Using Self-Adaptive Evolutionary Algorithms to Evolve Dynamism-Oriented Maps for a Real Time Strategy Game

9th International Conference on Large Scale Scientific Computations. The final publication is available at link.springer.comThis work presents a procedural content generation system that uses an evolutionary algorithm in order to generate interesting maps for a real-time strategy game, called Planet Wars. Interestingness is here captured by the dynamism of games (i.e., the extent to which they are action-packed). We consider two different approaches to measure the dynamism of the games resulting from these generated maps, one based on fluctuations in the resources controlled by either player and another one based on their confrontations. Both approaches rely on conducting several games on the map under scrutiny using top artificial intelligence (AI) bots for the game. Statistic gathered during these games are then transferred to a fuzzy system that determines the map's level of dynamism. We use an evolutionary algorithm featuring self-adaptation of mutation parameters and variable-length chromosomes (which means maps of different sizes) to produce increasingly dynamic maps.TIN2011-28627-C04-01, P10-TIC-608

Reduction of the size of datasets by using evolutionary feature selection: the case of noise in a modern city

Smart city initiatives have emerged to mitigate the negative effects of a very fast growth of urban areas. Most of the population in our cities are exposed to high levels of noise that generate discomfort and different health problems. These issues may be mitigated by applying different smart cities solutions, some of them require high accurate noise information to provide the best quality of serve possible. In this study, we have designed a machine learning approach based on genetic algorithms to analyze noise data captured in the university campus. This method reduces the amount of data required to classify the noise by addressing a feature selection optimization problem. The experimental results have shown that our approach improved the accuracy in 20% (achieving an accuracy of 87% with a reduction of up to 85% on the original dataset).Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. This research has been partially funded by the Spanish MINECO and FEDER projects TIN2016-81766-REDT (http://cirti.es), and TIN2017-88213-R (http://6city.lcc.uma.es)

Current OPEs in Superconformal Theories

It's well known that in conformal theories the two- and three-point functions of a subset of the local operators-the conformal primaries-suffice, via the operator product expansion (OPE), to determine all local correlation functions of operators. It's less well known that, in superconformal theories, the OPE of superdescendants is generally undetermined from those of the superprimaries, and there is no universal notion of superconformal blocks. We recall these and related aspects of 4d (S)CFTs, and then we focus on the super operator product expansion (sOPE) of conserved currents in 4d N=1 SCFTs. The current-current OPE J(x)J(0) has applications to general gauge mediation. We show how superconformal symmetry, when combined with current conservation, determines the OPE coefficients of superconformal descendants in terms of those of the superconformal primaries. We show that only integer-spin real superconformal primary operators of vanishing R-charge, and their descendants, appear in the sOPE. We also discuss superconformal blocks for four-point functions of the conserved currents.Comment: 37 pages, 1 figure. Corrected some formulas, added reference

Constraints on chiral operators in N=2 SCFTs

Open Access, © The Authors. Article funded by SCOAP3. This article is distributed under the terms of the Creative Commons Attribution License ( CC-BY 4.0 ), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited

Evolutionary optimisation of neural network models for fish collective behaviours in mixed groups of robots and zebrafish

Animal and robot social interactions are interesting both for ethological studies and robotics. On the one hand, the robots can be tools and models to analyse animal collective behaviours, on the other hand, the robots and their artificial intelligence are directly confronted and compared to the natural animal collective intelligence. The first step is to design robots and their behavioural controllers that are capable of socially interact with animals. Designing such behavioural bio-mimetic controllers remains an important challenge as they have to reproduce the animal behaviours and have to be calibrated on experimental data. Most animal collective behavioural models are designed by modellers based on experimental data. This process is long and costly because it is difficult to identify the relevant behavioural features that are then used as a priori knowledge in model building. Here, we want to model the fish individual and collective behaviours in order to develop robot controllers. We explore the use of optimised black-box models based on artificial neural networks (ANN) to model fish behaviours. While the ANN may not be biomimetic but rather bio-inspired, they can be used to link perception to motor responses. These models are designed to be implementable as robot controllers to form mixed-groups of fish and robots, using few a priori knowledge of the fish behaviours. We present a methodology with multilayer perceptron or echo state networks that are optimised through evolutionary algorithms to model accurately the fish individual and collective behaviours in a bounded rectangular arena. We assess the biomimetism of the generated models and compare them to the fish experimental behaviours.Comment: 10 pages, 4 figure

Probing the local nature of excitons and plasmons in few-layer MoS₂

Excitons and plasmons are the two most fundamental types of collective electronic excitations occurring in solids. Traditionally, they have been studied separately using bulk techniques that probe their average energetic structure over large spatial regions. However, as the dimensions of materials and devices continue to shrink, it becomes crucial to understand how these excitations depend on local variations in the crystal- and chemical structure on the atomic scale. Here, we use monochromated low-loss scanning-transmission-electron-microscopy electron-energy-loss spectroscopy, providing the best simultaneous energy and spatial resolution achieved to-date to unravel the full set of electronic excitations in few-layer MoS₂ nanosheets over a wide energy range. Using first-principles, many-body calculations we confirm the excitonic nature of the peaks at ~ 2 and ~ 3 eV in the experimental electron-energy-loss spectrum and the plasmonic nature of higher energy-loss peaks. We also rationalise the non-trivial dependence of the electron-energy-loss spectrum on beam and sample geometry such as the number of atomic layers and distance to steps and edges. Moreover, we show that the excitonic features are dominated by the long wavelength (q = 0) components of the probing field, while the plasmonic features are sensitive to a much broader range of q-vectors, indicating a qualitative difference in the spatial character of the two types of collective excitations. Our work provides a template protocol for mapping the local nature of electronic excitations that open new possibilities for studying photo-absorption and energy transfer processes on a nanometer scale

Towards Better Integration of Surrogate Models and Optimizers

Surrogate-Assisted Evolutionary Algorithms (SAEAs) have been proven to be very effective in solving (synthetic and real-world) computationally expensive optimization problems with a limited number of function evaluations. The two main components of SAEAs are: the surrogate model and the evolutionary optimizer, both of which use parameters to control their respective behavior. These parameters are likely to interact closely, and hence the exploitation of any such relationships may lead to the design of an enhanced SAEA. In this chapter, as a first step, we focus on Kriging and the Efficient Global Optimization (EGO) framework. We discuss potentially profitable ways of a better integration of model and optimizer. Furthermore, we investigate in depth how different parameters of the model and the optimizer impact optimization results. In particular, we determine whether there are any interactions between these parameters, and how the problem characteristics impact optimization results. In the experimental study, we use the popular Black-Box Optimization Benchmarking (BBOB) testbed. Interestingly, the analysis finds no evidence for significant interactions between model and optimizer parameters, but independently their performance has a significant interaction with the objective function. Based on our results, we make recommendations on how best to configure EGO

How to Blend a Robot within a Group of Zebrafish: Achieving Social Acceptance through Real-time Calibration of a Multi-level Behavioural Model

We have previously shown how to socially integrate a fish robot into a group of zebrafish thanks to biomimetic behavioural models. The models have to be calibrated on experimental data to present correct behavioural features. This calibration is essential to enhance the social integration of the robot into the group. When calibrated, the behavioural model of fish behaviour is implemented to drive a robot with closed-loop control of social interactions into a group of zebrafish. This approach can be useful to form mixed-groups, and study animal individual and collective behaviour by using biomimetic autonomous robots capable of responding to the animals in long-standing experiments. Here, we show a methodology for continuous real-time calibration and refinement of multi-level behavioural model. The real-time calibration, by an evolutionary algorithm, is based on simulation of the model to correspond to the observed fish behaviour in real-time. The calibrated model is updated on the robot and tested during the experiments. This method allows to cope with changes of dynamics in fish behaviour. Moreover, each fish presents individual behavioural differences. Thus, each trial is done with naive fish groups that display behavioural variability. This real-time calibration methodology can optimise the robot behaviours during the experiments. Our implementation of this methodology runs on three different computers that perform individual tracking, data-analysis, multi-objective evolutionary algorithms, simulation of the fish robot and adaptation of the robot behavioural models, all in real-time.Comment: 9 pages, 3 figure

Quantitative Mass Spectrometry Analysis Using PAcIFIC for the Identification of Plasma Diagnostic Biomarkers for Abdominal Aortic Aneurysm

BACKGROUND: Abdominal aortic aneurysm (AAA) is characterized by increased aortic vessel wall diameter (>1.5 times normal) and loss of parallelism. This disease is responsible for 1-4% mortality occurring on rupture in males older than 65 years. Due to its asymptomatic nature, proteomic techniques were used to search for diagnostic biomarkers that might allow surgical intervention under nonlife threatening conditions. METHODOLOGY/PRINCIPAL FINDINGS: Pooled human plasma samples of 17 AAA and 17 control patients were depleted of the most abundant proteins and compared using a data-independent shotgun proteomic strategy, Precursor Acquisition Independent From Ion Count (PAcIFIC), combined with spectral counting and isobaric tandem mass tags. Both quantitative methods collectively identified 80 proteins as statistically differentially abundant between AAA and control patients. Among differentially abundant proteins, a subgroup of 19 was selected according to Gene Ontology classification and implication in AAA for verification by Western blot (WB) in the same 34 individual plasma samples that comprised the pools. From the 19 proteins, 12 were detected by WB. Five of them were verified to be differentially up-regulated in individual plasma of AAA patients: adiponectin, extracellular superoxide dismutase, protein AMBP, kallistatin and carboxypeptidase B2. CONCLUSIONS/SIGNIFICANCE: Plasma depletion of high abundance proteins combined with quantitative PAcIFIC analysis offered an efficient and sensitive tool for the screening of new potential biomarkers of AAA. However, WB analysis to verify the 19 PAcIFIC identified proteins of interest proved inconclusive save for five proteins. We discuss these five in terms of their potential relevance as biological markers for use in AAA screening of population at risk

FeCo/Graphite Nanocrystals for Multi-Modality Imaging of Experimental Vascular Inflammation

BACKGROUND: FeCo/graphitic-carbon nanocrystals (FeCo/GC) are biocompatible, high-relaxivity, multi-functional nanoparticles. Macrophages represent important cellular imaging targets for assessing vascular inflammation. We evaluated FeCo/GC for vascular macrophage uptake and imaging in vivo using fluorescence and MRI. METHODS AND RESULTS: Hyperlipidemic and diabetic mice underwent carotid ligation to produce a macrophage-rich vascular lesion. In situ and ex vivo fluorescence imaging were performed at 48 hours after intravenous injection of FeCo/GC conjugated to Cy5.5 (n = 8, 8 nmol of Cy5.5/mouse). Significant fluorescence signal from FeCo/GC-Cy5.5 was present in the ligated left carotid arteries, but not in the control (non-ligated) right carotid arteries or sham-operated carotid arteries (p = 0.03 for ligated vs. non-ligated). Serial in vivo 3T MRI was performed at 48 and 72 hours after intravenous FeCo/GC (n = 6, 270 µg Fe/mouse). Significant T2* signal loss from FeCo/GC was seen in ligated left carotid arteries, not in non-ligated controls (p = 0.03). Immunofluorescence staining showed colocalization of FeCo/GC and macrophages in ligated carotid arteries. CONCLUSIONS: FeCo/GC accumulates in vascular macrophages in vivo, allowing fluorescence and MR imaging. This multi-functional high-relaxivity nanoparticle platform provides a promising approach for cellular imaging of vascular inflammation