Results 21 to 30 of about 601,707 (261)

Wave space interpretation of scattered ultrasound

open access: yesUltrasound in Medicine & Biology, 1988
Wave space is described nonmathematically for design and interpretation of ultrasound scattering experiments. By considering ultrasound scattering from a variety of targets and geometries, the wave space approach is shown to describe target characteristics in terms of spatial frequencies and their amplitudes.
R M, Lerner, R C, Waag
openaire   +2 more sources

LANGUAGE AND SPEECH IN THE SPACE OF CULTURE: INTERPRETATION AND REINTERPRETATION

open access: yesВестник Волгоградского государственного университета: Серия 2. Языкознание, 2017
The author focuses on the experience of the thought-speech activity of the subject. Its consideration is carried out through the prism of such areas of humanitarian science as the philosophy of language, ontology, psychology, cultural studies, and art ...
Polina S. Volkova
doaj   +1 more source

Age Differences in the Interpretation of Facial Emojis: Classification on the Arousal-Valence Space

open access: yesFrontiers in Psychology, 2022
Emojis are universal tools that are frequently used to express people’s emotional states throughout daily communications. They are often applied in various fields of research, such as consumer surveys, as indicators of users’ emotional states.
Gaku Kutsuzawa   +3 more
doaj   +1 more source

State-Space Interpretation of Model Predictive Control [PDF]

open access: yesAutomatica, 1994
A model predictive control technique based on a step response model is developed using state estimation techniques. The standard step response model is extended so that integrating systems can be treated within the same framework. Based on the modified step response model, it is shown how the state estimation techniques from stochastic optimal control ...
Jay H. Lee   +2 more
openaire   +2 more sources

Interpretable Latent Space to Enable Counterfactual Explanations

open access: yes, 2022
Many dimensionality reduction methods have been introduced to map a data space into one with fewer features and enhance machine learning models' capabilities. This reduced space, called latent space, holds properties that allow researchers to understand the data better and produce better models.
Bodria F.   +3 more
openaire   +4 more sources

On the meaning and interpretation of tomography in abstract Hilbert spaces [PDF]

open access: yesPhysics Letters A, 2006
Submitted to Phys.
Manʹko V. I.   +5 more
openaire   +4 more sources

Diversity and complexity in neural organoids

open access: yesFEBS Letters, EarlyView.
Neural organoid research aims to expand genetic diversity on one side and increase tissue complexity on the other. Chimeroids integrate multiple donor genomes within single organoids. Self‐organising multi‐identity organoids, exogenous cell seeding, or enforced assembly of region‐specific organoids contribute to tissue complexity.
Ilaria Chiaradia, Madeline A. Lancaster
wiley   +1 more source

Interpreting Disentangled Representations of Person-Specific Convolutional Variational Autoencoders of Spatially Preserving EEG Topographic Maps via Clustering and Visual Plausibility

open access: yesInformation, 2023
Dimensionality reduction and producing simple representations of electroencephalography (EEG) signals are challenging problems. Variational autoencoders (VAEs) have been employed for EEG data creation, augmentation, and automatic feature extraction.
Taufique Ahmed, Luca Longo
doaj   +1 more source

Conformal Invarians Interpreted in the Sitter Space

open access: yesMatemática Contemporânea, 2008
Work of both authors is partially supported by DGCYT and FEDER grant no.
Romero Fuster, M. C.   +1 more
openaire   +2 more sources

Modelling stem cell differentiation related processes—A practical overview for biologists

open access: yesFEBS Letters, EarlyView.
Stem cell differentiation is complex and difficult to control experimentally. This review introduces suitable computational modelling approaches that can support stem cell research, from mechanistic ODE and abstract models to multiscale and deep learning methods.
Ricco Zeegelaar   +4 more
wiley   +1 more source

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