Realism in the visual arts is both an old and current issue. Famously, when the Lumière brothers showed “The Arrival of a Train at La Ciotat” in 1895, the audience was so surprised that they ran away ...from the screen, toward the back of the room. This can be said to be the result of the high degree of realism conveyed by the film and the reality of the image of the train perceived by the audience.Today, the issue of realism has shifted from reconstructing the real world through computer-generated imagery to the pursuit of unrealistic “fictional realism” and “realism created by artists”. This shift has given rise to a range of new issues that need to be examined, from advanced image-creation technologies to the creative consciousness of artists.In this paper, I examine the approach toward realism in animation from the perspective of dynamic technology and its expressive power as well as by confirming the film theory of André Bazin. Specifically, I analyze realism in animation by comparing Disney’s 1994 and 2019 versions of the “The Lion King.” In addition, I discuss the creative process of the artist who made the 2017 experimental animated short “Ugly.”The purpose of this paper is to clarify the transformation of animated expression by examining computer graphics technology and its application in animation, as well as to suggest the direction of future activities by artists whose medium is computer graphics.
In this article, we investigate a communication and computation problem for industrial Internet of Things (IoT) networks, where <inline-formula><tex-math ...notation="LaTeX">K</tex-math></inline-formula> relays can help accomplish the computation tasks with the assist of <inline-formula><tex-math notation="LaTeX">M</tex-math></inline-formula> computational access points. In industrial IoT networks, latency and energy consumption are two important metrics of interest to measure the system performance. To enhance the system performance, a three-hierarchical optimization framework is proposed to reduce the latency and energy consumption, which involves bandwidth allocation, offloading, and relay selection. Specifically, we first optimize the bandwidth allocation by presenting three schemes for the second-hop wireless relaying. We then optimize the computation offloading based on the discrete particle swarm optimization algorithm. We further present three relay selection criteria by taking into account the tradeoff between the system performance and implementation complexity. Simulation results are finally demonstrated to show the effectiveness of the proposed three-hierarchical optimization framework.
Ensemble learning is an important group of machine learning techniques that aim to enhance the nonlinearity and generalization ability of a learning system by aggregating multiple learners. We found ...that ensemble techniques show great potential for improving the performance of traditional hyperspectral target detection algorithms, while at present, there are few previous works have been done on this topic. To this end, we propose an Ensemble based Constrained Energy Minimization (E-CEM) detector for hyperspectral image target detection. Classical hyperspectral image target detection algorithms like Constrained Energy Minimization (CEM), matched filter (MF) and adaptive coherence/cosine estimator (ACE) are usually designed based on constrained least square regression methods or hypothesis testing methods with Gaussian distribution assumption. However, remote sensing hyperspectral data captured in a real-world environment usually shows strong nonlinearity and non-Gaussianity, which will lead to performance degradation of these classical detection algorithms. Although some hierarchical detection models are able to learn strong nonlinear discrimination of spectral data, due to the spectrum changes, these models usually suffer from the instability in detection tasks. The proposed E-CEM is designed based on the classical CEM detection algorithm. To improve both of the detection nonlinearity and generalization ability, the strategies of “cascaded detection”, “random averaging” and “multi-scale scanning” are specifically designed. Experiments on one synthetic hyperspectral image and two real hyperspectral images demonstrate the effectiveness of our method. E-CEM outperforms the traditional CEM detector and other state-of-the-art detection algorithms. Our code will be made publicly available.
State-of-the-art two-stage object detectors apply a classifier to a sparse set of object proposals, relying on region-wise features extracted by RoIPool or RoIAlign as inputs. The region-wise ...features, in spite of aligning well with the proposal locations, may still lack the crucial context information which is necessary for filtering out noisy background detections, as well as recognizing objects possessing no distinctive appearances. To address this issue, we present a simple but effective Hierarchical Context Embedding (HCE) framework, which can be applied as a plug-and-play component, to facilitate the classification ability of a series of region-based detectors by mining contextual cues. Specifically, to advance the recognition of context-dependent object categories, we propose an image-level categorical embedding module which leverages the holistic image-level context to learn object-level concepts. Then, novel RoI features are generated by exploiting hierarchically embedded context information beneath both whole images and interested regions, which are also complementary to conventional RoI features. Moreover, to make full use of our hierarchical contextual RoI features, we propose the early-and-late fusion strategies ( i.e. , feature fusion and confidence fusion), which can be combined to boost the classification accuracy of region-based detectors. Comprehensive experiments demonstrate that our HCE framework is flexible and generalizable, leading to significant and consistent improvements upon various region-based detectors, including FPN, Cascade R-CNN, Mask R-CNN and PA-FPN. With simple modification, our HCE framework can be conveniently adapted to fit the structure of one-stage detectors, and achieve improved performance for SSD, RetinaNet and EfficientDet.
Human eyes can recognize person identities based on some small salient regions. However, such valuable salient information is often hidden when computing similarities of images with existing ...approaches. Moreover, many existing approaches learn discriminative features and handle drastic viewpoint change in a supervised way and require labeling new training data for a different pair of camera views. In this paper, we propose a novel perspective for person re-identification based on unsupervised salience learning. Distinctive features are extracted without requiring identity labels in the training procedure. First, we apply adjacency constrained patch matching to build dense correspondence between image pairs, which shows effectiveness in handling misalignment caused by large viewpoint and pose variations. Second, we learn human salience in an unsupervised manner. To improve the performance of person re-identification, human salience is incorporated in patch matching to find reliable and discriminative matched patches. The effectiveness of our approach is validated on the widely used VIPeR dataset and ETHZ dataset.
In this paper, we propose a novel approach of learning mid-level filters from automatically discovered patch clusters for person re-identification. It is well motivated by our study on what are good ...filters for person re-identification. Our mid-level filters are discriminatively learned for identifying specific visual patterns and distinguishing persons, and have good cross-view invariance. First, local patches are qualitatively measured and classified with their discriminative power. Discriminative and representative patches are collected for filter learning. Second, patch clusters with coherent appearance are obtained by pruning hierarchical clustering trees, and a simple but effective cross-view training strategy is proposed to learn filters that are view-invariant and discriminative. Third, filter responses are integrated with patch matching scores in RankSVM training. The effectiveness of our approach is validated on the VIPeR dataset and the CUHK01 dataset. The learned mid-level features are complementary to existing handcrafted low-level features, and improve the best Rank-1 matching rate on the VIPeR dataset by 14%.
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Nanoformulations with advantages in drug delivery, safety and pharmacodynamics have been booming as a promising strategy for cancer therapy. However, the traditional nanocarrier still ...suffers from the low drug loading capacity, potential systematic toxicity, unclear metabolism, and other uncertainties. To overcome these issues, carrier-free nanodrugs with desirable bioactivity were developed rapidly and drawn considerable attention. Meanwhile, the multifunctional self-delivery nanoarcheticture fabricated by a simple and “green” method, has significant advantages in synergistic cancer therapy and inhibition of multidrug resistant (MDR). Till now, carrier-free nanoparticles for tumor theranostics, phototherapy, chemotherapy, diagnose and synergistic therapy, have made outstanding progress. In this review, we make an integrated and exhaustive overview of lately reports on carrier-free nanodrug delivery systems formed by several active agents. We summarize the self-assembly and modified strategies, with emphasis on application superiority of carrier-free nanocrystal, and give new insight into the establishment of ideal nanosystems for cancer treatment.
Dysregulated extravillous trophoblast invasion and proliferation are known to increase the risk of recurrent spontaneous abortion (RSA); however, the underlying mechanism remains unclear. Herein, in ...our retrospective observational case-control study we show that villous samples from RSA patients, compared to healthy controls, display reduced succinate dehydrogenase complex iron sulfur subunit (SDHB) DNA methylation, elevated SDHB expression, and reduced succinate levels, indicating that low succinate levels correlate with RSA. Moreover, we find high succinate levels in early pregnant women are correlated with successful embryo implantation. SDHB promoter methylation recruited MBD1 and excluded c-Fos, inactivating SDHB expression and causing intracellular succinate accumulation which mimicked hypoxia in extravillous trophoblasts cell lines JEG3 and HTR8 via the PHD2-VHL-HIF-1α pathway; however, low succinate levels reversed this effect and increased the risk of abortion in mouse model. This study reveals that abnormal metabolite levels inhibit extravillous trophoblast function and highlights an approach for RSA intervention.
The electrochemical properties of transition metal oxides strongly depend on the coordination environment of metal atoms. Nevertheless, the relationship between the coordination environment of metal ...atoms and electrochemical performance of metal oxides is unclear, while the strategy of adjusting the coordination environment of metal atoms is rare. Herein, the engineering of the coordination environment of Mn atoms in manganese dioxides (MnO2) by using a triethanolamine (TEA) complex‐induced method is reported. The detailed experimental characterizations and density functional theory calculations show that the optimized Mn coordination environment with oxygen deficiency and more corner‐shared Mn–Mn shells results in apparent electron dislocation and forms an effective built‐in electrical field. As a result, the obtained MnO2‐TEA sample exhibits a high conductivity and an excellent ion diffusion capacity, with a remarkable specific capacitance of 417.5 F g−1 at 1 A g−1. At the power density of 450.0 W kg−1, the fabricated asymmetric supercapacitor delivers the maximal energy density (57.4 Wh kg−1). This work not only provides an effective strategy of adjusting the coordination environment of metal atoms in metal oxides, but also presents a deeper understanding of the electronic structure dependent electrochemical performance of electrode materials.
A simple triethanolamine complex method can significantly enhance the capacitive performance of MnO2 by adjusting the coordination environment of the Mn atoms, in which oxygen deficiency and more corner‐shared Mn–Mn shells (Mn–Mn2) are introduced to delocalize electrons and form a built‐in electrical field in MnO2, realizing a high conductivity and an excellent ion diffusion capacity.
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An environmentally benign and efficient hydrothermal carbonization method is widely applied for the preparation of carbon-based adsorbents. However, the adsorption capacity toward ...anionic species would be influenced due to the negatively charged surface of the traditional hydrothermal carbonaceous materials; moreover most of the carbonaceous materials were in the form of powder which restricted the practical applications. Herein, amino-rich hydrothermal carbon-coated electrospun polyacrylonitrile fiber (PAN@NC) adsorbents were obtained through one-step hydrothermal carbonization approach assisted by diethylenetriamine using polyacrylonitrile (PAN) fibers as the templates, which showed highly efficient adsorption for anionic pollutants. The PAN@NC fibers were characterized in detail to confirm their structures and composition. The flexible and robust PAN@NC fiber membrane exhibited high adsorption capacity and good regeneration and recycling ability toward the anionic metal ion Cr(VI) and herbicide 2,4-dichlorophenoxyacetic acid (2,4-D). According to the Langmuir model, the adsorption behaviors showed monolayer adsorption capacities of 290.70mg/g and 164.47mg/g for Cr(VI) and 2,4-D, respectively, which were higher than that of many other adsorbents. Recycling study indicated that the removal efficiencies for both pollutants retained above 90% after five cycles. These findings demonstrate that PAN@NC fibers are promising adsorbents for the removal of anionic pollutants from wastewater solutions.
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