Degradation of a quantum directional reference frame as a random walk

We investigate if the degradation of a quantum directional reference frame through repeated use can be modeled as a classical direction undergoing a random walk on a sphere. We demonstrate that the behaviour of the fidelity for a degrading quantum directional reference frame, defined as the average probability of correctly determining the orientation of a test system, can be fit precisely using such a model. Physically, the mechanism for the random walk is the uncontrollable back-action on the reference frame due to its use in a measurement of the direction of another system. However, we find that the magnitude of the step size of this random walk is not given by our classical model and must be determined from the full quantum description.Comment: 5 pages, no figures. Comments are welcome. v2: several changes to clarify the key results. v3: journal reference added, acknowledgements and references update

Generalization Error in Deep Learning

Deep learning models have lately shown great performance in various fields such as computer vision, speech recognition, speech translation, and natural language processing. However, alongside their state-of-the-art performance, it is still generally unclear what is the source of their generalization ability. Thus, an important question is what makes deep neural networks able to generalize well from the training set to new data. In this article, we provide an overview of the existing theory and bounds for the characterization of the generalization error of deep neural networks, combining both classical and more recent theoretical and empirical results

Accent processing in dementia

Accented speech conveys important nonverbal information about the speaker as well as presenting the brain with the problem of decoding a non-canonical auditory signal. The processing of non-native accents has seldom been studied in neurodegenerative disease and its brain basis remains poorly understood. Here we investigated the processing of non-native international and regional accents of English in cohorts of patients with Alzheimer's disease (AD; n=20) and progressive nonfluent aphasia (PNFA; n=6) in relation to healthy older control subjects (n=35). A novel battery was designed to assess accent comprehension and recognition and all subjects had a general neuropsychological assessment. Neuroanatomical associations of accent processing performance were assessed using voxel-based morphometry on MR brain images within the larger AD group. Compared with healthy controls, both the AD and PNFA groups showed deficits of non-native accent recognition and the PNFA group showed reduced comprehension of words spoken in international accents compared with a Southern English accent. At individual subject level deficits were observed more consistently in the PNFA group, and the disease groups showed different patterns of accent comprehension impairment (generally more marked for sentences in AD and for single words in PNFA). Within the AD group, grey matter associations of accent comprehension and recognition were identified in the anterior superior temporal lobe. The findings suggest that accent processing deficits may constitute signatures of neurodegenerative disease with potentially broader implications for understanding how these diseases affect vocal communication under challenging listening conditions

Patient-reported outcomes: pathways to better health, better services, and better societies

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordWhile the use of PROs in research is well established, many challenges lie ahead as their use is extended to other applications. There is consensus that health outcome evaluations that include PROs along with clinician-reported outcomes and administrative data are necessary to inform clinical and policy decisions. The initiatives presented in this paper underline evolving recognition that PROs play a unique role in adding the patient perspective alongside clinical (e.g., blood pressure) and organizational (e.g., admission rates) indicators for evaluating the effects of new products, selecting treatments, evaluating quality of care, and monitoring the health of the population. In this paper, we first explore the use of PRO measures to support drug approval and labeling claims. We critically evaluate the evidence and challenges associated with using PRO measures to improve healthcare delivery at individual and population levels. We further discuss the challenges associated with selecting from the abundance of measures available, opportunities afforded by agreeing on common metrics for constructs of interest, and the importance of establishing an evidence base that supports integrating PRO measures across the healthcare system to improve outcomes. We conclude that the integration of PROs as a key end point within individual patient care, healthcare organization and program performance evaluations, and population surveillance will be essential for evaluating whether increased healthcare expenditure is translating into better health outcomes.Jose M. Valderas was supported by an NIHR Clinician Scientist Award (NIHR/CS/010/024)

A randomized control trial evaluating fluorescent ink versus dark ink tattoos for breast radiotherapy.

OBJECTIVE: The purpose of this UK study was to evaluate interfraction reproducibility and body image score when using ultraviolet (UV) tattoos (not visible in ambient lighting) for external references during breast/chest wall radiotherapy and compare with conventional dark ink. METHODS: In this non-blinded, single-centre, parallel group, randomized control trial, patients were allocated to receive either conventional dark ink or UV ink tattoos using computer-generated random blocks. Participant assignment was not masked. Systematic (∑) and random (σ) setup errors were determined using electronic portal images. Body image questionnaires were completed at pre-treatment, 1 month and 6 months to determine the impact of tattoo type on body image. The primary end point was to determine that UV tattoo random error (σsetup) was no less accurate than with conventional dark ink tattoos, i.e. <2.8 mm. RESULTS: 46 patients were randomized to receive conventional dark or UV ink tattoos. 45 patients completed treatment (UV: n = 23, dark: n = 22). σsetup for the UV tattoo group was <2.8 mm in the u and v directions (p = 0.001 and p = 0.009, respectively). A larger proportion of patients reported improvement in body image score in the UV tattoo group compared with the dark ink group at 1 month [56% (13/23) vs 14% (3/22), respectively] and 6 months [52% (11/21) vs 38% (8/21), respectively]. CONCLUSION: UV tattoos were associated with interfraction setup reproducibility comparable with conventional dark ink. Patients reported a more favourable change in body image score up to 6 months following treatment. Advances in knowledge: This study is the first to evaluate UV tattoo external references in a randomized control trial

Earth as a Proxy Exoplanet: Deconstructing and Reconstructing Spectrophotometric Light Curves

Point-source spectrophotometric (single-point) light curves of Earth-like planets contain a surprising amount of information about the spatial features of those worlds. Spatially resolving these light curves is important for assessing time-varying surface features and the existence of an atmosphere, which in turn is critical to life on Earth and significant for determining habitability on exoplanets. Given that Earth is the only celestial body confirmed to harbor life, treating it as a proxy exoplanet by analyzing time-resolved spectral images provides a benchmark in the search for habitable exoplanets. The Earth Polychromatic Imaging Camera (EPIC) on the Deep Space Climate Observatory (DSCOVR) provides such an opportunity, with observations of ~5000 full-disk sunlit Earth images each year at 10 wavelengths with high temporal frequency. We disk-integrate these spectral images to create single-point light curves and decompose them into principal components (PCs). Using machine-learning techniques to relate the PCs to six preselected spatial features, we find that the first and fourth PCs of the single-point light curves, contributing ~83.23% of the light-curve variability, contain information about low and high clouds, respectively. Surface information relevant to the contrast between land and ocean reflectance is contained in the second PC, while individual land subtypes are not easily distinguishable (<0.1% total light-curve variation). We build an Earth model by systematically altering the spatial features to derive causal relationships to the PCs. This model can serve as a baseline for analyzing Earth-like exoplanets and guide wavelength selection and sampling strategies for future observations

Limited effect of patient and disease characteristics on compliance with hospital antimicrobial guidelines

Objective: Physicians frequently deviate from guidelines that promote prudent use of antimicrobials. We explored to what extent patient and disease characteristics were associated with compliance with guideline recommendations for three common infections. Methods: In a 1-year prospective observational study, 1,125 antimicrobial prescriptions were analysed for compliance with university hospital guidelines. Results: Compliance varied significantly between and within the groups of infections studied. Compliance was much higher for lower respiratory tract infections (LRTIs; 79%) than for sepsis (53%) and urinary tract infections (UTIs; 40%). Only predisposing illnesses and active malignancies were associated with more compliant prescribing, whereas alcohol/ intravenous drug abuse and serum creatinine levels > 130 mu mol/l were associated with less compliant prescribing. Availability of culture results had no impact on compliance with guidelines for sepsis but was associated with more compliance in UTIs and less in LRTIs. Narrowing initial broad-spectrum antimicrobial therapy to cultured pathogens was seldom practised. Most noncompliant prescribing concerned a too broad spectrum of activity when compared with guideline-recommended therapy. Conclusion: Patient characteristics had only a limited impact on compliant prescribing for a variety of reasons. Physicians seemed to practise defensive prescribing behaviour, favouring treatment success in current patients over loss of effectiveness due to resistance in future patients

OMERACT Filter 2.1: Elaboration of the Conceptual Framework for Outcome Measurement in Health Intervention Studies

Objective: The Outcome Measures in Rheumatology (OMERACT) Filter 2.0 framework was developed in 2014 to aid core outcome set development by describing the full universe of “measurable aspects of health conditions” from which core domains can be selected. This paper provides elaborations and updated concepts (OMERACT Filter 2.1). Methods: At OMERACT 2018, we discussed challenges in the framework application caused by unclear or ambiguous wording and terms and incompletely developed concepts. Results: The updated OMERACT Filter 2.1 framework makes benefits and harms explicit, clarifies concepts, and improves naming of various terms. Conclusion: We expect that the Filter 2.1 framework will improve the process of core set development

A Comparison of Machine Learning Methods for Cross-Domain Few-Shot Learning

We present an empirical evaluation of machine learning algorithms in cross-domain few-shot learning based on a fixed pre-trained feature extractor. Experiments were performed in five target domains (CropDisease, EuroSAT, Food101, ISIC and ChestX) and using two feature extractors: a ResNet10 model trained on a subset of ImageNet known as miniImageNet and a ResNet152 model trained on the ILSVRC 2012 subset of ImageNet. Commonly used machine learning algorithms including logistic regression, support vector machines, random forests, nearest neighbour classification, naïve Bayes, and linear and quadratic discriminant analysis were evaluated on the extracted feature vectors. We also evaluated classification accuracy when subjecting the feature vectors to normalisation using p-norms. Algorithms originally developed for the classification of gene expression data—the nearest shrunken centroid algorithm and LDA ensembles obtained with random projections—were also included in the experiments, in addition to a cosine similarity classifier that has recently proved popular in few-shot learning. The results enable us to identify algorithms, normalisation methods and pre-trained feature extractors that perform well in cross-domain few-shot learning. We show that the cosine similarity classifier and ℓ² -regularised 1-vs-rest logistic regression are generally the best-performing algorithms. We also show that algorithms such as LDA yield consistently higher accuracy when applied to ℓ² -normalised feature vectors. In addition, all classifiers generally perform better when extracting feature vectors using the ResNet152 model instead of the ResNet10 model

What we talk about when we talk about "global mindset": managerial cognition in multinational corporations

Recent developments in the global economy and in multinational corporations have placed significant emphasis on the cognitive orientations of managers, giving rise to a number of concepts such as “global mindset” that are presumed to be associated with the effective management of multinational corporations (MNCs). This paper reviews the literature on global mindset and clarifies some of the conceptual confusion surrounding the construct. We identify common themes across writers, suggesting that the majority of studies fall into one of three research perspectives: cultural, strategic, and multidimensional. We also identify two constructs from the social sciences that underlie the perspectives found in the literature: cosmopolitanism and cognitive complexity and use these two constructs to develop an integrative theoretical framework of global mindset. We then provide a critical assessment of the field of global mindset and suggest directions for future theoretical and empirical research