Generalized Bregman Divergence and Gradient of Mutual Information for Vector Poisson Channels

We investigate connections between information-theoretic and estimation-theoretic quantities in vector Poisson channel models. In particular, we generalize the gradient of mutual information with respect to key system parameters from the scalar to the vector Poisson channel model. We also propose, as another contribution, a generalization of the classical Bregman divergence that offers a means to encapsulate under a unifying framework the gradient of mutual information results for scalar and vector Poisson and Gaussian channel models. The so-called generalized Bregman divergence is also shown to exhibit various properties akin to the properties of the classical version. The vector Poisson channel model is drawing considerable attention in view of its application in various domains: as an example, the availability of the gradient of mutual information can be used in conjunction with gradient descent methods to effect compressive-sensing projection designs in emerging X-ray and document classification applications

Task-Driven Adaptive Statistical Compressive Sensing of Gaussian Mixture Models

A framework for adaptive and non-adaptive statistical compressive sensing is developed, where a statistical model replaces the standard sparsity model of classical compressive sensing. We propose within this framework optimal task-specific sensing protocols specifically and jointly designed for classification and reconstruction. A two-step adaptive sensing paradigm is developed, where online sensing is applied to detect the signal class in the first step, followed by a reconstruction step adapted to the detected class and the observed samples. The approach is based on information theory, here tailored for Gaussian mixture models (GMMs), where an information-theoretic objective relationship between the sensed signals and a representation of the specific task of interest is maximized. Experimental results using synthetic signals, Landsat satellite attributes, and natural images of different sizes and with different noise levels show the improvements achieved using the proposed framework when compared to more standard sensing protocols. The underlying formulation can be applied beyond GMMs, at the price of higher mathematical and computational complexity

Latent protein trees

Unbiased, label-free proteomics is becoming a powerful technique for measuring protein expression in almost any biological sample. The output of these measurements after preprocessing is a collection of features and their associated intensities for each sample. Subsets of features within the data are from the same peptide, subsets of peptides are from the same protein, and subsets of proteins are in the same biological pathways, therefore, there is the potential for very complex and informative correlational structure inherent in these data. Recent attempts to utilize this data often focus on the identification of single features that are associated with a particular phenotype that is relevant to the experiment. However, to date, there have been no published approaches that directly model what we know to be multiple different levels of correlation structure. Here we present a hierarchical Bayesian model which is specifically designed to model such correlation structure in unbiased, label-free proteomics. This model utilizes partial identification information from peptide sequencing and database lookup as well as the observed correlation in the data to appropriately compress features into latent proteins and to estimate their correlation structure. We demonstrate the effectiveness of the model using artificial/benchmark data and in the context of a series of proteomics measurements of blood plasma from a collection of volunteers who were infected with two different strains of viral influenza.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS639 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Were we stressed or was it just me – and does it even matter? : Efforts to disentangle individual and collective resilience within real and imagined stressors

Although resilience is a multi-level process, research largely focuses on the individual and little is known about how resilience may distinctly present at the group level. Even less is known about subjective conceptualizations of resilience at either level. Therefore, two studies sought to better understand how individuals conceptualize resilience both as an individual and as a group. Study 1 (N = 123) experimentally manipulated whether participants reported on either individual or group-based responses to real stressors and analysed their qualitative responses. For individual responses, subjective resilience featured active coping most prominently, whereas social support was the focus for group-based responses. As these differences might be attributable to the different stressors people remembered in either condition, Study 2 (N = 171) held a hypothetical stressor (i.e., natural disaster) constant. As expected, resilience at the group level emphasized maintaining group cohesion. Surprisingly, the group condition also reported increased likelihood to engage in blame, denial, and behavioural disengagement. Contrary to expectations, participants in the individual condition reported stronger desire to seek out new groups. The combined findings are discussed within the framework of resilience and social identity and highlight the necessity of accounting for multiple levels and subjective conceptualizations of resilience

Communications inspired linear discriminant analysis

We study the problem of supervised linear dimensionality reduction, taking an information-theoretic viewpoint. The linear projection matrix is designed by maximizing the mutual information between the projected signal and the class label. By harnessing a recent theoretical result on the gradient of mutual information, the above optimization problem can be solved directly using gradient descent, without requiring simplification of the objective function. Theoretical analysis and empirical comparison are made between the proposed method and two closely related methods, and comparisons are also made with a method in which Rényi entropy is used to define the mutual information (in this case the gradient may be computed simply, under a special parameter setting). Relative to these alternative approaches, the proposed method achieves promising results on real datasets. Copyright 2012 by the author(s)/owner(s)

Paradoxien des Digital Turn in der Architektur 1990–2015. Von den Verlockungen des Organischen: digitales Entwerfen zwischen informellem Denken und biomorphem Resultat

Vor dem Hintergrund der Einführung des Computers und der damit verbundenen Digitalisierung in der Architektur mit ihrer breiten Anwendung in den 1990er Jahren geht die Arbeit von der Frage aus, warum es im Formbildungsprozess eine Diskrepanz zwischen informellem Denken und biomorphem Resultat gibt. Es werden Paradoxien aufgedeckt, deren Fehlschlüsse zu einer Vielfalt von digitalen Strömungen bei gleichzeitiger Vereinheitlichung der Ausdrucksmittel führten. Im Mittelpunkt steht eine vergleichende und disziplinübergreifende Gegenüberstellung informeller und biomorpher Ansätze. Der informelle Ansatz findet seinen Ursprung im Konzept des Formlosen bei Georges Bataille in den 1920er Jahren und in der informellen Kunst in den 1950er/1960er Jahren. Der biomorphe Ansatz präsentiert sich in dieser Arbeit durch den Nachweis der Verlockungen, aufgrund derer die Architektur die Natur immer wieder als Vorbild nimmt. Es wird aufgezeigt, wo der aktuelle Architekturdiskurs in der Vermischung beider Ansätze feststeckt. Die Konklusion und der Ausblick bilden den Abschluss, in dem die "Unfreiheit" des Programmierens mit dem Wesen der Unbestimmtheit in einer postdigitalen Ära zusammengedacht wird. Dabei wird eine Antwort auf die Frage gegeben, warum sich das digitale Entwerfen vielfach einer biomorphen Formensprache bedient und wie ein Weg aussehen kann, der aus dieser Sackgasse herausführt