| 1. |
- Kasiuk, Julia, et al.
(author)
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The enhancement of low-temperature excitation of magnons via interlayer exchange coupling in perpendicularly magnetized [Co/Pd] multilayers
- 2024
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In: APPLIED PHYSICS LETTERS. - 0003-6951 .- 1077-3118. ; 124:19
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Journal article (peer-reviewed)abstract
- In this study, we analyze the correlation between magnetization and magnetoresistance of perpendicularly anisotropic [Co/Pd] multilayered films with different thicknesses of Pd layers t(Pd) = 0.6-2.0 nm in a wide range of temperatures, T = 4-300 K. We revealed that electron scattering by magnons makes a significant contribution to the magnetoresistance of the multilayers regardless of the layer thickness. Contrary to expectations, the effect of magnon magnetoresistance (MMR) increases with decreasing temperature below T = 50 K in the films with t(Pd) = 0.8 and 1.0 nm. The revealed low-temperature MMR increase, which is most pronounced in the [Co-0.5/Pd-1.0] multilayers, is associated with the enhanced magnon excitation due to antiferromagnetic exchange coupling between the Co layers. The latter ensures an atypical shape of the magnetization curves of the [Co-0.5/Pd-1.0] multilayers at low temperatures in a perpendicular magnetic field, which combine a quadratic hysteresis loop of a perpendicularly anisotropic ferromagnet and an anomalous magnetization drop resulting from a violation of the ordering of magnetic moments and their amplified oscillations initiated by the interlayer exchange coupling.
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| 2. |
- Tran, Khanh-Tung, et al.
(author)
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NeuProNet: neural profiling networks for sound classification
- 2024
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In: Neural Computing & Applications. - : Springer Nature. - 0941-0643 .- 1433-3058. ; 36:11, s. 5873-5887
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Journal article (peer-reviewed)abstract
- Real-world sound signals exhibit various aspects of grouping and profiling behaviors, such as being recorded from identical sources, having similar environmental settings, or encountering related background noises. In this work, we propose novel neural profiling networks (NeuProNet) capable of learning and extracting high-level unique profile representations from sounds. An end-to-end framework is developed so that any backbone architectures can be plugged in and trained, achieving better performance in any downstream sound classification tasks. We introduce an in-batch profile grouping mechanism based on profile awareness and attention pooling to produce reliable and robust features with contrastive learning. Furthermore, extensive experiments are conducted on multiple benchmark datasets and tasks to show that neural computing models under the guidance of our framework gain significant performance gaps across all evaluation tasks. Particularly, the integration of NeuProNet surpasses recent state-of-the-art (SoTA) approaches on UrbanSound8K and VocalSound datasets with statistically significant improvements in benchmarking metrics, up to 5.92% in accuracy compared to the previous SoTA method and up to 20.19% compared to baselines. Our work provides a strong foundation for utilizing neural profiling for machine learning tasks.
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