Simulating protection rackets: a case study of the Sicilian Mafia

Protection racketeering groups are powerful, deeply entrenched in multiple societies across the globe, and they harm the societies and economies in which they operate in multiple ways. These reasons make their dynamics important to understand and an objective of both scientific and application-oriented interest. Legal and social norm-based approaches arguably play significant roles in influencing protection racket dynamics. We propose an agent-based simulation model, the Palermo Scenario, to enrich our understanding of these influences and to test the effect of different policies on protection racket dynamics. Our model integrates the legal and the social norm-based approaches and uses a complex normative agent architecture that enables the analysis of both agents’ behaviours and mental normative representations driving behaviour. We demonstrate the usefulness of the model and the benefits of using this complex normative architecture through a case study of the Sicilian Mafia.     

A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem

Meta-heuristics are frequently used to tackle NP-hard combinatorial optimization problems. With this paper we contribute to the understanding of the success of 2-opt based local search algorithms for solving the traveling salesperson problem (TSP). Although 2-opt is widely used in practice, it is hard to understand its success from a theoretical perspective. We take a statistical approach and examine the features of TSP instances that make the problem either hard or easy to solve. As a measure of problem difficulty for 2-opt we use the approximation ratio that it achieves on a given instance. Our investigations point out important features that make TSP instances hard or easy to be approximated by 2-opt.     

Are My Neighbours Ageing Yet? Local Dimensions of Demographic Change in German Cities

Among the highly developed countries, demographic ageing is particularly advanced in Germany. Accentuated by migration, there is great regional variation in the progress of demographic change. Within urban areas, residential neighbourhoods differ considerably in their demographic composition and perspectives. Based on municipal data, this paper examines regional variability in the diversity and pace of neighbourhood-level ageing. Focusing on the Rhine-Ruhr conurbation in North Rhine-Westphalia, the analysis outlines at what pace ageing proceeds in a variety of neighbourhoods, which attract no constant influx of working-age migrants. After decades of suburbanisation, in the period from 1998 to 2008 regional migration concentrated on the central districts of large cities. More profoundly in the most prosperous cities growing in population than in other cities, childless working-age adults separate from other age groups and family types in selected central city quarters. The findings suggest that spatial differentials in ageing are likely to affect future location decisions and, thereby, regional and local prosperity.     

Expected runtimes of evolutionary algorithms for the Eulerian cycle problem

Evolutionary algorithms are randomized search heuristics, which are applied to problems whose structure is not well understood, as well as to problems in combinatorial optimization. They have successfully been applied to different kinds of arc routing problems. To start the analysis of evolutionary algorithms with respect to the expected optimization time on these problems, we consider the Eulerian cycle problem. We show that a variant of the well-known (1+1)$(1+1)$ EA working on the important encoding of permutations is able to find an Eulerian tour of an Eulerian graph in expected polynomial time. Altering the operator used for mutation in the considered algorithm, the expected optimization time changes from polynomial to exponential.     

Cytotoxicity of advanced glycation endproducts is mediated by oxidative stress

Non-enzymatic glycation of proteins with reducing sugars and subsequent transition metal catalysed oxidations leads to the formation of protein bound "advanced glycation endproducts" (AGEs). They accumulate on long-lived proteins and are for example structural components of the beta-amyloid plaques in Alzheimer's disease. Since the oxidation of glycated proteins as well as the interaction of AGEs with cell surface receptors produces superoxide radicals, it was tested in BHK 21 hamster fibroblast cells and SH-SY5Y human neuroblastoma cells if AGEs can exert cytotoxic effects on cells. Cell viability was assessed with three independent tests: MTT-assay (activity of the mitochondrial respiratory chain), lactate dehydrogenase assay (release of cytoplasmatic enzymes, membrane integrity) and Neutral Red assay (active uptake of a hydrophilic dye). Two model AGEs, chicken egg albumin-AGE and BSA-AGE, both caused significant cell death in a dose-dependent manner. The cytotoxic effects of AGEs could be attenuated by alpha-ketoglutarate and pyruvate, by antioxidants such as thioctic acid and N-acetylcysteine, and by aminoguanidine, an inhibitor of nitric oxide synthase. This suggests that reactive oxygen species as well as reactive nitrogen species contribute to AGE mediated cytotoxicity. Since AGEs accumulate on beta-amyloid plaques in AD over time, they may additionally contribute to oxidative stress, cell damage, functional loss and even neuronal cell death in the Alzheimer's disease brain.     

Scan-based volume animation driven by locally adaptive articulated registrations

This paper describes a complete system to create anatomically accurate example-based volume deformation and animation of articulated body regions, starting from multiple in vivo volume scans of a specific individual. In order to solve the correspondence problem across volume scans, a template volume is registered to each sample. The wide range of pose variations is first approximated by volume blend deformation (VBD), providing proper initialization of the articulated subject in different poses. A novel registration method is presented to efficiently reduce the computation cost while avoiding strong local minima inherent in complex articulated body volume registration. The algorithm highly constrains the degrees of freedom and search space involved in the nonlinear optimization, using hierarchical volume structures and locally constrained deformation based on the biharmonic clamped spline. Our registration step establishes a correspondence across scans, allowing a data-driven deformation approach in the volume domain. The results provide an occlusion-free person-specific 3D human body model, asymptotically accurate inner tissue deformations, and realistic volume animation of articulated movements driven by standard joint control estimated from the actual skeleton. Our approach also addresses the practical issues arising in using scans from living subjects. The robustness of our algorithms is tested by their applications on the hand, probably the most complex articulated region in the body, and the knee, a frequent subject area for medical imaging due to injuries.     

The Kemeny Constant for Finite Homogeneous Ergodic Markov Chains

A quantity known as the Kemeny constant, which is used to measure the expected number of links that a surfer on the World Wide Web, located on a random web page, needs to follow before reaching his/her desired location, coincides with the more well known notion of the expected time to mixing, i.e., to reaching stationarity of an ergodic Markov chain. In this paper we present a new formula for the Kemeny constant and we develop several perturbation results for the constant, including conditions under which it is a convex function. Finally, for chains whose transition matrix has a certain directed graph structure we show that the Kemeny constant is dependent only on the common length of the cycles and the total number of vertices and not on the specific transition probabilities of the chain.     

Automated bidding in computational markets: an application in market-based allocation of computing services

Autonomous agents are widely applied to automate interactions in robotics, e.g. for selling and purchasing goods on eBay, and in financial markets, e.g. in the form of quote machines and algorithmic traders. Current research investigates efficient economic mechanisms that fully automate the provisioning and usage processes of Grid-based services. On the one hand, consumers want to allocate resources on demand for their various applications, e.g. data sharing, stream processing, email, business applications and simulations. On the other hand, providers of Grid services want to govern business policies to meet their utilization and profit goals. The above-mentioned processes are not manually manageable, however, because decisions need to be taken within milliseconds. Therefore, such processes have to be automated to minimize human interactions. Hence, market mechanisms and strategic behavior play important roles when it comes to achieving automated and efficient allocation of Grid services. The paper begins by presenting a framework for automated bidding, providing a methodology for the design and implementation of configurable bidding strategies. Second, it presents a novel bidding strategy based on a reinforcement learning technique. This strategy is designed to automate the bid generation processes of consumers and providers in various market mechanisms. Third, the behavior and convergence of the strategy is evaluated in a centralized Continuous Double Auction and a decentralized on-line machine scheduling mechanism against selected benchmark bidding strategies. Fourth, we define a bidding language for communicating consumer and provider preferences to the market as well as report back the match of the market-based allocation process.