1,436 research outputs found

    An Optimal Algorithm for the Maximum-Density Segment Problem

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    We address a fundamental problem arising from analysis of biomolecular sequences. The input consists of two numbers wminw_{\min} and wmaxw_{\max} and a sequence SS of nn number pairs (ai,wi)(a_i,w_i) with wi>0w_i>0. Let {\em segment} S(i,j)S(i,j) of SS be the consecutive subsequence of SS between indices ii and jj. The {\em density} of S(i,j)S(i,j) is d(i,j)=(ai+ai+1+...+aj)/(wi+wi+1+...+wj)d(i,j)=(a_i+a_{i+1}+...+a_j)/(w_i+w_{i+1}+...+w_j). The {\em maximum-density segment problem} is to find a maximum-density segment over all segments S(i,j)S(i,j) with wminwi+wi+1+...+wjwmaxw_{\min}\leq w_i+w_{i+1}+...+w_j \leq w_{\max}. The best previously known algorithm for the problem, due to Goldwasser, Kao, and Lu, runs in O(nlog(wmaxwmin+1))O(n\log(w_{\max}-w_{\min}+1)) time. In the present paper, we solve the problem in O(n) time. Our approach bypasses the complicated {\em right-skew decomposition}, introduced by Lin, Jiang, and Chao. As a result, our algorithm has the capability to process the input sequence in an online manner, which is an important feature for dealing with genome-scale sequences. Moreover, for a type of input sequences SS representable in O(m)O(m) space, we show how to exploit the sparsity of SS and solve the maximum-density segment problem for SS in O(m)O(m) time.Comment: 15 pages, 12 figures, an early version of this paper was presented at 11th Annual European Symposium on Algorithms (ESA 2003), Budapest, Hungary, September 15-20, 200

    A Fuzzy Object-Oriented Tool Selection System

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    Contextual Label Projection for Cross-Lingual Structure Extraction

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    Translating training data into target languages has proven beneficial for cross-lingual transfer. However, for structure extraction tasks, translating data requires a label projection step, which translates input text and obtains translated labels in the translated text jointly. Previous research in label projection mostly compromises translation quality by either facilitating easy identification of translated labels from translated text or using word-level alignment between translation pairs to assemble translated phrase-level labels from the aligned words. In this paper, we introduce CLAP, which first translates text to the target language and performs contextual translation on the labels using the translated text as the context, ensuring better accuracy for the translated labels. We leverage instruction-tuned language models with multilingual capabilities as our contextual translator, imposing the constraint of the presence of translated labels in the translated text via instructions. We compare CLAP with other label projection techniques for creating pseudo-training data in target languages on event argument extraction, a representative structure extraction task. Results show that CLAP improves by 2-2.5 F1-score over other methods on the Chinese and Arabic ACE05 datasets.Comment: Work in Progres

    A junctionless SONOS nonvolatile memory device constructed with in situ-doped polycrystalline silicon nanowires

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    In this paper, a silicon-oxide-nitride-silicon nonvolatile memory constructed on an n+-poly-Si nanowire [NW] structure featuring a junctionless [JL] configuration is presented. The JL structure is fulfilled by employing only one in situ heavily phosphorous-doped poly-Si layer to simultaneously serve as source/drain regions and NW channels, thus greatly simplifying the manufacturing process and alleviating the requirement of precise control of the doping profile. Owing to the higher carrier concentration in the channel, the developed JL NW device exhibits significantly enhanced programming speed and larger memory window than its counterpart with conventional undoped-NW-channel. Moreover, it also displays acceptable erase and data retention properties. Hence, the desirable memory characteristics along with the much simplified fabrication process make the JL NW memory structure a promising candidate for future system-on-panel and three-dimensional ultrahigh density memory applications

    Digital twin-driven smart manufacturing: Connotation, reference model, applications and research issues

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    This paper reviews the recent development of Digital Twin technologies in manufacturing systems and processes, to analyze the connotation, application scenarios, and research issues of Digital Twin-driven smart manufacturing in the context of Industry 4.0. To understand Digital Twin and its future potential in manufacturing, we summarized the definition and state-of-the-art development outcomes of Digital Twin. Existing technologies for developing a Digital Twin for smart manufacturing are reviewed under a Digital Twin reference model to systematize the development methodology for Digital Twin. Representative applications are reviewed with a focus on the alignment with the proposed reference model. Outstanding research issues of developing Digital Twins for smart manufacturing are identified at the end of the paper
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