236 research outputs found
Non-local parameterization of atmospheric subgrid processes with neural networks
Subgrid processes in global climate models are represented by
parameterizations that are a major source of uncertainties in simulations of
climate. In recent years, it has been suggested that new machine-learning
parameterizations learned from high-resolution model output data could be
superior to traditional parameterizations. Currently, both traditional and
machine-learning parameterizations of subgrid processes in the atmosphere are
based on a single-column approach. Namely, the information used by these
parameterizations is taken from a single atmospheric column. However, a
single-column approach might not be ideal for the parameterization problem
since certain atmospheric phenomena, such as organized convective systems, can
cross multiple grid boxes and involve slantwise circulations that are not
purely vertical. Here we train neural networks using non-local inputs spanning
over 33 columns of inputs. We find that including the non-local inputs
substantially improves the prediction of subgrid tendencies of a range of
subgrid processes. The improvement is especially notable for cases associated
with mid-latitude fronts and convective instability. Using an explainable
artificial intelligence technique called layer-wise relevance propagation, we
find that non-local inputs from zonal and meridional winds contain information
that helps to improve the performance of the neural network parameterization.
Our results imply that use of non-local inputs has the potential to
substantially improve both traditional and machine-learning parameterizations.Comment: 31 pages, 17 figures (7 figures in the main file
A Weakly-Supervised Streaming Multilingual Speech Model with Truly Zero-Shot Capability
In this paper, we introduce our work of building a Streaming Multilingual
Speech Model (SM2), which can transcribe or translate multiple spoken languages
into texts of the target language. The backbone of SM2 is Transformer
Transducer, which has high streaming capability. Instead of human labeled
speech translation (ST) data, SM2 models are trained using weakly supervised
data generated by converting the transcriptions in speech recognition corpora
with a machine translation service. With 351 thousand hours of anonymized
speech training data from 25 languages, SM2 models achieve comparable or even
better ST quality than some recent popular large-scale non-streaming speech
models. More importantly, we show that SM2 has the truly zero-shot capability
when expanding to new target languages, yielding high quality ST results for
{source-speech, target-text} pairs that are not seen during training.Comment: submitted to ICASSP 202
A Discrete-Time Algorithm for Stiffness Extraction from sEMG and Its Application in Antidisturbance Teleoperation
© 2016 Peidong Liang et al. We have developed a new discrete-time algorithm of stiffness extraction from muscle surface electromyography (sEMG) collected from human operator's arms and have applied it for antidisturbance control in robot teleoperation. The variation of arm stiffness is estimated from sEMG signals and transferred to a telerobot under variable impedance control to imitate human motor control behaviours, particularly for disturbance attenuation. In comparison to the estimation of stiffness from sEMG, the proposed algorithm is able to reduce the nonlinear residual error effect and to enhance robustness and to simplify stiffness calibration. In order to extract a smoothing stiffness enveloping from sEMG signals, two enveloping methods are employed in this paper, namely, fast linear enveloping based on low pass filtering and moving average and amplitude monocomponent and frequency modulating (AM-FM) method. Both methods have been incorporated into the proposed stiffness variance estimation algorithm and are extensively tested. The test results show that stiffness variation extraction based on the two methods is sensitive and robust to attenuation disturbance. It could potentially be applied for teleoperation in the presence of hazardous surroundings or human robot physical cooperation scenarios
USB-NeRF: Unrolling Shutter Bundle Adjusted Neural Radiance Fields
Neural Radiance Fields (NeRF) has received much attention recently due to its
impressive capability to represent 3D scene and synthesize novel view images.
Existing works usually assume that the input images are captured by a global
shutter camera. Thus, rolling shutter (RS) images cannot be trivially applied
to an off-the-shelf NeRF algorithm for novel view synthesis. Rolling shutter
effect would also affect the accuracy of the camera pose estimation (e.g. via
COLMAP), which further prevents the success of NeRF algorithm with RS images.
In this paper, we propose Unrolling Shutter Bundle Adjusted Neural Radiance
Fields (USB-NeRF). USB-NeRF is able to correct rolling shutter distortions and
recover accurate camera motion trajectory simultaneously under the framework of
NeRF, by modeling the physical image formation process of a RS camera.
Experimental results demonstrate that USB-NeRF achieves better performance
compared to prior works, in terms of RS effect removal, novel view image
synthesis as well as camera motion estimation. Furthermore, our algorithm can
also be used to recover high-fidelity high frame-rate global shutter video from
a sequence of RS images
Evolution of Interlayer Coupling in Twisted MoS2 Bilayers
Van der Waals (vdW) coupling is emerging as a powerful method to engineer and
tailor physical properties of atomically thin two-dimensional (2D) materials.
In graphene/graphene and graphene/boron-nitride structures it leads to
interesting physical phenomena ranging from new van Hove singularities1-4 and
Fermi velocity renormalization5, 6 to unconventional quantum Hall effects7 and
Hofstadter's butterfly pattern8-12. 2D transition metal dichalcogenides
(TMDCs), another system of predominantly vdW-coupled atomically thin layers13,
14, can also exhibit interesting but different coupling phenomena because TMDCs
can be direct or indirect bandgap semiconductors15, 16. Here, we present the
first study on the evolution of interlayer coupling with twist angles in
as-grown MoS2 bilayers. We find that an indirect bandgap emerges in bilayers
with any stacking configuration, but the bandgap size varies appreciably with
the twist angle: it shows the largest redshift for AA- and AB-stacked bilayers,
and a significantly smaller but constant redshift for all other twist angles.
The vibration frequency of the out-of-plane phonon in MoS2 shows similar twist
angle dependence. Our observations, together with ab initio calculations,
reveal that this evolution of interlayer coupling originates from the repulsive
steric effects, which leads to different interlayer separations between the two
MoS2 layers in different stacking configurations
Structural and spectral dynamics of single-crystalline Ruddlesden-Popper phase halide perovskite blue light-emitting diodes.
Achieving perovskite-based high-color purity blue-emitting light-emitting diodes (LEDs) is still challenging. Here, we report successful synthesis of a series of blue-emissive two-dimensional Ruddlesden-Popper phase single crystals and their high-color purity blue-emitting LED demonstrations. Although this approach successfully achieves a series of bandgap emissions based on the different layer thicknesses, it still suffers from a conventional temperature-induced device degradation mechanism during high-voltage operations. To understand the underlying mechanism, we further elucidate temperature-induced device degradation by investigating the crystal structural and spectral evolution dynamics via in situ temperature-dependent single-crystal x-ray diffraction, photoluminescence (PL) characterization, and density functional theory calculation. The PL peak becomes asymmetrically broadened with a marked intensity decay, as temperature increases owing to [PbBr6]4- octahedra tilting and the organic chain disordering, which results in bandgap decrease. This study indicates that careful heat management under LED operation is a key factor to maintain the sharp and intense emission
SimCC: a Simple Coordinate Classification Perspective for Human Pose Estimation
The 2D heatmap-based approaches have dominated Human Pose Estimation (HPE)
for years due to high performance. However, the long-standing quantization
error problem in the 2D heatmap-based methods leads to several well-known
drawbacks: 1) The performance for the low-resolution inputs is limited; 2) To
improve the feature map resolution for higher localization precision, multiple
costly upsampling layers are required; 3) Extra post-processing is adopted to
reduce the quantization error. To address these issues, we aim to explore a
brand new scheme, called \textit{SimCC}, which reformulates HPE as two
classification tasks for horizontal and vertical coordinates. The proposed
SimCC uniformly divides each pixel into several bins, thus achieving
\emph{sub-pixel} localization precision and low quantization error. Benefiting
from that, SimCC can omit additional refinement post-processing and exclude
upsampling layers under certain settings, resulting in a more simple and
effective pipeline for HPE. Extensive experiments conducted over COCO,
CrowdPose, and MPII datasets show that SimCC outperforms heatmap-based
counterparts, especially in low-resolution settings by a large margin
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