Results 61 to 70 of about 8,525 (297)

Solid Harmonic Wavelet Bispectrum for Image Analysis

open access: yesAdvanced Science, EarlyView.
The Solid Harmonic Wavelet Bispectrum (SHWB), a rotation‐ and translation‐invariant descriptor that captures higher‐order (phase) correlations in signals, is introduced. Combining wavelet scattering, bispectral analysis, and group theory, SHWB achieves interpretable, data‐efficient representations and demonstrates competitive performance across texture,
Alex Brown   +3 more
wiley   +1 more source

Self-bilinear Map from One Way Encoding System and Indistinguishability Obfuscation [PDF]

open access: yes, 2015
The bilinear map whose domain and target sets are identical is called the self-bilinear map. Original self-bilinear maps are defined over cyclic groups. This brings a lot of limitations to construct secure self-bilinear schemes.
Baodian Wei   +3 more
core  

Shorter Verifier-Local Revocation Group Signatures from Bilinear Maps [PDF]

open access: yes, 2006
We propose a new computational complexity assumption from bilinear map, based on which we construct Verifier-Local Revocation group signatures with shorter lengths than previous ...
Zhou Sujing   +3 more
core   +1 more source

Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy

open access: yesAdvanced Science, EarlyView.
Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu   +4 more
wiley   +1 more source

Supporting Non-membership Proofs with Bilinear-map Accumulators [PDF]

open access: yes, 2008
In this short note, we present an extension of Nguyen\u27s bilinear-map based accumulator scheme to support \emph{non-membership witnesses} and corresponding \emph{non-membership proofs}, i.e., cryptographic proofs that an element has not been ...
Nikos Triandopoulos, Ivan Damgård
core  

Biolipid Film‐Fused Electrochemiluminescence for Multipurpose In Situ Bioassays

open access: yesAdvanced Science, EarlyView.
An ECL‐emissive, membrane‐interactive scaffold was fabricated, and facilely fused with natural and non‐native phospholipids into multifactorial mimicries of cytomembranes and vesicles for in vitro representative membrane‐process probing. Such a biointerface‐based, state‐sensitive ECL paradigm not only pinpointed proximal phenomena, including channeling
Jialiang Chen   +9 more
wiley   +1 more source

Decoding Spatial Heterogeneity and Multi‐Omics Regulation with Hierarchical Graph Learning

open access: yesAdvanced Science, EarlyView.
ABSTRACT Recent advances in spatial multi‐omics technologies have enabled the simultaneous profiling of multiple molecular layers within the same tissue slice, providing unprecedented opportunities to investigate tissue spatial organization. However, most existing computational methods identify spatial domains in a purely data‐driven manner, rarely ...
Jiazhou Chen   +6 more
wiley   +1 more source

Arens regularity of some bilinear maps

open access: yes, 2009
Let H be a Hilbert space. we show that the following statements are equivalent: (a) B(H) is finite dimension, (b) every left Banach module action l : B(H)×H → H, is Arens regular (c) every bilinear map f : B(H)*→ B(H) is Arens regular.
Gordji, M. Eshaghi
core   +1 more source

The arens triadjoints of some bilinear maps [PDF]

open access: yes, 2014
In this paper we study the Arens triadjoints of some bilinear maps on vector lattices. in particular, we prove that, for Archimedean vector lattices A and B, the Arens triadjoint i) T*** : A '' x A '' -> B '' of a positive orthosymmetric bilinear map T :
Yılmaz, Ruşen
core   +1 more source

Efficient In‐Hardware Matrix–Vector Multiplication and Addition Exploiting Bilinearity of Schottky Barrier Transistors Processed on Industrial FDSOI

open access: yesAdvanced Electronic Materials, EarlyView.
ABSTRACT Machine learning and Artificial Intelligence (AI) tasks have stretched traditional hardware to its limits. In‐hardware computation is a novel approach that aims to run complex operations, such as matrix–vector multiplication, directly at the device level for increased efficiency.
Juan P. Martinez   +10 more
wiley   +1 more source

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