What is Twitter

http://user.it.uu.se/~jarst116/slides/week4.pdf

Graph-Based Algorithms for Boolean Function Manipulation

The Two Sexes: Growing Up Apart: Coming Together

https://www.linkgroup.hu/docs/13PharmTher.pdf

Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review

In the process of elucidating Spencer’s First Principles and the cosmic theory of evolution, it has proved helpful already to touch on his treatment of social life in that general context. It is essential now, however, to address the theory as manifested in respect of ‘psychical’ and ‘social’ evolution, themselves intimately linked in his discussions.

Mind and Society

The topological structure of complex networks has fascinated researchers for several decades, and today we have a fairly good understanding of the types and reoccurring characteristics of many different complex networks. However, surprisingly little is known today about models to compare complex graphs, and quantitatively measure their similarity and dynamics. 
In the past a variety of node centralities, i.e., functions which assign values to nodes to represent their importance in the graph. Based on such centralities we propose a natural similarity measure for complex networks: the centrality distance $d_C$, the difference between two graphs with respect to a given node centrality $C$. Centrality distances can take the specific roles of the different nodes in the network into account, and have many interesting applications. 
As a case study, we investigate the evolution of networks with respect to centrality distances and their approximations based on closeness, betweenness, pagerank, clustering and degree centrality. In particular we study the dynamic behavior of six real-world social and physical networks and show that centrality distances can be used to effectively distinguish between randomly generated and actual evolutionary paths of dynamic complex networks.

Exploring the Graph of Graphs: Network Evolution and Centrality Distances.

This paper reports on a case study on analyzing, structuring and re-using real-time control algorithms which represent a significant amount of intellectual property. As a starting point, legacy code written in ADA together with a Windows-based testing framework is available. The goal is to migrate the code onto a real-time multi-core platform taking advantage of technological progress. We present a tool-supported three-step approach for such legacy control software: identifying and isolating the control algorithms, preparing these algorithms and their information exchange for execution within a modern execution framework for Linux written in C++, and validating the solution by a) performing regression testing to ensure partial correctness and b) validating its real-time properties.

Migrating legacy control software to multi-core hardware

This article studies how a mechanism designer can influence games by promising payments to the players depending on their mutual choice of strategies. First, we investigate the cost of implementing a desirable behavior and present algorithms to compute this cost. Whereas a mechanism designer can decide efficiently whether strategy profiles can be implemented at no cost at all our complexity analysis indicates that computing an optimal implementation is generally NP-hard. Second, we introduce and analyze the concept of leverage in a game. The leverage captures the benefits that a benevolent or a malicious mechanism designer can achieve by implementing a certain strategy profile region within economic reason, i.e., by taking the implementation cost into account. Mechanism designers can often manipulate games and change the social welfare by a larger extent than the amount of money invested. Unfortunately, computing the leverage turns out to be intractable as well in the general case.

Cost and complexity of harnessing games with payments

Communication networks for smart grids may consist of a mixture of legacy and new links using heterogeneous technologies, such as copper wires, optical fibers, wireless and powerline communication. If nodes are connected by two or more links, such as wireless and powerline, the sender of a message must decide on which link to transmit the next message. This paper considers the problem of dynamically selecting the link, based on success/failure (acknowledgement) of previous transmissions. The novel method is based on Markov (Gilbert-Elliott) channel models of lossy and time varying links. It specifies how to employ success/failure observations to rank the links optimally, with the objective function to maximize throughput. The theory of partially observable Markov decision problems (POMDP) provides the basic framework. We compare this new method with known linear learning strategies.

Dynamic selection of wireless/powerline links using Markov Decision Processes

The demand for real-time data storage in distributed control systems (DCSs) is growing. Yet, providing real-time DCS guarantees is challenging, especially when more and more sensor and actuator devices are connected to industrial plants and message loss needs to be taken into account.In this paper, we investigate how to build a shared memory abstraction for DCSs as a first step towards implementing different shared storage systems in a DCS context. We first prove that, in the presence of host crashes and message losses, the necessary guarantees of such an abstraction are impossible to implement using a traditional approach that has no access to the internals of existing DCS services, e.g., a modular approach where algorithms are built on top of existing software blocks like failure detectors. We propose a white-box approach that utilizes messages of existing services in any DCS as the sole means of communication. More precisely, we present TapeWorm, an algorithm that attaches itself to the heartbeat messages of the failure detector component in DCSs. We prove that TapeWorm implements the desired shared memory guarantees for applications running on a DCS. We also analyze the performance of TapeWorm and we showcase ways of adapting TapeWorm to various application needs and workloads.

/pdf/right-on-time-distributed-shared-memory-389u7zf9xs.pdf

Yvonne-Anne Pignolet

Papers

Exploring the Graph of Graphs: Network Evolution and Centrality Distances.

Migrating legacy control software to multi-core hardware

Cost and complexity of harnessing games with payments

Dynamic selection of wireless/powerline links using Markov Decision Processes

Right on Time Distributed Shared Memory