scholarly journals A corpus-driven approach to discourse organisation: from cues to complex markers

2017 ◽  
Vol 8 (1) ◽  
pp. 66-105
Author(s):  
Marie-Paule Péry-Woodley ◽  
Lydia-Mai Ho-Dac ◽  
Josette Rebeyrolle ◽  
Ludovic Tanguy ◽  
C`ecile Fabre
Keyword(s):  
Large Scale ◽  
Discourse Markers ◽  
Data Intensive ◽  
Multi Level ◽  
Mining Methods ◽  
The Rich ◽  
Intensive Approach ◽  
Rich Data ◽  
Descriptive Survey ◽  
Oriented Approach

This paper reports on an experiment implementing a data-intensive approach to discourse organisation. Its focus is on enumerative structures envisaged as a type of textual pattern in a sequentiality-oriented approach to discourse. On the basis of a large-scale annotation exercise calling upon automatic feature mark-up alongside manual annotation, we explore a method to identify complex discourse markers seen as configurations of cues. The presentation of the background to what is termed "multi-level annotation" is organised around four issues: linearity, complexity of discourse markers, top-down processing, granularity and the multi-level nature of discourse structures. In this context, enumerative structures seem to deserve scrutiny for a number of reasons: they are frequent structures appearing at different granularity levels, they are signalled by a variety of devices appearing to work together in complex ways, and they combine a textual role (discourse organisation) with an ideational role (categorisation). We describe the annotation procedure and experimental framework which resulted in nearly 1,000 enumerative structures being annotated in a diversified corpus of over 600,000 words. The results of two approaches to the rich data produced are then presented: firstly, a descriptive survey highlights considerable variation in length and composition, while showing enumerative structure to be a basic strategy resorted to in all three sub-corpora, and leads to a granularity-based typology of the annotated structures; secondly, recurrent cue configurations---our "complex~ markers"---are identified by the application of data mining methods. The paper ends with perspectives for further exploitation of the data, in particular with respect to the semantic characterisation of enumerative structures.

The three paradoxes of the energy transition - Assessing sustainability of large-scale solar photovoltaic through multi-level and multi-scalar perspective in Rwanda

2021 ◽  
Vol 288 ◽  
pp. 125519
Author(s):  
Carole Brunet ◽  
Oumarou Savadogo ◽  
Pierre Baptiste ◽  
Michel A. Bouchard ◽  
Céline Cholez ◽  
...  
Keyword(s):  
Large Scale ◽  
Energy Transition ◽  
Solar Photovoltaic ◽  
Multi Level

scholarly journals Mean-Field Models for EEG/MEG: From Oscillations to Waves

2021 ◽  
Author(s):  
Áine Byrne ◽  
James Ross ◽  
Rachel Nicks ◽  
Stephen Coombes
Keyword(s):  
Gap Junction ◽  
Large Scale ◽  
Mean Field ◽  
Coarse Grained ◽  
Neural Mass Model ◽  
Neuron Network ◽  
Mass Model ◽  
Mean Field Model ◽  
Neural Mass ◽  
The Rich

AbstractNeural mass models have been used since the 1970s to model the coarse-grained activity of large populations of neurons. They have proven especially fruitful for understanding brain rhythms. However, although motivated by neurobiological considerations they are phenomenological in nature, and cannot hope to recreate some of the rich repertoire of responses seen in real neuronal tissue. Here we consider a simple spiking neuron network model that has recently been shown to admit an exact mean-field description for both synaptic and gap-junction interactions. The mean-field model takes a similar form to a standard neural mass model, with an additional dynamical equation to describe the evolution of within-population synchrony. As well as reviewing the origins of this next generation mass model we discuss its extension to describe an idealised spatially extended planar cortex. To emphasise the usefulness of this model for EEG/MEG modelling we show how it can be used to uncover the role of local gap-junction coupling in shaping large scale synaptic waves.

scholarly journals A new multi-level algorithm for balanced partition problem on large scale directed graphs

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Xianyue Li ◽  
Yufei Pang ◽  
Chenxia Zhao ◽  
Yang Liu ◽  
Qingzhen Dong
Keyword(s):  
Large Scale ◽  
Directed Graphs ◽  
Vlsi Design ◽  
Graph Partition ◽  
Partition Problem ◽  
Multi Level ◽  
The Stability ◽  
Recursive Partition ◽  
Balanced Partition ◽  
Partition Method

AbstractGraph partition is a classical combinatorial optimization and graph theory problem, and it has a lot of applications, such as scientific computing, VLSI design and clustering etc. In this paper, we study the partition problem on large scale directed graphs under a new objective function, a new instance of graph partition problem. We firstly propose the modeling of this problem, then design an algorithm based on multi-level strategy and recursive partition method, and finally do a lot of simulation experiments. The experimental results verify the stability of our algorithm and show that our algorithm has the same good performance as METIS. In addition, our algorithm is better than METIS on unbalanced ratio.

scholarly journals A Proximal Algorithm for Fork-Choice in Distributed Ledger Technology for Context-Based Clustering on Edge Computing

2020 ◽  
Vol 2 (1) ◽  
pp. 92
Author(s):  
Rahim Rahmani ◽  
Ramin Firouzi ◽  
Sachiko Lim ◽  
Mahbub Alam
Keyword(s):  
Large Scale ◽  
Cluster Head ◽  
Edge Computing ◽  
Computing Technology ◽  
Proximal Algorithm ◽  
Data Intensive ◽  
Distributed Ledger ◽  
Smart Contract ◽  
Distributed Ledger Technology ◽  
Iot Devices

The major challenges of operating data-intensive of Distributed Ledger Technology (DLT) are (1) to reach consensus on the main chain as a set of validators cast public votes to decide on which blocks to finalize and (2) scalability on how to increase the number of chains which will be running in parallel. In this paper, we introduce a new proximal algorithm that scales DLT in a large-scale Internet of Things (IoT) devices network. We discuss how the algorithm benefits the integrating DLT in IoT by using edge computing technology, taking the scalability and heterogeneous capability of IoT devices into consideration. IoT devices are clustered dynamically into groups based on proximity context information. A cluster head is used to bridge the IoT devices with the DLT network where a smart contract is deployed. In this way, the security of the IoT is improved and the scalability and latency are solved. We elaborate on our mechanism and discuss issues that should be considered and implemented when using the proposed algorithm, we even show how it behaves with varying parameters like latency or when clustering.

Large scale gene regulatory network inference with a multi-level strategy

2016 ◽  
Vol 12 (2) ◽  
pp. 588-597 ◽  
Author(s):  
Jun Wu ◽  
Xiaodong Zhao ◽  
Zongli Lin ◽  
Zhifeng Shao
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Large Scale ◽  
Network Inference ◽  
Biological Processes ◽  
Molecular Processes ◽  
Gene Regulatory Network Inference ◽  
Cell Functions ◽  
Multi Level ◽  
Gene Regulatory

Transcriptional regulation is a basis of many crucial molecular processes and an accurate inference of the gene regulatory network is a helpful and essential task to understand cell functions and gain insights into biological processes of interest in systems biology.

scholarly journals Multi-purpose, multi-level feature modeling of large-scale industrial software systems

2016 ◽  
Vol 17 (3) ◽  
pp. 913-938 ◽  
Author(s):  
Daniela Rabiser ◽  
Herbert Prähofer ◽  
Paul Grünbacher ◽  
Michael Petruzelka ◽  
Klaus Eder ◽  
...  
Keyword(s):  
Large Scale ◽  
Feature Modeling ◽  
Software Systems ◽  
Multi Level ◽  
Industrial Software

scholarly journals GATECloud.net: a platform for large-scale, open-source text processing on the cloud

2013 ◽  
Vol 371 (1983) ◽  
pp. 20120071 ◽  
Author(s):  
Valentin Tablan ◽  
Ian Roberts ◽  
Hamish Cunningham ◽  
Kalina Bontcheva
Keyword(s):  
Cloud Computing ◽  
Language Processing ◽  
Large Scale ◽  
Virtual Machines ◽  
Cost Benefit Analysis ◽  
Text Processing ◽  
Cost Benefit ◽  
Data Intensive ◽  
On Demand ◽  
Usage Evaluation

Cloud computing is increasingly being regarded as a key enabler of the ‘democratization of science’, because on-demand, highly scalable cloud computing facilities enable researchers anywhere to carry out data-intensive experiments. In the context of natural language processing (NLP), algorithms tend to be complex, which makes their parallelization and deployment on cloud platforms a non-trivial task. This study presents a new, unique, cloud-based platform for large-scale NLP research—GATECloud. net. It enables researchers to carry out data-intensive NLP experiments by harnessing the vast, on-demand compute power of the Amazon cloud. Important infrastructural issues are dealt with by the platform, completely transparently for the researcher: load balancing, efficient data upload and storage, deployment on the virtual machines, security and fault tolerance. We also include a cost–benefit analysis and usage evaluation.

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