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Webb Finds Evidence for Neutron Star at Heart of Young Supernova Remnant
NASA’s James Webb Space Telescope has found the best evidence yet for emission from a neutron star at the site of a recently observed supernova. The supernova, known as SN 1987A, was a core-collapse supernova, meaning the compacted remains at its core formed either a neutron star or a black hole. Evidence for such a compact object has long been sought, and while indirect evidence for the presence of a neutron star has previously been found, this is the first time that the effects of high-energy emission from the probable young neutron star have been detected.
Supernovae – the explosive final death throes of some massive stars – blast out within hours, and the brightness of the explosion peaks within a few months. The remains of the exploding star will continue to evolve at a rapid rate over the following decades, offering a rare opportunity for astronomers to study a key astronomical process in real time.
Supernova 1987A
The supernova SN 1987A occurred 160,000 light-years from Earth in the Large Magellanic Cloud. It was first observed on Earth in February 1987, and its brightness peaked in May of that year. It was the first supernova that could be seen with the naked eye since Kepler's Supernova was observed in 1604.
About two hours prior to the first visible-light observation of SN 1987A, three observatories around the world detected a burst of neutrinos lasting only a few seconds. The two different types of observations were linked to the same supernova event, and provided important evidence to inform the theory of how core-collapse supernovae take place. This theory included the expectation that this type of supernova would form a neutron star or a black hole. Astronomers have searched for evidence for one or the other of these compact objects at the center of the expanding remnant material ever since.
Indirect evidence for the presence of a neutron star at the center of the remnant has been found in the past few years, and observations of much older supernova remnants –such as the Crab Nebula – confirm that neutron stars are found in many supernova remnants. However, no direct evidence of a neutron star in the aftermath of SN 1987A (or any other such recent supernova explosion) had been observed, until now.
Image: Supernova 1987A
Claes Fransson of Stockholm University, and the lead author on this study, explained: “From theoretical models of SN 1987A, the 10-second burst of neutrinos observed just before the supernova implied that a neutron star or black hole was formed in the explosion. But we have not observed any compelling signature of such a newborn object from any supernova explosion. With this observatory, we have now found direct evidence for emission triggered by the newborn compact object, most likely a neutron star.”
Webb’s Observations of SN 1987A
Webb began science observations in July 2022, and the Webb observations behind this work were taken on July 16, making the SN 1987A remnant one of the first objects observed by Webb. The team used the Medium Resolution Spectrograph (MRS) mode of Webb’s MIRI (Mid-Infrared Instrument), which members of the same team helped to develop. The MRS is a type of instrument known as an Integral Field Unit (IFU).
IFUs are able to image an object and take a spectrum of it at the same time. An IFU forms a spectrum at each pixel, allowing observers to see spectroscopic differences across the object. Analysis of the Doppler shift of each spectrum also permits the evaluation of the velocity at each position.
Spectral analysis of the results showed a strong signal due to ionized argon from the center of the ejected material that surrounds the original site of SN 1987A. Subsequent observations using Webb’s NIRSpec (Near-Infrared Spectrograph) IFU at shorter wavelengths found even more heavily ionized chemical elements, particularly five times ionized argon (meaning argon atoms that have lost five of their 18 electrons). Such ions require highly energetic photons to form, and those photons have to come from somewhere.
“To create these ions that we observed in the ejecta, it was clear that there had to be a source of high-energy radiation in the center of the SN 1987A remnant,” Fransson said. “In the paper we discuss different possibilities, finding that only a few scenarios are likely, and all of these involve a newly born neutron star.”
More observations are planned this year, with Webb and ground-based telescopes. The research team hopes ongoing study will provide more clarity about exactly what is happening in the heart of the SN 1987A remnant. These observations will hopefully stimulate the development of more detailed models, ultimately enabling astronomers to better understand not just SN 1987A, but all core-collapse supernovae.
These findings were published in the journal Science.
The James Webb Space Telescope is the world’s premier space science observatory. Webb is solving mysteries in our solar system, looking beyond to distant worlds around other stars, and probing the mysterious structures and origins of our universe and our place in it. Webb is an international program led by NASA with its partners, ESA (European Space Agency) and the Canadian Space Agency.
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Title: llm agents can autonomously hack websites.
Abstract: In recent years, large language models (LLMs) have become increasingly capable and can now interact with tools (i.e., call functions), read documents, and recursively call themselves. As a result, these LLMs can now function autonomously as agents. With the rise in capabilities of these agents, recent work has speculated on how LLM agents would affect cybersecurity. However, not much is known about the offensive capabilities of LLM agents. In this work, we show that LLM agents can autonomously hack websites, performing tasks as complex as blind database schema extraction and SQL injections without human feedback. Importantly, the agent does not need to know the vulnerability beforehand. This capability is uniquely enabled by frontier models that are highly capable of tool use and leveraging extended context. Namely, we show that GPT-4 is capable of such hacks, but existing open-source models are not. Finally, we show that GPT-4 is capable of autonomously finding vulnerabilities in websites in the wild. Our findings raise questions about the widespread deployment of LLMs.
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Our next-generation model: Gemini 1.5
Feb 15, 2024
The model delivers dramatically enhanced performance, with a breakthrough in long-context understanding across modalities.
A note from Google and Alphabet CEO Sundar Pichai:
Last week, we rolled out our most capable model, Gemini 1.0 Ultra, and took a significant step forward in making Google products more helpful, starting with Gemini Advanced . Today, developers and Cloud customers can begin building with 1.0 Ultra too — with our Gemini API in AI Studio and in Vertex AI .
Our teams continue pushing the frontiers of our latest models with safety at the core. They are making rapid progress. In fact, we’re ready to introduce the next generation: Gemini 1.5. It shows dramatic improvements across a number of dimensions and 1.5 Pro achieves comparable quality to 1.0 Ultra, while using less compute.
This new generation also delivers a breakthrough in long-context understanding. We’ve been able to significantly increase the amount of information our models can process — running up to 1 million tokens consistently, achieving the longest context window of any large-scale foundation model yet.
Longer context windows show us the promise of what is possible. They will enable entirely new capabilities and help developers build much more useful models and applications. We’re excited to offer a limited preview of this experimental feature to developers and enterprise customers. Demis shares more on capabilities, safety and availability below.
Introducing Gemini 1.5
By Demis Hassabis, CEO of Google DeepMind, on behalf of the Gemini team
This is an exciting time for AI. New advances in the field have the potential to make AI more helpful for billions of people over the coming years. Since introducing Gemini 1.0 , we’ve been testing, refining and enhancing its capabilities.
Today, we’re announcing our next-generation model: Gemini 1.5.
Gemini 1.5 delivers dramatically enhanced performance. It represents a step change in our approach, building upon research and engineering innovations across nearly every part of our foundation model development and infrastructure. This includes making Gemini 1.5 more efficient to train and serve, with a new Mixture-of-Experts (MoE) architecture.
The first Gemini 1.5 model we’re releasing for early testing is Gemini 1.5 Pro. It’s a mid-size multimodal model, optimized for scaling across a wide-range of tasks, and performs at a similar level to 1.0 Ultra , our largest model to date. It also introduces a breakthrough experimental feature in long-context understanding.
Gemini 1.5 Pro comes with a standard 128,000 token context window. But starting today, a limited group of developers and enterprise customers can try it with a context window of up to 1 million tokens via AI Studio and Vertex AI in private preview.
As we roll out the full 1 million token context window, we’re actively working on optimizations to improve latency, reduce computational requirements and enhance the user experience. We’re excited for people to try this breakthrough capability, and we share more details on future availability below.
These continued advances in our next-generation models will open up new possibilities for people, developers and enterprises to create, discover and build using AI.
Context lengths of leading foundation models
Highly efficient architecture
Gemini 1.5 is built upon our leading research on Transformer and MoE architecture. While a traditional Transformer functions as one large neural network, MoE models are divided into smaller "expert” neural networks.
Depending on the type of input given, MoE models learn to selectively activate only the most relevant expert pathways in its neural network. This specialization massively enhances the model’s efficiency. Google has been an early adopter and pioneer of the MoE technique for deep learning through research such as Sparsely-Gated MoE , GShard-Transformer , Switch-Transformer, M4 and more.
Our latest innovations in model architecture allow Gemini 1.5 to learn complex tasks more quickly and maintain quality, while being more efficient to train and serve. These efficiencies are helping our teams iterate, train and deliver more advanced versions of Gemini faster than ever before, and we’re working on further optimizations.
Greater context, more helpful capabilities
An AI model’s “context window” is made up of tokens, which are the building blocks used for processing information. Tokens can be entire parts or subsections of words, images, videos, audio or code. The bigger a model’s context window, the more information it can take in and process in a given prompt — making its output more consistent, relevant and useful.
Through a series of machine learning innovations, we’ve increased 1.5 Pro’s context window capacity far beyond the original 32,000 tokens for Gemini 1.0. We can now run up to 1 million tokens in production.
This means 1.5 Pro can process vast amounts of information in one go — including 1 hour of video, 11 hours of audio, codebases with over 30,000 lines of code or over 700,000 words. In our research, we’ve also successfully tested up to 10 million tokens.
Complex reasoning about vast amounts of information
1.5 Pro can seamlessly analyze, classify and summarize large amounts of content within a given prompt. For example, when given the 402-page transcripts from Apollo 11’s mission to the moon, it can reason about conversations, events and details found across the document.
Gemini 1.5 Pro can understand, reason about and identify curious details in the 402-page transcripts from Apollo 11’s mission to the moon.
Better understanding and reasoning across modalities
1.5 Pro can perform highly-sophisticated understanding and reasoning tasks for different modalities, including video. For instance, when given a 44-minute silent Buster Keaton movie , the model can accurately analyze various plot points and events, and even reason about small details in the movie that could easily be missed.
Gemini 1.5 Pro can identify a scene in a 44-minute silent Buster Keaton movie when given a simple line drawing as reference material for a real-life object.
Relevant problem-solving with longer blocks of code
1.5 Pro can perform more relevant problem-solving tasks across longer blocks of code. When given a prompt with more than 100,000 lines of code, it can better reason across examples, suggest helpful modifications and give explanations about how different parts of the code works.
Gemini 1.5 Pro can reason across 100,000 lines of code giving helpful solutions, modifications and explanations.
Enhanced performance
When tested on a comprehensive panel of text, code, image, audio and video evaluations, 1.5 Pro outperforms 1.0 Pro on 87% of the benchmarks used for developing our large language models (LLMs). And when compared to 1.0 Ultra on the same benchmarks, it performs at a broadly similar level.
Gemini 1.5 Pro maintains high levels of performance even as its context window increases. In the Needle In A Haystack (NIAH) evaluation, where a small piece of text containing a particular fact or statement is purposely placed within a long block of text, 1.5 Pro found the embedded text 99% of the time, in blocks of data as long as 1 million tokens.
Gemini 1.5 Pro also shows impressive “in-context learning” skills, meaning that it can learn a new skill from information given in a long prompt, without needing additional fine-tuning. We tested this skill on the Machine Translation from One Book (MTOB) benchmark, which shows how well the model learns from information it’s never seen before. When given a grammar manual for Kalamang , a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person learning from the same content.
As 1.5 Pro’s long context window is the first of its kind among large-scale models, we’re continuously developing new evaluations and benchmarks for testing its novel capabilities.
For more details, see our Gemini 1.5 Pro technical report .
Extensive ethics and safety testing
In line with our AI Principles and robust safety policies, we’re ensuring our models undergo extensive ethics and safety tests. We then integrate these research learnings into our governance processes and model development and evaluations to continuously improve our AI systems.
Since introducing 1.0 Ultra in December, our teams have continued refining the model, making it safer for a wider release. We’ve also conducted novel research on safety risks and developed red-teaming techniques to test for a range of potential harms.
In advance of releasing 1.5 Pro, we've taken the same approach to responsible deployment as we did for our Gemini 1.0 models, conducting extensive evaluations across areas including content safety and representational harms, and will continue to expand this testing. Beyond this, we’re developing further tests that account for the novel long-context capabilities of 1.5 Pro.
Build and experiment with Gemini models
We’re committed to bringing each new generation of Gemini models to billions of people, developers and enterprises around the world responsibly.
Starting today, we’re offering a limited preview of 1.5 Pro to developers and enterprise customers via AI Studio and Vertex AI . Read more about this on our Google for Developers blog and Google Cloud blog .
We’ll introduce 1.5 Pro with a standard 128,000 token context window when the model is ready for a wider release. Coming soon, we plan to introduce pricing tiers that start at the standard 128,000 context window and scale up to 1 million tokens, as we improve the model.
Early testers can try the 1 million token context window at no cost during the testing period, though they should expect longer latency times with this experimental feature. Significant improvements in speed are also on the horizon.
Developers interested in testing 1.5 Pro can sign up now in AI Studio, while enterprise customers can reach out to their Vertex AI account team.
Learn more about Gemini’s capabilities and see how it works .
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Collection 12 March 2023
Journal Top 100 - 2022
This collection highlights our most downloaded* research papers published in 2022. Featuring authors from around the world, these papers highlight valuable research from an international community.
You can also check out the Top 100 across various subject areas here .
*Data obtained from SN Insights, which is based on Digital Science’s Dimensions.
mRNA vaccine-induced antibodies more effective than natural immunity in neutralizing SARS-CoV-2 and its high affinity variants
- Dominic Esposito
Cats learn the names of their friend cats in their daily lives
- Saho Takagi
- Atsuko Saito
- Hika Kuroshima
Metformin administration is associated with enhanced response to transarterial chemoembolization for hepatocellular carcinoma in type 2 diabetes patients
- Woo Jin Jung
- Sangmi Jang
- Jin-Wook Kim
The impact of digital media on children’s intelligence while controlling for genetic differences in cognition and socioeconomic background
- Bruno Sauce
- Magnus Liebherr
- Torkel Klingberg
Life tables of annual life expectancy and mortality for companion dogs in the United Kingdom
- Kendy Tzu-yun Teng
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A once-ignored community of science sleuths now has the research community on its heels
A community of sleuths hunting for errors in scientific research have sent shockwaves through some of the most prestigious research institutions in the world — and the science community at large.
High-profile cases of alleged image manipulations in papers authored by the former president at Stanford University and leaders at the Dana-Farber Cancer Institute have made national media headlines, and some top science leaders think this could be just the start.
“At the rate things are going, we expect another one of these to come up every few weeks,” said Holden Thorp, the editor-in-chief of the Science family of scientific journals, whose namesake publication is one of the two most influential in the field.
The sleuths argue their work is necessary to correct the scientific record and prevent generations of researchers from pursuing dead-end topics because of flawed papers. And some scientists say it’s time for universities and academic publishers to reform how they address flawed research.
“I understand why the sleuths finding these things are so pissed off,” said Michael Eisen, a biologist, the former editor of the journal eLife and a prominent voice of reform in scientific publishing. “Everybody — the author, the journal, the institution, everybody — is incentivized to minimize the importance of these things.”
For about a decade, science sleuths unearthed widespread problems in scientific images in published papers, publishing concerns online but receiving little attention.
That began to change last summer after then-Stanford President Marc Tessier-Lavigne, who is a neuroscientist, stepped down from his post after scrutiny of alleged image manipulations in studies he helped author and a report criticizing his laboratory culture. Tessier-Lavigne was not found to have engaged in misconduct himself, but members of his lab appeared to manipulate images in dubious ways, a report from a scientific panel hired to examine the allegations said.
In January, a scathing post from a blogger exposed questionable work from top leaders at the Dana-Farber Cancer Institute , which subsequently asked journals to retract six articles and issue corrections for dozens more.
In a resignation statement , Tessier-Lavigne noted that the panel did not find that he knew of misconduct and that he never submitted papers he didn’t think were accurate. In a statement from its research integrity officer, Dana-Farber said it took decisive action to correct the scientific record and that image discrepancies were not necessarily evidence an author sought to deceive.
“We’re certainly living through a moment — a public awareness — that really hit an inflection when the Marc Tessier-Lavigne matter happened and has continued steadily since then, with Dana-Farber being the latest,” Thorp said.
Now, the long-standing problem is in the national spotlight, and new artificial intelligence tools are only making it easier to spot problems that range from decades-old errors and sloppy science to images enhanced unethically in photo-editing software.
This heightened scrutiny is reshaping how some publishers are operating. And it’s pushing universities, journals and researchers to reckon with new technology, a potential backlog of undiscovered errors and how to be more transparent when problems are identified.
This comes at a fraught time in academic halls. Bill Ackman, a venture capitalist, in a post on X last month discussed weaponizing artificial intelligence to identify plagiarism of leaders at top-flight universities where he has had ideological differences, raising questions about political motivations in plagiarism investigations. More broadly, public trust in scientists and science has declined steadily in recent years, according to the Pew Research Center .
Eisen said he didn’t think sleuths’ concerns over scientific images had veered into “McCarthyist” territory.
“I think they’ve been targeting a very specific type of problem in the literature, and they’re right — it’s bad,” Eisen said.
Scientific publishing builds the base of what scientists understand about their disciplines, and it’s the primary way that researchers with new findings outline their work for colleagues. Before publication, scientific journals consider submissions and send them to outside researchers in the field for vetting and to spot errors or faulty reasoning, which is called peer review. Journal editors will review studies for plagiarism and for copy edits before they’re published.
That system is not perfect and still relies on good-faith efforts by researchers to not manipulate their findings.
Over the past 15 years, scientists have grown increasingly concerned about problems that some researchers were digitally altering images in their papers to skew or emphasize results. Discovering irregularities in images — typically of experiments involving mice, gels or blots — has become a larger priority of scientific journals’ work.
Jana Christopher, an expert on scientific images who works for the Federation of European Biochemical Societies and its journals, said the field of image integrity screening has grown rapidly since she began working in it about 15 years ago.
At the time, “nobody was doing this and people were kind of in denial about research fraud,” Christopher said. “The common view was that it was very rare and every now and then you would find someone who fudged their results.”
Today, scientific journals have entire teams dedicated to dealing with images and trying to ensure their accuracy. More papers are being retracted than ever — with a record 10,000-plus pulled last year, according to a Nature analysis .
A loose group of scientific sleuths have added outside pressure. Sleuths often discover and flag errors or potential manipulations on the online forum PubPeer. Some sleuths receive little or no payment or public recognition for their work.
“To some extent, there is a vigilantism around it,” Eisen said.
An analysis of comments on more than 24,000 articles posted on PubPeer found that more than 62% of comments on PubPeer were related to image manipulation.
For years, sleuths relied on sharp eyes, keen pattern recognition and an understanding of photo manipulation tools. In the past few years, rapidly developing artificial intelligence tools, which can scan papers for irregularities, are supercharging their work.
Now, scientific journals are adopting similar technology to try to prevent errors from reaching publication. In January, Science announced that it was using an artificial intelligence tool called Proofig to scan papers that were being edited and peer-reviewed for publication.
Thorp, the Science editor-in-chief, said the family of six journals added the tool “quietly” into its workflow about six months before that January announcement. Before, the journal was reliant on eye-checks to catch these types of problems.
Thorp said Proofig identified several papers late in the editorial process that were not published because of problematic images that were difficult to explain and other instances in which authors had “logical explanations” for issues they corrected before publication.
“The serious errors that cause us not to publish a paper are less than 1%,” Thorp said.
In a statement, Chris Graf, the research integrity director at the publishing company Springer Nature, said his company is developing and testing “in-house AI image integrity software” to check for image duplications. Graf’s research integrity unit currently uses Proofig to help assess articles if concerns are raised after publication.
Graf said processes varied across its journals, but that some Springer Nature publications manually check images for manipulations with Adobe Photoshop tools and look for inconsistencies in raw data for experiments that visualize cell components or common scientific experiments.
“While the AI-based tools are helpful in speeding up and scaling up the investigations, we still consider the human element of all our investigations to be crucial,” Graf said, adding that image recognition software is not perfect and that human expertise is required to protect against false positives and negatives.
No tool will catch every mistake or cheat.
“There’s a lot of human beings in that process. We’re never going to catch everything,” Thorp said. “We need to get much better at managing this when it happens, as journals, institutions and authors.”
Many science sleuths had grown frustrated after their concerns seemed to be ignored or as investigations trickled along slowly and without a public resolution.
Sholto David, who publicly exposed concerns about Dana-Farber research in a blog post, said he largely “gave up” on writing letters to journal editors about errors he discovered because their responses were so insufficient.
Elisabeth Bik, a microbiologist and longtime image sleuth, said she has frequently flagged image problems and “nothing happens.”
Leaving public comments questioning research figures on PubPeer can start a public conversation over questionable research, but authors and research institutions often don’t respond directly to the online critiques.
While journals can issue corrections or retractions, it’s typically a research institution’s or a university’s responsibility to investigate cases. When cases involve biomedical research supported by federal funding, the federal Office of Research Integrity can investigate.
Thorp said the institutions need to move more swiftly to take responsibility when errors are discovered and speak plainly and publicly about what happened to earn the public’s trust.
“Universities are so slow at responding and so slow at running through their processes, and the longer that goes on, the more damage that goes on,” Thorp said. “We don’t know what happened if instead of launching this investigation Stanford said, ‘These papers are wrong. We’re going to retract them. It’s our responsibility. But for now, we’re taking the blame and owning up to this.’”
Some scientists worry that image concerns are only scratching the surface of science’s integrity issues — problems in images are simply much easier to spot than data errors in spreadsheets.
And while policing bad papers and seeking accountability is important, some scientists think those measures will be treating symptoms of the larger problem: a culture that rewards the careers of those who publish the most exciting results, rather than the ones that hold up over time.
“The scientific culture itself does not say we care about being right; it says we care about getting splashy papers,” Eisen said.
Evan Bush is a science reporter for NBC News. He can be reached at [email protected].
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