All You Need Is Boundary: Toward Arbitrary-Shaped Text Spotting

Recently, end-to-end text spotting that aims to detect and recognize text from cluttered images simultaneously has received particularly growing interest in computer vision. Different from the existing approaches that formulate text detection as bounding box extraction or instance segmentation, we localize a set of points on the boundary of each text instance. With the representation of such boundary points, we establish a simple yet effective scheme for end-to-end text spotting, which can read the text of arbitrary shapes. Experiments on three challenging datasets, including ICDAR2015, TotalText and COCO-Text demonstrate that the proposed method consistently surpasses the state-of-the-art in both scene text detection and end-to-end text recognition tasks.Comment: Accepted to AAAI202

Use of fiber materials to improve the durability of road structural concrete

Durability is an important property of concrete, and in road projects in cold regions of Belarus, often freeze-thaw cycles and salt erosion and other multiple factors act together to cause concrete damage. This paper focuses on the effect of using mineral admixtures and fibrous materials on improving the frost resistance of concrete

GANet: Goal Area Network for Motion Forecasting

Predicting the future motion of road participants is crucial for autonomous driving but is extremely challenging due to staggering motion uncertainty. Recently, most motion forecasting methods resort to the goal-based strategy, i.e., predicting endpoints of motion trajectories as conditions to regress the entire trajectories, so that the search space of solution can be reduced. However, accurate goal coordinates are hard to predict and evaluate. In addition, the point representation of the destination limits the utilization of a rich road context, leading to inaccurate prediction results in many cases. Goal area, i.e., the possible destination area, rather than goal coordinate, could provide a more soft constraint for searching potential trajectories by involving more tolerance and guidance. In view of this, we propose a new goal area-based framework, named Goal Area Network (GANet), for motion forecasting, which models goal areas rather than exact goal coordinates as preconditions for trajectory prediction, performing more robustly and accurately. Specifically, we propose a GoICrop (Goal Area of Interest) operator to effectively extract semantic lane features in goal areas and model actors' future interactions, which benefits a lot for future trajectory estimations. GANet ranks the 1st on the leaderboard of Argoverse Challenge among all public literature (till the paper submission), and its source codes will be released

A combined central and local voltage control strategy of soft open points in active distribution networks

With the increasing penetration of distributed generation (DG), the risk of voltage violations in active distribution networks (ADNs) has become a major concern for the system operator. Soft open point (SOP) is a flexible power electronic device which can realize accurate active and reactive power flow control. This paper proposes a combined central and local operation strategy of SOPs to realize voltage control in ADNs. The active power of SOPs is centrally adjusted based on the information and forecasting throughout the network, which aims to maintain the voltage within the limits in the global optimization. And the local control of reactive power based on real-time measurements can rapidly respond to the frequent voltage violations caused by the fluctuations of DG outputs. The potential benefits of SOPs are fully explored to reduce power losses and improve voltage profile of ADNs. By applying convex relaxation, the original mixed-integer nonlinear programming (MINLP) model is converted into an effectively solved mixed-integer second-order cone programming (MISOCP) model. Case studies on the PG&E 69-node distribution system are conducted to verify the effectiveness of the proposed method

A new opportunity for the emerging tellurium semiconductor: making resistive switching devices

Abstract: The development of the resistive switching cross-point array as the next-generation platform for high-density storage, in-memory computing and neuromorphic computing heavily relies on the improvement of the two component devices, volatile selector and nonvolatile memory, which have distinct operating current requirements. The perennial current-volatility dilemma that has been widely faced in various device implementations remains a major bottleneck. Here, we show that the device based on electrochemically active, low-thermal conductivity and low-melting temperature semiconducting tellurium filament can solve this dilemma, being able to function as either selector or memory in respective desired current ranges. Furthermore, we demonstrate one-selector-one-resistor behavior in a tandem of two identical Te-based devices, indicating the potential of Te-based device as a universal array building block. These nonconventional phenomena can be understood from a combination of unique electrical-thermal properties in Te. Preliminary device optimization efforts also indicate large and unique design space for Te-based resistive switching devices

The role of school organizational conditions in teacher psychological resilience and stress during COVID-19 pandemic: A moderated mediation model

Educational revisions facilitate the relief of teacher stress by means of enhancing school organizational conditions. However, limited research has explored the effects of school organizational conditions on teacher stress in China. Using a sample of 734 primary and secondary school teachers from 30 provinces or municipalities of China, this study examined the effects of school organizational conditions on teacher stress in China, with a particular focus on the mediating role of psychological resilience and moderating role of perceived COVID-19 crisis strength. The results demonstrated that school organizational conditions were negatively associated with teacher stress. Furthermore, psychological resilience partially mediated the relation between school organizational conditions and teacher stress. In addition, perceived COVID-19 crisis strength significantly moderated the direct and indirect relations between school organizational conditions and teacher stress. The relations between school organizational conditions and teacher stress and between school organizational conditions and psychological resilience were stronger for teachers who perceived low levels of COVID-19 crisis strength. However, the indirect relation between psychological resilience and stress was stronger for teachers who perceived high levels of COVID-19 crisis strength. Implications have been provided accordingly