Tootfinder

Evaluation and Comparison Semantics for ODRL
Jaime Osvaldo Salas, Paolo Pareti, Semih Yumu\c{s}ak, Soulmaz Gheisari, Luis-Daniel Ib\'a\~nez, George Konstantinidis
https://arxiv.org/abs/2509.05139

Evaluation and Comparison Semantics for ODRL
We consider the problem of evaluating, and comparing computational policies in the Open Digital Rights Language (ODRL), which has become the de facto standard for governing the access and usage of digital resources. Although preliminary progress has been made on the formal specification of the language's features, a comprehensive formal semantics of ODRL is still missing. In this paper, we provide a simple and intuitive formal semantics for ODRL that is based on query answering. Our semantics r…

SemSteDiff: Generative Diffusion Model-based Coverless Semantic Steganography Communication
Song Gao, Rui Meng, Xiaodong Xu, Haixiao Gao, Yiming Liu, Chenyuan Feng, Ping Zhang, Tony Q. S. Quek, Dusit Niyato
https://arxiv.org/abs/2509.04803

SemSteDiff: Generative Diffusion Model-based Coverless Semantic Steganography Communication
Semantic communication (SemCom), as a novel paradigm for future communication systems, has recently attracted much attention due to its superiority in communication efficiency. However, similar to traditional communication, it also suffers from eavesdropping threats. Intelligent eavesdroppers could launch advanced semantic analysis techniques to infer secret semantic information. Therefore, some researchers have designed Semantic Steganography Communication (SemSteCom) scheme to confuse semanti…

SGS-3D: High-Fidelity 3D Instance Segmentation via Reliable Semantic Mask Splitting and Growing
Chaolei Wang, Yang Luo, Jing Du, Siyu Chen, Yiping Chen, Ting Han
https://arxiv.org/abs/2509.05144

SGS-3D: High-Fidelity 3D Instance Segmentation via Reliable Semantic Mask Splitting and Growing
Accurate 3D instance segmentation is crucial for high-quality scene understanding in the 3D vision domain. However, 3D instance segmentation based on 2D-to-3D lifting approaches struggle to produce precise instance-level segmentation, due to accumulated errors introduced during the lifting process from ambiguous semantic guidance and insufficient depth constraints. To tackle these challenges, we propose splitting and growing reliable semantic mask for high-fidelity 3D instance segmentation (SGS…

Emergent Semantics Beyond Token Embeddings: Transformer LMs with Frozen Visual Unicode Representations
A. Bochkov
https://arxiv.org/abs/2507.04886 https://…

Emergent Semantics Beyond Token Embeddings: Transformer LMs with Frozen Visual Unicode Representations
Understanding the locus of semantic representation in large language models (LLMs) is crucial for interpretability and architectural innovation. The dominant paradigm posits that trainable input embeddings serve as foundational "meaning vectors." This paper challenges that view. We construct Transformer models where the embedding layer is entirely frozen, with vectors derived not from data, but from the visual structure of Unicode glyphs. These non-semantic, precomputed visual embeddings are fi…

React-tRace: A Semantics for Understanding React Hooks
Jay Lee, Joongwon Ahn, Kwangkeun Yi
https://arxiv.org/abs/2507.05234 https://a…

React-tRace: A Semantics for Understanding React Hooks
React has become the most widely used web front-end framework, enabling the creation of user interfaces in a declarative and compositional manner. Hooks are a set of APIs that manage side effects in functional components in React. However, their semantics are often seen as opaque to developers, leading to UI bugs. In this paper, we formalize the semantics of the essence of React Hooks we name React-tRace, providing a framework that clarifies their behavior. We demonstrate that our model capture…

R.B. And I hiked the Taughannock Falls Rim Trail this morning. So beautiful.
#hiking #nature #seenonmydogwwalk

China setzt erstmals schwimmende #Solaranlagen im offenen #Meer ein.
Die Offshore-Anlagen erzeugen Strom und ermöglichen gleichzeitig #Fischzucht.
Vor

China setzt auf schwimmende Solarparks im Meer
Mit Offshore-Solaranlagen will China die Energiewende an der Küste beschleunigen. Die schwimmenden Kraftwerke kombinieren Stromerzeugung mit Fischzucht.

SREC: Encrypted Semantic Super-Resolution Enhanced Communication
Zhidi Zhang, Rui Meng, Song Gao, Haixiao Gao, Xiaodong Xu
https://arxiv.org/abs/2509.04787 https://

SREC: Encrypted Semantic Super-Resolution Enhanced Communication
Semantic communication (SemCom), as a typical paradigm of deep integration between artificial intelligence (AI) and communication technology, significantly improves communication efficiency and resource utilization efficiency. However, the security issues of SemCom are becoming increasingly prominent. Semantic features transmitted in plaintext over physical channels are easily intercepted by eavesdroppers. To address this issue, this paper proposes Encrypted Semantic Super-Resolution Enhanced C…

BioLite U-Net: Edge-Deployable Semantic Segmentation for In Situ Bioprinting Monitoring
Usman Haider, Lukasz Szemet, Daniel Kelly, Vasileios Sergis, Andrew C. Daly, Karl Mason
https://arxiv.org/abs/2509.06690

BioLite U-Net: Edge-Deployable Semantic Segmentation for In Situ Bioprinting Monitoring
Bioprinting is a rapidly advancing field that offers a transformative approach to fabricating tissue and organ models through the precise deposition of cell-laden bioinks. Ensuring the fidelity and consistency of printed structures in real-time remains a core challenge, particularly under constraints imposed by limited imaging data and resource-constrained embedded hardware. Semantic segmentation of the extrusion process, differentiating between nozzle, extruded bioink, and surrounding backgrou…

KGRAG-SC: Knowledge Graph RAG-Assisted Semantic Communication
Dayu Fan, Rui Meng, Song Gao, Xiaodong Xu
https://arxiv.org/abs/2509.04801 https://arxiv.org/…

KGRAG-SC: Knowledge Graph RAG-Assisted Semantic Communication
The state-of-the-art semantic communication (SC) schemes typically rely on end-to-end deep learning frameworks that lack interpretability and struggle with robust semantic selection and reconstruction under noisy conditions. To address this issue, this paper presents KGRAG-SC, a knowledge graph-assisted SC framework that leverages retrieval-augmented generation principles. KGRAG-SC employs a multi-dimensional knowledge graph, enabling efficient semantic extraction through community-guided entit…