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🎵 Gumshoe
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https://samanthacrain.bandcamp.com/album/gumshoe
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Clinical semantics for lung cancer prediction
Luis H. John, Jan A. Kors, Jenna M. Reps, Peter R. Rijnbeek, Egill A. Fridgeirsson
https://arxiv.org/abs/2508.14627 https://…

Clinical semantics for lung cancer prediction
Background: Existing clinical prediction models often represent patient data using features that ignore the semantic relationships between clinical concepts. This study integrates domain-specific semantic information by mapping the SNOMED medical term hierarchy into a low-dimensional hyperbolic space using Poincaré embeddings, with the aim of improving lung cancer onset prediction. Methods: Using a retrospective cohort from the Optum EHR dataset, we derived a clinical knowledge graph from th…

Quantum Petri Nets with Event Structures semantics
Julien Saan Joachim (ENS Paris Saclay, LMF, Inria), Marc de Visme (LMF, Inria, CNRS, ENS Paris Saclay), Stefan Haar (Inria)
https://arxiv.org/abs/2508.14531

Quantum Petri Nets with Event Structures semantics
Classical Petri nets provide a canonical model of concurrency, with unfolding semantics linking nets, occurrence nets, and event structures. No comparable framework exists for quantum concurrency: existing ''quantum Petri nets'' lack rigorous concurrent and sound quantum semantics, analysis tools, and unfolding theory. We introduce Quantum Petri Nets (QPNs), Petri nets equipped with a quantum valuation compatible with the quantum event structure semantics of Clairambault, De Visme, and Winskel …

Disentangling concept semantics via multilingual averaging in Sparse Autoencoders
Cliff O'Reilly, Ernesto Jimenez-Ruiz, Tillman Weyde
https://arxiv.org/abs/2508.14275 https:…

Disentangling concept semantics via multilingual averaging in Sparse Autoencoders
Connecting LLMs with formal knowledge representation and reasoning is a promising approach to address their shortcomings. Embeddings and sparse autoencoders are widely used to represent textual content, but the semantics are entangled with syntactic and language-specific information. We propose a method that isolates concept semantics in Large Langue Models by averaging concept activations derived via Sparse Autoencoders. We create English text representations from OWL ontology classes, transla…

Driving Style Recognition Like an Expert Using Semantic Privileged Information from Large Language Models
Zhaokun Chen, Chaopeng Zhang, Xiaohan Li, Wenshuo Wang, Gentiane Venture, Junqiang Xi
https://arxiv.org/abs/2508.13881

Driving Style Recognition Like an Expert Using Semantic Privileged Information from Large Language Models
Existing driving style recognition systems largely depend on low-level sensor-derived features for training, neglecting the rich semantic reasoning capability inherent to human experts. This discrepancy results in a fundamental misalignment between algorithmic classifications and expert judgments. To bridge this gap, we propose a novel framework that integrates Semantic Privileged Information (SPI) derived from large language models (LLMs) to align recognition outcomes with human-interpretable …

MHSNet:An MoE-based Hierarchical Semantic Representation Network for Accurate Duplicate Resume Detection with Large Language Model
Yu Li, Zulong Chen, Wenjian Xu, Hong Wen, Yipeng Yu, Man Lung Yiu, Yuyu Yin
https://arxiv.org/abs/2508.13676

MHSNet:An MoE-based Hierarchical Semantic Representation Network for Accurate Duplicate Resume Detection with Large Language Model
To maintain the company's talent pool, recruiters need to continuously search for resumes from third-party websites (e.g., LinkedIn, Indeed). However, fetched resumes are often incomplete and inaccurate. To improve the quality of third-party resumes and enrich the company's talent pool, it is essential to conduct duplication detection between the fetched resumes and those already in the company's talent pool. Such duplication detection is challenging due to the semantic complexity, structural h…

On Splitting Lightweight Semantic Image Segmentation for Wireless Communications
Ebrahim Abu-Helalah, Jordi Serra, Jordi Perez-Romero
https://arxiv.org/abs/2507.14199

On Splitting Lightweight Semantic Image Segmentation for Wireless Communications
Semantic communication represents a promising technique towards reducing communication costs, especially when dealing with image segmentation, but it still lacks a balance between computational efficiency and bandwidth requirements while maintaining high image segmentation accuracy, particularly in resource-limited environments and changing channel conditions. On the other hand, the more complex and larger semantic image segmentation models become, the more stressed the devices are when process…

Out-of-bounds hydrodynamics in holographic anisotropic Dirac semimetals
Sebasti\'an Bahamondes, Ignacio Salazar Landea, Rodrigo Soto-Garrido
https://arxiv.org/abs/2507.13497

Out-of-bounds hydrodynamics in holographic anisotropic Dirac semimetals
We present a version of a strongly correlated 2+1-dimensional condensed matter system that features a thermal phase transition between a semimetal and an insulator through a semi-Dirac quantum critical region using AdS/CFT holography. We introduce backreaction into the bulk equations of motion to measure transport coefficients in the boundary; specifically the shear viscosity $η$. By explicitly breaking rotational symmetry we find a new instance of violation of the KSS-bound for the $η/s$ rat…

Strongly First Order Disjunctive Embedded Dependencies in Team Semantics
Pietro Galliani
https://arxiv.org/abs/2506.15367 https://arx…

Strongly First Order Disjunctive Embedded Dependencies in Team Semantics
First Order Team Semantics is a generalization of Tarskian Semantics in which formulas are satisfied with respect to sets of assignments. In Team Semantics, it is possible to extend First Order Logic via new types of atoms that describe dependencies between variables; some of these extensions are strictly more expressive than First Order Logic, while others are reducible to it. Many of the atoms studied in Team Semantics are inspired by Database Theory and belong in particular to the class of…

An LLM Agent-Based Complex Semantic Table Annotation Approach
Yilin Geng, Shujing Wang, Chuan Wang, Keqing He, Yanfei Lv, Ying Wang, Zaiwen Feng, Xiaoying Bai
https://arxiv.org/abs/2508.12868

An LLM Agent-Based Complex Semantic Table Annotation Approach
The Semantic Table Annotation (STA) task, which includes Column Type Annotation (CTA) and Cell Entity Annotation (CEA), maps table contents to ontology entities and plays important roles in various semantic applications. However, complex tables often pose challenges such as semantic loss of column names or cell values, strict ontological hierarchy requirements, homonyms, spelling errors, and abbreviations, which hinder annotation accuracy. To address these issues, this paper proposes an LLM-bas…