Energy Market Integration and Economic Convergence: Implications for East Asia

Energy Market Integration (EMI) has been pursued by East Asian countries in recent years, but how it could play a role in facilitating economic growth of countries in the region remains to be an empirical question. This paper uses the economic convergence analysis (including both the σ-convergence and β-convergence approaches) to examine the impact of EMI - measured by two newly constructed indexes (namely, the energy trade index and the energy market competition index) - at the country level, on dynamic economic growth path across countries, with a special interest to inform policy makings related to promoting EMI among East Asian countries. The result shows that a more integrated energy market may significantly reduce income disparity across countries and thus help poor countries to catch up with rich countries in economic development. Moreover, a comparison among the three regions including EU, NAFTA and EAS shows that EAS countries are more likely to achieve economic convergence along with the construction of EMI process. An important policy implication is that less developed countries in the EAS region can increase benefits from actively participating into the EMI process.

Nanonewton force generation and detection based on a sensitive torsion pendulum

In this paper, we introduce the experiment based on a sensitive torsion pendulum for measuring and calibrating small forces at nanonewton scale. The force standard for calibration is the universal gravitation between four masses separated by known distances. It is realized by two test masses suspended as the part of torsion pendulum and two source masses on a rotation table. Two force generation mechanisms, optical force from radiation pressure and electrostatic force by capacitive actuation unit, are designed and will be calibrated by the gravitation force. We present our recent results of radiation pressure measurements, and describe the design of capacitive displacement sensing/actuating unit.Comment: This work has been presented on Conference on Precision Electromagnetic Measurements, 8-13 June 2008, Broomfield, Colorad

Question Type Guided Attention in Visual Question Answering

Visual Question Answering (VQA) requires integration of feature maps with drastically different structures and focus of the correct regions. Image descriptors have structures at multiple spatial scales, while lexical inputs inherently follow a temporal sequence and naturally cluster into semantically different question types. A lot of previous works use complex models to extract feature representations but neglect to use high-level information summary such as question types in learning. In this work, we propose Question Type-guided Attention (QTA). It utilizes the information of question type to dynamically balance between bottom-up and top-down visual features, respectively extracted from ResNet and Faster R-CNN networks. We experiment with multiple VQA architectures with extensive input ablation studies over the TDIUC dataset and show that QTA systematically improves the performance by more than 5% across multiple question type categories such as "Activity Recognition", "Utility" and "Counting" on TDIUC dataset. By adding QTA on the state-of-art model MCB, we achieve 3% improvement for overall accuracy. Finally, we propose a multi-task extension to predict question types which generalizes QTA to applications that lack of question type, with minimal performance loss