Energy Dissipation Via Coupling With a Finite Chaotic Environment

We study the flow of energy between a harmonic oscillator (HO) and an external environment consisting of N two-degrees of freedom non-linear oscillators, ranging from integrable to chaotic according to a control parameter. The coupling between the HO and the environment is bilinear in the coordinates and scales with system size with the inverse square root of N. We study the conditions for energy dissipation and thermalization as a function of N and of the dynamical regime of the non-linear oscillators. The study is classical and based on single realization of the dynamics, as opposed to ensemble averages over many realizations. We find that dissipation occurs in the chaotic regime for a fairly small N, leading to the thermalization of the HO and environment a Boltzmann distribution of energies for a well defined temperature. We develop a simple analytical treatment, based on the linear response theory, that justifies the coupling scaling and reproduces the numerical simulations when the environment is in the chaotic regime.Comment: 7 pages, 10 figure

Energy transfer dynamics and thermalization of two oscillators interacting via chaos

We consider the classical dynamics of two particles moving in harmonic potential wells and interacting with the same external environment (HE), consisting of N non-interacting chaotic systems. The parameters are set so that when either particle is separately placed in contact with the environment, a dissipative behavior is observed. When both particles are simultaneously in contact with HE an indirect coupling between them is observed only if the particles are in near resonance. We study the equilibrium properties of the system considering ensemble averages for the case N=1 and single trajectory dynamics for N large. In both cases, the particles and the environment reach an equilibrium configuration at long times, but only for large N a temperature can be assigned to the system.Comment: 8 pages, 6 figure

Energy Dissipation Via Coupling With A Finite Chaotic Environment.

We study the flow of energy between a harmonic oscillator (HO) and an external environment consisting of N two-degrees-of-freedom nonlinear oscillators, ranging from integrable to chaotic according to a control parameter. The coupling between the HO and the environment is bilinear in the coordinates and scales with system size as 1/√N. We study the conditions for energy dissipation and thermalization as a function of N and of the dynamical regime of the nonlinear oscillators. The study is classical and based on a single realization of the dynamics, as opposed to ensemble averages over many realizations. We find that dissipation occurs in the chaotic regime for fairly small values of N, leading to the thermalization of the HO and the environment in a Boltzmann distribution of energies for a well-defined temperature. We develop a simple analytical treatment, based on the linear response theory, that justifies the coupling scaling and reproduces the numerical simulations when the environment is in the chaotic regime.8306111

Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation

Magnetic Resonance Imaging (MRI) is widely used in routine clinical diagnosis and treatment. However, variations in MRI acquisition protocols result in different appearances of normal and diseased tissue in the images. Convolutional neural networks (CNNs), which have shown to be successful in many medical image analysis tasks, are typically sensitive to the variations in imaging protocols. Therefore, in many cases, networks trained on data acquired with one MRI protocol, do not perform satisfactorily on data acquired with different protocols. This limits the use of models trained with large annotated legacy datasets on a new dataset with a different domain which is often a recurring situation in clinical settings. In this study, we aim to answer the following central questions regarding domain adaptation in medical image analysis: Given a fitted legacy model, 1) How much data from the new domain is required for a decent adaptation of the original network?; and, 2) What portion of the pre-trained model parameters should be retrained given a certain number of the new domain training samples? To address these questions, we conducted extensive experiments in white matter hyperintensity segmentation task. We trained a CNN on legacy MR images of brain and evaluated the performance of the domain-adapted network on the same task with images from a different domain. We then compared the performance of the model to the surrogate scenarios where either the same trained network is used or a new network is trained from scratch on the new dataset.The domain-adapted network tuned only by two training examples achieved a Dice score of 0.63 substantially outperforming a similar network trained on the same set of examples from scratch.Comment: 8 pages, 3 figure

Efficient Behavior of Small-World Networks

We introduce the concept of efficiency of a network, measuring how efficiently it exchanges information. By using this simple measure small-world networks are seen as systems that are both globally and locally efficient. This allows to give a clear physical meaning to the concept of small-world, and also to perform a precise quantitative a nalysis of both weighted and unweighted networks. We study neural networks and man-made communication and transportation systems and we show that the underlying general principle of their construction is in fact a small-world principle of high efficiency.Comment: 1 figure, 2 tables. Revised version. Accepted for publication in Phys. Rev. Let

Acceptability with general orderings

We present a new approach to termination analysis of logic programs. The essence of the approach is that we make use of general orderings (instead of level mappings), like it is done in transformational approaches to logic program termination analysis, but we apply these orderings directly to the logic program and not to the term-rewrite system obtained through some transformation. We define some variants of acceptability, based on general orderings, and show how they are equivalent to LD-termination. We develop a demand driven, constraint-based approach to verify these acceptability-variants. The advantage of the approach over standard acceptability is that in some cases, where complex level mappings are needed, fairly simple orderings may be easily generated. The advantage over transformational approaches is that it avoids the transformation step all together. {\bf Keywords:} termination analysis, acceptability, orderings.Comment: To appear in "Computational Logic: From Logic Programming into the Future

From controlling single processes to the complex automation of process chains by artificially intelligent control systems: the ControlInSteel project

The ControlInSteel project, a cooperation of four research institutes, revisited research projects of the last 20 years focusing on automation and control solutions applied to the downstream steel production route. During this investigation we found hints to those solutions, which were beneficial for specific problems. For our analysis, 46 projects were systematically reviewed. Taxonomies for the problem space, the solution space, the barriers and issues and the impact were developed and each project categorized along these taxonometrical dimensions. As a result, the interdependencies between solutions and impact could be analysed in a quantifiable way, which led to a new way of evaluating project success. It also brought new insights about the most promising techniques already applied and those techniques, that have been apparently not yet been applied to steel production, although being highly successful in other domains. This leads to potential future research chances for the steel production and their complex process chains. The paper will also finally demonstrate how a similar taxonometrical approach can be used to conserve expert knowledge in automation to feed a truly artificially intelligent control solution - not only exploiting machine learning methods but essentially using machine reasoning on top of the digitized expert knowledge to achieve improved process automation

integrated dynamic energy management for steel production

Abstract The steel industry is an important consumer of electrical energy having a significant impact on the electricity network and accounting to a significant part of production costs. Thus, there is the opportunity of closer cooperation between grid operators and steel industry to improve the power consumption prediction and actively contribute to a secure network operation. This paper aims to describe an overall dynamical approach for electricity demand monitoring and timely reactions to the grid situation, to avoid non flexible equipment disconnection, financial fines when deviating from energy contingent and contributing to the grid stability. Energy management, simulation, decision support procedures and process control tools will be integrated in an agent based system able to predict and manage power consumption