An Improved Tax Scheme for Selfish Routing

We study the problem of routing traffic for independent selfish users in a congested network to minimize the total latency. The inefficiency of selfish routing motivates regulating the flow of the system to lower the total latency of the Nash Equilibrium by economic incentives or penalties. When applying tax to the routes, we follow the definition of [Christodoulou et al, Algorithmica, 2014] to define ePoA as the Nash total cost including tax in the taxed network over the optimal cost in the original network. We propose a simple tax scheme consisting of step functions imposed on the links. The tax scheme can be applied to routing games with parallel links, affine cost functions and single-commodity networks to lower the ePoA to at most 4/3 - epsilon, where epsilon only depends on the discrepancy between the links. We show that there exists a tax scheme in the two link case with an ePoA upperbound less than 1.192 which is almost tight. Moreover, we design another tax scheme that lowers ePoA down to 1.281 for routing games with groups of links such that links in the same group are similar to each other and groups are sufficiently different

Research Notes : Taiwan : A spontaneous narrow-leaflet mutant

At the Asian Vegetable Research and Development Center, developing improved vegetable soybeans is one of the objectives. In accomplishing that objective, one of the crosses involved AGS 188, PI 157424 from Maturity Group IV and G 10381, cultivar \u27Kaohsiung\u27 No. 8 released by the Kaohsiung District Agricultural Improvement Station in Pingtung, Taiwan. The F1 and F2 progenies of the cross were normal

EVALUATION OF RECEIVING ABILITY OF TEENAGE MALE TABLE TENNIS PLAYERS IN TAIWAN

The purpose of this study was to evaluate the forehand receiving ability of teenage male table tennis players. Thirty-nine male players consist of skill levels from junior to senior high school students and national squads were selected. This assessment involves three tests: basic control, judgment, and match-like condition simulation. We found under the basic control test, the junior high school players performed poorer in downspin and left-side downspin in the aspect of accuracy (

On the Influencing Factors of Dictionary App Interface Design for the Elders

AbstractEnglish learning is becoming one of the popular movements towards the Globalization. In recent years especially, more people use smartphones to learn English. However, it was found in the current market that most dictionary apps were designed for the younger generation and neglected the needs of the elderly. The issue of memory over-load turned out to be the critical problem of the usability for the elderly, due to the complex menu structures. Thus this study is meant to explore a suitable menu structure for the senior user, and provide suggestions for the relative researches.The study results are:1.Gender: There is no significant between male and female in the operating performance.2.Menu structure: the performance of the hybrid structure is superior to the linear structure.3.Display mode: There is no significant between the horizontal and vertical display modes in operating performance.4.Task Complexity: A positive ratio between task complexity and menu topological structure was revealed, the harder the task complexity, the better performance of mixed structure can be expected

SD-Net: Symmetric-Aware Keypoint Prediction and Domain Adaptation for 6D Pose Estimation In Bin-picking Scenarios

Despite the success in 6D pose estimation in bin-picking scenarios, existing methods still struggle to produce accurate prediction results for symmetry objects and real world scenarios. The primary bottlenecks include 1) the ambiguity keypoints caused by object symmetries; 2) the domain gap between real and synthetic data. To circumvent these problem, we propose a new 6D pose estimation network with symmetric-aware keypoint prediction and self-training domain adaptation (SD-Net). SD-Net builds on pointwise keypoint regression and deep hough voting to perform reliable detection keypoint under clutter and occlusion. Specifically, at the keypoint prediction stage, we designe a robust 3D keypoints selection strategy considering the symmetry class of objects and equivalent keypoints, which facilitate locating 3D keypoints even in highly occluded scenes. Additionally, we build an effective filtering algorithm on predicted keypoint to dynamically eliminate multiple ambiguity and outlier keypoint candidates. At the domain adaptation stage, we propose the self-training framework using a student-teacher training scheme. To carefully distinguish reliable predictions, we harnesses a tailored heuristics for 3D geometry pseudo labelling based on semi-chamfer distance. On public Sil'eane dataset, SD-Net achieves state-of-the-art results, obtaining an average precision of 96%. Testing learning and generalization abilities on public Parametric datasets, SD-Net is 8% higher than the state-of-the-art method. The code is available at https://github.com/dingthuang/SD-Net