Results 51 to 60 of about 2,420 (204)

Cellular Snowballing: Cell Adhesion and Migration Drive the Self‐Assembly of Cell‐Microgel Biohybrid Spheroids

open access: yesAdvanced Science, EarlyView.
A new class of biohybrid spheroids is engineered through the self‐assembly of adherent cells and extracellular matrix‐mimetic hydrogel microparticles (microgels). By mimicking a snowballing effect, this approach enables scalable formation of porous, millimeter‐scale spheroids with enhanced cell viability and molecular diffusion.
Zaman Ataie   +7 more
wiley   +1 more source

Random Time‐Space Coding Metasurfaces for Spatial Control of the Temporal Statistics of Electromagnetic Fields

open access: yesAdvanced Science, EarlyView.
A probabilistic framework based on random time‐space coding metasurfaces enables control of the spatial distribution of electromagnetic fields temporal statistics. By tailoring the marginal and joint distributions of random codes, electromagnetic fields with desired mean and variance patterns are realized, enabling simultaneous transmission and jamming.
Jia Cheng Li   +3 more
wiley   +1 more source

Qubit Reduction and Quantum Speedup for Wireless Channel Assignment Problem

open access: yesIEEE Transactions on Quantum Engineering, 2023
In this article, we propose a novel method of formulating an NP-hard wireless channel assignment problem as a higher-order unconstrained binary optimization (HUBO), where the Grover adaptive search (GAS) is used to provide a quadratic speedup for solving
Yuki Sano   +2 more
doaj   +1 more source

An Algorithm for Unconstrained Quadratically Penalized Convex Optimization [PDF]

open access: yesCommunications in Statistics - Simulation and Computation, 2011
A descent algorithm, "Quasi-Quadratic Minimization with Memory" (QQMM), is proposed for unconstrained minimization of the sum, $F$, of a non-negative convex function, $V$, and a quadratic form. Such problems come up in regularized estimation in machine learning and statistics. In addition to values of $F$, QQMM requires the (sub)gradient of $V$.
openaire   +2 more sources

Fundamental Challenges, Physical Implementations, and Integration Strategies for Ising Machines in Large‐Scale Optimization Tasks

open access: yesAdvanced Electronic Materials, EarlyView.
Ising machines are emerging as specialized hardware solvers for computationally hard optimization problems. This review examines five major platforms—digital CMOS, analog CMOS, emerging devices, coherent optics, and quantum systems—highlighting physics‐rooted advantages and shared bottlenecks in scalability and connectivity.
Hyunjun Lee, Joon Pyo Kim, Sanghyeon Kim
wiley   +1 more source

Speedup of high-order unconstrained binary optimization using quantum $${{\mathbb{Z}}}_{2}$$ Z 2 lattice gauge theory

open access: yesCommunications Physics
An important and difficult problem in optimization is the high-order unconstrained binary optimization, which can represent many optimization problems more efficiently than quadratic unconstrained binary optimization, but how to quickly solve it has ...
Bi-Ying Wang   +5 more
doaj   +1 more source

Trust‐region filter algorithms utilizing Hessian information for gray‐box optimization

open access: yesAIChE Journal, EarlyView.
Abstract Optimizing industrial processes often involves gray‐box models that couple algebraic glass‐box equations with black‐box components lacking analytic derivatives. Such systems challenge derivative‐based solvers. The classical trust‐region filter (TRF) algorithm provides a robust framework but requires extensive parameter tuning and numerous ...
Gul Hameed   +4 more
wiley   +1 more source

Digital Annealer for quadratic unconstrained binary optimization: A comparative performance analysis

open access: yesApplied Soft Computing, 2022
Digital Annealer (DA) is a computer architecture designed for tackling combinatorial optimization problems formulated as quadratic unconstrained binary optimization (QUBO) models. In this paper, we present the results of an extensive computational study to evaluate the performance of DA in a systematic way in comparison to multiple state-of-the-art ...
Oylum Şeker   +2 more
openaire   +2 more sources

Factorization Machine‐Based Active Learning for Functional Materials Design with Optimal Initial Data

open access: yesAdvanced Intelligent Discovery, EarlyView.
This work investigates the optimal initial data size for surrogate‐based active learning in functional material optimization. Using factorization machine (FM)‐based quadratic unconstrained binary optimization (QUBO) surrogates and averaged piecewise linear regression, we show that adequate initial data accelerates convergence, enhances efficiency, and ...
Seongmin Kim, In‐Saeng Suh
wiley   +1 more source

Quadratic unconstrained binary optimization formulation for rectified-linear-unit-type functions [PDF]

open access: yesPhysical Review E, 2019
5 pages, 2 ...
Sato, Go   +4 more
openaire   +3 more sources

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