Evidence For Advective Flow From Multi-Wavelength Observations Of Nova Muscae

We model the UV/optical spectrum of the black hole binary Nova Muscae as a sum of black body emissions from the outer region of an accretion disk. We show for self-consistency that scattering effects in this region are not important. The black hole mass ($M \approx 6 M_\odot$), the inclination angle ($\mu \approx 0.5$) and the distance to the source ($D \approx 5$ kpc) have been constrained by optical observations during quiescence (Orosz et al. 1996). Using these values we find that the accretion rate during the peak was ${\dot M} \approx 8 \times 10^{19}$ g sec$^{-1}$ and subsequently decayed exponentially. We define a radiative fraction ($f$) to be the ratio of the X-ray energy luminosity to the total gravitational power dissipated for a keplerian accretion disk. We find that $f \approx 0.1$ and remains nearly constant during the Ultra-soft and Soft spectral states. Thus for these states, the inner region of the accretion disk is advection dominated. $f$ probably increased to $\approx 0.5$ during the Hard state and finally decreased to $\approx 0.03$ as the source returned to quiescence.Comment: 5 figures. uses aasms4.sty, accepted by Ap

Gluon Condensates, Chiral Symmetry Breaking and Pion Wave Function

We consider here chiral symmetry breaking in quantum chromodynamics arising from gluon condensates in vacuum. Through coherent states of gluons simulating a mean field type of approximation, we show that the off-shell gluon condensates of vacuum generate a mass-like contribution for the quarks, giving rise to chiral symmetry breaking. We next note that spontaneous breaking of global chiral symmetry links the four component quark field operator to the pion wave function. This in turn yields many hadronic properties in the light quark sector in agreement with experiments, leading to the conclusion that low energy hadron properties are primarily driven by the vacuum structure of quantum chromodynamics.Comment: 25 pages, IP/BBSR/92-76, revte

Relating Green's Functions in Axial and Lorentz Gauges using Finite Field-Dependent BRS Transformations

We use finite field-dependent BRS transformations (FFBRS) to connect the Green functions in a set of two otherwise unrelated gauge choices. We choose the Lorentz and the axial gauges as examples. We show how the Green functions in axial gauge can be written as a series in terms of those in Lorentz gauges. Our method also applies to operator Green's functions. We show that this process involves another set of related FFBRS transfomations that is derivable from infinitesimal FBRS. We suggest possible applications.Comment: 20 pages, LaTex, Section 4 expanded, typos corrected; last 2 references modified; (this) revised version to appear in J. Math. Phy

Mean field and Monte Carlo studies of the magnetization-reversal transition in the Ising model

Detailed mean field and Monte Carlo studies of the dynamic magnetization-reversal transition in the Ising model in its ordered phase under a competing external magnetic field of finite duration have been presented here. Approximate analytical treatment of the mean field equations of motion shows the existence of diverging length and time scales across this dynamic transition phase boundary. These are also supported by numerical solutions of the complete mean field equations of motion and the Monte Carlo study of the system evolving under Glauber dynamics in both two and three dimensions. Classical nucleation theory predicts different mechanisms of domain growth in two regimes marked by the strength of the external field, and the nature of the Monte Carlo phase boundary can be comprehended satisfactorily using the theory. The order of the transition changes from a continuous to a discontinuous one as one crosses over from coalescence regime (stronger field) to nucleation regime (weaker field). Finite size scaling theory can be applied in the coalescence regime, where the best fit estimates of the critical exponents are obtained for two and three dimensions.Comment: 16 pages latex, 13 ps figures, typos corrected, references adde

Ion-acoustic solitary waves and shocks in a collisional dusty negative ion plasma

We study the effects of ion-dust collisions and ion kinematic viscosities on the linear ion-acoustic instability as well as the nonlinear propagation of small amplitude solitary waves and shocks (SWS) in a negative ion plasma with immobile charged dusts. {The existence of two linear ion modes, namely the `fast' and `slow' waves is shown, and their properties are analyzed in the collisional negative ion plasma.} {Using the standard reductive perturbation technique, we derive a modified Korteweg-de Vries-Burger (KdVB) equation which describes the evolution of small amplitude SWS.} {The profiles of the latter are numerically examined with parameters relevant for laboratory and space plasmas where charged dusts may be positively or negatively charged.} It is found that negative ion plasmas containing positively charged dusts support the propagation of SWS with negative potential. However, the perturbations with both positive and negative potentials may exist when dusts are negatively charged. The results may be useful for the excitation of SWS in laboratory negative ion plasmas as well as for observation in space plasmas where charged dusts may be positively or negatively charged.Comment: 13 pages, 9 figures; To appear in Physical Review

Streaming Kernelization

Kernelization is a formalization of preprocessing for combinatorially hard problems. We modify the standard definition for kernelization, which allows any polynomial-time algorithm for the preprocessing, by requiring instead that the preprocessing runs in a streaming setting and uses $\mathcal{O}(poly(k)\log|x|)$ bits of memory on instances $(x,k)$. We obtain several results in this new setting, depending on the number of passes over the input that such a streaming kernelization is allowed to make. Edge Dominating Set turns out as an interesting example because it has no single-pass kernelization but two passes over the input suffice to match the bounds of the best standard kernelization

Gate Stack Dielectric Degradation of Rare-Earth Oxides Grown on High Mobility Ge Substrates

We report on the dielectric degradation of Rare-Earth Oxides (REOs), when used as interfacial buffer layers together with HfO2 high-k films (REOs/HfO2) on high mobility Ge substrates. Metal-Oxide-Semiconductor (MOS) devices with these stacks,show dissimilar charge trapping phenomena under varying levels of Constant- Voltage-Stress (CVS) conditions, which also influences the measured densities of the interface (Nit) and border (NBT) traps. In the present study we also report on C-Vg hysteresis curves related to Nit and NBT. We also propose a new model based on Maxwell-Wagner instabilities mechanism that explains the dielectric degradations (current decay transient behavior) of the gate stack devices grown on high mobility substrates under CVS bias from low to higher fields, and which is unlike to those used for other MOS devices. Finally, the time dependent degradation of the corresponding devices revealed an initial current decay due to relaxation, followed by charge trapping and generation of stress-induced leakage which eventually lead to hard breakdown after long CVS stressing.Comment: 19pages (double space), 7 figures, original research article, Submitted to JAP (AIP

Cross Pixel Optical Flow Similarity for Self-Supervised Learning

We propose a novel method for learning convolutional neural image representations without manual supervision. We use motion cues in the form of optical flow, to supervise representations of static images. The obvious approach of training a network to predict flow from a single image can be needlessly difficult due to intrinsic ambiguities in this prediction task. We instead propose a much simpler learning goal: embed pixels such that the similarity between their embeddings matches that between their optical flow vectors. At test time, the learned deep network can be used without access to video or flow information and transferred to tasks such as image classification, detection, and segmentation. Our method, which significantly simplifies previous attempts at using motion for self-supervision, achieves state-of-the-art results in self-supervision using motion cues, competitive results for self-supervision in general, and is overall state of the art in self-supervised pretraining for semantic image segmentation, as demonstrated on standard benchmarks