253 research outputs found
High Range Resolution Profile Construction Exploiting Modified Fractional Fourier Transformation
This paper addresses the discrimination of closely spaced high speed group targets with radar transmitting linear frequency modulation (LFM) pulses. The high speed target motion leads to range migration and target dispersion and thereby the discriminating capability of the high range resolution profile (HRRP) deteriorating significantly. An effective processing approach composed of stretch processing (SP), modified fractional Fourier transform (FrFT), and multiple signal classification (MUSIC) algorithm is proposed to deal with this problem. Firstly, SP is adopted to transform the received LFM with Doppler distortions into narrow band LFM signals. Secondly, based on the two-dimensional range/velocity plane constructed by the modified FrFT, the velocity of the high speed group target is estimated and compensated with just one single pulse. After the compensation of range migration and target dispersion simultaneously, the resolution of the HRRP achieved by single pulse transmission improves significantly in the high speed group targets scenarios. Finally, MUSIC algorithm with superresolution capability is utilized to make a more explicit discrimination between the scatterers in comparison with the conventional SP method. Simulation results show the effectiveness of the proposed scheme
Gravitational waves from holographic QCD phase transition with gluon condensate
In this paper, we discuss the holographic first order QCD phase transition
with gluon condensate and the generation of gravitational waves (GWs) from the
phase transition. The first order QCD phase transition is dual to the first
order Hawking-Page phase transition from holography. We study the first order
Hawking-Page phase transition from the thermal dilatonic phase to the dilatonic
black hole phase and find the phase transition temperature is proportional to
the gluon condensate. After substituting into the phenomenological value of
gluon condensate from QCD sum rules, we find . In further
research, we study the GWs generated from holographic cosmic first order QCD
phase transition with gluon condensate and the produced GWs might be detected
by the International Pulsar Timing Array, Square Kilometre Array and Big-Bang
Observer. Moreover, the gluon condensate suppresses the energy density of total
GWs and peak frequency.Comment: 16 pages, 2 figure
STATE-OF-ART Algorithms for Injectivity and Bounded Surjectivity of One-dimensional Cellular Automata
Surjectivity and injectivity are the most fundamental problems in cellular
automata (CA). We simplify and modify Amoroso's algorithm into optimum and make
it compatible with fixed, periodic and reflective boundaries. A new algorithm
(injectivity tree algorithm) for injectivity is also proposed. After our
theoretic analysis and experiments, our algorithm for injectivity can save much
space and 90\% or even more time compared with Amoroso's algorithm for
injectivity so that it can support the decision of CA with larger neighborhood
sizes. At last, we prove that the reversibility with the periodic boundary and
global injectivity of one-dimensional CA is equivalent
Securing Recommender System via Cooperative Training
Recommender systems are often susceptible to well-crafted fake profiles,
leading to biased recommendations. Among existing defense methods,
data-processing-based methods inevitably exclude normal samples, while
model-based methods struggle to enjoy both generalization and robustness. To
this end, we suggest integrating data processing and the robust model to
propose a general framework, Triple Cooperative Defense (TCD), which employs
three cooperative models that mutually enhance data and thereby improve
recommendation robustness. Furthermore, Considering that existing attacks
struggle to balance bi-level optimization and efficiency, we revisit poisoning
attacks in recommender systems and introduce an efficient attack strategy,
Co-training Attack (Co-Attack), which cooperatively optimizes the attack
optimization and model training, considering the bi-level setting while
maintaining attack efficiency. Moreover, we reveal a potential reason for the
insufficient threat of existing attacks is their default assumption of
optimizing attacks in undefended scenarios. This overly optimistic setting
limits the potential of attacks. Consequently, we put forth a Game-based
Co-training Attack (GCoAttack), which frames the proposed CoAttack and TCD as a
game-theoretic process, thoroughly exploring CoAttack's attack potential in the
cooperative training of attack and defense. Extensive experiments on three real
datasets demonstrate TCD's superiority in enhancing model robustness.
Additionally, we verify that the two proposed attack strategies significantly
outperform existing attacks, with game-based GCoAttack posing a greater
poisoning threat than CoAttack.Comment: arXiv admin note: text overlap with arXiv:2210.1376
Cooperative Retriever and Ranker in Deep Recommenders
Deep recommender systems (DRS) are intensively applied in modern web
services. To deal with the massive web contents, DRS employs a two-stage
workflow: retrieval and ranking, to generate its recommendation results. The
retriever aims to select a small set of relevant candidates from the entire
items with high efficiency; while the ranker, usually more precise but
time-consuming, is supposed to further refine the best items from the retrieved
candidates. Traditionally, the two components are trained either independently
or within a simple cascading pipeline, which is prone to poor collaboration
effect. Though some latest works suggested to train retriever and ranker
jointly, there still exist many severe limitations: item distribution shift
between training and inference, false negative, and misalignment of ranking
order. As such, it remains to explore effective collaborations between
retriever and ranker.Comment: 12pages, 4 figures, WWW'2
Interactive Graph Convolutional Filtering
Interactive Recommender Systems (IRS) have been increasingly used in various
domains, including personalized article recommendation, social media, and
online advertising. However, IRS faces significant challenges in providing
accurate recommendations under limited observations, especially in the context
of interactive collaborative filtering. These problems are exacerbated by the
cold start problem and data sparsity problem. Existing Multi-Armed Bandit
methods, despite their carefully designed exploration strategies, often
struggle to provide satisfactory results in the early stages due to the lack of
interaction data. Furthermore, these methods are computationally intractable
when applied to non-linear models, limiting their applicability. To address
these challenges, we propose a novel method, the Interactive Graph
Convolutional Filtering model. Our proposed method extends interactive
collaborative filtering into the graph model to enhance the performance of
collaborative filtering between users and items. We incorporate variational
inference techniques to overcome the computational hurdles posed by non-linear
models. Furthermore, we employ Bayesian meta-learning methods to effectively
address the cold-start problem and derive theoretical regret bounds for our
proposed method, ensuring a robust performance guarantee. Extensive
experimental results on three real-world datasets validate our method and
demonstrate its superiority over existing baselines
Multi-Scale Attention Networks for Pavement Defect Detection
Pavement defects such as cracks, net cracks, and pit slots can cause potential traffic safety problems. The timely detection and identification play a key role in reducing the harm of various pavement defects. Particularly, the recent development in deep learning-based CNNs has shown competitive performance in image detection and classification. To detect pavement defects automatically and improve effects, a multi-scale mobile attention-based network, which we termed MANet, is proposed to perform the detection of pavement defects. The architecture of the encoder-decoder is used in MANet, where the encoder adopts the MobileNet as the backbone network to extract pavement defect features. Instead of the original 3×3 convolution, the multi-scale convolution kernels are utilized in depth-wise separable convolution layers of the network. Further, the hybrid attention mechanism is separately incorporated into the encoder and decoder modules to infer the significance of spatial points and inter-channel relationship features for the input intermediate feature maps. The proposed approach achieves state-of-the-art performance on two publicly-available benchmark datasets, i.e., the Crack500 (500 crack images with 2,000×1,500 pixels) and CFD (118 crack images with 480×320 pixels) datasets. The mean intersection over union ( MIoU ) of the proposed approach on these two datasets reaches 0.7219 and 0.7788, respectively. Ablation experiments show that the multi-scale convolution and hybrid attention modules can effectively help the model extract high-level feature representations and generate more accurate pavement crack segmentation results. We further test the model on locally collected pavement crack images (131 images with 1024×768 pixels) and it achieves a satisfactory result. The proposed approach realizes the MIoU of 0.6514 on the local dataset and outperforms other compared baseline methods. Experimental findings demonstrate the validity and feasibility of the proposed approach and it provides a viable solution for pavement crack detection in practical application scenarios. Our code is available at https://github.com/xtu502/pavement-defects
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