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We study the satisfiability problem of symbolic finite automata and decompose it into the satisfiability problem of the theory of the input characters and the monadic second-order theory of the indices of accepted words. We use our decomposition to obtain ...
As big strides were being made in many science fields in the 1970s and 80s, faster computation for solving problems in molecular biology, semiconductor technology, aeronautics, particle physics, etc., was at the forefront of research. Parallel and super-co ...
. High-resolution simulations of particle-based kinetic plasma models typically require a high number of particles and thus often become computationally intractable. This is exacerbated in multi-query simulations, where the problem depends on a set of para ...
The SALUTE project aims at evaluating performance of electroacoustic metasurface, employing a surface array of controlled electroacoustic actuators, for smart acoustic lining under grazing turbulent flow to be used in UHBR Technologies Engines. Theoretical ...
Society of Photo-Optical Instrumentation Engineers (SPIE)2023
Three-dimensional inspection of nanostructures such as integrated circuits is important for security and reliability assurance. Two scanning operations are required: ptychographic to recover the complex transmissivity of the specimen, and rotation of the s ...
By the addition of entropic regularization, multimarginal optimal transport problems can be trans-formed into tensor scaling problems, which can be solved numerically using the multimarginal Sinkhorn algorithm. The main computational bottleneck of this alg ...
Reducing the computational time required by high-fidelity, full-order models (FOMs) for the solution of problems in cardiac mechanics is crucial to allow the translation of patient-specific simulations into clinical practice. Indeed, while FOMs, such as th ...
Machine learning (ML) models for molecules and materials commonly rely on a decomposition of the global target quantity into local, atom-centered contributions. This approach is convenient from a computational perspective, enabling large-scale ML-driven si ...
While the introduction of practical deep learning has driven progress across scientific fields, recent research highlighted that the requirement of deep learning for ever-increasing computational resources and data has potential negative impacts on the sci ...
The use of point clouds as an imaging modality has been rapidly growing, motivating research on compression methods to enable efficient transmission and storage for many applications. While compression standards relying on conven- tional techniques such as ...
