31 research outputs found
From Micro to Macro: Uncovering and Predicting Information Cascading Process with Behavioral Dynamics
Cascades are ubiquitous in various network environments. How to predict these
cascades is highly nontrivial in several vital applications, such as viral
marketing, epidemic prevention and traffic management. Most previous works
mainly focus on predicting the final cascade sizes. As cascades are typical
dynamic processes, it is always interesting and important to predict the
cascade size at any time, or predict the time when a cascade will reach a
certain size (e.g. an threshold for outbreak). In this paper, we unify all
these tasks into a fundamental problem: cascading process prediction. That is,
given the early stage of a cascade, how to predict its cumulative cascade size
of any later time? For such a challenging problem, how to understand the micro
mechanism that drives and generates the macro phenomenons (i.e. cascading
proceese) is essential. Here we introduce behavioral dynamics as the micro
mechanism to describe the dynamic process of a node's neighbors get infected by
a cascade after this node get infected (i.e. one-hop subcascades). Through
data-driven analysis, we find out the common principles and patterns lying in
behavioral dynamics and propose a novel Networked Weibull Regression model for
behavioral dynamics modeling. After that we propose a novel method for
predicting cascading processes by effectively aggregating behavioral dynamics,
and propose a scalable solution to approximate the cascading process with a
theoretical guarantee. We extensively evaluate the proposed method on a large
scale social network dataset. The results demonstrate that the proposed method
can significantly outperform other state-of-the-art baselines in multiple tasks
including cascade size prediction, outbreak time prediction and cascading
process prediction.Comment: 10 pages, 11 figure
SPAN: A Stochastic Projected Approximate Newton Method
Second-order optimization methods have desirable convergence properties.
However, the exact Newton method requires expensive computation for the Hessian
and its inverse. In this paper, we propose SPAN, a novel approximate and fast
Newton method. SPAN computes the inverse of the Hessian matrix via low-rank
approximation and stochastic Hessian-vector products. Our experiments on
multiple benchmark datasets demonstrate that SPAN outperforms existing
first-order and second-order optimization methods in terms of the convergence
wall-clock time. Furthermore, we provide a theoretical analysis of the
per-iteration complexity, the approximation error, and the convergence rate.
Both the theoretical analysis and experimental results show that our proposed
method achieves a better trade-off between the convergence rate and the
per-iteration efficiency.Comment: Appeared in the AAAI 2020, 25 pages, 6 figure
Seismic metamaterial surface for broadband Rayleigh waves attenuation
Elastic metamaterials (EMMs) have been widely studied owing to their advantages in controlling the propagation of elastic waves. In the past decade, considerable efforts have been made to attenuate Rayleigh waves using EMMs. However, the complex nonlinear behaviour of soil renders the realisation of existing EMMs challenging in practical engineering. To overcome this limitation, we present a new seismic metamaterial surface (SMMS) to isolate Rayleigh waves over a broad frequency range of 3.2–19.2 Hz. First, we discuss the propagation velocity of Rayleigh waves in EMMs and determine a reasonable design range for the effective dynamic material properties of the SMMS for seismic surface wave attenuation. Then, we construct a unit cell and demonstrate that these properties, both in band gaps and negative bands, lie within this specified design range. The negative bands are initially introduced into Rayleigh wave mitigation. Finally, a new type of SMMS is presented and validated using numerical results and scaled experiments. The results show that the vertical deformation on the surface can be decreased by more than 96 %. The findings reported here open new avenues for protecting engineering structures from low-frequency seismic vibrations
A Crack Segmentation Model Combining Morphological Network and Multiple Loss Mechanism
With the wide application of computer vision technology and deep-learning theory in engineering, the image-based detection of cracks in structures such as pipelines, pavements and dams has received more and more attention. Aiming at the problems of high cost, low efficiency and poor detection accuracy in traditional crack detection methods, this paper proposes a crack segmentation network by combining a morphological network and a multiple-loss mechanism. First, for improving the identification of cracks with different resolutions, the U-Net network is used to extract multi-scale features from the crack image. Second, for eliminating the effect of polarized light on the cracks under different illuminations, the extracted crack features are further morphologically processed by a white-top hat transform and a black-bottom hat transform. Finally, a multi-loss mechanism is designed to solve the problem of the inaccurate segmentation of cracks on a single scale. Extensive experiments are carried out on five open crack datasets: Crack500, CrackTree200, CFD, AEL and GAPs384. The experimental results showed that the average ODS, OIS, AIU, sODS and sOIS are 75.7%, 73.9%, 36.4%, 52.4% and 52.2%, respectively. Compared with state-of-the-art methods, the proposed method achieves better crack segmentation performance. Ablation experiments also verified the effectiveness of each module in the algorithm
A Crack Segmentation Model Combining Morphological Network and Multiple Loss Mechanism
With the wide application of computer vision technology and deep-learning theory in engineering, the image-based detection of cracks in structures such as pipelines, pavements and dams has received more and more attention. Aiming at the problems of high cost, low efficiency and poor detection accuracy in traditional crack detection methods, this paper proposes a crack segmentation network by combining a morphological network and a multiple-loss mechanism. First, for improving the identification of cracks with different resolutions, the U-Net network is used to extract multi-scale features from the crack image. Second, for eliminating the effect of polarized light on the cracks under different illuminations, the extracted crack features are further morphologically processed by a white-top hat transform and a black-bottom hat transform. Finally, a multi-loss mechanism is designed to solve the problem of the inaccurate segmentation of cracks on a single scale. Extensive experiments are carried out on five open crack datasets: Crack500, CrackTree200, CFD, AEL and GAPs384. The experimental results showed that the average ODS, OIS, AIU, sODS and sOIS are 75.7%, 73.9%, 36.4%, 52.4% and 52.2%, respectively. Compared with state-of-the-art methods, the proposed method achieves better crack segmentation performance. Ablation experiments also verified the effectiveness of each module in the algorithm