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In this work, we introduce SEESys, the first system to provide online pose error estimation for Simultaneous Localization and Mapping (SLAM). Unlike prior offline error estimation approaches, the SEESys framework efficiently collects real-time system features and delivers accurate pose error magnitude estimates with low latency. This enables real-time quality-of-service information for downstream applications. To achieve this goal, we develop a SLAM system run-time status monitor (RTS monitor) that performs feature collection with minimal overhead, along with a multi-modality attention-based Deep SLAM Error Estimator (DeepSEE) for error estimation. We train and evaluate SEESys using both public SLAM benchmarks and a diverse set of synthetic datasets, achieving an RMSE of 0.235 cm of pose error estimation, which is 15.8% lower than the baseline. Additionally, we conduct a case study showcasing SEESys in a real-world scenario, where it is applied to a real-time audio error advisory system for human operators of a SLAM-enabled device. The results demonstrate that SEESys provides error estimates with an average end-to-end latency of 37.3 ms, and the audio error advisory reduces pose tracking error by 25%. 

Manual building code compliance checking is a time-consuming, labor-intensive and error-prone process. Automated logic-based reasoning is an essential step in the automation of this process. There have been previous studies using logic programming languages for automated logic-based reasoning to support automated compliance checking (ACC) of building designs with building codes. As a high-performance implementation of the standard logic programming language, B-Prolog was widely used in these studies. However, due to the support of dynamic predicates and user-defined operators, the predicates’ functions vary according to different user definitions; therefore, B-Prolog is sometimes not reliable for building code reasoning. As a more expressive, scalable, and reliable alterative to B-Prolog, Picat, a logic-based multi-paradigm programming language, provides a new and potentially more powerful platform for automated logic-based reasoning in ACC. To explore the potential value of Picat in ACC, in this study, the authors compared Picat and B-Prolog performance in automatically checking 20 requirement rules in the 2015 International Building Code. The experimental results showed that the automated checking for building codes in the B-Prolog version was faster than that in the Picat version, whereas the Picat version was more reliable than the B-Prolog version. This could be the result of B-Prolog using unifica-tion and Picat using pattern matching for indexing rules. More potential applications of Picat in ACC domain need further research. Furthermore, this schema could be used in the teaching of ACC to graduate construction students, illustrating the need to focus on the reliability, predictability and scalability of the process, in order to provide a practical solution to improving code compliance checking processes. 

Thin-walled structures have been widely used in automotive and aerospace industries to improve the system crashworthiness and impact protection. However, during manufacturing, transporting and handling processes, initial geometric imperfections are inevitably introduced to the thin-walled structures, which imposes negative impacts to the mechanical performance and service life of the thin-walled structures. In this study, we have introduced structural imperfection with controlled geometry and dimension to thin-walled steel tubes and characterized the mechanical response of these empty tubes and LN-filled tubes by quasi-static compression tests. Results show, the structural imperfection reduces the energy absorption capacity of empty tubes by about 20%. As the tube is filled with LN, the structural imperfection does not affect the energy absorption capacity of LN filled tube. The enhanced imperfection resistance is attributed to the suppression of imperfection growth caused by the strong liquid-solid interaction between the LN and tube wall. These findings suggest that the LN filling material can effectively reduce the adverse impact of structural imperfection and shed light on future design of thin-walled energy absorption devices. 

Abstract The Gravitational-Wave Transient Catalog (GWTC) is a collection of short-duration (transient) gravitational-wave signals identified by the LIGO–Virgo–KAGRA Collaboration in gravitational-wave data produced by the eponymous detectors. The catalog provides information about the identified candidates, such as the arrival time and amplitude of the signal and properties of the signal’s source as inferred from the observational data. GWTC is the data release of this dataset, and version 4.0 extends the catalog to include observations made during the first part of the fourth LIGO–Virgo–KAGRA observing run up until 2024 January 31. This Letter marks an introduction to a collection of articles related to this version of the catalog, GWTC-4.0. The collection of articles accompanying the catalog provides documentation of the methods used to analyze the data, summaries of the catalog of events, observational measurements drawn from the population, and detailed discussions of selected candidates.