39 research outputs found

    Popularity Ratio Maximization: Surpassing Competitors through Influence Propagation

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    In this paper, we present an algorithmic study on how to surpass competitors in popularity by strategic promotions in social networks. We first propose a novel model, in which we integrate the Preferential Attachment (PA) model for popularity growth with the Independent Cascade (IC) model for influence propagation in social networks called PA-IC model. In PA-IC, a popular item and a novice item grab shares of popularity from the natural popularity growth via the PA model, while the novice item tries to gain extra popularity via influence cascade in a social network. The {\em popularity ratio} is defined as the ratio of the popularity measure between the novice item and the popular item. We formulate {\em Popularity Ratio Maximization (PRM)} as the problem of selecting seeds in multiple rounds to maximize the popularity ratio in the end. We analyze the popularity ratio and show that it is monotone but not submodular. To provide an effective solution, we devise a surrogate objective function and show that empirically it is very close to the original objective function while theoretically, it is monotone and submodular. We design two efficient algorithms, one for the overlapping influence and non-overlapping seeds (across rounds) setting and the other for the non-overlapping influence and overlapping seed setting, and further discuss how to deal with other models and problem variants. Our empirical evaluation further demonstrates that the proposed PRM-IMM method consistently achieves the best popularity promotion compared to other methods. Our theoretical and empirical analyses shed light on the interplay between influence maximization and preferential attachment in social networks.Comment: 22 pages, 8 figures, to be appear SIGMOD 202

    Flexible transit routing model considering passengers’ willingness to pay

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    This paper proposes an alternative flexible transit model with two levels of bus stops, A level and B level. A-level bus stops are fixed, while B-level bus stops are flexible and provide service only when passengers indicate a strong willingness to pay (WTP). This fare structure encourages passengers to choose bus stops with their mobile phones or computers. An optimization model of 0-1 integer-programming is formulated based on whether certain B-level stops can be serviced. With a numerical example, we compare the performance of the proposed traversing method and a tabu search algorithm, both of which are adapted to solve the model. Finally, a real case is provided to evaluate the proposed transit system against comparable systems (e.g., a fixed-route transit system and a taxi service), and the result shows that the flexible transit routing model will help both passengers and bus companies, thus creating a win-win situation

    Evaluating the impact of citations of articles based on knowledge flow patterns hidden in the citations.

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    The effective evaluation of the impact of a scholarly article is a significant endeavor; for this reason, it has garnered attention. From the perspective of knowledge flow, this paper extracted various knowledge flow patterns concealed in articles citation counts to describe the citation impact of the articles. First, the intensity characteristic of knowledge flow was investigated to distinguish the different citation vitality of articles. Second, the knowledge diffusion capacity was examined to differentiate the size of the scope of articles' influences on the academic environment. Finally, the knowledge transfer capacity was discussed to investigate the support degree of articles on the follow-up research. Experimental results show that articles got more citations recently have a higher knowledge flow intensity. The articles have various impacts on the academic environment and have different supporting effects on the follow-up research, representing the differences in their knowledge diffusion and knowledge transfer capabilities. Compared with the single quantitative index of citation frequency, these knowledge flow patterns can carefully explore the citation value of articles. By integrating the three knowledge flow patterns to examine the total citation impact of articles, we found that the articles exhibit distinct value of citation impact even if they were published in the same field, in the same year, and with similar citation frequencies

    Spatial Distribution Patterns and Driving Factors of Plant Biomass and Leaf N, P Stoichiometry on the Loess Plateau of China

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    Understanding the geographic patterns and potential drivers of leaf stoichiometry and plant biomass is critical for modeling the biogeochemical cycling of ecosystems and to forecast the responses of ecosystems to global changes. Therefore, we studied the spatial patterns and potential drivers of leaf stoichiometry and herb biomass from 15 sites spanning from south to north along a 500 km latitudinal gradient of the Loess Plateau. We found that leaf N and P stoichiometry and the biomass of herb plants varied greatly on the Loess Plateau, showing spatial patterns, and there were significant differences among the four vegetation zones. With increasing latitude (decreasing mean annual temperature and decreasing mean precipitation), aboveground and belowground biomass displayed an opening downward parabolic trend, while the root–shoot ratio gradually decreased. Furthermore, there were significant linear relationships between the leaf nitrogen (N) and phosphorus (P) contents and latitude and climate (mean annual rainfall and mean annual temperature). However, the leaf N/P ratio showed no significant latitudinal or climatic trends. Redundancy analysis and stepwise regression analysis revealed herb biomass and leaf N and P contents were strongly related to environmental driving factors (slope, soil P content and latitude, altitude, mean annual rainfall and mean annual temperature). Compared with global scale results, herb plants on the Loess Plateau are characterized by relatively lower biomass, higher N content, lower P content and a higher N/P ratio, and vegetative growth may be more susceptible to P limitation. These findings indicated that the remarkable spatial distribution patterns of leaf N and P stoichiometry and herb biomass were jointly regulated by the climate, soil properties and topographic properties, providing new insights into potential vegetation restoration strategies

    Spatial Distribution Patterns and Driving Factors of Plant Biomass and Leaf N, P Stoichiometry on the Loess Plateau of China

    No full text
    Understanding the geographic patterns and potential drivers of leaf stoichiometry and plant biomass is critical for modeling the biogeochemical cycling of ecosystems and to forecast the responses of ecosystems to global changes. Therefore, we studied the spatial patterns and potential drivers of leaf stoichiometry and herb biomass from 15 sites spanning from south to north along a 500 km latitudinal gradient of the Loess Plateau. We found that leaf N and P stoichiometry and the biomass of herb plants varied greatly on the Loess Plateau, showing spatial patterns, and there were significant differences among the four vegetation zones. With increasing latitude (decreasing mean annual temperature and decreasing mean precipitation), aboveground and belowground biomass displayed an opening downward parabolic trend, while the root–shoot ratio gradually decreased. Furthermore, there were significant linear relationships between the leaf nitrogen (N) and phosphorus (P) contents and latitude and climate (mean annual rainfall and mean annual temperature). However, the leaf N/P ratio showed no significant latitudinal or climatic trends. Redundancy analysis and stepwise regression analysis revealed herb biomass and leaf N and P contents were strongly related to environmental driving factors (slope, soil P content and latitude, altitude, mean annual rainfall and mean annual temperature). Compared with global scale results, herb plants on the Loess Plateau are characterized by relatively lower biomass, higher N content, lower P content and a higher N/P ratio, and vegetative growth may be more susceptible to P limitation. These findings indicated that the remarkable spatial distribution patterns of leaf N and P stoichiometry and herb biomass were jointly regulated by the climate, soil properties and topographic properties, providing new insights into potential vegetation restoration strategies

    Integrated Wavelength-Tuned Optical mm-Wave Beamformer with Doubled Delay Resolution

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    Integrated optical true time delay lines attract lots of attention for optically controlled mm-wave beam steering due to its low-loss/broadband performance and stable/compact system architecture. However, for remotely-controlled networks, the techniques that require local-site actively-tuned elements would make the network control more complicated. A passive design using wavelength-tuning is regarded as a promising candidate. In this paper, an integrated wavelength-tuned optical mm-wave beamformer with doubled delay resolution is proposed and demonstrated in a generic InP platform. A bidirectional looped-back arrayed waveguide grating (AWG) module acts as the stepwise tunable delay unit. By introducing an extra AWG router for delay mode (positive/negative) selection and a bidirectional optical interface, a pure λ-tuned, resolution-doubled optical delay network is realized without using any active component (e.g. heaters, current injection) at the local site. This photonic passive design is more beneficial to the remotely-controlled mm-wave beam steering system. Furthermore, the AWG router potentially allows multi-port wavelength switching to support the scaling-up of the network. The fabrication-caused delay error of <1.1 ps is experimentally verified on-chip. A further proof-of-concept 38-GHz fiber-wireless beam steering system with a 186° angular steering is experimentally demonstrated using QAM-4 modulation. Accurate mm-wave phase shifts originated from the proposed optical beamformer are obtained, which proves the effectiveness of the tunable integrated beamformer

    Spatio-Temporal Variation in Mountainous Landscape Changes: A Case Study of Shizhu County

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    The study of dynamic changes and spatial variation of landscape patterns is important to deeply understand the relationship between human activities and the natural environment. We selected a typical mountain area, Shizhu County, as the study area and analyzed the landscape’s dynamic changes and spatial variation in that area from 2000–2015. The results showed that cropland and forestland were the dominant landscape types in the study area. Cropland and grassland areas decreased, being mainly converted to forestland. Forestland and built-up land areas were increasing; the increase in built-up land was mainly due to the invasion into cropland areas, and the increase in forestland was mainly due to the conversion of cropland and grassland. Water bodies were affected by factors such as water storage in the Three Gorges Reservoir, and their area continued to increase. The change in landscape was most dramatic from 2005–2010, mainly due to the rapid increase in the areas of built-up land and water bodies and the rapid decrease in grassland area. There were apparent spatial variations in landscape distribution, patterns, and dynamic changes. Although water bodies were mainly distributed in the relatively gentle slope areas with an elevation of less than 200 m and a slope of 0°–6°, other landscapes were concentrated at an elevation higher than 500 m, a slope of 15°–35°, with a westerly or northwesterly aspect. These areas also had the most drastic landscape changes. At the type-level and the landscape-level, landscape indices showed greater variation with elevation and slope than with aspect. Finally, the variations with elevation, slope, and aspect differed among different landscape types

    Deep Fully Convolutional Embedding Networks for Hyperspectral Images Dimensionality Reduction

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    Due to the superior spatial–spectral extraction capability of the convolutional neural network (CNN), CNN shows great potential in dimensionality reduction (DR) of hyperspectral images (HSIs). However, most CNN-based methods are supervised while the class labels of HSIs are limited and difficult to obtain. While a few unsupervised CNN-based methods have been proposed recently, they always focus on data reconstruction and are lacking in the exploration of discriminability which is usually the primary goal of DR. To address these issues, we propose a deep fully convolutional embedding network (DFCEN), which not only considers data reconstruction but also introduces the specific learning task of enhancing feature discriminability. DFCEN has an end-to-end symmetric network structure that is the key for unsupervised learning. Moreover, a novel objective function containing two terms—the reconstruction term and the embedding term of a specific task—is established to supervise the learning of DFCEN towards improving the completeness and discriminability of low-dimensional data. In particular, the specific task is designed to explore and preserve relationships among samples in HSIs. Besides, due to the limited training samples, inherent complexity and the presence of noise in HSIs, a preprocessing where a few noise spectral bands are removed is adopted to improve the effectiveness of unsupervised DFCEN. Experimental results on three well-known hyperspectral datasets and two classifiers illustrate that the low dimensional features of DFCEN are highly separable and DFCEN has promising classification performance compared with other DR methods

    Intentional construction of high-performance SnO2 catalysts with a 3D porous structure for electrochemical reduction of CO2

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    Herein, SnO2-NC (SnO2-nanocube) and SnO2-NF (SnO2-nanoflake) electro-catalysts featuring a large specific surface area and 3D porous structure were successfully constructed via acid etching and sulfurization-desulphurization methods, respectively. As catalysts for the electrochemical reduction of CO2, the faradaic efficiency (FHCOO-+CO = 82.4%, 91.5%, respectively) and partial current density (j(HCOO-+CO) = 10.7 and 11.5 mA cm(-2), respectively) of SnO2-NCs and SnO2-NFs were enhanced in comparison with SnO2-NPs (SnO2-nanoparticles, FHCOO-+CO = 63.4%, j(HCOO-+CO) = 5.7 mA cm(-2)) at -1.0 V vs. RHE. The enhanced catalytic activity is attributed to their uniform 3D porous structure, high specific surface area and excellent wettability. Additionally, the morphology of SnO2-NCs and SnO2-NFs was largely preserved after electrolyzing for 12 h (after 12 h of electrolysis), indicating the effective buffering effect of the 3D structure in electrolysis. Naturally, the current density and faradaic efficiency of the SnO2-NC and SnO2-NF catalysts remained nearly unchanged after long-term stability measurements, revealing great stability
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