Disrupted orbital order and the pseudo-gap in layered 1T-TaS2_2

We present a state-of-the-art density functional theory (DFT) study which models crucial features of the partially disordered orbital order stacking in the prototypical layered transition metal dichalcogenide 1T-TaS2 . Our results not only show that DFT models with realistic assumptions about the orbital order perpendicular to the layers yield band structures which agree remarkably well with experiments. They also demonstrate that DFT correctly predicts the formation of an excitation pseudo-gap which is commonly attributed to Mott-Hubbard type electron-electron correlations. These results highlight the importance of interlayer interactions in layered transition metal dichalcogenides and serve as an intriguing example of how disorder within an electronic crystal can give rise to pseudo-gap features

Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning

We show that denoising of 3D point clouds can be learned unsupervised, directly from noisy 3D point cloud data only. This is achieved by extending recent ideas from learning of unsupervised image denoisers to unstructured 3D point clouds. Unsupervised image denoisers operate under the assumption that a noisy pixel observation is a random realization of a distribution around a clean pixel value, which allows appropriate learning on this distribution to eventually converge to the correct value. Regrettably, this assumption is not valid for unstructured points: 3D point clouds are subject to total noise, i. e., deviations in all coordinates, with no reliable pixel grid. Thus, an observation can be the realization of an entire manifold of clean 3D points, which makes a na\"ive extension of unsupervised image denoisers to 3D point clouds impractical. Overcoming this, we introduce a spatial prior term, that steers converges to the unique closest out of the many possible modes on a manifold. Our results demonstrate unsupervised denoising performance similar to that of supervised learning with clean data when given enough training examples - whereby we do not need any pairs of noisy and clean training data.Comment: Proceedings of ICCV 201

Organic or contract support? Investigating cost and performance in aircraft sustainment

Over the past 15 years, the United States Air Force (USAF) has shifted toward utilizing more Contracted Logistics Support (CLS) and away from organic maintenance in their aircraft fleets. Given operating and support costs comprise 53-65% of total life-cycle costs for USAF aircraft, understanding the implications of these sustainment decisions is imperative. Utilizing a maintenance cost per flying hour metric and performing regression analysis, we find the maintenance strategy decision (CLS, mixed, or organic) is the most significant driver. We then examine performance metrics in relation to two established aircraft availability targets. Analysis of variance reveals statistically significant differences between maintenance strategies, with CLS outperforming organic in relation to the targets

Improving Resource Management in the Afghan Air Force

The nascent Afghan Air Force (AAF) is rapidly changing with new platforms programmed and existing platforms expanding. As US and coalition forces draw down, the transition of financial responsibility from American to Afghan processes is on the horizon

Single-image Tomography: 3D Volumes from 2D Cranial X-Rays

As many different 3D volumes could produce the same 2D x-ray image, inverting this process is challenging. We show that recent deep learning-based convolutional neural networks can solve this task. As the main challenge in learning is the sheer amount of data created when extending the 2D image into a 3D volume, we suggest firstly to learn a coarse, fixed-resolution volume which is then fused in a second step with the input x-ray into a high-resolution volume. To train and validate our approach we introduce a new dataset that comprises of close to half a million computer-simulated 2D x-ray images of 3D volumes scanned from 175 mammalian species. Applications of our approach include stereoscopic rendering of legacy x-ray images, re-rendering of x-rays including changes of illumination, view pose or geometry. Our evaluation includes comparison to previous tomography work, previous learning methods using our data, a user study and application to a set of real x-rays