Self-Organized Ordering of Terms and Documents in NSF Awards Data

We present the results of an analysis of a text corpus of 129,000 abstracts of NSF-sponsored basic research projects between years 1990 and 2003. The methods used in the analysis include term extraction based on a reference corpus and an entropy measure, and the Self-Organizing Map algorithm for the formation of a term map and a document map. Methodologically, the basic approach is based on earlier developments, such as word category maps and the WEBSOM method, but in the level of details, we report several new aspects and quantitative comparison results between methodological variants in this article. The data covers a quite large proportion of US-based scientific research during recent years. The analysis results indicate the basic patterns discernable in the data, both at the level of the awards and at the terminology used in them

Learning Rate Adaptation for Differentially Private Learning

Peer reviewe

GPrank : an R package for detecting dynamic elements from genome-wide time series

Background: Genome-wide high-throughput sequencing (HIS) time series experiments are a powerful tool for monitoring various genomic elements over time. They can be used to monitor, for example, gene or transcript expression with RNA sequencing (RNA-seq), DNA methylation levels with bisulfite sequencing (BS-seq), or abundances of genetic variants in populations with pooled sequencing (Pool-seq). However, because of high experimental costs, the time series data sets often consist of a very limited number of time points with very few or no biological replicates, posing challenges in the data analysis. Results: Here we present the GPrank R package for modelling genome-wide time series by incorporating variance information obtained during pre-processing of the HIS data using probabilistic quantification methods or from a beta-binomial model using sequencing depth. GPrank is well-suited for analysing both short and irregularly sampled time series. It is based on modelling each time series by two Gaussian process (GP) models, namely, time-dependent and time-independent GP models, and comparing the evidence provided by data under two models by computing their Bayes factor (BF). Genomic elements are then ranked by their BFs, and temporally most dynamic elements can be identified. Conclusions: Incorporating the variance information helps GPrank avoid false positives without compromising computational efficiency. Fitted models can be easily further explored in a browser. Detection and visualisation of temporally most active dynamic elements in the genome can provide a good starting point for further downstream analyses for increasing our understanding of the studied processes.Peer reviewe

Co-Operation as an Asymmetric Form of Human-Computer Creativity. Case: Peace Machine

This theoretical paper identifies a need for a definition of asymmetric co-creativity where creativity is expected from the computational agent but not from the human user. Our co-operative creativity framework takes into account that the computational agent has a message to convey in a co-operative fashion, which introduces a trade-off on how creative the computer can be. The requirements of co-operation are identified from an interdisciplinary point of view. We divide cooperative creativity in message creativity, contextual creativity and communicative creativity. Finally these notions are applied in the context of the Peace Machine system concept.Peer reviewe