207 research outputs found
Self-supervised Multi-view Stereo via Effective Co-Segmentation and Data-Augmentation
Recent studies have witnessed that self-supervised methods based on view
synthesis obtain clear progress on multi-view stereo (MVS). However, existing
methods rely on the assumption that the corresponding points among different
views share the same color, which may not always be true in practice. This may
lead to unreliable self-supervised signal and harm the final reconstruction
performance. To address the issue, we propose a framework integrated with more
reliable supervision guided by semantic co-segmentation and data-augmentation.
Specially, we excavate mutual semantic from multi-view images to guide the
semantic consistency. And we devise effective data-augmentation mechanism which
ensures the transformation robustness by treating the prediction of regular
samples as pseudo ground truth to regularize the prediction of augmented
samples. Experimental results on DTU dataset show that our proposed methods
achieve the state-of-the-art performance among unsupervised methods, and even
compete on par with supervised methods. Furthermore, extensive experiments on
Tanks&Temples dataset demonstrate the effective generalization ability of the
proposed method.Comment: This paper is accepted by AAAI-21 with a Distinguished Paper Awar
Semi-supervised Deep Multi-view Stereo
Significant progress has been witnessed in learning-based Multi-view Stereo
(MVS) under supervised and unsupervised settings. To combine their respective
merits in accuracy and completeness, meantime reducing the demand for expensive
labeled data, this paper explores the problem of learning-based MVS in a
semi-supervised setting that only a tiny part of the MVS data is attached with
dense depth ground truth. However, due to huge variation of scenarios and
flexible settings in views, it may break the basic assumption in classic
semi-supervised learning, that unlabeled data and labeled data share the same
label space and data distribution, named as semi-supervised distribution-gap
ambiguity in the MVS problem. To handle these issues, we propose a novel
semi-supervised distribution-augmented MVS framework, namely SDA-MVS. For the
simple case that the basic assumption works in MVS data, consistency
regularization encourages the model predictions to be consistent between
original sample and randomly augmented sample. For further troublesome case
that the basic assumption is conflicted in MVS data, we propose a novel style
consistency loss to alleviate the negative effect caused by the distribution
gap. The visual style of unlabeled sample is transferred to labeled sample to
shrink the gap, and the model prediction of generated sample is further
supervised with the label in original labeled sample. The experimental results
in semi-supervised settings of multiple MVS datasets show the superior
performance of the proposed method. With the same settings in backbone network,
our proposed SDA-MVS outperforms its fully-supervised and unsupervised
baselines.Comment: This paper is accepted in ACMMM-2023. The code is released at:
https://github.com/ToughStoneX/Semi-MV
Mechanism of crack propagation for K9 glass
In order to study the mechanism of crack propagation, the varied cutting-depth scratch experiment is carried out and smoothed particle hydrodynamics (SPH) simulation method is used to assistant the investigation. The SPH simulation results reveal that crack will propagate in the direction where stress concentration exceeds the fracture toughness of K9 glass. The initial crack length in critical transition depth is calculated by combining the critical stress of fracture and the fracture toughness of K9 glass. Based on the effective plastic strain, the relation between scratching depth and crack depth is obtained. The recovery of crack tip is found and explained from the relationship between cutting depth and crack depth. Using the energy balance theory of Griffith, the variation of material internal energy is revealed. Comparing the scratching forces obtained from experiment and simulation, the validity of simulation results is verified. The phenomenon of crack delayed propagation is found in both experiment and simulation. The explanation of mechanism is given
A Unified Framework for Integrating Semantic Communication and AI-Generated Content in Metaverse
As the Metaverse continues to grow, the need for efficient communication and
intelligent content generation becomes increasingly important. Semantic
communication focuses on conveying meaning and understanding from user inputs,
while AI-Generated Content utilizes artificial intelligence to create digital
content and experiences. Integrated Semantic Communication and AI-Generated
Content (ISGC) has attracted a lot of attentions recently, which transfers
semantic information from user inputs, generates digital content, and renders
graphics for Metaverse. In this paper, we introduce a unified framework that
captures ISGC two primary benefits, including integration gain for optimized
resource allocation and coordination gain for goal-oriented high-quality
content generation to improve immersion from both communication and content
perspectives. We also classify existing ISGC solutions, analyze the major
components of ISGC, and present several use cases. We then construct a case
study based on the diffusion model to identify an optimal resource allocation
strategy for performing semantic extraction, content generation, and graphic
rendering in the Metaverse. Finally, we discuss several open research issues,
encouraging further exploring the potential of ISGC and its related
applications in the Metaverse.Comment: 8 pages, 6 figure
A Unified Blockchain-Semantic Framework for Wireless Edge Intelligence Enabled Web 3.0
Web 3.0 enables user-generated contents and user-selected authorities. With
decentralized wireless edge computing architectures, Web 3.0 allows users to
read, write, and own contents. A core technology that enables Web 3.0 goals is
blockchain, which provides security services by recording content in a
decentralized and transparent manner. However, the explosion of on-chain
recorded contents and the fast-growing number of users cause increasingly
unaffordable computing and storage resource consumption. A promising paradigm
is to analyze the semantic information of contents that can convey precisely
the desired meanings without consuming many resources. In this article, we
propose a unified blockchain-semantic ecosystems framework for wireless edge
intelligence-enabled Web 3.0. Our framework consists of six key components to
exchange semantic demands. We then introduce an Oracle-based proof of semantic
mechanism to implement on-chain and off-chain interactions of Web 3.0
ecosystems on semantic verification algorithms while maintaining service
security. An adaptive Deep Reinforcement Learning-based sharding mechanism on
Oracle is designed to improve interaction efficiency, which can facilitate Web
3.0 ecosystems to deal with varied semantic demands. Finally, a case study is
presented to show that the proposed framework can dynamically adjust Oracle
settings according to varied semantic demands.Comment: 8 pages, 5 figures, 1 tabl
Study on surrounding rock failure characteristics and control technology of gob-side entry retaining in"three hard" thin coal seam
This study aims to address existing problems of complex stress conditions and difficulties in retaining roadway so as to achieve long-term stability of roadway surrounding rock in gob-side entry in thin coal seam. Specifically, we investigated the 4301 working face of Liangshuijing Coal Mine in Huisen Coal Industry of Shaanxi Province through theoretical analysis, numerical simulation and engineering practice, with an aim to study the failure characteristics and control countermeasures of surrounding rock of gob-side entry retaining in shallow buried "triple hard" thin coal seam, and analyse the influence of structural parameters of roadside support on roadway stability. The results show that 1)maintaining the roadway through roadside filling body requires sufficient support strength and appropriate shrinkage to the roof and ensuring the overall stability of the roadway; 2)the mechanical behaviour of high water filling material under uniaxial compression can be divided into four stages: "uniform compaction, elastic deformation, dynamic instability and deterioration failure". The numerical simulation results show that in increasing the width of the filling body, the maximum stress first increases, then decreases and then increases, and a stable bearing stress core appears at 1.6 m. Theoretical calculation shows that the optimal height of roof cutting is 10.7 m, and it offers the roof cutting scheme and parameters of shaped blasting. Engineering practice shows that there is no large deformation and no obvious stress concentration in the 60 m range behind the working face. The surrounding rock exhibits good overall control effect and stable structure, and the effect of retaining roadway meets the design requirements
CHRONOS: Time-Aware Zero-Shot Identification of Libraries from Vulnerability Reports
Tools that alert developers about library vulnerabilities depend on accurate,
up-to-date vulnerability databases which are maintained by security
researchers. These databases record the libraries related to each
vulnerability. However, the vulnerability reports may not explicitly list every
library and human analysis is required to determine all the relevant libraries.
Human analysis may be slow and expensive, which motivates the need for
automated approaches. Researchers and practitioners have proposed to
automatically identify libraries from vulnerability reports using extreme
multi-label learning (XML).
While state-of-the-art XML techniques showed promising performance, their
experiment settings do not practically fit what happens in reality. Previous
studies randomly split the vulnerability reports data for training and testing
their models without considering the chronological order of the reports. This
may unduly train the models on chronologically newer reports while testing the
models on chronologically older ones. However, in practice, one often receives
chronologically new reports, which may be related to previously unseen
libraries. Under this practical setting, we observe that the performance of
current XML techniques declines substantially, e.g., F1 decreased from 0.7 to
0.24 under experiments without and with consideration of chronological order of
vulnerability reports.
We propose a practical library identification approach, namely CHRONOS, based
on zero-shot learning. The novelty of CHRONOS is three-fold. First, CHRONOS
fits into the practical pipeline by considering the chronological order of
vulnerability reports. Second, CHRONOS enriches the data of the vulnerability
descriptions and labels using a carefully designed data enhancement step.
Third, CHRONOS exploits the temporal ordering of the vulnerability reports
using a cache to prioritize prediction of...Comment: Accepted to the Technical Track of ICSE 202
3D phase field modeling of multi-dendrites evolution in solidification and validation by synchrotron x-ray tomography
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. In this paper, the dynamics of multi-dendrite concurrent growth and coarsening of an Al-15 wt.% Cu alloy was studied using a highly computationally efficient 3D phase field model and real-time synchrotron X-ray micro-tomography. High fidelity multi-dendrite simulations were achieved and the results were compared directly with the time-evolved tomography datasets to quantify the relative importance of multi-dendritic growth and coarsening. Coarsening mechanisms under different solidification conditions were further elucidated. The dominant coarsening mechanisms change from small arm melting and interdendritic groove advancement to coalescence when the solid volume fraction approaches ~0.70. Both tomography experiments and phase field simulations indicated that multi-dendrite coarsening obeys the classical Lifshitz–Slyozov–Wagner theory Rn − Rn0=kc(t − t0), but with a higher constant of n = 4.3
cDNA-AFLP analysis reveals differential gene expression in compatible interaction of wheat challenged with Puccinia striiformis f. sp. tritici
<p>Abstract</p> <p>Background</p> <p><it>Puccinia striiformis </it>f. sp. <it>tritici </it>is a fungal pathogen causing stripe rust, one of the most important wheat diseases worldwide. The fungus is strictly biotrophic and thus, completely dependent on living host cells for its reproduction, which makes it difficult to study genes of the pathogen. In spite of its economic importance, little is known about the molecular basis of compatible interaction between the pathogen and wheat host. In this study, we identified wheat and <it>P. striiformis </it>genes associated with the infection process by conducting a large-scale transcriptomic analysis using cDNA-AFLP.</p> <p>Results</p> <p>Of the total 54,912 transcript derived fragments (TDFs) obtained using cDNA-AFLP with 64 primer pairs, 2,306 (4.2%) displayed altered expression patterns after inoculation, of which 966 showed up-regulated and 1,340 down-regulated. 186 TDFs produced reliable sequences after sequencing of 208 TDFs selected, of which 74 (40%) had known functions through BLAST searching the GenBank database. Majority of the latter group had predicted gene products involved in energy (13%), signal transduction (5.4%), disease/defence (5.9%) and metabolism (5% of the sequenced TDFs). BLAST searching of the wheat stem rust fungus genome database identified 18 TDFs possibly from the stripe rust pathogen, of which 9 were validated of the pathogen origin using PCR-based assays followed by sequencing confirmation. Of the 186 reliable TDFs, 29 homologous to genes known to play a role in disease/defense, signal transduction or uncharacterized genes were further selected for validation of cDNA-AFLP expression patterns using qRT-PCR analyses. Results confirmed the altered expression patterns of 28 (96.5%) genes revealed by the cDNA-AFLP technique.</p> <p>Conclusion</p> <p>The results show that cDNA-AFLP is a reliable technique for studying expression patterns of genes involved in the wheat-stripe rust interactions. Genes involved in compatible interactions between wheat and the stripe rust pathogen were identified and their expression patterns were determined. The present study should be helpful in elucidating the molecular basis of the infection process, and identifying genes that can be targeted for inhibiting the growth and reproduction of the pathogen. Moreover, this study can also be used to elucidate the defence responses of the genes that were of plant origin.</p
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