The use of deep learning in image segmentation, classification and detection

Recent years have shown that deep learned neural networks are a valuable tool in the field of computer vision. This paper addresses the use of two different kinds of network architectures, namely LeNet and Network in Network (NiN). They will be compared in terms of both performance and computational efficiency by addressing the classification and detection problems. In this paper, multiple databases will be used to test the networks. One of them contains images depicting burn wounds from pediatric cases, another one contains an extensive number of art images and other facial databases were used for facial keypoints detection

High precision framework for Chaos Many-Body Engine

In this paper we present a C# 4.0 high precision framework for simulation of relativistic many-body systems. In order to benefit from, previously developed, chaos analysis instruments, all new modules were designed to be integrated with Chaos Many-Body Engine [1,3]. As a direct application, we used 46 digits precision for analyzing the Butterfly Effect of the gravitational force in a specific relativistic nuclear collision toy-model. Trying to investigate the average Lyapunov Exponent dependency on the incident momentum, an interesting case of intermittency was noticed. Based on the same framework, other high-precision simulations are currently in progress (e.g. study on the possibility of considering, hard to detect, extremely low frequency photons as one of the dark matter components)

Some phenomenological considerations on the nuclear collisions at high energies

We present some results obtained by applying the chaos theory on the numerical study of one threedimensional, relativistic, many-body quark system. The asymptotic freedom property is introduced by employing a harmonic term in the bi-particle potential. In this context, we used also the outcome of a semiclassical study, applied to the quark constituents of nucleons. Depending on the initial temperature parameter, the system can evolve toward an oscillating or an expansion regime. It is important to notice also a transition region, characterized by a partial fragmentation (higher degree of order). This effect can be observed near the critical temperature and is related to the partial overcoming of the potential barrier (corresponding to the farthest particles from the system). The degree of fragmentation is defined on the Shannon entropy basis and using the graphs theory. For analyzing the expansion tendency of one relativistic many-body system, we employed also the virial coefficient.Comment: 7 pages, 2 figures, preliminary results presented at Conference of Physics, Bucharest, 200

Semiclassical study on Proton and Neutron

Starting from the existing semiclassical studies on hydrogenoid atoms, we propose a similar intuitive exercise for the three-body quark systems corresponding to protons and neutrons. In the frame of this toy model we try to explain both the stabilities of proton and neutron with respect to the nuclear interaction, and the spectrum of nucleonic resonances with J=1/2. Our choice is motivated also by a good agreement obtained for the up and down quark rest masses report. Taking into account the deterministic chaotic behavior of many-body systems, the discussed exercise could be understood as an interesting particular case of a quantum three-body problem which admits a semiclassical treatment.Comment: 4 pages, 2 tables, National Conference of Physics (Romania 2005

Implementation of quark confinement, and retarded interactions algorithms for Chaos Many-Body Engine

In Grossu et al. (2012) we presented a Chaos Many-Body Engine (CMBE) toy-model for chaos analysis of relativistic nuclear collisions at 4.5 A GeV/c (the SKM 200 collaboration) which was later extended to Cu + Cu collisions at the maximum BNL energy. Inspired by existing quark billiards, the main goal of this work was extending CMBE to partons. Thus, we first implemented a confinement algorithm founded on some intuitive assumptions: 1) the system can be decomposed into a set of two or three-body quark white clusters; 2) the bi-particle force is limited to the domain of each cluster; 3) the physical solution conforms to the minimum potential energy requirement. Color conservation was also treated as part of the reactions logic module. As an example of use, we proposed a toy-model for p + p collisions at sqrt(s)=10 GeV and we compared it with HIJING. Another direction of interest was related to retarded interactions. Following this purpose, we implemented an Euler retarded algorithm and we tested it on a simple two-body system with attractive inverse-square-law force. First results suggest that retarded interactions may contribute to the Virial theorem anomalies (dark matter) encountered for gravitational systems (e.g. clusters of galaxies). On the other hand, the time reverse functionality implemented in CMBE v03 could be used together with retardation for analyzing the Loschmidt paradox. Regarding the application design, it is important to mention the code was refactored to SOLID. In this context, we have also written more than one hundred unit and integration tests, which represent an important indicator of application logic validity.Comment: Submission to CPC in progres

Code C# for chaos analysis of relativistic many-body systems with reactions

In this work we present a reactions module for "Chaos Many-Body Engine" (Grossu et al., 2010 [1]). Following our goal of creating a customizable, object oriented code library, the list of all possible reactions, including the corresponding properties (particle types, probability, cross-section, particles lifetime etc.), could be supplied as parameter, using a specific XML input file. Inspired by the Poincare section, we propose also the "Clusterization map", as a new intuitive analysis method of many-body systems. For exemplification, we implemented a numerical toy-model for nuclear relativistic collisions at 4.5 A GeV/c (the SKM200 collaboration). An encouraging agreement with experimental data was obtained for momentum, energy, rapidity, and angular {\pi}- distributions

Intermittency route to chaos for the nuclear billiard - a quantitative study

We extended a previous qualitative study of the intermittent behaviour of a chaotical nucleonic system, by adding a few quantitative analyses: of the configuration and kinetic energy spaces, power spectra, Shannon entropies, and Lyapunov exponents. The system is regarded as a classical "nuclear billiard" with an oscillating surface of a 2D Woods-Saxon potential well. For the monopole and dipole vibrational modes we bring new arguments in favour of the idea that the degree of chaoticity increases when shifting the oscillation frequency from the adiabatic to the resonance stage of the interaction. The order-chaos-order-chaos sequence is also thoroughly investigated and we find that, for the monopole deformation case, an intermittency pattern is again found. Moreover, coupling between one-nucleon and collective degrees of freedom is proved to be essential in obtaining chaotic states.Comment: Submitted to Physical Review C, APS REVTEX 4.1, 14 pages, 17 Postscript figure

Intermittency route to chaos for the nuclear billiard - a qualitative study

We analyze on a simple classical billiard system the onset of chaotical behaviour in different dynamical states. A classical version of the "nuclear billiard" with a 2D deep Woods-Saxon potential is used. We take into account the coupling between the single-particle and the collective degrees of freedom in the presence of dissipation for several vibrational multipolarities. For the considered oscillation modes an increasing divergence of the nucleonic trajectories from the adiabatic to the resonance regime was observed. Also, a peculiar case of intermittency is reached in the vicinity of the resonance, for the monopole case. We examine the order-to-chaos transition by performing several types of qualitative analysis including sensitive dependence on the initial conditions, single-particle phase space maps, fractal dimensions of Poincare maps and autocorrelation functions.Comment: Submitted to Physical Review C, APS REVTEX 4.1, 12 pages, 15 Postscript figures, title changed, a few references were removed and a few added, text added on the resonance condition, comments added to the "Fractal dimensions on the Poincare maps" subsection, the connection of the pkdr system with the logistic map and the first figure were remove

Study on Proton and Neutron

In this paper we study some phenomenological aspects, related to the proton and neutron stabilities. Working in the frame of the Isgur-Karl quark model, we obtained some encouraging results that could be considered as a premise for future more realistically discussions and analysis

Characterization of Gravitational Waves Signals Using Neural Networks

Gravitational wave astronomy has been already a well-established research domain for many years. Moreover, after the detection by LIGO/Virgo collaboration, in 2017, of the first gravitational wave signal emitted during the collision of a binary neutron star system, that was accompanied by the detection of other types of signals coming from the same event, multi-messenger astronomy has claimed its rights more assertively. In this context, it is of great importance in a gravitational wave experiment to have a rapid mechanism of alerting about potential gravitational waves events other observatories capable to detect other types of signals (e.g. in other wavelengths) that are produce by the same event. In this paper, we present the first progress in the development of a neural network algorithm trained to recognize and characterize gravitational wave patterns from signal plus noise data samples. We have implemented two versions of the algorithm, one that classifies the gravitational wave signals into 2 classes, and another one that classifies them into 4 classes, according to the mass ratio of the emitting source. We have obtained promising results, with 100% training and testing accuracy for the 2-class network and approximately 95% for the 4-class network. We conclude that the current version of the neural network algorithm demonstrates the ability of a well-configured and calibrated Bidirectional Long-Short Term Memory software to classify with very high accuracy and in an extremely short time gravitational wave signals, even when they are accompanied by noise. Moreover, the performance obtained with this algorithm qualifies it as a fast method of data analysis and can be used as a low-latency pipeline for gravitational wave observatories like the future LISA Mission.Comment: 51 pages, 29 figures. This work was presented at the 13th International LISA Symposium, 202