Inverse problems in dynamic cognitive modeling

Inverse problems for dynamical system models of cognitive processes comprise the determination of synaptic weight matrices or kernel functions for neural networks or neural/dynamic field models, respectively. We introduce dynamic cognitive modeling as a three tier top-down approach where cognitive processes are first described as algorithms that operate on complex symbolic data structures. Second, symbolic expressions and operations are represented by states and transformations in abstract vector spaces. Third, prescribed trajectories through representation space are implemented in neurodynamical systems. We discuss the Amari equation for a neural/dynamic field theory as a special case and show that the kernel construction problem is particularly ill-posed. We suggest a Tikhonov-Hebbian learning method as regularization technique and demonstrate its validity and robustness for basic examples of cognitive computations.

Modeling Information Diffusion in University Libraries: Assessing Peer Interaction Patterns as a Complex Dynamic System

This presentation was offered as part of the CUNY Library Assessment Conference, Reinventing Libraries: Reinventing Assessment, held at the City University of New York in June 2014.

Semiparametric modeling of the spatial distribution of occupational accident risk in the casual labor market, Piracicaba, southeast Brazil

The objective of this study was to estimate the spatial distribution of work accident risk in the informal work market in the urban zone of an industrialized city in southeast Brazil and to examine concomitant effects of age, gender, and type of occupation after controlling for spatial risk variation. The basic methodology adopted was that of a population-based case-control study with particular interest focused on the spatial location of work. Cases were all casual workers in the city suffering work accidents during a one-year period; controls were selected from the source population of casual laborers by systematic random sampling of urban homes. The spatial distribution of work accidents was estimated via a semiparametric generalized additive model with a nonparametric bidimensional spline of the geographical coordinates of cases and controls as the nonlinear spatial component, and including age, gender, and occupation as linear predictive variables in the parametric component. We analyzed 1,918 cases and 2,245 controls between 1/11/2003 and 31/10/2004 in Piracicaba, Brazil. Areas of significantly high and low accident risk were identified in relation to mean risk in the study region (p < 0.01). Work accident risk for informal workers varied significantly in the study area. Significant age, gender, and occupational group effects on accident risk were identified after correcting for this spatial variation. A good understanding of high-risk groups and high-risk regions underpins the formulation of hypotheses concerning accident causality and the development of effective public accident prevention policies.

Ecological modeling and pest population management: a possible and necessary connection in a changing world

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Modeling the spatial and temporal heterogeneity of deforestation-driven carbon emissions: the INPE-EM framework applied to the Brazilian Amazon

We present a generic spatially explicit modeling framework to estimate carbon emissions from deforestation (INPE-EM). The framework incorporates the temporal dynamics related to the deforestation process and accounts for the biophysical and socioeconomic heterogeneity of the region under study. We build an emission model for the Brazilian Amazon combining annual maps of new clearings, four maps of biomass, and a set of alternative parameters based on the recent literature. The most important results are as follows: (a) Using different biomass maps leads to large differences in estimates of emission; for the entire region of the Brazilian Amazon in the last decade, emission estimates of primary forest deforestation range from 0.21 to 0.26 similar to Pg similar to C similar to yr-1. (b) Secondary vegetation growth presents a small impact on emission balance because of the short duration of secondary vegetation. In average, the balance is only 5% smaller than the primary forest deforestation emissions. (c) Deforestation rates decreased significantly in the Brazilian Amazon in recent years, from 27 similar to Mkm2 in 2004 to 7 similar to Mkm2 in 2010. INPE-EM process-based estimates reflect this decrease even though the agricultural frontier is moving to areas of higher biomass. The decrease is slower than a non-process instantaneous model would estimate as it considers residual emissions (slash, wood products, and secondary vegetation). The average balance, considering all biomass, decreases from 0.28 in 2004 to 0.15 similar to Pg similar to C similar to yr-1 in 2009; the non-process model estimates a decrease from 0.33 to 0.10 similar to Pg similar to C similar to yr-1. We conclude that the INPE-EM is a powerful tool for representing deforestation-driven carbon emissions. Biomass estimates are still the largest source of uncertainty in the effective use of this type of model for informing mechanisms such as REDD+. The results also indicate that efforts to reduce emissions should focus not only on controlling primary forest deforestation but also on creating incentives for the restoration of secondary forests.

Spatial pattern detection modeling of thrips (Thrips tabaci) on onion fields

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Prediction and spatial variability of soil dynamic properties in sugar cane fields of São Paulo State Brazil

The objective of this work was to model and diagnose the spatial variability of soil load support capacity (SLSC) in sugar cane crop fields, as well as to evaluate the management impact on São Paulo State soil structure. The investigated variables were: pressure preconsolidation (sigma(p)), apparent cohesion () and internal friction angle (). The conclusions from the results were that the models and spatial dependence maps constitute important tools in the prediction and location of the mechanical internal strength of soils cultivated with sugar cane. They will help future soil management decisions so that soil structure sustainability will not be compromised.

Simulation of dynamic pinion course using Runge-Kutta's method and impact modeling

Once defined the relationship between the Starter Motor components and their functions, it is possible to develop a mathematical model capable to predict the Starter behavior during operation. One important aspect is the engagement system behavior. The development of a mathematical tool capable of predicting it is a valuable step in order to reduce the design time, cost and engineering efforts. A mathematical model, represented by differential equations, can be developed using physics laws, evaluating force balance and energy flow through the systems degrees of freedom. Another important physical aspect to be considered in this modeling is the impact conditions (particularly on the pinion and ring-gear contact). This work is a report of those equations application on available mathematical software and the resolution of those equations by Runge-Kutta's numerical integration method, in order to build an accessible engineering tool. Copyright © 2011 SAE International.

The fractional-nonlinear robotic manipulator: Modeling and dynamic simulations

In this paper, we applied the Riemann-Liouville approach and the fractional Euler-Lagrange equations in order to obtain the fractional-order nonlinear dynamics equations of a two link robotic manipulator. The aformentioned equations have been simulated for several cases involving: integer and non-integer order analysis, with and without external forcing acting and some different initial conditions. The fractional nonlinear governing equations of motion are coupled and the time evolution of the angular positions and the phase diagrams have been plotted to visualize the effect of fractional order approach. The new contribution of this work arises from the fact that the dynamics equations of a two link robotic manipulator have been modeled with the fractional Euler-Lagrange dynamics approach. The results reveal that the fractional-nonlinear robotic manipulator can exhibit different and curious behavior from those obtained with the standard dynamical system and can be useful for a better understanding and control of such nonlinear systems. © 2012 American Institute of Physics.

Modeling spatial distribution of cancer incidence in Cordoba, Argentina

Background: Cancer is the second leading cause of death in Argentina, and there is little knowledge about its incidence. The first study based on population-based cancer registry described spatial incidence and indicated that there existed at least county-level aggregation. The aim of the present work is to model the incidence patterns for the most incidence cancer in Córdoba Province, Argentina, using information from the Córdoba Cancer Registry by performing multilevel mixed model approach to deal with dependence and unobserved heterogeneity coming from the geo-reference cancer occurrence. Methods: Standardized incidence rates (world standard population) (SIR) by sex based on 5-year age groups were calculated for 109 districts nested on 26 counties for the most incidence cancers in Cordoba using 2004 database. A Poisson twolevel random effect model representing unobserved heterogeneity between first level-districts and second level-counties was fitted to assess the spatial distribution of the overall and site specific cancer incidence rates. Results: SIR cancer at Córdoba province shown an average of 263.53±138.34 and 200.45±98.30 for men and women, respectively. Considering the ratio site specific mean SIR to the total mean, breast cancer ratio was 0.25±0.19, prostate cancer ratio was 0.12±0.10 and lower values for lung and colon cancer for both sexes. The Poisson two-level random intercepts model fitted for SIR data distributed with overdispersion shown significant hierarchical structure for the cancer incidence distribution. Conclusions: a strong spatial-nested effect for the cancer incidence in Córdoba was observed and will help to begin the study of the factors associated with it.

Generalizing the dynamic field theory of spatial cognition across real and developmental time scales

Within cognitive neuroscience, computational models are designed to provide insights into the organization of behavior while adhering to neural principles. These models should provide sufficient specificity to generate novel predictions while maintaining the generality needed to capture behavior across tasks and/or time scales. This paper presents one such model, the Dynamic Field Theory (DFT) of spatial cognition, showing new simulations that provide a demonstration proof that the theory generalizes across developmental changes in performance in four tasks—the Piagetian A-not-B task, a sandbox version of the A-not-B task, a canonical spatial recall task, and a position discrimination task. Model simulations demonstrate that the DFT can accomplish both specificity—generating novel, testable predictions—and generality—spanning multiple tasks across development with a relatively simple developmental hypothesis. Critically, the DFT achieves generality across tasks and time scales with no modification to its basic structure and with a strong commitment to neural principles. The only change necessary to capture development in the model was an increase in the precision of the tuning of receptive fields as well as an increase in the precision of local excitatory interactions among neurons in the model. These small quantitative changes were sufficient to move the model through a set of quantitative and qualitative behavioral changes that span the age range from 8 months to 6 years and into adulthood. We conclude by considering how the DFT is positioned in the literature, the challenges on the horizon for our framework, and how a dynamic field approach can yield new insights into development from a computational cognitive neuroscience perspective.

Tests of the Dynamic Field Theory and the Spatial Precision Hypothesis: Capturing a Qualitative Developmental Transition in Spatial Working Memory

This study tested a dynamic field theory (DFT) of spatial working memory and an associated spatial precision hypothesis (SPH). Between 3 and 6 years of age, there is a qualitative shift in how children use reference axes to remember locations: 3-year-olds’ spatial recall responses are biased toward reference axes after short memory delays, whereas 6-year-olds’ responses are biased away from reference axes. According to the DFT and the SPH, quantitative improvements over development in the precision of excitatory and inhibitory working memory processes lead to this qualitative shift. Simulations of the DFT in Experiment 1 predict that improvements in precision should cause the spatial range of targets attracted toward a reference axis to narrow gradually over development, with repulsion emerging and gradually increasing until responses to most targets show biases away from the axis. Results from Experiment 2 with 3- to 5-year-olds support these predictions. Simulations of the DFT in Experiment 3 quantitatively fit the empirical results and offer insights into the neural processes underlying this developmental change.

Modeling and measuring the spatial relation "along": Regions, contours and fuzzy sets

The analysis of spatial relations among objects in an image is an important vision problem that involves both shape analysis and structural pattern recognition. In this paper, we propose a new approach to characterize the spatial relation along, an important feature of spatial configurations in space that has been overlooked in the literature up to now. We propose a mathematical definition of the degree to which an object A is along an object B, based on the region between A and B and a degree of elongatedness of this region. In order to better fit the perceptual meaning of the relation, distance information is included as well. In order to cover a more wide range of potential applications, both the crisp and fuzzy cases are considered. In the crisp case, the objects are represented in terms of 2D regions or ID contours, and the definition of the alongness between them is derived from a visibility notion and from the region between the objects. However, the computational complexity of this approach leads us to the proposition of a new model to calculate the between region using the convex hull of the contours. On the fuzzy side, the region-based approach is extended. Experimental results obtained using synthetic shapes and brain structures in medical imaging corroborate the proposed model and the derived measures of alongness, thus showing that they agree with the common sense. (C) 2011 Elsevier Ltd. All rights reserved.

The assessment of change in rural landscape and the analysis of the driving forces. Proposal of a spatial model for the rural built environment

The presented study carried out an analysis on rural landscape changes. In particular the study focuses on the understanding of driving forces acting on the rural built environment using a statistical spatial model implemented through GIS techniques. It is well known that the study of landscape changes is essential for a conscious decision making in land planning. From a bibliography review results a general lack of studies dealing with the modeling of rural built environment and hence a theoretical modelling approach for such purpose is needed. The advancement in technology and modernity in building construction and agriculture have gradually changed the rural built environment. In addition, the phenomenon of urbanization of a determined the construction of new volumes that occurred beside abandoned or derelict rural buildings. Consequently there are two types of transformation dynamics affecting mainly the rural built environment that can be observed: the conversion of rural buildings and the increasing of building numbers. It is the specific aim of the presented study to propose a methodology for the development of a spatial model that allows the identification of driving forces that acted on the behaviours of the building allocation. In fact one of the most concerning dynamic nowadays is related to an irrational expansion of buildings sprawl across landscape. The proposed methodology is composed by some conceptual steps that cover different aspects related to the development of a spatial model: the selection of a response variable that better describe the phenomenon under study, the identification of possible driving forces, the sampling methodology concerning the collection of data, the most suitable algorithm to be adopted in relation to statistical theory and method used, the calibration process and evaluation of the model. A different combination of factors in various parts of the territory generated favourable or less favourable conditions for the building allocation and the existence of buildings represents the evidence of such optimum. Conversely the absence of buildings expresses a combination of agents which is not suitable for building allocation. Presence or absence of buildings can be adopted as indicators of such driving conditions, since they represent the expression of the action of driving forces in the land suitability sorting process. The existence of correlation between site selection and hypothetical driving forces, evaluated by means of modeling techniques, provides an evidence of which driving forces are involved in the allocation dynamic and an insight on their level of influence into the process. GIS software by means of spatial analysis tools allows to associate the concept of presence and absence with point futures generating a point process. Presence or absence of buildings at some site locations represent the expression of these driving factors interaction. In case of presences, points represent locations of real existing buildings, conversely absences represent locations were buildings are not existent and so they are generated by a stochastic mechanism. Possible driving forces are selected and the existence of a causal relationship with building allocations is assessed through a spatial model. The adoption of empirical statistical models provides a mechanism for the explanatory variable analysis and for the identification of key driving variables behind the site selection process for new building allocation. The model developed by following the methodology is applied to a case study to test the validity of the methodology. In particular the study area for the testing of the methodology is represented by the New District of Imola characterized by a prevailing agricultural production vocation and were transformation dynamic intensively occurred. The development of the model involved the identification of predictive variables (related to geomorphologic, socio-economic, structural and infrastructural systems of landscape) capable of representing the driving forces responsible for landscape changes.. The calibration of the model is carried out referring to spatial data regarding the periurban and rural area of the study area within the 1975-2005 time period by means of Generalised linear model. The resulting output from the model fit is continuous grid surface where cells assume values ranged from 0 to 1 of probability of building occurrences along the rural and periurban area of the study area. Hence the response variable assesses the changes in the rural built environment occurred in such time interval and is correlated to the selected explanatory variables by means of a generalized linear model using logistic regression. Comparing the probability map obtained from the model to the actual rural building distribution in 2005, the interpretation capability of the model can be evaluated. The proposed model can be also applied to the interpretation of trends which occurred in other study areas, and also referring to different time intervals, depending on the availability of data. The use of suitable data in terms of time, information, and spatial resolution and the costs related to data acquisition, pre-processing, and survey are among the most critical aspects of model implementation. Future in-depth studies can focus on using the proposed model to predict short/medium-range future scenarios for the rural built environment distribution in the study area. In order to predict future scenarios it is necessary to assume that the driving forces do not change and that their levels of influence within the model are not far from those assessed for the time interval used for the calibration.