Digital phenotyping of coconut and morphological traits associated with eriophyid mite infestation

Observations were recorded on traits associated with mite infestation related at two stages of button on six different coconut cultivars over three years. Highly significant correlation was found between mite damage score with color or weight of tepal. Step-wise multiple regression of the data analysis showed color of inner tepal as major trait associated with infestation by eriophyid mite. Other traits are ratio of tepal weight to tepal area, per cent of buttons with pink discoloration or with resin, tepals of regular aestivation and gap between fruit and tepal. Digital phenotype data of 83 image files were used to calculate color signature and correlated the same to mite damage score over three years. Red spectral values were found to vary from 14 to 251, green values to 12 to 237 and blue to vary from 5 to 183. Spectral values red max, green max, 3* Red + Green max had high significant negative correlation (>-0.4) with mite damage. Color and firmness of fruits and tepals of three coconut varieties were further analyzed where, fruits and tepals of COD variety showed high red/green (a* value of Hunterlab) >12. Firmness of 3 month old tepal and fruit of Benualim (BGRT) tall variety was (penetrometer reading >38) higher than other varieties

Antiinflammatory Therapy with Canakinumab for Atherosclerotic Disease

Background: Experimental and clinical data suggest that reducing inflammation without affecting lipid levels may reduce the risk of cardiovascular disease. Yet, the inflammatory hypothesis of atherothrombosis has remained unproved. Methods: We conducted a randomized, double-blind trial of canakinumab, a therapeutic monoclonal antibody targeting interleukin-1β, involving 10,061 patients with previous myocardial infarction and a high-sensitivity C-reactive protein level of 2 mg or more per liter. The trial compared three doses of canakinumab (50 mg, 150 mg, and 300 mg, administered subcutaneously every 3 months) with placebo. The primary efficacy end point was nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death. RESULTS: At 48 months, the median reduction from baseline in the high-sensitivity C-reactive protein level was 26 percentage points greater in the group that received the 50-mg dose of canakinumab, 37 percentage points greater in the 150-mg group, and 41 percentage points greater in the 300-mg group than in the placebo group. Canakinumab did not reduce lipid levels from baseline. At a median follow-up of 3.7 years, the incidence rate for the primary end point was 4.50 events per 100 person-years in the placebo group, 4.11 events per 100 person-years in the 50-mg group, 3.86 events per 100 person-years in the 150-mg group, and 3.90 events per 100 person-years in the 300-mg group. The hazard ratios as compared with placebo were as follows: in the 50-mg group, 0.93 (95% confidence interval [CI], 0.80 to 1.07; P = 0.30); in the 150-mg group, 0.85 (95% CI, 0.74 to 0.98; P = 0.021); and in the 300-mg group, 0.86 (95% CI, 0.75 to 0.99; P = 0.031). The 150-mg dose, but not the other doses, met the prespecified multiplicity-adjusted threshold for statistical significance for the primary end point and the secondary end point that additionally included hospitalization for unstable angina that led to urgent revascularization (hazard ratio vs. placebo, 0.83; 95% CI, 0.73 to 0.95; P = 0.005). Canakinumab was associated with a higher incidence of fatal infection than was placebo. There was no significant difference in all-cause mortality (hazard ratio for all canakinumab doses vs. placebo, 0.94; 95% CI, 0.83 to 1.06; P = 0.31). Conclusions: Antiinflammatory therapy targeting the interleukin-1β innate immunity pathway with canakinumab at a dose of 150 mg every 3 months led to a significantly lower rate of recurrent cardiovascular events than placebo, independent of lipid-level lowering. (Funded by Novartis; CANTOS ClinicalTrials.gov number, NCT01327846.

A dynamic system framework for the decomposition method solving Support Vector Machines

The decomposition method is generally used to solve the quadratic program of Support Vector Machines. The rate of convergence of this method is largely dependant on the sequence of sub-problems solved. In order to study ways of increasing the convergence, we propose a dynamic system perspective to model the dynamics of the decomposition method. In particular, the minimization of a sub-problem can be viewed as an autonomous dissipative system in terms of second order differential equations. The gradients of the sub-problems and the inequality constraints are explicitly modelled as system variables. Using these models, we then define a general decomposition method as a non-autonomous system composed of sub-systems that operate for discrete time intervals. The dependance of this system on time is depicted by a time dependant permutation matrix which functions as an indicator for operating subsystem components

An inexact penalty method for the semiparametric Support Vector Machine classifier

The support vector machine (SVM) classifier has been a popular classification tool used for a variety of pattern recognition tasks. In this study, we compare the performance of a semiparametric SVM classifier derived using an inexact penalty method on the original SVM formulation. This semiparametric form can be easily solved using a sequential decomposition method. We compare the accuracy of the semiparametric SVM against the standard SVM classifier trained using the SMO algorithm. The results indicate that in some cases the semiparametric SVM can give better generalization results than a standard SVM. We also demonstrate several cases where our iterative algorithm solves the SVM problem faster than the SMO

An extrapolated Sequential Minimal Optimization Algorithm for Support Vector Machines

The sequential minimal optimization (SMO) algorithm is a popular algorithm used to solve the support vector machine problem due to its efficiency and ease of implementation. We investigate applying extrapolation methods to the SMO update method in order to increase the rate of convergence of this algorithm. We first show that the update method is Newtonian and that extrapolation ensures the update is norm reducing on the objective function. We also note that choosing the working set pair according to some partial order does result in slightly faster speedups in algorithm performance

Stability Analysis of the Decomposition Method for solving Support Vector Machines

In situations where processing memory is limited, the Support Vector Machine quadratic program can be decomposed into smaller sub-problems and solved sequentially. The convergence of this method has been proven previously through the use of a counting method. In this initial investigation, we approach the convergence analysis by treating the decomposed sub-problems as subsystems of a general system. The gradients of the subproblems and the inequality constraints are explicitly modelled as system variables. The change in these variables during optimization form a dynamic system modelled by vector differential equations. We show that the change in the objective function can be written as the energy in the system. This makes it a natural Lyapunov function which has an asymptotically stable point at the origin. The asymptotic stability of the whole system then follows under certain assumptions

Fast linear stationary methods for Automatically Biased Support Vector Machines

We present a new training algorithm, which is capable of providing Fast training for a new automatically biased SVM. We compare our agorithm to the well-known Sequential Minimal Optimization (SMO) algorithm. We then show that this method allows for the application of acceleration methods which further increases the rates of convergence

A new momentum minimization decomposition method for support vector machines

The Support Vector Machine classifier is a binary classifier applied to classify large datasets, which is ideal for the application of decomposition methods when processing memory is limited. However, the rates of convergence of the decomposition method are largely dependent on the sequence of decomposed problems solved. Unfortunately, choosing the optimal sequence of sub problems is dilcult due to the inability of the algorithm to consider the entire variable space at once. We propose a measure of iteration that we call momentum and derive a prediction method to minimize the momentum of the updated iterates hitting the boundary constraints. Our prediction method uses a rough heuristic set to choose an approximately optimal sub problem to solve. We show that this rough heuristic set could greatly improve the speed of the popular Sequential Minimal Optimization algorithm