A practical technique for the generation of highly uniform LIPSS

AbstractLaser-induced periodic surface structures (LIPSS) can be reliably produced with ultrashort (<10ps) laser pulses given fluence near the ablation threshold. Neat, parallel, uniform structures are harder to reproduce. Electrodynamic models show a field at normal incidence interacts with the surface resulting in periodicity in intensity along the surface in the direction of the incident E-field producing ridges and toughs on the surface orthogonal to the E-field. A completely smooth surface offers nothing to perturb the eventual periodic feature formation but is very difficult to achieve: we have demonstrated that simply avoiding surface roughness components near the frequency and direction of the emergent features significantly improves uniform feature production. An appropriate unidirectional polishing process can be realised using an inexpensive spinning cloth wheel. By using a cylindrical lens we were also able to process stainless steel surfaces at 5mm2s−1 so indicating useful industrial potential

FreezeOut: Accelerate Training by Progressively Freezing Layers

The early layers of a deep neural net have the fewest parameters, but take up the most computation. In this extended abstract, we propose to only train the hidden layers for a set portion of the training run, freezing them out one-by-one and excluding them from the backward pass. Through experiments on CIFAR, we empirically demonstrate that FreezeOut yields savings of up to 20% wall-clock time during training with 3% loss in accuracy for DenseNets, a 20% speedup without loss of accuracy for ResNets, and no improvement for VGG networks. Our code is publicly available at https://github.com/ajbrock/FreezeOutComment: Extended Abstrac

Generative and Discriminative Voxel Modeling with Convolutional Neural Networks

When working with three-dimensional data, choice of representation is key. We explore voxel-based models, and present evidence for the viability of voxellated representations in applications including shape modeling and object classification. Our key contributions are methods for training voxel-based variational autoencoders, a user interface for exploring the latent space learned by the autoencoder, and a deep convolutional neural network architecture for object classification. We address challenges unique to voxel-based representations, and empirically evaluate our models on the ModelNet benchmark, where we demonstrate a 51.5% relative improvement in the state of the art for object classification.Comment: 9 pages, 5 figures, 2 table

SMASH: One-Shot Model Architecture Search through HyperNetworks

Designing architectures for deep neural networks requires expert knowledge and substantial computation time. We propose a technique to accelerate architecture selection by learning an auxiliary HyperNet that generates the weights of a main model conditioned on that model's architecture. By comparing the relative validation performance of networks with HyperNet-generated weights, we can effectively search over a wide range of architectures at the cost of a single training run. To facilitate this search, we develop a flexible mechanism based on memory read-writes that allows us to define a wide range of network connectivity patterns, with ResNet, DenseNet, and FractalNet blocks as special cases. We validate our method (SMASH) on CIFAR-10 and CIFAR-100, STL-10, ModelNet10, and Imagenet32x32, achieving competitive performance with similarly-sized hand-designed networks. Our code is available at https://github.com/ajbrock/SMAS

Laser microsculpting for the generation of robust diffractive security markings on the surface of metals

AbstractWe report the development of a laser-based process for the direct writing (‘microsculpting’) of unique security markings (reflective phase holograms) on the surface of metals. In contrast to the common approaches used for unique marking of the metal products and components, e.g., polymer holographic stickers which are attached to metals as an adhesive tape, our process enables the generation of the security markings directly onto the metal surface and thus overcomes the problems with tampering and biocompatibility which are typical drawbacks of holographic stickers. The process uses 35ns laser pulses of wavelength 355nm to generate optically-smooth deformations on the metal surface using a localised laser melting process. Security markings (holographic structures) on 304-grade stainless steel surface are fabricated, and their resulted optical performance is tested using a He–Ne laser beam of 632.8nm wavelength