Study on the radiative decays of hch_c via intermediate meson loops model

Recently, the BESIII Collaboration reported two new decay processes $h_c(1P)\to \gamma \eta$ and $\gamma \eta^\prime$. Inspired by this measurement, we propose to study the radiative decays of $h_c$ via intermediate charmed meson loops in an effective Lagrangian approach. With the acceptable cutoff parameter range, the calculated branching ratios of $h_c(1P)\to \gamma \eta$ and $\gamma \eta^\prime$ are orders of $10^{-4}\sim 10^{-3}$ and $10^{-3} \sim 10^{-2}$, respectively. The ratio $R_{h_c}= \mathcal{B}( h_c\to \gamma \eta )/\mathcal{B}( h_c\to \gamma \eta^\prime )$ can reproduce the experimental measurements with the commonly acceptable $\alpha$ range. This ratio provide us some information on the $\eta-\eta^\prime$ mixing, which may be helpful for us to test SU(3)-flavor symmetries in QCD.Comment: 11 pages, 5 figures, accepted for publication in EPJ

Joint Visual Denoising and Classification using Deep Learning

Visual restoration and recognition are traditionally addressed in pipeline fashion, i.e. denoising followed by classification. Instead, observing correlations between the two tasks, for example clearer image will lead to better categorization and vice visa, we propose a joint framework for visual restoration and recognition for handwritten images, inspired by advances in deep autoencoder and multi-modality learning. Our model is a 3-pathway deep architecture with a hidden-layer representation which is shared by multi-inputs and outputs, and each branch can be composed of a multi-layer deep model. Thus, visual restoration and classification can be unified using shared representation via non-linear mapping, and model parameters can be learnt via backpropagation. Using MNIST and USPS data corrupted with structured noise, the proposed framework performs at least 20\% better in classification than separate pipelines, as well as clearer recovered images. The noise model and the reproducible source code is available at {\url{https://github.com/ganggit/jointmodel}}.Comment: 5 pages, 7 figures, ICIP 201

One-operator two-machine flow shop scheduling with setup times for machines and total completion time objective

In a manufacturing environment, when a worker or a machine switches from one type of operation to another, a setup time may be required. I propose a scheduling model with one operator and two machines. In this problem, a single operator completes a set of jobs requiring operations in a two-machine flow shop. The operator can perform only one operation at a time. When one machine is in use, the other is idle. Whenever the operator changes machine, a setup time is required. We consider the objective of total completion time. I formulate the problem as a linear integer programming with \u27 O\u27(\u27n\u273) 0-1 variables and \u27 O\u27(\u27n\u272) constraints. I also introduce some classes of valid inequalities. To obtain the exact solutions, Branch-and-Bound, Cut-and-Branch, Branch-and-Cut algorithms are used. For larger size problems, some heuristic procedures are proposed and the computational results are compared

Word Recognition with Deep Conditional Random Fields

Recognition of handwritten words continues to be an important problem in document analysis and recognition. Existing approaches extract hand-engineered features from word images--which can perform poorly with new data sets. Recently, deep learning has attracted great attention because of the ability to learn features from raw data. Moreover they have yielded state-of-the-art results in classification tasks including character recognition and scene recognition. On the other hand, word recognition is a sequential problem where we need to model the correlation between characters. In this paper, we propose using deep Conditional Random Fields (deep CRFs) for word recognition. Basically, we combine CRFs with deep learning, in which deep features are learned and sequences are labeled in a unified framework. We pre-train the deep structure with stacked restricted Boltzmann machines (RBMs) for feature learning and optimize the entire network with an online learning algorithm. The proposed model was evaluated on two datasets, and seen to perform significantly better than competitive baseline models. The source code is available at https://github.com/ganggit/deepCRFs.Comment: 5 pages, published in ICIP 2016. arXiv admin note: substantial text overlap with arXiv:1412.339

Driving effect of fiscal policy on regional innovation efficiency

This study uses a network data envelopment analysis (DEA) approach to measure phased innovation efficiency to explore how fiscal technology innovation policy drives the development of regional innovation. A game model is constructed that includes governments, enterprises, universities, and research institutes to explain the influence mechanism. The innovation process is decomposed into the transformation stage of scientific research results and their commercial application. A Tobit model is used to explain the effect of fiscal policy on innovation efficiency. These methods led to novel conclusions: (1) the growth rate of innovation efficiency in the first stage is greater with smaller regional differences, with larger regional differences in innovation efficiency in the second stage; (2) the intensity of fiscal R&D funding in science and technology has a significant positive effect on overall innovation efficiency and phased innovation efficiency; and (3) the positive effect of fiscal R&D funding is greater on the commercial application of scientific achievements. The targeting effect of fiscal innovation policy on industry–university research (IUR) cooperation needs to be improved through resource sharing, joint participation, sharing of achievements, and risk sharing

NEW AND IMPROVED TECHNIQUES FOR CHARACTERIZING BRITTLE ROCK RESPONSE

To improve the production from shale, stimulation technique such as hydraulic fracturing with proppants is essential. To maximize the effectiveness of hydraulic fracturing, brittle intervals with the minimum creep deformation are preferable as the target zone. A literature review on rock brittleness evaluation is first conducted to analyze the pros and cons of each assessment method. To capture the Class II behavior of brittle shale, damage-controlled compression test is improved using inelastic strain as the control parameter. Indentation test as a simple and fast brittleness evaluation method is used to indent on four lithologies, the indentation displacement (depth) is considered as the brittleness index; Hydraulic fractures are thought to initiate from tensile fractures, however, currently no brittleness indices are derived from tensile failure. This mismatch of failure mechanism renders existing brittleness indices not representative for application in hydraulic fracturing. To mitigate the mismatch, a new brittleness evaluation method is proposed, that is damage-controlled Brazilian test, brittleness of different lithologies have been measured and compared, Brittle-Ductile Transition and strength envelope are obtained, and fracture angle inclination is observed in different confining pressure. To assess the contribution of creep to closure rate and conductivity loss of hydraulic fractures in gas shale, the viscoelastic characteristics of shale have been investigated. A series of creep tests were conducted on reservoir shale core samples. First, a few uniaxial creep tests were performed on several selected samples, and then multistage triaxial creep tests were carried out at room temperature. Samples used in the tests come from three different gas shale reservoirs. Creep strain can be described by a power-law function of time. The clay and carbonate contents of these shale samples vary noticeably. Results indicate that rocks with more quartz and less clay have higher elastic moduli. Pseudo-steady creep rate increases linearly with deviatoric stress and higher confining pressures increase the amount of creep strain under the same deviatoric stress. Creep tests at elevated temperatures have also been carried out to show that temperature increase creep rate. The key findings and contribution of this dissertation include: Indentation test and damage controlled Brazilian test are effective and fast methods to evaluate the brittleness of rocks, the Crack Opening Displacement (COD) can be used as a brittleness index (reverse relation). The indentation depth (displacement) is the brittleness index of indentation test. An alternative damage (inelastic strain) controlled compression test is developed based on the original method (linear combination of load and displacement). A brittleness formulation ε_p/(ε_r l) is derived based on material characteristic length. The Brittle-Ductile Transition of tensile failure is first obtained from confined Brazilian test. Fracture angles in confined Brazilian test progressively increase with confining pressure. Brazilian discs no longer fail in tensile fracturing under high confining pressure when the minimum principal stress in the middle of the disc is compressive; this provides convincing laboratory evidence for the existence of hybrid fractures that constitute transition from extension to shear fractures. Clay content and TOC in shale determine their brittleness and creep properties. For the joint test, multistage compression tests, multistage shear tests and joint stiffness test have been combined to maximize the dataset from one single core plug