5,228 research outputs found
The HyperBagGraph DataEdron: An Enriched Browsing Experience of Multimedia Datasets
Traditional verbatim browsers give back information in a linear way according
to a ranking performed by a search engine that may not be optimal for the
surfer. The latter may need to assess the pertinence of the information
retrieved, particularly when she wants to explore other facets of a
multi-facetted information space. For instance, in a multimedia dataset
different facets such as keywords, authors, publication category, organisations
and figures can be of interest. The facet simultaneous visualisation can help
to gain insights on the information retrieved and call for further searches.
Facets are co-occurence networks, modeled by HyperBag-Graphs -- families of
multisets -- and are in fact linked not only to the publication itself, but to
any chosen reference. These references allow to navigate inside the dataset and
perform visual queries. We explore here the case of scientific publications
based on Arxiv searches.Comment: Extension of the hypergraph framework shortly presented in
arXiv:1809.00164 (possible small overlaps); use the theoretical framework of
hb-graphs presented in arXiv:1809.0019
Detecting Activations over Graphs using Spanning Tree Wavelet Bases
We consider the detection of activations over graphs under Gaussian noise,
where signals are piece-wise constant over the graph. Despite the wide
applicability of such a detection algorithm, there has been little success in
the development of computationally feasible methods with proveable theoretical
guarantees for general graph topologies. We cast this as a hypothesis testing
problem, and first provide a universal necessary condition for asymptotic
distinguishability of the null and alternative hypotheses. We then introduce
the spanning tree wavelet basis over graphs, a localized basis that reflects
the topology of the graph, and prove that for any spanning tree, this approach
can distinguish null from alternative in a low signal-to-noise regime. Lastly,
we improve on this result and show that using the uniform spanning tree in the
basis construction yields a randomized test with stronger theoretical
guarantees that in many cases matches our necessary conditions. Specifically,
we obtain near-optimal performance in edge transitive graphs, -nearest
neighbor graphs, and -graphs
A closed-form approach to Bayesian inference in tree-structured graphical models
We consider the inference of the structure of an undirected graphical model
in an exact Bayesian framework. More specifically we aim at achieving the
inference with close-form posteriors, avoiding any sampling step. This task
would be intractable without any restriction on the considered graphs, so we
limit our exploration to mixtures of spanning trees. We consider the inference
of the structure of an undirected graphical model in a Bayesian framework. To
avoid convergence issues and highly demanding Monte Carlo sampling, we focus on
exact inference. More specifically we aim at achieving the inference with
close-form posteriors, avoiding any sampling step. To this aim, we restrict the
set of considered graphs to mixtures of spanning trees. We investigate under
which conditions on the priors - on both tree structures and parameters - exact
Bayesian inference can be achieved. Under these conditions, we derive a fast an
exact algorithm to compute the posterior probability for an edge to belong to
{the tree model} using an algebraic result called the Matrix-Tree theorem. We
show that the assumption we have made does not prevent our approach to perform
well on synthetic and flow cytometry data
Resolving Structure in Human Brain Organization: Identifying Mesoscale Organization in Weighted Network Representations
Human brain anatomy and function display a combination of modular and
hierarchical organization, suggesting the importance of both cohesive
structures and variable resolutions in the facilitation of healthy cognitive
processes. However, tools to simultaneously probe these features of brain
architecture require further development. We propose and apply a set of methods
to extract cohesive structures in network representations of brain connectivity
using multi-resolution techniques. We employ a combination of soft
thresholding, windowed thresholding, and resolution in community detection,
that enable us to identify and isolate structures associated with different
weights. One such mesoscale structure is bipartivity, which quantifies the
extent to which the brain is divided into two partitions with high connectivity
between partitions and low connectivity within partitions. A second,
complementary mesoscale structure is modularity, which quantifies the extent to
which the brain is divided into multiple communities with strong connectivity
within each community and weak connectivity between communities. Our methods
lead to multi-resolution curves of these network diagnostics over a range of
spatial, geometric, and structural scales. For statistical comparison, we
contrast our results with those obtained for several benchmark null models. Our
work demonstrates that multi-resolution diagnostic curves capture complex
organizational profiles in weighted graphs. We apply these methods to the
identification of resolution-specific characteristics of healthy weighted graph
architecture and altered connectivity profiles in psychiatric disease.Comment: Comments welcom
The architecture of a video image processor for the space station
The architecture of a video image processor for space station applications is described. The architecture was derived from a study of the requirements of algorithms that are necessary to produce the desired functionality of many of these applications. Architectural options were selected based on a simulation of the execution of these algorithms on various architectural organizations. A great deal of emphasis was placed on the ability of the system to evolve and grow over the lifetime of the space station. The result is a hierarchical parallel architecture that is characterized by high level language programmability, modularity, extensibility and can meet the required performance goals
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Computing infrastructure issues in distributed communications systems : a survey of operating system transport system architectures
The performance of distributed applications (such as file transfer, remote login, tele-conferencing, full-motion video, and scientific visualization) is influenced by several factors that interact in complex ways. In particular, application performance is significantly affected both by communication infrastructure factors and computing infrastructure factors. Several communication infrastructure factors include channel speed, bit-error rate, and congestion at intermediate switching nodes. Computing infrastructure factors include (among other things) both protocol processing activities (such as connection management, flow control, error detection, and retransmission) and general operating system factors (such as memory latency, CPU speed, interrupt and context switching overhead, process architecture, and message buffering). Due to a several orders of magnitude increase in network channel speed and an increase in application diversity, performance bottlenecks are shifting from the network factors to the transport system factors.This paper defines an abstraction called an "Operating System Transport System Architecture" (OSTSA) that is used to classify the major components and services in the computing infrastructure. End-to-end network protocols such as TCP, TP4, VMTP, XTP, and Delta-t typically run on general-purpose computers, where they utilize various operating system resources such as processors, virtual memory, and network controllers. The OSTSA provides services that integrate these resources to support distributed applications running on local and wide area networks.A taxonomy is presented to evaluate OSTSAs in terms of their support for protocol processing activities. We use this taxonomy to compare and contrast five general-purpose commercial and experimental operating systems including System V UNIX, BSD UNIX, the x-kernel, Choices, and Xinu
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