60 research outputs found
Focus on Hiders: Exploring Hidden Threats for Enhancing Adversarial Training
Adversarial training is often formulated as a min-max problem, however,
concentrating only on the worst adversarial examples causes alternating
repetitive confusion of the model, i.e., previously defended or correctly
classified samples are not defensible or accurately classifiable in subsequent
adversarial training. We characterize such non-ignorable samples as "hiders",
which reveal the hidden high-risk regions within the secure area obtained
through adversarial training and prevent the model from finding the real worst
cases. We demand the model to prevent hiders when defending against adversarial
examples for improving accuracy and robustness simultaneously. By rethinking
and redefining the min-max optimization problem for adversarial training, we
propose a generalized adversarial training algorithm called Hider-Focused
Adversarial Training (HFAT). HFAT introduces the iterative evolution
optimization strategy to simplify the optimization problem and employs an
auxiliary model to reveal hiders, effectively combining the optimization
directions of standard adversarial training and prevention hiders. Furthermore,
we introduce an adaptive weighting mechanism that facilitates the model in
adaptively adjusting its focus between adversarial examples and hiders during
different training periods. We demonstrate the effectiveness of our method
based on extensive experiments, and ensure that HFAT can provide higher
robustness and accuracy
Silicon photonic MEMS switches based on split waveguide crossings
The continuous push for high-performance photonic switches is one of the most
crucial premises for the sustainable scaling of programmable and reconfigurable
photonic circuits for a wide spectrum of applications. Large-scale photonic
switches constructed with a large number of 22 elementary switches
impose stringent requirements on the elementary switches. In contrast to
conventional elementary switches based on mode interference or mode coupling,
here we propose and realize a brand-new silicon MEMS 22 elementary
switch based on a split waveguide crossing (SWX) consisting of two halves. With
this structure, the propagation direction of the incident light can be
manipulated to implement the OFF and ON states by splitting or combining the
two halves of the SWX, respectively. More specifically, we introduce
refractive-index engineering by incorporating subwavelength-tooth (SWT)
structures on both reflecting facets to further reduce the excess loss in the
ON state. Such a unique switching mechanism features a compact footprint on a
standard SOI wafer and enables excellent photonic performance with low excess
loss of 0.1-0.52/0.1-0.47dB and low crosstalk of -37/-22.5dB over an
ultrawide bandwidth of 1400-1700nm for the OFF/ON states in simulation, while
in experiment, excess loss of 0.15-0.52/0.42-0.66dB and crosstalk of
-45.5/-25dB over the bandwidth of 1525-1605 nm for the OFF/ON states have
been measured.Furthermore, excellent MEMS characteristics such as near-zero
steady-state power consumption, low switching energy of sub-pJ, switching speed
of {\mu}s-scale, durability beyond 10^9 switching cycles, and overall device
robustness have been achieved. Finally, a 1616 switch using Benes
topology has also been fabricated and characterized as a proof of concept,
further validating the suitability of the SWX switches for large-scale
integration
On the Cultivation Mode of Petroleum Engineering Professionals in Higher Vocational Education
In this essay, the author points out several problems during the cultivation of petroleum engineering professionals in higher vocational education, and brings up an innovative training model which involves “platform + module” in theoretical teaching and “basis + innovation” in practical teaching. Analysis are made on the reform of curriculum system, improvement in students’ professional accomplishment, strengthening of teaching staff construction featured as “double-Identity instructor with double capabilities”, acceleration in practice bases construction, improvement of teaching method of skills training , enhancement in professional skills training, etc.
Research Status of Oil Well Casing Damage Image Recognition Technology
Along with the production of the oil well, the casing damage has become more and more serious, which seriously affects the oil wells production, even resulting in the oil wells abandonment. The oil well casing damage usually occurs in the deep downhole place, and the imaging quality is generally low due to the complexity of downhole and the limitation of test equipment itself, it is difficult to identify the shape and parameter of the casing failure; therefore, how to accurately obtain the information of damaged parts becomes a big difficulty. Digital processing, image segmentation and edge tracing are carried out for the casing image by using image processing techniques. The physical dimension of the material object in the image is obtained by applying the image-forming principle, achieving the quantitative interpretation to the surface condition inside of the casing. The image recognition technology not only can quickly find the damaged parts but also can give the corresponding information for the damaged casing. In this paper, the image recognition technology of casing damages introduced. Key words: Casing; Image Recognition; Damage; Filterin
PaintSeg: Training-free Segmentation via Painting
The paper introduces PaintSeg, a new unsupervised method for segmenting
objects without any training. We propose an adversarial masked contrastive
painting (AMCP) process, which creates a contrast between the original image
and a painted image in which a masked area is painted using off-the-shelf
generative models. During the painting process, inpainting and outpainting are
alternated, with the former masking the foreground and filling in the
background, and the latter masking the background while recovering the missing
part of the foreground object. Inpainting and outpainting, also referred to as
I-step and O-step, allow our method to gradually advance the target
segmentation mask toward the ground truth without supervision or training.
PaintSeg can be configured to work with a variety of prompts, e.g. coarse
masks, boxes, scribbles, and points. Our experimental results demonstrate that
PaintSeg outperforms existing approaches in coarse mask-prompt, box-prompt, and
point-prompt segmentation tasks, providing a training-free solution suitable
for unsupervised segmentation
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