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Found 30 papers, 11 papers with code
ChangeMamba: Remote Sensing Change Detection with Spatio-Temporal State Space Model
For the change decoder, which is available in all three architectures, we propose three spatio-temporal relationship modeling mechanisms, which can be naturally combined with the Mamba architecture and fully utilize its attribute to achieve spatio-temporal interaction of multi-temporal features, thereby obtaining accurate change information.
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Multi-resolution CSI Feedback with deep learning in Massive MIMO System
In massive multiple-input multiple-output (MIMO) system, user equipment (UE) needs to send downlink channel state information (CSI) back to base station (BS).
GNOT: A General Neural Operator Transformer for Operator Learning
However, there are several challenges for learning operators in practical applications like the irregular mesh, multiple input functions, and complexity of the PDEs' solution.
SyntheWorld: A Large-Scale Synthetic Dataset for Land Cover Mapping and Building Change Detection
However, when it comes to remote sensing image processing, the creation of synthetic datasets becomes challenging due to the demand for larger-scale and more diverse 3D models.
Graph Contrastive Learning for Anomaly Detection
To achieve the contrastive objective, we design a graph neural network encoder that can infer and further remove suspicious links during message passing, as well as learn the global context of the input graph.
Exchange means change: an unsupervised single-temporal change detection framework based on intra- and inter-image patch exchange
Change detection (CD) is a critical task in studying the dynamics of ecosystems and human activities using multi-temporal remote sensing images.
Land-cover change detection using paired OpenStreetMap data and optical high-resolution imagery via object-guided Transformer
Optical high-resolution imagery and OpenStreetMap (OSM) data are two important data sources for land-cover change detection.
Binary Neural Network Aided CSI Feedback in Massive MIMO System
In massive multiple-input multiple-output (MIMO) system, channel state information (CSI) is essential for the base station to achieve high performance gain.
Binarized Aggregated Network with Quantization: Flexible Deep Learning Deployment for CSI Feedback in Massive MIMO System
Massive multiple-input multiple-output (MIMO) is one of the key techniques to achieve better spectrum and energy efficiency in 5G system.
DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training
Pre-training has been investigated to improve the efficiency and performance of training neural operators in data-scarce settings.
Deep learning for multi-label classification of coral conditions in the Indo-Pacific via underwater photogrammetry
A dataset containing over 20, 000 high-resolution coral images of different health conditions and stressors was constructed based on the field survey.
Aggregated Network for Massive MIMO CSI Feedback
In frequency division duplexing (FDD) mode, it is necessary to send the channel state information (CSI) from user equipment to base station.
Skorohod and Stratonovich integrals for controlled processes
Given a continuous Gaussian process $x$ which gives rise to a $p$-geometric rough path for $p\in (2, 3)$, and a general continuous process $y$ controlled by $x$, under proper conditions we establish the relationship between the Skorohod integral $\int_0^t y_s {\mathrm{d}}^\diamond x_s$ and the Stratonovich integral $\int_0^t y_s {\mathrm{d}} {\mathbf x}_s$.
Probability
On eigenvalues of the Brownian sheet matrix
We derive a system of stochastic partial differential equations satisfied by the eigenvalues of the symmetric matrix whose entries are the Brownian sheets.
Probability
Joint Transceiver Optimization for Wireless Communication PHY with Convolutional Neural Network
The simulation results show that the performance of neural network based system is superior to traditional modulation and equalization methods in terms of time complexity and bit error rate (BER) under fading channels.
Transition behavior of the seizure dynamics modulated by the astrocyte inositol triphosphate noise
Our simulation results show that the increase of the IP3 noise intensity induces the depolarization-block epileptic seizures together with an increase in neuronal firing frequency.
Simple Contrastive Representation Adversarial Learning for NLP Tasks
In this paper, adversarial training is performed to generate challenging and harder learning adversarial examples over the embedding space of NLP as learning pairs.
GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothing
Under the GSmooth framework, we present a scalable algorithm that uses a surrogate image-to-image network to approximate the complex transformation.
Single-scatter channel impulse response model of non-line-of-sight ultraviolet communications
Previous studies on the temporal characteristics of single-scatter transmission in non-line-of-sight (NLOS) ultraviolet communications (UVC) were based on the prolate-spheroidal coordinate system.
Bi-level Physics-Informed Neural Networks for PDE Constrained Optimization using Broyden's Hypergradients
In this paper, we present a novel bi-level optimization framework to resolve the challenge by decoupling the optimization of the targets and constraints.
DABERT: Dual Attention Enhanced BERT for Semantic Matching
To alleviate this problem, we propose a novel Dual Attention Enhanced BERT (DABERT) to enhance the ability of BERT to capture fine-grained differences in sentence pairs.
Improving Semantic Matching through Dependency-Enhanced Pre-trained Model with Adaptive Fusion
Transformer-based pre-trained models like BERT have achieved great progress on Semantic Sentence Matching.
Deep learning for full-field ultrasonic characterization
This study takes advantage of recent advances in machine learning to establish a physics-based data analytic platform for distributed reconstruction of mechanical properties in layered components from full waveform data.
Iterative-in-Iterative Super-Resolution Biomedical Imaging Using One Real Image
Deep learning-based super-resolution models have the potential to revolutionize biomedical imaging and diagnoses by effectively tackling various challenges associated with early detection, personalized medicine, and clinical automation.
Enhancing Secrecy Capacity in PLS Communication with NORAN based on Pilot Information Codebooks
At the same time, selecting a NORAN design with ideal security performance from a large number of design options is also a challenging problem.
Free Space Optical Communication for Inter-Satellite Link: Architecture, Potentials and Trends
The sixth-generation (6G) network is expected to achieve global coverage based on the space-air-ground integrated network, and the latest satellite network will play an important role in it.
A Fully Data-Driven Approach for Realistic Traffic Signal Control Using Offline Reinforcement Learning
Specifically, we combine well-established traffic flow theory with machine learning to construct a reward inference model to infer the reward signals from coarse-grained traffic data.
Optical Integrated Sensing and Communication: Architectures, Potentials and Challenges
Integrated sensing and communication (ISAC) is viewed as a crucial component of future mobile networks and has gained much interest in both academia and industry.
Change Detection Between Optical Remote Sensing Imagery and Map Data via Segment Anything Model (SAM)
In this study, we explore unsupervised multimodal change detection between two key remote sensing data sources: optical high-resolution imagery and OpenStreetMap (OSM) data.
OAG-Bench: A Human-Curated Benchmark for Academic Graph Mining
We envisage that OAG-Bench can serve as a common ground for the community to evaluate and compare algorithms in academic graph mining, thereby accelerating algorithm development and advancement in this field.
