Multi-Label Zero-Shot Learning with Structured Knowledge Graphs

In this paper, we propose a novel deep learning architecture for multi-label zero-shot learning (ML-ZSL), which is able to predict multiple unseen class labels for each input instance. Inspired by the way humans utilize semantic knowledge between objects of interests, we propose a framework that incorporates knowledge graphs for describing the relationships between multiple labels. Our model learns an information propagation mechanism from the semantic label space, which can be applied to model the interdependencies between seen and unseen class labels. With such investigation of structured knowledge graphs for visual reasoning, we show that our model can be applied for solving multi-label classification and ML-ZSL tasks. Compared to state-of-the-art approaches, comparable or improved performances can be achieved by our method.Comment: CVPR 201

THE CHARACTERISTICS OF DOUBLE KICK IN THE AIR DURING ACTIVE AND PASSIVE ATTACK FOR ELITE TAE-KWON-DO ATHLETES

The purpose of this study is to discuss the reaction time and movement time of the double kick in the air (DKA) of the active and passive attacks for elite tea-kwon-do athletes. The experiment takes six male tae-kwon-do athletes (average age: 20.5 yearold; average height: 174.8cm; average weight: 63kg) who won the national match in the past 2 years as the subjects. A high speed camera (120Hz) was used to shoot their movements, which is quantified by a Silicon Coach at the same time. Based on the experimental result, the DKA can be a long, short, or rapid jumping attack movement, whose vertical displacement is large and the rolling angle of the body in the air is large. Therefore, it is suitable for the combo attack after the active attack in the competition

Experimental demonstrations of high-Q superconducting coplanar waveguide resonators

We designed and successfully fabricated an absorption-type of superconducting coplanar waveguide (CPW) resonators. The resonators are made from a Niobium film (about 160 nm thick) on a high-resistance Si substrate, and each resonator is fabricated as a meandered quarter-wavelength transmission line (one end shorts to the ground and another end is capacitively coupled to a through feedline). With a vector network analyzer we measured the transmissions of the applied microwave through the resonators at ultra-low temperature (e.g., at 20 mK), and found that their loaded quality factors are significantly high, i.e., up to 10^6. With the temperature increases slowly from the base temperature (i.e., 20 mK), we observed the resonance frequencies of the resonators are blue shifted and the quality factors are lowered slightly. In principle, this type of CPW-device can integrate a series of resonators with a common feedline, making it a promising candidate of either the data bus for coupling the distant solid-state qubits or the sensitive detector of single photons.Comment: Accepted by Chinese Science Bulleti

Solving multiple-criteria R&D project selection problems with a data-driven evidential reasoning rule

In this paper, a likelihood based evidence acquisition approach is proposed to acquire evidence from experts'assessments as recorded in historical datasets. Then a data-driven evidential reasoning rule based model is introduced to R&D project selection process by combining multiple pieces of evidence with different weights and reliabilities. As a result, the total belief degrees and the overall performance can be generated for ranking and selecting projects. Finally, a case study on the R&D project selection for the National Science Foundation of China is conducted to show the effectiveness of the proposed model. The data-driven evidential reasoning rule based model for project evaluation and selection (1) utilizes experimental data to represent experts' assessments by using belief distributions over the set of final funding outcomes, and through this historic statistics it helps experts and applicants to understand the funding probability to a given assessment grade, (2) implies the mapping relationships between the evaluation grades and the final funding outcomes by using historical data, and (3) provides a way to make fair decisions by taking experts' reliabilities into account. In the data-driven evidential reasoning rule based model, experts play different roles in accordance with their reliabilities which are determined by their previous review track records, and the selection process is made interpretable and fairer. The newly proposed model reduces the time-consuming panel review work for both managers and experts, and significantly improves the efficiency and quality of project selection process. Although the model is demonstrated for project selection in the NSFC, it can be generalized to other funding agencies or industries.Comment: 20 pages, forthcoming in International Journal of Project Management (2019