499 research outputs found
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
Selenium status in the body and cardiovascular disease: a systematic review and meta-analysis
Background: Both experimental and observational studies have provided conflicting evidence on the associations of selenium with incidence and mortality of cardiovascular disease (CVD). The aim of this study was to evaluate the association between selenium status in the body and incidence and mortality of CVD by performing a systematic review and meta-analysis of observational studies and randomized controlled trials. Methods: A systematic search for articles in MEDLINE (Ovid), Embase, Web of Science (Thomson Reuters) and Cochrane library (Wiley) was conducted. Thirteen of the 1811 articles obtained from the databases met our inclusion criteria and were considered in the final analysis. The effect sizes were presented as weighted relative risk (RR) and 95% confidence intervals (CIs) using random-effects model. To detect dose-response relationships, we used meta-regression. Results: Overall, there was a reduced risk of CVD incidence (RR = 0.66; 95% CI: 0.40-1.09) and mortality (RR = 0.69; 95% CI: 0.57-0.84) in physiologically high selenium status compared to low selenium status in the body. There was a 15% (RR = 0.85, 95% CI: 0.76-0.94) decreased risk of CVD incidence per 10 µg increment in blood selenium concentration. In addition, a statistically significantly nonlinear dose-response relationship was found between CVD mortality and increased blood selenium concentration with the lowest risk at the 30-35 µg increment in blood selenium. Conclusions: Physiologically high selenium levels in the body are associated with decreased risk for CVD incidence and mortality, however, people should be cautious about the potential harmful effects from excessive intake of selenium.publishedVersio
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
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