1,017 research outputs found
A general approach to improve the bias stability of NMR gyroscope
In recent years, progress in improving the bias stability of NMR gyroscopes
has been hindered. Taking inspiration from the core idea of rotation modulation
in the strapdown inertial navigation system, we propose a general approach to
enhancing the bias stability of NMR gyroscopes that does not require
consideration of the actual physical sources. The method operates on the fact
that the sign of the bias does not follow that of the sensing direction of the
NMR gyroscope, which is much easier to modulate than with other types of
gyroscopes. We conducted simulations to validate the method's feasibility
Quantum fluctuations in the BCS-BEC crossover of two-dimensional Fermi gases
We present a theoretical study of the ground state of the BCS-BEC crossover
in dilute two-dimensional Fermi gases. While the mean-field theory provides a
simple and analytical equation of state, the pressure is equal to that of a
noninteracting Fermi gas in the entire BCS-BEC crossover, which is not
consistent with the features of a weakly interacting Bose condensate in the BEC
limit and a weakly interacting Fermi liquid in the BCS limit. The inadequacy of
the 2D mean-field theory indicates that the quantum fluctuations are much more
pronounced than those in 3D. In this work, we show that the inclusion of the
Gaussian quantum fluctuations naturally recovers the above features in both the
BEC and the BCS limits. In the BEC limit, the missing logarithmic dependence on
the boson chemical potential is recovered by the quantum fluctuations. Near the
quantum phase transition from the vacuum to the BEC phase, we compare our
equation of state with the known grand canonical equation of state of 2D Bose
gases and determine the ratio of the composite boson scattering length to the fermion scattering length . We find , in good agreement with the exact four-body calculation. We
compare our equation of state in the BCS-BEC crossover with recent results from
the quantum Monte Carlo simulations and the experimental measurements and find
good agreements.Comment: Published versio
Modality to Modality Translation: An Adversarial Representation Learning and Graph Fusion Network for Multimodal Fusion
Learning joint embedding space for various modalities is of vital importance
for multimodal fusion. Mainstream modality fusion approaches fail to achieve
this goal, leaving a modality gap which heavily affects cross-modal fusion. In
this paper, we propose a novel adversarial encoder-decoder-classifier framework
to learn a modality-invariant embedding space. Since the distributions of
various modalities vary in nature, to reduce the modality gap, we translate the
distributions of source modalities into that of target modality via their
respective encoders using adversarial training. Furthermore, we exert
additional constraints on embedding space by introducing reconstruction loss
and classification loss. Then we fuse the encoded representations using
hierarchical graph neural network which explicitly explores unimodal, bimodal
and trimodal interactions in multi-stage. Our method achieves state-of-the-art
performance on multiple datasets. Visualization of the learned embeddings
suggests that the joint embedding space learned by our method is
discriminative. code is available at:
\url{https://github.com/TmacMai/ARGF_multimodal_fusion}Comment: Accepted by AAAI-2020; code is available at:
https://github.com/TmacMai/ARGF_multimodal_fusio
Relation-dependent Contrastive Learning with Cluster Sampling for Inductive Relation Prediction
Relation prediction is a task designed for knowledge graph completion which
aims to predict missing relationships between entities. Recent subgraph-based
models for inductive relation prediction have received increasing attention,
which can predict relation for unseen entities based on the extracted subgraph
surrounding the candidate triplet. However, they are not completely inductive
because of their disability of predicting unseen relations. Moreover, they fail
to pay sufficient attention to the role of relation as they only depend on the
model to learn parameterized relation embedding, which leads to inaccurate
prediction on long-tail relations. In this paper, we introduce
Relation-dependent Contrastive Learning (ReCoLe) for inductive relation
prediction, which adapts contrastive learning with a novel sampling method
based on clustering algorithm to enhance the role of relation and improve the
generalization ability to unseen relations. Instead of directly learning
embedding for relations, ReCoLe allocates a pre-trained GNN-based encoder to
each relation to strengthen the influence of relation. The GNN-based encoder is
optimized by contrastive learning, which ensures satisfactory performance on
long-tail relations. In addition, the cluster sampling method equips ReCoLe
with the ability to handle both unseen relations and entities. Experimental
results suggest that ReCoLe outperforms state-of-the-art methods on commonly
used inductive datasets
Effects on Buildings of Surface Curvature Caused by Underground Coal Mining
Ground curvature caused by underground mining is one of the most obvious deformation quantities in buildings. To study the influence of surface curvature on buildings and predict the movement and deformation of buildings caused by ground curvature, a prediction model of the influence function on mining subsidence was used to establish the relationship between surface curvature and wall deformation. The prediction model of wall deformation was then established and the surface curvature was obtained from mining subsidence prediction software. Five prediction lines were set up in the wall from bottom to top and the predicted deformation of each line was used to calculate the crack positions in the wall. Thus, the crack prediction model was obtained. The model was verified by a case study from a coalmine in Shanxi, China. The results show that when the ground curvature is positive, the crack in the wall is shaped like a "V"; when the ground curvature is negative, the crack is shaped like a "∧". The conclusion provides the basis for a damage evaluation method for buildings in coalmine areas
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