Photon-number distributions of twin beams generated in spontaneous parametric down-conversion and measured by an intensified CCD camera

The measurement of photon-number statistics of fields composed of photon pairs, generated in spontaneous parametric down-conversion and detected by an intensified CCD camera is described. Final quantum detection efficiencies, electronic noises, finite numbers of detector pixels, transverse intensity spatial profiles of the detected beams as well as losses of single photons from a pair are taken into account in a developed general theory of photon-number detection. The measured data provided by an iCCD camera with single-photon detection sensitivity are analyzed along the developed theory. Joint signal-idler photon-number distributions are recovered using the reconstruction method based on the principle of maximum likelihood. The range of applicability of the method is discussed. The reconstructed joint signal-idler photon-number distribution is compared with that obtained by a method that uses superposition of signal and noise and minimizes photoelectron entropy. Statistics of the reconstructed fields are identified to be multi-mode Gaussian. Elements of the measured as well as the reconstructed joint signal-idler photon-number distributions violate classical inequalities. Sub-shot-noise correlations in the difference of the signal and idler photon numbers as well as partial suppression of odd elements in the distribution of the sum of signal and idler photon numbers are observed.Comment: 14 pages, 14 figure

Absolute detector calibration using twin beams

A method for the determination of absolute quantum detection efficiency is suggested based on the measurement of photocount statistics of twin beams. The measured histograms of joint signal-idler photocount statistics allow to eliminate an additional noise superimposed on an ideal calibration field composed of only photon pairs. This makes the method superior above other approaches presently used. Twin beams are described using a paired variant of quantum superposition of signal and noise.Comment: 3 pages, 2 figure

Multimedijski udžbenik o teoriji i primjeni elektromagnetizma

Teaching university courses, which deal with the phenomena of electromagnetic nature and their application, is rather difficult due to their abstract nature. Therefore, the teaching has to be accompanied by clear explanation, and by simulation illustrating the examined topics. That is why a multimedia textbook was developed, which presents the theoretical description of electromagnetic phenomena, and at the same time, enables to simulate them. The textbook is of a two-dimensional structure, which enables the book to be used by bachelor\u27s students and master\u27s ones for studying, by users of incorporated programs for guiding, and by programmers for developing their own applications. The textbook is completed by an explanatory indexing mechanism, which makes the book readable even for an inexperienced reader.Podučavanje sveučilišnih kolegija koji se bave fenomenima elektromagnetizma prilično je zahtjevno zbog apstraktnosti gradiva koje se iznosi. Podučavanje stoga valja popratiti jasnim objašnjenjima i simulacijama koje ilustriraju teme koje se obrađuju. Upravo je zato priređen multimedijski udžbenik koji donosi teorijski opis elektromagnetskih fenomena i istodobno omogućava njihovo simuliranje. Struktura udžbenika je dvodimenzionalna što omogućava njegovu primjenu na dodiplomskom i poslijediplomskom studiju korištenjem gotovih programa koji su dio udžbenika ili izradbom vlastitih programa od strane korisnika. Udžbenik je opremljen sustavom kazala s objašnjenjima, što olakšava korištenje i čitateljima koji ne poznaju područje

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Influence of surface roughness on the wake structure of a circular cylinder at Reynolds number 5× 103 to 12× 103

The flow around roughened circular cylinders and the wake behind them were studied in a wind tunnel flow using the Particle Image Velocimetry (PIV) method. The Reynolds number ranged from 5140 to 11800, and the real diameter of the cylinders including surface roughness ranged from 15.06 to 17.34 mm. The flow properties and forces around the roughened cylinders was evaluated by means of Strouhal number and coefficient of drag. The wake was analysed by means of mean velocities along the cylinder centreline, its width and the Proper Orthogonal Decomposition (POD) method. The added surface roughness was found to decrease the wake length and increase the wake width at lower Reynolds numbers. At higher Reynolds numbers, the added surface roughness did not decrease the wake length, but did increase the wake width, although with less effect. The POD analysis showed changes in the higher modes of the flow. The kinetic energy of the first two modes covers up to 50% of the total kinetic energy; the first two modes in the case of the smooth cylinder have the lowest kinetic energy, whereas the first two modes in the case of the roughest cylinder have the highest kinetic energy. The similarity between the POD modes of the smooth and roughened cylinders might be due the fact that the actual Reynolds number range was below the transitional one