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arXiv 2022年

作者： Anshu, Anurag Breuckmann, Nikolas P. Nirkhe, Chinmay School of Engineering and Applied Sciences Harvard University United States Department of Computer Science University College London United Kingdom Electrical Engineering and Computer Sciences University of California Berkeley United States

The NLTS (No Low-Energy Trivial State) conjecture of Freedman and Hastings [FH14] posits that there exist families of Hamiltonians with all low energy states of non-trivial complexity (with complexity measured by the quantum circuit depth preparing the state). We prove this conjecture by showing that the recently discovered families of constant-rate and linear-distance QLDPC codes correspond to NLTS local Hamiltonians. Copyright © 2022, The Authors. All rights reserved.

关键词： Hamiltonians

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Overall performance indicators for sustainability assessment and management in mining industry 7

Overall performance indicators for sustainability assessment...

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7th International Conference on Optimization and Applications, ICOA 2021

作者： Lamjahdi, Aicha Bouloiz, Hafida Gallab, Maryam Ibn Zohr University Laboratory of Systems Engineering and Decision-making Support National School of Applied Sciences Agadir Morocco ENSMR MIS - LISTD Laboratory Department of Computer Science Rabat Morocco

ISBN: (纸本)9781665441032

The mining industry is the source of the production of many consumer goods and equipment. Therefore, the companies that control this activity play a significant role in the global economy. However, it is an important source of pollution of water, air, soil and ecosystems by metals. It exploits fossil and non-renewable resources, requiring large amounts of energy and sometimes water. Thus, implementing a sustainable approach through an overall performance integration in manufacturing practices, is become a necessity in this sector. Hence, this research presents a collection of key performance indicators for overall performance evaluation and management in mining industry. The proposed indicators were established from literature on the basis of the sustainability triple bottom line of economic, social, and environmental factors. A survey is conducted then, in a Moroccan mining industry to validate the adaptability of the chosen indicators with the industry activities and practices. Future work will further develop an evaluation tool that will help managers to assess the overall performance in mining industry and to take the appropriate actions to increase its competitiveness. © 2021 IEEE.

关键词： Decision making

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Semantic Data Representation for Explainable Windows Malware Detection Models

arXiv

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arXiv 2024年

作者： Švec, Peter Balogh, Štefan Homola, Martin Kľuka, Ján Bisták, Tomáš Institute of Computer Science and Mathematics Faculty of Electrical Engineering and Information Technology Slovak University of Technology Ilkovičova 3 Bratislava Slovakia Department of Applied Informatics Faculty of Mathematics Physics and Informatics Comenius University in Bratislava Mlynská dolina Bratislava Slovakia

Ontologies are a standard tool for creating semantic schemata in many knowledge intensive domains of human interest. They are becoming increasingly important also in the areas that have been until very recently dominated by subsymbolic knowledge representation and machine-learning (ML) based data processing. One such area is information security, and specifically, malware detection. We thus propose PE Malware Ontology that offers a reusable semantic schema for Portable Executable (PE – the Windows binary format) malware files. This ontology is inspired by the structure of the EMBER dataset, which focuses on the static malware analysis of PE files. With this proposal, we hope to provide a unified semantic representation for the existing and future PE-malware datasets and facilitate the application of symbolic, neuro-symbolic, or otherwise explainable approaches in the PE-malware-detection domain, which may produce interpretable results described by the terms defined in our ontology. In addition, we also publish semantically treated EMBER data, including fractional datasets, to support the reproducibility of experiments on EMBER. We supplement our work with a preliminary case study, conducted using concept learning, to show the general feasibility of our approach. While we were not able to match the precision of the state-of-the-art ML tools, the learned malware discriminators were interesting and highly interpretable. Copyright © 2024, The Authors. All rights reserved.

关键词： Ontology

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Particle Swarm optimization Based Reliable Control Algorithm for Fuzzy Systems

Particle Swarm optimization Based Reliable Control Algorithm...

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International Conference on Control, Automation and Systems ( ICCAS)

作者： L. Ponnarasi P.B. Pankajavalli R. Sakthivel P. Selvaraj Department of Computer Science Sakthi College of Arts and Science College for Women Oddanchatram India Department of Computer Science Bharathiar University Coimbatore India Department of Applied Mathematics Bharathiar University Coimbatore India School of Electrical Engineering Chungbuk National University 1 Chungdae-ro Cheongju South Korea

This paper deals with the fault-tolerant control problem for T-S fuzzy systems based on the particle swarm optimization (PSO) method. To cope the stability and fault-tolerants simultaneously, a novel fault-tolerant control algorithm is developed. The required conditions of the addressed system are developed with the aid of Lyapunov stability theory. Precisely, the particle swarm optimization algorithm is implemented to optimize the fuzzy controller. Finally, simulation results are provided to demonstrate the effectiveness of the obtained results.

关键词： Fault tolerance Simulation Fault tolerant systems Reliability theory Stability analysis Takagi-Sugeno model Particle swarm optimization

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Stackelberg POMDP: A Reinforcement Learning Approach for Economic Design

arXiv

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arXiv 2022年

作者： Brero, Gianluca Eden, Alon Chakrabarti, Darshan Gerstgrasser, Matthias Greenwald, Amy Li, Vincent Parkes, David C. Data Science Initiative Brown University United States School of Computer Science and Engineering Hebrew University of Jerusalem Department of Industrial Engineering and Operations Research Columbia University United States John A. Paulson School of Engineering and Applied Sciences Harvard University United States Department of Computer Science Brown University United States

We introduce a reinforcement learning framework for economic design where the interaction between the environment designer and the participants is modeled as a Stackelberg game. In this game, the designer (leader) sets up the rules of the economic system, while the participants (followers) respond strategically. We integrate algorithms for determining followers' response strategies into the leader's learning environment, providing a formulation of the leader's learning problem as a POMDP that we call the Stackelberg POMDP. We prove that the optimal leader's strategy in the Stackelberg game is the optimal policy in our Stackelberg POMDP under a limited set of possible policies, establishing a connection between solving POMDPs and Stackelberg games. We solve our POMDP under a limited set of policy options via the centralized training with decentralized execution framework. For the specific case of followers that are modeled as no-regret learners, we solve an array of increasingly complex settings, including problems of indirect mechanism design where there is turn-taking and limited communication by *** demonstrate the effectiveness of our training framework through ablation *** also give convergence results for no-regret learners to a Bayesian version of a coarse-correlated equilibrium, extending known results to correlated types. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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How graph neural networks learn: lessons from training dynamics 24

How graph neural networks learn: lessons from training dynam...

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Proceedings of the 41st International Conference on Machine Learning

作者： Chenxiao Yang Qitian Wu David Wipf Ruoyu Sun Junchi Yan School of Artificial Intelligence & Department of Computer Science and Engineering & MoE Lab of AI Shanghai Jiao Tong University Amazon Web Services School of Data Science The Chinese University of Hong Kong Shenzhen and Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data

A long-standing goal in deep learning has been to characterize the learning behavior of black-box models in a more interpretable manner. For graph neural networks (GNNs), considerable advances have been made in formalizing what functions they can represent, but whether GNNs will learn desired functions during the optimization process remains less clear. To fill this gap, we study their training dynamics in function space. In particular, we find that the gradient descent optimization of GNNs implicitly leverages the graph structure to update the learned function, as can be quantified by a phenomenon which we call kernel-graph alignment. We provide theoretical explanations for the emergence of this phenomenon in the overparameterized regime and empirically validate it on real-world GNNs. This finding offers new interpretable insights into when and why the learned GNN functions generalize, highlighting their limitations in heterophilic graphs. Practically, we propose a parameter-free algorithm that directly uses a sparse matrix (i.e. graph adjacency) to update the learned function. We demonstrate that this embarrassingly simple approach can be as effective as GNNs while being orders-of-magnitude faster.

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A Fractal-Fractional Model for the MHD Flow of Casson Fluid in a Channel

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computers, Materials & Continua 2021年第5期67卷 1385-1398页

作者： Nadeem Ahmad Sheikh Dennis Ling Chuan Ching Thabet Abdeljawad Ilyas Khan Muhammad Jamil Kottakkaran Sooppy Nisar Fundamental and Applied Science Department Universiti Teknologi PETRONASPerak32610Malaysia Department of Mathematics City University of Science and Information TechnologyPeshawar25000Pakistan Department of Mathematics and General Sciences Prince Sultan University11586RiyadhSaudi Arabia Department of Medical Research China Medical University40402TaichungTaiwanChina Department of Computer Science and Information Engineering Asia UniversityTaichungTaiwanChina Faculty of Mathematics and Statistics Ton Duc Thang UniversityHo Chi Minh City72915Vietnam Department of Geosciences Universiti Teknologi PETRONASPerak32610Malaysia Department of Earth Sciences COMSATS University IslamabadAbbottabad CampusAbbottabad22010Pakistan Department of Mathematics College of Arts and SciencesPrince Sattam bin Abdulaziz UniversityWadi Aldawaser11991Saudi Arabia

An emerging definition of the fractal-fractional operator has been used in this study for the modeling of Casson fluid *** magnetohydrodynamics flow of Casson fluid has cogent in a channel where the motion of the upper plate generates the flow while the lower plate is at a static *** proposed model is non-dimensionalized using the Pi-Buckingham theorem to reduce the complexity in solving the model and computation *** non-dimensional fractal-fractional model with the power-law kernel has been solved through the Laplace transform *** mathcad software has been used for illustration of the influence of various parameters,i.e.,Hartman number,fractal,fractional,and Casson fluid parameters on the velocity of fluid *** graphs and tables,the results have been implemented and it is shown that the boundary conditions are fully *** results reveal that the flow velocity is decreasing with the increasing values of the Hartman number and is increasing with the increasing values of the Casson fluid *** findings of the fractal-fractional model have elucidated that the memory effect of the flow model has higher quality than the simple fractional and classical ***,to show the validity of the obtained closed-form solutions,special cases have been obtained which are in agreement with the already published solutions.

关键词： Fractal-fractional derivative Casson fluid MHD flow exact solutions

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Quantum LSTM Model for Estimation of Energy Expenditure in Human Aging using Wearable IoT Healthcare Technology

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IEEE Internet of Things Journal 2025年

作者： Tran, Bao-Nhi Dang Fahim, Muhammad McNiven, Bradley D. E. Guizani, Mohsen Shin, Hyundong Duong, Trung Q. Memorial University Faculty of Engineering and Applied Science St. John'sNLA1B 3X5 Canada Queen's University Belfast United Kingdom Mohammad Bin Zayed University of Artificial Intelligence Machine Learning Department Abu Dhabi United Arab Emirates Kyung Hee University Department of Electronic Engineering Yongin-si Gyeonggi-do17104 Korea Republic of Memorial University Faculty of Engineering and Applied Science St. John'sNLA1C 5S7 Canada Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom

Physical activity energy expenditure (PAEE) offers significant benefits for general healthcare monitoring and has the potential to promote healthy and active aging for elderly individuals. With recent advancements in quantum information and computation, quantum machine learning (QML) has emerged as a tool capable of improving upon the measurement of PAEE. In this paper, we propose a hybrid QML model to predict PAEE which consists of a classical long short-term memory (LSTM) model integrated with a variational quantum circuit (VQC). This model, which we refer to as the enhanced quantum long short-term memory linear (eQLSTML), was subsequently trained and tested using the publicly available GOTOV Human Physical Activity and Energy Expenditure Dataset for Older Individuals. In particular, we study the proposed eQLSTML model with different gate choices in the quantum circuit along with various embedding and layering techniques. Our results indicate our model to be superior in both performance comparisons and prediction when compared to traditional machine learning methods currently employed. Our findings indicate that combining QML approaches with wearable IoT healthcare devices provides a new avenue for personalized healthcare monitoring and an effective method for promoting healthy aging. © 2025 IEEE.

关键词： Quantum electronics

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Day-ahead inflow forecasting using causal empirical decomposition

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Journal of Hydrology 2022年第PartA期613卷

作者： Yousefi, Mojtaba Cheng, Xiaomei Gazzea, Michele Wierling, August Hubert Rajasekharan, Jayaprakash Helseth, Arild Farahmand, Hossein Arghandeh, Reza Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Science Bergen5063 Norway Smart Innovation Norway Halden1783 Norway Department of Electric Power Engineering Norwegian University of Science and Technology Trondheim7034 Norway Energy systems SINTEF Trondheim7034 Norway

It is essential to have accurate and reliable daily-inflow forecasting to improve short-term hydropower scheduling. This paper proposes a Causal multivariate Empirical mode Decomposition (CED) framework as a complementary pre-processing step for a day-ahead inflow forecasting problem. The idea behind CED is combining physics-based causal inference with signal processing-based decomposition to get the most relevant features among multiple time-series to the inflow values. The CED framework is validated for two areas in Norway with different meteorological and hydrological conditions. The validation results show that using CED as a pre-processing step significantly enhances (up to 70%) the forecasting accuracy for various state-of-the-art forecasting methods. © 2022 The Author(s)

关键词： Forecasting

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Single Loop Control of a Common DC-Bus-Configured Traction Motor Emulator Using State Feedback Linearization Method

Single Loop Control of a Common DC-Bus-Configured Traction M...

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2021 IEEE Southern Power Electronics Conference, SPEC 2021

作者： Kadam, Arvind H. Williamson, Sheldon S. Group Faculty of Engineering and Applied Science Department of Electrical Computer and Software Engineeirng 2000 Simcoe Street North OshawaONL1G 0C5 Canada

ISBN: (纸本)9781665436236

Nowadays, the LCL (inductor-capacitor-inductor) filter is an integral part of every power electronic converter system operating on current control. Though higher order, the LCL filter due to its high harmonic suppression capability are widely adopted in back-to-back converter systems or grid connected converter systems. Normally, with LCL filter, the control system follows double closed loop with outer loop controlling DC voltage and inner loop controlling AC current. However, for some special applications like common DC-bus-configured motor emulator (ME) system, the DC bus voltage is not required to be controlled. In addition, the presence of LCL filter introduces cross coupling of flux and torque component in case of AC motor emulation. The double closed loop control is generally adopted to decouple torque and flux components, however the controller design is more complex process. Therefore, this paper presents a single loop control of a common DC-bus-configured ME system with LCL filter based on state feedback linearization method. The decoupling equations in direct and quadrature axis are derived here. The simulation results presented for a 2.0 kW PMSM motor validate the stability as well as closed loop operation of the system with single loop control. © 2021 IEEE.

关键词： Synchronous motors

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