Tootfinder

As Cyber Threats Escalate, the National Vulnerability Database Is Falling Behind
The National Institute of Standards and Technology (NIST) is struggling.
It faces a growing backlog to process data in its vulnerability repository, which publicly shares information assessing and detailing mitigation solutions against new cyber exploits.
With nearly 1,800 new reported vulnerabilities sitting in a queue for analysis this year, delays in processing leave the United States increa…

LoRA-fine-tuned Large Vision Models for Automated Assessment of Post-SBRT Lung Injury
M. Bolhassani, B. Veasey, E. Daugherty, S. Keltner, N. Kumar, N. Dunlap, A. Amini
https://arxiv.org/abs/2509.12155 …

LoRA-fine-tuned Large Vision Models for Automated Assessment of Post-SBRT Lung Injury
This study investigates the efficacy of Low-Rank Adaptation (LoRA) for fine-tuning large Vision Models, DinoV2 and SwinV2, to diagnose Radiation-Induced Lung Injury (RILI) from X-ray CT scans following Stereotactic Body Radiation Therapy (SBRT). To evaluate the robustness and efficiency of this approach, we compare LoRA with traditional full fine-tuning and inference-only (no fine-tuning) methods. Cropped images of two sizes (50 mm3 and 75 mm3), centered at the treatment isocenter, in addition …

DMLDroid: Deep Multimodal Fusion Framework for Android Malware Detection with Resilience to Code Obfuscation and Adversarial Perturbations
Doan Minh Trung, Tien Duc Anh Hao, Luong Hoang Minh, Nghi Hoang Khoa, Nguyen Tan Cam, Van-Hau Pham, Phan The Duy
https://arxiv.org/abs/2509.11187

DMLDroid: Deep Multimodal Fusion Framework for Android Malware Detection with Resilience to Code Obfuscation and Adversarial Perturbations
In recent years, learning-based Android malware detection has seen significant advancements, with detectors generally falling into three categories: string-based, image-based, and graph-based approaches. While these methods have shown strong detection performance, they often struggle to sustain robustness in real-world settings, particularly when facing code obfuscation and adversarial examples (AEs). Deep multimodal learning has emerged as a promising solution, leveraging the strengths of mult…

Tensor Invariant Data-Assisted Control and Dynamic Decomposition of Multibody Systems
Mostafa Eslami, Maryam Babazadeh
https://arxiv.org/abs/2509.11688 https://

Tensor Invariant Data-Assisted Control and Dynamic Decomposition of Multibody Systems
The control of robotic systems in complex, shared collaborative workspaces presents significant challenges in achieving robust performance and safety when learning from experienced or simulated data is employed in the pipeline. A primary bottleneck is the reliance on coordinate-dependent models, which leads to profound data inefficiency by failing to generalize physical interactions across different frames of reference. This forces learning algorithms to rediscover fundamental physical principl…

Room temperature control of axial and basal antiferromagnetic anisotropies using strain
Jack Harrison, Junxiong Hu, Charles Godfrey, Jheng-Cyuan Lin, Tim A Butcher, J\"org Raabe, Simone Finizio, Hariom Jani, Paolo G Radaelli
https://arxiv.org/abs/2510.12222

Room temperature control of axial and basal antiferromagnetic anisotropies using strain
Antiferromagnetic materials are promising platforms for the development of ultra-fast spintronics and magnonics due to their robust magnetism, high-frequency relativistic dynamics, low-loss transport, and the ability to support topological textures. However, achieving deterministic control over antiferromagnetic order in thin films is a major challenge, due to the formation of multi-domain states stabilised by competing magnetic and destressing interactions. Thus, the successful implementation …

Robust Recovery and Control of Cyber-physical Discrete Event Systems under Actuator Attacks
Samuel Oliveira, Mostafa Tavakkoli Anbarani, Gregory Beal, Ilya Kovalenko, Marcelo Teixeira, Andr\'e B. Leal, R\^omulo Meira-G\'oes
https://arxiv.org/abs/2510.11405

Robust Recovery and Control of Cyber-physical Discrete Event Systems under Actuator Attacks
Critical real-world applications strongly rely on Cyber-physical systems (CPS), but their dependence on communication networks introduces significant security risks, as attackers can exploit vulnerabilities to compromise their integrity and availability. This work explores the topic of cybersecurity in the context of CPS modeled as discrete event systems (DES), focusing on recovery strategies following the detection of cyberattacks. Specifically, we address actuator enablement attacks and propo…

I was looking into the #DANDI archive, following a post of @… that mentioned it as a possibility for #Neuroscience data storage.. It looks like it could be useful i…

Those who follow me may be feeling overwhelmed by the number of appeals for help from people in #Gaza that I'm reposting.
I am sorry; I feel overwhelmed myself. I cannot help most of these people. My resources are very limited. I can't tell you whether one person's need is more urgent than another's.
But this need is real and desperate. Bombs are still falling; aid is sti…

Geometric Analysis of Magnetic Labyrinthine Stripe Evolution via U-Net Segmentation
Vin\'icius Yu Okubo, Kotaro Shimizu, B. S. Shivaran, Gia-Wei Chern, Hae Yong Kim
https://arxiv.org/abs/2509.11485

Geometric Analysis of Magnetic Labyrinthine Stripe Evolution via U-Net Segmentation
Labyrinthine stripe patterns are common in many physical systems, yet their lack of long-range order makes quantitative characterization challenging. We investigate the evolution of such patterns in bismuth-doped yttrium iron garnet (Bi:YIG) films subjected to a magnetic field annealing protocol. A U-Net deep learning model, trained with synthetic degradations including additive white Gaussian and Simplex noise, enables robust segmentation of experimental magneto-optical images despite noise an…

Time-To-Inconsistency: A Survival Analysis of Large Language Model Robustness to Adversarial Attacks
Yubo Li, Ramayya Krishnan, Rema Padman
https://arxiv.org/abs/2510.02712 http…

Time-To-Inconsistency: A Survival Analysis of Large Language Model Robustness to Adversarial Attacks
Large Language Models (LLMs) have revolutionized conversational AI, yet their robustness in extended multi-turn dialogues remains poorly understood. Existing evaluation frameworks focus on static benchmarks and single-turn assessments, failing to capture the temporal dynamics of conversational degradation that characterize real-world interactions. In this work, we present the first comprehensive survival analysis of conversational AI robustness, analyzing 36,951 conversation turns across 9 stat…