Integrating Neighborhood Geographic Distribution and Social Structure Influence for Social Media User Geolocation

Geolocating social media users aims to discover the real geographical locations of users from their publicly available data,which can support online location-based applications such as disaster alerts and local content recommen-dations.Social relationship-based methods represent a classical approach for geolocating social media.However,geographically proximate relationships are sparse and challenging to discern within social networks,thereby affecting the accuracy of user geolocation.To address this challenge,we propose user geolocation methods that integrate neighborhood geographical distribution and social structure influence(NGSI)to improve geolocation accuracy.Firstly,we propose a method for evaluating the homophily of locations based on the k-order neighbor-hood geographic distribution(k-NGD)similarity among users.There are notable differences in the distribution of k-NGD similarity between location-proximate and non-location-proximate users.Exploiting this distinction,we filter out non-location-proximate social relationships to enhance location homophily in the social network.To better utilize the location-proximate relationships in social networks,we propose a graph neural network algorithm based on the social structure influence.The algorithm enables us to perform a weighted aggregation of the information of users’multi-hop neighborhood,thereby mitigating the over-smoothing problem of user features and improving user geolocation performance.Experimental results on real social media dataset demonstrate that the neighborhood geographical distribution similarity metric can effectively filter out non-location-proximate social relationships.Moreover,compared with 7 existing social relationship-based user positioning methods,our proposed method can achieve multi-granularity user geolocation and improve the accuracy by 4.84%to 13.28%.

Serial time-encoded amplified microscopy for ultrafast imaging based on multi-wavelength laser

In this paper,a serial time-encoded amplified microscopy(STEAM)by employing a multi-wavelength laser as the light source is proposed and experimentally demonstrated.This system achieves ultrafast optical imaging with a tunable frame rate.The measuring range depends on the spectrum width of the multi-wavelength laser.Through tuning the speed of the modulating signal,the frame rate ranges from 100to 250 MHz.In addition,the spatial resolution can be improved by increasing the group velocity dispersion and reducing the wavelength spacing.Finally,with the development of photonic integrate circuits(PIC),the multi-wavelength laser source has the potential for integration on a photonic chip and thus the size of the proposed STEAM could be reduced in the future.

Optical length-change measurement based on an incoherent single-bandpass microwave photonic filter with high resolution

An optical length-change measurement technique is proposed based on an incoherent microwave photonic filter(MPF).The optical length under testing is inserted into an optical link of a single-bandpass MPF based on a polarization-processed incoherent light source.The key feature of the proposed technique is to transfer the length measurement in the optical domain to the electrical domain.In the electrical domain,the measurement resolution is extremely high thanks to the high-resolution measurement of microwave frequency response.In addition,since the MPF is a single-bandpass MPF,the optical length is uniquely determined by the central frequency of the MPF.A detailed investigation of the relation between the center frequency of the MPF and the optical length change is implemented.A measurement experiment is also demonstrated,and the experimental results show that the proposed technique has a measurement sensitivity of 1 GHz/mm with a high length-measurement resolution of 1 pm in theory.The proposed approach has the advantages of high sensitivity,high resolution,and immunity to power variation in electronic and optical links.