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Electroencephalography (EEG)-based multimodal learning integrates brain signals with complementary modalities to improve mental state assessment, providing great clinical potential. The effectiveness of such paradigms largely depends on the representation learning on heterogeneous modalities. For EEG-based paradigms, one promising approach is to leverage their hierarchical structures, as recent studies have shown that both EEG and associated modalities (e.g., facial expressions) exhibit hierarchical structures reflecting complex cognitive processes. However, Euclidean embeddings struggle to represent these hierarchical structures due to their […]

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Traffic sign detection in autonomous driving faces challenges including multi-scale objects, complex backgrounds, and limited edge-computing power. To address insufficient multi-scale feature representation and high false negatives for small traffic signs in YOLOv8n, this study proposes an improved algorithm integrating the VoVGSCSP module with a Multi-scale Contextual Attention (MCA) mechanism. The original C2f module is replaced with VoVGSCSP, enhancing feature representation through parallel residual branches and cross-stage connections. A lightweight neck, SlimNeck, is designed and combined with MCA, […]

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The Gielis superformula is a powerful parametric tool that generates an infinite variety of natural and organic curves and surfaces through a compact set of parameters. However, classical differential geometry has lacked a unified framework for analyzing their curvature, torsion, and intrinsic geometric properties. This study addresses this gap by developing a novel superelliptic geometric framework that integrates the superformula 6with the differential geometry of curves and surfaces. We define the superelliptic inner and cross products, the star […]

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The design and reliability assurance of embedded systems is a complex issue, since they need to handle not only digital behavior but also physical quantities such as time, cost, and sometimes randomness. In addition, since many embedded systems, such as networks and automobiles, have systems in which errors can be fatal, design verification for reliability assurance is an important research topic. With the above background, we adopt the approach of specifying and verifying embedded systems by formal models. […]

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Hyundai Motor Group is starting to look like a company building machines that act in the real world. The change centres on physical AI: Where AI is placed into robots and systems that move and respond in physical spaces. Current efforts are mainly focused on factory and industrial settings. Hyundai’s move into physical AI systems In an interview with Semafor, chairman Chung Eui-sun said robotics and AI will play a central role in Hyundai’s next phase of growth, […]

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Continual machine unlearning aims to remove the influence of data that should no longer be retained, while preserving the usefulness of the model on everything else. This setting becomes especially difficult when deletion requests arrive sequentially, because the model must repeatedly adapt without erasing previously retained knowledge. Low-Rank Adaptation (LoRA) offers an efficient way to implement such updates, but naively combining many sequential LoRA modules leads to parameter collision, causing textit{strong interference} between tasks. We propose a static […]

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Let Γ be a commutative with identity multiplicative hyperring and S ⊆ Γ be a multiplicatively closed subset of Γ. In this study, we will discuss the definition and general properties of weakly Γ-prime hyperideals. Let Ω be a hyperideal of Γ disjoint from S. We say that Ω is a weakly S− prime hyperideal of Γ if there exists an s ∈ S such that for all ϱ,σ ∈ Γ , if {0} ̸ = ϱ⋄σ ⊆ […]

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We study a class of wrapping operators acting on the space of formal languages over a fixed finite alphabet. The underlying space is equipped with a length-based ultrametric, in which two languages are close whenever they coincide on all sufficiently short words. We prove that every wrapping operator generated by a finite family of guards with positive total guard length is a contraction. As a consequence, Banach’s contraction principle yields existence and uniqueness of a fixed point for […]

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The preparation of annotated datasets remains a critical bottleneck in the machine learning (ML) pipeline. Existing tools are fragmented across cloud-hosted services, self-hosted web applications, and lightweight desktop tools—none simultaneously ad-dressing diverse annotation modalities, offline-first operation, integrated training, and serverless collaboration. We present Annotix, an open-source, cross-platform desktop application built on a Rust backend (Tauri 2) and React 19 frontend, designed to unify the entire ML data preparation workflow within a single privacy-preserving environ-ment. To evaluate its practical […]

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This paper builds upon a previous study of rotation sequences with four factors, in which the additional parameter is used for optimization, extending the result to generic rigid motions in three-dimensional Euclidean space. To do that in practice, one uses dual projective quaternions (dual Rodrigues’ vectors) describing screw motions and applies the well-known principle of transference in a rather straightforward manner. There are, however, some technicalities worth discussing, like the famous gimbal lock problem emerging in Euler-type decompositions. […]

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