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System size dependent topological zero modes in coupled topolectrical chains

Physical Review B 2022年第7期106卷 075158-075158页

作者： S. M. Rafi-Ul-Islam Zhuo Bin Siu Haydar Sahin Ching Hua Lee Mansoor B. A. Jalil Department of Electrical and Computer Engineering National University of Singapore Singapore 117583 Institute of High Performance Computing Agency for Science Technology and Research Singapore 138632 Department of Physics National University of Singapore Singapore 117551

In this paper, we demonstrate the emergence and disappearance of topological zero modes (TZMs) in a coupled topolectrical (TE) circuit lattice. Specifically, we consider non-Hermitian TE chains in which TZMs do not occur in the individual uncoupled chains, but emerge when these chains are coupled by interchain capacitors. The coupled system hosts TZMs which show size-dependent behaviors and vanish beyond a certain critical size. In addition, the emergence or disappearance of the TZMs in the open boundary condition spectra for a given size of the coupled system can be controlled by modulating the signs of its inverse decay length. Analytically, trivial and nontrivial phases of the coupled system can be distinguished by the differing ranks of their corresponding Laplacian matrix. The TE circuit framework enables the physical detection of the TZMs via electrical impedance measurements. Our work establishes the conditions for inducing TZMs and modulating their behavior in coupled TE chains.

关键词： Skin effect Topological insulators 1-dimensional systems Non-Hermitean systems Bloch wave theory Green's function methods Tight-binding model

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A Digital 3D TCAM Accelerator for the Inference Phase of Random Forest 23

A Digital 3D TCAM Accelerator for the Inference Phase of Ran...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Chieh-Lin Tsai Chun-Feng Wu Yuan-Hao Chang Han-Wen Hu Yung-Chun Lee Hsiang-Pang Li Tei-Wei Kuo Department of Computer Science and Information Engineering National Taiwan University Department of Computer Science National Yang Ming Chiao Tung University Institute of Information Science Academia Sinica Taiwan Macronix International Co. Ltd and Department of Electrical Engineering National Tsing Hua University Macronix International Co. Ltd Department of Computer Science and Information Engineering National Taiwan University and Mohamed Bin Zayed University of Artificial Intelligence and NTU High Performance and Scientific Computing Center National Taiwan University

ISBN: (纸本)9798350323481

Random forest is a popular ensemble machine-learning algorithm for classification and regression tasks. However, the irregular tree shapes and non-deterministic memory access patterns make it hard for the current von Neumann architecture to handle random forest efficiently. This paper proposes a digital 3D TCAM-based accelerator for the random forest, adopting the idea of processing-in-memory (PIM) to reduce data movement. By utilizing this accelerator, we propose a TCAM-based approach to provide real-time inference with low energy consumption, making it suitable for edge or embedded environments. In the experiments, the proposed approach achieves an average of 3.13 times higher throughput with 22 times more energy saving than the GPU approach.

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FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection

FishEye8K: A Benchmark and Dataset for Fisheye Camera Object...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Gochoo, Munkhjargal Otgonbold, Munkh-Erdene Ganbold, Erkhembayar Hsieh, Jun-Wei Chang, Ming-Ching Chen, Ping-Yang Dorj, Byambaa Al Jassmi, Hamad Batnasan, Ganzorig Alnajjar, Fady Abduljabbar, Mohammed Lin, Fang-Pang United Arab Emirates University Department of Computer Science and Software Engineering United Arab Emirates United Arab Emirates University Emirates Center for Mobility Research United Arab Emirates National Yang Ming Chiao Tung University College of AI and Green Energy Taiwan University at Albany - State University of New York NY United States National Yang Ming Chiao Tung University Department of Computer Science Taiwan Mongolian University of Science and Technology Mongolia National Center for High-Performance Computing Taiwan

ISBN: (纸本)9798350302493

With the advance of AI, road object detection has been a prominent topic in computer vision, mostly using perspective cameras. Fisheye lens provides omnidirectional wide coverage for using fewer cameras to monitor road intersections, however with view distortions. To our knowledge, there is no existing open dataset prepared for traffic surveillance on fisheye cameras. This paper introduces an open FishEye8K benchmark dataset for road object detection tasks, which comprises 157K bounding boxes across five classes (Pedestrian, Bike, Car, Bus, and Truck). In addition, we present benchmark results of State-of-The-Art (SoTA) models, including variations of YOLOv5, YOLOR, YOLO7, and YOLOv8. The dataset comprises 8,000 images recorded in 22 videos using 18 fisheye cameras for traffic monitoring in Hsinchu, Taiwan, at resolutions of 1080×1080 and 1280×1280. The data annotation and validation process were arduous and time-consuming, due to the ultra-wide panoramic and hemispherical fisheye camera images with large distortion and numerous road participants, particularly people riding scooters. To avoid bias, frames from a particular camera were assigned to either the training or test sets, maintaining a ratio of about 70:30 for both the number of images and bounding boxes in each class. Experimental results show that YOLOv8 and YOLOR outperform on input sizes 640×640 and 1280×1280, respectively. The dataset will be available on the GitHub link with PASCAL VOC, MS COCO, and YOLO annotation formats. The FishEye8K benchmark will provide significant contributions to the fisheye video analytics and smart city applications. © 2023 IEEE.

关键词： Object detection

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SpaceCube GHOST: A Resilient Processor for Low-Power, high-Reliability Space computing

SpaceCube GHOST: A Resilient Processor for Low-Power, High-R...

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IEEE Conference on Aerospace

作者： Noah Perryman Nicholas Franconi Gary Crum Christopher Wilson Alan D. George Department of Electrical and Computer Engineering NSF Center for Space High-Performance and Resilient Computing (SHREC) University of Pittsburgh Pittsburgh PA Science Data Processing Branch Code 587 Software Engineering Division NASA Goddard Space Flight Center Greenbelt MD

There is a direct need for high-visibility NASA missions that provide significant scientific impact or have a high mission class using completely radiation-hardened (rad-hard) electronics solutions, to enable artificial intelligence (AI) applications in harsh environments despite severe size, weight, and power (SWaP) constraints. For these missions, where current state-of-the-art solutions are too power-demanding or are incapable of surviving the intended radiation environment, an alternative rad-hard processing architecture that can leverage the control-flow capabilities of scalar processors while also incorporating the hardware-acceleration capabilities of an FPGA is of significant value. Therefore, in this research, we propose a miniaturized (3.5 in. × 3.5 in. form factor) processor card featuring the GR740 quad-core rad-hard processor and the CertusPro-NX-RT radiation-tolerant FPGA, called the SpaceCube GR740 Host for Onboard science and Telemetry (GHOST) architecture. The card will be designed to conform to the NASA Goddard Space Flight Center (GSFC) CubeSat Card Specification (CS2), which provides a common template to build new 1U CubeSat-sized cards compatible with a variety of other avionics designs. The GR740 features a fault-tolerant quad- processor LEON4FT SPARC V8 integer unit with a 7-stage pipeline and 4×4 KiB instruction and data caches. To extend the capabilities of the GR740, the CertusPro-NX-RT, a low-power radiation-tolerant FPGA, was combined to create a hybrid system architecture, providing the benefits of a programmable logic fabric. The GHOST architecture supports high-reliability, general-purpose processing and system monitoring via the GR740 while simultaneously increasing the AI-based application performance via a combination of the GR740 and CertusPro-NX-RT.

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Deep Neural Cellular Potts Models

arXiv

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arXiv 2025年

作者： Minartz, Koen d’Hondt, Tim Hillmann, Leon Starruß, Jörn Brusch, Lutz Menkovski, Vlado Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands Department of Applied Physics and Science Education Eindhoven University of Technology Eindhoven Netherlands Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands Center for Information Services and High Performance Computing TUD Dresden University of Technology Dresden Germany

The cellular Potts model (CPM) is a powerful computational method for simulating collective spatiotemporal dynamics of biological cells. To drive the dynamics, CPMs rely on physics-inspired Hamiltonians. However, as first principles remain elusive in biology, these Hamiltonians only approximate the full complexity of real multicellular systems. To address this limitation, we propose NeuralCPM, a more expressive cellular Potts model that can be trained directly on observational data. At the core of NeuralCPM lies the Neural Hamiltonian, a neural network architecture that respects universal symmetries in collective cellular dynamics. Moreover, this approach enables seamless integration of domain knowledge by combining known biological mechanisms and the expressive Neural Hamiltonian into a hybrid model. Our evaluation with synthetic and real-world multicellular systems demonstrates that NeuralCPM is able to model cellular dynamics that cannot be accounted for by traditional analytical Hamiltonians. Copyright © 2025, The Authors. All rights reserved.

关键词： Potts model

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Improving Software Defined Network Controllers in a Multi-Vendor Environment

SSRN

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SSRN 2023年

作者： Deneke, Bereketeab Berhanu Beyene, Asrat Mulatu Haile, Endalew Ayenew Department of Computer Science School of Graduate Studies St. Mary’s University Addis Ababa Ethiopia High Performance Computing & Big Data Analytics CoE Department of Electrical and Computer Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia Adama Science and Technology University School of Electrical Engineering and Computing Electrical Power and Control Engineering Adama Ethiopia

Software Defined Networking (SDN) is an emerging networking paradigm with the potential to foster innovation through programmable networks. SDNs are characterized by the separation of control and data planes where, in a logically centralized controller, it’s possible to make routing decisions on behalf of forwarding elements. For this, there are different protocols used like Open Flow, Forward and Control Element Separation (FORCES), Path Computation Element Protocol (PCEP), Network Configuration Protocol (NETCONF), and Interface to Routing System (IRS). Border Gateway Protocol (BGP) is one such protocol that can enable the network programmability promised by SDNs. The controllers used in BGP as control plane protocol leverages RESTCONF (RFC 8040) as management plane protocol to interact with physical routers, switches, and network service firewalls. Therefore, the aim of this work is to improve the performance of SDN controllers using BGP enabling a multi-vendor environment without requiring infrastructure upgrade showcasing how interoperability can be achieved. The proposed system is implemented using OpenDaylight controllers. The performance of the prototype is evaluated using Latency, Throughput, CPU consumption, and TTL (Time to Live). Based on the simulation results it is observed that the SDN controllers that use BGP show improvements in terms of Latency and Throughput. © 2023, The Authors. All rights reserved.

关键词： Controllers

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A K-Nearest Centroid Neighbor with atention Classifier 5

A K-Nearest Centroid Neighbor with atention Classifier

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5th International Conference on computer science and Artificial Intelligence, CSAI 2021

作者： Huang, Rui Ma, Ying Wang, Tian Li, GuoQi Yan, Ming Department of Computer and Information Engineering Xiamen University of Technology China Institute of High Performance Computing Agency for Science Technology and Research Singapore Singapore School of Information Science and Engineering Hunan First Normal University China Department of Precision Instrument Center for Brain Inspired Computing Research

ISBN: (纸本)9781450384155

Among classic algorithms of data mining, the K-nearest neighbor based methods are simple and effective pattern classification algorithms. However, most KNN-based methods do not fully take into account the impact of different training sample points on classification, lead to inaccurate classification. To address this issue, we propose a scheme named Attention-based local mean K-Nearest Centroid Neighbor Classifier (ALMKNCN), combining nearest centroid neighbor with attention mechanism, the influence of each training sample on the query sample is fully considered. Given the query pattern, we first calculate the local centroid mean vector for each class, and then use the idea of attention mechanism to calculate the weight of pseudo-distance between each class and test sample. Finally, based on attention coefficient, the distances between the query sample and local mean vectors are weighted to determine the class of the query sample. Extensive experiments on UCI and KEEL data sets are carried out by comparing ALMKNCN to the state-of-art KNN-based methods. The experimental results demonstrate that the proposed ALMKNCN outperforms the related competitive KNN-based methods with more effectiveness. © 2021 ACM.

关键词： Data mining

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Online Demand Peak Shaving with Machine-Learned Advice in Cyber-Physical Energy Systems

Online Demand Peak Shaving with Machine-Learned Advice in Cy...

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IEEE International Symposium on Dependable, Autonomic and Secure computing (DASC)

作者： Minxi Fengl Wei Li Boyu Qin Albert Y. Zomaya Centre for Distributed and High Performance Computing School of Computer Science The University of Sydney Australia Department of Electrical Engineering State Key Laboratory of Electrical Insulation and Power Equipment Xi'an Jiaotong University China

With the deep integration of cyber tools, control algorithms are increasingly employed in cyber-physical energy systems to enhance management, cost efficiency, and robustness. Effective demand load management is crucial in cyber-physical energy systems as it directly impacts operational costs. Failure to adequately manage spiky or seasonal demand loads can lead to significant expenses on monthly utility bills. In this study, we propose AMPAMOD, a randomized online algorithm with machine-learned advice, to optimize battery operations and mitigate highly dynamic peak loads. AMPAMOD utilizes limited advice from machine learning algorithms to guide our online algorithm and ensure cost-effective peak load management. The theoretical analysis shows that our solution has minimal advice complexity, a linear computational cost, and an improved competitive ratio. Finally, we conduct extensive trace-driven experiments on real-world datasets. AMPAMOD achieves a peak shaving success rate of over 90%, outperforming baselines by at least 50%. These experimental results confirm theoretical findings and demonstrate the potential of AMPAMOD.

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Critical hybridization of skin modes in coupled non-Hermitian chains

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Physical Review Research 2022年第1期4卷 013243-013243页

作者： S. M. Rafi-Ul-Islam Zhuo Bin Siu Haydar Sahin Ching Hua Lee Mansoor B. A. Jalil Department of Electrical and Computer Engineering National University of Singapore Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore Department of Physics National University of Singapore Singapore

Non-Hermitian topological systems exhibit a plethora of unusual topological phenomena including the extreme localization of eigenstates, known as the non-Hermitian skin effect (NHSE), which occurs in open chains. However, the non-Hermitian characteristics of coupled non-Hermitian chains remain largely unexplored. Here, we report on the eigenstate localization in coupled non-Hermitian chains with dissimilar inverse skin lengths in which the NHSE can be switched on and off by modulating the interchain coupling strength. In the limit of small interchain strength, the NHSE is present at both ends of the coupled system because of the weak hybridization of the eigenstates of the individual chains. The eigenspectrum under open boundary conditions (OBC) exhibits a discontinuous jump known as the critical NHSE (CNHSE) as the chain length increases. However, when the interchain coupling strength and hence, the hybridization between eigenstates become significant, the NHSE and CNHSE vanish. Instead, a peculiar “half-half skin localization” occurs in composite chains with opposite signs of inverse skin lengths, where half of the eigenstates are exponentially localized at one chain and the remainder of the eigenstates on the other chain. Our results provide new insights into the non-Hermitian phenomena in coupled systems.

关键词： Electronic structure Exotic phases of matter Skin effect Topological insulators Topological phase transition Topological phases of matter 1-dimensional systems Topological materials Band structure methods Bloch wave theory Lattice models in condensed matter Tight-binding model

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Quality Assessment of Screen Content Videos

Quality Assessment of Screen Content Videos

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International Conference on Pattern Recognition and Image Analysis (IPRIA)

作者： Hossein Motamednia Pooryaa Cheraaqee Azadeh Mansouri Ahmad Mahmoudi-Aznaveh Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Cyber Research Institute Shahid Beheshti University Tehran Iran

Perceptual quality assessment has always been challenging due to the difficulty in modeling the no-linear human visual system. With the diversity in the contents of multimedia signals, the conventional methods for traditional media seems no longer satisfying. One of these emerging media, is the screen content images/videos (SCINs), Containing texts and computer generated graphics, SCVs cannot be sufficiently expressed with features designed for natural sceneries. Therefore, new researches tried to devise objective quality assessment metrics, specificly for screen contents. Recently, a dataset was proposed for quality assessment of screen content videos. Since screen contents are full of structures that spread in cardinal directions, we were motivated to employ the horizontal and vertical subbands of the wavelet transform to characterize these types of visual contents. The features were incorporated in a full-reference method that showed promising results on the publicly available dataset for SCV quality assessment. The method can bo accessed via: https://***/motamedNia/QASCV.

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