Miracle-dz/UniFAR-FTUs huggingface dataset:
Nuclear reactor core modelling in multifunctional simulators

This thesis concentrates on the development of nuclear reactor core models for the APROS multifunctional simulation environment and use of the core models in various kinds of applications. The work was started in 1986 as a part of the development of the entire APROS simulation system. The aim was to create core models that would serve in a reliable manner in an interactive, modular and multifunctional simulator/plant analyser environment. One-dimensional and three-dimensional core neutronics models have been developed. Both models have two energy groups and six delayed neutron groups. The three-dimensional finite difference type core model is able to describe both BWRand PWR -type cores with quadratic fuel assemblies and VVER-type cores with hexagonal fuel assemblies. The oneand three-dimensional core neutronics models can be connected with the homogeneous, the five-equation or the six-equation thermal hydraulic models of APROS. The key feature of APROS is that the same physical models can be used in various applications. The nuclear reactor core models of APROS have been built in such a manner that the same models can be used in simulator and plant analyser applications, as well as in safety analysis. In the APROS environment the user can select the number of flow channels in the three-dimensional reactor core and either the homogeneous, the fiveor the six-equation thermal hydraulic model for these channels. The thermal hydraulic model and the number of flow channels have a decisive effect on the calculation time of the three-dimensional core model and thus, at present, these particular selections make the major difference between a safety analysis core model and a training simulator core model.
Walsh functions as surrogate model for pseudo-boolean optimization problems

Surrogate-modeling is about formulating quick-to-evaluate mathematical models, to approximate black-box and time-consuming computations or simulation tasks. Although such models are well-established to solve continuous optimization problems, very few investigations regard the optimization of combinatorial structures. These structures deal for instance with binary variables, allowing each compound in the representation of a solution to be activated or not. Still, this field of research is experiencing a sudden renewed interest, bringing to the community fresh algorithmic ideas for growing these particular surrogate models. This article proposes the first surrogate-assisted optimization algorithm (WSaO) based on the mathematical foundations of discrete Walsh functions, combined with the powerful grey-box optimization techniques in order to solve pseudo-boolean optimization problems. We conduct our experiments on a benchmark of combinatorial structures and demonstrate the accuracy, and the optimization efficiency of the proposed model. We finally highlight how Walsh surrogates may outperform the state-of-the-art surrogate models for pseudo-boolean functions.
Auto-CVE: a coevolutionary approach to evolve ensembles in automated machine learning

Automated Machine Learning (Auto-ML) is a growing field receiving a lot of attention. Several techniques are being developed to address the question of how to automate the process of defining machine learning pipelines, using diverse types of approaches and with relative success, but still this problem is far from being solved. Ensembles are frequently employed in machine learning given their better performance, when compared to the use of a single model, and higher robustness. However, until now, not much attention has been given to them in the Auto-ML field. In this sense, this work presents Auto-CVE (Automated Coevolutionary Voting Ensemble) a new approach to Auto-ML. Based on a coevolutionary model, it uses two populations (one of ensembles and another for components) to actively search for voting ensembles. When compared to the popular algorithm TPOT, Auto-CVE shows competitive results in both accuracy and computing time.
What's inside the black-box?: a genetic programming method for interpreting complex machine learning models

Interpreting state-of-the-art machine learning algorithms can be difficult. For example, why does a complex ensemble predict a particular class? Existing approaches to interpretable machine learning tend to be either local in their explanations, apply only to a particular algorithm, or overly complex in their global explanations. In this work, we propose a global model extraction method which uses multi-objective genetic programming to construct accurate, simplistic and model-agnostic representations of complex black-box estimators. We found the resulting representations are far simpler than existing approaches while providing comparable reconstructive performance. This is demonstrated on a range of datasets, by approximating the knowledge of complex black-box models such as 200 layer neural networks and ensembles of 500 trees, with a single tree.
Footprints of fitness functions in search-based software testing

Testing is technically and economically crucial for ensuring software quality. One of the most challenging testing tasks is to create test suites that will reveal potential defects in software. However, as the size and complexity of software systems increase, the task becomes more labour-intensive and manual test data generation becomes infeasible. To address this issue, researchers have proposed different approaches to automate the process of generating test data using search techniques; an area that is known as Search-Based Software Testing (SBST). SBST methods require a fitness function to guide the search to promising areas of the solution space. Over the years, a plethora of fitness functions have been proposed. Some methods use control information, others focus on goals. Deciding on what fitness function to use is not easy, as it depends on the software system under test. This work investigates the impact of software features on the effectiveness of different fitness functions. We propose the Mapping the Effectiveness of Test Automation (META) Framework which analyses the footprint of different fitness functions and creates a decision tree that enables the selection of the appropriate function based on software features.
POET: open-ended coevolution of environments and their optimized solutions

How can progress in machine learning and reinforcement learning be automated to generate its own never-ending curriculum of challenges without human intervention? The recent emergence of quality diversity (QD) algorithms offers a glimpse of the potential for such continual open-ended invention. For example, novelty search showcases the benefits of explicit novelty pressure, MAP-Elites and Innovation Engines highlight the advantage of explicit elitism within niches in an otherwise divergent process, and minimal criterion coevolution (MCC) reveals that problems and solutions can coevolve divergently. The Paired Open-Ended Trailblazer (POET) algorithm introduced in this paper combines these principles to produce a practical approach to generating an endless progression of diverse and increasingly challenging environments while at the same time explicitly optimizing their solutions. An intriguing implication is the opportunity to transfer solutions among environments, reflecting the view that innovation is a circuitous and unpredictable process. POET is tested in a 2-D obstacles course domain, where it generates diverse and sophisticated behaviors that create and solve a wide range of environmental challenges, many of which cannot be solved by direct optimization, or by a direct-path curriculum-building control algorithm. We hope that POET will inspire a new push towards open-ended discovery across many domains.
A surrogate model assisted (1+1)-ES with increased exploitation of the model

Surrogate models in black-box optimization can be exploited to different degrees. At one end of the spectrum, they can be used to provide inexpensive but inaccurate assessments of the quality of candidate solutions generated by the black-box optimization algorithm. At the other end, optimization of the surrogate model function can be used in the process of generating those candidate solutions themselves. The latter approach more fully exploits the model, but may be more susceptible to systematic model error. This paper examines the effect of the degree of exploitation of the surrogate model in the context of a simple (1 + 1)-ES. First, we analytically derive the potential gain from more fully exploiting surrogate models by using a spherically symmetric test function and a simple model for the error resulting from the use of surrogate models. We then observe the effects of increased exploitation in an evolution strategy employing Gaussian process surrogate models applied to a range of test problems. We find that the gain resulting from more fully exploiting surrogate models can be considerable.
Optimizing evolutionary CSG tree extraction

The extraction of 3D models represented by Constructive Solid Geometry (CSG) trees from point clouds is a common problem in reverse engineering pipelines as used by Computer Aided Design (CAD) tools. We propose three independent enhancements on state-of-the-art Genetic Algorithms (GAs) for CSG tree extraction: (1) A deterministic point cloud filtering mechanism that significantly reduces the computational effort of objective function evaluations without loss of geometric precision, (2) a graph-based partitioning scheme that divides the problem domain in smaller parts that can be solved separately and thus in parallel and (3) a 2-level improvement procedure that combines a recursive CSG tree redundancy removal technique with a local search heuristic, which significantly improves GA running times. We show in an extensive evaluation that our optimized GA-based approach provides faster running times and scales better with problem size compared to state-of-the-art GA-based approaches.
Single- and multi-objective game-benchmark for evolutionary algorithms

Despite a large interest in real-world problems from the research field of evolutionary optimisation, established benchmarks in the field are mostly artificial. We propose to use game optimisation problems in order to form a benchmark and implement function suites designed to work with the established COCO benchmarking framework. Game optimisation problems are real-world problems that are safe, reasonably complex and at the same time practical, as they are relatively fast to compute. We have created four function suites based on two optimisation problems previously published in the literature (TopTrumps and MarioGAN). For each of the applications, we implemented multiple instances of several scalable single- and multi-objective functions with different characteristics and fitness landscapes. Our results prove that game optimisation problems are interesting and challenging for evolutionary algorithms.
Towards better generalization in WLAN positioning systems with genetic algorithms and neural networks

The most widely used positioning system today is the GPS (Global Positioning System), which has many commercial, civil and military applications, being present in most smartphones. However, this system does not perform well in indoor locations, which poses a constraint for the positioning task on environments like shopping malls, office buildings, and other public places. In this context, WLAN positioning systems based on fingerprinting have attracted a lot of attention as a promising approach for indoor localization while using the existing infrastructure. This paper contributes to this field by presenting a methodology for developing WLAN positioning systems using genetic algorithms and neural networks. The fitness function of the genetic algorithm is based on the generalization capabilities of the network for test points that are not included in the training set. By using this approach, we have achieved state-of-the-art results with few parameters, and our method has shown to be less prone to overfitting than other techniques in the literature, showing better generalization in points that are not recorded on the radio map.
COEGAN: evaluating the coevolution effect in generative adversarial networks

Generative adversarial networks (GAN) present state-of-the-art results in the generation of samples following the distribution of the input dataset. However, GANs are difficult to train, and several aspects of the model should be previously designed by hand. Neuroevolution is a well-known technique used to provide the automatic design of network architectures which was recently expanded to deep neural networks. COEGAN is a model that uses neuroevolution and coevolution in the GAN training algorithm to provide a more stable training method and the automatic design of neural network architectures. COEGAN makes use of the adversarial aspect of the GAN components to implement coevolutionary strategies in the training algorithm. Our proposal was evaluated in the Fashion-MNIST and MNIST dataset. We compare our results with a baseline based on DCGAN and also with results from a random search algorithm. We show that our method is able to discover efficient architectures in the Fashion-MNIST and MNIST datasets. The results also suggest that COEGAN can be used as a training algorithm for GANs to avoid common issues, such as the mode collapse problem.
When mating improves on-line collective robotics

It has been long known from the theoretical work on evolution strategies, that recombination improves convergence towards better solution and improves robustness against selection error in noisy environment. We propose to investigate the effect of recombination in online embodied evolutionary robotics, where evolution is decentralized on a swarm of agents. We hypothesize that these properties can also be observed in these algorithms and thus could improve their performance. We introduce the (μ/μ, 1)-On-line EEA which use a recombination operator inspired from evolution strategies and apply it to learn three different collective robotics tasks, locomotion, item collection and item foraging. Different recombination operators are investigated and compared against a purely mutative version of the algorithm. The experiments show that, when correctly designed, recombination improves significantly the adaptation of the swarm in all scenarios.
Towards a better understanding of Protein-Protein Interaction Networks

Proteins participate in the majority of cellular processes. To determine the function of a protein it is not sufficient to solely know its sequence, its structure in isolation, or how it works individually. Additionally, we need to know how the protein interacts with other proteins in biological networks. This is because most of the proteins perform their main function through interactions. This thesis sets out to improve the understanding of protein-protein interaction networks (PPINs). For this, we propose three approaches: (1) Studying measures and methods used in social and complex networks. The methods, measures, and properties of social networks allow us to gain an understanding of PPINs via the comparison of different types of network families. We studied and evaluated models that describe social networks to see which models are useful in describing biological networks. We investigate the similarities and differences in terms of the network community profile and centrality measures. (2) Studying PPINs and their role in evolution. We are interested in the relationship of PPINs and the evolutionary changes between species. We investigate whether the centrality measures are correlated with the variability and similar-
Utility of platforms Viteks MS and Microflex LT for the identification of complex clinical isolates that require molecular methods for their taxonomic classification

Mass spectrometry has revolutionized the clinical microbiology field in America’s and Europe’s industrialized countries, for being a fast, reliable and inexpensive technique. Our study is based on the comparison of the performance of two commercial platforms, Microflex LT (Bruker Daltonics, Bremen, Germany) and Vitek MS (bioMérieux, Marcy l´Etoile, France) for the identification of unusual and hard-to-diagnose microorganisms in a Reference Laboratory in Argentina. During a four-month period (February–May 2018) the diagnostic efficiency and the concordance between both systems were assessed, and the results were compared with the polyphasic taxonomic identification of all isolates. The study included 265 isolates: 77 Gram-Negative Bacilli, 33 Gram-Positive Cocci, 40 Anaerobes, 35 Actinomycetales, 19 Fastidious Microorganisms and 61 Gram-Positive Bacilli. All procedures were practiced according to the manufacturer’s recommendations in each case by duplicate, and strictly in parallel. Other relevant factors, such as the utility of the recommended extraction protocols, reagent stability and connectivity were also evaluated. Both systems correctly identified the majority of the isolates to species and complex level (82%, 217/265). Vitex MS achieved a higher number of correct species-level identifications between the gram-positive microorganisms; however, it presented greater difficulty in the identification of non-fermenting bacilli and a higher number of incorrect identifications when the profile of the microorganism was not represented in the commercial database. Both platforms showed an excellent performance on the identification of anaerobic bacteria and fastidious species. Both systems enabled the fast and reliable identification of most of the tested isolates and were shown to be very practical for the user.
First Clinical Experience with a Carbon Fibre Reinforced PEEK Composite Plating System for Anterior Cervical Discectomy and Fusion

Carbon fibre reinforced polyether ether ketone (CFR-PEEK) is a suitable material to replace metal implants in orthopaedic surgery. The radiolucency of CFR-PEEK allows an optimal visualisation of the bone and soft tissue structures. We aimed to assess the performance and radiological and clinical outcomes of anterior cervical discectomy and fusion (ACDF) with CFR-PEEK anterior cervical plating (ACP) under first use clinical conditions. We retrospectively studied the prospectively-collected data of 42 patients who underwent ACDF with CFR-PEEK ACP between 2011 and 2016. We assessed clinical outcome (Odom’s criteria, complications) and radiological parameters (global and segmental cervical lordosis, Bridwell score for fusion, adjacent segment degeneration) preoperatively, immediately post-operatively, and after a 12-month follow-up period. Patients’ satisfaction was excellent, good, fair, and poor in 12, 19, 3, and 1 patients, respectively. Two patients developed dysphagia. No hardware failure occurred. Compared with preoperative radiographs, we observed a gain of global cervical lordosis and segmental lordosis (7.4 ± 10.1 and 5.6 ± 7.1 degrees, respectively) at the 12-month follow-up. Bridwell IF grades I, II, and III were observed in 22, 6, and 7 patients, respectively. The 12-month adjacent segment degeneration-free and adjacent segment disease-free survival rates were 93.1% and 96.3%, respectively. We observed a dysphagia rate of 5.7% and a reoperation rate of 4.8%. In conclusion, CFR-PEEK ACP shows positive outcomes in terms of implant safety, restoration of cervical lordosis, and functional recovery, and is suitable for ACDF.
Time course of the subjective visual vertical during sustained optokinetic and galvanic vestibular stimulation.

The brain is thought to use rotation cues from both the vestibular and optokinetic system to disambiguate the gravito-inertial force, as measured by the otoliths, into components of linear acceleration and gravity direction relative to the head. Hence, when the head is stationary and upright, an erroneous percept of tilt arises during optokinetic roll stimulation (OKS) or when an artificial canal-like signal is delivered by means of galvanic vestibular stimulation (GVS). It is still unknown how this percept is affected by the combined presence of both cues or how it develops over time. Here, we measured the time course of the subjective visual vertical (SVV), as a proxy of perceived head tilt, in human participants (n = 16) exposed to constant-current GVS (1 and 2 mA, cathodal and anodal) and constant-velocity OKS (30°/s CW and CCW), or their combination. In each trial, participants continuously adjusted the orientation of a visual line, which drifted randomly, to Earth-vertical. We found that both GVS and OKS evoke an exponential time course of the SVV. These time courses have different amplitudes and different time constants, 4 s and 7 s respectively, and combine linearly when the two stimulations are presented together. We discuss these results in the framework of observer theory and Bayesian state estimation.
A Third Emerging Stage for the Current Digital Society? Optimal Parenting Styles in Spain, the United States, Germany, and Brazil

We propose a new paradigm with three historical stages for an optimal parenting style (i.e., indulgent parenting style), which extends the traditional paradigm of only two stages (i.e., authoritarian and authoritative parenting styles). The three stages concur, at the same time, in different environments, context, and cultures. We studied the third stage for optimal parent–child relationships through the offspring’s personal and social well-being, with four adolescent samples from 11 to 19 years old (52.2% girls) from Spain (n = 689), the United States (n = 488), Germany (n = 606), and Brazil (n = 672). The offspring’s personal well-being was measured through self-esteem (academic, social, emotional, family, and physical), while social well-being was measured with the internalization of self-transcendence (universalism and benevolence) and conservation values (security, conformity, and tradition). The parent–child parenting style was measured through parental warmth and strictness, and the adolescents’ parents were classified into one of four groups (indulgent, authoritarian, authoritative, and neglectful). Remarkably, the greatest personal well-being was found for adolescents raised with higher parental warmth and lower parental strictness (i.e., indulgent), and the greatest social well-being was found for adolescents raised with higher parental warmth (i.e., indulgent and authoritative; p < 0.05 for all countries). Consistently, poorer personal well-being and social well-being were associated with less parental warmth (i.e., authoritarian and neglectful). Findings suggest that the parent–child relationships analyzed have a common pattern associated with personal and social well-being that coincide with a proposed third stage.