Search Results for author:
Found 45 papers, 13 papers with code
A strictly predefined-time convergent and anti-noise fractional-order zeroing neural network for solving time-variant quadratic programming in kinematic robot control
This paper proposes a strictly predefined-time convergent and anti-noise fractional-order zeroing neural network (SPTC-AN-FOZNN) model, meticulously designed for addressing time-variant quadratic programming (TVQP) problems.
A Predefined-Time Convergent and Noise-Tolerant Zeroing Neural Network Model for Time Variant Quadratic Programming With Application to Robot Motion Planning
This paper develops a predefined-time convergent and noise-tolerant fractional-order zeroing neural network (PTC-NT-FOZNN) model, innovatively engineered to tackle time-variant quadratic programming (TVQP) challenges.
Provincial allocation of China's commercial building operational carbon towards carbon neutrality
Overall, this study offers optimized provincial carbon allocation strategies within the commercial building sector in China via dynamic scenario simulations, with the goal of hitting the carbon peak target and progressing toward a low-carbon future for the building sector.
Mesa-Extrapolation: A Weave Position Encoding Method for Enhanced Extrapolation in LLMs
Our findings reveal that with meticulous weave position, PE can indeed be extended beyond effective range.
AutoFPDesigner: Automated Flight Procedure Design Based on Multi-Agent Large Language Model
AutoFPDesigner allows users to oversee and seamlessly participate in the design process.
AIPatient: Simulating Patients with EHRs and LLM Powered Agentic Workflow
Here, we developed AIPatient, an advanced simulated patient system with AIPatient Knowledge Graph (AIPatient KG) as the input and the Reasoning Retrieval-Augmented Generation (Reasoning RAG) agentic workflow as the generation backbone.
A multi-source data power load forecasting method using attention mechanism-based parallel cnn-gru
From the model-agnostic perspective, this paper proposes a parallel structure network to extract important information from both dynamic and static data.
Enhancing Autism Spectrum Disorder Early Detection with the Parent-Child Dyads Block-Play Protocol and an Attention-enhanced GCN-xLSTM Hybrid Deep Learning Framework
By learning these global features with spatio-temporal correlations, our 2sGCN-AxLSTM effectively analyzes dynamic human behavior patterns and demonstrates an unprecedented accuracy of 89. 6\% in early detection of ASD.
Cinemo: Consistent and Controllable Image Animation with Motion Diffusion Models
Diffusion models have achieved great progress in image animation due to powerful generative capabilities.
Advancements in Recommender Systems: A Comprehensive Analysis Based on Data, Algorithms, and Evaluation
Using 286 research papers collected from Web of Science, ScienceDirect, SpringerLink, arXiv, and Google Scholar databases, a systematic review methodology was adopted to review and summarize the current challenges and potential future developments in data, algorithms, and evaluation aspects of RSs.
TCMBench: A Comprehensive Benchmark for Evaluating Large Language Models in Traditional Chinese Medicine
To address this research gap, we introduce TCM-Bench, an comprehensive benchmark for evaluating LLM performance in TCM.
China's plug-in hybrid electric vehicle transition: an operational carbon perspective
This study is the first to adopt a bottom-up model to measure the real-world energy use and carbon dioxide emissions of China's top twenty selling PHEV models across different regions from 2020 to 2022.
A Minimal Set of Parameters Based Depth-Dependent Distortion Model and Its Calibration Method for Stereo Vision Systems
In this work, we propose a minimal set of parameters based depth-dependent distortion model (MDM), which considers the radial and decentering distortions of the lens to improve the accuracy of stereo vision systems and simplify their calibration process.
Large Language Models in Biomedical and Health Informatics: A Review with Bibliometric Analysis
Large Language Models (LLMs) have rapidly become important tools in Biomedical and Health Informatics (BHI), enabling new ways to analyze data, treat patients, and conduct research.
NPHardEval4V: A Dynamic Reasoning Benchmark of Multimodal Large Language Models
Understanding the reasoning capabilities of Multimodal Large Language Models (MLLMs) is an important area of research.
Latte: Latent Diffusion Transformer for Video Generation
We propose a novel Latent Diffusion Transformer, namely Latte, for video generation.
Inter-object Discriminative Graph Modeling for Indoor Scene Recognition
Leveraging object information within scenes to enhance the distinguishability of feature representations has emerged as a key approach in this domain.
SEINE: Short-to-Long Video Diffusion Model for Generative Transition and Prediction
The goal is to generate high-quality long videos with smooth and creative transitions between scenes and varying lengths of shot-level videos.
On the Convergence of Federated Averaging under Partial Participation for Over-parameterized Neural Networks
Federated learning (FL) is a widely employed distributed paradigm for collaboratively training machine learning models from multiple clients without sharing local data.
LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Models
To this end, we propose LaVie, an integrated video generation framework that operates on cascaded video latent diffusion models, comprising a base T2V model, a temporal interpolation model, and a video super-resolution model.
Ranked #4 on
Text-to-Video Generation
on EvalCrafter Text-to-Video (ECTV) Dataset
(using extra training data)
Semantic-embedded Similarity Prototype for Scene Recognition
Due to the high inter-class similarity caused by the complex composition and the co-existing objects across scenes, numerous studies have explored object semantic knowledge within scenes to improve scene recognition.
InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation
Specifically, we utilize a multi-scale approach to generate video-related descriptions.
Semantic-guided modeling of spatial relation and object co-occurrence for indoor scene recognition
Secondly, both spatial features from the SSRM and deep features from the Image Feature Extraction Module are allocated to each object, so as to distinguish the coexisting object across different scenes.
SRRM: Semantic Region Relation Model for Indoor Scene Recognition
Despite the remarkable success of convolutional neural networks in various computer vision tasks, recognizing indoor scenes still presents a significant challenge due to their complex composition.
Ranked #1 on
Scene Recognition
on MIT Indoor Scenes
(10-stage average accuracy metric)
LEO: Generative Latent Image Animator for Human Video Synthesis
Our key idea is to represent motion as a sequence of flow maps in the generation process, which inherently isolate motion from appearance.
Disjoint Masking with Joint Distillation for Efficient Masked Image Modeling
Masked image modeling (MIM) has shown great promise for self-supervised learning (SSL) yet been criticized for learning inefficiency.
Accounting for Temporal Variability in Functional Magnetic Resonance Imaging Improves Prediction of Intelligence
We compare the prediction performance for different intelligence measures based on static FC, dynamic FC, and region level time series acquired from the Adolescent Brain Cognitive Development (ABCD) study involving close to 7000 individuals.
High-dimensional Measurement Error Models for Lipschitz Loss
Our estimator is designed to minimize the $L_1$ norm among all estimators belonging to suitable feasible sets, without requiring any knowledge of the noise distribution.
FedSSO: A Federated Server-Side Second-Order Optimization Algorithm
In this work, we propose FedSSO, a server-side second-order optimization method for federated learning (FL).
TL-GAN: Improving Traffic Light Recognition via Data Synthesis for Autonomous Driving
Traffic light recognition, as a critical component of the perception module of self-driving vehicles, plays a vital role in the intelligent transportation systems.
Compressing Models with Few Samples: Mimicking then Replacing
Hence, previous methods optimize the compressed model layer-by-layer and try to make every layer have the same outputs as the corresponding layer in the teacher model, which is cumbersome.
Contrastive Attention Network with Dense Field Estimation for Face Completion
This multi-scale architecture is beneficial for the decoder to utilize discriminative representations learned from encoders into images.
Separating Boundary Points via Structural Regularization for Very Compact Clusters
VCC takes advantage of distributions of local relationships of samples near the boundary of clusters, so that they can be properly separated and pulled to cluster centers to form compact clusters.
Adaptive Deconvolution-based stereo matching Net for Local Stereo Matching
In deep learning-based local stereo matching methods, larger image patches usually bring better stereo matching accuracy.
Discovering key topics from short, real-world medical inquiries via natural language processing and unsupervised learning
Millions of unsolicited medical inquiries are received by pharmaceutical companies every year.
Unsupervised Contrastive Photo-to-Caricature Translation based on Auto-distortion
Considering the intuitive artifacts in the existing methods, we propose a contrastive style loss for style rendering to enforce the similarity between the style of rendered photo and the caricature, and simultaneously enhance its discrepancy to the photos.
Almost elementariness and fiberwise amenability for étale groupoids
In this paper, we introduce two new approximation properties for \'etale groupoids, almost elementariness and (ubiquitous) fiberwise amenability, inspired by Matui's and Kerr's notions of almost finiteness.
Operator Algebras Dynamical Systems 22A22 (primary) 46L35, 51F30, 37A55, 37B05 (Secondary)
Exploiting Style and Attention in Real-World Super-Resolution
Real-world image super-resolution (SR) is a challenging image translation problem.
Learning Multi-resolution Graph Edge Embedding for Discovering Brain Network Dysfunction in Neurological Disorders
Tremendous recent literature show that associations between different brain regions, i. e., brain connectivity, provide early symptoms of neurological disorders.
Fast and Incremental Loop Closure Detection Using Proximity Graphs
Visual loop closure detection, which can be considered as an image retrieval task, is an important problem in SLAM (Simultaneous Localization and Mapping) systems.
Batch Normalization Sampling
In this paper, we propose alleviating this problem through sampling only a small fraction of data for normalization at each iteration.
Crossbar-aware neural network pruning
Crossbar architecture based devices have been widely adopted in neural network accelerators by taking advantage of the high efficiency on vector-matrix multiplication (VMM) operations.
A brief introduction to the Grey Machine Learning
A short discussion on the priority of this new framework to the existing grey models and LSSVM have also been discussed in this paper.
