Behavior-oriented numerical modeling of nearshore oceanic current and application on sea harbor

590-602The West Guangdong longshore current (WG current) is a unique oceanic current system. Plenty of field survey datasets indicated that it flows uni-directionally from north-east to south-west in the entire year even during the south-west monsoon season. At present, the natural formation mechanism of the WG current remains controversial, and the traditional process-oriented modeling method could not deal with the dilemma of the scaling mismatch between the regional ocean circulation (several thousand kilometers) and harbor structure (several hundred meters). To solve this problem, in this paper, a behavior-oriented modeling concept was developed, wherein the contribution of the WG current was considered by incorporating additional net flow flux in the hydrodynamic model to separate it from the tidal currents. Through rigorous validations according to the site observed datasets, the proposed modeling concept was found to have good precision. Using the Jida Harbor as a real-life case, the modeling results showed that after the combination of the tidal current and WG current, the westward cross-flow speed in the approach channel could exceed 0.5 m/s, and at the harbor entrance the WG current induces an intense local circulation cell while ebbing, which may bring in additional maneuver risk to the ships

Distributional Domain-Invariant Preference Matching for Cross-Domain Recommendation

Learning accurate cross-domain preference mappings in the absence of overlapped users/items has presented a persistent challenge in Non-overlapping Cross-domain Recommendation (NOCDR). Despite the efforts made in previous studies to address NOCDR, several limitations still exist. Specifically, 1) while some approaches substitute overlapping users/items with overlapping behaviors, they cannot handle NOCDR scenarios where such auxiliary information is unavailable; 2) often, cross-domain preference mapping is modeled by learning deterministic explicit representation matchings between sampled users in two domains. However, this can be biased due to individual preferences and thus fails to incorporate preference continuity and universality of the general population. In light of this, we assume that despite the scattered nature of user behaviors, there exists a consistent latent preference distribution shared among common people. Modeling such distributions further allows us to capture the continuity in user behaviors within each domain and discover preference invariance across domains. To this end, we propose a Distributional domain-invariant Preference Matching method for non-overlapping Cross-Domain Recommendation (DPMCDR). For each domain, we hierarchically approximate a posterior of domain-level preference distribution with empirical evidence derived from user-item interactions. Next, we aim to build distributional implicit matchings between the domain-level preferences of two domains. This process involves mapping them to a shared latent space and seeking a consensus on domain-invariant preference by minimizing the distance between their distributional representations therein. In this way, we can identify the alignment of two non-overlapping domains if they exhibit similar patterns of domain-invariant preference.Comment: 9 pages, 5 figures, full research paper accepted by ICDM 202

Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities

The vast proliferation of sensor devices and Internet of Things enables the applications of sensor-based activity recognition. However, there exist substantial challenges that could influence the performance of the recognition system in practical scenarios. Recently, as deep learning has demonstrated its effectiveness in many areas, plenty of deep methods have been investigated to address the challenges in activity recognition. In this study, we present a survey of the state-of-the-art deep learning methods for sensor-based human activity recognition. We first introduce the multi-modality of the sensory data and provide information for public datasets that can be used for evaluation in different challenge tasks. We then propose a new taxonomy to structure the deep methods by challenges. Challenges and challenge-related deep methods are summarized and analyzed to form an overview of the current research progress. At the end of this work, we discuss the open issues and provide some insights for future directions

CT-Net: Arbitrary-Shaped Text Detection via Contour Transformer

Contour based scene text detection methods have rapidly developed recently, but still suffer from inaccurate frontend contour initialization, multi-stage error accumulation, or deficient local information aggregation. To tackle these limitations, we propose a novel arbitrary-shaped scene text detection framework named CT-Net by progressive contour regression with contour transformers. Specifically, we first employ a contour initialization module that generates coarse text contours without any post-processing. Then, we adopt contour refinement modules to adaptively refine text contours in an iterative manner, which are beneficial for context information capturing and progressive global contour deformation. Besides, we propose an adaptive training strategy to enable the contour transformers to learn more potential deformation paths, and introduce a re-score mechanism that can effectively suppress false positives. Extensive experiments are conducted on four challenging datasets, which demonstrate the accuracy and efficiency of our CT-Net over state-of-the-art methods. Particularly, CT-Net achieves F-measure of 86.1 at 11.2 frames per second (FPS) and F-measure of 87.8 at 10.1 FPS for CTW1500 and Total-Text datasets, respectively.Comment: This paper has been accepted by IEEE Transactions on Circuits and Systems for Video Technolog

Real-world outcomes of long-term prednisone and deflazacort use in patients with Duchenne muscular dystrophy: experience at a single, large care center

Aim: To assess outcomes among patients with Duchenne muscular dystrophy receiving deflazacort or prednisone in real-world practice. Methods: Clinical data for 435 boys with Duchenne muscular dystrophy from Cincinnati Children\u27s Hospital Medical Center were studied retrospectively using time-to-event and regression analyses. Results: Median ages at loss of ambulation were 15.6 and 13.5 years among deflazacort- and prednisone-initiated patients, respectively. Deflazacort was also associated with a lower risk of scoliosis and better ambulatory function, greater % lean body mass, shorter stature and lower weight, after adjusting for age and steroid duration. No differences were observed in whole body bone mineral density or left ventricular ejection fraction. Conclusion: This single center study adds to the real-world evidence associating deflazacort with improved clinical outcomes

Impact of neighbourhood-scale climate characteristics on building heating demand and night ventilation cooling potential

As buildings are main contributor to greenhouse gas emissions, it is important to assess the performance of existing buildings and assist the design of new sustainable buildings through building energy simulation. It is well known that by using local climate measurements for building energy simulation would provide more accurate result than by using other typical weather data, i.e. typical meteorological year (TMY). However, as different built forms/architectural layouts would also have impacts on neighbourhood-scale microclimate, it is worthy to quantify the difference it would make. In this study, we performed a year-long measurement with four weather stations surrounding a campus building in 2009 and 2010. Each station was placed in a typical type of built form, including a street canyon, a courtyard, a semi-closed courtyard and a relatively larger open area. Besides, two typical weather data files, typical meteorological year (TMY) and actual meteorological year (AMY) were taken as reference. Annual heating demand and natural ventilation cooling potential were calculated based on all 6 weather files. Our simulation results show that the variation in annual heating demand of different built forms could be between 1.1 - 7.3%, where the large open area has the highest heating demand and it of the courtyard is the lowest. The difference between on-site measurement and TMY in annual heating load is as high as 10.8%. While in summer, night ventilation cooling potential of the courtyard and the semi-closed form are the highest, and it of the street canyon is the lowest. Using TMY could underestimate the night ventilation cooling potential by 26 – 31% and using AMY could overestimate it by 9 – 14% in total. Overall speaking, the courtyard form shows good performance in reducing heating demand and enhancing night ventilation cooling, while the street canyon shows relatively poor performance in both aspects. These findings highlight the importance to understand the impact of neighbourhood-scale microclimate on building energy performance