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Article ; Online: S

Jo, Sangho / Jang, Ohtae / Bhattacharyya, Chaitali / Kim, Minjun / Lee, Taeseok / Jang, Yewon / Song, Haekang / Kwon, Hyukmin / Do, Saebyeol / Kim, Sungho

Sensors (Basel, Switzerland)

2024 Volume 24, Issue 7

Abstract: Several studies in computer vision have examined specular removal, which is crucial for object detection and recognition. This research has traditionally been divided into two tasks: specular highlight removal, which focuses on removing specular ... ...

Abstract	Several studies in computer vision have examined specular removal, which is crucial for object detection and recognition. This research has traditionally been divided into two tasks: specular highlight removal, which focuses on removing specular highlights on object surfaces, and reflection removal, which deals with specular reflections occurring on glass surfaces. In reality, however, both types of specular effects often coexist, making it a fundamental challenge that has not been adequately addressed. Recognizing the necessity of integrating specular components handled in both tasks, we constructed a specular-light (
Language	English
Publishing date	2024-04-03
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s24072286
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: S

Guan, Yanfei / Lee, Taegyo / Wang, Ke / Yu, Shu / McWilliams, J Christopher

Journal of chemical information and modeling

2023 Volume 63, Issue 12, Page(s) 3751–3760

Abstract: ... on both the Pfizer internal dataset and the USPTO public dataset to predict regioselectivity for S ...

Abstract	Fast and accurate prospective predictions of regioselectivity can significantly reduce the time and resources spent on unproductive transformations in the pharmaceutical industry. Density functional theory (DFT) reaction modeling through transition state theory (TST) and machine learning (ML) methods has been widely used to predict reaction outcomes such as selectivity. However, TST reaction modeling and ML methods are either time-consuming or data-dependent. Herein, we introduce a prototype seamlessly bridging ML and TST modeling by triggering resource-intensive but much less domain-sensitive DFT calculations only on less confident ML predictions. The proposed workflow was trained and tested on both the Pfizer internal dataset and the USPTO public dataset to predict regioselectivity for S
MeSH term(s)	Prospective Studies ; Density Functional Theory ; Drug Industry ; Machine Learning ; Workflow
Language	English
Publishing date	2023-06-05
Publishing country	United States
Document type	Journal Article
ZDB-ID	190019-5
ISSN	1549-960X ; 0095-2338
ISSN (online)	1549-960X
ISSN	0095-2338
DOI	10.1021/acs.jcim.3c00580
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Article ; Online: S

Mantell, Mark A / Lasky, Matthew R / Lee, Melissa / Remy, Matthew / Sanford, Melanie S

Organic letters

2021 Volume 23, Issue 13, Page(s) 5213–5217

Abstract: ... of electron rich arenes. First, an S ...

Abstract	This report describes the development of two photocatalytic methods for the pyridination of electron rich arenes. First, an S
Language	English
Publishing date	2021-06-23
Publishing country	United States
Document type	Journal Article
ISSN	1523-7052
ISSN (online)	1523-7052
DOI	10.1021/acs.orglett.1c01749
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Article ; Online: S

Lee, So Jeong / Morales-Colón, María T / Brooks, Allen F / Wright, Jay S / Makaravage, Katarina J / Scott, Peter J H / Sanford, Melanie S

The Journal of organic chemistry

2021 Volume 86, Issue 20, Page(s) 14121–14130

Abstract: This report describes a method for the nucleophilic radiofluorination of (hetero)aryl chlorides, (hetero)aryl triflates, and nitroarenes using a combination of [ ...

Abstract	This report describes a method for the nucleophilic radiofluorination of (hetero)aryl chlorides, (hetero)aryl triflates, and nitroarenes using a combination of [
MeSH term(s)	Fluorides ; Fluorine Radioisotopes ; Quaternary Ammonium Compounds ; Radiopharmaceuticals
Chemical Substances	Fluorine Radioisotopes ; Quaternary Ammonium Compounds ; Radiopharmaceuticals ; tetramethylammonium (H0W55235FC) ; Fluorides (Q80VPU408O)
Language	English
Publishing date	2021-09-10
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	123490-0
ISSN	1520-6904 ; 0022-3263
ISSN (online)	1520-6904
ISSN	0022-3263
DOI	10.1021/acs.joc.1c01491
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Article ; Online: S

You, Ye-Lim / Lee, Ji-Yeon / Choi, Hyeon-Son

Food science and biotechnology

2023 Volume 32, Issue 9, Page(s) 1225–1233

Abstract: Gomisin C is a lignan isolated from the fruit of S: Supplementary information: The online ...

Abstract	Gomisin C is a lignan isolated from the fruit of S Supplementary information: The online version contains supplementary material available at 10.1007/s10068-023-01263-8.
Language	English
Publishing date	2023-02-03
Publishing country	Korea (South)
Document type	Journal Article
ZDB-ID	2000008-X
ISSN	2092-6456 ; 1226-7708
ISSN (online)	2092-6456
ISSN	1226-7708
DOI	10.1007/s10068-023-01263-8
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Article ; Online: S

Lee, Yu-Hsuan / Ren, Daan / Jeon, Byungsun / Liu, Hung-Wen

Natural product reports

2023 Volume 40, Issue 9, Page(s) 1521–1549

Abstract: Covering: from 2000 up to the very early part of ... ...

Abstract	Covering: from 2000 up to the very early part of 2023
MeSH term(s)	S-Adenosylmethionine ; Methyltransferases/chemistry ; Catalysis
Chemical Substances	S-Adenosylmethionine (7LP2MPO46S) ; Methyltransferases (EC 2.1.1.-)
Language	English
Publishing date	2023-09-20
Publishing country	England
Document type	Journal Article ; Review ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2002546-4
ISSN	1460-4752 ; 0265-0568
ISSN (online)	1460-4752
ISSN	0265-0568
DOI	10.1039/d2np00086e
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Article ; Online: S

Das, Tandrila / Yang, Xinglin / Lee, Hwayoung / Garst, Emma H / Valencia, Estefania / Chandran, Kartik / Im, Wonpil / Hang, Howard C

ACS chemical biology

2022 Volume 17, Issue 8, Page(s) 2109–2120

Abstract: Interferon-induced transmembrane proteins (IFITM1, 2, and 3) are important antiviral proteins that are active against many viruses, including influenza A virus (IAV), dengue virus (DENV), Ebola virus (EBOV), Zika virus (ZIKV), and severe acute ... ...

Abstract	Interferon-induced transmembrane proteins (IFITM1, 2, and 3) are important antiviral proteins that are active against many viruses, including influenza A virus (IAV), dengue virus (DENV), Ebola virus (EBOV), Zika virus (ZIKV), and severe acute respiratory syndrome coronavirus (SARS-CoV). IFITM proteins exhibit specificity in activity, but their distinct mechanisms of action and regulation are unclear. Since
MeSH term(s)	Animals ; Antiviral Agents/pharmacology ; Cholesterol/metabolism ; Influenza A virus ; Lipoylation ; Membrane Proteins/metabolism ; Membrane Proteins/pharmacology ; SARS-CoV-2 ; Sterols/metabolism ; Zika Virus
Chemical Substances	Antiviral Agents ; Membrane Proteins ; Sterols ; Cholesterol (97C5T2UQ7J)
Language	English
Publishing date	2022-07-21
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ISSN	1554-8937
ISSN (online)	1554-8937
DOI	10.1021/acschembio.2c00176
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Article ; Online: Observation of a Resonant Structure near the D_{s}^{+}D_{s}^{-} Threshold in the B^{+}→D_{s}^{+}D_{s}^{-}K^{+} Decay.

Aaij, R / Abdelmotteleb, A S W / Abellan Beteta, C / Abudinén, F / Ackernley, T / Adeva, B / Adinolfi, M / Afsharnia, H / Agapopoulou, C / Aidala, C A / Aiola, S / Ajaltouni, Z / Akar, S / Akiba, K / Albrecht, J / Alessio, F / Alexander, M / Alfonso Albero, A / Aliouche, Z /

Alvarez Cartelle, P / Amalric, R / Amato, S / Amey, J L / Amhis, Y / An, L / Anderlini, L / Andersson, M / Andreianov, A / Andreotti, M / Andreou, D / Ao, D / Archilli, F / Artamonov, A / Artuso, M / Aslanides, E / Atzeni, M / Audurier, B / Bachmann, S / Bachmayer, M / Back, J J / Bailly-Reyre, A / Baladron Rodriguez, P / Balagura, V / Baldini, W / Baptista de Souza Leite, J / Barbetti, M / Barlow, R J / Barsuk, S / Barter, W / Bartolini, M / Baryshnikov, F / Basels, J M / Bassi, G / Batsukh, B / Battig, A / Bay, A / Beck, A / Becker, M / Bedeschi, F / Bediaga, I B / Beiter, A / Belavin, V / Belin, S / Bellee, V / Belous, K / Belov, I / Belyaev, I / Benane, G / Bencivenni, G / Ben-Haim, E / Berezhnoy, A / Bernet, R / Bernet Andres, S / Berninghoff, D / Bernstein, H C / Bertella, C / Bertolin, A / Betancourt, C / Betti, F / Bezshyiko, Ia / Bhasin, S / Bhom, J / Bian, L / Bieker, M S / Biesuz, N V / Bifani, S / Billoir, P / Biolchini, A / Birch, M / Bishop, F C R / Bitadze, A / Bizzeti, A / Blago, M P / Blake, T / Blanc, F / Blusk, S / Bobulska, D / Boelhauve, J A / Boente Garcia, O / Boettcher, T / Boldyrev, A / Bolognani, C S / Bolzonella, R / Bondar, N / Borgato, F / Borghi, S / Borsato, M / Borsuk, J T / Bouchiba, S A / Bowcock, T J V / Boyer, A / Bozzi, C / Bradley, M J / Braun, S / Brea Rodriguez, A / Brodzicka, J / Brossa Gonzalo, A / Brown, J / Brundu, D / Buonaura, A / Buonincontri, L / Burke, A T / Burr, C / Bursche, A / Butkevich, A / Butter, J S / Buytaert, J / Byczynski, W / Cadeddu, S / Cai, H / Calabrese, R / Calefice, L / Cali, S / Calladine, R / Calvi, M / Calvo Gomez, M / Campana, P / Campora Perez, D H / Campoverde Quezada, A F / Capelli, S / Capriotti, L / Carbone, A / Carboni, G / Cardinale, R / Cardini, A / Carli, I / Carniti, P / Carus, L / Casais Vidal, A / Caspary, R / Casse, G / Cattaneo, M / Cavallero, G / Cavallini, V / Celani, S / Cerasoli, J / Cervenkov, D / Chadwick, A J / Chapman, M G / Charles, M / Charpentier, Ph / Chavez Barajas, C A / Chefdeville, M / Chen, C / Chen, S / Chernov, A / Chernyshenko, S / Chobanova, V / Cholak, S / Chrzaszcz, M / Chubykin, A / Chulikov, V / Ciambrone, P / Cicala, M F / Cid Vidal, X / Ciezarek, G / Ciullo, G / Clarke, P E L / Clemencic, M / Cliff, H V / Closier, J / Cobbledick, J L / Coco, V / Coelho, J A B / Cogan, J / Cogneras, E / Cojocariu, L / Collins, P / Colombo, T / Congedo, L / Contu, A / Cooke, N / Corredoira, I / Corti, G / Couturier, B / Craik, D C / Crkovská, J / Cruz Torres, M / Currie, R / Da Silva, C L / Dadabaev, S / Dai, L / Dai, X / Dall'Occo, E / Dalseno, J / D'Ambrosio, C / Daniel, J / Danilina, A / d'Argent, P / Davies, J E / Davis, A / De Aguiar Francisco, O / de Boer, J / De Bruyn, K / De Capua, S / De Cian, M / De Freitas Carneiro Da Graca, U / De Lucia, E / De Miranda, J M / De Paula, L / De Serio, M / De Simone, D / De Simone, P / De Vellis, F / de Vries, J A / Dean, C T / Debernardis, F / Decamp, D / Dedu, V / Del Buono, L / Delaney, B / Dembinski, H-P / Denysenko, V / Deschamps, O / Dettori, F / Dey, B / Di Cicco, A / Di Nezza, P / Diachkov, I / Didenko, S / Dieste Maronas, L / Ding, S / Dobishuk, V / Dolmatov, A / Dong, C / Donohoe, A M / Dordei, F / Dos Reis, A C / Douglas, L / Downes, A G / Dudek, M W / Dufour, L / Duk, V / Durante, P / Durham, J M / Dutta, D / Dziurda, A / Dzyuba, A / Easo, S / Egede, U / Egorychev, V / Eidelman, S / Eirea Orro, C / Eisenhardt, S / Ejopu, E / Ek-In, S / Eklund, L / Ely, S / Ene, A / Epple, E / Escher, S / Eschle, J / Esen, S / Evans, T / Fabiano, F / Falcao, L N / Fan, Y / Fang, B / Farry, S / Fazzini, D / Feo, M / Fernandez Gomez, M / Fernez, A D / Ferrari, F / Ferreira Lopes, L / Ferreira Rodrigues, F / Ferreres Sole, S / Ferrillo, M / Ferro-Luzzi, M / Filippov, S / Fini, R A / Fiorini, M / Firlej, M / Fischer, K M / Fitzgerald, D S / Fitzpatrick, C / Fiutowski, T / Fleuret, F / Fontana, M / Fontanelli, F / Forty, R / Foulds-Holt, D / Franco Lima, V / Franco Sevilla, M / Frank, M / Franzoso, E / Frau, G / Frei, C / Friday, D A / Fu, J / Fuehring, Q / Fulghesu, T / Gabriel, E / Galati, G / Galati, M D / Gallas Torreira, A / Galli, D / Gambetta, S / Gan, Y / Gandelman, M / Gandini, P / Gao, Y / Garau, M / Garcia Martin, L M / Garcia Moreno, P / García Pardiñas, J / Garcia Plana, B / Garcia Rosales, F A / Garrido, L / Gaspar, C / Geertsema, R E / Gerick, D / Gerken, L L / Gersabeck, E / Gersabeck, M / Gershon, T / Giambastiani, L / Gibson, V / Giemza, H K / Gilman, A L / Giovannetti, M / Gioventù, A / Gironella Gironell, P / Giugliano, C / Giza, M A / Gizdov, K / Gkougkousis, E L / Gligorov, V V / Göbel, C / Golobardes, E / Golubkov, D / Golutvin, A / Gomes, A / Gomez Fernandez, S / Goncalves Abrantes, F / Goncerz, M / Gong, G / Gorelov, I V / Gotti, C / Grabowski, J P / Grammatico, T / Granado Cardoso, L A / Graugés, E / Graverini, E / Graziani, G / Grecu, A T / Greeven, L M / Grieser, N A / Grillo, L / Gromov, S / Gruberg Cazon, B R / Gu, C / Guarise, M / Guittiere, M / Günther, P A / Gushchin, E / Guth, A / Guz, Y / Gys, T / Hadavizadeh, T / Haefeli, G / Haen, C / Haimberger, J / Haines, S C / Halewood-Leagas, T / Halvorsen, M M / Hamilton, P M / Hammerich, J / Han, Q / Han, X / Hansen, E B / Hansmann-Menzemer, S / Hao, L / Harnew, N / Harrison, T / Hasse, C / Hatch, M / He, J / Heijhoff, K / Heinicke, K / Henderson, C / Henderson, R D L / Hennequin, A M / Hennessy, K / Henry, L / Herd, J H / Heuel, J / Hicheur, A / Hill, D / Hilton, M / Hollitt, S E / Horswill, J / Hou, R / Hou, Y / Hu, J / Hu, W / Hu, X / Huang, W / Huang, X / Hulsbergen, W / Hunter, R J / Hushchyn, M / Hutchcroft, D / Ibis, P / Idzik, M / Ilin, D / Ilten, P / Inglessi, A / Iniukhin, A / Ishteev, A / Ivshin, K / Jacobsson, R / Jage, H / Jaimes Elles, S J / Jakobsen, S / Jans, E / Jashal, B K / Jawahery, A / Jevtic, V / Jiang, E / Jiang, X / Jiang, Y / John, M / Johnson, D / Jones, C R / Jones, T P / Jost, B / Jurik, N / Juszczak, I / Kandybei, S / Kang, Y / Karacson, M / Karpenkov, D / Karpov, M / Kautz, J W / Keizer, F / Keller, D M / Kenzie, M / Ketel, T / Khanji, B / Kharisova, A / Kholodenko, S / Khreich, G / Kirn, T / Kirsebom, V S / Kitouni, O / Klaver, S / Kleijne, N / Klimaszewski, K / Kmiec, M R / Koliiev, S / Kondybayeva, A / Konoplyannikov, A / Kopciewicz, P / Kopecna, R / Koppenburg, P / Korolev, M / Kostiuk, I / Kot, O / Kotriakhova, S / Kozachuk, A / Kravchenko, P / Kravchuk, L / Krawczyk, R D / Kreps, M / Kretzschmar, S / Krokovny, P / Krupa, W / Krzemien, W / Kubat, J / Kucewicz, W / Kucharczyk, M / Kudryavtsev, V / Kunde, G J / Kupsc, A / Lacarrere, D / Lafferty, G / Lai, A / Lampis, A / Lancierini, D / Landesa Gomez, C / Lane, J J / Lane, R / Lanfranchi, G / Langenbruch, C / Langer, J / Lantwin, O / Latham, T / Lazzari, F / Lazzaroni, M / Le Gac, R / Lee, S H / Lefèvre, R / Leflat, A / Legotin, S / Lenisa, P / Leroy, O / Lesiak, T / Leverington, B / Li, A / Li, H / Li, K / Li, P / Li, P-R / Li, S / Li, T / Li, Y / Li, Z / Liang, X / Lin, C / Lin, T / Lindner, R / Lisovskyi, V / Litvinov, R / Liu, G / Liu, H / Liu, Q / Liu, S / Lobo Salvia, A / Loi, A / Lollini, R / Lomba Castro, J / Longstaff, I / Lopes, J H / Lopez Huertas, A / López Soliño, S / Lovell, G H / Lu, Y / Lucarelli, C / Lucchesi, D / Luchuk, S / Lucio Martinez, M / Lukashenko, V / Luo, Y / Lupato, A / Luppi, E / Lusiani, A / Lynch, K / Lyu, X-R / Ma, L / Ma, R / Maccolini, S / Machefert, F / Maciuc, F / Mackay, I / Macko, V / Mackowiak, P / Madhan Mohan, L R / Maevskiy, A / Maisuzenko, D / Majewski, M W / Malczewski, J J / Malde, S / Malecki, B / Malinin, A / Maltsev, T / Manca, G / Mancinelli, G / Mancuso, C / Manuzzi, D / Manzari, C A / Marangotto, D / Maratas, J M / Marchand, J F / Marconi, U / Mariani, S / Marin Benito, C / Marks, J / Marshall, A M / Marshall, P J / Martelli, G / Martellotti, G / Martinazzoli, L / Martinelli, M / Martinez Santos, D / Martinez Vidal, F / Massafferri, A / Materok, M / Matev, R / Mathad, A / Matiunin, V / Matteuzzi, C / Mattioli, K R / Mauri, A / Maurice, E / Mauricio, J / Mazurek, M / McCann, M / Mcconnell, L / McGrath, T H / McHugh, N T / McNab, A / McNulty, R / Mead, J V / Meadows, B / Meier, G / Melnychuk, D / Meloni, S / Merk, M / Merli, A / Meyer Garcia, L / Miao, D / Mikhasenko, M / Milanes, D A / Millard, E / Milovanovic, M / Minard, M-N / Minotti, A / Miralles, T / Mitchell, S E / Mitreska, B / Mitzel, D S / Mödden, A / Mohammed, R A / Moise, R D / Mokhnenko, S / Mombächer, T / Monk, M / Monroy, I A / Monteil, S / Morandin, M / Morello, G / Morello, M J / Moron, J / Morris, A B / Morris, A G / Mountain, R / Mu, H / Muhammad, E / Muheim, F / Mulder, M / Müller, K / Murphy, C H / Murray, D / Murta, R / Muzzetto, P / Naik, P / Nakada, T / Nandakumar, R / Nanut, T / Nasteva, I / Needham, M / Neri, N / Neubert, S / Neufeld, N / Neustroev, P / Newcombe, R / Nicolini, J / Niel, E M / Nieswand, S / Nikitin, N / Nolte, N S / Normand, C / Novoa Fernandez, J / Nunez, C / Oblakowska-Mucha, A / Obraztsov, V / Oeser, T / O'Hanlon, D P / Okamura, S / Oldeman, R / Oliva, F / Olivares, M E / Onderwater, C J G / O'Neil, R H / Otalora Goicochea, J M / Ovsiannikova, T / Owen, P / Oyanguren, A / Ozcelik, O / Padeken, K O / Pagare, B / Pais, P R / Pajero, T / Palano, A / Palutan, M / Pan, Y / Panshin, G / Paolucci, L / Papanestis, A / Pappagallo, M / Pappalardo, L L / Pappenheimer, C / Parker, W / Parkes, C / Passalacqua, B / Passaleva, G / Pastore, A / Patel, M / Patrignani, C / Pawley, C J / Pearce, A / Pellegrino, A / Pepe Altarelli, M / Perazzini, S / Pereima, D / Pereiro Castro, A / Perret, P / Petric, M / Petridis, K / Petrolini, A / Petrov, A / Petrucci, S / Petruzzo, M / Pham, H / Philippov, A / Piandani, R / Pica, L / Piccini, M / Pietrzyk, B / Pietrzyk, G / Pili, M / Pinci, D / Pisani, F / Pizzichemi, M / Placinta, V / Plews, J / Plo Casasus, M / Polci, F / Poli Lener, M / Poliakova, M / Poluektov, A / Polukhina, N / Polyakov, I / Polycarpo, E / Ponce, S / Popov, D / Popov, S / Poslavskii, S / Prasanth, K / Promberger, L / Prouve, C / Pugatch, V / Puill, V / Punzi, G / Qi, H R / Qian, W / Qin, N / Qu, S / Quagliani, R / Raab, N V / Rabadan Trejo, R I / Rachwal, B / Rademacker, J H / Rajagopalan, R / Rama, M / Ramos Pernas, M / Rangel, M S / Ratnikov, F / Raven, G / Rebollo De Miguel, M / Redi, F / Reich, J / Reiss, F / Remon Alepuz, C / Ren, Z / Renaudin, V / Resmi, P K / Ribatti, R / Ricci, A M / Ricciardi, S / Richardson, K / Richardson-Slipper, M / Rinnert, K / Robbe, P / Robertson, G / Rodrigues, A B / Rodrigues, E / Rodriguez Fernandez, E / Rodriguez Lopez, J A / Rodriguez Rodriguez, E / Rollings, A / Roloff, P / Romanovskiy, V / Romero Lamas, M / Romero Vidal, A / Roth, J D / Rotondo, M / Rudolph, M S / Ruf, T / Ruiz Fernandez, R A / Ruiz Vidal, J / Ryzhikov, A / Ryzka, J / Saborido Silva, J J / Sagidova, N / Sahoo, N / Saitta, B / Salomoni, M / Sanchez Gras, C / Sanderswood, I / Santacesaria, R / Santamarina Rios, C / Santimaria, M / Santovetti, E / Saranin, D / Sarpis, G / Sarpis, M / Sarti, A / Satriano, C / Satta, A / Saur, M / Savrina, D / Sazak, H / Scantlebury Smead, L G / Scarabotto, A / Schael, S / Scherl, S / Schiller, M / Schindler, H / Schmelling, M / Schmidt, B / Schmitt, S / Schneider, O / Schopper, A / Schubiger, M / Schulte, S / Schune, M H / Schwemmer, R / Sciascia, B / Sciuccati, A / Sellam, S / Semennikov, A / Senghi Soares, M / Sergi, A / Serra, N / Sestini, L / Seuthe, A / Shang, Y / Shangase, D M / Shapkin, M / Shchemerov, I / Shchutska, L / Shears, T / Shekhtman, L / Shen, Z / Sheng, S / Shevchenko, V / Shi, B / Shields, E B / Shimizu, Y / Shmanin, E / Shupperd, J D / Siddi, B G / Silva Coutinho, R / Simi, G / Simone, S / Singla, M / Skidmore, N / Skuza, R / Skwarnicki, T / Slater, M W / Smallwood, J C / Smeaton, J G / Smith, E / Smith, K / Smith, M / Snoch, A / Soares Lavra, L / Sokoloff, M D / Soler, F J P / Solomin, A / Solovev, A / Solovyev, I / Song, R / Souza De Almeida, F L / Souza De Paula, B / Spaan, B / Spadaro Norella, E / Spiridenkov, E / Spradlin, P / Sriskaran, V / Stagni, F / Stahl, M / Stahl, S / Stanislaus, S / Stein, E N / Steinkamp, O / Stenyakin, O / Stevens, H / Stone, S / Strekalina, D / Suljik, F / Sun, J / Sun, L / Sun, Y / Svihra, P / Swallow, P N / Swientek, K / Szabelski, A / Szumlak, T / Szymanski, M / Tan, Y / Taneja, S / Tanner, A R / Tat, M D / Terentev, A / Teubert, F / Thomas, E / Thompson, D J D / Thomson, K A / Tilquin, H / Tisserand, V / T'Jampens, S / Tobin, M / Tomassetti, L / Tonani, G / Tong, X / Torres Machado, D / Tou, D Y / Trifonova, E / Trilov, S M / Trippl, C / Tuci, G / Tully, A / Tuning, N / Ukleja, A / Unverzagt, D J / Ursov, E / Usachov, A / Ustyuzhanin, A / Uwer, U / Vagner, A / Vagnoni, V / Valassi, A / Valenti, G / Valls Canudas, N / van Beuzekom, M / Van Dijk, M / Van Hecke, H / van Herwijnen, E / Van Hulse, C B / van Veghel, M / Vazquez Gomez, R / Vazquez Regueiro, P / Vázquez Sierra, C / Vecchi, S / Velthuis, J J / Veltri, M / Venkateswaran, A / Veronesi, M / Vesterinen, M / Vieira, D / Vieites Diaz, M / Vilasis-Cardona, X / Vilella Figueras, E / Villa, A / Vincent, P / Volle, F C / Vom Bruch, D / Vorobyev, A / Vorobyev, V / Voropaev, N / Vos, K / Vrahas, C / Waldi, R / Walsh, J / Wan, G / Wang, C / Wang, J / Wang, M / Wang, R / Wang, X / Wang, Y / Wang, Z / Ward, J A / Watson, N K / Websdale, D / Wei, Y / Weisser, C / Westhenry, B D C / White, D J / Whitehead, M / Wiederhold, A R / Wiedner, D / Wilkinson, G / Wilkinson, M K / Williams, I / Williams, M / Williams, M R J / Williams, R / Wilson, F F / Wislicki, W / Witek, M / Witola, L / Wong, C P / Wormser, G / Wotton, S A / Wu, H / Wyllie, K / Xiang, Z / Xiao, D / Xie, Y / Xu, A / Xu, J / Xu, L / Xu, M / Xu, Q / Xu, Z / Yang, D / Yang, S / Yang, Y / Yang, Z / Yeomans, L E / Yeroshenko, V / Yeung, H / Yin, H / Yu, J / Yuan, X / Zaffaroni, E / Zavertyaev, M / Zdybal, M / Zenaiev, O / Zeng, M / Zhang, C / Zhang, D / Zhang, L / Zhang, S / Zhang, Y / Zharkova, A / Zhelezov, A / Zheng, Y / Zhou, T / Zhou, X / Zhou, Y / Zhovkovska, V / Zhu, X / Zhu, Z / Zhukov, V / Zou, Q / Zucchelli, S / Zuliani, D / Zunica, G

Physical review letters

2023 Volume 131, Issue 7, Page(s) 71901

Abstract: An amplitude analysis of the B^{+}→D_{s}^{+}D_{s}^{-}K^{+} decay is carried out to study ... referred to as X(3960), is observed in the D_{s}^{+}D_{s}^{-} invariant-mass spectrum with significance ... for an additional structure is found around 4140 MeV in the D_{s}^{+}D_{s}^{-} invariant mass, which might be caused ...

Abstract	An amplitude analysis of the B^{+}→D_{s}^{+}D_{s}^{-}K^{+} decay is carried out to study for the first time its intermediate resonant contributions, using proton-proton collision data collected with the LHCb detector at center-of-mass energies of 7, 8, and 13 TeV. A near-threshold peaking structure, referred to as X(3960), is observed in the D_{s}^{+}D_{s}^{-} invariant-mass spectrum with significance greater than 12 standard deviations. The mass, width, and the quantum numbers of the structure are measured to be 3956±5±10 MeV, 43±13±8 MeV, and J^{PC}=0^{++}, respectively, where the first uncertainties are statistical and the second systematic. The properties of the new structure are consistent with recent theoretical predictions for a state composed of cc[over ¯]ss[over ¯] quarks. Evidence for an additional structure is found around 4140 MeV in the D_{s}^{+}D_{s}^{-} invariant mass, which might be caused either by a new resonance with the 0^{++} assignment or by a J/ψϕ↔D_{s}^{+}D_{s}^{-} coupled-channel effect.
Language	English
Publishing date	2023-09-01
Publishing country	United States
Document type	Journal Article
ZDB-ID	208853-8
ISSN	1079-7114 ; 0031-9007
ISSN (online)	1079-7114
ISSN	0031-9007
DOI	10.1103/PhysRevLett.131.071901
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Book ; Online: S-CLIP

Mo, Sangwoo / Kim, Minkyu / Lee, Kyungmin / Shin, Jinwoo

Semi-supervised Vision-Language Pre-training using Few Specialist Captions

2023

Abstract: ... of image-text pairs available for training. To address this, we propose S-CLIP, a semi-supervised learning ... method for training CLIP that utilizes additional unpaired images. S-CLIP employs two pseudo-labeling ... of labels for supervision instead of the exact one. By combining these objectives, S-CLIP significantly ...

Abstract	Vision-language models, such as contrastive language-image pre-training (CLIP), have demonstrated impressive results in natural image domains. However, these models often struggle when applied to specialized domains like remote sensing, and adapting to such domains is challenging due to the limited number of image-text pairs available for training. To address this, we propose S-CLIP, a semi-supervised learning method for training CLIP that utilizes additional unpaired images. S-CLIP employs two pseudo-labeling strategies specifically designed for contrastive learning and the language modality. The caption-level pseudo-label is given by a combination of captions of paired images, obtained by solving an optimal transport problem between unpaired and paired images. The keyword-level pseudo-label is given by a keyword in the caption of the nearest paired image, trained through partial label learning that assumes a candidate set of labels for supervision instead of the exact one. By combining these objectives, S-CLIP significantly enhances the training of CLIP using only a few image-text pairs, as demonstrated in various specialist domains, including remote sensing, fashion, scientific figures, and comics. For instance, S-CLIP improves CLIP by 10% for zero-shot classification and 4% for image-text retrieval on the remote sensing benchmark, matching the performance of supervised CLIP while using three times fewer image-text pairs.
Keywords	Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
Subject code	006 ; 004
Publishing date	2023-05-23
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: S-LoRA

Sheng, Ying / Cao, Shiyi / Li, Dacheng / Hooper, Coleman / Lee, Nicholas / Yang, Shuo / Chou, Christopher / Zhu, Banghua / Zheng, Lianmin / Keutzer, Kurt / Gonzalez, Joseph E. / Stoica, Ion

Serving Thousands of Concurrent LoRA Adapters

2023

Abstract: ... during serving. To capitalize on these opportunities, we present S-LoRA, a system designed ... for the scalable serving of many LoRA adapters. S-LoRA stores all adapters in the main memory and fetches ... reduce fragmentation, S-LoRA proposes Unified Paging. Unified Paging uses a unified memory pool to manage ...

Abstract	The "pretrain-then-finetune" paradigm is commonly adopted in the deployment of large language models. Low-Rank Adaptation (LoRA), a parameter-efficient fine-tuning method, is often employed to adapt a base model to a multitude of tasks, resulting in a substantial collection of LoRA adapters derived from one base model. We observe that this paradigm presents significant opportunities for batched inference during serving. To capitalize on these opportunities, we present S-LoRA, a system designed for the scalable serving of many LoRA adapters. S-LoRA stores all adapters in the main memory and fetches the adapters used by the currently running queries to the GPU memory. To efficiently use the GPU memory and reduce fragmentation, S-LoRA proposes Unified Paging. Unified Paging uses a unified memory pool to manage dynamic adapter weights with different ranks and KV cache tensors with varying sequence lengths. Additionally, S-LoRA employs a novel tensor parallelism strategy and highly optimized custom CUDA kernels for heterogeneous batching of LoRA computation. Collectively, these features enable S-LoRA to serve thousands of LoRA adapters on a single GPU or across multiple GPUs with a small overhead. Compared to state-of-the-art libraries such as HuggingFace PEFT and vLLM (with naive support of LoRA serving), S-LoRA can improve the throughput by up to 4 times and increase the number of served adapters by several orders of magnitude. As a result, S-LoRA enables scalable serving of many task-specific fine-tuned models and offers the potential for large-scale customized fine-tuning services. The code is available at https://github.com/S-LoRA/S-LoRA
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Distributed ; Parallel ; and Cluster Computing
Subject code	004
Publishing date	2023-11-06
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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