102 research outputs found

    Efficient Algorithms for Attributed Graph Alignment with Vanishing Edge Correlation

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    Graph alignment refers to the task of finding the vertex correspondence between two positively correlated graphs. Extensive study has been done on polynomial-time algorithms for the graph alignment problem under the Erd\H{o}s--R\'enyi graph pair model, where the two graphs are Erd\H{o}s--R\'enyi graphs with edge probability quq_\mathrm{u}, correlated under certain vertex correspondence. To achieve exact recovery of the vertex correspondence, all existing algorithms at least require the edge correlation coefficient Ļu\rho_\mathrm{u} between the two graphs to satisfy Ļu>Ī±\rho_\mathrm{u} > \sqrt{\alpha}, where Ī±ā‰ˆ0.338\alpha \approx 0.338 is Otter's tree-counting constant. Moreover, it is conjectured in [1] that no polynomial-time algorithm can achieve exact recovery under weak edge correlation Ļu<Ī±\rho_\mathrm{u}<\sqrt{\alpha}. In this paper, we show that with a vanishing amount of additional attribute information, exact recovery is polynomial-time feasible under vanishing edge correlation Ļuā‰„nāˆ’Ī˜(1)\rho_\mathrm{u} \ge n^{-\Theta(1)}. We identify a local tree structure, which incorporates one layer of user information and one layer of attribute information, and apply the subgraph counting technique to such structures. A polynomial-time algorithm is proposed that recovers the vertex correspondence for all but a vanishing fraction of vertices. We then further refine the algorithm output to achieve exact recovery. The motivation for considering additional attribute information comes from the widely available side information in real-world applications, such as the user's birthplace and educational background on LinkedIn and Twitter social networks

    On the Feasible Region of Efficient Algorithms for Attributed Graph Alignment

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    Graph alignment aims at finding the vertex correspondence between two correlated graphs, a task that frequently occurs in graph mining applications such as social network analysis. Attributed graph alignment is a variant of graph alignment, in which publicly available side information or attributes are exploited to assist graph alignment. Existing studies on attributed graph alignment focus on either theoretical performance without computational constraints or empirical performance of efficient algorithms. This motivates us to investigate efficient algorithms with theoretical performance guarantee. In this paper, we propose two polynomial-time algorithms that exactly recover the vertex correspondence with high probability. The feasible region of the proposed algorithms is near optimal compared to the information-theoretic limits. When specialized to the seeded graph alignment problem under the seeded Erd\H{o}s--R\'{e}nyi graph pair model, the proposed algorithms extends the best known feasible region for exact alignment by polynomial-time algorithms

    High-Performance Inference Graph Convolutional Networks for Skeleton-Based Action Recognition

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    Recently, significant achievements have been made in skeleton-based human action recognition with the emergence of graph convolutional networks (GCNs). However, the state-of-the-art (SOTA) models used for this task focus on constructing more complex higher-order connections between joint nodes to describe skeleton information, which leads to complex inference processes and high computational costs, resulting in reduced model's practicality. To address the slow inference speed caused by overly complex model structures, we introduce re-parameterization and over-parameterization techniques to GCNs, and propose two novel high-performance inference graph convolutional networks, namely HPI-GCN-RP and HPI-GCN-OP. HPI-GCN-RP uses re-parameterization technique to GCNs to achieve a higher inference speed with competitive model performance. HPI-GCN-OP further utilizes over-parameterization technique to bring significant performance improvement with inference speed slightly decreased. Experimental results on the two skeleton-based action recognition datasets demonstrate the effectiveness of our approach. Our HPI-GCN-OP achieves an accuracy of 93% on the cross-subject split of the NTU-RGB+D 60 dataset, and 90.1% on the cross-subject benchmark of the NTU-RGB+D 120 dataset and is 4.5 times faster than HD-GCN at the same accuracy

    Noisy Computing of the OR\mathsf{OR} and MAX\mathsf{MAX} Functions

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    We consider the problem of computing a function of nn variables using noisy queries, where each query is incorrect with some fixed and known probability pāˆˆ(0,1/2)p \in (0,1/2). Specifically, we consider the computation of the OR\mathsf{OR} function of nn bits (where queries correspond to noisy readings of the bits) and the MAX\mathsf{MAX} function of nn real numbers (where queries correspond to noisy pairwise comparisons). We show that an expected number of queries of (1Ā±o(1))nlogā”1Ī“DKL(pāˆ„1āˆ’p) (1 \pm o(1)) \frac{n\log \frac{1}{\delta}}{D_{\mathsf{KL}}(p \| 1-p)} is both sufficient and necessary to compute both functions with a vanishing error probability Ī“=o(1)\delta = o(1), where DKL(pāˆ„1āˆ’p)D_{\mathsf{KL}}(p \| 1-p) denotes the Kullback-Leibler divergence between Bern(p)\mathsf{Bern}(p) and Bern(1āˆ’p)\mathsf{Bern}(1-p) distributions. Compared to previous work, our results tighten the dependence on pp in both the upper and lower bounds for the two functions

    Analysis of vibration attenuation characteristics of large thickness carbon fiber composite laminates

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    The vibration attenuation and damping characteristics of carbon fiber reinforced composite laminates with different thicknesses were investigated by hammering experiments under free boundary constraints in different directions. The dynamic signal testing and analysis system is applied to collect and analyze the vibration signals of the composite specimens, and combine the self-spectrum analysis and logarithmic decay method to identify the fundamental frequencies of different specimens and calculate the damping ratios of different directions of the specimens. The results showed that the overall stiffness of the specimen increased with the increase of the specimen thickness, and when the thickness of the sample increases from 24mm to 32mm, the fundamental frequency increases by 35.1%, the vibration showed the same vibration attenuation and energy dissipation characteristics in the 0Ā° and 90Ā° directions of the specimen, compared with the specimen in the 45Ā° direction, which was less likely to be excited and had poorer vibration attenuation ability, while the upper and lower surfaces of the same specimen showed slightly different attenuation characteristics to the vibration, the maximum difference of damping capacity between top and bottom surfaces of CFRP plates is about 70%

    Exploring public attention and sentiment toward carbon neutrality: evidence from Chinese social media Sina Weibo

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    IntroductionExploring the publicā€™s cognition toward carbon neutrality is conducive to improving the quality and effectiveness of policymaking, and promoting the realization of carbon neutrality goals. This study aims to explore the publicā€™s attention and sentiment toward carbon neutrality from the perspective of social psychology.MethodsUsing posts on carbon neutrality from the Chinese social media platform Sina Weibo as the data source, this study uses statistical analysis, the Mann-Kendall method, keyword analysis, the BERT model, and the LDA model to explore public attention and sentiment.ResultsThe results show that: (1) men, people living east of the Hu line (economically developed regions), and the public in the energy finance market are more concerned about carbon neutrality; (2) high public attention and great dynamic changes in public attention toward carbon neutrality could be trigged by highly credible government or international governmental organizationsā€™ information; (3) public sentiment toward carbon neutrality is mostly positive; however, specific topics affect public sentiment differently.DiscussionThe research results contribute to policymakersā€™ better understanding of the trend of public attention and sentiment toward carbon neutrality, and support improvements in the quality and impact of policymaking

    Large and tunable magnetoresistance in van der Waals ferromagnet/semiconductor junctions

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    Magnetic tunnel junctions (MTJs) with conventional bulk ferromagnets separated by a nonmagnetic insulating layer are key building blocks in spintronics for magnetic sensors and memory. A radically different approach of using atomically-thin van der Waals (vdW) materials in MTJs is expected to boost their figure of merit, the tunneling magnetoresistance (TMR), while relaxing the lattice-matching requirements from the epitaxial growth and supporting high-quality integration of dissimilar materials with atomically-sharp interfaces. We report TMR up to 192% at 10 K in all-vdW Fe3GeTe2/GaSe/Fe3GeTe2 MTJs. Remarkably, instead of the usual insulating spacer, this large TMR is realized with a vdW semiconductor GaSe. Integration of semiconductors into the MTJs offers energy-band-tunability, bias dependence, magnetic proximity effects, and spin-dependent optical-selection rules. We demonstrate that not only the magnitude of the TMR is tuned by the semiconductor thickness but also the TMR sign can be reversed by varying the bias voltages, enabling modulation of highly spin-polarized carriers in vdW semiconductors

    Large and tunable magnetoresistance in van der Waals Ferromagnet/Semiconductor junctions

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    Magnetic tunnel junctions (MTJs) with conventional bulk ferromagnets separated by a nonmagnetic insulating layer are key building blocks in spintronics for magnetic sensors and memory. A radically different approach of using atomically-thin van der Waals (vdW) materials in MTJs is expected to boost their figure of merit, the tunneling magnetoresistance (TMR), while relaxing the lattice-matching requirements from the epitaxial growth and supporting high-quality integration of dissimilar materials with atomically-sharp interfaces. We report TMR up to 192% at 10 K in all-vdW Fe3GeTe2/GaSe/Fe3GeTe2 MTJs. Remarkably, instead of the usual insulating spacer, this large TMR is realized with a vdW semiconductor GaSe. Integration of two-dimensional ferromagnets in semiconductor-based vdW junctions offers gate-tunability, bias dependence, magnetic proximity effects, and spin-dependent optical-selection rules. We demonstrate that not just the magnitude, but also the TMR sign is tuned by the applied bias or the semiconductor thickness, enabling modulation of highly spin-polarized carriers in vdW semiconductors

    CompactĀ andĀ robustĀ deepĀ learningĀ architecture forĀ fluorescenceĀ lifetimeĀ imagingĀ andĀ FPGA implementation

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    This paper reports a bespoke adder-based deep learning network for time-domain fluorescence lifetime imaging (FLIM). By leveraging the l1-norm extraction method, we propose a 1-D Fluorescence Lifetime AdderNet (FLAN) without multiplication-based convolutions to reduce the computational complexity. Further, we compressed fluorescence decays in temporal dimension using a log-scale merging technique to discard redundant temporal information derived as log-scaling FLAN (FLAN+LS). FLAN+LS achieves 0.11 and 0.23 compression ratios compared with FLAN and a conventional 1-D convolutional neural network (1-D CNN) while maintaining high accuracy in retrieving lifetimes. We extensively evaluated FLAN and FLAN+LS using synthetic and real data. A traditional fitting method and other non-fitting, high-accuracy algorithms were compared with our networks for synthetic data. Our networks attained a minor reconstruction error in different photon-count scenarios. For real data, we used fluorescent beads' data acquired by a confocal microscope to validate the effectiveness of real fluorophores, and our networks can differentiate beads with different lifetimes. Additionally, we implemented the network architecture on a field-programmable gate array (FPGA) with a post-quantization technique to shorten the bit-width, thereby improving computing efficiency. FLAN+LS on hardware achieves the highest computing efficiency compared to 1-D CNN and FLAN. We also discussed the applicability of our network and hardware architecture for other time-resolved biomedical applications using photon-efficient, time-resolved sensor

    Hardware inspired neural network for efficient time-resolved biomedical imaging

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    Convolutional neural networks (CNN) have revealed exceptional performance for fluorescence lifetime imaging (FLIM). However, redundant parameters and complicated topologies make it challenging to implement such networks on embedded hardware to achieve real-time processing. We report a lightweight, quantized neural architecture that can offer fast FLIM imaging. The forward-propagation is significantly simplified by replacing matrix multiplications in each convolution layer with additions and data quantization using a low bit-width. We first used synthetic 3-D lifetime data with given lifetime ranges and photon counts to assure correct average lifetimes can be obtained. Afterwards, human prostatic cancer cells incubated with gold nanoprobes were utilized to validate the feasibility of the network for real-world data. The quantized network yielded a 37.8% compression ratio without performance degradation. Clinical relevance - This neural network can be applied to diagnose cancer early based on fluorescence lifetime in a non-invasive way. This approach brings high accuracy and accelerates diagnostic processes for clinicians who are not experts in biomedical signal processin
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