Progressive ShallowNet for large scale dynamic and spontaneous facial behaviour analysis in children

COVID-19 has severely disrupted every aspect of society and left negative impact on our life. Resisting the temptation in engaging face-to-face social connection is not as easy as we imagine. Breaking ties within social circle makes us lonely and isolated, that in turns increase the likelihood of depression related disease and even can leads to death by increasing the chance of heart disease. Not only adults, children's are equally impacted where the contribution of emotional competence to social competence has long term implications. Early identification skill for facial behaviour emotions, deficits, and expression may help to prevent the low social functioning. Deficits in young children's ability to differentiate human emotions can leads to social functioning impairment. However, the existing work focus on adult emotions recognition mostly and ignores emotion recognition in children. By considering the working of pyramidal cells in the cerebral cortex, in this paper, we present progressive lightweight shallow learning for the classification by efficiently utilizing the skip-connection for spontaneous facial behaviour recognition in children. Unlike earlier deep neural networks, we limit the alternative path for the gradient at the earlier part of the network by increase gradually with the depth of the network. Progressive ShallowNet is not only able to explore more feature space but also resolve the over-fitting issue for smaller data, due to limiting the residual path locally, making the network vulnerable to perturbations. We have conducted extensive experiments on benchmark facial behaviour analysis in children that showed significant performance gain comparatively

Precursor- route ZnO films from mixed casting solvent for high performance aqueous electrolyte- gated transistors

We significantly improved the properties of semiconducting zinc oxide (ZnO) films resulting from the thermal conversion of a soluble precursor, zinc acetate (ZnAc), by using a mixed casting solvent for the precursor. ZnAc dissolves more readily in a 1:1 mix of ethanol (EtOH) and acetone than in either pure EtOH, pure acetone, or pure isopropanol, and ZnO films converted from mixed solvent cast ZnAc are more homogeneous. When gated with a biocompatible electrolyte, phosphate buffered saline (PBS), ZnO thin film transistors (TFTs) derived from mixed solvent cast ZnAc give 7 times larger field effect current than similar films derived from ZnAc cast from pure EtOH. Sheet resistance at VG = VD = 1V is 18 kΩ/▢, lower than for any organic TFT, and lower than for any water- gated ZnO TFT, reported to date

‘The Mosques Are the Biggest Problem We’ve Got Right Now’ – Key Agent and Survivor Accounts of Engaging Mosques with Domestic and Honour-Based Violence in the United Kingdom.

This article considers the role of mosques in addressing domestic violence (DV) and honor-based violence (HBV) in the United Kingdom. Utilizing data extracted from interviews with 38 key agents and survivors, this article will highlight that some mosques can be difficult to engage with when attempting to raise awareness on violence against women (VAW). Participants explained that the patriarchal nature of mosques contributes to this difficulty together with their exclusion of women within organizational structures. Some mosques also deny that VAW is even a problem within their communities. This is a worrying trend as those on the pulpit often possess significant powers of influence across large congregations and are perfectly placed to help provoke dialogue on these issues. Furthermore, it adds yet another layer of inequality experienced by Muslim women that makes reporting abuse and seeking intervention that much more difficult. In the face of this resistance, this article will consider some ways in which mosques can raise awareness about VAW and where Muslim women can access support. It will also explore additional strategies and recommendations in relation to overcoming mosques unwilling to support VAW initiatives

Fetal brain tissue annotation and segmentation challenge results.

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, gray matter, white matter, ventricles, cerebellum, brainstem, deep gray matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero

Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results

Segmentation is a critical step in analyzing the developing human fetal brain. There have been vast improvements in automatic segmentation methods in the past several years, and the Fetal Brain Tissue Annotation (FeTA) Challenge 2021 helped to establish an excellent standard of fetal brain segmentation. However, FeTA 2021 was a single center study, and the generalizability of algorithms across different imaging centers remains unsolved, limiting real-world clinical applicability. The multi-center FeTA Challenge 2022 focuses on advancing the generalizability of fetal brain segmentation algorithms for magnetic resonance imaging (MRI). In FeTA 2022, the training dataset contained images and corresponding manually annotated multi-class labels from two imaging centers, and the testing data contained images from these two imaging centers as well as two additional unseen centers. The data from different centers varied in many aspects, including scanners used, imaging parameters, and fetal brain super-resolution algorithms applied. 16 teams participated in the challenge, and 17 algorithms were evaluated. Here, a detailed overview and analysis of the challenge results are provided, focusing on the generalizability of the submissions. Both in- and out of domain, the white matter and ventricles were segmented with the highest accuracy, while the most challenging structure remains the cerebral cortex due to anatomical complexity. The FeTA Challenge 2022 was able to successfully evaluate and advance generalizability of multi-class fetal brain tissue segmentation algorithms for MRI and it continues to benchmark new algorithms. The resulting new methods contribute to improving the analysis of brain development in utero.Comment: Results from FeTA Challenge 2022, held at MICCAI; Manuscript submitted. Supplementary Info (including submission methods descriptions) available here: https://zenodo.org/records/1062864

Progressive ShallowNet for large scale dynamic and spontaneous facial behaviour analysis in children

COVID-19 has severely disrupted every aspect of society and left negative impact on our life. Resisting the temptation in engaging face-to-face social connection is not as easy as we imagine. Breaking ties within social circle makes us lonely and isolated, that in turns increase the likelihood of depression related disease and even can leads to death by increasing the chance of heart disease. Not only adults, children's are equally impacted where the contribution of emotional competence to social competence has long term implications. Early identification skill for facial behaviour emotions, deficits, and expression may help to prevent the low social functioning. Deficits in young children's ability to differentiate human emotions can leads to social functioning impairment. However, the existing work focus on adult emotions recognition mostly and ignores emotion recognition in children. By considering the working of pyramidal cells in the cerebral cortex, in this paper, we present progressive lightweight shallow learning for the classification by efficiently utilizing the skip-connection for spontaneous facial behaviour recognition in children. Unlike earlier deep neural networks, we limit the alternative path for the gradient at the earlier part of the network by increase gradually with the depth of the network. Progressive ShallowNet is not only able to explore more feature space but also resolve the over-fitting issue for smaller data, due to limiting the residual path locally, making the network vulnerable to perturbations. We have conducted extensive experiments on benchmark facial behaviour analysis in children that showed significant performance gain comparatively

Assessment of physiological states from contactless face video: a sparse representation approach

International audienc

Customised Design of A Patient Specific 3D Printed Whole Mandible Implant

In this study we investigate the design methodology for the creation of a patient specific, whole mandible implant based on a patient's medical imaging data. We tailor the implant as a treatment option for a patient who will undergo a mandibulectomy due to cancer infiltration of the jaw. We create a 3D representative model of the patient's skeletal structure from CT scan data, and us this to generate the implant from the patient's corrupt mandible. In this particular case study the cancer is restricted to the right region of the mandible, and so the left side is used in a symmetry matching approach to create the final model for manufacturing. The final design was 3D printed in medical grade titanium and finished using a mechanical polishing technique, the yield a near mirror finish. We found the final implant to be highly robust, and an excellent fit to a representative model of the patient's skeletal anatomy. We believe this approach to hold considerable potential for implementation as a treatment option for mandibular complications