Fractal dimension and anisotropy of soil CO2 emission in an agricultural field during fallow

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Local dimension and finite time prediction in coupled map lattices

Forecasting, for obvious reasons, often become the most important goal to be achieved. For spatially extended systems (e.g. atmospheric system) where the local nonlinearities lead to the most unpredictable chaotic evolution, it is highly desirable to have a simple diagnostic tool to identify regions of predictable behaviour. In this paper, we discuss the use of the bred vector (BV) dimension, a recently introduced statistics, to identify the regimes where a finite time forecast is feasible. Using the tools from dynamical systems theory and Bayesian modelling, we show the finite time predictability in two-dimensional coupled map lattices in the regions of low BV dimension. © Indian Academy of Sciences.

Análise espaço temporal da dimensão fractal de matas ciliares na alta bacia do rio Passa Cinco - centro Leste do Estado de São Paulo

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Estrutura de variabilidade espacial e temporal da emissão de CO2 e atributos do solo caracterizada por dimensão fractal em área de cana-de-açúcar

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

First valence, then arousal: the temporal dynamics of brain electric activity evoked by emotional stimuli

The temporal dynamics of the neural activity that implements the dimensions valence and arousal during processing of emotional stimuli were studied in two multi-channel ERP experiments that used visually presented emotional words (experiment 1) and emotional pictures (experiment 2) as stimulus material. Thirty-two healthy subjects participated (mean age 26.8 +/- 6.4 years, 24 women). The stimuli in both experiments were selected on the basis of verbal reports in such a way that we were able to map the temporal dynamics of one dimension while controlling for the other one. Words (pictures) were centrally presented for 450 (600) ms with interstimulus intervals of 1,550 (1,400) ms. ERP microstate analysis of the entire epochs of stimulus presentations parsed the data into sequential steps of information processing. The results revealed that in several microstates of both experiments, processing of pleasant and unpleasant valence (experiment 1, microstate #3: 118-162 ms, #6: 218-238 ms, #7: 238-266 ms, #8: 266-294 ms; experiment 2, microstate #5: 142-178 ms, #6: 178-226 ms, #7: 226-246 ms, #9: 262-302 ms, #10: 302-330 ms) as well as of low and high arousal (experiment 1, microstate #8: 266-294 ms, #9: 294-346 ms; experiment 2, microstate #10: 302-330 ms, #15: 562-600 ms) involved different neural assemblies. The results revealed also that in both experiments, information about valence was extracted before information about arousal. The last microstate of valence extraction was identical with the first microstate of arousal extraction.

Globale räumlich-zeitliche Klimavariabilität im Holozän (GHOST) [Global Holocene Spatial and Temporal Climate Variability]

Verbindung mariner Paläotemperatur-Kurven mit dreidimensionaler, gekoppelter Atmosphäre-Ozean Modellierung [Integrating marine multiproxy temperature estimates and three-dimensional coupled atmosphere/ocean modelling] Das Projekt war ein Beitrag zur Untersuchung des Klimas des Holozäns. Es basierte auf zwei Standbeinen: Der Heranziehung von weltweit verfügbaren, unbearbeiteten, aktualisierten und neu zusammengestellten marinen multiproxy Temperaturrekonstruktionen einerseits und der Verwendung von gekoppelten Zirkulationsmodellen für Atmosphäre und Ozean andererseits. Das Modell arbeitete mit relativ geringer Auflösung und Rechenzeit und ist für transiente Simulationen des Paläoklimas angepaßt. Für eine möglichst große globale Abdeckung der Zeitserien von Klimaproxies wurden Sedimentdaten herangezogen, die eine geringe aber dennoch höchstmögliche zeitliche Auflösung im Bereich von 50 bis 200 Jahren besitzen. Sowohl Datenrekonstruktion als auch gekoppelte Klimamodellierung erzeugten dreidimensionale Datensätze, zwei räumliche Dimensionen auf der Erdoberfläche, sowie die Zeit als dritte Dimension. Raumzeitliche Muster wurden im Rahmen des Projektes untersucht. Die eingehende Analyse rekonstruierter wie der Modell-Daten sollte einerseits das Verständnis für Klimaänderungen verbessern, die in Proxydaten gefunden werden und andererseits eine Validierung der Klimavariabilität im Modell ermöglichen. Die Musteranalyse ergab Einblicke in die Mechanismen, die zur Heterogenität von Erwärmung und Abkühlung im Holozän beitragen. Die Weiterführung der Klimasimulationen des Holozäns in die Zukunft der nächsten Jahrhunderte diente einer besseren Abschätzung der zukünftigen Klimaänderung.

The PSE-3D instability analysis methodology for flows depending strongly on two and weakly on the third spatial dimension

The present contribution discusses the development of a PSE-3D instability analysis algorithm, in which a matrix forming and storing approach is followed. Alternatively to the typically used in stability calculations spectral methods, new stable high-order finitedifference-based numerical schemes for spatial discretization 1 are employed. Attention is paid to the issue of efficiency, which is critical for the success of the overall algorithm. To this end, use is made of a parallelizable sparse matrix linear algebra package which takes advantage of the sparsity offered by the finite-difference scheme and, as expected, is shown to perform substantially more efficiently than when spectral collocation methods are used. The building blocks of the algorithm have been implemented and extensively validated, focusing on classic PSE analysis of instability on the flow-plate boundary layer, temporal and spatial BiGlobal EVP solutions (the latter necessary for the initialization of the PSE-3D), as well as standard PSE in a cylindrical coordinates using the nonparallel Batchelor vortex basic flow model, such that comparisons between PSE and PSE-3D be possible; excellent agreement is shown in all aforementioned comparisons. Finally, the linear PSE-3D instability analysis is applied to a fully three-dimensional flow composed of a counter-rotating pair of nonparallel Batchelor vortices.

The time dimension of neural network models

This review attempts to provide an insightful perspective on the role of time within neural network models and the use of neural networks for problems involving time. The most commonly used neural network models are defined and explained giving mention to important technical issues but avoiding great detail. The relationship between recurrent and feedforward networks is emphasised, along with the distinctions in their practical and theoretical abilities. Some practical examples are discussed to illustrate the major issues concerning the application of neural networks to data with various types of temporal structure, and finally some highlights of current research on the more difficult types of problems are presented.

A comparison of beta-amyloid deposition in the medial temporal lobe in sporadic Alzheimer's disease, Down's syndrome and normal elderly brains

The density of beta-amyloid (A beta) deposits was studied in the medial temporal lobe in non-demented individuals and in sporadic Alzheimer's disease (SAD) and Down's syndrome (DS). No A beta deposits were recorded in six of the non-demented cases, while in a further eight cases, these were confined to either the lateral occipitotemporal or parahippocampal gyrus. The mean density of A beta deposits in the cortex was greater in SAD and DS than in non-demented cases but with overlap between patient groups. The mean density of A beta deposits was greater in DS than SAD consistent with a gene dosage effect. The ratio of primitive to diffuse A beta deposits was greater in DS and in non-demented cases than in SAD and the ratio of classic to diffuse deposits was lowest in DS. In all groups, A beta deposits occurred in clusters which were often regularly distributed. In the cortex, the dimension of the A beta clusters was greater in SAD than in the non-demented cases and DS. The data suggest that the development of A beta pathology in the hippocampus could be a factor in the development of DS and SAD. Furthermore, the high density of A beta deposits, and in particular the high proportion of primitive type deposits, may be important in DS while the development of large clusters of A beta deposits may be a factor in SAD.

Necessary and sufficient checkpoint selection for temporal verification of high-confidence cloud workflow systems

On-time completion is an important temporal QoS (Quality of Service) dimension and one of the fundamental requirements for high-confidence workflow systems. In recent years, a workflow temporal verification framework, which generally consists of temporal constraint setting, temporal checkpoint selection, temporal verification, and temporal violation handling, has been the major approach for the high temporal QoS assurance of workflow systems. Among them, effective temporal checkpoint selection, which aims to timely detect intermediate temporal violations along workflow execution plays a critical role. Therefore, temporal checkpoint selection has been a major topic and has attracted significant efforts. In this paper, we will present an overview of work-flow temporal checkpoint selection for temporal verification. Specifically, we will first introduce the throughput based and response-time based temporal consistency models for business and scientific cloud workflow systems, respectively. Then the corresponding benchmarking checkpoint selection strategies that satisfy the property of “necessity and sufficiency” are presented. We also provide experimental results to demonstrate the effectiveness of our checkpoint selection strategies, and finally points out some possible future issues in this research area.

On Ordinal VC-Dimension and Some Notions of Complexity

We generalize the classical notion of Vapnik–Chernovenkis (VC) dimension to ordinal VC-dimension, in the context of logical learning paradigms. Logical learning paradigms encompass the numerical learning paradigms commonly studied in Inductive Inference. A logical learning paradigm is defined as a set W of structures over some vocabulary, and a set D of first-order formulas that represent data. The sets of models of ϕ in W, where ϕ varies over D, generate a natural topology W over W. We show that if D is closed under boolean operators, then the notion of ordinal VC-dimension offers a perfect characterization for the problem of predicting the truth of the members of D in a member of W, with an ordinal bound on the number of mistakes. This shows that the notion of VC-dimension has a natural interpretation in Inductive Inference, when cast into a logical setting. We also study the relationships between predictive complexity, selective complexity—a variation on predictive complexity—and mind change complexity. The assumptions that D is closed under boolean operators and that W is compact often play a crucial role to establish connections between these concepts. We then consider a computable setting with effective versions of the complexity measures, and show that the equivalence between ordinal VC-dimension and predictive complexity fails. More precisely, we prove that the effective ordinal VC-dimension of a paradigm can be defined when all other effective notions of complexity are undefined. On a better note, when W is compact, all effective notions of complexity are defined, though they are not related as in the noncomputable version of the framework.