Direction-Aware User Recommendation Based on Asymmetric Network Embedding

User recommendation aims at recommending users with potential interests in the social network. Previous works have mainly focused on the undirected social networks with symmetric relationship such as friendship, whereas recent advances have been made on the asymmetric relationship such as the following and followed by relationship. Among the few existing direction-aware user recommendation methods, the random walk strategy has been widely adopted to extract the asymmetric proximity between users. However, according to our analysis on real-world directed social networks, we argue that the asymmetric proximity captured by existing random walk based methods are insufficient due to the inbalance in-degree and out-degree of nodes. To tackle this challenge, we propose InfoWalk, a novel informative walk strategy to efficiently capture the asymmetric proximity solely based on random walks. By transferring the direction information into the weights of each step, InfoWalk is able to overcome the limitation of edges while simultaneously maintain both the direction and proximity. Based on the asymmetric proximity captured by InfoWalk, we further propose the qualitative (DNE-L) and quantitative (DNE-T) directed network embedding methods, capable of preserving the two properties in the embedding space. Extensive experiments conducted on six real-world benchmark datasets demonstrate the superiority of the proposed DNE model over several state-of-the-art approaches in various tasks.

Hybrid Metric K-Nearest Neighbor Algorithm and Applications

The K-Nearest Neighbor (KNN) algorithm is a classical machine learning algorithm. Most KNN algorithms are based on a single metric and do not further distinguish between repeated values in the range of K values, which can lead to a reduced classification effect and thus affect the accuracy of fault diagnosis. In this paper, a hybrid metric-based KNN algorithm is proposed to calculate a composite metric containing distance and direction information between test samples, which improves the discriminability of the samples. In the experiments, the hybrid metric KNN (HM-KNN) algorithm proposed in this paper is compared and validated with a variety of KNN algorithms based on a single distance metric on six data sets, and an HM-KNN application method is given for the forward gait stability control of a bipedal robot, where the abnormal motion is considered as a fault, and the distribution of zero moment points when the abnormal motion is generated is compared. The experimental results show that the algorithm has good data differentiation and generalization ability for different data sets, and it is feasible to apply it to the walking stability control of bipedal robots based on deep neural network control.

Deep Learning-Based Fast TOF-PET Image Reconstruction Using Direction Information

Deep learning has attracted attention for positron emission tomography (PET) image reconstruction task, however, it remains necessary to further improve the image quality. In this study, we propose a novel CNN-based fast time-of-flight PET (TOF-PET) image reconstruction method to fully utilize the direction information of coincidence events. The proposed method inputs view-grouped histo-images into a 3D CNN as a multi-channel image to use the direction information of coincidence events. We evaluated the proposed method using Monte Carlo simulation data obtained from a digital brain phantom. Compared to the case without it, when using direction information, the peak signal-to-noise ratio and structural similarity were improved by 1.2 dB and 0.02, at a coincidence time resolution of 300 ps. The calculation times of the proposed method were significantly faster than the conventional iterative reconstruction. These results indicate that the proposed method improves both the speed and image quality of TOF-PET image reconstruction.

Multisensory-motor integration in olfactory navigation of silkmoth, Bombyx mori, using virtual reality system

Most animals survive and thrive due to navigational behavior to reach their destinations. In order to navigate, it is important for animals to integrate information obtained from multisensory inputs and use that information to modulate their behavior. In this study, by using a virtual reality (VR) system for an insect, we investigated how the adult silkmoth integrates visual and wind direction information during female search behavior (olfactory behavior). According to the behavioral experiments using a VR system, the silkmoth had the highest navigational success rate when odor, vision, and wind information were correctly provided. However, the success rate of the search was reduced if the wind direction information provided was different from the direction actually detected. This indicates that it is important to acquire not only odor information but also wind direction information correctly. When the wind is received from the same direction as the odor, the silkmoth takes positive behavior; if the odor is detected but the wind direction is not in the same direction as the odor, the silkmoth behaves more carefully. This corresponds to a modulation of behavior according to the degree of complexity (turbulence) of the environment. We mathematically modeled the modulation of behavior using multisensory information and evaluated it using simulations. The mathematical model not only succeeded in reproducing the actual silkmoth search behavior but also improved the search success relative to the conventional odor-source search algorithm.

Multitarget Position-Sensitivity Security Transmission with OAM-DM Based on FDA

The multitarget position-sensitivity security transmission scheme with orbital angular momentum (OAM) directional modulation (DM) waveform using the uniform circular frequency diverse array (FDA) is proposed. The transmitter employs FDA to generate dual-mode range-dependent OAM beam pattern, and the direction information of OAM beam is modulated into the signal. According to the modulation method, orthogonal carrier frequency can be designed to realize multitarget position-dependent OAM pattern. In the undesired position, the intensity pattern and the phase front of the radio beam vary randomly with the digital transmission sequence. Because modulation waveform is position-dependent, the technique offers security, as the signal can be purposely distorted in other positions. The composite dual-mode OAM signal makes it more difficult for eavesdroppers to demodulate correct messages. The receiver with a single antenna employs the phase compensation and helical phase factor to restore the correct digital signal in the desired position. Numerical results show that multitarget position-sensitivity OAM-DM technology based on FDA offers the security transmission scheme.

Parameterization of magnetic vector potentials and fields for efficient multislice calculations of elastic electron scattering

The multislice method, which simulates the propagation of the incident electron wavefunction through a crystal, is a well established method for analysing the multiple scattering effects that an electron beam may undergo. The inclusion of magnetic effects into this method proves crucial towards simulating enhanced magnetic interaction of vortex beams with magnetic materials, calculating magnetic Bragg spots or searching for magnon signatures, to name a few examples. Inclusion of magnetism poses novel challenges to the efficiency of the multislice method for larger systems, especially regarding the consistent computation of magnetic vector potentials A and magnetic fields B over large supercells. This work presents a tabulation of parameterized magnetic (PM) values for the first three rows of transition metal elements computed from atomic density functional theory (DFT) calculations, allowing for the efficient computation of approximate A and B across large crystals using only structural and magnetic moment size and direction information. Ferromagnetic b.c.c. (body-centred cubic) Fe and tetragonal FePt are chosen to showcase the performance of PM values versus directly obtaining A and B from the unit-cell spin density by DFT. The magnetic fields of b.c.c. Fe are well described by the PM approach while for FePt the PM approach is less accurate due to deformations in the spin density. Calculations of the magnetic signal, namely the change due to A and B of the intensity of diffraction patterns, show that the PM approach for both b.c.c. Fe and FePt is able to describe the effects of magnetism in these systems to a good degree of accuracy.

Analysis of Multisensory-Motor Integration in Olfactory Navigation of Silkmoth, Bombyx mori, using Virtual Reality System

Most animals survive and thrive due to navigation behavior to reach their destinations. In order to navigate, it is important for animals to integrate information obtained from multisensory inputs and use that information to modulate their behavior. In this study, by using a virtual reality (VR) system for an insect, we investigated how an adult silkmoth integrates visual and wind direction information during female search behavior (olfactory behavior). According to the behavioral experiments using the VR system, the silkmoth had the highest navigation success rate when odor, vision, and wind information were correctly provided. However, we found that the success rate of the search signifcantly reduced if wind direction information was provided that was incorrect from the direction actually detected. This indicates that it is important to acquire not only odor information, but also wind direction information correctly. In other words, Behavior was modulated by the degree of co-incidence between the direction of arrival of the odor and the direction of arrival of the wind, and posture control (angular velocity control) was modulated by visual information. We mathematically modeled the modulation of behavior using multisensory information and evaluated it by simulation. As a result, the mathematical model not only succeeded in reproducing the actual female search behavior of the silkmoth, but can also improve search success relative to the conventional odor source search algorithm.

Distributed sensing of wind direction using fiber-optic cables

In the atmospheric boundary layer, phenomena exist with challenging properties such as spatial heterogeneity, particularly during stable weak wind situations. Studying spatially heterogeneous features requires spatially distributed measurements on fine spatial and temporal scales. Fiber-Optic Distributed Sensing (FODS) can provide spatially distributed measurements, simultaneously offering a spatial resolution on the order of decimeters and a temporal resolution on the order of seconds. While FODS has already been deployed to study various variables, FODS wind direction sensing has only been demonstrated in idealized wind tunnel experiments. We present the first distributed observations of FODS wind directions from field data. The wind direction sensing is accomplished by using pairs of actively heated fiber optic cables with cone-shaped microstructures attached to them. Here we present three different methods of calculating wind directions from the FODS measurements, two based on using combined wind speed and direction information and one deriving wind direction independently from FODS wind speed. For each approach, the effective temporal and spatial resolution is quantified using spectral coherence. With each method of calculating wind directions, temporal resolutions on the order of tens of seconds can be achieved. The accuracy of FODS wind directions was evaluated against a sonic anemometer, showing deviations of less than 15° most of the time. The applicability of FODS for wind direction measurements in different environmental conditions is tested by analysing the dependence of FODS wind direction accuracy and observable scales on environmental factors. Finally, we demonstrate the potential of this technique by presenting a period that displays spatial and temporal structures in the wind direction.

Toward the recognition of spacecraft feature components: A new benchmark and a new model

Countries are increasingly interested in spacecraft surveillance and recognition which play an important role in on-orbit maintenance, space docking, and other applications. Traditional detection methods, including radar, have many restrictions, such as excessive costs and energy supply problems. For many on-orbit servicing spacecraft, image recognition is a simple but relatively accurate method for obtaining sufficient position and direction information to offer services. However, to the best of our knowledge, few practical machine-learning models focusing on the recognition of spacecraft feature components have been reported. In addition, it is difficult to find substantial on-orbit images with which to train or evaluate such a model. In this study, we first created a new dataset containing numerous artificial images of on-orbit spacecraft with labeled components. Our base images were derived from 3D Max and STK software. These images include many types of satellites and satellite postures. Considering real-world illumination conditions and imperfect camera observations, we developed a degradation algorithm that enabled us to produce thousands of artificial images of spacecraft. The feature components of the spacecraft in all images were labeled manually. We discovered that direct utilization of the DeepLab V3+ model leads to poor edge recognition. Poorly defined edges provide imprecise position or direction information and degrade the performance of on-orbit services. Thus, the edge information of the target was taken as a supervisory guide, and was used to develop the proposed Edge Auxiliary Supervision DeepLab Network (EASDN). The main idea of EASDN is to provide a new edge auxiliary loss by calculating the L2 loss between the predicted edge masks and ground-truth edge masks during training. Our extensive experiments demonstrate that our network can perform well both on our benchmark and on real on-orbit spacecraft images from the Internet. Furthermore, the device usage and processing time meet the demands of engineering applications.