Search Results for author:
Found 28 papers, 7 papers with code
Data-driven Effective Modeling of Multiscale Stochastic Dynamical Systems
While the governing equations of the systems are unknown, bursts of observation data of the slow variables are available.
Modeling Unknown Stochastic Dynamical System Subject to External Excitation
When a sufficient amount of such I/O data are available, our method is capable of learning the unknown dynamics and producing an accurate predictive model for the stochastic responses of the system subject to arbitrary excitation signals not in the training data.
Asynchronous Large Language Model Enhanced Planner for Autonomous Driving
Despite real-time planners exhibiting remarkable performance in autonomous driving, the growing exploration of Large Language Models (LLMs) has opened avenues for enhancing the interpretability and controllability of motion planning.
A survey on fairness of large language models in e-commerce: progress, application, and challenge
This work presents a comprehensive survey on the applications and challenges of LLMs in e-commerce.
Unlocking the Power of Multi-institutional Data: Integrating and Harmonizing Genomic Data Across Institutions
The extracted latent features from the Bridge model consistently excel in predicting patient survival across six cancer types in GENIE BPC data.
Unleashing the Power of CNN and Transformer for Balanced RGB-Event Video Recognition
It is intuitive to combine them for high-performance RGB-Event based video recognition, however, existing works fail to achieve a good balance between the accuracy and model parameters, as shown in Fig.~\ref{firstimage}.
Modeling Unknown Stochastic Dynamical System via Autoencoder
We present a numerical method to learn an accurate predictive model for an unknown stochastic dynamical system from its trajectory data.
Cross-Dataset-Robust Method for Blind Real-World Image Quality Assessment
First, many individual models based on popular and state-of-the-art (SOTA) Swin-Transformer (SwinT) are trained on different real-world BIQA datasets respectively.
Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative Perception
Specifically, we analyze the performance changes of different methods under different bandwidths, providing a deep insight into the performance-bandwidth trade-off issue.
Supervised Auto-Encoding Twin-Bottleneck Hashing
Other existing methods construct the similarity graph and consider all points simultaneously.
Learning Stochastic Dynamical System via Flow Map Operator
Termed stochastic flow map learning (sFML), the new framework is an extension of flow map learning (FML) that was developed for learning deterministic dynamical systems.
Deep Reinforcement Learning for Localizability-Enhanced Navigation in Dynamic Human Environments
The planner learns to assign different importance to the geometric features and encourages the robot to navigate through areas that are helpful for laser localization.
Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021
This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels.
CAVE: Cerebral Artery-Vein Segmentation in Digital Subtraction Angiography
Further, it falls short in effectively separating arteries and veins as it disregards the temporal perspectives inherent in DSA.
Multi-channel end-to-end neural network for speech enhancement, source localization, and voice activity detection
In this study, a neural beamformer consisting of a beamformer and a novel multi-channel DCCRN is proposed for speech enhancement and source localization.
PO-ELIC: Perception-Oriented Efficient Learned Image Coding
In the past years, learned image compression (LIC) has achieved remarkable performance.
S3E-GNN: Sparse Spatial Scene Embedding with Graph Neural Networks for Camera Relocalization
In the GNN query module, the pose graph is transformed to form a embedding-aggregated reference graph for camera relocalization.
Post-Training Quantization for Cross-Platform Learned Image Compression
It has been witnessed that learned image compression has outperformed conventional image coding techniques and tends to be practical in industrial applications.
Dynamic COVID risk assessment accounting for community virus exposure from a spatial-temporal transmission model
COVID-19 pandemic has caused unprecedented negative impacts on our society, including further exposing inequity and disparity in public health.
Post-Training Quantization Is All You Need to Perform Cross-Platform Learned Image Compression
It has been witnessed that learned image compression has outperformed conventional image coding techniques and tends to be practical in industrial applications.
Multi-frame Joint Enhancement for Early Interlaced Videos
Early interlaced videos usually contain multiple and interlacing and complex compression artifacts, which significantly reduce the visual quality.
Ranked #1 on
Video Deinterlacing
on MSU Deinterlacer Benchmark
Learning Syntactic Dense Embedding with Correlation Graph for Automatic Readability Assessment
Deep learning models for automatic readability assessment generally discard linguistic features traditionally used in machine learning models for the task.
Study on $Z_{cs}$ and excited $B_s^0$ states in the chiral quark model
For $b\bar{s}q\bar{q}$ system with $J=0$, some resonance states are also found.
High Energy Physics - Phenomenology
Representation Learning for Integrating Multi-domain Outcomes to Optimize Individualized Treatment
This learning framework is based on the measurement theory in psychiatry for modeling multiple disease diagnostic measures as arising from the underlying causes (true mental states).
Representation Learning for Integrating Multi-domain Outcomes to Optimize Individualized Treatments
This learning framework is based on the measurement theory in psychiatry for modeling multiple disease diagnostic measures as arising from the underlying causes (true mental states).
UNIFUZZ: A Holistic and Pragmatic Metrics-Driven Platform for Evaluating Fuzzers
We hope that our findings can shed light on reliable fuzzing evaluation, so that we can discover promising fuzzing primitives to effectively facilitate fuzzer designs in the future.
Cryptography and Security
Global stability of some totally geodesic wave maps
We prove that wave maps that factor as $\mathbb{R}^{1+d} \overset{\varphi_{\text{S}}}{\to} \mathbb{R} \overset{\varphi_{\text{I}}}{\to} M$, subject to a sign condition, are globally nonlinear stable under small compactly supported perturbations when $M$ is a space-form.
Analysis of PDEs 35A23, 35B45, 35B35, 35L71
Integrated Server for Measurement-Device-Independent Quantum Key Distribution Network
Quantum key distribution (QKD), harnessing quantum physics and optoelectronics, may promise unconditionally secure information exchange in theory.
Quantum Physics
