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Author |
Conde, D.; Castillo, F.L.; Escobar, C.; García, C.; Garcia Navarro, J.E.; Sanz, V.; Zaldívar, B.; Curto, J.J.; Marsal, S.; Torta, J.M. |
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Title |
Forecasting Geomagnetic Storm Disturbances and Their Uncertainties Using Deep Learning |
Type |
Journal Article |
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Year |
2023 |
Publication |
Space Weather |
Abbreviated Journal |
Space Weather |
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Volume |
21 |
Issue |
11 |
Pages |
e2023SW003474 - 27pp |
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Keywords |
geomagnetic storms; deep learning; forecasting; SYM-H; uncertainties; hyper-parameter optimization |
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Abstract |
Severe space weather produced by disturbed conditions on the Sun results in harmful effects both for humans in space and in high-latitude flights, and for technological systems such as spacecraft or communications. Also, geomagnetically induced currents (GICs) flowing on long ground-based conductors, such as power networks, potentially threaten critical infrastructures on Earth. The first step in developing an alarm system against GICs is to forecast them. This is a challenging task given the highly non-linear dependencies of the response of the magnetosphere to these perturbations. In the last few years, modern machine-learning models have shown to be very good at predicting magnetic activity indices. However, such complex models are on the one hand difficult to tune, and on the other hand they are known to bring along potentially large prediction uncertainties which are generally difficult to estimate. In this work we aim at predicting the SYM-H index characterizing geomagnetic storms multiple-hour ahead, using public interplanetary magnetic field (IMF) data from the Sun-Earth L1 Lagrange point and SYM-H data. We implement a type of machine-learning model called long short-term memory (LSTM) network. Our scope is to estimate the prediction uncertainties coming from a deep-learning model in the context of forecasting the SYM-H index. These uncertainties will be essential to set reliable alarm thresholds. The resulting uncertainties turn out to be sizable at the critical stages of the geomagnetic storms. Our methodology includes as well an efficient optimization of important hyper-parameters of the LSTM network and robustness tests. |
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Address |
[Conde, D.; Escobar, C.; Garcia, C.; Garcia, J. E.; Sanz, V.; Zaldivar, B.] Univ Valencia, CSIC, Ctr Mixto, Inst Fis Corpuscular IFIC, Valencia, Spain, Email: Daniel.Conde@ific.uv.es |
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Amer Geophysical Union |
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English |
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WOS:001104189700001 |
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no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5804 |
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Author |
Schaffter, T. et al; Albiol, F.; Caballero, L. |
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Title |
Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms |
Type |
Journal Article |
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Year |
2020 |
Publication |
JAMA Network Open |
Abbreviated Journal |
JAMA Netw. Open |
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Volume |
3 |
Issue |
3 |
Pages |
e200265 - 15pp |
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Abstract |
Importance Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives. Objective To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms. Design, Setting, and Participants In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016. Main Outcomes and Measurements Algorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists' specificity with radiologists' sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists' recall assessment was developed and evaluated. Results Overall, 144231 screening mammograms from 85580 US women (952 cancer positive <= 12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166578 examinations from 68008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists' sensitivity, lower than community-practice radiologists' specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms and US radiologist assessments resulted in a higher area under the curve of 0.942 and achieved a significantly improved specificity (92.0%) at the same sensitivity. Conclusions and Relevance While no single AI algorithm outperformed radiologists, an ensemble of AI algorithms combined with radiologist assessment in a single-reader screening environment improved overall accuracy. This study underscores the potential of using machine learning methods for enhancing mammography screening interpretation. Question How do deep learning algorithms perform compared with radiologists in screening mammography interpretation? Findings In this diagnostic accuracy study using 144231 screening mammograms from 85580 women from the United States and 166578 screening mammograms from 68008 women from Sweden, no single artificial intelligence algorithm outperformed US community radiologist benchmarks; including clinical data and prior mammograms did not improve artificial intelligence performance. However, combining best-performing artificial intelligence algorithms with single-radiologist assessment demonstrated increased specificity. Meaning Integrating artificial intelligence to mammography interpretation in single-radiologist settings could yield significant performance improvements, with the potential to reduce health care system expenditures and address resource scarcity experienced in population-based screening programs. This diagnostic accuracy study evaluates whether artificial intelligence can overcome human mammography interpretation limits with a rigorous, unbiased evaluation of machine learning algorithms. |
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[Schaffter, Thomas; Hoff, Bruce; Yu, Thomas; Neto, Elias Chaibub; Friend, Stephen; Guinney, Justin] Sage Bionetworks, Computat Oncol, Seattle, WA USA, Email: gustavo@us.ibm.com |
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Amer Medical Assoc |
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English |
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ISSN |
2574-3805 |
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Notes |
WOS:000519249800002 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4683 |
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Author |
Ilner, A.; Blair, J.; Cabrera, D.; Markert, C.; Bratkovskaya, E. |
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Title |
Probing hot and dense nuclear matter with K*, (K)over-bar* vector mesons |
Type |
Journal Article |
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Year |
2019 |
Publication |
Physical Review C |
Abbreviated Journal |
Phys. Rev. C |
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Volume |
99 |
Issue |
2 |
Pages |
024914 - 22pp |
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Abstract |
We investigate the possibility of probing the hot and dense nuclear matter-created in relativistic heavyion collisions (HICs)-with strange vector mesons (K*, (K) over bar*). Our analysis is based on the nonequilibrium parton-hadron-string dynamics (PHSD) transport approach which incorporates partonic and hadronic degrees of freedom and describes the full dynamics of HIC on a microscopic level-starting from the primary nucleon-nucleon collisions to the formation of the strongly interacting quark gluon plasma (QGP), followed by dynamical hadronization of (anti)quarks as well as final hadronic elastic and inelastic interactions. This allows us to study the K* and (K) over bar* meson formation from the QGP as well as the in-medium effects related to the modification of their spectral properties during the propagation through the dense and hot hadronic environment in the expansion phase. We employ relativistic Breit-Wigner spectral functions for the K*, (K) over bar* mesons with self-energies obtained from a self-consistent coupled-channel G-matrix approach to study the role of in-medium effects on the K* and (K) over bar* meson dynamics in heavy-ion collisions from FAIR/NICA to LHC energies. According to our analysis most of the final K* /(K) over bar*'s, that can be observed experimentally by reconstruction of the invariant mass of pi + K((K) over bar) pairs, are produced during the late hadronic phase and originate dominantly from the K((K) over bar) + pi -> K*( (K) over bar*) formation channel. The amount of K*/ (K) over bar*'s, originating from the QGP channel is comparatively small even at LHC energies and those K* /(K) over bar*'s can hardly be reconstructed experimentally due to the rescattering of final pions and (anti)kaons. This mirrors the results from our previous study on the strange vector-meson production in heavy-ion collisions at RHIC energies. We demonstrate that K* /(K) over bar* in-medium effects should be visible at FAIR/NICA and BES RHIC energies, where the production of K* /(K) over bar*'s occurs at larger net-baryon densities. Finally, we present the experimental procedures to extract the information on the resonance masses and widths by fitting the final mass spectra at LHC energies. |
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Address |
[Ilner, Andrej; Bratkovskaya, Elena] Johann Wolfgang Goethe Univ Frankfurt Main, Inst Theoret Phys, D-60438 Frankfurt, Germany, Email: ilner@fias.uni-frankfurt.de |
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Amer Physical Soc |
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English |
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ISSN |
2469-9985 |
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Notes |
WOS:000459905400005 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
3925 |
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Permanent link to this record |
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Author |
BABAR Collaboration (Lees, J.P. et al); Martinez-Vidal, F.; Oyanguren, A. |
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Title |
Observation of the Decay D-0 -> K- pi(+) e(+) e(-) |
Type |
Journal Article |
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Year |
2019 |
Publication |
Physical Review Letters |
Abbreviated Journal |
Phys. Rev. Lett. |
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Volume |
122 |
Issue |
8 |
Pages |
081802 - 8pp |
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Abstract |
We report the observation of the rare charm decay D-0 -> K-pi(+)e(+)e(-), based on 468 fb(-1) of e(+)e(-) annihilation data collected at or close to the center-of-mass energy of the (sic)(4S) resonance with the BABAR detector at the SLAC National Accelerator Laboratory. We find the branching fraction in the invariant mass range 0.675 < m(e(+)e(-)) < 0.875 GeV/c(2) of the electron-positron pair to be B(D-0 -> K-pi(+)e(+)e(-)) = (4.0 +/- 0.5 +/- 0.2 +/- 0.1) x 10(-6), where the first uncertainty is statistical, the second systematic, and the third due to the uncertainty in the branching fraction of the decay D-0 -> K-pi(+)pi(+)pi(-) used as a normalization mode. The significance of the observation corresponds to 9.7 standard deviations including systematic uncertainties. This result is consistent with the recently reported D-0 -> K-pi(+)mu(+)mu(-) branching fraction, measured in the same invariant mass range, and with the value expected in the standard model. In a set of regions of m(e(+)e(-)), where long-distance effects are potentially small, we determine a 90% confidence level upper limit on the branching fraction B(D-0 -> K-pi(+)e(+)e(-)) < 3.1 x 10(-6). |
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[Lees, J. P.; Poireau, V.; Tisserand, V.] Univ Savoie, CNRS, IN2P3, Lab Annecy Le Vieux Phys Particules LAPP, F-74941 Annecy Le Vieux, France |
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Amer Physical Soc |
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English |
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0031-9007 |
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Notes |
WOS:000459920400005 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
3926 |
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Permanent link to this record |
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Author |
ATLAS Collaboration (Aaboud, M. et al); Alvarez Piqueras, D.; Bailey, A.J.; Barranco Navarro, L.; Cabrera Urban, S.; Castillo, F.L.; Castillo Gimenez, V.; Cerda Alberich, L.; Costa, M.J.; Escobar, C.; Estrada Pastor, O.; Ferrer, A.; Fiorini, L.; Fuster, J.; Garcia, C.; Garcia Navarro, J.E.; Gonzalez de la Hoz, S.; Higon-Rodriguez, E.; Jimenez Pena, J.; Lacasta, C.; Lozano Bahilo, J.J.; Madaffari, D.; Mamuzic, J.; Marti-Garcia, S.; Melini, D.; Mitsou, V.A.; Pedraza Lopez, S.; Rodriguez Bosca, S.; Rodriguez Rodriguez, D.; Salt, J.; Soldevila, U.; Sanchez, J.; Valero, A.; Valls Ferrer, J.A.; Vos, M. |
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Title |
Search for pairs of highly collimated photon-jets in pp collisions at root s=13 TeV with the ATLAS detector |
Type |
Journal Article |
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Year |
2019 |
Publication |
Physical Review D |
Abbreviated Journal |
Phys. Rev. D |
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Volume |
99 |
Issue |
1 |
Pages |
012008 - 29pp |
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Abstract |
Results of a search for the pair production of photon-jets-collimated groupings of photons-in the ATLAS detector at the Large Hadron Collider are reported. Highly collimated photon-jets can arise from the decay of new, highly boosted particles that can decay to multiple photons collimated enough to be identified in the electromagnetic calorimeter as a single, photonlike energy cluster. Data from proton-proton collisions at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 36.7 fb(-1), were collected in 2015 and 2016. Candidate photon-jet pair production events are selected from those containing two reconstructed photons using a set of identification criteria much less stringent than that typically used for the selection of photons, with additional criteria applied to provide improved sensitivity to photon-jets. Narrow excesses in the reconstructed diphoton mass spectra are searched for. The observed mass spectra are consistent with the Standard Model background expectation. The results are interpreted in the context of a model containing a new, high-mass scalar particle with narrow width, X, that decays into pairs of photon-jets via new, light particles, a. Upper limits are placed on the cross section times the product of branching ratios sigma x B(X -> aa) x B(a -> gamma gamma)(2) for 200 GeV < m(X) < 2 TeV and for ranges of m(a) from a lower mass of 100 MeV up to between 2 and 10 GeV, depending upon m(X). Upper limits are also placed on sigma x B(X -> aa) x B(a -> 3 pi(0))(2) for the same range of m(X) and for ranges of m(a) from a lower mass of 500 MeV up to between 2 and 10 GeV. |
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Address |
[Duvnjak, D.; Jackson, P.; Oliver, J. L.; Petridis, A.; Qureshi, A.; Sharma, A. S.; White, M. J.] Univ Adelaide, Dept Phys, Adelaide, SA, Australia |
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Amer Physical Soc |
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English |
Summary Language |
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Series Issue |
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Edition |
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ISSN |
2470-0010 |
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Notes |
WOS:000456035800008 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
3888 |
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Permanent link to this record |