On continuity of the entropy-based differently implicational algorithm

summary:Aiming at the previously-proposed entropy-based differently implicational algorithm of fuzzy inference, this study analyzes its continuity. To begin with, for the FMP (fuzzy modus ponens) and FMT (fuzzy modus tollens) problems, the continuous as well as uniformly continuous properties of the entropy-based differently implicational algorithm are demonstrated for the Tchebyshev and Hamming metrics, in which the R-implications derived from left-continuous t-norms are employed. Furthermore, four numerical fuzzy inference examples are provided, and it is found that the entropy-based differently implicational algorithm can obtain more reasonable solution in contrast with the fuzzy entropy full implication algorithm. Finally, in the entropy-based differently implicational algorithm, we point out that the first fuzzy implication reflects the effect of rule base, and that the second fuzzy implication embodies the inference mechanism

Development of sulphide semiconductor electrodes for visible light conversion

This work starts with the synthesis of relatively thick n-type CuInS2 film by a single-step electrodeposition followed by calcination in a mixture of N2 and H2 at 500 ºC without the need of toxic H2S gas for sulfurization. It was found that the heat treatment in reducing atmosphere suppresses the formation of oxide impurity of the electrodeposited Cu/In/S elements and increases the donor density of CuInS2 by increasing the sulfur vacancies at the grain surface. The resultant CuInS2 film from reducing gas calcination exhibited a fourfold enhancement in photoelectrochemical performance as compared to that from calcination in pure N2. Following the synthesis of the n-type CuInS2 film, a one-dimensional (1-D) CuInS2-ZnO composite was designed to improve the electron transport within the photoanode and capture the visible-light absorption for photoelectrochemical applications. Well-aligned ZnO nanorods were successfully coated with a uniform thin layer of CuInS2 nanoparticle photosensitizers using a tailored sequential pulsed-electrodeposition. The formation of CuInS2-ZnO heterojunction with well-matched band energy alignment and the superior electron mobility in ZnO nanorods led to remarkable improved photoelectrochemical performance of the electrode under visible light irradiation. The effects of deposition sequences and pulsing frequency in the sequential pulsed electrodeposition were found to be crucial in the photoelectrochemical properties of these photoelectrodes. Besides the ternary sulphide CuInS2, binary sulphide such as CdS was also studied for the application of photoelectrodes due to its advanced visible light activity. The last part of the thesis focuses on the development of one-dimensional CdS-ZnO composites prepared by pulsed electrodeposition with specific synthesis conditions. An ultrathin CdS film with a thickness less than 10 nm was formed on the pre-grown ZnO nanorod arrays. The CdS-ZnO heterojunction electrode yielded a significant improvement in the photoelectrochemical properties comparing to pure CdS and pristine ZnO nanorod arrays films. The application of pulsed electrodeposition in this work will provide a new platform into the development of decorating 1-D structure with various metal sulphide materials

Towards Automated Software Evolution of Data-Intensive Applications

Recent years have witnessed an explosion of work on Big Data. Data-intensive applications analyze and produce large volumes of data typically terabyte and petabyte in size. Many techniques for facilitating data processing are integrated into data-intensive applications. API is a software interface that allows two applications to communicate with each other. Streaming APIs are widely used in today\u27s Object-Oriented programming development that can support parallel processing. In this dissertation, an approach that automatically suggests stream code run in parallel or sequentially is proposed. However, using streams efficiently and properly needs many subtle considerations. The use and misuse patterns for stream codes are proposed in this dissertation. Modern software, especially for highly transactional software systems, generates vast logging information every day. The huge amount of information prevents developers from receiving useful information effectively. Log-level could be used to filter run-time information. This dissertation proposes an automated evolution approach for alleviating logging information overload by rejuvenating log levels according to developers\u27 interests. Machine Learning (ML) systems are pervasive in today\u27s software society. They are always complex and can process large volumes of data. Due to the complexity of ML systems, they are prone to classic technical debt issues, but how ML systems evolve is still a puzzling problem. This dissertation introduces ML-specific refactoring and technical debt for solving this problem

A Tool for Rejuvenating Feature Logging Levels via Git Histories and Degree of Interest

Logging is a significant programming practice. Due to the highly transactional nature of modern software applications, massive amount of logs are generated every day, which may overwhelm developers. Logging information overload can be dangerous to software applications. Using log levels, developers can print the useful information while hiding the verbose logs during software runtime. As software evolves, the log levels of logging statements associated with the surrounding software feature implementation may also need to be altered. Maintaining log levels necessitates a significant amount of manual effort. In this paper, we demonstrate an automated approach that can rejuvenate feature log levels by matching the interest level of developers in the surrounding features. The approach is implemented as an open-source Eclipse plugin, using two external plug-ins (JGit and Mylyn). It was tested on 18 open-source Java projects consisting of ~3 million lines of code and ~4K log statements. Our tool successfully analyzes 99.22% of logging statements, increases log level distributions by ~20%, and increases the focus of logs in bug fix contexts ~83% of the time. For further details, interested readers can watch our demonstration video (https://www.youtube.com/watch?v=qIULoAXoDv4).Comment: 4 pages, ICSE '22 (tool demo track

Poster: Towards safe refactoring for intelligent parallelization of Java 8 streams

The Java 8 Stream API sets forth a promising new programming model that incorporates functional-like, MapReduce-style features into a mainstream programming language. However, using streams correctly and efficiently may involve subtle considerations. In this poster, we present our ongoing work and preliminary results towards an automated refactoring approach that assists developers in writing optimal stream code. The approach, based on ordering and typestate analysis, determines when it is safe and advantageous to convert streams to parallel and optimize a parallel streams

Safe Automated Refactoring for Intelligent Parallelization of Java 8 Streams

Streaming APIs are becoming more pervasive in mainstream Object-Oriented programming languages and platforms. For example, the Stream API introduced in Java 8 allows for functional-like, MapReduce-style operations in processing both finite, e.g., collections, and infinite data structures. However, using this API efficiently involves subtle considerations such as determining when it is best for stream operations to run in parallel, when running operations in parallel can be less efficient, and when it is safe to run in parallel due to possible lambda expression side-effects. Also, streams may not run all operations in parallel depending on particular collectors used in reductions. In this paper, we present an automated refactoring approach that assists developers in writing efficient stream code in a semantics-preserving fashion. The approach, based on a novel data ordering and typestate analysis, consists of preconditions and transformations for automatically determining when it is safe and possibly advantageous to convert sequential streams to parallel, unorder or de-parallelize already parallel streams, and optimize streams involving complex reductions. The approach was implemented as a plug-in to the popular Eclipse IDE, uses the WALA and SAFE analysis frameworks, and was evaluated on 11 Java projects consisting of ∼642K lines of code. We found that 57 of 157 candidate streams (36.31%) were refactorable, and an average speedup of 3.49 on performance tests was observed. The results indicate that the approach is useful in optimizing stream code to their full potential

KCNK9 mediates the inhibitory effects of genistein on hepatic metastasis from colon cancer

Objective: The tyrosine-protein kinase inhibitor, genistein, can inhibit cell malignant transformation and has an antitumor effect on various types of cancer. It has been shown that both genistein and KNCK9 can inhibit colon cancer. This research aimed to investigate the suppressive effects of genistein on colon cancer cells and the association between the application of genistein and KCNK9 expression level. Methods: The Cancer Genome Atlas (TCGA) database was used to study the correlation between the KCNK9 expression level and the prognosis of colon cancer patients. HT29 and SW480 colon cancer cell lines were cultured to examine the inhibitory effects of KCNK9 and genistein on colon cancer in vitro, and a mouse model of colon cancer with liver metastasis was established to verify the inhibitory effect of genistein in vivo. Results: KCNK9 was overexpressed in colon cancer cells and was associated with a shorter Overall Survival (OS), a shorter Disease-Specific Survival (DFS), and a shorter Progression-Free Interval (PFI) of colon cancer patients. In vitro experiments showed that downregulation of KCNK9 or genistein application could suppress cell proliferation, migration, and invasion abilities, induce cell cycle quiescence, promote cell apoptosis, and reduce epithelial-mesenchymal transition of the colon cancer cell line. In vivo experiments revealed that silencing of KCNK9 or application of genistein could inhibit hepatic metastasis from colon cancer. Additionally, genistein could inhibit KCNK9 expression, thereby attenuating Wnt/β-catenin signaling pathway. Conclusion: Genistein inhibited the occurrence and progression of colon cancer through Wnt/β-catenin signaling pathway that could be mediated by KCNK9

Multiscale Motion-Aware and Spatial-Temporal-Channel Contextual Coding Network for Learned Video Compression

Recently, learned video compression has achieved exciting performance. Following the traditional hybrid prediction coding framework, most learned methods generally adopt the motion estimation motion compensation (MEMC) method to remove inter-frame redundancy. However, inaccurate motion vector (MV) usually lead to the distortion of reconstructed frame. In addition, most approaches ignore the spatial and channel redundancy. To solve above problems, we propose a motion-aware and spatial-temporal-channel contextual coding based video compression network (MASTC-VC), which learns the latent representation and uses variational autoencoders (VAEs) to capture the characteristics of intra-frame pixels and inter-frame motion. Specifically, we design a multiscale motion-aware module (MS-MAM) to estimate spatial-temporal-channel consistent motion vector by utilizing the multiscale motion prediction information in a coarse-to-fine way. On the top of it, we further propose a spatial-temporal-channel contextual module (STCCM), which explores the correlation of latent representation to reduce the bit consumption from spatial, temporal and channel aspects respectively. Comprehensive experiments show that our proposed MASTC-VC is surprior to previous state-of-the-art (SOTA) methods on three public benchmark datasets. More specifically, our method brings average 10.15\% BD-rate savings against H.265/HEVC (HM-16.20) in PSNR metric and average 23.93\% BD-rate savings against H.266/VVC (VTM-13.2) in MS-SSIM metric.Comment: 12pages,12 figure