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Träfflista för sökning "WFRF:(Carpenter P. T.) srt2:(2020)"

Sökning: WFRF:(Carpenter P. T.) > (2020)

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2.
  • Salami, B., et al. (författare)
  • LEGaTO: Low-Energy, Secure, and Resilient Toolset for Heterogeneous Computing
  • 2020
  • Ingår i: PROCEEDINGS OF THE 2020 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE 2020). - 1530-1591. - 9783981926347 ; , s. 169-174
  • Konferensbidrag (refereegranskat)abstract
    • The LEGaTO project leverages task-based programming models to provide a software ecosystem for Made in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engines. The aim is to attain one order of magnitude energy savings from the edge to the converged cloud/HPC, balanced with the security and resilience challenges. LEGaTO is an ongoing three-year EU H2020 project started in December 2017.
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3.
  • Basu, Anwesha, et al. (författare)
  • Evolution of collective and noncollective structures in Xe 123
  • 2020
  • Ingår i: Physical Review C. - : American Physical Society. - 2469-9985. ; 101:2
  • Tidskriftsartikel (refereegranskat)abstract
    • An experiment involving a heavy-ion-induced fusion-evaporation reaction was carried out where high-spin states of Xe123 were populated in the Se80(Ca48,5n)Xe123 reaction at 207 MeV beam energy. Gamma-ray coincidence events were recorded with the Gammasphere Ge detector array. The previously known level scheme was confirmed and enhanced with the addition of five new band structures and several interband transitions. Cranked Nilsson-Strutinsky (CNS) calculations were performed and compared with the experimental results in order to assign configurations to the bands.
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4.
  • Hollandi, R., et al. (författare)
  • nucleAIzer : A Parameter-free Deep Learning Framework for Nucleus Segmentation Using Image Style Transfer
  • 2020
  • Ingår i: Cell Systems. - : Elsevier BV. - 2405-4712. ; 10:5, s. 453-458.e6
  • Tidskriftsartikel (refereegranskat)abstract
    • Single-cell segmentation is typically a crucial task of image-based cellular analysis. We present nucleAIzer, a deep-learning approach aiming toward a truly general method for localizing 2D cell nuclei across a diverse range of assays and light microscopy modalities. We outperform the 739 methods submitted to the 2018 Data Science Bowl on images representing a variety of realistic conditions, some of which were not represented in the training data. The key to our approach is that during training nucleAIzer automatically adapts its nucleus-style model to unseen and unlabeled data using image style transfer to automatically generate augmented training samples. This allows the model to recognize nuclei in new and different experiments efficiently without requiring expert annotations, making deep learning for nucleus segmentation fairly simple and labor free for most biological light microscopy experiments. It can also be used online, integrated into CellProfiler and freely downloaded at www.nucleaizer.org. A record of this paper's transparent peer review process is included in the Supplemental Information. Microscopy image analysis of single cells can be challenging but also eased and improved. We developed a deep learning method to segment cell nuclei. Our strategy is adapting to unexpected circumstances automatically by synthesizing artificial microscopy images in such a domain as training samples.
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5.
  • Rudolph, D., et al. (författare)
  • Onset of high-spin rotational bands in the N = Z nucleus Ga-62
  • 2020
  • Ingår i: Physical Review C. Nuclear Physics. - : American Physical Society. - 0556-2813 .- 1089-490X. ; 102:1
  • Tidskriftsartikel (refereegranskat)abstract
    • The fusion-evaporation reaction Si-28 + Ca-40 at 122 MeV beam energy was used to populate high-spin states in the odd-odd N = Z nucleus Ga-62. With the combination of the Gammasphere spectrometer and the Microball CsI(Tl) charged-particle detector array the decay scheme of Ga-62 was extended beyond 10 MeV excitation energy. The onset of band structures was observed. These high-spin rotational states are interpreted and classified by means of cranked Nilsson-Strutinsky calculations.
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  • Resultat 1-5 av 5
 
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