Tootfinder

The third dimension of cities - relating building height, urban area, and population
Peiran Zhang, Liang Gao, Fabiano L. Ribeiro, Bin Jia, Ziyou Gao, Diego Rybski
https://arxiv.org/abs/2508.20114

The third dimension of cities - relating building height, urban area, and population
For decades, urban development was studied on two-dimensional maps, largely ignoring the third dimension. However, building height is crucial because it dramatically potentiates the interior space of cities. Here, using a newly released global building height dataset of 2903 cities across 42 countries in 2015, we develop a Cobb-Douglas model to simultaneously examine the relationship between urban population size and both horizontal and vertical urban extents. We find that, contrary to expectat…

VisionSafeEnhanced VPC: Cautious Predictive Control with Visibility Constraints under Uncertainty for Autonomous Robotic Surgery
Wang Jiayin, Wei Yanran, Jiang Lei, Guo Xiaoyu, Zheng Ayong, Zhao Weidong, Li Zhongkui
https://arxiv.org/abs/2508.18937

VisionSafeEnhanced VPC: Cautious Predictive Control with Visibility Constraints under Uncertainty for Autonomous Robotic Surgery
Autonomous control of the laparoscope in robot-assisted Minimally Invasive Surgery (MIS) has received considerable research interest due to its potential to improve surgical safety. Despite progress in pixel-level Image-Based Visual Servoing (IBVS) control, the requirement of continuous visibility and the existence of complex disturbances, such as parameterization error, measurement noise, and uncertainties of payloads, could degrade the surgeon's visual experience and compromise procedural saf…

GPUnion: Autonomous GPU Sharing on Campus
Yufang Li (The Hong Kong University of Science,Technology), Yuanbo Zhang (Sun Yat-sen University), Hanlong Liao (National University of Defense Technology), Guoming Tang (The Hong Kong University of Science,Technology), Deke Guo (Sun Yat-sen University)
https://arxiv.org/abs/2507.18928

GPUnion: Autonomous GPU Sharing on Campus
A pronounced imbalance in GPU resources exists on campus, where some laboratories own underutilized servers while others lack the compute needed for AI research. GPU sharing can alleviate this disparity, while existing platforms typically rely on centralized oversight and persistent allocation models, conflicting with the voluntary and autonomous nature of academic resource ownership. We present GPUnion, a campus-scale GPU sharing platform enabling voluntary participation while preserving full …

Equinox: Holistic Fair Scheduling in Serving Large Language Models
Zhixiang Wei, James Yen, Jingyi Chen, Ziyang Zhang, Zhibai Huang, Chen Chen, Xingzi Yu, Yicheng Gu, Chenggang Wu, Yun Wang, Mingyuan Xia, Jie Wu, Hao Wang, Zhengwei Qi
https://arxiv.org/abs/2508.16646

Equinox: Holistic Fair Scheduling in Serving Large Language Models
We address the limitations of current LLM serving with a dual-counter framework separating user and operator perspectives. The User Fairness Counter measures quality of service via weighted tokens and latency; the Resource Fairness Counter measures operational efficiency through throughput and GPU utilization. Since these metrics are only available post-execution, creating a scheduling paradox, we introduce a deterministic Mixture of Prediction Experts (MoPE) framework to predict user-perceived…

Error-resilient Reversal of Quantum Chaotic Dynamics Enabled by Scramblons
Yu-Chen Li, Tian-Gang Zhou, Shengyu Zhang, Ze Wu, Liqiang Zhao, Haochuan Yin, Xiaoxue An, Hui Zhai, Pengfei Zhang, Xinhua Peng
https://arxiv.org/abs/2506.19915

Error-resilient Reversal of Quantum Chaotic Dynamics Enabled by Scramblons
The emergence of the arrow of time in quantum many-body systems stems from the inherent tendency of Hamiltonian evolution to scramble quantum information and increase entanglement. While, in principle, one might counteract this temporal directionality by engineering a perfectly inverted Hamiltonian to reverse entanglement growth, such a scenario is fundamentally unstable because even minor imperfections in the backward evolution can be exponentially amplified, a hallmark of quantum many-body ch…

From Cancer Drivers to Cancer Keepers: Paradigm Shift and Clinical Implications
Xizhe Zhang, Weixiong Zhang
https://arxiv.org/abs/2508.16128 https://arxiv.…

From Cancer Drivers to Cancer Keepers: Paradigm Shift and Clinical Implications
Cancer research has traditionally focused on identifying driver genes, those with mutations that initiate tumorigenesis. The Cancer Driver Gene (CDG) paradigm, further supported by the observation of oncogene addiction in tumors, has successfully guided the development of targeted therapies. However, the limitations of this driver-centric view, highlighted by the broad emergence of frequent therapeutic resistance, the presence of driver mutations in healthy tissues or individuals, and the lack …

ClusterRCA: Network Failure Diagnosis in HPC Systems Using Multimodal Data
Yongqian Sun, Xijie Pan, Xiao Xiong, Lei Tao, Jiaju Wang, Shenglin Zhang, Yuan Yuan, Yuqi Li, Kunlin Jian
https://arxiv.org/abs/2506.20673

ClusterRCA: Network Failure Diagnosis in HPC Systems Using Multimodal Data
Network failure diagnosis is challenging yet critical for high-performance computing (HPC) systems. Existing methods cannot be directly applied to HPC scenarios due to data heterogeneity and lack of accuracy. This paper proposes a novel framework, called ClusterRCA, to localize culprit nodes and determine failure types by leveraging multimodal data. ClusterRCA extracts features from topologically connected network interface controller (NIC) pairs to analyze the diverse, multimodal data in HPC s…

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training
Talha Mehboob, Luanzheng Guo, Nathan Tallent, Michael Zink, David Irwin
https://arxiv.org/abs/2507.17904

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training
The exponential growth of large-scale AI models has led to computational and power demands that can exceed the capacity of a single data center. This is due to the limited power supplied by regional grids that leads to limited regional computational power. Consequently, distributing training workloads across geographically distributed sites has become essential. However, this approach introduces a significant challenge in the form of communication overhead, creating a fundamental trade-off betw…

Shelby: Decentralized Storage Designed to Serve
Guy Goren, Andrew Hariri, Timothy D. R. Hartley, Ravi Kappiyoor, Alexander Spiegelman, David Zmick
https://arxiv.org/abs/2506.19233

Shelby: Decentralized Storage Designed to Serve
Existing decentralized storage protocols fall short of the service required by real-world applications. Their throughput, latency, cost-effectiveness, and availability are insufficient for demanding workloads such as video streaming, large-scale data analytics, or AI training. As a result, Web3 data-intensive applications are predominantly dependent on centralized infrastructure. Shelby is a high-performance decentralized storage protocol designed to meet demanding needs. It achieves fast, re…

Crosslisted article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
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- Towards Integrated Energy-Communication-Transportation Hub: A Base-Station-Centric Design in 5G a...
Linfeng Shen, Guanzhen Wu, Cong Zhang, Xiaoyi Fan, Jiangchuan Liu