Nonlinear dynamics of river runoff elucidated by horizontal visibility graphs

Horizontal Visibility Graphs (HVGs) are a recently developed method to construct networks from time series. The values of the time series are considered as the nodes of the network and are linked to each other if there is no larger value between them, such as they can “see” each other. The network properties reflect the nonlinear dynamics of the time series. For some classes of stochastic processes and for periodic time series, analytical results can be obtained for network-derived quantities such as the degree distribution, the local clustering coefficient distribution, the mean path length, and others. HVGs have the potential to discern between deterministic-chaotic and correlated-stochastic time series. Here, we investigate the sensitivity of the HVG methodology to properties and pre-processing of real-world data, i.e., time series length, the presence of ties, and deseasonalization, using a set of around 150 runoff time series from managed rivers at daily resolution from Brazil with an average length of 65 years. We show that an application of HVGs on real-world time series requires a careful consideration of data pre-processing steps and analysis methodology before robust results and interpretations can be obtained. For example, one recent analysis of the degree distribution of runoff records reported pronounced sub-exponential “long-tailed” behavior of North American rivers, whereas another study of South American rivers showed hyper-exponential “short-tailed” behavior resembling correlated noise. We demonstrate, using the dataset of Brazilian rivers, that these apparently contradictory results can be reconciled by minor differences in data-preprocessing (here: small differences in subtracting the seasonal cycle). Hence, data-preprocessing that is conventional in hydrology (“deseasonalization”) changes long-term correlations and the overall runoff dynamics substantially, and we present empirical consequences and extensive simulations to investigate these issues from a HVG methodological perspective. After carefully accounting for these methodological aspects, the HVG analysis reveals that the river runoff dataset shows indeed complex behavior that appears to stem from a superposition of short-term correlated noise and “long-tailed behaviour,” i.e., highly connected nodes. Moreover, the construction of a dam along a river tends to increase short-term correlations in runoff series. In summary, the present study illustrates the (often substantial) effects of methodological and data-preprocessing choices for the interpretation of river runoff dynamics in the HVG framework and its general applicability for real-world time series.Fil: Lange, Holger. Norwegian Institute of Bioeconomy Research; NoruegaFil: Sippel, Sebastian. Norwegian Institute of Bioeconomy Research; NoruegaFil: Rosso, Osvaldo Aníbal. Instituto Universidad Escuela de Medicina del Hospital Italiano; Argentina. Universidad de Los Andes; Chile. Universidade Federal de Alagoas; Brasil. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

3D silicon pixel detectors for the ATLAS Forward Physics experiment

The ATLAS Forward Physics (AFP) project plans to install 3D silicon pixel detectors about 210 m away from the interaction point and very close to the beamline (2-3 mm). This implies the need of slim edges of about 100-200 $\mu$m width for the sensor side facing the beam to minimise the dead area. Another challenge is an expected non-uniform irradiation of the pixel sensors. It is studied if these requirements can be met using slightly-modified FE-I4 3D pixel sensors from the ATLAS Insertable B-Layer production. AFP-compatible slim edges are obtained with a simple diamond-saw cut. Electrical characterisations and beam tests are carried out and no detrimental impact on the leakage current and hit efficiency is observed. For devices without a 3D guard ring a remaining insensitive edge of less than 15 $\mu$m width is found. Moreover, 3D detectors are non-uniformly irradiated up to fluences of several 10$^{15}$ n$_{eq}$/cm$^2$ with either a focussed 23 GeV proton beam or a 23 MeV proton beam through holes in Al masks. The efficiency in the irradiated region is found to be similar to the one in the non-irradiated region and exceeds 97% in case of favourable chip-parameter settings. Only in a narrow transition area at the edge of the hole in the Al mask, a significantly lower efficiency is seen. A follow-up study of this effect using arrays of small pad diodes for position-resolved dosimetry via the leakage current is carried out.Comment: 10 pages, submitted to JINS

Hyperreconfigurable architectures and the partition into hypercontexts problem

Dynamically reconfigurable architectures or systems are able to reconfigure their function and/or structure to suit the changing needs of a computation during run time. The increasing flexibility of modern dynamically reconfigurable systems improves their adaptability to computational needs but also makes fast reconfiguration difficult because of the large amount of reconfiguration information which has to be transferred. However, even when a computation uses this flexibility it will not use it all the time. Therefore, we propose to make the potential for reconfiguration itself reconfigurable. Such architectures are called hyperreconfigurable. Different models of hyperreconfigurable architectures are proposed in this paper. We also study a fundamental problem that emerges on such architectures, namely, to determine for a given computation when and how the potential for reconfiguration should be changed during run time so that the reconfiguration overhead is minimal. It is shown that the general problem is NP-hard but fast polynomial time algorithms are given to solve this problem for special types of hyperreconfigurable architectures. We define two example hyperreconfigurable architectures and illustrate the introduced concepts for corresponding application problems

The Partition into Hypercontexts Problem for Hyperreconfigurable Architectures

Hyperreconfigurable architectures adapt their reconfiguration abilities during run time in order to achieve fast dynamic reconfiguration. Models for such architectures have been proposed that change their ability for reconfiguration during hyperreconfiguration steps and in ordinary reconfiguration steps reconfigure the actual contexts for a computation within the limits that have been set by the last hyperreconfiguration step. In this paper we study algorithmic aspects of how to optimally decide what hyperreconfiguration steps should be done during a computation in order to minimize the total time necessary for hyperreconfiguration and ordinary reconfiguration. It is shown that the general problem is NP-hard but fast polynomial time algorithms are given to solve this problem on different types of hyperreconfigurable architectures. These include newly introduced architectures that use a cache to store hypercontexts. We define an example hyperreconfigurable architecture and illustrate the introduced concepts for three application problems

Hyperreconfigurable architectures as flexible control systems

Dynamically reconfigurable architectures or systems are able to reconfigure their function and/or structure to suit changing needs of a computation during run time. The increasing flexibility of modern dynamically reconfigurable systems improves their adaptability but also makes fast reconfiguration difficult because of the large amount of necessary reconfiguration information. However, even when a computation uses this flexibility it will not use it all the time. Therefore, we propose to make the potential for reconfigurationitself reconfigurable. This allows for speeding up reconfiguration operations during phases where only parts of the total flexibility are required. Such architectures are called hyperreconfigurable and use two types of reconfiguration operations: hyperreconfigurations for changing the reconfiguration potential and ordinary reconfigurations for actually configuring a new context for a computation

Hyperreconfigurable architectures for fast run time reconfiguration

Dynamically reconfigurable architectures or systems are able to reconfigure their function and/or structure to suit changing needs of a computation during run time. The increasing flexibility of modern dynamically reconfigurable systems improves their adaptability but also makes fast reconfiguration difficult because of the large amount of necessary reconfiguration information. However, even when a computation uses this flexibility it is not use it all the time. Therefore, we propose to make the potential for reconfiguration itself reconfigurable. This allows for speeding up reconfiguration operations during phases where only parts of the total flexibility are required. Such architectures are called hyperreconfigurable and uses two types of reconfiguration operations: hyperreconfigurations for changing the reconfiguration potential and ordinary reconfigurations for actually configuring a new context for a computation