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Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data
Announce Type: replace Abstract: Noise-based certified machine unlearning currently faces a hard ceiling: the noise magnitude required to certify unlearning typically destroys model utility, particularly for large-scale deletion requests. While leveraging public data is a standard technique in differential privacy to relax this tension, its role in unlearning remains unexplored. We address this gap by introducing Asymmetric Langevin Unlearning (ALU), a framework that uses public data to...
Speaker Mining -- FAIR Data on Public Broadcasts for Question Answering
arXiv:2606.02905v1 Announce Type: new Abstract: Public broadcasts are at the center of civic discourse: Traditional television talk shows, alongside emerging podcast and web video formats, capture and guide the attention of our societies, shaping how citizens encounter politics, science, and societal issues. Yet, systematic or even simple analyses of these formats face similar challenges: guest and content metadata are scarce, fleeting, fragmented, and not standardized.
RO-LiDAR GeoQuickView: A Web Platform for Exploring Public LiDAR-Derived Elevation Data in Romania
arXiv:2606.08876v1 Announce Type: new Abstract: Public elevation data can support landscape research, environmental interpretation, planning, education, and public engagement, but their practical reuse is often limited by fragmented delivery and specialist processing requirements. This paper presents RO-LiDAR GeoQuickView, an independent, voluntary, and non-commercial Web-GIS initiative for exploring and reusing publicly accessible elevation data in Romania. The platform integrates...
In-Context Learning for the Imputation of Public Opinion Data with Large Language Models
arXiv:2606.09351v1 Announce Type: new Abstract: Large language models have been widely evaluated as simulators of individual survey responses. In practice, however, fully unobserved responses are rare; the dominant problem is partial non-response. Imputation aims to restore the overall structure of a survey dataset by filling in these missing values.
Health-Informed Computing: Estimating and Addressing the Public Health Impact of Data Centers
Announce Type: replace Abstract: The surging demand for artificial intelligence (AI) has led to a rapid expansion of energy-intensive data centers, contributing to criteria air pollutant emissions and raising public health concerns that have received comparatively limited attention in sustainability assessments. This paper introduces a principled methodology to model air pollutant emissions for data centers and estimate the public health impacts. Our findings reveal that the growing demand...
AI’s elite celebrated in Washington as the public sours on data centers and chatbots
WASHINGTON — Glitz, glamor and geopolitics were on the menu Wednesday night as Washington’s AI elite gathered to mingle and celebrate AI pioneers. Complete with a red carpet and a dancing humanoid robot, the event could have been mistaken for a science fiction Hollywood premiere. Even as AI becomes an increasing source of tension in communities across America, the night’s honorees were largely optimistic about the trajectory of AI and its potential impact on society.
NHS trust issues public apology to Nottingham attack victims over medical data breaches
An NHS trust has issued a public apology to victims of the Nottingham attack regarding medical data breaches. A medical director stated to a public inquiry that the data breaches caused "additional distress" to the victims.
NHS trust issues public apology to Nottingham attack victims over medical data breaches
An NHS trust has issued a public apology to victims of the Nottingham attack regarding medical data breaches. A medical director stated to a public inquiry that the data breaches caused "additional distress" to the victims.
rt2gtfs: A scalable framework for correcting public transport timetables using real-time data for accessibility analysis
arXiv:2603.11477v2 Announce Type: replace Abstract: Travel time is a fundamental component of accessibility measurement, yet most accessibility analyses rely on static timetable data that assume public transport services operate exactly as scheduled. Such representations overlook the substantial variability in travel times arising from operational conditions and service disruptions. In this paper, we present rt2gtfs, an open-source Python package for reconstructing empirical public transport...
Take Action: LAPD Removed Crime Location Data. Here's Why It Matters
Take Action: LAPD Removed Crime Location Data. Here's Why It Matters. Dear SpotCrime Subscriber, For years, residents across Los Angeles relied on public crime data to stay informed about safety in their neighborhoods.