Evolving a Behavioral Repertoire for a Walking Robot

Numerous algorithms have been proposed to allow legged robots to learn to walk. However, the vast majority of these algorithms is devised to learn to walk in a straight line, which is not sufficient to accomplish any real-world mission. Here we introduce the Transferability-based Behavioral Repertoire Evolution algorithm (TBR-Evolution), a novel evolutionary algorithm that simultaneously discovers several hundreds of simple walking controllers, one for each possible direction. By taking advantage of solutions that are usually discarded by evolutionary processes, TBR-Evolution is substantially faster than independently evolving each controller. Our technique relies on two methods: (1) novelty search with local competition, which searches for both high-performing and diverse solutions, and (2) the transferability approach, which com-bines simulations and real tests to evolve controllers for a physical robot. We evaluate this new technique on a hexapod robot. Results show that with only a few dozen short experiments performed on the robot, the algorithm learns a repertoire of con-trollers that allows the robot to reach every point in its reachable space. Overall, TBR-Evolution opens a new kind of learning algorithm that simultaneously optimizes all the achievable behaviors of a robot.Comment: 33 pages; Evolutionary Computation Journal 201

Implementing innovation in the UK National Health Service A case study in patient compliance

Missed appointments are a common form of patient non-compliance. One reason often given is they had been forgotten. We evaluate two recent innovations to address this problem introduced at one of the Scottish health boards to increase compliance at consultant-lead outpatient clinics. Patients with long waits were more likely to miss their appointment, but not, apparently, because they were more likely to forget it. Nevertheless, both innovations made a big difference and, combined, reduced recorded non-attendance rate by 90%. The administrative innovation changed the way appointments were made. At its most basic, bookings were made no more than five to seven weeks in advance, at no additional cost. Some of these patients also had to confirm their appointment. This reduced non-attendance still further, but at a cost of at least £150 per missed appointment avoided. The technological innovation used SMS and E-mail messages to send last minute reminders. It more than halved non-attendance, but the inability to collect contact details limited it applicability, and few of the patients reached provided contact details. Some fifty to one hundred messages have had to be sent to avoid one missed appointment. However, the use of the NHS network avoided significant recurrent costs.Outpatient services; Did not attend; SMS messages; Partial booking; Patient focussed booking

Fast Damage Recovery in Robotics with the T-Resilience Algorithm

Damage recovery is critical for autonomous robots that need to operate for a long time without assistance. Most current methods are complex and costly because they require anticipating each potential damage in order to have a contingency plan ready. As an alternative, we introduce the T-resilience algorithm, a new algorithm that allows robots to quickly and autonomously discover compensatory behaviors in unanticipated situations. This algorithm equips the robot with a self-model and discovers new behaviors by learning to avoid those that perform differently in the self-model and in reality. Our algorithm thus does not identify the damaged parts but it implicitly searches for efficient behaviors that do not use them. We evaluate the T-Resilience algorithm on a hexapod robot that needs to adapt to leg removal, broken legs and motor failures; we compare it to stochastic local search, policy gradient and the self-modeling algorithm proposed by Bongard et al. The behavior of the robot is assessed on-board thanks to a RGB-D sensor and a SLAM algorithm. Using only 25 tests on the robot and an overall running time of 20 minutes, T-Resilience consistently leads to substantially better results than the other approaches

In-House Counsel’s Role in the Structuring of Mortgage-Backed Securities

The authors introduce the financial crisis and the role played by mortgage-backed securities. Then describe the controversy at issue: whether, in order to own and enforce the mortgage loans backing those securities, a special-purpose vehicle “purchasing” mortgage loans must take physical delivery of the notes and security instruments in the precise manner specified by the sale agreement. Focusing on this controversy, the authors analyze (i) the extent, if any, that the controversy has merit; (ii) whether in-house counsel should have anticipated the controversy; and (iii) what, if anything, in-house counsel could have done to avert or, after it arose, to mitigate the controversy. Finally, the authors examine how the foregoing analysis can help to inform the broader issue of how in-house counsel should address complex legal transactions

MAGAN: Margin Adaptation for Generative Adversarial Networks

We propose the Margin Adaptation for Generative Adversarial Networks (MAGANs) algorithm, a novel training procedure for GANs to improve stability and performance by using an adaptive hinge loss function. We estimate the appropriate hinge loss margin with the expected energy of the target distribution, and derive principled criteria for when to update the margin. We prove that our method converges to its global optimum under certain assumptions. Evaluated on the task of unsupervised image generation, the proposed training procedure is simple yet robust on a diverse set of data, and achieves qualitative and quantitative improvements compared to the state-of-the-art

Plagiarism Avoidance in Academic Submissions

The purpose of this paper is to provide undergraduate students with an introductory resource focusing on plagiarism avoidance. It sets out to identify often confusing associated nuances. From the perspective of paraphrasing, the requirements attaching to the necessary provision of proper citations are explored. The paper concludes by pondering the topic of reliability of sources

Aspects of structure and functionality in legumin and vicilin from vicia faba seeds

Soy beans command a large proportion of the vegetable protein market of the food processing industry; however, their cultivation is limited by climatic factors. A crop able to compete in these markets, but suited to growth in cooler, temperate conditions would have political and economic advantages for Europe. One such crop under consideration is Vicia faba minor. A new faba bean line (IVS-G),developed by a breeding programme at Durham University, was compared to an established commercial variety (Maris Bead) for chemical, physical and functional characteristics. A selection of processing methods was used to produce proteinaceous materials equivalent to soy products already on the commercial market. The methods used were defatting, air-classification, and the production of isolates, proteinates and micellar protein preparations. The materials were assessed and compared to soy products for functionality in tests designed to assess solubility, gelling, foaming and emulsifying properties. The storage proteins, legumin and vicilin, are the major protein constituents of the bean seed, and their structural and functional properties were also investigated. The bean products had similar functional properties to equivalent soy products, when protein contents were also similar. However, the higher initial in vivo protein content of the soy bean does give the latter a competitive advantage. Also, the marketing of by-products of faba bean, eg. Starch and fibre, may prove difficult

Biomechanical and physiological investigations in the IBMPFD animal model

Inclusion body myopathy associated with Paget’s disease of bone and frontotemporal dementia (IBMPFD; OMIM 167320) is an autosomal dominant inherited multisystem disorder caused by mutations in the valosin-containing protein (VCP) gene. Knock-in mice expressing the common human p.R155H VCP mutation develop a progressive myopathy with ubiquitin-positive inclusion bodies, accumulation of abnormally shaped mitochondria in skeletal muscle and focal bone degradation reminiscent of Paget’s disease of bone. To further assess the physiological effects of this mutation in muscle, we compared the in vitro contractile properties of the extensor digitorum longus (EDL) (fast-twitch muscle) and soleus (slow-twitch muscle) from mice heterozygous for the p.R155H mutation in VCP and wild-type mice. Our results showed that fast-twitch muscle fibres isolated from VCPR155H/+ mutant mice ~12-15 months old not only fatigued faster and to a greater extent, but also recovered significantly slower and to a lesser degree than those of age-matched wild-type mice. Thereafter, the muscles seem to recover and by the time the mice were 27 months old, there was no difference in the fatigue resistance of mutant and wild type mice. These results suggest that VCP may be necessary for maintenance of glycolytic capacity in mouse fast-twitch muscle fibres at 12-15 months only. Investigation of oxidative capacity in 12 and 14 month old VCPR155H/+ mice revealed significantly lower mitochondrial enzyme activity (citrate synthase) in VCPR155H/+ mice at 14 months; concomitant with the reduction in fasttwitch fibre fatigue tolerance. Primary fibroblast cells isolated from our VCPR155H/+ mouse model showed reduced ability of mitochondrial networks to fragment when exposed to oxidative stress, indicating that intact VCP is required for the successful maintenance of mitochondrial network dynamics and quality control

Primes between consecutive powers

This paper updates the explicit interval estimate for primes between consecutive powers. It is shown that there is least one prime between $n^{155}$ and $(n+1)^{155}$ for all $n\geq 1$. This result is in part obtained with a new explicit version of Goldston's 1983 estimate for the error in the truncated Riemann--von Mangoldt explicit formula.Comment: This is the published versio

Uncertain Quality-Diversity: Evaluation methodology and new methods for Quality-Diversity in Uncertain Domains

Quality-Diversity optimisation (QD) has proven to yield promising results across a broad set of applications. However, QD approaches struggle in the presence of uncertainty in the environment, as it impacts their ability to quantify the true performance and novelty of solutions. This problem has been highlighted multiple times independently in previous literature. In this work, we propose to uniformise the view on this problem through four main contributions. First, we formalise a common framework for uncertain domains: the Uncertain QD setting, a special case of QD in which fitness and descriptors for each solution are no longer fixed values but distribution over possible values. Second, we propose a new methodology to evaluate Uncertain QD approaches, relying on a new per-generation sampling budget and a set of existing and new metrics specifically designed for Uncertain QD. Third, we propose three new Uncertain QD algorithms: Archive-sampling, Parallel-Adaptive-sampling and Deep-Grid-sampling. We propose these approaches taking into account recent advances in the QD community toward the use of hardware acceleration that enable large numbers of parallel evaluations and make sampling an affordable approach to uncertainty. Our final and fourth contribution is to use this new framework and the associated comparison methods to benchmark existing and novel approaches. We demonstrate once again the limitation of MAP-Elites in uncertain domains and highlight the performance of the existing Deep-Grid approach, and of our new algorithms. The goal of this framework and methods is to become an instrumental benchmark for future works considering Uncertain QD.Comment: Submitted to Transactions on Evolutionary Computatio