Modelling Emotion Based Reward Valuation with Computational Reinforcement Learning

We show that computational reinforcement learning can model human decision making in the Iowa Gambling Task (IGT). The IGT is a card game, which tests decision making under uncertainty. In our experiments, we found that modulating learning rate decay in Q-learning, enables the approximation of both the behaviour of normal subjects and those who are emotionally impaired by ventromedial prefrontal lesions. Outcomes observed in impaired subjects are modeled by high learning rate decay, while low learning rate decay replicates healthy subjects under otherwise identical conditions. The ventromedial prefrontal cortex has been associated with emotion based reward valuation, and, the value function in reinforcement learning provides an analogous assessment mechanism. Thus reinforcement learning can provide a good model for the role of emotional reward as a modulator of the learning rate

Predicting aquaplaning performance from tyre profile images with machine learning

The tread of a tyre consists of a profile (pattern of grooves, sipes, and blocks) mainly designed to improve wet performance and inhibit aquaplaning by providing a conduit for water to be expelled underneath the tyre as it makes contact with the road surface. Testing different tread profile designs is time consuming, as it requires fabrication and physical measurement of tyres. We propose a supervised machine learning method to predict tyres’ aquaplaning performance based on the tread profile described in geometry and rubber stiffness. Our method provides a regressor from the space of profile geometry, reduced to images, to aquaplaning performance. Experimental results demonstrate that image analysis and machine learning combined with other methods can yield improved prediction of aquaplaning performance, even using non-normalised data. Therefore this method has can potentially save substantial cost and time in tyre development. This investigation is based on data provided by Continental Reifen Deutschland GmbH

Frequencies of some B blood group alleles in laying hens from a selection and crossbreeding experiment

<p/

3d augmented mirror: a multimodal interface for string instrument learning and teaching with gesture support

Multimodal interfaces can open up new possibilities for music education, where the traditional model of teaching is based predominantly on verbal feedback. This paper explores the development and use of multimodal interfaces in novel tools to support music practice training. The design of multimodal interfaces for music education presents a challenge in several respects. One is the integration of multimodal technology into the music learning process. The other is the technological development, where we present a solution that aims to support string practice training with visual and auditory feedback. Building on the traditional function of a physical mirror as a teaching aid, we describe the concept and development of an "augmented mirror" using 3D motion capture technology

Accuracy and interpretability trade-offs in machine learning applied to safer gambling

Responsible gambling is an area of research and industry which seeks to understand the pathways to harm from gambling and implement programmes to reduce or prevent harm that gambling might cause. There is a growing body of research that has used gambling behavioural data to model and predict harmful gambling, and the industry is showing increasing interest in technologies that can help gambling operators to better predict harm and prevent it through appropriate interventions. However, industry surveys and feedback clearly indicate that in order to enable wider adoption of such data-driven methods, industry and policy makers require a greater understanding of how machine learning methods make these predictions. In this paper, we make use of the TREPAN algorithm for extracting decision trees from Neural Networks and Random Forests. We present the first comparative evaluation of predictive performance and tree properties for extracted trees, which is also the first comparative evaluation of knowledge extraction for safer gambling. Results indicate that TREPAN extracts better performing trees than direct learning of decision trees from the data. Overall, trees extracted with TREPAN from different models offer a good compromise between prediction accuracy and interpretability. TREPAN can produce decision trees with extended tests rules of different forms, so that interpretability depends on multiple factors. We present detailed results and a discussion of the trade-offs with regard to performance and interpretability and use in the gambling industry

The Need for Knowledge Extraction: Understanding Harmful Gambling Behavior with Neural Networks

Responsible gambling is a field of study that involves supporting gamblers so as to reduce the harm that their gambling activity might cause. Recently in the literature, machine learning algorithms have been introduced as a way to predict potentially harmful gambling based on patterns of gambling behavior, such as trends in amounts wagered and the time spent gambling. In this paper, neural network models are analyzed to help predict the outcome of a partial proxy for harmful gambling behavior: when a gambler “self-excludes”, requesting a gambling operator to prevent them from accessing gambling opportunities. Drawing on survey and interview insights from industry and public officials as to the importance of interpretability, a variant of the knowledge extraction algorithm TREPAN is proposed which can produce compact, human-readable logic rules efficiently, given a neural network trained on gambling data. To the best of our knowledge, this paper reports the first industrial-strength application of knowledge extraction from neural networks, which otherwise are black-boxes unable to provide the explanatory insights which are crucially required in this area of application. We show that through knowledge extraction one can explore and validate the kinds of behavioral and demographic profiles that best predict self-exclusion, while developing a machine learning approach with greater potential for adoption by industry and treatment providers. Experimental results reported in this paper indicate that the rules extracted can achieve high fidelity to the trained neural network while maintaining competitive accuracy and providing useful insight to domain experts in responsible gambling

Visualization of Tonal Harmony for Jazz Lead Sheets

Jazz improvisation is the extemporaneous expression of melody, and musicians commonly base their performances on chord progressions given by lead sheets. It is standard practice to commit a progression to memory by analyzing it for common patterns. This paper presents a visualization design intended to help reduce the amount of cognitive work needed to assimilate a song’s chords and harmonic patterns. It does this using color, shapes, and glyphs as visual variables to convey meaning about tonal centers, chord functions, and harmonic structure

Dynamics of direct inter-pack encounters in endangered African wild dogs

Aggressive encounters may have important life history consequences due to the potential for injury and death, disease transmission, dispersal opportunities or exclusion from key areas of the home range. Despite this, little is known of their detailed dynamics, mainly due to the difficulties of directly observing encounters in detail. Here, we describe detailed spatial dynamics of inter-pack encounters in African wild dogs (Lycaon pictus), using data from custom-built high-resolution GPS collars in 11 free-ranging packs. On average, each pack encountered another pack approximately every 7 weeks and met each neighbour twice each year. Surprisingly, intruders were more likely to win encounters (winning 78.6% of encounters by remaining closer to the site in the short term). However, intruders did tend to move farther than residents toward their own range core in the short-term (1 h) post-encounter, and if this were used to indicate losing an encounter, then the majority (73.3%) of encounters were won by residents. Surprisingly, relative pack size had little effect on encounter outcome, and injuries were rare (<15% of encounters). These results highlight the difficulty of remotely scoring encounters involving mobile participants away from static defendable food resources. Although inter-pack range overlap was reduced following an encounter, encounter outcome did not seem to drive this, as both packs shifted their ranges post-encounter. Our results indicate that inter-pack encounters may be lower risk than previously suggested and do not appear to influence long-term movement and ranging

Brachio-cephalic ('Gracz') fistula use for continuous hemofiltration in a hemodynamically unstable hemodialysis patient without venous vascular access: a case report

Even in patients with chronic renal failure and chronic intermittent hemodialysis, continuous venovenous hemofiltration (CVVH) is the most often practiced renal replacement technique in the intensive care unit. Although patients show less hemodynamic instability during CVVH than during hemodialysis, it requires a blood flow exceeding 200 ml/min in the extracorporeal circuit necessitating the use of large bore catheters. Vascular access in critically ill septic and edematous patients is sometimes difficult, or even impossible