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.

Randomization techniques for assessing the significance of gene periodicity results.

Abstract: Modern high-throughput measurement technologies such as DNA microarrays and next generation sequencers produce extensive datasets. With large datasets the emphasis has been moving from traditional statistical tests to new data mining methods that are capable of detecting complex patterns, such as clusters, regulatory networks, or time series periodicity. Study of periodic gene expression is an interesting research question that also is a good example of challenges involved in the analysis of high-throughput data in general. Unlike for classical statistical tests, the distribution of test statistic for data mining methods cannot be derived analytically. We describe the randomization based approach to significance testing, and show how it can be applied to detect periodically expressed genes. We present four randomization methods, three of which have previously been used for gene cycle data. We propose a new method for testing significance of periodicity in gene expression short time series data, such as from gene cycle and circadian clock studies. We argue that the underlying assumptions behind existing significance testing approaches are problematic and some of them unrealistic. We analyze the theoretical properties of the existing and proposed methods, showing how our method can be robustly used to detect genes with exceptionally high periodicity. We also demonstrate the large differences in the number of significant results depending on the chosen randomization methods and parameters of the testing framework. By reanalyzing gene cycle data from various sources, we show how previous estimates on the number of gene cycle controlled genes are not supported by the data. Our randomization approach combined with widely adopted Benjamini-Hochberg multiple testing method yields better predictive power and produces more accurate null distributions than previous methods. Existing methods for testing significance of periodic gene expression patterns are simplistic and optimistic. Our testing framework allows strict levels of statistical significance with more realistic underlying assumptions, without losing predictive power. As DNA microarrays have now become mainstream and new high-throughput methods are rapidly being adopted, we argue that not only there will be need for data mining methods capable of coping with immense datasets, but there will also be need for solid methods for significance testing.

Inferring task-relevant image regions from gaze data

Abstract: A number of studies have recently used eye movements of a user inspecting the content as implicit relevance feedback for proactive retrieval systems. Typically binary feedback for images or text paragraphs is inferred from the gaze pattern. We seek to make such feedback richer for image retrieval, by inferring which parts of the image the user found relevant. For this purpose, we present a novel Bayesian mixture model for inferring possible target regions directly from gaze data alone, and show how the relevance of those regions can then be inferred using a simple classifier that is independent of the content or the task

Towards psychological customization of information for individuals and social groups

Abstract: 1. INTRODUCTION When perceiving information via media and communications technologies, the mind is psychologically transported into a quasi-natural experience of the events described. This is called presence. In presence, information becomes the focused object of perception, while the immediate, external context, including the technological device, fades into the background (Biocca and Levy, 1995; Lombard and Ditton, 1997; Lombard et al, 2000). Various empirical studies show that information experienced in presence has real psychological effects on perceivers, such as emotion based on the events described or cognition of making sense of the events and learning about them (Reeves and Nass, 1996). When using collaborative technology for computer-mediated social interaction, the users experience a state called social presence during which users may, for instance, experience intimacy of interaction or feeling of togetherness in virtual space (Lombard and Ditton, 1997; Lombard et al, 2000). During social presence users also experience various other types of emotional and cognitive effects, such as interpersonal emotion, emotion based on being successful at the task at hand and learning from shared activities or shared information. However, in the context of HCI the psychological effects occurring in computer mediated social interaction have not been thoroughly researched. Moreover, there is a need to integrate and implement the mind-based individual-centric and social interaction-centric approaches to emotional and cognitive effects at the level of system design. Communication systems may be considered as consisting of three layers (Benkler, 2000). At the bottom is a

Maximum thick paths in static and dynamic environments

Abstract: We consider the problem of finding a large number of disjoint paths for unit disks moving amidst static or dynamic obstacles. The problem is motivated by the capacity estimation problem in air traffic management, in which one must determine how many aircraft can safely move through a domain while avoiding each other and avoiding ''no-fly zones'' and predicted weather hazards. For the static case we give efficient exact algorithms, based on adapting the ''continuous uppermost path'' paradigm. As a by-product, we establish a continuous analogue of Menger's Theorem. Next we study the dynamic problem in which the obstacles may move, appear and disappear, and otherwise change with time in a known manner; in addition, the disks are required to enter/exit the domain during prescribed time intervals. Deciding the existence of just one path, even for a 0-radius disk, moving with bounded speed is NP-hard, as shown by Canny and Reif [J. Canny, J.H. Reif, New lower bound techniques for robot motion planning problems, in: Proc. 28th Annu. IEEE Sympos. Found. Comput. Sci., 1987, pp. 49-60]. Moreover, we observe that determining the existence of a given number of paths is hard even if the obstacles are static, and only the entry/exit time intervals are specified for the disks. This motivates studying ''dual'' approximations, compromising on the radius of the disks and on the maximum speed of motion. Our main result is a pseudopolynomial-time dual-approximation algorithm. If K unit disks, each moving with speed at most 1, can be routed through an environment, our algorithm finds (at least) K paths for disks of radius somewhat smaller than 1 moving with speed somewhat larger than 1.