Dual adjacency matrix : exploring link groups in dense networks

Node grouping is a common way of adding structure and information to networks that aids their interpretation. However, certain networks benefit from the grouping of links instead of nodes. Link communities, for example, are a form of link groups that describe high-quality overlapping node communities. There is a conceptual gap between node groups and link groups that poses an interesting visualization challenge. We introduce the Dual Adjacency Matrix to bridge this gap. This matrix combines node and link group techniques via a generalization that also enables it to be coordinated with a node-link-contour diagram. These methods have been implemented in a prototype that we evaluated with an information scientist and neuroscientist via interviews and prototype walk-throughs. We demonstrate this prototype with the analysis of a trade network and an fMRI correlation network

Model-based segmentation and classification of trajectories (Extended abstract)

We present efficient algorithms for segmenting and classifying a trajectory based on a parameterized movement model like the Brownian bridge movement model. Segmentation is the problem of subdividing a trajectory into parts such that each art is homogeneous in its movement characteristics. We formalize this using the likelihood of the model parameter. We consider the case where a discrete set of m parameter values is given and present an algorithm to compute an optimal segmentation with respect to an information criterion in O(nm) time for a trajectory with n sampling points. Classification is the problem of assigning trajectories to classes. We present an algorithm for discrete classification given a set of trajectories. Our algorithm computes the optimal classification with respect to an information criterion in O(m^2 + mk(log m + log k)) time for m parameter values and k trajectories, assuming bitonic likelihood functions

Visual analytics in histopathology diagnostics: a protocol-based approach

Computer-Aided-Diagnosis (CAD) systems supporting the diagnostic process are widespread in radiology. Digital Pathology is still behind in the introduction of such solutions. Several studies investigated pathologists' behavior but only a few aimed to improve the diagnostic and report process with novel applications. In this work we designed and implemented a first protocol-based CAD viewer supported by visual analytics. The system targets the optimization of the diagnostic workflow in breast cancer diagnosis by means of three image analysis features that belong to the standard grading system (Nottingham Histologic Grade). A pathologist's routine was tracked during the examination of breast cancer tissue slides and diagnostic traces were analyzed from a qualitative perspective. Accordingly, a set of generic requirements was elicited to define the design and the implementation of the CAD-Viewer. A first qualitative evaluation conducted with five pathologists shows that the interface suffices the diagnostic workflow and diminishes the manual effort. We present promising evidence of the usefulness of our CAD-viewer and opportunities for its extension and integration in clinical practice. As a conclusion, the findings demonstrate that it is feasibile to optimize the Nottingham Grading workflow and, generally, the histological diagnosis by integrating computational pathology data with visual analytics techniques

The cinepheur: post-cinematic passage, post-perceptual passage

This thesis develops a hermeneutic commensurate with the aesthetic and ontological challenges of what Steven Shaviro describes as a post-cinematic media ecology, and Shane Denson describes as an emergent post-perceptual media ecology. I consider canonicity and cinephilia as frustrated efforts to contain and comprehend this new cinematic media object, offering a third unit of interpretation in their place, which I describe as the cinetopic anecdote. I associate the cinetopic anecdote with a particular way of moving between cinema and cinematic infrastructure, which I label cinetopic passage, and with a subject position that I label the cinepheur. Drawing on Walter Benjamin’s theory of the flâneur, I argue that the cinetopic anecdote precludes the extraction of a privileged cinematic moment in the manner characteristic of Christian Keathley’s cinephilic anecdote, but instead compels the cinepheur to instantiate, embody or physically recreate the infrastructural conditions that produced it, dovetailing production and consumption into what Axel Bruns has described as the emergent category of produsage; “unfinished artifacts, continuing process.” Having elaborated the cinetopic anecdote, I apply it to postmodern, post-cinematic and post-perceptual media ecologies, in order to evoke the peculiar forms of attachment and obsession bound up with the Criterion and Netflix platforms. In the process, I draw on Franco Moretti’s conception of distant reading to frame the cinetopic anecdote as a unit of distant viewing, offering distant viewings of Angela Christlieb and Stephen Kijak’s Cinemania, Sidney Lumet’s Garbo Talks and Pier Paolo Pasolini’s Salò, or The 120 Days of Sodom. Just as distant reading takes “the great unread” as its object of enquiry, so the cinetopic anecdote speaks to a media ecology preoccupied by the “great unviewed,” in which cinematic scarcity increasingly ramifies as an elegaic object

Partitioning the Heritability of Tourette Syndrome and Obsessive Compulsive Disorder Reveals Differences in Genetic Architecture

The direct estimation of heritability from genome-wide common variant data as implemented in the program Genome-wide Complex Trait Analysis (GCTA) has provided a means to quantify heritability attributable to all interrogated variants. We have quantified the variance in liability to disease explained

A comparison of two tree representations for data-driven volumetric image filtering

We compare two tree-based, hierarchical representations of volumetric gray-scale images for data-driven image filtering. One representation is the max-tree, in which tree nodes represent connected components of all level sets of a data set. The other representation is the watershed tree, consisting of nodes representing nested, homogeneous image regions. Region attribute-based filtering is achieved by pruning the trees. Visualization is used to compare both the filtered images and trees. In our comparison, we also consider flexibility, intuitiveness, and extendability of both tree representations