50 research outputs found
Indirect signals from light neutralinos in supersymmetric models without gaugino mass unification
We examine indirect signals produced by neutralino self-annihilations, in the
galactic halo or inside celestial bodies, in the frame of an effective MSSM
model without gaugino-mass unification at a grand unification scale. We compare
our theoretical predictions with current experimental data of gamma-rays and
antiprotons in space and of upgoing muons at neutrino telescopes. Results are
presented for a wide range of the neutralino mass, though our discussions are
focused on light neutralinos. We find that only the antiproton signal is
potentially able to set constraints on very low-mass neutralinos, below 20 GeV.
The gamma-ray signal, both from the galactic center and from high galactic
latitudes, requires significantly steep profiles or substantial clumpiness in
order to reach detectable levels. The up-going muon signal is largely below
experimental sensitivities for the neutrino flux coming from the Sun; for the
flux from the Earth an improvement of about one order of magnitude in
experimental sensitivities (with a low energy threshold) can make accessible
neutralino masses close to O, Si and Mg nuclei masses, for which resonant
capture is operative.Comment: 17 pages, 1 tables and 5 figures, typeset with ReVTeX4. The paper may
also be found at http://www.to.infn.it/~fornengo/papers/indirect04.ps.gz or
through http://www.astroparticle.to.infn.it/. Limit from BR(Bs--> mu+ mu-)
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Early Integration of Palliative Care into Standard Oncology Care: Evidence and Overcoming Barriers to Implementation
In Canada, widespread discussion of medical aid in dying (http://www.parl.gc.ca/HousePublications/Publication.[...
Improving retinanet for CT lesion detection with dense masks from weak recist labels.
Accurate, automated lesion detection in Computed Tomography (CT) is an important yet challenging task due to the large variation of lesion types, sizes, locations and appearances. Recent work on CT lesion detection employs two-stage region proposal based methods trained with centroid or bounding-box annotations. We propose a highly accurate and efficient one-stage lesion detector, by re-designing a RetinaNet to meet the particular challenges in medical imaging. Specifically, we optimize the anchor configurations using a differential evolution search algorithm. For training, we leverage the response evaluation criteria in solid tumors (RECIST) annotation which are measured in clinical routine. We incorporate dense masks from weak RECIST labels, obtained automatically using GrabCut, into the training objective, which in combination with other advancements yields new state-of-the-art performance. We evaluate our method on the public DeepLesion benchmark, consisting of 32,735 lesions across the body. Our one-stage detector achieves a sensitivity of 90.77% at 4 false positives per image, significantly outperforming the best reported methods by over 5%