Tootfinder

Früher stand das Silicon Valley für Innovation und Freiheit, heute zerstören Tech-Giganten die Demokratie. Die ehemalige EU-Abgeordnete Marietje Schaake kämpft gegen den zunehmenden Einfluss der Tech-Konzerne auf Politik. Sie warnt Medien davor, sich von Big Tech abhängig zu machen – und hofft auf einen Journalismus, der nicht länger auf die Narrative der Tech-Giganten hereinfällt.

Marietje Schaake: „Medien machen sich von mächtigen…
Marietje Schaake warnt im journalist-Interview Medien davor, sich von großen Tech Unternehmen abhängig zu machen.

Three Hot Jupiters transiting K-dwarfs with a significant heavy element mass
Y. G. C. Frensch, F. Bouchy, G. Lo Curto, S. Ulmer-Moll, S. G. Sousa, N. C. Santos, K. G. Stassun, C. N. Watkins, H. Chakraborty, K. Barkaoui, M. Battley, W. Ceva, K. A. Collins, T. Daylan, P. Evans, J. P. Faria, C. Farret Jentink, E. Fontanet, E. Frid\'en, G. Furesz, M. Gillon, N. Grieves, C. Hellier, E. Jehin, J. M. Jenkins, L. K. W. Kwok, D. W. Latham, B. Lavie, N. Law, A. W. Mann, F. Murgas, E. Palle, …

Three Hot Jupiters transiting K-dwarfs with a significant heavy element mass
Albeit at a lower frequency than around hotter stars, short-period gas giants around low-mass stars ($T_\mathrm{eff} < 4965$ K) do exist, despite predictions from planetary population synthesis models that such systems should be exceedingly rare. By combining data from TESS and ground-based follow-up observations, we seek to confirm and characterize giant planets transiting K dwarfs, particularly mid/late K dwarfs. Photometric data were obtained from the TESS mission, supplemented by ground-bas…

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

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

Datasheets Aren't Enough: DataRubrics for Automated Quality Metrics and Accountability
Genta Indra Winata, David Anugraha, Emmy Liu, Alham Fikri Aji, Shou-Yi Hung, Aditya Parashar, Patrick Amadeus Irawan, Ruochen Zhang, Zheng-Xin Yong, Jan Christian Blaise Cruz, Niklas Muennighoff, Seungone Kim, Hanyang Zhao, Sudipta Kar, Kezia Erina Suryoraharjo, M. Farid Adilazuarda, En-Shiun Annie Lee, Ayu Purwarianti, Derry Tanti Wijaya, Monojit Choudhury

Datasheets Aren't Enough: DataRubrics for Automated Quality Metrics and Accountability
High-quality datasets are fundamental to training and evaluating machine learning models, yet their creation-especially with accurate human annotations-remains a significant challenge. Many dataset paper submissions lack originality, diversity, or rigorous quality control, and these shortcomings are often overlooked during peer review. Submissions also frequently omit essential details about dataset construction and properties. While existing tools such as datasheets aim to promote transparency…

DV365: Extremely Long User History Modeling at Instagram
Wenhan Lyu, Devashish Tyagi, Yihang Yang, Ziwei Li, Ajay Somani, Karthikeyan Shanmugasundaram, Nikola Andrejevic, Ferdi Adeputra, Curtis Zeng, Arun K. Singh, Maxime Ransan, Sagar Jain
https://arxiv.org/abs/2506.00450

DV365: Extremely Long User History Modeling at Instagram
Long user history is highly valuable signal for recommendation systems, but effectively incorporating it often comes with high cost in terms of data center power consumption and GPU. In this work, we chose offline embedding over end-to-end sequence length optimization methods to enable extremely long user sequence modeling as a cost-effective solution, and propose a new user embedding learning strategy, multi-slicing and summarization, that generates highly generalizable user representation of …

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Enhanced Mesenchymal Stem Cell Response with Preserved Biocompatibility via (MnZn)Ferrite--Polyacrylonitrile Composite Nanofiber Membranes
Baran Sarac, Elham Sharifikolouei, Matej Micusik, Alessandro Scalia, Ziba Najmi, Andrea Cochis, Lia Rimondini, Gabriele Barrera, Marco Coisson, Selin G\"umr\"ukc\"u, Eray Y\"uce, A. Sezai Sarac

Enhanced Mesenchymal Stem Cell Response with Preserved Biocompatibility via (MnZn)Ferrite--Polyacrylonitrile Composite Nanofiber Membranes
This study focuses on the synthesis and characterization of advanced polymeric composite electrospun nanofibers (NFs) containing magnetic oxide nanoparticles (NPs). By leveraging the method of electrospinning, the research aims to investigate polymer composites with enhanced interfacial properties, improved double-layer capacitance, and adequate biocompatibility. Electrospun polyacrylonitrile (PAN) NFs embedded with Fe2O3 and MnZn ferrite NPs were comprehensively characterized using advanced te…

Meson-baryon scattering lengths without annihilation diagrams to order $p^4$ in heavy baryon chiral perturbation theory
Jing Ou-Yang, Ke Wang, Bo-Lin Huang
https://arxiv.org/abs/2506.22339

Meson-baryon scattering lengths without annihilation diagrams to order $p^4$ in heavy baryon chiral perturbation theory
We calculate the threshold $T$ matrices of the meson and baryon processes that have no annihilation diagrams: $π^{+}Σ^{+}$, $π^{+}Ξ^0$, $K^+p$, $K^+n$, and $\bar{K}^0Ξ^0$ to the fourth order in heavy baryon chiral perturbation theory. By performing least squares and Bayesian fits to the non-physical lattice QCD data, we determine the low-energy constants through both perturbative and non-perturbative iterative methods. By using these low-energy constants, we obtain the physical scattering …

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network
Yining Pang, Chenghan Li
https://arxiv.org/abs/2506.19871

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network
Insurance fraud detection represents a pivotal advancement in modern insurance service, providing intelligent and digitalized monitoring to enhance management and prevent fraud. It is crucial for ensuring the security and efficiency of insurance systems. Although AI and machine learning algorithms have demonstrated strong performance in detecting fraudulent claims, the absence of standardized defense mechanisms renders current systems vulnerable to emerging adversarial threats. In this paper, w…

A Start To End Machine Learning Approach To Maximize Scientific Throughput From The LCLS-II-HE
Aashwin Mishra, Matt Seaberg, Ryan Roussel, Fred Poitevin, Jana Thayer, Daniel Ratner, Auralee Edelen, Apurva Mehta
https://arxiv.org/abs/2505.23858

A Start To End Machine Learning Approach To Maximize Scientific Throughput From The LCLS-II-HE
With the increasing brightness of Light sources, including the Diffraction-Limited brightness upgrade of APS and the high-repetition-rate upgrade of LCLS, the proposed experiments therein are becoming increasingly complex. For instance, experiments at LCLS-II-HE will require the X-ray beam to be within a fraction of a micron in diameter, with pointing stability of a few nanoradians, at the end of a kilometer-long electron accelerator, a hundred-meter-long undulator section, and tens of meters l…