Measurements in two bases are sufficient for certifying high-dimensional entanglement

High-dimensional encoding of quantum information provides a promising method of transcending current limitations in quantum communication. One of the central challenges in the pursuit of such an approach is the certification of high-dimensional entanglement. In particular, it is desirable to do so without resorting to inefficient full state tomography. Here, we show how carefully constructed measurements in two bases (one of which is not orthonormal) can be used to faithfully and efficiently certify bipartite high-dimensional states and their entanglement for any physical platform. To showcase the practicality of this approach under realistic conditions, we put it to the test for photons entangled in their orbital angular momentum. In our experimental setup, we are able to verify 9-dimensional entanglement for a pair of photons on a 11-dimensional subspace each, at present the highest amount certified without any assumptions on the state.Comment: 11+14 pages, 2+7 figure

Changing indications and socio-demographic determinants of (adeno)tonsillectomy among children in England--are they linked? A retrospective analysis of hospital data.

OBJECTIVE: To assess whether increased awareness and diagnosis of obstructive sleep apnoea syndrome (OSAS) and national guidance on tonsillectomy for recurrent tonsillitis have influenced the socio-demographic profile of children who underwent tonsillectomy over the last decade. METHOD: Retrospective time-trends study of Hospital Episodes Statistics data. We examined the age, sex and deprivation level, alongside OSAS diagnoses, among children aged <16 years who underwent (adeno)tonsillectomy in England between 2001/2 and 2011/12. RESULTS: Among children aged <16 years, there were 29,697 and 27,732 (adeno)tonsillectomies performed in 2001/2 and 2011/12, respectively. The median age at (adeno)tonsillectomy decreased from 7 (IQR: 5-11) to 5 (IQR: 4-9) years over the decade. (Adeno)tonsillectomy rates among children aged 4-15 years decreased by 14% from 350 (95%CI: 346-354) in 2001/2 to 300 (95%CI: 296-303) per 100,000 children in 2011/12. However, (adeno)tonsillectomy rates among children aged <4 years increased by 58% from 135 (95%CI: 131-140) to 213 (95%CI 208-219) per 100,000 children in 2001/2 and 2011/2, respectively. OSAS diagnoses among children aged <4 years who underwent surgery increased from 18% to 39% between these study years and the proportion of children aged <4 years with OSAS from the most deprived areas increased from 5% to 12%, respectively. CONCLUSIONS: (Adeno)tonsillectomy rates declined among children aged 4-15 years, which reflects national guidelines recommending the restriction of the operation to children with more severe recurrent throat infections. However, (adeno)tonsillectomy rates among pre-school children substantially increased over the past decade and one in five children undergoing the operation was aged <4 years in 2011/12.The increase in surgery rates in younger children is likely to have been driven by increased awareness and detection of OSAS, particularly among children from the most deprived areas

Automatic Identification of Defects on Eggshell Through a Multispectral Vision System

The objective of this research was to develop an off-line artificial vision system to automatically detect defective eggshells, i.e., dirty or cracked eggshells, by employing multispectral images with the final purpose to adapt the system to an on-line grading machine. In particular, this work was focused to study the feasibility of identifying organic stains on brown eggshells (dirty eggshell), caused by blood, feathers, feces, etc., from natural stains, caused by deposits of pigments on the outer layer of clean eggshells. During the analysis a total of 384 eggs were evaluated (clean: 148, dirty: 236). Dirty samples were evaluated visually in order to classify them according to the kind of defect (blood, feathers, and white, clear or dark feces), and clean eggshells were classified on the basis of the colour of the natural stains (clear or dark). For each sample digital images were acquired by employing a Charged Coupled Device (CCD) camera endowed with 15 monochromatic filters (440-940 nm). A Matlab® function was developed in order to automate the process and analyze images, with the aim to classify samples as clean or dirty. The program was constituted by three major steps: first, the research of an opportune combination of monochromatic images in order to isolate the eggshell from the background; second, the detection of the dirt stains; third, the classification of the images samples into the dirty or clean group on the basis of geometric characteristics of the stains (area in pixel). The proposed classification algorithm was able to correctly classify near 98% of the samples with a very low processing time (0.05s). The robustness of the proposed classification was observed applying an external validation to a second set of samples (n = 178), obtaining similar percentage of correctly classified samples (97%)

On the dual representation of coherent risk measures

A classical result in risk measure theory states that every coherent risk measure has a dual representation as the supremum of certain expected value over a risk envelope. We study this topic in more detail. The related issues include: (1) Set operations of risk envelopes and how they change the risk measures, (2) The structure of risk envelopes of popular risk measures, (3) Aversity of risk measures and its impact to risk envelopes, and (4) A connection between risk measures in stochastic optimization and uncertainty sets in robust optimization

Probabilistic machine learning and artificial intelligence.

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.The author acknowledges an EPSRC grant EP/I036575/1, the DARPA PPAML programme, a Google Focused Research Award for the Automatic Statistician and support from Microsoft Research.This is the author accepted manuscript. The final version is available from NPG at http://www.nature.com/nature/journal/v521/n7553/full/nature14541.html#abstract

Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk.

Blood pressure is a heritable trait influenced by several biological pathways and responsive to environmental stimuli. Over one billion people worldwide have hypertension (≥140 mm Hg systolic blood pressure or  ≥90 mm Hg diastolic blood pressure). Even small increments in blood pressure are associated with an increased risk of cardiovascular events. This genome-wide association study of systolic and diastolic blood pressure, which used a multi-stage design in 200,000 individuals of European descent, identified sixteen novel loci: six of these loci contain genes previously known or suspected to regulate blood pressure (GUCY1A3-GUCY1B3, NPR3-C5orf23, ADM, FURIN-FES, GOSR2, GNAS-EDN3); the other ten provide new clues to blood pressure physiology. A genetic risk score based on 29 genome-wide significant variants was associated with hypertension, left ventricular wall thickness, stroke and coronary artery disease, but not kidney disease or kidney function. We also observed associations with blood pressure in East Asian, South Asian and African ancestry individuals. Our findings provide new insights into the genetics and biology of blood pressure, and suggest potential novel therapeutic pathways for cardiovascular disease prevention