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Found 11 papers, 3 papers with code
Ensuring Disturbance Rejection Performance by Synthesizing Grid-Following and Grid-Forming Inverters in Power Systems
To satisfy dynamic requirements of power systems, it is imperative for grid-tied inverters to ensure good disturbance rejection performance (DRP) under variable grid conditions.
Feynman Diagrams as Computational Graphs
We propose a computational graph representation of high-order Feynman diagrams in Quantum Field Theory (QFT), applicable to any combination of spatial, temporal, momentum, and frequency domains.
Characterizing the Role of Complex Power in Small-Signal Synchronization Stability of Multi-Converter Power Systems
In this paper, we focus on the impact regularity of complex power on the small-signal synchronization stability of the MCPS.
Obj-NeRF: Extract Object NeRFs from Multi-view Images
To solve this problem, in this paper, we propose Obj-NeRF, a comprehensive pipeline that recovers the 3D geometry of a specific object from multi-view images using a single prompt.
Enhancing Energy Efficiency for Reconfigurable Intelligent Surfaces with Practical Power Models
To address this issue, in this paper, we first utilize a practical power model for a RIS-assisted multi-user multiple-input single-output (MU-MISO) communication system, which takes into account the difference in power dissipation caused by ON-OFF states of RIS's PIN diodes.
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets
Recently, pre-trained foundation models have enabled significant advancements in multiple fields.
Extrinsic Factors Affecting the Accuracy of Biomedical NER
Biomedical named entity recognition (NER) is a critial task that aims to identify structured information in clinical text, which is often replete with complex, technical terms and a high degree of variability.
PopSparse: Accelerated block sparse matrix multiplication on IPU
Furthermore, static sparsity in general outperforms dynamic sparsity.
GPS++: Reviving the Art of Message Passing for Molecular Property Prediction
We present GPS++, a hybrid Message Passing Neural Network / Graph Transformer model for molecular property prediction.
GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property Prediction
This technical report presents GPS++, the first-place solution to the Open Graph Benchmark Large-Scale Challenge (OGB-LSC 2022) for the PCQM4Mv2 molecular property prediction task.
Privacy-Preserving Collaborative Operation of Networked Microgrids with the Local Utility Grid Based on Enhanced Benders Decomposition
Abstract—In this paper, a privacy-preserving decision-making the framework is developed for a collaborative operation of networked microgrids and the local utility grid, where the distribution system operator (DSO) interacts with microgrid master controllers (MCs) in a leader-followers fashion.
