Tootfinder

Localized and weighted versions of extremal problems
Binlong Li, Bo Ning
https://arxiv.org/abs/2509.17055 https://arxiv.org/pdf/2509.17055

Localized and weighted versions of extremal problems
Malec and Tompkins (EUJC, 2023) considered the localized versions of Turán-type problems, and proved a localized theorem on Erdős-Gallai Theorem on paths. Zhao and Zhang (JGT, 2025) gave a long proof of a localized version of Erdős-Gallai Theorem on cycles. In this paper, we consider several types of generalization of Turán-type problems, that is, localized versions, weighted versions, and generalized Turán-type problems, and their connectedness. We first present very short proofs for re…

Multi-needle Localization for Pelvic Seed Implant Brachytherapy based on Tip-handle Detection and Matching
Zhuo Xiao, Fugen Zhou, Jingjing Wang, Chongyu He, Bo Liu, Haitao Sun, Zhe Ji, Yuliang Jiang, Junjie Wang, Qiuwen Wu
https://arxiv.org/abs/2509.17931

Multi-needle Localization for Pelvic Seed Implant Brachytherapy based on Tip-handle Detection and Matching
Accurate multi-needle localization in intraoperative CT images is crucial for optimizing seed placement in pelvic seed implant brachytherapy. However, this task is challenging due to poor image contrast and needle adhesion. This paper presents a novel approach that reframes needle localization as a tip-handle detection and matching problem to overcome these difficulties. An anchor-free network, based on HRNet, is proposed to extract multi-scale features and accurately detect needle tips and han…

On tested Bousfield-Friedlander localizations
Niall Taggart
https://arxiv.org/abs/2507.17321 https://arxiv.org/pdf/2507.17321

On tested Bousfield-Friedlander localizations
We demonstrate that a Bousfield-Friedlander localization with a set of test morphisms in the sense introduced by Bandklayder, Bergner, Griffiths, Johnson, and Santhanam can also be characterized as a left Bousfield localization at the set of test morphisms. This viewpoint enables us to establish a homogeneous model structure associated with any calculus arising from a Bousfield-Friedlander localization of this form. As a corollary, we show that homogeneous functors in discrete calculus coincide…

Localizing Malicious Outputs from CodeLLM
Mayukh Borana, Junyi Liang, Sai Sathiesh Rajan, Sudipta Chattopadhyay
https://arxiv.org/abs/2509.17070 https://ar…

Localizing Malicious Outputs from CodeLLM
We introduce FreqRank, a mutation-based defense to localize malicious components in LLM outputs and their corresponding backdoor triggers. FreqRank assumes that the malicious sub-string(s) consistently appear in outputs for triggered inputs and uses a frequency-based ranking system to identify them. Our ranking system then leverages this knowledge to localize the backdoor triggers present in the inputs. We create nine malicious models through fine-tuning or custom instructions for three downstr…

RepILN: Reparameterized Inertial Localization Network
Shanshan Zhang, Tianshui Wen, Siyue Wang, Qi Zhang, Ziheng Zhou, Lingxiang Zheng, Yu Yang
https://arxiv.org/abs/2507.15293

RepILN: Reparameterized Inertial Localization Network
Inertial localization is regarded as a promising positioning solution for consumer-grade IoT devices due to its cost-effectiveness and independence from external infrastructure. However, data-driven inertial localization methods often rely on increasingly complex network architectures to improve accuracy, which challenges the limited computational resources of IoT devices. Moreover, these methods frequently overlook the importance of modeling long-term dependencies in inertial measurements - a …

Hybrid Baseband Simulation for Single-Channel Radar-Based Indoor Localization System
Sven Hinderer, Zheming Yin, Athanasios Papanikolaou, Jan Hesselbarth, Bin Yang
https://arxiv.org/abs/2509.15650

Hybrid Baseband Simulation for Single-Channel Radar-Based Indoor Localization System
Indoor localization with chirp sequence radar at millimeter wavelength offers high localization accuracy at low system cost. We propose a hybrid radar baseband signal simulator for our novel single-channel radar-based indoor localization system consisting of an active radar and passive reflectors as references. By combining ray tracing channel simulations with real measurements of the two-way antenna gain of the radar and accurate simulation of the radar cross section of chosen reflectors, real…

Beyond Diagnosis: Evaluating Multimodal LLMs for Pathology Localization in Chest Radiographs
Advait Gosai, Arun Kavishwar, Stephanie L. McNamara, Soujanya Samineni, Renato Umeton, Alexander Chowdhury, William Lotter
https://arxiv.org/abs/2509.18015

Beyond Diagnosis: Evaluating Multimodal LLMs for Pathology Localization in Chest Radiographs
Recent work has shown promising performance of frontier large language models (LLMs) and their multimodal counterparts in medical quizzes and diagnostic tasks, highlighting their potential for broad clinical utility given their accessible, general-purpose nature. However, beyond diagnosis, a fundamental aspect of medical image interpretation is the ability to localize pathological findings. Evaluating localization not only has clinical and educational relevance but also provides insight into a …

Bench-RNR: Dataset for Benchmarking Repetitive and Non-repetitive Scanning LiDAR for Infrastructure-based Vehicle Localization
Runxin Zhao, Chunxiang Wang, Hanyang Zhuang, Ming Yang
https://arxiv.org/abs/2509.15583

Bench-RNR: Dataset for Benchmarking Repetitive and Non-repetitive Scanning LiDAR for Infrastructure-based Vehicle Localization
Vehicle localization using roadside LiDARs can provide centimeter-level accuracy for cloud-controlled vehicles while simultaneously serving multiple vehicles, enhanc-ing safety and efficiency. While most existing studies rely on repetitive scanning LiDARs, non-repetitive scanning LiDAR offers advantages such as eliminating blind zones and being more cost-effective. However, its application in roadside perception and localization remains limited. To address this, we present a dataset for infrast…

StableGuard: Towards Unified Copyright Protection and Tamper Localization in Latent Diffusion Models
Haoxin Yang, Bangzhen Liu, Xuemiao Xu, Cheng Xu, Yuyang Yu, Zikai Huang, Yi Wang, Shengfeng He
https://arxiv.org/abs/2509.17993

StableGuard: Towards Unified Copyright Protection and Tamper Localization in Latent Diffusion Models
The advancement of diffusion models has enhanced the realism of AI-generated content but also raised concerns about misuse, necessitating robust copyright protection and tampering localization. Although recent methods have made progress toward unified solutions, their reliance on post hoc processing introduces considerable application inconvenience and compromises forensic reliability. We propose StableGuard, a novel framework that seamlessly integrates a binary watermark into the diffusion gen…