On the Behavior of the Homogeneous Self-Dual Model for Conic Convex Optimization

There is a natural norm associated with a starting point of the homogeneous self-dual (HSD) embedding model for conic convex optimization. In this norm two measures of the HSD model’s behavior are precisely controlled independent of the problem instance: (i) the sizes of ε-optimal solutions, and (ii) the maximum distance of ε-optimal solutions to the boundary of the cone of the HSD variables. This norm is also useful in developing a stopping-rule theory for HSD-based interior-point methods such as SeDuMi. Under mild assumptions, we show that a standard stopping rule implicitly involves the sum of the sizes of the ε-optimal primal and dual solutions, as well as the size of the initial primal and dual infeasibility residuals. This theory suggests possible criteria for developing starting points for the homogeneous self-dual model that might improve the resulting solution time in practice

Determination of dissociation constants of Strychnos alkaloids from Strychnos nux-vomica L. by capillary electrophoresis

An accurate capillary electrophoresis method was developed for the determination of dissociation constants of five Strychnos alkaloids from Strychnos nux-vomica L. The method relies on measuring the effective mobility of the solute as a function of the buffer pH. The mathematical relationship was strictly derived from the fundamental electrophoresis theory and the dissociation equilibrium of a weak base without any simplifications. Careful optimization of the running buffer permitted base-line resolution of the five structurally similar alkaloids.

Determination of the optical band-gap energy of cubic and hexagonal boron nitride using luminescence excitation spectroscopy

Sponsorship: EPSRC, STFC

Getting Constitutional Theory into Proportion: A Matter of Interpretation?

Gibbs, N., Getting Constitutional Theory into Proportion: A Matter of Interpretation?, Oxford Journal of Legal Studies, 27 (1), 175-191. RAE2008

The Oscillatory Squeeze flow rheometer: Comprehensive theory and a new experimental facility

Binding, David; Bell, D.; Walters, K., (2006) 'The Oscillatory Squeeze flow rheometer: Comprehensive theory and a new experimental facility', Rheologica Acta 46 pp.111-121 RAE2008

Constructing Universities as Organizations: University Reforms in Poland in the Light of Institutional Theory

This chapter shows that apart from changes at the systemic and institutional levels, successful reform implementation struggles with a gradual change in academic beliefs, attitudes and behaviours. Currently, visions of the university proposed by the Polish academic community and visions of it proposed by Polish reformers and policymakers (within ongoing reforms) are worlds apart. I shall study recent reforms in the context of specific academic self--protective narratives being produced in the last two decades (at the collective level of the academic profession) and in the context of the Ivory Tower university ideals predominant at the individual level (as studied comparatively through a large--scale European survey of the academic profession). Institutions change both swiftly, radically – and slowly, gradually. Research literature on institutional change until recently was focused almost exclusively on the role of radical changes caused by external shocks, leading to radical institutional reconfigurations. And research literature about the gradual, incremental institutional change have been emergent for about a decade and a half now (Mahoney and Thelen 2010; Streeck and Thelen 2005, 2009; Thelen 2003). Polish higher education provides interesting empirical grounds to test institutional theories. Both types of transformations (radical and gradual) may lead to equally permanent changes in the functioning of institutions, equally deep transformations of their fundamental rules, norms and operating procedures. Questions about institutional change are questions about characteristics of institutions undergoing changes. Endogenous institutional change is as important as exogenous change (Mahoney and Thelen 2010: 3). Moments in which there emerge opportunities of performing deep institutional reforms are short (in Poland these moments occurred in 2009-2012), and between them there are long periods of institutional stasis and stability (Pierson 2004: 134-135). The premises of theories of institutional change can be applied systematically to a system of higher education which shows an unprecedented rate of change and which is exposed to broad, fundamental reform programmes. There are many ways to discuss the Kudrycka reforms - and "constructing Polish universities as organizations" (rather than traditional academic "institutions") is one of more promising. In this account, Polish universities are under construction as organizations, and under siege as institutions. They are being rationalized as organizations, following instrumental rather than institutional logics. Polish academics in their views and attitudes are still following an institutional logic, while Polish reforms are following the new (New Public Management-led) instrumental logics. Both are on a collision course about basic values. Reforms and reformees seem to be worlds apart. I am discussing the the two contrasting visions of the university and describing the Kudrycka reforms as the reistitutionalization of the research mission of Polish universities. The core of reforms is a new level of funding and governance - the intermediary one (and no longer the state one), with four new peer-run institutions, with the KEJN, PKA and NCN in the lead. Poland has been beginning to follow the "global rules of the academic game" since 2009. I am also discussing two academic self-protection modes agains reforms: (Polish) "national academic traditions" and "institutional exceptionalism" (of Polish HE). Both discourses prevailed for two decades, none seems socially (and politically) acceptable any more. Old myths do not seem to fit new realities. In this context I am discussing briefly and through large-scale empirical data the low connectedness to the outside world of Polish HE institutions, low influence of the government on HE policies and the low level of academic entrepreneurialism, as seen through the EUROAC/CAP micro-level data. The conclusion is that the Kudrycka reforms are an imporant first step only - Poland is too slow in reforms, and reforms are both underfunded and inconsistent. Poland is still accumulating disadvantages as public funding and university reforms have not reached a critical point. Ever more efforts lead to ever less results, as macro-level data show. Consequently, it may be useful to construct universities as organizations in Poland to a higher degree than elsewhere in Europe, and especially in Western Europe.

Self-Organizing Hierarchical Knowledge Discovery by an ARTMAP Image Fusion System

Classifying novel terrain or objects front sparse, complex data may require the resolution of conflicting information from sensors working at different times, locations, and scales, and from sources with different goals and situations. Information fusion methods can help resolve inconsistencies, as when evidence variously suggests that an object's class is car, truck, or airplane. The methods described here consider a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an object's class is car, vehicle, and man-made. Underlying relationships among objects are assumed to be unknown to the automated system or the human user. The ARTMAP information fusion system used distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierarchical knowledge structures. The system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships.

Self-Organizing Information Fusion and Hierarchical Knowledge Discovery: A New Framework Using Artmap Neural Networks

Classifying novel terrain or objects from sparse, complex data may require the resolution of conflicting information from sensors woring at different times, locations, and scales, and from sources with different goals and situations. Information fusion methods can help resolve inconsistencies, as when eveidence variously suggests that and object's class is car, truck, or airplane. The methods described her address a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an object's class is car, vehicle, and man-made. Underlying relationships among classes are assumed to be unknown to the autonomated system or the human user. The ARTMAP information fusion system uses distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierachical knowlege structures. The fusion system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships. The procedure is illustrated with two image examples, but is not limited to image domain.

Self-Supervised ARTMAP

Computational models of learning typically train on labeled input patterns (supervised learning), unlabeled input patterns (unsupervised learning), or a combination of the two (semisupervised learning). In each case input patterns have a fixed number of features throughout training and testing. Human and machine learning contexts present additional opportunities for expanding incomplete knowledge from formal training, via self-directed learning that incorporates features not previously experienced. This article defines a new self-supervised learning paradigm to address these richer learning contexts, introducing a neural network called self-supervised ARTMAP. Self-supervised learning integrates knowledge from a teacher (labeled patterns with some features), knowledge from the environment (unlabeled patterns with more features), and knowledge from internal model activation (self-labeled patterns). Self-supervised ARTMAP learns about novel features from unlabeled patterns without destroying partial knowledge previously acquired from labeled patterns. A category selection function bases system predictions on known features, and distributed network activation scales unlabeled learning to prediction confidence. Slow distributed learning on unlabeled patterns focuses on novel features and confident predictions, defining classification boundaries that were ambiguous in the labeled patterns. Self-supervised ARTMAP improves test accuracy on illustrative lowdimensional problems and on high-dimensional benchmarks. Model code and benchmark data are available from: http://techlab.bu.edu/SSART/.

Sequential Memory with ART: A Self-Organizing Network Capable of Learning Sequences of Patterns

A model which extends the adaptive resonance theory model to sequential memory is presented. This new model learns sequences of events and recalls a sequence when presented with parts of the sequence. A sequence can have repeated events and different sequences can share events. The ART model is modified by creating interconnected sublayers within ART's F2 layer. Nodes within F2 learn temporal patterns by forming recency gradients within LTM. Versions of the ART model like ART I, ART 2, and fuzzy ART can be used.

Towards a Unified Theory of Neocortex: Laminar Cortical Circuits for Vision and Cognition

A key goal of computational neuroscience is to link brain mechanisms to behavioral functions. The present article describes recent progress towards explaining how laminar neocortical circuits give rise to biological intelligence. These circuits embody two new and revolutionary computational paradigms: Complementary Computing and Laminar Computing. Circuit properties include a novel synthesis of feedforward and feedback processing, of digital and analog processing, and of pre-attentive and attentive processing. This synthesis clarifies the appeal of Bayesian approaches but has a far greater predictive range that naturally extends to self-organizing processes. Examples from vision and cognition are summarized. A LAMINART architecture unifies properties of visual development, learning, perceptual grouping, attention, and 3D vision. A key modeling theme is that the mechanisms which enable development and learning to occur in a stable way imply properties of adult behavior. It is noted how higher-order attentional constraints can influence multiple cortical regions, and how spatial and object attention work together to learn view-invariant object categories. In particular, a form-fitting spatial attentional shroud can allow an emerging view-invariant object category to remain active while multiple view categories are associated with it during sequences of saccadic eye movements. Finally, the chapter summarizes recent work on the LIST PARSE model of cognitive information processing by the laminar circuits of prefrontal cortex. LIST PARSE models the short-term storage of event sequences in working memory, their unitization through learning into sequence, or list, chunks, and their read-out in planned sequential performance that is under volitional control. LIST PARSE provides a laminar embodiment of Item and Order working memories, also called Competitive Queuing models, that have been supported by both psychophysical and neurobiological data. These examples show how variations of a common laminar cortical design can embody properties of visual and cognitive intelligence that seem, at least on the surface, to be mechanistically unrelated.

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.

Thomas Davis's education policies: theory and practice

This thesis explores the education policies of Thomas Davis. On the eve of the Great Famine Ireland was economically impoverished and politically dependent. The Irish people had a subservient mentality, were mainly uneducated and were unaware of their potential. He believed that education would develop a self-reliant, self-sufficient people; it would create a new generation of leaders and citizens necessary to transform Ireland into a prosperous, independent nation. This thesis explores his education philosophy which was political in orientation; he called for reform of university education so that it would educate leaders who were knowledgeable, patriotic and responsible. He formulated a curriculum which consisted of knowledge that would have direct use and application in public life; his curriculum included moral philosophy, oratory, philological studies and history. His contribution to the debate on the Queens Colleges bill, 1845, is explored including his public disagreement with Daniel O’Connell on the principle of multi-denominational education. This work also examines his policies on learning methodologies and teaching methods. It provides details of his thoughts on learning by experience, by observation, book learning and learning in the home. It focuses on the deficiencies evident in the system of teaching and learning that operated in Trinity College Dublin and it provides an analysis of his preferred method of instruction: Lyceum teaching. This thesis also explores his national curriculum in history and Irish culture which was designed to forge a sense of national identity, to win support for repeal and to develop the principle of nationality. He formulated a national curriculum to counteract the absence of national knowledge in the state schools, to provide the people with a positive self-image and ultimately to empower them to reclaim Ireland and to develop it. Davis knew the power of education and he used it as an instrument of political and social change.

A theory of buyer-seller networks

This paper introduces a new model of exchange: networks, rather than markets, of buyers and sellers. It begins with the empirically motivated premise that a buyer and seller must have a relationship, a "link," to exchange goods. Networks - buyers, sellers, and the pattern of links connecting them - are common exchange environments. This paper develops a methodology to study network structures and explains why agents may form networks. In a model that captures characteristics of a variety of industries, the paper shows that buyers and sellers, acting strategically in their own self-interests, can form the network structures that maximize overall welfare.

Accelerating self-consistent field convergence with the augmented Roothaan-Hall energy function.

Based on Pulay's direct inversion iterative subspace (DIIS) approach, we present a method to accelerate self-consistent field (SCF) convergence. In this method, the quadratic augmented Roothaan-Hall (ARH) energy function, proposed recently by Høst and co-workers [J. Chem. Phys. 129, 124106 (2008)], is used as the object of minimization for obtaining the linear coefficients of Fock matrices within DIIS. This differs from the traditional DIIS of Pulay, which uses an object function derived from the commutator of the density and Fock matrices. Our results show that the present algorithm, abbreviated ADIIS, is more robust and efficient than the energy-DIIS (EDIIS) approach. In particular, several examples demonstrate that the combination of ADIIS and DIIS ("ADIIS+DIIS") is highly reliable and efficient in accelerating SCF convergence.