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Smart World Congress (SWC), IEEE

作者： Feiran You Hongyang Du Jiawen Kang Wei Ni Dusit Niyato Abbas Jamalipour School of Electrical and Computer Engineering The University of Sydney Sydney NSW Australia Data61 Commonwealth Scientific and Industrial Research Organization (CSIRO) Sydney NSW Australia Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong School of Automation Guangdong University of Technology China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

The rapid growth of Artificial Intelligence-Generated Content (AIGC) services has led to increased mobile user participation in related computations and interactions. This development has enabled AI-generated characters to interact with Virtual Reality (VR) users in real time, making the VR experience more interactive and personalized. In this paper, we consider an MEC system where VR users engage in AIGC services, focusing on the Generative Diffusion Model (GDM)based image generation tasks. Specifically, VR users initiate requests for computing resources, while computation offloading distributes the processing load across the MEC system. To manage AIGC edge computation offloading and cloudlet-VR user connections jointly, a Data Center Operator (DCO) employs a centralized Proximal Policy Optimization (PPO) algorithm. To protect VR users’ privacy while preserving PPO functionality, we employ the Generative Diffusion Model (GDM), specifically the Denoising Diffusion Implicit Model (DDIM), which first introduces noise to the PPO state, then conducts a denoising process to recover the state information. We further employ Inverse Reinforcement Learning (IRL) to infer rewards for the recovered states, using expert demonstrations trained by the PPO. The similarity between PPO-generated rewards and IRL-inferred rewards is then computed. Simulation results demonstrate that our proposed approach successfully achieves computation offloading while protecting VR users’ privacy within the PPO centralized management framework.

关键词： Privacy Solid modeling Simulation Noise reduction Noise Virtual reality Reinforcement learning Diffusion models Protection Optimization

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Minimal Model of Cellular Symmetry Breaking

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Physical Review Letters 2019年第18期123卷 188101-188101页

作者： Alexander Mietke V. Jemseena K. Vijay Kumar Ivo F. Sbalzarini Frank Jülicher Max Planck Institute for the Physics of Complex Systems 01187 Dresden Germany Chair of Scientific Computing for Systems Biology Faculty of Computer Science TU Dresden 01187 Dresden Germany Center for Systems Biology Dresden 01307 Dresden Germany Max Planck Institute of Molecular Cell Biology and Genetics 01307 Dresden Germany International Centre for Theoretical Sciences Tata Institute of Fundamental Research 560 089 Bengaluru India Cluster of Excellence Physics of Life TU Dresden 01307 Dresden Germany

The cell cortex, a thin film of active material assembled below the cell membrane, plays a key role in cellular symmetry-breaking processes such as cell polarity establishment and cell division. Here, we present a minimal model of the self-organization of the cell cortex that is based on a hydrodynamic theory of curved active surfaces. Active stresses on this surface are regulated by a diffusing molecular species. We show that coupling of the active surface to a passive bulk fluid enables spontaneous polarization and the formation of a contractile ring on the surface via mechanochemical instabilities. We discuss the role of external fields in guiding such pattern formation. Our work reveals that key features of cellular symmetry breaking and cell division can emerge in a minimal model via general dynamic instabilities.

关键词： Biological fluid dynamics Cell division Cell polarity Cell processes & properties Self-organization Active liquid films Active matter

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A Lightweight Feature Selection Method Based on Rankability

SSRN

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SSRN 2024年

作者： Kong, Lingping Velasquez, Juan D. Perfilieva, Irina Pant, Millie Pan, Jeng-Shyang Snášel, Václav Department of Computer Science VSB-Technical University of Ostrava Ostrava Czech Republic Department of Industrial Engineering University of Chile Santiago Chile Santiago Chile Institute for Research and Applications of Fuzzy Modeling Centre of Excellence IT4Innovations division of The University of Ostrava Ostrava Czech Republic Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Uttarakhand Roorkee247667 India School of Artificial Intelligence Nanjing University of Information Science and Technology China Instituto Sistemas Complejos de Ingeniería Santiago Chile Santiago Chile Faculty of Computer Science and Telecommunications Cracow University of Technology Kraków Poland Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Uttarakhand Roorkee247667 India College of Computer Science and Engineering Shandong University of Science and Technology China

Within the realm of data mining and machine learning, feature selection stands out as an essential technique for dimensionality reduction, presenting both popularity and complexity. Feature selection involves a nuanced trade-off that results in information loss. This distinction underscores the necessity for the development of lightweight, efficient, and practical methods capable of minimizing information loss while executing dimensional reduction. In this paper, we introduce a rankability-based feature selection method. The concept of rankability, proposed in 2019 and similar to the entropy concept, remains relatively unexplored. Our proposed method distinguishes itself by its lightweight complexity, eliminating the need for iterative *** evaluate the effectiveness of our approach, we conducted experiments on forty-one datasets and compared the results with four other algorithms using refined data with 10\%, 30\%, and 50\% feature dimension reduction. The findings reveal that our rankability-based feature selection method consistently outperforms the other four methods across a majority of datasets in terms of accuracy, precision, recall, and F1-macro performance. Specifically, the classification score exceeds 0.9 on accuracy for 50\% feature dimension reduction in twelve datasets, with nine datasets achieving scores over 0.9 in recall, F1-macro, and precision. Overall, our proposed feature selection method demonstrates promising performance. © 2024, The Authors. All rights reserved.

关键词： Feature Selection

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A non-perturbative approach to computing seismic normal modes in rotating planets

arXiv

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arXiv 2019年

作者： Shi, Jia Li, Ruipeng Xi, Yuanzhe Saad, Yousef de Hoop, Maarten V. Department of Earth Environmental and Planetary Sciences Rice University TX United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory CA United States Department of Mathematics Emory University AtlantaGA United States Department of Computer Science and Engineering University of Minnesota MN United States Department of Computational and Applied Mathematics Rice University TX United States

MSC Codes 86-08, 86-04, 85-04, 85-08, 85-10, 15A18, 65N25, 65N30A Continuous Galerkin method based approach is presented to compute the seismic normal modes of rotating planets. Special care is taken to separate out the essential spectrum in the presence of a fluid outer core using a polynomial filtering eigensolver. The relevant elastic-gravitational system of equations, including the Coriolis force, is subjected to a mixed finite-element method, while self-gravitation is accounted for with the fast multipole method. Our discretization utilizes fully unstructured tetrahedral meshes for both solid and fluid regions. The relevant eigenvalue problem is solved by a combination of several highly parallel and computationally efficient methods. We validate our three-dimensional results in the non-rotating case using analytical results for constant elastic balls, as well as numerical results for an isotropic Earth model from standard "radial" algorithms. We also validate the computations in the rotating case, but only in the slowly-rotating regime where perturbation theory applies, because no other independent algorithms are available in the general case. The algorithm and code are used to compute the point spectra of eigenfrequencies in several Earth and Mars models studying the effects of heterogeneity on a large range of scales. Copyright © 2019, The Authors. All rights reserved.

关键词： Seismology

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Weak Devices Matter: Model Heterogeneity in Resource-Constrained Federated Learning

Weak Devices Matter: Model Heterogeneity in Resource-Constra...

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IEEE International Conference on Communications (ICC)

作者： Tzu–Hsuan Peng Cheng-Wei Huang Ai-Chun Pang Te-Chuan Chiu Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan R.O.C Graduate Institute of Networking and Multimedia National Taiwan University Taipei Taiwan R.O.C High Performance and Scientific Computing Center National Taiwan University Taipei Taiwan R.O.C Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan R.O.C Department of Computer Science National Tsing Hua University Hsinchu Taiwan R.O.C

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Federated Learning (FL) is a promising framework for enabling edge intelligence while preserving privacy and enhancing communication efficiency. In real-world scenarios, limited and heterogeneous hardware resources among users threaten the overall training efficiency in FL. To address this issue, several model heterogeneity frameworks have emerged in recent years. However, existing studies primarily focus on mitigating idle times on powerful devices, overlooking the fact that strong devices may not always have a positive impact and can potentially become a burden instead. Furthermore, as the resource gap among clients widens or the number of weak devices increases, the model may be hindered by the influence of strong devices. Directly applying existing model heterogeneity frameworks leads to model performance degradation due to the neglect of contributions from weak devices. To make a performance breakthrough, we proposed a novel FL framework called Progressive Width Mixing (PWM), which extracts diverse features and balances the contribution of strong and weak devices and mitigates straggler effects under scenarios with various device distributions. To our best knowledge, we are the first model heterogeneity work that considered straggler effects from the weak devices' point of view. The experiments show that PWM maintains stability over different datasets and outperforms all baselines across various device distributions and non-IID scenarios. PWM makes a leap forward and surpasses all baselines by 7%, and mitigates the negative impact from the strong devices.

关键词： Performance evaluation Training Privacy Philosophical considerations Federated learning Pulse width modulation System recovery

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Current Studies and Applications of Shuffled Frog Leaping Algorithm: A Review

arXiv

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arXiv 2022年

作者： Maaroof, Bestan B. Rashid, Tarik A. Abdulla, Jaza M. Hassan, Bryar A. Alsadoon, Abeer Mohammadi, Mokhtar Khishe, Mohammad Mirjalili, Seyedali Information Technology Department College of Commerce University of Sulaimani KRI Iraq Computer Science and Engineering University of Kurdistan Hewler KRI Erbil Iraq Computer Science Department College of Science Komar University of Science and Technology KRI Sulaymaniyah Iraq Information Technology College of Commerce University of Sulaimani KRI Sulaymaniyah Iraq Kurdistan Institution for Strategic Studies and Scientific Research Sulaimani Iraq School of Computing and Mathematics Charles Sturt University Sydney Australia Information Technology Department Sydney Australia Department of Information Technology College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq Department of Marine Electronics and Communication Engineering Imam Khomeini Marine Science University Nowshahr Iran Centre for Artificial Intelligence Research and Optimization Torrens University Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of

Shuffled Frog Leaping Algorithm (SFLA) is one of the most widespread algorithms. It was developed by Eusuff and Lansey in 2006. SFLA is a population-based metaheuristic algorithm that combines the benefits of memetics with particle swarm optimization. It has been used in various areas, especially in engineering problems due to its implementation easiness and limited variables. Many improvements have been made to the algorithm to alleviate its drawbacks, whether they were achieved through modifications or hybridizations with other well-known algorithms. This paper reviews the most relevant works on this algorithm. An overview of the SFLA is first conducted, followed by the algorithm's most recent modifications and hybridizations. Next, recent applications of the algorithm are discussed. Then, an operational framework of SLFA and its variants is proposed to analyze their uses on different cohorts of applications. Finally, future improvements to the algorithm are suggested. The main incentive to conduct this survey to provide useful information about the SFLA to researchers interested in working on the algorithm's enhancement or application. © 2022, CC BY.

关键词： Particle swarm optimization (PSO)

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Quantum-parallel vectorized data encodings and computations on trapped-ions and transmons QPUs

arXiv

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arXiv 2023年

作者： Balewski, Jan Amankwah, Mercy G. Van Beeumen, Roel Bethel, E. Wes Perciano, Talita Camps, Daan National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA United States Department of Mathematics Case Western Reserve University ClevelandOH United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA United States Computer Science Department San Francisco State University San FranciscoCA United States Scientific Data Division Lawrence Berkeley National Laboratory BerkeleyCA United States

Compact quantum data representations are essential to the emerging field of quantum algorithms for data analysis. We introduce two new data encoding schemes, QCrank and QBArt, which have a high degree of quantum parallelism through uniformly controlled rotation gates. QCrank encodes a sequence of real-valued data as rotations of the data qubits, allowing for high storage density. QBArt directly embeds a binary representation of the data in the computational basis, requiring fewer quantum measurements and lending itself to well-understood arithmetic operations on binary data. We present several applications of the proposed encodings for different types of data. We demonstrate quantum algorithms for DNA pattern matching, Hamming weight calculation, complex value conjugation, and retrieving an O(400) bits image, all executed on the Quantinuum QPU. Finally, we use various cloud-accessible QPUs, including IBMQ and IonQ, to perform additional benchmarking experiments. Copyright © 2023, The Authors. All rights reserved.

关键词： Digital storage

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High-order semi-Lagrangian kinetic scheme for compressible turbulence

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Physical Review E 2021年第2期104卷 025301-025301页

作者： Dominik Wilde Andreas Krämer Dirk Reith Holger Foysi Department of Mechanical Engineering University of Siegen Paul-Bonatz-Straße 9-11 57076 Siegen-Weidenau Germany Institute of Technology Resource and Energy-efficient Engineering (TREE) Bonn-Rhein-Sieg University of Applied Sciences Grantham-Allee 20 53757 Sankt Augustin Germany Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 6 14195 Berlin Germany Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) Schloss Birlinghoven 53754 Sankt Augustin Germany

Turbulent compressible flows are traditionally simulated using explicit time integrators applied to discretized versions of the Navier-Stokes equations. However, the associated Courant-Friedrichs-Lewy condition severely restricts the maximum time-step size. Exploiting the Lagrangian nature of the Boltzmann equation's material derivative, we now introduce a feasible three-dimensional semi-Lagrangian lattice Boltzmann method (SLLBM), which circumvents this restriction. While many lattice Boltzmann methods for compressible flows were restricted to two dimensions due to the enormous number of discrete velocities in three dimensions, the SLLBM uses only 45 discrete velocities. Based on compressible Taylor-Green vortex simulations we show that the new method accurately captures shocks or shocklets as well as turbulence in 3D without utilizing additional filtering or stabilizing techniques other than the filtering introduced by the interpolation, even when the time-step sizes are up to two orders of magnitude larger compared to simulations in the literature. Our new method therefore enables researchers to study compressible turbulent flows by a fully explicit scheme, whose range of admissible time-step sizes is dictated by physics rather than spatial discretization.

关键词： Compressible flows Kinetic theory Turbulence Lattice-Boltzmann methods Navier-Stokes equation

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Stochastic Optimisation Framework using the Core Imaging Library and Synergistic Image Reconstruction Framework for PET Reconstruction

arXiv

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arXiv 2024年

作者： Papoutsellis, Evangelos da Costa-Luis, Casper Deidda, Daniel Delplancke, Claire Duff, Margaret Fardell, Gemma Gillman, Ashley Jorgensen, Jakob S. Kereta, Zeljko Ovtchinnikov, Evgueni Pasca, Edoardo Schramm, Georg Thielemans, Kris Finden Ltd Rutherford Appleton Laboratory Harwell Campus United Kingdom Scientific Computing Department Science & Technology Facilities Council Harwell Campus United Kingdom National Physical Laboratory Electricité de France Research and Development France Australian e-Health Res. Ctr. CSIRO BrisbaneQLD Australia Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Department of Computer Science University College London United Kingdom Department of Imaging and Pathology Division of Nuclear Medicine KU Leuven Leuven Belgium Institute of Nuclear Medicine University College London United Kingdom

We introduce a stochastic framework into the open- source Core Imaging Library (CIL) which enables easy development of stochastic algorithms. Five such algorithms from the literature are developed, Stochastic Gradient Descent, Stochastic Average Gradient (-Amélioré), (Loopless) Stochastic Variance Reduced Gradient. We showcase the functionality of the framework with a comparative study against a deterministic algorithm on a simulated 2D PET dataset, with the use of the open-source Synergistic Image Reconstruction Framework. We observe that stochastic optimisation methods can converge in fewer passes of the data than a standard deterministic algorithm1. © 2024, CC BY.

关键词： Positron emission tomography

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Predicting the electronic properties of 3D, million-atom semiconductor nanostructure architectures

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Journal of Physics: Conference Series 2006年第1期46卷

作者： A Zunger A Franceschetti G Bester W B Jones Kwiseon Kim P A Graf L-W Wang A Canning O Marques C Voemel J Dongarra J Langou S Tomov Materials Science Center NREL USA Scientific Computing Center NREL USA Computational Research Division LBNL USA Department of Computer Science University of Tennessee USA

The past ~10 years have witnessed revolutionary breakthroughs both in synthesis of quantum dots (leading to nearly monodispersed, defect-free nanostructures) and in characterization of such systems, revealing ultra narrow spectroscopic lines of <1 meV width, exposing new intriguing effects, such as multiple exciton generation, fine-structure splitting, quantum entanglement, multiexciton recombination and more. These discoveries have led to new technological applications including quantum computing and ultra-high efficiency solar cells. Our work in this project is based on two realizations/observations: First, that the dots exhibiting clean and rich spectroscopic and transport characteristics are rather big. Indeed, the phenomenology indicated above is exhibited only by the well-passivated defect-free quantum dots containing at least a few thousand atoms (colloidal) and even a few hundred thousand atoms (self assembled). Understanding the behavior of nanotechnology devices requires the study of even larger, million-atom systems composed of multiple components such as wires+dots+films. Second, first-principles many-body computational techniques based on current approaches (Quantum Monte-Carlo, GW, Bethe-Salpeter) are unlikely to be adaptable to such large structures and, at the same time, the effective mass-based techniques are too crude to provide insights on the many-body/atomistic phenomenology revealed by experiment. Thus, we have developed a set of methods that use an atomistic approach (unlike effective-mass based techniques) and utilize single-particle + many body techniques that are readily scalable to ~103−106 atom nanostructures. New mathematical and computational techniques have also been developed to accelerate our calculations and go beyond simple conjugate gradient based methods allowing us to study larger systems. In this short paper based on a poster presented at the DOE SciDAC06 conference we will present the overall structure as well as highlights of

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