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Cyber-physical systems tightly integrate computational resources with physical processes through sensing and actuating, widely penetrating various safety-critical domains, such as autonomous driving, medical monitoring, and industrial control. Unfortunately, they are susceptible to assorted attacks that can result in injuries or physical damage soon after the system is compromised. Consequently, we require mechanisms that swiftly recover their physical states, redirecting a compromised system to desired states to mitigate hazardous situations that can result from attacks. However, existing recovery studies have overlooked stochastic uncertainties that can be unbounded, making a recovery infeasible or invalidating safety and real-time guarantees. This paper presents a novel recovery approach that achieves the highest probability of steering the physical states of systems with stochastic uncertainties to a target set rapidly or within a given time. Further, we prove that our method is sound, complete, fast, and has low computational complexity if the target set can be expressed as a strip. Finally, we demonstrate the practicality of our solution through the implementation in multiple use cases encompassing both linear and nonlinear dynamics, including robotic vehicles, drones, and vehicles in high-fidelity simulators. 

Atmospheric chemistry models generally assume organic aerosol (OA) to be photochemically inert. Recent mechanisms for the oxidation of biogenic isoprene, a major source of secondary organic aerosol (iSOA), produce excessive OA in the absence of subsequent OA reactivity. At the same time, models underestimate atmospheric concentrations of formic and acetic acids for which OA degradation could provide a source. Here we show that the aqueous photooxidation of an isoprene-derived organosulfate (2-methyltriolsulfate or MTS), an important iSOA component, produces formic and acetic acids in high yields and at timescales competitive with deposition. Experimental data are well fit by a kinetic model in which three sequential oxidation reactions of the isoprene organosulfate produce two molar equivalents of formic acid and one of acetic acid. We incorporate this chemistry and that of 2-methyltetrol, another ubiquitous iSOA component, into the GEOS-Chem global atmospheric chemistry model. Simulations show that photooxidation and subsequent revolatilization of this iSOA may account for up to half of total iSOA loss globally, producing 4 Tg a−1 each of formic and acetic acids. This reduces model biases in gas-phase formic acid and total organic aerosol over the Southeast United States in summer by ∼30% and 60% respectively. While our study shows the importance of adding iSOA photochemical sinks into atmospheric models, uncertainties remain that warrant further study. In particular, improved understanding of reaction dependencies on particle characteristics and concentrations of particle-phase OH and other oxidants are needed to better simulate the effects of this chemistry on the atmospheric budgets of organic acids and iSOA. 

Path-specific effect analysis is a powerful tool in causal inference. This paper provides a definition of causal counterfactual path-specific importance score for the structural causal model (SCM). Different from existing path-specific effect definitions, which focus on the population level, the score defined in this paper can quantify the impact of a decision variable on an outcome variable along a specific pathway at the individual level. Moreover, the score has many desirable properties, including following the chain rule and being consistent. Finally, this paper presents an algorithm that can leverage these properties and find the k-most important paths with the highest importance scores in a causal graph effectively.

 

Ferroelectric materials are currently some of the most widely applied material systems and are constantly generating improved functions with higher efficiencies. Advancements in poly(vinylidene fluoride) (PVDF)–based polymer ferroelectrics provide flexural, coupling-efficient, and multifunctional material platforms for applications that demand portable, lightweight, wearable, and durable features. We highlight the recent advances in fluoropolymer ferroelectrics, their energetic cross-coupling effects, and emerging technologies, including wearable, highly efficient electromechanical actuators and sensors, electrocaloric refrigeration, and dielectric devices. These developments reveal that the molecular and nanostructure manipulations of the polarization-field interactions, through facile defect biasing, could introduce enhancements in the physical effects that would enable the realization of multisensory and multifunctional wearables for the emerging immersive virtual world and smart systems for a sustainable future.

 

Cyber-physical systems (CPSs) leverage computations to operate physical objects in real-world environments, and increasingly more CPS-based applications have been designed for life-critical applications. Therefore, any vulnerability in such a system can lead to severe consequences if exploited by adversaries. In this paper, we present a data predictive recovery system to safeguard the CPS from sensor attacks, assuming that we can identify compromised sensors from data. Our recovery system guarantees that the CPS will never encounter unsafe states and will smoothly recover to a target set within a conservative deadline. It also guarantees that the CPS will remain within the target set for a specified period. Major highlights of our paper include (i) the recovery procedure works on nonlinear systems, (ii) the method leverages uncorrupted sensors to relieve uncertainty accumulation, and (iii) an extensive set of experiments on various nonlinear benchmarks that demonstrate our framework's performance and efficiency. 

Squeezed light has long been used to enhance the precision of a single optomechanical sensor. An emerging set of proposals seeks to use arrays of optomechanical sensors to detect weak distributed forces, for applications ranging from gravity-based subterranean imaging to dark matter searches; however, a detailed investigation into the quantum-enhancement of this approach remains outstanding. Here, we propose an array of entanglement-enhanced optomechanical sensors to improve the broadband sensitivity of distributed force sensing. By coherently operating the optomechanical sensor array and distributing squeezing to entangle the optical fields, the array of sensors has a scaling advantage over independent sensors (i.e.,$$\sqrt{M}\to M$$$\sqrt{M}\to M$, whereMis the number of sensors) due to coherence as well as joint noise suppression due to multi-partite entanglement. As an illustration, we consider entanglement-enhancement of an optomechanical accelerometer array to search for dark matter, and elucidate the challenge of realizing a quantum advantage in this context.