Shimla: The tourist resorts of Kufri and Narkanda near Shimla experienced mild snow in the past 24 hours, the weather bureau said on Monday, triggering a rush of tourists.
The state capital remained cloudy a day earlier and experienced a sunny morning on Monday.
Shimla recorded the minimum temperature at 3.9 degrees Celsius, an official of the Met department told IANS.
Nearby tourist spots like Kufri, Fagu and Narkanda experienced mild spells of snowfall.
Khadrala and Theog in Shimla districts recorded 10 cm and 2.5 cm of snowfall, respectively.
"High-altitude areas of Lahaul and Spiti, Chamba, Mandi, Kullu, Kinnaur and Shimla districts have been experiencing mild to moderate snow since Sunday, while mid hills getting rain," the official said.
Kalpa in Kinnaur district recorded a low of minus three degrees Celsius while the temperature was nine degrees in Dharamsala, minus 0.6 degree in Manali.
Keylong in Lahaul-Spiti district was the coldest in the state at a low of 3.2 degrees below freezing point.
The Met Office forecast said western disturbances – storm systems originating in the Caspian Sea and moving across the Afghanistan-Pakistan region – would remain active till January 6, bringing more rain or snow.


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Schedule a free television recycling or e-waste pickup by sending an email to info@aesrecycling.com or call us at 714-579-5749.


text:
Global Glide
People of Peninsula
Shark Tank 2017
Peninsula Outlook
PHS Top 20
Choir Scrambles to Raise $60,000 in 6 Months
Lauren Gilman
PHS Choir Car Wash.
Lauren Gilman, Reporter
October 25, 2016|298 Views
As spring approaches, urgency sets in on Peninsula's choir: they have been invited to New York, and leave on April 2nd. They have been asked to perform "John Rutter's Last Requiem" at Carnegie Hall.
Field trips are a great way of exposing students to a new culture. Last spring, Peninsula Choir traveled to Memphis, Tennessee to listen to music and experience a newfound appreciation behind musical history. This spring, the choir takes to New York to perform at Carnegie Hall.
"In New York, we are going to go sing at Carnegie Hall. We're going to perform a requiem which is going to be a lot of fun, it's really expensive and a lot of music to learn," Choir President John Rooney said.
This amazing opportunity comes at a cost: $2,000 per student. Choir students must now work together to raise enough money to send 30 students on the trip of a lifetime. The choir class has come up with many ideas on how to raise money.
"We have planned to do a choir concert with a dinner before, we're going to do an auction in February, we're going to do takeovers for Fondi and for Applebee's, they've done a car wash already… and as many other events that we can," Choir VTO, Micah Cranford, said.
The choir class is also trying to master 64 pages of music, 54 minutes of performance, and seven movements. Our singers are going to be partnered with professional soloists and a highly esteemed orchestra. This is a huge amount of work to master, but our choir is optimistic and ready to take on the challenge.
In addition to performing the choir has other events planned.
"The performance is the huge part but we're also going to see a broadway show and we're going to go and see several historic sights, we're going to see the ground zero museum, see the statue of liberty, and we have this cool boat ride after our concert that we're going to have a dinner cruise around the statue of liberty," Mrs. Ellis said.
Choir has set their eyes on this goal and won't stop until it's achieved.
"It's going to be really fun, it's going to be a pretty big experience for all of us and it's going to be a good bonding time," Rooney said.
Lauren Gilman, Editor in Chief
Lauren Gilman signed up for Outlook this year, because she is taking the lead roll in the classroom as the editor. If she could interview one person she...
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The King County Coronavirus Outbreak
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The student news site of Peninsula High School.

redpajama_set_name: RedPajamaCommonCrawl


text:
Q: Having a difficulty with neo4j connection I'm having a problem connecting to neo4j on a machine that previously didn't have any problems.
I've purged the previous installation from the machine and performed a new install - although I noticed that once activated it appeared to have a record of my previous activity.
Now, when connecting to the Desktop console, I'm getting conflicting messages on the one hand, :server connect returns
You are connected as user neo4j
to bolt://localhost:11001
Connection credentials are stored in your web browser.

On the other hand, any Cypher code I try to enter returns:
BoltConnectionError
BoltConnectionError: No connection found, did you connect to Neo4j?
 BoltConnectionError: No connection found, did you connect to Neo4j?
:server connect
Connect to Neo4j
Database access requires an authenticated connection.

How can it be both?

redpajama_set_name: RedPajamaStackExchange


After 10 years of growth, Honeng Electronics accumulate significant amount of resources of customers and suppliers. We become a one of the leading companies in the electronic industry. Honeng Electronics always bring best quality and service to our customer around the globe, we developed Honeng Marketing Search(HMS) and Inventory Management System(IMS) to manage global supply chain in order to remain advantages among our competitors.
Keeping improving service to our customers and building up global sales network are the main focus for our company in next 10 years. We will build sales network across Southeast Asia, Europe and North America, and want to provide satisfactory service to our clients.
Working-Hard and Live-Happy is always the guide to Honeng's employees, we have been creating best working environment and good relationship for our staff, most of all, Honeng's employees can choose various paths to succeed in our company.


It's astonishing how tight a lid the major media has kept on the JFK assassination over the last 50 years. As a long-time writer on the subject, I've taken particular note of the parade of fake whistleblowers luring the unsuspecting researchers into rabbit holes salted with time bombs. Every generation brings a new wave of manufactured disinfo and the noise-to-signal ratio has been immense. All the noise is evidence of a conspiracy. The media was consolidated into a private cartel prior to WWII and now serves propaganda and pablum, and no longer capable of investigating deep politics in America as they are wired into the national security state.
After more than five decades, however, like a scene out of The Bridge on the River Kwai, the muddy water is descending on the JFK assassination and exposing lines leading into JM/Wave, the CIA's largest station outside Langley when Kennedy was killed, with over 2,000 professional mercenaries on its payroll. Once JFK approved a secret assassination program inside the station, that very same squad ended up killing him in a tragedy worthy of Sophocles or Shakespeare. The last remaining data dump of documents still being held for reasons was due out last year, and that release guaranteed new revelations, as well as new rabbit holes salted with time bombs, but, of course, the release was delayed once again for reasons of national security. Rest assured, this crime was committed by Ted Shackley, David Morales and John Roselli, among others, and covered-up by Allen Dulles, James Angleton and J. Edgar Hoover.
One of the more interesting dots I was recently able to connect was eyewitness Richard Carr's testimony concerning two men he saw standing in the window of the 6th Floor Texas Book Depository during the assassination, and how much they resembled Ted Shackley and David Morales. Carr got a closer look a few minutes later, when he witnessed them exit the back of the building and jump into a waiting Rambler station wagon. Miraculously, Carr survived two assassination attempts and was even able to kill one of his assailants.
Whether you are new to the case, or a long-time researcher, you'll find material in this book not covered anywhere else. As an insider in counterculture media, I was allowed a closer examination of many of the leading whistleblowers and concluded many were really spooks salting time bombs. I also believe the case needs to be distilled and not buried in a blizzard of details, so I try to keep the word length down to the minimum necessary to round all the necessary bases. Instead of letting the noise distract or mislead, I follow disinfo to its source, and study the structure of the disinfo matrix for clues. Everyone brings something to the table, especially the spooks.
Your analysis appears very similar to that set out by Daniel Sheehan which is a good thing. What was the role of Mac Wallace? Did Angleton have prior knowledge?
Of course Angleton had prior knowledge, and Mac Wallace killing JFK is a rabbit hole in my opinion and part of an op to blame the assassination on LBJ.
Wallace, yes I can believe that. Would Angleton (and Helms) left the disposal of LHO to a bozo like Jack Ruby? I'm not convinced.
Oswald was never supposed to be taken alive. Neither Angleton nor Helms were at the scene, and the situation was dynamic as it unfolded.
Do you have an archive of all Tin Whistle posts?
Everything is online and easily navigated by selecting the proper category through the box in the sidebar.


IOB's multi-annual research plan can be found below.
This evaluation analyses to what extent the Dutch policy on international corporate social responsibility (ICSR) achieved its intended results. The focus is on enhancing due diligence by (Dutch) companies in line with the OECD Guidelines on International Investment and Multinational Enterprises and the UN Guiding Principles on Business and Human Rights. We will review the realisation and effects of sector covenants, frameworks for private-sector development policies, frameworks for procurement by the government, guidelines for embassies, international cooperation and the development and enforcement of legislation in this area. The evaluation also includes country studies that explore the policy's impact in Bangladesh, Colombia, Ethiopia and India. Part of this evaluation is also a separate evaluation of the Netherlands' National Contact Point (NCP) OECD Guidelines 2012-2018.
The objective of this evaluation is to provide input for future strategic partnership programmes. This evaluation will look at four main aspects in the functioning of SP: (i) complementarity of MoFA's and CSO's roles; (ii) funding modalities; (iii) accountability towards donors and towards southern stakeholders; and (iv) ownership and relative autonomy of CSO's. The main questions of this evaluation are: how do SP function? what are the causes for this? what recommendations can we draw from this? This evaluation focuses on the programmes 'Dialogue and Dissent' and the 'SRHR partnerships', both of DSO, and 'Conflict mediation strategic partnerships' and 'Addressing Root Causes', both of DSH. This evaluation focuses on the functioning of strategic partnerships, and will not consider ultimate effectiveness, which will be dealt with in the final evaluations in 2020.


1. Which planet in the solar system is named after the Roman messenger to the Gods?
2. Which Shropshire town is named after the civil engineer who built viaducts, roads and bridges in the area?
3. The Russian ballerina Anna Pavlova was visiting which country in 1908, when she was presented with a fruit and cream merangue in her name?
4. Which subatomic particle is named after the Greek for "first"?
5. Named after four renaissance painters, what are the four names of the Teenage Mutant Ninja Turtles?
6. Which town in Yorkshire has liquorice based sweets named after it?
7. The Phillipines were named after which Philip II, king of which country?
8. What is the full name of the owner of the company AMSTRAD?
9. Volgograd, a Russian city on the river Volga, was known until 1961 as what name?
10. Dame Nellie Melba the Australian soprano was ill in the London Savoy in 1893, and would only eat one thing that was later named in her honour. What was this?


The US Army website was defaced as a result of the SEA's latest political hacking campaign.
The Syrian Electronic Army has taken responsibility for defacing the US Army's website.
On Monday, the official US Army website www.army.mil, used for news releases and special features, was taken down for several hours following defacement of the homepage.
SEA took responsibility for the hack, claiming the cyberattack was performed through targeting and intercepting content paths in the backend of the US Army website. The SEA discovered an exploit in the website's Control Panel which allowed the group to edit protected content delivery paths -- which in turn allowed them to deface the website's pages.
Pop-ups appeared criticizing the US Army and its activities in the Middle East. One, posted on Twitter, said: "Your commanders admit they are training the people they have sent you to die fighting."
The group called for the US military to "stop training terrorists in Turkey and Jordan," saying there "are sources confirmed to us [..] and there are voices in the US military which reject this training and considers it a big mistake."
"Today an element of the Army.mil service provider's content was compromised. After this came to our attention, the Army took appropriate preventive measures to ensure there was no breach of Army data by taking down the website temporarily."
This is far from the first time the Syrian Electronic Army has launched a political cyberattack against well-known targets. The Washington Post, Microsoft, eBay and PayPal have all been former victims of the hacktivist group, which used the hacks to rage against the United States government and allege that data was being sold to the US government by the Redmond giant.
"It's certainly embarrassing when cyber war takes down the US Army's website. But the larger issue is not this annoying vandalism. The Army needs to protect its sensitive data from hackers and ensure there are no repeats of the massive federal government breach that was reported just last week."


text:
The five games that will define Manchester United's season
It's crunch time at the top, and we believe there are fives games that will define the season for Man United.
With less than ten Premier League games remaining of the season, Manchester United have left themselves every possible chance of securing a top four-finish after a dreadful first-half of the campaign.
The Red Devils have been a completely different outfit since the appointment of Ole Gunnar Solskjær. Who has only tasted one league defeat since taking over from Jose Mourinho.
That solitary defeat at the hands of Arsenal not only ended Solskjær's unbeaten record, it has blown the race for the Champions League spots wide open.
Coupled with Tottenham Hotspur's wobble throughout March, there's going to be many twists and turns between now and the end of the campaign. And we've identified five games that could well define their entire season.
Wolverhampton Wanderers (A) 2/4/19
Some might say that Wolves are this season's surprise package, but in reality, they were always going to make a strong impression this season.
Nuno Espirito Santo's side are just outside the top six and have an array of talent they can call upon.
From United's perspective, they can expect a tough gig at Molineux. And although they'll be expected to clinch a win, every player will need to stand up and be counted for.
Barcelona (H/A) 9/4/19 & 16/4/19
No matter what United supporters make of the Champions League draw, they'll have a monumental task navigating their way past Barcelona.
The Spanish giants were paired with Solsjkær's side and they'll square off at Old Trafford in the first-leg. This will prove the Norwegian's biggest test to date.
The Red Devils will need to be at their very best to have any chance of progressing to the semi-finals. But the tie has all the ingredients to be a classic.
Everton (A) 21/4/19
The blue half of Merseyside haven't lived up to their billing this season. But their slight upturn in fortunes will suit them perfectly ahead of the visit of United.
Goodison Park will be packed out to the rafters and if United can craft out the win, they'll remain in the race for a top four finish with just four league games remaining.
Once again, it won't be easy against Everton. Marco Silva's side are susceptible to losing on home turf though, with five defeats on their own patch this season.
Manchester City (H) 24/4/19
If there's one game that could dictate United's finish to 2018/19, it'll be the crunch Manchester derby against neighbours City.
Pep Guardiola's side have really ramped it up in recent weeks. They are now locked in a fight with Liverpool for the Premier League crown.
The Red Devils will be absolutely gunning for the three points, even if it helps out their arch-rivals on the red half of Merseyside.
It really does promise to be one of the best Manchester Derbies in years. There is just so much up for grabs for both sides.
Chelsea (H) 28/4/19
West Londoners Chelsea are the penultimate visitors to Old Trafford at the end of April. So much could be decided with the outcome of this game.
Both sides will be firmly locked into the race for the Champions League places. The game could literally contain everything you'd come to expect.
It's likely that Arsenal and Tottenham will still be very much in the mix at this stage. So a positive result is an absolute necessity.
No matter what happens in these five games, there's no denying they're the standout fixtures that will shape United's season. Next season, expect Solskjær's side to be back in the title reckoning. But before then, check out all the Premier League markets on Betfair.


text:
Blaise Cendrars (La Chaux-de-Fonds, cantón de Neuchâtel, Suiza, 1 de septiembre de 1887 - París, 21 de enero de 1961), cuyo nombre real era Frédéric-Louis Sauser, fue un escritor suizo en lengua francesa. Adoptó la nacionalidad francesa en 1916.

Sus viajes, reales e imaginarios, son la fuente de inspiración principal de su poesía (La Prosa del transiberiano y de la pequeña Jehanne de Francia un magnífico poema de dos metros de altura en su edición original, ilustrado con colores simultáneos por Sonia Delaunay, 1913) y de su prosa (El oro, 1925, el trágico destino de Johann Augustus Sutter, millonario que se arruinó al encontrarse oro en un terreno de su propiedad; Bourlinguer, 1948).

Tras enrolarse en la Legión Extranjera, participó en la Primera Guerra Mundial en la que perdió el brazo derecho, amputado en 1915 por debajo  del codo (antebrazo). Este acontecimiento marcará su obra profundamente, puesto que si su brazo derecho es el que le sirve para su oficio de escritor también es el que usa como pianista. La guerra decidió su vocación.

Biografía

Los orígenes

Nacido en una familia burguesa de origen bernés, aunque francófona, durante su infancia, seguirá a sus padres entre Egipto, Nápoles y Basilea, y luego va a estudiar a un internado en Alemania del que se escapará. Descubierto en Neuchâtel, sus padres le fuerzan a cursar estudios en la Escuela de Comercio, lo que no es muy de su agrado.

A los 16 años, huye junto a un traficante, Rogovine. Cruza Alemania y llega a Moscú, en plena efervescencia revolucionaria, viaja en el Transiberiano para hacer negocios en Asia. Todo lo que vive en estos años influirá mucho en el sentido de su vida. Trabajó con un joyero suizo en San Petersburgo hasta 1907. En la biblioteca imperial, de la que es habitual, un bibliotecario, Sozonov, le anima a que se haga escritor. Empieza a anotar sus lecturas, sus pensamientos, costumbre que conservó a lo largo de toda su vida.

En esa época escribe La Leyenda de Novgorod o del oro gris, que el propio Sozonov traduce al ruso y del que imprime 14 ejemplares en blanco sobre papel negro. Hasta 1996, no se supo de ningún ejemplar de este mítico libro que se encontraba en todas sus bibliografías desde su primer libro "oficial" en 1912 (Secuencias). Muchos opinan incluso que ese libro nunca existió, acrecentando de ese modo la fama de fabulador que siempre le persiguió en vida e incluso después de muerto. Un poeta declaró haber encontrado un ejemplar en los anaqueles de un librero búlgaro en 1996. No se tiene la certeza de que el ejemplar sea auténtico.

Al regresar a Suiza, estudia medicina en Berna, pero no encuentra las respuestas a las preguntas que le preocupan acerca del ser humano, su psique o su comportamiento. Influido por el Latín Místico de Remy de Gourmont, escribe Secuencias, su primer libro de poesía.

Tras una corta estancia en París, regresa a San Petersburgo, en donde escribe su primera novela, Moganni Nameh que no se publicará hasta 1922, en 4 entregas en la revista Les Feuilles libres, y se sumerge en la lectura de Schopenhauer; una revelación se aparece en una frase de este que ilumina su relación con la realidad: "el mundo es mi representación". A partir de ese momento hace de su vida un poema y escribe la vida en su poesía.

Su estancia en los Estados Unidos le muestra el camino, nuevo y sometido a las leyes de la mecánica, de la velocidad, de la modernidad, en las que el mundo se embarca. Persiguiendo una forma adecuada de expresión, hambriento, alucinado, redacta su primer poema largo, Semana Santa en Nueva York que será siempre "su más hermosa noche de escritura": un poema considerado el fundador de la poesía moderna.

La vocación literaria

En 1912 regresa a París, pero ya sabe cuál es su verdadera vocación; funda una revista, Les Hommes Nouveaux junto a Emil Szyttya, un conocido anarquista. Publica antes Secuencias en su propia editorial. Estos poemas de juventud tienen una inspiración clásica, son un homenaje a la prosa de Remy de Gourmont, al que admira. Traba amistad con personalidades artísticas y literarias: Apollinaire, Chagall, Fernand Léger, Léopold Survage, Modigliani, Csaky, Oleksandr Arjípenko, Robert y Sonia Delaunay. Con esta publica en 1913 su Prosa del Transiberiano y de la pequeña Jehanne de Francia, primer libro simultáneo (el texto y la imagen están estrechamente relacionados para producir en el lector una nueva emoción artística) que originará una encendida polémica. Al estallar la guerra, se alista en la Legión Extranjera. Pierde en Champaña el brazo derecho.

Publica La Guerra en Luxemburgo (el jardín), y un largo texto en prosa: He matado, primer libro ilustrado por Fernand Léger, que se puede incluir entre las páginas más bellas que se hayan podido escribir acerca de la guerra:

Profundo hoy confirma su interés por una visión poética de la modernidad. Se interesa además por el cine y publica un guion: El fin del mundo filmado por el ángel de Notre-Dame, también ilustrado por su amigo, Fernand Léger. Su tercer poema "homérico", Panamá, o las aventuras de mis siete tíos aparecerá en 1918. A éste siguieron Diecinueve poemas elásticos, en donde recoge los poemas escritos antes de la guerra y que anuncian los Alcoholes de Apollinaire. Deja París, deseoso de encontrar una mayor libertad y decide trabajar en pueblecitos del sur de Francia, a la vez que se muestra interesado por el cine. Trabaja como ayudante de Abel Gance, y colabora en J'accuse (1919) y La Roue (1923) en las que incluso hizo de figurante, ya que con su brazo amputado era un herido muy verosímil. En 1920, trabaja de director en Roma, en donde dirige La venere nera. También escribe para los Ballets Suecos La Creación del Mundo, con música de Darius Milhaud y decorados y vestuario de Fernand Léger. Apasionado por África, al igual que otros muchos escritores de la época, será el primero que reunirá los textos más importantes de la tradición oral africana en su "Antología negra" que aparece en 1921. Los modernistas de São Paulo lo invitan en 1924 y viaja a Brasil, en donde conocerá entre otros al pintor Cicero Dias.

En 1924, publica su último libro de poemas: Kodak. En los años 1970 los estudiosos descubrieron que había inventado en esa ocasión la técnica del collage recortando y agrupando frases del Misterioso doctor Cornélius la novela popular de Gustave Le Rouge. Quiere mostrarlo así a su mejor amigo, que es también poeta.

En 1925, decide abandonar la poesía y dedicarse a la novela. Escribe El oro, éxito mundial que revoluciona el género de la novela. A esta novela seguirán Moravagine, El Plan de la Aguja y Las Confesiones de Dan Yack (que a punto está de ganar el Premio Goncourt) y tres volúmenes de cuentos.

También es el creador del género del "reportaje literario" con Ron - La aventura de Jean Galmot, y también Hollywood, la Meca del cine (ilustrado con 29 dibujos del natural de Jean Guérin).

El exilio. La producción de posguerra

En 1939, al estallar la Segunda Guerra Mundial trabajará como corresponsal de guerra para el ejército británico. De todos sus reportajes, que aparecen publicados en "Paris Soir", saldrá un libro, "Con el ejército inglés", que aparecerá en la lista Otto y cuya tirada quemarán en su casi totalidad los alemanes. La invasión alemana le afectará mucho, se exiliará a Aix-en-Provence y durante tres años no escribirá nada.

Tras ese largo silencio publicará El hombre fulminado, La mano cortada, Bourlinguer y La urbanización del cielo que forman una tetralogía. De hecho están escritas como si fueran obras musicales, con su ritmo, sus 'cuadros' numerados, los párrafos y las frases medidos: Cendrars se reencuentra con su formación musical, perenne en su escritura, pero que adopta una nueva dimensión en estas grandes novelas relacionadas con su vida. Tras su retorno a la literatura, Doisneau acude a Aix para realizar un reportaje fotográfico sobre Cendrars. Ilustra el artículo de Maximilien Vox que publica en 1945 la revista de la cámara de comercio franco-sueca La Porte ouverte con un título que resume a la perfección esos años de guerra: Cendrars, un elefante solitario. Cendrars escribirá 4 años después, en 1949, el texto del primer libro de Doisneau: La periferia de París. Es la revelación de un gran fotógrafo.

El 27 de octubre de 1949, contrae matrimonio con Raymone Duchâteau, su enamorada de siempre en Sigriswil en la región del Oberland bernés. Escribe, para incluirlo en su novela "La torre Eiffel sideral": "Deseaba decir a los jóvenes de hoy que les engañan, que la vida no es un dilema y que entre las dos ideologías opuestas entre las que se les fuerza a optar, está la vida, la vida, con sus turbadoras y milagrosas contradicciones, la vida y sus ilimitadas posibilidades, sus absurdos mucho más capaces de alegrarnos que las idioteces y simplezas de la "política", y que por lo que tienen que optar es por la vida, a pesar de la atracción del suicidio, individual o colectivo, y de su fulminante lógica científica. No hay más posible elección. ¡ Vivir !".

En 1950 regresa a París. En 1956 escribe su última obra, una novela: ¡Llévame al fin del mundo! Enfermo, alcanza el grado de Comendador de la Legión de honor en 1960, distinción que le impone André Malraux.

Muere el 21 de enero de 1961, justo después de haber obtenido in extremis su único galardón literario oficial en vida: el Gran Premio Literario de la Ciudad de París. En el momento de su muerte, Cendrars tenía más de 30 novelas en preparación.

Entre 1961 y 1994 los restos de Blaise Cendrars reposaron en el Cementerio de Batignolles de París. Pero en 1994 sus restos se trasladaron al cementerio de Le Tremblay-sur-Mauldre en Yvelines, donde la familia poseía desde 1918 una residencia, su "casita de campo".

Obras

Poesía

 1912 - La Pascua en Nueva York (Les Pâques à New York)
 1913
 Secuencias (Séquences)
 Prosa del Transiberiano y de la pequeña Jeanne de Francia (Prose du Transsibérien et de la petite Jeanne de france)
 1916 - La guerra en Luxemburgo (La Guerre au Luxembourg)
 1918 - Panamá, o las aventuras de mis siete tíos (Le Panama ou les aventures de mes sept oncles)
 1919 - Diecinueve poemas elásticos (Dix-neuf poèmes élastiques)
 1924
 Hojas de ruta (Feuilles de route. 1. Le Formose) >> Fund. Juan March, 2009
 Kodak. Documentales (Kodak. Documentaire)
 1957 - Desde el mundo entero al corazón del mundo (Du monde entier au cœur du monde)

Novelas y relatos cortos

 1908 - La Leyenda de Novgorod o del oro gris
 1918 - He matado (J'ai tué)
 1919 - El fin del mundo (La Fin du monde filmée par l'Ange N.-D.)
 1921 - Antología negra (Anthologie nègre). >> Ardora, 2010.
 1922 - Moganni Nameh (escrito antes de 1912)
 1925 - El oro (L'Or. La merveilleuse histoire du général Johann August Suter). >> Alianza, 2006.
 1926 - Moravagine. >> Alfaguara, 2004
 1928 - Cuentos negros para niños blancos (Petits Contes nègres por les enfants des Blancs) >> Espasa, 1988.
 1929
 El plan de la aguja  (Le Plan de l'Aiguille. Dan Yack)
 Las confesiones de Dan Yack (Les Confessions de Dan Yack)
 Una noche en la selva (Une nuit dans la forêt)
 1930
 Como los blancos antes fueron negros (Comment les Blancs sont d'anciens Noirs)
 Ron. La aventura de Jean Galmot (Rhum. L'aventure de Jean Galmot) >> Barataria, 2008
 1936 - Hollywood, la Meca del cine (Hollywood, La Mecque du cinéma)
 1937 - Historias ciertas (Histoires vraies)
 1938 - La vida peligrosa (La Vie dangereuse)
 1940
 De ultramar a índigo (D'Oultremer à Indigo)
 Con el ejército inglés (Chez l'armée anglaise)
 1945 - El hombre fulminado (L'Homme foudroyé). >> Nostromo, 1974
 1946 - La mano cortada (La Main coupée. >> Argos-Vergara, 1980.
 1948 - Bourlinguer
 1949 - La urbanización del cielo (Le Lotissement du ciel), basado en la biografía de José de Cupertino.
 1956 - ¡Llévame hasta el fin del mundo! (Emmène-moi au bout du monde!...). >> Argos-Vergara, 1980.
 1957 - Demasiado, es demasiado (Trop c'est trop)
 1959 - A la aventura (À l'aventure)

Otras
 1917 - Profundo hoy
 1923 - La venere nera (película)
 1923 - La creación del Mundo (libreto teatral)

Referencias

Enlaces externos

Escritores de Suiza del siglo XX
Escritores en francés del siglo XX
Poetas de Suiza
Corresponsales de guerra
Militares franceses de la Primera Guerra Mundial
Comendadores de la Legión de Honor
Poesía moderna
Fallecidos en París

redpajama_set_name: RedPajamaWikipedia


Sharp Leadership Book Clubs provide an opportunity for Sharp HealthCare leaders to read and discuss a book pertaining to outstanding leadership practices. The book clubs are open to Sharp HealthCare leads and above.
Book selections will change on a quarterly basis. For June 2019, the book will be "Treating People Well: The Extraordinary Power of Civility at Work and in Life" by by Lea Berman and Jeremy Bernard.


Heart of Los Angeles (HOLA) is in need of wishlist items for their clients. They accept all kind of toys as long as they refrain from any toys that resemble any type of weapons or promote violence, i.e. toy guns, etc. They have students in 1st - 12th grade. They will happily be able to pick up gift donations at your convenience as well. There are a few ways to donate: The first way is to check HOLA's Amazon Wishlist. This list can serve as a staff suggestion of what to buy and donate, or you can also purchase gifts right off the Wishlist and have it sent straight to HOLA's headquarters. Some Highly Requested Items from students: Slime, Play-Doh, Backpacks, American Doll, Board Games, Any type of Lego set, Books, Soccer Balls, Coloring set or painting, Instant Polaroid Cameras or the film set. (popular with middle schoolers and high school), Yogurt-Land gift cards (popular with middle schoolers and high school), hydro flask water Bottles, Nike socks for men or women (popular with middle schoolers and high school). If you don't want to give a traditional gift, HOLA is very much in need of general school supplies, art supplies, or athletics supplies i.e. basketballs, soccer balls, Gatorade, hydro flask water bottles for students, etc. Please contact Monica Aguilera at maguilera@heartofla.org if you have any questions.
United Friends of the Children is hosting a holiday party for the scholars in their program from ages 7 - college and their caregivers. They have games, raffles, food, and gifts! They are in need of gifts. Gifts can be dropped off before the event at their main office on Wilshire Blvd. They will need volunteers for the following: Line control at games and photo booth • Assist w/ arts and craft table • Assist w/ any give away • Food (serving and line control) • Drinks (refill and line control) • Set up / tear down and event check in. They will be in contact with volunteers as the event gets closer regarding logistics. For a full wishlist please contact Damaris Pereda.
Camp TLC is collecting toys and donations to help support their Camp LAkota Holiday Party. This is their largest party and strives to give the Wounded Knee School District (#4 poorest community in America) a special holiday. The officially reported poverty rate for American Indians living on Pine Ridge is 53.75%. The United States average is 15.6%. Two of the five "poorest communities in America" are located on the Pine Ridge Reservation. Last year, they surprised the school with gifts, this year if they are to raise enough funds, will host a Holiday Carnival for the families and their children. They are looking for donations of toys and monetary donations to help make this day special for the youth on Pine Ridge. If interested in helping, please contact 917.363.5154.
Camp TLC (Together Living a Challenge) is having a Holiday Party for their campers and the member of the East LA Rising Boys and Girls Club. Many of the campers and family rely on Camp TLC's generosity during this Holiday Season to help with gifts. Their campers reside in a kinship or foster care situation or have come from a traumatic circumstance. They try to make this time of year much brighter! They are looking for in kind donations of toys, etc. Monetary donations can be made via the website below or by calling 917.363.5154.
"Adopt" a Foster Child for the Holidays!


text:
London-Based Forensic Outreach Teams Up with FBI Codebreaker to Track Down Future Scientists
Industry News: London-Based Forensic Outreach Teams Up with FBI Codebreaker to Track Down Future Scientists
London-based Forensic Outreach, an online platform that focuses on public engagement in security and crime science, has collaborated with an FBI cryptanalyst and leading science education provider Ward's Science to create a unique code-breaking competition that aims to inspire students worldwide to learn more about cryptology and forensic science.
Mission: Decipher - An International Forensic Science & Codebreaking Competition for Secondary School Students, will run globally for six weeks for school pupils aged between 11 and 18.
London-based Forensic Outreach launched in 2001 and has since partnered with over 100 academic institutes to create and provide pupils and adults with the codebreaking, security and crime science workshops designed to inspire the next generation.
FBI codebreaker Deenen Hernandez has helped shape the competition with Shivani Lamba, CEO of Forensic Outreach. Through this initiative, they hope to get as many pupils as possible interested in cryptology and forensic science.
At 15, Forensic Outreach's CEO, Shivani Lamba, entered a cryptology project in a qualifying round for the Intel Science and Engineering Fair and was awarded a tour of CIA HQ. Codebreaking has always fascinated Shivani, and she joined Forensic Outreach whilst an undergraduate at University College London as a means to fund her degree during a period of financial hardship. In 2009, Shivani took over the company and spearheaded the organization's shift to delivering public engagement campaigns online.
Shivani Lamba, CEO at Forensic Outreach, explains: "Codebreaking has always captured the public's imagination -- it certainly has a place in mine. This competition also comes at an opportune moment -- when there's an increased awareness of the work at Bletchley Park and the fascinating life of Alan Turing. I'm delighted to be working with Ward's Science and FBI cryptanalyst Deneen Hernandez to inspire a new generation of scientists."
Deneen Hernandez, FBI cryptanalyst, said: "This competition is a fantastic way to engage students in the areas of codebreaking and forensic science -- and help them get their feet wet. My law enforcement background and training as an examiner allowed me to provide students with challenges that are perhaps rarely seen in the typical science classroom. In reality, cryptanalysis is often an unexplored area in forensic science -- and I'm confident that this competition will bring these concepts a little closer to a new generation of scientists."
A spokesperson from Ward's Science said: "Ward's Science has been dedicated to helping science teachers inspire students for over 150 years. We are thrilled to work with leading curriculum developer in the United Kingdom and Europe--Forensic Outreach as both of our companies have similar missions and share the same passion for science education. We hope the competition inspires students to continue their interest in forensics well after the competition finishes and go on to pursue careers in science and technology."
VWR International Ltd
Deputy Managing Editor
Industry News: Forensic science is unlocking the mysteries of fatal lightning strikes
Editorial Article: Top-rated lab products awarded new Seals of Quality
Expert Insight: Sample prep strategies for the clinical research lab
Editorial Article: Announcing the latest scientists to strike lucky in our festive giveaway
Editorial Article: 6 top reasons why scientists read reviews on SelectScience
Expert Insight: Free masterclass on oligo printing via enzymatic DNA synthesis


We are offering a gamut of towels such as bath towels, face towel, hand towel, towel for men, towel for women etc. Following ranges are made by utilizing premium quality form of cotton material which is procuring from well known industrialists. In view of premium qualities such as soft and cozy towel and durability these are demanding widely from India. You can pick up following absorbent & durable towel on market leading prices. A towel is thing of absorbent fabric which is used to wipe and drying a body or surface. Moreover, following towels can draws moisture as it comes in direct contact of body. These can be availed on various sizes, colors and customized designs.


Following the arrests of Ali Ahmadnia, the administrator of the Telegram channel Eslahat ("Reform") News, and other reformist journalists in recent days, Iranian parliament's Deputy Speaker Ali Motahari lashed out at the head of Iran's Intelligence Ministry, demanding that he be called in for questioning.
In an interview with Iranian Students' News Agency (ISNA), Motahari warned that if the ministry could not thoroughly explain the motives behind the recent arrests, he would summon the intelligence minister for questioning, or even ask parliament to begin the process of impeaching him.
Motahari said he had learned that a military intelligence agency had arrested [Persian link] approximately 12 administrators for reformist or pro-Rouhani Telegram channels. It is not yet clear which agency is responsible for the arrests.
He also pointed out that the arrests had taken place near the end of the of the Iranian calendar year when parliament is not in session, making it difficult to access information. The timing of the arrests was also suspect, he said, because of the approaching presidential election, scheduled for May 19, making it even more urgent for an explanation to be provided.
In a separate case, in February, the Revolutionary Court in the Kurdish city of Saghez sentenced four people to prison for "propaganda in support of Kurdish opposition parties" after they set up a group and channel on the Telegram messaging service.


What to Expect From the New iPhone Lineup Coming This Fall!
Apple will introduce three new iPhones later this year. While the exact date of release has yet to be announced, Apple typically sends out invitations in late August for their fall iPhone events. These events usually take place at the beginning of September, with most official launch dates occurring about a week-and-a-half later.
Of the three new iPhones being released, one will be a budget, 6.1-inch LCD version. This one will come with a larger screen than the iPhone 8 but with a similar price. It will include Face ID and Dual-SIM function. It is estimated the price will start at between $699 and $749, with upgrades available. For cost purposes, the 6.1-inch LCD model will have a single-lens camera. This less expensive iPhone is expected to be the best-seller among the trio being released.
Sources believe that two AMOLED iPhones will become available: a 5.8-inch version and a 6.5-inch version. The 5.8-inch model is thought to be a follow up to the iPhone X, whereas the large 6.5-inch model is currently being dubbed the iPhone X Plus. One of the new models is expected to feature a rear-facing camera with a triple-lens array. This would improve the iPhone's zoom capacity and picture quality, particularly in dimly lit environments. You will be able to upgrade storage to 512 GB, and support for the Apple Pencil stylus is also expected. Investment analysists are predicting that these phones could start at $1,100.
All three phones will likely have Face ID and could even omit the fingerprint sensor. The 6.1-inch LCD iPhone will probably be housed in an aluminum frame. The two more expensive OLED iPhones are rumored to feature more durable stainless-steel housing. Meanwhile, iPhone X will likely be discontinued as its sales have not been as high as Apple had hoped.


text:
\section{Introduction}
Deep neural networks are data hungry in nature and require large amounts of data for successful training. For imbalanced datasets, where several (potentially important) classes have a scarce representation, the learned models are biased towards highly abundant classes. This is because the scarce classes have less representations during training which results in a mismatch between the joint distribution model for training $p(x,y)$ and test sets $p(x',y')$. This leads to lower recall rates for rare classes, which are otherwise critically desirable in numerous scenarios. As an example, a malignant lesion is rare compared to benign ones, but should not be miss-classified.

\begin{figure}
    \centering
    \includegraphics[width=\linewidth,keepaspectratio=True]{figs/Figure_Intro_MM.pdf}
    \caption{Affinity Loss integrates classification and clustering in a single objective. It's flexible formulation in Euclidean space allows enforcing margin between classes, control over learned clusters, number of class-prototypes and the distance between class-prototypes. Such max-margin learning greatly helps in overcoming class imbalance by learning balanced classification regions and generalizable class boundaries. }
    \label{fig:intro_fig}
\end{figure}

The soft-max loss is a popular choice for  conventional recognition tasks. However, through extensive experiments, we show that it is less suitable to handle mismatch between train and test distributions. This is partly due to no direct enforcement of margins in the classification space and the lack of a principled approach to control intra-class variations and inter-class separation.
Here, we propose that max-margin learning can improve generalization which can help mitigate classifier bias towards more frequent classes by learning balanced representations for all classes. Remarkably, some recent efforts focus on introducing max-margin constraints within the soft-max loss function \cite{deng2018arcface, liu2016large, liu2017sphereface}. Since soft-max loss computes similarities in the angular domain (vector dot-product or cosine similarity), direct enforcement of angular margins is ill-posed and existing works either  involve approximations or make restricting assumptions (e.g., points lying on a hypersphere).

In this paper, we propose a novel loss formulation that enhances generalization by jointly reducing intra-class variations and maximizing inter-class distances. A notable difference from the previous works is the automatic learning of class representative prototypes in the Euclidean space with inherent flexibility to enforce certain geometric constraints on the learned prototypes. This is in contrast to soft-max loss where more abundant classes tend to occupy additional space in the projected feature space and rare classes get a skewed representation. The proposed objective is named the `Affinity loss function' as it is based on a Gaussian similarity metric defined in terms of Bergman divergence. The proposed loss formulation learns to map input images to a highly discriminative Euclidean space where the distance with class representative prototypes provides a direct similarity measure for each class. The class prototypes are key points in the  embedding space around which feature points are clustered  \cite{snell2017prototypical}.

The affinity loss function promotes the classifier to have a simpler, balanced and more generalizable inductive bias during training.
The proposed loss function thus provides the following advantages:
\begin{itemize}\setlength{\itemsep}{0pt}
\item An inherent mechanism to jointly cluster and classify feature vectors in the Euclidean space.

\item A tractable way to ensure uniformly spaced and equidistant class prototypes (when embedding dimension $d$ and prototype number $n$ are related as: $n < d+1$).

\item Along-with uniformly spaced prototypes, our formulation ensures that the clusters formed around the prototypes are uniformly shaped (in terms of second order moments).

\item The resulting classifier shows robustness against different levels of label noises and imbalances amongst classes
\end{itemize}

The proposed loss function is a differentiable module which is applicable to different network architectures, and complements the commonly deployed regularization techniques including dropout, weight decay and momentum. Through extensive evaluations on a number of datasets, we demonstrate that it achieves a highly balanced and generalizable classifier, leading to significant improvements over previous techniques.

\section{Related Work}

\textbf{Class-imbalanced Learning:}
Imbalanced datasets exhibit complex characteristics and learning from such data requires designing new techniques and paradigms. The existing class imbalance approaches can be divided into two main categories, \textbf{1)} data-level, and \textbf{2)} algorithm-level approaches. The data-level schemes modify the distribution of data e.g., by oversampling the minority classes \cite{shen2016relay,chawla2002smote, han2005borderline,he2009learning, jo2004class} or undersampling the majority classes \cite{kubat1997addressing,barandela2003restricted}. Such approaches are usually susceptible to redundancy and over-fitting (for over-sampling) and critical information loss (for under-sampling). In comparison, the algorithm level approaches improve the classifier itself e.g., through cost-sensitive learning. Such methods incorporate prior knowledge about classes based upon their significance or representation in the training data \cite{lawrence2012neural,richard1991neural, khan2017cost}. These methods have been applied to different classifiers including SVMs \cite{tang2009svms}, decision trees \cite{zhou2010multi} and boosting \cite{ting2000comparative}. Some works further explore ensemble of cost-sensitive classifiers to tackle imbalance \cite{huang2018discriminative,KRAWCZYK2014554}. A major challenge associated with these cost-sensitive methods is that the class-specific costs are only defined at the beginning, and they lack mechanisms to dynamically update the costs during the course of training

\textbf{Deep Imbalanced Learning:} Some recent attempts have been made to learn deep models from imbalanced data \cite{ jeatrakul2010classification,khan2017cost, castro2013novel,wang2016training,ng2016dual}. For example, the method in \cite{jeatrakul2010classification} first learns to under sample the training data using a neural network, followed by Synthetic Minority Oversampling TEchnique (SMOTE) based technique to re-balance the data. Deep models are trained to directly optimize the imbalanced classification accuracy in \cite{wang2016training,ng2016dual}. Wang et.~al. \cite{wang2017learning} propose a meta learning approach to progressively transfer the model parameters from majority towards less-frequent classes. Some works \cite{khan2017cost, castro2013novel} train cost sensitive deep networks, which alternatively optimize class costs and network weights. Continually determining class costs while training a deep model is still an open and challenging research problem, and makes optimization intractable in learning from large scale datasets \cite{huang2016learning}.

\textbf{Joint Loss Formulation:} Popular loss functions used for classification in deep networks include hinge loss, soft-max loss, Euclidean loss and contrastive loss \cite{khan2018guide}. A triplet loss could simultaneously perform recognition and clustering, however its training is prohibitive due to huge number of triplet combinations on large-scale datasets \cite{schroff2015facenet}. Since these loss functions are limited in their capability to achieve discriminability in feature space, recent literature explores the combination of multiple loss functions. To this end, \cite{sun2014deep} showed that the combination of soft-max and contrastive losses concurrently enforce intra-class compactness and inter-class separability. On a similar line,  \cite{wen2016discriminative} proposed `center loss' that uses separate objectives for classification and clustering.

\textbf{Max-margin Learning:} Margin-maximizing learning objectives have been traditionally used in machine learning. Hinge loss in Support vector machines is one of the pioneering max-margin learning framework \cite{hearst1998support}. Some recent works aim to integrate max-margin learning with cross-entropy loss function. Among these, Large-margin soft-max \cite{liu2016large} enforces inter-class separability directly on the dot-product similarity while SphereFace \cite{liu2017sphereface}  and ArcFace \cite{deng2018arcface} enforce multiplicative and additive angular margins on the hypersphere manifold, respectively. The hypersphere assumption for feature space makes the resulting loss less generalizable to applications other than face recognition. Furthermore, enforcing margin based separation in angular domain is an ill-posed problem and either requires approximations or assumptions (e.g., unit sphere) \cite{elsayed2018large}. THis paper proposes a new flexible loss function which simultaneously performs clustering and classification, and enables direct enforcement of the max-margin constraints. We describe the proposed loss formulation next.
\section{Max-margin Framework}
We propose a hybrid multi-task formulation to perform learning on imbalanced datasets. The proposed formulation combines classification and clustering in a single objective that minimizes intra-class variations while simultaneously achieving maximal inter-class separation. We first explain why traditional Soft-max Loss (SL) is unsuitable for large-margin learning and then introduce our novel objective function.

\subsection{Soft-max Loss}
Given an input-output pair $\{\mathbf{x}_i, y_i\}$, a deep neural network transforms input to a feature space representation $\mathbf{f}_i$ using a function $\mathcal{F}$ parameterized by $\theta$ i.e.,  $\mathbf{f} = \mathcal{F}(\mathbf{x}; \theta)$. The soft-max loss can then compute the discrepancy between prediction and ground-truth in the label space as follows:
\begin{align} \label{eq:softmax}
L_{sm} = \frac{1}{N}\sum\limits_i -\log \Big( \frac{\exp(\mathbf{w}^T_{y_i}\mathbf{f}_i)}{\sum_j \exp( \mathbf{w}^T_{j}\mathbf{f}_i)} \Big),
\end{align}
where $i \in [1,N],\; j \in [1,C]$, $N$ and $C$ are number of training examples and classes respectively. It is worth noting that we have included the last fully connected layer in the definition of soft-max loss  which will be useful in further analysis. Also, for the sake of brevity, we do not mention unit biases in Eq.~\ref{eq:softmax}.

Although soft-max loss is one of the most popular choices for multi-class classification, in the following discussion, we argue that it is not suitable for class imbalanced learning due to several limitations.

\paragraph{Limitations of SL:}
The loss function in Eq.~\ref{eq:softmax} computes inner vector product $\langle \mathbf{w}, \mathbf{f} \rangle$ which measures the projection of feature representation on to each of the class vectors $\mathbf{w}_j$. The goal is to perfectly align $\mathbf{f}_i$ with the correct class vector $\mathbf{w}_{y_i}$ such that the data likelihood is maximized. Due to the reliance of the oft-max loss on vector dot-product, it has the following limitations:
\begin{itemize}\setlength{\itemsep}{0em}
\item No inherent mechanism to ensure max margin constraints. Computation of inter-class margin for soft-max loss is intractable \cite{elsayed2018large}. Large margin constraints promote better generalization in imbalanced distributions and robustness against input perturbations \cite{cortes1995support}.
\item The learned projection vectors are not necessarily equi-spaced in the classification space. That is, ideally the angle between closest projection vectors should be equal (e.g., $\frac{2\pi}{k}$ in 2D where $k$ is the number of classes). However, in practice the projection vectors for majority classes occupy more angular space compared with minority classes. This has been visualized in Fig.~\ref{fig: vis} on imbalanced MNIST dataset, and leads to poor generalization to test samples.
\item The length $\parallel \textbf{w}_j \parallel_2$ of the learnt projection vectors for different classes is not necessarily the same. It has been shown in the literature that the minority class projection vectors are weaker (i.e., with less magnitude) compared with the majority classes \cite{liu2016large}. Cost-sensitive learning which artificially augments the magnitude of the minority class projection vectors has been shown to be effective for imbalance learning \cite{khan2017cost}.
\end{itemize}

\paragraph{Unsuitability of SL for Imbalanced Learning:}
We attribute the above limitations to not directly enforcing the max-margin constraints on the classification boundaries. Consider the definition of soft-max loss (Eq.~\ref{eq:softmax}) in terms of dot-products $\mathbf{w}^T\mathbf{f}$, we can simplify the expression as follows:
\begin{align}
L^i_{sm} &\propto  \sum_{j\neq y_i} \exp(\mathbf{w}_j^T \mathbf{f}_i - \mathbf{w}_{y_i}^T\mathbf{f}_i)
\end{align}
The decision boundary for a class pair $\{j,k\}$ is given by the case where $\mathbf{w}_j^T \mathcal{F}(\mathbf{x}) = \mathbf{w}_k^T \mathcal{F}(\mathbf{x})$, i.e., the class boundaries are shared between the pair of classes. Further, minimization of $L_{sm}^i$ requires $\mathbf{w}_j^T \mathcal{F}(\mathbf{x}) > \mathbf{w}_k^T \mathcal{F}(\mathbf{x}) : k \neq j$ for correct class assignment to $\mathbf{x}$. This is a `relative constraint' and therefore the soft-max loss $L_{sm}$ does not necessarily: (a) reduces intra-class variations, (b) enforces a margin between each class pair.  To address these issues, we propose our new loss function next.

\begin{figure*}[!ht]
\includegraphics[trim={16cm 19cm 7cm 11cm},clip,width=1\linewidth,keepaspectratio=True]{figs/vis/fig_vis.pdf}
\caption{2D feature space projections in terms of penultimate layer activations. The model is trained on imbalanced MNIST data (by retaining only 10\% of the samples for digits 0-4) using different losses: (a) soft-max loss learns floral petals in angular space, note that the minority class feature vectors are weaker (shorter in length) and occupy less angular space. (b) center loss reduces intra-class variations by performing clustering. However, the minority class vectors tend to be congested near the center and are confused amongst each other (c) the proposed affinity loss learns equi-spaced clusters of uniform shapes for both majority and minority classes.}
\label{fig: vis}
\end{figure*}

\subsection{Max-margin Learning with Hybrid Objective}

\noindent
\textbf{Euclidean space similarity measure:} Instead of computing similarities with class prototypes using vector dot-product, we propose to measure class similarities for an input feature in the Euclidean space using a Gaussian similarity measure in terms of Bergman divergence (squared $\ell^2$ distance):
\begin{align}\label{eq:gaussian}
   d(\textbf{f}_i, \textbf{w}_j) = \exp\Big(-\frac{\parallel\textbf{f}_i-\textbf{w}_j\parallel^2}{\sigma}\Big),
\end{align}
where, $\sigma$ denotes a weighting parameter.  This provides us: \textbf{(a)} the flexibility to directly enforce margin maximizing constraints, \textbf{(b)} have equi-spaced classification boundaries for multiple classes, \textbf{(c)} control the variance of learned clusters and therefore enhancing intra-class compactness, \textbf{(d)} the freedom to use standard distance measures in Euclidean domain to measure similarity and most importantly \textbf{(e)} simultaneous classification and clustering in a single objective function.

\begin{theorem}
The similarity function $d(\mathbf{a}, \mathbf{b})$ is a valid similarity metric for any real-valued inputs.
\end{theorem}
\begin{proof}
The real-valued similarity function $d(\mathbf{a}, \mathbf{b})$ will define a valid similarity metric if it satisfies the following conditions \cite{li2004similarity}:
\begin{itemize}\setlength{\itemsep}{0em}
    \item Non-negativity: $d(\mathbf{a}, \mathbf{b}) \geq 0$
    \item Symmetry: $d(\mathbf{a}, \mathbf{b}) = d(\mathbf{b}, \mathbf{a})$
    \item Equivalence: $d(\mathbf{a}, \mathbf{a}) = d(\mathbf{b}, \mathbf{b}) =  d(\mathbf{a}, \mathbf{b})$ iff $\mathbf{a} = \mathbf{b}$
    \item Self-similarity: $d(\mathbf{a}, \mathbf{a}) \geq d(\mathbf{a}, \mathbf{b})$
    \item Triangular similarity: $d(\mathbf{a}, \mathbf{b}) + d(\mathbf{b}, \mathbf{c}) \leq d(\mathbf{a}, \mathbf{c}) + d(\mathbf{b}, \mathbf{b})$
\end{itemize}
Since, all above conditions are true for $d(\cdot)$, therefore, it is a valid similarity metric.
\end{proof}

\noindent
\textbf{Relation between Dot-product and Gaussian Similarity:} The proposed Gaussian similarity measure is related to the dot-product as follows:
\begin{align}\label{eq:relation}
   d(\textbf{f}_i, \textbf{w}_j) &= \exp\Big(-\frac{\parallel\mathbf{f}_i\parallel^2 + \parallel\mathbf{w}_j\parallel^2 - 2  \langle\textbf{w}_j, \mathbf{f}\rangle}{\sigma}\Big), \\
   \langle\textbf{w}_j, \mathbf{f}\rangle & = \frac{\sigma \log d(\textbf{f}_i, \textbf{w}_j)  + \parallel\mathbf{f}_i\parallel^2 + \parallel\mathbf{w}_j\parallel^2 }{2}
\end{align}
Intuitively, the above relation implies the dependence of soft-max loss on the scale/magnitude of feature vectors and class prototypes. It leads to two conclusios:
(1)  It can be seen that $d(\textbf{f}_i, \textbf{w}_j)$ is bounded between $[0,1]$ since $\parallel\mathbf{f}_i\parallel^2 + \parallel\mathbf{w}_j\parallel^2 \geq 2  \langle\textbf{w}_j, \mathbf{f}\rangle$, while $\langle\textbf{w}_j, \mathbf{f}\rangle$ can have large magnitudes. (2) The Gaussian measure can be considered as an inverse chord distance when magnitudes of vectors are normalized to be equal. The dot product in that case is directly proportional to the Gaussian similarity and both similarity measures will behave similarly if no additional constraints are included in our proposed similarity measure. However, the main flexiblity with our formulation is the explicit introduction of margin constraints, which we introduce next.

\noindent
\textbf{Enforcing margin between classes:}
 Note that some variants of soft-max loss introduce angle based margin constraints \cite{liu2017sphereface,deng2018arcface}, however, the margins in angular domain are computationally expensive and implemented only as approximations due to intractability. Our formulation allows a more direct margin penalty in the loss function. The proposed max margin loss function based on  Eq.~\ref{eq:gaussian} is given by,
\begin{align}\label{eq:maxmarg}
    L_{mm} = \sum_{j} \max \big(0, \lambda + d(\textbf{f}_i, \textbf{w}_j) - d(\textbf{f}_i, \textbf{w}_{y_i})\big) \; : j \neq y_i,
\end{align}
where $d(\textbf{f}_i, \textbf{w}_j)$ is the similarity of the sample with its true class, $d(\textbf{f}_i, \textbf{w}_{y_i})$ is its similarity with other classes, and $\lambda$ is the enforced margin.

\noindent
\textbf{Uniform classification regions:} The soft-max loss does not ensure uniform classification regions for all classes. As a result, undersampled minority classes get a shrinked representation in the feature space compared to more frequent classes. To ensure equi-distant weight vectors, we propose to apply a regularization on the  learned class weights. This regularizer is termed as a `\emph{diversity regularizer}' as it enforces all class centers ($\mathbf{w}$) to be uniformly spread out in the feature space. The diversity regularizer is formally defined as follows:
\begin{align}
R(\mathbf{w}) &= \mathbb{E}[(\parallel\mathbf{w}_j- \mathbf{w}_k\parallel^2 - \mu)^2], \; s.t.\, j < k,\\
\mu &= \frac{2}{C^2-C} \sum\limits_{j<k} \parallel\mathbf{w}_j-\mathbf{w}_k\parallel^2
\end{align}
where $\mu$ is the mean distance between all class prototypes.

\textbf{Multi-centered learning:} For challenging classification problems, the feature space may be partitioned such that all samples belonging to the same class are not co-located in a single region. Therefore, clustering all same class samples with a single prototype (class center) will not be optimal in such cases. To resolve this limitation, we introduce a novel multi-centered learning paradigm based on our max-margin framework. Instead of learning a single projection vector $\mathbf{w}_j$ for each class, the proposed framework enables learning multiple projection vectors $\{\mathbf{w}_{t}\}_j$ per-class. Specifically, we can learn $m$ projection vectors per class, where similarity of a feature vector $\textbf{f}_i$ with a class $j$ is given by:
\begin{align}
   d(\textbf{f}_i, \textbf{w}_j) =\max \big\{\exp\big(-\frac{\parallel\textbf{f}_i-\textbf{w}_{j,t}\parallel^2}{\sigma}\big)\big\}, t=[1, m]
\end{align}
Max-margin loss is then defined similar to Eq.~\ref{eq:maxmarg} above. The overall loss function therefore becomes:
\begin{align}
    L = L_{mm} + R(\mathbf{w}).
\end{align}
The diversity regularizer for the multi-center case is enforced on the similarity between all $m*C$ prototypes.

\section{Experiments}

\begin{figure}
    \centering
    \includegraphics[width=\columnwidth,keepaspectratio=True,trim=3em 18em 3em 1em, clip=true]{figs/Data_Fig.pdf}
    \caption{Data Imbalance due to long-tail distribution.}
    \label{fig:data_plot}
\end{figure}

To demonstrate the effectiveness of the proposed affinity loss, we perform experiments on datasets which exhibit natural imbalance. These include Dermofit Image Library (DIL) for skin lesion classification and large scale image datasets for facial verification. We further extensively evaluate various components of the proposed approach by systematically generating imbalance and introducing different levels of label noise. Through these empirical evaluations, we provide an evidence of the robustness of the proposed method against different data imbalance levels and noisy training labels. A brief description about the evaluated datasets is presented next.

\subsection{Datasets}

 \noindent \textbf{Skin Melanoma Dataset (DIL):}
 Edinburgh Dermofit Image Library (DIL) contains $1300$ images belonging to $10$ skin lesion categories including melanomas, seborrhoeic keratosis and basal cell carcinomas. The images are based upon diagnosis from dermatologists and dermatopathologists. The number of images vary amongst categories (between $24$ and $331$, mean $130$, median $83$), and show significant imbalance, with $50\%$ of all images belonging to only top two classes (Fig.~\ref{fig:data_plot}). Similar to \cite{ballerini2013color}, we perform two experiments, considering five and ten class splits respectively, and report results for 3-fold cross validation.

\noindent \textbf{Face Recognition: }
Datasets used to train large scale face recognition models have natural imbalance. This is because the data is web-crawled, and images for some identities are easily available in abundance compared with others. For unconstrained face recognition, we train our model on VGG2 \cite{Cao18}, which is a large scale dataset with inherent class imbalance. We evaluate the trained network on four different datasets. These include two popular widely used benchmarks i.e., Labelled Faces in the Wild (LFW) \cite{LFWTechUpdate} and YouTube Faces (YTF) \cite{wolf2011face}. We further evaluate on Celebrities in Frontal Profile (CFP) \cite{cfp-paper} and Age Database (AgeDB) \cite{AgeDB}.

\noindent \textbf{VGG2:} facial image dataset \cite{Cao18} contains $3.31$ million images belonging to $8,631$ identities. The number of samples for each subject exhibit imbalance and vary from $80$ to $843$ with a mean of $362$. The data is collected from the Internet and has real-life variations in the form of ethnicites, head poses, illumination changes and age groups.

\noindent \textbf{LFW:} Labelled Faces in the Wild (LFW) \cite{LFWTechUpdate} contains $13,233$ static images of $5749$ individuals collected over the Internet in real-life situations. We follow the standard evaluation protocol `unrestricted with labeled outside data' \cite{LFWTechUpdate} and test on $6000$ pairs for face verification.

\noindent \textbf{YTF:} YouTube Faces (YTF) \cite{wolf2011face} has $3425$ videos belonging to $195$ different subjects. The length of video sequences varies between $48$ and $6070$ frames, with an average of $181.3$ frames. We follow the standard evaluation protocol for face verification on $5000$ video pairs.

\noindent \textbf{CFP:} contains $10$ frontal and $4$ profile view images for $500$ different identities \cite{cfp-paper}. Two evaluation protocols are used based upon the type of images in the gallery and probe: frontal-frontal (FF) and frontal-profile (FP). Each protocol has 10 runs, each with 700 face pairs (350 same and 350 different).

\noindent \textbf{AgeDB:} has $12,240$ images acquired in-the-wild for $440$ subjects \cite{AgeDB}. Along with variations across expression deformations, head poses and illumination conditions, a distinct feature of this dataset is the diversity across ages of the subjects, which ranges between $3$ and $101$ years, with an average of $49$ years. Test evaluation protocol has four groups with different age gaps (5, 10, 20 and 30 years). Each group contains ten splits, each having 600 face image pairs (300 same, 300 different). We use the most challenging split with 30 years gap.

 \noindent \textbf{Imbalanced MNIST:} Standard MNIST has $70,000$ handwritten images of digits (0-9), $60,000$ of these images are used for training (∼600/class) and the remaining 10,000 for testing (∼100/class). For this paper, we perform experiments on the standard evaluation split, as well as by systematically creating imbalance in the training set. For this, we reduce the even and odd digit samples to 10\% and 25\%. We further perform ablative study (Sec.~\ref{ablation}) by gradually introducing different imbalance ratios amongst classes and noise levels in the training labels

\subsection{Experimental Settings}

For experiments on DIL dataset, ResNet-18 backbone is used in combination with the proposed affinity loss.  For training the model to learn features for face verification tasks, we deploy Squeeze and Excitation (SE) networks \cite{hu2017squeeze} with ResNet-50 backbone and affinity loss. The face images are cropped and re-sized to $112\times112$ using multi-task cascaded Convolution Neural Network (CNN) \cite{7553523}. The model is trained using random horizontal flips as data augmentation.  The features extracted after the global pooling layer are then used for face verification evaluations on different datasets. The experiments on MNIST are performed on a simple network with four hidden layers having three convolution layers ($32$, $64$ and $128$ filters of $5\times5$), one fully connected layer ($128$ neurons), and an output layer. The model is trained with Stochastic Gradient Descent (SGD) optimizer with momentum and learning rate decay. For ablative study in Sec.~\ref{ablation}, we only change the output soft-max layer with the proposed Affinity loss layer and keep rest of the architecture fixed.

\subsection{Results and Analysis}

Table.~\ref{tab:DILexp} present our experimental results on DIL dataset. In Exp\#1, we report average performance for 3 fold cross validation on five classes (Actinic Keratosis, Basal Cell Carcinoma, Melanocytic Nevus, Squamous Cell Carcinoma and Seborrhoeic Keratosis). Compared with existing state of the art \cite{khan2017cost}, we achieve an absolute gain of $10.9\%$ on Exp\#1. For Exp\#2 on DIL dataset, all $10$ classes are considered. Evaluations on 3 fold cross validation in Table~\ref{tab:DILexp} show a significant performance improvement of $7.7\%$ for Exp\#2. Confusion matrix analysis for class-wise accuracy comparison in Fig~\ref{fig:conf_mat} shows that the performance boost is more pronounced for minority classes with lower representations. We attribute this to the capability of the proposed method to simultaneously optimize within class compactness by performing feature space clustering, and enhance inter-class separability by enforcing max-margin constraints. Our method achieves competitive performance on LFW and YTF datasets in Table~\ref{tab:lfw_ytf}. The performances on LFW and YTF are already saturated with many recent methods surpassing human-level results. The top performing methods on these datasets have been trained on much larger models with significantly more data and model parameters. Further evaluations on other facial recognition benchmarks achieve verification accuracies of 95.9\%, 99.5\% and 96.0\% on AgeDB30, CFP-FF and CFP-FP datasets respectively. These results prove the effectiveness of the proposed approach for large scale imbalanced learning. It is worth noting that our proposed Affinity loss does not require additional compute and memory and is easily scalable to larger datasets. This is in contrast to some of the existing loss formulations (such as triplet loss \cite{schroff2015facenet} and contrastive loss \cite{hadsell2006dimensionality}) which do enhance feature space discriminability, but suffer scalability to large data due to substantial possible combinations of training pairs, triplets or quintuplets.

\begin{table}
\centering
\scalebox{.76}{
\begin{tabular}{@{}lcc@{}}
  \toprule[0.4mm]
   \textbf{Methods} (using stand. split) &  \multicolumn{2}{c}{\textbf{Performances}}  \\
  \midrule
  Deeply Supervised Nets \cite{lee2015deeply} & \multicolumn{2}{c}{{99.6\%}}\\
  Generalized Pooling Func. \cite{lee2016generalizing} & \multicolumn{2}{c}{99.7\%} \\
  Maxout NIN \cite{chang2015batch} & \multicolumn{2}{c}{\textbf{99.8\%}} \\
  \midrule
  \textbf{Imbalanced} ($\downarrow$) &  CoSen CNN \cite{khan2017cost} & Affinity Loss \\
  \midrule
  Stand. split &  {99.3\%} & \textbf{99.6\%}\\

   10\% of  odd digits   & {98.6\%} & \textbf{99.3\%} \\

   10\%  of  even digits    & {98.4\%} & \textbf{99.3\%}\\

   25\%  of  odd digits  & {98.9\%}  & \textbf{99.4\%} \\

  25\% of  even digits  & {98.5\%} & \textbf{99.5\%} \\
  \bottomrule[0.4mm]
  \vspace{0.1mm}
\end{tabular}}
\caption{Evaluations on Imbalanced MNIST Database.}\vspace{-0.2cm}
\label{tab:MNISTexp}
\end{table}

\begin{table}
\centering
\scalebox{.85}{
\begin{tabular}{@{}lcc@{}}
  \toprule[0.4mm]
  \textbf{Methods}  &  \multicolumn{2}{c}{\textbf{Performances}}  \\

  (using stand. split) & Exp\#1 (5-classes) & Exp\#2 (10-classes) \\
  \midrule
  Hierarchical-KNN \cite{ballerini2013color} &{74.3 $\pm$ 2.5\%}  & 68.8 $\pm$ 2.0\%\\
  Hierarchical-Bayes \cite{ballerini2012non} & {69.6 $\pm$ 0.4\%} & 63.1 $\pm$ 0.6\% \\
  Flat-KNN \cite{ballerini2013color} & {69.8 $\pm$ 1.6\%} & 64.0 $\pm$ 1.3\%\\
  \midrule

  CoSen CNN \cite{khan2017cost}  & \sbest{80.2 $\pm$ 2.5\% }& \sbest{72.6 $\pm$ 1.6\%}  \\
 Affinity Loss & \best{91.1 $\pm$ 1.7\%} & \best{80.3  $\pm$ 2.1\%} \\
  \bottomrule[0.4mm]
  \vspace{0.1mm}
\end{tabular}
}
\caption{Evaluation on DIL Database.}\vspace{-0.2cm}
\label{tab:DILexp}
\end{table}

\begin{figure}
    \centering
    \begin{tabular}{cc}
    \includegraphics[width=.5\linewidth,keepaspectratio=True,trim=0em 0em 4em 2em, clip=true]{figs/tnnls.png}&
    \includegraphics[width=.5\linewidth,keepaspectratio=True,trim=0em 0em 4em 2em, clip=true]{figs/ours.png}  \\
    {\footnotesize (a) CosSen CNN \cite{khan2017cost}} & {\footnotesize (b) Affinity Loss }
    \end{tabular}
    \caption{Confusion matrices for Exp\#1 on DIL dataset. }
    \label{fig:conf_mat}
\end{figure}

\begin{table}[]
\centering
 \resizebox{.99\columnwidth}{!}{
\begin{tabular}{l c c c c}
\toprule[0.4mm]
\textbf{Methods}  &  \#\textbf{Models}  &  \textbf{Train Data}  & \textbf{LFW}             & \textbf{YTF} \\
  \midrule
DeepFace \cite{taigman2014deepface}   &  3  &  4M  & 97.35 & 91.4           \\
FaceNet \cite{schroff2015facenet}     &  1  & 200M & 99.63 & 95.4           \\
Web-scale \cite{taigman2015web}       &  4  & 4.5M & 98.37 & -              \\
VGG Face \cite{parkhi2015deep}        &  1  & 2.6M & 98.95 & 97.3           \\
DeepID2+ \cite{sun2015deeply}         &  25 & 0.3M & 99.47 & 93.2           \\
Baidu  \cite{liu2015targeting}        &  1  & 1.3M & 99.13 & -              \\
Center Face \cite{wen2016discriminative} & 1 & 0.7M & 99.28 & 94.9           \\
Marginal Loss \cite{deng2017marginal} &  1  & 4M & 99.48 & 95.98          \\
Noisy Softmax \cite{chen2017noisy}  &  1  & Ext. WebFace & 99.18  & 94.88          \\
Range Loss  \cite{zhang2017range}   &  1  &  1.5M & 99.52 & 93.7           \\
Augmentation \cite{masi2016we}      &  1  & WebFace & 98.06  & -              \\
Center Invariant Loss   \cite{wu2017deep}  &  1  & WebFace & 99.12 & 93.88   \\
Feature transfer  \cite{yin2018feature}    &  1  & 4.8M    & 99.37 & -   \\
Softmax+Contrastive \cite{sun2014deep} &  1   & WebFace   & 98.78  & 93.5           \\
Triplet Loss  \cite{schroff2015facenet} & 1   & WebFace  & 98.7  & 93.4           \\
Large Margin Softmax  \cite{liu2016large} & 1 & WebFace  & 99.10 & 94.0 \\
Center Loss  \cite{wen2016discriminative}   &  1  & WebFace & 99.05  & 94.4   \\
SphereFace  \cite{liu2017sphereface}        &  1  & WebFace & 99.42  & 95.0  \\
CosFace  \cite{Wang_2018_CVPR}   & 1  & WebFace  & 99.33    & 96.1           \\
LMLE  \cite{huang2016learning} &   1   & WebFace  & {99.51}    & 95.8          \\
Affinity Loss   &  1  & VGG2 & \textbf{99.65}    &  \textbf{97.3}              \\
\bottomrule[0.4mm]
\vspace{0.1mm}
\end{tabular}}
\caption{Face Verification Performance on LFW and YTF datasets.}\vspace{-0.2cm}
\label{tab:lfw_ytf}
\end{table}

\begin{figure}
    \centering
    \includegraphics[width=\columnwidth, trim=0.5em 0.5em 0.5em 0.5em, clip=true]{figs/Plot4.png}
    \caption{The effect of label noise on the soft-max and affinity loss functions. }
    \label{fig:noise}
\end{figure}

\subsection{Generalization}
To test the generalization of the proposed method for different imbalance levels, we gradually reduce the training set by changing the representation of the minority class samples on MNIST data. Specifically, we gradually alter the majority to minority class ratios (up-to $1:0.025$) by randomly dropping samples of the first five digits ($0-4$). Under these settings, we therefore have significantly lower representation for half of the classes. The experimental results in terms of error rates against fraction of retained minority class samples are shown in Fig.~\ref{fig:ratios_minority}. We also repeat these experiments for standard soft-max loss. The comparison in Fig.~\ref{fig:ratios_minority} demonstrates a consistently superior performance of the proposed loss function across all settings. The effect on achieved performance is more noticeable for larger imbalance levels between majority and minority classes. The proposed Affinity loss enhances inter-class separability irrespective of the class frequencies by enforcing margin maximization constraints. Soft-max loss does not have inherent margin learning characteristics. Further, compared with soft-max loss, where intra-class variations can vary across classes depending upon their representative samples, affinity loss learns uniformed sized clusters.
As visualized in Fig.~\ref{fig: vis}, feature space within class disparities are observed for soft-max loss with minority classes occupying compact regions compared with their majority counterparts. In comparison, our proposed loss formulation is flexible, and allows learnt class prototypes to be equi-spaced and form uniformly shaped clusters. This reduces bias towards the less frequent cases and enhances the overall generalization capabilities, thus yielding a more discriminatively learnt feature space and an improved performance

\begin{figure}
\centering
\includegraphics[width=0.8\linewidth,keepaspectratio=True,trim=1em 1.5em 1em 1em, clip=true]{figs/Plot3.png}
\caption{Robustness analysis against different imbalance levels (fraction of retained minority class samples)}
\label{fig:ratios_minority}
\end{figure}

\subsection{Robustness against Noisy Labels} \label{Robustness against Noisy Labels} For many real-world applications, the acquired data has noisy labels, and generalization of the learning methods against label noise is highly desirable \cite{sheng2008get,vinyals2016matching,huang2018discriminative}. To check the robustness of our proposed approach against noisy labels in the training data, we randomly flip the classes of MNIST training samples. The fraction of the miss-labelled samples is gradually increased from $10\%$ to $50\%$ with an increment of $10\%$. In order to avoid over-fitting on the noisy data, we deploy early stopping \cite{yao2007early}, and finish training when the performance on a held-out cross validation set starts to degrade. For comparison, we repeat all experiments using standard soft-max loss. The experimental results in Fig.~\ref{fig:noise} show that that the proposed Affinity loss performs better across the entire range of different noise levels. Although, the performance for both soft-max and affinity losses degrades with increasing noise factors, the proposed affinity loss shows more robustness, specially for larger noise ratios, with comparatively less performance degradation. The multi-centered learning in our loss provides flexibility to the noisy samples to associate themselves with class prototypes which are different from the non-noisy and clean samples.

\begin{SCfigure}
    \centering
    \includegraphics[width=.5\linewidth, trim=1em 0.5em 1em 0.5em, clip=true]{figs/Plot1.png}
    \caption{Effect of changing parameter $\sigma$ that controls the spread of clusters. Results on imbalanced MNIST show that increasing the cluster variance above a certain point results in overlapped clusters and higher error rate. }
    \label{fig:sigma}
\end{SCfigure}

\subsection{Ablation}
\label{ablation}

\noindent \textbf{Number of Cluster Centers:} A unique aspect of the proposed affinity loss is its multi-centered learning which provides us the flexibility to have multiple class prototypes for each class. Here, we perform experiments on the imbalanced MNIST dataset ($10\%$ representation for first five digits), by gradually changing the number of representative prototypes $m$ per class from $1$ to $20$. The experimental results in terms of error rates vs prototypes $m$ in Fig.~\ref{fig:clusters_differet} show that the best performance is achieved for $m \geq 5$. Fewer prototypes per class ($m \leq 5$) yield relatively poor performance. The proposed method performs consistently when prototypes are increased beyond $5$. Such multi-centered learning supports diversity in input samples. It is specifically helpful in scenarios with complex data distributions where large differences are observed amongst samples of the same class. Such diverse samples might not necessarily cluster around a single region, and could form multiple clusters by virtue of the proposed multi-centered learning mechanism. Furthermore, our experiments in Sec.~\ref{Robustness against Noisy Labels} show that by providing flexible class prototypes, multi-centered learning proves an effective and robust scheme against noisy samples.

\begin{figure}
    \centering
    \includegraphics[keepaspectratio=Ture,width=\linewidth,trim=1em 0.5em 1em 0.5em, clip=true]{figs/Plot2.png}
    \caption{Performance for different number of clusters per class.}
    \label{fig:clusters_differet}
\end{figure}

\noindent \textbf{Cluster Spread $\sigma$:} The parameter $\sigma$ in Eq.~\ref{eq:gaussian} determines the cluster spread and helps achieve uniform intra-class variations. Our 2D visualization of the learnt features in Fig.~\ref{fig: vis} demonstrate that the clusters for each class are uniformly sized for both the majority and minority classes. This is in contrast to the traditional soft-max loss, where shrinked feature space regions are observed for the minority classes. For our proposed loss formulation, the size of the cluster is directly related with the value of parameter $\sigma$, with larger $\sigma$ indicating larger variance for a cluster. We perform experiment on imbalanced MNIST dataset for different values of the the parameter $\sigma=\{.1,.5,1,5,1e1,2e1,5e1\}$. The results in Fig.~\ref{fig:sigma} show that the optimal performance is achieved for values of $\sigma$  between $5$ and $20$. Very high values of $\sigma$ results in larger cluster spreads causing overlaps and confusion amongst classes and lower classification performance.

\begin{table}[]
\centering
\scalebox{.95}{
\begin{tabular}{llc}
 \toprule[0.4mm]
 Distance $d$ & Similarity &Performance  \\
 \midrule
$||a-b||^1$ & $\exp{-(d/\sigma})$ & $99.3$\\
$||a-b||^2$ & $\exp{-(d/\sigma})$ & $99.3$ \\
$||a-b||^1$ & $1/(1+d)$ & $-$\\
$||a-b||^2$ & $1/(1+d)$ & $99.1$\\

\bottomrule

\end{tabular}}
\vspace{0.5em}
\caption{Evaluation with different combinations of distance and similarity measures.}
\label{tab:comb_sim_dis}
\end{table}

\noindent \textbf{Distance and Similarity Metrics:} Our original affinity loss formulation in Eq.~\ref{eq:gaussian} first computes the squared $\ell_2$ distance between the feature $\textbf{f}$ and class prototype $\textbf{w}$, which is then converted to a similarity measure using the Gaussian metric. In this experiment, we evaluate different combinations of distance and similarity metrics. $\ell_1$ and $\ell_2$ metrics are used to compute distances, whereas Gaussian and inverse distance (defined by $\frac{1}{1+x}$) are the two similarity measures. We perform these experiments on imbalanced MNIST data (by retaining 10\% samples for first five digits). Table.~\ref{tab:comb_sim_dis} shows our evaluation results. The proposed scheme works well with all combinations except for $\ell_1$ distance and Gaussian similarity, where it fails to converge. The best performance is achieved for Gaussian similarity in combination with squared $\ell_2$ distance.

\section{Conclusion}

Class imbalance is ubiquitous in natural data  and learning from such data is an unresolved challenge. The paper proposed a flexible loss formulation, aimed at producing a generalizable large margin classifier, to tackle class imbalance learning using deep networks. Based upon Euclid space affinity defined using Gaussian similarity on Bregmen divergence, the proposed loss jointly performs feature space clustering and max-margin classification. It enables learning uniform sized equi-spaced clusters in the feature space, thus enhancing between class separability and reducing intra-class variations. The proposed scheme complements existing regularizer such as weight decays, and can be incorporated with different architectural backbones without incurring additional compute overhead. Experimental evaluations validate the effectiveness of the affinity loss for face verification and image classification benchmarks involoving imbalanced data.

{\small
\bibliographystyle{ieee}

redpajama_set_name: RedPajamaArXiv


Hi! I am absolutely in love with the new Woodland Friends collection from FabScraps! My girls like this line even more than I do, so I created them a little keepsake box the store some of their trinkets.
A simple coat of paint and a clear sticker transformed the wood base of this box from plain to pretty.
The lid of this box was thin, clear plastic, so I piled on the layers of patterned paper, cardstock, and chipboard to help add some strength.
If you'd like the full supply list and/or detailed instructions for this project, please stop by the FabScraps blog.


Senators voted 95 to 3 Wednesday to restore military retiree benefits cut last year as part of a compromise budget deal, adopting a House bill that covers the move by extending reductions to Medicare.
The rollback of the military pension benefits — which, according to the Congressional Research Service, would strip a typical military enlisted person of about $69,000 in lifetime retirement benefits — is expected to cost about $6 billion.
The House voted Tuesday to restore the cost-of-living adjustment to military retirees who are younger than 62. The trim had come as part of December's bipartisan budget deal, and with veterans groups leading the charge, had been under bipartisan attack ever since.
House Republicans had floated the idea of tying the repeal of military pension cuts to a bill raising the federal government's borrowing authority, but ultimately passed a debt-ceiling increase free of riders.
The three senators who voted against the bill were Thomas R. Carper (D-Del.), Daniel Coats (R-Ind.) and Jeff Flake (R-Ariz.) .
The original Senate bill was sponsored by Sen. Mark Pryor (D-Ark.), who is facing a difficult reelection in November. Pryor's bill, which was set aside for the House legislation, would have restored the COLA benefits without replacing them with other budget trims.
The House bill that passed Tuesday also extended sequestration levels for Medicare spending — an unpopular provision among Democrats.
Before Wednesday's vote, the Senate set aside several controversial amendments offered as offsets to make the restoration deficit-neutral.
Some Republicans had flocked to a proposed amendment from Sen. Kelly Ayotte (R-N.H.) that would have covered the COLA by no longer allowing illegal immigrants to claim a child tax credit.
That proposal had been panned by the Democratic leadership and drew fire from Spanish-language media for much of the week.
"It's pretty clear . . . that amendment is so ill-advised and so unfair," Reid said Tuesday.
Democrats' proposals for paying for the benefit restoration included an amendment that would offset the costs by closing an offshore tax loophole — a measure brought by Sen. Jeanne Shaheen (D-N.H.) and supported by several Senate Democrats, including both senators from military-rich Virginia — which was seen as a nonstarter with Republicans.
In a separate amendment to the COLA bill, Sen. Rob Portman (R-Ohio) and Coats sought to provide a three-month extension to emergency unemployment benefits — which the Senate has already tried, and failed, to pass twice this year.
The cuts to military benefits were an unpopular provision of the budget deal, and lawmakers on both sides of the aisle have lobbied for the pension cuts to be restored.
Perhaps the most notable exception to the largely bipartisan support for restoring the benefits came from Rep. Paul Ryan (R-Wis.), who helped broker the budget deal in December.
In a statement issued after he cast one of the 19 Republican votes against the House bill to restore the benefits cuts, Ryan blasted the legislation as a way of dodging needed reforms to military retirement compensation.
The Veterans of Foreign Wars issued a statement praising the vote.


Mention edtech, and one of the first thoughts that comes to mind is collecting data to evaluate student progress. And according to experts who participated in a recent webinar, getting that data to educators so they can develop a more effective and personalized learning plan for each student is a crucial goal.
During a webinar hosted last month by edWeb.net, called "Get Smart with EdTech: Track Usage on Every Device," presenters Jeremy Bunkley, director of information technology services for the School District of Clay County, Florida, and Leo Brehm, product manager for CatchOn, an edtech software company, explained that understanding which technologies are being used in the classroom is one of the most important pieces of the edtech data equation.
1. Do you know all of the applications that are being used in your district?
Starting from the early days of online education tools, teachers have been downloading their favorite tools and sharing them with students. If asked what resources they are using, however, many will leave a few off of the list for fear that they will be told to stop using them. Thus, while a school may think it has 20 official edtech tools in the classroom, the unofficial list could be over 200.
This can cause several issues, from inequity in how students are taught to network issues as teachers access their personal playlists. In addition, professional development leaders can't possibly provide appropriate professional learning on using tools in the classroom when no two teachers use the same ones. Schools and districts need to have a system that collects data on all programs being used so that leadership can get a clearer picture of what's happening in the classroom.
2. Do you know how these applications are being used?
Tracking usage data can further help leaders refine professional development and understand where there may be a disconnect between teachers and school leaders. For example, if the school has adopted a specific reading program for K-3, and the teachers are using supplemental materials more than the program, the teachers might need further training on the adopted program. Data can also show where programs may be contributing to student success and where the resources show minimal impact.
3. Do you know if any of these applications violate student data privacy?
Sometimes, even approved edtech tools may have an add-on that doesn't follow your school's privacy standards. More important, when educators can download and add digital resources on their own, they may be giving companies unintended access to private student information.
Finally, school leaders should help teachers understand why they can't just download the latest fad, especially in light of current school budgets.
Jeremy Bunkley is the director of information technology services at the School District of Clay County in Northeast Florida. The district serves approximately 35,000 students and is comprised of 48 schools, including an adult education program and alternative schools. Since Jeremy's arrival a few years ago, he has rapidly transformed Clay's technology staff into a 21st century IT department that works closely with its digital instruction team to produce positive outcomes in the district's classrooms. He's formed a tight-knit team that shares a unified vision for Clay's students. Leo Brehm is an accomplished senior executive, consultant, board member, and thought leader with more than 22 years of success in the education and e-learning industries. Today, Leo serves as the product manager for CatchOn, Inc. He has been actively involved in the design and development of the CatchOn API, which empowers school districts to use data in aggregate with student performance, leveraging dashboarding business intelligence tools. He has worked for the Public Schools of Northborough and Southborough, Massachusetts as director of technology and digital learning; Newton, MA Public Schools as Director of IT and Library Media; Sharon, Massachusetts Public Schools as director of technology; and as a consultant at Creativemindz Consulting Group LLC. He has a proven history of dedication to the advancement of learning through a schooling model that is both student-centered and personalized.
Monica Cougan joined Education Networks of America in 2012 and oversees ENA's partner program and product marketing initiatives in her role as product marketing manager. Monica has more than 30 years of experience in education technology integration that she leverages to help assess, identify, and support the needs of today's education and library communities. She has extensive experience in building collaborative network opportunities which include assisting in project development, professional development, implementation and training, and the evaluation of program implementation. An educator at heart, Monica is a former middle school mathematics teacher and technology coordinator who has had teaching experience in grade levels ranging from preschool through college.
This broadcast was hosted by edWeb.net and sponsored by Education Networks of America and CatchOn . A recording of the webinar can be found here .


This class will introduce participants to various photography techniques that will allow them to expand their skill set and hone their ability to capture nature. Local photographer Andrew Doiron will introduce you to equipment used for wilderness photography and how you can learn to shoot such things as waterfalls and night skies to create compelling images. Learn how to do basic composition techniques and what to look for in a good photograph. Come check out his work at andydoiron.com/gallery/my-photography.


Volvo Penta's V6-280 engine is powering the sailing teams' RIBs in the Volvo Ocean Race 2017-18.
A highly efficient low-emission gasoline engine by Volvo Penta has been selected to power the rigid inflatable boats (RIBs) for use in the Volvo Ocean Race 2017-18. The next generation V6-280 sterndrive has been chosen as it provides excellent power and maneuverability, and low emissions. It is the cleanest engine available on the market in the gasoline segment.
The RIBs are used by each of the seven teams in the competition, to transport crew, equipment and guests from the Race Village ports to the guest sailing catamarans. Every team has two RIBs and each boat is used in an alternate port, so that the teams' guest sailing activities can be carried out quickly at the stopovers on the route around the world.
The V6-280 DPS aquamatic sterndrive is an integrated package comprised of a 6-cylinder, 4.3-liter, freshwater-cooled engine and duoprop twin counter-rotating propellers. The compact and lightweight unit has two catalytic converters, direct fuel injection, and variable valve timing, to ensure high fuel efficiency and optimum power and low emissions throughout the whole operating range. It provides excellent handling and impressive acceleration, and hydrocarbon (HC²) and nitrogen oxide (NOx) emissions are around 90 per cent lower than a typical outboard engine.
For the Volvo Ocean Race, the RIBs are also equipped with Volvo Penta's electronic vessel control (EVC; a complete system from helm to prop which links engines with functional controls and software), and Glass Cockpit (a system that integrates operational and monitoring controls in one customizable screen interface).


text:
Georges Bataille - Bibliography - Secondary Literature
Ades, Dawn, and Simon Baker, Undercover Surrealism: Georges Bataille and Documents. (Cambridge: The MIT Press, 2006).
Barthes, Roland. "The Metaphor of the Eye". In Critical Essays. Trans. Richard Howard. (Evanston, IL: Northwestern University Press, 1972). 239-248.
Blanchot, Maurice. "The Limit-Experience". In The Infinite Conversation. Trans. Susan Hanson. (Minneapolis: University of Minnesota Press, 1993). 202-229.
Blanchot, Maurice. The Unavowable Community. Trans. Pierre Joris. (Barrytown, NY: Station Hill Press, 1988).
Derrida, Jacques, "From Restricted to General Economy: A Hegelianism without Reserve," in Writing and Difference (London: Routledge, 1978).
Foucault, Michel. "A Preface to Transgression". Trans. Donald F. Bouchard and Sherry Simon. In Aesthetics, Method and Epistemology: Essential Works of Foucault, 1954-1984. Ed. James D. Faubion (New York: New Press, 1998). 103-122.
Hussey, Andrew, Inner Scar: The Mysticism of Georges Bataille (Amsterdam: Rudopi, 2000).
Krauss, Rosalind, No More Play in The Originality of the Avant-Garde and Other Modernist Myths (MIT Press, 1985).
Nancy, Jean-Luc, The Inoperative Community (Minneapolis & Oxford: University of Minnesota Press, 1991).
Roudinesco, Elisabeth, Jacques Lacan & Co.: a history of psychoanalysis in France, 1925-1985, 1990, Chicago: Chicago University Press.
Roudinesco, Elisabeth, Jacques Lacan, Outline of a Life, History of a System of Thought, 1999, New York, Columbia University Press.
Roudinesco, Elisabeth, Our Dark Side, A History of Perversion, Cambridge, Polity Press, 2009.
Sollers, Philippe, Writing and the Experience of Limits (Columbia University Press, 1982).
Sontag, Susan. "The Pornographic Imagination." Styles of Radical Will. (Picador, 1967).
Surya, Michel, Georges Bataille: an intellectual biography, trans. by Krzysztof Fijalkowski and Michael Richardson (London: Verso, 2002).
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