121 research outputs found
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
STAT1 employs myeloid cell-extrinsic mechanisms to regulate the neutrophil response and provide protection against invasive Klebsiella pneumoniae lung infection
Klebsiella pneumoniae (KP) is an extracellular Gram-negative bacterium that causes infections in the lower respiratory and urinary tracts and the bloodstream. STAT1 is a master transcription factor that acts to maintain T cell quiescence under homeostatic conditions. Although STAT1 helps defend against systemic spread of acute KP intrapulmonary infection, whether STAT1 regulation of T cell homeostasis impacts pulmonary host defense during acute bacterial infection and injury is less clear. Using a clinical KP respiratory isolate and a pneumonia mouse model, we found that STAT1 deficiency led to an early neutrophil-dominant transcriptional profile and neutrophil recruitment in the lung preceding widespread bacterial dissemination and lung injury development. Yet, myeloid cell STAT1 was dispensable for control of KP proliferation and dissemination, because myeloid cell-specific STAT1-deficient (LysMCre/WT;Stat1fl/fl) mice showed bacterial burden in the lung, liver, and kidney similar to that of their wild-type littermates. Surprisingly, IL-17-producing CD4+ T cells infiltrated Stat1-/- murine lungs early during KP infection. The increase in Th17 cells in the lung was not due to preexisting immunity against KP and was consistent with circulating rather than tissue-resident CD4+ T cells. However, blocking global IL-17 signaling with anti-IL-17RC administration led to increased proliferation and dissemination of KP, suggesting that IL-17 provided by other innate immune cells is essential in defense against KP. Contrastingly, depletion of CD4+ T cells reduced Stat1-/- murine lung bacterial burden, indicating that early CD4+ T cell activation in the setting of global STAT1 deficiency is pathogenic. Altogether, our findings suggest that STAT1 employs myeloid cell-extrinsic mechanisms to regulate neutrophil responses and provides protection against invasive KP by restricting nonspecific CD4+ T cell activation and immunopathology in the lung
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
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