Tootfinder

Auditable Shared Objects: From Registers to Synchronization Primitives
Hagit Attiya, Antonio Fern\'andez Anta, Alessia Milani, Alexandre Rapetti, Corentin Travers
https://arxiv.org/abs/2508.14506

Auditable Shared Objects: From Registers to Synchronization Primitives
Auditability allows to track operations performed on a shared object, recording who accessed which information. This gives data owners more control on their data. Initially studied in the context of single-writer registers, this work extends the notion of auditability to other shared objects, and studies their properties. We start by moving from single-writer to multi-writer registers, and provide an implementation of an auditable $n$-writer $m$-reader read / write register, with $O(n+m)$ ste…

Long-context Reference-based MT Quality Estimation
Sami Ul Haq, Chinonso Cynthia Osuji, Sheila Castilho, Brian Davis
https://arxiv.org/abs/2509.13980 https://

Long-context Reference-based MT Quality Estimation
In this paper, we present our submission to the Tenth Conference on Machine Translation (WMT25) Shared Task on Automated Translation Quality Evaluation. Our systems are built upon the COMET framework and trained to predict segment-level Error Span Annotation (ESA) scores using augmented long-context data. To construct long-context training data, we concatenate in-domain, human-annotated sentences and compute a weighted average of their scores. We integrate multiple human judgment datasets…

Generative Co-Design of Antibody Sequences and Structures via Black-Box Guidance in a Shared Latent Space
Yinghua Yao, Yuangang Pan, Xixian Chen
https://arxiv.org/abs/2508.11424

Generative Co-Design of Antibody Sequences and Structures via Black-Box Guidance in a Shared Latent Space
Advancements in deep generative models have enabled the joint modeling of antibody sequence and structure, given the antigen-antibody complex as context. However, existing approaches for optimizing complementarity-determining regions (CDRs) to improve developability properties operate in the raw data space, leading to excessively costly evaluations due to the inefficient search process. To address this, we propose LatEnt blAck-box Design (LEAD), a sequence-structure co-design framework that opt…

Enhancing Radiographic Disease Detection with MetaCheX, a Context-Aware Multimodal Model
Nathan He, Cody Chen
https://arxiv.org/abs/2509.12287 https://arxi…

Enhancing Radiographic Disease Detection with MetaCheX, a Context-Aware Multimodal Model
Existing deep learning models for chest radiology often neglect patient metadata, limiting diagnostic accuracy and fairness. To bridge this gap, we introduce MetaCheX, a novel multimodal framework that integrates chest X-ray images with structured patient metadata to replicate clinical decision-making. Our approach combines a convolutional neural network (CNN) backbone with metadata processed by a multilayer perceptron through a shared classifier. Evaluated on the CheXpert Plus dataset, MetaChe…

Demystifying Progressive Web Application Permission Systems
Mengxiao Wang, Guofei Gu
https://arxiv.org/abs/2509.13563 https://arxiv.org/pdf/2509.13563

Demystifying Progressive Web Application Permission Systems
Progressive Web Applications (PWAs) blend the advantages of web and native apps, offering features like offline access, push notifications, and installability. Beyond these, modern PWAs are increasingly granted system-level capabilities such as auto-start on login and shared context with native applications. However, their permission management remains poorly defined and inconsistently implemented across platforms and browsers. To investigate these gaps, we developed Permissioner, a cross-pla…

DeMeVa at LeWiDi-2025: Modeling Perspectives with In-Context Learning and Label Distribution Learning
Daniil Ignatev, Nan Li, Hugh Mee Wong, Anh Dang, Shane Kaszefski Yaschuk
https://arxiv.org/abs/2509.09524

DeMeVa at LeWiDi-2025: Modeling Perspectives with In-Context Learning and Label Distribution Learning
This system paper presents the DeMeVa team's approaches to the third edition of the Learning with Disagreements shared task (LeWiDi 2025; Leonardelli et al., 2025). We explore two directions: in-context learning (ICL) with large language models, where we compare example sampling strategies; and label distribution learning (LDL) methods with RoBERTa (Liu et al., 2019b), where we evaluate several fine-tuning methods. Our contributions are twofold: (1) we show that ICL can effectively predict anno…

Enhancing Manufacturing Knowledge Access with LLMs and Context-aware Prompting
Sebastian Monka, Irlan Grangel-Gonz\'alez, Stefan Schmid, Lavdim Halilaj, Marc Rickart, Oliver Rudolph, Rui Dias
https://arxiv.org/abs/2507.22619

Enhancing Manufacturing Knowledge Access with LLMs and Context-aware Prompting
Knowledge graphs (KGs) have transformed data management within the manufacturing industry, offering effective means for integrating disparate data sources through shared and structured conceptual schemas. However, harnessing the power of KGs can be daunting for non-experts, as it often requires formulating complex SPARQL queries to retrieve specific information. With the advent of Large Language Models (LLMs), there is a growing potential to automatically translate natural language queries into…

Understanding Demand for Shared Autonomous Micro-Mobility
Naroa Coretti Sanchez, Kent Larson
https://arxiv.org/abs/2508.03521 https://arxiv.org/pdf/2508.03…

Understanding Demand for Shared Autonomous Micro-Mobility
This study examines the behavioral and environmental implications of shared autonomous micro-mobility systems, focusing on autonomous bicycles and their integration with transit in the U.S. While prior research has addressed operational and lifecycle aspects, a critical gap remains in understanding which modes these services are likely to substitute, who is most inclined to adopt them, and how service attributes influence user decisions. We design a context-aware stated preference survey ground…

StoryGrid: A Tangible Interface for Student Expression
Tom Moher, Louis Gomez, Janet Kim, Claudia Hindo, Benjamin Watson, Stephen Fransen, Tim McEneany
https://arxiv.org/abs/2507.05600

StoryGrid: A Tangible Interface for Student Expression
StorySpace is a classroom-based design and presentation system for interactive multimedia posters. Employing the technology base first used in Eden's PITAboard [2002], StorySpace allows groups of learners to manipulate projected multimedia objects on a horizontal board using a small collection of shared physical tokens. In this paper, we present the ongoing design history of StorySpace in the context of its introduction within an urban high school literature class. Interface modifications based…

And the sunny context really helps to make space to consider our shared history.
Did I mention the harbour already 😉?

Embedding structures in continua: linear models and finite element discretizations
David Portillo, Ignacio Romero
https://arxiv.org/abs/2509.07735 https://…

Embedding structures in continua: linear models and finite element discretizations
This work describes models and numerical approximations that describe the mechanical behavior of deformable continua with embedded structural members, such as rigid bodies, beams, shells, etc. The continuum formulation extends an idea first presented in the context of the Arlequin method and constrains the kinematics of the two types of bodies to be compatible in the energy sense. In the article, we exploit the shared similarities of all structural theories to introduce a general framework for …

Network-Aware Control of AGVs in an Industrial Scenario: A Simulation Study Based on ROS 2 and Gazebo
Filippo Bragato, Tullia Fontana, Marco Giordani, Malte Schellmann, Josef Eichinger, Michele Zorzi
https://arxiv.org/abs/2509.06451

Network-Aware Control of AGVs in an Industrial Scenario: A Simulation Study Based on ROS 2 and Gazebo
Networked Control System (NCS) is a paradigm where sensors, controllers, and actuators communicate over a shared network. One promising application of NCS is the control of Automated Guided Vehicles (AGVs) in the industrial environment, for example to transport goods efficiently and to autonomously follow predefined paths or routes. In this context, communication and control are tightly correlated, a paradigm referred to as Joint Communication and Control (JCC), since network issues such as del…

Dynamic link switching induces stable synchronized states in sparse networks
Muhittin Cenk Eser, Everton S. Medeiros, Mustafa Riza, Maximilian Engel
https://arxiv.org/abs/2507.08007

Dynamic link switching induces stable synchronized states in sparse networks
The flow of information in networked systems composed of multiple interacting elements strongly depends on the level of connectivity among these elements. Sparse connectivity often hinders the emergence of states where information is globally shared, such as fully synchronized states. In this context, dynamically switching the existing network links among the system elements can facilitate the onset of synchronization. However, determining the optimal rate at which links should be switched to e…

Differentially Private Federated Quantum Learning via Quantum Noise
Atit Pokharel, Ratun Rahman, Shaba Shaon, Thomas Morris, Dinh C. Nguyen
https://arxiv.org/abs/2508.20310 http…

Differentially Private Federated Quantum Learning via Quantum Noise
Quantum federated learning (QFL) enables collaborative training of quantum machine learning (QML) models across distributed quantum devices without raw data exchange. However, QFL remains vulnerable to adversarial attacks, where shared QML model updates can be exploited to undermine information privacy. In the context of noisy intermediate-scale quantum (NISQ) devices, a key question arises: How can inherent quantum noise be leveraged to enforce differential privacy (DP) and protect model infor…

TokenLake: A Unified Segment-level Prefix Cache Pool for Fine-grained Elastic Long-Context LLM Serving
Bingyang Wu, Zili Zhang, Yinmin Zhong, Guanzhe Huang, Yibo Zhu, Xuanzhe Liu, Xin Jin
https://arxiv.org/abs/2508.17219

TokenLake: A Unified Segment-level Prefix Cache Pool for Fine-grained Elastic Long-Context LLM Serving
Prefix caching is crucial to accelerate multi-turn interactions and requests with shared prefixes. At the cluster level, existing prefix caching systems are tightly coupled with request scheduling to optimize cache efficiency and computation performance together, leading to load imbalance, data redundancy, and memory fragmentation of caching systems across instances. To address these issues, memory pooling is promising to shield the scheduler from the underlying cache management so that it can …

HEAS: Hierarchical Evolutionary Agent Simulation Framework for Cross-Scale Modeling and Multi-Objective Search
Ruiyu Zhang, Lin Nie, Xin Zhao
https://arxiv.org/abs/2508.15555 ht…

HEAS: Hierarchical Evolutionary Agent Simulation Framework for Cross-Scale Modeling and Multi-Objective Search
Hierarchical Evolutionary Agent Simulation (HEAS) is a Python framework that unifies layered agent-based modeling with evolutionary optimization and tournament evaluation in a single, reproducible workflow. HEAS represents models as hierarchies of lightweight processes ("streams") scheduled in deterministic layers that read and write a shared context, making cross-scale couplings explicit and auditable. A compact API and CLI-simulate, optimize, evaluate-expose single- and multi-objective evolut…

Overview of ADoBo at IberLEF 2025: Automatic Detection of Anglicisms in Spanish
Elena Alvarez-Mellado, Jordi Porta-Zamorano, Constantine Lignos, Julio Gonzalo
https://arxiv.org/abs/2507.21813

Overview of ADoBo at IberLEF 2025: Automatic Detection of Anglicisms in Spanish
This paper summarizes the main findings of ADoBo 2025, the shared task on anglicism identification in Spanish proposed in the context of IberLEF 2025. Participants of ADoBo 2025 were asked to detect English lexical borrowings (or anglicisms) from a collection of Spanish journalistic texts. Five teams submitted their solutions for the test phase. Proposed systems included LLMs, deep learning models, Transformer-based models and rule-based systems. The results range from F1 scores of 0.17 to 0.99…