Artificial intelligence for visually impaired

The eyes are an essential tool for human observation and perception of the world, helping people to perform their tasks. Visual impairment causes many inconveniences in the lives of visually impaired people. Therefore, it is necessary to focus on the needs of the visually impaired community. Researchers work from different angles to help visually impaired people live normal lives. The advent of the digital age has profoundly changed the lives of the visually impaired community, making life more convenient. Deep learning, as a promising technology, is also expected to improve the lives of visually impaired people. It is increasingly being used in the diagnosis of eye diseases and the development of visual aids. The earlier accurate diagnosis of the eye disease by the doctor, the sooner the patient can receive the appropriate treatment and the better chances of a cure. This paper summarises recent research on the development of artificial intelligence-based eye disease diagnosis and visual aids. The research is divided according to the purpose of the study into deep learning methods applied in diagnosing eye diseases and smart devices to help visually impaired people in their daily lives. Finally, a summary is given of the directions in which artificial intelligence may be able to assist the visually impaired in the future. In addition, this overview provides some knowledge about deep learning for beginners. We hope this paper will inspire future work on the subjects

Predictions of corporate bond excess returns

In this paper, we investigate the predictability of corporate bond excess returns using a comprehensive data sample for the period from January 1973 to December 2010. We find that corporate bond returns are more predictable than stock returns, and the predictability tends to be higher for low-grade bonds and short-maturity bonds. A forward rate factor captures substantial variations in expected bond excess returns. Furthermore, liquidity factors and a bond's credit spread have predictive power on corporate bond excess returns. Combining these variables with traditional predictors significantly improves the performance of the predictive model for corporate bond returns

Applicable artificial intelligence for brain disease: A survey

Brain diseases threaten hundreds of thousands of people over the world. Medical imaging techniques such as MRI and CT are employed for various brain disease studies. As artificial intelligence succeeded in image analysis, scientists employed artificial intelligence, especially deep learning technologies, to assist brain disease studies. The AI applications for brain disease studies can be divided into two categories. The first category is preprocessing, including denoising, registration, skull-stripping, intensity normalization, and data augmentation. The second category is the clinical application that contains lesion segmentation, disease detection, grade classification, and outcome prediction. In this survey, we reviewed over one hundred representative papers on how to apply AI to brain disease studies. We first introduced AI-based preprocessing for brain disease studies. Second, we reviewed the influential works of AI-based brain disease studies. At last, we also discussed three development trends in the future. We hope this survey will inspire both expert-level researchers and entry-level beginners

Fracture analysis of mode iii problems by the Trefftz boundary element approach

"Trefftz approach can be referred to the boundary-type solution procedure employing the regular T-complete function satisfying the governing equation. Since Trefftz [1] in 1926 applied his approach that was forced to satisfy the boundary condition to achieve the outcomes, numerous papers, concerning fundamentals, applications and analysis, have emerged in the literature. For the Trefftz boundary element method, Cheung et al [2] developed a direct formulation for solving two-dimensional potential problem. Kita et al [3] studied the same problem by the direct formulation and domain decomposition approach. Portela and Charafi [4] applied Trefftz Boundary element formulation to potential problems with thin internal or edge cavities. Sladek et al [5] presented a global and local Trefftz bpundary integral approach to solve Helmholtz equation. Domingues et al [6] extended the Trefftz boundary element approach to the analysis of linear elastic fracture mechanics. Most of the developments in the field can also be found in [7]."--p. 321

Extended reality for chronic pain relief

© 2019 Copyright held by the owner/author(s). Chronic pain is ongoing pain lasting for long periods of time after the initial injury or disease has healed. Chronic pain is difficult to treat and can affect the daily lives of patients. Distraction therapy is a proven way of relieving pain by redirecting the focus of patients' attention. Virtual reality is an effective platform for distraction therapy as it immerses the user visually, aurally, and even somewhat physically in a virtual world detached from reality. There is little research done on the effects that physical interactions have on pain management. This project aims to evaluate different types of extended reality (XR) interactions, including full body movement, for chronic pain patients to determine which is the best for pain relief. We are building a prototype for participants to interact both mentally and physically and measuring the reduction in subjective pain ratings at various points of the XR experience

Price discovery and persistent arbitrage violations in credit markets

This paper investigates price violations in credit markets using a data sample spanning from 2002 to 2016. We find that price violations are highly persistent during the crisis period, particularly for speculative-grade bonds. There is evidence that price distortions and market disintegration are linked to market-wide and firm-level impediments to arbitrage and limited capital provision. Higher firm-level impediments to arbitrage lead to less market integration, and more severe and persistent pricing discrepancies. Moreover, we find that the negative CDS basis persists in the postcrisis period, which is attributable to dealers’ lower capital commitment and deterioration in market-making quality

Mode III fracture analysis by Trefftz boundary element method

This paper presents a hybrid Trefftz (HT) boundary element method (BEM) by using two indirect techniques for mode III fracture problems. Two Trefftz complete functions of Laplace equation for normal elements and a special purpose Trefftz function for crack elements are proposed in deriving the Galerkin and the collocation techniques of HT BEM. Then two auxiliary functions are introduced to improve the accuracy of the displacement field near the crack tips, and stress intensity factor (SIF) is evaluated by local crack elements as well. Furthermore, numerical examples are given, including comparisons of the present results with the analytical solution and the other numerical methods, to demonstrate the efficiency for different boundary conditions and to illustrate the convergence influenced by several parameters. It shows that HT BEM by using the Galerkin and the collocation techniques is effective for mode III fracture problems

False signal injection attack detection of cyber physical systems by event-triggered distributed filtering over sensor networks

This paper is concerned with false signal injection attack detection mechanism using a novel distributed event-triggered filtering for cyber physical systems over sensor networks. By the Internet of Things, the classic physical systems are transformed to the networked cyber physical systems, which are built with a large number of distributed networked sensors. In order to save the precious network resources, a novel distributed event-triggered strategy is proposed. Under this strategy, to generate the localized residual signals, the event-triggered distributed fault detection filters are proposed. By Lyapunov- Krasovskii functional theory, the distributed fault detection filtering problem can be formulated as stability and an H∞H∞ performance of the residual system. Furthermore, a sufficient condition is derived such that the resultant residual system is stable while the transmission of the sampled data is reduced. Based on this condition, the codesign method of the fault detection filters and the transmission strategy is proposed. An illustrative example is given to show the effectiveness of the proposed method

ConvNet-CA: A Lightweight Attention-Based CNN for Brain Disease Detection

Attention-based convolutional networks have attracted great interest in recent years and achieved great success in improving representation capability of networks. However, most attention mechanisms are complicated and implemented by introducing a large number of extra parameters. In this study, we proposed a lightweight attention-based convolutional network (ConvNet-CA) that has a low computation complexity yet a high performance for brain disease detection. ConvNet-CA weights the importance of different channels in features maps and pays more attention to important channels by introducing an efficient channel attention mechanism. We evaluated ConvNet-CA on a publicly accessible benchmark dataset: Whole Brain Atlas. The brain diseases involved in this study are stroke, neoplastic disease, degenerative disease, and infectious disease. The experimental results showed that ConvNet-CA achieved highly competitive performance over state-of-the-art methods on distinguishing different types of brain diseases, with an overall multi-class classification accuracy of 94.88 ± 3.64%.</p

Price discovery and persistent arbitrage violations in credit markets

This paper investigates price violations in credit markets using a data sample spanning from 2002 to 2016. We find that price violations are highly persistent during the crisis period, particularly for speculative-grade bonds. There is evidence that price distortions and market disintegration are linked to market-wide and firm-level impediments to arbitrage and limited capital provision. Higher firm-level impediments to arbitrage lead to less market integration, and more severe and persistent pricing discrepancies. Moreover, we find that the negative CDS basis persists in the postcrisis period, which is attributable to dealers’ lower capital commitment and deterioration in market-making quality