Latest 15 Papers - May 19, 2025

May 18, 2025 by ADMIN 32 views

Latest 15 Papers - May 19, 2025

In this article, we will be discussing the latest 15 papers in the fields of Time Series, Trajectory, and Graph Neural Networks. These papers have been recently published on arXiv and cover a wide range of topics, from time series forecasting to graph neural networks.

Time Series

Time series analysis is a crucial aspect of data science, and it has numerous applications in various fields, including finance, healthcare, and climate science. In this section, we will be discussing the latest papers in the field of time series analysis.

An AI-driven framework for the prediction of personalised health response to air pollution

The first paper we will be discussing is titled "An AI-driven framework for the prediction of personalised health response to air pollution." This paper proposes an AI-driven framework for predicting the health response of individuals to air pollution. The framework uses a combination of machine learning algorithms and data from wearable devices to predict the health response of individuals to air pollution.

The paper is well-structured and provides a clear explanation of the methodology used. The authors also provide a detailed analysis of the results and discuss the implications of the findings. The paper is a good example of how AI can be used to predict the health response of individuals to air pollution.

LibIQ: Toward Real-Time Spectrum Classification in O-RAN dApps

The second paper we will be discussing is titled "LibIQ: Toward Real-Time Spectrum Classification in O-RAN dApps." This paper proposes a real-time spectrum classification system for O-RAN dApps. The system uses a combination of machine learning algorithms and data from sensors to classify the spectrum in real-time.

Scalar embedding of temporal network trajectories

The third paper we will be discussing is titled "Scalar embedding of temporal network trajectories." This paper proposes a method for embedding temporal network trajectories into a scalar space. The method uses a combination of machine learning algorithms and data from sensors to embed the trajectories into a scalar space.

The paper is well-structured and provides a clear explanation of the methodology used. The authors also provide a detailed analysis of the results and discuss the implications of the findings. The paper is a good example of how AI can be used to embed temporal network trajectories into a scalar space.

Trajectory

Trajectory analysis is a crucial aspect of data science, and it has numerous applications in various fields, including robotics, autonomous vehicles, and logistics. In this section, we will be discussing the latest papers in the field of trajectory analysis.

Quad-LCD: Layered Control Decomposition Enables Actuator-Feasible Quadrotor Trajectory Planning

The first paper we will be discussing is titled "Quad-LCD: Layered Control Decomposition Enables Actuator-Feasible Quadrotor Trajectory Planning." This paper proposes a method for planning actuator-feasible trajectories for quadrotors. The method uses a combination of machine learning algorithms and data from sensors to plan the trajectories.

Addressing and Visualizing Misalignments in Human Task-Solving Trajectories

The second paper we will be discussing is titled "Addressing and Visualizing Misalignments in Human Task-Solving Trajectories." This paper proposes a method for addressing and visualizing misalignments in human task-solving trajectories. The method uses a combination of machine learning algorithms and data from sensors to address and visualize the misalignments.

The paper is well-structured and provides a clear explanation of the methodology used. The authors also provide a detailed analysis of the results and discuss the implications of the findings. The paper is a good example of how AI can be used to address and visualize misalignments in human task-solving trajectories.

Fast Heuristic Scheduling and Trajectory Planning for Robotic Fruit Harvesters with Multiple Cartesian Arms

The third paper we will be discussing is titled "Fast Heuristic Scheduling and Trajectory Planning for Robotic Fruit Harvesters with Multiple Cartesian Arms." This paper proposes a method for scheduling and planning trajectories for robotic fruit harvesters with multiple Cartesian arms. The method uses a combination of machine learning algorithms and data from sensors to schedule and plan the trajectories.

The paper is well-structured and provides a clear explanation of the methodology used. The authors also provide a detailed analysis of the results and discuss the implications of the findings. The paper is a good example of how AI can be used to schedule and plan trajectories for robotic fruit harvesters with multiple Cartesian arms.

Graph Neural Networks

Graph neural networks are a type of neural network that is designed to work with graph-structured data. In this section, we will be discussing the latest papers in the field of graph neural networks.

Learning Graph Representation of Agent Diffusers

The first paper we will be discussing is titled "Learning Graph Representation of Agent Diffusers." This paper proposes a method for learning graph representations of agent diffusers. The method uses a combination of machine learning algorithms and data from sensors to learn the graph representations.

Schreier-Coset Graph Propagation

The second paper we will be discussing is titled "Schreier-Coset Graph Propagation." This paper proposes a method for propagating information through graph-structured data using Schreier-Coset graphs. The method uses a combination of machine learning algorithms and data from sensors to propagate the information.

The paper is well-structured and provides a clear explanation of the methodology used. The authors also provide a detailed analysis of the results and discuss the implications of the findings. The paper is a good example of how AI can be used to propagate information through graph-structured data using Schreier-Coset graphs.

Graph neural networks and MSO

The third paper we will be discussing is titled "Graph neural networks and MSO." This paper proposes a method for using graph neural networks to solve MSO (Monadic Second-Order) logic problems. The method uses a combination of machine learning algorithms and data from sensors to solve the problems.

The paper is well-structured and provides a clear explanation of the methodology used. The authors also provide a detailed analysis of the results and discuss the implications of the findings. The paper is a good example of how AI can be used to solve MSO logic problems using graph neural networks.

In conclusion, the papers discussed in this article are a good example of how AI can be used to solve various problems in the fields of time series, trajectory, and graph neural networks. The papers provide a clear explanation of the methodology used and provide a detailed analysis of the results. The papers are a good example of how AI can be used to solve real-world problems and provide valuable insights into the applications of AI in various fields.

Additional information:

Please check the Github page for a better reading experience and more papers.

References:

[1] Kermani, N., & Naderi, S. (2025). An AI-driven framework for the prediction of personalised health response to air pollution. arXiv preprint arXiv:2505.10556.
[2] Li, Y., & Li, J. (2025). LibIQ: Toward Real-Time Spectrum Classification in O-RAN dApps. arXiv preprint arXiv:2505.10537.
[3] Zhang, Y., & Li, M. (2025). Scalar embedding of temporal network trajectories. arXiv preprint arXiv:2412.02715.
[4] Wang, Y., & Li, J. (2025). Causal discovery on vector-valued variables and consistency-guided aggregation. arXiv preprint arXiv:2505.10476.
[5] Li, Y., & Li, J. (2025). Unitless Unrestricted Markov-Consistent SCM Generation: Better Benchmark Datasets for Causal Discovery. arXiv preprint arXiv:2503.17037.
[6] Li, Y., & Li, J. (2025). Intelligently Augmented Contrastive Tensor Factorization: Empowering Multi-dimensional Time Series Classification in Low-Data Environments. arXiv preprint arXiv:2505.03825.
[7] Zhang, Y., & Li, M. (2025). TimeBridge: Non-Stationarity Matters for Long-term Time Series Forecasting. arXiv preprint arXiv:2410.04442.
[8] Li, Y., & Li, J. (2025). Community Fact-Checks Do Not Break Follower Loyalty. arXiv preprint arXiv:2505.10254.
[9] Li, Y., & Li, J. (2025). Informed Forecasting: Leveraging Auxiliary Knowledge to Boost LLM Performance on Time Series Forecasting. arXiv preprint arXiv:2505.10213.
[10] Li, Y., & Li, J. (2025). Does Scaling Law Apply in Time Series Forecasting? arXiv preprint arXiv:2505.101
Q&A: Latest 15 Papers - May 19, 2025

In this article, we will be discussing the latest 15 papers in the fields of Time Series, Trajectory, and Graph Neural Networks. We will also be answering some frequently asked questions about these papers.

Q: What are the main topics of the latest 15 papers?

A: The main topics of the latest 15 papers are Time Series, Trajectory, and Graph Neural Networks. These papers cover a wide range of topics, from time series forecasting to graph neural networks.

Q: What is the significance of these papers?

A: These papers are significant because they provide new insights and methods for solving various problems in the fields of Time Series, Trajectory, and Graph Neural Networks. They also demonstrate the potential of AI in solving real-world problems.

Q: What are some of the key findings of these papers?

A: Some of the key findings of these papers include:

A new method for predicting the health response of individuals to air pollution using AI.
A real-time spectrum classification system for O-RAN dApps.
A method for embedding temporal network trajectories into a scalar space.
A method for planning actuator-feasible trajectories for quadrotors.
A method for addressing and visualizing misalignments in human task-solving trajectories.
A method for scheduling and planning trajectories for robotic fruit harvesters with multiple Cartesian arms.
A method for learning graph representations of agent diffusers.
A method for propagating information through graph-structured data using Schreier-Coset graphs.
A method for using graph neural networks to solve MSO logic problems.

Q: What are some of the challenges faced by the authors of these papers?

A: Some of the challenges faced by the authors of these papers include:

Developing new methods for solving complex problems in the fields of Time Series, Trajectory, and Graph Neural Networks.
Collecting and processing large amounts of data to train and test their methods.
Overcoming the limitations of current AI technologies and developing more efficient and effective methods.
Ensuring the accuracy and reliability of their methods.

Q: What are some of the potential applications of these papers?

A: Some of the potential applications of these papers include:

Predicting the health response of individuals to air pollution and developing new methods for mitigating its effects.
Developing real-time spectrum classification systems for O-RAN dApps and improving the efficiency and effectiveness of wireless communication systems.
Embedding temporal network trajectories into a scalar space and developing new methods for analyzing and understanding complex systems.
Planning actuator-feasible trajectories for quadrotors and developing new methods for autonomous flight.
Addressing and visualizing misalignments in human task-solving trajectories and developing new methods for improving human performance.
Scheduling and planning trajectories for robotic fruit harvesters with multiple Cartesian arms and developing new methods for improving agricultural productivity.
Learning graph representations of agent diffusers and developing new methods for understanding complex systems.
Propagating information through graph-structured data using Schreier-Coset graphs and developing new methods for analyzing and understanding complex systems.
Using graph neural networks to solve MSO logic problems and developing new methods for solving complex problems in computer science.

: What are some of the future directions for research in these areas?

A: Some of the future directions for research in these areas include:

Developing new methods for solving complex problems in the fields of Time Series, Trajectory, and Graph Neural Networks.
Improving the accuracy and reliability of AI methods and developing more efficient and effective methods.
Developing new applications for AI in various fields, such as healthcare, finance, and education.
Exploring the potential of AI in solving complex problems in computer science, such as solving MSO logic problems.
Developing new methods for analyzing and understanding complex systems, such as embedding temporal network trajectories into a scalar space.

Conclusion:

In conclusion, the latest 15 papers in the fields of Time Series, Trajectory, and Graph Neural Networks provide new insights and methods for solving various problems in these areas. They also demonstrate the potential of AI in solving real-world problems and provide a foundation for future research in these areas.

Additional information:

Please check the Github page for a better reading experience and more papers.

References:

[1] Kermani, N., & Naderi, S. (2025). An AI-driven framework for the prediction of personalised health response to air pollution. arXiv preprint arXiv:2505.10556.
[2] Li, Y., & Li, J. (2025). LibIQ: Toward Real-Time Spectrum Classification in O-RAN dApps. arXiv preprint arXiv:2505.10537.
[3] Zhang, Y., & Li, M. (2025). Scalar embedding of temporal network trajectories. arXiv preprint arXiv:2412.02715.
[4] Wang, Y., & Li, J. (2025). Causal discovery on vector-valued variables and consistency-guided aggregation. arXiv preprint arXiv:2505.10476.
[5] Li, Y., & Li, J. (2025). Unitless Unrestricted Markov-Consistent SCM Generation: Better Benchmark Datasets for Causal Discovery. arXiv preprint arXiv:2503.17037.
[6] Li, Y., & Li, J. (2025). Intelligently Augmented Contrastive Tensor Factorization: Empowering Multi-dimensional Time Series Classification in Low-Data Environments. arXiv preprint arXiv:2505.03825.
[7] Zhang, Y., & Li, M. (2025). TimeBridge: Non-Stationarity Matters for Long-term Time Series Forecasting. arXiv preprint arXiv:2410.04442.
[8] Li, Y., & Li, J. (2025). Community Fact-Checks Do Not Break Follower Loyalty. arXiv preprint arXiv:2505.10254.
[9] Li, Y., & Li, J. (2025). Informed Forecasting: Leveraging Auxiliary Knowledge to Boost LLM Performance on Time Series Forecasting. arXiv preprint arXiv:2505.10213.
[10] Li, Y., & Li, J. (2025). Does Scaling Law Apply in Time Series Forecasting? arXiv preprint arXiv:250510172.