An evolutionary algorithm for global optimization based on self-organizing maps

In this article, a new population-based algorithm for real-parameter global optimization is presented, which is denoted as self-organizing centroids optimization (SOC-opt). The proposed method uses a stochastic approach which is based on the sequential learning paradigm for self-organizing maps (SOMs). A modified version of the SOM is proposed where each cell contains an individual, which performs a search for a locally optimal solution and it is affected by the search for a global optimum. The movement of the individuals in the search space is based on a discrete-time dynamic filter, and various choices of this filter are possible to obtain different dynamics of the centroids. In this way, a general framework is defined where well-known algorithms represent a particular case. The proposed algorithm is validated through a set of problems, which include non-separable problems, and compared with state-of-the-art algorithms for global optimization.     

Development of a water-in-oil-in-water multiple emulsion system integrating biomimetic aqueous-core lipid nanodroplets for protein entity stabilization. Part II: process and product characterization

The aqueous-core enclosed in lipid nanoballoons integrating multiple emulsions of the type water-in-oil-in-water mimic, at least in theory, the environment within viable cells, thus being suitable for housing hydrophilic protein entities such as bioactive proteins, peptides and bacteriophage particles. This study reports a complete physicochemical characterization of optimized biomimetic aqueous-core lipid nanoballoons housing hydrophilic (BSA) protein entities, evolved from a statistical 2³×3¹ factorial design study (three variables at two levels and one variable at three levels) that was the subject of the first paper of a series of three, aiming at complete stabilization of the three-dimensional structure of protein entities attempted via housing the said molecular entities within biomimetic aqueous-core lipid nanoballoons integrating a multiple (W/O/W) emulsion. The statistical factorial design followed led to the production of an optimum W/O/W multiple emulsion possessing quite homogeneous particles with an average hydrodynamic size of (186.2?±?2.6) nm and average Zeta potential of (?36.5?±?0.9) mV, and exhibiting a polydispersity index of 0.206?±?0.014. Additionally, the results obtained for the diffusion coefficient of the lipid nanoballoons integrating the optimized W/O/W multiple emulsion were comparable and of the same order of magnitude (10^?12 m² s^?1) as those published by other authors since, typically, diffusion coefficients for molecules range from 10^?10 to 10^?7 m² s^?1, but diffusion coefficients for nanoparticles are typically of the order of magnitude of 10^?12 m² s^?1.     

High-resolution venography of the brain using magnetic resonance imaging

The purpose of this study was to evaluate a non-flow related magnetic resonance imaging method to visualize small veins independent of arteries in the human brain. A long TE, high-resolution 3D gradient echo MR acquisition was used to highlight venous information. The method is based on the paramagnetic property of deoxyhemoglobin and the resulting phase difference between veins and brain parenchyma at long echo times. The MR magnitude images were masked with a phase mask filter to enhance small structure visibility.. Venous information down to sub-pixel vessel diameters of several hundred microns is visible. Venous data are displayed in an angiographic manner using a minimum intensity projection algorithm. Both superficial veins and deep white matter veins are visible. The method has been successfully applied in volunteers. Preliminary results in patients with cerebral arteriovenous malformations indicate its potential in clinical applications. The proposed method is easy to implement and does not require administration of a contrast agent or application of specially designed rf pulses to highlight the veins. Rather it exploits the intrinsic magnetic properties (BOLD-effect) and the prolonged T ^_2* of venous blood. The method may be of diagnostic potential in the assessment of arteriovenous malformations or other vascular venous lesions. © 1998 Elsevier Science B.V. All rights reserved.     

In situ ray tracing and computational steering for interactive blood flow simulation

Recent algorithm and hardware developments have significantly improved our capability to interactively visualise time-varying flow fields. However, when visualising very large dynamically varying datasets interactively there are still limitations in the scalability and efficiency of these methods. Here we present a rendering pipeline which employs an efficient in situ ray tracing technique to visualise flow fields as they are simulated. The ray casting approach is particularly well suited for the visualisation of large and sparse time-varying datasets, where it is capable of rendering fluid flow fields at high image resolutions and at interactive frame rates on a single multi-core processor using OpenMP. The parallel implementation of our in situ visualisation method relies on MPI, requires no specialised hardware support, and employs the same underlying spatial decomposition as the fluid simulator. The visualisation pipeline allows the user to operate on a commodity computer and explore the simulation output interactively. Our simulation environment incorporates numerous features that can be utilised in a wide variety of research contexts.     

Semantic fusion of laser and vision in pedestrian detection

Fusion of laser and vision in object detection has been accomplished by two main approaches: (1) independent integration of sensor-driven features or sensor-driven classifiers, or (2) a region of interest (ROI) is found by laser segmentation and an image classifier is used to name the projected ROI. Here, we propose a novel fusion approach based on semantic information, and embodied on many levels. Sensor fusion is based on spatial relationship of parts-based classifiers, being performed via a Markov logic network. The proposed system deals with partial segments, it is able to recover depth information even if the laser fails, and the integration is modeled through contextual information—characteristics not found on previous approaches. Experiments in pedestrian detection demonstrate the effectiveness of our method over data sets gathered in urban scenarios.     

Experimental evaluation of pheromone models in ACOPlan

In this paper the system ACOPlan for planning with non uniform action cost is introduced and analyzed. ACOPlan is a planner based on the ant colony optimization framework, in which a colony of planning ants searches for near optimal solution plans with respect to an overall plan cost metric. This approach is motivated by the strong similarity between the process used by artificial ants to build solutions and the methods used by state?Cbased planners to search solution plans. Planning ants perform a stochastic and heuristic based search by interacting through a pheromone model. The proposed heuristic and pheromone models are presented and compared through systematic experiments on benchmark planning domains. Experiments are also provided to compare the quality of ACOPlan solution plans with respect to state of the art satisficing planners. The analysis of the results confirm the good performance of the Action?CAction pheromone model and points out the promising performance of the novel Fuzzy?CLevel?CAction pheromone model. The analysis also suggests general principles for designing performant pheromone models for planning and further extensions of ACOPlan to other optimization models.     

Robust model selection with flexible trimming

The forward search provides data-driven flexible trimming of a C_p statistic for the choice of regression models that reveals the effect of outliers on model selection. An informed robust model choice follows. Even in small samples, the statistic has a null distribution indistinguishable from an F distribution. Limits on acceptable values of the C_p statistic follow. Two examples of widely differing size are discussed. A powerful graphical tool is the generalized candlestick plot, which summarizes the information on all forward searches and on the choice of models. A comparison is made with the use of M-estimation in robust model choice.