Tootfinder

dbpedia_team: DBpedia athlete-team affiliations
Bipartite network of the affiliations (employment relations) between professional athletes and their teams, as extracted from Wikipedia by the DBpedia project.
This network has 935627 nodes and 1366466 edges.
Tags: Economic, Employment, Unweighted
https://netwo…

The overtures to "accountability" in national security legislation are never expected to actually do anything, since there's often no (legal) mechanism to establish standing.
https://mastodon.xyz/@privacyint/114926117300945444

A Novel Framework for Integrating 3D Ultrasound into Percutaneous Liver Tumour Ablation
Shuwei Xing, Derek W. Cool, David Tessier, Elvis C. S. Chen, Terry M. Peters, Aaron Fenster
https://arxiv.org/abs/2506.21162

A Novel Framework for Integrating 3D Ultrasound into Percutaneous Liver Tumour Ablation
3D ultrasound (US) imaging has shown significant benefits in enhancing the outcomes of percutaneous liver tumour ablation. Its clinical integration is crucial for transitioning 3D US into the therapeutic domain. However, challenges of tumour identification in US images continue to hinder its broader adoption. In this work, we propose a novel framework for integrating 3D US into the standard ablation workflow. We present a key component, a clinically viable 2D US-CT/MRI registration approach, le…

Counter-propagating spontaneous parametric down-conversion source in lithium niobate on insulator
Jost Kellner, Alessandra Sabatti, Tristan Kuttner, Robert J. Chapman, Rachel Grange
https://arxiv.org/abs/2506.21396

Counter-propagating spontaneous parametric down-conversion source in lithium niobate on insulator
Quantum photonic technologies rely on the ability to generate, manipulate, and interfere indistinguishable single photons on a scalable platform. Among the various approaches, spontaneous parametric down-conversion (SPDC) remains one of the most widely used methods for generating entangled or pure photon pairs. However most integrated SPDC sources relying on co-propagating geometries have a limited purity of heralded photons, or require lossy filtering. Type-2 SPDC processes can produce pure se…

From Tabula Rasa to Emergent Abilities: Discovering Robot Skills via Real-World Unsupervised Quality-Diversity
Luca Grillotti (AIRL, Imperial College London), Lisa Coiffard (AIRL, Imperial College London), Oscar Pang (AIRL, Imperial College London), Maxence Faldor (AIRL, Imperial College London), Antoine Cully (AIRL, Imperial College London)
https://

From Tabula Rasa to Emergent Abilities: Discovering Robot Skills via Real-World Unsupervised Quality-Diversity
Autonomous skill discovery aims to enable robots to acquire diverse behaviors without explicit supervision. Learning such behaviors directly on physical hardware remains challenging due to safety and data efficiency constraints. Existing methods, including Quality-Diversity Actor-Critic (QDAC), require manually defined skill spaces and carefully tuned heuristics, limiting real-world applicability. We propose Unsupervised Real-world Skill Acquisition (URSA), an extension of QDAC that enables rob…

Structuralist Approach to AI Literary Criticism: Leveraging Greimas Semiotic Square for Large Language Models
Fangzhou Dong, Yifan Zeng, Yingpeng Sang, Hong Shen
https://arxiv.org/abs/2506.21360

Structuralist Approach to AI Literary Criticism: Leveraging Greimas Semiotic Square for Large Language Models
Large Language Models (LLMs) excel in understanding and generating text but struggle with providing professional literary criticism for works with profound thoughts and complex narratives. This paper proposes GLASS (Greimas Literary Analysis via Semiotic Square), a structured analytical framework based on Greimas Semiotic Square (GSS), to enhance LLMs' ability to conduct in-depth literary analysis. GLASS facilitates the rapid dissection of narrative structures and deep meanings in narrative wor…

Quantification of mobile ions in perovskite solar cells with thermally activated ion current measurements
Moritz C. Schmidt, Agustin O. Alvarez, Riccardo Pallotta, Biruk A. Seid, Jeroen J. de Boer, Jarla Thiesbrummel, Felix Lang, Giulia Grancini, Bruno Ehrler
https://arxiv.org/abs/2508.19403

Quantification of mobile ions in perovskite solar cells with thermally activated ion current measurements
Mobile ions play a key role in the degradation of perovskite solar cells, making their quantification essential for enhancing device stability. Various electrical measurements have been applied to characterize mobile ions. However, discerning between different ionic migration processes can be difficult. Furthermore, multiple measurements at different temperatures are usually required to probe different ions and their activation energies. Here, we demonstrate a new characterization technique bas…

Retrieval-Confused Generation is a Good Defender for Privacy Violation Attack of Large Language Models
Wanli Peng, Xin Chen, Hang Fu, XinYu He, Xue Yiming, Juan Wen
https://arxiv.org/abs/2506.19889

Retrieval-Confused Generation is a Good Defender for Privacy Violation Attack of Large Language Models
Recent advances in large language models (LLMs) have made a profound impact on our society and also raised new security concerns. Particularly, due to the remarkable inference ability of LLMs, the privacy violation attack (PVA), revealed by Staab et al., introduces serious personal privacy issues. Existing defense methods mainly leverage LLMs to anonymize the input query, which requires costly inference time and cannot gain satisfactory defense performance. Moreover, directly rejecting the PVA …

Conditional Method Confidence Set
Lukas Bauer, Ekaterina Kazak
https://arxiv.org/abs/2505.21278 https://arxiv.org/pdf/2505.21278

Conditional Method Confidence Set
This paper proposes a Conditional Method Confidence Set (CMCS) which allows to select the best subset of forecasting methods with equal predictive ability conditional on a specific economic regime. The test resembles the Model Confidence Set by Hansen et al. (2011) and is adapted for conditional forecast evaluation. We show the asymptotic validity of the proposed test and illustrate its properties in a simulation study. The proposed testing procedure is particularly suitable for stress-testing …

FedProtoKD: Dual Knowledge Distillation with Adaptive Class-wise Prototype Margin for Heterogeneous Federated Learning
Md Anwar Hossen, Fatema Siddika, Wensheng Zhang, Anuj Sharma, Ali Jannesari
https://arxiv.org/abs/2508.19009

FedProtoKD: Dual Knowledge Distillation with Adaptive Class-wise Prototype Margin for Heterogeneous Federated Learning
Heterogeneous Federated Learning (HFL) has gained attention for its ability to accommodate diverse models and heterogeneous data across clients. Prototype-based HFL methods emerge as a promising solution to address statistical heterogeneity and privacy challenges, paving the way for new advancements in HFL research. This method focuses on sharing only class-representative prototypes among heterogeneous clients. However, these prototypes are often aggregated on the server using weighted averagin…