Hierarchical models

The totalitarianism inherent in corporate structures is defined and preserved by the hierarchy, and these structures stretch far beyond for-profit, private enterprises. In an attempt to justify arbitrary positions of power, organizations often portray them as "leadership" positions, deploying corporate doublespeak like "team leaders" or "officers" in their hierarchical arrangement. The problem with this is that leadership, in any true sense, is an absolute contradiction from power; and especially from arbitrary power. The acquisition of money and idleness that becomes synonymous with climbing the ladder makes leadership roles impossible for those who fill these positions to obtain.

The aims of this study were to translate into Spanish and analyze the psychometric characteristics of the Global Motivation Scale (GMS). In study 1, participants were 237 university students. Results of analysis of correlations between the seven subscales
of the GMS supported the presence of a continuum of self-determination, and confirmatory factor analysis confirmed the structure of seven factors correlated. In
addition, internal consistency values were acceptable. In study 2, participants were 139 university students. Results of analysis of correlations between self-esteem and the seven subscales of the GMS supported the criterion validity of the scale. In conclusion, results support the use of the Spanish version of the GMS for assessing global motivation in university students.

This thesis presents a novel hierarchical learning framework, Reinforcement Learning Optimal Control, for controlling nonlinear dynamical systems with continuous states and actions. The adapted approach mimics the neural computations that allow our brain to bridge across the divide between symbolic action-selection and low-level actuation control by operating at two levels of abstraction. First, current findings demonstrate that at the level of limb coordination human behaviour is explained by linear optimal feedback control theory, where cost functions match energy and timing constraints of tasks. Second, humans learn cognitive tasks involving learning symbolic level action selection, in terms of both model-free and model-based reinforcement learning algorithms. We postulate that the ease with which humans learn complex nonlinear tasks arises from combining these two levels of abstraction. The Reinforcement Learning Optimal Control framework learns the local task dynamics from naive experience using an expectation maximization algorithm for estimation of linear dynamical systems and forms locally optimal Linear Quadratic Regulators, producing continuous low-level control. A high-level reinforcement learning agent uses these available controllers as actions and learns how to combine them in state space, while maximizing a long term reward. The optimal control costs form training signals for high-level symbolic learner. The algorithm demonstrates that a small number of locally optimal linear controllers can be combined in a smart way to solve global nonlinear control problems and forms a proof-of-principle to how the brain may bridge the divide between low-level continuous control and high-level symbolic action selection. It competes in terms of computational cost and solution quality with state-of-the-art control, which is illustrated with solutions to benchmark problems.

In this heavily annotated article the provocative thesis is submitted that there is something fundamentally wrong with Western Sinology, or 'Chinakunde', or 'Zhongguoxue' (as distinct from 'Hanxue', which is a kind of old-fashioned philology). 'China experts' either pretend to be knowledgeable about everything related to China, in which case they cannot be taken seriously, or - eventually - admit not to be scientific all-rounders with respect to the country, in which case they cannot be called 'China experts'. The author, who graduated in Sinology from Leyden University and in economics from Erasmus University Rotterdam, not only believes that the study of China has to be taken to a higher level (a belief he expects few tenured professors of Chinese Studies/History will share); he also explains how this long overdue task can be accomplished. Sinologists should take the complexity turn. They should treat China as a 'Ganzheit' (not: 'Gesamtheit'), as a territory-bound, history-moulded and culture-soaked totality of identifiable yet interdependent (f)actors, as a whole intimately interconnected with its numerous parts, as a hypercomplex system of complex, adaptive and non-linear systems of political, military, legal, economic, financial, social, medical, educational, artistic or other nature. Firmly distancing itself from multidisciplinary research (which in practice is a matter of juxta- rather than composition), the new study of China requires a well-thought-out, balanced division of labour. Close collaboration with ICT-driven, China-oriented experts in the natural, social and human sciences willing to co-operate with each other is a sine qua non for comprehending the country that seems to be moving to the centre stage of world politics. The study of China should be mile-wide and mile-deep. The heyday of Sinology is yet to come!
An earlier version of this highly critical but undeniably constructive paper was rejected out of hand by the editors of leading 'Chinese/Asian Studies' journals. The author claims to have reason to suspect them (and other so-called China experts) of being 'bought by China'!

A strategy for adaptive control and energetic optimization of aerobic fermentors was implemented, with both air flow and agitation speed as manipulated variables. This strategy is separable in its components: control, optimization, estimation. We optimized parameter’s estimation (from the usual KLa correlation) using sinusoidal excitation of air flow and agitation speed. We have implemented parameter’s estimation trough recursive least squares algorithm with forgetting factor. We carried separate essays on control, optimization and estimation algorithms. We carried our essays using an original computational simulation environment, with noise and delay generating facilities for data sampling and filtering.

Our results show the convergence and robustness of the estimation algorithm used, improved with use of both forgetting factor and KLa dead-band facilities. Control algorithm used in our work compares favorably with PID using the integrated area criteria for deviation between oxygen molarity and critical molarity (set point). Optimization algorithm clearly reduces energetic consumption, respecting critical molarity. Integration of control, optimization and adaptive algorithms was implemented, but future work is needed for stability. Methods were defined and implemented for stability improvement. We have implemented data acquisition and computer manipulation of air flow and agitation speed for actual fermentors.

Radiosity is the most established technique for simulating high exchange between surfaces considered as perfect diffusers. This paper presents a new topological structure for use in the context of hierarchical radiosity combined with discontinuity meshing. This is mostly useful for a new strategy adopted for subdividing the elements of a scene consisted of convex polygons.The subdivision is done in a local optimization manner keeping the aspect ratios of the produced polygons low. The generated meshes give high visual accuracy.

Caesalpinia bonduc L. is an important medicinal plant threatened by overexploitation. In the present study, the impact of climate on seed morphology, germination capacity, seedling, and plant growth of C. bonduc were evaluated. A total of 2000 seeds were collected in Sudanian and Guinean climate zones of Africa and their length, width, thickness, weight and color were recorded. A hierarchical classification and canonical discriminant analysis were applied to the above traits of seeds from the different climatic zones. An analysis of variance with repeated measures was applied to seeds morphotypes identified by the hierarchical classification to test for the effect of these morphotypes on seed germination, seedling and plant growth. Hierarchical classification helped to identify four seed morphotypes. Canonical discriminant analysis performed on these morphotypes revealed highly significant differences. Morphotypes 1 and 3 comprised green seeds mainly from the Sudanian zone while morphotypes 2 and 4 gathered grey seeds mainly from Guinean zone. Morphotype 3 had the longest seeds while the shortest seeds were from morphotype 1. The heaviest seeds were found in morphotype 4 whereas the lightest ones were from morphotype 1. Seeds of morphotype 4 were the thickest and widest, while the slimmest and most narrow ones were grouped in morphotype 1. Morphotype 3, consisting of large green seeds mainly from Sudanian zone, was superior in terms of seedling and plant growth among all morphotypes and should be the best choice for planting purposes of the species.

Nonlinear optimal control problems are often solved with numerical methods that require knowledge of system's dynamics which may be difficult to infer, and that carry a large computational cost associated with iterative calculations. We present a novel neurobiologically inspired hierarchical learning framework, Reinforcement Learning Optimal Control, which operates on two levels of abstraction and utilises a reduced number of controllers to solve nonlinear systems with unknown dynamics in continuous state and action spaces. Our approach is inspired by research at two levels of abstraction: first, at the level of limb coordination human behaviour is explained by linear optimal feedback control theory. Second, in cognitive tasks involving learning symbolic level action selection, humans learn such problems using model-free and model-based reinforcement learning algorithms. We propose that combining these two levels of abstraction leads to a fast global solution of nonlinear control problems using reduced number of controllers. Our framework learns the local task dynamics from naive experience and forms locally optimal infinite horizon Linear Quadratic Regulators which produce continuous low-level control. A top-level reinforcement learner uses the controllers as actions and learns how to best combine them in state space while maximising a long-term reward. A single optimal control objective function drives high-level symbolic learning by providing training signals on desirability of each selected controller. We show that a small number of locally optimal linear controllers are able to solve global nonlinear control problems with unknown dynamics when combined with a reinforcement learner in this hierarchical framework. Our algorithm competes in terms of computational cost and solution quality with sophisticated control algorithms and we illustrate this with solutions to benchmark problems.

Recently, the field of International Relations has seen increased interest in international hierarchy, and also an upswing in the analytical study of imperial logics of rule. Nonetheless, existing structural models of empire focus on core-periphery dynamics, and so cannot explain polities that display elements of both core and periphery. Therefore, I offer the new concept of ‘semi-cores’. Semi-cores are a specific form of historical political associations whereby certain imperial provinces are different from the others in terms of the close relationships it maintains with the imperial metropolis. Semi-cores are different by virtue of being relatively similar. The conceptualisation of semi-cores is followed by a section illustrating its logic, examining the relatively unfamiliar cases of Scotland and Norway and their position within the Danish and British empires, respectively. Although being separate imperial provinces, these were tightly connected to an imperial core. This concept helps us better understand imperial logics, and in the process shows how cultural factors can be formalised into accounts of structural logics of rule, impacting our understanding of both historical and contemporary hierarchical international affairs.

Nonlinear optimal control problems are often solved with numerical methods that require knowledge of system's dynamics which may be difficult to infer, and that carry a large computational cost associated with iterative calculations. We present a novel neurobiologically inspired hierarchical learning framework, Reinforcement Learning Optimal Control, which operates on two levels of abstraction and utilises a reduced number of controllers to solve nonlinear systems with unknown dynamics in continuous state and action spaces. Our approach is inspired by research at two levels of abstraction: first, at the level of limb coordination human behaviour is explained by linear optimal feedback control theory. Second, in cognitive tasks involving learning symbolic level action selection, humans learn such problems using model-free and model-based reinforcement learning algorithms. We propose that combining these two levels of abstraction leads to a fast global solution of nonlinear contro...

We describe a hierarchical cluster and route procedure (HOGCR) for coordinating vehicle routing in large-scale post-disaster distribution and evacuation activities. The HOGCR is a multi-level clustering algorithm that groups demand nodes into smaller clusters at each planning level, enabling the optimal solution of cluster routing problems. The routing problems are represented as capacitated network flow models that are solved optimally and independently by CPLEX on a parallel computing platform. The HOGCR preserves the consistency among parent and child cluster solutions obtained at consecutive levels. We assess the performance of the algorithm by using large scale scenarios and find satisfactory results.

This study presents a review of the state-of-the-art on the coordination of generation and transmission expansion planning. First, the authors present the different investment and operation modelling approaches, with an emphasis on the centralised co-optimisation problem. Second, a comprehensive review of co-planning hierarchical equilibrium models, under a market environment, is carried out. The authors categorise the distinctive market approaches that usually represent the lower level of co-planning problems. They offer an updated and detailed classification of multilevel equilibrium models, based on their hierarchical and regulatory structure versus their equivalent reduced structure. Finally, the authors identify research gaps in the literature of each one of the mentioned model categories.

This paper presents a study on relationship between a structure of a hierarchical recognizer and the classification accuracy. Different systems of SVMs ordered by a binary tree structure are compared in a letters recognition task from UCI repository. First, models based a several joining rules are used to create different trees. Then, the recognition ratio and the time-consumption factor are analyzed to create the optimal decision tree. The method yields about 97 percent generalization in comparison to 80 percent generalization for the origin method used by the data set donors.

MANET is an autonomous system of mobile nodes attached by wireless links. It represents a complex and
dynamic distributed systems that consist of mobile wireless nodes that can freely self organize into an adhoc
network topology. The devices in the network may have limited transmission range therefore multiple
hops may be needed by one node to transfer data to another node in network. This leads to the need for an
effective routing protocol. In this paper we study various classifications of routing protocols and their types
for wireless mobile ad-hoc networks like DSDV, GSR, AODV, DSR, ZRP, FSR, CGSR, LAR, and Geocast
Protocols. In this paper we also compare different routing protocols on based on a given set of parameters
Scalability, Latency, Bandwidth, Control-overhead, Mobility impact.

The University of Michigan Center for Excellence in Health Statistics seeks to improve the quality of survey data available to federal, state, and local policy makers for health promotion and disease prevention. Survey methods have been called as one of the great inventions of the 20 th Century, and are widely used in health policy formulation for information about the health and health care utilization of the general population. Sound, accurate, and reliable data are required for policy analysts not only to assess the current state of health and health care utilization in the general population but also to develop models to predict trends and developments in the population's health. Improving the quality of health survey statistics leads to more effective health promotion and disease prevention policy. The Michigan Center for Excellence in Health Statistics (MiCEHS) is designed to conduct research on the survey process to improve the accuracy of data obtained from sample survey...

Underwater wireless sensor networks (UWSN), similar
to the terrestrial sensor networks, have different
challenges such as limited bandwidth, low battery p
ower, defective underwater channels, and high varia
ble
propagation delay. A crucial problem in UWSN is fin
ding an efficient route between a source and a
destination. Consequently, great efforts have been
made for designing efficient protocols while consid
ering
the unique characteristics of underwater communicat
ion. Several routing protocols are proposed for thi
s
issue and can be classified into geographic and non
-geographic routing protocols. In this paper we foc
us
on the geographic routing protocols. We introduce a
review and comparison of different algorithms
proposed recently in the literature. We also presen
ted a novel taxonomy of these routing in which the
protocols are classified into three categories (gre
edy, restricted directional flooding and hierarchic
al)
according to their forwarding strategies.

Content-based image retrieval (CBIR) is a new but widely adopted method for finding images from vast and unannotated image databases. As the network and development of multimedia technologies are becoming more popular, users are not satisfied with the traditional information retrieval techniques. So nowadays the content based image retrieval (CBIR) are becoming a source of exact and fast retrieval. In recent years, a variety of techniques have been developed to improve the performance of CBIR. Data clustering is an unsupervised method for extraction hidden pattern from huge data sets. With large data
sets, there is possibility of high dimensionality. Having both accuracy and efficiency for high dimensional data sets with enormous number of samples is a challenging arena. In this paper the clustering techniques are discussed and analysed. Also, we propose a method HDK that uses more than one clustering technique to improve the performance of CBIR.This method makes use of hierachical and divide and conquer KMeans clustering technique with equivalency and compatible relation concepts to improve the performance
of the K-Means for using in high dimensional datasets. It also introduced the feature like color, texture and shape for accurate and effective retrieval system.

Tungiasis is an important parasitic disease of global public health concern. However, there is limited data on its epidemiology in many poor-resource areas which prevent the implementation of targeted control measures. A cross-sectional study was implemented to determine the prevalence of Tunga spp. (Tunga penetrans) and associated determinants of infestation among humans in Central Kenya. We used a questionnaire to collate the information needed to determine the Multidimensional Poverty Index of the visited homesteads. Soil samples were taken randomly from each homestead to establish the type and percentage of each of the primary soil nutrients. From images taken of either the feet, hands, toes or fingers of each person in the visited homestead, categorization on infestation with Tunga penetrans, was done. For data analysis, we used WinBUGS, a Markov chain Monte Carlo method in complex Bayesian models. Reduced hierarchical Bayesian logistic (RHBL) model for the prevalence of jigger infestation was developed through the Bayesian approach. For a unit decrease in the poverty index at level 1 there was an increase in the prevalence of jigger infestation by 80.17%. A unit increase in clay percentage in the soil contributed to a 35.91 times increase in jigger infestation prevalence. Incorporating structured and unstructured random effects led to the spatial hierarchical Bayesian logistic (SHBL) model showing Murang’a county to have a higher prevalence (2.575 times) of jigger infestation compared to Nyeri county, while Kiambu county had a lower prevalence. From the models, a unit change in the poverty level and clay soil percentage increased the prevalence of jigger infestation substantially. It was used to identify the geographical areas where broad intervention strategies would be needed in Central, Kenya.

To date, there is a lack of empirical analysis about their effect on survey nonresponse in most developing countries, including Indonesia. To fill this research gap, this study aims
to extend previous research to explore the use of paradata in predicting survey non-participation in the 2007 Indonesian Family Life Survey (IFLS4). Findings suggest that non-response may be lowered if interviewers make an effort to contact respondents during weekday afternoons and evenings, and during the weekends. Non-response may
be also be lowered if survey organizations make special efforts to maintain contact with households with dependent children, disabled or sick individuals, and multiple working
adults, as well as households located in urban areas.

Clustering is the process of partitioning a set of data so that the data can be divided into subsets. Clustering is implemented so that same set of data can be collected on one side and other set of data can be collected on the other end. Clustering can be done using many methods like partitioning methods, hierarchical methods, density based method. Hierarchical method creates a hierarchical decomposition of the given set of data objects. In successive iteration, a cluster is split into smaller clusters, until eventually each object is in one cluster, or a termination condition holds. In this paper, partitioning method has been used with hierarchical method to form better and improved clusters. We have used various algorithms for getting better and improved clusters.

The aims of this study were to translate into Spanish and analyze the psychometric characteristics of the Global Motivation Scale (GMS). In study 1, participants were 237 university students. Results of analysis of correlations between the seven subscales of the GMS supported the presence of a continuum of self-determination, and confirmatory factor analysis confirmed the structure of seven factors correlated. In addition, internal consistency values were acceptable. In study 2, participants were 139 university students. Results of analysis of correlations between self-esteem and the seven subscales of the GMS supported the criterion validity of the scale. In conclusion, results support the use of the Spanish version of the GMS for assessing global motivation in university students.

Beta regression is a suitable choice for modelling continuous response variables tak-ing values on the unity interval. Data structures such as hierarchical, repeated measures and longitudinal typically induce extra variability and/or dependence and can be ac-counted for by the inclusion of random effects. Statistical inference typically requires numerical methods, possibly combined with sampling algorithms. This class of Beta mixed models is adopted for the analysis of two real problems with grouped data struc-tures. We focus on likelihood inference and describe the implemented algorithms. The first is a study on the life quality index of industry workers with data collected according to an hierarchical sampling scheme. The second is a study assessing the impact of hy-droelectric power plants upon measures of water quality indexes up, downstream and at the reservoirs of the dammed rivers, with a nested and longitudinal data structure. Re-sults from different algorithms are reporter ...

Generating class-agnostic object proposals followed by classification has recently become a common paradigm for object detection. Current state-of-the-art approaches typically generate generic objects, which serve as candidates for object classification. Since these object proposals are generic whereas the categories for classification are domain specific, there is a gap between the generation of object proposals and the classification of object proposals. In this paper, by taking advantages of the intrinsic structure and the complexity of each category of objects, we propose a novel tree-based hierarchical model to learn object proposals, from top proposals produced by the existing object proposals generation methods. First, we develop a tree-structured representation for each object to capture its hierarchical structure feature. Second, we propose a 23D compact feature vector to represent objects' visual features. Third, we formulate a learning schema which evaluates the objectness of each proposal. Experiments demonstrate the significant improvement of the proposed approach over the state-of-the-art method in terms of object detection rate. An application is proposed based on this approach to help children learn and recognize objects by their visual appearances and their sub-parts structures.

Extensive amount of data stored in medical documents require developing methods that help users to find what they are looking for effectively by organizing large amounts of information into a small number of meaningful clusters. The produced clusters contain groups of objects which are more similar to each other than to the members of any other group. Thus, the aim of high-quality document clustering algorithms is to determine a set of clusters in which the inter-cluster similarity is minimized and intra-cluster similarity is maximized. The most important feature in many clustering algorithms is treating the clustering problem as an optimization process, that is, maximizing or minimizing a particular clustering criterion function defined over the whole clustering solution. The only real difference between agglomerative algorithms is how they choose which clusters to merge. The main purpose of this paper is to compare different agglomerative algorithms based on the evaluation of the clusters quality produced by different hierarchical agglomerative clustering algorithms using different criterion functions for the problem of clustering medical documents. Our experimental results showed that the agglomerative algorithm that uses I1 as its criterion function for choosing which clusters to merge produced better clusters quality than the other criterion functions in term of entropy and purity as external measures.

The relevance of a Census is unquestionable for many National Statistical Systems due to its thematic
range and territorial scope. Nonetheless, its complexity leads to challenges in ensuring timeliness and
the quality of the results. The aim of this work is to identify potential causes of nonsampling errors
associated to the data collection process of the 2010 Brazilian Census.
We used data obtained from the field work monitoring system that provided information about
divergences observed between data collected by enumerators and supervisors. The latter carried out
follow-up interviews in households selected by the supervision/monitoring plan. In addition, human
resources databases containing socio-demographic information of enumerators and supervisors, such
as gender, age and educational level, were also brought to bear to enhance the analysis. Moreover, in
order to investigate associations between paradata and the socio-demographic profiles of the survey
respondents, Census microdata was also used.
The statistical analysis employed generalized hierarchical models in which three nested hierarchical
levels were taken into account, namely the supervisor, the enumerator and the respondent. The model
response variable was defined as the occurrence of a discrepancy (or divergence) between the
information collected by enumerators and their supervisors for at least one of the following respondent
characteristics: sex, age and literacy.
The results indicate that the different hierarchical levels investigated are relevant to decompose the
data variability and hence have to be considered in the analysis. We also found evidence of significant
associations between the occurrence of divergences (on data collected by enumerators and
supervisors) and socio-demographic characteristics of enumerators, supervisors and respondents. In
addition, the results revealed notable regional differences regarding the divergences of each level and
the associations with explanatory variables considered in the study.

This paper presents a report of a study carried out to develop a job coordination model for the active resources on a mobile wireless computational grid. This was with a view to addressing the problem of frequent disruptions in connections and high computation time for a job resulting from the mobility of the mobile resources actively processing tasks. The proposed framework is a load-balancing three tier hierarchical system configuration and scheduling policies employing mobile Agents coordinating messengers carrying data and instructions among the hierarchical structured nodes. The model achieves a remarkable performance as compared with theoretical values in that there were reduction in response times and latencies when simulated with various workloads. The proposed migration and checkpointing approach ensures that currently executing processes are not always migrated because of loss of signal only, but only with reduction in battery power of the mobile hosts within the allocated...

We describe a novel method of Bayesian inference for hierarchical or non-hierarchical equal variance normal Signal Detection Theory models with one or more criteria. The method is implemented as an open-source R package that uses the state-of-the-art Stan platform for sampling from posterior distributions. Our method can accommodate binary responses as well as additional ratings and an arbitrary number of nested or crossed random grouping factors. The SDT parameters can be regressed on additional predictors within the same model via intermediate unconstrained parameters, and the model can be extended by using automatically generated human-readable Stan code as a template. In the paper we explain how our method improves on other similar available methods, we give an overview of the package, demonstrate its use by providing a real-study data analysis walk-through, and show that the model successfully recovers known parameter values when fitted to simulated data. We also demonstrate that ignoring a hierarchical data structure may lead to severely biased estimates when fitting Signal Detection Theory models.

The hierarchical model of intrinsic and extrinsic motivation establishes three levels of generality to analyze human motivation (global, contextual, and situational). The dynamism of motivation is explained by means of the relations between the three levels. The purpose of this study is to test the reciprocal effects, top-down and bottom-up, between one motivational level and the adjacent level, as well as the stability of the global level (horizontal effect). A longitudinal design with six measurement time points across a 4-month interval was used. Participants were 142 undergraduate students. Results from path analysis using a Bayesian estimation method provided support for our hypotheses. Finally, the implications of the dynamic processes of influence among the hierarchical levels in academic setting are discussed.