177,590 research outputs found

    Adaptive Resonance: An Emerging Neural Theory of Cognition

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    Adaptive resonance is a theory of cognitive information processing which has been realized as a family of neural network models. In recent years, these models have evolved to incorporate new capabilities in the cognitive, neural, computational, and technological domains. Minimal models provide a conceptual framework, for formulating questions about the nature of cognition; an architectural framework, for mapping cognitive functions to cortical regions; a semantic framework, for precisely defining terms; and a computational framework, for testing hypotheses. These systems are here exemplified by the distributed ART (dART) model, which generalizes localist ART systems to allow arbitrarily distributed code representations, while retaining basic capabilities such as stable fast learning and scalability. Since each component is placed in the context of a unified real-time system, analysis can move from the level of neural processes, including learning laws and rules of synaptic transmission, to cognitive processes, including attention and consciousness. Local design is driven by global functional constraints, with each network synthesizing a dynamic balance of opposing tendencies. The self-contained working ART and dART models can also be transferred to technology, in areas that include remote sensing, sensor fusion, and content-addressable information retrieval from large databases.Office of Naval Research and the defense Advanced Research Projects Agency (N00014-95-1-0409, N00014-1-95-0657); National Institutes of Health (20-316-4304-5

    Modelling of spectral properties and population kinetics studies of inertial fusión and laboratory astrophysical plasmas

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    Fundamental research and modelling in plasma atomic physics continue to be essential for providing basic understanding of many different topics relevant to high-energy-density plasmas. The Atomic Physics Group at the Institute of Nuclear Fusion has accumulated experience over the years in developing a collection of computational models and tools for determining the atomic energy structure, ionization balance and radiative properties of, mainly, inertial fusion and laser-produced plasmas in a variety of conditions. In this work, we discuss some of the latest advances and results of our research, with emphasis on inertial fusion and laboratory-astrophysical applications

    Biologically Inspired Approaches to Automated Feature Extraction and Target Recognition

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    Ongoing research at Boston University has produced computational models of biological vision and learning that embody a growing corpus of scientific data and predictions. Vision models perform long-range grouping and figure/ground segmentation, and memory models create attentionally controlled recognition codes that intrinsically cornbine botton-up activation and top-down learned expectations. These two streams of research form the foundation of novel dynamically integrated systems for image understanding. Simulations using multispectral images illustrate road completion across occlusions in a cluttered scene and information fusion from incorrect labels that are simultaneously inconsistent and correct. The CNS Vision and Technology Labs (cns.bu.edulvisionlab and cns.bu.edu/techlab) are further integrating science and technology through analysis, testing, and development of cognitive and neural models for large-scale applications, complemented by software specification and code distribution.Air Force Office of Scientific Research (F40620-01-1-0423); National Geographic-Intelligence Agency (NMA 201-001-1-2016); National Science Foundation (SBE-0354378; BCS-0235298); Office of Naval Research (N00014-01-1-0624); National Geospatial-Intelligence Agency and the National Society of Siegfried Martens (NMA 501-03-1-2030, DGE-0221680); Department of Homeland Security graduate fellowshi

    DISCOV: A Neural Model of Colour Vision, with Applications to Image Processing and Classification

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    The DISCOV (Dimensionless Shunting Colour Vision) system models a cascade of primate colour vision cells: retinal ganglion, thalamic single opponent, and two classes of cortical double opponents. A unified model fotmalism derived from psychophysical axioms produces transparent network dynamics and principled parameter settings. DISCOV fits an array of physiological data for each cell type, and makes testable experimental predictions. Properties of DISCOV model cells are compared with properties of conesponding components in the alternative Neural Fusion model. A benchmark testbed demonstrates the marginal computational utility of each model cell type on a recognition task derived from orthophoto imagery.Air Force Office of Scientific Research (F49620-01-1-0423); National Geospatial-Intelligence Agency (NMA 201-01-1-2016); National Science Foundation (SBE-035437, DGE-0221680); Office of Naval Research (N00014-01-1-0624

    Subjective logic operators in trust assessment: an empirical study

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    Computational trust mechanisms aim to produce trust ratings from both direct and indirect information about agents' behaviour. Subjective Logic (SL) has been widely adopted as the core of such systems via its fusion and discount operators. In recent research we revisited the semantics of these operators to explore an alternative, geometric interpretation. In this paper we present principled desiderata for discounting and fusion operators in SL. Building upon this we present operators that satisfy these desirable properties, including a family of discount operators. We then show, through a rigorous empirical study, that specific, geometrically interpreted, operators significantly outperform standard SL operators in estimating ground truth. These novel operators offer real advantages for computational models of trust and reputation, in which they may be employed without modifying other aspects of an existing system

    Rayleigh—Taylor-Instability Evolution in Colliding-Plasma-Jet Experiments with Magnetic and Viscous Stabilization

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    The Rayleigh—Taylor instability causes mixing in plasmas throughout the universe, from micron-scale plasmas in inertial confinement fusion implosions to parsec-scale supernova remnants. The evolution of this interchange instability in a plasma is influenced by the presence of viscosity and magnetic fields, both of which have the potential to stabilize short-wavelength modes. Very few experimental observations of Rayleigh—Taylor growth in plasmas with stabilizing mechanisms are reported in the literature, and those that are reported are in sub-millimeter scale plasmas that are difficult to diagnose. Experimental observations in well-characterized plasmas are important for validation of computational models used to make design predictions for inertial confinement fusion efforts. This dissertation presents observations of instability growth during the interaction between a high Mach-number, initially unmagnetized plasma jet and a stagnated, magnetized plasma. A multi-frame fast camera captures Rayleigh—Taylor-instability growth while interferometry, spectroscopy, photodiode, and magnetic probe diagnostics are employed to estimate plasma parameters in the vicinity of the collision. As the instability grows, an evolution to longer mode wavelength is observed. Comparisons of experimental data with idealized magnetohydrodynamic simulations including a physical viscosity model suggest that the observed instability evolution is consistent with both magnetic and viscous stabilization. These data provide the opportunity to benchmark computational models used in astrophysics and fusion research

    Sparse Regression with Multi-type Regularized Feature Modeling

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    Within the statistical and machine learning literature, regularization techniques are often used to construct sparse (predictive) models. Most regularization strategies only work for data where all predictors are treated identically, such as Lasso regression for (continuous) predictors treated as linear effects. However, many predictive problems involve different types of predictors and require a tailored regularization term. We propose a multi-type Lasso penalty that acts on the objective function as a sum of subpenalties, one for each type of predictor. As such, we allow for predictor selection and level fusion within a predictor in a data-driven way, simultaneous with the parameter estimation process. We develop a new estimation strategy for convex predictive models with this multi-type penalty. Using the theory of proximal operators, our estimation procedure is computationally efficient, partitioning the overall optimization problem into easier to solve subproblems, specific for each predictor type and its associated penalty. Earlier research applies approximations to non-differentiable penalties to solve the optimization problem. The proposed SMuRF algorithm removes the need for approximations and achieves a higher accuracy and computational efficiency. This is demonstrated with an extensive simulation study and the analysis of a case-study on insurance pricing analytics

    A Survey on Semantic Processing Techniques

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    Semantic processing is a fundamental research domain in computational linguistics. In the era of powerful pre-trained language models and large language models, the advancement of research in this domain appears to be decelerating. However, the study of semantics is multi-dimensional in linguistics. The research depth and breadth of computational semantic processing can be largely improved with new technologies. In this survey, we analyzed five semantic processing tasks, e.g., word sense disambiguation, anaphora resolution, named entity recognition, concept extraction, and subjectivity detection. We study relevant theoretical research in these fields, advanced methods, and downstream applications. We connect the surveyed tasks with downstream applications because this may inspire future scholars to fuse these low-level semantic processing tasks with high-level natural language processing tasks. The review of theoretical research may also inspire new tasks and technologies in the semantic processing domain. Finally, we compare the different semantic processing techniques and summarize their technical trends, application trends, and future directions.Comment: Published at Information Fusion, Volume 101, 2024, 101988, ISSN 1566-2535. The equal contribution mark is missed in the published version due to the publication policies. Please contact Prof. Erik Cambria for detail
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