The Felon and the Angel Copier: Criminal Identity and the Promise of Photography in Victorian England and Wales

Ireland Richard, 'The Felon and the Angel Copier: Criminal Identity and the Promise of Photography in Victorian England and Wales', In: Policing and War in Europe, Criminal Justice History, (Westport, CT, Greenwood Press), volume 16, pp.53-86, 2002 RAE2008

Typability and Type Checking in the Second-Order Λ-Calculus Are Equivalent and Undecidable (Preliminary Draft)

We consider the problems of typability[1] and type checking[2] in the Girard/Reynolds second-order polymorphic typed λ-calculus, for which we use the short name "System F" and which we use in the "Curry style" where types are assigned to pure λ -terms. These problems have been considered and proven to be decidable or undecidable for various restrictions and extensions of System F and other related systems, and lower-bound complexity results for System F have been achieved, but they have remained "embarrassing open problems"[3] for System F itself. We first prove that type checking in System F is undecidable by a reduction from semi-unification. We then prove typability in System F is undecidable by a reduction from type checking. Since the reverse reduction is already known, this implies the two problems are equivalent. The second reduction uses a novel method of constructing λ-terms such that in all type derivations, specific bound variables must always be assigned a specific type. Using this technique, we can require that specific subterms must be typable using a specific, fixed type assignment in order for the entire term to be typable at all. Any desired type assignment may be simulated. We develop this method, which we call "constants for free", for both the λK and λI calculi.

Lightweight Modeling of Java Virtual Machine Security Constraints using Alloy

The Java programming language has been widely described as secure by design. Nevertheless, a number of serious security vulnerabilities have been discovered in Java, particularly in the component known as the Bytecode Verifier. This paper describes a method for representing Java security constraints using the Alloy modeling language. It further describes a system for performing a security analysis on any block of Java bytecodes by converting the bytes into relation initializers in Alloy. Any counterexamples found by the Alloy analyzer correspond directly to insecure code. Analysis of a real-world malicious applet is given to demonstrate the efficacy of the approach.

Modeling the Java Bytecode Verifier

The Java programming language has been widely described as secure by design. Nevertheless, a number of serious security vulnerabilities have been discovered in Java, particularly in the Bytecode Verifier, a critical component used to verify class semantics before loading is complete. This paper describes a method for representing Java security constraints using the Alloy modeling language. It further describes a system for performing a security analysis on any block of Java bytecodes by converting the bytes into relation initializers in Alloy. Any counterexamples found by the Alloy analyzer correspond directly to insecure code. Analysis of the approach in the context of known security exploits is provided. This type of analysis represents a significant departure from standard malware analysis methods based on signatures or anomaly detection.

Fuzzy ARTMAP, Slow Learning and Probability Estimation

A nonparametric probability estimation procedure using the fuzzy ARTMAP neural network is here described. Because the procedure does not make a priori assumptions about underlying probability distributions, it yields accurate estimates on a wide variety of prediction tasks. Fuzzy ARTMAP is used to perform probability estimation in two different modes. In a 'slow-learning' mode, input-output associations change slowly, with the strength of each association computing a conditional probability estimate. In 'max-nodes' mode, a fixed number of categories are coded during an initial fast learning interval, and weights are then tuned by slow learning. Simulations illustrate system performance on tasks in which various numbers of clusters in the set of input vectors mapped to a given class.

A Fuzzy ARTMAP Nonparametric Probability Estimator For Nonstationary Pattern Recognition Problem

An incremental, nonparametric probability estimation procedure using the fuzzy ARTMAP neural network is introduced. In slow-learning mode, fuzzy ARTMAP searches for patterns of data on which to build ever more accurate estimates. In max-nodes mode, the network initially learns a fixed number of categories, and weights are then adjusted gradually.

A Neural Network Architecture for Autonomous Learning, Recognition, and Prediction in a Nonstationary World

In a constantly changing world, humans are adapted to alternate routinely between attending to familiar objects and testing hypotheses about novel ones. We can rapidly learn to recognize and narne novel objects without unselectively disrupting our memories of familiar ones. We can notice fine details that differentiate nearly identical objects and generalize across broad classes of dissimilar objects. This chapter describes a class of self-organizing neural network architectures--called ARTMAP-- that are capable of fast, yet stable, on-line recognition learning, hypothesis testing, and naming in response to an arbitrary stream of input patterns (Carpenter, Grossberg, Markuzon, Reynolds, and Rosen, 1992; Carpenter, Grossberg, and Reynolds, 1991). The intrinsic stability of ARTMAP allows the system to learn incrementally for an unlimited period of time. System stability properties can be traced to the structure of its learned memories, which encode clusters of attended features into its recognition categories, rather than slow averages of category inputs. The level of detail in the learned attentional focus is determined moment-by-moment, depending on predictive success: an error due to over-generalization automatically focuses attention on additional input details enough of which are learned in a new recognition category so that the predictive error will not be repeated. An ARTMAP system creates an evolving map between a variable number of learned categories that compress one feature space (e.g., visual features) to learned categories of another feature space (e.g., auditory features). Input vectors can be either binary or analog. Computational properties of the networks enable them to perform significantly better in benchmark studies than alternative machine learning, genetic algorithm, or neural network models. Some of the critical problems that challenge and constrain any such autonomous learning system will next be illustrated. Design principles that work together to solve these problems are then outlined. These principles are realized in the ARTMAP architecture, which is specified as an algorithm. Finally, ARTMAP dynamics are illustrated by means of a series of benchmark simulations.

ARTMAP: Supervised Real-Time Learning and Classification of Nonstationary Data by a Self-Organizing Neural Network

This article introduces a new neural network architecture, called ARTMAP, that autonomously learns to classify arbitrarily many, arbitrarily ordered vectors into recognition categories based on predictive success. This supervised learning system is built up from a pair of Adaptive Resonance Theory modules (ARTa and ARTb) that are capable of self-organizing stable recognition categories in response to arbitrary sequences of input patterns. During training trials, the ARTa module receives a stream {a^(p)} of input patterns, and ARTb receives a stream {b^(p)} of input patterns, where b^(p) is the correct prediction given a^(p). These ART modules are linked by an associative learning network and an internal controller that ensures autonomous system operation in real time. During test trials, the remaining patterns a^(p) are presented without b^(p), and their predictions at ARTb are compared with b^(p). Tested on a benchmark machine learning database in both on-line and off-line simulations, the ARTMAP system learns orders of magnitude more quickly, efficiently, and accurately than alternative algorithms, and achieves 100% accuracy after training on less than half the input patterns in the database. It achieves these properties by using an internal controller that conjointly maximizes predictive generalization and minimizes predictive error by linking predictive success to category size on a trial-by-trial basis, using only local operations. This computation increases the vigilance parameter ρa of ARTa by the minimal amount needed to correct a predictive error at ARTb· Parameter ρa calibrates the minimum confidence that ARTa must have in a category, or hypothesis, activated by an input a^(p) in order for ARTa to accept that category, rather than search for a better one through an automatically controlled process of hypothesis testing. Parameter ρa is compared with the degree of match between a^(p) and the top-down learned expectation, or prototype, that is read-out subsequent to activation of an ARTa category. Search occurs if the degree of match is less than ρa. ARTMAP is hereby a type of self-organizing expert system that calibrates the selectivity of its hypotheses based upon predictive success. As a result, rare but important events can be quickly and sharply distinguished even if they are similar to frequent events with different consequences. Between input trials ρa relaxes to a baseline vigilance pa When ρa is large, the system runs in a conservative mode, wherein predictions are made only if the system is confident of the outcome. Very few false-alarm errors then occur at any stage of learning, yet the system reaches asymptote with no loss of speed. Because ARTMAP learning is self stabilizing, it can continue learning one or more databases, without degrading its corpus of memories, until its full memory capacity is utilized.

Fuzzy ARTMAP: A Neural Network Architecture for Incremental Supervised Learning of Analog Multidimensional Maps

A new neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors. The architecture, called Fuzzy ARTMAP, achieves a synthesis of fuzzy logic and Adaptive Resonance Theory (ART) neural networks by exploiting a close formal similarity between the computations of fuzzy subsethood and ART category choice, resonance, and learning. Fuzzy ARTMAP also realizes a new Minimax Learning Rule that conjointly minimizes predictive error and maximizes code compression, or generalization. This is achieved by a match tracking process that increases the ART vigilance parameter by the minimum amount needed to correct a predictive error. As a result, the system automatically learns a minimal number of recognition categories, or "hidden units", to met accuracy criteria. Category proliferation is prevented by normalizing input vectors at a preprocessing stage. A normalization procedure called complement coding leads to a symmetric theory in which the MIN operator (Λ) and the MAX operator (v) of fuzzy logic play complementary roles. Complement coding uses on-cells and off-cells to represent the input pattern, and preserves individual feature amplitudes while normalizing the total on-cell/off-cell vector. Learning is stable because all adaptive weights can only decrease in time. Decreasing weights correspond to increasing sizes of category "boxes". Smaller vigilance values lead to larger category boxes. Improved prediction is achieved by training the system several times using different orderings of the input set. This voting strategy can also be used to assign probability estimates to competing predictions given small, noisy, or incomplete training sets. Four classes of simulations illustrate Fuzzy ARTMAP performance as compared to benchmark back propagation and genetic algorithm systems. These simulations include (i) finding points inside vs. outside a circle; (ii) learning to tell two spirals apart; (iii) incremental approximation of a piecewise continuous function; and (iv) a letter recognition database. The Fuzzy ARTMAP system is also compared to Salzberg's NGE system and to Simpson's FMMC system.

An exploratory study on the impact of an early years' preschool intervention programme in the Republic of Ireland

Early years’ education has increasingly been identified as a mechanism to alleviate educational disadvantage in areas of social exclusion. Early years’ intervention programmes are now a common government social policy for addressing social problems (Reynolds, Mann, Miedel, and Smokowski, 1997). In particular, state provided early years’ programmes such as Head Start in the United States and Early Start in Ireland have been established to combat educational disadvantage for children experiencing poverty and socio-economic inequality. The focus of this research is on the long-term outcomes of an early years’ intervention programme in Ireland. It aims to assess whether participation in the programme enhances the life course of children at-risk of educational disadvantage. It involves an in-depth analysis of one Early Start project which was included in the original eight projects established by the Department of Education and Science in 1994. The study utilises a multi-group design to provide a detailed analysis of both the academic and social progress of programme participants. It examines programme outcomes from a number of perspectives by collecting the views of the three main stakeholders involved in the education process; students who participated in Early Start in 1994/5, their parents and their teachers. To contribute to understanding the impact of the programme from a community perspective interviews were also conducted with local community educators and other local early years’ services. In general, Early Start was perceived by all participants in this study as making a positive contribution to parent involvement in education and to strengthening educational capital in the local area. The study found that parents and primary school teachers identified aspects of school readiness as the main benefit of participation in Early Start and parents and teachers were very positive about the role of Early Start in preparing children for the transition to formal school. In addition to this, participation in Early Start appears to have made a positive contribution to academic attainment in Maths and Science at Junior Certificate level. Students who had participated in Early Start were also rated more highly by their second level teachers in terms of goal-setting and future orientation which are important factors in educational attainment. Early Start then can be viewed as providing a positive contribution to the long-term social and academic outcomes for its participants.

Flow stabilization with active hydrodynamic cloaks

We demonstrate that fluid flow cloaking solutions, based on active hydrodynamic metamaterials, exist for two-dimensional flows past a cylinder in a wide range of Reynolds numbers (Re's), up to approximately 200. Within the framework of the classical Brinkman equation for homogenized porous flow, we demonstrate using two different methods that such cloaked flows can be dynamically stable for Re's in the range of 5-119. The first highly efficient method is based on a linearization of the Brinkman-Navier-Stokes equation and finding the eigenfrequencies of the least stable eigenperturbations; the second method is a direct numerical integration in the time domain. We show that, by suppressing the von Kármán vortex street in the weakly turbulent wake, porous flow cloaks can raise the critical Reynolds number up to about 120 or five times greater than for a bare uncloaked cylinder. © 2012 American Physical Society.

Magnetic metamaterial superlens for increased range wireless power transfer.

The ability to wirelessly power electrical devices is becoming of greater urgency as a component of energy conservation and sustainability efforts. Due to health and safety concerns, most wireless power transfer (WPT) schemes utilize very low frequency, quasi-static, magnetic fields; power transfer occurs via magneto-inductive (MI) coupling between conducting loops serving as transmitter and receiver. At the "long range" regime - referring to distances larger than the diameter of the largest loop - WPT efficiency in free space falls off as (1/d)(6); power loss quickly approaches 100% and limits practical implementations of WPT to relatively tight distances between power source and device. A "superlens", however, can concentrate the magnetic near fields of a source. Here, we demonstrate the impact of a magnetic metamaterial (MM) superlens on long-range near-field WPT, quantitatively confirming in simulation and measurement at 13-16 MHz the conditions under which the superlens can enhance power transfer efficiency compared to the lens-less free-space system.

Observations of spatial flow patterns at the coral colony scale on a shallow reef flat

Although small-scale spatial flow variability can affect both larger-scale circulation patterns and biological processes on coral reefs, there are few direct measurements of spatial flow patterns across horizontal scales <100 m. Here flow patterns on a shallow reef flat were measured at scales from a single colony to several adjacent colonies using an array of acoustic Doppler velocimeters on a diver-operated traverse. We observed recirculation zones immediately behind colonies, reduced currents and elevated dissipation rates in turbulent wakes up to 2 colony diameters downstream and enhanced Reynolds stresses in shear layers around wake peripheries. Flow acceleration zones were observed above and between colonies. Coherent flow structures varied with incident flow speeds; recirculation zones were stronger and wakes were more turbulent in faster flows. Low-frequency (<0.03 Hz) flow variations, for which water excursions were large compared with the colony diameters (Keulegan-Carpenter number, KC >1), had similarspatial patterns to wakes, while higher-frequency variations (0.05-0.1 Hz, KC<1) had no observable spatial structure. On the reef flat, both drag and inertial forces exerted by coral colonies could have significant effects on flow, but within different frequency ranges; drag dominates for low-frequency flow variations and inertial forces dominate for higher frequency variations, including the wave band. Our scaling analyses suggest that spatial flow patterns at colony and patch scales could have important implications or both physical and biological processes at larger reef scales through their effects on forces exerted on the flow, turbulent mixing, and dispersion. © 2013. American Geophysical Union. All Rights Reserved.

Development of numerical techniques for near-field aeroacoustic computations

This work is concerned with the development of a numerical scheme capable of producing accurate simulations of sound propagation in the presence of a mean flow field. The method is based on the concept of variable decomposition, which leads to two separate sets of equations. These equations are the linearised Euler equations and the Reynolds-averaged Navier–Stokes equations. This paper concentrates on the development of numerical schemes for the linearised Euler equations that leads to a computational aeroacoustics (CAA) code. The resulting CAA code is a non-diffusive, time- and space-staggered finite volume code for the acoustic perturbation, and it is validated against analytic results for pure 1D sound propagation and 2D benchmark problems involving sound scattering from a cylindrical obstacle. Predictions are also given for the case of prescribed source sound propagation in a laminar boundary layer as an illustration of the effects of mean convection. Copyright © 1999 John Wiley & Sons, Ltd.

A semi-Lagrangian finite volume method for Newtonian contraction flows

A new finite volume method for solving the incompressible Navier--Stokes equations is presented. The main features of this method are the location of the velocity components and pressure on different staggered grids and a semi-Lagrangian method for the treatment of convection. An interpolation procedure based on area-weighting is used for the convection part of the computation. The method is applied to flow through a constricted channel, and results are obtained for Reynolds numbers, based on half the flow rate, up to 1000. The behavior of the vortex in the salient corner is investigated qualitatively and quantitatively, and excellent agreement is found with the numerical results of Dennis and Smith [Proc. Roy. Soc. London A, 372 (1980), pp. 393-414] and the asymptotic theory of Smith [J. Fluid Mech., 90 (1979), pp. 725-754].