Fast Predictive Image Registration

We present a method to predict image deformations based on patch-wise image appearance. Specifically, we design a patch-based deep encoder-decoder network which learns the pixel/voxel-wise mapping between image appearance and registration parameters. Our approach can predict general deformation parameterizations, however, we focus on the large deformation diffeomorphic metric mapping (LDDMM) registration model. By predicting the LDDMM momentum-parameterization we retain the desirable theoretical properties of LDDMM, while reducing computation time by orders of magnitude: combined with patch pruning, we achieve a 1500x/66x speed up compared to GPU-based optimization for 2D/3D image registration. Our approach has better prediction accuracy than predicting deformation or velocity fields and results in diffeomorphic transformations. Additionally, we create a Bayesian probabilistic version of our network, which allows evaluation of deformation field uncertainty through Monte Carlo sampling using dropout at test time. We show that deformation uncertainty highlights areas of ambiguous deformations. We test our method on the OASIS brain image dataset in 2D and 3D

Itinerant ferromagnetism and intrinsic anomalous Hall effect in amorphous iron-germanium

The amorphous iron-germanium system (a-FexGe1-x) lacks long-range structural order and hence lacks a meaningful Brillouin zone. The magnetization of a-FexGe1-x is well explained by the Stoner model for Fe concentrations x above the onset of magnetic order around x=0.4, indicating that the local order of the amorphous structure preserves the spin-split density of states of the Fe-3d states sufficiently to polarize the electronic structure despite k being a bad quantum number. Measurements reveal an enhanced anomalous Hall resistivity ρxyAH relative to crystalline FeGe; this ρxyAH is compared to density-functional theory calculations of the anomalous Hall conductivity to resolve its underlying mechanisms. The intrinsic mechanism, typically understood as the Berry curvature integrated over occupied k states but shown here to be equivalent to the density of curvature integrated over occupied energies in aperiodic materials, dominates the anomalous Hall conductivity of a-FexGe1-x (0.38≤x≤0.61). The density of curvature is the sum of spin-orbit correlations of local orbital states and can hence be calculated with no reference to k space. This result and the accompanying Stoner-like model for the intrinsic anomalous Hall conductivity establish a unified understanding of the underlying physics of the anomalous Hall effect in both crystalline and disordered systems

Brand awareness of new technology in the introduction stage: A study of Blu-Ray vs HD-DVD format

The introduction of a new technology into the marketplace generally is a risky endeavour for a company, however, when there are competing new technologies of which it is believed only one can survive, winning over customers is one of the major corporate battles to be fought. This paper presents results of a survey among 1495 people regarding their awareness of the two DVD competing formats (Blu-ray and HD-DVD) in the early stages of the recent DVD format war. The results reveal that in the early stages of the format war more people were aware of the HD-DVD than of the Blu-ray format. A model is presented that predicts format awareness from four consumer characteristic constructs and four demographic variables

Effect of non-vacuum thermal annealing on high indium content InGaN films deposited by pulsed laser deposition

InGaN films with 33% and 60% indium contents were deposited by pulsed laser deposition (PLD) at a low growth temperature of 300 °C. The films were then annealed at 500-800 °C in the non-vacuum furnace for 15 min with an addition of N(2) atmosphere. X-ray diffraction results indicate that the indium contents in these two films were raised to 41% and 63%, respectively, after annealing in furnace. In(2)O(3) phase was formed on InGaN surface during the annealing process, which can be clearly observed by the measurements of auger electron spectroscopy, transmission electron microscopy and x-ray photoelectron spectroscopy. Due to the obstruction of indium out-diffusion by forming In(2)O(3) on surface, it leads to the efficient increment in indium content of InGaN layer. In addition, the surface roughness was greatly improved by removing In(2)O(3) with the etching treatment in HCl solution. Micro-photoluminescence measurement was performed to analyze the emission property of InGaN layer. For the as-grown InGaN with 33% indium content, the emission wavelength was gradually shifted from 552 to 618 nm with increasing the annealing temperature to 800 °C. It reveals the InGaN films have high potential in optoelectronic applications

ECoFFeS: A Software Using Evolutionary Computation for Feature Selection in Drug Discovery

Feature selection is of particular importance in the field of drug discovery. Many methods have been put forward for feature selection during recent decades. Among them, evolutionary computation has gained increasing attention owing to its superior global search ability. However, there still lacks a simple and efficient software for drug developers to take advantage of evolutionary computation for feature selection. To remedy this issue, in this paper, a user-friendly and standalone software, named ECoFFeS, is developed. ECoFFeS is expected to lower the entry barrier for drug developers to deal with feature selection problems at hand by using evolutionary algorithms. To the best of our knowledge, it is the first software integrating a set of evolutionary algorithms (including two modified evolutionary algorithms proposed by the authors) with various evaluation combinations for feature selection. Specifically, ECoFFeS considers both single-objective and multi-objective evolutionary algorithms, and both regression- and classification-based models to meet different requirements. Five data sets in drug discovery are collected in ECoFFeS. In addition, to reduce the total analysis time, the parallel execution technique is incorporated into ECoFFeS. The source code of ECoFFeS can be available from https://github.com/JiaweiHuang/ECoFFeS/

Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data

Training deep fully convolutional neural networks (F-CNNs) for semantic image segmentation requires access to abundant labeled data. While large datasets of unlabeled image data are available in medical applications, access to manually labeled data is very limited. We propose to automatically create auxiliary labels on initially unlabeled data with existing tools and to use them for pre-training. For the subsequent fine-tuning of the network with manually labeled data, we introduce error corrective boosting (ECB), which emphasizes parameter updates on classes with lower accuracy. Furthermore, we introduce SkipDeconv-Net (SD-Net), a new F-CNN architecture for brain segmentation that combines skip connections with the unpooling strategy for upsampling. The SD-Net addresses challenges of severe class imbalance and errors along boundaries. With application to whole-brain MRI T1 scan segmentation, we generate auxiliary labels on a large dataset with FreeSurfer and fine-tune on two datasets with manual annotations. Our results show that the inclusion of auxiliary labels and ECB yields significant improvements. SD-Net segments a 3D scan in 7 secs in comparison to 30 hours for the closest multi-atlas segmentation method, while reaching similar performance. It also outperforms the latest state-of-the-art F-CNN models.Comment: Accepted at MICCAI 201

Treatment of Linear and Nonlinear Dielectric Property of Molecular Monolayer and Submonolayer with Microscopic Dipole Lattice Model: I. Second Harmonic Generation and Sum-Frequency Generation

In the currently accepted models of the nonlinear optics, the nonlinear radiation was treated as the result of an infinitesimally thin polarization sheet layer, and a three layer model was generally employed. The direct consequence of this approach is that an apriori dielectric constant, which still does not have a clear definition, has to be assigned to this polarization layer. Because the Second Harmonic Generation (SHG) and the Sum-Frequency Generation vibrational Spectroscopy (SFG-VS) have been proven as the sensitive probes for interfaces with the submonolayer coverage, the treatment based on the more realistic discrete induced dipole model needs to be developed. Here we show that following the molecular optics theory approach the SHG, as well as the SFG-VS, radiation from the monolayer or submonolayer at an interface can be rigorously treated as the radiation from an induced dipole lattice at the interface. In this approach, the introduction of the polarization sheet is no longer necessary. Therefore, the ambiguity of the unaccounted dielectric constant of the polarization layer is no longer an issue. Moreover, the anisotropic two dimensional microscopic local field factors can be explicitly expressed with the linear polarizability tensors of the interfacial molecules. Based on the planewise dipole sum rule in the molecular monolayer, crucial experimental tests of this microscopic treatment with SHG and SFG-VS are discussed. Many puzzles in the literature of surface SHG and SFG spectroscopy studies can also be understood or resolved in this framework. This new treatment may provide a solid basis for the quantitative analysis in the surface SHG and SFG studies.Comment: 23 pages, 3 figure