A Neural Algorithm of Artistic Style

In fine art, especially painting, humans have mastered the skill to create unique visual experiences through composing a complex interplay between the content and style of an image. Thus far the algorithmic basis of this process is unknown and there exists no artificial system with similar capabilities. However, in other key areas of visual perception such as object and face recognition near-human performance was recently demonstrated by a class of biologically inspired vision models called Deep Neural Networks. Here we introduce an artificial system based on a Deep Neural Network that creates artistic images of high perceptual quality. The system uses neural representations to separate and recombine content and style of arbitrary images, providing a neural algorithm for the creation of artistic images. Moreover, in light of the striking similarities between performance-optimised artificial neural networks and biological vision, our work offers a path forward to an algorithmic understanding of how humans create and perceive artistic imagery

Isospin violation in epsilon'

On the basis of a next-to-leading-order calculation in chiral perturbation theory, the first complete analysis of isospin breaking for direct CP violation in K^0 -> 2 pi decays is performed. We find a destructive interference between three different sources of isospin violation in the CP violation parameter epsilon'. Within the uncertainties of large-N_c estimates for the low-energy constants, the isospin violating correction for epsilon' is below 15 %.Comment: 4 page

Microcanonical Thermodynamics of First Order Phase Transitions studied in the Potts Model

Phase transitions of first and second order can easily be distinguished in small systems in the microcanonical ensemble. Configurations of phase coexistence, which are suppressed in the canonical formulation, carry important information about the main characteristics of first order phase transitions like the transition temperature, the latent heat, and the interphase surface tension. The characterisitc backbending of the micro- canonical caloric equation of state T(E) (not to be confused with the well known Van der Waals loops in ordinary thermodynamics) leading to a negative specific heat is intimatly linked to the interphase surface entropy.Comment: Latex, 4 eps-figures, graphicx.st

The Design of Mechanically Compatible Fasteners for Human Mandible Reconstruction

Mechanically compatible fasteners for use with thin or weakened bone sections in the human mandible are being developed to help reduce large strain discontinuities across the bone/implant interface. Materials being considered for these fasteners are a polyetherertherketone (PEEK) resin with continuous quartz or carbon fiber for the screw. The screws were designed to have a shear strength equivalent to that of compact/trabecular bone and to be used with a conventional nut, nut plate, or an expandable shank/blind nut made of a ceramic filled polymer. Physical and finite element models of the mandible were developed in order to help select the best material fastener design. The models replicate the softer inner core of trabecular bone and the hard outer shell of compact bone. The inner core of the physical model consisted of an expanding foam and the hard outer shell consisted of ceramic particles in an epoxy matrix. This model has some of the cutting and drilling attributes of bone and may be appropriate as an educational tool for surgeons and medical students. The finite element model was exercised to establish boundary conditions consistent with the stress profiles associated with mandible bite forces and muscle loads. Work is continuing to compare stress/strain profiles of a reconstructed mandible with the results from the finite element model. When optimized, these design and fastening techniques may be applicable, not only to other skeletal structures, but to any composite structure

Electromagnetism in nonleptonic weak interactions

We construct a low-energy effective field theory that permits the complete treatment of isospin-breaking effects in nonleptonic weak interactions to next-to-leading order. To this end, we enlarge the chiral Lagrangian describing strong and Delta S=1 weak interactions by including electromagnetic terms with the photon as additional dynamical degree of freedom. The complete and minimal list of local terms at next-to-leading order is given. We perform the one-loop renormalization at the level of the generating functional and specialize to K -> pi pi decays.Comment: 17 pages, 1 figure; 2 references added, final version for publication in Nucl. Phys.

<VAP> Green Function in the Resonance Region

We analyse the three-point function of vector, axial-vector and pseudoscalar currents. In the spirit of large N_C, a resonance dominated Green function is confronted with the leading high-energy behaviour from the operator product expansion. The matching is shown to be fully compatible with a chiral resonance Lagrangian and it allows to determine some of the chiral low-energy constants of O(p^6).Comment: 13 pages, 2 figures. Published version. Results and conclusions unchange

Neural system identification for large populations separating "what" and "where"

Neuroscientists classify neurons into different types that perform similar computations at different locations in the visual field. Traditional methods for neural system identification do not capitalize on this separation of 'what' and 'where'. Learning deep convolutional feature spaces that are shared among many neurons provides an exciting path forward, but the architectural design needs to account for data limitations: While new experimental techniques enable recordings from thousands of neurons, experimental time is limited so that one can sample only a small fraction of each neuron's response space. Here, we show that a major bottleneck for fitting convolutional neural networks (CNNs) to neural data is the estimation of the individual receptive field locations, a problem that has been scratched only at the surface thus far. We propose a CNN architecture with a sparse readout layer factorizing the spatial (where) and feature (what) dimensions. Our network scales well to thousands of neurons and short recordings and can be trained end-to-end. We evaluate this architecture on ground-truth data to explore the challenges and limitations of CNN-based system identification. Moreover, we show that our network model outperforms current state-of-the art system identification models of mouse primary visual cortex.Comment: NIPS 201