No Arabic abstract
The idea of predicting the future from the knowledge of the past is quite natural when dealing with systems whose equations of motion are not known. Such a long-standing issue is revisited in the light of modern ergodic theory of dynamical systems and becomes particularly interesting from a pedagogical perspective due to its close link with Poincares recurrence. Using such a connection, a very general result of ergodic theory - Kacs lemma - can be used to establish the intrinsic limitations to the possibility of predicting the future from the past. In spite of a naive expectation, predictability results to be hindered rather by the effective number of degrees of freedom of a system than by the presence of chaos. If the effective number of degrees of freedom becomes large enough, regardless the regular or chaotic nature of the system, predictions turn out to be practically impossible. The discussion of these issues is illustrated with the help of the numerical study of simple models.
We present a personal view of the state of the art in turbulence research. We summarize first the main achievements in the recent past, and then point ahead to the main challenges that remain for experimental and theoretical efforts.
The primordial abundance of 7Li as predicted by Big Bang Nucleosynthesis (BBN) is more than a factor 2 larger than what has been observed in metal-poor halo stars. Herein, we analyze the possibility that this discrepancy originates from incorrect assumptions about the nuclear reaction cross sections relevant for BBN. To do this, we introduce an efficient method to calculate the changes in the 7Li abundance produced by arbitrary (temperature dependent) modifications of the nuclear reaction rates. Then, considering that 7Li is mainly produced from 7Be via the electron capture process 7Be + e -> 7Li + nu_e, we assess the impact of the various channels of 7Be destruction. Differently from previous analysis, we consider the role of unknown resonances by using a complete formalism which takes into account the effect of Coulomb and centrifugal barrier penetration and that does not rely on the use of the narrow-resonance approximation. As a result of this, the possibility of a nuclear physics solution to the 7Li problem is significantly suppressed. Given the present experimental and theoretical constraints, it is unlikely that the 7Be + n destruction rate is underestimated by the 2.5 factor required to solve the problem. We exclude, moreover, that resonant destruction in the channels 7Be + t and 7Be + 3He can explain the 7Li puzzle. New unknown resonances in 7Be + d and 7Be + alpha could potentially produce significant effects. Recent experimental results have ruled out such a possibility for 7Be+d. On the other hand, for the 7Be + alpha channel very favorable conditions are required. The possible existence of a partially suitable resonant level in 11C is studied in the framework of a coupled-channel model and the possibility of a direct measurement is considered.
Understanding how light interacts at the nanoscale with metals, semiconductors, or ordinary dielectrics is pivotal if one is to properly engineer nano-antennas, filters and, more generally, devices that aim to harness the effects of new physical phenomena that manifest themselves at the nanoscale. We presently report experimental results on second and third harmonic generation from 20nm- and 70nm-thick gold layers, for TE- and TM-polarized incident light pulses. We highlight and discuss for the first time the relative roles bound electrons and an intensity dependent free electron density (hot electrons) play in third harmonic generation. While planar structures are generally the simplest to fabricate, metal layers that are only a few nanometers thick and partially transparent are almost never studied. Yet, transmission offers an additional reference point for comparison, which through relatively simple experimental measurements affords the opportunity to test the accuracy of available theoretical models. Our experimental results are explained well within the context of the microscopic hydrodynamic model that we employ to simulate second and third harmonic conversion efficiencies, and to simultaneously and uniquely predict the nonlinear dispersive properties of a gold nanolayer under pulsed illumination. Using our experimental observations and our model, based solely on the measured third harmonic power conversion efficiencies we predict |chi3|~10^(-18)-10^(-17)(m/V)^2, triggered mostly by hot electrons, without resorting to the implementation of a z-scan set-up.
Results regarding probable bifurcations from fixed points are presented in the context of general dynamical systems (real, random matrices), time-delay dynamical systems (companion matrices), and a set of mappings known for their properties as universal approximators (neural networks). The eigenvalue spectra is considered both numerically and analytically using previous work of Edelman et. al. Based upon the numerical evidence, various conjectures are presented. The conclusion is that in many circumstances, most bifurcations from fixed points of large dynamical systems will be due to complex eigenvalues. Nevertheless, surprising situations are presented for which the aforementioned conclusion is not general, e.g. real random matrices with Gaussian elements with a large positive mean and finite variance.
In industry, there exist plenty of scenarios where old gray photos need to be automatically colored, such as video sites and archives. In this paper, we present the HistoryNet focusing on historical persons diverse high fidelity clothing colorization based on fine grained semantic understanding and prior. Colorization of historical persons is realistic and practical, however, existing methods do not perform well in the regards. In this paper, a HistoryNet including three parts, namely, classification, fine grained semantic parsing and colorization, is proposed. Classification sub-module supplies classifying of images according to the eras, nationalities and garment types; Parsing sub-network supplies the semantic for person contours, clothing and background in the image to achieve more accurate colorization of clothes and persons and prevent color overflow. In the training process, we integrate classification and semantic parsing features into the coloring generation network to improve colorization. Through the design of classification and parsing subnetwork, the accuracy of image colorization can be improved and the boundary of each part of image can be more clearly. Moreover, we also propose a novel Modern Historical Movies Dataset (MHMD) containing 1,353,166 images and 42 labels of eras, nationalities, and garment types for automatic colorization from 147 historical movies or TV series made in modern time. Various quantitative and qualitative comparisons demonstrate that our method outperforms the state-of-the-art colorization methods, especially on military uniforms, which has correct colors according to the historical literatures.