289 research outputs found
Implementation of novel methods of global and nonsmooth optimization : GANSO programming library
We discuss the implementation of a number of modern methods of global and nonsmooth continuous optimization, based on the ideas of Rubinov, in a programming library GANSO. GANSO implements the derivative-free bundle method, the extended cutting angle method, dynamical system-based optimization and their various combinations and heuristics. We outline the main ideas behind each method, and report on the interfacing with Matlab and Maple packages. <br /
Reflexive Cones
Reflexive cones in Banach spaces are cones with weakly compact intersection
with the unit ball. In this paper we study the structure of this class of
cones. We investigate the relations between the notion of reflexive cones and
the properties of their bases. This allows us to prove a characterization of
reflexive cones in term of the absence of a subcone isomorphic to the positive
cone of \ell_{1}. Moreover, the properties of some specific classes of
reflexive cones are investigated. Namely, we consider the reflexive cones such
that the intersection with the unit ball is norm compact, those generated by a
Schauder basis and the reflexive cones regarded as ordering cones in a Banach
spaces. Finally, it is worth to point out that a characterization of reflexive
spaces and also of the Schur spaces by the properties of reflexive cones is
given.Comment: 23 page
The Parameterized Complexity of Centrality Improvement in Networks
The centrality of a vertex v in a network intuitively captures how important
v is for communication in the network. The task of improving the centrality of
a vertex has many applications, as a higher centrality often implies a larger
impact on the network or less transportation or administration cost. In this
work we study the parameterized complexity of the NP-complete problems
Closeness Improvement and Betweenness Improvement in which we ask to improve a
given vertex' closeness or betweenness centrality by a given amount through
adding a given number of edges to the network. Herein, the closeness of a
vertex v sums the multiplicative inverses of distances of other vertices to v
and the betweenness sums for each pair of vertices the fraction of shortest
paths going through v. Unfortunately, for the natural parameter "number of
edges to add" we obtain hardness results, even in rather restricted cases. On
the positive side, we also give an island of tractability for the parameter
measuring the vertex deletion distance to cluster graphs
Exponential Random Graph Modeling for Complex Brain Networks
Exponential random graph models (ERGMs), also known as p* models, have been
utilized extensively in the social science literature to study complex networks
and how their global structure depends on underlying structural components.
However, the literature on their use in biological networks (especially brain
networks) has remained sparse. Descriptive models based on a specific feature
of the graph (clustering coefficient, degree distribution, etc.) have dominated
connectivity research in neuroscience. Corresponding generative models have
been developed to reproduce one of these features. However, the complexity
inherent in whole-brain network data necessitates the development and use of
tools that allow the systematic exploration of several features simultaneously
and how they interact to form the global network architecture. ERGMs provide a
statistically principled approach to the assessment of how a set of interacting
local brain network features gives rise to the global structure. We illustrate
the utility of ERGMs for modeling, analyzing, and simulating complex
whole-brain networks with network data from normal subjects. We also provide a
foundation for the selection of important local features through the
implementation and assessment of three selection approaches: a traditional
p-value based backward selection approach, an information criterion approach
(AIC), and a graphical goodness of fit (GOF) approach. The graphical GOF
approach serves as the best method given the scientific interest in being able
to capture and reproduce the structure of fitted brain networks
Set optimization - a rather short introduction
Recent developments in set optimization are surveyed and extended including
various set relations as well as fundamental constructions of a convex analysis
for set- and vector-valued functions, and duality for set optimization
problems. Extensive sections with bibliographical comments summarize the state
of the art. Applications to vector optimization and financial risk measures are
discussed along with algorithmic approaches to set optimization problems
Inferring structural connectivity using Ising couplings in models of neuronal networks
Functional connectivity metrics have been widely used to infer the underlying structural connectivity in neuronal networks. Maximum entropy based Ising models have been suggested to discount the effect of indirect interactions and give good results in inferring the true anatomical connections. However, no benchmarking is currently available to assess the performance of Ising couplings against other functional connectivity metrics in the microscopic scale of neuronal networks through a wide set of network conditions and network structures. In this paper, we study the performance of the Ising model couplings to infer the synaptic connectivity in in silico networks of neurons and compare its performance against partial and cross-correlations for different correlation levels, firing rates, network sizes, network densities, and topologies. Our results show that the relative performance amongst the three functional connectivity metrics depends primarily on the network correlation levels. Ising couplings detected the most structural links at very weak network correlation levels, and partial correlations outperformed Ising couplings and cross-correlations at strong correlation levels. The result was consistent across varying firing rates, network sizes, and topologies. The findings of this paper serve as a guide in choosing the right functional connectivity tool to reconstruct the structural connectivity
Ratio of the Isolated Photon Cross Sections at \sqrt{s} = 630 and 1800 GeV
The inclusive cross section for production of isolated photons has been
measured in \pbarp collisions at GeV with the \D0 detector at
the Fermilab Tevatron Collider. The photons span a transverse energy ()
range from 7-49 GeV and have pseudorapidity . This measurement is
combined with to previous \D0 result at GeV to form a ratio
of the cross sections. Comparison of next-to-leading order QCD with the
measured cross section at 630 GeV and ratio of cross sections show satisfactory
agreement in most of the range.Comment: 7 pages. Published in Phys. Rev. Lett. 87, 251805, (2001
Search for Kaluza-Klein Graviton Emission in Collisions at TeV using the Missing Energy Signature
We report on a search for direct Kaluza-Klein graviton production in a data
sample of 84 of \ppb collisions at = 1.8 TeV, recorded
by the Collider Detector at Fermilab. We investigate the final state of large
missing transverse energy and one or two high energy jets. We compare the data
with the predictions from a -dimensional Kaluza-Klein scenario in which
gravity becomes strong at the TeV scale. At 95% confidence level (C.L.) for
=2, 4, and 6 we exclude an effective Planck scale below 1.0, 0.77, and 0.71
TeV, respectively.Comment: Submitted to PRL, 7 pages 4 figures/Revision includes 5 figure
Measurement of the average time-integrated mixing probability of b-flavored hadrons produced at the Tevatron
We have measured the number of like-sign (LS) and opposite-sign (OS) lepton
pairs arising from double semileptonic decays of and -hadrons,
pair-produced at the Fermilab Tevatron collider. The data samples were
collected with the Collider Detector at Fermilab (CDF) during the 1992-1995
collider run by triggering on the existence of and candidates
in an event. The observed ratio of LS to OS dileptons leads to a measurement of
the average time-integrated mixing probability of all produced -flavored
hadrons which decay weakly, (stat.)
(syst.), that is significantly larger than the world average .Comment: 47 pages, 10 figures, 15 tables Submitted to Phys. Rev.
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