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Michael W. Apple (Paterson, 20 d'agost de 1942) és un pedagog i sociòleg, a més d'una gran influència en l'àmbit educacional del . Treballa especialment en la comprensió i l'anàlisi de les relacions entre educació i poder.

Biografia
Va néixer el 20 d'agost del 1942 a Paterson nova Jersey, EUA a una família Comunista humil i pobre.

El seu avi matern d'origen rus, al  va emigrar a Anglaterra, per treballar en fàbriques de Manchester, i després als EUA. Va marxar de Rússia per raons polítiques, econòmiques i judicials, ja que en ser comunista sempre va estar en perill.

El seu avi assistia a cercles de treballadors on es parlava de política, Michael Apple l'acompanyava i d'aquesta manera es va introduir en aquest món. Però la política no era política a menys que estigués guiat per lectures com ara: llibres sobre política, d'història del món, obres de Marx… A més l'avi estava en contra de la religió, ja que creia que els oprimia. Per aquest motiu M.W. Apple en els seus escrits desconfia de la recerca de la "puresa" per a les explicacions de la política educativa.

La seva mare, Mimi Apple era una dona activista comunista i antiracista. Mai va finalitzar l'escola secundària però li encantava la poesia i els contes curts. Mimi li va posar de segon nom Whitman, per un escriptor de Poesia anomenat Walt Whitman, així que el seu nom complet és Michael Whitman Apple. La mare va ser dels membres fundadors del Congrés d'Igualtat Racial (CORE) i una activista en les comunitats pobres i negres de Paterson. Per aquest motiu Michael també va ser actiu en la CORE, a més de ser el director de publicitat amb només 15 anys. Ella també estava molt involucrada en la lluita per l'educació i l'ocupació, contra formes polítiques que negaven el poder a la gent pobre, i lluitava contra l'estructuració racial.

El pare, Harry, era un impressor socialista el qual treballava moltíssimes hores per poder treure a la seva família de la pobresa. Michael Apple i el seu germà petit passaven moltes hores cada setmana a la impremta. Per aquest motiu M.W. Apple de dia treballava a una impremta, d'una petita Universitat de mestres, en la qual a la nit també estudiava.

Harry i Mimi van treballar en política a la CORE en campanyes per triar al personal treballador i/o als candidats negres o llatins. Respectaven molt els sindicats i les possibilitats de les organitzacions col·lectives de persones. Ells creien que les característiques que havia d'adoptar un professor eren: honrar a la família tant en termes de classe (ell estava "davant"), en termes polítics (dir la veritat sobre la societat) i en termes intel·lectuals (sobre l'alfabetització crítica, donar-li a la gent el poder per comprendre el món i a la vegada, per canviar-ho).

A Michael W. Apple el govern li va finançar els estudis fins a graduar-se a la Universitat de Colúmbia, pel fet que tenien la necessitat d'un professorat per a formar nous docents.

És ex professor de primària i secundària a Nova Jersey, i va ser president del sindicat docent de l'escola "Teachers Union" del 1962 al 1966. A partir d'aquesta experiència li va sorgir una inquietud per la pedagogia i del canvi social dins l'àmbit educatiu.

Es va llicenciar en lletres a Glassboro State Collage l'any 1967. Tot seguit, l'any 1968 es va treure un màster d'arts a la Universitat de Colúmbia.

Va ser preceptor del Departament de Filosofia i ciències socials, a més d'instructor i investigador del Departament de currículum i docència durant l'any 1969 fins al 1970, a la Universitat de Colúmbia.

El 1970 es va doctorar en educació a la Universitat de Colúmbia. En aquest mateix any, a l'escola d'educació de la Universitat de Wisconsin-Madison, va exercir de professor adjunt del Departament de currículum i instrucció, fins a l'any 1973 on va adquirir un rang més, i va exercir de professor associat al Departament de currículum i instrucció fins al 1976, on seguidament va passar a ser professor fins al 1991, a continuació va ser professor de John Bascom, del 1991 fins a l'actualitat, al Departament de currículum i instrucció i estudis de política educativa.

A partir del seu recorregut en l'educació decideix centrar la seva teoria en l'especialització de l'educació i del poder, en la política cultural, en la teoria del currículum i de la investigació, en l'ensenyança crítica i sobretot en el desenvolupament democràtic.

Contextos socials i polítics

Pedagogia crítica
La pedagogia crítica es pot emmarcar dins de la perspectiva dual on trobem un conjunt de teories que consideren l'anàlisi tant de la importància dels sistemes i estructures del comportament de les persones i grups, com la capacitat d'aquests en incidir en els sistemes. Aquestes aportacions teòriques de la perspectiva dual, han generat pràctiques educatives d'èxit estudiantil.

«La pedagogia crítica es defineix com una instància de formació i aprenentatge ètic i polític que incideix a la forma de producció de subjectivitat, al procés de construcció i circulació de valors i en la generació de pràctiques socials.» (Piedad, 2011, p. 3).

Per als autors i autores que parteixen d'aquesta perspectiva, l'educació pot exercir una doble funció: preparar a les persones per a integrar-se en la societat i facilitar el coneixement crític necessari per a una major democratització en les societats. La pedagogia crítica per tant estudia i analitza com es pot contribuir a la superació de la desigualtat tenint en compte l'estructura i el sistema, i a la vegada tenint en compte als subjectes d'aquesta societat perquè puguin generar aquests processos de transformació.

Veiem que en la pedagogia crítica trobem autors com Paulo Freire, Michael Apple, Basil Bernstein, Henry Giroux, Donaldo Macedo, Joe L. Kincheloe y Shirley R. Steinberg, Ramón Flecha… entre d'altres, que treballen aquesta perspectiva dual per a poder construir una societat més justa i igualitària per a tothom, on aquelles persones i col·lectius que són més vulnerables per patir exclusió social puguin transformar la seva situació mitjançant l'educació.

Uns dels primers autors de la pedagogia crítica va ser Michael Apple, el qual analitza sociològicament l'educació donant lloc al qual s'ha anomenat model de la resistència, on les lluites dels treballadors, dones, pobres, negres, entre altres, demostren contínuament la possibilitat d'una acció concreta en el poder. Apple va proposar que era necessari una sociologia de l'educació centrada en els processos de resistència de l'escola, i que contribueixen a l'alumnat per a superar situacions de desigualtats socials i on puguin aconseguir l'èxit escolar.

Una altra aportació de Michael Apple és l'anàlisi del currículum educatiu i la proposta d'un currículum democràtic amb accés de tota la comunitat educativa i poder fer desaparèixer el currículum ocult.

Currículum ocult
El currículum ocult és aquell que reforça desigualtats socials en diferents aspectes, als estudiants durant la seva educació, i que no apareixen al currículum educatiu normatiu. A més el currículum ocult canvia constantment, segons evolucionen els coneixements i les creences de la societat.

Aquest tipus d'aprenentatge no només existeix dins de l'aula escolar, sinó que pot estar en tots els contextos socials, pel fet que està vinculat a experiències del propi o d'altres individus.

Michael Apple en la seva obra més destacada "Ideology and currículum" parla sobre aquest currículum ocult amagat dins del sistema educatiu i analitza el currículum escolar des de les arrels ideològiques i polítiques de la forma i el contingut del currículum educatiu, així com les relacions dels valors dominants de la societat.

Neoliberalisme i educació democràtica
Segons Apple existeixen dos problemes principals en l'educació institucional, primer són les reformes educatives neoliberals, i el segon les polítiques neoconservadores existents, les quals impliquen unes normes nacionals on se centra l'estudi. Per tant, fa una crítica a un estat, a un govern i a les seves principals institucions que no compleixen les seves promeses ni els seus valors fent així un acte de patriotisme i no democràtic. Per aquest motiu, Apple identifica als neoliberals com a element poderós dins d'una aliança de dretes.

A més, podem observar una forta influència de Marx i de les teories marxistes i neomarxistes, especialment teories de l'economia política de l'estat. Per aquest motiu, Apple diu que l'educació ha de ser vista des d'un punt polític, ja que en l'educació també es troben desigualtats en relacions del poder en la societat i en les relacions de domini i subordinació, per tant trobem diversos conflictes generats per aquestes relacions.

Les reformes dels neoliberals i neoconservadors (plans, programes d'estudi...) normalment disminueixen o empitjoren les desigualtats dels estudiants, ja que s'ha de ser molt cautelós a l'hora d'aplicar algunes reformes perquè aquestes qui realment les haurien de fer són aquelles persones que menys coses tenen, és a dir, a qui els afectaran aquestes reformes. Després de tot, no estem en igualtat de condicions i molts cops els beneficis no són per a tots. A més també hi ha una gran quantitat d'evidències internacionals on aquestes polítiques poden reproduir i empitjorar aquestes desigualtats de raça, classe i gènere, així com les estructures ja existents del poder econòmic i cultural. Per tant, en el llenguatge de reforma educativa sempre s'intenta fer allò que és millor, per a poder constituir les escoles democràtiques.

Michael W. Apple, parla sobre tot aquest tema en la seva obra "Les escoles democràtiques" on connecta teories educatives i enfocaments de les formes reals d'educació a les aules i com aquestes formes es transformen en fets molt importants. Aquest llibre és un bon mètode per mostrar el que vol dir a l'hora de parlar de la pràctica, és a dir, s'han de trobar maneres de parlar amb la gent i el que és més essencial, que els educadors crítics no ignorin la qüestió de la pràctica.

Pel que fa al neoliberalisme, Michael Apple també va estar fortament influenciat per Marx, i aquest concepte ho redueix tot a necessitats econòmiques i polítiques. Aquest concepte considera que tot el que és públic és dolent, i bo tot allò que és privat. Tals polítiques poden crear encara més desigualtats en l'educació. De fet, Apple creia que no es pot entendre què hi ha darrere del fet neoliberal tret que posem la raça i el gènere un al costat de l'altre com a forces superiors darrere d'això.

Una altra de les raons que té per defensar l'escola pública és que a diferència d'alguns marxistes que creuen que les escoles són fàbriques que produeixen treballadors dòcils pel capitalisme. Ell va demostrar que les escoles van ser i són escenaris de lluites antiracistes on les identitats activistes van ser i es formen i on van servir com a llocs crucials per a la formació de moviments contrahegemónics.

Obra més destacada «Ideology and curriculum»
Un dels seus llibres més rellevants va ser publicat el 1979, sota el nom de "ideology and curriculum", editada per John Eggleston, a l'editorial britànica Routledge i Kegan Paul en un format de vuit capítols, els quals parla d'una ideologia i un pla d'estudis que critiquen l'educació als Estats Units.

En aquest llibre Apple fa una anàlisi de com es reprodueix l'estructura ideològica de l'escola i la forma del control social, és a dir analitza la relació escola-societat per mitjà del pensament Marxista. Un dels temes que també tracta és el desenvolupament històric dels estudis sobre el currículum educatiu i de com hi ha un currículum ocult amagat en el mateix sistema educatiu.

En aquesta anàlisi, Michael Apple se centra en el pla d'estudis de Gramsci, Bourdieu i altres sociòlegs britànics, sobre les organitzacions educatives, per així enfocar l'abstracte paper d'aquestes de crear condicions que recolzen l'hegemonia ideològica.

Aquesta ideologia va ser una resposta al llibre de 1076 de Bowles i Gintis Schooling in Capitallist Amèrica, que va iniciar una conversa de la teoria de la reproducció social entre acadèmics radicals en l'educació a EEUU i a Gran Bretanya. Apple va descriure la ideologia com una investigació sobre "conjunt organitzat de significats i pràctiques, el sistema central, eficaç i dominant de significats, valors i accions que es viuen" en les escoles i l'ordre social.

Altres obres
 Ideology and Curriculum (1979).
 Cultural and Economic Reproduction in Education: Essays (1982).
 Education and power (1982).
 Teachers and texts: A political economy of class and gender relations in education (1986).
 The Curriculum: Problems, Politics, and Possibilities (1988).
 The politics of the textbook (1991).
 Official Knowledge: Democratic Education in a Conservative Age (1993).
 Democratic Schools (1995).
 Cultural politics and education (1996).
 Education/technology/power: Educational Computing As a Social practice (1998).
 Power/Knowledge/pedagogy: The Meaning Of Democratic Education In unsettling Times (1998).
 Power, Meaning, and Identity (1999).
 Educating the "right" way: markets, standards, god, and inequality (2001).
 The State and the Politics of Knowledge (2003).
 Critical Literacy: What Every American Needs to Know (2005).
 Global Crises, Social Justice, and Education (2009).
 The Routledge International Handbook of the Sociology of Education (2010).
 The Routledge International Handbook of Critical Education (2010).
 Can Education Change Society? (2012).
 The Struggle for Democracy in Education (2018).

Edicions en castellà
 Ideología y currículo. Madrid:  Akal, 1986.
 Educación y poder. Barcelona: Paidós, 1987.
 Maestros y textos una economía política de las relaciones de clase y de sexo en educación. Barcelona: Centro de Publicaciones del M.E.C. y Ediciones Paidós Ibérica, D.L. 1989
 El conocimiento oficial. La educación democrática en una era conservadora. Barcelona; Paidós 1996.
 Política cultura y educación. Madrid: Morata, 1996.
 Escuelas democráticas. Madrid: Morata 2005.
 Teoría crítica y educación. Buenos Aires: Miño y Dávila Editores, 2000.
 Educar <Como dios manda >  Mercados, niveles, religión y desigualdad. Barcelona: Ediciones Paidós, 2002.
 Poder, conocimiento y reforma educacional. Argentina: Axolotl libros, 2012.

Títols honorífics
 Doctorat Honorus Causa a la Universitat Nacional de Rosario (Rosario, Argentina).
 Doctorat Honorus Causa a la  Universitat de la Pampa (La Pampa, Argentina).
 Doctorat Honorus Causa, Acadèmic mundial i professor d'Estudis sobre Política Educativa a la Universitat de Londres, Institut d'Educació (Londres, Anglaterra).
 Acadèmic mundial i professor distingit d'Estudis de Política Educativa a la Universitat Normal de la Xina Oriental (Xangai, Xina).
 Doctor en lletres humanes a la Universitat McGill (Montreal, Canadà).

Premis
 Premi Distingit d'Assoliment Acadèmic, Escola d'Educació, Universitat de Wisconsin, Madison, 2006.
 Guardonat amb el Premi Doctor of Humane Letters de la Universitat McGill, guardonat amb el premi Doctor of Humane Letters de la Universidad de Rowan.
 Educar el camí "correcte" guardonat amb el Premi al llibre destacat per la Divisió B de l'Associació Nord-americana d'Investigació Educativa.
 Educar el camí "correcte" atorgat Premi Critics Choice per l'Associació.
 Nord-americana d'Estudis Educatius.
 Premi Lifetime Achievement Award, Divisió d'Estudis Curriculars de l'Associació.
 Nord-americana de Recerca Educativa, 1998.
 Premi al millor article de l'any, Consell Nacional de Mestres de Matemàtiques, 1992.

Referències

Enllaços externs
 Congrés d'Igualtat Racial (CORE)
 Glassboro State Collage
 Universitat de Wisconsin-Madison
 Model de resistència
 Ideology and curriculum
 Les escoles democràtiques
 John Eggleston

Bibliografia
 Apple, M. W. (2012). Curriculum Vitae. Retriev from {{format ref}} http://dm.education.wisc.edu/mwapple/pci/APPLE-VITA-Short-2012-1.pdf
 Apple, M., & King, N. (1977). What Do Schools Teach?. Curriculum Inquiry, 6(4), 341-358. doi:10.1080/03626784.1977.11075550
 Camdepadrós, R., & Pulido, C. (2009). La Sociología de la Educación desde la Pedagogía Crítica. Revista Electrónica Teoría de la Educación. Educación y Cultura en La Sociedad de la Información, 10(3), 64-189. retrieved from {{format ref}} http://www.usal.es/~teoriaeducacion/rev_numero_10_03/n10_03_camdepadros_pulido.pdf
 Gottesman, I. (2012). From Gouldner to Gramsci: The Making of Michael Apple's Ideology and Curriculum. Curriculum Inquiry, 42(5), 571-596. doi:10.1111/j.1467-873X.2012.00612.x
 McGregor, G. (2018). Cómo "correcto" continúa "haciendo lo malo" por nuestros jóvenes: reflexiones contemporáneas sobre el análisis de Michael Apple del "giro derechista" en educación. Educational Review, 70 (1), 84-90. doi:10.1080 /00131911.2018.1388610
 Ortega Valencia, P. (2011). La pedagogía crítica: Reflexiones entorno a sus prácticas y desafíos. Pedagogía y Saberes, 0(31), 26-33. doi:{{format ref}} http://dx.doi.org/10.17227/01212494.31pys26.33
 Peters, M. A. (2015). Interview with Michael Apple: the biography of a public intelectual. Open Review of Educational Research, 2(1), 105-117. doi:10.1080/2326555507.2015.1010174
 Sánchez-Redondo Morcillo, C. (1995). La concepción del curriculum escolar en Michael W Apple. revista de la Escuela Universitaria de Magisterio de Toledo, 20(1), 165-207.  Retrieved from  {{format ref}} https://ruidera.uclm.es/xmlui/bitstream/handle/10578/10413/La%20concepción%20del%20%20currículum%20escolar%20en%20Michael%20W.%20Apple.pdf?sequence=1&isAllowed=y
 Totten, S., & Pedersen, J. E. (2006). Researching and teaching social issues: the personal stories and pedagogical efforts of professors of education (1rd ed.). Lanham, MD: Rowman & Littlefield.

Pedagogs estatunidencs
Alumnes del Teachers College
Professors de la Universitat de Wisconsin-Madison
Persones de Nova Jersey
Doctors honoris causa per la Universitat de la Corunya
Sociòlegs estatunidencs


text:
Ease of Doing Business in Sri Lanka2022 Data | 2023 Forecast | 2008-2021 Historical
Sri Lanka is ranked 99 among 190 economies in the ease of doing business, according to the latest World Bank annual ratings. The rank of Sri Lanka improved to 99 in 2019 from 100 in 2018. source: World Bank
Ease of Doing Business in Sri Lanka averaged 99.92 from 2008 until 2019, reaching an all time high of 113 in 2014 and a record low of 83 in 2012. This page includes a chart with historical data for Ease of Doing Business in Sri Lanka. Ease of Doing Business in Sri Lanka - values, historical data and charts - was last updated on January of 2022.
Ease of Doing Business in Sri Lanka is expected to reach 105.00 by the end of 2020, according to Trading Economics global macro models and analysts expectations. In the long-term, the Ease of Doing Business in Sri Lanka is projected to trend around 105.00 in 2021, according to our econometric models.

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Ease of Doing Business in Sri Lanka
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Hello, sorry I disappeared for this week. I've been writing a lot of fiction and trying to figure out how to write this at the same time without it sapping all my energy for fiction. Let's try talking about TV for a while and see if that works.
So Nikki and I have been rewatching Lost and I'd highly recommend you guys all do the same if you've got a hole in your Netflix schedule. Not because it's a ton better or worse than I remember it being. It's still a deeply flawed show that was frequently capable of being one of the best things on TV. It's just a very different show 10 years later.
For starters you can see its mark on everything now. Not just in the way that every network is trying to make "the next Lost" by buying whatever pilot includes enough weird quasi-mystical bullshit to sustain a fanbase but in the way that creators now fundamentally make TV shows differently because of Lost.
Lost didn't invent sprawling casts deep mythologies or jumping around in time within episodes but it become a huge enough hit that it created a very clear mark of delineation. Before Lost shows deployed those ideas infrequently and carefully in order to not scare away their fan base. After Lost shows think of them as standard aspects of TV storytelling you can deploy whenever and expect the audience to keep up.
In fact Lost's use of those ideas is starting to look a little bit dated and hesitant. Sure it'll spend a whole episode abandoning the main cast to tell the story of some other group or person's time on the island but it seems to rush through those stories, scared the audience will think its a waste of their time in a way that sorta makes that a self-fulfilling prophesy. In 2005 spending a whole episode just showing what happened to a second group of survivors from the plane crash seemed revolutionary. Rewatching it with Nikki last night my reaction was "Jesus, nobody's getting any time to develop here. Why didn't they make this a two or three episode arc?"
It's also just a much more stylish show than I remembered it being. The flashbacks really allowed the show to jump around stylistically in a way that on first viewing seemed like a writer's dream but on a rewatch I now recognize as a production designer's nightmare. I'm not sure how much the designers on that show got paid but it was not enough. They do some damn fine work. Sun/Jin episodes are frequently kinda dull on a plot level but they're like the show decides to drop you into a little Wong Kar Wai episode every now and again, beautifully elaborate wallpaper and all.
And that pilot. Jesus do the production values on that pilot still hold up. In the middle of season two we're already getting to the point where the show's "random bullshit happens" ambitions outstrip its "random bullshit happens" budget with the crash of the tail end of the plane but God damn the shots of that plane breaking apart are still as harrowing as they ever were.
It's also weird how much of the chronology of this show I had totally wrong in my head. The french woman shows up and starts rambling about "the others" in like the 5th episode but Hurley's backstory is really late in the first season. That means we've already sat through three interminable hours of Kate's mysterious past of being a dangerous bad girl who's never dangerous or bad enough that you have to stop finding her sexually attractive before the numbers show up.
That reminds me, oh man, some of the characters. For a show that's finale refrain was "this was always a show about the characters judge us on that" a lot of these characters suck. Obviously a lot of this stuff still works or there'd just be no show. Charlie is still charming and adorable. Locke and by extension Terry O'Quinn still pretty much make the show they're so great. Saiyd holds up surprisingly well when they give him something to do besides act as a lightning rod for IMPORTANT! POST 9/11! ISSUES! which starts happening basically right away as the show realizes it actually has almost nothing valuable to say about terrorism/torture/national security. But the show's core triangle of Jack/Kate/Sawyer…
Nikki fundamentally hates all of them and I can't blame her. I keep trying to turn her around on Sawyer who's at least a fun guy when he's a jerk but then whenever I try and make that point the show dives back into how sad and angry he is on the inside and I just want to play a Papa Roach song over his sad face.
On a first watch I thought of Kate and Sawyer as interesting characters the show eventually lost the thread on. Instead their kinda boring characters from the word go propped up by how little we know about them. Their performances and everything said about them implied hidden depths that were supposed to be revealed later and the actual reveal was that the emperor had no clothes. In the end they were just cliched criminals with hearts of gold and boring backstories. I remember being super mad when Kate and Sawyer finally made out the first time. On this run through they seem made for each other.
And Jack. Jesus Christ, Jack. If, with the end of Breaking Bad, we've reached the end of the age of the anti-hero then can Don Draper signing off next year be the end of the age of televised daddy issues? It doesn't help that Christian Shepard is just a big old collection of inconsistent character actions in flashbacks demanded by the plot. Screaming at his son to not try to save everyone one minute and crying in a bar about how fantastically better than him his son is the next so that Jack can go on stupid Hero's quest riffs about his role as leader. (But then don't all fathers seem that way to their sons?…LOST) It also doesn't help that Lost actually named the main character's dad Christian Shepard. Presumably it was their second pick after Jesus McCarpenter was rejected as not obvious enough.
If it sounds like I'm just constantly ragging on this show I'm not. Freed of its responsibility as the BIGGEST GENRE SHOW ON TELEVISION Lost's many flaws become kinda charming. We've entered into the period of the second season where Lost will finally progress the plot forward to like, Shannon getting shot by somebody from the tail section of the plane, and then they spend 3 episodes on flashbacks to let you know all about the guy who loaded the dryer wrong so that Saiyd was only wearing his second favorite shirt when Shannon was shot instead of actually showing the fallout of the event.
At the time I could not stand this and its what eventually led to me giving up on the show in the third season (before catching back up in the final season in time for the finale because) but this time I find it weirdly adorable. It's like watching a three year old refuse to eat his vegetables when you're not actually responsible for feeding them. They're still being dumb but it's adorable and kinda hilarious when it's not your problem.
Same with Kate's backstory and the dragged out reveal of what's in the hatch and Boone's entire existence. Freed of all the expectations placed on it Lost's kind of a dumb show sometimes and you're allowed to actually enjoy it in all its inconsistent glory.
I'm still probably skipping the one that gives the backstory of all Jack's tattoos though.


Metallic nanostructures have been investigated with various applications especially for integration with light detection devices. The incident light can be manipulated by those nanostructures to enhance light absorption therefor improve device performance. However, previous studies focused on optical design. The electrical properties of these integrated light detection devices have not been fully considered. The photon generated carriers transport and collection are critical for light detection devices as well. An optimized device platform considering from both the optical and electrical aspects to fully utilize these nanostructures is highly desired for future light detection devices.
This dissertation targeted on three objectives, beginning with the fabrication process development of various nanostructures on different substrates. High quality nanostructures were achieved with minimum 20nm gap and 45nm line width. The second objective was developing the metallic fishnet nanostructures integrated Schottky contact a-Si solar cell to improve both light absorption and photon generated carrier collection. The fishnet was designed as the light trapping structure and 2D connected top contact to collect carriers. The third objective was developing metallic nanostructures integrated GeSn photodetectors. The H shape nano antennas were integrated on GeSn photodetectors. Multiple resonant absorption peaks at infrared range were observed using spectroscopic ellipsometry. However, there was no obvious photoresponse value improvement of developed solar cells and H shape antennas integrated GeSn photodetectors. For further investigation, interdigitated electrodes integrated GeSn photodetectors were designed. With less carrier transit time, the responsivity value of the integrated Ge0.991Sn0.009 photodetector was 72µA/W at 1.55µm at room temperature which was 6 times higher comparing to device without integration. Meanwhile, with the increased carrier life time by decreasing temperature, the responsivity value of integrated Ge0.93Sn0.07 detectors at 1.55µm at 100K was 8.5mA/W which was 200 times higher than the value at 300K. These results suggest relative large surface recombination rate was the dominant loss mechanism in metallic nanostructures integrated light detection devices, as the ratio of carrier life time and transit time determines the gain of photodetector. The light detection devices integrated with metallic nanostructures can be developed to maximize device performance with light trapping effect and carrier collection efficiency.
Huang, Liang, "Nanofabrication of Metallic Nanostructures and Integration with Light Detection Devices" (2014). Theses and Dissertations. 2247.


The Beau Rivage Condominiums is the only riverfront high rise nestled in Jacksonville, Florida's premier historic residential neighborhood, Avondale. Sweeping panoramic views of the Saint Johns River can be enjoyed from most units and from the spacious common social rooms and riverfront swimming pool.
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The Shoppes of Avondale — some of the city's best shopping (Underwood's Jewelers, the Hobbie Horse, J. Ashley, Anita's Garden Shop, Vue Optical, the Green Alligator toy store, to name but a few!) and restaurants such as the Fox diner, the Casbah, Biscottis, Blue Fish, the Brick, Mellow Mushroom Avondale and Mojo's #4 barbeque— all half a mile away!
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Restaurant Orsay — one of the most talked-about and successful restaurants in the city, located just one mile from the Beau Rivage.
But life isn't all eating, drinking and shopping, alas. We are also a mere six blocks away from St. Vincent's Medical Center, one of Jacksonville's largest hospitals.
The Beau Rivage Condominiums in Jacksonville, Florida. Modern living in an elegant high-rise, nestled amongst the charms and conveniences of Avondale — the best of all possible worlds.


text:
\section{Electronic Submission}
\label{submission}

Submission to the SCIS workshop will be via openreview; \href{https://openreview.net/group?id=ICML.cc/2022/Workshop/SCIS}{link}. Information about the submission process and \LaTeX\ templates
are available on the conference \href{https://sites.google.com/view/scis-workshop/home}{website}:

The guidelines below will be enforced for initial submissions and
camera-ready copies. Here is a brief summary:
\begin{itemize}
\item Submissions must be in PDF\@.
\item If your paper has appendices, submit the appendix together with the main body and the references \textbf{as a single file}. Reviewers will not look for appendices as a separate PDF file. So if you submit such an extra file, reviewers will very likely miss it.
\item Page limit: The main body of the paper has to be fitted to \textbf{4 pages}, excluding references and appendices; the space for the latter two is not limited.
\item \textbf{Do not include author information or acknowledgements} in your
    initial submission.
\item Your paper should be in \textbf{10 point Times font}.
\item Make sure your PDF file only uses Type-1 fonts.
\item Place figure captions \emph{under} the figure (and omit titles from inside
    the graphic file itself). Place table captions \emph{over} the table.
\item References must include page numbers whenever possible and be as complete
    as possible. Place multiple citations in chronological order.
\item Do not alter the style template; in particular, do not compress the paper
    format by reducing the vertical spaces.
\item Keep your abstract brief and self-contained, one paragraph and roughly
    4--6 sentences. Gross violations will require correction at the
    camera-ready phase. The title should have content words capitalized.
\end{itemize}

\textbf{Paper Deadline:} The deadline for paper submission that is
advertised on the conference website is strict. If your full,
anonymized, submission does not reach us on time, it will not be
considered for publication.

\textbf{Anonymous Submission:} The SCIS workshop uses double-blind review: no identifying
author information may appear on the title page or in the paper
itself. \cref{author info} gives further details.

\textbf{Submission:} SCIS will not accept any paper which overlaps substantially in technical content with conference papers previously published.
Papers under review under other conferences or workshops can be submitted.

\medskip

Authors must provide their manuscripts in \textbf{PDF} format.
Furthermore, please make sure that files contain only embedded Type-1 fonts
(e.g.,~using the program \texttt{pdffonts} in linux or using
File/DocumentProperties/Fonts in Acrobat). Other fonts (like Type-3)
might come from graphics files imported into the document.

Authors using \textbf{Word} must convert their document to PDF\@. Most
of the latest versions of Word have the facility to do this
automatically. Submissions will not be accepted in Word format or any
format other than PDF\@. Really. We're not joking. Don't send Word.

Those who use \textbf{\LaTeX} should avoid including Type-3 fonts.
Those using \texttt{latex} and \texttt{dvips} may need the following
two commands:

{\footnotesize
\begin{verbatim}
dvips -Ppdf -tletter -G0 -o paper.ps paper.dvi
ps2pdf paper.ps
\end{verbatim}}
It is a zero following the ``-G'', which tells dvips to use
the config.pdf file. Newer \TeX\ distributions don't always need this
option.

Using \texttt{pdflatex} rather than \texttt{latex}, often gives better
results. This program avoids the Type-3 font problem, and supports more
advanced features in the \texttt{microtype} package.

\textbf{Graphics files} should be a reasonable size, and included from
an appropriate format. Use vector formats (.eps/.pdf) for plots,
lossless bitmap formats (.png) for raster graphics with sharp lines, and
jpeg for photo-like images.

The style file uses the \texttt{hyperref} package to make clickable
links in documents. If this causes problems for you, add
\texttt{nohyperref} as one of the options to the \texttt{icml2022}
usepackage statement.

\subsection{Submitting Final Camera-Ready Copy}

The final versions of papers accepted for publication should follow the
same format and naming convention as initial submissions, except that
author information (names and affiliations) should be given. See
\cref{final author} for formatting instructions.

The footnote for a final version must be ``\textit{Published at the ICML 2022 Workshop on Spurious Correlations, Invariance, and Stability}.
Baltimore, Maryland, USA.
Copyright 2022 by the author(s).''

For those using the \textbf{\LaTeX} style file, this change (and others) is
handled automatically by simply changing
$\mathtt{\backslash usepackage\{icml2022\}}$ to
$$\mathtt{\backslash usepackage[accepted]\{icml2022\}}$$
Authors using \textbf{Word} must edit the
footnote on the first page of the document themselves.

Camera-ready copies should have a short title of the paper as running head on each page except the first one.
The running title consists of a
single line centered above a horizontal rule which is $1$~point thick.
The running head should be centered, bold and in $9$~point type. The
rule should be $10$~points above the main text.
For those using the
\textbf{\LaTeX} style file,
a shorter title can be supplied by using

\verb|\icmltitlerunning{...}|

just before $\mathtt{\backslash begin\{document\}}$.
Authors using \textbf{Word} must edit the header of the document themselves.

\section{Format of the Paper}

All submissions must follow the specified format.

\subsection{Dimensions}

The text of the paper should be formatted in two columns, with an
overall width of 6.75~inches, height of 9.0~inches, and 0.25~inches
between the columns. The left margin should be 0.75~inches and the top
margin 1.0~inch (2.54~cm). The right and bottom margins will depend on
whether you print on US letter or A4 paper, but all final versions
must be produced for US letter size.
Do not write anything on the margins.

The paper body should be set in 10~point type with a vertical spacing
of 11~points. Please use Times typeface throughout the text.

\subsection{Title}

The paper title should be set in 14~point bold type and centered
between two horizontal rules that are 1~point thick, with 1.0~inch
between the top rule and the top edge of the page. Capitalize the
first letter of content words and put the rest of the title in lower
case.

\subsection{Author Information for Submission}
\label{author info}

SCIS uses double-blind review, so author information must not appear. If
you are using \LaTeX\/ and the \texttt{icml2022.sty} file, use
\verb+\icmlauthor{...}+ to specify authors and \verb+\icmlaffiliation{...}+ to specify affiliations. (Read the TeX code used to produce this document for an example usage.) The author information
will not be printed unless \texttt{accepted} is passed as an argument to the
style file.
Submissions that include the author information will not
be reviewed.

\subsubsection{Self-Citations}

If you are citing published papers for which you are an author, refer
to yourself in the third person. In particular, do not use phrases
that reveal your identity (e.g., ``in previous work \cite{langley00}, we
have shown \ldots'').

Do not anonymize citations in the reference section. The only exception are manuscripts that are
not yet published (e.g., under submission).
If you choose to refer to
such unpublished manuscripts \cite{anonymous}, anonymized copies have
to be submitted
as Supplementary Material via openreview\@.
However, keep in mind that an SCIS
paper should be self contained and should contain sufficient detail
for the reviewers to evaluate the work. In particular, reviewers are
not required to look at the Supplementary Material when writing their
review (they are not required to look at more than the first $4$ pages of the submitted document).

\subsubsection{Camera-Ready Author Information}
\label{final author}

If a paper is accepted, a final camera-ready copy must be prepared.
For camera-ready papers, author information should start 0.3~inches below the
bottom rule surrounding the title. The authors' names should appear in 10~point
bold type, in a row, separated by white space, and centered. Author names should
not be broken across lines. Unbolded superscripted numbers, starting 1, should
be used to refer to affiliations.

Affiliations should be numbered in the order of appearance. A single footnote
block of text should be used to list all the affiliations. (Academic
affiliations should list Department, University, City, State/Region, Country.
Similarly for industrial affiliations.)

Each distinct affiliations should be listed once. If an author has multiple
affiliations, multiple superscripts should be placed after the name, separated
by thin spaces. If the authors would like to highlight equal contribution by
multiple first authors, those authors should have an asterisk placed after their
name in superscript, and the term ``\textsuperscript{*}Equal contribution"
should be placed in the footnote block ahead of the list of affiliations. A
list of corresponding authors and their emails (in the format Full Name
\textless{}email@domain.com\textgreater{}) can follow the list of affiliations.
Ideally only one or two names should be listed.

A sample file with author names is included in the ICML2022 style file
package. Turn on the \texttt{[accepted]} option to the stylefile to
see the names rendered. All of the guidelines above are implemented
by the \LaTeX\ style file.

\subsection{Abstract}

The paper abstract should begin in the left column, 0.4~inches below the final
address. The heading `Abstract' should be centered, bold, and in 11~point type.
The abstract body should use 10~point type, with a vertical spacing of
11~points, and should be indented 0.25~inches more than normal on left-hand and
right-hand margins. Insert 0.4~inches of blank space after the body. Keep your
abstract brief and self-contained, limiting it to one paragraph and roughly 4--6
sentences. Gross violations will require correction at the camera-ready phase.

\subsection{Partitioning the Text}

You should organize your paper into sections and paragraphs to help
readers place a structure on the material and understand its
contributions.

\subsubsection{Sections and Subsections}

Section headings should be numbered, flush left, and set in 11~pt bold
type with the content words capitalized. Leave 0.25~inches of space
before the heading and 0.15~inches after the heading.

Similarly, subsection headings should be numbered, flush left, and set
in 10~pt bold type with the content words capitalized. Leave
0.2~inches of space before the heading and 0.13~inches afterward.

Finally, subsubsection headings should be numbered, flush left, and
set in 10~pt small caps with the content words capitalized. Leave
0.18~inches of space before the heading and 0.1~inches after the
heading.

Please use no more than three levels of headings.

\subsubsection{Paragraphs and Footnotes}

Within each section or subsection, you should further partition the
paper into paragraphs. Do not indent the first line of a given
paragraph, but insert a blank line between succeeding ones.

You can use footnotes\footnote{Footnotes
should be complete sentences.} to provide readers with additional
information about a topic without interrupting the flow of the paper.
Indicate footnotes with a number in the text where the point is most
relevant. Place the footnote in 9~point type at the bottom of the
column in which it appears. Precede the first footnote in a column
with a horizontal rule of 0.8~inches.\footnote{Multiple footnotes can
appear in each column, in the same order as they appear in the text,
but spread them across columns and pages if possible.}

\begin{figure}[ht]
\vskip 0.2in
\begin{center}
\centerline{\includegraphics[width=\columnwidth]{icml_numpapers}}
\caption{Historical locations and number of accepted papers for International
Machine Learning Conferences (ICML 1993 -- ICML 2008) and International
Workshops on Machine Learning (ML 1988 -- ML 1992). At the time this figure was
produced, the number of accepted papers for ICML 2008 was unknown and instead
estimated.}
\label{icml-historical}
\end{center}
\vskip -0.2in
\end{figure}

\subsection{Figures}

You may want to include figures in the paper to illustrate
your approach and results. Such artwork should be centered,
legible, and separated from the text. Lines should be dark and at
least 0.5~points thick for purposes of reproduction, and text should
not appear on a gray background.

Label all distinct components of each figure. If the figure takes the
form of a graph, then give a name for each axis and include a legend
that briefly describes each curve. Do not include a title inside the
figure; instead, the caption should serve this function.

Number figures sequentially, placing the figure number and caption
\emph{after} the graphics, with at least 0.1~inches of space before
the caption and 0.1~inches after it, as in
\cref{icml-historical}. The figure caption should be set in
9~point type and centered unless it runs two or more lines, in which
case it should be flush left. You may float figures to the top or
bottom of a column, and you may set wide figures across both columns
(use the environment \texttt{figure*} in \LaTeX). Always place
two-column figures at the top or bottom of the page.

\subsection{Algorithms}

If you are using \LaTeX, please use the ``algorithm'' and ``algorithmic''
environments to format pseudocode. These require
the corresponding stylefiles, algorithm.sty and
algorithmic.sty, which are supplied with this package.
\cref{alg:example} shows an example.

\begin{algorithm}[tb]
   \caption{Bubble Sort}
   \label{alg:example}
\begin{algorithmic}
   \STATE {\bfseries Input:} data $x_i$, size $m$
   \REPEAT
   \STATE Initialize $noChange = true$.
   \FOR{$i=1$ {\bfseries to} $m-1$}
   \IF{$x_i > x_{i+1}$}
   \STATE Swap $x_i$ and $x_{i+1}$
   \STATE $noChange = false$
   \ENDIF
   \ENDFOR
   \UNTIL{$noChange$ is $true$}
\end{algorithmic}
\end{algorithm}

\subsection{Tables}

You may also want to include tables that summarize material. Like
figures, these should be centered, legible, and numbered consecutively.
However, place the title \emph{above} the table with at least
0.1~inches of space before the title and the same after it, as in
\cref{sample-table}. The table title should be set in 9~point
type and centered unless it runs two or more lines, in which case it
should be flush left.

\begin{table}[t]
\caption{Classification accuracies for naive Bayes and flexible
Bayes on various data sets.}
\label{sample-table}
\vskip 0.15in
\begin{center}
\begin{small}
\begin{sc}
\begin{tabular}{lcccr}
\toprule
Data set & Naive & Flexible & Better? \\
\midrule
Breast    & 95.9$\pm$ 0.2& 96.7$\pm$ 0.2& $\surd$ \\
Cleveland & 83.3$\pm$ 0.6& 80.0$\pm$ 0.6& $\times$\\
Glass2    & 61.9$\pm$ 1.4& 83.8$\pm$ 0.7& $\surd$ \\
Credit    & 74.8$\pm$ 0.5& 78.3$\pm$ 0.6&         \\
Horse     & 73.3$\pm$ 0.9& 69.7$\pm$ 1.0& $\times$\\
Meta      & 67.1$\pm$ 0.6& 76.5$\pm$ 0.5& $\surd$ \\
Pima      & 75.1$\pm$ 0.6& 73.9$\pm$ 0.5&         \\
Vehicle   & 44.9$\pm$ 0.6& 61.5$\pm$ 0.4& $\surd$ \\
\bottomrule
\end{tabular}
\end{sc}
\end{small}
\end{center}
\vskip -0.1in
\end{table}

Tables contain textual material, whereas figures contain graphical material.
Specify the contents of each row and column in the table's topmost
row. Again, you may float tables to a column's top or bottom, and set
wide tables across both columns. Place two-column tables at the
top or bottom of the page.

\subsection{Theorems and such}
The preferred way is to number definitions, propositions, lemmas, etc. consecutively, within sections, as shown below.
\begin{definition}
\label{def:inj}
A function $f:X \to Y$ is injective if for any $x,y\in X$ different, $f(x)\ne f(y)$.
\end{definition}
Using \cref{def:inj} we immediate get the following result:
\begin{proposition}
If $f$ is injective mapping a set $X$ to another set $Y$,
the cardinality of $Y$ is at least as large as that of $X$
\end{proposition}
\begin{proof}
Left as an exercise to the reader.
\end{proof}
\cref{lem:usefullemma} stated next will prove to be useful.
\begin{lemma}
\label{lem:usefullemma}
For any $f:X \to Y$ and $g:Y\to Z$ injective functions, $f \circ g$ is injective.
\end{lemma}
\begin{theorem}
\label{thm:bigtheorem}
If $f:X\to Y$ is bijective, the cardinality of $X$ and $Y$ are the same.
\end{theorem}
An easy corollary of \cref{thm:bigtheorem} is the following:
\begin{corollary}
If $f:X\to Y$ is bijective,
the cardinality of $X$ is at least as large as that of $Y$.
\end{corollary}
\begin{assumption}
The set $X$ is finite.
\label{ass:xfinite}
\end{assumption}
\begin{remark}
According to some, it is only the finite case (cf. \cref{ass:xfinite}) that is interesting.
\end{remark}

\subsection{Citations and References}

Please use APA reference format regardless of your formatter
or word processor. If you rely on the \LaTeX\/ bibliographic
facility, use \texttt{natbib.sty} and \texttt{icml2022.bst}
included in the style-file package to obtain this format.

Citations within the text should include the authors' last names and
year. If the authors' names are included in the sentence, place only
the year in parentheses, for example when referencing Arthur Samuel's
pioneering work \yrcite{Samuel59}. Otherwise place the entire
reference in parentheses with the authors and year separated by a
comma \cite{Samuel59}. List multiple references separated by
semicolons \cite{kearns89,Samuel59,mitchell80}. Use the `et~al.'
construct only for citations with three or more authors or after
listing all authors to a publication in an earlier reference \cite{MachineLearningI}.

Authors should cite their own work in the third person
in the initial version of their paper submitted for blind review.
Please refer to \cref{author info} for detailed instructions on how to
cite your own papers.

Use an unnumbered first-level section heading for the references, and use a
hanging indent style, with the first line of the reference flush against the
left margin and subsequent lines indented by 10 points. The references at the
end of this document give examples for journal articles \cite{Samuel59},
conference publications \cite{langley00}, book chapters \cite{Newell81}, books
\cite{DudaHart2nd}, edited volumes \cite{MachineLearningI}, technical reports
\cite{mitchell80}, and dissertations \cite{kearns89}.

Alphabetize references by the surnames of the first authors, with
single author entries preceding multiple author entries. Order
references for the same authors by year of publication, with the
earliest first. Make sure that each reference includes all relevant
information (e.g., page numbers).

Please put some effort into making references complete, presentable, and
consistent, e.g. use the actual current name of authors.
If using bibtex, please protect capital letters of names and
abbreviations in titles, for example, use \{B\}ayesian or \{L\}ipschitz
in your .bib file.

\section*{Accessibility}
Authors are kindly asked to make their submissions as accessible as possible for everyone including people with disabilities and sensory or neurological differences.
Tips of how to achieve this and what to pay attention to will be provided on the conference website \url{http://icml.cc/}.

\section*{Software and Data}

If a paper is accepted, we strongly encourage the publication of software and data with the
camera-ready version of the paper whenever appropriate. This can be
done by including a URL in the camera-ready copy. However, \textbf{do not}
include URLs that reveal your institution or identity in your
submission for review. Instead, provide an anonymous URL or upload
the material as ``Supplementary Material'' into the CMT reviewing
system. Note that reviewers are not required to look at this material
when writing their review.

\section*{Acknowledgements}

\textbf{Do not} include acknowledgements in the initial version of
the paper submitted for blind review.

If a paper is accepted, the final camera-ready version can (and
probably should) include acknowledgements. In this case, please
place such acknowledgements in an unnumbered section at the
end of the paper. Typically, this will include thanks to reviewers
who gave useful comments, to colleagues who contributed to the ideas,
and to funding agencies and corporate sponsors that provided financial
support.

\nocite{langley00}

\section{Introduction}
Exploiting structure in the data to infer latent variables and capture causal mechanisms is crucial for causal representation learning \cite{tcrl}. Such a representation would allow for counterfactual reasoning in a manner similar to that of humans, thereby moving away from models that rely on exploiting spurious correlations for prediction.

Causal mechanisms are usually modelled as Bayesian Networks or Directed Acyclic Graphs (DAG) and given information about the causal variables, one can learn the DAG with structure learning algorithms. Recently, there has been a flurry of works advancing structure learning algorithms \cite{lingam, notears, graphvae, dynotears, dibs, vcn, golem, bcdnets, dag_gflow} that learn the structure of a DAG given data samples (of causal variables). Most of these works cast the discrete optimization of learning a DAG into a continuous one that is optimized through gradient descent, thereby sidestepping the computational intractability arising from the super-exponential nature of DAG search in the discrete case. However, all approaches learn a causal DAG on the premise that one has full access to the true causal variables which might not be realistic. A more realistic assumption would be that we have partial or no access to true causal variables and that one has to infer the structure \textit{along with} the causal variables.

Here, we introduce a fully differentiable latent variable model, Decoder BCD, to study the problem of Bayesian structure learning in linear Gaussian additive noise models, from high dimensional data. We perform synthetic experiments to analyze why unsupervised causal discovery in latent variable models is difficult. Section 2 explains preliminaries for the setup and section 3 gives the problem setup. In section 4, we introduce Decoder BCD, a decoder model for Bayesian Causal Discovery in the latent space before discussing experiments and our findings in section 5. We discuss related work in section 6 before concluding in section 7.

\begin{figure*}
  \centering
  \includegraphics[height=8cm]{model.pdf} \label{model_img}
  \caption{An illustration of the latent variable (decoder) model for Bayesian causal discovery}
\end{figure*}

\section{Preliminaries}

\textbf{Structural Causal Models (SCM)}: We operate in the framework of SCM \cite{pearl} where node $Z_i$ represents a random causal variable with an independent noise variable $\epsilon_i \sim \mathcal{N}(0, \sigma_i^2)$, and parents $Pa_G(Z_i)$ corresponding to a DAG $G$. We focus primarily on the family of linear Gaussian additive noise models. However, for a DAG to be identifiable from data, one either has to observe a non-Gaussian setting, or, in the case of a Gaussian one, have an equal noise variance assumption (all $\sigma_i = \sigma$) \cite{peters_eq_noi_var}. Since we are in the Gaussian setting, we assume the latter. Thus, we have $d$ causal variables $Z = [Z_1, ... Z_d]$, and a joint distribution entailed by the DAG $G$ such that,
\begin{equation} \label{eq1}
P(Z_1, ... Z_d) = \prod_{i=1}^{d} {P(Z_i | Pa_G(Z_i)) }
\end{equation}
and the exact values of the $d$ random variables are given by $ z_i = f(Pa_G(z_i)) + \epsilon_i$, where $f(.)$ is a linear function. In our case, $f$ is the weighted sum of values taken on by random variables $Pa_G(Z_i)$, the weights given by the weighted adjacency matrix $W$, such that  $z = W^Tz + \epsilon$. \\

\textbf{Bayesian Causal Discovery Nets (BCD Nets)}: We build Decoder BCD, our latent variable decoder model, upon BCD Nets \cite{bcdnets}. Given samples of true causal variables, BCD Nets is a Bayesian structure learning method that obtains a posterior distribution over causal structures that best explains the data. Similar to other structure learning works \cite{dibs}, BCD Nets introduces the problem of structure learning as a continuous constrained optimization problem. However, the DAG is parameterized such that one always ends up with a DAG and is therefore a hard constraint (in contrast to DiBS which has a soft DAG constraint). This is achieved by formulating the weighted adjacency matrix as $W = {(P L P^T)}^T$ where $P$ is a permutation matrix and $L$ is strictly lower triangular. When $P$ is identity, this is equivalent to having a DAG of fixed ordering with each node $j$ having its possible parents only in the node range $[j+1, d]$. $P$ allows one to transition between node orderings by permuting the rows and columns of $L$. Apart from estimating $W$ (via $P$ and $L$), BCD Nets also infers $\Sigma$, the noise covariance, for the noise variables on each node in the DAG. \\

Thus, overall, BCD Nets formulates the Bayesian Structure Learning problem as inference of $P, L,$ and $\Sigma$ with a unique factorization of the posterior as $q_{\phi}(P, L, \Sigma)$ = $q_{\phi}(P | L, \Sigma) \cdot q_{\phi}(L, \Sigma)$. The model is trained on an ELBO loss (eq. \ref{bcd_elbo}) with a horseshoe prior on $L$, Gumbel Sinkhorn prior \cite{gs} on $P$, and a Gaussian prior on $\Sigma$.
\begin{equation} \label{bcd_elbo}
    \begin{aligned}
        \mathbb{E}_{(L, \Sigma) \sim q_{\phi}} \bigl[ & \mathbb{E}_{P \sim q_{\phi}(. |L, \Sigma)}  \bigl[ \log p(X | P, L, \Sigma) \\
        - & \log \frac{q_{\phi}(P | L, \Sigma)}{p(P | L, \Sigma)} \bigr] - \log \frac{q_{\phi}(L, \Sigma)}{p(L, \Sigma)} \bigr]
    \end{aligned}
\end{equation}
For finer details, we refer the reader to the original work \cite{bcdnets}. Our work focuses more on \emph{extending} BCD Nets to the high dimensional setting and studying unsupervised graph recovery in the latent space.

\section{The Problem Setup}

This work revolves around the Bayesian inference of causal variables $Z$ and the causal structure $G$. Given $n$ samples of high dimensional data $X \in \mathbb{R}^{n \times D}$, we wish to recover a distribution over graph structures $G$ -- the (weighted) adjacency matrix -- and the causal variables $Z \in \mathbb{R}^{n \times d}$. Our setup revolves mostly around the recovery in linear isotropic Gaussian additive noise SCM, which is identifiable. Models like DiBS \cite{dibs}, VCN \cite{vcn}, and BCD Nets \cite{bcdnets} can recover the Ground Truth (GT) DAG given only observational and interventional data. Given observational data, recovery is possible up to a Markov Equivalence Class (MEC).

We first generate a random ER \cite{er} DAG — with weighted adjacency matrix $W_{GT}$ with sparsity pattern $G_{GT}$, the adjacency matrix — and consider this the ground truth and set the noise covariance to be $\Sigma_{GT} = \sigma_{GT}^2 I$, since we have an isotropic Gaussian assumption. In our experiments, $\sigma_{GT}$ is usually set to $0.1$.

\textbf{Data generation of the true causal variables}: The data generation is done by an ancestral sampling process, compactly given by $z = W^Tz + \epsilon$. One could also get interventional data from this setup: \textbf{(i)} Choose the node set $N$ being intervened upon, \textbf{(ii)} for every node in $N$, zero out the particular column in $W$ to get the mutated DAG $\tilde{W}$ and \textbf{(iii)} perform ancestral sampling using $z = \tilde{W}^Tz + \epsilon$. This process is repeated multiple times to get $n$ samples of $z, $ and we will then organize these samples into a $(n, d)$ matrix and term it $z_{GT}$, or simply, $z$ \footnote{These variables will also be referred to, at times, as samples of true causal variables}.

\textbf{Generating the true high dimensional data}: We assume the observed low level data is a linear projection of the causal variables given by $X_{GT} = z_{GT}P'$ , where $P' \in \mathbb{R}^{d \times D}$ is a projection matrix and we have $D >> d$ in the real world. For most of the upcoming experiments, we will study recovery in the (simpler) limiting case where $d$ equal to $D$. This work studies recovery in latent space in a case where direct access to true causal variables is not given, in contrast to existing structure learning works.

\section{Decoder BCD}
BCD Nets performs Bayesian inference over $P, L, \Sigma$ given samples of true causal variables to best explain the data by training an ELBO loss (see eq. \ref{bcd_elbo}). Decoder BCD tries to infer the decoder parameters in addition to inferring $P, L, \Sigma$. It is trained over $X_{GT}$ instead of over $z_{GT}$ as in BCD Nets. Thus, we relax the assumption that we have access to samples of true causal variables. We can only access the high dimensional data that has to be explained by $(Z, G)$ and we have to fit a structure, $\hat{G}$, and estimate edge weights, $\hat{W}$, to fit our best guess of the causal variables, $\hat{z}$. Algorithm \ref{alg:cap} summarizes the inference mechanism of Decoder BCD. A diagrammatic overview is given in Figure \ref{model_img}.

\begin{algorithm}
\caption{Decoder BCD for causal discovery from high dimensional data} \label{alg:cap}
    1. Initialize random distributions for $P$, $L$, $\Sigma$ \\
    2. For \textbf{train\_steps}: \\
    \hspace{0.5cm} (i) Sample $\hat{P}, \hat{L}, \hat{\Sigma} \sim q_{\phi}(P, L, \Sigma)$ \\
    \hspace{0.5cm} (ii) $\hat{W} = (\hat{P}\hat{L}\hat{P^T})^T$ \\
    \hspace{0.5cm} (iii) Perform ancestral sampling: $\hat{z} = \hat{W}^T\hat{z} + \epsilon$; $\epsilon \sim \mathcal{N}(0, \hat{\sigma}^2)$ and $\hat{\Sigma} = \hat{\sigma}^2I$ \\
    \hspace{0.5cm} (iv) Decode $\hat{z}$ to obtain $\hat{X}$ \\
    (v) Update parameters of the distribution $P(P, L, \Sigma)$ with loss as $MSE(X, \hat{X})$ \\
    (vi) For supervised experiments, add an additional KL loss between true and posterior observational joint: $KL(q(z_1, ...z_d) || p(z_1, ...z_d))$
\end{algorithm}

\section{Experiments and Findings}

For all our experiments, for simplicity, we will stick to just learning the decoder and inferring the edge matrix $L$ since this makes the optimization simpler for our studies. Such an assumption of fixing the permutation $P$ to the GT, and thereby, the node orderings, is not unreasonable \cite{graphvae}. Here, the focus is solely on inferring the edges $L$ in latent space whilst learning a decoder. In all experiments, we train the model for $5000$ steps across $20$ random seeds, with a learning rate of 0.002 on ER-2 DAGs. We consider the case of the higher dimensional data being $D=10$ dimensions that is generated by data from a $d=6$ node underlying SCM.

\textbf{Metrics}: In our experiments, we refer to the expected Structural Hamming Distance across 64 samples of the inferred DAG as SHD, $MSE(L, \hat{L})$ is the MSE between predicted $\hat{L}$ and $L_{GT}$, AUROC (a value of 0.5 denotes a random baseline with null edges), and $KL(\text{true}\ ||\ \text{learned})$ refers to the KL divergence between the posterior observational joint and the GT observational joint distributions.

\subsection{Learning edge matrix $L$ with supervision}
For the supervised experiments, we add an additional KL loss on the inferred posterior observational joint $q(z_1,...z_d)$ and the prior observational GT joint distribution, $p(z_1,...z_d) \sim \mathcal{N}(\mu_z, \Sigma_z)$, where $\Sigma_z$ is calculated with $W_{GT}$ instead of with $\hat{W}$. The estimation of the prior and posterior observational joint distribution is detailed in \ref{a1}.

\begin{figure}[h]
\includegraphics[width=8.5cm]{_graph1.pdf}
\caption{Supervised learning of $L$ on 600 observational data points with $d=6, D=10$
\label{fig1}}
\end{figure}

\textbf{Finding 1}: From figure \ref{fig1}, we can see the expected SHD approaching 0 as $KL(\text{true}\ ||\ \text{learned})$ and $MSE(L, \hat{L})$ approach 0. Using a KL over the observational joint distribution results in complete graph recovery in the supervised case. This is expected since we provide a mild signal for the model to uncover the true causal variables. Instead of providing prior over the samples of true causal variables, we use the true observational joint distribution as a signal.

\subsection{Unsupervised learning of a single edge weight}
For this setting, instead of inferring the whole lower triangular edge matrix $L$, we infer only the last edge at position $(d, d-1)$. The other elements of the matrix are fixed to the GT and we observe graph recovery in this case.
This subsection is split into two parts to study recovery with (i) observational data and (ii) a mix of observational and interventional data, to analyze the effects of interventional data.

\begin{figure}[h]
\includegraphics[width=8.5cm]{_graph2.pdf}
\caption{Unsupervised learning of a single edge weight with 1800 observational data points \label{fig2}}
\end{figure}

We use 1800 observational data points for case (i) and 1800 data points (50-50 split of observational and interventional data) for case (ii). The interventional data generation process for single node and multi node interventions is detailed in \ref{a2}. The interventional values are fixed to 100.

\begin{figure}[h]
\includegraphics[width=8.5cm]{_graph3.pdf}
\caption{Unsupervised learning of a single edge weight with 900 observational and 900 interventional data points \label{fig2.2}}
\end{figure}

\textbf{Finding 2}: Figures \ref{fig2} and \ref{fig2.2} reveal that using observational and/or interventional data with single node or multi node interventions with fixed intervention values is not sufficient to learn to orient a single edge in the unsupervised case.

\subsection{Unsupervised learning of edge weight $L$}
In this experiment and in the next, we explore the problem of learning the entire lower triangular edge matrix $L$ in an unsupervised setting. First, we consider the learning problem with various amounts of observational data to analyze its effect on edge recovery in the latent space, which is shown in figure \ref{fig3.1}.

\begin{figure}[h]
\includegraphics[width=8.5cm]{_graph4.pdf}
\caption{Unsupervised learning of the edge weight matrix with various amounts of observational data \label{fig3.1}}
\end{figure}

Figure \ref{fig3.1} shows that all metrics diverge with training but there is no trend with respect to the amount of observational data that the model is given.

\begin{figure}[h]
\includegraphics[width=8.5cm]{_graph5.pdf}
\caption{Unsupervised learning of edge weights with random single node and multi node interventions \label{fig4.1}}
\end{figure}

We now consider learning $L$ using interventional targets as labels to learn the structure in the latent space with a mix of 300 observational data points and 3300 interventional data points. For this experiment, we retrain Decoder BCD multiple times from scratch — each time with the same 300 observational data points but with  more interventional data than the previous run. This helps us understand the usefulness of interventional data for graph recovery. This result is illustrated in figure \ref{fig4.1}. For the interventional data points, we chose to use a fixed intervention value of 100.0. The reason for this particular value was that we had to choose a value that is far from 0 -- the mean of all the nodes in the causal graph. Note that in a linear Gaussian additive noise SCM, if one has 0 mean of the error variables $\epsilon$, then all nodes in the graph have 0 mean.

\begin{figure}[h]
\includegraphics[width=8.5cm]{_graph6.pdf}
\caption{Unsupervised learning of edge weights with uniform single node and multi node interventions \label{fig4.3}}
\end{figure}

Finally, other than learning from randomly (single or multi) intervened nodes, we also performed an experiment to observe the effect of randomly chosen interventional values, rather than a fixed interventional value. Thus, instead of an intervention value of 100, we randomly sampled interventional values in $\text{Uniform}(-10.0, 10.0)$ for each data point and repeated our previous experiment for the same amount of observational (300) and interventional (3300) data points. We summarize our findings for this experiment in figure \ref{fig4.3}.

\textbf{Finding 4}: Figures \ref{fig4.1} and \ref{fig4.3} show that both single node and multi node interventions help in recovering the edge weights, measured across all the 4 metrics. However, multi node interventions with uniformly sampled intervention values results in the better inference of the structure and parameters of the latent SCM.

\section{Related Works}

To address the challenges of causal discovery, a variety of methods have been proposed. Some of these methods are based on structure learning using observational data and some take in to account interventional data \cite{dibs, ncmfai, ncmfui, dcdi}.

There has been an increasing focus on Bayesian structure learning \cite{daggnn, vcn, dibs, bcdnets, dag_gflow} to quantify epistemic uncertainty that is crucial for reinforcement learning and active learning settings. \citet{dds} follow almost exactly the same approach as \citet{bcdnets} except that they operate on nonlinear Gaussian SCM instead of a linear one. There also exist many maximum likelihood based methods, one such example is \cite{graphvae}. It is one of the few works that learn a structure in the latent space but they do not operate in a causality-based or SCM framework.

Markov Chain Monte Carlo (MCMC) is a popular technique for sampling from complex high dimensional probability distributions, such as the posterior distribution of DAGs. \cite{madigan1995bayesian} uses Metropolis-Hastings \cite{mhalgo} to predict the posterior distribution through Markov space to perform single edge addition or deletion. \cite{eaton2007exact} propose a hybrid MCMC algorithm that uses an exact score based algorithm. \cite{kuipers2022efficient} and \cite{viinikka2020towards} use more efficient MCMC samplers. \cite{dag_gflow} uses a novel class of probabilistic models, GFlowNets \cite{gflow}, which model distribution over discrete entities like DAGs to approximate the posterior in place of MCMC algorithms. Section \ref{mrw} discusses related work in more detail. Finally, our work involved randomly selecting nodes to intervene on while also randomly selecting the values for the interventions. \cite{iwah} uses a mutual information objective to learn where (which nodes) and how (with what values) to perform interventions in an active learning scenario to recover the edges more efficiently. However, unlike ours, their SCM is not in the latent space.

\section{Conclusion}
In this work, we introduced our latent variable model, Decoder BCD, and studied the causal representation learning problem. We explored the cases where edge recovery fails -- learning to orient a single edge or learning with only observational data. To address this, we propose using interventional targets as labels to allow recovery  of edges and edge weights in an SCM. Our experiments show that this is a promising direction for the unsupervised Bayesian causal discovery in latent space. However, our hypothesis of the observed data having a latent linear SCM and linear projection of the latent causal variables to higher dimension is a limitation (refer \ref{limitations}) when it comes to mechanisms in the real world. Future work should explore nonlinear projections of the causal variables as well as nonlinear and non-Gaussian SCMs. Finally, we discuss some key challenges for future study in Appendix \ref{challenges}.

\section*{Acknowledgements}

The authors are grateful to Nan Rosemary Ke, Anirudh Goyal, Tristan Deleu and Sébastien Lachapelle for fruitful discussions and feedback. The authors are also thankful to Compute Canada and CIFAR for the compute and funding that made this work possible.

redpajama_set_name: RedPajamaArXiv


text:
I suoi migliori risultati internazionali sono stati il 1º posto all'Universiade di Porto Alegre nel 1963 e il 2º posto ai Giochi del Mediterraneo di Napoli sempre nel 1963.

Biografia
Più volte nazionale italiano, indossa per la prima volta la maglia azzurra ai Campionati europei del 1962, a Belgrado.

L'anno successivo vince la medaglia d'oro all'Universiade di Porto Alegre e arriva secondo ai Giochi del Mediterraneo di Napoli.

Nel 1964, complice un problema alla schiena, non prendere parte alle Olimpiadi di Tokyo, nonostante le ottime prestazioni degli ultimi anni.

L'anno dopo prende parte alla Universiade di Budapest e nel 1967 a quella di Tokyo.

Terminata ancora giovane l'attività agonistica, causa i frequenti problemi alla schiena, inizia ad allenare presso vari club per poi entrare nel 1989 all'interno del Consiglio Nazionale della FIDAL, ricoprendo tra le altre cariche anche quella di vicepresidente. Attualmente ricopre la carica di presidente della sezione di Verona dell'Associazione Nazionale Atleti Olimpici e Azzurri d'Italia.

Palmarès

Campionati nazionali
 2 volte campione italiano nel lancio del disco (1962, 1964)

1962
  ai Campionati italiani assoluti, lancio del disco - 54,16 m

1963
  ai Campionati italiani assoluti, lancio del disco

1964
  ai Campionati italiani assoluti, lancio del disco - 53,76 m

1965
  ai Campionati italiani assoluti, lancio del disco

1966
  ai Campionati italiani assoluti, lancio del disco

Onorificenze

Note

Voci correlate
 Migliori prestazioni italiane nel lancio del disco

Collegamenti esterni

redpajama_set_name: RedPajamaWikipedia


Growing organizations that control more than one legal business entity are sometimes faced with the challenge of how to share data and perform intercompany transactions between partner companies.
The Intercompany integration solution for SAP Business One enables businesses running SAP Business One to manage Intercompany transactions for multiple companies by automatically replicating corresponding transactions across multiple company databases. Automating the replication of such transactions significantly reduces the effort required to generate Intercompany trading financial statements.
Intercompany Solution provides the flexibility in master data replication. User can decide the fields and masters that are to be replicated between each company.
The application allows to configure the exact fields in the master data as well as the objects that are to be replicated between branch companies.
Users can define data distribution templates to replicate specific global business partner, global item master, global bill of materials, and global price lists masters to certain branch companies.
Administrators can define the users who will be allowed to add, update, or remove global master data.
The administrator can configure the solution to replicate UDTs between branch companies similar to the master data replication process.
The Intercompany integration solution automatically creates an intercompany transaction as a reciprocal transaction in the SAP Business One application of another business unit and does so in a standardized and transparent manner. Consider a sales subsidiary that buys finished goods from a manufacturing plant. When the sales subsidiary creates a purchase order in its SAP Business One application, the solution automatically triggers the creation of a sales order transaction in the SAP Business One application of the manufacturing plant. The ability to automatically create a related, reciprocal transaction in the other company database eliminates manual data entry, improves employee productivity, and reduces the potential for errors from entering data again.
Intercompany Solution automates the flow of marketing documents across partner companies including Purchase Order to Sales Order, Delivery to Goods Receipt PO, A/R Invoice to A/P Invoice, Goods Return to Return and A/P Credit Memo to A/R Credit Memo.
Support for Item and Service Type Documents: Intercompany solution supports replication of service type document in intercompany trade.
Cancellation of Marketing Documents: If a user cancels or rejects a marketing document, the application automatically cancels the reciprocal document in the other company.
Document Total Comparison Engine: The document total comparison engine compares the document total amounts of the sender and receiver documents and alerts user of any discrepancies.
Tax Comparison Engine: The tax comparison engine compares the tax amounts (and details) calculated in the sender and receiver documents and alerts user of any discrepancies. Tax Comparison Engine is only relevant for the Brazil localization.
Replication of Serial Numbers and Batch Numbers: The Intercompany solution automatically replicates serial and batch numbers between the sender and receiving company.
The Intercompany solutions allocates income and expenses across business entities by automatically creating balanced GL postings to intercompany accounts.
GL Allocation Posting Templates: Commercial organizations may have allocation transactions presented in a template format, in which only the recorded amounts vary from transaction to transaction. This feature enables the user to create pre-defined templates for expenses or profit allocation that can be used to record repeatedly occurring transactions. For example, overall marketing expenditure to be allocated in a pre-defined percentage amongst the subsidiaries can be setup as a template.
Recurring GL Allocation Postings: Every business may have recurring transactions, say on daily, weekly, monthly or annual basis. This feature enables to create templates for recurring allocation postings, which can be executed based on the recurring frequency. Such recurring transactions can be configured in the Recurring G/L Allocation Postings screen. If the recurring transactions have been configured, the application prompts the user at application startup to post the scheduled recurring transactions.
Rejection of Journal Vouchers: If a journal voucher is rejected in the receiving company, the application creates a reversal journal entry in the sender company.
Another way to allocate expense amounts to other branch companies is by using the allocation feature on the A/P Service Invoice document. The application allows for allocation of expenses booked through AP service invoices across branch companies.
The Intercompany integration solution support following two methods of allocating expenses from an A/P service invoice.
Allocation by Invoice – In this method of allocation, system creates draft A/R invoice as a result of allocation in the sender company. On adding these draft A/R service invoices to the system will automatically create draft A/P service invoices in the receiver companies.
Allocation by G/L – This method of allocation is similar to G/L allocations. System creates the journal voucher in the receiving company as a result of allocation in the sender company.
Centralized Payments feature enables branch companies to delegate to other branch companies the task of making outgoing payments on their behalf. For example, the head office company could make vendor payments centrally on behalf of its subsidiary companies.
The Consolidation feature allows the journal entries of all branch companies to be consolidated into a consolidation company, enabling a single, easy way of running SAP Business One financial reports for the entire organization.
Consolidation of companies with different chart of account structures and different local currencies.
Consolidation of individual Accounts based on Historical Exchange Rates, Closing Exchange Rate or Average Exchange Rates.
Consolidation of Financial statements adjusted to reflect minority interests in companies within the group.
Set up multi-level consolidation hierarchy to enable group-wise reporting.
Set the application to automatically run the consolidation process based on defined schedules.
The application gives detailed visibility of inventory status of items across the organization. The application lists current inventory of all items in each subsidiary, including foreign subsidiaries – providing in-stock, committed, ordered, and available quantities.
The application provides an organization wide single view of all payables and receivables from trade between the business units and subsidiaries.
Consolidated sales for each operating entity can be viewed in a single report, to know how products are selling across various regions.
The report lists all incoming intercompany documents that are not yet accepted and posted by the receiving subsidiary.
Enables to execute the same queries across partner companies to generate ad-hoc reports in real-time by extracting the most updated data from the partner companies.
The entire Intercompany Solution can be setup and configured from a central web-based module. This eliminates the need to configure the solution individually for every participating company.
A web-based utility logs all inter-company transactions between different partner companies for effective audit control and troubleshooting.
Enable employees to restrict the creation of sales documents for customers and prompt a warning message based on the organization-wide consolidated credit limit and account balance.
Allow a business entity to accept or reject incoming intercompany transactions, and notify the sending business unit of the resulting decision.
Notify subsidiaries of intercompany transactions in real time with alert messages and e-mails. In case of inter-company trade the originating documents are emailed as an attachment to the outgoing email notification.
Supports Intercompany transactions in local and foreign currencies.
Supports SAP Business One companies located across multiple physical servers and geographies as well as deployments on a single machine.
Supports SAP Business One companies running on different localizations.
The terms used in the user interface of the intercompany add-on or administration console can be customized by importing excel-based language resource strings. Examples are field names, messages, screen titles, button names, and more.


text:
Q: Java synchronized blocks -- lock objects In the sample code
public class MsLunch {
private long c1 = 0;
private long c2 = 0;
private Object lock1 = new Object();
private Object lock2 = new Object();

public void inc1() {
    synchronized(lock1) {
        c1++;
    }
}

public void inc2() {
    synchronized(lock2) {
        c2++;
    }
}
}

on this page,
lock1 and lock2 are controlling the updates on c1 and c2 resply.
However,
    synchronized(lock2)

is acquiring the lock of the object lock1 and releasing it when the synchronized block
    synchronized(lock1) {
        c1++;
    }

is executed.
While this block is being executed, there may be
an update on member c1 of this object still-- and I don't
see how this update is being prevented by
synchronizing on lock1 as in the code.
It is the object lock1 that there is exclusive access on--
and nothing else(?)
So, how is the implementation
public void inc1() {
    synchronized(lock1) {
        c1++;
    }
}

in the above code different from
public void synchronized inc1() {
        c1++;
}

or even
public void inc1() {
    synchronized(c1) {
        //do something on c1
    }
}

when c1 is an object but not a primitive?
What am I missing here ?
Note: I saw
What is the difference between synchronized on lockObject and using this as the lock?
and
Java synchronized method lock on object, or method?
among some other discussions.

A: Implementation 1.
You are locking on the lock1 object.  Nothing else that needs a lock on lock1 can execute. Having your two methods lock on different objects means that the two methods can run concurrently, but no two threads can run the same method concurrently.
Implementation 2.
Making a method synchronized means that the entire method body is implicitly in a synchronized(this) block (or synchronized on the Class object if the method is static.  If both methods are synchronized, then one method would prevent the other from running at the same time, which is different then locking both methods on different objects.
Implementation 3.
If c1 is an object and not a primitive, then the semantics are very similar to implementation 1 - locking on an explicit object.

A: Your statement is as far as I know incorrect. The page you linked doesn't state what you claim in your question neither. This is how locks work: Locks do not prevent threads of accessing an object at all. Locks only prevents another thread of acquiring the same lock while another thread already has acquired that lock.
This means that this can happen:
Thread A: synchronize (lockObject)
Thread B: lockObject.toString();
Thread A: release the lock on lockObject

This is what happens when two Threads want the same lock at the same time:
Thread A: synchronize (lockObject)
Thread B: synchronize (lockObject) // This will block until (1) !
Thread A: do some stuff and then release lock on lockObject
Thread B: gets the lock (1)

However:
public void synchronized inc1() {
    c1++;
}

is exactly the same as:
public void inc1() {
    synchronized(this) {
        c1++;
    }
}

A: Marking a block or method synchronized without specifying a lock object synchronizes on the object who owns the method; in this case, the MsLunch instance. It's equivalent to synchronized(this). The purpose of the lock-object idiom is to break that lock up so that c1 and c2 can be manipulated separately. A synchronized block can only synchronize on an object, not a primitive.

A: synchronized(lock2){
  // do sth
}

is actually acquiring lock of object lock2
where as
public void synchronized inc1() {
        c1++;
}

acquires object on this object.
locks acquired by synchronized will be released once the program leaves the block.

A: I was having trouble with one thread hogging the lock
for(;;){synchronized(lockObject){
...
}}

I guess Java doesn't like to unlock it on its own the first chance it gets.
Using an java.util.concurrent.locks.ReentrantLock fixed the problem
static ReentrantLock lock = new ReentrantLock();
for(;;){
  lock.lock();
  ...
  lock.unlock();
}

redpajama_set_name: RedPajamaStackExchange