Results 71 to 80 of about 541,388 (266)

Reprogrammable, In‐Materia Matrix‐Vector Multiplication with Floppy Modes

Advanced Intelligent Systems, EarlyView.
This article describes a metamaterial that mechanically computes matrix‐vector multiplications, one of the fundamental operations in artificial intelligence models. The matrix multiplication is encoded in floppy modes, near‐zero force deformations of soft matter systems.
Theophile Louvet, Parisa Omidvar, Marc Serra‐Garcia   +2 more
wiley   +1 more source

AI in Neurology: Everything, Everywhere, All at Once Part 3: Surveillance, Synthesis, Simulation, and Systems

Annals of Neurology, EarlyView.
This final part 3 review builds on the practical applications discussed in part 2 and explores how artificial intelligence (AI) is transforming data management, neurological education, and neurological care across large healthcare networks and datasets. The review also highlights AI's role in real‐world and synthetic data, digital twins, and innovative
Matthew Rizzo
wiley   +1 more source

Ultrafast Demagnetization Through Femtosecond Generation of Non‐Thermal Magnons

Advanced Physics Research, EarlyView.
A fully ab initio parametrized model is developed to describe out‐of‐equilibrium magnon dynamics in ferromagnetic metals. Applying this model to ultrafast laser‐induced demagnetization, it is revealed that the generation of non‐thermal magnons provides a sizable demagnetization within 200 fs in excellent comparison with experiments, which establishes ...
Markus Weißenhofer, Peter M. Oppeneer
wiley   +1 more source

Conformal Interactions Between Matter and Higher‐Spin (Super)Fields

Fortschritte der Physik, Volume 71, Issue 1, January 2023., 2023
Abstract In even spacetime dimensions, the interacting bosonic conformal higher‐spin (CHS) theory can be realised as an induced action. The main ingredient in this definition is the model S[φ,h]$\mathcal {S}[\varphi ,h]$ describing a complex scalar field φ coupled to an infinite set of background CHS fields h, with S[φ,h]$\mathcal {S}[\varphi ,h ...
Sergei M. Kuzenko, Michael Ponds, Emmanouil S. N. Raptakis   +2 more
wiley   +1 more source

MCRB for Parameter Estimation from One-Bit Quantized and Oversampled Measurements [PDF]

arXiv
One-bit quantization has garnered significant attention in recent years for various signal processing and communication applications. Estimating model parameters from one bit quantized data can be challenging, particularly when the quantization process is explicitly accounted for in the estimator.
arxiv  

Optimization of Quantized Analog Signal Processing Using Genetic Algorithms and μ-Law

IEEE Open Journal of Circuits and Systems, 2022
Digital mismatch calibration for quantized analog (QA) signal processing is proposed for the first time. Since the proposed calibration mechanism does not require uniform QA slicer levels, non-uniform quantization can be applied to improve the system performance.
Qingnan Yu, Tony Chan Carusone, Antonio Liscidini   +2 more
openaire   +1 more source

Conditions for optimality of scalar feedback quantization

2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008
This paper presents novel results on scalar feedback quantization (SFQ) with uniform quantizers. We focus on general SFQ configurations where reconstruction is via a linear combination of frame vectors. Using a deterministic approach, we derive two necessary and sufficient conditions for SFQ to be optimal, i.e., to produce, for every input, a quantized
Graham C. Goodwin, Daniel E. Quevedo, Arie Feuer, Milan S. Derpich   +3 more
openaire   +5 more sources

Axion‐Like Interactions and CFT in Topological Matter, Anomaly Sum Rules and the Faraday Effect

Advanced Physics Research, EarlyView.
This review investigates the connection between chiral anomalies and their manifestation in topological materials, using both perturbative methods based on ordinary quantum field theory and conformal field theory (CFT). It emphasizes the role of CFT in momentum space for parity‐odd correlation functions, and their reconstruction by the inclusion of a ...
Claudio Corianò, Mario Cretì, Stefano Lionetti, Dario Melle, Riccardo Tommasi   +4 more
wiley   +1 more source

Pulse‐Level Quantum Robust Control with Diffusion‐Based Reinforcement Learning

Advanced Physics Research, EarlyView.
This paper proposes a diffusion‐based reinforcement learning method for pulse‐level quantum robust control (PQC‐DBRL) to enhance the robustness of pulse‐level quantum gate control. When evaluated across different Hamiltonian variations, PQC‐DBRL shows a smaller fidelity variance compared to GRAPE, indicating higher robustness against system parameter ...
Yuanjing Zhang, Tao Shang, Chenyi Zhang, Xueyi Guo   +3 more
wiley   +1 more source

Task-Based Quantizer Design for Sensing With Random Signals [PDF]

arXiv
In integrated sensing and communication (ISAC) systems, random signaling is used to convey useful information as well as sense the environment. Such randomness poses challenges in various components in sensing signal processing. In this paper, we investigate quantizer design for sensing in ISAC systems.
arxiv