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Found 91 papers, 31 papers with code
Disentangled Cascaded Graph Convolution Networks for Multi-Behavior Recommendation
Furthermore, these models overlook the personalized nature of user behavioral preferences by employing uniform transformation networks for all users and items.
Cluster-based Graph Collaborative Filtering
This model performs high-order graph convolution on cluster-specific graphs, which are constructed by capturing the multiple interests of users and identifying the common interests among them.
Waveform Design for Joint Communication and SAR Imaging Under Random Signaling
Conventional synthetic aperture radar (SAR) imaging systems typically employ deterministic signal designs, which lack the capability to convey communication information and are thus not suitable for integrated sensing and communication (ISAC) scenarios.
Deep Cooperation in ISAC System: Resource, Node and Infrastructure Perspectives
With the mobile communication system evolving into 6th-generation (6G), the Internet of Everything (IoE) is becoming reality, which connects human, big data and intelligent machines to support the intelligent decision making, reconfiguring the traditional industries and human life.
Few-shot Adaptation of Multi-modal Foundation Models: A Survey
Therefore, in this survey, we introduce and analyze the research advancements in few-shot adaptation methods for multi-modal models, summarizing commonly used datasets and experimental setups, and comparing the results of different methods.
Frame Structure and Protocol Design for Sensing-Assisted NR-V2X Communications
In this study, we propose novel frame structures that incorporate ISAC signals for three crucial stages in the NR-V2X system: initial access, connected mode, and beam failure and recovery.
6D Radar Sensing and Tracking in Monostatic Integrated Sensing and Communications System
Unlike most existing ISAC studies believing that only the radial velocity of far-field dynamic target can be measured based on one single base station (BS), we find that the sensing echo channel of MIMO-ISAC system actually includes the distance, horizontal angle, pitch angle, radial velocity, horizontal angular velocity, and pitch angular velocity of the dynamic target.
Understanding Before Recommendation: Semantic Aspect-Aware Review Exploitation via Large Language Models
Recommendation systems harness user-item interactions like clicks and reviews to learn their representations.
Reshaping the ISAC Tradeoff Under OFDM Signaling: A Probabilistic Constellation Shaping Approach
To cope with this issue, we characterize the random AF of OFDM communication signals, and demonstrate that the AF variance is determined by the fourth-moment of the constellation amplitudes.
Attribute-driven Disentangled Representation Learning for Multimodal Recommendation
To obtain robust and independent representations for each factor associated with a specific attribute, we first disentangle the representations of features both within and across different modalities.
Securing the Sensing Functionality in ISAC Networks: An Artificial Noise Design
Integrated sensing and communications (ISAC) systems employ dual-functional signals to simultaneously accomplish radar sensing and wireless communication tasks.
Generating Human-Centric Visual Cues for Human-Object Interaction Detection via Large Vision-Language Models
To capitalize on these rich Human-Centric Visual Cues, we propose a novel approach named HCVC for HOI detection.
Communication-Assisted Sensing in 6G Networks
Exploring the mutual benefit and reciprocity of sensing and communication (S\&C) functions is fundamental to realizing deeper integration for integrated sensing and communication (ISAC) systems.
Probabilistic Constellation Shaping for OFDM-Based ISAC Signaling
Integrated Sensing and Communications (ISAC) has garnered significant attention as a promising technology for the upcoming sixth-generation wireless communication systems (6G).
Parallel compressive super-resolution imaging with wide field-of-view based on physics enhanced network
By training the network with the prior OTF of an arbitrary 128x128-pixel region and fine-tuning the network with other OTFs within rest regions of FOV, we realize both mask optimization and super-resolution imaging with up to 1020x1500 wide FOV.
Identification of Ghost Targets for Automotive Radar in the Presence of Multipath
Colocated multiple-input multiple-output (MIMO) technology has been widely used in automotive radars as it provides accurate angular estimation of the objects with relatively small number of transmitting and receiving antennas.
Intelligent Reflective Surface Assisted Integrated Sensing and Wireless Power Transfer
Wireless sensing and wireless energy are enablers to pave the way for smart transportation and a greener future.
Sensing With Random Signals
Radar systems typically employ well-designed deterministic signals for target sensing.
Sensing as a Service in 6G Perceptive Mobile Networks: Architecture, Advances, and the Road Ahead
Sensing-as-a-service is anticipated to be the core feature of 6G perceptive mobile networks (PMN), where high-precision real-time sensing will become an inherent capability rather than being an auxiliary function as before.
Semantic-Guided Feature Distillation for Multimodal Recommendation
The teacher model first extracts rich modality features from the generic modality feature by considering both the semantic information of items and the complementary information of multiple modalities.
Successive Pose Estimation and Beam Tracking for mmWave Vehicular Communication Systems
Simulation results demonstrate that the proposed SPEBT scheme is capable of providing precise pose estimation information and accurate beam tracking output, while reducing the proportion of beam training overhead to less than 5% averagely.
Symbol-Level Precoding for MU-MIMO System with RIRC Receiver
The problem is solved using the alternating optimization (AO) method, and the optimal solution structures for transmit precoding and receive combining matrices are derived by using Lagrangian and Karush-Kuhn-Tucker (KKT) conditions, based on which, the original problem is transformed into an equivalent quadratic programming problem, enabling more efficient solutions.
Landslide Surface Displacement Prediction Based on VSXC-LSTM Algorithm
Landslide is a natural disaster that can easily threaten local ecology, people's lives and property.
LightGT: A Light Graph Transformer for Multimedia Recommendation
Considering its challenges in effectiveness and efficiency, we propose a novel Transformer-based recommendation model, termed as Light Graph Transformer model (LightGT).
Ranked #1 on
Multi-Media Recommendation
on Kwai
(Recall@10 metric)
Specific Beamforming for Multi-UAV Networks: A Dual Identity-based ISAC Approach
Beam alignment is essential to compensate for the high path loss in the millimeter-wave (mmWave) Unmanned Aerial Vehicle (UAV) network.
Faster-Than-Nyquist Symbol-Level Precoding for Wideband Integrated Sensing and Communications
In this paper, we present an innovative symbol-level precoding (SLP) approach for a wideband multi-user multi-input multi-output (MU-MIMO) downlink Integrated Sensing and Communications (ISAC) system employing faster-than-Nyquist (FTN) signaling.
Robust Spatiotemporal Traffic Forecasting with Reinforced Dynamic Adversarial Training
Therefore, improving the adversarial robustness of these models is crucial for ITS.
RemoteCLIP: A Vision Language Foundation Model for Remote Sensing
However, these models primarily learn low-level features and require annotated data for fine-tuning.
Ranked #2 on
Cross-Modal Retrieval
on RSITMD
(using extra training data)
Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated Graph Neural Network
To address these limitations, we propose Bkd-FedGNN, a benchmark for backdoor attacks on FedGNN.
Optimizing Long-term Value for Auction-Based Recommender Systems via On-Policy Reinforcement Learning
Auction-based recommender systems are prevalent in online advertising platforms, but they are typically optimized to allocate recommendation slots based on immediate expected return metrics, neglecting the downstream effects of recommendations on user behavior.
Waveform Design for Communication-Assisted Sensing in 6G Perceptive Networks
In particular, a pair of transmission schemes, namely, separated S&C and dual-functional waveform designs, are proposed to optimize the sensing QoS under the constraints of the rate-distortion and power budget.
Towards ISAC-Empowered Vehicular Networks: Framework, Advances, and Opportunities
Connected and autonomous vehicle (CAV) networks face several challenges, such as low throughput, high latency, and poor localization accuracy.
Few-shot Classification via Ensemble Learning with Multi-Order Statistics
To address this need, we prove theoretically that leveraging ensemble learning on the base classes can correspondingly reduce the true error in the novel classes.
Joint Beam Scheduling and Power Allocation for SWIPT in Mixed Near- and Far-Field Channels
Extremely large-scale array (XL-array) has emerged as a promising technology to enhance the spectrum efficiency and spatial resolution in future wireless networks, leading to a fundamental paradigm shift from conventional far-field communications towards the new near-field communications.
Multi-Behavior Recommendation with Cascading Graph Convolution Networks
Most existing multi-behavior models fail to capture such dependencies in a behavior chain for embedding learning.
Rethinking Estimation Rate for Wireless Sensing: A Rate-Distortion Perspective
Wireless sensing has been recognized as a key enabling technology for numerous emerging applications.
Symbol-Level Precoding for Integrated Sensing and Communications: A Faster-Than-Nyquist Approach
In this paper, we propose a novel symbol-level precoding (SLP) method for a multi-user multi-input multi-output (MU-MIMO) downlink Integrated Sensing and Communications (ISAC) system based on faster-than-Nyquist (FTN) signaling.
Learning to Agree on Vision Attention for Visual Commonsense Reasoning
Visual Commonsense Reasoning (VCR) remains a significant yet challenging research problem in the realm of visual reasoning.
ISAC-Enabled V2I Networks Based on 5G NR: How Much Can the Overhead Be Reduced?
The emergence of the fifth-generation (5G) New Radio (NR) brings additional possibilities to vehicle-to-everything (V2X) network with improved quality of services.
Radar Sensing via OTFS Signaling: A Delay Doppler Signal Processing Perspective
The recently proposed orthogonal time frequency space (OTFS) modulation multiplexes data symbols in the delay-Doppler (DD) domain.
Joint Beamforming Design for Dual-Functional MIMO Radar and Communication Systems Guaranteeing Physical Layer Security
The dual-functional radar and communication (DFRC) technique constitutes a promising next-generation wireless solution, due to its benefits in terms of power consumption, physical hardware, and spectrum exploitation.
Multi-queue Momentum Contrast for Microvideo-Product Retrieval
The booming development and huge market of micro-videos bring new e-commerce channels for merchants.
Joint Receiver Design for Integrated Sensing and Communications
In this letter, we investigate the joint receiver design for integrated sensing and communication (ISAC) systems, where the communication signal and the target echo signal are simultaneously received and processed to achieve a balanced performance between both functionalities.
Integrated Sensing and Communications with Reconfigurable Intelligent Surfaces
Integrated sensing and communications (ISAC) are envisioned to be an integral part of future wireless networks, especially when operating at the millimeter-wave (mmWave) and terahertz (THz) frequency bands.
On the Performance Gain of Integrated Sensing and Communications: A Subspace Correlation Perspective
In this paper, we shed light on the performance gain of integrated sensing and communications (ISAC) from the perspective of channel correlations between radar sensing and communication (S&C), namely ISAC subspace correlation.
RIS-assisted Integrated Sensing and Communications: A Subspace Rotation Approach
We first show that the sensing and communication (S&C) performance can be jointly improved due to the capability of the RISs to control the ISAC channel.
Sensing-Assisted Eavesdropper Estimation: An ISAC Breakthrough in Physical Layer Security
Using the resulting information about potential Eves, we formulate secrecy rate expressions, that are a function of the Eves' estimation accuracy.
Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Models
Remarkably, we also show that adversarial training with our proposed attacks can significantly improve the robustness of spatiotemporal traffic forecasting models.
Seventy Years of Radar and Communications: The Road from Separation to Integration
Radar and communications (R&C) as key utilities of electromagnetic (EM) waves have fundamentally shaped human society and triggered the modern information age.
Privacy-Preserving Synthetic Data Generation for Recommendation Systems
At the item level, a synthetic data generation module is proposed to generate a synthetic item corresponding to the selected item based on the user's preferences.
Intelligent Reflecting Surface Enabled Sensing: Cramér-Rao Bound Optimization
For the extended target case, we obtain the optimal transmit beamforming solution to minimize the CRB in closed form.
ISAC from the Sky: UAV Trajectory Design for Joint Communication and Target Localization
In this paper, we consider an airborne integrated sensing and communications (ISAC) system where a UAV, which acts both as a communication BS and a mono-static radar, flies over a given area to transmit downlink signal to a ground communication user.
ProtoCLIP: Prototypical Contrastive Language Image Pretraining
Based on this understanding, in this paper, Prototypical Contrastive Language Image Pretraining (ProtoCLIP) is introduced to enhance such grouping by boosting its efficiency and increasing its robustness against the modality gap.
A Simple Baseline for Adversarial Domain Adaptation-based Unsupervised Flood Forecasting
Specifically, training of FloodDAN includes two stages: in the first stage, we train a rainfall encoder and a prediction head to learn general transferable hydrological knowledge on large-scale source domain data; in the second stage, we transfer the knowledge in the pretrained encoder into the rainfall encoder of target domain through adversarial domain alignment.
Self-Supervised Music-Motion Synchronization Learning for Music-Driven Conducting Motion Generation
Therefore, we propose a novel Music Motion Synchronized Generative Adversarial Network (M2S-GAN), which generates motions according to the automatically learned music representations.
Cascading Residual Graph Convolutional Network for Multi-Behavior Recommendation
In particular, we design a cascading residual graph convolutional network structure, which enables our model to learn user preferences by continuously refining user embeddings across different types of behaviors.
Vehicular Connectivity on Complex Trajectories: Roadway-Geometry Aware ISAC Beam-tracking
In this paper, we propose sensing-assisted beamforming designs for vehicles on arbitrarily shaped roads by relying on integrated sensing and communication (ISAC) signalling. Specifically, we aim to address the limitations of conventional ISAC beam-tracking schemes that do not apply to complex road geometries.
Intelligent Reflecting Surface Enabled Sensing: Cramér-Rao Lower Bound Optimization
This paper investigates intelligent reflecting surface (IRS) enabled non-line-of-sight (NLoS) wireless sensing, in which an IRS is deployed to assist an access point (AP) to sense a target in its NLoS region.
Analysis Method of Strapdown Inertial Navigation Error Distribution Based on Covariance Matrix Decomposition
Compared with the Monte-Carlo method and other method based on covariance matrix, the proposed method uses more complete error model, considers the interaction effect of error sources and can be easily realized with less computation.
Disentangled Multimodal Representation Learning for Recommendation
However, existing methods ignore the fact that different modalities contribute differently towards a user's preference on various factors of an item.
Sensing as a Service in 6G Perceptive Networks: A Unified Framework for ISAC Resource Allocation
Then, we establish a unified framework for ISAC resource allocation, where the fairness and the comprehensiveness optimization criteria are considered for the aforementioned sensing services.
Integrated Human Activity Sensing and Communications
In this article, we provide a review of human related sensing in the context of ISAC.
An Experimental Proof of Concept for Integrated Sensing and Communications Waveform Design
This experimental work focuses on a dual-functional radar sensing and communication framework where a single radiation waveform, either omnidirectional or directional, can realize both radar sensing and communication functions.
Disentangled Graph Neural Networks for Session-based Recommendation
The session embedding is then generated by aggregating the item embeddings with attention weights of each item's factors.
Integrated Sensing and Communications for V2I Networks: Dynamic Predictive Beamforming for Extended Vehicle Targets
The first stage is exploited for ISAC transmission, where a wide beam is adopted for both communication and sensing.
MIMO-OFDM Dual-Functional Radar-Communication Systems: Low-PAPR Waveform Design
In this paper, we explore a dual-functional radar-communication (DFRC) system for achieving integrated sensing and communications (ISAC).
Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond
As the standardization of 5G is being solidified, researchers are speculating what 6G will be.
VirtualConductor: Music-driven Conducting Video Generation System
In this demo, we present VirtualConductor, a system that can generate conducting video from any given music and a single user's image.
Integrated Sensing and Communication from Learning Perspective: An SDP3 Approach
Characterizing the sensing and communication performance tradeoff in integrated sensing and communication (ISAC) systems is challenging in the applications of learning-based human motion recognition.
Significant Wave Height Prediction based on Wavelet Graph Neural Network
Several parallel graph neural networks are separately trained on wavelet decomposed data, and the reconstruction of each model's prediction forms the final SWH prediction.
Accelerating Edge Intelligence via Integrated Sensing and Communication
Conventionally, the sensing and communication stages are executed sequentially, which results in excessive amount of dataset generation and uploading time.
Towards Multi-Functional 6G Wireless Networks: Integrating Sensing, Communication and Security
At the same time, the sensing capability incorporated in the ISAC transmission offers unique opportunities to design secure ISAC techniques.
Secure Dual-Functional Radar-Communication Transmission: Exploiting Interference for Resilience Against Target Eavesdropping
We study security solutions for dual-functional radar communication (DFRC) systems, which detect the radar target and communicate with downlink cellular users in millimeter-wave (mmWave) wireless networks simultaneously.
Long-term Cross Adversarial Training: A Robust Meta-learning Method for Few-shot Classification Tasks
Although adversarial training (AT) methods such as Adversarial Query (AQ) can improve the adversarially robust performance of meta-learning models, AT is still computationally expensive training.
Integrating Sensing and Communications for Ubiquitous IoT: Applications, Trends and Challenges
Meanwhile, triggered by ISAC, we are also witnessing a paradigm shift in the ubiquitous IoT architecture, in which the sensing and communication layers are tending to converge into a new layer, namely, the signaling layer.
Hardware Efficient Joint Radar-Communications with Hybrid Precoding and RF Chain Optimization
In this paper, we aim to achieve energy efficient design with minimum hardware requirement for hybrid precoding, which enables a large number of antennas with minimal number of RF chains, and sub-arrayed multiple-input multiple-output (MIMO) radar based joint radar-communication (JRC) systems.
An Overview of Signal Processing Techniques for Joint Communication and Radar Sensing
Joint communication and radar sensing (JCR) represents an emerging research field aiming to integrate the above two functionalities into a single system, sharing a majority of hardware and signal processing modules and, in a typical case, sharing a single transmitted signal.
Feature-level Attentive ICF for Recommendation
To demonstrate the effectiveness of our method, we design a light attention neural network to integrate both item-level and feature-level attention for neural ICF models.
Interest-aware Message-Passing GCN for Recommendation
To form the subgraphs, we design an unsupervised subgraph generation module, which can effectively identify users with common interests by exploiting both user feature and graph structure.
Cramér-Rao Bound Optimization for Joint Radar-Communication Design
We employ the Cram\'er-Rao bound (CRB) as a performance metric of target estimation, under both point and extended target scenarios.
Learning to Select for MIMO Radar based on Hybrid Analog-Digital Beamforming
In this paper, we propose an energy-efficient radar beampattern design framework for a Millimeter Wave (mmWave) massive multi-input multi-output (mMIMO) system, equipped with a hybrid analog-digital (HAD) beamforming structure.
The GALAH+ Survey: Third Data Release
Based on kinematics, 4% are halo stars.
Astrophysics of Galaxies Solar and Stellar Astrophysics
A Review of Automated Diagnosis of COVID-19 Based on Scanning Images
The pandemic of COVID-19 has caused millions of infections, which has led to a great loss all over the world, socially and economically.
Deep Learning Based Single Sample Per Person Face Recognition: A Survey
Face recognition under this situation is referred to as single sample face recognition and poses significant challenges to the effective training of deep models.
Bayesian Predictive Beamforming for Vehicular Networks: A Low-overhead Joint Radar-Communication Approach
To accurately estimate the motion parameters of vehicles in real-time, we propose a novel message passing algorithm based on factor graph, which yields near optimal solution to the maximum a posteriori estimation.
A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects
In this review, we aim to provide novel ideas and prospects in this fast-growing field as much as possible.
A^2-GCN: An Attribute-aware Attentive GCN Model for Recommendation
Considering the fact that for different users, the attributes of an item have different influence on their preference for this item, we design a novel attention mechanism to filter the message passed from an item to a target user by considering the attribute information.
User Diverse Preference Modeling by Multimodal Attentive Metric Learning
To tackle this problem, in this paper, we propose a novel Multimodal Attentive Metric Learning (MAML) method to model user diverse preferences for various items.
Truncated nuclear norm regularization for low-rank tensor completion
It is proved that the recently proposed truncated nuclear norm (TNN) can replace the traditional nuclear norm, as an improved approximation to the rank of a matrix.
Double Weighted Truncated Nuclear Norm Regularization for Low-Rank Matrix Completion
The truncated nuclear norm regularization (TNNR) method is applicable in real-world scenarios.
Low-Rank Tensor Completion by Truncated Nuclear Norm Regularization
Currently, low-rank tensor completion has gained cumulative attention in recovering incomplete visual data whose partial elements are missing.
