Towards Analyzing Semantic Robustness of Deep Neural Networks

Despite the impressive performance of Deep Neural Networks (DNNs) on various vision tasks, they still exhibit erroneous high sensitivity toward semantic primitives (e.g. object pose). We propose a theoretically grounded analysis for DNN robustness in the semantic space. We qualitatively analyze different DNNs' semantic robustness by visualizing the DNN global behavior as semantic maps and observe interesting behavior of some DNNs. Since generating these semantic maps does not scale well with the dimensionality of the semantic space, we develop a bottom-up approach to detect robust regions of DNNs. To achieve this, we formalize the problem of finding robust semantic regions of the network as optimizing integral bounds and we develop expressions for update directions of the region bounds. We use our developed formulations to quantitatively evaluate the semantic robustness of different popular network architectures. We show through extensive experimentation that several networks, while trained on the same dataset and enjoying comparable accuracy, do not necessarily perform similarly in semantic robustness. For example, InceptionV3 is more accurate despite being less semantically robust than ResNet50. We hope that this tool will serve as a milestone towards understanding the semantic robustness of DNNs.Comment: Presented at European conference on computer vision (ECCV 2020) Workshop on Adversarial Robustness in the Real World ( https://eccv20-adv-workshop.github.io/ ) [best paper award]. The code is available at https://github.com/ajhamdi/semantic-robustnes

Nonlinear stochastic receding horizon control: stability, robustness and Monte Carlo methods for control approximation

© 2017, © 2017 Informa UK Limited, trading as Taylor & Francis Group. This work considers the stability of nonlinear stochastic receding horizon control when the optimal controller is only computed approximately. A number of general classes of controller approximation error are analysed including deterministic and probabilistic errors and even controller sample and hold errors. In each case, it is shown that the controller approximation errors do not accumulate (even over an infinite time frame) and the process converges exponentially fast to a small neighbourhood of the origin. In addition to this analysis, an approximation method for receding horizon optimal control is proposed based on Monte Carlo simulation. This method is derived via the Feynman–Kac formula which gives a stochastic interpretation for the solution of a Hamilton–Jacobi–Bellman equation associated with the true optimal controller. It is shown, and it is a prime motivation for this study, that this particular controller approximation method practically stabilises the underlying nonlinear process

An introduction to Wishart matrix moments

© 2018 Now Publishers Inc. All rights reserved. These lecture notes provide a comprehensive, self-contained introduction to the analysis of Wishart matrix moments. This study may act as an introduction to some particular aspects of random matrix theory, or as a self-contained exposition of Wishart matrix moments. Random matrix theory plays a central role in statistical physics, computational mathematics and engineering sciences, including data assimilation, signal processing, combinatorial optimization, compressed sensing, econometrics and mathematical finance, among numerous others. The mathematical foundations of the theory of random matrices lies at the intersection of combinatorics, non-commutative algebra, geometry, multivariate functional and spectral analysis, and of course statistics and probability theory. As a result, most of the classical topics in random matrix theory are technical, and mathematically difficult to penetrate for non-experts and regular users and practitioners. The technical aim of these notes is to review and extend some important results in random matrix theory in the specific context of real random Wishart matrices. This special class of Gaussian-type sample covariance matrix plays an important role in multivariate analysis and in statistical theory.We derive non-asymptotic formulae for the full matrix moments of real valued Wishart random matrices. As a corollary, we derive and extend a number of spectral and trace-type results for the case of non-isotropic Wishart random matrices. We also derive the full matrix moment analogues of some classic spectral and trace-type moment results. For example, we derive semi-circle and Marchencko-Pastur-type laws in the non-isotropic and full matrix cases. Laplace matrix transforms and matrix moment estimates are also studied, along with new spectral and trace concentration-type inequalities

Perturbations and projections of Kalman–Bucy semigroups

© 2017 Elsevier B.V. We analyse various perturbations and projections of Kalman–Bucy semigroups and Riccati equations. For example, covariance inflation-type perturbations and localisation methods (projections) are common in the ensemble Kalman filtering literature. In the limit of these ensemble methods, the regularised sample covariance tends toward a solution of a perturbed/projected Riccati equation. With this motivation, results are given characterising the error between the nominal and regularised Riccati flows and Kalman–Bucy filtering distributions. New projection-type models are also discussed; e.g. Bose–Mesner projections. These regularisation models are also of interest on their own, and in, e.g., differential games, control of stochastic/jump processes, and robust control

Controlling the shape and scale of triangular formations using landmarks and bearing-only sensing

© 2016 TCCT. This work considers the scenario where three agents that can sense only bearings use two landmarks to control their formation shape. We define a method of relating the known distance separating the landmarks back to the edge lengths of the triangular formation. The result is used to define a formation control law that incorporates inter-agent distance constraints. We prove a strong exponential convergence result and show how one can extend the controller such that global stability from any initial position is possible

Network consensus in the wasserstein metric space of probability measures

Distributed consensus in the Wasserstein metric space of probability measures on the real line is introduced in this work. Convergence of each agent's measure to a common measure is proven under a weak network connectivity condition. The common measure reached at each agent is one minimizing a weighted sum of its Wasserstein distance to all initial agent measures. This measure is known as the Wasserstein barycenter. Special cases involving Gaussian measures, empirical measures, and time-invariant network topologies are considered, where convergence rates and average-consensus results are given. This work has possible applicability in computer vision, machine learning, clustering, and estimation

A perturbation analysis of stochastic matrix Riccati diffusions

Matrix differential Riccati equations are central in filtering and optimal control theory. The purpose of this article is to develop a perturbation theory for a class of stochastic matrix Riccati diffusions. Diffusions of this type arise, for example, in the analysis of ensemble Kalman-Bucy filters since they describe the flow of certain sample covariance estimates. In this context, the random perturbations come from the fluctuations of a mean field particle interpretation of a class of nonlinear diffusions equipped with an interacting sample covariance matrix functional. The main purpose of this article is to derive non-asymptotic Taylor-type expansions of stochastic matrix Riccati flows with respect to some perturbation parameter. These expansions rely on an original combination of stochastic differential analysis and nonlinear semigroup techniques on matrix spaces. The results here quantify the fluctuation of the stochastic flow around the limiting deterministic Riccati equation, at any order. The convergence of the interacting sample covariance matrices to the deterministic Riccati flow is proven as the number of particles tends to infinity. Also presented are refined moment estimates and sharp bias and variance estimates. These expansions are also used to deduce a functional central limit theorem at the level of the diffusion process in matrix spaces

Upper respiratory tract symptoms and salivary immunoglobulin A of elite female gymnasts : a full year longitudinal field study

The aim of this study was to determine the frequency of upper respiratory tract symptoms (URS) in elite female gymnasts during a training season. In addition, we aimed to observe the extent to which salivary immunoglobulin A (sIgA) is associated with URS in these athletes, including potential effects of the season and timing of sample collection. Over one year, 18 elite female gymnasts completed URS and fatigue questionnaires weekly and provided 1 mL of saliva after a minimum 36 h of rest (morning or afternoon) to measure relative sIgA concentration (= mean absolute sIgA value of the week divided by the mean absolute sIgA value of the weeks without URS). Mean weekly URS and mean relative sIgA values per gymnast correlated negatively (r = -0.606, P = 0.022). Most URS were noted in the most fatigued gymnasts (7.4 ± 10.1 vs. 2.5 ± 5.6 (P < 0.001) for ‘normal’ and 2.1 ± 3.7 (P = 0.001) for ‘better than normal’ rested). In spring, relative sIgA was higher compared to autumn (112 ± 55 vs. 89 ± 41%, P < 0.001) and winter (92 ± 35%, P = 0.001), while during summer, relative sIgA appeared higher compared to autumn (110 ± 55 vs. 89 ± 41%, P = 0.016). The interaction effect with timing of sample collection showed higher relative sIgA values in morning samples in spring and summer compared to afternoon samples, with the inverse observed in autumn and winter (F = 3.565, P = 0.014). During a gymnastics season, lower relative sIgA values were linked to higher susceptibility to URS in elite gymnasts. However, relative sIgA values were influenced by season and timing of sample collection and thus should be considered when interpreting sIgA data

Controlled Sequential Monte Carlo

Sequential Monte Carlo methods, also known as particle methods, are a popular set of techniques to approximate high-dimensional probability distributions and their normalizing constants. They have found numerous applications in statistics and related fields as they can be applied to perform state estimation for non-linear non-Gaussian state space models and Bayesian inference for complex static models. Like many Monte Carlo sampling schemes, they rely on proposal distributions which have a crucial impact on their performance. We introduce here a class of controlled sequential Monte Carlo algorithms, where the proposal distributions are determined by approximating the solution to an associated optimal control problem using an iterative scheme. We provide theoretical analysis of our proposed methodology and demonstrate significant gains over state-of-the-art methods at a fixed computational complexity on a variety of applications

Dynamical study on polaron formation in a metal/polymer/metal structure

By considering a metal/polymer/metal structure within a tight-binding one-dimensional model, we have investigated the polaron formation in the presence of an electric field. When a sufficient voltage bias is applied to one of the metal electrodes, an electron is injected into the polymer chain, then a self-trapped polaron is formed at a few hundreds of femtoseconds while it moves slowly under a weak electric field (not larger than $$ V/cm). At an electric field between $1.0\times 10^4$ V/cm and $$ V/cm, the polaron is still formed, since the injected electron is bounded between the interface barriers for quite a long time. It is shown that the electric field applied at the polymer chain reduces effectively the potential barrier in the metal/polymer interface