Tootfinder

Interesting,
"One Token to rule them all - obtaining Global Admin in every Entra ID tenant via Actor tokens"
https://dirkjanm.io/obtaining-global-admin-in-every-entra-id-tenant-with-actor-tokens/

Morphology-optimized Multi-Scale Fusion: Combining Local Artifacts and Mesoscopic Semantics for Deepfake Detection and Localization
Chao Shuai, Gaojian Wang, Kun Pan, Tong Wu, Fanli Jin, Haohan Tan, Mengxiang Li, Zhenguang Liu, Feng Lin, Kui Ren
https://arxiv.org/abs/2509.13776

Morphology-optimized Multi-Scale Fusion: Combining Local Artifacts and Mesoscopic Semantics for Deepfake Detection and Localization
While the pursuit of higher accuracy in deepfake detection remains a central goal, there is an increasing demand for precise localization of manipulated regions. Despite the remarkable progress made in classification-based detection, accurately localizing forged areas remains a significant challenge. A common strategy is to incorporate forged region annotations during model training alongside manipulated images. However, such approaches often neglect the complementary nature of local detail and…

Learn to optimize for automatic proton PBS treatment planning for H&N cancers
Qingqing Wang, Liqiang Xiao, Chang Chang
https://arxiv.org/abs/2508.11085 https://

Learn to optimize for automatic proton PBS treatment planning for H&N cancers
Proton PBS treatment planning for H&N cancers involves numerous conflicting objectives, requiring significant effort from human planners to balance and satisfy multiple clinical goals during planning. To achieve this, experience-demanding objective parameter adjustment and computationally expensive inverse optimization are performed iteratively. Extensive efforts have been made to automatically adjust objective parameters, but the most time-consuming component, i.e., inverse optimization, still…

"While preparing for my Black Hat and DEF CON talks in July of this year, I found the most impactful #EntraID #vulnerability that I will probably ever find. This vulnerability could have allowed me to compromise every Entra ID tenant in the world (except probably those in national cloud deploymen…

Semi-Infinite Programming for Collision-Avoidance in Optimal and Model Predictive Control
Yunfan Gao, Florian Messerer, Niels van Duijkeren, Rashmi Dabir, Moritz Diehl
https://arxiv.org/abs/2508.12335 …

Semi-Infinite Programming for Collision-Avoidance in Optimal and Model Predictive Control
This paper presents a novel approach for collision avoidance in optimal and model predictive control, in which the environment is represented by a large number of points and the robot as a union of padded polygons. The conditions that none of the points shall collide with the robot can be written in terms of an infinite number of constraints per obstacle point. We show that the resulting semi-infinite programming (SIP) optimal control problem (OCP) can be efficiently tackled through a combinati…

An Introduction to Sliced Optimal Transport
Khai Nguyen
https://arxiv.org/abs/2508.12519 https://arxiv.org/pdf/2508.12519

An Introduction to Sliced Optimal Transport
Sliced Optimal Transport (SOT) is a rapidly developing branch of optimal transport (OT) that exploits the tractability of one-dimensional OT problems. By combining tools from OT, integral geometry, and computational statistics, SOT enables fast and scalable computation of distances, barycenters, and kernels for probability measures, while retaining rich geometric structure. This paper provides a comprehensive review of SOT, covering its mathematical foundations, methodological advances, computa…

Low-rank surrogate modeling and stochastic zero-order optimization for training of neural networks with black-box layers
Andrei Chertkov, Artem Basharin, Mikhail Saygin, Evgeny Frolov, Stanislav Straupe, Ivan Oseledets
https://arxiv.org/abs/2509.15113

Low-rank surrogate modeling and stochastic zero-order optimization for training of neural networks with black-box layers
The growing demand for energy-efficient, high-performance AI systems has led to increased attention on alternative computing platforms (e.g., photonic, neuromorphic) due to their potential to accelerate learning and inference. However, integrating such physical components into deep learning pipelines remains challenging, as physical devices often offer limited expressiveness, and their non-differentiable nature renders on-device backpropagation difficult or infeasible. This motivates the develo…

MOVER: Multimodal Optimal Transport with Volume-based Embedding Regularization
Haochen You, Baojing Liu
https://arxiv.org/abs/2508.12149 https://arxiv.org/…

MOVER: Multimodal Optimal Transport with Volume-based Embedding Regularization
Recent advances in multimodal learning have largely relied on pairwise contrastive objectives to align different modalities, such as text, video, and audio, in a shared embedding space. While effective in bi-modal setups, these approaches struggle to generalize across multiple modalities and often lack semantic structure in high-dimensional spaces. In this paper, we propose MOVER, a novel framework that combines optimal transport-based soft alignment with volume-based geometric regularization t…

An Efficient Medical Image Classification Method Based on a Lightweight Improved ConvNeXt-Tiny Architecture
Jingsong Xia, Yue Yin, Xiuhan Li
https://arxiv.org/abs/2508.11532 htt…

An Efficient Medical Image Classification Method Based on a Lightweight Improved ConvNeXt-Tiny Architecture
Intelligent analysis of medical imaging plays a crucial role in assisting clinical diagnosis. However, achieving efficient and high-accuracy image classification in resource-constrained computational environments remains challenging. This study proposes a medical image classification method based on an improved ConvNeXt-Tiny architecture. Through structural optimization and loss function design, the proposed method enhances feature extraction capability and classification performance while redu…

e-boost: Boosted E-Graph Extraction with Adaptive Heuristics and Exact Solving
Jiaqi Yin, Zhan Song, Chen Chen, Yaohui Cai, Zhiru Zhang, Cunxi Yu
https://arxiv.org/abs/2508.13020

e-boost: Boosted E-Graph Extraction with Adaptive Heuristics and Exact Solving
E-graphs have attracted growing interest in many fields, particularly in logic synthesis and formal verification. E-graph extraction is a challenging NP-hard combinatorial optimization problem. It requires identifying optimal terms from exponentially many equivalent expressions, serving as the primary performance bottleneck in e-graph based optimization tasks. However, traditional extraction methods face a critical trade-off: heuristic approaches offer speed but sacrifice optimality, while exac…