Vector Associative Maps: Unsupervised Real-time Error-based Learning and Control of Movement Trajectories

This article describes neural network models for adaptive control of arm movement trajectories during visually guided reaching and, more generally, a framework for unsupervised real-time error-based learning. The models clarify how a child, or untrained robot, can learn to reach for objects that it sees. Piaget has provided basic insights with his concept of a circular reaction: As an infant makes internally generated movements of its hand, the eyes automatically follow this motion. A transformation is learned between the visual representation of hand position and the motor representation of hand position. Learning of this transformation eventually enables the child to accurately reach for visually detected targets. Grossberg and Kuperstein have shown how the eye movement system can use visual error signals to correct movement parameters via cerebellar learning. Here it is shown how endogenously generated arm movements lead to adaptive tuning of arm control parameters. These movements also activate the target position representations that are used to learn the visuo-motor transformation that controls visually guided reaching. The AVITE model presented here is an adaptive neural circuit based on the Vector Integration to Endpoint (VITE) model for arm and speech trajectory generation of Bullock and Grossberg. In the VITE model, a Target Position Command (TPC) represents the location of the desired target. The Present Position Command (PPC) encodes the present hand-arm configuration. The Difference Vector (DV) population continuously.computes the difference between the PPC and the TPC. A speed-controlling GO signal multiplies DV output. The PPC integrates the (DV)·(GO) product and generates an outflow command to the arm. Integration at the PPC continues at a rate dependent on GO signal size until the DV reaches zero, at which time the PPC equals the TPC. The AVITE model explains how self-consistent TPC and PPC coordinates are autonomously generated and learned. Learning of AVITE parameters is regulated by activation of a self-regulating Endogenous Random Generator (ERG) of training vectors. Each vector is integrated at the PPC, giving rise to a movement command. The generation of each vector induces a complementary postural phase during which ERG output stops and learning occurs. Then a new vector is generated and the cycle is repeated. This cyclic, biphasic behavior is controlled by a specialized gated dipole circuit. ERG output autonomously stops in such a way that, across trials, a broad sample of workspace target positions is generated. When the ERG shuts off, a modulator gate opens, copying the PPC into the TPC. Learning of a transformation from TPC to PPC occurs using the DV as an error signal that is zeroed due to learning. This learning scheme is called a Vector Associative Map, or VAM. The VAM model is a general-purpose device for autonomous real-time error-based learning and performance of associative maps. The DV stage serves the dual function of reading out new TPCs during performance and reading in new adaptive weights during learning, without a disruption of real-time operation. YAMs thus provide an on-line unsupervised alternative to the off-line properties of supervised error-correction learning algorithms. YAMs and VAM cascades for learning motor-to-motor and spatial-to-motor maps are described. YAM models and Adaptive Resonance Theory (ART) models exhibit complementary matching, learning, and performance properties that together provide a foundation for designing a total sensory-cognitive and cognitive-motor autonomous system.

Dynamic Programming for Optimal Control of Set-Up Scheduling with Neural Network Modifications

This paper demonstrates an optimal control solution to change of machine set-up scheduling based on dynamic programming average cost per stage value iteration as set forth by Cararnanis et. al. [2] for the 2D case. The difficulty with the optimal approach lies in the explosive computational growth of the resulting solution. A method of reducing the computational complexity is developed using ideas from biology and neural networks. A real time controller is described that uses a linear-log representation of state space with neural networks employed to fit cost surfaces.

A Neural Network for Synthesizing the Pitch of an Acoustic Source

This article describes a neural network model capable of generating a spatial representation of the pitch of an acoustic source. Pitch is one of several auditory percepts used by humans to separate multiple sound sources in the environment from each other. The model provides a neural instantiation of a type of "harmonic sieve". It is capable of quantitatively simulating a large body of psychoacoustical data, including new data on octave shift perception.

Genetically Generated Neural Networks II: Searching for an Optimal Representation

Genetic Algorithms (GAs) make use of an internal representation of a given system in order to perform optimization functions. The actual structural layout of this representation, called a genome, has a crucial impact on the outcome of the optimization process. The purpose of this paper is to study the effects of different internal representations in a GA, which generates neural networks. A second GA was used to optimize the genome structure. This structure produces an optimized system within a shorter time interval.

A Spectral Network Model of Pitch Perception

A model of pitch perception, called the Spatial Pitch Network or SPINET model, is developed and analyzed. The model neurally instantiates ideas front the spectral pitch modeling literature and joins them to basic neural network signal processing designs to simulate a broader range of perceptual pitch data than previous spectral models. The components of the model arc interpreted as peripheral mechanical and neural processing stages, which arc capable of being incorporated into a larger network architecture for separating multiple sound sources in the environment. The core of the new model transforms a spectral representation of an acoustic source into a spatial distribution of pitch strengths. The SPINET model uses a weighted "harmonic sieve" whereby the strength of activation of a given pitch depends upon a weighted sum of narrow regions around the harmonics of the nominal pitch value, and higher harmonics contribute less to a pitch than lower ones. Suitably chosen harmonic weighting functions enable computer simulations of pitch perception data involving mistuned components, shifted harmonics, and various types of continuous spectra including rippled noise. It is shown how the weighting functions produce the dominance region, how they lead to octave shifts of pitch in response to ambiguous stimuli, and how they lead to a pitch region in response to the octave-spaced Shepard tone complexes and Deutsch tritones without the use of attentional mechanisms to limit pitch choices. An on-center off-surround network in the model helps to produce noise suppression, partial masking and edge pitch. Finally, it is shown how peripheral filtering and short term energy measurements produce a model pitch estimate that is sensitive to certain component phase relationships.

A Self-Organizing Neural Model of Motor Equivalent Reaching and Tool Use by a Multijoint Arm

This paper describes a self-organizing neural model for eye-hand coordination. Called the DIRECT model, it embodies a solution of the classical motor equivalence problem. Motor equivalence computations allow humans and other animals to flexibly employ an arm with more degrees of freedom than the space in which it moves to carry out spatially defined tasks under conditions that may require novel joint configurations. During a motor babbling phase, the model endogenously generates movement commands that activate the correlated visual, spatial, and motor information that are used to learn its internal coordinate transformations. After learning occurs, the model is capable of controlling reaching movements of the arm to prescribed spatial targets using many different combinations of joints. When allowed visual feedback, the model can automatically perform, without additional learning, reaches with tools of variable lengths, with clamped joints, with distortions of visual input by a prism, and with unexpected perturbations. These compensatory computations occur within a single accurate reaching movement. No corrective movements are needed. Blind reaches using internal feedback have also been simulated. The model achieves its competence by transforming visual information about target position and end effector position in 3-D space into a body-centered spatial representation of the direction in 3-D space that the end effector must move to contact the target. The spatial direction vector is adaptively transformed into a motor direction vector, which represents the joint rotations that move the end effector in the desired spatial direction from the present arm configuration. Properties of the model are compared with psychophysical data on human reaching movements, neurophysiological data on the tuning curves of neurons in the monkey motor cortex, and alternative models of movement control.

The impacts of past land-use on the ecology of an ancient woodland in south-west Ireland

A multi-disciplinary study was conducted to compare stands of ancient and secondary origin within a single wood, the Gearagh woodland, County Cork. These sites were compared with adjacent areas of grassland, which provided a reference for the former land-use (pasture) of the secondary woodland. A historical study confirmed that while the core of the Gearagh has been subject to minimal human interference, other sections have been cleared in the past for agricultural purposes. Investigations into soil structure and composition showed that soil properties in these secondary woodland areas were significantly altered by this past woodland clearance and conversion to agriculture, while the soil of the ancient woodland showed little signs of disturbance. The vegetation community also differed between the two woodland areas, partly due to altered environmental conditions. Many of the ancient woodland plant species were unable to form a persistent seed bank, while there was increased representation of species associated with more open-habitat conditions in the seed bank of the secondary woodland. While germination of woodland species was low in all sites, overall, seeds tended to germinate more successfully in the ancient woodland. The ancient woodland also provided a suitable habitat for many soil and ground detritivores, most notably enchytraeids, although earthworms were not abundant. Past agricultural use, however, changed the decomposer community considerably, with increased representation of earthworm species and a decline in the abundance of enchytraeids in the secondary stands. In conclusion, the legacies of historical agricultural activities can continue to significantly affect the structure and composition of present-day woodlands so that they may differ considerably from undisturbed ancient woodland stands, even within the same woodland. A greater understanding of the origin, development and ecological functioning of ancient woodlands should aid in determining future conservation and management requirements.

Towards a regional understanding of Irish traditional music

The geography of Irish traditional music is a complex, popular and largely unexplored element of the narrative of the tradition. Geographical concepts such as the region are recurrent in the discourse of Irish traditional music but regions and their processes are, for the most part, blurred or misunderstood. This thesis explores the geographical approach to the study of Irish traditional music focusing on the concept of the region and, in particular, the role of memory in the construction and diffusion of regional identities. This is a tripartite study considering people, place and music. Each of these elements impacts on our experience of the other. All societies have created music. Music is often associated with or derived from places. Some places construct or reinforce their identity through the music and musicians through which they are associated. The thesis challenges conventional discourse on regional styles that construct an imagined pattern of regions based on subtle musical differences that may, though are not always, shared by people in that region and focuses on the social networks through which the music is disseminated. The thesis also challenges the abandonment of regional styles and the concept of regions in understanding the complex geographies of Irish traditional music (Morton, 2001). It seeks to find a middle ground between discourse analysis, musical analysis, the experience of music and place, and the representation of music and place. The dissertation is divided into three parts. Part one considers the development of music geography, noting and critiquing the abandonment of useful paradigms in both geography and ethnomusicology in search of new ways of understanding. Of particular interest is the concept of the region but it also considers the study of landscape and the humanist approach in cultural geography. The second part focuses on the discourse and study of regions in Irish traditional music and the various agents and processes that shape the concept of the region in Irish traditional music. The final part presents a case study of the Sliabh Luachra region combining and applying the various perspectives and paradigms drawn from geographical, ethnomusicological and anthropological sources. It attempts to generate an understanding of Sliabh Luachra as a region in the Irish traditional music narrative that is based on a combination of musical, socio-cultural and locational/environmental factors.

The early years in Irish multigrade classes: trajectories of practice and identity

This study investigates how the experiences of Junior Infants are shaped in multigrade classes. Multigrade classes are composed of two or more grades within the same classroom with one teacher having responsibility for the instruction of all grades in this classroom within a time-tabled period (Little, 2001, Mason and Doepner, 1998). The overall aim of the research is to problematize the issues of early childhood pedagogy in multigrade classes in the context of children negotiating identities, positioning and power relations. A Case Study approach was employed to explore the perspectives of the teachers, children and their parents in eight multigrade schools. Concurrent with this, a nation-wide Questionnaire Survey was also conducted which gave a broader context to the case study findings. Findings from the research study suggest that institutional context is vitally important and finding the space to implement pedagogic practices is a highly complex matter for teachers. While a majority of teachers reported the benefits for younger children being in mixed-age settings alongside older children, only a minority of case study school teachers demonstrated how it is possible to promote classroom climates which were provided multiple opportunities for younger children to engage fully in classrooms. The findings reveal constraints on pedagogical practice which included: time pressures within the job, an increase in diversity in pupil population, meeting special needs, large class sizes, high pupil/teacher ratios, and planning/organisation of tasks which intensified the complexities of addressing the needs of children who differ significantly in age, cognitive, social and emotional levels. An emergent and recurrent theme of this study is the representation of Junior Infants as apprentices in their ‘communities of practice’ who contributed in peripheral ways to the practices of their groups (Lave and Wenger, 1991, Wenger, 1998). Through a continuous process of negotiation of meaning, these pupils learned the knowledge and skills within their communities of practice that empowered some to participate more fully than others. The children in their ‘figured worlds’ (Holland, Lachiotte, Skinner and Caine 1998) occupy identities which are influenced by established arrangements of resources and practices within that community as well as by their own agentive actions. Finally, the findings of the study also demonstrate how the dimension of power is central to the exercise of social relations and pedagogical practices in multigrade classes.

Manifestations of heavenly light and fire and the construction of a saint: sanctity, mission and salvation in Adomnán's Vita Columbae

This thesis examines the late seventh-century Latin Life of Columba (Vita Columbae) in a context sympathetic to the spiritual aims and formative intellectual background of its author, Adomnán of Iona. It argues that the Vita Columbae is a sophisticated work, shaped by Adomnán’s spiritual and theological concerns. This sophistication is revealed by a forensic examination of Adomnán’s representation of Columba’s sanctity through a series of miracles, in particular, miracle stories depicting divine manifestations of fire and light. This thesis considers the form and function of these miracle stories in the context of biblical, patristic and medieval interpretations of their archetypes, towards revealing the underlying influence of scriptural, hagiographical and monastic models of sanctity. Chapter one evaluates the function of the Vita Columbae, and outlines the core themes of sanctity which pervade the work, by considering the technical terminology and literary devices found in the opening prefaces in the context of the wider monastic tradition. Chapter two examines Adomnán’s use of biblical models of sanctity to establish Columba’s sanctity, and their relationship between these models and certain miraculous episodes in the Vita Columbae. Chapter three investigates Adomnán’s description of the Holy Spirit as an illuminating fire, and its significance for his portrayal of the saint, by means of a forensic examination of biblical, exegetical and hagiographical treatments of the image. Chapter four examines the missiological, soteriological and providential elements contributing to Adomnán’s portrayal of Columba’s sanctity, as conveyed through the presence of biblical models, particularly the image of the column of fire. Chapter five establishes the influence of monastic examinations of the contemplative life on Adomnán’s portrayal of Columba’s sanctity, and shows how that sanctity is confirmed in terms of his ability to contemplate divine light.

Cost savings from relaxation of operational constraints on a power system with high wind penetration

Wind energy is predominantly a nonsynchronous generation source. Large-scale integration of wind generation with existing electricity systems, therefore, presents challenges in maintaining system frequency stability and local voltage stability. Transmission system operators have implemented system operational constraints (SOCs) in order to maintain stability with high wind generation, but imposition of these constraints results in higher operating costs. A mixed integer programming tool was used to simulate generator dispatch in order to assess the impact of various SOCs on generation costs. Interleaved day-ahead scheduling and real-time dispatch models were developed to allow accurate representation of forced outages and wind forecast errors, and were applied to the proposed Irish power system of 2020 with a wind penetration of 32%. Savings of at least 7.8% in generation costs and reductions in wind curtailment of 50% were identified when the most influential SOCs were relaxed. The results also illustrate the need to relax local SOCs together with the system-wide nonsynchronous penetration limit SOC, as savings from increasing the nonsynchronous limit beyond 70% were restricted without relaxation of local SOCs. The methodology and results allow for quantification of the costs of SOCs, allowing the optimal upgrade path for generation and transmission infrastructure to be determined.

Standardizing clinical trials workflow representation in UML for international site comparison.

BACKGROUND: With the globalization of clinical trials, a growing emphasis has been placed on the standardization of the workflow in order to ensure the reproducibility and reliability of the overall trial. Despite the importance of workflow evaluation, to our knowledge no previous studies have attempted to adapt existing modeling languages to standardize the representation of clinical trials. Unified Modeling Language (UML) is a computational language that can be used to model operational workflow, and a UML profile can be developed to standardize UML models within a given domain. This paper's objective is to develop a UML profile to extend the UML Activity Diagram schema into the clinical trials domain, defining a standard representation for clinical trial workflow diagrams in UML. METHODS: Two Brazilian clinical trial sites in rheumatology and oncology were examined to model their workflow and collect time-motion data. UML modeling was conducted in Eclipse, and a UML profile was developed to incorporate information used in discrete event simulation software. RESULTS: Ethnographic observation revealed bottlenecks in workflow: these included tasks requiring full commitment of CRCs, transferring notes from paper to computers, deviations from standard operating procedures, and conflicts between different IT systems. Time-motion analysis revealed that nurses' activities took up the most time in the workflow and contained a high frequency of shorter duration activities. Administrative assistants performed more activities near the beginning and end of the workflow. Overall, clinical trial tasks had a greater frequency than clinic routines or other general activities. CONCLUSIONS: This paper describes a method for modeling clinical trial workflow in UML and standardizing these workflow diagrams through a UML profile. In the increasingly global environment of clinical trials, the standardization of workflow modeling is a necessary precursor to conducting a comparative analysis of international clinical trials workflows.

The role of tectonic uplift, climate, and vegetation in the long-term terrestrial phosphorous cycle

Phosphorus (P) is a crucial element for life and therefore for maintaining ecosystem productivity. Its local availability to the terrestrial biosphere results from the interaction between climate, tectonic uplift, atmospheric transport, and biotic cycling. Here we present a mathematical model that describes the terrestrial P-cycle in a simple but comprehensive way. The resulting dynamical system can be solved analytically for steady-state conditions, allowing us to test the sensitivity of the P-availability to the key parameters and processes. Given constant inputs, we find that humid ecosystems exhibit lower P availability due to higher runoff and losses, and that tectonic uplift is a fundamental constraint. In particular, we find that in humid ecosystems the biotic cycling seem essential to maintain long-term P-availability. The time-dependent P dynamics for the Franz Josef and Hawaii chronosequences show how tectonic uplift is an important constraint on ecosystem productivity, while hydroclimatic conditions control the P-losses and speed towards steady-state. The model also helps describe how, with limited uplift and atmospheric input, as in the case of the Amazon Basin, ecosystems must rely on mechanisms that enhance P-availability and retention. Our novel model has a limited number of parameters and can be easily integrated into global climate models to provide a representation of the response of the terrestrial biosphere to global change. © 2010 Author(s).

Economic return of clinical trials performed under the pediatric exclusivity program.

CONTEXT: In 1997, Congress authorized the US Food and Drug Administration (FDA) to grant 6-month extensions of marketing rights through the Pediatric Exclusivity Program if industry sponsors complete FDA-requested pediatric trials. The program has been praised for creating incentives for studies in children and has been criticized as a "windfall" to the innovator drug industry. This critique has been a substantial part of congressional debate on the program, which is due to expire in 2007. OBJECTIVE: To quantify the economic return to industry for completing pediatric exclusivity trials. DESIGN AND SETTING: A cohort study of programs conducted for pediatric exclusivity. Nine drugs that were granted pediatric exclusivity were selected. From the final study reports submitted to the FDA (2002-2004), key elements of the clinical trial design and study operations were obtained, and the cost of performing each study was estimated and converted into estimates of after-tax cash outflows. Three-year market sales were obtained and converted into estimates of after-tax cash inflows based on 6 months of additional market protection. Net economic return (cash inflows minus outflows) and net return-to-costs ratio (net economic return divided by cash outflows) for each product were then calculated. MAIN OUTCOME MEASURES: Net economic return and net return-to-cost ratio. RESULTS: The indications studied reflect a broad representation of the program: asthma, tumors, attention-deficit/hyperactivity disorder, hypertension, depression/generalized anxiety disorder, diabetes mellitus, gastroesophageal reflux, bacterial infection, and bone mineralization. The distribution of net economic return for 6 months of exclusivity varied substantially among products (net economic return ranged from -$8.9 million to $507.9 million and net return-to-cost ratio ranged from -0.68 to 73.63). CONCLUSIONS: The economic return for pediatric exclusivity is variable. As an incentive to complete much-needed clinical trials in children, pediatric exclusivity can generate lucrative returns or produce more modest returns on investment.

Task-driven adaptive statistical compressive sensing of gaussian mixture models

A framework for adaptive and non-adaptive statistical compressive sensing is developed, where a statistical model replaces the standard sparsity model of classical compressive sensing. We propose within this framework optimal task-specific sensing protocols specifically and jointly designed for classification and reconstruction. A two-step adaptive sensing paradigm is developed, where online sensing is applied to detect the signal class in the first step, followed by a reconstruction step adapted to the detected class and the observed samples. The approach is based on information theory, here tailored for Gaussian mixture models (GMMs), where an information-theoretic objective relationship between the sensed signals and a representation of the specific task of interest is maximized. Experimental results using synthetic signals, Landsat satellite attributes, and natural images of different sizes and with different noise levels show the improvements achieved using the proposed framework when compared to more standard sensing protocols. The underlying formulation can be applied beyond GMMs, at the price of higher mathematical and computational complexity. © 1991-2012 IEEE.