Lifeboat Foundation

What methods can be developed to help identify symptoms of myopia and its more serious version, myopic maculopathy? This is what a recent study published in JAMA Ophthalmology hopes to address as an international team of researchers investigated how artificial intelligence (AI) algorithms can be used to identify early signs of myopic maculopathy, as left untreated it can lead to irreversible damage to a person’s eyes. This study holds the potential to help researchers develop more effective options for identifying this worldwide disease, as it is estimated that approximately 50 percent of the global population will suffer from myopia by 2050.

“AI is ushering in a revolution that leverages global knowledge to improves diagnosis accuracy, especially in its earliest stage of the disease,” said Dr. Yalin Wang, who is a professor in the School of Computing and Augmented Intelligence at Arizona State University and a co-author on the study. “These advancements will reduce medical costs and improve the quality of life for entire societies.”

For the study, the researchers used a novel AI algorithm known as NN-MobileNet to scan retinal images and classify the severity of myopic maculopathy, which currently has five levels of severity in the medical field. The team then used deep neural networks to determine what’s known as the spherical equivalent, which is how eye doctors prescribe glasses and contacts to their patients. Combining these two methods enabled researchers to create a new AI algorithm capable of identifying early signs of myopic maculopathy.

https://rdcu.be/dVhCN

Imagine trying to understand the brain’s activity over time—an incredibly complex and dynamic process that happens at different speeds.

To solve this problem, we developed a pipeline called UnitMatch, which operates after spike sorting. Before applying UnitMatch, the user spike sorts each recording independently using their preferred algorithm. UnitMatch then deploys a naive Bayes classifier on the units’ average waveform in each recording and tracks units across recordings, assigning a probability to each match.

Continue reading “Tracking neurons across days with high-density probes” »

The improved accuracy of MNT reaction predictions provided by this model could facilitate the production of isotopes that are difficult to generate using other methods. These isotopes are valuable for scientific research and medical applications, such as diagnostics and treatments. According to Prof. Zhang, the goal is to keep the model comprehensive yet practical for experimental use.

This development represents a step forward in nuclear physics, contributing to the understanding of exotic nuclei production through MNT reactions. Further refinement of the model may enhance its utility in guiding future research and improving rare isotope production processes.

This research was conducted in collaboration with Beijing Normal University, Beijing Academy of Science and Technology, and the National Laboratory of Heavy Ion Accelerator of Lanzhou.

Koo and his team tested CREME on another AI-powered DNN genome analysis tool called Enformer. They wanted to know how Enformer’s algorithm makes predictions about the genome. Koo says questions like that are central to his work.

“We have these big, powerful models,” Koo said. “They’re quite compelling at taking DNA sequences and predicting gene expression. But we don’t really have any good ways of trying to understand what these models are learning. Presumably, they’re making accurate predictions because they’ve learned a lot of the rules about gene regulation, but we don’t actually know what their predictions are based off of.”

With CREME, Koo’s team uncovered a series of genetic rules that Enformer learned while analyzing the genome. That insight may one day prove invaluable for drug discovery. The investigators stated, “CREME provides a powerful toolkit for translating the predictions of genomic DNNs into mechanistic insights of gene regulation … Applying CREME to Enformer, a state-of-the-art DNN, we identify cis-regulatory elements that enhance or silence gene expression and characterize their complex interactions.” Koo added, “Understanding the rules of gene regulation gives you more options for tuning gene expression levels in precise and predictable ways.”

From artificial-intelligence algorithms to zebrafish, this book take a precautionary approach to assessing how sentient such entities are.

What I believe is that symmetry follows everything even mathematics but what explains it is the Fibonacci equation because it seems to show the grand design of everything much like physics has I believe the final parameter of the quantified parameter of infinity.

Recent explorations of unique geometric worlds reveal perplexing patterns, including the Fibonacci sequence and the golden ratio.

Augmented reality (AR) takes digital images and superimposes them onto real-world views. But AR is more than a new way to play video games; it could transform surgery and self-driving cars. To make the technology easier to integrate into common personal devices, researchers report in ACS Photonics how to combine two optical technologies into a single, high-resolution AR display. In an eyeglasses prototype, the researchers enhanced image quality with a computer algorithm that removed distortions.

The large language models that have increasingly taken over the tech world are not “cheap” in many ways. The most prominent LLMs, such as GPT-4, took some $100 million to build in the form of legal costs of accessing training data, computational power costs for what could be billions or trillions of parameters, the energy and water needed to fuel computation, and the many coders developing the training algorithms that must run cycle after cycle so the machine will “learn.”

But, if a researcher needs to do a specialized task that a machine could do more efficiently and they don’t have access to a large institution that offers access to generative AI tools, what other options are available? Say, a parent wants to prep their child for a difficult test and needs to show many examples of how to solve complicated math problems.

Building their own LLM is an onerous prospect for costs mentioned above, and making direct use of the big models like GPT-4 and Llama 3.1 might not immediately be suited for the complex reasoning in logic and math their task requires.

New study suggests that black holes may not be the featureless, structureless entities that Einstein’s general theory of relativity predicts them to be.

The frozen star is a recent proposal for a nonsingular solution of Einstein’s equations that describes an ultracompact object which closely resembles a black hole from an external perspective. The frozen star is also meant to be an alternative, classical description of an earlier proposal, the highly quantum polymer model. Here, we show that the thermodynamic properties of frozen stars closely resemble those of black holes: frozen stars radiate thermally, with a temperature and an entropy that are perturbatively close to those of black holes of the same mass. Their entropy is calculated using the Euclidean-action method of Gibbons and Hawking. We then discuss their dynamical formation by estimating the probability for a collapsing shell of “normal’’ matter to transition, quantum mechanically, into a frozen star.