Understanding the Robot Ecosystem: Don\u27t lose sight of either the trees or the forest

The robot sector in many countries has thrived recently thanks to government supports and innovations in various industries. This study, using the patent database to define the robot sector, reconfigures IO (Input-Output) data to analyze the relationships among various sectors. In particular, we consider the internal description of the robot sector (mesoscopic view—the trees) as well as the relationship between the robot and the non-robot sectors (macroscopic view—the forest), so that we can not only understand robot ecosystems in various dimensions but also develop policy insights. For the sake of systematic analysis of the intra- and inter-sector relations as well as the meso-macro links, this study constructs network models and employs several network measures. Our model and analysis present a good case study to understand the nature of the robot sector in terms of the business ecosystem. This novel approach also contributes to finding out a promising path that leverages the strengths of intra-sector relations and spreads the impact of the robot sector across the macro relations

Understanding the Robot Ecosystem: Don't lose sight of either the trees or the forest

The robot sector in many countries has thrived recently thanks to government supports and innovations in various industries. This study, using the patent database to define the robot sector, reconfigures IO (Input-Output) data to analyze the relationships among various sectors. In particular, we consider the internal description of the robot sector (mesoscopic view—the trees) as well as the relationship between the robot and the non-robot sectors (macroscopic view—the forest), so that we can not only understand robot ecosystems in various dimensions but also develop policy insights. For the sake of systematic analysis of the intra- and inter-sector relations as well as the meso-macro links, this study constructs network models and employs several network measures. Our model and analysis present a good case study to understand the nature of the robot sector in terms of the business ecosystem. This novel approach also contributes to finding out a promising path that leverages the strengths of intra-sector relations and spreads the impact of the robot sector across the macro relations

Elicitation and aggregation of data in knowledge intensive crowdsourcing

With the significant advance of internet and connectivity, crowdsourcing gained more popularity and various crowdsourcing platforms emerged. This project focuses on knowledge-intensive crowdsourcing, in which agents are presented with the tasks that require certain knowledge in domain. Knowledge-intensive crowdsourcing requires agents to have experiences on the specific domain. With the constraint of resources and its trait as sourcing from crowd, platform is likely to draw agents with different levels of expertise and knowledge and asking same task can result in bad performance. Some agents can give better information when they are asked with more general question or more knowledge-specific task or even other task in the same domain. With this intuition of hierarchy, this project depicts knowledge-structure in domain as tree structure and aims to propose methods on how to assign tasks to the agents to realize the ground truth of the data they are presented

Eichler integrals and harmonic weak Maass forms

Recently, K. Bringmann, P. Guerzhoy, Z. Kent and K. Ono studied the connection between Eichler integrals and the holomorphic parts of harmonic weak Maass forms on the full modular group. In this article, we extend their result to more general groups, namely, $H$-groups by employing the theory of supplementary functions introduced and developed by M. I. Knopp and S. Y. Husseini. In particular, we show that the set of Eichler integrals, which have polynomial period functions, is the same as the set of holomorphic parts of harmonic weak Maass forms of which the non-holomorphic parts are certain period integrals of cusp forms. From this we deduce relations among period functions for harmonic weak Maass forms

Power Allocation for Device-to-Device Interference Channel Using Truncated Graph Transformers

Power control for the device-to-device interference channel with single-antenna transceivers has been widely analyzed with both model-based methods and learning-based approaches. Although the learning-based approaches, i.e., datadriven and model-driven, offer performance improvement, the widely adopted graph neural network suffers from learning the heterophilous power distribution of the interference channel. In this paper, we propose a deep learning architecture in the family of graph transformers to circumvent the issue. Experiment results show that the proposed methods achieve the state-of-theart performance across a wide range of untrained network configurations. Furthermore, we show there is a trade-off between model complexity and generality.Comment: 6 pages, 5 figures. Accepted in IEEE International Mediterranean Conference on Communications and Networkin

Exact formulas for traces of singular moduli of higher level modular functions

Zagier proved that the traces of singular values of the classical j-invariant are the Fourier coefficients of a weight 3/2 modular form and Duke provided a new proof of the result by establishing an exact formula for the traces using Niebur's work on a certain class of non-holomorphic modular forms. In this short note, by utilizing Niebur's work again, we generalize Duke's result to exact formulas for traces of singular moduli of higher level modular functions.Comment: 8 page

Modeling of HVDC System to Improve Estimation of Transient DC Current and Voltages for AC Line-to-Ground FaultAn Actual Case Study in Korea

A new modeling method for high voltage direct current (HVDC) systems and associated controllers is presented for the power system simulator for engineering (PSS/E) simulation environment. The aim is to improve the estimation of the transient DC voltage and current in the event of an AC line-to-ground fault. The proposed method consists primary of three interconnected modules for (a) equation conversion; (b) control-mode selection; and (c) DC-line modeling. Simulation case studies were carried out using PSS/E and a power systems computer aided design/electromagnetic transients including DC (PSCAD/EMTDC) model of the Jeju-Haenam HVDC system in Korea. The simulation results are compared with actual operational data and the PSCAD/EMTDC simulation results for an HVDC system during single-phase and three-phase line-to-ground faults, respectively. These comparisons show that the proposed PSS/E modeling method results in the improved estimation of the dynamic variation in the DC voltage and current in the event of an AC network fault, with significant gains in computational efficiency, making it suitable for real-time analysis of HVDC systems.111Ysciescopu

PU-EdgeFormer: Edge Transformer for Dense Prediction in Point Cloud Upsampling

Despite the recent development of deep learning-based point cloud upsampling, most MLP-based point cloud upsampling methods have limitations in that it is difficult to train the local and global structure of the point cloud at the same time. To solve this problem, we present a combined graph convolution and transformer for point cloud upsampling, denoted by PU-EdgeFormer. The proposed method constructs EdgeFormer unit that consists of graph convolution and multi-head self-attention modules. We employ graph convolution using EdgeConv, which learns the local geometry and global structure of point cloud better than existing point-to-feature method. Through in-depth experiments, we confirmed that the proposed method has better point cloud upsampling performance than the existing state-of-the-art method in both subjective and objective aspects. The code is available at https://github.com/dohoon2045/PU-EdgeFormer.Comment: Accepted to ICASSP 202

SPGP: Structure Prototype Guided Graph Pooling

While graph neural networks (GNNs) have been successful for node classification tasks and link prediction tasks in graph, learning graph-level representations still remains a challenge. For the graph-level representation, it is important to learn both representation of neighboring nodes, i.e., aggregation, and graph structural information. A number of graph pooling methods have been developed for this goal. However, most of the existing pooling methods utilize k-hop neighborhood without considering explicit structural information in a graph. In this paper, we propose Structure Prototype Guided Pooling (SPGP) that utilizes prior graph structures to overcome the limitation. SPGP formulates graph structures as learnable prototype vectors and computes the affinity between nodes and prototype vectors. This leads to a novel node scoring scheme that prioritizes informative nodes while encapsulating the useful structures of the graph. Our experimental results show that SPGP outperforms state-of-the-art graph pooling methods on graph classification benchmark datasets in both accuracy and scalability.Comment: 18 pages, 6 figure