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Are teenage and adult smoking causally related? Recent anti-tobacco policy is predicated on the assumption that preventing teenagers from smoking will ensure that fewer adults smoke, but direct evidence in support of this assumption is scant. Using data from three nationally representative sources and instrumenting for teenage smoking with cigarette taxes experienced at ages 14-17, we document a strong positive relationship between teenage and adult smoking: deterring 10 teenagers from smoking through raising cigarette taxes roughly translates into 5 fewer adult smokers. We conclude that efforts to reduce teenage smoking can have long-lasting consequences on smoking participation and, presumably, health.

On shopping websites, product images of low quality negatively affect customer experience. Although there are plenty of work in detecting images with different defects, few efforts have been dedicated to correct those defects at scale. A major challenge is that there are thousands of product types and each has specific defects, therefore building defect specific models is unscalable. In this paper, we propose a unified Image-to-Image (I2I) translation model to correct multiple defects across different product types. Our model leverages an attention mechanism to hierarchically incorporate high-level defect groups and specific defect types to guide the network to focus on defect-related image regions. Evaluated on eight public datasets, our model reduces the Frechet Inception Distance (FID) by 24.6% in average compared with MoNCE, the state-of-the-art I2I method. Unlike public data, another practical challenge on shopping websites is that some paired images are of low quality. Therefore we design our model to be semi-paired by combining the L1 loss of paired data with the cycle loss of unpaired data. Tested on a shopping website dataset to correct three image defects, our model reduces (FID) by 63.2% in average compared with WS-I2I, the state-of-the art semi-paired I2I method., Comment: 6 pages, 6 figures, 3 tables. To be published in ICIP 2023

Jinxing Xia,1 Wei Huang,2 Fanbo Lu,1 Moyan Li,1 Bo Wang1 1Department of Clinical Laboratory, First Affiliated Hospital of Anhui Medical University, Hefei, People’s Republic of China; 2Department of Oncology, First Affiliated Hospital of Anhui Medical University, Hefei, People’s Republic of ChinaCorrespondence: Jinxing Xia; Bo Wang, Department of Clinical Laboratory, First Affiliated Hospital of Anhui Medical University, Hefei, People’s Republic of China, Email xiajinxing@ahmu.edu.cn; wangbo8711@126.comObjective: Invasive infections due to Candida spp. have unique epidemiology, strain distribution, antimicrobial susceptibility, and clinical features. This study aimed to compare and evaluate these characteristic variables between invasive Candida infection and colonization of critically ill patients in local China to potentially improve differential diagnosis and therapy.Methods: A total of 193 critically ill patients were recruited and followed up for the study, and 133 Candida isolates were obtained from invasive Candida-infected or -colonized subjects. The strains were identified to species level through matrix-assisted laser desorption/ionization–time-of-flight mass spectrometry, assisted by DNA sequencing. Candida susceptibility to common antifungals, including azoles, was determined by microbroth ATB Fungus 3 methodology. Azole resistance–related gene sequencing and homologous 3D-structure modeling were employed. Patient demographics and clinical risk factors were documented and comparatively analyzed from the hospital information-management system.Results: Non–C. albicans Candida (56%) principally caused invasive Candida infections, while C. albicans (55.17%) contributed more to Candida colonization in critically ill patients. Additional risk factors exerted significant impact on both Candida cohorts, primarily including invasive interventions, cancers, and concurrent infections in common. Most colonized Candida spp. harbored relatively higher sensi

The multi-output Gaussian process ($\mathcal{MGP}$) is based on the assumption that outputs share commonalities, however, if this assumption does not hold negative transfer will lead to decreased performance relative to learning outputs independently or in subsets. In this article, we first define negative transfer in the context of an $\mathcal{MGP}$ and then derive necessary conditions for an $\mathcal{MGP}$ model to avoid negative transfer. Specifically, under the convolution construction, we show that avoiding negative transfer is mainly dependent on having a sufficient number of latent functions $Q$ regardless of the flexibility of the kernel or inference procedure used. However, a slight increase in $Q$ leads to a large increase in the number of parameters to be estimated. To this end, we propose two latent structures that scale to arbitrarily large datasets, can avoid negative transfer and allow any kernel or sparse approximations to be used within. These structures also allow regularization which can provide consistent and automatic selection of related outputs.