Spatial Period-Doubling Agglomeration of a Core-Periphery Model with a System of Cities

The orientation and progress of spatial agglomeration for Krugman's core--periphery model are investigated in this paper. Possible agglomeration patterns for a system of cities spread uniformly on a circle are set forth theoretically. For example, a possible and most likely course predicted for eight cities is a gradual and successive one---concentration into four cities and then into two cities en route to a single city. The existence of this course is ensured by numerical simulation for the model. Such gradual and successive agglomeration, which is called spatial-period doubling, presents a sharp contrast with the agglomeration of two cities, for which spontaneous concentration to a single city is observed in models of various kinds. It exercises caution about the adequacy of the two cities as a platform of the spatial agglomerations and demonstrates the need of the study on a system of cities

Spatial Period-Doubling Agglomeration of a Core-Periphery Model with a System of Cities

The orientation and progress of spatial agglomeration for Krugman's core--periphery model are investigated in this paper. Possible agglomeration patterns for a system of cities spread uniformly on a circle are set forth theoretically. For example, a possible and most likely course predicted for eight cities is a gradual and successive one---concentration into four cities and then into two cities en route to a single city. The existence of this course is ensured by numerical simulation for the model. Such gradual and successive agglomeration, which is called spatial-period doubling, presents a sharp contrast with the agglomeration of two cities, for which spontaneous concentration to a single city is observed in models of various kinds. It exercises caution about the adequacy of the two cities as a platform of the spatial agglomerations and demonstrates the need of the study on a system of cities.Agglomeration of population; Bifurcation; Core-periphery model; Group theory; Spatial period doubling

Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence

Incremental learning (IL) has received a lot of attention recently, however, the literature lacks a precise problem definition, proper evaluation settings, and metrics tailored specifically for the IL problem. One of the main objectives of this work is to fill these gaps so as to provide a common ground for better understanding of IL. The main challenge for an IL algorithm is to update the classifier whilst preserving existing knowledge. We observe that, in addition to forgetting, a known issue while preserving knowledge, IL also suffers from a problem we call intransigence, inability of a model to update its knowledge. We introduce two metrics to quantify forgetting and intransigence that allow us to understand, analyse, and gain better insights into the behaviour of IL algorithms. We present RWalk, a generalization of EWC++ (our efficient version of EWC [Kirkpatrick2016EWC]) and Path Integral [Zenke2017Continual] with a theoretically grounded KL-divergence based perspective. We provide a thorough analysis of various IL algorithms on MNIST and CIFAR-100 datasets. In these experiments, RWalk obtains superior results in terms of accuracy, and also provides a better trade-off between forgetting and intransigence

Explanation-based learning for diagnosis

Diagnostic expert systems constructed using traditional knowledge-engineering techniques identify malfunctioning components using rules that associate symptoms with diagnoses. Model-based diagnosis (MBD) systems use models of devices to find faults given observations of abnormal behavior. These approaches to diagnosis are complementary. We consider hybrid diagnosis systems that include both associational and model-based diagnostic components. We present results on explanation-based learning (EBL) methods aimed at improving the performance of hybrid diagnostic problem solvers. We describe two architectures called EBL_IA and EBL(p). EBL_IA is a form fo "learning in advance" that pre-compiles models into associations. At run-time the diagnostic system is purely associational. In EBL(p), the run-time diagnosis system contains associational, MBD, and EBL components. Learned associational rules are preferred but when they are incomplete they may produce too many incorrect diagnoses. When errors cause performance to dip below a give threshold p, EBL(p) activates MBD and explanation-based "learning while doing". We present results of empirical studies comparing MBD without learning versus EBL_IA and EBL(p). The main conclusions are as follows. EBL_IA is superior when it is feasible but it is not feasible for large devices. EBL(p) can speed-up MBD and scale-up to larger devices in situations where perfect accuracy is not required

A Primal-Dual Augmented Lagrangian

Nonlinearly constrained optimization problems can be solved by minimizing a sequence of simpler unconstrained or linearly constrained subproblems. In this paper, we discuss the formulation of subproblems in which the objective is a primal-dual generalization of the Hestenes-Powell augmented Lagrangian function. This generalization has the crucial feature that it is minimized with respect to both the primal and the dual variables simultaneously. A benefit of this approach is that the quality of the dual variables is monitored explicitly during the solution of the subproblem. Moreover, each subproblem may be regularized by imposing explicit bounds on the dual variables. Two primal-dual variants of conventional primal methods are proposed: a primal-dual bound constrained Lagrangian (pdBCL) method and a primal-dual $\ell$1 linearly constrained Lagrangian (pd$\ell$1-LCL) method