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. In this paper, we present an multidimensional quantum convolutional classifier (MQCC) that performs multidimensional and multifeature quantum convolution with average and Euclidean pooling, thus adapting the CNN structure to a variational quantum algorithm (VQA). The experimental work was conducted using multidimensional data to validate the correctness and demonstrate the scalability of the proposed method utilizing both noisy and noise-free quantum simulations. We evaluated the MQCC model with reference to reported work on state-of-the-art quantum simulators from IBM Quantum and Xanadu using a variety of standard ML datasets. The experimental results show the favorable characteristics of our proposed techniques compared with existing work with respect to a number of quantitative metrics, such as the number of training parameters, cross-entropy loss, classification accuracy, circuit depth, and quantum gate count.

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. In addition, the deep circuits required to perform quantum convolution with a unity stride, especially for multidimensional data, increase the risk of violating decoherence constraints. In this work, we propose depth-optimized circuits for performing generalized multidimensional quantum convolution operations with unity stride targeting applications that process data with high dimensions, such as hyperspectral imagery and remote sensing. We experimentally evaluate and demonstrate the applicability of the proposed techniques by using real-world, high-resolution, multidimensional image data on a state-of-the-art quantum simulator from IBM Quantum.

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. The survey characterized the proportion of students who rotate at practice sites where tobacco is sold, directors' perceptions of tobacco sales in experiential sites, and the number of hours of tobacco education in their pharmacy curricula. KEY FINDINGS: Directors (n = 81; 63%) estimated that 69% of students rotate through sites where tobacco is sold. If given the opportunity to choose between two potential sites, where one sells tobacco and the other does not, 40% of directors would be unlikely to choose a site that sold tobacco. CONCLUSIONS: With respect to tobacco sales, pharmacy schools are largely noncompliant with guidelines and resolutions of professional organizations.

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. All eligible ventilated patients were enrolled. No clinical interventions were performed. Airway flow and pressure signals were sampled digitally for two hours. The frequency spectrum of the airway flow signal, processed to include only its expiratory phase, was calculated with the Cooley-Tukey Fast Fourier Transform method at 2.5 minute intervals. The amplitude ratio of the first harmonic peak (H1) to that of zero frequency (DC), or H1/DC, was taken as a measure of spectral organization. AI values were obtained at 30-minute intervals and compared to corresponding measures of H1/DC. RESULTS: The frequency spectrum of synchronized patients was characterized by sharply defined peaks spaced at multiples of mean respiratory rate. The spectrum of asynchronous patients was less organized, showing lower and wider H1 peaks and disappearance of higher frequency harmonics. H1/DC was inversely related to AI (n = 110; r2 = 0.57; P < 0.0001). Asynchrony, defined by AI > 10%, was associated H1/DC < 43% with 83% sensitivity and specificity. CONCLUSIONS: Spectral analysis of airway flow provides an automatic, non-invasive assessment of ventilator asynchrony at fixed short intervals. This method can be adapted to ventilator systems as a clinical monitor of asynchrony.

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. DESIGN AND SETTING: Multinational, prospective observational study conducted in six medical and surgical ICUs. PATIENTS: Consecutive sample of 106 adults requiring insertion of a pulmonary artery catheter (PAC). Average age was 59.5 +/- 15.5 years, APACHE II score was 15.5 +/- 6.7 (mean +/- SD). Main outcome measure was 28-day mortality. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: We drew blood samples from the proximal and distal ports of PACs every 6 h from the time of PAC insertion (Initial measurement) until its removal (Final measurement). Samples were analyzed for SO(2), [Lac], glucose concentration and blood gases. Hemodynamic measurements were obtained after blood samples. We monitored patients for 30.9 +/- 11.0 h. Overall mortality rate was 25.5%. More survivors had mean and final DeltaSO(2) > or = 0 and Delta[Lac] > or = 0 than decedents (p < 0.01; p < 0.05 respectively). On the average, DeltaSO(2) and Delta[Lac] were positive in survivors and negative in decedents. Survival odds ratios for final measurements of DeltaSO(2) > or = 0 and Delta[Lac] > or = 0 were 19.22 and 7.70, respectively (p < 0.05). CONCLUSIONS: A strong association exists between positive DeltaSO(2) and Delta[Lac] and survival in critically ill patients. Whether therapy aimed at increasing DeltaSO(2) and Delta[Lac] results in improved ICU survival remains to be determined.

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.