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Category: mathematics – Page 101

Foresight Existential Hope Group.
Program & apply to join: https://foresight.org/existential-hope/

In the Existential Hope-podcast (https://www.existentialhope.com), we invite scientists to speak about long-termism. Each month, we drop a podcast episode where we interview a visionary scientist to discuss the science and technology that can accelerate humanity towards desirable outcomes.

Xhope Special with Foresight Fellow Morgan Levine.

Morgan Levine is a ladder-rank Assistant Professor in the Department of Pathology at the Yale School of Medicine and a member of both the Yale Combined Program in Computational Biology and Bioinformatics, and the Yale Center for Research on Aging. Her work relies on an interdisciplinary approach, integrating theories and methods from statistical genetics, computational biology, and mathematical demography to develop biomarkers of aging for humans and animal models using high-dimensional omics data. As PI or co-Investigator on multiple NIH-, Foundation-, and University-funded projects, she has extensive experience using systems-level and machine learning approaches to track epigenetic, transcriptomic, and proteomic changes with aging and incorporate.
this information to develop measures of risk stratification for major chronic diseases, such as cancer and Alzheimer’s disease. Her work also involves development of systems-level outcome measures of aging, aimed at facilitating evaluation for geroprotective interventions.

Existential Hope.
A group of aligned minds who cooperate to build beautiful futures from a high-stakes time in human civilization by catalyzing knowledge around potential paths to get there and how to plug in.

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A team of researchers at the Sorbonne University of Paris reports a new way to emulate black hole and stellar accretion disks. In their paper published in the journal Physical Review Letters, the group describes using magnetic and electric fields to create a rotating disk made of liquid metal to emulate the behavior of material surrounding black holes and stars, which leads to the development of accretion disks.

Prior research has shown that massive objects have a gravitational reach that pulls in gas, dust and other material. And since such massive objects tend to spin, the material they pull in tends to swirl around the object as it moves closer. When that happens, gravity exerted by materials in the swirling mass tends to coalesce, resulting in an . Astrophysicists have been studying the dynamics of accretion disks for many years but have not been able to figure out how angular momentum is transferred from the inner parts of a given accretion disk to its outer parts as material in the disk moves ever closer to the central object.

Methods used to study accretion disks have involved the development of math formulas, and real-world models using liquids that swirl like eddies. None of the approaches has proven suitable, however, which has led researchers to look for new models. In this new effort, the researchers developed a method to generate an accretion disk made of bits spinning in the air.

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Watch over 2,400 documentaries for free for 30 days AND get a free Nebula account by signing up at https://curiositystream.com/upandatom and using the code “upandatom”. Once you sign up you’ll get an email about Nebula. If you don’t get one, contact the curiosity stream support team and they will set you up with a free Nebula account right away.

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Continue reading “The Physics of Self-Replication and Nanotechnology” | >

An encryption tool co-created by a University of Cincinnati math professor will soon safeguard the telecommunications, online retail and banking and other digital systems we use every day.

The National Institute of Standards and Technology chose four new encryption tools designed to thwart the next generation of hackers or thieves. One of them, called CRYSTALS-Kyber, is co-created by UC College of Arts and Sciences math professor Jintai Ding.

“It’s not just for today but for tomorrow,” Ding said. “This is information that you don’t want people to know even 30 or 50 years from now.”

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To try out our new course (and many others on math and science), go to https://brilliant.org/sabine. You can get started for free, and the first 200 will get 20% off the annual premium subscription.

Physicists have many theories for the beginning of our universe: A big bang, a big bounce, a black hole, a network, a collision of membranes, a gas of strings, and the list goes on. What does this mean? It means we don’t know how the universe began. And the reason isn’t just that we’re lacking data, the reason is that science is reaching its limits when we try to understand the initial condition of the entire universe.

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The Poplawski paper about how the universe might have been born from a black hole is here: https://link.springer.com/article/10.1007/s10714-021-02790-7

00:00 Intro.
00:25 The Big Bang Theory.
03:47 Why So Many Other Theories?
04:53 The Problem With Cosmology.
07:30 The Importance of Simplicity.
10:57 Stories of Creation.
15:35 Sponsor Message

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Mathematical models suggest that with just a few more genes, it might be possible to define hundreds of cellular identities, more than enough to populate the tissues of complex organisms. It’s a finding that opens the door to experiments that could bring us closer to understanding how, eons ago, the system that builds us was built.

The Limits of Mutual Repression

Developmental biologists have illuminated many tipping points and chemical signals that prompt cells to follow one developmental pathway or another by studying natural cells. But researchers in the field of synthetic biology often take another approach, explained Michael Elowitz, a professor of biology and bioengineering at Caltech and an author of the new paper: They build a system of cell-fate control from scratch to see what it can tell us about what such systems require.

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A new molecule created by a researcher at the University of Texas at Dallas kills a variety of difficult-to-treat cancers, including triple-negative breast cancer, by taking advantage of a weakness in cells that was not previously targeted by existing drugs.

The research, which was conducted using isolated cells, human cancer tissue, and mouse-grown human cancers, was recently published in Nature Cancer.

A co-corresponding author of the study and an associate professor of chemistry and biochemistry in the School of Natural Sciences and Mathematics at the University of Texas at Dallas, Dr. Jung-Mo Ahn has dedicated more than ten years of his career to developing small molecules that target protein-protein interactions in cells. He previously created potential therapeutic candidate compounds for treatment-resistant prostate cancer and breast cancer using a method called structure-based rational drug design.

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Meta is developing a machine learning model that scans these citations and cross-references their content to Wikipedia articles to verify that not only the topics line up, but specific figures cited are accurate.

This isn’t just a matter of picking out numbers and making sure they match; Meta’s AI will need to “understand” the content of cited sources (though “understand” is a misnomer, as complexity theory researcher Melanie Mitchell would tell you, because AI is still in the “narrow” phase, meaning it’s a tool for highly sophisticated pattern recognition, while “understanding” is a word used for human cognition, which is still a very different thing).

Meta’s model will “understand” content not by comparing text strings and making sure they contain the same words, but by comparing mathematical representations of blocks of text, which it arrives at using natural language understanding (NLU) techniques.

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