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Found 23 papers, 14 papers with code
Inter3D: A Benchmark and Strong Baseline for Human-Interactive 3D Object Reconstruction
Recent advancements in implicit 3D reconstruction methods, e. g., neural rendering fields and Gaussian splatting, have primarily focused on novel view synthesis of static or dynamic objects with continuous motion states.
Prediction-Enhanced Monte Carlo: A Machine Learning View on Control Variate
Despite being an essential tool across engineering and finance, Monte Carlo simulation can be computationally intensive, especially in large-scale, path-dependent problems that hinder straightforward parallelization.
OmniSR: Shadow Removal under Direct and Indirect Lighting
Shadows can originate from occlusions in both direct and indirect illumination.
Ranked #1 on
Shadow Removal
on INS Dataset
Causal Inference with Double/Debiased Machine Learning for Evaluating the Health Effects of Multiple Mismeasured Pollutants
This strategy results in potential inaccuracy in the actual personal exposure, introducing bias in estimating the health effects of air pollution and its constituents, especially when evaluating the causal effects of correlated multi-pollutant constituents measured with correlated error.
Tackling Noisy Clients in Federated Learning with End-to-end Label Correction
Intuitively, the performance degradation is dominated by clients with higher noise rates since their trained models contain more misinformation from data, thus it is necessary to devise an effective optimization scheme to mitigate the negative impacts of these noisy clients.
StaPep: an open-source tool for the structure prediction and feature extraction of hydrocarbon-stapled peptides
Many tools exist for extracting structural and physiochemical descriptors from linear peptides to predict their properties, but similar tools for hydrocarbon-stapled peptides are lacking. Here, we present StaPep, a Python-based toolkit designed for generating 2D/3D structures and calculating 21 distinct features for hydrocarbon-stapled peptides. The current version supports hydrocarbon-stapled peptides containing 2 non-standard amino acids (norleucine and 2-aminoisobutyric acid) and 6 nonnatural anchoring residues (S3, S5, S8, R3, R5 and R8). Then we established a hand-curated dataset of 201 hydrocarbon-stapled peptides and 384 linear peptides with sequence information and experimental membrane permeability, to showcase StaPep's application in artificial intelligence projects. A machine learning-based predictor utilizing above calculated features was developed with AUC of 0. 85, for identifying cell-penetrating hydrocarbon-stapled peptides. StaPep's pipeline spans data retrieval, cleaning, structure generation, molecular feature calculation, and machine learning model construction for hydrocarbon-stapled peptides. The source codes and dataset are freely available on Github: https://github. com/dahuilangda/stapep_package.
CAMEL2: Enhancing weakly supervised learning for histopathology images by incorporating the significance ratio
Histopathology image analysis plays a crucial role in cancer diagnosis.
Semantic-aware Generation of Multi-view Portrait Drawings
Our motivation is that facial semantic labels are view-consistent and correlate with drawing techniques.
Masked and Adaptive Transformer for Exemplar Based Image Translation
To overcome this challenge, we improve the accuracy of matching on the one hand, and diminish the role of matching in image generation on the other hand.
Joint Super-Resolution and Inverse Tone-Mapping: A Feature Decomposition Aggregation Network and A New Benchmark
Joint Super-Resolution and Inverse Tone-Mapping (joint SR-ITM) aims to increase the resolution and dynamic range of low-resolution and standard dynamic range images.
Resistance-Time Co-Modulated PointNet for Temporal Super-Resolution Simulation of Blood Vessel Flows
In this paper, a novel deep learning framework is proposed for temporal super-resolution simulation of blood vessel flows, in which a high-temporal-resolution time-varying blood vessel flow simulation is generated from a low-temporal-resolution flow simulation result.
LASOR: Learning Accurate 3D Human Pose and Shape Via Synthetic Occlusion-Aware Data and Neural Mesh Rendering
The lack of diverse and accurate pose and shape training data becomes a major bottleneck, especially for scenes with occlusions in the wild.
Ranked #2 on
3D Human Shape Estimation
on SSP-3D
Salient Objects in Clutter
This design bias has led to a saturation in performance for state-of-the-art SOD models when evaluated on existing datasets.
Temporal Modulation Network for Controllable Space-Time Video Super-Resolution
To well exploit the temporal information, we propose a Locally-temporal Feature Comparison (LFC) module, along with the Bi-directional Deformable ConvLSTM, to extract short-term and long-term motion cues in videos.
Topological Luttinger semimetallic phase accompanied with surface states realized in silicon
More importantly, the topological surface states in the LSM phase fill in the gap between the topological matters and silicon, which provide an opportunity to integrate the topological quantum devices and silicon chips together.
Materials Science
Image-based Textile Decoding
A pattern in which the warps and wefts cross on a grid is defined in a binary matrix.
Discovery of unconventional chiral charge order in kagome superconductor KV3Sb5
Here we use high-resolution scanning tunnelling microscopy (STM) to discover an unconventional charge order in a kagome material KV3Sb5, with both a topological band structure and a superconducting ground state.
Superconductivity Materials Science Strongly Correlated Electrons
Learnability and Robustness of Shallow Neural Networks Learned With a Performance-Driven BP and a Variant PSO For Edge Decision-Making
However, how fat is an SNN enough to solve a nonlinear decision-making problem in edge devices?
InSAR Phase Denoising: A Review of Current Technologies and Future Directions
Nowadays, interferometric synthetic aperture radar (InSAR) has been a powerful tool in remote sensing by enhancing the information acquisition.
CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation
In this research, we propose CAMEL, a weakly supervised learning framework for histopathology image segmentation using only image-level labels.
BAGS: An automatic homework grading system using the pictures taken by smart phones
To solve these problems, we propose BAGS, an automatic homework grading system which can effectively locate and recognize handwritten answers.
Shape-from-Mask: A Deep Learning Based Human Body Shape Reconstruction from Binary Mask Images
In this paper, we propose a deep learning based reconstruction of 3D human body shape from 2D orthographic views.
