Approaching a topological phase transition in Majorana nanowires

Recent experiments have produced mounting evidence of Majorana zero modes in nanowire-superconductor hybrids. Signatures of an expected topological phase transition accompanying the onset of these modes nevertheless remain elusive. We investigate a fundamental question concerning this issue: Do well-formed Majorana modes necessarily entail a sharp phase transition in these setups? Assuming reasonable parameters, we argue that finite-size effects can dramatically smooth this putative transition into a crossover, even in systems large enough to support well-localized Majorana modes. We propose overcoming such finite-size effects by examining the behavior of low-lying excited states through tunneling spectroscopy. In particular, the excited-state energies exhibit characteristic field and density dependence, and scaling with system size, that expose an approaching topological phase transition. We suggest several experiments for extracting the predicted behavior. As a useful byproduct, the protocols also allow one to measure the wire's spin-orbit coupling directly in its superconducting environment.Comment: 13 pages, 8 figure

Realization of an all-optical zero to π cross-phase modulation jump

We report on the experimental demonstration of an all-optical π cross-phase modulation jump. By performing a preselection, an optically induced unitary transformation, and then a postselection on the polarization degree of freedom, the phase of the output beam acquires either a zero or π phase shift (with no other possible values). The postselection results in optical loss in the output beam. An input state may be chosen near the resulting phase singularity, yielding a pi phase shift even for weak interaction strengths. The scheme is experimentally demonstrated using a coherently prepared dark state in a warm atomic cesium vapor

Reducing Reparameterization Gradient Variance

Optimization with noisy gradients has become ubiquitous in statistics and machine learning. Reparameterization gradients, or gradient estimates computed via the "reparameterization trick," represent a class of noisy gradients often used in Monte Carlo variational inference (MCVI). However, when these gradient estimators are too noisy, the optimization procedure can be slow or fail to converge. One way to reduce noise is to use more samples for the gradient estimate, but this can be computationally expensive. Instead, we view the noisy gradient as a random variable, and form an inexpensive approximation of the generating procedure for the gradient sample. This approximation has high correlation with the noisy gradient by construction, making it a useful control variate for variance reduction. We demonstrate our approach on non-conjugate multi-level hierarchical models and a Bayesian neural net where we observed gradient variance reductions of multiple orders of magnitude (20-2,000x)

Approximate Inference for Constructing Astronomical Catalogs from Images

We present a new, fully generative model for constructing astronomical catalogs from optical telescope image sets. Each pixel intensity is treated as a random variable with parameters that depend on the latent properties of stars and galaxies. These latent properties are themselves modeled as random. We compare two procedures for posterior inference. One procedure is based on Markov chain Monte Carlo (MCMC) while the other is based on variational inference (VI). The MCMC procedure excels at quantifying uncertainty, while the VI procedure is 1000 times faster. On a supercomputer, the VI procedure efficiently uses 665,000 CPU cores to construct an astronomical catalog from 50 terabytes of images in 14.6 minutes, demonstrating the scaling characteristics necessary to construct catalogs for upcoming astronomical surveys.Comment: accepted to the Annals of Applied Statistic

Implementation of Provably Stable MaxNet

MaxNet TCP is a congestion control protocol that uses explicit multi-bit signalling from routers to achieve desirable properties such as high throughput and low latency. In this paper we present an implementation of an extended version of MaxNet. Our contributions are threefold. First, we extend the original algorithm to give both provable stability and rate fairness. Second, we introduce the MaxStart algorithm which allows new MaxNet connections to reach their fair rates quickly. Third, we provide a Linux kernel implementation of the protocol. With no overhead but 24-bit price signals, our implementation scales from 32 bit/s to 1 peta-bit/s with a 0.001% rate accuracy. We confirm the theoretically predicted properties by performing a range of experiments at speeds up to 1 Gbit/sec and delays up to 180 ms on the WAN-in-Lab facility

Applications of MODIS Fluorescent Line Height Measurements to Monitor Water Quality Trends and Algal Bloom Activity

Recent advances in satellite and airborne remote sensing, such as improvements in sensor and algorithm calibrations, processing techniques and atmospheric correction procedures have provided for increased coverage of remote-sensing, ocean-color products for coastal regions. In particular, for the Moderate Resolution Imaging Spectrometer (MODIS) sensor calibration updates, improved aerosol retrievals and new aerosol models has led to improved atmospheric correction algorithms for turbid waters and have improved the retrieval of ocean color in coastal waters. This has opened the way for studying ocean phenomena and processes at finer spatial scales, such as the interactions at the land-sea interface, trends in coastal water quality and algal blooms. Human population growth and changes in coastal management practices have brought about significant changes in the concentrations of organic and inorganic, particulate and dissolved substances entering the coastal ocean. There is increasing concern that these inputs have led to declines in water quality and have increase local concentrations of phytoplankton, which cause harmful algal blooms. In two case studies we present MODIS observations of fluorescence line height (FLH) to 1) assess trends in water quality for Tampa Bay, Florida and 2) illustrate seasonal and annual variability of algal bloom activity in Monterey Bay, California as well as document estuarine/riverine plume induced red tide events. In a comprehensive analysis of long term (2003-2011) in situ monitoring data and satellite imagery from Tampa Bay we assess the validity of the MODIS FLH product against chlorophyll-a and a suite of water quality parameters taken in a variety of conditions throughout a large optically complex estuarine system. A systematic analysis of sampling sites throughout the bay is undertaken to understand how the relationship between FLH and in situ chlorophyll-a responds to varying conditions and to develop a near decadal trend in water quality changes. In situ monitoring locations that correlated well with satellite imagery were in depths greater than seven meters and were located over five kilometers from shore. Water quality parameter of total nitrogen, phosphorous, turbidity and biological oxygen demand had high correlations with these sites, as well. Satellite FLH estimates show improving water quality from 2003-2007 with a slight decline up through 2011. Dinoflagellate blooms in Monterey Bay, California (USA) have recently increased in frequency and intensity. Nine years of MODIS FLH observations are used to describe the annual and seasonal variability of bloom activity within the Bay. Three classes of MODIS algorithms were correlated against in situ chlorophyll measurements. The FLH algorithm provided the most robust estimate of bloom activity. Elevated concentrations of phytoplankton were evident during the months of August-November, a period during which increased occurrences of dinoflagellate blooms have been observed in situ. Seasonal patterns of FLH show the on- and offshore movement of areas of high phytoplankton biomass between oceanographic seasons. Higher concentrations of phytoplankton are also evident in the vicinity of the land-based nutrient sources and outflows, and the cyclonic bay-wide circulation can transport these nutrients to the northern Bay bloom incubation region. Both of these case studies illustrate the utility MODIS FLH observations in supporting management decisions in coastal and estuarine waters

Synthetic lethal analysis of Caenorhabditis elegans posterior embryonic patterning genes identifies conserved genetic interactions

Phenotypic robustness is evidenced when single-gene mutations do not result in an obvious phenotype. It has been suggested that such phenotypic stability results from 'buffering' activities of homologous genes as well as non-homologous genes acting in parallel pathways. One approach to characterizing mechanisms of phenotypic robustness is to identify genetic interactions, specifically, double mutants where buffering is compromised. To identify interactions among genes implicated in posterior patterning of the Caenorhabditis elegans embryo, we measured synthetic lethality following RNA interference of 22 genes in 15 mutant strains. A pair of homologous T-box transcription factors (tbx-8 and tbx-9) is found to interact in both C. elegans and C. briggsae, indicating that their compensatory function is conserved. Furthermore, a muscle module is defined by transitive interactions between the MyoD homolog hlh-1, another basic helix-loop-helix transcription factor, hnd-1, and the MADS-box transcription factor unc-120. Genetic interactions within a homologous set of genes involved in vertebrate myogenesis indicate broad conservation of the muscle module and suggest that other genetic modules identified in C. elegans will be conserved

Overview: Computer vision and machine learning for microstructural characterization and analysis

The characterization and analysis of microstructure is the foundation of microstructural science, connecting the materials structure to its composition, process history, and properties. Microstructural quantification traditionally involves a human deciding a priori what to measure and then devising a purpose-built method for doing so. However, recent advances in data science, including computer vision (CV) and machine learning (ML) offer new approaches to extracting information from microstructural images. This overview surveys CV approaches to numerically encode the visual information contained in a microstructural image, which then provides input to supervised or unsupervised ML algorithms that find associations and trends in the high-dimensional image representation. CV/ML systems for microstructural characterization and analysis span the taxonomy of image analysis tasks, including image classification, semantic segmentation, object detection, and instance segmentation. These tools enable new approaches to microstructural analysis, including the development of new, rich visual metrics and the discovery of processing-microstructure-property relationships.Comment: submitted to Materials and Metallurgical Transactions