A sufficient condition for a finite-time L2L_2 singularity of the 3d Euler Equations

A sufficient condition is derived for a finite-time $L_2$ singularity of the 3d incompressible Euler equations, making appropriate assumptions on eigenvalues of the Hessian of pressure. Under this condition $\ \lim_{ t \uparrow T_*} \sup \|\frac{ D \omega} { Dt}\|_{L_2(\Omega)} = \infty$, where $\Omega \subset \mathbb{R}$ moves with the fluid. In particular, $| \omega |$, $| \S_{ij} |$, and $| \P_{ij} |$ all become unbounded at one point $(x_1, T_1)$, $T_1$ being the first blow-up time in $L_2$

Barotropic Shelf Circulation Forced by an Isolated Oceanic Disturbance.

A \u27slowly varying\u27 and \u27isolated\u27 oceanic disturbance may locally drive the shelf circulation. This situation is analytically studied using a linear, steady-state, barotropic model. The solution has a dipolar structure over the shelf. This is consistent with an integral theorem of zero net relative angular momentum on the f-plane with a sloping topography, derived herein. It is found that the forced circulation patterns are controlled by the alongshore scale of the disturbance, magnitude of bottom stress, and geometry of the shelf. In particular, by generating significant relative vorticity due to the ageostrophic motion, the friction strongly influences the center position, the strength, and the size of the forced shelf motion. When large alongshore topographic variations are present, the combined effect of the friction and shelf curvature results in an asymmetry of the pressure field, with an intensified motion inshore

EAST: An Efficient and Accurate Scene Text Detector

Previous approaches for scene text detection have already achieved promising performances across various benchmarks. However, they usually fall short when dealing with challenging scenarios, even when equipped with deep neural network models, because the overall performance is determined by the interplay of multiple stages and components in the pipelines. In this work, we propose a simple yet powerful pipeline that yields fast and accurate text detection in natural scenes. The pipeline directly predicts words or text lines of arbitrary orientations and quadrilateral shapes in full images, eliminating unnecessary intermediate steps (e.g., candidate aggregation and word partitioning), with a single neural network. The simplicity of our pipeline allows concentrating efforts on designing loss functions and neural network architecture. Experiments on standard datasets including ICDAR 2015, COCO-Text and MSRA-TD500 demonstrate that the proposed algorithm significantly outperforms state-of-the-art methods in terms of both accuracy and efficiency. On the ICDAR 2015 dataset, the proposed algorithm achieves an F-score of 0.7820 at 13.2fps at 720p resolution.Comment: Accepted to CVPR 2017, fix equation (3

Vehicle Speed Aware Computing Task Offloading and Resource Allocation Based on Multi-Agent Reinforcement Learning in a Vehicular Edge Computing Network

For in-vehicle application, the vehicles with different speeds have different delay requirements. However, vehicle speeds have not been extensively explored, which may cause mismatching between vehicle speed and its allocated computation and wireless resource. In this paper, we propose a vehicle speed aware task offloading and resource allocation strategy, to decrease the energy cost of executing tasks without exceeding the delay constraint. First, we establish the vehicle speed aware delay constraint model based on different speeds and task types. Then, the delay and energy cost of task execution in VEC server and local terminal are calculated. Next, we formulate a joint optimization of task offloading and resource allocation to minimize vehicles' energy cost subject to delay constraints. MADDPG method is employed to obtain offloading and resource allocation strategy. Simulation results show that our algorithm can achieve superior performance on energy cost and task completion delay.Comment: 8 pages, 6 figures, Accepted by IEEE International Conference on Edge Computing 202

Document-Level Multi-Event Extraction with Event Proxy Nodes and Hausdorff Distance Minimization

Document-level multi-event extraction aims to extract the structural information from a given document automatically. Most recent approaches usually involve two steps: (1) modeling entity interactions; (2) decoding entity interactions into events. However, such approaches ignore a global view of inter-dependency of multiple events. Moreover, an event is decoded by iteratively merging its related entities as arguments, which might suffer from error propagation and is computationally inefficient. In this paper, we propose an alternative approach for document-level multi-event extraction with event proxy nodes and Hausdorff distance minimization. The event proxy nodes, representing pseudo-events, are able to build connections with other event proxy nodes, essentially capturing global information. The Hausdorff distance makes it possible to compare the similarity between the set of predicted events and the set of ground-truth events. By directly minimizing Hausdorff distance, the model is trained towards the global optimum directly, which improves performance and reduces training time. Experimental results show that our model outperforms previous state-of-the-art method in F1-score on two datasets with only a fraction of training time