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Proceedings ArticleDOI
20 Mar 2017-
TL;DR: This work presents a conceptually simple, flexible, and general framework for object instance segmentation, which extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition.
Abstract: We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without tricks, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition. Code will be made available.

9,492 citations


Proceedings ArticleDOI
07 Dec 2015-
Abstract: Rectified activation units (rectifiers) are essential for state-of-the-art neural networks. In this work, we study rectifier neural networks for image classification from two aspects. First, we propose a Parametric Rectified Linear Unit (PReLU) that generalizes the traditional rectified unit. PReLU improves model fitting with nearly zero extra computational cost and little overfitting risk. Second, we derive a robust initialization method that particularly considers the rectifier nonlinearities. This method enables us to train extremely deep rectified models directly from scratch and to investigate deeper or wider network architectures. Based on the learnable activation and advanced initialization, we achieve 4.94% top-5 test error on the ImageNet 2012 classification dataset. This is a 26% relative improvement over the ILSVRC 2014 winner (GoogLeNet, 6.66% [33]). To our knowledge, our result is the first to surpass the reported human-level performance (5.1%, [26]) on this dataset.

9,436 citations


Journal ArticleDOI
Peter A. R. Ade, Nabila Aghanim, Monique Arnaud, M. Ashdown, J. Aumont, Carlo Baccigalupi, A. J. Banday, R. B. Barreiro, James G. Bartlett, N. Bartolo, E. Battaner, Richard A. Battye, K. Benabed, Alain Benoit, A. Benoit-Lévy, J.-P. Bernard, Marco Bersanelli, P. Bielewicz, J. J. Bock, Anna Bonaldi, Laura Bonavera, J. R. Bond, Julian Borrill, François R. Bouchet, F. Boulanger, M. Bucher, Carlo Burigana, R. C. Butler, Erminia Calabrese, Jean-François Cardoso, A. Catalano, Anthony Challinor, A. Chamballu, Ranga-Ram Chary, H. C. Chiang, Jens Chluba, P. R. Christensen, Sarah E. Church, David L. Clements, S. Colombi, L. P. L. Colombo, C. Combet, A. Coulais, B. P. Crill, A. Curto, F. Cuttaia, Luigi Danese, R. D. Davies, R. J. Davis, P. de Bernardis, A. de Rosa, G. de Zotti, Jacques Delabrouille, F.-X. Désert, E. Di Valentino, Clive Dickinson, Jose M. Diego, Klaus Dolag, H. Dole, S. Donzelli, Olivier Doré, Marian Douspis, A. Ducout, Jo Dunkley, X. Dupac, George Efstathiou, F. Elsner, Torsten A. Ensslin, H. K. Eriksen, Marzieh Farhang, James R. Fergusson, Fabio Finelli, Olivier Forni, M. Frailis, A. A. Fraisse, E. Franceschi, A. Frejsel, S. Galeotta, S. Galli, K. Ganga, C. Gauthier, Martina Gerbino, Tuhin Ghosh, M. Giard, Y. Giraud-Héraud, Elena Giusarma, E. Gjerløw, J. González-Nuevo, Krzysztof M. Gorski, Serge Gratton, A. Gregorio, Alessandro Gruppuso, Jon E. Gudmundsson, Jan Hamann, F. K. Hansen, Duncan Hanson, D. L. Harrison, George Helou, Sophie Henrot-Versille, C. Hernández-Monteagudo, D. Herranz, S. R. Hildebrandt, E. Hivon, Michael P. Hobson, W. A. Holmes, Allan Hornstrup, W. Hovest, Zhiqi Huang, Kevin M. Huffenberger, G. Hurier, Andrew H. Jaffe, T. R. Jaffe, W. C. Jones, Mika Juvela, E. Keihänen, Reijo Keskitalo, Theodore Kisner, R. Kneissl, J. Knoche, Lloyd Knox, Martin Kunz, Hannu Kurki-Suonio, Guilaine Lagache, Anne Lähteenmäki, J.-M. Lamarre, Anthony Lasenby, Massimiliano Lattanzi, Charles R. Lawrence, J. P. Leahy, R. Leonardi, Julien Lesgourgues, François Levrier, Antony Lewis, Michele Liguori, P. B. Lilje, M. Linden-Vørnle, M. López-Caniego, Philip Lubin, J. F. Macías-Pérez, G. Maggio, Davide Maino, N. Mandolesi, A. Mangilli, A. Marchini, Peter G. Martin, M. Martinelli, E. Martínez-González, Silvia Masi, Sabino Matarrese, Pasquale Mazzotta, P. McGehee, Peter Meinhold, Alessandro Melchiorri, J.-B. Melin, L. Mendes, A. Mennella, M. Migliaccio, M. Millea, S. Mitra, M.-A. Miville-Deschênes, A. Moneti, L. Montier, Gianluca Morgante, Daniel J. Mortlock, Adam Moss, Dipak Munshi, J. A. Murphy, Pavel Naselsky, Federico Nati, Paolo Natoli, Calvin B. Netterfield, Hans Ulrik Nørgaard-Nielsen, F. Noviello, Dmitry Novikov, I. D. Novikov, C. A. Oxborrow, F. Paci, L. Pagano, F. Pajot, R. Paladini, Daniela Paoletti, Bruce Partridge, F. Pasian, G. Patanchon, T. J. Pearson, O. Perdereau, L. Perotto, Francesca Perrotta, Valeria Pettorino, F. Piacentini, M. Piat, E. Pierpaoli, Davide Pietrobon, Stéphane Plaszczynski, Etienne Pointecouteau, G. Polenta, L. Popa, G. W. Pratt, G. Prézeau, Simon Prunet, J.-L. Puget, Jörg P. Rachen, William T. Reach, Rafael Rebolo, M. Reinecke, Mathieu Remazeilles, C. Renault, A. Renzi, I. Ristorcelli, Graca Rocha, C. Rosset, M. Rossetti, G. Roudier, B. Rouillé d'Orfeuil, Michael Rowan-Robinson, Jose Alberto Rubino-Martin, Ben Rusholme, Najla Said, Valentina Salvatelli, L. Salvati, M. Sandri, D. Santos, M. Savelainen, Giorgio Savini, Douglas Scott, Michael Seiffert, Paolo Serra, E. P. S. Shellard, Locke D. Spencer, M. Spinelli, V. Stolyarov, R. Stompor, R. Sudiwala, R. A. Sunyaev, D. Sutton, A.-S. Suur-Uski, J.-F. Sygnet, J. A. Tauber, Luca Terenzi, L. Toffolatti, M. Tomasi, M. Tristram, T. Trombetti, M. Tucci, J. Tuovinen, M. Turler, G. Umana, Luca Valenziano, Jussi-Pekka Väliviita, B. Van Tent, P. Vielva, Fabrizio Villa, L. A. Wade, Benjamin D. Wandelt, Ingunn Kathrine Wehus, Martin White, Simon D. M. White, Althea Wilkinson, D. Yvon, Andrea Zacchei, Andrea Zonca 
Abstract: We present results based on full-mission Planck observations of temperature and polarization anisotropies of the CMB. These data are consistent with the six-parameter inflationary LCDM cosmology. From the Planck temperature and lensing data, for this cosmology we find a Hubble constant, H0= (67.8 +/- 0.9) km/s/Mpc, a matter density parameter Omega_m = 0.308 +/- 0.012 and a scalar spectral index with n_s = 0.968 +/- 0.006. (We quote 68% errors on measured parameters and 95% limits on other parameters.) Combined with Planck temperature and lensing data, Planck LFI polarization measurements lead to a reionization optical depth of tau = 0.066 +/- 0.016. Combining Planck with other astrophysical data we find N_ eff = 3.15 +/- 0.23 for the effective number of relativistic degrees of freedom and the sum of neutrino masses is constrained to < 0.23 eV. Spatial curvature is found to be |Omega_K| < 0.005. For LCDM we find a limit on the tensor-to-scalar ratio of r <0.11 consistent with the B-mode constraints from an analysis of BICEP2, Keck Array, and Planck (BKP) data. Adding the BKP data leads to a tighter constraint of r < 0.09. We find no evidence for isocurvature perturbations or cosmic defects. The equation of state of dark energy is constrained to w = -1.006 +/- 0.045. Standard big bang nucleosynthesis predictions for the Planck LCDM cosmology are in excellent agreement with observations. We investigate annihilating dark matter and deviations from standard recombination, finding no evidence for new physics. The Planck results for base LCDM are in agreement with BAO data and with the JLA SNe sample. However the amplitude of the fluctuations is found to be higher than inferred from rich cluster counts and weak gravitational lensing. Apart from these tensions, the base LCDM cosmology provides an excellent description of the Planck CMB observations and many other astrophysical data sets.

9,349 citations


Posted Content
TL;DR: The TensorFlow interface and an implementation of that interface that is built at Google are described, which has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields.
Abstract: TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms. A computation expressed using TensorFlow can be executed with little or no change on a wide variety of heterogeneous systems, ranging from mobile devices such as phones and tablets up to large-scale distributed systems of hundreds of machines and thousands of computational devices such as GPU cards. The system is flexible and can be used to express a wide variety of algorithms, including training and inference algorithms for deep neural network models, and it has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields, including speech recognition, computer vision, robotics, information retrieval, natural language processing, geographic information extraction, and computational drug discovery. This paper describes the TensorFlow interface and an implementation of that interface that we have built at Google. The TensorFlow API and a reference implementation were released as an open-source package under the Apache 2.0 license in November, 2015 and are available at www.tensorflow.org.

9,253 citations


Proceedings ArticleDOI
21 Jul 2017-
TL;DR: Conditional adversarial networks are investigated as a general-purpose solution to image-to-image translation problems and it is demonstrated that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks.
Abstract: We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Moreover, since the release of the pix2pix software associated with this paper, hundreds of twitter users have posted their own artistic experiments using our system. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without handengineering our loss functions either.

9,134 citations


Posted Content
TL;DR: This work proposes a Parametric Rectified Linear Unit (PReLU) that generalizes the traditional rectified unit and derives a robust initialization method that particularly considers the rectifier nonlinearities.
Abstract: Rectified activation units (rectifiers) are essential for state-of-the-art neural networks. In this work, we study rectifier neural networks for image classification from two aspects. First, we propose a Parametric Rectified Linear Unit (PReLU) that generalizes the traditional rectified unit. PReLU improves model fitting with nearly zero extra computational cost and little overfitting risk. Second, we derive a robust initialization method that particularly considers the rectifier nonlinearities. This method enables us to train extremely deep rectified models directly from scratch and to investigate deeper or wider network architectures. Based on our PReLU networks (PReLU-nets), we achieve 4.94% top-5 test error on the ImageNet 2012 classification dataset. This is a 26% relative improvement over the ILSVRC 2014 winner (GoogLeNet, 6.66%). To our knowledge, our result is the first to surpass human-level performance (5.1%, Russakovsky et al.) on this visual recognition challenge.

8,865 citations


Journal ArticleDOI
Theo Vos1, Amanuel Alemu Abajobir, Kalkidan Hassen Abate2, Cristiana Abbafati3, Kaja Abbas4, Foad Abd-Allah5, Rizwan Suliankatchi Abdulkader6, Abdishakur M. Abdulle7, Teshome Abuka Abebo8, Semaw Ferede Abera9, Semaw Ferede Abera10, Victor Aboyans, Laith J. Abu-Raddad11, Ilana N. Ackerman12, Abdu A. Adamu, Olatunji O. Adetokunboh13, Mohsen Afarideh14, Ashkan Afshin1, Sanjay K. Agarwal15, Rakesh Aggarwal16, Anurag Agrawal17, Anurag Agrawal18, Sutapa Agrawal19, Hamid Ahmadieh20, Muktar Beshir Ahmed2, Miloud Taki Eddine Aichour, Amani Nidhal Aichour, Ibtihel Aichour, Sneha Aiyar1, Rufus Akinyemi21, Rufus Akinyemi22, Nadia Akseer23, Faris Lami24, Fares Alahdab25, Ziyad Al-Aly26, Khurshid Alam27, Khurshid Alam28, Noore Alam, Tahiya Alam1, Deena Alasfoor, Kefyalew Addis Alene, Raghib Ali29, Reza Alizadeh-Navaei30, Ala'a Alkerwi, François Alla31, Peter Allebeck, Christine Allen1, Fatma Al-Maskari32, Rajaa Al-Raddadi, Ubai Alsharif33, Shirina Alsowaidi32, Khalid A Altirkawi34, Azmeraw T. Amare35, Azmeraw T. Amare36, Erfan Amini, Walid Ammar37, Yaw Ampem Amoako38, Hjalte H Andersen39, Carl Abelardo T. Antonio, Palwasha Anwari, Johan Ärnlöv40, Al Artaman41, Krishna Kumar Aryal, Hamid Asayesh42, Solomon Weldegebreal Asgedom43, Reza Assadi44, Tesfay Mehari Atey, Niguse Tadele Atnafu34, Sachin R Atre, Leticia Avila-Burgos, Euripide Frinel G Arthur Avokphako, Ashish Awasthi19, Umar Bacha38, Alaa Badawi45, Kalpana Balakrishnan46, Amitava Banerjee47, Marlena S Bannick48, Aleksandra Barac, Ryan M Barber, Suzanne Barker-Collo, Till Bärnighausen, Simón Barquera49, Lars Barregard, Lope H Barrero50, Sanjay Basu, Bob Battista51, Katherine E Battle40, Bernhard T. Baune27, Shahrzad Bazargan-Hejazi52, Shahrzad Bazargan-Hejazi53, Justin Beardsley, Neeraj Bedi54, Neeraj Bedi55, Ettore Beghi56, Yannick Béjot57, Bayu Begashaw Bekele, Michelle L. Bell58, Derrick A Bennett, Isabela M. Benseñor, Jennifer Benson19, Adugnaw Berhane59, Derbew Fikadu Berhe, Eduardo Bernabé, Balem Demtsu Betsu, Mircea Beuran, Addisu Shunu Beyene45, Neeraj Bhala60, Anil Bhansali1, Samir Bhatt, Zulfiqar A Bhutta23, Zulfiqar A Bhutta61, Sibhatu Biadgilign62, Burcu Kucuk Bicer63, Kelly Bienhoff, Boris Bikbov, Charles Birungi, Stan Biryukov, Donal Bisanzio64, Donal Bisanzio65, Habtamu Mellie Bizuayehu27, Dube Jara Boneya52, Dube Jara Boneya53, Soufiane Boufous, Rupert R A Bourne66, Alexandra Brazinova67, Traolach S. Brugha68, Rachelle Buchbinder, Lemma Negesa Bulto Bulto, Blair R. Bumgarner, Zahid A Butt, Lucero Cahuana-Hurtado, Ewan Cameron, Mate Car, Hélène Carabin69, Hélène Carabin70, Jonathan R. Carapetis, Rosario Cárdenas71, David O Carpenter60, Juan Jesus Carrero, Austin Carter1, Félix Carvalho, Daniel C Casey, Valeria Caso, Carlos A Castañeda-Orjuela, Chris D Castle, Ferrán Catalá-López72, Hsing-Yi Chang73, Jung-Chen Chang, Fiona J Charlson, Honglei Chen28, Mirriam Chibalabala, Chioma Ezinne Chibueze, Vesper Hichilombwe Chisumpa66, Abdulaal A Chitheer, Devasahayam J. Christopher74, Liliana G Ciobanu75, Massimo Cirillo76, Danny Colombara77, Cyrus Cooper78, Paolo Cortesi79, Michael H. Criqui80, John A. Crump81, Abel Fekadu Dadi82, Koustuv Dalal68, Lalit Dandona1, Lalit Dandona19, Rakhi Dandona1, Rakhi Dandona19, José Neves83, Dragos Virgil Davitoiu, Barbora de Courten12, Diego De Leo84, Barthelemy Kuate Defo85, Louisa Degenhardt1, Louisa Degenhardt86, Selina Deiparine1, Robert P. Dellavalle87, Robert P. Dellavalle88, Kebede Deribe89, Don C. Des Jarlais90, Subhojit Dey, Samath D Dharmaratne91, Samath D Dharmaratne1, Preet Kaur Dhillon92, Daniel Dicker93, Eric L. Ding94, Shirin Djalalinia, Huyen Phuc Do, E. Ray Dorsey95, Kadine Priscila Bender dos Santos96, Dirk Douwes-Schultz97, Kerrie E. Doyle, Tim Driscoll28, Manisha Dubey98, Bruce Bartholow Duncan99, Ziad El-Khatib, Jerisha Ellerstrand100, Ahmad Ali Enayati, Aman Yesuf Endries101, Sergey Petrovich Ermakov, Holly E. Erskine102, Babak Eshrati, Sharareh Eskandarieh14, Alireza Esteghamati, Kara Estep78, Fanuel Belayneh Bekele Fanuel103, Carla Sofia e Sa Farinha104, André Faro105, Farshad Farzadfar14, Mir Sohail Fazeli99, Valery L. Feigin1, Valery L. Feigin106, Seyed-Mohammad Fereshtehnejad107, Seyed-Mohammad Fereshtehnejad108, João C. Fernandes109, Alize J. Ferrari102, Tesfaye Regassa Feyissa82, Irina Filip110, Florian Fischer111, Christina Fitzmaurice1, Abraham D Flaxman30, Luisa Sorio Flor, Nataliya Foigt, Kyle J Foreman1, Richard C. Franklin89, Nancy Fullman1, Thomas Fürst112, João M. Furtado, Neal D. Futran, Emmanuela Gakidou84, Morsaleh Ganji, Alberto L Garcia-Basteiro, Teshome Gebre86, Teshome Gebre1, Tsegaye Tewelde Gebrehiwot1, Ayele Geleto88, Ayele Geleto87, Bikila Lencha Gemechu113, Hailay Abrha Gesesew89, Peter W. Gething29, Alireza Ghajar90, Katherine B Gibney, Paramjit Gill114, Richard F. Gillum1, Richard F. Gillum91, Ibrahim Abdelmageem Mohamed Ginawi, Ababi Zergaw Giref1, Melkamu Dedefo Gishu, Giorgia Giussani, William W Godwin115, Audra L Gold116, Ellen M Goldberg1, Philimon Gona117, Amador Goodridge28, Sameer Vali Gopalani118, Atsushi Goto, Alessandra C. Goulart119, Max Griswold99, Harish Chander Gugnani120, Rashmi Gupta, Rajeev Gupta, Tanush Gupta121, Vipin Gupta122, Nima Hafezi-Nejad14, Gessessew Bugssa Hailu, Alemayehu Hailu1, Randah R. Hamadeh123, Samer Hamidi124, Alexis J Handal125, Graeme J. Hankey126, Graeme J. Hankey127, Sarah Wulf Hanson, Yuantao Hao128, Hilda L Harb, Habtamu Abera Hareri102, Josep Maria Haro129, James D. Harvey14, Mohammad Sadegh Hassanvand, Rasmus Havmoeller108, Caitlin Hawley1, Simon I. Hay1, Roderick J. Hay130, Nathaniel J Henry, Ileana Heredia-Pi49, Julio C. Montañez Hernandez131, Pouria Heydarpour104, Hans W. Hoek132, Howard J. Hoffman59, Nobuyuki Horita, H. Dean Hosgood, Sorin Hostiuc133, Peter J. Hotez18, Damian G Hoy28, Aung Soe Htet, Guoqing Hu134, Hsiang Huang102, Chantal Huynh1, Kim Moesgaard Iburg, Ehimario U. Igumbor111, Chad Ikeda1, Caleb Mackay Salpeter Irvine1, Kathryn H. Jacobsen135, Nader Jahanmehr, Mihajlo Jakovljevic136, Simerjot K Jassal137, Mehdi Javanbakht21, Sudha P Jayaraman112, Panniyammakal Jeemon138, Paul N. Jensen, Vivekanand Jha29, Vivekanand Jha139, Guohong Jiang, Denny John140, Sarah Charlotte Johnson, Catherine O. Johnson1, Jost B. Jonas141, Mikk Jürisson142, Zubair Kabir143, Rajendra Kadel144, Amaha Kahsay, Ritul Kamal, Haidong Kan, Nadim E. Karam29, André Karch, Corine Karema145, Amir Kasaeian14, Getachew Mullu Kassa146, Nigussie Assefa Kassaw147, Nicholas J Kassebaum1, Anshul Kastor, Srinivasa Vittal Katikireddi148, Anil Kaul114, Norito Kawakami, Peter Njenga Keiyoro, Andre Pascal Kengne, Andre Keren, Yousef Khader149, Ibrahim A Khalil1, Ejaz Ahmad Khan150, Young-Ho Khang151, Ardeshir Khosravi, Jagdish Khubchandani1, Aliasghar Ahmad Kiadaliri152, Christian Kieling99, Yun Jin Kim1, Daniel Kim153, Pauline Kim1, Ruth W Kimokoti154, Yohannes Kinfu155, Adnan Kisa156, Adnan Kisa157, Katarzyna Kissimova-Skarbek, Mika Kivimäki, Ann Kristin Knudsen, Yoshihiro Kokubo, Dhaval Kolte158, Jacek A Kopec159, Soewarta Kosen, Parvaiz A Koul160, Ai Koyanagi, Michael Kravchenko161, Sanjay Krishnaswami1, Kristopher J Krohn1, G Anil Kumar19, Pushpendra Kumar124, Sanjiv Kumar125, Hmwe H Kyu1, Dharmesh Kumar Lal128, Ratilal Lalloo, Nkurunziza Lambert, Qing Lan129, Anders Larsson, Pablo M Lavados162, Janet L Leasher163, Paul H. Lee164, Jong-Tae Lee1, James Leigh28, Cheru Tesema Leshargie1, Janni Leung102, Ricky Leung, Miriam Levi165, Yichong Li, Yongmei Li, Darya Li Kappe166, Xiaofeng Liang, Misgan Legesse Liben, Stephen S Lim1, Shai Linn59, Patrick Y Liu167, Angela Liu168, Shiwei Liu, Yang Liu, Rakesh Lodha, Giancarlo Logroscino49, Stephanie J. London, Katharine J Looker169, Alan D. Lopez27, Alan D. Lopez1, Stefan Lorkowski170, Paulo A. Lotufo, Nicola Low171, Rafael Lozano1, Tim C.D. Lucas29, Erlyn Rachelle King Macarayan172, Hassan Magdy Abd El Razek173, Mohammed Magdy Abd El Razek, Mahdi Mahdavi174, Marek Majdan175, Reza Majdzadeh14, Azeem Majeed, Reza Malekzadeh14, Reza Malekzadeh176, Rajesh Malhotra1, Deborah Carvalho Malta, Abdullah Al Mamun, Helena Manguerra1, Treh Manhertz1, Ana Mantilla, Lorenzo G. Mantovani79, Chabila C Mapoma177, Laurie B. Marczak21, Jose Martinez-Raga178, Francisco Rogerlândio Martins-Melo, Ira Martopullo1, Winfried März179, Manu Raj Mathur19, Mohsen Mazidi180, Colm McAlinden, Madeline McGaughey181, John J. McGrath182, John J. McGrath102, Martin McKee, Claire McNellan143, Suresh Mehata144, Man Mohan Mehndiratta, Tefera Chane Mekonnen, Peter Memiah183, Ziad A. Memish93, Walter Mendoza184, Mubarek Abera Mengistie, Desalegn Tadese Mengistu, George A. Mensah59, Tuomo J. Meretoja, Atte Meretoja, Haftay Berhane Mezgebe, Renata Micha146, Anoushka Millear1, Ted R. Miller185, Ted R. Miller186, Edward J Mills148, Mojde Mirarefin, Erkin M. Mirrakhimov187, Awoke Misganaw1, Shiva Raj Mishra, Philip B. Mitchell86, Karzan Abdulmuhsin Mohammad188, Alireza Mohammadi, Kedir Endris Mohammed, Shafiu Mohammed, Sanjay K. Mohanty189, Ali H. Mokdad1, Sarah K Mollenkopf, Lorenzo Monasta, Marcella Montico, Maziar Moradi-Lakeh49, Paula Moraga, Rintaro Mori151, Chloe Morozoff, Shane D. Morrison, Mark Moses, Cliff Mountjoy-Venning51, Kalayu Birhane Mruts, Ulrich O Mueller, Kate Muller1, M. E. Murdoch153, Gudlavalleti V S Murthy1, Kamarul Imran Musa190, Jean B. Nachega1, Gabriele Nagel154, Mohsen Naghavi1, Aliya Naheed, Kovin Naidoo, Luigi Naldi, Vinay Nangia, Gopalakrishnan Natarajan191, Dumessa Edessa Negasa158, Ruxandra Irina Negoi, Ionut Negoi, Charles R. Newton192, Charles R. Newton29, Josephine W. Ngunjiri193, Trang Huyen Nguyen, Quyen Nguyen160, Cuong Tat Nguyen, Grant Nguyen161, Minh Nguyen1, Emma Nichols1, Dina Nur Anggraini Ningrum194, Dina Nur Anggraini Ningrum195, Sandra Nolte63, Vuong Minh Nong19, Bo Norrving152, Jean Jacques Noubiap, Martin O'Donnell, Felix Akpojene Ogbo28, In-Hwan Oh196, Anselm Okoro, Olanrewaju Oladimeji197, Olanrewaju Oladimeji198, Tinuke O Olagunju199, Andrew T Olagunju200, Andrew T Olagunju75, Helen E Olsen1, Bolajoko O. Olusanya, Jacob Olusegun Olusanya, Kanyin Ong1, John Nelson Opio1, Eyal Oren1, Eyal Oren201, Alberto Ortiz202, Aaron Osgood-Zimmerman102, Majdi Osman165, Mayowa O. Owolabi22, Mahesh Pa203, Rosana E. Pacella204, Adrian Pana174, Basant Kumar Panda, Christina Papachristou205, Christina Papachristou206, Eun-Kee Park207, Charles D. H. Parry13, Mahboubeh Parsaeian, Scott B. Patten169, George C Patton, Katherine R. Paulson1, Neil Pearce, David M. Pereira83, David M. Pereira208, Norberto Perico56, Konrad Pesudovs, Carrie Beth Peterson1, Max Petzold209, Michael R. Phillips210, David M. Pigott1, Julian David Pillay211, Christine Pinho, Dietrich Plass212, Martin A Pletcher173, Svetlana Popova23, Richie Poulton213, Farshad Pourmalek214, Dorairaj Prabhakaran19, Noela M Prasad175, Narayan Prasad14, Carrie Purcell1, Mostafa Qorbani, Reginald Quansah, Beatriz Paulina Ayala Quintanilla59, Rynaz H S Rabiee46, Amir Radfar215, Anwar Rafay, Kazem Rahimi29, Afarin Rahimi-Movaghar, Vafa Rahimi-Movaghar14, Mohammad Hifz Ur Rahman, Mahfuzar Rahman216, Rajesh Kumar Rai217, Sasa Rajsic, Usha Ram, Chhabi Lal Ranabhat179, Zane Rankin218, Puja C Rao1, P. V. Rao1, Salman Rawaf219, Sarah E Ray, Robert C. Reiner1, Nikolas Reinig1, Marissa B Reitsma1, Giuseppe Remuzzi56, Andre M. N. Renzaho, Serge Resnikoff220, Satar Rezaei166, Antonio Luiz Pinho Ribeiro221, Luca Ronfani, Gholamreza Roshandel222, Gholamreza Roshandel14, Gregory A. Roth, Ambuj Roy, Enrico Rubagotti223, George Mugambage Ruhago224, Soheil Saadat183, Nafis Sadat1, Mahdi Safdarian14, Sare Safi225, Saeid Safiri226, Rajesh Sagar, Ramesh Sahathevan2, Joseph Salama1, Huda Omer Ba Saleem227, Joshua A. Salomon228, Sundeep Salvi, Abdallah M. Samy229, Juan Sanabria230, Juan Sanabria231, Damian Santomauro, Itamar S. Santos, João Vasco Santos, Milena M Santric Milicevic, Benn Sartorius232, Maheswar Satpathy233, Monika Sawhney234, Sonia Saxena, Maria Inês Schmidt99, Ione Jayce Ceola Schneider235, Ben Schöttker236, David C. Schwebel, Falk Schwendicke237, Soraya Seedat, Sadaf G. Sepanlou14, Sadaf G. Sepanlou176, Edson Serván-Mori, Tesfaye Setegn14, Katya Anne Shackelford1, Amira Shaheen238, Masood Ali Shaikh, Mansour Shamsipour, Sheikh Mohammed Shariful Islam239, Sheikh Mohammed Shariful Islam28, Jayendra Sharma, Rajesh Sharma240, Jun She241, Peilin Shi242, Chloe Shields243, Girma Temam Shifa244, Mika Shigematsu59, Yukito Shinohara, Rahman Shiri245, Reza Shirkoohi, Shreya Shirude, Kawkab Shishani246, Mark G. Shrime247, Abla M. Sibai, Inga Dora Sigfusdottir248, Inga Dora Sigfusdottir132, Diego Augusto Santos Silva113, João Pedro Silva, Dayane Gabriele Alves Silveira249, Jasvinder A. Singh, Narinder Pal Singh250, Dhirendra N Sinha, Eirini Skiadaresi, Vegard Skirbekk251, Erica Leigh Slepak1, Amber Sligar, David L. Smith1, Mari Smith1, Badr Hasan Sobaih252, Eugene Sobngwi253, Reed J D Sorensen1, Tatiane Cristina Moraes Sousa, Luciano A. Sposato254, Chandrashekhar T Sreeramareddy255, Vinay Srinivasan1, Jeffrey D. Stanaway1, Vasiliki Stathopoulou221, Nicholas Steel191, Murray B. Stein, Dan J. Stein256, Dan J. Stein257, Timothy J. Steiner258, Timothy J. Steiner259, Caitlyn Steiner1, S. Steinke192, S. Steinke29, Mark A. Stokes239, Lars Jacob Stovner258, Bryan Strub1, Michelle L Subart1, Mu'awiyyah Babale Sufiyan260, Bruno F. Sunguya261, Bruno F. Sunguya262, Patrick J Sur1, Patrick J Sur263, Soumya Swaminathan264, Bryan L. Sykes265, Dillon O Sylte1, Rafael Tabarés-Seisdedos72, Getachew Redae Taffere1, Jukka Takala, Nikhil Tandon15, Mohammad Tavakkoli266, Nuno Taveira267, Hugh R. Taylor, Arash Tehrani-Banihashemi49, Tesfalidet Tekelab, Abdullah Sulieman Terkawi268, Dawit Jember Tesfaye14, Belay Tesssema1, Ornwipa Thamsuwan, Katie E Thomas1, Amanda G. Thrift12, Tenaw Yimer Tiruye28, Ruoyan Tobe-Gai196, Mette Christophersen Tollånes198, Mette Christophersen Tollånes197, Marcello Tonelli269, Roman Topor-Madry270, Roman Topor-Madry271, Miguel Tortajada272, Mathilde Touvier, Bach Xuan Tran273, Suryakant Tripathi1, Christopher Troeger1, Thomas Truelsen274, Derrick Tsoi1, Kald Beshir Tuem275, Emin Murat Tuzcu276, Stefanos Tyrovolas277, Kingsley N. Ukwaja, Eduardo A. Undurraga278, Eduardo A. Undurraga279, Chigozie Jesse Uneke204, Rachel L Updike1, Olalekan A. Uthman280, Benjamin S Chudi Uzochukwu281, Job F M van Boven282, Santosh Varughese74, Tommi Vasankari, S Venkatesh283, Narayanaswamy Venketasubramanian284, Ramesh Vidavalur, Francesco Saverio Violante285, Sergey K Vladimirov286, Vasiliy Victorovich Vlassov287, Stein Emil Vollset1, Fiseha Wadilo, Tolassa Wakayo83, Tolassa Wakayo208, Yuan-Pang Wang, Marcia R. Weaver, Scott Weichenthal288, Elisabete Weiderpass, Robert G. Weintraub289, Andrea Werdecker, Ronny Westerman161, Harvey Whiteford1, Harvey Whiteford102, Tissa Wijeratne290, Charles Shey Wiysonge256, Charles D.A. Wolfe291, Charles D.A. Wolfe292, Rachel Woodbrook158, Anthony D. Woolf293, Abdulhalik Workicho294, Denis Xavier130, Gelin Xu295, Simon Yadgir1, Mohsen Yaghoubi16, Bereket Yakob296, Lijing L Yan297, Yuichiro Yano298, Pengpeng Ye, Hassen Hamid Yimam299, Paul S. F. Yip300, Naohiro Yonemoto, Seok Jun Yoon301, Marcel Yotebieng302, Mustafa Z. Younis303, Mustafa Z. Younis304, Zoubida Zaidi, Maysaa El Sayed Zaki, Elias Asfaw Zegeye216, Zerihun Menlkalew Zenebe, Xueying Zhang1, Maigeng Zhou305, Ben Zipkin217, Sanjay Zodpey, Liesl Zühlke, Christopher J L Murray 
University of Washington1, Jimma University2, Sapienza University of Rome3, Virginia Tech4, Cairo University5, Manonmaniam Sundaranar University6, New York University Abu Dhabi7, Hawassa University8, University of Hohenheim9, Mekelle University10, Cornell University11, Monash University12, Stellenbosch University13, Tehran University of Medical Sciences14, All India Institute of Medical Sciences15, Sanjay Gandhi Post Graduate Institute of Medical Sciences16, Council of Scientific and Industrial Research17, Baylor College of Medicine18, Public Health Foundation of India19, Shahid Beheshti University20, Newcastle University21, University of Ibadan22, University of Toronto23, University of Baghdad24, Mayo Clinic25, Washington University in St. Louis26, University of Melbourne27, University of Sydney28, University of Oxford29, Mazandaran University of Medical Sciences30, University of Lorraine31, United Arab Emirates University32, Free University of Berlin33, King Saud University34, University of South Australia35, Bahir Dar University36, American University of Beirut37, Kwame Nkrumah University of Science and Technology38, Cleveland Clinic39, Dalarna University40, University of Manitoba41, Qom University of Medical Sciences42, University of Bordeaux43, Swedish Research Council44, Public Health Agency of Canada45, Carol Davila University of Medicine and Pharmacy46, University of Thessaly47, Arba Minch University48, Iran University of Medical Sciences49, Pontifical Xavierian University50, Baqiyatallah University of Medical Sciences51, University of California, Los Angeles52, Charles R. Drew University of Medicine and Science53, Gandhi Medical College54, Jazan University55, Mario Negri Institute for Pharmacological Research56, University of Burgundy57, Yale University58, National Institutes of Health59, University of Birmingham60, Aga Khan University61, Alexandria University62, Hacettepe University63, University of Nottingham64, RTI International65, Anglia Ruskin University66, Comenius University in Bratislava67, University of Leicester68, University of South Florida69, Emory University70, Universidad Autónoma Metropolitana71, Carlos III Health Institute72, National Health Research Institutes73, Christian Medical College & Hospital74, University of Adelaide75, University of Salerno76, Ohio State University77, University of Southampton78, University of Milan79, University of California, San Diego80, Durham University81, Flinders University82, University of Porto83, Griffith University84, Université de Montréal85, University of New South Wales86, United States Department of Veterans Affairs87, Anschutz Medical Campus88, Brighton and Sussex Medical School89, Icahn School of Medicine at Mount Sinai90, University of Peradeniya91, Australian Catholic University92, Alfaisal University93, Geisinger Medical Center94, University of Rochester95, Philippine Institute for Development Studies96, University of the West Indies97, World Food Programme98, Universidade Federal do Rio Grande do Sul99, University of Cambridge100, St. Paul's Hospital101, University of Queensland102, Johns Hopkins University103, Statistics Korea104, Universidade Federal de Sergipe105, Auckland University of Technology106, McGill University107, Karolinska Institutet108, Catholic University of Portugal109, Kaiser Permanente110, Bielefeld University111, University of Basel112, Addis Ababa University113, Coventry Health Care114, Medical University of Varna115, University of Cape Coast116, University of Massachusetts Boston117, University of Oklahoma118, University of São Paulo119, Saint James School of Medicine120, Yeshiva University121, Umeå University122, Arabian Gulf University123, Hamdan bin Mohammed e-University124, University of New Mexico125, Sir Charles Gairdner Hospital126, University of Western Australia127, Sun Yat-sen University128, University of Barcelona129, St. John's University130, University of Louisville131, Columbia University132, American Board of Legal Medicine133, Central South University134, George Mason University135, University of Kragujevac136, Kobe University137, Sree Chitra Thirunal Institute for Medical Sciences and Technology138, The George Institute for Global Health139, Sidi Mohamed Ben Abdellah University140, Capital Medical University141, University of Tartu142, University College Cork143, London School of Economics and Political Science144, Swiss Tropical and Public Health Institute145, College of Health Sciences, Bahrain146, University of Tsukuba147, University of Glasgow148, Jordan University of Science and Technology149, Health Services Academy150, Seoul National University151, Lund University152, Northeastern University153, Simmons College154, University of Canberra155, Tulane University156, University of Oslo157, Erasmus University Rotterdam158, University of British Columbia159, Sher-I-Kashmir Institute of Medical Sciences160, Russian Academy161, Tabriz University of Medical Sciences162, Nova Southeastern University163, Hong Kong Polytechnic University164, University of Florence165, Kermanshah University of Medical Sciences166, University of Texas at Austin167, University of Gilan168, University of Bristol169, University of Jena170, University of Bern171, University of the Philippines172, Damietta University173, Bucharest University of Economic Studies174, University of Trnava175, Shiraz University of Medical Sciences176, University of Zambia177, University of Colombo178, Medical University of Graz179, Chalmers University of Technology180, Opole University181, Aarhus University182, University College West183, United Nations184, Curtin University185, Pacific Institute186, Kyrgyz State Medical Academy187, Ishik University188, Islamic Azad University189, Universiti Sains Malaysia190, Madras Medical College191, Kenya Medical Research Institute192, University of Embu193, State University of Semarang194, Taipei Medical University195, Kyung Hee University196, University of Namibia197, Human Sciences Research Council198, McMaster University199, University of Lagos200, San Diego State University201, Foundation University, Islamabad202, Jagadguru Sri Shivarathreeswara University203, University of Chichester204, University of Malaya205, The Chinese University of Hong Kong206, Kosin University207, University of Cartagena208, University of Liverpool209, Shanghai Jiao Tong University210, Durban University of Technology211, Environment Agency212, Grant Medical College and Sir Jamshedjee Jeejeebhoy Group of Hospitals213, Tribhuvan University214, A.T. Still University215, BRAC University216, University of Göttingen217, University of the East218, Public Health England219, Brien Holden Vision Institute220, Universidade Federal de Minas Gerais221, Golestan University222, Southern University of Science and Technology223, Thomas Jefferson University224, Shahid Beheshti University of Medical Sciences and Health Services225, University of Maragheh226, University of Helsinki227, Stanford University228, Ain Shams University229, Case Western Reserve University230, Marshall University231, University of KwaZulu-Natal232, Utkal University233, University of North Carolina at Charlotte234, Universidade Federal de Santa Catarina235, German Cancer Research Center236, Charité237, An-Najah National University238, Deakin University239, University of Delhi240, Fudan University241, Tufts University242, Lancaster University243, Queensland University of Technology244, Finnish Institute of Occupational Health245, Washington State University246, Harvard University247, Reykjavík University248, University of Brasília249, Max Healthcare250, Nishtar Medical College251, King Khalid University252, University of Yaoundé I253, University of Western Ontario254, International Medical University255, South African Medical Research Council256, University of Cape Town257, Norwegian University of Science and Technology258, Imperial College London259, Ahmadu Bello University260, Muhimbili University of Health and Allied Sciences261, University of Dar es Salaam262, University of California, Riverside263, Trường ĐH Nguyễn Tất Thành264, University of California, Irvine265, New York Medical College266, University of Lisbon267, University of Virginia268, University of Calgary269, Jagiellonian University270, World Health Organization271, Autonomous University of Chile272, Hanoi Medical University273, University of Copenhagen274, International University, Cambodia275, Moscow Institute of Physics and Technology276, Norwegian Institute of Public Health277, Brandeis University278, Pontifical Catholic University of Chile279, University of Warwick280, Heidelberg University281, Australian National University282, Maimonides Medical Center283, National University of Singapore284, University of Bologna285, I.M. Sechenov First Moscow State Medical University286, National Research University – Higher School of Economics287, Duy Tan University288, Royal Children's Hospital289, La Trobe University290, King's College London291, Guy's and St Thomas' NHS Foundation Trust292, Royal Cornwall Hospital293, University of Groningen294, Nanjing University295, Charotar University of Science and Technology296, Duke University297, Northwestern University298, Drexel University299, University of Hong Kong300, Korea University301, University of Kinshasa302, Tsinghua University303, Jackson State University304, Chinese Center for Disease Control and Prevention305
16 Sep 2017-The Lancet
Abstract: Summary Background As mortality rates decline, life expectancy increases, and populations age, non-fatal outcomes of diseases and injuries are becoming a larger component of the global burden of disease. The Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) provides a comprehensive assessment of prevalence, incidence, and years lived with disability (YLDs) for 328 causes in 195 countries and territories from 1990 to 2016. Methods We estimated prevalence and incidence for 328 diseases and injuries and 2982 sequelae, their non-fatal consequences. We used DisMod-MR 2.1, a Bayesian meta-regression tool, as the main method of estimation, ensuring consistency between incidence, prevalence, remission, and cause of death rates for each condition. For some causes, we used alternative modelling strategies if incidence or prevalence needed to be derived from other data. YLDs were estimated as the product of prevalence and a disability weight for all mutually exclusive sequelae, corrected for comorbidity and aggregated to cause level. We updated the Socio-demographic Index (SDI), a summary indicator of income per capita, years of schooling, and total fertility rate. GBD 2016 complies with the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER). Findings Globally, low back pain, migraine, age-related and other hearing loss, iron-deficiency anaemia, and major depressive disorder were the five leading causes of YLDs in 2016, contributing 57·6 million (95% uncertainty interval [UI] 40·8–75·9 million [7·2%, 6·0–8·3]), 45·1 million (29·0–62·8 million [5·6%, 4·0–7·2]), 36·3 million (25·3–50·9 million [4·5%, 3·8–5·3]), 34·7 million (23·0–49·6 million [4·3%, 3·5–5·2]), and 34·1 million (23·5–46·0 million [4·2%, 3·2–5·3]) of total YLDs, respectively. Age-standardised rates of YLDs for all causes combined decreased between 1990 and 2016 by 2·7% (95% UI 2·3–3·1). Despite mostly stagnant age-standardised rates, the absolute number of YLDs from non-communicable diseases has been growing rapidly across all SDI quintiles, partly because of population growth, but also the ageing of populations. The largest absolute increases in total numbers of YLDs globally were between the ages of 40 and 69 years. Age-standardised YLD rates for all conditions combined were 10·4% (95% UI 9·0–11·8) higher in women than in men. Iron-deficiency anaemia, migraine, Alzheimer's disease and other dementias, major depressive disorder, anxiety, and all musculoskeletal disorders apart from gout were the main conditions contributing to higher YLD rates in women. Men had higher age-standardised rates of substance use disorders, diabetes, cardiovascular diseases, cancers, and all injuries apart from sexual violence. Globally, we noted much less geographical variation in disability than has been documented for premature mortality. In 2016, there was a less than two times difference in age-standardised YLD rates for all causes between the location with the lowest rate (China, 9201 YLDs per 100 000, 95% UI 6862–11943) and highest rate (Yemen, 14 774 YLDs per 100 000, 11 018–19 228). Interpretation The decrease in death rates since 1990 for most causes has not been matched by a similar decline in age-standardised YLD rates. For many large causes, YLD rates have either been stagnant or have increased for some causes, such as diabetes. As populations are ageing, and the prevalence of disabling disease generally increases steeply with age, health systems will face increasing demand for services that are generally costlier than the interventions that have led to declines in mortality in childhood or for the major causes of mortality in adults. Up-to-date information about the trends of disease and how this varies between countries is essential to plan for an adequate health-system response. Funding Bill & Melinda Gates Foundation, and the National Institute on Aging and the National Institute of Mental Health of the National Institutes of Health.

8,768 citations


Journal ArticleDOI
26 Jan 2017-Scientific Reports
TL;DR: It is found that intraflagellar transport 20 mediates the ability of Ror2 signaling to induce the invasiveness of tumors that lack primary cilia, and IFT20 regulates the nucleation of Golgi-derived microtubules by affecting the GM130-AKAP450 complex.
Abstract: Signaling through the Ror2 receptor tyrosine kinase promotes invadopodia formation for tumor invasion. Here, we identify intraflagellar transport 20 (IFT20) as a new target of this signaling in tumors that lack primary cilia, and find that IFT20 mediates the ability of Ror2 signaling to induce the invasiveness of these tumors. We also find that IFT20 regulates the nucleation of Golgi-derived microtubules by affecting the GM130-AKAP450 complex, which promotes Golgi ribbon formation in achieving polarized secretion for cell migration and invasion. Furthermore, IFT20 promotes the efficiency of transport through the Golgi complex. These findings shed new insights into how Ror2 signaling promotes tumor invasiveness, and also advance the understanding of how Golgi structure and transport can be regulated.

8,752 citations


Journal ArticleDOI
TL;DR: This document provides updated normal values for all four cardiac chambers, including three-dimensional echocardiography and myocardial deformation, when possible, on the basis of considerably larger numbers of normal subjects, compiled from multiple databases.
Abstract: The rapid technological developments of the past decade and the changes in echocardiographic practice brought about by these developments have resulted in the need for updated recommendations to the previously published guidelines for cardiac chamber quantification, which was the goal of the joint writing group assembled by the American Society of Echocardiography and the European Association of Cardiovascular Imaging. This document provides updated normal values for all four cardiac chambers, including three-dimensional echocardiography and myocardial deformation, when possible, on the basis of considerably larger numbers of normal subjects, compiled from multiple databases. In addition, this document attempts to eliminate several minor discrepancies that existed between previously published guidelines.

8,690 citations


Proceedings ArticleDOI
01 Oct 2017-
Abstract: Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. However, for many tasks, paired training data will not be available. We present an approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired examples. Our goal is to learn a mapping G : X → Y such that the distribution of images from G(X) is indistinguishable from the distribution Y using an adversarial loss. Because this mapping is highly under-constrained, we couple it with an inverse mapping F : Y → X and introduce a cycle consistency loss to push F(G(X)) ≈ X (and vice versa). Qualitative results are presented on several tasks where paired training data does not exist, including collection style transfer, object transfiguration, season transfer, photo enhancement, etc. Quantitative comparisons against several prior methods demonstrate the superiority of our approach.

8,605 citations


Journal ArticleDOI
TL;DR: Slow momentum for some cancers amenable to early detection is juxtaposed with notable gains for other common cancers, and it is notable that long‐term rapid increases in liver cancer mortality have attenuated in women and stabilized in men.
Abstract: Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States and compiles the most recent data on population-based cancer occurrence. Incidence data (through 2016) were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data (through 2017) were collected by the National Center for Health Statistics. In 2020, 1,806,590 new cancer cases and 606,520 cancer deaths are projected to occur in the United States. The cancer death rate rose until 1991, then fell continuously through 2017, resulting in an overall decline of 29% that translates into an estimated 2.9 million fewer cancer deaths than would have occurred if peak rates had persisted. This progress is driven by long-term declines in death rates for the 4 leading cancers (lung, colorectal, breast, prostate); however, over the past decade (2008-2017), reductions slowed for female breast and colorectal cancers, and halted for prostate cancer. In contrast, declines accelerated for lung cancer, from 3% annually during 2008 through 2013 to 5% during 2013 through 2017 in men and from 2% to almost 4% in women, spurring the largest ever single-year drop in overall cancer mortality of 2.2% from 2016 to 2017. Yet lung cancer still caused more deaths in 2017 than breast, prostate, colorectal, and brain cancers combined. Recent mortality declines were also dramatic for melanoma of the skin in the wake of US Food and Drug Administration approval of new therapies for metastatic disease, escalating to 7% annually during 2013 through 2017 from 1% during 2006 through 2010 in men and women aged 50 to 64 years and from 2% to 3% in those aged 20 to 49 years; annual declines of 5% to 6% in individuals aged 65 years and older are particularly striking because rates in this age group were increasing prior to 2013. It is also notable that long-term rapid increases in liver cancer mortality have attenuated in women and stabilized in men. In summary, slowing momentum for some cancers amenable to early detection is juxtaposed with notable gains for other common cancers.

8,578 citations


Posted Content
TL;DR: This work shows that it can significantly improve the acoustic model of a heavily used commercial system by distilling the knowledge in an ensemble of models into a single model and introduces a new type of ensemble composed of one or more full models and many specialist models which learn to distinguish fine-grained classes that the full models confuse.
Abstract: A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may be too computationally expensive to allow deployment to a large number of users, especially if the individual models are large neural nets. Caruana and his collaborators have shown that it is possible to compress the knowledge in an ensemble into a single model which is much easier to deploy and we develop this approach further using a different compression technique. We achieve some surprising results on MNIST and we show that we can significantly improve the acoustic model of a heavily used commercial system by distilling the knowledge in an ensemble of models into a single model. We also introduce a new type of ensemble composed of one or more full models and many specialist models which learn to distinguish fine-grained classes that the full models confuse. Unlike a mixture of experts, these specialist models can be trained rapidly and in parallel.

8,473 citations


Journal ArticleDOI
TL;DR: Quantitative assessments show that SegNet provides good performance with competitive inference time and most efficient inference memory-wise as compared to other architectures, including FCN and DeconvNet.
Abstract: We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet. This core trainable segmentation engine consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The architecture of the encoder network is topologically identical to the 13 convolutional layers in the VGG16 network [1] . The role of the decoder network is to map the low resolution encoder feature maps to full input resolution feature maps for pixel-wise classification. The novelty of SegNet lies is in the manner in which the decoder upsamples its lower resolution input feature map(s). Specifically, the decoder uses pooling indices computed in the max-pooling step of the corresponding encoder to perform non-linear upsampling. This eliminates the need for learning to upsample. The upsampled maps are sparse and are then convolved with trainable filters to produce dense feature maps. We compare our proposed architecture with the widely adopted FCN [2] and also with the well known DeepLab-LargeFOV [3] , DeconvNet [4] architectures. This comparison reveals the memory versus accuracy trade-off involved in achieving good segmentation performance. SegNet was primarily motivated by scene understanding applications. Hence, it is designed to be efficient both in terms of memory and computational time during inference. It is also significantly smaller in the number of trainable parameters than other competing architectures and can be trained end-to-end using stochastic gradient descent. We also performed a controlled benchmark of SegNet and other architectures on both road scenes and SUN RGB-D indoor scene segmentation tasks. These quantitative assessments show that SegNet provides good performance with competitive inference time and most efficient inference memory-wise as compared to other architectures. We also provide a Caffe implementation of SegNet and a web demo at http://mi.eng.cam.ac.uk/projects/segnet/ .

8,450 citations


Book ChapterDOI
Leslie Lamport1
Abstract: The concept of one event happening before another in a distributed system is examined, and is shown to define a partial ordering of the events. A distributed algorithm is given for synchronizing a system of logical clocks which can be used to totally order the events. The use of the total ordering is illustrated with a method for solving synchronization problems. The algorithm is then specialized for synchronizing physical clocks, and a bound is derived on how far out of synchrony the clocks can become.

8,352 citations


Proceedings ArticleDOI
07 Jun 2015-
TL;DR: A system that directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure offace similarity, and achieves state-of-the-art face recognition performance using only 128-bytes perface.
Abstract: Despite significant recent advances in the field of face recognition [10, 14, 15, 17], implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. In this paper we present a system, called FaceNet, that directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure of face similarity. Once this space has been produced, tasks such as face recognition, verification and clustering can be easily implemented using standard techniques with FaceNet embeddings as feature vectors.

8,289 citations


Posted Content
09 Sep 2016-arXiv: Learning
TL;DR: A scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs which outperforms related methods by a significant margin.
Abstract: We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin.

8,285 citations


Journal ArticleDOI
TL;DR: The 2016 World Health Organization Classification of Tumors of the Central Nervous System is both a conceptual and practical advance over its 2007 predecessor and is hoped that it will facilitate clinical, experimental and epidemiological studies that will lead to improvements in the lives of patients with brain tumors.
Abstract: The 2016 World Health Organization Classification of Tumors of the Central Nervous System is both a conceptual and practical advance over its 2007 predecessor. For the first time, the WHO classification of CNS tumors uses molecular parameters in addition to histology to define many tumor entities, thus formulating a concept for how CNS tumor diagnoses should be structured in the molecular era. As such, the 2016 CNS WHO presents major restructuring of the diffuse gliomas, medulloblastomas and other embryonal tumors, and incorporates new entities that are defined by both histology and molecular features, including glioblastoma, IDH-wildtype and glioblastoma, IDH-mutant; diffuse midline glioma, H3 K27M-mutant; RELA fusion-positive ependymoma; medulloblastoma, WNT-activated and medulloblastoma, SHH-activated; and embryonal tumour with multilayered rosettes, C19MC-altered. The 2016 edition has added newly recognized neoplasms, and has deleted some entities, variants and patterns that no longer have diagnostic and/or biological relevance. Other notable changes include the addition of brain invasion as a criterion for atypical meningioma and the introduction of a soft tissue-type grading system for the now combined entity of solitary fibrous tumor / hemangiopericytoma-a departure from the manner by which other CNS tumors are graded. Overall, it is hoped that the 2016 CNS WHO will facilitate clinical, experimental and epidemiological studies that will lead to improvements in the lives of patients with brain tumors.

8,183 citations


Journal ArticleDOI
01 Sep 2015-SoftwareX
TL;DR: GROMACS is one of the most widely used open-source and free software codes in chemistry, used primarily for dynamical simulations of biomolecules, and provides a rich set of calculation types.
Abstract: GROMACS is one of the most widely used open-source and free software codes in chemistry, used primarily for dynamical simulations of biomolecules. It provides a rich set of calculation types, prepa ...

8,050 citations


Journal ArticleDOI
B. P. Abbott1, Richard J. Abbott1, T. D. Abbott2, Matthew Abernathy1, Fausto Acernese3, Kendall Ackley4, C. Adams, Todd Adams5, Paolo Addesso6, Rana X. Adhikari1, V. B. Adya7, C. Affeldt7, M. Agathos, Kazuhiro Agatsuma, Nancy Aggarwal8, Odylio D. Aguiar9, Lloyd Paul Aiello3, A. Ain10, P. Ajith11, Benjamin William Allen12, A. Allocca3, P. A. Altin13, S. B. Anderson1, W. G. Anderson14, Koji Arai1, M. A. Arain4, M. C. Araya1, C. C. Arceneaux15, J. S. Areeda16, N. Arnaud17, K. G. Arun18, S. Ascenzi19, Gregory Ashton20, M. Ast21, S. M. Aston, P. Astone3, P. Aufmuth7, C. Aulbert7, Stanislav Babak7, P. Bacon22, M. K. M. Bader, P. T. Baker23, F. Baldaccini3, G. Ballardin, S. W. Ballmer24, J. C. Barayoga1, S. E. Barclay25, Barry C. Barish1, D. Barker, Fabrizio Barone3, B. Barr25, Lisa Barsotti8, M. Barsuglia22, D. Barta, James G. Bartlett, M. A. Barton, Imre Bartos26, Riccardo Bassiri27, A. Basti3, J. C. Batch, C. Baune7, Viswanath Bavigadda, Marco Bazzan3, B. Behnke7, M. Bejger, C. Belczynski28, A. S. Bell25, Chris J Bell25, B. K. Berger1, J. Bergman, G. Bergmann7, Christopher P. L. Berry29, D. Bersanetti3, Alessandro Bertolini, J. Betzwieser, Swetha Bhagwat24, R. Bhandare, I. A. Bilenko30, G. Billingsley1, J. Birch, R. Birney31, Ofek Birnholtz7, Sebastien Biscans8, A. Bisht, M. Bitossi, Christopher M. Biwer24, Marie-Anne Bizouard17, J. K. Blackburn1, Carl Blair32, David Blair32, R. M. Blair, Steven Bloemen33, O. Bock7, T. P. Bodiya8, M. Boer5, G. Bogaert5, C. Bogan7, Alejandro Bohé7, P. Bojtos34, Charlotte Bond29, François Bondu, R. Bonnand5, B. A. Boom, R. Bork1, V. Boschi3, Suvadeep Bose35, Y. Bouffanais22, A. Bozzi, C. Bradaschia3, Patrick Brady14, Vladimir B. Braginsky30, Marica Branchesi3, J. E. Brau36, Tristan Briant, A. Brillet5, M. Brinkmann7, V. Brisson17, P. Brockill14, A. F. Brooks1, Duncan A. Brown24, D. D. Brown29, N. M. Brown8, C. C. Buchanan2, Aaron Buikema8, Tomasz Bulik28, H. J. Bulten37, Alessandra Buonanno38, D. Buskulic5, C. Buy22, Robert L. Byer27, Miriam Cabero7, Laura Cadonati39, Gianpietro Cagnoli40, C. Cahillane1, J. Calderón Bustillo41, T. A. Callister1, Enrico Calloni42, Jordan Camp43, Kipp Cannon, Junwei Cao44, Collin D. Capano7, E. Capocasa22, F. Carbognani, S. Caride45, J. Casanueva Diaz17, C. Casentini19, Sarah Caudill14, Marco Cavaglia15, F. Cavalier17, R. Cavalieri, Giancarlo Cella3, Carlos Cepeda1, L. Cerboni Baiardi3, G. Cerretani3, E. Cesarini19, R. Chakraborty1, T. Chalermsongsak1, S. J. Chamberlin46, M. Chan25, Shiuh Chao47, P. Charlton48, E. Chassande-Mottin22, Hsiao-Wen Chen49, Yi Chen1, Chia-Liang Cheng47, Andrea Chincarini3, A. Chiummo, H. S. Cho50, M. Cho38, Jong H. Chow13, Nelson Christensen51, Q. Chu32, S. S. Y. Chua, S. Chung32, Giacomo Ciani4, F. Clara, John A. Clark39, F. Cleva5, E. Coccia19, Pierre-François Cohadon, A. Colla52, Christophe Collette53, L. R. Cominsky54, M. Constancio9, A. Conte52, L. Conti3, Douglas R. Cook, Thomas Corbitt2, Neil J. Cornish23, Alessandra Corsi45, Samuele Cortese, C. A. Costa9, Michael W. Coughlin51, S. B. Coughlin55, J.-P. Coulon5, S. T. Countryman26, P. Couvares1, E. E. Cowan39, David Coward32, M. J. Cowart, D. C. Coyne1, R. Coyne45, Kieran Craig25, Jolien D. E. Creighton14, Teviet Creighton56, Jonathan Cripe2, S. G. Crowder57, A. M. Cruise29, A. Cumming25, Liam Cunningham25, E. Cuoco, T. Dal Canton7, S. L. Danilishin25, S. D'Antonio3, Karsten Danzmann, N. S. Darman58, C. F. Da Silva Costa4, V. Dattilo, I. Dave, H. P. Daveloza56, M. Davier17, Gavin Davies25, E. J. Daw59, R. Day, Soumi De24, D.B. DeBra27, G. Debreczeni, Jerome Degallaix40, M. De Laurentis42, Samuel Deléglise, W. Del Pozzo29, T. Denker, Thomas Dent7, H. Dereli5, Vladimir Dergachev1, R. T. DeRosa, R. De Rosa42, R. DeSalvo3, Sanjeev Dhurandhar10, Marco Aurelio Diaz56, L. Di Fiore3, M. Di Giovanni52, A. Di Lieto3, S. Di Pace52, I. Di Palma7, A. Di Virgilio3, G. Dojcinoski60, V. Dolique40, F. Donovan8, K. L. Dooley15, S. Doravari7, R. Douglas25, T. P. Downes14, M. Drago3, R. W. P. Drever1, J. C. Driggers, Zhihui Du44, M. Ducrot5, S. E. Dwyer, T. B. Edo59, M. C. Edwards51, A. Effler, Heinz-Bernd Eggenstein7, P. Ehrens1, J. Eichholz4, S. S. Eikenberry4, W. Engels1, Reed Essick8, T. Etzel1, Matthew Evans8, Timothy Evans, R. Everett46, M. Factourovich26, V. Fafone19, H. Fair24, Stephen Fairhurst61, Xiaohui Fan44, Qi Fang32, S. Farinon3, Ben Farr49, Will M. Farr29, Marc Favata60, M. Fays61, H. Fehrmann7, Martin M. Fejer27, D. Feldbaum4, I. Ferrante3, E. C. Ferreira9, Federico Ferrini, F. Fidecaro3, Lee Samuel Finn46, I. Fiori, D. Fiorucci22, Rebecca Fisher24, R. Flaminio, M. Fletcher25, H. Fong, J.-D. Fournier5, S. Franco17, S. Frasca52, F. Frasconi3, Maik Frede7, Z. Frei34, Andreas Freise29, R. Frey36, V. Frey17, T. T. Fricke7, Peter Fritschel8, V. V. Frolov, Paul Fulda4, M. Fyffe, H. A. G. Gabbard15, Jonathan R. Gair62, Luca Gammaitoni3, S. G. Gaonkar10, F. Garufi42, Alberto Gatto22, G. Gaur, Neil Gehrels43, Gianluca Gemme3, Bruce Gendre5, E. Genin, A. Gennai3, Jimin George, László Á. Gergely63, V. Germain5, Abhirup Ghosh11, Archisman Ghosh11, Sourav Ghosh33, Joseph A. Giaime, K. D. Giardina, A. Giazotto3, Karl Gill64, A. Glaefke25, Joseph Gleason4, Evan Goetz65, R. Goetz4, László Gondán34, Gabriela Gonzalez2, J. M. Gonzalez Castro3, Achamveedu Gopakumar11, N. A. Gordon25, Michael L. Gorodetsky30, S. E. Gossan1, M. Gosselin, R. Gouaty5, C. Graef25, P. B. Graff38, M. Granata40, A. Grant25, Slawomir Gras8, C. Gray, G. Greco3, Andrew W. Green29, R. J. S. Greenhalgh66, Paul J. Groot33, Hartmut Grote7, S. Grunewald7, G. M. Guidi3, X. Guo44, A. Gupta10, M. K. Gupta, K. E. Gushwa1, E. K. Gustafson1, R. Gustafson65, J. J. Hacker16, B. R. Hall35, E. D. Hall1, G. D. Hammond25, M. Haney11, M. M. Hanke7, J. Hanks, Chad Hanna46, Mark Hannam61, J. Hanson, T. Hardwick2, Jan Harms3, Gregory M. Harry67, I. W. Harry7, M. J. Hart25, M. T. Hartman4, Carl-Johan Haster29, K. Haughian25, James Healy68, J. Heefner1, Antoine Heidmann, M. C. Heintze, Gerhard Heinzel7, H. Heitmann5, Patrice Hello17, G. Hemming, Martin Hendry25, Ik Siong Heng25, J. Hennig25, A. W. Heptonstall1, M. Heurs, Stefan Hild25, D. Hoak69, K. A. Hodge1, David Jonathan Hofman40, S. E. Hollitt70, K. Holt, Daniel E. Holz49, Philip F. Hopkins61, D. J. Hosken70, J. H. Hough25, E. A. Houston25, Eric Howell32, Yi-Ming Hu25, S. Huang47, E. A. Huerta71, D. Huet17, B. Hughey64, Sascha Husa41, S. H. Huttner25, T. Huynh-Dinh, A. Idrisy46, N. Indik7, D. R. Ingram, R. Inta45, H. N. Isa25, J.-M. Isac, Maximiliano Isi1, G. Islas16, T. Isogai8, Bala R. Iyer11, K. Izumi, M. Jacobson1, Thibaut Jacqmin, H. J. Jang50, Karan Jani39, Piotr Jaranowski72, S. Jawahar73, F. Jiménez-Forteza41, W. W. Johnson2, Nathan K. Johnson-McDaniel11, David Jones20, Roger Jones25, R. J. G. Jonker, Li Ju32, K. Haris74, C. V. Kalaghatgi61, V. Kalogera55, S. Kandhasamy15, G. Kang50, J. B. Kanner1, S. Karki36, M. Kasprzack, Erik Katsavounidis8, W. Katzman, S. Kaufer, Tejinder Kaur32, K. Kawabe, F. Kawazoe, Fabien Kéfélian5, M. S. Kehl, David Keitel41, D. B. Kelley24, W. Kells1, R. Kennedy59, Drew Keppel7, J. S. Key56, Alexander Khalaidovski7, F. Y. Khalili30, Imran Khan3, Sebastian Khan61, Zahoor Ali Khan, Efim A. Khazanov75, N. Kijbunchoo, Chi Woong Kim50, Jinsook Kim76, Kyungmin Kim77, Nam Gyu Kim50, Namjun Kim27, Y. M. Kim76, E. J. King70, P. J. King, D. L. Kinzel, J. S. Kissel, L. Kleybolte21, Sergey Klimenko4, S. M. Koehlenbeck7, Keiko Kokeyama2, S. Koley, V. Kondrashov1, Antonios Kontos8, Scott Koranda14, M. Korobko21, W. Z. Korth1, I. Kowalska28, D. B. Kozak1, V. Kringel7, Badri Krishnan7, A. Królak, C. Krueger, G. Kuehn7, P. Kumar, Rajesh Kumar25, L. Kuo47, A. Kutynia, P. Kwee7, B. D. Lackey24, M. Landry, J. S. Lange68, B. Lantz27, Paul D. Lasky78, Albert Lazzarini1, C. Lazzaro39, P. Leaci52, S. Leavey25, E. O. Lebigot44, Chang-Hwan Lee76, Hyun Lee77, Hyung Mok Lee79, Kejia Lee25, A. Lenon24, M. Leonardi3, J. R. Leong7, N. Leroy17, N. Letendre5, Yuri Levin78, B. M. Levine, Tjonnie G. F. Li1, Adam A. Libson8, Tyson Littenberg80, N. A. Lockerbie73, J. Logue25, A. L. Lombardi69, Lionel London61, J. E. Lord24, M. Lorenzini3, V. Loriette5, M. Lormand, G. Losurdo3, J. D. Lough, Carlos O. Lousto68, Geoffrey Lovelace16, Harald Lück, Andrew Lundgren7, J. Luo51, Ryan Lynch8, Y. Q. Ma32, Timothy MacDonald27, B. Machenschalk7, M. MacInnis8, D. M. Macleod2, F. Magaña-Sandoval24, R. M. Magee35, M. Mageswaran1, Ettore Majorana3, I. Maksimovic5, V. Malvezzi19, N. Man5, Ilya Mandel29, Vuk Mandic57, V. Mangano25, G. L. Mansell13, Magnus Manske14, M. Mantovani, Fabio Marchesoni, F. Marion5, Szabolcs Marka26, Zsuzsa Márka26, A. S. Markosyan27, E. Maros1, F. Martelli3, Lionel Martellini5, I. W. Martin25, R. M. Martin4, Denis Martynov1, J. N. Marx1, K. Mason8, A. Masserot5, T. J. Massinger24, M. Masso-Reid25, Fabrice Matichard8, L. Matone26, Nergis Mavalvala8, N. Mazumder35, G. Mazzolo7, R. L. McCarthy, David E. McClelland13, S. McCormick, S. C. McGuire81, G. McIntyre1, J. McIver1, D. J. McManus13, Sean T. McWilliams71, D. Meacher46, G. D. Meadors7, J. Meidam, Andrew Melatos58, G. Mendell, D. Mendoza-Gandara7, R. A. Mercer14, E. L. Merilh, M. Merzougui5, S. Meshkov1, C. Messenger25, Cody Messick46, P. M. Meyers57, F. Mezzani52, Haixing Miao29, C. Michel40, H. Middleton29, Eugeniy E. Mikhailov82, Leopoldo Milano42, John Miller8, Margaret Millhouse23, Y. Minenkov3, J. Ming7, S. Mirshekari83, Chandra Kant Mishra11, Subhasish Mitra10, V. P. Mitrofanov30, Guenakh Mitselmakher4, R. Mittleman8, A. Moggi3, M. Mohan, Satyanarayan Ray Pitambar Mohapatra8, M. Montani3, Blake Moore60, Christopher J. Moore84, D. Moraru, G. Moreno, S. R. Morriss56, Kasem Mossavi7, B. Mours5, C. M. Mow-Lowry29, C. L. Mueller4, Guido Mueller4, A. W. Muir61, Arunava Mukherjee11, Debnandini Mukherjee14, Soma Mukherjee56, N. Mukund10, A. Mullavey, Jesper Munch70, David Murphy26, P. G. Murray25, A. Mytidis4, I. Nardecchia19, L. Naticchioni52, R. K. Nayak85, V. Necula4, K. Nedkova69, Gijs Nelemans33, M. Neri3, A. Neunzert65, G. P. Newton25, T. T. Nguyen13, Alex B. Nielsen7, Samaya Nissanke33, Alexander H. Nitz7, F. Nocera, D. Nolting, M. E. Normandin56, L. K. Nuttall24, J. Oberling, Evan Ochsner14, J. O'Dell66, Eric Oelker8, G. H. Ogin86, John J. Oh, Seog Oh, F. Ohme61, M. Oliver41, P. Oppermann7, Richard J. Oram, B. O'Reilly, Richard O'Shaughnessy68, Christian D. Ott1, David J. Ottaway70, R. S. Ottens4, H. Overmier, Benjamin J. Owen45, Archana Pai74, S. A. Pai, J. R. Palamos36, O. V. Palashov75, C. Palomba3, A. Pal-Singh21, Howard Pan47, Yi Pan38, Chris Pankow55, Francesco Pannarale61, B. C. Pant, F. Paoletti, A. Paoli, Maria Alessandra Papa7, H. R. Paris27, William Parker, D. Pascucci25, A. Pasqualetti, R. Passaquieti3, D. Passuello3, B. Patricelli3, Z. Patrick27, B. L. Pearlstone25, M. Pedraza1, R. Pedurand40, Larne Pekowsky24, A. Pele, S. Penn87, A. Perreca1, Harald P. Pfeiffer, M. Phelps25, O. J. Piccinni52, M. Pichot5, M. Pickenpack7, F. Piergiovanni3, V. Pierro3, G. Pillant, L. Pinard40, Innocenzo M. Pinto3, Matthew Pitkin25, J. Poeld7, Rosa Poggiani3, P. Popolizio, A. Post7, Jade Powell25, J. Prasad10, V. Predoi61, S. S. Premachandra78, T. Prestegard57, Larry R. Price1, M. Prijatelj, Maria Ilaria Del Principe3, S. Privitera7, Reinhard Prix7, G. A. Prodi3, L. Prokhorov30, O. Puncken7, M. Punturo3, P. Puppo3, Michael Pürrer7, H. Qi14, Jiayi Qin32, V. Quetschke56, E. A. Quintero1, R. Quitzow-James36, F. J. Raab, D. S. Rabeling13, H. Radkins, P. Raffai34, S. Raja, Malik Rakhmanov56, C. R. Ramet, P. Rapagnani52, Vivien Raymond7, M. Razzano3, V. Re19, Jenny C. A. Read16, C. Reed, Tania Regimbau5, L. Rei3, Stuart Reid31, David H. Reitze4, H. Rew82, Susana Reyes24, F. Ricci52, K. Riles65, N. A. Robertson25, R. Robie25, F. Robinet17, A. Rocchi3, L. Rolland5, J. G. Rollins1, V. J. Roma36, Joseph D. Romano56, Rocco Romano3, Gleb Romanov82, J. H. Romie, D. Rosińska88, Sheila Rowan25, Albrecht Rüdiger7, P. Ruggi, Kris Ryan, Surabhi Sachdev1, T. Sadecki, Laleh Sadeghian14, L. Salconi, M. Saleem74, Francesco Salemi7, A. Samajdar85, L. Sammut78, Laura Sampson55, E. J. Sanchez1, V. Sandberg, B. Sandeen55, G. H. Sanders1, J. R. Sanders65, B. Sassolas40, Bangalore Suryanarayana Sathyaprakash61, Peter R. Saulson24, O. E. S. Sauter65, R. L. Savage, A. Sawadsky, P. Schale36, Roland Schilling7, Jochen Schmidt7, Patricia Schmidt1, Roman Schnabel21, R. M. S. Schofield36, A. Schönbeck21, E. Schreiber7, D. Schuette, Bernard F. Schutz61, John D. Scott25, Susan M. Scott13, D. Sellers, A. S. Sengupta89, D. Sentenac, V. Sequino19, A. M. Sergeev75, G. Serna16, Y. Setyawati33, A. Sevigny, Daniel A. Shaddock13, T. J. Shaffer, Sweta Shah33, M. S. Shahriar55, M. Shaltev7, Z. Shao1, B. Shapiro27, P. Shawhan38, A. Sheperd14, D. H. Shoemaker8, D. M. Shoemaker39, K. Siellez39, Xavier Siemens14, D. Sigg, A. D. Silva9, D. Simakov7, A. Singer1, Leo Singer43, Ajay Kumar Singh7, Robinjeet Singh2, A. Singhal3, Alicia M. Sintes41, B. J. J. Slagmolen13, J. R. Smith16, Mathew Smith1, Nicholas Smith1, Rory Smith1, Edwin J. Son, B. Sorazu25, Fiodor Sorrentino3, Tarun Souradeep10, A. K. Srivastava, A. Staley26, M. Steinke7, Jessica Steinlechner25, Sebastian Steinlechner25, D. Steinmeyer, B. C. Stephens14, Simon Stevenson29, Robert Stone56, Kenneth A. Strain25, N. Straniero40, G. Stratta3, N. A. Strauss51, S. E. Strigin30, R. Sturani83, A. L. Stuver, T. Summerscales90, L. Sun58, P. J. Sutton61, B. L. Swinkels, Marek Szczepanczyk64, M. Tacca22, D. Talukder36, David B. Tanner4, Márton Tápai63, S. P. Tarabrin7, Andrea Taracchini7, Robert Taylor1, T. Theeg7, M. P. Thirugnanasambandam1, Elizabeth R. Thomas29, M. Thomas, P. Thomas, K. A. Thorne, Kip S. Thorne1, Eric Thrane78, Shubhanshu Tiwari3, V. Tiwari61, K. V. Tokmakov73, C. Tomlinson59, M. Tonelli3, C. V. Torres56, C. I. Torrie1, D. Töyrä29, F. Travasso3, G. Traylor, Daniele Trifirò15, M. C. Tringali3, L. Trozzo91, Maggie Tse8, M. Turconi5, D. Tuyenbayev56, D. Ugolini92, C. S. Unnikrishnan11, A. L. Urban14, S. A. Usman24, H. Vahlbruch, G. Vajente1, G. Valdes56, Michele Vallisneri1, N. van Bakel, M. van Beuzekom, J. F. J. van den Brand37, C. Van Den Broeck, D. C. Vander-Hyde24, L. van der Schaaf, J. V. van Heijningen, A. A. van Veggel25, M. Vardaro3, S. Vass1, M. Vasúth, Ruslan Vaulin8, Alberto Vecchio29, G. Vedovato3, John Veitch29, P. J. Veitch70, K. Venkateswara93, D. Verkindt5, F. Vetrano3, A. Viceré3, Serena Vinciguerra29, D. J. Vine31, J-Y. Vinet5, Salvatore Vitale8, T. Vo24, H. Vocca3, C. Vorvick, D. V. Voss4, W. D. Vousden29, Sergey P. Vyatchanin30, A. R. Wade13, L. E. Wade94, Madeline Wade94, S. J. Waldman8, Michelle E. Walker2, L. Wallace1, S. Walsh7, G. Wang3, Hua Wang29, Meng Wang29, X. F. Wang44, Yanzhi Wang32, H. Ward25, R. L. Ward13, J. Warner, M. Was5, B. A. Weaver, L.-W. Wei95, L.-W. Wei5, M. Weinert7, A. J. Weinstein1, Rainer Weiss8, Timothy A Welborn, Linqing Wen32, Peter Weßels7, T. Westphal7, K. Wette7, James Whelan68, S. E. Whitcomb1, D. J. White59, Bernard F. Whiting4, K. Wiesner7, C. Wilkinson, Phil Willems1, L. Williams4, Roy Williams1, A. R. Williamson61, Joshua L. Willis96, Benno Willke, M. H. Wimmer, Lutz Winkelmann7, Walter Winkler7, C. C. Wipf1, A. G. Wiseman14, H. Wittel, Graham Woan25, John Worden, J. L. Wright25, G. Wu, Joshua Yablon55, I. Yakushin, William Yam8, H. Yamamoto1, C. C. Yancey38, M. J. Yap13, Haocun Yu8, M. Yvert5, A. K. Zadrożny, L. Zangrando3, Michele Zanolin64, J. P. Zendri3, M. Zevin55, Fan Zhang8, Lei Zhang1, Mi Zhang82, Y.-H. Zhang68, C. Zhao32, Minchuan Zhou55, Zifan Zhou55, Xing-Jiang Zhu32, Michael E Zucker8, S. E. Zuraw69, J. Zweizig1 
California Institute of Technology1, Louisiana State University2, Istituto Nazionale di Fisica Nucleare3, University of Florida4, Centre national de la recherche scientifique5, University of Salerno6, Max Planck Society7, Massachusetts Institute of Technology8, National Institute for Space Research9, Inter-University Centre for Astronomy and Astrophysics10, Tata Institute of Fundamental Research11, Leibniz University of Hanover12, Australian National University13, University of Wisconsin–Milwaukee14, University of Mississippi15, California State University, Fullerton16, University of Paris-Sud17, Chennai Mathematical Institute18, University of Rome Tor Vergata19, University of Southampton20, University of Hamburg21, Paris Diderot University22, Montana State University23, Syracuse University24, University of Glasgow25, Columbia University26, Stanford University27, University of Warsaw28, University of Birmingham29, Moscow State University30, University of the West of Scotland31, University of Western Australia32, Radboud University Nijmegen33, Eötvös Loránd University34, Washington State University35, University of Oregon36, VU University Amsterdam37, University of Maryland, College Park38, Georgia Institute of Technology39, Claude Bernard University Lyon 140, University of the Balearic Islands41, University of Naples Federico II42, Goddard Space Flight Center43, Tsinghua University44, Texas Tech University45, Pennsylvania State University46, National Tsing Hua University47, Charles Sturt University48, University of Chicago49, Korea Institute of Science and Technology Information50, Carleton College51, Sapienza University of Rome52, Free University of Brussels53, Sonoma State University54, Northwestern University55, University of Texas at Brownsville56, University of Minnesota System57, University of Melbourne58, University of Sheffield59, Montclair State University60, Cardiff University61, University of Edinburgh62, University of Szeged63, Embry–Riddle Aeronautical University64, University of Michigan65, Rutherford Appleton Laboratory66, American University67, Rochester Institute of Technology68, University of Massachusetts Amherst69, University of Adelaide70, West Virginia University71, Bial72, University of Strathclyde73, Indian Institutes of Science Education and Research74, Russian Academy of Sciences75, Pusan National University76, Hanyang University77, Monash University78, Seoul National University79, University of Alabama in Huntsville80, University of New South Wales81, College of William & Mary82, American Institute for Economic Research83, University of Cambridge84, Indian Institute of Science Education and Research, Kolkata85, Whitman College86, Hobart and William Smith Colleges87, University of Zielona Góra88, Indian Institutes of Technology89, Andrews University90, University of Siena91, Trinity University92, University of Washington93, Kenyon College94, Artemis95, Abilene Christian University96
TL;DR: This is the first direct detection of gravitational waves and the first observation of a binary black hole merger, and these observations demonstrate the existence of binary stellar-mass black hole systems.
Abstract: On September 14, 2015 at 09:50:45 UTC the two detectors of the Laser Interferometer Gravitational-Wave Observatory simultaneously observed a transient gravitational-wave signal. The signal sweeps upwards in frequency from 35 to 250 Hz with a peak gravitational-wave strain of $1.0 \times 10^{-21}$. It matches the waveform predicted by general relativity for the inspiral and merger of a pair of black holes and the ringdown of the resulting single black hole. The signal was observed with a matched-filter signal-to-noise ratio of 24 and a false alarm rate estimated to be less than 1 event per 203 000 years, equivalent to a significance greater than 5.1 {\sigma}. The source lies at a luminosity distance of $410^{+160}_{-180}$ Mpc corresponding to a redshift $z = 0.09^{+0.03}_{-0.04}$. In the source frame, the initial black hole masses are $36^{+5}_{-4} M_\odot$ and $29^{+4}_{-4} M_\odot$, and the final black hole mass is $62^{+4}_{-4} M_\odot$, with $3.0^{+0.5}_{-0.5} M_\odot c^2$ radiated in gravitational waves. All uncertainties define 90% credible intervals.These observations demonstrate the existence of binary stellar-mass black hole systems. This is the first direct detection of gravitational waves and the first observation of a binary black hole merger.

8,011 citations



Proceedings ArticleDOI
21 Jul 2017-
TL;DR: This paper exploits the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost and achieves state-of-the-art single-model results on the COCO detection benchmark without bells and whistles.
Abstract: Feature pyramids are a basic component in recognition systems for detecting objects at different scales. But pyramid representations have been avoided in recent object detectors that are based on deep convolutional networks, partially because they are slow to compute and memory intensive. In this paper, we exploit the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost. A top-down architecture with lateral connections is developed for building high-level semantic feature maps at all scales. This architecture, called a Feature Pyramid Network (FPN), shows significant improvement as a generic feature extractor in several applications. Using a basic Faster R-CNN system, our method achieves state-of-the-art single-model results on the COCO detection benchmark without bells and whistles, surpassing all existing single-model entries including those from the COCO 2016 challenge winners. In addition, our method can run at 5 FPS on a GPU and thus is a practical and accurate solution to multi-scale object detection. Code will be made publicly available.

7,876 citations


Posted Content
TL;DR: The authors present some updates to YOLO!
Abstract: We present some updates to YOLO! We made a bunch of little design changes to make it better. We also trained this new network that's pretty swell. It's a little bigger than last time but more accurate. It's still fast though, don't worry. At 320x320 YOLOv3 runs in 22 ms at 28.2 mAP, as accurate as SSD but three times faster. When we look at the old .5 IOU mAP detection metric YOLOv3 is quite good. It achieves 57.9 mAP@50 in 51 ms on a Titan X, compared to 57.5 mAP@50 in 198 ms by RetinaNet, similar performance but 3.8x faster. As always, all the code is online at this https URL

7,812 citations


Journal ArticleDOI
Monkol Lek, Konrad J. Karczewski1, Konrad J. Karczewski2, Eric Vallabh Minikel1, Eric Vallabh Minikel2, Kaitlin E. Samocha, Eric Banks1, Timothy Fennell1, Anne H. O’Donnell-Luria1, Anne H. O’Donnell-Luria2, Anne H. O’Donnell-Luria3, James S. Ware, Andrew J. Hill4, Andrew J. Hill2, Andrew J. Hill1, Beryl B. Cummings2, Beryl B. Cummings1, Taru Tukiainen2, Taru Tukiainen1, Daniel P. Birnbaum1, Jack A. Kosmicki, Laramie E. Duncan1, Laramie E. Duncan2, Karol Estrada1, Karol Estrada2, Fengmei Zhao1, Fengmei Zhao2, James Zou1, Emma Pierce-Hoffman1, Emma Pierce-Hoffman2, Joanne Berghout5, David Neil Cooper6, Nicole A. Deflaux7, Mark A. DePristo1, Ron Do, Jason Flannick1, Jason Flannick2, Menachem Fromer, Laura D. Gauthier1, Jackie Goldstein2, Jackie Goldstein1, Namrata Gupta1, Daniel P. Howrigan1, Daniel P. Howrigan2, Adam Kiezun1, Mitja I. Kurki1, Mitja I. Kurki2, Ami Levy Moonshine1, Pradeep Natarajan, Lorena Orozco, Gina M. Peloso1, Gina M. Peloso2, Ryan Poplin1, Manuel A. Rivas1, Valentin Ruano-Rubio1, Samuel A. Rose1, Douglas M. Ruderfer8, Khalid Shakir1, Peter D. Stenson6, Christine Stevens1, Brett Thomas2, Brett Thomas1, Grace Tiao1, María Teresa Tusié-Luna, Ben Weisburd1, Hong-Hee Won9, Dongmei Yu, David Altshuler1, David Altshuler10, Diego Ardissino, Michael Boehnke11, John Danesh12, Stacey Donnelly1, Roberto Elosua, Jose C. Florez2, Jose C. Florez1, Stacey Gabriel1, Gad Getz2, Gad Getz1, Stephen J. Glatt13, Christina M. Hultman14, Sekar Kathiresan, Markku Laakso15, Steven A. McCarroll2, Steven A. McCarroll1, Mark I. McCarthy16, Mark I. McCarthy17, Dermot P.B. McGovern18, Ruth McPherson19, Benjamin M. Neale2, Benjamin M. Neale1, Aarno Palotie, Shaun Purcell8, Danish Saleheen20, Jeremiah M. Scharf, Pamela Sklar, Patrick F. Sullivan14, Patrick F. Sullivan21, Jaakko Tuomilehto22, Ming T. Tsuang23, Hugh Watkins16, Hugh Watkins17, James G. Wilson24, Mark J. Daly2, Mark J. Daly1, Daniel G. MacArthur1, Daniel G. MacArthur2 
18 Aug 2016-Nature
TL;DR: The aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries generated as part of the Exome Aggregation Consortium (ExAC) provides direct evidence for the presence of widespread mutational recurrence.
Abstract: Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of predicted protein-truncating variants, with 72% of these genes having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human 'knockout' variants in protein-coding genes.

7,679 citations