Deep neural networks are widely used for nonlinear function approximation, with
applications ranging from computer vision to control. Although these networks involve the …

Abstract Recently, Multi-Robot Systems (MRS) have attained considerable recognition
because of their efficiency and applicability in different types of real-life applications. This …

Learning-enabled control systems have demonstrated impressive empirical performance on
challenging control problems in robotics, but this performance comes at the cost of reduced …

Deep neural networks have emerged as a widely used and effective means for tackling
complex, real-world problems. However, a major obstacle in applying them to safety-critical …

Formal approaches can provide strict correctness guarantees for the development of both
hardware and software systems. In this work, we examine state-of-the-art formal methods for …

The enhancements being developed by the Time-Sensitive Networking Task Group as part
of IEEE 802.1 emerge as the future of real-time communication over Ethernet networks for …

The Internet of Things (IoT) has brought about a new era of connected devices and systems,
with applications ranging from healthcare to transportation. However, the reliability and …

Given a neural network (NN) and a set of possible inputs to the network described by
polyhedral constraints, we aim to compute a safe over-approximation of the set of possible …

Computer-aided cryptography is an active area of research that develops and applies
formal, machine-checkable approaches to the design, analysis, and implementation of …

Toward verified artificial intelligence Page 1 46 COMMUNICATIONS OF THE ACM | JULY
2022 | VOL. 65 | NO. 7 contributed articles ILL US TRA TION B Y PETER CRO W THER A …