Tootfinder

dblp_cite: DBLP citations (2014)
Citations among papers contained in the DBLP computer science bibliography. 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 snapshot from May 2014.
This network has 12590 nodes and 49759 edges.
Tags: Informational, Citation, Unweighted

Works-magnet: Accelerating Metadata Curation for Open Science
Eric Jeangirard
https://arxiv.org/abs/2506.14430 https://arxiv.org/pdf/…

Works-magnet: Accelerating Metadata Curation for Open Science
The transition to Open Science necessitates robust and reliable metadata. While national initiatives, such as the French Open Science Monitor, aim to track this evolution using open data, reliance on proprietary databases persists in many places. Open platforms like OpenAlex still require significant human intervention for data accuracy. This paper introduces Works-magnet, a project by the French Ministry of Higher Education and Research (MESR) Data Science & Engineering Team. Works-magnet is d…

Global carbon emissions on track to exhaust 1.5°C budget in three years, study warns #environment

Global carbon emissions on track to exhaust 1.5°C budget in three years, study warns
The central estimate of the remaining carbon budget for 1.5°C is 130 billion tons of carbon dioxide (CO2) (from the beginning of 2025). This would be exhausted in a little more than three years at current levels of CO2 emissions, according to the latest Indicators of Global Climate Change study published in the journal Earth System Science Data, and the budget for 1.6°C or 1.7°C could be exceeded within nine years.

Minimizing Communication for Parallel Symmetric Tensor Times Same Vector Computation
Hussam Al Daas (STFC, Scientific Computing Department, Rutherford Appleton Laboratory, Didcot, UK), Grey Ballard (Wake Forest University, Computer Science Department, Winston-Salem, NC, USA), Laura Grigori (EPFL, Institute of Mathematics, Lausanne, Switzerland,PSI, Center for Scientific Computing, Theory,Data, Villigen, Switzerland), Suraj Kumar (Institut national de recherche en sciences et technologi…

Minimizing Communication for Parallel Symmetric Tensor Times Same Vector Computation
In this article, we focus on the parallel communication cost of multiplying the same vector along two modes of a $3$-dimensional symmetric tensor. This is a key computation in the higher-order power method for determining eigenpairs of a $3$-dimensional symmetric tensor and in gradient-based methods for computing a symmetric CP decomposition. We establish communication lower bounds that determine how much data movement is required to perform the specified computation in parallel. The core idea …

A new paper projecting Joshua tree habitat under future climate based on incredibly high-resolution distribution data, from Joshua Tree Genome Project collaborators at USGS. They estimate up to 80% loss of suitable habitat by 2100 under the worst-case climate scenario.
#JoshuaTree #science

Photoabsorption Cross Sections studied within the axially deformed Relativistic Quasiparticle Finite Amplitude Framework
C. Chen (Frontiers Science Center for Rare isotope, Lanzhou University, Lanzhou, China, School of Nuclear Science and Technology, Lanzhou University, Lanzhou, China), Y. F. Niu (Frontiers Science Center for Rare isotope, Lanzhou University, Lanzhou, China, School of Nuclear Science and Technology, Lanzhou University, Lanzhou, China), R. Xu (China Nuclear Data Center,…

Photoabsorption Cross Sections studied within the axially deformed Relativistic Quasiparticle Finite Amplitude Framework
Photoabsorption cross sections for 235 stable nuclei, ranging from $^{40}$Ca to $^{209}$Bi, were investigated by the quasiparticle finite amplitude method (QFAM) based on the axially deformed relativistic Hartree-Bogoliubov (RHB) approach using relativistic point-coupling interaction DD-PC1, with extensions to odd-A nuclei. GDR parameters based on the standard Lorentzian (SLO) model were extracted from QFAM results and compared with those from experimental data recommended by IAEA. Good agreeme…

glass: ordered set data structure for client-side order books
Viktor Krapivensky
https://arxiv.org/abs/2506.13991 https://arxiv.org/p…

glass: ordered set data structure for client-side order books
The "ordered set" abstract data type with operations "insert", "erase", "find", "min", "max", "next" and "prev" is ubiquitous in computer science. It is usually implemented with red-black trees, $B$-trees, or $B^+$-trees. We present our implementation of ordered set based on a trie. It only supports integer keys (as opposed to keys of any strict weakly ordered type) and is optimized for market data, namely for what we call sequential locality. The following is the list of what we believe to be …

Revisiting Randomization in Greedy Model Search
Xin Chen, Jason M. Klusowski, Yan Shuo Tan, Chang Yu
https://arxiv.org/abs/2506.15643 https://

Revisiting Randomization in Greedy Model Search
Combining randomized estimators in an ensemble, such as via random forests, has become a fundamental technique in modern data science, but can be computationally expensive. Furthermore, the mechanism by which this improves predictive performance is poorly understood. We address these issues in the context of sparse linear regression by proposing and analyzing an ensemble of greedy forward selection estimators that are randomized by feature subsampling -- at each iteration, the best feature is s…

A case study: the savings potential thanks to FAIR data in one Materials Science PhD project
Michael Seitz, Nick Garabedian, Ilia Bagov, Christian Greiner
https://arxiv.org/abs/2506.12043

A case study: the savings potential thanks to FAIR data in one Materials Science PhD project
The FAIR (Findable, Accessible, Interoperable, and Reusable) data principles have gained significant attention as a means to enhance data sharing, collaboration, and reuse across various domains. Here, we explore the potential benefits of implementing FAIR data practices within engineering projects, with a monetary focus in the German context, but by considering aspects which are relatively universal. By examining the FAIR-data aspect of a Materials Science and Engineering PhD project, it becom…

Microsoft has once again been named a Leader in the 2025 Gartner® Magic Quadrant™ for Data Science and Machine Learning (DSML) Platforms.
https://azure.microsoft.com/en-us/blog

Microsoft recognized for second consecutive year as a Leader in the 2025 Gartner® Magic Quadrant™ for Data Science and Machine Learning Platforms | Microsoft Azure Blog
Find out how Microsoft Azure Machine Learning can help you optimize business processes, improve customer experiences, and drive innovation.

"Violinist composes music from moth flight data to highlight insect decline"
#Music #Insects #Animals

Violinist composes music from moth flight data to highlight insect decline
Music and science collide in Moth x Human, a new composition by violinist Ellie Wilson that transforms moth flight data into a haunting tribute to these overlooked pollinators

MindGrab for BrainChop: Fast and Accurate Skull Stripping for Command Line and Browser
Armina Fani (Tri-Institutional Center for Translational Research in Neuroimaging and Data Science), Mike Doan (Tri-Institutional Center for Translational Research in Neuroimaging and Data Science), Isabelle Le (Tri-Institutional Center for Translational Research in Neuroimaging and Data Science), Alex Fedorov (Emory University), Malte Hoffmann (Harvard University), Chris Rorden (University of South C…

MindGrab for BrainChop: Fast and Accurate Skull Stripping for Command Line and Browser
We developed MindGrab, a parameter- and memory-efficient deep fully-convolutional model for volumetric skull-stripping in head images of any modality. Its architecture, informed by a spectral interpretation of dilated convolutions, was trained exclusively on modality-agnostic synthetic data. MindGrab was evaluated on a retrospective dataset of 606 multimodal adult-brain scans (T1, T2, DWI, MRA, PDw MRI, EPI, CT, PET) sourced from the SynthStrip dataset. Performance was benchmarked against Synth…

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

Procedures for Constraining Robotic Fiber Positioning for Highly Multiplexed Spectroscopic Surveys: The Case of FPS for SDSS-V
Ilija Medan, Tom Dwelly, Kevin R. Covey, Eleonora Zari, Michael R. Blanton, Joleen K. Carlberg, S. Drew Chojnowski, Alexander Ji, Yue Shen, John Donor, Jos\'e S\'anchez-Gallego, Sean Morrison, H\'ector J. Ibarra-Medel, Conor Sayres, Keivan G. Stassun

Procedures for Constraining Robotic Fiber Positioning for Highly Multiplexed Spectroscopic Surveys: The Case of FPS for SDSS-V
One crucial aspect of planning any large scale astronomical survey is constructing an observing strategy that maximizes reduced data quality. This is especially important for surveys that are rather heterogeneous and broad-ranging in their science goals. The Sloan Digital Sky Survey V (SDSS-V), which now utilizes the Focal Plane System (FPS) to robotically place fibers that feed the spectrographs, certainly meets these criteria. The addition of the FPS facilities an increase in survey efficienc…

Humans, Machine Learning, and Language Models in Union: A Cognitive Study on Table Unionability
Sreeram Marimuthu, Nina Klimenkova, Roee Shraga
https://arxiv.org/abs/2506.12990

Humans, Machine Learning, and Language Models in Union: A Cognitive Study on Table Unionability
Data discovery and table unionability in particular became key tasks in modern Data Science. However, the human perspective for these tasks is still under-explored. Thus, this research investigates the human behavior in determining table unionability within data discovery. We have designed an experimental survey and conducted a comprehensive analysis, in which we assess human decision-making for table unionability. We use the observations from the analysis to develop a machine learning framewor…

🔍 Wie wir aus Erfahrung lernen können: Thomas Bayes und die Diagnose von Erkrankungen in der Medizin
Risiko & Wahrscheinlichkeit: Die Schlüssel zu besseren Gesundheitsentscheidungen! 🩺 Wir erklären, wie Statistik & Medizin zusammenpassen.
⏲ Uhrzeit: 17:00-24:00 Uhr
🗓️ Datum: 21.06.2025
🧑‍🔬 Veranstalter: CIDAS Campus-Institut Data Science
📍 Veranstaltungsort: @…

Overview of statistical concepts in datascience: could be usefull in case you need some preformulated text to share with stakeholders... https://towardsdatascience.com/ultimate-guide-to-statistics-for-data-science-a3d8f1fd69a7

General Manager of Azure AI at Microsoft Don Scott shares a second consecutive year win in this post on the AI and Machine Learning portion of the official Azure blog. Gartner Group has identified Microsoft as a industry leader in data science and machine learning platforms.
"Microsoft recognized for second consecutive year as a Leader in the 2025 Gartner® Magic Quadrant™ for Data Science and Machine Learning Platforms"

"Applications of the Critical Incident Technique in Library and Information Science Research: A Literature Review" https://doi.org/10.1515/libri-2024-0065

Anastassia defends her PhD: Urban Data Science for Sustainable Mobility
https://nerds.itu.dk/2025/04/01/anastassia-defends-her-phd-urban-data-science-for-sustainable-mobility/
Congrats to @…

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

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

Our partner conference, MICES – Mix-Camp E-Commerce Search, takes place on June 18th at TUECHTIG in Berlin. Focusing on e-commerce search, MICES brings together experts from various fields such as IT, product management, UX design, search management, information retrieval, data science and search engine vendors to discuss challenges, share ideas, best practices and case studies in e-commerce search.
Register for free and learn more here:

MLOps with Microservices: A Case Study on the Maritime Domain
Renato Cordeiro Ferreira (Jheronimus Academy of Data Science, Technical University of Eindhoven, Tilburg University), Rowanne Trapmann (Jheronimus Academy of Data Science, Technical University of Eindhoven, Tilburg University), Willem-Jan van den Heuvel (Jheronimus Academy of Data Science, Technical University of Eindhoven, Tilburg University)

MLOps with Microservices: A Case Study on the Maritime Domain
This case study describes challenges and lessons learned on building Ocean Guard: a Machine Learning-Enabled System (MLES) for anomaly detection in the maritime domain. First, the paper presents the system's specification, and architecture. Ocean Guard was designed with a microservices' architecture to enable multiple teams to work on the project in parallel. Then, the paper discusses how the developers adapted contract-based design to MLOps for achieving that goal. As a MLES, Ocean Guard emplo…

CPN-Py: A Python-Based Tool for Modeling and Analyzing Colored Petri Nets
Alessandro Berti, Wil M. P. van der Aalst
https://arxiv.org/abs/2506.12238 https:…

CPN-Py: A Python-Based Tool for Modeling and Analyzing Colored Petri Nets
Colored Petri Nets (CPNs) are an established formalism for modeling processes where tokens carry data. Although tools like CPN Tools and CPN IDE excel at CPN-based simulation, they are often separate from modern data science ecosystems. Meanwhile, Python has become the de facto language for process mining, machine learning, and data analytics. In this paper, we introduce CPN-Py, a Python library that faithfully preserves the core concepts of Colored Petri Nets -- including color sets, timed tok…

SAS in ESA Datalabs: A New Platform for XMM-Newton Analysis
Esin G. Gulbahar, Camille M. Diez, Aitor Ibarra, Ivan Valtchanov, Richard Saxton, Ignacio de la Calle Perez, Jose Lopez-Miralles, Alejandro Gonzalez Ganzabal, Peter Kretschmar
https://arxiv.org/abs/2506.14444

SAS in ESA Datalabs: A New Platform for XMM-Newton Analysis
XMM-Newton is a cornerstone mission of the European Space Agency (ESA) for X-ray astronomy, providing high-quality X-ray data for astrophysical research since the start of the century. Its Science Analysis System (SAS) has been a reliable data reduction and analysis software, evolving throughout the years to meet changing user needs, while incorporating new methods. This paper presents the XMM-SAS Datalab, a tool within the cloud-based ESA Datalabs platform, designed to enhance the interactivit…

Breakthrough Listen: A Technosignature Search Around 27 Eclipsing Exoplanets Selected from the Transiting Exoplanet Survey Satellite Catalogue
R. Barrett (University of Southern Queensland), C. D. Tremblay (SETI Institute, CSIRO Astronomy and Space Science), B. Addison (University of Southern Queensland Centre for Astrophysics, Swinburne University of Technology Centre for Astrophysics and Supercomputing), D. C. Price (International Centre for Radio Astronomy Research, SKA Observatory …

Breakthrough Listen: A Technosignature Search Around 27 Eclipsing Exoplanets Selected from the Transiting Exoplanet Survey Satellite Catalogue
Here we analyse the archival data for a set of 27 Transiting Exoplanet Survey Satellite (TESS) Targets of Interest (TOIs) in search for artificially generated radio signals, or 'technosignatures', interrupted by occultation. Exoplanetary eclipses are notable events to observe in the search for technosignatures, as they mark the geometrical alignment of the target, its host star, and Earth. During an eclipse event, any signal emanating from the target of interest should cease for the duration of…

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…

SDSS-V Milky Way Mapper (MWM): ASPCAP Stellar Parameters and Abundances in SDSS-V Data Release 19
Szabolcs M\'esz\'aros, Paula Jofr\'e, Jennifer A. Johnson, Jonathan C. Bird, Andrew R. Casey, Katia Cunha, Nathan De Lee, Peter Frinchaboy, Guillaume Guiglion, Viola Heged\H{u}s, Alex P. Ji, Juna A. Kollmeier, Melissa K. Ness, Jonah Otto, Marc H. Pinsonneault, Alexandre Roman-Lopes, Amaya Sinha, Ying-Yi Song, Guy S. Stringfellow, Keivan G. Stassun, Jamie Tayar, Andrew Tkachenko…

SDSS-V Milky Way Mapper (MWM): ASPCAP Stellar Parameters and Abundances in SDSS-V Data Release 19
The goal of this paper is to describe the science verification of Milky Way Mapper (MWM) ASPCAP data products published in Data Release 19 (DR19) of the fifth phase of the Sloan Digital Sky Survey (SDSS-V). We compare MWM ASPCAP atmospheric parameters T$_{\rm eff}$, log g, 24 abundances of 21 elements (carbon, nitrogen, and oxygen have multiple sources for deriving their abundance values) and their uncertainties determined from APOGEE spectrograph spectra with those of the literature and evalua…

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

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

Monitoring of Drift Patterns in Image Data
Subhasish Basak, Anik Roy, Partha Sarathi Mukherjee
https://arxiv.org/abs/2506.14260 https://

Monitoring of Drift Patterns in Image Data
Sequential monitoring of images has broad applications across various domains, including climate science, ecosystem monitoring, medical diagnostics, and so forth. In many such applications, images acquired over time exhibit gradual changes, referred to as drifts, which pose significant challenges for monitoring. Rather than detecting only abrupt step changes, it is crucial to monitor and characterize these drift patterns. Despite its practical importance, the problem of drift monitoring in imag…

📣 Calling all GÉANT Project partners…
Got an idea for digital research, data transfer or secure storage solutions to support open science?
The 2025 GÉANT Above-the-Net Services Incubator is officially open for proposals!
This is your opportunity to:
✅ Develop and test your innovative idea
✅ Deliver impact to the whole community through new, shared, open-source services
✅ Help shape the future of GÉANT's Above-the-Net services portfolio
Learn more: …

GÉANT Project Above-the-Net Services Incubator: Call for Proposals | GÉANT CONNECT Online
The GÉANT (GN5-2) Project’s Above-the-Net Services incubator is looking for practical proposals around three areas of service concept investment being developed by the GN5-2 Above-the-Net Services team. These are: Digital Research Environment (DRE) Object Storage Infrastructure Data Movement Infrastructure Through the incubator, the GN5-2 project will offer person months (PMs) to project partners to develop

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

We're recruiting on our MSc Sociology and Data Analytics for next September at the University of Limerick @…

This MSc offers in depth knowledge of sociological analysis, research… | Faculty of Arts, Humanities and Social Sciences, University of Limerick
This MSc offers in depth knowledge of sociological analysis, research methods and social science analytics - including high level training in data, data science, and data mining, and a range of sequenced topics that include crime, law, and deviance, culture and media, health and well-being, political analysis and social movements, and stratification and social inequality. The combination of high level training in research methods and robust exposure to core topics in Sociology and the Social …

Pivoting the paradigm: the role of spreadsheets in K-12 data science
Oren Tirschwell, Nicholas Jon Horton
https://arxiv.org/abs/2506.03232 https://

Pivoting the paradigm: the role of spreadsheets in K-12 data science
Spreadsheet tools are widely accessible to and commonly used by K-12 students and teachers. They have an important role in data collection and organization. Beyond data organization, spreadsheets also make data visible and easy to interact with, facilitating student engagement in data exploration and analysis. Though not suitable for all circumstances, spreadsheets can and do help foster data and computing skills for K-12 students. This paper 1) reviews prior frameworks on K-12 data tools; 2) p…

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…

Teams of #PurdueFortWayne 🐘 students and faculty presented their projects on data science and industrial consulting at the Indiana Data Mine Symposium at #Purdue #WestLafayette

📢 Call for PhD and Postdocs: Using data science to improve epidemic preparedness
https://nerds.itu.dk/2025/03/25/call-for-phd-and-postdocs-using-data-science-to-improve-epidemic-preparedness/
We are loo…

Mapping correlations and coherence: adjacency-based approach to data visualization and regularity discovery
Guang-Xing Li
https://arxiv.org/abs/2506.05758 …

Mapping correlations and coherence: adjacency-based approach to data visualization and regularity discovery
The development of science has been transforming man's view towards nature for centuries. Observing structures and patterns in an effective approach to discover regularities from data is a key step toward theory-building. With increasingly complex data being obtained, revealing regularities systematically has become a challenge. Correlation is a most commonly-used and effective approach to describe regularities in data, yet for complex patterns, spatial inhomogeneity and complexity can often un…

Next investigation should be into the councillors themselves
Warrnambool council abandons peer-reviewed flood study, citing 'supposed science' - ABC News
https://www.abc.net.au/news/2025-06-05/regiona…

Warrnambool council abandons peer-reviewed flood study, questioning 'supposed science'
An independent flood study flagging high-risk areas in Victoria's south-west is abandoned after councillors decide they do not trust the data.

Explainer-guided Targeted Adversarial Attacks against Binary Code Similarity Detection Models
Mingjie Chen (Zhejiang University), Tiancheng Zhu (Huazhong University of Science,Technology), Mingxue Zhang (The State Key Laboratory of Blockchain,Data Security, Zhejiang University,Hangzhou High-Tech Zone), Yiling He (University College London), Minghao Lin (University of Southern California), Penghui Li (Columbia University), Kui Ren (The State Key Laboratory of Blockchain,Data Security, Z…

Explainer-guided Targeted Adversarial Attacks against Binary Code Similarity Detection Models
Binary code similarity detection (BCSD) serves as a fundamental technique for various software engineering tasks, e.g., vulnerability detection and classification. Attacks against such models have therefore drawn extensive attention, aiming at misleading the models to generate erroneous predictions. Prior works have explored various approaches to generating semantic-preserving variants, i.e., adversarial samples, to evaluate the robustness of the models against adversarial attacks. However, the…

Insights from a 30-Year international Partnership on Astronomical Archives
David R. Rodriguez, Maria Arevalo, Patrick Dowler, Javier Espinosa, Brian McLean, Chris Willott
https://arxiv.org/abs/2506.11888

Insights from a 30-Year international Partnership on Astronomical Archives
In an era where astronomical data is expanding at an unprecedented rate, the importance of data sharing and accessibility among astronomy archives cannot be overstated. Since the 1990s, an international partnership between the Space Telescope Science Institute (STScI), the European Space Astronomy Centre (ESAC), and the Canadian Astronomy Data Centre (CADC) has been focused on this endeavor, facilitating the exchange of data from the Hubble and James Webb Space Telescopes. We will present how…

Trustworthy Provenance for Big Data Science: a Modular Architecture Leveraging Blockchain in Federated Settings
Nicola Giuseppe Marchioro, Yannis Velegrakis, Valentine Anantharaj, Ian Foster, Sandro Luigi Fiore
https://arxiv.org/abs/2505.24675

Trustworthy Provenance for Big Data Science: a Modular Architecture Leveraging Blockchain in Federated Settings
Ensuring the trustworthiness and long-term verifiability of scientific data is a foundational challenge in the era of data-intensive, collaborative research. Provenance metadata plays a key role in this context, capturing the origin, transformation, and usage of research artifacts. However, existing solutions often fall short when applied to distributed, multi-institutional settings. This paper introduces a modular, domain-agnostic architecture for provenance tracking in federated environments,…

A Tale of Two Systems: Characterizing Architectural Complexity on Machine Learning-Enabled Systems
Renato Cordeiro Ferreira (University of S\~ao Paulo, Jheronimus Academy of Data Science, Technical University of Eindhoven, Tilburg University)
https://arxiv.org/abs/2506.11295

A Tale of Two Systems: Characterizing Architectural Complexity on Machine Learning-Enabled Systems
How can the complexity of ML-enabled systems be managed effectively? The goal of this research is to investigate how complexity affects ML-Enabled Systems (MLES). To address this question, this research aims to introduce a metrics-based architectural model to characterize the complexity of MLES. The goal is to support architectural decisions, providing a guideline for the inception and growth of these systems. This paper brings, side-by-side, the architecture representation of two systems that …

The future of gravitational wave science unlocking LIGO potential: AI-driven data analysis and exploration
Yong Xiao, Li, Zin Nandar Win, He Wang, Hla Myo Tun, Win Thu Zar
https://arxiv.org/abs/2506.04584

The future of gravitational wave science unlocking LIGO potential: AI-driven data analysis and exploration
The advent of gravitational wave astronomy (GW) has revolutionized the observation of cataclysmic cosmic events, such as black hole mergers and neutron star collisions. The Laser Interferometer Gravitational-Wave Observatory (LIGO) has been at the forefront of these discoveries. However, the immense volume and complexity of gravitational wave data present significant challenges for traditional analysis methods. This paper investigates the growing synergy between artificial intelligence (AI) and…

lol, basically every single example in this post shows how the LLM is just generating context that's not in the actual image. But somehow this is sold as being better than "classical" computer vision.
I don't know folks, if I actually wanted to do "data science", with focus on the "science" bit, I'd be disturbed by that. 🤷‍♂️
https://fosstodon.org/@Posit/114597245963405210

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

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

DELPHYNE: A Pre-Trained Model for General and Financial Time Series
Xueying Ding, Aakriti Mittal, Achintya Gopal
https://arxiv.org/abs/2506.06288 https://

DELPHYNE: A Pre-Trained Model for General and Financial Time Series
Time-series data is a vital modality within data science communities. This is particularly valuable in financial applications, where it helps in detecting patterns, understanding market behavior, and making informed decisions based on historical data. Recent advances in language modeling have led to the rise of time-series pre-trained models that are trained on vast collections of datasets and applied to diverse tasks across financial domains. However, across financial applications, existing ti…

KI4Demokratie: An AI-Based Platform for Monitoring and Fostering Democratic Discourse
Rudy Alexandro Garrido Veliz, Till Nikolaus Schaland, Simon Bergmoser, Florian Horwege, Somya Bansal, Ritesh Nahar, Martin Semmann, J\"org Forthmann, Seid Muhie Yimam
https://arxiv.org/abs/2506.09947…

KI4Demokratie: An AI-Based Platform for Monitoring and Fostering Democratic Discourse
Social media increasingly fuel extremism, especially right-wing extremism, and enable the rapid spread of antidemocratic narratives. Although AI and data science are often leveraged to manipulate political opinion, there is a critical need for tools that support effective monitoring without infringing on freedom of expression. We present KI4Demokratie, an AI-based platform that assists journalists, researchers, and policymakers in monitoring right-wing discourse that may undermine democratic va…

sp_infectious: Art exhibit dynamic contacts (2011)
This dataset contains the daily dynamic contact networks collected during the Infectious SocioPatterns event that took place at the Science Gallery in Dublin, Ireland, during the artscience exhibition INFECTIOUS: STAY AWAY. Each file in the downloadable package contains a tab-separated list representing the active contacts during 20-second intervals of one day of data collection. Each line has the form “t i j“, where i and j are the a…

Series B, Episode 04 - Horizon
CALLY: [Enters] Orac, where did you get this information? [Holds up a data card. Avon takes it]
ORAC: I was instructed to obtain anything relating to the planet. The data was obtained by cross-referencing prisoner and execution lists. It is standard procedure.
https://blake.torpidity.n…

Blakes7Bot | Search
Companion site to the Blakes7Bot Twitter, Mastodon, and Bluesky accounts. Automated lines of Blakes 7 dialog. Includes a script search facility.

Observable Covariance and Principal Observable Analysis for Data on Metric Spaces
Ece Karacam, Washington Mio, Osman Berat Okutan
https://arxiv.org/abs/2506.04003

Observable Covariance and Principal Observable Analysis for Data on Metric Spaces
Datasets consisting of objects such as shapes, networks, images, or signals overlaid on such geometric objects permeate data science. Such datasets are often equipped with metrics that quantify the similarity or divergence between any pair of elements turning them into metric spaces $(X,d)$, or a metric measure space $(X,d,μ)$ if data density is also accounted for through a probability measure $μ$. This paper develops a Lipschitz geometry approach to analysis of metric measure spaces based on…

Roadmap for electronic structure, anharmonicity, and electron-phonon calculations in locally disordered inorganic and hybrid halide perovskites
Marios Zacharias, George Volonakis, Laurent Pedesseau, Claudine Katan, Feliciano Giustino, Jacky Even
https://arxiv.org/abs/2506.10402

Roadmap for electronic structure, anharmonicity, and electron-phonon calculations in locally disordered inorganic and hybrid halide perovskites
The role of data in modern materials science becomes more valuable and accurate when effects such as electron-phonon coupling and anharmonicity are included, providing a more realistic representation of finite-temperature material behavior. Furthermore, positional polymorphism, characterized by correlated local atomic disorder usually not reported by standard diffraction techniques, is a critical yet underexplored factor in understanding the electronic structure and transport properties of ener…

Stream DaQ: Stream-First Data Quality Monitoring
Vasileios Papastergios, Anastasios Gounaris
https://arxiv.org/abs/2506.06147 https://

Stream DaQ: Stream-First Data Quality Monitoring
Data quality is fundamental to modern data science workflows, where data continuously flows as unbounded streams feeding critical downstream tasks, from elementary analytics to advanced artificial intelligence models. Existing data quality approaches either focus exclusively on static data or treat streaming as an extension of batch processing, lacking the temporal granularity and contextual awareness required for true streaming applications. In this paper, we present a novel data quality monit…

On Inverse Problems, Parameter Estimation, and Domain Generalization
Deborah Pereg
https://arxiv.org/abs/2506.06024 https://arxiv.org…

On Inverse Problems, Parameter Estimation, and Domain Generalization
Signal restoration and inverse problems are key elements in most real-world data science applications. In the past decades, with the emergence of machine learning methods, inversion of measurements has become a popular step in almost all physical applications, which is normally executed prior to downstream tasks that often involve parameter estimation. In this work, we analyze the general problem of parameter estimation in an inverse problem setting. First, we address the domain-shift problem b…

Nonlinear Causal Discovery for Grouped Data
Konstantin G\"obler, Tobias Windisch, Mathias Drton
https://arxiv.org/abs/2506.05120 https://

Nonlinear Causal Discovery for Grouped Data
Inferring cause-effect relationships from observational data has gained significant attention in recent years, but most methods are limited to scalar random variables. In many important domains, including neuroscience, psychology, social science, and industrial manufacturing, the causal units of interest are groups of variables rather than individual scalar measurements. Motivated by these applications, we extend nonlinear additive noise models to handle random vectors, establishing a two-step …

Beyond Static Responses: Multi-Agent LLM Systems as a New Paradigm for Social Science Research
Jennifer Haase, Sebastian Pokutta
https://arxiv.org/abs/2506.01839

Beyond Static Responses: Multi-Agent LLM Systems as a New Paradigm for Social Science Research
As large language models (LLMs) transition from static tools to fully agentic systems, their potential for transforming social science research has become increasingly evident. This paper introduces a structured framework for understanding the diverse applications of LLM-based agents, ranging from simple data processors to complex, multi-agent systems capable of simulating emergent social dynamics. By mapping this developmental continuum across six levels, the paper clarifies the technical and …

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

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

This https://arxiv.org/abs/2412.04854 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepe…

First constraint for atmospheric millicharged particles with the LUX-ZEPLIN experiment
We report on a search for millicharged particles (mCPs) produced in cosmic ray proton atmospheric interactions using data collected during the first science run of the LUX-ZEPLIN experiment. The mCPs produced by two processes -- meson decay and proton bremsstrahlung -- are considered in this study. This search utilized a novel signature unique to liquid xenon (LXe) time projection chambers (TPCs), allowing sensitivity to mCPs with masses ranging from 10 to 1000 MeV/c$^2$ and fractional charges …

This https://arxiv.org/abs/2312.16139 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Abnormal component analysis
At the crossway of machine learning and data analysis, anomaly detection aims at identifying observations that exhibit abnormal behaviour. Be it measurement errors, disease development, severe weather, production quality default(s) (items) or failed equipment, financial frauds or crisis events, their on-time identification and isolation constitute an important task in almost any area of industry and science. While a substantial body of literature is devoted to detection of anomalies, little att…

Series B, Episode 04 - Horizon
CALLY: [Enters] Orac, where did you get this information? [Holds up a data card. Avon takes it]
ORAC: I was instructed to obtain anything relating to the planet. The data was obtained by cross-referencing prisoner and execution lists. It is standard procedure.
https://blake.torpidity.n…

Blakes7Bot | Search
Companion site to the Blakes7Bot Twitter, Mastodon, and Bluesky accounts. Automated lines of Blakes 7 dialog. Includes a script search facility.

Savage-Dickey density ratio estimation with normalizing flows for Bayesian model comparison
Kiyam Lin, Alicja Polanska, Davide Piras, Alessio Spurio Mancini, Jason D. McEwen
https://arxiv.org/abs/2506.04339

Savage-Dickey density ratio estimation with normalizing flows for Bayesian model comparison
A core motivation of science is to evaluate which scientific model best explains observed data. Bayesian model comparison provides a principled statistical approach to comparing scientific models and has found widespread application within cosmology and astrophysics. Calculating the Bayesian evidence is computationally challenging, especially as we continue to explore increasingly more complex models. The Savage-Dickey density ratio (SDDR) provides a method to calculate the Bayes factor (eviden…

Medium writer Paolo Perrone curates a short list of interesting algorithms, the rationale behind them, along with graphs and diagrams to boot.
Algorithms that made this short list:
Wave Function Collapse
The Diffusion Model
Simulated Annealing
Sleep Sort
BOGO Sort
BOID
SHOR’s
Marching Cubes
Practical Byzantine Fault Tolerance and,
Boyer Moore
"The 10 Weirdest, Most Brilliant Algorithms Ever Devised and What They Actually Do&…

10 Weirdest Algorithms That Changed Tech Forever | Data Science Collective
Discover 10 unusual algorithms that defied logic, broke rules, and transformed the way we think about technology and problem-solving.

faculty_hiring: Faculty hiring networks (Comp. Sci., Business, History)
Three networks of faculty hiring in Computer Science Departments, Business Schools, and History Departments. Each node is a PhD-granting institution in the respective field, and a directed edge (i,j) indicates that a person received their PhD from node i and was tenure-track faculty at node j during time of collection (2011-2013). All data collected from faculty public rosters at the sampled institutions.
Thi…

This https://arxiv.org/abs/2503.10569 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Low-Rank Matrix Regression via Least-Angle Regression
Low-rank matrix regression is a fundamental problem in data science with various applications in systems and control. Nuclear norm regularization has been widely applied to solve this problem due to its convexity. However, it suffers from high computational complexity and the inability to directly specify the rank. This work introduces a novel framework for low-rank matrix regression that addresses both unstructured and Hankel matrices. By decomposing the low-rank matrix into rank-1 bases, the …

Millimeter-wave observations of Euclid Deep Field South using the South Pole Telescope: A data release of temperature maps and catalogs
M. Archipley, A. Hryciuk, L. E. Bleem, K. Kornoelje, M. Klein, A. J. Anderson, B. Ansarinejad, M. Aravena, L. Balkenhol, P. S. Barry, K. Benabed, A. N. Bender, B. A. Benson, F. Bianchini, S. Bocquet, F. R. Bouchet, E. Camphuis, M. G. Campitiello, J. E. Carlstrom, J. Cathey, C. L. Chang, S. C. Chapman, P. Chaubal, P. M. Chichura, A. Chokshi, T. -L. Chou…

Millimeter-wave observations of Euclid Deep Field South using the South Pole Telescope: A data release of temperature maps and catalogs
Context. The South Pole Telescope third-generation camera (SPT-3G) has observed over 10,000 square degrees of sky at 95, 150, and 220 GHz (3.3, 2.0, 1.4 mm, respectively) overlapping the ongoing 14,000 square-degree Euclid Wide Survey. The Euclid collaboration recently released Euclid Deep Field observations in the first quick data release (Q1). Aims. With the goal of releasing complementary millimeter-wave data and encouraging legacy science, we performed dedicated observations of a 57-square-…

Edge interventions can mitigate demographic and prestige disparities in the Computer Science coauthorship network
Kate Barnes, Mia Ellis-Einhorn, Carolina Ch\'avez-Ruelas, Nayera Hasan, Mohammad Fanous, Blair D. Sullivan, Sorelle Friedler, Aaron Clauset
https://arxiv.org/abs/2506.04435…

Edge interventions can mitigate demographic and prestige disparities in the Computer Science coauthorship network
Social factors such as demographic traits and institutional prestige structure the creation and dissemination of ideas in academic publishing. One place these effects can be observed is in how central or peripheral a researcher is in the coauthorship network. Here we investigate inequities in network centrality in a hand-collected data set of 5,670 U.S.-based faculty employed in Ph.D.-granting Computer Science departments and their DBLP coauthorship connections. We introduce algorithms for comb…

MetaInfoSci: An Integrated Web Tool for Scholarly Data Analysis
Kiran Sharmaa, Parul Khurana, Ziya Uddina
https://arxiv.org/abs/2506.09056 https://

MetaInfoSci: An Integrated Web Tool for Scholarly Data Analysis
The exponential increase in academic publications has made it increasingly difficult for researchers to remain up to date and systematically synthesize knowledge scattered across vast and fragmented research domains. Literature reviews, particularly those supported by bibliometric methods, have become essential in organizing prior findings and guiding future research directions. While numerous tools exist for bibliometric analysis and network science, there is currently no single platform that …

Symbolic Foundation Regressor on Complex Networks
Weiting Liu, Jiaxu Cui, Jiao Hu, En Wang, Bo Yang
https://arxiv.org/abs/2505.21879 https://

Symbolic Foundation Regressor on Complex Networks
In science, we are interested not only in forecasting but also in understanding how predictions are made, specifically what the interpretable underlying model looks like. Data-driven machine learning technology can significantly streamline the complex and time-consuming traditional manual process of discovering scientific laws, helping us gain insights into fundamental issues in modern science. In this work, we introduce a pre-trained symbolic foundation regressor that can effectively compress …

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

A Metrics-Oriented Architectural Model to Characterize Complexity on Machine Learning-Enabled Systems
Renato Cordeiro Ferreira (University of S\~ao Paulo, Jheronimus Academy of Data Science, Technical University of Eindhoven, Tilburg University)
https://arxiv.org/abs/2506.08153

A Metrics-Oriented Architectural Model to Characterize Complexity on Machine Learning-Enabled Systems
How can the complexity of ML-enabled systems be managed effectively? The goal of this research is to investigate how complexity affects ML-Enabled Systems (MLES). To address this question, this research aims to introduce a metrics-based architectural model to characterize the complexity of MLES. The goal is to support architectural decisions, providing a guideline for the inception and growth of these systems. This paper showcases the first step for creating the metrics-based architectural mode…

Benchmarking Adelaide
Great Australian Pods Podcast Directory: #GreatAusPods

Benchmarking Adelaide | Great Australian Pods Podcast Directory
Podcast: Benchmarking Adelaide Host: Sam Dighton Category: Society & Culture Sub-Category: Entrepreneurship, Local News & Stories, Mathematics & Data Science First Episode: 2024 We sit down with…

This https://arxiv.org/abs/2503.17945 has been replaced.
initial toot: https://mastoxiv.page/@a…

Deep Learning Assisted Denoising of Experimental Micrographs
Microstructure imaging is crucial in materials science, but experimental images often introduce noise that obscures critical structural details. This study presents a novel deep learning approach for robust microstructure image denoising, combining phase-field simulations, Fourier transform techniques, and an attention-based neural network. The innovative framework addresses dataset limitations by synthetically generating training data by combining computational phase-field microstructures with…

Replaced article(s) found for stat.OT. https://arxiv.org/list/stat.OT/new/
[1/1]:
A Mathematical Lens for Teaching Data Science
https://

The SPHEREx Sky Simulator: Science Data Modeling for the First All-Sky Near-Infrared Spectral Survey
Brendan P. Crill, Yoonsoo P. Bach, Sean A. Bryan, Jean Choppin de Janvry, Ari J. Cukierman, C. Darren Dowell, Spencer W. Everett, Candice Fazar, Tatiana Goldina, Zhaoyu Huai, Howard Hui, Woong-Seob Jeong, Jae Hwan Kang, Phillip M. Korngut, Jae Joon Lee, Daniel C. Masters, Chi H. Nguyen, Jeonghyun Pyo, Teresa Symons, Yujin Yang, Michael Zemcov, Rachel Akeson, Matthew L. N. Ashby, James J…

The SPHEREx Sky Simulator: Science Data Modeling for the First All-Sky Near-Infrared Spectral Survey
We describe the SPHEREx Sky Simulator, a software tool designed to model science data for NASA's SPHEREx mission that will carry out a series of all-sky spectrophotometric surveys at $\sim$6'' spatial resolution in 102 spectral channels spanning 0.75 to 5 $μ$m. The Simulator software implements models for astrophysical emission, instrument characteristics, and survey strategy to generate realistic infrared sky scenes as they will be observed by SPHEREx. The simulated data includes a variety of…

#Blakes7 Series B, Episode 06 - Trial
THANIA: We reserve our opening declaration, sir.
SAMOR: Very well. Enter prosecution data. [A clerk presses some buttons.]
https://blake.torpidity.net/m/206/53

Blakes7Bot | Search
Companion site to the Blakes7Bot Twitter, Mastodon, and Bluesky accounts. Automated lines of Blakes 7 dialog. Includes a script search facility.

faculty_hiring: Faculty hiring networks (Comp. Sci., Business, History)
Three networks of faculty hiring in Computer Science Departments, Business Schools, and History Departments. Each node is a PhD-granting institution in the respective field, and a directed edge (i,j) indicates that a person received their PhD from node i and was tenure-track faculty at node j during time of collection (2011-2013). All data collected from faculty public rosters at the sampled institutions.
Thi…

Challenging Spontaneous Quantum Collapse with XENONnT
E. Aprile, J. Aalbers, K. Abe, S. Ahmed Maouloud, L. Althueser, B. Andrieu, E. Angelino, D. Ant\'on Martin, S. R. Armbruster, F. Arneodo, L. Baudis, M. Bazyk, L. Bellagamba, R. Biondi, A. Bismark, K. Boese, A. Brown, G. Bruno, R. Budnik, C. Cai, C. Capelli, J. M. R. Cardoso, A. P. Cimental Ch\'avez, A. P. Colijn, J. Conrad, J. J. Cuenca-Garc\'ia, C. Curceanu, V. D'Andrea, L. C. Daniel Garcia, M. P. Decowski, A. Deist…

Challenging Spontaneous Quantum Collapse with XENONnT
We report on the search for X-ray radiation as predicted from dynamical quantum collapse with low-energy electronic recoil data in the energy range of 1-140 keV from the first science run of the XENONnT dark matter detector. Spontaneous radiation is an unavoidable effect of dynamical collapse models, which were introduced as a possible solution to the long-standing measurement problem in quantum mechanics. The analysis utilizes a model that for the first time accounts for cancellation effects i…

Analysis of points outcome in ATP Grand Slam Tennis using big data and machine learning
Martin Illum (Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Denmark), Hans Christian Bechs{\o}fft Mikkelsen (Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Denmark), Emil Hovad (Department of Applied Mathematics and Computer Science, Technical University of Denmark, …

Analysis of points outcome in ATP Grand Slam Tennis using big data and machine learning
Tennis is one of the world's biggest and most popular sports. Multiple researchers have, with limited success, modeled the outcome of matches using probability modelling or machine learning approaches. The approach presented here predicts the outcomes of points in tennis matches. This is based on given a probability of winning a point, based on the prior history of matches, the current match, the player rankings and if the points are started with a first or second. The use of historical public …

Motivando el uso y aprendizaje de Bash a trav\'es de concursos de programaci\'on
Luis Costero, Jorge Villarrubia, Francisco D. Igual
https://arxiv.org/abs/2506.00105

Motivando el uso y aprendizaje de Bash a través de concursos de programación
Command line learning and Bash usage are fundamental skills in systems administration, software development, and data science environments. However, their teaching has been neglected in many curricula, despite its relevance in the professional field. To address this gap, we developed an interactive competition that encourages students to improve their Bash skills through practical and competitive challenges. This gamified approach seeks to motivate autonomous learning and reinforce command line…

This https://arxiv.org/abs/2502.06753 has been replaced.
link: https://scholar.google.com/scholar?q=a

SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence era
Surrogate models are widely used to approximate complex systems across science and engineering to reduce computational costs. Despite their widespread adoption, the field lacks standardisation across key stages of the modelling pipeline, including data sampling, model selection, evaluation, and downstream analysis. This fragmentation limits reproducibility and cross-domain utility -- a challenge further exacerbated by the rapid proliferation of AI-driven surrogate models. We argue for the urgen…

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…

Replaced article(s) found for physics.soc-ph. https://arxiv.org/list/physics.soc-ph/new/
[1/1]:
Growth of Science and Women: Methodological Challenges of Using Structured Big Data

TFW you've spent ages trying to solve a tricky problem and it. just. does. not. work. You take a break, come back, try something new, nope still nothi... Wait! It's there, there it is! That's the data...
🥳🎊
That is why we love doing #science and in an increasingly packed day, the opportunity to work away uninterrupted* on a focused problem is increasingly rare. *That's* why I love fieldwork...
Details in a #FieldDiary soon.
*Uninterrupted time helped today by failure of all internet phone communication in Qaanaaq..
#Fieldwork #LifeOfAScientist #AcademicChatter

Generalised Time-Series Analysis of Fault Mechanics Using Explainable AI
Thomas King, Sergio Vinciguerra
https://arxiv.org/abs/2505.21312 https://

Generalised Time-Series Analysis of Fault Mechanics Using Explainable AI
Understanding how faults nucleate and grow is a critical problem in earthquake science and hazard assessment. This study examines fault development in Alzo granite under triaxial pressures ranging from 5 to 40 MPa by applying a Time Delay Neural Network (TDNN) to multi-parameter acoustic emission (AE) data. The TDNN integrates waveform-derived attributes, including peak delay and scattering attenuation, with occurrence-based metrics such as time distributions, Gutenberg-Richter b-values, and sp…

This https://arxiv.org/abs/2408.08294 has been replaced.
link: https://scholar.google.com/scholar?q=a

eGAD! double descent is explained by Generalized Aliasing Decomposition
A central problem in data science is to use potentially noisy samples of an unknown function to predict values for unseen inputs. In classical statistics, predictive error is understood as a trade-off between the bias and the variance that balances model simplicity with its ability to fit complex functions. However, over-parameterized models exhibit counterintuitive behaviors, such as "double descent" in which models of increasing complexity exhibit decreasing generalization error. Others may e…

faculty_hiring: Faculty hiring networks (Comp. Sci., Business, History)
Three networks of faculty hiring in Computer Science Departments, Business Schools, and History Departments. Each node is a PhD-granting institution in the respective field, and a directed edge (i,j) indicates that a person received their PhD from node i and was tenure-track faculty at node j during time of collection (2011-2013). All data collected from faculty public rosters at the sampled institutions.
Thi…

PDRs4All XIV: CH radical and $H_3^ $ molecular ion in the irradiated protoplanetary disk d203-506
I. Schroetter, O. Bern\'e, J. R. Goicoechea, J. H. Black, O. Roncero, F. Alarcon, P. Amiot, O. Asvany, C. Boersma, S. Br\"unken, J. Cami, L. Coudert, E. Dartois, A. Fuente, B. Gans, A. Gusdorf, U. Jacovella, M. A. Martin Drumel, T. Onaka, E. Peeters, E. Roueff, A. G. G. M. Tielens, M. Zannese

PDRs4All XIV: CH radical and $H_3^+$ molecular ion in the irradiated protoplanetary disk d203-506
Most protoplanetary disks experience a phase in which they are subjected to strong ultraviolet radiation from nearby massive stars. This UV radiation can substantially alter their chemistry by producing numerous radicals and molecular ions. In this Letter we present detailed analysis of the JWST-NIRSpec spectrum of the d203-506 obtained as part of the PDRs4All Early Release Science program. Using state-of-the-art spectroscopic data, we searched for species using a multi-molecule fitting tool, P…

This https://arxiv.org/abs/2304.05775 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Mapping compact memory-biased dynamics reveals overlapping communities
Researchers use networks to model relational data from complex systems and apply tools from network science to reveal their organisation and function. Many real-world systems, from social networks to protein-protein interactions and species distributions, exhibit overlapping flow-based communities that reflect their functional organisation. However, identifying such overlapping flow-based communities typically requires higher-order relational data, which is often unavailable. To address this ch…

sp_infectious: Art exhibit dynamic contacts (2011)
This dataset contains the daily dynamic contact networks collected during the Infectious SocioPatterns event that took place at the Science Gallery in Dublin, Ireland, during the artscience exhibition INFECTIOUS: STAY AWAY. Each file in the downloadable package contains a tab-separated list representing the active contacts during 20-second intervals of one day of data collection. Each line has the form “t i j“, where i and j are the a…

Hello there.
I, or maybe we, intend this to serve both as a diary and a reference.
We are a jack of many trades. As such, it is hard to squeeze into 1.5k symbols, and it is by no means comprehensive. Still, sometimes labels are helpful.
Here is about IT, interconnections between technologies (Fullstack, Data Science... sometimes even AI and ethics of it). Politics, because our life is inevitably tied to it (especially with

#gsa #nyc #columbia #climate
The original Charney Report that says doubling CO2 will yield 1.5-4.5 degrees C temperature change (
#NOAA 's #GFDL and #NASA 's #GISS - incidentally, gfdl was also in the news yesterday
https://www.propublica.org/article/trump-noaa-budget-cuts-climate-change-modeling-princeton-gfdl
" The gutting of NOAA was outlined earlier this month in a leaked memo from the Office of Management and Budget that detailed steep reductions at the Department of Commerce, which houses the science agency. ... NOAA’s overall funding would be slashed by 27%…
most significant target is the office of Oceanic and Atmospheric Research ⎯ a nerve center of global climate science, data collection and modeling, including the Geophysical Fluid Dynamics Laboratory ⎯ which would be cut by 74%. “At this funding level, OAR is eliminated as a line office,” the memo stated.”

This https://arxiv.org/abs/2502.00599 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_nuc…

Time Evolution of Prompt Gamma-Ray Emission in $^{252}$Cf(sf) and $^{233,235}$U($n$,f) Reactions
We investigate the time evolution of prompt fission $γ$ emission due to the presence of ns to ms isomers in the fragments produced in the neutron-induced fission of $^{233,235}$U and in the spontaneous fission of $^{252}$Cf. Calculations performed with the CGMF fission event generator are compared with recent experimental data on $^{252}$Cf(sf) and $^{235}$U($n$,f) obtained with the DANCE+NEUANCE setup at the Los Alamos Neutron Science Center. Of particular interest are the average $γ$-ray en…

This https://arxiv.org/abs/2502.17841 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Quantifying interdisciplinary synergy in higher STEM education
We propose a framework to quantify and utilize interdisciplinarity in science and engineering curricula in the case of university-level higher education. We analyze interdisciplinary relations by standardizing large-scale official educational data in Korea using a cutting-edge large language model and constructing knowledge maps for disciplines of scientific education. We design and evaluate single-field and integrated dual-field curricula by adapting pedagogical theory and utilizing informatio…