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2025 NFL Draft first-round pick contract tracker: Giants sign Abdul Carter and Jaxson Dart

https://www.cbssports.com/nfl/news/2025-nf

Querying Attack-Fault-Defense Trees: Property Specification in Smart Grid and Aerospace Case Studies
Reza Soltani, Stefano M. Nicoletti, Milan Lopuha\"a-Zwakenberg, Mari\"elle Stoelinga
https://arxiv.org/abs/2506.23789

Querying Attack-Fault-Defense Trees: Property Specification in Smart Grid and Aerospace Case Studies
This paper introduces AFDL, a logic-based framework for reasoning about safety, security, and defense interactions in Attack-Fault-Defense Trees, which is a model that captures all safety, security, and defense domains in a single framework. We showcase both AFDL and propose a structured domain specific query language, LangAFDL, which enables domain experts to express complex analysis goals through intuitive templates. LangAFDL supports both Boolean and quantified queries as well as minimal cut…

»Dein Gesicht gehört uns – Die Gefahr automatischer Gesichtserkennung:
Ein Computersystem, das sekundenschnell jeden einzelnen Menschen auf der Welt identifizieren kann: Das klingt mehr nach dem Roman «1984» von George Orwell als nach der Realität. Und doch: Die Software des US-Unternehmens Clearview AI ist schon sehr nahe daran, genau das zu schaffen.«
Wenn deren "Sicherheit" auch zur Bevölkerung Überwachung eingesetzt wird?
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Dein Gesicht gehört uns – Die Gefahr automatischer Gesichtserkennung - DOK - Play SRF
Ein Computersystem, das sekundenschnell jeden einzelnen Menschen auf der Welt identifizieren kann: Das klingt mehr nach dem Roman «1984» von George Orwell als nach der Realität. Und doch: Die Software des US-Unternehmens Clearview AI ist schon sehr nahe daran, genau das zu schaffen. Neue Technologien durchdringen unser Leben, zum Guten und manchmal auch zum Schlechten. Jaime Ramirez ist Leiter der Abteilung für Ermittlungen mithilfe künstlicher Intelligenz bei der Polizei von Miami – ein…

Rises for Measuring Local Distributivity in Lattices
Mohammad Abdulla, Tobias Hille, Dominik D\"urrschnabel, Gerd Stumme
https://arxiv.org/abs/2506.23168

Rises for Measuring Local Distributivity in Lattices
Distributivity is a well-established and extensively studied notion in lattice theory. In the context of data analysis, particularly within Formal Concept Analysis (FCA), lattices are often observed to exhibit a high degree of distributivity. However, no standardized measure exists to quantify this property. In this paper, we introduce the notion of rises in (concept) lattices as a means to assess distributivity. Rises capture how the number of attributes or objects in covering concepts change …

Exploring the change in scientific readability following the release of ChatGPT
Abdulkareem Alsudais
https://arxiv.org/abs/2506.21825 https://

Exploring the change in scientific readability following the release of ChatGPT
The rise and growing popularity of accessible large language models have raised questions about their impact on various aspects of life, including how scientists write and publish their research. The primary objective of this paper is to analyze a dataset consisting of all abstracts posted on arXiv.org between 2010 and June 7th, 2024, to assess the evolution of their readability and determine whether significant shifts occurred following the release of ChatGPT in November 2022. Four standard re…

Multi-to one-dimensional screening and semi-discrete optimal transport
Omar Abdul Halim, Brendan Pass
https://arxiv.org/abs/2506.21740 https://

Multi-to one-dimensional screening and semi-discrete optimal transport
We study the monopolist's screening problem with a multi-dimensional distribution of consumers and a one-dimensional space of goods. We establish general conditions under which solutions satisfy a structural condition known as nestedness, which greatly simplifies their analysis and characterization. Under these assumptions, we go on to develop a general method to solve the problem, either in closed form or with relatively simple numerical computations, and illustrate it with examples. These res…

Some invariant connections on symplectic reductive homogeneous spaces
Abdelhak Abouqateb, Othmane Dani
https://arxiv.org/abs/2506.23211 https://

Some invariant connections on symplectic reductive homogeneous spaces
A symplectic reductive homogeneous space is a pair $(G/H,Ω)$, where $G/H$ is a reductive homogeneous $G$-space and $Ω$ is a $G$-invariant symplectic form on it. The main examples include symplectic Lie groups, symplectic symmetric spaces, and flag manifolds. This paper focuses on the existence of a natural symplectic connection on $(G/H,Ω)$. First, we introduce a family $\{\nabla^{a,b}\}_{(a,b)\in\mathbb{R}^2}$ of $G$-invariant connection on $G/H$, and establish that $\nabla^{0,1}$ is flat i…

Learning-Based Hybrid Neural Receiver for 6G-V2X Communications
Osama Saleem, Mohammed Alfaqawi, Pierre Merdrignac, Abdelaziz Bensrhair, Soheyb Ribouh
https://arxiv.org/abs/2506.21983

Learning-Based Hybrid Neural Receiver for 6G-V2X Communications
Neural receiver models are proposed to jointly optimize multiple functionalities of wireless receivers; however, a comprehensive receiver model that replaces the entire physical layer blocks has not yet been presented in the literature. In this work, we introduce a novel hybrid neural receiver (H-NR) built on Transformer encoder blocks and Graph Neural Network (GNN), as part of an end-to-end wireless communication framework. In our communication framework, we assume vehicle to network (V2N) upl…

When GNNs Met a Word Equations Solver: Learning to Rank Equations (Extended Technical Report)
Parosh Aziz Abdulla, Mohamed Faouzi Atig, Julie Cailler, Chencheng Liang, Philipp R\"ummer
https://arxiv.org/abs/2506.23784

When GNNs Met a Word Equations Solver: Learning to Rank Equations (Extended Technical Report)
Nielsen transformation is a standard approach for solving word equations: by repeatedly splitting equations and applying simplification steps, equations are rewritten until a solution is reached. When solving a conjunction of word equations in this way, the performance of the solver will depend considerably on the order in which equations are processed. In this work, the use of Graph Neural Networks (GNNs) for ranking word equations before and during the solving process is explored. For this, a…

Demonstrating Interoperable Channel State Feedback Compression with Machine Learning
Dani Korpi, Rachel Wang, Jerry Wang, Abdelrahman Ibrahim, Carl Nuzman, Runxin Wang, Kursat Rasim Mestav, Dustin Zhang, Iraj Saniee, Shawn Winston, Gordana Pavlovic, Wei Ding, William J. Hillery, Chenxi Hao, Ram Thirunagari, Jung Chang, Jeehyun Kim, Bartek Kozicki, Dragan Samardzija, Taesang Yoo, Andreas Maeder, Tingfang Ji, Harish Viswanathan

Demonstrating Interoperable Channel State Feedback Compression with Machine Learning
Neural network-based compression and decompression of channel state feedback has been one of the most widely studied applications of machine learning (ML) in wireless networks. Various simulation-based studies have shown that ML-based feedback compression can result in reduced overhead and more accurate channel information. However, to the best of our knowledge, there are no real-life proofs of concepts demonstrating the benefits of ML-based channel feedback compression in a practical setting, …