Our model can capture both long- and short-term temporal patterns within each patient journey and effectively handle the high degree of missingness in EHR data without any imputation data generation.
We believe that the proposed PromptCast task as well as our PISA dataset could provide novel insights and further lead to new research directions in the domain of time-series representation learning and forecasting.
In this paper, we propose a novel pipeline that leverages language foundation models for temporal sequential pattern mining, such as for human mobility forecasting tasks.
Contrastive Learning (CL) is one of the most well-known approaches in SSL that attempts to learn general, informative representations of data.
Building models for health prediction based on Electronic Health Records (EHR) has become an active research area.
Unlike existing reviews of SSRL that have pre-dominately focused upon methods in the fields of CV or NLP for a single modality, we aim to provide the first comprehensive review of multimodal self-supervised learning methods for temporal data.
Disentangled representation learning offers useful properties such as dimension reduction and interpretability, which are essential to modern deep learning approaches.
As a decisive part in the success of Mobility-as-a-Service (MaaS), spatio-temporal predictive modeling for crowd movements is a challenging task particularly considering scenarios where societal events drive mobility behavior deviated from the normality.
Furthermore, unlike existing methods, we introduce a location prediction branch in MobTCast as an auxiliary task to model the geographical context and predict the next location.
Heterogeneity and irregularity of multi-source data sets present a significant challenge to time-series analysis.
In this work, we propose a VAE-based architecture for learning the disentangled representation from real spatio-temporal data for mobility forecasting.
In this framework, a specific pipeline is encoded as a candidate solution and a multi-objective evolutionary algorithm is applied under different population sizes to produce multiple Pareto optimal sets (POSs).
Existing parking recommendation solutions mainly focus on finding and suggesting parking spaces based on the unoccupied options only.
To model and forecast flight delays accurately, it is crucial to harness various vehicle trajectory and contextual sensor data on airport tarmac areas.
The pre-trained feature encoder is then fine-tuned in the downstream phase to perform cough classification.
We conducted a field study at a K-12 private school in the suburbs of Melbourne, Australia.
Specifically, we propose a service model of DaaS based on the dynamic spatiotemporal features of drones and their in-flight contexts.
Change Point Detection (CPD) methods identify the times associated with changes in the trends and properties of time series data in order to describe the underlying behaviour of the system.
This paper investigates the Cyber-Physical behavior of users in a large indoor shopping mall by leveraging anonymized (opt in) Wi-Fi association and browsing logs recorded by the mall operators.
Generative Adversarial Networks (GANs) have shown remarkable success in producing realistic-looking images in the computer vision area.
In this paper, we formulate the dynamic crime patrol planning problem for multiple police officers using check-ins, crime, incident response data, and POI information.
We compare the performance of flexgrid2vec with a set of state-of-the-art visual representation learning models on binary and multi-class image classification tasks.
In this paper, we propose a framework, called G-CREWE (Graph CompREssion With Embedding) to solve the network alignment problem.
Federated learning provides a compelling framework for learning models from decentralized data, but conventionally, it assumes the availability of labeled samples, whereas on-device data are generally either unlabeled or cannot be annotated readily through user interaction.
Extracting informative and meaningful temporal segments from high-dimensional wearable sensor data, smart devices, or IoT data is a vital preprocessing step in applications such as Human Activity Recognition (HAR), trajectory prediction, gesture recognition, and lifelogging.
One of the core challenges in open-plan workspaces is to ensure a good level of concentration for the workers while performing their tasks.
The main purpose of HVAC, maintaining appropriate thermal comfort, is crucial for the best utilisation of energy usage.
Point-of-Interest (POI) recommendation is one of the most important location-based services helping people discover interesting venues or services.
This paper introduces COLTRANE, ConvolutiOnaL TRAjectory NEtwork, a novel deep map inference framework which operates on GPS trajectories collected in various environments.
Our experiments on CelebA and LFW datasets show that the quality of the reconstructed images from the obfuscated features of the raw image is dramatically decreased from 0. 9458 to 0. 3175 in terms of multi-scale structural similarity.
The mobility dynamics inferred from Foursquare helps us understanding urban social events like crime In this paper, we propose a directed graph from the aggregated movement between regions using Foursquare data.
Deep learning has been extended to a number of new domains with critical success, though some traditional orienteering problems such as the Travelling Salesman Problem (TSP) and its variants are not commonly solved using such techniques.