Generating realistic scaled complex networks

Research on generative models is a central project in the emerging field of network science, and it studies how statistical patterns found in real networks could be generated by formal rules. Output from these generative models is then the basis for designing and evaluating computational methods on networks, and for verification and simulation studies. During the last two decades, a variety of models has been proposed with an ultimate goal of achieving comprehensive realism for the generated networks. In this study, we (a) introduce a new generator, termed ReCoN; (b) explore how ReCoN and some existing models can be fitted to an original network to produce a structurally similar replica, (c) use ReCoN to produce networks much larger than the original exemplar, and finally (d) discuss open problems and promising research directions. In a comparative experimental study, we find that ReCoN is often superior to many other state-of-the-art network generation methods. We argue that ReCoN is a scalable and effective tool for modeling a given network while preserving important properties at both micro- and macroscopic scales, and for scaling the exemplar data by orders of magnitude in size.Comment: 26 pages, 13 figures, extended version, a preliminary version of the paper was presented at the 5th International Workshop on Complex Networks and their Application

Nanoscale Heat Transfer from Magnetic Nanoparticles and Ferritin in an Alternating Magnetic Field

Recent suggestions of nanoscale heat confinement on the surface of synthetic and biogenic magnetic nanoparticles during heating by radio frequency-alternating magnetic fields have generated intense interest because of the potential utility of this phenomenon for noninvasive control of biomolecular and cellular function. However, such confinement would represent a significant departure from the classical heat transfer theory. Here, we report an experimental investigation of nanoscale heat confinement on the surface of several types of iron oxide nanoparticles commonly used in biological research, using an all-optical method devoid of the potential artifacts present in previous studies. By simultaneously measuring the fluorescence of distinct thermochromic dyes attached to the particle surface or dissolved in the surrounding fluid during radio frequency magnetic stimulation, we found no measurable difference between the nanoparticle surface temperature and that of the surrounding fluid for three distinct nanoparticle types. Furthermore, the metalloprotein ferritin produced no temperature increase on the protein surface nor in the surrounding fluid. Experiments mimicking the designs of previous studies revealed potential sources of the artifacts. These findings inform the use of magnetic nanoparticle hyperthermia in engineered cellular and molecular systems

The Semantic Web Paradigm for a Real-Time Agent Control (Part II)

This paper is the second part of The Semantic Web Paradigm for a Real-time Agent Control, and the goal is to present the predictability of a multiagent system used in a learning process for a control problem (MASLCP).learning process, fuzzy control, agent predictability

Large-scale gene-expression studies and the challenge of multiple sclerosis.

In multiple sclerosis, a complex neurodegenerative disorder, a combination of genetic and environmental factors results in inflammation and myelin damage. Recent transcription-profiling studies have found distinct gene-expression patterns in diseased tissue; such large-scale studies at different stages of the disease are contributing to understanding multiple sclerosis and developing effective therapy

ToyArchitecture: Unsupervised Learning of Interpretable Models of the World

Research in Artificial Intelligence (AI) has focused mostly on two extremes: either on small improvements in narrow AI domains, or on universal theoretical frameworks which are usually uncomputable, incompatible with theories of biological intelligence, or lack practical implementations. The goal of this work is to combine the main advantages of the two: to follow a big picture view, while providing a particular theory and its implementation. In contrast with purely theoretical approaches, the resulting architecture should be usable in realistic settings, but also form the core of a framework containing all the basic mechanisms, into which it should be easier to integrate additional required functionality. In this paper, we present a novel, purposely simple, and interpretable hierarchical architecture which combines multiple different mechanisms into one system: unsupervised learning of a model of the world, learning the influence of one's own actions on the world, model-based reinforcement learning, hierarchical planning and plan execution, and symbolic/sub-symbolic integration in general. The learned model is stored in the form of hierarchical representations with the following properties: 1) they are increasingly more abstract, but can retain details when needed, and 2) they are easy to manipulate in their local and symbolic-like form, thus also allowing one to observe the learning process at each level of abstraction. On all levels of the system, the representation of the data can be interpreted in both a symbolic and a sub-symbolic manner. This enables the architecture to learn efficiently using sub-symbolic methods and to employ symbolic inference.Comment: Revision: changed the pdftitl

Data stream mining techniques: a review

A plethora of infinite data is generated from the Internet and other information sources. Analyzing this massive data in real-time and extracting valuable knowledge using different mining applications platforms have been an area for research and industry as well. However, data stream mining has different challenges making it different from traditional data mining. Recently, many studies have addressed the concerns on massive data mining problems and proposed several techniques that produce impressive results. In this paper, we review real time clustering and classification mining techniques for data stream. We analyze the characteristics of data stream mining and discuss the challenges and research issues of data steam mining. Finally, we present some of the platforms for data stream mining