Search Results for author:
Found 257 papers, 114 papers with code
Efficient Hyper-parameter Search for Knowledge Graph Embedding
Based on the analysis, we propose an efficient two-stage search algorithm KGTuner, which efficiently explores HP configurations on small subgraph at the first stage and transfers the top-performed configurations for fine-tuning on the large full graph at the second stage.
Open3DVQA: A Benchmark for Comprehensive Spatial Reasoning with Multimodal Large Language Model in Open Space
We evaluate several SOTA MLLMs across various aspects of spatial reasoning, such as relative and absolute spatial relationships, situational reasoning, and object-centric spatial attributes.
Beyond Overfitting: Doubly Adaptive Dropout for Generalizable AU Detection
To prevent excessive feature dropout, a progressive training strategy is used, allowing for selective exclusion of sensitive features at any model layer.
Decoupled Doubly Contrastive Learning for Cross Domain Facial Action Unit Detection
To achieve the feature decoupling, D$^2$CA is trained to disentangle AU and domain factors by assessing the quality of synthesized faces in cross-domain scenarios when either AU or domain attributes are modified.
Causal Discovery and Inference towards Urban Elements and Associated Factors
Experimental results in urban mobility prediction tasks further show that the proposed method can effectively reduce confounding effects and enhance performance of urban computing tasks.
EPR-GAIL: An EPR-Enhanced Hierarchical Imitation Learning Framework to Simulate Complex User Consumption Behaviors
The core idea of our EPR-GAIL framework is to model user consumption behaviors as a complex EPR decision process, which consists of purchase, exploration, and preference decisions.
Causality Enhanced Origin-Destination Flow Prediction in Data-Scarce Cities
However, with the common issues of missing regional features and lacking OD flow data, it is quite daunting to predict OD flow in developing cities.
UrbanVideo-Bench: Benchmarking Vision-Language Models on Embodied Intelligence with Video Data in Urban Spaces
Large multimodal models exhibit remarkable intelligence, yet their embodied cognitive abilities during motion in open-ended urban 3D space remain to be explored.
A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrieval
This paper explores the transformative potential of large language model agents in enhancing search and recommendation systems.
AgentSociety Challenge: Designing LLM Agents for User Modeling and Recommendation on Web Platforms
The AgentSociety Challenge is the first competition in the Web Conference that aims to explore the potential of Large Language Model (LLM) agents in modeling user behavior and enhancing recommender systems on web platforms.
Predicting Cascade Failures in Interdependent Urban Infrastructure Networks
Experiments demonstrate $I^3$ achieves a 31. 94\% in terms of AUC, 18. 03\% in terms of Precision, 29. 17\% in terms of Recall, 22. 73\% in terms of F1-score boost in predicting infrastructure failures, and a 28. 52\% reduction in terms of RMSE for cascade volume forecasts compared to leading models.
Sample-efficient diffusion-based control of complex nonlinear systems
Complex nonlinear system control faces challenges in achieving sample-efficient, reliable performance.
Structure-prior Informed Diffusion Model for Graph Source Localization with Limited Data
The source localization problem in graph information propagation is crucial for managing various network disruptions, from misinformation spread to infrastructure failures.
Predicting the Energy Landscape of Stochastic Dynamical System via Physics-informed Self-supervised Learning
Energy landscapes play a crucial role in shaping dynamics of many real-world complex systems.
PLPHP: Per-Layer Per-Head Vision Token Pruning for Efficient Large Vision-Language Models
To address this challenge, we propose Per-Layer Per-Head Vision Token Pruning (PLPHP), a two-level fine-grained pruning method including Layer-Level Retention Rate Allocation and Head-Level Vision Token Pruning.
CityEQA: A Hierarchical LLM Agent on Embodied Question Answering Benchmark in City Space
To bridge this gap, we introduce CityEQA, a new task where an embodied agent answers open-vocabulary questions through active exploration in dynamic city spaces.
Understanding and Evaluating Hallucinations in 3D Visual Language Models
Recently, 3D-LLMs, which combine point-cloud encoders with large models, have been proposed to tackle complex tasks in embodied intelligence and scene understanding.
Defining and Evaluating Visual Language Models' Basic Spatial Abilities: A Perspective from Psychometrics
The Theory of Multiple Intelligences underscores the hierarchical nature of cognitive capabilities.
Invisible Walls in Cities: Leveraging Large Language Models to Predict Urban Segregation Experience with Social Media Content
We design a Reflective LLM Coder to digest social media content into insights consistent with real-world feedback, and eventually produce a codebook capturing key dimensions that signal segregation experience, such as cultural resonance and appeal, accessibility and convenience, and community engagement and local involvement.
Collaborative Deterministic-Diffusion Model for Probabilistic Urban Spatiotemporal Prediction
Existing spatiotemporal prediction models are predominantly deterministic, focusing on primary spatiotemporal patterns.
A Robust Attack: Displacement Backdoor Attack
As artificial intelligence becomes more prevalent in our lives, people are enjoying the convenience it brings, but they are also facing hidden threats, such as data poisoning and ad- versarial attacks.
Trend-encoded Probabilistic Multi-order Model: A Non-Machine Learning Approach for Enhanced Stock Market Forecasts
In recent years, the dominance of machine learning in stock market forecasting has been evident.
AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society
In this paper, we propose AgentSociety, a large-scale social simulator that integrates LLM-driven agents, a realistic societal environment, and a powerful large-scale simulation engine.
Learning Street View Representations with Spatiotemporal Contrast
In this work, we propose an innovative self-supervised learning framework that leverages temporal and spatial attributes of street view imagery to learn image representations of the dynamic urban environment for diverse downstream tasks.
Qwen2.5-1M Technical Report
By leveraging our inference framework, the Qwen2. 5-1M models achieve a remarkable 3x to 7x prefill speedup in scenarios with 1 million tokens of context.
Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction
In this paper, we introduce a novel perspective by emphasizing the role of noise in the denoising process.
One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion
Based on these common patterns, we can construct a general framework that enables a single model to address different tasks.
Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models
This innovative paradigm enables LLMs' to mimic complex human reasoning processes, such as tree search and reflective thinking.
A Survey on Responsible LLMs: Inherent Risk, Malicious Use, and Mitigation Strategy
While large language models (LLMs) present significant potential for supporting numerous real-world applications and delivering positive social impacts, they still face significant challenges in terms of the inherent risk of privacy leakage, hallucinated outputs, and value misalignment, and can be maliciously used for generating toxic content and unethical purposes after been jailbroken.
A Diffusive Data Augmentation Framework for Reconstruction of Complex Network Evolutionary History
The evolutionary processes of complex systems contain critical information regarding their functional characteristics.
DehazeGS: Seeing Through Fog with 3D Gaussian Splatting
In the inference stage, we eliminate the effects of scattering and attenuation on the Gaussians and directly project them onto a 2D plane to obtain a clear view.
SAM-Aware Graph Prompt Reasoning Network for Cross-Domain Few-Shot Segmentation
In contrast, the large-scale visual model SAM, pre-trained on tens of millions of images from various domains and classes, possesses excellent generalizability.
Position-aware Graph Transformer for Recommendation
Collaborative recommendation fundamentally involves learning high-quality user and item representations from interaction data.
Semantics Prompting Data-Free Quantization for Low-Bit Vision Transformers
Data-free quantization (DFQ), which facilitates model quantization without real data to address increasing concerns about data security, has garnered significant attention within the model compression community.
A Universal Model for Human Mobility Prediction
We propose a universal human mobility prediction model (named UniMob), which can be applied to both individual trajectory and crowd flow.
Re-Attentional Controllable Video Diffusion Editing
Therefore, the controllability of video editing remains a formidable challenge.
SmartAgent: Chain-of-User-Thought for Embodied Personalized Agent in Cyber World
To address this, we propose Chain-of-User-Thought (COUT), a novel embodied reasoning paradigm that takes a chain of thought from basic action thinking to explicit and implicit personalized preference thought to incorporate personalized factors into autonomous agent learning.
Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts
Urban flow prediction is a classic spatial-temporal forecasting task that estimates the amount of future traffic flow for a given location.
Noise Matters: Diffusion Model-based Urban Mobility Generation with Collaborative Noise Priors
In this paper, we propose CoDiffMob, a diffusion method for urban mobility generation with collaborative noise priors, we emphasize the critical role of noise in diffusion models for generating mobility data.
KAN See Your Face
With the advancement of face reconstruction (FR) systems, privacy-preserving face recognition (PPFR) has gained popularity for its secure face recognition, enhanced facial privacy protection, and robustness to various attacks.
Research on Optimal Portfolio Based on Multifractal Features
Providing optimal portfolio selection for investors has always been one of the hot topics in academia.
Understanding World or Predicting Future? A Comprehensive Survey of World Models
The concept of world models has garnered significant attention due to advancements in multimodal large language models such as GPT-4 and video generation models such as Sora, which are central to the pursuit of artificial general intelligence.
A Survey on Human-Centric LLMs
The rapid evolution of large language models (LLMs) and their capacity to simulate human cognition and behavior has given rise to LLM-based frameworks and tools that are evaluated and applied based on their ability to perform tasks traditionally performed by humans, namely those involving cognition, decision-making, and social interaction.
A Foundation Model for Unified Urban Spatio-Temporal Flow Prediction
In this paper, we build UniFlow, a foundational model for general urban flow prediction that unifies both grid-based and graphbased data.
UrbanDiT: A Foundation Model for Open-World Urban Spatio-Temporal Learning
This allows the model to unify both multi-data and multi-task learning, and effectively support a wide range of spatio-temporal applications.
Unveiling Hidden Details: A RAW Data-Enhanced Paradigm for Real-World Super-Resolution
Real-world image super-resolution (Real SR) aims to generate high-fidelity, detail-rich high-resolution (HR) images from low-resolution (LR) counterparts.
Long-Tailed Object Detection Pre-training: Dynamic Rebalancing Contrastive Learning with Dual Reconstruction
In this paper, we introduce a novel pre-training framework for object detection, called Dynamic Rebalancing Contrastive Learning with Dual Reconstruction (2DRCL).
LLM-assisted Explicit and Implicit Multi-interest Learning Framework for Sequential Recommendation
Recent studies have shown that the rich semantic information in the text can effectively supplement the deficiencies of behavioral data.
ASER: Activation Smoothing and Error Reconstruction for Large Language Model Quantization
Quantization stands as a pivotal technique for large language model (LLM) serving, yet it poses significant challenges particularly in achieving effective low-bit quantization.
Generalizing Hyperedge Expansion for Hyper-relational Knowledge Graph Modeling
To tackle this issue, in this paper, we generalize the hyperedge expansion in hypergraph learning and propose an equivalent transformation for HKG modeling, referred to as TransEQ.
Symbolic regression via MDLformer-guided search: from minimizing prediction error to minimizing description length
To solve this problem, we propose a novel search objective based on the minimum description length, which reflects the distance from the target and decreases monotonically as the search approaches the correct form of the target formula.
Towards Personalized Federated Learning via Comprehensive Knowledge Distillation
By applying knowledge distillation, we effectively transfer global generalized knowledge and historical personalized knowledge to the local model, thus mitigating catastrophic forgetting and enhancing the general performance of personalized models.
Enhancing ID-based Recommendation with Large Language Models
The basic idea of LLM4IDRec is that by employing LLM to augment ID data, if augmented ID data can improve recommendation performance, it demonstrates the ability of LLM to interpret ID data effectively, exploring an innovative way for the integration of LLM in ID-based recommendation.
Flexible Coded Distributed Convolution Computing for Enhanced Fault Tolerance and Numerical Stability in Distributed CNNs
Deploying Convolutional Neural Networks (CNNs) on resource-constrained devices necessitates efficient management of computational resources, often via distributed systems susceptible to latency from straggler nodes.
Zero-Shot Self-Consistency Learning for Seismic Irregular Spatial Sampling Reconstruction
In this paper, we proposed a zero-shot self-consistency learning strategy and employed an extremely lightweight network for seismic data reconstruction.
Synergizing LLM Agents and Knowledge Graph for Socioeconomic Prediction in LBSN
Motivated by the remarkable abilities of large language models (LLMs) in commonsense reasoning, embedding, and multi-agent collaboration, in this work, we synergize LLM agents and knowledge graph for socioeconomic prediction.
TrajAgent: An Agent Framework for Unified Trajectory Modelling
In TrajAgent, we first develop UniEnv, an execution environment with a unified data and model interface, to support the execution and training of various models.
FoMo: A Foundation Model for Mobile Traffic Forecasting with Diffusion Model
Mobile traffic forecasting allows operators to anticipate network dynamics and performance in advance, offering substantial potential for enhancing service quality and improving user experience.
EmbodiedCity: A Benchmark Platform for Embodied Agent in Real-world City Environment
To address it, in this paper, we construct a benchmark platform for embodied intelligence evaluation in real-world city environments.
OpenCity: A Scalable Platform to Simulate Urban Activities with Massive LLM Agents
Besides, the substantial speedup of OpenCity allows us to establish a urban simulation benchmark for LLM agents for the first time, comparing simulated urban activities with real-world data in 6 major cities around the globe.
DeltaDQ: Ultra-High Delta Compression for Fine-Tuned LLMs via Group-wise Dropout and Separate Quantization
To address the above issue, we propose a novel distribution-driven delta compression framework DeltaDQ, which utilizes Group-wise Dropout and Separate Quantization to achieve ultra-high compression for the delta weight.
HLM-Cite: Hybrid Language Model Workflow for Text-based Scientific Citation Prediction
Second, logical relationships between papers are implicit, and directly prompting an LLM to predict citations may result in surface-level textual similarities rather than the deeper logical reasoning.
AgentSquare: Automatic LLM Agent Search in Modular Design Space
We believe that the modular design space and AgentSquare search framework offer a platform for fully exploiting the potential of prior successful designs and consolidating the collective efforts of research community.
ShieldDiff: Suppressing Sexual Content Generation from Diffusion Models through Reinforcement Learning
There is a potential risk that T2I model can generate unsafe images with uncomfortable contents.
Fusion is all you need: Face Fusion for Customized Identity-Preserving Image Synthesis
To address this, we leverage the pre-trained UNet from Stable Diffusion to incorporate the target face image directly into the generation process.
Distilling Generative-Discriminative Representations for Very Low-Resolution Face Recognition
After that, the discriminative representation distillation further considers a pretrained face recognizer as the discriminative teacher to supervise the learning of the student head via cross-resolution relational contrastive distillation.
Spindle: Efficient Distributed Training of Multi-Task Large Models via Wavefront Scheduling
Recent foundation models are capable of handling multiple tasks and multiple data modalities with the unified base model structure and several specialized model components.
Learning Privacy-Preserving Student Networks via Discriminative-Generative Distillation
In this work, we propose a discriminative-generative distillation approach to learn privacy-preserving deep models.
Large-scale Urban Facility Location Selection with Knowledge-informed Reinforcement Learning
The facility location problem (FLP) is a classical combinatorial optimization challenge aimed at strategically laying out facilities to maximize their accessibility.
AgentMove: Predicting Human Mobility Anywhere Using Large Language Model based Agentic Framework
In AgentMove, we first decompose the mobility prediction task into three sub-tasks and then design corresponding modules to complete these subtasks, including spatial-temporal memory for individual mobility pattern mining, world knowledge generator for modeling the effects of urban structure and collective knowledge extractor for capturing the shared patterns among population.
A Population-to-individual Tuning Framework for Adapting Pretrained LM to On-device User Intent Prediction
Mobile devices, especially smartphones, can support rich functions and have developed into indispensable tools in daily life.
TDNetGen: Empowering Complex Network Resilience Prediction with Generative Augmentation of Topology and Dynamics
Predicting the resilience of complex networks, which represents the ability to retain fundamental functionality amidst external perturbations or internal failures, plays a critical role in understanding and improving real-world complex systems.
MIA-Tuner: Adapting Large Language Models as Pre-training Text Detector
Existing studies have partially addressed this need through an exploration of the pre-training data detection problem, which is an instance of a membership inference attack (MIA).
Perceive, Reflect, and Plan: Designing LLM Agent for Goal-Directed City Navigation without Instructions
In the absence of navigation instructions, such abilities are vital for the agent to make high-quality decisions in long-range city navigation.
Wireless-Powered Mobile Crowdsensing Enhanced by UAV-Mounted RIS: Joint Transmission, Compression, and Trajectory Design
Mobile crowdsensing (MCS) enables data collection from massive devices to achieve a wide sensing range.
PateGail: A Privacy-Preserving Mobility Trajectory Generator with Imitation Learning
In the training process, only the generated trajectories and their rewards obtained based on personal discriminators are shared between the server and devices, whose privacy is further preserved by our proposed perturbation mechanisms with theoretical proof to satisfy differential privacy.
UrbanWorld: An Urban World Model for 3D City Generation
Toward this problem, we propose UrbanWorld, the first generative urban world model that can automatically create a customized, realistic and interactive 3D urban world with flexible control conditions.
Robust Skin Color Driven Privacy Preserving Face Recognition via Function Secret Sharing
In this work, we leverage the pure skin color patch from the face image as the additional information to train an auxiliary skin color feature extractor and face recognition model in parallel to improve performance of state-of-the-art (SOTA) privacy-preserving face recognition (PPFR) systems.
EmT: A Novel Transformer for Generalized Cross-subject EEG Emotion Recognition
Furthermore, we design a temporal contextual transformer module (TCT) with two types of token mixers to learn the temporal contextual information.
CityBench: Evaluating the Capabilities of Large Language Models for Urban Tasks
The challenge in constructing a systematic evaluation benchmark for urban research lies in the diversity of urban data, the complexity of application scenarios and the highly dynamic nature of the urban environment.
CityGPT: Empowering Urban Spatial Cognition of Large Language Models
In this paper, we propose CityGPT, a systematic framework for enhancing the capability of LLMs on understanding urban space and solving the related urban tasks by building a city-scale world model in the model.
From Pixels to Progress: Generating Road Network from Satellite Imagery for Socioeconomic Insights in Impoverished Areas
The Sustainable Development Goals (SDGs) aim to resolve societal challenges, such as eradicating poverty and improving the lives of vulnerable populations in impoverished areas.
A GPU-accelerated Large-scale Simulator for Transportation System Optimization Benchmarking
However, existing microscopic traffic simulators are inefficient in large-scale scenarios and thus fail to support the adoption of these methods in large-scale transportation system optimization scenarios.
Effectively Compress KV Heads for LLM
The advent of pre-trained large language models (LLMs) has revolutionized various natural language processing tasks.
CityLight: A Universal Model for Coordinated Traffic Signal Control in City-scale Heterogeneous Intersections
Our method, CityLight, features a universal representation module that not only aligns the state representations of intersections by reindexing their phases based on their semantics and designing heterogeneity-preserving observations, but also encodes the narrowed relative traffic relation types to project the neighborhood intersections onto a uniform relative traffic impact space.
Explore then Determine: A GNN-LLM Synergy Framework for Reasoning over Knowledge Graph
The task of reasoning over Knowledge Graphs (KGs) poses a significant challenge for Large Language Models (LLMs) due to the complex structure and large amounts of irrelevant information.
Research on the Spatial Data Intelligent Foundation Model
This report focuses on spatial data intelligent large models, delving into the principles, methods, and cutting-edge applications of these models.
DFAMiner: Mining minimal separating DFAs from labelled samples
We propose DFAMiner, a passive learning tool for learning minimal separating deterministic finite automata (DFA) from a set of labelled samples.
Masked Face Recognition with Generative-to-Discriminative Representations
First, we leverage a generative encoder pretrained for face inpainting and finetune it to represent masked faces into category-aware descriptors.
Partial train and isolate, mitigate backdoor attack
On the other hand, the accuracy of models carrying backdoors on normal samples is no different from that of clean models. In this article, by observing the characteristics of backdoor attacks, We provide a new model training method (PT) that freezes part of the model to train a model that can isolate suspicious samples.
Modeling User Fatigue for Sequential Recommendation
In this paper, we propose to model user Fatigue in interest learning for sequential Recommendations (FRec).
EEG-Deformer: A Dense Convolutional Transformer for Brain-computer Interfaces
Effectively learning the temporal dynamics in electroencephalogram (EEG) signals is challenging yet essential for decoding brain activities using brain-computer interfaces (BCIs).
PagPassGPT: Pattern Guided Password Guessing via Generative Pretrained Transformer
Amidst the surge in deep learning-based password guessing models, challenges of generating high-quality passwords and reducing duplicate passwords persist.
Depression Detection on Social Media with Large Language Models
Depression detection aims to determine whether an individual suffers from depression by analyzing their history of posts on social media, which can significantly aid in early detection and intervention.
Contrastive Learning of Person-independent Representations for Facial Action Unit Detection
We formulate the self-supervised AU representation learning signals in two-fold: (1) AU representation should be frame-wisely discriminative within a short video clip; (2) Facial frames sampled from different identities but show analogous facial AUs should have consistent AU representations.
Identify Critical Nodes in Complex Network with Large Language Models
Identifying critical nodes in networks is a classical decision-making task, and many methods struggle to strike a balance between adaptability and utility.
Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlook
As cities continue to burgeon, Urban Computing emerges as a pivotal discipline for sustainable development by harnessing the power of cross-domain data fusion from diverse sources (e. g., geographical, traffic, social media, and environmental data) and modalities (e. g., spatio-temporal, visual, and textual modalities).
Large Language Model for Participatory Urban Planning
To deal with the different facilities needs of residents, we initiate a discussion among the residents in each community about the plan, where residents provide feedback based on their profiles.
Artificial Intelligence for Complex Network: Potential, Methodology and Application
Complex networks pervade various real-world systems, from the natural environment to human societies.
LLM4SBR: A Lightweight and Effective Framework for Integrating Large Language Models in Session-based Recommendation
However, constrained by high time and space costs, as well as the brief and anonymous nature of session data, the first LLM recommendation framework suitable for industrial deployment has yet to emerge in the field of SBR.
UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction
Urban spatio-temporal prediction is crucial for informed decision-making, such as traffic management, resource optimization, and emergence response.
Spatio-Temporal Few-Shot Learning via Diffusive Neural Network Generation
Spatio-temporal modeling is foundational for smart city applications, yet it is often hindered by data scarcity in many cities and regions.
Large Language Model-driven Meta-structure Discovery in Heterogeneous Information Network
ReStruct uses a grammar translator to encode the meta-structures into natural language sentences, and leverages the reasoning power of LLMs to evaluate their semantic feasibility.
Chain-of-Planned-Behaviour Workflow Elicits Few-Shot Mobility Generation in LLMs
We find mechanistic mobility models, such as gravity model, can effectively map mobility intentions to physical mobility behaviours.
Exploring Heterogeneity and Uncertainty for Graph-based Cognitive Diagnosis Models in Intelligent Education
Specifically, to explore heterogeneity, we propose a semantic-aware graph neural networks based CD model.
RIS-Enhanced Cognitive Integrated Sensing and Communication: Joint Beamforming and Spectrum Sensing
Cognitive radio (CR) and integrated sensing and communication (ISAC) are both critical technologies for the sixth generation (6G) wireless networks.
Social Physics Informed Diffusion Model for Crowd Simulation
In this paper, we propose a social physics-informed diffusion model named SPDiff to mitigate the above gap.
Estimating On-road Transportation Carbon Emissions from Open Data of Road Network and Origin-destination Flow Data
To relieve this issue by utilizing the strong pattern recognition of artificial intelligence, we incorporate two sources of open data representative of the transportation demand and capacity factors, the origin-destination (OD) flow data and the road network data, to build a hierarchical heterogeneous graph learning method for on-road carbon emission estimation (HENCE).
Synergy-of-Thoughts: Eliciting Efficient Reasoning in Hybrid Language Models
If these intuitive thoughts exhibit conflicts, SoT will invoke the reflective reasoning of scaled-up language models to emulate the intervention of System 2, which will override the intuitive thoughts and rectify the reasoning results.
Large Language Model Agent for Hyper-Parameter Optimization
Hyperparameter optimization is critical in modern machine learning, requiring expert knowledge, numerous trials, and high computational and human resources.
Privacy-Preserving Face Recognition in Hybrid Frequency-Color Domain
Lastly, secure multiparty computation is implemented for safely computing the embedding distance during model inference.
Large language model empowered participatory urban planning
Participatory urban planning is the mainstream of modern urban planning and involves the active engagement of different stakeholders.
UV-SAM: Adapting Segment Anything Model for Urban Village Identification
Urban villages, defined as informal residential areas in or around urban centers, are characterized by inadequate infrastructures and poor living conditions, closely related to the Sustainable Development Goals (SDGs) on poverty, adequate housing, and sustainable cities.
Short-Term Multi-Horizon Line Loss Rate Forecasting of a Distribution Network Using Attention-GCN-LSTM
Accurately predicting line loss rates is vital for effective line loss management in distribution networks, especially over short-term multi-horizons ranging from one hour to one week.
Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives
Finally, since this area is new and quickly evolving, we discuss the open problems and promising future directions.
Urban Generative Intelligence (UGI): A Foundational Platform for Agents in Embodied City Environment
Urban environments, characterized by their complex, multi-layered networks encompassing physical, social, economic, and environmental dimensions, face significant challenges in the face of rapid urbanization.
GLOP: Learning Global Partition and Local Construction for Solving Large-scale Routing Problems in Real-time
The recent end-to-end neural solvers have shown promise for small-scale routing problems but suffered from limited real-time scaling-up performance.
A Survey of Generative AI for Intelligent Transportation Systems: Road Transportation Perspective
Intelligent transportation systems are vital for modern traffic management and optimization, greatly improving traffic efficiency and safety.
Mixed Attention Network for Cross-domain Sequential Recommendation
Recent proposed cross-domain sequential recommendation models such as PiNet and DASL have a common drawback relying heavily on overlapped users in different domains, which limits their usage in practical recommender systems.
Inverse Learning with Extremely Sparse Feedback for Recommendation
In this paper, we propose a meta-learning method to annotate the unlabeled data from loss and gradient perspectives, which considers the noises in both positive and negative instances.
Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration
However, this hypothesis heavily relies on the overfitting of target models, which will be mitigated by multiple regularization methods and the generalization of LLMs.
Devil in the Landscapes: Inferring Epidemic Exposure Risks from Street View Imagery
Built environment supports all the daily activities and shapes our health.
A Practical Large-Scale Roadside Multi-View Multi-Sensor Spatial Synchronization Framework for Intelligent Transportation Systems
To address these challenges, our research introduces a parallel spatial transformation (PST)-based framework for large-scale, multi-view, multi-sensor scenarios.
ROAM: memory-efficient large DNN training via optimized operator ordering and memory layout
However, a memory-efficient execution plan that includes a reasonable operator execution order and tensor memory layout can significantly increase the models' memory efficiency and reduce overheads from high-level techniques.
Stance Detection with Collaborative Role-Infused LLM-Based Agents
Next, in the reasoning-enhanced debating stage, for each potential stance, we designate a specific LLM-based agent to advocate for it, guiding the LLM to detect logical connections between text features and stance, tackling the second challenge.
Ranked #1 on
Stance Detection
on P-Stance
EconAgent: Large Language Model-Empowered Agents for Simulating Macroeconomic Activities
Existing agent modeling typically employs predetermined rules or learning-based neural networks for decision-making.
Relation-aware Ensemble Learning for Knowledge Graph Embedding
Knowledge graph (KG) embedding is a fundamental task in natural language processing, and various methods have been proposed to explore semantic patterns in distinctive ways.
Ranked #1 on
Link Property Prediction
on ogbl-biokg
Dual-stage Flows-based Generative Modeling for Traceable Urban Planning
We design several experiments to indicate that our framework can outperform compared to other generative models for the urban planning task.
DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization
As a Neural Combinatorial Optimization method, DeepACO performs better than or on par with problem-specific methods on canonical routing problems.
Flash-LLM: Enabling Cost-Effective and Highly-Efficient Large Generative Model Inference with Unstructured Sparsity
Therefore, we propose Flash-LLM for enabling low-cost and highly-efficient large generative model inference with the sophisticated support of unstructured sparsity on high-performance but highly restrictive Tensor Cores.
Towards Generative Modeling of Urban Flow through Knowledge-enhanced Denoising Diffusion
To capture the effect of multiple factors on urban flow, such as region features and urban environment, we employ diffusion model to generate urban flow for regions under different conditions.
Alleviating Video-Length Effect for Micro-video Recommendation
Micro-videos platforms such as TikTok are extremely popular nowadays.
Central Similarity Multi-View Hashing for Multimedia Retrieval
Hash representation learning of multi-view heterogeneous data is the key to improving the accuracy of multimedia retrieval.
Learning and Optimization of Implicit Negative Feedback for Industrial Short-video Recommender System
Specifically, in short-video recommendation, the easiest-to-collect user feedback is the skipping behavior, which leads to two critical challenges for the recommendation model.
A Probabilistic Fluctuation based Membership Inference Attack for Diffusion Models
Membership Inference Attack (MIA) identifies whether a record exists in a machine learning model's training set by querying the model.
Edit Temporal-Consistent Videos with Image Diffusion Model
In addition to the utilization of a pretrained T2I 2D Unet for spatial content manipulation, we establish a dedicated temporal Unet architecture to faithfully capture the temporal coherence of the input video sequences.
Understanding and Modeling Passive-Negative Feedback for Short-video Sequential Recommendation
To enhance the robustness of our model, we then introduce a multi-task learning module to simultaneously optimize two kinds of feedback -- passive-negative feedback and traditional randomly-sampled negative feedback.
Uncertainty-aware Consistency Learning for Cold-Start Item Recommendation
Graph Neural Network (GNN)-based models have become the mainstream approach for recommender systems.
EduChat: A Large-Scale Language Model-based Chatbot System for Intelligent Education
Currently, EduChat is available online as an open-source project, with its code, data, and model parameters available on platforms (e. g., GitHub https://github. com/icalk-nlp/EduChat, Hugging Face https://huggingface. co/ecnu-icalk ).
A Satellite Imagery Dataset for Long-Term Sustainable Development in United States Cities
Especially satellite imagery is a potential data source for studying sustainable urban development.
NEON: Living Needs Prediction System in Meituan
To address these two challenges, we design a system of living NEeds predictiON named NEON, consisting of three phases: feature mining, feature fusion, and multi-task prediction.
Watch out Venomous Snake Species: A Solution to SnakeCLEF2023
The SnakeCLEF2023 competition aims to the development of advanced algorithms for snake species identification through the analysis of images and accompanying metadata.
Detecting Vulnerable Nodes in Urban Infrastructure Interdependent Network
Understanding and characterizing the vulnerability of urban infrastructures, which refers to the engineering facilities essential for the regular running of cities and that exist naturally in the form of networks, is of great value to us.
Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering
Automated Machine Learning (AutoML) techniques have recently been introduced to design Collaborative Filtering (CF) models in a data-specific manner.
OpenSiteRec: An Open Dataset for Site Recommendation
As a representative information retrieval task, site recommendation, which aims at predicting the optimal sites for a brand or an institution to open new branches in an automatic data-driven way, is beneficial and crucial for brand development in modern business.
Multi-Scale Simulation of Complex Systems: A Perspective of Integrating Knowledge and Data
In this survey, we will systematically review the literature on multi-scale simulation of complex systems from the perspective of knowledge and data.
Carbon emissions and sustainability of launching 5G mobile networks in China
We show that the decline in carbon efficiency leads to a carbon efficiency trap, estimated to cause additional carbon emissions of 23. 82 +- 1. 07 megatons in China.
Complexity-aware Large Scale Origin-Destination Network Generation via Diffusion Model
The Origin-Destination~(OD) networks provide an estimation of the flow of people from every region to others in the city, which is an important research topic in transportation, urban simulation, etc.
Origin-Destination Network Generation via Gravity-Guided GAN
Origin-destination (OD) flow, which contains valuable population mobility information including direction and volume, is critical in many urban applications, such as urban planning, transportation management, etc.
How Graph Convolutions Amplify Popularity Bias for Recommendation?
Through theoretical analyses, we identify two fundamental factors: (1) with graph convolution (\textit{i. e.,} neighborhood aggregation), popular items exert larger influence than tail items on neighbor users, making the users move towards popular items in the representation space; (2) after multiple times of graph convolution, popular items would affect more high-order neighbors and become more influential.
Road Planning for Slums via Deep Reinforcement Learning
Existing re-blocking or heuristic methods are either time-consuming which cannot generalize to different slums, or yield sub-optimal road plans in terms of accessibility and construction costs.
Spatio-temporal Diffusion Point Processes
To enhance the learning of each step, an elaborated spatio-temporal co-attention module is proposed to capture the interdependence between the event time and space adaptively.
Private Gradient Estimation is Useful for Generative Modeling
In this work, we present a new private generative modeling approach where samples are generated via Hamiltonian dynamics with gradients of the private dataset estimated by a well-trained network.
Model Checking Strategies from Synthesis Over Finite Traces
Our central result is that LTLf model checking of non-terminating transducers is \emph{exponentially harder} than that of terminating transducers.
Template-based eukaryotic genome editing directed by SviCas3
RNA-guided gene editing based on the CRISPR-Cas system is currently the most effective genome editing technique.
Simplifying Low-Light Image Enhancement Networks with Relative Loss Functions
In this paper, to make the learning easier in low-light image enhancement, we introduce FLW-Net (Fast and LightWeight Network) and two relative loss functions.
Decoupled Multimodal Distilling for Emotion Recognition
Specially, the representation of each modality is decoupled into two parts, i. e., modality-irrelevant/-exclusive spaces, in a self-regression manner.
Understanding Expressivity of GNN in Rule Learning
These results further inspire us to propose a novel labeling strategy to learn more rules in KG reasoning.
Robust Preference-Guided Denoising for Graph based Social Recommendation
Graph Neural Network(GNN) based social recommendation models improve the prediction accuracy of user preference by leveraging GNN in exploiting preference similarity contained in social relations.
Joint Beamforming for RIS-Assisted Integrated Sensing and Communication Systems
However, due to multiple performance metrics used for communication and sensing, the limited degrees-of-freedom (DoF) in optimizing ISAC systems poses a challenge.
Knowledge-infused Contrastive Learning for Urban Imagery-based Socioeconomic Prediction
To address such issues, in this paper, we propose a Knowledge-infused Contrastive Learning (KnowCL) model for urban imagery-based socioeconomic prediction.
Advancements in Federated Learning: Models, Methods, and Privacy
Based on these frameworks, we have instantiated FedOpt algorithms.
Learning to Simulate Daily Activities via Modeling Dynamic Human Needs
To enhance the fidelity and utility of the generated activity data, our core idea is to model the evolution of human needs as the underlying mechanism that drives activity generation in the simulation model.
Dual-interest Factorization-heads Attention for Sequential Recommendation
In this paper, we propose Dual-interest Factorization-heads Attention for Sequential Recommendation (short for DFAR) consisting of feedback-aware encoding layer, dual-interest disentangling layer and prediction layer.
Learning Symbolic Models for Graph-structured Physical Mechanism
The essence of our method is to model the formula skeleton with a message-passing flow, which helps transform the discovery of the skeleton into the search for the message-passing flow.
TAP: Accelerating Large-Scale DNN Training Through Tensor Automatic Parallelisation
However, finding a suitable model parallel schedule for an arbitrary neural network is a non-trivial task due to the exploding search space.
Physics-Informed Neural Networks for Prognostics and Health Management of Lithium-Ion Batteries
Physics-Informed Neural Network (PINN) is an efficient tool to fuse empirical or physical dynamic models with data-driven models.
Style Projected Clustering for Domain Generalized Semantic Segmentation
In contrast to existing methods, we instead utilize the difference between images to build a better representation space, where the distinct style features are extracted and stored as the bases of representation.
Distribution-Consistent Modal Recovering for Incomplete Multimodal Learning
Recovering missed modality is popular in incomplete multimodal learning because it usually benefits downstream tasks.
Towards Real World HDRTV Reconstruction: A Data Synthesis-based Approach
To solve this problem, we propose a learning-based data synthesis approach to learn the properties of real-world SDRTVs by integrating several tone mapping priors into both network structures and loss functions.
MAPDP: Cooperative Multi-Agent Reinforcement Learning to Solve Pickup and Delivery Problems
It is of great importance to efficiently provide high-quality solutions of cooperative PDP.
Revisiting and Advancing Chinese Natural Language Understanding with Accelerated Heterogeneous Knowledge Pre-training
Recently, knowledge-enhanced pre-trained language models (KEPLMs) improve context-aware representations via learning from structured relations in knowledge graphs, and/or linguistic knowledge from syntactic or dependency analysis.
Mutual Harmony: Sequential Recommendation with Dual Contrastive Network
Such user-centric recommendation will make it impossible for the provider to expose their new items, failing to consider the accordant interactions between user and item dimensions.
Intrinsically Motivated Reinforcement Learning based Recommendation with Counterfactual Data Augmentation
It is more challenging to balance the exploration and exploitation in DRL RS where RS agent need to deeply explore the informative trajectories and exploit them efficiently in the context of recommender systems.
Causal Inference in Recommender Systems: A Survey and Future Directions
Existing recommender systems extract user preferences based on the correlation in data, such as behavioral correlation in collaborative filtering, feature-feature, or feature-behavior correlation in click-through rate prediction.
DisenHCN: Disentangled Hypergraph Convolutional Networks for Spatiotemporal Activity Prediction
In particular, we first unify the fine-grained user similarity and the complex matching between user preferences and spatiotemporal activity into a heterogeneous hypergraph.
DVR: Micro-Video Recommendation Optimizing Watch-Time-Gain under Duration Bias
Recommender systems are prone to be misled by biases in the data.
Practitioners Versus Users: A Value-Sensitive Evaluation of Current Industrial Recommender System Design
Recommender systems are playing an increasingly important role in alleviating information overload and supporting users' various needs, e. g., consumption, socialization, and entertainment.
KGTuner: Efficient Hyper-parameter Search for Knowledge Graph Learning
While hyper-parameters (HPs) are important for knowledge graph (KG) learning, existing methods fail to search them efficiently.
Gating-adapted Wavelet Multiresolution Analysis for Exposure Sequence Modeling in CTR prediction
The exposure sequence is being actively studied for user interest modeling in Click-Through Rate (CTR) prediction.
A Review-aware Graph Contrastive Learning Framework for Recommendation
Second, while most current models suffer from limited user behaviors, can we exploit the unique self-supervised signals in the review-aware graph to guide two recommendation components better?
PICASSO: Unleashing the Potential of GPU-centric Training for Wide-and-deep Recommender Systems
However, we observe that GPU devices in training recommender systems are underutilized, and they cannot attain an expected throughput improvement as what it has achieved in CV and NLP areas.
Canonical Mean Filter for Almost Zero-Shot Multi-Task classification
Motivated by ANIL, we rethink the role of adaption in the feature extractor of CNAPs, which is a state-of-the-art representative few-shot method.
Disentangling Long and Short-Term Interests for Recommendation
Modeling user's long-term and short-term interests is crucial for accurate recommendation.
Neighboring Backdoor Attacks on Graph Convolutional Network
To address such a challenge, we set the trigger as a single node, and the backdoor is activated when the trigger node is connected to the target node.
Structure Enhanced Graph Neural Networks for Link Prediction
Graph Neural Networks (GNNs) have shown promising results in various tasks, among which link prediction is an important one.
LoSAC: An Efficient Local Stochastic Average Control Method for Federated Optimization
Federated optimization (FedOpt), which targets at collaboratively training a learning model across a large number of distributed clients, is vital for federated learning.
Progressive Feature Interaction Search for Deep Sparse Network
Deep sparse networks (DSNs), of which the crux is exploring the high-order feature interactions, have become the state-of-the-art on the prediction task with high-sparsity features.
Inhomogeneous Social Recommendation with Hypergraph Convolutional Networks
With the hypergraph convolutional networks, the social relations can be modeled in a more fine-grained manner, which more accurately depicts real users' preferences, and benefits the recommendation performance.
Spatio-Temporal Urban Knowledge Graph Enabled Mobility Prediction
Further, a complex embedding model with elaborately designed scoring functions is proposed to measure the plausibility of facts in STKG to solve the knowledge graph completion problem, which considers temporal dynamics of the mobility patterns and utilizes PoI categories as the auxiliary information and background knowledge.
Knowledge-driven Site Selection via Urban Knowledge Graph
Specifically, motivated by distilled knowledge and rich semantics in KG, we firstly construct an urban KG (UrbanKG) with cities' key elements and semantic relationships captured.
Improving Location Recommendation with Urban Knowledge Graph
We first conduct information propagation on two sub-graphs to learn the representations of POIs and users.
M6-10T: A Sharing-Delinking Paradigm for Efficient Multi-Trillion Parameter Pretraining
Recent expeditious developments in deep learning algorithms, distributed training, and even hardware design for large models have enabled training extreme-scale models, say GPT-3 and Switch Transformer possessing hundreds of billions or even trillions of parameters.
A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions
In this survey, we conduct a comprehensive review of the literature on graph neural network-based recommender systems.
TabGNN: Multiplex Graph Neural Network for Tabular Data Prediction
Tabular data prediction (TDP) is one of the most popular industrial applications, and various methods have been designed to improve the prediction performance.
Real-time Image Enhancer via Learnable Spatial-aware 3D Lookup Tables
One is a 1D weight vector used for image-level scenario adaptation, the other is a 3D weight map aimed for pixel-wise category fusion.
DGCN: Diversified Recommendation with Graph Convolutional Networks
These years much effort has been devoted to improving the accuracy or relevance of the recommendation system.
One-shot Transfer Learning for Population Mapping
Thus, obtaining fine-grained population distribution from coarse-grained distribution becomes an important problem.
Learning Fair Face Representation With Progressive Cross Transformer
To mitigate racial bias and meantime preserve robust FR, we abstract face identity-related representation as a signal denoising problem and propose a progressive cross transformer (PCT) method for fair face recognition.
Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Survey
For each problem, we comprehensively introduce the existing DRL solutions.
