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We performed inelastic neutron scattering experiments on single-crystal samples of the honeycomb lattice magnet, ilmenite NiTiO3. Below the Neel temperature of 22 K, spin wave excitations with a band energy of 3.7 meV were observed. The neutron energ

Externí odkaz: http://arxiv.org/abs/2411.11311

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Restoring Super-High Resolution GPS Mobility Data

Autor: Yonekura, Haruki, Ozeki, Ren, Rizk, Hamada, Yamaguchi, Hirozumi

This paper presents a novel system for reconstructing high-resolution GPS trajectory data from truncated or synthetic low-resolution inputs, addressing the critical challenge of balancing data utility with privacy preservation in mobility application

Externí odkaz: http://arxiv.org/abs/2410.12818

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Chiral-Split Magnon in Altermagnetic MnTe

Autor: Liu, Zheyuan, Ozeki, Makoto, Asai, Shinichiro, Itoh, Shinichi, Masuda, Takatsugu

Publikováno v: Phys. Rev. Lett. 133, 156702 (2024)

Altermagnetism is a newly discovered magnetic class named after the alternating spin polarizations in both real and reciprocal spaces. Like the spin-splitting of electronic bands, the magnon bands in altermagnets are predicted to exhibit alternating

Externí odkaz: http://arxiv.org/abs/2408.16490

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On odd covers of cliques and disjoint unions

Autor: Buchanan, Calum, Clifton, Alexander, Culver, Eric, Frankl, Péter, Nie, Jiaxi, Ozeki, Kenta, Rombach, Puck, Yin, Mei

Babai and Frankl posed the ``odd cover problem" of finding the minimum cardinality of a collection of complete bipartite graphs such that every edge of the complete graph of order $n$ is covered an odd number of times. In a previous paper with O'Neil

Externí odkaz: http://arxiv.org/abs/2408.08598

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Privacy-Preserved Taxi Demand Prediction System Utilizing Distributed Data

Autor: Ozeki, Ren, Yonekura, Haruki, Rizk, Hamada, Yamaguchi, Hirozumi

Accurate taxi-demand prediction is essential for optimizing taxi operations and enhancing urban transportation services. However, using customers' data in these systems raises significant privacy and security concerns. Traditional federated learning

Externí odkaz: http://arxiv.org/abs/2408.04931

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