Vital information matching in vision-and-language navigation.

With the rapid development of artificial intelligence technology, many researchers have begun to focus on visual language navigation, which is one of the most important tasks in multi-modal machine learning. The focus of this multi-modal field is how to fuse multiple inputs, which is crucial for the integrated feedback of intrinsic information. However, the existing models are only implemented through simple data augmentation or expansion, and are obviously far from being able to tap the intrinsic relationship between modalities.

Image Sensing and Processing with Convolutional Neural Networks.

Convolutional neural networks are a class of deep neural networks that leverage spatial information, and they are therefore well suited to classifying images for a range of applications [...

Using Social Signals to Predict Shoplifting: A Transparent Approach to a Sensitive Activity Analysis Problem.

Retail shoplifting is one of the most prevalent forms of theft and has accounted for over one billion GBP in losses for UK retailers in 2018. An automated approach to detecting behaviours associated with shoplifting using surveillance footage could help reduce these losses. Until recently, most state-of-the-art vision-based approaches to this problem have relied heavily on the use of black box deep learning models.

Structure-Aware Feature Disentanglement With Knowledge Transfer for Appearance-Changing Place Recognition.

Long-term visual place recognition (VPR) is challenging as the environment is subject to drastic appearance changes across different temporal resolutions, such as time of the day, month, and season. A wide variety of existing methods address the problem by means of feature disentangling or image style transfer but ignore the structural information that often remains stable even under environmental condition changes. To overcome this limitation, this article presents a novel structure-aware feature disentanglement network (SFDNet) based on knowledge transfer and adversarial learning.

Variations in the oral microbiome are associated with depression in young adults.

A growing body of evidence supports an important role for alterations in the brain-gut-microbiome axis in the aetiology of depression and other psychiatric disorders. The potential role of the oral microbiome in mental health has received little attention, even though it is one of the most diverse microbiomes in the body and oral dysbiosis has been linked to systemic diseases with an underlying inflammatory aetiology. This study examines the structure and composition of the salivary microbiome for the first time in young adults who met the DSM-IV criteria for depression (n = 40) and matched controls (n = 43) using 16S rRNA gene-based next generation sequencing.

An Efficient and Lightweight Deep Learning Model for Human Activity Recognition Using Smartphones.

Traditional pattern recognition approaches have gained a lot of popularity. However, these are largely dependent upon manual feature extraction, which makes the generalized model obscure. The sequences of accelerometer data recorded can be classified by specialized smartphones into well known movements that can be done with human activity recognition.

Emotion recognition with convolutional neural network and EEG-based EFDMs.

Electroencephalogram (EEG), as a direct response to brain activity, can be used to detect mental states and physical conditions. Among various EEG-based emotion recognition studies, due to the non-linear, non-stationary and the individual difference of EEG signals, traditional recognition methods still have the disadvantages of complicated feature extraction and low recognition rates. Thus, this paper first proposes a novel concept of electrode-frequency distribution maps (EFDMs) with short-time Fourier transform (STFT).

A predictive model for paediatric autism screening.

Autism spectrum disorder is an umbrella term for a group of neurodevelopmental disorders that is associated with impairments to social interaction, communication, and behaviour. Typically, autism spectrum disorder is first detected with a screening tool (e.g.

DENSE-INception U-net for medical image segmentation.

Background And Objective: Convolutional neural networks (CNNs) play an important role in the field of medical image segmentation. Among many kinds of CNNs, the U-net architecture is one of the most famous fully convolutional network architectures for medical semantic segmentation tasks. Recent work shows that the U-net network can be substantially deeper thus resulting in improved performance on segmentation tasks.

A Computational Model of Thalamocortical Dysrhythmia in People With Tinnitus.

Tinnitus is a problem that affects a diverse range of people. One common trait amongst people with tinnitus is the presence of hearing loss, which is apparent in over 90% of the cohort. It is postulated that the remainder of people with tinnitus have hidden hearing loss in the form of cochlear synaptopathy.

Biologically Inspired Intensity and Depth Image Edge Extraction.

In recent years, artificial vision research has moved from focusing on the use of only intensity images to include using depth images, or RGB-D combinations due to the recent development of low-cost depth cameras. However, depth images require a lot of storage and processing requirements. In addition, it is challenging to extract relevant features from depth images in real time.

Robust Microbial Markers for Non-Invasive Inflammatory Bowel Disease Identification.

Inflammatory Bowel Disease (IBD) is an umbrella term for a group of inflammatory diseases of the gastrointestinal tract, including Crohn's Disease and ulcerative colitis. Changes to the intestinal microbiome, the community of micro-organisms that resides in the human gut, have been shown to contribute to the pathogenesis of IBD. IBD diagnosis is often delayed due to its non-specific symptoms and because an invasive colonoscopy is required for confirmation, which leads to poor growth in children and worse treatment outcomes.

Bioinspired Approach to Modeling Retinal Ganglion Cells Using System Identification Techniques.

The processing capabilities of biological vision systems are still vastly superior to artificial vision, even though this has been an active area of research for over half a century. Current artificial vision techniques integrate many insights from biology yet they remain far-off the capabilities of animals and humans in terms of speed, power, and performance. A key aspect to modeling the human visual system is the ability to accurately model the behavior and computation within the retina.

Patient satisfaction with nurse-led chronic kidney disease clinics: A multicentre evaluation.

Background: There is growing international evidence that nurse-led chronic kidney disease (CKD) clinics provide a comprehensive approach to achieving clinical targets effective in slowing the progression of CKD. Across Queensland, Australia, these clinics have been established in many renal outpatient departments although patient satisfaction with these clinics is unknown.

Objectives: To measure patient satisfaction levels with CKD nurse-led clinics.

Detecting Wash Trade in Financial Market Using Digraphs and Dynamic Programming.

A wash trade refers to the illegal activities of traders who utilize carefully designed limit orders to manually increase the trading volumes for creating a false impression of an active market. As one of the primary formats of market abuse, a wash trade can be extremely damaging to the proper functioning and integrity of capital markets. The existing work focuses on collusive clique detections based on certain assumptions of trading behaviors.

Multiscale Edge Detection Using a Finite Element Framework for Hexagonal Pixel-Based Images.

In recent years, the processing of hexagonal pixel-based images has been investigated, and as a result, a number of edge detection algorithms for direct application to such image structures have been developed. We build on this paper by presenting a novel and efficient approach to the design of hexagonal image processing operators using linear basis and test functions within the finite element framework. Development of these scalable first order and Laplacian operators using this approach presents a framework both for obtaining large-scale neighborhood operators in an efficient manner and for obtaining edge maps at different scales by efficient reuse of the seven-point linear operator.

Adaptive hidden Markov model with anomaly States for price manipulation detection.

Price manipulation refers to the activities of those traders who use carefully designed trading behaviors to manually push up or down the underlying equity prices for making profits. With increasing volumes and frequency of trading, price manipulation can be extremely damaging to the proper functioning and integrity of capital markets. The existing literature focuses on either empirical studies of market abuse cases or analysis of particular manipulation types based on certain assumptions.

Temporal changes of diffusion patterns in mild traumatic brain injury via group-based semi-blind source separation.

Despite the emerging applications of diffusion tensor imaging (DTI) to mild traumatic brain injury (mTBI), very few investigations have been reported related to temporal changes in quantitative diffusion patterns, which may help to assess recovery from head injury and the long term impact associated with cognitive and behavioral impairments caused by mTBI. Most existing methods are focused on detection of mTBI affected regions rather than quantification of temporal changes following head injury. Furthermore, most methods rely on large data samples as required for statistical analysis and, thus, are less suitable for individual case studies.

Linguistic decision making for robot route learning.

Machine learning enables the creation of a nonlinear mapping that describes robot-environment interaction, whereas computing linguistics make the interaction transparent. In this paper, we develop a novel application of a linguistic decision tree for a robot route learning problem by dynamically deciding the robot's behavior, which is decomposed into atomic actions in the context of a specified task. We examine the real-time performance of training and control of a linguistic decision tree, and explore the possibility of training a machine learning model in an adaptive system without dual CPUs for parallelization of training and control.

Enhancement of fiber orientation distribution reconstruction in diffusion-weighted imaging by single channel blind source separation.

In diffusion-weighted imaging (DWI), reliable fiber tracking results rely on the accurate reconstruction of the fiber orientation distribution function (fODF) in each individual voxel. For high angular resolution diffusion imaging (HARDI), deconvolution-based approaches can reconstruct the complex fODF and have advantages in terms of computational efficiency and no need to estimate the number of distinct fiber populations. However, HARDI-based methods usually require relatively high b-values and a large number of gradient directions to produce good results.

Edge detecting for range data using Laplacian operators.

Feature extraction in image data has been investigated for many years, and more recently the problem of processing images containing irregularly distributed data has become prominent. Range data are now commonly used in the areas of image processing and computer vision. However, due to the data irregularity found in range images that occurs with a variety of image sensors, direct image processing, in particular edge detection, is a non-trivial problem.