This paper advocates a novel video saliency detection method based on the spatial-temporal saliency fusion and low-rank coherency guided saliency diffusion. In sharp contrast to the conventional ...methods, which conduct saliency detection locally in a frame-by-frame way and could easily give rise to incorrect low-level saliency map, in order to overcome the existing difficulties, this paper proposes to fuse the color saliency based on global motion clues in a batch-wise fashion. And we also propose low-rank coherency guided spatial-temporal saliency diffusion to guarantee the temporal smoothness of saliency maps. Meanwhile, a series of saliency boosting strategies are designed to further improve the saliency accuracy. First, the original long-term video sequence is equally segmented into many short-term frame batches, and the motion clues of the individual video batch are integrated and diffused temporally to facilitate the computation of color saliency. Then, based on the obtained saliency clues, inter-batch saliency priors are modeled to guide the low-level saliency fusion. After that, both the raw color information and the fused low-level saliency are regarded as the low-rank coherency clues, which are employed to guide the spatial-temporal saliency diffusion with the help of an additional permutation matrix serving as the alternative rank selection strategy. Thus, it could guarantee the robustness of the saliency map's temporal consistence, and further boost the accuracy of the computed saliency map. Moreover, we conduct extensive experiments on five public available benchmarks, and make comprehensive, quantitative evaluations between our method and 16 state-of-the-art techniques. All the results demonstrate the superiority of our method in accuracy, reliability, robustness, and versatility.
Taking 30 provinces in China from 2011 to 2020 as a research sample, this paper empirically tests the impact of digital village construction on carbon emissions. This study found that there is an ..."inverted U" curve relationship between digital rural construction and rural carbon emissions. Agricultural planting structure and agricultural technology efficiency are important ways for digital village construction to reduce agricultural carbon emissions. The study also found that the higher the level of economic development, the stronger the carbon emission reduction effect of digital village construction. In addition, there are also significant differences in the carbon emission reduction effect of digital village construction in regions with different environmental regulation intensities. Finally, in terms of the relationship between digital economic activities and carbon emission reduction, the research conclusions of this paper have important implications.
Dental plaque causes many common oral diseases (e.g., caries, gingivitis, and periodontitis). Therefore, plaque detection and control are extremely important for children's oral health. The ...objectives of this study were to design a deep learning-based artificial intelligence (AI) model to detect plaque on primary teeth and to evaluate the diagnostic accuracy of the model.
A conventional neural network (CNN) framework was adopted, and 886 intraoral photos of primary teeth were used for training. To validate clinical feasibility, 98 intraoral photos of primary teeth were assessed by the AI model. Additionally, tooth photos were acquired using a digital camera. One experienced pediatric dentist examined the photos and marked the regions containing plaque. Then, a plaque-disclosing agent was applied, and the areas with plaque were identified. After 1 week, the dentist drew the plaque area on the 98 photos taken by the digital camera again to evaluate the consistency of manual diagnosis. Additionally, 102 intraoral photos of primary teeth were marked to denote the plaque areas obtained by the AI model and the dentist to evaluate the diagnostic capacity of each approach based on lower-resolution photos. The mean intersection-over-union (MIoU) metric was employed to indicate detection accuracy.
The MIoU for detecting plaque on the tested tooth photos was 0.726 ± 0.165. The dentist's MIoU was 0.695 ± 0.269 when first diagnosing the 98 photos taken by the digital camera and 0.689 ± 0.253 after 1 week. Compared to the dentist, the AI model demonstrated a higher MIoU (0.736 ± 0.174), and the results did not change after 1 week. When the dentist and the AI model assessed the 102 intraoral photos, the MIoU was 0.652 ± 0.195 for the dentist and 0.724 ± 0.159 for the model. The results of a paired t-test found no significant difference between the AI model and human specialist (P > .05) in diagnosing dental plaque on primary teeth.
The AI model showed clinically acceptable performance in detecting dental plaque on primary teeth compared with an experienced pediatric dentist. This finding illustrates the potential of such AI technology to help improve pediatric oral health.
Salient motion detection is vital for security surveillance, pattern and motion recognition, traffic control, human–computer interaction, etc. Although such a subject has been very well investigated ...for analysis of stationary videos, many technical challenges still prevail when correctly handling and analyzing non-stationary videos recorded by hand-hold and pan-tilt-zoom cameras. To ameliorate, this paper develops a novel and robust salient motion detection method (especially valuable for quantitative analysis of non-stationary videos) by employing new computational strategies, including low-rank analysis aided by the divide-and-conquer approach, and exploration of the space–time semantic coherency. The key idea in our new approach is to respectively conduct multi-purpose low-rank analysis over a temporal series of well-decomposed frame-batches that have relatively-consistent backgrounds. First, we conduct bilateral random projection (BRP)-based low-rank analysis to accurately keep track of short-term stable-background observations, which consist of frames with similar global appearance and small local variations. Then, to eliminate the side effects due to visual variations induced by view angle changes, we incorporate the low-rank background prior into previous short-term observation to guide robust principal component analysis (RPCA) low-rank revealing based robust salient motion detection over current short-term observation. Meanwhile, a series of saliency clues extracted from the stabilized short-term observations are leveraged to expedite the proper updating of the low-rank background information, which enables us to effectively combat several obstinate problems. Finally, we conduct comprehensive experiments on the public CD2014 benchmark and other five non-stationary videos recorded from the hand-hold camera, and make extensive and quantitative evaluations with six state-of-the-art methods. Experimental results indicate that our method not only outperforms all other methods in the case of non-stationary videos but also obtains outstanding performance for stationary videos.
•We propose a versatile and robust background tracking method.•We integrate low-rank background prior with coherency revealing based on the aligned RPCA low-rank analysis.•We define a series of saliency clues via online exploration of the spatio-temporal coherency.
Taking 30 provinces in China from 2011 to 2020 as a research sample, this paper empirically tests the impact of digital village construction on carbon emissions. This study found that there is an ..."inverted U" curve relationship between digital rural construction and rural carbon emissions. Agricultural planting structure and agricultural technology efficiency are important ways for digital village construction to reduce agricultural carbon emissions. The study also found that the higher the level of economic development, the stronger the carbon emission reduction effect of digital village construction. In addition, there are also significant differences in the carbon emission reduction effect of digital village construction in regions with different environmental regulation intensities. Finally, in terms of the relationship between digital economic activities and carbon emission reduction, the research conclusions of this paper have important implications.
•We propose a novel deep variance network (DVN) by integrating subspaces with Bayesian network into CNN framework.•We propose a hierarchical Bayesian model for unbalance learning of inner-class ...heterogeneity and inter-class homogeneity.•We generate virtual samples to complete the unbalanced dataset in a top-down way from feature level to image level.
Convolutional neural network (CNN) has demonstrated its superior ability to achieve amazing accuracy in computer vision field. Nevertheless, for practical domain-specific image recognition tasks, it still remains difficult to obtain massive high-quality labeled datasets due to the strong requirements for extensive, tedious manual processing. Inspired by the well-known observation that human brain can accurately recognize objects without relying on massive congeneric examples, we propose a novel deep variance network (DVN) to further enhance the generalization ability of CNN in this paper, which could still produce higher recognition accuracy even with unbalanced training datasets than original CNN. The key idea of our DVN is built upon the intrinsic exploitation of inter-class homogeneity and intra-class heterogeneity. Towards such goal, we make the first attempt to incorporate a hierarchical Bayesian model into the powerful CNN framework, which can transfer the joint feature distribution from certain object’s complete training dataset to other object’s incomplete training dataset in an iterative way. In each training cycle, the CNN-resulted features are clustered into discrimination-related subspaces to guide the learning and adaptive adjustment of homogeneity and heterogeneity over unbalanced training datasets. In practice, we furnish several state-of-the-art deep networks with our proposed DVN, and conduct extensive experiments and comprehensive evaluations over CIFAR-10, MNIST, and SVHN benchmarks. The experiments have shown that, most of the furnished deep networks can benefit from our DVN, wherein they gain at most 6.9% accuracy improvement over CIFAR-10 benchmark, 52.83% error reduction over MNIST benchmark, and an improvement of 6.2% over SVHN datasets.
To understand the potential roles of terrestrial bamboo on controlling cyanobacterial blooms in aquatic systems, growth rates of the cyanobacterium Microcystis aeruginosa and its competitor algae ...were examined under different concentrations of bamboo extract. In mono-species cultures with unicellular algal strains, 5.0 g L
extract treatment suppressed M. aeruginosa growth, while it had little effect on the growth of green alga Scenedesmus obliquus or promoted the growth of diatom Nitzschia palea. In co-species cultures, the extract treatment increased the effect of S. obliquus and N. palea on the growth of M. aeruginosa. Under the extract treatment with a field-collected M. aeruginosa population, its cell density declined and its colony was etiolated and sank, while co-cultured N. palea increased explosively by invading the colony. These results suggest that bamboo forest stands along banks and artificially installed bamboo poles can affect the aquatic environment for phytoplankton community. Enhancing the growth of competitors, especially diatoms that can invade cyanobacterial colonies, by using extracts or by providing substrates for growth, was suggested to be the major way of the bloom control by bamboo.
This study presents financial network indicators that can be applied to inspect the financial contagion on real economy, as well as the spatial spillover and industry aggregation effects. We propose ...to design both a directed and undirected networks of financial sectors of top 20 countries in GDP based on symbolized transfer entropy and Pearson correlation coefficients. We examine the effect and usefulness of the network indicators by newly using them instead of the original Dow Jones financial sector as explanatory variables to construct the higher-order information spatial econometric models. The results demonstrate that the estimated accuracies obtained from both the two networks are improved significantly compared with the spatial econometric model using the original data. It indicates that the network indictors are more effective to capture the dynamic information of financial systems. And meanwhile, the accuracy based on the directed network is a little higher than the undirected network, which indicates the symbolized transfer entropy, i.e. the directed and weighted network, is more suitable and effective to reflect relationships in the financial field. In addition, the results also show that under the global financial crisis, the co-movement between financial sectors of a country/region and the global financial sector as well as between financial sectors and real economy sectors is increased. However, some sectors in particular Utilities and Healthcare are impacted slightly. This study tries to use the financial network indicators in modeling to study contagion channels on the real economy and the industry aggregation effects and suggest how network indicators can be practically used in financial fields.
In this study, biomorphic hard carbon is prepared from corn husk using the one-step carbonization method and used as a potassium-ion battery anode material. The biocarbon anode with extremely small ...specific surface area (3.4 m2 g−1) demonstrates better electrochemical performance than those with large specific surface areas. Particularly, at a current density of 0.1 A g−1, the carbon electrode delivers first discharge and charge capacities of 396.2 and 231.0 mAh g−1, respectively. After 100 cycles, the reversible capacity retention rate is 89.1%. Moreover, at a high current density of 1 A g−1, the reversible capacity reaches approximately 135.3 mAh g−1 after 500 cycles. This excellent performance is attributed to the enhancement of ion diffusion kinetics because of the hierarchically porous structures, effect of nitrogen and oxygen heteroatoms and rational interlayer spacing of biocarbon materials. In comparison, activated biocarbon exhibits lower reversible capacity, ascribed to the formation of more solid electrolyte interphase and destruction of hierarchical structures via KOH activation. Cyclic voltammetry calculation indicates that both capacitance and diffusion are responsible for the excellent K ion storage.
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•Hierarchical porous biocarbon was prepared from corn husk and used as the anode for potassium ion battery.•The unactivated carbon anodes exhibit higher capacity and cycling stability than those of activated carbon anodes.•Cyclic voltammetry calculation indicates that both capacitance and diffusion are responsible for the excellent K+ storage.
Although novel 3D animation techniques could be boosted by a large variety of deep learning methods, flexible automatic 3D applications (involving animated figures such as humans and low-life ...animals) are still rarely studied in 3D computer vision. This is due to lacking of arbitrary 3D data acquisition environment, especially those involving human populated scenes. Given a single image, the 3D animation aided by contextual inference is still plagued by limited reconstruction clues without prior knowledge pertinent to the identified figures/objects and/or their possible relationship w.r.t. the environment. To alleviate such difficulty in time-varying 3D animation, we devise a dynamic scene creation framework via a dynamic knowledge graph (DKG). The DKG encodes both temporal and spatial contextual clues to enable and facilitate human interactions with the affordance environment. Furthermore, we construct the DKG-driven variational auto-encoder (DVAE) upon animation kinematics knowledge conveyed by meta-motion sequences, which are disentangled from videos of prior scenes. It is then possible to utilize the DKG to induce the animations in certain scenes, thus, we could automatically and physically generate plausible 3D animations that afford vivid interactions among humans, low-and life animals in the environment. The extensive experimental results and comprehensive evaluations confirm our DKGs’ representation and modeling power towards new animation production in 3D graphics and vision applications.
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