Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image Generation

Quantum machine learning (QML) has received increasing attention due to its potential to outperform classical machine learning methods in problems pertaining classification and identification tasks. A subclass of QML methods is quantum generative adversarial networks (QGANs) which have been studied as a quantum counterpart of classical GANs widely used in image manipulation and generation tasks. The existing work on QGANs is still limited to small-scale proof-of-concept examples based on images with significant downscaling. Here we integrate classical and quantum techniques to propose a new hybrid quantum-classical GAN framework. We demonstrate its superior learning capabilities by generating $28 \times 28$ pixels grey-scale images without dimensionality reduction or classical pre/post-processing on multiple classes of the standard MNIST and Fashion MNIST datasets, which achieves comparable results to classical frameworks with three orders of magnitude less trainable generator parameters. To gain further insight into the working of our hybrid approach, we systematically explore the impact of its parameter space by varying the number of qubits, the size of image patches, the number of layers in the generator, the shape of the patches and the choice of prior distribution. Our results show that increasing the quantum generator size generally improves the learning capability of the network. The developed framework provides a foundation for future design of QGANs with optimal parameter set tailored for complex image generation tasks

Benchmarking Adversarially Robust Quantum Machine Learning at Scale

Machine learning (ML) methods such as artificial neural networks are rapidly becoming ubiquitous in modern science, technology and industry. Despite their accuracy and sophistication, neural networks can be easily fooled by carefully designed malicious inputs known as adversarial attacks. While such vulnerabilities remain a serious challenge for classical neural networks, the extent of their existence is not fully understood in the quantum ML setting. In this work, we benchmark the robustness of quantum ML networks, such as quantum variational classifiers (QVC), at scale by performing rigorous training for both simple and complex image datasets and through a variety of high-end adversarial attacks. Our results show that QVCs offer a notably enhanced robustness against classical adversarial attacks by learning features which are not detected by the classical neural networks, indicating a possible quantum advantage for ML tasks. Contrarily, and remarkably, the converse is not true, with attacks on quantum networks also capable of deceiving classical neural networks. By combining quantum and classical network outcomes, we propose a novel adversarial attack detection technology. Traditionally quantum advantage in ML systems has been sought through increased accuracy or algorithmic speed-up, but our work has revealed the potential for a new kind of quantum advantage through superior robustness of ML models, whose practical realisation will address serious security concerns and reliability issues of ML algorithms employed in a myriad of applications including autonomous vehicles, cybersecurity, and surveillance robotic systems.Comment: 10 pages, 5 Figure

Maternal fucosyltransferase 2 status affects the gut bifidobacterial communities of breastfed infants.

BackgroundIndividuals with inactive alleles of the fucosyltransferase 2 gene (FUT2; termed the 'secretor' gene) are common in many populations. Some members of the genus Bifidobacterium, common infant gut commensals, are known to consume 2'-fucosylated glycans found in the breast milk of secretor mothers. We investigated the effects of maternal secretor status on the developing infant microbiota with a special emphasis on bifidobacterial species abundance.ResultsOn average, bifidobacteria were established earlier and more often in infants fed by secretor mothers than in infants fed by non-secretor mothers. In secretor-fed infants, the relative abundance of the Bifidobacterium longum group was most strongly correlated with high percentages of the order Bifidobacteriales. Conversely, in non-secretor-fed infants, Bifidobacterium breve was positively correlated with Bifidobacteriales, while the B. longum group was negatively correlated. A higher percentage of bifidobacteria isolated from secretor-fed infants consumed 2'-fucosyllactose. Infant feces with high levels of bifidobacteria had lower milk oligosaccharide levels in the feces and higher amounts of lactate. Furthermore, feces containing different bifidobacterial species possessed differing amounts of oligosaccharides, suggesting differential consumption in situ.ConclusionsInfants fed by non-secretor mothers are delayed in the establishment of a bifidobacteria-laden microbiota. This delay may be due to difficulties in the infant acquiring a species of bifidobacteria able to consume the specific milk oligosaccharides delivered by the mother. This work provides mechanistic insight into how milk glycans enrich specific beneficial bacterial populations in infants and reveals clues for enhancing enrichment of bifidobacterial populations in at risk populations - such as premature infants

Prevalence and causes of prescribing errors: the prescribing outcomes for trainee doctors engaged in clinical training (PROTECT) study

Objectives Study objectives were to investigate the prevalence and causes of prescribing errors amongst foundation doctors (i.e. junior doctors in their first (F1) or second (F2) year of post-graduate training), describe their knowledge and experience of prescribing errors, and explore their self-efficacy (i.e. confidence) in prescribing. Method A three-part mixed-methods design was used, comprising: prospective observational study; semi-structured interviews and cross-sectional survey. All doctors prescribing in eight purposively selected hospitals in Scotland participated. All foundation doctors throughout Scotland participated in the survey. The number of prescribing errors per patient, doctor, ward and hospital, perceived causes of errors and a measure of doctors' self-efficacy were established. Results 4710 patient charts and 44,726 prescribed medicines were reviewed. There were 3364 errors, affecting 1700 (36.1%) charts (overall error rate: 7.5%; F1:7.4%; F2:8.6%; consultants:6.3%). Higher error rates were associated with : teaching hospitals (p&#60;0.001), surgical (p = &#60;0.001) or mixed wards (0.008) rather thanmedical ward, higher patient turnover wards (p&#60;0.001), a greater number of prescribed medicines (p&#60;0.001) and the months December and June (p&#60;0.001). One hundred errors were discussed in 40 interviews. Error causation was multi-factorial; work environment and team factors were particularly noted. Of 548 completed questionnaires (national response rate of 35.4%), 508 (92.7% of respondents) reported errors, most of which (328 (64.6%) did not reach the patient. Pressure from other staff, workload and interruptions were cited as the main causes of errors. Foundation year 2 doctors reported greater confidence than year 1 doctors in deciding the most appropriate medication regimen. Conclusions Prescribing errors are frequent and of complex causation. Foundation doctors made more errors than other doctors, but undertook the majority of prescribing, making them a key target for intervention. Contributing causes included work environment, team, task, individual and patient factors. Further work is needed to develop and assess interventions that address these.</p

The effect of a youth mental health service model on access to secondary mental healthcare for young people aged 14–25 years

Aims and method: The Norfolk Youth Service was created in 2012 in response to calls to redesign mental health services to better meet the needs of young people. The new service model transcends traditional boundaries by creating a single, ‘youth friendly’ service for young people aged 14–25 years. The aim of this study was to investigate the effect of the transition to this new model on patterns of referral, acceptance and service use. We analysed routinely collected data on young people aged 14–25 years referred for secondary mental healthcare in Norfolk before and after implementation of the youth mental health service. The number of referrals, their age and gender, proportion of referrals accepted and average number of service contacts per referral by age pre- and post-implementation were compared. Results: Referrals increased by 68% following implementation of the new service model, but the proportion of referrals accepted fell by 27 percentage points. Before implementation of the youth service, there was a clear discrepancy between the peak age of referral and the age of those seen by services. Following implementation, service contacts were more equitable across ages, with no marked discontinuity at age 18 years. Clinical implications: Our findings suggest that the transformation of services may have succeeded in reducing the ‘cliff edge’ in access to mental health services at the transition to adulthood. However, the sharp rise in referrals and reduction in the proportion of referrals accepted highlights the importance of considering possible unintended consequences of new service models. Declaration of interests: None

Women’s experiences of receiving care for pelvic organ prolapse: a qualitative study

Background Pelvic organ prolapse is a common urogenital condition affecting 41–50% of women over the age of 40. To achieve early diagnosis and appropriate treatment, it is important that care is sensitive to and meets women’s needs, throughout their patient journey. This study explored women’s experiences of seeking diagnosis and treatment for prolapse and their needs and priorities for improving person-centred care. Methods Twenty-two women receiving prolapse care through urogynaecology services across three purposefully selected NHS UK sites took part in three focus groups and four telephone interviews. A topic guide facilitated discussions about women’s experiences of prolapse, diagnosis, treatment, follow-up, interactions with healthcare professionals, overall service delivery, and ideals for future services to meet their needs. Data were analysed thematically. Results Three themes emerged relating to women’s experiences of a) Evaluating what is normal b) Hobson’s choice of treatment decisions, and c) The trial and error of treatment and technique. Women often delayed seeking help for their symptoms due to lack of awareness, embarrassment and stigma. When presented to GPs, their symptoms were often dismissed and unaddressed until they became more severe. Women reported receiving little or no choice in treatment decisions. Choices were often influenced by health professionals’ preferences which were subtly reflected through the framing of the offer. Women’s embodied knowledge of their condition and treatment was largely unheeded, resulting in decisions that were inconsistent with women’s preferences and needs. Physiotherapy based interventions were reported as helping women regain control over their symptoms and life. A need for greater awareness of prolapse and physiotherapy interventions among women, GPs and consultants was identified alongside greater focus on prevention, early diagnosis and regular follow-up. Greater choice and involvement in treatment decision making was desired. Conclusions As prolapse treatment options expand to include more conservative choices, greater awareness and education is needed among women and professionals about these as a first line treatment and preventive measure, alongside a multi-professional team approach to treatment decision making. Women presenting with prolapse symptoms need to be listened to by the health care team, offered better information about treatment choices, and supported to make a decision that is right for them

Towards quantum enhanced adversarial robustness in machine learning

Machine learning algorithms are powerful tools for data driven tasks such as image classification and feature detection, however their vulnerability to adversarial examples - input samples manipulated to fool the algorithm - remains a serious challenge. The integration of machine learning with quantum computing has the potential to yield tools offering not only better accuracy and computational efficiency, but also superior robustness against adversarial attacks. Indeed, recent work has employed quantum mechanical phenomena to defend against adversarial attacks, spurring the rapid development of the field of quantum adversarial machine learning (QAML) and potentially yielding a new source of quantum advantage. Despite promising early results, there remain challenges towards building robust real-world QAML tools. In this review we discuss recent progress in QAML and identify key challenges. We also suggest future research directions which could determine the route to practicality for QAML approaches as quantum computing hardware scales up and noise levels are reduced.Comment: 10 Pages, 4 Figure

HIF-1alpha and HIF-2alpha are differentially activated in distinct cell populations in retinal ischaemia.

BACKGROUND: Hypoxia plays a key role in ischaemic and neovascular disorders of the retina. Cellular responses to oxygen are mediated by hypoxia-inducible transcription factors (HIFs) that are stabilised in hypoxia and induce the expression of a diverse range of genes. The purpose of this study was to define the cellular specificities of HIF-1alpha and HIF-2alpha in retinal ischaemia, and to determine their correlation with the pattern of retinal hypoxia and the expression profiles of induced molecular mediators. METHODOLOGY/PRINCIPAL FINDINGS: We investigated the tissue distribution of retinal hypoxia during oxygen-induced retinopathy (OIR) in mice using the bio-reductive drug pimonidazole. We measured the levels of HIF-1alpha and HIF-2alpha proteins by Western blotting and determined their cellular distribution by immunohistochemistry during the development of OIR. We measured the temporal expression profiles of two downstream mediators, vascular endothelial growth factor (VEGF) and erythropoietin (Epo) by ELISA. Pimonidazole labelling was evident specifically in the inner retina. Labelling peaked at 2 hours after the onset of hypoxia and gradually declined thereafter. Marked binding to Müller glia was evident during the early hypoxic stages of OIR. Both HIF-1alpha and HIF-2alpha protein levels were significantly increased during retinal hypoxia but were evident in distinct cellular distributions; HIF-1alpha stabilisation was evident in neuronal cells throughout the inner retinal layers whereas HIF-2alpha was restricted to Müller glia and astrocytes. Hypoxia and HIF-alpha stabilisation in the retina were closely followed by upregulated expression of the downstream mediators VEGF and EPO. CONCLUSIONS/SIGNIFICANCE: Both HIF-1alpha and HIF-2alpha are activated in close correlation with retinal hypoxia but have contrasting cell specificities, consistent with differential roles in retinal ischaemia. Our findings suggest that HIF-2alpha activation plays a key role in regulating the response of Müller glia to hypoxia