Fabrication and transport critical currents of multifilamentary MgB2/Fe wires and tapes

Multifilamentary MgB2/Fe wires and tapes with high transport critical current densities have been fabricated using a straightforward powder-in-tube (PIT) process. After annealing, we measured transport jc values up to 1.1 * 105 A/cm2 at 4.2 K and in a field of 2 T in a MgB2/Fe square wire with 7 filaments fabricated by two-axial rolling, and up to 5 * 104 A/cm2 at 4.2 K in 1 T in a MgB2/Fe tape with 7 filaments. For higher currents these multifilamentary wires and tapes quenched due to insufficient thermal stability of filaments. Both the processing routes and deformation methods were found to be important factors for fabricating multifilamentary MgB2 wires and tapes with high transport jc values.Comment: 13 pages, 7 figure

Transport Properties and Exponential n-values of Fe/MgB2 Tapes With Various MgB2 Particle Sizes

Fe/MgB2 tapes have been prepared starting with pre-reacted binary MgB2 powders. As shown by resistive and inductive measurements, the reduction of particle size to a few microns by ball milling has little influence on Bc2, while the superconducting properties of the individual MgB2 grains are essentially unchanged. Reducing the particle size causes an enhancement of Birr from 14 to 16 T, while Jc has considerably increased at high fields, its slope Jc(B) being reduced. At 4.2K, values of 5.3*10^4 and 1.2*10^3 A/cm^2 were measured at 3.5 and 10 T, respectively, suggesting a dominant role of the conditions at the grain interfaces. A systematic variation of these conditions at the interfaces is undertaken in order to determine the limit of transport properties for Fe/MgB2 tapes. The addition of 5% Mg to MgB2 powder was found to affect neither Jc nor Bc2. For the tapes with the highest Jc values, very high exponential n factors were measured: n = 148, 89 and 17 at 3.5, 5 and 10T, respectively and measurements of critical current versus applied strain have been performed. The mechanism leading to high transport critical current densities of filamentary Fe/MgB2 tapes based on MgB2 particles is discussed.Comment: Presented at ICMC 2003, 25-28 May 200

Multi-task Image Classification via Collaborative, Hierarchical Spike-and-Slab Priors

Promising results have been achieved in image classification problems by exploiting the discriminative power of sparse representations for classification (SRC). Recently, it has been shown that the use of \emph{class-specific} spike-and-slab priors in conjunction with the class-specific dictionaries from SRC is particularly effective in low training scenarios. As a logical extension, we build on this framework for multitask scenarios, wherein multiple representations of the same physical phenomena are available. We experimentally demonstrate the benefits of mining joint information from different camera views for multi-view face recognition.Comment: Accepted to International Conference in Image Processing (ICIP) 201

Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks

Predicting the future health information of patients from the historical Electronic Health Records (EHR) is a core research task in the development of personalized healthcare. Patient EHR data consist of sequences of visits over time, where each visit contains multiple medical codes, including diagnosis, medication, and procedure codes. The most important challenges for this task are to model the temporality and high dimensionality of sequential EHR data and to interpret the prediction results. Existing work solves this problem by employing recurrent neural networks (RNNs) to model EHR data and utilizing simple attention mechanism to interpret the results. However, RNN-based approaches suffer from the problem that the performance of RNNs drops when the length of sequences is large, and the relationships between subsequent visits are ignored by current RNN-based approaches. To address these issues, we propose {\sf Dipole}, an end-to-end, simple and robust model for predicting patients' future health information. Dipole employs bidirectional recurrent neural networks to remember all the information of both the past visits and the future visits, and it introduces three attention mechanisms to measure the relationships of different visits for the prediction. With the attention mechanisms, Dipole can interpret the prediction results effectively. Dipole also allows us to interpret the learned medical code representations which are confirmed positively by medical experts. Experimental results on two real world EHR datasets show that the proposed Dipole can significantly improve the prediction accuracy compared with the state-of-the-art diagnosis prediction approaches and provide clinically meaningful interpretation

The U(1)A anomaly in noncommutative SU(N) theories

We work out the one-loop $U(1)_A$ anomaly for noncommutative SU(N) gauge theories up to second order in the noncommutative parameter $\theta^{\mu\nu}$. We set $\theta^{0i}=0$ and conclude that there is no breaking of the classical $U(1)_A$ symmetry of the theory coming from the contributions that are either linear or quadratic in $\theta^{\mu\nu}$. Of course, the ordinary anomalous contributions will be still with us. We also show that the one-loop conservation of the nonsinglet currents holds at least up to second order in $\theta^{\mu\nu}$. We adapt our results to noncommutative gauge theories with SO(N) and U(1) gauge groups.Comment: 50 pages, 5 figures in eps files. Some comments and references adde

Adaptive polarization-difference transient imaging for depth estimation in scattering media

Introducing polarization into transient imaging improves depth estimation in participating media, by discriminating reflective from scattered light transport and calculating depth from the former component only. Previous works have leveraged this approach under the assumption of uniform polarization properties. However, the orientation and intensity of polarization inside scattering media is nonuniform, both in the spatial and temporal domains. As a result of this simplifying assumption, the accuracy of the estimated depth worsens significantly as the optical thickness of the medium increases. In this Letter, we introduce a novel adaptive polarization-difference method for transient imaging, taking into account the nonuniform nature of polarization in scattering media. Our results demonstrate a superior performance for impulse-based transient imaging over previous unpolarized or uniform approaches

Anatomic Insights into Disrupted Small-World Networks in Pediatric Posttraumatic Stress Disorder.

Purpose To use diffusion-tensor (DT) imaging and graph theory approaches to explore the brain structural connectome in pediatric posttraumatic stress disorder (PTSD). Materials and Methods This study was approved by the relevant research ethics committee, and all participants’ parents or guardians provided informed consent. Twenty-four pediatric patients with PTSD and 23 control subjects exposed to trauma but without PTSD were recruited after the 2008 Sichuan earthquake. The structural connectome was constructed by using DT imaging tractography and thresholding the mean fractional anisotropy of 90 brain regions to yield 90 × 90 partial correlation matrixes. Graph theory analysis was used to examine the group-specific topologic properties, and nonparametric permutation tests were used for group comparisons of topologic metrics. Results Both groups exhibited small-world topology. However, patients with PTSD showed an increase in the characteristic path length (P = .0248) and decreases in local efficiency (P = .0498) and global efficiency (P = .0274). Furthermore, patients with PTSD showed reduced nodal centralities, mainly in the default mode, salience, central executive, and visual regions (P < .05, corrected for false-discovery rate). The Clinician-Administered PTSD Scale score was negatively correlated with the nodal efficiency of the left superior parietal gyrus (r = −0.446, P = .043). Conclusion The structural connectome showed a shift toward “regularization,” providing a structural basis for functional alterations of pediatric PTSD. These abnormalities suggest that PTSD can be understood by examining the dysfunction of large-scale spatially distributed neural networks