text:
EU Prize for Journalism Names its 2015 Winners
The European Union has acknowledged six Georgian Journalists for their professional standards and ethics.
On the evening of Wednesday 20 January, the winners of the 'EU Prize for Journalism 2015' were revealed in six categories at an award ceremony held in the National Parliamentary Library of Georgia.
The EU Prize for Journalism as well as the EUMM Special Prize for Peace Journalism recognize the works of the written press, TV, Radio and social media that reflect high journalistic standards and contribute to promoting a better understanding of the values of the European Union.
The Prize is to contribute to stimulating the debate about press and media performance by recognizing examples of high quality and unbiased journalism.
The Ambassador of the European Union, Janos Herman, delivered the welcome speech at the award ceremony and emphasized that this year the prize had a record amount of applications.
"This means that directions and the issue of human rights, investigations, minorities and conflicts are becoming popular among Georgian journalists. Journalism plays an essential role in upholding the values of democratic societies. Improving professional standards and respecting ethic principles in this profession is therefore in the interest of all of us and can contribute to bringing Georgia closer to the European Union," the Ambassador of the European Union said at the award ceremony.
Five awards were handed out in following categories:
Winner of the Best Investigative Story in print or online media was Zurab Vardiashvili for the story "Children of Poverty" published in the Liberal magazine.
The Best Investigative TV and/or Radio Coverage award was handed to Lia Toklikishvili (co-author Maia Gogoladze) for the documentary named "Town of the Gold-Scarified".
The award for the Best Online or Print Blog went to Anuna Bukia for her blog titled "The diary of a cancer patient".
The most Informative Online Media award was taken by newly established website www.chai-khana.org, which came in ahead of two other finalists ipn.ge and iset-pi.ge
The Best Documentary Photo Reflecting EU Values was awarded to Daro Sulakauri for her documentary photo "Early Marriages".
The winners of each category will be awarded a share of 7,500 EUR.
Within the framework of the EU Prize for Journalism 2015, the EU Monitoring Mission (EUMM) awarded the Special Prize for Peace Journalism, which recognizes reporting that promotes a peaceful solution on both sides of the conflict divide, acknowledging journalists who promote peace and social justice over violence, while addressing their audiences.
The EUMM Special Prize for Peace Journalism of this year went to Nino Chipchiuri for the article "The doctors are building bridges of health in the regions".
Her prize is a fellowship allowing her to participate in the Caucasus Program at the headquarters of the Institute for War and Peace Reporting (IWPR) in London.
The EU Prize for Journalism was launched in 2012. Every year, the Prize calls for new applications on the 3rd of May – the World Press Day.
Prizes are awarded by a Jury comprising three renowned European journalists, three reputed Georgian journalists or balanced public profiles, and one representative from the Delegation of the European Union to Georgia.
GEORGIA TODAY would like to congratulate its colleagues in Georgian media.


I published a story, "Detroit Muslims force women to sit in the back of buses" a few days ago, it was not correct, I had in fact, changed a few words that then affected the perspective of the story in a different manner. The actual headline was: "At Front of Brooklyn Bus, a Clash of Religious and Women's Rights" from a New York Times story and it was about the use of a private, but publicly-funded busline for the Orthodox Jewish Community of Brooklyn.
My motivations for sending out the changed story were to find out how people (Muslims and non-Muslims) would react to Muslims doing such a thing and how differently this story would be viewed BECAUSE Muslims were thought to be doing such a thing in this land of "Freedom and Democracy". My point was also to illustrate that the aberrations and the evils that are so magnified among Muslims, are really no different from those practiced by non-Muslims; that Muslims must be seen as being no different from any other faith, whether they are in the US or, "out there".
People contacted me back directly (most did not reply) and through some of the group discussion lists that I also sent the "story" to.
A few people saw through what I had done and one even saw through what I was trying to do, but the rest varied in their responses.
Some Muslims were disgusted and even suggested that Muslims who force women into such positions, should consider returning to the Old Country.
Some Muslims tried to defend the rights of people to practice their own traditions as long as nobody else was being hurt…the fact that women werebeing hurt in this scenario got buried under "tradition".
None of these reactions were particularly surprising.
The fact is that there are quite a few communities where gender-segregation is practiced, Orthodox Jews and Christian communities like the Amish come to mind immediately. There are entire communities where even speaking English, is seen as being the language of the undesirable "foreigners", as well as communities where non-English languages (German, Spanish, French, Hebrew etc.,) are used, more so than English. All of them are non-Muslim communities that have existed in this country for well over a hundred years, they continue to live and be seen as all-American communities.
But let some Muslims walk around in "Muslim garb" (anything perceived as Middle-Eastern clothing) and prepare for a maelstrom of vindictive opinions, threats to the individuals and to Muslims in general, screams that America is about to be swamped by the Muzlem hordes…even the FBI gets involved in "investigating" this possible threat to the Fatherland (Oops!, "Homeland").
One has to ask why such attitudes are so widely shared among REAL Americans.
The simple answer is the Muslims are terrible at public relations and because Muslims don't generally engage on the public platform (politics and other civic and civil life), we have no influence. Muslims also believe that all we have to do is show people the truth of what Islam is and who we are, that everything will be okay because Truth always triumphs.
On the other side of the coin, there is a mighty array of forces against Muslims. For the most part, these forces include Jews and Christians of the right wing. This alliance ignores facts, cook their own "facts" and depend heavily on sincere, impressionable Americans, to swallow false but powerfully emotional appeals to why Muslims and Islam must be feared and hated.
Jewish groups promote anti-Muslim sentiments because they fear Muslims might destroy Israel and better to fight them now, than to try and take them on when they are really effective later. Christian groups promote anti-Muslim sentiments because they revere the Biblical reference to "The Chosen People" (Jews) and "The Promised Land" (Israel) and they too, see Muslims as potential threats to Israel and to Christianity as a whole.
The Alliance between Right-Wing Christians and Jews grows in spite of the fact that these Christians believe all Jews must "return" to Israel, where they must all convert to Christianity or, be destroyed, before Judgment Day comes. Jews take the attitude that since the Torah does not have such a prediction, Judgment Day is unlikely to happen soon and why not take advantage of the situation while it is there? No Jew seems to ask what will happen if some day, the Right Wing Christians get powerful and get tired of waiting for Jews to voluntarily "return" to Israel.
Muslims' approach is short-term; go out there and inform people about Islam and Muslims and they will believe we are as American as they are, with the same diversity amongst us.
The "Judeo-Christian" Alliance on the other hand, takes both, the short-term and the long-term outlook.
In the short term, they bring in speakers ("experts") on Islam and Muslims who tell their audiences about the fantasy destructive nature of Islam and Muslims and how we Muslims are bent on destroying the so-called, "Judeo-Christian Civilization" and replace it with the other fantasy, "The Caliphate". "Islam must be stopped and eradicated from America before it destroys us", they say. Nobody questions what precisely is envisioned in this "Stop and Eradicate" measure.
In the long term, the Alliance is working on the minds of the future. Anti-Islam lectures and lessons are being presented in schools, thus, sowing the seeds of hate in our future generations. Anti-Islam and anti-Muslim lectures and presentation are being hammered into the minds of new entrants in Police, FBI, CIA and all branches of the military thus, ensuring an enduring hate of Muslims in American minds (here is just one example).
These efforts are extremely well-organized and extremely well-funded. Funds come from willing individuals to the tune of tens of MILLIONS of dollars every year; the funds are given without question and with the full knowledge that the money will be used to fight Islam and Muslims. The Alliance has effectively vetted politicians so they too, support the anti-Muslim goals and again, tens of MILLIONS of dollars are directed to anti-Islam efforts, funded entirely by the American tax-payer.
The Alliance is so successful that if the words "Muslim" or Islam" are uttered, the picture that pops up (even in the minds of many Muslims), is a foaming, turbaned Muslim fanatic with a knife in one hand and a gun in the other…possibly wearing a suicide-bomb vest.
Where are the Jews and Christians of good will?
In the face of this Goebbels-like onslaught, their number are shrinking every year. They continue to believe that their America has enduring values that will not get corrupted by the evil ones; that the Alliance should be ignored because is made up of a small number of idiots who can never succeed and America, the most civilized among The Civilized, will never persecute Muslims.
Does this picture evoke any older memories?
Most of us come from the Old country where anyone who raised his head, lost it. Most of us are keeping a low profile, doing our jobs and continue to hope that this ill-wind will also blow away. The fear of involvement is covered up by saying we should pray more and we should not get involved in "politics"…meaning anything to do with activism or the pursuit of civil rights. Rich Muslims pour hundreds of thousands of dollars in their localities, into building Mosques or, funding classes to learn about Islam, for their children, but nothing to counter the efforts of The Alliance.
It should not take a genius to figure out who will win this one-sided "War!" or, how badly the losers stand to lose.
My efforts right or wrong, are devoted to fighting anti-Muslim bigotry and in my fantasy I might live to see the tide turn against The Alliance.
Though something inside me do wish to react against what you've wrote in this post, I have a feeling that it is more based on what you didn't write that what you did write, 'cause I can't disagree, at least in general, with what you wrote. My "problem" though is that I don't know what that "you didn't wrote" is, so I refuse to attempt to guess it.
You are right in stating that there exist "an alliance" (though I wouldn't use that word, more seeing it as a "general notion among Christian and rightist Jews), attempting to promote anti-Muslim views, though it varies how strong the "anti-Muslim" part is.
Many Jews, at least here in Israel, are looking to Christian "friends" with skepticism. It's not that they (or we) view all Christians in a suspicious view, most are decent and good people, as is also the case with Jews and Muslims, but some groups and individuals do have an agenda, which one should be careful of. Many Jews, even rightist Jews, reacted against Beck coming to Israel, even though he has a pro-Israeli agenda.
There are also many Jews here, my self included, who attempt to create awareness of the close bonds and similarities there actually are among Islam and Judaism, and try to remind Jews that though there are Islamic groups who view Jews as their enemies, this doesn't mean that they are representative for the whole world's Muslim societies. Just as Jews are of different understandings and opinions, so is it with Muslims.
What is so sacred about arguing the difference between "some" and "all", but only as related to Jews? Did I not also lump Christians along with Jews?
I don't wish to get sucked into the whirlpool of the "SOME Jews vs. ALL Jews" argument. I did say RIGHT WING Jews and Christians and that, I had hoped, should suffice to say that I only mean those Jews and Christians who are on the right (wrong!) wing and not ALL Jews and Christians. It is quite likely that there may be some Jew or Christian who tells me s-he considers himself to be right wing, but is not prejudiced against Muslims. Okay, I'll accept that, but s-he is likely an army of one among the other right wingers.
I would have to be nuts to lump ALL Jews and Christians as being against Muslims or, not being wonderful people. I won't use the cliche of "some of my best friends…", but I will tell you that my family is aware that should I fail to return some day, it is likely I am the unwilling guest of our security forces (Police, FBI etc.,) and they should contact my most trusted friend and lawyer who is an Israel-supporting Jew.
One of my standard refrains is, "I wish all Jews and Christians were like some Muslims I know and I wish all Muslims were like some Jews and Christians I know".
No, I do not believe right-wing Jews and Christians collected together and signed an agreement of mutual aggression against Muslims. I used the word metaphorically to represent a common goal (of attacking Muslims and Islam). Their sub-groups, including many Churches and Synagues, bring in the same hate-mongers like Geert Wilders and Daniel Pipes, who spout the same bile and false representations of Islam and the Quran, to keep their naive audiences bug-eyed with fear. Right-wing Churches AND Synagogues routinely show video "documentaries" like "Obsession", "Fitna" and "The Third Jihad", to their congregations to instill greater fear and hate towards Muslims and Islam…all based on false or, distorted information about the Quran, Islam and Muslims.
As for what I did not write, you seem to have the advantage over me in that you can read my sub-conscious mind. As far as I am aware, I do not pull punches and I do not write anything I don't believe…nor do I fail to write what I do believe.
Perhaps you can help me out and tell me what I did not write, that you disagree with and then we can have a discussion in which both parties are aware of what is being said.
It is you, not I, who said I left out something and it is you who started an argument based on what I did not write…a most convoluted discussion, worthy of Alice in Wonderland. It reminds me of the torture-chamber interrogation where the victim continues to be tortured because he is accused of not telling all.


text:
One killed when car runs stop sign, gets hit by school bus on Milwaukee's north side
An 18-year-old male passenger in the car died at the scene of the crash in the 3700 block of West Sheridan Avenue.
One killed when car runs stop sign, gets hit by school bus on Milwaukee's north side An 18-year-old male passenger in the car died at the scene of the crash in the 3700 block of West Sheridan Avenue. Check out this story on jsonline.com: https://www.jsonline.com/story/news/2019/02/05/18-year-old-man-killed-milwaukee-crash-involving-car-school-bus/2784484002/
D. Kwas, Milwaukee Journal Sentinel Published 9:07 p.m. CT Feb. 5, 2019
An 18-year-old man was killed Tuesday when the car he was riding in ran a stop sign and was struck by a school bus.
The accident occurred about 4:30 p.m in the 3700 block of West Sheridan Avenue on the city's north side, the Milwaukee Police Department said in a news release.
The 18-year-old victim died at the scene. The driver of the car was arrested, police said.
The bus held only the driver and one passenger. The bus driver remained at the scene and was cooperating with police.
Stay in the know. Sign up to get NewsWatch delivered to your inbox every afternoon.
Read or Share this story: https://www.jsonline.com/story/news/2019/02/05/18-year-old-man-killed-milwaukee-crash-involving-car-school-bus/2784484002/
Two dozen arrested in Milwaukee, elsewhere in case of cocaine sent through mail
Trump rally: 24 hours of fans in line, protests, Packers shoutouts and a VP visit to Culver's
Former UW-Oshkosh administrators fined $75,500 in felony misconduct case
Pocan endorses Sanders, giving him a boost in Wisconsin
Wisconsin voters barely budging on impeachment, Trump and the 2020 Dems
First Trump supporters get into line more than 24 hours before Milwaukee rally

redpajama_set_name: RedPajamaCommonCrawl


text:
The following tables list by region the nominated candidates for the 2022 Ontario general election.

Abbreviations guide
Abbreviations of political parties used in these tables:

All – Ontario Alliance
CCP – Canadians' Choice Party
Consen. – Consensus Ontario
CPO – Ontario Centrist Party
Comm. – Communist Party of Canada (Ontario)
ERP – Electoral Reform Party
FPJ – Freedom of Choice, Peace & Justice Party
FPO – Freedom Party of Ontario
Green – Green Party of Ontario
Ind. – Independent
Liberal – Ontario Liberal Party
Libert. – Ontario Libertarian Party
Mod. – Ontario Moderate Party
N. Ont – Northern Ontario Party
NB – New Blue Party of Ontario
NDP – Ontario New Democratic Party
NOTA – None of the Above Party
PPCFO – People's Progressive Common Front of Ontario
PC – Progressive Conservative Party of Ontario

PBP – Public Benefit Party of Ontario
PCRP – Ontario Provincial Confederation of Regions Party
People – The Peoples Political Party
PSN – Party for People with Special Needs
PPO – Populist Party Ontario
SNSA – Stop the New Sex-Ed Agenda
TOP – Ontario Party

Number of candidates by party

 A Liberal candidate in Parry Sound—Muskoka was deregistered from the party before May 12, 2022, due to allegations of homophobic comments. A Liberal candidate in Chatham-Kent—Leamington was deregistered from the party for past homophobic comments on social media. A replacement candidate for the Liberals in Chatham-Kent—Leamington later withdrew from the race, citing personal attacks after the NDP alleged that the candidate had been registered using the previous candidate's signatures. Additionally, the Liberals did not field a candidate in Timmins due to "a technical issue".

 One New Blue candidate in Ottawa West—Nepean was deregistered on May 17, 2022, due to Canadian military rules.

Candidates and results
† = indicates that the incumbent is not seeking re-election
‡ = indicates that the incumbent is running for re-election in a different riding
§ = represents that the incumbent was refused nomination by their party
$ = represents that the incumbent was announced as nominated by their party but later chose to retire
# = represents that the incumbent was announced as nominated by their party but later lost that party's nomination through departure or expulsion from caucus
bold indicates party leader
italics = indicates a contestant for nomination or declared interest
strikethrough indicates a candidate that has been disqualified or withdrew

Eastern Ontario

|-
| style="background:whitesmoke;"|Bay of Quinte
||
|Todd Smith
|
|Alison Kelly
|
|Emilie Leneveu
|
|Erica Charlton
|
|Rob Collins
|
|Noah Wales
|
|
||
|Todd Smith
|-
| style="background:whitesmoke;"|Glengarry—Prescott—Russell
| |
|Stéphane Sarrazin
|
|Alicia Eglin
|
|Amanda Simard
|
|Thaila Riden
|
|Victor Brassard
|
|Stéphane Aubry
|
|
||
|Amanda Simard
|-
| style="background:whitesmoke;"|Hastings—Lennox and Addington
||
|Ric Bresee
|
|Eric DePoe
|
|Ted Darby
|
|Christina Wilson
|
|Joyce Reid
|
|Derek Sloan
|
|
||
|Daryl Kramp $
|-
| style="background:whitesmoke;"|Kingston and the Islands
|
|Gary Bennett
|
|Mary Rita Holland
||
|Ted Hsu
|
|Zachary Typhair
|
|Stephen Skyvington
|
|Shalea Beckwith
|
|Laurel Claus Johnson(Consen.)Shelley Joanne Galloway (Ind.)Sebastian Vaillancourt (Comm.)
||
|Ian Arthur †
|-
| style="background:whitesmoke;"|Lanark—Frontenac—Kingston
||
|John Jordan
|
|Drew Cumpson
|
|Amanda Pulker-Mok
|
|Marlene Spruyt
|
|Marcin Lewandowski
|
|Thomas Mulder
|
|Chelsea Hillier(PPO)Craig Timothy Massey Rogers(Ind.)
||
|Randy Hillier †
|-
| style="background:whitesmoke;"|Leeds—Grenville—Thousand Islands and Rideau Lakes
||
|Steve Clark
|
|Chris Wilson
|
|Josh Bennett
|
|Fiona Jager
|
|Dan Kitsch
|
|Glenn L. Malcolm
|
|Stephen Ireland(PPCFO)Dave Senger(PPO)Mark Snow(Libert.)
||
|Steve Clark
|-
| style="background:whitesmoke;"|Renfrew—Nipissing—Pembroke
||
|John Yakabuski
|
|Kurt Stoll
|
|Oliver A. Jacob
|
|Anna Dolan
|
|Thomas O'Connor
|
|Kade MacWilliams
|
|Murray Reid(PCRP)
||
|John Yakabuski
|-
| style="background:whitesmoke;"|Stormont—Dundas—South Glengarry
||
|Nolan Quinn
|
|Wendy Stephen
|
|Kirsten J. Gardner
|
|Jacqueline Milner
|
|Claude Tardif
|
|Remi Tremblay
|
|
||
|Jim McDonell $
|}

Ottawa

|-
| style="background:whitesmoke;"|Carleton
||
|Goldie Ghamari
|
|Kevin St. Denis
|
|Tom Dawson
|
|Cody Zulinski
|
|Rob Stocki
|
|Ethan Ferguson
|
|Chris Mark Beauchamp (NOTA)
||
|Goldie Ghamari
|-
| style="background:whitesmoke;"|Kanata—Carleton
||
|Merrilee Fullerton
|
|Melissa Coenraad
|
|Shahbaz Syed
|
|Pat Freel
|
|Jennifer Boudreau
|
|Brian Chuipka
|
|
||
|Merrilee Fullerton
|-
| style="background:whitesmoke;"|Nepean
||
|Lisa MacLeod
|
|Brian Double
|
|Tyler Watt
|
|Kaitlyn Tremblay
|
|Kathleen Corriveau
|
|Bryan Emmerson
|
|
||
|Lisa MacLeod
|-
| style="background:whitesmoke;"|Orléans
|
|Melissa Felián
|
|Gabe Bourdon
||
|Stephen Blais
|
|Michelle Petersen
|
|Liam Randall
|
|Vince Clements
|
|Ken Lewis(Libert.)
||
|Stephen Blais
|-
| style="background:whitesmoke;"|Ottawa Centre
|
|Scott Healey
||
|Joel Harden
|
|Katie Gibbs
|
|Shelby Bertrand
|
|Glen Armstrong
|
|
|
|Marc Adornato(NOTA)Thomas Borcsok(Ind.)Josh Rachlis(Ind.)Stuart Ryan (Comm.)Raymond Samuels(PPCFO)
||
|Joel Harden
|-
| style="background:whitesmoke;"|Ottawa South
|
|Edward Dinca
|
|Morgan Gay
||
|John Fraser
|
|Nira Dookeran
|
|Martin Ince
|
|Myles Dear
|
|Daniel Thomas(Ind.)Larry Wasslen (Comm.)
||
|John Fraser
|-
| style="background:whitesmoke;"|Ottawa—Vanier
|
|Patrick Mayangi
|
|Lyra Evans
||
|Lucille Collard
|
|Christian Proulx
|
|Michael Pastien
|
|Eric Armstrong-Giroux
|
|Coreen Corcoran(Libert.)Blake Hamilton(NOTA)David McGruer(FPO)
||
|Lucille Collard
|-
| style="background:whitesmoke;"|Ottawa West—Nepean
|
|Jeremy Roberts
||
|Chandra Pasma
|
|Sam Bhalesar
|
|Steven Warren
|
|Scott Blandford
|
|Vilteau Delvas
|
|
||
|Jeremy Roberts
|}

Central Ontario

|-
| style="background:whitesmoke;"|Barrie—Innisfil
||
|Andrea Khanjin
|
|Pekka Reinio
|
|John Olthuis
|
|Bonnie North
|
|Ashlyn Steele
|
|Grace Dean
|
|Benjamin Hughes(Ind.)Jake Tucker(PPCFO)
||
|Andrea Khanjin
|-
| style="background:whitesmoke;"|Barrie—Springwater—Oro-Medonte
||
|Doug Downey
|
|Beverley Patchell
|
|Jeff Lehman
|
|Elyse Robinson
|
|Hayden Hughes
|
|Gerry Auger
|
|
||
|Doug Downey
|-
| style="background:whitesmoke;"|Bruce—Grey—Owen Sound
||
|Rick Byers
|
|Karen Gventer
|
|Selwyn J. Hicks
|
|Danielle Valiquette
|
|Vince Grimaldi
|
|Suzanne Coles
|
|Reima Kaikkonen(Ind.)Joel Loughead(NOTA)Joseph Westover (PPO)
||
|Bill Walker $
|-
| style="background:whitesmoke;"|Dufferin—Caledon
||
|Sylvia Jones
|
|Tess Prendergast
|
|Bob Gordanier
|
|Laura Campbell
|
|Andrea Banyai
|
|Lily Nguyen
|
|Erickumar Emmanuel (Mod.)Kay Sayer (PBP)
||
|Sylvia Jones
|-
| style="background:whitesmoke;"|Haliburton—Kawartha Lakes—Brock
||
|Laurie Scott
|
|Barbara Doyle
|
|Don McBey
|
|Tom Regina
|
|Ben Prentice
|
|Kerstin Kelly
|
|Gene Balfour(Libert.)
||
|Laurie Scott
|-
| style="background:whitesmoke;"|Northumberland—Peterborough South
||
|David Piccini
|
|Kim McArthur-Jackson
|
|Jeff Kawzenuk
|
|Lisa Francis
|
|Joshua Chalhoub
|
|Vanessa Head
|
|
||
|David Piccini
|-
| style="background:whitesmoke;"|Peterborough—Kawartha
||
|Dave Smith
|
|Jen Deck
|
|Greg Dempsey
|
|Robert Gibson
|
|Rebecca Quinnell
|
|Tom Marazzo
|
|
||
|Dave Smith
|-
| style="background:whitesmoke;"|Simcoe—Grey
||
|Brian Saunderson
|
|Keith Nunn
|
|Ted Crysler
|
|Allan Kuhn
|
|David Ghobrial
|
|Rodney Sacrey
|
|Billy G Gordon(NOTA)
||
|Jim Wilson †
|-
| style="background:whitesmoke;"|Simcoe North
||
|Jill Dunlop
|
|Elizabeth Van Houtte
|
|Aaron Cayden Hiltz
|
|Krystal Brooks
|
|Mark Douris
|
|Aaron Macdonald
|
|William Joslin(Libert.)
||
|Jill Dunlop
|-
| style="background:whitesmoke;"|York—Simcoe
||
|Caroline Mulroney
|
|Spencer Yang Ki
|
|Walter Alvarez-Bardales
|
|Julie Stewart
|
|Brent Fellman
|
|Alana Hollander
|
|Franco Colavecchia(Mod.)Zachary Tisdale(Libert.)
||
|Caroline Mulroney
|}

905 Belt

Durham

|-
| style="background:whitesmoke;"|Ajax
||
|Patrice Barnes
|
|Christine Santos
|
|Amber Bowen
|
|Neil Runnalls
|
|Garry Reader
|
|Aaron Hopkins
|
|Intab Ali (Ind.)Allen Hadley (Ind.)
|
|Vacant
|-
| style="background:whitesmoke;"|Durham
||
|Todd McCarthy
|
|Chris Borgia
|
|Granville Anderson
|
|Mini Batra
|
|Spencer Ford
|
|Lou De Vuono
|
|Tony Stravato (Ind.)
||
|Lindsey Park #
|-
| style="background:whitesmoke;"|Oshawa
|
|Alex Down
||
|Jennifer French
|
|Catherine Mosca
|
|Katarina Dunham
|
|Daryl Janssen
|
|Dave Forsythe
|
|
||
|Jennifer French
|-
| style="background:whitesmoke;"|Pickering—Uxbridge
||
|Peter Bethlenfalvy
|
|Khalid Ahmed
|
|Ibrahim Daniyal
|
|Julia Rondinone
|
|Elizabeth Tallis
|
|Lisa Robinson
|
|Abir Dabbour (Ind.)Netalia Duboisky (Mod.)Hasan Syed (CPO)
||
|Peter Bethlenfalvy
|-
| style="background:whitesmoke;"|Whitby
||
|Lorne Coe
|
|Sara Labelle
|
|Aadil Mohammed
|
|Stephanie Leblanc
|
|Trystan Lackner
|
|Emil Labaj
|
|Christopher Rinella (Ind.)Douglas Thom (FPO)
||
|Lorne Coe
|}

Peel

|-
| style="background:whitesmoke;"|Brampton Centre
||
|Charmaine Williams
|
|Sara Singh
|
|Safdar Hussain
|
|Karitsa Tye
|
|Kathy Matusiak Costa
|
|
|
|
||
|Sara Singh
|-
| style="background:whitesmoke;"|Brampton East
||
|Hardeep Grewal
|
|Gurratan Singh
|
|Jannat Garewal
|
|Jamaal Blackwood
|
|Mike Bayer
|
|Paul Stark
|
|
||
|Gurratan Singh
|-
| style="background:whitesmoke;"|Brampton North
||
|Graham McGregor
|
|Sandeep Singh
|
|Harinder K Malhi
|
|Aneep Dhade
|
|Jerry Fussek
|
|Julia Bauman
|
|
||
|Kevin Yarde §
|-
| style="background:whitesmoke;"|Brampton South
||
|Prabmeet Singh Sarkaria
|
|Andria Barrett
|
|Marilyn Raphael
|
|Ines Espinoza
|
|Mike Mol
|
|
|
|Mehdi Pakzad (NOTA)
||
|Prabmeet Sarkaria
|-
| style="background:whitesmoke;"|Brampton West
||
|Amarjot Sandhu
|
|Navjit Kaur
|
|Rimmy Jhajj
|
|Pauline Thornham
|
|David Pardy
|
|Manjot Sekhon
|
|
||
|Amarjot Sandhu
|-
| style="background:whitesmoke;"|Mississauga Centre
||
|Natalia Kusendova
|
|Sarah Walji
|
|Sumira Malik
|
|Adriane Franklin
|
|Audrey Simpson
|
|Stephanie Wright
|
|Viktor Chornopyskyy (Mod.)Elie Diab (PPO)Greg Vezina (NOTA)
||
|Natalia Kusendova
|-
| style="background:whitesmoke;"|Mississauga East—Cooksville
||
|Kaleed Rasheed
|
|Khawar Hussain
|
|Dipika Damerla
|
|James Hea
|
|Mark Morrissey
|
|Gregory Tomchyshyn
|
|Wiktor Jachtholtz (Mod.)
||
|Kaleed Rasheed
|-
| style="background:whitesmoke;"|Mississauga—Erin Mills
||
|Sheref Sabawy
|
|Farina Hassan
|
|Imran Mian
|
|Michelle Angkasa
|
|Charles Wroblewski
|
|Laura E. Scarangella
|
|
||
|Sheref Sabawy
|-
| style="background:whitesmoke;"|Mississauga—Lakeshore
||
|Rudy Cuzzetto
|
|Julia Kole
|
|Elizabeth Mendes
|
|David Zeni
|
|Renata Cynarska
|
|George Cescon
|
|Brian Crombie (NOTA)
||
|Rudy Cuzzetto
|-
| style="background:whitesmoke;"|Mississauga—Malton
||
|Deepak Anand
|
|Waseem Ahmed
|
|Aman Gill
|
|Robert Chan
|
|Van Nguyen
|
|
|
|
||
|Deepak Anand
|-
| style="background:whitesmoke;"|Mississauga—Streetsville
||
|Nina Tangri
|
|Nicholas Rabba
|
|Jill Promoli
|
|Reead Rahamut
|
|Amir Kendic
|
|Christine Oliver

|
|Fourat Jajou (PPO)Len Little (NOTA)
||
|Nina Tangri
|}

York

|-
| style="background:whitesmoke;"|Aurora—Oak Ridges—Richmond Hill
||
|Michael Parsa
|
|Reza Pourzad
|
|Marjan Kasirlou
|
|Kevin Zheng
|
|Rosaria Wiseman
|
|Catherine Dellerba
|
|Igor Strelkov (Mod.)
||
|Michael Parsa
|-
| style="background:whitesmoke;"|King—Vaughan
||
|Stephen Lecce
|
|Samantha Sanchez
|
|Gillian Vivona
|
|Ren Guidolin
|
|Michael Di Mascolo
|
|Neil Killips
|
|Tatiana Babitch (Mod.)
||
|Stephen Lecce
|-
| style="background:whitesmoke;"|Markham—Stouffville
||
|Paul Calandra
|
|Kingsley Kwok
|
|Kelly Dunn
|
|Myles O'Brien
|
|Jennifer Gowland
|
|Michele Petit
|
|
||
|Paul Calandra
|-
| style="background:whitesmoke;"|Markham—Thornhill
||
|Logan Kanapathi
|
|Matthew Henriques
|
|Sandra Tam
|
|Zane Abulail
|
|Jennifer Gleason
|
|
|
|Mansoor Qureshi (CPO)
||
|Logan Kanapathi
|-
| style="background:whitesmoke;"|Markham—Unionville
||
|Billy Pang
|
|Senthil Mahalingam
|
|Emily Li
|
|Shanta Sundarason
|
|Trina Kollis
|
|Naz Obredor
|
|
||
|Billy Pang
|-
| style="background:whitesmoke;"|Newmarket—Aurora
||
|Dawn Gallagher Murphy
|
|Denis Heng
|
|Sylvain Roy
|
|Carolina Rodriguez
|
|Iwona Czarnecka
|
|Krista Mckenzie
|
|Yuri Duboisky (Mod.)
||
|Christine Elliott $
|-
| style="background:whitesmoke;"|Richmond Hill
||
|Daisy Wai
|
|Raymond Bhushan
|
|Roozbeh Farhadi
|
|Hasen Nicanfar
|
|Les Hoffman
|
|Ramtin Biouckzadeh
|
|Olga Rykova (Mod.)
||
|Daisy Wai
|-
| style="background:whitesmoke;"|Thornhill
||
|Laura Smith
|
|Jasleen Kambo
|
|Laura Mirabella
|
|Daniella Mikanovsky
|
|Yakov Zarkhine
|
|Igor Tvorogov
|
|Jacob Joel Ginsberg (Ind.)Hiten Patel Roman Pesis (FPJ) Aleksei Polyakov (Mod.)Brandon Ying (Ind.)
||
|Gila Martow †
|-
| style="background:whitesmoke;"|Vaughan—Woodbridge
||
|Michael Tibollo
|
|Will McCarty
|
|Steven Del Duca
|
|Philip James Piluris
|
|Luca Mele
|
|Gerrard Fortin
|
|Mario Greco (PPO)Walid Omrani (Mod.)
||
|Michael Tibollo
|}

Toronto

Scarborough

|-
| style="background:whitesmoke;"|Scarborough—Agincourt
||
|Aris Babikian
|
|Benjamin Lee Truong
|
|Soo Wong
|
|Jacqueline Scott
|
|Rane Vega
|
|Donny Morgan
|
|
||
|Aris Babikian
|-
| style="background:whitesmoke;"|Scarborough Centre
||
|David Smith
|
|Neethan Shan
|
|Mazhar Shafiq
|
|Fatima Faruq
|
|Hidie Jaber
|
|Raphael Rosch
|
|Paul Beatty(Ind.)Serge Korovitsyn(Libert.)Kostadinos Stefanis(Ind.)Maria Tzvetanova(Mod.)
||
|Christina Mitas $
|-
| style="background:whitesmoke;"|Scarborough—Guildwood
|
|Alicia Vianga
|
|Veronica Javier
||
|Mitzie Hunter
|
|Dean Boulding
|
|Opa Hope Day
|
|William Moore
|
|Kevin Clarke(People)
||
|Mitzie Hunter
|-
| style="background:whitesmoke;"|Scarborough North
||
|Raymond Cho
|
|Justin Kong
|
|Anita Anandarajan
|
|Tara McMahon
|
|James Bountrogiannis
|
|Pete Grusys
|
|Mark Dickson(NOTA)
||
| Raymond Cho
|-
| style="background:whitesmoke;"|Scarborough—Rouge Park
||
|Vijay Thanigasalam
|
|Felicia Samuel
|
|Manal Abdullahi
|
|Priyan De Silva
|
|Christopher Bressi
|
|Gordon Kerr
|
|Matthew Oliver(FPO)
||
|Vijay Thanigasalam
|-
| style="background:whitesmoke;"|Scarborough Southwest
|
|Bret Snider
||
|Doly Begum
|
|Lisa Patel
|
|Cara Brideau
|
|Peter Naus
|
|Barbara Everatt
|
|James McNair(NOTA)Michelle Parsons(Ind.)
||
|Doly Begum
|}

North York

|-
| style="background:whitesmoke;"|Don Valley East
|
|Sam Moini
|
|Mara-Elena Nagy
||
|Adil Shamji
|
|Rizwan Khan
|
|Denyse Twagiramariya
|
|Donald McMullen
|
|
|
|Svetlozar Aleksiev(Mod.)Stella Kargiannakis(Ind.)Dimitre Popov(Consen.)Wayne Simmons(FPO)
||
|Vacant
|-
| style="background:whitesmoke;"|Don Valley North
||
|Vincent Ke
|
|Ebrahim Astaraki
|
|Jonathan Tsao
|
|Ostap Soroka
|
|Jay Sobel
|
|
|
|
|
|
||
|Vincent Ke
|-
|style="background:whitesmoke;"|Don Valley West
|
|Mark Saunders
|
|Irwin Elman
||
|Stephanie Bowman
|
|Sheena Sharp
|
|Laurel Hobbs
|
|Kylie Mc Allister
|
|
|
|John Kittredge (Libert.)John Kladitis(Ind.)Paul Reddick(Consen.)
||
|Kathleen Wynne †
|-
| style="background:whitesmoke;"|Eglinton—Lawrence
||
|Robin Martin
|
|Natasha Doyle-Merrick
|
|Arlena Hebert
|
|Leah Tysoe
|
|Erwin Sniedzins
|
|Lauren Dearing
|
|Bryant Thompson
|
|Jonathan Davis(PBP)Sam Kaplun(Ind.)Derek Sharp(PSN)
||
|Robin Martin
|-
| style="background:whitesmoke;"|Willowdale
||
|Stan Cho
|
|Hal David Berman
|
|Paul Saguil
|
|Monica Henriques
|
|Joanne Csillag
|
|Gian Pietro Arella
|
|Ben Barone
|
|Birinder Singh Ahluwalia(Ind.)Lilya Eklishaeva(FPJ)Jaime Rodriguez(PPO)Charles Roddy Sutherland(Ind.)
||
|Stan Cho
|-
| style="background:whitesmoke;"|York Centre
||
|Michael Kerzner
|
|Frank Chu
|
|Shelley Brown
|
|Alison Lowney
|
|Don Pincivero
|
|Nick Balaskas
|
|Mark Dewdney
|
|Parviz Isgandarov(Mod.)Lionel Wayne Poizner(PSN)
||
|Roman Baber †
|}

Central Toronto and East York

|-
| style="background:whitesmoke;"|Beaches—East York
|
|Angela Kennedy
|
|Kate Dupuis
||
|Mary-Margaret McMahon
|
|Abhijeet Manay
|
|Stephen Roney
|
|John Ferguson
|

|Drew Garvie (Comm.)Joe Ring(NOTA)Bahman Yazdanfar(CCP)
||
|Rima Berns-McGown $
|-
| style="background:whitesmoke;"|Davenport
|
|Paul Spence
||
|Marit Stiles
|
|Jerry Levitan
|
|Karen Stephenson
|
|Mario Bilusic
|
|Diti Coutinho
|
|Nicholas Alexander(Ind.)Jack Copple (Comm.)Simon Fogel(Ind.)Nunzio Venuto(Libert.)
||
|Marit Stiles
|-
| style="background:whitesmoke;"|Parkdale—High Park
|
|Monika Frejlich
||
|Bhutila Karpoche
|
|Karim Bardeesy
|
|Patrick Macklem
|
|Danielle Height
|
|Craig Peskett
|
|Gunes Agduk(Comm.)Oliver Roberts(People)
||
|Bhutila Karpoche
|-
| style="background:whitesmoke;"|Spadina—Fort York
|
|Husain Neemuchwala
||
|Chris Glover
|
|Chi Nguyen
|
|Cara Des Granges
|
|Angela Asher
|
|
|
|Jan Osko(SNSA)
||
|Chris Glover
|-
| style="background:whitesmoke;"|Toronto Centre
|
|Jessica Goddard
||
|Kristyn Wong-Tam
|
|David Morris
|
|Nicki Ward
|
|Steve Hoehlmann
|
|
|
|Ivan Byard (Comm.)Jennifer Snell(SNSA)Ron Shaw(NOTA)
||
|Suze Morrison $
|-
| style="background:whitesmoke;"|Toronto—Danforth
|
|Colleen McCleery
||
|Peter Tabuns
|
|Mary Fragedakis
|
|Marcelo Levy
|
|Milton Kandias
|
|George Simopoulos
|
|Christopher Brophy(NOTA)Jennifer Moxon (Comm.)
||
|Peter Tabuns
|-
| style="background:whitesmoke;"|Toronto—St. Paul's
|
|Blake Libfeld
||
|Jill Andrew
|
|Nathan Stall
|
|Ian Lipton
|
|Yehuda Goldberg
|
|Christian Ivanov Mihaylov
|
|Zoë Alexandra (PPO)Margarita Sharapova(Mod.)
||
|Jill Andrew
|-
| style="background:whitesmoke;"|University—Rosedale
|
|Carl Qiu
||
|Jessica Bell
|
|Andrea Barrack
|
|Dianne Saxe
|
|James Leventakis
|
|
|
|John Kanary(SNSA)
||
|Jessica Bell
|}

Etobicoke and York

|-
| style="background:whitesmoke;"|Etobicoke Centre
||
|Kinga Surma
|
|Heather Vickers-Wong
|
|Noel Semple
|
|Brian MacLean
|
|Cathy Habus
|
|Mitchell Gilboy
|
|Richard M. Kiernicki (NOTA)Genadij Zaitsev(Mod.)
||
|Kinga Surma
|-
| style="background:whitesmoke;"|Etobicoke—Lakeshore
||
|Christine Hogarth
|
|Farheen Alim
|
|Lee Fairclough
|
|Thomas Yanuziello
|
|Mary Markovic
|
|
|
|Bill Denning(Ind.)Vitas Naudziunas(NOTA)
||
|Christine Hogarth
|-
| style="background:whitesmoke;"|Etobicoke North
||
|Doug Ford
|
|Aisha Jahangir
|
|Julie Lutete
|
|Gabriel Blanc
|
|Victor Ehikwe
|
|Andy D'Andrea
|
|Carol Royer (People)
||
|Doug Ford
|-
| style="background:whitesmoke;"|Humber River—Black Creek
|
|Paul Nguyen
||
|Tom Rakocevic
|
|Ida Li Preti
|
|Keith Berry
|
|Iulian Caunei
|
|Lee Miguel Gonzalez
|
|Knia Singh(Ind.)
||
|Tom Rakocevic
|-
| style="background:whitesmoke;"|York South—Weston
||
|Michael Ford
|
|Faisal Hassan
|
|Nadia Guerrera
|
|Ignacio Mongrell Gonzalez
|
|Tom Hipsz
|
|Ana Gabriela Ortiz
|
|James Michael Fields(Ind.)
||
|Faisal Hassan
|}

Hamilton, Halton and Niagara

Halton

|-
| style="background:whitesmoke;"|Burlington
||
|Natalie Pierre
|
|Andrew Drummond
|
|Mariam Manaa
|
|Kyle Hutton
|
|Allison McKenzie
|
|Sebastian Aldea
|
|
||
|Jane McKenna $
|-
| style="background:whitesmoke;"|Milton
||
|Parm Gill
|
|Katherine Cirlincione
|
|Sameera Ali
|
|Oriana Knox
|
|John Spina
|
|
|
|Masood Khan(Consen.)
||
|Parm Gill
|-
| style="background:whitesmoke;"|Oakville
||
|Stephen Crawford
|
|Maeve McNaughton
|
|Alison Gohel
|
|Bruno Sousa
|
|Mark Fraser Platt
|
|Alicia Bedford
|
|Stephen Kenneth Crawford (NOTA)Andrew Titov(Mod.)Silvio Ursomarzo(FPO)
||
|Stephen Crawford
|-
| style="background:whitesmoke;"|Oakville North—Burlington
||
|Effie Triantafilopoulos
|
|Rhyan Vincent-Smith
|
|Kaniz Mouli
|
|Ali Hosny
|
|Doru Marin Gordan
|
|Jill Service
|
|
||
|Effie Triantafilopoulos
|}

Hamilton

|-
| style="background:whitesmoke;"|Flamborough—Glanbrook
||
|Donna Skelly
|
|Allison Cillis
|
|Melisse Willems
|
|Mario Portak
|
|Paul Simoes
|
|Walt Juchniewicz
|
|Nikita Mahood(PPO)
||
|Donna Skelly
|-
| style="background:whitesmoke;"|Hamilton Centre
|
|Sarah Bokhari
||
|Andrea Horwath
|
|Ekaterini Dimakis
|
|Sandy Crawley
|
|John Chroust
|
|Brad Peace
|
|Nigel Cheriyan (Comm.)Nathalie Xian Yi Yan(Ind.)
||
|Andrea Horwath
|-
| style="background:whitesmoke;"|Hamilton East—Stoney Creek
||
|Neil Lumsden
|
|Zaigham Butt
|
|Jason Farr
|
|Cassie Wylie
|
|Jeffery Raulino
|
|Domenic DiLuca
|
|Paul Miller(Ind.)Cameron Rajewski(ERP)
||
|Paul Miller
|-
| style="background:whitesmoke;"|Hamilton Mountain
|
|Michael Spadafora
||
|Monique Taylor
|
|Chantale Lachance
|
|Janet Errygers
|
|Baylee Nguyen
|
|Andy Busa
|
|
||
|Monique Taylor
|-
| style="background:whitesmoke;"|Hamilton West—Ancaster—Dundas
|
|Fred Bennink
||
|Sandy Shaw
|
|Shubha Sandill
|
|Syam Chandra
|
|Lee Weiss
|
|Frank Thiessen
|
|
||
|Sandy Shaw
|}

Niagara

|-
| style="background:whitesmoke;"|Niagara Centre
|
|Fred Davies
||
|Jeff Burch
|
|Terry Flynn
|
|Michelle McArthur
|
|Gary Dumelie
|
|Vincent Gircys
|
|
||
|Jeff Burch
|-
| style="background:whitesmoke;"|Niagara Falls
|
|Bob Gale
||
|Wayne Gates
|
|Ashley Waters
|
|Tommy Ward
|
|Christine Lewis-Napolitano
|
|Wesley Kavanagh
|
|Devon St. Denis-Richard(NOTA)
||
|Wayne Gates
|-
| style="background:whitesmoke;"|Niagara West
||
|Sam Oosterhoff
|
|Dave Augustyn
|
|Doug Joyner
|
|Laura Garner
|
|Chris Arnew
|
|Dan Dale
|
|Stefanos Karatopis(Libert.)Jim Torma (PPO)
||
|Sam Oosterhoff
|-
| style="background:whitesmoke;"|St. Catharines
|
|Sal Sorrento
||
|Jennie Stevens
|
|Ryan Madill
|
|Michele Braniff
|
|Keith McDonald
|
|Michael Goddard
|
|Judi Falardeau(Libert.)J. Justin O'Donnell(All)Rin Simon(Comm.)
||
|Jennie Stevens
|}

Midwestern Ontario

|-
| style="background:whitesmoke;"|Brantford—Brant
||
|Will Bouma
|
|Harvey Bischof
|
|Ruby Toor
|
|Karleigh Csordas
|
|Tad Brudzinski
|
|Allan Wilson
|
|Leslie Bory (CCP)Rob Ferguson (Libert.)John Turmel (Ind.)
||
|Will Bouma
|-
| style="background:whitesmoke;"|Cambridge
||
|Brian Riddell
|
|Marjorie Knight
|
|Surekha Shenoy
|
|Carla Johnson
|
|Belinda Karahalios
|
|
|
|
||
|Belinda Karahalios
|-
| style="background:whitesmoke;"|Guelph
|
|Peter McSherry
|
|James Parr
|
|Raechelle Devereaux
||
|Mike Schreiner
|
|Will Lomker
|
|
|
|Juanita Burnett (Comm.)Paul Taylor (NOTA)
||
|Mike Schreiner
|-
| style="background:whitesmoke;"|Haldimand—Norfolk
|
|Ken Hewitt
|
|Sarah Lowe
|
|Aziz Chouhdery
|
|Erik Coverdale
|
|Nate Hawkins
|
|Sheldon Simpson
||
|Bobbi Ann Brady (Ind.)George McMorrow (Ind.)Thecla Ross(FPO)
||
|Toby Barrett $
|-
| style="background:whitesmoke;"|Huron—Bruce
||
|Lisa Thompson
|
|Laurie Hazzard
|
|Shelley Blackmore
|
|Matthew Van Ankum
|
|Matt Kennedy
|
|Gerrie Huenemoerder
|
|Bruce Eisen(All)Ronald Stephens(Ind.)
||
|Lisa Thompson
|-
| style="background:whitesmoke;"|Kitchener Centre
|
|Jim Schmidt
||
|Laura Mae Lindo
|
|Kelly Steiss
|
|Wayne Mak
|
|Peter Beimers
|
|
|
|
||
|Laura Mae Lindo
|-
| style="background:whitesmoke;"|Kitchener—Conestoga
||
|Mike Harris Jr.
|
|Karen Meissner
|
|Melanie Van Alphen
|
|Nasir Abdulle
|
|Jim Karahalios
|
|Elisabeth Perrin Snyder
|
|Jason Adair(PPO)
||
|Mike Harris Jr.
|-
| style="background:whitesmoke;"|Kitchener South—Hespeler
||
|Jess Dixon
|
|Joanne Weston
|
|Ismail Mohamed
|
|David Weber
|
|John Teat
|
|David Gillies
|
|
||
|Amy Fee †
|-
| style="background:whitesmoke;"|Oxford
||
|Ernie Hardeman
|
|Lindsay Wilson
|
|Mary Holmes
|
|Cheryle Rose Baker
|
|Connie Oldenburger
|
|Karl Toews
|
|
||
|Ernie Hardeman
|-
| style="background:whitesmoke;"|Perth—Wellington
||
|Matthew Rae
|
|Jo-Dee Burbach
|
|Ashley Fox
|
|Laura Bisutti
|
|Bob Hosken
|
|Sandy William MacGregor
|
|Robby Smink (FPO)
||
|Randy Pettapiece $
|-
| style="background:whitesmoke;"|Waterloo
|
|Andrew Aitken
||
|Catherine Fife
|
|Jennifer Tuck
|
|Shefaza Esmail
|
|Vladimir Voznyuk
|
|Benjamin Hufnagel
|
|Peter House(ERP)Christian Shingiro (Comm.)
||
|Catherine Fife
|-
| style="background:whitesmoke;"|Wellington—Halton Hills
||
|Ted Arnott
|
|Diane Ballantyne
|
|Tom Takacs
|
|Ryan Kahro
|
|Stephen Kitras
|
|
|
|Ron Patava(Consen.)
||
|Ted Arnott
|}

Southwestern Ontario

|-
| style="background:whitesmoke;"|Chatham-Kent—Leamington
||
|Trevor Jones
|
|Brock McGregor
|
|
|
|Jennifer Surerus
|
|Rhonda Jubenville
|
|Rick Nicholls
|
|
||
|Rick Nicholls
|-
| style="background:whitesmoke;"|Elgin—Middlesex—London
||
|Rob Flack
|
|Andy Kroeker
|
|Heather Jackson
|
|Amanda Stark
|
|Matt Millar
|
|Brigitte Belton
|
|Malichi Malé(Consen.)Dave Plumb(FPO)
||
|Vacant
|-
| style="background:whitesmoke;"|Essex
||
|Anthony Leardi
|
|Ron LeClair
|
|Manpreet Brar
|
|Nicholas Wendler
|
|Danielle Sylvester
|
|Frank Causarano
|
|Kevin Linfield(NOTA)
||
|Taras Natyshak †
|-
| style="background:whitesmoke;"|Lambton—Kent—Middlesex
||
|Monte McNaughton
|
|Vanessa Benoit
|
|Bruce Baker
|
|Wanda Dickey
|
|David Barnwell
|
|Aaron Istvan Vegh
|
|Dean Eve(NOTA)
||
|Monte McNaughton
|-
| style="background:whitesmoke;"|London—Fanshawe
|
|Jane Kovarikova
||
|Teresa Armstrong
|
|Zeba Hashmi
|
|Zack Ramsey
|
|Adriana A. Medina
|
|Doug Macdonald
|
|Stephen R. Campbell(NOTA)Dave Durnin (FPO)T Paul Plumb(Consen.)
||
|Teresa Armstrong
|-
| style="background:whitesmoke;"|London North Centre
|
|Jerry Pribil
||
|Terence Kernaghan
|
|Kate Graham
|
|Carol Dyck
|
|Tommy Caldwell
|
|Darrel Grant
|
|George Le Mac(Consen.)Paul McKeever(FPO)
||
|Terence Kernaghan
|-
| style="background:whitesmoke;"|London West
|
|Paul Paolatto
||
|Peggy Sattler
|
|Vanessa Lalonde
|
|Colleen McCauley
|
|Kristopher Hunt
|
|Cynthia Workman
|
|Jacques Y Boudreau(Libert.)Brad Harness(Consen.)Mike McMullen(FPO)
||
|Peggy Sattler
|-
| style="background:whitesmoke;"|Sarnia—Lambton
||
|Bob Bailey
|
|Dylan Stelpstra
|
|Mark Russell
|
|Mason Bourdeau
|
|Keith Benn
|
|Ian Orchard
|
|Carla Olson(PPO)
||
|Bob Bailey
|-
| style="background:whitesmoke;"|Windsor—Tecumseh
||
|Andrew Dowie
|
|Gemma Grey-Hall
|
|Gary Kaschak
|
|Melissa Coulbeck
|
|Sophia Sevo
|
|Steven Gifford
|
|Giovanni Abati(Ind.)Nick Babic(Ind)Laura Chesnik(Ind.)David Sylvestre(NOTA)
||
|Percy Hatfield †
|-
| style="background:whitesmoke;"|Windsor West
|
|John Leontowicz
||
|Lisa Gretzky
|
|Linda L McCurdy
|
|Krysta Glovasky-Ridsdale
|
|Joshua Griffin
|
|Jeremy Palko
|
|
||
|Lisa Gretzky
|}

Northern Ontario

Northeastern Ontario

|-
| style="background:whitesmoke;"|Algoma—Manitoulin
|
|Cheryl Fort
||
|Michael Mantha
|
|Tim Vine
|
|Maria Legault
|
|Ron Koski
|
|Frederick Weening
|
|
||
|Michael Mantha
|-
| style="background:whitesmoke;"|Mushkegowuk—James Bay
|
|Eric Côté
||
|Guy Bourgouin
|
|Matthew Pronovost
|
|Catherine Jones
|
|Michael Buckley
|
|
|
|Fauzia Sadiq(PCRP)
||
|Guy Bourgouin
|-
| style="background:whitesmoke;"|Nickel Belt
|
|Randy Hazlett
||
|France Gélinas
|
|Gilles Proulx
|
|Glenys Babcock
|
|Melanie Savoie
|
|Willy Schneider
|
|
||
|France Gélinas
|-
| style="background:whitesmoke;"|Nipissing
||
|Vic Fedeli
|
|Erika Lougheed
|
|Tanya Vrebosch
|
|Sean McClocklin
|
|Taylor Russell
|
|Joe Jobin
|
|Michelle Lashbrook (Libert.)Giacomo Vezina(NOTA)
||
|Vic Fedeli
|-
| style="background:whitesmoke;"|Parry Sound—Muskoka
||
|Graydon Smith
|
|Erin Horvath
|
|
|
|Matt Richter
|
|Doug Maynard
|
||Andrew John Cocks
|
|Daniel Predie Jr.(Ind.)Brad Waddell (PPO)
||
|Norm Miller $
|-
| style="background:whitesmoke;"|Sault Ste. Marie
||
|Ross Romano
|
|Michele McCleave-Kennedy
|
|Liam Hancock
|
|Keagan Gilfillan
|
|S. Pankhurst
|
|
|
|Naomi Sayers(Ind.)
||
|Ross Romano
|-
| style="background:whitesmoke;"|Sudbury
|
|Marc Despatie
||
|Jamie West
|
|David Farrow
|
|David Robinson
|
|Sheldon Pressey
|
|Jason Laface
|
|Adrien Berthier(Libert.)J. David Popescu (Ind.)
||
|Jamie West
|-
| style="background:whitesmoke;"|Timiskaming—Cochrane
|
|Bill Foy
||
|John Vanthof
|
|Brian Johnson
|
|Kris Rivard
|
|Garry Andrade
|
|Geoffrey Aitchison
|
|Eric Cummings (Libert.)Jeff Wilkinson(NOTA)
||
|John Vanthof
|-
| style="background:whitesmoke;"|Timmins
||
|George Pirie
|
|Gilles Bisson
|
|
|
|Elizabeth Lockhard
|
|David Farrell
|
|
|
|Nadia Sadiq(PCRP)
||
|Gilles Bisson
|}

Northwestern Ontario

|-
| style="background:whitesmoke;"|Kenora—Rainy River
||
|Greg Rickford
|
|JoAnne Formanek Gustafson
|
|Anthony Leek
|
|Catherine Kiewning
|
|Kelvin Boucher-Chicago
|
|Larry Breiland
|
|Richard A. Jonasson
|
|Mi'Azhikwan(Ind.)
||
|Greg Rickford
|-
| style="background:whitesmoke;"|Kiiwetinoong
|
|Dwight Monck
||
|Sol Mamakwa
|
|Manuela Michelizzi
|
|Suzette A. Foster
|
|Alex Dornn
|
|
|
|
|
|
||
|Sol Mamakwa
|-
| style="background:whitesmoke;"|Thunder Bay—Atikokan
||
|Kevin Holland
|
|Judith Monteith-Farrell
|
|Rob Barrett
|
|Eric Arner
|
|David Tommasini
|
|Dan Criger
|
|
|
|Kenneth Jones(N. Ont)
||
|Judith Monteith-Farrell
|-
| style="background:whitesmoke;"|Thunder Bay—Superior North
|
|Peng You
||
|Lise Vaugeois
|
|Shelby Ch'ng
|
|Tracey Allison MacKinnon
|
|Katherine Suutari
|
|Stephen Hufnagel
|
|Adam Cherry
|
|Andy Wolff(N. Ont)
||
|Michael Gravelle
|}

References

Candidates in Ontario provincial elections

redpajama_set_name: RedPajamaWikipedia


text:
The Magnetic Fields' Stephin Merritt to Star in a Live Video Game
By Emily Temple
We've been having warm and fuzzy feelings about Stephin Merritt ever since the Magnetic Fields' '90s revival track "Andrew in Drag" hit the internet, but turns out the oddball frontman just as keen on the future as he is on the past. In collaboration with D.I.Y. video arcade collective Babycastles, the Museum of Natural History is presenting Space Cruiser, a 200-person video game starring Merritt, available to play next Thursday only.
As The Observer explains, the planetarium at the Rose Center for Earth and Space has been re-imagined as a spaceship, which you will be able to control (with a bunch of other music and video game nerds). Babycastles co-founder Syed Salahuddin elucidated further: "A multi-player control deck at the center of the theater will offer a small crew the chance to navigate the spacecraft together, piloting through treacherous asteroid belts. The crew is an unassigned, free-form team that works together to pilot the spaceship, with various parts of the ship spread out over the theater while being guided by a on-board computer system that is voiced by Stephin Merritt of the Magnetic Fields." This may be crazy, or this may be totally awesome. The only way to find out is to buy a ticket (try the discount code "BEYOND") and let us know. [via Stereogum]


Hello and welcome to our dojo's website. We invite you to join us for an aikido seminar with Yoko Okamoto Sensei from Aikido Kyoto, September 29th, 30th, and October 1st, 2017. You can find information about our seminar on the events page.


Italy's economy entered a recession at the end of last year amid sluggish growth in the entire euro area, the BBC reports.
In the last quarter of 2018, Italy's economy shrank by 0.2 percent, after a 0.1 percent decline in the third quarter, according to Italy's statistics office.
PM Giuseppe Conte said that the decline was likely to continue into this year.
Economic growth in the 19-country eurozone only reached 0.2 percent in the final quarter of 2018, while for the entire 28-nation bloc the growth reached 0.3 percent.
In Italy, agriculture, forestry, fishing and industry all contributed to the economic decline, and rising net exports could not make up for the losses.
The European Commission forced the Italian government to revise its 2019 budget last month amid concerns that it would have a major impact on the country's debt levels.


text:
package com.freshplanet.nativeExtensions;

import android.util.Log;

import com.adobe.fre.FREContext;
import com.adobe.fre.FREFunction;
import com.adobe.fre.FREObject;

public class FetchStarterNotificationFunction implements FREFunction {

	private static String TAG = "NotifActivity";

	@Override
	public FREObject call(FREContext arg0, FREObject[] arg1) {
		if(NotificationActivity.isComingFromNotification == null) {
			Log.e(TAG, "fetchStarterNotificationFunction called before NotificationActivity was created - not supposed to happen");
			NotificationActivity.isComingFromNotification = false;
		}
		if (NotificationActivity.isComingFromNotification)
		{
			String params = NotificationActivity.notifParams;
			params = params == null ? "" : params;
			Log.d(TAG, "coming from notif :"+params);

			arg0.dispatchStatusEventAsync("APP_STARTING_FROM_NOTIFICATION", params);
		} else
		{
			Log.d(TAG, "not coming from notif");
		}
		return null;
	}

}

redpajama_set_name: RedPajamaGithub


The Institute of Human Virology Nigeria (IHVN), a leading indigenous non-governmental organization implementing a comprehensive family-focused HIV/AIDS prevention, care and treatment program in partnership with local and international organizations and the different tiers of the Government of Nigeria at national, state, local and site levels, is looking for a Financial Assistant. The Assistant will work in the Finance Department of the Institute to support its various thematic units in making sure that adequate measures are in place for controlling and safeguarding the assets of Institute.
Membership of the Institute of Chartered Accountants of Nigeria or similar profession qualification is not required but possession of such may be an added advantage.
Successful applicants will undergo financial and criminal background check and will be required to secure fraud insurance bond and a surety who must be a blood relative.
Photocopy and dispatch of vouchers to Head Office.


text:
\section{Introduction}
\label{sec:intro}

Computing the relative pose (position and orientation) between two cameras given an image pair is a fundamental computer vision task and an essential step in multiview structure from motion (SfM) pipelines \cite{ozyecsil2017survey, snavely2006photo,schoenberger2016sfm, wu2011visualsfm, wu2013towards}, with applications to autonomous robot navigation and to virtual and augmented reality. Classical techniques rely on keypoint extraction and matching using descriptors such as SIFT \cite{lowe2004distinctive,bay2006surf,rublee2011orb,calonder2010brief,rosten2006machine}, followed by robust estimation of the essential matrix using, e.g., RANSAC \cite{fischler1981random}. These methods can recover the relative pose very accurately. However, (1) they only recover five out of the six degrees of freedom of the relative pose; the distance between the two camera positions cannot be recovered from point correspondences; (2) they can be slow due to an exhaustive search of most consistent relative pose model by RANSAC, and (3) they may fail when the scene includes repetitive structures or when there are lighting differences.

To overcome these drawbacks, a number of recent papers have explored the use of deep neural networks for relative pose regression (RPR), see  discussion in Section~\ref{sec:related work}. These new methods use appearance and pose priors to guide the matching of point features and to select the likely relative pose given an input image pair. For instance, deep methods are capable of recovering the distance between two cameras by exploiting priors on the size of the observed objects. Nevertheless, current deep methods are not as accurate as the classical approach in many settings, and moreover, they mostly generalize to new image pairs only within the same scenes used for training.

Inspired partially by the classical techniques, we propose here a new, generalizable deep architecture for relative pose estimation. Our architecture includes three modules, a dense feature description module based on a pretrained LoFTR architecture \cite{sun2021loftr}, a matching and warping module, and a pose regressor. LoFTR is a powerful local feature matching architecture based on interleaved self- and cross-attention modules that has been shown to generalize across scenes and even across different datasets. Here we use LoFTR to produce a semi-dense feature embedding of the two input images. Given these descriptors and an encoding of the associated image coordinates, our second module selects and warps the likely point matches. Finally, the pose regressor uses these warped matches to output the full six degrees of freedom depicting the relative position and orientation of the two cameras. We test our method on various datasets and show that it achieves superior performance to existing, deep-based methods on challenging image pairs, including ones that depict novel scenes and even unseen datasets, in particular with limited training data.

\section{Related Work}
\label{sec:related work}

The pose between two images with
known intrinsic parameters can be recovered by decomposing the essential matrix \cite{hartley2003multiple}.
This yields the relative rotation and a scaleless translation vector. The essential
matrix is classically estimated by matching local features, such as \cite{lowe2004distinctive,bay2006surf,rublee2011orb,calonder2010brief,rosten2006machine}, followed by a numerical scheme, e.g., the 5-point solver \cite{nister2004efficient} inside robust estimator loop, namely RANSAC \cite{fischler1981random} and others \cite{barath2019magsac,barath2020magsac++,raguram2008comparative}. Despite the success of this classical approach, it lacks the ability to utilize priors, for example, to infer the unknown distance between the two cameras. These shortcomings have led to the introduction of deep-based approaches, which we review next.

\noindent\textbf{Deep feature extraction and matching}. Several studies have utilized  deep networks to detect and match point features in image pairs \cite{revaud2019r2d2,dusmanu2019d2,bhowmik2020reinforced,sarlin20superglue,tyszkiewicz2020disk,yi2016lift,truong2020glu,truong2020gocor,truong2021learning,sun2021loftr,jiang2021cotr}. Among these, LIFT \cite{yi2016lift} is an early approach that additionally assigns an orientation to each feature. Superglue \cite{sarlin20superglue} uses a Graph Neural Network (GNN) to match two sets of interest points; for example, on top of SuperPoint\cite{detone2018superpoint} keypoints and descriptors. A line of works utilizes a neural network for constructing a differentiable matching module (suggesting also matching confidence) \cite{truong2020glu,truong2020gocor,truong2021learning}. The recently proposed works LoFTR \cite{sun2021loftr} and COTR
\cite{jiang2021cotr} utilize self- and cross-attention layers and multiscale analysis to produce semi-dense, near pixel-wise  correspondences. Relative pose is then obtained by applying RANSAC to the extracted correspondences. Those methods were shown to produce accurate correspondences even in low-texture areas, demonstrating good generalization ability across scenes and datasets.  Still, those methods are not end-to-end approaches, and the relative position is recovered up to a scale factor.

\noindent\textbf{Deep RANSAC}. Recent works sought to construct a deep network architecture that can robustly estimate the essential matrix from point matches. Specifically, DSAC \cite{brachmann2017dsac} replaces RANSAC's hypothesis selection with a robust average of hypotheses, resulting in a differentiable counterpart of RANSAC. \cite{choy2020high} utilizes convolutions in high dimension to recover the essential matrix robustly.  \cite{ranftl2018deep,brachmann2019expert,brachmann2019neural} propose a deep-based scheme to distinguish between inliers and outliers, given putative correspondences, for tasks such as fundamental matrix estimation and camera re-localization.

\noindent\textbf{End-to-end relative pose regression (RPR)}.
A stream of deep learning methods attempts to predict the relative pose directly from two input images, circumventing the need to detect 2D putative matches explicitly.
Early works \cite{melekhov2017relative,laskar2017camera} used ResNet34 pre-trained for classification on the Imagenet dataset to produce holistic feature vectors that describe the two input images. An MLP module was then applied to these feature vectors to regress the relative position, with its magnitude, as well as the relative orientation. Subsequent works \cite{abouelnaga2021distillpose, zhou2020learn, winkelbauer2021learning, en2018rpnet,balntas2018relocnet} utilized a similar approach using modified loss functions and architectures. All of these methods have a limited cross-scene generalization ability.

More recently, \cite{rockwell20228} advocated the use of Vision Transformer (ViT) feature maps \cite{dosovitskiy2020image, vaswani2017attention} for relative pose regression. They further incorporated quadratic positional encoding to produce an architecture that mimics the Eight-Point Algorithm for essential matrix recovery \cite{hartley1997defense}. Lastly, Map-free  \cite{arnold2022map} used a residual UNet (ResUNet) architecture to produce a feature map for each image. A soft feature matching was obtained by computing a 4D correlation volume followed by softmax. This soft matching was then used to warp the features of one image into their corresponding locations in the other image. A regression module was finally used to estimate the relative pose. Overall, this pipeline is similar to ours, except that we replaced the UNet feature extractor with the more powerful LoFTR architecture and the soft feature matching with a hard one. These allow us to significantly improve accuracy and achieve cross scene generalization with limited training data.

\section{Method}
\label{sec:method}
\begin{figure*}
    \centering
    \includegraphics[width=\textwidth]{fig/network_architecture.pdf}
    \caption{Network architecture. Given an image pair, our network is composed of three modules (top), LoFTR which produces a coarse grid of features, a warping module, and a pose regression module. The bottom figure shows a detailed description of the warping and pose regression modules.} \label{fig:network_architecture}
\end{figure*}

Our goal, given two images of the same scene captured from different viewpoints, is to predict the relative position and orientation between the two respective cameras, including the distance between their centers. We achieve this goal by introducing a 3-module architecture, a feature extraction module, a matching and warping module, and a pose regression head. Below we describe the three modules and the loss function. See Fig. \ref{fig:network_architecture}, describing the architecture of our model.

\subsection{Feature extraction}

Our first module produces descriptors for each pixel in the two images in a coarse resolution of $1/8^{th}$ of the original image resolution in each dimension. For this module we use a sub-network of the LoFTR model \cite{sun2021loftr}, pre-trained on the Scannet dataset \cite{dai2017scannet}, depicting indoor scenes. The LoFTR architecture is applied to both images in parallel. Denote the input  images by $I_\mathrm{A}$ and $I_\mathrm{B}$. Each image is first fed to a feature pyramid network \cite{lin2017feature}, producing a pixel-wise feature map at the desired resolution. Next, a positional encoding is summed to these maps. The obtained maps are then fed into a sequence of interleaved self- and cross-attention layers, producing the final feature maps, $F_A(\mathbf{x}_i),F_B(\mathbf{x}_j) \in \mathbb{R}^C$, where $C$ denotes the number of feature channels, $1 \le i,j \le n$, $n=WH/64$ and $W$ and $H$ denote the width and height of the input images in their original resolution. Noted that the cross attention steps modify the feature maps to account for the features seen in the other image.

\subsection{Matching and warping}

Our next aim is to identify matching points. Given the coarse feature maps $F_A(\mathbf{x}_i)$ and $F_B(\mathbf{x}_j)$ (expressed as row vectors), we select for each point $\mathbf{x}_i$ in $I_\mathrm{A}$ a corresponding point $\mathbf{x}_i^*$ in $I_\mathrm{B}$ by maximizing the correlation between their respective feature vectors, $F_A(\mathbf{x}_i)$ and $F_B(\mathbf{x}_i^*)$. Specifically, the coordinate $\mathbf{x}_i^*$ in $I_\mathrm{B}$ is determined by
\begin{equation}
    \mathbf{x}_i^* = \argmax_j F_A(\mathbf{x}_i) F_B(\mathbf{x}_j)^T
\end{equation}

Additionally, we use the computed correlation matrix $F_AF_B^T$ to calculate a confidence score by applying softmax over each row of the 4-D correlation matrix, namely,
\begin{equation}
    conf(\mathbf{x}_i^*)=\underset{\mathrm{per \: row}}{\mathrm{softmax}}(F_AF_B^T)(x_i^*)
\end{equation}

We next concatenate corresponding features, append an encoding of their positions in the two images and the confidence score obtaining, a feature map of correspondences, namely,
\begin{equation}
    G(\mathbf{x}_i) = [F_A(\mathbf{x}_i),\mathbf{x}_i, F_B(\mathbf{x}_i^*), \mathbf{x}_i^*,\mathrm{conf}(\mathbf{x}_i^*)]
\end{equation}

Note that in this feature maps, the features in $F_b$ are warped to their corresponding features in $F_a$.

\subsection{Pose Regression module}

Given the warped feature map, $G(\mathbf{x}_i)$, we apply a residual convolutional block followed by an MLP, which is then split to produce a 4-dimensional quaternion vector $\hat \mathbf{q}$ depicting the relative orientation and a 3-dimensional vector $\hat \mathbf{t}$ depicting the (scaled) relative position. The magnitude of this 3-vector predicts the distance in meters between the two camera centers. To depict an orientation, the quaternion $\hat\mathbf{q}$ should be normalized, assuming the form $(\cos(\theta/2),\mathbf{v}\sin(\theta/2))$, where $\theta$ represents a rotation angle and $\mathbf{v}$ represents an axis of rotation. Since $\pm\hat\mathbf{q}$ represents the same rotation, we additionally set its first component to be positive.

\subsection{Loss Function}

We train the network in a supervised manner. Given image pairs with ground truth orientation specified by a quaternion $\mathbf{q}$ and position $\mathbf{t}$, we let
\begin{equation}
\mathcal{L}_{\mathbf{q}}(I_\mathrm{A},I_\mathrm{B}) = \left\|\frac{\hat\mathbf{q}}{\|\hat\mathbf{q}\|}-\mathbf{q}\right\|_{1},
\label{eq:loss_rot}
\end{equation}
\begin{equation}
\mathcal{L}_{\mathbf{t}}(I_\mathrm{A},I_\mathrm{B})=\|\hat{\mathbf{t}}-\mathbf{t}\|_{1},
\label{eq:loss_trans}
\end{equation}
and
\begin{equation}
\mathcal{L}_{\mathbf{t}_n}(I_\mathrm{A},I_\mathrm{B})=\left\|\frac{\hat{\mathbf{t}}}{\|\hat{\mathbf{t}}\|} - \frac{\mathbf{t}}{\|\mathbf{t}\|}\right\|_2,
\label{eq:loss_normtrans}
\end{equation}
where $\mathcal{L}_{\mathbf{q}}$ depicts the orientation error and $\mathcal{L}_{\mathbf{t}}$ and $\mathcal{L}_{\mathbf{t}_n}$ depict the position error (with the inter-camera distance both un-normalized and normalized). Balancing these terms can be challenging. Following \cite{kendall2017geometric}, we use learned weights as follows:
\begin{align}
\mathcal{L}_{\sigma}(I_\mathrm{A},I_\mathrm{B}) & = \exp \left(-s_\mathbf{q}\right) \mathcal{L}_{\mathbf{q}}(I_\mathrm{A},I_\mathrm{B}) + s_\mathbf{q}\\
&+ \exp \left(-s_{\mathbf{t}}\right) \mathcal{L}_{\mathbf{t}}(I_\mathrm{A},I_\mathrm{B}) + s_{\mathbf{t}}\\
&+ \exp \left(-s_{\mathbf{t}_n}\right) \mathcal{L}_{\mathbf{t}_n}(I_\mathrm{A},I_\mathrm{B}) + s_{\mathbf{t}_n},
\label{eq:loss2_learned}
\end{align}
where $s_\mathbf{q}$, $s_\mathbf{t}$, and $s_{\mathbf{t}_n}$ are scalars initialized at zero and optimized with gradient descent during training.

\section{Experiments}

\subsection{Datasets}
We train our network on the 7Scenes indoor dataset \cite{glocker2013real} and evaluate on the 7Scenes, University indoor \cite{laskar2017camera}, and the outdoor Cambridge landmarks \cite{kendall2015posenet} datasets.

\textbf{7Scenes \cite{glocker2013real}}. Microsoft 7-Scenes dataset is a widely used indoor dataset for visual relocalization that contains RGB-D images covering seven different indoor locations (rooms) scanned with KinectFusion. Each scene is provided with training and testing image sequences. We use the training dataset provided in \cite{laskar2017camera, melekhov2017relative}, consisting of roughly 40K pairs of images and 80K test pairs. See examples of pairs from this dataset in Fig. \ref{fig:7scenes}.
We note that the relative camera positions in this dataset are erroneously set to $\mathbf{t}_i-\mathbf{t}_j$ where $\mathbf{t}_i,\mathbf{t}_j$ represent the absolute position of the $i^\mathrm{th}$ and  $j^\mathrm{th}$ cameras, instead of $\mathbf{t}_i-R_{ij}^T\mathbf{t}_j$, where $R_{ij}$ denotes the relative orientation between cameras $i$ and $j$. We rectify this error in our experiments.

\textbf{University \cite{laskar2017camera}}. University is an indoor dataset consisting of five scenes. The scenes are organized in a similar way to 7Scenes. The dataset contains roughly 14K training pairs and 17K test pairs. See examples of pairs from this dataset in Fig.~\ref{fig:university}. Ground truth localization is computed with an off-the-shelf SfM method. To improve the ground truth localization, we recomputed the absolute position and orientation for each of the five scenes using Colmap \cite{schoenberger2016sfm} and removed both cross scene image pairs and pairs that involve images that Colmap failed to register. Note that SfM methods can only solve for camera positions only up to a global multiplicative scale.

\textbf{Cambridge Landmarks \cite{kendall2015posenet}}. An urban localization dataset capturing six outdoor scenes in Cambridge, UK. The ground truth poses are determined using a standard SfM method (with positions recovered up to a global scale). The images were captured at different times, affected by different lighting and weather condition and containing moving objects including vehicles and pedestrians. See examples of pairs in Fig.~\ref{fig:cambridge}.

\textbf{Scannet\cite{dai2017scannet}} Scannet is an indoor dataset consisted of 1613 monocular sequences with
ground truth poses and depth maps. This
dataset contains image pairs with wide baselines and extensive texture-less regions. We use the same train/test split used by LoFTR\cite{sun2021loftr}.

\label{sec:exp}
\subsection{Baselines}

We compare our approach to three recent RPR methods:

\textbf{RelPoseNet} \cite{laskar2017camera}. This method utilizes ResNet34 classification network pre-trained on the Imagenet dataset to extract a feature vector for each image. The two vectors are then concatenated and fed into an MLP that regresses the relative pose. The
method is trained end-to-end with a ground truth loss. The
training includes color jitter and random crop augmentations.

\textbf{8-Point \cite{rockwell20228}}. This method produces feature embedding using a pre-trained Res-Net18 \cite{he2016deep} followed by ViT-tiny \cite{dosovitskiy2020image} with an added bi-linear cross attention layer with a quadratic positional encoding. The output of these is passed through an MLP to regress the relative position and orientation. The method is trained end-to-end with a ground truth loss. The training data is augmented with color jitter and random grey scale.

\textbf{Map-free RPR}. This method is taken from the baselines suggested in \cite{arnold2022map}. It uses a residual UNet (ResUNet) architecture to produce a feature map for each image. A soft feature matching is obtained by computing a 4D correlation volume followed by softmax. This soft matching is then used to warp the features of one image into their corresponding locations in the other image. A regression module is finally used to estimate the relative pose. This method has no pre-trained parts.
We both trained this method from scratch using the quaternion and scaled translation parameterization (marked as "Map free rep" in the tables), and also tested the pre-trained model with weights obtained with training, using the $3D-3D$ method described in \cite{arnold2022map}, on the large Scannet dataset (marked as "Map free Scannet" in the tables)

\textbf{Our approach: implementation details}.
Our method is implemented using Pytorch\cite{paszke2019pytorch} and Kornia\cite{riba2020kornia}. We initialize the loss hyper-parameters with $s_\mathbf{q}=s_\mathbf{t}=s_{\mathbf{t}_n}=0$.
We train our method for 80 epochs using the ADAM optimizer \cite{kingma2014adam} with a learning rate of $1e^{-3}$ and StepLR scheduler with step size of 6 and $\gamma=0.9$.

For all methods we use the validation dataset to determine the best set of weights.
\subsection{Metrics and Evaluation}
For each method we measure the position error as the Euclidean distance between the predicted and ground truth position:
\begin{equation}
    t_{err}(\mathbf{t},\hat{\mathbf{t}}) = \Vert \hat{\mathbf{t}} - \mathbf{t} \Vert_2.
\end{equation}
For the University and Cambridge Landmarks datasets camera positions are known up to a global scale for each scene. In our experiments, therefore, we optimize for this scale for each method separately.
We measure orientation error as the angle between the predicted and ground truth quaternion:
\begin{equation}
    R_{err}(\mathbf{q},\hat{\mathbf{q}}) = 2\arccos\left(\left\vert \frac{\hat\mathbf{q}^T \mathbf{q}}{\|\hat\mathbf{q}\|}\right\vert\right).
\end{equation}
For both measures we report the median per scene for each method.

\begin{table*}[t]
\centering
\scriptsize
\begin{tabular}{|l|rr|rr|rr|rr|rr|rr|rr|rr|}
\hline
Method               & \multicolumn{2}{c|}{Chess}   & \multicolumn{2}{c|}{Fire}                          & \multicolumn{2}{c|}{Heads}    & \multicolumn{2}{c|}{Office}  & \multicolumn{2}{c|}{Pumpkin} & \multicolumn{2}{c|}{Red kitchen} & \multicolumn{2}{c|}{Stairs}   & \multicolumn{2}{c|}{Average}       \\
\hline
RelPoseNet\cite{melekhov2017relative}              & 8.149                 & 0.261 & 12.327 & 0.382                & 12.414 & 0.234                & 9.045 & 0.307                & 9.362 & 0.352                & 9.636 & 0.407                   & 12.259 & 0.367                & 10.456 & 0.342                 \\
8Point\cite{rockwell20228}               & \textbf{3.338}                 & 0.123 & 7.819  & 0.270                & 10.384 & 0.172                & 4.059 & 0.181                & 3.682 & 0.187                & 4.145 & 0.210                   & 5.018  & 0.263                & 5.492  & 0.214                 \\
Map-free rep.\cite{arnold2022map}             & 5.835                 & 0.154 & 12.207                      & 0.334                & 12.542 & 0.191                & 7.319 & 0.237                & 6.647 & 0.269                & 7.093 & 0.267                   & 10.167 & 0.313                & 8.830  & 0.268                 \\
Map-free Scannet \cite{arnold2022map}             & 5.445                 & 0.144 & 5.110                      & 0.149                & \textbf{3.406} & \textbf{0.112}                & \textbf{3.518} & \textbf{0.13}                & 5.506 & 0.266                & \textbf{3.754} & 0.185                   & \textbf{3.817} & \textbf{0.152}                & \textbf{4.365}  & 0.166                 \\
\textbf{Ours}        & 4.463                 & 0.147 & 7.206                      & 0.195                & 7.254  & 0.128                & 4.653 & 0.167                & 4.797 & 0.225                & 4.543 & 0.203                   & 5.840  & 0.238                & 5.537  & 0.186\\
\textbf{Ours-Scannet}        & 3.568                 & \textbf{0.115} & \textbf{4.946}                      & \textbf{0.142}                & 6.195  & 0.123                & 3.660 & 0.140                & \textbf{3.670} & \textbf{0.168}                & 4.019 & \textbf{0.149}                   & 6.344  & 0.225                & 4.630  & \textbf{0.158}\\
\hline
\end{tabular}
\caption{\textbf{7Scenes experiment}. The table shows the result of training and testing on the entire 7Scenes dataset with the train and test split specified in the text (except Map-free Scannet and Ours-Scannet, which are trained on Scannet). Each column shows orientation error in degrees (left) and position error in meters (right).}
\label{tab:7scenes}

\centering
\scriptsize
\centering
\begin{tabular}{|l|rr|rr|rr|rr|rr|rr|rr|rr|}
\hline
Method & \multicolumn{2}{c|}{Chess} & \multicolumn{2}{c|}{Fire} & \multicolumn{2}{c|}{Heads} & \multicolumn{2}{c|}{Office} & \multicolumn{2}{c|}{Pumpkin} & \multicolumn{2}{c|}{Red kitchen} & \multicolumn{2}{c|}{Stairs} & \multicolumn{2}{c|}{Average}  \\
\hline

RelPoseNet\cite{melekhov2017relative}                 & 16.129 & 0.388             & 16.070 & 0.378           & 14.091 & 0.341            & 15.947 & 0.364             & 14.940 & 0.365              & 15.705 & 0.471                 & 14.341 & 0.353             & 15.318 & 0.380               \\
8Point\cite{rockwell20228}                  & 14.785 & 0.372             & 16.591 & 0.347           & 14.913 & 0.339            & 13.238 & 0.360             & 13.224 & 0.382              & 13.154 & 0.419                 & 13.977 & 0.372             & 14.269 & 0.370               \\
Map-free rep.\cite{arnold2022map}                & 20.312 & 0.395             & 17.809 & 0.395           & 21.953 & 0.380            & 18.075 & 0.545             & 26.948 & 0.483              & 17.867 & 0.593                 & 20.284 & 0.369             & 20.464 & 0.451               \\
\textbf{Ours}       & \textbf{10.867}  & \textbf{0.304}             & \textbf{10.016} & \textbf{0.344}           & \textbf{10.277}  & \textbf{0.309}            & \textbf{7.416}  & \textbf{0.246}             & \textbf{8.100}  & \textbf{0.255}              & \textbf{8.040}  & \textbf{0.278}                 & \textbf{10.011} & \textbf{0.266}             & \textbf{9.247}  & \textbf{0.250}\\
\hline
\end{tabular}
\caption{\textbf{7Scenes: single scene training experiment}. This table shows the result of training on one of the seven scenes and testing on the other six scenes. The title in each column describes the training scene. Each column shows orientation error in degrees (left) and position error in meters (right).}
\label{tab:single_scenes}

\centering
\footnotesize
\begin{tabular}{|l|rr|rr|rr|rr|rr|}
\hline
Method                & \multicolumn{2}{c|}{Office}     & \multicolumn{2}{c|}{Meeting} & \multicolumn{2}{c|}{kitchen}    & \multicolumn{2}{c|}{Conference} & \multicolumn{2}{c|}{Average}         \\
\hline
RelPoseNet\cite{melekhov2017relative}               & 18.723         & 0.345          & 14.701 & 0.311               & 15.917         & 0.363          & 18.069         & 0.373          & 16.853         & 0.348           \\
8Point\cite{rockwell20228}                & 14.236         & 0.272          & 9.449  & 0.223               & 11.196         & 0.227          & 14.431         & 0.234          & 12.328         & 0.239           \\
Map-free rep.\cite{arnold2022map} & 12.241         & \textbf{0.246} & 10.613 & 0.218               & 11.734         & \textbf{0.172} & 12.479         & \textbf{0.222} & 11.767         & \textbf{0.214}  \\
Map-free- Scannet      & 5.317 & 0.452          & \textbf{3.522}  & 0.184               & \textbf{3.526} & 0.484          & 4.335 & 0.529          & 4.175 & 0.412           \\
\textbf{Ours}                  & 7.132          & 0.266          & 5.110  & \textbf{0.143}      & 6.998      & 0.311          & 5.510     & 0.280          & 6.188          & 0.250           \\
\textbf{Ours - Scannet}                  & \textbf{4.987}          & 0.360          & 3.536  & 0.165      & 3.791     & 0.420          & \textbf{3.311}     & 0.362          & \textbf{3.904}          & 0.327           \\
\hline
\end{tabular}
\caption{\textbf{University experiment}. The table shows the result of the methods trained on the 7Scenes dataset (except Map-free Scannet and Ours-Scannet, which are trained on Scannet) and tested on the University dataset.}
\label{tab:university}

\centering
\footnotesize
\begin{tabular}{|l|rr|rr|rr|rr|rr|}
\hline
method         & \multicolumn{2}{c|}{KingsCollege} & \multicolumn{2}{c|}{OldHospital} & \multicolumn{2}{c|}{ShopFacade} & \multicolumn{2}{c|}{StMarysChurch} & \multicolumn{2}{c|}{Average}     \\
\hline
RelPoseNet\cite{melekhov2017relative}     & 18.629         & 0.268            & 18.235         & 0.298           & 19.364         & 0.314          & 20.158         & 0.399             & 19.097         & 0.320           \\
8point\cite{rockwell20228}         & 7.890          & 0.213            & 9.675          & 0.165           & 11.463         & \textbf{0.196}          & 12.600         & 0.223             & 10.407         & 0.199           \\
Map-free rep.\cite{arnold2022map}  & 9.312          & 0.175            & 11.929         & 0.194           & 13.324         & 0.281          & 14.166         & 0.245             & 12.183         & 0.224           \\
Map-free- Scannet & 4.506 & 0.296            & \textbf{6.746} & 0.246  & \textbf{7.018} & 0.542 & \textbf{7.404} & 0.284    & \textbf{6.418} & 0.342  \\

\textbf{Ours}     & 6.842          & \textbf{0.145}   & 8.168          & \textbf{0.115}           & 12.218         & 0.289          & 12.502         & \textbf{0.195}             & 9.933          & \textbf{0.186} \\
\textbf{Ours - Scannet}   & \textbf{4.086}  & 0.209                   & 6.909  & 0.237                  & 7.388  & 0.400                 & 7.889  & 0.174                    & 6.568  & 0.255 \\
\hline
\end{tabular}
\caption{\textbf{Cambridge experiment}. The table shows the result obtained for the methods trained on the 7Scenes dataset (except Map-free Scannet and Ours-Scannet, which are trained on Scannet) and tested on the Cambridge outdoors dataset.}
\label{tab:cambridge}
\end{table*}

\subsection{Results}
In our first experiment, we trained and tested all methods on the 7Scenes dataset using the train/test split described above. In this setting, when trained on 7Scenes our method (marked as "Ours" in the tables) exhibits similar accuracy as the 8Point network, and both outperform RelPoseNet and Map-free (marked as "Map-free rep" in the tables). In addition, for completeness, we compare our model
when trained on the huge Scannet dataset (marked as "Ours-Scannet" in the tables)  with the pre-trained Map-free model on Scannet dataset (marked as "Map-free Scannet" in the tables).  In this case, we are comparable to Map-free. See results in Table \ref{tab:7scenes}. Our advantage over all algorithms becomes evident in the cross-scene and cross-dataset generalization experiments below.

The next experiment tests cross scene generalization. For this experiment we trained the methods on a mere single scene from the 7Scenes dataset and tested on the remaining 6 scenes. Results are shown in Table \ref{tab:single_scenes}. It can be seen that in this experiment our method significantly outperforms all the baseline methods.

The third and fourth experiments test cross dataset generalization. In the third experiment we train all methods on the 7Scene indoor dataset and test on the University indoor dataset. We note that the test pairs depict significantly different scenery and are captured with different cameras under different lighting setups. Our results are shown in Table \ref{tab:university}. When trained on 7Scenes our method produces significantly more accurate orientations than competing methods trained on the same dataset, and its position estimates are somewhat inferior to those of Map-free. When trained on Scannet our network produces the best orientations in comparison to all methods, and orientation somewhat better than the Map-free trained on Scannet.

In the last experiment we train all methods on either the 7Scene indoor or the Scannet datasets and test on the Cambridge outdoor dataset. The results are show in Table \ref{tab:cambridge}. The results show that, although our method is trained on indoor scenes only, it manages to generalize to outdoor scenes as well. Also, when trained on Scannet, our network predicts camera positions more accurately than Map-free trained on Scannet and achieves comparable orientation predictions, and both these methods are significantly superior to the other methods trained on 7scenes.

\begin{figure}[H]
     \includegraphics[width=1\columnwidth]{fig/7scenes_figure.png}
     \caption{Pairs of images from the \textbf{7Scenes} dataset, including scenes with repetitive patterns.} \label{fig:7scenes}

     \includegraphics[width=1\columnwidth]{fig/university_figure.png}
     \caption{Pairs of images from the Office, Kitchen and Meeting scenes from the \textbf{University} dataset. Notice the texture-less scenes and the middle pair from the Kitchen scene has no overlap.} \label{fig:university}

     \includegraphics[width=1\columnwidth]{fig/Cambridge_figure.png}
     \caption{Pairs of images from the Kings College, Old Hospital and Shop Facade from the \textbf{Cambridge Landmarks} dataset. Notice the different lighting conditions.}  \label{fig:cambridge}
 \end{figure}

\subsection{Ablation Study}

We perform an ablation study to analyze the effect of individual components of our method. In particular, (1) we replace the feature extraction module (2) we analyze the importance of the warping module.
For (1) we extract the feature maps from different stages of LoFTR \cite{sun2021loftr}. For (2) we evaluate the impact of the warping module. These experiments and additional implementation and datasets details are provided in the supplementary material.

\section{Conclusion}
\label{sec:conclusion}

We presented a deep neural network for relative pose regression. Given an image pair, our method uses the pre-trained LoFTR network to produce a semi-dense point feature map, a matching and warping model, and a convolutional regressor to estimate the six degrees of freedom in the relative position and orientation of the two cameras. Our experiments indicate that our method achieves favorable cross scene and cross dataset generalization, in particular, when the training data is limited, outperforming previous works.

\section*{Acknowledgment}
This research is partially supported by the Israeli Council for Higher Education (CHE) via the Weizmann Data Science Research Center and by research grants from the Estate of Tully and Michele Plesser and the Anita James Rosen Foundation. Further support is obtained from the Dan and Betty Kahn Michigan-Israel Partnership, and the U.S.-Israel Binational Science Foundation, grant number 2018680.

 \clearpage
{\small
\bibliographystyle{ieee_fullname}

redpajama_set_name: RedPajamaArXiv


text:
Lizzie Demands a Seat!: Elizabeth Jennings Fights for Streetcar Rights
by Beth Anderson (text) & illus. by E. B. Lewis
Calkins Creek. Jan. 2020. 32p. Tr $17.99. ISBN 9781629799391.
Gr 1-3–In 1854, when Lizzie Jennings was forced off a traditionally "whites only" streetcar, she went to court, winning the right for all black passengers to ride in the same car with white people on the Third Avenue Railroad in New York City. Anderson's account of Jennings's early civil rights triumph stresses the teacher and choir director's determination. An afterword explains how this free, educated, and wealthy black woman was uniquely positioned to succeed where an earlier court case had failed, and how the fight continued for 10 more years before all New York street car companies stopped having separate cars for black and white passengers. Set on spreads with full-bleed illustrations, the storytelling is straightforward and direct. Dialogue closely follows contemporary newspaper accounts to enliven the historical moment. The well-chosen language—"She'd been rejected, restricted, and refused by schools, restaurants, and theaters"—is a pleasure to read aloud. Departing from the somber palette he used for Jabari Asim's Preaching to the Chickens, Lewis employs pastel colors, shades of blues, pinks, and purples, and plenty of background yellow to portray the characters and their surroundings. This lightens the story and supports its positive outcome. Shadowy background figures remind careful readers of the larger community that supported Jennings and were affected. Pair with Nikki Giovanni's Rosa Parks for a reminder of how long this struggle continued.
VERDICT An important story beautifully told.
Reviewed by Kathleen Isaacs, Children's Literature Specialist, Pasadena, MD , Feb 01, 2020


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text:
Q: Implement dilation filter in PGM format files I'm currently doing a student project which does some image processings (dilation and erosion) on greyscale pgm file. I tried to implement dilation on the image object but got weird result. I expected the image object will be larger than the original image. But I got multiple copies of the object depending the kernel size I use.
Original Picture:

Dilation result:

This is my dilation source code
import numpy as np

def pgm_read(filename):
    """Read PGM file to a array"""
    # Performance Bottleneck: I/O system calls, parallel write/read
    try:
        with open(filename, 'r') as fp:
            lines = fp.readlines()
            header_info = lines[2].split()
            return np.array([line.strip('\n') for line in lines[4:]], dtype=np.int32).reshape(int(header_info[0]),
                                                                                int(header_info[1]))
    except OSError:
        print("An exception occurred")

def pgm_write(img, dest, header):
    """Write numpy array to PGM file"""
    try:
        header = "P2\n# test\n" + header + "\n80\n"

        f = open(dest, "w")
        f.write(header)
        f.close()
        with open(dest, "a") as f:
            for x in range(img.shape[0]):
                for y in range(img.shape[1]):
                    f.write(str(img[x][y]) + "\n")
    except OSError:
        print("Writing exception occurred")

def dilation(img):

    rows, cols = img.shape
    dilated_img = np.zeros((rows, cols), dtype=np.int32)

    kernel = np.ones((2, 2))
    rows2, cols2 = kernel.shape

    for x in range(rows):
        for y in range(cols):
            """Search the object within the image"""
            if img[x][y] == 1:
                # Convolve with kernel
                for i in range(rows2):
                    for j in range(cols2):
                        if kernel[i][j] == 1:
                            # Object Enlargement
                            c = x + i
                            d = y + j
                            if c < rows and d < cols:
                                dilated_img[c][d] = 1

    for x in range(rows):
        for y in range(cols):
            """Give the object brightest colour for debugging purpose"""
            if dilated_img[x][y] == 1:
                dilated_img[x][y] = 80

    return dilated_img

if __name__ == '__main__':
    a = pgm_read("Axial_68.pgm")
    a = dilation(a)
    target = "Axial_68_1.pgm"
    header = "265 490"
    pgm_write(a, target, header)

Axial_68.pgm
I am very certain my file reading and writing functions work properly as I can use it to read and write other source pgm file properly.
And one thing I found very weird behaviour is I could print half pgm file like this.

using code
    for x in range(rows // 2):
        for y in range(columns):
            arr[x][y] = 80

But when I use this code and expect half vertical using code:
    for x in range(rows):
        for y in range(columns // 2):
            arr[x][y] = 80

I got this:

I have tried this with a few other generated pgm files and all are same result.
I wonder if my dilation code has something to do with this weird behaviour.

A: It always helps displaying a matrix directly, rather than relying on routines that you wrote yourself to write it to file for examination. For example, adding
import matplotlib.pyplot as pp
#...
    a = pgm_read("Axial_68.pgm")
    pp.imshow(a)
    pp.show()

to your code immediately reveals that the input image is not read in correctly. Swapping the two dimensions in the reshape function call fixed it for me:
return np.array([line.strip('\n') for line in lines[4:]], dtype=np.int32).reshape(int(header_info[1]),
                                                                                int(header_info[0]))

I highly recommend that you use a library to read and write image files. For example imageio is a good option. Matplotlib itself also reads and writes various image file formats.
If you want to do image processing, get a library for that too. For example Pillow, OpenCV or DIPlib. These all also include image file reading and writing functionality.

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New Jersey's Clean Energy Program provides energy rebates for custom projects that reduce energy consumption by at least 25,000 kWh per year. The custom project program is for projects beyond standard projects such as upgrading a HVAC. It can cover projects such as server virtualization and deploying a data center energy management system that will turn off systems at scheduled times provided the energy savings is predicable, documented and meets all other program criteria.
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