Effectiveness of different trap design in mass trapping of Bothynoderes punctiventris Germar

The discovery of an aggregation attractant for Bothynoderes punctiventris Germ. raised several questions for possible improvements of IPM of Bothynoderes punctiventris in sugar beet. First results on exploration of possibilities for its use for monitoring purposes as well as for mass trapping of adults of the pest are described in this paper. Trap design effectiveness was evaluated in the overwintering fields of sugar beet weevil for two years in localities in Serbia and Hungary. Among trap designs tested it was proved that baited CSALOMON® TAL trap design was optimal

Acquisition of Chess Knowledge in AlphaZero

What is learned by sophisticated neural network agents such as AlphaZero? This question is of both scientific and practical interest. If the representations of strong neural networks bear no resemblance to human concepts, our ability to understand faithful explanations of their decisions will be restricted, ultimately limiting what we can achieve with neural network interpretability. In this work we provide evidence that human knowledge is acquired by the AlphaZero neural network as it trains on the game of chess. By probing for a broad range of human chess concepts we show when and where these concepts are represented in the AlphaZero network. We also provide a behavioural analysis focusing on opening play, including qualitative analysis from chess Grandmaster Vladimir Kramnik. Finally, we carry out a preliminary investigation looking at the low-level details of AlphaZero's representations, and make the resulting behavioural and representational analyses available online.Comment: 69 pages, 44 figure

Automatic Categorization of Human-coded and Evolved CoreWar Warriors

Abstract. CoreWar is a computer simulation devised in the 1980s where programs loaded into a virtual memory array compete for control over the virtual machine. These programs are written in a special-purpose assembly language called Redcode and referred to as warriors. A great variety of environments and battle strategies have emerged over the years, leading to formation of different warrior types. This paper deals with the problem of automatic warrior categorization, presenting results of classification based on several approaches to warrior representation, and offering insight into ambiguities concerning the identification of strategic classes. Over 600 human-coded warriors were annotated, forming a training set for classification. Several major classifiers were used, SVMs proving to be the most reliable, reaching accuracy of 84%. Classification of an evolved warrior set using the trained classifiers was also conducted. The obtained results proved helpful in outlining the issues with both automatic and manual Redcode program categorization

Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records

Early prediction of patient outcomes is important for targeting preventive care. This protocol describes a practical workflow for developing deep-learning risk models that can predict various clinical and operational outcomes from structured electronic health record (EHR) data. The protocol comprises five main stages: formal problem definition, data pre-processing, architecture selection, calibration and uncertainty, and generalizability evaluation. We have applied the workflow to four endpoints (acute kidney injury, mortality, length of stay and 30-day hospital readmission). The workflow can enable continuous (e.g., triggered every 6 h) and static (e.g., triggered at 24 h after admission) predictions. We also provide an open-source codebase that illustrates some key principles in EHR modeling. This protocol can be used by interdisciplinary teams with programming and clinical expertise to build deep-learning prediction models with alternate data sources and prediction tasks

AI for social good: unlocking the opportunity for positive impact

Advances in machine learning (ML) and artificial intelligence (AI) present an opportunity to build better tools and solutions to help address some of the world’s most pressing challenges, and deliver positive social impact in accordance with the priorities outlined in the United Nations’ 17 Sustainable Development Goals (SDGs). The AI for Social Good (AI4SG) movement aims to establish interdisciplinary partnerships centred around AI applications towards SDGs. We provide a set of guidelines for establishing successful long-term collaborations between AI researchers and application-domain experts, relate them to existing AI4SG projects and identify key opportunities for future AI applications targeted towards social good

Resting State fMRI Functional Connectivity Analysis Using Dynamic Time Warping

Traditional resting-state network concept is based on calculating linear dependence of spontaneous low frequency fluctuations of the BOLD signals of different brain areas, which assumes temporally stable zero-lag synchrony across regions. However, growing amount of experimental findings suggest that functional connectivity exhibits dynamic changes and a complex time-lag structure, which cannot be captured by the static zero-lag correlation analysis. Here we propose a new approach applying Dynamic Time Warping (DTW) distance to evaluate functional connectivity strength that accounts for non-stationarity and phase-lags between the observed signals. Using simulated fMRI data we found that DTW captures dynamic interactions and it is less sensitive to linearly combined global noise in the data as compared to traditional correlation analysis. We tested our method using resting-state fMRI data from repeated measurements of an individual subject and showed that DTW analysis results in more stable connectivity patterns by reducing the within-subject variability and increasing robustness for preprocessing strategies. Classification results on a public dataset revealed a superior sensitivity of the DTW analysis to group differences by showing that DTW based classifiers outperform the zero-lag correlation and maximal lag cross-correlation based classifiers significantly. Our findings suggest that analysing resting-state functional connectivity using DTW provides an efficient new way for characterizing functional networks