Multiport beamforming system based on reconfigurable waveguide phased antenna array for satellite communication applications

A multiport K/Ka-band beamforming system based on the reconfigurable waveguide phased antenna array is presented in this thesis. The waveguide structure is used to achieve low loss, wideband performance, and simple installation and maintenance. The antenna array is adopted to compensate for the high propagation loss in higher frequency, which also provided flexible functions for multi-user wireless communication applications. The reconfigurable waveguide transitions are the most crucial component in this beamforming system to achieve dual linear-polarized/left-handed circular-polarized/right-handed circular-polarized functions at K/Ka-band respectively by using the reconfigurable structure. It provided much better performance in bandwidth compared with the recent dual-band/dual-mode waveguide transitions. The waveguide antenna and antenna arrays are reconfigurable and replaceable to meet the design purposes and requirements for linear-polarized/left-handed circular-polarized/right-handed circular-polarized functions. The ultra-bandwidth from K-band to Ka-band provided advantages in saving cost and flexible functions due to the waveguide antenna array parts being applicable for both transmitting/receiving systems for K/Ka-band. This advanced beamforming system could provide many merits such as low loss, wideband, compact structure, high functional flexibility, lower cost, simpler installation, and easier maintenance by using the waveguide reconfigurable. These advantages are indicated by the sufficiently good performance in both the simulated and measured results in this thesis, which demonstrated that this beamforming system design is applicable for wireless communication applications in high frequency, especially for satellite communication applications with separated unlink and downlink systems. The potential and prospect for a MIMO beamforming system with a multilayer PCB feeding network are also demonstrated from the wideband performance of multilayer SICL power divider and SICL-to-waveguide transitions in this thesis to get a more flexible structure for a MIMO beamforming system and more compact structure

Exploring the Design Space of Immersive Urban Analytics

Recent years have witnessed the rapid development and wide adoption of immersive head-mounted devices, such as HTC VIVE, Oculus Rift, and Microsoft HoloLens. These immersive devices have the potential to significantly extend the methodology of urban visual analytics by providing critical 3D context information and creating a sense of presence. In this paper, we propose an theoretical model to characterize the visualizations in immersive urban analytics. Further more, based on our comprehensive and concise model, we contribute a typology of combination methods of 2D and 3D visualizations that distinguish between linked views, embedded views, and mixed views. We also propose a supporting guideline to assist users in selecting a proper view under certain circumstances by considering visual geometry and spatial distribution of the 2D and 3D visualizations. Finally, based on existing works, possible future research opportunities are explored and discussed.Comment: 23 pages,11 figure

Fine-Grained Named Entity Typing over Distantly Supervised Data Based on Refined Representations

Fine-Grained Named Entity Typing (FG-NET) is a key component in Natural Language Processing (NLP). It aims at classifying an entity mention into a wide range of entity types. Due to a large number of entity types, distant supervision is used to collect training data for this task, which noisily assigns type labels to entity mentions irrespective of the context. In order to alleviate the noisy labels, existing approaches on FGNET analyze the entity mentions entirely independent of each other and assign type labels solely based on mention sentence-specific context. This is inadequate for highly overlapping and noisy type labels as it hinders information passing across sentence boundaries. For this, we propose an edge-weighted attentive graph convolution network that refines the noisy mention representations by attending over corpus-level contextual clues prior to the end classification. Experimental evaluation shows that the proposed model outperforms the existing research by a relative score of upto 10.2% and 8.3% for macro f1 and micro f1 respectively

Similarities and differences of functional connectivity in drug-naïve, first-episode adolescent and young adult with major depressive disorder and schizophrenia

Major depressive disorder (MDD) and schizophrenia (SZ) are considered two distinct psychiatric disorders. Yet, they have considerable overlap in symptomatology and clinical features, particularly in the initial phases of illness. The amygdala and prefrontal cortex (PFC) appear to have critical roles in these disorders; however, abnormalities appear to manifest differently. In our study forty-nine drug-naïve, first-episode MDD, 45 drug-naïve, first-episode SZ, and 50 healthy control (HC) participants from 13 to 30 years old underwent resting-state functional magnetic resonance imaging. Functional connectivity (FC) between the amygdala and PFC was compared among the three groups. Significant differences in FC were observed between the amygdala and ventral PFC (VPFC), dorsolateral PFC (DLPFC), and dorsal anterior cingulated cortex (dACC) among the three groups. Further analyses demonstrated that MDD showed decreased amygdala-VPFC FC and SZ had reductions in amygdala-dACC FC. Both the diagnostic groups had significantly decreased amygdala-DLPFC FC. These indicate abnormalities in amygdala-PFC FC and further support the importance of the interaction between the amygdala and PFC in adolescents and young adults with these disorders. Additionally, the alterations in amygdala-PFC FC may underlie the initial similarities observed between MDD and SZ and suggest potential markers of differentiation between the disorders at first onset

RAHNet: Retrieval Augmented Hybrid Network for Long-tailed Graph Classification

Graph classification is a crucial task in many real-world multimedia applications, where graphs can represent various multimedia data types such as images, videos, and social networks. Previous efforts have applied graph neural networks (GNNs) in balanced situations where the class distribution is balanced. However, real-world data typically exhibit long-tailed class distributions, resulting in a bias towards the head classes when using GNNs and limited generalization ability over the tail classes. Recent approaches mainly focus on re-balancing different classes during model training, which fails to explicitly introduce new knowledge and sacrifices the performance of the head classes. To address these drawbacks, we propose a novel framework called Retrieval Augmented Hybrid Network (RAHNet) to jointly learn a robust feature extractor and an unbiased classifier in a decoupled manner. In the feature extractor training stage, we develop a graph retrieval module to search for relevant graphs that directly enrich the intra-class diversity for the tail classes. Moreover, we innovatively optimize a category-centered supervised contrastive loss to obtain discriminative representations, which is more suitable for long-tailed scenarios. In the classifier fine-tuning stage, we balance the classifier weights with two weight regularization techniques, i.e., Max-norm and weight decay. Experiments on various popular benchmarks verify the superiority of the proposed method against state-of-the-art approaches.Comment: Accepted by the ACM International Conference on Multimedia (MM) 202

HAMNER: Headword Amplified Multi-span Distantly Supervised Method for Domain Specific Named Entity Recognition

To tackle Named Entity Recognition (NER) tasks, supervised methods need to obtain sufficient cleanly annotated data, which is labor and time consuming. On the contrary, distantly supervised methods acquire automatically annotated data using dictionaries to alleviate this requirement. Unfortunately, dictionaries hinder the effectiveness of distantly supervised methods for NER due to its limited coverage, especially in specific domains. In this paper, we aim at the limitations of the dictionary usage and mention boundary detection. We generalize the distant supervision by extending the dictionary with headword based non-exact matching. We apply a function to better weight the matched entity mentions. We propose a span-level model, which classifies all the possible spans then infers the selected spans with a proposed dynamic programming algorithm. Experiments on all three benchmark datasets demonstrate that our method outperforms previous state-of-the-art distantly supervised methods.Comment: 9 pages, 2 figure

A Diffusion model for POI recommendation

Next Point-of-Interest (POI) recommendation is a critical task in location-based services that aim to provide personalized suggestions for the user's next destination. Previous works on POI recommendation have laid focused on modeling the user's spatial preference. However, existing works that leverage spatial information are only based on the aggregation of users' previous visited positions, which discourages the model from recommending POIs in novel areas. This trait of position-based methods will harm the model's performance in many situations. Additionally, incorporating sequential information into the user's spatial preference remains a challenge. In this paper, we propose Diff-POI: a Diffusion-based model that samples the user's spatial preference for the next POI recommendation. Inspired by the wide application of diffusion algorithm in sampling from distributions, Diff-POI encodes the user's visiting sequence and spatial character with two tailor-designed graph encoding modules, followed by a diffusion-based sampling strategy to explore the user's spatial visiting trends. We leverage the diffusion process and its reversed form to sample from the posterior distribution and optimized the corresponding score function. We design a joint training and inference framework to optimize and evaluate the proposed Diff-POI. Extensive experiments on four real-world POI recommendation datasets demonstrate the superiority of our Diff-POI over state-of-the-art baseline methods. Further ablation and parameter studies on Diff-POI reveal the functionality and effectiveness of the proposed diffusion-based sampling strategy for addressing the limitations of existing methods