Color Superconductivity at Moderate Density

The effect of color breaking on colored quarks' chiral condensates has been investigated at zero temperature and moderate baryon density. It is found that the influence of the diquark condensate on different colored quarks is very small.Comment: 4 pages, 1 figure in eps, talk given at XXXI International Symposium on Multiparticle Dynamics, Sept 1-7, 2001, Datong China. See http://ismd31.ccnu.edu.cn

Experience with Group Supervision

Supervision can take a few different forms. For example, it can be one-to-one supervision and it can also be group supervision. Group supervision is an important process within the scientific community. Many research groups use this form to supervise doctoral- and master students in groups. Some efforts have been made to study this process. For example, Samara (2002) studied the group supervision process in group writing. However, group supervision has not been studied thoroughly so far. This project aims at studying the group supervision from the community of practice point of view. The main research question is: What are the effects of group supervision on constructing a learning community

Research Progress and the Limiting Factors of Direct Seeding Rice in Central China

Symposium paper Part 2: Frontiers of sustainable rice production syste

Learning Segmentation Masks with the Independence Prior

An instance with a bad mask might make a composite image that uses it look fake. This encourages us to learn segmentation by generating realistic composite images. To achieve this, we propose a novel framework that exploits a new proposed prior called the independence prior based on Generative Adversarial Networks (GANs). The generator produces an image with multiple category-specific instance providers, a layout module and a composition module. Firstly, each provider independently outputs a category-specific instance image with a soft mask. Then the provided instances' poses are corrected by the layout module. Lastly, the composition module combines these instances into a final image. Training with adversarial loss and penalty for mask area, each provider learns a mask that is as small as possible but enough to cover a complete category-specific instance. Weakly supervised semantic segmentation methods widely use grouping cues modeling the association between image parts, which are either artificially designed or learned with costly segmentation labels or only modeled on local pairs. Unlike them, our method automatically models the dependence between any parts and learns instance segmentation. We apply our framework in two cases: (1) Foreground segmentation on category-specific images with box-level annotation. (2) Unsupervised learning of instance appearances and masks with only one image of homogeneous object cluster (HOC). We get appealing results in both tasks, which shows the independence prior is useful for instance segmentation and it is possible to unsupervisedly learn instance masks with only one image.Comment: 7+5 pages, 13 figures, Accepted to AAAI 201

Experimental Study of Influence of Movements on Airflow Under Stratum Ventilation

AbstractStratum ventilation, which could provide quality air in breathing zone and stratified thermal comfort, is a promising technology to meet the challenge of energy saving nowadays. This study is to find the influence of movements on airflow under stratum ventilation.The experiment is conducted in a full-scale chamber. A moving manikin is used to simulate the movements of an occupant. The results show that the moving manikin blocks some of the supply air when it is passing by the supply air inlet. However, the influence is local and disappears fast, mostly within 20 s

Policy Gradients for Probabilistic Constrained Reinforcement Learning

This paper considers the problem of learning safe policies in the context of reinforcement learning (RL). In particular, we consider the notion of probabilistic safety. This is, we aim to design policies that maintain the state of the system in a safe set with high probability. This notion differs from cumulative constraints often considered in the literature. The challenge of working with probabilistic safety is the lack of expressions for their gradients. Indeed, policy optimization algorithms rely on gradients of the objective function and the constraints. To the best of our knowledge, this work is the first one providing such explicit gradient expressions for probabilistic constraints. It is worth noting that the gradient of this family of constraints can be applied to various policy-based algorithms. We demonstrate empirically that it is possible to handle probabilistic constraints in a continuous navigation problem