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4th International Conference on Intelligent Systems and Pattern Recognition, ISPR 2024

作者： Sehairi, Kamal Bouhafs, Abdelkader Boucherit, Ibtissam Mama Bouwmans, Thierry Chouireb, Fatima Department of Physics Laboratory of Applied Sciences and Didactics École Normale Supérieure de Laghouat Laghouat Algeria Department of Electronics Telecommunication Signals and Systems Laboratory University Amar Telidji of Laghouat Laghouat Algeria Department of Computer Science Lab. Mathématiques Image et Applications Université de La Rochelle La Rochelle France

ISBN: (纸本)9783031821523

Identifying and locating objects in images and videos, including elements like traffic signs, vehicles, buildings, and people, constitutes a fundamental and demanding task in computer vision, known as object detection. Due to the higher computing complexity of this technique and the large amount of data carried by the video signal, it is nearly impossible for ordinary general-purpose processors GPPs or CPUs to run these techniques in real-time, especially for embedded systems applications. Therefore, special hardware that can acquire, control, or execute in parallel is required. These specialized hardware systems include Digital Signal Processors DSPs, Field Programmable Gate Arrays FPGAs, Visual Processing Units VPUs, Tensor Processing Units TPUs, Neural Processing Units NPUs or Graphics Processing Units GPUs. This work presents the benefits of accelerating traditional object detection methods on a high-end embedded system, the Jetson Nano Developer Kit. This single computer board is equipped with the Tegra K1 System on Chip SoC, which is composed of a quad-core ARM A15 and 192 cores of Kepler-embedded GPU. Computing acceleration was ensured via the use of the CUDA OpenCV library for both the Histogram of Oriented Gradients HOG and the Haar Cascade Classifier. For VGA resolution, results reveal that the GPU implementation on this embedded system is 1.4× faster than the CPU for the HOG method and 2× for the Haar Cascade Classifier method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： System-on-chip

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Qudit Dynamical Decoupling on a Superconducting Quantum Processor

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Physical Review Letters 2025年第5期134卷 050601-050601页

作者： Vinay Tripathi Noah Goss Arian Vezvaee Long B. Nguyen Irfan Siddiqi Daniel A. Lidar Department of Physics and Astronomy University of Southern California Los Angeles California 90089 USA Center for Quantum Information Science and Technology University of Southern California Los Angeles California 90089 USA Quantum Nanoelectronics Laboratory Department of Physics University of California at Berkeley Berkeley California 94720 USA Applied Math and Computational Research Division Lawrence Berkeley National Lab Berkeley California 94720 USA Department of Electrical and Computer Engineering University of Southern California Los Angeles California 90089 USA Department of Chemistry University of Southern California Los Angeles California 90089 USA

Multilevel qudit systems are increasingly being explored as alternatives to traditional qubit systems due to their denser information storage and processing potential. However, qudits are more susceptible to decoherence than qubits due to increased loss channels, noise sensitivity, and crosstalk. To address these challenges, we develop protocols for dynamical decoupling (DD) of qudit systems based on the Heisenberg-Weyl group. We implement and experimentally verify these DD protocols on a superconducting transmon processor that supports qudit operation based on qutrits (d=3) and ququarts (d=4). Specifically, we demonstrate single-qudit DD sequences to decouple qutrits and ququarts from system-bath-induced decoherence. We also introduce two-qudit DD sequences designed to suppress the detrimental cross-Kerr couplings between coupled qudits. This allows us to demonstrate a significant improvement in the fidelity of time-evolved qutrit Bell states. Our results highlight the utility of leveraging DD to enable scalable qudit-based quantum computing.

关键词： Qubits

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Data-Driven Methods for Weather Forecast 21st

Data-Driven Methods for Weather Forecast

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21st International Conference on Computational science, ICCS 2021

作者： Nino-Ruiz, Elias David Acevedo García, Felipe J. Applied Math and Computer Science Lab Computer Science Department Universidad del Norte Colombia Barranquilla Colombia

ISBN: (纸本)9783030779696

In this paper, we propose efficient and practical data-driven methods for weather forecasts. We exploit the information brought by historical weather datasets to build machine-learning-based models. These models are employed to produce numerical forecasts, which can be improved by injecting additional data via data assimilation. Our approaches’ general idea is as follows: given a set of time snapshots of some dynamical system, we group the data by time across multiple days. These groups are employed to build first-order Markovian models that reproduce dynamics from time to time. Our numerical models’ precision can be improved via sequential data assimilation. Experimental tests are performed by using the National-Centers-for-Environmental-Prediction Department-of-Energy Reanalysis II dataset. The results reveal that numerical forecasts can be obtained within reasonable error magnitudes in the L2 norm sense, and even more, observations can improve forecasts by order of magnitudes, in some cases. © 2021, Springer Nature Switzerland AG.

关键词： Dynamical systems

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Efficient oblivious transfer protocols

Efficient oblivious transfer protocols

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2001 Operating Section Proceedings, American Gas Association

作者： Naor, Moni Pinkas, Benny Dept. of Computer Science and Applied Math Weizmann Institute of Science Rehovot Israel STAR Lab. Intertrust Technologies

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HiPoNet: A Topology-Preserving Multi-View Neural Network For High Dimensional Point Cloud and Single-Cell Data

arXiv

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arXiv 2025年

作者： Viswanath, Siddharth Madhu, Hiren Bhaskar, Dhananjay Kovalic, Jake Johnson, Dave Ying, Rex Tape, Christopher Adelstein, Ian Perlmutter, Michael Krishnaswamy, Smita Department of Computer Science Yale University New Haven United States Department of Genetics Yale University New Haven United States Department of Applied Physics Yale University New Haven United States Program in Computing Boise State University Idaho United States Cell Communication Lab Department of Oncology University College London Cancer Institute London United Kingdom Department of Mathematics Yale University New Haven United States Department of Mathematics Boise State University Idaho United States Computational Biology and Bioinformatics Program Yale University New Haven United States Wu-Tsai Institute Yale University New Haven United States Program for Applied Math Yale University New Haven United States

In this paper, we propose HiPoNet, an end-to-end differentiable neural network for regression, classification, and representation learning on high-dimensional point clouds. Single-cell data can have high dimensionality exceeding the capabilities of existing methods for point clouds which are tailored for 3D data. Moreover, modern single-cell and spatial experiments now yield entire cohorts of datasets (i.e. one on every patient), necessitating models that can process large, high-dimensional point clouds at scale. Most current approaches build a single nearest-neighbor graph, discarding important geometric information. In contrast, HiPoNet forms higher-order simplicial complexes through learnable feature reweighting, generating multiple data views that disentangle distinct biological processes. It then employs simplicial wavelet transforms to extract multiscale features—capturing both local and global topology. We empirically show that these components preserve topological information in the learned representations, and that HiPoNet significantly outperforms state-of-the-art point-cloud and graph-based models on single cell. We also show an application of HiPoNet on spatial transcriptomics datasets using spatial co-ordinates as one of the views. Overall, HiPoNet offers a robust and scalable solution for high-dimensional data analysis. © 2025, CC BY.

关键词： Network topology

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Fairness in scheduling 6

Fairness in scheduling

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6th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 1995

作者： Ajtai, Miklos Aspnes, James Naor, Moni Rabani, Yuval Schulman, Leonard J. Waarts, Orli IBM Research Division Almaden Research Center United States Dept. of Computer Science Yale University United States Dept. of Applied Math and Computer Science Weizmann Institute Israel ICSI Berkeley Lab. for Computer Science MIT United States Department of Computer Science University of Toronto TorontoONM5S 1A4 Canada Computer Science Division U.C. Berkeley United States

ISBN: (纸本)0898713498

On-line machine scheduling has been studied extensively, but the fundamental issue of fairness in scheduling is still mostly open. In this paper we explore the issue in settings where there are long living processes which should be repeatedly scheduled for various tasks throughout the lifetime of a system. For any such instance we develop a notion of desired load of a process, which is a function of the tasks it participates in. The unfairness of a system is the maximum, taken over all processes, of the difference between the desired load and the actual load. An example of such a setting is the carpooi problem suggested by Fagin and Williams [17]. In this problem, a set of n people form a carpooi. On each day a subset of the people arrive and one of them is designated as the driver. A scheduling rule is required so that the driver will be determined in a 'fair' way. We investigate this problem under various assumptions on the input distribution. We also show that the carpooi problems can capture several other problems of fairness in scheduling.

关键词： Scheduling

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InGAN: Capturing and remapping the "DNA" of a natural image

arXiv

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arXiv 2018年

作者： Shocher, Assaf Bagon, Shai Isola, Phillip Irani, Michal Dept. of Computer Science and Applied Math Weizmann Institute of Science Computer Science and Artificial Intelligence Lab Massachusetts Institute of Technology

Generative Adversarial Networks (GANs) typically learn a distribution of images in a large image dataset, and are then able to generate new images from this distribution. However, each natural image has its own internal statistics, captured by its unique distribution of patches. In this paper we propose an "Internal GAN" (InGAN) – an image-specific GAN – which trains on a single input image and learns its internal distribution of patches. It is then able to synthesize a plethora of new natural images of significantly different sizes, shapes and aspect-ratios all with the same internal patch-distribution (same "DNA") as the input image. In particular, despite large changes in global size/shape of the image, all elements inside the image maintain their local size/shape. InGAN is fully unsupervised, requiring no additional data other than the input image itself. Once trained on the input image, it can remap the input to any size or shape in a single feedforward pass, while preserving the same internal patch distribution. InGAN provides a unified framework for a variety of tasks, bridging the gap between textures and natural images.1 Copyright © 2018, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Chaotic Associative Recalls for Fixed Point Attractor Patterns

Chaotic Associative Recalls for Fixed Point Attractor Patter...

引用

International Joint Conference on Neural Networks 2003

作者： Zhao, Liang Cáceres, Juan C.G. Szu, Harold Inst. of Math. and Computer Science University of São Paulo Sao Paulo Brazil Digital Media RF Lab. Sch. of Eng. and Applied Science George Washington University Washington DC United States

Human perception is a complex nonlinear dynamics. On the one hand it is periodic dynamics and on the other hand it is chaotic. Thus, we wish to propose a hybrid - the spatial chaotic dynamics for the associative recall to retrieve patterns, similar to Walter Freeman's discovery, and the fixed point dynamics for memory storage, similar to Hopfield and Grossberg's discoveries. In this model, each neuron in the network could be a chaotic map, whose phase space is divided into two states: one is periodic dynamic state with period-V, which is used to represent a V-value retrieved pattern;another is chaotic dynamic state. Firstly, patterns are stored in the memory by fixed point learning algorithm. In the retrieving process, all neurons are initially set in the chaotic region. Due to the ergodicity property of chaos, each neuron will approximate the periodic points covered by the chaotic attractor at same instants. When this occurs, the control is activated to drive the dynamic of each neuron to their corresponding stable periodic point. computer simulations confirm the theoretical prediction.

关键词： Pattern recognition

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Modeling stable matching problems with answer set programming

Modeling stable matching problems with answer set programmin...

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7th International Web Rule Symposium on Theory, Practice, and Applications of Rules on the Web, RuleML 2013

作者： De Clercq, Sofie Schockaert, Steven De Cock, Martine Nowé, Ann Dept. of Applied Math. CS and Stats Ghent University Ghent Belgium School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Computational Modeling Lab Vrije Universiteit Brussel Brussels Belgium

ISBN: (纸本)9783642396168

The Stable Marriage Problem (SMP) is a well-known matching problem first introduced and solved by Gale and Shapley [7]. Several variants and extensions to this problem have since been investigated to cover a wider set of applications. Each time a new variant is considered, however, a new algorithm needs to be developed and implemented. As an alternative, in this paper we propose an encoding of the SMP using Answer Set Programming (ASP). Our encoding can easily be extended and adapted to the needs of specific applications. As an illustration we show how stable matchings can be found when individuals may designate unacceptable partners and ties between preferences are allowed. Subsequently, we show how our ASP based encoding naturally allows us to select specific stable matchings which are optimal according to a given criterion. Each time, we can rely on generic and efficient off-the-shelf answer set solvers to find (optimal) stable matchings. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Encoding (symbols)

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Computational model and the human perception of emotional body language (EBL)

Computational model and the human perception of emotional bo...

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23rd Convention of the Society for the Study of Artificial Intelligence and Simulation of Behaviour, AISB 2009

作者： Fridin, Marina Barliya, Avi Schechtman, Edna De Gelder, Beatrice Flash, Tamar Dept. of Computer Science and Applied Math. Weizmann Institute of Science Rehovot Israel Lab. of Cognitive and Affective Neuroscience Tilburg Univ. Netherlands Dept. of Industrial Engineering and Management Ben Gurion Univ. Beer Sheva Israel

ISBN: (纸本)1902956850

Emotions are undeniably a central component of human existence. In recent years, the importance of developing systems which incorporate emotions into human-computer interaction (HCI) has been widely acknowledged. However, research on emotion recognition has been dominated by studies of facial expression of emotion. In comparison, the study of EBL has received relatively little attention. Here we study the phenomena of EBL, specifically of static body postures expressing emotions, from two different perspectives. First, we have built a computational model for the recognition of four basic emotions which achieves a relatively high recognition rate (70 %). Secondly, to study perception of EBL, we examined what body parts attract the observer's attention during the perception of EBL. This is done by tracking eye movements of human subjects during the observation of static postures expressing emotions. Although invaluable information can be inferred from motion, this study will show that information about static body posture is rich enough for both automatic recognition and human perception. The present study contributes in an applicative way both to the development of automatic recognition systems of EBL and provides insight into the nature of human recognition of EBL.

关键词： Eye movements

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