Sparse representation for pose invariant face recognition

Face recognition is easily affected by pose angle. In order to improve the obustness to pose angle, we need to solve the pose estimation, face synthesis and recognition problem. Sparse representation can represent a face image with linear combination of atom faces. In this paper, we construct different pose dictionaries using face images captured under the same pose angle to estimate pose angle and synthesize front face images for recognition. Experimental results show that sparse representation can estimate pose angle accurately, synthesize near frontal faces very well and significantly improve the recognition rate for large pose angles

Transverse-momentum-dependent wave functions and Soft functions at one-loop in Large Momentum Effective Theory

In large-momentum effective theory (LaMET), the transverse-momentum-dependent (TMD) light-front wave functions and soft functions can be extracted from the simulation of a four-quark form factor and equal-time correlation functions. In this work, using expansion by regions we provide a one-loop proof of TMD factorization of the form factor. For the one-loop validation, we also present a detailed calculation of ${\cal O}(\alpha_s)$ perturbative corrections to these quantities, in which we adopt a modern technique for the calculation of TMD form factor based the integration by part and differential equation. The one-loop hard functions are then extracted. Using lattice data from Lattice Parton Collaboration on quasi-TMDWFs, we estimate the effects from the one-loop matching kernel and find that the perturbative corrections depend on the operator to define the form factor, but are less sensitive to the transverse separation. These results will be helpful to precisely extract the soft functions and TMD wave functions from the first-principle in future

Does global value chain engagement improve export quality? Evidence from Chinese manufacturing firms

Using a firm-level data set of Chinese manufacturing sector, we examine whether global value chain (G.V.C.) engagement induces firms to upgrade the quality of the goods that they export. Empirical results show that G.V.C. participation has positive impact on export product quality, and this finding is consistent across several robustness checks. However, the influence of G.V.C. embedment on export quality presents an ‘inverted-U’ shape. The mechanism analysis demonstrates that the effect of G.V.C. participation on export quality is driven by competition effect and firms’ willingness to import high-quality intermediates. Furthermore, the quality effect of G.V.C. embedment differs depending on firm characteristics. This article therefore contributes to a better understanding of the benefits of participation in G.V.C.s for manufacturing firms from developing countries

Formation of Nanofoam carbon and re-emergence of Superconductivity in compressed CaC6

Pressure can tune material's electronic properties and control its quantum state, making some systems present disconnected superconducting region as observed in iron chalcogenides and heavy fermion CeCu2Si2. For CaC6 superconductor (Tc of 11.5 K), applying pressure first Tc increases and then suppresses and the superconductivity of this compound is eventually disappeared at about 18 GPa. Here, we report a theoretical finding of the re-emergence of superconductivity in heavily compressed CaC6. The predicted phase III (space group Pmmn) with formation of carbon nanofoam is found to be stable at wide pressure range with a Tc up to 14.7 K at 78 GPa. Diamond-like carbon structure is adhered to the phase IV (Cmcm) for compressed CaC6 after 126 GPa, which has bad metallic behavior, indicating again departure from superconductivity. Re-emerged superconductivity in compressed CaC6 paves a new way to design new-type superconductor by inserting metal into nanoporous host lattice.Comment: 31 pages, 12 figures, and 4 table

Terahertz Wave Guiding by Femtosecond Laser Filament in Air

Femtosecond laser filament generates strong terahertz (THz) pulse in air. In this paper, THz pulse waveform generated by femtosecond laser filament has been experimentally investigated as a function of the length of the filament. Superluminal propagation of THz pulse has been uncovered, indicating that the filament creates a THz waveguide in air. Numerical simulation has confirmed that the waveguide is formed because of the radially non-uniform refractive index distribution inside the filament. The underlying physical mechanisms and the control techniques of this type THz pulse generation method might be revisited based on our findings. It might also potentially open a new approach for long-distance propagation of THz wave in air.Comment: 5 pages, 6 figure

QED contributions to the Ξc−Ξc′\Xi_c-\Xi_c' mixing

We explore the QED corrections to the $\Xi_c-\Xi_c^{\prime}$ mixing within the framework of light-front quark model (LFQM) in the three-quark picture. After explicitly investigating the relation between the $\Xi_c-\Xi_c^{\prime}$ mixing and the flavor $\rm {SU(3)}$ and heavy quark symmetry breaking, we derive the QED contributions to the mixing angle. Numerical results indicate the QED contribution is smaller than the one from the mass difference between the strange and up/down quark provided by a recent Lattice QCD analysis. Adding these contributions together we find that at this stage the $\Xi_c-\Xi_c^{\prime}$ mixing is small and still incapable to account for the large $\rm {SU(3)}$ symmetry breaking in the semi-leptonic $\Xi_c$ decays.Comment: 7 pages, 4figure

Singing Voice Synthesis with Vibrato Modeling and Latent Energy Representation

This paper proposes an expressive singing voice synthesis system by introducing explicit vibrato modeling and latent energy representation. Vibrato is essential to the naturalness of synthesized sound, due to the inherent characteristics of human singing. Hence, a deep learning-based vibrato model is introduced in this paper to control the vibrato's likeliness, rate, depth and phase in singing, where the vibrato likeliness represents the existence probability of vibrato and it would help improve the singing voice's naturalness. Actually, there is no annotated label about vibrato likeliness in existing singing corpus. We adopt a novel vibrato likeliness labeling method to label the vibrato likeliness automatically. Meanwhile, the power spectrogram of audio contains rich information that can improve the expressiveness of singing. An autoencoder-based latent energy bottleneck feature is proposed for expressive singing voice synthesis. Experimental results on the open dataset NUS48E show that both the vibrato modeling and the latent energy representation could significantly improve the expressiveness of singing voice. The audio samples are shown in the demo website