43,870 research outputs found
The roles of consistency and exclusivity in perceiving body ownership and agency
No full textKe Ma · Bernhard Hommel · Hong Chen (2019) Psychological Researc
He ke ji you shi: [wu juan]. v.1
No full text[V.1]. 客還草 / 陳函輝著 -- 罌存 / 陳函輝著 -- [v.2]. 題徐霞客紀遊急就章 / 黃道周著 -- 率豆社約 / 陳函輝集補 -- 年評社集 / 陳函輝著.[V.1]. Ke huan cao / Chen Hanhui zhu -- Ying cun / Chen Hanhui zhu -- [v.2]. Ti xu xia ke ji you ji jiu zhang / Huang Daozhou zhu -- Lu dou she yue / Chen Hanhui ji bu -- Nian ping she ji / Chen Hanhui zhu.綫裝, 1函.框18.5x13.8公分, 8行17字, 白口, 左右雙邊, 無魚尾. 版心上鐫題名, 下鐫葉次, 《客還草》版心下鐫"小寒山", 《罌存》版心下鐫"閉戶吟", 《題徐霞客紀遊急就章》版心下鐫"石人集", 《率豆社約》版心下鐫"小寒山", 《年評社集》版心下鐫"東園公"題名據序.《客還草》卷端題下鐫"一名《司馬悔》"《罌存》卷端題下鐫"一名《閉戶吟》"《年評社集》卷端題下鐫"一名《東園公草》"鈐有"抱經樓"印.Xian zhuang, 1 han.Kuang 18.5 x 13.8 gong fen, 8 hang 17 zi. Bai kou, zuo you shuang bian, wu yu wei. Ban xin shang juan ti ming, xia juan ye ci, "Ke huan cao" ban xin xia juan "Xiaohanshan", "Ying cun" ban xin xia juan "Bi hu yin", "Ti xu xia ke ji you ji jiu zhang" ban xin xia juan "Shiren ji", "Lu dou she yue" ban xin xia juan "Xiaohanshan", "Nian ping she ji" ban xin xia juan "Dong yuan gong"Ti ming ju xu."Ke huan cao" juan duan ti xia juan "yi ming 'Sima hui'""Ying cun" juan duan ti xia juan "yi ming 'Bi hu yin'""Nian ping she ji" juan duan ti xia juan "yi ming 'Dong yuan gong cao'"Qian you "Bao jing lou" yin
On use of different feature sets for pattern classification: An alternative method
No full textWe propose an alternative method for the use of different feature sets in pattern classification. Unlike traditional methods, e.g. combination of multiple classifiers and use of a composite feature set, our method copes with the problem based on an idea of soft competition on different feature sets. A modular neural network architecture is proposed to implement the idea accordingly. The proposed architecture is interpreted as a generalized finite mixture model and, therefore, parameter estimation is treated as a maximum likelihood problem. An EM algorithm is derived for parameter estimation. Moreover, we propose a heuristic model selection method to fit the proposed architecture to a specific problem. Comparative results are presented for the real world problem of speaker identification.EI
Multiparty Selection
Get PDFGiven a sequence A of n numbers and an integer (target) parameter 1 ≤ i ≤ n, the (exact) selection problem is that of finding the i-th smallest element in A. An element is said to be (i,j)-mediocre if it is neither among the top i nor among the bottom j elements of S. The approximate selection problem is that of finding an (i,j)-mediocre element for some given i,j; as such, this variant allows the algorithm to return any element in a prescribed range. In the first part, we revisit the selection problem in the two-party model introduced by Andrew Yao (1979) and then extend our study of exact selection to the multiparty model. In the second part, we deduce some communication complexity benefits that arise in approximate selection. In particular, we present a deterministic protocol for finding an approximate median among k players
Locality-Sensitive Bucketing Functions for the Edit Distance
Get PDFMany bioinformatics applications involve bucketing a set of sequences where each sequence is allowed to be assigned into multiple buckets. To achieve both high sensitivity and precision, bucketing methods are desired to assign similar sequences into the same bucket while assigning dissimilar sequences into distinct buckets. Existing k-mer-based bucketing methods have been efficient in processing sequencing data with low error rate, but encounter much reduced sensitivity on data with high error rate. Locality-sensitive hashing (LSH) schemes are able to mitigate this issue through tolerating the edits in similar sequences, but state-of-the-art methods still have large gaps. Here we generalize the LSH function by allowing it to hash one sequence into multiple buckets. Formally, a bucketing function, which maps a sequence (of fixed length) into a subset of buckets, is defined to be (d₁, d₂)-sensitive if any two sequences within an edit distance of d₁ are mapped into at least one shared bucket, and any two sequences with distance at least d₂ are mapped into disjoint subsets of buckets. We construct locality-sensitive bucketing (LSB) functions with a variety of values of (d₁,d₂) and analyze their efficiency with respect to the total number of buckets needed as well as the number of buckets that a specific sequence is mapped to. We also prove lower bounds of these two parameters in different settings and show that some of our constructed LSB functions are optimal. These results provide theoretical foundations for their practical use in analyzing sequences with high error rate while also providing insights for the hardness of designing ungapped LSH functions
Random Sampling and Size Estimation Over Cyclic Joins
Get PDFComputing joins is expensive, and often unnecessary when the output size is large. In 1999, Chaudhuri et al. [Surajit Chaudhuri et al., 1999] posed the problem of random sampling over joins as a potentially effective approach to avoiding computing the join in full, while obtaining important statistical information about the join results. Unfortunately, no significant progress has been made in the last 20 years, except for the case of acyclic joins. In this paper, we present the first non-trivial result on sampling over cyclic joins. We show that after a linear-time preprocessing step, a join result can be drawn uniformly at random in expected time O(IN^ρ/OUT), where IN^ρ is known as the AGM bound of the join and OUT is its output size. This result holds for all joins on binary relations, as well as certain joins on relations of higher arity. We further show how this algorithm immediately leads to a join size estimation algorithm with the same running time
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