Search results for "force"
showing 10 items of 3423 documents
New evidence for chunk-based models in word segmentation.
2014
International audience; : There is large evidence that infants are able to exploit statistical cues to discover the words of their language. However, how they proceed to do so is the object of enduring debates. The prevalent position is that words are extracted from the prior computation of statistics, in particular the transitional probabilities between syllables. As an alternative, chunk-based models posit that the sensitivity to statistics results from other processes, whereby many potential chunks are considered as candidate words, then selected as a function of their relevance. These two classes of models have proven to be difficult to dissociate. We propose here a procedure, which lea…
Personality and reinforcement: An exploration using a maze-learning task
1995
A computerized maze learning task was investigated under control, reward and punishment, provided by differing financial reinforcement contingencies. The relationships between speed crossing the maze and anxiety and impulsivity personality traits were explored. Anxiety is hypothesized to reflect a behavioural inhibition system active in punishing environments; and impulsivity, to reflect an activation system active in rewarding environments. Of the measures of impulsivity taken, only one—venturesomeness from the I7—was associated significantly with increased maze crossing speed; this was found particularly in the reward condition and in males. Several anxiety variables were associated with …
Know your full potential: Quantitative Kelvin probe force microscopy on nanoscale electrical devices
2018
In this study we investigate the influence of the operation method in Kelvin probe force microscopy (KPFM) on the measured potential distribution. KPFM is widely used to map the nanoscale potential distribution in operating devices, e.g., in thin film transistors or on cross sections of functional solar cells. Quantitative surface potential measurements are crucial for understanding the operation principles of functional nanostructures in these electronic devices. Nevertheless, KPFM is prone to certain imaging artifacts, such as crosstalk from topography or stray electric fields. Here, we compare different amplitude modulation (AM) and frequency modulation (FM) KPFM methods on a reference s…
Can Interpretable Reinforcement Learning Manage Prosperity Your Way?
2022
Personalisation of products and services is fast becoming the driver of success in banking and commerce. Machine learning holds the promise of gaining a deeper understanding of and tailoring to customers’ needs and preferences. Whereas traditional solutions to financial decision problems frequently rely on model assumptions, reinforcement learning is able to exploit large amounts of data to improve customer modelling and decision-making in complex financial environments with fewer assumptions. Model explainability and interpretability present challenges from a regulatory perspective which demands transparency for acceptance; they also offer the opportunity for improved insight into and unde…
Deep Q-Learning With Q-Matrix Transfer Learning for Novel Fire Evacuation Environment
2021
We focus on the important problem of emergency evacuation, which clearly could benefit from reinforcement learning that has been largely unaddressed. Emergency evacuation is a complex task which is difficult to solve with reinforcement learning, since an emergency situation is highly dynamic, with a lot of changing variables and complex constraints that makes it difficult to train on. In this paper, we propose the first fire evacuation environment to train reinforcement learning agents for evacuation planning. The environment is modelled as a graph capturing the building structure. It consists of realistic features like fire spread, uncertainty and bottlenecks. We have implemented the envir…
Deep RTS: A Game Environment for Deep Reinforcement Learning in Real-Time Strategy Games
2018
Reinforcement learning (RL) is an area of research that has blossomed tremendously in recent years and has shown remarkable potential for artificial intelligence based opponents in computer games. This success is primarily due to the vast capabilities of convolutional neural networks, that can extract useful features from noisy and complex data. Games are excellent tools to test and push the boundaries of novel RL algorithms because they give valuable insight into how well an algorithm can perform in isolated environments without the real-life consequences. Real-time strategy games (RTS) is a genre that has tremendous complexity and challenges the player in short and long-term planning. The…
Expanding the Active Inference Landscape: More Intrinsic Motivations in the Perception-Action Loop
2018
Active inference is an ambitious theory that treats perception, inference and action selection of autonomous agents under the heading of a single principle. It suggests biologically plausible explanations for many cognitive phenomena, including consciousness. In active inference, action selection is driven by an objective function that evaluates possible future actions with respect to current, inferred beliefs about the world. Active inference at its core is independent from extrinsic rewards, resulting in a high level of robustness across e.g.\ different environments or agent morphologies. In the literature, paradigms that share this independence have been summarised under the notion of in…
Expert Q-learning: Deep Reinforcement Learning with Coarse State Values from Offline Expert Examples
2022
In this article, we propose a novel algorithm for deep reinforcement learning named Expert Q-learning. Expert Q-learning is inspired by Dueling Q-learning and aims at incorporating semi-supervised learning into reinforcement learning through splitting Q-values into state values and action advantages. We require that an offline expert assesses the value of a state in a coarse manner using three discrete values. An expert network is designed in addition to the Q-network, which updates each time following the regular offline minibatch update whenever the expert example buffer is not empty. Using the board game Othello, we compare our algorithm with the baseline Q-learning algorithm, which is a…
Cross-sublattice Spin Pumping and Magnon Level Attraction in van der Waals Antiferromagnets
2020
We theoretically study spin pumping from a layered van der Waals antiferromagnet in its canted ground state into an adjacent normal metal. We find that the resulting dc spin pumping current bears contributions along all spin directions. Our analysis allows for detecting intra- and cross-sublattice spin-mixing conductances via measuring the two in-plane spin current components. We further show that sublattice symmetry-breaking Gilbert damping can be realized via interface engineering and induces a dissipative coupling between the optical and acoustic magnon modes. This realizes magnon level attraction and exceptional points in the system. Furthermore, the dissipative coupling and cross-subla…
Concrete columns confined with fibre reinforced cementitious mortars: Experimentation and modelling
2014
Abstract The structural behaviour of concrete columns strengthened with a system made up of fibre nets embedded in an inorganic stabilized cementitious matrix under an uniaxial load was investigated. Medium size specimens with circular and square cross-section were cast and subjected to monotonic uniaxial compression, to investigate the efficiency of a p-Phenylene BenzobisOxazole (PBO) Fibre Reinforced Cementitious Mortar (FRCM) system in increasing both strength and ductility. The experimental results show that the confinement system adopted produced a noticeable increment in strength and ductility, though the low mechanical ratios of fibre considered were not always able to ensure hardeni…