Approximating the CIECAM02 color appearance model by means of neural networks

An artificial neural network used to realize the approximating problem of the color appearance model (CAM) CIECAM02 in color management is demonstrated. GretagMacbeth ColorChecker Charts, which now axe widely used in calibration of digital camera, are chosen as samples to implement the forward and reverse color appearance models. When the predictive results are evaluated, for forward model, the output color appearance space is converted to the uniform color space based on CAM and is evaluated, while for reverse model, because the prediction precision is insufficient, we try to convert the color appearance space, which is the cylinder space, to the cube space similar to the red, green, and blue (RGB) space, and the results show that the precision is obviously improved.     

Method of “optimum observables” and implementation of neural networks in physics investigations

A separation of a signal of various physics processes from an overwhelming background is one of the most important problems in contemporary high-energy physics. The application of various multivariate statistical methods, such as the neural-network method, has become one of the popular steps toward optimizing relevant analyses. The choice of optimum variables that would disclose distinctions between a signal and a background is one of the important elements in the application of neural networks. A universal method for determining an optimum set of such kinematical variables is described in the present article. The method is based on an analysis of Feynman diagrams contributing to signal and background processes. This method was successfully implemented in searches for single top-quark production with the D0 detector (Tevatron, Fermilab) in analyzing Run I and Run II data. Brief recommendations concerning an optimum implementation of the neural-network method in physics analysis are given on the basis of experience gained in searches for single top-quark production with the D0 detector.     

Discrimination of neutrons and γ-rays in liquid scintillator based on Elman neural network

In this work,a new neutron and γ(n/γ) discrimination method based on an Elman Neural Network(ENN) is proposed to improve the discrimination performance of liquid scintillator(LS) detectors.Neutron andγ data were acquired from an EJ-335 LS detector,which was exposed in a ~(241)Am-~9Be radiation field.Neutron and γ events were discriminated using two methods of artificial neural network including the ENN and a typical Back Propagation Neural Network(BPNN) as a control.The results show that the two methods have different n/γdiscrimination performances.Compared to the BPNN,the ENN provides an improved of Figure of Merit(FOM)in n/γ discrimination.The FOM increases from 0.907 ± 0.034 to 0.953 ± 0.037 by using the new method of the ENN.The proposed n/γ discrimination method based on ENN provides a new choice of pulse shape discrimination in neutron detection.     

Signal-background discrimination with convolutional neural networks in the Panda X-Ⅲ experiment using MC simulation

The Panda X-Ⅲ experiment will search for neutrinoless double beta decay of136 Xe with high pressure gaseous time projection chambers at the China Jin-Ping underground Laboratory. The tracking feature of gaseous detectors helps suppress the background level, resulting in the improvement of the detection sensitivity. We study a method based on the convolutional neural networks to discriminate double beta decay signals against the background from high energy gammas generated by214 Bi and208 Tl decays based on detailed Monte Carlo simulation. Using the 2-dimensional projections of recorded tracks on two planes, the method successfully suppresses the background level by a factor larger than 100 with a high signal efficiency. An improvement of 62%on the efficiency ratio of ?_s/(?_b)～1/2 is achieved in comparison with the baseline in the Panda X-Ⅲ conceptual design report.     

Error and attack tolerance of synchronization in Hindmarsh–Rose neural networks with community structure

Synchronization is one of the most important features observed in large-scale complex networks of interacting dynamical systems. As is well known, there is a close relation between the network topology and the network synchronizability. Using the coupled Hindmarsh–Rose neurons with community structure as a model network, in this paper we explore how failures of the nodes due to random errors or intentional attacks affect the synchronizability of community networks. The intentional attacks are realized by removing a fraction of the nodes with high values in some centrality measure such as the centralities of degree, eigenvector, betweenness and closeness. According to the master stability function method, we employ the algebraic connectivity of the considered community network as an indicator to examine the network synchronizability. Numerical evidences show that the node failure strategy based on the betweenness centrality has the most influence on the synchronizability of community networks. With this node failure strategy for a given network with a fixed number of communities, we find that the larger the degree of communities, the worse the network synchronizability; however, for a given network with a fixed degree of communities, we observe that the more the number of communities, the better the network synchronizability.     

Energy Distribution in the SU（1,1）Coherent State of Hydrogen

We Construct and SU(1,1)coherent state for hydrogen,we discuss its properties,and we give the energy distribution law of the atom.The coherent state may be used to describe the atomic states of the discrete spectrum.     

Effects of the switching time in OPS/OBS networks

In this paper we present an analytical model, which considers the effects of the switching time on the network performance in service differentiated optical packet/burst switching (OPS/OBS) networks. Our results indicate that the switching time must be less than 10 % of the packet/burst duration in order to avoid any significant reductions in the network performance. Furthermore, regarding a network with full     

On the role of Δ-resonance in the pion production in collisions of heavy intermediate-energy ions in the hydrodynamic approach

Pion production in heavy-ion collisions is considered within the hydrodynamic approach. It is shown that consideration of pion production as a result of Δ-resonance decay (Δ → N + π) leads to hardening of the high-energy “tails” of subthreshold pions.     

Model of a neural network inertial satellite navigation system capable of estimating the earth’s gravitational field gradient

A model for recognizing inertial and satellite data on an object’s motion that are delivered by a set of distributed onboard sensors (newtonmeters, gyros, satellite receivers) has been described. Specifically, the model is capable of estimating the parameters of the gravitational field.     

Possibility of Direct Observation of the Bloch—Siegert Shift in Coherent Dynamics of Multiphoton Raman Transitions

JETP Letters - Dust structures formed in exothermic chain plasma chemical processes initiated by pulsed gyrotron radiation in mixtures of metal and dielectric powders are studied. The composition...     

Electric quadrupole interactions and the γ–α phase transition in Ce: the role of conduction electrons

We present a theoretical model of the “isostructural" - phase transition in Ce which is based on quadrupolar interactions due to coupled charge density fluctuations of 4f electrons and of conduction electrons. The latter are treated in tight-binding approximation. The - transition is described as an orientational ordering of quadrupolar electronic densities in a structure. The quadrupolar order of the conduction electron densities is complementary to the quadrupolar order of 4f electron densities. The inclusion of conduction electrons leads to an increase of the lattice contraction at the - transition in comparison to the sole effect of 4f electrons. We calculate the Bragg scattering law and suggest synchrotron radiation experiments in order to check the structure. Received 21 September 1999 and Received in final form 2 May 2000     

Theoretical studies of three-dimensional potential energy surfaces using neural networks and rotational spectra of the Ar–N2 complex

ABSTRACT

A new three-dimensional potential energy surface (PES) of the Ar–N₂ van der Waals complex is constructed using the neural network method based on ab initio data points at the CCSD(T) level. The aug-cc-pVQZ basis set is employed for all atoms with midbond functions. The vibrationally averaged PES V₀₀ is characterised by a global T-shaped minimum which occurs at R = 3.715 Å, θ = 90.0° with a well depth of 98.779 cm^?1. Based on our three-dimensional PES, bound-state calculations are performed for three isotopomers of Ar–¹⁴N₂, Ar–¹⁵N₂, and Ar–¹⁴N¹⁵N, and several intermolecular vibrational states are assigned by analysing the wavefunctions. Moreover, the averaged structural parameters are calculated and the pure rotational transition frequencies with J = 0--6 are predicted. The spectroscopic constants are determined by fitting the rotational energy levels. The theoretical results are in good agreement with experimental data and this work gives more accurate results than those determined previously for the Ar–N₂ complex.     

Vulnerability to paroxysmal oscillations in delayed neural networks: a basis for nocturnal frontal lobe epilepsy?

Resonance can occur in bistable dynamical systems due to the interplay between noise and delay (τ) in the absence of a periodic input. We investigate resonance in a two-neuron model with mutual time-delayed inhibitory feedback. For appropriate choices of the parameters and inputs three fixed-point attractors co-exist: two are stable and one is unstable. In the absence of noise, delay-induced transient oscillations (referred to herein as DITOs) arise whenever the initial function is tuned sufficiently close to the unstable fixed-point. In the presence of noisy perturbations, DITOs arise spontaneously. Since the correlation time for the stationary dynamics is ～τ, we approximated a higher order Markov process by a three-state Markov chain model by rescaling time as t?→?2sτ, identifying the states based on whether the sub-intervals were completely confined to one basin of attraction (the two stable attractors) or straddled the separatrix, and then determining the transition probability matrix empirically. The resultant Markov chain model captured the switching behaviors including the statistical properties of the DITOs. Our observations indicate that time-delayed and noisy bistable dynamical systems are prone to generate DITOs as switches between the two attractors occur. Bistable systems arise transiently in situations when one attractor is gradually replaced by another. This may explain, for example, why seizures in certain epileptic syndromes tend to occur as sleep stages change.     

On the role of theΔ -isobar in deep inelastic scattering on the deuteron

The role of the nuclear structure effects, viz. the boundness of nucleons and-isobar components, in the deep inelastic scattering of leptons on the deuteron is considered. It is shown that small admixture of the-isobar configurations to the deuteron wave function may lead to significant effects in the spin structure functions of the deuteronb ₁ ^D andg ₁ ^D (x): up to 4% in g ₁ ^D (x) dx and up to 10% ing ₁ ^D (x) at smallx.One of the authors (A.U.) thanks Prof. F.C. Khanna for helpful discussions and for kind offering the information used in our calculations.     

Effect of helium-neon laser on fast excitatory postsynaptic potential of neurons in the isolated rat superior cervical ganglia

The aim of this study is to further measure the effect of 632.8-nm helium-neon laser on fast excitatory postsynaptic potential (f-EPSP) of postganglionic neurons in isolated rat superior cervical ganglia by means of intracellular recording techniques. The neurons with f-EPSP were irradiated by different power densities (1 - 5 mW/cm2) laser. Irradiated by the 2-mW/cm2 laser, the amplitude of the f-EPSP could augment (P < 0.05, paired t test) and even cause action potential at the end of the first 1 - 2 minutes, the f-EPSP could descend and last for 3 8 minutes. But the amplitude of the f-EPSP of neurons irradiated by the 5-mW/cm2 laser could depress for the irradiating periods. The results show that: 1) the variation of the amplitude of f-EPSP caused by laser is power density-dependent and time-dependent; 2) there exist the second-order phases in the interaction of the helium-neon laser with neurons. These findings may provide certain evidence in explanation of the mechanisms of clinical helium-neon lase     

The maximization of the network throughput ensuring free flow conditions in traffic and transportation networks: Breakdown minimization (BM) principle versus Wardrop’s equilibria

We have revealed general physical conditions for the maximization of thenetwork throughput at which free flow conditions are ensured, i.e., traffic breakdowncannot occur in the whole traffic or transportation network. A physical measure of thenetwork – network capacity is introduced that characterizes generalfeatures of the network with respect to the maximization of the network throughput. Thenetwork capacity allows us also to make a general proof of the deterioration of trafficsystem occurring when dynamic traffic assignment is performed in a network based on theclassical Wardrop’ user equilibrium (UE) and system optimum (SO) equilibrium.