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 $\mathcal{B}$ and a real-time semantic segmenter $\mathcal{S}$. The BGS segmenter $\mathcal{B}$ aims to construct background models and segments foreground objects. The real-time semantic segmenter $\mathcal{S}$ is used to refine the foreground segmentation outputs as feedbacks for improving the model updating accuracy. $\mathcal{B}$ and $\mathcal{S}$ work in parallel on two threads. For each input frame $I_t$, the BGS segmenter $\mathcal{B}$ computes a preliminary foreground/background (FG/BG) mask $B_t$. At the same time, the real-time semantic segmenter $\mathcal{S}$ extracts the object-level semantics ${S}_t$. Then, some specific rules are applied on ${B}_t$ and ${S}_t$ to generate the final detection ${D}_t$. Finally, the refined FG/BG mask ${D}_t$ 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 $p = 30\ \rm{GPa}$. 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