Stability analysis of the Eulerian-Lagrangian finite volume methods for nonlinear hyperbolic equations in one space dimension

In this paper, we construct a novel Eulerian-Lagrangian finite volume (ELFV) method for nonlinear scalar hyperbolic equations in one space dimension. It is well known that the exact solutions to such problems may contain shocks though the initial conditions are smooth, and direct numerical methods may suffer from restricted time step sizes. To relieve the restriction, we propose an ELFV method, where the space-time domain was separated by the partition lines originated from the cell interfaces whose slopes are obtained following the Rakine-Hugoniot junmp condition. Unfortunately, to avoid the intersection of the partition lines, the time step sizes are still limited. To fix this gap, we detect effective troubled cells (ETCs) and carefully design the influence region of each ETC, within which the partitioned space-time regions are merged together to form a new one. Then with the new partition of the space-time domain, we theoretically prove that the proposed first-order scheme with Euler forward time discretization is total-variation-diminishing and maximum-principle-preserving with {at least twice} larger time step constraints than the classical first order Eulerian method for Burgers' equation. Numerical experiments verify the optimality of the designed time step sizes.Comment: Submitted to Mathematics of Computatio

Efficient Deep Reinforcement Learning via Adaptive Policy Transfer

Transfer Learning (TL) has shown great potential to accelerate Reinforcement Learning (RL) by leveraging prior knowledge from past learned policies of relevant tasks. Existing transfer approaches either explicitly computes the similarity between tasks or select appropriate source policies to provide guided explorations for the target task. However, how to directly optimize the target policy by alternatively utilizing knowledge from appropriate source policies without explicitly measuring the similarity is currently missing. In this paper, we propose a novel Policy Transfer Framework (PTF) to accelerate RL by taking advantage of this idea. Our framework learns when and which source policy is the best to reuse for the target policy and when to terminate it by modeling multi-policy transfer as the option learning problem. PTF can be easily combined with existing deep RL approaches. Experimental results show it significantly accelerates the learning process and surpasses state-of-the-art policy transfer methods in terms of learning efficiency and final performance in both discrete and continuous action spaces.Comment: Accepted by IJCAI'202

Video Event Extraction via Tracking Visual States of Arguments

Video event extraction aims to detect salient events from a video and identify the arguments for each event as well as their semantic roles. Existing methods focus on capturing the overall visual scene of each frame, ignoring fine-grained argument-level information. Inspired by the definition of events as changes of states, we propose a novel framework to detect video events by tracking the changes in the visual states of all involved arguments, which are expected to provide the most informative evidence for the extraction of video events. In order to capture the visual state changes of arguments, we decompose them into changes in pixels within objects, displacements of objects, and interactions among multiple arguments. We further propose Object State Embedding, Object Motion-aware Embedding and Argument Interaction Embedding to encode and track these changes respectively. Experiments on various video event extraction tasks demonstrate significant improvements compared to state-of-the-art models. In particular, on verb classification, we achieve 3.49% absolute gains (19.53% relative gains) in F1@5 on Video Situation Recognition

Genome-Wide Identification and Characterization of Auxin Response Factor (ARF) Gene Family Involved in Wood Formation and Response to Exogenous Hormone Treatment in Populus trichocarpa

Auxin is a key regulator that virtually controls almost every aspect of plant growth and development throughout its life cycle. As the major components of auxin signaling, auxin response factors (ARFs) play crucial roles in various processes of plant growth and development. In this study, a total of 35 PtrARF genes were identified, and their phylogenetic relationships, chromosomal locations, synteny relationships, exon/intron structures, cis-elements, conserved motifs, and protein characteristics were systemically investigated. We also analyzed the expression patterns of these PtrARF genes and revealed that 16 of them, including PtrARF1, 3, 7, 11, 13–17, 21, 23, 26, 27, 29, 31, and 33, were preferentially expressed in primary stems, while 15 of them, including PtrARF2, 4, 6, 9, 10, 12, 18–20, 22, 24, 25, 28, 32, and 35, participated in different phases of wood formation. In addition, some PtrARF genes, with at least one cis-element related to indole-3-acetic acid (IAA) or abscisic acid (ABA) response, responded differently to exogenous IAA and ABA treatment, respectively. Three PtrARF proteins, namely PtrARF18, PtrARF23, and PtrARF29, selected from three classes, were characterized, and only PtrARF18 was a transcriptional self-activator localized in the nucleus. Moreover, Y2H and bimolecular fluorescence complementation (BiFC) assay demonstrated that PtrARF23 interacted with PtrIAA10 and PtrIAA28 in the nucleus, while PtrARF29 interacted with PtrIAA28 in the nucleus. Our results provided comprehensive information regarding the PtrARF gene family, which will lay some foundation for future research about PtrARF genes in tree development and growth, especially the wood formation, in response to cellular signaling and environmental cues