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  1. Free, publicly-accessible full text available May 1, 2024
  2. SITE (Ed.)
    Peer learning through pair programming is a type of collaborative learning that involves students working in pairs to discuss computer programming concepts or develop codes to solve problems. The Zoom breakout room method is applied to teach pair programming in a virtual classroom during the COVID-19 environment. By facilitating pair programming in a virtual learning environment, we gained valuable experience in promoting collaborative learning, active learning, and problem-based learning activities in a cloud setting. 
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  3. null (Ed.)
    The randomized midpoint method, proposed by (Shen and Lee, 2019), has emerged as an optimal discretization procedure for simulating the continuous time underdamped Langevin diffusion. In this paper, we analyze several probabilistic properties of the randomized midpoint discretization method, considering both overdamped and underdamped Langevin dynamics. We first characterize the stationary distribution of the discrete chain obtained with constant step-size discretization and show that it is biased away from the target distribution. Notably, the step-size needs to go to zero to obtain asymptotic unbiasedness. Next, we establish the asymptotic normality of numerical integration using the randomized midpoint method and highlight the relative advantages and disadvantages over other discretizations. Our results collectively provide several insights into the behavior of the randomized midpoint discretization method, including obtaining confidence intervals for numerical integrations. 
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  4. Large-scale concrete 3D printing and digital construction has brought enormous potential to expand the design space of building components (e.g., building envelope) for the integration of multiple architectural functionalities including energy saving. In this research, a modular 3D printed vertical concrete green wall system – namely the 3D-VtGW, was developed. The 3D-VtGW envelope was assembled with prefabricated (3D printed) multifunctional wall modular elements, which serves as the enclosure of the building as well as the backbone for a green wall system to improve building’s energy efficiency. Using this design concept and large-scale concrete 3D printing, a prototype commercial building was built in Nanjing, China. To quantify the energy-saving potential of the 3D-VtGW system, a thermal network model was developed to simulate the thermal behavior of buildings with 3D-VtGW system and for thermal comfort analysis. Whole-building energy simulation was carried out using Chinese Standard Weather Data (CSWD) o Nanjing, China. The simulation results indicate that the building with 3D-VtGW exhibited prominent potential for energy saving and improved thermal comfort. The integrated greenery system in 3D-VtGW largely reduces wall exterior surface temperature and through-wall heat flux via the combined effects of plant shading, evapotranspiration, and heat storage from soil. This study presents the immense opportunities brought by digital fabrication and construction to extend the design space and function integration in buildings. 
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  5. Free, publicly-accessible full text available March 1, 2024
  6. null (Ed.)
  7. Existing image-to-image transformation approaches primarily focus on synthesizing visually pleasing data. Generating images with correct identity labels is challenging yet much less explored. It is even more challenging to deal with image transformation tasks with large deformation in poses, viewpoints, or scales while preserving the identity, such as face rotation and object viewpoint morphing. In this paper, we aim at transforming an image with a fine-grained category to synthesize new images that preserve the identity of the input image, which can thereby benefit the subsequent fine-grained image recognition and few-shot learning tasks. The generated images, transformed with large geometric deformation, do not necessarily need to be of high visual quality but are required to maintain as much identity information as possible. To this end, we adopt a model based on generative adversarial networks to disentangle the identity related and unrelated factors of an image. In order to preserve the fine-grained contextual details of the input image during the deformable transformation, a constrained nonalignment connection method is proposed to construct learnable highways between intermediate convolution blocks in the generator. Moreover, an adaptive identity modulation mechanism is proposed to transfer the identity information into the output image effectively. Extensive experiments on the CompCars and Multi-PIE datasets demonstrate that our model preserves the identity of the generated images much better than the state-of-the-art image-to-image transformation models, and as a result significantly boosts the visual recognition performance in fine-grained few-shot learning. 
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