Products of Generalized Stochastic Sarymsakov Matrices

In the set of stochastic, indecomposable, aperiodic (SIA) matrices, the class of stochastic Sarymsakov matrices is the largest known subset (i) that is closed under matrix multiplication and (ii) the infinitely long left-product of the elements from a compact subset converges to a rank-one matrix. In this paper, we show that a larger subset with these two properties can be derived by generalizing the standard definition for Sarymsakov matrices. The generalization is achieved either by introducing an "SIA index", whose value is one for Sarymsakov matrices, and then looking at those stochastic matrices with larger SIA indices, or by considering matrices that are not even SIA. Besides constructing a larger set, we give sufficient conditions for generalized Sarymsakov matrices so that their products converge to rank-one matrices. The new insight gained through studying generalized Sarymsakov matrices and their products has led to a new understanding of the existing results on consensus algorithms and will be helpful for the design of network coordination algorithms

Incentive Mechanism of Enterprises Energy-saving and Emission Reduction Based on Rank Order Tournaments

AbstractThe article establishes the analytical framework of incentive mechanism and the rank order tournaments model that based on the relative performance for developing energy-saving and emission reduction by using the theory of principle-agent and the Malcomson model. We systematically analyzes the model, and proposes the corresponding policy suggestions

Compound-specific delta D and its hydrological and environmental implication in the lakes on the Tibetan Plateau

The hydrogen isotopic composition (delta D) of n-alkanes in lacustrine sediments is widely used in palaeoenvironmental studies, but the heterogeneous origins and relative contributions of these lipids provide challenges for the interpretation of the increasing dataset as an environment and climatic proxy. We systematically investigated n-alkane delta D values from 51 submerged plants (39 Potamogeton, 1 Myriophyllum, and 11 Ruppia), 13 algae (5 Chara, 3 Cladophora, and 5 Spirogyra) and 20 terrestrial plants (10 grasses and 10 shrubs) in and around 15 lakes on the Tibetan Plateau. Our results demonstrate that delta D values of C-29 n-alkane are correlated significantly with the lake water delta D values both for algae (R (2)=0.85, p < 0.01, n=9) and submerged plants (R (2)=0.90, p < 0.01, n=25), indicating that delta D values of these algae and submerged plants reflect the delta D variation of lake water. We find that apparent hydrogen isotope fractionation factors between individual n-alkanes and water (epsilon (a/w)) are not constant among different algae and submerged plants, as well as in a single genus under different liminological conditions, indicating that the biosynthesis or environmental conditions (e.g. salinity) may affect their delta D values. The delta D values of submerged plant Ruppia in the Xiligou Lake (a closed lake) are significant enriched in D than those of terrestrial grasses around the lake (one-way ANOVA, p < 0.01), but the algae Chara in the Keluke Lake (an open lake) display similar delta D values with grasses around the lake (one-way ANOVA, p=0.826 > 0.05), suggesting that the n-alkane delta D values of the algae and submerged plants record the signal of D enrichment in lake water relative to precipitation only in closed lakes in arid and semi-arid area. For each algae and submerged plant sample, we find uniformed delta D values of different chain length n-alkanes, implying that, in combination with other proxies such as Paq and Average Chain Length, the offset between the delta D values of different chain length n-alkanes can help determine the source of sedimentary n-alkanes as well as inferring the hydrological characteristics of an ancient lake basin (open vs closed lake)

Efficient Fully Convolution Neural Network for Generating Pixel Wise Robotic Grasps With High Resolution Images

This paper presents an efficient neural network model to generate robotic grasps with high resolution images. The proposed model uses fully convolution neural network to generate robotic grasps for each pixel using 400 $\times$ 400 high resolution RGB-D images. It first down-sample the images to get features and then up-sample those features to the original size of the input as well as combines local and global features from different feature maps. Compared to other regression or classification methods for detecting robotic grasps, our method looks more like the segmentation methods which solves the problem through pixel-wise ways. We use Cornell Grasp Dataset to train and evaluate the model and get high accuracy about 94.42% for image-wise and 91.02% for object-wise and fast prediction time about 8ms. We also demonstrate that without training on the multiple objects dataset, our model can directly output robotic grasps candidates for different objects because of the pixel wise implementation.Comment: Submitted to ROBIO 201

Social Network Developing Process Driven by Conflict in Mass Contingency Events

AbstractTwo evolutionary mechanisms of mass contingency events are discussed, which are the cognition structure and the social network structure of the vulnerable groups and also the important problem in public security engineering of developing country. The paper analyzes the developing process of social network driven by conflict. Because the abundant participants share the expensive protest cost, the opinion leaders or sponsors organize the social network in the vulnerable group to maintain their legitimate rights and interests. The theoretical research shows that the protest strategy is feasible as soon as the social network reaches the minimal numbers. The CHAM strike event in 2010 provides an excellent case to explain the three-phase developing process of mass contingency events and the hiberarchy social network driven by the conflict. Lastly, it makes the simulation analysis about the social network of the CHAM strike under the Netlogo platform, where the simulating result is in accordance with the theoretical analysis

The Influence of Fixed and Moving NPC on Pedestrians’ Avoidance Behaviors: VR-Based Experiments

Pedestrians have to take actions when crossing other pedestrians to avoid collisions. In this work, we focus on the differences of avoidance behaviors when a pedestrian crosses a moving and fixed intruder (NPC) in the virtual environment. The avoidance process is divided into three stages using the start avoidance point and maximum lateral deviation point. In moving NPC experiments, the distance from start avoidance point to the potential collision point (CP) first decreases and then increases as the intrusion angle increases. In standing NPC experiments, pedestrians start avoidance closer to the CP (average distance: 3.73m). In moving NPC experiments, the average maximum lateral offset distance (MLD) for the pedestrians to detour decreases with the intrusion angles decreases (Behind MLD ∈[1.09 m, 1.94 m], Front MLD ∈[1.13 m, 1.56 m]). In standing NPC experiments, the average MLD is 1.01m (left: 1.04m, right: 0.98m), which is the closest to the MLD of pedestrians at 180° intrusion angles. What’s more, at 30°, 60°, 90° and 120° intrusion angles, pedestrians avoiding behind the NPC require higher MLD than others avoiding in front of the NPC. Thus, more subjects prefer to avoid in front of the NPC under these conditions (88%, 86%, 78%, 69% of all). But the preference weakens and disappears at 150° and 180° intrusion angles due to the decrease of MLD. In standing NPC experiments, significant left-right preference is not found in pedestrians’ avoidance strategies (right: 46%, left: 54%). This article quantitatively analyses the difference between the influence of fixed and movement NPC on pedestrians’ avoidance strategies. The mechanism of pedestrian’s avoidance behavior is obtained by analyzing characteristic parameters, which is helpful to adjust pedestrian avoidance prediction models and design humanoid robots

Quantifying Learning and Competition among Crowdfunding Projects: Metrics and a Predictive Model

The performance of a crowdfunding project is highly situational-dependent. In this study, we quantify the interactions between crowdfunding projects in order to understand how these interactions can help predict the performance of crowdfunding campaigns. Specifically, we utilize Natural Language Processing (NLP) techniques to create a semi-automated system to label the associated product for each crowdfunding campaign. We also propose three sets of metrics to measure how crowdfunding projects learn from and compete with each other. Finally, we propose a machine learning model and demonstrate that the proposed metrics and the proposed model outperform other combinations when predicting the performance of crowdfunding projects