Learning the Roots of Visual Domain Shift

In this paper we focus on the spatial nature of visual domain shift, attempting to learn where domain adaptation originates in each given image of the source and target set. We borrow concepts and techniques from the CNN visualization literature, and learn domainnes maps able to localize the degree of domain specificity in images. We derive from these maps features related to different domainnes levels, and we show that by considering them as a preprocessing step for a domain adaptation algorithm, the final classification performance is strongly improved. Combined with the whole image representation, these features provide state of the art results on the Office dataset.Comment: Extended Abstrac

Reentrant ventricular arrhythmias in the late myocardial infarction period. 12. Spontaneous versus induced reentry and intramural versus epicardial circuits

One to 5 days after one-stage ligation of the left anterior descending coronary artery in dogs, reentrant excitation can be induced by programmed premature stimulation in the surviving electrophysiologically abnormal, thin epicardial layer overlying the infarct. In experiments in four dogs, reentrant excitation occurred “spontaneously” during a regular sinus or atria) rhythm. A tachycardia-dependent Wenckebach conduction sequence in a potentially reentrant pathway was the initiating mechanism for spontaneous reentrant tachycardias and was the basis for both manifest and concealed reentrant extrasystolic rhythms. In all dogs showing spontaneous reentry, reentrant excitation could also be induced by premature stimulation at cycle lengths much shorter than those associated with spontaneous reentry, and induced reentrant circuits were always different from those during spontaneous reentry. In two dogs, the reentrant circuit was located intramurally in close proximity to a patchy septal infarction.The study illustrates that irrespective of the anatomic localization of reentrant circuits (epicardial or intramural), their dimension (large or small) or their mechanism of initiation (programmed premature stimulation or “spontaneous”), reentrant excitation always occurred in a figure 8 configuration (or a modification thereof). The figure 8 model, rather than the ring model or the leading circle model, may be the common model of reentry in the mammalian heart

Deep Adversarial Attention Alignment for Unsupervised Domain Adaptation: The Benefit of Target Expectation Maximization

© 2018, Springer Nature Switzerland AG. In this paper, we make two contributions to unsupervised domain adaptation (UDA) using the convolutional neural network (CNN). First, our approach transfers knowledge in all the convolutional layers through attention alignment. Most previous methods align high-level representations, e.g., activations of the fully connected (FC) layers. In these methods, however, the convolutional layers which underpin critical low-level domain knowledge cannot be updated directly towards reducing domain discrepancy. Specifically, we assume that the discriminative regions in an image are relatively invariant to image style changes. Based on this assumption, we propose an attention alignment scheme on all the target convolutional layers to uncover the knowledge shared by the source domain. Second, we estimate the posterior label distribution of the unlabeled data for target network training. Previous methods, which iteratively update the pseudo labels by the target network and refine the target network by the updated pseudo labels, are vulnerable to label estimation errors. Instead, our approach uses category distribution to calculate the cross-entropy loss for training, thereby ameliorating the error accumulation of the estimated labels. The two contributions allow our approach to outperform the state-of-the-art methods by +2.6% on the Office-31 dataset

Are You Tampering With My Data?

We propose a novel approach towards adversarial attacks on neural networks (NN), focusing on tampering the data used for training instead of generating attacks on trained models. Our network-agnostic method creates a backdoor during training which can be exploited at test time to force a neural network to exhibit abnormal behaviour. We demonstrate on two widely used datasets (CIFAR-10 and SVHN) that a universal modification of just one pixel per image for all the images of a class in the training set is enough to corrupt the training procedure of several state-of-the-art deep neural networks causing the networks to misclassify any images to which the modification is applied. Our aim is to bring to the attention of the machine learning community, the possibility that even learning-based methods that are personally trained on public datasets can be subject to attacks by a skillful adversary.Comment: 18 page

Comparison of high versus low frequency cerebral physiology for cerebrovascular reactivity assessment in traumatic brain injury: a multi-center pilot study

Current accepted cerebrovascular reactivity indices suffer from the need of high frequency data capture and export for post-acquisition processing. The role for minute-by-minute data in cerebrovascular reactivity monitoring remains uncertain. The goal was to explore the statistical time-series relationships between intra-cranial pressure (ICP), mean arterial pressure (MAP) and pressure reactivity index (PRx) using both 10-s and minute data update frequency in TBI. Prospective data from 31 patients from 3 centers with moderate/severe TBI and high-frequency archived physiology were reviewed. Both 10-s by 10-s and minute-by-minute mean values were derived for ICP and MAP for each patient. Similarly, PRx was derived using 30 consecutive 10-s data points, updated every minute. While long-PRx (L-PRx) was derived via similar methodology using minute-by-minute data, with L-PRx derived using various window lengths (5, 10, 20, 30, 40, and 60 min; denoted L-PRx_5, etc.). Time-series autoregressive integrative moving average (ARIMA) and vector autoregressive integrative moving average (VARIMA) models were created to analyze the relationship of these parameters over time. ARIMA modelling, Granger causality testing and VARIMA impulse response function (IRF) plotting demonstrated that similar information is carried in minute mean ICP and MAP data, compared to 10-s mean slow-wave ICP and MAP data. Shorter window L-PRx variants, such as L-PRx_5, appear to have a similar ARIMA structure, have a linear association with PRx and display moderate-to-strong correlations (r ~ 0.700, p Peer reviewe

Exploring the mechanical properties of additively manufactured carbon-rich zirconia 3D microarchitectures

Two-photon lithography (TPL) is a promising technique for manufacturing ceramic microstructures with nanoscale resolution. The process relies on tailor-made precursor resins rich in metal-organic and organic constituents, which can lead to carbon-based residues incorporated within the ceramic microstructures. While these are generally considered unwanted impurities, our study reveals that the presence of carbon-rich residues in the form of graphitic and disordered carbon in tetragonal (t-) ZrO2 can benefit the mechanical strength of TPL microstructures. In order to achieve a better understanding of these effects, we deconvolute the structural and materials contributions to the strength of the 3D microarchitectures by comparing them to plain micropillars. We vary the organic content by different thermal treatments, resulting in different crystal structures. The highest compression strength of 3.73 ± 0.21 GPa and ductility are reached for the t-ZrO2 micropillars, which also contain the highest carbon content. This paradoxical finding opens up new perspectives and will foster the development of “brick and mortar”-like ceramic microarchitectures

Transport control by coherent zonal flows in the core/edge transitional regime

3D Braginskii turbulence simulations show that the energy flux in the core/edge transition region of a tokamak is strongly modulated - locally and on average - by radially propagating, nearly coherent sinusoidal or solitary zonal flows. The flows are geodesic acoustic modes (GAM), which are primarily driven by the Stringer-Winsor term. The flow amplitude together with the average anomalous transport sensitively depend on the GAM frequency and on the magnetic curvature acting on the flows, which could be influenced in a real tokamak, e.g., by shaping the plasma cross section. The local modulation of the turbulence by the flows and the excitation of the flows are due to wave-kinetic effects, which have been studied for the first time in a turbulence simulation.Comment: 5 pages, 5 figures, submitted to PR

Deep Over-sampling Framework for Classifying Imbalanced Data

Class imbalance is a challenging issue in practical classification problems for deep learning models as well as traditional models. Traditionally successful countermeasures such as synthetic over-sampling have had limited success with complex, structured data handled by deep learning models. In this paper, we propose Deep Over-sampling (DOS), a framework for extending the synthetic over-sampling method to exploit the deep feature space acquired by a convolutional neural network (CNN). Its key feature is an explicit, supervised representation learning, for which the training data presents each raw input sample with a synthetic embedding target in the deep feature space, which is sampled from the linear subspace of in-class neighbors. We implement an iterative process of training the CNN and updating the targets, which induces smaller in-class variance among the embeddings, to increase the discriminative power of the deep representation. We present an empirical study using public benchmarks, which shows that the DOS framework not only counteracts class imbalance better than the existing method, but also improves the performance of the CNN in the standard, balanced settings