209 research outputs found
New Technology and Experimental Study on Snow-Melting Heated Pavement System in Tunnel Portal
In recent years, with the rapid growth of economy and sharp rise of motor vehicles in China, the pavement skid resistance in tunnel portals has become increasingly important in cold region. However, the deicing salt, snow removal with machine, and other antiskid measures adopted by highway maintenance division have many limitations. To improve the treatment effect, we proposed a new snow-melting approach employing electric heat tracing, in which heating cables are installed in the structural layer of road. Through the field experiment, laboratory experiment, and numerical investigation, structure type, heating power, and preheating time of the flexible pavement heating system in tunnel portal were systematically analyzed, and advantages of electric heat tracing technology in improving the pavement skid resistance in tunnel portal were also presented. Therefore, such new technology, which offers new snow-melting methods for tunnel portal, bridge, mountainous area, and large longitudinal slope in cold region, has promising prospect for extensive application
Global-Local Stepwise Generative Network for Ultra High-Resolution Image Restoration
While the research on image background restoration from regular size of
degraded images has achieved remarkable progress, restoring ultra
high-resolution (e.g., 4K) images remains an extremely challenging task due to
the explosion of computational complexity and memory usage, as well as the
deficiency of annotated data. In this paper we present a novel model for ultra
high-resolution image restoration, referred to as the Global-Local Stepwise
Generative Network (GLSGN), which employs a stepwise restoring strategy
involving four restoring pathways: three local pathways and one global pathway.
The local pathways focus on conducting image restoration in a fine-grained
manner over local but high-resolution image patches, while the global pathway
performs image restoration coarsely on the scale-down but intact image to
provide cues for the local pathways in a global view including semantics and
noise patterns. To smooth the mutual collaboration between these four pathways,
our GLSGN is designed to ensure the inter-pathway consistency in four aspects
in terms of low-level content, perceptual attention, restoring intensity and
high-level semantics, respectively. As another major contribution of this work,
we also introduce the first ultra high-resolution dataset to date for both
reflection removal and rain streak removal, comprising 4,670 real-world and
synthetic images. Extensive experiments across three typical tasks for image
background restoration, including image reflection removal, image rain streak
removal and image dehazing, show that our GLSGN consistently outperforms
state-of-the-art methods.Comment: submmitted to Transactions on Image Processin
Investigation Progresses and Applications of Fractional Derivative Model in Geotechnical Engineering
Over the past couple of decades, as a new mathematical tool for addressing a number of tough problems, fractional calculus has been gaining a continually increasing interest in diverse scientific fields, including geotechnical engineering due primarily to geotechnical rheology phenomenon. Unlike the classical constitutive models in which simulation analysis gradually fails to meet the reasonable accuracy of requirement, the fractional derivative models have shown the merits of hereditary phenomena with long memory. Additionally, it is traced that the fractional derivative model is one of the most effective and accurate approaches to describe the rheology phenomenon. In relation to this, an overview aimed first at model structure and parameter determination in combination with application cases based on fractional calculus was provided. Furthermore, this review paper shed light on the practical application aspects of deformation analysis of circular tunnel, rheological settlement of subgrade, and relevant loess researches subjected to the achievements acquired in geotechnical engineering. Finally, concluding remarks and important future investigation directions were pointed out
CT-BERT: Learning Better Tabular Representations Through Cross-Table Pre-training
Tabular data -- also known as structured data -- is one of the most common
data forms in existence, thanks to the stable development and scaled deployment
of database systems in the last few decades. At present however, despite the
blast brought by large pre-trained models in other domains such as ChatGPT or
SAM, how can we extract common knowledge across tables at a scale that may
eventually lead to generalizable representation for tabular data remains a full
blank. Indeed, there have been a few works around this topic. Most (if not all)
of them are limited in the scope of a single table or fixed form of a schema.
In this work, we first identify the crucial research challenges behind tabular
data pre-training, particularly towards the cross-table scenario. We position
the contribution of this work in two folds: (i)-we collect and curate nearly 2k
high-quality tabular datasets, each of which is guaranteed to possess clear
semantics, clean labels, and other necessary meta information. (ii)-we propose
a novel framework that allows cross-table pre-training dubbed as CT-BERT.
Noticeably, in light of pioneering the scaled cross-table training, CT-BERT is
fully compatible with both supervised and self-supervised schemes, where the
specific instantiation of CT-BERT is very much dependent on the downstream
tasks. We further propose and implement a contrastive-learning-based and masked
table modeling (MTM) objective into CT-BERT, that is inspired from computer
vision and natural language processing communities but sophistically tailored
to tables. The extensive empirical results on 15 datasets demonstrate CT-BERT's
state-of-the-art performance, where both its supervised and self-supervised
setups significantly outperform the prior approaches
Controllable Textual Inversion for Personalized Text-to-Image Generation
The recent large-scale generative modeling has attained unprecedented
performance especially in producing high-fidelity images driven by text
prompts. Text inversion (TI), alongside the text-to-image model backbones, is
proposed as an effective technique in personalizing the generation when the
prompts contain user-defined, unseen or long-tail concept tokens. Despite that,
we find and show that the deployment of TI remains full of "dark-magics" -- to
name a few, the harsh requirement of additional datasets, arduous human efforts
in the loop and lack of robustness. In this work, we propose a much-enhanced
version of TI, dubbed Controllable Textual Inversion (COTI), in resolving all
the aforementioned problems and in turn delivering a robust, data-efficient and
easy-to-use framework. The core to COTI is a theoretically-guided loss
objective instantiated with a comprehensive and novel weighted scoring
mechanism, encapsulated by an active-learning paradigm. The extensive results
show that COTI significantly outperforms the prior TI-related approaches with a
26.05 decrease in the FID score and a 23.00% boost in the R-precision.Comment: 10 pages, 6 figures, 2 tables. Project Page:
https://github.com/jnzju/COT
Transformer-Based Visual Segmentation: A Survey
Visual segmentation seeks to partition images, video frames, or point clouds
into multiple segments or groups. This technique has numerous real-world
applications, such as autonomous driving, image editing, robot sensing, and
medical analysis. Over the past decade, deep learning-based methods have made
remarkable strides in this area. Recently, transformers, a type of neural
network based on self-attention originally designed for natural language
processing, have considerably surpassed previous convolutional or recurrent
approaches in various vision processing tasks. Specifically, vision
transformers offer robust, unified, and even simpler solutions for various
segmentation tasks. This survey provides a thorough overview of
transformer-based visual segmentation, summarizing recent advancements. We
first review the background, encompassing problem definitions, datasets, and
prior convolutional methods. Next, we summarize a meta-architecture that
unifies all recent transformer-based approaches. Based on this
meta-architecture, we examine various method designs, including modifications
to the meta-architecture and associated applications. We also present several
closely related settings, including 3D point cloud segmentation, foundation
model tuning, domain-aware segmentation, efficient segmentation, and medical
segmentation. Additionally, we compile and re-evaluate the reviewed methods on
several well-established datasets. Finally, we identify open challenges in this
field and propose directions for future research. The project page can be found
at https://github.com/lxtGH/Awesome-Segmenation-With-Transformer. We will also
continually monitor developments in this rapidly evolving field.Comment: Work in progress. Github:
https://github.com/lxtGH/Awesome-Segmenation-With-Transforme
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