5,954 research outputs found
Exploring Evolutionary Economic Geographies
Evolutionary approaches in economics have gathered increasing support over the last 25 years. Despite an impressive body of literature, economists are still far from formulating a coherent research paradigm. The multitude of approaches in evolutionary economics poses problems for the development of an evolutionary economic geography. For the most part, evolutionary economic geography imports selective concepts from evolutionary biology and economics and applies those concepts to specific problems within economic geography. We discuss a number of problems with this approach and suggest that a more powerful and appealing alternative requires the development of theoretically consistent models of evolutionary processes. This paper outlines the contours of an evolutionary model of economic dynamics where economic agents are located in different geographical spaces. We seek to show how competition between those agents, based on the core evolutionary principles of variety, selection and retention, may produce distinct economic regions sharing properties that differentiate them from competitors elsewhere. These arguments are extended to illustrate how the emergent properties of economic agents and places co-evolve and lead to different trajectories of economic development over space.evolutionary economics, economic geography, Generalized Darwinism, biological metaphors, self-organization
Income inequality and minimum consumption: implications for growth
We propose a model that recognizes hierarchical goods and income inequality among households. The model demonstrates that growth is impacted not by inequality per se, but "absolute" income distribution or the level of poverty underlying the income distribution. Specifically, when a large fraction of the population is below the threshold income necessary for subsistence, aggregate consumption is depressed. In low-income countries, high inequality of income retards consumption growth, whereas in high-income countries inequality may be neutral for growth. Cross-country regressions indicate a positive and significant relationship between the middle quintile share of income and aggregate consumption. In all cases analyzed, increasing income in the middle quintile increases consumption growthIncome distribution ; Consumption (Economics)
Human resource management implications of new forms of organizing
Adopting a process view, we explore the personnel (HRM) implications of new forms of organizing (NFOs). We review the characteristics of NFOs and explain how they require a renewed HRM approach. We illustrate the HRM approach with preliminary results from a European comparative study, and comment on the challenges ahead.Human resource management;
Mobile Robots
The objective of this book is to cover advances of mobile robotics and related technologies applied for multi robot systems' design and development. Design of control system is a complex issue, requiring the application of information technologies to link the robots into a single network. Human robot interface becomes a demanding task, especially when we try to use sophisticated methods for brain signal processing. Generated electrophysiological signals can be used to command different devices, such as cars, wheelchair or even video games. A number of developments in navigation and path planning, including parallel programming, can be observed. Cooperative path planning, formation control of multi robotic agents, communication and distance measurement between agents are shown. Training of the mobile robot operators is very difficult task also because of several factors related to different task execution. The presented improvement is related to environment model generation based on autonomous mobile robot observations
Spatial Aggregation: Theory and Applications
Visual thinking plays an important role in scientific reasoning. Based on the
research in automating diverse reasoning tasks about dynamical systems,
nonlinear controllers, kinematic mechanisms, and fluid motion, we have
identified a style of visual thinking, imagistic reasoning. Imagistic reasoning
organizes computations around image-like, analogue representations so that
perceptual and symbolic operations can be brought to bear to infer structure
and behavior. Programs incorporating imagistic reasoning have been shown to
perform at an expert level in domains that defy current analytic or numerical
methods. We have developed a computational paradigm, spatial aggregation, to
unify the description of a class of imagistic problem solvers. A program
written in this paradigm has the following properties. It takes a continuous
field and optional objective functions as input, and produces high-level
descriptions of structure, behavior, or control actions. It computes a
multi-layer of intermediate representations, called spatial aggregates, by
forming equivalence classes and adjacency relations. It employs a small set of
generic operators such as aggregation, classification, and localization to
perform bidirectional mapping between the information-rich field and
successively more abstract spatial aggregates. It uses a data structure, the
neighborhood graph, as a common interface to modularize computations. To
illustrate our theory, we describe the computational structure of three
implemented problem solvers -- KAM, MAPS, and HIPAIR --- in terms of the
spatial aggregation generic operators by mixing and matching a library of
commonly used routines.Comment: See http://www.jair.org/ for any accompanying file
Adaptive microstructure-informed tractography for accurate brain connectivity analyses
Human brain has been subject of deep interest for centuries, given it's central role in controlling and directing the actions and functions of the body as response to external stimuli. The neural tissue is primarily constituted of neurons and, together with dendrites and the nerve synapses, constitute the gray matter (GM) which plays a major role in cognitive functions. The information processed in the GM travel from one region to the other of the brain along nerve cell projections, called axons. All together they constitute the white matter (WM) whose wiring organization still remains challenging to uncover. The relationship between structure organization of the brain and function has been deeply investigated on humans and animals based on the assumption that the anatomic architecture determine the network dynamics. In response to that, many different imaging techniques raised, among which diffusion-weighted magnetic resonance imaging (DW-MRI) has triggered tremendous hopes and expectations. Diffusion-weighted imaging measures both restricted and unrestricted diffusion, i.e. the degree of movement freedom of the water molecules, allowing to map the tissue fiber architecture in vivo and non-invasively. Based on DW-MRI data, tractography is able to exploit information of the local fiber orientation to recover global fiber pathways, called streamlines, that represent groups of axons. This, in turn, allows to infer the WM structural connectivity, becoming widely used in many different clinical applications as for diagnoses, virtual dissections and surgical planning. However, despite this unique and compelling ability, data acquisition still suffers from technical limitations and recent studies have highlighted the poor anatomical accuracy of the reconstructions obtained with this technique and challenged its effectiveness for studying brain connectivity. The focus of this Ph.D. project is to specifically address these limitations and to improve the anatomical accuracy of the structural connectivity estimates. To this aim, we developed a global optimization algorithm that exploits micro and macro-structure information, introducing an iterative procedure that uses the underlying tissue properties to drive the reconstruction using a semi-global approach. Then, we investigated the possibility to dynamically adapt the position of a set of candidate streamlines while embedding the anatomical prior of trajectories smoothness and adapting the configuration based on the observed data. Finally, we introduced the concept of bundle-o-graphy by implementing a method to model groups of streamlines based on the concept that axons are organized into fascicles, adapting their shape and extent based on the underlying microstructure
Tractographie adaptative basée sur la microstructure pour des analyses précises de la connectivité cérébrale
Le cerveau est un sujet de recherche depuis plusieurs décennies, puisque son rÎle
est central dans la compréhension du genre humain. Le cerveau est composé de
neurones, oĂč leurs dendrites et synapses se retrouvent dans la matiĂšre grise alors que
les axones en constituent la matiĂšre blanche. Lâinformation traitĂ©e dans les diffĂ©rentes
rĂ©gions de la matiĂšre grise est ensuite transmise par lâintermĂ©diaire des axones afin
dâaccomplir diffĂ©rentes fonctions cognitives.
La matiĂšre blanche forme une structure dâinterconnections complexe encore dif-
ficile Ă comprendre et Ă Ă©tudier. La relation entre lâarchitecture et la fonction du
cerveau a Ă©tĂ© Ă©tudiĂ©e chez les humains ainsi que pour dâautres espĂšces, croyant que
lâarchitecture des axones dĂ©terminait la dynamique du rĂ©seau fonctionnel.
Dans ce mĂȘme objectif, lâImagerie par rĂ©sonance (IRM) est un outil formidable
qui nous permet de visualiser les tissus cérébraux de façon non-invasive. Plus partic-
uliĂšrement, lâIRM de diffusion permet dâestimer et de sĂ©parer la diffusion libre de celle
restreinte par la structure des tissus. Cette mesure de restriction peut ĂȘtre utilisĂ©e
afin dâinfĂ©rer lâorientation locale des faisceaux de matiĂšre blanche. Lâalgorithme de
tractographie exploite cette carte dâorientation pour reconstruire plusieurs connexions
de la matiĂšre blanche (nommĂ©es âstreamlinesâ).
Cette modĂ©lisation de la matiĂšre blanche permet dâestimer la connectivitĂ© cĂ©rĂ©brale
dite structurelle entre les diffĂ©rentes rĂ©gions du cerveau. Ces rĂ©sultats peuvent ĂȘtre
employĂ©s directement pour la planification chirurgicale ou indirectement pour lâanalyse
ou une Ă©valuation clinique.
Malgré plusieurs de ses limitations, telles que sa variabilité et son imprécision, la
tractographie reste lâunique moyen dâĂ©tudier lâarchitecture de la matiĂšre blanche ainsi
que la connectivité cérébrale de façon non invasive.
Lâobjectif de ce projet de doctorat est de rĂ©pondre spĂ©cifiquement Ă ces limitations
et dâamĂ©liorer la prĂ©cision anatomique des estimations de connectivitĂ© structurelle.
Dans ce but, nous avons dĂ©veloppĂ© un algorithme dâoptimisation globale qui exploite
les informations de micro et macrostructure, en introduisant une procédure itéra-
tive qui utilise les propriétés sous-jacentes des tissus pour piloter la reconstruction
en utilisant une approche semi-globale. Ensuite, nous avons étudié la possibilité
dâadapter dynamiquement la position dâun ensemble de lignes de courant candidates
tout en intégrant le préalable anatomique de la douceur des trajectoires et en adap-
tant la configuration en fonction des données observées. Enfin, nous avons introduit
le concept de bundle-o-graphy en mettant en Ćuvre une mĂ©thode pour modĂ©liser des
groupes de lignes de courant basées sur le concept que les axones sont organisés en
fascicules, en adaptant leur forme et leur Ă©tendue en fonction de la microstructure
sous-jacente.Abstract : Human brain has been subject of deep interest for centuries, given itâs central role in controlling and directing the actions and functions of the body as response to external stimuli. The neural tissue is primarily constituted of neurons and, together with dendrites and the nerve synapses, constitute the gray matter (GM) which plays a major role in cognitive functions. The information processed in the GM travel from one region to the other of the brain along nerve cell projections, called axons. All together they constitute the white matter (WM) whose wiring organization still remains challenging to uncover. The relationship between structure organization of the brain and function has been deeply investigated on humans and animals based on the assumption that the anatomic architecture determine the network dynamics. In response to that, many different imaging techniques raised, among which diffusion-weighted magnetic resonance imaging (DW-MRI) has triggered tremendous hopes and expectations. Diffusion-weighted imaging measures both restricted and unrestricted diffusion, i.e. the degree of movement freedom of the water molecules, allowing to map the tissue fiber architecture in vivo and non-invasively. Based on DW-MRI data, tractography is able to exploit information of the local fiber orien- tation to recover global fiber pathways, called streamlines, that represent groups of axons. This, in turn, allows to infer the WM structural connectivity, becoming widely used in many different clinical applications as for diagnoses, virtual dissections and surgical planning. However, despite this unique and compelling ability, data acqui- sition still suffers from technical limitations and recent studies have highlighted the poor anatomical accuracy of the reconstructions obtained with this technique and challenged its effectiveness for studying brain connectivity. The focus of this Ph.D. project is to specifically address these limitations and to improve the anatomical accuracy of the structural connectivity estimates. To this aim, we developed a global optimization algorithm that exploits micro and macro- structure information, introducing an iterative procedure that uses the underlying tissue properties to drive the reconstruction using a semi-global approach. Then, we investigated the possibility to dynamically adapt the position of a set of candidate streamlines while embedding the anatomical prior of trajectories smoothness and adapting the configuration based on the observed data. Finally, we introduced the concept of bundle-o-graphy by implementing a method to model groups of streamlines based on the concept that axons are organized into fascicles, adapting their shape and extent based on the underlying microstructure.Sommario : Il cervello umano Ăš oggetto di profondo interesse da secoli, dato il suo ruolo centrale
nel controllare e dirigere le azioni e le funzioni del corpo in risposta a stimoli
esterno. Il tessuto neurale Ăš costituito principalmente da neuroni che, insieme ai dendriti
e alle sinapsi nervose, costituiscono la materia grigia (GM), la quale riveste un
ruolo centrale nelle funzioni cognitive. Le informazioni processate nella GM viaggiano
da una regione allâaltra del cervello lungo estensioni delle cellule nervose, chiamate
assoni. Tutti insieme costituiscono la materia bianca (WM) la cui organizzazione
strutturale rimane tuttora sconosciuta. Il legame tra struttura e funzione del cervello
sono stati studiati a fondo su esseri umani e animali partendo dal presupposto che
lâarchitettura anatomica determini la dinamica della rete funzionale. In risposta a
ciĂČ, sono emerse diverse tecniche di imaging, tra cui la risonanza magnetica pesata
per diffusione (DW-MRI) ha suscitato enormi speranze e aspettative. Questa tecnica
misura la diffusione sia libera che ristretta, ovvero il grado di libertĂ di movimento
delle molecole dâacqua, consentendo di mappare lâarchitettura delle fibre neuronali
in vivo e in maniera non invasiva. Basata su dati DW-MRI, la trattografia Ăš in
grado di sfruttare le informazioni sullâorientamento locale delle fibre per ricostruirne
i percorsi a livello globale. Questo, a sua volta, consente di estrarre la connettivitĂ
strutturale della WM, utilizzata in diverse applicazioni cliniche come per diagnosi,
dissezioni virtuali e pianificazione chirurgica. Tuttavia, nonostante questa capacitĂ
unica e promettente, lâacquisizione dei dati soffre ancora di limitazioni tecniche
e recenti studi hanno messo in evidenza la scarsa accuratezza anatomica delle ricostruzioni
ottenute con questa tecnica, mettendone in dubbio lâefficacia per lo studio
della connettivitĂ cerebrale. Il focus di questo progetto di dottorato Ăš quello di affrontare in modo specifico
queste limitazioni e di migliorare lâaccuratezza anatomica delle stime di connettivitĂ
strutturale. A tal fine, abbiamo sviluppato un algoritmo di ottimizzazione globale che
sfrutta le informazioni sia micro che macrostrutturali, introducendo una procedura
iterativa che utilizza le proprietĂ del tessuto neuronale per guidare la ricostruzione utilizzando
un approccio semi-globale. Successivamente, abbiamo studiato la possibilitĂ
di adattare dinamicamente la posizione di un insieme di streamline candidate incorporando
il prior anatomico per cui devono seguire traiettorie regolari e adattando
la configurazione in base ai dati osservati. Infine, abbiamo introdotto il concetto
di bundle-o-graphy implementando un metodo per modellare gruppi di streamline
basato sul concetto che gli assoni sono organizzati in fasci, adattando la loro forma
ed estensione in base alla microstruttura sottostante
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