Querty processing for composite objects

Thesis (Ph.D. in Engineering)--University of Tsukuba, (A), no. 1116, 1993.3.2

Downregulation of CacyBP by CRISPR/dCas9-KRAB Prevents Bladder Cancer Progression

Bladder cancer (BCa) is a leading cause of cancer-related death in the world. CacyBP is initially described as a binding partner of calcyclin and has been shown to be involved in a wide range of cellular processes, including cell differentiation, proliferation, protein ubiquitination, cytoskeletal dynamics and tumorigenesis. In the present study, we found that CacyBP expression was significantly upregulated in BCa tissues compared with adjacent normal tissues. Moreover, its expression was negatively correlated with overall survival time. Secondly, CacyBP had higher expressions in BCa cell lines than normal urothelial cells which was consistent with the results of BCa tissues. Finally, knockdown of CacyBP by CRIPSR-dCas9-KRAB in T24 and 5,637 BCa cells inhibited cell proliferation and migration by CCK-8 assay and scratch assay, and promoted apoptosis by caspase-3/ELISA. These data elucidate that CacyBP is an important oncogene contributing to malignant behavior of BCa and provide a potentially molecular target for treatment of BCa

On the investment value of sell-side analyst recommendation revisions in the UK

This study conducts a comprehensive investigation into the investment value of sell-side analyst recommendation revisions in the UK, using a unique dataset from 1995 to 2013. Our rolling window analysis shows that, on average, upgrades fail to generate any significantly positive abnormal returns in any period of time, even before transaction costs. In addition, although downgrades could generate significantly negative abnormal gross returns over some periods of time, these observed significant returns disappear after accounting for transaction costs. Overall, our bootstrapping simulations confirm sell-side analysts’ lack of skill in making valuable up/downward revisions to cover the size of transaction costs, irrespective of whether these revisions are made by high-ranking brokerage houses or not. However, an industry-based analysis shows that, within two high-tech industry sectors, i.e., Health Care and Technology sectors, sell-side analysts possess certain skill in making valuable downgrades over some periods of time and, in particular, such skill is sufficient to offset transaction costs

Fairness in Recommendation: Foundations, Methods and Applications

As one of the most pervasive applications of machine learning, recommender systems are playing an important role on assisting human decision making. The satisfaction of users and the interests of platforms are closely related to the quality of the generated recommendation results. However, as a highly data-driven system, recommender system could be affected by data or algorithmic bias and thus generate unfair results, which could weaken the reliance of the systems. As a result, it is crucial to address the potential unfairness problems in recommendation settings. Recently, there has been growing attention on fairness considerations in recommender systems with more and more literature on approaches to promote fairness in recommendation. However, the studies are rather fragmented and lack a systematic organization, thus making it difficult to penetrate for new researchers to the domain. This motivates us to provide a systematic survey of existing works on fairness in recommendation. This survey focuses on the foundations for fairness in recommendation literature. It first presents a brief introduction about fairness in basic machine learning tasks such as classification and ranking in order to provide a general overview of fairness research, as well as introduce the more complex situations and challenges that need to be considered when studying fairness in recommender systems. After that, the survey will introduce fairness in recommendation with a focus on the taxonomies of current fairness definitions, the typical techniques for improving fairness, as well as the datasets for fairness studies in recommendation. The survey also talks about the challenges and opportunities in fairness research with the hope of promoting the fair recommendation research area and beyond.Comment: Accepted by ACM Transactions on Intelligent Systems and Technology (TIST

Efficient Methods for Aggregate Reverse Rank Queries

Given two data sets of user preferences and product attributes in addition to a set of query products, the aggregate reverse rank (ARR) query returns top-k users who regard the given query products as the highest aggregate rank than other users. ARR queries are designed to focus on product bundling in marketing. Manufacturers are mostly willing to bundle several products together for the purpose of maximizing benefits or inventory liquidation. This naturally leads to an increase in data on users and products. Thus, the problem of efficiently processing ARR queries become a big issue. In this paper, we reveal two limitations of the state-of-the-art solution to ARR query; that is, (a) It has poor efficiency when the distribution of the query set is dispersive. (b) It has to process a large portion user data. To address these limitations, we develop a cluster-and-process method and a sophisticated indexing strategy. From the theoretical analysis of the results and experimental comparisons, we conclude that our proposals have superior performance