Substantial gain enhancement for optical parametric amplification and oscillation in two-dimensional χ(2) nonlinear photonic crystals

We have analyzed optical parametric interaction in a 2D NPC. While in general the nonlinear coefficient is small compared to a 1D NPC, we show that at numerous orientations a multitude of reciprocal vectors contribute additively to enhance the gain in optical parametric amplification and oscillation in a 2D patterned crystal. In particular, we have derived the effective nonlinear coefficients for common-signal amplification and common-idler amplification for a tetragonal inverted domain pattern. We show that in the specific case of signal amplification with QPM by both G10 and G11, symmetry of the crystal results in coupled interaction with the corresponding signal amplification by G10 and G1,-1. As a consequence, this coupled utilization of all three reciprocal vectors leads to a substantial increase in parametric gain. Using PPLN we demonstrate numerically that a gain that comes close to that of a 1D QPM crystal could be realized in a 2D NPC with an inverted tetragonal domain pattern. This special mechanism produces two pairs of identical signal and idler beams propagating in mirror-imaged forward directions. In conjunction with this gain enhancement and multiple beams output we predict that there is a large pulling effect on the output wavelength due to dynamic signal build-up in the intrinsic noncollinear geometry of a 2D NPC OPO

Application of Cryogenic Treatment to Extend the Life of the TiAlN-Coated Tungsten Carbide Milling Cutter

Cutting tools are important to the manufacturing industry since they will affect production efficiency and product quality. Cryogenic treatment can improve the material properties by decreasing residual stress, stabilizing dimensional accuracy, and increasing wear resistance. The purpose of this study is to investigate the feasibility and effect of cryogenic treatment on the performance of TiAlN-coated tungsten carbide milling cutters for machining the Inconel alloy 625 in terms of different testing methods (e.g., hardness, wear resistance, residual stress, microstructure, and tool life test). Experimental results indicate that after cryogenic treatment there is less wear, the microstructure is denser, residual stress is decreased, the adhesion of coating and tungsten carbide is improved, and the tool life is effectively improved

Adversarial nets with perceptual losses for text-to-image synthesis

Recent approaches in generative adversarial networks (GANs) can automatically synthesize realistic images from descriptive text. Despite the overall fair quality, the generated images often expose visible flaws that lack structural definition for an object of interest. In this paper, we aim to extend state of the art for GAN-based text-to-image synthesis by improving perceptual quality of generated images. Differentiated from previous work, our synthetic image generator optimizes on perceptual loss functions that measure pixel, feature activation, and texture differences against a natural image. We present visually more compelling synthetic images of birds and flowers generated from text descriptions in comparison to some of the most prominent existing work

Adversarial Learning of Semantic Relevance in Text to Image Synthesis

We describe a new approach that improves the training of generative adversarial nets (GANs) for synthesizing diverse images from a text input. Our approach is based on the conditional version of GANs and expands on previous work leveraging an auxiliary task in the discriminator. Our generated images are not limited to certain classes and do not suffer from mode collapse while semantically matching the text input. A key to our training methods is how to form positive and negative training examples with respect to the class label of a given image. Instead of selecting random training examples, we perform negative sampling based on the semantic distance from a positive example in the class. We evaluate our approach using the Oxford-102 flower dataset, adopting the inception score and multi-scale structural similarity index (MS-SSIM) metrics to assess discriminability and diversity of the generated images. The empirical results indicate greater diversity in the generated images, especially when we gradually select more negative training examples closer to a positive example in the semantic space