Tissue multifractality and Born approximation in analysis of light scattering: a novel approach for precancers detection

Multifractal, a special class of complex self-affine processes, are under recent intensive investigations because of their fundamental nature and potential applications in diverse physical systems. Here, we report on a novel light scattering-based inverse method for extraction/quantification of multifractality in the spatial distribution of refractive index of biological tissues. The method is based on Fourier domain pre-processing via the Born approximation, followed by the Multifractal Detrended Fluctuation Analysis. The approach is experimentally validated in synthetic multifractal scattering phantoms, and tested on biopsy tissue slices. The derived multifractal properties appear sensitive in detecting cervical precancerous alterations through an increase of multifractality with pathology progression, demonstrating the potential of the developed methodology for novel precancer biomarker identification and tissue diagnostic tool. The novel ability to delineate the multifractal optical properties from light scattering signals may also prove useful for characterizing a wide variety of complex scattering media of non-biological origin

Solving variational inequalities defined on a domain with infinitely many linear constraints

We study a variational inequality problem whose domain is defined by infinitely many linear inequalities. A discretization method and an analytic center based inexact cutting plane method are proposed. Under proper assumptions, the convergence results for both methods are given. We also provide numerical examples to illustrate the proposed method

Multiagent cooperation for solving global optimization problems: an extendible framework with example cooperation strategies

This paper proposes the use of multiagent cooperation for solving global optimization problems through the introduction of a new multiagent environment, MANGO. The strength of the environment lays in itsflexible structure based on communicating software agents that attempt to solve a problem cooperatively. This structure allows the execution of a wide range of global optimization algorithms described as a set of interacting operations. At one extreme, MANGO welcomes an individual non-cooperating agent, which is basically the traditional way of solving a global optimization problem. At the other extreme, autonomous agents existing in the environment cooperate as they see fit during run time. We explain the development and communication tools provided in the environment as well as examples of agent realizations and cooperation scenarios. We also show how the multiagent structure is more effective than having a single nonlinear optimization algorithm with randomly selected initial points

Synthesis of Fluorine-18 Functionalized Nanoparticles for use as in vivo Molecular Imaging Agents

Nanoparticles containing fluorine-18 were prepared from block copolymers made by ring opening metathesis polymerization (ROMP). Using the fast initiating ruthenium metathesis catalyst (H_2IMes)(pyr)_2(Cl)_2Ru=CHPh, low polydispersity amphiphilic block copolymers were prepared from a cinnamoyl-containing hydrophobic norbornene monomer and a mesyl-terminated PEG-containing hydrophilic norbornene monomer. Self-assembly into micelles and subsequent cross-linking of the micelle cores by light-activated dimerization of the cinnamoyl groups yielded stable nanoparticles. Incorporation of fluorine-18 was achieved by nucleophilic displacement of the mesylates by the radioactive fluoride ion with 31% incorporation of radioactivity. The resulting positron-emitting nanoparticles are to be used as in vivo molecular imaging agents for use in tumor imaging

Janus II: a new generation application-driven computer for spin-system simulations

This paper describes the architecture, the development and the implementation of Janus II, a new generation application-driven number cruncher optimized for Monte Carlo simulations of spin systems (mainly spin glasses). This domain of computational physics is a recognized grand challenge of high-performance computing: the resources necessary to study in detail theoretical models that can make contact with experimental data are by far beyond those available using commodity computer systems. On the other hand, several specific features of the associated algorithms suggest that unconventional computer architectures, which can be implemented with available electronics technologies, may lead to order of magnitude increases in performance, reducing to acceptable values on human scales the time needed to carry out simulation campaigns that would take centuries on commercially available machines. Janus II is one such machine, recently developed and commissioned, that builds upon and improves on the successful JANUS machine, which has been used for physics since 2008 and is still in operation today. This paper describes in detail the motivations behind the project, the computational requirements, the architecture and the implementation of this new machine and compares its expected performances with those of currently available commercial systems.Comment: 28 pages, 6 figure

Toward quantifying the increasing role oceanic heat in sea ice loss in the new Arctic

Author Posting. © American Meteorological Society, 2015. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Bulletin of the American Meteorological Society 96 (2015): 2079–2105, doi:10.1175/BAMS-D-13-00177.1.The loss of Arctic sea ice has emerged as a leading signal of global warming. This, together with acknowledged impacts on other components of the Earth system, has led to the term “the new Arctic.” Global coupled climate models predict that ice loss will continue through the twenty-first century, with implications for governance, economics, security, and global weather. A wide range in model projections reflects the complex, highly coupled interactions between the polar atmosphere, ocean, and cryosphere, including teleconnections to lower latitudes. This paper summarizes our present understanding of how heat reaches the ice base from the original sources—inflows of Atlantic and Pacific Water, river discharge, and summer sensible heat and shortwave radiative fluxes at the ocean/ice surface—and speculates on how such processes may change in the new Arctic. The complexity of the coupled Arctic system, and the logistic and technological challenges of working in the Arctic Ocean, require a coordinated interdisciplinary and international program that will not only improve understanding of this critical component of global climate but will also provide opportunities to develop human resources with the skills required to tackle related problems in complex climate systems. We propose a research strategy with components that include 1) improved mapping of the upper- and middepth Arctic Ocean, 2) enhanced quantification of important process, 3) expanded long-term monitoring at key heat-flux locations, and 4) development of numerical capabilities that focus on parameterization of heat-flux mechanisms and their interactions.2016-06-0

Shadow Detection by Combined Photometric Invariants for Improved Foreground Segmentation

Detection and tracking of moving objects are the essential steps of many video understanding applications such as traffic monitoring, video surveillance and visual event recognition. Moving object detection process segments the scene into foreground (moving) and background regions. Moving cast shadows cause serious problems in this process because they can easily be misclassified as foreground. This misclassification may lead to drastic changes in the shapes of objects or merging of multiple objects. in this paper, we present a method to detect moving cast shadows to improve the performance of moving object detection. the foreground regions are processed in terms of intensity, chromaticity, and reflectance ratio. to further refine the results, compactness constraint is enforced on the foreground and shadow masks. the algorithm exploits spatial and spectral information; no a priori knowledge about camera, illumination or object/scene characteristics are required. Obtained results show better performance compared to other work in recent literature

A Framework for Trajectory based Visual Event Retrieval

This paper presents a framework of a system for the query and retrieval of video data based on video events in huge video repositories. the events are formulated using domain-independent event primitives which are represented by spatio-temporal relationships between objects in the video scenes. Complex events are expressible as combinations of simpler events. This facilitates support of event queries from a variety of points of view. in addition, the framework is expected to provide adaptability of the framework to multiple domains