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Weekly News Recap (18-24 April 2022)
Posted byPlatform for Peace and Humanity 24/04/2022 24/04/2022 Posted inWeekly News RecapTags:Domestic courts, ICRC, International courts, OHCHR, UN
© Photo by UN Ukraine via Flickr
Pakistan: Six Convicts Sentenced to Death for Killing a Sri Lankan National Accused of Committing Blasphemy
Egypt: Social Media Influencer's 10-Year Human Trafficking Sentence Reduced
Argentina: Napalpí Massacre Trial Opened After 98 Years of the Incidence
CAR: First SCC Trial Opened Against Alleged War Crimes and Crimes Against Humanity
UK: Court Officially Approved WikiLeaks Founder Assange's Extradition to the US
Netherlands: Ethiopian Refugee Appealed Conviction for War Crimes
DRC: 9 Militiamen Convicted of War Crimes Committed in the Province of Kasai Central
Rwanda: Chinese National Sentenced to 20 Years in Jail for Torturing Mine Workers
Bosnia: Wartime Commander of Interior Ministry Police Acquitted of War Crime Charges
Turkey: Activist Osman Kavala Made a Final Appeal Before the Court Pronounces a Verdict
UN: A New Action Plan to Protect Children in Yemen
Mediterranean: Drowning of Migrants and Asylum Seekers Continues
Brazil: Organisations Write Open Letter Inviting UN Experts to Investigate Continuing Police Abuse Against People of African Descent
Afghanistan: School Bombings – a Failure to Protect Ethnic and Religious Minorities
UN: Increased Humanitarian Aid by UN Agencies Across Ukraine
CAR: UN Humanitarian Coordinator Condemns Recent Attacks on Humanitarian Organizations
UNISFA: 'Trust Deficit' between Sudan and South Sudan Continues to be a Concern
ICRC: The Director of Operations Calls for Improved Humanitarian Access in Ukraine
OHCHR: UN Human Rights Experts Address the Urgency of Action in Israel and Occupied Palestinian Territory
NATO: Finland and Sweden Begin Discussions on Joining the North Atlantic Treaty Organization
On 18 April 2022, a court in Pakistan sentenced six men to death and several others to jail for murdering Priyantha Kumara Diyawadana, a Sri Lankan National, in 2021. Diyawadana worked as a manager of a Sri Lankan sports equipment factory and was accused of committing blasphemy by the workers in Pakistan's eastern Sialkot district. The AFP news agency was told by the local police that rumours were spread that Diyawadana had torn down a religious poster and had thrown it in a dustbin. In video clips shared on social media, Diyawadana was seen being beaten and set ablaze by a mob chanting slogans against blasphemy. According to the public prosecutor, the Anti-Terrorism Court in Lahore sentenced nine people to life imprisonment and pronounced five years' jail to one and two-year sentences to 72. The special anti-terror court was established to speed up the justice delivery system of such cases.
https://www.aljazeera.com/news/2022/4/18/pakistan-six-sentenced-to-death-for-lynching-sri-lankan-national
On 18 April 2022, in the case of a social media influencer Haneen Hossam who was convicted for the offence of human trafficking and sentenced to 10 years in jail, the court has reduced the sentence to three years. Along with the sentence, Hossam was also fined 200,000 Egyptian pounds ($10,740) by the Cairo Criminal Court. She was first arrested alongside Mawada al-Adham in 2020 for "attacking society's values" in online videos and was given a two-year jail sentence. In 2021, they were acquitted by the appeals court. Later, they were charged with human trafficking for reportedly spreading the word for girls to collaborate with them to make money online. Adham was sentenced to six years in jail. According to Hossam's lawyer, Hossam has already served 21 months behind the bar which is inclusive of the investigation period and thus she can be released in June or July. This case has sparked a debate on Egyptian authorities targeting female influencers in an attempt to control cyberspace by policing women's bodies and conduct.
https://www.aljazeera.com/news/2022/4/18/egypt-cuts-tiktok-influencer-sentence-to-3-years
On 19 April 2022, a landmark trial opened in the city of Resistencia in Argentina's northeast that seeks to hold the people who participated in the 1924 'Napalpí massacre' accountable. It has been 98 years since the massacre took place. In 1924, roughly 130 policemen and ranchers descended on the protesting residents of the Napalpí indigenous reservation, where Qom and Moqoit people lived under the conditions of semi-slavery, and killed more than around 300-500 people. It was due to a lack of defendants in the case that a trial could not be commenced until now. Although a federal judge had earlier ruled this incident of mass killing as a crime against humanity, the presiding judge observed that the case seeks to establish the truth and generate a sense of awareness among individuals to avoid such human right violation in the future. The people responsible for the massacre have long been dead. The Government calls the trial "a truth trial" which may lead to reparation being paid to the victims. Around 40 people will testify during the trial.
https://www.justiceinfo.net/en/90997-argentina-trial-opens-98-years-after-indigenous-massacre.html
On 19 April 2022, the first trial opened before the Special Criminal Court (SCC) in the Central African Republic (CAR). This day had long been awaited by victims of war crimes and crimes against humanity. The accused Issa Sallet Adoum, Yaouba Ousman and Mahamat Tahir, members of the Central African Republic rebel group 3R, were allegedly involved in the attacks that took place in the villages of Koundjili and Lemouna in May 2019 and are to be tried before the SCC. The attacks led to the death of many civilians. The SCC, which was set up in 2015, is conducting investigations with the International Criminal Court (ICC). The trial was postponed on Tuesday when the lawyers for the defendants chose not to appear before the court, boycotting the proceedings. The trial, however, will resume on 25 April. This trial is considered a milestone for the Central African Republic where millions of people have been forced to flee due to the decades-long conflict between the government forces and rebels.
https://www.amnesty.org/en/latest/news/2022/04/first-car-scc-trial-alleged-war-criminals-justice/
https://www.usnews.com/news/world/articles/2022-04-19/central-african-republic-war-crimes-trial-postponed-after-lawyer-no-show
On 20 April 2022, the Westminster Magistrates Court officially gave the approval order to extradite WikiLeaks founder Julian Assange to the United States, where he will be facing spying charges for publishing highly classified documents relating to the wars in Iraq and Afghanistan ten years ago. First, the order of the court will be sent to the United Kingdom's Home Secretary, Priti Patel who will decide to approve or reject the extradition request. Previously, a British judge ruled in favour of Mr Assange, citing the harsh conditions of US prisons which might pose a risk of Mr Assange harming himself. The US assured Mr Assange's safety in prison and the High Court overturned this decision. The Supreme Court, UK's highest court, denied Mr Assange permission to appeal the lower court's decision last month. Mr Assange has not yet exhausted all his legal remedies and can appeal the Home Secretary's decision once pronounced, for judicial review. If Mr Assange is convicted of all the charges, he could face imprisonment for up to 175 years.
https://www.forbes.com/sites/roberthart/2022/04/20/british-court-approves-julian-assanges-us-extradition-to-face-spying-charges/?sh=591299643f02
On 20 April 2022, the two-week appeal hearing of Eshetu Alemu, who was convicted of war crimes, came to an end. He had appealed before The Hague Court of Appeals against the lower court's verdict that sentenced him to life imprisonment. Mr Alemu argued that the lower court had refused to acknowledge exculpatory evidence during his 2017 trial. In the 2017 trial, the prosecution accused him of torturing and murdering civilians during the political violence that took place in the late 1970s in Ethiopia and further presented evidence claiming he supervised the execution of 75 people detained in a church. Mr Alemu denies all the charges and claims he was not a participant in the incidents. Mr Alemu is now a Dutch citizen, who was granted asylum in the Netherlands in the 1990s. In 2006, Ethiopia sentenced him to death for charges of genocide and crimes against humanity but the Netherland was not willing to extradite anyone facing the death penalty. Mr Alemu was arrested in 2015 by Dutch investigators. The Netherlands is prosecuting Mr Alemu under the principle of universal jurisdiction.
https://www.courthousenews.com/dutch-court-hears-appeal-of-ethiopian-refugee-convicted-of-war-crimes/
On 21 April 2022, the Kananga garrison military court pronounced 9 militiamen guilty of war crimes and crimes against humanity committed between April to May 2017 in several villages in the territory of Dimbelenge in central Kasai. The violence included armed clashes between Kamuina Nsapu movement and the DRC armed forces. All the defendants plead not guilty. Around 250 victims joined the proceedings as civil parties. The court, in order to create an accessible passage to justice for the victims, shifted the court to Bana Ba Ntumba where the actual crimes were committed. The 9 militiamen were convicted of war crimes and crimes against humanity for murder, torture and other inhumane acts, rape and pillage, as well as, other ordinary offences. The Court awarded compensation ranging from US$2,000 to US$20,000 to the victims and sentenced the nine men to death.
https://trialinternational.org/latest-post/guilty-verdict-in-the-trial-of-9-militiamen-suspected-of-war-crimes-in-bana-ba-ntumba/
https://www.justiceinfo.net/en/91073-kasai-justice-against-all-odds.html
On 21 April 2022, a Chinese national convicted of torturing mine workers was sentenced to 20 years in jail by a Rwandan court. In a video that went viral last year, Sun Shujun, the manager of the mine was found whipping a man tied to a pole. Mr Shujun acknowledge the assault and stated that he was frustrated with them for constantly stealing minerals from the mine. Mr Shujun further argued that he had paid one million Rwandan francs and had signed and sent a reconciliation letter to the victims. The prosecution argued that the victims accepted the payment as they were scared and traumatised by him. The court observed that Mr Shujun's actions amounted to a grave crime committed with malicious intent and thus pronounced a jail term of 20 years. Mr Shujun can appeal the verdict within 30 days. The Chinese embassy in Rwanda took note of the verdict and said that it has always urged Chinese nationals to abide by the local rules and regulations of Rwanda. Both Rwanda and China enjoy good relations.
https://www.aljazeera.com/news/2022/4/21/chinese-mine-manager-jailed-in-rwanda-for-torture
On 21 April 2022, the Bosnian state court, while pronouncing the first-instance verdict in the case of Dragan Vikic and three co-defendants who were accused of committing war crimes against the prisoners that were killed in Veliki Park in Sarajevo in 1992, held that the accused are not guilty. The prosecution charged Mr Vikic and Jusuf Pusina based on their position within the Bosnian interior ministry's police squad, with Mr Vikic, as the Commander Mr Pusina as the former assistant minister for uniformed police. According to the Prosecution, both men must have been aware that their subordinates were planning to murder eight Yugoslav People's Army prisoners and had failed to stop or punish them. The court observed that the testimonies of the witnesses and the pieces of evidence produced by the prosecution were unreliable and could not prove the guilt of the accused beyond a reasonable doubt. Further, the prosecution was unable to establish that Mr Vikic held command over the squad responsible for the crime. The court acknowledged that eight soldiers were denied their right to life and were killed in Veliki Park but this did not prove that the defendants were associated with the incident. The verdict is appealable.
https://balkaninsight.com/2022/04/21/sarajevo-wartime-police-chief-cleared-of-crimes-against-prisoners/
On 22 April 2022, in the case of a Paris-born philanthropist and activist Osman Kavala, 64, who was arrested when he arrived in Istanbul in 2017, a final appeal for freedom was made before the court. The court had estimated a verdict for this year-long trial by Friday but has now extended it until Monday next week. Mr Kavala has been imprisoned without conviction and has denied all the charges framed against him. The prosecution argued that Mr Kavala was responsible for financially aiding the 2013 attempt to overthrow Turkish President Tayyip Erdogan's government and for his involvement in the 2016 coup plot. In February 2020, Mr Kavala was acquitted of the 2013 protests charges but was detained again for the charges of participating in and aiding the 2016 coup attempt. The court will pronounce its verdict on Monday and a guilty verdict would mean Mr Kavala could be sentenced to life without the possibility of parole. This case has strained Ankara's ties with Western allies. According to human rights advocates, a verdict releasing Mr Kavala would mean the Turkish judicial system is free from Erdogan's pressure.
https://www.arabnews.com/node/2068441/middle-east
https://www.aljazeera.com/news/2022/4/22/turkeys-jailed-rights-defender-makes-final-appeal-before-verdict
On 18 April 2022, it was reported that the Action Plan that has been signed by the Houthis with the United Nations in order to put a stop to the recruitment of children in armed conflicts, and aspect of attacks on schools, children and hospitals. Virginia Gamba, the UN Special Representative for Children & Armed Conflict (CAAC), also commended the efforts and commitment that have been shown by Houthis towards the prevention of grave violations against children. The Houthis, in accordance with the Action Plan, have committed to putting an end to the recruitment of children and also to release the recruited children within six months and providing them with reintegration assistance. The UN Representative emphasised that the Action Plan should be implemented to the fullest so that improvement in the protection of children is visible. The Action Plan has also led to highlighting the humanitarian and economic needs of Yemen along with providing an opportunity to resume and renew the Yemeni political process. Phillipe Duamelle, UNICEF Representative called the signing of the Action Plan "an important milestone" for children affected by the Yemen conflict and said that he looked forward to its implementation as the agency continues to work with all parties for the welfare and safety of Yemeni children.
https://reliefweb.int/report/yemen/sana-authorities-yemen-sign-action-plan-un-prevent-grave-violations-against-children
https://www.hrw.org/news/2022/04/19/houthis-commit-end-violations-against-children-yemen
On 18 April 2022, Euro-Mediterranean Human Rights Monitor reported that the continuous and increased sinking and drowning of migrant and asylum seekers reflects the indifference of all nations and parties. On 15 April 2022, 35 migrants and asylum seekers capsized off the coast of Sabratha, Libya. Six bodies were recovered but 29 are reported missing. According to the International Organisation for Migration (IOM), since the beginning of the year, almost 560 migrants and asylum seekers have died or gone missing in the Mediterranean region. The number of migrants arriving in Europe via the Mediterranean has been 20 per cent more than in 2020; in 2021,116,573 migrants and asylum seekers have been crossing the Mediterranean Sea and 1,864 have died or gone missing. The Euro-Med Monitor emphasised that there should be a new mechanism in place through which migration and asylum seekers are protected from human trafficking while also developing such conditions that help in integrating migrants into their communities. The Monitor also underscored the need for all parties to abide by the United Nations Convention on the Law of the Sea (UNCLOS) 1982 and the International Convention for Search and Rescue, both of which urge and emphasise the need for assisting, rescuing people stranded and distressed at sea.
https://reliefweb.int/report/world/migrant-drownings-mediterranean-continue-disgraceful-moral-failure-governments
On 18 April 2022, hundreds of Brazilian organisations in an open letter to the Foreign Minister Carlos Alberto Franco França stated that the new UN International Independent Expert Mechanism to Advance Racial Justice and Equality in Law Enforcement should be invited for conducting a fact-finding mission in Brazil. The official communication of invitation comes important as discrimination and violence against black people have been frequent – more than 6400 people had been killed by police in 2020 and 80 per cent of those killed have been black people. Maria Laura Canineu, Brazil Director at Human Rights Watch emphasised that there was a need for the government to invite the UN experts for assessing the situation in Brazil as police abuse is a major issue. The experts providing recommendations on the issue would further ensure that police forces protect communities without discrimination and according to rule of law. The signatories to the open letter include groups and organisations focusing on racial equality, national human rights organisations and also communities that have been highly affected by police abuse.
https://www.hrw.org/news/2022/04/18/brazil-invite-un-experts-racial-justice
On 19 April 2022, it was reported that an all-boys high school Abdul Rahim Shahid in Kabul, Afghanistan, was bombed, killing at least 6 people and wounding 20. Reportedly the attack occurred when students were coming out of their morning classes. Shortly after the bombing at school, Mumtaz Education Centre was also bombed. UN Humanitarian Coordinator in Afghanistan, Ramiz Alakbarov condemned the attack and underscored that violence around or in schools was highly unacceptable and is prohibited under international humanitarian law. Amnesty International called for conducting a prompt investigation into the attacks which should be in compliance with international law. Samara Hamidi, Amnesty International's South Asia Campaigner urged that these acts are a reflection of the continuous violence that Afghans have to face every day and also show the failure on part of the de-facto authorities to protect religious and ethnic minorities.
https://www.amnesty.org/en/latest/news/2022/04/afghanistan-school-bombings-a-reprehensible-attack-on-religious-and-ethnic-minorities/
On 19 April 2022, UN World Food Programme stated that six million people were in need of food and economic assistance since the Russian invasion. UN agencies have stepped up their efforts to provide humanitarian aid across Ukraine. The World Health Organisation stated that it was doing its best to provide and deliver lifesaving supplies and assistance in remote areas. The agency has delivered 218 metric tonnes of emergency and medical supplies to the country. Furthermore, the WFP has delivered enough food to support 2 million people for two months and 1.7 million have been assisted by way of in-kind food assistance since 24 February, while $ 3.6 billion cash transfers have been made to keep the markets up and running. 113 tonnes of food have been delivered by the UN Agency which would be sufficient for 20,000 people for 10 days.
On 20 April 2022, a press release published by ReliefWeb stated that the UN Humanitarian Coordinator in the Central African Republic Denise Brown is 'deeply shocked and dismayed' by attacks aimed at humanitarian organizations. On 7 and 9 April 2022, six aid workers and a health district employee were injured during violence carried out by armed individuals. Moreover, one humanitarian organization was forced to suspend activities that were ensuring the access to clean water for approximately 11,000 people living in remote areas. These security incidents obstruct the delivery of humanitarian aid, required by more than half of the Central African Republic's (CAR) population. The incident is far from being isolated, as the country represents one of the most challenging missions in the world for humanitarian workers. Solely between 1 January and 15 April 2022, 43 incidents involving humanitarian organizations were reported, among which 11 aid workers were injured. In 2021, at least one incident per day affecting humanitarian workers was recorded with burglaries, robberies, and intrusions accounting for half of them.
https://reliefweb.int/report/central-african-republic/humanitarian-coordinator-strongly-condemns-two-attacks-humanitarian
https://reliefweb.int/sites/reliefweb.int/files/resources/HC%20Press%20release_Attacks%20against%20humanitarians_20042022_ENG_vf.pdf
On 21 April 2022, the United Nations Security Council was briefed by Peacekeeping chief Jean-Pierre Lacroix on the work of the UN Interim Security Force in the Abyei region. In this regard, Mr Lacroix urged the Council to extend the force's mandate for another six months. The deployment of a peacekeeping force has been authorized in 2011 in order to serve as a mitigation measure to the dispute between northern and southern Sudan over the Abyei area. Over the years, the operation facilitated dialogue between the pastoral Misseriya and nomadic Misseriya communities while also addressing recent incidents of violence. Although the overall security situation in the disputed, Abyei region between Sudan and South Sudan has remained calm Mr Lacroix told the Security Council that the "trust deficit" between the two main communities remains a major concern. He further mentioned the deteriorating humanitarian situation in Abeyi since his last briefing in October. The number of people requiring aid rose from 103,000 to 240,000, primarily due to deadly violence between Twic Dinka and Ngok Dinka communities earlier in the year.
On 21 April 2022, Dominik Stillhart, the director of operations for the International Committee of the Red Cross, addressed the issue of humanitarian access in Ukraine. Mr Stillhart's recent visit to Irpin and Bucha evinced the substantial level of destruction caused by urban warfare. Mentioning the severe consequences of only a few weeks of hostilities, he emphasized the urgent need for improvement and respect for International Humanitarian Law. The visit generated a stronger appreciation of the issue of restricted access to humanitarian aid. Mr Stillhart added that parties must do everything possible to ensure that civilians in conflict-affected cities have access to critical humanitarian aid. The International Committee of the Red Cross continues to work in order to ameliorate the consequences of damaged vital infrastructure and the resulting disruptions in water, electricity, and gas supplies. One of these efforts is the rehabilitation of a water pipeline connecting Kyiv and Irpin that will benefit over 200,000 people. Civilians, civilian infrastructure, medical facilities, vehicles, and personnel were mentioned in the press release. Furthermore, the International Committee of the Red Cross urged all parties to allow unimpeded humanitarian aid and safe passage for medical personnel and civilians seeking refuge.
https://www.icrcnewsroom.org/story/en/2007/ukraine-witnessing-urban-warfare-s-aftermath-in-irpin-and-bucha-calls-for-improved-humanitarian-access-as-fighting-intensifies-in-the-east/0/yJrb2DAeWL
On 22 April 2022, UN Special Rapporteur Michael Lynk addressed the precarious situation of human rights in the occupied Palestinian territory. Mr Lynk emphasized the necessity of adopting short- and long-term international measures and directing Israel to end the 15-year Gaza blockade by halting all settlements in the West Bank and removing checkpoints and permit restrictions and other barriers to Palestinian freedom of movement. Consequently, OHCHR Spokesperson Ravina Shamdasani emphasized, during a press briefing in Geneva, that the use of force by Israeli police must be thoroughly investigated without any delay. Ms Shamdasani's statement comes as a response to last week's tensions that occurred near the Al Aqsa Mosque compound, an edifice sacred to both Muslims and Jews. Reports state that over the weekend, more than 470 people were arrested in Jerusalem. Among them, approximately 180 Palestinians, including at least 27 children, were injured by Israeli forces. While reportedly everyone has been released, most have been barred from entering the Al Aqsa Mosque compound or the Old City of Jerusalem for the coming weeks. These developments come as out-turn after weeks of unrest in the occupied West Bank.
https://www.ohchr.org/en/press-briefing-notes/2022/04/press-briefing-notes-ukraine-and-israelopt
On 22 April 2022, the possibility of Finland and Sweden adhering to the North Atlantic Treaty Organization entered an advanced phase. Leaders from both countries travelled to Washington in order to meet with senior Biden administration officials and start discussing the accession perspective. During their visit, Petteri Orpo, the chair of Finland's centre-right National Coalition Party, and Ulf Kristersson, the leader of the opposition in Sweden's parliament and the head of the country's Moderate Party, met with officials and congressional staff in order to press for prompt support for the expansion, in the eventuality that both countries formally submit bids. Following Russian President Vladimir Putin's decision to launch a full-scale military invasion of Ukraine, the prospect of the two Nordic countries joining the military alliance represents a significant shift in their foreign policies. In order to accept a new member, all of the organization's current members must agree unanimously. Most observers expect Finland's parliament to approve membership accession by mid-May, and Sweden to announce its decision by the end of the month. The alliance is expected to make a formal announcement on both countries' membership at the upcoming summit in Madrid. Consequently, an expansion in Northern Europe could have significant geopolitical and military implications for the alliance, given the current tensions with Russia.
https://foreignpolicy.com/2022/04/22/finland-sweden-nato-membership-russia-ukraine-war/
Weekly News Recap (25 April-1 May 2022)


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text:
Q: npm install -g failing, getting npm ERR! code EACCES When I run npm install -g typescript the following message shows:
npm ERR! code EACCES
npm ERR! syscall mkdir
npm ERR! path /usr/local/lib/node_modules/typescript
npm ERR! errno -13
npm ERR! Error: EACCES: permission denied, mkdir '/usr/local/lib/node_modules/typescript'
npm ERR!  [Error: EACCES: permission denied, mkdir '/usr/local/lib/node_modules/typescript'] {
npm ERR!   errno: -13,
npm ERR!   code: 'EACCES',
npm ERR!   syscall: 'mkdir',
npm ERR!   path: '/usr/local/lib/node_modules/typescript'
npm ERR! }
npm ERR!
npm ERR! The operation was rejected by your operating system.
npm ERR! It is likely you do not have the permissions to access this file as the current user
npm ERR!
npm ERR! If you believe this might be a permissions issue, please double-check the
npm ERR! permissions of the file and its containing directories, or try running
npm ERR! the command again as root/Administrator.

npm ERR! A complete log of this run can be found in:
npm ERR!     /Users/alexz/.npm/_logs/2021-12-26T10_45_08_412Z-debug.log

I tried to check if I have successfully installed TypeScript, if I run npm ls typescript it shows:
alexz@ /Users/alexz
└── typescript@4.5.4

I am confused if I have installed TypeScript or not, because if I do tsc -v it tells me the tsc command is not found. How do I fix the above problem? Thanks!

A: It is a permission issue. You could read Resolving EACCES permissions errors when installing packages globally to change npm's permissions or you could use sudo command as below.

It may ask you your computer password.

sudo npm install -g typescript

redpajama_set_name: RedPajamaStackExchange


text:
Staunton resident won't contest sex offenses
By Michael Neary;
STAUNTON – Daniel Folk, 43, entered "no contest" pleas in Augusta Circuit Court on Thursday to charges of aggravated sexual battery, forcible sodomy and forcible inanimate object penetration.
The first charge carries a sentence of one to 20 years in prison. The others carry sentences of five years to life in prison, said Rupen Shah, chief deputy commonwealth's attorney for Augusta County.
Folk, who is being held at Middle River Regional Jail, was arrested in June after he was accused of molesting an 8-year-old girl.
Judge Victor Ludwig offered the opportunity to postpone the pleas and order a competency evaluation because Folk had reportedly attempted suicide on Sunday.
"I am concerned that anyone ... who attempts to take his life may not be in a position four days later to be entering pleas to charges, two of which carry maximum sentences of life, one of which carries a maximum sentence of 20 years," Ludwig said.
Folk, of Staunton, opted to move forward with the pleas.
"Your honor, the action that I took … in attempting to take my life has very little to do with the sentencing that I will be faced with, and has more to do with the guilt and the shame and the harm that I caused to a little girl that I promised I would protect," he said.
A sentencing hearing is scheduled for 2:30 to 3:30 p.m. April 17.

redpajama_set_name: RedPajamaCommonCrawl


text:
November 16, 2018 Education News
Majority of Americans now led by governors with proven record of championing public education
By Tim Walker
Education was a top-tier issue in the 2018 midterm elections across the nation. Its prominence in the Wisconsin governor's race was such that incumbent Scott Walker campaigned as a champion of public education. For Wisconsin's educators and parents, this was an outlandish sight given Walker's notorious and unmistakable record of slashing the state's education budget.
Wisconsin voters had a better choice in Tony Evers, the state superintendent of education and former teacher. Evers said he was running for governor "because I am goddamn sick and tired of Scott Walker gutting our public schools, insulting our hard-working educators, and destroying higher education in Wisconsin." Not only has Evers proposed an increase in school funding of $1.4 billion over two years, but he's also called for a phase-out of the state's school voucher program.
It was close but Evers emerged victorious, defeating Walker's bid for a third term. "We have a partner who's willing to listen to us, and we need to be there to provide the kind of advice and support that he will need to lead this state," said Wisconsin Education Association Council president Ron Martin.
Although the Wisconsin gubernatorial race attracted national attention, Evers was not the only staunchly pro-public education candidate elected governor in 2018. Far from it. Champions of public schools ran up an impressive string of victories – many in states whose schools had been weakened by reckless budget cuts and expansion of unaccountable charter schools and private school voucher programs.
Gavin Newsom in California, Jared Polis in Colorado, Brad Little in Idaho, JB Pritzker in Illinois, Laura Kelly in Kansas, Gretchen Whitmer in Michigan, Tim Walz in Minnesota, Steve Sisolak in Nevada, Michelle Lujan Grisham in New Mexico, Janet Mills in Maine, and Ned Lamont in Connecticut were all elected to their first terms. Tom Wolf in Pennsylvania and Kate Brown in Oregon were re-elected to second terms.
What all this means is come January, the majority of Americans will be led by governors with a proven track record of championing public education.
In addition to victories at the top of the ticket, educators helped elect a diverse slate of lieutenant governors (seven women, three African Americans, two Hispanic Americans, one Native American, and the first openly LGBTQ Lieutenant Governor) and flip the state legislative chambers into pro-public education majorities in six states. (Moreover, nearly 15 percent of all state legislative seats in the United States will be held by elected educators, according to an NEA analysis.)
Because education policy is decided by state legislatures and public education funding is a primary responsibility of the state, winning these contests was the primary focus of the National Education Association (NEA) and its affiliates. Overall, nearly 220,000 NEA members and education families were involved in getting out the vote up and down the ballot in the 2018 election.
One response to "Majority of Americans now led by governors with proven record of championing public education"
Charlene Shafer says:
I'm happy for the states who voted for & elected pro-education leaders. Texas didn't manage to do that; but, the people elected should be on notice. If they don't pass pro-education legislation, there will be changes happening in the next election.


There are many personal reasons to volunteer as a high school student, or once you're retired, but what about for us small business and start-up owners?
What about we, the few, the brave, the bold entrepreneurs who lay awake at 3am sweating in anticipation for the next round of funding or the completion of an upcoming important project? What is the advantage to try and jam even more activities into our already overcrowded days?
Well, the truth is that making time for volunteering can actually be the difference between successful small businesses and those that fail.
We're not just saying that, either; here are 7 reasons we've come up with based on our own experiences as well as credible secondary research.
Volunteers report being happier and less depressed.
As an entrepreneur, you likely spend a lot of time doing everything except relaxing. Experts agree that focusing on helping someone – or something – else can interrupt the usual stress-producing patterns our bodies become used to.
Another substantial piece of research has proven that individuals who begin volunteering earlier in life end up having a 44% lowered risk of developing age-related illness.
"The Apple co-founder Steve Jobs and companies like Pixar built open work environments that invited intermingling and the sharing of visions – you need that atmosphere too," writes Richard Branson on Entrepreneur.com in an article about fostering success in a start-up environment.
If you don't have access to an open office or networking area at your location, it might be a good idea to invest in one. In the meantime, volunteering within communities allows you the freedom to mingle and share ideas with people from other walks of life.
Sometimes just by working with new people and being an active listener you can find solutions to your own troubles (work-related or otherwise). You never know when your creative fire is going to ignite, or who is going to help you illuminate the darkness that's clouding a gloomy brainstorming process.
Don't just work in your community, live in your community. Build your reputation with as much care and integrity as you build your business.
Volunteering lets you show that you genuinely care about the state of your surroundings both culturally and economically. By helping out with causes you care about you will ensure that you are consistently helping to raise the collective floor of your community.
Why build a business in a place if you don't see it having a bright future? Volunteer to show that you care about the longevity of your community and its people, and in turn your community will reward you with ample opportunities to expand your company. It will also show others that you are a natural leader, which is an important quality and personality trait of a small business owner.
Hitting roadblocks? Maybe a project isn't panning out the way you intended it to?
Volunteering is a great way to earn that sense of accomplishment that we all need from time to time in order to keep our confidence up. Completing tasks for your volunteer organization can be especially effective at getting you back on track to being productive at your own business.
Volunteer positions often offer extensive training (coupled with experience), such as a crisis counsellor at a women's shelter. Using these opportunities to practice or hash out new ideas and skills can be a worthwhile endeavour not just for the person or organization you're helping, but for your own set of skills that you can bring back to your business.
You signed up for this. You're always busy. You expected to never have enough time, but you know you need to somehow still make time for your family and personal life. Volunteering can provide that balance and there are always opportunities to volunteer with your family.
While it might be a challenge to coordinate everyone's schedules, volunteering as a family has many worthwhile benefits. Children watch everything you do. By giving back to the community, you show them firsthand how volunteering makes a difference and how good it feels to help other people and enact change. It's also a valuable way for you to get to know organizations in the community and find resources and activities for your children and family.
Showing off your brand not as a business but as a human will let customers connect and relate to you in a way that a 'dollars and cents' attitude will not.
Furthermore, you should always strive to not just be a good business person, but also a good person. Yes, profits are important for any business, but as a small business owner, they should only act as the fuel that propels you towards doing great things for your community.
Did we miss any of the great reasons why small business owners should volunteer? We'd love to hear your comments and tips!
This blog post is brought to you by Chris Martin, Marketing Coordinator for Charity Republic - an Ontario-based social enterprise that fosters volunteerism and community engagement through simple and efficient software solutions for charities, non-profits, and the education sector.
Chris Martin is the Marketing Coordinator for Charity Republic, an Ontario-based social enterprise that fosters volunteerism and community engagement through simple and efficient software solutions for charities, non-profits, and the education sector.


text:
package com.weather.android.gson;

import com.google.gson.annotations.SerializedName;

/**
 * Created by Pan on 2017/8/28.
 */

public class Now {

    @SerializedName("tmp")
    public String temperature;

    @SerializedName("cond")
    public More more;

    public class More{
        @SerializedName("txt")
        public String info;
    }
}

redpajama_set_name: RedPajamaGithub


Copiah County recently completed a new $3.5 million community safe room, just north of Gallman Road.
The safe room can withstand 200 mile per hour winds, and can hold more than 900 people. During calmer weather, citizens can use it for receptions and other events.
The safe room will also provide the perfect shelter for people in neighboring Claiborne County, in the event of an emergency at the Grand Gulf Nuclear Plant.


text:
Q: utf8_unicode_ci seems not to be UTF8 I got something strange with my mySql database...
My tables are encode in "utf8_unicode_ci", but when I make a SELECT on it, data seems not to be UTF8 because json_encode give an empty string.
I have to re browse the result to re-encode the data in UTF8...
    $q = $this->db->prepare("SELECT ...");

    $q->execute();

    $data = $q->fetchAll();

    foreach ($data as $key => $value) {
        $data[$key] = utf8_encode($value);
    }

    return $data;

After that, json_encode do his job. But I don't understand why the SELECT result from my table encode in "utf8_unicode_ci" doesn't extract the data in UTF8 directly... It is very redundant to re-browse the array...
Thanks for help

A: Having the Mysql tables encoded in UTF-8 is not enough. That only ensures that the data is "stored" in UTF-8. When it is "passed" to external sources, even the passing pipe has to be UTF-8 encoded. You will need to set char set for the connection link identifier as well.
It works something like so:
$mysqli = new mysqli("server", "user", "password", "test");
$mysqli->set_charset("utf8");

Also check out SET NAMES utf8


text:
Home » Europe » Teen dies after two cars plunge into icy water after crashing on road
Teen dies after two cars plunge into icy water after crashing on road
A 19-year-old man has died after two cars crashed on a treacherous road and plunged into a freezing river in a village in Scotland. The vehicles span off a treacherously icy road and hurtled into the River Dee in Aboyne earlier today.
Emergency services and the Coastguard were called to the collision on the A93 at 8.05am this morning.
The 19-year-old driver of the Audi was pronounced dead at the scene. The 19-year-old driver of the Volkswagen Golf was taken to Raigmore Hospital where staff describe his condition as stable.
Sergeant Eoin Maxwell from the Divisional Road Policing Unit said: "Our thoughts are with the family and friends of the man who died.
"Our investigation is at an early stage and we would appeal to anyone who witnessed the crash take place, who has not yet spoken to police, to contact us. We are also keen to obtain any dashcam from motorists driving on the road around the time of the crash.
"We would like to thank the members of the public who assisted at the scene of the crash this morning."
He added: "Anyone with information should contact officers through 101 with reference 0568 of 19 December."
A Scottish Ambulance Service spokesman told the Press and Journal newspaper: "We received a call today at 8.04am to attend a road traffic collision on the A93, west of Aboyne.
"We dispatched two ambulances, our trauma team and specialist operations team (SORT) to the scene, with air support from the coastguard.
"Two patients were transported to Aberdeen Royal Infirmary."
Aberdeen Coastguard is supporting the air rescue team in the area.
Two fire engines, specialist resources and a boat team were also in attendance.
MORE FOLLOWS
Nigel Farage issues SERIOUS warning to May on ITV Peston – 'We'll FIGHT general election'
Opposition in Kyrgyzstan claim power after storming government buildings
Is Prince Harry's memoir ghostwritten? Why isn't Prince Harry writing his own book?
Argentina's President tells Putin 'the world needs unity and peace'
Cecily Strong leaves "Saturday Night Live" in her 11th season


text:
\section{Introduction}
Learning a semantical embedding metric to make  similar positive samples cluster together, while  dissimilar negative ones widen apart is an essential part for modern recognition tasks \cite{hadsell2006dimensionality,chopra2005learning}.
With the flourish of deep learning technologies \cite{krizhevsky2012imagenet,simonyan2014very,szegedy2015going}, deep metric learning has gained more attention in recent years \cite{hoffer2015deep,bell2015learning,schroff2015facenet,cui2016fine,sohn2016improved}.
By training deep neural networks discriminatively end-to-end, a more complex highly-nonlinear deep feature representation (from the input space to a lower dimensional semantical embedding metric space) can be learned.
The jointly learned deep feature representation and embedding metric yield significant improvement for recognition applications, such as 2D image retrieval \cite{wang2014learning, bell2015learning, oh2016deep} or classification \cite{weinberger2009distance, qian2015fine}, signature verification \cite{bromley1994signature},  face recognition \cite{chopra2005learning, weinberger2009distance, schroff2015facenet},  and sketch-based 3D shape cross-modality retrieval \cite{li2013shrec, wang2015sketch, xie2017learning}.

Despite the progress made,  most of the pre-existing loss objectives  \cite{bromley1994signature,chopra2005learning,hoffer2015deep,schroff2015facenet,bell2015learning} do have some limitations for metric learning.
Commonly used contrastive loss \cite{hadsell2006dimensionality,chopra2005learning} or triplet loss \cite{weinberger2009distance,chechik2010large}  only considers the semantical information within individual pairs or triplets of examples at each update,  while the interactions with the rest ones are ignored. It would bias the learned embedding metric and feature representation.
Moreover,  they do not give enough attention to hard positive or negative examples, by cause of the fact that these samples are often sparsely distributed and expensive to seek out. These \emph{hard samples} can strongly influence parameters during the network is learned to correct them.
As a consequence,  methods which neglect them often suffer from slow convergence rate and suboptimal performance.
Occasionally, such methods require expensive sampling techniques to accelerate the training process and boost the learning performance \cite{chechik2010large,schroff2015facenet,norouzi2012hamming,cui2016fine}.

In this paper,  we propose a novel batch-wise optimal transport loss objective for deep metric learning. It can learn an importance-driven distance metric via optimal transport programming from batches of samples simultaneously.
As we know,  the fundamental idea behind metric learning is minimizing the intra-category variations (or distances) while maximizing the inter-category variations (or distances).  Thus, those semantically similar positive samples with large ground distances and dissimilar negative examples with small ground distances should be regarded as \emph{hard samples}.  Such samples should be emphasized correctly  to accelerate the metric learning process.
Figure \ref{fig:motivation} illustrates our main idea of proposing  the new batch-wise optimal transport loss objective. As illustrated,
learning with the proposed loss can utilize all available semantical information of training batches simultaneously.
The introduced importance-driven distance metric is partly obtained as a solution to the optimal transport program \cite{vallender1974calculation,cuturi2013sinkhorn}. It can  mine and emphasize those \emph{hard samples} automatically.
Thus,  the convergence rate  of  distance metric learning process can be significantly improved.
We further develop the new loss objective in a deep metric learning manner.
The whole network can be trained discriminatively in an end-to-end fashion. The jointly learned semantical embedding metric and deep feature representation would be more robust to intra-class and inter-class variations.
We finally verify the performance of our proposed method applying to various visual recognition tasks, including 2D image recognition, sketch-based 3D shape cross-modality retrieval, and 3D shape recognition.
Experiment results on six widely used benchmark datasets,
i.e.,  \emph{MNIST},  \emph{CIFAR10},  \emph{SHREC13}, \emph{SHREC14},   \emph{ModelNet10} and  \emph{ModelNet40},  demonstrate the superiority of the proposed method.
Our method can achieve a state-of-the-art recognition performance with a notably fast convergence rate.

In a nutshell, our main contributions in the present work can be  summarized as follows:

(1) We propose a novel batch-wise optimal transport loss objective for learning an importance-driven distance metric to improve the existing pair-wise based loss objectives.

(2) We develop a deep metric learning method based on the proposed loss objective, which learns the importance-driven metric and deep feature representation jointly.

(3) We verify the superiority of our proposed method on visual recognition tasks, including 2D image recognition,  sketch-based 3D shape retrieval, and 3D shape recognition.

\begin{figure*}[htb]

	\centering

	\includegraphics[height=8.3cm,width=17.5cm]{Framework6.pdf}

	\caption{

		We formulate the proposed  loss into a deep metric learning framework.
		Given batches of each modality samples, we use \emph{LeNet-5} \cite{lecun1998gradient}, \emph{ResNet-50} \cite{he2016deep}, and  \emph{MVCNN} \cite{su2015multi} as $\bm {{f^1_{\text{CNN}}}}$ to extract deep CNN features for 2D images, 2D sketches, and 3D shapes, respectively. The metric network $\bm {{f^2_{\text{Metric}}}}$ consisting of four fully connected (FC) layers, i.e., 4096-2048-512-128 (two  FC layers 512-256 for \emph{LeNet-5}) is used to perform dimensionality reduction of the CNN features.  We add three sigmoid functions as activation among these FC layers to generate normalized and dense feature vectors.  The whole framework can be  end-to-end trained discriminatively with the new batch-wise optimal transport loss.  The highlighted
		importance-driven distance metrics
		${\bm T_{ij}}\bm M_{ij}^{+}$ and ${\bm T_{ij}}\bm M_{ij}^{-}$ are used for emphasizing {hard positive} and {negative samples}. It jointly learns the semantic embedding metric and deep feature representation for retrieval and classification.}
	\label{figFramework}

\end{figure*}

\section{Related Work}\label{sec2}
Recognition of 3D shapes is becoming prevalent with the advancement of modeling, digitizing, and visualizing techniques for 3D objects.
The increasing availability of 3D CAD models,  both on the Internet, e.g.,  \emph{Google 3D Warehouse} \cite{GoogleWarehouse} and \emph{Turbosquid} \cite{Turbosquid},  and in the domain-specific field, e.g., \emph{ModelNet} \cite{ModelNet} and \emph{SHREC} \cite{li2015comparison},
has led to the development of several scalable and efficient methods to study and analyze them, as well as to facilitate practical applications.
For 3D shape recognition, one fundamental issue is how to construct a determinative yet robust 3D shape descriptor and feature representation.
Compared to 2D images, 3D shapes have more complex geometric structures.  Their appearance can be affected significantly by innumerable variations such as  viewpoint, scale,  and deformation. These have brought great challenges into the recognition task.
A natural method is to construct a shape descriptor based on the native 3D structures, e.g., point clouds, polygon meshes, and volumetric grid.  Then, shapes can be represented with distances, angles, triangle areas, tetrahedra volumes, local shape diameters \cite{osada2002shape,chaudhuri2010data},  heat kernel signatures \cite{bronstein2011shape,kokkinos2012intrinsic},
extensions of  handcrafted SIFT, SURF \cite{knopp2010hough},  and learned 3D CNNs \cite{wu20153d,maturana2015voxnet} on 3D volumetric grids.
An alternative way is  describing a 3D shape by a collection of 2D view-based projections.
It can make use of  CNN models, which have been pre-trained on  large 2D image datasets such as ImageNet \cite{krizhevsky2012imagenet} and gained a decent ability of generalization \cite{donahue2014decaf,simonyan2014very,girshick2014rich}.
In this context, DeepPano \cite{shi2015deeppano} and PANORAMA-NN \cite{sfikas2017exploiting} are developed to convert
3D shapes into panoramic views, e.g., a cylinder projection around its principal axis.
Multi-view CNN (\emph{MVCNN}) \cite{su2015multi} groups multiple CNNs with a view-pooling structure to process and learn from all available 2D projections of a 3D shape jointly.

\section{Background}\label{sec3}
\noindent \textbf{Loss objective for metric learning:}
Metric learning aims to learn a semantical metric  from input samples.
Let $\bm x \in \bm X$ be an input sample.
The kernel function $f(\cdot;\bm \theta):\bm X \to \mathbb{R}^d$ takes input $\bm{x}$ and generates an feature representation or embedding $f(\bm x)$. In deep metric learning \cite{hadsell2006dimensionality,chopra2005learning,sohn2016improved},  kernel $f(\cdot;\bm \theta)$  is usually defined by a deep neural network, parameterized by a series of weights and bias $\bm \theta$.
Metric learning optimizes a discriminative loss objective to minimize  intra-class distances while maximizing  inter-class distances.
For example,  the contrastive loss in seminal Siamese network \cite{hadsell2006dimensionality,chopra2005learning} takes pairs of samples as input and trains two identical networks to learn a deep metric $\bm M_{ij}$ as
\begin{equation}
\mathcal{L}({\bm x_i},{\bm x_j};f) = {\bm y_{ij}}\bm M_{ij} + (\bm 1 - {\bm y_{ij}})\max \{ 0,\varepsilon - \bm M_{ij} \},
\end{equation}
where the label $\bm y_{ij} \in \{0,1\}$ indicates whether a pair of $(\bm x_i,\bm x_j)$
is from the same class or not. The margin parameter $\varepsilon$ imposes a threshold of the distances among dissimilar samples.
Conventionally,  the Euclidian metric $\bm M_{ij}=||(f({\bm x_i}) - f({\bm x_j})||_2^2$ in the feature embedding space  is used to denote the  distance of a pair of samples.
Triplet loss \cite{weinberger2009distance,chechik2010large} shares a similar idea with contrastive loss, but extends a pair of samples to a triplet. For a given query $\bm x_i$, a similar sample $\bm x_j$ to the query $\bm x_i$, and a dissimilar one $\bm x_k$, the triplet loss can be formulated as
\begin{equation}
\mathcal{L}(\bm x_i,\bm x_j,{\bm x_k};f) = \max \{ 0,{\bm M_{ij}} - {\bm M_{ik}} +  \varepsilon\}.
\end{equation}
Intuitively, it encourages the distance between the dissimilar pair $\bm M_{ik}=||(f({\bm x_i}) - f({\bm x_k})||_2^2$ to be larger than the distance between the similar pair $\bm M_{ij}=||(f({\bm x_i}) - f({\bm x_j})||_2^2$ by at least a margin $\varepsilon$.

\noindent \textbf{Optimal transport distances:}
Optimal transport distances \cite{cuturi2013sinkhorn}, also known as Wasserstein distances \cite{vallender1974calculation} or Earth Mover's distances \cite{rubner2000earth}, define a distance between two probability distributions according to principles
from optimal transport theory \cite{villani2008optimal, wu2011witsenhausen}.
Formally, let $\bm r$ and  $\bm c$ be  $n$-dimensional probability measures. The set of transportation plans between probability distributions $\bm r$ and $\bm c$
is defined as
$
U(\bm r,\bm c): = \{ \bm T \in {\mathbb R_{+}^{n \times n}} | \bm{T1} = \bm r,\bm{T^T1} = \bm c \},
$
where $\bm 1$ is an all-ones vector.
The set of transportation plans  $U(\bm r,\bm c)$ contains all nonnegative $n \times n$ elements with row and column sums $\bm r$ and $\bm c$,  respectively.

Give an  $n \times n$ ground distance matrix $\bm M$, the cost of mapping $\bm r$ to $\bm c$ using a transport matrix $\bm T$ can be quantified as $\langle \bm{T,M}\rangle$, where $\langle.,.\rangle$ stands for the Frobenius dot-product. Then the problem defined in Equation (\ref{OT_problem})
\begin{equation}\label{OT_problem}
{D_{\bm M}}(\bm r, \bm c): = \mathop {\min }\limits_{\bm T \in U(\bm r, \bm c)} \langle \bm T, \bm M\rangle ,
\end{equation}
is called an optimal transport problem between $\bm r$ and $\bm c$ given ground cost $\bm M$. The optimal transport distance ${D_M}(\bm r, \bm c)$ measures the cheapest way to transport the mass in probability measure $\bm r$ to match that in $\bm c$.

Optimal transport distances define a more powerful cross-bin metric to measure probabilities compared with some commonly used bin-to-bin metrics, e.g., Euclidean, Hellinger, and Kullback-Leibler divergences. However, the cost of computing ${D_M}$ is at least $\mathcal{O}(n^3log(n))$ when comparing two $n$-dimensional probability distributions in a general metric space \cite{pele2009fast}. To alleviate it, Cuturi \cite{cuturi2013sinkhorn} formulated a regularized transport problem by adding an entropy regularizer to  Equation (\ref{OT_problem}).
This makes the objective function strictly convex and allows it to be solved efficiently.
Particularly, given a transport matrix $\bm T$, let $h(\bm T)=-\sum\nolimits_{ij}   {{\bm T_{ij}}\log {\bm T_{ij}}} $ be the entropy of $\bm T$. For any $\lambda >0$, the regularized transport problem can be defined as
\begin{equation}\label{OT_problem_regularized}
{D^{\lambda}_{\bm M}}(\bm r, \bm c): = \mathop {\min }\limits_{\bm T \in U(\bm r, \bm c)} \langle \bm T, \bm M\rangle- \frac{1}{\lambda }h(\bm T),
\end{equation}
where the lager $\lambda$ is, the closer this relaxation ${D^{\lambda}_M}(\bm r, \bm c)$ is to original ${D_{\bm M}}(\bm r, \bm c)$. Cuturi \cite{cuturi2013sinkhorn} also proposed the Sinkhorn's algorithm \cite{sinkhorn1967diagonal} to solve Equation (\ref{OT_problem_regularized}) for the optimal transport $\bm T^*$. Specifically, let the matrix $\bm K= exp(-\lambda \bm M)$ and solve it for the scaling vectors $\bm u$ and $\bm v$ to a fixed-point by computing $\bm u=\bm r./\bm{Kv}$, $\bm v= \bm c ./ \bm {K^Tu} $ in an alternating way. These yield the optimal transportation plan $\bm T^*=diag(\bm u) \bm K diag(\bm v)$. This algorithm can be solved with complexity $\mathcal{O}(n^2)$ \cite{cuturi2013sinkhorn}, which is significantly faster than exactly solving the original optimal transport problem.

\section{Our Method}
In this section, we propose a deep metric learning scheme by using  principles of the optimal transport theory \cite{villani2008optimal}.
Currently, research works with optimal transport distance \cite{cuturi2013sinkhorn,cuturi2014fast,cuturi2014ground}  mainly focus  on theoretical analysis and simulation verification.
Thus, it is hard to apply them into a large-scale 3D shape recognition contest directly.
To this end, we  have done the following three works to construct a trainable
batch-wise optimal transport loss objective.

\subsection{Importance-driven Distance Metric Learning}\label{Re-defined the ground distances}

Assuming  we are given two batches of samples, each batch has $n$ examples $\bm{X} \in \mathbb R^{d \times n}$. Let $\bm x_i \in \mathbb{R}^d$ be the representation of the $i^{th}$ shape.
Additionally, let $\bm r$ and $\bm c$ be the $n$-dimensional probability vectors for  two batches, where $r_i$ and $c_i$ denote the number of times shape $i$ occurs in $\bm r$ and $\bm c$ (normalized overall  samples in $\bm r$ and $\bm c$). The optimal transport   introduces a transportation plan $\bm T \in \mathbb{R}^{n \times n}$ such that $\bm T_{ij}$ describes how much of $r_i$ should be transported to $c_j$.
As described in Equation (\ref{OT_problem_regularized}),  the optimal transport distance between batches $\bm r$ and $\bm c$ can be re-formulated as
\begin{equation}\label{our_OTD}
\begin{aligned}
& \quad  \bm D^{\lambda}_{\text{OT}}(\bm r,\bm c) =  \mathop {\min }\limits_{\bm T \ge 0} \sum\nolimits_{i.j = 1}^n {{\bm T_{ij}}} \bm M_{ij}-\frac{1}{\lambda }h(\bm T_{ij}) \\
& \text{s.t.} \quad   \sum\nolimits_{j = 1}^n {\bm T_{ij} }  = \bm r
\quad \! \text{and} \! \quad  \sum\nolimits_{i = 1}^n {{\bm T_{ij}} = \bm c \quad  \forall i,j.}
\end{aligned}
\end{equation}
The learned optimal transportation plan $\bm T^*$ is  a probability distribution \cite{cuturi2013sinkhorn}, which aims to find the least amount of cost needed to transport the mass from  batch  $\bm r$ to  batch $\bm c$. The unit of cost corresponds to transporting a sample by the unit of ground distance.
Thus, $\bm T^*$ solved by Equation (\ref{our_OTD})  prefers to assign higher importance values to samples with small ground distances while leaving fewer for others.

Utilizing such property, we define the importance-driven distance metric via imposing semantical information of samples.
Specifically, we first define the ground distances for a pair of similar positive samples
as
\begin{equation}\label{eq1}
\bm M^+ (\bm x_i,\bm x_j;f)=e^{- \gamma ||f(\bm x_i)- f(\bm x_j)||_2^2 },
\end{equation}
where $\gamma$ is a hype-parameter controlling the extent of rescaling.
This re-scaling operator shrinks  large Euclidian distance between similar samples.
After re-scaling  $\bm M^+$,  the learned $\bm T^*$ tends to put higher importance values on  those similar samples which have far Euclidian distances among each other (a.k.a.,  {\emph{hard postive samples}}), while putting lower on the others accordingly. Thus, it would accelerate the process that similar samples are getting close to each other.
For dissimilar negative samples, we  define the ground distances correspondingly as
\begin{equation}\label{eq2}
\bm M^-(\bm x_i,\bm x_j;f)=e^{- \gamma \text{max} \{0,\varepsilon -{ ||f(\bm x_i)- f(\bm x_j)||_2^2 }\}}.
\end{equation}
The hinge loss  $\text{max} \{0,\varepsilon -{ ||f(\bm x_i)- f(\bm x_j)||_2^2 }\}$ penalizes the dissimilar samples within the margin $\varepsilon$ and ignores the others. Thus, contrary to the above similar samples case,
here the learned $\bm T^*$  will pay higher importance values on those dissimilar samples with small Euclidian distances (a.k.a., {\emph{hard negative samples}}), while assigning fewer on the others. Thus, it could accelerate the process that dissimilar samples are getting apart to each other.

\subsection{Batch-wise Optimal Transport Loss Learning}

Based on the defined distances $\bm M^+, \bm M^-$,  and  optimal transportation plan $\bm T^*$, now we can formulate a batch-wise optimal transport loss  for metric learning. It can  be viewed as an $n$-pairs extension version of the contrastive loss or triplet loss.
We define the loss objective as
\begin{equation}\label{Eq:optimal transport loss}
\begin{aligned}
&  \quad  \quad  \quad \qquad \mathcal{L}(\bm x_i,\bm x_j;f) \! =\! \mathcal{L}^{+}+ \mathcal{L}^{-} \\
&=  \bm Y_{ij}\frac{1}{2}\sum\nolimits_{ij}^n {\bm T_{ij}}\bm M_{ij}^{+}\! + \! (\bm I_{ij}-\bm Y_{ij})\frac{1}{2}\sum\nolimits_{ij}^n {\bm T_{ij}}\bm M_{ij}^{-},
\end{aligned}
\end{equation}
where $\bm Y_{ij}$ is a binary label assigned to a pair of training batches. Let $\bm Y_{ij}=1$ if sample $\bm x_i$ and $\bm x_j$ are deemed similar, and $\bm Y_{ij}=0$ otherwise. An all-ones matrix is denoted as $\bm I\in \mathbb{R}^{n \times n}$  and $n$ is the size of each training batch.
In practice, ${\bm T_{ij}}\bm M_{ij}^{+}$ and ${\bm T_{ij}}\bm M_{ij}^{-}$ can be regarded as the importance-driven distance metric for  positive and negative samples, respectively. The optimal transportation plan ${\bm T^*}$  obtained by solving Equation (\ref{our_OTD}) is  a probability distribution of weights for emphasizing hard positive and negative samples during the loss objective optimization.
We just write the loss objective regarding only one pair of batches here for simplicity.  The overall data loss objective based on all training batches can be easily derived as $\sum \mathcal{L} $.

\subsection{Batch Gradient Descent  Optimization}
We further derive the back-propagation form of the batch-wise optimal transport loss objective.  The proposed  loss objective can be embedded into a deep metric learning framework,  so that the whole network can be trained discriminatively end-to-end via batch gradient descent.

Since the batch-wise optimal transport distance is a fully connected dense matrix of pairs-wise ground distance, its gradient can be  deduced  as network flow manner.  Specifically,  we compute the gradient of  corresponding  loss $\mathcal{L}(\bm x_i,\bm x_j;f)$ with respect to embedding representations $f(\bm x_i)$ and $f(\bm x_j)$ as follows,
\begin{small}
	\begin{equation}\label{gradient}

	\begin{aligned}
	& \frac{{\partial \mathcal L}}{{\partial {f(\bm x_i)}}} \!= \! \sum\limits_{j = 1}^n {{\bm T^*_{ij}}({f(\bm x_i)} - {f(\bm x_j)})({\bm Y_{ij}} - (\bm I_{ij} - {\bm Y_{ij}}){\bm \delta _{ij}})} \\
	& \frac{{\partial \mathcal L}}{{\partial {f(\bm x_j)}}}\! = \! - \!\sum\limits_{i = 1}^n {{\bm T^*_{ij}}({f(\bm x_i)}\! - \!{f(\bm x_j)})({\bm Y_{ij}}\! -\! (\bm I_{ij}\! - \!{\bm Y_{ij}}){\bm \delta _{ij}})},
	\end{aligned}
	\end{equation}
\end{small}where $\bm T^*$ is the optimizer obtained from  Equation (\ref{OT_problem_regularized}).
Motivated by the fast optimal distance computation \cite{cuturi2013sinkhorn,cuturi2014fast,frogner2015learning}, we relax the linear program in Equation (\ref{our_OTD}) using the regularized entropy as in Equation (\ref{OT_problem_regularized}).
It allows us to approximately solve  Equation (\ref{OT_problem_regularized}) in $\mathcal{O}(n^2)$ time via $\bm T^*={diag} (\bm u) \bm K diag (\bm v)$, where $n$ is the size of batch.

The $\bm \delta$ here is also a binary indicator assigned to the pairs.   Let $\bm \delta_{ij}=1$ when the Eucildian distance between  shape $\bm x_i$ and $\bm x_j$ is within the margin (i.e., $\varepsilon - ||f(\bm x_i) - f(\bm x_j)||_2^2 > 0$), and $\bm \delta_{ij}=0$ otherwise.
The  $f(\bm x_i)$ and $f(\bm x_j)$ are feature representations obtained through  deep neural networks.  Therefore, the gradient with respect to the network
can be  computed easily with the chain-rule in a back-propagation fashion,
as far as $\frac{{\partial \mathcal L}}{{\partial {f(\bm x_i)}}}$ and $\frac{{\partial \mathcal L}}{{\partial {f(\bm x_j)}}}$ are derived.
We also note that the defined ground distance $\bm M^+$ and $\bm M^-$  are just used to determine the optimal transportation plan $\bm T^*$ for re-weighting the importance of similar positive and dissimilar negative samples. We do not consider them as  variables to compute gradient in Equation (\ref{gradient}) for gradient updating.

\section{Experiments}\label{sec4}

In this section, we evaluated the performance of the proposed method with applications to 2D image recognition (i.e., retrieval and classification), sketch-based 3D shape retrieval, and 3D shape recognition tasks. Six widely used benchmark datasets were employed in our experiments, including \emph{MNIST} \cite{lecun1998gradient},  \emph{CIFAR10} \cite{krizhevsky2014cifar}, \emph{SHREC13} \cite{li2013shrec}, \emph{SHREC14} \cite{pickup2014shrec}, \emph{ModelNet10}, and \emph{ModelNet40} \cite{wu20153d}.

\begin{figure*}[htb]

	\centering

	\includegraphics[width=\linewidth]{2d3dplots.pdf}

	\caption{ \emph{Left:} Mean average precision (mAP) and classification accuracy curves of batch-wise optimal transport loss and pair-wise contrastive loss on 2D \emph{MNIST} dataset.  \emph{Middle:} Comparison of their mAP and accuracy curves on 2D \emph{CIFAR10} dataset.  \emph{Right:}  Comparison of their mAP curves on sketch-based 3D shapes \emph{SHREC13} and \emph{SHREC14} dataset.}
	\label{2dcurves}

\end{figure*}
\begin{table*}
	\parbox{.5\linewidth}{
		\centering
		\caption{Retrieval results on the \emph{SHREC13} benchmark dataset}\label{T2d3d1}
		\scalebox{.9}[1]{
			\begin{tabular}{c c c c c c c}
				\hline

				\textbf{Method} & NN & FT & ST & E & DCG & mAP \\ [0.5ex]
				\hline
				CDMR & 0.279 & 0.203 & 0.296 & 0.166 & 0.458 & 0.250 \\
				\hline
				SBR-VC & 0.164 & 0.097 & 0.149 & 0.085 & 0.348 & 0.116 \\
				\hline
				SP & 0.017 & 0.016 & 0.031 & 0.018 & 0.240 & 0.026 \\
				\hline
				FDC & 0.110 & 0.069 & 0.107 & 0.061 & 0.307 & 0.086 \\
				\hline
				Siamese & 0.405 & 0.403 & 0.548 & 0.287 & 0.607 & 0.469\\
				\hline
				LWBR & {0.712} &  {0.725} &  {0.725} &  \textbf{0.369} &  {0.814} & {0.752} \\
				\hline
				\textbf{Our Method} & \textbf{0.713} &  \textbf{0.728} &  \textbf{0.788} &  {0.366} &  \textbf{0.818} & \textbf{0.754} \\
				\hline

			\end{tabular}
		}
	}
	\hfill
	\parbox{.5\linewidth}{
		\centering
		\caption{Retrieval results on the \emph{SHREC14} benchmark dataset}\label{T2d3d2}
		\scalebox{.9}[1]{
			\begin{tabular}{c c c c c c c}
				\hline

				\textbf{Method} & NN & FT & ST & E & DCG & mAP \\ [0.5ex]
				\hline
				CDMR & 0.109 & 0.057 & 0.089 & 0.041 & 0.328 & 0.054 \\
				\hline
				SBR-VC & 0.095 & 0.050 & 0.081 & 0.037 & 0.319 & 0.050 \\
				\hline
				DB-VLAT & 0.160 & 0.115 & 0.170 & 0.079 & 0.376 & 0.131 \\
				\hline
				Siamese & 0.239 & 0.212 & 0.316 & 0.140 & 0.496 & 0.228\\
				\hline
				DCML & 0.272 & 0.275 & 0.345 & 0.171 & 0.498 & 0.286\\
				\hline
				LWBR & {0.403} &  {0.378} &  {0.455} &  {0.236} &  {0.581} & {0.401} \\
				\hline
				\textbf{Our Method} & \textbf{0.536} &  \textbf{0.564} &  \textbf{0.629} &  \textbf{0.305} &  \textbf{0.712} & \textbf{0.591} \\
				\hline

			\end{tabular}
		}
	}
\end{table*}
\begin{figure*}[htb]

	\centering
	\includegraphics[height=5.4cm,width=14cm]{ow_3d2.pdf}

	\caption{Mean average precision (mAP) curve with respect to the number of epochs evaluated of various methods on the \emph{ModelNet40} dataset. Left subfigure illustrates the mAP curves of four loss objectives for 3D shape retrieval, and right subfigure illustrates the mAP curves of three weighting modesc. The mAP have been observed every five epochs for 200 epochs in figures.}
	\label{ow_curve}

\end{figure*}
\begin{table*}[htb]

	\renewcommand{\arraystretch}{1}

	\begin{center}
		\caption{Comparisons of batch-wise optimal transport loss with other benchmark methods on the \emph{ModelNet40} dataset.}\label{table1}
		\scalebox{.88}[.9]{
			\begin{tabular}{c c c c c c c c c} \toprule
				~ & \multicolumn{8}{c}{ \textbf {Evaluation Criteria} } \\
				\cmidrule{3-9}\morecmidrules\cmidrule{3-9}
				&\textbf{ Methods}    & NN        & FT      & ST      & DCG    &   E   &  mAP (\%)  &  Accuracy (\%)  \\ \midrule
				\multirow{3}[0]*{\textbf{Pair-wise}}
				& Individual      & 0.8287    & 0.6544    & 0.7891    & 0.8562        & 0.5668      & 69.3\%    &  88.6\% \\
				& Triplets       & 0.8324    & 0.6968    & 0.8029    & 0.8629        & 0.5927      & 74.1\%    &  89.1\% \\
				& Random     & 0.8688    & 0.7948     & 0.9048    & 0.9140         & 0.6601      & 83.1\%  &  89.5\%\\
				\multirow{3}[0]*{\textbf{Batch-wise}}
				& Mean Weighted        & 0.8750    & 0.7986     & 0.9032    & 0.9158        & 0.6589         & 83.3\%         &  89.7\%     \\
				& Random Reweighted      & 0.8688    & 0.7673     & 0.8846    & 0.9051         & 0.6445      & 83.1\%         &  89.0\%     \\
				& \textbf{Optimal Reweighted}     & \textbf{0.8762 }   &\textbf{0.8013}    & \textbf{0.8991}  & \textbf{0.9178 } &\textbf{0.6560}   & \textbf{83.8 \%}  &  \textbf{90.3 \% }  \\
				\bottomrule
		\end{tabular} }
	\end{center}
	\vspace{-0.5cm}
\end{table*}
\begin{table*}[!htb]

	\renewcommand{\arraystretch}{1}

	\begin{center}
		\caption{Retrieval and classification results on the \emph{ModelNet10} and \emph{ModelNet40} datasets.}\label{table2}

		\scalebox{.9}[.9]{
			\begin{tabular}{l l c c c c}\toprule
				\textbf{Methods} & \textbf{Shape Descriptor} & \multicolumn{2}{c}{\textbf{\emph{ModelNet10}}} & \multicolumn{2}{c}{\textbf{\emph{ModelNet40}}} \\
				\cmidrule{3-6}\morecmidrules\cmidrule{3-6}
				&  &  mAP (\%)  &  Accuracy (\%) &  mAP (\%)  &  Accuracy (\%)  \\ \midrule
				(1) MVCNN   \cite{su2015multi}  & 2D View-based Descriptor (\#Views=12)   & N/A & N/A  & 80.2\%      & 89.5\%  \\
				& 2D View-based Descriptor (\#Views=80)  & N/A & N/A  & 79.5\%      & 90.1\%  \\ \midrule
				(2) GIFT \cite{bai2016gift}     & 2D View-based Descriptor (\#Views=64)   &\textbf{91.1 \%} & 92.3\%  & 81.9\%      & 83.1\%  \\  \midrule
				(3) 3DShapeNets \cite{wu20153d} & 3D Voxel Grid ($30\times30\times30$)    &68.3\%  & 83.5\%  & 49.2\%      & 77.0\%  \\\midrule
				(4) Geometry Image \cite{sinha2016deep}      & 2D Geometry Image   & 74.9\% &  88.4\%   & 51.3\%      &83.9\% \\\midrule
				(5) PANORAMA-NN \cite{sfikas2017exploiting}  & 2D  Panoramic View  & 87.4\% &  91.1\%    & 83.5\%      &\textbf{90.7 \% } \\\midrule
				(6) DeepPano \cite{shi2015deeppano}          & 2D  Panoramic View    & 84.1\% & 85.4\%  & 76.8\%      &77.6\%  \\\midrule
				\textbf{Our Method}             & 2D View-based Descriptor (\#Views=12)  & 87.5\%    & \textbf{93.7 \%}  & \textbf{83.8 \%}      & 90.3 \%  \\
				\bottomrule
		\end{tabular}}
	\end{center}
	\vspace{-0.4cm}
\end{table*}
\begin{figure}[htb]

	\centering

	\includegraphics[width=\linewidth]{ow_distance3.pdf}

	\caption{Illustration of  relationship between the ground distances $\bm M^*$ and the optimal transportation plan $\bm T^*$ on the \emph{ModelNet40} dataset.
		For two batches (each with batch size $32$) of samples, we visualize the values of the batch-wise ground distances matrix (i.e., $32 \times 32$)  and the corresponding optimal transportation plan. }
	\label{fig:distance}

\end{figure}

\subsection{Experimental settings}

\noindent \textbf{Architecture:}  Figure \ref{figFramework} illustrates network architecture of deep metric learning with our batch-wise loss objective.

\noindent \textbf{Datasets:}
The \emph{MNIST} \cite{lecun1998gradient} is a large handwritten digits  dataset, which has $60,000$ $28 \times 28$ black-and-white training images and 10,000 testing images.
The \emph{CIFAR10}  \cite{krizhevsky2014cifar} dataset  consists of  $60,000$ $32 \times 32$ RGB images in 10 different categories, with $6,000$ images per category. There are $50,000$ training images and $10,000$ test images.
\emph{SHREC13} \cite{li2013shrec} and  \emph{SHREC14} \cite{pickup2014shrec} are two large-scale datasets for sketch-based 3D shape retrieval.
\emph{SHREC13} contains  $7,200$ human-drawn sketches and $1,258$ 3D shapes from 90 different categories.  For each category,  $50$ sketches are used for training and remaining $30$ sketches are used for the test. There are $14$ 3D shapes per category generally. \emph{SHREC14}
is larger than \emph{SHREC13}, which has $13,680$ sketches and $8,987$ 3D shapes from $171$ categories.  Each of the categories has $53$ 3D shapes on average. There are $8,550$ sketches for training and $5,130$ for test.
\emph{ModelNet} \cite{ModelNet} is a large-scale 3D shape dataset, which contains $151,128$ 3D CAD models belonging to $660$ unique object categories \cite{wu20153d}. There are two subsets of \emph{ModelNet} can be used for evaluation. \emph{ModelNet10} contains $4,899$ 3D shapes from $10$ categories while \emph{ModelNet40} has $12,311$ shapes from 40 categories. In our experiments, we used
the same training and test splits as in \cite{wu20153d}. Specifically, we randomly selected $100$ unique shapes per category, where $80$ shapes were chosen for training and the remaining $20$ shapes for the test.

\noindent \textbf{Evaluations:}
For retrieval, we used Euclidian distance to measure the similarity of the shapes based on  their learned feature vectors output by the metric network as shown in Figure \ref{figFramework}.
Given a query from the test set, a ranked list of the remaining test samples was returned according to their distances to the query sample.
We used the evaluation metrics for retrieval as in \cite{xie2017learning} when presenting our results. The metrics include nearest neighbor (NN) \cite{cover1967nearest}, first tier (FT) \cite{tangelder2004survey}, second tier (ST) \cite{cornea20053d}, E-measure (E) \cite{chen2004adaptive}, discounted cumulated gain (DCG) \cite{jarvelin2008discounted},
and mean average precision (mAP) \cite{philbin2007object}.
For classification, we trained  one-vs-all linear SVMs \cite{chang2011libsvm} to classify 2D images and 3D shapes
using their features. The average category accuracy \cite{wu20153d} was used to evaluate the classification performance.

\noindent \textbf{Parameters settings:}
In our 2D image recognition, the learning rate and batch size  were $0.01$ and $64$ respectively. Our optimizer had a momentum of $0.9$ and $0$ weight decay rate. The regularized parameter $\lambda$ in Equation (\ref{our_OTD}) was set to be $0.01$ while
the re-scaling parameter $\gamma$ in Equation (\ref{eq1}) being $10$.
In the sketch-based 3D shape retrieval and 3D shape recognition experiments, the batch size was reduced to $32$. Meanwhile,  the learning rate, weight decay and momentum remained the same as what has been used in 2D experiments. We increased the regularized  parameter $\lambda$  to $10$,  which is the same as the re-scaling parameter $\gamma$.

\subsection{Evaluation of Proposed Method}

\subsubsection{2D Image Recognition}

Firstly, we empirically evaluated the effect of our proposed method on two broadly used 2D images benchmark datasets, i.e., \emph{MNIST} and  \emph{CIFAR10}.
As illustrated in Figure \ref{figFramework}, we used a \emph{Siamese}-like symmetrical network structure, which employed \emph{Lenet-5} as its base CNN to obtain
$256$-dimensional feature vectors
for the images in both datasets. Training images were randomly shuffled at the start of each epoch. In each training step, the optimal transportation $\bm T^*$ between two batches of image features was approximated by iterating the Sinkhorn's algorithm for 20 times. After each epoch, we computed all image features with the symmetrical network trained so far for classification and retrieval. The categorical accuracies provided by one-vs-rest linear SVMs and the retrieval mAPs given by the similarity measure based on the testing samples were recorded.

The left-hand and {middle} subfigures in Figure \ref{2dcurves} present accuracy and mAP curves of the batch-wise optimal transport loss learning concerning the number of epochs. These figures illustrate the relationship between the convergence rate and recognition performance.
Comparing with the pair-wise contrastive loss, our method posses a significantly faster convergence rate. On CIFAR10, it provides a retrieval mAP and a classification accuracy which are approximately $15 \%$ and $10 \%$ higher than the corresponding values achieved by the pair-wise loss at the end of $50$ epochs.
The empirical results indicate that the importance-driven distance metric learning can
effectively adjust the distribution of weights. It pays more attention to the hard positive and negative samples  during the training process.

\subsubsection{Sketch-based 3D Shape Retrieval}

We then evaluated our method for sketch-based 3D shape retrieval on two large-scale benchmark datasets, i.e.,  \emph{SHREC13} and \emph{SHREC14}.
The right-hand two subfigures in Figure \ref{2dcurves}  demonstrate the mAP curves of our batch-wise optimal transport loss as compared to the pair-wise loss objective.  As illustrated, our method is about $5$ times and $3$ times faster than LBWR on \emph{SHREC13} and \emph{SHREC14} respectively. Meanwhile, the retrieval performance is remarkably higher than the compared LBWR.

We also compared our method with several mainstream approaches for 3D shape retrieval, including CDMR \cite{furuya2013ranking}, SBR-VC \cite{li2013shrec}, SP \cite{sousa2010sketch}, FDC \cite{sousa2010sketch}, Siamese network \cite{wang2015sketch}, DCML \cite{dai2017deep}, DB-VLAT \cite{tatsuma2012large},  and LWBR \cite{xie2017learning}.
The evaluation criteria include  NN, FT, ST, E, DCG, and mAP.

As summarized in Table \ref{T2d3d1} and Table \ref{T2d3d2}, our batch-wise optimal transport loss  based method achieved the best retrieval performance with respect to all evaluation metrics on  \emph{SHREC13} and \emph{SHREC14}.
Among  compared methods, CDMR, DCML, Siamese network, and LWBR are all deep metric learning based approaches. They measured similarity based on pairs of samples and mapped data into an embedding metric space through different pooling schemes. In contrast,
our proposed batch-wise optimal transport loss objective can correctly re-weight the importance value of samples, mainly focus on those \emph{hard samples}.  Thus, our approach obtained better retrieval performance. Its mAP reaches 0.754, which is slightly better than LBWR and significantly better than other methods. Furthermore, the advantage of our approach is enlarged on \emph{SHREC14} because this dataset has more severe intra-class and cross-modality variations. As a consequence, the mAP of our proposed method is $0.591$, which is $0.190$, $0.305$,  and 0.363 higher than LBWR, DCML and Siamese network, respectively.

\subsubsection{3D Shapes Recognition}

We finally verified the proposed method for  3D shape recognition on two large-scale 3D shape datasets, i.e., \emph{ModelNet10} and \emph{ModelNet40}.  Pair-wise loss and triplet loss suffer from slow convergence rate because they are not capable of exploring all available semantical information within training batches simultaneously. To alleviate this problem, we used random sampling techniques (i.e., recurrently shuffle the training batches during each epoch) to loop over as many randomly sampled pairs as possible.
It is expected that the random pairs based loss objective could make full use of all information so that the finally learned semantic metric could be balanced correctly.
The left-hand subfigure in Figure \ref{ow_curve} presents the mAP curves of batch-wise optimal transport loss and other compared loss objectives for 3D shape retrieval. Similarly,
the batch-wise optimal transport loss objective still has significantly faster convergence rate and can achieve a decent retrieval performance within a small number of epochs (i.e., $5$ epochs).

We also examined two different probability distributions, i.e., uniformly distributed mean value ($\nu=\frac{1}{{{n^2}}}$) and random numbers in the interval $(0,1)$,
as  alternatives of the optimal transportation plan.
Uniformly distributed mean value weights in the batch-wise loss imply that samples  are equally important for later metric learning.  Uniformly distributed random weights randomly mark some samples as \emph{hard samples} within a pair of batches during the learning process.
The right-hand subfigure in  Figure \ref{ow_curve}  illustrates comparison results of the retrieval performance concerning the number of epochs for these three re-weighting strategies. It demonstrates that  the convergence rate of optimal re-weighted is much faster than the others.

The detailed comparison results are summarized in Table \ref{table1}. We compared the batch-wise optimal transport loss with other designed benchmark methods using NN, FT, ST, E, DCG and mAP on the \emph{ModelNet40} dataset. As illustrated  in Figure \ref{ow_curve} and  Table \ref{table1}, learning with batch-wise optimal transport loss objective has considerably faster convergence rate than other benchmark methods. It  takes only a few epochs (i.e., $5$ epochs) to achieve mAP at  $83.8 \%$ and accuracy at $90.3 \%$, which are better than  others after $200$ epochs.
It demonstrates that the learned optimal transportation plan can correctly re-weight the training samples according to their importance values during the metric learning process. Moreover,  solving Equation (\ref{our_OTD}) to learn optimal transportation plan ${\bm T^*}$ is not computational expensive in practice.
The average running time required by
one epoch of individual pair loss objective is $2.51$ seconds, and that of batch-wise optimal transport loss objective takes $9.02$ seconds.

Here, we analyzed the role of learned importance-driven distance metric $\bm T^* \bm M^*$ in our work. It is composed of the optimal transportation plan $ \bm T^* \in \mathbb{R}^{n \times n}$ and the semantical information embedded ground distances $\bm M^* \in \mathbb{R}^{n \times n}$. The element $\bm M_{ij}^*$ is filled with the distances of batch-wise similar positive samples $\bm M_{ij}^+$ and the distances of batch-wise dissimilar negative samples $\bm M_{ij}^-$.
The right-hand subfigure in Figure \ref{fig:distance} shows that  far similar positive and adjacent dissimilar negative samples (i.e., \emph{hard samples}) are sparsely distributed.
The left-hand subfigure  is the optimal transportation plan which is actually a probability distribution  \cite{cuturi2013sinkhorn}.
The color map reveals the learned metric ensures
higher importance weights to those few samples with small ground distance while giving less on the remaining ones.

In the end, we compared our method with state-of-the-art approaches for shape retrieval and classification, including  MVCNN \cite{su2015multi}, GIFT \cite{bai2016gift}, DeepPano \cite{shi2015deeppano} and et al.
The detailed comparison results are summarized in Table \ref{table2}.  Compared to these approaches,  our method based on batch-wise optimal transport loss learning can achieve the (almost) state-of-the-art performance on both tasks.

\section{Conclusion}\label{sec5}

In this paper, we proposed a novel batch-wise optimal transport loss objective to learn an importance-driven distance metric.  The learned distance metric can effectively emphasize \emph{hard samples} according to their importance weights.
We then formulated the proposed loss objective into an end-to-end deep metric learning network for recognition.
We evaluated the performance and versatility of our method with various visual recognition tasks, including 2D image recognition, 2D sketch-based 3D shape cross-modality retrieval, and multiview based 3D shape recognition.
The empirical results verified the proposed method could generically accelerate the convergence rate while achieving excellent recognition performance.
Our future work will involve facilitating such a trend and applying this importance-driven distance metric learning to more widespread applications.
For example, 3D point cloud classification,  segmentation,  3D scene reconstruction,   cross-modality correspondence among visual, audio,  and text.

{\small
	\bibliographystyle{ieee}

redpajama_set_name: RedPajamaArXiv


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text:
Q: Finding remainder when unknown $f(x)$ is divided to $g(x)$ When $f(x)$ is divided by $x - 2$ and $x + 3$, the remainders are 5 and -1, respectively. Find the remainder when $f(x)$ is divided by $x^2 + x - 6$
My method:
Since $x - 2$ and $x + 3$ are linears, dividing by quadratic will leave linear remainders.
Using the remainder theorem:
$$ f(x) = g(x) q(x) + r(x) $$
Where $g(x)$ is a divisor, $q(x)$ is a remainder, and $r(x)$ is a remainder
I let $ax + b$ here be the remainder.
So:
\begin{align}
f(x) &= g(x) (x - 2)(x + 3) + ax + b\\
f(2) &= g(2) (0)(5) + 2a+ b\\
f(-3) &= g(-3) (-5)(0) - 3a + b\\
\\
&5 = 2a + b\\
&-1 = -3a + b\\
\\
&...\\
\\
&a = \frac{6}{5}, b = \frac{13}{5}
\\
\end{align}
Then the remainder is $ax + b = \frac{6}{5}x + \frac{13}{5} $
Is it possible to find the $f(x)$ out of remainders?

A: No, there are infinite $f(x)$ which satisfy the given conditions:
$$f(x)=q(x)(x^2 + x - 6)+\frac{6}{5}x + \frac{13}{5}$$
with any polynomial $q(x)$.


text:
#pragma once
#include <aws/email/SES_EXPORTS.h>
#include <aws/core/utils/memory/stl/AWSString.h>

namespace Aws
{
namespace SES
{
namespace Model
{
  enum class DsnAction
  {
    NOT_SET,
    failed,
    delayed,
    delivered,
    relayed,
    expanded
  };

namespace DsnActionMapper
{
AWS_SES_API DsnAction GetDsnActionForName(const Aws::String& name);

AWS_SES_API Aws::String GetNameForDsnAction(DsnAction value);
} // namespace DsnActionMapper
} // namespace Model
} // namespace SES
} // namespace Aws


text:
Tripp Guin, Managing Partner
Tripp Guin is Principal and Broker in Charge with TRIPP Commercial, LLC. Mr. Guin's focus is on tenant representation, brokerage and Global Corporate Services. Mr. Guin has been a member of the Charlotte Region Commercial Board of Realtors Million Dollar Club for eight years. Mr. Guin has been listed as one of the Charlotte market's top Tenant Representation brokers. After joining the commercial real estate industry as a business development broker and tenant representative specialist, Mr. Guin has been consistently recognized as a top producer, winning the Silver Award from the Charlotte Region Commercial Board of Realtors for production between $2 and $10 Million in Revenues in 2005, 06, 08, 09, 10 and in 2006, 07 and 2011 he received the Gold Award for Production ($10 Million +). Also, in 2006, he was awarded the CRCBR's Rookie of the Year for the Charlotte Metro Area. Most recently in March of 2011, Mr. Guin was recognized by the Business Journal for the prestigious Forty under 40 award recognizing those that have committed to improve our community through their business endeavors and philanthropy. Prior to founding TRIPP Commercial, Mr. Guin was the founder and CEO of three companies: Referral Resource, Sales Force Partners and Carolinas Realty
He began his real estate brokerage career with the Charlotte office of Trammell Crow Company specializing as a Tenant Representation broker representing companies locally, regionally and nationally including Commercial Lease and Purchases, Financial Analysis, Transaction Management, Acquisition and Dispositions and Strategic Planning. After TCC was acquired by CBRE in 2006, Mr. Guin continued in brokerage until starting TRIPP Commercial, LLC January 2012.
A graduate of the College of Charleston, SC, where Mr. Guin played Varsity Basketball and was a Founding Father of Sigma Phi Epsilon. He is a native of Mt. Holly, NC, serves on the Board of Cornet Global. He is also a member of Business Leaders of Charlotte, Church at Charlotte, Providence Country Club and Y Guides. He is married with three children.


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Mission Concept
Observation Strategy
Quick Facts Mission Concept Observation Strategy
NASA partnership with the Indian Space Research Organisation (ISRO)
NASA Administrator Charles Bolden (left) and ISRO Chairman K. Radhakrishnan signing documents in Toronto on Sept. 30, 2014, to launch a joint Earth-observing satellite mission and establish a pathway for future joint missions to explore Mars. Credit: NASA
NISAR is a joint Earth-observing mission between NASA and the Indian Space Research Organization (ISRO) with the goal to make global measurements of the causes and consequences of land surface changes using advanced radar imaging. This mission concept and the resulting partnership are in response to the National Academy of Science's 2007 survey of Earth observational priorities for the next decade, known as the decadal survey. One of the top priorities identified in this survey was to gain data and insight in three Earth science domains: ecosystems, deformation of Earth's crust and cryospheric sciences.
As NASA and ISRO discussed the possibility of a joint radar mission, it became clear that this goal was of great interest to the ISRO science community. ISRO identified science and applications that were complementary to the primary mission objectives: agricultural monitoring and characterization, landslide studies, Himalayan glacier studies, soil moisture, coastal processes, coastal winds, and monitoring hazards. A second radar frequency was added to the mission to better fulfill these science requirements. NISAR will be the first satellite mission to use two different radar frequencies (L-band and S-band) to measure changes in our planet's surface less than a centimeter across. The partnership with India has been key to preserving as much science as possible.
On Sept. 30, 2014, NASA and ISRO signed a partnership to collaborate on and launch NISAR. The mission is targeted to launch in early 2023. NASA is providing the mission's L-band synthetic aperture radar, a high-rate communication subsystem for science data, GPS receivers, a solid-state recorder and payload data subsystem. ISRO is providing the spacecraft bus, the S-band radar, the launch vehicle and associated launch services.
Science Handbook
Science White Papers
Applications White Papers
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Helping Farmers from Space
NISAR: Watching the Earth Move Under Our Feet
Members of the integration and test team, from NASA's Jet Propulsion Laboratory and the Indian Space Research Organization, with NISAR's S-synthetic aperture radar instrument at JPL.
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Site Manager: Kalina Velev


I was arrested twice for a dui within a few months of each other a couple years ago. My lawyer was able to get them to do a CWOF and treat it as a first offense. I am now just trying to get my license and when I went to the RMV they told me that I needed an interlock device. The RMV explicitly states that in order to have an interlock device you must have "two convictions of a DUI". I do not believe I should have to get this device because neither of my DUI's were convictions they were CWOF. I researched it a bit an in accordance with Souza v Registrar of Motor Vehicles the Registry cannot use a CWOF as a conviction. That being said I would have zero convictions and should not need this device. Do I have a case against the RMV?
No. You're ignition interlock required. G.L. c. 90 § 24 ½ requires all repeat DUI offenders who are reinstating after January 1, 2006 to use a certified ignition interlock device during the entire term of any hardship license and for 2 years after getting the "H" restriction removed and a full reinstatement of your driver's license. A CWOF is considered a conviction for IID purposes.
The Driver Control Unit of the Registry of Motor Vehicles and the Division of Insurance Board of Appeal both take a zero-tolerance approach when it comes to ignition interlock requirements.