Crafting networks to achieve, or not achieve, chaotic states

The influence of networks topology on collective properties of dynamical systems defined upon it is studied in the thermodynamic limit. A network model construction scheme is proposed where the number of links, the average eccentricity and the clustering coefficient are controlled. This is done by rewiring links of a regular one dimensional chain according to a probability $p$ within a specific range $r$, that can depend on the number of vertices $N$. We compute the thermodynamic behavior of a system defined on the network, the $XY-$rotors model, and monitor how it is affected by the topological changes. We identify the network dimension $d$ as a crucial parameter: topologies with d\textless{}2 exhibit no phase transitions while ones with d\textgreater{}2 display a second order phase transition. Topologies with $d=2$ exhibit states characterized by infinite susceptibility and macroscopic chaotic/turbulent dynamical behavior. These features are also captured by $d$ in the finite size context

Critical behaviour of the XY -rotors model on regular and small world networks

We study the XY-rotors model on small networks whose number of links scales with the system size $N_{links}\sim N^{\gamma}$, where $1\le\gamma\le2$. We first focus on regular one dimensional rings in the microcanonical ensemble. For $\gamma<1.5$ the model behaves like short-range one and no phase transition occurs. For $\gamma>1.5$, the system equilibrium properties are found to be identical to the mean field, which displays a second order phase transition at a critical energy density $\varepsilon=E/N, \varepsilon_{c}=0.75$. Moreover for $\gamma_{c}\simeq1.5$ we find that a non trivial state emerges, characterized by an infinite susceptibility. We then consider small world networks, using the Watts-Strogatz mechanism on the regular networks parametrized by $\gamma$. We first analyze the topology and find that the small world regime appears for rewiring probabilities which scale as $p_{SW}\propto1/N^{\gamma}$. Then considering the XY-rotors model on these networks, we find that a second order phase transition occurs at a critical energy $\varepsilon_{c}$ which logarithmically depends on the topological parameters $p$ and $\gamma$. We also define a critical probability $p_{MF}$, corresponding to the probability beyond which the mean field is quantitatively recovered, and we analyze its dependence on $\gamma$

Onset of anomalous diffusion from local motion rules

Anomalous diffusion processes, in particular superdiffusive ones, are known to be efficient strategies for searching and navigation by animals and also in human mobility. One way to create such regimes are Lévy flights, where the walkers are allowed to perform jumps, the " flights " , that can eventually be very long as their length distribution is asymptotically power-law distributed. In our work, we present a model in which walkers are allowed to perform, on a 1D lattice, " cascades " of n unitary steps instead of one jump of a randomly generated length, as in the Lévy case, where n is drawn from a cascade distribution pn. We show that this local mechanism may give rise to superdiffusion or normal diffusion when pn is distributed as a power law. We also introduce waiting times that are power-law distributed as well and therefore the probability distribution scaling is steered by the two PDF's power-law exponents. As a perspective, our approach may engender a possible generalization of anomalous diffusion in context where distances are difficult to define, as in the case of complex networks, and also provide an interesting model for diffusion in temporal networks

Persistent Homology analysis of Phase Transitions

Persistent homology analysis, a recently developed computational method in algebraic topology, is applied to the study of the phase transitions undergone by the so-called XY-mean field model and by the phi^4 lattice model, respectively. For both models the relationship between phase transitions and the topological properties of certain submanifolds of configuration space are exactly known. It turns out that these a-priori known facts are clearly retrieved by persistent homology analysis of dynamically sampled submanifolds of configuration space.Comment: 10 pages; 10 figure

Emergence of a non trivial fluctuating phase in the XY model on regular networks

We study an XY-rotor model on regular one dimensional lattices by varying the number of neighbours. The parameter $2\ge\gamma\ge1$ is defined. $\gamma=2$ corresponds to mean field and $\gamma=1$ to nearest neighbours coupling. We find that for $\gamma<1.5$ the system does not exhibit a phase transition, while for $\gamma > 1.5$ the mean field second order transition is recovered. For the critical value $\gamma=\gamma_c=1.5$, the systems can be in a non trivial fluctuating phase for whichthe magnetisation shows important fluctuations in a given temperature range, implying an infinite susceptibility. For all values of $\gamma$ the magnetisation is computed analytically in the low temperatures range and the magnetised versus non-magnetised state which depends on the value of $\gamma$ is recovered, confirming the critical value $\gamma_{c}=1.5$

AI Watch : AI Uptake in Health and Healthcare, 2020

This document presents a sectoral analysis of AI in health and healthcare for AI Watch, the knowledge service of the European Commission monitoring the development, uptake and impact of Artificial Intelligence for Europe. Its main aim is to act as a benchmark for future editions of the report to be able to assess the changes in uptake and impact of AI in healthcare over time, in line with the mission of AI Watch. The report recognises that we are still at an early stage in the adoption of AI and that AI offers many opportunities in the short term for improved efficiency in administrative and operational processes and in the medium-long term for clinical applications, patients’ care, and increased citizen empowerment. At the same time, AI applications in this sensitive sector raise many ethical and societal issues and shaping the direction of development so that we can maximise the benefits whilst reducing the risks is a key issue. In the global context, Europe is well positioned with a strong research base and excellent health data, which is the pre-requisite for the development of beneficial AI applications. Where Europe is less well placed is in translating research and innovation into industrial applications and in venture capital funding able to support innovative companies to set themselves up and scale up once successful. There are however noticeable exception as the case of the BioNTech that is leading the development of one of the COVID-19 vaccines. It should also be noted that in AI-enabled health start-ups, many of them are in the area of drug discovery, i.e. the domain of BioNTech. Investment in education and training of the healthcare workforce as well as creating environments for multidisciplinary exchange of knowledge between software developers and health practitioners are other key areas. The report recognizes that there are many important policy developments already in the making that will shape future directions, including the European Strategy for Data which is setting up a common dataspace for health, a riskbased regulatory framework for AI to be put in place by the end of 2020, and the forthcoming launch of the Horizon Europe programme as well the Digital Europe Programme with large investments in AI, computing infrastructure, cybersecurity and training. The COVID-19 crisis has also acted as a booster to the adoption of AI in health and the digital transition of business, research, education and public administration. Furthermore, the unprecedented investments of the Recovery Plan agreed in July 2020 may fuel development in digital technologies and health beyond expectation. We are therefore at the junction of a potentially extraordinary period of change which we will be able to measure in future years against the baseline set by this report.JRC.B.6-Digital Econom

Synergistic roles of climate warming and human occupation in Patagonian megafaunal extinctions during the Last Deglaciation

The causes of Late Pleistocene megafaunal extinctions (60,000 to 11,650 years ago, hereafter 60 to 11.65 ka) remain contentious, with major phases coinciding with both human arrival and climate change around the world. The Americas provide a unique opportunity to disentangle these factors as human colonization took place over a narrow time frame (~15 to 14.6 ka) but during contrasting temperature trends across each continent. Unfortunately, limited data sets in South America have so far precluded detailed comparison. We analyze genetic and radiocarbon data from 89 and 71 Patagonian megafaunal bones, respectively, more than doubling the high-quality Pleistocene megafaunal radiocarbon data sets from the region.We identify a narrowmegafaunal extinction phase 12,280 ± 110 years ago, some 1 to 3 thousand years after initial human presence in the area. Although humans arrived immediately prior to a cold phase, the Antarctic Cold Reversal stadial, megafaunal extinctions did not occur until the stadial finished and the subsequent warming phase commenced some 1 to 3 thousand years later. The increased resolution provided by the Patagonian material reveals that the sequence of climate and extinction events in North and South America were temporally inverted, but in both cases, megafaunal extinctions did not occur until human presence and climate warming coincided. Overall, metapopulation processes involving subpopulation connectivity on a continental scale appear to have been critical for megafaunal species survival of both climate change and human impacts.Fil: Metcalf, Jessica L.. University of Adelaide; Australia. State University of Colorado Boulder; Estados UnidosFil: Turney, Chris. University of New South Wales; AustraliaFil: Barnett, Ross. University of Oxford; Reino Unido. Universidad de Copenhagen; DinamarcaFil: Martin, Fabiana. Universidad de Magallanes. Instituto de la Patagonia. Centro de Estudios del Hombre Austral; ChileFil: Bray, Sarah C.. University of Adelaide; Australia. University of South Australia; AustraliaFil: Vilstrup, Julia T.. Universidad de Copenhagen; DinamarcaFil: Orlando, Ludovic. Universidad de Copenhagen; DinamarcaFil: Salas-Gismondi, Rodolfo. Université de Montpellier. Institut des Sciences de l’Evolution; Francia. Universidad Nacional Mayor de San Marcos; PerúFil: Loponte, Daniel Marcelo. Secretaría de Cultura de la Nación. Dirección Nacional de Cultura y Museos. Instituto Nacional de Antropología y Pensamiento Latinoamericano; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Medina, Matias Eduardo. Centro de Estudios Históricos ; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: de Nigris, Mariana Eleonor. Secretaría de Cultura de la Nación. Dirección Nacional de Cultura y Museos. Instituto Nacional de Antropología y Pensamiento Latinoamericano; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Civalero, Maria Teresa. Secretaría de Cultura de la Nación. Dirección Nacional de Cultura y Museos. Instituto Nacional de Antropología y Pensamiento Latinoamericano; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fernández, Pablo Marcelo. Secretaría de Cultura de la Nación. Dirección Nacional de Cultura y Museos. Instituto Nacional de Antropología y Pensamiento Latinoamericano; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Gasco, Alejandra Valeria. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales. Laboratorio de Paleoecología Humana; ArgentinaFil: Duran, Victor Alberto. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales. Laboratorio de Paleoecología Humana; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Seymour, Kevin L.. Royal Ontario Museum. Department of Natural History; CanadáFil: Otaola, Clara. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Multidisciplinario de Historia y Ciencias Humanas; ArgentinaFil: Gil, Adolfo Fabian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Museo de Historia Natural de San Rafael - Ianigla | Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Museo de Historia Natural de San Rafael - Ianigla | Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Museo de Historia Natural de San Rafael - Ianigla; ArgentinaFil: Paunero, Rafael. Universidad Nacional de La Plata; ArgentinaFil: Prevosti, Francisco Juan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Universidad Nacional de La Rioja. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Universidad Nacional de Catamarca. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Secretaría de Industria y Minería. Servicio Geológico Minero Argentino. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Provincia de La Rioja. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja; ArgentinaFil: Bradshaw, Corey J. A.. University of Adelaide; AustraliaFil: Wheeler, Jane C.. Instituto de Investigación y Desarrollo de Camélidos Sudamericanos; PerúFil: Borrero, Luis Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Multidisciplinario de Historia y Ciencias Humanas; ArgentinaFil: Austin, Jeremy J.. University of Adelaide; AustraliaFil: Cooper, Alan. University of Adelaide; Australia. University of Oxford; Reino Unid

Artificial Intelligence and Digital Transformation: early lessons from the COVID-19 crisis

The COVID-19 pandemic has created an extraordinary medical, economic and social emergency. To contain the spread of the virus, many countries adopted a lock down policy closing schools and business and keeping people at home for several months. This resulted in a massive surge of activity online for education, business, public administration, research, social interaction. This report considers these recent developments and identifies some early lessons with respect to the present and future development of AI and digital transformation in Europe, focusing in particular on data, as this is an area of significant shifts in attitudes and policy. The report analyses the increasing use of AI in medicine and healthcare, the tensions in data sharing between individual rights and collective wellbeing, the search for technological solutions like contact tracing apps to help monitor the spread of the virus, and the potential concerns they raise. The forced transition to online showed the resilience of the Internet but also the disproportionate impact on already vulnerable groups like the elderly and children. The report concludes that the COVID-19 crisis has acted as a boost for AI adoption and data sharing, and created new opportunities. It has also amplified concerns for democracy and social inequality and showed Europe’s vulnerability on data and platforms, calling for action to address these crucial aspects.JRC.B.6-Digital Econom

Lien entre le seuil d'interaction à longue-portée et la topologie des réseaux.

Dans cette thèse, nous discutons l'influence d'un réseau qui possède une topologie non triviale sur les propriétés collectives d'un modèle hamiltonien pour spins,le modèle XY, défini sur ces réseaux.Nous nous concentrons d'abord sur la topologie des chaînes régulières et du réseau Petit Monde (Small World), créé avec le modèle Watt- Strogatz.Nous contrôlons ces réseaux par deux paramètres γ, pour le nombre d' interactions et p, la probabilité de ré-attacher un lien aléatoirement.On définit deux mesures, le chemin moyen ℓ et la connectivité C et nous analysons leur dépendance de (γ,p). Ensuite,nous considérons le comportement du modèle XY sur la chaîne régulière et nous trouvons deux régimes: un pour γ≺1,5,qui ne présente pas d'ordre longue portée et un pour γ≻1,5 où une transition de phase du second ordre apparaît.Nous observons l'existence d'un état métastable pour γc=1,5. Sur les réseaux Petit Monde,nous illustrons les conditions pour avoir une transition et comment son énergie critique εc(γ,p) dépend des paramètres (γ,p).Enfin,nous proposons un modèle de réseau où les liens d'une chaîne régulière sont ré-attachés aléatoirement avec une probabilité p dans un rayon spécifique r. Nous identifions la dimension du réseau d(p,r) comme un paramètre crucial:en le variant,il nous est possible de passer de réseaux avec d≺2 qui ne présentent pas de transition de phase à des configurations avec d≻2 présentant une transition de phase du second ordre, en passant par des régimes de dimension d=2 qui présentent des états caractérisés par une susceptibilité infinie et une dynamique chaotique.In this thesis we discuss the influence of a non trivial network topology on the collective properties of an Hamiltonian model defined on it, the XY -rotors model. We first focus on networks topology analysis, considering the regular chain and a Small World network, created with the Watt-Strogatz model. We parametrize these topologies via γ, giving the vertex degree and p, the probability of rewiring. We then define two topological parameters, the average path length ℓ and the connectivity C and we analize their dependence on γ and p. Secondly, we consider the behavior of the XY- model on the regular chain and we find two regimes: one for γ≺1.5, which does not display any long-range order and one for γ≻1.5 in which a second order phase transition of the magnetization arises. Moreover we observe the existence of a metastable state appearing for γc=1.5. Finally we illustrate in what conditions we retrieve the phase transition on Small World networks and how its critical energy εc(γ,p) depends on the topological parameters γ and p. In the last part, we propose a network model in which links of a regular chain are rewired according to a probability p within a specific range r. We identify a quantity, the network dimension d(p,r) as a crucial parameter. Varying this dimension we are able to cross over from topologies with d≺2 exhibiting no phase transitions to ones with d≻2 displaying a second order phase transition, passing by topologies with dimension d=2 which exhibit states characterized by infinite susceptibility and macroscopic chaotic dynamical behavior

Emergence of Long Range Order in the XY Model on Diluted Small World Networks

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