Leveraging Data Locality in Quantum Convolutional Classifiers.

Quantum computing (QC) has opened the door to advancements in machine learning (ML) tasks that are currently implemented in the classical domain. Convolutional neural networks (CNNs) are classical ML architectures that exploit data locality and possess a simpler structure than a fully connected multi-layer perceptrons (MLPs) without compromising the accuracy of classification. However, the concept of preserving data locality is usually overlooked in the existing quantum counterparts of CNNs, particularly for extracting multifeatures in multidimensional data.

Generalized Quantum Convolution for Multidimensional Data.

The convolution operation plays a vital role in a wide range of critical algorithms across various domains, such as digital image processing, convolutional neural networks, and quantum machine learning. In existing implementations, particularly in quantum neural networks, convolution operations are usually approximated by the application of filters with data strides that are equal to the filter window sizes. One challenge with these implementations is preserving the spatial and temporal localities of the input features, specifically for data with higher dimensions.

Attitudes of experiential education directors regarding tobacco sales in pharmacies in the USA.

Objectives: Accreditation guidelines in the USA suggest that experiential sites for pharmacy students should demonstrate 'a strong commitment to health promotion and illness prevention'; however, most community pharmacies sell tobacco products. This study aimed to determine the proportion of students rotating through advanced pharmacy practice experience (APPE) sites where tobacco is sold and experiential education directors' perception regarding the sales of tobacco in APPE sites.

Methods: A brief survey was distributed by mail to experiential education directors at US pharmacy schools.

Automatic detection of patient-ventilator asynchrony by spectral analysis of airway flow.

Introduction: Adequate ventilatory support of critically ill patients depends on prompt recognition of ventilator asynchrony, as asynchrony is associated with worse outcomes.We compared an automatic method of patient-ventilator asynchrony monitoring, based on airway flow frequency analysis, to the asynchrony index (AI) determined visually from airway tracings.

Methods: This was a prospective, sequential observational study of 110 mechanically ventilated adults.

Central venous to mixed venous blood oxygen and lactate gradients are associated with outcome in critically ill patients.

Objective: Blood O(2) saturation and lactate concentration gradients from superior vena cava (SVC) to pulmonary artery (PA) occur in critically ill patients. These gradients (DeltaSO(2) and Delta[Lac]) may be positive or negative. We tested the hypothesis that positive DeltaSO(2) and Delta[Lac] are associated with improved survival in critically ill patients.

Familial solitary fibrous tumor of the pleura: a case report.

This report describes the occurrence of solitary fibrous tumors of the pleura in a mother and her daughter. No other occurrence of this rare tumor in members of the same family has ever been reported.