text:
Josh Hader
Hader with the Milwaukee Brewers in 2019
San Diego Padres – No. 71
Born: (1994-04-07) April 7, 1994 (age 28)
Millersville, Maryland
Bats: Left
Throws: Left
MLB debut
June 10, 2017, for the Milwaukee Brewers
MLB statistics
(through 2022 season)
Win–loss record
Earned run average
Milwaukee Brewers (2017–2022)
San Diego Padres (2022–present)
4× All-Star (2018, 2019, 2021, 2022)
2× All-MLB First Team (2019, 2021)
3× NL Reliever of the Year (2018, 2019, 2021)
NL saves leader (2020)
Pitched a combined no-hitter on September 11, 2021
MLB records
Most consecutive outs via strikeout (16) on September 22, 2018
Most consecutive hitless appearances (12)
Fastest player to reach 400 strikeouts (accomplished in 234.2 IP)
Most consecutive saves to start a season (18)
Representing United States
2015 Toronto Team
Joshua Ronald Hader (born April 7, 1994) is an American professional baseball pitcher for the San Diego Padres of Major League Baseball (MLB). He has previously played in MLB for the Milwaukee Brewers. Hader is a four-time All-Star and three-time winner of the National League Reliever of the Year Award.
The Baltimore Orioles selected him in the 19th round of the 2012 MLB draft. He was traded twice before joining the Milwaukee Brewers organization in 2015. Hader played for the United States national baseball team in the Pan American Games in 2015, and appeared in the All-Star Futures Game in 2016. Traded to the Milwaukee Brewers organization in 2015, Hader made his MLB debut in 2017. In 2021, he became the fastest pitcher in major league history to reach 400 strikeouts. The Brewers traded Hader to the Padres in 2022.
Hader attended Old Mill High School in Millersville, Maryland, where he played for the school's baseball team. As a senior, Hader had a 10–0 win–loss record, a 0.30 earned run average (ERA), and 125 strikeouts. He also had a batting average above .400. After graduating from high school, Hader was prepared to enroll at Anne Arundel Community College.[1][2]
Baltimore Orioles (2012–2013)
The Baltimore Orioles selected Hader in the 19th round of the 2012 MLB draft, with the 582nd overall selection.[3][4] He signed with the Orioles for a $40,000 signing bonus, rather than attend Anne Arundel.[1][3] Beginning his professional career, Hader's fastball velocity increased, from 84–88 miles per hour (135–142 km/h) as a high school player, to 94–95 miles per hour (151–153 km/h) as he changed his workout routine.[5]
Hader played for the Gulf Coast Orioles in the Rookie-level Gulf Coast League and the Aberdeen Ironbirds of the Class A-Short Season New York–Penn League, allowing only 14 hits and nine walks while striking out 48 in 28+2⁄3 innings pitched.[2] In 2013, the Orioles assigned Hader to the Delmarva Shorebirds of the Class A South Atlantic League (SAL). He appeared in the SAL All-Star Game,[5] and through July 31, pitched to a 3–6 record with 79 strikeouts and a 2.65 ERA in 17 games started.[3]
Houston Astros (2013–2015)
On July 31, 2013, the day of the MLB trade deadline, the Orioles traded Hader and L. J. Hoes to the Houston Astros in exchange for Bud Norris.[1][3] He completed his 2013 season with the Quad Cities River Bandits of the Class A Midwest League.[6]
Pitching for the Lancaster JetHawks of the Class A-Advanced California League in 2014, Hader and two relief pitchers combined to throw a no-hitter on May 13.[7] After pitching to a 9–2 record with a league-leading 2.70 ERA and 112 strikeouts in 103+1⁄3 innings, the Astros promoted him to the Corpus Christi Hooks of the Class AA Texas League in August. He was named the California League Pitcher of the Year.[8] Hader returned to Corpus Christi in the 2015 season.[9] He was selected to play for the United States national baseball team in the 2015 Pan American Games.[10]
On July 30, 2015, the Astros traded Hader, Brett Phillips, Domingo Santana, and Adrian Houser to the Milwaukee Brewers for Carlos Gómez and Mike Fiers.[11] The Brewers assigned Hader to the Biloxi Shuckers of the Class AA Southern League.[12] He finished the season with a 3.03 ERA combined.
The Brewers invited him to spring training in 2016.[13] Hader began the 2016 season with Biloxi, and made his first start for the Colorado Springs Sky Sox of the Class AAA Pacific Coast League on June 12.[14] He was selected to appear in the 2016 All-Star Futures Game.[15] The Brewers added him to their 40-man roster after the season.[16]
Hader began the 2017 season with Colorado Springs. The Brewers promoted him to the major leagues on June 9.[17] He made his major league debut on June 10.[18]
Hader began 2018 in the Milwaukee bullpen. On April 30, Hader became the first pitcher ever to record eight strikeouts in an outing that was less than three innings long. He recorded a 2+2⁄3-inning save against the Cincinnati Reds, in which he faced nine batters, recording eight strikeouts and one walk.[19] Owning a 1.21 ERA with 83 strikeouts in 44 innings, Hader was named to the 2018 MLB All-Star Game.[20] Hader appeared in the 2018 All-Star Game at the top of the eighth inning. During his appearance, he gave up hits to Rangers' Shin-Soo Choo and Astros' George Springer before Mariners' Jean Segura hit a home run to left field to give the American All-Stars a 5–2 lead.[21][22] After giving up a single to Boston Red Sox baseman Mitch Moreland, Hader was replaced after 26 pitches, allowing four hits and one out over five batters.[23]
In 2018, Hader was 6–1 with 12 saves and a 2.43 ERA. He had 143 strikeouts in 81+1⁄3 innings. Among MLB pitchers who threw at least 20 innings, Hader held left-handed batters to the lowest batting average (.088).[24] He won the National League Reliever of the Year Award,[25] becoming the first non-full-time closer to win the award.
Hader began the 2019 season as the Brewers' closer following injuries to fellow relievers Jeremy Jeffress (shoulder strain) and Corey Knebel (Tommy John surgery). On March 30, Hader pitched an immaculate inning to earn the save against the St. Louis Cardinals, striking out Tyler O'Neill, Dexter Fowler, and Yairo Munoz on nine consecutive strikes, seven of which were swinging strikes.[26] Hader received the NL Reliever of the Month Award for both May and June.[27] In the 2019 National League Wild Card Game, Hader blew the save for the Brewers, giving up a three-run single to Juan Soto of the Washington Nationals, which, coupled with an error from right fielder Trent Grisham, led to Washington's go-ahead run scoring on the play and resulted in the Brewers eventually losing the game.[28] Hader finished his 2019 season with a 3–5 record, a 2.62 ERA, and 138 strikeouts over 72+2⁄3 relief innings. He won his second consecutive NL Reliever of the Year Award.[29]
Hader began the 2020 season with 12 consecutive hitless appearances, the longest such streak in major league history.[30] Hader finished the season with a 3.79 ERA and an NL-leading 13 saves in 15 chances.
On May 8, 2021, Hader reached 400 strikeouts faster than any other pitcher in MLB history, doing so in 234+2⁄3 innings pitched, breaking the prior record of 236 innings, which had been set by Craig Kimbrel in 2014.[31] For July, Hader was named NL Reliever of the Month, the fourth time he won the award.[32] On September 11, Hader pitched the ninth inning to close out a combined no-hitter against the Cleveland Indians. [33] He finished the 2021 season with 34 saves in 35 opportunities; he recorded a career-low 1.23 ERA with 102 strikeouts in 58+2⁄3 innings.
After beginning the 2022 season with 25 saves in 27 appearances and a 1.05 ERA, Hader's pitching declined during the summer, and on August 1, the Brewers traded him to the San Diego Padres for Taylor Rogers, Dinelson Lamet, Esteury Ruiz, and Robert Gasser.[34] He made his Padres debut the following day, earning the win against the Colorado Rockies.[35] However, his struggles continued, including a stretch of earning only 2 saves in 13 appearances, with an increased ERA of 6.52.
He regained his all-star form in September, and has been instrumental in the Padres postseason run. [36]
Hader is the son of Tom and Patricia Hader.[37] Hader is married to Maria Hader.[38] The couple's first child was born in June 2022.[39]
During the 2018 Major League Baseball All-Star Game, a series of tweets by Hader ranging from mid-2011 to late-2012 were discovered to contain racist, homophobic and sexist content.[40][41] Friends and family of Hader who were invited to the game were given blank jerseys to wear due to the controversy.[42][43] Hader issued an apology after the game and deleted his account on Twitter.[44][45]
Houston Astros award winners and league leaders
^ a b c Pleskoff, Bernie (August 1, 2013). "Newly acquired pitcher Josh Hader brings high upside to Houston Astros". MLB.com. Retrieved November 21, 2013.
^ a b Melewski, Steve (December 14, 2012). "A look at Josh Hader and his stunning velocity increase last summer". MASN. Retrieved November 21, 2013.
^ a b c d Connolly, Dan (July 31, 2013). "Josh Hader says 'it's pretty crazy' he's been traded from Orioles to Astros". The Baltimore Sun. Retrieved November 21, 2013.
^ Williams, Preston (June 6, 2012). "Old Mill pitcher Josh Hader drafted by Orioles". Washington Post. Archived from the original on December 3, 2013. Retrieved November 24, 2013.
^ a b Driver, David (June 23, 2013). "Old Mill's Hader has been impressive as Orioles' prospect". The Baltimore Sun. Retrieved November 21, 2013.
^ "YouTube: Quad-Cities River Bandits pitcher Josh Hader". Qctimes.com. September 15, 2013. Retrieved November 24, 2013.
^ "Houston Astros prospects Josh Hader, J.D. Osbourne, Daniel Minor combine on no-hitter for Lancaster JetHawks". MiLB.com. Retrieved July 8, 2015.
^ de Jesus Ortiz, Jose (August 22, 2014). "Astros minor leaguer Josh Hader named California League Pitcher of the Year". Houston Chronicle. Retrieved September 4, 2014.
^ "Corpus Christi Hooks lefty Josh Hader has used his laid back presence on and off the mound to go from a 19th round pick of the Orioles in 2012 to the Astros 2014 Minor League Pitcher of the Year. – Corpus Christi Hooks News". MiLB.com. June 2, 2015. Retrieved July 8, 2015.
^ "Hooks pitcher Josh Hader and catcher Roberto Peña will represent the United States and Puerto Rico, respectively, at the Pan-American Games July 10–19 in Toronto – Corpus Christi Hooks News". MiLB.com. July 6, 2015. Retrieved July 8, 2015.
^ "Carlos Gomez, Mike Fiers traded to Houston Astros for prospects". ESPN.com. July 30, 2015. Retrieved July 30, 2015.
^ "Hader masterful in Shuckers debut: Hader allows one hit, strikes out nine". Biloxi Sun Herald. August 4, 2015. Archived from the original on August 6, 2015. Retrieved August 5, 2015.
^ McCalvy, Adam (February 28, 2016). "Southpaw Hader has Majors starter potential: Prospect grateful for 'big opportunity' at first big league camp". MLB.com. Retrieved March 14, 2016.
^ Peng, Michael (June 12, 2016). "Hader allows one hit in Triple-A debut: Brewers No. 4 prospect fans nine, gives up two runs over six innings". MiLB.com. Retrieved June 19, 2017.
^ foxsports (June 28, 2016). "Brewers prospect Hader named to All-Star Futures Game". FOX Sports. Retrieved June 9, 2017.
^ McCalvy, Adam (November 18, 2016). "Brewers' Brinson, Hader added to 40-man roster". MLB.com. Retrieved June 9, 2017.
^ "Brewers summon lefty Josh Hader, place Travis Shaw on family leave". Milwaukee Journal Sentinel. June 9, 2017. Retrieved June 9, 2017.
^ Rosiak, Todd (June 15, 2017). "Brewers taking it slowly with Josh Hader". Milwaukee Journal Sentinel. Retrieved June 20, 2017.
^ "Josh Hader Becomes 1st Pitcher to Record 8 Ks in Fewer Than 3 Innings". April 30, 2018. Retrieved May 1, 2018.
^ Wagner, Andrew (July 9, 2018). "Lorenzo Cain, Christian Yelich, Josh Hader selected to All-Star Game; Jesus Aguilar needs fans' votes". Wisconsin State Journal. Retrieved July 18, 2018.
^ "Segura smacks go-ahead 3-run homer – ESPN Video". Retrieved July 18, 2018.
^ "All-Star Game 2018: Rare rally, error lead to Jean Segura's three-run shot". Retrieved July 18, 2018.
^ "All-Stars vs. All-Stars – Play-By-Play – July 17, 2018". ESPN.com. Retrieved July 18, 2018.
^ Splits Leaderboards | FanGraphs
^ "Brewers' Josh Hader wins 2018 Trevor Hoffman Award as top reliever in National League".
^ McCalvy, Adam (March 31, 2019). "Hader closes out win with immaculate inning". MLB.com. Retrieved March 31, 2019.
^ Harrigan, Thomas (July 3, 2019). "Josh Hader, Liam Hendriks Relievers of Month". MLB.com. Retrieved May 21, 2021.
^ "Brewers' NLDS dreams dashed by eventful 8th".
^ "Brewers' Josh Hader voted top relief pitcher in National League for second year in a row".
^ McCalvy, Adam (September 3, 2020). "Unhittable Hader sets Major League record". MLB.com. Retrieved September 3, 2020.
^ Castrovince, Anthony (May 8, 2021). "Hader becomes fastest to 400 career K's". MLB.com. Retrieved July 15, 2021.
^ Adler, David (July 2, 2021). "Relievers of Month for June: Trivino, Hader". Retrieved July 15, 2021.
^ Castrovince, Anthony (September 11, 2021). "Led by Burnes, Brewers spin combo no-hitter". MLB.com. Retrieved September 12, 2021.
^ Cassavell, AJ (August 1, 2022). "Padres acquire Hader from Crew in 5-player trade". Retrieved August 1, 2022.
^ "Hader gets win as SD sweeps doubleheader with walk-off HR". MLB.com. Retrieved August 3, 2022.
^ "Cronenworth, Padres rally to stun Dodgers 5-3 to reach NLCS". ESPN.com. October 15, 2022. Retrieved October 16, 2022.
^ Wagner, Bill. "Josh Hader makes major league debut with Brewers". Retrieved December 11, 2020.
^ "josh.hader". Instagram. Archived from the original on December 24, 2021. Retrieved December 11, 2020.
^ "Milwaukee Brewers reinstate closer Josh Hader from paternity list, put Aaron Ashby on IL". ESPN.com. Associated Press. June 20, 2022. Retrieved June 20, 2022.
^ Brewers' Josh Hader apologizes for uncovered trove of racist, sexist, and homophobic tweets – For The Win (USA Today)
^ "MLB all-star apologizes for history of racist tweets". ABC News. July 18, 2018. Retrieved July 18, 2018.
^ "Jeff Passan on Twitter". Retrieved July 18, 2018.
^ Brewers reliever Josh Hader remorseful for offensive tweets – ESPN
^ Hader 'deeply sorry' for previous tweets – ESPN Video
^ Burke, Caroline (July 18, 2018). "Josh Hader Locks Twitter Account After Alleged Old Tweets Surface". Retrieved July 18, 2018.
Wikimedia Commons has media related to Josh Hader.
Career statistics and player information from MLB, or ESPN, or Baseball Reference, or Fangraphs, or Baseball Reference (Minors), or Retrosheet
Josh Haderade™ on Twitter
Josh Hader on Instagram
Awards and achievements Preceded byTyler Gilbert No-hitter pitcher September 11, 2021 (with Corbin Burnes) Succeeded byTylor Megill Drew Smith Joely Rodríguez Seth Lugo Edwin Díaz
Major League Baseball Reliever of the Year Award
2014: Craig Kimbrel
2015: Mark Melancon
2016: Kenley Jansen
2018: Josh Hader
2020: Devin Williams
2014: Greg Holland
2015: Andrew Miller
2016: Zack Britton
2018: Edwin Díaz
2019: Aroldis Chapman
2020: Liam Hendriks
Sporting News NL Relief Pitcher of the Year Award
2013: Kimbrel
2015: Melancon
2016: Jansen
2018: Hader
2019: Yates
2020: Williams
National League season saves leaders
1969: Gladding
1970: Granger
1971: Giusti
1972: Carroll
1973: Marshall
1975: Eastwick & Hrabosky
1976: Eastwick
1977: Fingers
1979: Sutter
1983: Smith
1985: Reardon
1986: Worrell
1987: Bedrosian
1988: Franco
1989: M. Davis
1993: Myers
1996: Brantley & Worrell
1997: Shaw
1998: Hoffman
1999: Urbina
2000: Alfonseca
2001: Nen
2002: Smoltz
2003: Gagné
2004: Benítez & Isringhausen
2005: Cordero
2007: Valverde
2009: Bell
2010: Wilson
2011: Axford & Kimbrel
2012: Kimbrel & Motte
2016: Familia
2017: Holland & Jansen
2018: W. Davis
2019 All-MLB Team
1st Team
C: Realmuto
1B: Alonso
2B: LeMahieu
3B: Rendon
SS: Bogaerts
OF: Bellinger
Yelich
DH: Cruz
SP: Cole
deGrom
Verlander
RP: Hader
2nd Team
C: Grandal
1B: Freeman
2B: Altuve
3B:Bregman
SS: Semien
OF: Acuña Jr.
DH: Álvarez
SP: Flaherty
Greinke
Soroka
RP: Chapman
C: Pérez
1B: Guerrero Jr
2B: Semien
3B: Riley
SS: Tatís Jr.
OF: Harper
DH: Ohtani
SP: Buehler
Burnes
C: Posey
2B: Albies
3B: Devers
SS: Turner
OF: Castellanos
SP: Fried
Gausman
Urías
RP: Iglesias
San Diego Padres current roster
Active roster
2 Trent Grisham
4 Blake Snell
5 Wil Myers
7 Ha-seong Kim
9 Jake Cronenworth
10 Jurickson Profar
11 Yu Darvish
12 Luis Campusano
13 Manny Machado
16 Brandon Dixon
17 Brandon Drury
21 Nick Martinez
22 Juan Soto
24 Josh Bell
25 Tim Hill
26 Austin Nola
28 José Azócar
34 Craig Stammen
36 Pierce Johnson
38 Jorge Alfaro
44 Joe Musgrove
48 Steven Wilson
50 Adrián Morejón
52 Mike Clevinger
55 Sean Manaea
66 Luis Garcia
71 Josh Hader
74 Nabil Crismatt
75 Robert Suárez
Inactive roster
32 Eguy Rosario
33 Reiss Knehr
37 Matthew Batten
40 Ryan Weathers
49 Michel Báez
56 Ray Kerr
61 Efraín Contreras
65 Jose Castillo
77 Jay Groome
-- Angel Felipe
-- Brett Sullivan
Injured list
15 Drew Pomeranz
54 Austin Adams
Restricted list
23 Fernando Tatís Jr.
Manager 3 Bob Melvin
Bench 20 Ryan Christenson
Hitting 39 Michael Brdar
Batting Practice/Hitting Instructor 81 Morgan Burkhart
Pitching 57 Ruben Niebla
First Base/Outfield Instructor 46 David Macias
Third Base 18 Matt Williams
Bullpen 53 Ben Fritz
Coaching Assistant 86 Heberto Andrade
Quality Control 84 Ryan Flaherty
Catching 99Francisco Cervelli
Senior Advisor 00 Bryan Price
Game Planning/Coaching Assistant 83 Peter Summerville
Coaching Assistant 82 Justin Novak
Aberdeen IronBirds players
Baseball players at the 2015 Pan American Games
Baseball players from Maryland
Biloxi Shuckers players
Colorado Springs Sky Sox players
Corpus Christi Hooks players
Delmarva Shorebirds players
Gulf Coast Orioles players
Lancaster JetHawks players
Major League Baseball pitchers
Milwaukee Brewers players
National League All-Stars
Pan American Games medalists in baseball
Pan American Games silver medalists for the United States
People from Anne Arundel County, Maryland
Quad Cities River Bandits players
San Diego Padres players
Sportspeople from the Baltimore metropolitan area
United States national baseball team players
Medalists at the 2015 Pan American Games
Twitch (service) streamers


text:
Q: JBOSS fuse proxying the schema I tried to create router with JBoss Fuse/Camel and deployed in successfully.
<camelContext id="blueprintContext"
                trace="false"
                xmlns="http://camel.apache.org/schema/blueprint">
    <route id="httpBridge">
      <from uri="jetty:http://mysystem:8282/CreateAccountService?matchOnUriPrefix=true"/>
      <to uri="jetty:http://mysystem:8080/service/services/CreateAccountService?bridgeEndpoint=true&throwExceptionOnFailure=false"/>
    </route>
  </camelContext>

Able to access the service using proxy.
I have used xsd for data types in wsdl.
<wsdl:types>
<xsd:schema xmlns:fault="http://www.sample.project.com.au/common/message/FaultMessage/v1" xmlns:pref="http://www.sample.project.com.au/services/account/CreateAccount/CreateAccountRequest/v1" xmlns:pref1="http://www.sample.project.com.au/services/account/CreateAccount/CreateAccountResponse/v1" xmlns:pref2="http://www.sample.project.com.au/common/message/TechnicalException/v1" xmlns:soap="http://schemas.xmlsoap.org/wsdl/soap/" xmlns:tns="http://www.sample.project.com.au/services/account/CreateAccount/v1" xmlns:wsdl="http://schemas.xmlsoap.org/wsdl/" xmlns:xsd="http://www.w3.org/2001/XMLSchema" targetNamespace="http://www.sample.project.com.au/services/account/CreateAccount/v1">
<xsd:import namespace="http://www.sample.project.com.au/services/account/CreateAccount/CreateAccountRequest/v1" schemaLocation="http://mysystem:8080/service/services/CreateAccountService?xsd=CreateAccountRequest.xsd"></xsd:import>
<xsd:import namespace="http://www.sample.project.com.au/services/account/CreateAccount/CreateAccountResponse/v1" schemaLocation="http://mysystem:8080/service/services/CreateAccountService?xsd=CreateAccountResponse.xsd"></xsd:import>
</xsd:schema>
</wsdl:types>

http://mysystem:8282/CreateAccountService?wsdl (Proxy)
But, I can see all the xsd details in wsdl with
<xsd:import namespace="http://www.sample.project.com.au/services/account/CreateAccount/CreateAccountRequest/v1" schemaLocation="http://mysystem:8080/service/services/CreateAccountService?xsd=CreateAccountRequest.xsd"></xsd:import>

which is original url of the schema.
http://mysystem:8080/service/services/CreateAccountService?xsd=CreateAccountRequest.xsd

I want to hide this details also.
But, I was able to see all the schema with actual url details.
How can I create the proxy for all the contents in the URI.
If I am not clear with my question, please let me know.

A: 1)
You need to process the reply message (using message translator eip  http://camel.apache.org/message-translator.html) before returning it to the client, so you can replace the url in the message body from the actual url, to the proxy url
For example you can use a bean with a single method that does the search/replace
public String transformTheMessage(String body) {
  ...
  ... search for the url you want to replace
}

And then define a bean in the xml file and use it in the Camel route
<route id="httpBridge">
      <from uri="jetty:http://mysystem:8282/CreateAccountService?matchOnUriPrefix=true"/>
      <to uri="jetty:http://mysystem:8080/service/services/CreateAccountService?bridgeEndpoint=true&throwExceptionOnFailure=false"/>
      <to uri="bean:myBean"/>
    </route>

2)
Or check if the request is for the WSDL and do a content based router, and return the content from a file which you have on the proxy server - and the file has the proxy urls hardcoded etc.
http://camel.apache.org/content-based-router.html
3)
Or check out this example

*

*https://camel.apache.org/cxf-proxy-example.html

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text:
Facebook's revenue reportedly hits $1.6bn in first half of 2011
by Paul Sawers — in Facebook
Reuters reports that Facebook's revenue for the first half of 2011 hit $1.6bn, with its Net income falling just short of $500 million.
Reuters' source chose to remain anonymous, given that Facebook is still a private company and doesn't publicly disclose its financial details.
Last December, it was reported that Facebook was on track for $2bn revenue in 2011, and if this latest report proves accurate, then it's on course to more than meet that figure.
Facebook has previously been non-committal on its future IPO plans, but it has been rumored to be happening some time in 2012, which could see its valuation exceed the $100bn mark.
According to the Reuters' report, Facebook's first-half revenue roughly doubled-up on the same period the year before, and these results would line up the social network pretty well if it was to go public in 2012.
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Earlier this year, Reuters also reported that Facebook had generated $1.2bn in revenue in the first nine months of 2010, according to a document that was distributed by Goldman Sachs with a view towards courting potential investors in Facebook. Goldman Sachs itself invested $450m in the social network back in January, and placed a value of $50m on the company.
Read next: Media checkins more than a fad? GetGlue see 130% growth in activity in 3 months

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Having read this I thought it was exmlteery enlightening. I appreciate you spending some time and effort to put this article together. I once again find myself personally spending a significant amount of time both reading and commenting. But so what, it was still worth it!


Fashion week got off to an unofficial start last night, as GQ and the CFDA fêted their second crop of the Best New Menswear Designers in America. The nominees—André Benjamin of Benjamin Biy; Robert Geller; Alex Carleton of Rogues Gallery; David Mullen of Save Khaki; Sam Shipley and Jeff Halmos; and Yigal Azrouël—were on hand in the sprawling, sleekly appointed space inside Rockefeller Center, and all showed Fall 2009 looks exclusive to the event. The winner will be announced in a few weeks, and will then create a special capsule collection to be sold exclusively at Bloomingdale's and Levi.com for one month. The collection will be available from other retailers thereafter.


vii+99+[1 add] pages with frontispiece and diagrams. Octavo (8 1/4" x 5 3/4"). Head end pages gilt. First edition.
Wilhelm Steinitz was born at Prague, Bohemia (now the Czech Republic) on May 18, 1836, that is, he was a year older than Paul Morphy. They never played against each other. He moved to Vienna, Austria, as a young man and worked as a journalist. Steinitz turning point as a Chess player started in Vienna at the end of 1858, just when Morphy was finishing his triumphal trip to Europe. On 1861, he was considered the Austrian Champion. His international career starts with the strong tournament of London, 1862.
He won the Vienna city championship, and later that year he took sixth place at an international tournament in London, England. He then settled in London and made Chess his primary occupation. The London tournament was won by Anderssen that, after Paul Morphy disappearance and Staunton retirement, was considered the best player in the world. Steinitz was sixth, after Anderssen, Paulsen, Owen, MacDonnell and Dubois. We can infer therefore that, at that time, his strength was far weaker than Morphy's.
Steinitz was known at that time as a strong and very creative combinative player. In London, he played one of his best games against Mongredien, one of Morphy's rivals. Nobody could expect the turn that his style would take in a few years. In the following years, Wilhelm Steinitz play improved steadily. In 1863 he defeated Englishman Joseph Blackburne and Dubois. In 1866 he defeated both Adolf Anderssen of Germany and Henry Bird of England.
His defeat of Anderssen, then recognized as the world's best player, marked the beginning of Steinitz's reign as unofficial world champion. Although the next years were unfortunate for him, neither in the tournament of Paris, 1867 nor in Baden-Baden, 1870, he was able to obtain first prize. In this last tournament, Adolf Anderssen was first. It is around this moment that Wilhelm Steinitz style starts a revolutionary transformation.
From being mainly a tactical Chess player, he changed to become the first strategic player in chess history. His new idea was that victory can not be obtained just by the will of power, creating attacks when there are no justified reasons to do so. Instead, attack is the logical consequence of the accumulation of small advantages obtained in the previous moves. This advantages included better development, more space, better pawn structure, pair of bishops, etc.
Steinitz was the first one that understood that Chess has important constrains to creativity, it has a rigid internal logic, and only by understanding this logic is possible to win a game against strong players. He demonstrated the importance of these points of view both in his substantial writings and in his games. Steinitz's development marks the starting point of chess systematics. Before Steinitz, Chess theory was just at most a collection of tricks, especially tactical opening variations.
With Wilhelm Steinitz and his followers, the theory underlying not only opening, but also middle game and endgame and, most important, the logical connection between the different parts of the game, began to be understood. Although Steinitz theories have been demonstrated to be only guidelines, not to be followed with closed eyes, the rules that he developed are still the backbone of modern Chess strategy. This new and, for his contemporaries, totally strange style, brought him successes, as he never had before. Wins at London, 1872 and Vienna, 1873, easy victories in matches against Zukertort, 1872 (+7-1=4) and Blackburne, 1876 (+7-0=0). This was the highest point of Steinitz's career. His superiority over the rest of players was unquestionable. However, during the next six years after his match with Blackburne, he did not play at all.
After living in London for more than 20 years, Steinitz immigrated to the United States, where he continued to dominate the Chess world. However, his loss to Polish player Johann Zukertort in a London tournament in 1883 caused some to argue that Zukertort was world champion. The debate was settled in 1886 when the two played a championship match. What made this match particularly interesting is that they decided that the winner was to be considered officially "World champion", a title until then in-existent.
The match was very exciting, with Zukertort taking a strong lead in the first games, played in New York. However, Steinitz recovered in the next series, played in St. Louis, and in the last one, played in New Orleans, he definitively dominated. The final result was +10-5=5. Steinitz won easily and was crowned world champion. Wilhelm Steinitz was the first chess world champion.
After winning the title, Steinitz concentrated in his writings, he made important contributions to the literature of Chess, primarily by editing numerous Chess publications. One of his most prominent posts was with The International Chess Magazine from 1885 to 1891. In the next eight years, he only played matches. He won quite convincingly against Tchigorin (1889, +10-6=1) and Gunsberg (1890-1, +6-4=9).
The publication of Steinitz's "Modern Chess instructor", where several tactically doubtful opening lines were recommended because of their apparent strategic logic, led to a cable match against Tchigorin, that Steinitz lost. Tchigorin had his opportunity to be world champion in 1892, but he lost in dramatic fashion (final result, +10-8=5 for Steinitz). Steinitz was 56 already and it was clear however that his best chess was in the past.
He remained the official world champion until 1894. He played a match, in New York and Montreal, against a practically unknown player called Emanuel Lasker. Lasker won easily (+10 - 5 = 4). After this match, Steinitz's chess play clearly declined. He played still in some important tournaments (Hastings, 1895; St. Petersburg, 1895-6; Nuremberg, 1896) without success.
A second match against Lasker (Moscow, 1896-7) was arranged, but Wilhelm Steinitz lost overwhelmingly, +2-10=5. After a poor performance in London in 1899, Steinitz went insane and died a year later on August 12, 1900 at Wards Island, N.Y.
Page head margin stained, rebound in blue cloth with gold lettering to spine and cover.


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No matter whether you are buying or selling, the process is disruptive to your daily activities and we are here to minimize the noise. Our team of dedicated professionals will help you with your real estate purchase or sale. At J Phillips Commercial, we will help you fulfill your real estate goals with the least amount of negative impact to your operations. Our consulting team can even help you coordinate your move including property improvements and equipment relocation.


text:
\section{Introduction}

Image alignment is a fundamental research problem that has been studied for decades, which has been applied in various applications~\cite{BrownL03,zaragoza2013projective,guo2016joint,WronskiGEKKLLM19,liu2013bundled}. Commonly adopted registration methods include homography~\cite{gao2011constructing}, mesh-based deformation~\cite{zaragoza2013projective,LiuTYSZ16}, and optical flow~\cite{dosovitskiy2015FlowNet,EpicFlow_2015}. These methods look at the image contents for the registration, which often require rich textures~\cite{lin2017direct,zhang2020content} and similar illumination variations~\cite{sun2018pwc} for good results.

In contrast, gyroscopes can be used to align images, where image contents are no longer required~\cite{karpenko2011digital}. The gyroscope in a mobile phone provides the camera 3D rotations, which can be converted into homographies given camera intrinsic parameters for the image alignment~\cite{karpenko2011digital, hartley2003multiple}. In this way, the rotational motions can be compensated. We refer to this as gyro image alignment. One drawback is that translations cannot be handled by the gyro. Fortunately, rotational motions are prominent compared with translational motions~\cite{shan2007rotational}, especially when filming scenes or objects that are not close to the camera~\cite{liu2017hybrid}.

Compared with image-based methods, gyro-based methods are attractive. First, it is irrelevant to image contents, which largely improves the robustness. Second, gyros are widely available and can be easily accessed on our daily mobiles. Many methods have built their applications based on the gyros~\cite{huang2018online,guse2012gesture,zaki2020study}.

\begin{figure}[t]
\begin{center}
  \includegraphics[width=1\linewidth]{images/Teaser.pdf}
\end{center}
  \caption{(a) inputs without the alignment, (b) gyroscope alignment on a non-OIS camera, (c) gyroscope alignment on an OIS camera, and (d) our method on an OIS camera. We replace the red channel of one image with that of the other image, where misaligned pixels are visualized as colored ghosts.  The same visualization is applied for the rest of the paper.}
\label{fig:teaser}
\end{figure}

On the other hand, the cameras of smartphones keep evolving, where optical image stabilizer (OIS) becomes more and more popular, which promises less blurry images and smoother videos. It compensates for 2D pan and tilt motions of the imaging device through lens mechanics~\cite{chiu2007optimal,yeom2009optical}. However, OIS terminates the possibility of image registration by gyros. As the homography derived from the gyros is no longer correspond to the captured images, which have been adjusted by OIS with unknown quantities and directions. One may try to read pans and tilts from the camera module. However, this is not easy as it is bounded with the camera sensor, which requires assistance from professionals of the manufacturers~\cite{koo2009optical}.

In this work, we propose a deep learning method that compensates the OIS motion without knowing its readings, such that the gyro can be used for image alignment on OIS equipped cell-phones. Fig.~\ref{fig:teaser} shows an alignment example. Fig.~\ref{fig:teaser} (a) shows two input images. Fig.~\ref{fig:teaser} (b) is the gyro alignment produced by a non-OIS camera. As seen, the images can be well aligned with no OIS interferences. Fig.~\ref{fig:teaser} (c) is the gyro alignment produced by an OIS camera. Misalignments can be observed due to the OIS motion. Fig.~\ref{fig:teaser} (d) represents our OIS compensated result.

Two frames are denoted as $I_a$ and $I_b$, the motion from gyro between them as $G_{ab}$. The real motion (after OIS adjustment) between two frames is $G'_{ab}$. We want to find a mapping function that transforms $G_{ab}$ to $G'_{ab}$:
\begin{equation}
    \small
    G'_{ab} = f(G_{ab}).
\end{equation}

We propose to train a supervised convolutional neural network for this mapping. To achieve this, we record videos and their gyros as training data. The input motion $G_{ab}$ can be obtained directly given the gyro readings. However, obtaining the ground-truth labels for $G'_{ab}$ is non-trivial. We propose to estimate the real motion from the captured images. If we estimate a homography between them, then the translations are included, which is inappropriate for rotation-only gyros. The ground-truth should merely contain rotations between $I_a$ and $I_b$. Therefore, we estimate a fundamental matrix and decompose it for the rotation matrix~\cite{hartley2003multiple}. However, the cell-phone cameras are rolling shutter (RS) cameras, where different rows of pixels have slightly distinct rotations matrices. In this work, we propose a Fundamental Mixtures model that estimates an array of fundamental matrices for the RS camera, such that rotational motions can be extracted as the ground-truth. In this way, we can learn the mapping function.

For evaluations, we capture a testing dataset with various scenes, where we manually mark point correspondences for quantitative metrics. According to our experiments, our network can accurately recover the mapping, achieving gyro alignments comparable to non-OIS cameras. In summary, our contributions are:
\begin{itemize}
  \item We propose a new problem that compensates OIS motions for gyro image alignment on cell-phones. To the best of our knowledge, the problem is not explored yet, but important to many image and video applications.
  \item We propose a solution that learns the mapping function between gyro motions and real motions, where a Fundamental Mixtures model is proposed under the RS setting for the real motions.
  \item We propose a dataset for the evaluation. Experiments show that our method works well when compared with non-OIS cameras, and outperforming image-based opponents in challenging cases.
\end{itemize}

\section{Related Work}
\subsection{Image Alignments}
Homography~\cite{gao2011constructing}, mesh-based~\cite{zaragoza2013projective}, and optical flow~\cite{sun2018pwc} methods are the most commonly adopted motion models, which align images in a global, middle, and pixel level. They are often estimated by matching image features or optimize photometric loss~\cite{lin2017direct}. Apart from classical traditional features, such as SIFT~\cite{Lowe04}, SURF~\cite{BayTG06}, and ORB~\cite{RubleeRKB11}, deep features have been proposed for improving robustness, e.g., LIFT~\cite{YiTLF16} and SOSNet~\cite{TianYFWHB19}. Registration can also be realized by deep learning directly, such as deep homography estimation~\cite{le2020deep,zhang2020content}. In general, without extra sensors, these methods align images based on the image contents.

\subsection{Gyroscopes}
Gyroscope is important in helping estimate camera rotations during mobile capturing. The fusion of gyroscope and visual measurements have been widely applied in various applications, including but not limited to, image alignment and video stabilization~\cite{karpenko2011digital}, image deblurring~\cite{mustaniemi2019gyroscope}, simultaneous localization and mapping (SLAM)~\cite{huang2018online}, gesture-based user authentication on mobile devices~\cite{guse2012gesture}, and human gait recognition~\cite{zaki2020study}. In mobiles, one important issue is the synchronization between the timestamps of gyros and video frames, which requires gyro calibration~\cite{jia2013online}. In this work, we access the gyro data at the Hardware Abstraction Layer (HAL) of the android layout~\cite{siddha2012hardware}, to achieve accurate synchronizations.

\subsection{Optical Image Stabilizer}
Optical Image Stabilizer (OIS) has been around commercially since the mid-90s~\cite{sato1993control} and becomes more and more popular in our daily cell-phones. Both the image capturing and video recording can benefit from OIS, producing results with less blur and improved stability~\cite{koo2009optical}. It works by controlling the path of the image through the lens and onto the image sensor, which is achieved by measuring the camera shakes using sensors such as gyroscope, and move the lens horizontally or vertically to counteract shakes by electromagnet motors~\cite{chiu2007optimal,yeom2009optical}. Once a mobile is equipped with OIS, it cannot be turn-off easily~\cite{nasiri2012optical}. On one hand, OIS is good for daily users. On the other hand, it is not friendly to mobile developers who need gyros to align images. In this work, we enable the gyro image alignment on OIS cameras.

\section{Algorithm}
\begin{figure*}[t]
\begin{center}
\includegraphics[width=1\textwidth]{images/method_pipeline_v14.pdf}
\end{center}
\caption{The overview of our algorithm which includes (a) gyro-based flow estimator, (d) the fundamental-based flow estimator, and (b) neural network predicting an output flow. For each pair of frames $I_a$ and $I_b$, the homography array is computed using the gyroscope readings from $t_{I_{a}}$ to $t_{I_{b}}$, which is converted into the source motion $G_{ab}$ as the network input. On the other side, we estimate a Fundamental Mixtures model to produce the target flow $F_{ab}$ as the guidance. The network is then trained to produce the output $G'_{ab}$.}
\label{fig:pipeline}
\end{figure*}
Our method is built upon convolutional neural networks. It takes one gyro-based flow $G_{ab}$ from the source frame $I_a$ to the target frame $I_b$ as input, and produces OIS compensated flow $G'_{ab}$ as output. Our pipeline consists of three modules: a gyro-based flow estimator, a Fundamental Mixtures flow estimator, and a fully convolutional network that compensates the OIS motion. Fig.~\ref{fig:pipeline} illustrates the pipeline. First, the gyro-based flows are generated according to the gyro readings (Fig.~\ref{fig:pipeline}(a) and Sec.~\ref{sec:gyroflow}), then they are fed into a network to produce OIS compensated flows $G'_{ab}$ as output (Fig.~\ref{fig:pipeline}(b) and Sec.~\ref{sec:network}). To obtain the ground-truth rotations, we propose a Fundamental Mixtures model, so as to produce the Fundamental Mixtures flows $F_{ab}$ (Fig.~\ref{fig:pipeline} (d) and Sec.~\ref{sec:fundamental}) as the guidance to the network (Fig.~\ref{fig:pipeline} (c)). During the inference, the Fundamental Mixtures model is not required. The gyro readings are converted into gyro-based flows and fed to the network for compensation.

\begin{figure}[t]
\begin{center}
  \includegraphics[width=0.9\linewidth]{images/gyro_ts_v5.pdf}
\end{center}
  \caption{Illustration of rolling shutter frames. $t_{I_a}$ and $t_{I_b}$ are the frame starting time. $t_s$ is the camera readout time and $t_f$ denotes the frame period ($t_f > t_s$). $t_a(i)$ and $t_b(i)$ represent the starting time of patch $i$ in $I_a$ and $I_b$. }
\label{fig:gyro_ts}
\end{figure}

\subsection{Gyro-Based Flow}\label{sec:gyroflow}
We compute rotations by compounding gyro readings consisting of angular velocities and timestamps. In particular, we read them from the HAL of android architecture for synchronization. The rotation vector $n=\left(\omega_{x}, \omega_{y}, \omega_{z}\right) \in \mathbb{R}^{3}$ is computed from gyro readings between frames $I_a$ and $I_b$~\cite{karpenko2011digital}. The rotation matrix $R(t)\in SO(3)$ can be produced according to the Rodrigues Formula~\cite{dai2015euler}.

If the camera is global shutter, the homography is modeled as:
\begin{equation}
\small
\mathbf{H}(t)=\mathbf{K} \mathbf{R}(t) \mathbf{K}^{-1}\label{eq:globalH},
\end{equation}
\noindent where $K$ is the intrinsic camera matrix and $R(t)$ denotes the camera rotation from $I_a$ to $I_b$.

In an RS camera, every row of the image is exposed at a slightly different time. Therefore, Eq.(\ref{eq:globalH}) is not applicable since every row of the image has slightly different rotation matrices. In practice, assigning each row of pixels with a rotation matrix is unnecessary. We group several consecutive rows into a row patch and assign each patch with a rotation matrix. Fig.~\ref{fig:gyro_ts} shows an example. Let $t_s$ denotes the camera readout time that is the time duration between the exposure of the first row and the last row of pixels.

\begin{equation}
\small
t_{a}(i)=t_{I}+t_{s} \frac{i}{N},
\end{equation}

\noindent where $t_{a}(i)$ denotes the start of the exposure of the $i$-$th$ patch in $I_a$ as shown in Fig.~\ref{fig:gyro_ts}, $t_{I}$ denotes the starting timestamp of the corresponding frame, $N$ denotes the number of patches per frame. The end of the exposure is:

\begin{equation}
\small
t_{b}(i)=t_{a}(i)+t_{f},
\end{equation}

\noindent where $t_{f}=1/FPS$ is the frame period. Here, the homography between the $i$-th row at frame $I_a$ and $I_b$ can be modeled as:

\begin{equation}
\small
\mathbf{H}=\mathbf{K} \mathbf{R}\left(t_{b}\right) \mathbf{R}^{\top}\left(t_{a}\right) \mathbf{K}^{-1},
\end{equation}
where $\small\mathbf{R}\left(t_{b}\right) \mathbf{R}^{\top}\left(t_{a}\right)$ can be computed by accumulating rotation matrices from $t_a$ to $t_b$.

In our implementation, we divide the image into $6$ patches which computes a homography array containing $6$ horizontal homographies between two consecutive frames. We convert the homography array into a flow field~\cite{mustaniemi2019gyroscope} so that it can be fed as input to a convolutional neural network. For every pixel $p$ in the $I_a$, we have:

\begin{equation}
\small
\mathbf{p}^{\prime}=\mathbf{H}(t) \mathbf{p}, \quad \mathbf{(u,v)}=\mathbf{p}^{\prime}- \mathbf{p},
\label{eq:homo2flow}
\end{equation}
\noindent computing the offset for every pixel produces a gyro-based flow $G_{ab}$.

\subsection{Fundamental Mixtures}\label{sec:fundamental}

Before introducing our model of Fundamental Mixtures, we briefly review the process of estimating the fundamental matrix. If the camera is global-shutter, every row of the frame is imaged simultaneously at a time. Let $p_1$ and $p_2$ be the projections of the 3D point $X$ in the first and second frame, $p_1=P_{1} X$ and $p_2=P_{2} X$, where $P_{1}$ and $P_{2}$ represent the projection matrices. The fundamental matrix satisfies the equation~\cite{hartley2003multiple}:
\begin{equation}
\small
\mathbf{p}_{1}^{T} \mathbf{F} \mathbf{p}_{2}=0,
\label{F-matrix}
\end{equation}
where $p_1=(x_1, y_1, 1)^{T}$ and $p_2=\left(x_1^{\prime}, y_1^{\prime}, 1\right)^{T}$. Let $\mathbf{f}$ be the 9-element vector made up of $F$, then Eq.(\ref{F-matrix}) can be written as:
\begin{equation}
    \small
    \left(x_1^{\prime} x_1, x_1^{\prime} y_1, x_1^{\prime}, y_1^{\prime} x_1, y_1^{\prime} y_1, y_1^{\prime}, x_1, y_1, 1\right) \mathbf{f}=0,
    \label{dlt_F}
\end{equation}
given $n$ correspondences, yields a set of linear equations:
\begin{equation}
\small
\begin{aligned}
&A \mathbf{f}= \left[\begin{array}{ccc}
x_{1}^{\prime}p_1^{T} & y_{1}^{\prime}p_1^{T} & p_1^{T}  \\
\vdots & \vdots & \vdots \\
x_{n}^{\prime}p_n^{T} & y_{n}^{\prime}p_n^{T} & p_n^{T}
\end{array}\right] \mathbf{f} = 0.
\end{aligned}
\end{equation}
Using at least 8 matching points yields a homogenous linear system, which can be solved under the constraint $\|\mathbf{f}\|_{2}=1$ using the Singular Value Decomposition(SVD) of $A=U D V^{\top}$ where the last column of $V$ is the solution~\cite{hartley2003multiple}.

In the case of RS camera, projection matrices $P_{1}$ and $P_{2}$ vary across rows instead of being frame-global.  Eq.(\ref{F-matrix}) does not hold. Therefore, we introduce Fundamental Mixtures assigning each row patch with a fundamental matrix.

We detect FAST features~\cite{trajkovic1998fast} and track them by KLT~\cite{shi1994good} between frames. We modify the detection threshold for uniform feature distributions~\cite{grundmann2012calibration,guo2016joint}.

To model RS effects, we divide a frame into $N$ patches, resulting in $N$ unknown fundamental matrices $F_{i}$ to be estimated per frame. If we estimate each fundamental matrix independently, the discontinuity is unavoidable. We propose to smooth neighboring matrices during the estimation as shown in Fig.~\ref{fig:pipeline} (d), where a point $p_1$ not only contributes to its own patch but also influences its nearby patches weighted by the distance. The fundamental matrix for point $p$ is the mixture:

\begin{equation}
\small
F({p_1}) =\sum_{i=1}^{N} F_{i} w_{i}(p_1),
\end{equation}

\noindent where $w_{i}(p_1)$ is the gaussian weight with the mean equals to the middle of each patch, and the sigma $\sigma = 0.001 * h$, where $h$ represents the frame height.

To fit a Fundamental Mixtures $F_{i}$ given a pair of matching points $\left(p_{1}, p_{2}\right)$, we rewrite Eq.(\ref{F-matrix}) as:
\begin{equation}
    \small
    0=\mathbf{p}_{1}^{T} \mathbf{F_{p_1}} \mathbf{p}_{2}=\sum_{i=1}^{N} w_{i}(p_1) \cdot \mathbf{p}_{1}^{T} \mathbf{F_i} \mathbf{p}_{2},
    \label{p1Fp2}
\end{equation}
where $\mathbf{p}_{1}^{T} \mathbf{F}_{\mathbf{k}} \mathbf{p}_{2}$ can be transformed into:
\begin{equation}
\small
A_{p_1}^{i} f_{i} = \left(\begin{array}{ccc}
x_{1}^{\prime}p_1^{T} & y_{1}^{\prime}p_1^{T} & p_1^{T}
\end{array}\right) f_{i},
\label{dlt_ak}
\end{equation}
where $f_{i}$ denotes the vector formed by concatenating the columns of $F_{i}$. Combining Eq.(\ref{p1Fp2}) and Eq.(\ref{dlt_ak}) yields a $1 \times 9 i$ linear constraint:
\begin{equation}
    \small
    \underbrace{\left(w_{1}(p_1) A_{p_1}^{1} \ldots w_{i}(p_1) A_{p_1}^{i}\right)}_{A_{p_1}} \underbrace{\left(\begin{array}{c}
f_{1} \\
\vdots \\
f_{i}
\end{array}\right)}_{f}=A_{p_1} f=0.
\label{svd_f_mix}
\end{equation}
Aggregating all linear constraints $A_{p_j}$ for every match point $(p_j, p_{j+1})$ yields a homogenous linear system $A\mathbf{f}=0$ that can be solved under the constraint $\|\mathbf{f}\|_{2}=1$ via SVD.

For robustness, if the number of feature points in one patch is inferior to $8$, Eq.(\ref{svd_f_mix}) is under constrained. Therefore, we add a regularizer to constrain $\lambda\left\|A_{p}^{i}-A_{p}^{i-1}\right\|_{2}=0\small$ to the homogenous system with $\lambda = 1\small$.

\subsubsection{Rotation-Only Homography}
Given the fundamental matrix $F_i$ and the camera intrinsic $K$, we can compute the essential matrix $E_i$ of the $i$-th patch: $\mathbf{E_i}=\mathbf{K}^{T} \mathbf{F_i} \mathbf{K}\small.$ The essential matrix $E_i$~\cite{hartley2003multiple} can be decomposed into camera rotations and translations, where only rotations $R_i$ are retained. We use $R_i$ to form a rotation-only homography similar to Eq.(\ref{eq:globalH}) and convert the homography array into a flow field as Eq.(\ref{eq:homo2flow}). We call this flow field as Fundamental Mixtures Flow $F_{ab}$. Note that, $R_i$ is spatially smooth, as $F_i$ is smooth, so does $F_{ab}$.

\subsection{Network Structure}\label{sec:network}
The architecture of the network is shown in Fig.~\ref{fig:pipeline} that utilizes a backbone of UNet~\cite{ronneberger2015u} consists of a series of convolutional and downsampling layers with skip connections. The input to the network is gyro-based flow $G'_{ab}$ and the ground-truth target is Fundamental Mixtures flow $F_{ab}$. Our network aims to produce an optical flow of size $H \times W \times 2$ which compensates the motion generated by OIS between $G'_{ab}$ and $F_{ab}$. Besides, the network is fully convolutional which accepts input of arbitrary sizes.

Our network is trained on $9k$ rich-texture frames with resolution of $360$x$270$ pixels over $1k$ iterations by an Adam optimizer~\cite{kingma2014adam} whose $l_{r}=1.0 \times 10^{-4}$, $\beta_1=0.9$, $\beta_2=0.999$. The batch size is $8$, and for every $50$ epochs, the learning rate is reduced by $20\%$. The entire training process costs about $50$ hours. The implementation is in PyTorch and the network is trained on one NVIDIA RTX 2080 Ti.

\section{Experimental Results}

\subsection{Dataset}
Previously, there are some dedicated datasets which are designed to evaluate the homography estimation~\cite{zhang2020content} or the image deblurring with the artificial-generated gyroscope-frame pair~\cite{mustaniemi2019gyroscope}, whether none of them combine real gyroscope readings with corresponding video frames. So we propose a new dataset and benchmark GF4.

\begin{figure}[t]
\begin{center}
   \includegraphics[width=1\linewidth]{images/dataset_v2.pdf}
\end{center}
   \caption{A glance at our evaluation dataset. Our dataset contains 4 categories, regular(RE), low-texture(LT), low-light(LL) and moving-foreground(MF). Each category contains $350$ pairs, a total of $1400$ pairs, with synchronized gyroscope readings.}
\label{fig:dataset}
\end{figure}

\noindent\textbf{Training Set} To train our network, we record a set of videos with the gyroscope readings using a hand-held cellphone. We choose scenes with rich textures so that sufficient feature points can be detected to calculate the Fundamental Mixtures model. The videos last $300$ seconds, yielding $9$,$000$ frames in total. Note that, the scene type is not important as long as it can provide enough features as needed for Fundamental Mixtures estimation.

\begin{figure}[t]
\begin{center}
   \includegraphics[width=1\linewidth]{images/data_annotation_v4.pdf}
\end{center}
   \caption{We mark the correspondences manually in our evaluation set for quantitative metrics. For each pair, we mark $6 \sim 8$ point matches. }
\vspace{-5pt}
\label{fig:annotation}
\end{figure}

\noindent\textbf{Evaluation Set} For the evaluation, we capture scenes with different types, to compare with image-based registration methods. Our dataset contains $4$ categories, including regular (RE), low-texture (LT), low-light (LL), and moving-foregrounds (MF) frame-gyroscope pairs. Each scene contains $350$ pairs. So, there are $1400$ pairs in the dataset. We show some examples in Fig.~\ref{fig:dataset}. For quantitative evaluation, we manually mark $6\sim8$ point correspondences per pair, distributing uniformly on frames. Fig.~\ref{fig:annotation} shows some examples.

\subsection{Comparisons with non-OIS camera}

Our purpose is to enable gyro image alignment on OIS cameras. Therefore, we compare our method with non-OIS cameras. In general, our method should perform equally well as non-OIS cameras, if the OIS motion could be compensated successfully. For comparison, ideally, we should use one camera with OIS turn on and off. However, the OIS cannot be turned off easily. Therefore, we use two cell-phones with similar camera intrinsics, one with OIS and one without, and capture the same scene twice with similar motions. Fig.~\ref{fig:qualitycompare_ois} shows some examples. Fig.~\ref{fig:qualitycompare_ois} (a) shows the input frames. Fig.~\ref{fig:qualitycompare_ois} (b) shows the gyro alignment on a non-OIS camera. As seen, images can be well aligned. Fig.~\ref{fig:qualitycompare_ois} (c) shows the gyro alignment on an OIS camera. Due to the OIS interferences, images cannot be aligned directly using the gyro. Fig.~\ref{fig:qualitycompare_ois} (d) shows our results. With OIS compensation, images can be well aligned on OIS cameras.

\begin{table}[t]
    \small
    \centering
    \resizebox*{0.45 \textwidth}{!}
    {
    	\begin{tabular}{lccc}
    		\toprule
    		 &Non-OIS Camera & OIS Camera & Ours\\
    		\midrule
            Geometry Distance &0.688 & 1.038 & 0.709 \\
    		\bottomrule
    	\end{tabular}
	}
	\caption{Comparisons with non-OIS camera. }
	\vspace{-10pt}
	\label{tab:non-ois}
\end{table}

We also calculate quantitative values. Similarly, we mark the ground-truth for evaluation. The average geometry distance between the warped points and the manually labeled GT points are computed as the error metric (the lower the better). Table~\ref{tab:non-ois} shows the results. Our result $0.709$ is comparable with non-OIS camera $0.688$ (slightly worse), while no compensation yields $1.038$, which is much higher.

\subsection{Comparisons with Image-based Methods}

\begin{figure*}
\begin{center}
\includegraphics[width=1\textwidth]{images/ois_on_off_gyro_align_v8.pdf}
\end{center}
\vspace{-10pt}
\caption{Comparisons with non-OIS cameras. (a) input two frames. (b) gyro alignment results on the non-OIS camera. (c) gyro alignment results on the OIS camera. (d) our OIS compensation results. Without OIS compensation, clear misalignment can be observed in (c) whereas our method can solve this problem and be comparable with non-OIS results in (b).}
\label{fig:qualitycompare_ois}
\end{figure*}

\begin{figure*}[h]
\begin{center}
  \includegraphics[width=1\linewidth]{images/quality_exp_v8.pdf}
\end{center}
  \caption{Comparisons with image-based methods. We compare with SIFT~\cite{Lowe04} + RANSAC~\cite{FischlerB81}, Meshflow~\cite{LiuTYSZ16}, and the recent deep homography~\cite{zhang2020content} method. We show examples covering all scenes in our evaluation dataset. Our method can align images robustly while image-based methods contain some misaligned regions.  }
\label{fig:qualitycompare}
\end{figure*}

Although it is a bit unfair to compare with image-based methods as we adopt additional hardware. We desire to show the importance and robustness of the gyro-based alignment, to highlight the importance of enabling this capability on OIS cameras.

\begin{table*}[h]
\small
\label{tab:results}
	\centering
	\resizebox*{1.0 \textwidth}{!}
	{
	\begin{tabular}{>{\arraybackslash}p{5cm}
    >{\centering\arraybackslash}p{2.4cm}
    >{\centering\arraybackslash}p{2.4cm}
    >{\centering\arraybackslash}p{2.4cm}
    >{\centering\arraybackslash}p{2.4cm}
    >{\centering\arraybackslash}p{2.4cm}}
		\toprule
		1) &RE& LT& LL& MF& Avg \\
		\midrule
		2) $\mathcal{I}_{3 \times 3}$ & 7.098(+2785.37\%)  & 7.055(+350.80\%)   & 7.035(+519.55\%)   & 7.032(+767.08\%)   & 7.055(+313.97\%)\\
		\midrule
		3) SIFT~\cite{Lowe04}+ RANSAC~\cite{FischlerB81} & 0.340(+38.21\%)      & 6.242(+298.85\%)   & 2.312(+103.58\%)   & 1.229(+51.54\%)    & 2.531(+48.49\%)  \\

		4) SIFT~\cite{Lowe04} + MAGSAC~\cite{barath2019magsac} &\textcolor[rgb]{1,0,0}{0.213(\textminus13.41\%)}      & 5.707(+264.66\%)   & 2.818(+148.17\%)   & \textcolor[rgb]{0,0,1}{0.811(+0.00\%)}     & 2.387(+40.08\%) \\

		5) ORB~\cite{rublee2011orb} + RANSAC~\cite{FischlerB81} & 0.653(+165.45\%)     & 6.874(+339.23\%)   & \textcolor[rgb]{0,0,1}{1.136(+0.00\%)}     & 2.27(+179.28\%)   & 2.732(+60.30\%)  \\

		6) ORB~\cite{rublee2011orb} + MAGSAC~\cite{barath2019magsac} &0.919(+273.58\%)     & 6.859(+338.27\%)   & 1.335(+17.60\%)    & 2.464(+203.82\%)   & 2.894(+69.83\%) \\

		7) SOSNET~\cite{tian2019sosnet} + RANSAC~\cite{FischlerB81} & \textcolor[rgb]{0,0,1}{0.246(+0.00\%)}       & 5.946(+279.94\%)   & 1.977(+74.11\%)    & 0.907(+11.84\%)    & 2.269(+33.14\%)  \\

		8) SOSNET~\cite{tian2019sosnet} + MAGSAC~\cite{barath2019magsac} & 0.309(+25.61\%)      & 5.585(+256.87\%)   & 1.972(+73.67\%)    & 1.142(+40.81\%)    & 2.252(+32.14\%) \\

		9) SURF~\cite{BayTG06} + RANSAC~\cite{FischlerB81} & 0.343(+39.43\%)      & 3.161(+101.98\%)   & 2.213(+94.89\%)    & 1.420(+75.09\%)    & 1.784(+4.69\%)\\

		10) SURF~\cite{BayTG06} + MAGSAC~\cite{barath2019magsac} & 0.307(+24.80\%)      & 3.634(+132.20\%)   & 2.246(+97.78\%)    & 1.267(+56.23\%)    & 1.863(+9.34\%)\\

		\midrule
		11) MeshFlow~\cite{LiuTYSZ16} & 0.843(+242.68\%)     & 7.042(+349.97\%)   & 1.729(+52.27\%)    & 1.109(+36.74\%)    & 2.681(+57.30\%)\\

		\midrule
		12) Deep Homography~\cite{zhang2020content} & 1.342(+445.53\%) & \textcolor[rgb]{0,0,1}{1.565(+0.00\%)} & 2.253(+98.41\%) & 1.657(+104.32\%) & \textcolor[rgb]{0,0,1}{1.704(+0.00\%)}  \\

		\midrule
		13) Ours & 0.609(+147.56\%)     &\textcolor[rgb]{1,0,0}{1.01(\textminus35.27\%)}    &\textcolor[rgb]{1,0,0}{0.637(\textminus43.90\%)}    &\textcolor[rgb]{1,0,0}{0.736(\textminus9.25\%)}     &\textcolor[rgb]{1,0,0}{0.749(\textminus56.07\%)}\\
		\bottomrule
	\end{tabular}
	}
	\caption{Quantitative comparisons on the evaluation dataset. The best performance is marked in red and the second-best is in blue. }
\label{table:quality}
\end{table*}

\subsubsection{Qualitative Comparisons}
Firstly, we compare our method with one frequently used traditional feature-based algorithm, i.e. SIFT~\cite{Lowe04} and RANSAC~\cite{FischlerB81} that compute a global homography, and another feature-based algorithm, i.e. Meshflow~\cite{LiuTYSZ16} that deforms a mesh for the non-linear motion representation. Moreover, we compare our method with the recent deep homography method~\cite{zhang2020content}.

Fig.~\ref{fig:qualitycompare} (a) shows a regular example where all the methods work well. Fig.~\ref{fig:qualitycompare} (b), SIFT+RANSAC fails to find a good solution, so does deep homography, while Meshflow works well. One possible reason is that a single homography cannot cover the large depth variations.  Fig.~\ref{fig:qualitycompare} (c) illustrates a moving-foreground example that SIFT+RANSAC and Meshflow cannot work well, as few features are detected on the background, whereas Deep Homography and our method can align the background successfully. A similar example is shown in Fig.~\ref{fig:qualitycompare} (d), SIFT+RANSAC and Deep Homography fail. Meshflow works on this example as sufficient features are detected in the background. In contrast, our method can still align the background without any difficulty. Because we do not need the image contents for the registration. Fig.~\ref{fig:qualitycompare}(e) is an example of low-light scenes, and Fig.~\ref{fig:qualitycompare}(f) is a low-texture scene. All the image-based methods fail as no high-quality features can be extracted, whereas our method is robust.

\subsubsection{Quantitative Comparisons}

We also compare our method with other feature-based methods quantitatively, i.e., the geometry distance. For the feature descriptors, we choose SIFT~\cite{Lowe04}, ORB~\cite{rublee2011orb}, SOSNet~\cite{TianYFWHB19}, SURF~\cite{BayTG06}. For the outlier rejection algorithms, we choose RANSAC~\cite{FischlerB81} and MAGSAC~\cite{barath2019magsac}. The errors for each category are shown in Table~\ref{table:quality} followed by the overall averaged error, where $\mathcal{I}_{3 \times 3}$ refers to a $3 \times 3$ identity matrix as a reference. In particular, feature-based methods sometimes crash, when the error is larger than $\mathcal{I}_{3 \times 3}$ error, we set the error equal to $\mathcal{I}_{3 \times 3}$ error. Regarding the motion model, from $3$) to $10$) and $12$) are single homography, $11$) is mesh-based, and $13$) is a homography array. In Table~\ref{table:quality}, we mark the best performance in red and the second-best in blue.

As shown, except for comparing to feature-based methods in RE scenes, our method outperforms the others for all categories. It is reasonable because, in regular(RE) scenes, a set of high-quality features is detected which allows to output a good solution. In contrast, gyroscopes can only compensate for rotational motions, which decreases scores to some extent. For the rest scenes, our method beats the others with an average error being lower than the $2$nd best by $56.07\%$. Especially for low-light(LL) scenes, our method computes an error which is at least lower than the $2$nd best by $43.9\%$.

\subsection{Ablation Studies}
\subsubsection{Fully Connected Neural Network}

\begin{figure}[h]
\begin{center}
  \includegraphics[width=1\linewidth]{images/h4pt_pipeline_v5.pdf}
\end{center}
  \caption{Regression of the homography array using the fully connected network. For each pair of frames $I_a$ and $I_b$, a homography array is computed by using gyro readings, which is fed to the network. On the other side, a Fundamental Mixtures model is produced as targets to guide the training process.}
\label{fig:h4pt}
\end{figure}

Our network is fully convolutional, where we convert gyroscope data into homography arrays, and then into flow fields as image input to the network. However, there is another option where we can directly input homography arrays as input. Similarly, on the other side, the Fundamental Mixtures are converted into rotation-only homography arrays and then used as guidance. Fig.~\ref{fig:h4pt} shows the pipeline, where we test two homography representations, including $3 \times 3$ homography matrix elements and $H_{4pt}$ representation from~\cite{detone2016deep} that represents homography by 4 motion vectors. The network is fully connected and L2 loss is adopted for the regression. We adopt the same training data as described above. The result is that neither representations converge, where the $H_{4pt}$ is slightly better than directly regressing matrix elements.

Perhaps, there exist other representations or network structures that may work well or even better than our current proposal. Here, as the first try, we have proposed a working pipeline and want to leave the improvements as future works.

\subsubsection{Global Fundamental vs. Mixtures}

To verify the effectiveness of our Fundamental Mixtures model, we compare it with a global fundamental matrix. Here, we choose the evaluation dataset of the regular scenes to alleviate the feature problem. We estimate global fundamental matrix and Fundamental Mixtures, then convert to the rotation-only homographies, respectively. Finally, we align the images with rotation-only homographies accordingly. An array of homographies from Fundamental Mixtures
produces an error of \textbf{0.451}, which is better than an error of \textbf{0.580} produced by a single homography from a global fundamental matrix. It indicates that the Fundamental Mixtures model is functional in the case of RS cameras.

Moreover, we generate GT with the two methods and train our network, respectively. As shown in Table~\ref{table:GT_F_Fmix}, the network trained on Fundamental Mixtures-based GT outperforms the global fundamental matrix, which demonstrates the effectiveness of our Fundamental Mixtures.
\begin{table}[h]
    \small
    \centering
    \resizebox{0.48 \textwidth}{!}
    {\begin{tabular}{lccccc}
        \toprule
        Ground Truth         & RE    & LT    & LL    & MF    & Avg   \\ \midrule
        Global Fundamental   & 0.930  &1.189 & 0.769 & 0.917 & 0.951 \\
        Fundamental Mixtures & 0.609 & 1.010  & 0.637 & 0.736 & 0.749 \\ \bottomrule
    \end{tabular}}
    \caption{The performance of networks trained on two different GT.}
    \label{table:GT_F_Fmix}
\end{table}

\subsubsection{Backbone}
\begin{table}[h]
	\centering
    \small
	\begin{tabular}{lccccc}
		\toprule
		 &RE& LT& LL& MF& Avg \\
		\midrule
		R2UNet\cite{alom2018recurrent} &0.713 &1.006 &0.652 &0.791 &0.791\\
		AttUNet\cite{oktay2018attention} &0.896 &1.058 &0.752 &0.993 &0.925\\
		R2AttUNet\cite{alom2018recurrent} &0.651 &1.014 &0.668 &0.722 &0.764\\
		\midrule
		Ours &0.609 & 1.01 &0.637 &0.736 &0.749\\
		\bottomrule
	\end{tabular}
	\caption{The performance of networks with different backbones.}
	\label{tab:backbone}
\end{table}
We choose the UNet~\cite{ronneberger2015u} as our network backbone, we also test several other variants~\cite{alom2018recurrent,oktay2018attention}. Except for AttUNet~\cite{oktay2018attention}, performances are similar, as shown in Table~\ref{tab:backbone}.

\section{Conclusion}
We have presented a DeepOIS pipeline for the compensation of OIS motions for gyroscope image registration. We have captured the training data as video frames as well as their gyro readings by an OIS camera and then calculated the ground-truth motions with our proposed Fundamental Mixtures model under the setting of rolling shutter cameras. For the evaluation, we have manually marked point correspondences on our captured dataset for quantitative metrics. The results show that our compensation network works well when compared with non-OIS cameras and outperforms other image-based methods. In summary, a new problem is proposed and we show that it is solvable by learning the OIS motions, such that gyroscope can be used for image registration on OIS cameras. We hope our work can inspire more researches in this direction.

redpajama_set_name: RedPajamaArXiv


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text:
Home Breaking How reliable are the labels covid-free, some restaurants and shops?
How reliable are the labels covid-free, some restaurants and shops?
T. S. V. MADRID Updated: Save Send news by mail electrónicoTu name *
what commercial Strategy to attract customers or health guarantee? Establishments that hang the usual certificates free of coronavirus ("covid free" sold in its space, there is no risk of infection for disinfection constant as well as by other measures such as the use of ozone systems . However, citizens are not free of risks, and these seals may not guarantee the non-existence of the virus in your local. With respect to this trading strategy, the National Business Association for Environmental Health -that represents the sector of pest control services Spanish – has been pointed out that are not appropriate and refer that generate expectations inposibles meet and involve a health hazard by generating a false sense of security.
"At the same time between any local, certified 'covid-free", an infected person, especially if it is an asymptomatic patient who may not even know who is a carrier, and cough, the space would already be contaminated," explains Monge. "Right now," adds the director general of ANECPLA, Milagros Fernández de Lezeta, "there are many seals who are trying to introduce in the market, the majority of whom are not based in any proceedings, nor have a system of audit to ensure that practices that are carried out correspond to protocols of quality previously established, but only respond to a target market".
Other organizations had already previously warned. A few weeks ago, the Spanish Confederation of Commerce (CEC) said in a note that these labels "are not a guarantee of safety and may be counterproductive to the cause that consumers to relax the precautionary measures". On the basis of this premise, the spokesperson of the CEC advises to the traders who incorporate these labels in their windows, because-to date – it is impossible to ensure that a store is free of coronavirus.
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The tangle of seals that proliferates in the present nor does it help to gain clarity or security. The "covid free joins the launch of the "Safe Tourism Certified", a certification promoted by the Institute for Spanish Tourism Quality ( ICTE) that hotels can incorporate only after passing an audit.
Surcharge for the disinfection
Another of the inconveniences for the consumer comes from the hand of the surcharges that tend to go hand in hand with the disinfection of the premises. When inicioó the desescalada, some users complained to the asoaciaciones of consumption that I was taking a "supplement Covid" of up to 60 euros for the costs of disinfection of workshops of car repairs, hair salons or dental clinics, among other facilities.
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text:
Ariella Azoulay: Looting, Destruction, Photography and Museums: the Imperial Origins of Democracy
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Lecture by Ariella Azoulay.
Ariella Azoulay (Tel Aviv, 1962) is a writer, video-essay director, exhibition curator and one of the most innovative voices in the field of the history and theory of photography, as well as visual culture studies. Since the publication of The Civil Contract of Photography (Zone Books, 2008), she has become an essential reference when considering the effects and political influence of photographic archives.
One of the most influential ideas deriving from her work as a researcher has been that of potential history, as a result of her analysis of the long conflict marking the history of Israel and Palestine. According to the author, potential history should be understood as a set of unrealised possibilities in the course of history and as future possibilities not yet envisaged in the account of the events of the past. In short, this idea awakens the latent polyphony in civil relations, which in the case of Palestine, would have allowed a coexistence.
Her most recent research: Gestures of Slavery: The Archivists (on the appropriation of ethnographic objects in the African continent during the colonial period); The Historical Potential of the Heritage of Museums (a film that proposes the use ethnographic objects as global passports for people of the African continent who find themselves without the right of citizenship once they leave Africa); (In) Documented and The Natural History of Violation (on the invisibility of the large-scale rapes perpetrated at the end of the Second World War in Germany); ¡Basta! (on the Universal Declaration of Human Rights as an action led by UNESCO without correspondence with the rights demanded by the workers' struggles); Books Out of Place and Masterpieces (on the policy of devolution and the return of the cultural heritage usurped from Jewish communities during Nazism); and The Potential History of Palestine (an exercise exposing the possibilities of civil cohabitation despite the conflict).
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Darivka is located 20km north-east of Kherson. The settlement was founded in 1780. According to the census of 1886, only 169 people lived in the village. Little is known about the local Jewish community as the majority of Jews of this region lived in Jewish agricultural colonies where they would frequently resettled to cultivate the deserted lands. Darivka was occupied by German troops at the end of August, 1941. By that time a lot of Jews from north-eastern regions of Ukraine were evacuated to Darivka by the Soviet authorities.
Without any available archives, Yahad's team managed to find out what happened to the Jewish refugees evacuated to Darivka. The Jewish refugees, generally women and children, were brought to Darivka in trucks and were settled in the local Klub building. When the Germans arrived, they took them to a ditch located north-east of the village, in the direction of Kalanchak, to be shot. Beside the Jews, about 20 Roma were murdered as well in a ditch close to the village of Kalanchak.


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Because You d Eunice
Drama Comedy 19821h 15m
Moments in four different years in the lives of the Harper family beginning with 1955 as a hopeful Eunice prepares to go to a party, and her brother Phillip, a recent college graduate, departs for New York In 1963, a married Eunice and her husband Ed Higgins help Mama welcome novelist Phillip home for a visit. In 1973, a divorced Eunice is living with Mama. In 1978, Eunice, Ellen, and Phillip return from Mama's funeral and Eunice makes a painful realization about her life.Eunice featuring Carol Burnett and Vicki Lawrence is not currently available to stream, rent, or buy but you can add it to your want to see list for updates. It's a comedy and drama movie with a high IMDb audience rating of 8.0 (254 votes).
Where to Watch Eunice
Eunice is not available to watch online.
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Not available to rent or buy.
#11109 Ranked in Comedy Movies
Eunice has a high IMDb audience rating of 8.0 (254 votes). The movie is somewhat popular with Reelgood users lately.
About Eunice
Eunice Overview
Moments in four different years in the lives of the Harper family beginning with 1955 as a hopeful Eunice prepares to go to a party, and her brother Phillip, a recent college graduate, departs for New York In 1963, a married Eunice and her husband Ed Higgins help Mama welcome novelist Phillip home for a visit. In 1973, a divorced Eunice is living with Mama. In 1978, Eunice, Ellen, and Phillip return from Mama's funeral and Eunice makes a painful realization about her life.
Related Genres: DramaComedy
Original Release Date: Mar 15, 1982


Singapore is one of the most advanced and important countries for information security. However, a likely new local law is dividing opinions: any ethical hacker who wants to operate in the country will have to take a license.
Ethical hacking is the term used for the individual who modifies the internal aspects of devices, programs and computer networks for the "common good" without taking any kind of advantage.
The law is not yet implemented, however, if any hacker is caught – or if any individual wants to help some company via penetration tests, for example – acting without the license, will have to pay $114 Thousand and face up to two years in prison.
As noted by Joon Ian Wong of Quartz, the law is likely to be sanctioned. This is because it "is in line with the reputation of extreme order" present in Singapore – Wong lived in the country for a while.
The law is a real danger even for the local telecommunications market. Usually, teens and youngsters start hacking out of curiosity. They track videos on YouTube, read forum tutorials, chat online, and virtually conduct their studies on the internet. Most of them test the tools as a way of learning. With the law, they can be arrested.
In addition to the arrest of young hackers entering the world, the law preventing young people from testing tools ultimately inhibits their willingness to act in this segment. With that, many young people who wish to work professionally in the areas of technology and telecommunications will be discouraged.
Going further: the law could lead to better hackers. More applications and tools that hide IPs and identities can be developed. If the license is paid, ethical hackers without pocket money will start working off the government radar – and there are VPNs, Tors, encryption and various technologies to shut down the authorities.


Seawinds is arranged over three floors, so lets venture down to the lower ground floor. Here you will find accommodation comprising a generous double bedroom with fitted wardrobes and window to the front framing the sea views. Next along is the study, a wine store and modern shower room. You have complete private access to this floor via a lovely sun terrace at the front of the house. This arrangement has great flexibility within the home and would be ideal for the extended/multi generation family. You can utilise the lower ground floor as a private floor or simply cherished space away from the rest of the house providing the perfect guest accommodation or workspace.