text:
How many must die? Pope blasts Russia war, appeals for peace
AP , Wednesday 14 Sep 2022
Pope Francis told the Russian Orthodox hierarchy and other faith leaders Wednesday that religion must never be used to justify the ``evil'' of war, and asked at an outdoor Mass in Kazakhstan, ``How many deaths will it take?'' for peace to prevail in Ukraine.
Pope Francis speaks at the 7th Congress of Leaders of World and Traditional Religions, in Nur-Sultan, Kazakhstan, Wednesday, Sep. 14, 2022. Pope Francis is on the second day of his three-day trip to Kazakhstan. AP
Ukraine's Zelensky visits recently retaken, devastated city
EU chief Von der Leyen to visit Ukraine to stress support
Ukraine shifting balances
An increasingly frail Francis made the appeal during his first full day in the former Soviet republic of Kazakhstan, where he opened a global interfaith conference and ministered to the tiny Catholic community in the majority Muslim country.
In the conference audience of imams, patriarchs, rabbis and muftis was Metropolitan Anthony, in charge of foreign relations for the Russian Orthodox Church, which has firmly backed Russia's invasion of Ukraine.
His boss, Patriarch Kirill, was supposed to have participated in the congress but canceled last month.
Kirill has supported Russia's invasion on spiritual and ideological grounds, calling it a ``metaphysical'' battle with the West. He has blessed Russian soldiers going into war and invoked the idea that Russians and Ukrainians are one people.
Francis didn't mention Russia or Ukraine in his remarks to the Kazakh conference. But he insisted that faith leaders themselves must take the lead in promoting a culture of peace, since it would be hypocritical to expect that non-believers would promote peace if religious leaders don't.
``If the creator, to whom we have devoted our lives, is the author of human life, how can we who call ourselves believers consent to the destruction of that life?'' he asked.
``Mindful of the wrongs and errors of the past, let us unite our efforts to ensure that the Almighty will never again be held hostage to the human thirst for power.''
Francis then laid down a challenge to all those in the room to commit themselves to resolving disputes through dialogue and negotiation, not with arms.
``May we never justify violence. May we never allow the sacred to be exploited by the profane. The sacred must never be a prop for power, nor power a prop for the sacred!''
He made the appeal more explicit in an afternoon outdoor Mass for Kazakhstan's tiny Catholic community, in which he asked for prayers for ``beloved Ukraine.''
``How many deaths will it still take before conflict yields to dialogue for the good of people, nations and all humanity?'' he asked. ``The one solution is peace and the only way to arrive at peace is through dialogue.''
Kirill sent a message to the congress read aloud by Anthony. In it, the Russian patriarch didn't refer to the war but in general to problems over the past two decades caused by ``attempts to build a world without relying on moral values.''
The Russian patriarch has blasted the West's secular mentality and claimed the seeds of the Ukraine conflict were sown by foreign threats to Russia's borders.
He has depicted the conflict as a struggle against a foreign liberal establishment purportedly demanding countries hold ``gay parades'' as the price of admission to a world of excess consumption and freedom.
``These attempts have led not only to the loss of the concept of justice in international relations, but also to brutal confrontation, military conflicts, the spread of terrorism and extremism in different parts of the world,'' Kirill said in his message.
Suggesting he felt Russia was the victim of a smear campaign, he denounced the spread of misinformation and the ``distortion of historical facts'' and ``manipulation of mass consciousness'' to spread messages of ``hatred towards entire peoples, cultures and religions.''
In addition to the Russian Orthodox delegation, the religious leaders included Sheikh Ahmed al-Tayeb, the grand imam of Al-Azhar, the seat of Sunni learning in Cairo, who warmly greeted Francis with a kiss on the cheek when the pope arrived in a wheelchair.
Al-Tayeb used his speech to the conference to complain that traditional religions had been replaced by a culture of lust and gay marriage. ``This is not acceptable, not even for animals and beasts, let alone for people with pure hearts and sound minds,'' he said.
The Right Rev. Jo Bailey Wells, the Anglican bishop of Dorking and one of only a half dozen women heading delegations, lamented that women represent half the world's population but are hardly represented in religious leadership.
``My expectation is it will be a challenge to those present to empower women in the family context and in public society,'' she told reporters.
Before Kirill bowed out, there had been speculation that Francis could meet with Kirill on the sidelines of the congress. The two met for the first time in 2016 in Cuba, the first-ever meeting of a pope and Russian patriarch, and spoke by videoconference in the early weeks of the war.
Francis afterward publicly criticized Kirill's justification of Russia's invasion and warned that he mustn't become ``Putin's altar boy.''
Speaking to reporters after he met with Francis, Anthony said Francis' ``altar boy'' comment didn't go over well in Moscow. ``It wasn't expected and clearly it's not useful for the unity of Christians,'' he said. ``It was a surprise. But we know we have to move on.''
He said a Kirill-Francis meeting is still possible, but insisted it had to be well-prepared ahead of time and must produce a concrete joint statement, as was issued after the Havana meeting.
In addition to the meeting with Anthony, Francis also was meeting with al-Tayeb, the head of Russia's religious council of Muslims and other Orthodox, Jewish, Lutheran and Muslim leaders.
Another visitor in Kazakhstan on Wednesday was apparently not on Francis' agenda. Chinese President Xi Jinping arrived in Nur-Sultan on his first state visit outside China since early in the coronavirus pandemic.
Vatican and Kazakh officials said they didn't expect Xi would meet with the pope during his brief visit to a key economic and political ally in the region.
Also In Humanitarian
Human rights champions in Belarus, Russia, Ukraine win Nobel Peace Prize
Ukraine nuclear workers recount abuse, threats from Russians
20 dead after attack on civilian convoy in Ukraine: governor
Finland to ban entry to Russian tourists starting midnight
Over 194,000 Russians flee call-up to neighboring countries
German population hits a record 84M due to Ukraine refugees
What happens after Russia's 'referendums' in Ukraine?
EU using Med countries like a refugee 'parking lot': Greece


text:
Nighttime view of the 1892 New Orleans Hotel and Spring Street downtown
-1 - 11 ºC
Northwest Arkansas (XNA)
Boone County (HRO)
An Arkansas city with character, both outdoors and underground
Situated in the heart of the Ozark Mountains, Eureka Springs is both a historic town with lots of character and a nature-lover's paradise, with winding mountainside streets, meters of forests, and picturesque lakes and rivers. Underground, a maze of caves and caverns await exploration.
A trip to Eureka Springs' Turpentine Creek Wildlife Refuge is not to be missed. Though most of these big cats (lions, tigers and cougars) aren't native to Arkansas, or anywhere close, they are at home in recreated habitats, where you can observe them from only three meters away. There are three lakes and two rivers to explore around Eureka Springs, and Belle of the Ozarks boat service cruises over one of them, Beaver Lake, with a leisurely 20-kilometer tour. You'll pass an Indian burial ground, a wildlife sanctuary and a huge game preserve. Other popular pastimes on the waterways include sailing, swimming and skiing. Skip the mainland and go straight to the sky with Ozark Mountain Zip Lines. A 90-meter-high jag in the air that takes you through the lush Ozark forest above hardwood trees and limestone cliffs.
Underground Adventure
There's just as much going on below the Ozark landscape of rolling hills and forests, a whole rich underworld, in fact. The many caves and caverns here are considered "living" because they drip with water and are still evolving, which keeps them cool year-round. Explore this subterranean land full of lakes, mazes and rock formations at Onyx Cave Park, where spelunkers will discover cave creatures, crystals and fossils. Another way to experience the complex cave system is by sleeping in one.
Eureka Springs Treehouses, Caves, Castles and Hobbits is an accommodation like no other, offering lodgings that keep guests underground all night.
Something to Talk About
Eureka Springs has character, for sure, but the characters define the place's spirit. Like any historic town, Eureka Springs has its legends and lore, which is embodied by Quigley's Castle, the eccentric home built from scratch by the late Elise Quigley (it must be seen to be believed). Catch a show at the Intrigue Theater, where an illusionist and a medium bring some macabre fun to your night. If that's not enough, take a ghost tour at the Crescent Hotel, a stylish Victorian-era lodging reported to be the most haunted hotel in the USA.
At St. Elizabeth's Catholic Church, instead of using a door, you enter through the bell tower.
An Arkansas Cultural Tour: Where History Meets Legends of Americana
How to Explore Arkansas like an Outdoor Adventurer
Little Rock, Arkansas: A Warm Welcome to the South
Entertainment in Arkansas: From Hot Springs and Cave Tours to the Birthplace of Blues
The Mississippi Delta: Birthplace of the Blues
Clarksdale, Mississippi: Discovering Blues History
Shopping, Dining and History in Charming St. Charles
St. Charles, Missouri: 3 Days of Dining and Historic Charm
Official Eureka Springs Travel Site
eurekasprings.org

redpajama_set_name: RedPajamaCommonCrawl


Pingdom's website and performance monitoring software helps you to improve the reliability of your site and deliver a faster, more functional customer experience. Here are some things you can expect from the Pingdom/Grow integration.
Before you get started you will need your Pingdom username, password, and application key.
Log in to your Grow account and click Add Metric. Select Pingdom from the list of data sources, then press the blue Connect button.
In order to connect your account, you'll need your Pingdom username, password, and application key.
You can get your application key by logging in to My Pingdom, then going to Sharing > The Pingdom API.
Below is a complete list of the reports available from Pingdom, along with a description of the fields available with each report.
Account Settings: Settings data for accounts, including name, company, description, email, phone, address, account created, public reports code, settings saved, country, region ID, timezone, timezone offset, date format, time format, number format, etc.
Actions: All communications sent within the selected date range, including contact name, contact ID, check ID, action date, via, status, message short, message full, sent to, and charged.
Checks: All used checks, including ID, created date, name, host name, use legacy notifications, resolution, type, ipv6, last error date, last test date, last response date, status, alert policy, etc.
Contacts: All contacts, including contact ID, name, and type.
Credits: All remaining checks and SMS credits, including check limit, default check limit, transaction check limit, available checks, used default, used transaction, available SMS, available SMS tests, autofill SMS, autofill SMS amount, max SMS overage, available RUM sites, used RUM sites, max RUM page views, available alerting full users, etc.Outages: All status changes for the specified check(s) within the selected date range, including status, from time, to time, check name, and check ID.
Performance by Interval: Page performance data for the selected check(s)s and date range, grouped by hour, day, or week. Includes from time, to time, uptime, downtime, average response time, unmonitored, etc.
Response Time Average: Response time metrics for the selected check(s) and date range, optionally split by country and/or probe. Includes avg response, from time, to time, check name, and check ID.
Some Pingdom date fields use a date format called Unix Epoch. You can convert these fields to a more useful format using SQL.


Our large pool is an eight lane 25 yard lap pool with temperatures between 80-82 degrees. Our smaller pool is also heated with temperatures ranging between 88-90 degrees. We have a hydrolift available for those individuals that need assistance entering the water.
Water Works is an outdoor pool with zero depth pool entrance with a frog slide in shallow water. We also have two additional slides for those that have passed our swim test. Open Memorial Day - Labor Day.
Ages 10-12 receive cardiovascular instruction only.
Ages 13-14 receive cardiovascular and strength machine instruction.
Teaches participants how to use the cardio and weight machines.
The FitStart Program is a 6 week program for members 15 and older as an introduction to fitness and the equipment. It begins with an initial fitness consultation with one of our Wellness Coaches. Then, you will meet once a week with your Wellness Coach to continue to learn equipment usage. Schedule these appointments at our Membership Services Desk.
Children between the ages of 6-12 may utilize the track with a parent or guardian (guardian must be over 18 years). Strollers are allowed on the track during hours that child watch is not available.
STRIVE for Youth Fitness!!!!!! Improve the health and well being of your family!!!! The continuous circuit training is designed for children between the ages of 6 and 12. The perfect combination of games and resistance Training. The ultimate solution for youth fitness. Parents enjoy your workout ( 2 hr limit) while your kids have fun too!


Fascination over the letter "y". Why you ask? The intersection and counter parts that form "y" connects two diagonals into one straight line. Breaking apart the letter "y" and adding significant pieces, (only when necessary to another letter form) I created a new typeface given the name, "WEYE". It is pronounced exactly like the word "why" but giving it a unique look and a direct emphasis to the letter "y". This typeface is a modified version of Helvetica Neue.
There are two different styles and two different weights. Regular shows the parts of the "y" added/modified to the structure of the letter form. Sliced is when those parts are taken off, giving the letter form a unique style.
Type design based off the letter "Y". Modified from Helvetica Neue.


text:
Q: S function for 2 D look up Table, Similar to "sfix_look1_dyn.mex32" I need some information about "sfix_look1_dyn.mex32" file. Currently I am using this mex file, with the help of masked s function block. I am using this block only for 1 D look up related calculations. But if I want to use similar kind of logic for 2 D Look up table then is it possible to use same mex file ? Basically I want to know is there any way out so that, for 2 D lookup I can give values of Table Data as Inputs.

A: AFAIK you can't use same mex file or tlc for 2-D lookup, its for Dynamic lookup 1D Block
However, you can use n-D Lookup Table block

redpajama_set_name: RedPajamaStackExchange


text:
BERTRAM, Jessie Brown, after Hill & Adamson.
Collection of portraits and views from Edinburgh and St Andrews,
1843–47, printed circa 1916.
50 carbon prints, most approximately 6 x 8 inches, each mounted on thick card approximately 10¼ x 16½ inches, within border ruled in brown ink; many titled and numbered in pencil below, several with pencil annotations in a later hand on verso (another later numbering system is indicated in square brackets in descriptions below); in original wood box with hinged opening front panel and lid, original paper label pasted inside lid 'J. BERTRAM, Platinotype and Carbon printer, 148 Rose Street, Edinburgh'; with a small gelatin silver 'passport' style photograph of an older woman with Foreign Office stamps in ink and blind on lower section, annotated 'Jessie Bertram' (partially obscured) in ink on verso.
US $68108 €56181
David Octavius Hill and Robert Adamson are known primarily for their portraits of 'men of the cloth' taken during a short burst of intense photographic activity between 1843 and 1847. Their portraits of women and children are fewer but equally memorable and perhaps more appreciated today, as are their Newhaven fishing portraits, their landscapes and city views. This body of work was kept in the public's attention by the efforts of their successors, the photographers and collector/ publishers Thomas and James Craig Annan, Andrew Elliot, Alfred Stieglitz and, by no means least, the Edinburgh photographer Jessie Bertram (1881–1954). It was she who reprinted this selection of their photographs which were advertised by the publisher, Andrew Elliot, as being made from the original calotype negatives. She used the permanent carbon process, producing beautiful prints that remain strong and rich in tone 100 years later. The selection offered here, although numbering 50 which suggests a complete set from the evidence of other groupings, comes in a wood box supplied by the photographer with space for more examples and a label indicating that she also offered photographs made using the platinum process, another method noted for its permanency. Found among the mounted prints was a small passport-style photograph of a middle-aged woman, surely the photographer?


text:
Home > FACULTY_SCHOLARSHIP > 949
Single Point of Entry and the Bankruptcy Alternative
David A. Skeel Jr., University of PennsylvaniaFollow
This Essay, which will appear in Across the Great Divide: New Perspectives on the Financial Crisis, a Brookings Institution and Hoover Institution book, begins with a brief overview of concerns raised by the Lehman Brothers bankruptcy about the adequacy of our existing architecture for resolving the financial distress of systemically important financial institutions. The principal takeaway of the first section is that Title II as enacted left most of these issues unanswered. By contrast, the FDIC's new single point of entry strategy, which is introduced in the second section, can be seen as addressing nearly all of them. The third and fourth sections point out some of the limitations of single point of entry, first by highlighting potential pitfalls and distortions and then by explaining that single point of entry does not end the too-big-to-fail problem and would not reduce worrisome concentration in the financial services industry. The final section turns to bankruptcy, which remains the strategy of choice for resolving even systemically important financial institutions and considers how a single-entry-style strategy could be used in bankruptcy. Indeed, the strategy harkens back to the original procedure used to reorganize American railroads well over a century ago.
in, Across the Great Divide: New Perspectives on the Financial Crisis (Brookings-Hoover, Forthcoming)
Skeel, David A. Jr., "Single Point of Entry and the Bankruptcy Alternative" (2014). Faculty Scholarship at Penn Law. 949.
https://scholarship.law.upenn.edu/faculty_scholarship/949
Banking and Finance Law Commons, Bankruptcy Law Commons, Corporate Finance Commons, Economic Policy Commons, Finance Commons, Finance and Financial Management Commons, Law and Economics Commons, Policy History, Theory, and Methods Commons, Political Economy Commons, Public Policy Commons


text:
Q: QGIS 2.14 fails to install on Ubuntu 14.04 My previously working installation of QGIS 2.14 on Ubuntu 14.04 suddenly failed to start QGIS.  I have been trying to reinstall QGIS without success.  I have tried several ways to completely uninstall first, including this -
sudo apt-get autoremove
sudo apt-get autoclean
sudo apt-get -f install
sudo apt-get autoremove qgis
sudo apt-get --purge remove qgis

Then, on trying to reinstall QGIS, install fails with this error -
Setting up qgis-providers (1:2.14.0+20trusty-ubuntugis) ...
/usr/lib/qgis/crssync: symbol lookup error: /usr/lib/libqgis_core.so.2.14.0: undefined symbol: initGEOS_rdpkg: error processing package qgis-providers (--configure): subprocess installed post-installation script returned error exit status 127

So qgis-providers is not getting configured.  I have no idea what the undefined symbol issue might be.
I was using the qgis debian repository (http://qgis.org/debian) + the Ubuntugis repository (ppa.launchpad.net/ubuntugis/ubuntugis-unstable/ubuntu).  Also tried qgis ubuntu repos (http://qgis.org/ubuntugis).  Tried only debian qgis repos (with ubuntugis turned off.  All combinations fail configuring qgis-providers with same error.
I was able to install qgis 2.0 from the standard Ubuntu repository apparently without problem, but I find that my QGIS project files saved from 2.14 will not open in 2.0.
This has me stumped.  I thought for sure I could just reinstall to recover, but after several frustrating hours, I could sure use some help or clues to some resolution.


text:
Knock-and-Announce Supreme Court Case Law
In Hudson v. Michigan (2006) 547 U.S. 586, the United States Supreme Court confronted the question of "whether the exclusionary rule is appropriate for violation of the knock-and-announce requirement." (Id. at p. 590.) A majority of the court concluded that "the social costs of applying the exclusionary rule to knock-and-announce violations are considerable; the incentive to such violations is minimal to begin with, and the extant deterrences against them are substantial--incomparably greater than the factors deterring warrantless entries when Mapp v. Ohio (1961) 367 U.S. 643 was decided. Resort to the massive remedy of suppressing evidence of guilt is unjustified." (Hudson, at p. 599.) Justice Scalia wrote the main opinion, which consisted of four parts. (Hudson v. Michigan, supra, 547 U.S. at pp. 588-602.) Justices Breyer, Stevens, Souter, and Ginsburg dissented. (Id. at p. 604.) Justice Kennedy declined to join part IV of Justice Scalia's opinion, but concurred in the judgment and joined in parts I through III, noting that "the Court's holding is fully supported by" those parts. (Id. at p. 604.) Justice Kennedy specifically described the court's holding as follows: "In the specific context of the knock-and-announce requirement, a violation is not sufficiently related to the later discovery of evidence to justify suppression." (Id. at p. 603.) Thus, five justices joined the ruling that the exclusionary rule is not available as a remedy for a violation of the knock-and-announce requirement.


text:
Rome, Naples et Florence est le titre d'un récit de voyage de Stendhal.

Histoire du texte
Le premier texte est édité en 1817 sous le titre Rome, Naples et Florence, puis une deuxième édition parait l'année suivante et une troisième en 1826. Dans cette dernière, Stendhal procède à une véritable refonte du texte original en augmentant le nouveau volume d'anecdotes romanesques : une comparaison entre les textes de 1817 et 1826 laisse vite apparaître que les lieux et les dates sont souvent imaginés.

Le titre ne correspond pas exactement au contenu du livre : le voyage de l'auteur se déroule également à travers plusieurs autres villes italiennes, notamment Milan et Bologne qui occupent une grande partie de l'ouvrage. Dans son récit, négligeant souvent la description des monuments, Stendhal dépeint plutôt la sensibilité et les mœurs des Italiens.

Résumé

Milan

À peine arrivé, le soir du , il court à La Scala – qu'il appelle le premier théâtre du monde - où l'on donnait encore une représentation de La Testa di Bronzo. Passionné d'opéra, il relate longuement l'ambiance des loges où s'y déroulent des discussions (Stendhal parle le milanais) et anecdotes quelquefois confidentielles, parties de tarot et soupers de carnaval : sans oublier de décrire, l'habitude des Milanais qui, avant d'aller à La Scala, ont pour usage de parader, soit à pieds, soit à cheval ou en carrosse, sur le Corso (situé entre la Porta Rense et le bastion de la ).

À Milan, ville à laquelle il associe souvent le souvenir de Napoléon I, il aime flâner chez les marchands d'estampes ou bien au  (aujourd'hui Corso Vittorio Emanuele) où l'on rencontre les plus jolies femmes. En sédiole (landau) et sans cicerone, il se déplace dans le Milanais et décrit une architecture qui lui semble plus vivante en Italie que la peinture et la sculpture. Chaque soir, son bonheur est aussi de revoir le Duomo éclairé par une belle lune qui lui offre un aspect d'une beauté unique au monde. Tout comme Charles de Brosses et presque un siècle plus tard, il visite, entre autres, la fameuse église de la Madone et finit ses courses par La Cène de Léonard de Vinci au couvent delle Grazie. Il se rend de nombreuses fois au musée de Brera et se promène également à Marignan sur le champ de gloire de François I. Depuis 1815, Milan est la capitale du royaume lombard-vénitien, et à l'entrée solennelle de l'empereur François I dans la ville, prévue le , Stendhal ne lui prédit aucun succès.
En , en quittant Milan pour Pavie et son université, il dit des Milanais : « Je n'ai jamais rencontré de peuple qui convienne si bien à mon âme ».

Bologne

Après être passé aussi par Plaisance, et Parme pour y admirer les fresques du Corrège, il rejoint Bologne fin décembre où il séjournera plusieurs semaines.
À peine arrivé, il va sur-le-champ voir la fameuse tour qui penche, la Garisenda ainsi que l'église San Domenico où repose la dépouille du saint. Durant la journée, dès qu'il a une demi-heure, il monte au musée de la ville pour y voir, souvent, un seul tableau telle la sainte Cécile de Raphaël, et aux Carracci, il leur reconnaît une force de caractère presque égale à leur talent. Le reste de son temps, il baguenaude sous les portiques de saint Pétrone ou il se promène dans le jardin de la Montagnola. Les jours de pluie, il lit ses chers historiens du Moyen Âge. En soirée (le théâtre n'est pas ouvert), il aime coqueter dans les salons et se mêler aux secrets des Italiennes qu'il trouve bien plus puissantes que partout ailleurs. La liberté des propos y est aussi grande qu'à Londres et cette ville en terre papale lui semble avoir beaucoup plus d'esprit et d'originalité que Milan : cependant, il n'oublie jamais qu'en société, l'esprit qui amuse un Français incommode souvent un Italien.

Florence

Il traverse la Toscane, aux collines cultivées comme un jardin, écrit-il, et il arrive le  à Florence où l'idée d'être dans cette ville, le met déjà dans une sorte d'extase. Et aussitôt après les formalités du passeport, il se rend à Santa Croce où son cœur bat la chamade devant les sépultures de personnages illustres. Il visite aussi l'église del Carmine, San Lorenzo et Santa Maria del Fiore, et sur la plus belle place de Florence, rien ne peut être comparé au palazzo Vecchio. Au Teatro del Cocomero où tout est pauvre des décorations aux chanteurs, il assiste au Il barbiere di Siviglia de Gioachino Rossini. Florence est d'une rare propreté où l'on respire dans ses rues le beau idéal du Moyen Âge. Il trouve le Florentin comme le plus poli et le plus soigneux des hommes, cependant il est fortement frappé par l'idiome toscan qu'il trouve désagréable. Les paysans de Toscane sont, pour lui, les gens les plus civilisés du monde et il passe chez eux, à Castelfiorentino, une soirée imprévue et délicieuse à écouter des contes.

Naples

Depuis Florence, il partage la calèche d'un ami milanais jusqu'à Naples. Il traverse Rome, en trois heures, en apercevant de loin la coupole de Saint-Pierre puis poursuit son itinéraire par la via Appia qui offre une vue magnifique sur la campagne romaine traversée par une suite de longs vestiges d'aqueducs. À Terracina, lors d'une halte dans une auberge, parmi un groupe de voyageurs, il rencontre fortuitement Gioachino Rossini avec lequel il passe la plus aimable de ses soirées. Le , la descente vers Naples et la mer, par une large route, est grandiose. Le premier édifice rencontré est l'Albergo dei Poveri, puis le palazzo degli Studi et à gauche la via Toledo qui est la rue la plus peuplée et la plus gaie. Il trouve un logement au septième étage d'un palais avec vue sur la mer et le Vésuve. À Naples, il va habiter cinq mois. Le , il assiste à l'ouverture du San Carlo qu'il décrit comme folies, torrents de peuple et salle éblouissante, et qu'il estime quelques soirées plus tard tout à fait inférieur à La Scala.
Mélomane impénitent, Stendhal, lors des relâches (trois soirs par semaine) du San Carlo, fréquente aussi les théâtres Nuovo, La Fenice et les Fiorentini. Au printemps, l'ascension du Vésuve l'épuise énormément et à Pompéi où il dit se rendre plusieurs fois, il se sent transporté dans l'Antiquité. Il regrette la visite de Caserte mais est séduit par Portici et Capodimonte. Dès le mois d'avril, il arpente plusieurs autres sites du royaume des Deux-Siciles tels Paestum, Crotone, la Calabre et une partie des Pouilles.

Rome

Il arrive à Rome le  août, mais par crainte de la fièvre (le Tibre n'est toujours pas endigué), il préfère dormir à Castel Gandolfo. À la chapelle Sixtine, il assiste à une messe du pape où il compare le chant des castrats à un charivari des plus insupportables. Il fréquente comme dans chaque ville les théâtres notamment l'Argentina et le Valle. À Rome, la censure rend les comédies plates et le rire s'exprime principalement dans des pièces de marionnettes qui souvent s'improvisent : il passe une soirée des plus agréables au théâtre des marionnettes du palais Fiano.

Annexes

Notes et références

Postérité
"Son livre le plus riche", lettre de Nietzsche à Peter Gast, datée du

Bibliographie
 Rome, Naples et Florence [par] Stendhal. Texte établi et annoté par Daniel Muller, préf. de Charles Maurras, Paris, E. Champion 1919. Tome I et Tome II
 Édition originale de 1826, volume II, numérisée sur Gallica.
 Rome, Naples et Florence de Stendhal. Illustré par les peintres du Romantisme, Paris, Éditions Diane de Selliers, 2002. (Voir présentation en ligne)

Articles connexes
 Liste de récits de voyage en Italie
 Itinéraire d'Italie

Récit de voyage en Italie
Œuvre de Stendhal
Livre paru en 1817

redpajama_set_name: RedPajamaWikipedia


Property disposals earn property £22m in exceptional profit to lift pre-tax profit to £64.6m.
Property giant Hammerson has blamed financing and void costs at three new developments for a 3.4% fall in pre-tax profit for the six months to 30 June 2004.
Overall pre-tax profit rose by 36% to £64.6m as Hammerson made £22m exceptional profit disposing of property worth £245m. As a result of the disposals property income fell by £14m, but Hammerson reported that net rental income increased by 5.5% on a like-for-like basis.


text:
Jump to main content [Access key C]
Jump to primary navigation [Access key N]
Skip navigation [Access key S]
LEDS Global Partnership
Agriculture, Forestry and Land Use
Subnational Integration
Climate Helpdesk
New report release : Crunching Numbers !
Crunching Numbers- Quantifying the sustainable development co-benefits of Mexico's climate Commitments
The government of Mexico has set a new tone in domestic political discussions by bringing forward an ambitious social and economic development agenda – putting development ambitions and people of all social and economic backgrounds in the centre of the debate, reaffirming its continuing international commitment to combat global warming with determined national climate action.
In coordination with German Development Cooperation and SD Strategies, this study is presented as an essential first step to break the pattern of working in silos, by quantifying a selection of important social, economic and environmental co-benefits of climate action in various sectors and their impact for the achievement of the 2030 Agenda. The aim of this study being to provide country-specific intelligence on the interrelations between climate action and the achievement of the 2030 Agenda for Sustainable Development and the full scope of benefits.
Understanding and measuring co-benefits contributes to identifying opportunities to accelerate progress, guiding decision decision making, avoiding the duplication of efforts, and reducing the costs of implementation.
The study goes beyond existing research by quantifying the co-benefits of current and prospective NDC measures and their contribution to development
objectives and SDG targets. By exploring existing synergies, it showcases how ambitious climate action can contribute to other Mexican development
priorities. By verifying and quantifying the magnitude and significance of climate action for economic, social and environmental development
objectives, this report strengthens the case for climate and development policy integration.
This study analyses six priority co-benefits resulting from the implementation of three current and two potential Mexican NDC commitments . The study builds on a prior qualitative analysis on the inter-linkages and possible co-benefits between Mexico's NDC and the 2030 Agenda for Sustainable Development.
This report is the result of a collaboration between the government of Mexico, SD Strategies and the GIZ.
You can download the report here.
by: LEDSGP Global | posted: 8am, March 9, 2020
Author(s): Dean Gioutsos and Alexander Ochs
Organisation(s): GIZ, Gobierno de Mexico, SD Strategies
English Español powered by


text:
Q: A transport-level error has occurred when receiving results from the server. An existing connection was forcibly closed by the remote host We have an application in Azure which connects to SQL Server on-premise over hybrid connection
Every 4-5 requests from 1000 would fail with error 10054:

A transport-level error has occurred when receiving results from the server.
An existing connection was forcibly closed by the remote host

What we've tried:

*

*restarted app in Azure

*increased ConnectTimeOut and CommandTimeOut in application

*rebooted Windows hosting SQL Server

*installed OS and SQL Server patches on-prem

*checked SQL Server error log, no related errors

*sysadmin checked firewall/switches - ZERO packet loss or drops

*performed update of firmware and drivers (network, sas, raid)

Also checked this:

*

*remote access in SQL Server is turned on, obviously

*SQL Server Browser turned off, but there is only one default instance

*hybrid connection manager is up to date

*SqlClient version is up to date (System.Data.SqlClient: 4.0.0.0)

*databases auto_close = OFF

*sessions are not getting killed by anyone

*server does not go to sleep or energy saving mode

*TCP chimney is disabled

*SPN config is OK with Kerberos Configuration Manager

None of above helped. Resolution:
https://learn.microsoft.com/en-us/previous-versions/sql/sql-server-2008-r2/ms187005(v=sql.105)?redirectedfrom=MSDN
Although link is for Windows Server 2003, it worked for Windows Server 2016
Solution was to add registry entry SynAttackProtect - DWORD - 00000000
HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Services\Tcpip\Parameters\
As we understand, requests from Azure were too rapid sometimes so system thought it was under attack ? We added registry entry and stopped getting error 10054. Team is happy and already moved on to other tasks
I thought posting above information on DBA SE may help other guys troubleshooting this issue
Question:
Is this solution optimal, as it seem to open server to SYN-flood and denial-of-service attacks ?
Are there any alternatives in fixing 10054 error ?


The Battery Chair was created from The Battery Conservancy Americas Design Competition in 2012. Canadian designer Andrew Jones was selected by a jury which reviewed 679 submissions from 15 countries.
Manufactured by Maglin Site Furniture, the winning chair is a solid steel frame powder-coated in blue. It is moveable within the new two-acre Battery Oval lawn so visitors can sit where they choose.
Donors can dedicate chairs to honor or remember loved ones or celebrate the momentous events of life. A steel plaque will be engraved and affixed discreetly to the back of The Battery Chair, to become part of the permanent collection on The Battery Oval. Fill out the form below or see the PDF Version Here.
Thank you for supporting The Battery. If you have questions, or would like to dedicate more than one chair, please call us anytime during business hours at 212-344-3495.


text:
Бубликово () — село,
Гуриновский сельский совет,
Белопольский район,
Сумская область,
Украина.

Код КОАТУУ — 5920683002. Население по переписи 2001 года составляет 42 человека .

Географическое положение
Село Бубликово находится на расстоянии в 4 км от рек Крыга и Павловка.
На расстоянии в 1 км расположены сёла Алексенки, Гуриновка и Кандыбино.

Примечания


Hailing from the sunny shores of Australia, Raj is a Be Well Health Coach, nutritionist, yoga instructor + teacher at The Class by Taryn Toomey. She is passionate about all things mind, body + soul, and feels motivated to share her knowledge with anyone who will listen. With a strong curiosity towards Ayurveda and ancient tradition, she works to constantly find the balance between optimum health + a little fries + red wine on the weekends.
Here's how to stay on track with your fitness goals and still have a great holiday season.
Whether you already over-ate at the various "Friendsgivings" you attended or are preparing to indulge on the big day, this concoction of goodness is just what you need to support your gut health and bring you right back to that wellness track.
Post workout, it is very important to supply your body with nutrients and minerals that will help the body recover.
Having the tools to self-soothe and remain in a state of trust are crucial to getting the most out of your days.
There are several offerings on the market — from one-day retreats in Malibu to three-week breaks in India — and choosing the right one can feel overwhelming. But we've got your back.


text:
Q: Live Migration of Repository Root Our VisualSVN server (SE) currently stores its repositories in "C:\Repositories" but given the growth of our repositories the C: drive is starting to fill up. We would like to move the entire repository root to the second drive in the server (D:) which has plenty of space for the foreseeable future.
I have found this question which tells me how to change the root, but as that question concerned a fresh install the preservation of existing data wasn't a concern. After some research I've found that there are several ways of preserving the existing repositories:

*

*straight-up file copy in explorer: should preserve all data but I'm hesitant to trust Windows with this task

*svnadmin dump to a dumpfile, then load the dumpfile at the new location

*svnadmin hotcopy the repositories to the new location

I'd like to have your input on which approach would be preferable. I'd prefer the hotcopy method if it's robust enough as dumping all repositories would take quite a while. The dump method is described here but seems to include steps that are unnecessary for my situation as I'm keeping everything on the same server.
Additionally, I'd like to ask how we would best proceed with this migration to keep downtime to a minimum. I'd like to prevent users from committing changes while the files are being copied. Can I just shut the SVN service down while I'm changing the root and copying the files?
Finally, I'd like to use this opportunity to upgrade the server from 2.5.2 to 2.5.8. Should I do this after the file copy? Should I first restart the service and then shut it down again so it processes and checks the moved repositories?

A: I recommend you to use svnadmin hotcopy for each repository or xcopy /E /H C:\Repositories D:\Repositories. Don't forget to copy authorization files (authz, authz-windows and htpasswd). You have to stop VisualSVN Server before migration.
svnadmin dump is not required and as you mentioned really time consuming process.
It better to upgrade VisualSVN Server after repositories migration and required reconfiguration. In some upgrades VisualSVN Server needs access to repositories folder for upgrading.

A: These are some steps you can use. The use of this python script will make your task easier and benefit you in the future for taking daily backups of your repository.

*

*On VisualSVNServer, go to properties and change the Repositories Root to your new location. Assuming "D:\Repositories".

*Download Python 2.7 (if you don't already have it installed).

*Download "hot-backup.py"

*Execute the following from a command window.
hot-backup.py C:\Repositories\<YourRepoName>  D:\Repositories\<YourRepoName>

*Upgrade your SVN Version
This should do the trick. Good luck  !!!

A: *

*Xcopy SVN-Parent to new location

*Edit (by hand) httpd.conf of VisualSVN Server, SVNParentPath parameter

*Restart VisualSVN Server from it's MMC-applet


text:
\section{Introduction}
\label{Introduction}
Object detection in aerial images serves as an essential step for numerous applications such as urban planning, traffic surveillance,  port management, and maritime rescue \cite{azimi2018towards,zhang2018toward}. The aerial images are taken from the bird's-eye view. Detecting objects in aerial images is a challenging task as the objects typically have different scales and textures, and the background is complex. Moreover, the objects are usually densely packed and displayed in arbitrary directions. Consequently, applying the horizontal bounding boxes to oriented object detection would lead to misalignment between the detected bounding boxes and the objects \cite{ding2019learning}. To deal with this problem, oriented bounding boxes are preferred for capturing objects in aerial images.

\begin{figure}[t!]
\begin{center}
   \includegraphics[width=\linewidth]{images/pic1.pdf}
\end{center}
\caption{Oriented bounding box (OBB)  descriptions for (a) baseline method, termed Center+$wh$+$\theta$, where $w,h,\theta$ are the width, height and angle of an OBB. Note that $w$ and $h$ of the OBBs are measured in different rotating coordinate systems for each object; (b) the proposed method, where $\textbf{t}, \textbf{r}, \textbf{b}, \textbf{l}$ are the top, right, bottom and left box boundary-aware vectors. The box boundary-aware vectors are defined in four quadrants of the Cartesian coordinate system for all the arbitrarily oriented objects; (c) illustrates the corner cases where the vectors are very close to the $xy$-axes.}
\label{fig:figure1}
\end{figure}

Current oriented object detection methods are mainly derived from the two-stage anchor-based detectors \cite{girshick2014rich,girshick2015fast,ren2015faster}. Generally, in the first stage, those detectors spread anchor boxes on the feature maps densely and then regress the offsets between the target box and the anchor box parameters in order to provide region proposals. In the second stage, the region-of-interest (ROI) features are pooled to refine the box parameters and classify the object categories. Notably, they use the center, width, height, and angle as the descriptions of an oriented bounding box. The angle is learned either in the first stage or in the second stage. For instance, R$^2$CNN \cite{jiang2017r2cnn}, Yang et al. \cite{yang2018position}, and ROI Transformer \cite{ding2019learning} regress the angle parameters from the pooled horizontal region proposal features in the second stage; similarly, R$^2$PN \cite{zhang2018toward}, R-DFPN \cite{yang2018automatic} and ICN \cite{azimi2018towards} generate oriented region proposals in the first stage. These oriented object detection methods share the same drawbacks with the anchor-based detectors. For example, the design of the anchor boxes is complicated; and the choices of aspect-ratios and the size of the anchor boxes need to be tuned carefully. Besides, the extreme imbalance between the positive and negative anchor boxes would induce slow training and sub-optimal performance \cite{duan2019centernet}. Moreover, the crop-and-regress strategies in the second stage are computationally expensive \cite{zhou2019bottom}. Recently, the keypoint-based object detectors \cite{law2018cornernet,zhou2019bottom,duan2019centernet} have been developed to overcome the disadvantages of anchor-based solutions \cite{ren2015faster,liu2016ssd,yi2019assd} in the horizontal object detection task. In particular, these methods detect the corner points of the bounding boxes and then group these points by comparing embedding distances or center distances of the points. Such strategies have demonstrated improved performance, yet with one weakness that the grouping process is time-consuming. To address this issue, Zhou's CenterNet \cite{zhou2019objects} suggests detecting the object center and regressing the width ($w$) and height ($h$) of the bounding box directly, which achieves faster speed at comparable accuracy. Intuitively, Zhou's CenterNet can be extended to the oriented object detection task by learning an additional angle $\theta$ together with $w$ and $h$ (see Fig.~\ref{fig:figure1}a). However, as the parameters $w$ and $h$ are measured in different rotating coordinate systems for each arbitrarily oriented object, jointly learning those parameters may be challenging for the model.

In this paper, we extend Zhou's CenterNet to the oriented object detection task. However, instead of regressing the $w$, $h$ and $\theta$ at the center points, we learn the box boundary-aware vectors (BBAVectors, Fig.~\ref{fig:figure1}b) to capture the rotational bounding boxes of the objects. The BBAVectors are distributed in the four quadrants of the Cartesian coordinate system.
Empirically we show that this design is superior to directly predicting the spatial parameters of bounding boxes.
In practice, we observe that in the corner cases, where the vectors are very close to the boundary of the quadrants (i.e., $xy$-axes in Fig.~\ref{fig:figure1}c), it would be difficult for the network to differentiate the vector types. To deal with this problem, we group the oriented bounding box (OBB) into two categories and handle them separately. Specifically, we have two types of boxes: horizontal bounding box (HBB) and rotational bounding box (RBB), where RBB refers to all oriented bounding boxes except the horizontal ones. We summarize our contributions as follows:
\begin{itemize}
    \item We propose the box boundary-aware vectors (BBAVectors) to describe the OBB. This strategy is simple yet effective. The BBAVectors are measured in the same Cartesian coordinate system for all the arbitrarily oriented objects. Compared to the baseline method that learns the width, height and angle of the OBBs, the BBAVectors achieve better performance.

    \item We extend the center keypoint-based object detector to the oriented object detection task. Our model is single-stage and anchor box free, which is fast and accurate. It achieves state-of-the-art performances on the DOTA and HRSC2016 datasets.

\end{itemize}

\section{Related Work}
\label{Related Work}
\subsection{Oriented Object Detection}
The horizontal object detectors, such as R-CNN \cite{ma2015hierarchical}, fast R-CNN \cite{girshick2015fast}, faster R-CNN \cite{ren2015faster}, SSD \cite{liu2016ssd}, YOLO \cite{redmon2016you}, are designed for horizontal objects detection. These methods generally use the horizontal bounding boxes (HBB) to capture the objects in natural images. Different from the horizontal object detection task, oriented object detection relies on oriented bounding boxes (OBB) to capture the arbitrarily oriented objects. Current oriented object detection methods are generally extended from the horizontal object detectors. For example, R$^2$CNN \cite{jiang2017r2cnn} uses the region proposal network (RPN) to produce the HBB of the text and combines different scales of pooled ROI features to regress the parameters of OBB. R$^2$PN \cite{zhang2018toward} incorporates the box orientation parameter into the RPN network and develops a rotated RPN network. R$^2$PN also utilizes a rotated ROI pooling to refine the box parameters.  R-DFPN \cite{yang2018automatic} employs the Feature Pyramid Network (FPN) \cite{lin2017feature} to combine multi-scale features and boost the detection performance. Based on the DFPN backbone, Yang \textit{et al}. \cite{yang2018position} further propose an adaptive ROI Align method for the second-stage box regression. RoI Transformer \cite{ding2019learning} learns the spatial transformation from the HBBs to OBBs. ICN \cite{azimi2018towards} develops an Image Cascade Network that enhances the semantic features before adopting R-DFPN. RRD \cite{liao2018rotation} uses active rotation filters to encode the rotation information. Gliding Vertex \cite{xu2020gliding} glide the vertex of the horizontal bounding boxes to capture the oriented bounding boxes. All these methods are based on anchor boxes. Overall, the anchor-based detectors first spread a large amount of anchor boxes on the feature maps densely, and then regress the offsets between the target boxes and the anchor boxes. Such anchor-based strategies suffer from the imbalance issue between positive and negative anchor boxes. The issue would lead to slow training and sub-optimal detection performances \cite{law2018cornernet}.

\begin{figure*}[t!]
\begin{center}
   \includegraphics[width=0.9\linewidth]{images/pic2.pdf}
\end{center}
\caption{The overall architecture and the oriented bounding box (OBB) descriptions of the proposed method. The input image is resized to $608\times 608$ before being fed to the network. The architecture is built on a U-shaped network. Skip connections are adopted to combine feature maps in the up-sampling process. The output of the architecture involves four maps: the heatmap $P$, offset map $O$, box parameter map $B$, and orientation map $\alpha$. The locations of the center points are inferred from the heatmap and offset map. At the center points, the box boundary-aware vectors (BBAVectors) are learned. The resolution of the output maps is $152\times 152$. HBBs refer to the horizontal bounding boxes. RBBs indicate all oriented bounding boxes except the HBBs. The symbols $\textbf{t},\textbf{r},\textbf{b}, \textbf{l}$ refer to the top, right, bottom and left vectors of BBAVectors, $w_e$ and $h_e$ are the external width and height of an OBB. The decoded OBBs are shown in red bounding boxes.}
\label{fig:figure2}
\end{figure*}

\subsection{Keypoint-Based Object Detection}
The keypoint-based object detectors \cite{law2018cornernet,zhou2019bottom,zhou2019objects,yi2019multi} capture the objects by detecting the keypoints and therefore provide anchor-free solutions. Keypoint detection is extensively employed in the face landmark detection \cite{merget2018robust} and pose estimation \cite{newell2016stacked,sun2018integral}. In the horizontal object detection task, the keypoint-based detection methods propose to detect the corner points or the center points of the objects and extract the box size information from these points. Cornernet \cite{law2018cornernet} is one of the pioneers. It captures the top-left and bottom-right corner points of the HBB using heatmaps. The corner points are grouped for each object by comparing the embedding distances of the points. Duan's CenterNet \cite{duan2019centernet} detects both corner points and center points. ExtremeNet \cite{zhou2019bottom} locates the extreme and center points of the boxes. These two methods both use the center information to group the box points. However, the post-grouping process in these methods is time-consuming. To address this problem, Zhou's CenterNet \cite{zhou2019objects} proposes to regress the width and height of the bounding box at the center point without a post-grouping process, which makes the prediction faster. The keypoint-based object detectors show advantages over the anchor-based ones in terms of speed and accuracy, yet the keypoint-based detectors are barely applied to oriented object detection task.

\paragraph{Baseline method.}  In this paper, we extend Zhou's CenterNet to the oriented object detection task. In particular, we first build a baseline method that directly regresses the width $w$ and height $h$ as well as the orientation angle $\theta$ of the bounding boxes. We term this baseline method as Center$+wh+\theta$ (see Fig.~\ref{fig:figure1}a). We compare the proposed method with Center$+wh+\theta$ to demonstrate the advantages of box boundary-aware vectors.

\section{Method}
In this section, we first describe the overall architecture of the proposed method, and then explain the output maps in detail. The output maps are gathered and decoded to generate the oriented bounding boxes of the objects.

\subsection{Architecture}
\label{sub:architecture}
The proposed network (see Fig.~\ref{fig:figure2}) is built on a U-shaped architecture \cite{ronneberger2015u}. We use the ResNet101 Conv1-5 \cite{he2016deep} as the backbone. At the top of the backbone network, we up-sample the feature maps and output a feature map that is 4 times smaller (scale $s=4$) than the input image. In the up-sampling process, we combine a deep layer with a shallow layer through skip connections to share both the high-level semantic information and low-level finer details. In particular, we first up-sample a deep layer to the same size of the shallow layer through bilinear interpolation. The up-sampled features map is refined through a $3\times3$ convolutional layer. The refined feature map is then concatenated with the shallow layer, followed by a $1\times1$ convolutional layer to refine the channel-wise features. Batch normalization and ReLU activation are used in the latent layers. Suppose an input RGB image is $I\in\mathbb{R}^{3\times H\times W}$, where $H$ and $W$ are the height and width of the image. The output feature map $X\in \mathbb{R}^{C\times \frac{H}{s}\times \frac{W}{s}}$ ($C=256$ in this paper) is then transformed into four branches: heatmap ($P\in \mathbb{R}^{K\times \frac{H}{s}\times \frac{W}{s}}$), offset ($O\in \mathbb{R}^{2\times\frac{H}{s}\times \frac{W}{s}}$), box parameter ($B\in \mathbb{R}^{10\times \frac{H}{s}\times \frac{W}{s}}$), and the orientation map ($\alpha\in \mathbb{R}^{1\times \frac{H}{s}\times \frac{W}{s}}$), where $K$ is the number of dataset categories and $s=4$ refers to the scale. The transformation is implemented with two convolutional layers with $3\times3$ kernels and $256$ channels.

\subsection{Heatmap}
\label{sub:heatmap}
Heatmap is generally utilized to localize particular keypoints in the input image, such as the joints of humans and the facial landmarks \cite{merget2018robust,newell2016stacked,sun2018integral}. In this work, we use the heatmap to detect the center points of arbitrarily oriented objects in the aerial images. Specifically, the heatmap $P\in \mathbb{R}^{K\times \frac{H}{s}\times \frac{W}{s}}$ used in this work has $K$ channels, with each corresponding to one object category. The map at each channel is passed through a sigmoid function. The predicted heatmap value at a particular center point is regarded as the confidence of the object detection.

\paragraph{Groundtruth} Suppose $\textbf{c}=(c_x, c_y)$ is the center point of an oriented bounding box, we place a 2D Gaussian  exp$({-\frac{(p_x-c_x)^2+(p_y-c_y)^2}{2\sigma^2}})$ (see Fig.~\ref{fig:figure2}) around each $\textbf{c}$ to form the groundtruth heatmap $\hat{P}\in \mathbb{R}^{K\times \frac{H}{s}\times \frac{W}{s}}$, where $\sigma$ is a box size-adaptive standard deviation \cite{zhou2019objects,law2018cornernet}, point $\hat{\mathbf{p}} = (p_x,p_y)$ indexes the pixel points on $\hat{P}$.

\paragraph{Training Loss} When training the heatmaps, only the center points $\textbf{c}$ are positive. All the other points including the points in the Gaussian bumps are negative. Directly learning the positive center points would be difficult due to the imbalance issue. To handle this problem, following the work of \cite{law2018cornernet}, we decrease the penalty for the points inside the Gaussian bumps and use the variant focal loss to train the heatmap:
\begin{equation}
    L_{h}= -\frac{1}{N}\sum_{i}
                \begin{cases}
           (1-p_{i})^\alpha\log(p_{i}) & \text{if } \hat{p}_{i}=1\\
               (1-\hat{p}_{i})^\beta p_{i}^\alpha \log(1-p_{i}) & \text{otherwise},\\
               \end{cases}
\end{equation}
where $\hat{p}$  and $p$ refer to the ground-truth and the predicted heatmap values, $i$ indexes the pixel locations on the feature map, $N$ is the number of objects, $\alpha$ and $\beta$ are the hyperparameters that control the contribution of each point. We choose $\alpha=2$ and $\beta=4$ empirically as in \cite{law2018cornernet}.

\subsection{Offset}
\label{sub:offsets}
In the inference stage, the peak points are extracted from the predicted heatmap $P$ as the center point locations of the objects. These center points $\textbf{c}$ are integers. However, down-scaling a point from the input image to the output heatmap generates a floating-point number. To compensate for the difference between the quantified floating center point and the integer center point, we predict an offset map $O\in \mathbb{R}^{2\times\frac{H}{s}\times \frac{W}{s}}$. Given a ground-truth center point $\Bar{\mathbf{c}} = (\Bar{c}_x,\Bar{c}_y)$ on the input image, the offset between the scaled floating center point and the quantified center point is:
\begin{equation}
    \textbf{o} = (\frac{\Bar{c}_x}{s}-\lfloor{\frac{\Bar{c}_x}{s}}\rfloor, \frac{\Bar{c}_y}{s}-\lfloor{\frac{\Bar{c}_y}{s}}\rfloor),
\end{equation}
The offset is optimized with a smooth $L_1$ loss \cite{girshick2015fast}:
\begin{equation}
    L_{o} = \frac{1}{N}\sum_{k=1}^{N}\text{Smooth}_{L_1}(\mathbf{o}_k-\hat{\mathbf{o}}_k),
\end{equation}
where $N$ is the total number of objects, $\hat{\mathbf{o}}$ refers to the ground-truth offsets, $k$ indexes the objects. The smooth $L_1$ loss can be expressed as:
\begin{equation}
    \text{Smooth}_{L_1}(x) = \begin{cases}
    0.5x^2 &\text{if }|x|<1\\
    |x|-0.5 & \text{otherwise}.
    \end{cases}
\end{equation}

\subsection{Box Parameters}
To capture the oriented bounding boxes, one natural and straightforward way is to detect the width $w$, and height $h$, and angle $\theta$ of an OBB from the center point. We term this baseline method as Center$+wh+\theta$ (see Fig.~\ref{fig:figure1}a). This method has several disadvantages. First, a small angle variation has marginal influence on the total loss in training, but it may induce a large IOU difference between the predicted box and the ground-truth box. Second, for each object, the $w$ and $h$ of its OBB are measured in an individual rotating coordinate system that has an angle $\theta$ with respect to the $y$-axis. Therefore, it is challenging for the network to jointly learn the box parameters for all the objects. In this paper, we propose to use the box boundary-aware vectors (BBAVectors, see Fig.~\ref{fig:figure1}b) to describe the OBB. The BBAVectors contain the top $\textbf{t}$, right $\textbf{r}$, bottom $\textbf{b}$ and left $\textbf{l}$ vectors from the center points of the objects. In our design, the four types of vectors are distributed in four quadrants of the Cartesian coordinate system. All the arbitrarily oriented objects share the same coordinate system, which would facilitate the transmission of mutual information and therefore improve the generalization ability of the model. We intentionally design the four vectors instead of two (i.e., $\textbf{t}$ and $\textbf{b}$, or $\textbf{r}$ and $\textbf{l}$) to enable more mutual information to be shared when some local features are obscure and weak.

The box parameters are defined as $\textbf{b}=[\textbf{t},\textbf{r},\textbf{b},\textbf{l},w_e, h_e]$, where $\textbf{t},\textbf{r},\textbf{b},\textbf{l}$ are the BBAVectors, $w_e$ and $h_e$ are the external horizontal box size of an OBB, as described in Fig.~\ref{fig:figure2}. The details of $w_e$ and $h_e$ are explained in Section \ref{sub: orientation}. Totally, the box parameter map $B\in \mathbb{R}^{10\times \frac{H}{s}\times \frac{W}{s}}$ has $10$ channels with $2\times4$ vectors and $2$ external size parameters. We also use a smooth $L_1$ loss to regress the box parameters at the center point:
\begin{equation}
    L_{b} = \frac{1}{N}\sum_{k=1}^{N}\text{Smooth}_{L_1}(\mathbf{b}_k- \hat{\mathbf{b}}_k),
\end{equation}
where $\mathbf{b}$ and $\hat{\mathbf{b}}$ are the predicted and ground-truth box parameters, respectively.

\subsection{Orientation}
\label{sub: orientation}
In practice, we observe that the detection would fail in situations where the objects nearly align with $xy$-axes (see Fig.~\ref{fig:figure4}b). The reason would be that at the boundary of the quadrant, the types of the vectors are difficult to be differentiated. We term this problem as corner cases (see Fig.~\ref{fig:figure1}c). To address this issue, in this work we group OBBs into two categories and process them separately. In particular, the two types of boxes are HBB and RBB, where RBB involves all the rotation bounding boxes except the horizontal ones. The benefit of such a classification strategy is that we transform the corner cases into the horizontal ones, which can be dealt with easily. When the network encounters the corner cases, the orientation category and the external size ($w_e$ and $h_e$ in Fig.~\ref{fig:figure2}) can help the network to capture the accurate OBB. The additional external size parameters also enrich the descriptions of an OBB.

We define the orientation map as $\alpha\in \mathbb{R}^{1\times \frac{H}{s}\times \frac{W}{s}}$. The output map is finally processed by a sigmoid function. To create the ground-truth of the orientation class $\hat{\alpha}$, we define:
\begin{equation}
    \hat{\alpha}=\begin{cases}
    1 \text{ (RBB)} & \text{IOU(OBB, HBB)}<0.95\\
    0 \text{ (HBB)}& \text{otherwise},
    \end{cases}
\end{equation}
where IOU is the intersection-over-union between the oriented bounding box (OBB) and the horizontal bounding box (HBB). The orientation class is trained with the binary cross-entropy loss:
\begin{equation}
    L_{\alpha} = -\frac{1}{N}\sum_i^N(\hat{\alpha}_i\log(\alpha_i)+(1-\hat{\alpha}_i)\log(1-\alpha_i)),
\end{equation}
where $\alpha$ and $\hat{\alpha}$ are the predicted and the ground-truth orientation classes, respectively.

\input{tables/table1.tex}

\section{Experiments}
\subsection{Datasets}
We evaluate our method on two public aerial image datasets: DOTA \cite{xia2018dota} and HRSC2016 \cite{icpram17}.

\paragraph{DOTA.} We use DOTA-v1.0  \cite{xia2018dota} dataset for the oriented object detection. It contains 2,806 aerial images with a wide variety of scales, orientations, and shapes of objects. These images are collected from different sensors and platforms. The image resolution ranges from $800\times800$ to $4000\times4000$. The fully-annotated images contain 188,282 instances. The DOTA-V1.0 has 15 categories: Plane, Baseball Diamond (BD),    Bridge,    Ground Track Field (GTF), Small Vehicle (SV), Large Vehicle    (LV), Ship, Tennis Court (TC), Basketball Court (BC), Storage Tank (ST), Soccer-Ball Field (SBF), Roundabout (RA), Harbor, Swimming Pool (SP) and Helicopter (HC). The DOTA images involve the crowd and small objects in a large image. For accurate detection, we use the same algorithm as ROI Transformer \cite{ding2019learning} to crop the original images into patches. In particular, the images are cropped into $600\times600$ patches with a stride of 100.  The input images have two scales $0.5$ and $1$.  The trainval set and testing set contain 69,337 and 35,777 images after the cropping, respectively. The trainval set refers to both training and validation sets \cite{ding2019learning}. Following the previous works \cite{azimi2018towards,ding2019learning}, we train the network on the trainval set and test on the testing set. The detection results of cropped images are merged as the final results. Non-maximum-suppression (NMS) with a 0.1 IOU threshold is applied to the final detection results to discard repetitive detection. The testing dataset is evaluated through the online server.

\noindent\paragraph{HRSC2016.} Ship detection in aerial images is important for port management, cargo transportation, and maritime rescue \cite{yang2018automatic}. The HRSC2016 \cite{icpram17} is a ship dataset collected from Google Earth, which contains 1,061 images with ships in various appearances. The image sizes range from $300\times300$ to $1500\times900$. The dataset has 436 training images, $181$ validation images, and $444$ testing images. We train the network on the training set and use the validation set to stop the training when the loss on the validation set no longer decreases. The detection performance of the proposed method is reported on the testing set.

\subsection{Implementation Details}
We resize the input images to $608\times 608$ in the training and testing stage, giving an output resolution of $152\times 152$. We implement our method with PyTorch. The backbone weights are pre-trained on the ImageNet dataset. The other weights are initialized under the default settings of PyTorch. We adopt the standard data augmentations to the images in the training process, which involve random flipping and random cropping within scale range $[0.9,1.1]$. We use Adam \cite{kingma2014adam} with an initial learning rate of $1.25\times 10^{-4}$ to optimize the total loss $L=L_h+L_o+L_b+L_{\alpha}$. The network is trained with a batch size of 20 on four NVIDIA GTX 1080 Ti GPUs. We train the network for about 40 epochs on the DOTA dataset and 100 epochs on the HRSC2016 dataset. We additionally report an experiment with a larger batch size 48 on 4 NVIDIA Quadro RTX 6000 GPUs, we mark the results with symbol $*$ in Table \ref{Table:Table1}. The speed of the proposed network is measured on a single NVIDIA TITAN X GPU on the HRSC2016 dataset.

\subsection{Testing Details}
To extract the center points, we apply an NMS on the output heatmaps through a $3\times 3$ max-pooling layer. We pick the top 500 center points from the heatmaps and take out the offsets ($\textbf{o}$), box parameters ($\textbf{b}$), and orientation class ($\alpha$) at each center point ($\textbf{c}$). The heatmap value is used as the detection confidence score. We first adjust the center points by adding the offsets $\tilde{\mathbf{c}} =\mathbf{c} + \textbf{o}$. Next, we rescale the obtained integer center points on the heatmaps by $\bar{\mathbf{c}} = s\tilde{\mathbf{c}}, s=4$. We obtain the RBB when $\alpha>0.5$, and we get the HBB otherwise. We use the top-left ($\textbf{tl}$), top-right ($\textbf{tr}$), bottom-right ($\textbf{br}$) and bottom-left ($\textbf{bl}$) points of the bounding box as the final decoded points. Specifically, for a center point $\bar{\mathbf{c}}$, the decoded RBB points are obtained from:
\begin{align}
    \textbf{tl} &= (\textbf{t}+\textbf{l})+\bar{\mathbf{c}}, \quad
    \textbf{tr} = (\textbf{t}+\textbf{r})+\bar{\mathbf{c}}\nonumber\\
    \textbf{br} &= (\textbf{b}+\textbf{r})+\bar{\mathbf{c}},
    \quad
    \textbf{bl} = (\textbf{b}+\textbf{l})+\bar{\mathbf{c}}.
\end{align}
For a HBB, the points are:
\begin{align}
    \textbf{tl} &= (\bar{c}_x-w_e/2, \bar{c}_y-h_e/2),

    ~\textbf{tr} = (\bar{c}_x+w_e/2, \bar{c}_y-h_e/2)\nonumber\\
    \textbf{br} &= (\bar{c}_x+w_e/2, \bar{c}_y+h_e/2),

    ~\textbf{bl} = (\bar{c}_x-w_e/2, \bar{c}_y+h_e/2).
\end{align}
We gather the RBBs and HBBs as the final detection results.

\subsection{Comparison with the State-of-the-arts}
We compare the performance of the proposed method with the state-of-the-art algorithms on the DOTA and HRSC2016 datasets. To study the impact of orientation classification, we define two versions of the the proposed method: BBAVectors$+r$ and BBAVectors$+rh$. BBAVectors$+r$ only learns the box boundary-aware vectors to detect OBB, which contains box parameters $b=[\textbf{t}, \textbf{r}, \textbf{b}, \textbf{l}]$. BBAVectors$+rh$ additionally learns the orientation class $\alpha$ and the external size parameters ($w_e$ and $w_h$).

\begin{figure*}[tbh!]
\begin{center}
   \includegraphics[width=0.92\linewidth]{images/pic3.pdf}
\end{center}
\caption{Visualization of the detection results of BBAVectors+$rh$ on DOTA dataset.}
\label{fig:figure3}
\end{figure*}

\paragraph{DOTA.} The detection results on the DOTA dataset are illustrated in Table~\ref{Table:Table1}. YOLOv2 \cite{redmon2017yolo9000} and FR-O \cite{xia2018dota} are trained on HBB \cite{xia2018dota} and their performances are comparably lower than the other methods. Notably, although the one-stage detector YOLOv2 runs faster, its accuracy is lower than the two-stage anchor-based detectors. R-DFPN \cite{yang2018automatic} learns the angle parameter from faster R-CNN \cite{ren2015faster} and improves performance from 54.13\% to 57.94\%. R$^2$CNN \cite{jiang2017r2cnn} pools multiple sizes of region proposals at the output of RPN and improves the accuracy from 57.94\% to 60.67\%. Yang \textit{et al}. \cite{yang2018position} use the adaptive ROI align to extract objects and achieve 1.62\% improvement over 60.67\%. ICN \cite{azimi2018towards} adopts the Image Cascaded Network to enrich features before R-DFPN and boost the performance from 62.29\% to 68.16\%. ROI Transformer  \cite{ding2019learning} transfers the horizontal ROIs to oriented ROIs by learning the spatial transformation, raising the accuracy from 68.16\% to 69.56\%. Different from these methods, the proposed method offers a new concept of oriented object detection, a keypoint-based detection method with box boundary-aware vectors. As shown in Table \ref{Table:Table1}, Without orientation classification, the BBAVectors$+r$ improves 2.05\% over 69.59\% of ROI Transformer+FPN \cite{lin2017feature} and 3.87\% over 67.74\% of ROI Transformer without FPN. As the proposed method is single-stage, this result demonstrates the detection advantages of the keypoint-based method over the anchor-based method. With the orientation classification and additional external OBB size parameters ($w_e$ and $h_e$), the proposed BBAVectors$+rh$ achieves 72.32\% mAP, which exceeds ROI Transformer+FPN by 2.76\%. Besides, BBAVectors$+rh$ runs faster than ROI Transformer (see Table~\ref{Table:Table2}). With a larger training batch size, the BBAVectors$+rh^*$ achieves about 3 points higher than BBAVectors$+rh$. The visualization of the detection results of BBAVectors$+rh$ on DOTA dataset is illustrated in Fig.~\ref{fig:figure3}. The background in the aerial images is complicated and the objects are arbitrarily oriented with different sizes and scales. However, the proposed method is robust to capture the objects even for the tiny and crowd small vehicles.

\paragraph{HRSC2016.} The performance comparison results between the proposed method and the state-of-the-arts on HRSC2016 dataset is illustrated in Table~\ref{Table:Table2}. The R$^2$PN \cite{zhang2018toward} learns the rotated region proposals based on VGG16 backbone, achieving 79.6\% AP. RRD \cite{liao2018rotation} adopts activate rotating filters and improves the accuracy from 79.6\% to 84.3\%. ROI Transformer   \cite{ding2019learning} without FPN produces 86.2\%, while BBAVectors+$r$ achieves 88.2\%. BBAVectors$+rh$ performs slightly higher (0.4\% over 88.2\%) than BBAVectors$+r$.
In the inference stage, the proposed method achieves 12.88 FPS on a single NVIDIA TITAN X GPU, which is 2.18x faster than ROI Transformer.

\input{tables/table2.tex}

\begin{figure}[tbh!]
\begin{center}
   \includegraphics[width=0.95\linewidth]{images/pic4.pdf}
\end{center}
\caption{Comparison of BBAVectors+$r$ and BBAVectors+$rh$.}
\label{fig:figure4}
\end{figure}

\subsection{Ablation Studies}
\label{sub:ablation studies}
We compare the performances of the proposed BBAVectors$+r$ and BBAVectors$+rh$ to study the impact of orientation classification. As we mentioned before, BBAVectors$+r$ refers to box parameters $b=[\textbf{t}, \textbf{r}, \textbf{b}, \textbf{l}]$.  BBAVectors$+rh$ corresponds to $b=[\textbf{t}, \textbf{r}, \textbf{b}, \textbf{l}, w_e, h_e, \alpha]$. BBAVectors$+r$ has $274.01$MB parameters with $25.82\times 10^{9}$ FLOPs, while BBAVectors$+rh$ has $276.28$MB parameters and $27.68\times 10^{9}$ FLOPs.

As can be seen in Fig.~\ref{fig:figure4}, the BBAVectors$+r$ can hardly capture the bounding boxes that nearly align with the $xy$-axes. These are the corner cases as discussed above. The reason for the failure detection would be that it is difficult for the network to differentiate the type of vectors near the quadrant boundary (i.e., classification boundary). To address this problem, we separate the OBB into RBB and HBB by learning an orientation class $\alpha$ and we use the external parameters ($w_e$ and $w_h$) to describe HBB. As illustrated in Fig.~\ref{fig:figure4}, the BBAVectors$+rh$ excels in capturing the oriented bounding box at the corner cases. On the DOTA dataset (see Table~\ref{Table:Table1}), the BBAVectors$+rh$ improves 0.71\% over BBAVectors$+r$. On the HRSC2016 dataset (see Table~\ref{Table:Table2}), BBAVectors$+rh$ achieves 0.4\% improvement over BBAVectors$+r$.

\subsection{Comparison with Baseline}
To explore the advantage of the box boundary-aware vectors, we also compare our method with the baseline Center+$wh$+$\theta$ (see Fig.~\ref{fig:figure1}a), which employs the width ($w$), height ($h$) and angle ($\theta\in(-90^\circ,-0^\circ]$) as the descriptions of OBB. Note that the baseline method shares the same architecture as the proposed method except for the output box parameter and orientation map. The training procedure is the same as the proposed method. Here we do not explicitly handle the corner cases for a fair comparison. From Table~\ref{Table:Table3}, we can see that the proposed method performs 4.82\% and 2.74\% better than Center+$wh$+$\theta$ on HRSC2016 and DOTA datasets, respectively. The results suggest that the box boundary-aware vectors are better for oriented object detection than learning the $w,h,\theta$ of OBB directly. The reason would be that the box boundary-aware vectors are learned in the same Cartesian coordinate systems, while in the baseline method, the size parameters ($w$ and $h$) of an OBB are measured in different rotating coordinate systems that have an angle $\theta$ with respect to the $y$-axis. Jointly learning those parameters would be difficult for the baseline method.

\input{tables/table3.tex}

\section{Conclusion}
In this paper, we propose a new oriented object detection method based on box boundary-aware vectors and center points detection. The proposed method is single-stage and is free of anchor boxes.  The proposed box boundary-aware vectors perform better in capturing the oriented bounding boxes than the baseline method that directly learns the width, height, and angle of the oriented bounding box. The results on the HRSC2016 and DOTA datasets demonstrate the superiority of the proposed method over the state-of-the-arts.

\input{paper24.bbl}

\end{document}

redpajama_set_name: RedPajamaArXiv


text:
\section{Introduction}

Many cosmologists now recognize that it is not sufficient just to know the dynamical laws of physics (sometimes misleadingly called the `theory of everything,' which we do not know yet, though superstring/M theory is one leading candidate that is only partially understood).  One also needs to know the quantum state (or the boundary conditions giving it), which is so far even less understood.  However, even that is insufficient, since we also need to know how to get probabilities (or perhaps more strictly, measures) of observations from the quantum state \cite{Page:2008ns}.

There are plausible postulates for some aspects of the dynamical laws and for the quantum state that, when coupled with simple ideas for the rules for getting the probabilities or measures of observations, lead to the disturbing conclusion that the measure seems to be concentrated on Boltzmann Brain observations, observations of Boltzmann Brains (BBs) that arise from relatively rapid thermal fluctuations \cite{Rees, Dyson:2002pf, Goheer:2002vf, Albrecht:2004ke, Vilenkin:2006qg, DeSimone:2008if, Carroll:2010zz} or quantum fluctuations \cite{Page:2005ur, Page:2006dt, Page:2006hr, Page:2006ys, Page:2006nt, Davenport:2010jy}, rather than observations of Ordinary Observers (OOs) that arise from relatively slow evolution.  The seemingly plausible way in which Boltzmann Brain domination arises is that Ordinary Observers are believed to require something like radiation from a nearby star (e.g., from the Sun in our case), but stars have finite lifetimes and (leaving out Boltzmann stars, which would occur much, much less frequently than Boltzmann brains) exisr only during a finite time during the history of the universe.  Therefore, Ordinary Observers can collectively exist only for a finite period of the history of the universe, whereas Boltzmann Brains can collectively exist over a much, much longer time according to the latest preferred theories about the universe, Thus the total number of Boltzmann Brains over all of spacetime can greatly dominate over the number of Ordinary Observers.  Therefore, assuming that each suitable configuration of an observer (whether a BB or an OO) leads to a comparable probability or measure for the corresponding observation, the much greater number of BBs over OOs makes the total probability or measure of BBs much greater than that of OOs.

In many simple models, the total measure of BB observations is so much greater than that of OOs that even the restricted class of BB observations that are exactly like ours has much greater measure than the OO observations exactly like ours.  Therefore, if we accept such models, we should think with high probability that we are the much more probable BBs.

Sean Carroll has recently argued \cite{Carroll:2017gkl} that although we cannot rule out such a model on statistical grounds (since it does predict that almost all observations like ours are made by Boltzmann Brains), we should rule it out for being cognitively unstable, because if we accept it and believe that our observations come from purely random thermal or quantum fluctuations, that would give us no reason for believing the model that has such random thermal or quantum fluctuations.  Therefore, as Carroll puts it, we should ``reject cosmological models that would be dominated by Boltzmann Brains (or at least Boltzmann Observers among those who have data just like ours), not because we have empirical evidence against them, but because they are cognitively unstable and therefore self-undermining and unworthy of serious consideration.''

However, for a Bayesian who believes that a Bayesian analysis should in principle be sufficient in science (assuming that one can calculate the probability of one's observation for all theories for which one assigns a significant prior probability, which of course is no mean task, and leaving aside the inevitable subjectivity of the prior probabilities), it seems a bit disturbing to have to invoke a different principle (cognitive instability) to rule out Boltzmann Brain theories.

Here I shall show that a Bayesian analysis by itself is in principle sufficient to give very low posterior probabilities to theories that predict domination by Boltzmann Brain observations.

\section{A Bayesian analysis of Ordinary Observer vs.\ Boltzmann Brain theories}

In a Bayesian analysis, if there are a set of possible observations $O_j$ and
theories $T_i$ with prior probabilities $P(T_i)$, such that the
probability of observation $O_j$ in theory $T_i$ is $P(O_j|T_i)$, then the posterior probability of theory $T_i$, given observation $O_j$, is
\be
P(T_i|O_j) = \frac{P(O_j|T_i)P(T_i)}{\sum_k P(O_j|T_k)P(T_k)}.
\n{Bayes}
\ee

Now let us suppose there are two sets of possible observations, a number $N_O$ of observations $O_j$ with $1 \leq j \leq N_O$ that are relatively ordered observations that Ordinary Observers can have, and a number $N_D$ of observations $O_j$ with $N_O+1 \leq j \leq N_O+N_D$ that are relatively disordered observations that only Boltzmann Brains have.  Since there can be thermal or quantum fluctuations to arbitrary Boltzmann Brain configurations (including configurations the same as what Ordinary Observers have), I shall assume that Boltzmann Brains can also have any of the relatively ordered observations as well.  Since presumably there are far more relatively disordered observations than ordered ones, I shall assume that $N_D \gg N_O$.  I shall further assume that we use one of our observations to test the different theories, and that this observation is a relatively ordered one, say for concreteness the observation $O_1$.

Next, suppose we have two theories to test against our observation $O_1$, theories $T_1$ and $T_2$.  Let theory $T_1$ be the theory in which there are $N_{OO}$ Ordinary Observers and no Boltzmann Brains.  Assume for inessential simplicity that each Ordinary Observer has the same number $m$ of observations, and that each of these $m$ observations for one single Ordinary Observer is distinct.  (For a human who lives about two billion seconds, this might be of the order of $10^{10}$ if there are roughly five observations per second, but the actual number does not matter.)  Assume that the universe of theory $T_1$ is sufficiently large that the number of observational occurrences, $mN_{OO}$, is the positive integer $n$ multiplied by the set of $N_O$ ordered observations, so that $mN_{OO} = nN_O$.  (The integer $n$ is the number of occurrences of each relatively ordered observation $O_j$ with $1 \leq j \leq N_O$.)  Further assume, again for inessential simplicity, that each of the $N_O$ relatively ordinary observations occurs the same number of times $n$.

Now suppose that theory $T_1$ specifies that each occurrence of one of the $m$ observations of each one of the $N_{OO}$ Ordinary Observers makes a unit contribution of the measure to the corresponding relatively ordered observation $O_j$ with $1 \leq j \leq N_O$.  Then the total measure of all observations in this theory is $mN_{OO} = nN_O$, and each of the $N_O$ relatively ordered observations that have positive measure in this theory has measure $n$.

For Bayes' theorem, we take the conditional probability $P(O_j|T_i)$ to be the normalized measure that the theory $T_i$ assigns to the observation $O_j$, so that for each theory, the sum of the conditional probabilities or normalized measures over all observations is unity.  Therefore, for theory $T_1$, the conditional probability or normalized measure for each relatively ordered observation is $P(O_j|T_i) = 1/N_O$ (for $1 \leq j \leq N_O$), and for each relatively disordered observation is $P(O_j|T_i) = 0$ (for $N_O+1 \leq j \leq N_O+N_D$).  In particular, since theory $T_1$ is being tested by the relatively ordered observation $O_1$, its likelihood is
\be
P(O_1|T_1) = \frac{1}{N_O}.
\n{T1}
\ee

Let theory $T_2$ be the theory that not only has $N_{OO}$ Ordinary Observers that each have $m$ observations, with these $mN_{OO} = nN_O$ total number of instances of observations equally spread among the $N_O$ relatively ordered observations so that they give a unnormalized measure $n$ for each relatively ordered observation $O_j$ with $1 \leq j \leq N_O$, but that theory $T_2$ also has $N_{BB}$ Boltzmann Brains.  Since Boltzmann Brains are short-lived thermal or quantum fluctuations, assume for inessential simplicity that each Boltzmann Brain makes only one observation, but also assume that the number $N_{BB}$ of Boltzmann Brains is some positive integer $p$ multiplied by the total number of possible observations, $N_O+N_D$, and that this number is much larger than the number of occurrences $mN_{OO} = nN_O$ of relatively ordered observations by Ordinary Observations, so that $N_{BB} = p(N_O+N_D) \gg mN_{OO} = nN_O$.  Assume that theory $T_2$ has the Boltzmann Brains contribute a total unnormalized measure $N_{BB} = p(N_O+N_D)$ that for simplicity is equally spread among all $N_O+N_D$ possible observations, so that each possible observation gains an unnormalized measure $p$ from Boltzmann Brains.

Then when we add the measure contributed by Ordinary Observers, $n$ for each of the $N_O$ relatively ordered observations, to the measure contributed by Boltzmann Brains, $p$ for each of the $N_O+N_D$ total number of observations, we get that the unnormalized measure is $n+p$ for each of the $N_O$ relatively ordered observations and $p$ for each of the $N_D$ relatively disordered observations.  The total unnormalized measure in theory $T_2$ is thus $(n+p)N_O + p N_D$.  Therefore, the normalized measure for each of the $N_O$ relatively ordered observations (including the observation $O_1$ being used to test the thoery) is $(n+p)/[(n+p)N_O + p N_D]$, and the normalized measure for each of the $N_D$ relatively disordered observations is $p/[(n+p)N_O + p N_D]$.  This implies that when tested by the relatively ordered observation $O_1$, theory $T_2$ has a likelihood
\be
P(O_1|T_2) = \frac{n+p}{(n+p)N_O + p N_D}.
\n{T2}
\ee

Eqs.\ (\ref{T1}) and (\ref{T2}) give the ratios of these likelihoods as
\be
\frac{P(O_1|T_1)}{P(O_1|T_2)} = 1 + \frac{pN_D}{(n+p)N_O}
=\left(1+\frac{N_D}{N_O}\right)
\frac{1+mN_{OO}/N_{BB}}{1+(1+N_D/N_O)mN_{OO}/N_{BB}}.
\n{likerat}
\ee
Furthermore, using Bayes' theorem Eq.\ (\ref{Bayes}), the ratio of the posterior probabilities of the Ordinary Observer theory $T_1$ and of the Boltzmann Brain theory $T_2$ is
\be
\frac{P(T_1|O_1)}{P(T_2|O_1)} = \frac{P(T_1)}{P(T_2)}
\left(1+\frac{N_D}{N_O}\right)
\frac{1+mN_{OO}/N_{BB}}{1+(1+N_D/N_O)mN_{OO}/N_{BB}}.
\n{postrat}
\ee

Surely the number $N_D$ of possible relatively disordered observations is much greater than the number $N_O$ of possible relatively ordered observations, or $N_D/N_O \gg 1$.  But initially plausible Boltzmann Brain theories tend to have the number $N_{BB}$ of Boltzmann Brains {\em enormously} larger than the number $N_{OO}$ of Ordinary Observers, and even enormously larger than the number of Ordinary Observers multiplied by the number $m$ of observations they each make during their lifetime (this $m$ being in comparison with 1 observation assumed here for each Boltzmann Brain during its typically very short lifetime), so that $N_{BB}/(mN_{OO}) \gg\gg 1$.  Therefore, I shall assume that
\be
\frac{N_{BB}}{mN_{OO}} \gg \frac{N_D}{N_O} \gg 1.
\n{ineq}
\ee
As a consequence, we get that the likelihood of the Ordinary Observer theory $T_1$ is much greater than the likelihood of the Boltzmann Brain theory $T_2$,
\be
P(O_1|T_1) \gg P(O_1|T_2)
\n{likecom}
\ee

The idea is that because Boltzmann Brains add extra unnormalized measure to the observations in a way that is less for the relatively ordered observation $O_1$ being used to test the theory than for elements of a set of other observations (the relatively disordered ones), they dilute the normalized measure of the observation $O_1$ and therefore reduce the likelihood of the theory that includes such Boltzmann Brains.

Of course, this is not the whole story, because to get the ratio of the posterior probabilities $P(T_1|O_1)$ and $P(T_2|O_1)$ as given by Eq.\ (\ref{postrat}), one must also include the ratio of prior probabilities $P(T_1)$ and $P(T_2)$.  The main reason that Boltzmann Brains have been seen as a problem is that they are predicted by what naively seems to be simpler theories than Ordinary Observer theories that avoid Boltzmann Brains, so one often takes $P(T_1) < P(T_2)$.  However, because the ratio of likelihoods is usually so much in favor of the Ordinary Observer theory, even if $P(T_1) < P(T_2)$, one ends up with the Ordinary Observer theory having the greater posterior probability:  $P(T_1|O_1) > P(T_2|O_1)$ if
\be
\frac{P(T_2)}{P(T_1)} < \left(1+\frac{N_D}{N_O}\right)
\frac{1+mN_{OO}/N_{BB}}{1+(1+N_D/N_O)mN_{OO}/N_{BB}}.
\n{ineq2}
\ee
The right hand side of this inequality is generally so large that it should not be hard to find a theory $T_1$ that is not so complex that its prior probability would reasonably be assigned so low a value that the inequality (\ref{ineq2}) would be violated.   If indeed (\ref{ineq2}) holds, then without invoking cognitive instability or needing to use it for setting the prior probabilities (which can be set by using Occam's razor to assign higher prior probabilities to simpler theories, so long as one does not make the ratio of priors on the left side of the inequality (\ref{ineq2}) larger than the right hand side), this straightforward Bayesian analysis assigns Boltzmann Brain theories, in which Boltzmann Brains strongly dominate over Ordinary Observers, lower posterior probabilities than Ordinary Observer theories, in which Boltzmann Brains do not dominate over Ordinary Observers.

Thus Bayes keep Boltzmann Brains at bay.

\section*{Acknowlegments}

I am grateful for conversations about Boltzmann Brains with a number of colleagues, such as Raphael Bousso, Sean Carroll, and Alan Guth.  I am also grateful that, so far as predicted by partial theories that seem to have the highest posterior probabilities, I have not had conversations about colleagues with Boltzmann Brains.

This research was supported in part by the Natural Sciences and Engineering Research Council of Canada, and in part by Perimeter Institute for Theoretical Physics, which hosted me during part of this work.  Research at Perimeter Institute is supported by the Government of Canada through the Department of Innovation, Science and Economic Development and by the Province of Ontario through the Ministry of Research, Innovation and Science.  I am also grateful for the hospitality of Sergei Dubovsky and Matthew Kleban at the Center for Cosmology and Particle Physics at New York University, where this paper was completed.

\baselineskip 4pt


Data from the Hubble space telescope has helped determine that a planet orbiting a nearby star likely shares Earth's deep-blue tones, but the similarities stop there, according to USA Today.
NASA said Wednesday morning in an announcement that the planet, 63 light-years from Earth, would look like a deep blue dot if we were close enough to view it directly.
What causes the blue color of planet HD 189733b? It's likely glass, NASA explains, raining sideways in the atmosphere according to the Los Angeles Times.
Fox News states that as far as planets go, HD 189733b, a giant, sizzling Jupiter-like world that swoops around its parent star every 2.2 days, couldn't be more different from Earth.
Astronomers weren't specifically thinking about HD 189733b's color per se when they requested observation time on the Hubble Space Telescope, according to Fox News.
They were following up previous studies showing the planet had clouds with an attempt to learn more about what is in its atmosphere.
As HD 189733b moved around its star, astronomers used Hubble's light-splitting spectrograph to home in on specific wavelengths of light reflecting off the planet's surface.
"Other factors may be photochemical aerosols — i.e. smog — and absorption by other atoms or molecules than sodium," though presently are no specific candidates," he added.
They continued on to say that the planet is likely gravitationally locked with one side permanently facing its star and the other in darkness. That dichotomy can generate wild winds that surpass 4,350 mph.
"I think of this planet in some ways as being about as alien a planet as you could possibly imagine," astronomer Heather Knutson, with the California Institute of Technology (Caltech) in Pasadena, told Discovery News.
"If we could actually see it in person, I think we would find that there's no good comparison we could make with anything that we're familiar with. That's what makes it interesting," she added.
What do you think of this new blue planet?


text:
Chocobo Gp Mobile App Announced
Stupidly-Named Chocobo GP' Mobile Game Announced
Chocodon't'
Square Enix has just announced a mobile game that's inspired by the upcoming kart racer Chocobo GP. Sounds pretty cool, right? Well, it does sound alright but there's just one big problem: it's named Chocobo GP'. No, I didn't misspell it and I didn't somehow stutter through my keyboard. It's truly one of the stupidest names I've ever heard, but hey, it's current year. Let's embrace Chocobo GP' in all it's idiotic glory.
Anyways, Chocobo... GP' is now available on both iOS and Android mobile devices. Not so interestingly, in the press release it says that it's meant to be read as Chocobo GP Dash, which is infuriating because it's a much better name, but whatever. I'm fine with this.
Players will navigate through 35 different courses and prevent their Chocobo from losing momentum. There's also a Ghost option that will allow players to race against themselves to try and beat their own records, which is always a neat feature to see. Players will only have to worry about to controls, so those that are obsessed with the stupid name can think about it while playing Chocobo GP'.
You'll be able to customize your rollers with the coins you receive upon finishing each level, and Android 6 / iOS 11 or higher is required to play the game. Both operating systems are fairly old, so that shouldn't be that big of a deal.
Chocobo GP, the game that people are actually excited about, is releasing on March 22, 2022, on the Nintendo Switch. If you want to get your Chocobo fix in the meantime, be sure to check out Chocobo's Mystery Dungeon, which we found to be a pretty good experience if a little grindy, or try out the Darwin Award nominee for bad naming Chocobo GP'.
For more information on everything Square Enix names, stay tuned to TechRaptor.
What do you think of the game-that-shall-not-be-named? Let us know in the comments!
TechRaptor Recommends
Chocobo's Mystery Dungeon: Every Buddy! Review - Cute Chocobo Meets Tough Dungeons
How To Use Xbox Cloud Gaming On Android
Too Quick To Judge: Final Fantasy Origin Is A Lot Of Fun
Patrick Perrault
Writer for TechRaptor, who hopes to gain valuable experience in a constantly changing industry.


On Monday, we carried a Web Crawler headline on Senator Ted Cruz's (R-TX) proposal to halt family separations for those illegally crossing the U.S. border with their children.
While the number of such people is comparatively small, the separations are obviously undesirable.
But it does happen, when the reasons cited by DHS are present. Cruz's proposal is the sensible one I would advocate: to ensure that we can halt the separations as quickly as possible, by expanding family accommodations; and to accelerate the processing of arriving migrants by adding judges and other administrative capacity.
This proposal is too sensible and effective for the Democrats. The main reason, apparently, is that it would enable us to stop the family separations while also continuing to enforce the law.
Instead of making sure illegal border-crossers are stopped and processed, the Democrats want Trump to do what Obama did: use his pen and phone to dial back enforcement, so that illegal border-crossers are basically just released into the United States because there's no capacity to hold and process them as the law says we're supposed to.
I am not exaggerating here. That's what Senate Minority Leader Chuck Schumer (D-NY) said on Tuesday, in a comprehensive rejection of Republican proposals to halt family separations.
Remember that, next time someone starts complaining that Trump is introducing executive imperialism. Trump and Republicans in Congress want to mount a constitutional, collaborative effort to enforce the law while eliminating an undesirable side effect.
It's the Democrats who want the president to unilaterally not enforce the law.
Schumer's opposition to a legislative fix means there likely won't be a quick end to the emotional images of immigrant children being separated from their families unless Trump backs down and reverses his "zero tolerance" policy.
But why should Trump have to reverse his "zero tolerance" policy? What kind of vicious, deceptive wording is that? Can you name other laws for which we should not have zero-tolerance policies? Let's do that with environmental laws and equal-opportunity laws. Sign memos in advance stating which clear violations of the laws we will tolerate. Any takers?
Grand theft? Embezzlement? Vehicular manslaughter? Murder? A little bit of any of these is OK, because some people do feel like there are benefits to stealing, driving drunk, and hacking spouses to death, or shooting uncooperative armed-robbery victims? Let the president, or a state governor, decide how much is the right amount?
Schumer obviously doesn't care about children or family separations at the border. He cares about extorting Trump, with emotional images of children, to get Trump to gut the enforcement of U.S. law. Remember that.


As you may have noticed from our social channels, Vantage had the opportunity to join TechCrunch at CES for its Hardware Battlefield competition. It was a busy week of introducing Snap to CES attendees, giving flight demos, preparing for our TechCrunch presentation and checking out other emerging technologies.
TechCrunch gifted us a booth space in Eureka Park — where hundreds of other startups were exhibiting— and gave us six minutes to present on the main stage with 14 other startups.
It was exciting to introduce Snap to a larger audience and get feedback from a panel of prominent industry experts. We enjoyed walking the show floor and checking out other emerging technologies like Nikon's new, 360-degree camera and the machine that will allow you to create custom latte art — we're a team of avid coffee drinkers.
But in looking at the competition within the drone market we're more confident than ever. Snap brings a unique combination of portability, safety and ease-of-use, not to mention the 4K sensor and custom micro-gimbal, which will allow for exceptional footage. Snap is extremely well positioned within the market and we're working tirelessly to bring the product to your doorsteps this spring.
How do FAA regulations impact Snap?
According to the FAA, owners must register their Small Unmanned Aircraft System (sUAS) if it weighs more than .55lbs (250g).
Snap weighs 1.1lbs (500g), which means that if you pre-order or purchase Snap you must register with the FAA.
To register, visit this website and create an account. Until January 20, 2016 the registration process is free. After that date the registration fee will be $5. We encourage all of those who have pre-ordered Snap to register now in order to avoid those costs.
As always, thank you for your support! We look forward to giving you more updates about Snap in the coming months.