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Found 9 papers, 5 papers with code
UDiFF: Generating Conditional Unsigned Distance Fields with Optimal Wavelet Diffusion
In this work, we present UDiFF, a 3D diffusion model for unsigned distance fields (UDFs) which is capable to generate textured 3D shapes with open surfaces from text conditions or unconditionally.
A Co-training Approach for Noisy Time Series Learning
Based on this, we create two views for the input time series through two different encoders.
Missing Data Imputation with Graph Laplacian Pyramid Network
Data imputation is a prevalent and important task due to the ubiquitousness of missing data.
Quantifying Space-Time Load Shifting Flexibility in Electricity Markets
The power grid is undergoing significant restructuring driven by the adoption of wind/solar power and the incorporation of new flexible technologies that can shift load in space and time (e. g., data centers, battery storage, and modular manufacturing).
Robust Attributed Graph Alignment via Joint Structure Learning and Optimal Transport
As the graphs to be aligned are usually constructed from different sources, the inconsistency issues of structures and features between two graphs are ubiquitous in real-world applications.
Pricing and Remunerating Electricity Storage Flexibility Using Virtual Links
Ambitious renewable portfolio standards motivate the incorporation of energy storage resources (ESR) as sources of flexibility.
Exploring the Impacts of Power Grid Signals on Data Center Operations using a Receding-Horizon Scheduling Model
An obstacle that arises here is the lack of computationally-tractable DC operation models that can capture objectives, constraints, and information flows that arise at the interface of DCs and the power grid.
Remunerating Space-Time, Load-Shifting Flexibility from Data Centers in Electricity Markets
Central to our study is the concept of virtual links, which provide non-physical pathways that can be used by DaCes to shift power loads (by shifting computing loads) across space and time.
Data Augmentation for Object Detection via Progressive and Selective Instance-Switching
For guiding our IS to obtain better object performance, we explore issues of instance imbalance and class importance in datasets, which frequently occur and bring adverse effect on detection performance.
