Sparse Nerves in Practice

Topological data analysis combines machine learning with methods from algebraic topology. Persistent homology, a method to characterize topological features occurring in data at multiple scales is of particular interest. A major obstacle to the wide-spread use of persistent homology is its computational complexity. In order to be able to calculate persistent homology of large datasets, a number of approximations can be applied in order to reduce its complexity. We propose algorithms for calculation of approximate sparse nerves for classes of Dowker dissimilarities including all finite Dowker dissimilarities and Dowker dissimilarities whose homology is Cech persistent homology. All other sparsification methods and software packages that we are aware of calculate persistent homology with either an additive or a multiplicative interleaving. In dowker_homology, we allow for any non-decreasing interleaving function $\alpha$. We analyze the computational complexity of the algorithms and present some benchmarks. For Euclidean data in dimensions larger than three, the sizes of simplicial complexes we create are in general smaller than the ones created by SimBa. Especially when calculating persistent homology in higher homology dimensions, the differences can become substantial

The relation between Hardy's non-locality and violation of Bell inequality

We give a analytic quantitative relation between Hardy's non-locality and Bell operator. We find that Hardy's non-locality is a sufficient condition for violation of Bell inequality, the upper bound of Hardy's non-locality allowed by information causality just correspond to Tsirelson bound of Bell inequality, and the upper bound of Hardy's non-locality allowed by the principle of no-signaling just correspond to the algebraic maximum of Bell operator. Then we study the Cabello's argument of Hardy's non-locality (a generalization of Hardy's argument) and find a similar relation between it and violation of Bell inequality. Finally, we give a simple derivation of the bound of Hardy's non-locality under the constraint of information causality with the aid of above derived relation between Hardy's non-locality and Bell operator, this bound is the main result of a recent work of Ahanj \emph{et al.} [Phys. Rev. A {\bf81}, 032103(2010)].Comment: 4 pages, no figure, minor chang

Observation of Muon Neutrino Disappearance with the MINOS Detectors in the NuMI Neutrino Beam

This Letter reports results from the MINOS experiment based on its initial exposure to neutrinos from the Fermilab NuMI beam. The rates and energy spectra of charged current ν_μ interactions are compared in two detectors located along the beam axis at distances of 1 and 735 km. With 1.27×10^(20) 120 GeV protons incident on the NuMI target, 215 events with energies below 30 GeV are observed at the Far Detector, compared to an expectation of 336±14 events. The data are consistent with ν_μ disappearance via oscillations with Δm_(32)^2|=2.74_(-0.26)^(+0.44)×10^(-3)  eV^2 and sin^2(2θ_(23))>0.87 (68% C.L.)

Donor Heart Allocation

The limited number of donor hearts is one of the greatest and persistent challenges to heart transplantation. Allocation of this precious resource requires the integration of objective data, clinical intuition, and moral fairness. Institution of an allocation system by UNOS has provided important structure to the allocation methodology. The system must be periodically reviewed and reorganized to ensure it is reflective of current patient disease and clinical practice and builds upon the previous knowledge paradigms. Since the establishment of the 2006 allocation system, not only has there been a dramatic increase in the number of heart transplant candidates, but also a dramatic increase in the number of patients qualifying as high-priority candidates. To address these changes, UNOS Thoracic Organ Transplantation Committee was tasked with providing a revised allocation system. The resulting system aims to improve waitlist mortality and post-transplant outcomes by better prioritizing the highest acuity patients while improving the geographic distribution of organ offers

Role of Cloud Computing Technology in Agriculture Fields

Use of Cloud computing technology in agricultural areas has greater chance in the overall development of India. An effective implementation of cloud computing is encouraging in agricultural sector. Cloud Computing is emerging today as a commercial infrastructure that eliminates the need for maintaining expensive computing hardware, software, Information technology, staff, infrastructure, recourses and their maintenance. Cloud computing is a network-based environment that focuses on sharing computations, Cloud computing networks access to a shared pool of configurable networks, servers, storage, service, applications & other important computing resources. In modern era of cloud computing technology very helpful for centralized the all-agricultural related data bank (Soil-related, weather, Research, Crop, Farmers, Agriculture marketing, fertilizers and pesticide information) in the cloud.  In this paper, also discuss Computing model, characteristics, deployment model, cloud service model, cloud benefits and challenge of cloud computing in agriculture field. Keywords: Cloud computing, Community model, Hybrid model, Public model, Private model, Agriculture, IaaS, Paas & Saa

First observations of separated atmospheric ν_μ and ν̅ _μ events in the MINOS detector

The complete 5.4 kton MINOS far detector has been taking data since the beginning of August 2003 at a depth of 2070 meters water-equivalent in the Soudan mine, Minnesota. This paper presents the first MINOS observations of ν_μ and ν̅ _μ charged-current atmospheric neutrino interactions based on an exposure of 418 days. The ratio of upward- to downward-going events in the data is compared to the Monte Carlo expectation in the absence of neutrino oscillations, giving R^(data)_(up/down/R^(MC)_(up/down) = 0:62^(+0.19)_(0:14)(stat.) ± 0.02(sys.). An extended maximum likelihood analysis of the observed L/E distributions excludes the null hypothesis of no neutrino oscillations at the 98% confidence level. Using the curvature of the observed muons in the 1.3 T MINOS magnetic field ν_μ and ν̅ _μ interactions are separated. The ratio of ν̅ _μ to ν_μ events in the data is compared to the Monte Carlo expectation assuming neutrinos and antineutrinos oscillate in the same manner, giving R^(data)_(ν_μ/ν̅ _μ) / R^(MC)_(ν_μ/ν̅ _μ) = 0.96^(+0:38)_(0.27)(stat.) ± 0.15(sys.), where the errors are the statistical and systematic uncertainties. Although the statistics are limited, this is the first direct observation of atmospheric neutrino interactions separately for ν_μ and ν̅ _μ