{"results":[{"id":"ss_210b0a3d76e93079cc51b03c4115fde545eea966","title":"GPQA: A Graduate-Level Google-Proof Q\u0026A Benchmark","authors":[{"name":"David Rein"},{"name":"Betty Li Hou"},{"name":"Asa Cooper Stickland"},{"name":"Jackson Petty"},{"name":"Richard Yuanzhe Pang"},{"name":"Julien Dirani"},{"name":"Julian Michael"},{"name":"Samuel R. Bowman"}],"abstract":"We present GPQA, a challenging dataset of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry. We ensure that the questions are high-quality and extremely difficult: experts who have or are pursuing PhDs in the corresponding domains reach 65% accuracy (74% when discounting clear mistakes the experts identified in retrospect), while highly skilled non-expert validators only reach 34% accuracy, despite spending on average over 30 minutes with unrestricted access to the web (i.e., the questions are\"Google-proof\"). The questions are also difficult for state-of-the-art AI systems, with our strongest GPT-4 based baseline achieving 39% accuracy. If we are to use future AI systems to help us answer very hard questions, for example, when developing new scientific knowledge, we need to develop scalable oversight methods that enable humans to supervise their outputs, which may be difficult even if the supervisors are themselves skilled and knowledgeable. The difficulty of GPQA both for skilled non-experts and frontier AI systems should enable realistic scalable oversight experiments, which we hope can help devise ways for human experts to reliably get truthful information from AI systems that surpass human capabilities.","source":"Semantic Scholar","year":2023,"language":"en","subjects":["Computer Science"],"url":"https://www.semanticscholar.org/paper/210b0a3d76e93079cc51b03c4115fde545eea966","is_open_access":true,"citations":2243,"published_at":"","score":97},{"id":"ss_2bc814b27312dbf0f4d54950aef651f58a185cdc","title":"Software for the frontiers of quantum chemistry: An overview of developments in the Q-Chem 5 package","authors":[{"name":"E. Epifanovsky"},{"name":"A. Gilbert"},{"name":"Xintian Feng"},{"name":"Joonho Lee"},{"name":"Yuezhi Mao"},{"name":"N. Mardirossian"},{"name":"Pavel Pokhilko"},{"name":"Alec F. White"},{"name":"Marc P. Coons"},{"name":"A. L. Dempwolff"},{"name":"Zhengting Gan"},{"name":"D. Hait"},{"name":"P. Horn"},{"name":"Leif D Jacobson"},{"name":"I. Kaliman"},{"name":"J. Kussmann"},{"name":"A. Lange"},{"name":"K. Lao"},{"name":"Daniel S. Levine"},{"name":"Jie Liu"},{"name":"Simon McKenzie"},{"name":"Adrian F. Morrison"},{"name":"K. Nanda"},{"name":"F. Plasser"},{"name":"D. Rehn"},{"name":"M. Vidal"},{"name":"Zhi-Qiang You"},{"name":"Ying Zhu"},{"name":"B. Alam"},{"name":"Benjamin J. Albrecht"},{"name":"Abdulrahman Aldossary"},{"name":"Ethan C Alguire"},{"name":"J. H. Andersen"},{"name":"V. Athavale"},{"name":"Dennis Barton"},{"name":"K. Begam"},{"name":"Andrew Behn"},{"name":"Nicole Bellonzi"},{"name":"Yves A. Bernard"},{"name":"E. Berquist"},{"name":"Hugh G. A. Burton"},{"name":"A. Carreras"},{"name":"Kevin Carter-Fenk"},{"name":"Romit Chakraborty"},{"name":"Alan D. Chien"},{"name":"K. D. Closser"},{"name":"Vale Cofer-Shabica"},{"name":"Saswata Dasgupta"},{"name":"Marc de Wergifosse"},{"name":"Jia Deng"},{"name":"M. Diedenhofen"},{"name":"Hainam Do"},{"name":"S. Ehlert"},{"name":"Po-Tung Fang"},{"name":"S. Fatehi"},{"name":"Qing Feng"},{"name":"Triet Friedhoff"},{"name":"James R Gayvert"},{"name":"Qinghui Ge"},{"name":"Gergely Gidofalvi"},{"name":"Matthew B Goldey"},{"name":"J. Gomes"},{"name":"Cristina E. Gonzalez-Espinoza"},{"name":"Sahil Gulania"},{"name":"A. Gunina"},{"name":"M. W. Hanson-Heine"},{"name":"Phillip H P Harbach"},{"name":"A. Hauser"},{"name":"Michael F. Herbst"},{"name":"Mario Hernández Vera"},{"name":"Manuel Hodecker"},{"name":"Z. C. Holden"},{"name":"Shannon Houck"},{"name":"Xu-Feng Huang"},{"name":"Kerwin Hui"},{"name":"B. Huynh"},{"name":"M. Ivanov"},{"name":"Ádám Jász"},{"name":"Hyunjun Ji"},{"name":"Hanjie Jiang"},{"name":"B. Kaduk"},{"name":"S. Kähler"},{"name":"K. Khistyaev"},{"name":"Jaehoon Kim"},{"name":"Gergely Kis"},{"name":"P. Klunzinger"},{"name":"Zsuzsanna Koczor-Benda"},{"name":"Joong Hoon Koh"},{"name":"D. Kosenkov"},{"name":"Laura Koulias"},{"name":"T. Kowalczyk"},{"name":"C. M. Krauter"},{"name":"Karl Y Kue"},{"name":"A. Kunitsa"},{"name":"T. Kus"},{"name":"István Ladjánszki"},{"name":"A. Landau"},{"name":"K. Lawler"},{"name":"Daniel Lefrancois"},{"name":"S. Lehtola"},{"name":"Run R. Li"},{"name":"Yi‐Pei Li"},{"name":"Jiashu Liang"},{"name":"M. Liebenthal"},{"name":"Hung-Hsuan Lin"},{"name":"You-Sheng Lin"},{"name":"Fenglai Liu"},{"name":"Kuan-Yu Liu"},{"name":"Matthias Loipersberger"},{"name":"A. Luenser"},{"name":"A. Manjanath"},{"name":"P. Manohar"},{"name":"E. Mansoor"},{"name":"S. Manzer"},{"name":"Shan-Ping Mao"},{"name":"A. Marenich"},{"name":"Thomas Markovich"},{"name":"S. Mason"},{"name":"S. Maurer"},{"name":"Peter F McLaughlin"},{"name":"M. Menger"},{"name":"J. Mewes"},{"name":"Stefanie A. Mewes"},{"name":"Pierpaolo Morgante"},{"name":"J. W. Mullinax"},{"name":"Katherine J. Oosterbaan"},{"name":"G. Paran"},{"name":"Alexander C Paul"},{"name":"Suranjan K Paul"},{"name":"Fabijan Pavošević"},{"name":"Zheng Pei"},{"name":"Stefan Prager"},{"name":"E. Proynov"},{"name":"Á. Rák"},{"name":"E. Ramos‐Cordoba"},{"name":"Bhaskar Rana"},{"name":"A. E. Rask"},{"name":"Adam Rettig"},{"name":"R. M. Richard"},{"name":"F. Rob"},{"name":"Elliot Rossomme"},{"name":"Tarek Scheele"},{"name":"Maximilian Scheurer"},{"name":"Matthias Schneider"},{"name":"Nickolai Sergueev"},{"name":"S. Sharada"},{"name":"W. Skomorowski"},{"name":"David W. Small"},{"name":"Christopher J. Stein"},{"name":"Yu-Chuan Su"},{"name":"Eric Sundstrom"},{"name":"Z. Tao"},{"name":"Jonathan Thirman"},{"name":"G. Tornai"},{"name":"T. Tsuchimochi"},{"name":"N. Tubman"},{"name":"S. Veccham"},{"name":"Oleg A. Vydrov"},{"name":"J. Wenzel"},{"name":"Jonathon Witte"},{"name":"A. Yamada"},{"name":"Kun Yao"},{"name":"Sina Yeganeh"},{"name":"Shane R. Yost"},{"name":"Alexander Zech"},{"name":"Igor Ying Zhang"},{"name":"Xing Zhang"},{"name":"Yu Zhang"},{"name":"D. Zuev"},{"name":"Alán Aspuru-Guzik"},{"name":"A. Bell"},{"name":"N. Besley"},{"name":"K. Bravaya"},{"name":"B. Brooks"},{"name":"D. Casanova"},{"name":"Jeng-Da Chai"},{"name":"S. Coriani"},{"name":"C. Cramer"},{"name":"G. Cserey"},{"name":"A. DePrince"},{"name":"R. DiStasio"},{"name":"A. Dreuw"},{"name":"B. Dunietz"},{"name":"T. Furlani"},{"name":"W. Goddard"},{"name":"S. Hammes‐Schiffer"},{"name":"T. Head‐Gordon"},{"name":"W. Hehre"},{"name":"Chao‐Ping Hsu"},{"name":"Thomas-C. Jagau"},{"name":"Yousung Jung"},{"name":"A. Klamt"},{"name":"Jing Kong"},{"name":"D. Lambrecht"},{"name":"Wanzhen Liang"},{"name":"N. Mayhall"},{"name":"C. W. McCurdy"},{"name":"J. Neaton"},{"name":"C. Ochsenfeld"},{"name":"John A. Parkhill"},{"name":"R. Peverati"},{"name":"V. Rassolov"},{"name":"Y. Shao"},{"name":"L. Slipchenko"},{"name":"T. Stauch"},{"name":"R. P. Steele"},{"name":"Joseph E. Subotnik"},{"name":"A. Thom"},{"name":"A. Tkatchenko"},{"name":"D. Truhlar"},{"name":"T. Van Voorhis"},{"name":"T. Wesołowski"},{"name":"K. B. Whaley"},{"name":"H. Woodcock"},{"name":"P. Zimmerman"},{"name":"S. Faraji"},{"name":"P. Gill"},{"name":"M. Head‐Gordon"},{"name":"J. Herbert"},{"name":"A. Krylov"}],"abstract":"This article summarizes technical advances contained in the fifth major release of the Q-Chem quantum chemistry program package, covering developments since 2015. A comprehensive library of exchange–correlation functionals, along with a suite of correlated many-body methods, continues to be a hallmark of the Q-Chem software. The many-body methods include novel variants of both coupled-cluster and configuration-interaction approaches along with methods based on the algebraic diagrammatic construction and variational reduced density-matrix methods. Methods highlighted in Q-Chem 5 include a suite of tools for modeling core-level spectroscopy, methods for describing metastable resonances, methods for computing vibronic spectra, the nuclear–electronic orbital method, and several different energy decomposition analysis techniques. High-performance capabilities including multithreaded parallelism and support for calculations on graphics processing units are described. Q-Chem boasts a community of well over 100 active academic developers, and the continuing evolution of the software is supported by an “open teamware” model and an increasingly modular design.","source":"Semantic Scholar","year":2021,"language":"en","subjects":["Medicine"],"doi":"10.1063/5.0055522","url":"https://www.semanticscholar.org/paper/2bc814b27312dbf0f4d54950aef651f58a185cdc","pdf_url":"https://aip.scitation.org/doi/pdf/10.1063/5.0055522","is_open_access":true,"citations":984,"published_at":"","score":94.52},{"id":"ss_7617d4015f2521bce6abad3adb8135c272123bc9","title":"The Belle II Physics Book","authors":[{"name":"E. Kou"},{"name":"P. Urquijo"},{"name":"W. Altmannshofer"},{"name":"F. Beaujean"},{"name":"G. Bell"},{"name":"M. Beneke"},{"name":"I. Bigi"},{"name":"F. Blanke"},{"name":"C. Bobeth"},{"name":"M. Bona"},{"name":"N. Brambilla"},{"name":"V. Braun"},{"name":"J. Brod"},{"name":"A. Buras"},{"name":"H. Y. Cheng"},{"name":"C. Chiang"},{"name":"G. Colangelo"},{"name":"H. Czyz"},{"name":"A. Datta"},{"name":"F. Fazio"},{"name":"T. Deppisch"},{"name":"M. Dolan"},{"name":"S. Fajfer"},{"name":"T. Feldmann"},{"name":"S. Godfrey"},{"name":"M. Gronau"},{"name":"Y. Grossman"},{"name":"F. Guo"},{"name":"U. Haisch"},{"name":"C. Hanhart"},{"name":"S. Hashimoto"},{"name":"S. Hirose"},{"name":"J. Hisano"},{"name":"L. Hofer"},{"name":"M. Hoferichter"},{"name":"W. Hou"},{"name":"T. Huber"},{"name":"S. Jahn"},{"name":"M. Jamin"},{"name":"J. Jones"},{"name":"M. Jung"},{"name":"A. Kagan"},{"name":"F. Kahlhoefer"},{"name":"J. Kamenik"},{"name":"T. Kaneko"},{"name":"Y. Kiyo"},{"name":"A. Kokulu"},{"name":"N. Kosnik"},{"name":"A. Kronfeld"},{"name":"Z. Ligeti"},{"name":"H. Logan"},{"name":"C. Lu"},{"name":"V. Lubicz"},{"name":"F. Mahmoudi"},{"name":"K. Maltman"},{"name":"M. Misiak"},{"name":"S. Mishima"},{"name":"K. Moats"},{"name":"B. Moussallam"},{"name":"A. Nefediev"},{"name":"U. Nierste"},{"name":"D. Nomura"},{"name":"N. Offen"},{"name":"S. Olsen"},{"name":"E. Passemar"},{"name":"A. Paul"},{"name":"G. Paz"},{"name":"A. Petrov"},{"name":"A. Pich"},{"name":"A. Polosa"},{"name":"J. Pradler"},{"name":"S. Prelovsek"},{"name":"M. Procura"},{"name":"G. Ricciardi"},{"name":"D. Robinson"},{"name":"P. Roig"},{"name":"S. Schacht"},{"name":"K. Schmidt-Hoberg"},{"name":"J. Schwichtenberg"},{"name":"S. Sharpe"},{"name":"J. Shigemitsu"},{"name":"N. Shimizu"},{"name":"Y. Shimizu"},{"name":"L. Silvestrini"},{"name":"S. Simula"},{"name":"C. Smith"},{"name":"P. Stoffer"},{"name":"D. Straub"},{"name":"F. Tackmann"},{"name":"M. Tanaka"},{"name":"A. Tayduganov"},{"name":"G. Tetlalmatzi-Xolocotzi"},{"name":"T. Teubner"},{"name":"A. Vairo"},{"name":"D. Dyk"},{"name":"J. Virto"},{"name":"Z. Was"},{"name":"R. Watanabe"},{"name":"I. Watson"},{"name":"J. Zupan"},{"name":"R. Zwicky"},{"name":"F. Abudinén"},{"name":"I. Adachi"},{"name":"K. Adamczyk"},{"name":"P. Ahlburg"},{"name":"H. Aihara"},{"name":"A. Aloisio"},{"name":"L. Andricek"},{"name":"N. A. Kỳ"},{"name":"M. Arndt"},{"name":"D. Asner"},{"name":"H. Atmacan"},{"name":"T. Aushev"},{"name":"V. Aushev"},{"name":"R. Ayad"},{"name":"T. Aziz"},{"name":"S. Baehr"},{"name":"S. Bahinipati"},{"name":"P. Bambade"},{"name":"Y. Ban"},{"name":"M. Barrett"},{"name":"J. Baudot"},{"name":"P. Behera"},{"name":"K. Belous"},{"name":"M. Bender"},{"name":"J. Bennett"},{"name":"M. Berger"},{"name":"E. Bernieri"},{"name":"F. Bernlochner"},{"name":"M. Bessner"},{"name":"D. Besson"},{"name":"S. Bettarini"},{"name":"V. Bhardwaj"},{"name":"B. Bhuyan"},{"name":"T. Bilka"},{"name":"S. Bilmis"},{"name":"S. Bilokin"},{"name":"G. Bonvicini"},{"name":"A. Bozek"},{"name":"M. Bračko"},{"name":"P. Branchini"},{"name":"N. Braun"},{"name":"R. Briere"},{"name":"T. Browder"},{"name":"L. Burmistrov"},{"name":"S. Bussino"},{"name":"L. Cao"},{"name":"G. Caria"},{"name":"G. Casarosa"},{"name":"C. Cecchi"},{"name":"D. Červenkov"},{"name":"M. Chang"},{"name":"P. Chang"},{"name":"R. Cheaib"},{"name":"V. Chekelian"},{"name":"Y. Chen"},{"name":"B. Cheon"},{"name":"K. Chilikin"},{"name":"K. Cho"},{"name":"J. Choi"},{"name":"S. Choi"},{"name":"S. Choudhury"},{"name":"D. Cinabro"},{"name":"L. Cremaldi"},{"name":"D. Cuesta"},{"name":"S. Cunliffe"},{"name":"N. Dash"},{"name":"E. D. L. C. Burelo"},{"name":"E. Lucia"},{"name":"G. Nardo"},{"name":"M. Nuccio"},{"name":"G. D. Pietro"},{"name":"A. Hernandez"},{"name":"B. Deschamps"},{"name":"M. Destefanis"},{"name":"S. Dey"},{"name":"F. Capua"},{"name":"S. Carlo"},{"name":"J. Dingfelder"},{"name":"Z. Doležal"},{"name":"I. D. Jiménez"},{"name":"T. Dong"},{"name":"D. Dossett"},{"name":"S. Duell"},{"name":"S. Eidelman"},{"name":"D. Epifanov"},{"name":"J. Fast"},{"name":"T. Ferber"},{"name":"S. Fiore"},{"name":"A. Fodor"},{"name":"F. Forti"},{"name":"A. Frey"},{"name":"O. Frost"},{"name":"B. G. Fulsom"},{"name":"M. Gabriel"},{"name":"N. Gabyshev"},{"name":"E. Ganiev"},{"name":"X. Gao"},{"name":"B. Gao"},{"name":"R. Garg"},{"name":"A. Garmash"},{"name":"V. Gaur"},{"name":"A. Gaz"},{"name":"T. Gessler"},{"name":"U. Gebauer"},{"name":"M. Gelb"},{"name":"A. Gellrich"},{"name":"D. Getzkow"},{"name":"R. Giordano"},{"name":"A. Giri"},{"name":"A. Glazov"},{"name":"B. Gobbo"},{"name":"R. Godang"},{"name":"O. Gogota"},{"name":"P. Goldenzweig"},{"name":"B. Golob"},{"name":"W. Gradl"},{"name":"E. Graziani"},{"name":"M. Greco"},{"name":"D. Greenwald"},{"name":"S. Gribanov"},{"name":"Y. Guan"},{"name":"E. Guido"},{"name":"A. Guo"},{"name":"S. Halder"},{"name":"K. Hara"},{"name":"O. Hartbrich"},{"name":"T. Hauth"},{"name":"K. Hayasaka"},{"name":"H. Hayashii"},{"name":"C. Hearty"},{"name":"I. Cruz"},{"name":"M. Villanueva"},{"name":"A. Hershenhorn"},{"name":"T. Higuchi"},{"name":"M. Hoek"},{"name":"S. Hollitt"},{"name":"N. Van"},{"name":"C.-L. Hsu"},{"name":"Y. Hu"},{"name":"K. Huang"},{"name":"T. Iijima"},{"name":"K. Inami"},{"name":"G. Inguglia"},{"name":"A. Ishikawa"},{"name":"R. Itoh"},{"name":"Y. Iwasaki"},{"name":"M. Iwasaki"},{"name":"P. Jackson"},{"name":"W. Jacobs"},{"name":"I. Jaegle"},{"name":"H. Jeon"},{"name":"X. Ji"},{"name":"S. Jia"},{"name":"Y. Jin"},{"name":"C. Joo"},{"name":"M. Kuenzel"},{"name":"I. Kadenko"},{"name":"James Kahn"},{"name":"H. Kakuno"},{"name":"A. B. Kaliyar"},{"name":"J. Kandra"},{"name":"K. Kang"},{"name":"T. Kawasaki"},{"name":"C. Ketter"},{"name":"M. Khasmidatul"},{"name":"H. Kichimi"},{"name":"J. Kim"},{"name":"K. Kim"},{"name":"H. Kim"},{"name":"D. Kim"},{"name":"K. Kim"},{"name":"Y. Kim"},{"name":"T. Kimmel"},{"name":"H. Kindo"},{"name":"K. Kinoshita"},{"name":"T. Konno"},{"name":"A. Korobov"},{"name":"S. Korpar"},{"name":"D. Kotchetkov"},{"name":"R. Kowalewski"},{"name":"P. Križan"},{"name":"R. Kroeger"},{"name":"J. Krohn"},{"name":"P. Krokovny"},{"name":"W. Kuehn"},{"name":"T. Kuhr"},{"name":"R. Kulasiri"},{"name":"M. Kumar"},{"name":"R. Kumar"},{"name":"T. Kumita"},{"name":"A. Kuzmin"},{"name":"Y. Kwon"},{"name":"S. Lacaprara"},{"name":"Y. Lai"},{"name":"K. Lalwani"},{"name":"J. Lange"},{"name":"S. Lee"},{"name":"J. Lee"},{"name":"P. Leitl"},{"name":"D. Levit"},{"name":"S. Levonian"},{"name":"S. Li"},{"name":"L. Li"},{"name":"Y. Li"},{"name":"Y. Li"},{"name":"Q. Li"},{"name":"L. Gioi"},{"name":"J. Libby"},{"name":"Z. Liptak"},{"name":"D. Liventsev"},{"name":"S. Longo"},{"name":"A. Loos"},{"name":"G. L. Castro"},{"name":"M. Lubej"},{"name":"T. Lueck"},{"name":"F. Luetticke"},{"name":"T. Luo"},{"name":"F. Mueller"},{"name":"T. Mueller"},{"name":"C. MacQueen"},{"name":"Y. Maeda"},{"name":"M. Maggiora"},{"name":"S. Maity"},{"name":"E. Manoni"},{"name":"S. Marcello"},{"name":"C. Marinas"},{"name":"M. M. Hernández"},{"name":"A. Martini"},{"name":"D. Matvienko"},{"name":"J. McKenna"},{"name":"F. Meier"},{"name":"M. Merola"},{"name":"F. Metzner"},{"name":"C. Miller"},{"name":"K. Miyabayashi"},{"name":"H. Miyake"},{"name":"H. Miyata"},{"name":"R. Mizuk"},{"name":"G. Mohanty"},{"name":"H. Moon"},{"name":"T. Moon"},{"name":"A. Mordà"},{"name":"T. Morii"},{"name":"M. Mrvar"},{"name":"G. Muroyama"},{"name":"R. Mussa"},{"name":"I. Nakamura"},{"name":"T. Nakano"},{"name":"M. Nakao"},{"name":"H. Nakayama"},{"name":"H. Nakazawa"},{"name":"T. Nanut"},{"name":"M. Naruki"},{"name":"K. Nath"},{"name":"M. Nayak"},{"name":"N. Nellikunnummel"},{"name":"D. Neverov"},{"name":"C. Niebuhr"},{"name":"J. Ninkovic"},{"name":"S. Nishida"},{"name":"K. Nishimura"},{"name":"M. Nouxman"},{"name":"G. Nowak"},{"name":"K. Ogawa"},{"name":"Y. Onishchuk"},{"name":"H. Ono"},{"name":"Y. Onuki"},{"name":"P. Pakhlov"},{"name":"G. Pakhlova"},{"name":"B. Pal"},{"name":"E. Paoloni"},{"name":"H. Park"},{"name":"C. Park"},{"name":"B. Paschen"},{"name":"A. Passeri"},{"name":"S. Paul"},{"name":"T. Pedlar"},{"name":"M. Perelló"},{"name":"I. Peruzzi"},{"name":"R. Pestotnik"},{"name":"L. Piilonen"},{"name":"L. Lerma"},{"name":"V. Popov"},{"name":"K. Prasanth"},{"name":"E. Prencipe"},{"name":"M. Prim"},{"name":"M. Purohit"},{"name":"A. Rabusov"},{"name":"R. Rasheed"},{"name":"S. Reiter"},{"name":"M. Remnev"},{"name":"P. Resmi"},{"name":"I. Ripp-Baudot"},{"name":"M. Ritter"},{"name":"M. Ritzert"},{"name":"G. Rizzo"},{"name":"L. Rizzuto"},{"name":"S. Robertson"},{"name":"D. R. Perez"},{"name":"J. Roney"},{"name":"C. Rosenfeld"},{"name":"A. Rostomyan"},{"name":"N. Rout"},{"name":"S. Rummel"},{"name":"G. Russo"},{"name":"D. Sahoo"},{"name":"Y. Sakai"},{"name":"M. Salehi"},{"name":"D. Sanders"},{"name":"S. Sandilya"},{"name":"A. Sangal"},{"name":"L. Santelj"},{"name":"J. Sasaki"},{"name":"Y. Sato"},{"name":"V. Savinov"},{"name":"B. Scavino"},{"name":"M. Schram"},{"name":"H. Schreeck"},{"name":"J. Schueler"},{"name":"C. Schwanda"},{"name":"A. Schwartz"},{"name":"R. Seddon"},{"name":"Y. Seino"},{"name":"K. Senyo"},{"name":"O. Seon"},{"name":"I. Seong"},{"name":"M. Sevior"},{"name":"C. Sfienti"},{"name":"M. Shapkin"},{"name":"C. Shen"},{"name":"M. Shimomura"},{"name":"J. Shiu"},{"name":"B. Shwartz"},{"name":"A. Sibidanov"},{"name":"F. Simon"},{"name":"J. Singh"},{"name":"R. Sinha"},{"name":"S. Skambraks"},{"name":"K. Smith"},{"name":"R. Sobie"},{"name":"A. Soffer"},{"name":"A. Sokolov"},{"name":"E. Solovieva"},{"name":"B. Spruck"},{"name":"S. Stanič"},{"name":"M. Starič"},{"name":"N. Starinsky"},{"name":"U. Stolzenberg"},{"name":"Z. Stottler"},{"name":"R. Stroili"},{"name":"J. Strube"},{"name":"J. Stypuła"},{"name":"M. Sumihama"},{"name":"K. Sumisawa"},{"name":"T. Sumiyoshi"},{"name":"D. Summers"},{"name":"W. Sutcliffe"},{"name":"S. Suzuki"},{"name":"M. Tabata"},{"name":"M. Takahashi"},{"name":"M. Takizawa"},{"name":"U. Tamponi"},{"name":"J. Tan"},{"name":"S. Tanaka"},{"name":"K. Tanida"},{"name":"N. Taniguchi"},{"name":"Y. Tao"},{"name":"P. Taras"},{"name":"G. T. Muñoz"},{"name":"F. Tenchini"},{"name":"U. Tippawan"},{"name":"E. Torassa"},{"name":"K. Trabelsi"},{"name":"T. Tsuboyama"},{"name":"M. Uchida"},{"name":"S. Uehara"},{"name":"T. Uglov"},{"name":"Y. Unno"},{"name":"S. Uno"},{"name":"Y. Ushiroda"},{"name":"Y. Usov"},{"name":"S. Vahsen"},{"name":"R. Tonder"},{"name":"G. Varner"},{"name":"K. Varvell"},{"name":"A. Vinokurova"},{"name":"L. Vitale"},{"name":"M. Vos"},{"name":"A. Vossen"},{"name":"E. Waheed"},{"name":"H. Wakeling"},{"name":"K. Wan"},{"name":"M. Wang"},{"name":"X. Wang"},{"name":"B. Wang"},{"name":"A. Warburton"},{"name":"J. Webb"},{"name":"S. Wehle"},{"name":"C. Wessel"}],"abstract":"We present the physics program of the Belle II experiment, located on the intensity frontier SuperKEKB e+e- collider. Belle II collected its first collisions in 2018, and is expected to operate for the next decade. It is anticipated to collect 50/ab of collision data over its lifetime. This book is the outcome of a joint effort of Belle II collaborators and theorists through the Belle II theory interface platform (B2TiP), an effort that commenced in 2014. The aim of B2TiP was to elucidate the potential impacts of the Belle II program, which includes a wide scope of physics topics: B physics, charm, tau, quarkonium, electroweak precision measurements and dark sector searches. It is composed of nine working groups (WGs), which are coordinated by teams of theorist and experimentalists conveners: Semileptonic and leptonic B decays, Radiative and Electroweak penguins, phi_1 and phi_2 (time-dependent CP violation) measurements, phi_3 measurements, Charmless hadronic B decay, Charm, Quarkonium(like), tau and low-multiplicity processes, new physics and global fit analyses. This book highlights \"golden- and silver-channels\", i.e. those that would have the highest potential impact in the field. Theorists scrutinised the role of those measurements and estimated the respective theoretical uncertainties, achievable now as well as prospects for the future. Experimentalists investigated the expected improvements with the large dataset expected from Belle II, taking into account improved performance from the upgraded detector.","source":"Semantic Scholar","year":2018,"language":"en","subjects":["Physics"],"doi":"10.1093/ptep/ptz106","url":"https://www.semanticscholar.org/paper/7617d4015f2521bce6abad3adb8135c272123bc9","pdf_url":"https://academic.oup.com/ptep/article-pdf/2019/12/123C01/32693980/ptz106.pdf","is_open_access":true,"citations":1020,"published_at":"","score":92},{"id":"ss_1aa57fd9f06e73c5fb05f070cc88b2f7cc3aa6d3","title":"Offloading in Mobile Edge Computing: Task Allocation and Computational Frequency Scaling","authors":[{"name":"T. Dinh"},{"name":"Jianhua Tang"},{"name":"Q. La"},{"name":"Tony Q. S. Quek"}],"abstract":"","source":"Semantic Scholar","year":2017,"language":"en","subjects":["Computer Science"],"doi":"10.1109/TCOMM.2017.2699660","url":"https://www.semanticscholar.org/paper/1aa57fd9f06e73c5fb05f070cc88b2f7cc3aa6d3","is_open_access":true,"citations":842,"published_at":"","score":86.25999999999999},{"id":"ss_7fd1ce2f7bc7d15e08a4298b8181254e97159d9d","title":"The CMS experiment at the CERN LHC","authors":[{"name":"S. Chatrchyan"},{"name":"G. Hmayakyan"},{"name":"V. Khachatryan"},{"name":"A. Sirunyan"},{"name":"W. Adam"},{"name":"T. Bauer"},{"name":"T. Bergauer"},{"name":"H. Bergauer"},{"name":"M. Dragicevic"},{"name":"J. Erö"},{"name":"M. Friedl"},{"name":"R. Frühwirth"},{"name":"V. Ghete"},{"name":"P. Glaser"},{"name":"C. Hartl"},{"name":"N. Hoermann"},{"name":"J. Hrubec"},{"name":"S. Hänsel"},{"name":"M. Jeitler"},{"name":"K. Kastner"},{"name":"M. Krammer"},{"name":"M. Markytan"},{"name":"I. Mikulec"},{"name":"B. Neuherz"},{"name":"Tobias Nöbauer"},{"name":"M. Oberegger"},{"name":"M. Padrta"},{"name":"M. Pernicka"},{"name":"P. Porth"},{"name":"H. Rohringer"},{"name":"S. Schmid"},{"name":"T. Schreiner"},{"name":"R. Stark"},{"name":"H. Steininger"},{"name":"J. Strauss"},{"name":"A. Taurok"},{"name":"D. Uhl"},{"name":"W. Waltenberger"},{"name":"G. Walzel"},{"name":"E. Widl"},{"name":"C. Wulz"},{"name":"V. Petrov"},{"name":"V. Prosolovich"},{"name":"V. Chekhovsky"},{"name":"O. Dvornikov"},{"name":"I. Emeliantchik"},{"name":"A. Litomin"},{"name":"V. Makarenko"},{"name":"I. Marfin"},{"name":"V. Mossolov"},{"name":"N. Shumeiko"},{"name":"A. Solin"},{"name":"R. Stefanovitch"},{"name":"S. Gonzalez"},{"name":"A. Tikhonov"},{"name":"A. Fedorov"},{"name":"M. Korzhik"},{"name":"O. Missevitch"},{"name":"R. Zuyeuski"},{"name":"W. Beaumont"},{"name":"M. Cardaci"},{"name":"E. D. Langhe"},{"name":"E. Wolf"},{"name":"E. Delmeire"},{"name":"S. Ochesanu"},{"name":"M. Tasevsky"},{"name":"P. Mechelen"},{"name":"J. D’Hondt"},{"name":"S. D. Weirdt"},{"name":"O. Devroede"},{"name":"R. Goorens"},{"name":"S. Hannaert"},{"name":"J. Heyninck"},{"name":"J. Maes"},{"name":"M. Mozer"},{"name":"S. Tavernier"},{"name":"W. Doninck"},{"name":"L. V. Lancker"},{"name":"P. Mulders"},{"name":"I. Villella"},{"name":"C. Wastiels"},{"name":"C. Yu"},{"name":"O. Bouhali"},{"name":"O. Charaf"},{"name":"B. Clerbaux"},{"name":"P. D. Harenne"},{"name":"G. Lentdecker"},{"name":"J. Dewulf"},{"name":"S. Elgammal"},{"name":"R. Gindroz"},{"name":"G. Hammad"},{"name":"T. Mahmoud"},{"name":"L. Neukermans"},{"name":"M. Pins"},{"name":"R. Pins"},{"name":"S. Rugovac"},{"name":"J. Stefanescu"},{"name":"V. Sundararajan"},{"name":"C. Velde"},{"name":"P. Vanlaer"},{"name":"J. Wickens"},{"name":"M. Tytgat"},{"name":"S. Assouak"},{"name":"J. Bonnet"},{"name":"G. Bruno"},{"name":"J. Caudron"},{"name":"B. D. Callatay"},{"name":"De Jeneret"},{"name":"S. Visscher"},{"name":"P. Demin"},{"name":"D. Favart"},{"name":"C. Felix"},{"name":"B. Florins"},{"name":"E. Forton"},{"name":"A. Giammanco"},{"name":"G. Grégoire"},{"name":"M. Jonckman"},{"name":"D. Kcira"},{"name":"T. Keutgen"},{"name":"V. Lemaitre"},{"name":"D. Michotte"},{"name":"O. Militaru"},{"name":"S. Ovyn"},{"name":"T. Pierzchala"},{"name":"K. Piotrzkowski"},{"name":"V. Roberfroid"},{"name":"X. Rouby"},{"name":"N. Schul"},{"name":"O. Aa"},{"name":"N. Beliy"},{"name":"E. Daubie"},{"name":"P. Herquet"},{"name":"G. Alves"},{"name":"M. Pol"},{"name":"M. Souza"},{"name":"M. Vaz"},{"name":"D. Damiao"},{"name":"V. Oguri"},{"name":"A. Santoro"},{"name":"A. Sznajder"},{"name":"E. Gregores"},{"name":"R. Iope"},{"name":"S. Novaes"},{"name":"T. Tomei"},{"name":"T. Anguelov"},{"name":"G. Antchev"},{"name":"I. Atanasov"},{"name":"J. Damgov"},{"name":"N. Darmenov"},{"name":"L. Dimitrov"},{"name":"V. Genchev"},{"name":"P. Iaydjiev"},{"name":"A. Marinov"},{"name":"S. Piperov"},{"name":"S. Stoykova"},{"name":"G. Sultanov"},{"name":"R. Trayanov"},{"name":"I. Vankov"},{"name":"C. Cheshkov"},{"name":"A. Dimitrov"},{"name":"M. Dyulendarova"},{"name":"I. Glushkov"},{"name":"V. Kozhuharov"},{"name":"L. Litov"},{"name":"M. Makariev"},{"name":"E. Marinova"},{"name":"S. Markov"},{"name":"M. Mateev"},{"name":"I. Nasteva"},{"name":"B. Pavlov"},{"name":"P. Petev"},{"name":"P. Petkov"},{"name":"V. Spassov"},{"name":"Z. Toteva"},{"name":"V. Velev"},{"name":"V. Verguilov"},{"name":"J. Bian"},{"name":"Guo-ming Chen"},{"name":"He-Sheng Chen"},{"name":"M. Chen"},{"name":"C. Jiang"},{"name":"B. Liu"},{"name":"X. Shen"},{"name":"H. Sun"},{"name":"J. Tao"},{"name":"Jian Wang"},{"name":"Mingming Yang"},{"name":"Zhiqin Zhang"},{"name":"W. Zhao"},{"name":"H. Zhuang"},{"name":"Y. Ban"},{"name":"J. Cai"},{"name":"Y. Ge"},{"name":"S. Liu"},{"name":"H. Liu"},{"name":"L. Liu"},{"name":"S. Qian"},{"name":"Q. Wang"},{"name":"Z. Xue"},{"name":"Zongchang Yang"},{"name":"Y. Ye"},{"name":"J. Ying"},{"name":"P. Li"},{"name":"J. Liao"},{"name":"Z. Xue"},{"name":"D. Yan"},{"name":"H. Yuan"},{"name":"C. Montoya"},{"name":"J. Sanabria"},{"name":"N. Godinovic"},{"name":"I. Puljak"},{"name":"I. Sorić"},{"name":"Z. Antunović"},{"name":"M. Dželalija"},{"name":"K. Marasovic"},{"name":"V. Brigljevic"},{"name":"K. Kadija"},{"name":"S. Morović"},{"name":"R. Fereos"},{"name":"C. Nicolaou"},{"name":"A. Papadakis"},{"name":"F. Ptochos"},{"name":"P. Razis"},{"name":"D. Tsiakkouri"},{"name":"Z. Zinonos"},{"name":"A. Hektor"},{"name":"M. Kadastik"},{"name":"K. Kannike"},{"name":"É. Lippmaa"},{"name":"M. Müntel"},{"name":"M. Raidal"},{"name":"L. Rebane"},{"name":"P. Aarnio"},{"name":"E. Anttila"},{"name":"K. Banzuzi"},{"name":"P. Bulteau"},{"name":"S. Czellár"},{"name":"N. Eiden"},{"name":"C. Eklund"},{"name":"P. Engstrom"},{"name":"A. Heikkinen"},{"name":"A. Honkanen"},{"name":"J. Härkönen"},{"name":"V. Karimäki"},{"name":"H. Katajisto"},{"name":"R. Kinnunen"},{"name":"J. Klem"},{"name":"J. Kortesmaa"},{"name":"M. Kotamäki"},{"name":"A. Kuronen"},{"name":"T. Lampén"},{"name":"K. Lassila-Perini"},{"name":"V. Lefébure"},{"name":"S. Lehti"},{"name":"T. Lindén"},{"name":"P. Luukka"},{"name":"S. Michal"},{"name":"F. Brígido"},{"name":"T. Mäenpää"},{"name":"T. Nyman"},{"name":"J. Nysten"},{"name":"E. Pietarinen"},{"name":"K. Skog"},{"name":"K. Tammi"},{"name":"E. Tuominen"},{"name":"J. Tuominiemi"},{"name":"D. Ungaro"},{"name":"T. Vanhala"},{"name":"L. Wendland"},{"name":"C. Williams"},{"name":"M. Iskanius"},{"name":"A. Korpela"},{"name":"G. Polese"},{"name":"T. Tuuva"},{"name":"G. Bassompierre"},{"name":"A. Bazan"},{"name":"P. David"},{"name":"J. Ditta"},{"name":"G. Drobychev"},{"name":"N. Fouque"},{"name":"J. Guillaud"},{"name":"V. Hermel"},{"name":"A. Karneyeu"},{"name":"T. L. Flour"},{"name":"S. Lieunard"},{"name":"M. Maire"},{"name":"P. Mendiburu"},{"name":"P. Nédélec"},{"name":"J. Peigneux"},{"name":"M. Schneegans"},{"name":"D. Sillou"},{"name":"J. Vialle"},{"name":"M. Anfreville"},{"name":"J. Bard"},{"name":"P. Besson"},{"name":"E. Bougamont"},{"name":"M. Boyer"},{"name":"P. Brédy"},{"name":"R. Chipaux"},{"name":"M. Dejardin"},{"name":"D. Denegri"},{"name":"J. Descamps"},{"name":"B. Fabbro"},{"name":"J. Faure"},{"name":"S. Ganjour"},{"name":"F. Gentit"},{"name":"A. Givernaud"},{"name":"P. Gras"},{"name":"G. H. Monchenault"},{"name":"P. Jarry"},{"name":"C. Jeanney"},{"name":"F. Kircher"},{"name":"M. Lemaire"},{"name":"Y. Lemoigne"},{"name":"B. Levésy"},{"name":"E. Locci"},{"name":"J. Lottin"},{"name":"I. Mandjavidze"},{"name":"M. Mur"},{"name":"J. Pansart"},{"name":"A. Payn"},{"name":"J. Rander"},{"name":"J. Reymond"},{"name":"J. Rolquin"},{"name":"F. Rondeaux"},{"name":"A. Rosowsky"},{"name":"J. Rousse"},{"name":"Z. Sun"},{"name":"J. Tartas"},{"name":"A. Lysebetten"},{"name":"P. Venault"},{"name":"P. Verrecchia"},{"name":"M. Anduze"},{"name":"J. Badier"},{"name":"S. Baffioni"},{"name":"M. Bercher"},{"name":"C. Bernet"},{"name":"U. Berthon"},{"name":"J. Bourotte"},{"name":"A. Busata"},{"name":"P. Busson"},{"name":"M. Cerutti"},{"name":"D. Chamont"},{"name":"C. Charlot"},{"name":"C. Collard"},{"name":"A. Debraine"},{"name":"D. Decotigny"},{"name":"L. Dobrzyński"},{"name":"O. Ferreira"},{"name":"Y. Geerebaert"},{"name":"J. Gilly"},{"name":"C. Gregory"},{"name":"L. G. Riveros"},{"name":"M. Haguenauer"},{"name":"A. Karar"},{"name":"B. Koblitz"},{"name":"D. Lecouturier"},{"name":"A. Mathieu"},{"name":"G. Milleret"},{"name":"P. Miné"},{"name":"P. Paganini"},{"name":"P. Poilleux"},{"name":"N. Pukhaeva"},{"name":"N. Regnault"},{"name":"T. Romanteau"},{"name":"I. Semeniouk"},{"name":"Y. Sirois"},{"name":"C. Thiebaux"},{"name":"J. Vanel"},{"name":"A. Zabi"},{"name":"J. Agram"},{"name":"A. Albert"},{"name":"L. Anckenmann"},{"name":"J. Andrea"},{"name":"F. Anstotz"},{"name":"A. Bergdolt"},{"name":"J. Berst"},{"name":"R. Blaes"},{"name":"D. Bloch"},{"name":"J. Brom"},{"name":"J. Cailleret"},{"name":"F. Charles"},{"name":"E. Christophel"},{"name":"G. Claus"},{"name":"J. Coffin"},{"name":"C. Colledani"},{"name":"J. Croix"},{"name":"E. Dangelser"},{"name":"N. Dick"},{"name":"F. Didierjean"},{"name":"F. Drouhin"},{"name":"W. Duliński"},{"name":"J. Ernenwein"},{"name":"R. Fang"},{"name":"J. Fontaine"},{"name":"G. Gaudiot"},{"name":"W. Geist"},{"name":"D. Gelé"},{"name":"T. Goeltzenlichter"},{"name":"U. Goerlach"},{"name":"P. Graehling"},{"name":"L. Gross"},{"name":"C. Hu"},{"name":"J. M. Helleboid"},{"name":"T. Henkes"},{"name":"M. Hoffer"},{"name":"C. Hoffmann"},{"name":"J. Hosselet"},{"name":"L. Houchu"},{"name":"Y. Hu"},{"name":"D. Huss"},{"name":"C. Illinger"},{"name":"F. Jeanneau"},{"name":"P. Juillot"},{"name":"T. Kachelhoffer"},{"name":"M. Kapp"},{"name":"H. Kettunen"},{"name":"L. Ayat"},{"name":"A. Bihan"},{"name":"A. Lounis"},{"name":"C. Maazouzi"},{"name":"V. Mack"},{"name":"P. Majewski"},{"name":"D. Mangeol"},{"name":"J. Michel"},{"name":"S. Moreau"},{"name":"C. Olivetto"},{"name":"A. Pallarès"},{"name":"Y. Patois"},{"name":"P. P. vorio"},{"name":"C. Racca"},{"name":"Y. Riahi"},{"name":"I. Ripp-Baudot"},{"name":"P. Schmitt"},{"name":"J. Schunck"},{"name":"G. Schuster"},{"name":"B. Schwaller"},{"name":"M. Sigward"},{"name":"J. Sohler"},{"name":"J. Speck"},{"name":"R. Strub"},{"name":"T. Todorov"},{"name":"R. Turchetta"},{"name":"P. Hove"},{"name":"D. Vintache"},{"name":"A. Zghiche"},{"name":"M. Ageron"},{"name":"J. Augustin"},{"name":"C. Baty"},{"name":"G. Baulieu"},{"name":"M. Bedjidian"},{"name":"J. Blaha"},{"name":"A. Bonnevaux"},{"name":"G. Boudoul"},{"name":"P. Brunet"},{"name":"E. Chabanat"},{"name":"E. Chabert"},{"name":"R. Chierici"},{"name":"V. Chorowicz"},{"name":"C. Combaret"},{"name":"D. Contardo"},{"name":"R. D. Negra"},{"name":"P. Depasse"},{"name":"O. Drapier"},{"name":"M. Dupanloup"},{"name":"T. Dupasquier"},{"name":"H. Mamouni"},{"name":"N. Estre"},{"name":"J. Fay"},{"name":"S. Gascon"},{"name":"N. Giraud"},{"name":"C. Girerd"},{"name":"G. Guillot"},{"name":"R. Haroutunian"},{"name":"B. Ille"},{"name":"M. Lethuillier"},{"name":"N. Lumb"},{"name":"C. Martin"},{"name":"H. Mathez"},{"name":"G. Maurelli"},{"name":"S. Muanza"},{"name":"P. Pangaud"},{"name":"S. Perriès"},{"name":"O. Ravat"},{"name":"E. Schibler"},{"name":"F. Schirra"},{"name":"G. Smadja"},{"name":"S. Tissot"},{"name":"B. Trocmé"},{"name":"S. Vanzetto"},{"name":"J. Walder"},{"name":"Y. Bagaturia"},{"name":"D. Mjavia"},{"name":"A. Mzhavia"},{"name":"Z. Tsamalaidze"},{"name":"V. Roǐnishvili"},{"name":"R. Adolphi"},{"name":"G. Anagnostou"},{"name":"R. Brauer"},{"name":"W. Braunschweig"}],"abstract":"","source":"Semantic Scholar","year":2008,"language":"en","subjects":["Physics"],"doi":"10.1088/1748-0221/3/08/S08004","url":"https://www.semanticscholar.org/paper/7fd1ce2f7bc7d15e08a4298b8181254e97159d9d","pdf_url":"http://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1281\u0026context=physicsbloom","is_open_access":true,"citations":7168,"published_at":"","score":82},{"id":"ss_3d8862d02931c63e24952c2af346b6260795ad32","title":"Ubiquity and dominance of oxygenated species in organic aerosols in anthropogenically‐influenced Northern Hemisphere midlatitudes","authors":[{"name":"Q. Zhang"},{"name":"J. Jimenez"},{"name":"M. Canagaratna"},{"name":"J. Allan"},{"name":"H. Coe"},{"name":"I. Ulbrich"},{"name":"M. Alfarra"},{"name":"A. Takami"},{"name":"A. Middlebrook"},{"name":"Yele Sun"},{"name":"K. Džepina"},{"name":"E. Dunlea"},{"name":"K. Docherty"},{"name":"P. DeCarlo"},{"name":"D. Salcedo"},{"name":"T. Onasch"},{"name":"J. Jayne"},{"name":"T. Miyoshi"},{"name":"A. Shimono"},{"name":"S. Hatakeyama"},{"name":"N. Takegawa"},{"name":"Y. Kondo"},{"name":"J. Schneider"},{"name":"F. Drewnick"},{"name":"S. Borrmann"},{"name":"S. Weimer"},{"name":"K. Demerjian"},{"name":"P. I. Williams"},{"name":"K. Bower"},{"name":"R. Bahreini"},{"name":"R. Bahreini"},{"name":"L. Cottrell"},{"name":"R. Griffin"},{"name":"J. Rautiainen"},{"name":"J. Y. Sun"},{"name":"Yan Zhang"},{"name":"D. Worsnop"}],"abstract":"","source":"Semantic Scholar","year":2007,"language":"en","subjects":["Biology","Geology","Environmental Science"],"doi":"10.1029/2007GL029979","url":"https://www.semanticscholar.org/paper/3d8862d02931c63e24952c2af346b6260795ad32","pdf_url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2007GL029979","is_open_access":true,"citations":2031,"published_at":"","score":81},{"id":"ss_3647f20633e713384bfef4e9c246a3b4b51c89ef","title":"Ultrasensitive terahertz sensing with high-Q Fano resonances in metasurfaces","authors":[{"name":"Ranjan Singh"},{"name":"W. Cao"},{"name":"I. Al-Naib"},{"name":"Longqing Cong"},{"name":"W. Withayachumnankul"},{"name":"Weili Zhang"}],"abstract":"High quality factor resonances are extremely promising for designing ultra-sensitive refractive index label-free sensors, since it allows intense interaction between electromagnetic waves and the analyte material. Metamaterial and plasmonic sensing have recently attracted a lot of attention due to subwavelength confinement of electromagnetic fields in the resonant structures. However, the excitation of high quality factor resonances in these systems has been a challenge. We excite an order of magnitude higher quality factor resonances in planar terahertz metamaterials that we exploit for ultrasensitive sensing. The low-loss quadrupole and Fano resonances with extremely narrow linewidths enable us to measure the minute spectral shift caused due to the smallest change in the refractive index of the surrounding media. We achieve sensitivity levels of 7.75 × 103 nm/refractive index unit (RIU) with quadrupole and 5.7 × 104 nm/RIU with the Fano resonances which could be further enhanced by using thinner substrates. These findings would facilitate the design of ultrasensitive real time chemical and biomolecular sensors in the fingerprint region of the terahertz regime.","source":"Semantic Scholar","year":2014,"language":"en","subjects":["Physics"],"doi":"10.1063/1.4895595","url":"https://www.semanticscholar.org/paper/3647f20633e713384bfef4e9c246a3b4b51c89ef","pdf_url":"https://dr.ntu.edu.sg/bitstream/10356/102411/1/Ultrasensitive%20terahertz%20sensing%20with%20high-Q%20Fano%20resonances%20in%20metasurfaces.pdf","is_open_access":true,"citations":610,"published_at":"","score":76.3},{"id":"ss_4a597a081721e436e20b4e85197072e22aaecfad","title":"From $r$ to $Q^*$: Your Language Model is Secretly a Q-Function","authors":[{"name":"Rafael Rafailov"},{"name":"Joey Hejna"},{"name":"Ryan Park"},{"name":"Chelsea Finn"}],"abstract":"Reinforcement Learning From Human Feedback (RLHF) has been critical to the success of the latest generation of generative AI models. In response to the complex nature of the classical RLHF pipeline, direct alignment algorithms such as Direct Preference Optimization (DPO) have emerged as an alternative approach. Although DPO solves the same objective as the standard RLHF setup, there is a mismatch between the two approaches. Standard RLHF deploys reinforcement learning in a specific token-level MDP, while DPO is derived as a bandit problem in which the whole response of the model is treated as a single arm. In this work we rectify this difference. We theoretically show that we can derive DPO in the token-level MDP as a general inverse Q-learning algorithm, which satisfies the Bellman equation. Using our theoretical results, we provide three concrete empirical insights. First, we show that because of its token level interpretation, DPO is able to perform some type of credit assignment. Next, we prove that under the token level formulation, classical search-based algorithms, such as MCTS, which have recently been applied to the language generation space, are equivalent to likelihood-based search on a DPO policy. Empirically we show that a simple beam search yields meaningful improvement over the base DPO policy. Finally, we show how the choice of reference policy causes implicit rewards to decline during training. We conclude by discussing applications of our work, including information elicitation in multi-turn dialogue, reasoning, agentic applications and end-to-end training of multi-model systems.","source":"Semantic Scholar","year":2024,"language":"en","subjects":["Computer Science"],"url":"https://www.semanticscholar.org/paper/4a597a081721e436e20b4e85197072e22aaecfad","is_open_access":true,"citations":247,"published_at":"","score":75.41},{"id":"ss_bf89341e16fc0a379ab5e4c6370cf7ea4e9afd03","title":"Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions","authors":[{"name":"Yevgen Chebotar"},{"name":"Q. Vuong"},{"name":"A. Irpan"},{"name":"Karol Hausman"},{"name":"F. Xia"},{"name":"Yao Lu"},{"name":"Aviral Kumar"},{"name":"Tianhe Yu"},{"name":"Alexander Herzog"},{"name":"Karl Pertsch"},{"name":"K. Gopalakrishnan"},{"name":"Julian Ibarz"},{"name":"Ofir Nachum"},{"name":"S. Sontakke"},{"name":"Grecia Salazar"},{"name":"Huong Tran"},{"name":"Jodilyn Peralta"},{"name":"Clayton Tan"},{"name":"D. Manjunath"},{"name":"Jaspiar Singht"},{"name":"Brianna Zitkovich"},{"name":"Tomas Jackson"},{"name":"Kanishka Rao"},{"name":"Chelsea Finn"},{"name":"S. Levine"}],"abstract":"In this work, we present a scalable reinforcement learning method for training multi-task policies from large offline datasets that can leverage both human demonstrations and autonomously collected data. Our method uses a Transformer to provide a scalable representation for Q-functions trained via offline temporal difference backups. We therefore refer to the method as Q-Transformer. By discretizing each action dimension and representing the Q-value of each action dimension as separate tokens, we can apply effective high-capacity sequence modeling techniques for Q-learning. We present several design decisions that enable good performance with offline RL training, and show that Q-Transformer outperforms prior offline RL algorithms and imitation learning techniques on a large diverse real-world robotic manipulation task suite. The project's website and videos can be found at https://qtransformer.github.io","source":"Semantic Scholar","year":2023,"language":"en","subjects":["Computer Science"],"doi":"10.48550/arXiv.2309.10150","url":"https://www.semanticscholar.org/paper/bf89341e16fc0a379ab5e4c6370cf7ea4e9afd03","pdf_url":"https://arxiv.org/pdf/2309.10150","is_open_access":true,"citations":145,"published_at":"","score":71.35},{"id":"arxiv_2510.04569","title":"Risk-Sensitive Option Market Making with Arbitrage-Free eSSVI Surfaces: A Constrained RL and Stochastic Control Bridge","authors":[{"name":"Jian'an Zhang"}],"abstract":"We formulate option market making as a constrained, risk-sensitive control problem that unifies execution, hedging, and arbitrage-free implied-volatility surfaces inside a single learning loop. A fully differentiable eSSVI layer enforces static no-arbitrage conditions (butterfly and calendar) while the policy controls half-spreads, hedge intensity, and structured surface deformations (state-dependent rho-shift and psi-scale). Executions are intensity-driven and respond monotonically to spreads and relative mispricing; tail risk is shaped with a differentiable CVaR objective via the Rockafellar--Uryasev program. We provide theory for (i) grid-consistency and rates for butterfly/calendar surrogates, (ii) a primal--dual grounding of a learnable dual action acting as a state-dependent Lagrange multiplier, (iii) differentiable CVaR estimators with mixed pathwise and likelihood-ratio gradients and epi-convergence to the nonsmooth objective, (iv) an eSSVI wing-growth bound aligned with Lee's moment constraints, and (v) policy-gradient validity under smooth surrogates. In simulation (Heston fallback; ABIDES-ready), the agent attains positive adjusted P\\\u0026L on most intraday segments while keeping calendar violations at numerical zero and butterfly violations at the numerical floor; ex-post tails remain realistic and can be tuned through the CVaR weight. The five control heads admit clear economic semantics and analytic sensitivities, yielding a white-box learner that unifies pricing consistency and execution control in a reproducible pipeline.","source":"arXiv","year":2025,"language":"en","subjects":["q-fin.TR"],"url":"https://arxiv.org/abs/2510.04569","pdf_url":"https://arxiv.org/pdf/2510.04569","is_open_access":true,"published_at":"2025-10-06T08:11:16Z","score":69},{"id":"arxiv_2506.04658","title":"Can Artificial Intelligence Trade the Stock Market?","authors":[{"name":"Jędrzej Maskiewicz"},{"name":"Paweł Sakowski"}],"abstract":"The paper explores the use of Deep Reinforcement Learning (DRL) in stock market trading, focusing on two algorithms: Double Deep Q-Network (DDQN) and Proximal Policy Optimization (PPO) and compares them with Buy and Hold benchmark. It evaluates these algorithms across three currency pairs, the S\u0026P 500 index and Bitcoin, on the daily data in the period of 2019-2023. The results demonstrate DRL's effectiveness in trading and its ability to manage risk by strategically avoiding trades in unfavorable conditions, providing a substantial edge over classical approaches, based on supervised learning in terms of risk-adjusted returns.","source":"arXiv","year":2025,"language":"en","subjects":["q-fin.TR","cs.LG","q-fin.CP"],"url":"https://arxiv.org/abs/2506.04658","pdf_url":"https://arxiv.org/pdf/2506.04658","is_open_access":true,"published_at":"2025-06-05T05:59:10Z","score":69},{"id":"arxiv_2407.04521","title":"Unified continuous-time q-learning for mean-field game and mean-field control problems","authors":[{"name":"Xiaoli Wei"},{"name":"Xiang Yu"},{"name":"Fengyi Yuan"}],"abstract":"This paper studies the continuous-time q-learning in mean-field jump-diffusion models when the population distribution is not directly observable. We propose the integrated q-function in decoupled form (decoupled Iq-function) from the representative agent's perspective and establish its martingale characterization, which provides a unified policy evaluation rule for both mean-field game (MFG) and mean-field control (MFC) problems. Moreover, we consider the learning procedure where the representative agent updates the population distribution based on his own state values. Depending on the task to solve the MFG or MFC problem, we can employ the decoupled Iq-function differently to characterize the mean-field equilibrium policy or the mean-field optimal policy respectively. Based on these theoretical findings, we devise a unified q-learning algorithm for both MFG and MFC problems by utilizing test policies and the averaged martingale orthogonality condition. For several financial applications in the jump-diffusion setting, we obtain the exact parameterization of the decoupled Iq-functions and the value functions, and illustrate our q-learning algorithm with satisfactory performance.","source":"arXiv","year":2024,"language":"en","subjects":["math.OC","cs.LG","q-fin.CP"],"url":"https://arxiv.org/abs/2407.04521","pdf_url":"https://arxiv.org/pdf/2407.04521","is_open_access":true,"published_at":"2024-07-05T14:06:59Z","score":68},{"id":"arxiv_2412.00036","title":"Beyond Monte Carlo: Harnessing Diffusion Models to Simulate Financial Market Dynamics","authors":[{"name":"Andrew Lesniewski"},{"name":"Giulio Trigila"}],"abstract":"We propose a highly efficient and accurate methodology for generating synthetic financial market data using a diffusion model approach. The synthetic data produced by our methodology align closely with observed market data in several key aspects: (i) they pass the two-sample Cramer - von Mises test for portfolios of assets, and (ii) Q - Q plots demonstrate consistency across quantiles, including in the tails, between observed and generated market data. Moreover, the covariance matrices derived from a large set of synthetic market data exhibit significantly lower condition numbers compared to the estimated covariance matrices of the observed data. This property makes them suitable for use as regularized versions of the latter. For model training, we develop an efficient and fast algorithm based on numerical integration rather than Monte Carlo simulations. The methodology is tested on a large set of equity data.","source":"arXiv","year":2024,"language":"en","subjects":["q-fin.CP","cs.AI","cs.CE","q-fin.PM"],"url":"https://arxiv.org/abs/2412.00036","pdf_url":"https://arxiv.org/pdf/2412.00036","is_open_access":true,"published_at":"2024-11-21T17:39:23Z","score":68},{"id":"ss_714c207bf089d9b4d773fbceaae7e06461a59c3d","title":"Deterministic design of wavelength scale, ultra-high Q photonic crystal nanobeam cavities.","authors":[{"name":"Q. Quan"},{"name":"M. Lončar"}],"abstract":"Photonic crystal nanobeam cavities are versatile platforms of interest for optical communications, optomechanics, optofluidics, cavity QED, etc. In a previous work [Appl. Phys. Lett. 96, 203102 (2010)], we proposed a deterministic method to achieve ultrahigh Q cavities. This follow-up work provides systematic analysis and verifications of the deterministic design recipe and further extends the discussion to air-mode cavities. We demonstrate designs of dielectric-mode and air-mode cavities with Q \u003e 10⁹, as well as dielectric-mode nanobeam cavities with both ultrahigh-Q (\u003e 10⁷) and ultrahigh on-resonance transmissions (T \u003e 95%).","source":"Semantic Scholar","year":2011,"language":"en","subjects":["Physics","Medicine"],"doi":"10.1364/OE.19.018529","url":"https://www.semanticscholar.org/paper/714c207bf089d9b4d773fbceaae7e06461a59c3d","pdf_url":"https://doi.org/10.1364/oe.19.018529","is_open_access":true,"citations":430,"published_at":"","score":67.9},{"id":"ss_069f6760e7e45df6cccb006905b64742607ff55f","title":"Fuzzy Q-Learning for multi-agent decentralized energy management in microgrids","authors":[{"name":"P. Kofinas"},{"name":"A. Dounis"},{"name":"G. Vouros"}],"abstract":"Abstract This study proposes a cooperative multi-agent system for managing the energy of a stand-alone microgrid. The multi-agent system learns to control the components of the microgrid so as this to achieve its purposes and operate effectively, by means of a distributed, collaborative reinforcement learning method in continuous actions-states space. Stand-alone microgrids present challenges regarding guaranteeing electricity supply and increasing the reliability of the system under the uncertainties introduced by the renewable power sources and the stochastic demand of the consumers. In this article we consider a microgrid that consists of power production, power consumption and power storage units: the power production group includes a Photovoltaic source, a fuel cell and a diesel generator; the power consumption group includes an electrolyzer unit, a desalination plant and a variable electrical load that represent the power consumption of a building; the power storage group includes only the Battery bank. We conjecture that a distributed multi-agent system presents specific advantages to control the microgrid components which operate in a continuous states and actions space: For this purpose we propose the use of fuzzy Q-Learning methods for agents representing microgrid components to act as independent learners, while sharing state variables to coordinate their behavior. Experimental results highlight both the effectiveness of individual agents to control system components, as well as the effectiveness of the multi-agent system to guarantee electricity supply and increase the reliability of the microgrid.","source":"Semantic Scholar","year":2018,"language":"en","subjects":["Computer Science"],"doi":"10.1016/J.APENERGY.2018.03.017","url":"https://www.semanticscholar.org/paper/069f6760e7e45df6cccb006905b64742607ff55f","is_open_access":true,"citations":192,"published_at":"","score":67.75999999999999},{"id":"crossref_10.3390/agriculture13112057","title":"SpikoPoniC: A Low-Cost Spiking Neuromorphic Computer for Smart Aquaponics","authors":[{"name":"Ali Siddique"},{"name":"Jingqi Sun"},{"name":"Kung Jui Hou"},{"name":"Mang I. Vai"},{"name":"Sio Hang Pun"},{"name":"Muhammad Azhar Iqbal"}],"abstract":"Aquaponics is an emerging area of agricultural sciences that combines aquaculture and hydroponics in a symbiotic way to enhance crop production. A stable smart aquaponic system requires estimating the fish size in real time. Though deep learning has shown promise in the context of smart aquaponics, most smart systems are extremely slow and costly and cannot be deployed on a large scale. Therefore, we design and present a novel neuromorphic computer that uses spiking neural networks (SNNs) for estimating not only the length but also the weight of the fish. To train the SNN, we present a novel hybrid scheme in which some of the neural layers are trained using direct SNN backpropagation, while others are trained using standard backpropagation. By doing this, a blend of high hardware efficiency and accuracy can be achieved. The proposed computer SpikoPoniC can classify more than 84 million fish samples in a second, achieving a speedup of at least 3369× over traditional general-purpose computers. The SpikoPoniC consumes less than 1100 slice registers on Virtex 6 and is much cheaper than most SNN-based hardware systems. To the best of our knowledge, this is the first SNN-based neuromorphic system that performs smart real-time aquaponic monitoring.","source":"CrossRef","year":2023,"language":"en","subjects":null,"doi":"10.3390/agriculture13112057","url":"https://doi.org/10.3390/agriculture13112057","is_open_access":true,"citations":10,"published_at":"","score":67.3},{"id":"ss_c50517040585ca5e88d7ba1d5b9e95b19e6f2df9","title":"Cannabinoids inhibit N- and P/Q-type calcium channels in cultured rat hippocampal neurons.","authors":[{"name":"W. Twitchell"},{"name":"Sean P. Brown"},{"name":"K. Mackie"}],"abstract":"","source":"Semantic Scholar","year":1997,"language":"en","subjects":["Chemistry","Medicine"],"doi":"10.1152/JN.1997.78.1.43","url":"https://www.semanticscholar.org/paper/c50517040585ca5e88d7ba1d5b9e95b19e6f2df9","is_open_access":true,"citations":572,"published_at":"","score":67.16},{"id":"arxiv_2306.16208","title":"Continuous-time q-learning for mean-field control problems","authors":[{"name":"Xiaoli Wei"},{"name":"Xiang Yu"}],"abstract":"This paper studies the q-learning, recently coined as the continuous time counterpart of Q-learning by Jia and Zhou (2023), for continuous time Mckean-Vlasov control problems in the setting of entropy-regularized reinforcement learning. In contrast to the single agent's control problem in Jia and Zhou (2023), the mean-field interaction of agents renders the definition of the q-function more subtle, for which we reveal that two distinct q-functions naturally arise: (i) the integrated q-function (denoted by $q$) as the first-order approximation of the integrated Q-function introduced in Gu, Guo, Wei and Xu (2023), which can be learnt by a weak martingale condition involving test policies; and (ii) the essential q-function (denoted by $q_e$) that is employed in the policy improvement iterations. We show that two q-functions are related via an integral representation under all test policies. Based on the weak martingale condition and our proposed searching method of test policies, some model-free learning algorithms are devised. In two examples, one in LQ control framework and one beyond LQ control framework, we can obtain the exact parameterization of the optimal value function and q-functions and illustrate our algorithms with simulation experiments.","source":"arXiv","year":2023,"language":"en","subjects":["cs.LG","math.OC","q-fin.CP"],"url":"https://arxiv.org/abs/2306.16208","pdf_url":"https://arxiv.org/pdf/2306.16208","is_open_access":true,"published_at":"2023-06-28T13:43:46Z","score":67},{"id":"arxiv_2311.05743","title":"Advancing Algorithmic Trading: A Multi-Technique Enhancement of Deep Q-Network Models","authors":[{"name":"Gang Hu"}],"abstract":"This study enhances a Deep Q-Network (DQN) trading model by incorporating advanced techniques like Prioritized Experience Replay, Regularized Q-Learning, Noisy Networks, Dueling, and Double DQN. Extensive tests on assets like BTC/USD and AAPL demonstrate superior performance compared to the original model, with marked increases in returns and Sharpe Ratio, indicating improved risk-adjusted rewards. Notably, convolutional neural network (CNN) architectures, both 1D and 2D, significantly boost returns, suggesting their effectiveness in market trend analysis. Across instruments, these enhancements have yielded stable and high gains, eclipsing the baseline and highlighting the potential of CNNs in trading systems. The study suggests that applying sophisticated deep learning within reinforcement learning can greatly enhance automated trading, urging further exploration into advanced methods for broader financial applicability. The findings advocate for the continued evolution of AI in finance.","source":"arXiv","year":2023,"language":"en","subjects":["q-fin.CP"],"url":"https://arxiv.org/abs/2311.05743","pdf_url":"https://arxiv.org/pdf/2311.05743","is_open_access":true,"published_at":"2023-11-09T21:02:03Z","score":67},{"id":"arxiv_2303.08615","title":"Characteristic Function of the Tsallis $q$-Gaussian and Its Applications in Measurement and Metrology","authors":[{"name":"Viktor Witkovský"}],"abstract":"The Tsallis $q$-Gaussian distribution is a powerful generalization of the standard Gaussian distribution and is commonly used in various fields, including non-extensive statistical mechanics, financial markets and image processing. It belongs to the $q$-distribution family, which is characterized by a non-additive entropy. Due to their versatility and practicality, $q$-Gaussians are a natural choice for modeling input quantities in measurement models. This paper presents the characteristic function of a linear combination of independent $q$-Gaussian random variables and proposes a numerical method for its inversion. The proposed technique makes it possible to determine the exact probability distribution of the output quantity in linear measurement models, with the input quantities modeled as independent $q$-Gaussian random variables. It provides an alternative computational procedure to the Monte Carlo method for uncertainty analysis through the propagation of distributions.","source":"arXiv","year":2023,"language":"en","subjects":["stat.CO","physics.data-an","q-fin.CP","quant-ph","stat.AP"],"doi":"10.3390/metrology3020012","url":"https://arxiv.org/abs/2303.08615","pdf_url":"https://arxiv.org/pdf/2303.08615","is_open_access":true,"published_at":"2023-03-15T13:42:35Z","score":67}],"total":1506400,"page":1,"page_size":20,"sources":["arXiv","CrossRef","Semantic Scholar"],"query":"q-fin.CP"}