Dark Side Augmentation: Generating Diverse Night Examples for Metric Learning

Image retrieval methods based on CNN descriptors rely on metric learning from a large number of diverse examples of positive and negative image pairs. Domains, such as night-time images, with limited availability and variability of training data suffer from poor retrieval performance even with methods performing well on standard benchmarks. We propose to train a GAN-based synthetic-image generator, translating available day-time image examples into night images. Such a generator is used in metric learning as a form of augmentation, supplying training data to the scarce domain. Various types of generators are evaluated and analyzed. We contribute with a novel light-weight GAN architecture that enforces the consistency between the original and translated image through edge consistency. The proposed architecture also allows a simultaneous training of an edge detector that operates on both night and day images. To further increase the variability in the training examples and to maximize the generalization of the trained model, we propose a novel method of diverse anchor mining. The proposed method improves over the state-of-the-art results on a standard Tokyo 24/7 day-night retrieval benchmark while preserving the performance on Oxford and Paris datasets. This is achieved without the need of training image pairs of matching day and night images. The source code is available at https://github.com/mohwald/gandtr .Comment: 11 pages, 4 figures, 8 table

Reversible Random Sequential Adsorption of Dimers on a Triangular Lattice

We report on simulations of reversible random sequential adsorption of dimers on three different lattices: a one-dimensional lattice, a two-dimensional triangular lattice, and a two-dimensional triangular lattice with the nearest neighbors excluded. In addition to the adsorption of particles at a rate K+, we allow particles to leave the surface at a rate K-. The results from the one-dimensional lattice model agree with previous results for the continuous parking lot model. In particular, the long-time behavior is dominated by collective events involving two particles. We were able to directly confirm the importance of two-particle events in the simple two-dimensional triangular lattice. For the two-dimensional triangular lattice with the nearest neighbors excluded, the observed dynamics are consistent with this picture. The two-dimensional simulations were motivated by measurements of Ca++ binding to Langmuir monolayers. The two cases were chosen to model the effects of changing pH in the experimental system.Comment: 9 pages, 10 figure

Modern machine-learning can support diagnostic differentiation of central and peripheral acute vestibular disorders

BACKGROUND Diagnostic classification of central vs. peripheral etiologies in acute vestibular disorders remains a challenge in the emergency setting. Novel machine-learning methods may help to support diagnostic decisions. In the current study, we tested the performance of standard and machine-learning approaches in the classification of consecutive patients with acute central or peripheral vestibular disorders. METHODS 40 Patients with vestibular stroke (19 with and 21 without acute vestibular syndrome (AVS), defined by the presence of spontaneous nystagmus) and 68 patients with peripheral AVS due to vestibular neuritis were recruited in the emergency department, in the context of the prospective EMVERT trial (EMergency VERTigo). All patients received a standardized neuro-otological examination including videooculography and posturography in the acute symptomatic stage and an MRI within 7~days after symptom onset. Diagnostic performance of state-of-the-art scores, such as HINTS (Head Impulse, gaze-evoked Nystagmus, Test of Skew) and ABCD2 (Age, Blood, Clinical features, Duration, Diabetes), for the differentiation of vestibular stroke vs. peripheral AVS was compared to various machine-learning approaches: (i) linear logistic regression (LR), (ii) non-linear random forest (RF), (iii) artificial neural network, and (iv) geometric deep learning (Single/MultiGMC). A prospective classification was simulated by ten-fold cross-validation. We analyzed whether machine-estimated feature importances correlate with clinical experience. RESULTS Machine-learning methods (e.g., MultiGMC) outperform univariate scores, such as HINTS or ABCD2, for differentiation of all vestibular strokes vs. peripheral AVS (MultiGMC area-under-the-curve (AUC): 0.96 vs. HINTS/ABCD2 AUC: 0.71/0.58). HINTS performed similarly to MultiGMC for vestibular stroke with AVS (AUC: 0.86), but more poorly for vestibular stroke without AVS (AUC: 0.54). Machine-learning models learn to put different weights on particular features, each of which is relevant from a clinical viewpoint. Established non-linear machine-learning methods like RF and linear methods like LR are less powerful classification models (AUC: 0.89 vs. 0.62). CONCLUSIONS Established clinical scores (such as HINTS) provide a valuable baseline assessment for stroke detection in acute vestibular syndromes. In addition, machine-learning methods may have the potential to increase sensitivity and selectivity in the establishment of a correct diagnosis

Universality in the Screening Cloud of Dislocations Surrounding a Disclination

A detailed analytical and numerical analysis for the dislocation cloud surrounding a disclination is presented. The analytical results show that the combined system behaves as a single disclination with an effective fractional charge which can be computed from the properties of the grain boundaries forming the dislocation cloud. Expressions are also given when the crystal is subjected to an external two-dimensional pressure. The analytical results are generalized to a scaling form for the energy which up to core energies is given by the Young modulus of the crystal times a universal function. The accuracy of the universality hypothesis is numerically checked to high accuracy. The numerical approach, based on a generalization from previous work by S. Seung and D.R. Nelson ({\em Phys. Rev A 38:1005 (1988)}), is interesting on its own and allows to compute the energy for an {\em arbitrary} distribution of defects, on an {\em arbitrary geometry} with an arbitrary elastic {\em energy} with very minor additional computational effort. Some implications for recent experimental, computational and theoretical work are also discussed.Comment: 35 pages, 21 eps file

Reaction-kinetics of organo-clay hybrid films: in-situ IRRAS and AFM studies

In this paper we have reported the reaction kinetics of nano dimensional clay saponite and hectorite with an amphiphilic cation octadecyl rhodamine B (RhB) in hybrid Langmuir monolayer at the air-water interface. The surface pressure-molecular area (pi-A) isotherms were strongly influenced by the presence of clay with the lift-off area of the cationic amphiphile shifted to progressively larger area. In-situ infrared reflection absorption spectroscopy (IRRAS) was used to demonstrate the reaction kinetics. Time taken to complete the reaction kinetics for RhB-hectorite hybrid films is larger than RhB-saponite hybrid films. Atomic force microscopic images of hybrid Langmuir-Blodgett films give compelling visual evidence of the incorporation of clay platelets into the hybrid films and density of which increases with the progress of reaction kinetics.Comment: 11 pages, 5 figure

Phospholipid Composition Modulates Carbon Nanodiamond-Induced Alterations in Phospholipid Domain Formation

This document is the Accepted Manuscript version of a Published Work that appeared in final form in Langmuir, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see http://doi.org/10.1021/la504923j.The focus of this work is to elucidate how phospholipid composition can modulate lipid nanoparticle interactions in phospholipid monolayer systems. We report on alterations in lipid domain formation induced by anionically engineered carbon nanodiamonds (ECNs) as a function of lipid headgroup charge and alkyl chain saturation. Using surface pressure vs area isotherms, monolayer compressibility, and fluorescence microscopy, we found that anionic ECNs induced domain shape alterations in zwitterionic phosphatidylcholine lipids, irrespective of the lipid alkyl chain saturation, even when the surface pressure vs area isotherms did not show any significant changes. Bean-shaped structures characteristic of dipalmitoylphosphatidylcholine (DPPC) were converted to multilobed, fractal, or spiral domains as a result of exposure to ECNs, indicating that ECNs lower the line tension between domains in the case of zwitterionic lipids. For membrane systems containing anionic phospholipids, ECN-induced changes in domain packing were related to the electrostatic interactions between the anionic ECNs and the anionic lipid headgroups, even when zwitterionic lipids are present in excess. By comparing the measured size distributions with our recently developed theory derived by minimizing the free energy associated with the domain energy and mixing entropy, we found that the change in line tension induced by anionic ECNs is dominated by the charge in the condensed lipid domains. Atomic force microscopy images of the transferred anionic films confirm that the location of the anionic ECNs in the lipid monolayers is also modulated by the charge on the condensed lipid domains. Because biological membranes such as lung surfactants contain both saturated and unsaturated phospholipids with different lipid headgroup charges, our results suggest that when studying potential adverse effects of nanoparticles on biological systems the role of lipid compositions cannot be neglected