text:
Go Back to the List April 06, 2020
Malta Sees 4% Drop In Foreign Students Studying English
Malta has seen a decrease of 4 per cent year-on-year in the number of foreign students studying English at its licensed English Language Teaching (ELT) schools.
According to Malta's National Statistics Office (NSO), in the year 2019, only 83,610 students came to Malta for English Language studies, compared to the 87,112 students who came in 2018. So, there was a decrease of 4.0% in the year 2019.
Though there was a slight decrease in the total number of foreign students coming to Malta for studying the English language, Maltese ELTs saw an increase in the number of non-EU students from the previous year.
Russia, Brazil and Japan were the main origin countries in 2019 for non-EU ELT students in Malta, contributing 5,997, 5,000 and 4,106 students respectively.
In each of Russia, Brazil and Japan's case, the student numbers were an increase from the 2018 figures, when they accounted for 5,396, 4,360 and 3,508 ELT students in Malta respectively.
According to StudyTravel Network, this can be attributed to the Maltese government introducing working rights for non-EU English Language students.
Nearly half of foreign students studying English in Malta come from EU
The biggest share of foreign students who were studying English in Malta came from other EU countries, viz. Italy (18,335), Germany (9,764), and France (8,878).
Together, students from these countries made up 44.2% of the total number of students who came to Malta to pursue English Language studies.
Overall, in 2019, 56,597 foreign students pursuing English studies in Malta came from the EU, and 27,013 from non-EU countries. This compared to a total of 62,957 and 24,155 respectively in 2018.
Of the total ELT student population, a majority was female (60.1%). Most of the students were aged 15 years or less, and only 6,224 students were aged 50 and above.
In 2019, 70.3% of the ELT student population attended the 'General English: Standard' course while 15.9% chose the 'Intensive English' course.
Written by Zahra Hamdani,
Source link: https://www.globaleducationtimes.org/news/europe/malta-sees-4-drop-in-foreign-students-studying-english/1275/ | https://www.globaleducationtimes.org/news/europe/malta-sees-4-drop-in-foreign-students-studying-english/1275/
Unis say vaccination must but long gap, no doses back home worry international students...


On July 9, Palimpsest author and Victoria's Poet Laureate Yvonne Blomer will read in St. Marten's church as part of the Atlin Arts and Music Festival. Yukon poet Joanna Lilley will also read, and host Lily Gontard will facilitate some poetic conversation.
If you can't make it to Atlin, don't sweat: the event will be available on podcast. Stay tuned!


The Citizens Coinage Advisory Committee (CCAC) met on September 21, 2012, at United States Mint Headquarters, to review and discuss designs for the Code Talkers Recognition Congressional Gold Medals for members of the Crow Creek Sioux and Sisseton Sioux tribes. In attendance were Chairman Gary Marks, Heidi Wastweet, Donald Scarinci, Thomas Uram, Jeanne Stevens-Sollman, Mike Ross, Erik Jansen, as was Dr. Jim Adams, senior historian for the Smithsonian Institution National Museum of the American Indian, and Ron Harrigal and Don Everhart from the U.S. Mint. Mr. Everhart was videoconferenced in from the Philadelphia Mint; all others attended in person.
After the designs were presented by Mr. Harrigal, there was a motion from Donald Scarinci for the committee to recommend the preferences of the Crow Creek Sioux tribe: obverse design 1 and reverse design 1. Heidi Wastweet had a question regarding the areas of polish and frost on reverse design 1; she also said that design reminded her of the symbol for toxic waste. The committee, with a 4-3 vote, approved the motion and by doing so, recommended those two designs for this tribe's medal.
After the vote, Jeanne Stevens-Sollman made a comment, followed by an actual motion, that the phrases "Act of Congress 2008" and "World War I – World War II" should be transposed on the reverse design; the committee unanimously approved that motion.
The obverse designs for the Sisseton Wahpeton Sioux created more discussion from the committee. Some, like Ms. Wastweet and Erik Jansen, saw the eagle in obverse design 1 as attacking the soldier, a design element that Ms. Wastweet had also noted had been seen in two previous medal designs. Others, like Ms. Stevens-Sollman and Mr. Scarinci, saw the eagle as sheltering the soldier. The votes by the committee: obverse design 1 (with 21 points) and reverse design 2 (with 18 points).
The Commission of Fine Arts also reviewed the design candidates on September 20, 2012. For their comments and recommendations, please refer to this article.
All of the design candidates provided by the United States Mint for the third round of Code Talker Congressional Gold Medals are shown below.


text:
Q: getting exception java.lang.ArrayIndexOutOfBoundsException while running this code? I am getting an exception java.lang.ArrayIndexOutOfBoundsException while passing list[array.length] reference to s.
import java.io.*;

public class Test
{
  public void demo(String [] list)
   {
   try
   {
    String s=list[list.length];// error
   }
   catch(ArrayIndexOutOfBoundsException  e)
   {
  System.out.println("The exception is"+e);
   }
   finally
   {
      System.out.println("Finally here.");
   }
}
 public static void main(String [] args)
 {
   Test t=new Test();
   String [] list={"one","two"};
   t.demo(list);
   System.out.println("Done!");
 }
}

A: If you have an array of length 5 you will have elements on the index 0-4 and you are trying to access the 5th element.
If you want to access the last element:
String s = list[list.length - 1];

A: String s=list[list.length];

arrays are indexed from 0 so last item is list.length-1

A: Index in most programming languages starts in zero. So, you can use index in the range [0, length - 1]. But, in the line
String s = list[list.length];

you are using an invalid index. It should be (at max):
String s = list[list.length - 1];

A: in Java arrays are zero based. so you have to write:
String s=list[list.length-1];

list.length gives you the number of elements in the array (2) but the last element in the array is on index 1

A: You are getting the Exception due to the line :
String s=list[list.length];

Always remember :
Last-index_of_an_Array  = Array-length - 1 // because array index starts from 0

So, try :
String s=list[list.length - 1];

A:  String [] list={"one","two"};
 list[0] value is "one";
 list[1] value is "two";
 list.length value is 2
 String s=list[list.length];// error

because
 String s=list[2];// error
java.lang.ArrayIndexOutOfBoundsException

redpajama_set_name: RedPajamaStackExchange


text:
The Vine is a bus rapid transit (BRT) route in Vancouver, Washington that is operated by C-Tran. The  line runs from downtown Vancouver to the Vancouver Mall, serving 34 stations primarily on Fourth Plain Boulevard. It opened on January 8, 2017, becoming the first bus rapid transit system in the Portland metropolitan area.

The corridor was identified as a possible BRT route in 2005 and was originally named the Fourth Plain BRT Project. The routing was approved for BRT development in 2012 by C-Tran, the Vancouver City Council, and the Federal Transit Administration and construction began in August 2015. The $53 million project is primarily funded by a Federal Transit Administration grant that was secured in late 2015. The Vine replaced two bus routes that carried over 6,000 trips daily. A second line in The Vine system, running on Mill Plain Boulevard, is planned to open in 2023.

Route

The Vine begins at Turtle Place, a former park that was once home to a C-Tran bus station, located on 7th Street between Washington and Main streets in downtown Vancouver and one block east of Esther Short Park. Within downtown Vancouver, buses travel in a one-way pair, southbound on Washington Street and northbound on Broadway Street, before turning east onto McLoughlin Boulevard and crossing under Interstate 5. The Vine then stops at the Marshall/Luepke Community Center on the east side of the freeway and turns onto Fort Vancouver Way, heading northeast to serve the campus of Clark College with two stops as well as the Vancouver campus of the VA Medical Center. The route turns eastward once again at Fourth Plain Boulevard, following the corridor as it parallels the State Route 500 freeway to the north. At Thurston Way, The Vine turns north towards its final approach to the Vancouver Mall, where the line terminates.

Along the route, The Vine has several queue jumps installed to give buses priority at traffic signals.

Stations

The Vine serves 34 stations located in the city of Vancouver, Washington, primarily on Fourth Plain Boulevard between Downtown Vancouver and Vancouver Mall, located approximately  apart. Stations consist of a  platform that is raised for level boarding, and includes shelters and windscreens, ticket vending machines, real-time arrival signs.

Service and fares

The Vine runs every 10 minutes during peak hours and 15 minutes during off-peak periods and on weekends. Buses run from 4:30 a.m. to 12:40 am on weekdays and from 6:00 am to 12:25 am on weekends and holidays. A $1.80 adult fare, the same as existing local C-Tran service, is charged to ride The Vine.

The Vine accepts the Hop Fastpass contactless smart card fare system, a new system available throughout the Portland–Vancouver metropolitan area in coordination with TriMet and the Portland Streetcar. The system has been in public beta testing since February 2017 and is scheduled to be launched for the general public on July 1, 2017.  Hop card readers have been installed at all Vine stations.

C-Tran also runs a shuttle bus, route 60, from Downtown Vancouver to Jantzen Beach, Hayden Island and the Delta Park/Vanport light rail station to cross the Columbia River like former routes 4 and 44.

Fleet
The Vine uses a fleet of ten New Flyer Xcelsior XDE60 diesel-electric hybrid buses that measure  long and carry up to 100 people. The articulated buses are low-floor, have three doors, and include three interior bicycle racks. The first buses were delivered in April 2016.

History

The Southwest Washington Regional Transportation Council (RTC) began studying high-capacity transit for Vancouver and Clark County in 2008, and determined that bus rapid transit would be viable on four main corridors: Highway 99, Fourth Plain Boulevard, Interstate 205, and Mill Plain Boulevard. C-Tran, the county's transit agency, adopted a 20-year long-range plan in 2010 that recommended building the first bus rapid transit line on Fourth Plain. The Fourth Plain corridor had been served by local routes 4 and 44, the two busiest in the C-Tran system, which continued to northern Portland, Oregon.

Design concepts for a Fourth Plan bus rapid transit service were presented in 2011 and 2012, and a locally-preferred alternative was adopted by C-Tran, the Vancouver City Council, and RTC in 2012.

On November 6, 2012, C-Tran placed a 0.1 percent sales tax increase on the general election ballot to fund a light rail extension from Portland to Downtown Vancouver via a new bridge, as well as operating costs of the Fourth Plain bus rapid transit project. While the ballot measure was rejected, the bus rapid transit project moved forward and was granted Federal Transit Administration (FTA) funding in 2014. Opponents of the project filed a lawsuit in the Clark County Superior Court in 2014 to prevent C-Tran from receiving federal funds, arguing that the project did not meet the definition of "high-capacity transit" as required in the ballot measure language. The suit was dismissed in 2015, with the judge ruling in favor of C-Tran.

The project was named "The Vine" after a public naming contest in 2014, beating out other candidates by "evoking greenery, leaves and branches". A groundbreaking ceremony was held on August 24, 2015, kicking off construction of the $53 million project. The FTA confirmed its $38.5 million commitment to the project in September; the rest of the project is funded by C-Tran, and grants from the Washington State Department of Transportation and RTC.

In October 2016, C-Tran announced that The Vine would open on January 8, 2017. C-Tran held a community celebration on January 7, 2017, including a street fair and preview rides attended by 200 people. Service began on January 8, 2017 using  buses in place of the service's articulated buses, and stopping at route 4 stops rather than stations, due to a winter storm. The articulated buses, which lack drop-down tire chains that would allow for operations in winter conditions, debuted the following day instead. In its first year of service, The Vine carried 45 percent more riders than Route 4 and operating costs decreased by 21 percent.

Mill Plain line

The Vine was the first bus rapid transit system to open in the Portland metropolitan area, and was followed in 2022 by the Frequent Express service between Portland and Gresham. In February 2018, the C-Tran Board of Directors approved a design contract for a potential bus rapid transit project on Mill Plain Boulevard, an east–west corridor to the south of The Vine's Fourth Plain Boulevard. Preliminary plans for the project was approved in 2019 and was followed by agreements with the city government on property acquisition and upgrading fiber optic lines along the corridor.

The Mill Plain line, covering  from the shared terminus at Turtle Place in Downtown Vancouver to Clark College's Columbia Tech Center near Southeast 192nd Avenue, will have 37 stations and is anticipated to cost $50 million. Half of the anticipated costs will be covered by a grant from the Federal Transit Administration that was announced in May 2020. Project construction began in September 2021 and the line is scheduled to begin service in 2023.

Future expansion

, C-Tran is also planning a third BRT line along Main Street and Old Highway 99 between Vancouver, Hazel Dell, and Salmon Creek.

References

External links

Project website

2017 establishments in Washington (state)
2017 in transport
Bus rapid transit in Washington (state)
Transportation in Vancouver, Washington

redpajama_set_name: RedPajamaWikipedia


text:
Ben Bova Online
Most of a century in tech and Science Fiction!
A life in search of the future!
GRAND TOUR & Projects
Writing Tips!
Nonfiction/Orion/Articles
Ben's Booksale, his PERSONAL stash!
Ebooks from ReAnimus Press
Dr. Ben Bova not only writes about the future; he has helped to create it. The author of more than 130 futuristic novels and nonfiction books, he has been involved in science and high technology since the very beginnings of the space program. President Emeritus of the National Space Society and a past president of Science-fiction and Fantasy Writers of America. Dr. Bova is a frequent commentator on radio and television, and a widely-popular lecturer. He has also been an award-winning editor and an executive in the aerospace industry.
He is author of 140 futuristic novels and nonfiction books.
• Involved in science and high technology since the 1950's
• President Emeritus of the National Space Society
• Past president of Science Fiction Writers of America
• Recipient, Lifetime Achievement Award, Arthur C. Clarke Foundation
• Frequent radio and television commentator, widely-popular lecturer
• Award-winning editor and executive in the aerospace industry.
• Guest of Honor, World Science Fiction Convention 2000 in Chicago.
• Winner of the John W. Campbell Award for TITAN in 2006.
Ben Bova EARTH!
A wave of lethal gamma radiation is expanding from the core of the Milky Way galaxy at the speed of light, killing everything in its path.
The countdown to when the death wave will reach Earth and the rest of the solar system is at two thousand years.
Humans were helped by the Predecessors, who provided shielding generators that can protect the solar system. In return, the Predecessors asked humankind's help to save other intelligent species that are in danger of being annihilated.
But what of Earth? With the Death Wave no longer a threat to humanity, humans have spread out and colonized all the worlds of the solar system. The technology of the Predecessors has made Earth a paradise, at least on the surface. But a policy of exiling discontented young people to the outer planets and asteroid mines has led to a deep divide between the new worlds and the homeworld, and those tensions are about to explode into open war.
Award-winning writer Ben Bova returns with Power Failure, another tense political thriller starring Jake Ross, science advisor to Senator Frank Tomlinson, as they continue their complex power maneuvers in this near future science fiction novel.
Dr. Jake Ross came to Washington to try to make a difference, but he's learned the only way to get something done in Washington, assuming your ideals survive the corrosive atmosphere, is to gather power. Ross has gathered a great deal, riding in the wake of Frank Tomlinson.
But now Tomlinson has decided to shoot for the moon. If they win, they get it all. If they lose, the game is over for Jake Ross.
In the Power trilogy, Bova's vision of a future powered by solar satellite transmission is tantalizingly within reach.
Jake Ross Series
#1 Power Play
#2 Power Surge
#3 Power Failure
SURVIVAL now in stores!
Ben Bova continues his hard SF Star Quest series which began with Death Wave and Apes and Angels. In Survival, a human team sent to scout a few hundred lightyears in front of the death wave encounters a civilization far in advance of our own, a civilization of machine intelligences.
These sentient, intelligent machines have existed for eons, and have survived earlier "death waves," gamma ray bursts from the core of the galaxy. They are totally self-sufficient, completely certain that the death wave cannot harm them, and utterly uninterested in helping to save other civilizations, organic or machine.
(Publisher) Click link above to purchase!
EARTH, another novel in the Grand Tour series is finished and at the publisher too!. We're looking at what's been going on at home while the human race is expanding out to the stars.
~Ben
Several links with interviews of Ben on Youtube from RME Productions on the Writers Tips page!
A very comprehensive review of Apes and Angels on SFRevu.
Copyright © 2020 Ben Bova Online — Primer WordPress theme by GoDaddy

redpajama_set_name: RedPajamaCommonCrawl


text:
Findbiographies
Film & Theater Personalities
Sportspersons
Home » Musicians
Jerry Garcia Biography
Jerry Garcia was a famous American musician, known for his adept guitar skills. Explore this biography to learn more about his profile, childhood, life and timeline.
Also known as: Jerome, John
Birthday: 01 August 1942
Died: 09 August 1995
Spouse/Ex-: Carolyn Garcia
Education: San Francisco Art Institute
Jerry Garcia is fondly remembered for his exquisite guitar work, singing and songwriter capabilities during his associations with the band, the 'Grateful Dead'. He was one of the co-founders of the band and was a musician with them for a whopping thirty years. In his long and fruitful career, he had the opportunity to work with some of the best and the most prestigious music labels in the country like Arista, Warner Bros., Acoustic Disc and Rhino. The band rose to popularity and became so successful that Garcia and his band decided to launch their very own record company called the 'Grateful Dead'. Although he is widely associated with his band, he also released a number of solo albums and worked with a number of other artistes during his tenure as a musician. He was also ranked no. 13 in the famed Rolling Stones Magazines under the category of '100 Greatest Guitarists of All Time', which also featured as the magazine's cover story. Aside his career, he suffered quite a bit health-wise due to his unstable weight and was also a victim of substance abuse. He was sent to a rehabilitation center, where he stayed till his death. Scroll further for more.
Jerry Garcia was born Jerome John on August 1, 1942 in San Francisco, California. He was the second of the two sons born to Joe and Ruth Marie Garcia.
He began taking piano lessons as a child and he would often play and sing at his father's bar. However, after the untimely death of his father and a couple of other tragic incidents during childhood, his mother took over his late father's bar and the children were sent to live with their maternal grandparents.
He attended Monroe schools and was introduced to the banjo, his first stringed instrument. As a young boy, he was introduced to a wide spectrum of musical genres such as rock and roll, blues, jazz, country and bluegrass.
He studied at Denman Junior High School and Balboa High School in 1958, where he joined his school band, Chords.
Jerry Garcia joined the United States Army but was quickly discharged on December 14, 1960.
After a near-death car accident, he began playing the guitar and was introduced to Robert Hunter, who became the lyrical collaborator for 'Grateful Dead' in April, 1961. He performed in his first concert with Hunter and also played in a band called 'Wildwood Boys'. It was during this time he met David Nelson and Phil Lesh.
He soon began teaching and playing the banjo and the acoustic guitar and from 1962-1964, he sang a number of old-time folk and bluegrass music. It was around this time that psychedelic LSD was gaining popularity, which Garcia believes, changed his career and life for the better.
Garcia came up with the name 'Grateful Dead' for his newly formed band with Phil Lesh and Bill Kreutzmann, and despite the band members not liking the name, news of a new band had spread and the name stuck.
He served as the lead guitarist and singer for the band and composed popular songs like 'Franklin's Tower', 'Scarlet Begonias' and 'Dark Star'. He became extremely popular as he was known for his 'soulful guitar improvisations'.
The band toured from 1965 almost until Garcia's death in 1995. During their three decade span, the band had performed in 2314 shows. His guitar-playing melded elements of Celtic, rock, bluegrass, country and jazz.
In addition to being the lead guitarist of 'Grateful Dead', he collaborated with various other side projects, the most notable project being, the 'Jerry Garcia Band' and 'Legion of Mary'.
He played over 50 studio albums of varied genres and many artists sought help for their albums from Garcia such as Warren Zevon, Ken Nordine, Pete Sears, Bruce Hornsby and Bob Dylan.
In 1995, Garcia played on three tracks for the CD, 'Blue Incantation' by guitarist, Sanjay Mishra, which was his last studio collaboration.
Wake of the Flood
Dylan & the Dead
Without A Net
The Arista Years
The Very Best of Grateful Dead
Almost Acoustic
Before Time Began
Old and in the Way
Side Trips, Volume One
Mother McCree's Uptown Jug Champions
Ranked 13th in Rolling Stone's '100 Greatest Guitarists of All Time'
When he was young, he lost a couple of his fingers and faced personal losses which affected his psychological health, like the untimely death of his father.
He was also addicted to cocaine, heroin and LSD for which he was sent to rehabilitation centers for the longest time. He was also arrested on numerous occasions for the same.
He married Sara Ruppenthal on April 23, 1963. The couple had one child.
Carolyn Adams gave birth to Garcia's second and third daughters in the 70's. The couple only married in 1981.
Garcia suffered from a number of illnesses during his lifetime, which were mainly due to weight-related problems. He went into a diabetic coma in 1986.
He met Deborah Koons, while he was in a relationship with his second wife and fell in love with her. She eventually became his third wife. He then met Barbara Meier, whom he married on February 14, 1994, a year before his death.
He passed away on August 9, 1995 after he suffered a heart attack.
Following his death, a number of movies and documentaries were based on his life and many concerts such as 'Comes a Time: A Celebration of the Music and Spirit of Jerry Garcia', was held in his remembrance. Numerous other musical festivals across the world hold annual events in the memory of this great personality.
Findbiographies.com Editors
The Findbiographies.com website
https://www.findbiographies.com/jerry-garcia-49.php
(American)
(British)
(Austrian)
Celebrities By Profession
© Findbiographies.com - 2019 Privacy Policy | Terms Of Service


When you decide to locate, expand or build in Oakville, our team is here with you every step of the way. We offer assistance with site selection, industry research and the development approvals process. Please do not hesitate to contact us with your questions.
Have a question? Want to stay up-to-date on what's happening in Oakville? There are several ways to stay in touch with Oakville Economic Development.
Sign up for our e-newsletter for regular updates on new developments, building permits and other exciting news.
The Invest Oakville website is presented by the Town of Oakville's Economic Development department. We're here to help.


text:
Inger Haldis Løite, född 27 september 1958 i Gjerstad, Norge, är en norsk politiker i Arbeiderpartiet. Hon var invald i Stortinget för Aust-Agder åren 2005–2009.

Externa länkar
 Inger Løite på Stortingets webbplats

Norska politiker
Födda 1958
Kvinnor
Levande personer


text:
Affordable housing developer buys frat house in…
Affordable homes builder buys downtown San Jose frat house
First Community Housing buys frat residence near SJSU, might build supportive housing for homeless
George Avalos / Bay Area News Group
Sigma Nu Fraternity house on South 11th Street between East San Fernando Street and East San Antonio Street in downtown San Jose. A builder of affordable homes has bought a fraternity house in downtown San Jose a short distance from the state university's campus.
By George Avalos | gavalos@bayareanewsgroup.com | Bay Area News Group
PUBLISHED: December 1, 2020 at 5:45 a.m. | UPDATED: December 4, 2020 at 3:52 p.m.
SAN JOSE — A builder of affordable homes has bought a fraternity house a short distance from the San Jose State University campus, a deal that could help the developer create housing for people who were formerly homeless.
First Community Housing has bought the downtown site where the Sigma Nu Fraternity had been operating a house for its Zeta Iota Chapter, Santa Clara County property documents filed on Nov. 30 show.
"We have been exploring sites for supportive housing in downtown San Jose just based on the fact that this is where the homeless problem is most concentrated," said Geoffrey Morgan, president of First Community Housing.
The three-story house is located on South 11th Street between East San Fernando Street and East San Antonio Street.
The building is now vacant after the university and the Sigma Nu Fraternity suspended the Zeta Iota chapter in July.
First Community Housing is an increasingly busy developer of affordable homes in the South Bay, including several projects in San Jose.
In the most recent deal pulled off by First Community Housing, an affiliate of the nonprofit corporation paid $5.6 million for the fraternity house, the property documents show.
First Community Housing is eyeing the development of 120 supportive housing units for the formerly homeless now that the nonprofit has completed its purchase of the Sigma Nu house.
The fraternity house property could become part of a larger site where the supportive housing development would spout. Morgan said the nonprofit is discussing the purchase of additional property adjacent to the frat house site.
"Well-managed supportive housing with good services provides the safest conditions possible for the recently homeless," Morgan said.
New York City-based Local Initiatives Support provided a $5.6 million loan to finance the property purchase by Lighthouse at Grace, the affiliate of First Community Housing. Local Initiatives Support specializes in property loans for affordable housing builders.
San Jose-based First Community Housing is working on multiple affordable housing projects in or next to downtown San Jose. These include:
— The Villages at Roosevelt, a proposed complex that would consist of 125 affordable residences at 995 E. Santa Clara St.
— Roosevelt Park Apartments at 21 N. 21st St., consisting of 80 affordable residences.
— McEvoy Apartments at 280 McEvoy St., would contain 365 residential units and be built a short distance from the footprint of Google's proposed transit village.
Google Endeavor, a unit of the tech titan, has provided $29 million in financing for the McEvoy Apartments.
Sigma Nu Fraternity house at 155 S. 11th St. in downtown San Jose. A builder of affordable homes has bought a fraternity house in downtown San Jose a short distance from the state university's campus.
Sigma Nu Fraternity house on South 11th Street between East San Fernando Street and East San Antonio Street in downtown San Jose. A builder of affordable homes has bought a fraternity house in downtown San Jose a short distance from the state university's campus.
Front of Sigma Nu Fraternity house in downtown San Jose at 155 S. 11th St. A builder of affordable homes has bought a fraternity house in downtown San Jose a short distance from the state university's campus.
Coronavirus jolts Silicon Valley office market
Homes, offices, proposed near San Jose BART stop
In the most recent deal, the seller was an entity headed by the Sigma Nu fraternity. The property was last purchased in 1975. .
Although the frat house is currently empty, First Community Housing is determined to use at least a portion of it to demonstrate that the site hasn't been abandoned.
"We are going to be a little bit of retrofitting so we can have a leasing office for some of our projects," Morgan said. "Right now it is just vacant, and we want to put it to use pretty quickly. We want to actively manage it."
Morgan said he has done volunteer work to help serve the homeless in downtown San Jose.
"The purchase of the Sigma Nu house really advances the cause to solve one of the worst problems in San Jose," Morgan said.
George Avalos | Business reporter
George Avalos is a business reporter for the Bay Area News Group.
gavalos@bayareanewsgroup.com
Follow George Avalos	@georgeavalos
More in Housing
East Bay city to post rent increases, eviction details online


Why are u against me?
Oh! My Child, its none.
time to make up for that.
Call her all night all day long.
Found out she was buried under stone.
all I could see was dark black sky.
something was unsettling, said my gut.
How she must have felt?
when I had said - leave me be.


text:
Het Octaaffeest is het grootste religieuze feest in het groothertogdom Luxemburg. Het gaat gepaard met bedevaarten vanuit het groothertogdom Luxemburg, Belgisch Luxemburg, Noord-Lotharingen en de Eifel naar de kathedraal van Luxemburg. Het is een Maria-feest, dat eindigt met een katholieke processie bijgewoond door de groothertogelijke familie en autoriteiten van Luxemburg. Het Maria-beeld dat in de processie wordt gedragen, heeft de naam Consolatrix Afflictorum, Patrona Civitatis et Patriae Luxemburgensis. Dit betekent troosteres der bedroefden en patrones van de stad en het land Luxemburg.

De naam Octaaf komt van de kerkelijke periode voor 8 dagen. Het Octaaffeest vindt plaats tussen de 3e zondag en de 5e zondag na Pasen. Strikt gezien is het een dubbel-octaaf.

De oorsprong gaat terug tot de Contrareformatie in de Spaanse Nederlanden. In 1642 bouwde de Jezuïet Jacques Broquart, samen met studenten uit het Jezuïetencollege van Luxemburg, een kapel ter ere van Maria. De kapel was gelegen aan de stadsmuren van Luxemburg, meer bepaald aan de Nei Paart (Nieuwe Poort), een belangrijke toegangspoort tot de stad. Aangezien Jacques Broquart genezen was van de pest, werd de kapel een bedevaartsoord voor heel het (Spaanse) hertogdom Luxemburg. In 1666 werd het Mariabeeld patrones van de stad Luxemburg en vond de eerste processie plaats. In 1678 werd het Mariabeeld patrones van het hertogdom Luxemburg en het graafschap Chiny.

De Franse bezetters vernielden de Jezuïetenkapel eind 18e eeuw. Het beeld werd verplaatst naar de Jezuïetenkerk van Luxemburg, die sinds midden de 19e eeuw de kathedraal van Luxemburg werd, omwille van het nieuw opgerichte bisdom Luxemburg.

Luxemburgse feestdag
Evenement in Luxemburg (land)
Mariabedevaart


5 posts tagged with medicinebone.
I posted a solo version of this tune back in January 2010, but this is the Medicine Bone version, recorded at a gig the other night right here in ol' Tokyo town. Video at YouTube.
Another tune from the same recent Medicine Bone show as this one, with special guest Ken Kawashima on harmonica. Video at YouTube and Vimeo.
From a recent live show with my band, Medicine Bone. The key we did this in wasn't the best for me, so I ended up kinda screaming it. And the recording is dirty and clipping. So, it turned out just about right. Oh, and the best thing is that old buddy Ken Kawashima (hear him with me here, here and here) was in town and graced us with some of his superb blues harpery. Video at YouTube and Vimeo.
Another live recording from my new band, Medicine Bone (previously). This is one of band-mate Ken Shima's songs, which he's singing here. I'm on walking drums. I love drumming to this tune. Video of this same performance on YouTube.


Why gold price went high?
For the first time in history, price of gold touched a new high today, with a sovereign of the yellow metal being sold at Rs 25,302 in Chennai. While one gram is being sold at Rs 3,219, customers are shocked. However, increasing their shock, traders have said the rates will increase further.
According to Jayanthilal, who heads gold and diamond merchants association, since the economy of the United States is on a downward trend, big investors have started investing in gold. This has impacted in the global gold market." Stating that this has led to increase in gold prices in India, he said in the coming days, the price will increase more.
Gold, which is a symbol of prestige and wealth, is a soft, dense, yellow metal. It is a chemical element. Its chemical symbol is Au. Its atomic number is 79. As a precious metal, it has been used for many jewels. In its purest form, it is a bright, slightly reddish yellow, dense, soft, malleable, and ductile metal. Chemically, gold is a transition metal and a group 11 element. It is one of the least reactive chemical elements and is solid under standard conditions.
Gold often occurs in free elemental (native) form, as nuggets or grains, in rocks, in veins, and in alluvial deposits. It occurs in a solid solution series with the native element silver (as electrum) and also naturally alloyed with copper and palladium. Less commonly, it occurs in minerals as gold compounds, often with tellurium (gold tellurides).


Moldtech has recently supplied a modular mould system for wetcast box culverts for a client in Poland.
The mould has a height of 1 meter and allows producing precast box culverts and half-culverts with dimensions between 1.5x2 m and 3x2 m.
The mould is manually operated and allows simple and fast assembly, adjustment and operation.
The system allows manufacturing box culverts with different thicknesses for all sections. These moulds are always supplied with 2 sets of modular bases to allow producing up to 2 pieces per day.
This client already has several moulds of the same type supplied by Moldtech and this mould has been manufactured according to his specific requirements.
These systems are widely configurable and allow future extensions, which allows many possible configurations and multiple manufacturing sizes, by combining different elements of all moulds, this makes it easy for the client adapting its production to the market changing demands.


text:
\section{Approach} \label{sec:approach}

Our approach first constructs a ground-satellite feature
dictionary that captures the relationship between the two views using
an existing database from the area. Second, we learn projection
matrices for the each of the two views so as to arrive at a feature
embedding that is more location-discriminative. Given a query ground
image, we then use the dictionary and the learned projections to
arrive at a distribution over the location in georeferenced satellite
images of the environment in which
the ground image was taken. Next, we describe these three steps in detail.

\subsection{Ground-Satellite Image Feature Dictionary}

The role of the ground-satellite dictionary is to capture the
relationships that exists between feature-based representations of
ground images and their corresponding overhead view. Specifically, we
define our ground-satellite image dictionary using three types of
features. The first consists of pixel-level RGB intensity, which we
smooth using bilateral filtering to preserve color
transitions~\cite{fastbilateral}. The second takes the form of
edge potentials for which we use a structured forest-based
method~\cite{edgedet} that is more robust to non-semantic noise (as
compared to classic gradient-based edge detectors) and can be computed
in real-time. The third feature type we use are neural, pixel-wise,
dense, semantic attributes. For these, we use fully-convolutional
neural networks~\cite{fcn} trained on ImageNet~\cite{imagenet} and
fine-tuned on PASCAL VOC~\cite{pascalvoc2012}. Next, we describe the process by which we construct the
ground-satellite dictionary, which we depict in Figure~\ref{fig:dictionary}.

For each ground image in the database, we identify the corresponding
satellite image centered on and oriented with the ground image
pose. We then compute pixel-wise features for both images. Next, we
compute the ground image features $g^{dict}_{i}$ on a
fixed-interval 2D grid (to ensure an efficient dictionary size), and
project these sampled points onto the satellite image using the
stereo-based depth estimate~\cite{stereo}.\footnote{For the
  experimental evaluation, we use the stereo pair only to estimate
  depth. We only use images from one camera to generate the
  dictionary, learn the projections, and estimate pose.} Sampled points that fall
outside the satellite image region are rejected. We record the
satellite features corresponding to the remaining projected points,
which we denote as $s^{dict}_{i}$. We repeat this process for all ground-satellite image
pairs to form our one-to-one ground-satellite image feature
dictionary. We also store dense satellite features even if they do not
appear in the dictionary, so that they do not need to be recomputed
during the localization phase. We store the dictionary with two $k$-d
trees for fast retrieval.

\subsection{Location-Discriminative Projection Learning}

The goal of location-discriminative projection learning is to identify
two linear projections $W_g$ and $W_s$ that transform the ground and
satellite image features
such that those that are physically close are also nearby in the
projected space. Nominally,
we can
model this projection learning task as
an optimization over a loss function that expresses the distance
in location between each feature point and its nearest neighbor
in the projected space. In the case of the ground image, this results
in a loss of the form
\begin{equation}
  W_g=\argmin_{W}\sum\limits_{i}\Delta L\bigl(i,\argmin_{k\in \mathcal{N}(i)}f_g(i,k,W)\bigr),
  \label{eqn:cost}
\end{equation}
where $\Delta L(i,k)$ is the
scalar difference in location (ignoring orientation for simplicity) between two feature
points, $\mathcal{N}(i)$ is a neighborhood around feature $i$ in
feature space, and
\mbox{$f_g(i,k,W) = \lVert Wg^{dict}_{i}-Wg^{dict}_{k}\rVert_{2}$}. We
consider a feature-space neighborhood for computational efficiency and
have found a $\mathcal{N}(i)=20$ neighborhood to be
effective in our reported experiments. A similar definition is used for $W_s$.

In practice, however, this loss function is difficult to
optimize. Instead, we treat the objective as a ranking problem and optimize
over a surrogate loss function that employs the hinge loss,
\begin{equation}
        \mathcal{L} \!=\! \sum\limits_{i}\!\left(\!f_g(i,k^*,W) \!-\!\!\! \min\limits_{k\in \mathcal{N}(i)}\!\!\Big(f_g(i,k,W)\!-\!m(i,k)\Big)\!\right)_{+}\!\!\!\!
        \label{eqn:hingeloss}
\end{equation}
where \mbox{$k^* = \argmin_{k}\Delta L(i,k)$} is the nearest feature
with regards to metric location, \mbox{$m(i,k) = \Delta L(i,k) - \Delta L(i,k^*)$}, and
\mbox{$(x)_+ = \textrm{max}(0,x)$} denotes the hinge loss.  Intuitively, we would like
the distance $f_g(i,k^*,W)$ to be smaller than any other distance $f_g(i,k,W)$ by a margin
\mbox{$m(i,k)$}.  We minimize the loss function~\eqref{eqn:hingeloss}
using stochastic gradient descent with Adam~\cite{adam} as the weight
update algorithm. Algorithm~\ref{algo:proj} describes the process we
use to learn a projection matrix, which is repeated twice for both $W_g$,
and $W_s$.
\begin{algorithm}[!t]
    \caption{Location-discriminative projection learning\!}

    \textbf{Input:} $\{g^{dict}_i\}$, $\{L(i)\}$\\
    \textbf{Output:} $W$\\
    \begin{algorithmic}[1]

        \STATE Initialize $W=\mathbb{I}$ and $t=0$
        \FOR{$epi$=1:\textsc{MaxIter}}
			\FOR{each $i$}
				\STATE $t = epi \times i_{max} + i$
				\IF{$ f_g(i,k^*\!,W) \!-\!\!\! \min\limits_{k\in \mathcal{N}(i)}\!\!\Big(\!f_g(i,k,W)\!-\!m(i,k)\!\Big) \!>\! 0$}
					\STATE Compute $\partial \mathcal{L}_t$ as \mbox{$ \partial \Big(f_g(i,k^*,W) - \min\limits_{k\in \mathcal{N}(i)}f_g(i,k,W) \Big) / \partial W$}
					\STATE Compute $\Delta W_t$ as \mbox{$\textsc{Adam}(\{\partial \mathcal{L}_0, \partial \mathcal{L}_1, \ldots, \partial \mathcal{L}_t \})$ }~\cite{adam}
					\STATE Update $W \leftarrow W - \Delta W_t$
				\ENDIF
			\ENDFOR
			\IF{convergence}
				\STATE break
			\ENDIF

        \ENDFOR
    \end{algorithmic}
    \label{algo:proj}
\end{algorithm}

\subsection{Localization}
\begin{figure}[!t]
    \centering
    \subfigure[]{\includegraphics[width=0.45\linewidth]{./figures/fig1a.pdf}} \hfil%
    \subfigure[]{\includegraphics[width=0.45\linewidth]{./figures/fig1b.pdf}}
    \caption{Localization by (a) image-to-image matching using
      two-image reference database. By estimating the
      ground-satellite co-occurrence, our method (b) yields a more fine-grained distribution over the camera's location, without the
      need for a larger ground image database. $I_q$ denotes the query
      ground image. $I_i^{db}$ denotes a database ground image, and
      $L_{i,0}^{db}$. $L_{i,1}^{db}$ and $L_{i,2}^{db}$
      are the interpolated locations along the vehicle path. Our
      method is able to evaluate localization possibilities of
      $L_{i,1}^{db}$ and $L_{i,2}^{db}$ without knowing their ground
      images.}
    \label{fig:interpolation}
\end{figure}
In our final localization step, we compute the probability
$P(L|I^{q})$ that a given query ground image $I^q$ was taken at a
particular position and orientation $L$, where we interpolate the
database locations along the vehicle path to get a larger number of
location candidates $L$ (Fig.~\ref{fig:interpolation}). In order to arrive at
this likelihood, we first extract features for the query image $I^q$
and then retrieve the precomputed dense features for the satellite
image associated with pose $L$. Next, we sample the query ground image
features with a 2D grid and find their corresponding satellite image
features by projecting using the query stereo image, as if $I^{q}$ was
centered and oriented at pose $L$. After rejecting projected samples that
lie outside the satellite image, we obtain a set of ground-satellite
image feature pairs, where the $n^{th}$ pair is denoted as
$(g^{q}_{n},s^{L}_{n})$.

For each ground-satellite image feature pair, we evaluate their
co-occurrence score according to the size of the intersection of their
respective database neighbor sets in the projected feature space. To
do so, we first transform the features to their corresponding projected
spaces as $W_gg^{q}_{n}$ and
$W_ss^{L}_{n}$. Next, we retrieve their $M$-nearest neighbors, each for
the transformed ground and satellite images, among the dictionary
features projected into the projected space using the Approximate Nearest Neighbor
algorithm~\cite{ann}. The retrieved neighbor index sets are denoted as
$\{id_g^m(W_gg^{q}_{n})\}$ and $\{id_s^m(W_ss^{L}_{n})\}$. The
Euclidean feature distances between the query and database index $m$
are denoted as $\{d_g^m(W_gg^{q}_{n})\}$ and $\{d_s^m(W_ss^{L}_{n})\}$
for the ground and satellite images, respectively.  A single-pair
co-occurrence score is expressed as the consistency between the two
retrieved sets
\begin{equation}
  \textrm{score}(s^{L}_{n}|g^{q}_{n})=\!\!\!\!\sum\limits_{(m_1,m_2)\in
    I}\!\!\!\!\Big(d_g^{m_1}(W_gg^{q}_{n})\cdot d_s^{m_2}(W_ss^{L}_{n})\Big)^{-1},
\end{equation}
where $I=\{id_g^m(W_gg^{q}_{n})\}\cap \{id_s^m(W_ss^{L}_{n})\}$ denotes
all the $(m_1,m_2)$ pairs that are in the intersection of the two sets.
We then define the desired probability over the pose $L$ for the
query image $I^{q}$ as
\begin{equation}
  P(L|I^{q})=\frac{1}{C}\sum\limits_{n}\textrm{score}(s^{L}_{n}|g^{q}_{n}),
\end{equation}
where $C$ is a normalizing factor. We interpolate the database
vehicle path with $L_{i,j}^{db}$ (Fig.~\ref{fig:interpolation})
and infer the final location as that where $P(L|I^{q})$ is
maximized.

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

We presented a multimodal learning method that performs accurate
visual localization by exploiting the availability of satellite
imagery. Our approach takes a ground image as input and outputs the
vehicle's corresponding location on a georeferenced satellite image
using a learned ground-satellite image dictionary
embedding. We proposed an algorithm for
estimating the co-occurrence probabilities between the ground and
satellite images. We also described a ranking-based technique that
learns location-discriminative feature projection matrices, improving
the ability of our method to accurately localize a given
ground image. We evaluated our method on multiple public datasets,
which demonstrate its ability to accurately perform visual localization.

Our future work will focus on the inclusion of additional features
that enable learning with a smaller amount of data, and on learning
general ground-to-satellite relationships that generalize across
different environments.

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

Autonomous vehicles have recently received a great deal of attention
in the robotics, intelligent transportation, and artificial
intelligence communities. Accurate estimation of a vehicle's location
is a key capability to realizing autonomous operation. Currently, many
vehicles employ Global Positioning System (GPS) receivers to estimate
their absolute, georeferenced pose. However, most commercial GPS
systems suffer from limited precision and are sensitive to multipath
effects (e.g., in the so-called ``urban canyons'' formed by tall
buildings), which can introduce significant biases that are difficult
to detect. Visual place recognition seeks to overcome this limitation
by identifying a camera's (coarse) location in an a priori known
environment (typically in combination with map-based localization,
which uses visual recognition for loop-closure). Visual place
recognition, however, is challenging due to the appearance variations
that result from environment and perspective changes (e.g., parked
cars that are no longer present, illumination changes, weather
variations), and the perceptual aliasing that results from different
areas having similar appearance. A number of techniques have been
proposed of late that make significant progress towards overcoming
these challenges~\citep{cummins08, park10, cummins11, churchill12,
  milford12, johns13, sunderhauf13, naseer14, lynen14, mcmanus14,
  hansen14, sunderhauf15, sunderhauf15a}.

Satellite imagery provides an alternative source of information that
can be employed as a reference for vehicle localization. High
resolution, georeferenced, satellite images that densely cover the
world are becoming increasingly accessible and well-maintained, as
exemplified by the databases available via Google
Maps~\citep{google-maps} and the USGS~\cite{usgs}. Algorithms that are
able to reason over the correspondence between ground and satellite
imagery can exploit this availability to achieve wide-area camera
localization~\citep{jacobs07, bansal11, saticcvw11, lin13,
  viswanathan14, saticra15}.
\begin{figure}[!t]
    \centering
    \includegraphics[height=1.3in]{./figures/demo1.pdf}\hfil
    \includegraphics[height=1.3in]{./figures/demo2.pdf}%
    \caption{Given a ground image (left), our method
      outputs the vehicle location (blue) on the satellite image
      (left), along the known vehicle path (orange).} \label{fig:ground-satellite-example}
\end{figure}

In this paper, we present a multi-view learning method that performs
accurate vision-based localization with the aid of satellite
imagery. Our system takes as input an outdoor stereo ground image and
returns the location of the stereo pair in a georeferenced satellite
image (Fig.~\ref{fig:ground-satellite-example}), assuming access to a
database of ground (stereo) and satellite images of the environment
(e.g., such as those acquired during a previous environment
visit). Instead of matching the query ground image against the
database of ground images, as is typically done for visual place
recognition, we estimate the co-occurrence probability of the query
ground image and the local satellite image at a particular location,
i.e., the probability over the ground viewpoint in the satellite
image. This allows us to identify a more precise distribution over
locations by interpolating the vehicle path with sampled local
satellite images.  In this way, our approach uses readily available
satellite images for localization, which improves accuracy without
requiring a dense database of ground images. Our method includes a
listwise ranking algorithm to learn effective feature projection
matrices that increase the features' discriminative power in terms of
location, and thus further improve localization accuracy.

The novel contributions of this paper are:
\begin{itemize}
\item We propose a strategy for localizing a camera based upon an
    estimate of the ground image-satellite image co-occurrence, which
    improves localization accuracy without ground image database
    expansion.

\item We describe a ranking-loss method that learns general feature
    projections to effectively capture the relationship between
    ground and satellite imagery.
\end{itemize}

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

Several approaches exist that address the related problem of
localizing a ground image by matching it against an Internet-based
database of geotagged ground images. These methods typically employ visual
features~\citep{hays08,zheng09} or a combination of visual and
textual (i.e., image tags) features~\citep{crandall09}. These techniques
have proven effective at identifying the location of query images over
impressively large areas~\citep{hays08}. However, their reliance
upon available reference images limits their use to regions
with sufficient coverage (e.g., those visited by tourists) and their
accuracy depends on the spatial density of this coverage. Meanwhile,
several methods~\citep{schindler07,zamir10,chen11a,majdik13} treat
vision-based localization as a problem of image retrieval against a
database of street view images. \citet{majdik13} propose a method that
localizes a micro aerial vehicle within urban scenes by matching
against virtual views generated from a Google Street View
image database.

In similar fashion to our work, previous methods have investigated
visual localization of ground images against a collection of
georeferenced satellite
images~\citep{jacobs07,bansal11,lin13,viswanathan14}. \citet{bansal11}
describe a method that localizes street view images relative to a
collection of geotagged oblique aerial and satellite images. Their
method uses the combination of satellite and aerial images to extract
building facades and their locations. Localization then follows by
matching these facades against those in the query ground
image. Meanwhile, \citet{lin13} leverage the availability of land use
attributes and propose a cross-view learning approach that learns the
correspondence between features from ground-level images, overhead
images, and land cover data. \citet{viswanathan14} describe an
algorithm that warps panoramic ground images to obtain a projected bird's eye
view of the ground that they then match to a grid of satellite
locations. The inferred poses are then used as observations in a
particle filter for tracking. Other
methods~\citep{saticra15,saticcvw11} focus on extracting
orthographical texture patterns (e.g., road lane markings on the
ground plane) and then match these observed patterns with the
satellite image. These approaches perform well, but rely on the
existence of clear, non-occluded visual textures. Meanwhile, other
work has considered the related task of of visual localization of a
ground robot relative to images acquired with an aerial
vehicle~\citep{stentz02,kelly06}.

A great deal of attention has been paid in the robotics and vision
communities to the related problem of visual place recognition~\citep{cummins08,
  park10, cummins11, churchill12, milford12, johns13, sunderhauf13,
  naseer14, lynen14, mcmanus14, hansen14, sunderhauf15, sunderhauf15a}. The biggest
challenges to visual place recognition arise due to variations in image
appearance that result from changes in viewpoint, environment
structure, and illumination, as well as to perceptual aliasing, which are
both typical of real-world environments. Much of the work seeks to mitigate some of these challenges by using
interest point detectors and descriptors that are robust to transformations in scale and
rotation, as well as to slight variations in illumination (e.g., SIFT~\cite{sift} and
SURF~\cite{surf}).  Place
recognition then follows as image retrieval, i.e., image-to-image
matching-based search against a database (with various methods to improve
efficiency)~\citep{wolf05,li06,filliat07,schindler07}. While these
methods have demonstrated reasonable performance despite some appearance
variations, they are prone to failure when the environment is
perceptually aliased. Under these conditions, feature descriptors are
no longer sufficiently discriminative, which results in false matches
(notably, when the query image corresponds to a environment location
that is not in the map). When used for data association in a
downstream SLAM framework, these erroneous loop closures can result in
estimator divergence.

\begin{figure*}[!t]
    \centering
    \includegraphics[width=0.87\textwidth]{figures/fig2.pdf}
    \caption{Illustration of our ground and satellite feature dictionary learning process.}
    \label{fig:dictionary}
\end{figure*}
The FAB-MAP algorithm by Cummins and Newman \cite{cummins08,cummins11} is
designed to address challenges that arise as a result of perceptual
aliasing. To do so, FAB-MAP learns a generative model of region
appearance using a bag-of-words representation that expresses the
commonality of certain features. By essentially modeling this
perceptual ambiguity, the authors are able to reject invalid image
matches despite significant aliasing, while correctly recognizing
those that are valid. Alternatively, other methods achieve
robustness to perceptual aliasing and appearance variations by
treating visual place recognition as a problem of matching image
sequences~\citep{koch10,milford12,johns13,sunderhauf13,naseer14},
whereby imposing joint consistency reduces the likelihood of false
matches. The robustness of image retrieval methods can be further
improved by increasing the space of appearance variations spanned by
the database~\citep{neubert13,mcmanus14a}. However, approaches that
achieve invariance proportional to the richness of their training
necessarily require larger databases to achieve robustness. Our method
similarly learns a probabilistic model that we can then use for
matching, though our likelihood model is over
the location of the query in the georeferenced satellite
image. Rather than treating localization as image retrieval, whereby we find
the nearest map image for a given query, we instead leverage the
availability of satellite images to estimate the interpolated
position. This provides additional robustness to viewpoint variation,
particularly as a result of increased separation between the
database images (e.g., on the order of 10\,m).

Meanwhile, recent attention in visual place recognition has focused on
the particularly challenging problem of identifying matches in the
event that there are significant appearance variations due to large
illumination changes (e.g., matching a query image taken at night to a
database image taken during the day) and seasonal changes (e.g.,
matching a query image with snow to one taken during
summer)~\citep{neubert13,mcmanus14,maddern14,lowry14,sunderhauf15,sunderhauf15a}. While
some of these variations can be captured with a sufficiently rich
database~\citep{neubert13}, this comes at the cost of requiring a
great deal of training data and its ability to generalize is not
clear~\citep{sunderhauf15}. \citet{mcmanus14a} seek to overcome the
brittleness of point-based features to environment
variation~\citep{valgren07, glover10, milford12, naseer14} by learning
a region-based detector to improve invariance to
appearance changes. Alternatively, \citet{sunderhauf15}  build on the
recent success of deep neural networks and eschew
traditional features in favor of ones that can be learned from
large corpora of images~\citep{russakovski14}. Their framework first
detects candidate landmarks in an image using state-of-the-art
proposal methods, and then employs a convolutional neural network to generate
features for each landmark. These features provide robustness to
appearance and viewpoint variations that enables accurate place
recognition under challenging environmental conditions. We also take advantage of this recent
development in convolutional neural networks for image
segmentation to achieve effective pixel-wise semantic
feature extractors.

Related to our approach of projection matrix learning is a long
history of machine learning research with ranking loss objective
functions~\cite{hang11}. Our approach is also related to the field of
multi-view learning~\cite{xu13}, as we learn the relationship between ground
and satellite image views.

\section{Experimental Results} \label{sec:results}

We evaluate our method on the widely-used, publicly available
KITTI~\cite{kitti} and Malaga-Urban~\cite{malaga} datasets.

\subsection{KITTI Dataset}

\begin{figure}[!t]
    \centering
    \includegraphics[width=0.475\linewidth]{./figures/city1.pdf}\hfil
    \includegraphics[width=0.475\linewidth]{./figures/city2.pdf}\\
    \vspace{0.25cm}
    \includegraphics[height=2.0in]{./figures/citymap.pdf}
    \caption{Examples from the KITTI-City dataset that include (top)
      two ground images and (bottom) a satellite image with a curve
      denoting the vehicle path and arrows that indicate the poses
      from which the ground images were taken.}
    \label{fig:kitti-city}
\end{figure}
We conduct two experiments on the KITTI dataset. In the first
experiment, we use five raw data sequences from the KITTI-City
category. Together, these sequences involve $1067$ total images,
each with a resolution of $1242\times 375$. The total driving distance
for these sequences is $822.9$\,m. We randomly select $40\%$ of the
images as the database image set, and the rest as the query image
set. Figure~\ref{fig:kitti-city} provides examples of these images and the
vehicle's path. In the second KITTI experiment, we consider a scenario in which the
vehicle initially navigates an environment and later uses the
resulting learned database for localization when revisiting the area.
We emulate this scenario using a long sequence drawn from
the KITTI-Residential category, where we use the ground images from
the first pass through the environment to generate the database, and
images from the second visit as the query set. For the long
KITTI-Residential sequences, we downsample the
database and query sets at a rate of approximately one image per second
to reduce the overlap in viewpoints, which facilitates
  the independence assumptions of FAB-MAP\citep{cummins11}. This
results in $3654$ total images ($3376$ images for the database and
$278$ images for the query), each with a resolution of
$1241\times 376$, with a total of $3080$\,m travelled by the vehicle
($2847$\,m and $233$\,m for the database and query,
respectively). Figure~\ref{fig:kitty-residential} shows example ground
images and the database/query data split. Note that while the
ground-satellite dictionary is generated using images from the
downsampled database, projection learning was performed using the
full-framerate imagery along the database path.
\begin{figure}[!t]
    \centering
    \includegraphics[width=0.475\linewidth]{./figures/resi1.pdf}\hfil
    \includegraphics[width=0.475\linewidth]{./figures/resi2.pdf}%
    \vspace{0.25cm}
    \includegraphics[height=2.0in]{./figures/resimap.pdf}%
    \caption{Examples from the KITTI-Residential dataset that include
      (top) two ground images and (bottom) a satellite image. The
      yellow and cyan paths denote the vehicle path during the first and second visit, respectively. Green arrows
      indicate the pose from which the images were taken.}
\label{fig:kitty-residential}
\end{figure}

We evaluate three variations of our method on these datasets as a
means of ablating individual components, and compare against two
existing approaches, which serve as baselines. One baseline that we
consider is a publicly available implementation of
FAB-MAP~\cite{cummins11},\footnote{\url{https://github.com/arrenglover/openfabmap}}
for which we use SURF features with a
cluster-size of $0.45$, which yields $2611$ and $3800$ bag-of-words
for the two experiments, respectively. We consider a second baseline
that performs exhaustive matching over a dense set of SURF features
for image retrieval. We further refine these matches using
RANSAC-based~\citep{fischler81} image-to-image homography estimation
to identify a set of geometrically consistent inlier features. We use
the average feature distance over these inliers as the final
measurement of image-to-image similarity. We refer to this baseline as
Exhaustive Feature Matching (EFM). The first variation of our method
that we consider eschews satellite images and instead performs ground
image retrieval using our image features (as opposed to SURF), which
we refer to as Ours-Ground-Only (Ours-GO). The second ablation
consists of our proposed framework with satellite images, but without
learning the location-discriminative feature projection, which we
refer to as Ours-No-Projection (Ours-NP). Lastly, we consider our full
method that utilizes satellite images and all images along the
database path for projection learning. For all methods,
  we identify the final position as a weighted average of the top
  three location matches. We also considered an
experiment in which we include query images taken from regions
outside those captured in our learned database and found that none of
the methods produced any false positives, which we define as
location estimates that are more than $10$\,m from ground-truth.

\begin{table}[!th]
    \centering
    \caption{KITTI localization error (meters)}
    \label{tab:kitti}
    \begin{tabularx}{0.75\linewidth}{lcc}
      \toprule
      Method                   &  KITTI-City         & KITTI-Residential\\
      \midrule
      FAB-MAP \cite{cummins11} & 1.24 (0.69)          & 2.29 (1.55)         \\
      EFM                      & 0.87 (0.15)          & 1.18 (0.91)         \\
      Ours-GO                  & 0.81 (0.07)          & 1.13 (0.81)         \\
      Ours-NP                  & 0.41 (0.20)          & 0.62 (0.33)         \\
      Ours-full                & \textbf{0.39} (0.22) & \textbf{0.42} (0.20)\\
      \bottomrule
    \end{tabularx}
\end{table}
Table~\ref{tab:kitti} compares the localization error for each of the
methods on the two KITTI-based experiments, with our method
outperforming the FAB-MAP and EFM baselines. Ours-GO achieves lower
error than the two SURF-based methods, which shows the effectiveness
of our proposed features at discriminating between ground images,
especially when there is overlap between images.\footnote{We note that
  this may violate independence assumptions that
  are made when learning the generative model for FAB-MAP~\citep{cummins11}.}
Ours-NP further reduces the error by interpolating the trajectory
between two adjacent ground database images (as described in
Fig.~\ref{fig:interpolation}) and evaluating ground-satellite
co-occurrence probabilities, which brings in more localization
information. Ours-full achieves the lowest error, which demonstrates
the effectiveness of using re-ranking to learn the
location-discriminative projection matrices. Note that Ours-NP and
Ours-full use stereo to compute depth when learning the
ground-satellite image dictionary, whereas FAB-MAP does not use stereo.

\subsection{Malaga-Urban Dataset}
\begin{figure}[!t]
    \centering
    \includegraphics[width=0.95\linewidth]{figures/malaga}
    \caption{Data split for the Malaga sequence where yellow, cyan, and purple denote the database, revisit query, and the outside query set, respectively.}
    \label{fig:malaga}
\end{figure}
We also evaluate our framework on the Malaga-Urban
dataset~\cite{malaga}, where we adopt the setup similar to
KITTI-Residential, using the first vehicle pass of an area as the
database set, and the second visit as the query set. In addition, we
also set aside images taken from a path outside the area represented
in the
database to evaluate each method's ability to handle negative queries. We used the
longest sequence, Malaga-10, which contains $18203$ images, each with a
resolution of $1024\times 768$. We downsample the database and query
sets at approximately one frame per second. The total driving distance
is $6.08$\,km, with $4.96$\,km for the database set, $583.5$\,m as the inside
query set, and $534.3$\,m as the outside query set. Figure~\ref{fig:malaga}
depicts the data splits.

\begin{figure}[!t]
    \centering%
    \subfigure[GPS trajectory error]{\includegraphics[width=0.475\linewidth]{./figures/malagabad}\label{fig:malaga-examples-trajectory}}\hfil
    %
    \subfigure[Reference image]{\includegraphics[width=0.475\linewidth]{./figures/ref}\label{fig:malaga-examples-reference}}\\
    %
    \subfigure[GPS-based match]{\includegraphics[width=0.475\linewidth]{./figures/dis328cm}\label{fig:malaga-examples-gps}}\hfil
    %
    \subfigure[Correct match]{\includegraphics[width=0.475\linewidth]{./figures/dis548cm}\label{fig:malaga-examples-correct}}%
    \caption{Deficiency of the ground-truth location tags in the Malaga
      dataset.}
\label{fig:malaga-examples}
\end{figure}
Unlike the KITTI datasets, the quality of the ground-truth location
tags in the Malaga dataset is relatively
poor. Figure~\ref{fig:malaga-examples} conveys the deficiencies in the
GPS tags provided with the data. Figure~\ref{fig:malaga-examples-trajectory} shows a portion of the
ground-truth trajectory, where the GPS data incorrectly suggests that
the vehicle took an infeasible path through the environment. As
further evidence, the GPS location tags suggest that the image in
Figure~\ref{fig:malaga-examples-gps} is $3.28$\,m away from the
reference image in Figure~\ref{fig:malaga-examples-reference} and constitutes
the nearest match. However, the image in Figure~\ref{fig:malaga-examples-correct} is
actually a closer match to the reference image (note the white trashcan in
the lower-right), despite being $5.48$\,m away according to the GPS
data. Due to the limited accuracy of the ground-truth locations, we
evaluate the methods in terms of place recognition (i.e., their ability to
localize the camera within $10$\,m of the ground-truth location) as
opposed to localization error.

\begin{figure}[!tb]
    \center
    \includegraphics[width=0.95\linewidth]{figures/roc_malaga}
    \caption{Precision-recall curve of different methods on the Malaga dataset.}
    \label{fig:malaga-precision-recall}
\end{figure}
We compare the full version of our algorithm to the FAB-MAP and Exhaustive
Feature Matching (EFM) methods as before. We set the bag-of-words size
for FAB-MAP to $2589$. We define true positives as images that are
identified as inliers and localized within $10$\,m of their
ground-truth locations. We picked optimal thresholds for each
method based on the square area under their precision-recall
curves (Fig.~\ref{fig:malaga-precision-recall}). Table~\ref{tab:malaga}
summarizes the precision and recall statistics for the different
methods.
\begin{table}[!t]
    \centering
    \caption{Optimal precision-recall of different methods.}
    \label{tab:malaga}
    \begin{tabularx}{0.55\linewidth}{lcc}
      \toprule
      Method                   & Precision                      & Recall            \\
      \midrule
      FAB-MAP \cite{cummins11}    & 49.3\%                            & 61.6\%               \\
      EFM                      & 89.4\%                            & 43.6\%            \\
      Ours-full                     & \textbf{90.2\%}                   & \textbf{86.6\%}            \\
      \bottomrule
\end{tabularx}
\end{table}

The results demonstrate that our method is able to correctly classify
most images as being inliers or outliers and subsequently estimate the
location of the inlier images. The EFM method
achieves comparable precision, however the computational
expense of doing exhaustive feature matching makes it intractable for
real-time use in all but trivially small environments. Note that using
only inlier images, the average location errors (with standard deviation) in
meters when rough localization succeeds for FAB-MAP, EFM, and our method
are $3.45 \, (2.16)$, $3.65 \, (2.21)$, and $3.33 \, (2.08)$, respectively. Although
our method achieves better accuracy, it is difficult to draw strong
conclusions due to the aforementioned deficiencies in the ground-truth
data.  We believe the improvement in accuracy of our method will
be more significant if accurate ground-truth location tags are
available, similar to what we have observed in our KITTI experiments.

redpajama_set_name: RedPajamaArXiv


Regardless of whether you are scheduled to attend either the Better Business Bureau's (BBB) Arbitration or the Texas lemon law administrative hearing (in order to get a lemon vehicle buyback or replacement), it is important to be prepared with essential documents that will help you win your case.
Before proceeding further, I must caution you that although I am an attorney who concentrates my practice to Texas lemon law related cases, that the information found here is not and should not be treated as legal advice. The reason is because every case, regardless of how similar, is different. Therefore, this information is simply a public service presentation and you should contact a Texas lemon law attorney as soon as possible in the event that you have recently purchased a new lemon car.
Because both of the above-mentioned programs generally do not have an attorney fee shifting provision that requires the car manufacturer to pay for your attorney fees if you prevail — having an attorney represent you in these hearing can be cost prohibitive. Under Texas lemon law, the only time the attorney fee shifting can be invoked by the consumer is when the automobile manufacturer is represented by an attorney at the hearing.
Documentation is key at the hearing. You must have written proof that your car meets the rebuttable presumption of the program. For example, if you are arguing that your car is a lemon because it has been subject to repairs for the illumination of the check engine light, then having for documented repair invoices issued by the service center will solidify your case, rather than relying on the arbitrator or administrative judge to take your word for it.
10) your notes in outline form so that you can concisely present your argument to the hearing officer.
Good luck on your upcoming hearing!


text:
Home » Latest News in Nigeria » Defection: All juicy appointments went to Katsina, Lagos - Saraki
Defection: All juicy appointments went to Katsina, Lagos - Saraki
The President of the Senate, Bukola Saraki, on Tuesday met his supporters in Ilorin, where he told them that he dumped the All Progressives Congress because of their insistence that he should do so and the guidance he received from God.
He further alleged that Kwara State was marginalised in terms of appointments by the APC.
Saraki spoke to journalists in Ilorin after a meeting attended by the state governor, all the members of the National Assembly from the state, 23 members of the state house of assembly and local government chairmen and members of the state executive council.
Saraki said, "We discussed this issue last week when I came home. You told me that you were tired of the APC, but I asked you to exercise patience. I told you that I will seek God's guidance and feed you back on my return to Ilorin.
"The Federal Government appointed over 200 persons into juicy offices without allotting any slot to me or (Speaker, House of Representatives, Yakubu) Dogara. Everything went to Katsina, Katsina, Lagos, Lagos. If not for the love I have for Nigeria, we would have scattered everything. They don't want us in their party. They don't like us in Kwara.
"What I am after is for my people to enjoy dividend of democracy. I want us to be in the party that will love my people. Wherever we are, we will win elections. The governor told me he's tired of the APC.
"Those we are talking to in the Peoples Democratic Party have understood that we need to move Nigeria forward. There is no cause for alarm. We should forget about personal ambition as we move to a new party. What should be paramount in our minds is Nigeria's progress. We will set up our committee which will be meeting with the PDP committee. I've met with great stakeholders in the PDP such as Prof. Sulaiman, Ajibola Simon and Yekini Alabi and we have all agreed to work together. We should cooperate with the PDP people who we are joining."


text:
The voice of Stephen Hawking was sent into space
science/
A Message from the English giant of astrophysics Stephen Hawking sent to the nearest black hole during a solemn funeral in Westminster Abbey, which took place on Friday, June 15. A specially written composition with his famous synthesized voice was broadcast into space by European space Agency.
The wheelchair-bound Professor, who dedicated their lives to unraveling the mysteries of the Universe and the struggle with disability, was buried along with other great scientists, Isaac Newton and Charles Darwin.
"the Stream will be transmitted in the direction of the nearest black hole 1A 0620-00, which is in a binary system with a rather ordinary orange dwarf star," said daughter Lucy Hawking. "This message of peace and hope, unity and the need to live together with harmony on this planet".
The Death of Hawking caused a flurry of messages about grief, from Queen Elizabeth II to NASA, which reflected his impact as a scientist and became a beacon for people who spend their whole life struggling with amyotrophic lateral sclerosis. Hocking died March 14 at age 76.
British actor Benedict Cumberbatch, who played Hawking in a BBC drama, and astronaut Tim Peak spoke at the ceremony of commemoration of the great scientist. Also a speech was made by Martin Rees, and Kip Thorne, colleagues and Nobel laureates in astrophysics. At the funeral Hawking has gathered a lot of famous people connected with it or just vdohnovitsya his work. Professor Brian Cox said that Hawking has inspired him at the age of 20 years for his book "a brief history of time".
"I passed physics a-levels, but was into more music, and as if trying to decide what to do, music or physics? Then my mother found out about this book and my dad bought me it for Christmas. I read it and became convinced that physics I'm going to do all my life."
The Voice of Hawking sent to the black hole with a radio telescope in Spain.
ScienceStephen HawkingPhysics
What will be the shelter for the first Martian colonists?
Mars is not the friendliest planet for humans While the Red Planet is roaming rovers, researchers are pondering the construction of shelters and materials needed by future Martian colonists. The authors of the new paper suggest that we could use one ...
New proof of string theory discovered
Just a few years ago, it seemed that string theory was the new theory of everything. But today the string universe raises more questions than answers String theory is designed to combine all our knowledge of the Universe and explain it. When she appe...
What is the four-dimensional space?
Modeling camera motion in four-dimensional space. View the world in different dimensions changes the way we perceive everything around, including time and space. Think about the difference between two dimensions and three dimensions is easy, but what...
CERN is starting a major upgrade to extract more data from the collisions of atoms
the world's largest particle accelerator is launching a major update which would allow us to extract 10 times more data and help in revealing the secrets of physics. European organization for nuclear research, aka CERN, began work...
Scientists have successfully extracted water from the air in the desert
the Scientists were able to collect water from dry desert air. They only require heat from the sun. The invention can change the lives of the 2.1 billion people who lack clean drinking water. The main advantage of the invention is...
How difficult is it to conquer the quantum nature of matter?
Matt Trusheim includes a switch in a dark laboratory, and powerful green laser illuminates a tiny diamond, held in place under the lens of a microscope. On the computer screen appears an image of a diffuse gas cloud dotted with br...


text:
Feature: InfoGraphic
InfoGraphic: Past New Years Eve Stunts
As part of MTV's 2011 New Years Eve celebration, reality television star Snooki will be encased inside a large ball and dropped to countdown the final seconds of 2010. What were some of the more outlandish stunts from past New Years Eves?
2004 Live via-satellite from the Abu-Ghraib prison in Afghanistan, New Years revelers all over the United States watch as Army personnel lead prisoners one-by-one on all fours, leash around thier neck, onto a stage where cast-iron balls are repeatedly dropped onto their groins.
1990 After reaching speeds of over 250mph, an Empire State Building elevator accidentally crashed during Downtown Julie Brown's televised stunt of descending the landmark's 102 stories to count down the final minute and a half of 1989, killing herself and an MTV cameraman.
1986 Filling in for an ailing Dick Clark, Rockin' New Years Eve host Max Headroom stutters his way through the final countdown of 1986, causing the official start of 1987 to be delayed for 53 minutes.
1980 Dallas star Larry Hagman is shot 10 times to count down the final seconds of 1979.
1974 After suffering through the Vietnam War and Watergate scandal, America was able to laugh again when a New Years Eve prank saw funnyman Buddy Hackett precariously re-position the Times Square ball directly over the head of an oblivious Gerald Ford, striking the president over the head.
1907 The Times Square New Years Eve ball is created by electrician Walter F. Palmer, putting an end to the previous long-standing New Years Eve tradition of John D. Rockefeller pushing ten Irish immigrants off the top of the New York Times building to ring out the final seconds of the year.


text:
Are videos overtaking photos? – Not so fast…
For a long time, videos have been heralded as the Next Big Thing, the capture format that was going to replace still photography.
But technology disruption often progresses more slowly than one might think. Or much faster than one might think. Or ends up stopping dead in its tracks. So where are we at with videos?
Back in 2013, I published a report for the then popular tech publication and market analysis firm, GigaOm, called Action! Roll 'em: Personal video poised to take off. The report started with this paragraph, which I could still use today:
To date, personal video hasn't exploded the way photos have. But, like photos, personal video has the potential to break out from its role as a storehouse for memories of life 's key moments and become an integral part of consumers' real-time social sharing habits.
The report highlighted the potential of solutions for short-form videos, such as Vine and Instagram, and hybrid photo-video solutions, such as Animoto and Magisto, to be the driving force behind the expected growth in consumer video capture, sharing and watching.
All the major social networks (from Facebook to Snapchat and anything in between) have now embraced video in a big way, to no small extent supported by influencers who promote themselves – and their brand sponsors – through eye-catching video clips. TikTok tops 2 billion downloads with their easy to (re)create music video app. Magisto was acquired last year by Vimeo, reportedly for $200M. Twitter made the biggest mistake in its history by abandoning Vine (Vine's founders are back at it with Byte, launched earlier this year). Quibi recently launched its smartphone-only equivalent of Netflix for watching short-form videos.
And the list goes on. While there is no shortage of apps and sites to create, edit, watch, share, or comment on videos, is today's average consumer also on a trajectory to capture more and more video? Or is she more of a happy watcher or sharer of videos created by others – professionals, influencers, and super young consumers?
That question has been nagging me for a while now, in particular, because when I look around or observe younger family members or their friends or their kids, it does seem that when there's anything interesting going on in their lives their default behavior is still taking photos, possibly in a hybrid format such as Instagram Boomerangs, Apple Live Photos, or animated GIFs (we called this format phodeos in our 2018 report; breaking news announced last week: Facebook is slated to acquire Giphy, see also below).
For this reason, we added a few video-related questions to a broader survey that we recently conducted among 1,248 US respondents aged 18 years or older who take at least 5 photos a month.
We found that the median number of smartphone photos taken per month is 25 vs. 4 for videos. This means that half of the respondents take fewer than 4 videos; the other half take more than 4 videos, compared to 25 for photos.
(The average numbers for both capture formats are naturally higher as, for instance, high-volume photographers might shoot more than 25 photos above the average of 25, but low-volume photographers can't shoot more than 25 below the average of 25. On average, our respondents take 63 photos vs. 11 videos per month; indicating a discrepancy between photos and videos comparable to what we report for the median numbers).
A few comments to put these differences in perspective:
Everything else being equal, there's a natural reason why one would take more photos than videos: a single video could contain multiple share-worthy moments, which would require numerous separate shots to capture as photos instead.
Also, our survey was among respondents 18 years or older. Had it included teenagers or tweens the number of videos captured might be higher.
We also analyzed statistically significant differences among specific respondent segments: Respondents in households with children take on average significantly more photos and videos than those without children. The same is true for respondents under 35 vs. those who are 35 or older. Female respondents take significantly more photos on average per month than males (there are no significant differences for gender in regard to taking videos).
While video is not yet a dominant capture format, is it catching up with photos?
Our survey indicates that's not the case. In fact, the numbers show the trend is in the opposite direction: photos are more on the rise than videos. More respondents believe they're taking more or way more photos now compared to a year ago than they believe is the case for taking videos.
This is not because taking videos is decreasing – video capture is also on the rise compared to a year ago:
48% indicated 4 ("more") or 5 ("way more") in regard to taking photos, compared to 32% for videos:
Where does this leave us?
Our survey shows that for now at least, consumers are clearly not substituting taking photos for taking videos.
But could that change in the future? There are important market and technology drivers that could trigger consumers to take more videos instead of photos. One such market driver is the exploding video watching and (re) sharing of video behavior. While we might not take that many more consumer videos, we certainly watch and (re)share them more than ever on YouTube and a range of social media outlets, including those mentioned earlier. Technology drivers include more and more smartphone cameras capable of capturing 4K at 30 fps or higher, video compression technologies that keep improving, 5G that will make transferring large video files a breeze, and innovative apps that keep making the video editing process easier and more versatile.
But are these drivers enough to tilt the balance? Will they result in an inevitable move from photo to video capture, similar to how we moved from B&W to color photography, from film to digital photography, or from standalone cameras to smartphone photography? Will still photography at some point become a niche use case and video capture the norm?
Or will capturing and editing of anything but the shortest form videos always be too time-consuming and/or intrusive? Or is there something inherent to the simplicity and beauty of capturing a single moment through a photo that can never be replaced by a video?
Maybe – but let's not assume this too easily! People said similar things about other legacy photography methods they loved: B&W over color photography, film over digital photography, and digital cameras over smartphones. Still, to a large extent these legacy methods have been replaced by more versatile, cheaper, or easier alternatives (with the jury still being out regarding to what degree digital cameras will become niche products).
Fewer life events are best illustrated by videos than photos. Photo by Bruno Cervera from Pexels
The answer – you guessed it – is that we don't know yet. But we do know who'll give the first indication of that answer: teenagers, tweens and possibly even younger kids. When they stop taking photos and it's all video, we'll know that we – the older bunch – will follow suit sooner or later. With the Instaxes and Polaroids of the world still going strong, and photos still a major format shared on the Snapchats and Instagrams of the world, we're not there yet. And maybe we'll never be.
Sony. Going hybrid. Sony is announcing a hybrid image sensor/AI chip, the IMX500. Or as Sony calls it, the world's First Intelligent Vision Sensors with AI Processing Functionality. The benefits? Conceivably, there are many. Instead of capturing the image first and then analyzing it, this hybrid sensor could capture the image the way the AI wants it, for instance by cropping out everything in the photo but the parts it recognizes and has been told to report — only the flowers, but never the stems, if that's what you want. Or cameras in public places that preemptively blur faces or license plates. Or smart home devices that recognize individuals without ever saving or sending any image data. In addition, Sony is partnering with Microsoft to develop a smart camera managed app powered by Azure Internet of Things (IoT) and cognitive services that it hopes to use alongside the IMX500 sensor to provide new video analytics use cases for enterprise customers.
Samsung. Going small. What, launching a 50MP sensor when you already have one at 108MP buzzing in the Galaxy S20 Ultra? Samsung's ISOCELL GN1 sensor has other tricks up its sleeve: it combines Dual-Pixel autofocus with the company's Tetracell technology, allowing for increased light sensitivity in low light as well as precise and fast ("DSLR-level") autofocus performance in all light conditions.
Chatbooks. Security breach. As reported by The Dead Pixels Society, Nate Quigley, CEO of Chatbooks, advised users the company had a security breach on March 26, which the company became aware of on May 5. No credit card information was compromised, but users are requested to change their passwords.
Facebook & Giphy. You can sell yourself too early to Facebook or too late. Instagram did the former (sold for $1B, now worth $100B+); Giphy just did the latter for somewhere between $300M-$400M while its last valuation in 2017 was $600M after it closed a round of $150M. It shows the new reality in startup-funding-exit land?
Apple & NextVR. You can sell yourself too early to Apple or too late. After having raised $115M over the years and having failed to raise a round C last year and consequently laying off 40% of its staff, sports VR technology provider, NextVR is acquired by Apple for $100M. Too late, is the verdict.
Facebook and Instagram. Going grand. What was missing in your life? The ability to buy stuff directly from a business' Facebook Page or Instagram profile. As of today, you can. But there is more to come. Facebook is also launching what it's calling a "universal product recognition model" that uses AI to identify consumer goods, from furniture to fast fashion to fast cars. It's the first step toward a future where the products in every image on its site can be identified and potentially shopped for. "We want to make anything and everything on the platform shoppable, whenever the experience feels right," Manohar Paluri, head of Applied Computer Vision at Facebook, told The Verge. "It's a grand vision." No kidding, that was a real quote.
DJI. Oops, these pesky patents. A US judge has ruled that DJI violated the patent of Autel Robotics, a smaller drone maker, and he is recommending that most DJI drone models be pulled from store shelves and blocked from being imported. If the judge's ruling and recommendation are upheld by the full ITC commission, the DJI drones could be kicked out of the US as early as July.
main image: Photo by Terje Sollie from Pexels


text:
Q: Найти итем в ItemsControl WPF Имеется ItemsControl, который выводит список radiobutton и textblock:
<ItemsControl x:Name="itemImages" Grid.Row="1">
    <ItemsControl.ItemsPanel>
         <ItemsPanelTemplate>
              <WrapPanel/>
         </ItemsPanelTemplate>
    </ItemsControl.ItemsPanel>
    <ItemsControl.ItemTemplate>
         <DataTemplate DataType="{x:Type local:ImageModel}">
              <StackPanel>
                   <RadioButton Style="{DynamicResource RadioButtonImageModel}" GroupName="1">
                   <Image Source="{Binding Path=Path}" Width="64"/>
                   </RadioButton>
                   <TextBlock HorizontalAlignment="Center" Text="{Binding Path=Name}"/>
              </StackPanel>
         </DataTemplate>
    </ItemsControl.ItemTemplate>
</ItemsControl>

Каким образом я могу отследить выбранный radiobutton в коде?

A: Если использование ItemsControl не принципиально, то просто используйте ListBox с таким же переопределением шаблона для данных. В RadioButton используйте привязку IsChecked к IsSelected ListBoxItem-a:
<ListBox x:Name="itemImages" Grid.Row="1" SelectedItem={Binding <!--здесь будет ваш выбранный объект -->}>
    <ListBox.ItemsPanel>
         <ItemsPanelTemplate>
              <WrapPanel/>
         </ItemsPanelTemplate>
    </ListBox.ItemsPanel>
    <ListBox.ItemTemplate>
         <DataTemplate DataType="{x:Type local:ImageModel}">
              <StackPanel>
                   <RadioButton Style="{DynamicResource RadioButtonImageModel}" GroupName="1"
                            IsChecked="{Binding RelativeSource={RelativeSource AncestorType=ListBoxItem}, Path=IsSelected}">
                   <Image Source="{Binding Path=Path}" Width="64"/>
                   </RadioButton>
                   <TextBlock HorizontalAlignment="Center" Text="{Binding Path=Name}"/>
              </StackPanel>
         </DataTemplate>
    </ListBox.ItemTemplate>
</ListBox>


Delphi H2 Change manual valve to sol valve - Duration: 5 minutes, 53 seconds.
How to Install a Diverter Valve to Your Faucet for use with a Water Ionizer - Duration: 2 minutes.
How to - Replace Your Filters and Reset Filter Counters in any H2 ionizer - Duration: 6 minutes, 16 seconds.
How To - Install Your Undersink Kit With Your Water Ionizer - Duration: 8 minutes, 4 seconds.
elita Pure Non Electric Water Ionizer Installation Video - Duration: 15 minutes.
Get The Most Out of Your H2 - Duration: 5 minutes, 22 seconds.
Ensure you invest in best value Water Ionizer! Be a Smart Shopper - Duration: 4 minutes, 51 seconds.
AlkaViva Australia Vesta H2 Ioniser - Duration: 117 seconds.
AlkaViva Australia Elita Ioniser - Duration: 2 minutes, 33 seconds.
Unbiased Water Ionizer Review Independent EPA Certified Lab Results for AlkaViva Australia - Duration: 9 minutes, 42 seconds.
Dr OZ Explains how the Antioxidants of Kangen Water Fights Cancer - Duration: 5 minutes, 57 seconds.


The Olivia's Garden design is not available on a SteelSeries Rival 600 Gaming Mouse Skin yet but we will make one for you with this design when you order this "Make from Design" product.
iStyles SteelSeries Rival 600 Gaming Mouse Skin design of Pattern, Floral design, Flower, Botany, Design, Visual arts, Textile, Plant, Wildflower, Pedicel with gray, green, pink, yellow, red, blue colors.
Added SteelSeries Rival 600 Gaming Mouse Skin to your shopping cart.


text:
Home News "Nobody Prosecuted For Deborah's Murder" – Ex-NERC Boss Laments
"Nobody Prosecuted For Deborah's Murder" – Ex-NERC Boss Laments
Several months after Islamic extremists murdered Christian woman, Deborah Samuel, her killers are yet to be brought to book.
A former chairman of the Nigerian Electricity Regulatory Commission, Sam Amadi stated this while lamenting about injustice in Nigeria.
Deborah, a Christian 200-level student of Shehu Shagari College of Education Sokoto, was killed by fellow students in May for allegedly blaspheming Prophet Muhammad.
On his Instagram page on Monday, Amadi lamented that nobody had been prosecuted for the student's murder because Nigeria had continued to appease "religious fundamentalists".
He wrote, "Nobody has been arrested and prosecuted for the murder of Deborah. Nobody will. This is because Nigeria is managed as a theocratic neo-feudal state. Nigeria's path to development passes through dismantling the religious construction of the foundation of the Nigerian state. All through, Nigeria has continued to appease religious fundamentalists."
The rights activist alleged that the Inspector General of Police, Usman Baba, and the Sokoto State governor, Aminu Tambuwal, did nothing to prosecute the killers so they would not annoy "some crazy religious fundamentalists".
Amadi added, "Neither the IGP nor Governor Tambuwal has done anything to arrest and prosecute those who killed Deborah because if we do so, we annoy some crazy religious fundamentalists, who may be crazy fellows. The backlash of such annoyance will be politically costly.
"Nigeria is trapped in backwardness of undue intermingling with religion. As long as it continues on this part, it cannot be a coherent modern democratic state."
Amadi's post comes shortly after the presidential candidate of the Peoples Democratic Party, Atiku Abubakar, revealed why he deleted his tweet condemning Deborah's murder.
Previous articleWakanda Forever soundtrack: Tems, Rihanna clinch first Golden Globe nomination
Next article"My turning point" – Singer Yinka Ayefele marks 25 years of surviving accident
Two Nigerians arrested in Canada for allegedly selling $500k worth of fraudulently issued plane tickets
Boko Haram commander Abu Illiya, 32 others killed in fresh troops' onslaught
Just In: INEC extends PVC collection by one week


When most travelers think of Southeast Asia, they conjure up images of well-traveled Thailand, or perhaps Vietnam, Laos and Cambodia. But Myanmar (Burma) has recently opened its doors to the world, offering a much more pristine and well-preserved cultural and nature experience. This includes Buddhist cultural treasures, traditional rural villages, unspoiled tropical landscapes, fascinating wildlife, and pioneer eco-lodges. Myanmar is truly one of the last places on Earth that captures the spirit of nature and the nature of human spirit, all in one place.
For a truly rare foray into the hidden natural treasures of Southeast Asia, we've even thrown in the best nature-based slice of Thailand, which you can experience on the same amazing journey as your Myanmar adventure. Check out Natural Habitat Adventures' unique two-country itinerary, which includes visits to the golden temples of Yangon and Bagan, national parks and natural highlights of Myanmar, and three Thai national parks that comprise some of Southeast Asia's richest wildlife enclaves.
Here are a few of our favorite things to see and do on an intimate guided excursion to Myanmar, as captured in the images below. For more photos of Myanmar and Thailand, follow our Expedition Leader Court Whelan on Instagram at @whelancourtland.
Discover Thailand's Khao Sok National Park, where unique wildlife inhabits the world's oldest rain forest amid spectacular karst mountains and jade-green lakes.
Travel to Costa Rica with WWF & NHA!
From mountains to ocean, lush rain forests to stark volcanoes, explore a variety of habitats that is home to a wide array of wildlife in Costa Rica!