Les individus diffèrent les uns des autres dans leur manière générale de se comporter au fil du temps et entre les contextes, ainsi que dans leur habileté à ajuster leurs comportements lorsqu’un changement environnemental survient. Encore aujourd’hui, les causes proximales et ultimes de ces différences ne sont pas bien comprises. Certains défendent que différents compromis de risque liant les types comportementaux rapide/proactif-lent/réactif aux styles cognitifs rapide/inflexible-exact/flexible pourraient en partie expliquer le maintien de ces différences. Toutefois, il semble y avoir des écarts dans la littérature quant à l’existence et la nature d’un tel compromis vitesse-exactitude. Une explication serait que ce genre de relations entre la personnalité et la cognition peuvent être modérées par différents facteurs. Ici, nous explorons la possibilité que l’impulsivité d’action, l’impulsivité de choix et l’incertitude attendue interagissent sur les performances d’apprentissage et de renversement de l’apprentissage chez l’humain. Pour évaluer leur impulsivité, les participants devaient remplir le questionnaire d’impulsivité de Barratt et effectuer une tâche de signal de stop ainsi qu’une tâche de dévaluation temporelle expérientielle. Puis, leurs performances d’apprentissage et de renversement de l’apprentissage ont été mesurées avec une nouvelle tâche de renversement de l’apprentissage, sous différents niveaux d’incertitude attendue. Les résultats démontrent que les patrons de performances d’apprentissage et de renversement de l’apprentissage associés à l’impulsivité d’action dépendent de l’incertitude attendue et de l’impulsivité de choix. Ils appuient également l’idée que l’impulsivité ne serait pas inadaptée per se, ce qui pourrait en partie expliquer le maintien des différences interindividuelles d’impulsivité et de flexibilité. / Individuals differ in how they each generally behave across time and contexts, as well as in their ability to acquire new information, and flexibly adjust their behavioral responses when a change in contingencies occurs. Still today, the proximal and ultimate causes of these differences are not well understood. In recent years, some advocate that divergent risk-reward trade-offs linking fast/proactive-slow/reactive behavioral types to fast/inflexible-accurate/flexible cognitive styles could partly explain the maintenance of these differences. However, it seems that there is a discrepancy in the literature about the existence and nature of such a speed-accuracy trade-off. One explanation could be that the link between personality and cognition is moderated by different factors. Thus, we propose here an exploratory study on how impulsive actions, impulsive choices, and expected uncertainty may interact altogether on learning and reversal learning performances in humans. To assess their impulsivity, participants had to fill out the Barratt impulsiveness scale questionnaire and to complete both a stop signal task and an experiential discounting task. Then, their learning and reversal learning performances were measured in a new reversal learning task, under different levels of reinforcer uncertainty. Results show that learning and reversal learning performances patterns linked to action impulsivity depend on expected uncertainty and choice impulsivity. In addition, they also support the idea that these dimensions of impulsivity are not maladaptive per se, which may provide another line of explanation for the maintenance of variation in impulsivities and flexibility.

In der numerischen Strömungssimulation (Computational Fluid Dynamics, CFD) stellt die Berechnungsgeschwindigkeit einen kritischen Faktor dar. Insbesondere bei transienten Berechnungen oder bei der Simulation von umfangreichen Modellen können Berechnungen auf Hochleistungsrechnern mit mehreren hundert Kernen schnell zu einer zeitintensiven Aufgabe werden, die Tage oder sogar Wochen in Anspruch nimmt. Der Vortrag bietet einen detaillierten Einblick in die Möglichkeiten der GPU-Beschleunigung in Ansys Fluent und beleuchtet das Potenzial dieser innovativen Technologie. Zu Beginn wird der neue GPU-Solver in Ansys Fluent vorgestellt. Dieser Gleichungslöser nutzt die Rechenkapazität von Grafikprozessoren (GPUs), um CFD-Berechnungen durch extreme Parallelisierung effizienter durchzuführen als herkömmliche CPU-basierte Solver. Ein zusätzlicher Vorteil dieser Methode ist die signifikante Reduzierung des Energieverbrauchs und der Hardware-Investitionskosten. Im Anschluss werden Benchmarks von CPU- gegenüber GPU-basierten Lösungen anhand verschiedener Anwendungsfälle präsentiert. Diese Benchmarks verdeutlichen die Leistungsfähigkeit und Effizienz von GPU-Solvern im Vergleich zu CPU-Solvern. So kann beispielsweise die Außenumströmung eines Fahrzeugs mit dem Coupled GPU Solver zehnmal schneller auf einer Nvidia A100 GPU berechnet werden als auf herkömmlicher HPC-Hardware mit 48 Kernen. Der Vortrag bietet auch einen Überblick über den aktuellen Funktionsumfang und die zukünftige Entwicklungsroadmap von Ansys Fluent. Dies gibt einen Einblick in die aktuellen Funktionen des Tools und die geplanten Entwicklungen für die Zukunft. Ein weiterer wichtiger Aspekt sind die Lizenz- und Hardwareanforderungen. Dies hilft, die notwendigen Ressourcen für die Implementierung dieser Technologie in eigenen Projekten zu verstehen. Abschließend bietet der Vortrag einen Ausblick auf die Anwendung von Künstlicher Intelligenz (KI) für CFD. Mit der fortschreitenden Entwicklung der KI-Technologie eröffnen sich neue Möglichkeiten für die Verbesserung und Beschleunigung von CFD-Berechnungen. Insgesamt bietet der Vortrag einen umfassenden Überblick über die Anwendung von GPU-Beschleunigung in moderner CFD-Software und die zukünftigen Entwicklungen in diesem Bereich. / Calculation speed is a critical factor in computational fluid dynamics (CFD). Especially for transient calculations or the simulation of extensive models, calculations on high-performance computers with several hundred cores can quickly become a time-consuming task that takes days or even weeks. The presentation offers a detailed insight into the possibilities of GPU acceleration in Ansys Fluent and highlights the potential of this innovative technology. At the beginning, the new GPU solver in Ansys Fluent will be introduced. This solver uses the computing power of graphics processing units (GPUs) to perform CFD calculations more efficiently than conventional CPU-based solvers through extreme parallelization. An additional advantage of this method is the significant reduction in energy consumption and hardware investment costs. Subsequently, benchmarks of CPU- versus GPU-based solutions will be presented based on different use cases. These benchmarks illustrate the performance and efficiency of GPU solvers compared to CPU solvers. For example, the external airflow of a vehicle can be calculated ten times faster with the Coupled GPU Solver on an Nvidia A100 GPU than on conventional HPC hardware with 48 cores. The presentation will also provide an overview of the current range of functions and the future development roadmap.

La prise de décision est un processus computationnel fondamental dans de nombreux aspects du comportement animal. Le modèle le plus souvent rencontré dans les études portant sur la prise de décision est appelé modèle de diffusion. Depuis longtemps, il explique une grande variété de données comportementales et neurophysiologiques dans ce domaine. Cependant, un autre modèle, le modèle d’urgence, explique tout aussi bien ces mêmes données et ce de façon parcimonieuse et davantage encrée sur la théorie. Dans ce travail, nous aborderons tout d’abord les origines et le développement du modèle de diffusion et nous verrons comment il a été établi en tant que cadre de travail pour l’interprétation de la plupart des données expérimentales liées à la prise de décision. Ce faisant, nous relèveront ses points forts afin de le comparer ensuite de manière objective et rigoureuse à des modèles alternatifs. Nous réexaminerons un nombre d’assomptions implicites et explicites faites par ce modèle et nous mettrons alors l’accent sur certains de ses défauts. Cette analyse servira de cadre à notre introduction et notre discussion du modèle d’urgence. Enfin, nous présenterons une expérience dont la méthodologie permet de dissocier les deux modèles, et dont les résultats illustrent les limites empiriques et théoriques du modèle de diffusion et démontrent en revanche clairement la validité du modèle d'urgence. Nous terminerons en discutant l'apport potentiel du modèle d'urgence pour l'étude de certaines pathologies cérébrales, en mettant l'accent sur de nouvelles perspectives de recherche. / Decision‐making is a computational process of fundamental importance to many aspects of animal behavior. The prevailing model in the experimental study of decision‐making is the drift‐diffusion model, which has a long history and accounts for a broad range of behavioral and neurophysiological data. However, an alternative model – called the urgency‐gating model – has been offered which can account equally well for much of the same data in a more parsimonious and theoretically‐sound manner. In what follows, we will first trace the origins and development of the DDM, as well as give a brief overview of the manner in which it has supplied an explanatory framework for a large number of behavioral and physiological studies in the domain of decision‐making. In so doing, we will attempt to build a strong and clear case for its strengths so that it can be fairly and rigorously compared to potential alternative models. We will then re‐examine a number of the implicit and explicit theoretical assumptions made by the drift‐diffusion model, as well as highlight some of its empirical shortcomings. This analysis will serve as the contextual backdrop for our introduction and discussion of the urgency‐gating model. Finally, we present a novel experiment, the methodological design of which uniquely affords a decisive empirical dissociation of the models, the results of which illustrate the empirical and theoretical shortcomings of the drift‐diffusion model and instead offer clear support for the urgency‐gating model. We finish by discussing the potential for the urgency gating model to shed light on a number of clinical disorders, highlighting a number of future directions for research.

Este trabalho apresenta a formulação de um sistema híbrido de apoio à decisão que, através de técnicas adaptativas, permite que múltiplos dispositivos sejam utilizados de forma colaborativa para encontrar uma solução para um problema de tomada de decisão. É proposta uma estratégia particular para o trabalho colaborativo que restringe o acesso aos dispositivos mais lentos com base na dificuldade encontrada pelos dispositivos mais rápidos para solucionar um problema específico. As soluções encontradas por cada dispositivo são propagadas aos demais, permitindo que cada um deles agregue estas novas soluções com o auxílio de técnicas adaptativas. É feito um estudo sobre aprendizagem de máquina mediante incertezas para verificar e minimizar os impactos negativos que uma nova solução, possivelmente errônea, possa ter. O sistema híbrido proposto é apresentado numa aplicação particular, utilizando testes padronizados para compará-lo com os dispositivos individuais que o compõem e com sistemas híbridos de mesma finalidade. Através destes testes, é mostrado que dispositivos consolidados, mesmo que de naturezas distintas, podem ser utilizados de maneira colaborativa, permitindo não só calibrar um compromisso entre o tempo de resposta e a taxa de acerto, mas também evoluir de acordo com o histórico de problemas processados. / This work presents a formulation of a hybrid decision-making system that employs adaptive techniques as a way to coordinate multiple devices in order to make a collaborative decision. The strategy proposed here is to restrict the use of slower devices, based on how difficult the specific problem is - easier problems may be solved on faster devices. Each device is able to learn through solutions given by the others, aggregating new knowledge with the aid of adaptive techniques. In order to evaluate and minimize the negative impact those new solutions may have, a study concerning machine learning under uncertainty is carried out. A particular application of this system has been tested and compared, not only to each individual device that is part of the system itself, but to similar hybrid systems as well. It is shown that even devices of distinct natures may be reused in a collaborative manner, making it possible to calibrate the trade-off between hit rate and response time, and to evolve according to the input stimuli received as well.

Este trabalho apresenta a formulação de um sistema híbrido de apoio à decisão que, através de técnicas adaptativas, permite que múltiplos dispositivos sejam utilizados de forma colaborativa para encontrar uma solução para um problema de tomada de decisão. É proposta uma estratégia particular para o trabalho colaborativo que restringe o acesso aos dispositivos mais lentos com base na dificuldade encontrada pelos dispositivos mais rápidos para solucionar um problema específico. As soluções encontradas por cada dispositivo são propagadas aos demais, permitindo que cada um deles agregue estas novas soluções com o auxílio de técnicas adaptativas. É feito um estudo sobre aprendizagem de máquina mediante incertezas para verificar e minimizar os impactos negativos que uma nova solução, possivelmente errônea, possa ter. O sistema híbrido proposto é apresentado numa aplicação particular, utilizando testes padronizados para compará-lo com os dispositivos individuais que o compõem e com sistemas híbridos de mesma finalidade. Através destes testes, é mostrado que dispositivos consolidados, mesmo que de naturezas distintas, podem ser utilizados de maneira colaborativa, permitindo não só calibrar um compromisso entre o tempo de resposta e a taxa de acerto, mas também evoluir de acordo com o histórico de problemas processados. / This work presents a formulation of a hybrid decision-making system that employs adaptive techniques as a way to coordinate multiple devices in order to make a collaborative decision. The strategy proposed here is to restrict the use of slower devices, based on how difficult the specific problem is - easier problems may be solved on faster devices. Each device is able to learn through solutions given by the others, aggregating new knowledge with the aid of adaptive techniques. In order to evaluate and minimize the negative impact those new solutions may have, a study concerning machine learning under uncertainty is carried out. A particular application of this system has been tested and compared, not only to each individual device that is part of the system itself, but to similar hybrid systems as well. It is shown that even devices of distinct natures may be reused in a collaborative manner, making it possible to calibrate the trade-off between hit rate and response time, and to evolve according to the input stimuli received as well.