Direct CP violation in τ±→K±ρ0(ω)ντ→K±π+π−ντ\tau^\pm\rightarrow K^\pm \rho^0 (\omega)\nu_\tau \rightarrow K^\pm \pi^+\pi^-\nu_\tau

We study the direct CP violation in the $\tau^\pm\rightarrow K^\pm \rho^0 (\omega)\nu_\tau \rightarrow K^\pm \pi^+\pi^-\nu_\tau$ decay process in the Standard Model. An interesting mechanism involving the charge symmetry violating mixing between $\rho^0$ and $\omega$ is applied to enlarge the CP asymmetry. With this mechanism, the maximum differential and localized integrated CP asymmetries can reach $-(5.6^{+2.9}_{-1.7})\times10^{-12}$ and $6.3^{+2.4}_{-3.3}\times 10^{-11}$, respectively, which still leave plenty room for CP-violating New Physics to be discovered through this process

A deep-neural-network-based hybrid method for semi-supervised classification of polarimetric SAR data

This paper proposes a deep-neural-network-based semi-supervised method for polarimetric synthetic aperture radar (PolSAR) data classification. The proposed method focuses on achieving a well-trained deep neural network (DNN) when the amount of the labeled samples is limited. In the proposed method, the probability vectors, where each entry indicates the probability of a sample associated with a category, are first evaluated for the unlabeled samples, leading to an augmented training set. With this augmented training set, the parameters in the DNN are learned by solving the optimization problem, where the log-likelihood cost function and the class probability vectors are used. To alleviate the “salt-and-pepper” appearance in the classification results of PolSAR images, the spatial interdependencies are incorporated by introducing a Markov random field (MRF) prior in the prediction step. The experimental results on two realistic PolSAR images demonstrate that the proposed method effectively incorporates the spatial interdependencies and achieves the good classification accuracy with a limited number of labeled samples

Formability and Performance of Al-Zn-Mg-Cu Alloys with Different Initial Tempers in Creep Aging Process

The initial temper may directly affect the deformation behavior and material performance in creep age forming (CAF) process. Five heat treatment states are selected as the initial tempers for CAF, namely, solution, peak-aging (T6), over-aging (T73), retrogression and re-solution. The formability and performance of an Al-Zn-Mg-Cu alloy with the above initial tempers in creep aging process are investigated via using creep and stress relaxation aging tests, mechanical property tests, corrosion resistance tests and microstructure analysis. The differences of formability are attributed to the inhibitions of different distributed matrix precipitates (MPts) on the dislocation movement, namely, the more coarsening the MPts is, the easier the dislocation movement. During creep aging process, the mechanical properties are improved for the solution, retrogression and re-solution tempers with fine MPts, but reduced for the T6 and T73 tempers due to coarsening of MPts. Since the distribution of grain boundary precipitates (GBPs) becomes discontinuous, the corrosion resistances of the creep aged specimens are enhanced for all initial tempers. Taking both mechanical properties and corrosion resistances into account, the re-solution temper may be a preferable choice to achieve high performance of the components beyond the precise shape in CAF

Self-excited Threshold Poisson Autoregression

This paper studies theory and inference of an observation-driven model for time series of counts. It is assumed that the observations follow a Poisson distribution conditioned on an accompanying intensity process, which is equipped with a two-regime structure according to the magnitude of the lagged observations. The model remedies one of the drawbacks of the Poisson autoregression model by allowing possibly negative correlation in the observations. Classical Markov chain theory and Lyapunov's method are utilized to derive the conditions under which the process has a unique invariant probability measure and to show a strong law of large numbers of the intensity process. Moreover the asymptotic theory of the maximum likelihood estimates of the parameters is established. A simulation study and a real data application are considered, where the model is applied to the number of major earthquakes in the world

New Supernova Candidates from SDSS-DR7 of Spectral Survey

The letter presents 25 discovered supernova candidates from SDSS-DR7 with our dedicated method, called Sample Decrease, and 10 of them were confirmed by other research groups, and listed in this letter. Another 15 are first discovered including 14 type Ia and one type II based on Supernova Identification (SNID) analysis. The results proved that our method is reliable, and the description of the method and some detailed spectra analysis procedures were also presented in this letter.Comment: 6 pages, 3 figure

Land Use Information Quick Mapping Based on UAV Low- Altitude Remote Sensing Technology and Transfer Learning

Obtaining surface spatio-temporal data rapidly, automatically and accurately is an important issue in agriculture informationization and intellectualization. Samples obtained by conventional manual visual interpretation are difficult to adapt the demands of land resources information extraction. Low altitude remote sensing technology as a kind of emerging technology for earth observation in recent years. Based on this, spatio-temporal data mining technology was introduced, and knowledge transfer learning mechanism was used, a novel landuse information classification method based on knowledge transfer learning (KTLC) was proposed. Firstly, new image was segmented by improved mean shift algorithm to obtain image objects. Secondly, the vector boundary of the objects and former historical landuse thematic map were matched and nested, invariant objects were obtained through overlay analysis, and purification of invariant object was finished by spectral and spatial information threshold filtering. The historical features category knowledge of thematic map was transferred to the new image objects. Finally, current images classification mapping was completed based on decision tree, and landuse classification mapping results were completed by the KTLC and eCognition for landuse information mapping classification (EC). The experimental results showed that KTLC could obtain accuracies equivalent to EC, and also outperforms EC in terms of efficiency

Semi-supervised classification of polarimetric SAR images using Markov random field and two-level Wishart mixture model

In this work, we propose a semi-supervised method for classification of polarimetric synthetic aperture radar (PolSAR) images. In the proposed method, a 2-level mixture model is constructed by associating each component density with a unique Wishart mixture model (instead of a single Wishart distribution as that in the conventional Wishart mixture model). This modeling scheme facilitates the accurate description of data for the categories, each of which includes multiple subcategories. The learning algorithm for the proposed model is developed based on variational inference and all the update equations are obtained in closed form. In the learning algorithm, the spatial interdependencies are incorporated by imposing a Markov random field prior on the indicator variable to alleviate the speckle effect on the classification results. The experimental results demonstrate the improved performance of the proposed method compared with the unsupervised version and supervised version of the proposed model as well as an existing method for semi-supervised classification

SVDinsTN: An Integrated Method for Tensor Network Representation with Efficient Structure Search

Tensor network (TN) representation is a powerful technique for data analysis and machine learning. It practically involves a challenging TN structure search (TN-SS) problem, which aims to search for the optimal structure to achieve a compact representation. Existing TN-SS methods mainly adopt a bi-level optimization method that leads to excessive computational costs due to repeated structure evaluations. To address this issue, we propose an efficient integrated (single-level) method named SVD-inspired TN decomposition (SVDinsTN), eliminating the need for repeated tedious structure evaluation. By inserting a diagonal factor for each edge of the fully-connected TN, we calculate TN cores and diagonal factors simultaneously, with factor sparsity revealing the most compact TN structure. Experimental results on real-world data demonstrate that SVDinsTN achieves approximately $10^2\sim{}10^3$ times acceleration in runtime compared to the existing TN-SS methods while maintaining a comparable level of representation ability