467 research outputs found
Let Graph be the Go Board: Gradient-free Node Injection Attack for Graph Neural Networks via Reinforcement Learning
Graph Neural Networks (GNNs) have drawn significant attentions over the years
and been broadly applied to essential applications requiring solid robustness
or vigorous security standards, such as product recommendation and user
behavior modeling. Under these scenarios, exploiting GNN's vulnerabilities and
further downgrading its performance become extremely incentive for adversaries.
Previous attackers mainly focus on structural perturbations or node injections
to the existing graphs, guided by gradients from the surrogate models. Although
they deliver promising results, several limitations still exist. For the
structural perturbation attack, to launch a proposed attack, adversaries need
to manipulate the existing graph topology, which is impractical in most
circumstances. Whereas for the node injection attack, though being more
practical, current approaches require training surrogate models to simulate a
white-box setting, which results in significant performance downgrade when the
surrogate architecture diverges from the actual victim model. To bridge these
gaps, in this paper, we study the problem of black-box node injection attack,
without training a potentially misleading surrogate model. Specifically, we
model the node injection attack as a Markov decision process and propose
Gradient-free Graph Advantage Actor Critic, namely G2A2C, a reinforcement
learning framework in the fashion of advantage actor critic. By directly
querying the victim model, G2A2C learns to inject highly malicious nodes with
extremely limited attacking budgets, while maintaining a similar node feature
distribution. Through our comprehensive experiments over eight acknowledged
benchmark datasets with different characteristics, we demonstrate the superior
performance of our proposed G2A2C over the existing state-of-the-art attackers.
Source code is publicly available at: https://github.com/jumxglhf/G2A2C}.Comment: AAAI 2023. v2: update acknowledgement section. arXiv admin note:
substantial text overlap with arXiv:2202.0938
The influence of lipids on the fate of nitrogen during hydrothermal liquefaction of protein-containing biomass
Nitrogen (N) in the bio-crude obtained from hydrothermal liquefaction (HTL) of protein-containing biomass not only reduces the heating value of fuels, but also increases cost for upgrading to meet the existing fuel standards. Considerable work so far had been focused on N-containing heterocycles formed via Maillard reactions. However, limited information is available on the influence of lipids, as the amides formation could compete with the Maillard reactions, further affecting the fate of N. The objective of this work is therefore to identify the influence of lipids on the nitrogen distribution in the different product phases, with a particular focus on the reaction of N-containing compounds, trying to achieve deeper understanding about reaction mechanism of HTL.
In this study, we tested a set of model compounds (lactose as model carbohydrate, lysine as model protein, palmitic acid as model component of a lipid) to conduct HTL. The model compounds were treated individually and in mixtures at 250 - 350 °C for batch reaction times of 20 min. We investigated the N-distribution in the different HTL-products, mainly focusing on the bio-crude. At 300 °C, only 4.9 wt.% of N distribution (defined as the amount of N in the product relative to that in the feedstocks) is found from HTL of single lysine, while 43.6 wt.% of that is obtained from HTL of the ternary mixture. This is most likely because the higher yield (54.1wt.%) of bio-crude produced from mixture. Specific N-containing compounds in the bio-crude were quantified. With addition of lipids, less yields of typical Maillard reaction products like pyrazines and caprolactam, generated from HTL of carbohydrates and proteins, were obtained, while amides are revealed with significant yield of 2.1 wt.%, indicating that in the presence of lipids, amide formation competes with the generation of Maillard reaction products. These results provide valuable insights for the transformation of nitrogen as well as the reaction pathways of complex systems such as sewage sludge, micro algae, food waste and on the like
Multi-scale Analysis based Image Fusion
Image fusion provides a better view than that provided by any of the individual source images. The aim of multi-scale analysis is to find a kind of optimal representation for high dimensional information expression. Based on the nonlinear approximation, the principle and ways of image fusion are studied, and its development, current and future challenges are reviewed in this paper.The 2nd International Conference on Intelligent Systems and Image Processing 2014 (ICISIP2014), September 26-29, 2014, Nishinippon Institute of Technology, Kitakyushu, Japa
Multi-scale Analysis based Image Fusion
The 2nd International Conference on Intelligent Systems and Image Processing 2014 (ICISIP2014), September 26-29, 2014, Nishinippon Institute of Technology, Kitakyushu, JapanImage fusion provides a better view than that provided by any of the individual source images. The aim of multi-scale analysis is to find a kind of optimal representation for high dimensional information expression. Based on the nonlinear approximation, the principle and ways of image fusion are studied, and its development, current and future challenges are reviewed in this paper
Nest-DGIL: Nesterov-optimized Deep Geometric Incremental Learning for CS Image Reconstruction
Proximal gradient-based optimization is one of the most common strategies for
solving image inverse problems as well as easy to implement. However, these
techniques often generate heavy artifacts in image reconstruction. One of the
most popular refinement methods is to fine-tune the regularization parameter to
alleviate such artifacts, but it may not always be sufficient or applicable due
to increased computational costs. In this work, we propose a deep geometric
incremental learning framework based on second Nesterov proximal gradient
optimization. The proposed end-to-end network not only has the powerful
learning ability for high/low frequency image features,but also can
theoretically guarantee that geometric texture details will be reconstructed
from preliminary linear reconstruction.Furthermore, it can avoid the risk of
intermediate reconstruction results falling outside the geometric decomposition
domains and achieve fast convergence. Our reconstruction framework is
decomposed into four modules including general linear reconstruction, cascade
geometric incremental restoration, Nesterov acceleration and post-processing.
In the image restoration step,a cascade geometric incremental learning module
is designed to compensate for the missing texture information from different
geometric spectral decomposition domains. Inspired by overlap-tile strategy, we
also develop a post-processing module to remove the block-effect in
patch-wise-based natural image reconstruction. All parameters in the proposed
model are learnable,an adaptive initialization technique of physical-parameters
is also employed to make model flexibility and ensure converging smoothly. We
compare the reconstruction performance of the proposed method with existing
state-of-the-art methods to demonstrate its superiority. Our source codes are
available at https://github.com/fanxiaohong/Nest-DGIL.Comment: 15 page
Adaptive Sparse Pairwise Loss for Object Re-Identification
Object re-identification (ReID) aims to find instances with the same identity
as the given probe from a large gallery. Pairwise losses play an important role
in training a strong ReID network. Existing pairwise losses densely exploit
each instance as an anchor and sample its triplets in a mini-batch. This dense
sampling mechanism inevitably introduces positive pairs that share few visual
similarities, which can be harmful to the training. To address this problem, we
propose a novel loss paradigm termed Sparse Pairwise (SP) loss that only
leverages few appropriate pairs for each class in a mini-batch, and empirically
demonstrate that it is sufficient for the ReID tasks. Based on the proposed
loss framework, we propose an adaptive positive mining strategy that can
dynamically adapt to diverse intra-class variations. Extensive experiments show
that SP loss and its adaptive variant AdaSP loss outperform other pairwise
losses, and achieve state-of-the-art performance across several ReID
benchmarks. Code is available at https://github.com/Astaxanthin/AdaSP.Comment: Accepted by CVPR 202
DiP: Learning Discriminative Implicit Parts for Person Re-Identification
In person re-identification (ReID) tasks, many works explore the learning of
part features to improve the performance over global image features. Existing
methods extract part features in an explicit manner, by either using a
hand-designed image division or keypoints obtained with external visual
systems. In this work, we propose to learn Discriminative implicit Parts (DiPs)
which are decoupled from explicit body parts. Therefore, DiPs can learn to
extract any discriminative features that can benefit in distinguishing
identities, which is beyond predefined body parts (such as accessories).
Moreover, we propose a novel implicit position to give a geometric
interpretation for each DiP. The implicit position can also serve as a learning
signal to encourage DiPs to be more position-equivariant with the identity in
the image. Lastly, a set of attributes and auxiliary losses are introduced to
further improve the learning of DiPs. Extensive experiments show that the
proposed method achieves state-of-the-art performance on multiple person ReID
benchmarks
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