Publications

Measuring the centroid of a spectral line is a common problem in astronomy. Many methods have been devised to overcome limitations due to either noise in the spectra or asymmetric profiles, the most common of which are the intensity weighted averages (first moment) or fits of analytical (typically Gaussian) profiles. If the spectral line can be considered a single component, we demonstrate that a simple quadratic fit to the pixel of maximum intensity and its two neighboring pixels provides a robust measure of the line centroid. This approach allows for a sub-velocity resolution precision on the line centroid, without be biases by noise or asymmetric features in the line profile and outperforming traditional methods in most situations.

Measuring the centroid of a spectral line is a common problem in astronomy. Many methods have been devised to overcome limitations due to either noise in the spectra or asymmetric profiles, the most common of which are the intensity weighted averages (first moment) or fits of analytical (typically Gaussian) profiles. If the spectral line can be considered a single component, we demonstrate that a simple quadratic fit to the pixel of maximum intensity and its two neighboring pixels provides a robust measure of the line centroid. This approach allows for a sub-velocity resolution precision on the line centroid, without be biases by noise or asymmetric features in the line profile and outperforming traditional methods in most situations.

A key challenge in precision medicine lies in understanding molecular-level underpinnings of complex human disease. Biological networks in multicellular organisms can generate hypotheses about disease genes, pathways, and their behavior in disease-related tissues. Diverse functional genomic data, including expression, protein-protein interaction, and relevant sequence and literature information, can be utilized to build integrative networks that provide both genome-wide coverage as well as contextual specificity and accuracy. By carefully extracting the relevant signal in thousands of heterogeneous functional genomics experiments through integrative analysis, these networks model how genes work together in specific contexts to carry out cellular processes, thereby contributing to a molecular-level understanding of complex human disease and paving the way toward better therapy and drug treatment. Here, we discuss current methods to build context-specific integrative networks, focusing on tissue-specific networks. We highlight applications of these networks in predicting tissue-specific molecular response, identifying candidate disease genes, and increasing power by amplifying the disease signal in quantitative genetics data. Altogether, these exciting developments enable biomedical scientists to characterize disease from pathophysiology to cellular system and, finally, to specific gene alterations-making significant strides toward the goal of precision medicine.

Converting a noisy parallax measurement into a posterior belief over distance requires inference with a prior. Usually this prior represents beliefs about the stellar density distribution of the Milky Way. However, multi-band photometry exists for a large fraction of the \textsl{\small{Gaia}} \textsl{\small{TGAS}} Catalog and is incredibly informative about stellar distances. Here we use \textsl{\small{2MASS}} colors for 1.4 million \textsl{\small{TGAS}} stars to build a noise-deconvolved empirical prior distribution for stars in color--magnitude space. This model contains no knowledge of stellar astrophysics or the Milky Way, but is precise because it accurately generates a large number of noisy parallax measurements under an assumption of stationarity; that is, it is capable of combining the information from many stars. We use the Extreme Deconvolution (\textsl{\small{XD}}) algorithm---an Empirical Bayes approximation to a full hierarchical model of the true parallax and photometry of every star---to construct this prior. The prior is combined with a \textsl{\small{TGAS}} likelihood to infer a precise photometric parallax estimate and uncertainty (and full posterior) for every star. Our parallax estimates are more precise than the \textsl{\small{TGAS}} catalog entries by a median factor of 1.2 (14% are more precise by a factor >2) and are more precise than previous Bayesian distance estimates that use spatial priors. We validate our parallax inferences using members of the Milky Way star cluster M67, which is not visible as a cluster in the \textsl{\small{TGAS}} parallax estimates, but appears as a cluster in our posterior parallax estimates. Our results, including a parallax posterior pdf for each of 1.4 million \textsl{\small{TGAS}} stars, are available in companion electronic tables.

The emerging field of computational acoustic monitoring aims at retrieving high-level information from acoustic scenes recorded by some network of sensors. These networks gather large amounts of data requiring analysis. To decide which parts to inspect further, we need tools that automatically mine the data, identifying recurring patterns and isolated events. This requires a similarity measure for acoustic scenes that does not impose strong assumptions on the data. The state of the art in audio similarity measurement is the “bag-of-frames” approach, which models a recording using summary statistics of short-term audio descriptors, such as mel-frequency cepstral coefficients (MFCCs). They successfully characterise static scenes with little variability in auditory content, but cannot accurately capture scenes with a few salient events superimposed over static background. To overcome this issue, we propose a two-scale representation which describes a recording using clusters of scattering coefficients. The scattering coefficients capture short-scale structure, while the cluster model captures longer time scales, allowing for more accurate characterization of sparse events. Evaluation within the acoustic scene similarity framework demonstrates the interest of the proposed approach.

Achieving macroscopic directed migration of microscale swimmers in a fluid is an
important step towards utilizing their autonomous motion. It has been experimentally shown that
directed motion can be induced, without any external fields, by certain geometrically asymmetric
obstacles due to interaction between their boundaries and the swimmers. In this paper, we propose
a kinetic-type model to study swimming and directional migration of microscale bimetallic rods in
a periodic array of posts with noncircular cross-sections. Both rod position and orientation are
taken into account; rod trapping and release on the post boundaries are modeled by empirically
characterizing curvature and orientational dependence of the boundary absorption and desorption.
Intensity of the directed rod migration, which we call the normalized net flux, is then defined and
computed given the geometry of the post array. We numerically study the effect of post spacings on
the flux; we also apply shape optimization to find better post shapes that can induce stronger flux.
Inspired by preliminary numerical results on two candidate posts, we perform an approximate analysis
on a simplified model to show the key geometric features that a good post should have. Based on
this, three new candidate shapes are proposed which give rise to large fluxes. This approach provides
an effective tool and guidance for experimentally designing new devices that induce strong directed
migration of microscale swimmers.

We introduce the Loschmidt-amplitude as a powerful tool to perform spectroscopy of generic many-body wave functions. We use our machinery to interrogate the wave function obtained after (one or multiple) Kibble-Zurek ramps within the transverse field quantum Ising model. Known results for the scaling of defects or regarding the preference of the ramp to populate the lowest parts of the multi-magnon bands are confirmed explicitly. We obtain a more complete understanding of the population of defects on the level of the many-body wave function as well as of the effects of magnon-magnon interaction or finite size corrections. We add to the Kibble-Zurek picture the aspect of quantum coherence and its influence on the defect dynamics.

Luminous blue variables are massive, evolved stars that exhibit large variations in luminosity and size on timescales from months to years, with high associated rates of mass loss1,2,3,4,5. In addition to this on-going variability, these stars exhibit outburst phases, during which their size increases and as a result their effective temperature decreases, typically to about 9,000 kelvin3,6. Outbursts are believed to be caused by the radiation force on the cooler, more opaque, outer layers of the star balancing or even exceeding the force of gravity, although the exact mechanisms are unknown and cannot be determined using one-dimensional, spherically symmetric models of stars because such models cannot determine the physical processes that occur in this regime7. Here we report three-dimensional simulations of massive, radiation-dominated stars, which show that helium opacity has an important role in triggering outbursts and setting the observed effective temperature during outbursts of about 9,000 kelvin. It probably also triggers the episodic mass loss at rates of 10−7 to 10−5 solar masses per year. The peak in helium opacity is evident in our three-dimensional simulations only because the density and temperature of the stellar envelope (the outer part of the star near the photosphere) need to be determined self-consistently with convection, which cannot be done in one-dimensional models that assume spherical symmetry. The simulations reproduce observations of long-timescale variability, and predict that convection causes irregular oscillations in the radii of the stars and variations in brightness of 10–30 per cent on a typical timescale of a few days. The amplitudes of these short-timescale variations are predicted to be even larger for cooler stars (in the outburst phase). This short-timescale variability should be observable with high-cadence observations.

A variety of problems in acoustic and electromagnetic scattering require the evaluation of impedance or layered media Green's functions. Given a point source located in an unbounded half-space or an infinitely extended layer, Sommerfeld and others showed that Fourier analysis combined with contour integration provides a systematic and broadly effective approach, leading to what is generally referred to as the Sommerfeld integral representation. When either the source or target is at some distance from an infinite boundary, the number of degrees of freedom needed to resolve the scattering response is very modest. When both are near an interface, however, the Sommerfeld integral involves a very large range of integration and its direct application becomes unwieldy. Historically, three schemes have been employed to overcome this difficulty: the method of images, contour deformation, and asymptotic methods of various kinds. None of these methods make use of classical layer potentials in physical space, despite their advantages in terms of adaptive resolution and high-order accuracy. The reason for this is simple: layer potentials are impractical in layered media or half-space geometries since they require the discretization of an infinite boundary. In this paper, we propose a hybrid method which combines layer potentials (physical-space) on a finite portion of the interface together with a Sommerfeld-type (Fourier) correction. We prove that our method is efficient and rapidly convergent for arbitrarily located sources and targets, and show that the scheme is particularly effective when solving scattering problems for objects which are close to the half-space boundary or even embedded across a layered media interface.

The expressive variability in producing a musical note conveys information essential to the modeling of orchestration and style. As such, it plays a crucial role in computer-assisted browsing of massive digital music corpora. Yet, although the automatic recognition of a musical instrument from the recording of a single "ordinary" note is considered a solved problem, automatic identification of instrumental playing technique (IPT) remains largely underdeveloped. We benchmark machine listening systems for query-by-example browsing among 143 extended IPTs for 16 instruments, amounting to 469 triplets of instrument, mute, and technique. We identify and discuss three necessary conditions for significantly outperforming the traditional mel-frequency cepstral coefficient (MFCC) baseline: the addition of second-order scattering coefficients to account for amplitude modulation, the incorporation of long-range temporal dependencies, and metric learning using large-margin nearest neighbors (LMNN) to reduce intra-class variability. Evaluating on the Studio On Line (SOL) dataset, we obtain a precision at rank 5 of 99.7\% for instrument recognition (baseline at 89.0\%) and of 61.0\% for IPT recognition (baseline at 44.5\%). We interpret this gain through a qualitative assessment of practical usability and visualization using nonlinear dimensionality reduction.