“Today, tools like drones, digital recorders, and artificial intelligence are helping us listen to the sounds of nature in unprecedented ways,” writes Vox, citing Karen Bakker, author of the new book Sounds of Life: How Digital Technology Is Bringing Us Closer to the Worlds of Animals and Plants.

But how far will this lead?
Automated listening posts have been set up in ecosystems around the planet, from rainforests to the depths of the ocean, and miniaturization has allowed scientists to stick microphones onto animals as small as honeybees. “Combined, these digital devices function like a planetary-scale hearing aid: enabling humans to observe and study nature’s sounds beyond the limits of our sensory capabilities,” Bakker writes.

All those devices create a ton of data, which would be impossible to go through manually. So researchers in the fields of bioacoustics (which studies sounds made by living organisms) and ecoacoustics (which studies the sounds made by entire ecosystems) are turning to artificial intelligence to sift through the piles of recordings, finding patterns that might help us understand what animals are saying to each other. There are now databases of whale songs and honeybee dances, among others, that Bakker writes could one day turn into “a zoological version of Google Translate.”

In an interview with Vox, the author points out that already “We can use artificial intelligence-enabled robots to speak animal languages and essentially breach the barrier of interspecies communication. Researchers are doing this in a very rudimentary way with honeybees and dolphins and to some extent with elephants.

“Now, this raises a very serious ethical question…”
I’ll give you one example. A research team in Germany encoded honeybee signals into a robot that they sent into a hive. That robot is able to use the honeybees’ waggle dance communication to tell the honeybees to stop moving, and it’s able to tell those honeybees where to fly to for a specific nectar source. The next stage in this research is to implant these robots into honeybee hives so the hives accept these robots as members of their community from birth. And then we would have an unprecedented degree of control over the hive; we’ll have essentially domesticated that hive in a way we’ve never done so before. This creates the possibility of exploitive use of animals. And there’s a long history of the military use of animals, so that’s one path that I think raises a lot of alarm bells.

Read more of this story at Slashdot.

Source: Slashdot – Can Technology Help Us Talk to Animals?

The Jerusalem Post reports that “Certain star clusters do not seem to be following current understandings of Isaac Newton’s laws of gravity, according to new research published on Wednesday.” (Specifically open star clusters, formed when thousands of stars are born in a gas cloud within a short time period.)

The researchers examined data on each stars’ velocity, direction of motion and age using data from the European Space Agency’s Gaia mission that allowed them to count the stars accurately for the very first time. The results?

As the clusters dissolve, the stars accumulate on two “tidal tails:” one pulled behind the cluster and the other pushed forward. “According to Newton’s laws of gravity, it’s a matter of chance in which of the tails a lost star ends up,” explains Dr. Jan Pflamm-Altenburg of the Helmholtz Institute of Radiation and Nuclear Physics at the University of Bonn. “So both tails should contain about the same number of stars. However, in our work we were able to prove for the first time that this is not true: In the clusters we studied, the front tail always contains significantly more stars nearby to the cluster than the rear tail….”

When the researchers looked at the data, they found that it did not fit Newton’s law of gravity and instead fit better with an alternate theory called Modified Newtonian Dynamics (MOND).

“Put simply, according to MOND, stars can leave a cluster through two different doors,” explained Prof. Dr. Pavel Kroupa of the Helmholtz Institute of Radiation and Nuclear Physics. “One leads to the rear tidal tail, the other to the front. However, the first is much narrower than the second — so it’s less likely that a star will leave the cluster through it. Newton’s theory of gravity, on the other hand, predicts that both doors should be the same width….”

The simulations also coincided with the Gaia data in terms of how long the star clusters typically survive, which is much shorter than would be expected according to Newton’s laws….
The MOND theory is controversial as modifications to Newton’s laws of gravity would have far-reaching consequences for other areas of physics as well, although they would solve many problems facing cosmology.

Thanks to long-time Slashdot reader schwit1 for submitting the story!

Read more of this story at Slashdot.

Source: Slashdot – Behavior of Star Clusters Challenges Newton’s Laws of Gravity

It’s been one week since Python 3.11 was released — and it’s “faster than ever!” So says Jay Miller, a developer on Microsoft’s six-person “Faster CPython” team (which includes Python creator Guido van Rossum, and offers assistance to other core developers). Miller reports that Python 3.11 “has already seen speedups of 10-60% in some areas of the language,” and offers an inside look at team.

First, how the team came together:

In 2020, Core Developer Mark Shannon drafted an Implementation plan for speeding up CPython (the most common implementation) by five times. This plan proposed a 4-stage process that, as Python’s creator Guido van Rossum says, “was an effort that was too much for one volunteer to accomplish”.

“Right from the start, my thought was well, we should try to see if Microsoft can hire Mark and a small team of people to support him.” In the previous year Van Rossum came out of retirement and joined Microsoft as a Distinguished Engineer. “It was an important effort and it was too much for one person.” Microsoft was open to the idea and a team of 6 engineers, including Van Rossum were established. That team has assisted other core developers in acting on this plan.

But the blog post also looks at how the team functions:

Every contributor that made the switch from part-time to full-time contribution mentioned being able to get deeper into their work on the language…. The team meets regularly to discuss these things. “All six of us meet every Monday,” says Van Rossum. “There’s always more than enough to talk about. That is very different than as a core dev community getting together for a Sprint twice a year, like one day after the conference. That is a very special event, of course, but it doesn’t feed me throughout the year.” Van Rossum believes that knowledge of one another and their collaborative work gave the team a “leg up” because everyone “knows what communication styles people have and what everybody’s weaknesses and strengths are….”

Shannon’s original 4 stage plan has continued to evolve to have continuous optimizations for the next several years. “To make that as smooth as possible, you have to think in terms of smaller steps, right?” says [team member] Michael Droettboom. Droettboom has worked on long-term projects in the scientific community including the Hubble Space Telescope and more recently the James Web Space Telescope…. “We hope we can bring some knowledge from really large proprietary systems into what we develop for the Community.” says Droettboom. “I think that’s really valuable because then you’re not just doing it in the abstract. Not just imagining what’s going to make Python faster for real use cases, but actually measuring it.” [Team member] Brandt Bucher adds in that developers working with these teams can test the impact of changes, “getting useful insights and contributions from people who maintain large, diverse codebases….” Many of the team’s meetings feature core developers from other teams and companies.

The blog post highlights specific activities of team members:

L Pereira is working on a change to how integers are represented inside Python, and “intends to change smaller integers to use native computation instead of the slower algorithms for arbitrarily large numbers.”
Irit Katriel implemented the new Exception groups and except* features in Python 3.11, and reports that “By simplifying the interpreter’s internal representation of raised exceptions, I reduced the time it takes to raise and catch an exception by about 10%.” Brandt Bucher (who helped create structural pattern matching for Python 3.10) is working on a Specialized Adaptive Interpreter (and tools like Specialist to help users move to Python).
And they’ve already begun working on features for future versions of Python. “You can also find out more about what the Faster CPython Team has in mind for 3.12 in their ideas repo on Github.”

Read more of this story at Slashdot.

Source: Slashdot – Python is Getting Faster. How a Team at Microsoft is Helping

Developer Dan Leveille received “a sketchy voicemail from a random number about a class action lawsuit settlement…” he posted on Twitter. “I thought it was a scam and almost ignored it.”

But he didn’t — and ended up with $8,064.88 in his Venmo account.

Back in 2019 a lawsuit by U.S. developers accused Apple of “profit-killing” App Store commissions, reports TechForge Media. Apple settled that suit by agreeing to create a $100 million Small Developer Assistance Fund (for developers who sold in Apple’s app store between June of 2015 and April of 2021). And this month Apple has finally started sending out those payments, Apple Insider reports:

Developers had until May 20 to submit a request to an independent administrator to become a “Settlement Class Member.” If they met the criteria, the developers stood to receive a payment from $250 to $30,000 in value….

Along with the fund, the settlement also introduced a number of changes to App Store policies, including modifications relating to customer and developer communication, new pricing tiers, and a promise by Apple to continue offering its 15% reduced App Store commission for at least three years.

Read more of this story at Slashdot.

Source: Slashdot – Apple’s 0 Million ‘Small Developer Assistance Fund’ Surprises Developers With Payouts

Dr. Bik is a microbiologist who has worked at Stanford University and for the Dutch National Institute for Health who is “blessed” with “what I’m told is a better-than-average ability to spot repeating patterns,” according to their new Op-Ed in the New York Times.

In 2014 they’d spotted the same photo “being used in two different papers to represent results from three entirely different experiments….”

Although this was eight years ago, I distinctly recall how angry it made me. This was cheating, pure and simple. By editing an image to produce a desired result, a scientist can manufacture proof for a favored hypothesis, or create a signal out of noise. Scientists must rely on and build on one another’s work. Cheating is a transgression against everything that science should be. If scientific papers contain errors or — much worse — fraudulent data and fabricated imagery, other researchers are likely to waste time and grant money chasing theories based on made-up results…..

But were those duplicated images just an isolated case? With little clue about how big this would get, I began searching for suspicious figures in biomedical journals…. By day I went to my job in a lab at Stanford University, but I was soon spending every evening and most weekends looking for suspicious images. In 2016, I published an analysis of 20,621 peer-reviewed papers, discovering problematic images in no fewer than one in 25. Half of these appeared to have been manipulated deliberately — rotated, flipped, stretched or otherwise photoshopped. With a sense of unease about how much bad science might be in journals, I quit my full-time job in 2019 so that I could devote myself to finding and reporting more cases of scientific fraud.

Using my pattern-matching eyes and lots of caffeine, I have analyzed more than 100,000 papers since 2014 and found apparent image duplication in 4,800 and similar evidence of error, cheating or other ethical problems in an additional 1,700. I’ve reported 2,500 of these to their journals’ editors and — after learning the hard way that journals often do not respond to these cases — posted many of those papers along with 3,500 more to PubPeer, a website where scientific literature is discussed in public….

Unfortunately, many scientific journals and academic institutions are slow to respond to evidence of image manipulation — if they take action at all. So far, my work has resulted in 956 corrections and 923 retractions, but a majority of the papers I have reported to the journals remain unaddressed.

Manipulated images “raise questions about an entire line of research, which means potentially millions of dollars of wasted grant money and years of false hope for patients.” Part of the problem is that despite “peer review” at scientific journals, “peer review is unpaid and undervalued, and the system is based on a trusting, non-adversarial relationship. Peer review is not set up to detect fraud.”

But there’s other problems.

Most of my fellow detectives remain anonymous, operating under pseudonyms such as Smut Clyde or Cheshire. Criticizing other scientists’ work is often not well received, and concerns about negative career consequences can prevent scientists from speaking out. Image problems I have reported under my full name have resulted in hateful messages, angry videos on social media sites and two lawsuit threats….

Things could be about to get even worse. Artificial intelligence might help detect duplicated data in research, but it can also be used to generate fake data. It is easy nowadays to produce fabricated photos or videos of events that never happened, and A.I.-generated images might have already started to poison the scientific literature. As A.I. technology develops, it will become significantly harder to distinguish fake from real.

Science needs to get serious about research fraud.
Among their proposed solutions? “Journals should pay the data detectives who find fatal errors or misconduct in published papers, similar to how tech companies pay bounties to computer security experts who find bugs in software.”

Read more of this story at Slashdot.

Source: Slashdot – ‘Science Has a Nasty Photoshopping Problem’