Darwinian Biolinguistics : Theory and History of a Naturalistic Philosophy of Language and Pragmatics / / by Antonino Pennisi, Alessandra Falzone

• Record Nr.: UNINA9910155294503321
• Autore: Pennisi Antonino
• Pubbl/distr/stampa: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016
• ISBN: 3-319-47688-2
• Edizione: [1st ed. 2016.]
• Descrizione fisica: 1 online resource (IX, 301 p. 12 illus.)
• Collana: Perspectives in Pragmatics, Philosophy & Psychology, , 2214-3807 ; ; 12
• Disciplina: 401.43
• Soggetti: Semantics; Language and languages—Philosophy; Psycholinguistics; Philosophy of Language
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: Acknowledgements -- 1. Introduction -- Part one – History and State of the art -- 2. Chomsky and Biolinguistics -- 3. The last Chomsky and the Evolutionary Perspective -- 3. 4. The Update of the Biolinguistics Agenda -- 5. Another Biolinguistics History: From Aristotle to Darwin and Broca -- Part two – Towards a Darwinian Biolinguistics -- 6. Comparing two Models: CBM vs DBM -- 7. The Nature of the Species-specificity of Human Language -- 8. Genetic Fundamentals -- 9. Morphological Fundamentals -- 10. Neurocerebral Fundamentals -- Part three – Extended Performativity: from brain Plasticity to Linguistic Pragmatics -- 11. Performance -- 12. Functional Plasticity -- 13. Evolutionary Plasticity -- 14. Bio-linguistic Plasticity and Origin of Language -- 15. The Boundaries of Biolinguistics -- 16. Pragmatics and Biolinguistics -- Bibliography.
• Sommario/riassunto: This book proposes a radically evolutionary approach to biolinguistics that consists in considering human language as a form of species-specific intelligence entirely embodied in the corporeal structures of Homo sapiens. The book starts with a historical reconstruction of two opposing biolinguistic models: the Chomskian Biolinguistic Model ; (CBM) and the Darwinian Biolinguistic Model (DBM). The second part compares the two models and develops into a complete reconsideration of the traditional biolinguistic issues in an evolutionary perspective, highlighting their potential influence on the paradigm of biologically oriented cognitive science. The third part formulates the philosophical, evolutionary and experimental basis of an extended theory of linguistic performativity within a naturalistic perspective of pragmatics of verbal language. The book proposes a model in which the continuity between human and non-human primates is linked to the gradual development of the articulatory and neurocerebral structures, and to a kind of prelinguistic pragmatics which characterizes the common nature of social learning. In contrast, grammatical, semantic and pragmatic skills that mark the learning of historical-natural languages are seen as a rapid acceleration of cultural evolution. The book makes clear that this acceleration will not necessarily favour the long-term adaptations for Homo sapiens. .

Positron Emission Particle Tracking : A Comprehensive Guide

• Record Nr.: UNINA9911009379403321
• Autore: Windows-Yule Kit
• Pubbl/distr/stampa: Bristol : , : Institute of Physics Publishing, , 2022; ©2022
• ISBN: 9780750344135; 075034413X
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (699 pages)
• Collana: IOP Ebooks Series
• Altri autori (Persone): ParkerDavid; MangerSamuel; NicuşanAndrei L; HeraldMatthew T
• Soggetti: Positron beams; Tracers (Chemistry)
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Intro -- Preface -- Acknowledgement -- Author biographies -- Kit Windows-Yule -- Leonard Nicuşan -- Matthew T Herald -- Samuel Manger -- David Parker -- Chapter 0 Using the book -- 0.1 The 'User' -- 0.2 The 'Researcher' -- 0.3 The 'Developer' -- 0.4 The 'Expert' -- Chapter 1 Imaging particulate and multiphase systems -- 1.1 Particulate and multiphase systems: why do they matter? -- 1.2 The importance of imaging -- 1.3 Particle and flow imaging: an overview -- References -- Chapter 2 The fundamentals of PEPT -- 2.1 Positron emission… -- 2.2 …particle tracking -- 2.2.1 Interactive example: PEPT-an idealised case -- PEPT: an idealised case -- Monte Carlo line of response generation -- Triangulate tracer's location -- Spatial error versus number of LoRs used -- 2.3 A more realistic picture -- 2.3.1 Issue 1: false coincidences -- 2.3.2 Issue 2: positron flight -- 2.3.3 Issue 3: imperfect detectors -- 2.3.4 Issue 4: finite detection rate -- 2.3.5 The real picture -- 2.3.6 Interactive example: sources of error in PEPT -- Sources of error in PEPT -- Monte Carlo line of response generation -- Adding noise: scattered events and spread -- Triangulate tracer's location -- Spatial error versus noise ratio -- 2.4 Not just particle tracking -- References -- Chapter 3 A history of PEPT -- 3.1 Adding the 'P': from PET to PEPT (origins to 1989) -- 3.2 MWPC PEPT (1989-1999) -- 3.3 Out with the old, in with the new (1999-2002) -- 3.4 Positron cameras of all shapes and sizes (2002-present) -- 3.5 PEPT elsewhere -- 3.6 The future-SuperPEPT, MicroPEPT and beyond -- References -- Chapter 4 Comparison with other techniques -- 4.1 Quasi-two-dimensional techniques -- 4.1.1 Particle tracking velocimetry (PTV) -- 4.1.2 Particle imaging velocimetry (PIV) -- 4.1.3 Photoelastic imaging -- 4.1.4 X-ray radiography -- 4.2 Tomographic techniques -- 4.2.1 X-ray computed tomography.; 4.2.2 Single-photon emission computed tomography (SPECT) -- 4.2.3 Positron emission tomography (PET) -- 4.2.4 Electrical capacitance tomography (ECT) -- 4.2.5 Magnetic resonance imaging (MRI) -- 4.2.6 Refractive index matched scanning (RIMS) -- 4.3 Three-dimensional particle-tracking techniques -- 4.3.1 Radioactive particle tracking (RPT) -- 4.3.2 Magnetic particle tracking (MPT) -- 4.4 Non-imaging techniques -- Angle of repose (AOR) tests -- Tapped density tests-the Carr index and Hausner ratio -- Flow through an orifice -- Shear testing -- Powder rheometry -- 4.5 Numerical simulation -- 4.5.1 The discrete element method (DEM) -- A simple discrete element method simulation -- Define system of differential equations -- Numerically solving the ODE: Euler method -- Numerically solving the ODE: adaptive integration schemes -- 4.5.2 Computational fluid dynamics (CFD) -- 4.5.3 The Monte Carlo method -- A simple Monte Carlo simulation -- How many random samples? -- 4.6 Other techniques -- References -- Chapter 5 Tracers and detectors -- 5.1 Creating tracers -- 5.1.1 Introduction-the ideal tracer -- 5.1.2 Positron-emitting nuclides and direct activation -- 5.1.3 Indirect activation -- 5.1.4 Handling and coating -- 5.2 Detector systems -- 5.2.1 Introduction -- 5.2.2 Scintillation detectors -- 5.2.3 PEPT system geometries -- 5.2.4 Examples of PEPT systems -- 5.2.5 Future developments -- 5.3 Modelling PEPT systems -- 5.3.1 How it works -- 5.3.2 Existing GATE models -- 5.3.3 Modelling a PEPT detector -- 5.3.4 Defining a PEPT tracer -- 5.3.5 Recreating an experiment -- 5.3.6 Studying a PEPT system using GATE -- References -- Chapter 6 Pre-processing: PEPT data and algorithms -- 6.1 Understanding PEPT data -- 6.1.1 Interactive example: PEPT data format -- PEPT Data Format -- Initialise raw line of response data -- Visualising a sample of LoRs.; Temporal resolution? -- 6.2 Available algorithms -- 6.2.1 The Birmingham algorithm -- 6.2.2 Interactive example: the Birmingham ; algorithm -- Interactive PEPT analysis example using the Birmingham method [1] -- This Jupyter Notebook -- Initialise raw line of response data -- Find minimum distance point -- Remove the farthest lines of response -- Iteratively remove the farthest LoRs and recompute MDP -- Complete Birmingham Method code -- High-performance Birmingham Method implementation -- 6.2.3 The line-density method -- 6.2.4 Interactive example: the line-density method -- Interactive PEPT analysis example using the line density algorithm [4] -- This Jupyter Notebook -- Initialise raw line of response data -- Voxelise the lines of response -- Fit 1D Gaussians around the peak -- Complete line density method code -- 6.2.5 The G-means clustering algorithm -- 6.2.6 Interactive example: the G-means algorithm -- Interactive PEPT analysis example using the clustering (G-means) algorithm [6] -- This Jupyter Notebook -- Initialise raw line of response data -- Voxelise the lines of response -- High pass filter -- Cluster voxels with G-means -- Complete clustering (G-means) algorithm code -- Multiple particle tracking -- 6.2.7 Feature-point identification (FPI) -- 6.2.8 Interactive example: FPI -- Interactive PEPT analysis example using the feature point identification algorithm [17] -- This Jupyter Notebook -- Initialise raw line of response data -- Voxelise the lines of response -- Subtract convolved matrix and blur -- Extract voxel peaks -- Complete FPI algorithm code -- Multiple particle tracking -- High-performance FPI algorithm implementation -- 6.2.9 Spatiotemporal B-spline reconstruction (SBSR) -- 6.2.10 Voronoi tesselation method -- 6.2.11 Interactive example: Voronoi tesselation.; Interactive PEPT analysis example using the Voronoi tesselation method [30] -- This Jupyter Notebook -- Initialise raw line of response data -- Discretise the lines of response -- Voronoi tesselation -- Gather points under consideration (PUCs) -- Local filtering based on the local outlier factor -- Global filtering -- Clustering the remaining PUCs -- Extract final tracer locations -- Complete Voronoi tesselation algorithm code -- 6.2.12 The triangulation method -- 6.2.13 Interactive example: triangulation method -- Interactive PEPT analysis example using the triangulation method [37] -- This Jupyter Notebook -- Initialise raw line of response data -- Calculate the LoR distance matrix -- Cluster LoRs closer than the tracer radius -- Find centroids of clustered LoRs' cutpoints -- Complete triangulation method code -- 6.2.14 PEPT using machine learning (PEPT-ML) -- 6.2.15 Interactive example: PEPT-ML -- Interactive PEPT analysis example using the PEPT-ML algorithm [38] -- This Jupyter Notebook -- Initialise raw line of response data -- Find cutpoints -- Cluster cutpoints with HDBSCAN -- Compute cluster centres -- Complete PEPT-ML algorithm code -- Second pass of clustering -- Multiple particle tracking -- High-performance PEPT-ML algorithm implementation -- 6.2.16 PEPT using expectation-maximisation (PEPT-EM) -- 6.2.17 Interactive example: PEPT-EM -- Interactive PEPT analysis examples using PEPT-EM [44] -- This Jupyter Notebook -- Initialise Raw Line of Response Data -- Calculate MDP and assign a weight to each LoR -- Recalculate MDP with previous weights -- Complete PEPT-EM algorithm code -- 6.2.18 The K-medoids method -- 6.2.19 Interactive example: K-medoids method -- Interactive PEPT analysis example using the K-medoids method [45] -- This Jupyter Notebook -- Initialise raw line of response data -- Find cutpoints.; Filter cutpoints with far nearest neighbors -- Cluster filtered cutpoints using K-medoids -- Compute clusters' centroids -- Complete K-medoids method mode -- Multiple particle pracking -- 6.2.20 The multiple location-allocation algorithm (MLAA) -- 6.2.21 Interactive example: the multiple location-allocation algorithm (MLAA) -- ; Interactive PEPT analysis example using the multiple location-allocation algorithm (MLAA) [49] -- This Jupyter Notebook -- Initialise raw line of response data -- Voxelise the lines of response -- Voxel global thresholding -- The location-allocation algorithm -- Complete multiple location-allocation algorithm code -- Multiple particle tracking -- 6.3 From finding tracers to tracking trajectories -- 6.3.1 Interactive example: the effects of sample size and overlap -- Effect of sample size and overlap -- Prelude -- Initialise raw line of response data -- Effect of sample size -- Effect of overlap -- 6.3.2 Trajectory extraction -- 6.3.3 Interactive example: filtering trajectories -- Filtering trajectories -- Initialising lines of response -- Filtering based on spatial error -- Filtering based on nearest neighbours -- Filtering using the PEPT Library -- 6.3.4 Interactive example: separating trajectories -- Separating trajectories -- Initialising lines of response -- PTV-based trajectory separation -- Clustering-based trajectory separation -- 6.4 Horses for courses: comparing algorithm capability for differing tasks -- References -- Chapter 7 Post-processing: extracting physical information from PEPT data -- 7.1 Particle trajectories -- 7.1.1 Single-particle trajectories and their interpretation -- 7.1.2 Interactive example: plotting single particle trajectories -- Tutorial: Using PEPT data to plot single particle trajectories -- Setting up -- Importing data -- Plotting data -- Using the pept library.; 7.1.3 Interactive example: multiple-particle data.
• Sommario/riassunto: This book provides both an accessible introduction to, and a comprehensive overview of, the PEPT technique, replete with interactive examples, usable algorithms and real PEPT data, allowing the reader to gain a deep understanding and practical, working knowledge of the methodology.