arXiv subjects

John Coleman

Publications and source records attributed to John Coleman.

7 recordsLinked to original sources

arXiv2024

Stabilization of vertical motion of a vehicle on bumpy terrain using deep reinforcement learning

Stabilizing vertical dynamics for on-road and off-road vehicles is an important research area that has been looked at mostly from the point of view of ride comfort. The advent of autonomous vehicles now shifts the focus more towards developing stabilizing techniques from the point of view of onboard proprioceptive and exteroceptive sensors whose real-time measurements influence the performance of an autonomous vehicle. The current solutions to this problem of managing the vertical oscillations usually limit themselves to the realm of active suspension systems without much consideration to modulating the vehicle velocity, which plays an important role by the virtue of the fact that vertical and longitudinal dynamics of a ground vehicle are coupled. The task of stabilizing vertical oscillations for military ground vehicles becomes even more challenging due lack of structured environments, like city roads or highways, in off-road scenarios. Moreover, changes in structural parameters of the vehicle, such as mass (due to changes in vehicle loading), suspension stiffness and damping values can have significant effect on the controller's performance. This demands the need for developing deep learning based control policies, that can take into account an extremely large number of input features and approximate a near optimal control action. In this work, these problems are addressed by training a deep reinforcement learning agent to minimize the vertical acceleration of a scaled vehicle travelling over bumps by controlling its velocity.

cs.RO↗

arXiv2024

Statistics in Phonetics

Phonetics is the scientific field concerned with the study of how speech is produced, heard and perceived. It abounds with data, such as acoustic speech recordings, neuroimaging data, or articulatory data. In this paper, we provide an introduction to different areas of phonetics (acoustic phonetics, sociophonetics, speech perception, articulatory phonetics, speech inversion, sound change, and speech technology), an overview of the statistical methods for analyzing their data, and an introduction to the signal processing methods commonly applied to speech recordings. A major transition in the statistical modeling of phonetic data has been the shift from fixed effects to random effects regression models, the modeling of curve data (for instance via GAMMs or FDA methods), and the use of Bayesian methods. This shift has been driven in part by the increased focus on large speech corpora in phonetics, which has been driven by machine learning methods such as forced alignment. We conclude by identifying opportunities for future research.

stat.AP↗

arXiv2023

The Common Workflow Scheduler Interface: Status Quo and Future Plans

Nowadays, many scientific workflows from different domains, such as Remote Sensing, Astronomy, and Bioinformatics, are executed on large computing infrastructures managed by resource managers. Scientific workflow management systems (SWMS) support the workflow execution and communicate with the infrastructures' resource managers. However, the communication between SWMS and resource managers is complicated by a) inconsistent interfaces between SMWS and resource managers and b) the lack of support for workflow dependencies and workflow-specific properties. To tackle these issues, we developed the Common Workflow Scheduler Interface (CWSI), a simple yet powerful interface to exchange workflow-related information between a SWMS and a resource manager, making the resource manager workflow-aware. The first prototype implementations show that the CWSI can reduce the makespan already with simple but workflow-aware strategies up to 25%. In this paper, we show how existing workflow resource management research can be integrated into the CWSI.

cs.DC↗

arXiv2022

A General Framework for Pairwise Unbiased Learning to Rank

Pairwise debiasing is one of the most effective strategies in reducing position bias in learning-to-rank (LTR) models. However, limiting the scope of this strategy, are the underlying assumptions required by many pairwise debiasing approaches. In this paper, we develop an approach based on a minimalistic set of assumptions that can be applied to a much broader range of user browsing patterns and arbitrary presentation layouts. We implement the approach as a simplified version of the Unbiased LambdaMART and demonstrate that it retains the underlying unbiasedness property in a wider variety of settings than the original algorithm. Finally, using simulations with "golden" relevance labels, we will show that the simplified version compares favourably with the original Unbiased LambdaMART when the examination of different positions in a ranked list is not assumed to be independent.

cs.IR↗

arXiv2005

Whitehead's Principle of Relativity - Unpublished Lectures by J. L. Synge, FRS

In Appendix B, there is a proposal about the Limb Effect which could be of great significance for Solar Physics. In his PRINCIPLE OF RELATIVITY, Cambridge University Press, 1922 (republished Dover, 2004) A. N. Whitehead, making no use of ''curved'' space-time, derived the same values as Einstein for the red-shift, perihelion advance and the deflection of light. Synge expounds Whitehead's arguments in modern notation making it more accessible for contemporary Physicists but also obtains the Shapiro retardation of electromagnetic radiation by a gravitational field. Synge compares Einstein's GRT and Whitehead's PR, finding the observational evidence equally strong for both theories. He alluded to Whitehead's prediction of a Limb Effect remarking that to pursue its consequences would require a knowledge of QM which did not exist in 1922. Possibly this was not done because of physicist's Obsession with GRT since 1919.

physics.gen-ph↗

arXiv1997

Stochastic phonological grammars and acceptability

In foundational works of generative phonology it is claimed that subjects can reliably discriminate between possible but non-occurring words and words that could not be English. In this paper we examine the use of a probabilistic phonological parser for words to model experimentally-obtained judgements of the acceptability of a set of nonsense words. We compared various methods of scoring the goodness of the parse as a predictor of acceptability. We found that the probability of the worst part is not the best score of acceptability, indicating that classical generative phonology and Optimality Theory miss an important fact, as these approaches do not recognise a mechanism by which the frequency of well-formed parts may ameliorate the unacceptability of low-frequency parts. We argue that probabilistic generative grammars are demonstrably a more psychologically realistic model of phonological competence than standard generative phonology or Optimality Theory.

cmp-lg↗

arXiv1997

Multilingual phonological analysis and speech synthesis

We give an overview of multilingual speech synthesis using the IPOX system. The first part discusses work in progress for various languages: Tashlhit Berber, Urdu and Dutch. The second part discusses a multilingual phonological grammar, which can be adapted to a particular language by setting parameters and adding language-specific details.

cmp-lg↗