1,743 research outputs found
Driving force induced transition in thermal behavior of grain boundary migration in Ni
Grain boundaries (GBs) that show higher mobility at lower temperatures (i.e.,
anti-thermal or non-Arrhenius behavior) have attracted significant interest in
recent years. In this study, we use atomistic simulations to systematically
investigate the effect of driving force on GB mobility based on a set of
bicrystalline models in Ni. It is found that the thermal behavior of GB
migration strongly depends on temperature and the magnitude of driving forces.
When the driving force is at the zero-driving force limit as induced solely by
thermal fluctuations, the mobility of all GBs investigated in the current study
shows a transition from thermally activated to anti-thermal behavior when the
temperature is increased. As the driving force increases, the transition
temperature at which the mobility peaks would gradually decrease so that for
some GBs only the anti-thermal behavior would be detected. Energy analysis
further reveals that the transition temperature (Ttrans) is linearly related to
both energy barrier per area (E) from NEB simulation and the fitted apparent
activation (Q) energy, and both E and Q are lowered as the driving force
increases. Our work supports the previous theoretical models for GB migration
based on both classical thermal activation and disconnection nucleation.
Furthermore, the current study can be used to improve both models by
considering the influence of driving force with a simple fix to how the energy
barrier for GB migration should be considered. It is expected that this work
advances the current understanding of general GB migration and sheds some light
on a unified theoretical framework in the near future
Fine structure characterization of zero-valent iron nanoparticles for decontamination of nitrites and nitrates in wastewater and groundwater
The main objectives of the present study were to investigate the chemical reduction of nitrate or nitrite species by zero-valent iron nanoparticle (ZVIN) in aqueous solution and related reaction kinetics or mechanisms using fine structure characterization. This work also exemplifies the utilization of field emission-scanning electron microscope (FE-SEM), transmission electron microscopy (TEM), and x-ray diffraction (XRD) to reveal the speciation and possible reaction pathway in a very complex adsorption and redox reaction process. Experimentally, ZVIN of this study was prepared by sodium borohydride reduction method at room temperature and ambient pressure. The morphology of as-synthesized ZVIN shows that the nearly ball and ultrafine particles ranged of 20-50 nm were observed with FE-SEM or TEM analysis. The kinetic model of nitrites or nitrates reductive reaction by ZVIN is proposed as a pseudo first-order kinetic equation. The nitrite and nitrate removal efficiencies using ZVIN were found 65-83% and 51-68%, respectively, based on three different initial concentrations. Based on the XRD pattern analyses, it is found that the quantitative relationship between nitrite and Fe(III) or Fe(II) is similar to the one between nitrate and Fe( III) in the ZVIN study. The possible reason is due to the faster nitrite reduction by ZVIN. In fact, the occurrence of the relative faster nitrite reductive reaction suggested that the passivation of the ZVIN have a significant contribution to iron corrosion. The extended x-ray absorption fine structure (EXAFS) or x-ray absorption near edge structure (XANES) spectra show that the nitrites or nitrates reduce to N-2 or NH3 while oxidizing the ZVIN to Fe2O3 or Fe3O4 electrochemically. It is also very clear that decontamination of nitrate or nitrite species in groundwater via the in-situ remediation with a ZVIN permeable reactive barrier would be environmentally attractive
Realization of generalized quantum searching using nuclear magnetic resonance
According to the theoretical results, the quantum searching algorithm can be
generalized by replacing the Walsh-Hadamard(W-H) transform by almost any
quantum mechanical operation. We have implemented the generalized algorithm
using nuclear magnetic resonance techniques with a solution of chloroform
molecules. Experimental results show the good agreement between theory and
experiment.Comment: 11 pages,3 figure. Accepted by Phys. Rev. A. Scheduled Issue: 01 Mar
200
Location-aware Graph Convolutional Networks for Video Question Answering
We addressed the challenging task of video question answering, which requires
machines to answer questions about videos in a natural language form. Previous
state-of-the-art methods attempt to apply spatio-temporal attention mechanism
on video frame features without explicitly modeling the location and relations
among object interaction occurred in videos. However, the relations between
object interaction and their location information are very critical for both
action recognition and question reasoning. In this work, we propose to
represent the contents in the video as a location-aware graph by incorporating
the location information of an object into the graph construction. Here, each
node is associated with an object represented by its appearance and location
features. Based on the constructed graph, we propose to use graph convolution
to infer both the category and temporal locations of an action. As the graph is
built on objects, our method is able to focus on the foreground action contents
for better video question answering. Lastly, we leverage an attention mechanism
to combine the output of graph convolution and encoded question features for
final answer reasoning. Extensive experiments demonstrate the effectiveness of
the proposed methods. Specifically, our method significantly outperforms
state-of-the-art methods on TGIF-QA, Youtube2Text-QA, and MSVD-QA datasets.
Code and pre-trained models are publicly available at:
https://github.com/SunDoge/L-GC
Generating Visually Aligned Sound from Videos
We focus on the task of generating sound from natural videos, and the sound
should be both temporally and content-wise aligned with visual signals. This
task is extremely challenging because some sounds generated \emph{outside} a
camera can not be inferred from video content. The model may be forced to learn
an incorrect mapping between visual content and these irrelevant sounds. To
address this challenge, we propose a framework named REGNET. In this framework,
we first extract appearance and motion features from video frames to better
distinguish the object that emits sound from complex background information. We
then introduce an innovative audio forwarding regularizer that directly
considers the real sound as input and outputs bottlenecked sound features.
Using both visual and bottlenecked sound features for sound prediction during
training provides stronger supervision for the sound prediction. The audio
forwarding regularizer can control the irrelevant sound component and thus
prevent the model from learning an incorrect mapping between video frames and
sound emitted by the object that is out of the screen. During testing, the
audio forwarding regularizer is removed to ensure that REGNET can produce
purely aligned sound only from visual features. Extensive evaluations based on
Amazon Mechanical Turk demonstrate that our method significantly improves both
temporal and content-wise alignment. Remarkably, our generated sound can fool
the human with a 68.12% success rate. Code and pre-trained models are publicly
available at https://github.com/PeihaoChen/regnetComment: Published in IEEE Transactions on Image Processing, 2020. Code,
pre-trained models and demo video: https://github.com/PeihaoChen/regne
RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning
We study unsupervised video representation learning that seeks to learn both
motion and appearance features from unlabeled video only, which can be reused
for downstream tasks such as action recognition. This task, however, is
extremely challenging due to 1) the highly complex spatial-temporal information
in videos; and 2) the lack of labeled data for training. Unlike the
representation learning for static images, it is difficult to construct a
suitable self-supervised task to well model both motion and appearance
features. More recently, several attempts have been made to learn video
representation through video playback speed prediction. However, it is
non-trivial to obtain precise speed labels for the videos. More critically, the
learnt models may tend to focus on motion pattern and thus may not learn
appearance features well. In this paper, we observe that the relative playback
speed is more consistent with motion pattern, and thus provide more effective
and stable supervision for representation learning. Therefore, we propose a new
way to perceive the playback speed and exploit the relative speed between two
video clips as labels. In this way, we are able to well perceive speed and
learn better motion features. Moreover, to ensure the learning of appearance
features, we further propose an appearance-focused task, where we enforce the
model to perceive the appearance difference between two video clips. We show
that optimizing the two tasks jointly consistently improves the performance on
two downstream tasks, namely action recognition and video retrieval.
Remarkably, for action recognition on UCF101 dataset, we achieve 93.7% accuracy
without the use of labeled data for pre-training, which outperforms the
ImageNet supervised pre-trained model. Code and pre-trained models can be found
at https://github.com/PeihaoChen/RSPNet.Comment: Accepted by AAAI-2021. Code and pre-trained models can be found at
https://github.com/PeihaoChen/RSPNe
Mechanism of dissolution and oxidation of stibnite mediated by the coupling of iron and typical antimony oxidizing bacteria
Antimony oxidizing bacteria (SbOB) and iron oxides are the main driving factors to the weathering dissolution and oxidation of stibnite (Sb2S3) waste ore. The characteristics of the dissolution and oxidation process of stibnite in the absence of strain AO-1 and iron oxides, Pseudomonas sp. AO-1-mediated (AO-1-mediated), Fe (Fe, Fe2(SO4)3, and FeS2) -mediated, and coupled-mediated groups (Fe+AO-1, Fe2(SO4)3+AO-1, FeS2+AO-1) under various pH values were examined through sequential batch experiments. The results showed that all the AO-1-mediated, Fe-mediated and coupled-mediated can promote the dissolution and oxidation of stibnite, and the promotion effect increased with the rise of pH. The order of contribution to the dissolution of stibnite under the coupling mediation is as follows: coupling effect (42.4-78.2%) > chemical effect (19.4-56.6%) > biological effect (0.9-2.4%). In addition, the dissolution and oxidation mechanisms of stibnite were further investigated and analyzed in combination with scanning electron microscopy (SEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). This study has important implications for elucidating the source control and geochemical behavior of antimony pollution in antimony mining areas
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