Learning Optimization-inspired Image Propagation with Control Mechanisms and Architecture Augmentations for Low-level Vision

In recent years, building deep learning models from optimization perspectives has becoming a promising direction for solving low-level vision problems. The main idea of most existing approaches is to straightforwardly combine numerical iterations with manually designed network architectures to generate image propagations for specific kinds of optimization models. However, these heuristic learning models often lack mechanisms to control the propagation and rely on architecture engineering heavily. To mitigate the above issues, this paper proposes a unified optimization-inspired deep image propagation framework to aggregate Generative, Discriminative and Corrective (GDC for short) principles for a variety of low-level vision tasks. Specifically, we first formulate low-level vision tasks using a generic optimization objective and construct our fundamental propagative modules from three different viewpoints, i.e., the solution could be obtained/learned 1) in generative manner; 2) based on discriminative metric, and 3) with domain knowledge correction. By designing control mechanisms to guide image propagations, we then obtain convergence guarantees of GDC for both fully- and partially-defined optimization formulations. Furthermore, we introduce two architecture augmentation strategies (i.e., normalization and automatic search) to respectively enhance the propagation stability and task/data-adaption ability. Extensive experiments on different low-level vision applications demonstrate the effectiveness and flexibility of GDC.Comment: 15 page

Immunotherapy: A promising novel endometriosis therapy

Endometriosis is a common disease of the female reproductive system and has malignant features. Although endometriosis by itself is a benign disease, its erosive growth characteristics lead to severe pelvic pain and female infertility. Unfortunately, several aspects of the pathogenesis of endometriosis are still unclear. Furthermore, the clinical therapeutic methods are unsatisfactory. The recurrence rate of endometriosis is high. Accumulating evidence suggests that the onset and development of endometriosis are closely related to the abnormal function of the female autoimmune system, especially the function of some immune cells such as the aggregation of neutrophils, abnormal differentiation of macrophages, decreased cytotoxicity of NK cells, and abnormal function of T- and B-cell lines. Therefore, immunotherapy is probably a novel therapeutic strategy for endometriosis besides surgery and hormone therapy. However, information regarding the clinical application of immunotherapy in the treatment of endometriosis is very limited. This article aimed to review the effects of existing immunomodulators on the development of endometriosis, including immune cell regulators and immune factor regulators. These immunomodulators clinically or experimentally inhibit the pathogenesis and development of endometriosis lesions by acting on the immune cells, immune factors, or immune-related signaling pathways. Thus, immunotherapy is probably a novel and effective clinical treatment choice for endometriosis. Experimental studies of the detailed mechanism of immunotherapy and large-scale clinical studies about the effectiveness and safety of this promising therapeutic method are required in the future

Distribution of Transmissible Amyloid Proteins in the Liver with Apolipoprotein A-II Amyloidosis

Article信州医学雑誌 64(4): 183-194(2016)journal articl

Laser induced arc dynamics destabilization in laser-arc hybrid welding

The interaction between laser and arc plasma is a central issue in laser-arc hybrid welding. We report a new interaction phenomenon called laser destabilizing arc dynamics in kilowatt fiber laser-TIG hybrid welding of 316L stainless steel. We found the laser action significantly oscillates the arc tail with a 1–3 kHz high frequency. Direct numerical simulation demonstrates that the destabilization mechanism is due to the high-speed oscillated metal vapor ejecting from the mesoscopic keyhole. More interestingly, the high-speed metal vapor could contrict the arc plasma by physical shielding. This provides a fundamentally different explanation from the generally adopted metal vapor ionization theory for laser constrict arc plasma phenomenon. Also, the results substantiate that the arc plasma cannot easily enter into the keyhole because of the violent metal vapor

Are We Building on the Rock? On the Importance of Data Preprocessing for Code Summarization

Code summarization, the task of generating useful comments given the code, has long been of interest. Most of the existing code summarization models are trained and validated on widely-used code comment benchmark datasets. However, little is known about the quality of the benchmark datasets built from real-world projects. Are the benchmark datasets as good as expected? To bridge the gap, we conduct a systematic research to assess and improve the quality of four benchmark datasets widely used for code summarization tasks. First, we propose an automated code-comment cleaning tool that can accurately detect noisy data caused by inappropriate data preprocessing operations from existing benchmark datasets. Then, we apply the tool to further assess the data quality of the four benchmark datasets, based on the detected noises. Finally, we conduct comparative experiments to investigate the impact of noisy data on the performance of code summarization models. The results show that these data preprocessing noises widely exist in all four benchmark datasets, and removing these noisy data leads to a significant improvement on the performance of code summarization. We believe that the findings and insights will enable a better understanding of data quality in code summarization tasks, and pave the way for relevant research and practice

Fasciolopsis buski (Digenea: Fasciolidae) from China and India may represent distinct taxa based on mitochondrial and nuclear ribosomal DNA sequences

Sequences of primers used to amplify fragments of Fasciolopsis buski mitochondrial genome. (DOCX 17 kb

Genetic characterization, species differentiation and detection of Fasciola spp. by molecular approaches

Liver flukes belonging to the genus Fasciola are among the causes of foodborne diseases of parasitic etiology. These parasites cause significant public health problems and substantial economic losses to the livestock industry. Therefore, it is important to definitively characterize the Fasciola species. Current phenotypic techniques fail to reflect the full extent of the diversity of Fasciola spp. In this respect, the use of molecular techniques to identify and differentiate Fasciola spp. offer considerable advantages. The advent of a variety of molecular genetic techniques also provides a powerful method to elucidate many aspects of Fasciola biology, epidemiology, and genetics. However, the discriminatory power of these molecular methods varies, as does the speed and ease of performance and cost. There is a need for the development of new methods to identify the mechanisms underpinning the origin and maintenance of genetic variation within and among Fasciola populations. The increasing application of the current and new methods will yield a much improved understanding of Fasciola epidemiology and evolution as well as more effective means of parasite control. Herein, we provide an overview of the molecular techniques that are being used for the genetic characterization, detection and genotyping of Fasciola spp.