Tootfinder

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…

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…

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…

Constraints on cosmology and baryonic feedback with joint analysis of Dark Energy Survey Year 3 lensing data and ACT DR6 thermal Sunyaev-Zel'dovich effect observations
S. Pandey, J. C. Hill, A. Alarcon, O. Alves, A. Amon, D. Anbajagane, F. Andrade-Oliveira, N. Battaglia, E. Baxter, K. Bechtol, M. R. Becker, G. M. Bernstein, J. Blazek, S. L. Bridle, E. Calabrese, H. Camacho, A. Campos, A. Carnero Rosell, M. Carrasco Kind, R. Cawthon, C. Chang, R. Chen, P. Chintalapati, A. Choi, J. C…

Constraints on cosmology and baryonic feedback with joint analysis of Dark Energy Survey Year 3 lensing data and ACT DR6 thermal Sunyaev-Zel'dovich effect observations
We present a joint analysis of weak gravitational lensing (shear) data obtained from the first three years of observations by the Dark Energy Survey and thermal Sunyaev-Zel'dovich (tSZ) effect measurements from a combination of Atacama Cosmology Telescope (ACT) and Planck data. A combined analysis of shear (which traces the projected mass) with the tSZ effect (which traces the projected gas pressure) can jointly probe both the distribution of matter and the thermodynamic state of the gas, accou…

Reduction Techniques for Survival Analysis
Johannes Piller, L\'ea Orsini, Simon Wiegrebe, John Zobolas, Lukas Burk, Sophie Hanna Langbein, Philip Studener, Markus Goeswein, Andreas Bender
https://arxiv.org/abs/2508.05715

Reduction Techniques for Survival Analysis
In this work, we discuss what we refer to as reduction techniques for survival analysis, that is, techniques that "reduce" a survival task to a more common regression or classification task, without ignoring the specifics of survival data. Such techniques particularly facilitate machine learning-based survival analysis, as they allow for applying standard tools from machine and deep learning to many survival tasks without requiring custom learners. We provide an overview of different reduction …

Analysis: Nvidia's $500B US spending plan has accelerated the US AI server ecosystem development, with at least eight suppliers unveiling new investment plans (Nikkei Asia)
https://asia.nikkei.com/Business/Technol…

Trump's first 'Made in the USA' triumph won't be the iPhone
AI data center suppliers flock to the US to serve Nvidia, Microsoft and others

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…

GloBIAS: strengthening the foundations of BioImage Analysis
A. A. Corbat (BioImage Informatics Unit, Science for Life Laboratory and Department of Information Technology, Uppsala University, Sweden), C. G. Walther (German BioImaging, Gesellschaft f\"ur Mikroskopie und Bildanalyse e.V., Konstanz, Germany, University of Vienna, Vienna, Austria), L. R. de la Ballina (Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Montebe…

GloBIAS: strengthening the foundations of BioImage Analysis
There is a global need for BioImage Analysis (BIA) as advances in life sciences increasingly rely on cutting-edge imaging systems that have dramatically expanded the complexity and dimensionality of biological images. Turning these data into scientific discoveries requires people with effective data management skills and knowledge of state-of-the-art image processing and data analysis, in other words, BioImage Analysts. The Global BioImage Analysts' Society (GloBIAS) aims to enhance the profile…

A Comprehensive Analysis of COVID-19 Detection Using Bangladeshi Data and Explainable AI
Shuvashis Sarker
https://arxiv.org/abs/2506.07234 https://

A Comprehensive Analysis of COVID-19 Detection Using Bangladeshi Data and Explainable AI
COVID-19 is a rapidly spreading and highly infectious virus which has triggered a global pandemic, profoundly affecting millions across the world. The pandemic has introduced unprecedented challenges in public health, economic stability, and societal structures, necessitating the implementation of extensive and multifaceted health interventions globally. It had a tremendous impact on Bangladesh by April 2024, with around 29,495 fatalities and more than 2 million confirmed cases. This study focu…

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

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…

This https://arxiv.org/abs/2506.01348 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Distributionally Robust Learning in Survival Analysis
We introduce an innovative approach that incorporates a Distributionally Robust Learning (DRL) approach into Cox regression to enhance the robustness and accuracy of survival predictions. By formulating a DRL framework with a Wasserstein distance-based ambiguity set, we develop a variant Cox model that is less sensitive to assumptions about the underlying data distribution and more resilient to model misspecification and data perturbations. By leveraging Wasserstein duality, we reformulate the …

For all the other passengers and crews of #Dreamliner‎s around the globe, I hope that #AirIndia flight 171 indeed didn't crash due to general issues with the aircraft type. 🧐 #Boeing
“Th…

Air India crash investigation focuses on movement of engine fuel control switches
Data-driven reporting and analysis on the strategy and technology of flying.

From July 1 if a new maintainer isn't found then tcpflow will be archived. From the GitHub page:
> tcpflow is a program that captures data transmitted as part of TCP connections (flows), and stores the data in a way that is convenient for protocol analysis and debugging.
https://github.com/simsong/tc…

Algorithmic Analysis of GTFS-RT vehicle position accuracy
Joshua Wong
https://arxiv.org/abs/2506.06479 https://arxiv.org/pdf/2506.064…

Algorithmic Analysis of GTFS-RT vehicle position accuracy
This paper presents three novel algorithms for calculating geodesic intersections on an ellipsoid. These algorithms are applied in a case study analyzing real-time transit data in California to assess vehicle position drift. The analysis reveals that while certain data anomalies can be corrected, large-scale discrepancies persist. The paper concludes by proposing a set of practical solutions that can be implemented by either data producers or consumers to significantly improve positional accura…

This year at #CCN2025 we will be showcasing our research on the classification of Mental Workload 🥵 Spatial Effects using Riemannian Manifold.
📅 When: Wednesday, August 13, 1:00 – 4:00 pm
📍 Where: CCN 2025 Conference Venue, de Brug & E-Hall
📋 What: Poster B152

• It leverages advanced mathematical techniques to better understand and classify mental workloads, offerin…

Classification of Mental Workload Spatial Effects using Riemannian Manifold | Next-generation neural computations
This study investigates the use of Riemannian geometry to classify mental workload from an EEG dataset collected in an aeronautical context. The analysis, based on EEG data recorded from 16 participants performing a Simon task, aimed to differentiate low and high workload conditions. Using covariance matrices and a Minimum Distance to Mean (MDM) classifier, the results demonstrate spatial effects of mental workload irrespective of the investigated spectral domain. This demonstrates that spatial…

This https://arxiv.org/abs/2410.08911 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Test-driven Software Experimentation with LASSO: an LLM Prompt Benchmarking Example
Empirical software engineering faces a critical gap: the lack of standardized tools for rapid development and execution of Test-Driven Software Experiments (TDSEs) -- that is, experiments that involve the execution of software subjects and the observation and analysis of their "de facto" run-time behavior. In this paper we present a general-purpose analysis platform called LASSO that provides a minimal set of domain-specific languages and data structures to conduct TDSEs. By empowering users wi…

Topological Sequence Analysis of Genomes: Delta Complex approaches
Jian Liu, Li Shen, Dong Chen, Guo-Wei Wei
https://arxiv.org/abs/2507.05452 https://

Topological Sequence Analysis of Genomes: Delta Complex approaches
Algebraic topology has been widely applied to point cloud data to capture geometric shapes and topological structures. However, its application to genome sequence analysis remains rare. In this work, we propose topological sequence analysis (TSA) techniques by constructing $Δ$-complexes and classifying spaces, leading to persistent homology, and persistent path homology on genome sequences. We also develop $Δ$-complex-based persistent Laplacians to facilitate the topological spectral analysis…

New #preprint by Alex Koplenig and me:
"Statistical errors undermine claims about the evolution of polysynthetic languages". (#PNAS (#Linguistics

Thermodynamic Analysis of Transverse Momentum Spectra in Pb-Pb Collisions at 2.76 TeV: Centrality Dependence of Temperature, Freezeout Parameters and Non-Extensitivity
M. Waqas, Hassan Ali Khan, Wolfgang Bietenholz, Muhammad Ajaz, Jihane Ben Slimane, Haifa I. Alrebdi, A. Haj Ismail
https://arxiv.org/abs/2507.07369

Thermodynamic Analysis of Transverse Momentum Spectra in Pb-Pb Collisions at 2.76 TeV: Centrality Dependence of Temperature, Freezeout Parameters and Non-Extensitivity
We study properties of Pb-Pb collisions at 2.76 TeV in mid-rapidity, $|y|<0.5$, based on data by the ALICE Collaboration. In particular, we examine the transverse momentum ($p_T$) spectra of positively charged (identified) hadrons, $π^+$, $K^+$ and $p$, generated in various centrality intervals. We perform individual fits using the thermodynamically consistent Tsallis distribution to extract the following quantities: the non-extensitivity parameter, $q$, the effective temperature, $T…

A Simple PTAS for Weighted $k$-means and Sensor Coverage
Akash Pareek, Supratim Shit
https://arxiv.org/abs/2508.06460 https://arxiv.org/pdf/2508.06460

A Simple PTAS for Weighted $k$-means and Sensor Coverage
Clustering is a fundamental technique in data analysis, with the $k$-means being one of the widely studied objectives due to its simplicity and broad applicability. In many practical scenarios, data points come with associated weights that reflect their importance, frequency, or confidence. Given a weighted point set $P \subset R^d$, where each point $p \in P$ has a positive weight $w_p$, the goal is to compute a set of $k$ centers $C = \{ c_1, c_2, \ldots, c_k \} \subset R^d$ that minimizes th…

Operator theoretic measure of causality in linear dynamical systems
Ankit Srivastava, Louis Cattafesta, Scott Dawson
https://arxiv.org/abs/2506.08118 https…

Operator theoretic measure of causality in linear dynamical systems
This paper introduces Linear Operator Causality Analysis (LOCA), a physics-based framework for analyzing causality in linear dynamical systems, with a focus on fluid mechanics. Unlike data-driven statistical methods like Granger causality, LOCA uses the matrix exponential from the system's governing equations to provide a more rigorous and interpretable measure of causal interactions. Our framework reveals that, under certain conditions, common data-driven measures effectively approximate the s…

Analysis: Mail Online is among major news brands most-impacted by Google's AI Overviews, with 68.8% of the top 100 keyword searches resulting in no site visits (Charlotte Tobitt/Press Gazette)
https://press…

How Google AI Overviews is fuelling zero-click searches for top publishers
New Similarweb data shows likes of Mail Online and People see high proportion of AI Overviews for their key search terms.

Brazil’s ANPD Preliminary Study on Generative AI highlights the dual nature of data protection law: balancing rights with technological innovation
https://fpf.org/blog/brazils-anpd-prel

Differential Attention for Multimodal Crisis Event Analysis
Nusrat Munia, Junfeng Zhu, Olfa Nasraoui, Abdullah-Al-Zubaer Imran
https://arxiv.org/abs/2507.05165

Differential Attention for Multimodal Crisis Event Analysis
Social networks can be a valuable source of information during crisis events. In particular, users can post a stream of multimodal data that can be critical for real-time humanitarian response. However, effectively extracting meaningful information from this large and noisy data stream and effectively integrating heterogeneous data remains a formidable challenge. In this work, we explore vision language models (VLMs) and advanced fusion strategies to enhance the classification of crisis data in…

Investigating non-Keplerian motion in flare events with astrometric data
Fengting Xie, Qing-Hua Zhu, Xin Li
https://arxiv.org/abs/2507.07411 https://

Investigating non-Keplerian motion in flare events with astrometric data
The GRAVITY interferometer has achieved microarcsecond precision in near-infrared interferometry, enabling tracking of flare centroid motion in the strong gravitational field near the Sgr A*. It might be promising to serve as a unique laboratory for exploring the accretion matter near black holes or testing Einstein's gravity. Recent studies debated whether there is a non-Keplerian motion of the flares in the GRAVITY dataset. It motivates us to present a comprehensive analysis based on error es…

A Unified Ontology for Scalable Knowledge Graph-Driven Operational Data Analytics in High-Performance Computing Systems
Junaid Ahmed Khan, Andrea Bartolini
https://arxiv.org/abs/2507.06107

A Unified Ontology for Scalable Knowledge Graph-Driven Operational Data Analytics in High-Performance Computing Systems
Modern high-performance computing (HPC) systems generate massive volumes of heterogeneous telemetry data from millions of sensors monitoring compute, memory, power, cooling, and storage subsystems. As HPC infrastructures scale to support increasingly complex workloads-including generative AI-the need for efficient, reliable, and interoperable telemetry analysis becomes critical. Operational Data Analytics (ODA) has emerged to address these demands; however, the reliance on schema-less storage s…

Superposed Parameterised Quantum Circuits
Viktoria Patapovich, Mo Kordzanganeh, Alexey Melnikov
https://arxiv.org/abs/2506.08749 https://

Superposed Parameterised Quantum Circuits
Quantum machine learning has shown promise for high-dimensional data analysis, yet many existing approaches rely on linear unitary operations and shared trainable parameters across outputs. These constraints limit expressivity and scalability relative to the multi-layered, non-linear architectures of classical deep networks. We introduce superposed parameterised quantum circuits to overcome these limitations. By combining flip-flop quantum random-access memory with repeat-until-success protocol…

A pictures says more than 1000 words. How much more can an audio representation of your data tell you? #rstats

sonify: Data Sonification - Turning Data into Sound
Sonification (or audification) is the process of representing data by sounds in the audible range. This package provides the R function sonify() that transforms univariate data, sampled at regular or irregular intervals, into a continuous sound with time-varying frequency. The ups and downs in frequency represent the ups and downs in the data. Sonify provides a substitute for R's plot function to simplify data analysis for the visually impaired.

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 …

The Open Cluster Chemical Abundances and Mapping Survey: VIII. Galactic Chemical Gradient and Azimuthal Analysis from SDSS/MWM DR19
Jonah M. Otto, Peter M Frinchaboy, Natalie R. Myers, James W. Johnson, John Donor, Ahabar Hossain, Szabolcs M\'esz\'aros, Katia Cunha, Binod Bhattarai, Gail Zasowski, Sarah R. Loebman, Alessa I. Wiggins, Adrian M. Price-Whelan, Taylor Spoo, Diogo Souto, Dmitry Bizyaev, Kaike Pan, Andrew K. Saydjari

The Open Cluster Chemical Abundances and Mapping Survey: VIII. Galactic Chemical Gradient and Azimuthal Analysis from SDSS/MWM DR19
The Open Cluster Chemical Abundances and Mapping (OCCAM) survey seeks to curate a large, comprehensive, uniform dataset of open clusters and member stars to constrain key Galactic parameters. This eighth entry from the OCCAM survey, based on the newly released SDSS-V/MWM Data Release 19 (DR19), has established a sample of 164 high quality open clusters that are used to constrain the radial and azimuthal gradients of the Milky Way. The DR19 cluster sample [Fe/H] abundances are roughly consistent…

Mediation Analysis for Sparse and Irregularly Spaced Longitudinal Outcomes with Application to the MrOS Sleep Study
Rui Ren, Haoyi Yang, Qian Xiao, Lingzhou Xue, Yuan Huang
https://arxiv.org/abs/2506.07953

Mediation Analysis for Sparse and Irregularly Spaced Longitudinal Outcomes with Application to the MrOS Sleep Study
Mediation analysis has become a widely used method for identifying the pathways through which an independent variable influences a dependent variable via intermediate mediators. However, limited research addresses the case where mediators are high-dimensional and the outcome is represented by sparse, irregularly spaced longitudinal data. To address these challenges, we propose a mediation analysis approach for scalar exposures, high-dimensional mediators, and sparse longitudinal outcomes. This …

On the computational feasibility of Bayesian end-to-end analysis of LiteBIRD simulations within Cosmoglobe
R. Aurvik, M. Galloway, E. Gjerl{\o}w, U. Fuskeland, A. Basyrov, M. Bortolami, M. Brilenkov, P. Campeti, H. K. Eriksen, L. T. Hergt, D. Herman, M. Monelli, L. Pagano, G. Puglisi, N. Raffuzzi, N. -O. Stutzer, R. M. Sullivan, H. Thommesen, D. J. Watts, I. K. Wehus, D. Adak, E. Allys, A. Anand, J. Aumont, C. Baccigalupi, M. Ballardini, A. J. Banday, R. B. Barreiro, N. Bartolo, S. Bas…

On the computational feasibility of Bayesian end-to-end analysis of LiteBIRD simulations within Cosmoglobe
We assess the computational feasibility of end-to-end Bayesian analysis of the JAXA-led LiteBIRD experiment by analysing simulated time ordered data (TOD) for a subset of detectors through the Cosmoglobe and Commander3 framework. The data volume for the simulated TOD is 1.55 TB, or 470 GB after Huffman compression. From this we estimate a total data volume of 238 TB for the full three year mission, or 70 TB after Huffman compression. We further estimate the running time for one Gibbs sample, fr…

AI-Based Reconstruction from Inherited Personal Data: Analysis, Feasibility, and Prospects
Mark Zilberman
https://arxiv.org/abs/2507.03059 https://

AI-Based Reconstruction from Inherited Personal Data: Analysis, Feasibility, and Prospects
This article explores the feasibility of creating an "electronic copy" of a deceased researcher by training artificial intelligence (AI) on the data stored in their personal computers. By analyzing typical data volumes on inherited researcher computers, including textual files such as articles, emails, and drafts, it is estimated that approximately one million words are available for AI training. This volume is sufficient for fine-tuning advanced pre-trained models like GPT-4 to replicate a res…

Long-term exposure to outdoor air pollution linked to increased risk of dementia
https://www.cam.ac.uk/research/news/long-term-exposure-to-outdoor-air-pollution-linked-to-increased-risk-of-dementia
Air pol…

Long-term exposure to outdoor air pollution linked to increased risk of dementia
An analysis of studies incorporating data from almost 30 million people has highlighted the role that air pollution – including that coming from car exhaust

An Architecture for Privacy-Preserving Telemetry Scheme
Kenneth Odoh
https://arxiv.org/abs/2507.06350 https://arxiv.org/pdf/2507.0635…

An Architecture for Privacy-Preserving Telemetry Scheme
We present a privacy-preserving telemetry aggregation scheme. Our underlying frequency estimation routine works within the framework of differential privacy. The design philosophy follows a client-server architecture. Furthermore, the system uses a local differential privacy scheme where data gets randomized on the client before submitting the request to the resource server. This scheme allows for data analysis on de-identified data by carefully adding noise to prevent re-identification attacks…

An analysis finds Chinese firms aim to buy 115K US-sanctioned Nvidia H100 and H200 chips for 39 data centers across China's western deserts, mostly in Xinjiang (Bloomberg)

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

Customs and Border Protection
is asking companies to pitch tools
for performing deep analysis on the contents of devices seized at the US border.
https://www.wired.com/story/cbp-wants-new-tech-to-search-for-hidden-data-on-seiz…

CBP Wants New Tech to Search for Hidden Data on Seized Phones
Customs and Border Protection is asking companies to pitch tools for performing deep analysis on the contents of devices seized at the US border.

A flexible, GPU-accelerated approach for the joint characterization of LISA instrumental noise and Stochastic Gravitational Wave Backgrounds
Alessandro Santini, Martina Muratore, Jonathan Gair, Olaf Hartwig
https://arxiv.org/abs/2507.06300

A flexible, GPU-accelerated approach for the joint characterization of LISA instrumental noise and Stochastic Gravitational Wave Backgrounds
LISA data analysis represents one of the most challenging tasks ahead for the future of gravitational-wave (GW) astronomy. Characterizing the instrument's noise properties while fitting for all the other detectable sources is a key requirement of any robust inference pipeline. Noise estimation will also play a crucial role in searches and parameter estimation of cosmological and astrophysical stochastic signals. Previous studies have tackled this topic by assuming perfect knowledge of the spect…

This https://arxiv.org/abs/2207.03997 has been replaced.
link: https://scholar.google.com/scholar?q=a

A Tutorial on Event Detection using Social Media Data Analysis: Applications, Challenges, and Open Problems
In recent years, social media has become one of the most popular platforms for communication. These platforms allow users to report real-world incidents that might swiftly and widely circulate throughout the whole social network. A social event is a real-world incident that is documented on social media. Social gatherings could contain vital documentation of crisis scenarios. Monitoring and analyzing this rich content can produce information that is extraordinarily valuable and help people and …

\textit{QuantMCP}: Grounding Large Language Models in Verifiable Financial Reality
Yifan Zeng
https://arxiv.org/abs/2506.06622 https://

\textit{QuantMCP}: Grounding Large Language Models in Verifiable Financial Reality
Large Language Models (LLMs) hold immense promise for revolutionizing financial analysis and decision-making, yet their direct application is often hampered by issues of data hallucination and lack of access to real-time, verifiable financial information. This paper introduces QuantMCP, a novel framework designed to rigorously ground LLMs in financial reality. By leveraging the Model Context Protocol (MCP) for standardized and secure tool invocation, QuantMCP enables LLMs to accurately interfac…

IRAS 22272 5435 (V354 Lac): Multicolor Photometry of a Variable Carbon-rich post-AGB Star and a Dust Formation Episode
N. P. Ikonnikova, V. I. Shenavrin, G. V. Komissarova, M. A. Burlak
https://arxiv.org/abs/2507.06777

IRAS 22272+5435 (V354 Lac): Multicolor Photometry of a Variable Carbon-rich post-AGB Star and a Dust Formation Episode
We present new UBV photometric observations (2009-2024) of the carbon-rich post-AGB star IRAS 22272+5435 (V354 Lac), combined with our data spanning over 30 years (1991-2024). The star shows pulsations with two closely spaced periods, 127 and 132 d, which cause brightness variations of variable amplitude. We also detect low-amplitude oscillations with a 661-day period, of uncertain origin. Color analysis reveals both temperature variations during pulsations and spectral anomalies. For the first…

Speech Quality Assessment Model Based on Mixture of Experts: System-Level Performance Enhancement and Utterance-Level Challenge Analysis
Xintong Hu, Yixuan Chen, Rui Yang, Wenxiang Guo, Changhao Pan
https://arxiv.org/abs/2507.06116

Speech Quality Assessment Model Based on Mixture of Experts: System-Level Performance Enhancement and Utterance-Level Challenge Analysis
Automatic speech quality assessment plays a crucial role in the development of speech synthesis systems, but existing models exhibit significant performance variations across different granularity levels of prediction tasks. This paper proposes an enhanced MOS prediction system based on self-supervised learning speech models, incorporating a Mixture of Experts (MoE) classification head and utilizing synthetic data from multiple commercial generation models for data augmentation. Our method buil…

This https://arxiv.org/abs/2411.06564 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Distributionally Robust Adaptive Beamforming
As a fundamental technique in array signal processing, beamforming plays a crucial role in amplifying signals of interest (SoI) while mitigating interference plus noise (IPN). When uncertainties exist in the signal model or the data size of snapshots is limited, the performance of beamformers significantly degrades. In this article, we comprehensively study the conceptual system, theoretical analysis, and algorithmic design for robust beamforming against uncertainties in the assumed snapshot or…

"Customs and Border Protection is asking companies to pitch tools for performing deep analysis on the contents of devices seized at the US border."
https://www.wired.com/story/cbp-wants-new-tech-to-search-for-hidden-data-on-seized-phones/…

CBP Wants New Tech to Search for Hidden Data on Seized Phones
Customs and Border Protection is asking companies to pitch tools for performing deep analysis on the contents of devices seized at the US border.

RCA Copilot: Transforming Network Data into Actionable Insights via Large Language Models
Alexander Shan, Jasleen Kaur, Rahul Singh, Tarun Banka, Raj Yavatkar, T. Sridhar
https://arxiv.org/abs/2507.03224

RCA Copilot: Transforming Network Data into Actionable Insights via Large Language Models
Ensuring the reliability and availability of complex networked services demands effective root cause analysis (RCA) across cloud environments, data centers, and on-premises networks. Traditional RCA methods, which involve manual inspection of data sources such as logs and telemetry data, are often time-consuming and challenging for on-call engineers. While statistical inference methods have been employed to estimate the causality of network events, these approaches alone are similarly challengi…

Towards Serverless Processing of Spatiotemporal Big Data Queries
Diana Baumann, Tim C. Rese, David Bermbach
https://arxiv.org/abs/2507.06005 https://

Towards Serverless Processing of Spatiotemporal Big Data Queries
Spatiotemporal data are being produced in continuously growing volumes by a variety of data sources and a variety of application fields rely on rapid analysis of such data. Existing systems such as PostGIS or MobilityDB usually build on relational database systems, thus, inheriting their scale-out characteristics. As a consequence, big spatiotemporal data scenarios still have limited support even though many query types can easily be parallelized. In this paper, we propose our vision of a nativ…

Progress and new challenges in image-based profiling
Erik Serrano, John Peters, Jesko Wagner, Rebecca E. Graham, Zhenghao Chen, Brian Feng, Gisele Miranda, Alexandr A. Kalinin, Loan Vulliard, Jenna Tomkinson, Cameron Mattson, Michael J. Lippincott, Ziqi Kang, Divya Sitani, Dave Bunten, Srijit Seal, Neil O. Carragher, Anne E. Carpenter, Shantanu Singh, Paula A. Marin Zapata, Juan C. Caicedo, Gregory P. Way

Progress and new challenges in image-based profiling
For over two decades, image-based profiling has revolutionized cellular phenotype analysis. Image-based profiling processes rich, high-throughput, microscopy data into unbiased measurements that reveal phenotypic patterns powerful for drug discovery, functional genomics, and cell state classification. Here, we review the evolving computational landscape of image-based profiling, detailing current procedures, discussing limitations, and highlighting future development directions. Deep learning h…

Transfer Learning and Explainable AI for Brain Tumor Classification: A Study Using MRI Data from Bangladesh
Shuvashis Sarker
https://arxiv.org/abs/2506.07228

Transfer Learning and Explainable AI for Brain Tumor Classification: A Study Using MRI Data from Bangladesh
Brain tumors, regardless of being benign or malignant, pose considerable health risks, with malignant tumors being more perilous due to their swift and uncontrolled proliferation, resulting in malignancy. Timely identification is crucial for enhancing patient outcomes, particularly in nations such as Bangladesh, where healthcare infrastructure is constrained. Manual MRI analysis is arduous and susceptible to inaccuracies, rendering it inefficient for prompt diagnosis. This research sought to ta…

Scalable Language Agnostic Taint Tracking using Explicit Data Dependencies
Sedick David Baker Effendi, Xavier Pinho, Andrei Michael Dreyer, Fabian Yamaguchi
https://arxiv.org/abs/2506.06247

Scalable Language Agnostic Taint Tracking using Explicit Data Dependencies
Taint analysis using explicit whole-program data-dependence graphs is powerful for vulnerability discovery but faces two major challenges. First, accurately modeling taint propagation through calls to external library procedures requires extensive manual annotations, which becomes impractical for large ecosystems. Second, the sheer size of whole-program graph representations leads to serious scalability and performance issues, particularly when quick analysis is needed in continuous development…

A Novel, Human-in-the-Loop Computational Grounded Theory Framework for Big Social Data
Lama Alqazlan, Zheng Fang, Michael Castelle, Rob Procter
https://arxiv.org/abs/2506.06083

A Novel, Human-in-the-Loop Computational Grounded Theory Framework for Big Social Data
The availability of big data has significantly influenced the possibilities and methodological choices for conducting large-scale behavioural and social science research. In the context of qualitative data analysis, a major challenge is that conventional methods require intensive manual labour and are often impractical to apply to large datasets. One effective way to address this issue is by integrating emerging computational methods to overcome scalability limitations. However, a critical conc…

Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modeling
Haoyu Wu, Diankun Wu, Tianyu He, Junliang Guo, Yang Ye, Yueqi Duan, Jiang Bian
https://arxiv.org/abs/2507.07982

Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modeling
Videos inherently represent 2D projections of a dynamic 3D world. However, our analysis suggests that video diffusion models trained solely on raw video data often fail to capture meaningful geometric-aware structure in their learned representations. To bridge this gap between video diffusion models and the underlying 3D nature of the physical world, we propose Geometry Forcing, a simple yet effective method that encourages video diffusion models to internalize latent 3D representations. Our ke…

Research on integrated intelligent energy management system based on big data analysis and machine learning
Jinzhou Xu, Yadan Zhang, Paola Tapia
https://arxiv.org/abs/2508.05583

Research on integrated intelligent energy management system based on big data analysis and machine learning
The application of big data is one of the significant features of integrated smart energy. Applying it to the file management of integrated smart energy projects is of great significance for improving the efficiency of project management and control. This article first discussed the benefits and challenges of implementing big data analysis in document management and control of integrated smart energy projects. In addition, an implementation framework for big data analysis in integrated smart en…

All-Sky Cosmic-Ray Anisotropy Update at Multiple Energies
Juan Carlos D\'iaz-V\'elez (for the HAWC,IceCube Collaborations), Riya Yogesh Kore (for the HAWC,IceCube Collaborations), Paolo Desiati (for the HAWC,IceCube Collaborations), Ferris Wolf (for the HAWC,IceCube Collaborations)
https://arxiv.org/abs/2507.07070…

All-Sky Cosmic-Ray Anisotropy Update at Multiple Energies
We present preliminary results on an updated full-sky analysis of the cosmic-ray arrival direction distribution with data collected by the High-Altitude Water Cherenkov (HAWC) Observatory and IceCube Neutrino Observatory with complementary field of views covering a large fraction of the sky. This study extends the energy range to higher energies. The HAWC Observatory, located at 19$^{\circ}$N has analyzed 8 years of cosmic-ray data over an energy range between 3.0 TeV and 1.0 PeV and confirms a…

This https://arxiv.org/abs/2505.18344 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Sample Complexity of Diffusion Model Training Without Empirical Risk Minimizer Access
Diffusion models have demonstrated state-of-the-art performance across vision, language, and scientific domains. Despite their empirical success, prior theoretical analyses of the sample complexity suffer from poor scaling with input data dimension or rely on unrealistic assumptions such as access to exact empirical risk minimizers. In this work, we provide a principled analysis of score estimation, establishing a sample complexity bound of $\widetilde{\mathcal{O}}(ε^{-6})$. Our approach lever…

KeyDroid: A Large-Scale Analysis of Secure Key Storage in Android Apps
Jenny Blessing, Ross J. Anderson, Alastair R. Beresford
https://arxiv.org/abs/2507.07927

KeyDroid: A Large-Scale Analysis of Secure Key Storage in Android Apps
Most contemporary mobile devices offer hardware-backed storage for cryptographic keys, user data, and other sensitive credentials. Such hardware protects credentials from extraction by an adversary who has compromised the main operating system, such as a malicious third-party app. Since 2011, Android app developers can access trusted hardware via the Android Keystore API. In this work, we conduct the first comprehensive survey of hardware-backed key storage in Android devices. We analyze 490 11…

Bayesian Bootstrap-based Gaussian Copula Model for Mixed Data with High Missing Rates
Seongmin Kim, Jeunghun Oh, Hungkuk Ko, Jeongmin Park, Jaeyong Lee
https://arxiv.org/abs/2507.06785

Bayesian Bootstrap-based Gaussian Copula Model for Mixed Data with High Missing Rates
Missing data is a common issue in various fields such as medicine, social sciences, and natural sciences, and it poses significant challenges for accurate statistical analysis. Although numerous imputation methods have been proposed to address this issue, many of them fail to adequately capture the complex dependency structure among variables. To overcome this limitation, models based on the Gaussian copula framework have been introduced. However, most existing copula-based approaches do not ac…

The z=1.03 Merging Cluster SPT-CL J0356-5337: New Strong Lensing Analysis with HST and MUSE
Grace Smith, Guillaume Mahler, Kate Napier, Keren Sharon, Matthew Bayliss, Bradford Benson, Lindsey Bleem, Benjamin Floyd, Vittorio Ghirardini, Michael D. Gladders, Gourav Khullar, Tim Schrabback
https://arxiv.org/abs/2507.07404

The z=1.03 Merging Cluster SPT-CL J0356-5337: New Strong Lensing Analysis with HST and MUSE
We present a strong lensing analysis and reconstruct the mass distribution of SPT-CL J0356-5337, a galaxy cluster at redshift z = 1.034. Our model supersedes previous models by making use of new multi-band HST data and MUSE spectroscopy. We identify two additional lensed galaxies to inform a more well-constrained model using 12 sets of multiple images in 5 separate lensed sources. The three previously-known sources were spectroscopically confirmed by Mahler et al. (2020) at redshifts of z = 2.3…

SkimROOT: Accelerating LHC Data Filtering with Near-Storage Processing
Narangerelt Batsoyol, Jonathan Guiang, Diego Davila, Aashay Arora, Philip Chang, Frank W\"urthwein, Steven Swanson
https://arxiv.org/abs/2506.04507

SkimROOT: Accelerating LHC Data Filtering with Near-Storage Processing
Data analysis in high-energy physics (HEP) begins with data reduction, where vast datasets are filtered to extract relevant events. At the Large Hadron Collider (LHC), this process is bottlenecked by slow data transfers between storage and compute nodes. To address this, we introduce SkimROOT, a near-data filtering system leveraging Data Processing Units (DPUs) to accelerate LHC data analysis. By performing filtering directly on storage servers and returning only the relevant data, SkimROOT min…

IRAS 16475-4609: A Young Compact HII Region Sculpting Its Molecular Environment
Felipe Navarete, Sean D. Points, Augusto Damineli
https://arxiv.org/abs/2506.08180

IRAS 16475-4609: A Young Compact HII Region Sculpting Its Molecular Environment
We present a near-infrared spectroscopic and imaging analysis of the star-forming region IRAS 16475-4609, based on TripleSpec/SOAR spectroscopy and NEWFIRM/CTIO imaging, complemented by archival radio and sub-millimeter data. Our spectroscopic analysis indicates that the central source is an early B-type star (B0-B0.7V) powering a compact HII region characterized by strong HI and HeI recombination lines, and molecular H$_2$ emission. We derive a distance of 3.51$\pm$0.74 kpc, consistent with th…

Testing time order and Leggett-Garg inequalities with noninvasive measurements on public quantum computers
Tomasz Rybotycki, Tomasz Bia{\l}ecki, Josep Batle, Bart{\l}omiej Zglinicki, Adam Szereszewski, Wolfgang Belzig, Adam Bednorz
https://arxiv.org/abs/2507.07904

Testing time order and Leggett-Garg inequalities with noninvasive measurements on public quantum computers
We present two protocols, with one and two two-qubit gates, to noninvasively measure two incompatible observables on a qubit. Both protocols are completed with a projective measurement. By gathering sufficiently large statistics, we have been able to violate Leggett-Garg inequality and time-order invariance. The detailed analysis of the data on 10 qubit sets from 5 devices available on IBM Quantum and one on IonQ reveals violations beyond 5 standard deviations in almost all cases. The noninvasi…

Stability and Extension of Steady and Ranging Persistence
Yann-Situ Gazull
https://arxiv.org/abs/2506.07911 https://arxiv.org/pdf/250…

Stability and Extension of Steady and Ranging Persistence
Persistent homology is a topological data analysis tool that has been widely generalized, extending its scope outside the field of topology. Among its extensions, steady and ranging persistence was developed to study a wide variety of graph properties. Precisely, given a feature of interest on graphs, it is possible to build two types of persistence (steady and ranging persistence) that follow the evolution of the feature along graph filtrations. This study extends steady and ranging persistenc…

Analysis: Community Notes submissions on X fell from a high of 120,000 in January to fewer than 60,000 in May; Musk now rarely mentions the feature (Joe Murphy/NBC News)
https://www.nbcnews.com/tech/social-media/x-twitter-community-not…

Use of Community Notes on Elon Musk's X has plummeted in 2025
Half as many crowdsourced Community Notes were created in May than were created in January.

QUEST: Query Optimization in Unstructured Document Analysis
Zhaoze Sun, Qiyan Deng, Chengliang Chai, Kaisen Jin, Xinyu Guo, Han Han, Ye Yuan, Guoren Wang, Lei Cao
https://arxiv.org/abs/2507.06515

QUEST: Query Optimization in Unstructured Document Analysis
Most recently, researchers have started building large language models (LLMs) powered data systems that allow users to analyze unstructured text documents like working with a database because LLMs are very effective in extracting attributes from documents. In such systems, LLM-based extraction operations constitute the performance bottleneck of query execution due to the high monetary cost and slow LLM inference. Existing systems typically borrow the query optimization principles popular in rel…

Group structure as a foundation for entropies
Henrik Jeldtoft Jensen, Piergiulio Tempesta
https://arxiv.org/abs/2507.06847 https://ar…

Group structure as a foundation for entropies
Entropy can signify different things: For instance, heat transfer in thermodynamics or a measure of information in data analysis. Many entropies have been introduced and it can be difficult to ascertain their different importance and merits. Here we consider entropy in an abstract sense, as a functional on a probability space and we review how being able to handle the trivial case of non-interacting systems together with the subtle requirement of extensivity allows a systematic classification o…

Determining $\alpha_s(m_Z)$ from the Heavy Jet Mass distribution
Miguel A. Benitez
https://arxiv.org/abs/2506.07723 https://arxiv.org…

Determining $α_s(m_Z)$ from the Heavy Jet Mass distribution
We present a state-of-the-art analysis on determining the strong coupling constant $α_s(m_Z)$ from available $e^+e^-$ data for the Heavy Jet Mass (HJM) distribution. Dijet resummation is supplemented with additional resummation of shoulder logarithms appearing around the symmetric 3-jet configuration of the HJM spectrum. In addition to $α_s(m_Z)$ we further obtain numerical results for two non-perturbative parameters, $Ω_1^ρ$ and $Θ_1$, encompassing effects from dijet and trijet hadronizat…

Analysis: Nvidia has the biggest weight in the S&P 500 of any individual stock since 1981 and the highest P/E as the index's top stock since Microsoft in 1999 (Luke Kawa/Sherwood News)
https://sherwood.news/markets/nvidia-i

Nvidia is dominating the S&P 500 more than any company in at least 44 years
Its weight in the benchmark US stock index has reached unparalleled heights, based on data from Apollo Global Management going back to 1981.

MatBYIB: A Matlab-based code for Bayesian inference of extreme mass-ratio inspiral binary with arbitrary eccentricity
Gen-Liang Li, Shu-Jie Zhao, Huai-Ke Guo, Jing-Yu Su, Zhen-Heng Lin
https://arxiv.org/abs/2506.05954

MatBYIB: A Matlab-based code for Bayesian inference of extreme mass-ratio inspiral binary with arbitrary eccentricity
Accurate parameter estimation(PE) of gravitational waves(GW) is essential for GW data analysis. In extreme mass-ratio inspiral binary(EMRI) systems, orbital eccentricity is a critical parameter for PE. However, current software for for PE of GW often neglects the direct estimation of orbital eccentricity. To fill this gap, we have developed the MatBYIB, a MATLAB-based software package for PE of GW with arbitrary eccentricity. The MatBYIB employs the Analytical Kludge (AK) waveform as a computat…

LISTEN: Lightweight Industrial Sound-representable Transformer for Edge Notification
Changheon Han, Yun Seok Kang, Yuseop Sim, Martin Byung-Guk Jun, Hyung Wook Park
https://arxiv.org/abs/2507.07879

LISTEN: Lightweight Industrial Sound-representable Transformer for Edge Notification
Deep learning-based machine listening is broadening the scope of industrial acoustic analysis for applications like anomaly detection and predictive maintenance, thereby improving manufacturing efficiency and reliability. Nevertheless, its reliance on large, task-specific annotated datasets for every new task limits widespread implementation on shop floors. While emerging sound foundation models aim to alleviate data dependency, they are too large and computationally expensive, requiring cloud …

Differentially Private Explanations for Clusters
Amir Gilad, Tova Milo, Kathy Razmadze, Ron Zadicario
https://arxiv.org/abs/2506.05900 https://

Differentially Private Explanations for Clusters
The dire need to protect sensitive data has led to various flavors of privacy definitions. Among these, Differential privacy (DP) is considered one of the most rigorous and secure notions of privacy, enabling data analysis while preserving the privacy of data contributors. One of the fundamental tasks of data analysis is clustering , which is meant to unravel hidden patterns within complex datasets. However, interpreting clustering results poses significant challenges, and often necessitates an…

PAST: A multimodal single-cell foundation model for histopathology and spatial transcriptomics in cancer
Changchun Yang, Haoyang Li, Yushuai Wu, Yilan Zhang, Yifeng Jiao, Yu Zhang, Rihan Huang, Yuan Cheng, Yuan Qi, Xin Guo, Xin Gao
https://arxiv.org/abs/2507.06418

PAST: A multimodal single-cell foundation model for histopathology and spatial transcriptomics in cancer
While pathology foundation models have transformed cancer image analysis, they often lack integration with molecular data at single-cell resolution, limiting their utility for precision oncology. Here, we present PAST, a pan-cancer single-cell foundation model trained on 20 million paired histopathology images and single-cell transcriptomes spanning multiple tumor types and tissue contexts. By jointly encoding cellular morphology and gene expression, PAST learns unified cross-modal representati…

Submillimeter and Mid-Infrared Variability of Young Stellar Objects in the M17 HII Region
Zhiwei Chen, Doug Johnstone, Carlos Contreras Pe\~na, Jeong-Eun Lee, Sheng-Yuan Liu, Gregory Herczeg, Steve Mairs, Geumsook Park, Kee-Tae Kim, Mi-Ryang Kim, Keping Qiu, Yao-Te Wang, Xu Zhang, Megan Reiter, the JCMT Transient Team
https://ar…

Submillimeter and Mid-Infrared Variability of Young Stellar Objects in the M17 HII Region
We conducted a comprehensive analysis of young stellar object (YSO) variability at submillimeter and mid-infrared (mid-IR) wavelengths for the M\,17 \ion{H}{2} region, using 3.5 years monitoring data from the JCMT Transient Survey at $450$ and $850\,μ$m and 9 years mid-IR monitoring data from the NEOWISE mission. Our study encompasses observations of 198 and 164 bright submillimeter peaks identified within the deep JCMT coadded maps at 450 and $850\,μ$m, and 66 YSOs seen by NEOWISE W2 that we…

AgenticData: An Agentic Data Analytics System for Heterogeneous Data
Ji Sun, Guoliang Li, Peiyao Zhou, Yihui Ma, Jingzhe Xu, Yuan Li
https://arxiv.org/abs/2508.05002 https://

AgenticData: An Agentic Data Analytics System for Heterogeneous Data
Existing unstructured data analytics systems rely on experts to write code and manage complex analysis workflows, making them both expensive and time-consuming. To address these challenges, we introduce AgenticData, an innovative agentic data analytics system that allows users to simply pose natural language (NL) questions while autonomously analyzing data sources across multiple domains, including both unstructured and structured data. First, AgenticData employs a feedback-driven planning tech…

Is Your Training Pipeline Production-Ready? A Case Study in the Healthcare Domain
Daniel Lawand (University of S\~ao Paulo), Lucas Quaresma (University of S\~ao Paulo), Roberto Bolgheroni (University of S\~ao Paulo), Alfredo Goldman (University of S\~ao Paulo), Renato Cordeiro Ferreira (University of S\~ao Paulo, Jheronimus Academy of Data Science, Technical University of Eindhoven, Tilburg University)

Is Your Training Pipeline Production-Ready? A Case Study in the Healthcare Domain
Deploying a Machine Learning (ML) training pipeline into production requires robust software engineering practices. This differs significantly from experimental workflows. This experience report investigates this challenge in SPIRA, a project whose goal is to create an ML-Enabled System (MLES) to pre-diagnose insufficiency respiratory via speech analysis. The first version of SPIRA's training pipeline lacked critical software quality attributes. This paper presents an overview of the MLES, then…

Designing Parallel Algorithms for Community Detection using Arachne
Fuhuan Li, Zhihui Du, David A. Bader
https://arxiv.org/abs/2507.06471 https://

Designing Parallel Algorithms for Community Detection using Arachne
The rise of graph data in various fields calls for efficient and scalable community detection algorithms. In this paper, we present parallel implementations of two widely used algorithms: Label Propagation and Louvain, specifically designed to leverage the capabilities of Arachne which is a Python-accessible, open-source framework for large-scale graph analysis. Our implementations achieve substantial speedups over existing Python-based tools like NetworkX and igraph, which lack efficient paral…

From Fads to Classics -- Analyzing Video Game Trend Evolutions through Steam Tags
Nicolas Grelier, Johannes Pfau, Nicolas Mathieu, St\'ephane Kaufmann
https://arxiv.org/abs/2506.08881

From Fads to Classics -- Analyzing Video Game Trend Evolutions through Steam Tags
The video game industry deals with a fast-paced, competitive and almost unpredictable market. Trends of genres, settings and modalities change on a perpetual basis, studios are often one big hit or miss away from surviving or perishing, and hitting the pulse of the time has become one of the greatest challenges for industrials, investors and other stakeholders. In this work, we aim to support the understanding of video game trends over time based on data-driven analysis, visualization and inter…

This https://arxiv.org/abs/2503.09714 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Full-Shape analysis of the power spectrum and bispectrum of DESI DR1 LRG and QSO samples
We present the first joint analysis of the power spectrum and bispectrum using the Data Release 1 (DR1) of the Dark Energy Spectroscopic Instrument (DESI), focusing on Luminous Red Galaxies (LRGs) and quasars (QSOs) across a redshift range of $0.4\leq z\leq2.1$. By combining the two- and three-point statistics, we are able to partially break the degeneracy between the logarithmic growth rate, $f(z)$, and the amplitude of dark matter fluctuations, $σ_\textrm{s8}(z)$, which cannot be measured se…

Replaced article(s) found for physics.data-an. https://arxiv.org/list/physics.data-an/new
[1/1]:
- A COMPASS to Model Comparison and Simulation-Based Inference in Galactic Chemical Evolution
Berkay Gunes, Sven Buder, Tobias Buck

State of the Ice Model in the IceCube Observatory
Dmitry Chirkin (for the IceCube Collaboration), Martin Rongen (for the IceCube Collaboration)
https://arxiv.org/abs/2507.06341

State of the Ice Model in the IceCube Observatory
IceCube is a neutrino observatory located at the South Pole that uses Antarctic ice as a medium for detection of Cherenkov photons. As such, analysis of the data relies on our understanding of the properties of ice within and around the instrumented volume. Over the years we have made significant progress in understanding the glacial ice and now have a comprehensive model that covers many of the relevant aspects of the photon propagation in it. In this report we give a historical overview of th…

A neutrino data analysis of extra-dimensional theories with massive bulk fields
Philipp Eller, Manuel Ettengruber, Alan Zander
https://arxiv.org/abs/2508.04274 https://

A neutrino data analysis of extra-dimensional theories with massive bulk fields
We present a global neutrino oscillation analysis of models with a single large extra dimension in which right-handed neutrinos possess bulk Dirac masses. Two scenarios are considered: Large Extra Dimensions with bulk masses and the Dark Dimension framework, both predicting a tower of sterile Kaluza-Klein states that mix with active neutrinos. Using data from MINOS/MINOS+, KamLAND, and Daya Bay, we perform a joint likelihood analysis. No signatures of these theories were found. Therefore, we co…

Edge-assisted Parallel Uncertain Skyline Processing for Low-latency IoE Analysis
Chuan-Chi Lai, Yan-Lin Chen, Bo-Xin Liu, Chuan-Ming Liu
https://arxiv.org/abs/2508.04596 https:/…

Edge-assisted Parallel Uncertain Skyline Processing for Low-latency IoE Analysis
Due to the Internet of Everything (IoE), data generated in our life become larger. As a result, we need more effort to analyze the data and extract valuable information. In the cloud computing environment, all data analysis is done in the cloud, and the client only needs less computing power to handle some simple tasks. However, with the rapid increase in data volume, sending all data to the cloud via the Internet has become more expensive. The required cloud computing resources have also becom…

An analysis of Crunchbase and PitchBook data shows that in 2025 so far at least 36 tech startups hit a $1B valuation, including seven in June and six in May (Dominic-Madori Davis/TechCrunch)
https://techcrunch.com/2025/07/06/7-new-tech-unicorns-w…

At least 36 new tech unicorns were minted in 2025 so far | TechCrunch
With AI igniting an investor frenzy, every month, more startups obtain unicorn status. Using data from Crunchbase and PitchBook, TechCrunch tracked down

Replaced article(s) found for physics.data-an. https://arxiv.org/list/physics.data-an/new
[1/1]:
- Self-consistent gravity model for inferring node mass in flow networks
Daekyung Lee, Wonguk Cho, Heetae Kim, Gunn Kim, Hyeong-Chai Jeong, Beom Jun Kim

PROVSYN: Synthesizing Provenance Graphs for Data Augmentation in Intrusion Detection Systems
Yi Huang, Wajih UI Hassan, Yao Guo, Xiangqun Chen, Ding Li
https://arxiv.org/abs/2506.06226

PROVSYN: Synthesizing Provenance Graphs for Data Augmentation in Intrusion Detection Systems
Provenance graph analysis plays a vital role in intrusion detection, particularly against Advanced Persistent Threats (APTs), by exposing complex attack patterns. While recent systems combine graph neural networks (GNNs) with natural language processing (NLP) to capture structural and semantic features, their effectiveness is limited by class imbalance in real-world data. To address this, we introduce PROVSYN, an automated framework that synthesizes provenance graphs through a three-phase pipel…

Tensor Stochastic Regression for High-dimensional Time Series via CP Decomposition
Shibo Li, Yao Zheng
https://arxiv.org/abs/2506.06919 https://

Tensor Stochastic Regression for High-dimensional Time Series via CP Decomposition
As tensor-valued data become increasingly common in time series analysis, there is a growing need for flexible and interpretable models that can handle high-dimensional predictors and responses across multiple modes. We propose a unified framework for high-dimensional tensor stochastic regression based on CANDECOMP/PARAFAC (CP) decomposition, which encompasses vector, matrix, and tensor responses and predictors as special cases. Tensor autoregression naturally arises as a special case within th…

Observational Insights on DBI K-essence Models Using Machine Learning and Bayesian Analysis
Samit Ganguly, Arijit Panda, Eduardo Guendelman, Debashis Gangopadhyay, Abhijit Bhattacharyya, Goutam Manna
https://arxiv.org/abs/2506.05674

Observational Insights on DBI K-essence Models Using Machine Learning and Bayesian Analysis
We present a comparative observational data analysis of two DBI-type k-essence scalar field models for late-time cosmic acceleration: one treats dark energy with standard matter, while the other unifies dark energy and dark matter within a single scalar field, each governed by distinct non-canonical Lagrangians. The background dynamics are formulated via modified Friedmann and scalar field equations, constrained by Pantheon+ SNe Ia, Hubble, and BAO data, using Bayesian inference (NUTS in NumPyr…

Generative Neural Network for Simulating Radio Emission from Extensive Air Showers
Pranav Sampathkumar, Tim Huege, Andreas Haungs, Ralph Engel
https://arxiv.org/abs/2507.07713

Generative Neural Network for Simulating Radio Emission from Extensive Air Showers
Cosmic ray shower detection using large radio arrays has gained significant traction in recent years. With massive improvements in signal modelling and microscopic simulations, the analysis of incoming events is still severely limited by the simulation cost of radio emission to interpret the data. In this work, we show that a neural network can be used for simulating such radio pulses. We also demonstrate how such a neural network can be used for $X_\mathrm{max}$ reconstruction, while retaining…

Resonant leptogenesis in inverse see-saw framework with modular $S_4$ symmetry
Abhishek, V. Suryanarayana Mummidi
https://arxiv.org/abs/2507.06610 https://…

Resonant leptogenesis in inverse see-saw framework with modular $S_4$ symmetry
This work introduces a model for lepton mass generation and flavor mixing, realized through a (2,3) inverse seesaw structure within a modular \( S_4 \) symmetry framework. The model employs modular forms to construct the lepton Yukawa couplings, thereby significantly simplifying the model by reducing its complexity. A detailed numerical analysis demonstrates consistency with current neutrino oscillation data, yielding constrained predictions for the mixing angles and CP-violating phases. The Di…

Analysis: Community Notes submissions on X fell from a high of 120,000 in January to fewer than 60,000 in May; Musk now rarely mentions the feature (Joe Murphy/NBC News)
https://www.nbcnews.com/tech/social-media/x-twitter-community-not…

Use of Community Notes on Elon Musk's X has plummeted in 2025
Half as many crowdsourced Community Notes were created in May than were created in January.

Layered, Overlapping, and Inconsistent: A Large-Scale Analysis of the Multiple Privacy Policies and Controls of U.S. Banks
Lu Xian, Van Tran, Lauren Lee, Meera Kumar, Yichen Zhang, Florian Schaub
https://arxiv.org/abs/2507.05415

Layered, Overlapping, and Inconsistent: A Large-Scale Analysis of the Multiple Privacy Policies and Controls of U.S. Banks
Privacy policies are often complex. An exception is the two-page standardized notice that U.S. financial institutions must provide under the Gramm-Leach-Bliley Act (GLBA). However, banks now operate websites, mobile apps, and other services that involve complex data sharing practices that require additional privacy notices and do-not-sell opt-outs. We conducted a large-scale analysis of how U.S. banks implement privacy policies and controls in response to GLBA; other federal privacy policy requ…

A Statistical Framework for Co-Mediators of Zero-Inflated Single-Cell RNA-Seq Data
Seungjun Ahn, Zhigang Li
https://arxiv.org/abs/2507.06113 https://

A Statistical Framework for Co-Mediators of Zero-Inflated Single-Cell RNA-Seq Data
Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of cellular heterogeneity, enabling detailed molecular profiling at the individual cell level. However, integrating high-dimensional single-cell data into causal mediation analysis remains challenging due to zero inflation and complex mediator structures. We propose a novel mediation framework leveraging zero-inflated negative binomial models to characterize cell-level mediator distributions and beta regression for zero-inflati…

[2025-07-10 Thu (UTC), no new articles found for physics.data-an Data Analysis, Statistics and Probability]
toXiv_bot_toot

Computationally Intensive Research: Advancing a Role for Secondary Analysis of Qualitative Data
Kaveh Mohajeri, Amir Karami
https://arxiv.org/abs/2506.04230

Computationally Intensive Research: Advancing a Role for Secondary Analysis of Qualitative Data
This paper draws attention to the potential of computational methods in reworking data generated in past qualitative studies. While qualitative inquiries often produce rich data through rigorous and resource-intensive processes, much of this data usually remains unused. In this paper, we first make a general case for secondary analysis of qualitative data by discussing its benefits, distinctions, and epistemological aspects. We then argue for opportunities with computationally intensive seconda…

When AIOps Become "AI Oops": Subverting LLM-driven IT Operations via Telemetry Manipulation
Dario Pasquini, Evgenios M. Kornaropoulos, Giuseppe Ateniese, Omer Akgul, Athanasios Theocharis, Petros Efstathopoulos
https://arxiv.org/abs/2508.06394

When AIOps Become "AI Oops": Subverting LLM-driven IT Operations via Telemetry Manipulation
AI for IT Operations (AIOps) is transforming how organizations manage complex software systems by automating anomaly detection, incident diagnosis, and remediation. Modern AIOps solutions increasingly rely on autonomous LLM-based agents to interpret telemetry data and take corrective actions with minimal human intervention, promising faster response times and operational cost savings. In this work, we perform the first security analysis of AIOps solutions, showing that, once again, AI-driven …

This https://arxiv.org/abs/2212.07917 has been replaced.
link: https://scholar.google.com/scholar?q=a

An Analysis of Variance of the Pantheon+ Dataset: Systematics in the Covariance Matrix?
We investigate the statistics of the available Pantheon+ dataset. Noticing that the $χ^2$ value for the best-fit $Λ$CDM model to the real data is small, we quantify how significant its smallness is by calculating the distribution of $χ^2$ values for the best-fit $Λ$CDM model fit to mock Pantheon+-like datasets, using the provided covariance matrix. We further investigate the distribution of the residuals of the Pantheon+ dataset with respect to the best-fit $Λ$CDM model, and notice that th…

Akaike information criterion for segmented regression models
Kazuki Nakajima, Yoshiyuki Ninomiya
https://arxiv.org/abs/2506.08760 https://

Akaike information criterion for segmented regression models
In segmented regression, when the regression function is continuous at the change-points that are the boundaries of the segments, it is also called joinpoint regression, and the analysis package developed by \cite{KimFFM00} has become a standard tool for analyzing trends in longitudinal data in the field of epidemiology. In addition, it is sometimes natural to expect the regression function to be discontinuous at the change-points, and in the field of epidemiology, this model is used in \cite{J…

[2025-07-11 Fri (UTC), no new articles found for physics.data-an Data Analysis, Statistics and Probability]
toXiv_bot_toot

Galaxy Cluster Mass Estimation Through The Splashback Radius
Lucas Gabriel-Silva, Laerte Sodr\'e Jr
https://arxiv.org/abs/2506.07425 https://

Galaxy Cluster Mass Estimation Through The Splashback Radius
We present an analysis of the splashback radius ($R_{\text{sp}}$) and the associated splashback mass ($M_{\text{sp}}$) for a sample of galaxy clusters using SDSS spectroscopic data and mock simulations. $R_{\text{sp}}$ marks a physical boundary between the virialized core and the outer infall regions of clusters, providing a robust measure of cluster mass accretion history without being affected by pseudo-evolution. We model the cumulative galaxy number profile of clusters, testing different ha…

[2025-08-11 Mon (UTC), no new articles found for physics.data-an Data Analysis, Statistics and Probability]
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