Automated Reachability Analysis of Neural Network-Controlled Systems via Adaptive Polytopes

Over-approximating the reachable sets of dynamical systems is a fundamental problem in safety verification and robust control synthesis. The representation of these sets is a key factor that affects the computational complexity and the approximation error. In this paper, we develop a new approach for over-approximating the reachable sets of neural network dynamical systems using adaptive template polytopes. We use the singular value decomposition of linear layers along with the shape of the activation functions to adapt the geometry of the polytopes at each time step to the geometry of the true reachable sets. We then propose a branch-and-bound method to compute accurate over-approximations of the reachable sets by the inferred templates. We illustrate the utility of the proposed approach in the reachability analysis of linear systems driven by neural network controllers

ReachLipBnB: A branch-and-bound method for reachability analysis of neural autonomous systems using Lipschitz bounds

We propose a novel Branch-and-Bound method for reachability analysis of neural networks in both open-loop and closed-loop settings. Our idea is to first compute accurate bounds on the Lipschitz constant of the neural network in certain directions of interest offline using a convex program. We then use these bounds to obtain an instantaneous but conservative polyhedral approximation of the reachable set using Lipschitz continuity arguments. To reduce conservatism, we incorporate our bounding algorithm within a branching strategy to decrease the over-approximation error within an arbitrary accuracy. We then extend our method to reachability analysis of control systems with neural network controllers. Finally, to capture the shape of the reachable sets as accurately as possible, we use sample trajectories to inform the directions of the reachable set over-approximations using Principal Component Analysis (PCA). We evaluate the performance of the proposed method in several open-loop and closed-loop settings

A Case of Kingella Kingae Endocarditis Complicated by Native Mitral Valve Rupture

We report a case of Kingella kingae endocarditis in a patient with a history of recent respiratory tract infection and dental extraction. This case is remarkable for embolic and vasculitic phenomena in association with a large valve vegetation and valve perforation. Kingella kingae is an organism known to cause endocarditis, however early major complications are uncommon. Our case of Kingella endocarditis behaved in a virulent fashion necessitating a combined approach of intravenous antibiotic therapy and a  valve replacement. It highlights the importance of expedited investigation for endocarditis in patients with Kingella bacteraemia

Certified Robustness via Dynamic Margin Maximization and Improved Lipschitz Regularization

To improve the robustness of deep classifiers against adversarial perturbations, many approaches have been proposed, such as designing new architectures with better robustness properties (e.g., Lipschitz-capped networks), or modifying the training process itself (e.g., min-max optimization, constrained learning, or regularization). These approaches, however, might not be effective at increasing the margin in the input (feature) space. As a result, there has been an increasing interest in developing training procedures that can directly manipulate the decision boundary in the input space. In this paper, we build upon recent developments in this category by developing a robust training algorithm whose objective is to increase the margin in the output (logit) space while regularizing the Lipschitz constant of the model along vulnerable directions. We show that these two objectives can directly promote larger margins in the input space. To this end, we develop a scalable method for calculating guaranteed differentiable upper bounds on the Lipschitz constant of neural networks accurately and efficiently. The relative accuracy of the bounds prevents excessive regularization and allows for more direct manipulation of the decision boundary. Furthermore, our Lipschitz bounding algorithm exploits the monotonicity and Lipschitz continuity of the activation layers, and the resulting bounds can be used to design new layers with controllable bounds on their Lipschitz constant. Experiments on the MNIST, CIFAR-10, and Tiny-ImageNet data sets verify that our proposed algorithm obtains competitively improved results compared to the state-of-the-art.Comment: 37th Conference on Neural Information Processing Systems (NeurIPS 2023

Iatrogenic meningitis caused by Neisseria sicca/subflava after intrathecal contrast injection, Australia

We report a case of invasive Neisseria sicca/subflava meningitis after a spinal injection procedure during which a face mask was not worn by the proceduralist. The report highlights the importance of awareness of, and adherence to, guidelines for protective face mask use during procedures that require sterile conditions

In silico Investigation on the Inhibiting Role of Nicotine/Caffeine by Blocking the S Protein of SARS-CoV-2 Versus ACE2 Receptor

In this paper, we studied the in silico interaction of angiotensin-converting enzyme 2 (ACE2) human receptor with two bioactive compounds, i.e., nicotine and caffeine, via molecular dynamic (MD) simulations. The simulations reveal the efficient blocking of ACE2 by caffeine and nicotine in the exposure to the spike (S) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We have selected the two most important active sites of ACE2-S protein, i.e., 6LZG and 6VW1, which are critically responsible in the interaction of S protein to the receptor and thus, we investigated their interaction with nicotine and caffeine through MD simulations. Caffeine and nicotine are interesting structures for interactions because of their similar structure to the candidate antiviral drugs. Our results reveal that caffeine or nicotine in a specific molar ratio to 6LZG shows a very strong interaction and indicate that caffeine is more efficient in the interaction with 6LZG and further blocking of this site against S protein binding. Further, we investigated the interaction of ACE2 receptor- S protein with nicotine or caffeine when mixed with candidate or approved antiviral drugs for SARS-CoV-2 therapy. Our MD simulations suggest that the combination of caffeine with ribavirin shows a stronger interaction with 6VW1, while in case of favipiravir+nicotine, 6LZG shows potent efficacy of these interaction, proposing the potent efficacy of these combinations for blocking ACE2 receptor against SARS-CoV-2

Empty substrate integrated waveguide technology for E plane high-frequency and high-performance circuits

Substrate integrated circuits (SIC) have attracted much attention in the last years because of their great potential of low cost, easy manufacturing, integration in a circuit board, and higher-quality factor than planar circuits. A first suite of SIC where the waves propagate through dielectric have been first developed, based on the well-known substrate integrated waveguide (SIW) and related technological implementations. One step further has been made with a new suite of empty substrate integrated waveguides, where the waves propagate through air, thus reducing the associated losses. This is the case of the empty substrate integrated waveguide (ESIW) or the air-filled substrate integrated waveguide (air-filled SIW). However, all these SIC are H plane structures, so classical H plane solutions in rectangular waveguides have already been mapped to most of these new SIC. In this paper a novel E plane empty substrate integrated waveguide (ESIW-E) is presented. This structure allows to easily map classical E plane solutions in rectangular waveguide to this new substrate integrated solution. It is similar to the ESIW, although more layers are needed to build the structure. A wideband transition (covering the frequency range between 33 GHz and 50 GHz) from microstrip to ESIW-E is designed and manufactured. Measurements are successfully compared with simulation, proving the validity of this new SIC. A broadband high-frequency phase shifter (for operation from 35 GHz to 47 GHz) is successfully implemented in ESIW-E, thus proving the good performance of this new SIC in a practical application.This work was supported by the Ministerio de Economía y Competitividad, Spanish Goverment, under research projects TEC2013-47037-C5-3-R, TEC2013-47037-C5-1-R, AYA2013-49759-EXP, and CSD2010-00064