654 research outputs found
Kosp2e: Korean Speech to English Translation Corpus
Most speech-to-text (S2T) translation studies use English speech as a source,
which makes it difficult for non-English speakers to take advantage of the S2T
technologies. For some languages, this problem was tackled through corpus
construction, but the farther linguistically from English or the more
under-resourced, this deficiency and underrepresentedness becomes more
significant. In this paper, we introduce kosp2e (read as `kospi'), a corpus
that allows Korean speech to be translated into English text in an end-to-end
manner. We adopt open license speech recognition corpus, translation corpus,
and spoken language corpora to make our dataset freely available to the public,
and check the performance through the pipeline and training-based approaches.
Using pipeline and various end-to-end schemes, we obtain the highest BLEU of
21.3 and 18.0 for each based on the English hypothesis, validating the
feasibility of our data. We plan to supplement annotations for other target
languages through community contributions in the future.Comment: Interspeech 2021 Camera-read
Investigating an Effective Character-level Embedding in Korean Sentence Classification
conference pape
Separable PINN: Mitigating the Curse of Dimensionality in Physics-Informed Neural Networks
Physics-informed neural networks (PINNs) have emerged as new data-driven PDE
solvers for both forward and inverse problems. While promising, the expensive
computational costs to obtain solutions often restrict their broader
applicability. We demonstrate that the computations in automatic
differentiation (AD) can be significantly reduced by leveraging forward-mode AD
when training PINN. However, a naive application of forward-mode AD to
conventional PINNs results in higher computation, losing its practical benefit.
Therefore, we propose a network architecture, called separable PINN (SPINN),
which can facilitate forward-mode AD for more efficient computation. SPINN
operates on a per-axis basis instead of point-wise processing in conventional
PINNs, decreasing the number of network forward passes. Besides, while the
computation and memory costs of standard PINNs grow exponentially along with
the grid resolution, that of our model is remarkably less susceptible,
mitigating the curse of dimensionality. We demonstrate the effectiveness of our
model in various PDE systems by significantly reducing the training run-time
while achieving comparable accuracy. Project page:
https://jwcho5576.github.io/spinn/Comment: To appear in NeurIPS 2022 Workshop on The Symbiosis of Deep Learning
and Differential Equations (DLDE) - II, 12 pages, 5 figures, full paper:
arXiv:2306.1596
Separable Physics-Informed Neural Networks
Physics-informed neural networks (PINNs) have recently emerged as promising
data-driven PDE solvers showing encouraging results on various PDEs. However,
there is a fundamental limitation of training PINNs to solve multi-dimensional
PDEs and approximate highly complex solution functions. The number of training
points (collocation points) required on these challenging PDEs grows
substantially, but it is severely limited due to the expensive computational
costs and heavy memory overhead. To overcome this issue, we propose a network
architecture and training algorithm for PINNs. The proposed method, separable
PINN (SPINN), operates on a per-axis basis to significantly reduce the number
of network propagations in multi-dimensional PDEs unlike point-wise processing
in conventional PINNs. We also propose using forward-mode automatic
differentiation to reduce the computational cost of computing PDE residuals,
enabling a large number of collocation points (>10^7) on a single commodity
GPU. The experimental results show drastically reduced computational costs (62x
in wall-clock time, 1,394x in FLOPs given the same number of collocation
points) in multi-dimensional PDEs while achieving better accuracy. Furthermore,
we present that SPINN can solve a chaotic (2+1)-d Navier-Stokes equation
significantly faster than the best-performing prior method (9 minutes vs 10
hours in a single GPU), maintaining accuracy. Finally, we showcase that SPINN
can accurately obtain the solution of a highly nonlinear and multi-dimensional
PDE, a (3+1)-d Navier-Stokes equation.Comment: arXiv admin note: text overlap with arXiv:2211.0876
Development of multistage 10-m shuttle run test for VO2max estimation in healthy adults
Background and objective: The disadvantage of the traditional 20-m
multistage shuttle run test (MST) is that it requires a long space for
measurements and does not include various age groups to develop the test.
Therefore, we developed a new MST to improve the spatial limitation by reducing
the measurement to a 10-m distance and to resolve the bias via uniform
distributions of gender and age.
Material and methods: Study subjects included 120 healthy adults (60
males and 60 females) aged 20 to 50 years. All subjects performed a graded
maximal exercise test (GXT) and a 10-m MST at five-day intervals. We developed a
regression model using 70% of the subject’s data and performed a
cross-validation test using 30% of the data.
Results: The male
regression model’s coefficient of determination (R2) was 58.8%, and the
standard error of estimation (SEE) was 4.17 mL/kg/min. The female
regression model’s R2 was 69.2%, and the SEE was 3.39 mL/kg/min.
The 10-m MST showed a high correlation with GXT on the VO2max (males: 0.816;
females: 0.821). In the cross-validation test for the developed regression
models, the male’s SEE was 4.38 mL/kg/min, and the female’s SEE
was 4.56 mL/kg/min.
Conclusion: Thus, the 10-m MST is an accurate and valid method for
estimating the VO2max. Therefore, the 10-m MST developed by us can be used
when the existing 20-m MST cannot be used due to spatial limitations and can be
applied to both men and women in their 20s and 50s
Quantitative agreement of Dzyaloshinskii-Moriya interactions for domain-wall motion and spin-wave propagation
The magnetic exchange interaction is the one of the key factors governing the
basic characteristics of magnetic systems. Unlike the symmetric nature of the
Heisenberg exchange interaction, the interfacial Dzyaloshinskii-Moriya
interaction (DMI) generates an antisymmetric exchange interaction which offers
challenging opportunities in spintronics with intriguing antisymmetric
phenomena. The role of the DMI, however, is still being debated, largely
because distinct strengths of DMI have been measured for different magnetic
objects, particularly chiral magnetic domain walls (DWs) and non-reciprocal
spin waves (SWs). In this paper, we show that, after careful data analysis,
both the DWs and SWs experience the same strength of DMI. This was confirmed by
spin-torque efficiency measurement for the DWs, and Brillouin light scattering
measurement for the SWs. This observation, therefore, indicates the unique role
of the DMI on the magnetic DW and SW dynamics and also guarantees the
compatibility of several DMI-measurement schemes recently proposed.Comment: 24 pages, 5 figure
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