Symmetrical 2-D hermite-gaussian square launch for high bit rate transmission in multimode fiber links

A 2-D Hermite-Gaussian square launch is demonstrated to show improved systems capacity over multimode fiber links. It shows a bandwidth improvement over both center and offset launches and exhibits ±5 ìm misalignment tolerance. © OSA/OFC/NFOEC 2011.

Developing ISO 14649-based conversational programming system for multi-channel complex machine tools

A multi-channel complex machine tool (MCCM) is a versatile machining system equipped with more than two spindles and turrets for both turning and milling operations. Despite the potential of such a tool, the value of the hardware is largely dependent on how the machine tools are effectively programmed for machining. In this paper we consider a shop-floor programming system based on ISO 14649 (called e-CAM), the international standard for the interface between computer-aided manufacture (CAM) and computer numerical control (CNC). To be deployed in practical industrial usage a great deal of research has to be carried out. In this paper we present: 1) Design consideration for an e-CAM system, 2) The architecture design of e-CAM, 3) Major algorithms to fulfill the modules defined in the architecture, and 4) Implementation details.

Fault tolerant control using Gaussian processes and model predictive control

Essential ingredients for fault-tolerant control are the ability to represent system behaviour following the occurrence of a fault, and the ability to exploit this representation for deciding control actions. Gaussian processes seem to be very promising candidates for the first of these, and model predictive control has a proven capability for the second. We therefore propose to use the two together to obtain fault-tolerant control functionality. Our proposal is illustrated by several reasonably realistic examples drawn from flight control. © 2013 IEEE.

First principles interatomic potential for tungsten based on Gaussian process regression

An accurate description of atomic interactions, such as that provided by first principles quantum mechanics, is fundamental to realistic prediction of the properties that govern plasticity, fracture or crack propagation in metals. However, the computational complexity associated with modern schemes explicitly based on quantum mechanics limits their applications to systems of a few hundreds of atoms at most. This thesis investigates the application of the Gaussian Approximation Potential (GAP) scheme to atomistic modelling of tungsten - a bcc transition metal which exhibits a brittle-to-ductile transition and whose plasticity behaviour is controlled by the properties of $\frac{1}{2} \langle 111 \rangle$ screw dislocations. We apply Gaussian process regression to interpolate the quantum-mechanical (QM) potential energy surface from a set of points in atomic configuration space. Our training data is based on QM information that is computed directly using density functional theory (DFT). To perform the fitting, we represent atomic environments using a set of rotationally, permutationally and reflection invariant parameters which act as the independent variables in our equations of non-parametric, non-linear regression. We develop a protocol for generating GAP models capable of describing lattice defects in metals by building a series of interatomic potentials for tungsten. We then demonstrate that a GAP potential based on a Smooth Overlap of Atomic Positions (SOAP) covariance function provides a description of the $\frac{1}{2} \langle 111 \rangle$ screw dislocation that is in agreement with the DFT model. We use this potential to simulate the mobility of $\frac{1}{2} \langle 111 \rangle$ screw dislocations by computing the Peierls barrier and model dislocation-vacancy interactions to QM accuracy in a system containing more than 100,000 atoms.

Characterization and expression analysis of Paralichthys olivaceus voltage-dependent anion channel (VDAC) gene in response to virus infection

Voltage-dependent anion channel (VDAC, also known as mitochondrial porin) is acknowledged to play an important role in stress-induced mammalian apoptosis. In this study, Paralichthys olivaceus VDAC (PoVDAC) gene was identified as a virally induced gene from Scophthalmus Maximus Rhabdovirus (SMRV)-infected flounder embryonic cells (FEC). The full length of PoVDAC cDNA is 1380 bp with an open reading frame of 852 bp encoding a 283 amino acid protein. The deduced PoVDAC contains one alpha-helix, 13 transmembrane beta-strands and one eukaryotic mitochondrial porin signature motif. Constitutive expression of PoVDAC was confirmed in all tested tissues by real-time PCR. Further expression analysis revealed PoVDAC mRNA was upregulated by viral infection. We prepared fish antiserum against recombinant VDAC proteins and detected the PoVDAC in heart lysates from flounder as a 32 kDa band on western blot. Overexpression of PoVDAC in fish cells induced apoptosis. Immunofluoresence localization indicated that the significant distribution changes of PoVDAC have occurred in virus-induced apoptotic cells. This is the first report on the inductive expression of VDAC by viral infection, suggesting that PoVDAC might be mediated flounder antiviral immune response through induction of apoptosis. (c) 2007 Elsevier Ltd. All rights reserved.

Gaussian processes for POMDP-based dialogue manager optimization

A partially observable Markov decision process (POMDP) has been proposed as a dialog model that enables automatic optimization of the dialog policy and provides robustness to speech understanding errors. Various approximations allow such a model to be used for building real-world dialog systems. However, they require a large number of dialogs to train the dialog policy and hence they typically rely on the availability of a user simulator. They also require significant designer effort to hand-craft the policy representation. We investigate the use of Gaussian processes (GPs) in policy modeling to overcome these problems. We show that GP policy optimization can be implemented for a real world POMDP dialog manager, and in particular: 1) we examine different formulations of a GP policy to minimize variability in the learning process; 2) we find that the use of GP increases the learning rate by an order of magnitude thereby allowing learning by direct interaction with human users; and 3) we demonstrate that designer effort can be substantially reduced by basing the policy directly on the full belief space thereby avoiding ad hoc feature space modeling. Overall, the GP approach represents an important step forward towards fully automatic dialog policy optimization in real world systems. © 2013 IEEE.

Second-order rate region of constant-composition codes for the multiple-access channel

This paper presents an achievable second-order rate region for the discrete memoryless multiple-access channel. The result is obtained using a random-coding ensemble in which each user's codebook contains codewords of a fixed composition. It is shown that this ensemble performs at least as well as i.i.d. random coding in terms of second-order asymptotics, and an example is given where a strict improvement is observed. © 2013 IEEE.

20 Gb/s mode-group-division multiplexing employing Hermite-Gaussian launches over worst-case multimode fiber links

For the first time, mode group division multiplexing is achieved in a multimode fiber link using a 2-D Hermite-Gaussian mode launch. 20 Gb/s error-free transmission is achieved over a 250 m worst-case OM1 multimode fiber link. © OSA 2014.

Bayesian inference and learning in Gaussian process state-space models with Particle MCMC

State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference and learning (i.e. state estimation and system identification) in nonlinear nonparametric state-space models. We place a Gaussian process prior over the state transition dynamics, resulting in a flexible model able to capture complex dynamical phenomena. To enable efficient inference, we marginalize over the transition dynamics function and, instead, infer directly the joint smoothing distribution using specially tailored Particle Markov Chain Monte Carlo samplers. Once a sample from the smoothing distribution is computed, the state transition predictive distribution can be formulated analytically. Our approach preserves the full nonparametric expressivity of the model and can make use of sparse Gaussian processes to greatly reduce computational complexity.

Genomic organization, gene duplication, and expression analysis of interleukin-1 beta in channel catfish (Ictalurus punctatus)

Interleukin-1 beta (IL-1 beta) is one of the pivotal early response pro-inflammatory cytokines that enables organisms to respond to infection and induces a cascade of reactions leading to inflammation. In spite of its importance and two decades of studies in the mammalian species, genes encoding IL-1 beta were not identified from non-mammalian species until recently. Recent research, particularly with genomic approaches, has led to sequencing of IL-1 beta from many species. Clinical studies also Suggested IL-1 beta as an immunoreagulatory molecule potentially useful for enhancing vaccination. However, no IL-1 beta genes have been identified from channel catfish, the primary aquaculture species from the United States. In this study, we identified two distinct cDNAs encoding catfish IL-1 beta. Their encoding genes were identified, sequenced, and characterized. The catfish IL-1 beta genes were assigned to bacterial artificial chromosome (BAC) clones. Genomic studies indicated that the IL-1 beta genes were tandemly duplicated on the same chromosome. Phylogenetic analysis of various IL-1 beta genes indicated the possibility of recent species-specific gene duplications in channel catfish, and perhaps also in swine and carp. Expression analysis indicated that both IL-1 beta genes were expressed, but exhibited distinct expression profiles in various catfish tissues, and after bacterial infection with Edwardsiella ictaluri. (c) 2005 Elsevier Ltd. All rights reserved.

NK-lysin of channel catfish: Gene triplication, sequence variation, and expression analysis

Antimicrobial peptides (AMPs) are important components of the host innate immune response against microbial invasion. In addition to the previously known four classes of antimicrobial peptides, a fifth class of antimicrobial peptides has been recently identified to include NK-lysins that have a globular three-dimensional structure and are larger with 74-78 amino acid residues. NK-lysin has been shown to harbor antimicrobial activities against a wide spectrum of microorganisms including bacteria, fungi, protozoa, and parasites. To date, NK-lysin genes have been reported from only a limited number of organisms. We previously identified a NK-lysin cDNA in channel catfish. Here we report the identification of two noveltypes of NK-lysin transcripts in channel catfish. Altogether, three distinct NK-lysin transcripts exist in channel catfish. In this work, their encoding genes were identified, sequenced, and characterized. We provide strong evidence that the catfish NK-lysin gene is tripled in the same genomic neighborhood. All three catfish NK-lysin genes are present in the same genomic region and are tightly linked on the same chromosome, as the same BAC clones harbor all three copies of the NK-lysin genes. All three NK-lysin genes are expressed, but exhibit distinct expression profiles in various tissues. In spite of the existence of a single copy of NK-lysin gene in the human genome, and only a single hit from the pufferfish,genome, there are two tripled clusters of NK-lysin genes on chromosome 17 of zebrafish in addition to one more copy on its chromosome 5. The similarity in the genomic arrangement of the tripled NK-lysin genes in channel catfish and zebrafish suggest similar evolution of NK-lysin genes. (c) 2005 Elsevier Ltd. All rights reserved.

Classification using log Gaussian Cox processes

McCullagh and Yang (2006) suggest a family of classification algorithms based on Cox processes. We further investigate the log Gaussian variant which has a number of appealing properties. Conditioned on the covariates, the distribution over labels is given by a type of conditional Markov random field. In the supervised case, computation of the predictive probability of a single test point scales linearly with the number of training points and the multiclass generalization is straightforward. We show new links between the supervised method and classical nonparametric methods. We give a detailed analysis of the pairwise graph representable Markov random field, which we use to extend the model to semi-supervised learning problems, and propose an inference method based on graph min-cuts. We give the first experimental analysis on supervised and semi-supervised datasets and show good empirical performance.

A derivation of the source-channel error exponent using nonidentical product distributions

This paper studies the random-coding exponent of joint source-channel coding for a scheme where source messages are assigned to disjoint subsets (referred to as classes), and codewords are independently generated according to a distribution that depends on the class index of the source message. For discrete memoryless systems, two optimally chosen classes and product distributions are found to be sufficient to attain the sphere-packing exponent in those cases where it is tight. © 2014 IEEE.

Cell types, network homeostasis, and pathological compensation from a biologically plausible ion channel expression model.

How do neurons develop, control, and maintain their electrical signaling properties in spite of ongoing protein turnover and perturbations to activity? From generic assumptions about the molecular biology underlying channel expression, we derive a simple model and show how it encodes an "activity set point" in single neurons. The model generates diverse self-regulating cell types and relates correlations in conductance expression observed in vivo to underlying channel expression rates. Synaptic as well as intrinsic conductances can be regulated to make a self-assembling central pattern generator network; thus, network-level homeostasis can emerge from cell-autonomous regulation rules. Finally, we demonstrate that the outcome of homeostatic regulation depends on the complement of ion channels expressed in cells: in some cases, loss of specific ion channels can be compensated; in others, the homeostatic mechanism itself causes pathological loss of function.