The semileptonic baryonic decay Ds+→ppˉe+νeD_s^+\to p\bar p e^+ \nu_e

The decay $D_s^+\to p \bar p e^+\nu_e$ with a proton-antiproton pair in the final state is unique in the sense that it is the only semileptonic baryonic decay which is physically allowed in the charmed meson sector. Its measurement will test our basic knowledge on semileptonic $D_s^+$ decays and the low-energy $p\bar p$ interactions. Taking into account the major intermediate state contributions from $\eta, \eta', f_0(980)$ and $X(1835)$, we find that its branching fraction is at the level of $10^{-9} \sim 10^{-8}$. The location and the nature of $X(1835)$ state are crucial for the precise determination of the branching fraction. We wish to trigger a new round of a careful study with the upcoming more data in BESIII as well as the future super tau-charm factory.Comment: final version, accepted for publication in Phys. Lett.

Branching fractions of semileptonic DD and DsD_s decays from the covariant light-front quark model

Based on the predictions of the relevant form factors from the covariant light-front quark model, we show the branching fractions for the $D (D_s) \to (P,\,S,\,V,\,A)\,\ell\nu_\ell$ ($\ell=e$ or $\mu$) decays, where $P$ denotes the pseudoscalar meson, $S$ the scalar meson with a mass above 1 GeV, $V$ the vector meson and $A$ the axial-vector one. Comparison with the available experimental results are made, and we find an excellent agreement. The predictions for other decay modes can be tested in a charm factory, e.g., the BESIII detector. The future measurements will definitely further enrich our knowledge on the hadronic transition form factor as well as the inner structure of the even-parity mesons ($S$ and $A$).Comment: Predictions on D-> K1(1270), K1(1400) l nu rates correcte

Beyond Classification: Latent User Interests Profiling from Visual Contents Analysis

User preference profiling is an important task in modern online social networks (OSN). With the proliferation of image-centric social platforms, such as Pinterest, visual contents have become one of the most informative data streams for understanding user preferences. Traditional approaches usually treat visual content analysis as a general classification problem where one or more labels are assigned to each image. Although such an approach simplifies the process of image analysis, it misses the rich context and visual cues that play an important role in people's perception of images. In this paper, we explore the possibilities of learning a user's latent visual preferences directly from image contents. We propose a distance metric learning method based on Deep Convolutional Neural Networks (CNN) to directly extract similarity information from visual contents and use the derived distance metric to mine individual users' fine-grained visual preferences. Through our preliminary experiments using data from 5,790 Pinterest users, we show that even for the images within the same category, each user possesses distinct and individually-identifiable visual preferences that are consistent over their lifetime. Our results underscore the untapped potential of finer-grained visual preference profiling in understanding users' preferences.Comment: 2015 IEEE 15th International Conference on Data Mining Workshop

Distributed Bootstrap for Simultaneous Inference Under High Dimensionality

We propose a distributed bootstrap method for simultaneous inference on high-dimensional massive data that are stored and processed with many machines. The method produces a $\ell_\infty$-norm confidence region based on a communication-efficient de-biased lasso, and we propose an efficient cross-validation approach to tune the method at every iteration. We theoretically prove a lower bound on the number of communication rounds $\tau_{\min}$ that warrants the statistical accuracy and efficiency. Furthermore, $\tau_{\min}$ only increases logarithmically with the number of workers and intrinsic dimensionality, while nearly invariant to the nominal dimensionality. We test our theory by extensive simulation studies, and a variable screening task on a semi-synthetic dataset based on the US Airline On-time Performance dataset. The code to reproduce the numerical results is available at GitHub: https://github.com/skchao74/Distributed-bootstrap.Comment: arXiv admin note: text overlap with arXiv:2002.0844

Deformation Processes of Metallic Open-Cell Foam Supported Sheet Metals

Sandwich panel has been widely applied to enhance the stiffness to weight performance of components in many industries. The manufacturing procedure of curved metal sandwich panels typically consists of forming the sheet and core material into prescribed shapes and applying the adhesive to bond the material in shaped molds. An alternative manufacturing method is to apply the conventional sheet metal forming technique to deform the flat sandwich panel into a curved panel. However, the face sheet will significantly limit the formability of the sandwich panel. To solve the problem, one face sheet was removed in the sandwich panel to increase the formability, then the metal sheet and the metallic open-cell foam were selected as the face sheet and the core material to form the metallic open-cell foam supported sheet metals. The main objective of this study is to develop a proper forming method to deform the metallic open-cell foam supported sheet metal without failure occurring. Two forming processes, press brake bending and hydroforming, which can reduce the contact stress to avoid the structure damage were investigated. Experiments were designed to understand the possible failure modes and the failure mechanism. Through the parametric study in the experimental results, the effects of material dimensions, material properties, and test parameters were analyzed to establish a failure criterion. In addition, a finite element analysis with a proper foam model was implemented to further inspect the failure mechanism and develop a guideline for the selection of materials and test parameters. For the press brake bending process, the experiment results have shown that the supported sheet metal can be successfully bent into a curved panel within small thickness reduction. The prediction in both geometric hoop strain failure criterion and shear strain failure in the finite element analysis were matched and agreed with the experimental result. For the hydroforming process, the experimental result indicated that the major failure mode is the adhesive failure. The early adhesive failure at the perimeter of the attached foam disc caused the open-cell foam to separate from the sheet metal. The required adhesive strength to the attainable dome height relationship was given by finite element analysis