Enhancing Traffic Prediction with Learnable Filter Module

Modeling future traffic conditions often relies heavily on complex spatial-temporal neural networks to capture spatial and temporal correlations, which can overlook the inherent noise in the data. This noise, often manifesting as unexpected short-term peaks or drops in traffic observation, is typically caused by traffic accidents or inherent sensor vibration. In practice, such noise can be challenging to model due to its stochastic nature and can lead to overfitting risks if a neural network is designed to learn this behavior. To address this issue, we propose a learnable filter module to filter out noise in traffic data adaptively. This module leverages the Fourier transform to convert the data to the frequency domain, where noise is filtered based on its pattern. The denoised data is then recovered to the time domain using the inverse Fourier transform. Our approach focuses on enhancing the quality of the input data for traffic prediction models, which is a critical yet often overlooked aspect in the field. We demonstrate that the proposed module is lightweight, easy to integrate with existing models, and can significantly improve traffic prediction performance. Furthermore, we validate our approach with extensive experimental results on real-world datasets, showing that it effectively mitigates noise and enhances prediction accuracy

DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model

Pervasive integration of GPS-enabled devices and data acquisition technologies has led to an exponential increase in GPS trajectory data, fostering advancements in spatial-temporal data mining research. Nonetheless, GPS trajectories contain personal geolocation information, rendering serious privacy concerns when working with raw data. A promising approach to address this issue is trajectory generation, which involves replacing original data with generated, privacy-free alternatives. Despite the potential of trajectory generation, the complex nature of human behavior and its inherent stochastic characteristics pose challenges in generating high-quality trajectories. In this work, we propose a spatial-temporal diffusion probabilistic model for trajectory generation (DiffTraj). This model effectively combines the generative abilities of diffusion models with the spatial-temporal features derived from real trajectories. The core idea is to reconstruct and synthesize geographic trajectories from white noise through a reverse trajectory denoising process. Furthermore, we propose a Trajectory UNet (Traj-UNet) deep neural network to embed conditional information and accurately estimate noise levels during the reverse process. Experiments on two real-world datasets show that DiffTraj can be intuitively applied to generate high-fidelity trajectories while retaining the original distributions. Moreover, the generated results can support downstream trajectory analysis tasks and significantly outperform other methods in terms of geo-distribution evaluations

MOELoRA: An MOE-based Parameter Efficient Fine-Tuning Method for Multi-task Medical Applications

The recent surge in the field of Large Language Models (LLMs) has gained significant attention in numerous domains. In order to tailor an LLM to a specific domain such as a web-based healthcare system, fine-tuning with domain knowledge is necessary. However, two issues arise during fine-tuning LLMs for medical applications. The first is the problem of task variety, where there are numerous distinct tasks in real-world medical scenarios. This diversity often results in suboptimal fine-tuning due to data imbalance and seesawing problems. Additionally, the high cost of fine-tuning can be prohibitive, impeding the application of LLMs. The large number of parameters in LLMs results in enormous time and computational consumption during fine-tuning, which is difficult to justify. To address these two issues simultaneously, we propose a novel parameter-efficient fine-tuning framework for multi-task medical applications called MOELoRA. The framework aims to capitalize on the benefits of both MOE for multi-task learning and LoRA for parameter-efficient fine-tuning. To unify MOE and LoRA, we devise multiple experts as the trainable parameters, where each expert consists of a pair of low-rank matrices to maintain a small number of trainable parameters. Additionally, we propose a task-motivated gate function for all MOELoRA layers that can regulate the contributions of each expert and generate distinct parameters for various tasks. To validate the effectiveness and practicality of the proposed method, we conducted comprehensive experiments on a public multi-task Chinese medical dataset. The experimental results demonstrate that MOELoRA outperforms existing parameter-efficient fine-tuning methods. The implementation is available online for convenient reproduction of our experiments