68 research outputs found
Numerical simulation on the influence of stirrups during electrochemical repair
Stirrup in the concrete structure has noteworthy effect on the electrochemical chloride removal (ECR), which was always ignored in the numerical simulation. Taking the impact of stirrup into consideration has disadvantages on improving the accurate of modeling results. In this paper, a three dimensional (3D) numerical model considering the impact of stirrup on ECR treatment was established, and an experiment was numerically studied to explore the validity of the model. The difference between the residual chloride concentration of simulation and experiment in most region of the concrete structure is within ± 25%. Based on this model, the effect of stirrups on ECR’s efficiency were explored. The results of numerical model shows that the stirrup has shielding effect on the chloride migration of the region between the stirrups, while in the region near the stirrup, it has positive on the chloride removal.
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Saliency-Enabled Coding Unit Partitioning and Quantization Control for Versatile Video Coding
The latest video coding standard, versatile video coding (VVC), has greatly improved coding efficiency over its predecessor standard high efficiency video coding (HEVC), but at the expense of sharply increased complexity. In the context of perceptual video coding (PVC), the visual saliency model that utilizes the characteristics of the human visual system to improve coding efficiency has become a reliable method due to advances in computer performance and visual algorithms. In this paper, a novel VVC optimization scheme compliant PVC framework is proposed, which consists of fast coding unit (CU) partition algorithm and quantization control algorithm. Firstly, based on the visual saliency model, we proposed a fast CU division scheme, including the redetermination of the CU division depth by calculating Scharr operator and variance, as well as the executive decision for intra sub-partitions (ISP), to reduce the coding complexity. Secondly, a quantization control algorithm is proposed by adjusting the quantization parameter based on multi-level classification of saliency values at the CU level to reduce the bitrate. In comparison with the reference model, experimental results indicate that the proposed method can reduce about 47.19% computational complexity and achieve a bitrate saving of 3.68% on average. Meanwhile, the proposed algorithm has reasonable peak signal-to-noise ratio losses and nearly the same subjective perceptual quality
Empowering Graph Representation Learning with Test-Time Graph Transformation
As powerful tools for representation learning on graphs, graph neural
networks (GNNs) have facilitated various applications from drug discovery to
recommender systems. Nevertheless, the effectiveness of GNNs is immensely
challenged by issues related to data quality, such as distribution shift,
abnormal features and adversarial attacks. Recent efforts have been made on
tackling these issues from a modeling perspective which requires additional
cost of changing model architectures or re-training model parameters. In this
work, we provide a data-centric view to tackle these issues and propose a graph
transformation framework named GTrans which adapts and refines graph data at
test time to achieve better performance. We provide theoretical analysis on the
design of the framework and discuss why adapting graph data works better than
adapting the model. Extensive experiments have demonstrated the effectiveness
of GTrans on three distinct scenarios for eight benchmark datasets where
suboptimal data is presented. Remarkably, GTrans performs the best in most
cases with improvements up to 2.8%, 8.2% and 3.8% over the best baselines on
three experimental settings
Single-Cell Multimodal Prediction via Transformers
The recent development of multimodal single-cell technology has made the
possibility of acquiring multiple omics data from individual cells, thereby
enabling a deeper understanding of cellular states and dynamics. Nevertheless,
the proliferation of multimodal single-cell data also introduces tremendous
challenges in modeling the complex interactions among different modalities. The
recently advanced methods focus on constructing static interaction graphs and
applying graph neural networks (GNNs) to learn from multimodal data. However,
such static graphs can be suboptimal as they do not take advantage of the
downstream task information; meanwhile GNNs also have some inherent limitations
when deeply stacking GNN layers. To tackle these issues, in this work, we
investigate how to leverage transformers for multimodal single-cell data in an
end-to-end manner while exploiting downstream task information. In particular,
we propose a scMoFormer framework which can readily incorporate external domain
knowledge and model the interactions within each modality and cross modalities.
Extensive experiments demonstrate that scMoFormer achieves superior performance
on various benchmark datasets. Remarkably, scMoFormer won a Kaggle silver medal
with the rank of 24/1221 (Top 2%) without ensemble in a NeurIPS 2022
competition. Our implementation is publicly available at Github.Comment: CIKM 202
Characterization of isoprene-derived secondary organic aerosols at a rural site in North China Plain with implications for anthropogenic pollution effects
Isoprene is the most abundant non-methane volatile organic compound (VOC) and the largest contributor to secondary organic aerosol (SOA) burden on a global scale. In order to examine the influence of high concentrations of anthropogenic pollutants on isoprene-derived SOA (SOA(i)) formation, summertime PM2.5 filter samples were collected with a three-hour sampling interval at a rural site in the North China Plain (NCP), and determined for SOA(i) tracers and other chemical species. RO2+NO pathway derived 2-methylglyceric acid presented a relatively higher contribution to the SOA, due to the high-NOx (similar to 20 ppb) conditions in the NCP that suppressed the reactive uptake of RO2+HO2 reaction derived isoprene epoxydiols. Compared to particle acidity and water content, sulfate plays a dominant role in the heterogeneous formation process of SOA(i). Diurnal variation and correlation of 2-methyltetrols with ozone suggested an important effect of isoprene ozonolysis on SOA(i) formation. SOA(i) increased linearly with levoglucosan during June 10-18, which can be attributed to an increasing emission of isoprene caused by the field burning of wheat straw and a favorable aqueous SOA formation during the aging process of the biomass burning plume. Our results suggested that isoprene oxidation is highly influenced by intensive anthropogenic activities in the NCP
Single Cells Are Spatial Tokens: Transformers for Spatial Transcriptomic Data Imputation
Spatially resolved transcriptomics brings exciting breakthroughs to
single-cell analysis by providing physical locations along with gene
expression. However, as a cost of the extremely high spatial resolution, the
cellular level spatial transcriptomic data suffer significantly from missing
values. While a standard solution is to perform imputation on the missing
values, most existing methods either overlook spatial information or only
incorporate localized spatial context without the ability to capture long-range
spatial information. Using multi-head self-attention mechanisms and positional
encoding, transformer models can readily grasp the relationship between tokens
and encode location information. In this paper, by treating single cells as
spatial tokens, we study how to leverage transformers to facilitate spatial
tanscriptomics imputation. In particular, investigate the following two key
questions: (1) , and (2) . By answering these two questions, we present a transformer-based
imputation framework, SpaFormer, for cellular-level spatial transcriptomic
data. Extensive experiments demonstrate that SpaFormer outperforms existing
state-of-the-art imputation algorithms on three large-scale datasets while
maintaining superior computational efficiency
Multi-Granularity Detector for Vulnerability Fixes
With the increasing reliance on Open Source Software, users are exposed to
third-party library vulnerabilities. Software Composition Analysis (SCA) tools
have been created to alert users of such vulnerabilities. SCA requires the
identification of vulnerability-fixing commits. Prior works have proposed
methods that can automatically identify such vulnerability-fixing commits.
However, identifying such commits is highly challenging, as only a very small
minority of commits are vulnerability fixing. Moreover, code changes can be
noisy and difficult to analyze. We observe that noise can occur at different
levels of detail, making it challenging to detect vulnerability fixes
accurately.
To address these challenges and boost the effectiveness of prior works, we
propose MiDas (Multi-Granularity Detector for Vulnerability Fixes). Unique from
prior works, Midas constructs different neural networks for each level of code
change granularity, corresponding to commit-level, file-level, hunk-level, and
line-level, following their natural organization. It then utilizes an ensemble
model that combines all base models to generate the final prediction. This
design allows MiDas to better handle the noisy and highly imbalanced nature of
vulnerability-fixing commit data. Additionally, to reduce the human effort
required to inspect code changes, we have designed an effort-aware adjustment
for Midas's outputs based on commit length. The evaluation results demonstrate
that MiDas outperforms the current state-of-the-art baseline in terms of AUC by
4.9% and 13.7% on Java and Python-based datasets, respectively. Furthermore, in
terms of two effort-aware metrics, EffortCost@L and Popt@L, MiDas also
outperforms the state-of-the-art baseline, achieving improvements of up to
28.2% and 15.9% on Java, and 60% and 51.4% on Python, respectively
Deep Learning in Single-Cell Analysis
Single-cell technologies are revolutionizing the entire field of biology. The
large volumes of data generated by single-cell technologies are
high-dimensional, sparse, heterogeneous, and have complicated dependency
structures, making analyses using conventional machine learning approaches
challenging and impractical. In tackling these challenges, deep learning often
demonstrates superior performance compared to traditional machine learning
methods. In this work, we give a comprehensive survey on deep learning in
single-cell analysis. We first introduce background on single-cell technologies
and their development, as well as fundamental concepts of deep learning
including the most popular deep architectures. We present an overview of the
single-cell analytic pipeline pursued in research applications while noting
divergences due to data sources or specific applications. We then review seven
popular tasks spanning through different stages of the single-cell analysis
pipeline, including multimodal integration, imputation, clustering, spatial
domain identification, cell-type deconvolution, cell segmentation, and
cell-type annotation. Under each task, we describe the most recent developments
in classical and deep learning methods and discuss their advantages and
disadvantages. Deep learning tools and benchmark datasets are also summarized
for each task. Finally, we discuss the future directions and the most recent
challenges. This survey will serve as a reference for biologists and computer
scientists, encouraging collaborations.Comment: 77 pages, 11 figures, 15 tables, deep learning, single-cell analysi
Differential Expression of Three Cryptosporidium Species-Specific MEDLE Proteins
Cryptosporidium parvum and Cryptosporidium hominis share highly similar proteomes, with merely ~3% divergence in overall nucleotide sequences. Cryptosporidium-specific MEDLE family is one of the major differences in gene content between the two species. Comparative genomic analysis indicated that MEDLE family may contribute to differences in host range among Cryptosporidium spp. Previous studies have suggested that CpMEDLE-1 encoded by cgd5_4580 and CpMEDLE-2 encoded by cgd5_4590 are potentially involved in the invasion of C. parvum. In this study, we expressed in Escherichia coli, the C. hominis-specific member of the MEDLE protein family, ChMEDLE-1 encoded by chro.50507, and two C. parvum-specific members, CpMEDLE-3 encoded by cgd5_4600 and CpMEDLE-5 encoded by cgd6_5480. Quantitative PCR, immunofluorescence staining and in vitro neutralization assay were conducted to assess their biologic characteristics. The expression of the cgd5_4600 gene was high during 12–48 h of the in vitro culture, while the expression of cgd6_5480 was the highest at 2 h. ChMEDLE-1 and CpMEDLE-3 proteins were mostly located in the anterior and mid-anterior region of sporozoites and merozoites, whereas CpMEDLE-5 was expressed over the entire surface of these invasive stages. Polyclonal antibodies against MEDLE proteins had different neutralization efficiency, reaching approximately 50% for ChMEDLE-1 and 60% for CpMEDLE-3, but only 20% for CpMEDLE-5. The differences in protein and gene expression and neutralizing capacity indicated the MEDLE proteins may have different roles during Cryptosporidium invasion and growth
Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation
Open international challenges are becoming the de facto standard for
assessing computer vision and image analysis algorithms. In recent years, new
methods have extended the reach of pulmonary airway segmentation that is closer
to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation,
limited effort has been directed to quantitative comparison of newly emerged
algorithms driven by the maturity of deep learning based approaches and
clinical drive for resolving finer details of distal airways for early
intervention of pulmonary diseases. Thus far, public annotated datasets are
extremely limited, hindering the development of data-driven methods and
detailed performance evaluation of new algorithms. To provide a benchmark for
the medical imaging community, we organized the Multi-site, Multi-domain Airway
Tree Modeling (ATM'22), which was held as an official challenge event during
the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed
pulmonary airway annotation, including 500 CT scans (300 for training, 50 for
validation, and 150 for testing). The dataset was collected from different
sites and it further included a portion of noisy COVID-19 CTs with ground-glass
opacity and consolidation. Twenty-three teams participated in the entire phase
of the challenge and the algorithms for the top ten teams are reviewed in this
paper. Quantitative and qualitative results revealed that deep learning models
embedded with the topological continuity enhancement achieved superior
performance in general. ATM'22 challenge holds as an open-call design, the
training data and the gold standard evaluation are available upon successful
registration via its homepage.Comment: 32 pages, 16 figures. Homepage: https://atm22.grand-challenge.org/.
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