Controlling precedence in sequential stimulus presentation with Euler tours

It is important to control for stimulus history in experiments probing responses to and similarity between sequentially presented stimuli. We present a method for stimulus order randomisation that guarantees identical precedence across stimuli. Generating sequences through sampling Euler tours allows for perfectly uniform stimulus history. This deconfounds the stimulus history from the present stimulus and maximises sensitivity to stimulus history effects including repetition suppression.

Genotyping of Foot-and-Mouth Disease Viruses Collected in Sudan Between 2009 and 2018

Foot-and-mouth disease (FMD) is widely distributed in Sudan where outbreaks occur on an annual basis especially during the winter months (December-February). This study aimed to increase our understanding of the epidemiological patterns of FMD in Sudan and connections to neighbouring countries by characterising the genetic sequences of FMD viruses (FMDV) collected from seven Sudanese states over a 10-year period (between 2009 and 2018). FMDV was detected in 91 of the 265 samples using an antigen-detection ELISA. Three serotypes were detected: O (46.2%), A (34.1%), and SAT 2 (19.8%). Fifty-three of these samples were submitted for sequence analyses, generating sequences that were characterised as belonging to O/EA-3 (n=18), A/AFRICA/G-IV (n=23) and SAT 2/VII/Alx-12 (n=12) viral lineages. Phylogenetic analyses provided evidence that FMDV lineages were maintained within Sudan, and also highlighted epidemiological connections to FMD outbreaks reported in neighbouring countries in East and North Africa (such as Ethiopia and Egypt). This study motivates continued FMD surveillance in Sudan to monitor the circulating viral lineages and broader initiatives to improve our understanding of the epidemiological risks in the region.

Multiscale stick-breaking mixture models

Bayesian nonparametric density estimation is dominated by single-scale methods, typically exploiting mixture model specifications, exception made for Pólya trees prior and allied approaches. In this paper we focus on developing a novel family of multiscale stick-breaking mixture models that inherits some of the advantages of both single-scale nonparametric mixtures and Pólya trees. Our proposal is based on a mixture specification exploiting an infinitely deep binary tree of random weights that grows according to a multiscale generalization of a large class of stick-breaking processes; this multiscale stick-breaking is paired with specific stochastic processes generating sequences of parameters that induce stochastically ordered kernel functions. Properties of this family of multiscale stick-breaking mixtures are described. Focusing on a Gaussian specification, a Markov Chain Monte Carlo algorithm for posterior computation is introduced. The performance of the method is illustrated analyzing both synthetic and real datasets consistently showing competitive results both in scenarios favoring single-scale and multiscale methods. The results suggest that the method is well suited to estimate densities with varying degree of smoothness and local features.

A General-Purpose Uniform Random Number Package

As computer capacities and simulation technologies advance, simulation has become the method of choice for modeling and analysis. The fundamental advantage of simulation is that it can tolerate far less restrictive modeling assumptions, leading to an underlying model that is more reflective of reality and thus more valid, leading to better decisions. Simulation studies are typically preceded by transforming in a more or less complicated way of a sequence of numbers between 0 and 1 produced by a pseudorandom generator into an observation of the measure of interest. Random numbers are a fundamental resource in science and technology. A facility for generating sequences of pseudorandom numbers is a fundamental part of computer simulation systems. Furthermore, random number generators also play an important role in cryptography and in the blockchain ecosystem. All samples of the sequence are generated independently of each other, and the value of the next sample in the sequence cannot be predicted, regardless of how many samples have already been produced.

Prediction of Answer Keywords using Char-RNN

<p>Generating sequences of characters using a Recurrent Neural Network (RNN) is a tried and tested method for creating unique and context aware words, and is fundamental in Natural Language Processing tasks. These type of Neural Networks can also be used a question-answering system. The main drawback of most of these systems is that they work from a factoid database of information, and when queried about new and current information, the responses are usually bleak. In this paper, the author proposes a novel approach to finding answer keywords from a given body of news text or headline, based on the query that was asked, where the query would be of the nature of current affairs or recent news, with the use of Gated Recurrent Unit (GRU) variant of RNNs. Thus, this ensures that the answers provided are relevant to the content of query that was put forth.</p>