Dublin City University

About: Dublin City University is a education organization based out in Dublin, Ireland. It is known for research contribution in the topics: Machine translation & Laser. The organization has 5904 authors who have published 17178 publications receiving 389376 citations. The organization is also known as: National Institute for Higher Education, Dublin & DCU.

Distributed scheduling scheme for video streaming over multi-channel multi-radio multi-hop wireless networks

Abstract: An important issue of supporting multi-user video streaming over wireless networks is how to optimize the systematic scheduling by intelligently utilizing the available network resources while, at the same time, to meet each video's Quality of Service (QoS) requirement. In this work, we study the problem of video streaming over multi-channel multi-radio multihop wireless networks, and develop fully distributed scheduling schemes with the goals of minimizing the video distortion and achieving certain fairness. We first construct a general distortion model according to the network?s transmission mechanism, as well as the rate distortion characteristics of the video. Then, we formulate the scheduling as a convex optimization problem, and propose a distributed solution by jointly considering channel assignment, rate allocation, and routing. Specifically, each stream strikes a balance between the selfish motivation of minimizing video distortion and the global performance of minimizing network congestions. Furthermore, we extend the proposed scheduling scheme by addressing the fairness problem. Unlike prior works that target at users' bandwidth or demand fairness, we propose a media-aware distortion-fairness strategy which is aware of the characteristics of video frames and ensures max-min distortion-fairness sharing among multiple video streams. We provide extensive simulation results which demonstrate the effectiveness of our proposed schemes.

The Trouble with Transparency: A Critical Review of Openness in e-Government

Abstract: Transparency in public administration is generally held to be desirable, something to be fostered and enabled. This long standing idea has gained considerable further momentum with the emergence of e-government and the affordances of computing in general and the Internet in particular. This paper examines the argument that transparency may, in certain and not uncommon circumstances, be inimical to good government and good governance and suggests that the importance of understanding why this is so has increased as information and communications technology permeates government and society. It suggests that in an electronic age, the scope and nature of transparency needs to be carefully managed, and that expectations of the benefits of ICT enabled transparency may be too high.

Word Embedding based Generalized Language Model for Information Retrieval

Abstract: Word2vec, a state-of-the-art word embedding technique has gained a lot of interest in the NLP community. The embedding of the word vectors helps to retrieve a list of words that are used in similar contexts with respect to a given word. In this paper, we focus on using the word embeddings for enhancing retrieval effectiveness. In particular, we construct a generalized language model, where the mutual independence between a pair of words (say t and t') no longer holds. Instead, we make use of the vector embeddings of the words to derive the transformation probabilities between words. Specifically, the event of observing a term t in the query from a document d is modeled by two distinct events, that of generating a different term t', either from the document itself or from the collection, respectively, and then eventually transforming it to the observed query term t. The first event of generating an intermediate term from the document intends to capture how well does a term contextually fit within a document, whereas the second one of generating it from the collection aims to address the vocabulary mismatch problem by taking into account other related terms in the collection. Our experiments, conducted on the standard TREC collection, show that our proposed method yields significant improvements over LM and LDA-smoothed LM baselines.

Synaptobrevin I mediates exocytosis of CGRP from sensory neurons and inhibition by botulinum toxins reflects their anti-nociceptive potential

Abstract: Calcitonin-gene-related peptide (CGRP), a potent vasodilator that mediates inflammatory pain, is elevated in migraine; nevertheless, little is known about its release from sensory neurons. In this study, CGRP was found to occur in the majority of neurons from rat trigeminal ganglia, together with the three exocytotic SNAREs [SNAP25, syntaxin 1 and the synaptobrevin (Sbr, also known as VAMP) isoforms] and synaptotagmin. Ca(2+)-dependent CGRP release was evoked with K(+)-depolarisation and, to lower levels, by capsaicin or bradykinin from neurons that contain the vanilloid receptor 1 and/or bradykinin receptor 2. Botulinum neurotoxin (BoNT) type A cleaved SNAP25 and inhibited release triggered by K(+) > bradykinin >> capsaicin. Unlike BoNT type D, BoNT type B did not affect exocytosis, even though the neurons possess its receptor and Sbr II and Sbr III got proteolysed (I is resistant in rat) but, in mouse neurons, it additionally cleaved Sbr I and blocked transmitter release. Sbr I and II were found in CGRP-containing vesicles, and each was shown to separately form a SNARE complex. These new findings, together with punctate staining of Sbr I and CGRP in neurites, implicate isoform Sbr I in exocytosis from large dense-core vesicles together with SNAP25 (also, probably, syntaxin 1 because BoNT type C1 caused partial cleavage and inhibition); this differs from Sbr-II-dependent release of transmitters from small synaptic vesicles. Such use of particular Sbr isoform(s) by different neurons raises the functional implications for other cells previously unrecognised.