Emerging research suggests exposure to wildfire smoke may alter the immune system for years.
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Thursday, June 27, 2019
Wednesday, June 26, 2019
Drag-and-drop data analytics
In the Iron Man movies, Tony Stark uses a holographic computer to project 3-D data into thin air, manipulate them with his hands, and find fixes to his superhero troubles. In the same vein, researchers from MIT and Brown University have now developed a system for interactive data analytics that runs on touchscreens and lets everyone — not just billionaire tech geniuses — tackle real-world issues.
For years, the researchers have been developing an interactive data-science system called Northstar, which runs in the cloud but has an interface that supports any touchscreen device, including smartphones and large interactive whiteboards. Users feed the system datasets, and manipulate, combine, and extract features on a user-friendly interface, using their fingers or a digital pen, to uncover trends and patterns.
In a paper being presented at the ACM SIGMOD conference, the researchers detail a new component of Northstar, called VDS for “virtual data scientist,” that instantly generates machine-learning models to run prediction tasks on their datasets. Doctors, for instance, can use the system to help predict which patients are more likely to have certain diseases, while business owners might want to forecast sales. If using an interactive whiteboard, everyone can also collaborate in real-time.
The aim is to democratize data science by making it easy to do complex analytics, quickly and accurately.
“Even a coffee shop owner who doesn’t know data science should be able to predict their sales over the next few weeks to figure out how much coffee to buy,” says co-author and long-time Northstar project lead Tim Kraska, an associate professor of electrical engineering and computer science in at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and founding co-director of the new Data System and AI Lab (DSAIL). “In companies that have data scientists, there’s a lot of back and forth between data scientists and nonexperts, so we can also bring them into one room to do analytics together.”
VDS is based on an increasingly popular technique in artificial intelligence called automated machine-learning (AutoML), which lets people with limited data-science know-how train AI models to make predictions based on their datasets. Currently, the tool leads the DARPA D3M Automatic Machine Learning competition, which every six months decides on the best-performing AutoML tool.
Joining Kraska on the paper are: first author Zeyuan Shang, a graduate student, and Emanuel Zgraggen, a postdoc and main contributor of Northstar, both of EECS, CSAIL, and DSAIL; Benedetto Buratti, Yeounoh Chung, Philipp Eichmann, and Eli Upfal, all of Brown; and Carsten Binnig who recently moved from Brown to the Technical University of Darmstadt in Germany.
An “unbounded canvas” for analytics
The new work builds on years of collaboration on Northstar between researchers at MIT and Brown. Over four years, the researchers have published numerous papers detailing components of Northstar, including the interactive interface, operations on multiple platforms, accelerating results, and studies on user behavior.
Northstar starts as a blank, white interface. Users upload datasets into the system, which appear in a “datasets” box on the left. Any data labels will automatically populate a separate “attributes” box below. There’s also an “operators” box that contains various algorithms, as well as the new AutoML tool. All data are stored and analyzed in the cloud.
The researchers like to demonstrate the system on a public dataset that contains information on intensive care unit patients. Consider medical researchers who want to examine co-occurrences of certain diseases in certain age groups. They drag and drop into the middle of the interface a pattern-checking algorithm, which at first appears as a blank box. As input, they move into the box disease features labeled, say, “blood,” “infectious,” and “metabolic.” Percentages of those diseases in the dataset appear in the box. Then, they drag the “age” feature into the interface, which displays a bar chart of the patient’s age distribution. Drawing a line between the two boxes links them together. By circling age ranges, the algorithm immediately computes the co-occurrence of the three diseases among the age range.
“It’s like a big, unbounded canvas where you can lay out how you want everything,” says Zgraggen, who is the key inventor of Northstar’s interactive interface. “Then, you can link things together to create more complex questions about your data.”
Approximating AutoML
With VDS, users can now also run predictive analytics on that data by getting models custom-fit to their tasks, such as data prediction, image classification, or analyzing complex graph structures.
Using the above example, say the medical researchers want to predict which patients may have blood disease based on all features in the dataset. They drag and drop “AutoML” from the list of algorithms. It’ll first produce a blank box, but with a “target” tab, under which they’d drop the “blood” feature. The system will automatically find best-performing machine-learning pipelines, presented as tabs with constantly updated accuracy percentages. Users can stop the process at any time, refine the search, and examine each model’s errors rates, structure, computations, and other things.
According to the researchers, VDS is the fastest interactive AutoML tool to date, thanks, in part, to their custom “estimation engine.” The engine sits between the interface and the cloud storage. The engine leverages automatically creates several representative samples of a dataset that can be progressively processed to produce high-quality results in seconds.
“Together with my co-authors I spent two years designing VDS to mimic how a data scientist thinks,” Shang says, meaning it instantly identifies which models and preprocessing steps it should or shouldn’t run on certain tasks, based on various encoded rules. It first chooses from a large list of those possible machine-learning pipelines and runs simulations on the sample set. In doing so, it remembers results and refines its selection. After delivering fast approximated results, the system refines the results in the back end. But the final numbers are usually very close to the first approximation.
“For using a predictor, you don’t want to wait four hours to get your first results back. You want to already see what’s going on and, if you detect a mistake, you can immediately correct it. That’s normally not possible in any other system,” Kraska says. The researchers’ previous user study, in fact, “show that the moment you delay giving users results, they start to lose engagement with the system.”
The researchers evaluated the tool on 300 real-world datasets. Compared to other state-of-the-art AutoML systems, VDS’ approximations were as accurate, but were generated within seconds, which is much faster than other tools, which operate in minutes to hours.
Next, the researchers are looking to add a feature that alerts users to potential data bias or errors. For instance, to protect patient privacy, sometimes researchers will label medical datasets with patients aged 0 (if they do not know the age) and 200 (if a patient is over 95 years old). But novices may not recognize such errors, which could completely throw off their analytics.
“If you’re a new user, you may get results and think they’re great,” Kraska says. “But we can warn people that there, in fact, may be some outliers in the dataset that may indicate a problem.”
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A new way to make droplets bounce away
In many situations, engineers want to minimize the contact of droplets of water or other liquids with surfaces they fall onto. Whether the goal is keeping ice from building up on an airplane wing or a wind turbine blade, or preventing heat loss from a surface during rainfall, or preventing salt buildup on surfaces exposed to ocean spray, making droplets bounce away as fast as possible and minimizing the amount of contact with the surface can be key to keeping systems functioning properly.
Now, a study by researchers at MIT demonstrates a new approach to minimizing the contact between droplets and surfaces. While previous attempts, including by members of the same team, have focused on minimizing the amount of time the droplet spends in contact with the surface, the new method instead focuses on the spatial extent of the contact, trying to minimize how far a droplet spreads out before bouncing away.
The new findings are described in the journal ACS Nano in a paper by MIT graduate student Henri-Louis Girard, postdoc Dan Soto, and professor of mechanical engineering Kripa Varanasi. The key to the process, they explain, is creating a series of raised ring shapes on the material’s surface, which cause the falling droplet to splash upward in a bowl-shaped pattern instead of flowing out flat across the surface.
The work is a followup on an earlier project by Varanasi and his team, in which they were able to reduce the contact time of droplets on a surface by creating raised ridges on the surface, which disrupted the spreading pattern of impacting droplets. But the new work takes this farther, achieving a much greater reduction in the combination of contact time and contact area of a droplet.
In order to prevent icing on an airplane wing, for example, it is essential to get the droplets of impacting water to bounce away in less time than it takes for the water to freeze. The earlier ridged surface did succeed in reducing the contact time, but Varanasi says “since then, we found there’s another thing at play here,” which is how far the drop spreads out before rebounding and bouncing off. “Reducing the contact area of the impacting droplet should also have a dramatic impact on transfer properties of the interaction,” Varanasi says.
The team initiated a series of experiments that demonstrated that raised rings of just the right size, covering the surface, would cause the water spreading out from an impacting droplet to splash upward instead, forming a bowl-shaped splash, and that the angle of that upward splash could be controlled by adjusting the height and profile of those rings. If the rings are too large or too small compared to the size of the droplets, the system becomes less effective or doesn’t work at all, but when the size is right, the effect is dramatic.
It turns out that reducing the contact time alone is not sufficient to achieve the greatest reduction in contact; it’s the combination of the time and area of contact that’s critical. In a graph of the time of contact on one axis, and the area of contact on the other axis, what really matters is the total area under the curve — that is, the product of the time and the extent of contact. The area of the spreading was “was another axis that no one has touched” in previous research, Girard says. “When we started doing so, we saw a drastic reaction,” reducing the total time-and-area contact of the droplet by 90 percent. “The idea of reducing contact area by forming ‘waterbowls’ has far greater effect on reducing the overall interaction than by reducing contact time alone,” Varanasi says.
As the droplet starts to spread out within the raised circle, as soon as it hits the circle’s edge it begins to deflect. “Its momentum is redirected upward,” Girard says, and although it ends up spreading outward about as far as it would have otherwise, it is no longer on the surface, and therefore not cooling the surface off, or leading to icing, or blocking the pores on a “waterproof” fabric.
Credit: Henri-Louis Girard, Dan Soto, and Kripa Varanas
The rings themselves can be made in different ways and from different materials, the researchers say — it’s just the size and spacing that matters. For some tests, they used rings 3-D printed on a substrate, and for others they used a surface with a pattern created through an etching process similar to that used in microchip manufacturing. Other rings were made through computer controlled milling of plastic.
While higher-velocity droplet impacts generally can be more damaging to a surface, with this system the higher velocities actually improve the effectiveness of the redirection, clearing even more of the liquid than at slower speeds. That’s good news for practical applications, for example in dealing with rain, which has relatively high velocity, Girard says. “It actually works better the faster you go,” he says.
In addition to keeping ice off airplane wings, the new system could have a wide variety of applications, the researchers say. For example, “waterproof” fabrics can become saturated and begin to leak when water fills up the spaces between the fibers, but when treated with the surface rings, fabrics kept their ability to shed water for longer, and performed better overall, Girard says. “There was a 50 percent improvement by using the ring structures,” he says.
The research was supported by MIT’s Deshpande Center for Technological Innovation.
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Rappers want fellow artist Loon out of prison and are petitioning the White House for it
A group of rappers have banded together to rally behind Harlem rapper Loon and petitioned President Trump to advocate for his release from prison where he’s serving a 14-year sentence.
Born Chauncey Lamont Hawkins, the “How You Want That” rapper was once signed to Puff Daddy’s Bad Boy Records but was arrested in 2011 and in 2013 was convicted on drug charges. Loon is serving time for conspiracy with intent to traffic one or more kilos of heroin and his fellow rap artists thinks he’s got a chance to get out.
READ MORE: Meek Mill, now a soldier for reform, gets recognition for his stance against injustice in the system
One precedent to site in a case like this is Kim Kardashian who met with Trump and ultimately had a hand in getting Alice Johnson her freedom after her drug conviction. Kardashian has been working with the Buried Alive Project to help win freedom for at least 17 inmates.
Vibe reports that a number of artists signed a letter addressed to Trump on June 13, including Snoop Dogg, Kevin Garnett, Jason Flom, movie producer Scott Budnick, Faith Evans, Stevie J, Roc Nation rapper Freeway, model Jeremy Meeks.
Loon has since changed his name to Amir Junaid Muhadith, converted to Islam and put his music career behind him in 2008.
In April he wrote a statement about his fight to get released:
“This administration’s commitment to criminal justice reform has given me hope that I might see justice in my case,” Loon said, according to AllHipHop.
Loon told Vibe: “It is only through the overwhelming push by this administration to change the state of our criminal justice system that real progress is finally being made.”
“Alongside an extraordinary group of individuals such as Weldon Angelos, Jason Flom, Faith Evans, Kevin Garnett, Jessica Jackson Sloan at #cut50 and so many others who are not only advocating on my behalf but seek to support broader change for a broken and unjust system. It is through my own desire for change and the support of so many that I return back to society as an asset to my community, a loving husband and father, and an advocate in our battle for real criminal justice reform.”
READ MORE: Cardi B’s post-indictment Instagram photo teases new movie ‘Hustlers’
In the petition which was organized by former producer Weldon Angelos, they wrote to Trump:
“We strongly urge you to grant him [Loon] a presidential commutation of sentence without delay,” he wrote. “It’s ridiculous that this talented individual was given such a long sentence for merely making an introduction. What purpose is served by keeping him in prison? He completely changed his life around years before he was indicted. This is just another example of a wasteful and destructive criminal justice system.”
If things don’t work out. Loon is scheduled to be out anyway in November 2021.
The post Rappers want fellow artist Loon out of prison and are petitioning the White House for it appeared first on theGrio.
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