Embedding problems has received a lot of attention in the last decades by various research dealt with many inter-connection networks. Through embedding the existing parallel algorithm of an interconnection network can...
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This article describes the application of parallel computing techniques for efficiently processing large volumes of data from ITS. This is a relevant problem in nowadays societies, especially when working under the no...
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Multiwinner voting captures a wide variety of settings, from parliamentary elections in democratic systems to product placement in online shopping platforms. There is a large body of work dealing with axiomatic charac...
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ISBN:
(纸本)1577358872
Multiwinner voting captures a wide variety of settings, from parliamentary elections in democratic systems to product placement in online shopping platforms. There is a large body of work dealing with axiomatic characterizations, computational complexity, and algorithmic analysis of multiwinner voting rules. Although many challenges remain, significant progress has been made in showing existence of fair and representative outcomes as well as efficient algorithmic solutions for many commonly studied settings. However, much of this work focuses on single-shot elections, even though in numerous real-world settings elections are held periodically and repeatedly. Hence, it is imperative to extend the study of multiwinner voting to temporal settings. Recently, there have been several efforts to address this challenge. However, these works are difficult to compare, as they model multi-period voting in very different ways. We propose a unified framework for studying temporal fairness in this domain, drawing connections with various existing bodies of work, and consolidating them within a general framework. We also identify gaps in existing literature, outline multiple opportunities for future work, and put forward a vision for the future of multiwinner voting in temporal settings.
This paper presents an accurate and fast 3D global localization method, 3D-BBS, that extends the existing branch-and-bound (BnB)-based 2D scan matching (BBS) algorithm. To reduce memory consumption, we utilize a spars...
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ISBN:
(纸本)9798350384581;9798350384574
This paper presents an accurate and fast 3D global localization method, 3D-BBS, that extends the existing branch-and-bound (BnB)-based 2D scan matching (BBS) algorithm. To reduce memory consumption, we utilize a sparse hash table for storing hierarchical 3D voxel maps. To improve the processing cost of BBS in 3D space, we propose an efficient roto-translational space branching. Furthermore, we devise a batched BnB algorithm to fully leverage GPU parallelprocessing. Through experiments in simulated and real environments, we demonstrated that the 3D-BBS enabled accurate global localization with only a 3D LiDAR scan roughly aligned in the gravity direction and a 3D pre-built map. This method required only 878 msec on average to perform global localization and outperformed state-of-the-art global registration methods in terms of accuracy and processing speed.
Abstract: The article presents the results of research on the development of a method for modeling data transmission paths in specialized computing systems for the purposes of assessing the impact of possible degradat...
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Tracking people in multi-camera surveillance systems is challenging due to disparate perspectives, large volumes of data, and high computation demands. This paper presents a distributed cooperative pipeline for pedest...
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ISBN:
(纸本)9798350374292;9798350374285
Tracking people in multi-camera surveillance systems is challenging due to disparate perspectives, large volumes of data, and high computation demands. This paper presents a distributed cooperative pipeline for pedestrian tracking that exploits the spatial and temporal redundancy within and across the video feeds from multiple synchronized cameras. It consists of three key components: 1) a lightweight policy network trained online in a self-supervised manner on each camera, 2) a sparse backbone processing unit purpose-built for parallelprocessing of selected regions of all cameras, and 3) an online clustering algorithm for object association. Utilizing online distributed reinforcement learning, the fully end-to-end trainable pipeline can accelerate any tracking-by-detection method by reducing detection costs across multiple perspectives. MVSparse has been evaluated using two multi-camera multi-target pedestrian tracking datasets, WildTrack and MultiviewX. It reduces the amount of processed regions by up to 52% and 39% with only moderate degradation of 1% and 0.1% in tracking accuracy on the two datasets, respectively. On a real-world testbed comprising four NVIDIA Jetson TX2 and a GPU server, MVSparse accelerates the end-to-end process and reduces the communication overheads by 1.88 and 1.60 X with only 2.27% and 3.17% degradation in tracking accuracy on the two datasets, respectively.
The FastECPP algorithm is currently the fastest approach to prove the primality of general numbers, and has the additional benefit of creating certificates that can be checked independently and with a lower complexity...
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ISBN:
(纸本)9783031645280;9783031645297
The FastECPP algorithm is currently the fastest approach to prove the primality of general numbers, and has the additional benefit of creating certificates that can be checked independently and with a lower complexity. This article shows how by parallelising over a linear number of cores, its quartic time complexity becomes a cubic wallclock time complexity;and it presents the algorithmic choices of the FastECPP implementation in the author's Cm software https://www. ***/cm/ which has been written with massive parallelisation over MPI in mind, and which has been used to establish a new primality record for the "repunit" (10(86453) - 1)/9.
Equivalence testing, a fundamental problem in the field of distribution testing, seeks to infer if two unknown distributions on [n] are the same or far apart in the total variation distance. Conditional sampling has e...
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Equivalence testing, a fundamental problem in the field of distribution testing, seeks to infer if two unknown distributions on [n] are the same or far apart in the total variation distance. Conditional sampling has emerged as a powerful query model and has been investigated by theoreticians and practitioners alike, leading to the design of optimal algorithms albeit in a sequential setting (also referred to as adaptive tester). Given the profound impact of parallel computing over the past decades, there has been a strong desire to design algorithms that enable high parallelization. Despite significant algorithmic advancements over the last decade, parallelizable techniques (also termed non-adaptive testers) have (O) over tilde (log(12) n) query complexity, a prohibitively large complexity to be of practical usage. Therefore, the primary challenge is whether it is possible to design algorithms that enable high parallelization while achieving efficient query complexity. Our work provides an affirmative answer to the aforementioned challenge: we present a highly parallelizable tester with a query complexity of (O) over tilde (log n), achieved through a single round of adaptivity, marking a significant stride towards harmonizing parallelizability and efficiency in equivalence testing.
Pairwise learning, an important domain within machine learning, addresses loss functions defined on pairs of training examples, including those in metric learning and AUC maximization. Acknowledging the quadratic grow...
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ISBN:
(纸本)1577358872
Pairwise learning, an important domain within machine learning, addresses loss functions defined on pairs of training examples, including those in metric learning and AUC maximization. Acknowledging the quadratic growth in computation complexity accompanying pairwise loss as the sample size grows, researchers have turned to online gradient descent (OGD) methods for enhanced scalability. Recently, an OGD algorithm emerged, employing gradient computation involving prior and most recent examples, a step that effectively reduces algorithmic complexity to O(T), with T being the number of received examples. This approach, however, con-fines itself to linear models while assuming the independence of example arrivals. We introduce a lightweight OGD algorithm that does not require the independence of examples and generalizes to kernel pairwise learning. Our algorithm builds the gradient based on a random example and a moving aver-age representing the past data, which results in a sub-linear regret bound with a complexity of O(T). Furthermore, through the integration of O((root) T log T) random Fourier features, the complexity of kernel calculations is effectively minimized. Several experiments with real-world datasets show that the proposed technique outperforms kernel and linear algorithms in offline and online scenarios.
Sparse matrix-vector multiplications (SpMV) are notoriously challenging to accelerate due to their highly irregular data access pattern. Although a fully customized static accelerator design may be adequate for small ...
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