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Category: information science – Page 45

Exploring the frontiers of neuromorphic engineering: A journey into brain-inspired computing

Neuromorphic engineering is a cutting-edge field that focuses on developing computer hardware and software systems inspired by the structure, function, and behavior of the human brain. The ultimate goal is to create computing systems that are significantly more energy-efficient, scalable, and adaptive than conventional computer systems, capable of solving complex problems in a manner reminiscent of the brain’s approach.

This interdisciplinary field draws upon expertise from various domains, including neuroscience, computer science, electronics, nanotechnology, and materials science. Neuromorphic engineers strive to develop computer chips and systems incorporating artificial neurons and synapses, designed to process information in a parallel and distributed manner, akin to the brain’s functionality.

Key challenges in neuromorphic engineering encompass developing algorithms and hardware capable of performing intricate computations with minimal energy consumption, creating systems that can learn and adapt over time, and devising methods to control the behavior of artificial neurons and synapses in real-time.

Researchers Say Quantum Machine Learning, Quantum Optimization Could Enhance The Design And Efficiency of Clinical Trials

Despite the promising findings, the study acknowledges several limitations of quantum computing. One of the primary challenges is hardware noise, which can reduce the accuracy of quantum computations. Although error correction methods are improving, quantum computing has not yet reached the level of fault tolerance needed for widespread commercial use. Additionally, the study notes that while quantum computing has shown promise in PBPK/PD modeling and site selection, further research is needed to fully realize its potential in these areas.

Looking ahead, the study suggests several future directions for research. One of the key areas for improvement is the integration of quantum algorithms with existing clinical trial infrastructure. This will require collaboration between researchers, pharmaceutical companies and regulators to ensure that quantum computing can be effectively applied in real-world clinical settings. Additionally, the study calls for more work on developing quantum algorithms that can handle the inherent variability in biological data, particularly in genomics and personalized medicine.

The research was conducted by a team from several prominent institutions. Hakan Doga, Aritra Bose, and Laxmi Parida are from IBM Research and IBM Quantum. M. Emre Sahin is affiliated with The Hartree Centre, STFC, while Joao Bettencourt-Silva is based at IBM Research, Dublin, Ireland. Anh Pham, Eunyoung Kim, Anh Pham, Eunyoung Kim and Alan Andress are from Deloitte Consulting LLP. Sudhir Saxena and Radwa Soliman are from GNQ Insilico Inc. Jan Lukas Robertus is affiliated with Imperial College London and Royal Brompton and Harefield Hospitals and Hideaki Kawaguchi is from Keio University. Finally, Daniel Blankenberg is from the Lerner Research Institute, Cleveland Clinic.

Numerical simulation of deformable droplets in three-dimensional, complex-shaped microchannels

The physics of drop motion in microchannels is fundamental to provide insights when designing applications of drop-based microfluidics. In this paper, we develop a boundary-integral method to simulate the motion of drops in microchannels of finite depth with flat walls and fixed depth but otherwise arbitrary geometries. To reduce computational time, we use a moving frame that follows the droplet throughout its motion. We provide a full description of the method, including our channel-meshing algorithm, which is a combination of Monte Carlo techniques and Delaunay triangulation, and compare our results to infinite-depth simulations. For regular geometries of uniform cross section, the infinite-depth limit is approached slowly with increasing depth, though we show much faster convergence by scaling with maximum vs average velocities. For non-regular channel geometries, features such as different branch heights can affect drop partitioning, breaking the symmetric behavior usually observed in regular geometries. Moreover, non-regular geometries also present challenges when comparing the results for deep and infinite-depth channels. To probe inertial effects on drop motion, the full Navier–Stokes equations are first solved for the entire channel, and the tabulated solution is then used as a boundary condition at the moving-frame surface for the Stokes flow inside the moving frame. For moderate Reynolds numbers up to Re = 5, inertial effects on the undisturbed flow are small even for more complex geometries, suggesting that inertial contributions in this range are likely small. This work provides an important tool for the design and analysis of three-dimensional droplet-based microfluidic devices.

AI can reduce a 100,000-equation quantum problem to just 4 equations

The Hubbard model is a studied model in condensed matter theory and a formidable quantum problem. A team of physicists used deep learning to condense this problem, which previously required 100,000 equations, into just four equations without sacrificing accuracy. The study, titled “Deep Learning the Functional Renormalization Group,” was published on September 21 in Physical Review Letters.

Dominique Di Sante is the lead author of this study. Since 2021, he holds the position of Assistant Professor (tenure track) at the Department of Physics and Astronomy, University of Bologna. At the same time, he is a Visiting Professor at the Center for Computational Quantum Physics (CCQ) at the Flatiron Institute, New York, as part of a Marie Sklodowska-Curie Actions (MSCA) grant that encourages, among other things, the mobility of researchers.

He and colleagues at the Flatiron Institute and other international researchers conducted the study, which has the potential to revolutionize the way scientists study systems containing many interacting electrons. In addition, if they can adapt the method to other problems, the approach could help design materials with desirable properties, such as superconductivity, or contribute to clean energy production.

How Big Data is Saving Earth from Asteroids: A Cosmic Shield

As technology advances, Big Data will play an increasingly important role in protecting Earth from asteroids. By harnessing the power of data analytics, AI, and machine learning, scientists can monitor and predict asteroid movements with greater accuracy than ever before. This enables us to develop early warning systems and potentially deflect asteroids before they can cause harm. Aspiring data scientists interested in contributing to such significant fields can gain the necessary skills by enrolling in a data science course in Chennai, where they can learn to utilize these advanced tools and techniques.

AI Innovations in Diagnosing Myopic Maculopathy

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.

Tracking neurons across days with high-density probes

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.

We tested UnitMatch on sequences of Neuropixels recordings from multiple regions of the mouse brain and found that it reliably tracked neurons across weeks. Its performance compares well to the concatenated method and to curation by human experts, while being much faster and applicable to longer sequences of recordings.

Because UnitMatch relies only on each unit’s spike waveform, and not on any functional properties, it can be used to test whether these properties change over time. Indeed, while units can maintain firing properties such as inter-spike interval (ISI) distribution10,11,12,19,20,28,29 and sensory, cognitive or motor correlates11,13,14,15,24,28,29,31,38, the stability of these properties cannot be assumed. In fact, it is often the question being investigated6,7,19,21,22,23,25,27,28,38,39,40.

New insights into exotic nuclei creation using Langevin equation model

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 , 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 , 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.

CRISPR CREME: An AI Treat to Enable Virtual Genomic Experiments

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.”