Learning Spatiotemporal Manifold Representation for Probabilistic Land Deformation Prediction.

Landslides refer to occurrences of massive ground movements due to geological (and meteorological) factors, and can have disastrous impacts on property, economy, and even lead to the loss of life. The advances in remote sensing provide accurate and continuous terrain monitoring, enabling the study and analysis of land deformation which, in turn, can be used for land deformation prediction. Prior studies either rely on predefined factors and patterns or model static land observations without considering the subtle interactions between different point locations and the dynamic changes of the surface conditions, causing the prediction model to be less generalized and unable to capture the temporal deformation characteristics.

MetaGeo: A General Framework for Social User Geolocation Identification With Few-Shot Learning.

Identifying the geolocation of social media users is an important problem in a wide range of applications, spanning from disease outbreaks, emergency detection, local event recommendation, to fake news localization, online marketing planning, and even crime control and prevention. Researchers have attempted to propose various models by combining different sources of information, including text, social relation, and contextual data, which indeed has achieved promising results. However, existing approaches still suffer from certain constraints, such as: 1) a very few samples are available and 2) prediction models are not easy to be generalized for users from new regions-which are challenges that motivate our study.

Conditional MaxRS Query for Evolving Spatial Data.

We address the problem of maintaining the correct answer-sets to a novel query- Maximizing Range-Sum (C-MaxRS)-for spatial data. Given a set of 2D point objects, possibly with associated weights, the traditional MaxRS problem determines an optimal placement for an axes-parallel rectangle so that the number-or, the weighted sum-of the objects in its interior is maximized. The peculiarities of C-MaxRS is that in many practical settings, the objects from a particular set-e.

Adversarial Human Trajectory Learning for Trip Recommendation.

The problem of trip recommendation has been extensively studied in recent years, by both researchers and practitioners. However, one of its key aspects-understanding human mobility-remains under-explored. Many of the proposed methods for trip modeling rely on empirical analysis of attributes associated with historical points-of-interest (POIs) and routes generated by tourists while attempting to also intertwine personal preferences-such as contextual topics, geospatial, and temporal aspects.

MetaMove: On Improving Human Mobility Classification and Prediction via Metalearning.

Despite offering efficient solutions to a plethora of novel challenges, existing approaches on mobility modeling require a large amount of labeled data when training effective and application-specific models. This renders them inapplicable to certain scenarios, where only a few samples are observed, and data types are unseen during training. To address these issues, we present a novel mobility learning method-MetaMove, the first metalearning-based model generalizing mobility prediction and classification in a unified framework.

Toward Discriminating and Synthesizing Motion Traces Using Deep Probabilistic Generative Models.

Mining knowledge from human mobility, such as discriminating motion traces left by different anonymous users, also known as the trajectory-user linking (TUL) problem, is an important task in many applications requiring location-based services (LBSs). However, it inevitably raises an issue that may be aggravated by TUL, i.e.

Recommendation via Collaborative Autoregressive Flows.

Although it is one of the most widely used methods in recommender systems, Collaborative Filtering (CF) still has difficulties in modeling non-linear user-item interactions. Complementary to this, recently developed deep generative model variants (e.g.