Results 51 to 60 of about 326,660 (252)

The Invariant Subspace Problem [PDF]

open access: yes, 2023
The invariant subspace problem is solved correcting my earlier attempts [6]-[12].Comment: I thank deeply Prof. Charles Akemann (UCSB), Prof.
Lee, Sa Ge
core   +1 more source

Causal‐Guided Ultra‐Long‐Term Time Series Forecasting Via Anticipated Covariates

open access: yesAdvanced Science, EarlyView.
Often treated as unknown, information from the future remains underutilized.We demonstrate that in a coupled dynamical system, providing the future state of the effect enables accurate forecasting of the cause for a long timesteps. A time series forecasting paradigm that introduces anticipated covariates to represent such known future states is ...
Jintong Zhao   +4 more
wiley   +1 more source

Invariant Subspaces and Exact Solutions for Nonlinear Variable-Coefficient Beam-Type Equations

open access: yesMathematics
This paper investigates a class of generalized nonlinear variable-coefficient beam-type evolution equations motivated by the modeling of nonhomogeneous elastic structures with nonlinear effects.
Manal Badgaish
doaj   +1 more source

On The Normality Set of Linear Operators

open access: yesمجلة بغداد للعلوم, 2020
In this paper, the Normality set  will be investigated. Then, the study highlights some concepts properties and important results. In addition, it will prove that every operator with normality set has non trivial invariant subspace of  .
Laith K. Shaakir, Anas A. Hijab
doaj   +1 more source

On pole placement and invariant subspaces [PDF]

open access: yes2013 XXIV International Conference on Information, Communication and Automation Technologies (ICAT), 2013
The classical eigenvalue assignment problem is revisited in this note. We derive an analytic expression for pole placement which represents a slight generalization of the celebrated Bass-Gura and Ackermann formulae, and also is closely related to the modal procedure of Simon and Mitter.
openaire   +5 more sources

An invariant subspace theorem [PDF]

open access: yes, 1980
In this paper it is proved that every operator on a complex Hilbert space whose spectrum is a spectral set has a nontrivial invariant ...
Agler, Jim
core   +1 more source

Lipidomic Profile Reconstruction of Therapeutic Membrane Targets Using Physics‐Based Optimization with Limited Activity Data

open access: yesAdvanced Science, EarlyView.
Using a minimal set of experimentally labeled antimicrobial and non‐antimicrobial peptides, we reconstruct the effective lipid composition of a pathogenic membrane ‐demonstrated at the example of bacterial membranes‐ through a physics‐based evolutionary molecular dynamics (Evo‐MD) approach. Optimization based on peptide insertion reveals broad‐spectrum
Maximilian Krebs, Herre Jelger Risselada
wiley   +1 more source

Cross-View Feature Learning via Structures Unlocking Based on Robust Low-Rank Constraint

open access: yesIEEE Access, 2020
The cross-view multimedia are widely existed and attract many attentions in recent years. Nevertheless, it is noted that the phenomenon, that data in different classes from same view are more similar than that in same class from different views, is ...
Ao Li   +5 more
doaj   +1 more source

Latent Diffusion Process With Mechanistic Guidance For Designing Functionally Graded Metamaterials With Perfect Connectivity

open access: yesAdvanced Science, EarlyView.
A latent diffusion‐based framework is proposed for designing functionally graded metamaterials with perfect connectivity. By integrating vector‐quantized latent representations with mechanistic guidance, the framework enables accurate inverse design toward target elastic properties.
Jongbin Yu, Dosung Lee, Namjung Kim
wiley   +1 more source

SemanticST: A Scalable Multi‐Contextual Graph Learning Framework for Uncovering Spatial Niches and Robust Multi‐Sample Integration in Spatial Transcriptomics

open access: yesAdvanced Science, EarlyView.
Technical limitations often let dominant signals overshadow rare cell types and fine‐grained heterogeneity in spatial transcriptomics. SemanticST, a graph neural network using multi‐semantic graph fusion and a novel min‐cut loss, recovers these subtle patterns.
Roxana Zahedi   +7 more
wiley   +1 more source

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