Reinforcement Learning for Robot Navigation with Adaptive Forward Simulation Time (AFST) in a Semi-Markov Model

Deep reinforcement learning (DRL) algorithms have proven effective in robot navigation, especially in unknown environments, by directly mapping perception inputs into robot control commands. However, most existing methods ignore the local minimum problem in navigation and thereby cannot handle complex unknown environments. In this paper, we propose the first DRL-based navigation method modeled by a semi-Markov decision process (SMDP) with continuous action space, named Adaptive Forward Simulation Time (AFST), to overcome this problem. Specifically, we reduce the dimensions of the action space and improve the distributed proximal policy optimization (DPPO) algorithm for the specified SMDP problem by modifying its GAE to better estimate the policy gradient in SMDPs. Experiments in various unknown environments demonstrate the effectiveness of AFST

The effects of a treatment combination of anti-VEGF injections, laser coagulation and cryotherapy on patients with type 3 Coat’s disease

Abstract Background To examined the curative effect of vitreous injection with ranibizumab,laser coagulation and cryotherapy in treating stage 3 Coats’ disease with exudative retinal detachment. Methods Seventeen patients with stage 3 Coats’ disease were enrolled in the study. All eyes were treated with vitreous injection of ranibizumab as initial treatment, and subsequent treatment depended on the absorption of subretinal fluid, Including cryotherapy and laser photocoagulation. Repeat treatment for the two treatment intervals occurred in ≥1 month. The mean follow-up time was 24.12 ± 5.99 months. The main data evaluation and outcome measurements included the patient’s vision, intraocular pressure(IOP), optical coherence tomography (OCT), slit lamp examination, indirect ophthalmoscopy, color Doppler imaging (CDI) and color fundus image analysis. The following variables were compared between groups: abnormal vascular changes, subretinal fluid and exudate absorption, retinal reattachment and complications. The final follow-up results were used to determine the effectiveness of treatment. Results Of the 17 patients included, 88.24% were male and 11.76% were female. Visual acuity was less than 0.02 in 12 eyes before surgery and 8 eyes after surgery. Visual acuity improved in 7 eyes, accounting for 41.18% of cases, and remained unchanged in 7 eyes, accounting for 41.18% of cases. Three patients were too young to undergo the operation, accounting for 17.65% of cases. The best vision was 0.1. Patients were treated 1 to 5 times for an average of 2.82 ± 0.95 times each. There was no statistically significant difference (t = 1.580, p = 0.135) between the preoperative and postoperative intraocular pressures. However, there was a statistically significant difference between the preoperative and postoperative retinal detachment height (2- related samples Wilcoxon signed rank test with z = 3.517, p = 0.000). The results further showed that all patients had different degrees of subretinal fluid absorption, and some of the new blood vessels subsided. All patients were successfully treated with laser and cryosurgery. No ocular or systemic complications were observed during follow-up. Conclusions Intravitreal ranibizumab (IVR), laser coagulation and cryotherapy were effective in the treatment of Coats’ disease with exudative retinal detachment. Trial registration number We retrospectively registered our study, The trial registration number (TRN) is ChiCTR-ONC-17011161 and date of registration is April 16, 2017

Universal ocular screening of 481 infants using wide-field digital imaging system

Abstract Background Universal ocular screening of infants is not a standard procedure in children’s health care system in China. This pilot study investigated prevalence of ocular abnormalities of 6 weeks-age infants using wide-field digital imaging system. Methods Infants aged 6 weeks around were consecutively enrolled in a public hospital between April 2015 and August 2016. All the infants who were enrolled in the study underwent vision assessment, eye position examination, external eye check, pupillary light reflex, red reflex examination, anterior and posterior ocular segments were examined using flashlight, ophthalmoscope, and wide-field digital imaging system. Results A total of 481 infants at 45.1 ± 6.1 days after birth were enrolled in the study. 198 infants had abnormal findings (41.2%). Retinal white spots and retinal white areas were the most common findings (42.9% of abnormalities and 17.7% of all infants screened). The second major finding was retinal hemorrhage (16.2% of abnormalities and 6.7% of all infants screened). Other abnormal findings include retinal pigmentation, concomitant exotropia, neonatal dacryocystitis, retinopathy of prematurity, ‘albinism-like fundus’, congenital nasolacrimal duct obstruction, familial exudative vitreoretinopathy, immature retina, corneal dermoid tumor, large physiologic cupping of optic disc, congenital persistent pupillary membrane, entropion trichiasis, subconjunctival hemorrhage, congenital cataract, vitreous hemorrhage, ptosis and choroidal nevus. Intervention of any form was required in 22 infants, which accounted for 11.1% of abnormalities detected and 4.6% of all infants screened. Conclusion Universal ocular screening is not only necessary for preterm infants but also for full-term infants. Addition of red reflex examination with wide-field digital imaging system can enhance the sensitivity of screening for ocular fundus abnormities. Further study with a long-term follow-up is needed in the future

Speculative text mining for document-level sentiment classification

Many existing solutions perform document-level sentiment classification based on local document only, ignoring other texts that might contribute to better classification accuracy. In this paper, we propose a novel speculative sentiment classification model named SSC. In SSC, we speculate that users with similar rating behaviours are more likely to write documents of similar sentiments toward a product. The motivation of SSC, therefore, is to exploit those speculative similar documents for improving classification accuracy. The proposed SSC model consists of three main components, namely, user-product interaction (UPI) component, document encoding (DE) component, and speculative similar document (SSD) component. The UPI component models user-product interactions, and encodes user/product ratings behaviours into user/product embeddings. The DE component utilizes learned user/product embeddings to capture the informative word vectors for comprising more accurate document representations. The SSD component aggregates documents written by similar users toward the same product for speculative sentiment classification. Because the user similarities are calculated based on user embeddings that encode user rating behaviours, the aggregated documents are more likely to have similar sentiments. The three components are seamlessly integrated into a unified model. In the unified manner, these three components are jointly optimized, and they mutually complement each other to enhance sentiment classification. We conduct extensive experiments on three public datasets, and demonstrate the advantage of the proposed SSC model over state-of-the-art baselines

Passivity-Based Design of Passive Damping for LCL-Type Grid-Connected Inverters to Achieve Full-Frequency Passive Output Admittance

The frequency-domain passivity theory suggests that incorporating passive output admittance in grid-connected inverters (GCIs) can effectively mitigate system instability issues. This theory has been widely recognized and applied in design guidelines for active damping (AD) and passive damping (PD) methods tailored explicitly for LCL-Type GCIs. However, AD methods have a limitation in that they can only reshape the output admittance of GCIs to be passive (with a nonnegative real part) up to the Nyquist frequency. This limitation arises from the inherent characteristics of the digital control system. Similarly, existing passivity-based PD design cases primarily focus on addressing instability issues that occur below the Nyquist frequency. To overcome this limitation and achieve full-frequency passive output admittance, this article introduces a resistor-capacitor (RC) branch-based PD scheme. It provides a comprehensive design guideline that can be applied to different scenarios involving inverter-side or grid-side current control, regardless of whether AD methods are utilized. By incorporating this RC-PD scheme, the designed GCIs achieve full-frequency passive output admittance, effectively preventing potential harmonic oscillations both below and above the Nyquist frequency. Finally, comparative experimental results for different cases are demonstrated to verify the effectiveness and superiority of the proposed approach.</p

Image-Based Crack Detection Method for FPSO Module Support

Floating Production Storage and Offloading (FPSO) is essential offshore equipment for developing offshore oil and gas. Due to the complex sea conditions, FPSOs will be subjected to long-term alternate loads under some circumstances. Thus, it is inevitable that small cracks occur in the upper part of the module pier. Those cracks may influence the structure’s safety evaluation. Therefore, this paper proposes a method for the FPSO module to support crack identification based on the PSPNet model. The main idea is to introduce an attention mechanism into the model with Mobilenetv2 as the backbone of the PSPNet, which can fuse multiple feature maps and increase context information. The detail feature loss caused by multiple convolutions and compressions in the original model was solved by applying the proposed method. Moreover, the attention mechanism is introduced to enhance the extraction of adequate information and suppress invalid information. The mPA value and MIoU value of the improved model increased by 2.4% and 1.8%, respectively, through verification on FPSO datasets

Hierarchical text interaction for rating prediction

Traditional recommender systems encounter several challenges such as data sparsity and unexplained recommendation. To address these challenges, many works propose to exploit semantic information from review data. However, these methods have two major limitations in terms of the way to model textual features and capture textual interaction. For textual modeling, they simply concatenate all the reviews of a user/item into a single review. However, feature extraction at word/phrase level can violate the meaning of the original reviews. As for textual interaction, they defer the interactions to the prediction layer, making them fail to capture complex correlations between users and items. To address those limitations, we propose a novel Hierarchical Text Interaction model (HTI) for rating prediction. In HTI, we propose to model low-level word semantics and high-level review representations hierarchically. The hierarchy allows us to exploit textual features at different granularities. To further capture complex user–item interactions, we propose to exploit semantic correlations between each user–item pair at different hierarchies. At word level, we propose an attention mechanism specialized to each user–item pair, and capture the important words for representing each review. At review level, we mutually propagate textual features between the user and item, and capture the informative reviews. The aggregated review representations are integrated into a collaborative filtering framework for rating prediction. Experiments on five real-world datasets demonstrate that HTI outperforms state-of-the-art models by a large margin. Further case studies provide a deep insight into HTI's ability to capture semantic correlations at different levels of granularities for rating prediction