The mechanisms of temporal inference

The properties of a temporal language are determined by its constituent elements: the temporal objects which it can represent, the attributes of those objects, the relationships between them, the axioms which define the default relationships, and the rules which define the statements that can be formulated. The methods of inference which can be applied to a temporal language are derived in part from a small number of axioms which define the meaning of equality and order and how those relationships can be propagated. More complex inferences involve detailed analysis of the stated relationships. Perhaps the most challenging area of temporal inference is reasoning over disjunctive temporal constraints. Simple forms of disjunction do not sufficiently increase the expressive power of a language while unrestricted use of disjunction makes the analysis NP-hard. In many cases a set of disjunctive constraints can be converted to disjunctive normal form and familiar methods of inference can be applied to the conjunctive sub-expressions. This process itself is NP-hard but it is made more tractable by careful expansion of a tree-structured search space

Bayesian Nonparametric Inference of Switching Linear Dynamical Systems

Many complex dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes. We consider two such models: the switching linear dynamical system (SLDS) and the switching vector autoregressive (VAR) process. Our Bayesian nonparametric approach utilizes a hierarchical Dirichlet process prior to learn an unknown number of persistent, smooth dynamical modes. We additionally employ automatic relevance determination to infer a sparse set of dynamic dependencies allowing us to learn SLDS with varying state dimension or switching VAR processes with varying autoregressive order. We develop a sampling algorithm that combines a truncated approximation to the Dirichlet process with efficient joint sampling of the mode and state sequences. The utility and flexibility of our model are demonstrated on synthetic data, sequences of dancing honey bees, the IBOVESPA stock index, and a maneuvering target tracking application.Comment: 50 pages, 7 figure

A sticky HDP-HMM with application to speaker diarization

We consider the problem of speaker diarization, the problem of segmenting an audio recording of a meeting into temporal segments corresponding to individual speakers. The problem is rendered particularly difficult by the fact that we are not allowed to assume knowledge of the number of people participating in the meeting. To address this problem, we take a Bayesian nonparametric approach to speaker diarization that builds on the hierarchical Dirichlet process hidden Markov model (HDP-HMM) of Teh et al. [J. Amer. Statist. Assoc. 101 (2006) 1566--1581]. Although the basic HDP-HMM tends to over-segment the audio data---creating redundant states and rapidly switching among them---we describe an augmented HDP-HMM that provides effective control over the switching rate. We also show that this augmentation makes it possible to treat emission distributions nonparametrically. To scale the resulting architecture to realistic diarization problems, we develop a sampling algorithm that employs a truncated approximation of the Dirichlet process to jointly resample the full state sequence, greatly improving mixing rates. Working with a benchmark NIST data set, we show that our Bayesian nonparametric architecture yields state-of-the-art speaker diarization results.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS395 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Automated design of minimum drag light aircraft fuselages and nacelles

The constrained minimization algorithm of Vanderplaats is applied to the problem of designing minimum drag faired bodies such as fuselages and nacelles. Body drag is computed by a variation of the Hess-Smith code. This variation includes a boundary layer computation. The encased payload provides arbitrary geometric constraints, specified a priori by the designer, below which the fairing cannot shrink. The optimization may include engine cooling air flows entering and exhausting through specific port locations on the body

Longitudinal multivariate tensor- and searchlight-based morphometry using permutation testing

Tensor based morphometry [1] was used to detect statistically significant regions of neuroanatomical change over time in a comparison between 36 probable Alzheimer's Disease patients and 20 age- and sexmatched controls. Baseline and twelve-month repeat Magnetic Resonance images underwent tied spatial normalisation [10] and longitudinal high-dimensional warps were then estimated. Analyses involved univariate and multivariate data derived from the longitudinal deformation fields. The most prominent findings were expansion of the fluid spaces, and contraction of the hippocampus and temporal region. Multivariate measures were notably more powerful, and have the potential to identify patterns of morphometric difference that would be overlooked by conventional mass-univariate analysis

Preliminary Results from the Caltech Core-Collapse Project (CCCP)

We present preliminary results from the Caltech Core-Collapse Project (CCCP), a large observational program focused on the study of core-collapse SNe. Uniform, high-quality NIR and optical photometry and multi-epoch optical spectroscopy have been obtained using the 200'' Hale and robotic 60'' telescopes at Palomar, for a sample of 50 nearby core-collapse SNe. The combination of both well-sampled optical light curves and multi-epoch spectroscopy will enable spectroscopically and photometrically based subtype definitions to be disentangled from each other. Multi-epoch spectroscopy is crucial to identify transition events that evolve among subtypes with time. The CCCP SN sample includes every core-collapse SN discovered between July 2004 and September 2005 that was visible from Palomar, found shortly (< 30 days) after explosion (based on available pre-explosion photometry), and closer than ~120 Mpc. This complete sample allows, for the first time, a study of core-collapse SNe as a population, rather than as individual events. Here, we present the full CCCP SN sample and show exemplary data collected. We analyze available data for the first ~1/3 of the sample and determine the subtypes of 13 SNe II based on both light curve shapes and spectroscopy. We discuss the relative SN II subtype fractions in the context of associating SN subtypes with specific progenitor stars.Comment: To appear in the proceedings of the meeting "The Multicoloured Landscape of Compact Objects and their Explosive Origins", Cefalu, Italy, June 2006, to be published by AIP, Eds. L. Burderi et a

Picosecond timing of Microwave Cherenkov Impulses from High-Energy Particle Showers Using Dielectric-loaded Waveguides

We report on the first measurements of coherent microwave impulses from high-energy particle-induced electromagnetic showers generated via the Askaryan effect in a dielectric-loaded waveguide. Bunches of 12.16 GeV electrons with total bunch energy of $\sim 10^3-10^4$ GeV were pre-showered in tungsten, and then measured with WR-51 rectangular (12.6 mm by 6.3 mm) waveguide elements loaded with solid alumina ($Al_2 O_3$) bars. In the 5-8 GHz $TE_{10}$ single-mode band determined by the presence of the dielectric in the waveguide, we observed band-limited microwave impulses with amplitude proportional to bunch energy. Signals in different waveguide elements measuring the same shower were used to estimate relative time differences with 2.3 picosecond precision. These measurements establish a basis for using arrays of alumina-loaded waveguide elements, with exceptional radiation hardness, as very high precision timing planes for high-energy physics detectors.Comment: 16 pages, 15 figure