1,306 research outputs found
Toward Robustness in Multi-label Classification: A Data Augmentation Strategy against Imbalance and Noise
Multi-label classification poses challenges due to imbalanced and noisy
labels in training data. We propose a unified data augmentation method, named
BalanceMix, to address these challenges. Our approach includes two samplers for
imbalanced labels, generating minority-augmented instances with high diversity.
It also refines multi-labels at the label-wise granularity, categorizing noisy
labels as clean, re-labeled, or ambiguous for robust optimization. Extensive
experiments on three benchmark datasets demonstrate that BalanceMix outperforms
existing state-of-the-art methods. We release the code at
https://github.com/DISL-Lab/BalanceMix.Comment: This paper was accepted at AAAI 2024. We upload the full version of
our paper on arXiv due to the page limit of AAA
Sound Demixing Challenge 2023 Music Demixing Track Technical Report: TFC-TDF-UNet v3
In this report, we present our award-winning solutions for the Music Demixing
Track of Sound Demixing Challenge 2023. First, we propose TFC-TDF-UNet v3, a
time-efficient music source separation model that achieves state-of-the-art
results on the MUSDB benchmark. We then give full details regarding our
solutions for each Leaderboard, including a loss masking approach for
noise-robust training. Code for reproducing model training and final
submissions is available at github.com/kuielab/sdx23.Comment: 5 pages, 4 table
Implicit Stacked Autoregressive Model for Video Prediction
Future frame prediction has been approached through two primary methods:
autoregressive and non-autoregressive. Autoregressive methods rely on the
Markov assumption and can achieve high accuracy in the early stages of
prediction when errors are not yet accumulated. However, their performance
tends to decline as the number of time steps increases. In contrast,
non-autoregressive methods can achieve relatively high performance but lack
correlation between predictions for each time step. In this paper, we propose
an Implicit Stacked Autoregressive Model for Video Prediction (IAM4VP), which
is an implicit video prediction model that applies a stacked autoregressive
method. Like non-autoregressive methods, stacked autoregressive methods use the
same observed frame to estimate all future frames. However, they use their own
predictions as input, similar to autoregressive methods. As the number of time
steps increases, predictions are sequentially stacked in the queue. To evaluate
the effectiveness of IAM4VP, we conducted experiments on three common future
frame prediction benchmark datasets and weather\&climate prediction benchmark
datasets. The results demonstrate that our proposed model achieves
state-of-the-art performance
Functionality-Driven Musculature Retargeting
We present a novel retargeting algorithm that transfers the musculature of a
reference anatomical model to new bodies with different sizes, body
proportions, muscle capability, and joint range of motion while preserving the
functionality of the original musculature as closely as possible. The geometric
configuration and physiological parameters of musculotendon units are estimated
and optimized to adapt to new bodies. The range of motion around joints is
estimated from a motion capture dataset and edited further for individual
models. The retargeted model is simulation-ready, so we can physically simulate
muscle-actuated motor skills with the model. Our system is capable of
generating a wide variety of anatomical bodies that can be simulated to walk,
run, jump and dance while maintaining balance under gravity. We will also
demonstrate the construction of individualized musculoskeletal models from
bi-planar X-ray images and medical examinations.Comment: 15 pages, 20 figure
Microfluidic device for analyzing preferential chemotaxis and chemoreceptor sensitivity of bacterial cells toward carbon sources
We present a novel microfluidic device that enables high sensitive analyses of the chemotactic response of motile bacterial cells (Escherichia coli) that swim toward a preferred nutrient by sorting and concentrating them. The device consists of the Y-shaped microchannel that has been widely used in chemotaxis studies to attract cells toward a high concentration and a concentrator array integrated with arrowhead-shaped ratchet structures beside the main microchannel to trap and accumulate them. Since the number of accumulated cells in the concentrator array continuously increases with time, the device makes it possible to increase the sensitivity of detecting chemotactic responses of the cells about 10 times greater than Y-shaped channel devices in 60 min. In addition, the device can characterize the relative chemotactic sensitivity of chemoreceptors to chemoeffectors by comparing the number of cells in the concentrator array at different distances from the channel junction. Since the device allows the analysis of both the chemotactic responses and the sensitivity of chemoreceptors with high resolution, we believe that not only can the device be broadly used for various microbial chemotaxis assays but it also can further the advancement of microbiology and even synthetic biology.close9
Adaptive Noise Reduction Algorithm to Improve R Peak Detection in ECG Measured by Capacitive ECG Sensors
Electrocardiograms (ECGs) can be conveniently obtained using capacitive ECG sensors. However, motion noise in measured ECGs can degrade R peak detection. To reduce noise, properties of reference signal and ECG measured by the sensors are analyzed and a new method of active noise cancellation (ANC) is proposed in this study. In the proposed algorithm, the original ECG signal at QRS interval is regarded as impulsive noise because the adaptive filter updates its weight as if impulsive noise is added. As the proposed algorithm does not affect impulsive noise, the original signal is not reduced during ANC. Therefore, the proposed algorithm can conserve the power of the original signal within the QRS interval and reduce only the power of noise at other intervals. The proposed algorithm was verified through comparisons with recent research using data from both indoor and outdoor experiments. The proposed algorithm will benefit a noise reduction of noisy biomedical signal measured from sensors.11Ysciescopu
Waveguide Holography: Towards True 3D Holographic Glasses
We present a novel near-eye display concept which consists of a waveguide
combiner, a spatial light modulator, and a laser light source. The proposed
system can display true 3D holographic images through see-through
pupil-replicating waveguide combiner as well as providing a large eye-box. By
modeling the coherent light interaction inside of the waveguide combiner, we
demonstrate that the output wavefront from the waveguide can be controlled by
modulating the wavefront of input light using a spatial light modulator. This
new possibility allows combining a holographic display, which is considered as
the ultimate 3D display technology, with the state-of-the-art pupil replicating
waveguides, enabling the path towards true 3D holographic augmented reality
glasses
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