Transport from the Recycler Ring to the Antiproton Source Beamlines

In the post-Nova era, the protons are directly transported from the Booster ring to the Recycler ring rather than the Main Injector. For Mu2e and g-2 project, the Debuncher ring will be modified into a Delivery ring to deliver the protons to both Mu2e and g-2 experiemnts. Therefore, It requires the transport of protons from the Recycler Ring to the Delivery ring. A new transfer line from the Recycler ring to the P1 beamline will be constructed to transport proton beam from the Recycler Ring to existing Antiproton Source beamlines. This new beamline provides a way to deliver 8 GeV kinetic energy protons from the Booster to the Delivery ring, via the Recycler, using existing beam transport lines, and without the need for new civil construction. This paper presents the Conceptual Design of this new beamline.Comment: 3 pp. 3rd International Particle Accelerator Conference (IPAC 2012) 20-25 May 2012. New Orleans, Louisian

Exact Algorithms for Maximum Independent Set

We show that the maximum independent set problem (MIS) on an $n$-vertex graph can be solved in $1.1996^nn^{O(1)}$ time and polynomial space, which even is faster than Robson's $1.2109^{n}n^{O(1)}$-time exponential-space algorithm published in 1986. We also obtain improved algorithms for MIS in graphs with maximum degree 6 and 7, which run in time of $1.1893^nn^{O(1)}$ and $1.1970^nn^{O(1)}$, respectively. Our algorithms are obtained by using fast algorithms for MIS in low-degree graphs in a hierarchical way and making a careful analyses on the structure of bounded-degree graphs

Fuck revisited.

This paper is a follow up to the investigation of McEnery, Baker and Hardie (2000) into the use of the word fuck in spoken British English. Both that paper and this are based on the British National Corpus. However, at the time of writing in 2000, the analysis of fuck in the written BNC had not been completed, hence the 2000 paper focussed on spoken English alone. In doing so, it explored the way fuck varied with respect to a range of meta-data encoded in the spoken BNC, principally age, sex and social class. We have now explored the written section of the BNC, and have explored the distribution of fuck with respect to a subset of the metadata encoded in the written BNC, namely domain, author gender, author age, audience gender, audience age, audience level, reception status, medium of text and date of creation. As some of these features have clear analogues in the spoken BNC (most clearly age and sex) comparisons between the work presented here and the earlier work on spoken English will be presented wherever possible. Throughout, unless otherwise stated, references to the frequency of usage of features in spoken language are taken from McEnery, Baker and Hardie (ibid)

Generic Wavefunction Description of Fractional Quantum Anomalous Hall States and Fractional Topological Insulators

We propose a systematical approach to construct generic fractional quantum anomalous Hall (FQAH) states, which are generalizations of the fractional quantum Hall states to lattice models with zero net magnetic field and full lattice translation symmetry. Local and translationally invariant Hamiltonians can also be constructed, for which the proposed states are unique ground states. Our result demonstrates that generic chiral topologically ordered states can be realized in lattice models, without requiring magnetic translation symmetry and Landau level structure. We further generalize our approach to the time-reversal invariant analog of fractional quantum Hall states--fractional topological insulators, and provide the first explicit wavefunction description of fractional topological insulators in the absence of spin conservation.Comment: 4.5 pages, 2 figure

A Deep Learning Approach to Examine Ischemic ST Changes in Ambulatory ECG Recordings.

Patients with suspected acute coronary syndrome (ACS) are at risk of transient myocardial ischemia (TMI), which could lead to serious morbidity or even mortality. Early detection of myocardial ischemia can reduce damage to heart tissues and improve patient condition. Significant ST change in the electrocardiogram (ECG) is an important marker for detecting myocardial ischemia during the rule-out phase of potential ACS. However, current ECG monitoring software is vastly underused due to excessive false alarms. The present study aims to tackle this problem by combining a novel image-based approach with deep learning techniques to improve the detection accuracy of significant ST depression change. The obtained convolutional neural network (CNN) model yields an average area under the curve (AUC) at 89.6% from an independent testing set. At selected optimal cutoff thresholds, the proposed model yields a mean sensitivity at 84.4% while maintaining specificity at 84.9%