Search Results for author:
Found 19 papers, 4 papers with code
STANKER: Stacking Network based on Level-grained Attention-masked BERT for Rumor Detection on Social Media
Rumor detection on social media puts pre-trained language models (LMs), such as BERT, and auxiliary features, such as comments, into use.
CSST Strong Lensing Preparation: a Framework for Detecting Strong Lenses in the Multi-color Imaging Survey by the China Survey Space Telescope (CSST)
To overcome these challenges, we have developed a framework based on a hierarchical visual Transformer with a sliding window technique to search for strong lensing systems within entire images.
Decision-Oriented Learning for Future Power System Decision-Making under Uncertainty
This paper first elaborates on the mismatch between more accurate forecasts and more optimal decisions in the power system caused by statistical-based learning (SBL) and explains how DOL resolves this problem.
Coordinated Planning of Offshore Charging Stations and Electrified Ships: A Case Study on Shanghai-Busan Maritime Route
Despite the success of electric vehicles on land, electrification of maritime ships is challenged by the dilemma of range anxiety and cargo-carrying capacity.
Adaptive Decision-Objective Loss for Forecast-then-Optimize in Power Systems
However, the implementation of decision loss in power systems faces challenges in 1) accommodating multi-stage decision-making problems where upstream optimality cannot guarantee final optimality; 2) adapting to dynamic environments such as changing parameters and nature of the problem like continuous or discrete optimization tasks.
AoI-Delay Tradeoff in Mobile Edge Caching: A Mixed-Order Drift-Plus-Penalty Algorithm
Mobile edge caching (MEC) is a promising technique to improve the quality of service (QoS) for mobile users (MU) by bringing data to the network edge.
Probabilistic Forecast-based Portfolio Optimization of Electricity Demand at Low Aggregation Levels
Using probabilistic load forecasts produced by either ARMA-GARCH models or kernel density estimation (KDE), we propose three approaches to creating a portfolio of residential households' demand: Forecast Validated, Seasonal Residual, and Seasonal Similarity.
A Joint Model and Data Driven Method for Distributed Estimation
To this end, we propose a joint model and data driven distributed estimation method by designing the optimal quantizers and fusion center (FC) based on the Bayesian and minimum mean square error (MMSE) criterions.
Risk-Aware Objective-Based Forecasting in Inertia Management
The objective-based forecasting considers the asymmetric and non-linear impacts of forecasting errors on decision objectives, thus improving the effectiveness of its downstream decision-making process.
Self-Supervised Monocular Depth Estimation with Self-Reference Distillation and Disparity Offset Refinement
In this work, we propose two novel ideas to improve self-supervised monocular depth estimation: 1) self-reference distillation and 2) disparity offset refinement.
URCDC-Depth: Uncertainty Rectified Cross-Distillation with CutFlip for Monocular Depth Estimation
Specifically, we use the depth estimates from the Transformer branch and the CNN branch as pseudo labels to teach each other.
Ranked #13 on
Monocular Depth Estimation
on KITTI Eigen split
Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report
The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing.
Synthetic Power Analyses: Empirical Evaluation and Application to Cognitive Neuroimaging
We propose synthetic power analyses; a framework for estimating statistical power at various sample sizes, and empirically explore the performance of synthetic power analysis for sample size selection in cognitive neuroscience experiments.
Towards Comprehensive Monocular Depth Estimation: Multiple Heads Are Better Than One
In this work, we investigate into the phenomenon and propose to integrate the strengths of multiple weak depth predictor to build a comprehensive and accurate depth predictor, which is critical for many real-world applications, e. g., 3D reconstruction.
Probing black hole microstructure with the kinetic turnover of phase transition
By treating black hole as the macroscopic stable state on the free energy landscape, we propose that the stochastic dynamics of the black hole phase transition can be effectively described by the Langevin equation or equivalently by the Fokker-Planck equation in phase space.
General Relativity and Quantum Cosmology
Hawking radiation, local temperatures, and nonequilibrium thermodynamics of the black holes with non-killing horizon
Therefore, the black hole with non-killing horizon can be overall in non-equilibrium steady state while the Hawking temperature of the black funnel can be viewed as the local temperature and the corresponding Hawking radiation can be regarded as being in the local equilibrium with the horizon of the black funnel.
General Relativity and Quantum Cosmology
Few shot domain adaptation for in situ macromolecule structural classification in cryo-electron tomograms
Motivation: Cryo-Electron Tomography (cryo-ET) visualizes structure and spatial organization of macromolecules and their interactions with other subcellular components inside single cells in the close-to-native state at sub-molecular resolution.
Consistent Classification with Generalized Metrics
We propose a framework for constructing and analyzing multiclass and multioutput classification metrics, i. e., involving multiple, possibly correlated multiclass labels.
