Search Results for author:
Found 91 papers, 35 papers with code
Dynamic Energy Flow Analysis of Integrated Electricity and Gas Systems: A Semi-Analytical Approach
Ensuring the safe and reliable operation of integrated electricity and gas systems (IEGS) requires dynamic energy flow (DEF) simulation tools that achieve high accuracy and computational efficiency.
Spatiotemporal Trajectory Tracking Method for Vehicles Incorporating Lead-Lag Judgement
In the domain of intelligent transportation systems, especially within the context of autonomous vehicle control, the preemptive holistic collaborative system has been presented as a promising solution to bring a remarkable enhancement in traffic efficiency and a substantial reduction in the accident rate, demonstrating a great potential of development.
Machine Learning Modeling for Multi-order Human Visual Motion Processing
While recent advances in computer vision (CV) have enabled DNN-based models to accurately estimate optical flow in naturalistic images, a significant disparity remains between CV models and the biological visual system in both architecture and behavior.
Self-adaptive vision-language model for 3D segmentation of pulmonary artery and vein
Exploiting the generalization ability of these pre-trained foundation models on downstream tasks, such as segmentation, leads to unexpected performance with a relatively small amount of labeled data.
Distilling Desired Comments for Enhanced Code Review with Large Language Models
Human evaluation confirms that both models identify issues more accurately and tend to generate review comments that better describe the issues contained in the code than the base LLMs do.
VLABench: A Large-Scale Benchmark for Language-Conditioned Robotics Manipulation with Long-Horizon Reasoning Tasks
General-purposed embodied agents are designed to understand the users' natural instructions or intentions and act precisely to complete universal tasks.
IMNet: Interference-Aware Channel Knowledge Map Construction and Localization
This paper presents a novel two-stage method for constructing channel knowledge maps (CKMs) specifically for A2G (Aerial-to-Ground) channels in the presence of non-cooperative interfering nodes (INs).
Preemptive Holistic Collaborative System and Its Application in Road Transportation
To address this challenge, we propose the Preemptive Holistic Collaborative System (PHCS) framework.
FedGraph: A Research Library and Benchmark for Federated Graph Learning
Federated graph learning is an emerging field with significant practical challenges.
SoccerNet 2024 Challenges Results
The SoccerNet 2024 challenges represent the fourth annual video understanding challenges organized by the SoccerNet team.
Nonlinear PDE Constrained Optimal Dispatch of Gas and Power: A Global Linearization Approach
To address it, we propose a globally linearized gas network model based on the Koopman operator theory, avoiding the commonly used local linearization and spatial discretization.
Depth-guided Texture Diffusion for Image Semantic Segmentation
By integrating this enriched depth map with the original RGB image into a joint feature embedding, our method effectively bridges the disparity between the depth map and the image, enabling more accurate semantic segmentation.
Correspondence-Guided SfM-Free 3D Gaussian Splatting for NVS
In SfM-free methods, inaccurate initial poses lead to misalignment issue, which, under the constraints of per-pixel image loss functions, results in excessive gradients, causing unstable optimization and poor convergence for NVS.
Enhancing Expressway Ramp Merge Safety and Efficiency via Spatiotemporal Cooperative Control
Subsequently, an advanced vehicle conflict risk evaluation model is constructed.
Spatio-temporal cooperative control Method of Highway Ramp Merge Based on Vehicle-road Coordination
The merging area of highway ramps faces multiple challenges, including traffic congestion, collision risks, speed mismatches, driver behavior uncertainties, limited visibility, and bottleneck effects.
A Reflective LLM-based Agent to Guide Zero-shot Cryptocurrency Trading
The utilization of Large Language Models (LLMs) in financial trading has primarily been concentrated within the stock market, aiding in economic and financial decisions.
Quantifying AI Psychology: A Psychometrics Benchmark for Large Language Models
Inspired by psychometrics, this paper presents a framework for investigating psychology in LLMs, including psychological dimension identification, assessment dataset curation, and assessment with results validation.
1+1>2: Can Large Language Models Serve as Cross-Lingual Knowledge Aggregators?
This paper introduces a method to enhance the multilingual performance of LLMs by aggregating knowledge from diverse languages.
Neural Gaffer: Relighting Any Object via Diffusion
Single-image relighting is a challenging task that involves reasoning about the complex interplay between geometry, materials, and lighting.
Uncertainty-guided Optimal Transport in Depth Supervised Sparse-View 3D Gaussian
3D Gaussian splatting has demonstrated impressive performance in real-time novel view synthesis.
GaussianPrediction: Dynamic 3D Gaussian Prediction for Motion Extrapolation and Free View Synthesis
In this paper, we introduce GaussianPrediction, a novel framework that empowers 3D Gaussian representations with dynamic scene modeling and future scenario synthesis in dynamic environments.
Stability-Oriented Prediction Horizons Design of Generalized Predictive Control for DC/DC Boost Converter
This paper introduces a novel approach in designing prediction horizons on a generalized predictive control for a DC/DC boost converter.
GeneAvatar: Generic Expression-Aware Volumetric Head Avatar Editing from a Single Image
Recently, we have witnessed the explosive growth of various volumetric representations in modeling animatable head avatars.
Neuromorphic Event-Driven Semantic Communication in Microgrids
Synergies between advanced communications, computing and artificial intelligence are unraveling new directions of coordinated operation and resiliency in microgrids.
TaxDiff: Taxonomic-Guided Diffusion Model for Protein Sequence Generation
In this work, we propose TaxDiff, a taxonomic-guided diffusion model for controllable protein sequence generation that combines biological species information with the generative capabilities of diffusion models to generate structurally stable proteins within the sequence space.
Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models
Sora is a text-to-video generative AI model, released by OpenAI in February 2024.
I Think, Therefore I am: Benchmarking Awareness of Large Language Models Using AwareBench
Do large language models (LLMs) exhibit any forms of awareness similar to humans?
A Deep Q-Network Based on Radial Basis Functions for Multi-Echelon Inventory Management
This paper addresses a multi-echelon inventory management problem with a complex network topology where deriving optimal ordering decisions is difficult.
TrustLLM: Trustworthiness in Large Language Models
This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions.
SVDTree: Semantic Voxel Diffusion for Single Image Tree Reconstruction
We cast the 3D information inference problem to a semantic voxel diffusion process which converts an input image of a tree to a novel Semantic Voxel Structure (SVS) in 3D space.
DMR: Decomposed Multi-Modality Representations for Frames and Events Fusion in Visual Reinforcement Learning
We explore visual reinforcement learning (RL) using two complementary visual modalities: frame-based RGB camera and event-based Dynamic Vision Sensor (DVS).
TIDE: Test Time Few Shot Object Detection
Few-shot object detection (FSOD) aims to extract semantic knowledge from limited object instances of novel categories within a target domain.
Principles from Clinical Research for NLP Model Generalization
The NLP community typically relies on performance of a model on a held-out test set to assess generalization.
A Causal Disentangled Multi-Granularity Graph Classification Method
Graph data widely exists in real life, with large amounts of data and complex structures.
MSFormer: A Skeleton-multiview Fusion Method For Tooth Instance Segmentation
Furthermore, the segmentation accuracy is improved by 2. 4%-5. 5% with the increasing volume of training data.
UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting
To address these issues, we propose UniTime for effective cross-domain time series learning.
Ranked #5 on
Time Series Forecasting
on ETTh1 (336) Multivariate
Effects of Human Adversarial and Affable Samples on BERT Generalization
BERT-based models have had strong performance on leaderboards, yet have been demonstrably worse in real-world settings requiring generalization.
MetaAgents: Simulating Interactions of Human Behaviors for LLM-based Task-oriented Coordination via Collaborative Generative Agents
We propose a novel framework that equips collaborative generative agents with human-like reasoning abilities and specialized skills.
MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use
However, in scenarios where LLMs serve as intelligent agents, as seen in applications like AutoGPT and MetaGPT, LLMs are expected to engage in intricate decision-making processes that involve deciding whether to employ a tool and selecting the most suitable tool(s) from a collection of available tools to fulfill user requests.
Highly Efficient SNNs for High-speed Object Detection
Then, we propose a scale-aware pseudoquantization scheme to guarantee the correctness of the compact ANN to SNN.
An Adaptive Spatial-Temporal Local Feature Difference Method for Infrared Small-moving Target Detection
Detecting small moving targets accurately in infrared (IR) image sequences is a significant challenge.
A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions
However, these methods often suffer from data imbalance, a common issue in graph data where certain segments possess abundant data while others are scarce, thereby leading to biased learning outcomes.
TinyDet: Accurate Small Object Detection in Lightweight Generic Detectors
Small object detection requires the detection head to scan a large number of positions on image feature maps, which is extremely hard for computation- and energy-efficient lightweight generic detectors.
Neural Insights for Digital Marketing Content Design
In digital marketing, experimenting with new website content is one of the key levers to improve customer engagement.
Towards Blind Watermarking: Combining Invertible and Non-invertible Mechanisms
The CIN is composed of the invertible part to achieve high imperceptibility and the non-invertible part to strengthen the robustness against strong noise attacks.
ClimateNeRF: Extreme Weather Synthesis in Neural Radiance Field
Physical simulations produce excellent predictions of weather effects.
Personalized Saliency in Task-Oriented Semantic Communications: Image Transmission and Performance Analysis
Semantic communication, as a promising technology, has emerged to break through the Shannon limit, which is envisioned as the key enabler and fundamental paradigm for future 6G networks and applications, e. g., smart healthcare.
Reversing Skin Cancer Adversarial Examples by Multiscale Diffusive and Denoising Aggregation Mechanism
Crucially, to further reverse adversarial noises and suppress redundant injected noises, a novel multiscale denoising mechanism is carefully designed that aggregates image information from neighboring scales.
elBERto: Self-supervised Commonsense Learning for Question Answering
Commonsense question answering requires reasoning about everyday situations and causes and effects implicit in context.
1000x Faster Camera and Machine Vision with Ordinary Devices
By treating vidar as spike trains in biological vision, we have further developed a spiking neural network-based machine vision system that combines the speed of the machine and the mechanism of biological vision, achieving high-speed object detection and tracking 1, 000x faster than human vision.
Enhancing and Dissecting Crowd Counting By Synthetic Data
In this article, we propose a simulated crowd counting dataset CrowdX, which has a large scale, accurate labeling, parameterized realization, and high fidelity.
BBA-net: A bi-branch attention network for crowd counting
Our method can integrate the pedestrian's head and body information to enhance the feature expression ability of the density map.
Large-scale protein-protein post-translational modification extraction with distant supervision and confidence calibrated BioBERT
However, by combining high confidence and low variation to identify high quality predictions, tuning the predictions for precision, we retained 19% of the test predictions with 100% precision.
TransBoost: A Boosting-Tree Kernel Transfer Learning Algorithm for Improving Financial Inclusion
The prosperity of mobile and financial technologies has bred and expanded various kinds of financial products to a broader scope of people, which contributes to advocating financial inclusion.
Monocular Road Planar Parallax Estimation
It implies planar parallax and can be combined with the road plane serving as a reference to estimate the 3D structure by warping the consecutive frames.
The Report on China-Spain Joint Clinical Testing for Rapid COVID-19 Risk Screening by Eye-region Manifestations
The image-level performance of COVID-19 prescreening model in the China-Spain multicenter study achieved an AUC of 0. 913 (95% CI, 0. 898-0. 927), with a sensitivity of 0. 695 (95% CI, 0. 643-0. 748), a specificity of 0. 904 (95% CI, 0. 891 -0. 919), an accuracy of 0. 875(0. 861-0. 889), and a F1 of 0. 611(0. 568-0. 655).
SPAN: Subgraph Prediction Attention Network for Dynamic Graphs
This paper proposes a novel model for predicting subgraphs in dynamic graphs, an extension of traditional link prediction.
Learning point embedding for 3D data processing
Among 2D convolutional networks on point clouds, point-based approaches consume point clouds of fixed size directly.
Rapid COVID-19 Risk Screening by Eye-region Manifestations
It is still nontrivial to develop a new fast COVID-19 screening method with the easier access and lower cost, due to the technical and cost limitations of the current testing methods in the medical resource-poor districts.
Refiner: Refining Self-attention for Vision Transformers
Vision Transformers (ViTs) have shown competitive accuracy in image classification tasks compared with CNNs.
Ranked #175 on
Image Classification
on ImageNet
Certificate complexity and symmetry of nested canalizing functions
For both Boolean values $b\in\{0, 1\}$, we obtain a formula for $b$-certificate complexity and consequently, we develop a direct proof of the certificate complexity formula of an NCF.
Combinatorics Discrete Mathematics
Unusual heat transport of the Kitaev material Na$_2$Co$_2$TeO$_6$: putative quantum spin liquid and low-energy spin excitations
We studied the field dependent thermal conductivity ($\kappa$) of Na$_2$Co$_2$TeO$_6$, a compound considered as the manifestation of the Kitaev model based on the high-spin $d^7$ Co$^{2+}$ ions.
Strongly Correlated Electrons
Extremal solution and Liouville theorem for anisotropic elliptic equations
We study the quasilinear Dirichlet boundary problem \begin{equation}\nonumber \left\{ \begin{aligned} -Qu&=\lambda e^{u} \quad \mbox{in}\quad\Omega\\ u&=0 \quad \mbox{on}\quad\partial\Omega,\\ \end{aligned} \right.
Analysis of PDEs
Stacked Homography Transformations for Multi-View Pedestrian Detection
This task is confronted with two challenges: how to establish the 3D correspondences from views to the BEV map and how to assemble occupancy information across views.
Ranked #2 on
Multiview Detection
on CVCS
(MODA (1m) metric)
AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep Learning
Synchronization is a key step in data-parallel distributed machine learning (ML).
AutoPose: Searching Multi-Scale Branch Aggregation for Pose Estimation
Such simplification limits the fusion of information at different scales and fails to maintain high-resolution representations.
AFDet: Anchor Free One Stage 3D Object Detection
High-efficiency point cloud 3D object detection operated on embedded systems is important for many robotics applications including autonomous driving.
FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search
In this paper, we propose a Fast Network Adaptation (FNA++) method, which can adapt both the architecture and parameters of a seed network (e. g. an ImageNet pre-trained network) to become a network with different depths, widths, or kernel sizes via a parameter remapping technique, making it possible to use NAS for segmentation and detection tasks a lot more efficiently.
The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systems
This paper presents and characterizes an Open Application Repository for Federated Learning (OARF), a benchmark suite for federated machine learning systems.
Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space
We hope that our first pre-trained big VAE language model itself and results can help the NLP community renew the interests of deep generative models in the era of large-scale pre-training, and make these principled methods more practical.
Fast Neural Network Adaptation via Parameter Remapping and Architecture Search
In our experiments, we conduct FNA on MobileNetV2 to obtain new networks for both segmentation and detection that clearly out-perform existing networks designed both manually and by NAS.
FasterSeg: Searching for Faster Real-time Semantic Segmentation
We present FasterSeg, an automatically designed semantic segmentation network with not only state-of-the-art performance but also faster speed than current methods.
Ranked #1 on
Semantic Segmentation
on BDD
Neural Architecture Search
Real-Time Semantic Segmentation
+1
An Image Based Visual Servo Approach with Deep Learning for Robotic Manipulation
With the powerful learning capabilities of convolutional neural networks(CNN), autonomous learning to extract features from images and fitting the nonlinear relationships from image space to task space is achieved, which can greatly facilitate the image based visual servo procedure.
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
By systematically summarizing the existing federated learning systems, we present the design factors, case studies, and future research opportunities.
Object Detection in Video with Spatial-temporal Context Aggregation
Without any bells and whistles, our method obtains 80. 3\% mAP on the ImageNet VID dataset, which is superior over the previous state-of-the-arts.
Densely Connected Search Space for More Flexible Neural Architecture Search
We revisit the search space design in most previous NAS methods and find the number and widths of blocks are set manually.
Ranked #91 on
Neural Architecture Search
on ImageNet
The iMaterialist Fashion Attribute Dataset
The dataset is constructed from over one million fashion images with a label space that includes 8 groups of 228 fine-grained attributes in total.
Joint Learning of Neural Networks via Iterative Reweighted Least Squares
We formulate this problem as joint learning of multiple copies of the same network architecture and enforce the network weights to be shared across these networks.
Graph Transformer
Graph neural networks (GNN) have gained increasing research interests as a mean to the challenging goal of robust and universal graph learning.
Truth Inference at Scale: A Bayesian Model for Adjudicating Highly Redundant Crowd Annotations
As we show, datasets produced by crowd-sourcing are often not of this type: the data is highly redundantly annotated ($\ge 5$ annotations per instance), and the vast majority of workers produce high quality outputs.
Massively Multilingual Transfer for NER
In cross-lingual transfer, NLP models over one or more source languages are applied to a low-resource target language.
Pixel-Anchor: A Fast Oriented Scene Text Detector with Combined Networks
Recently, semantic segmentation and general object detection frameworks have been widely adopted by scene text detecting tasks.
A Galaxy-Scale Fountain of Cold Molecular Gas Pumped by a Black Hole
The entire scenario is therefore consistent with a galaxy-spanning "fountain", wherein cold gas clouds drain into the black hole accretion reservoir, powering jets and bubbles that uplift a cooling plume of low-entropy multiphase gas, which may stimulate additional cooling and accretion as part of a self-regulating feedback loop.
Astrophysics of Galaxies
Computer Analysis of Architecture Using Automatic Image Understanding
Here we show that computer analysis of building images can perform quantitative analysis of architecture, and quantify similarities between city architectural styles in a quantitative fashion.
Graph-based regularization for regression problems with alignment and highly-correlated designs
This work considers a high-dimensional regression setting in which a graph governs both correlations among the covariates and the similarity among regression coefficients -- meaning there is \emph{alignment} between the covariates and regression coefficients.
Learning how to Active Learn: A Deep Reinforcement Learning Approach
Active learning aims to select a small subset of data for annotation such that a classifier learned on the data is highly accurate.
Automatic Spatially-aware Fashion Concept Discovery
This paper proposes an automatic spatially-aware concept discovery approach using weakly labeled image-text data from shopping websites.
Learning Unified Embedding for Apparel Recognition
To ease the training difficulty, a novel learning scheme is proposed by using the output from specialized models as learning targets so that L2 loss can be used instead of triplet loss.
SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-rays
Through this adversarial process the critic network learns the higher order structures and guides the segmentation model to achieve realistic segmentation outcomes.
Cold, clumpy accretion onto an active supermassive black hole
Supermassive black holes in galaxy centres can grow by the accretion of gas, liberating energy that might regulate star formation on galaxy-wide scales.
Astrophysics of Galaxies High Energy Astrophysical Phenomena
Community Detection with Node Attributes and its Generalization
This new model can recover communities with higher accuracy even when node attributes and communities are uncorre- lated.
