89 research outputs found
Quench levels and transient beam losses in LHC magnets
The last evaluation of quench levels related to transient beam losses was done in 1987 [1]. The subject is reevaluated with more detailed approach of the thermodynamics of the superconducting cables in response to a transient heat load associated to beam losses
Depth-dependent critical behavior in V2H
Using X-ray diffuse scattering, we investigate the critical behavior of an
order-disorder phase transition in a defective "skin-layer" of V2H. In the
skin-layer, there exist walls of dislocation lines oriented normal to the
surface. The density of dislocation lines within a wall decreases continuously
with depth. We find that, because of this inhomogeneous distribution of
defects, the transition effectively occurs at a depth-dependent local critical
temperature. A depth-dependent scaling law is proposed to describe the
corresponding critical ordering behavior.Comment: 5 pages, 4 figure
Depth-dependent ordering, two-length-scale phenomena and crossover behavior in a crystal featuring a skin-layer with defects
Structural defects in a crystal are responsible for the "two length-scale"
behavior, in which a sharp central peak is superimposed over a broad peak in
critical diffuse X-ray scattering. We have previously measured the scaling
behavior of the central peak by scattering from a near-surface region of a V2H
crystal, which has a first-order transition in the bulk. As the temperature is
lowered toward the critical temperature, a crossover in critical behavior is
seen, with the temperature range nearest to the critical point being
characterized by mean field exponents. Near the transition, a small two-phase
coexistence region is observed. The values of transition and crossover
temperatures decay with depth. An explanation of these experimental results is
here proposed by means of a theory in which edge dislocations in the
near-surface region occur in walls oriented in the two directions normal to the
surface. The strain caused by the dislocation lines causes the ordering in the
crystal to occur as growth of roughly cylindrically shaped regions. After the
regions have reached a certain size, the crossover in the critical behavior
occurs, and mean field behavior prevails. At a still lower temperature, the
rest of the material between the cylindrical regions orders via a weak
first-order transition.Comment: 12 pages, 8 figure
Proton Collimation in TeV Colliders
In high intensity proton colliders with superconducting magnets, quenches induced by beam losses are unavoidable in the absence of a collimation system. We will show that a single stage system cannot suffice at TeV energies. We will discuss a two-stage collimation system at first as an optical system then considering true scattering in collimator jaws. Expected performance at LHC are presented. then finally, we present the preliminary measurements done at 120 GeV/c in the SPS ring with a simplified three stage collimation system
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
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
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