C*-algebras associated to C*-correspondences and applications to mirror quantum spheres

The structure of the C*-algebras corresponding to even-dimensional mirror quantum spheres is investigated. It is shown that they are isomorphic to both Cuntz-Pimsner algebras of certain C*-correspondences and C*-algebras of certain labelled graphs. In order to achieve this, categories of labelled graphs and C*-correspondences are studied. A functor from labelled graphs to C*-correspondences is constructed, such that the corresponding associated C*-algebras are isomorphic. Furthermore, it is shown that C*-correspondences for the mirror quantum spheres arise via a general construction of restricted direct sum

Practical I-Voting on Stellar Blockchain

In this paper, we propose a privacy-preserving i-voting system based on the public Stellar Blockchain network. We argue that the proposed system satisfies all requirements stated for a robust i-voting system including transparency, verifiability, and voter anonymity. The practical architecture of the system abstracts a voter from blockchain technology used underneath. To keep user privacy, we propose a privacy-first protocol that protects voter anonymity. Additionally, high throughput and low transaction fees allow handling large scale voting at low costs. As a result we built an open-source, cheap, and secure system for i-voting that uses public blockchain, where everyone can participate and verify the election process without the need to trust a central authority. The main contribution to the field is a method based on a blind signature used to construct reliable voting protocol. The proposed method fulfills all requirements defined for i-voting systems, which is challenging to achieve altogether.The work was supported partially by founds of Department of Computer Architecture, Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, and Conselleria of Innovation, Universities, Science and Digital Society, of the Community of Valencia, Spain, under project AICO/2020/206. The development of the project has been also supported by the grant founded by Stellar Community Found

Automation of large scale transient protein expression in mammalian cells

Traditional mammalian expression systems rely on the time-consuming generation of stable cell lines; this is difficult to accommodate within a modern structural biology pipeline. Transient transfections are a fast, cost-effective solution, but require skilled cell culture scientists, making man-power a limiting factor in a setting where numerous samples are processed in parallel. Here we report a strategy employing a customised CompacT SelecT cell culture robot allowing the large-scale expression of multiple protein constructs in a transient format. Successful protocols have been designed for automated transient transfection of human embryonic kidney (HEK) 293T and 293S GnTI⁻ cells in various flask formats. Protein yields obtained by this method were similar to those produced manually, with the added benefit of reproducibility, regardless of user. Automation of cell maintenance and transient transfection allows the expression of high quality recombinant protein in a completely sterile environment with limited support from a cell culture scientist. The reduction in human input has the added benefit of enabling continuous cell maintenance and protein production, features of particular importance to structural biology laboratories, which typically use large quantities of pure recombinant proteins, and often require rapid characterisation of a series of modified constructs. This automated method for large scale transient transfection is now offered as a Europe-wide service via the P-cube initiative

リチウムイオン電池材料における異相界面構造解析

学位の種別: 課程博士審査委員会委員 : (主査)東京大学教授 幾原 雄一, 東京大学教授 山田 淳夫, 東京大学教授 枝川 圭一, 東京大学准教授 阿部 英司, 東京大学准教授 柴田 直哉University of Tokyo(東京大学

Over the moon or sick as a parrot? The effects of football results on a club's share price

International audienceThis paper considers the impact of match results on the stock returns of English football clubs. We propose that the magnitude of the response to a given result depends on the importance of the game, which is measured in two ways. First, we consider the extent to which the clubs are close rivals vying for similar league positions, as winning such games is particularly significant. Second, we argue that each individual game becomes more important for those clubs likely to be promoted or relegated as the season draws to a close, since a given match will have increasing information content concerning the final league position of the club. Using a fairly large panel comprising data for 19 clubs, we find some support for the notion that stock prices are more affected by the results of important matches than matches of lesser importance. We also observe that the difference between the number of points the club secures from a given match, and the number it was expected to secure, affects its stock price, as does the number of goals that the club under question scores in the match, relative to its competitor

Review of the Complexity of Managing Big Data of the Internet of Things

There is a growing awareness that the complexity of managing Big Data is one of the main challenges in the developing field of the Internet of Things (IoT). Complexity arises from several aspects of the Big Data life cycle, such as gathering data, storing them onto cloud servers, cleaning and integrating the data, a process involving the last advances in ontologies, such as Extensible Markup Language (XML) and Resource Description Framework (RDF), and the application of machine learning methods to carry out classifications, predictions, and visualizations. In this review, the state of the art of all the aforementioned aspects of Big Data in the context of the Internet of Things is exposed. The most novel technologies in machine learning, deep learning, and data mining on Big Data are discussed as well. Finally, we also point the reader to the state-of-the-art literature for further in-depth studies, and we present the major trends for the future.The authors acknowledge the support from the research center Internet of Things and People (IOTAP) at Malmö University in Sweden. This work was also supported by the Spanish Research Agency (AEI) and the European Regional Development Fund (ERDF) under project CloudDriver4Industry TIN2017-89266-R