On the Relationship Between the Generalized Equality Classifier and ART 2 Neural Networks

In this paper, we introduce the Generalized Equality Classifier (GEC) for use as an unsupervised clustering algorithm in categorizing analog data. GEC is based on a formal definition of inexact equality originally developed for voting in fault tolerant software applications. GEC is defined using a metric space framework. The only parameter in GEC is a scalar threshold which defines the approximate equality of two patterns. Here, we compare the characteristics of GEC to the ART2-A algorithm (Carpenter, Grossberg, and Rosen, 1991). In particular, we show that GEC with the Hamming distance performs the same optimization as ART2. Moreover, GEC has lower computational requirements than AR12 on serial machines

On the Relationship Between the Generalized Equality Classifier and ART 2 Neural Networks

In this paper, we introduce the Generalized Equality Classifier (GEC) for use as an unsupervised clustering algorithm in categorizing analog data. GEC is based on a formal definition of inexact equality originally developed for voting in fault tolerant software applications. GEC is defined using a metric space framework. The only parameter in GEC is a scalar threshold which defines the approximate equality of two patterns. Here, we compare the characteristics of GEC to the ART2-A algorithm (Carpenter, Grossberg, and Rosen, 1991). In particular, we show that GEC with the Hamming distance performs the same optimization as ART2. Moreover, GEC has lower computational requirements than AR12 on serial machines

Software reliability experiments data analysis and investigation

The objectives are to investigate the fundamental reasons which cause independently developed software programs to fail dependently, and to examine fault tolerant software structures which maximize reliability gain in the presence of such dependent failure behavior. The authors used 20 redundant programs from a software reliability experiment to analyze the software errors causing coincident failures, to compare the reliability of N-version and recovery block structures composed of these programs, and to examine the impact of diversity on software reliability using subpopulations of these programs. The results indicate that both conceptually related and unrelated errors can cause coincident failures and that recovery block structures offer more reliability gain than N-version structures if acceptance checks that fail independently from the software components are available. The authors present a theory of general program checkers that have potential application for acceptance tests

A dual-processor multi-frequency implementation of the FINDS algorithm

This report presents a parallel processing implementation of the FINDS (Fault Inferring Nonlinear Detection System) algorithm on a dual processor configured target flight computer. First, a filter initialization scheme is presented which allows the no-fail filter (NFF) states to be initialized using the first iteration of the flight data. A modified failure isolation strategy, compatible with the new failure detection strategy reported earlier, is discussed and the performance of the new FDI algorithm is analyzed using flight recorded data from the NASA ATOPS B-737 aircraft in a Microwave Landing System (MLS) environment. The results show that low level MLS, IMU, and IAS sensor failures are detected and isolated instantaneously, while accelerometer and rate gyro failures continue to take comparatively longer to detect and isolate. The parallel implementation is accomplished by partitioning the FINDS algorithm into two parts: one based on the translational dynamics and the other based on the rotational kinematics. Finally, a multi-rate implementation of the algorithm is presented yielding significantly low execution times with acceptable estimation and FDI performance

Eliminating Redundant Training Data Using Unsupervised Clustering Techniques

Training data for supervised learning neural networks can be clustered such that the input/output pairs in each cluster are redundant. Redundant training data can adversely affect training time. In this paper we apply two clustering algorithms, ART2 -A and the Generalized Equality Classifier, to identify training data clusters and thus reduce the training data and training time. The approach is demonstrated for a high dimensional nonlinear continuous time mapping. The demonstration shows six-fold decrease in training time at little or no loss of accuracy in the handling of evaluation data

Scholars\u27 Views on Improving Border Policy

On April 29, the Center for Canadian-American Studies at Western Washington University hosted a conference, Bridging Distances: Past and Future Perspectives on Canada-US Relations, to mark the program’s 40th anniversary.* Participating scholars and practitioners were asked to comment on future trends, opportunities and challenges in the relationship. Plenary remarks were given by Ian Brodie, former chief of staff to Prime Minister Harper, and the luncheon address was delivered by David Emerson, chair of the Energy Policy Institute of Canada and former minister in two governments. Panelists were convened to discuss issues covering the following areas: • Both Sides Now: Parallel Lines Across Binational Pasts • Border Tensions-Trade Mobility and Security • Contending Perspectives, Energy and the Environment • The US in a Shifting World: How Canada Fits It. This Brief discusses ideas about the border that surfaced in the conference presentations and discussions

Sudden Floods, Prudential Regulation and Stability in an Open Economy

We develop a dynamic stochastic model of a middle-income, small open economy with a two-level banking intermediation structure, a risk-sensitive regulatory capital regime, and imperfect capital mobility. Firms borrow from a domestic bank and the bank borrows on world capital markets, in both cases subject to an endogenous premium. A sudden flood in capital flows generates an expansion in credit and activity, and asset price pressures. Countercyclical regulation, in the form of a Basel III-type rule based on real credit gaps, is effective at promoting macroeconomic stability (defined in terms of the volatility of a weighted average of inflation and the output gap) and financial stability (defined in terms of the volatility of a composite index of the nominal exchange rate and house prices). However, because the gain in terms of reduced volatility may exhibit diminishing returns, a countercyclical regulatory rule may need to be supplemented by other, more targeted, macroprudential instruments.

Capital requirements and business cycles with credit market imperfections

The business cycle effects of bank capital regulatory regimes are examined in a New Keynesian model with credit market imperfections and a cost channel of monetary policy. Key features of the model are that bank capital increases incentives for banks to monitor borrowers, thereby reducing the probability of default, and excess capital generates benefits in terms of reduced regulatory scrutiny. Basel I and Basel II-type regulatory regimes are defined, and the model is calibrated for a middle-income country. Simulations of supply and demand shocks show that, depending on the elasticity that relates the repayment probability to the capital-loan ratio, a Basel II-type regime may be less procyclical than a Basel I-type regime.Banks&Banking Reform,Debt Markets,Access to Finance,Economic Theory&Research,Emerging Markets

Capital Regulation, Monetary Policy and Financial Stability

This paper examines the roles of bank capital regulation and monetary policy in mitigating procyclicality and promoting macroeconomic and financial stability. The analysis is based on a dynamic stochastic model with imperfect credit markets. Macroeconomic (financial) stability is defined in terms of the volatility of nominal income (real house prices). Numerical experiments show that even if monetary policy can react strongly to inflation deviations from target, combining a credit-augmented interest rate rule and a Basel III-type countercyclical capital regulatory rule may be optimal for promoting overall economic stability. The greater the degree of interest rate smoothing, and the stronger the policymaker’s concern with macroeconomic stability, the larger is the sensitivity of the regulatory rule to credit growth gaps.

Canada-US Border Securitization: Implications for Binational Cooperation

State borders are critical junctions where oppositional dynamics of exclusion and inclusion are played out. In the last eight years, transnational congruence inherent in economic globalization has clashed directly with the assertion of territorial security by the United States. Borders, harkening to the geopolitics of past centuries, are once again asserted to be sites of vulnerability and lines for maintaining control over people and territory. Border enforcement emphasizes controlling movement of undesirable people and goods, but it is also about ensuring domestic stability and countering challenges to the status quo. Given a history in which immigrants are as likely seen to be threats to national security as welcomed sources of assets and skills, border concerns and border control processes invariably breed anxiety about internal social and cultural boundaries as well. By differentiating the other, borders and their supporting narratives reinforce them. In addition to immigrants and refugees, people considered sufficiently different from prevailing norms are also affected. While national border policies affect the nation as a whole, border regions are disproportionately impacted. Border regions are the locus of cross border social and economic relations, the first point of contact and interaction between nations. As such, they serve to mediate perceptions of, as well as actual, relationship between countries. Their functions as social and economic conduits are constrained as border controls are intensified. Borders, under these conditions, serve to weaken relationships, and impede cross­border cooperation in such areas as commerce, environment, and public health. But the costs of border restrictions are far more than material and environmental alone. They involve social and psychological costs of growing suspicions, reluctance to engage, or slowed momentum for investing further in well established transboundary networks for working in common to solve complex problems. Focusing particularly on the Canada-U.S. border, this paper examines the impact of tighter border policies and enforcement processes on cross-border interaction, as well as their implications for binational and multinational security challenges. Among the questions that will guide the discussion are: What impact do exclusionary border policies have on host societies? How do border policies impact conceptions of border lands and binational cooperation? What problems are inherent in the often heralded trend toward smarter borders