194 research outputs found
Combining Background Subtraction Algorithms with Convolutional Neural Network
Accurate and fast extraction of foreground object is a key prerequisite for a
wide range of computer vision applications such as object tracking and
recognition. Thus, enormous background subtraction methods for foreground
object detection have been proposed in recent decades. However, it is still
regarded as a tough problem due to a variety of challenges such as illumination
variations, camera jitter, dynamic backgrounds, shadows, and so on. Currently,
there is no single method that can handle all the challenges in a robust way.
In this letter, we try to solve this problem from a new perspective by
combining different state-of-the-art background subtraction algorithms to
create a more robust and more advanced foreground detection algorithm. More
specifically, an encoder-decoder fully convolutional neural network
architecture is trained to automatically learn how to leverage the
characteristics of different algorithms to fuse the results produced by
different background subtraction algorithms and output a more precise result.
Comprehensive experiments evaluated on the CDnet 2014 dataset demonstrate that
the proposed method outperforms all the considered single background
subtraction algorithm. And we show that our solution is more efficient than
other combination strategies
Environmental Hydraulics in the New Millennium: Historical Evolution and Recent Research Trends
Environmental Hydraulics (EH) is the scientific study of environmental water flows and their related transport and transformation processes in natural water systems. This review provides some remarks about the historical development of EH throughout three different paradigms or ages, namely, the Public Health Age, the Water Quality Age, and finally the Integrated Environmental Hydraulics Age. We further evaluate how EH research has changed in the last 20 years through a bibliometric analysis of the proceedings of the International Symposium on Environmental Hydraulics (ISEH) and Environmental Fluid Mechanics (EFMC) journal articles conducted using Citespace
and Leximancer. Authors and affiliations are analyzed to identify patterns of collaboration, followed by an analysis of the temporal evolution of the EFMC impact index as well as its highly‐cited articles. Finally, the major EH topics are identified with a comparison between the topics extracted from the two different sources. As the EH field is becoming rapidly global, some topics were confirmed
to have attracted more interest in EH such as Flow Condition, Numerical Modelling, Experimental Measurements. It is hoped that our findings could provide a reference for students, academics, and policy‐makers related to EH
ESTformer: Transformer Utilizing Spatiotemporal Dependencies for EEG Super-resolution
Towards practical applications of Electroencephalography (EEG) data,
lightweight acquisition devices, equipped with a few electrodes, result in a
predicament where analysis methods can only leverage EEG data with extremely
low spatial resolution. Recent methods mainly focus on using mathematical
interpolation methods and Convolutional Neural Networks for EEG
super-resolution (SR), but they suffer from high computation costs, extra bias,
and few insights in spatiotemporal dependency modeling. To this end, we propose
the ESTformer, an EEG SR framework utilizing spatiotemporal dependencies based
on the Transformer. The ESTformer applies positional encoding methods and the
Multi-head Self-attention mechanism to the space and time dimensions, which can
learn spatial structural information and temporal functional variation. The
ESTformer, with the fixed masking strategy, adopts a mask token to up-sample
the low-resolution (LR) EEG data in case of disturbance from mathematical
interpolation methods. On this basis, we design various Transformer blocks to
construct the Spatial Interpolation Module (SIM) and the Temporal
Reconstruction Module (TRM). Finally, the ESTformer cascades the SIM and the
TRM to capture and model spatiotemporal dependencies for EEG SR with fidelity.
Extensive experimental results on two EEG datasets show the effectiveness of
the ESTformer against previous state-of-the-art methods and verify the
superiority of the SR data to the LR data in EEG-based downstream tasks of
person identification and emotion recognition. The proposed ESTformer
demonstrates the versatility of the Transformer for EEG SR tasks
Background Subtraction with Real-time Semantic Segmentation
Accurate and fast foreground object extraction is very important for object
tracking and recognition in video surveillance. Although many background
subtraction (BGS) methods have been proposed in the recent past, it is still
regarded as a tough problem due to the variety of challenging situations that
occur in real-world scenarios. In this paper, we explore this problem from a
new perspective and propose a novel background subtraction framework with
real-time semantic segmentation (RTSS). Our proposed framework consists of two
components, a traditional BGS segmenter and a real-time semantic
segmenter . The BGS segmenter aims to construct
background models and segments foreground objects. The real-time semantic
segmenter is used to refine the foreground segmentation outputs
as feedbacks for improving the model updating accuracy. and
work in parallel on two threads. For each input frame , the
BGS segmenter computes a preliminary foreground/background
(FG/BG) mask . At the same time, the real-time semantic segmenter
extracts the object-level semantics . Then, some specific
rules are applied on and to generate the final detection
. Finally, the refined FG/BG mask is fed back to update the
background model. Comprehensive experiments evaluated on the CDnet 2014 dataset
demonstrate that our proposed method achieves state-of-the-art performance
among all unsupervised background subtraction methods while operating at
real-time, and even performs better than some deep learning based supervised
algorithms. In addition, our proposed framework is very flexible and has the
potential for generalization
A possible role of crustacean cardioactive peptide in regulating immune response in hepatopancreas of mud crab
Crustacean cardioactive peptide (CCAP), a cyclic amidated non-apeptide, is widely found in arthropods. The functions of CCAP have been revealed to include regulation of heart rate, intestinal peristalsis, molting, and osmotic pressure. However, to date, there has not been any report on the possible involvement of CCAP in immunoregulation in crustaceans. In this study, a CCAP precursor (designated as Sp-CCAP) was identified in the commercially important mud crab Scylla paramamosain, which could be processed into four CCAP-associated peptides and one mature peptide (PFCNAFTGC-NH2). Bioinformatics analysis indicated that Sp-CCAP was highly conserved in crustaceans. RT-PCR results revealed that Sp-CCAP was expressed in nerve tissues and gonads, whereas the Sp-CCAP receptor gene (Sp-CCAPR) was expressed in 12 tissues of S. paramamosain, including hepatopancreas. In situ hybridization further showed that an Sp-CCAPR-positive signal is mainly localized in the F-cells of hepatopancreas. Moreover, the mRNA expression level of Sp-CCAPR in the hepatopancreas was significantly up-regulated after lipopolysaccharide (LPS) or polyriboinosinic polyribocytidylic acid [Poly (I:C)] challenge. Meanwhile, the mRNA expression level of Sp-CCAPR, nuclear transcription factor NF-kappa B homologs (Sp-Dorsal and Sp-Relish), member of mitogen-activated protein kinase (MAPK) signaling pathway (Sp-P38), pro-inflammatory cytokines factor (Sp-TNFSF and Sp-IL16), and antimicrobial peptide (Sp-Lysozyme, Sp-ALF, Sp-ALF4, and Sp-ALF5) in the hepatopancreas were all up-regulated after the administration of synthetic Sp-CCAP mature peptide both in vivo and in vitro. The addition of synthetic Sp-CCAP mature peptide in vitro also led to an increase in nitric oxide (NO) concentration and an improved bacterial clearance ability in the hepatopancreas culture medium. The present study suggested that Sp-CCAP signaling system might be involved in the immune responses of S. paramamosain by activating immune molecules on the hepatopancreas. Collectively, our findings shed new light on neuroendocrine-immune regulatory system in arthropods and could potentially provide a new strategy for disease prevention and control for mud crab aquaculture
Pyramiding stacking of multigenes (PSM): a simple, flexible and efficient multigene stacking system based on Gibson assembly and gateway cloning
Genetic engineering of complex metabolic pathways and multiple traits often requires the introduction of multiple genes. The construction of plasmids carrying multiple DNA fragments plays a vital role in these processes. In this study, the Gibson assembly and Gateway cloning combined Pyramiding Stacking of Multigenes (PSM) system was developed to assemble multiple transgenes into a single T-DNA. Combining the advantages of Gibson assembly and Gateway cloning, the PSM system uses an inverted pyramid stacking route and allows fast, flexible and efficient stacking of multiple genes into a binary vector. The PSM system contains two modular designed entry vectors (each containing two different attL sites and two selectable markers) and one Gateway-compatible destination vector (containing four attR sites and two negative selection markers). The target genes are primarily assembled into the entry vectors via two parallel rounds of Gibson assembly reactions. Then, the cargos in the entry constructs are integrated into the destination vector via a single tube Gateway LR reaction. To demonstrate PSM’s capabilities, four and nine gene expression cassettes were respectively assembled into the destination vector to generate two binary expression vectors. The transgenic analysis of these constructs in Arabidopsis demonstrated the reliability of the constructs generated by PSM. Due to its flexibility, simplicity and versatility, PSM has great potential for genetic engineering, synthetic biology and the improvement of multiple traits
Anomalous thermal transport across the superionic transition in ice
Superionic ices with highly mobile protons within the stable oxygen
sub-lattice occupy an important proportion of the phase diagram of ice and
widely exist in the interior of icy giants and throughout the universe.
Understanding the thermal transport in superionic ice is vital for the thermal
evolution of icy planets. However, it is highly challenging due to the extreme
thermodynamic conditions and dynamical nature of protons, beyond the capability
of the traditional lattice dynamics and empirical potential molecular dynamics
approaches. In this work, by utilizing the deep potential molecular dynamics
approach, we investigate the thermal conductivity of ice-VII and superionic
ice-VII" along the isobar of . A non-monotonic trend of
thermal conductivity with elevated temperature is observed. Through heat flux
decomposition and trajectory-based spectra analysis, we show that the
thermally-activated proton diffusion in ice-VII and superionic ice-VII"
contribute significantly to heat convection, while the broadening in
vibrational energy peaks and significant softening of transverse acoustic
branches lead to a reduction in heat conduction. The competition between proton
diffusion and phonon scattering results in anomalous thermal transport across
the superionic transition in ice. This work unravels the important role of
proton diffusion in the thermal transport of high-pressure ice. Our approach
provides new insights into modeling the thermal transport and atomistic
dynamics in superionic materials.Comment: 5 figure
Reaction mechanism between small-sized Ce clusters and water molecules: An ab initio investigation on Ce\u3csub\u3e\u3ci\u3en\u3c/i\u3e\u3c/sub\u3e+H\u3csub\u3e2\u3c/sub\u3eO
Reactions of small-sized cerium clusters Cen (n = 1–3) with a single water molecule are systematically investigated theoretically. The ground state structures of the Cen/H2O complex and the reaction pathways between Cen + H2O are predicted. Our results show the size-dependent reactivity of small-sized Ce clusters. The calculated reaction energies and reaction barriers indicate that the reactivity between Cen and water becomes higher with increasing cluster size. The predicted reaction pathways show that the single Ce atom and the Ce2 and Ce3 clusters can all easily react with H2O and dissociate the water molecule. Under UV-irradiation, the reaction of a Ce atom with a single H2O molecule may even release an H2 molecule. The reaction of either Ce2 or Ce3 with a single H2O molecule can fully dissociate the H2O into H and O atoms while it is bonded with the Ce cluster. The electronic configuration and oxidation states of the Ce atoms in the products and the higher occupied molecular orbitals are analyzed by using the natural bond orbital (NBO) analysis method, from which the high reactivity between the reaction products of Cen + H2O and an additional H2O molecule is predicted. Our results offer deeper molecular insights into the chemical reactivity of Ce, which could be helpful for developing more efficient Ce-doped or Ce-based catalysts.
Includes supplementary materials
Three-step Formation of Diamonds in Shock-compressed Hydrocarbons: Decomposition, Species Separation, and Nucleation
The accumulation and circulation of carbon-hydrogen dictate the chemical
evolution of ice giant planets. Species separation and diamond precipitation
have been reported in carbon-hydrogen systems, verified by static and
shock-compression experiments. Nevertheless, the dynamic formation processes
for the above-mentioned phenomena are still insufficiently understood. Here,
combing deep learning model, we demonstrate that diamonds form through a
three-step process involving decomposition, species separation and nucleation
procedures. Under shock condition of 125 GPa and 4590 K, hydrocarbons are
decomposed to give hydrogen and low-molecular-weight alkanes (CH4 and C2H6),
which escape from the carbon chains resulting in C/H species separation. The
remaining carbon atoms without C-H bonds accumulate and nucleate to form
diamond crystals. The process of diamond growth is found to associated with a
critical nucleus size where dynamic energy barrier plays a key role. These
dynamic processes for diamonds formation are insightful in establishing the
model for ice giant planet evolution.Comment: 5 figure
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