136 research outputs found
The anisotropic Kerr nonlinear refractive index of the beta-barium borate (\beta-BaB2O4) nonlinear crystal
We study the anisotropic nature of the Kerr nonlinear response in a
beta-barium borate (\beta-BaB2O4, BBO) nonlinear crystal. The focus is on
determining the relevant cubic tensor components that affect
interaction of type I cascaded second-harmonic generation. Various experiments
in the literature are analyzed and we correct the data from some of the
experiments for contributions from cascading as well as for updated material
parameters. We find that the Kerr nonlinear tensor component responsible for
self-phase modulation in cascading is considerably larger than what has been
used to date. We evaluate the impact of using such a cubic anisotropic response
in ultrafast cascading experiments.Comment: Updated version, comments on experiments from the literature welcom
Generalized Nonlinear Wave Equation in Frequency Domain
We interpret the forward Maxwell equation with up to third order induced
polarizations and get so called nonlinear wave equation in frequency domain
(NWEF), which is based on Maxwell wave equation and using slowly varying
spectral amplitude approximation. The NWEF is generalized in concept as it
directly describes the electric field dynamics rather than the envelope
dynamics and because it concludes most current-interested nonlinear processes
such as three-wave mixing, four-wave-mixing and material Raman effects. We give
two sets of NWEF, one is a 1+1D equation describing the (approximated) planar
wave propagation in nonlinear bulk material and the other corresponds to the
propagation in a waveguide structure.Comment: Equation Derivation
Soliton compression to few-cycle pulses with a high quality factor by engineering cascaded quadratic nonlinearities
We propose an efficient approach to improve few-cycle soliton compression
with cascaded quadratic nonlinearities by using an engineered multi-section
structure of the nonlinear crystal. By exploiting engineering of the cascaded
quadratic nonlinearities, in each section soliton compression with a low
effective order is realized, and high-quality few-cycle pulses with large
compression factors are feasible. Each subsequent section is designed so that
the compressed pulse exiting the previous section experiences an overall
effective self-defocusing cubic nonlinearity corresponding to a modest soliton
order, which is kept larger than unity to ensure further compression. This is
done by increasing the cascaded quadratic nonlinearity in the new section with
an engineered reduced residual phase mismatch. The low soliton orders in each
section ensure excellent pulse quality and high efficiency. Numerical results
show that compressed pulses with less than three-cycle duration can be achieved
even when the compression factor is very large, and in contrast to standard
soliton compression, these compressed pulses have minimal pedestal and high
quality factor
Nonlinear wave equation in frequency domain: accurate modeling of ultrafast interaction in anisotropic nonlinear media
We interpret the purely spectral forward Maxwell equation with up to 3 order induced polarizations for pulse propagation and interactions in
quadratic nonlinear crystals. The interpreted equation, also named nonlinear
wave equation in frequency domain, includes both quadratic and cubic
nonlinearities, delayed Raman effects and anisotropic nonlinearities. The full
potential of this wave equation is demonstrated by investigating simulations of
solitons generated in the process of ultrafast cascaded second-harmonic
generation. We show that a balance in the soliton delay can be achieved due to
competition between self-steepening, Raman effects and self-steepening-like
effects from cascading originating in the group-velocity mismatch between the
pump and second harmonic. We analyze the first-order contributions, and show
that this balance can be broken to create fast or slow pulses. Through further
simulations we demonstrate few-cycle compressed solitons in extremely short
crystals, where spectral phenomena such as blue/red shifting, non-stationary
radiation in accordance with the non-local phase matching condition and
dispersive-wave generation are observed and marked, which help improving the
experimental knowledge of cascading nonlinear soliton pulse compression
RobustMQ: Benchmarking Robustness of Quantized Models
Quantization has emerged as an essential technique for deploying deep neural
networks (DNNs) on devices with limited resources. However, quantized models
exhibit vulnerabilities when exposed to various noises in real-world
applications. Despite the importance of evaluating the impact of quantization
on robustness, existing research on this topic is limited and often disregards
established principles of robustness evaluation, resulting in incomplete and
inconclusive findings. To address this gap, we thoroughly evaluated the
robustness of quantized models against various noises (adversarial attacks,
natural corruptions, and systematic noises) on ImageNet. The comprehensive
evaluation results empirically provide valuable insights into the robustness of
quantized models in various scenarios, for example: (1) quantized models
exhibit higher adversarial robustness than their floating-point counterparts,
but are more vulnerable to natural corruptions and systematic noises; (2) in
general, increasing the quantization bit-width results in a decrease in
adversarial robustness, an increase in natural robustness, and an increase in
systematic robustness; (3) among corruption methods, \textit{impulse noise} and
\textit{glass blur} are the most harmful to quantized models, while
\textit{brightness} has the least impact; (4) among systematic noises, the
\textit{nearest neighbor interpolation} has the highest impact, while bilinear
interpolation, cubic interpolation, and area interpolation are the three least
harmful. Our research contributes to advancing the robust quantization of
models and their deployment in real-world scenarios.Comment: 15 pages, 7 figure
Isolation and Induction: Training Robust Deep Neural Networks against Model Stealing Attacks
Despite the broad application of Machine Learning models as a Service
(MLaaS), they are vulnerable to model stealing attacks. These attacks can
replicate the model functionality by using the black-box query process without
any prior knowledge of the target victim model. Existing stealing defenses add
deceptive perturbations to the victim's posterior probabilities to mislead the
attackers. However, these defenses are now suffering problems of high inference
computational overheads and unfavorable trade-offs between benign accuracy and
stealing robustness, which challenges the feasibility of deployed models in
practice. To address the problems, this paper proposes Isolation and Induction
(InI), a novel and effective training framework for model stealing defenses.
Instead of deploying auxiliary defense modules that introduce redundant
inference time, InI directly trains a defensive model by isolating the
adversary's training gradient from the expected gradient, which can effectively
reduce the inference computational cost. In contrast to adding perturbations
over model predictions that harm the benign accuracy, we train models to
produce uninformative outputs against stealing queries, which can induce the
adversary to extract little useful knowledge from victim models with minimal
impact on the benign performance. Extensive experiments on several visual
classification datasets (e.g., MNIST and CIFAR10) demonstrate the superior
robustness (up to 48% reduction on stealing accuracy) and speed (up to 25.4x
faster) of our InI over other state-of-the-art methods. Our codes can be found
in https://github.com/DIG-Beihang/InI-Model-Stealing-Defense.Comment: Accepted by ACM Multimedia 202
Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling
Quantization of transformer language models faces significant challenges due
to the existence of detrimental outliers in activations. We observe that these
outliers are asymmetric and concentrated in specific channels. To address this
issue, we propose the Outlier Suppression+ framework. First, we introduce
channel-wise shifting and scaling operations to eliminate asymmetric
presentation and scale down problematic channels. We demonstrate that these
operations can be seamlessly migrated into subsequent modules while maintaining
equivalence. Second, we quantitatively analyze the optimal values for shifting
and scaling, taking into account both the asymmetric property and quantization
errors of weights in the next layer. Our lightweight framework can incur
minimal performance degradation under static and standard post-training
quantization settings. Comprehensive results across various tasks and models
reveal that our approach achieves near-floating-point performance on both small
models, such as BERT, and large language models (LLMs) including OPTs, BLOOM,
and BLOOMZ at 8-bit and 6-bit settings. Furthermore, we establish a new state
of the art for 4-bit BERT
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