The reliable generation of random numbers has become a central component of information and communications technology. In fact, random number generators, algorithms or devices that can produce random sequences of numbers, are now helping to secure communications between different devices, produce statistical samples, and for various other applications.
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Science publisher Springer Nature has developed two new AI tools to detect fake research and duplicate images in scientific papers, helping to protect the integrity of published studies.
The growing number of cases of fake research is already putting a strain on the scientific publishing industry, according to Springer Nature. Following a pilot phase, the publisher is now rolling out two AI tools to identify papers with AI-generated fake content and problematic images — both red flags for research integrity issues.
The first tool, called “Geppetto,” detects AI-generated content, a telltale sign of “paper mills” producing fake research papers. The tool divides the paper into sections and uses its own algorithms to check the consistency of the text in each section.
This review spotlights the revolutionary role of deep learning (DL) in expanding the understanding of RNA is a fundamental biomolecule that shapes and regulates diverse phenotypes including human diseases. Understanding the principles governing the functions of RNA is a key objective of current biology. Recently, big data produced via high-throughput experiments have been utilized to develop DL models aimed at analyzing and predicting RNA-related biological processes. This review emphasizes the role of public databases in providing these big data for training DL models. The authors introduce core DL concepts necessary for training models from the biological data. By extensively examining DL studies in various fields of RNA biology, the authors suggest how to better leverage DL for revealing novel biological knowledge and demonstrate the potential of DL in deciphering the complex biology of RNA.
This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.
Colin Jacobs, PhD, assistant professor in the Department of Medical Imaging at Radboud University Medical Center in Nijmegen, The Netherlands, and Kiran Vaidhya Venkadesh, a second-year PhD candidate with the Diagnostic Image Analysis Group at Radboud University Medical Center discuss their 2021 Radiology study, which used CT images from the National Lung Cancer Screening Trial (NLST) to train a deep learning algorithm to estimate the malignancy risk of lung nodules.
Posted in cosmology, information science, mathematics, physics, singularity
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Isaac Newton’s Universal Law of Gravitation tells us that there is a singularity to be found within a black hole, but scientists and mathematicians have found a number of issues with Newton’s equations. They don’t always accurately represent reality. Einstein’s General Theory of Relativity is a more complete theory of gravity. So does using the General Theory of Relativity eliminate the singularity? No. Not only does it concur with Newton’s Universal Law of Gravitation but it also reveals a second singularity, not at the center of the black hole but at the event horizon.
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Officials of the U.S. Defense Advanced Research Projects Agency (DARPA) in Arlington, Va., has issued a solicitation (DARPA-PA-23–03-11) for the Defense Applications of Innovative Remote Sensing (DAIRS) project.
Primary emphasis will be in the high frequency (HF) band nominally at 4 to 15 MHz. Key applications in this frequency band are SWOTHR for aircraft, ship, and boat tracking, oceanographic SWOTHR, and sounding for ionospheric characterization.
When it comes to quantum computing, that chilling effect on research and development would enormously jeopardize U.S. national security. Our projects received ample funding from defense and intelligence agencies for good reason. Quantum computing may soon become the https://www.cyberdefensemagazine.com/quantum-security-is-nat...at%20allow, codebreaking%20attacks%20against%20traditional%20encryption" rel="noopener" class="">gold standard technology for codebreaking and defending large computer networks against cyberattacks.
Adopting the proposed march-in framework would also have major implications for our future economic stability. While still a nascent technology today, quantum computing’s ability to rapidly process huge volumes of data is set to revolutionize business in the coming decades. It may be the only way to capture the complexity needed for future AI and machine learning in, say, self-driving vehicles. It may enable companies to hone their supply chains and other logistical operations, such as manufacturing, with unprecedented precision. It may also transform finance by allowing portfolio managers to create new, superior investment algorithms and strategies.
Given the technology’s immense potential, it’s no mystery why China committed what is believed to be more than https://www.mckinsey.com/featured-insights/sustainable-inclu…n-quantum” rel=“noopener” class=””>$15 billion in 2022 to develop its quantum computing capacity–more than double the budget for quantum computing of EU countries and eight times what the U.S. government plans to spend.
Mark Hamilton, an MIT Ph.D. student in electrical engineering and computer science and affiliate of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), wants to use machines to understand how animals communicate. To do that, he set out first to create a system that can learn human language “from scratch.”
1. Privacy is important, but not always guaranteed. Grantcharov realized very quickly that the only way to get surgeons to use the black box was to make them feel protected from possible repercussions. He has designed the system to record actions but hide the identities of both patients and staff, even deleting all recordings within 30 days. His idea is that no individual should be punished for making a mistake.
The black boxes render each person in the recording anonymous; an algorithm distorts people’s voices and blurs out their faces, transforming them into shadowy, noir-like figures. So even if you know what happened, you can’t use it against an individual.
But this process is not perfect. Before 30-day-old recordings are automatically deleted, hospital administrators can still see the operating room number, the time of the operation, and the patient’s medical record number, so even if personnel are technically de-identified, they aren’t truly anonymous. The result is a sense that “Big Brother is watching,” says Christopher Mantyh, vice chair of clinical operations at Duke University Hospital, which has black boxes in seven operating rooms.
While carbon nanotubes are the materials that have received most of the attention so far, they have proved very difficult to manufacture and control, so scientists are eager to find other compounds that could be used to create nanowires and nanotubes with equally interesting properties, but easier to handle.
So, Chiara Cignarella, Davide Campi and Nicola Marzari thought to use computer simulations to parse known three-dimensional crystals, looking for those that—based on their structural and electronic properties —look like they could be easily “exfoliated,” essentially peeling away from them a stable 1-D structure. The same method has been successfully used in the past to study 2D materials, but this is the first application to their 1-D counterparts.
The researchers started from a collection of over 780,000 crystals, taken from various databases found in the literature and held together by van der Waals forces, the sort of weak interactions that happen when atoms are close enough for their electrons to overlap. Then they applied an algorithm that considered the spatial organization of their atoms looking for the ones that incorporated wire-like structures, and calculated how much energy would be necessary to separate that 1-D structure from the rest of the crystal.
