Tootfinder

You might want to fine-tune your notification settings:
“Apple Gave Governments Data on Thousands of Push Notifications” - https://www.404media.co/apple-gave-governments-data-on-thousands-of-push-notifications/

Apple Gave Governments Data on Thousands of Push Notifications
Push notification data can sometimes include the unencrypted content of notifications. Requests include from the U.S., U.K., Germany, and Israel.

Apple's July 2022 to June 2024 transparency report shows it gave governments data on thousands of push notifications, which can include unencrypted content (Joseph Cox/404 Media)
https://www.404media.co/apple-gave-governments-data-on-tho…

Apple Gave Governments Data on Thousands of Push Notifications
Push notification data can sometimes include the unencrypted content of notifications. Requests include from the U.S., U.K., Germany, and Israel.

Data Sharing for Research Tracker
https://fpf.org/blog/data-sharing-for-research-tracker/
@…
Co-authored by Hannah Babinski, for…

This looks very cool.
'OpenAIRE in collaboration with Area Science Park organizes a hands-on workshop titled “Where LEGO Meets FAIR Data,” designed to introduce the principles of FAIR data through a creative, interactive simulation using LEGO metaphors.'
https://www.

"Apple provided governments around the world with data related to thousands of push notifications sent to its devices, which can identify a target’s specific device or in some cases include unencrypted content."
https://www.404media.co/apple-gave-governm

Apple Gave Governments Data on Thousands of Push Notifications
Push notification data can sometimes include the unencrypted content of notifications. Requests include from the U.S., U.K., Germany, and Israel.

A coalition of seven leading civil society organisations has called on the European Commission to revoke the United Kingdom's data adequacy status, citing what they describe as a sustained and systemic erosion of privacy and data protection standards in the UK.
https://www.

Europe urged to revoke UK's data adequacy status
Government's practices are increasingly diverging from the EU's data protection framework, argue civil society groups

Do you need inspiration how to present a dataset in a clear figure and what package to use? Check out #rstats

From data to Viz | Find the graphic you need
A classification of chart types based on their input data format.

This https://arxiv.org/abs/2501.14755 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Data-Juicer 2.0: Cloud-Scale Adaptive Data Processing for and with Foundation Models
The burgeoning field of foundation models necessitates advanced data processing mechanisms capable of harnessing vast and valuable data with various types used by these models. Nevertheless, the current landscape presents unique challenges that traditional data processing frameworks struggle to handle effectively, particularly in handling the complexity of multimodal data. In response, we present Data-Juicer 2.0, a data processing system backed by 100+ data processing operators spanning text, i…

Trade-offs in Data Memorization via Strong Data Processing Inequalities
Vitaly Feldman, Guy Kornowski, Xin Lyu
https://arxiv.org/abs/2506.01855 https://

Trade-offs in Data Memorization via Strong Data Processing Inequalities
Recent research demonstrated that training large language models involves memorization of a significant fraction of training data. Such memorization can lead to privacy violations when training on sensitive user data and thus motivates the study of data memorization's role in learning. In this work, we develop a general approach for proving lower bounds on excess data memorization, that relies on a new connection between strong data processing inequalities and data memorization. We then demonst…

Amend Investigatory Powers Act: bar Govt from protected, private customer data
Amend the Act to specifically and completely bar any Government from attempting to compel any company to give them access to customers' data, which we think undermines privacy. We don't want any companies to pull privacy features from the UK market, potentially as a result of the Act and its use.

FDA tells drugmakers to redo studies over data integrity issue
(*CRO misconduct issue)
https://www.statnews.com/pharmalot/2025/04/04/fda-research-cro-raptim-india-data/

FDA tells drugmakers to redo studies run by a contract research firm due to data integrity issues
Citing "objectionable conditions," the agency said there were problems with the way Raptim Research tested medicines in India.

Mind Security, which offers AI-powered automated data loss prevention services to help prevent breaches, raised a $30M Series A (Kyt Dotson/SiliconANGLE)
https://siliconangle.com/2025/06/04/mind-raises-30m-help-businesses-prevent-da…

Mind raises $30M to help businesses prevent data leaks using AI - SiliconANGLE
Mind raises $30M to help businesses prevent data leaks using AI - SiliconANGLE

"Only two European states have net zero military emissions target, data shows"
#Europe #Climate #ClimateChange #Emissions

I have contributed a public statement in the currently open consultation on EU data retention plans: https://www.mayrhofer.eu.org/post/on-mass-data-retention/
You might want to do so as well.

On data retention laws: Why this is (still) not a good balance between mass surveillance and law enforcement | René Mayrhofer
The latest effort to legalize mass data retention of communication traffic metadata is still problematic. I try to make my most pressing argument why the balance between preventing harm and causing potential harm is not met.

An Algorithmic Pipeline for GDPR-Compliant Healthcare Data Anonymisation: Moving Toward Standardisation
Hamza Khan, Lore Menten, Liesbet M. Peeters
https://arxiv.org/abs/2506.02942

An Algorithmic Pipeline for GDPR-Compliant Healthcare Data Anonymisation: Moving Toward Standardisation
High-quality real-world data (RWD) is essential for healthcare but must be transformed to comply with the General Data Protection Regulation (GDPR). GDPRs broad definitions of quasi-identifiers (QIDs) and sensitive attributes (SAs) complicate implementation. We aim to standardise RWD anonymisation for GDPR compliance while preserving data utility by introducing an algorithmic method to identify QIDs and SAs and evaluate utility in anonymised datasets. We conducted a systematic literature review…

What Does Information Science Offer for Data Science Research?: A Review of Data and Information Ethics Literature
Brady D. Lund, Ting Wang
https://arxiv.org/abs/2506.03165

What Does Information Science Offer for Data Science Research?: A Review of Data and Information Ethics Literature
This paper reviews literature pertaining to the development of data science as a discipline, current issues with data bias and ethics, and the role that the discipline of information science may play in addressing these concerns. Information science research and researchers have much to offer for data science, owing to their background as transdisciplinary scholars who apply human-centered and social-behavioral perspectives to issues within natural science disciplines. Information science resea…

PSA: We had some image loading issues since yesterday on datasci.social, soon to be resolved by itself. Thx for understanding.
Reason: Due to migrating to a more stable provider: An additional cache for images was added that also stores the data in an efficient manner, but due to the large initial load some of its requests are timing out to the backend. We are increasing the timeouts, and also manually pushing data into it so it has data before the initial request.

Apple Gave Governments Data on Thousands of Push Notifications https://www.404media.co/apple-gave-governments-data-on-thousands-of-push-notifications/

This https://arxiv.org/abs/2504.20369 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Perception-aware Sampling for Scatterplot Visualizations
Visualizing data is often a crucial first step in data analytics workflows, but growing data sizes pose challenges due to computational and visual perception limitations. As a result, data analysts commonly down-sample their data and work with subsets. Deriving representative samples, however, remains a challenge. This paper focuses on scatterplots, a widely-used visualization type, and introduces a novel sampling objective -- perception-awareness -- aiming to improve sample efficacy by targeti…

U.S. Is Trimming Back Its Collection of Consumer Price Data (Ben Casselman/New York Times)
https://www.nytimes.com/2025/06/04/business/bls-price-data-collection.html
http://www.memeorandum.com/250604/p181#a250604p181

RT @…
#Hackathon with a 💰 £4,000 prize pot will take place from 9am-3pm (CEST) on 11, 12 and 16 June.
Entrants will be expected to use new data collected via our web-first panel survey to produce policy-relevant insights.
Registration closes…

Hackathon: Analysing CRONOS data and communicating key insights to policy makers | Infra4NextGen
Save the date for our next Hackathon on reusing, analysing and visualizing Infra4NextGen data to win prizes in various categories!

The most energetic transients - tidal disruptions of high-mass stars: #ExtremeNuclearTransients (ENTs) are the most energetic transients yet observed.

twitter_higgs: Twitter, Higgs boson (2012)
Data on tweets related to the announcement of the discovery of a new fundamental particle with the features of the Higgs boson on 4th July 2012. Data covers 1-7 July 2012, and includes four types of networks: followers, retweets, replies, and mentions.
This network has 304691 nodes and 563069 edges.
Tags: Social, Online, Weighted, Multilayer

@… このURLの、クリックして登録フォームがでるやつを使って。まだどれか生きてるから。

“Legitimate interests is now our legal basis for using your information to improve Meta Products.”
Alright, Zuck. 😂
Here’s what's included:
https://www.facebook.com/privacy/policy/?section_id=18.4-LegitimateInterestsWeRely

Users should be in control of what data their browser extensions can access, which is why Mozilla plans to build a data collection consent directly into Firefox's add-on installation flow
https://blog.mozilla.org/addons/2025/0

Rethinking the effects of data contamination in Code Intelligence
Zhen Yang, Hongyi Lin, Yifan He, Jie Xu, Zeyu Sun, Shuo Liu, Pengpeng Wang, Zhongxing Yu, Qingyuan Liang
https://arxiv.org/abs/2506.02791

Rethinking the effects of data contamination in Code Intelligence
In recent years, code intelligence has gained increasing importance in the field of automated software engineering. Meanwhile, the widespread adoption of Pretrained Language Models (PLMs) and Large Language Models (LLMs) has raised concerns regarding data contamination and its potential impact on model performance evaluation. This paper presents a systematic empirical study to investigate the fine-grained data contamination on code intelligence tasks. Our study involves diverse representative P…

Common Corpus: The Largest Collection of Ethical Data for LLM Pre-Training
Pierre-Carl Langlais, Carlos Rosas Hinostroza, Mattia Nee, Catherine Arnett, Pavel Chizhov, Eliot Krzystof Jones, Ir\`ene Girard, David Mach, Anastasia Stasenko, Ivan P. Yamshchikov
https://arxiv.org/abs/2506.01732

Common Corpus: The Largest Collection of Ethical Data for LLM Pre-Training
Large Language Models (LLMs) are pre-trained on large amounts of data from different sources and domains. These data most often contain trillions of tokens with large portions of copyrighted or proprietary content, which hinders the usage of such models under AI legislation. This raises the need for truly open pre-training data that is compliant with the data security regulations. In this paper, we introduce Common Corpus, the largest open dataset for language model pre-training. The data assem…

Here's a sort of rebuttal to AI 2027 that feels more informed than most of the ones I've read:
https://www.lesswrong.com/posts/XiMRyQcEyKCryST8T/slowdown-after-2028-compute-rlvr-uncertainty-moe-data-wall…

Ukraine's military intelligence agency (HUR) has gained access to sensitive data of Russia's strategic aircraft manufacturer Tupolev
Tupolev, a Soviet-era aerospace firm now fully integrated into Russia's defense-industrial complex, has been under international sanctions since 2022 for its role in Russia's war against Ukraine.
Its bombers have been widely used to launch long-range cruise missiles against Ukrainian cities and infrastructure.
According to the so…

'There is nothing secret left' — Ukraine hacks Russia's Tupolev aircraft manufacturer, source claims
HUR's cyber corps accessed over 4.4 gigabytes (GB) of internal data, including official correspondence, personnel files, home addresses, resumes, purchase records, and closed meeting minutes, a source claimed.

Second-order AAA algorithms for structured data-driven modeling
Michael S. Ackermann, Ion Victor Gosea, Serkan Gugercin, Steffen W. R. Werner
https://arxiv.org/abs/2506.02241

Second-order AAA algorithms for structured data-driven modeling
The data-driven modeling of dynamical systems has become an essential tool for the construction of accurate computational models from real-world data. In this process, the inherent differential structures underlying the considered physical phenomena are often neglected making the reinterpretation of the learned models in a physically meaningful sense very challenging. In this work, we present three data-driven modeling approaches for the construction of dynamical systems with second-order diffe…

Multimodal Financial Foundation Models (MFFMs): Progress, Prospects, and Challenges
Xiao-Yang Liu Yanglet, Yupeng Cao, Li Deng
https://arxiv.org/abs/2506.01973

Multimodal Financial Foundation Models (MFFMs): Progress, Prospects, and Challenges
Financial Large Language Models (FinLLMs), such as open FinGPT and proprietary BloombergGPT, have demonstrated great potential in select areas of financial services. Beyond this earlier language-centric approach, Multimodal Financial Foundation Models (MFFMs) can digest interleaved multimodal financial data, including fundamental data, market data, data analytics, macroeconomic, and alternative data (e.g., natural language, audio, images, and video). In this position paper, presented at the MFF…

If you're (like me) trying to create a Local Place Plan for your locality, and are struggling to analyse data from the Place Standard Tool, I've written a wee #Clojure program, leveraging Google's Gemini #LLM, to do it for you.
If you're just trying to analyse data from some other spre…

Eighteen Exoplanet Host Stars from the NPOI Data Archive
Ellyn K. Baines, Jeremy Jones, James H. Clark III, Henrique R. Schmitt, Jordan M. Stone
https://arxiv.org/abs/2506.02934

Eighteen Exoplanet Host Stars from the NPOI Data Archive
During the course of publishing angular diameters from the Navy Precision Optical Interferometer data archive, we found we had data on 17 confirmed exoplanet host stars and one exoplanet candidate (HD 20902/alpha Per). Here, we update our previously published stellar radii with more precise Gaia parallaxes when available, and use our radius and effective temperature measurements to fit each star's mass and age using MIST models. The mass changed by more than 10% for 9 of the 18 stars. Combining…

This https://arxiv.org/abs/2503.17414 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Opportunities and Challenges of Frontier Data Governance With Synthetic Data
Synthetic data, or data generated by machine learning models, is increasingly emerging as a solution to the data access problem. However, its use introduces significant governance and accountability challenges, and potentially debases existing governance paradigms, such as compute and data governance. In this paper, we identify 3 key governance and accountability challenges that synthetic data poses - it can enable the increased emergence of malicious actors, spontaneous biases and value drift.…

Differentially Private Distribution Release of Gaussian Mixture Models via KL-Divergence Minimization
Hang Liu, Anna Scaglione, Sean Peisert
https://arxiv.org/abs/2506.03467

Differentially Private Distribution Release of Gaussian Mixture Models via KL-Divergence Minimization
Gaussian Mixture Models (GMMs) are widely used statistical models for representing multi-modal data distributions, with numerous applications in data mining, pattern recognition, data simulation, and machine learning. However, recent research has shown that releasing GMM parameters poses significant privacy risks, potentially exposing sensitive information about the underlying data. In this paper, we address the challenge of releasing GMM parameters while ensuring differential privacy (DP) guar…

This https://arxiv.org/abs/2506.01696 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Missing Data in Signal Processing and Machine Learning: Models, Methods and Modern Approaches
The goal of this tutorial is to provide an overview of recent methods for handling missing data in signal processing methods, from their origins to the challenges ahead. Missing data approaches are grouped by three main categories: i) missing-data imputation, ii) estimation with missing values and iii) prediction with missing values. We focus on methodological and experimental results through specific case studies on real-world applications. Promising and future research directions, including a…

Labelling Data with Unknown References
Adrian de Wynter
https://arxiv.org/abs/2506.03083 https://arxiv.org/pdf/2506.03083

Labelling Data with Unknown References
An evaluator is trustworthy when there exists some agreed-upon way to measure its performance as a labeller. The two ways to establish trustworthiness are either by testing it, or by assuming the evaluator `knows' somehow the way to label the corpus. However, if labelled references (e.g., a development set) are unavailable, neither of these approaches work: the former requires the data, and the latter is an assumption, not evidence. To address this, we introduce an algorithm (the `No-Data Algor…

Splatting Physical Scenes: End-to-End Real-to-Sim from Imperfect Robot Data
Ben Moran, Mauro Comi, Steven Bohez, Tom Erez, Zhibin Li, Leonard Hasenclever
https://arxiv.org/abs/2506.04120

Splatting Physical Scenes: End-to-End Real-to-Sim from Imperfect Robot Data
Creating accurate, physical simulations directly from real-world robot motion holds great value for safe, scalable, and affordable robot learning, yet remains exceptionally challenging. Real robot data suffers from occlusions, noisy camera poses, dynamic scene elements, which hinder the creation of geometrically accurate and photorealistic digital twins of unseen objects. We introduce a novel real-to-sim framework tackling all these challenges at once. Our key insight is a hybrid scene represen…

Incorporating Correlated Nugget Effects in Multivariate Spatial Models: An Application to Argo Ocean Data
Damilya Saduakhas, David Bolin, Xiaotian Jin, Alexandre B. Simas, Jonas Wallin
https://arxiv.org/abs/2506.03042

Incorporating Correlated Nugget Effects in Multivariate Spatial Models: An Application to Argo Ocean Data
Accurate analysis of global oceanographic data, such as temperature and salinity profiles from the Argo program, requires geostatistical models capable of capturing complex spatial dependencies. This study introduces Gaussian and non-Gaussian hierarchical multivariate Matérn-SPDE models with correlated nugget effects to account for small-scale variability and measurement error correlations. Using simulations and Argo data, we demonstrate that incorporating correlated nugget effects significant…

Signals as a First-Class Citizen When Querying Knowledge Graphs
Tobias Schwarzinger, Gernot Steindl, Thomas Fr\"uhwirth, Thomas Preindl, Konrad Diwold, Katrin Ehrenm\"uller, Fajar J. Ekaputra
https://arxiv.org/abs/2506.03826

Signals as a First-Class Citizen When Querying Knowledge Graphs
Cyber-Physical Systems (CPSs) tightly integrate computation with physical entities, often generating vast amounts of time series data from thousands of sensors. Although knowledge graphs offer a powerful means to contextualize these data, existing approaches to integrating knowledge graphs with time series data lack a concept to model the continuous temporal values inherent in CPSs. This gap can make expressing computations on the sensor data cumbersome. In this work, we propose the integration…

https://coincentral.com/coinbase-hid-massive-data-breach-for-months-before-going-public/
“Coinbase knew about a TaskUs employee data breach in January but only disclosed it publicly in May after rejecting hackers' ransom demands.”

Coinbase Hid Massive Data Breach for Months Before Going Public - CoinCentral
Coinbase knew about a TaskUs employee data breach in January but only disclosed it publicly in May after rejecting hackers' ransom demands.

Trump Taps Palantir to Create Master Database on Every American | The New Republic
https://newrepublic.com/post/195904/trump-palantir-data-americans
Voor iedereen die zich nog afvroeg of privacy in de VS nog een rol van betekenis speelt of dat er …

A data model to connect the ESO Data Processing System (EDPS) to ELT data archives
Hugo Buddelmeijer, Gijs Verdoes Kleijn
https://arxiv.org/abs/2506.00899 …

A data model to connect the ESO Data Processing System (EDPS) to ELT data archives
We extend the data-driven approach for astronomical pipelines to the METIS instrument and integrate the ESO Data Processing System (EDPS) by adapting it to support backward chaining.

Carbon-Aware Temporal Data Transfer Scheduling Across Cloud Datacenters
Elvis Rodrigues, Jacob Goldverg, Tevfik Kosar
https://arxiv.org/abs/2506.04117 http…

Carbon-Aware Temporal Data Transfer Scheduling Across Cloud Datacenters
Inter-datacenter communication is a significant part of cloud operations and produces a substantial amount of carbon emissions for cloud data centers, where the environmental impact has already been a pressing issue. In this paper, we present a novel carbon-aware temporal data transfer scheduling framework, called LinTS, which promises to significantly reduce the carbon emission of data transfers between cloud data centers. LinTS produces a competitive transfer schedule and makes scaling decisi…

Combining social relations and interaction data in Recommender System with Graph Convolution Collaborative Filtering
Tin T. Tran, Vaclav Snasel, Loc Tan Nguyen
https://arxiv.org/abs/2506.02834

Combining social relations and interaction data in Recommender System with Graph Convolution Collaborative Filtering
A recommender system is an important subject in the field of data mining, where the item rating information from users is exploited and processed to make suitable recommendations with all other users. The recommender system creates convenience for e-commerce users and stimulates the consumption of items that are suitable for users. In addition to e-commerce, a recommender system is also used to provide recommendations on books to read, movies to watch, courses to take or websites to visit. Simi…

Analysis of Multiple Long Run Relations in Panel Data Models with Applications to Financial Ratios
Alexander Chudik, M. Hashem Pesaran, Ron P. Smith
https://arxiv.org/abs/2506.02135

Analysis of Multiple Long Run Relations in Panel Data Models with Applications to Financial Ratios
This paper provides a new methodology for the analysis of multiple long run relations in panel data models where the cross section dimension, $n$, is large relative to the time series dimension, $T$. For panel data models with large $n$ researchers have focused on panels with a single long run relationship. The main difficulty has been to eliminate short run dynamics without generating significant uncertainty for identification of the long run. We overcome this problem by using non-overlapping …

@… So long as you are using the same user data (settings) you will get that. If you want it to run independently specific another directory to house user data with --user-data-dir=
@…

The {esquisse} package makes it easy to plot your data in different ways with a drag and drop interface: #rstats

Explore and Visualize Your Data Interactively
A shiny gadget to create ggplot2 figures interactively with drag-and-drop to map your variables to different aesthetics. You can quickly visualize your data accordingly to their type, export in various formats, and retrieve the code to reproduce the plot.

🇺🇦 #NowPlaying on KEXP's #VarietyMix
Radiohead:
🎵 Weird Fishes/Arpeggi
#Radiohead
https://ghostdata.bandcamp.com/track/radiohead-weird-fishes-arpeggi-ghost-data-remix
https://open.spotify.com/track/4wajJ1o7jWIg62YqpkHC7S

Decentralized COVID-19 Health System Leveraging Blockchain
Lingsheng Chen, Shipeng Ye, Xiaoqi Li
https://arxiv.org/abs/2506.02674 https://

Decentralized COVID-19 Health System Leveraging Blockchain
With the development of the Internet, the amount of data generated by the medical industry each year has grown exponentially. The Electronic Health Record (EHR) manages the electronic data generated during the user's treatment process. Typically, an EHR data manager belongs to a medical institution. This traditional centralized data management model has many unreasonable or inconvenient aspects, such as difficulties in data sharing, and it is hard to verify the authenticity and integrity of the…

SMOTE-DP: Improving Privacy-Utility Tradeoff with Synthetic Data
Yan Zhou, Bradley Malin, Murat Kantarcioglu
https://arxiv.org/abs/2506.01907 https://

SMOTE-DP: Improving Privacy-Utility Tradeoff with Synthetic Data
Privacy-preserving data publication, including synthetic data sharing, often experiences trade-offs between privacy and utility. Synthetic data is generally more effective than data anonymization in balancing this trade-off, however, not without its own challenges. Synthetic data produced by generative models trained on source data may inadvertently reveal information about outliers. Techniques specifically designed for preserving privacy, such as introducing noise to satisfy differential priva…

Trump Administration Backs Off Effort to Collect Data on Food Stamp Recipients (Zach Montague/New York Times)
https://www.nytimes.com/2025/06/03/us/politics/trump-administration-personal-data-food-stamp-recipients.html?unlocked_article_code=1.ME8.Ii3t.2hf3Htm_rfho&smid=url-share
http://www.memeorandum.com/250603/p131#a250603p131

This https://arxiv.org/abs/2412.03824 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Towards Data Governance of Frontier AI Models
Data is essential to train and fine-tune today's frontier artificial intelligence (AI) models and to develop future ones. To date, academic, legal, and regulatory work has primarily addressed how data can directly harm consumers and creators, such as through privacy breaches, copyright infringements, and bias and discrimination. Our work, instead, focuses on the comparatively neglected question of how data can enable new governance capacities for frontier AI models. This approach for "frontier …

This https://arxiv.org/abs/2505.19746 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Improving Heart Rejection Detection in XPCI Images Using Synthetic Data Augmentation
Accurate identification of acute cellular rejection (ACR) in endomyocardial biopsies is essential for effective management of heart transplant patients. However, the rarity of high-grade rejection cases (3R) presents a significant challenge for training robust deep learning models. This work addresses the class imbalance problem by leveraging synthetic data generation using StyleGAN to augment the limited number of real 3R images. Prior to GAN training, histogram equalization was applied to sta…

I really like this approach
https://infosec.exchange/@masek/114624183034377452

Dynamical Dark Energy from $F(R)$ Gravity Models Unifying Inflation with Dark Energy: Confronting the Latest Observational Data
S. D. Odintsov, V. K. Oikonomou, G. S. Sharov
https://arxiv.org/abs/2506.02245

Dynamical Dark Energy from $F(R)$ Gravity Models Unifying Inflation with Dark Energy: Confronting the Latest Observational Data
A class of viable $F(R)$ gravity models which can provide a unified description of inflation with the dark energy era is confronted with the latest observational data on the dark energy era. These models have the unique characteristic that the de Sitter scalaron mass in the Einstein frame counterpart theory is a monotonic function of the curvature, which renders them viable descriptions for both the inflationary and the late-time acceleration eras. We also compare these models with other well-k…

I honestly don’t get how this is possible in 2025.
There’s no excuse for not having protected backups of everything critical. That was true 30 years ago, it is true today. Back then a bigger piece of the risk was hardware, less malware. Today it is unlikely for a disk failure to take your data because we all learned that RAID was worthwhile. But RAID is not a backup and backups today need to be resistant to intentional malicious deletion.

cora: CORA citations (1998)
Citations among papers indexed by CORA, from 1998, an early computer science research paper search engine. If a paper i cites a paper j also in this data set, then a directed edge connects i to j. (Papers not in the data set are excluded.) Self-loops may be present. The dates of these snapshots are uncertain.
This network has 23166 nodes and 91500 edges.
Tags: Informational, Citation, Unweighted

https://www.reuters.com/legal/legalindustry/marriott-wins-us-appeals-order-striking-down-data-breach-class-action-2025-06-03/
Marriott wins US appeals order striking down data breach class action

Nonsmooth data error estimates for exponential Runge--Kutta methods and applications to split exponential integrators
Qiumei Huang, Alexander Ostermann, Gangfan Zhong
https://arxiv.org/abs/2506.02778

Nonsmooth data error estimates for exponential Runge--Kutta methods and applications to split exponential integrators
We derive error bounds for exponential Runge-Kutta discretizations of parabolic equations with nonsmooth initial data. Our analysis is carried out in a framework of abstract semilinear evolution equations with operators having non-dense domain. In particular, we investigate nonsmooth data error estimates for the Allen-Cahn and the Burgers' equation. As an application, we apply these nonsmooth data error estimates to split exponential integrators and derive a convergence result in terms of the d…

Nearly 3,000 North Face website customer accounts breached as retail incidents continue https://therecord.media/north-face-customer-accounts-data-breach-notification

Optical spectroscopic signatures of the red giant evolutionary state
Ella Xi Wang, Melissa Ness, Thomas Nordlander, Andrew R. Casey, Sarah Martell, Marc Pinsonneault, Xiaoting Fu, Dennis Stello, Claudia Reyes, Marc Hon, Madeleine McKenzie, Mingjie Jian, Jie Yu, Sven Buder, Karin Lind, Joss Bland-Hawthorn, Daniel B. Zucker, Pradosh Barun Das, Richard de Grijs, Michael Hayden

Optical spectroscopic signatures of the red giant evolutionary state
Modern spectroscopic surveys output large data volumes. Theoretical models provide a means to transform the information encoded in these data to measurements of physical stellar properties. However, in detail the models are incomplete and simplified, and prohibit interpretation of the fine details in spectra. Instead, the available data provide an opportunity to use data-driven, differential analysis techniques, as a means towards understanding spectral signatures. We deploy such an analysis to…

Analysis of Server Throughput For Managed Big Data Analytics Frameworks
Emmanouil Anagnostakis, Polyvios Pratikakis
https://arxiv.org/abs/2506.03854 https:…

Analysis of Server Throughput For Managed Big Data Analytics Frameworks
Managed big data frameworks, such as Apache Spark and Giraph demand a large amount of memory per core to process massive volume datasets effectively. The memory pressure that arises from the big data processing leads to high garbage collection (GC) overhead. Big data analytics frameworks attempt to remove this overhead by offloading objects to storage devices. At the same time, infrastructure providers, trying to address the same problem, attribute more memory to increase memory per instance le…

If you just need a pretty figure from a dataset and not the full power of R, have a look at #gui

Optimization of Functional Materials Design with Optimal Initial Data in Surrogate-Based Active Learning
Seongmin Kim, In-Saeng Suh
https://arxiv.org/abs/2506.03329

Optimization of Functional Materials Design with Optimal Initial Data in Surrogate-Based Active Learning
The optimization of functional materials is important to enhance their properties, but their complex geometries pose great challenges to optimization. Data-driven algorithms efficiently navigate such complex design spaces by learning relationships between material structures and performance metrics to discover high-performance functional materials. Surrogate-based active learning, continually improving its surrogate model by iteratively including high-quality data points, has emerged as a cost-…

Brookfield says it plans to invest ~$10B to build an AI data center in the Swedish city of Strangnas, after committing to spend €20B in France earlier in 2025 (Supantha Mukherjee/Reuters)
https://www.reuters.com/technology/brookfi

AI Data Development: A Scorecard for the System Card Framework
Tadesse K. Bahiru, Haileleol Tibebu, Ioannis A. Kakadiaris
https://arxiv.org/abs/2506.02071 …

AI Data Development: A Scorecard for the System Card Framework
Artificial intelligence has transformed numerous industries, from healthcare to finance, enhancing decision-making through automated systems. However, the reliability of these systems is mainly dependent on the quality of the underlying datasets, raising ongoing concerns about transparency, accountability, and potential biases. This paper introduces a scorecard designed to evaluate the development of AI datasets, focusing on five key areas from the system card framework data development life cy…

This https://arxiv.org/abs/2503.04149 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Dynamic Benchmarking of Reasoning Capabilities in Code Large Language Models Under Data Contamination
The rapid evolution of code largelanguage models underscores the need for effective and transparent benchmarking of their reasoning capabilities. However, the current benchmarking approach heavily depends on publicly available, human-created datasets. The widespread use of these fixed benchmark datasets makes the benchmarking process to be static and thus particularly susceptible to data contamination, an unavoidable consequence of the extensive data collection processes used to train Code LLMs…

No such thing as a slow cybersecurity news day anymore, so don't miss today's Metacurity for the critical infosec developments you should know, including
--Top cyber vendors hope to clean up crazy threat group naming practices,
--Coinbase knew of data leak in January,
--Prolific swatter pleads guilty,
--Cartier confirms data breach,
--Abilene gropes for recovery after rejecting ransom payment,
--North Face customers' data stolen in credential s…

Top cyber vendors hope to clean up crazy threat group naming practices
Coinbase knew of data leak in January, Prolific swatter pleads guilty, Cartier confirms data breach, Abilene gropes for recovery after rejecting ransom payment, North Face customers' data stolen in credential stuffing attacks, $11.5m stolen from BitoPro hot wallets, much more

Simulating Complex Crossectional and Longitudinal Data using the simDAG R Package
Robin Denz, Nina Timmesfeld
https://arxiv.org/abs/2506.01498 https://

Simulating Complex Crossectional and Longitudinal Data using the simDAG R Package
Generating artificial data is a crucial step when performing Monte-Carlo simulation studies. Depending on the planned study, complex data generation processes (DGP) containing multiple, possibly time-varying, variables with various forms of dependencies and data types may be required. Simulating data from such DGP may therefore become a difficult and time-consuming endeavor. The simDAG R package offers a standardized approach to generate data from simple and complex DGP based on the definition …

product_space: Atlas of Economic Complexity export network
Two networks of economic products, where a pair of products are connected if they are exported at similar rates by the same countries. The data are a projection from a bipartite network of nations and the products they export. Edges weights represent a similarity score (called "proximity"). Data based on UN Comtrade worldwide trade patterns. SITC network based on the Standard International Trade Classification and HS …

A Dynamic Framework for Semantic Grouping of Common Data Elements (CDE) Using Embeddings and Clustering
Madan Krishnamurthy, Daniel Korn, Melissa A Haendel, Christopher J Mungall, Anne E Thessen
https://arxiv.org/abs/2506.02160

A Dynamic Framework for Semantic Grouping of Common Data Elements (CDE) Using Embeddings and Clustering
This research aims to develop a dynamic and scalable framework to facilitate harmonization of Common Data Elements (CDEs) across heterogeneous biomedical datasets by addressing challenges such as semantic heterogeneity, structural variability, and context dependence to streamline integration, enhance interoperability, and accelerate scientific discovery. Our methodology leverages Large Language Models (LLMs) for context-aware text embeddings that convert CDEs into dense vectors capturing semant…

The Curse of Dimensionality: De-identification Challenges in the Sharing of Highly Dimensional Datasets
https://fpf.org/blog/the-curse-of-dimensionality-de-identification-challenges-in-the-sharing-of-highl…

This https://arxiv.org/abs/2505.18458 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDB_…

A Survey of LLM $\times$ DATA
The integration of large language model (LLM) and data management (DATA) is rapidly redefining both domains. In this survey, we comprehensively review the bidirectional relationships. On the one hand, DATA4LLM, spanning large-scale data processing, storage, and serving, feeds LLMs with high quality, diversity, and timeliness of data required for stages like pre-training, post-training, retrieval-augmented generation, and agentic workflows: (i) Data processing for LLMs includes scalable acquisit…

This https://arxiv.org/abs/2505.19193 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Interpretable Graph Learning Over Sets of Temporally-Sparse Data
Real-world medical data often includes measurements from multiple signals that are collected at irregular and asynchronous time intervals. For example, different types of blood tests can be measured at different times and frequencies, resulting in fragmented and unevenly scattered temporal data. Similar issues of irregular sampling of different attributes occur in other domains, such as monitoring of large systems using event log files or the spread of fake news on social networks. Effectively …

This https://arxiv.org/abs/2505.19641 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

SynLogic: Synthesizing Verifiable Reasoning Data at Scale for Learning Logical Reasoning and Beyond
Recent advances such as OpenAI-o1 and DeepSeek R1 have demonstrated the potential of Reinforcement Learning (RL) to enhance reasoning abilities in Large Language Models (LLMs). While open-source replication efforts have primarily focused on mathematical and coding domains, methods and resources for developing general reasoning capabilities remain underexplored. This gap is partly due to the challenge of collecting diverse and verifiable reasoning data suitable for RL. We hypothesize that logica…

Mitigating Data Poisoning Attacks to Local Differential Privacy
Xiaolin Li, Ninghui Li, Boyang Wang, Wenhai Sun
https://arxiv.org/abs/2506.02156 https://…

Mitigating Data Poisoning Attacks to Local Differential Privacy
The distributed nature of local differential privacy (LDP) invites data poisoning attacks and poses unforeseen threats to the underlying LDP-supported applications. In this paper, we propose a comprehensive mitigation framework for popular frequency estimation, which contains a suite of novel defenses, including malicious user detection, attack pattern recognition, and damaged utility recovery. In addition to existing attacks, we explore new adaptive adversarial activities for our mitigation de…

Google says hackers loosely affiliated with the Com in the US, the UK, and Europe breached 20 companies in the US and Europe to steal Salesforce data (Margi Murphy/Bloomberg)
https://www.bloomberg.com/news/articles/20

Big Data-Driven Fraud Detection Using Machine Learning and Real-Time Stream Processing
Chen Liu, Hengyu Tang, Zhixiao Yang, Ke Zhou, Sangwhan Cha
https://arxiv.org/abs/2506.02008 …

Big Data-Driven Fraud Detection Using Machine Learning and Real-Time Stream Processing
In the age of digital finance, detecting fraudulent transactions and money laundering is critical for financial institutions. This paper presents a scalable and efficient solution using Big Data tools and machine learning models. We utilize realtime data streaming platforms like Apache Kafka and Flink, distributed processing frameworks such as Apache Spark, and cloud storage services AWS S3 and RDS. A synthetic dataset representing real-world Anti-Money Laundering (AML) challenges is employed t…

Dynamically derived morphology from the recurrence patterns of close binary stars using Kepler data
Anisha R. V. Kashyap, D. Pawar, R. Misra, G. Ambika, Sandip V George
https://arxiv.org/abs/2506.04111

Dynamically derived morphology from the recurrence patterns of close binary stars using Kepler data
In this work, we propose a novel method to classify close binary stars, derived from the dynamical structure inherent in their light curves. We apply the technique to light curves of binaries from the revised Kepler Eclipsing binary catalog, selecting close binaries which have the standard morphology parameter, $c$, $\gt 0.5$ corresponding to semi-detached, over-contact and ellipsoidal systems. Using the method of time delay embedding, we recreate the non-linear dynamics underlying the data and…

The Com strikes again.
Google Warns Hackers Stealing Salesforce Data From Companies
https://www.bloomberg.com/news/articles/20

Google warns of cybercriminals targeting Salesforce app to steal data, extort companies https://therecord.media/google-warns-cybercriminals-targeting-salesforce-apps

Integrating Expert Knowledge and Recursive Bayesian Inference: A Framework for Spatial and Spatio-Temporal Data Challenges
Mario Figueira, David Conesa, Antonio L\'opez-Qu\'ilez, H{\aa}vard Rue
https://arxiv.org/abs/2506.00221

Integrating Expert Knowledge and Recursive Bayesian Inference: A Framework for Spatial and Spatio-Temporal Data Challenges
Expert elicitation is a critical approach for addressing data scarcity across various disciplines. But moreover, it can also complement big data analytics by mitigating the limitations of observational data, such as incompleteness and reliability issues, thereby enhancing model estimates through the integration of disparate or conflicting data sources. The paper also outlines various strategies for integrating prior information within the Integrated Nested Laplace Approximation method and propo…

Wall Street giants like Blackstone, KKR, and BlackRock are pouring hundreds of billions into AI data centers, creating concerns of "oversupply" and a bubble (Maureen Farrell/New York Times)
https://www.nytimes.com/2025/06/02/business/ai-da…

Are A.I. Data Centers a Sure Thing or the Next Real Estate Bubble?
Private equity firms like Blackstone are using their clients’ money to buy and build data centers to fuel the artificial intelligence boom.

CityPulse: Real-Time Traffic Data Analytics and Congestion Prediction
Idriss Djiofack Teledjieu, Irzum Shafique
https://arxiv.org/abs/2506.01971 https://…

CityPulse: Real-Time Traffic Data Analytics and Congestion Prediction
CityPulse is a proof-of-concept big data pipeline designed to enable real-time urban mobility analytics using scalable, containerized components -- without reliance on physical sensor infrastructure. The system simulates the ingestion of 11 million traffic-related records representing urban phenomena such as vehicle congestion, GPS coordinates, and weather conditions. Data is ingested through a Dockerized Apache Kafka cluster, coordinated by ZooKeeper, and processed in real time using Apache Sp…

scDataset: Scalable Data Loading for Deep Learning on Large-Scale Single-Cell Omics
Davide D'Ascenzo, Sebastiano Cultrera di Montesano
https://arxiv.org/abs/2506.01883

scDataset: Scalable Data Loading for Deep Learning on Large-Scale Single-Cell Omics
Modern single-cell datasets now comprise hundreds of millions of cells, presenting significant challenges for training deep learning models that require shuffled, memory-efficient data loading. While the AnnData format is the community standard for storing single-cell datasets, existing data loading solutions for AnnData are often inadequate: some require loading all data into memory, others convert to dense formats that increase storage demands, and many are hampered by slow random disk access…

Vintage NPOI: New and Updated Angular Diameters for 145 Stars
Ellyn K. Baines, James H. Clark III, Bradley I. Kingsley, Henrique R. Schmitt, Jordan M. Stone
https://arxiv.org/abs/2506.02912

Vintage NPOI: New and Updated Angular Diameters for 145 Stars
We present new or updated angular diameters, physical radii, and effective temperatures for 145 stars from the Navy Precision Optical Interferometer data archive. We used data from 1996 to late-2021, and we describe the differences between early and late data, which hinge upon an update of the beam combiner in 2002. We came across several sub-categories of stars of interest: 13 of our stars are promising targets for the Habitable World Observatory and therefore require as much study as possible…

D-Rex: Heterogeneity-Aware Reliability Framework and Adaptive Algorithms for Distributed Storage
Maxime Gonthier (University of Chicago, Argonne National Laboratory), Dante D. Sanchez-Gallegos (Universidad Carlos III de Madrid), Haochen Pan (University of Chicago), Bogdan Nicolae (Argonne National Laboratory), Sicheng Zhou (Southern University of Science and Technology), Hai Duc Nguyen (University of Chicago, Argonne National Laboratory), Valerie Hayot-Sasson (University of Chicago, Ar…

D-Rex: Heterogeneity-Aware Reliability Framework and Adaptive Algorithms for Distributed Storage
The exponential growth of data necessitates distributed storage models, such as peer-to-peer systems and data federations. While distributed storage can reduce costs and increase reliability, the heterogeneity in storage capacity, I/O performance, and failure rates of storage resources makes their efficient use a challenge. Further, node failures are common and can lead to data unavailability and even data loss. Erasure coding is a common resiliency strategy implemented in storage systems to mi…

Germany's data protection commission fines Vodafone a record €45M due to data privacy violations linked to malicious behavior by third-party sales agents (Karin Matussek/Bloomberg)
https://www.bloomberg.com/news/articles/20

This https://arxiv.org/abs/2505.23470 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Refining Labeling Functions with Limited Labeled Data
Programmatic weak supervision (PWS) significantly reduces human effort for labeling data by combining the outputs of user-provided labeling functions (LFs) on unlabeled datapoints. However, the quality of the generated labels depends directly on the accuracy of the LFs. In this work, we study the problem of fixing LFs based on a small set of labeled examples. Towards this goal, we develop novel techniques for repairing a set of LFs by minimally changing their results on the labeled examples suc…

Tel Aviv-based Speedata, which is designing analytics processing units for big data workloads, raised a $44M Series B, aims to showcase its first APU this month (Kate Park/TechCrunch)
https://techcrunch.com/2025/06/03/speedata…

Speedata, a chip startup competing with Nvidia, raises a $44M Series B | TechCrunch
Speedata, a Tel Aviv-based startup developing an analytics processing unit (APU) designed to accelerate big data analytic and AI workloads, has raised a Founded by six researcher, Speedata wants to build the first-ever purpose-build chip designed specifically for big data workloads.

Investigating Timing-Based Information Leakage in Data Flow-Driven Real-Time Systems
Mohammad Fakhruddin Babar, Zain A. H. Hammadeh, Mohammad Hamad, Monowar Hasan
https://arxiv.org/abs/2506.01991

Investigating Timing-Based Information Leakage in Data Flow-Driven Real-Time Systems
Leaking information about the execution behavior of critical real-time tasks may lead to serious consequences, including violations of temporal constraints and even severe failures. We study information leakage for a special class of real-time tasks that have two execution modes, namely, typical execution (which invokes the majority of times) and critical execution (to tackle exceptional conditions). The data flow-driven applications inherit such a multimode execution model. In this paper, we i…

This https://arxiv.org/abs/2505.21938 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Practical Adversarial Attacks on Stochastic Bandits via Fake Data Injection
Adversarial attacks on stochastic bandits have traditionally relied on some unrealistic assumptions, such as per-round reward manipulation and unbounded perturbations, limiting their relevance to real-world systems. We propose a more practical threat model, Fake Data Injection, which reflects realistic adversarial constraints: the attacker can inject only a limited number of bounded fake feedback samples into the learner's history, simulating legitimate interactions. We design efficient attack …

This https://arxiv.org/abs/2505.24603 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches
Gaussian sketching, which consists of pre-multiplying the data with a random Gaussian matrix, is a widely used technique for multiple problems in data science and machine learning, with applications spanning computationally efficient optimization, coded computing, and federated learning. This operation also provides differential privacy guarantees due to its inherent randomness. In this work, we revisit this operation through the lens of Renyi Differential Privacy (RDP), providing a refined pri…

Brazil is piloting dWallet, a digital wallet program that allows users to monetize their data, the first nationwide initiative of its kind in the world (Gabriel Daros/Rest of World)
https://restofworld.org/2025/brazil-dwallet-user-data-pilot/

In a world first, Brazilians will soon be able to sell their digital data
Brazil is piloting dWallet, that lets citizens earn money from their data, but experts warn it could deepen inequality.

SubMIT: A Physics Analysis Facility at MIT
Josh Bendavid (Massachusetts Institute of Technology, CERN), Mariarosaria D'Alfonso (Massachusetts Institute of Technology), Jan Eysermans (Massachusetts Institute of Technology), Chad Freer (Massachusetts Institute of Technology), Maxim Goncharov (Massachusetts Institute of Technology), Matthew Heine (Massachusetts Institute of Technology), Luca Lavezzo (Massachusetts Institute of Technology), Marianne Moore (Massachusetts Institute of Te…

SubMIT: A Physics Analysis Facility at MIT
The recently completed SubMIT platform is a small set of servers that provide interactive access to substantial data samples at high speeds, enabling sophisticated data analyses with very fast turnaround times. Additionally, it seamlessly integrates massive processing resources for large-scale tasks by connecting to a set of powerful batch processing systems. It serves as an ideal prototype for an Analysis Facility tailored to meet the demanding data and computational requirements anticipated d…

This https://arxiv.org/abs/2505.17226 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Secure and Private Federated Learning: Achieving Adversarial Resilience through Robust Aggregation
Federated Learning (FL) enables collaborative machine learning across decentralized data sources without sharing raw data. It offers a promising approach to privacy-preserving AI. However, FL remains vulnerable to adversarial threats from malicious participants, referred to as Byzantine clients, who can send misleading updates to corrupt the global model. Traditional aggregation methods, such as simple averaging, are not robust to such attacks. More resilient approaches, like the Krum algorithm…

23andMe plans to hold a new auction for its DNA data, opening with a $305M bid led by Anne Wojcicki, after the ex-CEO challenged Regeneron's winning bid in May (Steven Church/Bloomberg)
https://www.bloomberg.com/news/articles/20

This https://arxiv.org/abs/2505.11197 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Modeling Cell Dynamics and Interactions with Unbalanced Mean Field Schrödinger Bridge
Modeling the dynamics from sparsely time-resolved snapshot data is crucial for understanding complex cellular processes and behavior. Existing methods leverage optimal transport, Schrödinger bridge theory, or their variants to simultaneously infer stochastic, unbalanced dynamics from snapshot data. However, these approaches remain limited in their ability to account for cell-cell interactions. This integration is essential in real-world scenarios since intercellular communications are fundamen…