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Found 83 papers, 40 papers with code
A Survey on Semantic Communications in Internet of Vehicles
Internet of Vehicles (IoV), as the core of intelligent transportation system, enables comprehensive interconnection between vehicles and their surroundings through multiple communication modes, which is significant for autonomous driving and intelligent traffic management.
Unifying and Optimizing Data Values for Selection via Sequential-Decision-Making
Data selection has emerged as a crucial downstream application of data valuation.
PPO-Based Vehicle Control for Ramp Merging Scheme Assisted by Enhanced C-V2X
On-ramp merging presents a critical challenge in autonomous driving, as vehicles from merging lanes need to dynamically adjust their positions and speeds while monitoring traffic on the main road to prevent collisions.
Enhanced SPS Velocity-adaptive Scheme: Access Fairness in 5G NR V2I Networks
Vehicle-to-Infrastructure (V2I) technology enables information exchange between vehicles and road infrastructure.
What Kind of Visual Tokens Do We Need? Training-free Visual Token Pruning for Multi-modal Large Language Models from the Perspective of Graph
Recent Multimodal Large Language Models(MLLMs) often use a large number of visual tokens to compensate their visual shortcoming, leading to excessive computation and obvious visual redundancy.
Cross-View Image Set Geo-Localization
Therefore, we propose a novel task: Cross-View Image Set Geo-Localization (Set-CVGL), which gathers multiple images with diverse perspectives as a query set for localization.
Cross-View Geo-Localization with Street-View and VHR Satellite Imagery in Decentrality Settings
To address this limitation, we introduce DReSS (Decentrality Related Street-view and Satellite-view dataset), a novel dataset designed to evaluate cross-view geo-localization with a large geographic scope and diverse landscapes, emphasizing the decentrality issue.
Accelerating Multimodal Large Language Models via Dynamic Visual-Token Exit and the Empirical Findings
In particular, we reveal that visual tokens will stop contributing to reasoning when the text tokens receive enough image information, yielding obvious visual redundancy.
DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoV
Therefore, this paper analyzes the effects of multi-priority queues and NOMA on AoI in the C-V2X vehicular communication system and proposes an energy consumption and AoI optimization method based on DRL.
Discrete Cosine Transform-Based Joint Spectral-Spatial Information Compression and Band Correlation Calculation for Hyperspectral Feature Extraction
We argue that increasing the spectral information in the extracted features is the key to addressing the over-smoothing problem in the classification map.
Ranked #1 on
Hyperspectral Image Classification
on Houston
(OA@10%perclass metric)
Movable Antenna Enhanced Networked Full-Duplex Integrated Sensing and Communication System
These challenges can be overcome by networked FD ISAC framework.
Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement Learning
The proposed approach leverages the semantic information to optimize the allocation of communication resources.
V2X-Assisted Distributed Computing and Control Framework for Connected and Automated Vehicles under Ramp Merging Scenario
Firstly, a centralized cooperative trajectory planning problem is formulated subject to the safely constraints and traffic performance in ramp merging scenario, where the trajectories of all vehicles are jointly optimized.
A Comprehensive Survey on Joint Resource Allocation Strategies in Federated Edge Learning
Federated Edge Learning (FEL), an emerging distributed Machine Learning (ML) paradigm, enables model training in a distributed environment while ensuring user privacy by using physical separation for each user data.
Blockchain-Enabled Variational Information Bottleneck for Data Extraction Based on Mutual Information in Internet of Vehicles
The Internet of Vehicles (IoV) network can address the issue of limited computing resources and data processing capabilities of individual vehicles, but it also brings the risk of privacy leakage to vehicle users.
Fit and Prune: Fast and Training-free Visual Token Pruning for Multi-modal Large Language Models
In this paper, we propose a novel and training-free approach for the effective visual token pruning of MLLMs, termed FitPrune, which can quickly produce a complete pruning recipe for MLLMs according to a pre-defined budget.
WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks
Wireless networks are increasingly facing challenges due to their expanding scale and complexity.
DRL-Based Federated Self-Supervised Learning for Task Offloading and Resource Allocation in ISAC-Enabled Vehicle Edge Computing
Our improved algorithm offloads partial task to RSU and optimizes energy consumption by adjusting transmission power, CPU frequency, and task assignment ratios, balancing local and RSU-based training.
DRL-Based Resource Allocation for Motion Blur Resistant Federated Self-Supervised Learning in IoV
In the Internet of Vehicles (IoV), Federated Learning (FL) provides a privacy-preserving solution by aggregating local models without sharing data.
Mobility-Aware Federated Self-supervised Learning in Vehicular Network
Federated Learning (FL) is an advanced distributed machine learning approach, that protects the privacy of each vehicle by allowing the model to be trained on multiple devices simultaneously without the need to upload all data to a road side unit (RSU).
Reconfigurable Intelligent Surface Aided Vehicular Edge Computing: Joint Phase-shift Optimization and Multi-User Power Allocation
To address the scheme, we propose an innovative deep reinforcement learning (DRL) framework that combines the Deep Deterministic Policy Gradient (DDPG) algorithm for optimizing RIS phase-shift coefficients and the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm for optimizing the power allocation of vehicle user (VU).
Digital Twin Vehicular Edge Computing Network: Task Offloading and Resource Allocation
With the increasing demand for multiple applications on internet of vehicles.
Joint Optimization of Age of Information and Energy Consumption in NR-V2X System based on Deep Reinforcement Learning
To address this, 3GPP has developed Vehicle-to-Everything (V2X) specifications based on 5G New Radio (NR) technology, where Mode 2 Side-Link (SL) communication resembles Mode 4 in LTE-V2X, allowing direct communication between vehicles.
Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing
Federated Learning (FL) can protect the privacy of the vehicles in vehicle edge computing (VEC) to a certain extent through sharing the gradients of vehicles' local models instead of local data.
Resource Allocation for Twin Maintenance and Computing Task Processing in Digital Twin Vehicular Edge Computing Network
By integrating DT with VEC, a virtual vehicle DT can be created in the VEC server to monitor the real-time operating status of vehicles.
A Machine Learning and Explainable AI Framework Tailored for Unbalanced Experimental Catalyst Discovery
To address these challenges, we introduce a robust machine learning and explainable AI (XAI) framework to accurately classify the catalytic yield of various compositions and identify the contributions of individual components.
Graph Neural Networks and Deep Reinforcement Learning Based Resource Allocation for V2X Communications
In the rapidly evolving landscape of Internet of Vehicles (IoV) technology, Cellular Vehicle-to-Everything (C-V2X) communication has attracted much attention due to its superior performance in coverage, latency, and throughput.
Channel Characterization of IRS-assisted Resonant Beam Communication Systems
The resonant beam communication (RBC) system, which employs spatially separated laser cavities as the transmitter and receiver, is a high-speed OWC technology capable of self-alignment without tracking.
Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learning
This paper focuses on the Age of Information (AoI) as a key metric for data freshness and explores task offloading issues for vehicles under RSU communication resource constraints.
CAT: Interpretable Concept-based Taylor Additive Models
Importantly, it can explain model predictions through high-level concepts that human can understand.
Proceedings of The second international workshop on eXplainable AI for the Arts (XAIxArts)
This second international workshop on explainable AI for the Arts (XAIxArts) brought together a community of researchers in HCI, Interaction Design, AI, explainable AI (XAI), and digital arts to explore the role of XAI for the Arts.
Deep-Reinforcement-Learning-Based AoI-Aware Resource Allocation for RIS-Aided IoV Networks
Reconfigurable Intelligent Surface (RIS) is a pivotal technology in communication, offering an alternative path that significantly enhances the link quality in wireless communication environments.
Reconfigurable Intelligent Surface Assisted VEC Based on Multi-Agent Reinforcement Learning
Vehicular edge computing (VEC) is an emerging technology that enables vehicles to perform high-intensity tasks by executing tasks locally or offloading them to nearby edge devices.
IMFL-AIGC: Incentive Mechanism Design for Federated Learning Empowered by Artificial Intelligence Generated Content
Federated learning (FL) has emerged as a promising paradigm that enables clients to collaboratively train a shared global model without uploading their local data.
Semantic-Aware Spectrum Sharing in Internet of Vehicles Based on Deep Reinforcement Learning
This optimization encompasses the optimal link of V2V and V2I sharing strategies, the transmission power for vehicles sending semantic information and the length of transmitted semantic symbols, aiming at maximizing HSSE of V2I and enhancing success rate of effective semantic information transmission (SRS) of V2V.
WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence
To empower LLMs with knowledge and expertise in the wireless domain, this paper proposes WirelessLLM, a comprehensive framework for adapting and enhancing LLMs to address the unique challenges and requirements of wireless communication networks.
Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing
In this scheme, vehicle s mobility, channel conditions with temporal variations, computational resources with temporal variations, different data amount, transmission channel status of vehicles as well as Byzantine attacks were taken into account. Simulation results show that the proposed scheme effectively improves the safety and accuracy of the global model.
Not All Attention is Needed: Parameter and Computation Efficient Transfer Learning for Multi-modal Large Language Models
In this paper, we propose a novel parameter and computation efficient tuning method for Multi-modal Large Language Models (MLLMs), termed Efficient Attention Skipping (EAS).
Blockchain-Enabled Variational Information Bottleneck for IoT Networks
In Internet of Things (IoT) networks, the amount of data sensed by user devices may be huge, resulting in the serious network congestion.
Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation Network
Finally, we propose a multi-agent deep reinforcement learning (MADRL) based algorithm to decide where the predicted popular contents are collaboratively cached among SBSs.
Efficient Toxic Content Detection by Bootstrapping and Distilling Large Language Models
Furthermore, student LMs fine-tuned with rationales extracted via DToT outperform baselines on all datasets with up to 16. 9\% accuracy improvement, while being more than 60x smaller than conventional LLMs.
URLLC-Awared Resource Allocation for Heterogeneous Vehicular Edge Computing
Vehicular edge computing (VEC) is a promising technology to support real-time vehicular applications, where vehicles offload intensive computation tasks to the nearby VEC server for processing.
Parameter and Computation Efficient Transfer Learning for Vision-Language Pre-trained Models
In this paper, we aim at parameter and computation efficient transfer learning (PCETL) for VLP models.
FedDD: Toward Communication-efficient Federated Learning with Differential Parameter Dropout
FedDD consists of two key modules: dropout rate allocation and uploaded parameter selection, which will optimize the model parameter uploading ratios tailored to different clients' heterogeneous conditions and also select the proper set of important model parameters for uploading subject to clients' dropout rate constraints.
General-Purpose Multi-Modal OOD Detection Framework
To reach this goal, we propose a general-purpose weakly-supervised OOD detection framework, called WOOD, that combines a binary classifier and a contrastive learning component to reap the benefits of both.
Approximated Prompt Tuning for Vision-Language Pre-trained Models
Prompt tuning is a parameter-efficient way to deploy large-scale pre-trained models to downstream tasks by adding task-specific tokens.
Adapting Pre-trained Language Models to Vision-Language Tasks via Dynamic Visual Prompting
In addition, we also experiment DVP with the recently popular adapter approach to keep the most parameters of PLMs intact when adapting to VL tasks, helping PLMs achieve a quick shift between single- and multi-modal tasks.
Deep Reinforcement Learning Based Vehicle Selection for Asynchronous Federated Learning Enabled Vehicular Edge Computing
Furthermore, as vehicles have different local training time due to various sizes of local data and their different computing capabilities, asynchronous federated learning (AFL) is employed to facilitate the RSU to update the global model immediately after receiving a local model to reduce the aggregation delay.
Deep Reinforcement Learning Based Power Allocation for Minimizing AoI and Energy Consumption in MIMO-NOMA IoT Systems
It is critical to determine the optimal policy including sample collection requirements and power allocation to minimize the AoI and energy consumption of MIMO-NOMA IoT system, where the transmission rate is not a constant in the SIC process and the noise is stochastic in the MIMO-NOMA channel.
Unsupervised Domain Adaptation on Person Re-Identification via Dual-level Asymmetric Mutual Learning
The knowledge transfer between two networks is based on an asymmetric mutual learning manner.
HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Association
Federated learning (FL) is a promising paradigm that enables collaboratively learning a shared model across massive clients while keeping the training data locally.
Olive Branch Learning: A Topology-Aware Federated Learning Framework for Space-Air-Ground Integrated Network
The space-air-ground integrated network (SAGIN), one of the key technologies for next-generation mobile communication systems, can facilitate data transmission for users all over the world, especially in some remote areas where vast amounts of informative data are collected by Internet of remote things (IoRT) devices to support various data-driven artificial intelligence (AI) services.
Symphony in the Latent Space: Provably Integrating High-dimensional Techniques with Non-linear Machine Learning Models
This paper revisits building machine learning algorithms that involve interactions between entities, such as those between financial assets in an actively managed portfolio, or interactions between users in a social network.
CycleTrans: Learning Neutral yet Discriminative Features for Visible-Infrared Person Re-Identification
Visible-infrared person re-identification (VI-ReID) is a task of matching the same individuals across the visible and infrared modalities.
Higher-order accurate two-sample network inference and network hashing
Two-sample hypothesis testing for network comparison presents many significant challenges, including: leveraging repeated network observations and known node registration, but without requiring them to operate; relaxing strong structural assumptions; achieving finite-sample higher-order accuracy; handling different network sizes and sparsity levels; fast computation and memory parsimony; controlling false discovery rate (FDR) in multiple testing; and theoretical understandings, particularly regarding finite-sample accuracy and minimax optimality.
Asynchronous Federated Learning for Edge-assisted Vehicular Networks
For the traditional federated learning (FL), vehicles train the data locally to obtain a local model and then upload the local model to the RSU to update the global model, thus the data privacy can be protected through sharing model parameters instead of data.
Time-Dependent Performance Modeling for Platooning Communications at Intersection
In this paper, we construct the time-dependent model to evaluate the platooning communication performance at the intersection based on the initial movement characteristics.
Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning
In addition, we consider the mobility of vehicles and propose a deep reinforcement learning algorithm to obtain the optimal cooperative caching location for the predicted popular contents in order to optimize the content transmission delay.
A Deep Reinforcement Learning Approach for Online Parcel Assignment
In this paper, we investigate the online parcel assignment (OPA) problem, in which each stochastically generated parcel needs to be assigned to a candidate route for delivery to minimize the total cost subject to certain business constraints.
Discover Cross-Modality Nuances for Visible-Infrared Person Re-Identification
In this paper, we propose a joint Modality and Pattern Alignment Network (MPANet) to discover cross-modality nuances in different patterns for visible-infrared person Re-ID, which introduces a modality alleviation module and a pattern alignment module to jointly extract discriminative features.
Deep Reinforcement Learning with Spatio-temporal Traffic Forecasting for Data-Driven Base Station Sleep Control
To meet the ever increasing mobile traffic demand in 5G era, base stations (BSs) have been densely deployed in radio access networks (RANs) to increase the network coverage and capacity.
Double Quarter Wave Crab Cavity Wire Stretching Measurement at BNL
The wire stretching measurement was completed on the prototype Double Quarter Wave (DQW) crab cavity for operation practice and calibration of the measurement system.
Accelerator Physics
FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring
In this paper, we propose FedHome, a novel cloud-edge based federated learning framework for in-home health monitoring, which learns a shared global model in the cloud from multiple homes at the network edges and achieves data privacy protection by keeping user data locally.
Delay Sensitive Task Offloading in the 802.11p Based Vehicular Fog Computing Systems
Furthermore, the long-term reward of the system (i. e., jointly considers the transmission delay, computing delay, available resources, and diversity of vehicles and tasks) becomes a significantly important issue for providers.
Networking and Internet Architecture
Time-dependent Performance Analysis of the 802.11p-based Platooning Communications Under Disturbance
However, one vehicle in platoons inevitably suffers from a disturbance resulting from the leader vehicle acceleration/deceleration, wind gust and uncertainties in a platoon control system, i. e., aerodynamics drag and rolling resistance moment etc.
Networking and Internet Architecture
Rosella: A Self-Driving Distributed Scheduler for Heterogeneous Clusters
We evaluate Rosella with a variety of workloads on a 32-node AWS cluster.
BATS: A Spectral Biclustering Approach to Single Document Topic Modeling and Segmentation
In this work, we reexamine the inter-related problems of "topic identification" and "text segmentation" for sparse document learning, when there is a single new text of interest.
Learning Task-oriented Disentangled Representations for Unsupervised Domain Adaptation
This drawback limits the flexibility of UDA in complicated open-set tasks where no labels are shared between domains.
DeepCP: Deep Learning Driven Cascade Prediction Based Autonomous Content Placement in Closed Social Network
Accordingly, we further propose a novel autonomous content placement mechanism CP-GAN which adopts the generative adversarial network (GAN) for agile placement decision making to reduce the content access latency and enhance users' QoE.
HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning
We further formulate a joint computation and communication resource allocation and edge association problem for device users under HFEL framework to achieve global cost minimization.
Distributed, Parallel, and Cluster Computing
Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework
Internet of Things (IoT) have widely penetrated in different aspects of modern life and many intelligent IoT services and applications are emerging.
Equity2Vec: End-to-end Deep Learning Framework for Cross-sectional Asset Pricing
Pricing assets has attracted significant attention from the financial technology community.
PD-GAN: Adversarial Learning for Personalized Diversity-Promoting Recommendation
This paper proposes Personalized Diversity-promoting GAN (PD-GAN), a novel recommendation model to generate diverse, yet relevant recommendations.
Bandit Learning for Diversified Interactive Recommendation
Interactive recommender systems that enable the interactions between users and the recommender system have attracted increasing research attentions.
Adaptive Reduced Rank Regression
We study the low rank regression problem $\my = M\mx + \epsilon$, where $\mx$ and $\my$ are $d_1$ and $d_2$ dimensional vectors respectively.
Video-based Person Re-identification with Two-stream Convolutional Network and Co-attentive Snippet Embedding
Recently, the applications of person re-identification in visual surveillance and human-computer interaction are sharply increasing, which signifies the critical role of such a problem.
Optical Flow Estimation
Video-Based Person Re-Identification
Recent Advances in Diversified Recommendation
With the rapid development of recommender systems, accuracy is no longer the only golden criterion for evaluating whether the recommendation results are satisfying or not.
Diversity-Promoting Deep Reinforcement Learning for Interactive Recommendation
Interactive recommendation that models the explicit interactions between users and the recommender system has attracted a lot of research attentions in recent years.
A Swarming Approach to Optimize the One-hop Delay in Smart Driving Inter-platoon Communications
In the second step, we first set the minimum average one-hop delay found in the first step as the initial optimization goal and then adopt the swarming approach again to get the one-hop delay of each backbone vehicle balance to the minimum average one-hop delay.
Networking and Internet Architecture
Towards Non-Parametric Learning to Rank
Next, we fix the problem by introducing a new algorithm with features constructed from "global information" of the data matrix.
Parameter-free $\ell_p$-Box Decoding of LDPC Codes
The Alternating Direction Method of Multipliers (ADMM) decoding of Low Density Parity Check (LDPC) codes has received many attentions due to its excellent performance at the error floor region.
Information Theory Information Theory
Improving Deep Neural Network with Multiple Parametric Exponential Linear Units
In this paper, we first propose a new activation function, Multiple Parametric Exponential Linear Units (MPELU), aiming to generalize and unify the rectified and exponential linear units.
Computational Curiosity (A Book Draft)
This book discusses computational curiosity, from the psychology of curiosity to the computational models of curiosity, and then showcases several interesting applications of computational curiosity.
