Tootfinder

Achieving Hilbert-Schmidt Independence Under R\'enyi Differential Privacy for Fair and Private Data Generation
Tobias Hyrup, Emmanouil Panagiotou, Arjun Roy, Arthur Zimek, Eirini Ntoutsi, Peter Schneider-Kamp
https://arxiv.org/abs/2508.21815

Achieving Hilbert-Schmidt Independence Under Rényi Differential Privacy for Fair and Private Data Generation
As privacy regulations such as the GDPR and HIPAA and responsibility frameworks for artificial intelligence such as the AI Act gain traction, the ethical and responsible use of real-world data faces increasing constraints. Synthetic data generation has emerged as a promising solution to risk-aware data sharing and model development, particularly for tabular datasets that are foundational to sensitive domains such as healthcare. To address both privacy and fairness concerns in this setting, we p…

The Grammar of FAIR: A Granular Architecture of Semantic Units for FAIR Semantics, Inspired by Biology and Linguistics
Lars Vogt, Barend Mons
https://arxiv.org/abs/2509.26434 ht…

The Grammar of FAIR: A Granular Architecture of Semantic Units for FAIR Semantics, Inspired by Biology and Linguistics
The FAIR Principles aim to make data and knowledge Findable, Accessible, Interoperable, and Reusable, yet current digital infrastructures often lack a unifying semantic framework that bridges human cognition and machine-actionability. In this paper, we introduce the Grammar of FAIR: a granular and modular architecture for FAIR semantics built on the concept of semantic units. Semantic units, comprising atomic statement units and composite compound units, implement the principle of semantic modu…

Channel Estimation and Data Detection in DS-Spread Channels: A Unified Framework, Novel Algorithms, and Waveform Comparison
Niladri Halder, Chandra R. Murthy
https://arxiv.org/abs/2508.21373

Channel Estimation and Data Detection in DS-Spread Channels: A Unified Framework, Novel Algorithms, and Waveform Comparison
We present a unified receiver processing framework for communication over delay-scale (DS)-spread channels that arise in underwater acoustic (UWA) communications that addresses both channel estimation (CE) and data detection for different modulation waveforms, namely OFDM, OTFS, OCDM, and ODSS, through a common input--output relation. Using this framework, we conduct a fair and comprehensive comparative study of these waveforms under DS-spread UWA channels and similar receiver complexities. W…

Dynamic Necklace Splitting
Rishi Advani, Abolfazl Asudeh, Mohsen Dehghankar, Stavros Sintos
https://arxiv.org/abs/2510.00162 https://arxiv.org/pdf/2510.001…

Dynamic Necklace Splitting
The necklace splitting problem is a classic problem in fair division with many applications, including data-informed fair hash maps. We extend necklace splitting to a dynamic setting, allowing for relocation, insertion, and deletion of beads. We present linear-time, optimal algorithms for the two-color case that support all dynamic updates. For more than two colors, we give linear-time, optimal algorithms for relocation subject to a restriction on the number of agents. Finally, we propose a ran…

Achieving Fair Skin Lesion Detection through Skin Tone Normalization and Channel Pruning
Zihan Wei, Tapabrata Chakraborti
https://arxiv.org/abs/2509.22712 https://

Achieving Fair Skin Lesion Detection through Skin Tone Normalization and Channel Pruning
Recent works have shown that deep learning based skin lesion image classification models trained on unbalanced dataset can exhibit bias toward protected demographic attributes such as race, age,and gender. Current bias mitigation methods usually either achieve high level of fairness with the degradation of accuracy, or only improve the model fairness on a single attribute. Additionally usually most bias mitigation strategies are either pre hoc through data processing or post hoc through fairnes…

WTMAD-4: A Fair Weighting Scheme for GMTKN55
Kyle R. Bryenton, Erin R. Johnson
https://arxiv.org/abs/2509.23498 https://arxiv.org/pdf/2509.23498

WTMAD-4: A Fair Weighting Scheme for GMTKN55
The GMTKN55 data set is a collection of standard benchmarks used in molecular quantum chemistry that spans small- and large-molecule thermochemistry, reaction barriers, and non-covalent interactions. Herein, we identify a flaw in the weighted mean absolute deviation (WTMAD) definitions commonly used to quantify performance of various electronic-structure methods for the GMTKN55 set, which under-weight some of its component benchmarks by orders of magnitude. A new WTMAD-4 metric is proposed, bas…

Fair Universe Higgs Uncertainty Challenge
Ragansu Chakkappai (Universit\'e Paris-Saclay, CNRS/IN2P3, IJCLab, Orsay, France, ChaLearn, USA), Wahid Bhimji (Lawrence Berkeley National Laboratory, Berkeley, USA), Paolo Calafiura (Lawrence Berkeley National Laboratory, Berkeley, USA), Po-Wen Chang (Lawrence Berkeley National Laboratory, Berkeley, USA), Yuan-Tang Chou (University of Washington, Seattle, USA), Sascha Diefenbacher (Lawrence Berkeley National Laboratory, Berkeley, USA), Jor…

Fair Universe Higgs Uncertainty Challenge
This competition in high-energy physics (HEP) and machine learning was the first to strongly emphasise uncertainties in $(H \rightarrow τ^+ τ^-)$ cross-section measurement. Participants were tasked with developing advanced analysis techniques capable of dealing with uncertainties in the input training data and providing credible confidence intervals. The accuracy of these intervals was evaluated using pseudo-experiments to assess correct coverage. The dataset is now published in Zenodo, and t…

Wait-free Replicated Data Types and Fair Reconciliation
Petr Kuznetsov, Maxence Perion, Sara Tucci-Piergiovanni
https://arxiv.org/abs/2508.18193 https://ar…

Wait-free Replicated Data Types and Fair Reconciliation
Replication is a standard way to maintain availability of shared data in fault-prone distributed systems. To make sure that the data replicas are up-to-date, they need to synchronize, which typically means engaging the replicas in waiting for coherent responses from each other. The amount of waiting depends on the consistency and availability guarantees we impose on the system. The folklore CAP theory states that strong consistency (the set of replicas create an illusion of one correct server) …

Towards Enhancing Data Equity in Public Health Data Science
Yiran Wang, Alicia E. Boyd, Lillian Rountree, Yi Ren, Kate Nyhan, Ruchit Nagar, Jackson Higginbottom, Megan L. Ranney, Harsh Parikh, Bhramar Mukherjee
https://arxiv.org/abs/2508.20301

Towards Enhancing Data Equity in Public Health Data Science
Data-driven decisions shape public health policies and practice, yet persistent disparities in data representation skew insights and undermine interventions. To address this, we advance a structured roadmap that integrates public health data science with computer science and is grounded in reflexivity. We adopt data equity as a guiding concept: ensuring the fair and inclusive representation, collection, and use of data to prevent the introduction or exacerbation of systemic biases that could le…

Replaced article(s) found for physics.data-an. https://arxiv.org/list/physics.data-an/new
[1/1]:
- FAIR Universe HiggsML Uncertainty Dataset and Competition
Lisa Benato, et al.

From real-time calibrations to smart HV tuning for FAIR
Valentin Kladov, Johan Messchendorp, James Ritman
https://arxiv.org/abs/2509.17653 https://arxiv.or…

From real-time calibrations to smart HV tuning for FAIR
Real-time data processing of the next generation of experiments at FAIR requires reliable event reconstruction and thus depends heavily on in-situ calibration procedures. Previously, we developed a neural-network-based approach that predicts calibration parameters from continuously available environmental and operational data and validated it on the HADES Multiwire Drift Chambers (MDCs), achieving fast predictions as accurate as offline calibrations. In this work, we introduce several methodolo…

Managing, Analyzing and Sharing Research Data with Gen3 Data Commons
Craig Barnes, Kyle Burton, Michael S. Fitzsimons, Hara Prasad Juvvala, Brienna Larrick, Christopher Meyer, Pauline Ribeyre, Ao Liu, Clint Malson, Noah Metoki-Shlubsky, Andrii Prokhorenkov, Jawad Qureshi, Radhika Reddy, L. Philip Schumm, Mingfei Shao, Trevar Simmons, Alexander VanTol, Peter Vassilatos, Aarti Venkat, Robert L. Grossman

Managing, Analyzing and Sharing Research Data with Gen3 Data Commons
Gen3 is an open-source data platform for building data commons. A data commons is a cloud-based data platform for managing, analyzing, and sharing data with a research community. Gen3 has been used to build over a dozen data commons that in aggregate contain over 28 PB of data and 64 million FAIR data objects. To set up a Gen3 data commons, you first define a data model. Gen3 then autogenerates 1) a data portal for searching and exploring data in the commons; 2) a data portal for submitting dat…

is everyone doing #FediCircles now? fair enough, I suppose.
https://data.natty.sh/fedi-circles/
(for the record, this isn't super accurate IMO :P)

is everyone doing #FediCircles now? fair enough, I suppose.
https://data.natty.sh/fedi-circles/
(for the record, this isn't super accurate IMO :P)

Fair Play in the Newsroom: Actor-Based Filtering Gender Discrimination in Text Corpora
Stefanie Urchs, Veronika Thurner, Matthias A{\ss}enmacher, Christian Heumann, Stephanie Thiemichen
https://arxiv.org/abs/2508.13169

Fair Play in the Newsroom: Actor-Based Filtering Gender Discrimination in Text Corpora
Large language models are increasingly shaping digital communication, yet their outputs often reflect structural gender imbalances that originate from their training data. This paper presents an extended actor-level pipeline for detecting and mitigating gender discrimination in large-scale text corpora. Building on prior work in discourse-aware fairness analysis, we introduce new actor-level metrics that capture asymmetries in sentiment, syntactic agency, and quotation styles. The pipeline supp…

Teaching RDM in a smart advanced inorganic lab course and its provision in the DALIA platform
Alexander Hoffmann, Jochen Ortmeyer, Fabian Fink, Charles Tapley Hoyt, Jonathan D. Geiger, Paul Kehrein, Torsten Schrade, Sonja Herres-Pawlis
https://arxiv.org/abs/2509.18902

IoDResearch: Deep Research on Private Heterogeneous Data via the Internet of Data
Zhuofan Shi, Zijie Guo, Xinjian Ma, Gang Huang, Yun Ma, Xiang Jing
https://arxiv.org/abs/2510.01553

IoDResearch: Deep Research on Private Heterogeneous Data via the Internet of Data
The rapid growth of multi-source, heterogeneous, and multimodal scientific data has increasingly exposed the limitations of traditional data management. Most existing DeepResearch (DR) efforts focus primarily on web search while overlooking local private data. Consequently, these frameworks exhibit low retrieval efficiency for private data and fail to comply with the FAIR principles, ultimately resulting in inefficiency and limited reusability. To this end, we propose IoDResearch (Internet of D…

Sequential Cohort Selection
Hortence Phalonne Nana, Christos Dimitrakakis
https://arxiv.org/abs/2508.16386 https://arxiv.org/pdf/2508.16386

Sequential Cohort Selection
We study the problem of fair cohort selection from an unknown population, with a focus on university admissions. We start with the one-shot setting, where the admission policy must be fixed in advance and remain transparent, before observing the actual applicant pool. In contrast, the sequential setting allows the policy to be updated across stages as new applicant data becomes available. This is achieved by optimizing admission policies using a population model, trained on data from previous a…

Responsible AI Adoption in the Public Sector: A Data-Centric Taxonomy of AI Adoption Challenges
Anastasija Nikiforova, Martin Lnenicka, Ulf Melin, David Valle-Cruz, Asif Gill, Cesar Casiano Flores, Emyana Sirait, Mariusz Luterek, Richard Michael Dreyling, Barbora Tesarova
https://arxiv.org/abs/2510.09634

Responsible AI Adoption in the Public Sector: A Data-Centric Taxonomy of AI Adoption Challenges
Despite Artificial Intelligence (AI) transformative potential for public sector services, decision-making, and administrative efficiency, adoption remains uneven due to complex technical, organizational, and institutional challenges. Responsible AI frameworks emphasize fairness, accountability, and transparency, aligning with principles of trustworthy AI and fair AI, yet remain largely aspirational, overlooking technical and institutional realities, especially foundational data and governance. …

PyCFRL: A Python library for counterfactually fair offline reinforcement learning via sequential data preprocessing
Jianhan Zhang, Jitao Wang, Chengchun Shi, John D. Piette, Donglin Zeng, Zhenke Wu
https://arxiv.org/abs/2510.06935

PyCFRL: A Python library for counterfactually fair offline reinforcement learning via sequential data preprocessing
Reinforcement learning (RL) aims to learn and evaluate a sequential decision rule, often referred to as a "policy", that maximizes the population-level benefit in an environment across possibly infinitely many time steps. However, the sequential decisions made by an RL algorithm, while optimized to maximize overall population benefits, may disadvantage certain individuals who are in minority or socioeconomically disadvantaged groups. To address this problem, we introduce PyCFRL, a Python librar…

Surfacing from replies:
Yes, this should be bigger. Yes, the fair thing would be for them to (1) pay the same per-work infringement fines that courts have made individuals pay and (2) have to destroy all data derived from the copyrighted works and start over. (Say what you will about copyright’s existence, but if it exists, companies above all should have to follow the rules.)
However, courts are far too much in the pocket of business to do that. This is vastly better than a loss. My hope is it can be the sharp end of a wedge. And…

A Systematic Review of FAIR-compliant Big Data Software Reference Architectures
Jo\~ao Pedro de Carvalho Castro, Maria J\'ulia Soares De Grandi, Cristina Dutra de Aguiar
https://arxiv.org/abs/2509.14370

A Systematic Review of FAIR-compliant Big Data Software Reference Architectures
To meet the standards of the Open Science movement, the FAIR Principles emphasize the importance of making scientific data Findable, Accessible, Interoperable, and Reusable. Yet, creating a repository that adheres to these principles presents significant challenges. Managing large volumes of diverse research data and metadata, often generated rapidly, requires a precise approach. This necessity has led to the development of Software Reference Architectures (SRAs) to guide the implementation pro…

Improved Streaming Algorithm for Fair $k$-Center Clustering
Longkun Guo, Zeyu Lin, Chaoqi Jia, Chao Chen
https://arxiv.org/abs/2510.05937 https://arxiv.org…

Improved Streaming Algorithm for Fair $k$-Center Clustering
Many real-world applications pose challenges in incorporating fairness constraints into the $k$-center clustering problem, where the dataset consists of $m$ demographic groups, each with a specified upper bound on the number of centers to ensure fairness. Focusing on big data scenarios, this paper addresses the problem in a streaming setting, where data points arrive one by one sequentially in a continuous stream. Leveraging a structure called the $λ$-independent center set, we propose a one-p…

LEO: An Open-Source Platform for Linking OMERO with Lab Notebooks and Heterogeneous Metadata Sources
Rodrigo Escobar D\'iaz Guerrero, Jamile Mohammad Jafari, Tobias Meyer-Zedler, Michael Schmitt, Juergen Popp, Thomas Bocklitz
https://arxiv.org/abs/2508.00654

LEO: An Open-Source Platform for Linking OMERO with Lab Notebooks and Heterogeneous Metadata Sources
In the interdisciplinary field of microscopy research, managing and integrating large volumes of data stored across disparate platforms remains a major challenge. Data types such as bioimages, experimental records, and spectral information are often maintained in separate repositories, each following different management standards. However, linking these data sources across the research lifecycle is essential to align with the FAIR principles of data management: Findability, Accessibility, Inte…

The propaganda is so wild. When I was younger, they'd lie about things 20-30 years prior, with the assumption that people will have forgotten. Now they are constantly attempting to rewrite history that happened a mere 5 years ago, thinking that they're slick.
To quote Orwell: "The Party told you to reject the evidence of your eyes and ears. It was their final, most essential command."

Information-Theoretic Fairness with A Bounded Statistical Parity Constraint
Amirreza Zamani, Abolfazl Changizi, Ragnar Thobaben, Mikael Skoglund
https://arxiv.org/abs/2508.12847

Information-Theoretic Fairness with A Bounded Statistical Parity Constraint
In this paper, we study an information-theoretic problem of designing a fair representation that attains bounded statistical (demographic) parity. More specifically, an agent uses some useful data $X$ to solve a task $T$. Since both $X$ and $T$ are correlated with some sensitive attribute or secret $S$, the agent designs a representation $Y$ that satisfies a bounded statistical parity and/or privacy leakage constraint, that is, such that $I(Y;S) \leq ε$. Here, we relax the perfect demographic …

Disentangled Deep Smoothed Bootstrap for Fair Imbalanced Regression
Samuel Stocksieker, Denys pommeret, Arthur Charpentier
https://arxiv.org/abs/2508.13829 https://

Disentangled Deep Smoothed Bootstrap for Fair Imbalanced Regression
Imbalanced distribution learning is a common and significant challenge in predictive modeling, often reducing the performance of standard algorithms. Although various approaches address this issue, most are tailored to classification problems, with a limited focus on regression. This paper introduces a novel method to improve learning on tabular data within the Imbalanced Regression (IR) framework, which is a critical problem. We propose using Variational Autoencoders (VAEs) to model and define…

Understanding Computer Science Students' Career Fair Experiences: Goals, Preparation, and Outcomes
Briana Lee, Samantha Limon, Alyssia Chen, Kenny Ka'aiakamanu-Quibilan, Anthony Peruma
https://arxiv.org/abs/2509.10717

Understanding Computer Science Students' Career Fair Experiences: Goals, Preparation, and Outcomes
The technology industry offers exciting and diverse career opportunities, ranging from traditional software development to emerging fields such as artificial intelligence, cybersecurity, and data science. Career fairs play a crucial role in helping Computer Science (CS) students understand the various career pathways available to them in the industry. However, limited research exists on how CS students experience and benefit from these events. Through a survey of 86 students, we investigate the…

Improving the FAIRness and Sustainability of the NHGRI Resources Ecosystem
Larry Babb, Carol Bult, Vincent J. Carey, Robert J. Carroll, Benjamin C. Hitz, Chris J. Mungall, Heidi L. Rehm, Michael C. Schatz, Alex Wagner, NHGRI Resource Workshop Community
https://arxiv.org/abs/2508.13498

Improving the FAIRness and Sustainability of the NHGRI Resources Ecosystem
In 2024, NHGRI-funded genomic resource projects completed a Self-Assessment Tool (SAT) and interviews to evaluate their application of FAIR (Findable, Accessible, Interoperable, Reusable) principles and sustainability. Key challenges were identified in metadata tools, data curation, variant identifiers, and data processing. Addressing these needs, we engaged the community through webinars and discussions, leading to a two-day workshop in March 2025. The workshop developed targeted recommendatio…

A Framework for FAIR and CLEAR Ecological Data and Knowledge: Semantic Units for Synthesis and Causal Modelling
Lars Vogt, Birgitta K\"onig-Ries, Tim Alamenciak, Joshua I. Brian, Carlos Alberto Arnillas, Lotte Korell, Robert Fr\"uhst\"uckl, Tina Heger
https://arxiv.org/abs/2508.08959…

A Framework for FAIR and CLEAR Ecological Data and Knowledge: Semantic Units for Synthesis and Causal Modelling
Ecological research increasingly relies on integrating heterogeneous datasets and knowledge to explain and predict complex phenomena. Yet, differences in data types, terminology, and documentation often hinder interoperability, reuse, and causal understanding. We present the Semantic Units Framework, a novel, domain-agnostic semantic modelling approach applied here to ecological data and knowledge in compliance with the FAIR (Findable, Accessible, Interoperable, Reusable) and CLEAR (Cognitively…

Generating CodeMeta using declarative mapping rules: An open-ended approach using ShExML
Herminio Garc\'ia-Gonz\'alez
https://arxiv.org/abs/2510.09172 https://

Generating CodeMeta using declarative mapping rules: An open-ended approach using ShExML
Nowadays, software is one of the cornerstones when conducting research in several scientific fields which employ computer-based methodologies to answer new research questions. However, for these experiments to be completely reproducible, research software should comply with the FAIR principles, yet its metadata can be represented following different data models and spread across different locations. In order to bring some cohesion to the field, CodeMeta was proposed as a vocabulary to represent…

Replaced article(s) found for physics.soc-ph. https://arxiv.org/list/physics.soc-ph/new
[1/1]:
- Learning Optimal and Fair Policies for Online Allocation of Scarce Societal Resources from Data C...
Bill Tang, \c{C}a\u{g}{\i}l Ko\c{c}yi\u{g}it, Eric Rice, Phebe Vayanos
…

Transparent and Fair Profiling in Employment Services: Evidence from Switzerland
Tim R\"az
https://arxiv.org/abs/2509.11847 https://arxiv.org/pdf/2509…

Transparent and Fair Profiling in Employment Services: Evidence from Switzerland
Long-term unemployment (LTU) is a challenge for both jobseekers and public employment services. Statistical profiling tools are increasingly used to predict LTU risk. Some profiling tools are opaque, black-box machine learning models, which raise issues of transparency and fairness. This paper investigates whether interpretable models could serve as an alternative, using administrative data from Switzerland. Traditional statistical, interpretable, and black-box models are compared in terms of p…

Crosslisted article(s) found for eess.SP. https://arxiv.org/list/eess.SP/new
[1/1]:
- Estimating Fair Graphs from Graph-Stationary Data
Madeline Navarro, Andrei Buciulea, Samuel Rey, Antonio G. Marques, Santiago Segarra

LLM-Based Information Extraction to Support Scientific Literature Research and Publication Workflows
Samy Ateia, Udo Kruschwitz, Melanie Scholz, Agnes Koschmider, Moayad Almohaishi
https://arxiv.org/abs/2510.04749

LLM-Based Information Extraction to Support Scientific Literature Research and Publication Workflows
The increasing volume of scholarly publications requires advanced tools for efficient knowledge discovery and management. This paper introduces ongoing work on a system using Large Language Models (LLMs) for the semantic extraction of key concepts from scientific documents. Our research, conducted within the German National Research Data Infrastructure for and with Computer Science (NFDIxCS) project, seeks to support FAIR (Findable, Accessible, Interoperable, and Reusable) principles in scienti…

Enhancing Fairness in Autoencoders for Node-Level Graph Anomaly Detection
Shouju Wang, Yuchen Song, Sheng'en Li, Dongmian Zou
https://arxiv.org/abs/2508.10785 https://

Enhancing Fairness in Autoencoders for Node-Level Graph Anomaly Detection
Graph anomaly detection (GAD) has become an increasingly important task across various domains. With the rapid development of graph neural networks (GNNs), GAD methods have achieved significant performance improvements. However, fairness considerations in GAD remain largely underexplored. Indeed, GNN-based GAD models can inherit and amplify biases present in training data, potentially leading to unfair outcomes. While existing efforts have focused on developing fair GNNs, most approaches target…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/4]:
- Learning Optimal and Fair Policies for Online Allocation of Scarce Societal Resources from Data C...
Bill Tang, \c{C}a\u{g}{\i}l Ko\c{c}yi\u{g}it, Eric Rice, Phebe Vayanos