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While the microscopic laws of physics are often symmetric under time reversal, most natural processes that we observe are not. The emergent asymmetry between typical and time-reversed processes is referred to as the arrow of time. In quantum physics, an arrow of time emerges when a sequence of measurements is performed on a system. We introduce quantum control tools that can yield dynamics more consistent with time flowing backward than forward. The control tools are based on the explicit construction of a Hamiltonian that can replicate the stochastic trajectories of a monitored quantum system. Such a Hamiltonian can reverse the effect of monitoring and, via a feedback process, generate trajectories consistent with a reversed arrow of time. It can also be used to simulate the backward-in-time dynamics of an open quantum system. Finally, we design a feedback-driven continuous measurement engine powered by the energy pumped into the system by the monitoring process. We show that the engine can operate under experimentally realizable conditions with feedback delay and finite-efficiency measurements. 

Abstract Adjusting for covariates in randomized controlled trials can enhance the credibility and efficiency of treatment effect estimation. However, handling numerous covariates and their complex (nonlinear) transformations poses a challenge. Motivated by the case study of the Best Apnea Interventions for Research (BestAIR) trial data from the National Sleep Research Resource (NSRR), where the number of covariates (p=114) is comparable to the sample size (N=196), we propose a principled covariate adjustment with variable selection (COADVISE) framework. COADVISE enables variable selection for covariates most relevant to the outcome while accommodating both linear and nonlinear adjustments. This framework ensures consistent estimates with improved efficiency over unadjusted estimators and provides robust variance estimation, even under outcome model misspecification. We demonstrate efficiency gains through theoretical analysis, extensive simulations, and a re-analysis of the BestAIR trial data to compare alternative variable selection strategies, offering cautionary recommendations. A user-friendly R package, Coadvise, is available to facilitate practical implementation. 

Soil moisture plays a crucial role in hydrological processes and serves as a key driver of rainfall-induced landslides, especially in regions with steep terrain and intense precipitation. Traditional landslide risk models often oversimplify soil moisture and infiltration dynamics, which limits their predictive accuracy. This study presents a deep learning-based framework for generating high-resolution, spatiotemporal Surface Soil Moisture (SSM) maps for Prince George’s County, Maryland—a region highly susceptible to rainfall-triggered landslides—aimed at improving infiltration modeling and landslide prediction. A Convolutional Long Short-Term Memory (ConvLSTM) network integrates static spatial features (elevation, slope, soil type) with multi-temporal meteorological variables (precipitation, temperature, humidity, wind speed, evapotranspiration) and vegetation indices. The model is trained using dense SSM maps derived from Sentinel-1 SAR data processed through a change detection algorithm, providing a physically meaningful alternative to sparse in-situ observations. To address data imbalance, a two-pass patch extraction strategy was implemented to enhance representation of high-SSM conditions. The framework leverages high-performance computing resources to process large-scale, multi-temporal raster datasets efficiently. Evaluation results show strong predictive performance, with the two-day model achieving R2 = 0.72, correlation = 0.85, RMSE = 0.154, and MAE = 0.103. The results demonstrate the model’s capability to produce fine-resolution, wall-to-wall SSM maps that capture the spatial and temporal dynamics of surface soil moisture, supporting the development of early warning systems and landslide hazard mitigation strategies. 

This paper addresses the problem of monitoring structures with potential emergent damage through adaptive sensing provided by teams of mobile robots. Advantages of mobile robot teams for structural health monitoring include: 1. Multiple views of a given structure, 2. Adaptive movements that focus attention in response to observed conditions,3. Heterogeneous sensing and movement, and 4. Federated health monitoring and prognosis assessment through networked sharing and processing of information. Towards this end three cases of the use of mobile robot teams will be presented: 1. Heterogeneous robot teams for home and small building maintenance – Identifying, diagnosing and mitigating damage to homes and small buildings is a vexing set of problems for the owners. As an aid small controlled bristlebots and quadruped robot dogs (QRDs) carry sensors throughout a small building, assess conditions, provide prognoses and networked links to repair options; 2. Culverts are primary components of stormwater and flood prevention infrastructure. Inspecting small culverts is difficult for humans and large culverts are accessible but dangerous due to issues of confined spaces. Low-cost mobile robots have emerged as a competitive inspection option for accessible culverts with straight or short runs that permit wireless telemetry. Longer culverts and those with bends, branches and drop inlets pose challenges to the telemetry. Teams of robots extend the range of inspection through multi-hop video and control telemetry; 3. Ground penetrating radar (GPR) is a method of sensing subsurface infrastructure conditions with high-frequency electromagnetic waves. Conventional GPRs operate in a suboptimal monostatic or bistatic mode, are tedious to operate and have limitations in sensing congested utility subsurface conditions. Coordinated multistatic ground penetrating radar operated with mobile robot teams alleviates some of these concerns and provide better subsurface assessments with automated methods that focus attention on subsurface features of interest. Results from laboratory and field tests of these robot teams, as well as organizing principles of control and automated information processing are presented.