Semantic Image Synthesis via Adversarial Learning

In this paper, we propose a way of synthesizing realistic images directly with natural language description, which has many useful applications, e.g. intelligent image manipulation. We attempt to accomplish such synthesis: given a source image and a target text description, our model synthesizes images to meet two requirements: 1) being realistic while matching the target text description; 2) maintaining other image features that are irrelevant to the text description. The model should be able to disentangle the semantic information from the two modalities (image and text), and generate new images from the combined semantics. To achieve this, we proposed an end-to-end neural architecture that leverages adversarial learning to automatically learn implicit loss functions, which are optimized to fulfill the aforementioned two requirements. We have evaluated our model by conducting experiments on Caltech-200 bird dataset and Oxford-102 flower dataset, and have demonstrated that our model is capable of synthesizing realistic images that match the given descriptions, while still maintain other features of original images.Comment: Accepted to ICCV 201

Stiffness identification of boundary conditions by using thin-layer element for parameterization

The stiffness of boundary conditions in mechanical structures is difficult to represent. A method is proposed to model the mechanical performance of the clamped boundary condition based on thin-layer element. Firstly, the contact surface of clamped boundary condition is parameterized by using isotropic thin-layer element to model the normal and tangential contact stiffness. Secondly, material parameter of thin-layer element is identified by using experimental modal data, parameter identification is transformed as an optimization problem. Experimental investigation is undertaken to verify the proposed method by employing an aluminum honeycomb panel, the numerical model of which is constructed by using the equivalent theory. Thin-layer elements with different properties are used to simulate the mechanical properties in different area of the boundary conditions, and the experimental modal data is adopted to identify the material parameters. Results show that the width to thickness ratio of the thin layer element has a great influence on the identification results

Candidates for Bariatric Surgery: Morbidly Obese Patients with Pulmonary Dysfunction

Obesity is a well-known major risk factor of cardiovascular disease and is associated with various comorbidities. The impact of obesity on pulmonary function remains unclear. Reductions in chest wall compliance and respiratory muscle strength due to a high percent body fat and localized fat distribution contributes to impaired pulmonary function and the occurrence of adverse respiratory symptoms. Dietary modifications and pharmaceutical agents are not effective in the long-term treatment of obesity. Treatment of morbidly obese patients using bariatric surgery has increased each year, especially after the introduction of video laparoscopic techniques. Effective weight loss after bariatric surgery may improve cardiovascular disease risk factors, including diabetes, hypertension, dyslipidemia, atherosclerosis, inflammation, chronic kidney disease, obstructive sleep apnea, and obesity hypoventilation syndrome. Bariatric surgery has also been associated with significantly improved respiratory symptoms and pulmonary function. We currently present a review of principal studies that evaluated the effects of obesity on pulmonary function and the identification of anthropometric factors of obesity that correspond to the reversal of respiratory symptoms and impaired pulmonary function after bariatric surgery