Peripheral fillings of relatively hyperbolic groups

A group theoretic version of Dehn surgery is studied. Starting with an arbitrary relatively hyperbolic group $G$ we define a peripheral filling procedure, which produces quotients of $G$ by imitating the effect of the Dehn filling of a complete finite volume hyperbolic 3--manifold $M$ on the fundamental group $\pi_1(M)$. The main result of the paper is an algebraic counterpart of Thurston's hyperbolic Dehn surgery theorem. We also show that peripheral subgroups of $G$ 'almost' have the Congruence Extension Property and the group $G$ is approximated (in an algebraic sense) by its quotients obtained by peripheral fillings. Various applications of these results are discussed.Comment: The difference with the previous version is that Proposition 3.2 is proved for quasi--geodesics instead of geodesics. This allows to simplify the exposition in the last section. To appear in Invent. Mat

Application of semantic control to a class of pursuer-evader problems

AbstractIn this article, we describe our work in developing a comprehensive software system for tactical decision aiding for an evader faced with multiple pursuers. The objective is to provide the evader with defensive maneuver decisions that maximize its chances of survival.We have developed a hierarchical semantic controller consisting of a System Identifier, a Goal Selector and an Adapter. This system is implemented on a 386DX personal computer via object-oriented programming, knowledge-based systems, Analytical Hierarchy Process, optimal control, and differential game methodologies.The viability of our Semantic Control approach to the evasive action selection problem has been shown and its operation has been tested against pursuers which follow either pure pursuit or proportional guidance strategies. The user is included in the decision process via approval of setpoints. Displays of the engagement and effect of coverage by countermeasures provide a visual reinforcement of the recommendation made. Use of semantic controllers as TDA support systems for man-in-the-loop, pursuer-evader problems on a PC with current software and hardware technology is feasible. The ability to deploy the system on a portable PC permits the use of the technology in a wide variety of applications

Bi-Objective Community Detection (BOCD) in Networks using Genetic Algorithm

A lot of research effort has been put into community detection from all corners of academic interest such as physics, mathematics and computer science. In this paper I have proposed a Bi-Objective Genetic Algorithm for community detection which maximizes modularity and community score. Then the results obtained for both benchmark and real life data sets are compared with other algorithms using the modularity and MNI performance metrics. The results show that the BOCD algorithm is capable of successfully detecting community structure in both real life and synthetic datasets, as well as improving upon the performance of previous techniques.Comment: 11 pages, 3 Figures, 3 Tables. arXiv admin note: substantial text overlap with arXiv:0906.061

miR-23~27~24 clusters control effector T cell differentiation and function

Coordinated repression of gene expression by evolutionarily conserved microRNA (miRNA) clusters and paralogs ensures that miRNAs efficiently exert their biological impact. Combining both loss- and gain-of-function genetic approaches, we show that the miR-23~27~24 clusters regulate multiple aspects of T cell biology, particularly helper T (Th) 2 immunity. Low expression of this miRNA family confers proper effector T cell function at both physiological and pathological settings. Further studies in T cells with exaggerated regulation by individual members of the miR-23~27~24 clusters revealed that miR-24 and miR-27 collaboratively limit Th2 responses through targeting IL-4 and GATA3 in both direct and indirect manners. Intriguingly, although overexpression of the entire miR-23 cluster also negatively impacts other Th lineages, enforced expression of miR-24, in contrast to miR-23 and miR-27, actually promotes the differentiation of Th1, Th17, and induced regulatory T cells, implying that under certain conditions, miRNA families can fine tune the biological effects of their regulation by having individual members antagonize rather than cooperate with each other. Together, our results identify a miRNA family with important immunological roles and suggest that tight regulation of miR-23~27~24 clusters in T cells is required to maintain optimal effector function and to prevent aberrant immune responses

Robust signatures of solar neutrino oscillation solutions

With the goal of identifying signatures that select specific neutrino oscillation parameters, we test the robustness of global oscillation solutions that fit all the available solar and reactor experimental data. We use three global analysis strategies previously applied by different authors and also determine the sensitivity of the oscillation solutions to the critical nuclear fusion cross section, S_{17}(0), for the production of 8B. The favored solutions are LMA, LOW, and VAC in order of g.o.f. The neutral current to charged current ratio for SNO is predicted to be 3.5 +- 0.6 (1 sigma), which is separated from the no-oscillation value of 1.0 by much more than the expected experimental error. The predicted range of the day-night difference in charged current rates is (8.2 +- 5.2)% and is strongly correlated with the day-night effect for neutrino-electron scattering. A measurement by SNO of either a NC to CC ratio > 3.3 or a day-night difference > 10%, would favor a small region of the currently allowed LMA neutrino parameter space. The global oscillation solutions predict a 7Be neutrino-electron scattering rate in BOREXINO and KamLAND in the range 0.66 +- 0.04 of the BP00 standard solar model rate, a prediction which can be used to test both the solar model and the neutrino oscillation theory. Only the LOW solution predicts a large day-night effect(< 42%) in BOREXINO and KamLAND. For the KamLAND reactor experiment, the LMA solution predicts 0.44 of the standard model rate; we evaluate 1 sigma and 3 sigma uncertainties and the first and second moments of the energy spectrum.Comment: Included predictions for KamLAND reactor experiment and updated to include 1496 days of Super-Kamiokande observation

A Deep Learning Framework for the Detection and Quantification of Reticular Pseudodrusen and Drusen on Optical Coherence Tomography

PURPOSE: The purpose of this study was to develop and validate a deep learning (DL) framework for the detection and quantification of reticular pseudodrusen (RPD) and drusen on optical coherence tomography (OCT) scans. METHODS: A DL framework was developed consisting of a classification model and an out-of-distribution (OOD) detection model for the identification of ungradable scans; a classification model to identify scans with drusen or RPD; and an image segmentation model to independently segment lesions as RPD or drusen. Data were obtained from 1284 participants in the UK Biobank (UKBB) with a self-reported diagnosis of age-related macular degeneration (AMD) and 250 UKBB controls. Drusen and RPD were manually delineated by five retina specialists. The main outcome measures were sensitivity, specificity, area under the receiver operating characteristic (ROC) curve (AUC), kappa, accuracy, intraclass correlation coefficient (ICC), and free-response receiver operating characteristic (FROC) curves. RESULTS: The classification models performed strongly at their respective tasks (0.95, 0.93, and 0.99 AUC, respectively, for the ungradable scans classifier, the OOD model, and the drusen and RPD classification models). The mean ICC for the drusen and RPD area versus graders was 0.74 and 0.61, respectively, compared with 0.69 and 0.68 for intergrader agreement. FROC curves showed that the model's sensitivity was close to human performance. CONCLUSIONS: The models achieved high classification and segmentation performance, similar to human performance. TRANSLATIONAL RELEVANCE: Application of this robust framework will further our understanding of RPD as a separate entity from drusen in both research and clinical settings

Observational constraint on generalized Chaplygin gas model

We investigate observational constraints on the generalized Chaplygin gas (GCG) model as the unification of dark matter and dark energy from the latest observational data: the Union SNe Ia data, the observational Hubble data, the SDSS baryon acoustic peak and the five-year WMAP shift parameter. It is obtained that the best fit values of the GCG model parameters with their confidence level are $A_{s}=0.73^{+0.06}_{-0.06}$ ($1\sigma$) $^{+0.09}_{-0.09}$ $(2\sigma)$, $\alpha=-0.09^{+0.15}_{-0.12}$ ($1\sigma$) $^{+0.26}_{-0.19}$ $(2\sigma)$. Furthermore in this model, we can see that the evolution of equation of state (EOS) for dark energy is similar to quiessence, and its current best-fit value is $w_{0de}=-0.96$ with the $1\sigma$ confidence level $-0.91\geq w_{0de}\geq-1.00$.Comment: 9 pages, 5 figure

Cosmological constraints on the generalized holographic dark energy

We use the Markov ChainMonte Carlo method to investigate global constraints on the generalized holographic (GH) dark energy with flat and non-flat universe from the current observed data: the Union2 dataset of type supernovae Ia (SNIa), high-redshift Gamma-Ray Bursts (GRBs), the observational Hubble data (OHD), the cluster X-ray gas mass fraction, the baryon acoustic oscillation (BAO), and the cosmic microwave background (CMB) data. The most stringent constraints on the GH model parameter are obtained. In addition, it is found that the equation of state for this generalized holographic dark energy can cross over the phantom boundary wde =-1.Comment: 14 pages, 5 figures. arXiv admin note: significant text overlap with arXiv:1105.186

Revisiting Generalized Chaplygin Gas as a Unified Dark Matter and Dark Energy Model

In this paper, we revisit generalized Chaplygin gas (GCG) model as a unified dark matter and dark energy model. The energy density of GCG model is given as $\rho_{GCG}/\rho_{GCG0}=[B_{s}+(1-B_{s})a^{-3(1+\alpha)}]^{1/(1+\alpha)}$, where $\alpha$ and $B_s$ are two model parameters which will be constrained by type Ia supernova as standard candles, baryon acoustic oscillation as standard rulers and the seventh year full WMAP data points. In this paper, we will not separate GCG into dark matter and dark energy parts any more as adopted in the literatures. By using Markov Chain Monte Carlo method, we find the result: $\alpha=0.00126_{- 0.00126- 0.00126}^{+ 0.000970+ 0.00268}$ and $B_s= 0.775_{- 0.0161- 0.0338}^{+ 0.0161+ 0.0307}$.Comment: 6 pages, 4 figure

Combined constraints on modified Chaplygin gas model from cosmological observed data: Markov Chain Monte Carlo approach

We use the Markov Chain Monte Carlo method to investigate a global constraints on the modified Chaplygin gas (MCG) model as the unification of dark matter and dark energy from the latest observational data: the Union2 dataset of type supernovae Ia (SNIa), the observational Hubble data (OHD), the cluster X-ray gas mass fraction, the baryon acoustic oscillation (BAO), and the cosmic microwave background (CMB) data. In a flat universe, the constraint results for MCG model are, $\Omega_{b}h^{2}=0.02263^{+0.00184}_{-0.00162}$ ($1\sigma$) $^{+0.00213}_{-0.00195}$ $(2\sigma)$, $B_{s}=0.7788^{+0.0736}_{-0.0723}$ ($1\sigma$) $^{+0.0918}_{-0.0904}$ $(2\sigma)$, $\alpha=0.1079^{+0.3397}_{-0.2539}$ ($1\sigma$) $^{+0.4678}_{-0.2911}$ $(2\sigma)$, $B=0.00189^{+0.00583}_{-0.00756}$ ($1\sigma$) $^{+0.00660}_{-0.00915}$ $(2\sigma)$, and $H_{0}=70.711^{+4.188}_{-3.142}$ ($1\sigma$) $^{+5.281}_{-4.149}$ $(2\sigma)$.Comment: 12 pages, 1figur