A high-fidelity multi-temporal point cloud updating method integrating semantic, geometric and texture information under a unified semantic-geometric collaborative constraint framework, which provides an effective solution for high-fidelity multi-temporal point cloud updating and supports digital twin city construction...
Findings demonstrate that Phys-GCN provides an accurate and efficient graph-based and physics-informed surrogate for steady flow-field reconstruction on non-uniform, unstructured meshes, thereby laying a solid foundation for future extensions to more complex three-dimensional and compressible flow configurations.
Hao-Ran Xie, Hao Zhou, Chang-Hao Yu et al.· SAE technical paper series· 0 citations
This paper introduces the Dynamic Topological Dependency Reasoning Network (DTTRN), a novel neural architecture designed for real-time learning and adaptation of complex system topology dependencies. Traditional dependency analysis methods rely on static, pre-defined topologies, limiting their effectiveness in dynamic...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to neural network optimization that dynamically adapts the network topology based on learned dependencies. The core idea is to leverage reinforcement learning, where two agents collaborate: one adjusts connection weights and the other modifies the network's structure (adding, removi...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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The central conclusion is that computational sophistication cannot repair absent chemical selectivity or non-independent validation: credible intelligent sensing requires mechanism-grounded signals, information-appropriate models, and validation at the level of the intended deployment claim.
Zhao-Jun Wu, Zhuang Sun, Kaiqiang Sun et al.· Chemical Communications· 0 citations
The proliferation of Internet of Things (IoT) devices has expanded the attack surface for sophisticated, polymorphic malware. Traditional intrusion detection systems (IDSs) and standard graph neural networks (GNNs) struggle with the heterogeneity of IoT traffic, adversarial structural perturbations, and the scarcity of...
Isaac Osei Asante, Farouk Abass· IEEE Internet of Things Jour...· 0 citations
This survey provides a first systematic overview of the emerging area of vision meets graphs, which treats visual depictions of graphs as first-class inputs for reasoning and learning, and organize existing work into three threads.
Xinjian Zhao, Wei Pang, Zhixuan Yu et al.· Proceedings of the Thirty-Fi...· 2 citations
The results show that learned ISAC schedulers should be evaluated using constraint-aware feasibility metrics in addition to conventional accuracy measures, and show that feasibility alone does not guarantee robustness to false data injection attacks.
Mehdi Zafari, I. Mohammadi, A. Swindlehurst· Proceedings of the 17th ACM...· 0 citations
Learned graph simulators provide an efficient alternative to high-fidelity solvers for granular dynamics. However, granular motion depends strongly on inter-granular contact history, which is difficult to preserve when particle contacts form, break, and rearrange. Existing simulators mainly store temporal information i...
Chang-Jian Zhou, Negin Yousefpour, Jie Qi et al.· 0 citations
LevelSyn is a novel physical-aware logic synthesis framework that integrates hierarchical representation learning with a wirelength-driven optimization engine that significantly outperforms state-of-the-art methods on the EPFL benchmark suite.
Jing-Yi Zhou, Zhengyuan Shi, Zi-Yang Zheng et al.· 0 citations
Graph neural networks (GNNs) are a class of neural networks suitable for learning on graph-structured data. Their application to spatial data is a natural extension, however its relatively unclear which message-passing operations, architectural configurations, and graph representation is best suited for classifying cha...
With the rapid development of the Internet of Things, computation intensive directed acyclic graph (DAG) tasks have become increasingly common in cloud-edge-end collaborative environments. However, cloud, edge, and end nodes are highly heterogeneous in computing capacity, network bandwidth, and energy consumption, whic...
Yan Qi, Chen-Wei Wang, Zi-Han Shen et al.· 0 citations
Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word.
Microsoft Research Blog· microsoft.comJul 13, 2026
Cryptographic code supports vital protections in modern computing systems. Learn how a new method helps verify code as developers write it while preserving speed and adaptability as it gets implemented and evolves. The post Verifying Rust cryptography in SymCrypt, from standards to code appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduJul 6, 2026
PhD student Rachel Sava, winner of the Envisioning the Future of Computing Prize, explores transformative improvements and dystopian risks of neural technology.
