J/\Psi \to \phi \pi \pi (K \bar{K}) decays, chiral dynamcis and OZI violation

We have studied the invariant mass distributions of the \pi\pi and K \bar{K} systems for invariant masses up to 1.2 GeV from the J/\Psi \to \phi \pi\pi(K\bar{K}) decays. The approach exploits the connection between these processes and the \pi\pi and K\bar{K} strange and non-strange scalar form factors by considering the \phi meson as a spectator. The calculated scalar form factors are then matched with the ones from next-to-leading order chiral perturbation theory, including the calculation of the the K\bar{K} scalar form factors. Final state interactions in the J/\Psi \to \phi \pi\pi (K\bar{K}) processes are taken into account as rescattering effects in the system of the two pseudoscalar mesons. A very good agreement with the experimental data from DM2 and MARK-III is achieved. Furthermore, making use of SU(3) symmetry, the S-wave contribution to the \pi^+\pi^- event distribution in the J/\Psi \to \omega \pi^+\pi^- reaction is also given and the data up to energies of about 0.7 GeV are reproduced. These decays of the J/\Psi to a vector and a pair of pseudoscalars turn out to be very sensitive to OZI violating physics which we parametrize in terms of a direct OZI violation parameter and the chiral perturbation theory low energy constants L_4^r and L_6^r. These constants all come out very different from zero, lending further credit to the statement that the OZI rule is not operative in the scalar 0^{++} channel.Comment: revtex, 21 pages, 10 figures, extended discussion of the model in section 2 and some minor corrections, version accepted for publication in Nucl. Phys.

Analysis of the nature of the ϕ→γπη\phi\to\gamma\pi\eta and ϕ→γπ0π0\phi\to\gamma\pi^0\pi^0 decays

We study interference patterns in the $\phi\to(\gamma a_0+\pi^0\rho)\to\gamma\pi\eta$ and $\phi\to(\gamma f_0+\pi^0\rho)\to\gamma \pi^0\pi^0$ reactions. Taking into account the interference, we fit the experimental data and show that the background reaction does not distort the $\pi^0\eta$ spectrum in the decay $\phi\to\gamma\pi\eta$ everywhere over the energy region and does not distort the $\pi^0\pi^0$ spectrum in the decay $\phi\to\gamma\pi^0\pi^0$ in the wide region of the $\pi^0\pi^0$ system invariant mass, $m_{\pi\pi}>670$ MeV, or when the photon energy is less than 300 MeV. We discuss the details of the scalar meson production in the radiative decays and note that there are reasonable arguments in favor of the one-loop mechanism $\phi\to K^+K^-\to\gamma a_0$ and $\phi\to K^+K^-\to\gamma f_0$. We discuss also distinctions between the four-quark, molecular, and two-quark models and argue that the Novosibirsk data give evidence in favor of the four-quark nature of the scalar $a_0(980)$ and $f_0(980)$ mesons.Comment: 15 pages, 7 figures, title is changed, a few clarifying remarks are added, accepted for publication in Physical Review

Deep Domain Adaptation for Detecting Bomb Craters in Aerial Images

The aftermath of air raids can still be seen for decades after the devastating events. Unexploded ordnance (UXO) is an immense danger to human life and the environment. Through the assessment of wartime images, experts can infer the occurrence of a dud. The current manual analysis process is expensive and time-consuming, thus automated detection of bomb craters by using deep learning is a promising way to improve the UXO disposal process. However, these methods require a large amount of manually labeled training data. This work leverages domain adaptation with moon surface images to address the problem of automated bomb crater detection with deep learning under the constraint of limited training data. This paper contributes to both academia and practice (1) by providing a solution approach for automated bomb crater detection with limited training data and (2) by demonstrating the usability and associated challenges of using synthetic images for domain adaptation

Deep Domain Adaptation for Detecting Bomb Craters in Aerial Images

The aftermath of air raids can still be seen for decades after the devastating events. Unexploded ordnance (UXO) is an immense danger to human life and the environment. Through the assessment of wartime images, experts can infer the occurrence of a dud. The current manual analysis process is expensive and time-consuming, thus automated detection of bomb craters by using deep learning is a promising way to improve the UXO disposal process. However, these methods require a large amount of manually labeled training data. This work leverages domain adaptation with moon surface images to address the problem of automated bomb crater detection with deep learning under the constraint of limited training data. This paper contributes to both academia and practice (1) by providing a solution approach for automated bomb crater detection with limited training data and (2) by demonstrating the usability and associated challenges of using synthetic images for domain adaptation.Comment: 56th Annual Hawaii International Conference on System Sciences (HICSS-56

Deep Domain Adaptation for Detecting Bomb Craters in Aerial Images

The aftermath of air raids can still be seen for decades after the devastating events. Unexploded ordnance (UXO) is an immense danger to human life and the environment. Through the assessment of wartime images, experts can infer the occurrence of a dud. The current manual analysis process is expensive and time-consuming, thus automated detection of bomb craters by using deep learning is a promising way to improve the UXO disposal process. However, these methods require a large amount of manually labeled training data. This work leverages domain adaptation with moon surface images to address the problem of automated bomb crater detection with deep learning under the constraint of limited training data. This paper contributes to both academia and practice (1) by providing a solution approach for automated bomb crater detection with limited training data and (2) by demonstrating the usability and associated challenges of using synthetic images for domain adaptation

Moderne Steueralgorithmen für Forstkräne mittels künstlichen neuronalen Netzen imitieren und optimieren = Imitate and optimize modern control algorithms for forestry cranes by means of artificial neural networks

Moderne hydrostatische Arbeitsantriebe für Land- und Forstmaschinen erfordern komplexe Steueralgorithmen. Im Gegenzug bieten diese gegenüber dem Stand der Technik signifikante energetische und steuerungstechnische Vorteile, wie eine reduzierte Schwingungsneigung oder die Implementierung einer variablen Leistungsbegrenzung. Neue Algorithmen sind daher essenziell zur nachhaltigen Optimierung zukünftiger Maschinen. Am Beispiel der elektrohydraulischen Bedarfsstromsteuerung eines Forstkrans wird dargestellt, wie ein bestehender Steueralgorithmus automatisiert in ein künstliches neuronales Netz (KNN) überführt und anschließend durch den Patternsearch-Algorithmus optimiert werden kann. Die KNN-Steuerung weist bereits nach 41 Generationen optimierter Parametersätze ein der Referenzsteuerung vergleichbares Verhalten auf. Mit diesem Ansatz ist es möglich, deterministische Algorithmen in stochastische Algorithmen mit vergleichbaren Übertragungsfunktionen zu überführen, die anschließend mit Methoden des maschinellen Lernens optimiert werden können

Third harmonic ICRF heating of Deuterium beam ions on ASDEX Upgrade

We report on recent experiments on the ASDEX Upgrade (AUG) tokamak (major radius R ≈1.65 m, minor radius a ≈ 0.5 m) with third harmonic ICRF heating of deuterium beam ions. Prior to this work, the scheme has been developed and applied on the JET tokamak, the largest currently operating tokamak (R ≈ 3 m, a ≈ 1 m), for fusion product studies and for testing alpha particle diagnostics in preparation of ITER [1]. The experiments reported here demonstrate that this scheme can also be used in medium size tokamaks such as AUG despite their reduced fast ion confinement.This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training programme 2014-2018 under grant agreement No 633053. The views and opinions expressed herein do not necessarily reflect those of the European Commission.Postprint (published version

Heat as a tracer for understanding transport processes in fractured media: Theory and field assessment from multiscale thermal push-pull tracer tests

International audienceThe characterization and modeling of heat transfer in fractured media is particularly challenging as the existence of fractures at multiple scales induces highly localized flow patterns. From a theoretical and numerical analysis of heat transfer in simple conceptual models of fractured media, we show that flow channeling has a significant effect on the scaling of heat recovery in both space and time. The late time tailing of heat recovery under channeled flow is shown to diverge from the TðtÞ / t 21:5 behavior expected for the classical parallel plate model and follow the scaling TðtÞ / 1=tðlog tÞ 2 for a simple channel modeled as a tube. This scaling, which differs significantly from known scalings in mobile-immobile systems, is of purely geometrical origin: late time heat transfer from the matrix to a channel corresponds dimensionally to a radial diffusion process, while heat transfer from the matrix to a plate may be considered as a one-dimensional process. This phenomenon is also manifested on the spatial scaling of heat recovery as flow channeling affects the decay of the thermal breakthrough peak amplitude and the increase of the peak time with scale. These findings are supported by the results of a field experimental campaign performed on the fractured rock site of Ploemeur. The scaling of heat recovery in time and space, measured from thermal breakthrough curves measured through a series of push-pull tests at different scales, shows a clear signature of flow channeling. The whole data set can thus be successfully represented by a multichannel model parametrized by the mean channel density and aperture. These findings, which bring new insights on the effect of flow channeling on heat transfer in fractured rocks, show how heat recovery in geothermal tests may be controlled by fracture geometry. In addition, this highlights the interest of thermal push-pull tests as a complement to solute tracers tests to infer fracture aperture and geometry

Anomalous transport in disordered fracture networks: Spatial Markov model for dispersion with variable injection modes

We investigate tracer transport on random discrete fracture networks that are characterized by the statistics of the fracture geometry and hydraulic conductivity. While it is well known that tracer transport through fractured media can be anomalous and particle injection modes can have major impact on dispersion, the incorporation of injection modes into effective transport modeling has remained an open issue. The fundamental reason behind this challenge is that-even if the Eulerian fluid velocity is steady-the Lagrangian velocity distribution experienced by tracer particles evolves with time from its initial distribution, which is dictated by the injection mode, to a stationary velocity distribution. We quantify this evolution by a Markov model for particle velocities that are equidistantly sampled along trajectories. This stochastic approach allows for the systematic incorporation of the initial velocity distribution and quantifies the interplay between velocity distribution and spatial and temporal correlation. The proposed spatial Markov model is characterized by the initial velocity distribution, which is determined by the particle injection mode, the stationary Lagrangian velocity distribution, which is derived from the Eulerian velocity distribution, and the spatial velocity correlation length, which is related to the characteristic fracture length. This effective model leads to a time-domain random walk for the evolution of particle positions and velocities, whose joint distribution follows a Boltzmann equation. Finally, we demonstrate that the proposed model can successfully predict anomalous transport through discrete fracture networks with different levels of heterogeneity and arbitrary tracer injection modes. © 2017 Elsevier Ltd.PKK and SL acknowledge a grant (16AWMP- B066761-04) from the AWMP Program funded by the Ministry of Land, Infrastructure and Transport of the Korean government and the support from Future Research Program (2E27030) funded by the Korea Institute of Science and Technology (KIST). PKK and RJ acknowledge a MISTI Global Seed Funds award. MD acknowledges the support of the European Research Council (ERC) through the project MHetScale (617511). TLB acknowledges the support of European Research Council (ERC) through the project Re- activeFronts (648377). RJ acknowledges the support of the US Department of Energy through a DOE Early Career Award (grant DE-SC0009286). The data to reproduce the work can be obtained from the corresponding author.N

Comparing Dynamics: Deep Neural Networks versus Glassy Systems

We analyze numerically the training dynamics of deep neural networks (DNN) by using methods developed in statistical physics of glassy systems. The two main issues we address are the complexity of the loss-landscape and of the dynamics within it, and to what extent DNNs share similarities with glassy systems. Our findings, obtained for different architectures and data-sets, suggest that during the training process the dynamics slows down because of an increasingly large number of flat directions. At large times, when the loss is approaching zero, the system diffuses at the bottom of the landscape. Despite some similarities with the dynamics of mean-field glassy systems, in particular, the absence of barrier crossing, we find distinctive dynamical behaviors in the two cases, thus showing that the statistical properties of the corresponding loss and energy landscapes are different. In contrast, when the network is under-parametrized we observe a typical glassy behavior, thus suggesting the existence of different phases depending on whether the network is under-parametrized or over-parametrized