165 research outputs found
Speaker-following Video Subtitles
We propose a new method for improving the presentation of subtitles in video
(e.g. TV and movies). With conventional subtitles, the viewer has to constantly
look away from the main viewing area to read the subtitles at the bottom of the
screen, which disrupts the viewing experience and causes unnecessary eyestrain.
Our method places on-screen subtitles next to the respective speakers to allow
the viewer to follow the visual content while simultaneously reading the
subtitles. We use novel identification algorithms to detect the speakers based
on audio and visual information. Then the placement of the subtitles is
determined using global optimization. A comprehensive usability study indicated
that our subtitle placement method outperformed both conventional
fixed-position subtitling and another previous dynamic subtitling method in
terms of enhancing the overall viewing experience and reducing eyestrain
Stable Feature Selection from Brain sMRI
Neuroimage analysis usually involves learning thousands or even millions of
variables using only a limited number of samples. In this regard, sparse
models, e.g. the lasso, are applied to select the optimal features and achieve
high diagnosis accuracy. The lasso, however, usually results in independent
unstable features. Stability, a manifest of reproducibility of statistical
results subject to reasonable perturbations to data and the model, is an
important focus in statistics, especially in the analysis of high dimensional
data. In this paper, we explore a nonnegative generalized fused lasso model for
stable feature selection in the diagnosis of Alzheimer's disease. In addition
to sparsity, our model incorporates two important pathological priors: the
spatial cohesion of lesion voxels and the positive correlation between the
features and the disease labels. To optimize the model, we propose an efficient
algorithm by proving a novel link between total variation and fast network flow
algorithms via conic duality. Experiments show that the proposed nonnegative
model performs much better in exploring the intrinsic structure of data via
selecting stable features compared with other state-of-the-arts
Continual Driving Policy Optimization with Closed-Loop Individualized Curricula
The safety of autonomous vehicles (AV) has been a long-standing top concern,
stemming from the absence of rare and safety-critical scenarios in the
long-tail naturalistic driving distribution. To tackle this challenge, a surge
of research in scenario-based autonomous driving has emerged, with a focus on
generating high-risk driving scenarios and applying them to conduct
safety-critical testing of AV models. However, limited work has been explored
on the reuse of these extensive scenarios to iteratively improve AV models.
Moreover, it remains intractable and challenging to filter through gigantic
scenario libraries collected from other AV models with distinct behaviors,
attempting to extract transferable information for current AV improvement.
Therefore, we develop a continual driving policy optimization framework
featuring Closed-Loop Individualized Curricula (CLIC), which we factorize into
a set of standardized sub-modules for flexible implementation choices: AV
Evaluation, Scenario Selection, and AV Training. CLIC frames AV Evaluation as a
collision prediction task, where it estimates the chance of AV failures in
these scenarios at each iteration. Subsequently, by re-sampling from historical
scenarios based on these failure probabilities, CLIC tailors individualized
curricula for downstream training, aligning them with the evaluated capability
of AV. Accordingly, CLIC not only maximizes the utilization of the vast
pre-collected scenario library for closed-loop driving policy optimization but
also facilitates AV improvement by individualizing its training with more
challenging cases out of those poorly organized scenarios. Experimental results
clearly indicate that CLIC surpasses other curriculum-based training
strategies, showing substantial improvement in managing risky scenarios, while
still maintaining proficiency in handling simpler cases
Understanding thermal induced escape mechanism of optically levitated sphere in vacuum
The escape phenomenon, mainly caused by thermal effects, is known as an
obstacle to the further practical application of optical levitation system in
vacuum. Irregular photophoresis induced by thermal effects can act as an
amplifier of Brownian motion. Studies on this topic provide interpretation for
particle escaping phenomenon during the pressure decreasing process, as well as
valuable insights into the micro- and nanoscale thermal effects in optical trap
in vacuum. In this paper, we derive and test a dynamic model for the motion of
an optically levitated particle in a non-equilibrium state and demonstrate the
escaping mechanism of heated particles. The result of theoretical
investigations is consistent with experimental escape at 0.1mbar. This work
reveals and provides a theoretical basis for the stable operation of laser
levitated oscillator in high vacuum and pave the way for the practicability of
ultra-sensitive sensing devices
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