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Nobuhiro Ito

Publications and source records attributed to Nobuhiro Ito.

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Edge-controlled non-Hermitian skin effect in the modified Haldane model

The hybrid skin-topological effect (HSTE) arises from the interplay between the non-Hermitian skin modes and topologically protected edge states. Here, we investigate the HSTE associated with antichiral edge states in a modified Haldane nanoribbon with gain and loss applied exclusively at the zigzag edges. We show that in antichiral systems, the HSTE originates from an imbalance of effective gain and loss between edge states and counter-propagating bulk modes, revealing a mechanism distinct from that in conventional chiral systems. Remarkably, in sufficiently narrow ribbons, gain or loss applied to only one edge induces a skin effect in the states localized at the opposite edge, demonstrating a non-Hermitian nonlocal antichiral skin effect. We further show that edge-localized dissipation can induce bulk skin modes only when $\mathcal{PT}$ symmetry is broken, while the bulk non-Hermitian skin effect is strictly forbidden in the $\mathcal{PT}$-symmetric regime. By tuning the gain and loss applied solely at the edges, both the emergence and localization direction of bulk skin modes can be controlled. Our results establish a symmetry-based mechanism for controlling non-Hermitian skin effects via edge dissipation in antichiral systems.

cond-mat.mes-hall

From Natural Language Instructions to Complex Processes: Issues in Chaining Trigger Action Rules

Automation services for complex business processes usually require a high level of information technology literacy. There is a strong demand for a smartly assisted process automation (IPA: intelligent process automation) service that enables even general users to easily use advanced automation. A natural language interface for such automation is expected as an elemental technology for the IPA realization. The workflow targeted by IPA is generally composed of a combination of multiple tasks. However, semantic parsing, one of the natural language processing methods, for such complex workflows has not yet been fully studied. The reasons are that (1) the formal expression and grammar of the workflow required for semantic analysis have not been sufficiently examined and (2) the dataset of the workflow formal expression with its corresponding natural language description required for learning workflow semantics did not exist. This paper defines a new grammar for complex workflows with chaining machine-executable meaning representations for semantic parsing. The representations are at a high abstraction level. Additionally, an approach to creating datasets is proposed based on this grammar.

cs.AI