Android operating system always occupies the highest market share in mobile operating systems. Security analysis on Android operating systems often focuses on analyzing applications (APK files) when installed on the p...
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Hierarchical clustering is a stronger extension of one of today's most influential unsupervised learning methods: clustering. the goal of this method is to create a hierarchy of clusters, thus constructing cluster...
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ISBN:
(纸本)9781450375184
Hierarchical clustering is a stronger extension of one of today's most influential unsupervised learning methods: clustering. the goal of this method is to create a hierarchy of clusters, thus constructing cluster evolutionary history and simultaneously finding clusterings at all resolutions. We propose four traits of interest for hierarchical clustering algorithms: (1) empirical performance, (2) theoretical guarantees, (3) balance (the minimum ratio between cluster sizes), and (4) scalability. While a number of algorithms are designed to achieve one to two of these traits at a time, there exist none that achieve all *** by Bateni et al.'s scalable and empirically successful Affinity Clustering [NeurIPs 2017], we introduce Affinity's successor, Matching Affinity Clustering. Like its predecessor, Matching Affinity Clustering maintains strong empirical performance, even outperforming Affinity when the dataset is size 2n and clusters are balanced, and uses Massively parallel Communication as its distributed model. Designed to maintain provably balanced clusters, we show that our algorithm achieves a (1/3-ε)-approximation for Moseley and Wang's revenue (the dual to Dasgupta's cost) when the data set is of size 2n, and a (1/9-ε)-approximation in general. We prove the former approximation is tight, and also that Affinity Clustering cannot do better than a 1/O(n)-approximation. In addition, we see that our algorithm empirically performs similarly to Affinity Clustering and k-Means, outperforming many state-of-the-art serial algorithms. Along the way, we also introduce an efficient k-sized maximum matching algorithm in the MPC model.
Inductor winding is often composed as parallel-connected wires to suppress the copper loss. However, in high frequency inductors, the proximity effect can cause concentrated AC current distribution, hindering suppress...
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ISBN:
(纸本)9781467388634
Inductor winding is often composed as parallel-connected wires to suppress the copper loss. However, in high frequency inductors, the proximity effect can cause concentrated AC current distribution, hindering suppression of the copper loss. therefore, optimization of the physical inductor structure requires predicting the AC current distribution. Although simulators are commonly employed for predicting the AC current distribution, simple analytical methods are also required for effective design or invention of the inductor structure with less copper loss. the purpose of this paper is to propose a novel analysis method of the AC current distribution in parallel-connected wires of high frequency inductors. the proposed method is based on a novel insight that the AC current is distributed to give an extremum of the magnetic co-energy contributed by the AC flux and the AC current under the given total AC current. Experiments are presented in this paper to verify the proposed method.
In the last decades, many kinds of task execution models such as grid and cloud computing have been developed. In such distributedsystems, each task is processed by respective processor in multicored computers e.g., ...
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ISBN:
(纸本)9781479932177
In the last decades, many kinds of task execution models such as grid and cloud computing have been developed. In such distributedsystems, each task is processed by respective processor in multicored computers e.g., household PCs which we can easily harness in recent years. If there is one policy to automatically decide the "best" combination and the number of processors (and computers), we effectively utilize those computational resources, thereby large number of jobs can be executed in parallel. In this paper, we propose a method for mapping of execution units for such environments. the method adopts a remapping technology after processor-execution unit mapping[11] is finished. Experimental comparisons by a simulation show the advantages of the proposed method.
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