On Nonparametric Residual Variance Estimation

In this paper, the problem of residual variance estimation is examined. The problem is analyzed in a general setting which covers non-additive heteroscedastic noise under non-iid sampling. To address the estimation problem, we suggest a method based on nearest neighbor graphs and we discuss its convergence properties under the assumption of a Hölder continuous regression function. The universality of the estimator makes it an ideal tool in problems with only little prior knowledge available.     

Overview of the local production process of raw milk butter in Wallonia (Belgium)

This study aims to describe the procedures and practices used in local production of raw milk butter. The demand for local products is increasing; hence, there is a need to describe the practices used in the artisanal production of raw milk butter. Therefore, a survey of 147 raw milk butter producers was carried out. The results from the survey indicate that there is not one single way to produce butter at artisanal level. In terms of maturation, six temperature sequences were distinguished. Attention is required at every step of production starting from breeding.     

Good edit similarity learning by loss minimization

Similarity functions are a fundamental component of many learning algorithms. When dealing with string or tree-structured data, measures based on the edit distance are widely used, and there exist a few methods for learning them from data. However, these methods offer no theoretical guarantee as to the generalization ability and discriminative power of the learned similarities. In this paper, we propose an approach to edit similarity learning based on loss minimization, called GESL. It is driven by the notion of (?,??,??)-goodness, a theory that bridges the gap between the properties of a similarity function and its performance in classification. Using the notion of uniform stability, we derive generalization guarantees that hold for a large class of loss functions. We also provide experimental results on two real-world datasets which show that edit similarities learned with GESL induce more accurate and sparser classifiers than other (standard or learned) edit similarities.     

Analyzing visually impaired people’s touch gestures on smartphones

Multimedia Tools and Applications - We present an analysis of how visually impaired people perform gestures on touch-screen smartphones and report their preferences, explaining the procedure and...     

Quantitative luminescence mapping of Cu(In,Ga)Se2 thin‐film solar cells

We investigate photoluminescence and electroluminescence (PL and EL) emission images from Cu(In,Ga)Se₂‐based solar cells by means of a Hyperspectral Imager. Using the generalized Planck's law, maps of the effective quasi‐Fermi level splitting Δμ_eff in absolute values are obtained. A good agreement is found between the spatially averaged splitting in PL and the global open‐circuit voltage. However, from a local carrier transport discussion, we conclude that the equality does not hold locally. The spatial variations are rather attributed to local depth variations of the quasi‐Fermi level splitting due to material properties spatial fluctuations. By comparing PL and EL emissions, we discuss qualitatively the local effective lifetimes and collection efficiencies. Copyright © 2014 John Wiley & Sons, Ltd.     

MuscleBuilder: A modeling tool for human anatomy

A traditional multi-layered approach is adopted to human body modeling and deformation. The model is split into three general anatomical structures: the skeleton, musculature and skin. It is shown that each of these layers is modeled and deformed by using fast, procedural, ad-hoc methods that can painlessly be reimplemented. The modeling approach is generic enough to handle muscles of varying shape, size and characteristics and does not break in extreme skeleton poses. It is also described that the integrated MuscleBuilder system whose main features are: i) easy and quick creation of muscle deformation models; ii) automatic deformation of an overlying skin. It is shown that visually realistic results can be obtained at interactive frame rates with very little input from the designer.     

Efficacy and stability of ammoniation process as aflatoxin B1 decontamination technology in rice

Rice, a cereal widely used in Venezuela for human and animal nutrition, is susceptible to aflatoxin contamination in the field and during storage. Therefore, the goal of this research was the evaluation of the efficacy and permanence of the ammoniation process through high pressure/high temperature (HP/HT) and atmospheric pressure/moderate temperature (AP/MT) conditions applied to rice samples artificially contaminated with aflatoxin B1. For this purpose a 2k design was drawn up considering the temperature, the rice moisture and the process time as variables. Under both sets of conditions, aflatoxin B1 concentration was reduced in a range of 90% to 100%. After in vitro stomach digestion simulation, toxin reversion ranged from 0% to 19%. In conclusion, the process efficacy and permanence were achieved through the use of high temperature and long process time for both sets of conditions (HP/HT and AP/MT), respectively.     

3D object recognition based on a geometrical topology model and extreme learning machine

In this paper, one geometrical topology hypothesis is present based on the optimal cognition principle, and the single-hidden layer feedforward neural network with extreme learning machine (ELM) is used for 3D object recognition. It is shown that the proposed approach can identify the inherent distribution and the dependence structure for each 3D object along multiple view angles by evaluating the local topological segments with a dipole topology model and developing the relevant mathematical criterion with ELM algorithm. The ELM ensemble is then used to combine the individual single-hidden layer feedforward neural network of each 3D object for performance improvements. The simulation results have shown the excellent performance and the effectiveness of the developed scheme.     

Effect of Oxime Ether Incorporation in Acyl Indole Derivatives on PPAR Subtype Selectivity

Compounds that simultaneously activate peroxisome proliferator‐activated receptor (PPAR) subtypes α and γ have the potential to effectively treat dyslipidemia and type 2 diabetes (T2D) in a single pharmaceutically active molecule. The frequently observed side effects of selective PPARγ agonists, such as edema and weight gain, were expected to be overcome by using additive PPARα activity, leading to dual PPARα/γ agonists with balanced activity for both subtypes. Herein we report the discovery, synthesis, and optimization of a new series of α‐ethoxyphenylpropionic acid bearing 5‐ or 6‐substituted indoles. The incorporation of oxime ethers on the carbonyl portion of the benzoyl group can bring the PPARα/γ potency ratio equal to or slightly greater than one, as is the case for compounds 20 c and 21 a . Compound 20 c shows high efficacy in an ob/ob mouse model of T2D and dyslipidemia, similar to that of rosiglitazone and tesaglitazar, but with a significant increase in body weight gain. In contrast, compound 21 a , less potent as a dual PPARα/γ activator than 20 c , showed an interesting pharmacological profile, as it elicits a decrease in body weight relative to reference compounds.