Helsinki Institute for Information Technology

About: Helsinki Institute for Information Technology is a facility organization based out in Espoo, Finland. It is known for research contribution in the topics: Population & Bayesian network. The organization has 630 authors who have published 1962 publications receiving 63426 citations.

Edit Distance: Sketching, Streaming, and Document Exchange

Abstract: We show that in the document exchange problem, where Alice holds x e {0, 1}n and Bob holds y e {0, 1}n, Alice can send Bob a message of size O(K(log2 K + log n)) bits such that Bob can recover x using the message and his input y if the edit distance between x and y is no more than K, and output "error" otherwise. Both the encoding and decoding can be done in time O(n + poly(K)). This result significantly improves on the previous communication bounds under polynomial encoding/decoding time. We also show that in the referee model, where Alice and Bob hold x and y respectively, they can compute sketches of x and y of sizes poly(K log n) bits (the encoding), and send to the referee, who can then compute the edit distance between x and y together with all the edit operations if the edit distance is no more than K, and output "error" otherwise (the decoding). To the best of our knowledge, this is the first result for sketching edit distance using poly(K log n) bits. Moreover, the encoding phase of our sketching algorithm can be performed by scanning the input string in one pass. Thus our sketching algorithm also implies the first streaming algorithm for computing edit distance and all the edits exactly using poly(K log n) bits of space.

A Markov chain approach to reconstruction of long haplotypes.

Abstract: Haplotypes are important for association based gene mapping, but there are no practical laboratory methods for obtaining them directly from DNA samples. We propose simple Markov models for reconstruction of haplotypes for a given sample of multilocus genotypes. The models are aimed specifically for long marker maps, where linkage disequilibrium between markers may vary and be relatively weak. Such maps are ultimately used in chromosome or genome-wide association studies. Haplotype reconstruction with standard Markov chains is based on linkage disequilibrium (LD) between neighboring markers. Markov chains of higher order can capture LD in a neighborhood of a given size. We introduce a more flexible and robust model, MC-VL, which is based on a Markov chain of variable order. Experimental validation of the Markov chain methods on both a wide range of simulated data and real data shows that they clearly out perform previous methods on genetically long marker maps and are highly competitive with short maps, too. MC-VL performs well across different data sets and settings while avoiding the problem of manually choosing an appropriate order for the Markov chain, and it has low computational complexity.

Structure discovery in Bayesian networks by sampling partial orders

Abstract: We present methods based on Metropolis-coupled Markov chain Monte Carlo (MC3) and annealed importance sampling (AIS) for estimating the posterior distribution of Bayesian networks. The methods draw samples from an appropriate distribution of partial orders on the nodes, continued by sampling directed acyclic graphs (DAGs) conditionally on the sampled partial orders. We show that the computations needed for the sampling algorithms are feasible as long as the encountered partial orders have relatively few down-sets. While the algorithms assume suitable modularity properties of the priors, arbitrary priors can be handled by dividing the importance weight of each sampled DAG by the number of topological sorts it has|we give a practical dynamic programming algorithm to compute these numbers. Our empirical results demonstrate that the presented partial-order-based samplers are superior to previous Markov chain Monte Carlo methods, which sample DAGs either directly or via linear orders on the nodes. The results also suggest that the convergence rate of the estimators based on AIS are competitive to those of MC3. Thus AIS is the preferred method, as it enables easier large-scale parallelization and, in addition, supplies good probabilistic lower bound guarantees for the marginal likelihood of the model.

InspirationWall: Supporting Idea Generation Through Automatic Information Exploration

Abstract: Collaborative idea generation leverages social interactions and knowledge sharing to spark diverse associations and produce creative ideas. Information exploration systems expand the current context by suggesting novel but related concepts. In this paper we introduce InspirationWall, an unobtrusive display that leverages speech recognition and information exploration to enhance an ongoing idea generation session with automatically retrieved concepts that relate to the conversation. We evaluated the system in six idea generation sessions of 20 minutes with small groups of two people. Preliminary results suggest that InspirationWall contrasts the decay of idea productivity over time and can thus represent an effective way to enhance idea generation activities.