Goos-H\"{a}nchen-Like Shifts in Atom Optics

We consider the propagation of a matter wavepacket of two-level atoms through a square potential created by a super-Gaussian laser beam. We explore the matter wave analog of Goos-H\"{a}nchen shift within the framework of atom optics where the roles of atom and light is exchanged with respect to conventional optics. Using a vector theory, where atoms are treated as particles possessing two internal spin components, we show that not only large negative but also large positive Goos-H\"{a}nchen shifts can occur in the reflected atomic beam.Comment: 7 pages, 4 figure

Spin relaxation and coherence times for electrons at the Si/SiO2 interface

While electron spins in silicon heterostructures make attractive qubits, little is known about the coherence of electrons at the Si/SiO2 interface. We report spin relaxation (T1) and coherence (T2) times for mobile electrons and natural quantum dots at a 28Si/SiO2 interface. Mobile electrons have short T1 and T2 of 0.3 us at 5 K. In line with predictions, confining electrons and cooling increases T1 to 0.8 ms at 350 mK. In contrast, T2 for quantum dots is around 10 us at 350 mK, increasing to 30 us when the dot density is reduced by a factor of two. The quantum dot T2 is shorter than T1, indicating that T2 is not controlled by T1 at 350 mK but is instead limited by an extrinsic mechanism. The evidence suggests that this extrinsic mechanism is an exchange interaction between electrons in neighboring dots.Comment: Extended with more experiments and rewritten. 6 pages, 5 figures, to be submitted to Phys. Rev.

A general U-block model-based design procedure for nonlinear polynomial control systems

The proposition of U-model concept (in terms of providing concise and applicable solutions for complex problems) and a corresponding basic U-control design algorithm was originated in the first author's PhD thesis. The term of U-model appeared (not rigorously defined) for the first time in the first author's other journal paper, which established a framework for using linear polynomial control system design approaches to design nonlinear polynomial control systems (in brief, linear polynomial approaches → nonlinear polynomial plants). This paper represents the next milestone work - using linear state-space approaches to design nonlinear polynomial control systems (in brief, linear state-space approaches → nonlinear polynomial plants). The overall aim of the study is to establish a framework, defined as the U-block model, which provides a generic prototype for using linear state-space-based approaches to design the control systems with smooth nonlinear plants/processes described by polynomial models. For analysing the feasibility and effectiveness, sliding mode control design approach is selected as an exemplary case study. Numerical simulation studies provide a user-friendly step-by-step procedure for the readers/users with interest in their ad hoc applications. In formality, this is the first paper to present the U-model-oriented control system design in a formal way and to study the associated properties and theorems. The previous publications, in the main, have been algorithm-based studies and simulation demonstrations. In some sense, this paper can be treated as a landmark for the U-model-based research from intuitive/heuristic stage to rigour/formal/comprehensive studies

Target Contour Recovering for Tracking People in Complex Environments

Recovering people contours from partial occlusion is a challenging problem in a visual tracking system. Partial occlusions would bring about unreasonable contour changes of the target object. In this paper, a novel method is presented to detect partial occlusion on people contours and recover occluded portions. Unlike other occlusion detection methods, the proposed method is only based on contours, which makes itself more flexible to be extended for further applications. Experiments with synthetic images demonstrate the accuracy of the method for detecting partial occlusions, and experiments on real-world video sequence are also carried out to prove that the method is also good enough to be used to recover target contours

Visual focus of attention estimation using eye center localization

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Spiral magnetic structure in spin-5/2 frustrated trimerized chains in SrMn3P4O14

We study a spin-5/2 antiferromagnetic trimerized chain substance SrMn3P4O14 using neutron powder diffraction experiments. The coplanar spiral magnetic structure appears below T_N1 = 2.2(1) K. Values of several magnetic structure parameters change rapidly at T_N2 = 1.75(5) K, indicating another phase transition, although the magnetic structures above and below T_N2 are the qualitatively same. The spiral magnetic structure can be explained by frustration between nearest-neighbor and next-nearest-neighbor exchange interactions in the trimerized chains.Comment: submitted to Phys. Rev.

Identifying network communities with a high resolution

Community structure is an important property of complex networks. An automatic discovery of such structure is a fundamental task in many disciplines, including sociology, biology, engineering, and computer science. Recently, several community discovery algorithms have been proposed based on the optimization of a quantity called modularity (Q). However, the problem of modularity optimization is NP-hard, and the existing approaches often suffer from prohibitively long running time or poor quality. Furthermore, it has been recently pointed out that algorithms based on optimizing Q will have a resolution limit, i.e., communities below a certain scale may not be detected. In this research, we first propose an efficient heuristic algorithm, Qcut, which combines spectral graph partitioning and local search to optimize Q. Using both synthetic and real networks, we show that Qcut can find higher modularities and is more scalable than the existing algorithms. Furthermore, using Qcut as an essential component, we propose a recursive algorithm, HQcut, to solve the resolution limit problem. We show that HQcut can successfully detect communities at a much finer scale and with a higher accuracy than the existing algorithms. Finally, we apply Qcut and HQcut to study a protein-protein interaction network, and show that the combination of the two algorithms can reveal interesting biological results that may be otherwise undetectable.Comment: 14 pages, 5 figures. 1 supplemental file at http://cic.cs.wustl.edu/qcut/supplemental.pd

Strange Nonchaotic Attractors In A Periodically Forced Piecewise Linear System With Noise

Acknowledgments This work is supported by the National Natural Science Foundation of China (NNSFC) (Nos. 12072291, 11732014 and 12172306)Peer reviewedPostprin

Automatic Segmentation, Localization, and Identification of Vertebrae in 3D CT Images Using Cascaded Convolutional Neural Networks

This paper presents a method for automatic segmentation, localization, and identification of vertebrae in arbitrary 3D CT images. Many previous works do not perform the three tasks simultaneously even though requiring a priori knowledge of which part of the anatomy is visible in the 3D CT images. Our method tackles all these tasks in a single multi-stage framework without any assumptions. In the first stage, we train a 3D Fully Convolutional Networks to find the bounding boxes of the cervical, thoracic, and lumbar vertebrae. In the second stage, we train an iterative 3D Fully Convolutional Networks to segment individual vertebrae in the bounding box. The input to the second networks have an auxiliary channel in addition to the 3D CT images. Given the segmented vertebra regions in the auxiliary channel, the networks output the next vertebra. The proposed method is evaluated in terms of segmentation, localization, and identification accuracy with two public datasets of 15 3D CT images from the MICCAI CSI 2014 workshop challenge and 302 3D CT images with various pathologies introduced in [1]. Our method achieved a mean Dice score of 96%, a mean localization error of 8.3 mm, and a mean identification rate of 84%. In summary, our method achieved better performance than all existing works in all the three metrics