The Quantum Monad on Relational Structures

Homomorphisms between relational structures play a central role in finite model theory, constraint satisfaction, and database theory. A central theme in quantum computation is to show how quantum resources can be used to gain advantage in information processing tasks. In particular, non-local games have been used to exhibit quantum advantage in boolean constraint satisfaction, and to obtain quantum versions of graph invariants such as the chromatic number. We show how quantum strategies for homomorphism games between relational structures can be viewed as Kleisli morphisms for a quantum monad on the (classical) category of relational structures and homomorphisms. We use these results to exhibit a wide range of examples of contextuality-powered quantum advantage, and to unify several apparently diverse strands of previous work

Transnational economic governance

What role do contract enforcement institutions provided by the state play for economic development? This question has often been addressed. However, empirical research in this field looks predominantly at transactions that are conducted domestically. Less research exists regarding the enforceability of contracts in cross-border transactions. In other words, research that addresses the institutional foundations of international exchange processes is still in its infancy. The following case study investigates the contract enforcement institutions that enable German customers to purchase software in Asian and East European Countries. This paper`s main argument is that nation states are not capable of providing a workable legal infrastructure for cross-border transactions. Instead, economic actors create their own informal mechanisms in order to enforce their contracts. Particularly important are relational contracts and reputational networks. Furthermore, the empirical evidence shows that German enterprises comprehensively use the opportunities offered by new developments in information and communication technology, when it comes to the initiation and control of their foreign business relations. Due to such technical innovation, it therefore seems that both reputational networks and relational contracts gain more and more efficiency compared to state private law. --

Broadcasting Convolutional Network for Visual Relational Reasoning

In this paper, we propose the Broadcasting Convolutional Network (BCN) that extracts key object features from the global field of an entire input image and recognizes their relationship with local features. BCN is a simple network module that collects effective spatial features, embeds location information and broadcasts them to the entire feature maps. We further introduce the Multi-Relational Network (multiRN) that improves the existing Relation Network (RN) by utilizing the BCN module. In pixel-based relation reasoning problems, with the help of BCN, multiRN extends the concept of `pairwise relations' in conventional RNs to `multiwise relations' by relating each object with multiple objects at once. This yields in O(n) complexity for n objects, which is a vast computational gain from RNs that take O(n^2). Through experiments, multiRN has achieved a state-of-the-art performance on CLEVR dataset, which proves the usability of BCN on relation reasoning problems.Comment: Accepted paper at ECCV 2018. 24 page

A review of relational capabilities on supply chain performance in textile and apparel industry

In today’s dynamic business environment, the competition is no longer between firms, but between supply chains to gain competitive advantages.Its nature changed the business environment not only sensitive to tangible resources, but also intangible resources. The trends have made industrial practitioners pay special attention to the concept of relational capabilities as a critical factor in improving supply chain performance.The motivation of this study is derived from the concept of relational capability, which includes a supplier partnership, customer relationship, information sharing, and information quality in supply chain management to achieve reliable, responsive, agile, and cost effective supply chain performance in the textile and apparel industry.Textile and apparel industry has a remarkable position as a confident industry, starting with the supplier of the processing of raw materials to the delivery of completed products to the end user, with impressive value added at each process.Obviously, textile and apparel provide necessary protection and image for people, thus it is one of the most basic necessities for human being. Current supply chain environment accentuated the necessity for relational capability to smooth en the supply chain management. The power of relational capability in managing the supply chain gains attention from researchers and practitioners, because the business benefits from enhanced organizations’ supply chain performance.Hence, this study provides a unique conceptual diagram expected to aid researchers and practitioners to create a more comprehensive understanding of the linkages between relational capability and supply chain performance. This is the main objective of this study, which is attempting to identify and explain the relationship between relational capability and supply chain performance in the textile and apparel industry

Exploration of a large database of French notarial acts with social network methods

International audienceThis article illustrates how mathematical and statistical tools designed to handle relational data may be useful to help decipher the most important features and defects of a large historical database and to gain knowledge about a corpus made of several thousand documents. Such a relational model is generally enough to address a wide variety of problems, including most databases containing relational tables. In mathematics, it is referred to as a 'network' or a 'graph'. The article's purpose is to emphasize how a relevant relational model of a historical corpus can serve as a theoretical framework which makes available automatic data mining methods designed for graphs. By such methods, for one thing, consistency checking can be performed so as to extract possible transcription errors or interpretation errors during the transcription automatically. Moreover, when the database is so large that a human being is unable to gain much knowledge by even an exhaustive manual exploration, relational data mining can help elucidate the database's main features. First, the macroscopic structure of the relations between entities can be emphasized with the help of network summaries automatically produced by classification methods. A complementary point of view is obtained via local summaries of the relation structure: a set of network-related indicators can be calculated for each entity, singling out, for instance, highly connected entities. Finally, visualisation methods dedicated to graphs can be used to give the user an intuitive understanding of the database. Additional information can be superimposed on such network visualisations, making it possible intuitively to link the relations between entities using attributes that describe each entity. This overall approach is here illustrated with a huge corpus of medieval notarial acts, containing several thousand transactions and involving a comparable number of persons

Relational Leadership Strategies in U.S. Banking

This qualitative study aimed to gain an understanding of relational leadership strategies currently employed among banking leaders developing quality workplace relationships. A quality workplace environment is where individual differences are nurtured, information is not suppressed or spun but instead openly shared, and where employees feel the company adds value to them, rather than only expecting it from them (Goffee & Jones; 2013; Katz & Miller, 2014; Tan, 2019). This study employed Uhl-Bien’s (2006) relational leadership model to explore the research question: What are relational leadership strategies commonly practiced by banking leaders to foster quality workplace environments? The study employed a qualitative design utilizing narratives. Narrative inquiry provided a method to discover leadership strategies. Narratives from publicly available and accessible sources were collected and analyzed. An extensive literature review highlighted authors pointing out distinctions that correspond to relational leaders’ characteristics as culture creators (Antonakis, 2012; Bass, 1985, 1999; Burns, 1978; Greenleaf, 1977; Hollander, 1992, 2010), influencers (Bass, 1985, 1999; Burns, 1978; House, 1976), inclusive (Dachler & Hosking, 1995a; Dansereau et al., 1975; Graen, 2016), and engaging (Antonakis, 2012; Carter et al., 2015; Dewar et al., 2020; Hosking & Pluut, 2010; Katz & Miller, 2014). Using narrative inquiry, 12 strategies were identified after reviewing the data. The strategies gleaned were reviewed for alignment with relational leaders’ key characteristics as culture creators, influencers, inclusive, and engaging. The emergent themes indicate a connection between relational leaders’ strategies for creating quality workplace environments and Uhl-Bien’s (2006) relational leadership theory. To create culture, relational leaders use clear language; they are forward-looking and build trust through feedback and collaboration. As influencers, relational leaders use empathy and emotional connection; they are honest and transparent and use straight talk when communicating. As inclusive, relational leaders create diverse teams; they focus on teamwork and people development and create psychological safety. Finally, as engagers, relational leaders empower employees; they establish connections and encourage collaboration and communication. Further research would provide additional insights. Furthermore, research including banking institutions outside the U.S. might produce information on relational leadership practices worldwide. Last, a quantitative or a mixed-methods study may yield critical supplementary data

Ontology-based knowledge representation of experiment metadata in biological data mining

According to the PubMed resource from the U.S. National Library of Medicine, over 750,000 scientific articles have been published in the ~5000 biomedical journals worldwide in the year 2007 alone. The vast majority of these publications include results from hypothesis-driven experimentation in overlapping biomedical research domains. Unfortunately, the sheer volume of information being generated by the biomedical research enterprise has made it virtually impossible for investigators to stay aware of the latest findings in their domain of interest, let alone to be able to assimilate and mine data from related investigations for purposes of meta-analysis. While computers have the potential for assisting investigators in the extraction, management and analysis of these data, information contained in the traditional journal publication is still largely unstructured, free-text descriptions of study design, experimental application and results interpretation, making it difficult for computers to gain access to the content of what is being conveyed without significant manual intervention. In order to circumvent these roadblocks and make the most of the output from the biomedical research enterprise, a variety of related standards in knowledge representation are being developed, proposed and adopted in the biomedical community. In this chapter, we will explore the current status of efforts to develop minimum information standards for the representation of a biomedical experiment, ontologies composed of shared vocabularies assembled into subsumption hierarchical structures, and extensible relational data models that link the information components together in a machine-readable and human-useable framework for data mining purposes

The Consistency of Relational Database and Object-Relational Database in GIS Applications

This final year project involves a research and a simple Geographical Information System (GIS) application that will show GIS, spatial data, spatial database management, which focused on relational and object-relational database management system. The main objectives of this project is to study and gain deeper understanding on the implementation of the two types of databases in GIS applications, to compare the level of performance between the databases in a GIS application and to determine the most suggested database to be implemented in a GIS application. The scope of the study will focus on integrating a GIS application that implements Malacca spatial database with two different database management system, namely relational database and objectrelational database system. The performance of each database system will be identified and compared. Rapid Development environment methodology will be utilized in the research on the performance of relational and object-relational databases in GIS applications and also in the development of an application that will implement the database with GIS applications. This methodology basically involved overlapping Planning, Analysis, Design and Implementation phases. Database development design process involved conceptual, logical and physical design stages. This report also includes discussions on the consistency of relational database as well as of the objectrelational database in GIS applications. This report suggests GIS application developer to choose object-relational database in order to manage both spatial and attributes data for the applications efficiently. Furthermore, the result from the object-relational database will be more consistent and reliable compared to a relational database and the performance is better. Recommendations for continuing this project are to compare and determine the level of consistency between relational database and object-relational database in World Wide Web environment or with multi-user accessing the database concurrently in order to study on the effects to the level of consistency and also to develop a map query interface

Weighted Random Walk Sampling for Multi-Relational Recommendation

In the information overloaded web, personalized recommender systems are essential tools to help users find most relevant information. The most heavily-used recommendation frameworks assume user interactions that are characterized by a single relation. However, for many tasks, such as recommendation in social networks, user-item interactions must be modeled as a complex network of multiple relations, not only a single relation. Recently research on multi-relational factorization and hybrid recommender models has shown that using extended meta-paths to capture additional information about both users and items in the network can enhance the accuracy of recommendations in such networks. Most of this work is focused on unweighted heterogeneous networks, and to apply these techniques, weighted relations must be simplified into binary ones. However, information associated with weighted edges, such as user ratings, which may be crucial for recommendation, are lost in such binarization. In this paper, we explore a random walk sampling method in which the frequency of edge sampling is a function of edge weight, and apply this generate extended meta-paths in weighted heterogeneous networks. With this sampling technique, we demonstrate improved performance on multiple data sets both in terms of recommendation accuracy and model generation efficiency