scholarly journals An a posteriori measure of network modularity

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 130 ◽  
Author(s):  
Timothée Poisot
Keyword(s):  
Real World ◽  
Ecological Networks ◽  
A Posteriori ◽  
Large Dataset ◽  
Network Modularity

Measuring modularity is important to understand the structure of networks, and has an important number of real-world implications. However, several measures exists to assess the modularity, and give both different modularity values and different modules composition. In this article, I propose an a posteriori measure of modularity, which represents the ratio of interactions between members of the same modules vs. members of different modules. I apply this measure to a large dataset of 290 ecological networks, to show that it gives new insights about their modularity.

Generating Pseudo-Data to Enhance the Performance of Classification-Based Engineering Design: A Preliminary Investigation

2020 ◽  
Author(s):  
Xianping Du ◽  
Onur Bilgen ◽  
Hongyi Xu
Keyword(s):  
Machine Learning ◽  
Engineering Design ◽  
Real World ◽  
Surrogate Model ◽  
Preliminary Investigation ◽  
Learning Model ◽  
Classification Model ◽  
Design Decision ◽  
Large Dataset ◽  
Machine Learning Model

Abstract Machine learning for classification has been used widely in engineering design, for example, feasible domain recognition and hidden pattern discovery. Training an accurate machine learning model requires a large dataset; however, high computational or experimental costs are major issues in obtaining a large dataset for real-world problems. One possible solution is to generate a large pseudo dataset with surrogate models, which is established with a smaller set of real training data. However, it is not well understood whether the pseudo dataset can benefit the classification model by providing more information or deteriorates the machine learning performance due to the prediction errors and uncertainties introduced by the surrogate model. This paper presents a preliminary investigation towards this research question. A classification-and-regressiontree model is employed to recognize the design subspaces to support design decision-making. It is implemented on the geometric design of a vehicle energy-absorbing structure based on finite element simulations. Based on a small set of real-world data obtained by simulations, a surrogate model based on Gaussian process regression is employed to generate pseudo datasets for training. The results showed that the tree-based method could help recognize feasible design domains efficiently. Furthermore, the additional information provided by the surrogate model enhances the accuracy of classification. One important conclusion is that the accuracy of the surrogate model determines the quality of the pseudo dataset and hence, the improvements in the machine learning model.

scholarly journals Harnessing tipping points in complex ecological networks

2019 ◽  
Vol 16 (158) ◽  
pp. 20190345 ◽  
Author(s):  
Junjie Jiang ◽  
Alan Hastings ◽  
Ying-Cheng Lai
Keyword(s):  
Real World ◽  
Management Strategy ◽  
Ecological Networks ◽  
Tipping Point ◽  
Active Management ◽  
Reduced Model ◽  
Tipping Points ◽  
Two Dimensional ◽  
Pollinator Species ◽  
Global Extinction

Complex and nonlinear ecological networks can exhibit a tipping point at which a transition to a global extinction state occurs. Using real-world mutualistic networks of pollinators and plants as prototypical systems and taking into account biological constraints, we develop an ecologically feasible strategy to manage/control the tipping point by maintaining the abundance of a particular pollinator species at a constant level, which essentially removes the hysteresis associated with a tipping point. If conditions are changing so as to approach a tipping point, the management strategy we describe can prevent sudden drastic changes. Additionally, if the system has already moved past a tipping point, we show that a full recovery can occur for reasonable parameter changes only if there is active management of abundance, again due essentially to removal of the hysteresis. This recovery point in the aftermath of a tipping point can be predicted by a universal, two-dimensional reduced model.

scholarly journals Enhanced inference of ecological networks by parameterizing ensembles of population dynamics models constrained with prior knowledge

BMC Ecology ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Chen Liao ◽  
Joao B. Xavier ◽  
Zhenduo Zhu
Keyword(s):  
Population Dynamics ◽  
Prior Knowledge ◽  
Ictalurus Punctatus ◽  
Real World ◽  
Silver Carp ◽  
Network Models ◽  
Ecological Networks ◽  
Illinois River ◽  
Series Data ◽  
Positive Interaction

Abstract Background Accurate network models of species interaction could be used to predict population dynamics and be applied to manage real world ecosystems. Most relevant models are nonlinear, however, and data available from real world ecosystems are too noisy and sparsely sampled for common inference approaches. Here we improved the inference of generalized Lotka–Volterra (gLV) ecological networks by using a new optimization algorithm to constrain parameter signs with prior knowledge and a perturbation-based ensemble method. Results We applied the new inference to long-term species abundance data from the freshwater fish community in the Illinois River, United States. We constructed an ensemble of 668 gLV models that explained 79% of the data on average. The models indicated (at a 70% level of confidence) a strong positive interaction from emerald shiner (Notropis atherinoides) to channel catfish (Ictalurus punctatus), which we could validate using data from a nearby observation site, and predicted that the relative abundances of most fish species will continue to fluctuate temporally and concordantly in the near future. The network shows that the invasive silver carp (Hypophthalmichthys molitrix) has much stronger impacts on native predators than on prey, supporting the notion that the invader perturbs the native food chain by replacing the diets of predators. Conclusions Ensemble approaches constrained by prior knowledge can improve inference and produce networks from noisy and sparsely sampled time series data to fill knowledge gaps on real world ecosystems. Such network models could aid efforts to conserve ecosystems such as the Illinois River, which is threatened by the invasion of the silver carp.

scholarly journals Enhanced inference of ecological networks by parameterizing ensembles of population dynamics models constrained with prior knowledge

2019 ◽  
Author(s):  
Chen Liao ◽  
Joao B. Xavier ◽  
Zhenduo Zhu
Keyword(s):  
Population Dynamics ◽  
Prior Knowledge ◽  
Ictalurus Punctatus ◽  
Real World ◽  
Silver Carp ◽  
Network Models ◽  
Ecological Networks ◽  
Illinois River ◽  
Series Data ◽  
Positive Interaction

AbstractBackgroundAccurate network models of species interaction could be used to predict population dynamics and be applied to manage real world ecosystems. Most relevant models are nonlinear, however, and data available from real world ecosystems are too noisy and sparsely sampled for common inference approaches. Here we improved the inference of generalized Lotka-Volterra (gLV) ecological networks by using a new optimization algorithm to constrain parameter signs with prior knowledge and a perturbation-based ensemble method.ResultsWe applied the new inference to long-term species abundance data from the freshwater fish community in the Illinois River, United States. We constructed an ensemble of 668 gLV models that explained 79% of the data on average. The models indicated (at a 70% level of confidence) a strong positive interaction from emerald shiner (Notropis atherinoides) to channel catfish (Ictalurus punctatus), which we could validate using data from a nearby observation site, and predicted that the relative abundances of most fish species will continue to fluctuate temporally and concordantly in the near future. The network shows that the invasive silver carp (Hypophthalmichthys molitrix) has much stronger impacts on native predators than on prey, supporting the notion that the invader perturbs the native food chain by replacing the diets of predators.ConclusionsEnsemble approaches constrained by prior knowledge can improve inference and produce networks from noisy and sparsely sampled time series data to fill knowledge gaps on real world ecosystems. Such network models could aid efforts to conserve ecosystems such as the Illinois River, which is threatened by the invasion of the silver carp.

scholarly journals Real-World Contribution of Electrification and Replacement Scenarios to the Fleet Emissions in West Midland Boroughs, UK

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 332
Author(s):  
Louisa K. Osei ◽  
Omid Ghaffarpasand ◽  
Francis D. Pope
Keyword(s):  
Remote Sensing ◽  
Real World ◽  
Mitigation Strategies ◽  
Large Dataset ◽  
Rural And Urban ◽  
Fleet Composition ◽  
Emission Reduction Potential ◽  
West Midlands ◽  
Vehicle Fleet ◽  
Fleet Replacement

This study reports the likely real-world effects of fleet replacement with electric vehicles (EVs) and higher efficiency EURO 6 vehicles on the exhaustive emissions of NOx, PM, and CO2 in the seven boroughs of the West Midlands (WM) region, UK. National fleet composition data, local EURO distributions, and traffic compositions were used to project vehicle fleet compositions for different roads in each borough. A large dataset of real-world emission factors including over 90,000 remote-sensing measurements, obtained from remote sensing campaigns in five UK cities, was used to parameterize the emission profiles of the studied scenarios. Results show that adoption of the fleet electrification approach would have the highest emission reduction potential on urban roads in WM boroughs. It would result in maximum reductions ranging from 35.0 to 37.9%, 44.3 to 48.3%, and 46.9 to 50.3% for NOx, PM, and CO2, respectively. In comparison, the EURO 6 replacement fleet scenario would lead to reductions ranging from 10.0 to 10.4%, 4.0 to 4.2%, and 6.0 to 6.4% for NOx, PM, and CO2, respectively. The studied mitigation scenarios have higher efficacies on motorways compared to rural and urban roads because of the differences in traffic fleet composition. The findings presented will help policymakers choose climate and air quality mitigation strategies.

scholarly journals Aggregating Crowd Wisdom with Side Information via a Clustering-based Label-aware Autoencoder

2020 ◽  
Author(s):  
Li'ang Yin ◽  
Yunfei Liu ◽  
Weinan Zhang ◽  
Yong Yu
Keyword(s):  
Real World ◽  
State Of The Art ◽  
Side Information ◽  
Map Estimation ◽  
A Posteriori ◽  
Label Noise ◽  
Generative Process ◽  
Crowd Wisdom ◽  
Object Features ◽  
Noisy Labels

Aggregating crowd wisdom infers true labels for objects, from multiple noisy labels provided by various sources. Besides labels from sources, side information such as object features is also introduced to achieve higher inference accuracy. Usually, the learning-from-crowds framework is adopted. However, the framework considers each object in isolation and does not make full use of object features to overcome label noise. In this paper, we propose a clustering-based label-aware autoencoder (CLA) to alleviate label noise. CLA utilizes clusters to gather objects with similar features and exploits clustering to infer true labels, by constructing a novel deep generative process to simultaneously generate object features and source labels from clusters. For model inference, CLA extends the framework of variational autoencoders and utilizes maximizing a posteriori (MAP) estimation, which prevents the model from overfitting and trivial solutions. Experiments on real-world tasks demonstrate the significant improvement of CLA compared with the state-of-the-art aggregation algorithms.

scholarly journals Evidence for alternate stable States in Collapsing Ecological Networks

2016 ◽  
Author(s):  
Suresh Babu ◽  
Gitanjali Yadav
Keyword(s):  
Complex Systems ◽  
Real World ◽  
Restoration Ecology ◽  
Invasion Biology ◽  
Ecological Networks ◽  
Stable States ◽  
Critical Transitions ◽  
Stable Equilibria ◽  
To Come

ABSTRACTBackgroundThere has been considerable interest and progress in our perception of organized complexity in recent years. Recurrent debates on the dynamics and stability of complex systems have enriched our understanding of these systems, but generalities in the relationship between structure and dynamics are hard to come by. Although traditionally an arena for theoreticians, much of this research has been invigorated by demonstration of the existence of alternate stable equilibria in real world ecosystems such as lakes, coral reefs, forests and grasslands.ResultsLinking up systems thinking with recent advances in our understanding of ecological networks opens up exciting possibilities. In an attempt to obtain general patterns of behaviour of complex systems, we have analyzed the response of eighty-six real world ecological networks to targeted extinctions, and the findings suggest that most networks are robust to loss of specialists until specific thresholds are reached in terms of geodesics. If the extinctions persist, a state change or ‘flip’ occurs and the structural properties are altered drastically, although the network does not collapse. Further, we find that as opposed to simpler networks, larger networks have several such alternate states that ensure their long-term persistence and that indeed complexity does endow resilience to such networks.ConclusionsThis is the first report of critical transitions in ecological networks and the implications of these findings for complex systems characterized by networks are likely to be profound with immediate significance in conservation biology, invasion biology and restoration ecology.

Multi-Objective Stochastic Optimization Algorithms to Calibrate Microsimulation Models

2019 ◽  
Vol 2673 (4) ◽  
pp. 743-752 ◽  
Author(s):  
Mohammad Karimi ◽  
Maryam Miriestahbanati ◽  
Hamed Esmaeeli ◽  
Ciprian Alecsandru
Keyword(s):  
Particle Swarm Optimization ◽  
Stochastic Optimization ◽  
Simulation Model ◽  
Optimization Algorithm ◽  
Real World ◽  
A Priori ◽  
A Posteriori ◽  
Swarm Optimization ◽  
Calibration Process ◽  
Multi Objective

The calibration process for microscopic models can be automatically undertaken using optimization algorithms. Because of the random nature of this problem, the corresponding objectives are not simple concave functions. Accordingly, such problems cannot easily be solved unless a stochastic optimization algorithm is used. In this study, two different objectives are proposed such that the simulation model reproduces real-world traffic more accurately, both in relation to longitudinal and lateral movements. When several objectives are defined for an optimization problem, one solution method may aggregate the objectives into a single-objective function by assigning weighting coefficients to each objective before running the algorithm (also known as an a priori method). However, this method does not capture the information exchange among the solutions during the calibration process, and may fail to minimize all the objectives at the same time. To address this limitation, an a posteriori method (multi-objective particle swarm optimization, MOPSO) is employed to calibrate a microscopic simulation model in one single step while minimizing the objectives functions simultaneously. A set of traffic data collected by video surveillance is used to simulate a real-world highway in VISSIM. The performance of the a posteriori-based MOPSO in the calibration process is compared with a priori-based optimization methods such as particle swarm optimization, genetic algorithm, and whale optimization algorithm. The optimization methodologies are implemented in MATLAB and connected to VISSIM using its COM interface. Based on the validation results, the a posteriori-based MOPSO leads to the most accurate solutions among the tested algorithms with respect to both objectives.

scholarly journals Bursting the Filter Bubble: Fairness-Aware Network Link Prediction

2020 ◽  
Vol 34 (01) ◽  
pp. 841-848
Author(s):  
Farzan Masrour ◽  
Tyler Wilson ◽  
Heng Yan ◽  
Pang-Ning Tan ◽  
Abdol Esfahanian
Keyword(s):  
Social Networking ◽  
Real World ◽  
Link Prediction ◽  
Representation Learning ◽  
Experimental Results ◽  
Adversarial Network ◽  
Network Link ◽  
Network Modularity ◽  
Filter Bubble ◽  
Real World Datasets

Link prediction is an important task in online social networking as it can be used to infer new or previously unknown relationships of a network. However, due to the homophily principle, current algorithms are susceptible to promoting links that may lead to increase segregation of the network—an effect known as filter bubble. In this study, we examine the filter bubble problem from the perspective of algorithm fairness and introduce a dyadic-level fairness criterion based on network modularity measure. We show how the criterion can be utilized as a postprocessing step to generate more heterogeneous links in order to overcome the filter bubble problem. In addition, we also present a novel framework that combines adversarial network representation learning with supervised link prediction to alleviate the filter bubble problem. Experimental results conducted on several real-world datasets showed the effectiveness of the proposed methods compared to other baseline approaches, which include conventional link prediction and fairness-aware methods for i.i.d data.

Speeding Up Data Manipulation Tasks with Alternative Implementations

2021 ◽  
Vol 30 (4) ◽  
pp. 1-28
Author(s):  
Yida Tao ◽  
Shan Tang ◽  
Yepang Liu ◽  
Zhiwu Xu ◽  
Shengchao Qin
Keyword(s):  
Real World ◽  
Performance Optimization ◽  
Input Output ◽  
Real World Data ◽  
Large Dataset ◽  
Data Manipulation ◽  
Performance Improvements ◽  
Specific Input ◽  
Data Volume ◽  
New Perspective

As data volume and complexity grow at an unprecedented rate, the performance of data manipulation programs is becoming a major concern for developers. In this article, we study how alternative API choices could improve data manipulation performance while preserving task-specific input/output equivalence. We propose a lightweight approach that leverages the comparative structures in Q&A sites to extracting alternative implementations. On a large dataset of Stack Overflow posts, our approach extracts 5,080 pairs of alternative implementations that invoke different data manipulation APIs to solve the same tasks, with an accuracy of 86%. Experiments show that for 15% of the extracted pairs, the faster implementation achieved >10x speedup over its slower alternative. We also characterize 68 recurring alternative API pairs from the extraction results to understand the type of APIs that can be used alternatively. To put these findings into practice, we implement a tool, AlterApi7 , to automatically optimize real-world data manipulation programs. In the 1,267 optimization attempts on the Kaggle dataset, 76% achieved desirable performance improvements with up to orders-of-magnitude speedup. Finally, we discuss notable challenges of using alternative APIs for optimizing data manipulation programs. We hope that our study offers a new perspective on API recommendation and automatic performance optimization.

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