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

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

Diagnostiek, behandeling en follow-up van het intermediair en hooggradig Non-Hodgkin Lymfoom. Kosten van protocollaire en niet-protocollaire behandelingen.

In dit onderzoek is een berekening gemaakt van de gemiddelde kosten van diagnostiek, behandeling en follow-up van patiënten met een non-Hodgkin lymfoom (NHL). Dit rapport is opgesteld in het kader van de opdracht van het ministerie van Volksgezondheid, Welzijn en Sport (VWS) aan het iMTA om in samenwerking met alle betrokken beroepsgroepen een landelijke klinische richtlijn voor het NHL te ontwikkelen, waarbij kosten-effectiviteitsoverwegingen in ogenschouw zijn genomen. In het voorliggende rapport wordt ten eerste een indicatie gegeven van de gemiddelde kosten in verschillende behandelingsgroepen (protocollair en niet-protocollair). Voorts biedt het rapport inzicht in specifieke kostenposten die ten behoeve van NHL-patiënten gemaakt worden, bijvoorbeeld de kosten van verschillende diagnostische opties

Information-Theoretic Compressive Measurement Design

An information-theoretic projection design framework is proposed, of interest for feature design and compressive measurements. Both Gaussian and Poisson measurement models are considered. The gradient of a proposed information-theoretic metric (ITM) is derived, and a gradient-descent algorithm is applied in design; connections are made to the information bottleneck. The fundamental solution structure of such design is revealed in the case of a Gaussian measurement model and arbitrary input statistics. This new theoretical result reveals how ITM parameter settings impact the number of needed projection measurements, with this verified experimentally. The ITM achieves promising results on real data, for both signal recovery and classification