The geometry of the double-pulsar system J0737-3039 from systematic intensity variations

The recent discovery of J0737-3039A & B-two pulsars in a highly relativistic orbit around one another - offers an unprecedented opportunity to study the elusive physics of pulsar radio emission. The system contains a rapidly rotating pulsar with a spin period of 22.7 ms and a slow companion with a spin period of 2.77 s, hereafter referred to as 'A' and 'B', respectively. A unique property of the system is that the pulsed radio flux from B increases systematically by almost two orders-of-magnitude during two short portions of each orbit. Here, we describe a geometrical model of the system that simultaneously explains the intensity variations of B and makes definitive and testable predictions for the future evolution of the emission properties of both stars. Our model assumes that B's pulsed radio flux increases when illuminated by emission from A. This model provides constraints on the spin axis orientation and emission geometry of A and predicts that its pulse profile will evolve considerably over the next several years due to geodetic precession until it disappears entirely in 15-20 years

Learning functional object categories from a relational spatio-temporal representation

Abstract. We propose a framework that learns functional objectcategories from spatio-temporal data sets such as those abstracted from video. The data is represented as one activity graph that encodes qualitative spatio-temporal patterns of interaction between objects. Event classes are induced by statistical generalization, the instances of which encode similar patterns of spatio-temporal relationships between objects. Equivalence classes of objects are discovered on the basis of their similar role in multiple event instantiations. Objects are represented in a multidimensional space that captures their role in all the events. Unsupervised learning in this space results in functional object-categories. Experiments in the domain of food preparation suggest that our techniques represent a significant step in unsupervised learning of functional object categories from spatio-temporal patterns of object interaction.

Spatial data stream multiplexing scheme for high-throughput WLANs

A novel scheme using spatial data stream multiplexing (SDSM) in the upcoming multiple-input multipleoutput (MIMO)-based IEEE 802.11n physical layer is proposed. It is shown that with SDSM, the same data rate can be achieved by using less number of transmit and receive antennas and therefore this scheme can reduce the number of antennas which results in reducing mutual coupling effects, hardware costs and implementation complexities. The maximum data rates that can be achieved using a 2 * 2 MIMO system is 270 Mbps and for a 4 * 4 MIMO system is 540 Mbps. The same data rates can be achieved using the SDSM technique which reduces the 2 * 2 MIMO system to 1 * 1 SISO system and the 4 * 4 MIMO system to a 2 * 2 MIMO system

Modelling stock volatilities during financial crises: A time varying coefficient approach

We examine how the most prevalent stochastic properties of key financial time series have been affected during the recent financial crises. In particular we focus on changes associated with the remarkable economic events of the last two decades in the volatility dynamics, including the underlying volatility persistence and volatility spillover structure. Using daily data from several key stock market indices, the results of our bivariate GARCH models show the existence of time varying correlations as well as time varying shock and volatility spillovers between the returns of FTSE and DAX, and those of NIKKEI and Hang Seng, which became more prominent during the recent financial crisis. Our theoretical considerations on the time varying modelwhich provides the platformupon which we integrate our multifaceted empirical approaches are also of independent interest. In particular, we provide the general solution for time varying asymmetric GARCH specifications, which is a long standing research topic. This enables us to characterize these models by deriving, first, their multistep ahead predictors, second, the first two time varying unconditional moments, and third, their covariance structure.Open Access funded by European Research Council under a Creative Commons license

Photometric Redshifts for an Optical/Near-Infrared Catalogue in the Chandra Deep Field South

Photometric redshifts have proven a powerful tool in identifying galaxies over a large range of lookback times. We have been generalising this technique to incorporate the selection of candidate high redshift QSOs. We have applied this to a large optical/near-infrared imaging survey in 6 wavebands aiming to push farther in redshift (and fainter in luminosity) than previous studies. We believe that study of these very faint and distant objects provides valuable insights into galaxy formation and evolution. Here we present work in progress and preliminary results for a catalogue of objects detected as part of the Las Campanas Infrared Survey. This is a stepping stone to the type of survey data that will become available in the next few years from projects such as UKIDSS and VISTA.Comment: 4 pages LaTeX, submitted to the "Eurokiel 2002: Galaxy Evolution III: From Simple Models to Self Consistant Approaches" Conference Proceeding

Non-invasive imaging of subsurface paint layers with optical coherence tomography

Optical coherence tomography (OCT) systems are fast scanning infrared Michelson interferometers designed for the non-invasive examination of the interiors of the eye and subsurface structures of biological tissues. OCT has recently been applied to the non-invasive examinations of the stratigraphy of paintings and museum artefacts. So far this is the only technique capable of imaging non-invasively the subsurface structure of paintings and painted objects. Unlike the traditional method of paint cross-section examination where sampling is required, the non-invasive and non-contact nature of the technique enables the examination of the paint cross-section anywhere on a painting, as there is no longer an issue with conservation ethics regarding the taking of samples from historical artefacts. A range of applications of the technique including the imaging of stratigraphy of paintings and painted artefacts, the imaging of underdrawings to the analysis of the optical properties of paint and varnish layers is presented. Future projects on the application of OCT to art conservation are discussed

3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation

Model architectures have been dramatically increasing in size, improving performance at the cost of resource requirements. In this paper we propose 3DQ, a ternary quantization method, applied for the first time to 3D Fully Convolutional Neural Networks (F-CNNs), enabling 16x model compression while maintaining performance on par with full precision models. We extensively evaluate 3DQ on two datasets for the challenging task of whole brain segmentation. Additionally, we showcase our method's ability to generalize on two common 3D architectures, namely 3D U-Net and V-Net. Outperforming a variety of baselines, the proposed method is capable of compressing large 3D models to a few MBytes, alleviating the storage needs in space critical applications.Comment: Accepted to MICCAI 201

An Exact Formula for the Average Run Length to False Alarm of the Generalized Shiryaev-Roberts Procedure for Change-Point Detection under Exponential Observations

We derive analytically an exact closed-form formula for the standard minimax Average Run Length (ARL) to false alarm delivered by the Generalized Shiryaev-Roberts (GSR) change-point detection procedure devised to detect a shift in the baseline mean of a sequence of independent exponentially distributed observations. Specifically, the formula is found through direct solution of the respective integral (renewal) equation, and is a general result in that the GSR procedure's headstart is not restricted to a bounded range, nor is there a "ceiling" value for the detection threshold. Apart from the theoretical significance (in change-point detection, exact closed-form performance formulae are typically either difficult or impossible to get, especially for the GSR procedure), the obtained formula is also useful to a practitioner: in cases of practical interest, the formula is a function linear in both the detection threshold and the headstart, and, therefore, the ARL to false alarm of the GSR procedure can be easily computed.Comment: 9 pages; Accepted for publication in Proceedings of the 12-th German-Polish Workshop on Stochastic Models, Statistics and Their Application

Characterizations of probability distributions via bivariate regression of record values

Bairamov et al. (Aust N Z J Stat 47:543-547, 2005) characterize the exponential distribution in terms of the regression of a function of a record value with its adjacent record values as covariates. We extend these results to the case of non-adjacent covariates. We also consider a more general setting involving monotone transformations. As special cases, we present characterizations involving weighted arithmetic, geometric, and harmonic means.Comment: accepted in Metrik