4,913 research outputs found
Screening magnetic two-dimensional atomic crystals with nontrivial electronic topology
To date only a few two-dimensional (2D) magnetic crystals were experimentally
confirmed, such as CrI3 and CrGeTe3, all with very low Curie temperatures (TC).
High-throughput first-principles screening over a large set of materials yields
89 magnetic monolayers including 56 ferromagnetic (FM) and 33 antiferromagnetic
compounds. Among them, 24 FM monolayers are promising candidates possessing TC
higher than that of CrI3. High TC monolayers with fascinating electronic phases
are identified: (i) quantum anomalous and valley Hall effects coexist in a
single material RuCl3 or VCl3, leading to a valley-polarized quantum anomalous
Hall state; (ii) TiBr3, Co2NiO6 and V2H3O5 are revealed to be half-metals. More
importantly, a new type of fermion dubbed type-II Weyl ring is discovered in
ScCl. Our work provides a database of 2D magnetic materials, which could guide
experimental realization of high-temperature magnetic monolayers with exotic
electronic states for future spintronics and quantum computing applications.Comment: 12 pages, 4 figure
FLASH: Fast Bayesian Optimization for Data Analytic Pipelines
Modern data science relies on data analytic pipelines to organize
interdependent computational steps. Such analytic pipelines often involve
different algorithms across multiple steps, each with its own hyperparameters.
To achieve the best performance, it is often critical to select optimal
algorithms and to set appropriate hyperparameters, which requires large
computational efforts. Bayesian optimization provides a principled way for
searching optimal hyperparameters for a single algorithm. However, many
challenges remain in solving pipeline optimization problems with
high-dimensional and highly conditional search space. In this work, we propose
Fast LineAr SearcH (FLASH), an efficient method for tuning analytic pipelines.
FLASH is a two-layer Bayesian optimization framework, which firstly uses a
parametric model to select promising algorithms, then computes a nonparametric
model to fine-tune hyperparameters of the promising algorithms. FLASH also
includes an effective caching algorithm which can further accelerate the search
process. Extensive experiments on a number of benchmark datasets have
demonstrated that FLASH significantly outperforms previous state-of-the-art
methods in both search speed and accuracy. Using 50% of the time budget, FLASH
achieves up to 20% improvement on test error rate compared to the baselines.
FLASH also yields state-of-the-art performance on a real-world application for
healthcare predictive modeling.Comment: 21 pages, KDD 201
Enhancing the geometric quantum discord in the Heisenberg {\it XX} chain by Dzyaloshinsky-Moriya interaction
We studied the trance distance, the Hellinger distance, and the Bures
distance geometric quantum discords (GQDs) for a two-spin Heisenberg {\it XX}
chain with the Dzyaloshinsky-Moriya (DM) interaction and the external magnetic
fields. We found that considerable enhancement of the GQDs can be achieved by
introducing the DM interaction, and their maxima were obtained in the limiting
case . The external magnetic fields and the increase of
the temperature can also enhance the GQDs to some extent for certain special
cases.Comment: 6 pages, 4 figure
Hierarchical Neural Network Architecture In Keyword Spotting
Keyword Spotting (KWS) provides the start signal of ASR problem, and thus it
is essential to ensure a high recall rate. However, its real-time property
requires low computation complexity. This contradiction inspires people to find
a suitable model which is small enough to perform well in multi environments.
To deal with this contradiction, we implement the Hierarchical Neural
Network(HNN), which is proved to be effective in many speech recognition
problems. HNN outperforms traditional DNN and CNN even though its model size
and computation complexity are slightly less. Also, its simple topology
structure makes easy to deploy on any device.Comment: To be submitted in part to IEEE ICASSP 201
Graph Regularized Low Rank Representation for Aerosol Optical Depth Retrieval
In this paper, we propose a novel data-driven regression model for aerosol
optical depth (AOD) retrieval. First, we adopt a low rank representation (LRR)
model to learn a powerful representation of the spectral response. Then, graph
regularization is incorporated into the LRR model to capture the local
structure information and the nonlinear property of the remote-sensing data.
Since it is easy to acquire the rich satellite-retrieval results, we use them
as a baseline to construct the graph. Finally, the learned feature
representation is feeded into support vector machine (SVM) to retrieve AOD.
Experiments are conducted on two widely used data sets acquired by different
sensors, and the experimental results show that the proposed method can achieve
superior performance compared to the physical models and other state-of-the-art
empirical models.Comment: 16 pages, 6 figure
Learning to Write Stylized Chinese Characters by Reading a Handful of Examples
Automatically writing stylized Chinese characters is an attractive yet
challenging task due to its wide applicabilities. In this paper, we propose a
novel framework named Style-Aware Variational Auto-Encoder (SA-VAE) to flexibly
generate Chinese characters. Specifically, we propose to capture the different
characteristics of a Chinese character by disentangling the latent features
into content-related and style-related components. Considering of the complex
shapes and structures, we incorporate the structure information as prior
knowledge into our framework to guide the generation. Our framework shows a
powerful one-shot/low-shot generalization ability by inferring the style
component given a character with unseen style. To the best of our knowledge,
this is the first attempt to learn to write new-style Chinese characters by
observing only one or a few examples. Extensive experiments demonstrate its
effectiveness in generating different stylized Chinese characters by fusing the
feature vectors corresponding to different contents and styles, which is of
significant importance in real-world applications.Comment: Accepted by IJCAI 201
Multipath IP Routing on End Devices: Motivation, Design, and Performance
Most end devices are now equipped with multiple network interfaces.
Applications can exploit all available interfaces and benefit from multipath
transmission. Recently Multipath TCP (MPTCP) was proposed to implement
multipath transmission at the transport layer and has attracted lots of
attention from academia and industry. However, MPTCP only supports TCP-based
applications and its multipath routing flexibility is limited. In this paper,
we investigate the possibility of orchestrating multipath transmission from the
network layer of end devices, and develop a Multipath IP (MPIP) design
consisting of signaling, session and path management, multipath routing, and
NAT traversal. We implement MPIP in Linux and Android kernels. Through
controlled lab experiments and Internet experiments, we demonstrate that MPIP
can effectively achieve multipath gains at the network layer. It not only
supports the legacy TCP and UDP protocols, but also works seamlessly with
MPTCP. By facilitating user-defined customized routing, MPIP can route traffic
from competing applications in a coordinated fashion to maximize the aggregate
user Quality-of-Experience.Comment: 12 pages, 9 figure
Temperature effect on the coupling between coherent longitudinal phonons and plasmons in n- and p-type GaAs
The coupling between longitudinal optical (LO) phonons and plasmons plays a
fundamental role in determining the performance of doped semiconductor devices.
In this work, we report a comparative investigation into the dependence of the
coupling on temperature and doping in n- and p-type GaAs by using ultrafast
optical phonon spectroscopy. A suppression of coherent oscillations has been
observed in p-type GaAs at lower temperature, strikingly different from n-type
GaAs and other materials in which coherent oscillations are strongly enhanced
by cooling. We attribute this unexpected observation to a cooling-induced
elongation of the depth of the depletion layer which effectively increases the
screening time of surface field due to a slow diffusion of photoexcited
carriers in p-type GaAs. Such an increase breaks the requirement for the
generation of coherent LO phonons and, in turn, LO phonon-plasmon coupled modes
because of their delayed formation in time.Comment: 18 pages, 4 figure
Pixel-Adaptive Convolutional Neural Networks
Convolutions are the fundamental building block of CNNs. The fact that their
weights are spatially shared is one of the main reasons for their widespread
use, but it also is a major limitation, as it makes convolutions content
agnostic. We propose a pixel-adaptive convolution (PAC) operation, a simple yet
effective modification of standard convolutions, in which the filter weights
are multiplied with a spatially-varying kernel that depends on learnable, local
pixel features. PAC is a generalization of several popular filtering techniques
and thus can be used for a wide range of use cases. Specifically, we
demonstrate state-of-the-art performance when PAC is used for deep joint image
upsampling. PAC also offers an effective alternative to fully-connected CRF
(Full-CRF), called PAC-CRF, which performs competitively, while being
considerably faster. In addition, we also demonstrate that PAC can be used as a
drop-in replacement for convolution layers in pre-trained networks, resulting
in consistent performance improvements.Comment: CVPR 2019. Video introduction: https://youtu.be/gsQZbHuR64
Out-of-time-order correlators and quantum phase transitions in the Rabi and Dicke model
The out-of-time-order correlators (OTOCs) is used to study the quantum phase
transitions (QPTs) between the normal phase and the superradiant phase in the
Rabi and few-body Dicke models with large frequency ratio of theatomic level
splitting to the single-mode electromagnetic radiation field frequency. The
focus is on the OTOC thermally averaged with infinite temperature, which is an
experimentally feasible quantity. It is shown that thecritical points can be
identified by long-time averaging of the OTOC via observing its local minimum
behavior. More importantly, the scaling laws of the OTOC for QPTs are revealed
by studying the experimentally accessible conditions with finite frequency
ratio and finite number of atoms in the studied models. The critical exponents
extracted from the scaling laws of OTOC indicate that the QPTs in the Rabi and
Dicke models belong to the same universality class.Comment: 9 pages, 10 figures, v3: published version added; v2: supplemental
material added, more results adde
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