CompSciRN: Algorithms (Topic)最新文献_第2页

EU Trade Secrets Law and Algorithmic Transparency 欧盟商业秘密法和算法透明度

CompSciRN: Algorithms (Topic) Pub Date : 2019-03-31 DOI: 10.2139/ssrn.3363178

Mariateresa Maggiolino

引用次数: 8

Shrinking the Upper Confidence Bound: A Dynamic Product Selection Problem for Urban Warehouses 缩小置信上限:城市仓库的动态产品选择问题

CompSciRN: Algorithms (Topic) Pub Date : 2019-02-26 DOI: 10.2139/ssrn.3342761

Rong Jin, D. Simchi-Levi, L. Wang, Xinshang Wang, Sen Yang

{"title":"Shrinking the Upper Confidence Bound: A Dynamic Product Selection Problem for Urban Warehouses","authors":"Rong Jin, D. Simchi-Levi, L. Wang, Xinshang Wang, Sen Yang","doi":"10.2139/ssrn.3342761","DOIUrl":"https://doi.org/10.2139/ssrn.3342761","url":null,"abstract":"The recent rising popularity of ultrafast delivery services on retail platforms fuels the increasing use of urban warehouses, whose proximity to customers makes fast deliveries viable. The space limit in urban warehouses poses a problem for such online retailers: the number of stock keeping units (SKUs) they carry is no longer “the more, the better,” yet it can still be significantly large, reaching hundreds or thousands in a product category. In this paper, we study algorithms for dynamically selecting a large number of products (i.e., SKUs) with top customer purchase probabilities on the fly, from an ocean of potential products to offer on retailers’ ultrafast delivery platforms. We distill the product selection problem into a semibandit model with linear generalization. There are in total N arms corresponding to N products, each with a feature vector of dimension d. The player pulls K arms in each period and observes the bandit feedback from each of the pulled arms. We focus on the setting where K is much greater than the number of total time periods T or the dimension of product features d. We first analyze a standard Upper Confidence Bound (UCB) algorithm and show its regret bound can be expressed as the sum of a T-independent part and a T-dependent part, which we refer to as “fixed cost” and “variable cost,” respectively. To reduce the fixed cost for large K values, we propose a novel online learning algorithm, which iteratively shrinks the upper confidence bounds within each period, and show its fixed cost is reduced by a factor of d. Moreover, we test the algorithms on an industrial data set from Alibaba Group. Experimental results show that our new algorithm reduces the total regret of the standard UCB algorithm by at least 10%. This paper was accepted by J. George Shanthikumar, big data analytics.","PeriodicalId":106276,"journal":{"name":"CompSciRN: Algorithms (Topic)","volume":"70 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124452959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Ships Matching Based on an Adaptive Acoustic Spectrum Signature Detection Algorithm 基于自适应声学频谱特征检测算法的船舶匹配

CompSciRN: Algorithms (Topic) Pub Date : 2018-08-01 DOI: 10.2139/ssrn.3389706

Dahai Cheng, Huigang Xu, Ruiliang Gong, Huan Huang

{"title":"Ships Matching Based on an Adaptive Acoustic Spectrum Signature Detection Algorithm","authors":"Dahai Cheng, Huigang Xu, Ruiliang Gong, Huan Huang","doi":"10.2139/ssrn.3389706","DOIUrl":"https://doi.org/10.2139/ssrn.3389706","url":null,"abstract":"In this paper, an acoustic spectrum signature tracks matching algorithm based on the Manhattan distance and the Euclidean distance of signature vectors, and a multi-frame fusion algorithm are proposed for reliable real time detection and matching of boat generated acoustic signal spectrum signatures. The experiments results have shown that the proposed tracks matching algorithm has the ability to discriminate the tracks from different ships and the ability of matching of the tracks from the same ship; and the spectrum signature detection algorithm has captured the critical features of ship generated acoustic signals. In the process of signal spectrum signature detection, the observation of time and frequency space is structured by dividing input digitalized acoustic signal into multiple frames and each frame is transformed into the frequency domain by FFT. Then, a normalization of signal spectrum vector is carried out to make the detection process more robust. After that, an adaptive median Constant False Alarm Rate (AMCFAR) algorithm is used for the detection and extraction of boat generated spectrum signature, in which an extreme low constant false alarm rate is kept with relative high detection rate. Finally, the frame detections are accumulated to build up the track spectrum signatures.","PeriodicalId":106276,"journal":{"name":"CompSciRN: Algorithms (Topic)","volume":"157 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127966975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Solutions of Volterra Singular Time-Fractional PIDEs Volterra奇异时间分数型PIDEs的解

CompSciRN: Algorithms (Topic) Pub Date : 2018-07-01 DOI: 10.2139/ssrn.3270463

M. Al‐Smadi, Omar Abu Arqub, S. Momani

引用次数: 0

Fast and Precautious: Order Controls for Trade Execution 快速和预防性:交易执行的订单控制

CompSciRN: Algorithms (Topic) Pub Date : 2017-04-05 DOI: 10.2139/ssrn.2977667

Brian M Bulthuis, Julio Concha, Tim Leung, Brian Ward

引用次数: 1

Implementation of Scheduling Algorithms for on Chip Communications 片上通信调度算法的实现

CompSciRN: Algorithms (Topic) Pub Date : 2013-01-18 DOI: 10.5120/10151-4986

Saravanakumar K. Rajasekar, R. Rangarajan, W. Dally, W. Dally, Zhizhou Fu, Xiang Ling, Yun-Lung Lee, J. Jou, Yen-Yu Chen, Eung S. Shin, V. Mooney