102 research outputs found
Efficient Algorithms for Attributed Graph Alignment with Vanishing Edge Correlation
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 , correlated
under certain vertex correspondence. To achieve exact recovery of the vertex
correspondence, all existing algorithms at least require the edge correlation
coefficient between the two graphs to satisfy
, where 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 .
In this paper, we show that with a vanishing amount of additional attribute
information, exact recovery is polynomial-time feasible under vanishing edge
correlation . 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
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
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 and Functions
We consider the problem of computing a function of variables using noisy
queries, where each query is incorrect with some fixed and known probability . Specifically, we consider the computation of the
function of bits (where queries correspond to noisy readings of the bits)
and the function of real numbers (where queries correspond
to noisy pairwise comparisons). We show that an expected number of queries of
is
both sufficient and necessary to compute both functions with a vanishing error
probability , where denotes the
Kullback-Leibler divergence between and
distributions. Compared to previous work, our results tighten the dependence on
in both the upper and lower bounds for the two functions
Analysis of vibration attenuation characteristics of large thickness carbon fiber composite laminates
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
IntroductionExploring the publicās 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 publicās 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
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
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
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
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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