Guidelines, identity and competing needs: The effect of signage design guidelines on uniformity and variety in urban retail business districts

This study examines the competing needs of business owners and urban districts in communicating their respective graphic identities to potential customers through retail signage, and explores how the implementation of design guidelines and design regulations can impact which identity is emphasized. Graphic identity is the means by which we recognize businesses or districts. This study examines existing design guidelines in search of an effective balance between uniformity and variety in urban retail signage systems that allow both the district and the business owner to communicate their message through graphic identity. This issue should be of interest to those who prepare urban design guidelines and to anyone engaged in the design or redesign of urban retail signage

CAFA: Class-Aware Feature Alignment for Test-Time Adaptation

Despite recent advancements in deep learning, deep neural networks continue to suffer from performance degradation when applied to new data that differs from training data. Test-time adaptation (TTA) aims to address this challenge by adapting a model to unlabeled data at test time. TTA can be applied to pretrained networks without modifying their training procedures, enabling them to utilize a well-formed source distribution for adaptation. One possible approach is to align the representation space of test samples to the source distribution (\textit{i.e.,} feature alignment). However, performing feature alignment in TTA is especially challenging in that access to labeled source data is restricted during adaptation. That is, a model does not have a chance to learn test data in a class-discriminative manner, which was feasible in other adaptation tasks (\textit{e.g.,} unsupervised domain adaptation) via supervised losses on the source data. Based on this observation, we propose a simple yet effective feature alignment loss, termed as Class-Aware Feature Alignment (CAFA), which simultaneously 1) encourages a model to learn target representations in a class-discriminative manner and 2) effectively mitigates the distribution shifts at test time. Our method does not require any hyper-parameters or additional losses, which are required in previous approaches. We conduct extensive experiments on 6 different datasets and show our proposed method consistently outperforms existing baselines

Elevated IFNA1 and suppressed IL12p40 associated with persistent hyperinflammation in COVID-19 pneumonia

IntroductionDespite of massive endeavors to characterize inflammation in COVID-19 patients, the core network of inflammatory mediators responsible for severe pneumonia stillremain remains elusive. MethodsHere, we performed quantitative and kinetic analysis of 191 inflammatory factors in 955 plasma samples from 80 normal controls (sample n = 80) and 347 confirmed COVID-19 pneumonia patients (sample n = 875), including 8 deceased patients. ResultsDifferential expression analysis showed that 76% of plasmaproteins (145 factors) were upregulated in severe COVID-19 patients comparedwith moderate patients, confirming overt inflammatory responses in severe COVID-19 pneumonia patients. Global correlation analysis of the plasma factorsrevealed two core inflammatory modules, core I and II, comprising mainly myeloid cell and lymphoid cell compartments, respectively, with enhanced impact in a severity-dependent manner. We observed elevated IFNA1 and suppressed IL12p40, presenting a robust inverse correlation in severe patients, which was strongly associated with persistent hyperinflammation in 8.3% of moderate pneumonia patients and 59.4% of severe patients. DiscussionAberrant persistence of pulmonary and systemic inflammation might be associated with long COVID-19 sequelae. Our comprehensive analysis of inflammatory mediators in plasmarevealed the complexity of pneumonic inflammation in COVID-19 patients anddefined critical modules responsible for severe pneumonic progression

Guidelines, identity and competing needs: The effect of signage design guidelines on uniformity and variety in urban retail business districts

This study examines the competing needs of business owners and urban districts in communicating their respective graphic identities to potential customers through retail signage, and explores how the implementation of design guidelines and design regulations can impact which identity is emphasized. Graphic identity is the means by which we recognize businesses or districts. This study examines existing design guidelines in search of an effective balance between uniformity and variety in urban retail signage systems that allow both the district and the business owner to communicate their message through graphic identity. This issue should be of interest to those who prepare urban design guidelines and to anyone engaged in the design or redesign of urban retail signage.</p

WEAK CARLEMAN ESTIMATES WITH TWO LARGE PARAMETERS FOR SECOND ORDER OPERATORS AND APPLICATIONS TO ELASTICITY WITH RESIDUAL STRESS

Abstract. We derive weak Carleman estimates with two large parameters for a general partial differential operator of second order under pseudo-convexity conditions on the weight function. We use these estimates to derive most natural Carleman type estimates for the (anisotropic) system of elasticity with residual stress and give applications to uniqueness and stability of the continuation and identification of the residual stress from boundary measurements. We give explicit sufficient pseudo-convexity conditions. Proofs use differential quadratic forms and Fourier analysis, combined with special (micro)localization arguments. 1. Introduction. W

Ultra-compact terahertz 50:50 power splitter designed by a perceptron based algorithm

We designed and simulated an ultra-compact 1 × 2 power splitter operating in the terahertz region. A machine learning approach was implemented to design the photonic device. The designed power splitter has a footprint of 500 µm × 500 µm. We calculated the insertion loss using a three-dimensional finite difference time domain method. The calculated insertion loss was less than 4 dB over the operating wavelength range of 275–325 µm. The machine learning algorithm implemented in this work can be applied to the inverse design of various photonic devices.11