Interactive Behavior Prediction for Heterogeneous Traffic Participants in the Urban Road: A Graph-Neural-Network-Based Multitask Learning Framework

A unified modeling framework for lane change intention recognition and vehicle status prediction

Trajectory regression, which aims to predict the travel time of arbitrary trajectories on road networks, attracts significant attention in various applications of traffic systems these years. In this paper, we tackle this problem with a multitask learning (MTL) framework. To take the temporal nature of the problem into consideration, we divide the regression problem into a set of sub-tasks of distinct time periods, then the problem can be treated in a multi-task learning framework. Further, we propose a novel regularization term in which we exploit the block sparse structure to augment the robustness of the model. In addition, we incorporate the spatial smoothness over road links and thus achieve a spatial-temporal framework. An accelerated proximal algorithm is adopted to solve the convex but non-smooth problem, which will converge to the global optimum. Experiments on both synthetic and real data sets demonstrate the effectiveness of the proposed method.

In this work we utilize implied volatility predictions from options on individual securities to predict the average volatility of an ensemble of securities. We cast this as a problem of online learning with expert advice, which has been studied extensively in the Machine Learning community. In this setting we treat each security as an expert and adaptively maintain a set of weights to combine the implied volatility predictions of all securities in our ensemble. We also consider options of different term lengths, treating each term length as a separate learning task in a Multi-task Learning (MTL) framework. In this MTL framework information is shared between the learning tasks to improve the overall learning process. We conduct experiments using historical option pricing data and corresponding historical volatility records. Using the straight mean of the implied volatility predictions as a benchmark strategy, we demonstrate improved combined predictions with a basic online learning algorithm and further improvement with our MTL framework.

In this paper, based on the widely available GPS trajectory big data that records the driving behaviours implicitly, we propose a multi-task learning (MTL) framework called \textsf{semi-Traj2Graph} to recognize the fine-grained driving styles in the temporal dimension accurately. The MTL framework can incorporate the learning capability of graph representation in extracting high-level and interpretable features regarding complex driving behaviours and semi-supervised in exploiting unlabelled data and reducing labelling effort. More specifically, in the graph representation learning, a multi-view graph is first built to capture a more complete view of driving behaviours from the raw GPS trajectory data, then graph convolutional neural networks (Graph-CNNs) are applied. In the semi-supervised learning, a pseudo-label labelling is adopted to make use of the unlabelled data. We evaluate the proposed framework extensively based on two taxi trajectory datasets collected from the city of Beijing and Chongqing, China, respectively. Experimental results show that \textsf{semi-Traj2Graph} outperforms compared to other baselines, achieving an overall accuracy of around 90\%. We also implement the framework on users' smartphones via the collaborative cloud-edge computation manner to demonstrate the system usability in real case

ADMET property prediction via multi-task graph learning under adaptive auxiliary task selection

Objective. Deep learning (DL)-based automated contouring and treatment planning has been proven to improve the efficiency and accuracy of radiotherapy. However, conventional radiotherapy treatment planning process has the automated contouring and treatment planning as separate tasks. Moreover in DL, the contouring and dose prediction tasks for automated treatment planning are done independently.Approach. In this study, we applied the multi-task learning (MTL) approach in order to seamlessly integrate automated contouring and voxel-based dose prediction tasks, as MTL can leverage common information between the two tasks and be able to increase the efficiency of the automated tasks. We developed our MTL framework using the two datasets: in-house prostate cancer dataset and the publicly available head and neck cancer dataset, OpenKBP.Main results. Compared to the sequential DL contouring and treatment planning tasks, our proposed method using MTL improved the mean absolute difference of dose volume histogram metrics of prostate and head and neck sites by 19.82% and 16.33%, respectively. Our MTL model for automated contouring and dose prediction tasks demonstrated enhanced dose prediction performance while maintaining or sometimes even improving the contouring accuracy. Compared to the baseline automated contouring model with the Dice score coefficients of 0.818 for prostate and 0.674 for head and neck datasets, our MTL approach achieved average scores of 0.824 and 0.716 for these datasets, respectively.Significance. Our study highlights the potential of the proposed automated contouring and planning using MTL to support the development of efficient and accurate automated treatment planning for radiotherapy.

Healthcare professionals and individual users use wearable devices equipped with various sensors for healthcare management. Recently, the joint usage of artificial intelligence and these wearable sensors has played an essential role in healthcare management by providing a wide range of applications such as fitness tracking, gym activity monitoring, patient rehabilitation monitoring, and disease detection. These tasks eventually aim to enhance personal well-being and better manage the user’s physical health by monitoring different activity types and body weight changes. Here, we present an efficient multi-task learning framework based on commercial smart insoles that can solve three tasks related to physical health management: activity classification, speed estimation, and body weight estimation. Our multi-task framework converts the sensor data from the smart insole to a recurrence plot, which shows significant performance improvement compared to processing the raw time series data. In addition, we utilized a modified MobileNetV2 as our backbone network, which has a total parameter of less than 100K and a computational budget of 0.34G of multiply-accumulate operations. Furthermore, we collected a vast dataset from 72 users carrying out 16 experiments, which contains the largest number of people for multi-task learning purposes using smart insoles. Extensive experiments show that the proposed multi-task learning framework is extremely efficient while outperforming or leading to comparable performance against single-task models.

Due to the recent vast availability of transportation traffic data, major research efforts have been devoted to traffic prediction, which is useful in many applications such as urban planning, traffic management and navigations systems. Current prediction methods that independently train a model per traffic sensor cannot accurately predict traffic in every situation (e.g., rush hours, constructions and accidents) because there may not exist sufficient training samples per sensor for all situations. To address this shortcoming, our core idea is to explore the commonalities of prediction tasks across multiple sensors who behave similarly in a specific traffic situation. Instead of building a model independently per sensor, we propose a Multi-Task Learning (MTL) framework that aims to first automatically identify the traffic situations and then simultaneously build one forecasting model for similar-behaving sensors per traffic situation. The key innovation here is that instead of the straightforward application of MTL where each "task" corresponds to a sensor, we relate each MTL's "task" to a traffic situation. Specifically, we first identify these traffic situations by running clustering algorithms on all sensors' data. Subsequently, to enforce the commonalities under each identified situation, we use the group Lasso regularization in MTL to select a common set of features for the prediction tasks, and we adapt efficient FISTA algorithm with guaranteed convergence rate. We evaluated our methods with a large volume of real-world traffic sensor data; our results show that by incorporating traffic situations, our proposed MTL framework performs consistently better than naively applying MTL per sensor. Moreover, our holistic approach, under different traffic situations, outperforms all the best traffic prediction approaches for a given situation by up to 18% and 30% in short and long term predictions, respectively.

High-resolution LAI data can provide more detailed information for remote sensing applications. However, for deep learning-based LAI retrieval methods, a training dataset composed of sensor data with a moderate or low spatial resolution cannot be directly applied to derive LAI from high-spatial-resolution sensor data due to distribution discrepancies between different sensor data. Following in this insight, we proposed a multi-task adversarial transfer learning framework, named DANN_MT. This framework leverages an adaptive adversarial training strategy to extract domain-invariant features between MODIS and Landsat surface reflectance data, enabling the retrieval of high-resolution LAI. To address the incomplete consistency between tasks of generating corresponding moderate-resolution LAI and high-resolution LAI, the DANN_MT model is designed with multi-task learning. Comparison results suggest that the retrieved high-resolution LAI exhibit reasonable spatial distribution and seasonality. Direct validation demonstrates that the retrieved high-resolution LAI (RMSE for the GBOV sites: 0.86, RMSE for the IMAGINES sites: 0.82) have a higher accuracy than the SNAP LAI (RMSE for the GBOV sites: 0.93, RMSE for the IMAGINES sites: 0.95). These conclusions reveal that the knowledge learned from the moderate-spatial-resolution dataset is successfully transferred to produce the high-spatial-resolution LAI from Landsat surface reflectance using the proposed DANN_MT model.

Retinal diseases represent a leading cause of visual impairment and blindness worldwide, with early and accurate diagnosis being crucial for preventing irreversible vision loss. Although deep learning techniques have achieved significant advances in fundus image analysis, existing methods predominantly focus on single tasks, treating vessel segmentation and disease diagnosis as independent problems without fully leveraging their intrinsic relationships. Furthermore, the lack of transparency in deep model predictions limits clinical adoption; while full interpretability remains an open challenge, post-hoc techniques such as Grad-CAM can provide partial transparency by highlighting influential image regions. This study presents V-MNet, a vessel-guided multi-task deep learning framework that simultaneously achieves retinal vessel segmentation and multi-disease classification, and provides visual transparency through Grad-CAM-based class activation mapping to support clinical decision-making. The framework comprises three core modules: a shared encoder extracts multi-scale feature representations; a segmentation decoder employs a U-Net-style architecture to generate vessel masks; and a classification decoder incorporates an innovative vessel-guided mechanism that explicitly transfers structural priors from the segmentation branch to the classification task, enabling the model to precisely localize pathological regions. Concurrently, an integrated Grad-CAM module generates post-hoc class activation maps for each disease category, highlighting spatially relevant lesion regions for clinician review. Comprehensive experiments were conducted on four public datasets-RFMiD, ODIR-5K, DRIVE, and EyePACS-light-v2. Experiments demonstrate that V-MNet achieves a Dice coefficient of 0.831 and AUC of 0.985 for vessel segmentation tasks, and an average AUC of 0.978 with F1-score of 0.935 for multi-disease classification tasks, significantly outperforming single-task baseline models and existing state-of-the-art methods. Ablation studies systematically quantify the performance contributions of multi-task learning and the vessel-guided mechanism, confirming the effectiveness of the framework's core innovations. V-MNet demonstrates broad application potential as a computer-aided diagnostic tool by jointly leveraging vascular structure and disease pathology for superior performance and visual transparency. The vessel-guided multi-task design effectively exploits the intrinsic relationship between vessel segmentation and disease classification, while the integrated Grad-CAM module addresses the lack of model transparency, facilitating clinical adoption and supporting clinical decision-making.

We present a semi-supervised multitask learning (MTL) framework, where we have multiple partially labeled data manifolds, each defining a classification task for which we wish to design a semi-supervised classifier. These different data sets may be observed simultaneously, or over the sensor lifetime. We propose a soft sharing prior over the parameters of all classifiers and learn all tasks jointly. The soft-sharing prior enables any task to robustly borrow information from related tasks. The semi-supervised MTL combines the advantages of semi-supervised learning and multitask learning, thus further improving the generalization performance of each classifier. Our MTL (or life-long learning) framework is based on our previous semi-supervised learning formulation, termed neighborhood-based classifier (NeBC) [1]. The performance of the semi-supervised MTL is validated by experimental results on several sensing data sets.

This paper proposes a novel Ordinal Regression Multi-Task Learning (OR-MTL) framework to address challenges in multi-task diagnosis of PD in Gas-Insulated Switchgear (GIS). GIS PD diagnosis typically involves tasks such as discharge-type identification and severity assessment, which is essentially an ordinal regression problem facing challenges such as high label noise and inconsistent ranking of prediction outcomes. To address these challenges, the OR-MTL framework introduces two key innovations: a dynamic task-weighting strategy based on excess risk estimation, which mitigates the negative impact of label noise on multi-task learning weight allocation, and an ordinal regression loss function based on conditional probability, which ensures consistent prediction ranking through conditional probability chains. Experiments on GIS PD datasets demonstrate that the excess risk-based task-weighting strategy exhibits superior robustness compared to traditional methods in high-noise environments, while the proposed ranking consistency loss function significantly improves the accuracy of severity assessment and reduces errors. Ablation studies further validate the effectiveness of the complete OR-MTL framework. This research not only provides an efficient solution for GIS PD diagnosis but also offers new insights and methodologies for multi-task learning involving ordinal regression tasks.

In this paper, a multiple kernel and multiple task learning framework (MKMTL) is proposed. To improve the interpretability of input data and adapt to different data sets, a weighted data-dependent kernel function is proposed and extended to multiple kernel functions. To fully reveal and utilize the shared information among different radar targets, multi-task learning framework is proposed. In this paper, a larger class of mixed norm penalty is adopted. It can increase the flexibility of MKMTL model. To verify the performance of the proposed model, measured MSTAR SAR public database is conducted. Experimental results demonstrate that the proposed method can effectively utilize the shared or potential information among different tasks and exhibits a better recognition performance compared with several popular existing recognition methods.KeywordsMulti-kernel learning (MKL)Multi-task learning (MTL)Radar target recognitionSynthetic aperture radar (SAR)

BackgroundAccurately predicting drug-target binding affinity (DTA) in silico plays an important role in drug discovery. Most of the computational methods developed for predicting DTA use machine learning models, especially deep neural networks, and depend on large-scale labelled data. However, it is difficult to learn enough feature representation from tens of millions of compounds and hundreds of thousands of proteins only based on relatively limited labelled drug-target data. There are a large number of unknown drugs, which never appear in the labelled drug-target data. This is a kind of out-of-distribution problems in bio-medicine. Some recent studies adopted self-supervised pre-training tasks to learn structural information of amino acid sequences for enhancing the feature representation of proteins. However, the task gap between pre-training and DTA prediction brings the catastrophic forgetting problem, which hinders the full application of feature representation in DTA prediction and seriously affects the generalization capability of models for unknown drug discovery.ResultsTo address these problems, we propose the GeneralizedDTA, which is a new DTA prediction model oriented to unknown drug discovery, by combining pre-training and multi-task learning. We introduce self-supervised protein and drug pre-training tasks to learn richer structural information from amino acid sequences of proteins and molecular graphs of drug compounds, in order to alleviate the problem of high variance caused by encoding based on deep neural networks and accelerate the convergence of prediction model on small-scale labelled data. We also develop a multi-task learning framework with a dual adaptation mechanism to narrow the task gap between pre-training and prediction for preventing overfitting and improving the generalization capability of DTA prediction model on unknown drug discovery. To validate the effectiveness of our model, we construct an unknown drug data set to simulate the scenario of unknown drug discovery. Compared with existing DTA prediction models, the experimental results show that our model has the higher generalization capability in the DTA prediction of unknown drugs.ConclusionsThe advantages of our model are mainly attributed to two kinds of pre-training tasks and the multi-task learning framework, which can learn richer structural information of proteins and drugs from large-scale unlabeled data, and then effectively integrate it into the downstream prediction task for obtaining a high-quality DTA prediction in unknown drug discovery.

Attribute hierarchy based multi-task learning for fine-grained image classification