Normal Kernels Research Articles

RGB-D joint modelling with scene geometric information for indoor semantic segmentation

This paper focuses on the problem of RGB-D semantic segmentation for indoor scenes. We introduce a novel gravity direction detection method based on vertical lines fitting combined 2D vision information and 3D geometric information to improve the original HHA depth encoding. Then to fuse two-stream networks of deep convolutional networks from RGB and depth encoding, we propose a joint modelling method by learning a weighted summing layer to fuse the prediction results. Finally, to refine the pixel-wise score maps, we adopt fully-connected CRF as a post-processing and propose a pairwise potential function combined normal kernel to explore geometric information. Experimental results show our proposed approach achieves state-of-the-art performance of RGB-D semantic segmentation on public dataset.

Credit scoring analysis using kernel discriminant

Credit scoring model is an important tool for reducing the risk of wrong decisions when granting credit facilities to applicants. This paper investigate the performance of kernel discriminant model in assessing customer credit risk. Kernel discriminant analysis is a non- parametric method which means that it does not require any assumptions about the probability distribution of the input. The main ingredient is a kernel that allows an efficient computation of Fisher discriminant. We use several kernel such as normal, epanechnikov, biweight, and triweight. The models accuracy was compared each other using data from a financial institution in Indonesia. The results show that kernel discriminant can be an alternative method that can be used to determine who is eligible for a credit loan. In the data we use, it shows that a normal kernel is relevant to be selected for credit scoring using kernel discriminant model. Sensitivity and specificity reach to 0.5556 and 0.5488 respectively.

Classification accuracy on the family planning participation status using kernel discriminant analysis

Population growth in Indonesia has increased every year. According to the population census conducted by the Central Bureau of Statistics (BPS) in 2010, the population of Indonesia has reached 237.6 million people. Therefore, to control the population growth rate, the government hold Family Planning or Keluarga Berencana (KB) program for couples of childbearing age. The purpose of this program is to improve the health of mothers and children in order to manifest prosperous society by controlling births while ensuring control of population growth. The data used in this study is the updated family data of Semarang city in 2016 that conducted by National Family Planning Coordinating Board (BKKBN). From these data, classifiers with kernel discriminant analysis will be obtained, and also classification accuracy will be obtained from that method. The result of the analysis showed that normal kernel discriminant analysis gives 71.05 % classification accuracy with 28.95 % classification error. Whereas triweight kernel discriminant analysis gives 73.68 % classification accuracy with 26.32 % classification error. Using triweight kernel discriminant for data preprocessing of family planning participation of childbearing age couples in Semarang City of 2016 can be stated better than with normal kernel discriminant.

Range contraction and increasing isolation of a polar bear subpopulation in an era of sea-ice loss.

Climate change is expected to result in range shifts and habitat fragmentation for many species. In the Arctic, loss of sea ice will reduce barriers to dispersal or eliminate movement corridors, resulting in increased connectivity or geographic isolation with sweeping implications for conservation. We used satellite telemetry, data from individually marked animals (research and harvest), and microsatellite genetic data to examine changes in geographic range, emigration, and interpopulation connectivity of the Baffin Bay (BB) polar bear (Ursus maritimus) subpopulation over a 25‐year period of sea‐ice loss. Satellite telemetry collected from n = 43 (1991–1995) and 38 (2009–2015) adult females revealed a significant contraction in subpopulation range size (95% bivariate normal kernel range) in most months and seasons, with the most marked reduction being a 70% decline in summer from 716,000 km2 (SE 58,000) to 211,000 km2 (SE 23,000) (p < .001). Between the 1990s and 2000s, there was a significant shift northward during the on‐ice seasons (2.6° shift in winter median latitude, 1.1° shift in spring median latitude) and a significant range contraction in the ice‐free summers. Bears in the 2000s were less likely to leave BB, with significant reductions in the numbers of bears moving into Davis Strait (DS) in winter and Lancaster Sound (LS) in summer. Harvest recoveries suggested both short and long‐term fidelity to BB remained high over both periods (83–99% of marked bears remained in BB). Genetic analyses using eight polymorphic microsatellites confirmed a previously documented differentiation between BB, DS, and LS; yet weakly differentiated BB from Kane Basin (KB) for the first time. Our results provide the first multiple lines of evidence for an increasingly geographically and functionally isolated subpopulation of polar bears in the context of long‐term sea‐ice loss. This may be indicative of future patterns for other polar bear subpopulations under climate change.

Bayesian Semiparametric Multivariate Density Deconvolution

ABSTRACTWe consider the problem of multivariate density deconvolution when interest lies in estimating the distribution of a vector valued random variable X but precise measurements on X are not available, observations being contaminated by measurement errors U. The existing sparse literature on the problem assumes the density of the measurement errors to be completely known. We propose robust Bayesian semiparametric multivariate deconvolution approaches when the measurement error density of U is not known but replicated proxies are available for at least some individuals. Additionally, we allow the variability of U to depend on the associated unobserved values of X through unknown relationships, which also automatically includes the case of multivariate multiplicative measurement errors. Basic properties of finite mixture models, multivariate normal kernels, and exchangeable priors are exploited in novel ways to meet modeling and computational challenges. Theoretical results showing the flexibility of the proposed methods in capturing a wide variety of data-generating processes are provided. We illustrate the efficiency of the proposed methods in recovering the density of X through simulation experiments. The methodology is applied to estimate the joint consumption pattern of different dietary components from contaminated 24 h recalls. Supplementary materials for this article are available online.

Generalized Skew-Normal Negentropy and Its Application to Fish Condition Factor Time Series

The problem of measuring the disparity of a particular probability density function from a normal one has been addressed in several recent studies. The most used technique to deal with the problem has been exact expressions using information measures over particular distributions. In this paper, we consider a class of asymmetric distributions with a normal kernel, called Generalized Skew-Normal (GSN) distributions. We measure the degrees of disparity of these distributions from the normal distribution by using exact expressions for the GSN negentropy in terms of cumulants. Specifically, we focus on skew-normal and modified skew-normal distributions. Then, we establish the Kullback–Leibler divergences between each GSN distribution and the normal one in terms of their negentropies to develop hypothesis testing for normality. Finally, we apply this result to condition factor time series of anchovies off northern Chile.

Impact of Soaking Temperature and Duration on Fissure Incidence of Rough Rice Kernels

Internal stresses owing to moisture and temperature gradients often result in the development of rice kernel fissures. Fissured rough rice kernels tend to break upon milling and potentially reduce the market value of rice. This work was conducted on the premise that fissures may be healed by soaking in water at a specific temperature and duration. Fissured rough rice kernels of a long‐grain cultivar, Wells, were selected by X‐ray imaging. Fissured kernels were soaked in a water bath at six soaking temperatures (22, 60, 65, 70, 75, and 80°C) and three soaking durations (1, 2, and 3 h) and then gently dried for characterization. X‐ray images revealed that soaking at 75°C for 3 h healed up to 70.0% of the fissured kernels. Soaking at 22, 60, or 65°C did not result in healing. For normal kernels, soaking at different temperatures for 3 h created fissures. Bending tests using a texture analyzer showed that brown rice breaking force increased from 18.5 N (fissured kernels) to 43.7 N (healed kernels). Soaking rough rice in water at a temperature slightly above its onset gelatinization temperature may potentially heal fissures.

“Antioxidant activity and characterization of protein fractions and hydrolysates from normal and quality protein maize kernels”

Maize is the main crop cultivated worldwide with more than 1 billion metric tons produced annually and is one of the most relevant sources of protein for human consumption in developing countries. Proteins and peptides isolated from maize exert relevant antioxidant activity which is increased by enzymatic hydrolysis. However, there is limited information about the antioxidant potential of proteins isolated from Quality Protein Maize (QPM) varieties and their hydrolysates. The aim of this research was to determine the differences in protein profile and antioxidant activity of protein fractions and hydrolysates between a hybrid white maize (Asgrow 773) and a QPM variety (CML-502). The biophysical evaluation and the total protein quantification by Kjeldahl and fractions by ninhydrin were consistent with the changes due to the breeding process of the QPM material. The antioxidant potential of the hydrolysates obtained from albumins and globulins had a 3-fold increase in both maize varieties. The prolamins hydrolysates presented an increase of 7-fold for the normal variety and 2-fold for the QPM variety. The results of this research allow indicate that the QPM varieties are a source of antioxidant peptides and promising candidates in the search for proteins and peptides with other bioactivities.

An improved time-dependent Boundary Element Method for two-dimensional acoustic problems in a subsonic uniform flow

In this paper, we have developed an improved formulation of two-dimensional Convected Boundary Element Method (CBEM) for radiation and propagation acoustic problems in a uniform mean flow with arbitrary orientation. The improved CBEM approach is derived from an advanced form of time-space two-dimensional Boundary Integral Equation (BIE) according to new Sommerfeld Radiation Conditions (SRC) with arbitrary mean flow. The acoustic variables of these formulations are expressed only in terms of the acoustic field as well as its normal and tangential derivatives. The multiplication operators are based explicitly on the two-dimensional Green's function and its convected normal derivative kernel. The proposed terms significantly reduce the presence of flow quantities incorporated in the classical integral formulations. Precisely, the convected kernel only requires the evaluation of two terms instead of several terms in conventional formulations due to the flow effects in the temporal and spatial derivatives. Also, for the singular integrations, the kernels containing logarithmic and weak singularities are converted to regular forms and evaluated partially analytically and numerically. The formulation is derived to be easy to implement as a numerical tool for computational codes of acoustic mediums with arbitrary mean flow. The accuracy and robustness of this technique is assessed through several examples such as the two-dimensional monopole, dipole and quadrupole sources in a uniform mean flow. An application of the improved formulation coupled with Particular Dirichlet-to-Neumann operators (PDtN) has been presented to describe the acoustic field inside two-dimensional infinite ducts in a uniform mean flow. The numerical results are compared to analytical, conventional BEM and Finite Element Method (FEM) formulations.

Powered embarrassing parallel MCMC sampling in Bayesian inference, a weighted average intuition

Although the Markov Chain Monte Carlo (MCMC) is very popular in parameter inference, the alleviation of the burden of calculation is crucial due to the limit of processors, memory, and disk bottleneck. This is especially true in terms of handling big data. In recent years, researchers have developed a parallel MCMC algorithm, in which full data are partitioned into subdatasets. Samples are drawn from the subdatasets independently at different machines without communication. In the extant literature, all machines are deemed to be identical. However, due to the heterogeneity of the data put into different machines, and the random nature of MCMC, the assumption of “identical machines” is questionable. Here we propose a Powered Embarrassing Parallel MCMC (PEPMCMC) algorithm, in which the full data posterior density is the product of the sub-posterior densities (posterior densities of different subdatasets) raised by some constraint powers. This is proven to be equivalent to a weighted averaging procedure. In our work, the powers are determined based on a maximum likelihood criterion, which leads to finding a maximum likelihood point within the convex hull of the estimates from different machines. We prove the asymptotic exactness and apply it to several cases to verify its strength in comparison with the unparallel and unpowered parallel algorithms. Furthermore, the connection between normal kernel density and parametric density estimations under certain conditions is investigated.

A new derivative with normal distribution kernel: Theory, methods and applications

New approach of fractional derivative with a new local kernel is suggested in this paper. The kernel introduced in this work is the well-known normal distribution that is a very common continuous probability distribution. This distribution is very important in statistics and also highly used in natural science and social sciences to portray real-valued random variables whose distributions are not known. Two definitions are suggested namely Atangana–Gómez Averaging in Liouville–Caputo and Riemann–Liouville sense. We presented some relationship with existing integrals transform operators. Numerical approximations for first and second order approximation are derived in detail. Some Applications of the new mathematical tools to describe some real world problems are presented in detail. This is a new door opened the field of statistics, natural and socials sciences.

AstroABC : An Approximate Bayesian Computation Sequential Monte Carlo sampler for cosmological parameter estimation

Given the complexity of modern cosmological parameter inference where we are faced with non-Gaussian data and noise, correlated systematics and multi-probe correlated data sets, the Approximate Bayesian Computation (ABC) method is a promising alternative to traditional Markov Chain Monte Carlo approaches in the case where the Likelihood is intractable or unknown. The ABC method is called "Likelihood free" as it avoids explicit evaluation of the Likelihood by using a forward model simulation of the data which can include systematics. We introduce astroABC, an open source ABC Sequential Monte Carlo (SMC) sampler for parameter estimation. A key challenge in astrophysics is the efficient use of large multi-probe datasets to constrain high dimensional, possibly correlated parameter spaces. With this in mind astroABC allows for massive parallelization using MPI, a framework that handles spawning of jobs across multiple nodes. A key new feature of astroABC is the ability to create MPI groups with different communicators, one for the sampler and several others for the forward model simulation, which speeds up sampling time considerably. For smaller jobs the Python multiprocessing option is also available. Other key features include: a Sequential Monte Carlo sampler, a method for iteratively adapting tolerance levels, local covariance estimate using scikit-learn's KDTree, modules for specifying optimal covariance matrix for a component-wise or multivariate normal perturbation kernel, output and restart files are backed up every iteration, user defined metric and simulation methods, a module for specifying heterogeneous parameter priors including non-standard prior PDFs, a module for specifying a constant, linear, log or exponential tolerance level, well-documented examples and sample scripts. This code is hosted online at https://github.com/EliseJ/astroABC

Local linear smoothers using inverse Gaussian regression

Local linear fitting has been widely used for estimating the univariate regression function, it has numerous fantastic properties like minimax efficiency and boundary correction. The asymmetric kernel functions match the support of the explanatory variables, and we discover the inverse Gaussian (IG) kernel function is identical to the normal kernel with variable bandwidth. Based on these, this paper proposes a new local linear estimation procedure with the IG kernel when the covariates are supported on \((0,\infty )\). Asymptotic theories of the proposed estimator are systematically studied, including the conditional mean squared error, the asymptotic normality and the uniform almost sure convergence. A simulation study and a data example indicate that the proposed estimator works efficiently.

ESTIMASI DENSITAS KERNEL ADJUSTED: STUDI SIMULASI

Kernel adjusted density estimation is a modification of the regular kernel density estimation. The modification is applied to a kernel function. This kernel function is derived from the location-scale transformation. Simulation study shows that this estimation have better results than the regular estimation because it has smaller MSE value. In addition, if normal kernel is used as a kernel function then the curve estimation will be smoother than other kernel function such as uniform kernel and Epachenikov kernel.

A spatial generalized ordered-response model with skew normal kernel error terms with an application to bicycling frequency

This paper proposes a new spatial generalized ordered response model with skew-normal kernel error terms and an associated estimation method. It contributes to the spatial analysis field by allowing a flexible and parametric skew-normal distribution for the kernel error term in traditional specifications of the spatial model. The resulting model is estimated using Bhat's (2011) maximum approximate composite marginal likelihood (MACML) inference approach. The model is applied to an analysis of bicycling frequency, using data from the 2014 Puget Sound household travel survey undertaken in the Puget Sound region in the State of Washington in the United States. Our results underscore the important effects of demographic variables, as well as the miles of bicycle lanes in an individual's immediate residential neighborhood, on bicycling propensity. An interesting finding is that women and young individuals (18–34 years of age) in particular “warm up” to bicycling as more investment is made in bicycling infrastructure, thus leading not only to a larger pool of bicyclists due to bicycling infrastructure enhancements, but also a more diverse and inclusive one. The results highlight the importance of introducing social dependence effects and non-normal kernel error terms from a policy standpoint. Specifically, our results suggest that ignoring these effects, as has been done by all earlier bicycling studies, can underestimate the impacts of bicycling infrastructure improvements and public campaigns on bicycle use frequency, potentially leading to under-investments in bicycling infrastructure projects.

Kernel estimation of the tail index of a right-truncated Pareto-type distribution

An asymptotically normal kernel estimator for the positive tail index of right-truncated data is introduced. A simulation study shows that the proposed estimator performs much better than the existing ones in terms of bias.

Development of Flattened Rice Flakes As Value Added Snacks Using Dehydrated Spices Powder As Natural Preservatives

Flattened rice defines as dehusked boiled rice which flattened into flat form flakes. Thicknesses of these flakes varied almost translucently thin near to four times thicker than normal rice kernel. The sample developed by dusting of dehydrated refined spice mixes of bay leaf, zinger, and garlic powder. Phytochemical component of spices having anti-oxidative properties works as natural preservative for cereal products. Proximate values of zinger , garlic, bay leaves and flattened rice were moisture, fat, protein, carbohydrate, energy and fiber contents (%) ranged from 5-8g, 2-8g, 7- 16.5g, 54-81g, 316-356 Kcal and 3- 26.3g, respectively. 1 % (w/w) spices powder mix were incorporated as natural preservative into flattened rice designed as one control (S0) and four treatments S1, S2, S3 and S4 samples. Physical properties of the samples were water activity (aw) and texture, resulted as S4 of 0.201 to 0.223 &amp; 5.342 to 5.122 and S0 of 0.298 to 0.342 &amp; 4.196 to 3.853. Moisture content and microbiological count for S4 and S0 were 8 to 8.2% &amp; 13 to 15%; 40 to 42 &amp; 40 to 80 cfu/g. The results are the indicator for crispiness and prolonged shelf life of developed snacks (S4). Sensory evaluation (N=30) judged natural spices powder mixed flattened rice snacks (S4) as significantly (P&lt;0.01) superior for appearance, flavor, taste and overall acceptability than other treatment group

Two‐Dimensional Kernel Smoothing of Mortality Surface: An Evaluation of Cohort Strength

Accurate mortality forecasts are of primary interest to insurance companies, pension providers and government welfare systems owing to the rapid increase in life expectancy during the past few decades. Existing mortality models in the literature tend to project future mortality rates by extracting the observed patterns in the mortality surface. Patterns found in the cohort dimension have received a considerable amount of attention and are included in many models of mortality. However, to our knowledge very few studies have considered an evaluation and comparison of cohort patterns across different countries. Moreover, the answer to the question of how the incorporation of the cohort effect affects the forecasting performance of mortality models still remains unclear. In this paper we introduce a new way of incorporating the cohort effect at the beginning of the estimation stage via the implementation of kernel smoothing techniques. Bivariate standard normal kernel density is used and we capture the cohort effect by assigning greater weights along the diagonals of the mortality surface. Based on the results from our empirical study, we compare and discuss the differences in cohort strength across a range of developed countries. Further, the fitting and forecasting results demonstrate the superior performance of our model when compared to some well‐known mortality models in the literature under a majority of circumstances. Copyright © 2016 John Wiley &amp; Sons, Ltd.

Geometrical on-the-fly shock detection in smoothed particle hydrodynamics

We present an on-the-fly geometrical approach for shock detection and Mach number calculation in simulations employing smoothed particle hydrodynamics (SPH). We utilize pressure gradients to select shock candidates and define up- and downstream positions. We obtain hydrodynamical states in the up- and downstream regimes with a series of normal and inverted kernel weightings parallel and perpendicular to the shock normals. Our on-the-fly geometrical Mach detector incorporates well within the SPH formalism and has low computational cost. We implement our Mach detector into the simulation code gadget and alongside many SPH improvements. We test our shock finder in a sequence of shock tube tests with successively increasing Mach numbers exceeding by far the typical values inside galaxy clusters. For all shocks, we resolve the shocks well and the correct Mach numbers are assigned. An application to a strong magnetized shock tube gives stable results in full magnetohydrodynamic setups. We simulate a merger of two idealized galaxy clusters and study the shock front. Shock structures within the merging clusters as well as the cluster shock are well captured by our algorithm and assigned correct Mach numbers.

Boron Nutrition Under Intermittent Flooding and Drying Condition Seems Sustainable Nutrient Management Technique in Rice

Abstract Water saving rice cultivation is emerging technique to couple with irrigation water shortage due to climate change all over the world. Major issue in these techniques is to compromise yield and quality fatalities because of higher unfilled grain due to nutrients deficiency. Boron fertilization seems to be big management technique to improve rice agriculture due to having imperative role in pollen viability. Thus, a field experiment was conducted to see the impact of boron fertilization both with basal and foliar application methods in water saving rice cultivation systems. Boron, with basal (3 kg borax/acre) and foliar (2% boron) was applied at different growth stages in rice crop grown under various rice cultivation systems; flooded rice, intermittent flooding and drying and aerobic rice. Boron fertilization both with basal and foliar application technique resulted in improved crop performance in all cultivation systems. Rice plants recorded highest yield, yield attributing parameters like productive tillers, panicle length and grain weight with boron fertilization. Quality parameters like sterile kernels, abortive kernels, opaque kernels were significantly reduced with boron fertilization in all rice cultivation systems. Furthermore, normal kernels were enhanced with basal and foliar application of boron nutrition. Likewise, maximum water use efficiency was recorded in foliar application of boron at panicle stage under intermittent flooding and drying condition. Foliar application of boron nutrition at panicle initiation stage was found to be most appropriate in water saving rice cultivation systems.