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.

Learning loops – interactions between guided reflection and experience-based learning in a serious game activity

Abstract: In a study on experience-based learning in serious games, 45 players were tested for topic comprehension by a questionnaire administered before and after playing the single-player serious game Peacemaker (Impact Games 2007). Players were divided into two activity conditions: 20 played a 1-h game with a 3-min half-time break to complete an affect self-report form while 25 also participated in a 20-min reflective group discussion during their half-time break. During the discussion, they were asked by an experimenter to reflect on a set of topics related to the game. We present the analysis of the questionnaires, which illustrates that contrary to our expectations the reflection period had a negative effect on the learning of the players as judged by their performance on closed-form questions at levels 1–5 (out of 6) on the Bloom taxonomy of learning outcomes. The questionnaire also included a few open questions which gave the players a possibility to display deep (level 6) learning. The players did not differ significantly between conditions regarding the questions measuring deep learning.

Gap Filling as Exact Path Length Problem

Abstract: One of the last steps in a genome assembly project is filling the gaps between consecutive contigs in the scaffolds. This problem can be naturally stated as finding an s-t path in a directed graph whose sum of arc costs belongs to a given range (the estimate on the gap length). Here s and t are any two contigs flanking a gap. This problem is known to be NP-hard in general. Here we derive a simpler dynamic programming solution than already known, pseudo-polynomial in the maximum value of the input range. We implemented various practical optimizations to it, and compared our exact gap-filling solution experimentally to popular gap-filling tools. Summing over all the bacterial assemblies considered in our experiments, we can in total fill 76% more gaps than the best previous tool, and the gaps filled by our method span 136% more sequence. Furthermore, the error level of the newly introduced sequence is comparable to that of the previous tools. The experiments also show that our exact approach does not easily scale to larger genomes, where the problem is in general difficult for all tools.

Improved approximations for two-stage min-cut and shortest path problems under uncertainty

Abstract: In this paper, we study the robust and stochastic versions of the two-stage min-cut and shortest path problems introduced in Dhamdhere et al. (in How to pay, come what may: approximation algorithms for demand-robust covering problems. In: FOCS, pp 367---378, 2005), and give approximation algorithms with improved approximation factors. Specifically, we give a 2-approximation for the robust min-cut problem and a 4-approximation for the stochastic version. For the two-stage shortest path problem, we give a $$3.39$$ 3.39 -approximation for the robust version and $$6.78$$ 6.78 -approximation for the stochastic version. Our results significantly improve the previous best approximation factors for the problems. In particular, we provide the first constant-factor approximation for the stochastic min-cut problem. Our algorithms are based on a guess and prune strategy that crucially exploits the nature of the robust and stochastic objective. In particular, we guess the worst-case second stage cost and based on the guess, select a subset of costly scenarios for the first-stage solution to address. The second-stage solution for any scenario is simply the min-cut (or shortest path) problem in the residual graph. The key contribution is to show that there is a near-optimal first-stage solution that completely satisfies the subset of costly scenarios that are selected by our procedure. While the guess and prune strategy is not directly applicable for the stochastic versions, we show that using a novel LP formulation, we can adapt a guess and prune algorithm for the stochastic versions. Our algorithms based on the guess and prune strategy provide insights about the applicability of this approach for more general robust and stochastic versions of combinatorial problems.

Local Approximability of Max-Min and Min-Max Linear Programs

Abstract: In a max-min LP, the objective is to maximise ω subject to A x≤1, C x≥ω 1, and x≥0. In a min-max LP, the objective is to minimise ρ subject to A x≤ρ 1, C x≥1, and x≥0. The matrices A and C are nonnegative and sparse: each row a i of A has at most ΔI positive elements, and each row c k of C has at most ΔK positive elements. We study the approximability of max-min LPs and min-max LPs in a distributed setting; in particular, we focus on local algorithms (constant-time distributed algorithms). We show that for any ΔI ≥2, ΔK ≥2, and e>0 there exists a local algorithm that achieves the approximation ratio ΔI (1−1/ΔK )+e. We also show that this result is the best possible: no local algorithm can achieve the approximation ratio ΔI (1−1/ΔK ) for any ΔI ≥2 and ΔK ≥2.