Development and Validation of a Slinky Ground Heat Exchanger Model

Ground source heat pump systems are an energy efficient heating and cooling technology for residential and commercial buildings. The main barrier to adoption is the higher investment cost compared to conventional systems. Where available land area permits, horizontal ground heat exchangers are generally less expensive than vertical borehole-type ground heat exchangers (GHXs). A further cost reduction can be made by using SlinkyTM ground heat exchangers, which require less trench space and hence reduce the installation cost, in many cases. It is desirable to formulate an accurate model for simulation purposes; such simulations can be used in both design tools and in energy analysis programs.The model formulated in this paper relies on analytical ring source solutions to compute temperature response functions for both horizontal and vertical SlinkyTM heat exchangers. The algorithm used to calculate the response factors have several features that significantly increase computation speed. The thermal effect of the detailed ground heat balance on the GHXs is considered by superimposing the undisturbed ground temperature calculated using a numerical model. For use in whole-building simulations where the GHX may be connected to other components, the model is formulated to calculate both heat transfer rate and exiting fluid temperature, given entering fluid temperature. The model has been validated against the previously published experimental data; and is implemented in a whole-building energy simulation program EnergyPlus.Mechanical Engineerin

A robust method for reliability updating with equality information using sequential adaptive importance sampling

Reliability updating refers to a problem that integrates Bayesian updating technique with structural reliability analysis and cannot be directly solved by structural reliability methods (SRMs) when it involves equality information. The state-of-the-art approaches transform equality information into inequality information by introducing an auxiliary standard normal parameter. These methods, however, encounter the loss of computational efficiency due to the difficulty in finding the maximum of the likelihood function, the large coefficient of variation (COV) associated with the posterior failure probability and the inapplicability to dynamic updating problems where new information is constantly available. To overcome these limitations, this paper proposes an innovative method called RU-SAIS (reliability updating using sequential adaptive importance sampling), which combines elements of sequential importance sampling and K-means clustering to construct a series of important sampling densities (ISDs) using Gaussian mixture. The last ISD of the sequence is further adaptively modified through application of the cross entropy method. The performance of RU-SAIS is demonstrated by three examples. Results show that RU-SAIS achieves a more accurate and robust estimator of the posterior failure probability than the existing methods such as subset simulation.Comment: 38 pages, 6 tables, 9 figure

OperARtistry: An AR-based Interactive Application to Assist the Learning of Chinese Traditional Opera (Xiqu) Makeup

Chinese Traditional Opera (Xiqu) is an important type of intangible cultural heritage and one key characteristic of Xiqu is its visual effects on face achieved via makeup. However, Xiqu makeup process, especially the eye-area makeup process, is complex and time-consuming, which poses a learning challenge for potential younger inheritors. We introduce OperARtistry, an interactive application based on Augmented Reality (AR) that offers in-situ Xiqu makeup guidance for beginners. Our application provides a step-by-step guide for Xiqu eye-area makeup, incorporating AR effects at each stage. Furthermore, we conducted an initial user study (n=6) to compare our approach with existing video-based tutorials to assess the effectiveness and usefulness of our approach. Our findings show that OperARtisty helped participants achieve high-quality eye-area makeup effects with less learning time.Comment: 11 pages, 9 figures, In Proceedings of The Eleventh International Symposium of Chinese CHI (Chinese CHI 2023

Efficient quantum compression for identically prepared states with arbitrary dimentional

In this paper, we present an efficient quantum compression method for identically prepared states with arbitrary dimentional

A Dive into SAM Prior in Image Restoration

The goal of image restoration (IR), a fundamental issue in computer vision, is to restore a high-quality (HQ) image from its degraded low-quality (LQ) observation. Multiple HQ solutions may correspond to an LQ input in this poorly posed problem, creating an ambiguous solution space. This motivates the investigation and incorporation of prior knowledge in order to effectively constrain the solution space and enhance the quality of the restored images. In spite of the pervasive use of hand-crafted and learned priors in IR, limited attention has been paid to the incorporation of knowledge from large-scale foundation models. In this paper, we for the first time leverage the prior knowledge of the state-of-the-art segment anything model (SAM) to boost the performance of existing IR networks in an parameter-efficient tuning manner. In particular, the choice of SAM is based on its robustness to image degradations, such that HQ semantic masks can be extracted from it. In order to leverage semantic priors and enhance restoration quality, we propose a lightweight SAM prior tuning (SPT) unit. This plug-and-play component allows us to effectively integrate semantic priors into existing IR networks, resulting in significant improvements in restoration quality. As the only trainable module in our method, the SPT unit has the potential to improve both efficiency and scalability. We demonstrate the effectiveness of the proposed method in enhancing a variety of methods across multiple tasks, such as image super-resolution and color image denoising.Comment: Technical Repor

Toward Real-World Light Field Super-Resolution

Deep learning has opened up new possibilities for light field super-resolution (SR), but existing methods trained on synthetic datasets with simple degradations (e.g., bicubic downsampling) suffer from poor performance when applied to complex real-world scenarios. To address this problem, we introduce LytroZoom, the first real-world light field SR dataset capturing paired low- and high-resolution light fields of diverse indoor and outdoor scenes using a Lytro ILLUM camera. Additionally, we propose the Omni-Frequency Projection Network (OFPNet), which decomposes the omni-frequency components and iteratively enhances them through frequency projection operations to address spatially variant degradation processes present in all frequency components. Experiments demonstrate that models trained on LytroZoom outperform those trained on synthetic datasets and are generalizable to diverse content and devices. Quantitative and qualitative evaluations verify the superiority of OFPNet. We believe this work will inspire future research in real-world light field SR.Comment: CVPRW 202

Entanglement as the cross-symmetric part of quantum discord

In this paper, we show that the minimal quantum discord over "cross-symmetric" state extensions is an entanglement monotone. In particular, we show that the minimal Bures distance of discord over cross-symmetric extensions is equivalent to the Bures distance of entanglement. At last, we refute a long-held but unstated convention that only contractive distances can be used to construct entanglement monotones by showing that the entanglement quantifier induced by the Hilbert-Schmidt distance, which is not contractive under quantum operations, is also an entanglement monotone.Comment: 9 pages, 1 figure. arXiv admin note: text overlap with arXiv:2012.0383

Generalized Equivariance and Preferential Labeling for GNN Node Classification

Existing graph neural networks (GNNs) largely rely on node embeddings, which represent a node as a vector by its identity, type, or content. However, graphs with unattributed nodes widely exist in real-world applications (e.g., anonymized social networks). Previous GNNs either assign random labels to nodes (which introduces artefacts to the GNN) or assign one embedding to all nodes (which fails to explicitly distinguish one node from another). Further, when these GNNs are applied to unattributed node classification problems, they have an undesired equivariance property, which are fundamentally unable to address the data with multiple possible outputs. In this paper, we analyze the limitation of existing approaches to node classification problems. Inspired by our analysis, we propose a generalized equivariance property and a Preferential Labeling technique that satisfies the desired property asymptotically. Experimental results show that we achieve high performance in several unattributed node classification tasks

TreeGen: A Tree-Based Transformer Architecture for Code Generation

A code generation system generates programming language code based on an input natural language description. State-of-the-art approaches rely on neural networks for code generation. However, these code generators suffer from two problems. One is the long dependency problem, where a code element often depends on another far-away code element. A variable reference, for example, depends on its definition, which may appear quite a few lines before. The other problem is structure modeling, as programs contain rich structural information. In this paper, we propose a novel tree-based neural architecture, TreeGen, for code generation. TreeGen uses the attention mechanism of Transformers to alleviate the long-dependency problem, and introduces a novel AST reader (encoder) to incorporate grammar rules and AST structures into the network. We evaluated TreeGen on a Python benchmark, HearthStone, and two semantic parsing benchmarks, ATIS and GEO. TreeGen outperformed the previous state-of-the-art approach by 4.5 percentage points on HearthStone, and achieved the best accuracy among neural network-based approaches on ATIS (89.1%) and GEO (89.6%). We also conducted an ablation test to better understand each component of our model