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Search Results: 1 - 10 of 200494 matches for " P. Rubinov "
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Hamamatsu PMT R7056 Study for the Extinction Monitoring System of the Mu2e Experiment at Fermilab
S. Boi,A. Dyshkant,D. Hedin,E. Johnson,E. Prebys,P. Rubinov
Physics , 2015,
Abstract: The Mu2e experiment at Fermilab proposes to search for the coherent neutrino-less conversion of muons to electrons in the presence of a nucleus. The experimental signature for an aluminum target is an isolated 105 MeV electron exiting the stopping target no earlier than ~700 ns after the pulse of proton beam hits the production target. Any protons that hit the production target in between the pulses can lead to fake conversion electrons during the measurement period. We define the beam extinction as the ratio of the number of protons striking the production target between pulses to the number striking the target during the pulses. It has been established that an extinction of approximately 10-10 is required to reduce the backgrounds to an acceptable level. It would be desirable to measure the extinction of the beam coming out of the accelerator in a minute or less. Studies for the fast extinction monitor based on Hamamatsu PMT R7056 is the subject of this presentation.
-porosity in monotonic analysis with applications to optimization
A. M. Rubinov
Abstract and Applied Analysis , 2005, DOI: 10.1155/aaa.2005.287
Abstract: We introduce and study some metric spaces of increasing positivelyhomogeneous (IPH) functions, decreasing functions, and conormal(upward) sets. We prove that the complements of the subset ofstrictly increasing IPH functions, of the subset of strictlydecreasing functions, and of the subset of strictly conormal setsare σ-porous in corresponding spaces. Some applications tooptimization are given.
On Convex Polytopes in the d-dimensional Space Containing and Avoiding Zero
Alexander Kelmans,Anatoliy Rubinov
Computer Science , 2012,
Abstract: The goal of this paper is to establish certain inequalities between the numbers of convex polytopes in the d-dimensional space "containing" and "avoiding" zero provided that their vertex sets are subsets of a given finite set of points in the space.
Weight-conserving characterization of complex functional brain networks
Mikail Rubinov,Olaf Sporns
Quantitative Biology , 2011, DOI: 10.1016/j.neuroimage.2011.03.069
Abstract: Complex functional brain networks are large networks of brain regions and functional brain connections. Statistical characterizations of these networks aim to quantify global and local properties of brain activity with a small number of network measures. Important functional network measures include measures of modularity (measures of the goodness with which a network is optimally partitioned into functional subgroups) and measures of centrality (measures of the functional influence of individual brain regions). Characterizations of functional networks are increasing in popularity, but are associated with several important methodological problems. These problems include the inability to characterize densely connected and weighted functional networks, the neglect of degenerate topologically distinct high-modularity partitions of these networks, and the absence of a network null model for testing hypotheses of association between observed nontrivial network properties and simple weighted connectivity properties. In this study we describe a set of methods to overcome these problems. Specifically, we generalize measures of modularity and centrality to fully connected and weighted complex networks, describe the detection of degenerate high-modularity partitions of these networks, and introduce a weighted-connectivity null model of these networks. We illustrate our methods by demonstrating degenerate high-modularity partitions and strong correlations between two complementary measures of centrality in resting-state functional magnetic resonance imaging (MRI) networks from the 1000 Functional Connectomes Project, an open-access repository of resting-state functional MRI datasets. Our methods may allow more sound and reliable characterizations and comparisons of functional brain networks across conditions and subjects.
Disruption of the three-body gravitational systems: Lifetime statistics
Victor Orlov,Alexey Rubinov,Ivan Shevchenko
Physics , 2010, DOI: 10.1111/j.1365-2966.2010.17239.x
Abstract: We investigate statistics of the decay process in the equal-mass three-body problem with randomized initial conditions. Contrary to earlier expectations of similarity with "radioactive decay", the lifetime distributions obtained in our numerical experiments turn out to be heavy-tailed, i.e. the tails are not exponential, but algebraic. The computed power-law index for the differential distribution is within the narrow range, approximately from -1.7 to -1.4, depending on the virial coefficient. Possible applications of our results to studies of the dynamics of triple stars known to be at the edge of disruption are considered.
A New Proton CT Scanner
G. Coutrakon,G. Blazey,S. Boi,A. Dyshkant,B. Erdelyi,D. Hedin,E. Johnson,J. Krider,V. Rykalin,S. A. Uzunyan,V. Zutshi,R. Fordt,G. Sellberg,J . E. Rauch,M. Roman,P. Rubinov,P. Wilson,M. Naimuddin
Physics , 2014,
Abstract: The design, construction, and preliminary testing of a second generation proton CT scanner is presented. All current treatment planning systems at proton therapy centers use X-ray CT as the primary imaging modality for treatment planning to calculate doses to tumor and healthy tissues. One of the limitations of X-ray CT is in the conversion of X-ray attenuation coefficients to relative (proton) stopping powers, or RSP. This results in more proton range uncertainty, larger target volumes and therefore, more dose to healthy tissues. To help improve this, we present a novel scanner capable of high dose rates, up to 2~MHz, and large area coverage, 20~x~24~cm$^2$, for imaging an adult head phantom and reconstructing more accurate RSP values.
Development of a proton Computed Tomography Detector System
Md. Naimuddin,G. Coutrakon,G. Blazey,S. Boi,A. Dyshkant,B. Erdelyi,D. Hedin,E. Johnson,J. Krider,V. Rukalin,S. A. Uzunyan,V. Zutshi,R. Fordt,G. Sellberg,J. E. Rauch,M. Roman,P. Rubinov,P. Wilson
Physics , 2015,
Abstract: Computer tomography is one of the most promising new methods to image abnormal tissues inside the human body. Tomography is also used to position the patient accurately before radiation therapy. Hadron therapy for treating cancer has become one of the most advantageous and safe options. In order to fully utilize the advantages of hadron therapy, there is a necessity of performing radiography with hadrons as well. In this paper we present the development of a proton computed tomography system. Our second-generation proton tomography system consists of two upstream and two downstream trackers made up of fibers as active material and a range detector consisting of plastic scintillators. We present details of the detector system, readout electronics, and data acquisition system as well as the commissioning of the entire system. We also present preliminary results from the test beam of the range detector.
Neurobiologically Realistic Determinants of Self-Organized Criticality in Networks of Spiking Neurons
Mikail Rubinov ,Olaf Sporns,Jean-Philippe Thivierge,Michael Breakspear
PLOS Computational Biology , 2011, DOI: 10.1371/journal.pcbi.1002038
Abstract: Self-organized criticality refers to the spontaneous emergence of self-similar dynamics in complex systems poised between order and randomness. The presence of self-organized critical dynamics in the brain is theoretically appealing and is supported by recent neurophysiological studies. Despite this, the neurobiological determinants of these dynamics have not been previously sought. Here, we systematically examined the influence of such determinants in hierarchically modular networks of leaky integrate-and-fire neurons with spike-timing-dependent synaptic plasticity and axonal conduction delays. We characterized emergent dynamics in our networks by distributions of active neuronal ensemble modules (neuronal avalanches) and rigorously assessed these distributions for power-law scaling. We found that spike-timing-dependent synaptic plasticity enabled a rapid phase transition from random subcritical dynamics to ordered supercritical dynamics. Importantly, modular connectivity and low wiring cost broadened this transition, and enabled a regime indicative of self-organized criticality. The regime only occurred when modular connectivity, low wiring cost and synaptic plasticity were simultaneously present, and the regime was most evident when between-module connection density scaled as a power-law. The regime was robust to variations in other neurobiologically relevant parameters and favored systems with low external drive and strong internal interactions. Increases in system size and connectivity facilitated internal interactions, permitting reductions in external drive and facilitating convergence of postsynaptic-response magnitude and synaptic-plasticity learning rate parameter values towards neurobiologically realistic levels. We hence infer a novel association between self-organized critical neuronal dynamics and several neurobiologically realistic features of structural connectivity. The central role of these features in our model may reflect their importance for neuronal information processing.
Symbiotic relationship between brain structure and dynamics
Mikail Rubinov, Olaf Sporns, Cees van Leeuwen, Michael Breakspear
BMC Neuroscience , 2009, DOI: 10.1186/1471-2202-10-55
Abstract: We show that coupled chaotic dynamics generate ordered and modular functional patterns, even on a random underlying structural connectivity. Consequently, structural connectivity becomes more modular as it rewires towards these functional patterns. Functional networks reflect the underlying structural networks on slow time scales, but significantly less so on faster time scales. In spite of ordered functional topology, structural networks remain robustly interconnected – and therefore small-world – due to the presence of central, inter-modular hub nodes. The noisy dynamics of these hubs enable them to persist despite ongoing rewiring and despite their comparative absence in functional networks.Our results outline a theoretical mechanism by which brain dynamics may facilitate neuroanatomical self-organization. We find time scale dependent differences between structural and functional networks. These differences are likely to arise from the distinct dynamics of central structural nodes.Modular small-world network topology may represent a basic organizational principle of neuroanatomical connectivity across multiple spatial scales [1-6]. Small-world networks are clustered (like ordered networks), and efficiently interconnected (like random networks) [1]. Modular networks are characterized by the presence of highly interconnected groups of nodes (modules) [7]. Hence a modular small-world connectivity reconciles the opposing demands of segregation and integration of functionally specialized brain areas [8] in the face of spatial wiring constraints [9]. However the mechanisms underlying the emergence of small-world connectivity in a developing nervous system remain unknown. In this study, we utilize nonlinear dynamical and network analyses to shed light on such mechanisms. We do this by using a model which examines the influence of neuronal dynamics on the underlying structural connectivity.Cortical structure and dynamics are highly interdependent. On relatively fast time
G-coupling functions
Daniel Morales-Silva,Alexander M. Rubinov,Wilfredo Sosa
Mathematics , 2012, DOI: 10.1080/02331930701761557
Abstract: GAP functions are useful for solving optimization problems, but the literature contains a variety of different concepts of GAP functions. It is interesting to point out that these concepts have many similarities. Here we introduce G-coupling functions, thus presenting a way to take advantage of these common properties.
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