Semileptonic B→D∗∗B \to D^{**} decays in Lattice QCD : a feasibility study and first results

We compute the decays ${B\to D^\ast_0}$ and ${B\to D^\ast_2}$ with finite masses for the $b$ and $c$ quarks. We first discuss the spectral properties of both the $B$ meson as a function of its momentum and of the $D^\ast_0$ and $D^\ast_2$ at rest. We compute the theoretical formulae leading to the decay amplitudes from the three-point and two-point correlators. We then compute the amplitudes at zero recoil of ${B\to D^\ast_0}$ which turns out not to be vanishing contrary to what happens in the heavy quark limit. This opens a possibility to get a better agreement with experiment. To improve the continuum limit we have added a set of data with smaller lattice spacing. The ${B\to D^\ast_2}$ vanishes at zero recoil and we show a convincing signal but only slightly more than 1 sigma from 0. In order to reach quantitatively significant results, we plan to fully exploit smaller lattice spacings as well as another lattice regularization.Comment: 31 pages with 15 figures ; sections 5 and 6 revised and update

The relationship between clinical insight and cognitive and affective empathy and their influence on community functioning in schizophrenia -

Thesis. M.A. American University of Beirut. Department of Psychology, 2015. T:6379Advisor : Dr. Tima Al Jamil, Clinical Assistant Professor, Psychology ; Members of Committee : Dr. Nadia Slobodenyuk, Assistant Professor, Psychology ; Dr. Alaa Hijazi, Assistant Professor, Psychology.Includes bibliographical references (leaves 103-122)Schizophrenia remains one of the most challenging psychiatric disorders to understand and treat in spite of decades of investigation and attempts of researchers in the field to bring patients to remission and functionality. Examining aspects such as clinical insight and domains of social cognition, such as cognitive and affective empathy are novel attempts at understanding and improving functioning in the community for individuals with schizophrenia. This proposal examined the relationship between clinical insight and cognitive and affective empathy in schizophrenia, and the predictive value of each on community functioning. The differences between healthy controls and patients on measures of cognitive and affective empathy were also examined. The study employed a cross-sectional survey design whereby a series of questionnaires and behavioral tasks assessing clinical insight, cognitive and affective empathy, and community functioning were administered to 22 participants with first episode and chronic schizophrenia. Questionnaires and behavioral tasks assessing cognitive and affective empathy were also administered to 21 healthy controls. Clinical insight emerged as a significant predictor of global community functioning, whereas cognitive and affective empathy contributed only to sub-domains of community functioning. Cognitive and affective empathy were both correlated with and predictive of clinical insight. Findings suggest intact affective empathy compared to more compromised cognitive empathic abilities which can be targeted in future psychotherapies to help improve overall insight into their mental illness as well as overall empathic capacities

Unknown Health States Recognition With Collective Decision Based Deep Learning Networks In Predictive Maintenance Applications

At present, decision making solutions developed based on deep learning (DL) models have received extensive attention in predictive maintenance (PM) applications along with the rapid improvement of computing power. Relying on the superior properties of shared weights and spatial pooling, Convolutional Neural Network (CNN) can learn effective representations of health states from industrial data. Many developed CNN-based schemes, such as advanced CNNs that introduce residual learning and multi-scale learning, have shown good performance in health state recognition tasks under the assumption that all the classes are known. However, these schemes have no ability to deal with new abnormal samples that belong to state classes not part of the training set. In this paper, a collective decision framework for different CNNs is proposed. It is based on a One-vs-Rest network (OVRN) to simultaneously achieve classification of known and unknown health states. OVRN learn state-specific discriminative features and enhance the ability to reject new abnormal samples incorporated to different CNNs. According to the validation results on the public dataset of Tennessee Eastman Process (TEP), the proposed CNN-based decision schemes incorporating OVRN have outstanding recognition ability for samples of unknown heath states, while maintaining satisfactory accuracy on known states. The results show that the new DL framework outperforms conventional CNNs, and the one based on residual and multi-scale learning has the best overall performance

Determination of the moments of the proton charge density

A global analysis of proton electric form factor experimental data from Rosenbluth separation and low squared four-momentum transfer experiments is discussed for the evaluation of the spatial moments of the proton charge density based on the recently published integral method \cite{Hob20}. Specific attention is paid to the evaluation of the systematic errors of the method, particularly the sensitivity to the choice of the mathematical expression of the form factor fitting function. Within this comprehensive analysis of proton electric form factor data, the moments of the proton charge density are determined for integer order moments, particularly: $\langle r^2 \rangle$=0.682(02)$_{Sta.}$(11)$_{Sys.}$~fm$^2$, $\langle r^3 \rangle$=0.797(10)$_{Sta.}$(58)$_{Sys.}$~fm$^3$, and $\langle r^4 \rangle$=1.02(05)$_{Sta.}$(31)$_{Sys.}$~fm$^4$. This analysis leads to the proton charge radius 0.8459(12)$_{Sta.}$(76)$_{Sys.}$~fm once relativistic effects are taken into account.Comment: 10 pages, 3 figure