430 research outputs found
Distributionally Robust Semi-Supervised Learning for People-Centric Sensing
Semi-supervised learning is crucial for alleviating labelling burdens in
people-centric sensing. However, human-generated data inherently suffer from
distribution shift in semi-supervised learning due to the diverse biological
conditions and behavior patterns of humans. To address this problem, we propose
a generic distributionally robust model for semi-supervised learning on
distributionally shifted data. Considering both the discrepancy and the
consistency between the labeled data and the unlabeled data, we learn the
latent features that reduce person-specific discrepancy and preserve
task-specific consistency. We evaluate our model in a variety of people-centric
recognition tasks on real-world datasets, including intention recognition,
activity recognition, muscular movement recognition and gesture recognition.
The experiment results demonstrate that the proposed model outperforms the
state-of-the-art methods.Comment: 8 pages, accepted by AAAI201
Asymmetric magnetization splitting in diamond domain structure: Dependence on exchange interaction and anisotropy
The distributions of magnetization orientation for both Landau and diamond
domain structures in nano-rectangles have been investigated by micromagnetic
simulation with various exchange coefficient and anisotropy constant. Both
symmetric and asymmetric magnetization splitting are found in diamond domain
structure, as well as only symmetric magnetization splitting in Landau
structure. In the Landau structure, the splitting angle increases with the
exchange coefficient but decreases slightly with the anisotropy constant,
suggesting that the exchange interaction mainly contributes to the
magnetization splitting in Landau structure. However in the diamond structure,
the splitting angle increases with the anisotropy constant but derceases with
the exchange coefficient, indicating that the magnetization splitting in
diamond structure is resulted from magnetic anisotropy.Comment: 5 pages, 5 figure
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