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Advances in construction robotics represent a potential shift in building design and construction. In general, construction robotics are usually deployed directly onto construction sites without systematically evaluating the design constructability for robotic applications. Literature on constructability suggest that ignoring it during design will cause rework, inefficiency, and higher cost. Although previous studies have widely discussed design constructability, they mainly focus on traditional human craft-based construction methods. Whereas a gap still exists in design constructability assessment for construction robotics. This paper presents an initial analytical framework for constructability assessment for construction robotics during the design phase. Specifically, we summarize factors that impact robotic constructability based on robotic features, design features, work constraints, and piloted an automated constructability checking system for robotics. Additionally, this study takes CANVAS, a drywall finishing robot, as case study to create a framework in simulation environment and the results demonstrate the potential value of the proposed framework. 

The development of robotics in the Architecture, Engineering, and Construction (AEC) industry has emerged in recent years in response to technology advances and industry challenges such as workforce shortages. Construction robotics has the potential to increase construction productivity and accuracy as well as reduce accidents and costs. However, their introduction to construction sites creates new challenges. Previous studies have shown that robots can cause major changes in construction workflow, scope, and methods. Construction robotics introduce key changes to the work process and the sequence of construction tasks. The traditional planning approach for work break down structure and scheduling assigns resources for construction activities based on human labor and craft methods. Despite this, the capabilities of robotics relative to construction resource planning, sequencing, and work scope has not been fully studied. To address this, the implementation of robotics in construction projects needs a new approach to organizing work packages (WP). With the inclusion of robotics as a resource, planning parameters such as methods and sequence will change both the scope and accordingly the work packaging for construction. This paper aims to systematically identify the potential impacts of robots on construction processes, as well as how those changes influences work packaging. The methodology is based on data integration and content analysis from literature review and collected interviews with project participants about real-world construction projects. The paper discusses how construction robots impact the work package approach and categorizes the affected factors. These factors include the work area, sequence and priority of construction activities, safety management, allocation of risk responsibility for tasks, interaction with other trades, and required materials. 

Using transdimensional plasmonic materials (TDPM) within the framework of fluctuational electrodynamics, we demonstrate nonlocality in dielectric response alters near-field heat transfer at gap sizes on the order of hundreds of nanometers. Our theoretical study reveals that, opposite to the local model prediction, propagating waves can transport energy through the TDPM. However, energy transport by polaritons at shorter separations is reduced due to the metallic response of TDPM stronger than that predicted by the local model. Our experiments conducted for a configuration with a silica sphere and a doped silicon plate coated with an ultrathin layer of platinum as the TDPM show good agreement with the nonlocal near-field radiation theory. Our experimental work in conjunction with the nonlocal theory has important implications in thermophotovoltaic energy conversion, thermal management applications with metal coatings, and quantum-optical structures. 

The drawing process is crucial to understanding the final result of a drawing. There has been a long history of understanding human drawing; what kinds of strokes people use and where they are placed. An area of interest in Artificial Intelligence is developing systems that simulate human behavior in drawing. However, there has been little work done to understand the order of strokes in the drawing process. Without sufficient understanding of natural drawing order, it is difficult to build models that can generate natural drawing processes. In this paper, we present a study comparing multiple types of stroke orders to confirm findings from previous work and demonstrate that multiple orderings of the same set of strokes can be perceived as human-drawn and different stroke order types achieve different perceived naturalness depending on the type of image prompt. 

Transfer learning on graphs drawn from varied distributions (domains) is in great demand across many applications. Emerging methods attempt to learn domain-invariant representations using graph neural networks (GNNs), yet the empirical performances vary and the theoretical foundation is limited. This paper aims at designing theory-grounded algorithms for graph domain adaptation (GDA). (i) As the first attempt, we derive a model-based GDA bound closely related to two GNN spectral properties: spectral smoothness (SS) and maximum frequency response (MFR). This is achieved by cross-pollinating between the OT-based (optimal transport) DA and graph filter theories. (ii) Inspired by the theoretical results, we propose algorithms regularizing spectral properties of SS and MFR to improve GNN transferability. We further extend the GDA theory into the more challenging scenario of conditional shift, where spectral regularization still applies. (iii) More importantly, our analyses of the theory reveal which regularization would improve performance of what transfer learning scenario, (iv) with numerical agreement with extensive real-world experiments: SS and MFR regularizations bring more benefits to the scenarios of node transfer and link transfer, respectively. In a nutshell, our study paves the way toward explicitly constructing and training GNNs that can capture more transferable representations across graph domains. Codes are released at https://github.com/Shen-Lab/GDA-SpecReg.