Results 231 to 240 of about 842,651 (374)
Solid Harmonic Wavelet Bispectrum for Image Analysis
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
Multi-Center Prototype Feature Distribution Reconstruction for Class-Incremental SAR Target Recognition. [PDF]
Zhang K, Wu B, Li P, Kang Z, Zhang L.
europepmc +1 more source
This study performs pan‐viromic profiling of 14,529 samples from 5,710 domestic herbivores across five Chinese provinces, establishing the DhCN‐Virome (1,085,360 viral metagenomes). It reveals species/sample‐specific viromic signatures and cross‐species transmission dynamics, aiding unified disease control.
Yue Sun +19 more
wiley +1 more source
Towards Effective Open-set Graph Class-incremental Learning
Jiazhen Chen +5 more
openalex +1 more source
Exploiting Fine-Grained Prototype Distribution for Boosting Unsupervised Class Incremental Learning [PDF]
Jiaming Liu +6 more
openalex +1 more source
A programmable 2048‐element circular ultrasound array combined with a compact acoustic lens produces a thin “sound sheet” over a large field of view, and records echoes with wide angular diversity across the ring aperture. Coherence‐enhanced beamforming converts full‐matrix data into high‐contrast tomographic slices, delivering near‐diffraction‐limited
Qiu‐De Zhang +11 more
wiley +1 more source
Fine-grained few-shot class-incremental identification of medicinal plants via frequency-aware contrastive learning. [PDF]
Tan C, Qin Z, Tang Z, Huang Y, Li K.
europepmc +1 more source
Generalizable Two-Branch Framework for Image Class-Incremental Learning [PDF]
Chao Wu, Xiaobin Chang, Ruixuan Wang
openalex +1 more source
Integrating Spatial Proteogenomics in Cancer Research
Xx xx. ABSTRACT Background: Spatial proteogenomics marks a paradigm shift in oncology by integrating molecular analysis with spatial information from both spatial proteomics and other data modalities (e.g., spatial transcriptomics), thereby unveiling tumor heterogeneity and dynamic changes in the microenvironment.
Yida Wang +13 more
wiley +1 more source

