Disentangling Adversarial Robustness and Generalization

Obtaining deep networks that are robust against adversarial examples and generalize well is an open problem. A recent hypothesis even states that both robust and accurate models are impossible, i.e., adversarial robustness and generalization are conflicting goals. In an effort to clarify the relationship between robustness and generalization, we assume an underlying, low-dimensional data manifold and show that: 1. regular adversarial examples leave the manifold; 2. adversarial examples constrained to the manifold, i.e., on-manifold adversarial examples, exist; 3. on-manifold adversarial examples are generalization errors, and on-manifold adversarial training boosts generalization; 4. regular robustness and generalization are not necessarily contradicting goals. These assumptions imply that both robust and accurate models are possible. However, different models (architectures, training strategies etc.) can exhibit different robustness and generalization characteristics. To confirm our claims, we present extensive experiments on synthetic data (with known manifold) as well as on EMNIST, Fashion-MNIST and CelebA.Comment: Conference on Computer Vision and Pattern Recognition 201

The Zebrafish (Danio rerio) Is a Relevant Model for Studying Sex-Specific Effects of 17β-Estradiol in the Adult Heart

Cardiovascular diseases are a major cause of morbidity and mortality, and there are significant sex differences therein. However, the underlying mechanisms are poorly understood. The steroid hormone 17β-estradiol (E2) is thought to play a major role in cardiovascular sex differences and to be protective, but this may not hold true for males. We aimed at assessing whether the zebrafish is an appropriate model for the study of E2 effects in the heart. We hypothesized that E2 regulates the cardiac contractility of adult zebrafish in a sex-specific manner. Male and female zebrafish were treated with vehicle (control) or E2 and the cardiac contractility was measured 0, 4, 7 and 14 days after treatment initiation using echocardiography. There was no significant effect on the heart rate by E2. Notably, there was a significant decrease in the ejection fraction of male zebrafish treated with E2 compared with controls. By contrast, there was no major difference in the ejection fraction between the two female groups. The dramatic effect in male zebrafish occurred as early as 4 days post treatment initiation. Although there was no significant difference in stroke volume and cardiac output between the two male groups, these were significantly higher in female zebrafish treated with E2 compared with controls. Gene expression analysis revealed that the levels of estrogen receptors were comparable among all groups. In conclusion, our data demonstrate that the adult zebrafish heart robustly responds to E2 and this occurs in a sex-specific manner. Given the benefits of using zebrafish as a model, new targets may be identified for the development of novel cardiovascular therapies for male and female patients. This would contribute towards the realization of personalized medicine

Disentangling Adversarial Robustness and Generalization

Obtaining deep networks that are robust against adversarial examples and generalize well is an open problem. A recent hypothesis even states that both robust and accurate models are impossible, i.e., adversarial robustness and generalization are conflicting goals. In an effort to clarify the relationship between robustness and generalization, we assume an underlying, low-dimensional data manifold and show that: 1. regular adversarial examples leave the manifold; 2. adversarial examples constrained to the manifold, i.e., on-manifold adversarial examples, exist; 3. on-manifold adversarial examples are generalization errors, and on-manifold adversarial training boosts generalization; 4. regular robustness and generalization are not necessarily contradicting goals. These assumptions imply that both robust and accurate models are possible. However, different models (architectures, training strategies etc.) can exhibit different robustness and generalization characteristics. To confirm our claims, we present extensive experiments on synthetic data (with known manifold) as well as on EMNIST, Fashion-MNIST and CelebA.Comment: Conference on Computer Vision and Pattern Recognition 201

OPTIC: Orbiting Plutonian Topographic Image Craft Proposal for an Unmanned Mission to Pluto

The proposal for an unmanned probe to Pluto is presented and described. The Orbiting Plutonian Topographic Image Craft's (OPTIC's) trip will take twenty years and after its arrival, will begin its data collection which includes image and radar mapping, surface spectral analysis, and magnetospheric studies. This probe's design was developed based on the request for proposal of an unmanned probe to Pluto requirements. The distinct problems which an orbiter causes for each subsystem of the craft are discussed. The final design revolved around two important factors: (1) the ability to collect and return the maximum quantity of information on the Plutonian system; and (2) the weight limitations which the choice of an orbiting craft implied. The velocity requirements of this type of mission severely limited the weight available for mission execution-owing to the large portion of overall weight required as fuel to fly the craft with present technology. The topics covered include: (1) scientific instrumentation; (2) mission management; (3) power and propulsion; (4) attitude and articulation control; (5) structural subsystems; and (6) command, control, and communication