Semantic trajectories, which enrich spatial-temporal data with textual information such as trip purposes or location activities, are key for identifying outlier behaviors critical to healthcare, social security, and urban planning. Traditional outlier detection relies on heuristic rules, which requires domain knowledge and limits its ability to identify unseen outliers. Besides, there lacks a comprehensive approach that can jointly consider multi-modal data across spatial, temporal, and textual dimensions. Addressing the need for a domain-agnostic model, we propose the Transferable Outlier Detection for Human Semantic Trajectories (TOD4Traj) framework.TOD4Traj first introduces a modality feature unification module to align diverse data feature representations, enabling the integration of multi-modal information and enhancing transferability across different datasets. A contrastive learning module is further pro-posed for identifying regular mobility patterns both temporally and across populations, allowing for a joint detection of outliers based on individual consistency and group majority patterns. Our experimental results have shown TOD4Traj's superior performance over existing models, demonstrating its effectiveness and adaptability in detecting human trajectory outliers across various datasets.

The present paper aims at analyzing the topological content of the complex trajectories that weeder-autonomous robots follow in operation. We will prove that the topological descriptors of these trajectories are affected by the robot environment as well as by the robot state, with respect to maintenance operations. Topological Data Analysis will be used for extracting the trajectory descriptors, based on homology persistence. Then, appropriate metrics will be applied in order to compare that topological representation of the trajectories, for classifying them or for making efficient pattern recognition.

Senior author and data science lead Professor Aldo Faisal, Director of Imperial's Centre in AI for Healthcare at the Departments of Computing and Bioengineering, said: "In the ever-changing landscape of the pandemic, clinicians are constantly learning and adapting to patient needs, which themselves change every day. Critically, we have set up a standing digital service evaluation of UK ICUs, getting day-by-day treatment data from ICUs across the nations. Our machine learning tool could help track patient progress in real time and help inform ICU guidelines by filling the gaps of patient care – reflecting back to clinicians to identify best practice quickly and benefit from sharing.

Researchers used AI to identify which daily changing clinical parameters best predict intervention responses in critically ill COVID-19 patients. The investigators used machine learning to predict which patients might get worse and not respond positively to being turned onto their front in intensive care units (ICUs) – a technique known as proning that is commonly used in this setting to improve oxygenation of the lungs. While the AI model was used on a retrospective cohort of patient data collected during the pandemic's first wave, the study demonstrates the ability of AI methods to predict patient outcomes using routine clinical information used by ICU medics. The researchers say the approach, where each patient's data were analysed day-by-day instead of only on admission, could be used to improve guidelines in clinical practice going forward. It could be applied to potential future waves of the pandemic and other diseases treated in similar clinical settings. This is the first study that examines daily COVID-19 patient data, using AI to understand the clinical response to the rapidly changing needs of patients in ICUs.

--Discovering human mobility patterns with geo-location data collected from smartphone users has been a hot research topic in recent years. In this paper, we attempt to discover daily mobile patterns based on GPS data. We view this problem from a probabilistic perspective in order to explore more information from the original GPS data compared to other conventional methods. A non-parameter Bayesian modeling method, Infinite Gaussian Mixture Model, is used to estimate the probability density for the daily mobility. Then, we use Kullback-Leibler divergence as the metrics to measure the similarity of different probability distributions. And combining Infinite Gaussian Mixture Model and Kullback-Leibler divergence, we derived an automatic clustering algorithm to discover mobility patterns for each individual user without setting the number of clusters in advance. In the experiments, the effectiveness of our method is validated on the real user data collected from different users. The results show that the IGMM-based algorithm outperforms the GMM-based algorithm. We also test our methods on the dataset with different lengths to discover the minimum data length for discovering mobility patterns. I NTRODUCTION S MARTPHONEdevices are equipped with multiple sensors that can record user behavior on the handsets. With the help of a large-scale smartphone usage data, researchers are able to study human behavior in the real world.