Plasmon hybridization engineering in self-organized anisotropic metasurfaces

Nano Research - The engineering of self-organized plasmonic metasurfaces is demonstrated using a maskless technique with defocused ion-beam sputtering and kinetically controlled deposition. The...     

Microalbuminuria, arterial hypertension and the cardiovascular risk

Both the Entamoeba histolytica lectin, a virulence factor for the causative agent of amebiasis, and the mammalian hepatic lectin bind to N-acetylgalactosamine (GalNAc) and galactose (Gal) nonreducing termini on oligosaccharides, with preference for GalNAc. Polyvalent GalNAc-derivatized neoglycoproteins have >1000-fold enhanced binding affinity for both lectins (Adler,P., Wood,S.J., Lee,Y.C., Lee,R.T., Petri,W.A.,Jr. and Schnaar,R.L.,1995, J. Biol. Chem ., 270, 5164-5171). Substructural specificity studies revealed that the 3-OH and 4-OH groups of GalNAc were required for binding to both lectins, whereas only the E.histolytica lectin required the 6-OH group. Whereas GalNAc binds with 4-fold lower affinity to the E.histolytica lectin than to the mammalian hepatic lectin, galactosamine and N-benzoyl galactosamine bind with higher affinity to the E. histolytica lectin. Therefore, a synthetic scheme for converting polyamine carriers to poly-N-acyl galactosamine derivatives (linked through the galactosamine primary amino group) was developed to test whether such ligands would bind the E.histolytica lectin with high specificity and high affinity. Contrary to expectations, polyvalent derivatives including GalN6lys5, GalN4desmosine, GalN4StarburstTMdendrimer, and GalN8StarburstTMdendrimer demonstrated highly enhanced binding to the mammalian hepatic lectin but little or no enhancement of binding to the E.histolytica lectin. We propose that the mammalian hepatic lectin binds with greatest affinity to GalNAc "miniclusters," which mimic branched termini of N-linked oligosaccharides, whereas the E.histolytica lectin binds most effectively to "maxiclusters," which may mimic more widely spaced GalNAc residues on intestinal mucins.     

Support environment for active rule design

The lack of tools for rule generation, analysis, and run-time monitoring appears one of the main obstacles to the widespreading of active database applications. This paper describes a complete tool environment for assisting the design of active rules applications; the tools were developed at Politecnico di Milano in the context of the IDEA Project, a 4-years Esprit project sponsored by the European Commission which was launched in June 1992. We describe tools for active rule generation, analysis, debugging, and browsing; rules are defined in Chimera, a conceptual design model and language for the specification of active rules applications. We also introduce a tool for mapping from Chimera into Oracle, a relational product supporting triggers.Most of the tools described in this paper are fully implemented and currently in operation (beta-testing) within the companies participating to the IDEA Project, with the exception of two of them (called Argonaut-V and Pandora), which will be completed by the end of 1996.Research presented in this paper is supported by Esprit project P6333 IDEA, and by ENEL contract VDS 1/94: Integrity Constraint Management     

Two is better than one: A diploid genotype for neural networks

In nature the genotype of many organisms exhibits diploidy, i.e., it includes two copies of every gene. In this paper we describe the results of simulations comparing the behavior of haploid and diploid populations of ecological neural networks living in both fixed and changing environments. We show that diploid genotypes create more variability in fitness in the population than haploid genotypes and buffer better environmental change; as a consequence, if one wants to obtain good results for both average and peak fitness in a single population one should choose a diploid population with an appropriate mutation rate. Some results of our simulations parallel biological findings.     

Interactive teaching of task-oriented robot grasps

This paper focuses on the problem of grasp stability and grasp quality analysis. An elegant way to evaluate the stability of a grasp is to model its wrench space. However, classical grasp quality measures suffer from several disadvantages, the main drawback being that they are not task related. Indeed, constructive approaches for approximating the wrench space including also task information have been rarely considered. This work presents an effective method for task-oriented grasp quality evaluation based on a novel grasp quality measure. We address the general case of multifingered grasps with point contacts with friction.The proposed approach is based on the concept of programming by demonstration and interactive teaching, wherein an expert user provides in a teaching phase a set of exemplar grasps appropriate for the task. Following this phase, a representation of task-related grasps is built. During task planning and execution, a grasp could be either submitted interactively for evaluation by a non-expert user or synthesized by an automatic planning system. Grasp quality is then assessed based on the proposed measure, which takes into account grasp stability along with its suitability for the task. To enable real-time evaluation of grasps, a fast algorithm for computing an approximation of the quality measure is also proposed. Finally, a local grasp optimization technique is described which can amend uncertainties arising in supplied grasps by non-expert users or assist in planning more valuable grasps in the neighborhood of candidate ones.The paper reports experiments performed in virtual reality with both an anthropomorphic virtual hand and a three-fingered robot hand. These experiments suggest the effectiveness and task relevance of the proposed grasp quality measure.     

Sensitivity analysis of the rice model WARM in Europe: Exploring the effects of different locations,climates and methods of analysis on model sensitivity to crop parameters

Sensitivity analysis studies how the variation in model outputs can be due to different sources of variation. This issue is addressed, in this study, as an application of sensitivity analysis techniques to a crop model in the Mediterranean region. In particular, an application of Morris and Sobol' sensitivity analysis methods to the rice model WARM is presented. The output considered is aboveground biomass at maturity, simulated at five rice districts of different countries (France, Greece, Italy, Portugal, and Spain) for years characterized by low, intermediate, and high continentality. The total effect index of Sobol' (that accounts for the total contribution to the output variation due a given parameter) and two Morris indices (mean μ and standard deviation σ of the ratios output changes/parameter variations) were used as sensitivity metrics. Radiation use efficiency (RUE), optimum temperature (T_opt), and leaf area index at emergence (LAI_ini) ranked in most of the combinations site × year as first, second and third most relevant parameters. Exceptions were observed, depending on the sensitivity method (e.g. LAI_ini resulted not relevant by the Morris method), or site-continentality pattern (e.g. with intermediate continentality in Spain, LAI_ini and T_opt were second and third ranked; with low continentality in Portugal, RUE was outranked by T_opt). Low σ values associated with the most relevant parameters indicated limited parameter interactions. The importance of sensitivity analyses by exploring site × climate combinations is discussed as pre-requisite to evaluate either novel crop-modelling approaches or the application of known modelling solutions to conditions not explored previously. The need of developing tools for sensitivity analysis within the modelling environment is also emphasized.     

Boosting learning and inference in Markov logic through metaheuristics

Markov Logic (ML) combines Markov networks (MNs) and first-order logic by attaching weights to first-order formulas and using these as templates for features of MNs. State-of-the-art structure learning algorithms in ML maximize the likelihood of a database by performing a greedy search in the space of structures. This can lead to suboptimal results because of the incapability of these approaches to escape local optima. Moreover, due to the combinatorially explosive space of potential candidates these methods are computationally prohibitive. We propose a novel algorithm for structure learning in ML, based on the Iterated Local Search (ILS) metaheuristic that explores the space of structures through a biased sampling of the set of local optima. We show through real-world experiments that the algorithm improves accuracy and learning time over the state-of-the-art algorithms. On the other side MAP and conditional inference for ML are hard computational tasks. This paper presents two algorithms for these tasks based on the Iterated Robust Tabu Search (IRoTS) metaheuristic. The first algorithm performs MAP inference and we show through extensive experiments that it improves over the state-of-the-art algorithm in terms of solution quality and inference time. The second algorithm combines IRoTS steps with simulated annealing steps for conditional inference and we show through experiments that it is faster than the current state-of-the-art algorithm maintaining the same inference quality.