Commensurate Dy magnetic ordering associated with incommensurate lattice distortion in orthorhombic DyMnO3

Synchrotron x-ray diffraction and resonant magnetic scattering experiments on single crystal DyMnO3 have been carried out between 4 and 40 K. Below TN(Dy) = 5K, the Dy magnetic moments order in a commensurate structure with propagation vector 0.5 b*. Simultaneous with the Dy magnetic ordering, an incommensurate lattice modulation with propagation vector 0.905 b* evolves while the original Mn induced modulation is suppressed and shifts from 0.78 b* to 0.81 b*. This points to a strong interference of Mn and Dy induced structural distortions in DyMnO3 besides a magnetic coupling between the Mn and Dy magnetic moments.Comment: submitted to Phys. Rev. B Rapid Communication

Detection of Physical Adversarial Attacks on Traffic Signs for Autonomous Vehicles

Current vision-based detection models within Autonomous Vehicles, can be susceptible to changes within the physical environment, which cause unexpected issues. Physical attacks on traffic signs could be malicious or naturally occurring, causing incorrect identification of the traffic sign which can drastically alter the behaviour of the autonomous vehicle. We propose two novel deep learning architectures which can be used as detection and mitigation strategy for environmental attacks. The first is an autoencoder which detects anomalies within a given traffic sign, and the second is a reconstruction model which generates a clean traffic sign without any anomalies. As the anomaly detection model has been trained on normal images, any abnormalities will provide a high reconstruction error value, indicating an abnormal traffic sign. The reconstruction model is a Generative Adversarial Network (GAN) and consists of two networks; a generator and a discriminator. These map the input traffic sign image into a meta representation as the output. By using anomaly detection and reconstruction models as mitigation strategies, we show that the performance of the other models in pipelines such as traffic sign recognition models can be significantly improved. In order to evaluate our models, several types of attack circumstances were designed and on average, the anomaly detection model achieved 0.84 accuracy with a 0.82 F1-score in real datasets whereas the reconstruction model improved performance of traffic sign recognition model from average F1-score 0.41 to 0.641

AMNet: Memorability Estimation with Attention

In this paper we present the design and evaluation of an end-to-end trainable, deep neural network with a visual attention mechanism for memorability estimation in still images. We analyze the suitability of transfer learning of deep models from image classification to the memorability task. Further on we study the impact of the attention mechanism on the memorability estimation and evaluate our network on the SUN Memorability and the LaMem datasets. Our network outperforms the existing state of the art models on both datasets in terms of the Spearman's rank correlation as well as the mean squared error, closely matching human consistency

Glass Transition in the Polaron Dynamics of CMR Manganites

Neutron scattering measurements on a bilayer manganite near optimal doping show that the short-range polarons correlations are completely dynamic at high T, but then freeze upon cooling to a temperature T* 310 K. This glass transition suggests that the paramagnetic/insulating state arises from an inherent orbital frustration that inhibits the formation of a long range orbital- and charge-ordered state. Upon further cooling into the ferromagnetic-metallic state (Tc=114 K), where the polarons melt, the diffuse scattering quickly develops into a propagating, transverse optic phonon.Comment: 4 pages, 4 figures. Physical Review Letters (in Press

The structure of intercalated water in superconducting Na0.35_{0.35}CoO2⋅_{2}\cdot1.37D2_{2}O: Implications for the superconducting phase diagram

We have used electron and neutron powder diffraction to elucidate the structural properties of superconducting \NaD. Our measurements show that our superconducting sample exhbits a number of supercells ranging from ${1/3}a^{*}$ to ${1/15}a^{*}$, but the most predominant one, observed also in the neutron data, is a double hexagonal cell with dimensions \dhx. Rietveld analysis reveals that \deut\space is inserted between CoO$_{2}$ sheets as to form a layered network of NaO$_{6}$ triangular prisms. Our model removes the need to invoke a 5K superconducting point compound and suggests that a solid solution of Na is possible within a constant amount of water $y$.Comment: 4 pages, 3 figure

Conic Multi-Task Classification

Traditionally, Multi-task Learning (MTL) models optimize the average of task-related objective functions, which is an intuitive approach and which we will be referring to as Average MTL. However, a more general framework, referred to as Conic MTL, can be formulated by considering conic combinations of the objective functions instead; in this framework, Average MTL arises as a special case, when all combination coefficients equal 1. Although the advantage of Conic MTL over Average MTL has been shown experimentally in previous works, no theoretical justification has been provided to date. In this paper, we derive a generalization bound for the Conic MTL method, and demonstrate that the tightest bound is not necessarily achieved, when all combination coefficients equal 1; hence, Average MTL may not always be the optimal choice, and it is important to consider Conic MTL. As a byproduct of the generalization bound, it also theoretically explains the good experimental results of previous relevant works. Finally, we propose a new Conic MTL model, whose conic combination coefficients minimize the generalization bound, instead of choosing them heuristically as has been done in previous methods. The rationale and advantage of our model is demonstrated and verified via a series of experiments by comparing with several other methods.Comment: Accepted by European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD)-201

Metro-Line Crossing Minimization: Hardness, Approximations, and Tractable Cases

Crossing minimization is one of the central problems in graph drawing. Recently, there has been an increased interest in the problem of minimizing crossings between paths in drawings of graphs. This is the metro-line crossing minimization problem (MLCM): Given an embedded graph and a set L of simple paths, called lines, order the lines on each edge so that the total number of crossings is minimized. So far, the complexity of MLCM has been an open problem. In contrast, the problem variant in which line ends must be placed in outermost position on their edges (MLCM-P) is known to be NP-hard. Our main results answer two open questions: (i) We show that MLCM is NP-hard. (ii) We give an $O(\sqrt{\log |L|})$-approximation algorithm for MLCM-P

Relation between crystal and magnetic structures of the layered manganites La2-2xSr1+2xMn2O7 (0.30 =< x =< 0.50)

Comprehensive neutron-powder diffraction and Rietveld analyses were carried out to clarify the relation between the crystal and magnetic structures of La2-2xSr1+2xMn2O7 (0.30 =< x =< 0.50). The Jahn-Teller (JT) distortion of Mn-O6 octahedra, i.e., the ratio of the averaged apical Mn-O bond length to the equatorial Mn-O bond length, is Delta_JT=1.042(5) at x=0.30, where the magnetic easy-axis at low temperature is parallel to the c axis. As the JT distortion becomes suppressed with increasing x, a planar ferromagnetic structure appears at x =< 0.32, which is followed by a canted antiferromagnetic (AFM) structure at x =< 0.39. The canting angle between neighboring planes continuously increases from 0 deg (planar ferromagnet: 0.32 =< x < 0.39) to 180 deg (A-type AFM: x=0.48 where Delta_JT=1.013(5)). Dominance of the A-type AF structure with decrease of JT distortion can be ascribed to the change in the eg orbital state from d3z^2-r^2 to dx^2-y^2