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Relaxation-Free Min-k-Partition for PCI Assignment in 5G Networks
Yeqing Qiu, Chengpiao Huang, Ye Xue, Zhipeng Jiang, Qingjiang Shi, Dong Zhang, Zhi-Quan Luo
https://arxiv.org/abs/2506.10362

Relaxation-Free Min-k-Partition for PCI Assignment in 5G Networks
Physical Cell Identity (PCI) is a critical parameter in 5G networks. Efficient and accurate PCI assignment is essential for mitigating mod-3 interference, mod-30 interference, collisions, and confusions among cells, which directly affect network reliability and user experience. In this paper, we propose a novel framework for PCI assignment by decomposing the problem into Min-3-Partition, Min-10-Partition, and a graph coloring problem, leveraging the Chinese Remainder Theorem (CRT). Furthermore,…

EmbodiedGen: Towards a Generative 3D World Engine for Embodied Intelligence
Wang Xinjie, Liu Liu, Cao Yu, Wu Ruiqi, Qin Wenkang, Wang Dehui, Sui Wei, Su Zhizhong
https://arxiv.org/abs/2506.10600

EmbodiedGen: Towards a Generative 3D World Engine for Embodied Intelligence
Constructing a physically realistic and accurately scaled simulated 3D world is crucial for the training and evaluation of embodied intelligence tasks. The diversity, realism, low cost accessibility and affordability of 3D data assets are critical for achieving generalization and scalability in embodied AI. However, most current embodied intelligence tasks still rely heavily on traditional 3D computer graphics assets manually created and annotated, which suffer from high production costs and li…

Instance-Based Transfer Learning with Similarity-Aware Subject Selection for Cross-Subject SSVEP-Based BCIs
Ziwen Wang, Yue Zhang, Zhiqiang Zhang, Sheng Quan Xie, Alexander Lanzon, William P. Heath, Zhenhong Li
https://arxiv.org/abs/2506.10933

Instance-Based Transfer Learning with Similarity-Aware Subject Selection for Cross-Subject SSVEP-Based BCIs
Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) can achieve high recognition accuracy with sufficient training data. Transfer learning presents a promising solution to alleviate data requirements for the target subject by leveraging data from source subjects; however, effectively addressing individual variability among both target and source subjects remains a challenge. This paper proposes a novel transfer learning framework, termed instance-based task-relat…

Replaced article(s) found for q-bio.QM. https://arxiv.org/list/q-bio.QM/new/
[1/1]:
DeepGDel: Deep Learning-based Gene Deletion Prediction Framework for Growth-Coupled Production in...

The Freight Multimodal Transport Problem with Buses and Drones: An Integrated Approach for Last-Mile Delivery
E Su, Hu Qin, Jiliu Li, Rui Zhang
https://arxiv.org/abs/2506.10311

The Freight Multimodal Transport Problem with Buses and Drones: An Integrated Approach for Last-Mile Delivery
This paper proposes a novel freight multimodal transport problem with buses and drones, where buses are responsible for transporting parcels to lockers at bus stops for storage, while drones are used to deliver each parcel from the locker to the corresponding customer. The integrated bus-drone system synergistically expands drone service coverage using the bus network to ensure efficient final delivery. Minimizing the total operational costs while satisfying customer demands necessitates the jo…

Jamie Raskin: “**And the minute that somebody in public office thinks that they’re a king, they’re a queen, they’re an emperor, they’re a dictator, that it is time for the people to evict, eject, reject, impeach, try, convict, and start all over again. Because the most important words of our Constitution are the three first words of the Constitution: ‘We the people**’”
This Democrat just reminded us why we all must declare ‘No Kings’
https://www.dailykos.com/stories/2025/6/13/2327843/-This-Democrat-just-reminded-us-why-we-all-must-declare-No-Kings

BugGen: A Self-Correcting Multi-Agent LLM Pipeline for Realistic RTL Bug Synthesis
Surya Jasper, Minh Luu, Evan Pan, Aakash Tyagi, Michael Quinn, Jiang Hu, David Kebo Houngninou
https://arxiv.org/abs/2506.10501

BugGen: A Self-Correcting Multi-Agent LLM Pipeline for Realistic RTL Bug Synthesis
Hardware complexity continues to strain verification resources, motivating the adoption of machine learning (ML) methods to improve debug efficiency. However, ML-assisted debugging critically depends on diverse and scalable bug datasets, which existing manual or automated bug insertion methods fail to reliably produce. We introduce BugGen, a first of its kind, fully autonomous, multi-agent pipeline leveraging Large Language Models (LLMs) to systematically generate, insert, and validate realisti…

Transformer IMU Calibrator: Dynamic On-body IMU Calibration for Inertial Motion Capture
Chengxu Zuo, Jiawei Huang, Xiao Jiang, Yuan Yao, Xiangren Shi, Rui Cao, Xinyu Yi, Feng Xu, Shihui Guo, Yipeng Qin
https://arxiv.org/abs/2506.10580

Transformer IMU Calibrator: Dynamic On-body IMU Calibration for Inertial Motion Capture
In this paper, we propose a novel dynamic calibration method for sparse inertial motion capture systems, which is the first to break the restrictive absolute static assumption in IMU calibration, i.e., the coordinate drift RG'G and measurement offset RBS remain constant during the entire motion, thereby significantly expanding their application scenarios. Specifically, we achieve real-time estimation of RG'G and RBS under two relaxed assumptions: i) the matrices change negligibly in a short tim…

Skin Deep (PC)
There's a new pirate queen in space, and she has a hit out on you, insurance commando extraordinaire Nina Pasadena. Get out there and save those cats!
TL;DR: a fun FPS stealth-ish romp with a good sense of humor and mechanics.
Skin Deep is weird, but in a good way. For some reason this universe is populated with voxel cats that crew space ships, and it's your job to get tucked away in cryosleep until pirates hijack them. Then you're woken up and h…

[2025-06-13 Fri (UTC), 5 new articles found for q-bio.QM Quantitative Methods]
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