144 research outputs found
CopyScope: Model-level Copyright Infringement Quantification in the Diffusion Workflow
Web-based AI image generation has become an innovative art form that can
generate novel artworks with the rapid development of the diffusion model.
However, this new technique brings potential copyright infringement risks as it
may incorporate the existing artworks without the owners' consent. Copyright
infringement quantification is the primary and challenging step towards
AI-generated image copyright traceability. Previous work only focused on data
attribution from the training data perspective, which is unsuitable for tracing
and quantifying copyright infringement in practice because of the following
reasons: (1) the training datasets are not always available in public; (2) the
model provider is the responsible party, not the image. Motivated by this, in
this paper, we propose CopyScope, a new framework to quantify the infringement
of AI-generated images from the model level. We first rigorously identify
pivotal components within the AI image generation pipeline. Then, we propose to
take advantage of Fr\'echet Inception Distance (FID) to effectively capture the
image similarity that fits human perception naturally. We further propose the
FID-based Shapley algorithm to evaluate the infringement contribution among
models. Extensive experiments demonstrate that our work not only reveals the
intricacies of infringement quantification but also effectively depicts the
infringing models quantitatively, thus promoting accountability in AI
image-generation tasks
A Rectangular Planar Spiral Antenna for GIS Partial Discharge Detection
A rectangular planar spiral antenna sensor was designed for detecting the partial discharge in gas insulation substations (GIS). It can expediently receive electromagnetic waves leaked from basin-type insulators and can effectively suppress low frequency electromagnetic interference from the surrounding environment. Certain effective techniques such as rectangular spiral structure, bow-tie loading, and back cavity structure optimization during the antenna design process can miniaturize antenna size and optimize voltage standing wave ratio (VSWR) characteristics. Model calculation and experimental data measured in the laboratory show that the antenna possesses a good radiating performance and a multiband property when working in the ultrahigh frequency (UHF) band. A comparative study between characteristics of the designed antenna and the existing quasi-TEM horn antenna was made. Based on the GIS defect simulation equipment in the laboratory, partial discharge signals were detected by the designed antenna, the available quasi-TEM horn antenna, and the microstrip patch antenna, and the measurement results were compared
WM-NET: Robust Deep 3D Watermarking with Limited Data
The goal of 3D mesh watermarking is to embed the message in 3D meshes that
can withstand various attacks imperceptibly and reconstruct the message
accurately from watermarked meshes. Traditional methods are less robust against
attacks. Recent DNN-based methods either introduce excessive distortions or
fail to embed the watermark without the help of texture information. However,
embedding the watermark in textures is insecure because replacing the texture
image can completely remove the watermark. In this paper, we propose a robust
deep 3D mesh watermarking WM-NET, which leverages attention-based convolutions
in watermarking tasks to embed binary messages in vertex distributions without
texture assistance. Furthermore, our WM-NET exploits the property that
simplified meshes inherit similar relations from the original ones, where the
relation is the offset vector directed from one vertex to its neighbor. By
doing so, our method can be trained on simplified meshes(limited data) but
remains effective on large-sized meshes (size adaptable) and unseen categories
of meshes (geometry adaptable). Extensive experiments demonstrate our method
brings 50% fewer distortions and 10% higher bit accuracy compared to previous
work. Our watermark WM-NET is robust against various mesh attacks, e.g. Gauss,
rotation, translation, scaling, and cropping
LSTM Learning with Bayesian and Gaussian Processing for Anomaly Detection in Industrial IoT
The data generated by millions of sensors in Industrial Internet of Things (IIoT) is extremely dynamic, heterogeneous, and large scale. It poses great challenges on the real-time analysis and decision making for anomaly detection in IIoT. In this paper, we propose a LSTM-Gauss-NBayes method, which is a synergy of the long short-term memory neural network (LSTM-NN) and the Gaussian Bayes model for outlier detection in IIoT. In a nutshell, the LSTM-NN builds model on normal time series. It detects outliers by utilising the predictive error for the Gaussian Naive Bayes model. Our method exploits advantages of both LSTM and Gaussian Naive Bayes models, which not only has strong prediction capability of LSTM for future time point data, but also achieves an excellent classification performance of Gaussian Naive Bayes model through the predictive error. Empirical studies demonstrate our solution outperforms the best-known competitors, which is a preferable choice for detecting anomalies
DoseDiff: Distance-aware Diffusion Model for Dose Prediction in Radiotherapy
Treatment planning is a critical component of the radiotherapy workflow,
typically carried out by a medical physicist using a time-consuming
trial-and-error manner. Previous studies have proposed knowledge-based or deep
learning-based methods for predicting dose distribution maps to assist medical
physicists in improving the efficiency of treatment planning. However, these
dose prediction methods usuallylack the effective utilization of distance
information between surrounding tissues andtargets or organs-at-risk (OARs).
Moreover, they are poor in maintaining the distribution characteristics of ray
paths in the predicted dose distribution maps, resulting in a loss of valuable
information obtained by medical physicists. In this paper, we propose a
distance-aware diffusion model (DoseDiff) for precise prediction of dose
distribution. We define dose prediction as a sequence of denoising steps,
wherein the predicted dose distribution map is generated with the conditions of
the CT image and signed distance maps (SDMs). The SDMs are obtained by a
distance transformation from the masks of targets or OARs, which provide the
distance information from each pixel in the image to the outline of the targets
or OARs. Besides, we propose a multiencoder and multi-scale fusion network
(MMFNet) that incorporates a multi-scale fusion and a transformer-based fusion
module to enhance information fusion between the CT image and SDMs at the
feature level. Our model was evaluated on two datasets collected from patients
with breast cancer and nasopharyngeal cancer, respectively. The results
demonstrate that our DoseDiff outperforms the state-of-the-art dose prediction
methods in terms of both quantitative and visual quality
EFFL: Egalitarian Fairness in Federated Learning for Mitigating Matthew Effect
Recent advances in federated learning (FL) enable collaborative training of
machine learning (ML) models from large-scale and widely dispersed clients
while protecting their privacy. However, when different clients' datasets are
heterogeneous, traditional FL mechanisms produce a global model that does not
adequately represent the poorer clients with limited data resources, resulting
in lower accuracy and higher bias on their local data. According to the Matthew
effect, which describes how the advantaged gain more advantage and the
disadvantaged lose more over time, deploying such a global model in client
applications may worsen the resource disparity among the clients and harm the
principles of social welfare and fairness. To mitigate the Matthew effect, we
propose Egalitarian Fairness Federated Learning (EFFL), where egalitarian
fairness refers to the global model learned from FL has: (1) equal accuracy
among clients; (2) equal decision bias among clients. Besides achieving
egalitarian fairness among the clients, EFFL also aims for performance
optimality, minimizing the empirical risk loss and the bias for each client;
both are essential for any ML model training, whether centralized or
decentralized. We formulate EFFL as a constrained multi-constrained
multi-objectives optimization (MCMOO) problem, with the decision bias and
egalitarian fairness as constraints and the minimization of the empirical risk
losses on all clients as multiple objectives to be optimized. We propose a
gradient-based three-stage algorithm to obtain the Pareto optimal solutions
within the constraint space. Extensive experiments demonstrate that EFFL
outperforms other state-of-the-art FL algorithms in achieving a
high-performance global model with enhanced egalitarian fairness among all
clients
Optimized sample preparation for two-dimensional gel electrophoresis of soluble proteins from chicken bursa of Fabricius
<p>Abstract</p> <p>Background</p> <p>Two-dimensional gel electrophoresis (2-DE) is a powerful method to study protein expression and function in living organisms and diseases. This technique, however, has not been applied to avian bursa of Fabricius (BF), a central immune organ. Here, optimized 2-DE sample preparation methodologies were constructed for the chicken BF tissue. Using the optimized protocol, we performed further 2-DE analysis on a soluble protein extract from the BF of chickens infected with virulent avibirnavirus. To demonstrate the quality of the extracted proteins, several differentially expressed protein spots selected were cut from 2-DE gels and identified by matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS).</p> <p>Results</p> <p>An extraction buffer containing 7 M urea, 2 M thiourea, 2% (w/v) 3-[(3-cholamidopropyl)-dimethylammonio]-1-propanesulfonate (CHAPS), 50 mM dithiothreitol (DTT), 0.2% Bio-Lyte 3/10, 1 mM phenylmethylsulfonyl fluoride (PMSF), 20 U/ml Deoxyribonuclease I (DNase I), and 0.25 mg/ml Ribonuclease A (RNase A), combined with sonication and vortex, yielded the best 2-DE data. Relative to non-frozen immobilized pH gradient (IPG) strips, frozen IPG strips did not result in significant changes in the 2-DE patterns after isoelectric focusing (IEF). When the optimized protocol was used to analyze the spleen and thymus, as well as avibirnavirus-infected bursa, high quality 2-DE protein expression profiles were obtained. 2-DE maps of BF of chickens infected with virulent avibirnavirus were visibly different and many differentially expressed proteins were found.</p> <p>Conclusion</p> <p>These results showed that method C, in concert extraction buffer IV, was the most favorable for preparing samples for IEF and subsequent protein separation and yielded the best quality 2-DE patterns. The optimized protocol is a useful sample preparation method for comparative proteomics analysis of chicken BF tissues.</p
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