321 research outputs found
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Synthesis and crystal growth of complex oxides with novel physical properties
The synthesis and crystal growth of complex oxides, particularly those with perovskite structures and their derivatives, have been a subject of intense interest due to the rich diversity of their physical properties. The focus of this dissertation lies in the exploration of the interplay between the structure, magnetism, and electronic properties within these crystalline materials, with a spotlight on the rare earth orthoferrites, RFeO₃. We delve into the profound implications of the crystal and magnetic structure relationships, with an emphasis on the effects of octahedral tilting on the canted spin structure. The ensuing weak ferromagnetism in these materials sets the stage for an array of fascinating spin phenomena, extending from fundamental spin structure and spin excitations to the application-centric aspects such as the spin Hall effect, inverse spin Hall effect, and the spin Seebeck effect. This research not only strengthens our understanding of the magnetic and structural attributes of these complex oxides, but also aims to unveil potential pathways to exploit these phenomena in the realm of spintronics. A major aspect of this dissertation involves exploring the topological nature of magnon bands, which offers a fresh perspective towards the behavior of magnetic materials. The dissertation is organized into six chapters, encompassing the connection between crystal structure and magnetic structure, an overview of spintronics and magnon band topology, detailed descriptions of experimental techniques, investigation of the spin Seebeck effect, examination of the magnon Hall effect in quantum magnets, and an in-depth study of 3d and 5d perovskite oxides. This work provides significant contributions to the existing knowledge base in the field of complex oxides, and could potentially catalyze the design of future magnetic devices.Materials Science and Engineerin
CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations
Geo-tagged images are publicly available in large quantities, whereas labels
such as object classes are rather scarce and expensive to collect. Meanwhile,
contrastive learning has achieved tremendous success in various natural image
and language tasks with limited labeled data. However, existing methods fail to
fully leverage geospatial information, which can be paramount to distinguishing
objects that are visually similar. To directly leverage the abundant geospatial
information associated with images in pre-training, fine-tuning, and inference
stages, we present Contrastive Spatial Pre-Training (CSP), a self-supervised
learning framework for geo-tagged images. We use a dual-encoder to separately
encode the images and their corresponding geo-locations, and use contrastive
objectives to learn effective location representations from images, which can
be transferred to downstream supervised tasks such as image classification.
Experiments show that CSP can improve model performance on both iNat2018 and
fMoW datasets. Especially, on iNat2018, CSP significantly boosts the model
performance with 10-34% relative improvement with various labeled training data
sampling ratios.Comment: In: ICML 2023, Jul 23 - 29, 2023, Honolulu, Hawaii, US
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