762 research outputs found
Many-body ground state localization and coexistence of localized and extended states in an interacting quasiperiodic system
We study the localization problem of one-dimensional interacting spinless
fermions in an incommensurate optical lattice, which changes from an extended
phase to a nonergoic many-body localized phase by increasing the strength of
the incommensurate potential. We identify that there exists an intermediate
regime before the system enters the many-body localized phase, in which both
the localized and extended many-body states coexist, thus the system is divided
into three different phases, which can be characterized by normalized
participation ratios of the many-body eigenstates and distributions of natural
orbitals of the corresponding one-particle density matrix. This is very
different from its noninterating limit, in which all eigenstaes undergo a
delocaliztion-localization transtion when the strength of the incommensurate
potential exceeds a critical value.Comment: 5 pages, 6 figure
Skeleton based action recognition using translation-scale invariant image mapping and multi-scale deep cnn
This paper presents an image classification based approach for skeleton-based
video action recognition problem. Firstly, A dataset independent
translation-scale invariant image mapping method is proposed, which transformes
the skeleton videos to colour images, named skeleton-images. Secondly, A
multi-scale deep convolutional neural network (CNN) architecture is proposed
which could be built and fine-tuned on the powerful pre-trained CNNs, e.g.,
AlexNet, VGGNet, ResNet etal.. Even though the skeleton-images are very
different from natural images, the fine-tune strategy still works well. At
last, we prove that our method could also work well on 2D skeleton video data.
We achieve the state-of-the-art results on the popular benchmard datasets e.g.
NTU RGB+D, UTD-MHAD, MSRC-12, and G3D. Especially on the largest and challenge
NTU RGB+D, UTD-MHAD, and MSRC-12 dataset, our method outperforms other methods
by a large margion, which proves the efficacy of the proposed method
Physics-informed machine learning of redox flow battery based on a two-dimensional unit cell model
In this paper, we present a physics-informed neural network (PINN) approach
for predicting the performance of an all-vanadium redox flow battery, with its
physics constraints enforced by a two-dimensional (2D) mathematical model. The
2D model, which includes 6 governing equations and 24 boundary conditions,
provides a detailed representation of the electrochemical reactions, mass
transport and hydrodynamics occurring inside the redox flow battery. To solve
the 2D model with the PINN approach, a composite neural network is employed to
approximate species concentration and potentials; the input and output are
normalized according to prior knowledge of the battery system; the governing
equations and boundary conditions are first scaled to an order of magnitude
around 1, and then further balanced with a self-weighting method. Our numerical
results show that the PINN is able to predict cell voltage correctly, but the
prediction of potentials shows a constant-like shift. To fix the shift, the
PINN is enhanced by further constrains derived from the current collector
boundary. Finally, we show that the enhanced PINN can be even further improved
if a small number of labeled data is available.Comment: 7 figure
- …