MATSuMoTo: The MATLAB Surrogate Model Toolbox For Computationally Expensive Black-Box Global Optimization Problems

MATSuMoTo is the MATLAB Surrogate Model Toolbox for computationally expensive, black-box, global optimization problems that may have continuous, mixed-integer, or pure integer variables. Due to the black-box nature of the objective function, derivatives are not available. Hence, surrogate models are used as computationally cheap approximations of the expensive objective function in order to guide the search for improved solutions. Due to the computational expense of doing a single function evaluation, the goal is to find optimal solutions within very few expensive evaluations. The multimodality of the expensive black-box function requires an algorithm that is able to search locally as well as globally. MATSuMoTo is able to address these challenges. MATSuMoTo offers various choices for surrogate models and surrogate model mixtures, initial experimental design strategies, and sampling strategies. MATSuMoTo is able to do several function evaluations in parallel by exploiting MATLAB's Parallel Computing Toolbox.Comment: 13 pages, 7 figure

On the Acquisition of Polarity Items: 11- to 12-Year-Olds' Comprehension of German NPIs and PPIs

Existing work on the acquisition of polarity-sensitive expressions (PSIs) suggests that children show an early sensitivity to the restricted distribution of negative polarity items (NPIs), but may be delayed in the acquisition of positive polarity items (PPIs). However, past studies primarily targeted PSIs that are highly frequent in children’s language input. In this paper, we report an experimental investigation on children’s comprehension of two NPIs and two PPIs in German. Based on corpus data indicating that the four tested PSIs are present in child-directed speech but rare in young children’s utterances, we conducted an auditory rating task with adults and 11- to 12-year-old children. The results demonstrate that, even at 11–12 years of age, children do not yet show a completely target-like comprehension of the investigated PSIs. While they are adult-like in their responses to one of the tested NPIs, their responses did not demonstrate a categorical distinction between licensed and unlicensed PSI uses for the other tested expressions. The effect was led by a higher acceptance of sentences containing unlicensed PSIs, indicating a lack of awareness for their distributional restrictions. The results of our study pose new questions for the developmental time scale of the acquisition of polarity items.Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659Universität Osnabrück (3158)Peer Reviewe

Surrogate Optimization of Deep Neural Networks for Groundwater Predictions

Sustainable management of groundwater resources under changing climatic conditions require an application of reliable and accurate predictions of groundwater levels. Mechanistic multi-scale, multi-physics simulation models are often too hard to use for this purpose, especially for groundwater managers who do not have access to the complex compute resources and data. Therefore, we analyzed the applicability and performance of four modern deep learning computational models for predictions of groundwater levels. We compare three methods for optimizing the models' hyperparameters, including two surrogate model-based algorithms and a random sampling method. The models were tested using predictions of the groundwater level in Butte County, California, USA, taking into account the temporal variability of streamflow, precipitation, and ambient temperature. Our numerical study shows that the optimization of the hyperparameters can lead to reasonably accurate performance of all models (root mean squared errors of groundwater predictions of 2 meters or less), but the ''simplest'' network, namely a multilayer perceptron (MLP) performs overall better for learning and predicting groundwater data than the more advanced long short-term memory or convolutional neural networks in terms of prediction accuracy and time-to-solution, making the MLP a suitable candidate for groundwater prediction.Comment: submitted to Journal of Global Optimization; main paper: 25 pages, 19 figures, 1 table; online supplement: 11 pages, 18 figures, 3 table

FORMAÇÃO INICIAL DE PROFESSORES PARA USO DAS TECNOLOGIAS: A APROPRIAÇÃO DO CONHECIMENTO TECNOLÓGICO EXPRESSO NO PROJETO PEDAGÓGICO DE CURSO

FORMAÇÃO INICIAL DE PROFESSORES PARA USO DAS TECNOLOGIAS: A APROPRIAÇÃO DO CONHECIMENTO TECNOLÓGICO EXPRESSO NO PROJETO PEDAGÓGICO DE CURS

Role of Cystathionine Gamma-Lyase in Immediate Renal Impairment and Inflammatory Response in Acute Ischemic Kidney Injury

Hydrogen sulfide (H2S) is known to act protectively during renal ischemia/reperfusion injury (IRI). However, the role of the endogenous H2S in acute kidney injury (AKI) is largely unclear. Here, we analyzed the role of cystathionine gamma-lyase (CTH) in acute renal IRI using CTH-deficient (Cth−/−) mice whose renal H2S levels were approximately 50% of control (wild- type) mice. Although levels of serum creatinine and renal expression of AKI marker proteins were equivalent between Cth−/− and control mice, histological analysis revealed that IRI caused less renal tubular damage in Cth−/− mice. Flow cytometric analysis revealed that renal population of infiltrated granulocytes/macrophages was equivalent in these mice. However, renal expression levels of certain inflammatory cytokines/adhesion molecules believed to play a role in IRI were found to be lower after IRI only in Cth−/− mice. Our results indicate that the systemic CTH loss does not deteriorate but rather ameliorates the immediate AKI outcome probably due to reduced inflammatory responses in the kidney. The renal expression of CTH and other H2S-producing enzymes was markedly suppressed after IRI, which could be an integrated adaptive response for renal cell protection