Discriminative training of the hidden vector state model for semantic parsing

In this paper, we discuss how discriminative training can be applied to the hidden vector state (HVS) model in different task domains. The HVS model is a discrete hidden Markov model (HMM) in which each HMM state represents the state of a push-down automaton with a finite stack size. In previous applications, maximum-likelihood estimation (MLE) is used to derive the parameters of the HVS model. However, MLE makes a number of assumptions and unfortunately some of these assumptions do not hold. Discriminative training, without making such assumptions, can improve the performance of the HVS model by discriminating the correct hypothesis from the competing hypotheses. Experiments have been conducted in two domains: the travel domain for the semantic parsing task using the DARPA Communicator data and the Air Travel Information Services (ATIS) data and the bioinformatics domain for the information extraction task using the GENIA corpus. The results demonstrate modest improvements of the performance of the HVS model using discriminative training. In the travel domain, discriminative training of the HVS model gives a relative error reduction rate of 31 percent in F-measure when compared with MLE on the DARPA Communicator data and 9 percent on the ATIS data. In the bioinformatics domain, a relative error reduction rate of 4 percent in F-measure is achieved on the GENIA corpus.

Ontology-based protein-protein interactions extraction from literature using the hidden vector state model

This paper proposes a novel framework of incorporating protein-protein interactions (PPI) ontology knowledge into PPI extraction from biomedical literature in order to address the emerging challenges of deep natural language understanding. It is built upon the existing work on relation extraction using the Hidden Vector State (HVS) model. The HVS model belongs to the category of statistical learning methods. It can be trained directly from un-annotated data in a constrained way whilst at the same time being able to capture the underlying named entity relationships. However, it is difficult to incorporate background knowledge or non-local information into the HVS model. This paper proposes to represent the HVS model as a conditionally trained undirected graphical model in which non-local features derived from PPI ontology through inference would be easily incorporated. The seamless fusion of ontology inference with statistical learning produces a new paradigm to information extraction.

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.

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.

Optical bend sensor for vector curvature measurement based on Bragg grating in eccentric core polymer optical fibre

We present an optical bend sensor based on a Bragg grating written in an eccentric core polymer optical fibre. The grating wavelength shifts are studied as a function of bend curvature and fibre orientation and the device exhibits strong fibre orientation dependence, wide bend curvature range of ± 22.7 m-1 and high bend sensitivity of 63 pm/m-1, which is 80 times higher than the reported sensor based on an offset-FBG in standard single mode silica fibre.

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.

Spiral attractor created by vector solitons

Mode-locked lasers emitting a train of femtosecond pulses called dissipative solitons are an enabling technology for metrology, high-resolution spectroscopy, fibre optic communications, nano-optics and many other fields of science and applications. Recently, the vector nature of dissipative solitons has been exploited to demonstrate mode locked lasing with both locked and rapidly evolving states of polarisation. Here, for an erbium-doped fibre laser mode locked with carbon nanotubes, we demonstrate the first experimental and theoretical evidence of a new class of slowly evolving vector solitons characterized by a double-scroll chaotic polarisation attractor substantially different from Lorenz, Rössler and Ikeda strange attractors. The underlying physics comprises a long time scale coherent coupling of two polarisation modes. The observed phenomena, apart from the fundamental interest, provide a base for advances in secure communications, trapping and manipulation of atoms and nanoparticles, control of magnetisation in data storage devices and many other areas. © 2014 CIOMP. All rights reserved.

Bound state vector solitons with locked and precessing states of polarization

We report experimental observation of new tightly and loosely bound state vector solitons with locked and precessing states of polarization in a carbon nanotube mode locked fiber laser in the anomalous dispersion regime. ©2013 Optical Society of America.

Polarisation dynamics of vector soliton molecules in mode locked fibre laser

Two fundamental laser physics phenomena - dissipative soliton and polarisation of light are recently merged to the concept of vector dissipative soliton (VDS), viz. train of short pulses with specific state of polarisation (SOP) and shape defined by an interplay between anisotropy, gain/loss, dispersion, and nonlinearity. Emergence of VDSs is both of the fundamental scientific interest and is also a promising technique for control of dynamic SOPs important for numerous applications from nano-optics to high capacity fibre optic communications. Using specially designed and developed fast polarimeter, we present here the first experimental results on SOP evolution of vector soliton molecules with periodic polarisation switching between two and three SOPs and superposition of polarisation switching with SOP precessing. The underlying physics presents an interplay between linear and circular birefringence of a laser cavity along with light induced anisotropy caused by polarisation hole burning.

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.

The Localization Procedures of the Vector of Weighting Coefficients for Fuzzy Models of Choice

The author analyzes the localization procedures of the vector of weighting coefficients which are based on presenting the function of value by additive reduction adapted to fuzzy models of choice.

Vector Combinatorial Problems in a Space of Combinations with Linear Fractional Functions of Criteria

The paper considers vector discrete optimization problem with linear fractional functions of criteria on a feasible set that has combinatorial properties of combinations. Structural properties of a feasible solution domain and of Pareto–optimal (efficient), weakly efficient, strictly efficient solution sets are examined. A relation between vector optimization problems on a combinatorial set of combinations and on a continuous feasible set is determined. One possible approach is proposed in order to solve a multicriteria combinatorial problem with linear- fractional functions of criteria on a set of combinations.

Efficient Computing of some Vector Operations over GF(3) and GF(4)

The problem of efficient computing of the affine vector operations (addition of two vectors and multiplication of a vector by a scalar over GF (q)), and also the weight of a given vector, is important for many problems in coding theory, cryptography, VLSI technology etc. In this paper we propose a new way of representing vectors over GF (3) and GF (4) and we describe an efficient performance of these affine operations. Computing weights of binary vectors is also discussed.