424 research outputs found
Systematic Review for Water Network Failure Models and Cases
As estimated in the American Society of Civil Engineers 2017 report, in the United States, there are approximately 240,000 water main pipe breaks each year. To help estimate pipe breaks and maintenance frequency, a number of physically-based and statistically-based water main failure prediction models have been developed in the last 30 years. Precious review papers focused more on the evolution of failure models rather than modeling results. However, the modeling results of different models applied in case studies are worth reviewing as well.
In this review, we focus on research papers after Year 2008 and collect latest cases without repetition. A total of 64 papers are qualified following the selection criteria. Detailed information on models and cases are summarized and compared. Chapter 2 provides a summary and review of failure models and discusses the limitation of current models. Chapter 3 provides a comprehensive review of collected cases, which include network characteristics and factors. Chapter 4 focuses on the main findings from collected papers. We conclude with insights and suggestions for future model selection for pipe failure analysis
Robust inference of the context specific structure and temporal dynamics of gene regulatory network
CILIATE: Towards Fairer Class-based Incremental Learning by Dataset and Training Refinement
Due to the model aging problem, Deep Neural Networks (DNNs) need updates to
adjust them to new data distributions. The common practice leverages
incremental learning (IL), e.g., Class-based Incremental Learning (CIL) that
updates output labels, to update the model with new data and a limited number
of old data. This avoids heavyweight training (from scratch) using conventional
methods and saves storage space by reducing the number of old data to store.
But it also leads to poor performance in fairness. In this paper, we show that
CIL suffers both dataset and algorithm bias problems, and existing solutions
can only partially solve the problem. We propose a novel framework, CILIATE,
that fixes both dataset and algorithm bias in CIL. It features a novel
differential analysis guided dataset and training refinement process that
identifies unique and important samples overlooked by existing CIL and enforces
the model to learn from them. Through this process, CILIATE improves the
fairness of CIL by 17.03%, 22.46%, and 31.79% compared to state-of-the-art
methods, iCaRL, BiC, and WA, respectively, based on our evaluation on three
popular datasets and widely used ResNet models
LRRU: Long-short Range Recurrent Updating Networks for Depth Completion
Existing deep learning-based depth completion methods generally employ
massive stacked layers to predict the dense depth map from sparse input data.
Although such approaches greatly advance this task, their accompanied huge
computational complexity hinders their practical applications. To accomplish
depth completion more efficiently, we propose a novel lightweight deep network
framework, the Long-short Range Recurrent Updating (LRRU) network. Without
learning complex feature representations, LRRU first roughly fills the sparse
input to obtain an initial dense depth map, and then iteratively updates it
through learned spatially-variant kernels. Our iterative update process is
content-adaptive and highly flexible, where the kernel weights are learned by
jointly considering the guidance RGB images and the depth map to be updated,
and large-to-small kernel scopes are dynamically adjusted to capture
long-to-short range dependencies. Our initial depth map has coarse but complete
scene depth information, which helps relieve the burden of directly regressing
the dense depth from sparse ones, while our proposed method can effectively
refine it to an accurate depth map with less learnable parameters and inference
time. Experimental results demonstrate that our proposed LRRU variants achieve
state-of-the-art performance across different parameter regimes. In particular,
the LRRU-Base model outperforms competing approaches on the NYUv2 dataset, and
ranks 1st on the KITTI depth completion benchmark at the time of submission.
Project page: https://npucvr.github.io/LRRU/.Comment: Published in ICCV 202
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