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.

Quantitative Power Consumption Analysis of a Multi-tier Wireless Multimedia Sensor Network

Abstract: Wireless multimedia sensor networks provide a platform of interconnected devices that are able to ubiquitously retrieve multimedia content. The provided content may include video and audio streams and still images in addition to traditional scalar data such as temperature, humidity or light intensity. In this paper, we implement a video surveillance application on a multi-tier wireless multimedia sensor network. The key contributions of this work are the implementation of the given network architecture on Libelium Waspmote platform and the evaluation of power consumption between single-tier and multi-tier architectures. We report power consumption of the system with respect to different scenarios and states such as video acquisition, processing, and uploading. The results demonstrate that with the selected hardware platform, the total energy consumption of a video surveillance scenario can be halved by using multi-tier compared with a single-tier architecture.

HYDROSYS: a mixed reality platform for on-site visualization of environmental data

Abstract: Modern mobile devices and networks facilitate the development of increasingly graphical and complex mobile GIS or geomedia applications. We advance the state-of-the-art with the HYDROSYS system, a heterogeneous platform integrating multiple data sources and components, allowing interactive environmental data analysis and monitoring in the field. Our key contributions are 1) scalable on-the-fly streaming of environmental sensor data, 2) highly graphical mixed reality representations embedding multivariate sensor data visualizations, including access to results of near real time simulations and other physically-based computations, 3) support for semantically meaningful public participation. We balance the use of standardized formats and optimized mobile transmissions. Our system builds on use cases ranging from hydrological professionals and municipal authorities to the environmentally-aware public. We present the challenges, solutions and implementation of the system, providing observations on potential bottlenecks.

Group-sparse Embeddings in Collective Matrix Factorization

Abstract: CMF is a technique for simultaneously learning low-rank representations based on a collection of matrices with shared entities. A typical example is the joint modeling of user-item, item-property, and user-feature matrices in a recommender system. The key idea in CMF is that the embeddings are shared across the matrices, which enables transferring information between them. The existing solutions, however, break down when the individual matrices have low-rank structure not shared with others. In this work we present a novel CMF solution that allows each of the matrices to have a separate low-rank structure that is independent of the other matrices, as well as structures that are shared only by a subset of them. We compare MAP and variational Bayesian solutions based on alternating optimization algorithms and show that the model automatically infers the nature of each factor using group-wise sparsity. Our approach supports in a principled way continuous, binary and count observations and is efficient for sparse matrices involving missing data. We illustrate the solution on a number of examples, focusing in particular on an interesting use-case of augmented multi-view learning.

Genomic Epidemiology and Evolution of Escherichia coli in Wild Animals in Mexico

Abstract: Escherichia coli is a common bacterial species in the gastrointestinal tracts of warm-blooded animals and humans. Pathogenicity and antimicrobial resistance in E. coli may emerge via host switching from animal reservoirs. Despite its potential clinical importance, knowledge of the population structure of commensal E. coli within wild hosts and the epidemiological links between E. coli in nonhuman hosts and E. coli in humans is still scarce. In this study, we analyzed the whole-genome sequencing data of a collection of 119 commensal E. coli strains recovered from the guts of 55 mammal and bird species in Mexico and Venezuela in the 1990s. We observed low concordance between the population structures of E. coli isolates colonizing wild animals and the phylogeny, taxonomy, and ecological and physiological attributes of the host species, with distantly related E. coli strains often colonizing the same or similar host species and distantly related host species often hosting closely related E. coli strains. We found no evidence for recent transmission of E. coli genomes from wild animals to either domesticated animals or humans. However, multiple livestock- and human-related virulence factor genes were present in E. coli of wild animals, including virulence factors characteristic of Shiga toxin-producing E. coli (STEC) and atypical enteropathogenic E. coli (aEPEC), where several isolates from wild hosts harbored the locus of enterocyte effacement (LEE) pathogenicity island. Moreover, E. coli isolates from wild animal hosts often harbored known antibiotic resistance determinants, including those against ciprofloxacin, aminoglycosides, tetracyclines, and beta-lactams, with some determinants present in multiple, distantly related E. coli lineages colonizing very different host animals. We conclude that genome pools of E. coli colonizing the guts of wild animals and humans share virulence and antibiotic resistance genes, underscoring the idea that wild animals could serve as reservoirs for E. coli pathogenicity in human and livestock infections. IMPORTANCEEscherichia coli is a clinically important bacterial species implicated in human- and livestock-associated infections worldwide. The bacterium is known to reside in the guts of humans, livestock, and wild animals. Although wild animals are recognized as potential reservoirs for pathogenic E. coli strains, the knowledge of the population structure of E. coli in wild hosts is still scarce. In this study, we used fine resolution of whole-genome sequencing to provide novel insights into the evolution of E. coli genomes from a small yet diverse collection of strains recovered within a broad range of wild animal species (including mammals and birds), the coevolution of E. coli strains with their hosts, and the genetics of pathogenicity of E. coli strains in wild hosts in Mexico. Our results provide evidence for the clinical importance of wild animals as reservoirs for pathogenic strains and highlight the need to include nonhuman hosts in the surveillance programs for E. coli infections.

Generative Modeling for Maximizing Precision and Recall in Information Visualization

Abstract: Information visualization has recently been formulated as an information retrieval problem, where the goal is to find similar data points based on the visualized nonlinear projection, and the visualization is optimized to maximize a compromise between (smoothed) precision and recall. We turn the visualization into a generative modeling task where a simple user model parameterized by the data coordinates is optimized, neighborhood relations are the observed data, and straightforward maximum likelihood estimation corresponds to Stochastic Neighbor Embedding (SNE). While SNE maximizes pure recall, adding a mixture component that “explains away” misses allows our generative model to focus on maximizing precision as well. The resulting model is a generative solution to maximizing tradeoffs between precision and recall. The model outperforms earlier models in terms of precision and recall and in external validation by unsupervised classification.