Jacobian Ensembles Improve Robustness Trade-offs to Adversarial Attacks

Deep neural networks have become an integral part of our software infrastructure and are being deployed in many widely-used and safety-critical applications. However, their integration into many systems also brings with it the vulnerability to test time attacks in the form of Universal Adversarial Perturbations (UAPs). UAPs are a class of perturbations that when applied to any input causes model misclassification. Although there is an ongoing effort to defend models against these adversarial attacks, it is often difficult to reconcile the trade-offs in model accuracy and robustness to adversarial attacks. Jacobian regularization has been shown to improve the robustness of models against UAPs, whilst model ensembles have been widely adopted to improve both predictive performance and model robustness. In this work, we propose a novel approach, Jacobian Ensembles-a combination of Jacobian regularization and model ensembles to significantly increase the robustness against UAPs whilst maintaining or improving model accuracy. Our results show that Jacobian Ensembles achieves previously unseen levels of accuracy and robustness, greatly improving over previous methods that tend to skew towards only either accuracy or robustness

The Fourth Positive System of Carbon Monoxide in the Hubble Space Telescope Spectra of Comets

The rich structure of the Fourth Positive System (A-X) of carbon monoxide accounts for many of the spectral features seen in long slit HST-STIS observations of comets 153P/Ikeya-Zhang, C/2001 Q4 (NEAT), and C/2000 WM1 (LINEAR), as well as in the HST-GHRS spectrum of comet C/1996 B2 Hyakutake. A detailed CO fluorescence model is developed to derive the CO abundances in these comets by simultaneously fitting all of the observed A-X bands. The model includes the latest values for the oscillator strengths and state parameters, and accounts for optical depth effects due to line overlap and self-absorption. The model fits yield radial profiles of CO column density that are consistent with a predominantly native source for all the comets observed by STIS. The derived CO abundances relative to water in these comets span a wide range, from 0.44% for C/2000 WM1 (LINEAR), 7.2% for 153P/Ikeya-Zhang, 8.8% for C/2001 Q4 (NEAT) to 20.9% for C/1996 B2 (Hyakutake). The subtraction of the CO spectral features using this model leads to the first identification of a molecular hydrogen line pumped by solar HI Lyman-beta longward of 1200A in the spectrum of comet 153P/Ikeya-Zhang. (Abridged)Comment: 12 pages, 11 figures, ApJ accepte

Semantic-based policy engineering for autonomic systems

This paper presents some important directions in the use of ontology-based semantics in achieving the vision of Autonomic Communications. We examine the requirements of Autonomic Communication with a focus on the demanding needs of ubiquitous computing environments, with an emphasis on the requirements shared with Autonomic Computing. We observe that ontologies provide a strong mechanism for addressing the heterogeneity in user task requirements, managed resources, services and context. We then present two complimentary approaches that exploit ontology-based knowledge in support of autonomic communications: service-oriented models for policy engineering and dynamic semantic queries using content-based networks. The paper concludes with a discussion of the major research challenges such approaches raise

On the spatial Markov property of soups of unoriented and oriented loops

We describe simple properties of some soups of unoriented Markov loops and of some soups of oriented Markov loops that can be interpreted as a spatial Markov property of these loop-soups. This property of the latter soup is related to well-known features of the uniform spanning trees (such as Wilson's algorithm) while the Markov property of the former soup is related to the Gaussian Free Field and to identities used in the foundational papers of Symanzik, Nelson, and of Brydges, Fr\"ohlich and Spencer or Dynkin, or more recently by Le Jan

Decomposition techniques for policy refinement.

The automation of policy refinement, whilst promising great benefits for policy-based management, has hitherto received relatively little treatment in the literature, with few concrete approaches emerging. In this paper we present initial steps towards a framework for automated distributed policy refinement for both obligation and authorization policies. We present examples drawn from military scenarios, describe details of our formalism and methods for action decomposition, and discuss directions for future research. © 2010 IEEE.Accepted versio

Grounding Aleatoric Uncertainty for Unsupervised Environment Design

Adaptive curricula in reinforcement learning (RL) have proven effective for producing policies robust to discrepancies between the train and test environment. Recently, the Unsupervised Environment Design (UED) framework generalized RL curricula to generating sequences of entire environments, leading to new methods with robust minimax regret properties. Problematically, in partially-observable or stochastic settings, optimal policies may depend on the ground-truth distribution over aleatoric parameters of the environment in the intended deployment setting, while curriculum learning necessarily shifts the training distribution. We formalize this phenomenon as curriculum-induced covariate shift (CICS), and describe how its occurrence in aleatoric parameters can lead to suboptimal policies. Directly sampling these parameters from the ground-truth distribution avoids the issue, but thwarts curriculum learning. We propose SAMPLR, a minimax regret UED method that optimizes the ground-truth utility function, even when the underlying training data is biased due to CICS. We prove, and validate on challenging domains, that our approach preserves optimality under the ground-truth distribution, while promoting robustness across the full range of environment settings