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

Midline Shift is Unrelated to Subjective Pupillary Reactivity Assessment on Admission in Moderate and Severe Traumatic Brain Injury.

BACKGROUND: This study aims to determine the relationship between pupillary reactivity, midline shift and basal cistern effacement on brain computed tomography (CT) in moderate-to-severe traumatic brain injury (TBI). All are important diagnostic and prognostic measures, but their relationship is unclear. METHODS: A total of 204 patients with moderate-to-severe TBI, documented pupillary reactivity, and archived neuroimaging were included. Extent of midline shift and basal cistern effacement were extracted from admission brain CT. Mean midline shift was calculated for each ordinal category of pupillary reactivity and basal cistern effacement. Sequential Chi-square analysis was used to calculate a threshold midline shift for pupillary abnormalities and basal cistern effacement. Univariable and multiple logistic regression analyses were performed. RESULTS: Pupils were bilaterally reactive in 163 patients, unilaterally reactive in 24, and bilaterally unreactive in 17, with mean midline shift (mm) of 1.96, 3.75, and 2.56, respectively (p = 0.14). Basal cisterns were normal in 118 patients, compressed in 45, and absent in 41, with mean midline shift (mm) of 0.64, 2.97, and 5.93, respectively (p < 0.001). Sequential Chi-square analysis identified a threshold for abnormal pupils at a midline shift of 7-7.25 mm (p = 0.032), compressed basal cisterns at 2 mm (p < 0.001), and completely effaced basal cisterns at 7.5 mm (p < 0.001). Logistic regression revealed no association between midline shift and pupillary reactivity. With effaced basal cisterns, the odds ratio for normal pupils was 0.22 (95% CI 0.08-0.56; p = 0.0016) and for at least one unreactive pupil was 0.061 (95% CI 0.012-0.24; p < 0.001). Basal cistern effacement strongly predicted midline shift (OR 1.27; 95% CI 1.17-1.40; p < 0.001). CONCLUSIONS: Basal cistern effacement alone is associated with pupillary reactivity and is closely associated with midline shift. It may represent a uniquely useful neuroimaging marker to guide intervention in traumatic brain injury

Apollo experience report: Development of guidance targeting techniques for the command module and launch vehicle

The development of the guidance targeting techniques for the Apollo command module and launch vehicle is discussed for four types of maneuvers: (1) translunar injection, (2) translunar midcourse, (3) lunar orbit insertion, and (4) return to earth. The development of real-time targeting programs for these maneuvers and the targeting procedures represented are discussed. The material is intended to convey historically the development of the targeting techniques required to meet the defined target objectives and to illustrate the solutions to problems encountered during that development

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

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

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