The Darin Gap migration crisis in six graphs, and one map – The New Humanitarian

A record 520,000 migrants crossed the treacherous jungle corridor connecting Colombia and Panam known as the Darin Gap in 2023. Less than a decade ago, that figure was only a few thousand, but the number has been doubling annually in recent years, and a further surge is expected in 2024.

2023 has broken all records. It has been a huge, terrible maelstrom, Elas Cornejo, who runs Fe y Alegra, an NGO promoting education and social advancement for migrants in Panam, told The New Humanitarian. And we expect a new increase [in 2024].

Services like Fe y Alegra on both sides of the Colombia-Panama border are becoming engulfed as the needs of vast numbers of vulnerable people traversing dangerous territory overwhelm local communities and aid groups trying to help.

The migrants take the 97-kilometre jungle trek over steep and muddy terrain and along fast-flowing rivers because it is the only overland route from South America into Central America. Once in Panam, where government reception centres are overrun, most hope to head northwards through Mexico to the southern US border, but these journeys are also full of risks.

Read more: The challenges facing the humanitarian response

The few humanitarian agencies and organisations operating on the ground in and around the Darin Gap are struggling to meet the soaring needs of those crossing, not least because of the insecurity in the region.

The Colombian side of the jungle is mostly controlled by the Gulf Clan a criminal organisation involved in drug and human trafficking that made an estimated $57 million from extortion along the migration route in just 10 months last year. The cartel controls most aspects of the route, determining who can assist and therefore heavily restricting the humanitarian response. In Panam, several international organisations help the migrants who reach the Indigenous communities of Bajo Chiquito and Canan Membrillo, and in government-run reception centres at the edge of the jungle, in San Vicente and Lajas Blancas. Those facilities, however, are meant to host less than 1,000 people per day. Instead, in 2023, they were receiving up to 5,500.

Diana Romero, emergency specialist at UNICEF Panam, told The New Humanitarian that coming up with the right emergency response hasn't been easy in a high-income country that was unprepared to deal with such needs. Panama had not faced situations of disasters or crises, so they didnt have the implementation partners needed, she said. In 2019, there were no local humanitarian teams, because there never was a demand for that. There were no specialists in WASH, gender, or nutrition.

As they cross the Darin Gap and beyond, migrants face unchecked abuses by criminal groups, rampant sexual violence, a cascade of physical and mental health impacts, and worse: Between January 2021 and March 2023, Panamanian authorities found a reported 124 bodies on the route, mostly through drowning, but thats thought to be a fraction of the real number of deaths, as many go unreported.

Many making these difficult journeys are escaping regional violence and economic crises in countries like Venezuela, Haiti, and Cuba, but increasing numbers have also been coming from countries in the Middle East, Africa, and Asia, including China.

With no sign of a let-up in 2024, here are six graphs (and one map) that show the scale and evolving nature of the crisis, with analysis to unpack those trends.

A number of factors caused the dramatic 2023 uptick in Darin Gap crossings. Changes in migration policies across the region have made it more difficult for those trying to reach the United States from South America to cross borders legally. Several countries imposed visa restrictions on Venezuelans and Haitians, even as countries such as Chile and Peru militarised their borders, pushing migrants to leave northward. In 2023, US President Joe Bidens administration ended Title 42 a pandemic-era border restriction which motivated more people to head to the United States even though Biden soon adopted measures making it extremely difficult for them to seek asylum, and ramped up deportations. The lack of adequate integration policies has also been a driver. Among Haitians and Venezuelans in the Darin, many are migrating for the second time, from countries such as Brazil and Chile where they faced xenophobia, obstacles to regularise their status, and poor job opportunities. In April, Panam, Colombia and the United States agreed on a tripartite plan to open up new regular migration routes to stem the flow, but so far no progress has been made.

From 2019 to 2022, most migrants crossing the Darin were Haitian and Cuban, but in the past two years Venezuelans have taken the lead, and the number of Ecuadorians seeking to escape from violence and poverty has also significantly increased. However, far from all the migrants crossing the Darin are Latin American, and the growing presence of migrants from other continents is garnering the attention of humanitarians, who must now cater their responses to those who dont speak Spanish and are foreigners to the region. Chinese, Afghans, Indians, and nationals of different African countries have to confront language and cultural barriers, as well as the other dangers.

The journey through the Darin Gap usually starts in the Colombian ports of Necocl or Turbo, where local communities offer maritime transportation to the towns of Acand or Capurgan. Migrants are charged high amounts of money for every section of the trip. After crossing by boat, they must pay again to be allowed to continue through the jungle to the Panamanian side. There are three main paths leading to the government-run reception centres of Lajas Blancas and San Vicente, through the communities of Bajo Chiquito or Canan Membrillo. The crossing lasts from 5 to 15 days and total costs range from $435 to more than $1,000 per person. There is also a more expensive VIP route, mostly used by Chinese. Migrants and asylum seekers then continue their trip to the Temporary Attention Center for Migrants (CATEM) in Costa Rica, from where, since October, they are directly transferred by bus to the Costa Rica-Nicaragua border. Many, however, run out of money before starting the trek and remain stranded in Turbo and Necocl, where they are vulnerable to extortion, violence, and human trafficking.

According to Diana Romero, emergency specialist at UNICEF Panam, one in five migrants crossing the Darin is a child half of them under the age of five. Although there are no accurate figures, there are many reports of children dying during the trek. The number of unaccompanied children is of particular concern. In 2022, UNICEF assisted about 1,000 unaccompanied minors, but in 2023 that figure reached 3,300. Of those, 67% were teenagers, 21% children aged between 6 and 12, and of the rest, 10% are babies, Romero said. Often, younger children get separated from their relatives during the trek only managing to reunite later on. According to Francisco Pulido, Plan Internationals director of humanitarian action and stabilisation in Colombia, teenagers tend to travel in friend groups often motivated by misinformation shared on social media. In other cases, the entire family cannot afford to continue the trek so parents leave their children in camps, hoping to send them money to follow on later.

Most of the medical cases that aid organisations come across and treat are related to the dangers of the jungle itself, or due to the lack of access to clean water and food en route. Theres no data available, but humanitarian groups say there has also been a rising number of migrants travelling with pre-existing chronic conditions psychiatric disorders, diabetes, hypertension, or asthma. These people often require emergency assistance because their medications get lost or stolen.

The traumatic experience of those crossing the Darin is also causing high numbers of mental health consultations. According to a recent Action Against Hunger report, women bear the brunt, and are often carrying children with no support. While survivors of sexual violence may suffer from depression, suicidal thoughts, and sleep disorders, others feel the emotional burdens and stress of caring for the family in such extreme conditions.

During 2022, Mdecins Sans Frontires (MSF) treated 232 survivors of sexual violence in the Darin Gap. Between January and November 2023, that number had soared to 462. According to what patients tell us, the modus operandi is getting crueller, Cristina Zugasti, MSF representative in Panam, told The New Humanitarian. Large groups are being kidnapped, forced to lay down face to the ground, and then robbed, physically attacked, and sexually abused. MSF figures, she added, are much lower than the reality. Many cases remain unreported because survivors don't see sexual attacks as a medical emergency, and they also don't want to delay the arrivals to their destinations. Threats from the perpetrators are another reason for survivors not to seek assistance.

Reported from Santiago, Chile by Daniela Mohor, with data visualisation from Zurich, Switzerland by Sofa Kuan.

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The Darin Gap migration crisis in six graphs, and one map - The New Humanitarian

Idaho White Nationalism: Inside a New Class of Republican Power – The Daily Dot

So this place is Satans temple, Dan Gookin said ironically. The cozy confines of the pub in downtown Coeur dAlene, Idaho dont bear any resemblance to a place for worshiping anything but a cold pint or bangers and mash.

Gookin explained that they used to have a poster for Menstruatin with Satan, a fundraiser for menstrual supplies organized by the Satanic Temple of Idaho. The Satanic Temple is a non-theistic organization that encourages benevolence and empathy, rejects tyranny, and advocates for bodily autonomy. In recent years, its become best known for fighting for reproductive freedom. Members dont worship or even believe in Satan.

Nevertheless, it drives conservative Christians wild.

Gookin has a frank manner and strong, clear voice. He tends to speak quickly with a serious delivery belied by the occasional flash of a dry wit. On an evening in late November, he said the poster convinced some local right-wingers that the pub is affiliated with the dark lord, a ridiculous, inaccurate assumption thats also convenient for his purposes. They wont step foot in the place.

We had campaign meetings here because we knew that there would be no spies, Gookin said. See, we can talk freely in here because we know there will never be a wacko anywhere near us.

The whackos are the Kootenai County Republican Central Committee (KCRCC) and their allies. Gookin, a conservative best known nationally for creating the For Dummies books, is a longtime city councilman and KCRCC member. These days hes persona non grata with the committee, not that he seems to mind.

They didnt count on me calling them out, Gookin said on a recent episode of his YouTube show, Kootenai Rants.

Idaho Republicans are in the midst of a civil war between the far-right wing and relative moderates like Gookin. In recent years, far-right extremists have moved to the heavily white and conservative state as part of an ideological migration that accelerated during the pandemic. Far-right comedian Owen Benjamin now lives about an hour-and-a-half north of Coeur dAlene.

Rather than reject the extremists, some powerful Republicans have embraced the Holocaust deniers and white nationalists whove made Idaho their home. This outrages many longtime locals of the county that famously defeated the neo-Nazi Aryan Nations decades ago. Gookin and other conservatives are fighting back in the press, election booth, and courts.

Its an uphill battle; the opposition is well-funded, organized, and willing to get its hands dirty. It even has a network of print and online publications steadily pumping propaganda into the information ecosystem.

This story is part of a series exploring far-right figures and groups impact on communities theyve relocated to in Idaho, West Virginia, Florida, and Maine; and what, if anything, those communities are doing about it. The Daily Dot spent the last several months visiting these communities, talking to locals, consulting historic and public records, and interviewing experts on extremism.

As the 2024 election approaches, the far-right will become more visible and vocal.

Former President Donald Trumps 2016 victory emboldened the neo-Nazis and white supremacists who coalesced at the deadly Unite the Right rally. His 2020 defeat inspired militias, conspiracy theorists, and hate groups to attack democracy. Both corresponded with increases in hate, antisemitism, and white supremacy that came screaming from the internet into the real world.

They may have failed on Jan. 6, 2021, but theyre back, mobilized, and ready to fight. Seizing control of places like Coeur dAlene is one of the ways theyre plotting their comeback.

Gookin isnt cowed. We need to fight this.

The week after Thanksgiving, Coeur dAlene was decked out in 1.5 million holiday lights sparkling off the lake and into the darkness beyond. Business was in full swing in the town of 55,000. Each night sold-out boats took excited children to see Santa Claus while adults packed into warm bars and restaurants for a bite and a bit to take the edge off.

Washington is less than an hour west and in another political world compared to Idaho, one of the most consistently Republican states in America. More Idahoans voted for Trump in 2020 than 2016. The state hasnt voted for a Democratic presidential candidate since Lyndon Johnson, and it chose Richard Nixon (R) over John F. Kennedy (D) in the election before that.

Its also a longtime harbor for racists.

From the mid-1970s to the turn of the century, the white supremacist Aryan Nations had a 20-acre compound in Kootenai County, which encompasses Coeur dAlene. Aryan Nations declared bankruptcy following a $6.3 million verdict against it in a case brought by a mother and son who were shot at and beaten by its security guards.

Fluffy white snow blanketed Coeur dAlene as Kate Bitz, 38, recalled hearing stories about all-ages punk shows turning into brawls when skinheads showed up and seeing news coverage of white supremacists marching down Sherman Avenue when she was growing up just across the border in Washington. On outings to Farragut State Park, theyd sometimes have to make a snap decision if the guys with the white power tattoos are showing up, do we leave and give them the whole beach, or stay.

Growing up in a hotbed of extremism led Bitz to a career opposing it. She works for the advocacy nonprofit Western States Center.

Bitz isnt surprised that the far-right is resurging. Idaho is the longtime home of a variety of extremists, ranging from evangelicals to neo-Nazis. People forget how multifaceted it was, she said, adding, This has all happened before in a different form.

Extremist groups have been active in Idaho for decades, Bitz said. For example, Northwest Front was described by Politico as Americas worst racists in 2015; racist mass murderer Dylann Roof highlighted the group in his manifesto. Northwest Front has been encouraging people to move to the Pacific Northwest to create a white ethno-state for years. American Redoubt, which has been described as white Christian nationalist (it identifies as a non-racist preparedness movement for Christian patriots), has been recruiting people to move to the area for over a decade. Idaho GOP Chair Dorothy Moon is a member of the far-right John Birch Society.

Now theres a new crop of extremists.

David Reilly and Vincent James Foxx are two of the most notorious newcomers in Idaho politics. Theyre part of far-right efforts to take control from the bottom up via the precinct strategy championed by Steve Bannon. Both are affiliated with white nationalist Nick Fuentes. Reilly has professed being a fan of Fuentes and reportedly attended his CPAC alternative, America First Political Action Conference. Foxx is the national treasurer of Fuentes America First organization.

Reilly became the focus of a scandal about his attendance of Unite the Right in 2017. He subsequently resigned from his fathers radio station where he was a host. InvestigateWest reports he sported a pin with the logo of the neo-Nazi Identity Evropa to the rally. In his resignation letter, Reilly denied being racist, white supremacist, or a neo-Nazi. A judge later threw out his lawsuit against a Pennsylvania-based news outlet and individuals he claimed had defamed him by calling him racist.

In recent years, Reilly called himself a Fuentes stan. Reilly is also purportedly an ally of the Unite the Right marcher best known for the catchphrase Hitler did nothing wrong. He has a lengthy history of antisemitic posts on X, formerly known as Twitter. Reilly did not respond to interview requests.

Reilly made his way to Idaho a few years ago.

In 2021, Reilly sought a seat on an Idaho school board, which he lost with 47% of the vote. (KCRCC endorsed him.) During the campaign, a group from his Pennsylvania hometown urged people to vote against him because of his involvement in Unite the Right.

When Reilly left our community, he acknowledged himself, not even McDonalds would hire [him]. Please consider if you, the voter, would want to hire Reilly to create policy for your schools, Bloomsburg Stand Against Hate wrote.

He didnt have as much trouble finding employment in Idaho.

During his failed 2022 gubernatorial campaign, anti-government militant Ammon Bundy paid $30,000 to a firm the Inlander reports was linked to Reilly. KCRCC also paid Reillys company $11,000 for videos.

Bitz said of KCRCC Chair Brent Regans association with the men, I think he sees Reilly and Vincent James as his pet white nationalists who he can push consulting money to during elections.

Regan did not respond to interview requests.

In December, InvestigateWest reported that Idaho Freedom Foundation (IFF), which Regan also chairs, employs Reilly to help with its communications strategy. The piece noted that Reilly has claimed Jews invented terrorism and control the media.

In response to the story, Regan penned an op-ed claiming he has no authority over IFFs hiring decisions and claiming its Jewish president, Wayne Hoffman, interviewed Reilly. I believe it is fair to say that Wayne Hoffmans sensitivity to anti-Semitism is greater than mine so that if he is okay with Reilly, so am I and so should you, Regan wrote. He also denied that Reilly is antisemitic or a white supremacist.

Regans editorial made no mention of Unite the Right.

Last week, amid rising criticism, IFF announced that Hoffman had been replaced with a far-right former lawmaker. It did not say if Hoffman quit or was fired.

Holocaust denier Foxx is another white nationalist who found more welcoming pastures in Idaho in recent years. In 2017, ProPublica described Foxx as a 31-year-old video blogger and livestreamer with a fondness for white supremacists and radical right-wing politics. It reported that Foxx was essentially an unofficial propagandist for Rise Above Movement (RAM), a violent, racist group at the center of much of the violence at Unite the Right. Three members were convicted for violence they committed at Unite the Right.

He didnt merely document RAMs violence, per ProPublica. The outlet reports that Foxx could be heard screaming, Get that f*cking cuck! in a YouTube video he posted of a RAM member and several others pummeling a man in California. Identity Evropa founder Nathan Damigo fought alongside RAM that day.

In 2021, Foxx moved from California to Idaho.

He was photographed with then-Lt. Gov. Janice McGeachin (R); Media Matters for America reported he said he had deep connections to her. Last January, he gave a speech to a group of north Idaho Republicans in which the Southern Poverty Law Center reports he echoed the racist great replacement conspiracy theory that whites are being intentionally displaced by nonwhite immigrants. In September, a former school board member who was once a KCRCC committeewoman claimed he said political leaders convinced him to move there.

Since becoming an Idahoan, Foxx has continued to espouse white nationalist talking points. He did not respond to interview requests.

Foxx is the national treasurer of Fuentes America First organization. In 2022, Foxx gushed great clip!! of a video of Ye (formerly Kanye West) praising Hitler. After Fuentes infamously had dinner with Trump, Foxx bragged, We have in fact infiltrated the mainstream flank of the GOP. Just look at what Tucker Carlson is talking about lately. We have parts of the nation talking about secession, talking about banning gay marriage. Last month, Foxx posted a video of actor Michael Rapaport claiming people would be thrown off a building for asking where to find an LGBTQ business in Gaza. Foxx captioned it, Wait a minute. Do I love Gaza now??!

Right Wing Watch unearthed a video of him saying, We are the Christian Taliban and we will not stop until The Handmaids Tale is a reality and even worse than that.

Last year, Foxx ran for chair of the Idaho Young Republicans. In his pitch for votes, he advocated using the precinct strategy to install extremists in positions throughout the state.

He lost.

People agree that Foxx and Reilly are just the tip of the spear.

Sarah Lynch is the executive director of North Idaho Pride Alliance (NIPA). Over coffee at Evans Brothers Coffee, a cheerful space on the same street where white supremacists used to march during Aryan Nations heyday, Lynch said after she and her wife moved to the area, she noticed it was a weird mix of like Nazis and granola hippies.

The darker side of the picturesque town was front and center in June 2022 when 31 members of the white nationalist Patriot Front were arrested en route to Pride in the Park in Coeur dAlene.

The incident stunned the nation. Patriot Front is one of the most active white supremacist groups in America and it often posts photos of its activities in Idaho. But a few dozen men in riot gear in the back of a U-Haul is a significant escalation from sneaking around at night to spray paint stencils and hang banners, which the hate group usually sticks to.

All the men were charged with conspiracy to riot; many have been convicted or pled guilty since then. Charges were dismissed against Patriot Front leader Thomas Rousseau last fall.

Police officers were doxed and received death threats after the arrests. Police Chief Lee White told media that they got 100 calls afterwardhalf from supporters and half from critics.

While Patriot Front generated headlines and fear, Lynch said it couldve been much worse.

Despite all the hateful rhetoric that was going on last year, and despite the events that occurred, we still had our largest ever Pride in the Park. It was our first one back since COVID, there were over 2,500 people there, Lynch said.

Lynch, a retired veteran with a Ph.D. in public safety, said that theyd established a communication line with law enforcement before the event, which has strengthened with time. The arrests also spurred some local and state officials to publicly support LGBTQ equality. Mayor Jim Hammond (R) declared June as Pride Month. Weeks before Lynch sat down for coffee, Hammond was named a Pillar of Idaho for his public stance against extremism.

These developments may have some feeling optimistic, but it isnt all sunshine and rainbows in Kootenai County.

Lynch said some families with queer children have moved away; others have said their queer adult relatives wont even come home for Thanksgiving because they dont feel safe there.

She described the homophobic and transphobic segment of the extreme far-right as a very loud minority.

As long as nobody else stands up and says anything, then thats the only narrative thats heard, she said.

Several years ago, Army veteran Sam Rowland moved back to the area where he was born. Rowland, a musician, has a thick red beard and eyes that seem older than his 39 years. He did a couple tours in Iraq; he said Coeur dAlene reminded him of the small town in Saudi Arabia where he grew up.

Then 2020 happened and it exposed itself. He paused. It re-exposed itself.

During the civil rights protests inspired by George Floyds murder, people took to the streets of Coeur dAlene to protect the community from antifa. Photos from the publication that Reilly purportedly runs show heavily armed men, most of whom appear to be white, gathered on the sidewalk downtown.

Rowland said some wore insignias identifying themselves as members of militia-type groups like the III Percenters. Prominent white supremacists were out there, he said. I was followed home.

He and others said that churches in the area have become breeding grounds for extremism, with pastors making little to no effort to separate politics from theology.

Rowland sees whats happening in Coeur dAlene as part of a larger strategy. You have to take the little towns first, he said.

It appears that they would like to have it turned into a very conservative quasi-religious institution that still has the benefit of public funding.

A large Coeur dAlene rejects hate sign hangs in the window of Crown & Thistle Pub. Jennifer and Ben Drake spent years making plans for the British-style pub, which served its first half-pint in 2019. Every detail, from the cask ales to the 120-year-old bar and the menu, which includes bangers made by Ben and a delectable Guinness short rib pie, is designed to make you feel like youre steps away from London Bridge, albeit in a snug in northern Idaho. (A snug is an enclosed booth from when it was faux pas for women to be seen drinking alcohol in public.)

Jennifers family has been in Coeur dAlene for five generations. Running the Crown & Thistle in her hometown is the fulfillment of a dream first glimpsed attending the University of St. Andrews in Scotland. Its come with nightmares that have nothing to do with Scotch eggs or ales.

Shes the type of person who stands up for what she thinks is right. Rejecting hate aligns with those values.

Over the din of the suppertime crowd on a snowy Friday night in December, Ben said they originally put up an 8 x 11 sign. Then, he said, We started getting hate mail.

They brushed it off, deciding to increase the size of the sign each time they received another hateful missive.

When she was a kid, Jen said the town was united against the Aryan Nations. Now theyre divided between people who fall in line and those who take a stand.

Both Drakes are Republicans. Yet theyve ended up on the opposite side of Regan and the partys radical flank.

Theyve infiltrated the community to the point that they say they are the community, Jen said.

The incidents, Jen said, escalated gradually. People call them liberals online. They dogpile the pub with one-star reviews. Insane misinformation floats from the internet to the streets.

They honestly think Im a Satan-worshiping communist witch, Jen said in a pained voice. And its too much for me. Im Lutheran. Im tired.

As chair of both IFF and KCRCC, Brent Regan is a powerful force in Idaho politics. IFF rates politicians based on their voting records; the more conservative, the higher the rating. KCRCC recruits and endorses candidates. These efforts have been effective. Various positions of power in Kootenai County are now held by people who score high on IFFs ideological purity tests and have the KCRCC stamp of approval.

Several people said that the candidates might check the right boxes, but they can struggle to govern effectively. They pointed to North Idaho College (NIC), whose board is under far-right control.

NIC has been hemorrhaging money since they took over. Worse, the 90-year-old community colleges accreditation is hanging by a thread.

A bust of Patrick Stewart circa Star Trek gazed down from the shelf in Dan Englishs office at Healing Hearts, the mental healthcare clinic he runs with his wife. A quilt hangs on the wall by his desk; English mentioned with endearing husbandly pride that his wife made it. Bagpipes softly played holiday music as English shared memories of the town where he was born and raised.

English, the lone Democrat on the city council, has been an elected official in Coeur dAlene for 30 years. He previously served on the school board and as clerk-auditor. He describes himself as an election geek who enjoys crunching data. The numbers from 2020 were extremely illuminating to him.

Eighty-five-plus [percent] had been a registered voter here less than like, you know, two years or four years or something. So its no wonder they have a hard time passing bonds for schools, he said.

English said that some of the transplants are from the extreme right and others are more traditional conservatives. The newcomers include a lot of retired police, so many from Los Angeles, in fact, that the area is sometimes called LAPD North. Theres also a contingent of liberals. The combination creates what he calls a weird melting pot.

It pains him to see his hometown torn apart by politics.

The sad part is how much time, energy, and financial resources is wasted over these ideology battles, or just peoples inflated ego, like the college, English said.

After the far-right took over NICs board, it fired the college president, who sued for wrongful termination and received a $500,000 settlement. NIC later put his replacement on leave; a court in a separate lawsuit determined this was without cause and ordered it to reinstate him and for the school to pay his attorneys fees.

Between litigation with the president it was deemed to have placed on leave without cause and a separate case the local newspaper brought over public records (NIC lost that too), attorneys fees, travel costs for officials from the accreditation agency, and training for the board itself, the Coeur dAlene Press reports that its spent $1.2 million. An Idaho Statesman columnist recently referred to this as an incompetence tax.

Now English says NIC cant afford the light bill to keep the library open a few extra hours on Sundays.

Its ironic that people get elected are a lot of those, anti-education, anti-science, and yet they want to be in positions of monitoring educators, he said. It appears that they would like to have it turned into a very conservative quasi-religious institution that still has the benefit of public funding.

Education has been thrust into the forefront of the conservative culture wars across the country.

KCRCC candidates won control of the library board last year by campaigning on reducing childrens access to sexually explicit books. During the campaign, KCRCC reportedly circulated a letter falsely accusing the incumbents of giving kids access to graphic books with text and pictures describing every imaginable sex act, books so explicit that if you were to give them to a child, you would be committing a crime.

They may have gone too far. The two women who allege they were smeareda lawyer and a longtime member of the library boardare suing Regan and KCRCC for defamation.

City councilman Gookin is also wrapped up in a defamation suit with KCRCC. Its suing him over what he characterizes as mean tweets. KCRCC claims that Regan has demonstrated profound ill will and malice toward many KCRCC officers and affiliated candidatesin particular, KCRCCs chairman, Brent Regan on his YouTube show, Kootenai Rants, and posts on X.

The KCRCC appreciates that Gookin is entitled to engage in speech that is protected by the First Amendment, the complaint states. However, his recent statements have crossed the line from protected speech into unprotected defamation because they accuse KCRCC of rigging its 2023 candidate rating and vetting process, perpetrating a fraud on its members, and violating campaign finance lawsthings which simply have not happened.

Gookin views their case as an attack on his free speech right to criticize them. He seems eager to have his day in court.

Its ping-pong time, he said in an email earlier this month.

Gookin describes the political migrants who are pushing Idaho further to the right as people who were p*ssed off living in more liberal areas. He said this migratory pattern accelerated during the pandemic because they thought theyd have more freedom there. (The libertarian Cato Institute actually ranks Idaho 49th in personal freedom.)

But it didnt absolve their anger.

They hate our governor. They hate our legislators. They hate elected officials like me, they hate people whove made it a conservative state, Gookin said. And they want to replace them with their own people who, like we see in Washington, D.C., are incompetent and incapable of governing.

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Idaho White Nationalism: Inside a New Class of Republican Power - The Daily Dot

Scientists tested 10 meals to find the perfect food for space travel – Livescience.com

Maintaining a balanced diet can be hard enough on Earth, but it's even more difficult in space especially when you're talking about long-haul missions. Although space-based agriculture has made strides in recent years, growing fresh crops in space is no easy feat, and each bit of food or water stored in a spacecraft adds mass, thus weighing down the vessel during its journey out of orbit.

Scientists recently studied possible nutrient-dense meals fit for long-term space travel, such as potential Mars missions, that both satisfy astronauts' nutritional needs and taste better than existing alternatives. They tested 10 dishes to see which would be the optimal meal for male astronauts; they plan to specifically study meals for female astronauts in the future. The best meal would help space travelers get the calories and variety of nutrients they need during their odysseys and use crops that could be grown in space with minimal water.

Ultimately, the best space meal turned out to be a hearty kale salad, according to their study, published Dec. 13 in the journal ACS Food Science & Technology.

"These assessments are essential steps toward feasibility in long-term human space missions, for example, to Mars," the authors wrote.

Space travelers have different nutritional requirements than people on Earth do. That's because astronauts face unique stressors, including the vibration, noise, weightlessness, cosmic radiation and drastic temperature changes inherent to spaceflight. Research suggests that a male astronaut needs to consume around 2.6 pounds (1.2 kilograms) of food per day to maintain their body weight and energy levels. That diet should include more than double the carbohydrates and proteins than a typical person on Earth would require.

Related: NASA reveals first image of 'space tomatoes' that went missing on the ISS for 8 months, and they're gross

With this in mind, the team assessed a variety of nutrient-dense ingredients using a statistical model, which also measured the foods' capability of being grown in space or stored for a long time in a spacecraft. This model yielded 10 "space dishes"; four were vegetarian, and six were made with plants and meat.

Compared with plants, meat options typically provide a higher concentration of certain key nutrients, such as protein and vitamin B12. However, the storage of animal products "requires a large space for long-term space missions," making them tough ingredients to regularly include in an astronaut's diet, the study's authors wrote. (In addition, there aren't yet efficient methods for growing lab-grown meat, although the field is advancing.)

The team couldn't include baked goods like bread, because crumbs can float around in microgravity and damage equipment in the spacecraft.

Crops, on the other hand, could be grown during space travel. Considering all of these factors, the researchers' models determined that the optimal dish to meet astronauts' nutritional needs while being feasible for space travel is a vegetarian salad made with soybeans, poppy seeds, barley, kale, peanuts, sweet potato and sunflower seeds but notably, no salad dressing.

"I think their choice was very well done," Kathleen Carter, a nutritional researcher at Central State University in Ohio who was not involved in the study, told Live Science. "I think that as we start extending our time in space, we're going to have to go to more plant-based. We're going to have to be able to grow our own resources."

Beyond nutritional value, the researchers studied another factor in the ideal astronaut meal: taste. They fed four volunteers the optimized space salad and recorded their feedback on its palatability. Overall, the results were positive, with one volunteer saying they "enjoyed the sweet taste of the potatoes and freshness crunch."

However, the researchers flagged some key limitations with this meal option.

While some plants, including Chinese cabbage and tomatoes, have been cultivated in space in recent decades, there still isn't a reliable and efficient cultivation system to maximize output in this environment, they noted in the study. Additionally, the optimized salad is still missing some of the vitamins and minerals an astronaut would need each day, though these could be provided through supplements, the authors wrote.

Future studies should also consider the cultural and individual dietary requirements of each astronaut, Carter said. Their space menu would need to accomodate any allergies, personal preferences or dietary restrictions, she added.

"Different cultures are going to want different types of foods," Carter said. "Making sure that food looks good, that it tastes good [and] that it's something that they really want to eat, in addition to being very nutrient dense, is going to be very important."

The researchers plan to use their models to design meals for female astronauts and to incorporate more crops into its algorithm, according to a statement.

Ever wonder why some people build muscle more easily than others or why freckles come out in the sun? Send us your questions about how the human body works to community@livescience.com with the subject line "Health Desk Q," and you may see your question answered on the website!

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Scientists tested 10 meals to find the perfect food for space travel - Livescience.com

Starfield Fan Discovers Early Starmap, Hints At Harder Space Travel – TheGamer

Starfield's space travel is pretty simple. You open a map, select a dot, and zoom off. There's no fuel and you won't stumble into any obstacles, aside from the odd raider, but originally it was much more gruelling.

Todd Howard said in 2022 that, during development, "Your ship would run out of fuel and the game would just stop", pointing to more realistic intergalactic travel. The concept was scrapped, with the grav drive being used to limit how far you can go instead. But now we know a little bit more about that old system.

As reported by GamesRadar, a dataminer uncovered a pre-launch starmap with UI elements pointing to fuel consumption and potential hazards. You can see it in the Reddit post embedded below.

On the right-hand side of the starmap we can see the "Jump Data" tab which details how long it will take to travel the distance you've selected. Underneath, it lists how much fuel a jump will consume, with a handy little bar displaying how much you currently have and what will be left after.

Underneath all of these stats are the problems you can encounter on your journey. In this case, we see solar radiation which will result in "light hull damage" and micrometeoroids that "can cause catastrophic stop". It's unclear how you would counter these problems, but upgrading your ship would likely have increased your odds, making it more difficult to reach higher-level areas from the start as you'd be stuck with a scrappy little vessel.

We were playing that and it became very punitive to the player. Your ship would run out of fuel and the game would just stop. You just want to get back to what you're doing. So we recently changed it where the fuel in your ship and the grav drive limits how far you can go at once, but it doesn't run out of fuel.

Interestingly, Bethesda didn't just cut features that made travel tougher, but quality-of-life elements too. In the starmap, we can see a filter system that dataminer redsaltyborger says "appears to be an overlay for economy/trade". It would have also highlighted potential hazards, which is likely why it was cut, but fans are calling on Bethesda to return the feature so that they can see where star yards and trade authority vendors are.

As for whether Starfield will ever see such arduous space travel return as an optional difficulty mode, Howard did say that it could surface in a future update, perhaps akin to Fallout 4 and Skyrim's survival mode. He also said that a mod might bring it back, so it could be up to the community to restore these scrapped ideas.

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Starfield Fan Discovers Early Starmap, Hints At Harder Space Travel - TheGamer

Faster Space Travel with Advanced Technologies – BNN Breaking

Revolutionising Space Travel: Faster Times and Advanced Technology

In a significant stride towards expedited interplanetary travel, a new online calculator for Lamberts Targeting Problem (LTP) is allowing for the generation of launch and arrival v-infinity pork-chop plots for space missions between solar system targets. This development was discussed by Scott Manley, who highlighted the potential for faster travel times from Earth to Mars.

The SpaceX Starship, with its facility for orbital refueling, has the potential to carry additional fuel, thereby augmenting its speed and minimizing travel time to Mars. A spreadsheet elucidating the Delta-V calculations for the SpaceX Starship demonstrates the potential for increased velocity, primarily due to the performance of SpaceXs Raptor engines and the anticipated improvements with the new LEET 1337 engines.

These engines are postulated to be simpler, lighter, and more cost-effective, with a higher production volume. SpaceX is also contemplating the possibility of larger fuel tanks for the Starship. The travel time and fuel estimates are premised on a low earth orbit refueling scenario, but could see significant enhancement with the introduction of a reusable tug, which would propel the Mars-bound Starship to near Earth escape velocity, conserving onboard fuel for deceleration and landing phases.

Affordability in space travel is a critical factor, and SpaceX aims to reduce costs by manufacturing cheaper, fully reusable ships and engines, and by producing methane fuel from natural gas or by utilizing solar power on Earth and Mars. The key innovations include reducing the cost of ships and engines by factors of 100 to 1000 and achieving full reusability. Aerobraking in the Martian atmosphere is also being explored as a fuel-free method for landing on Mars, although it has a maximum effective speed.

The discussion also references academic papers providing an approximate analytical solution to the LTP, noting that the solar system bodies are assumed to move in Keplerian orbits and that the calculations can have errors of up to 15-20% when considering very inefficient transfer arcs.

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Faster Space Travel with Advanced Technologies - BNN Breaking

New study uses machine learning to bridge the reality gap in quantum devices – University of Oxford

A study led by the University of Oxford has used the power of machine learning to overcome a key challenge affecting quantum devices. For the first time, the findings reveal a way to close the reality gap: the difference between predicted and observed behaviour from quantum devices. The results have been published in Physical Review X.

Functional variability is presumed to be caused by nanoscale imperfections in the materials that quantum devices are made from. Since there is no way to measure these directly, this internal disorder cannot be captured in simulations, leading to the gap in predicted and observed outcomes.

To address this, the research group used a physics-informed machine learning approach to infer these disorder characteristics indirectly. This was based on how the internal disorder affected the flow of electrons through the device.

Lead researcher Associate Professor Natalia Ares (Department of Engineering Science, University of Oxford) said: As an analogy, when we play crazy golf the ball may enter a tunnel and exit with a speed or direction that doesnt match our predictions. But with a few more shots, a crazy golf simulator, and some machine learning, we might get better at predicting the balls movements and narrow the reality gap.

Associate Professor Ares added: In the crazy golf analogy, it would be equivalent to placing a series of sensors along the tunnel, so that we could take measurements of the balls speed at different points. Although we still cant see inside the tunnel, we can use the data to inform better predictions of how the ball will behave when we take the shot.

Not only did the new model find suitable internal disorder profiles to describe the measured current values, it was also able to accurately predict voltage settings required for specific device operating regimes.

Co-author David Craig, a PhD student at the Department of Materials, University of Oxford, added, Similar to how we cannot observe black holes directly but we infer their presence from their effect on surrounding matter, we have used simple measurements as a proxy for the internal variability of nanoscale quantum devices. Although the real device still has greater complexity than the model can capture, our study has demonstrated the utility of using physics-aware machine learning to narrow the reality gap.

The study 'Bridging the reality gap in quantum devices with physics-aware machine learning has been published in Physical Review X.

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New study uses machine learning to bridge the reality gap in quantum devices - University of Oxford

Daily AI Roundup: Biggest Machine Learning, Robotic And Automation Updates – AiThority

This is our AI Daily Roundup. We are covering the top updates from around the world. The updates will feature state-of-the-art capabilities inartificial intelligence (AI),Machine Learning, Robotic Process Automation, Fintech, and human-system interactions.

We cover the role of AI Daily Roundup and its application in various industries and daily lives.

Ahead ofNRF2024, the retail industrys largest event, Google Cloud debuted several new AI andgenerative AI-powered technologies to help retailers personalize online shopping, modernize operations, and transform in-store technology rollouts.

Quantiphi, a leading AI-first digital engineering company andLambda, the GPU cloud and AI infrastructure company founded by deep learning engineers, have partnered to provide tailored AI solutions to enterprise customers and digital AI natives across multiple industries.

Quanta Computer Inc., a trailblazer in advanced technology solutions, andAmbarella, Inc., an edge AI semiconductor company,announced duringCESthe expansion of their strategic partnership. This collaboration is being broadened to include development with Ambarellas CV3-AD, CV7 and new N1 series AI systems-on-chip (SoCs), marking a significant capabilities advancement for cutting-edge AI products.

Patronus AI announced it is partnering with MongoDB to bring automated LLM evaluation and testing to enterprise customers. The joint offering will combine Patronus AIs capabilities with MongoDBs Atlas Vector Search product.

In a strategic move that anticipates the imminent shift indigital advertising,ZeotapData, the leading provider of people-based digital audiences, has announced a partnership with Illuma, the leader in AI-powered expansion and optimisation. This collaboration offers a new tactic in the face of third-party cookie deprecation.

[To share your insights with us, please write tosghosh@martechseries.com]

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Daily AI Roundup: Biggest Machine Learning, Robotic And Automation Updates - AiThority

Toyota’s Robots Are Learning to Do HouseworkBy Copying Humans – WIRED

As someone who quite enjoys the Zen of tidying up, I was only too happy to grab a dustpan and brush and sweep up some beans spilled on a tabletop while visiting the Toyota Research Lab in Cambridge, Massachusetts last year. The chore was more challenging than usual because I had to do it using a teleoperated pair of robotic arms with two-fingered pincers for hands.

Courtesy of Toyota Research Institute

As I sat before the table, using a pair of controllers like bike handles with extra buttons and levers, I could feel the sensation of grabbing solid items, and also sense their heft as I lifted them, but it still took some getting used to.

After several minutes tidying, I continued my tour of the lab and forgot about my brief stint as a teacher of robots. A few days later, Toyota sent me a video of the robot Id operated sweeping up a similar mess on its own, using what it had learned from my demonstrations combined with a few more demos and several more hours of practice sweeping inside a simulated world.

Autonomous sweeping behavior. Courtesy of Toyota Research Institute

Most robotsand especially those doing valuable labor in warehouses or factoriescan only follow preprogrammed routines that require technical expertise to plan out. This makes them very precise and reliable but wholly unsuited to handling work that requires adaptation, improvisation, and flexibilitylike sweeping or most other chores in the home. Having robots learn to do things for themselves has proven challenging because of the complexity and variability of the physical world and human environments, and the difficulty of obtaining enough training data to teach them to cope with all eventualities.

There are signs that this could be changing. The dramatic improvements weve seen in AI chatbots over the past year or so have prompted many roboticists to wonder if similar leaps might be attainable in their own field. The algorithms that have given us impressive chatbots and image generators are also already helping robots learn more efficiently.

The sweeping robot I trained uses a machine-learning system called a diffusion policy, similar to the ones that power some AI image generators, to come up with the right action to take next in a fraction of a second, based on the many possibilities and multiple sources of data. The technique was developed by Toyota in collaboration with researchers led by Shuran Song, a professor at Columbia University who now leads a robot lab at Stanford.

Toyota is trying to combine that approach with the kind of language models that underpin ChatGPT and its rivals. The goal is to make it possible to have robots learn how to perform tasks by watching videos, potentially turning resources like YouTube into powerful robot training resources. Presumably they will be shown clips of people doing sensible things, not the dubious or dangerous stunts often found on social media.

If you've never touched anything in the real world, it's hard to get that understanding from just watching YouTube videos, Russ Tedrake, vice president of Robotics Research at Toyota Research Institute and a professor at MIT, says. The hope, Tedrake says, is that some basic understanding of the physical world combined with data generated in simulation, will enable robots to learn physical actions from watching YouTube clips. The diffusion approach is able to absorb the data in a much more scalable way, he says.

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Toyota's Robots Are Learning to Do HouseworkBy Copying Humans - WIRED

Use of Non-invasive Machine Learning to Help Predict the Chronic Degree of Lupus Nephritis – Lupus Foundation of America

Using a non-invasive machine learning model based on ultrasound radiomic imaging to analyze features of the kidneys, such as shape and texture, researchers were able to predict the degree of kidney injury in people with lupus nephritis, (LN, lupus-related kidney disease). Currently, a renal biopsy, an invasive test which can cause bleeding, pain and other outcomes, is the most common form of assessing a persons chronic degree of LN.

Using radiomics, the ultrasound images of 136 people with LN who had renal biopsies were examined. The images were divided into two groups, a training set and a validation set, and seven machine learning models were constructed based on five ultrasound-based radiomics to establish prediction models. The Xgboost model performed the best in the training and test sets.

Knowing the degree of kidney injury in people with LN can be useful to clinicians as they develop an individuals treatment plan. Learn more about lupus and the kidneys.

Read the study

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Use of Non-invasive Machine Learning to Help Predict the Chronic Degree of Lupus Nephritis - Lupus Foundation of America

Deep fake, AI and face swap in video edit. Deepfake and machine learning. Facial tracking, detection and recognition … – Frederick News Post

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Deep fake, AI and face swap in video edit. Deepfake and machine learning. Facial tracking, detection and recognition ... - Frederick News Post

Machine-learning model for predicting oliguria in critically ill patients | Scientific Reports – Nature.com

Subjects

This retrospective cohort study used the electronic health record data of consecutive patients admitted to the ICU at Chiba University Hospital, Japan, from November 2010 to March 2019. The annual number of patients admitted to the 22-bed surgical/medical ICU ranged from 1,541 to 1,832. We excluded patients on maintenance dialysis and those without a documented body weight. This study was approved by the Ethical Review Board of Chiba University Graduate School of Medicine (approval number: 3380) in accordance with the Declaration of Helsinki. The Ethical Review Board of Chiba University Graduate School of Medicine waived the requirement for written informed consent in accordance with the Ethical Guidelines for Medical and Health Research Involving Human Subjects in Japan.

We defined oliguria as urine output of less than 0.5mL/kg/h according to the Kidney Disease: Improving Global Outcomes stage I criteria. AKI was diagnosed based on an increase in serum creatinine level of at least 0.3mg/dL from the baseline or oliguria38.

Patient records from the ICU data system contained 1,031 input variables, including (A) physiological measurements acquired every minute (heart rate, blood pressure, respiratory rate, peripheral oxygen saturation, and body temperature), (B) blood tests (complete blood count, biochemistry, coagulation, and blood gas analysis), (C) name and dosage of medications, (D) type and amount of blood transfusion, (E) patient observation record, and (F) patient care record. The minute-by-minute time-series tables were aggregated into hourly time-series tables. In the process of aggregating the tables, the median value was used for physiological measurements and the blood test values were obtained from the most recent test. For patient excretion values, urine and stool volumes were calculated as one-hour sums. The following six calculated variables were added to the dataset: hourly intake, hourly output, hourly total balance, hourly urine volume (mL/kg), oliguria (urine volume of less than 0.5mL/kg/h), and oliguria for six consecutive hours. A total of 222 background information variables, including age, sex, and admission diagnosis, were also added to the dataset. Consequently, the dataset contained 1,127 variables. We treated the missing values as a separate group or excluded them from the analysis. To remove potential collinearity values, we performed a multicollinearity test and analyzed the data without these values.

The dataset was randomly divided: 80% for training and 20% for testing. We developed a sequential machine-learning model to predict oliguria at any given time during the ICU stay using hourly variables and baseline information (Fig.1). For the values that were not continuously obtained, we used the most recent ones for the model development. The input variables were updated to encompass a 1-h window of the preceding values for the physiological measurements, blood tests, and medications. The primary and secondary outcome variables were oliguria at 6 and 72h after an arbitrary time point from ICU admission to discharge, respectively. Accordingly, we used variables recorded until 6 or 72h before ICU discharge corresponding to each outcome variable. The outcome variable was not incorporated as a predictor in the final model. After constructing the algorithm with the training data, the model predictions were validated using the test data. We validated the model performance with a fivefold cross validation. To ensure that the estimated model probabilities aligned with the actual probabilities of oliguria occurrence, we plotted the calibration curve of the model. The curve indicated that our model was well calibrated (Supplementary File 1: Fig. S4).

We selected four representative machine learning classifiers: LightGBM, category boosting (CatBoost), random forest, and extreme gradient boosting (XGboost). Before developing the prediction model, we compared the computational performances and model accuracies using the four classifiers (Supplementary File 1: Table S2). To develop the machine learning algorithm, we used a cloud computer (Google Collaboratory memory 25GB) to evaluate the accuracy of the model. The AUC values based on the receiver operating calibrating curves, sensitivity, specificity, and F1 score were calculated. Among the machine learning classifiers, LightGBM showed the best computation speed and AUC and the second-best F1 score with a marginal difference from XGboost (XGboost 0.899, LightGBM 0.896). Based on these results, we decided to use LightGBM for the analysis in this study. After developing a prediction model with all the variables, we reduced the number of variables for prediction by selecting clinically relevant variables (Supplementary File: Table S2). Subsequently, we compared the performances of the LightGBM model using the selected variables and all the variables. As a sensitivity analysis, we re-analyzed the data using a different computer environment, Amazon Web Service Sagemaker. The computer settings included the following: image: Data Science 3.0, kernel: python 3, and instance type: ml.t3.medium (memory 64GB).

To evaluate the important variables contributing to building the prediction model, we used the SHAP value. The SHAP value indicates the impact of each feature on the model output, with higher interpretability in machine learning models. We expressed the SHAP value as an absolute number with a positive or negative association between the variable and outcome. SHAP individual force plots showed several features at scale with a color bar that indicated the feature contribution to the onset of oliguria in individual instances, enhancing the interpretability regarding the connection between traits and the occurrence of oliguria. For the subgroup analyses, we compared the accuracies of the models in predicting oliguria based on sex, age (65 or>66years), and furosemide administration. To quantify the differences in the AUC plots of the two groups, the absolute values of the differences in the AUCs of each group from 6 to 72h were summed and averaged to obtain the MAE.

Data were expressed as medians with interquartile ranges for continuous values and as absolute numbers and percentages for categorical values. A P value<0.05 was considered as statistically significant. The main Python packages used in the analysis to create the machine learning algorithms were Python 3.10.11, pandas 1.5.3, numpy 1.22.4, matplotlib 3.7.1, scikit-learn 1.2.2, XGboost 1.7.2, lightgbm 2.2.3, catboost 1.1.1, and shap 0.41.0.

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Machine-learning model for predicting oliguria in critically ill patients | Scientific Reports - Nature.com

Machine learning approaches for estimating interfacial tension between oil/gas and oil/water systems: a performance … – Nature.com

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Machine learning and computer vision can boost urban renewal – Hello Future Orange – Hello Future

Monday 8th of January 2024

Reading time: 3 min

In the 2010s, the city of New York set an example for urban authorities when it used big data to optimise public services. Since then, progress in machine learning has led to further advances in the field of data analysis. A new computer vision project has notably demonstrated how Google Street View images can now be used to monitor urban decay.

In a ground-breaking project in the 2010s, the city of New York reorganized a wide range of public services to take into account the analysis of big data collected by local authorities. These included measures to prune the citys trees, and to investigate buildings with high levels of fire risk, properties managed by slumlords, and restaurants illegally dumping cooking oil into public sewers. Since then, progress in the field of machine learning has continued to extend the potential for data-driven public initiatives, and scientists are also investigating the use of new data sources on which they could be based, among them two researchers from the universities of Stanford (California) and Notre-Dame (Indiana), who recently presented a new approach for the monitoring of urban decay in the journal Scientific Reports.

We wanted to highlight the flexibility of the approach rather than propose a method with a fixed set of features.

The algorithm developed by their project identifies eight visual features of urban decay in street-view images: potholes, barred or broken windows, dilapidated facades, tents, weeds, graffiti, garbage, and utility markings. Until now, the researchers note, the measurement of urban change has largely centred on quantifying urban growth, primarily by examining land use, land cover dynamics and changes in urban infrastructure.

The idea of their project was not so much to show all that can be done with street-view images, but rather to test the use of a single algorithm trained on data from several cities, and if necessary to retrain it without modifying its underlying structure. At the same time, it should be noted that the data being used was not collected by public authorities, but from a new source: Big data and machine learning are increasingly being used for public policies, points out Yong Suk Lee, an assistant professor at Notre-Dame, specializing in technology and urban economics. Our proposed method is complementary to these approaches. Our paper highlights the potential to add street-viewImages to the increasing toolkit of urban data analytics.

As the researchers explain, the automated analysis of images can facilitate the evaluation of the scope of deterioration: The measurement of urban decay is further complicated by the fact that on the ground measurements of urban environments are often expensive to collect, and can at times be more difficult, and even dangerous, to collect in deteriorating parts of the city..

The research project focused on images from three urban areas: the Tenderloin and Mission districts in San Francisco, Colonia Doctores and the historic centre of Mexico City, and the western part of South Bend, Indiana, an average size American town.

A single algorithm (YOLO) was trained twice on, on two different corpora. The first of these was composed of manually collected pictures from the streets of San Francisco and images of graffiti captured in Athens (Greece) from the STORM corpus. This dataset also included Google Street View shots of San Francisco, Los Angeles and Oakland with homeless peoples tents and tarps, and images of Mexico City. All of these were sourced from a multiyear period to measure ongoing change. Subsequently the Mexican pictures were withdrawn to create a second training dataset.

We initially worked with US data but decided to compare if adding data from Mexico City made a difference, explains Yong Suk Lee. Not surprisingly, the larger consolidated data set was better. Also, we tried different model sizes (number of parameters) to see the trade-offs between speed and performance. For example, the algorithm was better able to detect potholes and broken windows in San Francisco when the training data included images from Mexico City.

However, due to a lack of similar images of in its training corpus, the algorithm significantly underperformed when tested on more suburban spaces in South Bend, although it was largely successful in following local changes signalled by dilapidated facades and weeds. The results showed that towns of this type require a specially adapted training corpus. The features identifying decay could differ in other places. That is what we wanted to convey as well, by comparing different cities, points out the Notre-Dame researcher. We wanted to highlight the flexibility of the approach rather than propose a method with a fixed set of features. With its inherent flexibility and a vast amount of readily available source data in Google Street View, this new approach will likely feature many more future research projects.

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The 3 Best Machine Learning Stocks to Buy in January 2024 – InvestorPlace

Machine learning is transforming sectors including healthcare and transportation, offering lucrative opportunities in the best machine learning stocks. However, investors should approach cautiously, as not all stocks in this sector ensure returns. Discernment is key, as many firms claim advanced machine learning needs more solid business models or definitive applications.

Moreover, this sector branches into specialized niches, including data analysis and artificial intelligence (AI), with machine learning being a key driver. Some businesses have made remarkable strides in this space, demonstrating commendable growth and innovation. Their work within machine learning is remarkable, effectively reshaping the way we interact with technology. Subsequently, Statista projects that the machine-learning market will reach $204.30 billion by 2024.

Furthermore, machine learning stocks are gaining momentum, reflecting a growing fascination with AI. This expanding field holds substantial growth prospects, offering investors opportunities to support the innovators shaping our tech future. For those seeking the next breakthrough, machine learning stocks could be the secret to forge the billionaires of tomorrow.

Source: Claudio Divizia / Shutterstock.com

Amazon (NASDAQ:AMZN) has impressively evolved from a garage startup to the worlds second-largest company by revenue. A significant part of its 2023 success was achieving the fastest delivery speeds ever, particularly boosting its appeal in the consumables and everyday essentials market.

Impressively, Amazon shows robust growth in its financial performance, notably in the third quarter, with EPS of 94 cents, smashing the 60 cents forecast. The company revenue soared by 12.6% year over year (YOY) to $143.1 billion, beating expectations by $1.54 billion and showcasing its market strength and efficiency.

Furthermore, Amazon is boosting its Prime Video game, bringing in a pro from Walt Disney for its advertising push. Additionally, Amazon has been focused on developing a platform that appeals to businesses for machine learning purposes, creating a workflow pipeline to onboard companies of various sizes. This effort leverages AWS cloud technology to build AI models.

Source: Sergio Photone / Shutterstock.com

Nvidia (NASDAQ:NVDA) is pushing the frontiers of quantum computing with its cuQuantum project, revolutionizing qubit simulation.

Simultaneously, its spicing up the AI realm with the Omniverse Cloud, enabling developers to master Isaac AMRs for sophisticated, AI-enhanced robotics. This fusion of high-tech and utility delivers innovation with a snazzy edge.

In the third quarter, Nvidias financials were impressive. Their non-GAAP earnings per share soared to $4.02, surpassing estimates by 63 cents. Revenue rocketed to $18.12 billion, up an astonishing 205.6% YOY. Also, data center revenue hit a new high of $14.51 billion, cementing Nvidias strong standing in the tech sector.

Furthermore, unveiling the GeForce RTX 4090D GPU in China gave Nvidias stock an additional boost. Analyst Vivek Arya, holding a confident $700 price target, forecasts the company will generate an impressive $100 billion incremental free cash flow over 2024 and 2025. Nvidia is not just playing in the tech arena; its setting new benchmarks, making it a standout choice for investors.

Source: Pamela Marciano / Shutterstock.com

Advanced Micro Devices (NASDAQ:AMD), with a market capitalization of 244 billion, solidifies its prominent status in the semiconductor sector. Endorsed by investment firm UBS alongside Micron Technology (NASDAQ:MU) for 2024, AMDs robust market presence and growth prospects are recognized, signaling a promising future.

Financially, In the third quarter, AMDs non-GAAP earnings per share reached 70 cents, exceeding estimates by 2 cents. Revenue rose to $5.8 billion, a 4.1% increase from last year, beating expectations by $110 million. Particularly, client segment revenue, driven by robust Ryzen mobile processor sales, soared to $1.5 billion, up 42% YOY.

Moreover, AMD isnt just riding the wave. Its making its own with the MI300 chips, poised as rivals to Nvidias H100. This strategic move has attracted tech giants like Meta Platforms (NASDAQ:META) and Microsoft (NASDAQ:MSFT), who are lining up for AMDs innovative chips. In the high-stakes semiconductor game, AMD is not just playing. Its setting the pace.

On the date of publication, Muslim Farooque did not have (either directly or indirectly) any positions in the securities mentioned in this article.The opinions expressed in this article are those of the writer, subject to the InvestorPlace.comPublishing Guidelines.

Muslim Farooque is a keen investor and an optimist at heart. A life-long gamer and tech enthusiast, he has a particular affinity for analyzing technology stocks. Muslim holds a bachelors of science degree in applied accounting from Oxford Brookes University.

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Data Science Salon Seattle Spotlights Generative AI and Machine Learning in Retail and E-commerce – GlobeNewswire

SEATTLE, Jan. 11, 2024 (GLOBE NEWSWIRE) -- Data Science Salon (DSS), recognized as the most diverse data science and machine learning community in the U.S., is delighted to announce its upcoming Seattle event. Scheduled for January 24th, 2024, at the modern Block 41 venue, DSSSEA is designed to spark transformative and innovative conversations about the application of AI and Machine Learning in the retail and e-commerce sectors.

DSS Seattle is dedicated to unraveling the complexities and potential of generative AI and machine learning within retail and e-commerce. Industry professionals will gather to explore pivotal topics, including:

This one-day, 200-person conference provides expert talks with leading data scientists from prominent companies such as Nordstrom, eBay, Amazon, Pinterest and Google and ample opportunities for networking, and collaborative discussions. All sessions will be recorded and made available on-demand within two hours post-event, ensuring that the insights and learnings are accessible to a wider audience beyond the day of the conference. Pre-recorded virtual sessions will also be available prior to the event to get our attendees ready for all DSSSEA has to offer.

I am thrilled to be speaking about experimentation at the Data Science Salon in Seattle. I hope to learn about the latest trends and techniques in data science experimentation, and to share my own experiences and insights with fellow attendees. I am excited to connect with like-minded professionals and to further develop my skills in this fast-paced and rapidly evolving field, says Benjamin Skrainka, Data Science Manager at eBay and virtual speaker for DSSSEA.

We invite data science practitioners, retail strategists, and e-commerce specialists to join us at DSSSEA for a day of identifying new ways to use AI and ML in your field. Registration is now open.

For more information and to reserve your seat for the in-person or on-demand event, please visit https://www.datascience.salon/seattle/.

About Data Science Salon Data Science Salon elevates the conversation in data science and machine learning by connecting industry experts and practitioners in a collaborative, community-focused environment. With a commitment to diversity and the advancement of the field, DSS is shaping the future of data-driven decision-making.

For Media and Sponsorship Inquiries: Anna Anisin Phone: +1 305-215-4527 Email: anna.a@formulatedby.com

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Plagiarism Detection Tools Offer a False Sense of Accuracy The Markup – The Markup

When Katherine Pickering Antonova became a history professor in 2008, she got access to the plagiarism detection software tools Turnitin and SafeAssign. At first blush, she thought the technology would be great. She had just finished a graduate program where she had manually graded papers as a teaching assistant, meticulously checking students suspect phrases to see if any showed up elsewhere.

But her first use of the plagiarism checkers gave her a jolt. The software suggested the majority of her students had copied portions of their essays.

Soon she realized the lie in how the tools were described to her. Its not tracking plagiarism at all, Pickering Antonova said. Its just flagging matching text. Those two concepts have different standards; plagiarism is a subjective assessment of misconduct, but scholars may have matching words in their academic articles for a variety of legitimate reasons.

Plagiarism checkers are built into The City University of New Yorks learning management system, where faculty members post assignments and students submit them. As at many colleges throughout the country, scanning for plagiarism in submitted assignments is the default. But fed up with false flags and the countless hours required to check potentially plagiarized passages against the source material Turnitin and SafeAssign highlight, Pickering Antonova gave up on the tools entirely a couple years ago.

The bots are literally worse than useless, she said. They do harm, and they dont find anything I couldnt find by myself.

Some experts agree that Claudine Gay, Harvards ousted president and a widely respected political scientist, recently became the latest victim of this technology. She was forced to step down from the presidency after an accuser flagged nearly 50 examples from her writing that they called plagiarism. But many of the examples looked a lot like what Pickering Antonova considered a waste of her time when she was grading student work.

The Voting Rights Act of 1965 is often cited as one of the most significant pieces of civil rights legislation passed in our nations history, Gay wrote in one paper. Her accuser says she plagiarized David Canons description of the landmark lawbut as the Washington Free Beacon reported in publishing the allegations, Canon himself disagrees, arguing Gay had done nothing wrong.

The controversy over Gays alleged plagiarism has roiled the academic community, and while much of the attention has been on the political maneuvering behind her ouster and the definition of plagiarism, some scholars have commented on the detection software that was likely behind it. The fact is, however, that students, not academics, bear the brunt of the tools shoddy analyses. Turnitin is the industry leader in marshaling text analysis tools to assess academic integrity, boasting partnerships with more than 20,000 institutions globally and a repository of over 1.8 billion student paper submissions (and still counting).

The companies that are marketing plagiarism detection tools tend to acknowledge their limitations. While they may be referred to as plagiarism checkers, the products are described as highlighting text similarities or duplicate content. They scan billions of webpages and scholarly articles looking for those matches and surface them for a reviewer. Some, like Grammarlys, are marketed to writers and offer to help people add proper citations where they may have forgotten them. It isnt meant to police plagiarism, but rather help writers avoid it. Turnitin specifically says its Similarity Report does not check for plagiarism.

Still, the tools are frequently used to justify giving students zeroes on their assignmentsand the students most likely to get such dismissive grading are those at less-selective institutions, where faculty are overstretched and underpaid.

For her part, Pickering Antonova came to feel guilty about putting students through the stress of seeing their Turnitin results.

They see their paper is showing up 60 percent plagiarized, and they have a heart attack, she said.

Plagiarism does not carry a legal definition. Institutions create their own plagiarism policies, and academic fields have norms about how to credit and cite sources in scholarly text. Plagiarism checkers are not designed with such nuance. It is up to users to follow up their algorithmic output with good, human judgment.

Jo Guldi, a professor of quantitative methods at Emory University, recently published The Dangerous Art of Text Mining: A Methodology for Digital History and jumped into the Gay plagiarism controversy with a now-deleted post on X before Christmas. She pointed out that computers can search for five-word overlaps in text but argued that such repetition does not equal plagiarism: the technology of text mining can be used to destroy the career of any scholar at any time, she wrote.

By phone, Guldi said that while she didnt cover plagiarism detection in her book, the parallel is clear. Her book traces bad conclusions reached because people fail to critically analyze the data. She, too, has used Turnitin in her classes and recognized the findings cannot be taken at face value.

You look at them and you see you have to apply judgment, she said. Its always a judgment call.

Many scholars, including those Gay is supposed to have plagiarized, have come to Gays defense over the course of the last month, arguing the text similarities highlighted do not rise to the level of plagiarism.

Machine Learning

Stanford study found AI detectors are biased against non-native English speakers

Yet her accuser has identified nearly 50 examples of overlap, pairing her writing with that of other scholars and insisting there is a pattern of academic misconduct. The sheer number of examplesand promise of more to comehelped seal Gays fate. And some scholars worry anyone with enemies could be next.

Ian Bogost, a professor at Washington University in St. Louis, mulled in The Atlantic what a full-bore plagiarism war could look like, running his own dissertation through iThenticate, a checker run by the same company as Turnitin that is marketed to researchers, publishers, and scholars.

Bill Ackman, a billionaire Harvard megadonor, signaled his commitment to participating in such a war after Business Insider launched its own grenade, publishing an analysis last week that accused his wife, Neri Oxman, of plagiarizing parts of her dissertation. Oxman got her Ph.D. at MIT in 2010 before joining the faculty and then leaving to become an entrepreneur. Suspecting someone from MIT encouraged Business Insider to take a closer look at her dissertation, Ackman posted on X that he was going to begin a review of the work of all current @MIT faculty members, President Kornbluth, other officers of the Corporation, and its board members for plagiarism.

He later added, Why would we stop at MIT? Dont we have to do a deep dive into academic integrity at Harvard as well? What about Yale, Princeton, Stanford, Penn, Dartmouth? You get the point.

Its unclear which tool Gays accuser used to identify their examples, but experts agree the accusations seem to come from a text comparison algorithm. A Markup analysis of five of Gays papers in the Grammarly and EasyBib plagiarism checkers did not turn up any of the plagiarism accusations that have surfaced in recent months. Grammarlys tool did flag instances of text overlap between Gays writing and other scholars, sometimes because they were citing her paper, but sometimes because the two authors were simply describing similar things. Gays 2017 political science paper A Room for Ones Own? is the subject of more than half a dozen accusations of plagiarism that Grammarly didnt flagbut the tool did, for example, suggest her line The estimated coefficients and standard errors from the may have been plagiarized from an article about diabetes in Bali.

Analyzing the same paper, Turnitin ignored several of the lines included in complaints against her but it did flag four from two academic papers. It also found other similarities, suggesting, for example, that the phrase receive a 10-year stream of tax credits warranted review.

Credit:Turnitin

David Smith, an associate professor of computer science at Northeastern University, has studied natural language processing and computational linguistics. He said plagiarism detection tools tend to start with what is called a null model. The algorithm is given very few assumptions and simply told to identify matching words across texts. To find examples in Gays writing, he said, it basically took people looking through the really low-precision output of these models.

Machine Learning

A Markup examination of a typical college shows how students are subject to a vast and growing array of watchful tech, including homework trackers, test-taking software, and even license platereaders

Somebody could have trained a better model that had higher precision, Smith said. That doesnt seem to be how it went in this case.

The result was a long list of plagiarism accusations most scholars found baffling.

Turnitin introduced its similarity check in 2000. Since then, plagiarism analyses have become the norm for editors of some academic journals as well as many college and university faculty members. Yet the tool is not universal. Many users, like Pickering Antonova, have decided the software isnt worth the time and dont align with their teaching goals. This has created two distinct classes of people: those who are subjected to plagiarism checkers and those who are not. For professional academics, Gays case highlights the concern that anyone with a high profile who makes the wrong enemy could quickly become part of the former group.

For students, its often just a matter of their schools norms. Plagiarism checkers can seem like a straightforward assessment of the originality of student work, reporting a percentage of the paper that may have been plagiarized. For faculty members who dont have the time to look at the dozens of false flags, it can be easy to rely on the total percentage and grade accordingly.

This behavior worries Smith, the computer scientist. Getting a quantification makes it easier to just judge a lot of student papers at scale, he said. Thats not whats going on in the Claudine Gay case but is troubling about whats going on with students subjection to these methods.

Tech companies have produced a steady stream of new tools for educators concerned with students cheating, including AI detectors that followed the widespread adoption of ChatGPT. With each new tool comes a promise of scientific accuracy and cutting-edge analysis of unbiased data.

But as Claudine Gays case demonstratesand the threat of the plagiarism wars promisesplagiarism detection is far from precise.

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Plagiarism Detection Tools Offer a False Sense of Accuracy The Markup - The Markup

Minimizing the Reality Gap in Quantum Devices with Machine Learning – AZoQuantum

A major obstacle facing quantum devices has been solved by a University of Oxford study that leveraged machine learning capabilities. The results show how to bridge the reality gap, or the discrepancy between expected and observed behavior from quantum devices, for the first time. Physical Review X has published the findings.

Image Credit:metamorworks/Shutterstock.com

Numerous applications, such as drug development, artificial intelligence, financial forecasting, and climate modeling, might be significantly improved by quantum computing. However, this will necessitate efficient methods for combining and scaling separate quantum bits (also known as qubits). Inherent variability, which occurs when even seemingly similar units display distinct behaviors, is a significant obstacle to this.

It is assumed that nanoscale flaws in the materials utilized to create quantum devices are the source of functional variability. This internal disorder cannot be represented in simulations since these cannot be measured directly, which accounts for the discrepancy between expected and observed results.

The study team addressed this by indirectly inferring certain disease traits through the use of a physics-informed machine learning technique. This was predicated on how the devices intrinsic instability impacted the electron flow.

As an analogy, when we play crazy golf the ball may enter a tunnel and exit with a speed or direction that doesnt match our predictions. But with a few more shots, a crazy golf simulator, and some machine learning, we might get better at predicting the balls movements and narrow the reality gap.

Natalia Ares, Study Lead Researcher and Associate Professor, Department of Engineering Science, University of Oxford

One quantum dot device was used as a test subject, and the researchers recorded the output current across it at various voltage settings. A simulation was run using the data to determine the difference between the measured current and the theoretical current in the absence of an internal disturbance.

The simulation was forced to discover an internal disorder arrangement that could account for the results at all voltage levels by monitoring the current at numerous distinct voltage settings. Deep learning was combined with statistical and mathematical techniques in this method.

Ares added, In the crazy golf analogy, it would be equivalent to placing a series of sensors along the tunnel, so that we could take measurements of the balls speed at different points. Although we still cant see inside the tunnel, we can use the data to inform better predictions of how the ball will behave when we take the shot.

The novel model not only identified appropriate internal disorder profiles to explain the observed current levels, but it also demonstrated the ability to precisely forecast the voltage settings necessary for particular device operating regimes.

Most importantly, the model offers a fresh way to measure the differences in variability between quantum devices. This could make it possible to predict device performance more precisely and aid in the development of ideal materials for quantum devices. It could guide compensatory strategies to lessen the undesirable consequences of material flaws in quantum devices.

Similar to how we cannot observe black holes directly but we infer their presence from their effect on surrounding matter, we have used simple measurements as a proxy for the internal variability of nanoscale quantum devices. Although the real device still has greater complexity than the model can capture, our study has demonstrated the utility of using physics-aware machine learning to narrow the reality gap.

David Craig, Study Co-Author and PhD Student, Department of Materials, University of Oxford

Craig, D. L., et. al. (2023) Bridging the Reality Gap in Quantum Devices with Physics-Aware Machine Learning. Physical Review X. doi:10.1103/PhysRevX.14.011001

Source: https://www.ox.ac.uk/

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Machine Learning for Predicting Oliguria in Intensive Care Units | Healthcare News – Medriva

Intensive care units (ICUs) are critical environments that deal with high-risk patients, where early detection of complications can significantly improve patient outcomes. Oliguria, a condition characterized by low urine output, is a common concern in ICUs and often signals acute kidney injury (AKI). Early prediction of oliguria can lead to timely intervention and better management of patients. Recent studies have shown that machine learning, a branch of artificial intelligence, can be effectively used to predict the onset of oliguria in ICU patients.

A retrospective cohort study aimed to develop and evaluate a machine learning algorithm for predicting oliguria in ICU patients. The study used electronic health record data from 9,241 patients admitted to the ICU between 2010 and 2019. The machine learning model demonstrated high accuracy in predicting the onset of oliguria at 6 hours and 72 hours with Area Under the Curve (AUC) values of 0.964 and 0.916, respectively. This suggests that the machine learning model can be a valuable tool for early identification of patients at risk of developing oliguria, enabling prompt intervention and optimal management of AKI.

The machine learning model identified several important variables for predicting oliguria. These included urine values, severity scores (SOFA score), serum creatinine, oxygen partial pressure, fibrinogen, fibrin degradation products, interleukin 6, and peripheral temperature. By taking into account these variables, the model was able to provide accurate predictions. The use of machine learning also allows for the continuous update and improvement of the model as more data becomes available, increasing its predictive accuracy over time.

Interestingly, the models accuracy varied based on several factors, including sex, age, and furosemide administration. This highlights the complex nature of predicting oliguria and the need for personalized, patient-specific models. It also underlines the potential of machine learning to adapt and learn from varying patient characteristics, providing more precise and individualized predictions.

The utilization of machine learning is not limited to predicting oliguria. Another study aimed to develop a machine learning model for early prediction of adverse events and treatment effectiveness in patients with hyperkalemia, a condition characterized by high levels of potassium in the blood. This study, too, achieved promising results, underscoring the potential of machine learning to revolutionize various aspects of patient care in the ICU setting.

The use of machine learning models in healthcare, and particularly in intensive care units, is a promising avenue for improving patient outcomes. By predicting the onset of conditions like oliguria, these models can provide critical early warnings that allow healthcare providers to intervene promptly. However, its crucial to remember that these models are tools to assist clinicians and not replace their judgment. As research continues and more data becomes available, these models are expected to become even more accurate and valuable in the future.

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Machine Learning for Predicting Oliguria in Intensive Care Units | Healthcare News - Medriva

Machine Learning in Business: 5 things a Data Science course won’t teach you – Towards Data Science

The author shares some important aspects of Applied Machine Learning that can be overlooked in formal Data Science education.

If you feel that I used a clickbaity title for this article, Id agree with you but hear me out! I have managed multiple junior data scientists over the years and in the last few years I have been teaching an applied Data Science course to Masters and PhD students. Most of them have great technical skills but when it comes to applying Machine Learning to real-world business problems, I realized there were some gaps.

Below are the 5 elements that I wish data scientists were more aware of in a business context:

Im hoping that reading this will be helpful to junior and mid-level data scientists to grow their career!

In this piece, I will focus on a scenario where data scientists are tasked with deploying machine learning models to predict customer behavior. Its worth noting that the insights can be applicable to scenarios involving product or sensor behaviors as well.

Lets start with the most critical of all: the What that you are trying to predict. All subsequent steps data cleaning, preprocessing, algorithm, feature engineering, hyperparameters optimization become futile unless you are focusing on the right target.

In order to be actionable, the target must represent a behavior, not a data point.

Ideally, your model aligns with a business use case, where actions or decisions will be based on its output. By making sure the target you are using is a good representation of a customer behavior, it is easy for the business to understand and utilize these models outputs.

Read more from the original source:
Machine Learning in Business: 5 things a Data Science course won't teach you - Towards Data Science

Genomic prediction in multi-environment trials in maize using statistical and machine learning methods | Scientific … – Nature.com

Phenotypic data

The data are composed of 265 single cross hybrids from the maize breeding program of Embrapa Maize and Sorghum evaluated in eight combinations of trials/locations/years under irrigated trials (WW) and water stress (WS) conditions at two locations in Brazil (JanabaMinas Gerais and TeresinaPiau) over two years (2010 and 2011). The hybrids were obtained from crosses between 188 inbred lines and two testers. The inbred lines belong to heterotic groups: dent (85 inbred lines), flint (86 inbred lines), and an additional group, referred to as group C (17 inbred lines), which is unrelated to the dent and flint origins. The two testers are inbred lines belonging to the flint (L3) and dent (L228-3) groups. Among the inbred lines, 120 were crossed with both testers, 52 were crossed with the L228-3 tester only, and 16 lines were crossed with the L3 tester only. Silva et al. (2020) evaluated the genetic diversity and heterotic groups in the same database. These authors showed the existence of subgroups within each heterotic group. Therefore, once these groups were not genetically well defined and the breeding program from Embrapa Maize was in the beginning, the effect of allelic substitution in both groups are assumed to be the same. More details on the experimental design and procedures can be found in Dias et al.13,30.

The experiment originally included 308 entries, but hybrids that were not present in all environments were also removed to evaluate the genomic prediction within each environment, resulting in a total of 265 hybrids for analysis. Each trial consisted of 308 maize single cross hybrids, randomly divided into six sets: sets 13 for crosses with L3 (61, 61, and 14 hybrids each), and sets 46 for crosses with L228-3 (80, 77, and 15 hybrids each). Four checks (commercial maize cultivars) were included in each set, and the experiment was designed in completely randomized blocks. Between trials, hybrids within each set remained the same, but hybrids and checks were randomly allocated into groups of plots within each set. This allocation varied between replicates of sets and between trials. The WS trials had three replications, except for the set containing 15 hybrids and the trials evaluated in 2010, which had two replications. All WW trials, except for the trial in 2011, had two replicates.

Two agronomic traits related to drought tolerance were analyzed: grain yield (GY) and female flowering time (FFT). GY was determined by weighing all grains in each plot, adjusted for 13% grain moisture, and converted to tons per hectare (t/ha), accounting for differences in plot sizes across trials. FFT was measured as the number of days from sowing until the stigmas appeared in 50% of the plants. A summary of means, standard deviations, and ranges of both evaluated traits are available in Table 1.

To conduct the analyses, hybrids considered as outliers were removed (i.e., hybrids that presented phenotypic values greater than 1.5interquartile range above the third quartile or below the first quartile) for the GY and FFT traits. The variations in predictive abilities among hybrids of T2, T1, and T0 are widely recognized31. However, the primary aim of our study was to compare different prediction methodologies in MET assays. In this study, there were 240 T2 hybrids and 68 T1 hybrids, with T2 hybrids had both parents evaluated in different hybrid combinations, while hybrids being single-cross hybrids sharing one parent with the tested hybrids. Given the realistic nature of our scenario, we have a limited and imbalanced distribution of these hybrid groups, making a fair comparison challenging. Consequently, we opted to construct a training set comprising T2 and T0 hybrids.

To correct the phenotypic values for experimental design effects, each trial (WW and WS) and environment were analyzed independently to obtain the Best Linear Unbiased Estimator (eBLUEs) for each hybrid, for the two traits evaluated. The estimates were obtained based on the following model:

$${varvec{y}} = 1mu + user2{ X}_{1} {varvec{r}} + {varvec{X}}_{2} {varvec{s}} + {varvec{X}}_{3} {varvec{h}} + {varvec{e}}$$

(1)

where (user2{y }left( {n times 1} right)) is the phenotype vector for (f) replicates, (t) sets of (p) hybrids, and (n) is the number of observations; (mu) is the mean; (user2{r }left( {f times 1} right)) is the fixed effect vector of the replicates; (user2{s }left( {t times 1} right)) is the fixed effect vector of the sets; ({varvec{h}}) (left( {p times 1} right)) is the fixed effect vector of the hybrids; and (user2{e }left( {k times 1} right)) is the residue vector, with (user2{ e} sim ,MVNleft( {0,{varvec{I}}sigma_{e}^{2} } right)), where ({varvec{I}}) is an identity matrix of corresponding order, and (sigma_{e}^{2}) the residual variance. ({varvec{X}}_{{1user2{ }}} left( {k times f} right)), ({varvec{X}}_{{2user2{ }}} left( {k times t} right)) e ({varvec{X}}_{{3user2{ }}} left( {k times p} right)) represents incidence matrices for their respective effects. The eBLUES of each environment were used in further analyses.

A total of 57,294 Single Nucleotide Polymorphisms (SNPs) markers were obtained from 188 inbred lines, and two testers used as parents of the 265 single cross hybrids. The genotyping by sequencing (GBS) strategyare detailed in Dias et al.13. For the quality control, SNPs were discarded if: the minor allele frequency was smaller than 5%, more than 20% of missing genotypes were found, and/or there were more than 5% of heterozygous genotypes. After filtering, missing data were imputed using NPUTE. Then, for each SNP, the genotypes of the hybrids were inferred based on the genotype of their parents (inbred line and tester). The number of SNPs per chromosome ranged from 3121 (chromosome 10) to 7705 (chromosome 1), totalizing 47,127 markers.

The additive and dominance genomic relationship matrices were constructed32 based on information from the SNPs using the package AGHmatrix33, following VanRaden34 and Vitezica et al., respectively.

Genomic predictions were performed using the Genomic Best Line Unbiased Prediction (GBLUP) method using the package AsReml v. 436. Two groups were considered: the first group comprised four environments under WW conditions, and the second included four environments under WS conditions. The linear model is described below:

$$overline{user2{y}} = mu 1 + user2{ Xb} + {varvec{Z}}_{1} {varvec{u}}_{{varvec{a}}} + {varvec{Z}}_{2} {varvec{u}}_{{varvec{d}}} + {varvec{e}}$$

(2)

where (user2{overline{y} }left( {pq times 1} right)) is the vector of eBLUES previously estimated for each environment with (p) hybrids and (q) environments;(mu) is the mean; (user2{b }left( {q times 1} right)) is the vector of environmental effects (fixed); ({varvec{u}}_{{user2{a }}} left( {pq times 1} right)) is the vector of individual additive genetic values nested within environments (random), with ({varvec{u}}_{{varvec{a}}} sim MVNleft( {0,left[ {{varvec{I}}_{{varvec{q}}} sigma_{{u_{a} }}^{2} + rho_{a} left( {{varvec{J}}_{{varvec{q}}} - {varvec{I}}_{{varvec{q}}} } right)} right] otimes {varvec{A}}} right)), where ({varvec{A}}) is the genomic relationship matrix between individuals for additive effects, (rho_{a}) is the additive genetic correlation coefficient between environments, ({varvec{I}}_{{varvec{q}}} user2{ }left( {q times q} right)) is an identity matrix, ({varvec{J}}_{{varvec{q}}} user2{ }left( {q times q} right)) is a matrix of ones, and (otimes) denotes the Kronecker product; ({varvec{u}}_{{varvec{d}}}) (left( {pq times 1} right)) is the vector of individual dominance genetic values nested within environments (random), with ({varvec{u}}_{{varvec{d}}} sim MVNleft( {0,left[ {{varvec{I}}_{{varvec{q}}} sigma_{{u_{d} }}^{2} + rho_{d} left( {{varvec{J}}_{{varvec{q}}} - {varvec{I}}_{{varvec{q}}} } right)} right] otimes {varvec{D}}} right)), where ({varvec{D}}) is the genomic relationship matrix between individuals for dominance effects, (rho_{{varvec{d}}}) is the dominance correlation coefficient between environments; ({varvec{e}}) (left( {pq times 1} right)) is the random residuals vector with ({varvec{e}}sim MVNleft( {0,{varvec{I}}sigma_{e}^{2} } right)). The capital letters (user2{X }left( {pq times q} right),user2{ Z}_{1} left( {pq times pq} right)) and ({varvec{Z}}_{2} user2{ }left( {pq times pq} right)) represent the incidence matrices for their respective effects, (1user2{ }left( {pq times 1} right)) is a vector of ones. The (co)variance components were obtained using the residual maximum likelihood method (REML)37.

Two alternative models were also used. The first for genomic prediction retained only additive effects by removing ({varvec{u}}_{{varvec{d}}}) from Eq.(2). The second model was used to estimate the genetic parameters within each environment separately.

The significance of random effects was tested using the Likelihood Ratio Test (LRT)38, given by:

$$LRT = 2*left( {LogL_{c} - LogL_{r} } right)$$

(3)

where (LogL_{c}) is the logarithm of the likelihood function of the complete model (with all effects included), and (LogL_{r}) is the logarithm of the restricted likelihood function of the reduced model (without the effect under test). Effect significance was tested by LRT using the chi-square (X2) probability density function with a degree of freedom and significance level of 5%39.

The narrow-sense heritability (({ }h^{2})), the proportion of variance explained by dominance effects ((d^{2})), and the broad-sense heritability (left( {H^{2} } right)) for each trait were estimated following Falconer and Mackay 199635.

Similar to the previous topic, the trials were divided between WW and WS conditions, and the potential of regression trees (RT) was explored using the following three algorithms: bagging, random forest, and boosting22. Bagging (Bag) is a methodology that aims to reduce the RT variance22. In other words, it consists of obtaining D samples with available sampling replacement, thus obtaining D models (hat{f}^{1} left( x right), hat{f}^{2} left( x right), ldots , hat{f}^{D} left( x right)), and finally use the generated models to obtain an average, given by:

$$hat{f}_{medio} left( x right) = frac{1}{D}mathop sum limits_{d = 1}^{D} hat{f}^{d} left( x right)$$

(4)

This decreases the variability obtained in the decision trees. The number of trees used in Bag is not a parameter that will result in overfitting of the model. In practice, a number of trees is used until the error has stabilized22. The number of trees sampled for Bag was set at 500 trees.

Random forest (RF) was proposed by HO40 and it is an improvement of Bag to avoid the high correlation of the trees and to improve the accuracy in the selection of individuals. RF changes only the number of predictor variables used in each split. That is, each time a split in a tree is considered, a random sample of (m) variables is chosen as candidates from the complete set of (p) variables. Hastie et al.21 suggest that the number of predictor variables used in each partition is equal to (m = frac{p}{3}) for regression trees. The number of trees for the RF was set at 500.

Boosting uses RT by adjusting the residual of an initial model. The residual is updated with each tree that grows sequentially from the previous tree's residual, and the response variable involves a combination of a large number of trees, such that:

$$hat{f}left( x right) = mathop sum limits_{b = 1}^{B} {uplambda } hat{f}^{b} left( x right)$$

(5)

The function (hat{f}left( . right)) refers to the final tree combined with sequentially adjusted trees, and is the shrinkage parameter that controls the learning rate of the method. Furthermore, this method needs to be adjusted with several splits in each of the trees. This parameter controls the complexity of the Boost and is known as the depth. For Boosting, the number of trees sampled was 250, with a learning rate of 0.1 and a depth of 3.

To perform hybrid prediction for each environment based on MET dataset, we propose the incorporation of location and year information in which the experiments were carried out as factors in the data input file together with SNPs markers as predictors in machine learning methodologies. As a response variable, the eBLUEs previously estimated by Eq.(1) were used.

For the construction of the bagging and random forest models, the randomForest function from the package randomForest41 was used. Finally, the package's gbm function gbm42 was used for boosting. All analyzes were implemented in the software R43.

Genomic predictions were carried out following Burgueo et al.16, considering two different prediction problems, CV1 and CV2, which simulate two possible scenarios a breeder can face. In CV1, the ability of the algorithms to predict the performance of hybrids that have not yet been evaluated in any field trial was evaluated. Thus, predictions derived from the CV1 scenario are entirely based on phenotypic and genotypic records from other related hybrids. In CV2, the ability of the algorithms to predict the performance of hybrids using data collected in other environments was evaluated. It simulates the prediction problem found in incomplete MET trials. Here, information from related individuals is used, and the prediction can benefit from genetic relationships between hybrids and correlations between environments. Within the CV2 scenario, two different situations of data imbalance were evaluated. In the first, called CV2 (50%), the tested hybrids were not present in half of the environments, while in the second, called CV2 (25%), the tested hybrids were not present in only 25% of the environments. Table 2 provides a hypothetical representation of this CV1, CV2 (50%), and CV2 (25%) validation scheme.

To separate the training and validation sets, the k-folds procedure was used, considering (k = 5). The set of 265 hybrids was divided into five groups, with 80% of the hybrids considered as the training population, and the remaining 20% hybrids considered as the validation population. The hybrids were separated into sets proportionally containing all the crosses performed (DentDent, DentFlint, FlintFlint, CDent, CFlint). The cross-validation process was performed separately for each trait, condition (WS or WW) and scenario (CV1, CV2-50% and CV2-25%) and was repeated five times to assess the predictive ability of the analyses.

The predictive ability within each environment for the conditions (WS and WW) was estimated by the Pearson correlation coefficient44 between the corrected phenotypic values (eBLUES) of Eq.(1) for each environment and the GEBVs predicted by each fitted method.

The authors confirm that all methods were carried out by relevant guidelines in the method section. The authors also confirm that the handling of the plant materials used in the study complies with relevant institutional, national, and international guidelines and legislation.

The authors confirm that the appropriate permissions and/or licenses for collection of plant or seed specimens are taken.

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Genomic prediction in multi-environment trials in maize using statistical and machine learning methods | Scientific ... - Nature.com