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Found 38 papers, 14 papers with code
CLIO: Role-interactive Multi-event Head Attention Network for Document-level Event Extraction
Transforming the large amounts of unstructured text on the Internet into structured event knowledge is a critical, yet unsolved goal of NLP, especially when addressing document-level text.
Open-Source AI-based SE Tools: Opportunities and Challenges of Collaborative Software Learning
We introduce a governance framework centered on federated learning (FL), designed to foster the joint development and maintenance of open-source AI code models while safeguarding data privacy and security.
Synergizing Spatial Optimization with Large Language Models for Open-Domain Urban Itinerary Planning
In this paper, we for the first time propose the task of Open-domain Urban Itinerary Planning (OUIP) for citywalk, which directly generates itineraries based on users' requests described in natural language.
LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoning
Large language models (LLMs) have demonstrated significant poten- tial for many downstream tasks, including those requiring human- level intelligence, such as vulnerability detection.
ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputation
The exploitation of the inherent structures of spatiotemporal data enables our model to learn balanced signal-noise representations, making it versatile for a variety of imputation problems.
Normalizing flow-based deep variational Bayesian network for seismic multi-hazards and impacts estimation from InSAR imagery
Onsite disasters like earthquakes can trigger cascading hazards and impacts, such as landslides and infrastructure damage, leading to catastrophic losses; thus, rapid and accurate estimates are crucial for timely and effective post-disaster responses.
Evaluating the Robustness of Test Selection Methods for Deep Neural Networks
Testing deep learning-based systems is crucial but challenging due to the required time and labor for labeling collected raw data.
The Relationship Between Speech Features Changes When You Get Depressed: Feature Correlations for Improving Speed and Performance of Depression Detection
The results show that the models used in the experiments improve in terms of training speed and performance when fed with feature correlation matrices rather than with feature vectors.
Contextualizing MLP-Mixers Spatiotemporally for Urban Data Forecast at Scale
Spatiotemporal urban data (STUD) displays complex correlational patterns.
LMs: Understanding Code Syntax and Semantics for Code Analysis
We break down the abilities needed for artificial intelligence~(AI) models to address SE tasks related to code analysis into three categories: 1) syntax understanding, 2) static behavior understanding, and 3) dynamic behavior understanding.
A Black-Box Attack on Code Models via Representation Nearest Neighbor Search
Furthermore, the perturbation of adversarial examples introduced by RNNS is smaller compared to the baselines in terms of the number of replaced variables and the change in variable length.
Beyond Prediction: On-street Parking Recommendation using Heterogeneous Graph-based List-wise Ranking
In this paper, we first time propose an on-street parking recommendation (OPR) task to directly recommend a parking space for a driver.
Maximin Headway Control of Automated Vehicles for System Optimal Dynamic Traffic Assignment in General Networks
Specifically, we aim to search for the optimal time headway between AVs on each link that achieves the network-wide system optimal dynamic traffic assignment (SO-DTA).
Demonstration-guided Deep Reinforcement Learning for Coordinated Ramp Metering and Perimeter Control in Large Scale Networks
This study considers two representative control approaches: ramp metering for freeways and perimeter control for homogeneous urban roads, and we aim to develop a deep reinforcement learning (DRL)-based coordinated control framework for large-scale networks.
Unveiling Code Pre-Trained Models: Investigating Syntax and Semantics Capacities
These structures are fundamental to understanding code.
Myopia prediction for adolescents via time-aware deep learning
Result: The mean absolute prediction error on the testing set was 0. 273-0. 257 for spherical equivalent, ranging from 0. 189-0. 160 to 0. 596-0. 473 if we consider different lengths of historical records and different prediction durations.
Estimating and Mitigating the Congestion Effect of Curbside Pick-ups and Drop-offs: A Causal Inference Approach
Curb space is one of the busiest areas in urban road networks.
Estimating probabilistic dynamic origin-destination demands using multi-day traffic data on computational graphs
The proposed framework is cast into the computational graph and a reparametrization trick is developed to estimate the mean and standard deviation of the probabilistic dynamic OD demand simultaneously.
Characterizing and Understanding the Behavior of Quantized Models for Reliable Deployment
The results reveal that 1) data with distribution shifts happen more disagreements than without.
Few-Sample Traffic Prediction with Graph Networks using Locale as Relational Inductive Biases
Currently, most of the state-of-the-art prediction models are based on graph neural networks (GNNs), and the required training samples are proportional to the size of the traffic network.
Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Cities
To the best of our knowledge, we are the first to employ adversarial multi-domain adaptation for network-wide traffic forecasting problems.
Attacking Deep Reinforcement Learning-Based Traffic Signal Control Systems with Colluding Vehicles
In view of this, this paper first time formulates a novel task in which a group of vehicles can cooperatively send falsified information to "cheat" DRL-based ATCS in order to save their total travel time.
Turning Traffic Monitoring Cameras into Intelligent Sensors for Traffic Density Estimation
The proposed framework consists of two major components: camera calibration and vehicle detection.
Deep Learning Model of Dock by Dock Process Significantly Accelerate the Process of Docking-based Virtual Screening
Howerver, a majority of compouds with low docking scores could waste most of the computational resources.
Network-wide link travel time and station waiting time estimation using automatic fare collection data: A computational graph approach
This paper focuses on an essential and hard problem to estimate the network-wide link travel time and station waiting time using the automatic fare collection (AFC) data in the URT system, which is beneficial to better understand the system-wide real-time operation state.
Adversarial Diffusion Attacks on Graph-based Traffic Prediction Models
The diffusion attack aims to select and attack a small set of nodes to degrade the performance of the entire prediction model.
Line Flow based SLAM
To address these problems, we leverage a line flow to encode the coherence of line segment observations of the same 3D line along the temporal dimension, which has been neglected in prior SLAM systems.
Short-term origin-destination demand prediction in urban rail transit systems: A channel-wise attentive split-convolutional neural network method
The proposed model contributes to the development of short-term OD flow prediction, and it also lays the foundations of real-time URT operation and management.
Missing Not at Random in Matrix Completion: The Effectiveness of Estimating Missingness Probabilities Under a Low Nuclear Norm Assumption
Recently, various papers have shown that we can reduce this bias in MNAR matrix completion if we know the probabilities of different matrix entries being missing.
High-Resolution Traffic Sensing with Autonomous Vehicles
The last decades have witnessed the breakthrough of autonomous vehicles (AVs), and the perception capabilities of AVs have been dramatically improved.
Test Selection for Deep Learning Systems
This raises the question of how we can automatically select candidate test data to test deep learning models.
Estimating multi-class dynamic origin-destination demand through a forward-backward algorithm on computational graphs
Provided with some observations of vehicular flow for each class in a large-scale transportation network, how to estimate the multi-class spatio-temporal vehicular flow, in terms of time-varying Origin-Destination (OD) demand and path/link flow, remains a big challenge.
Probabilistic representation and inverse design of metamaterials based on a deep generative model with semi-supervised learning strategy
To better unveil this implicit relationship and thus facilitate metamaterial design, we propose to represent metamaterials and model the inverse design problem in a probabilistically generative manner.
Optics
Estimating multi-year 24/7 origin-destination demand using high-granular multi-source traffic data
A GPU-based stochastic projected gradient descent method is proposed to efficiently solve the multi-year 24/7 DODE problem.
A deep learning approach to real-time parking occupancy prediction in spatio-temporal networks incorporating multiple spatio-temporal data sources
The case study also shows that, in generally, the prediction model works better for business areas than for recreational locations.
Efficient Traffic-Sign Recognition with Scale-aware CNN
The paper presents a Traffic Sign Recognition (TSR) system, which can fast and accurately recognize traffic signs of different sizes in images.
CompNet: Neural networks growing via the compact network morphism
We explored $CompNet$, in which case we morph a well-trained neural network to a deeper one where network function can be preserved and the added layer is compact.
An Equivalence of Fully Connected Layer and Convolutional Layer
The article is helpful for the beginners of the neural network to understand how fully connected layer and the convolutional layer work in the backend.
