Decompositions of injection patterns for nodal flow allocation in renewable electricity networks

The large-scale integration of fluctuating renewable power generation represents a challenge to the technical and economical design of a sustainable future electricity system. In this context, the increasing significance of long-range power transmission calls for innovative methods to understand the emerging complex flow patterns and to integrate price signals about the respective infrastructure needs into the energy market design. We introduce a decomposition method of injection patterns. Contrary to standard flow tracing approaches, it provides nodal allocations of link flows and costs in electricity networks by decomposing the network injection pattern into market-inspired elementary import/export building blocks. We apply the new approach to a simplified data-driven model of a European electricity grid with a high share of renewable wind and solar power generation.     

Still a bridge too far? Biolinguistic questions for grounding language on brains

In this paper we discuss how Fibonacci growth patterns are apparent in the structure of human language. We moreover show how familiar dynamics yielding these sorts of patterns in nature may be taken to apply, at some level of abstraction, for the human faculty of language. The overall picture casts doubts on any simplistic treatment of language behavior, of the sort stemming from classical behaviorism in psychology (which is again popular in contemporary computational models). Instead, it appears to be more profitable to study language as a complex dynamic system, emerging in human brains for physical reasons which are yet to be fully comprehended, but which in the end disfavor any dualistic approach to the study of mind in general, and the human mind in particular.     

A morphological based method to calculate the centroid spots of Hartmann patterns

We have designed a morphological filter in order to calculate the centroid spots of Hartmann patterns. This filter is based on four directional structuring elements. Each element takes into account the general morphology of the spots of the Hartmann pattern. The efficiency of the filter was tested by using a simulated pattern of spots. Finally, we apply this filter to experimental Hartmann patterns obtained for small convex lenses.     

Systematic search of the rotation-compatible polar phases in perovskite oxides: A first-principles study

In many perovskite oxides, ferroelectricity is intimately related to octahedral rotation patterns, which can suppress or enhance polar distortions. Using first-principles density functional theory, we investigate the relation between octahedral rotation patterns and polar instability. Based on the rotation patterns commonly observed in perovskite oxides, we present a workflow that allows to systematically and efficiently search for the unstable polar phonon modes and identify metastable polar structures. We apply the workflow to investigate rotation-pattern-dependent polar phases of CaSnO₃ and find metastable polar structures by changing rotation patterns. Further investigation of the polar R3c structures shows sizable polarization comparable to the conventional ferroelectrics. We discuss substrate materials having the potential to stabilize the polar structure. Our work provides an efficient way to identify new polar phases by changing the octahedral rotation patterns, which will be useful to design new functional materials in the thin-film/substrate configuration utilizing interfacial coupling.     

Linguistic analysis of the human heartbeat using frequency and rank order statistics

Complex physiologic signals may carry unique dynamical signatures that are related to their underlying mechanisms. We present a method based on rank order statistics of symbolic sequences to investigate the profile of different types of physiologic dynamics. We apply this method to heart rate fluctuations, the output of a central physiologic control system. The method robustly discriminates patterns generated from healthy and pathologic states, as well as aging. Furthermore, we observe increased randomness in the heartbeat time series with physiologic aging and pathologic states and also uncover nonrandom patterns in the ventricular response to atrial fibrillation.     

Long-range multifilamentation of femtosecond laser pulses versus air pressure

We examine the nonlinear dynamics of femtosecond filaments in air at different pressures. Emphasis is placed on the changes in multiple filamentation patterns produced by terawatt laser pulses. Principal modifications induced by pressure variations apply to the onset distance, size, and number of the filaments inside the laser bundle.     

Beam propagation in elliptical fibres

We apply an operator method, based on the scalar wave equation, to study the propagation of a Gaussian light beam in an optical fibre with an arbitrary, elliptically symmetric refractive-index profile. We discover that ray optical results, while not capable of predicting the rapid distortion of an optical beam in a non-ideal refractive-index profile, successfully simulate the general form of the optical power distributions, even in non-ideal profiles. We find that the propagation patterns for elliptical fibres with and without a strongly scattering central index dip show several unexpected similarities.     

Information-Efficient,Off-Center Sampling Results in Improved Precision in 3D Single-Particle Tracking Microscopy

In this work, we present a 3D single-particle tracking system that can apply tailored sampling patterns to selectively extract photons that yield the most information for particle localization. We demonstrate that off-center sampling at locations predicted by Fisher information utilizes photons most efficiently. When performing localization in a single dimension, optimized off-center sampling patterns gave doubled precision compared to uniform sampling. A ~20% increase in precision compared to uniform sampling can be achieved when a similar off-center pattern is used in 3D localization. Here, we systematically investigated the photon efficiency of different emission patterns in a diffraction-limited system and achieved higher precision than uniform sampling. The ability to maximize information from the limited number of photons demonstrated here is critical for particle tracking applications in biological samples, where photons may be limited.     

Multiplexing encrypted data by using polarized light

We investigate the feasibility of multiplexing, employing polarized light, a set of security encrypted data. The encryption approach is based on the double random pure-phase enciphering method. Phase conjugation operation is conducted in the reconstruction stage with the aid of a photorefractive crystal which stores the encrypted information. When storing each encrypted image, a polarization change is introduced in the system. This induces decorrelation on the speckle patterns inside the storing medium. We apply this approach for multiple image encryption. We show experimental results that confirm our approach.     

Outer-totalistic cellular automata on graphs

We present an intuitive formalism for implementing cellular automata on arbitrary topologies. By that means, we identify a symmetry operation in the class of elementary cellular automata. Moreover, we determine the subset of topologically sensitive elementary cellular automata and find that the overall number of complex patterns decreases under increasing neighborhood size in regular graphs. As exemplary applications, we apply the formalism to complex networks and compare the potential of scale-free graphs and metabolic networks to generate complex dynamics.     

Behavioural breaks in the heterogeneous agent model: The impact of herding,overconfidence, and market sentiment

The main aim of this work is to incorporate selected findings from behavioural finance into a Heterogeneous Agent Model using the Brock and Hommes (1998) [34] framework. Behavioural patterns are injected into an asset pricing framework through the so-called ‘Break Point Date’, which allows us to examine their direct impact. In particular, we analyse the dynamics of the model around the behavioural break. Price behaviour of 30 Dow Jones Industrial Average constituents covering five particularly turbulent US stock market periods reveals interesting patterns in this aspect. To replicate it, we apply numerical analysis using the Heterogeneous Agent Model extended with the selected findings from behavioural finance: herding, overconfidence, and market sentiment. We show that these behavioural breaks can be well modelled via the Heterogeneous Agent Model framework and they extend the original model considerably. Various modifications lead to significantly different results and model with behavioural breaks is also able to partially replicate price behaviour found in the data during turbulent stock market periods.     

Learning by message passing in networks of discrete synapses

We show that a message-passing process allows us to store in binary "material" synapses a number of random patterns which almost saturate the information theoretic bounds. We apply the learning algorithm to networks characterized by a wide range of different connection topologies and of size comparable with that of biological systems (e.g., [EQUATION: SEE TEXT]). The algorithm can be turned into an online-fault tolerant-learning protocol of potential interest in modeling aspects of synaptic plasticity and in building neuromorphic devices.     

Exponential Distribution for the Occurrence of Rare Patterns in Gibbsian Random Fields

We study the distribution of the occurrence of rare patterns in sufficiently mixing Gibbs random fields on the lattice ^d, d2. A typical example is the high temperature Ising model. This distribution is shown to converge to an exponential law as the size of the pattern diverges. Our analysis not only provides this convergence but also establishes a precise estimate of the distance between the exponential law and the distribution of the occurrence of finite patterns. A similar result holds for the repetition of a rare pattern. We apply these results to the fluctuation properties of occurrence and repetition of patterns: We prove a central limit theorem and a large deviation principle.     

Lifshitz Asymptotics via Linear Coupling of Disorder

We present a simple method for proving Lifshitz asymptotics for random Schrödinger operators and apply it to the Anderson and Poisson model.     

Susceptible-infected-recovered epidemics in populations with heterogeneous contact rates

Heterogeneity of contact patterns is recognized as an important feature for realistic modeling of many epidemics. During an outbreak, the frequency of contacts can vary a great deal from person to person and period to period. Contact heterogeneity has been shown to have a large impact on epidemic thresholds and the final size of epidemics. We develop and apply a model which incorporates an arbitrary distribution of contact rates. The model consists of a low-dimensional system of ordinary differential equations which incorporates arbitrary heterogeneity by making use of generating functions of the contact rate distribution. We show further how this model can be applied to the study of simple intervention strategies, such as quarantine of public venues with probability proportional to size. The dynamic model allows us to investigate the effects of gradually implementing such strategies in response to an ongoing epidemic, and we investigate these strategies using data on the contact patterns within a large US city.     

两相流相空间多元图重心轨迹动力学特征

本文提出了一种新的混沌时间序列高维相空间多元图重心轨迹动力学特征提取方法. 在确定了最佳嵌入维数和延迟时间后, 将相空间中高维矢量点映射到二维平面的雷达图上, 相应地将相空间中高维矢量点变换为对应的几何多边形. 通过提取几何多边形的重心位置得到重心轨迹动力学演化特性, 并利用重心轨迹矩特征量区分不同性质的混沌时间序列. 在此基础上, 处理分析了气液两相流电导传感器动态信号, 发现高维相空间多元图重心轨迹矩特征量不仅可以辨识泡状流、段塞流和混状流, 而且为流型动力学演化机理提供了新的分析途径.     

Entanglement as a precondition for secure quantum key distribution

We demonstrate that a necessary precondition for an unconditionally secure quantum key distribution is that both sender and receiver can use the available measurement results to prove the presence of entanglement in a quantum state that is effectively distributed between them. One can thus systematically search for entanglement using the class of entanglement witness operators that can be constructed from the observed data. We apply such analysis to two well-known quantum key distribution protocols, namely, the 4-state protocol and the 6-state protocol. As a special case, we show that, for some asymmetric error patterns, the presence of entanglement can be proven even for error rates above 25% (4-state protocol) and 33% (6-state protocol).     

Time-dependent scaling patterns in high frequency financial data

We measure the influence of different time-scales on the intraday dynamics of financial markets. This is obtained by decomposing financial time series into simple oscillations associated with distinct time-scales. We propose two new time-varying measures of complexity: 1) an amplitude scaling exponent and 2) an entropy-like measure. We apply these measures to intraday, 30-second sampled prices of various stock market indices. Our results reveal intraday trends where different time-horizons contribute with variable relative amplitudes over the course of the trading day. Our findings indicate that the time series we analysed have a non-stationary multifractal nature with predominantly persistent behaviour at the middle of the trading session and anti-persistent behaviour at the opening and at the closing of the session. We demonstrate that these patterns are statistically significant, robust, reproducible and characteristic of each stock market. We argue that any modelling, analytics or trading strategy must take into account these non-stationary intraday scaling patterns.