HEAO-A nominal scanning observation schedule

The HEAO-A observatory, scheduled for launch in late June 1977, will spend most of its orbital lifetime in a scanning mode, spining from 0.03 to 0.1 rpm about an axis aligned with the sun. The dates of availability in the scan band are given for a list of 248 X-ray sources. Celestial maps of source locations and scan planes, and examples of the nighttime elevation of available sources are presented. This document is intended to aid ground-based observers in planning coordinated observations with HEAO-A

Semiclassical Accuracy in Phase Space for Regular and Chaotic Dynamics

A phase-space semiclassical approximation valid to $O(\hbar)$ at short times is used to compare semiclassical accuracy for long-time and stationary observables in chaotic, stable, and mixed systems. Given the same level of semiclassical accuracy for the short time behavior, the squared semiclassical error in the chaotic system grows linearly in time, in contrast with quadratic growth in the classically stable system. In the chaotic system, the relative squared error at the Heisenberg time scales linearly with $\hbar_{\rm eff}$, allowing for unambiguous semiclassical determination of the eigenvalues and wave functions in the high-energy limit, while in the stable case the eigenvalue error always remains of the order of a mean level spacing. For a mixed classical phase space, eigenvalues associated with the chaotic sea can be semiclassically computed with greater accuracy than the ones associated with stable islands.Comment: 9 pages, 6 figures; to appear in Physical Review

Extent and mechanism of sealing in transected giant axons of squid and earthworms

Transected axons are often assumed to seal at their cut ends by the formation of continuous membrane barriers that allow for the restoration of function in the axonal stumps. We have used several electrophysiological measures (membrane potential, input resistance, injury current density) and several morphological measures (phase-contrast, video-enhanced differential interference contrast, light, and electron microscopies) of living and fixed material to assess the extent and mechanism of sealing within hours after transecting giant axons of squid (Loligo pealeiand Sepioteuthis lessoniana) and earthworms (Lumbricus terrestris). Our electrophysiological data suggest that the proximal and distal ends of transected squid giant axons do not completely seal within 2.5 hr in physiological saline. In contrast, the same set of measures suggest that proximal and distal ends of transected earthworm giant axons seal within 1 hr in physiological saline. Our morphological data show that the cut ends of both squid and earthworm axons constrict, but that a 20- 70-am-diameter opening always remains at the cut end that is filled with vesicles. Axonal transection induces the formation of vesicles that are observed in the axoplasm within minutes in standard salines and that rapidly migrate to the cut ends. These injury-induced vesicles are loosely packed near the cut ends of squid giant axons, which do not functionally seal within 2.5 hr of transection. In contrast, vesicles formed a tightly packed plug at the cut ends of earthworm medial giant axons, which do functionally seal within 1 hr of transection in physiological saline. Since we detect no single continuous membrane that spans the cut end, sealing does not appear to occur by the fusion of constricted axolemmal membrane or the formation of a membranous partition at the cut end. Rather, our data are consistent with the hypothesis that a tightly packed vesicular plug is responsible for sealing of earthworm giant axons.This work was supported in part by NIH Grant NS31256 and ONR Grant N00014-90-J-1137 to H.M.F., an NIAAA fellowship to T.L.K., and an ATP grant to G.D.B.Neuroscienc

A 3D Coarse-to-Fine Framework for Volumetric Medical Image Segmentation

In this paper, we adopt 3D Convolutional Neural Networks to segment volumetric medical images. Although deep neural networks have been proven to be very effective on many 2D vision tasks, it is still challenging to apply them to 3D tasks due to the limited amount of annotated 3D data and limited computational resources. We propose a novel 3D-based coarse-to-fine framework to effectively and efficiently tackle these challenges. The proposed 3D-based framework outperforms the 2D counterpart to a large margin since it can leverage the rich spatial infor- mation along all three axes. We conduct experiments on two datasets which include healthy and pathological pancreases respectively, and achieve the current state-of-the-art in terms of Dice-S{\o}rensen Coefficient (DSC). On the NIH pancreas segmentation dataset, we outperform the previous best by an average of over 2%, and the worst case is improved by 7% to reach almost 70%, which indicates the reliability of our framework in clinical applications.Comment: 9 pages, 4 figures, Accepted to 3D

Faster Methods for Contracting Infinite 2D Tensor Networks

We revisit the corner transfer matrix renormalization group (CTMRG) method of Nishino and Okunishi for contracting two-dimensional (2D) tensor networks and demonstrate that its performance can be substantially improved by determining the tensors using an eigenvalue solver as opposed to the power method used in CTMRG. We also generalize the variational uniform matrix product state (VUMPS) ansatz for diagonalizing 1D quantum Hamiltonians to the case of 2D transfer matrices and discuss similarities with the corner methods. These two new algorithms will be crucial to improving the performance of variational infinite projected entangled pair state (PEPS) methods.Comment: 20 pages, 5 figures, V. Zauner-Stauber previously also published under the name V. Zaune

Double Exchange in a Magnetically Frustrated System

This work examines the magnetic order and spin dynamics of a double-exchange model with competing ferromagnetic and antiferromagnetic Heisenberg interactions between the local moments. The Heisenberg interactions are periodically arranged in a Villain configuration in two dimensions with nearest-neighbor, ferromagnetic coupling $J$ and antiferromagnetic coupling $-\eta J$. This model is solved at zero temperature by performing a $1/\sqrt{S}$ expansion in the rotated reference frame of each local moment. When $\eta$ exceeds a critical value, the ground state is a magnetically frustrated, canted antiferromagnet. With increasing hopping energy $t$ or magnetic field $B$, the local moments become aligned and the ferromagnetic phase is stabilized above critical values of $t$ or $B$. In the canted phase, a charge-density wave forms because the electrons prefer to sit on lines of sites that are coupled ferromagnetically. Due to a change in the topology of the Fermi surface from closed to open, phase separation occurs in a narrow range of parameters in the canted phase. In zero field, the long-wavelength spin waves are isotropic in the region of phase separation. Whereas the average spin-wave stiffness in the canted phase increases with $t$ or $\eta$, it exhibits a more complicated dependence on field. This work strongly suggests that the jump in the spin-wave stiffness observed in Pr$_{1-x}$Ca$_x$MnO$_3$ with $0.3 \le x \le 0.4$ at a field of 3 T is caused by the delocalization of the electrons rather than by the alignment of the antiferromagnetic regions.Comment: 28 pages, 12 figure

Scaling and Universality of the Complexity of Analog Computation

We apply a probabilistic approach to study the computational complexity of analog computers which solve linear programming problems. We analyze numerically various ensembles of linear programming problems and obtain, for each of these ensembles, the probability distribution functions of certain quantities which measure the computational complexity, known as the convergence rate, the barrier and the computation time. We find that in the limit of very large problems these probability distributions are universal scaling functions. In other words, the probability distribution function for each of these three quantities becomes, in the limit of large problem size, a function of a single scaling variable, which is a certain composition of the quantity in question and the size of the system. Moreover, various ensembles studied seem to lead essentially to the same scaling functions, which depend only on the variance of the ensemble. These results extend analytical and numerical results obtained recently for the Gaussian ensemble, and support the conjecture that these scaling functions are universal.Comment: 22 pages, latex, 12 eps fig

Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation

We aim at segmenting small organs (e.g., the pancreas) from abdominal CT scans. As the target often occupies a relatively small region in the input image, deep neural networks can be easily confused by the complex and variable background. To alleviate this, researchers proposed a coarse-to-fine approach, which used prediction from the first (coarse) stage to indicate a smaller input region for the second (fine) stage. Despite its effectiveness, this algorithm dealt with two stages individually, which lacked optimizing a global energy function, and limited its ability to incorporate multi-stage visual cues. Missing contextual information led to unsatisfying convergence in iterations, and that the fine stage sometimes produced even lower segmentation accuracy than the coarse stage. This paper presents a Recurrent Saliency Transformation Network. The key innovation is a saliency transformation module, which repeatedly converts the segmentation probability map from the previous iteration as spatial weights and applies these weights to the current iteration. This brings us two-fold benefits. In training, it allows joint optimization over the deep networks dealing with different input scales. In testing, it propagates multi-stage visual information throughout iterations to improve segmentation accuracy. Experiments in the NIH pancreas segmentation dataset demonstrate the state-of-the-art accuracy, which outperforms the previous best by an average of over 2%. Much higher accuracies are also reported on several small organs in a larger dataset collected by ourselves. In addition, our approach enjoys better convergence properties, making it more efficient and reliable in practice.Comment: Accepted to CVPR 2018 (10 pages, 6 figures