Localization in log-gamma polymers with boundaries

Consider the directed polymer in one space dimension in log-gamma environment with boundary conditions, introduced by Sepp{\"a}l{\"a}inen. In the equilibrium case, we prove that the end point of the polymer converges in law as the length increases, to a density proportional to the exponent of a zero-mean random walk. This holds without space normalization, and the mass concentrates in a neighborhood of the minimum of this random walk. We have analogous results out of equilibrium as well as for the middle point of the polymer with both ends fixed. The existence and the identification of the limit relies on the analysis of a random walk seen from its infimum.Comment: 33 pages, 3 figure

XRay: Enhancing the Web's Transparency with Differential Correlation

Today's Web services - such as Google, Amazon, and Facebook - leverage user data for varied purposes, including personalizing recommendations, targeting advertisements, and adjusting prices. At present, users have little insight into how their data is being used. Hence, they cannot make informed choices about the services they choose. To increase transparency, we developed XRay, the first fine-grained, robust, and scalable personal data tracking system for the Web. XRay predicts which data in an arbitrary Web account (such as emails, searches, or viewed products) is being used to target which outputs (such as ads, recommended products, or prices). XRay's core functions are service agnostic and easy to instantiate for new services, and they can track data within and across services. To make predictions independent of the audited service, XRay relies on the following insight: by comparing outputs from different accounts with similar, but not identical, subsets of data, one can pinpoint targeting through correlation. We show both theoretically, and through experiments on Gmail, Amazon, and YouTube, that XRay achieves high precision and recall by correlating data from a surprisingly small number of extra accounts.Comment: Extended version of a paper presented at the 23rd USENIX Security Symposium (USENIX Security 14

The Influence of Natural Sounds on California Ground Squirrel (Otospermophilus beecheyi) Vigilance and Predator Detection

Many animals rely on the acoustical environment for functions spanning mate attraction, navigation and predator and prey detection. However, recent research suggests that the context of the acoustic environment can greatly influence the propagation and reception of acoustic signals and cues, potentially interfering with the ability of animals to perceive important environmental cues. Here, we sought to determine whether natural sounds influence vigilance and predator detection in the California ground squirrel (Otospermophilus beecheyi). In a manipulative field experiment, we measured squirrel vigilance behavior under three conditions: playback of river rapid noise, playback of cicada chorus noise and a control, unmanipulated sound treatment. Under each condition, we also measured squirrel flight initiation distance (FID), defined as the distance at which an animal flees from an approaching threat. This behavior was in response to an approaching robotic coyote, which simulated a common predator in our study area. Our study is poised to not only determine whether natural sounds influence key behaviors in a common mammal, but will provide needed information on whether natural sounds and human-made sounds cause similar perceptual limitations and behavioral responses in acoustically-oriented animals. For example, California ground squirrels are known to increase vigilance in the presence of anthropogenic noise, but it has yet to be determined how natural noises, with differing frequencies and power, affect behavior. We hope this study will shed light on the differences between these conditions

ARK: Aggregation of Reads by K-Means for Estimation of Bacterial Community Composition.

MOTIVATION: Estimation of bacterial community composition from high-throughput sequenced 16S rRNA gene amplicons is a key task in microbial ecology. Since the sequence data from each sample typically consist of a large number of reads and are adversely impacted by different levels of biological and technical noise, accurate analysis of such large datasets is challenging. RESULTS: There has been a recent surge of interest in using compressed sensing inspired and convex-optimization based methods to solve the estimation problem for bacterial community composition. These methods typically rely on summarizing the sequence data by frequencies of low-order k-mers and matching this information statistically with a taxonomically structured database. Here we show that the accuracy of the resulting community composition estimates can be substantially improved by aggregating the reads from a sample with an unsupervised machine learning approach prior to the estimation phase. The aggregation of reads is a pre-processing approach where we use a standard K-means clustering algorithm that partitions a large set of reads into subsets with reasonable computational cost to provide several vectors of first order statistics instead of only single statistical summarization in terms of k-mer frequencies. The output of the clustering is then processed further to obtain the final estimate for each sample. The resulting method is called Aggregation of Reads by K-means (ARK), and it is based on a statistical argument via mixture density formulation. ARK is found to improve the fidelity and robustness of several recently introduced methods, with only a modest increase in computational complexity. AVAILABILITY: An open source, platform-independent implementation of the method in the Julia programming language is freely available at https://github.com/dkoslicki/ARK. A Matlab implementation is available at http://www.ee.kth.se/ctsoftware

Combining guilt-by-association and guilt-by-profiling to predict Saccharomyces cerevisiae gene function

BackgroundLearning the function of genes is a major goal of computational genomics. Methods for inferring gene function have typically fallen into two categories: 'guilt-by-profiling', which exploits correlation between function and other gene characteristics; and 'guilt-by-association', which transfers function from one gene to another via biological relationships.ResultsWe have developed a strategy ('Funckenstein') that performs guilt-by-profiling and guilt-by-association and combines the results. Using a benchmark set of functional categories and input data for protein-coding genes in Saccharomyces cerevisiae, Funckenstein was compared with a previous combined strategy. Subsequently, we applied Funckenstein to 2,455 Gene Ontology terms. In the process, we developed 2,455 guilt-by-profiling classifiers based on 8,848 gene characteristics and 12 functional linkage graphs based on 23 biological relationships.ConclusionFunckenstein outperforms a previous combined strategy using a common benchmark dataset. The combination of 'guilt-by-profiling' and 'guilt-by-association' gave significant improvement over the component classifiers, showing the greatest synergy for the most specific functions. Performance was evaluated by cross-validation and by literature examination of the top-scoring novel predictions. These quantitative predictions should help prioritize experimental study of yeast gene functions

Ask the locals: multi-way local pooling for image recognition

International audienceInvariant representations in object recognition systems are generally obtained by pooling feature vectors over spatially local neighborhoods. But pooling is not local in the feature vector space, so that widely dissimilar features may be pooled together if they are in nearby locations. Recent approaches rely on sophisticated encoding methods and more specialized codebooks (or dictionaries), e.g., learned on subsets of descriptors which are close in feature space, to circumvent this problem. In this work, we argue that a common trait found in much recent work in image recognition or retrieval is that it leverages locality in feature space on top of purely spatial locality. We propose to apply this idea in its simplest form to an object recognition system based on the spatial pyramid framework, to increase the performance of small dictionaries with very little added engineering. State of- the-art results on several object recognition benchmarks show the promise of this approach

An essential function of the mitogen‐activated protein kinase Erk2 in mouse trophoblast development

The closely related mitogen-activated protein kinase isoforms extracellular signal-regulated kinase 1 (ERK1) and ERK2 have been implicated in the control of cell proliferation, differentiation and survival. However, the specific in vivo functions of the two ERK isoforms remain to be analysed. Here, we show that disruption of the Erk2 locus leads to embryonic lethality early in mouse development after the implantation stage. Erk2 mutant embryos fail to form the ectoplacental cone and extra-embryonic ectoderm, which give rise to mature trophoblast derivatives in the fetus. Analysis of chimeric embryos showed that Erk2 functions in a cell-autonomous manner during the development of extra-embryonic cell lineages. We also found that both Erk2 and Erk1 are widely expressed throughout early-stage embryos. The inability of Erk1 to compensate for Erk2 function suggests a specific function for Erk2 in normal trophoblast development in the mouse, probably in regulating the proliferation of polar trophectoderm cells

Robust hybrid estimation and rejection of multi-frequency signals

We consider the problem of output regulation for LTI systems in the presence of unknown exosystems. The knowledge about the multi-frequency signals exosystem consists in the maximum number of frequencies and their maximal value. The control scheme relies on two main components: an estimation algorithm, to reconstruct the signal generated by the exosystem, and a controller, to enforce the output regulation property to the closed-loop system. To tackle the first task, we propose a hybrid observer for the estimation of the (possibly piece-wise continuous) number and values of the frequencies contained in the exogenous signal. The hybrid observer is particularly appealing for numerical implementations, and it is combined with a self-tuning algorithm of the free parameters (gains), thus improving its performance even in case of noisy measurements. Semi-global exponential convergence of the estimation error is provided. As far as the second task is concerned, a robust hybrid regulator is designed for practical rejection of the multi-frequency disturbance signal acting on the plant. The result is achieved by exploiting the frequencies estimated by the hybrid observer. The effectiveness of the proposed control scheme is shown by means of numerical simulations

Fabrication of Low-Cost Paper-Based Microfluidic Devices by Embossing or Cut-and-Stack Methods

This communication describes the use of embossing, and “cut-and-stack” methods of assembly, to generate microfluidic devices from omniphobic paper, and demonstrates that fluid flowing through these devices behaves similarly to fluid in an open-channel microfluidic device. The porosity of the paper to gasses allows processes not possible in devices made using PDMS or other non-porous materials. Droplet generators and phase separators, for example, could be made by embossing “T”-shaped channels on paper. Vertical stacking of embossed or cut layers of omniphobic paper generated three-dimensional systems of microchannels. The gas permeability of the paper allowed fluid in the microchannel to contact and exchange with environmental or directed gases. An aqueous stream of water containing a pH-indicator, as one demonstration, changed color upon exposure to air containing HCl or NH3 gases.Chemistry and Chemical Biolog

ARK: Aggregation of Reads by K-Means for Estimation of Bacterial Community Composition

Motivation: Estimation of bacterial community composition from high-throughput sequenced 16S rRNA gene amplicons is a key task in microbial ecology. Since the sequence data from each sample typically consist of a large number of reads and are adversely impacted by different levels of biological and technical noise, accurate analysis of such large datasets is challenging. Results: There has been a recent surge of interest in using compressed sensing inspired and convex-optimization based methods to solve the estimation problem for bacterial community composition. These methods typically rely on summarizing the sequence data by frequencies of low-order k-mers and matching this information statistically with a taxonomically structured database. Here we show that the accuracy of the resulting community composition estimates can be substantially improved by aggregating the reads from a sample with an unsupervised machine learning approach prior to the estimation phase. The aggregation of reads is a pre-processing approach where we use a standard K-means clustering algorithm that partitions a large set of reads into subsets with reasonable computational cost to provide several vectors of first order statistics instead of only single statistical summarization in terms of k-mer frequencies. The output of the clustering is then processed further to obtain the final estimate for each sample. The resulting method is called Aggregation of Reads by K-means (ARK), and it is based on a statistical argument via mixture density formulation. ARK is found to improve the fidelity and robustness of several recently introduced methods, with only a modest increase in computational complexity