Annotation Graphs and Servers and Multi-Modal Resources: Infrastructure for Interdisciplinary Education, Research and Development

Annotation graphs and annotation servers offer infrastructure to support the analysis of human language resources in the form of time-series data such as text, audio and video. This paper outlines areas of common need among empirical linguists and computational linguists. After reviewing examples of data and tools used or under development for each of several areas, it proposes a common framework for future tool development, data annotation and resource sharing based upon annotation graphs and servers.Comment: 8 pages, 6 figure

Flow transitions in two-dimensional foams

For sufficiently slow rates of strain, flowing foam can exhibit inhomogeneous flows. The nature of these flows is an area of active study in both two-dimensional model foams and three dimensional foam. Recent work in three-dimensional foam has identified three distinct regimes of flow [S. Rodts, J. C. Baudez, and P. Coussot, Europhys. Lett. {\bf 69}, 636 (2005)]. Two of these regimes are identified with continuum behavior (full flow and shear-banding), and the third regime is identified as a discrete regime exhibiting extreme localization. In this paper, the discrete regime is studied in more detail using a model two dimensional foam: a bubble raft. We characterize the behavior of the bubble raft subjected to a constant rate of strain as a function of time, system size, and applied rate of strain. We observe localized flow that is consistent with the coexistence of a power-law fluid with rigid body rotation. As a function of applied rate of strain, there is a transition from a continuum description of the flow to discrete flow when the thickness of the flow region is approximately 10 bubbles. This occurs at an applied rotation rate of approximately $0.07 {\rm s^{-1}}$

Integrating detectors and their application to infrared astronomy

The work contained in this thesis is concerned with the performance of infrared integrating detector arrays, within the context of astronomical spectroscopy.A linear array of thirty-two InSb photo diodes is investigated. It is found to exhibit good capacitance and dark current uniformity across the array. By applying the principle of charge conservation to the multiplexed readout arrangement of this device, the signal response of the detector to different levels of illumination is derived. It is found from this, and confirmed experimentally, that the device has a highly linear radiation response over a range of reverse biases.The interaction between dark current and photo-current is studied,primarily by the application of a simple model. The results indicate that the effective signal gain of a detector can vary in the situation where dark current dominates the discharge processes, since in this instance accurate dark current subtraction becomes difficult.The predictions of the model are compared with experiments performed on two integrating arrays; one under study in the laboratory,and the other installed in the low background environment of a cooled grating spectrometer. Finally, suggestions are presented of ways of avoiding this problem, the simplest of which involves utilizing, where possible, low dark current detector materials.The importance of achieving good dark current uniformity with arrays is stressed, since this will improve the ability to flat-field faint object spectra.To illustrate the importance of these devices, infrared spectra obtained with array detectors, covering a range of astronomical objects,are presented and discussed

Multi-Task Dynamical Systems

Time series datasets are often composed of a variety of sequences from the same domain, but from different entities, such as individuals, products, or organizations. We are interested in how time series models can be specialized to individual sequences (capturing the specific characteristics) while still retaining statistical power by sharing commonalities across the sequences. This paper describes the multi-task dynamical system (MTDS); a general methodology for extending multi-task learning (MTL) to time series models. Our approach endows dynamical systems with a set of hierarchical latent variables which can modulate all model parameters. To our knowledge, this is a novel development of MTL, and applies to time series both with and without control inputs. We apply the MTDS to motion-capture data of people walking in various styles using a multi-task recurrent neural network (RNN), and to patient drug-response data using a multi-task pharmacodynamic model.Comment: 52 pages, 17 figure

Multi-Task Time Series Analysis applied to Drug Response Modelling

Time series models such as dynamical systems are frequently fitted to a cohort of data, ignoring variation between individual entities such as patients. In this paper we show how these models can be personalised to an individual level while retaining statistical power, via use of multi-task learning (MTL). To our knowledge this is a novel development of MTL which applies to time series both with and without control inputs. The modelling framework is demonstrated on a physiological drug response problem which results in improved predictive accuracy and uncertainty estimation over existing state-of-the-art models.Comment: To appear in AISTATS 201

Multi-Task Dynamical Systems

Time series datasets are often composed of a variety of sequences from the same domain, but from different entities, such as individuals, products, or organizations. We are interested in how time series models can be specialized to individual sequences (capturing the specific characteristics) while still retaining statistical power by sharing commonalities across the sequences. This paper describes the multi-task dynamical system (MTDS); a general methodology for extending multi-task learning (MTL) to time series models. Our approach endows dynamical systems with a set of hierarchical latent variables which can modulate all model parameters. To our knowledge, this is a novel development of MTL, and applies to time series both with and without control inputs. We apply the MTDS to motion-capture data of people walking in various styles using a multi-task recurrent neural network (RNN), and to patient drug-response data using a multi-task pharmacodynamic model.Comment: 52 pages, 17 figure

The Relationships Between Restrictive/Repetitive Behaviours, Intolerance of Uncertainty, and Anxiety in Autism:A Systematic Review and Meta-Analysis

Autistic people are more likely to experience anxiety than their non-autistic peers. Understanding mechanisms underpinning anxiety in autism is a vital aspect of developing effective interventions. Intolerance of uncertainty (IU) and restrictive/repetitive behaviours (RRBs) are proposed to contribute to anxiety for autistic people. This paper includes the first meta-analysis to investigate the associations between all three of these variables. A systematic search identified 33 papers that measured anxiety, IU and RRBs in 8347 autistic participants. Evidence was found for positive correlations between all three variables. Analysis of average participant age demonstrated that the relationship between anxiety and IU was stronger in younger participants. No significant differences were found between the associations in studies that included participants with intellectual disabilities and those that did not. A quality assessment framework identified methodological threats to validity. Most studies had good methods of recruitment; however, many anxiety and IU measurement tools were unvalidated in autistic populations. Results suggest that IU and RRBs should be considered when designing anxiety interventions for autistic people, however, the role of RRBs in particular needs to be investigated further to prevent interventions from taking away important coping strategies due to misunderstanding of causal relationships

The Velocity Dispersion -- Temperature Correlation from a Limited Cluster Sample

Most studies of correlations between X-ray and optical properties of galaxy clusters have used the largest samples of data available, regardless of the morphological types of clusters included. Given the increasing evidence that morphology is related to a cluster's degree of dynamical evolution, we approach the study of X-ray and optical correlations differently. We evaluate the relationship between velocity dispersion and temperature for a limited set of galaxy clusters taken from Bird (1994), which all possess dominant central galaxies and which have been explicitly corrected for the presence of substructure. We find that $\sigma _r \propto T^{0.61 \pm 0.13}$. We use a Monte Carlo computer routine to estimate the significance of this deviation from the $\sigma _r \propto T^{0.5}$ relationship predicted by the virial theorem. We find that the simulated correlation is steeper than the observed value only 4\% of the time, suggesting that the deviation is significant. The combination of protogalactic winds and dynamical friction reproduces nearly exactly the observed relationship between $\sigma _r$ and $T$.Comment: 27 pages, LaTeX, requires aasms.sty and epsf.tex; accepted for publication in The Astrophysical Journal. 2 PostScript figures, as well as PostScript version of complete paper, available by anonymous ftp from ftp://kula.phsx.ukans.edu

Rapid determination of LISA sensitivity to extreme mass ratio inspirals with machine learning

Gravitational wave observations of the inspiral of stellar-mass compact objects into massive black holes (MBHs), extreme mass ratio inspirals (EMRIs), enable precision measurements of parameters such as the MBH mass and spin. The Laser Interferometer Space Antenna is expected to detect sufficient EMRIs to probe the underlying source population, testing theories of the formation and evolution of MBHs and their environments. Population studies are subject to selection effects that vary across the EMRI parameter space, which bias inference results if unaccounted for. This bias can be corrected, but evaluating the detectability of many EMRI signals is computationally expensive. We mitigate this cost by (i) constructing a rapid and accurate neural network interpolator capable of predicting the signal-to-noise ratio of an EMRI from its parameters, and (ii) further accelerating detectability estimation with a neural network that learns the selection function, leveraging our first neural network for data generation. The resulting framework rapidly estimates the selection function, enabling a full treatment of EMRI detectability in population inference analyses. We apply our method to an astrophysically motivated EMRI population model, demonstrating the potential selection biases and subsequently correcting for them. Accounting for selection effects, we predict that LISA will measure the MBH mass function slope to a precision of 8.8%, the CO mass function slope to a precision of 4.6%, the width of the MBH spin magnitude distribution to a precision of 10% and the event rate to a precision of 12% with EMRIs at redshifts below z=6.Comment: 12 pages, 4 figure