Causes, mechanisms, and remedies of top-down cracking of asphalt pavements : state-of-the-art

2021 ◽

Vol 17 (1) ◽

pp. 1-19

Author(s):

My Kieu ◽

Andrew D. Bagdanov ◽

Marco Bertini

Keyword(s):

Domain Adaptation ◽

State Of The Art ◽

Pedestrian Detection ◽

Challenging Problem ◽

Top Down ◽

Bottom Up ◽

Security Applications ◽

Lighting Conditions ◽

Initial Layers ◽

Single Modality

Pedestrian detection is a canonical problem for safety and security applications, and it remains a challenging problem due to the highly variable lighting conditions in which pedestrians must be detected. This article investigates several domain adaptation approaches to adapt RGB-trained detectors to the thermal domain. Building on our earlier work on domain adaptation for privacy-preserving pedestrian detection, we conducted an extensive experimental evaluation comparing top-down and bottom-up domain adaptation and also propose two new bottom-up domain adaptation strategies. For top-down domain adaptation, we leverage a detector pre-trained on RGB imagery and efficiently adapt it to perform pedestrian detection in the thermal domain. Our bottom-up domain adaptation approaches include two steps: first, training an adapter segment corresponding to initial layers of the RGB-trained detector adapts to the new input distribution; then, we reconnect the adapter segment to the original RGB-trained detector for final adaptation with a top-down loss. To the best of our knowledge, our bottom-up domain adaptation approaches outperform the best-performing single-modality pedestrian detection results on KAIST and outperform the state of the art on FLIR.

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Bottom-up and Top-down: Bidirectional Additive Net for Edge Detection

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/83 ◽

2020 ◽

Author(s):

Lianli Gao ◽

Zhilong Zhou ◽

Heng Tao Shen ◽

Jingkuan Song

Keyword(s):

Edge Detection ◽

Spatial Information ◽

New Records ◽

State Of The Art ◽

Top Down ◽

Bottom Up ◽

Image Edge Detection ◽

Universal Network ◽

Image Edge ◽

Hierarchical Representations

Image edge detection is considered as a cornerstone task in computer vision. Due to the nature of hierarchical representations learned in CNN, it is intuitive to design side networks utilizing the richer convolutional features to improve the edge detection. However, there is no consensus way to integrate the hierarchical information. In this paper, we propose an effective and end-to-end framework, named Bidirectional Additive Net (BAN), for image edge detection. In the proposed framework, we focus on two main problems: 1) how to design a universal network for incorporating hierarchical information sufficiently; and 2) how to achieve effective information flow between different stages and gradually improve the edge map stage by stage. To tackle these problems, we design a consecutive bottom-up and top-down architecture, where a bottom-up branch can gradually remove detailed or sharp boundaries to enable accurate edge detection and a top-down branch offers a chance of error-correcting by revisiting the low-level features that contain rich textual and spatial information. And attended additive module (AAM) is designed to cumulatively refine edges by selecting pivotal features in each stage. Experimental results show that our proposed methods can improve the edge detection performance to new records and achieve state-of-the-art results on two public benchmarks: BSDS500 and NYUDv2.

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Complete Bottom-Up Predicate Invention in Meta-Interpretive Learning

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/320 ◽

2020 ◽

Author(s):

Céline Hocquette ◽

Stephen H. Muggleton

Keyword(s):

State Of The Art ◽

Order Logic ◽

Learning Performance ◽

Sample Complexity ◽

Logic Programs ◽

Top Down ◽

Bottom Up ◽

Predicate Invention ◽

Feature Discovery ◽

Second Order Logic

Predicate Invention in Meta-Interpretive Learning (MIL) is generally based on a top-down approach, and the search for a consistent hypothesis is carried out starting from the positive examples as goals. We consider augmenting top-down MIL systems with a bottom-up step during which the background knowledge is generalised with an extension of the immediate consequence operator for second-order logic programs. This new method provides a way to perform extensive predicate invention useful for feature discovery. We demonstrate this method is complete with respect to a fragment of dyadic datalog. We theoretically prove this method reduces the number of clauses to be learned for the top-down learner, which in turn can reduce the sample complexity. We formalise an equivalence relation for predicates which is used to eliminate redundant predicates. Our experimental results suggest pairing the state-of-the-art MIL system Metagol with an initial bottom-up step can significantly improve learning performance.

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A Goal-Driven Tree-Structured Neural Model for Math Word Problems

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/736 ◽

2019 ◽

Cited By ~ 1

Author(s):

Zhipeng Xie ◽

Shichao Sun

Keyword(s):

Word Problems ◽

State Of The Art ◽

Neural Model ◽

Neural Models ◽

Top Down ◽

Feedforward Networks ◽

Whole Process ◽

Expression Tree ◽

Generate Solution ◽

Human Problem

Most existing neural models for math word problems exploit Seq2Seq model to generate solution expressions sequentially from left to right, whose results are far from satisfactory due to the lack of goal-driven mechanism commonly seen in human problem solving. This paper proposes a tree-structured neural model to generate expression tree in a goal-driven manner. Given a math word problem, the model first identifies and encodes its goal to achieve, and then the goal gets decomposed into sub-goals combined by an operator in a top-down recursive way. The whole process is repeated until the goal is simple enough to be realized by a known quantity as leaf node. During the process, two-layer gated-feedforward networks are designed to implement each step of goal decomposition, and a recursive neural network is used to encode fulfilled subtrees into subtree embeddings, which provides a better representation of subtrees than the simple goals of subtrees. Experimental results on the dataset Math23K have shown that our tree-structured model outperforms significantly several state-of-the-art models.

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Direct laser-patterned MXene–perovskite image sensor arrays for visible-near infrared photodetection

Materials Horizons ◽

10.1039/d0mh00537a ◽

2020 ◽

Vol 7 (7) ◽

pp. 1901-1911 ◽

Cited By ~ 3

Author(s):

Aobo Ren ◽

Jihua Zou ◽

Huagui Lai ◽

Yixuan Huang ◽

Liming Yuan ◽

...

Keyword(s):

Near Infrared ◽

Performance Metrics ◽

State Of The Art ◽

Sensor Arrays ◽

Image Sensor ◽

Top Down ◽

Solution Processed ◽

Direct Laser ◽

Infrared Photodetection ◽

Art Performance

Solution-processed MXene–perovskite image sensor arrays are realized by a top-down method, which combine desirable manufacturing advantages and state-of-the-art performance metrics.

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Recycling Tire Rubber in Asphalt Pavements: State of the Art

Sustainability ◽

10.3390/su12219076 ◽

2020 ◽

Vol 12 (21) ◽

pp. 9076

Author(s):

Saud A. Alfayez ◽

Ahmed R. Suleiman ◽

Moncef L. Nehdi

Keyword(s):

State Of The Art ◽

Permanent Deformation ◽

Engineering Properties ◽

Asphalt Pavements ◽

Future Research ◽

Tire Rubber ◽

Durability Assessment ◽

Overall Performance ◽

Pavement Construction ◽

Critical Overview

The use of recycled tire rubber in asphalt pavements to improve the overall performance, economy, and sustainability of pavements has gained considerable attention over the last few decades. Several studies have indicated that recycled tire rubber can reduce the permanent deformation of flexible pavements and enhance its resistance to rutting, reduce pavement construction and maintenance costs, and improve the resistance to fatigue damage. This paper provides a systematic and critical overview of the research on and practice of using recycled tire rubber in asphalt pavements in terms of engineering properties, performance, and durability assessment. This critical analysis of the state-of-the-art should enhance the understanding of using recycled tire rubber in asphalt pavements, define pertinent recommendations, identify knowledge gaps, and highlight the need for concerted future research.

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