Exactly solvable one-qubit driving fields generated via non-linear equations

Using the Hubbard representation for $SU(2)$ we write the time-evolution operator of a two-level system in the disentangled form. This allows us to map the corresponding dynamical law into a set of non-linear coupled equations. In order to find exact solutions, we use an inverse approach and find families of time-dependent Hamiltonians whose off-diagonal elements are connected with the Ermakov equation. The physical meaning of the so-obtained Hamiltonians is discussed in the context of the nuclear magnetic resonance phenomeno

Microwave Induced Instability Observed in BSCCO 2212 in a Static Magnetic Field

We have measured the microwave dissipation at 10 GHz through the imaginary part of the susceptibility, $\chi^"$, in a BSCCO 2212 single crystal in an external static magnetic field $H$ parallel to the c-axis at various fixed temperatures. The characteristics of $\chi^"(H)$ exhibit a sharp step at a field $H_{step}$ which strongly depends on the amplitude of the microwave excitation $h_{ac}$. The characteristics of $h_{ac}$ vs. $H_{step}$, qualitatively reveal the behavior expected for the magnetic field dependence of Josephson coupling.Comment: 4 pages, 3 Postscript figure

3D Shape Prediction on Convolutional Deep Belief Networks

The field of image recognition software has grown immensely in recent years with the emergence of new deep learning techniques. Deep belief networks inspired by Hinton [11] were one of the earliest methodologies of deep learning in the late 2000s. More recently, convolutional neural networks have been used in deep learning techniques, architecture, and software to identify patterns in imagery in order to make predictions such as classification, image segmentation, etc. Traditional two-dimensional, or 2D, images stored as picture files, typically contain red, green, and blue color data for each individual pixel in the picture. However, more recent commercial 2.5D or depth cameras have become more readily available such as the Microsoft Kinect, which is capable of capturing both RGB and depth (RGB-D) data. With the new depth dimension that can be captured from these cameras, objects are no longer limited to a flat dimension and the volumetric shape of the object can now be used to aid in recognizing that particular object. In this project, I will utilize a convolutional deep belief network in order to observe the effects of rotation and sliding window stride when conducting classification on 3D models. An early study conducted named 3D ShapeNets experimented with this idea utilizing 3D computer aided design (CAD) model data in order to classify 3D models [2]. Extending from this research, the results from my research experiment showed an adverse correlation between angle granularity and recognition accuracy. Moreover, in regards to sliding window stride length, the training time increased substantially but had little effect on overall 3D model classification

Atlas de la flora alóctona de Madrid, I. Monilophyta-Gymnospermae

En este artículo se incluye la primera parte del atlas de flora alóctona de la Comunidad de Madrid, que incluye los taxones de helechos y gimnospermas. Únicamente se han considerado taxones que crecen fuera de zonas urbanas, parques y jardines, tanto introducidos como naturalizados. En total se han analizado 49 taxones, 2 de helechos y 47 de gimnospermas (33 Pinaceae y 14 Cupressaceae). De ellos, se han considerado 34 taxones (1 Salviniaceae, 22 Pinaceae, 11 Cupressaceae) incluyendo mapas de distribución, mientras que en otros 15 su presencia es dudosa en la actualidad o están únicamente localizadas en zonas urbanas. Dominan las especies de Pinaceae como resultado de las plantaciones forestales realizadas. Buena parte de las especies consideradas tienen capacidad para naturalizarse (se tiene constancia en 19 de ellas), pero por lo general su capacidad de expansión es limitada, no siendo invasoras; únicamente Azolla filiculoides Lam. tiene un comportamiento invasor, aunque su distribución en Madrid parece estable

Effects of smoking on vital capacity in healthy students

INTRODUCTION: Although the tobacco consumption has been reported to obstruct the effects of physical culture in young adults, there are few reports that include physical and laboratory evidence of this. Health education appears not to prevent impairment of the vital capacity associated with tobacco consumption. PURPOSE: The purpose of this study was estimate the effect of tobacco consumption on vital capacity after four months of participation in a theoretical-practical program on movement fundamentals.METHODS: Preexperimental design of two measurements. Lung function and a physical test were performed on seventeen healthy students. Course-Navette test was carried out to estimate vital capacity (heart rate at rest, maximum heart rate, physical level, VO2 max, distance and average speed). Forced expiratory volume in one second (FEV1) was measured by Welch Allyn Schiller spirometer. The sample was divided on the consumption of cigarettes (12 consumers vs. 5 abstainers). T-tests were used to evaluate the differences between groups. RESULTS: Participants were men of 20.94 years (SD = 2.69, 18-19 years) with normal body complexion (Body mass index = 24.51 kg/m 2 (SD = 1.69). There were no baseline differences between groups regarding age or body composition (p \u3e .05). Differences in all parameters related to vital capacity were observed in the abstainers group (p \u3c .01), except for maximal heart rate and resting heart rate (p \u3e .05). The smokers group decreased their results in both tests but without significant differences. CONCLUSION: Tobacco consumption affected the vital capacity of young adults despite the participation in theoretical-practical program of fundamentals of the movement. Tobacco abstinence coupled with participation in an educational program increased vital capacity measured with a physical and a laboratory test

Collapse of Non-Axisymmetric Cavities

A round disk with a harmonic disturbance impacts on a water surface and creates a non-axisymmetric cavity which collapses under the influence of hydrostatic pressure. We use disks deformed with mode m=2 to m=6. For all mode numbers we find clear evidence for a phase inversion of the cavity wall during the collapse. We present a fluid dynamics video showing high speed imaging of different modes, pointing out the characteristic features during collapse

Dispersionful analogues of Benney's equations and NN-wave systems

We recall Krichever's construction of additional flows to Benney's hierarchy, attached to poles at finite distance of the Lax operator. Then we construct a ``dispersionful'' analogue of this hierarchy, in which the role of poles at finite distance is played by Miura fields. We connect this hierarchy with $N$-wave systems, and prove several facts about the latter (Lax representation, Chern-Simons-type Lagrangian, connection with Liouville equation, $\tau$-functions).Comment: 12 pages, latex, no figure

Infinite Hopf family of elliptic algebras and bosonization

Elliptic current algebras E_{q,p}(\hat{g}) for arbitrary simply laced finite dimensional Lie algebra g are defined and their co-algebraic structures are studied. It is shown that under the Drinfeld like comultiplications, the algebra E_{q,p}(\hat{g}) is not co-closed for any g. However putting the algebras E_{q,p}(\hat{g}) with different deformation parameters together, we can establish a structure of infinite Hopf family of algebras. The level 1 bosonic realization for the algebra E_{q,p}(\hat{g}) is also established.Comment: LaTeX, 11 pages. This is the new and final versio

A deep neural network for positioning and inter-crystal scatter identification in multiplexed PET detectors

Objective: Conventional event positioning algorithms in light-sharing PET detectors are often limited by edge effects and the impact of inter-crystal scattering (ICS). This study explores the feasibility of deep neural network (DNN) techniques for more precise event positioning in finely segmented and highly multiplexed PET detectors with light-sharing. Approach: A DNN was designed for crystal localisation, and trained/tested with light distributions of photoelectric (P) and Compton/photoelectric (CP) events simulated using optical GATE and an efficient analytical method. Using the statistical properties of ICS events from simulation, an energy-guided positioning algorithm was built into the DNN, enabling selection of the unique or first crystal of interaction in P and CP events, respectively. Performance of the DNN was compared with Anger logic using light distributions from simulated 511-keV point sources near the PET detector. Results: Despite coarse photodetector data due to signal multiplexing, the DNN demonstrated a crystal classification accuracy of 90% for P events and 82% for CP events. For crystal positioning, the DNN outperformed Anger logic by at least 34% and 14% for P and CP events, respectively. Further improvement is somewhat constrained by the physics, specifically, the ratio of backward to forward scattering of gamma rays within the crystal array being close to 1. This prevents selecting the first crystal of interaction in CP events with a high degree of certainty. Significance: Light-sharing and multiplexed PET detectors are common in high-resolution PET, yet event positioning can be poor due to edge effects and ICS events. Our study shows that DNN-based event positioning can enhance 2D coincidence event positioning accuracy by nearly a factor of 2 compared to Anger logic. However, further improvements are difficult to foresee without timing information