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A new class of regularized preconditioners for double saddle-point problems
Achraf Badahmane
https://arxiv.org/abs/2508.19469 https://arxiv.org/pdf/2508.19…

A new class of regularized preconditioners for double saddle-point problems
The block structure of double saddle-point problems has prompted extensive research into efficient preconditioners. This paper introduces a novel class of three-by-three block preconditioners tailored for such systems from the time-dependent Maxwell equations or liquid crystal director modeling. The main motivation of this work is to highlight the limitations of recent preconditioners under high Reynolds numbers, as the original studies did not explore this scenario, and to demonstrate that our…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/7]:
- A Novel Perturb-ability Score to Mitigate Evasion Adversarial Attacks on Flow-Based ML-NIDS
Mohamed elShehaby, Ashraf Matrawy

Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming
Humza Ashraf, Syed Muhammad Danish, Aris Leivadeas, Yazan Otoum, Zeeshan Sattar
https://arxiv.org/abs/2508.08332

Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming
Large Language Models (LLMs) are widely used for code generation. However, commercial models like ChatGPT require significant computing power, which leads to high energy use and carbon emissions. This has raised concerns about their environmental impact. In this study, we evaluate open-source Small Language Models (SLMs) trained explicitly for code generation and compare their performance and energy efficiency against large LLMs and efficient human-written Python code. The goal is to investigat…

Replaced article(s) found for cs.CR. https://arxiv.org/list/cs.CR/new
[1/2]:
- A Novel Perturb-ability Score to Mitigate Evasion Adversarial Attacks on Flow-Based ML-NIDS
Mohamed elShehaby, Ashraf Matrawy

Transportation Cyber Incident Awareness through Generative AI-Based Incident Analysis and Retrieval-Augmented Question-Answering Systems
Ostonya Thomas, Muhaimin Bin Munir, Jean-Michel Tine, Mizanur Rahman, Yuchen Cai, Khandakar Ashrafi Akbar, Md Nahiyan Uddin, Latifur Khan, Trayce Hockstad, Mashrur Chowdhury
https://arxiv.org/abs/2508.025…

Transportation Cyber Incident Awareness through Generative AI-Based Incident Analysis and Retrieval-Augmented Question-Answering Systems
Technological advancements have revolutionized numerous industries, including transportation. While digitalization, automation, and connectivity have enhanced safety and efficiency, they have also introduced new vulnerabilities. With 95% of data breaches attributed to human error, promoting cybersecurity awareness in transportation is increasingly critical. Despite numerous cyberattacks on transportation systems worldwide, comprehensive and centralized records of these incidents remain scarce. …

Modified Cubic B-spline Based Differential Quadrature Methods for Time-fractional Black-Scholes Equation
Nizamudheen V, Riyasudheen TK, Noufal Asharaf, Shefeeq T
https://arxiv.org/abs/2508.06780

Modified Cubic B-spline Based Differential Quadrature Methods for Time-fractional Black-Scholes Equation
The time-fractional Black-Scholes equation (TFBSE) is intended to price the options for which the underlying price fluctuates within a correlated fractal transmission system. Although the TFBSE is an influential approach for grasping the long-term memory traits of financial markets, the non-local nature of fractional derivatives makes significant challenges in finding an accurate solution. We perform an efficient use of the differential quadrature method (DQM) based on modified cubic B-splines …