A survey of multi-task learning methods in chemoinformatics

Despite the increasing volume of available data, the proportion of experimentally measured data remains small compared to the virtual chemical space of possible chemical structures. Therefore, there is a strong interest in simultaneously predicting different ADMET and biological properties of molecules, which are frequently strongly correlated with one another. Such joint data analyses can increase the accuracy of models by exploiting their common representation and identifying common features between individual properties. In this work we review the recent developments in multi-learning approaches as well as cover the freely available tools and packages that can be used to perform such studies

Active Learning for Image Recognition Using a Visualization-Based User Interface

Limberg C, Krieger K, Wersing H, Ritter H. Active Learning for Image Recognition Using a Visualization-Based User Interface. In: Tetko IV, Kůrková V, Karpov P, Theis F, eds. Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning. Lecture Notes in Computer Science. Vol 11728. Cham: Springer; 2019: 495-506

Electrophysical Characteristics of a Polymer Composite Based on Ultrahigh Molecular Weight Polyethylene with CuO Nanoparticles

Методом импедансной спектроскопии исследованы электрофизические свойства композитного материала на основе сверхвысокомолекулярного полиэтилена с ограниченной массовой концентрацией 0,5 мас.% оксида меди CuO в диапазоне частот от 102 до 108 Гц. Предполагается, что введение в состав полимера малых концентраций наночастиц способствует более равномерному их осаждению на поверхностях полимерных гранул. Это позволяет в процессе тестирования таких образцов выявить наиболее вероятные механизмы их поляризации и протекания электрического тока в относительно однородном ансамбле наночастиц в полимерной матрице. Установлено, что внедряемые в полимерную матрицу наночастицы незначительно влияют на процессы электрической поляризации, но приводят к появлению частотно-зависимой проводимости в широком диапазоне частот. Этот процесс сопровождается существенным возрастанием диэлектрических потерь. Электрофизические характеристики полученных композитов обсуждаются с учётом переноса электрических зарядов (ионов или электронов) как по внутренней, так и по поверхностной структуре наночастиц CuOThe electrophysical properties of a composite material based on ultrahigh molecular weight polyethylene with a limited mass concentration of 0.5 wt% copper oxide CuO in the frequency range from 102 to 108 Hz were studied by impedance spectroscopy. It is assumed that the introduction of low concentrations of nanoparticles into the polymer composition contributes to their more uniform deposition on the surfaces of polymer granules. This makes it possible to reveal the most probable mechanisms of their polarization and the flow of electric current in a relatively homogeneous ensemble of nanoparticles in a polymer matrix during testing of such samples. It has been established that nanoparticles introduced into the polymer matrix have little effect on the processes of electric polarization, but lead to the appearance of frequency-dependent conductivity in a wide frequency range. This process is accompanied by a significant increase in dielectric losses. The electrophysical characteristics of the resulting composites are discussed taking into account the transfer of electric charges (ions or electrons) both along the internal and surface structures of CuO nanoparticle

Критика Юркевичем утилітаризму

This work focuses on studying players behaviour in interactive narratives with the aim to simulate their choices. Besides sub-optimal player behaviour due to limited knowledge about the environment, the difference in each player's style and preferences represents a challenge when trying to make an intelligent system mimic their actions. Based on observations from players interactions with an extract from the interactive fiction Anchorhead, we created a player profile to guide the behaviour of a generic player model based on the BDI (Belief-Desire-Intention) model of agency. We evaluated our approach using qualitative and quantitative methods and found that the player profile can improve the performance of the BDI player model. However, we found that players self-assessment did not yield accurate data to populate their player profile under our current approach.Comment: CHI Play 201

CATMoS: Collaborative Acute Toxicity Modeling Suite.

BACKGROUND: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals. OBJECTIVES: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50≤50mg/kg)], and nontoxic chemicals (LD50>2,000mg/kg). METHODS: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. RESULTS: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results. DISCUSSION: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495

Human-assisted neuroevolution through shaping, advice and examples

Many different methods for combining human expertise with machine learning in general, and evolutionary computation in particular, are possible. Which of these methods work best, and do they outperform human design and machine design alone? In order to answer this question, a humansubject experiment for comparing human-assisted machine learning methods was conducted. Three different approaches, i.e. advice, shaping, and demonstration, were employed to assist a powerful machine learning technique (neuroevolution) on a collection of agent training tasks, and contrasted with both a completely manual approach (scripting) and a completely hands-off one (neuroevolution alone). The results show that, (1) human-assisted evolution outperforms a manual scripting approach, (2) unassisted evolution perform