Patterns versus Characters in Subword-aware Neural Language Modeling

Words in some natural languages can have a composite structure. Elements of this structure include the root (that could also be composite), prefixes and suffixes with which various nuances and relations to other words can be expressed. Thus, in order to build a proper word representation one must take into account its internal structure. From a corpus of texts we extract a set of frequent subwords and from the latter set we select patterns, i.e. subwords which encapsulate information on character $n$-gram regularities. The selection is made using the pattern-based Conditional Random Field model with $l_1$ regularization. Further, for every word we construct a new sequence over an alphabet of patterns. The new alphabet's symbols confine a local statistical context stronger than the characters, therefore they allow better representations in ${\mathbb{R}}^n$ and are better building blocks for word representation. In the task of subword-aware language modeling, pattern-based models outperform character-based analogues by 2-20 perplexity points. Also, a recurrent neural network in which a word is represented as a sum of embeddings of its patterns is on par with a competitive and significantly more sophisticated character-based convolutional architecture.Comment: 10 page

Spreading dynamics on spatially constrained complex brain networks

The study of dynamical systems defined on complex networks provides a natural framework with which to investigate myriad features of neural dynamics and has been widely undertaken. Typically, however, networks employed in theoretical studies bear little relation to the spatial embedding or connectivity of the neural networks that they attempt to replicate. Here, we employ detailed neuroimaging data to define a network whose spatial embedding represents accurately the folded structure of the cortical surface of a rat brain and investigate the propagation of activity over this network under simple spreading and connectivity rules. By comparison with standard network models with the same coarse statistics, we show that the cortical geometry influences profoundly the speed of propagation of activation through the network. Our conclusions are of high relevance to the theoretical modelling of epileptic seizure events and indicate that such studies which omit physiological network structure risk simplifying the dynamics in a potentially significant way

Holographic Resonant Laser Printing of metasurfaces using plasmonic template

Laser printing with a spatial light modulator (SLM) has several advantages over conventional raster-writing and dot-matrix display (DMD) writing: multiple pixel exposure, high power endurance and existing software for computer generated holograms (CGH). We present a technique for the design and manufacturing of plasmonic metasurfaces based on ultrafast laser printing with an SLM. As a proof of principle, we have used this technique to laser print a plasmonic metalens as well as high resolution plasmonic color decorations. The high throughput holographic resonant laser printing (HRLP) approach enables on-demand mass-production of customized metasurfaces.Comment: Supplementary information is available upon request to author

Cognitive-based methods to facilitate learning of software applications via E-learning systems

E-learning systems, which are used for teaching complex software, can facilitate learning if they provide an appropriate teaching approach that decreases learners’ cognitive load in addition to providing practical knowledge. We believe there is lack of cognitively guided educational recommendations on how to effectively and efficiently design such learning platforms. We thus provide an integrative review paper that overviews relevant literature to cognitive load theory to provide practical solutions and an empirically validated framework to decrease learners’ cognitive load and improve the learning of complex software through E-learning systems. The solutions (which contain practical examples) are proposed based on different concepts of cognitive load theory including using analogies, worked examples and infographics to facilitate schema acquisition; keeping learners’ concentration on the target tools by preventing split-attention and redundancy effects and applying the training wheel method; using interactive videos based on embodied cognition theory and finally considering the modality and transient information effects in designing E-learning systems. These solutions are related to adapting the learning platform to human cognitive structures and can lead to increased learning performance by preventing working memory from being overwhelmed, thus facilitating the formation of schemas and resulting in more efficient and reliable learning with less effort

Observation of correlations up to the micrometer scale in sliding charge-density waves

High-resolution coherent x-ray diffraction experiment has been performed on the charge density wave (CDW) system K$_{0.3}$MoO$_3$. The $2k_F$ satellite reflection associated with the CDW has been measured with respect to external dc currents. In the sliding regime, the $2k_F$ satellite reflection displays secondary satellites along the chain axis which corresponds to correlations up to the micrometer scale. This super long range order is 1500 times larger than the CDW period itself. This new type of electronic correlation seems inherent to the collective dynamics of electrons in charge density wave systems. Several scenarios are discussed.Comment: 4 pages, 3 figures Typos added, references remove

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

Ballistic magnon heat conduction and possible Poiseuille flow in the helimagnetic insulator Cu2_2OSeO3_3

We report on the observation of magnon thermal conductivity $\kappa_m\sim$ 70 W/mK near 5 K in the helimagnetic insulator Cu$_2$OSeO$_3$, exceeding that measured in any other ferromagnet by almost two orders of magnitude. Ballistic, boundary-limited transport for both magnons and phonons is established below 1 K, and Poiseuille flow of magnons is proposed to explain a magnon mean-free path substantially exceeding the specimen width for the least defective specimens in the range 2 K $<T<$ 10 K. These observations establish Cu$_2$OSeO$_3$ as a model system for studying long-wavelength magnon dynamics.Comment: 10pp, 9 figures, accepted PRB (Editor's Suggestion

Conservation Laws in Cellular Automata

If X is a discrete abelian group and B a finite set, then a cellular automaton (CA) is a continuous map F:B^X-->B^X that commutes with all X-shifts. If g is a real-valued function on B, then, for any b in B^X, we define G(b) to be the sum over all x in X of g(b_x) (if finite). We say g is `conserved' by F if G is constant under the action of F. We characterize such `conservation laws' in several ways, deriving both theoretical consequences and practical tests, and provide a method for constructing all one-dimensional CA exhibiting a given conservation law.Comment: 19 pages, LaTeX 2E with one (1) Encapsulated PostScript figure. To appear in Nonlinearity. (v2) minor changes/corrections; new references added to bibliograph

Topological gravity on the lattice

In this paper we show that a particular twist of $\mathcal{N}=4$ super Yang-Mills in three dimensions with gauge group SU(2) possesses a set of classical vacua corresponding to the space of flat connections of the {\it complexified} gauge group $SL(2,C)$. The theory also contains a set of topological observables corresponding to Wilson loops wrapping non-trivial cycles of the base manifold. This moduli space and set of topological observables is shared with the Chern Simons formulation of three dimensional gravity and we hence conjecture that the Yang-Mills theory gives an equivalent description of the gravitational theory. Unlike the Chern Simons formulation the twisted Yang-Mills theory possesses a supersymmetric and gauge invariant lattice construction which then provides a possible non-perturbative definition of three dimensional gravity.Comment: 10 page