Competition of Orbital Antiferromagnetism With Q-Triplet-Pairing in the Ferromagnetic Superconductor UGe2_2

Within a multicomponent BCS-like framework we study the coexistence and competition of various CDW and SC order parameters in the presence of a strong ferromagnetic background. We find that the competition of unconventional CDW, also called orbital antiferromagnet, with SC at finite momentum (${\bf Q}$-triplet pairing) shows unique characteristics like an extreme sensitivity on the deviations from nesting. We argue that pressure in UGe$_2$ creates deviations from the nesting and report a phase diagram in qualitative agreement with the observed behavior of UGe$_2$.Comment: M2S Rio 200

Patterns of coexisting superconducting and particle-hole condensates

We have studied systematically the influence of particle-hole symmetric and asymmetric kinetic terms on the ordered phases that we may observe competing or coexisting in a tetragonal system. We show that there are precise patterns of triplets of ordered phases that are accessible (i.e. it is impossible to observe two of them without the third one). We found a systematic way to predict these patterns of states and tested it by identifying at least 16 different patterns of three order parameters that necessarily coexist in the presence of the kinetic terms. We show that there are two types of general equations governing the competition of all these triplets of order parameters and we provide them.Comment: Published versio

Observing extreme events in incomplete state spaces with application to rainfall estimation from satellite images

International audienceReconstructing the dynamics of nonlinear systems from observations requires the complete knowledge of its state space. In most cases, this is either impossible or at best very difficult. Here, by using a toy model, we investigate the possibility of deriving useful insights about the variability of the system from only a part of the complete state vector. We show that while some of the details of the variability might be lost, other details, especially extreme events, are successfully recovered. We then apply these ideas to the problem of rainfall estimation from satellite imagery. We show that, while reducing the number of observables reduces the correlation between actual and inferred precipitation amounts, good estimates for extreme events are still recoverable

Anti-persistence in the global temperature anomaly field

In this study, low-frequency variations in temperature anomaly are investigated by mapping temperature anomaly records onto random walks. We show evidence that global overturns in trends of temperature anomalies occur on decadal time-scales as part of the natural variability of the climate system. Paleoclimatic summer records in Europe and New-Zealand provide further support for these findings as they indicate that anti-persistence of temperature anomalies on decadal time-scale have occurred in the last 226 yrs. Atmospheric processes in the subtropics and mid-latitudes of the SH and interactions with the Southern Oceans seem to play an important role to moderate global variations of temperature on decadal time-scales

Node-weighted measures for complex networks with spatially embedded, sampled, or differently sized nodes

When network and graph theory are used in the study of complex systems, a typically finite set of nodes of the network under consideration is frequently either explicitly or implicitly considered representative of a much larger finite or infinite region or set of objects of interest. The selection procedure, e.g., formation of a subset or some kind of discretization or aggregation, typically results in individual nodes of the studied network representing quite differently sized parts of the domain of interest. This heterogeneity may induce substantial bias and artifacts in derived network statistics. To avoid this bias, we propose an axiomatic scheme based on the idea of node splitting invariance to derive consistently weighted variants of various commonly used statistical network measures. The practical relevance and applicability of our approach is demonstrated for a number of example networks from different fields of research, and is shown to be of fundamental importance in particular in the study of spatially embedded functional networks derived from time series as studied in, e.g., neuroscience and climatology.Comment: 21 pages, 13 figure

Investigating the topology of interacting networks - Theory and application to coupled climate subnetworks

Network theory provides various tools for investigating the structural or functional topology of many complex systems found in nature, technology and society. Nevertheless, it has recently been realised that a considerable number of systems of interest should be treated, more appropriately, as interacting networks or networks of networks. Here we introduce a novel graph-theoretical framework for studying the interaction structure between subnetworks embedded within a complex network of networks. This framework allows us to quantify the structural role of single vertices or whole subnetworks with respect to the interaction of a pair of subnetworks on local, mesoscopic and global topological scales. Climate networks have recently been shown to be a powerful tool for the analysis of climatological data. Applying the general framework for studying interacting networks, we introduce coupled climate subnetworks to represent and investigate the topology of statistical relationships between the fields of distinct climatological variables. Using coupled climate subnetworks to investigate the terrestrial atmosphere's three-dimensional geopotential height field uncovers known as well as interesting novel features of the atmosphere's vertical stratification and general circulation. Specifically, the new measure "cross-betweenness" identifies regions which are particularly important for mediating vertical wind field interactions. The promising results obtained by following the coupled climate subnetwork approach present a first step towards an improved understanding of the Earth system and its complex interacting components from a network perspective

Estimating the Fractal Dimension, K_2-entropy, and the Predictability of the Atmosphere

The series of mean daily temperature of air recorded over a period of 215 years is used for analysing the dimensionality and the predictability of the atmospheric system. The total number of data points of the series is 78527. Other 37 versions of the original series are generated, including ``seasonally adjusted'' data, a smoothed series, series without annual course, etc. Modified methods of Grassberger and Procaccia are applied. A procedure for selection of the ``meaningful'' scaling region is proposed. Several scaling regions are revealed in the ln C(r) versus ln r diagram. The first one in the range of larger ln r has a gradual slope and the second one in the range of intermediate ln r has a fast slope. Other two regions are settled in the range of small ln r. The results lead us to claim that the series arises from the activity of at least two subsystems. The first subsystem is low-dimensional (d_f=1.6) and it possesses the potential predictability of several weeks. We suggest that this subsystem is connected with seasonal variability of weather. The second subsystem is high-dimensional (d_f>17) and its error-doubling time is about 4-7 days. It is found that the predictability differs in dependence on season. The predictability time for summer, winter and the entire year (T_2 approx. 4.7 days) is longer than for transition-seasons (T_2 approx. 4.0 days for spring, T_2 approx. 3.6 days for autumn). The role of random noise and the number of data points are discussed. It is shown that a 15-year-long daily temperature series is not sufficient for reliable estimations based on Grassberger and Procaccia algorithms.Comment: 27 pages (LaTex version 2.09) and 15 figures as .ps files, e-mail: [email protected]

A Complement Receptor C5a Antagonist Regulates Epithelial to Mesenchymal Transition and Crystallin Expression After Lens Cataract Surgery in Mice

Purpose: To evaluate the effects of complement employing a mouse model for secondary cataract. Methods: The role of complement receptor C5a (CD88) was evaluated after cataract surgery in mice. An antagonist specific to C5a receptor was administered intraperitoneally to mice. Epithelial to mesenchymal transition (EMT) was evaluated by alpha-smooth muscle actin (α-SMA) staining and proliferation by bromodeoxyuridine (5-bromo-2\u27- deoxyuridine, BrdU) incorporation. Gene expression patterns was examined by microarray analysis and quantitative polymerase chain reaction (QPCR). Results: We found that administration of a C5aR antagonist in C57BL/6J mice decreases EMT, as evidenced by α-SMA expression, and cell proliferation. Gene expression by microarray analysis reveals discreet steps of gene regulation in the two major stages that of EMT and lens fiber differentiation in vivo. A hallmark of the microarray analysis is that the antagonist seems to be a novel stage-specific regulator of crystallin genes. At week two, which is marked by lens fiber differentiation genes encoding 12 crystallins and 3 lens-specific structural proteins were severely down-regulated. Conclusions: These results suggest a possible therapeutic role of an antagonist to C5aR in preventing secondary cataracts after surgery. Also these results suggest that crystallin gene expression can be regulated by pro-inflammatory events in the eye

Local Difference Measures between Complex Networks for Dynamical System Model Evaluation

Acknowledgments We thank Reik V. Donner for inspiring suggestions that initialized the work presented herein. Jan H. Feldhoff is credited for providing us with the STARS simulation data and for his contributions to fruitful discussions. Comments by the anonymous reviewers are gratefully acknowledged as they led to substantial improvements of the manuscript.Peer reviewedPublisher PD