Tootfinder

Waltz: Temperature-Aware Cooperative Compression for High-Performance Compression-Based CSDs
Dingcui Yu, Yunpeng Song, Yiyang Huang, Yumiao Zhao, Yina Lv, Chundong Wang, Youtao Zhang, Liang Shi
https://arxiv.org/abs/2509.05365

Waltz: Temperature-Aware Cooperative Compression for High-Performance Compression-Based CSDs
Data compression is widely adopted for modern solid-state drives (SSDs) to mitigate both storage capacity and SSD lifetime issues. Researchers have proposed compression schemes at different system layers, including device-side solutions like CCSDs ( c ompression-based c omputational SSDs) and compression supported by host-side, like F2FS (flash-friendly file system). We conduct quantitative studies to understand how host-side and device-side compression schemes affect the temperature and perfor…

Joint Probing and Scheduling for Cache-Aided Hybrid Satellite-Terrestrial Networks
Zhou Zhang, Yizhu Wang, Saman Atapattu, Sumei Sun
https://arxiv.org/abs/2510.04492 https://

Joint Probing and Scheduling for Cache-Aided Hybrid Satellite-Terrestrial Networks
Caching is crucial in hybrid satellite-terrestrial networks to reduce latency, optimize throughput, and improve data availability by storing frequently accessed content closer to users, especially in bandwidth-limited satellite systems, requiring strategic Medium Access Control (MAC) layer. This paper addresses throughput optimization in satellite-terrestrial integrated networks through opportunistic cooperative caching. We propose a joint probing and scheduling strategy to enhance content retr…

MARCO: A Cooperative Knowledge Transfer Framework for Personalized Cross-domain Recommendations
Lili Xie, Yi Zhang, Ruihong Qiu, Jiajun Liu, Sen Wang
https://arxiv.org/abs/2510.04508

MARCO: A Cooperative Knowledge Transfer Framework for Personalized Cross-domain Recommendations
Recommender systems frequently encounter data sparsity issues, particularly when addressing cold-start scenarios involving new users or items. Multi-source cross-domain recommendation (CDR) addresses these challenges by transferring valuable knowledge from multiple source domains to enhance recommendations in a target domain. However, existing reinforcement learning (RL)-based CDR methods typically rely on a single-agent framework, leading to negative transfer issues caused by inconsistent doma…

Resolving Tangling in Multi-Conformer Refinement via Iterative Projections
Avinash Mandaiya, Veit Elser
https://arxiv.org/abs/2509.05189 https://arxiv.org/…

Resolving Tangling in Multi-Conformer Refinement via Iterative Projections
The advent of advanced crystallographic techniques has shifted structural biology from static, single-conformer models toward probing protein dynamics. Extracting cooperative motions from temporally and spatially averaged electron density maps requires both high-resolution data and refinement algorithms capable of handling conformational heterogeneity. However, current refinement protocols often fail due to the tangling phenomenon, in which conformational states become improperly intertwined du…

The new USGS Cooperative National Geologic Map web tool was designed to also be mobile friendly,
allowing users to explore geologic data from smartphones and tablets, in addition to larger devices
https://ngmdb.usgs.gov/nationalgeology

Cooperative National Geologic Map | NGMDB
Explore the Cooperative National Geologic Map, a unified view of our Nation's geology, with interactive layers and geologic data.

Multi-Agent Guided Policy Search for Non-Cooperative Dynamic Games
Jingqi Li, Gechen Qu, Jason J. Choi, Somayeh Sojoudi, Claire Tomlin
https://arxiv.org/abs/2509.24226 https://

Multi-Agent Guided Policy Search for Non-Cooperative Dynamic Games
Multi-agent reinforcement learning (MARL) optimizes strategic interactions in non-cooperative dynamic games, where agents have misaligned objectives. However, data-driven methods such as multi-agent policy gradients (MA-PG) often suffer from instability and limit-cycle behaviors. Prior stabilization techniques typically rely on entropy-based exploration, which slows learning and increases variance. We propose a model-based approach that incorporates approximate priors into the reward function a…

Research Challenges and Progress in the End-to-End V2X Cooperative Autonomous Driving Competition
Ruiyang Hao, Haibao Yu, Jiaru Zhong, Chuanye Wang, Jiahao Wang, Yiming Kan, Wenxian Yang, Siqi Fan, Huilin Yin, Jianing Qiu, Yao Mu, Jiankai Sun, Li Chen, Walter Zimmer, Dandan Zhang, Shanghang Zhang, Mac Schwager, Wei Huang, Xiaobo Zhang, Ping Luo, Zaiqing Nie
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Research Challenges and Progress in the End-to-End V2X Cooperative Autonomous Driving Competition
With the rapid advancement of autonomous driving technology, vehicle-to-everything (V2X) communication has emerged as a key enabler for extending perception range and enhancing driving safety by providing visibility beyond the line of sight. However, integrating multi-source sensor data from both ego-vehicles and infrastructure under real-world constraints, such as limited communication bandwidth and dynamic environments, presents significant technical challenges. To facilitate research in this…

HYCO: Hybrid-Cooperative Learning for Data-Driven PDE Modeling
Lorenzo Liverani, Matthys Steynberg, Enrique Zuazua
https://arxiv.org/abs/2509.14123 https://

HYCO: Hybrid-Cooperative Learning for Data-Driven PDE Modeling
We present Hybrid-Cooperative Learning (HYCO), a hybrid modeling framework that iteratively integrates physics-based and data-driven models through a mutual regularization mechanism. Unlike traditional approaches that impose physical constraints directly on synthetic models, HYCO treats the physical and synthetic components as co-trained agents: the physical and synthetic models are nudged toward agreement, while the synthetic model is enhanced to better fit the available data. This cooperative…

CoPAD : Multi-source Trajectory Fusion and Cooperative Trajectory Prediction with Anchor-oriented Decoder in V2X Scenarios
Kangyu Wu, Jiaqi Qiao, Ya Zhang
https://arxiv.org/abs/2509.15984

CoPAD : Multi-source Trajectory Fusion and Cooperative Trajectory Prediction with Anchor-oriented Decoder in V2X Scenarios
Recently, data-driven trajectory prediction methods have achieved remarkable results, significantly advancing the development of autonomous driving. However, the instability of single-vehicle perception introduces certain limitations to trajectory prediction. In this paper, a novel lightweight framework for cooperative trajectory prediction, CoPAD, is proposed. This framework incorporates a fusion module based on the Hungarian algorithm and Kalman filtering, along with the Past Time Attention (…

Synesthesia of Machines (SoM)-Based Task-Driven MIMO System for Image Transmission
Sijiang Li, Rongqing Zhang, Xiang Cheng, Jian Tang
https://arxiv.org/abs/2509.02031 https://…

Synesthesia of Machines (SoM)-Based Task-Driven MIMO System for Image Transmission
To support cooperative perception (CP) of networked mobile agents in dynamic scenarios, the efficient and robust transmission of sensory data is a critical challenge. Deep learning-based joint source-channel coding (JSCC) has demonstrated promising results for image transmission under adverse channel conditions, outperforming traditional rule-based codecs. While recent works have explored to combine JSCC with the widely adopted multiple-input multiple-output (MIMO) technology, these approaches …

Machine learning-based multimodal prognostic models integrating pathology images and high-throughput omic data for overall survival prediction in cancer: a systematic review
Charlotte Jennings (National Pathology Imaging Cooperative, Leeds Teaching Hospitals NHS Trust, Leeds, UK), Andrew Broad (National Pathology Imaging Cooperative, Leeds Teaching Hospitals NHS Trust, Leeds, UK), Lucy Godson (National Pathology Imaging Cooperative, Leeds Teaching Hospitals NHS Trust, Leeds, UK), Emily…

Machine learning-based multimodal prognostic models integrating pathology images and high-throughput omic data for overall survival prediction in cancer: a systematic review
Multimodal machine learning integrating histopathology and molecular data shows promise for cancer prognostication. We systematically reviewed studies combining whole slide images (WSIs) and high-throughput omics to predict overall survival. Searches of EMBASE, PubMed, and Cochrane CENTRAL (12/08/2024), plus citation screening, identified eligible studies. Data extraction used CHARMS; bias was assessed with PROBAST+AI; synthesis followed SWiM and PRISMA 2020. Protocol: PROSPERO (CRD42024594745)…

Energy-Efficient Precoding for Dense VCSEL-Based OWC Systems Under a Cooperative Broadcast Model
Hossein Safi, Asim Ihsan, Hossien B. Eldeeb, Bastien Bechadergue, Iman Tavakkolnia, Harald Haas
https://arxiv.org/abs/2508.18871

Energy-Efficient Precoding for Dense VCSEL-Based OWC Systems Under a Cooperative Broadcast Model
As 6G and beyond aim for sustainable, high-capacity wireless connectivity, optical wireless communication (OWC) has emerged as a compelling solution.Recent advances in vertical-cavity surface-emitting laser (VCSEL) arrays have significantly enhanced OWC performance, enabling high-speed, low-power data transmission. However, dense VCSEL deployments introduce challenges related to interference and energy efficiency (EE). This paper proposes a scalable precoding framework for EE maximization in fu…

Cooperative Base Station Assignment and Resource Allocation for 6G ISAC Network
Jiajia Liao, Luping Xiang, Shida Zhong, Lixia Xiao, Haochen Liu, Kun Yang
https://arxiv.org/abs/2509.10240

Cooperative Base Station Assignment and Resource Allocation for 6G ISAC Network
In the upcoming 6G networks, integrated sensing and communications (ISAC) will be able to provide a performance boost in both perception and wireless connectivity. This paper considers a multiple base station (BS) architecture to support the comprehensive services of data transmission and multi-target sensing. In this context, a cooperative BS assignment and resource allocation (CBARA) strategy is proposed in this paper, aiming at jointly optimizing the communication and sensing (C&S) performan…

Data Fusion for BS-UE Cooperative MIMO-OFDM ISAC
Yixin Ding, Haoyu Jiang, Xiaoli Xu, Yanan Liang, Yong Zeng
https://arxiv.org/abs/2509.12646 https://arxiv.…

Data Fusion for BS-UE Cooperative MIMO-OFDM ISAC
Integrated sensing and communication (ISAC) is a promising technique for expanding the functionalities of wireless networks with enhanced spectral efficiency. The 3rd Generation Partnership Project (3GPP) has defined six basic sensing operation modes in wireless networks. To further enhance the sensing capability of wireless networks, this paper proposes a new sensing operation mode, i.e., the base station (BS) and user equipment (UE) cooperative sensing. Specifically, after decoding the commun…

Crosslisted article(s) found for math.AP. https://arxiv.org/list/math.AP/new
[1/1]:
- HYCO: Hybrid-Cooperative Learning for Data-Driven PDE Modeling
Lorenzo Liverani, Matthys Steynberg, Enrique Zuazua

Graph Learning for Cooperative Cell-Free ISAC Systems: From Optimization to Estimation
Peng Jiang, Ming Li, Rang Liu, Qian Liu
https://arxiv.org/abs/2507.06612

Graph Learning for Cooperative Cell-Free ISAC Systems: From Optimization to Estimation
Cell-free integrated sensing and communication (ISAC) systems have emerged as a promising paradigm for sixth-generation (6G) networks, enabling simultaneous high-rate data transmission and high-precision radar sensing through cooperative distributed access points (APs). Fully exploiting these capabilities requires a unified design that bridges system-level optimization with multi-target parameter estimation. This paper proposes an end-to-end graph learning approach to close this gap, modeling t…