： Stano, Martin Benesova, Wanda Marták, Lukáš Samuel Department of Informatics Informational Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava842 16 Slovakia Department of Computer Engineering and Applied Informatics Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava842 16 Slovakia Institute of Computational Perception Johannes Kepler University Linz4040 Austria LIT Artificial Intelligence Lab Johannes Kepler University Linz4040 Austria

In many practical applications, deep neural networks have been typically deployed to operate as a black box predictors. Despite high amount of work on interpretability and high demand on reliability of these systems, they typically still have to include a human actor in the loop, to validate the decisions and handle unpredictable failures and unexpected corner cases. This is true in particular for failure-critical application domains, such as medical diagnosis. We present a novel approach to explain and support an interpretation of a decision-making process to a human expert operating a deep learning system based on Convolutional Neural Network (CNN). By modeling activation statistics on selected layers of a trained CNN via Gaussian Mixture Models (GMM), we develop a novel perceptual code in binary vector space that describes how input sample is processed by the CNN. By measuring distances between pairs of samples in this perceptual encoding space, for any new input sample, we can now retrieve a set of most perceptually similar and dissimilar samples from an existing atlas of labeled samples, to support and clarify the decision made by the CNN model. Possible uses of this approach include for example Computer-Aided Diagnosis (CAD) systems working with medical imaging data, such as Magnetic Resonance Imaging (MRI) or Computed Tomography (CT) scans. We demonstrate the viability of our method in the domain of medical imaging for patient condition diagnosis, as proposed decision explanation method via similar ground truth domain examples (e.g. from existing diagnosis archives) will be interpretable by the operating medical personnel. Our results indicate that our method is capable of detecting distinct prediction strategies which enables to identify most similar predictions from an Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Bottom-Up Meta-Policy Search

arXiv

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arXiv 2019年

作者： Melo, Luckeciano C. Maximo, Marcos R.O.A. da Cunha, Adilson M. Autonomous Computational Systems Lab Computer Science Division Aeronautics Institute of Technology São José dos Campos Brazil Software Engineering Research Group Computer Science Division Aeronautics Institute of Technology São José dos Campos Brazil

Despite of the recent progress in agents that learn through interaction, there are several challenges in terms of sample efficiency and generalization across unseen behaviors during training. To mitigate these problems, we propose and apply a first-order Meta-Learning algorithm called Bottom-Up Meta-Policy Search (BUMPS), which works with two-phase optimization procedure: firstly, in a meta-training phase, it distills few expert policies to create a meta-policy capable of generalizing knowledge to unseen tasks during training;secondly, it applies a fast adaptation strategy named Policy Filtering, which evaluates few policies sampled from the meta-policy distribution and selects which best solves the task. We conducted all experiments in the RoboCup 3D Soccer Simulation domain, in the context of kick motion learning. We show that, given our experimental setup, BUMPS works in scenarios where simple multi-task Reinforcement Learning does not. Finally, we performed experiments in a way to evaluate each component of the algorithm. Copyright © 2019, The Authors. All rights reserved.

关键词： Reinforcement learning

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Pressured-induced superconductivity extending across the topological phase transition in thallium-based topological materials TlBi(S1-xSex)2

arXiv

引用

arXiv 2022年

作者： Pei, Cuiying Huang, Peihao Zhu, Peng Liu, Linlin Wang, Qi Zhao, Yi Gao, Lingling Li, Changhua Cao, Weizheng Lv, Jian Li, Xiang Wang, Zhiwei Yao, Yugui Yan, Binghai Felser, Claudia Chen, Yulin Liu, Hanyu Qi, Yanpeng School of Physical Science and Technology ShanghaiTech University Shanghai201210 China State Key Laboratory of Superhard Materials International Center for Computational Method and Software College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China School of Physics Beijing Institute of Technology Beijing100081 China Beijing Key Lab of Nanophotonics and Ultrafine Optoelectronic Systems Beijing Institute of Technology Beijing100081 China Material Science Center Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing314011 China ShanghaiTech Laboratory for Topological Physics ShanghaiTech University Shanghai201210 China Department of Condensed Matter Physics Weizmann Institute of Science Rehovot7610001 Israel Max Planck Institute for Chemical Physics of Solids Dresden01187 Germany Department of Physics Clarendon Laboratory University of Oxford Parks Road OxfordOX1 3PU United Kingdom Shanghai Key Laboratory of High-resolution Electron Microscopy ShanghaiTech University Shanghai201210 China

The coexistence of superconductivity and topology holds the potential to realize exotic quantum states of matter. Here we report that superconductivity induced by high pressure in three thallium-based materials, covering the phase transition from a normal insulator (TlBiS2) to a topological insulator (TlBiSe2) through a Dirac semimetal (TlBiSeS). By increasing the pressure up to 60 GPa, we observe superconductivity phase diagrams with maximal Tc values at 6.0~8.1 K. Our density-functional theory calculations reveal topological surface states in superconductivity phases for all three compounds. Our study paves the path to explore topological superconductivity and topological phase transitions. © 2022, CC BY-NC-ND.

关键词： Topology

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Pressure-Induced Superconductivity in Topological Heterostructure (PbSe)5(Bi2Se3)6

arXiv

引用

arXiv 2023年

作者： Pei, Cuiying Zhu, Peng Li, Bingtan Zhao, Yi Gao, Lingling Li, Changhua Zhu, Shihao Zhang, Qinghua Ying, Tianping Gu, Lin Gao, Bo Gou, Huiyang Yao, Yansun Sun, Jian Liu, Hanyu Chen, Yulin Wang, Zhiwei Yao, Yugui Qi, Yanpeng School of Physical Science and Technology ShanghaiTech University Shanghai201210 China School of Physics Beijing Institute of Technology Beijing100081 China Beijing Key Lab of Nanophotonics and Ultrafine Optoelectronic Systems Beijing Institute of Technology Beijing100081 China Material Science Center Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing314011 China State Key Laboratory of Superhard Materials International Center for Computational Method and Software College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing100190 China Center for High Pressure Science and Technology Advanced Research Beijing100094 China Department of Physics and Engineering Physics University of Saskatchewan SaskatoonSKS7N 5E2 Canada National Laboratory of Solid State Microstructures School of Physics Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing210093 China ShanghaiTech Laboratory for Topological Physics ShanghaiTech University Shanghai201210 China Department of Physics Clarendon Laboratory University of Oxford Parks Road OxfordOX1 3PU United Kingdom Shanghai Key Laboratory of High-Resolution Electron Microscopy ShanghaiTech University Shanghai201210 China

Recently, the natural heterostructure of (PbSe)5(Bi2Se3)6 has been theoretically predicted and experimentally confirmed as a topological insulator. In this work, we induce superconductivity in (PbSe)5(Bi2Se3)6 by implementing high pressure. As increasing pressure up to 10 GPa, superconductivity with Tc ~ 4.6 K suddenly appears, followed by an abrupt decrease. Remarkably, upon further compression above 30 GPa, a new superconducting state arises, where pressure raises the Tc to an unsaturated 6.0 K within the limit of our research. Combining XRD and Raman spectroscopies, we suggest that the emergence of two distinct superconducting states occurs concurrently with the pressure-induced structural transition in this topological heterostructure (PbSe)5(Bi2Se3)6. © 2023, CC BY-NC-ND.

关键词： Topology

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