Q parameter scaling in scalar and vector models with dominant signal-noise and noise-noise beating terms

The Q parameter scales differently with the noise power for the signal-noise and the noise-noise beating terms in scalar and vector models. Some procedures for including noise in the scalar model largely under-estimate the Q parameter.

Clinical evaluation of the Grand Seiko Auto Ref/Keratometer WAM-5500

Purpose: A clinical evaluation of the Grand Seiko Auto Ref/Keratometer WAM-5500 (Japan) was performed to evaluate validity and repeatability compared with non-cycloplegic subjective refraction and Javal–Schiotz keratometry. An investigation into the dynamic recording capabilities of the instrument was also conducted. Methods: Refractive error measurements were obtained from 150 eyes of 75 subjects (aged 25.12 ± 9.03 years), subjectively by a masked optometrist, and objectively with the WAM-5500 at a second session. Keratometry measurements from the WAM-5500 were compared to Javal–Schiotz readings. Intratest variability was examined on all subjects, whilst intertest variability was assessed on a subgroup of 44 eyes 7–14 days after the initial objective measures. The accuracy of the dynamic recording mode of the instrument and its tolerance to longitudinal movement was evaluated using a model eye. An additional evaluation of the dynamic mode was performed using a human eye in relaxed and accommodated states. Results: Refractive error determined by the WAM-5500 was found to be very similar (p = 0.77) to subjective refraction (difference, -0.01 ± 0.38 D). The instrument was accurate and reliable over a wide range of refractive errors (-6.38 to +4.88 D). WAM-5500 keratometry values were steeper by approximately 0.05 mm in both the vertical and horizontal meridians. High intertest repeatability was demonstrated for all parameters measured: for sphere, cylinder power and MSE, over 90% of retest values fell within ±0.50 D of initial testing. In dynamic (high-speed) mode, the root-mean-square of the fluctuations was 0.005 ± 0.0005 D and a high level of recording accuracy was maintained when the measurement ring was significantly blurred by longitudinal movement of the instrument head. Conclusion: The WAM-5500 Auto Ref/Keratometer represents a reliable and valid objective refraction tool for general optometric practice, with important additional features allowing pupil size determination and easy conversion into high-speed mode, increasing its usefulness post-surgically following accommodating intra-ocular lens implantation, and as a research tool in the study of accommodation.

Verification of the standard model of the photorefractive nonlinearity in BSO crystals

On the basis of the standard model for the photorefractive nonlinearity we investigate whether a systematic description of the dependence of two-beam energy exchange on beam polarization and grating vector K is possible. Our result is that there is good agreement between theory and experiment with respect to the polarization properties and semi-quantitative agreement with respect to the K-dependence of the energy exchange.

A novel framework of training hidden Markov support vector machines from lightly-annotated data

Natural language understanding (NLU) aims to map sentences to their semantic mean representations. Statistical approaches to NLU normally require fully-annotated training data where each sentence is paired with its word-level semantic annotations. In this paper, we propose a novel learning framework which trains the Hidden Markov Support Vector Machines (HM-SVMs) without the use of expensive fully-annotated data. In particular, our learning approach takes as input a training set of sentences labeled with abstract semantic annotations encoding underlying embedded structural relations and automatically induces derivation rules that map sentences to their semantic meaning representations. The proposed approach has been tested on the DARPA Communicator Data and achieved 93.18% in F-measure, which outperforms the previously proposed approaches of training the hidden vector state model or conditional random fields from unaligned data, with a relative error reduction rate of 43.3% and 10.6% being achieved.

Feature LDA:a supervised topic model for automatic detection of web API documentations from the web

Web APIs have gained increasing popularity in recent Web service technology development owing to its simplicity of technology stack and the proliferation of mashups. However, efficiently discovering Web APIs and the relevant documentations on the Web is still a challenging task even with the best resources available on the Web. In this paper we cast the problem of detecting the Web API documentations as a text classification problem of classifying a given Web page as Web API associated or not. We propose a supervised generative topic model called feature latent Dirichlet allocation (feaLDA) which offers a generic probabilistic framework for automatic detection of Web APIs. feaLDA not only captures the correspondence between data and the associated class labels, but also provides a mechanism for incorporating side information such as labelled features automatically learned from data that can effectively help improving classification performance. Extensive experiments on our Web APIs documentation dataset shows that the feaLDA model outperforms three strong supervised baselines including naive Bayes, support vector machines, and the maximum entropy model, by over 3% in classification accuracy. In addition, feaLDA also gives superior performance when compared against other existing supervised topic models.

The retrograde delivery of adenovirus vector carrying the gene for brain-derived neurotrophic factor protects neurons and oligodendrocytes from apoptosis in the chronically compressed spinal cord of twy/twy mice

STUDY DESIGN: The twy/twy mouse undergoes spontaneous chronic mechanical compression of the spinal cord; this in vivo model system was used to examine the effects of retrograde adenovirus (adenoviral vector [AdV])-mediated brain-derived neurotrophic factor (BDNF) gene delivery to spinal neural cells. OBJECTIVE: To investigate the targeting and potential neuroprotective effect of retrograde AdV-mediated BDNF gene transfection in the chronically compressed spinal cord in terms of prevention of apoptosis of neurons and oligodendrocytes. SUMMARY OF BACKGROUND DATA: Several studies have investigated the neuroprotective effects of neurotrophins, including BDNF, in spinal cord injury. However, no report has described the effects of retrograde neurotrophic factor gene delivery in compressed spinal cords, including gene targeting and the potential to prevent neural cell apoptosis. METHODS: AdV-BDNF or AdV-LacZ (as a control gene) was injected into the bilateral sternomastoid muscles of 18-week old twy/twy mice for retrograde gene delivery via the spinal accessory motor neurons. Heterozygous Institute of Cancer Research mice (+/twy), which do not undergo spontaneous spinal compression, were used as a control for the effects of such compression on gene delivery. The localization and cell specificity of ß-galactosidase expression (produced by LacZ gene transfection) and BDNF expression in the spinal cord were examined by coimmunofluorescence staining for neural cell markers (NeuN, neurons; reactive immunology protein, oligodendrocytes; glial fibrillary acidic protein, astrocytes; OX-42, microglia) 4 weeks after gene injection. The possible neuroprotection afforded by retrograde AdV-BDNF gene delivery versus AdV-LacZ-transfected control mice was assessed by scoring the prevalence of apoptotic cells (terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling-positive cells) and immunoreactivity to active caspases -3, -8, and -9, p75, neurofilament 200 kD (NF), and for the oligodendroglial progenitor marker, NG2. RESULTS.: Four weeks after injection, the retrograde delivery of the LacZ marker gene was identified in cervical spinal neurons and some glial cells, including oligodendrocytes in the white matter of the spinal cord, in both the twy/twy mouse and the heterozygous Institute of Cancer Research mouse (+/twy). In the compressed spinal cord of twy/twy mouse, AdV-BDNF gene transfection resulted in a significant decrease in the number of terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling-positive cells present in the spinal cord and a downregulation in the caspase apoptotic pathway compared with AdV-LacZ (control) gene transfection. There was a marked and significant increase in the areas of the spinal cord of AdV-BDNF-injected mice that were NF- and NG2-immunopositive compared with AdV-LacZ-injected mice, indicating the increased presence of neurons and oligodendrocytes in response to BDNF transfection. CONCLUSION: Our results demonstrate that targeted retrograde BDNF gene delivery suppresses apoptosis in neurons and oligodendrocytes in the chronically compressed spinal cord of twy/twy mouse. Further work is required to establish whether this method of gene delivery may provide neuroprotective effects in other situations of compressive spinal cord injury.

LRD : Latent Relation Discovery for vector space expansion and information retrieval

In this paper, we propose a text mining method called LRD (latent relation discovery), which extends the traditional vector space model of document representation in order to improve information retrieval (IR) on documents and document clustering. Our LRD method extracts terms and entities, such as person, organization, or project names, and discovers relationships between them by taking into account their co-occurrence in textual corpora. Given a target entity, LRD discovers other entities closely related to the target effectively and efficiently. With respect to such relatedness, a measure of relation strength between entities is defined. LRD uses relation strength to enhance the vector space model, and uses the enhanced vector space model for query based IR on documents and clustering documents in order to discover complex relationships among terms and entities. Our experiments on a standard dataset for query based IR shows that our LRD method performed significantly better than traditional vector space model and other five standard statistical methods for vector expansion.

Verification of the standard model of the photorefractive nonlinearity in BSO crystals

On the basis of the standard model for the photorefractive nonlinearity we investigate whether a systematic description of the dependence of two-beam energy exchange on beam polarization and grating vector K is possible. Our result is that there is good agreement between theory and experiment with respect to the polarization properties and semi-quantitative agreement with respect to the K-dependence of the energy exchange.

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

Background - The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. Results - We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides. Conclusion - As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.

Leontief Model Analysis with Fuzzy Parameters by Basic Matrixes Method

The basic matrixes method is suggested for the Leontief model analysis (LM) with some of its components indistinctly given. LM can be construed as a forecast task of product’s expenses-output on the basis of the known statistic information at indistinctly given several elements’ meanings of technological matrix, restriction vector and variables’ limits. Elements of technological matrix, right parts of restriction vector LM can occur as functions of some arguments. In this case the task’s dynamic analog occurs. LM essential complication lies in inclusion of variables restriction and criterion function in it.

NLSE-based model of a random distributed feedback fiber laser

In this work we propose a NLSE-based model of power and spectral properties of the random distributed feedback (DFB) fiber laser. The model is based on coupled set of non-linear Schrödinger equations for pump and Stokes waves with the distributed feedback due to Rayleigh scattering. The model considers random backscattering via its average strength, i.e. we assume that the feedback is incoherent. In addition, this allows us to speed up simulations sufficiently (up to several orders of magnitude). We found that the model of the incoherent feedback predicts the smooth and narrow (comparing with the gain spectral profile) generation spectrum in the random DFB fiber laser. The model allows one to optimize the random laser generation spectrum width varying the dispersion and nonlinearity values: we found, that the high dispersion and low nonlinearity results in narrower spectrum that could be interpreted as four-wave mixing between different spectral components in the quasi-mode-less spectrum of the random laser under study could play an important role in the spectrum formation. Note that the physical mechanism of the random DFB fiber laser formation and broadening is not identified yet. We investigate temporal and statistical properties of the random DFB fiber laser dynamics. Interestingly, we found that the intensity statistics is not Gaussian. The intensity auto-correlation function also reveals that correlations do exist. The possibility to optimize the system parameters to enhance the observed intrinsic spectral correlations to further potentially achieved pulsed (mode-locked) operation of the mode-less random distributed feedback fiber laser is discussed.

Derivation of Context-free Stochastic L-Grammar Rules for Promoter Sequence Modeling Using Support Vector Machine

Formal grammars can used for describing complex repeatable structures such as DNA sequences. In this paper, we describe the structural composition of DNA sequences using a context-free stochastic L-grammar. L-grammars are a special class of parallel grammars that can model the growth of living organisms, e.g. plant development, and model the morphology of a variety of organisms. We believe that parallel grammars also can be used for modeling genetic mechanisms and sequences such as promoters. Promoters are short regulatory DNA sequences located upstream of a gene. Detection of promoters in DNA sequences is important for successful gene prediction. Promoters can be recognized by certain patterns that are conserved within a species, but there are many exceptions which makes the promoter recognition a complex problem. We replace the problem of promoter recognition by induction of context-free stochastic L-grammar rules, which are later used for the structural analysis of promoter sequences. L-grammar rules are derived automatically from the drosophila and vertebrate promoter datasets using a genetic programming technique and their fitness is evaluated using a Support Vector Machine (SVM) classifier. The artificial promoter sequences generated using the derived L- grammar rules are analyzed and compared with natural promoter sequences.

Reshoring:opportunities and limits for manufacturing in the UK - the case of the auto sector

In recent years, offshoring and outsourcing have transformed fundamentally nationally based auto sectors into global networks of design, production and distribution across the global value chains coordinated by the major automotive Original Equipment Manufacturers (OEMs). As manufacturing activities tended to be shifted to low-labour cost locations in Asia, Africa and Latin America, high-end design, R&D, product development have stayed anchored mostly to high-cost and high knowledge-intensive home economy locations (perhaps with the except of some design and styling activities which are often located in major end markets around the world. However, very recently the weaknesses of and risks inherent in such global value chains (GVCs) have been exposed, triggering attempts to rethink their nature and also raising possibilities to reshore some manufacturing activities to home countries. A combination of a more competitive exchange rate (despite the very recent appreciation of sterling), increased transport costs, rising wages in key areas of China, and a greater awareness of supply chain resilience have all contributed to a perceived change in some business fundamentals. The potential for some supply chain relocalisation also links in with the servitisation of manufacturing including the auto sector and shift to a hybrid model where manufacturing and services are increasingly intertwined. However, there are limits as to how far this can go and these raise some important questions and issues over the possible role for industrial policy.

Fast and slowly evolving vector solitons in mode-locked fibre lasers

We report on a new vector model of an erbium-doped fibre laser mode locked with carbon nanotubes. This model goes beyond the limitations of the previously used models based on either coupled nonlinear Schrödinger or Ginzburg-Landau equations. Unlike the previous models, it accounts for the vector nature of the interaction between an optical field and an erbium-doped active medium, slow relaxation dynamics of erbium ions, linear birefringence in a fibre, linear and circular birefringence of a laser cavity caused by in-cavity polarization controller and light-induced anisotropy caused by elliptically polarized pump field. Interplay of aforementioned factors changes coherent coupling of two polarization modes at a long time scale and so results in a new family of vector solitons (VSs) with fast and slowly evolving states of polarization. The observed VSs can be of interest in secure communications, trapping and manipulation of atoms and nanoparticles, control of magnetization in data storage devices and many other areas.

Long period grating in multicore optical fiber:an ultra-sensitive vector bending sensor for low curvatures

Long period grating was UV inscribed into a multicore fiber consisting of 120 single mode cores. The multicore fiber that hosts the grating was fusion spliced into a single mode fiber at both ends. The splice creates a taper transition between the two types of fiber that produces a nonadiabatic mode evolution; this results in the illumination of all the modes in the multicore fiber. The spectral characteristics of this fiber device as a function of curvature were investigated. The device yielded a significant spectral sensitivity as high as 1.23 nm/m-1 and 3.57 dB/m-1 to the ultra-low curvature values from 0 to 1 m-1. This fiber device can also distinguish the orientation of curvature experienced by the fiber as the long period grating attenuation bands producing either a blue or red wavelength shift. The finite element method (FEM) model was used to investigate the modal behavior in multicore fiber and to predict the phase-matching curves of the long period grating inscribed into multicore fiber. © 2014 Optical Society of America.