text:
Сергей Николаевич Искюль (род. 12 октября 1946; Ленинград, СССР) — советский и российский историк, доктор исторических наук. Ведущий научный сотрудник Санкт-Петербургского института истории РАН.

Член Общества по изучению XVIII века (с момента его образования), Российского исторического общества (с 2017) и секции художественного перевода Союза писателей Санкт-Петербурга.

Биография
В 1964 году поступил на Исторический факультет Ленинградского государственного университета. С того же года работал в Научной библиотеке им. Горького при ЛГУ, а с декабря 1965 года в Ленинградском отделении Института истории АН СССР (с 1992 года — Санкт-Петербургский филиал Института российской истории РАН, с 2000 — Санкт-Петербургский институт истории РАН) научно-техническим сотрудником архива. В 1969 году окончил Истфак ЛГУ. С июля 1973 года — и. о. младшего научного сотрудника, а с октября того же года — младший научный сотрудник.

Первой крупной историко-источниковедческой работой Искюля было участие его в числе авторского коллектива (З. Н. Савельев, М. П. Ирошников, Л. Н. Семёнова, А. И. Юхт) под руководством С. Н. Валка и Н. Е. Носова в подготовке к изданию публикации трудов В. Н. Татищева, вышедшего в свет в 1979 году.

В 1982 году защитил диссертацию на соискание учёной степени кандидата исторических наук по теме «Германский вопрос во внешней политике России: 1807—1812 гг.». С июля 1986 года — научный сотрудник, а с декабря 1992 — старший научный сотрудник. В 1993 году проходил стажировку в Национальном архиве Франции. В 1997 году защитил докторскую диссертацию по теме «Внешняя политика России и германские государства 1801—1812 гг.». С августа того же года (по настоящее время) — ведущий научный сотрудник Санкт-Петербургского института истории РАН. С ноября 2013 года — заведующий Научно-историческим архивом.

Неоднократно получал стипендии Дома наук о Человеке (Париж). Также участвовал в международных конференциях, включая  в Даремском университете (Англия). В сентябре 2018 года под руководством Искюля проводились работы в архивах и библиотеках Парижа по проекту «Князь А. Б Куракин и его миссия в Париже в 1810—1812 гг.».

Награды и премии
 Премия имени митрополита Московского и Коломенского Макария (Булгакова) (2016—2017) — за работу «Война и мiръ в России в 1812 г.» (СПб., 2017), вторая премия в номинации «История России».

Библиография
Монографии

Переводы и публикации исторических источников
 Артур Юнг. Путешествие по Франции 1789 / предисл., комм. ; перев. с анг. ,  // Звезда. 1989. № 7.
 Мабли Г.-Б. де. Об изучении истории. О том, как писать историю / перев. с фр., статья, комм. , . М., 1993.
 Р.-Т. Вильсон. Дневник. Письма (1812—1813) / статья, перев. с англ, комм. , . СПб., 1995.
 Максимы и мысли узника Святой Елены. Рукопись, найденная в бумагах Лас Каза / перев. с фр., статья, комм. . СПб., 1995; 2-е изд. СПб., 2007; 3-е изд. СПб., 2008; 4-е изд. 2012.
 Внешнеполитическое ведомство России в 1805 г. (Отчёт товарища министра иностранных дел) / вступ. статья, подг. текста, комм.  // Русское прошлое. 1996. № 6.
 Юнг А. Дневники путешествий по Франции. 1787, 1788 и 1789 гг. / вступ. статья, комм., перев. с англ. , . СПб., 1996.
 Вяземский П. А. Записная книжка / вступ статья, подгот. текста, комм. . СПб., 1999.
 Из писем российских путешественников XVIII в. (братья Демидовы в Германии) // Русско-немецкие научные и культурные связи. СПб., 2000.
 Ганновер и внешняя политика России начала XIX века. Записка ганноверского дипломата 1801 г. // Русское прошлое. 2001. № 9.
 Путешествие братьев Демидовых по Европе. Письма и подневные журналы. 1750—1761 гг. / подг. текста, комм., отв. ред. . М., 2006.
 Вольтер. Анекдоты о царе Петре Великом / пер. с фр., вступ. статья, комм.  // Русское прошлое. 2006. № 10.
 Екатерина II. Pro et contra. / сост., вступ. ст., комм. . СПб: Изд. Христ. гуманит. Академии. 2006.
 Долгоруков П. В. Записки… / науч. ред. перев. с фр., статья, комм. . СПб., 2007.
 Каталог документов Архива СПбИИ РАН, относящихся к эпохе Просвещения // Les Archives de l'Est et la France des Lumière. Vol. I. Guide des Archives. Sous la direction de G.Dulac. Ferney, 2007.
 Вильсон Р. Повествование о случившемся во время нашествия на Россию Наполеона Бонапарта в России и при отступлении французской армии в 1812 году / перев. с англ., науч. ред., вступ. статья, комм. . М., 2008.
 Валишевский К. Александр I. История царствования / науч. ред. перев., комм., послесл. . СПб.: Вита Нова. 2011. Т. I—III.
 Фёдор Ростопчин. 1812 / перев. с фр., предисл., прим.  // Звезда. 2012. № 8.
 Эрстрём Э. Г. 1812 год. Путешествие из Москвы в Нижний Новгород / ред. перев. со швед., комм. , . Нижний Новгород, 2013.
 Агатон де Фэн. Записи 1812 года, служащие к истории императора Наполеона / ред. пер. с фр., статья и комм.  // Звезда. 2015. № 9—11. С. 79—131, 71—123, 94—119.
 Максимы и мысли узника Святой Елены. Рукопись, найденная в бумагах Лас Каза / сост. . М: РИМИС. 2015. — 206 с.
 Нарышкина Н. Ф. 1812 год, граф Ростопчин и его время. / пер. с фр. под ред. ; отв. ред. . СПб: Петрополис. 2016. — 600 с.
 Себастьен-Рок Никола Шамфор. Максимы и масли. Характеры и анекдоты / изд. подг. . СПб: Петрополис. 2017. — 366 с.
 Вяземский П. А. Старая записная книжка / сост., вступ. статья, комм. . СПб.: Петрополис. 2018. — 356 с.
 Фредерик Гийом де Водонкур. Записки, касающиеся истории войны 1812 года между Францией и Россией и принадлежащие перу штабного офицера французской армии / пер. Д. Соловьёва; под ред. С. Искюля; ред. перевода, статья, комм. С. Искюль. СПб.: Петрополис, 2019. 464 с.
 Эмманюэль Огюст Дьедонне Мариус Жозеф граф де Лас Каз. Максимы и мысли узника Святой Елены. Рукопись, найденная в бумагах Лас-Каза / изд. подг., статья, пер., комм. С. Н. Искюль. СПб: Петрополис. 2019. 207 с.
 Великий князь Николай Михайлович. Дипломатические сношения между Россией и Францией по донесениям послов Императоров Александра I и Наполеона. 1809—1812 гг. Депеши и письма князя А. Б. Куракина / подг. С. Н. Искюль; отв. ред. А. В. Торкунов. М.: Индрик, 2020. Т. I. — 846 с.

Основные статьи
 Вестфальский архив в Государственной публичной библиотеке им. М. Е. Салтыкова-Щедрина // Археографический ежегодник за 1971 год. М., 1972.
 Русские листовки в Германии 1813 г. // Ежегодник германской истории. М. 1979.
 Russland und die Oldenburger Krise, 1810—1811 // Oldenburger Jahrbuch. Bd. 85. 1985.
 Der Aufstand im Grosserzogtum Berg // Zeitschrift des Bergischen Geschichtsvereins. Bd. 92. 1985.
 Революция в освещении геттингенского журнала Revolutions-Almanach // Французская революция XVIII века. М., 1988.
 К истории общественного сознания в Германии в эпоху освободительной воны 1813 г. // Ежегодник германской истории. М., 1988.
 Briefe westfälischer Soldeten aus Russland in die Heimat // Jahrbuch für Geschichte der sozialistischen Länder Europas. Bd. 33. 1989.
 Труды великого князя Николая Михайловича по внешнеполитической истории России // Книга в России: Проблемы источниковедения и историографии. СПб., 1991.
 Review: Alexander I as Reformer: A Soviet Revisionist View // The Russian Review. 1991. Vol. 50, № 2, Pp. 211—214. (В соавт. с. К. С. Шульцем)
 Lettres inédites de Grimm à Catherine II // . 1991. № 10. pp. 41—55.
 Deutsch-russische Beziehungen und das frazösische Bündnis (1807—1808) // Jahrbücher für Geschichte Osteuropas. Bd. 40. Heft 2. München, 1992.
 Проект манифеста против Пруссии. 1805 г. // Вспомогательные исторические дисциплины. 1994. Т. 25. С. 152—166.
 Москва 1812 г. в записках современника (Московский «погром» глазами очевидца) // Новый часовой. 1994. № 2.
 Тильзитский мир: Россия и германские государства // Отечественная история. 1994. № 4—5. С. 228—236.
 Германская политика России и французская гегемония (дипломатическая записка 1804 года) // Русское прошлое. 1994. № 5. С. 338—356.
 Неопубликованный очерк великого князя Николая Михайловича «Императрица Елисавета Алексеевна» // . 1995. Т. 36. № 3. С. 345—349.
 Les liaisons dynastiques entre le Duché Schleswig-Holstein-Gottorp et la Russie au début XVIII siècle // Wolfenbütteler Forschungen. Bd. 74. 1997.
 Немецкие брошюры 1813 г. о русском солдате // Освободительное движение в России: Межвузовский сборник статей. Вып. 16. Саратов, 1997.
 Die Mission des Fürsten Nikolay G.Repnin-Wolkonskij in Kassel 1809—1810 // Europa in der Frühen Neuzeit. Festschrift für Günter Mühlpfordt. Bd. 3. Weimar. 1997. S. 327—356.
 «Время и мир изменились». Дипломатическая записка начала XIX в. о положении Германии в Европе и о характере её отношений с Россией // Исторический архив. 1998. № 1.
 La mission diplomatique de Lev Jakovlev en Westphalie (1810—1812) // Francia. Bd. 25/2. 1998.
 Первая жена наследника престола // Немцы в России. Петербургские немцы. СПб., 1999.
 Германия глазами русской путешественницы конца XVIII в.: дневник Е. П. Дивовой // Русские и немцы в XVIII веке. Встреча культур. М. 2000.
 Вольтер. История Российской империи при Петре Великом // Вопросы истории. 2001. № 8. С. 164—166.
 Война 1812 года и русское общество («Осведомительные письма» тайной полиции) // Русско-французские культурные связи в эпоху Просвещения: Материалы и исследования. М., 2001.
 Петербургское общество и тайная полиция. К истории войны 1812 г. // К истории войны 1812 года: Материалы Всероссийской научной конференции. 30 мая — 1 июня 2002 г. Саратов. 2002.
 Немцы в российской государственности XVIII—начала XIX вв.: историко-культурные аспекты // Немцы в государственности России. Вып. 7 серии «Русско-немецкие контакты в биографии Петербурга». СПб., 2004.
 Вестфальская миссия Л. А. Яковлева (1810—1812 гг.) // Клио. 2005. № 2. С. 116—124.
 Вольтер и его «История Российской империи» // Отечественная история и историческая мысль в России XIX—XX веков: Сборник статей к 75-летию А. Н. Цамутали. СПб., 2006.
 На пути к сословному раскрепощению (Манифест от 18 февраля 1762 г.) // Государство и общество в России XV — начала XX века. СПб., 2007.
 Б. Берг. Бурхард Кристоф фон Миних. Оценка, изображение и исследование его деятельности в русской и немецкой историографии // Вопросы истории. 2007. № 10. С. 172—175.
 К характеристике русского общества в 1812 г. // Власть, общество и реформы в России в XIX—начале XX века: Исследования, историография, источниковедение. СПб., 2009.
 1812 год: «…К отправлению из Петербурга в случае опасности» // История Петербурга. 2011. № 5 (63).
 Review: Marie-Pierre Rey, L'effroyable tragédie. Une nouvelle histoire de la campagne de Russie. Paris: Flammarion, 2012, 391 p. // Cahiers du monde russe. 2011. Т. 52. № 4. С. 682—686.
 Французы в Москве // Французский ежегодник. 2012: 200-летний Юбилей Отечественной войны 1812 г. М., 2012. С. 47—98.
 Рецензия: Damamme J.-C. Les Aigles en hiver. Russie 1812. Paris: Plon, 2009. — 818 p. // Труды кафедры истории Нового и новейшего времени. 2012. № 8. С. 131—135.
 «России не было нужды побеждать Наполеона» / Диалог о книге. «Россия против Наполеона: Борьба за Европу. 1897—1814» Доминика Ливена // Российская история. 2013. № 6. С. 3—51.
 Несколько слов по поводу книги Д. Ливена // Союз дворян Union de la Noblesse Russe. 2014−3. № 130. С. 20—28.
 Граф Поль-Филипп де Сегюр и его книга о русской кампании Великой армии // Союз дворян Union de la Noblesse Russe. 2015-2. № 133. С. 5—20.
 Франция, Россия и тернии тильзитского «брака» // Петербургский исторический журнал. 2015. № 1. С. 150—172.
 Санкт-Петербург в 1812 году // Эпоха 1812 года: Исследования. Источники. Историография. М.: ГИМ 2015. Вып. XIII. С. 193—219 (1 п.л.).
 В Петербурге перед лицом опасности. 1812 // Звезда. 2015. № 5. С. 175—220.
 Постюбилейные размышления о книге английского историка // Петербургский исторический журнал. 2016. № 4 (1 а.л.).
 Подделка с выставки, или о чём Александр I не писал Наполеону на исходе мая 1808 г. // Вспомогательные исторические дисциплины. 2017. Т. 36. № 36. С. 27—39.
 Немцы в войне 1812 г. в России // Германия — Россия: Вехи совместной истории в коллективной памяти / под общей ред. Х. Альтрихтера, В. В. Ищенко, Х. Мёллера, А. О. Чубарьяна. Т. 1I. XVIII век. М., 2018. С. 355—367.
 «Перекрёстные браки» и династические связи России с дворами «Третьей Германии» // Германия — Россия: Вехи совместной истории в коллективной памяти. С. 247—259. Т. 1. XVIII век. М., 2018. (В соавт. с К. Шарф)
 «От Аустерлица до Тильзита. Наполеон на вершине своей власти. 1805—1807» // Германия — Россия: Вехи совместной истории в коллективной памяти. С. 343—355. Т. 1. XVIII век. М., 2018. (В соавт. с К. Шарф)
 Первая официальная история войны 1812 года // История и культура. Статьи. Сообщения. Публикации. Вып. 15 (15). СПб., 2018. С. 113—129.
 Die Deutschen im Krieg von 1812 in Rußland // Deutschland-Russland. Stationen gemeinsamer Geschichte, Orte der Erinnerung. Hrsg. von H. Altrichter, W. Ischtschenko, H. Möller, A. Tschubarjan. De Gruzter Oldenburg, 2018. Bd. I Hrsg. von H.Möller, C.Scharf, W Dudarew und Maja Lawrinowitsch. — 410 S. S. 359—370.
 Von Austerlitz nach Tilsit. Napoleon und dem Höhepunkt seiner Macht 1805—1807 // Deutschland-Russland. Stationen gemeinsamer Geschichte, Orte der Erinnerung. S. 333—344. (In Zusammenarb. mit C. Scharf).
 Наполеон — читатель Макиавелли? // Петербургская библиотечная школа. 2019. № 3 (68). С. 3—9.
 Wechselheiraten. Rußlands dynastische Beziehungen zum «Dritten Deutschland» // Deutschland-Russland. Stationen gemeinsamer Geschichte, Orte der Erinnerung. S. 241—252. (In Zusammenarb. mit C.Scharf).
 Русско-французская конвенция 1810 г. о судьбе Польши и русско-французские отношения // . Vol. 68 (2020). N 2. С. 25—40.
 Une lettre suspecte du tsar Alexandre à Napoléon à propos de la mort de sa fille présumée, la grande duchesse Elisabeth Alexandrovna // Revue d'Institut Napoléon. 2018. Paris, 2021. № 217. Pp. 29—60.
 Выдумка о разговоре на аванпостах в 1812 г., едва не ставшая историческим фактом // Эпоха 1812 года. Исследования. Источники. Историография: Сборник материалов. М., 2021. Вып. XVIII. С. 58—68.
 Рецензия: Наполеон Бонапарт: третья и последняя биография на русском языке (Н. А. Троицкий. Наполеон Великий. Т. 1. Гражданин Бонапарт. 526 с.; Т. 2. Император Наполеон. 549 с. Подг. к публ., вступ. ст. М. В. Ковалева, Ю. Г. Степанова. М., 2020) // Новая и новейшая история. 2021. № 3. С. 232—237.

Примечания

Литература

Рецензии

Ссылки

Выпускники исторического факультета Санкт-Петербургского государственного университета
Сотрудники Санкт-Петербургского института истории РАН
Лауреаты Макарьевской премии


text:
Ryan Bishop, Kristoffer Gansing, Jussi Parikka, Elvia Wilk (Eds.)across & beyond
A transmediale Reader on Post-digital Practices, Concepts, and Institutions
Contributions by Morehshin Allahyari and Daniel Rourke, Jamie Allen and David Gauthier, Clemens Apprich and Ned Rossiter, Tatiana Bazzichelli, Benjamin Bratton, Florian Cramer, Dieter Daniels, Geoffroy de Lagasnerie, Daphne Dragona, Keller Easterling, Olga Goriunova, Louis Henderson, Geraldine Juarez, Olia Lialina, Alessandro Ludovico, Rosa Menkman, Julian Oliver and Danja Vasiliev, Erica Scourti, Cornelia Sollfrank, Telekommunisten (Baruch Gottlieb and Dmytri Kleiner), Tiziana Terranova, YoHa (Graham Harwood and Matsuko Yokokoji)
This collection of art and theory analyzes today's post-digital conditions for critical media practices—across and beyond the analog and the digital, the human and the nonhuman. The contributions also look across and beyond the field of media art, staking out new paths for understanding and working in the transversal territories between theory, technology, and art. The concept of the post-digital is a way to critically take account of, contextualize, and shift the coordinates of new technologies as part of contemporary culture. The post-digital condition is not merely a theoretical issue but also a situation that affects conceptual and practice-based work.
The program of the transmediale festival in Berlin, celebrating its thirtieth year in 2017, has reflected these changes, and this book gathers new contributions from leading international theorists and artists of media and art who have taken part in the festival program over its past five editions. Divided into the thematic sections "Imaginaries," "Interventions," and "Ecologies," this book is not a document of the festival itself, it is rather a stand-alone exploration of the ongoing themes of transmediale in a book format.
The reader is developed as a collaboration between transmediale and Winchester School of Art, University of Southampton.
Copublished with transmediale e.V.
Design by the Laboratory of Manuel Bürger (Stefanie Ackermann, Manuel Bürger)
January 2017, English
14.11 x 21.1 cm, 352 pages, 52 b/w ill., softcover

redpajama_set_name: RedPajamaCommonCrawl


The concept was using two rustic ranch gable forms then contrasting it with a refined shed form. The character of the gable forms have rustic shake roof, vertical barn board siding, exposed timbers and headers. The shed form is juxtaposed with the smooth horizontal siding wrapping the exterior corners, no trim around the windows, smooth metal roofing. The horizontal siding continues inside the house and provides warmth to the interiors.


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Animal Health Market Research Reports
Animal Health Market Research Reports & Consulting
Animal health industry comprises various products and services used for maintaining well-being of companion animals and food producing animals. Animal health products mainly include parasiticides, antimicrobials & antibiotics, vaccines, feed additives, growth promoters, diagnostics products, and other pharmaceuticals. This also covers several products used to enhance livestock productivity. Use of animal health products is based on approach of minimizing disease, preventative measures aiming to keep animals healthy or to limit the spread of disease within an animal population, and regular monitoring of health and welfare of animals.
Globally, the rising demand for animal food products, increasing investments from government organizations and private players, growing concerns of zoonotic diseases, worldwide regulation for the prevention of spread of diseases, growing pet adoption & rising pet insurance, and increasing veterinary healthcare expenditures are the key factors driving the market growth for animal health market. Increasing consolidation in the animal health industry, innovation in animal health products, and high growth in emerging markets are the key trends expected to define the animal health market landscape in the coming years.
Under the animal health domain, MarketsandMarkets provides extensive research reports on parasiticides, vaccines, growth promoters, and diagnostic products, diagnostic and imaging equipment, and testing services among others.
Animal health industry comprises various products and services used for maintaining well-being of companion animals and food producing animals. Animal health products mainly include parasiticides, antimicrobials & antibiotic ....see more
Veterinary Imaging Market by Product (Ultrasound (2D, 3D, Doppler), X-Ray (CR, DR), MRI, CT, Software, Reagent), Therapeutic Area (Orthopedics, Gynecology, Oncology, Dentistry), Animal (Small, Large), End User (Hospital, Clinic) - Global Forecast to 2026
The global veterinary imaging market is expected to grow from USD 1.6 billion in 2021 to USD 2.3 billion by 2026, at a compound annual growth rate (CAGR) of 7.0% during the forecast period. The prominent players GE Healthcare (US), Agfa-Gevaert N.V. (Belgium), Carestream Health (US), Esaote S.p.A (Italy), IDEXX Laboratories, Inc. (US), Mindray Medical International Limited (China), Canon Inc. (Japan), Heska Corporation (US), Siemens Healthineers (Germany), FUJIFILM Holdings Corporation (Japan), Konica Minolta Inc. (Japan), Samsung Electronics Co., Ltd. (South Korea), E.I. Medical Imaging (US), IMV imaging (UK), SEDECAL (Spain). Other players in the veterinary imaging market are Clarius Mobile Health (Canada), Shenzhen Ricso Technology Co. Ltd. (China), DRAMIŃSKI S.A. (Poland), Epica Animal Health (US), and ClearVet Digital Radiography Systems (US), MinXray Inc. (US), Hallmarq Veterinary Imaging (UK), Shantou Institute of Ultrasonic Instruments Co., Ltd (China), Sonoscape Medical Corp. (China), and Reproscan (US).
Veterinary Ultrasound Market by Type (2D, 3D-4D, Doppler), Product (Portable), Technology (Digital, Contrast Imaging), Animal Type (Small, Large), Application (Obstetrics, Cardiology, and Orthopedic), End User (Vet Clinics) - Global Forecast to 2026
The global veterinary ultrasound market is projected to reach USD 456 million by 2026, at a CAGR of 7.0% during the forecast period. The products and services market is dominated by a few globally established players such as include GE Healthcare (US), Esaote SpA (Italy), Shenzhen Mindray Bio-medical Electronics Co., Ltd. (China), Siemens Healthineers (Germany), FUJIFILM Holdings Corporation (Japan).
Animal Growth Promoters and Performance Enhancers Market by Type (Antibiotic and Non-antibiotic (Hormones, Acidifiers, Feed Enzymes, Probiotics & Prebiotics, Phytogenic)), Animal Type (Poultry, Porcine, Livestock, Aquaculture) - Global Forecast to 2026
The animal growth promoters and performance enhancers market industry is projected to grow from USD 16.2 billion in 2021 and to reach USD 21.4 billion by 2026, at a Compound Annual Growth Rate (CAGR) of 5.7% during the forecast period. Prominent players in the Animal Growth Promoters and Performance Enhancers Market include are Cargill, Incorporated (US), Royal DSM N.V. (Netherlands), Elanco Animal Health Incorporated (US), Boehringer Ingelheim Group (Germany), Merck & Co., Inc (US), Alltech Corporation (US), Archer-Daniels-Midland Company (ADM) (US), Vetoquinol S.A. (France), Bupo Animal Health Pty Ltd. (South Africa) and Novus International, Inc. (US).
Animal Parasiticides Market by Type (Ectoparasiticides (Pour-ons & Spot-ons, Tablets, Sprays), Endoparasiticides (Oral Solids, Injectables, Feed Additives), Endectocides), Animal (Companion Animals, Livestock), End User, & Region- Global Forecast to 2026
The animal parasiticides market is expected to grow from USD 9.7 billion in 2021 to USD 13.7 billion by 2026, at a compound annual growth rate (CAGR) of 7.2% during the forecast period.Zoetis Inc. (US), Elanco Animal Health Incorporated (US), Virbac (France), Merck & Co., Inc. (US), Boehringer Ingelheim GmbH (Germany), Ceva Santé Animale (France), Vetoquinol S.A. (France), PetIQ, Inc. (US), Norbrook (Ireland), and Bimeda Animal Health (US).
Veterinary Diagnostics Market by Product(Instruments, Consumables), Technology (Immunodiagnostics (Lateral Flow, ELISA), Clinical Biochemistry, PCR, Hematology, Urinalysis), Animal (Dog, Cat, Cattle, Pig, Poultry, Horse), End User - Global Forecast to 2026
The veterinary diagnostics market is expected to grow from USD 2.5 billion in 2021 to USD 3.9 billion by 2026, at a compound annual growth rate (CAGR) of 9.4%. during the forecast period.Thermo Fisher Scientific Inc. (US), Zoetis Inc. (US), IDEXX Laboratories Inc. (US), NEOGEN Corporation (US), Bio-Rad Laboratories Inc. (US), bioMérieux SA (France), Virbac (France), Heska Corporation (US), Agrolabo S.p.A. (Italy), INDICAL BIOSCIENCE GmbH (Germany), Randox Laboratories Ltd. (Ireland), IDvet (France), Biopanda Reagents (UK), Bionote, Inc. (South Korea), BioChek (Netherlands), Fassisi GmbH (Germany), Biogal Galed Labs (Israel), Alvedia (France), SKYER, Inc. (South Korea), and Shenzhen Bioeasy Biotechnology Co., Ltd. (China).
Companion Animal Pharmaceuticals Market by Indication (Infectious Diseases, Antibiotics, Parasiticides, Vaccines, Pain), Animal Type (Dogs, Cats, Horses), Distribution Channel (Veterinary Hospitals & Clinics), COVID-19 Impact - Global Forecast to 2026
The global companion animal pharmaceuticals market is expected to grow from USD 12.8 billion in 2021 to USD 18.5 billion by 2026, at a compound annual growth rate (CAGR) of 7.6% during the forecast period.Zoetis Inc. (US), Merck & Co., Inc. (US), Boehringer Ingelheim GmbH (Germany), Elanco Animal Health Incorporated (US), Ceva Santé Animale (France), Virbac (France), Vetoquinol S.A. (France), Dechra Pharmaceuticals plc (UK), Chanelle Pharma (Ireland), Kyoritsu Seiyaku (Japan), Zydus Animal Health and Investments Limited (India), Tianjin Ringpu Bio-Technology Co., Ltd. (China), HIPRA (Spain), Norbrook Holdings Limited (UK), Inovet (Belgium), Endovac Animal Health (US), ECO Animal Health Group plc (UK), Indian Immunologicals Ltd. (India), ALS Pvt. Ltd. (India), and Lutim Pharma Pvt. Ltd. (India).
Asia-Pacific Animal Health Market by product (Parasiticides (Ectoparasiticides), Antimicrobial & Antibiotic, Vaccines, Feed Additive( Vitamin, Amino Acids, Antioxidant), Growth Promoter) Animal (Livestock, Companion), End-user (Hospitals) - Forecast to 2025
The Asia-Pacific Animal Health Market is expected to grow from USD 15.6 billion in 2020 to USD 22.1 billion by 2025, at a CAGR of 7.2% during the forecast period. Some of the prominent players in this market are Boehringer Ingelheim (Germany), Zoetis Inc. (US), Elanco Animal Health (US), Merck & Co. Inc (US), Phibro Animal Health (US), Virbac (France), Vetoquinol S.A (France), Ceva Sante Animale (France), Dechra Pharmaceuticals Plc. (UK), Kyoritsu Seiyako (Japan), Tianjin Ringpu (China), China Animal Husbandry (China), and Jinyu Bio-Technology (China).
Veterinary Monitoring Equipment Market by Type (Vital Signs Monitor, Anesthesia Monitor, ECG Monitor), Target Area (Respiratory diseases, Weight Monitoring), Animal Type (Dogs, Cats, Equines), End User (Veterinary Clinic), Region - Global Forecast to 2025
The Veterinary Monitoring Equipment Market is expected to grow from USD 326 million in 2019 to USD 570 million by 2025, at a CAGR of 9.8% during the forecast period. Prominent players in the veterinary monitoring equipment market include Smiths Group plc. (UK), DRE Veterinary (US), Digicare Biomedical Technology (US), Midmark Corporation (US), Medtronic plc. (Ireland), Bionet America, Inc. (Korea), SonoScape Medical Corporation (China), Hallowell EMC (US), MinXray, Inc. (US), Hallmarq Veterinary Imaging Ltd. (UK), Esaote S.p.A (Italy), Agfa-Gevaert N.V. (Belgium), Nonin Medical, Inc. (US), Shenzhen Mindray Bio-Medical Electronics Co., Ltd. (China), Covetrus, Inc. (US), Vetland Medical Sales & Services LLC (US), Masimo Corporation (US), Burtons Medical Equipment Ltd. (UK), Vetronic Services (UK), and IMV Technologies (France).
Veterinary Vaccines Market by Disease (Swine Pneumonia, Avian Influenza, Rabies, Coccidiosis, Brucellosis, Canine Distemper), Type (Poultry, Livestock, Aquaculture, Porcine, Canine), Technology (Inactivated, Toxoid, Recombinant) - Global Forecast to 2025
The veterinary vaccines market is expected to grow from USD 8.0 billion in 2020 to USD 11.3 billion by 2025 at a compound annual growth rate (CAGR) of 7.2% during the forecast period.The prominent players in the veterinary vaccines market include Zoetis (US), Merck (US), Boehringer Ingelheim (Germany), Elanco (US), Ceva (France), Phibro Animal Health Corporation (US), and Virbac SA (France). Major players have focused on product launches, agreements, and partnerships to increase their shares in the global veterinary vaccines market.
Companion Animal Diagnostics Market by Technology (Immunodiagnostic, Clinical Biochemistry, Hematology, Urine Analysis), Application (Clinical Pathology, Virology, Bacteriology, Parasitology), Animal (Dog, Cat, Horse), End-User - Global Forecast to 2025
The companion, animal diagnostics market, is expected to grow USD 3.0 billion by 2025, at a CAGR of 9.8% during the forecasting period. The key players operating in the global companion, animal diagnostics market, are IDEXX Laboratories, Inc. (US), Zoetis, Inc. (US), Thermo Fisher Scientific, Inc. (US), Virbac (France), Heska Corporation (US), Neogen Corporation (US), bioMérieux SA (France), IDvet (France), INDICAL Bioscience GmbH (Germany) [QIAGEN N.V. (Netherlands)], and Randox Laboratories Ltd. (UK).
Veterinary Infectious Disease Diagnostics Market by Technology (Immunodiagnostics (Lateral Flow Assays, ELISA), Molecular Diagnostics), Animal (Companion, Farm), Pathogen (Bacteria, Viral, Parasite), Enduser (Lab, Hospital, Clinics, PoC) - Forecast to 2024
The Veterinary Infectious Disease Diagnostics Market is expected to grow from USD 1.4 billion in 2019 to USD 2.2 billion by 2024, at a CAGR of 8.4% during the forecast period. The major players operating in the veterinary infectious disease diagnostics market are IDEXX Laboratories, Inc. (US), Zoetis, Inc. (US), QIAGEN N.V. (Indical GmbH) (Germany), Thermo Fisher Scientific, Inc. (US), Neogen Corporation (US), Virbac (France), bioMérieux SA (France), IDVet (France), Randox Laboratories, Ltd. (UK), Heska Corporation (US), Creative Diagnostics (US), URIT Medical Electronic Group Co., Ltd (China), NTBIO Diagnostics, Inc. (Canada), Bio-X Diagnostics (Belgium), Agrolabo S.p.A (Italy), Eurolyser Diagnostica (Austria), Eurofins Technologies (Hungary), MEGACOR Diagnostik GmbH (Austria), AusDiagnostics Pty, Ltd. (Australia), Demedetic Diagnostics GmbH (Germany), Chembio Diagnostic Systems, Inc. (US), DRG Diagnostics GmbH (Germany), XpressBio (US), InBios International, Inc. (US), and Genesig (Primerdesign) (UK).
Veterinary Rapid Test Market by Product (Rapid Test Kit, Rapid Test Readers), Application (Viral, Bacterial, Parasite, Allergies), and Animal Type (Companion (Cat, Dog, Horse), Livestock (Cattle, Swine, Poultry)) - Global Forecast to 2024
The global veterinary rapid tests market is projected to reach USD 854 million by 2024 from USD 573 million in 2019, at a CAGR of 8.3%. Factors contributing to the growth of this market include the increasing prevalence of zoonotic diseases among companion and livestock animals and the growing need for the rapid detection of these diseases. The prominent players in this market are Zoetis, Inc. (US), IDEXX Laboratories, Inc. (US), Heska Corporation (US), Biopanda Reagents Ltd. (UK), Fassisi GmbH (Germany), Virbac (France), BioNote, Inc. (South Korea), SWISSAVANS AG (Switzerland), MEGACOR Diagnostik GmbH (Austria), and Woodley Equipment Company (UK).
Veterinary X-ray Market by Technology (Direct, Computed, Film), Type (Digital, Analog), Mobility (Fixed, Portable), Animal (Companion, Large Animal), Application (Trauma, Oncology, Dental), End User (Clinic, Hospital) - Forecast to 2023
The veterinary X-ray market is projected to reach USD 872 million by 2023 from USD 640 million in 2018, at a CAGR of 6.4%. Rising animal health expenditure and growing demand for pet insurance, increasing number of veterinary practitioners in developed regions, and growth in the companion animals market are the primary drivers for the veterinary X-ray market during the forecast period. The prominent players in the veterinary X-ray market are IDEXX (US), Fujifilm (Japan), Onex Corporation (Canada), Sedecal (Spain), Agfa-Gevaert (Belgium), Sound Technologies (US), Fujifilm Holdings Corporation (Japan), Canon, Inc (Japan), Examion (Germany), Konica Minolta (US), DRE Veterinary (US), and Heska Corporation (US).
Veterinary Equipment and Disposables Market by Product (Consumables, Anesthesia Machines, Ventilators, Patient Monitoring, Oxygen Masks, Infusion Pumps), Animal (Cats, Dogs, Equines, Bovines), End User (Hospitals, Clinics) - Global Forecast to 2023
The global veterinary equipment and disposables market is projected to 2.40 billion by 2023 from USD 1.63 billion in 2018, at a CAGR of 8.0% during the forecast period of 2018 to 2023. Rising animal health expenditure and a growing demand for pet insurance, increasing number of veterinary practitioners in developed regions, and growth in the companion animals market are the primary drivers for the global veterinary equipment and disposables market during the forecast period. The prominent players in the veterinary equipment and disposables market are Mindray Medical International Limited (China), Smiths Group plc (UK), Nonin Medical (US), Digicare Biomedical Technology, Inc. (US), B. Braun Melsungen AG (Germany), Henry Schein (US), Vetland Medical Sales and Services, LLC (US), Hallowell Engineering & Manufacturing Corporation (US), Infiniti Medical, LLC (US), DRE Veterinary (US), and Midmark Corporation (US).
Veterinary Pain Management Market by Product (Drugs (NSAIDs, Opioids), Device (Laser, Electromagnetic)), Application (Osteoarthritis, Cancer), Animal Type (Dog, Cat, Horse, Cattle), Distribution Channel (Hospital, Pharmacy) - Global Forecast to 2023
The veterinary pain management market is projected to reach USD 1.66 Billion by 2023 from USD 1.13 Billion in 2018, at a CAGR of 8.0% during the forecast period. Factors such as growth in companion animal adoption, increasing number of veterinary practitioners and their rising income levels in developed economies, growing prevalence of animal disease causing pain and inflammation, and the rising adoption of pet insurance with growing animal health expenditure are expected to drive the growth of the market. However, increasing pet care costs may be a key restraining factor for the market in the next five years. The prominent players in the veterinary pain management market are Zoetis (US), Merck Animal Health (US), Elanco (US), Chanelle (Ireland), Boehringer Ingelheim (Germany), Vetoquinol (France), Bayer (Germany), Norbrook (Ireland), Ceva Santé Animale (France), K-Laser (US), Assisi (US), and Dechra Pharmaceuticals (UK). New product launches and acquisitions were the major strategies adopted by these payers to maintain their positions in the veterinary pain management market


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Recovery & Stigma
This past weekend, I sat in a swanky hotel lobby in lower Manhattan, waiting for five friends to come down from the meeting room where one of our event's keynote sessions had just concluded. Women were descending into the lobby in groups, pairs, or alone. They were happily chatting, punctuating their sentences with laughter. They were in their twenties, all the way up into their golden years. Dressed in suits, yoga pants, and everything in-between. They were, on the whole, gorgeous. Any observer oblivious to our reason for being there would have assumed it was a health and fitness conference of some sort. I suppose it was, if you really think about it. We weren't learning new workout methods or sampling the latest supplements, but our work was indeed all about profound, long-term wellness - the kind that outlives the latest fitness trends and whose only membership requirement is willingness. I thought, as I gazed out at these women, my sisters in recovery, this is the hottest ticket in town, and any smart people who want to meet a woman with depth, faith, wisdom- should be sitting in this lobby right now. (Seriously - if you are on the market, stay tuned to the next #SheRecovers conference!).
Of course, unless you are yourself in recovery, you may not know that someone who is actively working on their sobriety (not to be confused with merely abstaining from their addictive behavior or substance) is such a good catch. Stigma is real, and I got to experience if first-hand a couple of weeks ago. I was running errands and an acquaintance came over to me to chat. At some point, she told me she was really angry with me when she heard I was in recovery from alcohol, because I had "put her daughter in danger" when she was in my presence. I was shocked by her assumptions, and angered, especially because she did not seem interested in hearing the truth (to be fair, she wasn't mean, she just wasn't interested in listening). I thought of this unpleasant interaction as I experienced this past weekend's gathering with 500 other women who were dedicating themselves to figuring out what led to their seeking solace in a bottle, a pill, disordered eating, abusive relationships, while doing the hard work it takes to avoid making the same mistakes. As I marveled on the wisdom I heard and saw and felt from these women, some of whom have become cherished friends over the past year, I thought about that misguided woman who probably was voicing the fear felt and lies believed by those who are not in recovery. I thought, gosh, if only more people knew how important these voices in this room are, not only to help others struggling with addiction, but indeed, to help this insane world that is only becoming more and more imbalanced and disconnected. If only more of us were in a position to come forward and show our faces, and live our recovery out loud! How f-ed up is it that so many of us are afraid to come forward?!
About 25 of us went for a run at 6:30am on Saturday, before the conference resumed. Yes, 6:30am on a Saturday. In NYC, party capital of the world. I point this out because if you are holding any stigma, I want you to ask yourself, if you were in a swanky hotel for 3 days in NYC, would you be bright-eyed and bushy-tailed at 6:30am on Saturday, when your conference doesn't begin till 10am? Sober is badass. In fact, I kind of hate the word sober, because it sounds so... sober, as in serious, subdued, "showing no excessive or extreme qualities of fancy or emotion." As our colorful, blinking (Heroes in Recovery had donated bracelets with blinking lights for our run), smiley, and eventually, sweaty group made our way along the Hudson River, the only thing sober about our jovial tribe was our livers and other alcohol-and-other-drug-free organs. As I would be several times throughout the weekend, I was struck by the thought that if I had know that this was what living the sober life looked and felt like, I may not have postponed it for so long! While I may not have hit any shocking rock bottom, in fact I probably drank about as much as you do, if all your alcohol-glorifying Facebook posts reflect your attitudes toward alcohol and its ability to enhance celebration, soften the edge, escape the ugh - I definitely feared that aspiring for long-term sobriety would pretty much sentence me to a life of prudish, unremarkable, convent-worthy behavior. Gosh, I couldn't have been more wrong. At some point I thought of Karl Marx's statement that religion is the opiate of the masses, and thought how today, alcohol is the opiate of the masses! What a privilege, and so much fun, it is to be a part of this community that one day at a time, is choosing to live in a way that is thoughtful, mindful, conscious. To move through the world awake.
The thing is, I can't blame that woman for her assumptions about me. Just like I can't blame myself for having made assumptions about what sobriety would be like. We don't see enough examples, or hear from enough people, of what exactly recovery looks like. The wonder. The camaraderie. The fun and laughter. The glow. The courage and strength. THE CONNECTION. The freedom. The authenticity! Most of us are only privy to the examples of dis-ease, and how it manifests itself, with usually only the extreme examples making their way into the public eye. It has often struck me how absurd it is that very often, addictive behavior happens openly, especially if it involves alcohol, a socially-sanctioned drug, but once we cross over to sobriety and recovery, we hide in basements and private chat rooms. No wonder most people don't have a fucking clue about two very important points: 1) you don't have to be a hopeless drunk in order to decide that alcohol is stupid and needs to be questioned and 2) choosing sobriety and working on recovery is an incredibly powerful, empowering thing to do, for yourself and for the world at large. It's the most badass thing you can do. Don't get me wrong - it often isn't nearly as pretty as the women on the stage or in the audience of She Recovers NYC. The recovery journey often feels - and probably looks - just as awful as mile 20 of a marathon. But that's a good thing, because it means we are no longer running from stuff or denying it. The only way to get to the glory of the "I did it!" Is to keep going, one foot, breath, day at a time. And holy crap it's worth it.
Here are some of the nuggets I got from our keynote speakers:
"Sobriety is no bullshit... To people not in recovery it means not drinking, but it's actually much more than that. It's figuring out who we were before the world told us who we were supposed to be."
- Glennon Melton Doyle
"Anything that I use to escape a perceived intolerable reality, is something that can turn into addiction."
- Nikki Myers
"Pain is inevitable. Suffering is optional. It's in the story I make up about the event."
"I drank to feel the way other people look."
- Elizabeth Vargas
"We should be grateful for all the situations that make us uncomfortable, for without them we wouldn't know what needs to be healed."
- Gabby Bernstein
"Say nice things to yourself, because you're the only one listening."
"Suffering gives you x-ray vision into the suffering of other people."
- Marianne Williamson
"This will pass one day, and I will embrace this pain because of the person I will be because of having gone through it."
"If you desensitize yourself to your own suffering, you desensitize yourself to others' suffering - and then the whole world suffers."
"It's a process, but at some point it becomes spewing. At one point it's 'allowing our feelings,' but at some point it becomes self-indulgence."
"The psyche has an immune system just like the body. And grief is the bulwark."
"We are so concerned about the chemicals in our gut, but not the ones we are putting in our brain."
If you are wondering if sobriety is something you should explore, perhaps start by asking yourself, why not? Sometimes one of the biggest obstacles standing between us and what we need to do for our health and happiness, is actually stigma. The assumptions we have about something we really don't know much about, which causes fear. For example, since I began my recovery journey, I have learned that the only thing that all of us in recovery have in common, is the fact that we were engaging in behavior that gave us a shameful, sinking feeling. The actual quantity or frequency was irrelevant - for some of us it was daily, for others monthly. For some, it was gallons, for others, occasional glasses. But if we could have some way to measure the sinking in our souls, we were pretty much in the same tank of ugh - or headed that way. If this is striking a chord with you, listen to it. That is your truth speaking. Follow it, or at least notice it. And if you want to explore any of this, please reach out to me. You don't have to do this alone.
Jenna (Jade Love)
I simply love this. I'm struck by the mother who judged you and see that I'm terrified of the stigma. But I'm getting over it and need friends like you to help me tell my story.
Hi Jenna (Jade),
Thanks so much for reading and commenting. Stigma is real, and I fully understand someone's fear of coming forward with their story. Every time we speak our truth, we risk that vulnerability. It can be really disheartening to feel judged and misunderstood, and to feel unloved- so many of us are people pleasers, which is part of our problem after all! Stigma is rooted in ignorance, so when we do open up, and show people what addiction and other brain health issues really look like, and especially, what recovery means and looks like- we lessen the darkness by broadening the light. And quite frankly, those who really matter, will be curious and loving enough to ask questions, and broaden their compassion. This woman may have been a dark cloud for me, but there have been hundreds to her one, who have come forward with "me too" or "I get it and I love you." Anyway, keep doing what you're doing ?


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Q: Можно ли вызывать функции класса из его конструктора? Можно ли вызывать функции класса из его конструктора или объект ещё не определён?

A: Хотя время жизни объекта в C++ начинается по завершении конструирования (если есть конструктор), конструктор является специальной функцией, которая в стандарте имеет свои условности ([class.cdtor]). Во время исполнения конструктора, объект находится в состоянии конструирования, и в этом состоянии можно вызывать любые функции-члены, а также виртуальные функции этого класса.
Важно понимать, что при начале исполнения тела конструктора, все конструкторы базовых классов (если есть) уже завершились, а все члены класса, которые имеют конструкторы, так же уже были сконструированы. Поэтому вызов функций-членов не может привести ни к какому непредвиденному результату. Другое дело, если вызывать функции-члены в списке инициализации конструктора — это может привести к неопределённому поведению.

redpajama_set_name: RedPajamaStackExchange


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The AUSIT Excellence Awards recognise initiatives/activities by individuals and organisations operating in all sectors of the translating and interpreting industry. They encourage best practice, professionalism, dedication to quality, innovation and outstanding contributions. The Awards celebrate organisations and individuals/teams in the following categories:
Excellence in Interpreting
This award recognises an interpreter or interpreters with appropriate credentials or qualifications for outstanding performance in a particular assignment, a series of related assignments or a project. The following elements are included in the assessment: linguistic and interpreting skills; professionalism and ethical conduct; outcomes/purpose fulfilment.
Excellence in Literary Translation
This award recognises a translator or translators with appropriate credentials or qualifications for the outstanding translation of a literary work (prose, poetry or drama). It may be given either for a single book-length translation of outstanding quality or for the entire body of a translator's work in the domain of literary translation (LOTE>English).
Excellence in Non-Literary Translation
This award recognises a translator or translators with appropriate credentials or qualifications for a single translation of outstanding quality or for the entire body of a translator's work in the scientific/technical/non-fiction domain (LOTE>English).
Outstanding Contribution to Translation & Interpreting
This award is for an individual, team or organisation for consistently outstanding performance in conducting activities or developing initiatives benefiting the industry as a whole, including end-users and practitioners. Areas of activity may include:
Supporting/enhancing the discipline and/or the profession
Outstanding Contribution to Interpreting and Translating: Working with Languages of Limited Diffusion
This award is presented to a practitioner, team of practitioners, language service provider, organisation, business or institution that is part of, related to, or involved in some aspect of interpreting and/or translating using an Indigenous language or a language of limited diffusion, in recognition of their work on a particular assignment, project or initiative that benefits the community as a whole, including end-users and practitioners.
Nominations for the AUSIT Excellence Awards
The coveted AUSIT Excellence Awards are highly regarded and submissions are encouraged from AUSIT members as well as all translators & interpreters from Australia and New Zealand. The key selection criteria for initiatives recognised by AUSIT through these awards include innovation, role model value, benefits delivered to users and/or practitioners and their level of impact on the industry as a whole.
The awards ceremony is generally held every two years. For details regarding the application process and eligibility, please refer to the Nomination Form below:
Excellence Awards Nomination Form
History of the AUSIT Excellence Awards
The Awards were originally launched in 2004 with one category only – Outstanding Contribution to the Translating and Interpreting Industry. The categories of Excellence in Interpreting and Excellence in Translation were added in 2005, and the category of Outstanding Contribution to Indigenous Interpreting was added in 2009. The Awards were not organised in 2015 and 2017 as AUSIT was preparing for hosting the International Federation of Translators Congress in Brisbane in 2017. Instead, between 2015-2017 a committee reviewed the procedure and guidelines, and in 2018, the present categories were established.
Chin Communications Team – Charles Qin, Xin Jin & John Zhou, for their work on the ANZSOG project.
Runners-up, highly commended:
MaryJane Kwon,  for facilitating communications between NT Beverages and Jace, a South Korean firm.
James Grieve (French language), for the entire body of work; Kevin Windle (Multiple languages), for the entire body of work.
Outstanding Contribution to Translating and Interpreting
Anna Kenny, for the lasting impact her training has had on the quality of interpreter services.
Outstanding Contribution to T&I Working with Languages of Limited Diffusion
No entry received.
Chin Communications Team – Charles Qin, Joseph Li Tu & Bruce Leifu Song, for their work on the Dairy Australia Greater China Scholarship Program; Yveline Piller & John Benson, for their work on the Australia-Africa Partnership Facility project.
Excellence in Translation
Catherine Manning Muir, for translating into English one of the earliest examples of pre-independence Indonesian literature: Hikayat Siti Mariah (The Saga of Siti Mariah).
Runner-up, highly commended:
Elizabeth Meister (German language), for her work on Cate Shortland's multi-award-winning feature film Lore.
Outstanding Contribution to the Translating and Interpreting Industry
SBS Subtitling and PPU Preparation, for their ground-breaking deployment of LOTE subtitles in programs aimed to speak directly to multiple generations of multilingual Australians about important social issues.
Outstanding Contribution to Indigenous Interpreting
Industrial & General (I&G) Pty Ltd, for the use of portable VOIP recording technology through regional and remote Australia which resulted in significant gains for Indigenous interpreting and translation.
Xin Jin (Mandarin language), for leading the interpreting team at the Australian Pavilion, World Expo 2010 Shanghai.
Nadesan Sundaresan (Tamil language), for a series of interpreting assignments in a detention facility for asylum seekers; Rona Zhang (Mandarin language), for interpreting at the Australian Pavilion, World Expo 2010 Shanghai.
Dalia Matar (Arabic language), for translating and subtitling work on the award-winning SBS Dateline program Iraq's Deadly Legacy.
Rekha Rajvanshi for the translation from English into Hindi of Dreaming Stories – Australian indigenous oral histories rendered as animations.
Transcultural & Language Service (TALS) Department at Northern Health (Vic), for its centralised language service model and transcultural training for Northern Health staff in how better to work with interpreters.
Sign Language Communications (SLC) (NSW), for the 'John Ferris Interpreter Internship', pairing newly accredited Auslan interpreters with experienced mentors.
TAFE SA, for the Diploma of Interpreting course designed specifically for Anangu-speaking students living in remote communities in South Australia.
Mona Jabbour (Arabic language), for interpreting between counsellors and traumatised asylum seekers and refugees.
Suzan Piper (Indonesian language), for her translation and subtitling of the award-winning documentary The Burning Season.
Queensland Health Multicultural Services, for the Queensland Health Interpreter Service Model.
Kimberley Interpreting Service (KIS), for the development and implementation of a multi-faceted professional development plan and interpreters' handbook.
Shaperai Jallya (Dari and Farsi languages), for interpretation in court proceedings for a man with mental health problems; JC Lloyd-Southwell (Spanish and several other languages), for simultaneous court and conference interpreting at the International Tribunal for the former Yugoslavia in the Hague.
Marc Orlando, for translation into French of an 11,000-word feature article on the war in Iraq written in English by Jon Stephens, published in Metro magazine. The translation won the 2006 International Prize for War Correspondents in Bayeux.
Victor Xu (Mandarin language), for interpreting a series of training sessions for Chinese government officials on income modelling and forecasting as part of the World Wide Project Management Services initiative for the China-Australia Governance Program.
SBS Subtitling Unit, for developing innovative procedures in subtitling.
Merril Clayton, for translating from German a series of medical documents; Sue Jollow, for translating from Chinese of insurance documents relating to a multi-million-dollar wind power project.
Terry Chesher, for her outstanding contribution to translation and interpreting through her work on the professional development programme for AUSIT NSW.
NABS (National AUSLAN Interpreter Payment and Booking Service), for its interactive interpreter training resources; NSW Multicultural Health Communication Services and I&G Media, for their joint submission: Can Creativity Be Translated – Towards Good Practice in Translating Radio Commercials – The Emphysema Test containing the innovative approach in developing guidelines to ensure that script translations matched the original creative talent.
Vannessa Hearman and Liang Joo Leow (Indonesian language), for their simultaneous interpreting for Channel Seven of the Corby verdict from Denpasar on a live news feed – first time ever in Australia.
Charles Qin (Mandarin language), for simultaneous interpreting as part of the inaugural visit of Chinese President Hu Jintao; Christoula Nicolaou (Greek language), for interpreting work at the Melbourne Greek Film Festival 2005; Vera Oujaimi (Arabic language), for a series of interpreting assignments with Victoria Police.
David Holm (Chinese languages/dialects), for annotated translations and transcriptions of Zhuang cosmological texts in the book Killing a Buffalo for the Ancestors (published by Southeast Asia Publications, Northern Illinois University, 2003).
Victoria Jourdan, for the translation into Spanish of the 88-page guidebook Crystal Oracle – Guidance from the Heart of the Earth; Leviseda Bun Douglas, for translation into Khmer of published information for the NSW Multicultural Health Communication Service.
The Victorian Department of Education & Training for the Talking in Tune – a Guide to Working with Interpreters in Schools package (video, hard copy guide and user notes).
Victorian Deaf Society (VicDeaf), for their Auslan Interpreter professional development programme; The Victorian Department of Justice's Diversity Issues Unit; for its Justice Online Directory for Justice Information; Melbourne Metropolitan Ambulance Service, for making their 4 Steps for Life CPR programme available in languages other than English.
Outstanding Contribution to the Translating and Interpreting Industry Small team/individual, winner:
LOTE Marketing, for the talking book Every Child is Important - part of the Australian Childhood Foundation's campaign – managing the translation of the content into seven languages.
Organisation winner:
All Graduates, for introducing an online interpreter booking system: a first in Australia.
Organisation, highly commended:
Web Communications Unit of the Victorian Department of Human Services, for creating a Health Translations Online Directory.
David Connor (multiple languages), for lifetime achievement as one of the institute's founding members and former AUSIT Vic/Tas office-bearer who for many years served the translating and interpreting industry in a variety of roles.


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text:
\section{Introduction}

Federated learning (FL) \cite{mcmahan2017communication} enables the collaborative training from datasets residing on distributed clients with the help of a parameter server. In numerous previous works, including \cite{smith2017federated,li2020federated,sattler2019robust,li2019convergence}, the superiority of FL has been validated through numerical results and convergence analysis in independently identically distributed (IID) and non-IID datasets. While the recent literature related to FL primarily addresses communication efficiency, fairness, robustness, privacy of FL, and personalization, almost all of the previous works have assumed that the training datasets at clients are perfectly ready to be used for training.

However, the annotation step should not be overlooked or ignored for the practical implementations of FL, like other machine learning (ML), since the cost for labeling is generally high and might be even dominant over the FL itself. Considering this problem, we study the annotation strategies in the FL framework, where the clients participating in FL should label their datasets prior to FL execution. For the annotations, we apply active learning (AL) \cite{settles2009active} at each client participating in FL. Because labeling all the instances is rarely a practical or cost-effective, AL aims to maximize the model's performance based on the fewest samples by selectively sampling and labeling the most informative instances.

To validate the proposed method, we establish a FL framework with the annotation step, where various active learning strategies in the FL are compared: 1) conventional FL with random sampling, 2) client-level separated active learning (S-AL), and 3) the proposed federated active learning (F-AL). In the F-AL, the clients collaboratively execute the AL to select the instances that are considered informative to FL in a distributed optimization manner. For the S-AL and F-AL, the state-of-the-art AL algorithms are incorporated.

The AL certainly outperforms the random sampling in the centralized learning. However, to the best of the authors' knowledge, there has been no work for considering the AL in the FL framework and investigating the effect of AL on the performance of FL. This work demonstrates that AL can surprisingly reduce the cost of labeling for FL, and the cost-saving is fascinating in the FL environments. Furthermore, we show that the proposed F-AL considerably improves the performance of AL in the FL environment. We summarize our contributions below:

\begin{itemize}
    \item We establish a general FL framework combining with the annotation step. We evaluate the three types of methods: conventional FL with random sampling, S-AL, and F-AL. With the S-AL, the clients independently apply AL in their datasets. The F-AL encourages the clients' collaboration for AL.

    \item We empirically demonstrate that the AL is effective in the FL environment through various experiments with AL algorithms and datasets. The numerical result indicates that the AL methods outperform random sampling in terms of test accuracy of global FL models.

    \item
    We demonstrate that F-AL outperforms the other methods. We highlight that the F-AL magnifies the benefit of AL in the FL environment.

\end{itemize}

\section{Related Work \& Background}

\subsection{Federated Learning}
FL can be categorized into cross-device FL and cross-silo FL \cite{kairouz2019advances}. In both of FL, data is locally generated and stored while the data is centrally managed and distributed to clients in the setting of datacenter distributed learning. The cross-device FL supposes that the clients are an enormous number of mobile or IoT devices connected by wi-fi or slow connections. Therefore, uplink communication is the main bottleneck of performance. Furthermore, it generally encounters fresh training samples which are never seen before since most clients participate only once in an entire FL process.

On the other hand, cross-silo FL typically supposes that the distribution scale is $2$-$100$ clients, which are generally different organizations or geo-distributed data centers such as hospitals or banks. Therefore,  it supposes that all clients are available during the whole FL process, and the clients' datasets are repeatedly used for training from round to round. The performance degradation due to communication bottleneck is not as severe as the case of cross-device FL. Instead, the performance heavily depends on the number or quality of the training dataset \cite{fenza2021data}.

FL can be executed in various ways in terms of optimization strategy of the knowledge among the clients. The most classic algorithms in FL are federated stochastic gradient descent (FedSGD), or federated averaging (FedAvg) \cite{mcmahan2017communication} which are based on the averaging of the clients' parameters. Beyond the vanilla algorithms, FedProx \cite{li2020federated} and FedDF \cite{lin2020ensemble} tackles the systems and statistical heterogeneity, FedMA \cite{wang2020federated} and FetchSGD \cite{rothchild2020fetchsgd} alleviate the communication bottleneck, and TERM \cite{li2020tilted} and Ditto \cite{li2021ditto} are related to the fairness and robustness in personalized FL.

\subsection{Active Learning}
AL selects the informative instances to be labeled prior to the other instances and aims to maximize the model's performance based on the fewest samples. It has been demonstrated that AL can considerably reduce the number of labeling samples and alleviate the heavy burden of cost for annotation \cite{settles2009active,ren2021survey}. In fact, it has been proved that an effective AL strategy can theoretically obtain exponential acceleration in the efficiency of labeling \cite{balcan2009agnostic}. Even when it is applied in the area of deep learning (DL), the cost saving in the annotation is much more fascinating since DL has its explicit limitation due to the high cost of labeling the numerous instances, even brutal in the professional field that requires rich knowledge \cite{bengio2007greedy,krizhevsky2012imagenet}.

The sampling strategies of AL can be categorized into uncertainty-based sampling, representation-based sampling, other sampling strategies leverage the characteristic of deep learning such as learning loss (LL) \cite{yoo2019learning}, Monte-Carlo dropout (MC-dropout) \cite{gal2016dropout}, adversarial active learning \cite{sinha2019variational,kim2021task}, and hybrid sampling using the strategies jointly. Uncertainty-based sampling \cite{lewis1994sequential,beluch2018power} queries the instances which are the most uncertain to the model trained on the current training samples. Representation-based sampling \cite{geifman2017deep,sener2017active} measures the representativeness of unlabeled samples and encourages the sampling strategy to select the instances from different areas of the distribution. Since the sampling strategy only concerned with uncertainty may skew the model due to the similarity of the sampled instances in a particular distribution, the balance between uncertainty and representativeness is one of the main issues in the performance of AL strategies \cite{sener2017active}.

Furthermore, most of the recent work related to AL focuses on the AL strategy for DL by leveraging the aspects of ML model such as estimated training loss \cite{yoo2019learning}, length of gradient \cite{freytag2014selecting} and MC dropout \cite{gal2016dropout} for uncertainty estimation. Adversarial active learning \cite{sinha2019variational,wang2020dual,zhang2020state,kim2021task} trains a generative adversarial network (GAN) structured auxiliary network which learns a low dimensional latent space and discriminates the labeled and unlabeled samples in order to select unlabeled instances which are most different from the labeled instances. Furthermore, \cite{cho2021mcdal} recently proposed Maximum Classifier Discrepancy for Active Learning (MCDAL) which is the first work that leverages classifier discrepancy for sampling in active learning.

\section{Problem Definition}\label{sec:problem}
This section provides FL framework where the annotation step is included before the execution of FL. We first introduce the FL environment comprising a parameter server and clients. The annotation step in the FL framework is described and formulated in more detail. Furthermore, we provide the AL which the clients execute in the annotation step.

\subsection{Federated Learning Environment}
We consider a cross-silo FL comprising a parameter server and $M$ clients. The clients store their own local dataset $\mathcal{U}_{m}$, $m=1,\dots,M$ which are the unlabeled datasets. Before the start of FL, each $m$-th client selects the instances from $\mathcal{U}_{m}$ and labels the instances to obtain $\mathcal{D}_{m}=\left\{\bm{x}_{i},\bm{y}_{i}\right\}$ where $\mathcal{L}_{m}=\left\{\bm{x}_{i}\right\}$ is the selected instance from $\mathcal{U}_{m}$ and $\bm{y}_{i}$ is the label of $\bm{x}_{i}$. We denote the sampling function as $\mathcal{A}\left(\cdot\right)$ and the selected instances, $\mathcal{L}_{m}$, as
\begin{equation}\label{sampling}
\mathcal{L}_{m}=\mathcal{A}\left(\mathcal{U}_{m}\right),
\end{equation}
for $m=1,\dots,M$.

Let $\bm{\theta} \in \mathbb{R}^{D}$ denote the global model to be optimized in FL. The local loss $F_{m} \left( \bm{\theta}\right)$ at the $m$-th client is $F_{m} \left( \bm{\theta}\right)= \frac{1}{\left|\mathcal{D}_{m} \right|} \sum_{\bm{u} \in \mathcal{D}_{m}} f \left( \bm{\theta}, \bm{u} \right)$,
where $\mathcal{D}_{m}$ is the labeled dataset at the $m$-th client and $f\left(\cdot\right)$ is the loss function determined by the network model. Accordingly, the global loss $F\left(\bm{\theta}\right)$ is defined as $
F\left(\bm{\theta}\right)=\frac{1}{ \left|\bigcup_{m=1}^{M} \mathcal{D}_{m} \right|} \sum_{\bm{u} \in \bigcup_{m=1}^{M} \mathcal{D}_{m}} f \left( \bm{\theta}, \bm{u} \right)$.
The goal of FL is to train the optimized parameter $\bm{\theta}^{*}$ minimizing the global loss, namely
$\bm{\theta}^{*}=\underset{\theta}{\mathrm{argmin}} ~ F\left(\bm{\theta}\right)$.

The FedSGD \cite{mcmahan2017communication} is applied for the FL updates, where $\bm{\theta}^{*}$ is obtained through iterative stochastic gradient descent (SGD) allowing the parallel computation of gradients at the clients. The parameter vector $\bm{\theta}_{t}$ at the $t$-th iteration is updated according to $\bm{\theta}_{t+1} = \bm{\theta}_{t}  - \eta_{t}  \sum _{m=1}^{M} \frac{n_{m}}{n}\bm{g}_{m }\left(\bm{\theta}_{t}\right)$,
where $\eta_{t}$ is the learning rate at the $t$-th iteration, $n=\sum_{m=1}^{M} n_{m}$, $n_{m}=\left|\mathcal{D}_{m} \right|$  and $\bm{g}_{m }\left(\bm{\theta}_{t}\right) \in \mathbb{R}^{D}$ is the stochastic gradient of $\bm{\theta}_{t}$ computed at the $m$-th client as
$\bm{g}_{m}\left(\bm{\theta}_{t}\right)=\frac{1}{\left|\mathcal{D}_{m}\right|} \sum_{\bm{u} \in \mathcal{D}_{m}}  \nabla f\left(\bm{\theta}_{t},\bm{u}\right)$. The update is equivalently given by
\begin{equation}\label{average}
\bm{\theta}_{t+1} = \sum_{m=1}^{M}\frac{n_{m}}{n} \bm{\theta}^{m}_{t+1},
\end{equation}
where
\begin{equation}\label{update}
\bm{\theta}^{m}_{t+1} =  \bm{\theta}_{t} - \eta_{t} \bm{g}_{m }\left(\bm{\theta}_{t}\right).
\end{equation}
In this work, the FedAvg \cite{mcmahan2017communication} computes the converged solution at each client by repeating \eqref{update} multiple times before the average. The overall FL framework is summarized in Algorithm \ref{alg:FedAvg}.

\begin{figure}[ht]
\vskip 0.2in
\begin{center}
\centerline{\includegraphics[width=0.5\columnwidth]{overview.png}}
\caption{Annotation strategies for federated learning.}
\label{overview}
\end{center}
\vskip -0.2in
\end{figure}

\begin{algorithm}[tb]
   \caption{Federated Learning with annotation step}
   \label{alg:FedAvg}
\begin{algorithmic}
   \STATE {\bfseries Input:} unlabeled datasets, $\left\{\mathcal{U}_{m}\right\}_{m=1}^{M}$
   \STATE \phantom{{\bfseries Input:}} initialized model, $\bm{\theta}_{1}$
   \STATE \phantom{{\bfseries Input:}} learning rate, $\left\{\eta_{t}\right\}_{t}$
   \begin{ALC@g}
   \STATE {\bfseries Annotation step:}
   \begin{ALC@g}
   \FOR{$m=1$ {\bfseries to} $M$}
   \STATE annotate $\mathcal{L}_{m}=\mathcal{A}\left(\mathcal{U}_{m}\right)$ to obtain $\mathcal{D}_{m}$
   \ENDFOR
   \end{ALC@g}
   \STATE {\bfseries FL step:}
   \begin{ALC@g}
   \FOR{each round $t=1,2,\dots$ }
   \STATE {\bfseries Client executes:}
   \begin{ALC@g}
   \STATE do multiple iterations of \eqref{update}
   \end{ALC@g}
   \STATE {\bfseries Server executes:}
   \begin{ALC@g}
   \STATE average model parameters as in \eqref{average}
   \end{ALC@g}
   \ENDFOR
   \end{ALC@g}
   \STATE {\bfseries return} $\texttt{FedAvg}\left(\left\{ \mathcal{D}_{m} \right\}_{m}^{M} \;\middle|\; \bm{\theta}_{1} ,\left\{\eta_{t}\right\}_{t} \right)$
   \end{ALC@g}
\end{algorithmic}
\end{algorithm}

\subsection{Active Learning}\label{sec:problem:AL}
In the proposed FL framework, we introduce the sampling function, $\mathcal{A}\left(\cdot\right)$, which finds the instances to be labeled from the unlabeled dataset prior to the process of FL. For an example of random sampling, the acquired sample instances from the unlabeled dataset $\mathcal{U}$ is
$\mathcal{A}\left(\mathcal{U}\right)=\texttt{random}(\mathcal{U}, b)$,
where $\texttt{random}\left(\mathcal{U}, b\right)$ is to randomly choose the $b$ instances from $\mathcal{U}$. In terms of the sampling function, the goal of AL is to find the best sampling function which selects the most informative and effective instances to the performance of the main task.

Most of the AL algorithms generally searches instances with the highest score in the unlabeled data pool \cite{mccallumzy1998employing} as
\begin{equation}
\mathcal{A}\left(\mathcal{U}\right)= \underset{\mathcal{L} \subseteq \mathcal{U},~\left| \mathcal{L} \right|=b, ~ x \in \mathcal{L}}{\mathrm{argmax}} ~ S\left(x\right),
\end{equation}
where $b$ is the budget of sampling, and $S\left(x\right)$ is the score function of $x$. The score function of effective AL should perfectly reflect the potential informativeness of instances in the unlabeled dataset. Hence, the AL algorithms can be described by how to design $S\left(\cdot\right)$. The score includes uncertainty, representativeness such as diversity, density, training loss, and dissimilarity to the labeled dataset.

Since the informativeness depends on the current labeled dataset, the score function is also conditioned on the current state of the labeled dataset.
Hence, the score is generally calculated based on the trained model with the current labeled dataset, namely
\begin{equation}\label{scorefunction}
S\left(x\right)=S\left(x\; \middle| \; \mathcal{D}\right)=S\left(x\; \middle| \; \bm{\phi} \left(\mathcal{D}\right) \right),
\end{equation}
where $\bm{\phi} \left(\mathcal{D}\right)$ is the auxiliary model that is trained with the labeled dataset $\mathcal{D}$, starting from the randomly initialized model. Furthermore, AL adopts multiple rounds for sampling and gradually samples from the unlabeled dataset. When it is desired to add $b$ instances to be labeled after $K$ rounds, it samples $b/K$ instances at each round. We summarize the description of AL algorithm, $\mathcal{A}\left(\cdot\right)$, in Algorithm 2.

\begin{algorithm}[tb]
   \caption{Active Learning, $\mathcal{A}\left(\cdot\right)$}
   \label{alg:AL}
\begin{algorithmic}
   \STATE {\bfseries Input:} unlabeled dataset, $\mathcal{U}^{1}$
   \STATE \phantom{{\bfseries Input:}} initially labeled dataset, $\mathcal{D}^{1}$
   \STATE \phantom{{\bfseries Input:}} number of AL round, $K$
   \STATE \phantom{{\bfseries Input:}} initialized models for $\bm{\phi}$, $\{\bm{\phi}^{k}\}_{k=1}^{K}$
   \STATE \phantom{{\bfseries Input:}} number of annotation budget, $b$
   \begin{ALC@g}
   \FOR{$k=1$ {\bfseries to} $K$}
   \STATE train $\bm{\phi}\left(\mathcal{D}^{k}\right)$, starting from $\bm{\phi}^{k}$
   \STATE sample $\mathcal{L}^{k}$,
   \begin{equation}\label{samplinginAL}
   \mathcal{L}^{k}=\underset{\mathcal{L} \subseteq \mathcal{U}^{k},~\left| \mathcal{L} \right|=\frac{b}{K}, ~ x \in \mathcal{L}}{\mathrm{argmax}} ~ S\left(x\; \middle| \; \bm{\phi}\left(\mathcal{D}^{k}\right) \right)
   \end{equation}
   \STATE $\hat{\mathcal{D}}^{k}=\texttt{annotate}\left(\mathcal{L}^{k}\right)$
   \STATE $\mathcal{D}^{k+1 }   = \mathcal{D}^{k} \cup \hat{\mathcal{D}}^{k}$
   \STATE $\mathcal{U}^{k+1 } = \mathcal{U}^{k} - \mathcal{L}^{k}$
   \ENDFOR
   \STATE {\bfseries return} $\mathcal{D}^{K+1}$ with size of $\left|\mathcal{D}^{1}\right|+b$
   \end{ALC@g}
\end{algorithmic}
\end{algorithm}

\begin{algorithm}[tb]
   \caption{Federated Active Learning, $\mathcal{A}\left(\cdot\right)$}
   \label{alg:FAL}
\begin{algorithmic}
   \STATE {\bfseries Input:} unlabeled dataset, $\left\{\mathcal{U}^{1}_{m}\right\}_{m=1}^{M}$
   \STATE \phantom{{\bfseries Input:}} initially labeled dataset, $\left\{\mathcal{D}^{1}_{m}\right\}_{m=1}^{M}$
   \STATE \phantom{{\bfseries Input:}} number of AL round, $K$
   \STATE \phantom{{\bfseries Input:}} initialized models for $\bm{\phi}$, $\{\bm{\phi}^{k}\}_{k=1}^{K}$
   \STATE \phantom{{\bfseries Input:}} number of annotation budget, $\left\{b_{m}\right\}_{m=1}^{M}$
   \begin{ALC@g}
   \FOR{$k=1$ {\bfseries to} $K$}
   \STATE {\bfseries FL step:}
   \begin{ALC@g}
   \STATE train $\bm{\phi}^{k}_{FL}$, starting from $\bm{\phi}^{k}$
   \begin{equation}
\bm{\phi}_{FL}^{k}= \texttt{FedAvg}\left(\left\{ \mathcal{D}_{m}^{k} \right\}_{m=1}^{M} \;\middle|\; \bm{\phi}^{k} ,\left\{\eta^{k}_{t}\right\}_{t} \right)
\end{equation}
   \end{ALC@g}
   \STATE {\bfseries Sampling step:}
   \begin{ALC@g}
   \FOR{$m=1$ {\bfseries to} $M$}
   \STATE sample $\mathcal{L}^{k}_{m}$,
   \begin{equation}\label{samplinginFAL}
   \mathcal{L}^{k}_{m}=\underset{\mathcal{L} \subseteq \mathcal{U}^{k}_{m},~\left| \mathcal{L} \right|=\frac{b_{m}}{K}, ~ x \in \mathcal{L}}{\mathrm{argmax}} ~ S\left(x\; \middle| \; \bm{\phi}^{k}_{FL}\right)
   \end{equation}
   \STATE $\hat{\mathcal{D}}^{k}_{m}=\texttt{annotate}\left(\mathcal{L}_{m}^{k}\right)$
   \STATE $\mathcal{D}_{m}^{k+1 }   = \mathcal{D}_{m}^{k} \cup \hat{\mathcal{D}}_{m}^{k}$
   \STATE $\mathcal{U}_{m}^{k+1 } = \mathcal{U}_{m}^{k} - \mathcal{L}_{m}^{k}$
   \ENDFOR
   \end{ALC@g}
   \ENDFOR
   \STATE {\bfseries return} $\mathcal{D}_{m}^{K+1}$ with size of $\left|\mathcal{D}_{m}^{1}\right|+b_{m}$, $m=1,\dots,M$
   \end{ALC@g}
\end{algorithmic}
\end{algorithm}
\section{Federated Active Learning (F-AL)}
This section introduces AL methods in the FL framework: S-AL and F-AL. In the benchmark scheme of conventional FL adopting random sampling, we set $\mathcal{A}\left(\mathcal{U}\right)=\texttt{random}\left(\mathcal{U},b\right)$ in the Algorithm 1, as we introduce in the Section \ref{sec:problem:AL}.

\subsection{Separate Active Learning (S-AL)}
In S-AL, the clients separately perform the AL before the FL execution. With S-AL, the $m$-th client applies $\mathcal{A}\left(\cdot\right)$ of Algorithm \ref{alg:AL} to its unlabeled dataset at the annotation step in the FL framework. The S-AL directly leverages the AL in the FL framework, including the annotation step, and might seem straightforward. However, no related work establishes AL in FL and investigates the effect of AL in FL.

\subsection{Federated Active Learning (F-AL)}
In S-AL, the clients independently accomplish AL and achieve the instances which are informative to the local datasets as in $\eqref{samplinginAL}$. At the $k$-th round, the $m$-th client selects $x$ with the highest score, $S\left(x\; \middle| \; \bm{\phi}\left(\mathcal{D}_{m}^{k}\right) \right)$, where $\mathcal{D}_{m}^{k}$ denotes $\mathcal{D}^{k}$ in the Algorithm \ref{alg:AL} in the perspective of $m$-th client.

Since the clients execute FL after the annotation step, however, it should be the main objective to obtain instances which are informative to the aggregate labeled dataset, $\mathcal{D}^{k}_{total}=\bigcup_{m=1}^{M} \mathcal{D}_{m}^{k}$ as in \eqref{scorefunction}. Therefore, the score function in F-AL is conditioned on $\mathcal{D}^{k}_{total}$ and defined as $S\left(x\; \middle| \; \bm{\phi}\left(\mathcal{D}_{total}^{k}\right) \right)$. But, $\bm{\phi}\left(\mathcal{D}_{total}^{k}\right)$ cannot be built because $\mathcal{D}_{m}^{k}$, $m=1,\dots,M$ should not be compiled to satisfy the constraint of FL. Thus, we replace $\bm{\phi}\left(\mathcal{D}_{total}^{k}\right)$ with the model trained by FL, $\bm{\phi}_{FL}^{k}$, which is
\begin{equation}\label{FALmodel}
\bm{\phi}_{FL}^{k}= \texttt{FedAvg}\left(\left\{ \mathcal{D}_{m}^{k} \right\}_{m=1}^{M} \;\middle|\; \bm{\phi}^{k} ,\left\{\eta^{k}_{t}\right\}_{t} \right).
\end{equation}
Accordingly, with F-AL, clients carry out FL to obtain the score function that represents the informativeness of the aggregate labeled dataset.

As a more clear perspective for the explanation, uncertainty \cite{lewis1994sequential,beluch2018power} can illustrate the ground why $\bm{\phi}_{FL}^{k}$ should be leveraged for the calculation of score function in order to improve FL performance. If the AL applies uncertainty-based sampling or the sampling related to uncertainty, referred as to task aware AL in \cite{kim2021task}, it utilizes the uncertainty score, which is measured by the main task model trained with the current labeled dataset. Therefore, the auxiliary model is the main task model, namely, $\bm{\phi}\left(\mathcal{D}^{k}\right)=\bm{\theta}\left(\mathcal{D}^{k}\right)$ in Algorithm \ref{alg:AL} or the set of auxiliary models includes the main tasks model in the case of several auxiliary models. Hence, we remark that the auxiliary model should also be obtained through FL since the main task model is trained by FL.

After attaining $\bm{\phi}_{FL}^{k}$ in F-AL, the instances can be ideally sampled as
\begin{equation} \label{samplingideal}
   \mathcal{L}^{k}=\underset{\mathcal{L} \subseteq \mathcal{U}^{k},~\left| \mathcal{L} \right|=\frac{b}{K}, ~ x \in \mathcal{L}}{\mathrm{argmax}} ~ S\left(x\; \middle| \; \bm{\phi}_{FL}^{k}  \right),
\end{equation}
where $\mathcal{U}^{k}= \bigcup_{m=1}^{M} \mathcal{U}^{k}_{m}$, $b=\sum_{m=1}^{M} b_{m}$. Under the annotation workload condition that the $m$-th client annotates $b_{m}/K$ instances at each round, we have $\mathcal{L}^{k}= \bigcup_{m=1}^{M} \mathcal{L}^{k}_{m}$, where
\begin{equation}\label{samplinginFAL2}
\mathcal{L}^{k}_{m}=\underset{\mathcal{L} \subseteq \mathcal{U}^{k}_{m},~\left| \mathcal{L} \right|=\frac{b_{m}}{K}, ~ x \in \mathcal{L}}{\mathrm{argmax}} ~ S\left(x\; \middle| \; \bm{\phi}^{k}_{FL}\right).
\end{equation}
Therefore, each $m$-th client samples $\mathcal{L}_{m}^{k}$ as in \eqref{samplinginFAL2} and follows the remaining steps in Algorithm \ref{alg:AL}. In fact, the sampling step in $\eqref{samplingideal}$ can be executed at the server by exchanging the scores and indices of instances. However, we do not go any further since it might break the fairness of annotation workload among clients.
\section{Experiments}
This section provides the implementation details and the numerical results with related discussion. We compare the
performance of FL using the random sampling, S-AL, and the proposed F-AL in image classification tasks. The annotation strategies are applied for the annotation step in the Algorithm \ref{alg:FedAvg}, where the test accuracy of the obtained model is measured for the performance metric. For the image classification tasks, we evaluate the performances of the annotation strategies on the classical public datasets, Fashion-MNIST \cite{xiao2017fashion}, CIFAR-10 \cite{krizhevsky2009learning}, and CIFAR-100 \cite{krizhevsky2009learning}. The Fashion-MNIST dataset is a more challenging alternative dataset for the MNIST dataset. It consists of a training dataset of 60,000 images for 10 types of clothing and a test dataset of 10,000 images. CIFAR-10 and CIFAR-100 contain 50,000 training images and 10,000 test images. CIFAR-10 has 10 classes, while CIFAR-100 has 100 classes.

\subsection{Active learning algorithms}
First, we evaluate the performance of annotation strategies when the AL algorithm is the recently proposed Maximum Classifier Discrepancy for Active Learning (MCDAL) \cite{cho2021mcdal} which is one of the state-of-the-art AL algorithms. It utilizes the prediction discrepancies between two auxiliary classifiers after learning the auxiliary classifiers to maximize the discrepancies. It replaces the classic uncertainty with the discrepancies in the predictions of the auxiliary classifiers. It empirically demonstrates that this approach outperforms the state-of-the-art AL algorithms on the several
image classifications, including CIFAR-10 and CIFAR-100.

For more discussion, we evaluate the performance of annotation strategies for the various kinds of AL algorithms to achieve consistency in performance comparison. The first category of the AL algorithms uncertainty-related AL algorithms. This category of AL algorithms includes the classic uncertainty-based sampling with maximum entropy \cite{lewis1994sequential}, MC-dropout with maximum entropy \cite{gal2016dropout}, Learning Loss (LL) \cite{yoo2019learning}, and MCDAL \cite{cho2021mcdal}. The other AL algorithms are the core-set approach \cite{sener2017active} and variational adversarial active learning (VAAL). The core-set approach is the most widely used AL among the representative-based AL in the literature, and the VAAL represents the recent adversarial AL algorithms \cite{wang2020dual,zhang2020state,kim2021task}. All of the algorithms consider the main task model as the auxiliary model for AL. In the LL, MCDAL, and VAAL algorithms, the auxiliary models are additionally assumed and trained for AL. Therefore, the other models can be trained by FL in addition to the main task models for F-AL. For the evaluation of LL, however, we locally train the auxiliary model because no improvements are observed with the FL of auxiliary models in our experiments.

\subsection{Implementation details}
In the experiments, we assume that $M=5$ clients respectively have disjoint $10000$ images where $10 \%$ of the dataset is initially labeled. In our active learning setup, the $10 \%$ of the dataset is added to the labeled dataset at the sampling step of each round. We repeat this AL rounds until the total dataset is labeled. Hence, we set $b=10000$, $K=10$, and measure the test accuracy of FL model at each $k$-th round of AL, $\texttt{FedAvg}\left(\left\{ \mathcal{D}^{k}_{m} \right\}_{m}^{M} \;\middle|\; \bm{\theta}^{k}_{1} ,\left\{\eta_{t}^{k}\right\}_{t} \right)$.

We apply the Resnet-18 \cite{he2016deep} for the base architecture of main task model for all the exemplary tasks.
In the FL implementation, the main task models are optimized by SGD with the learning rate of $5 \times 10^{-2}$ and learning rate decay of $0.997$ per global iteration. The number of the local epoch is $1$, and the global iteration ends when the training loss at the clients decrease below thresholds, $1 \times 10 ^{-3}$, $5 \times 10 ^{-4}$, and $1.5 \times 10 ^{-3}$ for Fashion-MNIST, CIFAR-10, and CIFAR-100, respectively. In the independent learning for S-AL, we use SGD with a learning rate $1 \times 10^{-2}$ and step decay of $0.997$ at every epoch. Independent learning follows the same stopping criteria as FL. We use random horizontal flips for data augmentations. For the result of the experiments, we use the average accuracy of three runs.

\begin{figure}[t]
\vskip 0.2in
\begin{center}
\centerline{\includegraphics[width=0.4\columnwidth]{repre_mnist.png}}
\caption{Test accuracies of global model trained by FL per rounds on Fashion-MNIST.}
\label{repre_mnist}
\end{center}
\vskip -0.2in
\end{figure}

\begin{figure}[t]
\vskip 0.2in
\begin{center}
\centerline{\includegraphics[width=0.4\columnwidth]{repre_cifar10.png}}
\caption{Test accuracies of global model trained by FL per rounds on CIFAR-10.}
\label{repre_cifar10}
\end{center}
\vskip -0.2in
\end{figure}

\begin{figure}[t]
\vskip 0.2in
\begin{center}
\centerline{\includegraphics[width=0.4\columnwidth]{repre_cifar100.png}}
\caption{Test accuracies of global model trained by FL per rounds on CIFAR-100.}
\label{repre_cifar100}
\end{center}
\vskip -0.2in
\end{figure}

\subsection{Performance comparison}
Fig. \ref{repre_mnist}-\ref{repre_cifar100} illustrate the performance of random sampling (conventional FL), S-AL (benchmark), and F-AL (ours) which are the annotation strategies for FL. The AL algorithm is MCDAL, and the datasets are Fashion-MNIST, CIFAR-10, and CIFAR-100. Full budget in the figures denotes the performance of FL when all the clients have $100 \%$ labeled dataset. On the Fashion-MNIST, F-AL and S-AL considerably outperform random sampling, and the proposed F-AL shows the best performance compared to the other strategies. In particular, the average improvement compared to random sampling is $1.1 \%$ and $1.6 \%$ for S-AL and F-AL, respectively, at the 2nd round and 3rd round, before converging to the performance of the full budget. On the CIFAR-10, F-AL and S-AL outperform random sampling, and the proposed F-AL shows the best performance compared to the other strategies, same as the case of Fashion-MNIST. The average improvement compared to random sampling is $1.6 \%$ and $2.4 \%$ for S-AL and F-AL, respectively, before the 10th round. At the half of the rounds, the improvement is $2.3 \%$ and $3.9 \%$ for S-AL and F-AL, respectively when the performance of random sampling is $79.5 \%$.

On the CIFAR-100, it is also observed that the F-AL and S-AL show better performance than the performance of random sampling. The average improvement compared to random sampling is $1.1 \%$ and $2.0 \%$ for S-AL and F-AL, respectively, before the 10th round and the improvements are $2.0 \%$ and $3.2 \%$ at the $7$th round while the test accuracy of random sampling is $42.3 \%$. Fig. \ref{repre_mnist}-\ref{repre_cifar100} demonstrate that the proposed F-AL outperforms the baseline methods in the image classification of Fashion-MNIST, CIFAR-10, and CIFAR-100.

\subsection{Extended results for various AL algorithms} In order to demonstrate that our proposed F-AL outperforms the baseline methods for the general AL algorithms, we extend the experiment with MCDAL in Fig. \ref{several_mnist}-\ref{several_cifar100}. We first consider uncertainty-related AL algorithms: uncertainty-based sampling, MC-dropout with maximum entropy, LL, and MCDAL. Fig. \ref{several_mnist}-\ref{several_cifar100} illustrate that F-AL outperforms S-AL and random sapling for the considered AL algorithms. The only conflicting case is when LL is applied on the CIFAR-100, as observed in Fig. \ref{several_cifar100}.

Through Fig. \ref{several_mnist}-\ref{several_cifar100}, we can compare the performance of the AL algorithms when they are applied for the FL environment. In Fig. \ref{several_mnist}, uncertainty-based sampling and MC-dropout show comparable performance, better than MCDAL and LL in both cases of S-AL and F-AL. In Fig. \ref{several_cifar10}, uncertainty-based sampling and MC-dropout show the best performance, and LL shows poor performance compared to the other algorithms, similar to the result in Fashion-MNIST. Fig. \ref{several_cifar100} illustrates that uncertainty-based sampling and MC-dropout outperform random sampling while MCDAL and LL show comparable performance with random sampling. As we observed in Fig. \ref{repre_cifar100}, F-MCDAL outperforms random sampling, but F-AL does not show considerable improvement for LL. In Table \ref{others_cifar10}, we consider the other categories of AL algorithms, which are the representative-based AL and adversarial AL. We evaluate the performance of random sampling, S-AL, and F-AL on CIFAR-10 when the AL algorithms are core-set approach and VAAL. Table \ref{others_cifar10} shows that both of core-set approach and VAAL show poor performance and are even worse than random sampling, while F-VAAL shows comparable performance with random sampling. F-AL does not show improvement when the core-set approach is applied for the AL algorithm.

\begin{figure}[t]
\vskip 0.2in
\begin{center}
\centerline{\includegraphics[width=0.4\columnwidth]{several_mnist.png}}
\caption{Test accuracies of global model trained by FL per rounds on Fashion-MNIST (uncertainty-related AL algorithms).}
\label{several_mnist}
\end{center}
\vskip -0.2in
\end{figure}

\begin{figure}[t]
\vskip 0.2in
\begin{center}
\centerline{\includegraphics[width=0.4\columnwidth]{several_cifar10.png}}
\caption{Test accuracies of global model trained by FL per rounds on CIFAR-10 (uncertainty-related AL algorithms).}
\label{several_cifar10}
\end{center}
\vskip -0.2in
\end{figure}

\begin{figure}[t]
\vskip 0.2in
\begin{center}
\centerline{\includegraphics[width=0.4\columnwidth]{several_cifar100.png}}
\caption{Test accuracies of global model trained by FL per rounds on CIFAR-100 (uncertainty-related AL algorithms).}
\label{several_cifar100}
\end{center}
\vskip -0.2in
\end{figure}

\begin{table*}[t]
\caption{Test accuracies of global model trained by FL per rounds on CIFAR-10 (core-set approach and VAAL).}
\label{others_cifar10}
\vskip 0.15in
\begin{center}
\begin{small}
\begin{sc}
\begin{tabular}{lccccccccccr}
\toprule
 \phantom{s} &\multicolumn{10}{c}{Labeled Data ($\%$)}   \\
methods    & $10\%$ & $20\%$ & $30\%$ & $40\%$ & $50\%$ & $60\%$ & $70\%$ & $80\%$ & $90\%$ & $100\%$ \\
\midrule
F-VAAL     &$0.571$ &$0.660$ &$0.720$ &$0.757$ &$0.778$ &$0.794$ &$0.808$ &$0.827$ &$0.839$ &$0.850$\\
VAAL     &$0.571$ &$0.634$ &$0.689$ &$0.737$ &$0.763$ &$0.789$ &$0.808$ &$0.819$ &$0.840$ &$0.850$\\
F-coreset     &$0.571$ &$0.652$ &$0.699$ &$0.742$ &$0.768$ &$0.791$ &$0.807$ &$0.820$ &$0.838$ &$0.850$\\
Coreset    &$0.571$ &$0.655$ &$0.700$ &$0.745$ &$0.770$ &$0.792$ &$0.806$ &$0.819$ &$0.838$ &$0.850$\\
Random     &$0.571$ &$0.662$ &$0.716$ &$0.755$ &$0.781$ &$0.795$ &$0.808$ &$0.827$ &$0.837$ &$0.850$\\
\bottomrule
\end{tabular}
\end{sc}
\end{small}
\end{center}
\vskip -0.1in
\end{table*}

\subsection{Discussion} In Fig. \ref{several_mnist}-\ref{several_cifar100} and Table \ref{others_cifar10}, it was first observed that uncertainty-based sampling and MC-dropout, which directly utilizes the uncertainty, show the best performance across most rounds of AL, and they have the largest performance increase by F-AL. In the previous literature \cite{yoo2019learning,cho2021mcdal}, it is validated that the LL and MCDAL outperform the classic uncertainty-based sampling and MC-dropout, contrary to the results in our experiments. In fact, LL and MCDAL learn the classifiers for discrepancy and the loss prediction module, respectively, in addition to the main task model, using the \textit{unlabeled dataset}. Compared to the large-scale dataset stored at one client in the literature \cite{yoo2019learning,cho2021mcdal}, multiple clients relatively have a much less number of instances in the unlabeled dataset for the distributed setting, e.g., $ 5$ clients respectively have $20 \%$ of the total dataset in our experiments. This insufficiency of the unlabeled dataset in the FL environment causes the worse performance degradation compared to the classical uncertainty-based AL algorithms.

With VAAL, the sampling step is implemented only by the variational autoencoder (VAE) and discriminator, trained by both the labeled dataset and the unlabeled dataset. Therefore, the performance is degraded by the insufficient unlabeled dataset, compared to the performance evaluated on the large-scale datasets \cite{sinha2019variational}. It is remarkable that F-VAAL outperforms VAAL, as observed in
TABLE \ref{others_cifar10}, since the improvement comes from the development of VAE and discriminator with F-AL. The core-set approach generally shows excellent performance under the large-scale dataset \cite{sener2017active} since the representativeness-based AL algorithm alleviates the problem of uncertainty-based sampling which selects the similar instances near the decision boundary. However, the problem is suppressed in the distributed setting and the core-set approach performs poor in our FL environment. With the core-set approach, only the feature extraction of main task model is utilized for sampling, so that the core-set approach is rarely improved by the main task model, developed by F-AL. Furthermore, core-set approach requires whole datasets among clients to be stored at one site for the collaborative sampling in \eqref{samplingideal}. It means that the collaboratively sampled instances with the core-set approach should be
\begin{equation}
   \mathcal{L}^{k}=\underset{\mathcal{L} \subseteq \mathcal{U}^{k},~\left| \mathcal{L} \right|=\frac{b}{K}}{\mathrm{argmax}} ~ S\left(\mathcal{L}\; \middle| \; \bm{\phi}_{FL}^{k}  \right),
\end{equation}
but it cannot be obtained from the local computation as the \eqref{samplinginFAL2}, since it requires $\mathcal{U}^{k}$ in contrast to the FL constraint. Hence, F-AL cannot improve the performance of the core-set approach.

\begin{figure}[t]
\vskip 0.2in
\begin{center}
\centerline{\includegraphics[width=0.4\columnwidth]{solo_cifar10.png}}
\caption{Test accuracies of model trained by independent learning per rounds on CIFAR-10.}
\label{IL_cifar10}
\end{center}
\vskip -0.2in
\end{figure}

\textbf{Performance of independent learning}
Through Fig. \ref{repre_mnist}-\ref{several_cifar100} and Table \ref{others_cifar10}, it has been demonstrated that AL is effective in FL environment, and the proposed F-AL outperforms the conventional random sampling and S-AL. For more discussion, we investigate the effect of F-AL in the perspective of local dataset. For this, each client solely trains the main task model with the local dataset after achieving the labeled dataset via the AL strategies. Fig. \ref{IL_cifar10} illustrates the average test accuracy of the models trained at the clients on CIFAR-10 when the AL algorithms are the uncertainty-related AL algorithms which show relatively significant performance increase by F-AL compared to the core-set approach and VAAL. It is observed that F-AL considerably decreases the performance of IL. In contrast, the S-AL certainly outperforms random sampling since S-AL samples the informative instances to the current local dataset. With F-AL, the clients collaborate to sample the informative instances to the aggregate datasets, not the local dataset. It becomes a solid constraint to the sampling of clients in the perspective of local datasets since each client with F-AL does not sample the instances that are not informative to the aggregate dataset even though the instances are informative to its datasets. As illustrated in Fig. \ref{example}, the aggregate dataset, which is sampled by F-AL, performs excellent for FL even though the sampled instances can be biased at the distribution of local datasets.

\begin{figure}[t]
\vskip 0.2in
\begin{center}
\centerline{\includegraphics[width=0.4\columnwidth]{example5.png}}
\caption{Distributions of the sampled instances }
\label{example}
\end{center}
\vskip -0.2in
\end{figure}
\section{Conclusion}
In this paper, we focused on the active learning (AL) and sampling strategies into the FL framework to reduce the annotation workload. In our proposed federated active learning (F-AL) method, the clients collaboratively perform the AL to obtain the instances that can maximally improve the global model of FL. We empirically demonstrate that F-AL outperforms conventional random sampling strategy, client-level separate AL (S-AL) for the various AL algorithms on the image classification applications such as Fashion-MNIST, CIFAR-10, and CIFAR-100.

\section*{Acknowledgements}

redpajama_set_name: RedPajamaArXiv


text:
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