Learning task specific distributed paragraph representations using a 2-tier convolutional neural network

We introduce a type of 2-tier convolutional neural network model for learning distributed paragraph representations for a special task (e.g. paragraph or short document level sentiment analysis and text topic categorization). We decompose the paragraph semantics into 3 cascaded constitutes: word representation, sentence composition and document composition. Specifically, we learn distributed word representations by a continuous bag-of-words model from a large unstructured text corpus. Then, using these word representations as pre-trained vectors, distributed task specific sentence representations are learned from a sentence level corpus with task-specific labels by the first tier of our model. Using these sentence representations as distributed paragraph representation vectors, distributed paragraph representations are learned from a paragraph-level corpus by the second tier of our model. It is evaluated on DBpedia ontology classification dataset and Amazon review dataset. Empirical results show the effectiveness of our proposed learning model for generating distributed paragraph representations.

Quantifying short-term dynamics of Parkinson's disease using self-reported symptom data from an internet social network

Background: Parkinson’s disease (PD) is an incurable neurological disease with approximately 0.3% prevalence. The hallmark symptom is gradual movement deterioration. Current scientific consensus about disease progression holds that symptoms will worsen smoothly over time unless treated. Accurate information about symptom dynamics is of critical importance to patients, caregivers, and the scientific community for the design of new treatments, clinical decision making, and individual disease management. Long-term studies characterize the typical time course of the disease as an early linear progression gradually reaching a plateau in later stages. However, symptom dynamics over durations of days to weeks remains unquantified. Currently, there is a scarcity of objective clinical information about symptom dynamics at intervals shorter than 3 months stretching over several years, but Internet-based patient self-report platforms may change this. Objective: To assess the clinical value of online self-reported PD symptom data recorded by users of the health-focused Internet social research platform PatientsLikeMe (PLM), in which patients quantify their symptoms on a regular basis on a subset of the Unified Parkinson’s Disease Ratings Scale (UPDRS). By analyzing this data, we aim for a scientific window on the nature of symptom dynamics for assessment intervals shorter than 3 months over durations of several years. Methods: Online self-reported data was validated against the gold standard Parkinson’s Disease Data and Organizing Center (PD-DOC) database, containing clinical symptom data at intervals greater than 3 months. The data were compared visually using quantile-quantile plots, and numerically using the Kolmogorov-Smirnov test. By using a simple piecewise linear trend estimation algorithm, the PLM data was smoothed to separate random fluctuations from continuous symptom dynamics. Subtracting the trends from the original data revealed random fluctuations in symptom severity. The average magnitude of fluctuations versus time since diagnosis was modeled by using a gamma generalized linear model. Results: Distributions of ages at diagnosis and UPDRS in the PLM and PD-DOC databases were broadly consistent. The PLM patients were systematically younger than the PD-DOC patients and showed increased symptom severity in the PD off state. The average fluctuation in symptoms (UPDRS Parts I and II) was 2.6 points at the time of diagnosis, rising to 5.9 points 16 years after diagnosis. This fluctuation exceeds the estimated minimal and moderate clinically important differences, respectively. Not all patients conformed to the current clinical picture of gradual, smooth changes: many patients had regimes where symptom severity varied in an unpredictable manner, or underwent large rapid changes in an otherwise more stable progression. Conclusions: This information about short-term PD symptom dynamics contributes new scientific understanding about the disease progression, currently very costly to obtain without self-administered Internet-based reporting. This understanding should have implications for the optimization of clinical trials into new treatments and for the choice of treatment decision timescales.

Racing for radical innovation:how motorsport companies harness network diversity for discontinuous innovation

The motorsport industry is a significant part of the UK economy. According to industry estimates approximately 4,500 companies are involved in the UK Motorsport and Performance Engineering Industry and its wide-ranging support activities. The industry has an annual turnover of £6.0 billion, and contributes £3.6 billion worth of exports. The Motorsport Industry Association estimates that the support side of the sector alone "involving events management, public relations, marketing, sponsorship and a host of other support functions" accounts for approximately £1.7 billion of the yearly industry total. And in terms of employment, UK Motorsport supports 38,500 full and part-time jobs, including 25,000 engineers.

Communities and emerging semantics in semantic link network:discovery and learning

The World Wide Web provides plentiful contents for Web-based learning, but its hyperlink-based architecture connects Web resources for browsing freely rather than for effective learning. To support effective learning, an e-learning system should be able to discover and make use of the semantic communities and the emerging semantic relations in a dynamic complex network of learning resources. Previous graph-based community discovery approaches are limited in ability to discover semantic communities. This paper first suggests the Semantic Link Network (SLN), a loosely coupled semantic data model that can semantically link resources and derive out implicit semantic links according to a set of relational reasoning rules. By studying the intrinsic relationship between semantic communities and the semantic space of SLN, approaches to discovering reasoning-constraint, rule-constraint, and classification-constraint semantic communities are proposed. Further, the approaches, principles, and strategies for discovering emerging semantics in dynamic SLNs are studied. The basic laws of the semantic link network motion are revealed for the first time. An e-learning environment incorporating the proposed approaches, principles, and strategies to support effective discovery and learning is suggested.

A simulation study of service differentiation for the bandwidth request scheme in fixed IEEE 802.16 network

Various flexible mechanisms related to quality of service (QoS) provisioning have been specified for uplink traffic at the medium access control (MAC) layer in the IEEE 802.16 standards. Among the mechanisms, contention based bandwidth request scheme can be used to indicate bandwidth demands to the base station for the non-real-time polling and best-effort services. These two services are used for most applications with unknown traffic characteristics. Due to the diverse QoS requirements of those applications, service differentiation (SD) is anticipated over the contention based bandwidth request scheme. In this paper we investigate the SD with the bandwidth request scheme by means of assigning different channel access parameters and bandwidth allocation priorities at different packets arrival probability. The effectiveness of the differentiation schemes is evaluated by simulations. It is observed that the initial backoff window can be efficient in SD, and if combined with the bandwidth allocation priority, the SD performances will be better.

Reconfiguration of supply chain network:an ISM-based roadmap to performance

Purpose: Short product life cycle and/or mass customization necessitate reconfiguration of operational enablers of supply chain (SC) from time to time in order to harness high levels of performance. The purpose of this paper is to identify the key operational enablers under stochastic environment on which practitioner should focus while reconfiguring a SC network. Design/methodology/approach: The paper used interpretive structural modeling (ISM) approach that presents a hierarchy-based model and the mutual relationships among the enablers. The contextual relationship needed for developing structural self-interaction matrix (SSIM) among various enablers is realized by conducting experiments through simulation of a hypothetical SC network. Findings: The research identifies various operational enablers having a high driving power towards assumed performance measures. In this regard, these enablers require maximum attention and of strategic importance while reconfiguring SC. Practical implications: ISM provides a useful tool to the SC managers to strategically adopt and focus on the key enablers which have comparatively greater potential in enhancing the SC performance under given operational settings. Originality/value: The present research realizes the importance of SC flexibility under the premise of reconfiguration of the operational units in order to harness high value of SC performance. Given the resulting digraph through ISM, the decision maker can focus the key enablers for effective reconfiguration. The study is one of the first efforts that develop contextual relations among operational enablers for SSIM matrix through integration of discrete event simulation to ISM. © Emerald Group Publishing Limited.

The mammalian phosphatidylinositol 3-phosphate 5-kinase (PIKfyve) regulates endosome-to-TGN retrograde transport

The yeast gene fab1 and its mammalian orthologue Pip5k3 encode the phosphatidylinositol 3-phosphate [PtdIns(3)P] 5-kinases Fab1p and PIKfyve, respectively, enzymes that generates phosphatidylinositol 3,5-bisphosphate [PtdIns(3,5)P(2)]. A shared feature of fab1Delta yeast cells and mammalian cells overexpressing a kinase-dead PIKfyve mutant is the formation of a swollen vacuolar phenotype: a phenotype that is suggestive of a conserved function for these enzymes and their product, PtdIns(3,5)P(2), in the regulation of endomembrane homeostasis. In the current study, fixed and live cell imaging has established that, when overexpressed at low levels in HeLa cells, PIKfyve is predominantly associated with dynamic tubular and vesicular elements of the early endosomal compartment. Moreover, through the use of small interfering RNA, it has been shown that suppression of PIKfyve induces the formation of swollen endosomal structures that maintain their early and late endosomal identity. Although internalisation, recycling and degradative sorting of receptors for epidermal growth factor and transferrin was unperturbed in PIKfyve suppressed cells, a clear defect in endosome to trans-Golgi-network (TGN) retrograde traffic was observed. These data argue that PIKfyve is predominantly associated with the early endosome, from where it regulates retrograde membrane trafficking to the TGN. It follows that the swollen endosomal phenotype observed in PIKfyve-suppressed cells results primarily from a reduction in retrograde membrane fission rather than a defect in multivesicular body biogenesis.

Contradictory reasoning network:an EEG and FMRI study

Contradiction is a cornerstone of human rationality, essential for everyday life and communication. We investigated electroencephalographic (EEG) and functional magnetic resonance imaging (fMRI) in separate recording sessions during contradictory judgments, using a logical structure based on categorical propositions of the Aristotelian Square of Opposition (ASoO). The use of ASoO propositions, while controlling for potential linguistic or semantic confounds, enabled us to observe the spatial temporal unfolding of this contradictory reasoning. The processing started with the inversion of the logical operators corresponding to right middle frontal gyrus (rMFG-BA11) activation, followed by identification of contradictory statement associated with in the right inferior frontal gyrus (rIFG-BA47) activation. Right medial frontal gyrus (rMeFG, BA10) and anterior cingulate cortex (ACC, BA32) contributed to the later stages of process. We observed a correlation between the delayed latency of rBA11 response and the reaction time delay during inductive vs. deductive reasoning. This supports the notion that rBA11 is crucial for manipulating the logical operators. Slower processing time and stronger brain responses for inductive logic suggested that examples are easier to process than general principles and are more likely to simplify communication. © 2014 Porcaro et al.

Developing user involvement in a UK cancer network:professionals’ and users’ perspectives

This paper explores the results of a consensus development exercise that explored diverse perspectives and sought to identify key principles for the development of user involvement in a cancer network. The exercise took place within one of 34 UK cancer networks and was a collaboration between the NHS, two universities and two voluntary sector organizations. The paper explores professionals’ and users’ perspectives on user involvement with reference to the current sociopolitical context of user participation. British policy documents have placed increasing emphasis on issues of patient and public participation in the evaluation and development of health services, and the issue of lay participation represents an important aspect of a critical public health agenda. The project presented here shows that developing user involvement may be a complex task, with lack of consensus on key issues representing a significant barrier. Further, the data suggest that professional responses can partly be understood in relation to specific occupational standpoints and strategies that potentially allow professionals to define and limit users’ involvement. The implications of these findings and the impact of the consensus development process itself are discussed.

Managing supply networks:organizational roles in network management

This paper describes research findings on the roles that organizations can adopt in managing supply networks. Drawing on extensive empirical data, it is demonstrated that organizations may be said to be able to manage supply networks, provided a broad view of ‘managing’ is adopted. Applying role theory, supply network management interventions were clustered into sets of linked activities and goals that constituted supply network management roles. Six supply network management roles were identified – innovation facilitator, co-ordinator, supply policy maker and implementer, advisor, information broker and supply network structuring agent. The findings are positioned in the wider context of debates about the meaning of management, the contribution of role theory to our understanding of management, and whether inter-organizational networks can be managed.

Network learning:an empirically-derived conceptual model of learning by groups of organizations

Building on a previous conceptual article, we present an empirically derived model of network learning - learning by a group of organizations as a group. Based on a qualitative, longitudinal, multiple-method empirical investigation, five episodes of network learning were identified. Treating each episode as a discrete analytic case, through cross-case comparison, a model of network learning is developed which reflects the common, critical features of the episodes. The model comprises three conceptual themes relating to learning outcomes, and three conceptual themes of learning process. Although closely related to conceptualizations that emphasize the social and political character of organizational learning, the model of network learning is derived from, and specifically for, more extensive networks in which relations among numerous actors may be arms-length or collaborative, and may be expected to change over time.

Exploring chain, network and cluster collaborative practices:implications for SMEs

Collaboration among enterprises has been rendered as one of the most important issues in the business agenda, either as a result of the globalisation and deregulation of markets or as a result of the Information and Communication Technology (ICT) revolution. Both factors have created a business reality where success in the collaboration practices followed, may result in improvements in the competitive position of enterprises. This paper starts from the basic business activity of the individual enterprise, looks into the chain, network and cluster collaborative practices and analyses their characteristics and the implications for Small-Medium Enterprises (SMEs). In addition, it provides insights regarding the opportunities, benefits, requirements and risks related to each collaborative practice. This paper finally argues that different collaboration practices are required, as enterprises and the industrial sectors where they operate, present distinctive characteristics.

Neural network DEA for measuring the efficiency of mutual funds

Efficiency in the mutual fund (MF), is one of the issues that has attracted many investors in countries with advanced financial market for many years. Due to the need for frequent study of MF's efficiency in short-term periods, investors need a method that not only has high accuracy, but also high speed. Data envelopment analysis (DEA) is proven to be one of the most widely used methods in the measurement of the efficiency and productivity of decision making units (DMUs). DEA for a large dataset with many inputs/outputs would require huge computer resources in terms of memory and CPU time. This paper uses neural network back-ropagation DEA in measurement of mutual funds efficiency and shows the requirements, in the proposed method, for computer memory and CPU time are far less than that needed by conventional DEA methods and can therefore be a useful tool in measuring the efficiency of a large set of MFs. Copyright © 2014 Inderscience Enterprises Ltd.

Topological versus chemical ordering in network glasses at intermediate and extended length scales

Atomic ordering in network glasses on length scales longer than nearest-neighbour length scales has long been a source of controversy(1-6). Detailed experimental information is therefore necessary to understand both the network properties and the fundamentals of glass formation. Here we address the problem by investigating topological and chemical ordering in structurally disordered AX2 systems by applying the method of isotopic substitution in neutron diffraction to glassy ZnCl2. This system may be regarded as a prototypical ionic network forming glass, provided that ion polarization effects are taken into account(7), and has thus been the focus of much attention(8-14). By experiment, we show that both the topological and chemical ordering are described by two length scales at distances greater than nearest-neighbour length scales. One of these is associated with the intermediate range, as manifested by the appearance in the measured diffraction patterns of a first sharp diffraction peak at 1.09( 3) angstrom(-1); the other is associated with an extended range, which shows ordering in the glass out to 62( 4) angstrom. We also find that these general features are characteristic of glassy GeSe2, a prototypical covalently bonded network material(15,16). The results therefore offer structural insight into those length scales that determine many important aspects of supercooled liquid and glass phenomenology(11).

Large-scale molecular dynamics simulations of HLA-A*0201 complexed with a tumor-specific antigenic peptide:can the α3 and β2m domains be neglected?

Large-scale massively parallel molecular dynamics (MD) simulations of the human class I major histo-compatibility complex (MHC) protein HLA-A*0201 bound to a decameric tumor-specific antigenic peptide GVY-DGREHTV were performed using a scalable MD code on high-performance computing platforms. Such computational capabilities put us in reach of simulations of various scales and complexities. The supercomputing resources available Large-scale massively parallel molecular dynamics (MD) simulations of the human class I major histocompatibility complex (MHC) protein HLA-A*0201 bound to a decameric tumor-specific antigenic peptide GVYDGREHTV were performed using a scalable MD code on high-performance computing platforms. Such computational capabilities put us in reach of simulations of various scales and complexities. The supercomputing resources available for this study allow us to compare directly differences in the behavior of very large molecular models; in this case, the entire extracellular portion of the peptide–MHC complex vs. the isolated peptide binding domain. Comparison of the results from the partial and the whole system simulations indicates that the peptide is less tightly bound in the partial system than in the whole system. From a detailed study of conformations, solvent-accessible surface area, the nature of the water network structure, and the binding energies, we conclude that, when considering the conformation of the α1–α2 domain, the α3 and β2m domains cannot be neglected. © 2004 Wiley Periodicals, Inc. J Comput Chem 25: 1803–1813, 2004