Aalto University

About: Aalto University is a education organization based out in Espoo, Finland. It is known for research contribution in the topics: Computer science & Context (language use). The organization has 9969 authors who have published 32648 publications receiving 829626 citations. The organization is also known as: TKK & Aalto-korkeakoulu.

Resource Sharing Optimization for Device-to-Device Communication Underlaying Cellular Networks

Abstract: We consider Device-to-Device (D2D) communication underlaying cellular networks to improve local services. The system aims to optimize the throughput over the shared resources while fulfilling prioritized cellular service constraints. Optimum resource allocation and power control between the cellular and D2D connections that share the same resources are analyzed for different resource sharing modes. Optimality is discussed under practical constraints such as minimum and maximum spectral efficiency restrictions, and maximum transmit power or energy limitation. It is found that in most of the considered cases, optimum power control and resource allocation for the considered resource sharing modes can either be solved in closed form or searched from a finite set. The performance of the D2D underlay system is evaluated in both a single-cell scenario, and a Manhattan grid environment with multiple WINNER II A1 office buildings. The results show that by proper resource management, D2D communication can effectively improve the total throughput without generating harmful interference to cellular networks.

Theorising from Case Studies: Towards a Pluralist Future for International Business Research

Abstract: The literature on case studies, both in the field of international business (IB) and in the social sciences more generally, has tended to focus on the methods of data production and analysis suited to this research strategy. In contrast, in this paper we investigate methods of theorising from case studies. We seek to understand how case researchers theorise, and how future IB research might utilise case studies for theorising. By means of a qualitative content analysis of case studies published in Journal of International Business Studies, Academy of Management Journal and Journal of Management Studies, we construct a typology of theorising from case studies. Two dimensions of the case study, namely causal explanation and contextualisation, form the basis for our typology. We distinguish four methods of theorising – inductive theory-building, interpretive sensemaking, natural experiment and contextualised explanation – only the first of which has been widely used in JIBS in the period that we investigate. On the basis of our own qualitative analysis, we show the limitations of inductive theory-building, and argue that greater utilisation of the other methods of theorising would enhance the case study's explanatory power and potential for contextualisation. We argue for a more pluralist future for IB research.

A Survey on Knowledge Graphs: Representation, Acquisition and Applications

Abstract: Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction toward cognition and human-level intelligence. In this survey, we provide a comprehensive review of the knowledge graph covering overall research topics about: 1) knowledge graph representation learning; 2) knowledge acquisition and completion; 3) temporal knowledge graph; and 4) knowledge-aware applications and summarize recent breakthroughs and perspective directions to facilitate future research. We propose a full-view categorization and new taxonomies on these topics. Knowledge graph embedding is organized from four aspects of representation space, scoring function, encoding models, and auxiliary information. For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning are reviewed. We further explore several emerging topics, including metarelational learning, commonsense reasoning, and temporal knowledge graphs. To facilitate future research on knowledge graphs, we also provide a curated collection of data sets and open-source libraries on different tasks. In the end, we have a thorough outlook on several promising research directions.