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Found 29 papers, 2 papers with code
Spectrum Sharing in Satellite-Terrestrial Integrated Networks: Frameworks, Approaches, and Opportunities
Furthermore, advanced SS approaches related to interference management in STINs and performance metrics of SS in STINs are introduced.
Fine-Tuning and Deploying Large Language Models Over Edges: Issues and Approaches
Additionally, large-scale foundation models have expanded to create images, audio, videos, and multi-modal contents, further emphasizing the need for efficient deployment.
Heavy-Ball Momentum Accelerated Actor-Critic With Function Approximation
In this work, we first propose a heavy-ball momentum based advantage actor-critic (\mbox{HB-A2C}) algorithm by integrating the heavy-ball momentum into the critic recursion that is parameterized by a linear function.
Joint Source-Channel Optimization for UAV Video Coding and Transmission
Unlike existing methods of modeling UAV video source coding and channel transmission separately, we investigate the joint source-channel optimization issue for video coding and transmission.
Pilot-Aided Joint Time Synchronization and Channel Estimation for OTFS
This letter proposes a pilot-aided joint time synchronization and channel estimation (JTSCE) algorithm for orthogonal time frequency space (OTFS) systems.
Joint Radar Sensing, Location, and Communication Resources Optimization in 6G Network
In the communication phase, a subcarrier allocation method based on subcarrier weights is proposed.
Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectories
Mobile Edge Computing (MEC) broadens the scope of computation and storage beyond the central network, incorporating edge nodes close to end devices.
Spin: An Efficient Secure Computation Framework with GPU Acceleration
Accuracy and efficiency remain challenges for multi-party computation (MPC) frameworks.
Cut-and-Paste: Subject-Driven Video Editing with Attention Control
This paper presents a novel framework termed Cut-and-Paste for real-word semantic video editing under the guidance of text prompt and additional reference image.
CropCap: Embedding Visual Cross-Partition Dependency for Image Captioning
Transformer-based approaches to image captioning have shown great success by utilizing long-term dependency for visual embedding.
IDVT: Interest-aware Denoising and View-guided Tuning for Social Recommendation
Specifically, the denoising process considers both social network structure and user interaction interests in a global view.
DRGame: Diversified Recommendation for Multi-category Video Games with Balanced Implicit Preferences
It is centered on multi-category video games, consisting of two {components}: Balance-driven Implicit Preferences Learning for data pre-processing and Clustering-based Diversified Recommendation {Module} for final prediction.
Accelerating Wireless Federated Learning via Nesterov's Momentum and Distributed Principle Component Analysis
A wireless federated learning system is investigated by allowing a server and workers to exchange uncoded information via orthogonal wireless channels.
DeepMA: End-to-end Deep Multiple Access for Wireless Image Transmission in Semantic Communication
Semantic communication is a new paradigm that exploits deep learning models to enable end-to-end communications processes, and recent studies have shown that it can achieve better noise resiliency compared with traditional communication schemes in a low signal-to-noise (SNR) regime.
Long-Range Zero-Shot Generative Deep Network Quantization
We find it is because: 1) a normal generator is hard to obtain high diversity of synthetic data, since it lacks long-range information to allocate attention to global features; 2) the synthetic images aim to simulate the statistics of real data, which leads to weak intra-class heterogeneity and limited feature richness.
Network Topology Inference based on Timing Meta-Data
If the data timings and ACK timings of two nodes -- say node 1 and node 2, respectively -- are causally related, this may be taken as evidence that node 1 is communicating to node 2 (which sends back ACK packets to node 1).
Uncertainty Minimization for Personalized Federated Semi-Supervised Learning
Since federated learning (FL) has been introduced as a decentralized learning technique with privacy preservation, statistical heterogeneity of distributed data stays the main obstacle to achieve robust performance and stable convergence in FL applications.
Towards Feature Distribution Alignment and Diversity Enhancement for Data-Free Quantization
To obtain high inter-class separability of semantic features, we cluster and align the feature distribution statistics to imitate the distribution of real data, so that the performance degradation is alleviated.
Proximal Policy Optimization-based Transmit Beamforming and Phase-shift Design in an IRS-aided ISAC System for the THz Band
Then the joint optimization of transmit beamforming and phase-shift design is achieved by gradient-based, primal-dual proximal policy optimization (PPO) in the multi-user multiple-input single-output (MISO) scenario.
AI-aided Traffic Control Scheme for M2M Communications in the Internet of Vehicles
Due to the rapid growth of data transmissions in internet of vehicles (IoV), finding schemes that can effectively alleviate access congestion has become an important issue.
Arbitrary Virtual Try-On Network: Characteristics Preservation and Trade-off between Body and Clothing
Based on this dataset, we then propose the Arbitrary Virtual Try-On Network (AVTON) that is utilized for all-type clothes, which can synthesize realistic try-on images by preserving and trading off characteristics of the target clothes and the reference person.
A Simple Approach to Automated Spectral Clustering
First, we propose to find the most reliable affinity matrix via grid search or Bayesian optimization among a set of candidates given by different AMC methods with different hyperparameters, where the reliability is quantified by the \textit{relative-eigen-gap} of graph Laplacian introduced in this paper.
SiamCorners: Siamese Corner Networks for Visual Tracking
By tracking a target as a pair of corners, we avoid the need to design the anchor boxes.
Evidence of topological nodal lines and surface states in the centrosymmetric superconductor SnTaS2
Utilizing angle-resolved photoemission spectroscopy and first-principles calculations, here, we demonstrate the existence of topological nodal-line states and drumheadlike surface states in centrosymmetric superconductor SnTaS2, which is a type-II superconductor with a critical transition temperature of about 3 K. The valence bands from Ta 5d orbitals and the conduction bands from Sn 5p orbitals cross each other, forming two nodal lines in the vicinity of the Fermi energy without the inclusion of spin-orbit coupling (SOC), protected by the spatial-inversion symmetry and time-reversal symmetry.
Superconductivity
Energy Efficient User Clustering, Hybrid Precoding and Power Optimization in Terahertz MIMO-NOMA Systems
Considering the power consumption and implementation complexity, the hybrid precoding scheme based on the sub-connection structure is adopted.
Semi-DerainGAN: A New Semi-supervised Single Image Deraining Network
Although supervised deep deraining networks have obtained impressive results on synthetic datasets, they still cannot obtain satisfactory results on real images due to weak generalization of rain removal capacity, i. e., the pre-trained models usually cannot handle new shapes and directions that may lead to over-derained/under-derained results.
Compressed DenseNet for Lightweight Character Recognition
LDB is a convolutional block similarly as dense block, but it can reduce the computation cost and weight size to (1/L, 2/L), compared with original ones, where L is the number of layers in blocks.
A Coarse-to-Fine Multi-stream Hybrid Deraining Network for Single Image Deraining
To obtain the negative rain streaks during training process more accurately, we present a new module named dual path residual dense block, i. e., Residual path and Dense path.
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services.
