Amir Rothschild

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Group testing is a long studied problem in combinatorics: A small set of <i>r</i> ill people should be identified out of the whole (<i>n</i> people) by using only queries (tests) of the form &#x201C;Does set X contain an ill human?&#x201D; In this paper we provide an explicit construction of a testing scheme which is better (smaller) than any known explicit(More)
We present solutions for the k-mismatch pattern matching problem with don’t cares. Given a text t of length n and a pattern p of length m with don’t care symbols and a bound k, our algorithms find all the places that the pattern matches the text with at most k mismatches. We first give an Θ (n(k + logm log k) log n) time randomised algorithm which finds the(More)
UDP-glucose: protein transglucosylase (UPTG, EC 2.4.1.112) catalyzes the first step of protein-bound alpha-glucan synthesis in potato tuber and developing maize endosperm. The presence of a non-dialyzable, heat labile protein responsible for low levels of UPTG activity in developing maize endosperm was investigated. UPTG activity in 5-day old maize(More)
A thermolabile UPTG inhibitor protein (IP) was isolated and purified from a developing maize endosperm preparation. High homology of two internal peptides of IP with known plant sucrose synthase (SS) sequences suggested that IP might be related somehow with SS. IP and SS activities were found in the same preparation and showed thermolability between 60-65(More)
We consider the classic problem of pattern matching with few mismatches in the presence of promiscuously matching wildcard symbols. Given a text t of length n and a pattern p of length m with optional wildcard symbols and a bound k, our algorithm finds all the alignments for which the pattern matches the text with Hamming distance at most k and also returns(More)
Efficient handling of sparse data is a key challenge in Computer Science. Binary convolutions, such as polynomial multiplication or the Walsh Transform are a useful tool in many applications and are efficiently solved. In the last decade, several problems required efficient solution of sparse binary convolutions. Both randomized and deterministic algorithms(More)
This paper presents a new technique for deterministic length reduction. This technique improves the running time of the algorithm presented in [?] for performing fast convolution in sparse data. While the regular fast convolution of vectors V1, V2 whose sizes are N1, N2 respectively, takes O(N1 logN2) using FFT, using the new technique for length reduction,(More)
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