Search

Acceleration of urbanisation is posing great challenges to sustainable development. Growing accessibility to big data and artificial intelligence (AI) technologies have revolutionised many fields and offered great potential for addressing pressing urban problems. However, using these technologies without explicitly considering responsibilities would bring new societal and environmental issues. To maximise the benefits of big data and AI while minimising potential issues, we envisage a conceptual framework of Responsible Urban Intelligence (RUI) and advocate an agenda for action. We first define RUI as consisting of three major components including urban problems, enabling technologies, and responsibilities; then introduce transparency, fairness, and eco-friendliness as the three dimensions of responsibilities which naturally link with the human, space, and time dimensions of cities; and further develop a four-stage implementation framework for responsibilities as consisting of solution design, data preparation, model building, and practical application; and finally present a research agenda for RUI addressing challenging issues including data and model transparency, tension between performance and fairness, and solving urban problems in an eco-friendly manner.

Brand logo detection is a special aspect of machine vision. However, Video logo detection benchmarks are scarce in the public domain. We exploit the power of a deep convolutional neural network (DCNN) and leverage established datasets related to existing applications to develop a deep-transfer active-learning (DTAL) algorithm to select the most valuable samples so that the smallest number possible needs to be labeled to achieve maximum performance improvements for video object detection model training. By exploiting the possible shared deep feature space between static and video datasets through transfer learning based on highly adaptable DCNN features, DTAL implements diversity-based active learning to select the most informative samples from a sequence of similar image frames for video object detection. We successfully apply the new DTAL algorithm to implement active learning for logo detection from live streaming sports videos as well as pedestrian and face detection from video data. We show that DTAL is a better active-learning method than state-of-the-art deep-learning-based active-learning object detection techniques. We also contribute one of the largest video-based logo resources, the Sports Match Video Logo (SMVL) dataset, to facilitate general logo detection research using transfer- and active-learning applications for video object detection. [ABSTRACT FROM AUTHOR]

Brain oedema is a common pathological phenomenon following many diseases and may lead to severe secondary damage. Astrocytes are the most numerous cells in the brain. Five aquaporins (AQPs) have been found in mature astrocytes, which play crucial roles in water transportation. However, most studies have focused on AQP4 or AQP9 and whether another aquaporin such as AQP5 involved in brain oedema is unclear. Here, we addressed the issue that the expression pattern of AQP5 in rat astrocytes in vitro was altered in the hypotonic condition through some mitogen‐activated protein kinases (MAPK) pathways. Primary astrocytes were randomly divided into the control group and the hypotonic group. Cell viability was evaluated by MTT test. Immunofluorescence, Western blotting and real‐time PCR were used to detect the expression of AQP5. Western blotting was used to detect the variation of MAPK pathway. The present study demonstrated that incubation of astrocytes in the hypotonic medium produced an increase inAQP5 expression, and AQP5 peaked at 6–12 h after hypotension solution exposure. In addition, MAPK pathways were set in motion under hypotension, but not all branches. Only the p38 inhibitor can inhibit AQP5 expression in cultured astrocytes. AQP5 is directly related to the extracellular hypotonic stimuli in astrocytes, which could be regulated through the p38 MAPK pathway. [ABSTRACT FROM AUTHOR]

Image clustering is a very useful technique that is widely applied to various areas, including remote sensing. Recently, visual representations by self-supervised learning have greatly improved the performance of image clustering. To further improve the well-trained clustering models, this paper proposes a novel method by first ranking samples within each cluster based on the confidence in their belonging to the current cluster and then using the ranking to formulate a weighted cross-entropy loss to train the model. For ranking the samples, we developed a method for computing the likelihood of samples belonging to the current clusters based on whether they are situated in densely populated neighborhoods, while for training the model, we give a strategy for weighting the ranked samples. We present extensive experimental results that demonstrate that the new technique can be used to improve the state-of-the-art image clustering models, achieving accuracy performance gains ranging from 2.1 % to 15.9 % . Performing our method on a variety of datasets from remote sensing, we show that our method can be effectively applied to remote sensing images. [ABSTRACT FROM AUTHOR]

In supervised deep learning, learning good representations for remote-sensing images (RSI) relies on manual annotations. However, in the area of remote sensing, it is hard to obtain huge amounts of labeled data. Recently, self-supervised learning shows its outstanding capability to learn representations of images, especially the methods of instance discrimination. Comparing methods of instance discrimination, clustering-based methods not only view the transformations of the same image as "positive" samples but also similar images. In this paper, we propose a new clustering-based method for representation learning. We first introduce a quantity to measure representations' discriminativeness and from which we show that even distribution requires the most discriminative representations. This provides a theoretical insight into why evenly distributing the images works well. We notice that only the even distributions that preserve representations' neighborhood relations are desirable. Therefore, we develop an algorithm that translates the outputs of a neural network to achieve the goal of evenly distributing the samples while preserving outputs' neighborhood relations. Extensive experiments have demonstrated that our method can learn representations that are as good as or better than the state of the art approaches, and that our method performs computationally efficiently and robustly on various RSI datasets. [ABSTRACT FROM AUTHOR]

Facial attributes in StyleGAN generated images are entangled in the latent space which makes it very difficult to independently control a specific attribute without affecting the others. Supervised attribute editing requires annotated training data which is difficult to obtain and limits the editable attributes to those with labels. Therefore, unsupervised attribute editing in an disentangled latent space is key to performing neat and versatile semantic face editing. In this paper, we present a new technique termed Structure-Texture Independent Architecture with Weight Decomposition and Orthogonal Regularization (STIA-WO) to disentangle the latent space for unsupervised semantic face editing. By applying STIA-WO to GAN, we have developed a StyleGAN termed STGAN-WO which performs weight decomposition through utilizing the style vector to construct a fully controllable weight matrix to regulate image synthesis, and employs orthogonal regularization to ensure each entry of the style vector only controls one independent feature matrix. To further disentangle the facial attributes, STGAN-WO introduces a structure-texture independent architecture which utilizes two independently and identically distributed (i.i.d.) latent vectors to control the synthesis of the texture and structure components in a disentangled way. Unsupervised semantic editing is achieved by moving the latent code in the coarse layers along its orthogonal directions to change texture related attributes or changing the latent code in the fine layers to manipulate structure related ones. We present experimental results which show that our new STGAN-WO can achieve better attribute editing than state of the art methods. [ABSTRACT FROM AUTHOR]

In order to compare the effects of iopromide and isoxazole on postoperative contrast-induced nephropathy in patients with renal insufficiency, the paper searches for randomized controlled trials and retrospective cohort studies comparing the effects of iopromide and iodixanol on renal function in patients with renal insufficiency after surgery. The data are extracted from eligible studies. We tried to assess the incidence of contrast-agent nephropathy, preoperative and postoperative serum creatinine indicators, and mortality. This paper includes 8 studies with a total of 1243 patients. The incidence of contrast-induced nephropathy in the iopromide group is higher than that in the iodixanol group, and there is no significant difference between the two groups in postoperative mortality and preoperative serum creatinine expression. Sensitivity analysis and funnel chart show that our research is robust and has low publication bias. Our research shows that in patients with renal insufficiency, the incidence of contrast-medium nephropathy in the iopromide group is higher than that in the iodixanol group. Iodixanol is safer and has less effect on patients' serum creatinine levels. [ABSTRACT FROM AUTHOR]