DETECTING COMMUNITY STRUCTURE： FROM PARSIMONY TO WEIGHTED PARSIMONY

Community detection has attracted a great deal of attention in recent years. A parsimony criterion for detecting this structure means that as minimal as possible number of inserted and deleted edges is needed when we make the network considered become a disjoint union of cliques. However, many small groups of nodes are obtained by directly using this criterion to some networks especially for sparse ones. In this paper we propose a weighted parsimony model in which a weight coefficient is introduced to balance the inserted and deleted edges to ensure the obtained subgraphs to be reasonable communities. Some benchmark testing examples are used to validate the effectiveness of the proposed method. It is interesting that the weight here can be determined only by the topological features of the network. Meanwhile we make some comparison of our model with maximizing modularity Q and modularity density D on some of the benchmark networks, although sometimes too many or a little less numbers of communities are obtained with Q or D, a proper number of communities are detected with the weighted model. All the computational results confirm its capability for community detection for the small or middle size networks.

On the principles of Parsimony and Self-consistency for the emergence of intelligence

Ten years into the revival of deep networks and artificial intelligence,we propose a theoretical framework that sheds light on understanding deep networks within a bigger picture of intelligence in general.We introduce two fundamental principles,Parsimony and Self-consistency,which address two fundamental questions regarding intelligence:what to learn and how to learn,respectively.We believe the two principles serve as the cornerstone for the emergence of intelligence,artificial or natural.While they have rich classical roots,we argue that they can be stated anew in entirely measurable and computable ways.More specifically,the two principles lead to an effective and efficient computational framework,compressive closed-loop transcription,which unifies and explains the evolution of modern deep networks and most practices of artificial intelligence.While we use mainly visual data modeling as an example,we believe the two principles will unify understanding of broad families of autonomous intelligent systems and provide a framework for understanding the brain.

Community Detection with the Weighted Parsimony Criterion

Community detection in networks has been studied extensively in the last decade. Many criteria, expressing the quality of the partitions obtained, as well as a few exact algorithms and a large number of heuristics have been proposed. The parsimony criterion consists in minimizing the number of edges added or removed from the given network in order to transform it into a set of disjoint cliques.Recently Zhang, Qiu and Zhang have proposed a weighted parsimony model in which a weight coefficient is introduced to balance the numbers of inserted and deleted edges. These authors propose rules to select a good value of the coefficient, use simulated annealing to find optimal or near-optimal solutions and solve a series of real and artificial instances. In the present paper, an algorithm is proposed for solving exactly the weighted parsimony problem for all values of the parameter. This algorithm is based on iteratively solving the problem for a set of given values of the parameter using a row generation algorithm. This procedure is combined with a search procedure to find all lowest breakpoints of the value curve(i.e., the weighted sum of inserted and deleted edges). Computational results on a series of artificial and real world networks from the literature are reported. It appears that several partitions for the same network may be informative and that the set of solutions usually contains at least one intuitively appealing partition.

On the Parsimony of Some Model-Selection Criteria in Econometric Modeling

The preference of a model-selection criterion to simple models can be define as it parsimony. Among the three aspects of the principle of Parsimony Sawyer (1980) expressed. it seems that most studies on this subject are more concerned with the equal-fitting or consistency than with the angmentation rules. In this paper an analytical approach will be developed to check foe compliance of some model-selection criteria with the rules. The most important consequence is the breach of one augmentation rule. Namely. all criteria examined except C2 will make it easier to augment a larger model with a fixed number of variables than to enlarge a smaller model with the same variables.Criterion C2 is found to be of good quality. It will had the exact model among a set of nested alterntives, choose a smaller one when models of equal-fitting are compared, and follow both augmentation rule 1 and rule 2. The results obtained in this paper are generally consistent with those resulting from Sawyer’s simulation approach, while the analytical way developed by the authors be applied under more general condition.

Phylogenetic placement of Cynomorium in Rosales inferred from sequences of the inverted repeat region of the chloroplast genome

Cynomorium is a herbaceous holoparasite that has been placed in Santalales, Saxifragales, Myrtales, or Sapindales. The inverted repeat （IR） region of the chloroplast genome region is slow evolving and, unlike mitochondrial genes, the chloroplast genome experiences few horizontal gene transfers between the host and parasite. Thus, in the present study, we used sequences of the IR region to test the phylogenetic placements of Cynomorium. Phylogenetic analyses of the chloroplast IR sequences generated largely congruent ordinal relationships with those from previous studies of angiosperm phylogeny based on single or multiple genes. Santalales was closely related to Caryophyllales and asterids. Saxifragales formed a clade where Peridiscus was sister to the remainder of the order, whereas Paeonia was sister to the woody clade of Saxifragales. Cynomorium is not closely related to Santalales, Saxifragales, Myrtales, or Sapindales; instead, it is included in Rosales and sister to Rosaceae. The various placements of the holoparasite on the basis of different regions of the mitochondrial genome may indicate the heterogeneous nature of the genome in the parasite. However, it is unlikely that the placement of Cynomorium in Rosales is the result of chloroplast gene transfer because Cynomorium does not parasitize on rosaceous plants and there is no chloroplast gene transfer between Cynomorium and Nitraria, a confirmed host of Cynomorium and a member of Sapindales.

Qualification of Three Analytical Wake Models

The decrease of wind velocity （wake losses） in downstream area of wind turbine is generally quantified using wake models. The overall estimated power of wind farm varies according to reliability of wake model used, however it＇s unclear which model is most appropriate and able to give a high performance in predicting wind velocity deficit. In this subject, a qualification of three analytical wake models （Jensen, lshihara and Frandsen） based on three principal criteria is presented in this paper： （i） the parsimony which characterizes the inverse of model complexity, （ii） the accuracy of estimation in which wake model is compared with the experimental data and （iii） imprecision that is related to assumptions and uncertainty on the value of variables considered in each model. This qualitative analysis shows the inability of wake models to predict wind velocity deficit due to the big uncertainty of variables considered and it sensitivity to wind farm characteristic.

Science writing in the real world

The objective of this contribution is to consider guides to technical writing.Since the professional writes what he does and does what he writes,guides to how you execute the one relate to how you perform the other,so this article is about more than just writing.While there is need for idiosyncrasy and individualism,there are some rules.Documents must have an explicit purpose stated at the outset.By their nature,documents in the applied sciences and business address real-world problems,but elsewhere activity may be laissez faire for which the cost-effectiveness in yielding innovations is contestable.A hallmark of written science and technology is that every statement is capable of being tested and capable of being shown to be wrong,and that methods yield repeatable results.Caution should be observed in requiring authoritative referencing for every notion,partly because of the unsatisfying infinite regress in searching for ultimate sources,and partly to avoid squashing innovation.It is not only the content of messages that matters,but reliability too.Probability theory must be built into design to assure that strong inference can be drawn from outcomes.Research,business and infrastructure projects must substitute the frequent optimistic ’everything goes according to plan’(EGAP) with a more realistic ’most likely development’(MLD) and the risks of even that not happening.A cornerstone of science and technology is parsimony.No description,experiment,explanation,hypothesis,idea,instrument,machine,method,model,prediction,statement,technique,test or theory should be more elaborate than necessary to satisfy its purpose.Antifragility- the capacity to survive and benefit from shocks- must be designed into project and organizational structure and function by manipulating such factors as complexity and interdependent to evade failure in a turbulent and unpredictable world.The role of writing is to integrate these issues,and communicate them so that the stakeholders share a vision before,during and after the project.

Linear-in-Parameter Models Based on Parsimonious Genetic Programming Algorithm and Its Application to Aero-Engine Start Modeling

A novel Parsimonious Genetic Programming （PGP） algorithm together with a novel aero-engine optimum data-driven dynamic start process model based on PGP is proposed. In application of this method, first, the traditional Genetic Programming（GP） is used to generate the nonlinear input-output models that are represented in a binary tree structure; then, the Orthogonal Least Squares algorithm （OLS） is used to estimate the contribution of the branches of the tree （refer to basic function term that cannot be decomposed anymore according to special rule） to the accuracy of the model, which contributes to eliminate complex redundant subtrees and enhance GP＇s convergence speed; and finally, a simple, reliable and exact linear-in-parameter nonlinear model via GP evolution is obtained. The real aero-engine start process test data simulation and the comparisons with Support Vector Machines （SVM） validate that the proposed method can generate more applicable, interpretable models and achieve comparable, even superior results to SVM.

Parsimonious Model for Blood Glucose Level Monitoring in Type 2 Diabetes patients

To establish the parsimonious model for blood glucose monitoring in patients with type 2 diabetes receiving oral hypoglycemic agent treatment. One hundred and fifty-nine adult Chinese type 2 diabetes patients were randomized to receive rapid-acting or sustained-release gliclazide therapy for 12 weeks.

Improved scheme to accelerate support vector regression

The computational cost of support vector regression in the training phase is O （N^3）, which is very expensive for a large scale problem. In addition, the solution of support vector regression is of parsimoniousness, which has relation to a part of the whole training data set. Hence, it is reasonable to reduce the training data set. Aiming at the scheme based on k-nearest neighbors to reduce the training data set with the computational complexity O （kMN^2）, an improved scheme is proposed to accelerate the reducing phase, which cuts down the computational complexity from O （kMN^2） to O （MN^2）. Finally, experimental results on benchmark data sets validate the effectiveness of the improved scheme.

A Novel Parsimonious Neurofuzzy Model Applied to Railway Carriage System Identification and Fault Diagnosis

In this paper, we suggest a novel parsimonious neurofuzzy model realized by RBFNs for railway carriage system identification and fault diagnosis. To overcome the curse of dimensionality resulting from high dimensional input variables, in our developed model the features extracted from the available observations are regarded as the input variables by adopting the higher-order statistics(HOS) technique. Such a constructed model is also applied to a practical railway carriage system, simulation results indicate that the developed neurofuzzy model possesses strong identification and fault diagnosis ability.

Phylogenetic Analysis of RhoGAP Domain-Containing Proteins

Proteins containing an Rho GTPase-activating protein （RhoGAP） domain work as molecular switches involved in the regulation of diverse cellular functions. The ability of these GTPases to regulate a wide number of cellular processes comes from their interactions with multiple effectors and inhibitors, including the RhoGAP family, which stimulates their intrinsic GTPase activity. Here, a phylogenetic approach was applied to study the evolutionary relationship among 59 RhoGAP domain-containing proteins. The sequences were aligned by their RhoGAP domains and the phylogenetic hypotheses were generated using Maximum Parsimony and Bayesian analyses. The character tracing of two traits, GTPase activity and presence of other domains, indicated a significant phylogenetic signal for both of them.

A Predictive,Two-Parameter Model for the Movement of Reservoir Landslides

Monitoring data show that many landslides in the Three Gorges region,China,undergo step-like displacements in response to the managed,quasi-sinusoidal annual variations in reservoir level.This behavior is consistent with motion initiating when the reservoir water level falls below a critical level that is intrinsic to each landslide,with the subsequent displacement rate of the landslide being proportional to the water depth below that critical level.Most motion terminates when the water level rises back above the critical level,so the annual step size is the time integral of the instantaneous displacement rate.These responses are incorporated into a differential equation that is easily calibrated with monitoring data,allowing prediction of landslide movement from actual or anticipated reservoir level changes.Model successes include(1)initiation and termination of the annual sliding steps at the critical reservoir level,producing a series of steps;(2)prediction of variable step size,year to year;and(3)approximate prediction of the shape and size of each annual step.Annual rainfall correlates poorly with step size,probably because its effect on groundwater levels is dwarfed by the 30 m annual variations in the level of the Three Gorges Reservoir.Viscous landslide behavior is suggested.