arXiv subjects

David Martin

Publications and source records attributed to David Martin.

29 records · Page 2Linked to original sources

arXiv2020

Multiple Transits during a Single Conjunction: Identifying Transiting Circumbinary Planetary Candidates from TESS

We present results of a study on identifying circumbinary planet candidates that produce multiple transits during one conjunction with eclipsing binary systems. The occurrence of these transits enables us to estimate the candidates' orbital periods, which is crucial as the periods of the currently known transiting circumbinary planets are significantly longer than the typical observational baseline of TESS. Combined with the derived radii, it also provides valuable information needed for follow-up observations and subsequent confirmation of a large number of circumbinary planet candidates from TESS. Motivated by the discovery of the 1108-day circumbinary planet Kepler-1647, we show the application of this technique to four of Kepler's circumbinary planets that produce such transits. Our results indicate that in systems where the circumbinary planet is on a low-eccentricity orbit, the estimated planetary orbital period is within <10-20% of the true value. This estimate is derived from photometric observations spanning less than 5% of the planet's period, demonstrating the strong capability of the technique. Capitalizing on the current and future eclipsing binaries monitored by NASA's TESS mission, we estimate that hundreds of circumbinary planets candidates producing multiple transits during one conjunction will be detected in the TESS data. Such a large sample will enable statistical understanding of the population of planets orbiting binary stars and shed new light on their formation and evolution.

astro-ph.EP↗

arXiv2020

Fluctuation-induced phase separation in metric and topological models of collective motion

We study the role of noise on the nature of the transition to collective motion in dry active matter. Starting from field theories that predict a continuous transition at the deterministic level, we show that fluctuations induce a density-dependent shift of the onset of order, which in turns changes the nature of the transition into a phase-separation scenario. Our results apply to a range of systems, including the topological models in which particles interact with a fixed number of nearest neighbors, which were believed so far to exhibit a continuous onset of order. Our analytical predictions are confirmed by numerical simulations of fluctuating hydrodynamics and microscopic models.

cond-mat.soft↗

arXiv2020

Learning Self-Awareness for Autonomous Vehicles: Exploring Multisensory Incremental Models

The technology for autonomous vehicles is close to replacing human drivers by artificial systems endowed with high-level decision-making capabilities. In this regard, systems must learn about the usual vehicle's behavior to predict imminent difficulties before they happen. An autonomous agent should be capable of continuously interacting with multi-modal dynamic environments while learning unseen novel concepts. Such environments are not often available to train the agent on it, so the agent should have an understanding of its own capacities and limitations. This understanding is usually called self-awareness. This paper proposes a multi-modal self-awareness modeling of signals coming from different sources. This paper shows how different machine learning techniques can be used under a generic framework to learn single modality models by using Dynamic Bayesian Networks. In the presented case, a probabilistic switching model and a bank of generative adversarial networks are employed to model a vehicle's positional and visual information respectively. Our results include experiments performed on a real vehicle, highlighting the potentiality of the proposed approach at detecting abnormalities in real scenarios.

eess.IV↗

arXiv2020

Anomaly Detection in Video Data Based on Probabilistic Latent Space Models

This paper proposes a method for detecting anomalies in video data. A Variational Autoencoder (VAE) is used for reducing the dimensionality of video frames, generating latent space information that is comparable to low-dimensional sensory data (e.g., positioning, steering angle), making feasible the development of a consistent multi-modal architecture for autonomous vehicles. An Adapted Markov Jump Particle Filter defined by discrete and continuous inference levels is employed to predict the following frames and detecting anomalies in new video sequences. Our method is evaluated on different video scenarios where a semi-autonomous vehicle performs a set of tasks in a closed environment.

cs.CV↗

arXiv2018

On the parallelization of stellar evolution codes

Multidimensional nucleosynthesis studies with hundreds of nuclei linked through thousands of nuclear processes are still computationally prohibitive. To date, most nucleosynthesis studies rely either on hydrostatic/hydrodynamic simulations in spherical symmetry, or on post-processing simulations using temperature and density versus time profiles directly linked to huge nuclear reaction networks. Parallel computing has been regarded as the main permitting factor of computationally intensive simulations. This paper explores the different pros and cons in the parallelization of stellar codes, providing recommendations on when and how parallelization may help in improving the performance of a code for astrophysical applications. We report on different parallelization strategies succesfully applied to the spherically symmetric, Lagrangian, implicit hydrodynamic code SHIVA, extensively used in the modeling of classical novae and type I X-ray bursts. Speed-up factors of 26 and 35 have been obtained with a parallelized version of SHIVA, in a 200-shell simulation of a type I X-ray burst carried out with two nuclear reaction networks: a reduced one, consisting of 324 isotopes and 1392 reactions, and a more extended network with 606 nuclides and 3551 nuclear interactions. Maximum speed-ups of 41 (324-isotope network) and 85 (606-isotope network), are also predicted for 200 cores, stressing that the number of shells of the computational domain constitutes an effective upper limit for the maximum number of cores that could be used in a parallel application.

astro-ph.SR↗

arXiv2018

Operation of normal-conducting RF cavities in multi-tesla magnetic fields for muon ionization cooling: a feasibility demonstration

Ionization cooling is the preferred method for producing bright muon beams. This cooling technique requires the operation of normal conducting, radio-frequency (RF) accelerating cavities within the multi-tesla fields of DC solenoid magnets. Under these conditions, cavities exhibit increased susceptibility to RF breakdown, which can damage channel components and imposes limits on channel length and transmission efficiency. We present a solution to the problem of breakdown in strong magnetic fields. We report, for the first time, stable high-vacuum, copper cavity operation at gradients above 50 MV/m and in an external magnetic field of three tesla. This eliminates a significant technical risk that has previously been inherent in ionization cooling channel designs.

physics.acc-ph↗

arXiv2018

Hierarchy of GANs for learning embodied self-awareness model

In recent years several architectures have been proposed to learn embodied agents complex self-awareness models. In this paper, dynamic incremental self-awareness (SA) models are proposed that allow experiences done by an agent to be modeled in a hierarchical fashion, starting from more simple situations to more structured ones. Each situation is learned from subsets of private agent perception data as a model capable to predict normal behaviors and detect abnormalities. Hierarchical SA models have been already proposed using low dimensional sensorial inputs. In this work, a hierarchical model is introduced by means of a cross-modal Generative Adversarial Networks (GANs) processing high dimensional visual data. Different levels of the GANs are detected in a self-supervised manner using GANs discriminators decision boundaries. Real experiments on semi-autonomous ground vehicles are presented.

cs.CV↗

arXiv2018

Learning Multi-Modal Self-Awareness Models for Autonomous Vehicles from Human Driving

This paper presents a novel approach for learning self-awareness models for autonomous vehicles. The proposed technique is based on the availability of synchronized multi-sensor dynamic data related to different maneuvering tasks performed by a human operator. It is shown that different machine learning approaches can be used to first learn single modality models using coupled Dynamic Bayesian Networks; such models are then correlated at event level to discover contextual multi-modal concepts. In the presented case, visual perception and localization are used as modalities. Cross-correlations among modalities in time is discovered from data and are described as probabilistic links connecting shared and private multi-modal DBNs at the event (discrete) level. Results are presented on experiments performed on an autonomous vehicle, highlighting potentiality of the proposed approach to allow anomaly detection and autonomous decision making based on learned self-awareness models.

cs.CV↗

arXiv2018

A Multi-perspective Approach To Anomaly Detection For Self-aware Embodied Agents

This paper focuses on multi-sensor anomaly detection for moving cognitive agents using both external and private first-person visual observations. Both observation types are used to characterize agents' motion in a given environment. The proposed method generates locally uniform motion models by dividing a Gaussian process that approximates agents' displacements on the scene and provides a Shared Level (SL) self-awareness based on Environment Centered (EC) models. Such models are then used to train in a semi-unsupervised way a set of Generative Adversarial Networks (GANs) that produce an estimation of external and internal parameters of moving agents. Obtained results exemplify the feasibility of using multi-perspective data for predicting and analyzing trajectory information.

cs.CV↗

arXiv2017

The balance of growth and risk in population dynamics

Essential to each other, growth and exploration are jointly observed in populations, be it alive such as animals and cells or inanimate such as goods and money. But their ability to move, crucial to cope with uncertainty and optimize returns, is tempered by the space/time properties of the environment. We investigate how the environment shape optimal growth and population distribution in such conditions. We uncover a trade-off between risks and returns by revisiting a common growth model over general graphs. Our results reveal a rich and nuanced picture: fruitful strategies commonly lead to risky positions, but this tension may nonetheless be alleviated by the geometry of the explored space. The applicability of our conclusions is subsequently illustrated over an empirical study of financial data.

cond-mat.stat-mech↗

arXiv2014

Performance Improvements for Nuclear Reaction Network Integration

Aims: The aim of this work is to compare the performance of three reaction network integration methods used in stellar nucleosynthesis calculations. These are the Gear's backward differentiation method, Wagoner's method (a 2nd-order Runge-Kutta method), and the Bader-Deuflehard semi-implicit multi-step method. Methods: To investigate the efficiency of each of the integration methods considered here, a test suite of temperature and density versus time profiles is used. This suite provides a range of situations ranging from constant temperature and density to the dramatically varying conditions present in white dwarf mergers, novae, and x-ray bursts. Some of these profiles are obtained separately from full hydrodynamic calculations. The integration efficiencies are investigated with respect to input parameters that constrain the desired accuracy and precision. Results: Gear's backward differentiation method is found to improve accuracy, performance, and stability in integrating nuclear reaction networks. For temperature-density profiles that vary strongly with time, it is found to outperform the Bader-Deuflehard method (although that method is very powerful for more smoothly varying profiles). Wagoner's method, while relatively fast for many scenarios, exhibits hard-to-predict inaccuracies for some choices of integration parameters owing to its lack of error estimations.

astro-ph.SR↗