Correlation between crystalline order and vitrification in colloidal monolayers

We investigate experimentally the relationship between local structure and dynamical arrest in a quasi-2d colloidal model system which approximates hard discs. We introduce polydispersity to the system to suppress crystallisation. Upon compression, the increase in structural relaxation time is accompanied by the emergence of local hexagonal symmetry. Examining the dynamical heterogeneity of the system, we identify three types of motion : "zero-dimensional" corresponding to beta-relaxation, "one-dimensional" or stringlike motion and "two-dimensional" motion. The dynamic heterogeneity is correlated with the local order, that is to say locally hexagonal regions are more likely to be dynamically slow. However we find that lengthscales corresponding to dynamic heterogeneity and local structure do not appear to scale together approaching the glass transition.Comment: 13 papes, to appear in J. Phys.: Condens. Matte

Entropy-based active learning for object recognition

Most methods for learning object categories require large amounts of labeled training data. However, obtaining such data can be a difficult and time-consuming endeavor. We have developed a novel, entropy-based ldquoactive learningrdquo approach which makes significant progress towards this problem. The main idea is to sequentially acquire labeled data by presenting an oracle (the user) with unlabeled images that will be particularly informative when labeled. Active learning adaptively prioritizes the order in which the training examples are acquired, which, as shown by our experiments, can significantly reduce the overall number of training examples required to reach near-optimal performance. At first glance this may seem counter-intuitive: how can the algorithm know whether a group of unlabeled images will be informative, when, by definition, there is no label directly associated with any of the images? Our approach is based on choosing an image to label that maximizes the expected amount of information we gain about the set of unlabeled images. The technique is demonstrated in several contexts, including improving the efficiency of Web image-search queries and open-world visual learning by an autonomous agent. Experiments on a large set of 140 visual object categories taken directly from text-based Web image searches show that our technique can provide large improvements (up to 10 x reduction in the number of training examples needed) over baseline techniques

Automated analysis of radar imagery of Venus: handling lack of ground truth

Lack of verifiable ground truth is a common problem in remote sensing image analysis. For example, consider the synthetic aperture radar (SAR) image data of Venus obtained by the Magellan spacecraft. Planetary scientists are interested in automatically cataloging the locations of all the small volcanoes in this data set; however, the problem is very difficult and cannot be performed with perfect reliability even by human experts. Thus, training and evaluating the performance of an automatic algorithm on this data set must be handled carefully. We discuss the use of weighted free-response receiver-operating characteristics (wFROCs) for evaluating detection performance when the “ground truth” is subjective. In particular, we evaluate the relative detection performance of humans and automatic algorithms. Our experimental results indicate that proper assessment of the uncertainty in “ground truth” is essential in applications of this nature

Late-time expansion in the semiclassical theory of the Hawking radiation

We give a detailed treatment of the back-reaction effects on the Hawking spectrum in the late-time expansion within the semiclassical approach to the Hawking radiation. We find that the boundary value problem defining the action of the modes which are regular at the horizon admits in general the presence of caustics. We show that for radii less that a certain critical value $r_c$ no caustic occurs for all values of the wave number and time and we give a rigorous lower bound on such a critical value. We solve the exact system of non linear equations defining the motion, by an iterative procedure rigorously convergent at late times. The first two terms of such an expansion give the $O(\omega/M)$ correction to the Hawking spectrum.Comment: 17 pages, 1 figure, LaTex, typos corrected, one intermediate formula adde

Method and apparatus for analyzing material properties using reflected ultrasound

A method and apparatus which assesses the mechanical properties of a material by launching an ultrasound signal at the material while varying the angle of incidence and analyzing the amplitude of the ultrasound wave reflected by the material. The method and apparatus correlates extrema (maxima or minima inflection points) in the reflected angle with the angle of incidence of the transmitted signal to identify critical angles of incidence. The velocity of the pressure wave in the material has been found to be a function of a first critical angle corresponding to a first maxima as the angle of incidence is increased in the range 0.degree.-90.degree.. The velocity of the shear wave in the material has been found to be a function of a second critical angle corresponding to a second maxima following the first maxima. Young's modulus of elasticity, Poisson's modulus, and density can be approximated using the velocity of the pressure wave and shear wave for isotropic materials. A third critical angle corresponding to a minima after the first critical angle (reflected amplitude approaching o) has been found particularly useful in conjunction with the first and second critical angles in assessing bone density and in determining whether the second critical point is at a maximum or an inflection point. The extension of the method in which the plane of scattering is rotated around the normal to bone while keeping the point of observation fixed has been found particularly useful in assessing the mechanical properties of anisotropic materials such as cortical bone.Board of Regents, University of Texas Syste

Automated analysis of quantitative image data using isomorphic functional mixed models, with application to proteomics data

Image data are increasingly encountered and are of growing importance in many areas of science. Much of these data are quantitative image data, which are characterized by intensities that represent some measurement of interest in the scanned images. The data typically consist of multiple images on the same domain and the goal of the research is to combine the quantitative information across images to make inference about populations or interventions. In this paper we present a unified analysis framework for the analysis of quantitative image data using a Bayesian functional mixed model approach. This framework is flexible enough to handle complex, irregular images with many local features, and can model the simultaneous effects of multiple factors on the image intensities and account for the correlation between images induced by the design. We introduce a general isomorphic modeling approach to fitting the functional mixed model, of which the wavelet-based functional mixed model is one special case. With suitable modeling choices, this approach leads to efficient calculations and can result in flexible modeling and adaptive smoothing of the salient features in the data. The proposed method has the following advantages: it can be run automatically, it produces inferential plots indicating which regions of the image are associated with each factor, it simultaneously considers the practical and statistical significance of findings, and it controls the false discovery rate.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS407 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

cis-acting sequences and trans-acting factors in the localization of mRNA for mitochondrial ribosomal proteins

mRNA localization is a conserved post-transcriptional process crucial for a variety of systems. Although several mechanisms have been identified, emerging evidence suggests that most transcripts reach the protein functional site by moving along cytoskeleton elements. We demonstrated previously that mRNA for mitochondrial ribosomal proteins are asymmetrically distributed in the cytoplasm, and that localization in the proximity of mitochondria is mediated by the 3′-UTR. Here we show by biochemical analysis that these mRNA transcripts are associated with the cytoskeleton through the microtubule network. Cytoskeleton association is functional for their intracellular localization near the mitochondrion, and the 3′-UTR is involved in this cytoskeleton-dependent localization. To identify the minimal elements required for localization, we generated DNA constructs containing, downstream from the GFP gene, deletion mutants of mitochondrial ribosomal protein S12 3′-UTR, and expressed them in HeLa cells. RT-PCR analysis showed that the localization signals responsible for mRNA localization are located in the first 154 nucleotides. RNA pulldown assays, mass spectrometry, and RNP immunoprecipitation assay experiments, demonstrated that mitochondrial ribosomal protein S12 3′-UTR interacts specifically with TRAP1 (tumor necrosis factor receptor-associated protein1), hnRNPM4 (heterogeneous nuclear ribonucleoprotein M4), Hsp70 and Hsp60 (heat shock proteins 70 and 60), and α-tubulin in vitro and in vivo

What Twitter Profile and Posted Images Reveal About Depression and Anxiety

Previous work has found strong links between the choice of social media images and users' emotions, demographics and personality traits. In this study, we examine which attributes of profile and posted images are associated with depression and anxiety of Twitter users. We used a sample of 28,749 Facebook users to build a language prediction model of survey-reported depression and anxiety, and validated it on Twitter on a sample of 887 users who had taken anxiety and depression surveys. We then applied it to a different set of 4,132 Twitter users to impute language-based depression and anxiety labels, and extracted interpretable features of posted and profile pictures to uncover the associations with users' depression and anxiety, controlling for demographics. For depression, we find that profile pictures suppress positive emotions rather than display more negative emotions, likely because of social media self-presentation biases. They also tend to show the single face of the user (rather than show her in groups of friends), marking increased focus on the self, emblematic for depression. Posted images are dominated by grayscale and low aesthetic cohesion across a variety of image features. Profile images of anxious users are similarly marked by grayscale and low aesthetic cohesion, but less so than those of depressed users. Finally, we show that image features can be used to predict depression and anxiety, and that multitask learning that includes a joint modeling of demographics improves prediction performance. Overall, we find that the image attributes that mark depression and anxiety offer a rich lens into these conditions largely congruent with the psychological literature, and that images on Twitter allow inferences about the mental health status of users.Comment: ICWSM 201