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Found 80 papers, 13 papers with code
IUCL at WASSA 2022 Shared Task: A Text-only Approach to Empathy and Emotion Detection
Our system, IUCL, participated in the WASSA 2022 Shared Task on Empathy Detection and Emotion Classification.
LiDDA: Data Driven Attribution at LinkedIn
Data Driven Attribution, which assigns conversion credits to marketing interactions based on causal patterns learned from data, is the foundation of modern marketing intelligence and vital to any marketing businesses and advertising platform.
Joint Resource Management for Energy-efficient UAV-assisted SWIPT-MEC: A Deep Reinforcement Learning Approach
The integration of simultaneous wireless information and power transfer (SWIPT) technology in 6G Internet of Things (IoT) networks faces significant challenges in remote areas and disaster scenarios where ground infrastructure is unavailable.
SocioVerse: A World Model for Social Simulation Powered by LLM Agents and A Pool of 10 Million Real-World Users
Social simulation is transforming traditional social science research by modeling human behavior through interactions between virtual individuals and their environments.
Easi3R: Estimating Disentangled Motion from DUSt3R Without Training
Recent advances in DUSt3R have enabled robust estimation of dense point clouds and camera parameters of static scenes, leveraging Transformer network architectures and direct supervision on large-scale 3D datasets.
A Weighted Predict-and-Optimize Framework for Power System Operation Considering Varying Impacts of Uncertainty
Integrating prediction and optimization enhances decision-making quality by yielding near optimal solutions.
GarmentPile: Point-Level Visual Affordance Guided Retrieval and Adaptation for Cluttered Garments Manipulation
Cluttered garments manipulation poses significant challenges due to the complex, deformable nature of garments and intricate garment relations.
ProBench: Benchmarking Large Language Models in Competitive Programming
To bridge the gap for high-level code reasoning assessment, we propose ProBench to benchmark LLMs in competitive programming, drawing inspiration from the International Collegiate Programming Contest.
On the Sum Rate and User Fairness of STAR-RIS Aided Communications
A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided communication system is investigated.
TrustRAG: Enhancing Robustness and Trustworthiness in RAG
Retrieval-Augmented Generation (RAG) systems enhance large language models (LLMs) by integrating external knowledge sources, enabling more accurate and contextually relevant responses tailored to user queries.
Online Low-Carbon Workload, Energy, and Temperature Management of Distributed Data Centers
Data centers have become one of the major energy consumers, making their low-carbon operations critical to achieving global carbon neutrality.
Brain-to-Text Benchmark '24: Lessons Learned
Speech brain-computer interfaces aim to decipher what a person is trying to say from neural activity alone, restoring communication to people with paralysis who have lost the ability to speak intelligibly.
Feat2GS: Probing Visual Foundation Models with Gaussian Splatting
Under Feat2GS, we conduct extensive experiments to probe the 3D awareness of several VFMs, and investigate the ingredients that lead to a 3D aware VFM.
Fully Open Source Moxin-7B Technical Report
Recently, Large Language Models (LLMs) have undergone a significant transformation, marked by a rapid rise in both their popularity and capabilities.
Joint Beamforming for STAR-RIS in Near-Field Communications
A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided near-field multiple-input multiple-output (MIMO) communication framework is proposed.
STAR-RIS Aided Integrated Sensing, Computing, and Communication for Internet of Robotic Things
A computation rate maximization problem is formulated to optimize the sensing and receive beamformers at the BS and the STAR-RIS coefficients under the BS power constraint, the sensing signal-to-noise ratio constraint, and STAR-RIS coefficients constraints.
STAR-RIS in Cognitive Radio Networks
The secondary network sum rate maximization problem is first formulated for the STAR-RIS aided MIMO CR system.
A Differentially Private Quantum Neural Network for Probabilistic Optimal Power Flow
The stochastic nature of renewable energy and load demand requires efficient and accurate solutions for probabilistic optimal power flow (OPF).
Translating Electrocardiograms to Cardiac Magnetic Resonance Imaging Useful for Cardiac Assessment and Disease Screening: A Multi-Center Study AI for ECG to CMR Translation Study
Trained on 159, 819 samples from five cohorts, including the UK Biobank (n=42, 483) and MIMIC-IV-ECG (n=164, 550), and externally validated on independent clinical datasets (n=3, 767), CardioNets achieved strong performance across disease screening and phenotype estimation tasks.
A capacity renting framework for shared energy storage considering peer-to-peer energy trading of prosumers with privacy protection
Shared energy storage systems (ESS) present a promising solution to the temporal imbalance between energy generation from renewable distributed generators (DGs) and the power demands of prosumers.
ET-SEED: Efficient Trajectory-Level SE(3) Equivariant Diffusion Policy
We theoretically extend equivariant Markov kernels and simplify the condition of equivariant diffusion process, thereby significantly improving training efficiency for trajectory-level SE(3) equivariant diffusion policy in an end-to-end manner.
ElectionSim: Massive Population Election Simulation Powered by Large Language Model Driven Agents
We also introduce PPE, a poll-based presidential election benchmark to assess the performance of our framework under the U. S. presidential election scenario.
Results of the Big ANN: NeurIPS'23 competition
The 2023 Big ANN Challenge, held at NeurIPS 2023, focused on advancing the state-of-the-art in indexing data structures and search algorithms for practical variants of Approximate Nearest Neighbor (ANN) search that reflect the growing complexity and diversity of workloads.
EqvAfford: SE(3) Equivariance for Point-Level Affordance Learning
Humans perceive and interact with the world with the awareness of equivariance, facilitating us in manipulating different objects in diverse poses.
Preferred-Action-Optimized Diffusion Policies for Offline Reinforcement Learning
Meanwhile, based on the diffusion model, preferred actions within the same behavior distribution are automatically generated through the critic function.
STYLE: Improving Domain Transferability of Asking Clarification Questions in Large Language Model Powered Conversational Agents
However, they still struggle to deliver promising performance on unseen domains, struggling to achieve effective domain transferability.
RLGNet: Repeating-Local-Global History Network for Temporal Knowledge Graph Reasoning
To address this issue, we propose a multi-scale hybrid architecture model based on ensemble learning, called RLGNet (Repeating-Local-Global History Network).
Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods
For each role, we summarize the methodologies, analyze the specific RL challenges that are mitigated, and provide insights into future directions.
TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models
Traffic prediction constitutes a pivotal facet within the purview of Intelligent Transportation Systems (ITS), and the attainment of highly precise predictions holds profound significance for efficacious traffic management.
Distributed Stackelberg Equilibrium Seeking for Networked Multi-Leader Multi-Follower Games with A Clustered Information Structure
In this case, the leaders cannot fully anticipate the collective rational response of all followers with its local information.
CRISIS ALERT:Forecasting Stock Market Crisis Events Using Machine Learning Methods
Historically, the economic recession often came abruptly and disastrously.
Economic Forces in Stock Returns
Driven by such motivation, we conduct an attribution analysis based on the general framework of their model to further prove the importance of the economic factors and identify the specific identity of significant factors.
Applications of Large Scale Foundation Models for Autonomous Driving
Since DARPA Grand Challenges (rural) in 2004/05 and Urban Challenges in 2007, autonomous driving has been the most active field of AI applications.
A Multi-timescale and Chance-Constrained Energy Dispatching Strategy of Integrated Heat-Power Community with Shared Hybrid Energy Storage
In the integrated heat-power system with coupling heat-power generators and demands, the key challenges lie in the interaction between heat and power, the inherent uncertainty of renewable energy and consumers' demands, and the multi-timescale scheduling of heat and power.
Neural Image Compression Using Masked Sparse Visual Representation
A set of semantic-class-dependent basis codebooks are learned, which are weighted combined to generate a rich latent feature for high-quality reconstruction.
Stochastic Optimization of Coupled Power Distribution-Urban Transportation Network Operations with Autonomous Mobility on Demand Systems
Then, a bi-level power-traffic flow (PTF) model is proposed to characterize the interaction of power distribution networks (PDNs) and urban transportation networks (UTNs).
Eva-KELLM: A New Benchmark for Evaluating Knowledge Editing of LLMs
In this paper, we propose Eva-KELLM, a new benchmark for evaluating knowledge editing of LLMs.
An Overview about Emerging Technologies of Autonomous Driving
Since DARPA started Grand Challenges in 2004 and Urban Challenges in 2007, autonomous driving has been the most active field of AI applications.
Hierarchical Game for Coupled Power System with Energy Sharing and Transportation System
This necessitates new business models in the power sector to hedge against uncertainties while imposing a strong coupling between the connected power and transportation networks.
Latent Semantic Diffusion-based Channel Adaptive De-Noising SemCom for Future 6G Systems
Upon the designed DNSC architecture, we further combine adversarial learning, variational autoencoder, and diffusion model to propose the Latent Diffusion DNSC (Latent-Diff DNSC) scheme to realize intelligent online de-noising.
Approach to Learning Generalized Audio Representation Through Batch Embedding Covariance Regularization and Constant-Q Transforms
Preliminary results show (1) the proposed BECR can incur a more dispersed embedding on the test set, (2) BECR improves the PaSST model without extra computation complexity, and (3) STFT preprocessing outperforms CQT in all tasks we tested.
Automatic Sleep Stage Classification with Cross-modal Self-supervised Features from Deep Brain Signals
This sleep stage classification model could be adapted to chronic and continuous monitor sleep for Parkinson's patients in daily life, and potentially utilized for more precise treatment in deep brain-machine interfaces, such as closed-loop deep brain stimulation.
t-SMILES: A Scalable Fragment-based Molecular Representation Framework for De Novo Molecule Generation
Effective representation of molecules is a crucial factor affecting the performance of artificial intelligence models.
Sizing Grid-Connected Wind Power Generation and Energy Storage with Wake Effect and Endogenous Uncertainty: A Distributionally Robust Method
In this paper, a bi-objective distributionally robust optimization (DRO) model is proposed to determine the capacities of wind power generation and ESSs considering the wake effect.
Local-to-Global Registration for Bundle-Adjusting Neural Radiance Fields
Neural Radiance Fields (NeRF) have achieved photorealistic novel views synthesis; however, the requirement of accurate camera poses limits its application.
Place Recognition under Occlusion and Changing Appearance via Disentangled Representations
Place recognition is a critical and challenging task for mobile robots, aiming to retrieve an image captured at the same place as a query image from a database.
Landmark Tracking in Liver US images Using Cascade Convolutional Neural Networks with Long Short-Term Memory
We further tested the proposed model on 69 landmarks from the testing dataset that has a similar image pattern to the training pattern, resulting in a mean tracking error of 0. 94+/-0. 83 mm.
Distributed Coordination of Charging Stations with Shared Energy Storage in a Distribution Network
Though charging stations can install energy storage to reduce their impacts on the grid, the conventional "one charging station, one energy storage" method may be uneconomical due to the high upfront cost of energy storage.
Zero-shot Cross-Linguistic Learning of Event Semantics
Typologically diverse languages offer systems of lexical and grammatical aspect that allow speakers to focus on facets of event structure in ways that comport with the specific communicative setting and discourse constraints they face.
Distributed Coordination of Charging Stations Considering Aggregate EV Power Flexibility
However, effective management of charging stations is challenging due to individual EV owners' privacy concerns, competing interests of different stations, and the coupling distribution network constraints.
Hybrid Reinforcement Learning for STAR-RISs: A Coupled Phase-Shift Model Based Beamformer
Despite the coupled nature of the phase-shift model, the formulated problem is solved by invoking a hybrid continuous and discrete phase-shift control policy.
UV Volumes for Real-time Rendering of Editable Free-view Human Performance
Neural volume rendering enables photo-realistic renderings of a human performer in free-view, a critical task in immersive VR/AR applications.
Smart Director: An Event-Driven Directing System for Live Broadcasting
To our best knowledge, our system is the first end-to-end automated directing system for multi-camera sports broadcasting, completely driven by the semantic understanding of sports events.
Towards Transactive Energy: An Analysis of Information-related Practical Issues
The development of distributed energy resources, such as rooftop photovoltaic (PV) panels, batteries, and electric vehicles (EVs), has decentralized our power system operation, where transactive energy markets empower local energy exchanges.
Dynamic Flow Equilibrium of Transportation and Power Distribution Networks Considering Flexible Traveling Choices and Voltage Quality Improvement
The increasing global spread of electric vehicles (EVs) has introduced significant interdependence between transportation and power networks.
A New Look at AI-Driven NOMA-F-RANs: Features Extraction, Cooperative Caching, and Cache-Aided Computing
Non-orthogonal multiple access (NOMA) enabled fog radio access networks (NOMA-F-RANs) have been taken as a promising enabler to release network congestion, reduce delivery latency, and improve fog user equipments' (F-UEs') quality of services (QoS).
Hallucinated Neural Radiance Fields in the Wild
This paper studies the problem of hallucinated NeRF: i. e., recovering a realistic NeRF at a different time of day from a group of tourism images.
Storage and Transmission Capacity Requirements of a Remote Solar Power Generation System
Large solar power stations usually locate in remote areas and connect to the main grid via a long transmission line.
DCT and DST Filtering with Sparse Graph Operators
These sparse operators can be viewed as graph filters operating in the DCT domain, which allows us to approximate any DCT graph filter by a MPGF, leading to a design with more degrees of freedom than the conventional PGF approach.
Artificial Intelligence Driven UAV-NOMA-MEC in Next Generation Wireless Networks
To this end, the fundamentals of this framework are first introduced.
Integrated 3C in NOMA-enabled Remote-E-Health Systems
A novel framework is proposed to integrate communication, control and computing (3C) into the fifth-generation and beyond (5GB) wireless networks for satisfying the ultra-reliable low-latency connectivity requirements of remote-e-Health systems.
Deep Learning for Latent Events Forecasting in Twitter Aided Caching Networks
A novel Twitter context aided content caching (TAC) framework is proposed for enhancing the caching efficiency by taking advantage of the legibility and massive volume of Twitter data.
Learning-Based Predictive Control via Real-Time Aggregate Flexibility
To be used effectively, an aggregator must be able to communicate the available flexibility of the loads they control, as known as the aggregate flexibility to a system operator.
Optimization and Control Systems and Control Systems and Control
Intelligent Reflecting Surface Aided Multi-Cell NOMA Networks
This paper proposes a novel framework of resource allocation in intelligent reflecting surface (IRS) aided multi-cell non-orthogonal multiple access (NOMA) networks, where a sum-rate maximization problem is formulated.
Path Design and Resource Management for NOMA enhanced Indoor Intelligent Robots
Our simulation results demonstrate that 1) With the aid of NOMA techniques, the communication reliability of IRs is effectively improved; 2) The radio map is qualified to be a virtual training environment, and its statistical channel state information improves training efficiency by about 30%; 3) The proposed DT-DPG algorithm is superior to the conventional deep deterministic policy gradient (DDPG) algorithm in terms of optimization performance, training time, and anti-local optimum ability.
Multi-Agent Reinforcement Learning in NOMA-aided UAV Networks for Cellular Offloading
Afterward, a mutual deep Q-network (MDQN) algorithm is proposed to jointly determine the optimal 3D trajectory and power allocation of UAVs.
NOMA in UAV-aided cellular offloading: A machine learning approach
A novel framework is proposed for cellular offloading with the aid of multiple unmanned aerial vehicles (UAVs), while non-orthogonal multiple access (NOMA) technique is employed at each UAV to further improve the spectrum efficiency of the wireless network.
Machine Learning Empowered Trajectory and Passive Beamforming Design in UAV-RIS Wireless Networks
The energy consumption minimizing problem is formulated by jointly designing the movement of the UAV, phase shifts of the RIS, power allocation policy from the UAV to MUs, as well as determining the dynamic decoding order.
An Ultra Lightweight CNN for Low Resource Circuit Component Recognition
In this paper, we present an ultra lightweight system that can effectively recognize different circuit components in an image with very limited training data.
Resource Allocation for Multi-Cell IRS-Aided NOMA Networks
This paper proposes a novel framework of resource allocation in multi-cell intelligent reflecting surface (IRS) aided non-orthogonal multiple access (NOMA) networks, where an IRS is deployed to enhance the wireless service.
Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies
Since DARPA Grand Challenges (rural) in 2004/05 and Urban Challenges in 2007, autonomous driving has been the most active field of AI applications.
Artificial Intelligence Aided Next-Generation Networks Relying on UAVs
Artificial intelligence (AI) assisted unmanned aerial vehicle (UAV) aided next-generation networking is proposed for dynamic environments.
RIS Enhanced Massive Non-orthogonal Multiple Access Networks: Deployment and Passive Beamforming Design
A novel framework is proposed for the deployment and passive beamforming design of a reconfigurable intelligent surface (RIS) with the aid of non-orthogonal multiple access (NOMA) technology.
OpenArray v1.0: a simple operator library for the decoupling of ocean modeling and parallel computing
In this work, we design a simple computing library to bridge the gap and decouple the work of ocean modeling from parallel computing.
MIMO Assisted Networks Relying on Intelligent Reflective Surfaces
The network's SE and EE are also derived.
Information Theory Signal Processing Information Theory
Investigating Multilingual Abusive Language Detection: A Cautionary Tale
Based on our findings, we can conclude that a multilingual optimization of classifiers is not possible even in settings where comparable data sets are used.
Destruction and Construction Learning for Fine-Grained Image Recognition
In this paper, we propose a novel "Destruction and Construction Learning" (DCL) method to enhance the difficulty of fine-grained recognition and exercise the classification model to acquire expert knowledge.
A k-Nearest Neighbor Approach towards Multi-level Sequence Labeling
For the experiments we perform a comparison between using pseudo-data and real world data.
Convolution Forgetting Curve Model for Repeated Learning
The status of forgetting (i. e. the output in the working memory model) is regarded as output function or the convolution result of the memory ability and learning material.
