Measurement of the Bottom-Strange Meson Mixing Phase in the Full CDF Data Set

We report a measurement of the bottom-strange meson mixing phase \beta_s using the time evolution of B0_s -> J/\psi (->\mu+\mu-) \phi (-> K+ K-) decays in which the quark-flavor content of the bottom-strange meson is identified at production. This measurement uses the full data set of proton-antiproton collisions at sqrt(s)= 1.96 TeV collected by the Collider Detector experiment at the Fermilab Tevatron, corresponding to 9.6 fb-1 of integrated luminosity. We report confidence regions in the two-dimensional space of \beta_s and the B0_s decay-width difference \Delta\Gamma_s, and measure \beta_s in [-\pi/2, -1.51] U [-0.06, 0.30] U [1.26, \pi/2] at the 68% confidence level, in agreement with the standard model expectation. Assuming the standard model value of \beta_s, we also determine \Delta\Gamma_s = 0.068 +- 0.026 (stat) +- 0.009 (syst) ps-1 and the mean B0_s lifetime, \tau_s = 1.528 +- 0.019 (stat) +- 0.009 (syst) ps, which are consistent and competitive with determinations by other experiments.Comment: 8 pages, 2 figures, Phys. Rev. Lett 109, 171802 (2012

Study of CP violation in Dalitz-plot analyses of B0 --> K+K-KS, B+ --> K+K-K+, and B+ --> KSKSK+

We perform amplitude analyses of the decays $B^0 \to K^+K^-K^0_S$, $B^+ \rightarrow K^+K^-K^+$, and $B^+ \to K^0_S K^0_S K^+$, and measure CP-violating parameters and partial branching fractions. The results are based on a data sample of approximately $470\times 10^6$ $B\bar{B}$ decays, collected with the BABAR detector at the PEP-II asymmetric-energy $B$ factory at the SLAC National Accelerator Laboratory. For $B^+ \to K^+K^-K^+$, we find a direct CP asymmetry in $B^+ \to \phi(1020)K^+$ of $A_{CP}= (12.8\pm 4.4 \pm 1.3)$, which differs from zero by $2.8 \sigma$. For $B^0 \to K^+K^-K^0_S$, we measure the CP-violating phase $\beta_{\rm eff} (\phi(1020)K^0_S) = (21\pm 6 \pm 2)^\circ$. For $B^+ \to K^0_S K^0_S K^+$, we measure an overall direct CP asymmetry of $A_{CP} = (4 ^{+4}_{-5} \pm 2)$. We also perform an angular-moment analysis of the three channels, and determine that the $f_X(1500)$ state can be described well by the sum of the resonances $f_0(1500)$, $f_2^{\prime}(1525)$, and $f_0(1710)$.Comment: 35 pages, 68 postscript figures. v3 - minor modifications to agree with published versio

Shrinking a large dataset to identify variables associated with increased risk of Plasmodium falciparum infection in Western Kenya

Large datasets are often not amenable to analysis using traditional single-step approaches. Here, our general objective was to apply imputation techniques, principal component analysis (PCA), elastic net and generalized linear models to a large dataset in a systematic approach to extract the most meaningful predictors for a health outcome. We extracted predictors for Plasmodium falciparum infection, from a large covariate dataset while facing limited numbers of observations, using data from the People, Animals, and their Zoonoses (PAZ) project to demonstrate these techniques: data collected from 415 homesteads in western Kenya, contained over 1500 variables that describe the health, environment, and social factors of the humans, livestock, and the homesteads in which they reside. The wide, sparse dataset was simplified to 42 predictors of P. falciparum malaria infection and wealth rankings were produced for all homesteads. The 42 predictors make biological sense and are supported by previous studies. This systematic data-mining approach we used would make many large datasets more manageable and informative for decision-making processes and health policy prioritization

A Planned Jefferson Lab Experiment on Spin-Flavor Decomposition

Experiment E04-113 at Jefferson Lab Hall C plans to measure the beam-target double-spin asymmetries in semi-inclusive deep-inelastic $\vec p(e, e^\prime h)X$ and $\vec d(e, e^\prime h)X$ reactions ($h=\pi^+, \pi^-, K^+$ or$K^-$) with a 6 GeV polarized electron beam and longitudinally polarized NH$_3$ and LiD targets. The high statistic data will allow a spin-flavor decomposition in the region of $x=0.12 \sim 0.41$ at $Q^2=1.21\sim 3.14$ GeV$^2$. Especially, leading-order and next-to-leading order spin-flavor decomposition of $\Delta u_v$, $\Delta d_v$ and $\Delta \bar{u} - \Delta \bar{d}$ will be extracted based on the measurement of the combined asymmetries $A_{1N}^{\pi^+ - \pi^-}$. The possible flavor asymmetry of the polarized sea will be addressed in this experiment.Comment: 4 pages, 2 figures, contribution paper to SPIN2004 conferenc

Performance of BPSK subcarrier intensity modulation free-space optical communications using a log-normal atmospheric turbulence model

In this paper, we present simulation results for the bit error rate (BER) performance and the fading penalty of a BPSK - subcarrier intensity modulation (BPSK-SIM) free-space optical (FSO) communication link in a log-normal atmospheric turbulence model. The results obtained are based on the Monte-Carlo simulation. Multiple subcarrier modulation schemes offer increased system throughput and require no knowledge of the channel fading in deciding what symbol has been received. In an atmospheric channel with a fading strength 2 l ? of 0.1 obtaining a BER of 10-6 using a 2-subcarrier system will require a signal-tonoise (SNR) of 23.1 dB. The required SNR increases with the fading strength and at a BER of 10-9 the fading penalty due to the atmospheric turbulence is ~ 41 dB for 9 . 0 2 = l ? . The comparative studies of the OOK and BPSK-SIM schemes showed that for similar electrical SNR, BPSK-SIM offered improved performance across all range of turbulence variance

Sparse 3D convolutional neural networks

We have implemented a convolutional neural network designed for processing sparse three-dimensional input data. The world we live in is three dimensional so there are a large number of potential applications including 3D object recognition and analysis of space-time objects. In the quest for efficiency, we experiment with CNNs on the 2D triangular-lattice and 3D tetrahedral-lattice.Comment: BMVC 201

R-CNN minus R

Deep convolutional neural networks (CNNs) have had a major impact in most areas of image understanding, including object category detection. In object detection, methods such as R-CNN have obtained excellent results by integrating CNNs with region proposal generation algorithms such as selective search. In this paper, we investigate the role of proposal generation in CNN-based detectors in order to determine whether it is a necessary modelling component, carrying essential geometric information not contained in the CNN, or whether it is merely a way of accelerating detection. We do so by designing and evaluating a detector that uses a trivial region generation scheme, constant for each image. Combined with SPP, this results in an excellent and fast detector that does not require to process an image with algorithms other than the CNN itself. We also streamline and simplify the training of CNN-based detectors by integrating several learning steps in a single algorithm, as well as by proposing a number of improvements that accelerate detection

Linear Global Translation Estimation with Feature Tracks

This paper derives a novel linear position constraint for cameras seeing a common scene point, which leads to a direct linear method for global camera translation estimation. Unlike previous solutions, this method deals with collinear camera motion and weak image association at the same time. The final linear formulation does not involve the coordinates of scene points, which makes it efficient even for large scale data. We solve the linear equation based on $L_1$ norm, which makes our system more robust to outliers in essential matrices and feature correspondences. We experiment this method on both sequentially captured images and unordered Internet images. The experiments demonstrate its strength in robustness, accuracy, and efficiency.Comment: Changes: 1. Adopt BMVC2015 style; 2. Combine sections 3 and 5; 3. Move "Evaluation on synthetic data" out to supplementary file; 4. Divide subsection "Evaluation on general data" to subsections "Experiment on sequential data" and "Experiment on unordered Internet data"; 5. Change Fig. 1 and Fig.8; 6. Move Fig. 6 and Fig. 7 to supplementary file; 7 Change some symbols; 8. Correct some typo

Rule Of Thumb: Deep derotation for improved fingertip detection

We investigate a novel global orientation regression approach for articulated objects using a deep convolutional neural network. This is integrated with an in-plane image derotation scheme, DeROT, to tackle the problem of per-frame fingertip detection in depth images. The method reduces the complexity of learning in the space of articulated poses which is demonstrated by using two distinct state-of-the-art learning based hand pose estimation methods applied to fingertip detection. Significant classification improvements are shown over the baseline implementation. Our framework involves no tracking, kinematic constraints or explicit prior model of the articulated object in hand. To support our approach we also describe a new pipeline for high accuracy magnetic annotation and labeling of objects imaged by a depth camera.Comment: To be published in proceedings of BMVC 201

Deranged calcium signaling and neurodegeneration in spinocerebellar ataxia type 3

Spinocerebellar ataxia type 3 (SCA3), also known as Machado-Joseph disease (MJD), is an autosomal-dominant neurodegenerative disorder caused by a polyglutamine expansion in ataxin-3 (SCA3, MJD1) protein. In biochemical experiments we demonstrate that mutant SCA3exp specifically associated with the type 1 inositol 1,4,5-trisphosphate receptor (InsP3R1), an intracellular calcium (Ca2+) release channel. In electrophysiological and Ca2+ imaging experiments we show that InsP3R1 are sensitized to activation by InsP3 in the presence of mutant SCA3exp. We found that feeding SCA3-YAC-84Q transgenic mice with dantrolene, a clinically relevant stabilizer of intracellular Ca2+ signaling, improved their motor performance and prevented neuronal cells loss in pontine nuclei and substantia nigra regions. Our results indicate that deranged Ca2+ signaling may play an important role in SCA3 pathology and that Ca2+ signaling stabilizers such as dantrolene may be considered as potential therapeutic drugs for treatment of SCA3 patients