Research on Target Tracking Algorithm of Twin Networks Integrating Attention Mechanism

Aiming at the current stage of the twin network target tracking algorithm, the tracking target is occluded, the tracking is affected by illumination, and the target's scale change from far to near or from near to far causes tracking failure. This article will optimize and improve from two directions. The twin neural network first uses an adaptive detailed feature extraction, adds a residual network to the twin network, and embeds a detailed feature retention module in each layer, amplifies the changes in the target feature, and retains the important structure of the original target feature Details: Secondly, the introduction of a spatial attention mechanism allows the main branch to pay more attention to the area to be matched, improves the ability to distinguish features, and makes the tracking effect better. In order to verify the effectiveness of this experiment, this experiment was tested on the data set OTB2015. The experiment proved that the proposed algorithm performs better in accuracy and success rate.

Electromagnetic resonance of nonlinear vacuum in one-dimensional cavity

Nonlinear corrections on electromagnetic fields in vacuum have been expected. In this study, we have theoretically considered nonlinear Maxwell’s equations in a one-dimensional cavity for a classical light and external static electromagnetic fields. A general solution for the electromagnetic corrective components including that of a longitudinal standing wave was derived after a linearization. The main purpose is to give a detailed feature of the previously reported resonant behavior [Shibata, Euro. Phys. J. D 74:215 (2020)], such as the effect of external static fields and the polarization fluctuation. These results favor the development of new and effective method for experiment. Graphic abstract

Interaction Networks Converging on Immunosuppressive Roles of Granzyme B: Special Niches Within the Tumor Microenvironment

Granzyme B is a renowned effector molecule primarily utilized by CTLs and NK cells against ill-defined and/or transformed cells during immunosurveillance. The overall expression of granzyme B within tumor microenvironment has been well-established as a prognostic marker indicative of priming immunity for a long time. Until recent years, increasing immunosuppressive effects of granzyme B are unveiled in the setting of different immunological context. The accumulative evidence confounded the roles of granzyme B in immune responses, thereby arousing great interests in characterizing detailed feature of granzyme B-positive niche. In this paper, the granzyme B-related regulatory effects of major suppressor cells as well as the tumor microenvironment that defines such functionalities were longitudinally summarized and discussed. Multiplex networks were built upon the interactions among different transcriptional factors, cytokines, and chemokines that regarded to the initiation and regulation of granzyme B-mediated immunosuppression. The conclusions and prospect may facilitate better interpretations of the clinical significance of granzyme B, guiding the rational development of therapeutic regimen and diagnostic probes for anti-tumor purposes.

A Triple Adversary Network Driven by Hybrid High-Order Attention for Domain Adaptation

How to bridge the knowledge gap between the annotated source domain and the unlabeled target domain is a basic challenge to domain adaptation. The existing approaches can relieve this gap by feature alignments across domains; however, aligning non-transferable features may lead to negative shift confusing the knowledge learning on target domains. In this paper, a triple adversary network is proposed on the basis of a high-order attention, hopefully to solve the problem. The proposed architecture focuses on the detailed feature alignment by a hybrid high-order attention using a fast iteration algorithm. In addition, an orthogonal loss of two complementary modules is applied to constrain the mutual exclusion of foreground and background features. Finally, a triple adversarial strategy is introduced to further improve the training convergence for the composed architectures. Numeric experiments on datasets of Digits, Office-31 and Office-home illuminate that the proposed network can effectively improve the state-of-art domain adaptations with superior transferring performance.

Application of 3-D Sensor Tracking Imaging in Detailed Feature Extraction of Motion Damage Action

In order to study the theoretical significance and practical value of the application of three-dimensional sensor tracking imaging in motion damage action detail feature extraction, the key technologies such as accurate segmentation of moving targets, regional feature extraction, target description and robust tracking are studied and discussed. The results show that the robustness of particle filter tracking algorithm in complex scenes can be effectively improved by using the separable feature information of the current tracking scene to track the target. From this it can be seen that the Bandelet transform based on geometric flow divides the image into regions, counts the histogram of the intensity distribution of the strip wave in all directions, and extracts the statistical features. Meanwhile, histogram features are normalized in order to maintain the robustness and selectivity of features.

A Novel LiDAR Data Classification Algorithm Combined CapsNet with ResNet

LiDAR data contain feature information such as the height and shape of the ground target and play an important role for land classification. The effect of convolutional neural network (CNN) for feature extraction on LiDAR data is very significant, however CNN cannot resolve the spatial relationship of features adequately. The capsule network (CapsNet) can identify the spatial variations of features and is widely used in supervised learning. In this article, the CapsNet is combined with the residual network (ResNet) to design a deep network-ResCapNet for improving the accuracy of LiDAR classification. The capsule network represents the features by vectors, which can account for the direction of the features and the relative position between the features. Therefore, more detailed feature information can be extracted. ResNet protects the integrity of information by passing input information to the output directly, which can solve the problem of network degradation caused by information loss in the traditional CNN propagation process to a certain extent. Two different LiDAR data sets and several classic machine learning algorithms are used for comparative experiments. The experimental results show that ResCapNet proposed in this article `improve the performance of LiDAR classification.

Mid-infrared spectroscopy of zodiacal emission with AKARI/IRC

Interplanetary dust (IPD) is thought to be recently supplied from asteroids and comets. Grain properties of the IPD can give us information about the environment in the proto-solar system, and can be traced from the shapes of silicate features around 10$\, \mu$m seen in the zodiacal emission spectra. We analyzed mid-infrared slit-spectroscopic data of the zodiacal emission in various sky directions obtained with the Infrared Camera on board the Japanese AKARI satellite. After we subtracted the contamination due to instrumental artifacts, we successfully obtained high signal-to-noise spectra and have determined detailed shapes of excess emission features in the 9–12$\, \mu$m range in all sky directions. According to a comparison between the feature shapes averaged over all directions and the absorption coefficients of candidate minerals, the IPD was found to typically include small silicate crystals, especially enstatite grains. We also found variations in the feature shapes and the related grain properties among the different sky directions. From investigations of the correlation between feature shapes and the brightness contributions from dust bands, the IPD in dust bands seems to have a size frequency distribution biased toward large grains and shows indications of hydrated minerals. The spectra at higher ecliptic latitudes showed a stronger excess, which indicates an increase in the fraction of small grains included in the line of sight at higher ecliptic latitudes. If we focus on the dependence of detailed feature shapes on ecliptic latitudes, the IPD at higher ecliptic latitudes was found to have a lower olivine/(olivine + pyroxene) ratio for small amorphous grains. The variation of the mineral composition of the IPD in different sky directions may imply different properties of the IPD from different types of parent bodies, because the spatial distribution of the IPD depends on the type of the parent body.

Perceived Usefulness of Emoticons, Emojis, and Stickers in Text Messaging

In text-based communication, which lacks nonverbal cues, various techniques for expressing communicative intent are now available. Most prominently used are emoticons, emojis, and stickers. Although previous studies have separately examined emoticons and emojis, few have compared their features, and also included comparison with stickers. The authors conducted a survey targeting 300 Japanese young adults to investigate the features of emoticons, emojis, and stickers from the viewpoint of their perceived usefulness. The authors also examined the effects of gender and text-messaging dependency on ratings of the perceived usefulness of these graphical symbols. This study revealed a detailed feature list for each type of symbol. The existence of characteristic roles for each type of symbol is discussed. This study also confirmed the effects of gender and text-messaging dependency on symbol usefulness ratings.