5,926 research outputs found
Approximate symmetry reduction approach: infinite series reductions to the KdV-Burgers equation
For weak dispersion and weak dissipation cases, the (1+1)-dimensional
KdV-Burgers equation is investigated in terms of approximate symmetry reduction
approach. The formal coherence of similarity reduction solutions and similarity
reduction equations of different orders enables series reduction solutions. For
weak dissipation case, zero-order similarity solutions satisfy the Painlev\'e
II, Painlev\'e I and Jacobi elliptic function equations. For weak dispersion
case, zero-order similarity solutions are in the form of Kummer, Airy and
hyperbolic tangent functions. Higher order similarity solutions can be obtained
by solving linear ordinary differential equations.Comment: 14 pages. The original model (1) in previous version is generalized
to a more extensive form and the incorrect equations (35) and (36) in
previous version are correcte
Min-Max Similarity: A Contrastive Learning Based Semi-Supervised Learning Network for Surgical Tools Segmentation
Segmentation of images is a popular topic in medical AI. This is mainly due
to the difficulty to obtain a significant number of pixel-level annotated data
to train a neural network. To address this issue, we proposed a semi-supervised
segmentation network based on contrastive learning. In contrast to the previous
state-of-the-art, we introduce a contrastive learning form of dual-view
training by employing classifiers and projectors to build all-negative, and
positive and negative feature pairs respectively to formulate the learning
problem as solving min-max similarity problem. The all-negative pairs are used
to supervise the networks learning from different views and make sure to
capture general features, and the consistency of unlabeled predictions is
measured by pixel-wise contrastive loss between positive and negative pairs. To
quantitative and qualitative evaluate our proposed method, we test it on two
public endoscopy surgical tool segmentation datasets and one cochlear implant
surgery dataset which we manually annotate the cochlear implant in surgical
videos. The segmentation performance (dice coefficients) indicates that our
proposed method outperforms state-of-the-art semi-supervised and fully
supervised segmentation algorithms consistently. The code is publicly available
at: https://github.com/AngeLouCN/Min_Max_Similarit
PromptCARE: Prompt Copyright Protection by Watermark Injection and Verification
Large language models (LLMs) have witnessed a meteoric rise in popularity
among the general public users over the past few months, facilitating diverse
downstream tasks with human-level accuracy and proficiency. Prompts play an
essential role in this success, which efficiently adapt pre-trained LLMs to
task-specific applications by simply prepending a sequence of tokens to the
query texts. However, designing and selecting an optimal prompt can be both
expensive and demanding, leading to the emergence of Prompt-as-a-Service
providers who profit by providing well-designed prompts for authorized use.
With the growing popularity of prompts and their indispensable role in
LLM-based services, there is an urgent need to protect the copyright of prompts
against unauthorized use.
In this paper, we propose PromptCARE, the first framework for prompt
copyright protection through watermark injection and verification. Prompt
watermarking presents unique challenges that render existing watermarking
techniques developed for model and dataset copyright verification ineffective.
PromptCARE overcomes these hurdles by proposing watermark injection and
verification schemes tailor-made for prompts and NLP characteristics. Extensive
experiments on six well-known benchmark datasets, using three prevalent
pre-trained LLMs (BERT, RoBERTa, and Facebook OPT-1.3b), demonstrate the
effectiveness, harmlessness, robustness, and stealthiness of PromptCARE.Comment: To Appear in the 45th IEEE Symposium on Security and Privacy 2024,
code is available at: https://github.com/grasses/PromptCAR
Approximate perturbed direct homotopy reduction method: infinite series reductions to two perturbed mKdV equations
An approximate perturbed direct homotopy reduction method is proposed and
applied to two perturbed modified Korteweg-de Vries (mKdV) equations with
fourth order dispersion and second order dissipation. The similarity reduction
equations are derived to arbitrary orders. The method is valid not only for
single soliton solution but also for the Painlev\'e II waves and periodic waves
expressed by Jacobi elliptic functions for both fourth order dispersion and
second order dissipation. The method is valid also for strong perturbations.Comment: 8 pages, 1 figur
RMT: Rule-based Metamorphic Testing for Autonomous Driving Models
Deep neural network models are widely used for perception and control in
autonomous driving. Recent work uses metamorphic testing but is limited to
using equality-based metamorphic relations and does not provide expressiveness
for defining inequality-based metamorphic relations. To encode real world
traffic rules, domain experts must be able to express higher order relations
e.g., a vehicle should decrease speed in certain ratio, when there is a vehicle
x meters ahead and compositionality e.g., a vehicle must have a larger
deceleration, when there is a vehicle ahead and when the weather is rainy and
proportional compounding effect to the test outcome. We design RMT, a
declarative rule-based metamorphic testing framework. It provides three
components that work in concert:(1) a domain specific language that enables an
expert to express higher-order, compositional metamorphic relations, (2)
pluggable transformation engines built on a variety of image and graphics
processing techniques, and (3) automated test generation that translates a
human-written rule to a corresponding executable, metamorphic relation and
synthesizes meaningful inputs.Our evaluation using three driving models shows
that RMT can generate meaningful test cases on which 89% of erroneous
predictions are found by enabling higher-order metamorphic relations.
Compositionality provides further aids for generating meaningful, synthesized
inputs-3012 new images are generated by compositional rules. These detected
erroneous predictions are manually examined and confirmed by six human judges
as meaningful traffic rule violations. RMT is the first to expand automated
testing capability for autonomous vehicles by enabling easy mapping of traffic
regulations to executable metamorphic relations and to demonstrate the benefits
of expressivity, customization, and pluggability
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