Automatically extracting polarity-bearing topics for cross-domain sentiment classification

Joint sentiment-topic (JST) model was previously proposed to detect sentiment and topic simultaneously from text. The only supervision required by JST model learning is domain-independent polarity word priors. In this paper, we modify the JST model by incorporating word polarity priors through modifying the topic-word Dirichlet priors. We study the polarity-bearing topics extracted by JST and show that by augmenting the original feature space with polarity-bearing topics, the in-domain supervised classifiers learned from augmented feature representation achieve the state-of-the-art performance of 95% on the movie review data and an average of 90% on the multi-domain sentiment dataset. Furthermore, using feature augmentation and selection according to the information gain criteria for cross-domain sentiment classification, our proposed approach performs either better or comparably compared to previous approaches. Nevertheless, our approach is much simpler and does not require difficult parameter tuning.

Creating demand for better health by using social marketing techniques

The most significant environmental change to support people who want to give up smoking is the legislation to ban smoking in public places. Following Scotland in March 2006, and Wales and Northern Ireland in April 2007, England moves one step closer to being smoke free on 1 July 2007, when it becomes illegal to smoke in almost every enclosed public place and workplace. Social marketing will be used to support this health promoting policy and will become more prominent in the design of health promotion campaigns of the future. Social marketing is not a new approach to promoting health but its adoption by the Government does represent a paradigm shift in the challenge to change public opinion and social norms. As a result some behaviours, like smoking or excessive alcohol consumption, will no longer be socially acceptable. The Department of Health has decided that social marketing should be used in England to guide all future health promotion efforts directed at achieving behavioural goals. This paradigm shift was announced in Chapter 2 of the “Choosing health” White Paper with its emphasis on the consumer, noting that a wide range of lifestyle choices are marketed to people, although health as a commodity itself has not been marketed. The DoH has an internal social marketing development unit to integrate social marketing principles into its work and ensure that providers deliver. The National Centre for Social Marketing has funding to provide ongoing support, to build capacity and capability in the workforce. This article describes the distinguishing features of the social marketing approach. It seeks to answer some questions. Is this really a new idea, a paradigm shift, or simply a change in terminology? What do the marketing principles offer that is new, or are they merely familiar ideas repackaged in marketing jargon? Will these principles be more effective than current health promotion practice and, if so, how does it work? Finally, what are the implications for community pharmacy?

Simultaneous spatial and spectral transparency in ultralong fiber lasers

We demonstrate that ultralong Raman lasers can be used to generate a transmission medium with simultaneous transparency over the spatial and the spectral domains. Numerical calculations show this cross-domain transparency to be preserved when the medium is used for transmitting high-intensity signals, which makes ultralong lasers an ideal experimental test bed for the study of multifrequency nonlinear interactions in optical fiber waveguides. Full spatiospectral transparency is experimentally obtained over a 20 nm x 20 km window.

West Midlands English:Birmingham and the Black Country

This volume focuses on the closely allied yet differing linguistic varieties of Birmingham and its immediate neighbour to the west, the industrial heartland of the Black Country. Both of these areas rose to economic prominence and success during the Industrial Revolution, and both have suffered economically and socially as a result of post-war industrial decline. The industrial heritage of both areas has meant that tight knit and socially homogeneous individual areas in each region have demonstrated in many respects little linguistic change over time, and have continued to exhibit linguistic features, especially morphological constructions, peculiar to these areas or now restricted to these areas. At the same time, immigration from other areas of the British Isles over time, from Commonwealth countries and later from EU member states, together with increased social mobility, have meant that newly developing structures and more widespread UK linguistic phenomena have spread into these varieties. This volume provides a clear description of the structure of the linguistic varieties spoken in the two areas. Following the structure of the Dialects of English volumes, it provides: •A comprehensive overview of the phonological, grammatical and lexical structure of both varieties, as well as similarities between the two varieties and distinguishing features •Thorough discussion of the historical and social factors behind the development of the varieties and the stigma attached to these varieties •Discussion of the unusual situation of the Black Country as an area undefined in geographical and administrative terms, existing only in the imagination •Examples of the variety from native speakers of differing ethnicities, ages and genders •An annotated bibliography for further consultation

Challenges for expatriate managers:an exploration of cross-cultural management, adjustment, and training issues faced by Anglo-American managers in Hong Kong

This field work study furthers understanding about expatriate management, in particular, the nature of cross-cultural management in Hong Kong involving Anglo-American expatriate and Chinese host national managers, the important features of adjustment for expatriates living and working there, and the type of training which will assist them to adjust and to work successfully in this Asian environment. Qualitative and quantitative data on each issue was gathered during in-depth interviews in Hong Kong, using structured interview schedules, with 39 expatriate and 31 host national managers drawn from a cross-section of functional areas and organizations. Despite the adoption of Western technology and the influence of Western business practices, micro-level management in Hong Kong retains a cultural specificity which is consistent with the norms and values of Chinese culture. There are differences in how expatriates and host nationals define their social roles, and Hong Kong's recent colonial history appears to influence cross-cultural interpersonal interactions. The inability of the spouse and/or family to adapt to Hong Kong is identified as a major reason for expatriate assignments to fail, though the causes have less to do with living away from family and friends, than with Hong Kong's highly urbanized environment and the heavy demands of work. Culture shock is not identified as a major problem, but in Hong Kong micro-level social factors require greater adjustment than macro-level societal factors. The adjustment of expatriate managers is facilitated by a strong orientation towards career development and hard work, possession of technical/professional expertise, and a willingness to engage in a process of continuous 'active learning' with respect to the host national society and culture. A four-part model of manager training suitable for Hong Kong is derived from the study data. It consists of a pre-departure briefing, post-arrival cross-cultural training, language training in basic Cantonese and in how to communicate more effectively in English with non-native speakers, and the assignment of a mentor to newly arrived expatriate managers.

Functional split and crosslinking of the membrane domain of the β subunit of proton-translocating transhydrogenase from Escherichia coli

Proton pumping nicotinamide nucleotide transhydrogenase from Escherichia coli contains an α subunit with the NAD(H)-binding domain I and a β subunit with the NADP(H)-binding domain III. The membrane domain (domain II) harbors the proton channel and is made up of the hydrophobic parts of the α and β subunits. The interface in domain II between the α and the β subunits has previously been investigated by cross-linking loops connecting the four transmembrane helices in the α subunit and loops connecting the nine transmembrane helices in the β subunit. However, to investigate the organization of the nine transmembrane helices in the β subunit, a split was introduced by creating a stop codon in the loop connecting transmembrane helices 9 and 10 by a single mutagenesis step, utilizing an existing downstream start codon. The resulting enzyme was composed of the wild-type α subunit and the two new peptides β1 and β2. As compared to other split membrane proteins, the new transhydrogenase was remarkably active and catalyzed activities for the reduction of 3-acetylpyridine-NAD + by NADPH, the cyclic reduction of 3-acetylpyridine-NAD + by NADH (mediated by bound NADP(H)), and proton pumping, amounting to about 50-107% of the corresponding wild-type activities. These high activities suggest that the α subunit was normally folded, followed by a concerted folding of β1 + β2. Cross-linking of a βS105C-βS237C double cysteine mutant in the functional split cysteine-free background, followed by SDS-PAGE analysis, showed that helices 9, 13, and 14 were in close proximity. This is the first time that cross-linking between helices in the same β subunit has been demonstrated.

Cross-sectional studies in prescribing research

A cross-sectional study aims to describe the overall picture of a phenomenon, a situational problem, an attitude or an issue, by asking a cross-section of a given population at one specified moment in time. This paper describes the key features of the cross-sectional survey method. It begins by highlighting the main principles of the method, then discusses stages in the research process, drawing on two surveys of primary care pharmacists to illustrate some salient points about planning, sampling frames, definition and conceptual issues, research instrument design and response rates. Four constraints in prescribing studies were noted. First the newness of the subject meant a low basis of existing knowledge to design a questionnaire. Second, there was no public existing database for the sampling frame, so a pragmatic sampling exercise was used. Third, the definition of a Primary Care Pharmacist (PCP) [in full] and respondents recognition of that name and identification with the new role limited the response. Fourth, a growing problem for all surveys, but particularly with pharmacists and general practitioners (GP) [in full] is the growing danger of survey fatigue, which has a negative impact on response levels.

Organizing innovation: complementarities between cross-functional teams

Cross-functional teams play a potentially important part in the innovation process enabling knowledge sharing, the development of trust and overcoming spatial and organizational barriers. Using a supermodularity approach, we focus on potential complementarities which may arise when cross-functional teams are used in different elements of the innovation process in UK and German manufacturing plants. Using optimal combinations of cross-functional teams in the innovation process increases innovation success in the UK by 29.5 per cent compared to 9.5 per cent in Germany. Patterns of complementarity are complex, however, but are more uniform in the UK than in Germany. The most uniform complementarities are between product design and development and production engineering, with little synergy evident between the more technical phases of the innovation process and the development of marketing strategy. In strategic terms, our results suggest the value of using cross-functional teams for the more technical elements of the innovation process but that the development of marketing strategy should remain the domain of specialists.

All-optical processing using cross-phase modulation

The following thesis presents results obtained from both numerical simulation and laboratory experimentation (both of which were carried out by the author). When data is propagated along an optical transmission line some timing irregularities can occur such as timing jitter and phase wander. Traditionally these timing problems would have been corrected by converting the optical signal into the electrical domain and then compensating for the timing irregularity before converting the signal back into the optical domain. However, this thesis posses a potential solution to the problem by remaining completely in the optical domain, eliminating the need for electronics. This is desirable as not only does optical processing reduce the latency effect that their electronic counterpart have, it also holds the possibility of an increase in overall speed. A scheme was proposed which utilises the principle of wavelength conversion to dynamically convert timing irregularities (timing jitter and phase wander) into a change in wavelength (this occurs on a bit-by-bit level and so timing jitter and phase wander can be compensated for simultaneously). This was achieved by optically sampling a linearly chirped, locally generated clock source (the sampling function was achieved using a nonlinear optical loop mirror). The data, now with each bit or code word having a unique wavelength, is then propagated through a dispersion compensation module. The dispersion compensation effectively re-aligns the data in time and so thus, the timing irregularities are removed. The principle of operation was tested using computer simulation before being re-tested in a laboratory environment. A second stage was added to the device to create 3R regeneration. The second stage is used to simply convert the timing suppressed data back into a single wavelength. By controlling the relative timing displacement between stage one and stage two, the wavelength that is finally produced can be controlled.

Self-training from labeled features for sentiment analysis

Sentiment analysis concerns about automatically identifying sentiment or opinion expressed in a given piece of text. Most prior work either use prior lexical knowledge defined as sentiment polarity of words or view the task as a text classification problem and rely on labeled corpora to train a sentiment classifier. While lexicon-based approaches do not adapt well to different domains, corpus-based approaches require expensive manual annotation effort. In this paper, we propose a novel framework where an initial classifier is learned by incorporating prior information extracted from an existing sentiment lexicon with preferences on expectations of sentiment labels of those lexicon words being expressed using generalized expectation criteria. Documents classified with high confidence are then used as pseudo-labeled examples for automatical domain-specific feature acquisition. The word-class distributions of such self-learned features are estimated from the pseudo-labeled examples and are used to train another classifier by constraining the model's predictions on unlabeled instances. Experiments on both the movie-review data and the multi-domain sentiment dataset show that our approach attains comparable or better performance than existing weakly-supervised sentiment classification methods despite using no labeled documents.

Learning sentiment classification model from labeled features

We propose a novel framework where an initial classifier is learned by incorporating prior information extracted from an existing sentiment lexicon. Preferences on expectations of sentiment labels of those lexicon words are expressed using generalized expectation criteria. Documents classified with high confidence are then used as pseudo-labeled examples for automatical domain-specific feature acquisition. The word-class distributions of such self-learned features are estimated from the pseudo-labeled examples and are used to train another classifier by constraining the model's predictions on unlabeled instances. Experiments on both the movie review data and the multi-domain sentiment dataset show that our approach attains comparable or better performance than exiting weakly-supervised sentiment classification methods despite using no labeled documents.

Organizing innovation:complementarities between cross-functional teams

Cross-functional teams play a potentially important part in the innovation process enabling knowledge sharing, the development of trust and overcoming spatial and organizational barriers. Using a supermodularity approach, we focus on potential complementarities which may arise when cross-functional teams are used in different elements of the innovation process in UK and German manufacturing plants. Using optimal combinations of cross-functional teams in the innovation process increases innovation success in the UK by 29.5 per cent compared to 9.5 per cent in Germany. Patterns of complementarity are complex, however, but are more uniform in the UK than in Germany. The most uniform complementarities are between product design and development and production engineering, with little synergy evident between the more technical phases of the innovation process and the development of marketing strategy. In strategic terms, our results suggest the value of using cross-functional teams for the more technical elements of the innovation process but that the development of marketing strategy should remain the domain of specialists.

Surface catalysed Suzuki-Miyaura cross-coupling by Pd nanoparticles:an operando XAS study

Size-controlled, catalytically active PVP-stabilised Pd nanoparticles have been studied by operando liquid phase XAS during the Suzuki cross-coupling of iodonanisole and phenylboronic acid in MeOH-toluene using KOMe base. XAS reveals nanoparticles are stable to metal leaching throughout the reaction, with surface density Pd defect sites directly implicated in the catalytic cycle. The efficacy of popular selective chemical and structural poisons for distinguishing heterogeneous and homogeneous contributions in Pd catalysed cross-couplings is also explored. © 2010 The Royal Society of Chemistry.

No-reference image quality assessment in contourlet domain

The target of no-reference (NR) image quality assessment (IQA) is to establish a computational model to predict the visual quality of an image. The existing prominent method is based on natural scene statistics (NSS). It uses the joint and marginal distributions of wavelet coefficients for IQA. However, this method is only applicable to JPEG2000 compressed images. Since the wavelet transform fails to capture the directional information of images, an improved NSS model is established by contourlets. In this paper, the contourlet transform is utilized to NSS of images, and then the relationship of contourlet coefficients is represented by the joint distribution. The statistics of contourlet coefficients are applicable to indicate variation of image quality. In addition, an image-dependent threshold is adopted to reduce the effect of content to the statistical model. Finally, image quality can be evaluated by combining the extracted features in each subband nonlinearly. Our algorithm is trained and tested on the LIVE database II. Experimental results demonstrate that the proposed algorithm is superior to the conventional NSS model and can be applied to different distortions. © 2009 Elsevier B.V. All rights reserved.

Event trigger identification for biomedical events extraction using domain knowledge

Motivation: In molecular biology, molecular events describe observable alterations of biomolecules, such as binding of proteins or RNA production. These events might be responsible for drug reactions or development of certain diseases. As such, biomedical event extraction, the process of automatically detecting description of molecular interactions in research articles, attracted substantial research interest recently. Event trigger identification, detecting the words describing the event types, is a crucial and prerequisite step in the pipeline process of biomedical event extraction. Taking the event types as classes, event trigger identification can be viewed as a classification task. For each word in a sentence, a trained classifier predicts whether the word corresponds to an event type and which event type based on the context features. Therefore, a well-designed feature set with a good level of discrimination and generalization is crucial for the performance of event trigger identification. Results: In this article, we propose a novel framework for event trigger identification. In particular, we learn biomedical domain knowledge from a large text corpus built from Medline and embed it into word features using neural language modeling. The embedded features are then combined with the syntactic and semantic context features using the multiple kernel learning method. The combined feature set is used for training the event trigger classifier. Experimental results on the golden standard corpus show that >2.5% improvement on F-score is achieved by the proposed framework when compared with the state-of-the-art approach, demonstrating the effectiveness of the proposed framework. © 2014 The Author 2014. The source code for the proposed framework is freely available and can be downloaded at http://cse.seu.edu.cn/people/zhoudeyu/ETI_Sourcecode.zip.