122 research outputs found
Effective Image Tampering Localization via Semantic Segmentation Network
With the widespread use of powerful image editing tools, image tampering
becomes easy and realistic. Existing image forensic methods still face
challenges of low accuracy and robustness. Note that the tampered regions are
typically semantic objects, in this letter we propose an effective image
tampering localization scheme based on deep semantic segmentation network.
ConvNeXt network is used as an encoder to learn better feature representation.
The multi-scale features are then fused by Upernet decoder for achieving better
locating capability. Combined loss and effective data augmentation are adopted
to ensure effective model training. Extensive experimental results confirm that
localization performance of our proposed scheme outperforms other
state-of-the-art ones
MSDRP: A Deep Learning Model Based on Multisource Data for Predicting Drug Response
Motivation: Cancer heterogeneity drastically affects cancer therapeutic outcomes. Predicting drug response in vitro is expected to help formulate personalized therapy regimens. In recent years, several computational models based on machine learning and deep learning have been proposed to predict drug response in vitro. However, most of these methods capture drug features based on a single drug description (e.g. drug structure), without considering the relationships between drugs and biological entities (e.g. target, diseases, and side effects). Moreover, most of these methods collect features separately for drugs and cell lines but fail to consider the pairwise interactions between drugs and cell lines.
Results: In this paper, we propose a deep learning framework, named MSDRP for drug response prediction. MSDRP uses an interaction module to capture interactions between drugs and cell lines, and integrates multiple associations/interactions between drugs and biological entities through similarity network fusion algorithms, outperforming some state-of-the-art models in all performance measures for all experiments. The experimental results of de novo test and independent test demonstrate the excellent performance of our model for new drugs. Furthermore, several case studies illustrate the rationality for using feature vectors derived from drug similarity matrices from multisource data to represent drugs and the interpretability of our model
Three-dimensional subsurface defect shape reconstruction and visualisation by pulsed thermography
Defects detected by most thermographic inspection are represented in the form of 2D image, which might limit the understanding of where the defects initiate and how they grow over time. This paper introduces a novel technique to rapidly estimate the defect depth and thickness simultaneously based on one single-side inspection. For the first time, defects are reconstructed and visualised in the form of a 3D image using cost-effective and rapid pulsed thermography technology. The feasibility and effectiveness of the proposed solution is demonstrated through inspecting a composite specimen and a steel specimen with semi-closed airgaps. For the composite specimen, this technique can deliver comparatively low averaged percentage error of the estimated total 3D defect volume of less than 10%
A clustering approach to detect faults with multi-component degradations in aircraft fuel systems
Accurate fault diagnosis and prognosis can significantly increase the safety and reliability of engineering systems and also reduce the maintenance costs. There is very limited relative research reported on the fault diagnosis of a complex system with multi-component degradation. The Complex Systems (CS) problem, which features multiple components simultaneously and nonlinearly interacting with each other and corresponding environment on multiple levels, has become an essential challenge in system engineering. In CS, even a single component degradation could cause misidentification of the fault severity level and lead to serious consequences. This paper introduces a new test rig to simulate multi-component degradations of the aircraft fuel system. A data analysis approach based on machine learning classification of both the time and frequency domain features is then proposed to detect and identify the fault severity level of CS with multi-component degradation. Results show that a) the fault can be sensitively detected with an accuracy > 99%; b) the severity of fault can be identified with an accuracy of 100%
Low-complexity full-field ultrafast nonlinear dynamics prediction by a convolutional feature separation modeling method
The modeling and prediction of the ultrafast nonlinear dynamics in the
optical fiber are essential for the studies of laser design, experimental
optimization, and other fundamental applications. The traditional propagation
modeling method based on the nonlinear Schr\"odinger equation (NLSE) has long
been regarded as extremely time-consuming, especially for designing and
optimizing experiments. The recurrent neural network (RNN) has been implemented
as an accurate intensity prediction tool with reduced complexity and good
generalization capability. However, the complexity of long grid input points
and the flexibility of neural network structure should be further optimized for
broader applications. Here, we propose a convolutional feature separation
modeling method to predict full-field ultrafast nonlinear dynamics with low
complexity and high flexibility, where the linear effects are firstly modeled
by NLSE-derived methods, then a convolutional deep learning method is
implemented for nonlinearity modeling. With this method, the temporal relevance
of nonlinear effects is substantially shortened, and the parameters and scale
of neural networks can be greatly reduced. The running time achieves a 94%
reduction versus NLSE and an 87% reduction versus RNN without accuracy
deterioration. In addition, the input pulse conditions, including grid point
numbers, durations, peak powers, and propagation distance, can be flexibly
changed during the predicting process. The results represent a remarkable
improvement in the ultrafast nonlinear dynamics prediction and this work also
provides novel perspectives of the feature separation modeling method for
quickly and flexibly studying the nonlinear characteristics in other fields.Comment: 15 pages,9 figure
A machine learning-based clustering approach to diagnose multi-component degradation of aircraft fuel systems
Accurate fault diagnosis and prognosis can significantly reduce maintenance costs, increase the safety and availability of engineering systems that have become increasingly complex. It has been observed that very limited researches have been reported on fault diagnosis where multi-component degradation are presented. This is essentially a challenging Complex Systems problem where features multiple components interacting simultaneously and nonlinearly with each other and its environment on multiple levels. Even the degradation of a single component can lead to a misidentification of the fault severity level. This paper introduces a new test rig to simulate the multi-component degradation of the aircraft fuel system. A machine learning-based data analytical approach based on the classification of clustering features from both time and frequency domains is proposed. The scope of this framework is the identification of the location and severity of not only the system fault but also the multi-component degradation. The results illustrate that (a) the fault can be detected with accuracy > 99%; (b) the severity of fault can be identified with an accuracy of almost 100%; (c) the degradation level can be successfully identified with the R-square value > 0.9
Learning Domain-Aware Detection Head with Prompt Tuning
Domain adaptive object detection (DAOD) aims to generalize detectors trained
on an annotated source domain to an unlabelled target domain. However, existing
methods focus on reducing the domain bias of the detection backbone by
inferring a discriminative visual encoder, while ignoring the domain bias in
the detection head. Inspired by the high generalization of vision-language
models (VLMs), applying a VLM as the robust detection backbone following a
domain-aware detection head is a reasonable way to learn the discriminative
detector for each domain, rather than reducing the domain bias in traditional
methods. To achieve the above issue, we thus propose a novel DAOD framework
named Domain-Aware detection head with Prompt tuning (DA-Pro), which applies
the learnable domain-adaptive prompt to generate the dynamic detection head for
each domain. Formally, the domain-adaptive prompt consists of the
domain-invariant tokens, domain-specific tokens, and the domain-related textual
description along with the class label. Furthermore, two constraints between
the source and target domains are applied to ensure that the domain-adaptive
prompt can capture the domains-shared and domain-specific knowledge. A prompt
ensemble strategy is also proposed to reduce the effect of prompt disturbance.
Comprehensive experiments over multiple cross-domain adaptation tasks
demonstrate that using the domain-adaptive prompt can produce an effectively
domain-related detection head for boosting domain-adaptive object detection
Balancing Logit Variation for Long-tailed Semantic Segmentation
Semantic segmentation usually suffers from a long-tail data distribution. Due
to the imbalanced number of samples across categories, the features of those
tail classes may get squeezed into a narrow area in the feature space. Towards
a balanced feature distribution, we introduce category-wise variation into the
network predictions in the training phase such that an instance is no longer
projected to a feature point, but a small region instead. Such a perturbation
is highly dependent on the category scale, which appears as assigning smaller
variation to head classes and larger variation to tail classes. In this way, we
manage to close the gap between the feature areas of different categories,
resulting in a more balanced representation. It is noteworthy that the
introduced variation is discarded at the inference stage to facilitate a
confident prediction. Although with an embarrassingly simple implementation,
our method manifests itself in strong generalizability to various datasets and
task settings. Extensive experiments suggest that our plug-in design lends
itself well to a range of state-of-the-art approaches and boosts the
performance on top of them
Gradient Information and Regularization for Gene Expression Programming to Develop Data-Driven Physics Closure Models
Learning accurate numerical constants when developing algebraic models is a
known challenge for evolutionary algorithms, such as Gene Expression
Programming (GEP). This paper introduces the concept of adaptive symbols to the
GEP framework by Weatheritt and Sandberg (2016) to develop advanced physics
closure models. Adaptive symbols utilize gradient information to learn locally
optimal numerical constants during model training, for which we investigate two
types of nonlinear optimization algorithms. The second contribution of this
work is implementing two regularization techniques to incentivize the
development of implementable and interpretable closure models. We apply
regularization to ensure small magnitude numerical constants and devise a novel
complexity metric that supports the development of low complexity models via
custom symbol complexities and multi-objective optimization. This extended
framework is employed to four use cases, namely rediscovering Sutherland's
viscosity law, developing laminar flame speed combustion models and training
two types of fluid dynamics turbulence models. The model prediction accuracy
and the convergence speed of training are improved significantly across all of
the more and less complex use cases, respectively. The two regularization
methods are essential for developing implementable closure models and we
demonstrate that the developed turbulence models substantially improve
simulations over state-of-the-art models
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