Search results for "NETWORKS"

showing 10 items of 3260 documents

Frontières des communautés polarisées : application à l'étude des théories complotistes autour des vaccins

2021

Les données des réseaux sociaux sont de plus en plus utilisées pour en extraire de la valeur, dans des domaines tels que le marketing, la politique ou la sociologie. Celles-ci peuvent être représentées sous forme de graphes, en modélisant précisément les interactions à travers des liens dirigés et pondérés. Dans l'analyse des données des réseaux sociaux, l'étude des communautés est une étape essentielle. Toutefois, pour une interprétation fine des phénomènes, il est également nécessaire d'étudier leurs interactions et de pouvoir détecter des traces de polarisation. Nous proposons une méthode qui permet d'évaluer l'antagonisme des communautés et d'identifier leurs frontières dans des réseaux…

communitiespolarizationcommunities boundariessocial networkscommunautésréseaux sociaux[INFO.INFO-SI] Computer Science [cs]/Social and Information Networks [cs.SI]frontières de communautéspolarisation[INFO] Computer Science [cs]graph mining
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Creative Network Communities in the Translocal Space of Digital Networks

2013

What should sociological research be in the age of Web 2.0? Considering that the task of “network sociology” is not only empirical research but also the interpretation of tendencies of the network culture, this research explores the rise of network communities within Eastern and Western Europe in the early Internet era. I coined the term creative networks to distinguish these early creative and social activities from today’s popular social networking. Thus I aimed to interpret the meaning of social action; the motivation of creative community actors, their main fields of activities and social organization forms; and the potential that these early developments contain for the future sustaina…

communitiessocial networkssocial dynamicnetworkcreative networkssocio-technical formations
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A primer on statistically validated networks

2019

In this contribution we discuss some approaches of network analysis providing information about single links or single nodes with respect to a null hypothesis taking into account the heterogeneity of the system empirically observed. With this approach, a selection of nodes and links is feasible when the null hypothesis is statistically rejected. We focus our discussion on approaches using i) the so-called disparity filter and ii) statistically validated network in bipartite networks. For both methods we discuss the importance of using multiple hypothesis test correction. Specific applications of statistically validated networks are discussed. We also discuss how statistically validated netw…

complex networks statistically validated networksSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)
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Computing temporal sequences associated with dynamic patterns on the C. elegans connectome

2021

AbstractUnderstanding how the structural connectivity of a network constrains the dynamics it is able to support is a very active and open area of research. We simulated the plausible dynamics resulting from the known C. elegans connectome using a recent model and theoretical analysis that computes the dynamics of neurobiological networks by focusing on how local interactions among connected neurons give rise to the global dynamics in an emergent way, independent of the biophysical or molecular details of the cells themselves. We studied the dynamics which resulted from stimulating a chemosensory neuron (ASEL) in a known feeding circuit, both in isolation and embedded in the full connectome…

computational modelingDorsumC. elegans modelComputer scienceCognitive Neurosciencegraph theoryNeuroscience (miscellaneous)Spatial geometrylcsh:RC321-57103 medical and health sciencesCellular and Molecular Neuroscience0302 clinical medicineDevelopmental Neuroscienceconnectome analysismedicinelcsh:Neurosciences. Biological psychiatry. Neuropsychiatry030304 developmental biologyOriginal Research0303 health sciencesGraph theoryMotor neuronmedicine.anatomical_structurenetworks (circuits)ConnectomeNeuronNeuroscience030217 neurology & neurosurgeryNeuroscience
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LightSleepNet: A Lightweight Deep Model for Rapid Sleep Stage Classification with Spectrograms.

2021

Deep learning has achieved unprecedented success in sleep stage classification tasks, which starts to pave the way for potential real-world applications. However, due to its enormous size, deployment of deep neural networks is hindered by high cost at various aspects, such as computation power, storage, network bandwidth, power consumption, and hardware complexity. For further practical applications (e.g., wearable sleep monitoring devices), there is a need for simple and compact models. In this paper, we propose a lightweight model, namely LightSleepNet, for rapid sleep stage classification based on spectrograms. Our model is assembled by a much fewer number of model parameters compared to…

computational modelingmallintaminentrainingpower demandsignaalinkäsittelyunitutkimusdeep learningsyväoppiminenbiological system modelingbrain modelingElectroencephalographyneuroverkotDeep LearningEEGNeural Networks ComputerSleep StagessleepSleepAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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Does ns2 Really Simulate MPLS Networks?

2008

Multi Protocol Label Switching (MPLS) is being used in many corporate networks and public infrastructures and as a backbone technology of many Autonomous Systems. Because of its importance, what is needed is to find out simulators able to simulate MPLS networks whose results reflect the real environment as much as possible. In this article, we will show real measurements from the Railway Infrastructure Administrator (ADIF, the Spanish railway infrastructure manager) MPLS network and we will compare them with the results obtained by the freeware simulator ns2. We will check the level of reliability provided by the simulator and we will know under which parameters its results will be similar …

computer.internet_protocolbusiness.industryComputer scienceReliability (computer networking)ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKSMultiprotocol Label SwitchingDiscrete event simulationbusinesscomputerComputer networkFourth International Conference on Autonomic and Autonomous Systems (ICAS'08)
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An Early Stage Researcher's Primer on Systems Medicine Terminology

2021

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. Thes…

computer_scienceGraphs and NetworksmedicineGlossary[SDV]Life Sciences [q-bio]Comprehensive ReviewTerminology03 medical and health sciences0302 clinical medicineSDG 3 - Good Health and Well-beingMachine learningGeneticsmultiscale data science[INFO]Computer Science [cs]systems medicinemulti-scale modellingMulti-scale modellingComputingMilieux_MISCELLANEOUS030304 developmental biologyInterdisciplinarityMulti-scale data science0303 health scienceshealthGeneral MedicineHuman bodySciences bio-médicales et agricolesData science[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]multiscale modeling3. Good healthTerm (time)Systems medicinemachine learningmulti-scale data scienceSystems medicineMedicine/dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_being030217 neurology & neurosurgeryNetwork and Systems Medicine
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An Optimized Architecture for CGA Operations and Its Application to a Simulated Robotic Arm

2022

Conformal geometric algebra (CGA) is a new geometric computation tool that is attracting growing attention in many research fields, such as computer graphics, robotics, and computer vision. Regarding the robotic applications, new approaches based on CGA have been proposed to efficiently solve problems as the inverse kinematics and grasping of a robotic arm. The hardware acceleration of CGA operations is required to meet real-time performance requirements in embedded robotic platforms. In this paper, we present a novel embedded coprocessor for accelerating CGA operations in robotic tasks. Two robotic algorithms, namely, inverse kinematics and grasping of a human-arm-like kinematics chain, ar…

conformal geometric algebraSettore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioniapplication-specific processorsComputer Networks and CommunicationsHardware and ArchitectureControl and Systems EngineeringSignal Processingcomputational geometryFPGA-based prototypingElectrical and Electronic Engineeringapplication-specific processors; Clifford Algebra; computational geometry; conformal geometric algebra; FPGA-based prototyping; grasping; human-like robotic arms; inverse kinematics
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Questions and controversies in the study of time-varying functional connectivity in resting fMRI.

2020

The brain is a complex, multiscale dynamical system composed of many interacting regions. Knowledge of the spatiotemporal organization of these interactions is critical for establishing a solid understanding of the brain’s functional architecture and the relationship between neural dynamics and cognition in health and disease. The possibility of studying these dynamics through careful analysis of neuroimaging data has catalyzed substantial interest in methods that estimate time-resolved fluctuations in functional connectivity (often referred to as “dynamic” or time-varying functional connectivity; TVFC). At the same time, debates have emerged regarding the application of TVFC analyses to re…

confound regression strategiesComputer scienceBrain networksRest1.1 Normal biological development and functioningdynamic connectivityReviewDynamical systemlcsh:RC321-57103 medical and health sciencesFunctional connectivity0302 clinical medicineArtificial IntelligenceUnderpinning researchBehavioral and Social Sciencestate fmricognitive controlmotion correctionReview Articleslcsh:Neurosciences. Biological psychiatry. Neuropsychiatry030304 developmental biologyindividual-differencesRest (physics)0303 health sciencesApplied MathematicsGeneral NeuroscienceResting fmriFunctional connectivitytest-retest reliabilityfMRINeurosciencesComputer Science ApplicationsMental HealthNeurologicalwhole-brainNeurosciencedefault mode030217 neurology & neurosurgeryBrain dynamics
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One and Two Dimensional Convolutional Neural Networks for Seizure Detection Using EEG Signals

2021

Deep learning for the automated detection of epileptic seizures has received much attention during recent years. In this work, one dimensional convolutional neural network (1D-CNN) and two dimensional convolutional neural network (2D-CNN) are simultaneously used on electroencephalogram (EEG) data for seizure detection. Firstly, using sliding windows without overlap on raw EEG to obtain the definite one-dimension time EEG segments (1D-T), and continuous wavelet transform (CWT) for 1D-T signals to obtain the two-dimension time-frequency representations (2D-TF). Then, 1D-CNN and 2D-CNN model architectures are used on 1D-T and 2D-TF signals for automatic classification, respectively. Finally, t…

convolutional neural networks (CNN)Computer scienceseizure detection02 engineering and technologyneuroverkotElectroencephalographyConvolutional neural network0202 electrical engineering electronic engineering information engineeringmedicineEEGContinuous wavelet transformSignal processingArtificial neural networkmedicine.diagnostic_testbusiness.industryelectroencephalogram (EEG)signaalinkäsittelyDeep learningtime-frequency representationtideep learningsignaalianalyysi020206 networking & telecommunicationsPattern recognitionkoneoppiminenBenchmark (computing)020201 artificial intelligence & image processingArtificial intelligencebusinessepilepsia
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