Part of: Volume: 32nd International Symposium on Temporal Representation and Reasoning (TIME 2025)
Series: Leibniz International Proceedings in Informatics (LIPIcs)
Conference: International Symposium on Temporal Representation and Reasoning (TIME)
Our paper on “Early experiences and views of older adults living with sensing technology at home: A qualitative study” is available as a pre-print here.
This study explored older adults’ perceptions and lived experiences of sensing technologies integrated into their home environments. Using semi-structured interviews and in-home observations, we examined how older individuals interacted with motion, magnetic, and physiological sensors embedded in their everyday routines.
and it is a companion paper to our modelling paper currently available on arXiv as arXiv:2507.08701.
Congratulations to Alex Hyman, who has been offered a full studentship to do a PhD in Artificial Intelligence. He will be joining AIML in September 2025.
In this work, we introduce a framework for representing and reasoning about the Activities of Daily Living of older adults living independently at home. The framework integrates data from sensors and contextual information that aggregates semi-structured interviews, home layouts and sociological observations from the participants.
Pre-print: arXiv:2507.08701.
Congratulations to James Vaughan who has passed his PhD viva with minor corrections. His thesis was on “Adaptable Latent Semantics for Automated Reasoning in Large Theories” and his examiners were Alexander Bolotov (University of Westminster) and Paul Jackson (Edinburgh).
The Disability Technology report, which explores the potential for data-driven digital technologies to reduce barriers that disabled people encounter in their everyday lives, was launched on the 23rd of June at the Royal Society.
The report’s steering committee included Professor Sir Bernard Silverman FRS (Chair), Dr Vint Cerf FRS, Professor Jacques Fleuriot, Dr Hamied Haroon, Dr Louise Hickman, Professor Catherine Holloway, Prateek Madhav, Professor Paul Upchurch, Professor Seralynne Vann and Professor Mike Wald.
Additional information:
Our new paper, Cluster and survival analysis of UK biobank data reveals associations between physical multimorbidity clusters and subsequent depression, has just been published in Nature Communications Medicine. The results have been highlighted by the MRC on the UKRI website and featured in its newsletter. See:
UKRI News: https://www.ukri.org/news/multiple-chronic-illnesses-linked-to-higher-risk-of-depression
Edinburgh University post: https://www.ed.ac.uk/news/multiple-chronic-illnesses-could-double-risk-of-depression
and
Full paper at: https://doi.org/10.1038/s43856-025-00825-7
Code at: https://github.com/laurendelong21/clusterMed
Our paper come out in Nature Scientific Reports has just appeared. It investigates the relationships between in-hospital activity, multimorbidity and post-discharge outcomes in older adults. It’s available at: https://doi.org/10.1038/s41598-025-92940-7.
Formally Verified Neurosymbolic Trajectory Learning via Tensor-based Linear Temporal Logic on Finite Traces
Astract:
We present a novel formalisation of tensor semantics for linear temporal logic on finite traces (LTLf), with formal proofs of correctness carried out in the theorem prover Isabelle/HOL. We demonstrate that this formalisation can be integrated into a neurosymbolic learning process by defining and verifying a differentiable loss function for the LTLf constraints, and automatically generating an implementation that integrates with PyTorch. We show that, by using this loss, the process learns to satisfy pre-specified logical constraints. Our approach offers a fully rigorous framework for constrained training, eliminating many of the inherent risks of ad-hoc, manual implementations of logical aspects directly in an “unsafe” programming language such as Python, while retaining efficiency in implementation.
In this entry we formalise a framework for process composition based on actions that are specified by their input and output resources. We verify their correctness by translating compositions of process into deductions of intuitionistic linear logic. As part of the verification we derive simple conditions on the compositions which ensure well-formedness of the corresponding deduction.
We describe an earlier version of this formalisation in our article Linear Resources in Isabelle/HOL, which also includes a formalisation of manufacturing processes in the simulation game Factorio.