Optimized logarithmic phase masks used to generate defocus invariant modulation transfer function for wavefront coding system

In a previous Letter [Opt. Lett. 33, 1171 (2008)], we proposed an improved logarithmic phase mask by making modifications to the original one designed by Sherif. However, further studies in another paper [Appl. Opt. 49, 229 (2010)] show that even when the Sherif mask and the improved one are optimized, their corresponding defocused modulation transfer functions (MTFs) are still not stable with respect to focus errors. So, by further modifying their phase profiles, we design another two logarithmic phase masks that exhibit more stable defocused MTF. However, with the defocus-induced phase effect considered, we find that the performance of the two masks proposed in this Letter is better than the Sherif mask, but worse than our previously proposed phase mask, according to the Hilbert space angle. (C) 2010 Optical Society of America

Performance of an improved logarithmic phase mask with optimized parameters in a wavefront-coding system

In two papers [Proc. SPIE 4471, 272-280 (2001) and Appl. Opt. 43, 2709-2721 (2004)], a logarithmic phase mask was proposed and proved to be effective in extending the depth of field; however, according to our research, this mask is not that perfect because the corresponding defocused modulation transfer function has large oscillations in the low-frequency region, even when the mask is optimized. So, in a previously published paper [Opt. Lett. 33, 1171-1173 (2008)], we proposed an improved logarithmic phase mask by making a small modification. The new mask can not only eliminate the drawbacks to a certain extent but can also be even less sensitive to focus errors according to Fisher information criteria. However, the performance comparison was carried out with the modified mask not being optimized, which was not reasonable. In this manuscript, we optimize the modified logarithmic phase mask first before analyzing its performance and more convincing results have been obtained based on the analysis of several frequently used metrics. (C) 2010 Optical Society of America

Profiling of Complex Microbial Populations by Denaturing Gradient Gel Electrophoresis Analysis of Polymerase Chain Reaction-Amplified Genes Coding for 16S rRNA

We describe a new molecular approach to analyzing the genetic diversity of complex microbial populations. This technique is based on the separation of polymerase chain reaction-amplified fragments of genes coding for 16S rRNA, all the same length, by denaturing gradient gel electrophoresis (DGGE). DGGE analysis of different microbial communities demonstrated the presence of up to 10 distinguishable bands in the separation pattern, which were most likely derived from as many different species constituting these populations, and thereby generated a DGGE profile of the populations. We showed that it is possible to identify constituents which represent only 1% of the total population. With an oligonucleotide probe specific for the V3 region of 16S rRNA of sulfate-reducing bacteria, particular DNA fragments from some of the microbial populations could be identified by hybridization analysis. Analysis of the genomic DNA from a bacterial biofilm grown under aerobic conditions suggests that sulfate-reducing bacteria, despite their anaerobicity, were present in this environment. The results we obtained demonstrate that this technique will contribute to our understanding of the genetic diversity of uncharacterized microbial populations.

The complete mitochondrial genome of the mantid shrimp Oratosquilla oratoria (Crustacea: Malacostraca: Stomatopoda): Novel non-coding regions features and phylogenetic implications of the Stomatopoda

The complete mitochondrial (mt) genome sequence of Oratosquilla oratoria (Crustacea: Malacostraca: Stomatopoda) was determined; a circular molecule of 15,783 bp in length. The gene content and arrangement are consistent with the pancrustacean ground pattern. The mt control region of O. oratoria is characterized by no GA-block near the 3' end and different position of [TA(A)]n-blocks compared with other reported Stomatopoda species. The sequence of the second hairpin structure is relative conserved which suggests this region may be a synapomorphic character for the Stomatopoda. In addition, a relative large intergenic spacer (101 bp) with higher A + T content than that in control region was identified between the tRNA(Glu) and tRNA(Phe) genes. Phylogenetic analyses based on the current dataset of complete mt genomes strongly support the Stomatopoda is closely related to Euphausiacea. They in turn cluster with Penaeoidea and Caridea clades while other decapods form a separate group, which rejects the monophyly of Decapoda. This challenges the suitability of Stomatopoda as an outgroup of Decapoda in phylogenetic analyses. The basal position of Stomatopoda within Eumalacostraca according to the morphological characters is also questioned. (C) 2010 Elsevier Inc. All rights reserved.

(Semi-)Predictive Discretization During Model Selection

In this paper, we present an approach to discretizing multivariate continuous data while learning the structure of a graphical model. We derive the joint scoring function from the principle of predictive accuracy, which inherently ensures the optimal trade-off between goodness of fit and model complexity (including the number of discretization levels). Using the so-called finest grid implied by the data, our scoring function depends only on the number of data points in the various discretization levels. Not only can it be computed efficiently, but it is also independent of the metric used in the continuous space. Our experiments with gene expression data show that discretization plays a crucial role regarding the resulting network structure.

Review of "Coding for Success" implementation.

Coding for Success was published in 2007 and described how bar coding and similar technologies can be used to improve patient safety, reduce costs and improve efficiency. This review aims to outline progress made since 2007, and was recommended by the Health Select Committee in its 2009 report on Patient Safety.

Hierarchical Multi-classification with Predictive Clustering Trees in Functional Genomics

Struyf, J., Dzeroski, S. Blockeel, H. and Clare, A. (2005) Hierarchical Multi-classification with Predictive Clustering Trees in Functional Genomics. In proceedings of the EPIA 2005 CMB Workshop

Statistical Evaluation of the Predictive Toxicology Challenge 2000-2001

Toivonen, H., Srinivasan, A., King, R. D., Kramer, S. and Helma, C. (2003) Statistical Evaluation of the Predictive Toxicology Challenge 2000-2001. Bioinformatics 19: 1183-1193

Non-commutative symbolic coding

Gohm, Rolf; Kummerer, B.; Lang, T., (2006) 'Non-commutative symbolic coding', Ergodic Theory and Dynamical Systems 26(5) pp.1521-1548 RAE2008

Differentiated Predictive Fair Service for TCP Flows

The majority of the traffic (bytes) flowing over the Internet today have been attributed to the Transmission Control Protocol (TCP). This strong presence of TCP has recently spurred further investigations into its congestion avoidance mechanism and its effect on the performance of short and long data transfers. At the same time, the rising interest in enhancing Internet services while keeping the implementation cost low has led to several service-differentiation proposals. In such service-differentiation architectures, much of the complexity is placed only in access routers, which classify and mark packets from different flows. Core routers can then allocate enough resources to each class of packets so as to satisfy delivery requirements, such as predictable (consistent) and fair service. In this paper, we investigate the interaction among short and long TCP flows, and how TCP service can be improved by employing a low-cost service-differentiation scheme. Through control-theoretic arguments and extensive simulations, we show the utility of isolating TCP flows into two classes based on their lifetime/size, namely one class of short flows and another of long flows. With such class-based isolation, short and long TCP flows have separate service queues at routers. This protects each class of flows from the other as they possess different characteristics, such as burstiness of arrivals/departures and congestion/sending window dynamics. We show the benefits of isolation, in terms of better predictability and fairness, over traditional shared queueing systems with both tail-drop and Random-Early-Drop (RED) packet dropping policies. The proposed class-based isolation of TCP flows has several advantages: (1) the implementation cost is low since it only requires core routers to maintain per-class (rather than per-flow) state; (2) it promises to be an effective traffic engineering tool for improved predictability and fairness for both short and long TCP flows; and (3) stringent delay requirements of short interactive transfers can be met by increasing the amount of resources allocated to the class of short flows.

A Quantitative Evaluation of the AVITEWRITE Model of Handwriting Learning

Much sensory-motor behavior develops through imitation, as during the learning of handwriting by children. Such complex sequential acts are broken down into distinct motor control synergies, or muscle groups, whose activities overlap in time to generate continuous, curved movements that obey an intense relation between curvature and speed. The Adaptive Vector Integration to Endpoint (AVITEWRITE) model of Grossberg and Paine (2000) proposed how such complex movements may be learned through attentive imitation. The model suggest how frontal, parietal, and motor cortical mechanisms, such as difference vector encoding, under volitional control from the basal ganglia, interact with adaptively-timed, predictive cerebellar learning during movement imitation and predictive performance. Key psycophysical and neural data about learning to make curved movements were simulated, including a decrease in writing time as learning progresses; generation of unimodal, bell-shaped velocity profiles for each movement synergy; size scaling with isochrony, and speed scaling with preservation of the letter shape and the shapes of the velocity profiles; an inverse relation between curvature and tangential velocity; and a Two-Thirds Power Law relation between angular velocity and curvature. However, the model learned from letter trajectories of only one subject, and only qualitative kinematic comparisons were made with previously published human data. The present work describes a quantitative test of AVITEWRITE through direct comparison of a corpus of human handwriting data with the model's performance when it learns by tracing human trajectories. The results show that model performance was variable across subjects, with an average correlation between the model and human data of 89+/-10%. The present data from simulations using the AVITEWRITE model highlight some of its strengths while focusing attention on areas, such as novel shape learning in children, where all models of handwriting and learning of other complex sensory-motor skills would benefit from further research.

Fusion Artmap: A Neural Network Architecture for Multi-Channel Data Fusion and Classification

Fusion ARTMAP is a self-organizing neural network architecture for multi-channel, or multi-sensor, data fusion. Single-channel Fusion ARTMAP is functionally equivalent to Fuzzy ART during unsupervised learning and to Fuzzy ARTMAP during supervised learning. The network has a symmetric organization such that each channel can be dynamically configured to serve as either a data input or a teaching input to the system. An ART module forms a compressed recognition code within each channel. These codes, in turn, become inputs to a single ART system that organizes the global recognition code. When a predictive error occurs, a process called paraellel match tracking simultaneously raises vigilances in multiple ART modules until reset is triggered in one of them. Parallel match tracking hereby resets only that portion of the recognition code with the poorest match, or minimum predictive confidence. This internally controlled selective reset process is a type of credit assignment that creates a parsimoniously connected learned network. Fusion ARTMAP's multi-channel coding is illustrated by simulations of the Quadruped Mammal database.

Dynamic Binding of Visual Contours Without Temporal Coding

Recognition of objects in complex visual scenes is greatly simplified by the ability to segment features belonging to different objects while grouping features belonging to the same object. This feature-binding process can be driven by the local relations between visual contours. The standard method for implementing this process with neural networks uses a temporal code to bind features together. I propose a spatial coding alternative for the dynamic binding of visual contours, and demonstrate the spatial coding method for segmenting an image consisting of three overlapping objects.

Neural Pattern Recognition With Multi-Scale Pyramidal Coding and Selective Attention

A new neural network architecture for spatial patttern recognition using multi-scale pyramida1 coding is here described. The network has an ARTMAP structure with a new class of ART-module, called Hybrid ART-module, as its front-end processor. Hybrid ART-module, which has processing modules corresponding to each scale channel of multi-scale pyramid, employs channels of finer scales only if it is necesssary to discriminate a pattern from others. This process is effected by serial match tracking. Also the parallel match tracking is used to select the spatial location having most salient feature and limit its attention to that part.