Convex Structuring Element Decomposition for Single Scan Binary Mathematical Morphology

International audienceThis paper presents a structuring element decomposition method and a corresponding morphological erosion algorithm able to compute the binary erosion of an image using a single regular pass whatever the size of the convex structuring element. Similarly to classical dilation-based methods, the proposed decomposition is iterative and builds a growing set of structuring elements. The novelty consists in using the set union instead of the Minkowski sum as the elementary structuring element construction operator. At each step of the construction, already-built elements can be joined together in any combination of translations and set unions. There is no restrictions on the shape of the structuring element that can be built. Arbitrary shape decompositions can be obtained with existing genetic algorithms with an homogeneous construction method. This paper, however, addresses the problem of convex shape decomposition with a deterministic method

Low-rank plus sparse decomposition for exoplanet detection in direct-imaging ADI sequences. The LLSG algorithm

Context. Data processing constitutes a critical component of high-contrast exoplanet imaging. Its role is almost as important as the choice of a coronagraph or a wavefront control system, and it is intertwined with the chosen observing strategy. Among the data processing techniques for angular differential imaging (ADI), the most recent is the family of principal component analysis (PCA) based algorithms. It is a widely used statistical tool developed during the first half of the past century. PCA serves, in this case, as a subspace projection technique for constructing a reference point spread function (PSF) that can be subtracted from the science data for boosting the detectability of potential companions present in the data. Unfortunately, when building this reference PSF from the science data itself, PCA comes with certain limitations such as the sensitivity of the lower dimensional orthogonal subspace to non-Gaussian noise. Aims. Inspired by recent advances in machine learning algorithms such as robust PCA, we aim to propose a localized subspace projection technique that surpasses current PCA-based post-processing algorithms in terms of the detectability of companions at near real-time speed, a quality that will be useful for future direct imaging surveys. Methods. We used randomized low-rank approximation methods recently proposed in the machine learning literature, coupled with entry-wise thresholding to decompose an ADI image sequence locally into low-rank, sparse, and Gaussian noise components (LLSG). This local three-term decomposition separates the starlight and the associated speckle noise from the planetary signal, which mostly remains in the sparse term. We tested the performance of our new algorithm on a long ADI sequence obtained on β Pictoris with VLT/NACO. Results. Compared to a standard PCA approach, LLSG decomposition reaches a higher signal-to-noise ratio and has an overall better performance in the receiver operating characteristic space. This three-term decomposition brings a detectability boost compared to the full-frame standard PCA approach, especially in the small inner working angle region where complex speckle noise prevents PCA from discerning true companions from noise

Exoplanet Imaging Data Challenge, phase II: Characterization of exoplanet signals in high-contrast images

Today, there exists a wide variety of algorithms dedicated to high-contrast imaging, especially for the detection and characterisation of exoplanet signals. These algorithms are tailored to address the very high contrast between the exoplanet signal(s), which can be more than two orders of magnitude fainter than the bright starlight residuals in coronagraphic images. The starlight residuals are inhomogeneously distributed and follow various timescales that depend on the observing conditions and on the target star brightness. Disentangling the exoplanet signals within the starlight residuals is therefore challenging, and new post-processing algorithms are striving to achieve more accurate astrophysical results. The Exoplanet Imaging Data Challenge is a community-wide effort to develop, compare and evaluate algorithms using a set of benchmark high-contrast imaging datasets. After a first phase ran in 2020 and focused on the detection capabilities of existing algorithms, the focus of this ongoing second phase is to compare the characterisation capabilities of state-of-the-art techniques. The characterisation of planetary companions is two-fold: the astrometry (estimated position with respect to the host star) and spectrophotometry (estimated contrast with respect to the host star, as a function of wavelength). The goal of this second phase is to offer a platform for the community to benchmark techniques in a fair, homogeneous and robust way, and to foster collaborations.Comment: Submitted to SPIE Astronomical Telescopes + Instrumentation 2022, Adaptive Optics Systems VIII, Paper 12185-

Investigating rare haematological disorders - A celebration of 10 years of the Sherlock Holmes symposia

The Sherlock Holmes symposia have been educating haematologists on the need for prompt recognition, diagnosis and treatment of rare haematological diseases for 10 years. These symposia, which are supported by an unrestricted educational grant from Sanofi Genzyme, encourage haematologists to consider rare disorders in differential diagnoses. Improvement in rare disease awareness is important because diagnostics and the availability of effective therapies have improved considerably, meaning that rare haematological diseases can be accurately diagnosed and successfully managed, particularly if they are identified early. The Sherlock Holmes symposia programme includes real-life interactive clinical cases of rare haematological disorders that require awareness from the physician, to be diagnosed at an early stage. The audience are encouraged to examine each case as if they were detectives, look for clues from the clinical history and presentation, consider the potential causes, assess which tests would be required to make a definitive diagnosis and suggest optimal treatment options. To celebrate the 10-year anniversary of the Sherlock Holmes symposia, this article describes a number of clinical cases that include anaemia, thrombocytopaenia and splenomegaly among the presenting symptoms, to illustrate the importance of rigorous differential diagnosis in the identification of rare haematological disorders

Beyond the Premier: Assessing Action Spotting Transfer Capability Across Diverse Domains

peer reviewedFootball stands as one of the most successful sports in history thanks to the plethora of professional leagues broadcasted worldwide followed by avid fans, further fueled by the abundance of amateur and grassroots leagues across nearly every country, encompassing countless players who devote their time to the sport. Despite the tremendous amount of visual data available worldwide for developing automatic systems to extract game events, most efforts focus on the few professional league matches. However, the recording quality and broadcast editing vary considerably across leagues, creating a disparity in the analytical capabilities of deep learning models. This paper delves into an analysis of how action spotting models transfer to diverse domains, analyzing the performance gap between various types of broadcasts. In particular, we investigate the transfer capability of state-of-the-art action spotting models across leagues, from amateur to professional, and broadcast quality, from AI-piloted camera to professional broadcast editing. Our analysis shows that transferring across leagues is challenging, with the most impactful feature been broadcasting editing quality. This analysis paper therefore seeks to spotlight this pressing issue and catalyze future research endeavors in the field of domain adaptation for action spotting methods

A randomized phase III study of carfilzomib vs low-dose corticosteroids with optional cyclophosphamide in relapsed and refractory multiple myeloma (FOCUS)

This randomized, phase III, open-label, multicenter study compared carfilzomib monotherapy against low-dose corticosteroids and optional cyclophosphamide in relapsed and refractory multiple myeloma (RRMM). Relapsed and refractory multiple myeloma patients were randomized (1:1) to receive carfilzomib (10-min intravenous infusion; 20 mg/m(2) on days 1 and 2 of cycle 1; 27 mg/m(2) thereafter) or a control regimen of low-dose corticosteroids (84 mg of dexamethasone or equivalent corticosteroid) with optional cyclophosphamide (1400 mg) for 28-day cycles. The primary endpoint was overall survival (OS). Three-hundred and fifteen patients were randomized to carfilzomib (n=157) or control (n=158). Both groups had a median of five prior regimens. In the control group, 95% of patients received cyclophosphamide. Median OS was 10.2 (95% confidence interval (CI) 8.4-14.4) vs 10.0 months (95% CI 7.7-12.0) with carfilzomib vs control (hazard ratio=0.975; 95% CI 0.760-1.249; P=0.4172). Progression-free survival was similar between groups; overall response rate was higher with carfilzomib (19.1 vs 11.4%). The most common grade ⩾3 adverse events were anemia (25.5 vs 30.7%), thrombocytopenia (24.2 vs 22.2%) and neutropenia (7.6 vs 12.4%) with carfilzomib vs control. Median OS for single-agent carfilzomib was similar to that for an active doublet control regimen in heavily pretreated RRMM patients

Exoplanet imaging data challenge, phase II: characterization of exoplanet signals in high-contract images

peer reviewedToday, there exists a wide variety of algorithms dedicated to high-contrast imaging, especially for the detection and characterisation of exoplanet signals. These algorithms are tailored to address the very high contrast between the exoplanet signal(s), which can be more than two orders of magnitude fainter than the bright starlight residuals in coronagraphic images. The starlight residuals are inhomogeneously distributed and follow various timescales that depend on the observing conditions and on the target star brightness. Disentangling the exoplanet signals within the starlight residuals is therefore challenging, and new post-processing algorithms are striving to achieve more accurate astrophysical results. The Exoplanet Imaging Data Challenge is a community-wide effort to develop, compare and evaluate algorithms using a set of benchmark high-contrast imaging datasets. After a first phase ran in 2020 and focused on the detection capabilities of existing algorithms, the focus of this ongoing second phase is to compare the characterisation capabilities of state-of-the-art techniques. The characterisation of planetary companions is two-fold: the astrometry (estimated position with respect to the host star) and spectrophotometry (estimated contrast with respect to the host star, as a function of wavelength). The goal of this second phase is to offer a platform for the community to benchmark techniques in a fair, homogeneous and robust way, and to foster collaborations

Direct exoplanet imaging with small-angle Vortex coronagraphs

Vortex coronagraphs are among the most promising solutions to perform high contrast imaging at small angular separations from bright stars. They enhance the dynamic range at very small inner working angle (down to the diffraction limit of the telescope) and provide a clear 360 degree discovery space for high-contrast direct imaging of exoplanets. In this talk, we will report on the first scientific results obtained with Vortex coronagraphs installed on 10-m class telescopes (i.e., the VLT and the LBT) and on the recent installation of one Vortex at Keck. We will describe the in-lab and on-sky performance of the Vortex, and describe the lessons learned after a few years of operation. Finally, we will discuss the prospects of our vortices for future extremely large telescopes and space missions.VORTE