Non-Gaussianity of a single scalar field in general covariant Ho\v{r}ava-Lifshitz gravity

In this paper, we study non-Gaussianity generated by a single scalar field in slow-roll inflation in the framework of the non-relativistic general covariant Ho\v{r}ava-Lifshitz theory of gravity with the projectability condition and an arbitrary coupling constant $\lambda$, where $\lambda$ characterizes the deviation of the theory from general relativity (GR) in the infrared. We find that the leading effect of self-interaction, in contrary to the case of minimal scenario of GR, is in general of the order $\hat{\alpha}_{n} \epsilon^{3/2}$, where $\epsilon$ is a slow-roll parameter, and $\hat{\alpha}_{n} (n = 3, 5)$ are the dimensionless coupling coefficients of the six-order operators of the Lifshitz scalar, and have no contributions to power spectra and indices of both scalar and tensor. The bispectrum, comparing with the standard one given in GR, is enhanced, and gives rise to a large value of the nonlinearity parameter $f_{\text{NL}}$.We study how the modified dispersion relation with high order moment terms affects the evaluation of the mode function and in turn the bispectrum, and show explicitly that the mode function takes various asymptotic forms during different periods of its evolution. In particular, we find that it is in general of superpositions of oscillatory functions, instead of plane waves like in the minimal scenario of GR. This results in a large enhancement of the folded shape in the bispectrum.Comment: Added new references and corrected some typos. 5 figures, revtex4. Phys. Rev. D86, 103523 (2012

Web3D learning framework for 3D shape retrieval based on hybrid convolutional neural networks

With the rapid development of Web3D technologies, sketch-based model retrieval has become an increasingly important challenge, while the application of Virtual Reality and 3D technologies has made shape retrieval of furniture over a web browser feasible. In this paper, we propose a learning framework for shape retrieval based on two Siamese VGG-16 Convolutional Neural Networks (CNNs), and a CNN-based hybrid learning algorithm to select the best view for a shape. In this algorithm, the AlexNet and VGG-16 CNN architectures are used to perform classification tasks and to extract features, respectively. In addition, a feature fusion method is used to measure the similarity relation of the output features from the two Siamese networks. The proposed framework can provide new alternatives for furniture retrieval in the Web3D environment. The primary innovation is in the employment of deep learning methods to solve the challenge of obtaining the best view of 3D furniture, and to address cross-domain feature learning problems. We conduct an experiment to verify the feasibility of the framework and the results show our approach to be superior in comparison to many mainstream state-of-the-art approaches

Formation Control with Unknown Directions and General Coupling Coefficients

Generally, the normal displacement-based formation control has a sensing mode that requires the agent not only to have certain knowledge of its direction, but also to gather its local information characterized by nonnegative coupling coefficients. However, the direction may be unknown in the sensing processes, and the coupling coefficients may also involve negative ones due to some circumstances. This paper introduces these phenomena into a class of displacement-based formation control problem. Then, a geometric approach have been employed to overcome the difficulty of analysis on the introduced phenomena. The purpose of this approach is to construct some convex polytopes for containing the effects caused by the unknown direction, and to analyze the non-convexity by admitting the negative coupling coefficients in a certain range. Under the actions of these phenomena, the constructed polytopes are shown to be invariant in view of the contractive set method. It means that the convergence of formation shape can be guaranteed. Subsequently, an example is given to examine the applicability of derived result

Application of BEMD in Extraction of Regional and Local Gravity Anomalies Reflecting Geological Structures Associated with Mineral Resources

The bi-dimensional empirical mode decomposition (BEMD) method is an adaptive analysis method for nonlinear and nonstationary data. With the sifting process of BEMD, the data can be decomposed into a series of bi-dimensional intrinsic mode functions (BIMFs), which may present the relative local feature of the data. In this study, the BEMD method was successfully used for analyzing the Bouguer gravity data of Gejiu tin-copper polymetallic ore field in Yunnan Province and Tongshi gold field in Western Shandong Uplift Block to extract different-scale anomalies. In these two cases, regional and local components were separated, which can reflect the geological structures at different depths and some intrusive bodies which may be associated with mineral deposits. The results reveals the spatial distribution relationship between the different intrusive bodies and the various types of mineral deposits in the aforementioned two study area, which provide some reliable evidence for exploration of new concealed mineral deposits

TiNi-based thin films for MEMS applications

In this paper, some critical issues and problems in the development of TiNi thin films were discussed, including preparation and characterization considerations, residual stress and adhesion, frequency improvement, fatigue and stability, as well as functionally graded or composite thin film design. Different types of MEMS applications were reviewed and the prospects for future advances in fabrication process and device development were discussed.Singapore-MIT Alliance (SMA

Isolation and in Vitro Probiotic Characteristics of Akkermansia muciniphila from Maternal and Infant Feces in Three Different Regions

In this study, a combination of an improved mucin enriched medium with real-time polymerase chain reaction (real-time PCR) was used to test 48 samples of maternal and infant feces for Akkermansia muciniphila (Akk). Under optimized conditions, 24 Akk strains were isolated from eight positive samples. All these strains were confirmed as Akk by 16S rRNA gene sequencing and PCR with Akk-specific primers. Repetitive extragenic palindrome-polymerase chain reaction (rep-PCR) fingerprinting classified the 24 strains into four genotypic groups. Subsequently, these strains were tested in vitro for simulated gastrointestinal fluid tolerance, hydrophobicity, antibiotic susceptibility, and glycan utilization capacity. The results showed that strains HN18D-1, HN18D-3, and WW48D1-13 had the highest tolerance to simulated gastric and intestinal fluids. All Akk strains were resistant to vancomycin, clindamycin, kanamycin and erythromycin. Xylooligosaccharides and soybean oligosaccharides had prebiotic effects on the Akk strains. Collectively, Akk isolates HN18D-1, HN18D-3 and WW48D1-13 can be used as potential probiotic candidates for subsequent in-depth studies