Tootfinder

Twenty-seven states and DC sue 23andMe to oppose the sale of DNA data from its customers without their direct consent (Rylee Kirk/New York Times)
https://www.nytimes.com/2025/06/10/business/23andme-data-lawsuit.html

23andMe Customers Did Not Expect Their DNA Data Would Be Sold, Lawsuit Claims
The genetic-testing company, which collected DNA data from users, is for sale in bankruptcy court. Now, 27 states and the District of Columbia oppose selling the data without express consent.

Dirty Data in the Newsroom: Comparing Data Preparation in Journalism and Data Science
Stephen Kasica, Charles Berret, Tamara Munzner
https://arxiv.org/abs/2507.07238

Dirty Data in the Newsroom: Comparing Data Preparation in Journalism and Data Science
The work involved in gathering, wrangling, cleaning, and otherwise preparing data for analysis is often the most time consuming and tedious aspect of data work. Although many studies describe data preparation within the context of data science workflows, there has been little research on data preparation in data journalism. We address this gap with a hybrid form of thematic analysis that combines deductive codes derived from existing accounts of data science workflows and inductive codes arisin…

Web 3.0 Requires Data Integrity
New integrity-focused standards are necessary to enable the trusted AI services of tomorrow.
🔐 https://cacm.acm.org/opinion/web-3-0-requires-data-integrity/

{nplyr} has helper functions to work on nested dataframes: #rstats #datascience

Wow. https://themarkup.org/pixel-hunt/2025/04/28/how-california-sent-residents-personal-health-data-to-linkedin My opinion of LinkedIn was already vanishingly low (one way to ensure I won't read something is to po…

https://www.theguardian.com/society/2025/jun/11/public-health-bodies-urged-launch-period-tracking-apps-protect-data
Public health bodies urged to launch period tracking apps to protect data

Understanding and Improving Data Repurposing
J. Parsons, R. Lukyanenko, B. Greenwood, C. Cooper
https://arxiv.org/abs/2506.09073 https://

Understanding and Improving Data Repurposing
We live in an age of unprecedented opportunities to use existing data for tasks not anticipated when those data were collected, resulting in widespread data repurposing. This commentary defines and maps the scope of data repurposing to highlight its importance for organizations and society and the need to study data repurposing as a frontier of data management. We explain how repurposing differs from original data use and data reuse and then develop a framework for data repurposing consisting o…

The Duty Comes From the Data: Rethinking Platform Liability in the Age of Algorithmic Harm
https://musictechpolicy.com/2025/07/05

The Duty Comes From the Data: Rethinking Platform Liability in the Age of Algorithmic Harm
Platforms like TikTok and YouTube claim immunity under Section 230, but when they exploit user data to push harmful content, they cross into tort liability. It’s time to hold platforms accountable …

NFL, Genius Sports extend, expand data deal https://www.espn.com/nfl/story/_/id/45493514/nfl-extends-expands-exclusive-data-deal-genius-sports

From @… for @… :
https://www.

Airlines Don't Want You to Know They Sold Your Flight Data to DHS
A contract obtained by 404 Media shows that an airline-owned data broker forbids the feds from revealing it sold them detailed passenger data.

Analysing semantic data storage in Distributed Ledger Technologies for Data Spaces
Juan Cano-Benito, Andrea Cimmino, Sven Hertling, Heiko Paulheim, Ra\'ul Garc\'ia-Castro
https://arxiv.org/abs/2507.07116

Analysing semantic data storage in Distributed Ledger Technologies for Data Spaces
Data spaces are emerging as decentralised infrastructures that enable sovereign, secure, and trustworthy data exchange among multiple participants. To achieve semantic interoperability within these environments, the use of semantic web technologies and knowledge graphs has been proposed. Although distributed ledger technologies (DLT) fit as the underlying infrastructure for data spaces, there remains a significant gap in terms of the efficient storage of semantic data on these platforms. This p…

President Trump's spending bill could limit local control over zoning and environmental regulations for AI data centers, worrying state lawmakers (Molly Taft/Wired)
https://www.wired.com/story/a-political-battle-is-brewing-over-data-centers/…

A Political Battle Is Brewing Over Data Centers
An AI-related provision in the “Big Beautiful Bill” could restrict state-level legislation of energy-hungry data centers—and is raising bipartisan objections across the US.

Just scrolling through the "Tree Preservation Orders" MHCLG has published geo-data for.
https://www.planning.data.gov.uk/dataset/tree

arxiv_citation: arXiv citation networks (1993-2003)
Citations among papers posted on arxiv.org under the hep-ph and hep-th categories, between 1993 and 2003. This time begins a few months after axiv was launched. 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.) These data were originally released as part of the 2003 KDD Cup.
This network has 27770 nodes and 352807 edges.
Tags: Informational,…

Airlines Don't Want You to Know They Sold Your Flight Data to DHS https://www.404media.co/airlines-dont-want-you-to-know-they-sold-your-flight-data-to-dhs/

Very excited about this! Code to access GRIN will help lots of Google Books partners, and the example might open other doors, as well as the obvious benefits of access to data!
'Institutional Books 1.0: A 242B token dataset from Harvard Library's collections, refined for accuracy and usability' https://arxiv.org/abs/2506…

Institutional Books 1.0: A 242B token dataset from Harvard Library's collections, refined for accuracy and usability
Large language models (LLMs) use data to learn about the world in order to produce meaningful correlations and predictions. As such, the nature, scale, quality, and diversity of the datasets used to train these models, or to support their work at inference time, have a direct impact on their quality. The rapid development and adoption of LLMs of varying quality has brought into focus the scarcity of publicly available, high-quality training data and revealed an urgent need to ground the steward…

It's almost like the only way to prevent your entire life from being used against you is for someone to pass a law...
https://www.404media.co/airlines-dont-want-you-to-know-they-sold-your-flight-data-to-dhs/

Airlines Don't Want You to Know They Sold Your Flight Data to DHS
A contract obtained by 404 Media shows that an airline-owned data broker forbids the feds from revealing it sold them detailed passenger data.

CFMI: Flow Matching for Missing Data Imputation
Vaidotas Simkus, Michael U. Gutmann
https://arxiv.org/abs/2506.09258 https://arxiv.or…

CFMI: Flow Matching for Missing Data Imputation
We introduce conditional flow matching for imputation (CFMI), a new general-purpose method to impute missing data. The method combines continuous normalising flows, flow-matching, and shared conditional modelling to deal with intractabilities of traditional multiple imputation. Our comparison with nine classical and state-of-the-art imputation methods on 24 small to moderate-dimensional tabular data sets shows that CFMI matches or outperforms both traditional and modern techniques across a wide…

RADAR: Benchmarking Language Models on Imperfect Tabular Data
Ken Gu, Zhihan Zhang, Kate Lin, Yuwei Zhang, Akshay Paruchuri, Hong Yu, Mehran Kazemi, Kumar Ayush, A. Ali Heydari, Maxwell A. Xu, Girish Narayanswamy, Yun Liu, Ming-Zher Poh, Yuzhe Yang, Mark Malhotra, Shwetak Patel, Hamid Palangi, Xuhai Xu, Daniel McDuff, Tim Althoff, Xin Liu
https://

RADAR: Benchmarking Language Models on Imperfect Tabular Data
Language models (LMs) are increasingly being deployed to perform autonomous data analyses. However, their data awareness -- the ability to recognize, reason over, and appropriately handle data artifacts such as missing values, outliers, and logical inconsistencies -- remains underexplored. These artifacts are especially common in real-world tabular data and, if mishandled, can significantly compromise the validity of analytical conclusions. To address this gap, we present RADAR, a benchmark for…

Trump's DOJ makes its most sweeping demand for election data yet (NPR)
https://www.npr.org/2025/06/11/nx-s1-5426097/trump-justice-department-voter-data-colorado
http://www.memeorandum.com/250611/p142#a250611p142

States sue to block 23andMe from auctioning genetic data in bankruptcy plan | Courthouse News Service
https://www.courthousenews.com/states-sue-to-block-23andme-from-auctioning-genetic-data-in-bankruptcy-plan/

Check out today's Metacurity for a concise round-up of the critical infosec developments you should know, including
--Cambridge researchers warn that private companies are harvesting period tracker data
--United Natural Foods says system restoration likely by June 15,
--Gabbard wants feds to use private sector for intel tech needs,
--States sue to stop sale of 23andMe DNA data,
--Microsoft issues at least 67 patches,
--Microsoft fixes zero day exploited…

Cambridge researchers warn that private companies are harvesting period tracker data
United Natural Foods expects system restoration by 6/15, Gabbard wants feds to use private sector for intel tech needs, States sue to stop sale of 23andMe DNA data, Microsoft issues at least 67 patches, Microsoft fixes zero day exploited by Stealth Falcon, so much more

Calculating water/energy usage for "AI" per token is a bit problematic: A data center has a massive base load even if nobody uses it just by sheer existence. And since we have no actual data for any of the popular platforms all numbers floating around are problematic and not very useful.
Like how much power does one of those servers NVIDIA cards really save if its utilization is only 50%? And are the overhead costs actually counted?

Data-Driven Nonlinear Regulation: Gaussian Process Learning
Telema Harry, Martin Guay, Shimin Wang, Richard D. Braatz
https://arxiv.org/abs/2506.09273 http…

Data-Driven Nonlinear Regulation: Gaussian Process Learning
This article addresses the output regulation problem for a class of nonlinear systems using a data-driven approach. An output feedback controller is proposed that integrates a traditional control component with a data-driven learning algorithm based on Gaussian Process (GP) regression to learn the nonlinear internal model. Specifically, a data-driven technique is employed to directly approximate the unknown internal model steady-state map from observed input-output data online. Our method does …

Error-Guided Pose Augmentation: Enhancing Rehabilitation Exercise Assessment through Targeted Data Generation
Omar Sherif, Ali Hamdi
https://arxiv.org/abs/2506.09833

Error-Guided Pose Augmentation: Enhancing Rehabilitation Exercise Assessment through Targeted Data Generation
Effective rehabilitation assessment is essential for monitoring patient progress, particularly in home-based settings. Existing systems often face challenges such as data imbalance and difficulty detecting subtle movement errors. This paper introduces Error-Guided Pose Augmentation (EGPA), a method that generates synthetic skeleton data by simulating clinically relevant movement mistakes. Unlike standard augmentation techniques, EGPA targets biomechanical errors observed in rehabilitation. Comb…

Consent for Processing Personal Data in the Age of AI: Key Updates Across Asia-Pacific
https://fpf.org/blog/consent-for-processing-personal-data-in-the-age-of-ai-key-updates-across-asia-pacific/

こいつ…動くぞ！
（ぞーぺんのiOS版を作る、技術検証のやつです。まだ長いよ！）
QT: https://fedibird.com/@takke/114669428324725545

Lightweight Electronic Signatures and Reliable Access Control Included in Sensor Networks to Prevent Cyber Attacks from Modifying Patient Data
Mishall Al-Zubaidie
https://arxiv.org/abs/2506.08828

Lightweight Electronic Signatures and Reliable Access Control Included in Sensor Networks to Prevent Cyber Attacks from Modifying Patient Data
Digital terrorism is a major cause of securing patient/healthcare providers data and information. Sensitive topics that may have an impact on a patient's health or even national security include patient health records and information on healthcare providers. Health databases and data sets have been continually breached by many, regular assaults, as well as local and remote servers equipped with wireless sensor networks (WSNs) in diverse locations. The problem was addressed by some contemporary …

Gaussian copula correlation network analysis with application to multi-omics data
Ekaterina Tomilina (MaIAGE, GABI), Florence Jaffr\'ezic (GABI), Gildas Mazo (MaIAGE)
https://arxiv.org/abs/2506.08586

Gaussian copula correlation network analysis with application to multi-omics data
Reconstructing gene regulatory networks from large-scale heterogeneous data is a key challenge in biology. In multi-omics data analysis, networks based on pairwise statistical association measures remain popular, as they are easy to build and understand. In the presence of mixed-type (discrete and continuous) data, however, the choice of good association measures remains an important issue. We propose here a novel approach based on the Gaussian copula, the parameters of which represent the link…

A five-month investigation found that data centers in Mexico, Chile, South Africa, the Netherlands, and the U.S. overhyped their economic benefits and downplayed environmental damage, especially their water use, even in drought zones.
https://www.themaybe.org/research/data-cen

Where Cloud Meets Cement

Calibrated Lanthanide Atomic Data for Kilonova Radiative Transfer. I. Atomic Structure and Opacities
Andreas Fl\"ors, Ricardo Ferreira da Silva, Jos\'e P. Marques, Jorge M. Sampaio, Gabriel Mart\'inez-Pinedo
https://arxiv.org/abs/2507.07785

Calibrated Lanthanide Atomic Data for Kilonova Radiative Transfer. I. Atomic Structure and Opacities
The early spectra of the kilonova (KN) AT2017gfo following the binary neutron star merger GW170817 exhibit numerous features shaped by r-process nucleosynthesis products. Although a few species were tentatively detected, no third-peak elements were unambiguously identified, as the amount of atomic data required for radiative transfer modeling is immense. Although comprehensive atomic data, including atomic opacities, is now available for many elements, wavelength-calibrated data remains limited…

Linking Data Citation to Repository Visibility: An Empirical Study
Fakhri Momeni, Janete Saldanha Bach, Brigitte Mathiak, Peter Mutschke
https://arxiv.org/abs/2506.09530

Linking Data Citation to Repository Visibility: An Empirical Study
In today's data-driven research landscape, dataset visibility and accessibility play a crucial role in advancing scientific knowledge. At the same time, data citation is essential for maintaining academic integrity, acknowledging contributions, validating research outcomes, and fostering scientific reproducibility. As a critical link, it connects scholarly publications with the datasets that drive scientific progress. This study investigates whether repository visibility influences data citatio…

Data Exfiltration in plain sight.
“Internet dead zones”, of course!
NOT!
https://www.thedailybeast.com/elon-musks-doge-goons-surreptitiously-transmitted-reams-of-white-house-data/

Real-Time Network Traffic Forecasting with Missing Data: A Generative Model Approach
Lei Deng, Wenhan Xu, Jingwei Li, Danny H. K. Tsang
https://arxiv.org/abs/2506.09647

Real-Time Network Traffic Forecasting with Missing Data: A Generative Model Approach
Real-time network traffic forecasting is crucial for network management and early resource allocation. Existing network traffic forecasting approaches operate under the assumption that the network traffic data is fully observed. However, in practical scenarios, the collected data are often incomplete due to various human and natural factors. In this paper, we propose a generative model approach for real-time network traffic forecasting with missing data. Firstly, we model the network traffic fo…

RIVM update rioolwaarden en percentage positief.
We lijken in deze 16e golf andermaal op een plateau beland te zijn van ongeveer 280. Echter, wegens Pinksteren loopt de verwerking van de data zo'n 3 dagen achter en de dagwaarden variëren nogal, dus houd daar rekening mee.
Er zitten dan ook maar 2 nieuwe dagen in de data: 4 en 5 juni, met resp. 35% en 25% van de meetstations.
#qp2t…

Exploring non-cold dark matter in a scenario of dynamical dark energy with DESI DR2 data
Tian-Nuo Li, Peng-Ju Wu, Guo-Hong Du, Yan-Hong Yao, Jing-Fei Zhang, Xin Zhang
https://arxiv.org/abs/2507.07798

Exploring non-cold dark matter in a scenario of dynamical dark energy with DESI DR2 data
Recent observations of DESI hint that dark matter (DM) may not be cold but have a non-zero equation of state (EoS) parameter, and that dark energy (DE) may not be a cosmological constant. In this work, we explore the possibility of a non-zero DM EoS parameter within the framework of dynamical DE. We perform analysis by using the latest baryon acoustic oscillation (BAO) data from DESI DR2, the cosmic microwave background (CMB) data from Planck, and the type Ia supernova (SN) data from DESY5 and …

Gradual Metaprogramming
Tianyu Chen, Darshal Shetty, Jeremy G. Siek, Chao-Hong Chen, Weixi Ma, Arnaud Venet, Rocky Liu
https://arxiv.org/abs/2506.09043 htt…

Gradual Metaprogramming
Data engineers increasingly use domain-specific languages (DSLs) to generate the code for data pipelines. Such DSLs are often embedded in Python. Unfortunately, there are challenges in debugging the generation of data pipelines: an error in a Python DSL script is often detected too late, after the execution of the script, and the source code location that triggers the error is hard to pinpoint. In this paper, we focus on the F3 DSL of Meta (Facebook), which is a DSL embedded in Python (so it …

Data-driven Kinematic Modeling in Soft Robots: System Identification and Uncertainty Quantification
Zhanhong Jiang, Dylan Shah, Hsin-Jung Yang, Soumik Sarkar
https://arxiv.org/abs/2507.07370

Data-driven Kinematic Modeling in Soft Robots: System Identification and Uncertainty Quantification
Precise kinematic modeling is critical in calibration and controller design for soft robots, yet remains a challenging issue due to their highly nonlinear and complex behaviors. To tackle the issue, numerous data-driven machine learning approaches have been proposed for modeling nonlinear dynamics. However, these models suffer from prediction uncertainty that can negatively affect modeling accuracy, and uncertainty quantification for kinematic modeling in soft robots is underexplored. In this w…

This https://arxiv.org/abs/2506.06155 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

A Novel Large-scale Crop Dataset and Dual-stream Transformer Method for Fine-grained Hierarchical Crop Classification from Integrated Hyperspectral EnMAP Data and Multispectral Sentinel-2 Time Series
Fine-grained crop classification is crucial for precision agriculture and food security monitoring. It requires simultaneous capture of both phenological dynamics (obtained from multi-temporal satellite data like Sentinel-2) and subtle spectral variations (demanding nanometer-scale spectral resolution from hyperspectral imagery). Research combining these two modalities remains scarce currently due to challenges in hyperspectral data acquisition and crop types annotation costs. To address these …

Stop battling confusing Google Sheets charts when you've got different types of data! 🙅‍♀️ There's a much better way to show everything clearly.
My new video dives deep into Combo Charts, making even wildly different scales (like baby names & wind energy! 🌬️👶 – yes, really!) look perfectly clear with dual axes. It’s all about making your data understandable at a glance.
Check out the full tutorial for the how-to:

Neurosymbolic Feature Extraction for Identifying Forced Labor in Supply Chains
Zili Wang, Frank Montabon, Kristin Yvonne Rozier
https://arxiv.org/abs/2507.07217

Neurosymbolic Feature Extraction for Identifying Forced Labor in Supply Chains
Supply chain networks are complex systems that are challenging to analyze; this problem is exacerbated when there are illicit activities involved in the supply chain, such as counterfeit parts, forced labor, or human trafficking. While machine learning (ML) can find patterns in complex systems like supply chains, traditional ML techniques require large training data sets. However, illicit supply chains are characterized by very sparse data, and the data that is available is often (purposely) co…

High Signal: Data Science | Career | AI
Great Australian Pods Podcast Directory: #GreatAusPods

Hi-d maps: An interactive visualization technique for multi-dimensional categorical data
Radi Muhammad Reza, Benjamin A Watson
https://arxiv.org/abs/2507.07890

Hi-d maps: An interactive visualization technique for multi-dimensional categorical data
In this paper, we present Hi-D maps, a novel method for the visualization of multi-dimensional categorical data. Our work addresses the scarcity of techniques for visualizing a large number of data-dimensions in an effective and space-efficient manner. We have mapped the full data-space onto a 2D regular polygonal region. The polygon is cut hierarchically with lines parallel to a user-controlled, ordered sequence of sides, each representing a dimension. We have used multiple visual cues such as…

Virtru, which offers data security services to clients like the US DOD and Salesforce, raised a $50M Series D led by Iconiq Capital at a $500M valuation (Allie Garfinkle/Fortune)
https://fortune.com/2025/07/11/exclusi

Exclusive: Virtru, data security company, raises $50 million Series D as valuation hits $500 million
Virtru has recently raised a $50 million Series D, led by Iconiq Capital, Fortune has exclusively learned.

Interesting stuff. This touches on a lot of work that was on my todo list. Especially estimating the "interestingness" of data by measuring the maximum compute needed to reach the optimal compression.
AIT is definitely becoming practically relevant.
https://arxiv.org/abs/2507.07995

Single-pass Adaptive Image Tokenization for Minimum Program Search
According to Algorithmic Information Theory (AIT) -- Intelligent representations compress data into the shortest possible program that can reconstruct its content, exhibiting low Kolmogorov Complexity (KC). In contrast, most visual representation learning systems use fixed-length representations for all inputs, ignoring variations in complexity or familiarity. Recent adaptive tokenization methods address this by allocating variable-length representations but typically require test-time search o…

Bardzo udany "film z dnia" vanlifera :) oczywiście nie mój, ale to jeden z autentyczniejszych kanałów, które śledzę i nowa forma POV w tym odcinku https://youtu.be/VTNR2vFPXcI

#vanlife #yt

GW170817 Viable Einstein-Gauss-Bonnet Inflation Compatible with the Atacama Cosmology Telescope Data
S. D. Odintsov, V. K. Oikonomou
https://arxiv.org/abs/2506.08193

GW170817 Viable Einstein-Gauss-Bonnet Inflation Compatible with the Atacama Cosmology Telescope Data
In this work we investigate several Einstein-Gauss-Bonnet models that are compatible with the GW170817 event, the Atacama Cosmology Telescope data and the BICEP/Keck updated Planck constraints on the tensor-to-scalar ratio. We consider two distinct classes of Einstein-Gauss-Bonnet theories, which are equally successful for GW170817-compatible model building and we examine their viability against the Atacama Cosmology Telescope data and the updated Planck constraints on the tensor-to-scalar rati…

arxiv_citation: arXiv citation networks (1993-2003)
Citations among papers posted on arxiv.org under the hep-ph and hep-th categories, between 1993 and 2003. This time begins a few months after axiv was launched. 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.) These data were originally released as part of the 2003 KDD Cup.
This network has 27770 nodes and 352807 edges.
Tags: Informational,…

MBTModelGenerator: A software tool for reverse engineering of Model-based Testing (MBT) models from clickstream data of web applications
Sasidhar Matta, Vahid Garousi
https://arxiv.org/abs/2506.08179

MBTModelGenerator: A software tool for reverse engineering of Model-based Testing (MBT) models from clickstream data of web applications
Automated testing has become a standard practice in software engineering, yet the creation of test models and suites remains labor-intensive. To reduce this effort, we developed an open-source tool that automatically generates Model-Based Testing (MBT) models from clickstream data collected during user interaction with web applications. The tool captures UI events, transforms them into state-transition models, and exports the result in a format compatible with the GraphWalker MBT tool. This ena…

An Introduction to Solving the Least-Squares Problem in Variational Data Assimilation
I. Dau\v{z}ickait\.e, M. A. Freitag, S. G\"urol, A. S. Lawless, A. Ramage, J. A. Scott, J. M. Tabeart
https://arxiv.org/abs/2506.09211

An Introduction to Solving the Least-Squares Problem in Variational Data Assimilation
Variational data assimilation is a technique for combining measured data with dynamical models. It is a key component of Earth system state estimation and is commonly used in weather and ocean forecasting. The approach involves a large-scale generalized nonlinear least-squares problem. Solving the resulting sequence of sparse linear subproblems requires the use of sophisticated numerical linear algebra methods. In practical applications, the computational demands severely limit the number of it…

Incentive Mechanism for Mobile Crowd Sensing with Assumed Bid Cost Reverse Auction
Jowa Yangchin, Ningrinla Marchang
https://arxiv.org/abs/2507.07688 https…

Incentive Mechanism for Mobile Crowd Sensing with Assumed Bid Cost Reverse Auction
Mobile Crowd Sensing (MCS) is the mechanism wherein people can contribute in data collection process using their own mobile devices which have sensing capabilities. Incentives are rewards that individuals get in exchange for data they submit. Reverse Auction Bidding (RAB) is a framework that allows users to place bids for selling the data they collected. Task providers can select users to buy data from by looking at bids. Using the RAB framework, MCS system can be optimized for better user util…

J'ai découvert les noms "Wide data" et "Long data" : #BuisnessIntelligence

Next stop in our NLP timeline is 2013, the introduction of low dimensional dense word vectors - so-called "word embeddings" - based on distributed semantics, as e.g. word2vec by Mikolov et al. from Google, which enabled representation learning on text.
T. Mikolov et al. (2013). Efficient Estimation of Word Representations in Vector Space.
…

Efficient Estimation of Word Representations in Vector Space
We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Fur…

Replaced article(s) found for physics.data-an. https://arxiv.org/list/physics.data-an/new
[1/1]:
- Orthogonal projections of hypercubes
Yoshiaki Horiike, Shin Fujishiro

A Saddle Point Algorithm for Robust Data-Driven Factor Model Problems
Shabnam Khodakaramzadeh, Soroosh Shafiee, Gabriel de Albuquerque Gleizer, Peyman Mohajerin Esfahani
https://arxiv.org/abs/2506.09776

A Saddle Point Algorithm for Robust Data-Driven Factor Model Problems
We study the factor model problem, which aims to uncover low-dimensional structures in high-dimensional datasets. Adopting a robust data-driven approach, we formulate the problem as a saddle-point optimization. Our primary contribution is a general first-order algorithm that solves this reformulation by leveraging a linear minimization oracle (LMO). We further develop semi-closed form solutions (up to a scalar) for three specific LMOs, corresponding to the Frobenius norm, Kullback-Leibler diver…

This https://arxiv.org/abs/2407.00976 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

ODIN: Open Data In Neurophysiology: Advancements, Solutions & Challenges
Across the life sciences, an ongoing effort over the last 50 years has made data and methods more reproducible and transparent. This openness has led to transformative insights and vastly accelerated scientific progress. For example, structural biology and genomics have undertaken systematic collection and publication of protein sequences and structures over the past half-century, and these data have led to scientific breakthroughs that were unthinkable when data collection first began. We beli…

Gradient-Weighted, Data-Driven Normalization for Approximate Border Bases -- Concept and Computation
Hiroshi Kera, Achim Kehrein
https://arxiv.org/abs/2506.09529

Gradient-Weighted, Data-Driven Normalization for Approximate Border Bases -- Concept and Computation
This paper studies the concept and the computation of approximately vanishing ideals of a finite set of data points. By data points, we mean that the points contain some uncertainty, which is a key motivation for the approximate treatment. A careful review of the existing border basis concept for an exact treatment motivates a new adaptation of the border basis concept for an approximate treatment. In the study of approximately vanishing polynomials, the normalization of polynomials plays a vit…

KramaBench: A Benchmark for AI Systems on Data-to-Insight Pipelines over Data Lakes
Eugenie Lai, Gerardo Vitagliano, Ziyu Zhang, Sivaprasad Sudhir, Om Chabra, Anna Zeng, Anton A. Zabreyko, Chenning Li, Ferdi Kossmann, Jialin Ding, Jun Chen, Markos Markakis, Matthew Russo, Weiyang Wang, Ziniu Wu, Michael J. Cafarella, Lei Cao, Samuel Madden, Tim Kraska

KramaBench: A Benchmark for AI Systems on Data-to-Insight Pipelines over Data Lakes
Constructing real-world data-to-insight pipelines often involves data extraction from data lakes, data integration across heterogeneous data sources, and diverse operations from data cleaning to analysis. The design and implementation of data science pipelines require domain knowledge, technical expertise, and even project-specific insights. AI systems have shown remarkable reasoning, coding, and understanding capabilities. However, it remains unclear to what extent these capabilities translate…

arxiv_citation: arXiv citation networks (1993-2003)
Citations among papers posted on arxiv.org under the hep-ph and hep-th categories, between 1993 and 2003. This time begins a few months after axiv was launched. 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.) These data were originally released as part of the 2003 KDD Cup.
This network has 27770 nodes and 352807 edges.
Tags: Informational,…

The Allen Institute for AI launches FlexOlmo, an LLM architecture that lets data owners control and remove their training data from a model even after training (Will Knight/Wired)
https://www.wired.com/story/flexolmo-ai-model-lets-data-owners-take-cont…

A New Kind of AI Model Lets Data Owners Take Control
A novel approach from the Allen Institute for AI enables data to be removed from an artificial intelligence model even after it has already been used for training.

ErrorEraser: Unlearning Data Bias for Improved Continual Learning
Xuemei Cao, Hanlin Gu, Xin Yang, Bingjun Wei, Haoyang Liang, Xiangkun Wang, Tianrui Li
https://arxiv.org/abs/2506.09347

ErrorEraser: Unlearning Data Bias for Improved Continual Learning
Continual Learning (CL) primarily aims to retain knowledge to prevent catastrophic forgetting and transfer knowledge to facilitate learning new tasks. Unlike traditional methods, we propose a novel perspective: CL not only needs to prevent forgetting, but also requires intentional forgetting.This arises from existing CL methods ignoring biases in real-world data, leading the model to learn spurious correlations that transfer and amplify across tasks. From feature extraction and prediction resul…

This https://arxiv.org/abs/2502.07732 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

When Incentives Backfire, Data Stops Being Human
Progress in AI has relied on human-generated data, from annotator marketplaces to the wider Internet. However, the widespread use of large language models now threatens the quality and integrity of human-generated data on these very platforms. We argue that this issue goes beyond the immediate challenge of filtering AI-generated content -- it reveals deeper flaws in how data collection systems are designed. Existing systems often prioritize speed, scale, and efficiency at the cost of intrinsic …

Conditional Unigram Tokenization with Parallel Data
Gianluca Vico, Jind\v{r}inch Libovick\'y
https://arxiv.org/abs/2507.07824 https://

Conditional Unigram Tokenization with Parallel Data
We introduce conditional unigram tokenization, a novel approach that extends unigram tokenization by conditioning target token probabilities on source-language tokens from parallel data. Given a fixed source tokenizer, our method learns a target tokenizer that maximizes cross-lingual semantic alignment. We evaluate our tokenizer on four language pairs across different families and resource levels, examining intrinsic properties and downstream performance on machine translation and language mode…

Towards Provenance-Aware Earth Observation Workflows: the openEO Case Study
H. Omidi, L. Sacco, V. Hutter, G. Irsiegler, M. Claus, M. Schobben, A. Jacob, M. Schramm, S. Fiore
https://arxiv.org/abs/2506.08597

Towards Provenance-Aware Earth Observation Workflows: the openEO Case Study
Capturing the history of operations and activities during a computational workflow is significantly important for Earth Observation (EO). The data provenance helps to collect the metadata that records the lineage of data products, providing information about how data are generated, transferred, manipulated, by whom all these operations are performed and through which processes, parameters, and datasets. This paper presents an approach to improve those aspects, by integrating the data provenance…

Qantas says 5.7 million affected by breach, leaked info not enough to access frequent flyer accounts https://therecord.media/qantas-airline-data-breach-frequent-flyer-numbers

US customs duties top $100 billion for first time in a fiscal year (David Lawder/Reuters)
https://www.reuters.com/business/trumps-tariff-collections-expected-grow-june-us-budget-data-2025-07-11/
http://www.memeorandum.com/250711/p103#a250711p103

Learning event-triggered controllers for linear parameter-varying systems from data
Renjie Ma, Su Zhang, Wenjie Liu, Zhijian Hu, Peng Shi
https://arxiv.org/abs/2506.08366

Learning event-triggered controllers for linear parameter-varying systems from data
Nonlinear dynamical behaviours in engineering applications can be approximated by linear-parameter varying (LPV) representations, but obtaining precise model knowledge to develop a control algorithm is difficult in practice. In this paper, we develop the data-driven control strategies for event-triggered LPV systems with stability verifications. First, we provide the theoretical analysis of $θ$-persistence of excitation for LPV systems, which leads to the feasible data-based representations. T…

»Bitcoin Depot breach exposes data of nearly 27,000 crypto users:
Bitcoin Depot, an operator of Bitcoin ATMs, is notifying customers of a data breach incident that has exposed their sensitive information.«
Who ever believed in digital security with Bitcoin service providers?
🤷‍♂️

This https://arxiv.org/abs/2410.16316 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

A Computational Harmonic Detection Algorithm to Detect Data Leakage through EM Emanation
Unintended electromagnetic emissions, called EM emanations, can be exploited to recover sensitive information, posing security risks. Metal shielding, used by defense organizations to prevent data leakage, is costly and impractical for widespread use. This issue is particularly significant for IoT devices due to their sheer volume and varied deployment environments. Therefore, there is a research need for an automated detection method to monitor facilities and address data leakage promptly. To …

Integrated Galaxy Light from Stacking $10^5$ Random Pointings in the Dark Energy Survey Data
Jenna E. Moore, Seth H. Cohen, Philip Mauskopf, Evan Scannapieco
https://arxiv.org/abs/2506.08162

Integrated Galaxy Light from Stacking $10^5$ Random Pointings in the Dark Energy Survey Data
We present a new technique for measuring the integrated galaxy light (IGL) with stacked image data from the Dark Energy Survey (DES). We extract $1\times1$ arcminute cutouts from nearly 100,000 randomly selected positions in the g, r, i, z, and Y bands from the DES data release 2 (DR2) maps. We generate source catalogs and masks for each cutout and the images are subsequently stacked to generate deep images of the sky both with and without sources. The IGL is then calculated by taking the diffe…

This https://arxiv.org/abs/2506.02791 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

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…

Replaced article(s) found for physics.data-an. https://arxiv.org/list/physics.data-an/new
[1/1]:
- Fuzzy permutation time irreversibility for nonequilibrium analysis of complex system
Wenpo Yao

Salesforce is restricting third-party companies from long-term indexing and storing of Slack messages, which would hamper rival enterprise AI firms like Glean (The Information)
https://www.theinformation.com/articles/salesforce-blocks-ai-rivals…

Salesforce Blocks AI Rivals From Using Slack Data
Slack, an instant-messaging service popular with businesses, recently blocked other software firms from searching or storing Slack messages even if their customers permit them to do so, according to a public disclosure from Slack’s owner, Salesforce. The move, which hasn’t previously been ...

Algorithmic Complexity Attacks on All Learned Cardinality Estimators: A Data-centric Approach
Yingze Li, Xianglong Liu, Dong Wang, Zixuan Wang, Hongzhi Wang, Kaixing Zhang, Yiming Guan
https://arxiv.org/abs/2507.07438

Algorithmic Complexity Attacks on All Learned Cardinality Estimators: A Data-centric Approach
Learned cardinality estimators show promise in query cardinality prediction, yet they universally exhibit fragility to training data drifts, posing risks for real-world deployment. This work is the first to theoretical investigate how minimal data-level drifts can maximally degrade the accuracy of learned estimators. We propose data-centric algorithmic complexity attacks against learned estimators in a black-box setting, proving that finding the optimal attack strategy is NP-Hard. To address th…

(Trying this again, only this time with the right threat group. It's only Monday 🤪)
I had always assumed Salt Typhoon hit Comcast.
https://www.nextgov.com/cybersecurity/2025…

US agencies assessed Chinese telecom hackers likely hit data center and residential internet providers
Data center giant Digital Realty and mass media titan Comcast were documented as likely victims of the Salt Typhoon cyberespionage group, people familiar say, marking a potentially major expansion of the group’s initial telecom hacking campaign discovered last year.

Generalization Error Analysis for Attack-Free and Byzantine-Resilient Decentralized Learning with Data Heterogeneity
Haoxiang Ye, Tao Sun, Qing Ling
https://arxiv.org/abs/2506.09438

Generalization Error Analysis for Attack-Free and Byzantine-Resilient Decentralized Learning with Data Heterogeneity
Decentralized learning, which facilitates joint model training across geographically scattered agents, has gained significant attention in the field of signal and information processing in recent years. While the optimization errors of decentralized learning algorithms have been extensively studied, their generalization errors remain relatively under-explored. As the generalization errors reflect the scalability of trained models on unseen data and are crucial in determining the performance of …

Expediting data extraction using a large language model (LLM) and scoping review protocol: a methodological study within a complex scoping review
James Stewart-Evans, Emma Wilson, Tessa Langley, Andrew Prayle, Angela Hands, Karen Exley, Jo Leonardi-Bee
https://arxiv.org/abs/2507.06623

Expediting data extraction using a large language model (LLM) and scoping review protocol: a methodological study within a complex scoping review
The data extraction stages of reviews are resource-intensive, and researchers may seek to expediate data extraction using online (large language models) LLMs and review protocols. Claude 3.5 Sonnet was used to trial two approaches that used a review protocol to prompt data extraction from 10 evidence sources included in a case study scoping review. A protocol-based approach was also used to review extracted data. Limited performance evaluation was undertaken which found high accuracy for the tw…

citeseer: CiteSeer citations (2014)
Citations among papers indexed by the CiteSeer digital library. 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.
This network has 384413 nodes and 1751463 edges.
Tags: Informational, Citation, Unweighted

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning
Rami Darwish, Mahmoud Abdelsalam, Sajad Khorsandroo, Kaushik Roy
https://arxiv.org/abs/2507.07258

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning
As IoT ecosystems continue to expand across critical sectors, they have become prominent targets for increasingly sophisticated and large-scale malware attacks. The evolving threat landscape, combined with the sensitive nature of IoT-generated data, demands detection frameworks that are both privacy-preserving and resilient to data heterogeneity. Federated Learning (FL) offers a promising solution by enabling decentralized model training without exposing raw data. However, standard FL algorithm…

Check out today's Metacurity for the critical infosec developments you should know, including
--UK's NCA arrested four people for M&S, Co-Op cyberattacks
--Russian hoops player Kasatkin busted in France in connection with ransomware,
--McDonald's employee chatbot was riddled with absurd flaws,
--Hackers stole $40m from GMX protocol,
--Customer data exposed in Bitcoin Depot breach,
--Hackers run scam messages in old Mt. Gox wallets,
--Ni…

UK's NCA arrested four people for M&S, Co-Op cyberattacks
Russian hoops player Kasatkin busted in France in connection with ransomware, McDonald's employee chatbot was riddled with absurd flaws, Hackers stole $40m from GMX protocol, Customer data exposed in Bitcoin Depot breach, Hackers run scam messages in old Mt. Gox wallets, much more

Mycelium: A Transformation-Embedded LSM-Tree
Holly Casaletto, Jeff Lefevre, Aldrin Montana, Peter Alvaro
https://arxiv.org/abs/2506.08923 https://

Mycelium: A Transformation-Embedded LSM-Tree
Compaction is a necessary, but often costly background process in write-optimized data structures like LSM-trees that reorganizes incoming data that is sequentially appended to logs. In this paper, we introduce Transformation-Embedded LSM-trees (TE-LSM), a novel approach that transparently embeds a variety of data transformations into the compaction process. While many others have sought to reduce the high cost of compaction, TE-LSMs leverage the opportunity to embed other useful work to amorti…

»Data Broker – Warum der Datenhandel trotz #DSGVO floriert:
Mit jedem Klick im Netz werden massenweise #Daten generiert. Der #Handel damit ist lukrativ – und ein potenzielles

Data Broker: Warum der Datenhandel trotz DSGVO floriert - t3n – digital pioneers
Mit jedem Klick im Netz werden massenweise Daten generiert. Der Handel damit ist lukrativ – und ein potenzielles Sicherheitsrisiko. Wir erklären das Geschäftsmodell der Data Broker und was die Rechtsprechung dazu sagt. Sie heißen Adsquare, Zeotap, Equifax oder Liveramp und wissen mehr über einen als man selbst. Data Broker vermengen Daten ihrer Firmenkunden mit durch […]

This https://arxiv.org/abs/2506.04929 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

ConECT Dataset: Overcoming Data Scarcity in Context-Aware E-Commerce MT
Neural Machine Translation (NMT) has improved translation by using Transformer-based models, but it still struggles with word ambiguity and context. This problem is especially important in domain-specific applications, which often have problems with unclear sentences or poor data quality. Our research explores how adding information to models can improve translations in the context of e-commerce data. To this end we create ConECT -- a new Czech-to-Polish e-commerce product translation dataset c…

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

Universal Embeddings of Tabular Data
Astrid Franz, Frederik Hoppe, Marianne Michaelis, Udo G\"obel
https://arxiv.org/abs/2507.05904 https://

Universal Embeddings of Tabular Data
Tabular data in relational databases represents a significant portion of industrial data. Hence, analyzing and interpreting tabular data is of utmost importance. Application tasks on tabular data are manifold and are often not specified when setting up an industrial database. To address this, we present a novel framework for generating universal, i.e., task-independent embeddings of tabular data for performing downstream tasks without predefined targets. Our method transforms tabular data into …

Israeli data security startup Cyera raised $540M led by Georgian, Greenoaks, and Lightspeed at a $6B valuation, up from $3B in November 2024 after raising $300M (Steven Scheer/Reuters)
https://www.reuters.com/world/middle-east/

Never a let-up in cybersecurity developments, so don't miss today's Metacurity for the most critical infosec developments you should know, including
--US grocery distributor United Natural Foods is the latest retail-related cyber victim
--M&S reopens website to shoppers,
--Google account phone numbers could have been brute-forced,
--TX and IL warn of breach-related data exposure,
--NHS blood supply still short a year after ransomware attack,
--C…

US grocery distributor United Natural Foods is the latest retail-related cyber victim
M&S reopens website to shoppers, Google account phone numbers could have been brute-forced, TX and IL warn of breach-related data exposure, NHS blood supply still short a year after ransomware attack, Comcast and Digital Realty were Salt Typhoon victims, Ofcom probes 4chan safety, much more

PrivTru: A Privacy-by-Design Data Trustee Minimizing Information Leakage
Lukas Gehring, Florian Tschorsch
https://arxiv.org/abs/2506.06124 https://

PrivTru: A Privacy-by-Design Data Trustee Minimizing Information Leakage
Data trustees serve as intermediaries that facilitate secure data sharing between independent parties. This paper offers a technical perspective on Data trustees, guided by privacy-by-design principles. We introduce PrivTru, an instantiation of a data trustee that provably achieves optimal privacy properties. Therefore, PrivTru calculates the minimal amount of information the data trustee needs to request from data sources to respond to a given query. Our analysis shows that PrivTru minimizes i…

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 …

LGND, which uses vector embeddings to analyze geospatial data and has an enterprise app to query it, raised a $9M seed led by Javelin Venture Partners (Tim De Chant/TechCrunch)
https://techcrunch.com/2025/07/10/lgnd-wants-to-make-chatgpt-for-the-earth/

Exclusive: LGND wants to make ChatGPT for the Earth | TechCrunch
LGND has raised $9 million to convert geographic data into vector embeddings to power AI models that understand the Earth.

This https://arxiv.org/abs/2506.00759 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Understanding and Mitigating Cross-lingual Privacy Leakage via Language-specific and Universal Privacy Neurons
Large Language Models (LLMs) trained on massive data capture rich information embedded in the training data. However, this also introduces the risk of privacy leakage, particularly involving personally identifiable information (PII). Although previous studies have shown that this risk can be mitigated through methods such as privacy neurons, they all assume that both the (sensitive) training data and user queries are in English. We show that they cannot defend against the privacy leakage in cro…

Learning Obfuscations Of LLM Embedding Sequences: Stained Glass Transform
Jay Roberts, Kyle Mylonakis, Sidhartha Roy, Kaan Kale
https://arxiv.org/abs/2506.09452

Learning Obfuscations Of LLM Embedding Sequences: Stained Glass Transform
The high cost of ownership of AI compute infrastructure and challenges of robust serving of large language models (LLMs) has led to a surge in managed Model-as-a-service deployments. Even when enterprises choose on-premises deployments, the compute infrastructure is typically shared across many teams in order to maximize the return on investment. In both scenarios the deployed models operate only on plaintext data, and so enterprise data owners must allow their data to appear in plaintext on a …

Will Cathcart says WhatsApp plans to support Apple in its legal case against the UK Home Office over weakening encryption, which may "set a dangerous precedent" (Zoe Kleinman/BBC)
https://www.bbc.com/news/articles/cgmjrn42wdwo

WhatsApp backs Apple in its legal row with the UK over user data
The messaging app says the UK government's demands over data access could set a "dangerous precedent."

Sources: JPMorgan Chase told fintech companies it will start charging fees for access to customers' account data, which could drastically reshape the industry (Bloomberg)
https://www.bloomberg.com/news/articles/20

FlexOlmo: Open Language Models for Flexible Data Use
Weijia Shi, Akshita Bhagia, Kevin Farhat, Niklas Muennighoff, Pete Walsh, Jacob Morrison, Dustin Schwenk, Shayne Longpre, Jake Poznanski, Allyson Ettinger, Daogao Liu, Margaret Li, Dirk Groeneveld, Mike Lewis, Wen-tau Yih, Luca Soldaini, Kyle Lo, Noah A. Smith, Luke Zettlemoyer, Pang Wei Koh, Hannaneh Hajishirzi, Ali Farhadi, Sewon Min

FlexOlmo: Open Language Models for Flexible Data Use
We introduce FlexOlmo, a new class of language models (LMs) that supports (1) distributed training without data sharing, where different model parameters are independently trained on closed datasets, and (2) data-flexible inference, where these parameters along with their associated data can be flexibly included or excluded from model inferences with no further training. FlexOlmo employs a mixture-of-experts (MoE) architecture where each expert is trained independently on closed datasets and la…

So are sports gambling and horse-racing authorities the latest vertical to be hit by Scattered Spider? Yesterday, bookmakers Paddy Power and Betfair admitted to a cyberattack, an incident that follows an attack last month on the British Horseracing Authority.

Personal information belonging to customers of Paddy Power and Betfair compromised in data breach

Synthetic Tabular Data: Methods, Attacks and Defenses
Graham Cormode, Samuel Maddock, Enayat Ullah, Shripad Gade
https://arxiv.org/abs/2506.06108 https://

Synthetic Tabular Data: Methods, Attacks and Defenses
Synthetic data is often positioned as a solution to replace sensitive fixed-size datasets with a source of unlimited matching data, freed from privacy concerns. There has been much progress in synthetic data generation over the last decade, leveraging corresponding advances in machine learning and data analytics. In this survey, we cover the key developments and the main concepts in tabular synthetic data generation, including paradigms based on probabilistic graphical models and on deep learni…

sp_colocation: Social co-locations (2018)
Network of colocations between peoople, based on the information on which RFID readers received information from the RFID tags. Namely, we define two individuals to be in co-presence if the same exact set of readers have received signals from both individuals during a 20s time window.
This network has 81 nodes and 150126 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata

Athena: Enhancing Multimodal Reasoning with Data-efficient Process Reward Models
Shuai Wang, Zhenhua Liu, Jiaheng Wei, Xuanwu Yin, Dong Li, Emad Barsoum
https://arxiv.org/abs/2506.09532

Athena: Enhancing Multimodal Reasoning with Data-efficient Process Reward Models
We present Athena-PRM, a multimodal process reward model (PRM) designed to evaluate the reward score for each step in solving complex reasoning problems. Developing high-performance PRMs typically demands significant time and financial investment, primarily due to the necessity for step-level annotations of reasoning steps. Conventional automated labeling methods, such as Monte Carlo estimation, often produce noisy labels and incur substantial computational costs. To efficiently generate high-q…

The data industry is consolidating, with Databricks' $1B Neon purchase, Salesforce's $8B Informatica deal, and more, fueled by the need for quality data for AI (Rebecca Szkutak/TechCrunch)
https://techcrunch.com/2025/07/07/ai-i

AI is forcing the data industry to consolidate — but that's not the whole story | TechCrunch
While AI may be the catalyst behind the recent wave of data company M&A, the market was ripe for consolidation.