Sanguthevar Rajasekaran

Optimal and sublogarithmic time randomized parallel sorting algorithms

This paper assumes a parallel RAM (random access machine) model which allows both concurrent reads and concurrent writes of a global memory.The main result is an optimal randomized parallel algorithm for INTEGER_SORT (i.e., for sorting n integers in the range

Accelerating materials property predictions using machine learning

[1,n]

Minimotif Miner: a tool for investigating protein function

). This algorithm costs only logarithmic time and is the first known that is optimal: the product of its time and processor bounds is upper bounded by a linear function of the input size. Also given is a deterministic sublogarithmic time algorithm for prefix sum. In addition this paper presents a sublogarithmic time algorithm for obtaining a random permutation of n elements in parallel. And finally, sublogarithmic time algorithms for GENERAL_SORT and INTEGER_SORT are presented. Our sub-logarithmic GENERAL_SORT algorithm is also optimal.

Optimal Routing Algorithms for Mesh-Connected Processor Arrays

The materials discovery process can be significantly expedited and simplified if we can learn effectively from available knowledge and data. In the present contribution, we show that efficient and accurate prediction of a diverse set of properties of material systems is possible by employing machine (or statistical) learning methods trained on quantum mechanical computations in combination with the notions of chemical similarity. Using a family of one-dimensional chain systems, we present a general formalism that allows us to discover decision rules that establish a mapping between easily accessible attributes of a system and its properties. It is shown that fingerprints based on either chemo-structural (compositional and configurational information) or the electronic charge density distribution can be used to make ultra-fast, yet accurate, property predictions. Harnessing such learning paradigms extends recent efforts to systematically explore and mine vast chemical spaces, and can significantly accelerate the discovery of new application-specific materials.

Fast and Practical Algorithms for Planted (l, d) Motif Search

In addition to large domains, many short motifs mediate functional post-translational modification of proteins as well as protein-protein interactions and protein trafficking functions. We have constructed a motif database comprising 312 unique motifs and a web-based tool for identifying motifs in proteins. Functional motifs predicted by MnM can be ranked by several approaches, and we validated these scores by analyzing thousands of confirmed examples and by confirming prediction of previously unidentified 14-3-3 motifs in EFF-1.

A transaction mapping algorithm for frequent itemsets mining

We show that there is a randomizedoblivious algorithm for routing any (partial) permutation on ann ×n grid in 2n +O(logn) parallel communication steps. The queues will not grow larger than Θ(logn/log logn) with high probability. We then modify this to obtain a (nonoblivious) algorithm with the same running time such that the size of the queues is bounded by a constant with high probability. For permutations withlocality, where each packet has to travel a distance at mostL, a generalization of the algorithm routes in time proportional toL with high probability. Finally, we identify a class of meshlike networks that have optimal or near-optimal diameter. These meshes have the potential of being adapted to run existing sorting and routing algorithms with corresponding reduction in their running times.

Exact algorithms for planted motif problems.

We consider the planted (I, d) motif search problem, which consists of finding a substring of length I that occurs in a set of input sequences {si,. ..,sn} with up to d errors, a problem that arises from the need to find transcription factor-binding sites in genomic information. We propose a sequence of practical algorithms, which start based on the ideas considered in PMS1. These algorithms are exact, have little space requirements, and are able to tackle challenging instances with bigger d, taking less time in the instances reported solved by exact algorithms. In particular, one of the proposed algorithms, PMSprune, is able to solve the challenging instances, such as (17, 6) and (19, 7), which were not previously reported as solved in the literature.

Sorting, selection, and routing on the array with reconfigurable optical buses

In this paper, we present a novel algorithm for mining complete frequent itemsets. This algorithm is referred to as the TM (transaction mapping) algorithm from hereon. In this algorithm, transaction ids of each itemset are mapped and compressed to continuous transaction intervals in a different space and the counting of itemsets is performed by intersecting these interval lists in a depth-first order along the lexicographic tree. When the compression coefficient becomes smaller than the average number of comparisons for intervals intersection at a certain level, the algorithm switches to transaction id intersection. We have evaluated the algorithm against two popular frequent itemset mining algorithms, FP-growth and dEclat, using a variety of data sets with short and long frequent patterns. Experimental data show that the TM algorithm outperforms these two algorithms.

Minimotif miner 2nd release: a database and web system for motif search

The problem of identifying meaningful patterns (i.e., motifs) from biological data has been studied extensively due to its paramount importance. Three versions of this problem have been identified in the literature. One of these three problems is the planted (l, d)-motif problem. Several instances of this problem have been posed as a challenge. Numerous algorithms have been proposed in the literature that address this challenge. Many of these algorithms fall under the category of heuristic algorithms. In this paper we present algorithms for the planted (l, d)-motif problem that always find the correct answer(s). Our algorithms are very simple and are based on some ideas that are fundamentally different from the ones employed in the literature. We believe that the techniques we introduce in this paper will find independent applications.

Randomized routing, selection, and sorting on the OTIS-mesh

In this paper, we present efficient algorithms for sorting, selection, and packet routing on the AROB (Array with Reconfigurable Optical Buses) model. One of our sorting algorithms sorts n general keys in O(1) time on an AROB of size n/sup /spl epsiv///spl times/n for any constant /spl epsiv/>0. We also show that selection from out of n elements can be done in randomized O(1) time employing n processors. Our routing algorithm can route any h-relation in randomized O(h) time. All these algorithms are clearly optimal.