A study by OIST researchers has developed a physics-based model to reproduce and predict individual ant movement. The model uses stochastic, or random, modeling to analyze video recordings of ants exploring laboratory environments, allowing for the derivation of predictions about their behavior in new environments.
USC is leading a national research team developing AI to predict turbulence, a challenge in physics and engineering that affects technologies daily. The approach could make scientific simulations faster and more accurate, enabling researchers to tackle complex problems.
A five-year study reveals that prolonged nitrogen deposition affects subtropical forest soil microbiomes, with a dynamic temporal shift in microbial community assembly and network stability. Dissolved organic matter quality is crucial in predicting bacterial network complexity.
Researchers found evidence of Native American dice making and use dating back 12,000 years, with binary lots crafted from bone featuring markings for counting sides. This discovery challenges traditional views on the origins of probability theory.
Researchers at Rice University developed a mathematical model that sheds light on ovarian aging and menopause timing. The study reveals synchronized and predictable patterns of follicle depletion, accelerating during midlife, which helps explain why menopause typically occurs at similar ages for most women.
Full Waveform Inversion (FWI) technology provides unprecedented precision in seismic imaging, breaking resolution limitations of traditional methods. It characterizes complex structures within the Earth's interior and offers higher-resolution subsurface models.
A new study from Rice University researchers introduces a theoretical framework that quantitatively predicts menopause timing by analyzing how ovarian follicles transition through different stages. The model explains why menopause occurs and sheds light on individual variability and cross-population differences, providing insights that...
Researchers have developed a new field called stochastic thermodynamics, which provides tools to investigate and quantify the energetics of computational systems far from thermal equilibrium. This can help minimize heat losses and optimize energy efficiency in computing.
Researchers have found that variability in when and how cells divide during embryo development leads to more optimal arrangements of cells, promoting robust tissue formation. This study challenges traditional views on the role of cell division variability in embryonic development.
Researchers introduce mathematical equations revealing minimum and maximum predicted energy cost of computational processes with randomness, offering insights into computing energy-cost bounds. The framework offers a way to calculate lower bounds on the energy cost of unpredictable finish situations.
Scientists have discovered that aging clocks measure stochastic changes in cells, rather than damage accumulation. This finding suggests that aging can be predicted using the variation in cellular processes.
A study discovers that traditional Chinese ice-ray lattice designs can provide unique stiffness and strength under asymmetric loads, offering an alternative to conventional gridshells. The research also explores the potential of integrating complex geometry into facade design and micro-scale material design.
Researchers have discovered that a tiny disturbance in a fluid system can amplify into large-scale patterns of randomness, making it difficult to predict turbulent flows. This phenomenon, known as spontaneous stochasticity, occurs regardless of the initial disturbance and has implications for weather forecasting and astrophysics.
A new study by EPFL researchers Antonia Sclocchi and Matthieu Wyart examines the impact of changing batch size and learning rate on stochastic gradient descent. They identify three regimes that affect AI learning differently, from exploratory to efficient approaches.
Chemical simulations can be sped up by resetting them, a new study from Tel Aviv University found. This technique, called stochastic resetting, overcomes the timescale problem, allowing for more accurate predictions of slow processes.
Researchers develop epigenetic clocks based on regional disorder of DNA methylation patterns, identifying common responses and critical differences from canonical clocks. These findings suggest a fundamental decoupling of epigenetic aging processes.
Uncertainty in thermodynamic parameters can influence experiment outcomes, suggesting a new approach to modifying stochastic thermodynamics equations. This oversight has implications for various systems, including cells and optical tweezers, where precision is limited by the uncertainty in system parameters.
Researchers developed a new method to estimate gradients and derivatives on quantum computers, enabling faster computations. This technique can be applied to various fields such as cryptography, optimization, and materials science.
Researchers developed a photoelectrochemical technique to precisely tune the lasing wavelength of microdisk lasers with subnanometric accuracy. The new approach facilitates the fabrication of micro- and nano-laser batches with precise emission wavelengths.
A team of researchers developed a computational simulation that explains key mechanism of DNA segregation, providing new insights into the distribution of genetic information during bacterial cell division. The study reveals fundamental biochemical principles relevant to synthetic biology and medical applications.
A new source-device-independent quantum random number generator (QRNG) protocol has been developed, operating securely and independently of source devices. This allows for practical applications in secure quantum information tasks, with a reported generation rate of 4 megabits per second.
Researchers from Washington University in St. Louis and Purdue University used single-cell data to develop a new framework for understanding the relationship between cell growth, DNA replication, and division in bacteria. They found that individual cells can exquisitely coordinate these processes, despite the 'noisiness' of each process.
Lehigh University researchers are developing a model to understand the impact of grain growth on material properties. The project aims to create new materials informatics methods, innovative stochastic differential equations, and models of grain growth to improve material performance and reliability.
Scientists at Tokyo Institute of Technology have developed a new processor architecture, STATICA, that can efficiently solve combinatorial optimization problems. The proposed system is fully connected and considers all spin-to-spin interactions, enabling parallel updating using stochastic cell automata, which reduces calculation time.
A new mathematical framework developed by NYU Tandon researchers predicts that interaction among diverse individuals can speed consensus. The model accounts for varying individual performance and communication connections within a group.
Researchers developed an analytical model to validate the capability of break junction techniques in extracting conductance information from single-molecule systems. The study explores the feasibility of using this technique to analyze molecular assembly, diffusion, and reaction processes in weak interaction systems.
Researchers from Universidad Carlos III de Madrid have patented a Kleroterion device made of methacrylate, ensuring randomness in the entire process. The new design offers advantages such as ease of transport and transparency, making it a simpler and more reliable method for selecting people.
Researchers at Tokyo Institute of Technology have developed a model that parallels the kinetic theory of molecules, providing a solid foundation for understanding price fluctuations in stable financial markets. The study also aims to examine unstable markets vulnerable to external shocks.
Researchers find that deltas exhibit a high diversity of delivery pathways to the sea, adapting to maintain their existence. The study's findings suggest an 'optimality principle' governing the behavior of rivers deltas across different environments.
Researchers have used a new mathematical model to study why computer networks and electronic equipment fail when handling multiple requests. The model takes into account the unreliability of servers and predicts system performance, providing insights for optimizing real-world systems.
Researchers at SISSA have developed a mathematical model of protein synthesis, revealing that the process is more stochastic than previously thought. This new understanding sheds light on the random component influencing protein translation times, which can affect the efficiency of the process.
A new study by USGS researchers Tom Parsons and Eric Geist analyzed the timing of world's largest earthquakes over 100 years, finding intervals between quakes similar to those expected from a random process. This suggests that global great earthquakes are occurring at random, rather than triggering each other.
Researchers discovered a new way for quantum computers to simulate stochastic processes, which are used to model phenomena like stock market movements and gas diffusion. This finding suggests that quantum theory might not yet be optimized, leaving room for further exploration of a deeper theory.
Ivan Corwin, a recent Ph.D. from NYU, and Jack Thorne, a Harvard University PhD candidate, were selected for their research achievements in probability, algebraic number theory, and representation theory. They will receive funding for one to five years to support their mathematical research.
A new study by Northwestern University researchers found that ineffective algorithms and profiles hinder online dating. In contrast, mobile dating enables potential partners to meet face-to-face quickly, which is crucial for finding compatibility.
Researchers have developed a comprehensive three-dimensional model to predict droplet size and spray characteristics in effervescent atomization. The study reveals the influence of operating conditions and liquid properties on atomization performance.
A new model shows that stochastic resonance occurs when the potential has sufficiently steep walls, but breaks down otherwise. This phenomenon could contribute to improving image resolution and understanding of biological systems.
A new mathematical model can detect financial bubbles in real-time using tick data and statistical techniques. The model estimates asset price volatility to determine if a bubble is forming, which can help prevent economic collapses.
Researchers observed paracrine signaling in Bacillus subtilis, a novel mechanism for maintaining two differentiated cell populations within a bacterial community. This discovery opens doors to developing strategies to reverse differentiation in antibiotic persister cells.
Salmonella bacteria use random molecular processes during cell division to form two groups that engage in job-sharing, with one group sacrificing itself to generate a greater common good. This phenomenon allows the bacteria to improve their chances of survival and spread in the gut.
Christopher Hoffman has been awarded a prestigious $70,000 AMS Centennial Fellowship to continue his research on probabilistic models from statistical physics. He will focus on stochastic growth processes at the Institut Mittag-Leffler and Centre de Recherches Mathematiques.
The National Academies Keck FUTURES INITIATIVE provides seed funding to researchers to pursue new ideas and connections. The grants aim to bridge the gap between bold new ideas and federal funding programs, enabling researchers to develop a line of inquiry and compete for larger awards.