Deterministic Small-World Networks

Many real life networks, such as the World Wide Web, transportation systems, biological or social networks, achieve both a strong local clustering (nodes have many mutual neighbors) and a small diameter (maximum distance between any two nodes). These networks have been characterized as small-world networks and modeled by the addition of randomness to regular structures. We show that small-world networks can be constructed in a deterministic way. This exact approach permits a direct calculation of relevant network parameters allowing their immediate contrast with real-world networks and avoiding complex computer simulations.Comment: 6 pages, 1 figur

On a Particularity in Model-Based Search

Giving positive feedback to good solutions is a common base technique in modelbased search algorithms, such as Ant Colony Optimization, Estimation of Distribution Algorithms, or Neural Networks. In particular, the reinforcement of components of good solutions by positive feedback is known as a successful technique in tackling hard combinatorial optimization problems. We show by a simple model-based search algorithm for the node-weighted k-cardinality tree problem that this strategy doesn't guarantee steadily increasing performance of the algorithm in general. It is rather possible that for some "problem"-"probabilistic model" combinations the average performance of the system is decreasing and even the average probability of sampling good solutions is decreasing over time. The result is proven analytically and the consequences are studied in some empirical case studies

Metaheuristics for Group Shop Scheduling

Abstract. The Group Shop Scheduling Problem (GSP) is a generalization of the classical Job Shop and Open Shop Scheduling Problems. In the GSP there are m machines and n jobs. Each job consists of a set of operations, which must be processed on specified machines without preemption. The operations of each job are partitioned into groups on which a total precedence order is given. The problem is to order the operations on the machines and on the groups such that the maximal completion time (makespan) of all operations is minimized. The main goal of this paper is to provide a fair comparison of five metaheuristi

Hybrid Metaheuristics8th International Workshop, HM 2013, Ischia, Italy, May 23-25, 2013. Proceedings /

X, 213 p. 41 illus.online resource

Evolution of nodule stiffness might predict response to local ablative therapy: A series of patients with hepatocellular carcinoma.

Early information on treatment response of HCC to local ablative therapy is crucial. Elastography as a non-invasive method has recently been shown to play a potential role in distinguishing between benign and malignant liver lesions. Elastography of hepatocellular carcinoma (HCC) in early response to local ablative therapy has not been studied to date.We prospectively included a cohort of 14 patients with diagnosis of HCC who were treated with local ablative therapy (transarterial chemoembolization, TACE and/or radiofrequency ablation, RFA). We used 2D shear-wave elastography (RT 2D-SWE) to examine stiffness of HCC lesion before and 3, 30 and 90 days after local ablative therapy. Contrast-enhanced imaging after 90 days was performed to evaluate treatment response. Primary endpoint was stiffness of HCC in response to local ablative therapy. Secondary end point was tumor recurrence.Stiffness of HCC nodules and liver showed no significant difference prior to local ablative therapy. As early as three days after treatment, stiffness of responding HCC was significantly higher compared to non-responding. Higher stiffness before treatment was significantly associated with tumor recurrence.Nodule stiffness in general and RT 2D-SWE in particular could provide a useful tool for early prediction of HCC response to local ablative therapy