Modelling strong interactions and longitudinally polarized vector boson scattering

We study scattering of the electroweak gauge bosons in 5D warped models. Within two different models we determine the precise manner in which the Higgs boson and the vector resonances ensure the unitarity of longitudinal vector boson scattering. We identify three separate scales that determine the dynamics of the scattering process in all cases. For a quite general background geometry of 5D, these scales can be linked to a simple functional of the warp factor. The models smoothly interpolate between a `composite' Higgs limit and a Higgsless limit. By holographic arguments, these models provide an effective description of vector boson scattering in 4D models with a strongly coupled electroweak breaking sector.Comment: 30 pages, no figure

Is Kant a Kantian constitutivist?

This thesis argues that Kant is too imprecise about his metaethics for it to be possible to settle whether or not he is a constitutivist. The first chapter argues for a definition of constitutivism, and distinguishes constitutivism from a position dubbed “agentialism.” Constitutivism rejects the ontology of robust forms of metanormative realism, but still seeks to secure the objectivity and categoricity of its norms. It does so by claiming that conforming to those norms is the only or the best way of pursuing an aim which agents cannot help but have. This definition is motivated by appeals to the literature, and by an appeal specifically to an argument of Christine Korsgaard’s against a rationalist conception of normative facts as knowledge to be applied. For assistance in defining agentialism, a parallel is explored between the metaethical literature and the literature on the normativity of logical laws. Agentialism is defined as a family of views which, like constitutivist ones, reject a robust realist ontology but still seek to secure the objectivity and categoricity of their norms. However, instead of the authority of those norms’ being grounded in an inescapable aim, some other explanation is offered which ties together being an agent and being subject to those norms. Henry Allison, Oliver Sensen, and Jens Timmermann are suggested to be advocates of agentialist readings of Kant. The second chapter collects a range of passages from Kant’s corpus which could be taken to be evidence of his constitutivism. Most of these are loaned from the work of Korsgaard, Barbara Herman, Andrews Reath, and Stephen Engstrom. The readings of those four authors are compared, so as to illustrate the ways in which one can disagree about Kant’s theory while agreeing that it should be read as constitutivist. The third chapter argues that all of the passages collected in the second are equally consistent with an agentialist, and so nonconstitutivist, interpretation of Kant’s metaethics. Constitutivist readings of Kant are, however, defended against objections. The conclusion is ultimately drawn that there is insufficient evidence to settle the question of whether Kant is a constitutivist or an agentialist

Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus

In systems of multiple agents, identifying the cause of observed agent dynamics is challenging. Often, these agents operate in diverse, non-stationary environments, where models rely on hand-crafted environment-specific features to infer influential regions in the system's surroundings. To overcome the limitations of these inflexible models, we present GP-LAPLACE, a technique for locating sources and sinks from trajectories in time-varying fields. Using Gaussian processes, we jointly infer a spatio-temporal vector field, as well as canonical vector calculus operations on that field. Notably, we do this from only agent trajectories without requiring knowledge of the environment, and also obtain a metric for denoting the significance of inferred causal features in the environment by exploiting our probabilistic method. To evaluate our approach, we apply it to both synthetic and real-world GPS data, demonstrating the applicability of our technique in the presence of multiple agents, as well as its superiority over existing methods.Comment: KDD '18 Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Pages 1254-1262, 9 pages, 5 figures, conference submission, University of Oxford. arXiv admin note: text overlap with arXiv:1709.0235

Detection of a Novel, and Likely Ancestral, Tn916-Like Element from a Human Saliva Metagenomic Library

Tn916 is a conjugative transposon (CTn) and the first reported and most well characterised of the Tn916/Tn1545 family of CTns. Tn916-like elements have a characteristic modular structure and different members of this family have been identified based on similarities and variations in these modules. In addition to carrying genes encoding proteins required for their conjugation, Tn916-like elements also carry accessory, antimicrobial resistance genes; most commonly the tetracycline resistance gene, tet(M). Our study aimed to identify and characterise tetracycline resistance genes from the human saliva metagenome using a functional metagenomic approach. We identified a tetracycline-resistant clone, TT31, the sequencing of which revealed it to encode both tet(M) and tet(L). Comparison of the TT31 sequence with the accessory, regulation, and recombination modules of other Tn916-like elements indicated that a partial Tn916-like element encoding a truncated orf9 was cloned in TT31. Analysis indicated that a previous insertion within the truncated orf9 created the full length orf9 found in most Tn916-like transposons; demonstrating that orf9 is, in fact, the result of a gene fusion event. Thus, we hypothesise that the Tn916-like element cloned in TT31 likely represents an ancestral Tn916

Velocity production in elite BMX riders: a field based study using a SRM power meter

The aim of this study was to analyze the production of velocity in bicycle motocross (BMX) compared to other cycling disciplines. Six elite BMX riders, 5 males and 1 female who competed and trained regularly for a period of 12 yrs ± 2 agreed to take part in this study. Each rider performed 3, 50-m sprint tests and a single 200 m fatigue test. The riders’ peak power, fatigue index, power to weight ratio, and cycling revolution per minute were analyzed using a Schoberer Rad Messtechnik (SRM) BMX power meter. The BMX riders’ peak power and power to weight ratio were all found to be similar to those in other sprint cycling events. Peak power outputs of 1539 ± 148 W and 1030 W were recorded with mean power to weight ratios of 21.29 ± 0.84 W·kg-1 and 16.65 W·kg-1 . The BMX riders’ power fatigue index was found to be higher than other sprint events as riders fatigued at a greater rate. Mean fatigue index was 61.19 ± 5.97 W·sec-1 for the male riders and 53.04 W·sec-1 for the female rider. A notable finding of this study was the relationship of cycling cadence (rev·min-1 ), peak power (Watts) and velocity (mi·h-1 ). This relationship suggests once a BMX rider achieves peak power their pedaling cadence becomes the major contributory factor to velocity production.N/

On sequential Bayesian inference for continual learning

Sequential Bayesian inference can be used for continual learning to prevent catastrophic forgetting of past tasks and provide an informative prior when learning new tasks. We revisit sequential Bayesian inference and assess whether using the previous task’s posterior as a prior for a new task can prevent catastrophic forgetting in Bayesian neural networks. Our first contribution is to perform sequential Bayesian inference using Hamiltonian Monte Carlo. We propagate the posterior as a prior for new tasks by approximating the posterior via fitting a density estimator on Hamiltonian Monte Carlo samples. We find that this approach fails to prevent catastrophic forgetting, demonstrating the difficulty in performing sequential Bayesian inference in neural networks. From there, we study simple analytical examples of sequential Bayesian inference and CL and highlight the issue of model misspecification, which can lead to sub-optimal continual learning performance despite exact inference. Furthermore, we discuss how task data imbalances can cause forgetting. From these limitations, we argue that we need probabilistic models of the continual learning generative process rather than relying on sequential Bayesian inference over Bayesian neural network weights. Our final contribution is to propose a simple baseline called Prototypical Bayesian Continual Learning, which is competitive with the best performing Bayesian continual learning methods on class incremental continual learning computer vision benchmarks