Identification of Cj1051c as a Major determinant for the restriction barrier of Campylobacter jejuni StrainNCTC11168

Campylobacter jejuni is a leading cause of human diarrheal illness in the world, and research on it has benefitted greatly by the completion of several genome sequences and the development of molecular biology tools. However, many hurdles remain for a full understanding of this unique bacterial pathogen. One of the most commonly used strains for genetic work with C. jejuni is NCTC11168. While this strain is readily transformable with DNA for genomic recombination, transformation with plasmids is problematic. In this study, we have identified a determinant of this to be cj1051c, predicted to encode a restriction-modification type IIG enzyme. Knockout mutagenesis of this gene resulted in a strain with a 1,000-fold-enhanced transformation efficiency with a plasmid purified from a C. jejuni host. Additionally, this mutation conferred the ability to be transformed by plasmids isolated from an Escherichia coli host. Sequence analysis suggested a high level of variability of the specificity domain between strains and that this gene may be subject to phase variation. We provide evidence that cj1051c is active in NCTC11168 and behaves as expected for a type IIG enzyme. The identification of this determinant provides a greater understanding of the molecular biology of C. jejuni as well as a tool for plasmid work with strain NCTC11168. © 2012, American Society for Microbiology.

Syllable language models for Mandarin speech recognition: exploiting character language models.

Mandarin Chinese is based on characters which are syllabic in nature and morphological in meaning. All spoken languages have syllabiotactic rules which govern the construction of syllables and their allowed sequences. These constraints are not as restrictive as those learned from word sequences, but they can provide additional useful linguistic information. Hence, it is possible to improve speech recognition performance by appropriately combining these two types of constraints. For the Chinese language considered in this paper, character level language models (LMs) can be used as a first level approximation to allowed syllable sequences. To test this idea, word and character level n-gram LMs were trained on 2.8 billion words (equivalent to 4.3 billion characters) of texts from a wide collection of text sources. Both hypothesis and model based combination techniques were investigated to combine word and character level LMs. Significant character error rate reductions up to 7.3% relative were obtained on a state-of-the-art Mandarin Chinese broadcast audio recognition task using an adapted history dependent multi-level LM that performs a log-linearly combination of character and word level LMs. This supports the hypothesis that character or syllable sequence models are useful for improving Mandarin speech recognition performance.

Discriminative spoken language understanding using word confusion networks

Current commercial dialogue systems typically use hand-crafted grammars for Spoken Language Understanding (SLU) operating on the top one or two hypotheses output by the speech recogniser. These systems are expensive to develop and they suffer from significant degradation in performance when faced with recognition errors. This paper presents a robust method for SLU based on features extracted from the full posterior distribution of recognition hypotheses encoded in the form of word confusion networks. Following [1], the system uses SVM classifiers operating on n-gram features, trained on unaligned input/output pairs. Performance is evaluated on both an off-line corpus and on-line in a live user trial. It is shown that a statistical discriminative approach to SLU operating on the full posterior ASR output distribution can substantially improve performance both in terms of accuracy and overall dialogue reward. Furthermore, additional gains can be obtained by incorporating features from the previous system output. © 2012 IEEE.

Characterization of vertical Mo/diamond Schottky barrier diode from non-ideal I-V and C-V measurements based on MIS model

In this study, we investigated non-ideal characteristics of a diamond Schottky barrier diode with Molybdenum (Mo) Schottky metal fabricated by Microwave Plasma Chemical Vapour Deposition (MPCVD) technique. Extraction from forward bias I-V and reverse bias C- 2-V measurements yields ideality factor of 1.3, Schottky barrier height of 1.872 eV, and on-resistance of 32.63 mö·cm2. The deviation of extracted Schottky barrier height from an ideal value of 2.24 eV (considering Mo workfunction of 4.53 eV) indicates Fermi level pinning at the interface. We attributed such non-ideal behavior to the existence of thin interfacial layer and interface states between metal and diamond which forms Metal-Interfacial layer-Semiconductor (MIS) structure. Oxygen surface treatment during fabrication process might have induced them. From forward bias C-V characteristics, the minimum thickness of the interfacial layer is approximately 0.248 nm. Energy distribution profile of the interface state density is then evaluated from the forward bias I-V characteristics based on the MIS model. The interface state density is found to be uniformly distributed with values around 1013 eV - 1·cm- 2. © 2013 Elsevier B.V.

Language model cross adaptation for LVCSR system combination

State-of-the-art large vocabulary continuous speech recognition (LVCSR) systems often combine outputs from multiple sub-systems that may even be developed at different sites. Cross system adaptation, in which model adaptation is performed using the outputs from another sub-system, can be used as an alternative to hypothesis level combination schemes such as ROVER. Normally cross adaptation is only performed on the acoustic models. However, there are many other levels in LVCSR systems' modelling hierarchy where complimentary features may be exploited, for example, the sub-word and the word level, to further improve cross adaptation based system combination. It is thus interesting to also cross adapt language models (LMs) to capture these additional useful features. In this paper cross adaptation is applied to three forms of language models, a multi-level LM that models both syllable and word sequences, a word level neural network LM, and the linear combination of the two. Significant error rate reductions of 4.0-7.1% relative were obtained over ROVER and acoustic model only cross adaptation when combining a range of Chinese LVCSR sub-systems used in the 2010 and 2011 DARPA GALE evaluations. © 2012 Elsevier Ltd. All rights reserved.

Paraphrastic language models

In natural languages multiple word sequences can represent the same underlying meaning. Only modelling the observed surface word sequence can result in poor context coverage, for example, when using n-gram language models (LM). To handle this issue, this paper presents a novel form of language model, the paraphrastic LM. A phrase level transduction model that is statistically learned from standard text data is used to generate paraphrase variants. LM probabilities are then estimated by maximizing their marginal probability. Significant error rate reductions of 0.5%-0.6% absolute were obtained on a state-ofthe-art conversational telephone speech recognition task using a paraphrastic multi-level LM modelling both word and phrase sequences.

Design and optimization of planar mesa termination for diamond Schottky barrier diodes

In this paper, we present planar mesa termination structure with high k dielectric Al2O3 for high-voltage diamond Schottky barrier diode. Analysis, design, and optimization are carried out by simulations using finite element technology computer-aided design (TCAD) Sentaurus Device software. The performances of planar mesa termination structure are compared to those of conventional field plate termination structure. It is found that optimum geometry of planar mesa terminated diode requires shorter metal plate extension (1/3 of the field plate terminated diode). Consequently, planar mesa terminated diode can be designed with bigger Schottky contact to increase its current carrying capability. Breakdown performance of field plate termination structure is limited at 1480 V due to peak electric field at the corner of Schottky contact (no oxide breakdown occurs). In contrast, peak electric field in planar mesa termination structure only occurs in the field oxide such that its breakdown performance is highly dependent on the oxide material. Due to Al2O3 breakdown, planar mesa termination structure suffers premature breakdown at 1440 V. Considering no oxide breakdown occurs, planar mesa termination structure can realize higher breakdown voltage of 1751 V. Therefore, to fully realize the potential of planar mesa terminated diode, it is important to choose suitable high k dielectric material with sufficient breakdown electric field for the field oxide. © 2013 Elsevier B.V.

Paraphrastic language models and combination with neural network language models

In natural languages multiple word sequences can represent the same underlying meaning. Only modelling the observed surface word sequence can result in poor context coverage, for example, when using n-gram language models (LM). To handle this issue, paraphrastic LMs were proposed in previous research and successfully applied to a US English conversational telephone speech transcription task. In order to exploit the complementary characteristics of paraphrastic LMs and neural network LMs (NNLM), the combination between the two is investigated in this paper. To investigate paraphrastic LMs' generalization ability to other languages, experiments are conducted on a Mandarin Chinese broadcast speech transcription task. Using a paraphrastic multi-level LM modelling both word and phrase sequences, significant error rate reductions of 0.9% absolute (9% relative) and 0.5% absolute (5% relative) were obtained over the baseline n-gram and NNLM systems respectively, after a combination with word and phrase level NNLMs. © 2013 IEEE.

Automatic construction and natural-language description of nonparametric regression models

Copyright © 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. This paper presents the beginnings of an automatic statistician, focusing on regression problems. Our system explores an open-ended space of statistical models to discover a good explanation of a data set, and then produces a detailed report with figures and natural- language text. Our approach treats unknown regression functions non- parametrically using Gaussian processes, which has two important consequences. First, Gaussian processes can model functions in terms of high-level properties (e.g. smoothness, trends, periodicity, changepoints). Taken together with the compositional structure of our language of models this allows us to automatically describe functions in simple terms. Second, the use of flexible nonparametric models and a rich language for composing them in an open-ended manner also results in state- of-the-art extrapolation performance evaluated over 13 real time series data sets from various domains.

Is UK Planning a Barrier to Energy Efficient Heritage Retrofit: A Comparative Analysis of a Selection of London Boroughs

This paper is part of a larger PhD research project examining the apparent conflict in UK planning between energy efficiency and conservation for the retrofit of the thermal envelope of the existing building stock. Review of the literature shows that the UK will not meet its 2050 emission reduction target without substantial improvement to the energy performance of the thermal envelope of the existing building stock and that significantly, 40% of the existing stock has heritage status and may be exempted from Building Regulations. A review of UK policy and legislation shows that there are clear national priorities towards reducing emissions and addressing climate change, yet also shows a movement towards local decision making and control. This paper compares the current status of thirteen London Boroughs in respect to their position on thermal envelope retrofit for heritage and traditionally constructed buildings. Data collection is through ongoing surveys and interviews that compare statistical data, planning policies, sustainability and environmental priorities, and Officer decision-making. This paper finds that there is a lack of consistency in application of planning policy across Boroughs and suggests that this is a barrier to the up-take of energy efficient retrofit. Various recommendations are suggested at both national and local level which could help UK planning and planning officers deliver more energy efficient heritage retrofits.

Unified form language: A domain-specific language for weak formulations of partial differential equations

We present the Unified Form Language (UFL), which is a domain-specific language for representing weak formulations of partial differential equations with a view to numerical approximation. Features of UFL include support for variational forms and functionals, automatic differentiation of forms and expressions, arbitrary function space hierarchies formultifield problems, general differential operators and flexible tensor algebra. With these features, UFL has been used to effortlessly express finite element methods for complex systems of partial differential equations in near-mathematical notation, resulting in compact, intuitive and readable programs. We present in this work the language and its construction. An implementation of UFL is freely available as an open-source software library. The library generates abstract syntax tree representations of variational problems, which are used by other software libraries to generate concrete low-level implementations. Some application examples are presented and libraries that support UFL are highlighted. © 2014 ACM.