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.

Speciation trajectories in recombining bacterial species.

Abstract: It is generally agreed that bacterial diversity can be classified into genetically and ecologically cohesive units, but what produces such variation is a topic of intensive research. Recombination may maintain coherent species of frequently recombining bacteria, but the emergence of distinct clusters within a recombining species, and the impact of habitat structure in this process are not well described, limiting our understanding of how new species are created. Here we present a model of bacterial evolution in overlapping habitat space. We show that the amount of habitat overlap determines the outcome for a pair of clusters, which may range from fast clonal divergence with little interaction between the clusters to a stationary population structure, where different clusters maintain an equilibrium distance between each other for an indefinite time. We fit our model to two data sets. In Streptococcus pneumoniae, we find a genomically and ecologically distinct subset, held at a relatively constant genetic distance from the majority of the population through frequent recombination with it, while in Campylobacter jejuni, we find a minority population we predict will continue to diverge at a higher rate. This approach may predict and define speciation trajectories in multiple bacterial species.

Genomic signatures of domestication in Old World camels

Abstract: Domestication begins with the selection of animals showing less fear of humans. In most domesticates, selection signals for tameness have been superimposed by intensive breeding for economical or other desirable traits. Old World camels, conversely, have maintained high genetic variation and lack secondary bottlenecks associated with breed development. By re-sequencing multiple genomes from dromedaries, Bactrian camels, and their endangered wild relatives, here we show that positive selection for candidate genes underlying traits collectively referred to as ‘domestication syndrome’ is consistent with neural crest deficiencies and altered thyroid hormone-based signaling. Comparing our results with other domestic species, we postulate that the core set of domestication genes is considerably smaller than the pan-domestication set – and overlapping genes are likely a result of chance and redundancy. These results, along with the extensive genomic resources provided, are an important contribution to understanding the evolutionary history of camels and the genomic features of their domestication. Robert R. Fitak et al. investigate the genetic basis for domestication in camels. They found that the positive selection of candidate domestication genes is consistent with neural crest deficiencies and altered thyroid hormone-based signaling. Their work provides insights to the evolutionary history of camels and genetics of domestication.

The fragmentation of attention in mobile interaction, and what to do with it

Abstract: to \" make place \" for using their mobile devices; for instance, a pedestrian might need to stop walking to write a text message—a situation we're all familiar with nowadays. In a line of research on mobile cognition, our goal has been first to understand how serious this \" multitasking craziness \" [2], or fragmentation of attention as we see it [4], is, and also to explore some possibilities to counter this unwanted phenomenon. T Th he e u un nb be ea ar ra ab bl le e c co os st t o of f m mo ob bi il li it ty y.. To make a long story short, we conducted a field experiment (see Figure 1) to investigate the seriousness and extent of fragmentation. The data conveyed the impulsive and drastically short-term nature of attention \" in the wild. \" Two measures represented in Figure 2 are particularly illuminating: the span of attention and the frequency of shifting. In mobile situations, continuous attention to the mobile device fragmented to bursts of just four to eight seconds from the 16 seconds of the laboratory, and attention to the mobile device had to be interrupted several attention shifts, by glancing the environment up to eight times during a page loading (in comparison to under one in the laboratory condition)! (Moreover, the real differences are most likely even more whopping since results from our laboratory condition probably exhibited a ceiling effect.) Others' recent findings also suggest that attention in the office is much, much less fragmented, the span being approximately three minutes [2], depending on the way it is operationalized. Clearly, going mobile really takes multitasking to an extreme where interaction and attention break down to bursts of just a few seconds. Interestingly, we observed several strategies that users adopted to compensate for this unwanted situation. In general, the simple strategies can be described as strategic withdrawals of resources from less important tasks (e.g., slowing down walking, or postponing and refusing tasks). More sophisticated strategies were enabled by users' preknowledge of the particular situation. For example, when a metro leaves from the station, travelers \" preprogram \" themselves to what is to be expected; in this case to the announcement of the destination station. After this calibration, only brief sampling is required to observe that the task is proceeding normally. Given all this, it is interesting to note that …

A Deep Interpretation of Classifier Chains

Abstract: In the “classifier chains” (CC) approach for multi-label classification, the predictions of binary classifiers are cascaded along a chain as additional features. This method has attained high predictive performance, and is receiving increasing analysis and attention in the recent multi-label literature, although a deep understanding of its performance is still taking shape. In this paper, we show that CC gets predictive power from leveraging labels as additional stochastic features, contrasting with many other methods, such as stacking and error correcting output codes, which use label dependence only as kind of regularization. CC methods can learn a concept which these cannot, even supposing the same base classifier and hypothesis space. This leads us to connections with deep learning (indeed, we show that CC is competitive precisely because it is a deep learner), and we employ deep learning methods – showing that they can supplement or even replace a classifier chain. Results are convincing, and throw new insight into promising future directions.

Multi-label Classification with Meta-Labels

Abstract: The area of multi-label classification has rapidly developed in recent years. It has become widely known that the baseline binary relevance approach can easily be outperformed by methods which learn labels together. A number of methods have grown around the label power set approach, which models label combinations together as class values in a multi-class problem. We describe the label-power set-based solutions under a general framework of meta-labels and provide some theoretical justification for this framework which has been lacking, explaining how meta-labels essentially allow a random projection into a space where non-linearities can easily be tackled with established linear learning algorithms. The proposed framework enables comparison and combination of related approaches to different multi-label problems. We present a novel model in the framework and evaluate it empirically against several high-performing methods, with respect to predictive performance and scalability, on a number of datasets and evaluation metrics. This deployment obtains competitive accuracy for a fraction of the computation required by the current meta-label methods for multi-label classification.