Humans approximately spend a third of their life sleeping, which makes monitoring sleep an integral part of well-being. In this paper, a 34-layer deep residual ConvNet architecture for end-to-end sleep staging is proposed. The network takes raw single channel electroencephalogram (Fpz-Cz) signal as input and yields hypnogram annotations for each 30s segments as output. Experiments are carried out for two different scoring standards (5 and 6 stage classification) on the expanded PhysioNet Sleep-EDF dataset, which contains multi-source data from hospital and household polysomnography setups. The performance of the proposed network is compared with that of the state-of-the-art algorithms in patient independent validation tasks. The experimental results demonstrate the superiority of the proposed network compared to the best existing method, providing a relative improvement in epoch-wise average accuracy of 6.8% and 6.3% on the household data and multi-source data, respectively. Codes are made publicly available on Github.

Semi-supervised classification on graph-structured data has received increasing attention, where labels are only available for a small subset of data such as social networks and citation networks. This problem is challenging due to the irregularity of graphs. Graph convolutional neural networks (GCN) have been recently proposed to address such kinds of problems, which feed the graph topology into the network to guide operations such as graph convolution. Nevertheless, in most cases where the graphs are not given, they are empirically constructed manually, which tends to be sub-optimal. Hence, we propose Graph Learning Neural Networks (GLNN), which exploits the optimization of graphs (the adjacency matrix in particular) and integrates into the GCN for semi-supervised node classification. Leveraging on spectral graph theory, this essentially combines both graph learning and graph convolution into a unified framework. Specifically, we represent features of social/citation networks as graph signals, and propose the objective of graph learning from the graph-signal prior, sparsity constraint and properties of a valid adjacency matrix via maximum a posteriori estimation. The optimization objective is then integrated into the loss function of the GCN, leading to joint learning of the adjacency matrix and high-level features. Experimental results show that our proposed GLNN outperforms state-of-the-art approaches over widely adopted social network datasets and citation network datasets.

Deep neural networks can be powerful tools, but require careful application-specific design to ensure that the most informative relationships in the data are learnable. In this paper, we apply deep neural networks to the nonlinear spatiotemporal physics problem of vehicle traffic dynamics. We consider problems of estimating macroscopic quantities (e.g., the queue at an intersection) at a lane level. First-principles modeling at the lane scale has been a challenge due to complexities in modeling social behaviors like lane changes, and those behaviors' resultant macro-scale effects. Following domain knowledge that upstream/downstream lanes and neighboring lanes affect each others' traffic flows in distinct ways, we apply a form of neural attention that allows the neural network layers to aggregate information from different lanes in different manners. Using a microscopic traffic simulator as a testbed, we obtain results showing that an attentional neural network model can use information from nearby lanes to improve predictions, and, that explicitly encoding the lane-to-lane relationship types significantly improves performance. We also demonstrate the transfer of our learned neural network to a more complex road network, discuss how its performance degradation may be attributable to new traffic behaviors induced by increased topological complexity, and motivate learning dynamics models from many road network topologies.

Functional neuroimaging techniques using resting-state functional MRI (rs-fMRI) have accelerated progress in brain disorders and dysfunction studies. Since, there are the slight differences between healthy and disorder brains, investigation in the complex topology of human brain functional networks is difficult and complicated task with the growth of evaluation criteria. Recently, graph theory and deep learning applications have spread widely to understanding human cognitive functions that are linked to gene expression and related distributed spatial patterns. Irregular graph analysis has been widely applied in many brain recognition domains, these applications might involve both node-centric and graph-centric tasks. In this paper, we discuss about individual Variational Autoencoder and Graph Convolutional Network (GCN) for the region of interest identification areas of brain which do not have normal connection when apply certain tasks. Here, we identified a framework of Graph Auto-Encoder (GAE) with hyper sphere distributer for functional data analysis in brain imaging studies that is underlying non-Euclidean structure, in learning of strong rigid graphs among large scale data. In addition, we distinguish the possible mode correlations in abnormal brain connections. Keywords: Functional brain networks, Graph theory, Generative Models, resting-state fMRI, VAE&GAN.

Celiac Disease (CD) and Environmental Enteropathy (EE) are common causes of malnutrition and adversely impact normal childhood development. CD is an autoimmune disorder that is prevalent worldwide and is caused by an increased sensitivity to gluten. Gluten exposure destructs the small intestinal epithelial barrier, resulting in nutrient mal-absorption and childhood under-nutrition. EE also results in barrier dysfunction but is thought to be caused by an increased vulnerability to infections. EE has been implicated as the predominant cause of under-nutrition, oral vaccine failure, and impaired cognitive development in low-and-middle-income countries. Both conditions require a tissue biopsy for diagnosis, and a major challenge of interpreting clinical biopsy images to differentiate between these gastrointestinal diseases is striking histopathologic overlap between them. In the current study, we propose a convolutional neural network (CNN) to classify duodenal biopsy images from subjects with CD, EE, and healthy controls. We evaluated the performance of our proposed model using a large cohort containing 1000 biopsy images. Our evaluations show that the proposed model achieves an area under ROC of 0.99, 1.00, and 0.97 for CD, EE, and healthy controls, respectively. These results demonstrate the discriminative power of the proposed model in duodenal biopsies classification.

Recent models of emotion recognition strongly rely on supervised deep learning solutions for the distinction of general emotion expressions. However, they are not reliable when recognizing online and personalized facial expressions, e.g., for person-specific affective understanding. In this paper, we present a neural model based on a conditional adversarial autoencoder to learn how to represent and edit general emotion expressions. We then propose Grow-When-Required networks as personalized affective memories to learn individualized aspects of emotion expressions. Our model achieves state-of-the-art performance on emotion recognition when evaluated on \textit{in-the-wild} datasets. Furthermore, our experiments include ablation studies and neural visualizations in order to explain the behavior of our model.

This paper proposes a new model, called condition-transforming variational autoencoder (CTVAE), to improve the performance of conversation response generation using conditional variational autoencoders (CVAEs). In conventional CVAEs , the prior distribution of latent variable z follows a multivariate Gaussian distribution with mean and variance modulated by the input conditions. Previous work found that this distribution tends to become condition independent in practical application. In our proposed CTVAE model, the latent variable z is sampled by performing a non-lineartransformation on the combination of the input conditions and the samples from a condition-independent prior distribution N (0; I). In our objective evaluations, the CTVAE model outperforms the CVAE model on fluency metrics and surpasses a sequence-to-sequence (Seq2Seq) model on diversity metrics. In subjective preference tests, our proposed CTVAE model performs significantly better than CVAE and Seq2Seq models on generating fluency, informative and topic relevant responses.

Fine-grained Named Entity Recognition is a task whereby we detect and classify entity mentions to a large set of types. These types can span diverse domains such as finance, healthcare, and politics. We observe that when the type set spans several domains the accuracy of the entity detection becomes a limitation for supervised learning models. The primary reason being the lack of datasets where entity boundaries are properly annotated, whilst covering a large spectrum of entity types. Furthermore, many named entity systems suffer when considering the categorization of fine grained entity types. Our work attempts to address these issues, in part, by combining state-of-the-art deep learning models (ELMo) with an expansive knowledge base (Wikidata). Using our framework, we cross-validate our model on the 112 fine-grained entity types based on the hierarchy given from the Wiki(gold) dataset.

Tools and methods for automatic image segmentation are rapidly developing, each with its own strengths and weaknesses. While these methods are designed to be as general as possible, there are no guarantees for their performance on new data. The choice between methods is usually based on benchmark performance whereas the data in the benchmark can be significantly different than that of the user. We introduce a novel Deep Learning method which, given an image and a proposed corresponding segmentation, estimates the Intersection over Union measure (IoU) with respect to the unknown ground truth. We refer to this method as a Quality Assurance Network - QANet. The QANet is designed to give the user an estimate of the segmentation quality on the users own, private, data without the need for human inspection or labelling. It is based on the RibCage Network architecture, originally proposed as a discriminator in an adversarial network framework. Promising IoU prediction results are demonstrated based on the Cell Segmentation Benchmark. The code is freely available at: ANONYMOUS.

Artificial Intelligence (AI) has the capability to provide radiologists with tools to improve their productivity, decision making and effectiveness and will lead to quicker diagnosis and improved patient outcomes. It will initially deploy as a diverse collection of assistive tools to augment, quantify and stratify the information available to the diagnostician, and offer a major opportunity to enhance and augment the radiology reading. It will improve access to medical record information and give radiologists more time to think about what is going on with patients, diagnose more complex cases, collaborate with patient care teams, and perform more invasive procedures. Deep Learning algorithms in particular will form the foundation for decision and workflow support tools and diagnostic capabilities. Algorithms will provide software the ability to "learn" by example on how to execute a task, then automatically execute those tasks as well as interpret new data.