Leading University of Portsmouth physicist’s study indicates that gravity or gravitational force is the result of a computational process within the universe.
Category: computing – Page 72
Gravity May Be a Clue That The Universe Is a Giant Computer
If we were living in a computer simulation, would we be able to tell we were living in a computer simulation? It’s a question that’s difficult to answer, but physicist Melvin Vopson of the University of Portsmouth in the UK believes that he may have found a clue.
According to his latest study, gravity could be a product of computational processes within the Universe, a by-product of the Universe’s attempt to keep information and matter neatly organized in space and time.
“My findings in this study fit with the thought that the Universe might work like a giant computer, or our reality is a simulated construct,” Vopson says.
Sci-Fi: Dreaming or Downloading?
🚀 THE FUTURE OF SCI-FI: UPLIFTING OR JUST UPLOADING? 🚀
Welcome back, gang! Egotastic FunTime is blasting into another galactic rant—this time asking the big question:
Has sci-fi lost its soul? 🌌
From Star Trek’s hopeful utopias to today’s server-farmed dystopias, we’re cracking open the hard drive of the future and asking if we’re still dreaming… or just buffering forever. 🤖✨
Why is modern sci-fi obsessed with uploading instead of uplifting?
Is humanity evolving or just ghosting itself with tech?
Where did the wonder go—and can we get it back?
Grab your neural nodes and sarcastic side-eyes, because we’re deep-diving into the state of sci-fi, tech anxiety, and how imagination might just save us yet.
Computational mechanism underlying switching of motor actions
Author summary Humans exhibit a remarkable ability to regulate their actions in response to changing environmental demands. An essential aspect of action regulation is action inhibition that occurs when stopping unwanted or inappropriate actions. However, everyday life rarely calls for complete inhibition of responses without switching behavior to adapt to new situations. Despite extensive research to understand how the brain switches actions, the computations underlying the switching process and how it relates to the selecting and stopping processes remain elusive. Part of this challenge lies in the fact that these processes are rarely studied together, making it difficult to develop a unified theory that explains the computational aspects of the action regulation mechanism. The current study aims to delineate the computations underlying action regulation functions that involve inhibitory control, explore how these functions interrelate, and how they can be implemented within brain networks, opening new avenues for future neurophysiological investigations.
Computational analysis clarifies cancer risk for families with genetic variants
QIMR Berghofer-led research has shown that new advanced computational prediction tools can improve the accuracy of genetic testing for families affected by an inherited condition that significantly increases their risk of developing cancer, paving the way to better targeted care.
The findings have been published in the American Journal of Human Genetics alongside complementary studies by international collaborators, which together show how incorporating the new computational biology tools with existing modeling methods improved the predictive power of genetic test results.
Computational tools are used to predict if and how a genetic variant is likely to impact the function of the protein encoded by the gene.
Neuromorphic system uses quantum effects to find optimal solutions to complex problems
It’s easy to solve a 3×3 Rubik’s cube, says Shantanu Chakrabartty, the Clifford W. Murphy Professor and vice dean for research and graduate education in the McKelvey School of Engineering at Washington University in St. Louis. Just learn and memorize the steps then execute them to arrive at the solution.
Computers are already good at this kind of procedural problem solving. Now, Chakrabartty and his collaborators have developed a tool that can go beyond procedure to discover new solutions to complex optimization problems in logistics to drug discovery.
Chakrabartty and his collaborators introduced NeuroSA, a problem-solving neuromorphic architecture modeled on how human neurobiology functions, but that leverages quantum mechanical behavior to find optimal solutions—guaranteed—and find those solutions more reliably than state-of-the-art methods.
A new computational framework illuminates the hidden ecology of diseased tissues
To understand what drives disease progression in tissues, scientists need more than just a snapshot of cells in isolation—they need to see where the cells are, how they interact, and how that spatial organization shifts across disease states. A computational method called MESA (Multiomics and Ecological Spatial Analysis), detailed in a study published in Nature Genetics, is helping researchers study diseased tissues in more meaningful ways.
The work details the results of a collaboration among researchers from MIT, Stanford University, Weill Cornell Medicine, the Ragon Institute of MGH, MIT, and Harvard, and the Broad Institute of MIT and Harvard, and was led by the Stanford team.
MESA brings an ecology-inspired lens to tissue analysis. It offers a pipeline to interpret spatial omics data—the product of cutting-edge technology that captures molecular information along with the location of cells in tissue samples. This data provides a high-resolution map of tissue “neighborhoods,” and MESA helps make sense of the structure of that map.
Dual scalable annealing processors overcome capacity and precision limits
Combinatorial optimization problems (COPs) arise in various fields such as shift scheduling, traffic routing, and drug development. However, they are challenging to solve using traditional computers in a practical timeframe.
Alternatively, annealing processors (APs), which are specialized hardware for solving COPs, have gained significant attention. They are based on the Ising model, in which COP variables are presented as magnetic spins and constraints as interactions between spins. Solutions are obtained by finding the spin state that minimizes the energy of the system.
There are two types of Ising models, the sparsely-coupled model and the fully-coupled model. Sparsely-coupled models offer high scalability by allowing more spins, but require COPs to be transformed to fit the model. Fully-coupled models, on the other hand, allow any COP to be mapped directly without transformation, making them highly desirable.