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When I enlisted in 1988, if you were Asian American, not only were you a rare bird in the U.S. Army, you were one of a kind in the infantry, and you were on your own, for all intents and purposes.^Me standing in front of our battalion HQ or “starship.” at Ft. Benning, Sand Hill during “Family Day.”^Yours truly after a five-mile run in the Georgia heat & humidity standing in front of my bunk during 11B OSUT at Ft. Benning, July 1988, D co., 2/19 Inf. Treadwell Barracks.For example, in my 11B OSUT basic training company at Ft. Benning, out of 200 soldiers, and approximately 20 cadre, I was the only Korean American. Our company did have one Laotian and one Cambodian, and one biracial person who was vaguely “half Chinese,” but acted and identified as white and resented any implication or suggestion that he might be Asian as well. So, out of 220 people, just three — including myself — identified as Asian in your prototypical OSUT infantry company in 1988, which means we’re talking a meta-minority here representing just two percent of the company, so yes, we were definitely under-represented. (In 1988, Asian Americans were about 4.2 percent of the U.S. population.)^During Family Day, July of 1988. My late dad took this pic of me at the Ft. Benning maingate. Didn’t even know this gate existed as we arrived in the pitch dark.Needless to say, looking back then some 30 years ago, if it was same-race solidarity and pan-Asian espirit de corps you were looking for, the U.S. military — and specifically the U.S. Army, and especially the infantry — was the worst place to find it and perhaps one of the worst possible places you could be as a young Asian American. But that’s not why I joined.^Me in the middle with my ARNG squad at Ft. Chaffee, AR ca. 1989.Nevertheless, it goes without saying that being the phenotypical outsider in a highly conformist and typically anti-Asian environment back then meant you were going to be subjected to a lot of anti-Asian discrimination, overt and covert racism, casual bigotry, bad racial jokes, and the non-stop barrage of stereotypical racial epithets and comments that would challenge even the most thick-skinned individual. But that was just the beginning.^Me standing in one of the most favorite places at Benning during OSUT, the dreaded and cherished ‘dfac’ at Treadwell Barracks.^Me in the middle with my unit when we deployed to Germany in 1990 for the Gulf War. This is Rhein-Main AB in Frankfurt a.M. Germany.The main problem with being the only person of your race in a very large and predominately white or black-dominated institution that isn’t known exactly for open-minded or progressive thinking is that you can easily become a scapegoat for anything and be falsely accused of everything, unless someone has your back, or knows that your reputation, conduct, performance and track record is flawless beyond reproach and unimpeachable to the point it would’ve been difficult to impugn your integrity. Of course, that’s easier said than done, b/c in this type of environment, people — not just your superiors, but also your peers and colleagues — are watching you like a hawk, just waiting for you to screw up so they can turn you in. Why? To get promotion points or simply out of jealousy, spite, racism, you name it!^Me in the back of a deuce-and-a-half with my M-60 GPMG during JRTC at Ft.Chaffee, AR, ca. 1989.^Standing outside our company area at Ft.Benning during family day.Typically, and this happens a lot in the Army, all someone has to say if something comes up missing/stolen/broken/SNAFU’d/etc., or more commonly if they themselves committed an infraction and are on the brink of being held accountable is, “that G**k did it!”, or, “I think it was probably that Asian kid who f*cked it up,” or, “That Korean private probably stole it.” And now, because you’re the only Asian and only Korean kid in the entire company/battalion/regiment/brigade, you’re now in a “world of sh*t.”^Squadmates during OSUT/basic training at Ft. Benning. That’s me on the right.On the other hand, it doesn’t work the same way if you are white or black, b/c if someone says, “That white guy did it…” you better not only have the accused’s rank, full name, unit, and serial number, as well as be able to identify him positively in a line-up, but solid proof and direct evidence that that person did it, b/c it’s not going to fly any other way.^Ft. Lewis, 1991. (I’m 2nd from left.)But back in the 1980s, when there was an entire officer and NCO corps raised on stories of Vietcong/North Korean/Japanese/Chinese as the main enemy of the U.S, it was lazy thinking to think it was probably that Asian soldier in your command who stole/broke or committed whatever illegal act. After all, as I’ve heard so many times before, “My grandaddy fought the Japs at Iwo Jima, my Uncle fought the g**ks at the Chosin Reservoir, and my daddy fought them again in Viet Nam!” So of course, it is just little ole’ me who’s gonna stab you in the back cos that’s why I joined the Army, right? LOL.^I took this picture as I was about to board a CH-47 with my platoon for an FTX at Ft. Lewis ca. 1991.Anyway, this was 30 years ago, and I’d like to believe things have changed a little, but I’m not going to hold my breath as the racially motivated hazing and subsequent Suicide of U.S. Marine Lcpl Harry Lew and the Suicide of U.S. Army Private Danny Chen both in 2011 in Afghanistan indicate that not much has changed from when I remember things, especially in the infantry in the 1980s and early ’90s when I was in.^Back from Ft. Benning in August 1988 after 13 weeks of 11B OSUT with my mom and sister.Regardless, time moves forward, and despite such discouraging incidents, recruitment of the current crop of Asian Americans are up overall, as many service-minded Asian Americans are now getting the hint that the best way to serve in the U.S. Armed Forces is to serve as an officer. To wit:In 2009, the Army had Asian Americans serving as 4.4 percent of its commissioned officers, and 3.5 percent of its enlisted personnel.In 2010, Asian Americans made up 3.7 percent of active duty service members, mostly in the Army and Navy, and 3.9 percent of the officers.In 2012, there were about 65,000 immigrants serving in the U.S. armed forces; of those, about 23 percent were from the Philippines.Due to the numerous Filipinos serving in the Navy, when seen together, they've been described as the "Filipino Mafia"Compare these low numbers with the fact that Asian Americans are currently 5.6% of the U.S. population. While Asian Americans are under represented in the enlisted ranks, they are closer to the civilian population in the officer ranks at around 4.4% of U.S. Army officers being Asian Americans. Also, 8 percent of USMA’s class of 2018 are Asian American, according to data released by the academy. (Generally, 8~10 % of the cadets are Asian American at West Point, with 80~90% being Korean Americans, oddly enough.)The latest stats for all the services in the U.S. military show that Asians accounted for just 3.8 percent of enlisted men and women and 4.4 percent of officers, according to a 2013 demographics report prepared for the Department of Defense.That said, lest we forget, Asian Americans in whatever capacity, not only continue to serve in the U.S. Armed Forces, but have also paid the ultimate sacrifice too. Lest we forget, there have been over 47 Asian-American U.S. military personnel who gave their lives in combat during OIF/OEF over the past 15 years in Iraq and Afghanistan. While I cannot remember everyone of their names, in addition to Marine Lcpl Harry Lew and Army Private Danny Chen, there were 9 other Chinese Americans (for a total of 11 Chinese-American KIAs), 2 Hmong-American KIAs, one Japanese-American KIA, 11 Vietnamese-American KIAs, and 22 Korean-American KIAs during OIF/OEF from 2002–2011 for a total of 47 Asian-American KIAs since 9/11 that I can name offhand. (There may be others. If so, please feel free to drop me a line with their name and details.) Those 47 Asian-American heroes are listed here as a memorial and reminder of their sacrifice to our nation:22 Korean Americans KIA during OIF/OEF after 9/11/2001:22) USAF Major Walter D. Gray, 38, an adopted Korean American, assigned as an U.S. Air Force Air Liaison Officer with 13th Air Support Ops Squadron, attached to the U.S. Army’s 4th Infantry Division, Fort Carson, CO. KIA: 08 AUG 2012, Sarkowi, Afghanistan due to a suicide VBIED attack on their base, killing Major Gray along with 4th ID’s CSM and another U.S. Army major in single attack.^USAF Major Walter D. Gray, an adopted Korean American, KIA 08 AUG 2012, Afghanistan, OEF.21) Army SGT Kyle B. McClain, 25, an adopted Korean American, assigned to 1433rd Engr. Co., 507th Engr. Bde, 177th MP Bde. KIA 01 AUG 2012, Salim-Aka Afghanistan.^Army SGT Kyle B. McClain, an adopted Korean American, KIA 01 AUG 2012, Afghanistan, OEF.20) Army Specialist Jinsu Lee, 34, Chatsworth, CA, assigned to 2–27th Infantry Regiment, 3rd BCT, 25th ID, Schofield Barracks, HI. KIA: 05 August 2011 due to hostile fire at FOB Bostick, Afghanistan.^Army SPC Jinsu Lee, CA, KIA 05 AUG 2011 at the age of 34, Afghanistan, OEF.19) Army SGT Jeffrey Chul-Soon Sherer, 29, assigned to 1–24th Infantry Regiment, 1st Stryker Brigade, 25th ID. KIA: 02 JUNE 2011, Shah-Joy, Kandahar, Afghanistan as a result of an IED attack to his Stryker vehicle.^Army SGT Jeffrey C.S. Sherer, an adopted Korean American, KIA 02 June, 2011 at the age of 29, Afghanistan, OEF.18) Army SFC Daehan Park, 36, of Watertown, Conn.;Died March 12, 2011 serving during Operation Enduring Freedom.Unit: assigned to 3rd Battalion, 1st Special Forces Group (3-1 SFG), Joint Base Lewis-McChord, WA. MOS: 11B20 (infantry squad leader) & 18E (Special Operations Communications Sergeant).SFC Park, a.k.a. “Michael Schneider,” was killed in action on 12 March 2011 in Wardak Province, Afghanistan due to injuries sustained when enemy forces attacked his unit with an IED.^Army SFC Daehan Park, 3–1st SFG(A), KIA 12 March 2011 OEF.17) Army Sgt. Daniel Lim, 23, of Cypress, CA. Died July 24, 2010 serving during Operation Enduring Freedom.Unit: assigned to 5-3rd Field Artillery Regiment, 17th Fires Brigade, Joint Base Lewis-McChord, WA.MOS: 13M, MLRS CrewmanSergeant Daniel Lim was killed in action on July 24, 2010 in Qalat, Afghanistan due to an IED attack on his vehicle. Also killed with Lim were Staff Sgt. Conrad A. Mora, SPC Joseph A. Bauer and PFC. Andrew L. Hand.^Army SGT Daniel Lim, 5–3rd FA, 17 FB, KIA 24 July 2011 OEF.16) Army PFC Benjamin J. Park, 25 of Fairfax Station, VADied June 18, 2010 serving during Operation Enduring Freedom.Unit: assigned to the 1-502nd Infantry Regiment, 2nd BCT, 101stAirborne Division (…Air Assault), Fort Campbell, KY;MOS: 11B, Infantryman;KIA June 18, 2010: Zhari district, Kandahar, Afghanistan, of injuries sustained when insurgents attacked his unit with an IED.^Army PFC Benjamin J. Park, 1–502nd Infantry Regt., 2nd BCT, 101st Airborne Div., KIA 18 June 2010, OEF.15) Army SPC Shinwoo Kim, 23, of Fullerton , CA .Unit: assigned to 2-12 Infantry, 2nd BCT, 2nd ID, Ft. Carson,… CO.MOS: Army MedicKIA 6-28-07 from IED. SPC Kim was killed along with four other members of his squad from IED wounds in Iraq during OIF.^Army SPC Shinwoo Kim, 2–12th Infantry Regt., 2nd BCT, 2nd ID, KIA 28 June 2007, OIF.14) Army SFC Nathan L. Winder, 32, of Blanding , UT.Unit: assigned to 2-1st Special Forces Group (Airborne), Ft. Lewis WA .MOS: Special Forces MedicKIA 6-26-07 in Diwaniyah , Iraq , of a shot to the neck sustained from enemy small-arms fire.^Army SFC Nathan L. Winder, an adopted Korean-American, 2–1st SFG(A), KIA 26 June 2007, OIF.13) Army Spec. Louis G. Kim, 19, Covina , CAUnit: 1-26th Inf, 2nd BCT, 1st ID, Schweinfurt , Germany .KIA: 2-20-2007, Ramadi Iraq , from small-arms fire during combat operations.^Army SPC Louis G. Kim, 1–26th Infantry Regt., 2nd BCT, 1st ID., KIA 20 Feb 2007, OIF.12) Army Sgt. Jae S. Moon, 21, Levittown , PA. Unit: 2-12 Inf, 2nd BCT, 2nd ID, Ft. Carson, CO. KIA: 12-25-2006, Baghdad due to an IED attack.^Army SGT Jae-sik Moon, 2–12th Infantry Regt., 2nd BCT, 2nd ID, KIA 25 DEC 2006, OIF.11) Marine Lcpl. Minhee Andy Kim, 20, Ann Arbor , MIUnit: 1-24th Marines, 4th MarDiv, USMCR.KIA: 11-1-2006, Anbar Province , from small-arms fire during combat operations.^ Marine Lcpl. Minhee Andy Kim; 1-24th Marines, 4th MarDiv, USMCR; KIA 01 NOV 2006; OIF.10) Army PFC Jang-ho Kim, 20, Placentia , CA. Unit: 1-26 Inf, 2nd BCT, 1st ID, Schweinfurt , Germany. KIA: 11-13-2006, Baghdad/OIF due to an IED.^Army PFC Jang-ho Kim; 1-26 Inf, 2nd BCT, 1st ID; KIA 13 NOV 2006; OIF.9) Army Sgt. Kyu H. Chay, 34, Fayettville , NC. Unit: 1-3rd Special Forces Group (Airborne), Ft. Bragg , NC. KIA: 10-28-2006, Oruzgan Province, Afghanistan, IED.^Army SGT Kyu H. Chay, 1-3rd Special Forces Group (Airborne). KIA: 28 OCT 2006.8. Marine Lcpl. Kun Y. Kim, 20, Atlanta , GA. Unit: 3-8 Marines, 2nd MarDiv, II MEF, Camp Lejeune , NC. KIA: 4-2-2006, Anbar Province, Iraq, during combat ops.^Marine Lcpl. Kun Y. Kim; 3-8 Marines, 2nd MarDiv, II MEF; KIA 2 APRIL 2006; OIF.7) Navy QM2 (SEAL) James Suh, 28, Deerfield Beach , FLUnit: SDV-Team 1, Pearl Harbor , HIKIA: 6-28-2005, mountains of eastern Afghanistan , MH-47 Chinook helicopter crash during “Operation Redwings.”^Navy QM2 (SEAL) James Suh; SEAL Delivery Vehicle Team One (SDV Team 1). KIA: 28 JUNE 2005.6) Army PFC Samuel S. Lee, 19, Anaheim , CA. Unit: 1-506 IN, 2nd ID, Camp Greaves , Korea. KIA: 3-28-2005, Ramadi , Iraq , non-combat incident.^Army PFC Samuel S. Lee, 1–506 Infantry Regiment, 2nd ID. KIA: 28 MARCH 2005.5) Army PFC Min-soo Choi, 21, RiverVale, NJ. Unit: 6-8 Cav, 4th Bde, 3rd ID, Ft. Stewart, GA. MOS: 11B. KIA: 2-26-2005, Abertha , Iraq , IED.^Army PFC Min-soo Choi, MOS 11B, 6-8 Cav, 4th Bde, 3rd ID; KIA 26 FEB 2005; OIF.4) Marine Cpl. In-Chul Kim, 23, Warren , MI. Unit: 9th Com Btn, 1st MEF, PendletonKIA: 12-7-2004, Anbar Province , Iraq , vehicle accident.^Cpl In-Chul Kim; 9th Com Btn, 1st MEF; KIA 7 DEC 2004; OIF.3) Army Pvt Jeung-jin Na Kim, 23, Honolulu , HIUnit: 2-17 FA, 2nd ID, Camp Hovey , KoreaKIA: 10-6-2004, Ramadi , Iraq , small-arms fire during combat operations.^Army PVT Jeung-jin Na Kim; 2-17 FA, 2nd ID; KIA 06 OCT 2004; OIF.2) Marine Cpl. Bum R. Lee, 21, Sunnyvale , CAUnit: 2-4 Marines, 1st MarDiv, 1st MEF, Camp Pendleton. KIA: 6-2-2004, Anbar Province , Iraq during combat operations.^Cpl. Bum R. Lee; 2-4 Marines, 1st MarDiv, 1st MEF; KIA 02 JUNE 2004; OIF.1) Marine Lcpl. Brad S. Shuder, 21, El Dorado , CA. Unit: 2-1 Marines, 1st MarDiv, 1st MEF,Camp Pendleton. KIA: 4-12-2004, Anbar Province , Iraq from enemy mortar fire.^Marine Lcpl. Brad S. Schuder, an adopted Korean American, 2-1 Marines, 1st MarDiv, 1st MEF; KIA 12 April 2004; OIF.11 Vietnamese-Americans KIA during OIF/OEF after 9/11/2001: (List may not be complete):11) Marine Lcpl. Tevan L. Nguyen: 21, of Hutto, Texas; assigned to the 3rd Battalion, 5th Marine Regiment, 1st Marine Division, I Marine Expeditionary Force, Camp Pendleton, Calif.; died Dec. 28, 2010 in Helmand province, Afghanistan, while conducting combat operations.^Marine Lcpl. Tevan L. Nguyen: 21, 3–5th Marine, 1st MarDiv, 1 MEF, KIA 28 DEC 2010, OEF.10) Army PFC Tan Q. Ngo: 20, of Beaverton, Ore.; assigned to the 1st Battalion, 4th Infantry Regiment, Hohenfels, Germany; died Aug. 27, 2008 in Kandahar, Afghanistan, when his mounted patrol received small arms and rocket-propelled grenade fire.^Army PFC Tan Q. Ngo: assigned to 1–4th Infantry Regiment, Hohenfels, Germany, KIA: 27 AUG 2008 in Kandahar, Afghanistan due RPG attack.9) Army Staff SGT Du Hai Tran: 30, of Reseda, Calif.; assigned to the Fires Squadron, 2nd Stryker Cavalry Regiment, Vilseck, Germany; died June 20, 2008 in Balad, Iraq, of wounds sustained when an improvised explosive device detonated near his unit while on patrol during combat operations.^Army Staff SGT Du Hai Tran, 30, of Reseda, Calif.; assigned to the Fires Squadron, 2nd Stryker Cavalry Regiment, Vilseck, Germany; KIA 20 June 2008 in Balad, Iraq, OIF.8) Army SPC Dan H. Nguyen: 24, of Sugar Land, Texas; assigned to the 1st Battalion, 12th Cavalry Regiment, 3rd Brigade Combat Team, 1st Cavalry Division, Fort Hood, Texas; died May 8, 2007 in Tahrir, Iraq while trying to rescue a fellow soldier when his unit was attacked by enemy forces.^Army Spc. Dan H. Nguyen, 24 of Sugarland, Texas, KIA 08 MAY 2007) while trying to rescue a soldier in Iraq during OIF.7) Army SGT Long N. Nguyen: 27, of Portland, Ore.; assigned to the 141st Brigade Support Battalion, Oregon ARNG, Portland, Ore.; died Feb. 10, 2007 of a non-combat-related wound in Mazar-e Sharif, Afghanistan.^Army Sgt. Long N. Nguyen, 27, of Portland, Oregon of the 41st Infantry Brigade Support Battalion, OR ARNG, died 10 FEB 2007 in Mazar-e-Sharif, Afghanistan, of a non-combat related wound.6) Army SFC Tung M. Nguyen: 38, of Tracy, Calif.; assigned to the 2nd Battalion, 3rd Special Forces Group (Airoborne), Fort Bragg, N.C.; died Nov. 14, 2006 of injuries suffered when his unit came in contact with enemy forces using small arms fire during combat operations in Baghdad.^Army SGT Tung. M. Nguyen, 38, of Tracy, CA, assigned to 2–3rd SFG(A), Fort Bragg, NC, KIA: 14 NOV 2006 due to combat operations in Baghdad during OIF.5) Army SPC Quoc-Binh Tran: 26, of Mission Viejo, Calif.; assigned to the 181st Support Battalion, California Army National Guard, San Bernardino, Calif.; died Nov. 7, 2004, of wounds sustained due to an IED during convoy operations in Baghdad.^Army SPC Quoc-Binh Tran: 26, of Mission Viejo, Calif.; assigned to the 181st Support Battalion, California ARNG, San Bernardino, Calif.; died 07 NOV, 2004.4) Marine Lcpl. Andrew S. Dang: 20, of Foster City, Calif.; assigned to 1st Combat Engineer Battalion, 1st Marine Division, I Marine Expeditionary Force, Camp Pendleton, Calif.; killed March 22, 2004 by hostile fire near Ramadi, Iraq.^Marine Lcpl. Andrew S. Dang: 20, of Foster City, Calif.; assigned to 1st Combat Engineer Battalion, 1st MarDiv, I MEF, Camp Pendleton, CA.KIA: 22 MAR, 2004.3) Marine Lcpl. Victor R. Lu: 22, of Los Angeles; assigned to 3rd Battalion, 5th Marine Regiment, 1st Marine Division, I Marine Expeditionary Force, Marine Corps Base Camp Pendleton, Calif.; killed Nov. 13, 2004 by enemy action in Anbar province, Iraq. Victor Ronald Huyen Lu’s parents were Vietnamese refugees and his father was a former South Vietnamese Army officer who fled Viet Nam in 1975.^Marine Lcpl. Victor R. Lu: 22, of Los Angeles; assigned to 3-5th Marine, 1st MarDiv, I MEF, MCB Camp Pendleton, CA. KIA: 13 NOV 20042) Marine Cpl. Binh N. Le: 20, of Alexandria, Va.; assigned to 5th Battalion, 10th Marine Regiment, 2nd Marine Division, II Marine Expeditionary Force, Camp Lejeune, N.C.; died Dec. 3, 2004 of injuries sustained in enemy action in Anbar province, Iraq.^Marine Cpl. Binh N. Le: 20, of Alexandria, Va.; assigned to 5-10th Marine, 2nd MarDiv, II MEF, Camp Lejeune, N.C.; died 03 DEC 2004.1 ) Marine Lcpl. Alan Dinh Lam: 19, of Snow Camp, N.C; assigned to the 8th Communication Battalion, 2nd Marine Expeditionary Brigade, Camp Lejeune, N.C.; killed on April 22, 2003, in a non-hostile accident when a RPG launcher being fired for familiarization malfunctioned near Kut, Iraq. The incident is under investigation.^Marine Lcpl. Alan Dinh Lam: 19, of Snow Camp, N.C; assigned to the 8th Communication Battalion, 2nd Marine Expeditionary Brigade, Camp Lejeune, N.C.; KIA on April 22, 2003,11 Chinese Americans KIA during OIF/OEF after 9/11/2001. (List may not be complete.)11) Marine Lcpl. Harry Lew:10) Army PFC Danny Chen:9) Army SGT Yihjyh L. Chen:8) Army PFC Ming Sun:7) Army SPC Roger S. Lee:6) Army SSG Edmund L. Lo:5) Navy Corpsman HM2: Xin Qi:4) Army CWO-3: Cornell C. Chao: 36, of Orange City, Calif.; assigned to the 4th Battalion, 227th Aviation Regiment, 1st Air Cavalry Brigade, 1st Cavalry Division, Fort Hood, Texas; died Jan 28, 2007 of wounds sustained when his helicopter crashed during combat operations in Najaf, Iraq. Also killed was Army Capt. Mark T. Resh.^Army CWO-3: Cornell C. Chao: 36, of Orange City, Calif.; assigned to the 4-227th Aviation, 1st AirCav Bde, 1st Cav Div, Fort Hood, Texas; died 28 Jan 2007 during helicopter combat operations in Iraq during OIF.3) Marine Lcpl. Jeffrey Lam: 22, of Queens, N.Y.; assigned to the 6th Communications Battalion, 4th Force Service Support Group, Marine Corps Reserve, Brooklyn, N.Y.; killed Nov. 8, 2004 in a non-hostile vehicle incident in Anbar province, Iraq.^Marine Lcpl. Jeffrey Lam: 22, of Queens, N.Y.; assigned to the 6th Coms Btn, 4th FSSG, USMCR, Brooklyn, N.Y. KIA: 08 NOV 8, 20042) Army SGT Elijah Tai-Wah Wong, 42, Mesa, AZ, of the Arizona ARNG 363rd EOD Co., KIA: 09 FEB 2004, in Sinjar, Iraq during EOD operations.^Army SGT Elijah Tai-Wah Wong, AZ ARNG, 363rd EOD, KIA: 09 FEB 2004, OIF.1 ) Army SPC Doron Chan: 20, of Highland, N.Y.; assigned to 411th Engineer Brigade, Army Reserve, New Windsor, N.Y.; killed March 18, 2004, when his convoy vehicle was cut off by a civilian vehicle near Balad, Iraq. Chan’s vehicle swerved, crossed into oncoming traffic and flipped over.^Army SPC Doron Chan: 20, of Highland, N.Y.; assigned to 411th Engineer Brigade, USAR, New Windsor, N.Y. KIA 08 March 2004,2 Hmong-Americans KIA in OIF/OEF since 9–11–2001:2) Army SPC Qixing Hwjhuam Lee: 20, of Minneapolis; assigned to 1st Battalion, 66th Armor Regiment, 1st Brigade, 4th Infantry Division, Fort Hood, Texas; killed Aug. 27, 2006 when an improvised explosive device detonated near his M2A3 Bradley Vehicle during combat operations in Taji, Iraq. Also killed were Sgt. Moises Jazmin, Spc. Shaun A. Novak and Spc. Tristan C. Smith.August 27, 2006, OIF.^Army SPC Qixing H. Lee, KIA 27 AUG 2006.1 ) Army Specialist, Thai Vue: 22, of Willows, Calif.; assigned to the 127th Military Police Company, 709th Military Police Battalion, 18th Military Police Brigade, V Corps, Hanau, Germany; killed June 18, 2004 when a mortar round hit the motor pool where he was working in Baghdad.^Army SPC Thai Vue, 18 JUNE 2004.1 Japanese-American KIA in OIF/OEF since 9/11/2001:1 ) USAF Capt. Reid K. Nishizuka: 30, of Kailua, Hawaii, assigned to 427th Reconnaissance Squadron, Beale Air Force Base, Calif.; died April 27, 2013, in the crash of an MC-12 aircraft near Kandahar Airfield, Afghanistan. Also killed in the incident were Capt. Brandon L. Cyr, of Woodbridge, Va., Staff Sgt. Richard A. Dickson, of Rancho Cordova, Calif.; and Staff Sgt. Daniel N. Fannin, of Morehead, Ky.^USAF Capt. Reid K. Nishizuka, KIA 27 APRIL 2013.

Here are some resources to start learning Deep Learning:Free Online BooksDeep Learning by Yoshua Bengio, Ian Goodfellow and Aaron Courville (05/07/2015)Neural Networks and Deep Learning by Michael Nielsen (Dec 2014)Deep Learning by Microsoft Research (2013)Deep Learning Tutorial by LISA lab, University of Montreal (Jan 6 2015)neuraltalk by Andrej Karpathy : numpy-based RNN/LSTM implementationAn introduction to genetic algorithmsArtificial Intelligence: A Modern ApproachDeep Learning in Neural Networks: An OverviewCoursesMachine Learning - Stanford by Andrew Ng in Coursera (2010-2014)Machine Learning - Caltech by Yaser Abu-Mostafa (2012-2014)Machine Learning - Carnegie Mellon by Tom Mitchell (Spring 2011)Neural Networks for Machine Learning by Geoffrey Hinton in Coursera (2012)Neural networks class by Hugo Larochelle from Université de Sherbrooke (2013)Deep Learning Course by CILVR lab @ NYU (2014)A.I - Berkeley by Dan Klein and Pieter Abbeel (2013)A.I - MIT by Patrick Henry Winston (2010)Vision and learning - computers and brains by Shimon Ullman, Tomaso Poggio, Ethan Meyers @ MIT (2013)Convolutional Neural Networks for Visual Recognition - Stanford by Fei-Fei Li, Andrej Karpathy (2015)Deep Learning for Natural Language Processing - StanfordNeural Networks - usherbrookeMachine Learning - Oxford (2014-2015)Deep Learning - Nvidia (2015)Videos and LecturesHow To Create A Mind By Ray KurzweilDeep Learning, Self-Taught Learning and Unsupervised Feature Learning By Andrew NgRecent Developments in Deep Learning By Geoff HintonThe Unreasonable Effectiveness of Deep Learning by Yann LeCunDeep Learning of Representations by Yoshua bengioPrinciples of Hierarchical Temporal Memory by Jeff HawkinsMachine Learning Discussion Group - Deep Learning w/ Stanford AI Lab by Adam CoatesMaking Sense of the World with Deep Learning By Adam CoatesDemystifying Unsupervised Feature Learning By Adam CoatesVisual Perception with Deep Learning By Yann LeCunThe Next Generation of Neural Networks By Geoffrey Hinton at GoogleTechTalksThe wonderful and terrifying implications of computers that can learn By Jeremy Howard at TEDxBrusselsUnsupervised Deep Learning - Stanford by Andrew Ng in Stanford (2011)Natural Language Processing By Chris Manning in StanfordPapersImageNet Classification with Deep Convolutional Neural NetworksUsing Very Deep Autoencoders for Content Based Image RetrievalLearning Deep Architectures for AICMU’s list of papersNeural Networks for Named Entity Recognition zipTraining tricks by YBGeoff Hinton's reading list (all papers)Supervised Sequence Labelling with Recurrent Neural NetworksStatistical Language Models based on Neural NetworksTraining Recurrent Neural NetworksRecursive Deep Learning for Natural Language Processing and Computer VisionBi-directional RNNLSTMGRU - Gated Recurrent UnitGFRNN . .LSTM: A Search Space OdysseyA Critical Review of Recurrent Neural Networks for Sequence LearningVisualizing and Understanding Recurrent NetworksWojciech Zaremba, Ilya Sutskever, An Empirical Exploration of Recurrent Network ArchitecturesRecurrent Neural Network based Language ModelExtensions of Recurrent Neural Network Language ModelRecurrent Neural Network based Language Modeling in Meeting RecognitionDeep Neural Networks for Acoustic Modeling in Speech RecognitionSpeech Recognition with Deep Recurrent Neural NetworksReinforcement Learning Neural Turing MachinesLearning Phrase Representations using RNN Encoder-Decoder for Statistical Machine TranslationGoogle - Sequence to Sequence Learning with Nneural NetworksMemory NetworksPolicy Learning with Continuous Memory States for Partially Observed Robotic ControlMicrosoft - Jointly Modeling Embedding and Translation to Bridge Video and LanguageNeural Turing MachinesAsk Me Anything: Dynamic Memory Networks for Natural Language ProcessingTutorialsUFLDL Tutorial 1UFLDL Tutorial 2Deep Learning for NLP (without Magic)A Deep Learning Tutorial: From Perceptrons to Deep NetworksDeep Learning from the Bottom upTheano TutorialNeural Networks for MatlabUsing convolutional neural nets to detect facial keypoints tutorialTorch7 TutorialsThe Best Machine Learning Tutorials On The WebVGG Convolutional Neural Networks PracticalDatasetsMNIST Handwritten digitsGoogle House Numbers from street viewCIFAR-10 and CIFAR-1004.IMAGENETTiny Images 80 Million tiny images6.Flickr Data 100 Million Yahoo datasetBerkeley Segmentation Dataset 500UC Irvine Machine Learning RepositoryFlickr 8kFlickr 30kMicrosoft COCOVQAImage QAAT&T Laboratories Cambridge face databaseAVHRR PathfinderAir Freight - The Air Freight data set is a ray-traced image sequence along with ground truth segmentation based on textural characteristics. (455 images + GT, each 160x120 pixels). (Formats: PNG)Amsterdam Library of Object Images - ALOI is a color image collection of one-thousand small objects, recorded for scientific purposes. In order to capture the sensory variation in object recordings, we systematically varied viewing angle, illumination angle, and illumination color for each object, and additionally captured wide-baseline stereo images. We recorded over a hundred images of each object, yielding a total of 110,250 images for the collection. (Formats: png)Annotated face, hand, cardiac & meat images - Most images & annotations are supplemented by various ASM/AAM analyses using the AAM-API. (Formats: bmp,asf)Image Analysis and Computer GraphicsBrown University Stimuli - A variety of datasets including geons, objects, and "greebles". Good for testing recognition algorithms. (Formats: pict)CAVIAR video sequences of mall and public space behavior - 90K video frames in 90 sequences of various human activities, with XML ground truth of detection and behavior classification (Formats: MPEG2 & JPEG)Machine Vision UnitCCITT Fax standard images - 8 images (Formats: gif)CMU CIL's Stereo Data with Ground Truth - 3 sets of 11 images, including color tiff images with spectroradiometry (Formats: gif, tiff)CMU PIE Database - A database of 41,368 face images of 68 people captured under 13 poses, 43 illuminations conditions, and with 4 different expressions.CMU VASC Image Database - Images, sequences, stereo pairs (thousands of images) (Formats: Sun Rasterimage)Caltech Image Database - about 20 images - mostly top-down views of small objects and toys. (Formats: GIF)Columbia-Utrecht Reflectance and Texture Database - Texture and reflectance measurements for over 60 samples of 3D texture, observed with over 200 different combinations of viewing and illumination directions. (Formats: bmp)Computational Colour Constancy Data - A dataset oriented towards computational color constancy, but useful for computer vision in general. It includes synthetic data, camera sensor data, and over 700 images. (Formats: tiff)Computational Vision LabContent-based image retrieval database - 11 sets of color images for testing algorithms for content-based retrieval. Most sets have a description file with names of objects in each image. (Formats: jpg)Efficient Content-based Retrieval GroupDensely Sampled View Spheres - Densely sampled view spheres - upper half of the view sphere of two toy objects with 2500 images each. (Formats: tiff)Computer Science VII (Graphical Systems)Digital Embryos - Digital embryos are novel objects which may be used to develop and test object recognition systems. They have an organic appearance. (Formats: various formats are available on request)Univerity of Minnesota Vision LabEl Salvador Atlas of Gastrointestinal VideoEndoscopy - Images and Videos of his-res of studies taken from Gastrointestinal Video endoscopy. (Formats: jpg, mpg, gif)FG-NET Facial Aging Database - Database contains 1002 face images showing subjects at different ages. (Formats: jpg)FVC2000 Fingerprint Databases - FVC2000 is the First International Competition for Fingerprint Verification Algorithms. Four fingerprint databases constitute the FVC2000 benchmark (3520 fingerprints in all).Biometric Systems Lab - University of BolognaFace and Gesture images and image sequences - Several image datasets of faces and gestures that are ground truth annotated for benchmarkingGerman Fingerspelling Database - The database contains 35 gestures and consists of 1400 image sequences that contain gestures of 20 different persons recorded under non-uniform daylight lighting conditions. (Formats: mpg,jpg)Language Processing and Pattern RecognitionGroningen Natural Image Database - 4000+ 1536x1024 (16 bit) calibrated outdoor images (Formats: homebrew)ICG Testhouse sequence - 2 turntable sequences from ifferent viewing heights, 36 images each, resolution 1000x750, color (Formats: PPM)Institute of Computer Graphics and VisionIEN Image Library - 1000+ images, mostly outdoor sequences (Formats: raw, ppm)INRIA's Syntim images database - 15 color image of simple objects (Formats: gif)INRIAINRIA's Syntim stereo databases - 34 calibrated color stereo pairs (Formats: gif)Image Analysis Laboratory - Images obtained from a variety of imaging modalities -- raw CFA images, range images and a host of "medical images". (Formats: homebrew)Image Analysis LaboratoryImage Database - An image database including some texturesJAFFE Facial Expression Image Database - The JAFFE database consists of 213 images of Japanese female subjects posing 6 basic facial expressions as well as a neutral pose. Ratings on emotion adjectives are also available, free of charge, for research purposes. (Formats: TIFF Grayscale images.)ATR Research, Kyoto, JapanJISCT Stereo Evaluation - 44 image pairs. These data have been used in an evaluation of stereo analysis, as described in the April 1993 ARPA Image Understanding Workshop paper ``The JISCT Stereo Evaluation'' by R.C.Bolles, H.H.Baker, and M.J.Hannah, 263--274 (Formats: SSI)MIT Vision Texture - Image archive (100+ images) (Formats: ppm)MIT face images and more - hundreds of images (Formats: homebrew)Machine Vision - Images from the textbook by Jain, Kasturi, Schunck (20+ images) (Formats: GIF TIFF)Mammography Image Databases - 100 or more images of mammograms with ground truth. Additional images available by request, and links to several other mammography databases are provided. (Formats: homebrew)ftp://ftp.cps.msu.edu/pub/prip - many images (Formats: unknown)Middlebury Stereo Data Sets with Ground Truth - Six multi-frame stereo data sets of scenes containing planar regions. Each data set contains 9 color images and subpixel-accuracy ground-truth data. (Formats: ppm)Middlebury Stereo Vision Research Page - Middlebury CollegeModis Airborne simulator, Gallery and data set - High Altitude Imagery from around the world for environmental modeling in support of NASA EOS program (Formats: JPG and HDF)NIST Fingerprint and handwriting - datasets - thousands of images (Formats: unknown)NIST Fingerprint data - compressed multipart uuencoded tar fileNLM HyperDoc Visible Human Project - Color, CAT and MRI image samples - over 30 images (Formats: jpeg)National Design Repository - Over 55,000 3D CAD and solid models of (mostly) mechanical/machined engineerign designs. (Formats: gif,vrml,wrl,stp,sat)Geometric & Intelligent Computing LaboratoryOSU (MSU) 3D Object Model Database - several sets of 3D object models collected over several years to use in object recognition research (Formats: homebrew, vrml)OSU (MSU/WSU) Range Image Database - Hundreds of real and synthetic images (Formats: gif, homebrew)OSU/SAMPL Database: Range Images, 3D Models, Stills, Motion Sequences - Over 1000 range images, 3D object models, still images and motion sequences (Formats: gif, ppm, vrml, homebrew)Signal Analysis and Machine Perception LaboratoryOtago Optical Flow Evaluation Sequences - Synthetic and real sequences with machine-readable ground truth optical flow fields, plus tools to generate ground truth for new sequences. (Formats: ppm,tif,homebrew)Vision Research Groupftp://ftp.limsi.fr/pub/quenot/opflow/testdata/piv/ - Real and synthetic image sequences used for testing a Particle Image Velocimetry application. These images may be used for the test of optical flow and image matching algorithms. (Formats: pgm (raw))LIMSI-CNRS/CHM/IMM/visionLIMSI-CNRSPhotometric 3D Surface Texture Database - This is the first 3D texture database which provides both full real surface rotations and registered photometric stereo data (30 textures, 1680 images). (Formats: TIFF)SEQUENCES FOR OPTICAL FLOW ANALYSIS (SOFA) - 9 synthetic sequences designed for testing motion analysis applications, including full ground truth of motion and camera parameters. (Formats: gif)Computer Vision GroupSequences for Flow Based Reconstruction - synthetic sequence for testing structure from motion algorithms (Formats: pgm)Stereo Images with Ground Truth Disparity and Occlusion - a small set of synthetic images of a hallway with varying amounts of noise added. Use these images to benchmark your stereo algorithm. (Formats: raw, viff (khoros), or tiff)Stuttgart Range Image Database - A collection of synthetic range images taken from high-resolution polygonal models available on the web (Formats: homebrew)Department Image UnderstandingThe AR Face Database - Contains over 4,000 color images corresponding to 126 people's faces (70 men and 56 women). Frontal views with variations in facial expressions, illumination, and occlusions. (Formats: RAW (RGB 24-bit))Purdue Robot Vision LabThe MIT-CSAIL Database of Objects and Scenes - Database for testing multiclass object detection and scene recognition algorithms. Over 72,000 images with 2873 annotated frames. More than 50 annotated object classes. (Formats: jpg)The RVL SPEC-DB (SPECularity DataBase) - A collection of over 300 real images of 100 objects taken under three different illuminaiton conditions (Diffuse/Ambient/Directed). -- Use these images to test algorithms for detecting and compensating specular highlights in color images. (Formats: TIFF )Robot Vision LaboratoryThe Xm2vts database - The XM2VTSDB contains four digital recordings of 295 people taken over a period of four months. This database contains both image and video data of faces.Centre for Vision, Speech and Signal ProcessingTraffic Image Sequences and 'Marbled Block' Sequence - thousands of frames of digitized traffic image sequences as well as the 'Marbled Block' sequence (grayscale images) (Formats: GIF)IAKS/KOGSU Bern Face images - hundreds of images (Formats: Sun rasterfile)U Michigan textures (Formats: compressed raw)U Oulu wood and knots database - Includes classifications - 1000+ color images (Formats: ppm)UCID - an Uncompressed Colour Image Database - a benchmark database for image retrieval with predefined ground truth. (Formats: tiff)UMass Vision Image Archive - Large image database with aerial, space, stereo, medical images and more. (Formats: homebrew)UNC's 3D image database - many images (Formats: GIF)USF Range Image Data with Segmentation Ground Truth - 80 image sets (Formats: Sun rasterimage)University of Oulu Physics-based Face Database - contains color images of faces under different illuminants and camera calibration conditions as well as skin spectral reflectance measurements of each person.Machine Vision and Media Processing UnitUniversity of Oulu Texture Database - Database of 320 surface textures, each captured under three illuminants, six spatial resolutions and nine rotation angles. A set of test suites is also provided so that texture segmentation, classification, and retrieval algorithms can be tested in a standard manner. (Formats: bmp, ras, xv)Machine Vision GroupUsenix face database - Thousands of face images from many different sites (circa 994)View Sphere Database - Images of 8 objects seen from many different view points. The view sphere is sampled using a geodesic with 172 images/sphere. Two sets for training and testing are available. (Formats: ppm)PRIMA, GRAVIRVision-list Imagery Archive - Many images, many formatsWiry Object Recognition Database - Thousands of images of a cart, ladder, stool, bicycle, chairs, and cluttered scenes with ground truth labelings of edges and regions. (Formats: jpg)3D Vision GroupYale Face Database - 165 images (15 individuals) with different lighting, expression, and occlusion configurations.Yale Face Database B - 5760 single light source images of 10 subjects each seen under 576 viewing conditions (9 poses x 64 illumination conditions). (Formats: PGM)Center for Computational Vision and ControlFrameworksCaffeTorch7Theanocuda-convnetconvetjsCcvNuPICDeepLearning4JBrainDeepLearnToolboxDeepnetDeeppyJavaNNhebelMocha.jlOpenDLcuDNNMGLKUnet.jlNvidia DIGITS - a web app based on CaffeNeon - Python based Deep Learning FrameworkKeras - Theano based Deep Learning LibraryChainer - A flexible framework of neural networks for deep learningRNNLM ToolkitRNNLIB - A recurrent neural network librarychar-rnnMatConvNet: CNNs for MATLABMinerva - a fast and flexible tool for deep learning on multi-GPUMiscellaneousGoogle Plus - Deep Learning CommunityCaffe Webinar100 Best Github Resources in Github for DLWord2VecCaffe DockerFileTorontoDeepLEarning convnetgfx.jsTorch7 Cheat sheetMisc from MIT's 'Advanced Natural Language Processing' courseMisc from MIT's 'Machine Learning' courseMisc from MIT's 'Networks for Learning: Regression and Classification' courseMisc from MIT's 'Neural Coding and Perception of Sound' courseImplementing a Distributed Deep Learning Network over SparkA chess AI that learns to play chess using deep learning.Reproducing the results of "Playing Atari with Deep Reinforcement Learning" by DeepMindWiki2Vec. Getting Word2vec vectors for entities and word from Wikipedia DumpsThe original code from the DeepMind article + tweaksGoogle deepdream - Neural Network artAn efficient, batched LSTM.A recurrent neural network designed to generate classical music.

How Demonetisation was planned?Background: Both India and Pakistan was taking the Paper from de la rue paper and Louisenthal . Remember both Pakistan and India was taking the Bank note paper from same de la rue and Louisenthal . Pakistan was using this opportunity to bring the fake notes into India to support terrorism and elections in India. India is unable to stop taking the bank note papers from de la rue and LouisenthalPakistan using the thefted ink, was printing the fake notes of Indian currency. Pakistan do not send the currency directly. It indirectly sends the currency through other nation like thisThe money was never cleansed right from 1947. We need to stop the fake note entry through different nation. It would approximately take 150 rupees to transfer 1000 rupee fake note into India. Before Narendra Modi take this decision “250 out of every 10 lakh notes in circulation were fake.” The enormity of the illegal trade in Indo-Bangla border can be gauged from the fact that BSF since January 2015 to November 2016 seized fake Indian currency notes with face value of Rs 3,96,72,500 and had apprehended 42 smugglers.So, stopping this fake notes which were transferring from Pakistan has to be stopped. Yeah!!! Even after demonetisation, fake notes were found. But, Modi had made Pakistan suffer by making this note ban. Who knows even China may also been involved in Fake note.Now coming to actual how demonetisation was implemented?The idea of Demonetisation to Narendra Modi was given by Anil Bokil.He was working on this topic of demonetisation right from 1999. But what he says that the highest denomination in India must be 50 rupees. He says 70% of the population of India can survive in just 2$ per day. He said this sentence during the start of 2000s (This may not work Today, May be 2$ was enough during 2000s)In 2013, soon after Modi was declared the Bharatiya Janata Party’s (BJP’s) prime ministerial candidate, Bokil went to Ahmedabad with his colleagues and sought to make a small presentation about the ArthaKranti proposal.The office of the then Gujarat chief minister gave Bokil 10 minutes. “By the time I was done, I realised that he had listened to me for 90 minutes. He said nothing after I had made my presentation," Bokil recalls .There have been a few follow-up meetings with Modi since then, in 2014, 2015, and even this year when, as the prime minister, Modi met Bokil with financial services secretary Hasmukh Adhia .“The first point of ArthaKranti proposal is a complete withdrawal of existing taxation system except the customs and import duties. The second point is tax on transactions routed through a bank, which will be the single point tax deducted at source on the credit/receiving account only. Third, cash transactions will not attract any tax. And the fourth point was withdrawal of high-denomination currency notes," Bokil says adding that Modi has done the fourth thing first. “That’s probably his way of doing things. Nobody among us imagined that there would be a politician who would do this. But Modi has proved us wrong," Bokil says .“As I told you, Modi has done the surgery without administering anaesthesia. It was always going to be painful. But believe me, 95% of those who are queuing up aren’t complaining. Eventually, they would understand why this was necessary," he asserts“Bokil met Rahul Gandhi in 2013 in Delhi,” says Malkar, 52 . That means he has met every PM candidate, but Narendra Modi has accepted this proposal.He also met Pratibha Patil, the then governor of Rajasthan in March 2007, who, impressed with his work, wrote to Somnath Chatterjee, the then Speaker of Lok Sabha, urging him to give the ArthaKranti team an opportunity to present their model before the Parliament. Since then, this team has been trying to influence politicians.The First step to make demonetisation is to make accounts in India: Narendra Modi implemented Jan Dhan accounts on 28th august 2014 where you can have zero balance account and absolutely no need of KYC .The pressure on bank employees was so high, they were working from 8 AM to 8 PM.The editorial from 'Officer's Voice', says, “The pressure on bank employees and officer to open accounts under the scheme is tremendous. The employees working in branched with allotted villages are required to work from 8am to 8pm on every Saturday till 26 January 2015 and conduct camps to enable people to open accounts under the scheme. The public sector banks (PSBs) are already under tremendous pressure due to acute staff shortage due to absence of recruitment during the past two decades and large scale retirement and resignations under voluntary retirement scheme (VRS).”There was so much pressure and few disadvantages also which you read here Jan Dhan Yojana and the rush to open more bank accountsBy Jan 2015, nearly 12 crores Jan dhan accounts were made and by 1st week of November, 25 crore Jan dhan accounts were made . By this Scheme, nearly 30 crores account were made post 2014 till the beginning of Demonetisation .People actually never came to open the account in the beginning. But, Modi said he will give 5000 rupee OD, if they maintain the account for 6 months . When Modi said about this OD, people rushed to the bank to make the bank accounts. Those 80–85% people who were not there in banking sector were given free zero balance account . It is a common sense that No one will keep zero balance account. Later, people put money in that and Because of this 20,000 crores were deposited by August 2015.Since, there will be a chance of opening more than one jan Dhan account (because of getting 5000 rupee OD), Narendra Modi asked the people to link the Aadhaar card to the Jan Dhan account so that the money which was given by Village accountant through NREGA scheme will be given deposited directly to the account. That means, if you need the money directly to your account, then you need to link your addhar to the account. thereby decreasing the misuse of the Jan Dhan account. This applied for rural areas. But, how to convince the urban people to link the addhar to the Jan dhan account. Narendra Modi asked the urban people to link the addhar so that the subsidy for the gas will be given directly to the account and people need not wait near the gas station to get the subsidy.Now, accounts were made and Aadhar linking were made from both rural and urban people. But, during Demonetisation, one more problem will occur. How to use money if the notes were banned?.. So, Modi introduced the concept of BHIM(Bharat interface for money). BHIM came into existence on 30th December 2016 and it was named after DR. B R Ambedkar and is intended to facilitate e-payments directly through banks as part of the 2016 bank note demonetisation and drive towards cashless transactions .Every problem was thought from every side so that no protest can happen when demonetisation was announced.Today because of this, electricity, gas bill, Petrol pump, etc can be given without touching your wallet.Now, those people who were keeping black money will go to Jewellery shop for transaction.The Government of India introduced changes in PAN requirements in December 2015 which came into effect from 1st January 2016. For various financial transactions, PAN was made essential in order to curb the flow of black money and widen the taxpayer base . PAN was made mandatory under Rule 114B for the following transactions :Purchase and sale of immovable property exceeding Rs 10 lakhs. This was previously Rs.5 lakhs.Purchase and sale of four wheelers.Life Insurance Premiums exceeding Rs.50,000 per yearFixed deposits held at any bank exceeding Rs 50,000 or Rs 5 lakh in a year .Opening an account with any bank except Pradhan Mantri Dhan Yojana accounts.Cash payments related to foreign travel and currency exchange exceeding Rs.50,000.One-time hotel bill payments of Rs.50,000 or more paid with cash.Cash purchase of bank drafts, pay orders or banker’s cheques above Rs.50,000 per day.Cash deposit in any bank account of Rs.50,000 or more in a single day.Applying for a credit card.Purchase of bonds or mutual funds above Rs.50,000.Opening demat accounts.Purchase of gold jewellery or bullion worth Rs.2 lakh or more by cash or card.By this, only Poor people and middle class people will get benefited. Poor people will get money directly to the Jan Dhan bank account. There will be no middle man to give subsidy. Middle class people were getting the benefit of BHIM and was get rid of the Subsidy problem.Now, its time for those rich people who were holding black money.Modi never gave call for demonetisation immediately. He gave chance to those black money holders to repay the black money with 45% tax This step is called as Income declaration scheme 2016. According to income tax department 64275 declarations were filed upto the midnight of 30th September, 2016 with an aggregate of Rs.65250 Crore worth of hitherto undeclared incomes in the form of cash and other assets being declared. Rs.16,000 crores received as tax out of one such system of Non-filers of Monitoring System (NMS). .2 Lakh crores were not added into the list of 65,250 crores .According to the press released by the ministry of finance“Among the declarations received, there were two sets of declarations of high value which were not taken on record in the above figure because these declarations were found to be suspicious in nature being filed by persons of small means. A family of four declarants namely, Mr. Abdul Razzaque Mohammed Sayed (self), Mr.Mohammed Aarif Abdul Razzaque Sayed (son), Shrimati Rukhsana Abdul Razzaque Sayed (wife) and Ms.Noorjahan Mohammed Sayed (sister) who were shown as residents of Flat no. 4 , Ground Floor , Jubilee Court, 269-B, T.P.S-III, Linking Road, Bandra (W), Mumbai, filed a total declaration of Rs. Two lakh crore (Rs. 2,00,000 crore). Three out of the four PAN numbers were originally in Ajmer which were migrated to Mumbai in September 2016, where the declarations were filed. The other declaration was filed by one Mr. Mahesh Kumar Champaklal Shah resident of 206, MangalJyot Tower, Jodhpur Gram Satellite, Ahmedabad for an amount of Rs.Thirteen Thousand Eight Hundred and Sixty crore (Rs.13,860 crore).These declarations from Mumbai and Ahmedabad were kept pending for investigation about the genuineness of the same and were not included in the total value of declarations announced on 1st October, 2016. After due enquiry, it was found that these declarants were persons of suspicious nature and very small means and the declarations could have been misused.Therefore, after due consideration, the Income Tax Department decided by 30th November, 2016 , to rejectthese two sets of declarations of Rs.Two Lakh Crore and Rs.Thirteen Thousand Eight Hundred and Sixty Crore respectively.”That means 2,00,000 + 13,860 + 16000 + 65250 = 2,95,000 came as the black money. 16,000 is the amount that never returned after demonetisation.Now, coming to the printing of 2000 rupee notes. Modi asked Raghuram Rajan for new currency printing. Raghuram Ram suggested 5000 ruppes notes and 10000 rupees notes . Modi accepted that and added 2000 rupees currency notes. So, plates for 500, 1000, 2000, 5000 and 10000 rupees note plates were getting ready. That means even Raghuram Rajan and all the designers was knowing that new currency will be coming to place .Next step was to remove Raghuram Rajan. So, Modi silently gave retirement to Raghuram Rajan and made Urjit Patel as the new Governor . I don’t know why Modi decided to remove Raghuram Rajan and was quietly watching Raghuram Rajan to come out of the seat. He made Urjit patel as the new governor of RBI.When Urjit Patel became a new Governor. Narendra Modi started printing new 2000 and 500 rupee currency notes in Mysore. All the paper which was needed for the printing new notes was purely from Indian made . This is a part of Make in India. People were only knowing that new 2000 rupee notes were coming, But the concept of demonetisation was completely new to them.after printing the money, Narendra Modi spoke with Armed forces people. Even Ajit Doval was present during the meeting . People would have started wondering why Narendra Modi spoke with Armed forces before demonetisation. I don’t know why he spoke with Armed forced on the same day. But, I assume Gangsters in India may do protest. To avoid that he may took the help of Armed forces. I don’t know how much secrecy maintained by Armed forces chief . For so many months, cell phones were not allowed to bring inside the parliament. On Tuesday November 8th 2016 and announced the demonetisation.Another comedy is that Rahul Gandhi gave the greatest statement ““PM did not ask anyone, not even Finance Minister or the Chief Economic Adviser, before taking this decision. But there were many in party (BJP) and many industrialists, who were aware. Big deposits were made in banks before this decision,”My dear readers, kindly observe the above statement. Rahul says Few BJP people knows about demonetisation, but finance minister and chief economic adviser do not know about Demonetisation. That means, if Finance minister and chief economic adviser knows previously about demonetisation, they would have informed about that to Rahul Gandhi privately(ofcourse).Some are often accusing Modi that Modi had told about demonetisation to BJP. However, a news came to know that “Cash worth Rs 91.50 lakh in recalled currency denominations of Rs 1,000 and Rs 500 were seized from a vehicle of Maharashtra Minister for Cooperation Subhash Deshmukh who looks after the cooperative sector ” and Subhash deshmukh is the Cabinet minister of Maharashtra.Narendra Modi purposefully brought the Pan card usage for buying Gold worth more than 2 Lakhs. Many raids happened on jewellery shops because many black money holders will go to Jewellery shops to buy Gold for the conversion of black money .Some may get a question where the persons are burning the notes. Will it affect the Government?. No!!!. The amount that never returned will directly goes to the treasury.Demonetisation was accepted in India:Mamata Banerjee and Kejriwal started rally against demonetisation. Not even 30 people were present for that Rally. (Proof: Just count in the video, how many people participated. Remove Police, remove leaders. check only people who participated)https://youtu.be/ljsu4fjB8h8https://youtu.be/jByT3jAZabohttps://youtu.be/8mvdDrPnrKoIn the video, media people were afraid to show how many people were present. They didn’t show how many people have arrived to that rally.Mamata Banerjee and Kejriwal gave 3 days time to roll back demonetisation. They are waiting even today :)According to Local circle survey, “Only 51% found the implementation to be good given the circumstances while 24% citizens have explicitly called out the implementation as poor. Another 25% citizens called the implementation average.”and “While citizens are not so happy with the implementation, support for the cause of demonetisation remains strong with 79% saying they don’t mind the inconvenience, another 18% saying its been painful but they still support it. Only 3% citizens said that they were against this step of the Government.” … (only 3% were against Modi because they were having Black money)Effects:Before demonetisation, Cards were mainly used for ATM withdrawal only. Now, cards were used everywhere. Every engineering college students do use Digital transaction only. I do digital transaction for just 1 rupee also.Some says Digital transaction is failure in India. But, here is the RBI report (Reserve Bank of India (Click here for report from RBI website))UPI payment during 2016–17 was 69 billion. UPI payment during 2017–18 was 1098 billion. You can very well check the website of official RBI.According to annual report of RBI dated Aug 29, 2018.NEFT (national electronic fund transfer) during 2016–17 was 1,20,000 and during 2017–18 was 1,72,000 billion.According to RBI, "The share of electronic transactions in the total volume of retail payments increased to 92.6 per cent in 2017-18, up from 88.9 per cent in the previous year with a corresponding reduction in the share of paper based clearing instruments from 11.1 per cent in 2016-17 to 7.4 per cent in 2017-18"As per RBI reports, digital transactions have grown 13.5 per cent from Rs 109.82 trillion in August to Rs 124.69 trillion in September; the highest was in March at Rs 149.59 trillion transactions.”How much money returned?RBI says 99.3% demonetised notes were returned; major points from central bank's annual reportBut, I don’t believe that India had got 99% of the money because I’m not able to get official answer from RBI.Why demonetisation by Modi?Narendra Modi answer this.Impact on Direct tax collection:What income tax department had said “The effect of Demonetisation is also clearly visible in the growth in Direct Tax Collections. Collection of Advance Tax under Personal Income Tax (i.e. other than Corporate Tax) as on 05.08.2017 showed a growth of about 41.79% over the corresponding period in F.Y. 2016-2017. Collection of Self Assessment Tax under Personal Income Tax showed a growth of 34.25% over the corresponding period in F.Y. 2016-2017.”It is not the whole decade, but it increased to the much extent after demonetisation.but, number of Taxpayers has increased by more than 1 crores.https://www.incometaxindia.gov.in/Documents/Direct%20Tax%20Data/time-series-data-2017-18.pdf (See the Darkened one)Effect on Indian GDPIndia’s GDP according to World bank2014 - 7.41%2015- 8.15%2016- 7.11%2017- 6.62% (0.42% decrease)2018- 7.3%(Narendra Modi is maintaining the GDP when compared to the previous Government. Previous Government GDP in 2010 was ~10% and in 2013, it was ~3.5%… Why sudden decrease in GDP? it is not a problem at all unless congress government who have a sharp decrease in GDP since Nov 2010 to May 30, 2014).out of 100%, atleast 1% of the people will retaliate. of course, those who have black money.Terrorism and Naxalism:two naxal with 25 lakhs in hand came to the bank for the exchange of notes and after questioning got caught .According to Ministry of home affairs,“ The declining trend which started in 2011 continued in 2016 as well. The last two and a half years has seen an unprecedented improvement in the LWE scenario across the country. There has been an overall 07% reduction in violent incidents (1136 to 1048) and 30% reduction (397 to 278) in LWE related deaths since end-2013. Over the same period there has been an increase of 50% in encounters (218 to 328) and an unprecedented 122% increase (100 to 222) in elimination of armed Maoists cadres. On the other hand, there has been a 43% reduction (115 to 65) in casualties to Security Forces personnel. The figures are the reflection of operation being conducted by the SF and the capacity building measures undertaken by the MHA. At the same time, the developmental outreach by the Government of India has seen an increasingly large number of LWE cadres shunning the path of violence and returning to the mainstream. Compared to 2013, there has been an increase of 411% (282 to 1442) in surrenders by LWE cadres in 2016 .In comparison to 2015, the year 2016 saw a decline of 3% (1089 to 1048) in incidents of violence while the number of deaths increased by 21% (230 to 278), which is mainly contributed by increase in death of civilians. 123 out of the 278 deaths are attributable to killing of alleged ‘police informers’ by the Maoists. On the operational front, 222 LWE cadres were eliminated in 2016 as compared to just 89 in 2015. 1840 LWE cadres were arrested and 1442 surrendered in 2016 as against 1668 and 570 respectively in 2015. The numbers of arms recovered have also increased from 724 to 800 and arms list due to snatchings dropped from 18 to 03 .Please give feedback and mention about necessary changes.Thank you..Regards,Gagan Indavara