Numerical Investigation of Graph Spectra and Information Interpretability of Eigenvalues

We undertake an extensive numerical investigation of the graph spectra of thousands regular graphs, a set of random Erd\"os-R\'enyi graphs, the two most popular types of complex networks and an evolving genetic network by using novel conceptual and experimental tools. Our objective in so doing is to contribute to an understanding of the meaning of the Eigenvalues of a graph relative to its topological and information-theoretic properties. We introduce a technique for identifying the most informative Eigenvalues of evolving networks by comparing graph spectra behavior to their algorithmic complexity. We suggest that extending techniques can be used to further investigate the behavior of evolving biological networks. In the extended version of this paper we apply these techniques to seven tissue specific regulatory networks as static example and network of a na\"ive pluripotent immune cell in the process of differentiating towards a Th17 cell as evolving example, finding the most and least informative Eigenvalues at every stage.Comment: Forthcoming in 3rd International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO), Lecture Notes in Bioinformatics, 201

Combining Contrast Invariant L1 Data Fidelities with Nonlinear Spectral Image Decomposition

This paper focuses on multi-scale approaches for variational methods and corresponding gradient flows. Recently, for convex regularization functionals such as total variation, new theory and algorithms for nonlinear eigenvalue problems via nonlinear spectral decompositions have been developed. Those methods open new directions for advanced image filtering. However, for an effective use in image segmentation and shape decomposition, a clear interpretation of the spectral response regarding size and intensity scales is needed but lacking in current approaches. In this context, $L^1$ data fidelities are particularly helpful due to their interesting multi-scale properties such as contrast invariance. Hence, the novelty of this work is the combination of $L^1$-based multi-scale methods with nonlinear spectral decompositions. We compare $L^1$ with $L^2$ scale-space methods in view of spectral image representation and decomposition. We show that the contrast invariant multi-scale behavior of $L^1-TV$ promotes sparsity in the spectral response providing more informative decompositions. We provide a numerical method and analyze synthetic and biomedical images at which decomposition leads to improved segmentation.Comment: 13 pages, 7 figures, conference SSVM 201

Statistical Inferences for Polarity Identification in Natural Language

Information forms the basis for all human behavior, including the ubiquitous decision-making that people constantly perform in their every day lives. It is thus the mission of researchers to understand how humans process information to reach decisions. In order to facilitate this task, this work proposes a novel method of studying the reception of granular expressions in natural language. The approach utilizes LASSO regularization as a statistical tool to extract decisive words from textual content and draw statistical inferences based on the correspondence between the occurrences of words and an exogenous response variable. Accordingly, the method immediately suggests significant implications for social sciences and Information Systems research: everyone can now identify text segments and word choices that are statistically relevant to authors or readers and, based on this knowledge, test hypotheses from behavioral research. We demonstrate the contribution of our method by examining how authors communicate subjective information through narrative materials. This allows us to answer the question of which words to choose when communicating negative information. On the other hand, we show that investors trade not only upon facts in financial disclosures but are distracted by filler words and non-informative language. Practitioners - for example those in the fields of investor communications or marketing - can exploit our insights to enhance their writings based on the true perception of word choice

Routine characterization and interpretation of complex alkali feldspar intergrowths

Almost all alkali feldspar crystals contain a rich inventory of exsolution, twin, and domain microtextures that form subsequent to crystal growth and provide a record of the thermal history of the crystal and often of its involvement in replacement reactions, sometimes multiple. Microtextures strongly influence the subsequent behavior of feldspars at low temperatures during diagenesis and weathering. They are central to the retention or exchange of trace elements and of radiogenic and stable isotopes. This review is aimed at petrologists and geochemists who wish to use alkali feldspar microtextures to solve geological problems or who need to understand how microtextures influence a particular process. We suggest a systematic approach that employs methods available in most well founded laboratories. The crystallographic relationships of complex feldspar intergrowths were established by the 1970s, mainly using single-crystal X-ray diffraction, but such methods give limited information on the spatial relationships of the different elements of the microtexture, or of the mode and chronology of their formation, which require the use of microscopy. We suggest a combination of techniques with a range of spatial resolution and strongly recommend the use of orientated sections. Sections cut parallel to the perfect (001) and (010) cleavages are the easiest to locate and most informative. Techniques described are light microscopy; scanning electron microscopy using both backscattered and secondary electrons, including the use of surfaces etched in the laboratory; electron-probe microanalysis and analysis by energy-dispersive spectrometry in a scanning electron microscope; transmission electron microscopy. We discuss the use of cathodoluminescence as an auxiliary technique, but do not recommend electron-backscattered diffraction for feldspar work. We review recent publications that provide examples of the need for great care and attention to pre-existing work in microtextural studies, and suggest several topics for future work

On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models

This study investigates the effects of Markov chain Monte Carlo (MCMC) sampling in unsupervised Maximum Likelihood (ML) learning. Our attention is restricted to the family of unnormalized probability densities for which the negative log density (or energy function) is a ConvNet. We find that many of the techniques used to stabilize training in previous studies are not necessary. ML learning with a ConvNet potential requires only a few hyper-parameters and no regularization. Using this minimal framework, we identify a variety of ML learning outcomes that depend solely on the implementation of MCMC sampling. On one hand, we show that it is easy to train an energy-based model which can sample realistic images with short-run Langevin. ML can be effective and stable even when MCMC samples have much higher energy than true steady-state samples throughout training. Based on this insight, we introduce an ML method with purely noise-initialized MCMC, high-quality short-run synthesis, and the same budget as ML with informative MCMC initialization such as CD or PCD. Unlike previous models, our energy model can obtain realistic high-diversity samples from a noise signal after training. On the other hand, ConvNet potentials learned with non-convergent MCMC do not have a valid steady-state and cannot be considered approximate unnormalized densities of the training data because long-run MCMC samples differ greatly from observed images. We show that it is much harder to train a ConvNet potential to learn a steady-state over realistic images. To our knowledge, long-run MCMC samples of all previous models lose the realism of short-run samples. With correct tuning of Langevin noise, we train the first ConvNet potentials for which long-run and steady-state MCMC samples are realistic images.Comment: Code available at: https://github.com/point0bar1/ebm-anatom

Using feedback requests to actively involve assessees in peer assessment : effects on the assessor’s feedback content and assessee’s agreement with feedback

Criticizing the common approach of supporting peer assessment through providing assessors with an explication of assessment criteria, recent insights on peer assessment call for support focusing on assessees, who often assume a passive role of receivers of feedback. Feedback requests, which require assessees to formulate their specific needs for feedback, have therefore been put forward as an alternative to supporting peer assessment, even though there is little known about their exact impact on feedback. Operationalizing effective feedback as feedback that (1) elaborates on the evaluation and (2) to which the receiver is agreeable, the present study examines how these two variables are affected by feedback requests, compared to an explanation of assessment criteria in the form of a content checklist. Situated against the backdrop of a writing task for 125 first-year students in an educational studies program at university, the study uses a 2 x 2 factorial design that resulted in four conditions: a control, feedback request, content checklist, and combination condition. The results underline the importance of taking message length into account when studying the effects of support for peer assessment. Although feedback requests did not have an impact on the raw number of elaborations, the proportion of informative elaborations within feedback messages was significantly higher in conditions that used a feedback request. In other words, it appears that the feedback request stimulated students to write more focused messages. In comparison with feedback content, the use of a feedback request did, however, not have a significant effect on agreement with feedback.peer assessment; feedback request; feedback content; agreement with feedbac