Publications

Abstract

Brain processing depends on the interactions between neuronal groups. Those interactions are governed by the pattern of anatomical connections and by yet unknown mechanisms that modulate the effective strength of a given connection. We found that the mutual influence among neuronal groups depends on the phase relation between rhythmic activities within the groups. Phase relations supporting interactions between the groups preceded those interactions by a few milliseconds, consistent with a mechanistic role. These effects were specific in time, frequency, and space, and we therefore propose that the pattern of synchronization flexibly determines the pattern of neuronal interactions.

Abstract

Set is widely used as a kind of basic data structure. However, when it is used for large scale data set the cost of storage, search and transport is overhead. The bloom filter uses a fixed size bit string to represent elements in a static set, which can reduce storage space and search cost that is a fixed constant. The time-space efficiency is achieved at the cost of a small probability of false positive in membership query. However, for many applications the space savings and locating time constantly outweigh this drawback. Dynamic bloom filter (DBF) can support concisely representation and approximate membership queries of dynamic set instead of static set. It has been proved that DBF not only possess the advantage of standard bloom filter, but also has better features when dealing with dynamic set. This paper proposes a time-dependent multiple bit-strings bloom filter (TMBF) which roots in the DBF and targets on dynamic incremental set. TMBF uses multiple bit-strings in time order to present a dynamic increasing set and uses backward searching to test whether an element is in a set. Based on the system logs from a real P2P file sharing system, the evaluation shows a 20% reduction in searching cost compared to DBF.

Abstract

This article reports on results from a broad crosslinguistic study based on data from thirty-five signed languages around the world. The study is the first of its kind, and the typological generalizations presented here cover the domain of interrogative structures as they appear across a wide range of geographically and genetically distinct signed languages. Manual and nonmanual ways of marking basic types of questions in signed languages are investigated. As a result, it becomes clear that the range of crosslinguistic variation is extensive for some subparameters, such as the structure of question-word paradigms, while other parameters, such as the use of nonmanual expressions in questions, show more similarities across signed languages. Finally, it is instructive to compare the findings from signed language typology to relevant data from spoken languages at a more abstract, crossmodality level.

Abstract

This article presents a typology of negative constructions across a substantial number of sign languages from around the globe. After situating the topic within the wider context of linguistic typology, the main negation strategies found across sign languages are described. Nonmanual negation includes the use of head movements and facial expressions for negation and is of great importance in sign languages as well as particularly interesting from a typological point of view. As far as manual signs are concerned, independent negative particles represent the dominant strategy, but there are also instances of irregular negation in most sign languages. Irregular negatives may take the form of suppletion, cliticisation, affixing, or internal modification of a sign. The results of the study lead to interesting generalisations about similarities and differences between negatives in signed and spoken languages.

Abstract

Genome-wide association studies using thousands to hundreds of thousands of single nucleotide polymorphism (SNP) markers and region-wide association studies using a dense panel of SNPs are already in use to identify disease susceptibility genes and to predict disease risk in individuals. Because these tasks become increasingly important, three different data sets were provided for the Genetic Analysis Workshop 15, thus allowing examination of various novel and existing data mining methods for both classification and identification of disease susceptibility genes, gene by gene or gene by environment interaction. The approach most often applied in this presentation group was random forests because of its simplicity, elegance, and robustness. It was used for prediction and for screening for interesting SNPs in a first step. The logistic tree with unbiased selection approach appeared to be an interesting alternative to efficiently select interesting SNPs. Machine learning, specifically ensemble methods, might be useful as pre-screening tools for large-scale association studies because they can be less prone to overfitting, can be less computer processor time intensive, can easily include pair-wise and higher-order interactions compared with standard statistical approaches and can also have a high capability for classification. However, improved implementations that are able to deal with hundreds of thousands of SNPs at a time are required.

Abstract

The Corpus NGT