This work proposes a unified framework based on Graph Neural Networks for simultaneous bacteria tracking, division detection, and life cycle state classification and introduces a network-based approach for segmentation error correction using division and multi-frame correspondence predictions.
Moritz Kunzmann, M. C. Elizondo-Cantú, I. Bischofs et al.· Medical Image Analysis· 0 citations
We present PROTEUS, an 18 mm2 programmable general-purpose digital compute-in-memory (GP-DCIM) accelerator integrating 4 Mb resistive random access memory (RRAM) and 2.6 Mb tensor static random access memory (SRAM) with a 32-bit hierarchical DCIM instruction set architecture (ISA). PROTEUS features fine-grained 1-D mat...
Luqi Zheng, A. M. Bavani, Mufeng Chen et al.· IEEE Journal of Solid-State...· 0 citations
Air quality forecasting has attracted increasing attention as global air pollution worsens. Spatiotemporal graph neural networks have become a leading paradigm, thanks to their ability to capture complex spatial and temporal dynamics in Air Quality Index (AQI) data. However, existing methods remain limited by weak mode...
Binwu Wang, Zhiqing Cui, Guangjun Wang et al.· IEEE Transactions on Knowled...· 0 citations
Out-of-distribution (OOD) anomalies pose serious threats to the reliability and security of Internet of Things (IoT) systems. As graph neural networks (GNNs) have become a dominant framework for modeling the relational structures inherent in IoT networks, detecting OOD nodes on graphs has emerged as an essential requir...
Liting Wang, Da Li, Zhiyun Lin· IEEE Internet of Things Jour...· 0 citations
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This paper proposes a fault location method for DC distribution networks (DCDNs) based on graph neural networks (GNNs), which integrates the fault line selection (FLS) and fault distance estimation (FDE) that are conventionally handled independently. The proposed method focuses on the analysis of feeders, including FLS...
Lai Wei, Kai Liao, Bo Li et al.· IEEE Transactions on Smart G...· 0 citations
An integrated computational framework combining machine learning (ML), deep learning (DL), and structure-based docking with experimental validation identifies AO65 as a promising lead for further TDP1-focused investigation.
Huang Zeng, Man-Yi Zhang, Bo Qiu et al.· RSC Advances· 0 citations
FAST is presented, a holistic framework that accelerates end-to-end TGNN training by jointly optimizing sampling, memory I/O, and computation and designs thread-efficient graph operators tailored to sparse temporal subgraphs, improving GPU cache locality and reducing the latency of aggregation and edge softmax.
Terahertz (THz) communication is an essential component of sixth-generation (6G) wireless networks; however, its use is constrained by molecular absorption, ultrawideband beam squint, high- $\kappa $ Rician fading, and beam misalignment. The multiplicative fading penalty of passive RIS design can be avoided, and reliab...
Mian Muhammad Kamal, Syed Zain Ul Abideen, Ijaz Khan et al.· IEEE Internet of Things Jour...· 0 citations
Urban Internet of Thing (IoT) networks face severe reliability threats from diverse wireless interference, including jamming and spoofing, which are difficult to detect and localize in multipath-rich environments. Existing schemes often suffer from high false alarms, poor generalization, and low localization accuracy....
Qian Wang, Kai Cheng, Hong Mei et al.· IEEE Internet of Things Jour...· 0 citations
The growing integration of renewable energy sources (RESs), such as photovoltaic (PV) and wind (WD), has significantly increased the variability of net load (NL), posing critical challenges on the net load forecasting. In this paper, a novel physics-informed series-aware graph Transformer (PISAGT) model is proposed for...
Chang-Sen Feng, Shuai Zhang, Licheng Wang et al.· IEEE Transactions on Smart G...· 0 citations
Visible light positioning (VLP), due to its widespread infrastructure deployment and high accuracy, has emerged as a highly promising key technology for Internet of Things (IoT). Current research mainly relies on either data-driven methods based on fingerprint features or model-driven methods based on geometric localiz...
Hao Zhang, Xiansheng Yang, Xinyu Li et al.· IEEE Internet of Things Jour...· 0 citations
This work introduces a non-linear Laplacian operator specific to signed and directed networks (NLSD) and proposes an efficient spectral GNN framework (NLSD-GNN), which not only integrates signed and directional data effectively but also achieves superior performance across diverse datasets.
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
MIT News · Artificial Intelligence· news.mit.eduJul 15, 2026
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.
