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Person re-identification is an important technique towards automatic recognition of a person across non-overlapping cameras. In this paper, a novel patch selection method based on parsing and saliency detection is proposed. The algorithm is divided into two stages. The first stage, primary selection: Deep Decompositional Network (DNN) is adopted to parse a pedestrian image into semantic regions, then sliding window and color matching techniques are proposed to select pedestrian patches and remove background patches. The second stage, secondary selection: saliency detection is utilized to select reliable patches according to saliency map. Finally, PHOG, HSV and SIFT features are extracted from these patches and fused with the global feature LOMO to compensate for the inherent errors of saliency detection. By applying the proposed method on such datasets as VIPeR, PRID2011, CUHK01, CUHK03, PRID 450S and iLIDS-VID, it is found that the proposed descriptor can produce results superior to many state-of-the-art feature representation methods for person identification. [ABSTRACT FROM AUTHOR]

Identification of the material from which an object is made is of significant value for effective robotic grasping and manipulation. Characteristics of the material can be retrieved using different sensory modalities: vision based, tactile based or sound based. Compressibility, surface texture and thermal properties can each be retrieved from physical contact with an object using tactile sensors. This paper presents a method for collecting data using a biomimetic fingertip in contact with various materials and then using these data to classify the materials both individually and into groups of their type. Following acquisition of data, principal component analysis (PCA) is used to extract features. These features are used to train seven different classifiers and hybrid structures of these classifiers for comparison. For all materials, the artificial systems were evaluated against each other, compared with human performance and were all found to outperform human participants’ average performance. These results highlighted the sensitive nature of the BioTAC sensors and pave the way for research that requires a sensitive and accurate approach such as vital signs monitoring using robotic systems. [ABSTRACT FROM AUTHOR]

Deep learning methods for salient object detection (SOD) have been studied actively and promisingly. However, it is still challenging for the studies with two aspects. The first one is a single type of label from the network to convey limit information, which leads to the poor generalization ability of the network. The second one is the difficulty to improve the accuracy and detect details of target. To address these challenges, we develop a novel approach via joint weakly supervised, unsupervised and supervised learning for SOD (WUSL–SOD), which differs from existing methods just based on ground-truth or other sparse labels. Specifically, to optimize the objective of the image, the unsupervised learning module (ULM) is designed to generate coarse saliency feature and suppress background noises via attention guiding mechanism. Then, we propose the weakly supervised learning module (WLM) based on scribbles for producing relatively accurate saliency feature. Note that this structure is used to enhance the details and remedy the deficiency of scribbles in WLM. For further refining information from the ULM and WLM, we propose a supervised learning module (SLM), which is not only applied to process and refine information from the ULM and WLM, but also enhance the image details and capture the entire target area. Furthermore, we also exchange information between the SLM and the WLM to obtain more accurate saliency maps. Extensive experiments on five datasets demonstrate that the proposed approach can effectively outperform the state-of-the-art approaches and achieve real-time. [ABSTRACT FROM AUTHOR]

Fast image processing is a key element in achieving real-time image and video analysis. We propose a novel framework based on a square spiral (denoted as “squiral”) architecture to facilitate fast image processing. Unlike conventional image pixel addressing schemes, where the pixel indices are based on two-dimensional Cartesian coordinates, the spiral addressing scheme enables the image pixel indices to be stored in a one dimensional vector, thereby accelerating the subsequent processing. We refer to the new framework as “Squiral” Image Processing (SIP). Firstly we introduce the approach for SIP conversion that transforms a standard 2D image to a 1D vector according to the proposed “squiral” architecture. Secondly we propose a non-overlapping convolution technique for SIP-based convolution, in which the SIP addressing scheme is incorporated by simulating the phenomenon of eye tremor in the human visual system. Furthermore, we develop a strategy to extend the SIP framework to be multiscale. The performance of the proposed framework is evaluated by the application of SIP-based approaches to edge and corner detection. The results demonstrate the efficiency of the proposed SIP framework compared with standard 2D convolution. [ABSTRACT FROM AUTHOR]

For one-shot segmentation, prototype learning is extensively used. However, using only one RGB prototype to represent all information in the support image may lead to ambiguities. To this end, we propose a one-shot segmentation network based on multi-modal prototype learning that uses depth information to complement RGB information. Specifically, we propose a multi-modal fusion and refinement block (MFRB) and multi-modal prototype learning block (MPLB). MFRB fuses RGB and depth features to generate multi-modal features and refined depth features, which are used by MPLB, to generate multi-modal information prototypes, depth information prototypes, and global information prototypes. Furthermore, we introduce self-attention to capture global context information in RGB and depth images. By integrating self-attention, MFRB, and MPLB, we propose the multi-modal prototype learning network (MMPL-Net), which adapts to the ambiguity of visual information in the scene. Finally, we construct a one-shot RGB-D segmentation dataset called OSS-RGB-D-5 i . Experiments using OSS-RGB-D-5 i show that our proposed method outperforms several state-of-the-art techniques with fewer labeled images and generalizes well to previously unseen objects. [ABSTRACT FROM AUTHOR]

To enable fast reliable feature matching or tracking in scenes, features need to be discrete and meaningful, and hence edge or corner features, commonly called interest points are often used for this purpose. Experimental research has illustrated that biological vision systems use neuronal circuits to extract particular features such as edges or corners from visual scenes. Inspired by this biological behaviour, this paper proposes a biologically inspired spiking neural network for the purpose of image feature extraction. Standard digital images are processed and converted to spikes in a manner similar to the processing that transforms light into spikes in the retina. Using a hierarchical spiking network, various types of biologically inspired receptive fields are used to extract progressively complex image features. The performance of the network is assessed by examining the repeatability of extracted features with visual results presented using both synthetic and real images. [ABSTRACT FROM AUTHOR]

In this paper, a data assimilation network is proposed to tackle the challenges of domain generalization for person re-identification (ReID). Most of the existing research efforts only focus on single-dataset issues, and the trained models are difficult to generalize to unseen scenarios. This paper presents a distinctive idea to improve the generality of the model by assimilating three types of images: style-variant images, misaligned images and unlabeled images. The latter two are often ignored in the previous domain generalization ReID studies. In this paper, a non-local convolutional block attention module is designed for assimilating the misaligned images, and an attention adversary network is introduced to correct it. A progressive augmented memory is designed for assimilating the unlabeled images by progressive learning. Moreover, we propose an attention adversary difference loss for attention correction, and a labeling-guide discriminative embedding loss for progressive learning. Rather than designing a specific feature extractor that is robust to style shift as in most previous domain generalization work, we propose a data assimilation meta-learning procedure to train the proposed network, so that it learns to assimilate style-variant images. It is worth mentioning that we add an unlabeled augmented dataset to the source domain to tackle the domain generalization ReID tasks. Extensive experiments demonstrate that our approach significantly outperforms the state-of-the-art domain generalization methods. [ABSTRACT FROM AUTHOR]

• A novel convolutional neural network is proposed for salient object detection. • Employing guiding module enhances the availability of contextual information. • Experimental results indicate that the proposed module achieves better metric performance. Salient object detection is a fundamental computer vision task. Deep learning techniques have been successfully applied for salient object detection, however, the saliency maps still suffer from incomplete predictions owing to the difficulty and complexity of objects. We propose a novel convolutional neural network based on aggregating contextual information that can improve the accuracy of salient object detection. In order to obtain high-level global information for fine-level feature maps, we add a pyramid pooling module to the proposed network. Moreover, building a guiding module that consists of four context-information feature modules, the algorithm can gain more effective information and enrich the details of saliency maps. Extensive experiments conducted using six benchmark datasets demonstrate that the proposed method outperforms existing state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Visual place recognition (VPR) in environments subject to extreme appearance variation due to changing weather, illumination or seasons is a challenging task. Recent works have shown that features learned from CNNs can achieve promising performance. However, most of the existing methods concentrate so much on the image itself that they neglect the architecture of the network, especially different filters that may carry more meaningful information. In this paper, we develop a learnable feature map filtering (FMF) module constrained by triplet loss to re-calibrate the weight of the individual feature map. In this way, specific feature maps that encode invariant characteristics of location are extracted. Moreover, to make full use of the rich global mutual information that resides in the sample set, we propose an influence-based graph attention network (IB-GAT) with a verification subnet to better incorporate the relations among samples during the training process. Different from conventional GAT approaches, IB-GAT enables feature nodes to attend over the influence of other nodes instead of the original feature. Thus refined features with more discriminative power could be generated. Extensive experiments have been conducted on six public VPR datasets with varying appearances. Ablation analysis verifies the potential efficacy of the FMF module and the IB-GAT components. The experimental results also demonstrate that the proposed methods can achieve better performance than the current state of the art. [ABSTRACT FROM AUTHOR]

• Development of a fuzzy classification system to ascertain an overall health status. • Determination of human BPM, RR and rhythm analysis via robotic tactile sensing. • Determination of human CRT and health status via use of tactile sensing and vision. • Pilot study to evaluate accuracy of vital sign measurement and fuzzy triage on human subjects. The ability to quickly and accurately triage a person's medical condition in an emergency situation or other critical scenarios could mean the difference between life and death. Endowing a robotic system with vision and tactile capabilities, similar to those of medical professionals, and thus enabling robots to assess a patient's status in an emergency is a highly sought after characteristic in healthcare robotics. This paper presents a novel fuzzy triage system exploiting visual and tactile sensing, to equip a robot with the skills to accurately determine key vital signs in humans. There are three key signs of human health: respiratory rate, pulse rate (Beats Per Minute (BPM)) and capillary refill time. Using ground truth from a medical professional, the fuzzy triage system is trained and validated initially with informed synthetic data and then further evaluated using vital signs data collected from subjects in a pilot study. Results from this pilot study indicate that the fuzzy triage system is capable of classifying a patient's health using the novel approaches for collecting BPM, Respiratory Rate (RR) and Capillary Refill Time (CRT) which replicate, to some extent, the approaches used by medical professionals for measuring vital signs. Furthermore, the intelligent system proved capable of determining whether a pulse was regular or arrhythmic, whether respiratory rate was regular or irregular, and determining the subject's capillary refill time. Such results imply that this system could ultimately be used, for example, in a home assistance robot for elderly or disabled persons, or as a first responder robot. Ultimately the aim would be that these methods could be utilised by robotic systems in emergency scenarios or disaster zones. [ABSTRACT FROM AUTHOR]

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. Subtle but significant differences in alpha and beta diversity of the salivary microbiome were observed, with clear separation of depressed and healthy control cohorts into distinct clusters. A total of 21 bacterial taxa were found to be differentially abundant in the depressed cohort, including increased Neisseria spp. and Prevotella nigrescens, while 19 taxa had a decreased abundance. In this preliminary study we have shown that the composition of the oral microbiome is associated with depression in young adults. Further studies are now warranted, particuarly investigations into whether such shifts play any role in the underling aetiology of depression. [ABSTRACT FROM AUTHOR]

Metal defect inspection is critical for maintaining product quality and ensuring production safety. However, the vast majority of existing defect segmentation methods rely heavily on large-scale datasets that only cater to specific defects, making them unsuitable for the industrial sector, where training samples are often limited. To address these challenges, we propose a bilateral guidance network for one-shot metal defect segmentation that leverages the perceptual consistency of background regions within industrial images to distinguish foreground and background regions. Our model uses an interactive feature reweighting scheme that models the inter- and self-dependence of foreground and background feature maps, enabling us to build robust pixel-level correspondences. Our proposed method demonstrates good domain adaptability and accurately segments defects in multiple materials, such as steel, leather, and carpet, among others. Additionally, we have incorporated a multi-scale receptive field encoder to enhance the model's ability to perceive objects of varying scales, providing a comprehensive solution for industrial defect segmentation. Experimental results indicate that our proposed method has the potential to be effective in a variety of real-world applications where defects may not be immediately visible or where large amounts of labeled data are not readily available. With only one shot, our method achieves the state-of-the-art performance of 41.62% mIoU and 70.30% MPA on the Defect-3 i dataset. [ABSTRACT FROM AUTHOR]

Person re-identification plays an important role in searching for a specific person in a camera network with non-overlapping cameras. The most critical problem for re-identification is feature representation. In this paper, a multi-level cross-view consistent feature learning framework is proposed for person re-identification. First, local deep, LOMO and SIFT features are extracted to form multi-level features. Specifically, local features from the lower and higher layers of a convolutional neural network (CNN) are extracted, these features complement each other as they extract apparent and semantic properties. Second, an ID-based cross-view multi-level dictionary learning (IDB-CMDL) is carried out to obtain sparse and discriminant feature representation. Third, a cross-view consistent word learning is performed to get the cross-view consistent BoVW histograms from sparse feature representation. Finally, a multi-level metric learning fuses multiple BoVW histograms, and learns the sample distance in the subspace for ranking. Experiments on the public CUHK03, Market1501, and DukeMTMC-ReID datasets show results that are superior to many state-of-the-art methods for person re-identification. [ABSTRACT FROM AUTHOR]

• A novel densely connection inception convolutional neural network based on U-Net architecture is proposed for medical image segmentation tasks. • The modified Inception-res module combining inception architecture and residual connection is used to make the proposed network deeper and wider. • The densely connection is used in the network to avoid gradient vanishing or redundant computation during network training. • Apply the proposed network to CT and MRI medical segmentation tasks and make evaluation with other segmentation methods. 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. Though adding more layers directly into network is a popular way to make a network deeper, it may lead to gradient vanishing or redundant computation during training. A novel CNN architecture is proposed that integrates the Inception-Res module and densely connecting convolutional module into the U-net architecture. The proposed network model consists of the following parts: firstly, the Inception-Res block is designed to increase the width of the network by replacing the standard convolutional layers; secondly, the Dense-Inception block is designed to extract features and make the network more deep without additional parameters; thirdly, the down-sampling block is adopted to reduce the size of feature maps to accelerate learning and the up-sampling block is used to resize the feature maps. The proposed model is tested on images of blood vessel segmentations from retina images, the lung segmentation of CT Data from the benchmark Kaggle datasets and the MRI scan brain tumor segmentation datasets from MICCAI BraTS 2017. The experimental results show that the proposed method can provide better performance on these two tasks compared with the state-of-the-art algorithms. The results reach an average Dice score of 0.9857 in the lung segmentation. For the blood vessel segmentation, the results reach an average Dice score of 0.9582. For the brain tumor segmentation, the results reach an average Dice score of 0.9867. The experiments highlighted that combining the inception module with dense connections in the U-Net architecture is a promising approach for semantic medical image segmentation. [ABSTRACT FROM AUTHOR]

Weakly supervised salient object detection (SOD) is a challenging task and has drawn much attention from several research perspectives, it has revealed two problems while driving the rapid development of saliency detection. (1) Large divergence in the characteristics of saliency regions in terms of location, shape and size makes them difficult to recognize. (2) The properties of convolutional neural networks dictate that it is insensitive to various transformations, which will lead to hardly balance the application of various disturbances. To tackle these limitations, this paper proposes a novel seminar learning framework with consistent transformation ensembling (SLF-CT) for scribble supervised SOD. The framework consists of the teacher–student model and the student–student model for segmenting the salient objects. Specifically, we first design a cross attention guided network (CAGNet) as a baseline model for saliency prediction. Then we assign CAGNet to the teacher–student model, where the teacher network is based on the exponential moving average and guides the training of the student network. Moreover, we adopt multiple pseudo labels to transfer the information among students from different conditions. To further enhance the regularization of the network, a consistency transformation mechanism is also incorporated, which encourages the saliency prediction and input image of the network to be consistent. The experimental results demonstrate that the proposed approach performs favorably comparable with the state-of-the-art weakly supervised methods. As far as we know, the proposed approach is the first application of seminar learning in the SOD area. [ABSTRACT FROM AUTHOR]

Technical and quantitative analysis in financial trading use mathematical and statistical tools to help investors decide on the optimum moment to initiate and close orders. While these traditional approaches have served their purpose to some extent, new techniques arising from the field of computational intelligence such as machine learning and data mining have emerged to analyse financial information. While the main financial engineering research has focused on complex computational models such as Neural Networks and Support Vector Machines, there are also simpler models that have demonstrated their usefulness in applications other than financial trading, and are worth considering to determine their advantages and inherent limitations when used as trading analysis tools. This paper analyses the role of simple machine learning models to achieve profitable trading through a series of trading simulations in the FOREX market. It assesses the performance of the models and how particular setups of the models produce systematic and consistent predictions for profitable trading. Due to the inherent complexities of financial time series the role of attribute selection, periodic retraining and training set size are discussed in order to obtain a combination of those parameters not only capable of generating positive cumulative returns for each one of the machine learning models but also to demonstrate how simple algorithms traditionally precluded from financial forecasting for trading applications presents similar performances as their more complex counterparts. The paper discusses how a combination of attributes in addition to technical indicators that has been used as inputs of the machine learning-based predictors such as price related features, seasonality features and lagged values used in classical time series analysis are used to enhance the classification capabilities that impacts directly into the final profitability. [ABSTRACT FROM AUTHOR]

Salient object detection (SOD) is a challenging and fundamental research in computer vision and image processing. Since the cost of pixel-level annotations is high, scribble annotations are usually used as weak supervisions. However, scribble annotations are too sparse and always located inside the objects with lacking annotations close to the semantic boundaries, which can't make confident predictions. To alleviate these issues, we propose a novel and effective scribble-based weakly supervised approach for SOD, named complementary characteristics fusion network (CCFNet). To be more specific, we design an edge fusion module (EFM) by taking account of local and high-level semantic information to equip our model, which would be beneficial to enhance the power of aggregating edge information. Then to achieve the complementary role of different features, a series of feature correlation modules (FCMs) are employed to strengthen the localization information and details learning. This is based on low-level, high-level global and edge information, which will complement each other to obtain relatively complete salient regions. Alternatively, to encourage the network to learn structural information and further improve the results of saliency maps in foreground and background, we propose a self-supervised salient detection (SSD) loss. Extensive experiments using five benchmark datasets demonstrate that our proposed approach performs favorably against the state-of-the-art weakly supervised algorithms, and even surpasses the performance of those fully supervised. • We propose an edge fusion module using the local and high-level semantic information. • Feature correlation module is employed to make full use of the complementary different features. • We propose a self-supervised salient detection loss to learn structural information. • The proposed method performs competitive against the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Most of existing salient object detection models are based on fully convolutional network (FCN), which learn multi-scale/level semantic information through convolutional layers to obtain high-quality predicted saliency maps. However, convolution is locally interactive, it is difficult to capture remote dependencies, and FCN-based methods suffer from coarse object boundaries. In this paper, to solve these problems, we propose a novel transformer framework for salient object detection (named TF-SOD), which mainly consists of the encoder part of the FCN, fusion module (FM), transformer module (TM) and feature decoder module (FDM). Specifically, FM is a bridge connecting the encoder and TM and provides some foresight for the non-local interaction of TM. Besides, FDM can efficiently decode the non-local features output by TM and achieve deep fusion with local features. This architecture enables the network to achieve a close integration of local and non-local interactions, making information complementary to each other, deeply mining the associated information between features. Furthermore, we also propose a novel edge reinforcement learning strategy, which can effectively suppress edge blurring from local and global aspects by means of powerful network architecture. Extensive experiments using five datasets demonstrate that the proposed method outperforms 19 state-of-the-art methods [ABSTRACT FROM AUTHOR]

Although methods based on the fully convolutional neural networks (FCNs) have shown strong advantages in the field of salient object detection, the existing methods still have two challenging issues: insufficient multi-level feature fusion ability and boundary blur. To overcome these issues, we propose a novel salient object detection method based on a multi-feature fusion cross network (denoted MFC-Net). Firstly, to overcome the issue of insufficient multi-level feature fusion ability, inspired by the connection mode of human brain neurons, we propose a novel cross network framework, combined with contextual feature transfer modules (CFTMs) to integrate, enhance and transmit multi-level feature information in an iterative manner. Secondly, to address the issue of blurred boundaries, we effectively enhance the edge features of saliency map by a simple edge enhancement strategy. Thirdly, to reduce the loss of information caused by the saliency map generated by multi-level feature fusion, we use feature fusion modules (FFMs) to learn contextual feature information from multiple angles and then output the resulting saliency map. Finally, a hybrid loss function fully supervises the network at the pixel and object level, optimizing the network performance. The proposed MFC-Net has been evaluated using five benchmark datasets. The performance evaluation demonstrates that the proposed method outperforms other state-of-the-art methods, which proves the superiority of MFC-Net approach. [ABSTRACT FROM AUTHOR]

Place recognition is an essential component to address the problem of visual navigation and SLAM. The long-term place recognition is challenging as the environment exhibits significant variations across different times of the days, months, and seasons. In this paper, we view appearance changes as multiple domains and propose a Feature Disentanglement Network (FDNet) based on a convolutional auto-encoder and adversarial learning to extract two independent deep features — content and appearance. In our network, the content feature is learned which only retains the content information of images through the competition with the discriminators and content encoder. Besides, we utilize the triplets loss to make the appearance feature encode the appearance information. The generated content features are directly used to measure the similarity of images without dimensionality reduction operations. We use datasets that contain extreme appearance changes to carry out experiments, which show how meaningful recall at 100% precision can be achieved by our proposed method where existing state-of-art approaches often get worse performance. • A novel Feature Disentanglement Network (FDNet) for vPR. • Appearance and content factors are disentangled through adversarial training. • Content features provide place recognition robust to extreme condition changes. • Disentangled features are appearance-invariant and show discriminative ability. [ABSTRACT FROM AUTHOR]

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). Residual block based deep convolutional neural network (CNN) is proposed for automatic feature extraction and emotion classification with EFDMs. Aim at the shortcomings of the small amount of EEG samples and the challenge of differences in individual emotions, which makes it difficult to construct a universal model, this paper proposes a cross-datasets emotion recognition method of deep model transfer learning. Experiments carried out on two publicly available datasets. The proposed method achieved an average classification score of 90.59% based on a short length of EEG data on SEED, which is 4.51% higher than the baseline method. Then, the pre-trained model was applied to DEAP through deep model transfer learning with a few samples, resulted an average accuracy of 82.84%. Finally, this paper adopts the gradient weighted class activation mapping (Grad-CAM) to get a glimpse of what features the CNN has learned during training from EFDMs and concludes that the high frequency bands are more favorable for emotion recognition. • Proposed a novel concept of EFDMs with STFT based on multiple channel EEG signals. • Constructed four residual blocks based CNN for emotion recognition. • Performed cross-datasets emotion recognition based on deep model transfer learning. • Studied the number of training samples used for cross-datasets emotion recognition. • Obtained the key EEG information automatically based on EFDMs and Grad-CAM. [ABSTRACT FROM AUTHOR]

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. Explicitly, probabilistic knowledge transfer (PKT) is employed to transfer knowledge obtained from the Canny edge detector to the structure encoder. An appearance teacher module is then designed to ensure that the learning of appearance encoder does not only rely on metric learning. The generated content features with structural information are used to measure the similarity of images. We finally evaluate the proposed approach and compare it to state-of-the-art place recognition methods using six datasets with extreme environmental changes. Experimental results demonstrate the effectiveness and improvements achieved using the proposed framework. Source code and some trained models will be available at http://www.tianshu.org.cn.

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. In this paper, to overcome these challenges, a novel multi-modal matching feedback self-tuning model is proposed, which is a novel neural network called Vital Information Matching Feedback Self-tuning Network (VIM-Net). Our VIM-Net network is mainly composed of two matching feedback modules, a visual matching feedback module (V-mat) and a trajectory matching feedback module (T-mat). Specifically, V-mat matches the target information of visual recognition with the entity information extracted by the command; T-mat matches the serialized trajectory feature with the direction of movement of the command. Ablation experiments and comparative experiments are conducted on the proposed model using the Matterport3D simulator and the Room-to-Room (R2R) benchmark datasets, and the final navigation effect is shown in detail. The results prove that the model proposed in this paper is indeed effective on the task., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Jia, Yu, Ru, Yang and Coleman.)

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. Feature selection algorithms are often applied to microbial communities to identify bacterial groups that drive disease. It has been shown that aggregating Ensemble Feature Selection (EFS) can improve the robustness of feature selection algorithms, which is defined as the variation of feature selector output caused by small changes to the dataset. In this work, we apply a two-step filter and an EFS process to generate robust feature subsets that can non-invasively predict IBD subtypes from high-resolution microbiome data. The predictive power of the robust feature subsets is the highest reported in literature to date. Furthermore, we identify five biologically plausible bacterial species that have not previously been implicated in IBD aetiology.

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., Method: This was a cross-sectional study undertaken at five clinics located in metropolitan, regional and remote hospitals in Queensland. Participants were >18 years of age (no upper age limit) with CKD (non-dialysis) who attended CKD nurse-led clinics over a six month period (N = 873). They completed the Nurse Practitioner Patient Satisfaction questionnaire which was modified for CKD., Results: The response rate was 64.3 % (n = 561); half of the respondents were male (55.5 %), there was a median age range of 71-80 years (43.5 %) and most respondents were pensioners or retired (84.2 %). While the majority reported that they were highly satisfied with the quality of care provided by the nurse (83.8 %), we detected differences in some aspects of satisfaction between genders, age groups and familiarity with the nurse. Overall, patients' comments were highly positive with a few improvements to the service being suggested; these related to car-parking, providing more practical support, and having accessible locations., Conclusion: In an era of person-centred care, it is important to measure patient satisfaction using appropriate and standardised questionnaires. Our results highlight that, to improve services, communication strategies should be optimised in nurse-led clinics., (© 2017 European Dialysis and Transplant Nurses Association/European Renal Care Association.)