MyLife: An online personal memory album

In this demo, we illustrate the formation, retrieval, and playback of autobiographical memory in an online personal memory album named MyLife. The memory in MyLife consists of pictorial snapshots of one's life together with the associated context, namely time, location, people, activity, imagery, and emotion. MyLife allows direct import of memories from other online personal photo repositories. For memory retrieval, users can use not only exact cues, but also partial, vague, inaccurate, and random ones. The retrieved memories are then played back as a movie-like slide show with various visual effects and background music. MyLife holds high potential in both research and daily usage. In particular, it provides the sense of nostalgia to the elderly users and thus may help to improve their psychological health and well-being.NRF (Natl Research Foundation, S’pore)Accepted versio

Identification of H3K4me1-associated proteins at mammalian enhancers.

Enhancers act to regulate cell-type-specific gene expression by facilitating the transcription of target genes. In mammalian cells, active or primed enhancers are commonly marked by monomethylation of histone H3 at lysine 4 (H3K4me1) in a cell-type-specific manner. Whether and how this histone modification regulates enhancer-dependent transcription programs in mammals is unclear. In this study, we conducted SILAC mass spectrometry experiments with mononucleosomes and identified multiple H3K4me1-associated proteins, including many involved in chromatin remodeling. We demonstrate that H3K4me1 augments association of the chromatin-remodeling complex BAF to enhancers in vivo and that, in vitro, H3K4me1-marked nucleosomes are more efficiently remodeled by the BAF complex. Crystal structures of the BAF component BAF45C indicate that monomethylation, but not trimethylation, is accommodated by BAF45C's H3K4-binding site. Our results suggest that H3K4me1 has an active role at enhancers by facilitating binding of the BAF complex and possibly other chromatin regulators

Probabilistic guided exploration for reinforcement learning in self-organizing neural networks

Exploration is essential in reinforcement learning, which expands the search space of potential solutions to a given problem for performance evaluations. Specifically, carefully designed exploration strategy may help the agent learn faster by taking the advantage of what it has learned previously. However, many reinforcement learning mechanisms still adopt simple exploration strategies, which select actions in a pure random manner among all the feasible actions. In this paper, we propose novel mechanisms to improve the existing knowledge-based exploration strategy based on a probabilistic guided approach to select actions. We conduct extensive experiments in a Minefield navigation simulator and the results show that our proposed probabilistic guided exploration approach significantly improves the convergence rate.NRF (Natl Research Foundation, S’pore)Accepted versio

Non-intrusive robust human activity recognition for diverse age groups

Many elderly prefer to live independently at their own homes. However, how to use modern technologies to ensure their safety presents vast challenges and opportunities. Being able to non-intrusively sense the activities performed by the elderly definitely has great advantages in various circumstances. Non-intrusive activity recognition can be performed using the embedded sensors in modern smartphones. However, not many activity recognition models are robust enough that allow the subjects to carry the smartphones in different pockets with unrestricted orientations and varying deviations. Moreover, to the best of our knowledge, no existing literature studied the difference between the youth and the elderly groups in terms of human activity recognition using smartphones. In this paper, we present our approach to perform robust activity recognition using only the accelerometer readings collected from the smartphone. First, we tested our model on two published data sets and found its performance is encouraging when compared against other models. Furthermore, we applied our model on two newly collected data sets: one consists of only young subjects (mean age = 22.5) and the other consists of only elderly subjects (mean age = 70.5). The experimental results show convincing prediction accuracy for both within and across diverse age groups. This paper fills the blank of elderly activity recognition using smartphones and shows promising results, which will serve as the groundwork of our future extensions to the current model.NRF (Natl Research Foundation, S’pore)Accepted versio

Progressive sequence matching for ADL plan recommendation

Activities of Daily Living (ADLs) are indicatives of a person's lifestyle. In particular, daily ADL routines closely relate to a person's well-being. With the objective of promoting active lifestyles, this paper presents an agent system that provides recommendations of suitable ADL plans (i.e., selected ADL sequences) to individual users based on the more active lifestyles of the others. Specifically, we develop a set of quantitative measures, named wellness scores, spanning the evaluation across the physical, cognitive, emotion, and social aspects based on his or her ADL routines. Then we propose an ADL sequence learning model, named Recommendation ADL ART, or RADLART, which proactively recommends healthier choices of activities based on the learnt associations among the user profiles, ADL sequence, and wellness scores. For empirical evaluation, extensive simulations have been conducted to assess the improvement in wellness scores for synthetic users with different acceptance rates of the provided recommendations. Experiments on real users further show that recommendations given by RADLART are generally more acceptable by the users because it takes into considerations of both the user profiles and the performed activities.NRF (Natl Research Foundation, S’pore)Accepted versio

Silver assistants for aging-in-place

In this demo, we present an assembly of silver assistants for supporting Aging-In-Place (AIP). The virtual agents are designed to serve around the clock to complement human care within the intelligent home environment. Residing in different platforms with ubiquitous access, the agents collaboratively provide holistic care to the elderly users. The demonstration is shown in a 3-D virtual home replicating a typical 5-room apartment in Singapore. Sensory inputs are stored in a knowledge base named Situation Awareness Model (SAM). Therefore, the capabilities of the agents can always be extended by expanding the knowledge defined in SAM. Using the simulation system, we can rapidly conduct various types of experiments to test and evaluate whether the silver assistants have effectively and reliably fulfilled their duties when serving the elderly.NRF (Natl Research Foundation, S’pore)Accepted versio