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arXiv 2023年

作者： Mao, Shunyuan Dong, Ruobing Lu, Lu Yi, Kwang Moo Wang, Sifan Perdikaris, Paris Department of Physics and Astronomy University of Victoria VictoriaBCV8P 5C2 Canada Department of Chemical and Biomolecular Engineering University of Pennsylvania PhiladelphiaPA19104 United States Department of Computer Science University of British Columbia VancouverBCV6T 1Z4 Canada Graduate Group in Applied Mathematics and Computational Science University of Pennsylvania PhiladelphiaPA19104 United States Department of Mechanical Engineering and Applied Mechanics University of Pennsylvania PhiladelphiaPA19104 United States

We develop a tool, which we name Protoplanetary Disk Operator Network (PPDONet), that can predict the solution of disk-planet interactions in protoplanetary disks in real-time. We base our tool on Deep Operator Networks (DeepONets), a class of neural networks capable of learning non-linear operators to represent deterministic and stochastic differential equations. With PPDONet we map three scalar parameters in a disk-planet system - the Shakura & Sunyaev viscosity α, the disk aspect ratio h0, and the planet-star mass ratio q - to steady-state solutions of the disk surface density, radial velocity, and azimuthal velocity. We demonstrate the accuracy of the PPDONet solutions using a comprehensive set of tests. Our tool is able to predict the outcome of disk-planet interaction for one system in less than a second on a laptop. A public implementation of PPDONet is available at https://***/smao-astro/PPDONet. Copyright © 2023, The Authors. All rights reserved.

关键词： Open source software

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Degree-targeted cascades in modular, degree-heterogeneous networks

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Physical Review Research 2022年第1期4卷 013040-013040页

作者： Jordan Snyder Weiran Cai Raissa M. D'Souza Graduate Group in Applied Mathematics University of California Davis California 95616 USA Department of Applied Mathematics University of Washington Seattle Washington 98195 USA Department of Computer Science University of California Davis California 95616 USA Department of Mechanical and Aerospace Engineering University of California Davis California 95616 USA Santa Fe Institute Santa Fe New Mexico 87501 USA

The dynamics of cascading activation, such as rapid changes in public opinion and the outbreak of disease epidemics, have a crucial dependence on the connectivity patterns among the agents. We study cascading dynamics in modular, degree-heterogeneous networks, and consider the impact of intramodule seeding strategy on intermodule spread. Specifically, we establish that although activating the highest-degree nodes is more effective than random selection at growing a cascade locally, there is a critical level of intermodule connectivity required for a cascade to cross from one module to another, irrespective of the seeding strategy. We present an analytical proof of this statement for the case that each module has the same degree distribution and all module pairs have the same intermodule connectivity, while our simulation results suggest its validity for more general situations, including a ring of modules. Interestingly, we find that on a network comprised of two modules, this critical level is primarily determined by the degree distribution of the alter module, as opposed to the seed module. Our analytical approach extends a method developed by Gleeson, but is able to capture different seeding strategies using only one dynamical variable per module, namely, the conditional exposure probability. Our work shows that the possibility of a global cascade depends sensitively on intermodule connectivity, and less on the intramodule seeding strategy. This suggests, for example, that slight changes to intermodule connectivity can be a feasible intervention strategy to promote or inhibit global cascades.

关键词： Attacks on networks Cascades Community structure Degree distributions Spreading Scale free & inhomogeneous networks Mean field theory Threshold & cascade models

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A Survey on Explainable Artificial Intelligence for Cybersecurity

arXiv

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arXiv 2023年

作者： Rjoub, Gaith Bentahar, Jamal Wahab, Omar Abdel Mizouni, Rabeb Song, Alyssa Cohen, Robin Otrok, Hadi Mourad, Azzam King Hussein School of Computing Sciences Princess Sumaya University for Technology Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Computer and Software Engineering Polytechnique Montréal Montréal Canada Electrical Engineering and Computer Science Department Khalifa University Abu Dhabi United Arab Emirates Department of EECS Khalifa University Abu Dhabi United Arab Emirates David R. Cheriton School of Computer Science University of Waterloo Canada Cyber Security Systems and Applied AI Research Center Dept. of CSM Lebanese American University Lebanon Division of Science New York University Abu Dhabi United Arab Emirates

The "black-box" nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a rapidly growing research field that aims to create machine learning models that can provide clear and interpretable explanations for their decisions and actions. In the field of cybersecurity, XAI has the potential to revolutionize the way we approach network and system security by enabling us to better understand the behavior of cyber threats and to design more effective defenses. In this survey, we review the state of the art in XAI for cybersecurity and explore the various approaches that have been proposed to address this important problem. The review follows a systematic classification of cybersecurity threats and issues in networks and digital systems. We discuss the challenges and limitations of current XAI methods in the context of cybersecurity and outline promising directions for future research. © 2023, CC BY.

关键词： Cybersecurity

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Work fluctuations due to partial thermalizations in two-level systems

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Physical Review E 2021年第4期103卷 042141-042141页

作者： Maria Quadeer Kamil Korzekwa Marco Tomamichel Centre for Quantum Software and Information School of Computer Science University of Technology Sydney Ultimo NSW 2007 Australia Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University 30-348 Kraków Poland Department of Electrical and Computer Engineering & Centre for Quantum Technologies National University of Singapore Singapore 119077

We study work extraction processes mediated by finite-time interactions with an ambient bath—partial thermalizations—as continuous-time Markov processes for two-level systems. Such a stochastic process results in fluctuations in the amount of work that can be extracted and is characterized by the rate at which the system parameters are driven in addition to the rate of thermalization with the bath. We analyze the distribution of work for the case in which the energy gap of a two-level system is driven at a constant rate. We derive analytic expressions for average work and a lower bound for the variance of work showing that such processes cannot be fluctuation-free in general. We also observe that an upper bound for the Monte Carlo estimate of the variance of work can be obtained using Jarzynski's fluctuation-dissipation relation for systems initially in equilibrium. Finally, we analyze work extraction cycles by modifying the Carnot cycle, incorporating processes involving partial thermalizations, and we obtain efficiency at maximum power for such finite-time work extraction cycles under different sets of constraints.

关键词： Fluctuation theorems Irreversible processes Nonequilibrium statistical mechanics

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EEG Epileptic Seizure Detection Based on Fused Brain Functional Networks 2nd

EEG Epileptic Seizure Detection Based on Fused Brain Functio...

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2nd International Conference on applied Intelligence, ICAI 2024

作者： Zhou, Pengfei Pan, Yijie Zhang, Xun Shou, Jibin Guo, Jiayang Xu, Meiyan Gu, Peipei College of Software Engineering Zhengzhou University of Light Industry Zhengzhou450000 China Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo China Zhejiang Moorgen Group Co. Ltd. Zhejiang310000 China Department of Hematology School of Medicine Xiamen University Xiamen361000 China National Institute for Data Science in Health and Medicine Xiamen University Xiamen361000 China College of Computer Science Minnan Normal University Zhangzhou363000 China

ISBN: (纸本)9789819619061

Epilepsy, characterized by abnormal neuronal discharges, is a widespread neurological disorder. Traditional detection methods primarily focus on extracting time-frequency features from raw electroencephalogram (EEG) signals, often overlooking the complex interactions between brain regions. To address this limitation, we propose an automated approach for epileptic seizure detection. We first construct a fused brain network (TFPP) by integrating the Pearson Correlation Coefficient (PCC) and Phase Lag Index (PLV) across both temporal and frequency domains, enhancing EEG signals by capturing synchronized activities across different brain regions. Next, we introduce the CNN_sk_BiLSTM model, which leverages Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks to sequentially extract spatial and temporal features. The SKAttention mechanism further refines feature extraction across multiple scales. Using the CHB-MIT scalp EEG dataset, the proposed method achieved an average sensitivity of 90.86%, specificity of 98.79%, and accuracy of 97.88%. These results confirm that fused brain functional networks effectively integrate the intra-channel characteristics of EEG signals, demonstrating the superior performance of the proposed approach in epileptic seizure detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electroencephalography

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Self-Adaptive Radiative Cooler for Maximizing Year-Round Energy Saving of households

Self-Adaptive Radiative Cooler for Maximizing Year-Round Ene...

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CLEO: science and Innovations, S and I 2022

作者： Li, Jiachen Tang, Kechao Dong, Kaichen Gordon, Madeleine P. Reichertz, Finnegan G. Kim, Hyungjin Rho, Yoonsoo Wang, Qingjun Lin, Chang-Yu Grigoropoulos, Costas P. Javey, Ali Urban, Jeffrey J. Yao, Jie Levinson, Ronnen Wu, Junqiao Department of Materials Science and Engineering University of California BerkeleyCA94720 United States Division of Materials Sciences Lawrence Berkeley National Laboratory BerkeleyCA94720 United States School of Integrated Circuits Peking University Beijing100871 China Applied Science and Technology Graduate Group University of California BerkeleyCA94720 United States The Molecular Foundry Lawrence Berkeley National Laboratory BerkeleyCA94720 United States East Bay Innovation Academy 3800 Mountain Blvd. OaklandCA94619 United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Department of Mechanical Engineering University of California BerkeleyCA94720 United States Heat Island Group Lawrence Berkeley National Laboratory BerkeleyCA94720 United States

We present an approach to household thermal regulation and energy saving from an all-season perspective by developing a mechanically flexible and energy-free coating that automatically adapts its thermal emittance to ... 详细信息

ISBN: (纸本)9781557528209

关键词： Energy conservation

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A Brief Review of Explainable Artificial Intelligence in Healthcare

SSRN

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SSRN 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalo, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Swinburne University of Technology HawthornVIC3122 Australia Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, The Authors. All rights reserved.

关键词： Decision making

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Q-Map: Quantum Circuit Implementation of Boolean Functions

arXiv

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arXiv 2023年

作者： Hajjdiab, Hassan Khalil, Ashraf Eleuch, Hichem Computer Science and Software Engineering Department Concordia University MontrealQC Canada College of Technological Innovation Zayed University Abu Dhabi United Arab Emirates Department of applied physics and astronomy University of Sharjah Sharjah United Arab Emirates College of Arts and Sciences Abu Dhabi University Abu Dhabi59911 United Arab Emirates Institute for Quantum Science and Engineering Texas A&M University College StationTX77843 United States

Quantum computing has gained attention in recent years due to the significant progress in quantum computing technology. Today many companies like IBM, Google and Microsoft have developed quantum computers and simulators for research and commercial use. The development of quantum techniques and algorithms is essential to exploit the full power of quantum computers. In this paper we propose a simple visual technique (we call Q-Map) for quantum realisation of classical Boolean logic circuits. The proposed method utilises concepts from Boolean algebra to produce a quantum circuit with minimal number of quantum gates. © 2023, CC BY.

关键词： Boolean algebra

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Data-driven approach for identifying the factors related to debt collector performance

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Journal of Open Innovation: Technology, Market, and Complexity 2024年第4期10卷

作者： Sivamayilvelan, Keerthana Rajasekar, Elakkiya Balachandran, Santhi Kotecha, Ketan Vairavasundaram, Subramaniyaswamy School of Computing SASTRA Deemed University Thanjavur 613401 India xPERT Research Group Department of Computer Science Birla Institute of Technology & Science Pilani - Dubai Campus Dubai 345055 United Arab Emirates School of Arts Sciences Humanities & Education (SASHE) SASTRA Deemed University Thanjavur 613401 India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Pune India School of Computer Science and Engineering Vellore Institute of Technology Vellore 632014 India

The company's success and growth heavily rely on its workforce's performance, yet the evaluation of employees has been only partially and inconclusively executed so far. The primary goal of this research is to build an open innovation framework for analyzing the performance of the debt collector. We have developed the Reinforcement Learning based Continual Learning (RLC) approach for evaluating the performance by analyzing the metrics such as visit patterns and collection percentage. We have used the private debt collection dataset to assess the debt collector's performance. We formulated hypotheses derived from insights gained during exploratory data analysis and subsequently validated them through statistical testing. Whether there are noticeable distinctions among debt collectors in terms of visitation frequency, collection rates, and collection modes. This proposed open innovation framework for analyzing the debt collector performance provides significant variation in terms of collection rate. The proposed EDQN-CL achieved a 13.56 % higher classification rate than the existing algorithm for categorizing the debt collector performance. © 2024 The Authors

关键词： And machine learning Continual learning Debt collection Debt collector performance Deep reinforcement learning Statistical analysis

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Object Detection at Sea Using Ensemble Methods Across Spectral Ranges

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IFAC-PapersOnLine 2021年第16期54卷 1-6页

作者： Frederik E.T. Schöller Martin K. Plenge-Feidenhans’l Jonathan D. Stets Mogens Blanke Automation and Control Group Department of Electrical Engineering Technical University of Denmark DK-2800 Kgs. Lyngby Denmark Section of Image Analysis and Computger Graphics Department of Applied Mathematics and Computer Science DTU

Having the option of a temporally unmanned bridge when conditions allow, while maintaining or even enhancing navigational safety, is a long term aim in the maritime industry. Such a system requires excellent perception of the environment using an array of sensors. This paper investigates performance of object detection at sea using electro-optical sensors in relevant spectral ranges and discusses how missed detection risk is minimised for objects within navigation range. Using a combination of cameras in visible, near- and far infrared ranges, convolutional neural networks are employed for object detection. Ensemble techniques are suggested to minimise the amount of missed detections and it is shown how optimisation of confidence thresholds can be used to increase performance. The results are based on image data from vessels in near-coast operation in Danish waters.

关键词： Multi-modal sensor fusion Deep learning Detection performance Object detection at sea computer vision Autonomous Marine Vehicles

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