We study the notion of learning in an oblivious changing environment. Existing online learning algorithms which minimize regret are shown to converge to the average of all locally optimal solutions. We propose a new performance metric, strengthening the standard metric of regret, to capture convergence to locally optimal solutions, and propose efficient algorithms which provably converge at the...

We propose adaptive nonlinear auto-associative modeling (ANAM) based on Locally Linear Embedding algorithm (LLE) for learning intrinsic principal features of each concept separately and recognition thereby. Unlike traditional supervised manifold learning algorithm, the proposed ANAM algorithm has several advantages: 1) it implicitly embodies discriminant information because the suboptimal param...

Integration of infant- and child-feeding index (ICFI) addressing the multidimensional child-feeding practices into one age-specific summary index is gaining importance. This cross-sectional study was aimed at understanding the association between the ICFI and the nutritional status of 259 children, aged 6-23 months, who attended the paediatric outpatient department of the Dhaka Medical College ...

The longitudinal relationship between stunting and wasting in children is poorly characterized. Instances of wasting or poor weight gain may precede linear growth retardation. We analyzed longitudinal anthropometric data for 1599 children from 8 cohort studies to determine the effect of wasting [weight-for-length Z-score (WLZ) < -2] and variability in WLZ in the first 17 mo on length-for-age Z-...

The current therapy for glioblastoma multiforme involves total surgical resection followed by combination of radiation therapy and temozolomide. Unfortunately, the efficacy for such current therapy is limited, and newer approaches are sorely needed to treat this deadly disease. We have recently described the isolation of bacterial proteins and peptides with anticancer activity. In phase I human...

A novel 3D face recognition framework based on Multiple Kernel Learning (MKL) is proposed in this work. As a first step, preprocessing is applied in order to extract relevant information and remove noise from 3D face scans. Next, a surface normals and Locally Adaptive Regression Kernels (LARK) features are extracted and a kernel function is associated with them. Finally, the corresponding kerne...

An adaptive version of growing and pruning RBF neural network has been used to predict the system output and implement Linear Model-Based Predictive Controller (LMPC) and Non-linear Model-based Predictive Controller (NMPC) strategies. A radial-basis neural network with growing and pruning capabilities is introduced to carry out on-line model identification.An Unscented Kal...

Understanding and interpreting large data sets is an important but challenging operation in many technical disciplines. Computer visualization has become a valuable tool to help portray characteristics of large data sets. In software visualization, illustrating the operation of very large programs or programs working on very large data sets has remained one of the key open problems. Here, we in...

This paper describes some improvements over the original Space-Optimized Tree technique for the visualization and manipulation of very large hierarchies. The new system uses an improved algorithm to calculate geometrical layouts and it also provides better navigation capability. We introduce our new layout algorithm that can make more consistence of the display than the original layout techniqu...

A numerical method for the fully adaptive sampling and interpolation of linear PDEs with random data is presented. It is based on the idea that the solution of the PDE with stochastic data can be represented as conditional expectation of a functional of a corresponding stochastic differential equation (SDE). The spatial domain is decomposed by a non-uniform grid and a classical Euler scheme is ...