Online Variance Reduction for Stochastic Optimization

Modern stochastic optimization methods often rely on uniform sampling which is agnostic to the underlying characteristics of the data. This might degrade the convergence by yielding estimates that suffer from a high variance. A possible remedy is to employ non-uniform importance sampling techniques, which take the structure of the dataset into account. In this work, we investigate a recently proposed setting which poses variance reduction as an online optimization problem with bandit feedback. We devise a novel and efficient algorithm for this setting that finds a sequence of importance sampling distributions competitive with the best fixed distribution in hindsight, the first result of this kind. While we present our method for sampling datapoints, it naturally extends to selecting coordinates or even blocks of thereof. Empirical validations underline the benefits of our method in several settings.Comment: COLT 201

Pairwise Confusion for Fine-Grained Visual Classification

Fine-Grained Visual Classification (FGVC) datasets contain small sample sizes, along with significant intra-class variation and inter-class similarity. While prior work has addressed intra-class variation using localization and segmentation techniques, inter-class similarity may also affect feature learning and reduce classification performance. In this work, we address this problem using a novel optimization procedure for the end-to-end neural network training on FGVC tasks. Our procedure, called Pairwise Confusion (PC) reduces overfitting by intentionally {introducing confusion} in the activations. With PC regularization, we obtain state-of-the-art performance on six of the most widely-used FGVC datasets and demonstrate improved localization ability. {PC} is easy to implement, does not need excessive hyperparameter tuning during training, and does not add significant overhead during test time.Comment: Camera-Ready version for ECCV 201

Evaluation of the capability of the simulated dual energy X-ray absorptiometry-based two-dimensional finite element models for predicting vertebral failure loads

Prediction of the vertebral failure load is of great importance for the prevention and early treatment of bone fracture. However, an efficient and effective method for accurately predicting the failure load of vertebral bones is still lacking. The aim of the present study was to evaluate the capability of the simulated dual energy X-ray absorptiometry (DXA)-based finite element (FE) model for predicting vertebral failure loads. Thirteen dissected spinal segments (T11/T12/L1) were scanned using a HR-pQCT scanner and then were mechanically tested until failure. The subject-specific three-dimensional (3D) and two-dimensional (2D) FE models of T12 were generated from the HR-pQCT scanner and the simulated DXA images, respectively. Additionally, the areal bone mineral density (aBMD) and areal bone mineral content (aBMC) of T12 were calculated. The failure loads predicted by the simulated DXA-based 2D FE models were more moderately correlated with the experimental failure loads (R  = 0.66) than the aBMC (R  = 0.61) and aBMD (R  = 0.56). The 2D FE models were slightly outperformed by the HR-pQCT-based 3D FE models (R  = 0.71). The present study demonstrated that the simulated DXA-based 2D FE model has better capability for predicting the vertebral failure loads than the densitometric measurements but is outperformed by the 3D FE model. The 2D FE model is more suitable for clinical use due to the low radiation dose and low cost, but it remains to be validated by further in vitro and in vivo studies. [Abstract copyright: Copyright © 2019. Published by Elsevier Ltd.

Acute lower limb ischemia due to thrombo-embolic arterial occlusions in two previously healthy men with markedly elevated Lp(a)

Lipoprotein (a) (Lp(a)) is a well-documented risk factor for atherosclerotic cardiovascular disease. Its role in acute thrombo-embolic occlusions of peripheral arteries is not known. We describe two cases of multiple, acute, peripheral arterial occlusions in two previously healthy men with markedly elevated Lp(a). Both cases had unsatisfactory results after percutaneous and surgical revascularization procedures. Experience yielded in these two cases suggests that when an unfavorable outcome occurs in a peripheral artery disease patient in the absence of the regular risk factors, Lp(a) should be determined and its role investigated

Stable fourfold configurations for small vacancy clusters in silicon from ab initio calculations

Using density-functional-theory calculations, we have identified new stable configurations for tri-, tetra-, and penta-vacancies in silicon. These new configurations consist of combinations of a ring-hexavacancy with three, two, or one interstitial atoms, respectively, such that all atoms remain fourfold. As a result, their formation energies are lower by 0.6, 1.0, and 0.6 eV, respectively, than the ``part of a hexagonal ring'' configurations, believed up to now to be the lowest-energy states

Motion-Induced Magnetic Resonance of Rb Atoms in a Periodic Magnetostatic Field

We demonstrate that transitions between Zeeman-split sublevels of Rb atoms are resonantly induced by the motion of the atoms (velocity: about 100 m/s) in a periodic magnetostatic field (period: 1 mm) when the Zeeman splitting corresponds to the frequency of the magnetic field experienced by the moving atoms. A circularly polarized laser beam polarizes Rb atoms with a velocity selected using the Doppler effect and detects their magnetic resonance in a thin cell, to which the periodic field is applied with the arrays of parallel current-carrying wires.Comment: 4 pages, 4 figures; minor corrections, Ref. [9] removed, published in PR