09/01/1925 Letter from L\u27Evenement

Letter from Florian Fortin, Director of L\u27Evenement, to Louis-Philippe Gagné.https://digitalcommons.usm.maine.edu/fac-lpg-letters-1920-1939/1000/thumbnail.jp

Measurements of fiducial and differential cross sections for Higgs boson production in the diphoton decay channel at s√=8 TeV with ATLAS

Measurements of fiducial and differential cross sections are presented for Higgs boson production in proton-proton collisions at a centre-of-mass energy of s√=8 TeV. The analysis is performed in the H → γγ decay channel using 20.3 fb−1 of data recorded by the ATLAS experiment at the CERN Large Hadron Collider. The signal is extracted using a fit to the diphoton invariant mass spectrum assuming that the width of the resonance is much smaller than the experimental resolution. The signal yields are corrected for the effects of detector inefficiency and resolution. The pp → H → γγ fiducial cross section is measured to be 43.2 ±9.4(stat.) − 2.9 + 3.2 (syst.) ±1.2(lumi)fb for a Higgs boson of mass 125.4GeV decaying to two isolated photons that have transverse momentum greater than 35% and 25% of the diphoton invariant mass and each with absolute pseudorapidity less than 2.37. Four additional fiducial cross sections and two cross-section limits are presented in phase space regions that test the theoretical modelling of different Higgs boson production mechanisms, or are sensitive to physics beyond the Standard Model. Differential cross sections are also presented, as a function of variables related to the diphoton kinematics and the jet activity produced in the Higgs boson events. The observed spectra are statistically limited but broadly in line with the theoretical expectations

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

Fiducial and differential cross sections of Higgs boson production measured in the four-lepton decay channel in pp collisions at √s = 8 TeV with the ATLAS detector

Measurements of fiducial and differential cross sections of Higgs boson production in the H→ZZ∗ → 4ℓ decay channel are presented. The cross sections are determined within a fiducial phase space and corrected for detection efficiency and resolution effects. They are based on 20.3 fb−¹ of pp collision data, produced at √s = 8 TeV centre-of-mass energy at the LHC and recorded by the ATLAS detector. The differential measurements are performed in bins of transverse momentum and rapidity of the four-lepton system, the invariant mass of the subleading lepton pair and the decay angle of the leading lepton pair with respect to the beam line in the four-lepton rest frame, as well as the number of jets and the transverse momentum of the leading jet. The measured cross sections are compared to selected theoretical calculations of the Standard Model expectations. No significant deviation from any of the tested predictions is found

Search for the Standard Model Higgs boson decay to μ+μ− with the ATLAS detector

A search is reported for Higgs boson decay to μ+μ−μ+μ− using data with an integrated luminosity of 24.8 fb−124.8 fb−¹ collected with the ATLAS detector in pp collisions at √s=7 and 8 TeV at the CERN Large Hadron Collider. The observed dimuon invariant mass distribution is consistent with the Standard Model background-only hypothesis in the 120–150 GeV search range. For a Higgs boson with a mass of 125.5 GeV, the observed (expected) upper limit at the 95% confidence level is 7.0 (7.2) times the Standard Model expectation. This corresponds to an upper limit on the branching ratio BR(H→μ+μ−)of 1.5×10−31.5×10−3

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

Emotion regulation of others' positive and negative emotions is related to distinct patterns of heart rate variability and situational empathy.

Although emotion regulation has been proposed to be crucial for empathy, investigations on emotion regulation have been primarily limited to intrapersonal processes, leaving the interpersonal processes of self-regulation rather unexplored. Moreover, studies showed that emotion regulation and empathy are related with increased autonomic activation. How emotion regulation and empathy are related at the autonomic level, and more specifically during differently valenced social situations remains an open question. Healthy adults viewed a series of short videos illustrating a target who was expressing positive, negative, or no emotions during a social situation (Positive, Negative, or Neutral Social Scenes). Prior to each video, participants were instructed to reappraise their own emotions (Up-regulation, Down-regulation, or No-regulation). To assess autonomic activation, RR intervals (RRI), high frequency (HF) components of heart rate variability (HRV), and electrodermal activity phasic responses (EDRs) were calculated. Situational empathy was measured through a visual analogue scale. Participants rated how empathic they felt for a specific target. Up- and Down-regulation were related to an increase and a decrease in situational empathy and an increase in RRI and HF, respectively, compared to the control condition (No-regulation). This suggests increased activity of the parasympathetic branch during emotion regulation of situational empathic responses. Positive compared to Negative Social Scenes were associated with decreased situational empathy, in addition to a slightly but non-significantly increased HF. Altogether, this study demonstrates that emotion regulation may be associated with changes in situational empathy and autonomic responses, preferentially dominated by the parasympathetic branch and possibly reflecting an increase of regulatory processes. Furthermore, the current study provides evidence that empathy for different emotional valences is associated with distinct changes in situational empathy and autonomic responses

Foundry photonic process extension with bandgap tuning using selective area growth

The extension of a photonic integrated circuit foundry process flow is proposed by integrating selective area growth (SAG) to enable bandgap tuning for each individual active building block. The process adaptations and the impact on performance are reviewed in terms of morphology requirements and topology reduction. This platform extension enables bandgap tuning for a set of active devices to cover the wavelength range from 1453 to 1651 nm. Integration is demonstrated in combination with active-passive butt-joint technology to create the most comprehensive range of generic building blocks. Performance and limitations of the range of achievable band-edges within the same monolithic wafer are studied for amplifiers and extended cavity lasers