Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics

Achieving an effective fine-grained appearance variation over 2D facial images, whilst preserving facial identity, is a challenging task due to the high complexity and entanglement of common 2D facial feature encoding spaces. Despite these challenges, such fine-grained control, by way of disentanglement is a crucial enabler for data-driven racial bias mitigation strategies across multiple automated facial analysis tasks, as it allows to analyse, characterise and synthesise human facial diversity. In this paper, we propose a novel GAN framework to enable fine-grained control over individual race-related phenotype attributes of the facial images. Our framework factors the latent (feature) space into elements that correspond to race-related facial phenotype representations, thereby separating phenotype aspects (e.g. skin, hair colour, nose, eye, mouth shapes), which are notoriously difficult to annotate robustly in real-world facial data. Concurrently, we also introduce a high quality augmented, diverse 2D face image dataset drawn from CelebA-HQ for GAN training. Unlike prior work, our framework only relies upon 2D imagery and related parameters to achieve state-of-the-art individual control over race-related phenotype attributes with improved photo-realistic output

Lilia, A Showcase for Fast Bootstrap of Conversation-Like Dialogues Based on a Goal-Oriented System

International audienceRecently many works have proposed to cast human-machine interaction in a sentence generation scheme. Neural networks models can learn how to generate a probable sentence based on the user's statement along with a partial view of the dialogue history. While appealing to some extent, these approaches require huge training sets of general-purpose data and lack a principled way to intertwine language generation with information retrieval from back-end resources to fuel the dialogue with actualised and precise knowledge. As a practical alternative, in this paper, we present Lilia, a showcase for fast bootstrap of conversation-like dialogues based on a goal-oriented system. First, a comparison of goal-oriented and conversational system features is led, then a conversion process is described for the fast bootstrap of a new system, finalised with an on-line training of the system's main components. Lilia is dedicated to a chitchat task, where speakers exchange viewpoints on a displayed image while trying collaboratively to derive its author's intention. Evaluations with user trials showed its efficiency in a realistic setup

An exploration of dropout with RNNs for natural language inference.

Dropout is a crucial regularization technique for the Recurrent Neural Network (RNN) models of Natural Language Inference (NLI). However, dropout has not been evaluated for the effectiveness at different layers and dropout rates in NLI models. In this paper, we propose a novel RNN model for NLI and empirically evaluate the effect of applying dropout at different layers in the model. We also investigate the impact of varying dropout rates at these layers. Our empirical evaluation on a large (Stanford Natural Language Inference (SNLI)) and a small (SciTail) dataset suggest that dropout at each feed-forward connection severely affects the model accuracy at increasing dropout rates. We also show that regularizing the embedding layer is efficient for SNLI whereas regularizing the recurrent layer improves the accuracy for SciTail. Our model achieved an accuracy 86.14% on the SNLI dataset and 77.05% on SciTail

Collaborative denoising autoencoder for high glycated haemoglobin prediction.

A pioneering study is presented demonstrating that the presence of high glycated haemoglobin (HbA1c) levels in a patient’s blood can be reliably predicted from routinely collected clinical data. This paves the way for performing early detection of Type-2 Diabetes Mellitus (T2DM). This will save healthcare providers a major cost associated with the administration and assessment of clinical tests for HbA1c. A novel collaborative denoising autoencoder framework is used to address this challenge. The framework builds an independent denoising autoencoder model for the high and low HbA1c level, which extracts feature representations in the latent space. A baseline model using just three features: patient age together with triglycerides and glucose level achieves 76% F1-score with an SVM classifier. The collaborative denoising autoencoder uses 78 features and can predict HbA1c level with 81% F1-score

Exploring Emotion Representation to Support Dialogue in Police Training on Child Interviewing

Police officers when dealing with interviewing children have to cope with a complex set of emotions from a vulnerable witness. Triggers for recognising those emotions and how to build rapport are often the basis of learning exercises. However, current training pulls together the full complexity of emotions during role-playing which can be over-whelming and reduce appropriate learning focus. Interestingly a serious game’s interface can provide valuable training not because it represents full complex, multimedia interactions but because it can restrict emotional complexity and increase focus during the interactions on key factors for emotional recognition. The focus of this paper is to report on a specific aspect that was explored during the development of a serious game that aims to address the current police-training needs of child interviewing techniques, where the recognition of emotions plays an important role in understanding how to build rapport with children. The review of literature reveals that emotion recognition, through facial expressions, can contribute significantly to the perceived quality of communication. For this study an ‘emotions map’ was created and tested by 41 participants to be used in the development of a targeted interface design to support the different levels of emotion recognition. The emotions identified were validated with a 70 % agreement across experts and non-experts highlighting the innate role of emotion recognition. A discussion is made around the role of emotions and game-based systems to support their identification for work-based training. As part of the graphical development of the Child Interview Stimulator (CIS) we examined different levels of emotional recognition that can be used to support the in-game graphical representation of a child’s response during a police interview

Improving the multiobjective evolutionary algorithm based on decomposition with new penalty schemes

It has been increasingly reported that the multiobjective optimization evolutionary algorithm based on decomposition (MOEA/D) is promising for handling multiobjective optimization problems (MOPs). MOEA/D employs scalarizing functions to convert an MOP into a number of single-objective subproblems. Among them, penalty boundary intersection (PBI) is one of the most popular decomposition approaches and has been widely adopted for dealing with MOPs. However, the original PBI uses a constant penalty value for all subproblems and has difficulties in achieving a good distribution and coverage of the Pareto front for some problems. In this paper, we investigate the influence of the penalty factor on PBI, and suggest two new penalty schemes, i.e., adaptive penalty scheme and subproblem-based penalty scheme (SPS), to enhance the spread of Pareto-optimal solutions. The new penalty schemes are examined on several complex MOPs, showing that PBI with the use of them is able to provide a better approximation of the Pareto front than the original one. The SPS is further integrated into two recently developed MOEA/D variants to help balance the population diversity and convergence. Experimental results show that it can significantly enhance the algorithm�s performance. © 2016, Springer-Verlag Berlin Heidelberg

S100A7-Downregulation Inhibits Epidermal Growth Factor-Induced Signaling in Breast Cancer Cells and Blocks Osteoclast Formation

S100A7 is a small calcium binding protein, which has been shown to be differentially expressed in psoriatic skin lesions, as well as in squamous cell tumors of the skin, lung and breast. Although its expression has been correlated to HER+ high-grade tumors and to a high risk of progression, the molecular mechanisms of these S100A7-mediated tumorigenic effects are not well known. Here, we showed for the first time that epidermal growth factor (EGF) induces S100A7 expression in both MCF-7 and MDA-MB-468 cell lines. We also observed a decrease in EGF-directed migration in shRNA-downregulated MDA-MB-468 cell lines. Furthermore, our signaling studies revealed that EGF induced simultaneous EGF receptor phosphorylation at Tyr1173 and HER2 phosphorylation at Tyr1248 in S100A7-downregulated cell lines as compared to the vector-transfected controls. In addition, reduced phosphorylation of Src at tyrosine 416 and p-SHP2 at tyrosine 542 was observed in these downregulated cell lines. Further studies revealed that S100A7-downregulated cells had reduced angiogenesis in vivo based on matrigel plug assays. Our results also showed decreased tumor-induced osteoclastic resorption in an intra-tibial bone injection model involving SCID mice. S100A7-downregulated cells had decreased osteoclast number and size as compared to the vector controls, and this decrease was associated with variations in IL-8 expression in in vitro cell cultures. This is a novel report on the role of S100A7 in EGF-induced signaling in breast cancer cells and in osteoclast formation

Nuclear S100A7 Is Associated with Poor Prognosis in Head and Neck Cancer

Tissue proteomic analysis of head and neck squamous cell carcinoma (HNSCC) and normal oral mucosa using iTRAQ (isobaric tag for relative and absolute quantitation) labeling and liquid chromatography-mass spectrometry, led to the identification of a panel of biomarkers including S100A7. In the multi-step process of head and neck tumorigenesis, the presence of dysplastic areas in the epithelium is proposed to be associated with a likely progression to cancer; however there are no established biomarkers to predict their potential of malignant transformation. This study aimed to determine the clinical significance of S100A7 overexpression in HNSCC.Immunohistochemical analysis of S100A7 expression in HNSCC (100 cases), oral lesions (166 cases) and 100 histologically normal tissues was carried out and correlated with clinicopathological parameters and disease prognosis over 7 years for HNSCC patients. Overexpression of S100A7 protein was significant in oral lesions (squamous cell hyperplasia/dysplasia) and sustained in HNSCC in comparison with oral normal mucosa (p(trend)<0.001). Significant increase in nuclear S100A7 was observed in HNSCC as compared to dysplastic lesions (p = 0.005) and associated with well differentiated squamous cell carcinoma (p = 0.031). Notably, nuclear accumulation of S100A7 also emerged as an independent predictor of reduced disease free survival (p = 0.006, Hazard ratio (HR = 7.6), 95% CI = 1.3-5.1) in multivariate analysis underscoring its relevance as a poor prognosticator of HNSCC patients.Our study demonstrated nuclear accumulation of S100A7 may serve as predictor of poor prognosis in HNSCC patients. Further, increased nuclear accumulation of S100A7 in HNSCC as compared to dysplastic lesions warrants a large-scale longitudinal study of patients with dysplasia to evaluate its potential as a determinant of increased risk of transformation of oral premalignant lesions

Selective laser trabeculoplasty: past, present, and future

Over the past two decades, selective laser trabeculoplasty (SLT) has increasingly become an established laser treatment used to lower intraocular pressure in open-angle glaucoma and ocular hypertensive patients. In this review we trace the origins of SLT from previous argon laser trabeculoplasty and review the current role it has in clinical practice. We outline future directions of SLT research and introduce emerging technologies that are further developing this intervention in the treatment paradigm of glaucoma.Eye advance online publication, 5 January 2018; doi:10.1038/eye.2017.273