281 research outputs found
Topological phase transitions in multi-component superconductors
We study the phase transition between a trivial and a time-reversal-invariant
topological superconductor in a single-band system. By analyzing the interplay
of symmetry, topology and energetics, we show that for a generic normal state
band structure, the phase transition occurs via extended intermediate phases in
which even- and odd-parity pairing components coexist. For inversion-symmetric
systems, the coexistence phase spontaneously breaks time-reversal symmetry. For
noncentrosymmetric superconductors, the low-temperature intermediate phase is
time-reversal breaking, while the high-temperature phase preserves
time-reversal symmetry and has topologically protected line nodes. Furthermore,
with approximate rotational invariance, the system has an emergent symmetry, and novel topological defects, such as half vortex lines
binding Majorana fermions, can exist. We analytically solve for the dispersion
of the Majorana fermion and show that it exhibit small and large velocities at
low and high energies. Relevance of our theory to superconducting pyrochlore
oxide CdReO and half-Heusler materials is discussed.Comment: 14 pages, 7 figures; to appear on Phys. Rev. Let
Asset Pricing and Cost of Equity for US Banking Sector by CAPM and TFPM from 1987-2011
Although Capital Asset Pricing Model (CAPM), one-factor model, has strong theoretical basis and is easy to use and understand, analysts also consider other alternative models, such as Three Factor Pricing Model (TFPM) developed by Fama and French (1993). Because some differences between actual return and estimated return could be explained by the effect of capital size and book-to-market ratio. The objective of using these two similar but complementary models is to estimate the cost of equity for the US banking sector. In order to do the estimation, we would conduct the estimation of parameters for both individual bank and the whole banking sector
An improved Siamese network for face sketch recognition
Face sketch recognition identifies the face photo from a large face sketch dataset. Some traditional methods are typically used to reduce the modality gap between face photos and sketches and gain excellent recognition rate based on a pseudo image which is synthesized using the corresponded face photo. However, these methods cannot obtain better high recognition rate for all face sketch datasets, because the use of extracted features cannot lead to the elimination of the effect of different modalities' images. The feature representation of the deep convolutional neural networks as a feasible approach for identification involves wider applications than other methods. It is adapted to extract the features which eliminate the difference between face photos and sketches. The recognition rate is high for neural networks constructed by learning optimal local features, even if the input image shows geometric distortions. However, the case of overfitting leads to the unsatisfactory performance of deep learning methods on face sketch recognition tasks. Also, the sketch images are too simple to be used for extracting effective features. This paper aims to increase the matching rate using the Siamese convolution network architecture. The framework is used to extract useful features from each image pair to reduce the modality gap. Moreover, data augmentation is used to avoid overfitting. We explore the performance of three loss functions and compare the similarity between each image pair. The experimental results show that our framework is adequate for a composite sketch dataset. In addition, it reduces the influence of overfitting by using data augmentation and modifying the network structure
DualFormer: Local-Global Stratified Transformer for Efficient Video Recognition
While transformers have shown great potential on video recognition with their
strong capability of capturing long-range dependencies, they often suffer high
computational costs induced by the self-attention to the huge number of 3D
tokens. In this paper, we present a new transformer architecture termed
DualFormer, which can efficiently perform space-time attention for video
recognition. Concretely, DualFormer stratifies the full space-time attention
into dual cascaded levels, i.e., to first learn fine-grained local interactions
among nearby 3D tokens, and then to capture coarse-grained global dependencies
between the query token and global pyramid contexts. Different from existing
methods that apply space-time factorization or restrict attention computations
within local windows for improving efficiency, our local-global stratification
strategy can well capture both short- and long-range spatiotemporal
dependencies, and meanwhile greatly reduces the number of keys and values in
attention computation to boost efficiency. Experimental results verify the
superiority of DualFormer on five video benchmarks against existing methods. In
particular, DualFormer achieves 82.9%/85.2% top-1 accuracy on Kinetics-400/600
with ~1000G inference FLOPs which is at least 3.2x fewer than existing methods
with similar performance. We have released the source code at
https://github.com/sail-sg/dualformer.Comment: Accepted by ECCV 202
UrbanFM: Inferring Fine-Grained Urban Flows
Urban flow monitoring systems play important roles in smart city efforts
around the world. However, the ubiquitous deployment of monitoring devices,
such as CCTVs, induces a long-lasting and enormous cost for maintenance and
operation. This suggests the need for a technology that can reduce the number
of deployed devices, while preventing the degeneration of data accuracy and
granularity. In this paper, we aim to infer the real-time and fine-grained
crowd flows throughout a city based on coarse-grained observations. This task
is challenging due to two reasons: the spatial correlations between coarse- and
fine-grained urban flows, and the complexities of external impacts. To tackle
these issues, we develop a method entitled UrbanFM based on deep neural
networks. Our model consists of two major parts: 1) an inference network to
generate fine-grained flow distributions from coarse-grained inputs by using a
feature extraction module and a novel distributional upsampling module; 2) a
general fusion subnet to further boost the performance by considering the
influences of different external factors. Extensive experiments on two
real-world datasets, namely TaxiBJ and HappyValley, validate the effectiveness
and efficiency of our method compared to seven baselines, demonstrating the
state-of-the-art performance of our approach on the fine-grained urban flow
inference problem
MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecasting
Multivariate time series forecasting poses an ongoing challenge across
various disciplines. Time series data often exhibit diverse intra-series and
inter-series correlations, contributing to intricate and interwoven
dependencies that have been the focus of numerous studies. Nevertheless, a
significant research gap remains in comprehending the varying inter-series
correlations across different time scales among multiple time series, an area
that has received limited attention in the literature. To bridge this gap, this
paper introduces MSGNet, an advanced deep learning model designed to capture
the varying inter-series correlations across multiple time scales using
frequency domain analysis and adaptive graph convolution. By leveraging
frequency domain analysis, MSGNet effectively extracts salient periodic
patterns and decomposes the time series into distinct time scales. The model
incorporates a self-attention mechanism to capture intra-series dependencies,
while introducing an adaptive mixhop graph convolution layer to autonomously
learn diverse inter-series correlations within each time scale. Extensive
experiments are conducted on several real-world datasets to showcase the
effectiveness of MSGNet. Furthermore, MSGNet possesses the ability to
automatically learn explainable multi-scale inter-series correlations,
exhibiting strong generalization capabilities even when applied to
out-of-distribution samples.Comment: 13 pages, 12 figure
Anomaly Detection by Adapting a pre-trained Vision Language Model
Recently, large vision and language models have shown their success when
adapting them to many downstream tasks. In this paper, we present a unified
framework named CLIP-ADA for Anomaly Detection by Adapting a pre-trained CLIP
model. To this end, we make two important improvements: 1) To acquire unified
anomaly detection across industrial images of multiple categories, we introduce
the learnable prompt and propose to associate it with abnormal patterns through
self-supervised learning. 2) To fully exploit the representation power of CLIP,
we introduce an anomaly region refinement strategy to refine the localization
quality. During testing, the anomalies are localized by directly calculating
the similarity between the representation of the learnable prompt and the
image. Comprehensive experiments demonstrate the superiority of our framework,
e.g., we achieve the state-of-the-art 97.5/55.6 and 89.3/33.1 on MVTec-AD and
VisA for anomaly detection and localization. In addition, the proposed method
also achieves encouraging performance with marginal training data, which is
more challenging
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