331 research outputs found
Topological phase transition from periodic edge states in moir\'e superlattices
Topological mosaic pattern (TMP) can be formed in two-dimensional (2D)
moir\'e superlattices, a set of periodic and spatially separated domains with
distinct topologies give rise to periodic edge states on the domain walls. In
this study, we demonstrate that these periodic edge states play a crucial role
in determining global topological properties. By developing a continuum model
for periodic edge states with C6z and C3z rotational symmetry, we predict that
a global topological phase transition at the charge neutrality point (CNP) can
be driven by the size of domain walls and moir\'e periodicity. The Wannier
representation analysis reveals that these periodic edge states are
fundamentally chiral px +- ipy orbitals. The interplay between on-site chiral
orbital rotation and neighboring hopping among chiral orbitals leads to band
inversion and a topological phase transition. Our work establishes a general
model for tuning local and global topological phases, paving the way for future
research on strongly correlated topological flat minibands within topological
mosaic pattern
Collaborative Optimization of Car-flow Organization for Freight Trains Based on Adjacent Technical Stations
This paper proposes a collaborative optimization model of car-flow organization for freight trains based on adjacent technical stations to minimize the average dwell time of train cars in a yard. To solve the car-flow organization problems, a priority-based hump sequence, which depends on the cars available in two adjacent technical stations, is adopted. Furthermore, a meta-heuristic algorithm based on the genetic algorithm and the taboo search algorithm is adopted to solve the model, and the introduction of the active scheduling method improves the efficiency of the algorithm. Finally, the model is applied to the car-flow organization problem of two adjacent technical stations, and the results are compared with those from a single technical station without collaboration. The results demonstrate that collaborative car-flow organization between technical stations significantly reduces the average dwell time at the stations, thereby improving the utilization rate of railroad equipment. In addition, the results indicate that the hybrid genetic algorithm can rapidly determine the train hump and marshalling schemes
THE INFLUENCE OF THE INNOVATIVE DEVELOPMENT OF TRADITIONAL HANDICRAFT ART ON STABILIZING THE MOOD OF PATIENTS WITH MENTAL ILLNESS
GammaE: Gamma Embeddings for Logical Queries on Knowledge Graphs
Embedding knowledge graphs (KGs) for multi-hop logical reasoning is a
challenging problem due to massive and complicated structures in many KGs.
Recently, many promising works projected entities and queries into a geometric
space to efficiently find answers. However, it remains challenging to model the
negation and union operator. The negation operator has no strict boundaries,
which generates overlapped embeddings and leads to obtaining ambiguous answers.
An additional limitation is that the union operator is non-closure, which
undermines the model to handle a series of union operators. To address these
problems, we propose a novel probabilistic embedding model, namely Gamma
Embeddings (GammaE), for encoding entities and queries to answer different
types of FOL queries on KGs. We utilize the linear property and strong boundary
support of the Gamma distribution to capture more features of entities and
queries, which dramatically reduces model uncertainty. Furthermore, GammaE
implements the Gamma mixture method to design the closed union operator. The
performance of GammaE is validated on three large logical query datasets.
Experimental results show that GammaE significantly outperforms
state-of-the-art models on public benchmarks
An adaptive method for inertia force identification in in cantilever under moving mass
The present study is concerned with the adaptive method based on wavelet transform to identify the inertia force between moving mass and cantilever. The basic model of cantilever is described and a classical identification method is introduced. Then the approximate equations about the model of cantilever can be obtained by the identification method. However, the order of modal adapted in the identification methods is usually constant which may make the identification results unsatisfied. As is known, the frequency of the highest order of modal is usually higher than the frequency of the input force in forward calculation methods. Therefore, wavelet transform is applied to decompose the data of deflection. The proportion of the low frequency component is chosen as the parameter of a binary function to decide the order of modal. The calculation results show that the adaptive method adapted in this paper is efficient to improve the accuracy of the inertia force between the moving mass and cantilever, and also the relationship between the proportion of low frequency component and the order of modal is indicated
An interpretability framework for Similar case matching
Similar Case Matching (SCM) plays a pivotal role in the legal system by
facilitating the efficient identification of similar cases for legal
professionals. While previous research has primarily concentrated on enhancing
the performance of SCM models, the aspect of interpretability has been
neglected. To bridge the gap, this study proposes an integrated pipeline
framework for interpretable SCM. The framework comprises four modules: judicial
feature sentence identification, case matching, feature sentence alignment, and
conflict resolution. In contrast to current SCM methods, our framework first
extracts feature sentences within a legal case that contain essential
information. Then it conducts case matching based on these extracted features.
Subsequently, our framework aligns the corresponding sentences in two legal
cases to provide evidence of similarity. In instances where the results of case
matching and feature sentence alignment exhibit conflicts, the conflict
resolution module resolves these inconsistencies. The experimental results show
the effectiveness of our proposed framework, establishing a new benchmark for
interpretable SCM
Identifying Solar Flare Precursors Using Time Series of SDO/HMI Images and SHARP Parameters
We present several methods towards construction of precursors, which show
great promise towards early predictions, of solar flare events in this paper. A
data pre-processing pipeline is built to extract useful data from multiple
sources, Geostationary Operational Environmental Satellites (GOES) and Solar
Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI), to prepare
inputs for machine learning algorithms. Two classification models are
presented: classification of flares from quiet times for active regions and
classification of strong versus weak flare events. We adopt deep learning
algorithms to capture both the spatial and temporal information from HMI
magnetogram data. Effective feature extraction and feature selection with raw
magnetogram data using deep learning and statistical algorithms enable us to
train classification models to achieve almost as good performance as using
active region parameters provided in HMI/Space-Weather HMI-Active Region Patch
(SHARP) data files. Case studies show a significant increase in the prediction
score around 20 hours before strong solar flare events
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