1,246 research outputs found
A Feasible Algorithm for Designing Biorthogonal Bivariate Vector-valued Finitely Supported Wavelets
AbstractWavelet analysis has been developed a new branch for over twenty years. The concept of vector-valued binary wavelets with two-scale dilation factor associated with an orthogonal vector-valued scaling function is introduced. The existence of orthogonal vector-valued wavelets with two-scale is discussed. A necessary and sufficient condition is provided by means of vector-valued multiresolution analysis and paraunitary vector filter bank theory. An algorithm for constructing a sort of orthogonal vector-valued wavelets with compact support is proposed, and their orthogonal properties are investigated
An Improved Entity Similarity Measurement Method
To facilitate the integration of learning resources categorized under different ontology representations, the techniques of ontology mapping can be applied. Through many algorithms and systems have been proposed for ontology mapping, they do not have an automatic weighting strategy on class features to automate the ontology mapping process. A novel method of computing the feature weights is proposed by feature semantic analysis, defining characteristics of the different entities similarity calculation model and weight calculation model. The results show that it makes the ontology mapping process more automatic while retaining satisfying accuracy. Improve ontology mapping effectiveness
Counterfactual Cross-modality Reasoning for Weakly Supervised Video Moment Localization
Video moment localization aims to retrieve the target segment of an untrimmed
video according to the natural language query. Weakly supervised methods gains
attention recently, as the precise temporal location of the target segment is
not always available. However, one of the greatest challenges encountered by
the weakly supervised method is implied in the mismatch between the video and
language induced by the coarse temporal annotations. To refine the
vision-language alignment, recent works contrast the cross-modality
similarities driven by reconstructing masked queries between positive and
negative video proposals. However, the reconstruction may be influenced by the
latent spurious correlation between the unmasked and the masked parts, which
distorts the restoring process and further degrades the efficacy of contrastive
learning since the masked words are not completely reconstructed from the
cross-modality knowledge. In this paper, we discover and mitigate this spurious
correlation through a novel proposed counterfactual cross-modality reasoning
method. Specifically, we first formulate query reconstruction as an aggregated
causal effect of cross-modality and query knowledge. Then by introducing
counterfactual cross-modality knowledge into this aggregation, the spurious
impact of the unmasked part contributing to the reconstruction is explicitly
modeled. Finally, by suppressing the unimodal effect of masked query, we can
rectify the reconstructions of video proposals to perform reasonable
contrastive learning. Extensive experimental evaluations demonstrate the
effectiveness of our proposed method. The code is available at
\href{https://github.com/sLdZ0306/CCR}{https://github.com/sLdZ0306/CCR}.Comment: Accepted by ACM MM 202
Raman scattering study of electron-doped PrCaFeAs superconductors
Temperature-dependent polarized Raman spectra of electron-doped
superconducting PrCaFeAs () single crystals
are reported. All four allowed by symmetry even-parity phonons are identified.
Phonon mode of B symmetry at 222 cm, which is associated with the
c-axis motion of Fe ions, is found to exhibit an anomalous frequency hardening
at low temperatures, that signals non-vanishing electron-phonon coupling in the
superconducting state and implies that the superconducting gap magnitude
meV.Comment: 4 pages, 3 figure
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