95 research outputs found

    Robust and Real-time Deep Tracking Via Multi-Scale Domain Adaptation

    Full text link
    Visual tracking is a fundamental problem in computer vision. Recently, some deep-learning-based tracking algorithms have been achieving record-breaking performances. However, due to the high complexity of deep learning, most deep trackers suffer from low tracking speed, and thus are impractical in many real-world applications. Some new deep trackers with smaller network structure achieve high efficiency while at the cost of significant decrease on precision. In this paper, we propose to transfer the feature for image classification to the visual tracking domain via convolutional channel reductions. The channel reduction could be simply viewed as an additional convolutional layer with the specific task. It not only extracts useful information for object tracking but also significantly increases the tracking speed. To better accommodate the useful feature of the target in different scales, the adaptation filters are designed with different sizes. The yielded visual tracker is real-time and also illustrates the state-of-the-art accuracies in the experiment involving two well-adopted benchmarks with more than 100 test videos.Comment: 6 page

    Anisotropic mass segregation: two-component mean-field model

    Full text link
    Galactic nuclei, the densest stellar environments in the Universe, exhibit a complex geometrical structure. The stars orbiting the central supermassive black hole follow a mass segregated distribution both in the radial distance from the center and in the inclination angle of the orbital planes. The latter distribution may represent the equilibrium state of vector resonant relaxation (VRR). In this paper, we build simple models to understand the equilibrium distribution found previously in numerical simulations. Using the method of maximising the total entropy and the quadrupole mean-field approximation, we determine the equilibrium distribution of axisymmetric two-component gravitating systems with two distinct masses, semimajor axes, and eccentricities. We also examine the limiting case when one of the components dominates over the total energy and angular momentum, approximately acting as a heat bath, which may represent the surrounding astrophysical environment such as the tidal perturbation from the galaxy, a massive perturber, a gas torus, or a nearby stellar system. Remarkably, the bodies above a critical mass in the subdominant component condense into a disk in a ubiquitous way. We identify the system parameters where the transition is smooth and where it is discontinuous. The latter cases exhibit a phase transition between an ordered disk-like state and a disordered nearly spherical distribution both in the canonical and in the microcanonical ensembles for these long-range interacting systems.Comment: 25 pages, 24 figures, accepted by PR

    Efficient Anomaly Detection with Budget Annotation Using Semi-Supervised Residual Transformer

    Full text link
    Anomaly Detection is challenging as usually only the normal samples are seen during training and the detector needs to discover anomalies on-the-fly. The recently proposed deep-learning-based approaches could somehow alleviate the problem but there is still a long way to go in obtaining an industrial-class anomaly detector for real-world applications. On the other hand, in some particular AD tasks, a few anomalous samples are labeled manually for achieving higher accuracy. However, this performance gain is at the cost of considerable annotation efforts, which can be intractable in many practical scenarios. In this work, the above two problems are addressed in a unified framework. Firstly, inspired by the success of the patch-matching-based AD algorithms, we train a sliding vision transformer over the residuals generated by a novel position-constrained patch-matching. Secondly, the conventional pixel-wise segmentation problem is cast into a block-wise classification problem. Thus the sliding transformer can attain even higher accuracy with much less annotation labor. Thirdly, to further reduce the labeling cost, we propose to label the anomalous regions using only bounding boxes. The unlabeled regions caused by the weak labels are effectively exploited using a highly-customized semi-supervised learning scheme equipped with two novel data augmentation methods. The proposed method outperforms all the state-of-the-art approaches using all the evaluation metrics in both the unsupervised and supervised scenarios. On the popular MVTec-AD dataset, our SemiREST algorithm obtains the Average Precision (AP) of 81.2% in the unsupervised condition and 84.4% AP for supervised anomaly detection. Surprisingly, with the bounding-box-based semi-supervisions, SemiREST still outperforms the SOTA methods with full supervision (83.8% AP) on MVTec-AD.Comment: 20 pages,6 figure

    Research on the Improved Combinatorial Prediction Model of Steel Price Based on Time Series

    Get PDF
    Accurately predicting the price change of steel (main building materials) is an effective means to control and manage the cost of construction projects. It is one of the ways for construction enterprises to reasonably allocate building materials, save resources, reduce carbon emissions and reduce environmental pollution. Based on the monthly historical price data of 100 steel rebar (16 mm) from November 2010 to February 2019, the separation and retrieval process of the four components in the time series are improved. The improved multiplicative and additive models were used to make separate predictions, and the reasonable weight is given to combine the multiplication and addition model by the reciprocal of variance method. Finally, an improved prediction model of steel bar price combination with higher prediction accuracy is obtained. The prediction results show that the improved multiplication model and addition model have higher prediction accuracy, their MAPE are 2.62% and 2.36% respectively. Moreover, the prediction accuracy of the combined model is even higher, its MAPE is 2.29%. The prediction accuracy of the improved composite model is higher than that of the individual models. The improved combined prediction model of reinforcement price based on time series method can provide some reference and help for cost control and management in construction engineering, further reduce resource waste and construction non-point source pollution

    C. elegans fatty acid two-hydroxylase regulates intestinal homeostasis by affecting heptadecenoic acid production

    Get PDF
    Background/Aims: The hydroxylation of fatty acids at the C-2 position is the first step of fatty acid α-oxidation and generates sphingolipids containing 2-hydroxy fatty acyl moieties. Fatty acid 2-hydroxylation is catalyzed by Fatty acid 2-hydroxylase (FA2H) enzyme. However, the precise roles of FA2H and fatty acid 2-hydroxylation in whole cell homeostasis still remain unclear. Methods: Here we utilize Caenorhabditis elegans as the model and systemically investigate the physiological functions of FATH-1/C25A1.5, the highly conserved worm homolog for mammalian FA2H enzyme. Immunostaining, dye-staining and translational fusion reporters were used to visualize FATH-1 protein and a variety of subcellular structures. The “click chemistry” method was employed to label 2-OH fatty acid in vivo. Global and tissue-specific RNAi knockdown experiments were performed to inactivate FATH-1 function. Lipid analysis of the fath-1 deficient mutants was achieved by mass spectrometry. Results: C. elegans FATH-1 is expressed at most developmental stages and in most tissues. Loss of fath-1 expression results in severe growth retardation and shortened lifespan. FATH-1 function is crucially required in the intestine but not the epidermis with stereospecificity. The “click chemistry” labeling technique showed that the FATH-1 metabolites are mainly enriched in membrane structures preferable to the apical side of the intestinal cells. At the subcellular level, we found that loss of fath-1 expression inhibits lipid droplets formation, as well as selectively disrupts peroxisomes and apical endosomes. Lipid analysis of the fath-1 deficient animals revealed a significant reduction in the content of heptadecenoic acid, while other major FAs remain unaffected. Feeding of exogenous heptadecenoic acid (C17: 1), but not oleic acid (C18: 1), rescues the global and subcellular defects of fath-1 knockdown worms. Conclusion: Our study revealed that FATH-1 and its catalytic products are highly specific in the context of chirality, C-chain length, spatial distribution, as well as the types of cellular organelles they affect. Such an unexpected degree of specificity for the synthesis and functions of hydroxylated FAs helps to regulate protein transport and fat metabolism, therefore maintaining the cellular homeostasis of the intestinal cells. These findings may help our understanding of FA2H functions across species, and offer potential therapeutical targets for treating FA2H-related diseases

    The Development Direction of Research on Chinese Technological Innovation

    Full text link
    The theoretical study of Chinese technological innovation assumes the perspective of prototype structure of scientific being, and consists of four directions of investigations: ontological study of innovation cognition, behavior study of innovation cognition, subjects of innovation cognition, and subjective material foundation of innovation cognition. The study aims to obtain technological innovation capacity through construction of an external technological innovation system. However, an external system would necessarily result in redundancy of systems and decreased operability. For the purpose of environmental friendliness and resource conservation, it is vital for our technological innovation to ignite the intrinsic innovation vitality of the system. Therefore the endogenous mechanism of innovation is the scientific direction to take for the theoretical study of technological innovation

    Morphological and Comparative Transcriptome Analysis of Three Species of Five-Needle Pines: Insights Into Phenotypic Evolution and Phylogeny

    Get PDF
    Pinus koraiensis, Pinus sibirica, and Pinus pumila are the major five-needle pines in northeast China, with substantial economic and ecological values. The phenotypic variation, environmental adaptability and evolutionary relationships of these three five-needle pines remain largely undecided. It is therefore important to study their genetic differentiation and evolutionary history. To obtain more genetic information, the needle transcriptomes of the three five-needle pines were sequenced and assembled. To explore the relationship of sequence information and adaptation to a high mountain environment, data on needle morphological traits [needle length (NL), needle width (NW), needle thickness (NT), and fascicle width (FW)] and 19 climatic variables describing the patterns and intensity of temperature and precipitation at six natural populations were recorded. Geographic coordinates of altitude, latitude, and longitude were also obtained. The needle morphological data was combined with transcriptome information, location, and climate data, for a comparative analysis of the three five-needle pines. We found significant differences for needle traits among the populations of the three five-needle pine species. Transcriptome analysis showed that the phenotypic variation and environmental adaptation of the needles of P. koraiensis, P. sibirica, and P. pumila were related to photosynthesis, respiration, and metabolites. Analysis of orthologs from 11 Pinus species indicated a closer genetic relationship between P. koraiensis and P. sibirica compared to P. pumila. Our study lays a foundation for genetic improvement of these five-needle pines and provides insights into the adaptation and evolution of Pinus species

    CT Imaging Findings of Pulmonary Artery Stenosis: A Pictorial Review

    Get PDF
    Pulmonary artery stenosis represents a group of disorders involving main, branch or peripheral pulmonary arteries with pain, dyspnea, hemoptysis or even no symptoms. Early diagnosis and timely intervention are crucial for reducing mortality, but timely diagnosis is challenging due to the non-specific symptoms. Computed tomography pulmonary angiography (CTPA) is useful in the diagnosis because it can provide more details about abnormal changes in the lumen, vessel wall and adjacent mediastinal structures. Congenital and acquired pulmonary artery anomalies have some characteristics on CTPA, which can be useful for differential diagnosis. Awareness of these conditions is important for radiologists. This pictorial review provides an overview of CTPA imaging features of pulmonary artery stenosis

    A novel method to estimate the absorption rate constant for two-compartment model fitted drugs without intravenous pharmacokinetic data

    Get PDF
    The in vivo performances of most drugs after extravascular administration are fitted well with the two-compartment pharmacokinetic (PK) model, but the estimation of absorption rate constant (ka) for these drugs becomes difficult during unavailability of intravenous PK data. Herein, we developed a novel method, called the direct method, for estimating the ka values of drugs without using intravenous PK data, by proposing a new PK parameter, namely, maximum apparent rate constant of disposition (kmax). The accuracy of the direct method in ka estimation was determined using the setting parameters (k12, k21, and k10 values at high, medium, and low levels, respectively) and clinical data. The results showed that the absolute relative error of ka estimated using the direct method was significantly lower than that obtained using both the Loo-Riegelman method and the statistical moment method for the setting parameters. Human PK studies of telmisartan, candesartan cilexetil, and tenofovir disoproxil fumarate indicated that the ka values of these drugs were accurately estimated using the direct method based on good correlations between the ka values and other PK parameters that reflected the absorption properties of drugs in vivo (Tmax, Cmax, and Cmax/AUC0-t). This novel method can be applied in situations where intravenous PK data cannot be obtained and is expected to provide valuable support for PK evaluation and in vitro-in vivo correlation establishment
    corecore