59 research outputs found
Guided Filtering based Pyramidal Stereo Matching for Unrectified Images
Stereo matching deals with recovering quantitative
depth information from a set of input images, based on the visual
disparity between corresponding points. Generally most of the
algorithms assume that the processed images are rectified. As
robotics becomes popular, conducting stereo matching in the
context of cloth manipulation, such as obtaining the disparity
map of the garments from the two cameras of the cloth folding
robot, is useful and challenging. This is resulted from the fact of
the high efficiency, accuracy and low memory requirement under
the usage of high resolution images in order to capture the details
(e.g. cloth wrinkles) for the given application (e.g. cloth folding).
Meanwhile, the images can be unrectified. Therefore, we propose
to adapt guided filtering algorithm into the pyramidical stereo
matching framework that works directly for unrectified images.
To evaluate the proposed unrectified stereo matching in terms of
accuracy, we present three datasets that are suited to especially
the characteristics of the task of cloth manipulations. By com-
paring the proposed algorithm with two baseline algorithms on
those three datasets, we demonstrate that our proposed approach
is accurate, efficient and requires low memory. This also shows
that rather than relying on image rectification, directly applying
stereo matching through the unrectified images can be also quite
effective and meanwhile efficien
Efficient Prediction of Time- and Angle-Resolved Photoemission Spectroscopy Measurements on a Non-Equilibrium BCS Superconductor
We study how time- and angle-resolved photoemission (tr-ARPES) reveals the
dynamics of BCS-type, s-wave superconducting systems with time-varying order
parameters. Approximate methods are discussed, based on previous approaches to
either optical conductivity or quantum dot transport, in order to enable
computationally efficient prediction of photoemission spectra. One use of such
predictions is to enable extraction of the underlying order parameter dynamics
from experimental data, which is topical given the rapidly growing use of
tr-ARPES in studying unconventional superconductivity. The methods considered
model the two-time lesser Green's functions with an approximated lesser
self-energy that describes relaxation by coupling of the system to two types of
baths. The approach primarily used here also takes into consideration the
relaxation of the excited states into equilibrium by explicitly including the
level-broadening of the retarded and advanced Green's functions. We present
equilibrium and non-equilibrium calculations of tr-ARPES spectrum from our
model and discuss the signatures of different types of superconducting
dynamics.Comment: 13 pages, 11 figure
Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks
Unlearnable example attacks are data poisoning techniques that can be used to
safeguard public data against unauthorized use for training deep learning
models. These methods add stealthy perturbations to the original image, thereby
making it difficult for deep learning models to learn from these training data
effectively. Current research suggests that adversarial training can, to a
certain degree, mitigate the impact of unlearnable example attacks, while
common data augmentation methods are not effective against such poisons.
Adversarial training, however, demands considerable computational resources and
can result in non-trivial accuracy loss. In this paper, we introduce the
UEraser method, which outperforms current defenses against different types of
state-of-the-art unlearnable example attacks through a combination of effective
data augmentation policies and loss-maximizing adversarial augmentations. In
stark contrast to the current SOTA adversarial training methods, UEraser uses
adversarial augmentations, which extends beyond the confines of
perturbation budget assumed by current unlearning attacks and defenses. It also
helps to improve the model's generalization ability, thus protecting against
accuracy loss. UEraser wipes out the unlearning effect with error-maximizing
data augmentations, thus restoring trained model accuracies. Interestingly,
UEraser-Lite, a fast variant without adversarial augmentations, is also highly
effective in preserving clean accuracies. On challenging unlearnable CIFAR-10,
CIFAR-100, SVHN, and ImageNet-subset datasets produced with various attacks, it
achieves results that are comparable to those obtained during clean training.
We also demonstrate its efficacy against possible adaptive attacks. Our code is
open source and available to the deep learning community:
https://github.com/lafeat/ueraser.Comment: UEraser introduces adversarial augmentations to suppress unlearnable
example attacks and outperforms current defense
Accelerating In-Browser Deep Learning Inference on Diverse Edge Clients through Just-in-Time Kernel Optimizations
Web applications are increasingly becoming the primary platform for AI
service delivery, making in-browser deep learning (DL) inference more
prominent. However, current in-browser inference systems fail to effectively
utilize advanced web programming techniques and customize kernels for various
client devices, leading to suboptimal performance.
To address the issues, this paper presents the first in-browser inference
system, nn-JIT.web, which enables just-in-time (JIT) auto-generation of
optimized kernels for both CPUs and GPUs during inference. The system achieves
this by using two novel web programming techniques that can significantly
reduce kernel generation time, compared to other tensor compilers such as TVM,
while maintaining or even improving performance. The first technique,
Tensor-Web Compiling Co-Design, lowers compiling costs by unifying tensor and
web compiling and eliminating redundant and ineffective compiling passes. The
second technique, Web-Specific Lite Kernel Optimization Space Design, reduces
kernel tuning costs by focusing on web programming requirements and efficient
hardware resource utilization, limiting the optimization space to only dozens.
nn-JIT.web is evaluated for modern transformer models on a range of client
devices, including the mainstream CPUs and GPUs from ARM, Intel, AMD and
Nvidia. Results show that nn-JIT.web can achieve up to 8.2x faster within 30
seconds compared to the baselines across various models
Sciences for The 2.5-meter Wide Field Survey Telescope (WFST)
The Wide Field Survey Telescope (WFST) is a dedicated photometric survey
facility under construction jointly by the University of Science and Technology
of China and Purple Mountain Observatory. It is equipped with a primary mirror
of 2.5m in diameter, an active optical system, and a mosaic CCD camera of 0.73
Gpix on the main focus plane to achieve high-quality imaging over a field of
view of 6.5 square degrees. The installation of WFST in the Lenghu observing
site is planned to happen in the summer of 2023, and the operation is scheduled
to commence within three months afterward. WFST will scan the northern sky in
four optical bands (u, g, r, and i) at cadences from hourly/daily to
semi-weekly in the deep high-cadence survey (DHS) and the wide field survey
(WFS) programs, respectively. WFS reaches a depth of 22.27, 23.32, 22.84, and
22.31 in AB magnitudes in a nominal 30-second exposure in the four bands during
a photometric night, respectively, enabling us to search tremendous amount of
transients in the low-z universe and systematically investigate the variability
of Galactic and extragalactic objects. Intranight 90s exposures as deep as 23
and 24 mag in u and g bands via DHS provide a unique opportunity to facilitate
explorations of energetic transients in demand for high sensitivity, including
the electromagnetic counterparts of gravitational-wave events detected by the
second/third-generation GW detectors, supernovae within a few hours of their
explosions, tidal disruption events and luminous fast optical transients even
beyond a redshift of 1. Meanwhile, the final 6-year co-added images,
anticipated to reach g about 25.5 mag in WFS or even deeper by 1.5 mag in DHS,
will be of significant value to general Galactic and extragalactic sciences.
The highly uniform legacy surveys of WFST will also serve as an indispensable
complement to those of LSST which monitors the southern sky.Comment: 46 pages, submitted to SCMP
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