Lenia and Expanded Universe

We report experimental extensions of Lenia, a continuous cellular automata family capable of producing lifelike self-organizing autonomous patterns. The rule of Lenia was generalized into higher dimensions, multiple kernels, and multiple channels. The final architecture approaches what can be seen as a recurrent convolutional neural network. Using semi-automatic search e.g. genetic algorithm, we discovered new phenomena like polyhedral symmetries, individuality, self-replication, emission, growth by ingestion, and saw the emergence of "virtual eukaryotes" that possess internal division of labor and type differentiation. We discuss the results in the contexts of biology, artificial life, and artificial intelligence.Comment: 8 pages, 5 figures, 1 table; submitted to ALIFE 2020 conferenc

A comparative study on the effects of providing customized versus conventional oral hygiene instructions to visually impaired adults

Includes bibliographical references (p. 25-26).Questionnaire in English and Chinese.published_or_final_versio

Crossing the dark matter soliton core: a possible reversed orbital precession

The ultra-light dark matter (ULDM) model has become a popular dark matter scenario nowadays. The mass of the ULDM particles is extremely small so that they can exhibit wave properties in the central dark matter halo region. Numerical simulations show that a soliton core with an almost constant mass density would be formed inside the ULDM halo. If our Galactic Centre has a dark matter soliton core, some of the stars orbiting about the supermassive black hole (Sgr A*) would be crossing the soliton core boundary. In this article, we report the first theoretical study on how the dark matter soliton core near the Sgr A* could affect the surrounding stellar orbital precession. We show that some particular stellar orbital precession may become retrograde in direction, which is opposite to the prograde direction predicted by General Relativity. We anticipate that future orbital data of the stars S2, S12 and S4716 can provide crucial tests for the ULDM model for $m \sim 10^{-19}-10^{-17}$ eV.Comment: Accepted in Phys. Rev.

A new method to constrain annihilating dark matter

Recent indirect searches of dark matter using gamma-ray, radio, and cosmic-ray data have provided some stringent constraints on annihilating dark matter. In this article, we propose a new indirect method to constrain annihilating dark matter. By using the data of the G2 cloud near the Galactic supermassive black hole Sgr A*, we can get stringent constraints on the parameter space of dark matter mass and the annihilation cross section, especially for the non-leptophilic annihilation channels $b\bar{b}$ and $W^{\pm}$. For the thermal annihilation cross section, the lower bounds of dark matter mass can be constrained up to TeV order for the non-leptophilic channels with the standard spike index $\gamma_{\rm sp}=7/3$.Comment: Accepted in MNRAS Letter

Investigating the capability of UAV imagery for AI-assisted mapping of Refugee Camps in East Africa

Refugee camps and informal settlements provide accommodation to some of the most vulnerable populations, with many of them located in Sub-Saharan Africa. Many of these settlements lack up-to-date geoinformation that we take for granted in developed world. Having up-to-date maps on their dimension, spatial layout is important. They are essential tools for assisting administration tasks such as crisis intervention, infrastructure development, and population estimates which encourage economic productivity. In the OpenStreetMap ecosystem, there is a disparity between built-up being digitised in the developed and the developing areas. This data inequality are results of multiple reasons ranging from a lack of commercial interest to knowledge gaps in data contributors and such disparity can be reduced with the help of assisted mapping technology. Very High Resolution remote sensing imagery and Machine Learning based methods can exploit the textural, spectral, and morphological characteristics and are commonly used to extract information from these complex environments. In particular recent advances in Deep Learning based Computer Vision have achieved significant results. This study is connected to a larger initiative to open-source the AI assisted mapping platform in the current Humanitarian OpenStreetMap Team's ecosystem, to investigate the capabilities of applying Deep Learning for building footprint delineation in refugee camps based on open-data Unmaned Aerial Vehicle (UAV) imagery from partner organisation OpeAerialMap. The objective of this study is to test the U-Net and several variations of the architectures' performance for building footprint segmentation, The performance of the different Deep Learning models on datasets of various complexity were collected. A comparison of the models' responses using class-based accuracy assessments metrics allows detail evaluation into how the different architectures and experiment setup respond to data quality. Given the computation and resources constraint of this project, the result suggests that increase in architectural depths corresponds with increase in precision. Models that were initialised on pre-trained weights from ImageNet could reduce recall. Lastly, to our surprise, the transferability of a competition winning network trained on similar resolution but on formal building performs worse than many models trained from scratch. This study showcased the ability to use Deep Learning semantic segmentation to perform building footprint delineation in complex humanitarian applications. Having increased access to open-data Very High Resolution UAV imagery from the OpenAerialMap initiative is an advantage to building AI-assisted humanitarian mapping. The study demonstrated a careful and rigourous approach to model evaluation. Yet, the variation of the study results not only emphasised the complexity of Deep Learning based methods, but also indicate the direction for further investigation that would be justifiable when further resources becomes available