Development and deployment of a low-cost, mobile-ready, air quality sensor system: progress toward distributed networks and autonomous aerial sampling

NASA Astrophysics Data System (ADS)

Hersey, S. P.; DiVerdi, R.; Gadtaula, P.; Sheneman, T.; Flores, K.; Chen, Y. H.; Jayne, J. T.; Cross, E. S.

2017-12-01

Throughout the 2016-2017 academic year, a new partnership between Olin College of Engineering and Aerodyne Research, Inc. developed an affordable, self-contained air quality monitoring instrument called Modulair. The Modulair instrument is based on the same operating principles as Aerodyne's newly-developed ARISense integrated sensor system, employing electrochemical sensors for gas-phase measurements of CO, NO, NO2, and O3 and an off-the-shelf optical particle counter for particle concentration, number, and size distribution information (0.4 < dp < 17 microns). High Dimensional Model Representation (HDMR) has been used to model the interference derived from relative humidity and temperature as well as the cross-sensitivity of the electrochemical sensors to non-target gas-phase species. The aim of the modeling effort is to provide transparent and robust calibration of electrical signals to pollutant concentrations from a set of electrochemical sensors. Modulair was designed from the ground-up, with custom electronics - including a more powerful microcontroller, a fully re-designed housing and a device-specific backend with a mobile, cloud-based data management system for real-time data posting and analysis. Open source tools and software were utilized in the development of the instrument. All initial work was completed by a team of undergraduate students as part of the Senior Capstone Program in Engineering (SCOPE) at Olin College. Deployment strategies for Modulair include distributed, mobile measurements and drone-based aerial sampling. Design goals for the drone integration include maximizing airborne sampling time and laying the foundation for software integration with the drone's autopilot system to allow for autonomous plume sampling across concentration gradients. Modulair and its flexible deployments enable real-time mapping of air quality data at exposure-relevant spatial scales, as well as regular, autonomous characterization of sources and dispersion of atmospheric pollutants. We will present an overview of the Modulair instrument and results from benchtop and field validation, including mobile and drone-based plume sampling in the Boston area.

Advanced Stochastic Collocation Methods for Polynomial Chaos in RAVEN

NASA Astrophysics Data System (ADS)

Talbot, Paul W.

As experiment complexity in fields such as nuclear engineering continually increases, so does the demand for robust computational methods to simulate them. In many simulations, input design parameters and intrinsic experiment properties are sources of uncertainty. Often small perturbations in uncertain parameters have significant impact on the experiment outcome. For instance, in nuclear fuel performance, small changes in fuel thermal conductivity can greatly affect maximum stress on the surrounding cladding. The difficulty quantifying input uncertainty impact in such systems has grown with the complexity of numerical models. Traditionally, uncertainty quantification has been approached using random sampling methods like Monte Carlo. For some models, the input parametric space and corresponding response output space is sufficiently explored with few low-cost calculations. For other models, it is computationally costly to obtain good understanding of the output space. To combat the expense of random sampling, this research explores the possibilities of using advanced methods in Stochastic Collocation for generalized Polynomial Chaos (SCgPC) as an alternative to traditional uncertainty quantification techniques such as Monte Carlo (MC) and Latin Hypercube Sampling (LHS) methods for applications in nuclear engineering. We consider traditional SCgPC construction strategies as well as truncated polynomial spaces using Total Degree and Hyperbolic Cross constructions. We also consider applying anisotropy (unequal treatment of different dimensions) to the polynomial space, and offer methods whereby optimal levels of anisotropy can be approximated. We contribute development to existing adaptive polynomial construction strategies. Finally, we consider High-Dimensional Model Reduction (HDMR) expansions, using SCgPC representations for the subspace terms, and contribute new adaptive methods to construct them. We apply these methods on a series of models of increasing complexity. We use analytic models of various levels of complexity, then demonstrate performance on two engineering-scale problems: a single-physics nuclear reactor neutronics problem, and a multiphysics fuel cell problem coupling fuels performance and neutronics. Lastly, we demonstrate sensitivity analysis for a time-dependent fuels performance problem. We demonstrate the application of all the algorithms in RAVEN, a production-level uncertainty quantification framework.

Exploring the limits of knowledge on boreal peatland development using a new model: the Holocene Peatland Model

NASA Astrophysics Data System (ADS)

Quillet, Anne; Garneau, Michelle; Frolking, Steve; Roulet, Nigel; Peng, Changhui

2010-05-01

The Holocene Peatland Model (HPM) (Frolking et al. 2009, Frolking et al. in prep.) is a recently developed tool integrating up-to-date knowledge on peatland dynamics that explores peatland development and carbon dynamics on a millennial timescale. HPM combines the water and carbon cycles with net primary production and peat decomposition and takes the multiple feedbacks into account. The model remains simple and few site-specific inputs are needed. HPM simulates the transient development of the peatland and delivers peat age, peat depth, peat composition, carbon accumulation and water table depth for each simulated year. Evaluating the ability of the model to reproduce peatland development can be achieved in several manners. Commonly one could choose to compare simulations results with observations from field data. However, we argue that the overall response of the model does not give much information about the value of the model design. Modelling of peatlands dynamics requires a lot of information regarding the behaviour of a peatland system within its environment (including allogenic changes in climate, hydrological conditions, nutrient availability or autogenic processes such as microtopographical effects). The actual state of knowledge does not cover all processes, interactions or feedbacks and a lot of peatland properties are neither well defined nor measured yet, so that estimates have been needed to build the model. The work presented here aims at analyzing the role of the model parameterization on the simulation results. To do so, a sensitivity analysis is performed with a Monte-Carlo analysis and with help of the GUI-HDMR software (Ziehn and Tomlin, 2009). This method ranks the parameters and combinations of them according to their influence on simulation results. The results will emphasize how the simulation is sensitive to the parameter values. First, the distribution of outputs gives insight into the possible responses of the simulation to HPM's assemblage of current knowledge. Second, the importance of some parameters on simulation results points out certain gaps in the current understanding of peatland dynamics. Thus, this study helps determine some avenues that should be explored in future in order to improve peatlands dynamics understanding. Frolking S, NT Roulet, A Quillet, E Tuittila, JL Bubier. 2009. Simulating long-term carbon and water dynamics in northern peatlands Eos Trans. AGU, 90(52), Fall Meet. Suppl., Abstract PP12B-05. Frolking S, NT Roulet, E Tuittila, JL Bubier, A Quillet. XXXX. A new model of Holocene peatland net primary production, decomposition, and peat accumulation. in prep. Ziehn T, AS Tomlin. 2009. GUI-HDMR - A solftware tool for global sensitivity analysis of complex models. Environmental Modelling & Software, 24, 775-785.

Species richness of Eurasian Zephyrus hairstreaks (Lepidoptera: Lycaenidae: Theclini) with implications on historical biogeography: An NDM/VNDM approach

PubMed Central

Yago, Masaya; Settele, Josef; Li, Xiushan; Ueshima, Rei; Grishin, Nick V.; Wang, Min

2018-01-01

Aim A database based on distributional records of Eurasian Zephyrus hairstreaks (Lepidoptera: Lycaenidae: Theclini) was compiled to analyse their areas of endemism (AoEs), species richness and distribution patterns, to explore their locations of past glacial refugia and dispersal routes. Methods Over 2000 Zephyrus hairstreaks occurrences are analysed using the NDM/VNDM algorithm, for the recognition of AoEs. Species richness was calculated by using the option ‘Number of different classes’ to count the different classes of a variable presented in each 3.0°×3.0° grid cell, and GIS software was used to visualize distribution patterns of endemic species. Results Centres of species richness of Zephyrus hairstreaks are situated in the eastern Qinghai-Tibet Plateau (EQTP), Hengduan Mountain Region (HDMR) and the Qinling Mountain Region (QLMR). Latitudinal gradients in species richness show normal distribution with the peak between 25° N and 35° N in the temperate zone, gradually decreasing towards the poles. Moreover, most parts of central and southern China, especially the area of QLMR-EQTP-HDMR, were identified as AoEs that may have played a significant role as refugia during Quaternary global cooling. There are four major distributional patterns of Zephyrus hairstreaks in Eurasia: Sino-Japanese, Sino-Himalayan, high-mountain and a combined distribution covering all three patterns. Conclusions Zephyrus hairstreaks probably originated at least 23–24 Myr ago in E. Asia between 25° N to 35° N in the temperate zone. Cenozoic orogenies caused rapid speciation of this tribe and extrusion of the Indochina block resulted in vicariance between the Sino-Japanese and the Sino-Himalayan patterns. The four distribution patterns provided two possible dispersal directions: Sino-Japanese dispersal and Sino-Himalayan dispersal. PMID:29351314

Species richness of Eurasian Zephyrus hairstreaks (Lepidoptera: Lycaenidae: Theclini) with implications on historical biogeography: An NDM/VNDM approach.

PubMed

Zhuang, Hailing; Yago, Masaya; Settele, Josef; Li, Xiushan; Ueshima, Rei; Grishin, Nick V; Wang, Min

2018-01-01

A database based on distributional records of Eurasian Zephyrus hairstreaks (Lepidoptera: Lycaenidae: Theclini) was compiled to analyse their areas of endemism (AoEs), species richness and distribution patterns, to explore their locations of past glacial refugia and dispersal routes. Over 2000 Zephyrus hairstreaks occurrences are analysed using the NDM/VNDM algorithm, for the recognition of AoEs. Species richness was calculated by using the option 'Number of different classes' to count the different classes of a variable presented in each 3.0°×3.0° grid cell, and GIS software was used to visualize distribution patterns of endemic species. Centres of species richness of Zephyrus hairstreaks are situated in the eastern Qinghai-Tibet Plateau (EQTP), Hengduan Mountain Region (HDMR) and the Qinling Mountain Region (QLMR). Latitudinal gradients in species richness show normal distribution with the peak between 25° N and 35° N in the temperate zone, gradually decreasing towards the poles. Moreover, most parts of central and southern China, especially the area of QLMR-EQTP-HDMR, were identified as AoEs that may have played a significant role as refugia during Quaternary global cooling. There are four major distributional patterns of Zephyrus hairstreaks in Eurasia: Sino-Japanese, Sino-Himalayan, high-mountain and a combined distribution covering all three patterns. Zephyrus hairstreaks probably originated at least 23-24 Myr ago in E. Asia between 25° N to 35° N in the temperate zone. Cenozoic orogenies caused rapid speciation of this tribe and extrusion of the Indochina block resulted in vicariance between the Sino-Japanese and the Sino-Himalayan patterns. The four distribution patterns provided two possible dispersal directions: Sino-Japanese dispersal and Sino-Himalayan dispersal.

Four dimensional variational assimilation of in-situ and remote-sensing aerosol data

NASA Astrophysics Data System (ADS)

Nieradzik, L. P.; Elbern, H.

2012-04-01

Aerosols play an increasingly important role in atmospheric modelling. They have a strong influence on the radiative transfer balance and a significant impact on human health. Their origin is various and so are its effects. Most of the measurement sites in Europe account for an integrated aerosol load PMx (Particulate Matter of less than x μm in diameter) which does not give any qualitative information on the composition of the aerosol. Since very different constituents contribute to PMx, like e.g. mineral dust derived from desert storms or sea salt, it is necessary to make aerosol forecasts not only of load, but also type resolved. The method of four dimensional variational data assimilation (4Dvar) is a widely known technique to enhance forecast skills of CTMs (Chemistry-Transport-Models) by ingesting in-situ and, especially, remote-sensing measurements. The EURAD-IM (EURopean Air pollution Dispersion - Inverse Model), containing a full adjoint gas-phase model, has been expanded with an adjoint of the MADE (Modal Aerosol Dynamics model for Europe) to optimise initial and boundary values for aerosols using 4Dvar. A forward and an adjoint radiative transfer model is driven by the EURAD-IM as mapping between BLAOT (Boundary Layer Aerosol Optical Thickness) and internal aerosol species. Furthermore, its condensation scheme has been bypassed by an HDMR (High-Dimensional-Model-Representation) to ensure differentiability. In this study both in-situ measured PMx as well as satellite retrieved aerosol optical thicknesses have been assimilated and the effect on forecast performance has been investigated. The source of BLAOT is the aerosol retrieval system SYNAER (SYNergetic AErosol Retrieval) from DLR-DFD that retrieves AOT by making use of both AATSR/SCIAMACHY and AVHRR/GOME-2 data respectively. Its strengths are a large spatial coverage, near real-time availability, and the classification of five intrinsic aerosol species, namely water-solubles, water-insolubles, soot, sea salt, and mineral dust which are furthermore size resolved in terms of modes. The skill of the aerosol 4Dvar system was tested in two episodes: 1) July through August 2003, a dry period with strong wildfire activity in Europe, and 2) October through November 2008, the period of the ZEPTER-2 (Second ZEPpelin based Tropospheric photochemical chemistry expERiment) measurement campaign in the area of Lake Constance. In the latter case one-way nesting has been applied from a horizontal grid resolution of 45 km down to 5 km. Overall, the results showed a significant increase in forecast quality of tropospheric aerosol loads.

Use of electrochemical sensors for measurement of air pollution: correcting interference response and validating measurements

NASA Astrophysics Data System (ADS)

Cross, Eben S.; Williams, Leah R.; Lewis, David K.; Magoon, Gregory R.; Onasch, Timothy B.; Kaminsky, Michael L.; Worsnop, Douglas R.; Jayne, John T.

2017-09-01

The environments in which we live, work, and play are subject to enormous variability in air pollutant concentrations. To adequately characterize air quality (AQ), measurements must be fast (real time), scalable, and reliable (with known accuracy, precision, and stability over time). Lower-cost air-quality-sensor technologies offer new opportunities for fast and distributed measurements, but a persistent characterization gap remains when it comes to evaluating sensor performance under realistic environmental sampling conditions. This limits our ability to inform the public about pollution sources and inspire policy makers to address environmental justice issues related to air quality. In this paper, initial results obtained with a recently developed lower-cost air-quality-sensor system are reported. In this project, data were acquired with the ARISense integrated sensor package over a 4.5-month time interval during which the sensor system was co-located with a state-operated (Massachusetts, USA) air quality monitoring station equipped with reference instrumentation measuring the same pollutant species. This paper focuses on validating electrochemical (EC) sensor measurements of CO, NO, NO2, and O3 at an urban neighborhood site with pollutant concentration ranges (parts per billion by volume, ppb; 5 min averages, ±1σ): [CO] = 231 ± 116 ppb (spanning 84-1706 ppb), [NO] = 6.1 ± 11.5 ppb (spanning 0-209 ppb), [NO2] = 11.7 ± 8.3 ppb (spanning 0-71 ppb), and [O3] = 23.2 ± 12.5 ppb (spanning 0-99 ppb). Through the use of high-dimensional model representation (HDMR), we show that interference effects derived from the variable ambient gas concentration mix and changing environmental conditions over three seasons (sensor flow-cell temperature = 23.4 ± 8.5 °C, spanning 4.1 to 45.2 °C; and relative humidity = 50.1 ± 15.3 %, spanning 9.8-79.9 %) can be effectively modeled for the Alphasense CO-B4, NO-B4, NO2-B43F, and Ox-B421 sensors, yielding (5 min average) root mean square errors (RMSE) of 39.2, 4.52, 4.56, and 9.71 ppb, respectively. Our results substantiate the potential for distributed air pollution measurements that could be enabled with these sensors.

Standard model of knowledge representation

NASA Astrophysics Data System (ADS)

Yin, Wensheng

2016-09-01

Knowledge representation is the core of artificial intelligence research. Knowledge representation methods include predicate logic, semantic network, computer programming language, database, mathematical model, graphics language, natural language, etc. To establish the intrinsic link between various knowledge representation methods, a unified knowledge representation model is necessary. According to ontology, system theory, and control theory, a standard model of knowledge representation that reflects the change of the objective world is proposed. The model is composed of input, processing, and output. This knowledge representation method is not a contradiction to the traditional knowledge representation method. It can express knowledge in terms of multivariate and multidimensional. It can also express process knowledge, and at the same time, it has a strong ability to solve problems. In addition, the standard model of knowledge representation provides a way to solve problems of non-precision and inconsistent knowledge.

Reevaluating the two-representation model of numerical magnitude processing.

PubMed

Jiang, Ting; Zhang, Wenfeng; Wen, Wen; Zhu, Haiting; Du, Han; Zhu, Xiangru; Gao, Xuefei; Zhang, Hongchuan; Dong, Qi; Chen, Chuansheng

2016-01-01

One debate in mathematical cognition centers on the single-representation model versus the two-representation model. Using an improved number Stroop paradigm (i.e., systematically manipulating physical size distance), in the present study we tested the predictions of the two models for number magnitude processing. The results supported the single-representation model and, more importantly, explained how a design problem (failure to manipulate physical size distance) and an analytical problem (failure to consider the interaction between congruity and task-irrelevant numerical distance) might have contributed to the evidence used to support the two-representation model. This study, therefore, can help settle the debate between the single-representation and two-representation models.

Lexical is as lexical does: computational approaches to lexical representation

PubMed Central

Woollams, Anna M.

2015-01-01

In much of neuroimaging and neuropsychology, regions of the brain have been associated with ‘lexical representation’, with little consideration as to what this cognitive construct actually denotes. Within current computational models of word recognition, there are a number of different approaches to the representation of lexical knowledge. Structural lexical representations, found in original theories of word recognition, have been instantiated in modern localist models. However, such a representational scheme lacks neural plausibility in terms of economy and flexibility. Connectionist models have therefore adopted distributed representations of form and meaning. Semantic representations in connectionist models necessarily encode lexical knowledge. Yet when equipped with recurrent connections, connectionist models can also develop attractors for familiar forms that function as lexical representations. Current behavioural, neuropsychological and neuroimaging evidence shows a clear role for semantic information, but also suggests some modality- and task-specific lexical representations. A variety of connectionist architectures could implement these distributed functional representations, and further experimental and simulation work is required to discriminate between these alternatives. Future conceptualisations of lexical representations will therefore emerge from a synergy between modelling and neuroscience. PMID:25893204

Calibrating cellular automaton models for pedestrians walking through corners

NASA Astrophysics Data System (ADS)

Dias, Charitha; Lovreglio, Ruggiero

2018-05-01

Cellular Automata (CA) based pedestrian simulation models have gained remarkable popularity as they are simpler and easier to implement compared to other microscopic modeling approaches. However, incorporating traditional floor field representations in CA models to simulate pedestrian corner navigation behavior could result in unrealistic behaviors. Even though several previous studies have attempted to enhance CA models to realistically simulate pedestrian maneuvers around bends, such modifications have not been calibrated or validated against empirical data. In this study, two static floor field (SFF) representations, namely 'discrete representation' and 'continuous representation', are calibrated for CA-models to represent pedestrians' walking behavior around 90° bends. Trajectory data collected through a controlled experiment are used to calibrate these model representations. Calibration results indicate that although both floor field representations can represent pedestrians' corner navigation behavior, the 'continuous' representation fits the data better. Output of this study could be beneficial for enhancing the reliability of existing CA-based models by representing pedestrians' corner navigation behaviors more realistically.

Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation

PubMed Central

Khaligh-Razavi, Seyed-Mahdi; Kriegeskorte, Nikolaus

2014-01-01

Inferior temporal (IT) cortex in human and nonhuman primates serves visual object recognition. Computational object-vision models, although continually improving, do not yet reach human performance. It is unclear to what extent the internal representations of computational models can explain the IT representation. Here we investigate a wide range of computational model representations (37 in total), testing their categorization performance and their ability to account for the IT representational geometry. The models include well-known neuroscientific object-recognition models (e.g. HMAX, VisNet) along with several models from computer vision (e.g. SIFT, GIST, self-similarity features, and a deep convolutional neural network). We compared the representational dissimilarity matrices (RDMs) of the model representations with the RDMs obtained from human IT (measured with fMRI) and monkey IT (measured with cell recording) for the same set of stimuli (not used in training the models). Better performing models were more similar to IT in that they showed greater clustering of representational patterns by category. In addition, better performing models also more strongly resembled IT in terms of their within-category representational dissimilarities. Representational geometries were significantly correlated between IT and many of the models. However, the categorical clustering observed in IT was largely unexplained by the unsupervised models. The deep convolutional network, which was trained by supervision with over a million category-labeled images, reached the highest categorization performance and also best explained IT, although it did not fully explain the IT data. Combining the features of this model with appropriate weights and adding linear combinations that maximize the margin between animate and inanimate objects and between faces and other objects yielded a representation that fully explained our IT data. Overall, our results suggest that explaining IT requires computational features trained through supervised learning to emphasize the behaviorally important categorical divisions prominently reflected in IT. PMID:25375136

Single or Dual Representations for Reading and Spelling?

ERIC Educational Resources Information Center

Holmes, Virginia M.; Babauta, Mariko L.

2005-01-01

Neuropsychological models postulate that the memory representation acquired for use in reading words is separate from the one acquired for use in spelling, while developmental models assume that the same representation is developed for access in both reading and spelling. The dual-representation model contends that there is often more precise…

Probing Lexical Representations: Simultaneous Modeling of Word and Reader Contributions to Multidimensional Lexical Representations

ERIC Educational Resources Information Center

Goodwin, Amanda P.; Gilbert, Jennifer K.; Cho, Sun-Joo; Kearns, Devin M.

2014-01-01

The current study models reader, item, and word contributions to the lexical representations of 39 morphologically complex words for 172 middle school students using a crossed random-effects item response model with multiple outcomes. We report 3 findings. First, results suggest that lexical representations can be characterized by separate but…

Representation primitives, process models and patient data in computer-interpretable clinical practice guidelines: a literature review of guideline representation models.

PubMed

Wang, Dongwen; Peleg, Mor; Tu, Samson W; Boxwala, Aziz A; Greenes, Robert A; Patel, Vimla L; Shortliffe, Edward H

2002-12-18

Representation of clinical practice guidelines in a computer-interpretable format is a critical issue for guideline development, implementation, and evaluation. We studied 11 types of guideline representation models that can be used to encode guidelines in computer-interpretable formats. We have consistently found in all reviewed models that primitives for representation of actions and decisions are necessary components of a guideline representation model. Patient states and execution states are important concepts that closely relate to each other. Scheduling constraints on representation primitives can be modeled as sequences, concurrences, alternatives, and loops in a guideline's application process. Nesting of guidelines provides multiple views to a guideline with different granularities. Integration of guidelines with electronic medical records can be facilitated by the introduction of a formal model for patient data. Data collection, decision, patient state, and intervention constitute four basic types of primitives in a guideline's logic flow. Decisions clarify our understanding on a patient's clinical state, while interventions lead to the change from one patient state to another.

Fixed versus mixed RSA: Explaining visual representations by fixed and mixed feature sets from shallow and deep computational models.

PubMed

Khaligh-Razavi, Seyed-Mahdi; Henriksson, Linda; Kay, Kendrick; Kriegeskorte, Nikolaus

2017-02-01

Studies of the primate visual system have begun to test a wide range of complex computational object-vision models. Realistic models have many parameters, which in practice cannot be fitted using the limited amounts of brain-activity data typically available. Task performance optimization (e.g. using backpropagation to train neural networks) provides major constraints for fitting parameters and discovering nonlinear representational features appropriate for the task (e.g. object classification). Model representations can be compared to brain representations in terms of the representational dissimilarities they predict for an image set. This method, called representational similarity analysis (RSA), enables us to test the representational feature space as is (fixed RSA) or to fit a linear transformation that mixes the nonlinear model features so as to best explain a cortical area's representational space (mixed RSA). Like voxel/population-receptive-field modelling, mixed RSA uses a training set (different stimuli) to fit one weight per model feature and response channel (voxels here), so as to best predict the response profile across images for each response channel. We analysed response patterns elicited by natural images, which were measured with functional magnetic resonance imaging (fMRI). We found that early visual areas were best accounted for by shallow models, such as a Gabor wavelet pyramid (GWP). The GWP model performed similarly with and without mixing, suggesting that the original features already approximated the representational space, obviating the need for mixing. However, a higher ventral-stream visual representation (lateral occipital region) was best explained by the higher layers of a deep convolutional network and mixing of its feature set was essential for this model to explain the representation. We suspect that mixing was essential because the convolutional network had been trained to discriminate a set of 1000 categories, whose frequencies in the training set did not match their frequencies in natural experience or their behavioural importance. The latter factors might determine the representational prominence of semantic dimensions in higher-level ventral-stream areas. Our results demonstrate the benefits of testing both the specific representational hypothesis expressed by a model's original feature space and the hypothesis space generated by linear transformations of that feature space.

Models as Feedback: Developing Representational Competence in Chemistry

ERIC Educational Resources Information Center

Padalkar, Shamin; Hegarty, Mary

2015-01-01

Spatial information in science is often expressed through representations such as diagrams and models. Learning the strengths and limitations of these representations and how to relate them are important aspects of developing scientific understanding, referred to as "representational competence." Diagram translation is particularly…

From Sensory Signals to Modality-Independent Conceptual Representations: A Probabilistic Language of Thought Approach

PubMed Central

Erdogan, Goker; Yildirim, Ilker; Jacobs, Robert A.

2015-01-01

People learn modality-independent, conceptual representations from modality-specific sensory signals. Here, we hypothesize that any system that accomplishes this feat will include three components: a representational language for characterizing modality-independent representations, a set of sensory-specific forward models for mapping from modality-independent representations to sensory signals, and an inference algorithm for inverting forward models—that is, an algorithm for using sensory signals to infer modality-independent representations. To evaluate this hypothesis, we instantiate it in the form of a computational model that learns object shape representations from visual and/or haptic signals. The model uses a probabilistic grammar to characterize modality-independent representations of object shape, uses a computer graphics toolkit and a human hand simulator to map from object representations to visual and haptic features, respectively, and uses a Bayesian inference algorithm to infer modality-independent object representations from visual and/or haptic signals. Simulation results show that the model infers identical object representations when an object is viewed, grasped, or both. That is, the model’s percepts are modality invariant. We also report the results of an experiment in which different subjects rated the similarity of pairs of objects in different sensory conditions, and show that the model provides a very accurate account of subjects’ ratings. Conceptually, this research significantly contributes to our understanding of modality invariance, an important type of perceptual constancy, by demonstrating how modality-independent representations can be acquired and used. Methodologically, it provides an important contribution to cognitive modeling, particularly an emerging probabilistic language-of-thought approach, by showing how symbolic and statistical approaches can be combined in order to understand aspects of human perception. PMID:26554704

MODELS FOR THE COMPLEX REPRESENTATIONS OF THE GROUPS \\mathrm{GL}(n,\\,q)

NASA Astrophysics Data System (ADS)

Klyachko, Alexander A.

1984-02-01

The main result of the paper consists in the construction of a model of the full linear group over a finite field, i.e. its representations such that each irreducible representation occurs as a component precisely once. The series of representations thus constructed has the well-known Gel'fand-Graev representation as first term.Bibliography: 12 titles.

Space-time modeling using environmental constraints in a mobile robot system

NASA Technical Reports Server (NTRS)

Slack, Marc G.

1990-01-01

Grid-based models of a robot's local environment have been used by many researchers building mobile robot control systems. The attraction of grid-based models is their clear parallel between the internal model and the external world. However, the discrete nature of such representations does not match well with the continuous nature of actions and usually serves to limit the abilities of the robot. This work describes a spatial modeling system that extracts information from a grid-based representation to form a symbolic representation of the robot's local environment. The approach makes a separation between the representation provided by the sensing system and the representation used by the action system. Separation allows asynchronous operation between sensing and action in a mobile robot, as well as the generation of a more continuous representation upon which to base actions.

Reading visually embodied meaning from the brain: Visually grounded computational models decode visual-object mental imagery induced by written text.

PubMed

Anderson, Andrew James; Bruni, Elia; Lopopolo, Alessandro; Poesio, Massimo; Baroni, Marco

2015-10-15

Embodiment theory predicts that mental imagery of object words recruits neural circuits involved in object perception. The degree of visual imagery present in routine thought and how it is encoded in the brain is largely unknown. We test whether fMRI activity patterns elicited by participants reading objects' names include embodied visual-object representations, and whether we can decode the representations using novel computational image-based semantic models. We first apply the image models in conjunction with text-based semantic models to test predictions of visual-specificity of semantic representations in different brain regions. Representational similarity analysis confirms that fMRI structure within ventral-temporal and lateral-occipital regions correlates most strongly with the image models and conversely text models correlate better with posterior-parietal/lateral-temporal/inferior-frontal regions. We use an unsupervised decoding algorithm that exploits commonalities in representational similarity structure found within both image model and brain data sets to classify embodied visual representations with high accuracy (8/10) and then extend it to exploit model combinations to robustly decode different brain regions in parallel. By capturing latent visual-semantic structure our models provide a route into analyzing neural representations derived from past perceptual experience rather than stimulus-driven brain activity. Our results also verify the benefit of combining multimodal data to model human-like semantic representations. Copyright © 2015 Elsevier Inc. All rights reserved.

Method and apparatus for modeling interactions

DOEpatents

Xavier, Patrick G.

2000-08-08

A method and apparatus for modeling interactions between bodies. The method comprises representing two bodies undergoing translations and rotations by two hierarchical swept volume representations. Interactions such as nearest approach and collision can be modeled based on the swept body representations. The present invention can serve as a practical tool in motion planning, CAD systems, simulation systems, safety analysis, and applications that require modeling time-based interactions. A body can be represented in the present invention by a union of convex polygons and convex polyhedra. As used generally herein, polyhedron includes polygon, and polyhedra includes polygons. The body undergoing translation can be represented by a swept body representation, where the swept body representation comprises a hierarchical bounding volume representation whose leaves each contain a representation of the region swept by a section of the body during the translation, and where the union of the regions is a superset of the region swept by the surface of the body during translation. Interactions between two bodies thus represented can be modeled by modeling interactions between the convex hulls of the finite sets of discrete points in the swept body representations.

Incorporating linguistic knowledge for learning distributed word representations.

PubMed

Wang, Yan; Liu, Zhiyuan; Sun, Maosong

2015-01-01

Combined with neural language models, distributed word representations achieve significant advantages in computational linguistics and text mining. Most existing models estimate distributed word vectors from large-scale data in an unsupervised fashion, which, however, do not take rich linguistic knowledge into consideration. Linguistic knowledge can be represented as either link-based knowledge or preference-based knowledge, and we propose knowledge regularized word representation models (KRWR) to incorporate these prior knowledge for learning distributed word representations. Experiment results demonstrate that our estimated word representation achieves better performance in task of semantic relatedness ranking. This indicates that our methods can efficiently encode both prior knowledge from knowledge bases and statistical knowledge from large-scale text corpora into a unified word representation model, which will benefit many tasks in text mining.

Incorporating Linguistic Knowledge for Learning Distributed Word Representations

PubMed Central

Wang, Yan; Liu, Zhiyuan; Sun, Maosong

2015-01-01

Combined with neural language models, distributed word representations achieve significant advantages in computational linguistics and text mining. Most existing models estimate distributed word vectors from large-scale data in an unsupervised fashion, which, however, do not take rich linguistic knowledge into consideration. Linguistic knowledge can be represented as either link-based knowledge or preference-based knowledge, and we propose knowledge regularized word representation models (KRWR) to incorporate these prior knowledge for learning distributed word representations. Experiment results demonstrate that our estimated word representation achieves better performance in task of semantic relatedness ranking. This indicates that our methods can efficiently encode both prior knowledge from knowledge bases and statistical knowledge from large-scale text corpora into a unified word representation model, which will benefit many tasks in text mining. PMID:25874581

Building Cognition: The Construction of Computational Representations for Scientific Discovery.

PubMed

Chandrasekharan, Sanjay; Nersessian, Nancy J

2015-11-01

Novel computational representations, such as simulation models of complex systems and video games for scientific discovery (Foldit, EteRNA etc.), are dramatically changing the way discoveries emerge in science and engineering. The cognitive roles played by such computational representations in discovery are not well understood. We present a theoretical analysis of the cognitive roles such representations play, based on an ethnographic study of the building of computational models in a systems biology laboratory. Specifically, we focus on a case of model-building by an engineer that led to a remarkable discovery in basic bioscience. Accounting for such discoveries requires a distributed cognition (DC) analysis, as DC focuses on the roles played by external representations in cognitive processes. However, DC analyses by and large have not examined scientific discovery, and they mostly focus on memory offloading, particularly how the use of existing external representations changes the nature of cognitive tasks. In contrast, we study discovery processes and argue that discoveries emerge from the processes of building the computational representation. The building process integrates manipulations in imagination and in the representation, creating a coupled cognitive system of model and modeler, where the model is incorporated into the modeler's imagination. This account extends DC significantly, and we present some of the theoretical and application implications of this extended account. Copyright © 2014 Cognitive Science Society, Inc.

Taking representation seriously: rethinking bioethics through Clint Eastwood's Million Dollar Baby.

PubMed

Braswell, Harold

2011-06-01

In this article, I propose a new model for understanding the function of representation in bioethics. Bioethicists have traditionally judged representations according to a mimetic paradigm, in which representations of bioethical dilemmas are assessed based on their correspondence to the "reality" of bioethics itself. In this article, I argue that this mimetic paradigm obscures the interaction between representation and reality and diverts bioethicists from analyzing the tensions in the representational object itself. I propose an anti-mimetic model of representation that is attuned to how representations can both maintain and potentially subvert dominant conceptions of bioethics. I illustrate this model through a case study of Clint Eastwood's film Million Dollar Baby. By focusing attention on the film's lack of adherence bioethical procedures and medical science, critics missed how an analysis of its representational logic provides a means of reimagining both bioethics and medical practice. In my conclusion, I build off this case study to assess how an incorporation of representational studies can deepen-and be deepened by-recent calls for interdisciplinarity in bioethics.

Unfixing Design Fixation: From Cause to Computer Simulation

ERIC Educational Resources Information Center

Dong, Andy; Sarkar, Somwrita

2011-01-01

This paper argues that design fixation, in part, entails fixation at the level of meta-representation, the representation of the relation between a representation and its reference. In this paper, we present a mathematical model that mimics the idea of how fixation can occur at the meta-representation level. In this model, new abstract concepts…

A Multi-factorial Model for Examining Racial and Ethnic Disparities in Acute Asthma Visits by Children

PubMed Central

Feldman, Jonathan M.; Serebrisky, Denise; Spray, Amanda

2012-01-01

Background Causes of children’s asthma health disparities are complex. Parents’ asthma illness representations may play a role. Purpose The study aims to test a theoretically based, multi-factorial model for ethnic disparities in children’s acute asthma visits through parental illness representations. Methods Structural equation modeling investigated the association of parental asthma illness representations, sociodemographic characteristics, health care provider factors, and social–environmental context with children’s acute asthma visits among 309 White, Puerto Rican, and African American families was conducted. Results Forty-five percent of the variance in illness representations and 30% of the variance in acute visits were accounted for. Statistically significant differences in illness representations were observed by ethnic group. Approximately 30% of the variance in illness representations was explained for whites, 23% for African Americans, and 26% for Puerto Ricans. The model accounted for >30% of the variance in acute visits for African Americans and Puerto Ricans but only 19% for the whites. Conclusion The model provides preliminary support that ethnic heterogeneity in asthma illness representations affects children’s health outcomes. PMID:22160799

A Comparison of Reasoning Processes in a Collaborative Modelling Environment: Learning about genetics problems using virtual chat

NASA Astrophysics Data System (ADS)

Pata, Kai; Sarapuu, Tago

2006-09-01

This study investigated the possible activation of different types of model-based reasoning processes in two learning settings, and the influence of various terms of reasoning on the learners’ problem representation development. Changes in 53 students’ problem representations about genetic issue were analysed while they worked with different modelling tools in a synchronous network-based environment. The discussion log-files were used for the “microgenetic” analysis of reasoning types. For studying the stages of students’ problem representation development, individual pre-essays and post-essays and their utterances during two reasoning phases were used. An approach for mapping problem representations was developed. Characterizing the elements of mental models and their reasoning level enabled the description of five hierarchical categories of problem representations. Learning in exploratory and experimental settings was registered as the shift towards more complex stages of problem representations in genetics. The effect of different types of reasoning could be observed as the divergent development of problem representations within hierarchical categories.

A roadmap for improving the representation of photosynthesis in Earth System Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rogers, Alistair; Medlyn, Belinda E.; Dukes, Jeffrey S.

Accurate representation of photosynthesis in terrestrial biosphere models (TBMs) is essential for robust projections of global change. However, current representations vary markedly between TBMs, contributing uncertainty projections of global carbon fluxes.

A roadmap for improving the representation of photosynthesis in Earth System Models

DOE PAGES

Rogers, Alistair; Medlyn, Belinda E.; Dukes, Jeffrey S.; ...

2016-11-28

Accurate representation of photosynthesis in terrestrial biosphere models (TBMs) is essential for robust projections of global change. However, current representations vary markedly between TBMs, contributing uncertainty projections of global carbon fluxes.

Dependency-based Siamese long short-term memory network for learning sentence representations

PubMed Central

Zhu, Wenhao; Ni, Jianyue; Wei, Baogang; Lu, Zhiguo

2018-01-01

Textual representations play an important role in the field of natural language processing (NLP). The efficiency of NLP tasks, such as text comprehension and information extraction, can be significantly improved with proper textual representations. As neural networks are gradually applied to learn the representation of words and phrases, fairly efficient models of learning short text representations have been developed, such as the continuous bag of words (CBOW) and skip-gram models, and they have been extensively employed in a variety of NLP tasks. Because of the complex structure generated by the longer text lengths, such as sentences, algorithms appropriate for learning short textual representations are not applicable for learning long textual representations. One method of learning long textual representations is the Long Short-Term Memory (LSTM) network, which is suitable for processing sequences. However, the standard LSTM does not adequately address the primary sentence structure (subject, predicate and object), which is an important factor for producing appropriate sentence representations. To resolve this issue, this paper proposes the dependency-based LSTM model (D-LSTM). The D-LSTM divides a sentence representation into two parts: a basic component and a supporting component. The D-LSTM uses a pre-trained dependency parser to obtain the primary sentence information and generate supporting components, and it also uses a standard LSTM model to generate the basic sentence components. A weight factor that can adjust the ratio of the basic and supporting components in a sentence is introduced to generate the sentence representation. Compared with the representation learned by the standard LSTM, the sentence representation learned by the D-LSTM contains a greater amount of useful information. The experimental results show that the D-LSTM is superior to the standard LSTM for sentences involving compositional knowledge (SICK) data. PMID:29513748

Building Hierarchical Representations for Oracle Character and Sketch Recognition.

PubMed

Jun Guo; Changhu Wang; Roman-Rangel, Edgar; Hongyang Chao; Yong Rui

2016-01-01

In this paper, we study oracle character recognition and general sketch recognition. First, a data set of oracle characters, which are the oldest hieroglyphs in China yet remain a part of modern Chinese characters, is collected for analysis. Second, typical visual representations in shape- and sketch-related works are evaluated. We analyze the problems suffered when addressing these representations and determine several representation design criteria. Based on the analysis, we propose a novel hierarchical representation that combines a Gabor-related low-level representation and a sparse-encoder-related mid-level representation. Extensive experiments show the effectiveness of the proposed representation in both oracle character recognition and general sketch recognition. The proposed representation is also complementary to convolutional neural network (CNN)-based models. We introduce a solution to combine the proposed representation with CNN-based models, and achieve better performances over both approaches. This solution has beaten humans at recognizing general sketches.

Profile of Students’ Mental Model Change on Law Concepts Archimedes as Impact of Multi-Representation Approach

NASA Astrophysics Data System (ADS)

Taher, M.; Hamidah, I.; Suwarma, I. R.

2017-09-01

This paper outlined the results of an experimental study on the effects of multi-representation approach in learning Archimedes Law on students’ mental model improvement. The multi-representation techniques implemented in the study were verbal, pictorial, mathematical, and graphical representations. Students’ mental model was classified into three levels, i.e. scientific, synthetic, and initial levels, based on the students’ level of understanding. The present study employed the pre-experimental methodology, using one group pretest-posttest design. The subject of the study was 32 eleventh grade students in a Public Senior High School in Riau Province. The research instrument included model mental test on hydrostatic pressure concept, in the form of essay test judged by experts. The findings showed that there was positive change in students’ mental model, indicating that multi-representation approach was effective to improve students’ mental model.

Information Object Definition–based Unified Modeling Language Representation of DICOM Structured Reporting

PubMed Central

Tirado-Ramos, Alfredo; Hu, Jingkun; Lee, K.P.

2002-01-01

Supplement 23 to DICOM (Digital Imaging and Communications for Medicine), Structured Reporting, is a specification that supports a semantically rich representation of image and waveform content, enabling experts to share image and related patient information. DICOM SR supports the representation of textual and coded data linked to images and waveforms. Nevertheless, the medical information technology community needs models that work as bridges between the DICOM relational model and open object-oriented technologies. The authors assert that representations of the DICOM Structured Reporting standard, using object-oriented modeling languages such as the Unified Modeling Language, can provide a high-level reference view of the semantically rich framework of DICOM and its complex structures. They have produced an object-oriented model to represent the DICOM SR standard and have derived XML-exchangeable representations of this model using World Wide Web Consortium specifications. They expect the model to benefit developers and system architects who are interested in developing applications that are compliant with the DICOM SR specification. PMID:11751804

A Knowledge-Based Representation Scheme for Environmental Science Models

NASA Technical Reports Server (NTRS)

Keller, Richard M.; Dungan, Jennifer L.; Lum, Henry, Jr. (Technical Monitor)

1994-01-01

One of the primary methods available for studying environmental phenomena is the construction and analysis of computational models. We have been studying how artificial intelligence techniques can be applied to assist in the development and use of environmental science models within the context of NASA-sponsored activities. We have identified several high-utility areas as potential targets for research and development: model development; data visualization, analysis, and interpretation; model publishing and reuse, training and education; and framing, posing, and answering questions. Central to progress on any of the above areas is a representation for environmental models that contains a great deal more information than is present in a traditional software implementation. In particular, a traditional software implementation is devoid of any semantic information that connects the code with the environmental context that forms the background for the modeling activity. Before we can build AI systems to assist in model development and usage, we must develop a representation for environmental models that adequately describes a model's semantics and explicitly represents the relationship between the code and the modeling task at hand. We have developed one such representation in conjunction with our work on the SIGMA (Scientists' Intelligent Graphical Modeling Assistant) environment. The key feature of the representation is that it provides a semantic grounding for the symbols in a set of modeling equations by linking those symbols to an explicit representation of the underlying environmental scenario.

Modeling a flexible representation machinery of human concept learning.

PubMed

Matsuka, Toshihiko; Sakamoto, Yasuaki; Chouchourelou, Arieta

2008-01-01

It is widely acknowledged that categorically organized abstract knowledge plays a significant role in high-order human cognition. Yet, there are many unknown issues about the nature of how categories are internally represented in our mind. Traditionally, it has been considered that there is a single innate internal representation system for categorical knowledge, such as Exemplars, Prototypes, or Rules. However, results of recent empirical and computational studies collectively suggest that the human internal representation system is apparently capable of exhibiting behaviors consistent with various types of internal representation schemes. We, then, hypothesized that humans' representational system as a dynamic mechanism, capable of selecting a representation scheme that meets situational characteristics, including complexities of category structure. The present paper introduces a framework for a cognitive model that integrates robust and flexible internal representation machinery. Three simulation studies were conducted. The results showed that SUPERSET, our new model, successfully exhibited cognitive behaviors that are consistent with three main theories of the human internal representation system. Furthermore, a simulation study on social cognitive behaviors showed that the model was capable of acquiring knowledge with high commonality, even for a category structure with numerous valid conceptualizations.

Character recognition using a neural network model with fuzzy representation

NASA Technical Reports Server (NTRS)

Tavakoli, Nassrin; Seniw, David

1992-01-01

The degree to which digital images are recognized correctly by computerized algorithms is highly dependent upon the representation and the classification processes. Fuzzy techniques play an important role in both processes. In this paper, the role of fuzzy representation and classification on the recognition of digital characters is investigated. An experimental Neural Network model with application to character recognition was developed. Through a set of experiments, the effect of fuzzy representation on the recognition accuracy of this model is presented.

Automated Diagnosis Coding with Combined Text Representations.

PubMed

Berndorfer, Stefan; Henriksson, Aron

2017-01-01

Automated diagnosis coding can be provided efficiently by learning predictive models from historical data; however, discriminating between thousands of codes while allowing a variable number of codes to be assigned is extremely difficult. Here, we explore various text representations and classification models for assigning ICD-9 codes to discharge summaries in MIMIC-III. It is shown that the relative effectiveness of the investigated representations depends on the frequency of the diagnosis code under consideration and that the best performance is obtained by combining models built using different representations.

System for conversion between the boundary representation model and a constructive solid geometry model of an object

DOEpatents

Christensen, Noel C.; Emery, James D.; Smith, Maurice L.

1988-04-05

A system converts from the boundary representation of an object to the constructive solid geometry representation thereof. The system converts the boundary representation of the object into elemental atomic geometrical units or I-bodies which are in the shape of stock primitives or regularized intersections of stock primitives. These elemental atomic geometrical units are then represented in symbolic form. The symbolic representations of the elemental atomic geometrical units are then assembled heuristically to form a constructive solid geometry representation of the object usable for manufacturing thereof. Artificial intelligence is used to determine the best constructive solid geometry representation from the boundary representation of the object. Heuristic criteria are adapted to the manufacturing environment for which the device is to be utilized. The surface finish, tolerance, and other information associated with each surface of the boundary representation of the object are mapped onto the constructive solid geometry representation of the object to produce an enhanced solid geometry representation, particularly useful for computer-aided manufacture of the object.

Point clouds in BIM

NASA Astrophysics Data System (ADS)

Antova, Gergana; Kunchev, Ivan; Mickrenska-Cherneva, Christina

2016-10-01

The representation of physical buildings in Building Information Models (BIM) has been a subject of research since four decades in the fields of Construction Informatics and GeoInformatics. The early digital representations of buildings mainly appeared as 3D drawings constructed by CAD software, and the 3D representation of the buildings was only geometric, while semantics and topology were out of modelling focus. On the other hand, less detailed building representations, with often focus on ‘outside’ representations were also found in form of 2D /2,5D GeoInformation models. Point clouds from 3D laser scanning data give a full and exact representation of the building geometry. The article presents different aspects and the benefits of using point clouds in BIM in the different stages of a lifecycle of a building.

A sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnson, J. D.; Oberkampf, William Louis; Helton, Jon Craig

2006-10-01

Evidence theory provides an alternative to probability theory for the representation of epistemic uncertainty in model predictions that derives from epistemic uncertainty in model inputs, where the descriptor epistemic is used to indicate uncertainty that derives from a lack of knowledge with respect to the appropriate values to use for various inputs to the model. The potential benefit, and hence appeal, of evidence theory is that it allows a less restrictive specification of uncertainty than is possible within the axiomatic structure on which probability theory is based. Unfortunately, the propagation of an evidence theory representation for uncertainty through a modelmore » is more computationally demanding than the propagation of a probabilistic representation for uncertainty, with this difficulty constituting a serious obstacle to the use of evidence theory in the representation of uncertainty in predictions obtained from computationally intensive models. This presentation describes and illustrates a sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory. Preliminary trials indicate that the presented strategy can be used to propagate uncertainty representations based on evidence theory in analysis situations where naive sampling-based (i.e., unsophisticated Monte Carlo) procedures are impracticable due to computational cost.« less

Neural Population Dynamics during Reaching Are Better Explained by a Dynamical System than Representational Tuning

PubMed Central

Dann, Benjamin

2016-01-01

Recent models of movement generation in motor cortex have sought to explain neural activity not as a function of movement parameters, known as representational models, but as a dynamical system acting at the level of the population. Despite evidence supporting this framework, the evaluation of representational models and their integration with dynamical systems is incomplete in the literature. Using a representational velocity-tuning based simulation of center-out reaching, we show that incorporating variable latency offsets between neural activity and kinematics is sufficient to generate rotational dynamics at the level of neural populations, a phenomenon observed in motor cortex. However, we developed a covariance-matched permutation test (CMPT) that reassigns neural data between task conditions independently for each neuron while maintaining overall neuron-to-neuron relationships, revealing that rotations based on the representational model did not uniquely depend on the underlying condition structure. In contrast, rotations based on either a dynamical model or motor cortex data depend on this relationship, providing evidence that the dynamical model more readily explains motor cortex activity. Importantly, implementing a recurrent neural network we demonstrate that both representational tuning properties and rotational dynamics emerge, providing evidence that a dynamical system can reproduce previous findings of representational tuning. Finally, using motor cortex data in combination with the CMPT, we show that results based on small numbers of neurons or conditions should be interpreted cautiously, potentially informing future experimental design. Together, our findings reinforce the view that representational models lack the explanatory power to describe complex aspects of single neuron and population level activity. PMID:27814352

Neural Population Dynamics during Reaching Are Better Explained by a Dynamical System than Representational Tuning.

PubMed

Michaels, Jonathan A; Dann, Benjamin; Scherberger, Hansjörg

2016-11-01

Recent models of movement generation in motor cortex have sought to explain neural activity not as a function of movement parameters, known as representational models, but as a dynamical system acting at the level of the population. Despite evidence supporting this framework, the evaluation of representational models and their integration with dynamical systems is incomplete in the literature. Using a representational velocity-tuning based simulation of center-out reaching, we show that incorporating variable latency offsets between neural activity and kinematics is sufficient to generate rotational dynamics at the level of neural populations, a phenomenon observed in motor cortex. However, we developed a covariance-matched permutation test (CMPT) that reassigns neural data between task conditions independently for each neuron while maintaining overall neuron-to-neuron relationships, revealing that rotations based on the representational model did not uniquely depend on the underlying condition structure. In contrast, rotations based on either a dynamical model or motor cortex data depend on this relationship, providing evidence that the dynamical model more readily explains motor cortex activity. Importantly, implementing a recurrent neural network we demonstrate that both representational tuning properties and rotational dynamics emerge, providing evidence that a dynamical system can reproduce previous findings of representational tuning. Finally, using motor cortex data in combination with the CMPT, we show that results based on small numbers of neurons or conditions should be interpreted cautiously, potentially informing future experimental design. Together, our findings reinforce the view that representational models lack the explanatory power to describe complex aspects of single neuron and population level activity.

Picture this: The value of multiple visual representations for student learning of quantum concepts in general chemistry

NASA Astrophysics Data System (ADS)

Allen, Emily Christine

Mental models for scientific learning are often defined as, "cognitive tools situated between experiments and theories" (Duschl & Grandy, 2012). In learning, these cognitive tools are used to not only take in new information, but to help problem solve in new contexts. Nancy Nersessian (2008) describes a mental model as being "[loosely] characterized as a representation of a system with interactive parts with representations of those interactions. Models can be qualitative, quantitative, and/or simulative (mental, physical, computational)" (p. 63). If conceptual parts used by the students in science education are inaccurate, then the resulting model will not be useful. Students in college general chemistry courses are presented with multiple abstract topics and often struggle to fit these parts into complete models. This is especially true for topics that are founded on quantum concepts, such as atomic structure and molecular bonding taught in college general chemistry. The objectives of this study were focused on how students use visual tools introduced during instruction to reason with atomic and molecular structure, what misconceptions may be associated with these visual tools, and how visual modeling skills may be taught to support students' use of visual tools for reasoning. The research questions for this study follow from Gilbert's (2008) theory that experts use multiple representations when reasoning and modeling a system, and Kozma and Russell's (2005) theory of representational competence levels. This study finds that as students developed greater command of their understanding of abstract quantum concepts, they spontaneously provided additional representations to describe their more sophisticated models of atomic and molecular structure during interviews. This suggests that when visual modeling with multiple representations is taught, along with the limitations of the representations, it can assist students in the development of models for reasoning about abstract topics such as atomic and molecular structure. There is further gain if students' difficulties with these representations are targeted through the use additional instruction such as a workbook that requires the students to exercise their visual modeling skills.

A study on model fidelity for model predictive control-based obstacle avoidance in high-speed autonomous ground vehicles

NASA Astrophysics Data System (ADS)

Liu, Jiechao; Jayakumar, Paramsothy; Stein, Jeffrey L.; Ersal, Tulga

2016-11-01

This paper investigates the level of model fidelity needed in order for a model predictive control (MPC)-based obstacle avoidance algorithm to be able to safely and quickly avoid obstacles even when the vehicle is close to its dynamic limits. The context of this work is large autonomous ground vehicles that manoeuvre at high speed within unknown, unstructured, flat environments and have significant vehicle dynamics-related constraints. Five different representations of vehicle dynamics models are considered: four variations of the two degrees-of-freedom (DoF) representation as lower fidelity models and a fourteen DoF representation with combined-slip Magic Formula tyre model as a higher fidelity model. It is concluded that the two DoF representation that accounts for tyre nonlinearities and longitudinal load transfer is necessary for the MPC-based obstacle avoidance algorithm in order to operate the vehicle at its limits within an environment that includes large obstacles. For less challenging environments, however, the two DoF representation with linear tyre model and constant axle loads is sufficient.

Probabilistic Graphical Model Representation in Phylogenetics

PubMed Central

Höhna, Sebastian; Heath, Tracy A.; Boussau, Bastien; Landis, Michael J.; Ronquist, Fredrik; Huelsenbeck, John P.

2014-01-01

Recent years have seen a rapid expansion of the model space explored in statistical phylogenetics, emphasizing the need for new approaches to statistical model representation and software development. Clear communication and representation of the chosen model is crucial for: (i) reproducibility of an analysis, (ii) model development, and (iii) software design. Moreover, a unified, clear and understandable framework for model representation lowers the barrier for beginners and nonspecialists to grasp complex phylogenetic models, including their assumptions and parameter/variable dependencies. Graphical modeling is a unifying framework that has gained in popularity in the statistical literature in recent years. The core idea is to break complex models into conditionally independent distributions. The strength lies in the comprehensibility, flexibility, and adaptability of this formalism, and the large body of computational work based on it. Graphical models are well-suited to teach statistical models, to facilitate communication among phylogeneticists and in the development of generic software for simulation and statistical inference. Here, we provide an introduction to graphical models for phylogeneticists and extend the standard graphical model representation to the realm of phylogenetics. We introduce a new graphical model component, tree plates, to capture the changing structure of the subgraph corresponding to a phylogenetic tree. We describe a range of phylogenetic models using the graphical model framework and introduce modules to simplify the representation of standard components in large and complex models. Phylogenetic model graphs can be readily used in simulation, maximum likelihood inference, and Bayesian inference using, for example, Metropolis–Hastings or Gibbs sampling of the posterior distribution. [Computation; graphical models; inference; modularization; statistical phylogenetics; tree plate.] PMID:24951559

Improving Representational Competence with Concrete Models

ERIC Educational Resources Information Center

Stieff, Mike; Scopelitis, Stephanie; Lira, Matthew E.; DeSutter, Dane

2016-01-01

Representational competence is a primary contributor to student learning in science, technology, engineering, and math (STEM) disciplines and an optimal target for instruction at all educational levels. We describe the design and implementation of a learning activity that uses concrete models to improve students' representational competence and…

The Role of Task Understanding on Younger and Older Adults' Performance.

PubMed

Frank, David J; Touron, Dayna R

2016-12-16

Age-related performance decrements have been linked to inferior strategic choices. Strategy selection models argue that accurate task representations are necessary for choosing appropriate strategies. But no studies to date have compared task representations in younger and older adults. Metacognition research suggests age-related deficits in updating and utilizing strategy knowledge, but other research suggests age-related sparing when information can be consolidated into a coherent mental model. Study 1 validated the use of concept mapping as a tool for measuring task representation accuracy. Study 2 measured task representations before and after a complex strategic task to test for age-related decrements in task representation formation and updating. Task representation accuracy and task performance were equivalent across age groups. Better task representations were related to better performance. However, task representation scores remained fairly stable over the task with minimal evidence of updating. Our findings mirror those in the mental model literature suggesting age-related sparing of strategy use when information can be integrated into a coherent mental model. Future research should manipulate the presence of a unifying context to better evaluate this hypothesis. © The Author 2016. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Arithmetic Problems at School: When There Is an Apparent Contradiction between the Situation Model and the Problem Model

ERIC Educational Resources Information Center

Coquin-Viennot, Daniele; Moreau, Stephanie

2007-01-01

Background: Understanding and solving problems involves different levels of representation. On the one hand, there are logico-mathematical representations, or problem models (PMs), which contain information such as "the size of the flock changed from 31 sheep to 42" while, on the other hand, there are more qualitative representations, or…

A Nakanishi-based model illustrating the covariant extension of the pion GPD overlap representation and its ambiguities

NASA Astrophysics Data System (ADS)

Chouika, N.; Mezrag, C.; Moutarde, H.; Rodríguez-Quintero, J.

2018-05-01

A systematic approach for the model building of Generalized Parton Distributions (GPDs), based on their overlap representation within the DGLAP kinematic region and a further covariant extension to the ERBL one, is applied to the valence-quark pion's case, using light-front wave functions inspired by the Nakanishi representation of the pion Bethe-Salpeter amplitudes (BSA). This simple but fruitful pion GPD model illustrates the general model building technique and, in addition, allows for the ambiguities related to the covariant extension, grounded on the Double Distribution (DD) representation, to be constrained by requiring a soft-pion theorem to be properly observed.

Improved water-level forecasting for the Northwest European Shelf and North Sea through direct modelling of tide, surge and non-linear interaction

NASA Astrophysics Data System (ADS)

Zijl, Firmijn; Verlaan, Martin; Gerritsen, Herman

2013-07-01

In real-time operational coastal forecasting systems for the northwest European shelf, the representation accuracy of tide-surge models commonly suffers from insufficiently accurate tidal representation, especially in shallow near-shore areas with complex bathymetry and geometry. Therefore, in conventional operational systems, the surge component from numerical model simulations is used, while the harmonically predicted tide, accurately known from harmonic analysis of tide gauge measurements, is added to forecast the full water-level signal at tide gauge locations. Although there are errors associated with this so-called astronomical correction (e.g. because of the assumption of linearity of tide and surge), for current operational models, astronomical correction has nevertheless been shown to increase the representation accuracy of the full water-level signal. The simulated modulation of the surge through non-linear tide-surge interaction is affected by the poor representation of the tide signal in the tide-surge model, which astronomical correction does not improve. Furthermore, astronomical correction can only be applied to locations where the astronomic tide is known through a harmonic analysis of in situ measurements at tide gauge stations. This provides a strong motivation to improve both tide and surge representation of numerical models used in forecasting. In the present paper, we propose a new generation tide-surge model for the northwest European Shelf (DCSMv6). This is the first application on this scale in which the tidal representation is such that astronomical correction no longer improves the accuracy of the total water-level representation and where, consequently, the straightforward direct model forecasting of total water levels is better. The methodology applied to improve both tide and surge representation of the model is discussed, with emphasis on the use of satellite altimeter data and data assimilation techniques for reducing parameter uncertainty. Historic DCSMv6 model simulations are compared against shelf wide observations for a full calendar year. For a selection of stations, these results are compared to those with astronomical correction, which confirms that the tide representation in coastal regions has sufficient accuracy, and that forecasting total water levels directly yields superior results.

System for conversion between the boundary representation model and a constructive solid geometry model of an object

DOEpatents

Christensen, N.C.; Emery, J.D.; Smith, M.L.

1985-04-29

A system converts from the boundary representation of an object to the constructive solid geometry representation thereof. The system converts the boundary representation of the object into elemental atomic geometrical units or I-bodies which are in the shape of stock primitives or regularized intersections of stock primitives. These elemental atomic geometrical units are then represented in symbolic form. The symbolic representations of the elemental atomic geometrical units are then assembled heuristically to form a constructive solid geometry representation of the object usable for manufacturing thereof. Artificial intelligence is used to determine the best constructive solid geometry representation from the boundary representation of the object. Heuristic criteria are adapted to the manufacturing environment for which the device is to be utilized. The surface finish, tolerance, and other information associated with each surface of the boundary representation of the object are mapped onto the constructive solid geometry representation of the object to produce an enhanced solid geometry representation, particularly useful for computer-aided manufacture of the object. 19 figs.

Information object definition-based unified modeling language representation of DICOM structured reporting: a case study of transcoding DICOM to XML.

PubMed

Tirado-Ramos, Alfredo; Hu, Jingkun; Lee, K P

2002-01-01

Supplement 23 to DICOM (Digital Imaging and Communications for Medicine), Structured Reporting, is a specification that supports a semantically rich representation of image and waveform content, enabling experts to share image and related patient information. DICOM SR supports the representation of textual and coded data linked to images and waveforms. Nevertheless, the medical information technology community needs models that work as bridges between the DICOM relational model and open object-oriented technologies. The authors assert that representations of the DICOM Structured Reporting standard, using object-oriented modeling languages such as the Unified Modeling Language, can provide a high-level reference view of the semantically rich framework of DICOM and its complex structures. They have produced an object-oriented model to represent the DICOM SR standard and have derived XML-exchangeable representations of this model using World Wide Web Consortium specifications. They expect the model to benefit developers and system architects who are interested in developing applications that are compliant with the DICOM SR specification.

A roadmap for improving the representation of photosynthesis in Earth system models.

PubMed

Rogers, Alistair; Medlyn, Belinda E; Dukes, Jeffrey S; Bonan, Gordon; von Caemmerer, Susanne; Dietze, Michael C; Kattge, Jens; Leakey, Andrew D B; Mercado, Lina M; Niinemets, Ülo; Prentice, I Colin; Serbin, Shawn P; Sitch, Stephen; Way, Danielle A; Zaehle, Sönke

2017-01-01

Accurate representation of photosynthesis in terrestrial biosphere models (TBMs) is essential for robust projections of global change. However, current representations vary markedly between TBMs, contributing uncertainty to projections of global carbon fluxes. Here we compared the representation of photosynthesis in seven TBMs by examining leaf and canopy level responses of photosynthetic CO 2 assimilation (A) to key environmental variables: light, temperature, CO 2 concentration, vapor pressure deficit and soil water content. We identified research areas where limited process knowledge prevents inclusion of physiological phenomena in current TBMs and research areas where data are urgently needed for model parameterization or evaluation. We provide a roadmap for new science needed to improve the representation of photosynthesis in the next generation of terrestrial biosphere and Earth system models. No claim to original US Government works New Phytologist © 2016 New Phytologist Trust.

Mental Models in Expert Physics Reasoning.

ERIC Educational Resources Information Center

Roschelle, Jeremy; Greeno, James G.

Proposed is a relational framework for characterizing experienced physicists' representations of physics problem situations and the process of constructing these representations. A representation includes a coherent set of relations among: (1) a mental model of the objects in the situation, along with their relevant properties and relations; (2) a…

Unitary Transformations in the Quantum Model for Conceptual Conjunctions and Its Application to Data Representation

PubMed Central

Veloz, Tomas; Desjardins, Sylvie

2015-01-01

Quantum models of concept combinations have been successful in representing various experimental situations that cannot be accommodated by traditional models based on classical probability or fuzzy set theory. In many cases, the focus has been on producing a representation that fits experimental results to validate quantum models. However, these representations are not always consistent with the cognitive modeling principles. Moreover, some important issues related to the representation of concepts such as the dimensionality of the realization space, the uniqueness of solutions, and the compatibility of measurements, have been overlooked. In this paper, we provide a dimensional analysis of the realization space for the two-sector Fock space model for conjunction of concepts focusing on the first and second sectors separately. We then introduce various representation of concepts that arise from the use of unitary operators in the realization space. In these concrete representations, a pair of concepts and their combination are modeled by a single conceptual state, and by a collection of exemplar-dependent operators. Therefore, they are consistent with cognitive modeling principles. This framework not only provides a uniform approach to model an entire data set, but, because all measurement operators are expressed in the same basis, allows us to address the question of compatibility of measurements. In particular, we present evidence that it may be possible to predict non-commutative effects from partial measurements of conceptual combinations. PMID:26617556

Unitary Transformations in the Quantum Model for Conceptual Conjunctions and Its Application to Data Representation.

PubMed

Veloz, Tomas; Desjardins, Sylvie

2015-01-01

Quantum models of concept combinations have been successful in representing various experimental situations that cannot be accommodated by traditional models based on classical probability or fuzzy set theory. In many cases, the focus has been on producing a representation that fits experimental results to validate quantum models. However, these representations are not always consistent with the cognitive modeling principles. Moreover, some important issues related to the representation of concepts such as the dimensionality of the realization space, the uniqueness of solutions, and the compatibility of measurements, have been overlooked. In this paper, we provide a dimensional analysis of the realization space for the two-sector Fock space model for conjunction of concepts focusing on the first and second sectors separately. We then introduce various representation of concepts that arise from the use of unitary operators in the realization space. In these concrete representations, a pair of concepts and their combination are modeled by a single conceptual state, and by a collection of exemplar-dependent operators. Therefore, they are consistent with cognitive modeling principles. This framework not only provides a uniform approach to model an entire data set, but, because all measurement operators are expressed in the same basis, allows us to address the question of compatibility of measurements. In particular, we present evidence that it may be possible to predict non-commutative effects from partial measurements of conceptual combinations.

GraDit: graph-based data repair algorithm for multiple data edits rule violations

NASA Astrophysics Data System (ADS)

Ode Zuhayeni Madjida, Wa; Gusti Bagus Baskara Nugraha, I.

2018-03-01

Constraint-based data cleaning captures data violation to a set of rule called data quality rules. The rules consist of integrity constraint and data edits. Structurally, they are similar, where the rule contain left hand side and right hand side. Previous research proposed a data repair algorithm for integrity constraint violation. The algorithm uses undirected hypergraph as rule violation representation. Nevertheless, this algorithm can not be applied for data edits because of different rule characteristics. This study proposed GraDit, a repair algorithm for data edits rule. First, we use bipartite-directed hypergraph as model representation of overall defined rules. These representation is used for getting interaction between violation rules and clean rules. On the other hand, we proposed undirected graph as violation representation. Our experimental study showed that algorithm with undirected graph as violation representation model gave better data quality than algorithm with undirected hypergraph as representation model.

Network Analysis of Students' Use of Representations in Problem Solving

NASA Astrophysics Data System (ADS)

McPadden, Daryl; Brewe, Eric

2016-03-01

We present the preliminary results of a study on student use of representations in problem solving within the Modeling Instruction - Electricity and Magnetism (MI-E&M) course. Representational competence is a critical skill needed for students to develop a sophisticated understanding of college science topics and to succeed in their science courses. In this study, 70 students from the MI-E&M, calculus-based course were given a survey of 25 physics problem statements both pre- and post- instruction, covering both Newtonian Mechanics and Electricity and Magnetism (E&M). For each problem statement, students were asked which representations they would use in that given situation. We analyze the survey results through network analysis, identifying which representations are linked together in which contexts. We also compare the representation networks for those students who had already taken the first-semester Modeling Instruction Mechanics course and those students who had taken a non-Modeling Mechanics course.

Do Knowledge-Component Models Need to Incorporate Representational Competencies?

ERIC Educational Resources Information Center

Rau, Martina Angela

2017-01-01

Traditional knowledge-component models describe students' content knowledge (e.g., their ability to carry out problem-solving procedures or their ability to reason about a concept). In many STEM domains, instruction uses multiple visual representations such as graphs, figures, and diagrams. The use of visual representations implies a…

Representational Translation with Concrete Models in Organic Chemistry

ERIC Educational Resources Information Center

Stull, Andrew T.; Hegarty, Mary; Dixon, Bonnie; Stieff, Mike

2012-01-01

In representation-rich domains such as organic chemistry, students must be facile and accurate when translating between different 2D representations, such as diagrams. We hypothesized that translating between organic chemistry diagrams would be more accurate when concrete models were used because difficult mental processes could be augmented by…

Impact of the Second Semester University Modeling Instruction Course on Students' Representation Choices

ERIC Educational Resources Information Center

McPadden, Daryl; Brewe, Eric

2017-01-01

Representation use is a critical skill for learning, problem solving, and communicating in science, especially in physics where multiple representations often scaffold the understanding of a phenomenon. University Modeling Instruction, which is an active-learning, research-based introductory physics curriculum centered on students' use of…

Deep Residual Network Predicts Cortical Representation and Organization of Visual Features for Rapid Categorization.

PubMed

Wen, Haiguang; Shi, Junxing; Chen, Wei; Liu, Zhongming

2018-02-28

The brain represents visual objects with topographic cortical patterns. To address how distributed visual representations enable object categorization, we established predictive encoding models based on a deep residual network, and trained them to predict cortical responses to natural movies. Using this predictive model, we mapped human cortical representations to 64,000 visual objects from 80 categories with high throughput and accuracy. Such representations covered both the ventral and dorsal pathways, reflected multiple levels of object features, and preserved semantic relationships between categories. In the entire visual cortex, object representations were organized into three clusters of categories: biological objects, non-biological objects, and background scenes. In a finer scale specific to each cluster, object representations revealed sub-clusters for further categorization. Such hierarchical clustering of category representations was mostly contributed by cortical representations of object features from middle to high levels. In summary, this study demonstrates a useful computational strategy to characterize the cortical organization and representations of visual features for rapid categorization.

Cognition and procedure representational requirements for predictive human performance models

NASA Technical Reports Server (NTRS)

Corker, K.

1992-01-01

Models and modeling environments for human performance are becoming significant contributors to early system design and analysis procedures. Issues of levels of automation, physical environment, informational environment, and manning requirements are being addressed by such man/machine analysis systems. The research reported here investigates the close interaction between models of human cognition and models that described procedural performance. We describe a methodology for the decomposition of aircrew procedures that supports interaction with models of cognition on the basis of procedures observed; that serves to identify cockpit/avionics information sources and crew information requirements; and that provides the structure to support methods for function allocation among crew and aiding systems. Our approach is to develop an object-oriented, modular, executable software representation of the aircrew, the aircraft, and the procedures necessary to satisfy flight-phase goals. We then encode in a time-based language, taxonomies of the conceptual, relational, and procedural constraints among the cockpit avionics and control system and the aircrew. We have designed and implemented a goals/procedures hierarchic representation sufficient to describe procedural flow in the cockpit. We then execute the procedural representation in simulation software and calculate the values of the flight instruments, aircraft state variables and crew resources using the constraints available from the relationship taxonomies. The system provides a flexible, extensible, manipulative and executable representation of aircrew and procedures that is generally applicable to crew/procedure task-analysis. The representation supports developed methods of intent inference, and is extensible to include issues of information requirements and functional allocation. We are attempting to link the procedural representation to models of cognitive functions to establish several intent inference methods including procedural backtracking with concurrent search, temporal reasoning, and constraint checking for partial ordering of procedures. Finally, the representation is being linked to models of human decision making processes that include heuristic, propositional and prescriptive judgement models that are sensitive to the procedural content in which the valuative functions are being performed.

Implementation multi representation and oral communication skills in Department of Physics Education on Elementary Physics II

NASA Astrophysics Data System (ADS)

Kusumawati, Intan; Marwoto, Putut; Linuwih, Suharto

2015-09-01

The ability of multi representation has been widely studied, but there has been no implementation through a model of learning. This study aimed to determine the ability of the students multi representation, relationships multi representation capabilities and oral communication skills, as well as the application of the relations between the two capabilities through learning model Presentatif Based on Multi representation (PBM) in solving optical geometric (Elementary Physics II). A concurrent mixed methods research methods with qualitative-quantitative weights. Means of collecting data in the form of the pre-test and post-test with essay form, observation sheets oral communication skills, and assessment of learning by observation sheet PBM-learning models all have a high degree of respectively validity category is 3.91; 4.22; 4.13; 3.88. Test reliability with Alpha Cronbach technique, reliability coefficient of 0.494. The students are department of Physics Education Unnes as a research subject. Sequence multi representation tendency of students from high to low in sequence, representation of M, D, G, V; whereas the order of accuracy, the group representation V, D, G, M. Relationship multi representation ability and oral communication skills, comparable/proportional. Implementation conjunction generate grounded theory. This study should be applied to the physics of matter, or any other university for comparison.

A model for process representation and synthesis. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Thomas, R. H.

1971-01-01

The problem of representing groups of loosely connected processes is investigated, and a model for process representation useful for synthesizing complex patterns of process behavior is developed. There are three parts, the first part isolates the concepts which form the basis for the process representation model by focusing on questions such as: What is a process; What is an event; Should one process be able to restrict the capabilities of another? The second part develops a model for process representation which captures the concepts and intuitions developed in the first part. The model presented is able to describe both the internal structure of individual processes and the interface structure between interacting processes. Much of the model's descriptive power derives from its use of the notion of process state as a vehicle for relating the internal and external aspects of process behavior. The third part demonstrates by example that the model for process representation is a useful one for synthesizing process behavior patterns. In it the model is used to define a variety of interesting process behavior patterns. The dissertation closes by suggesting how the model could be used as a semantic base for a very potent language extension facility.

Representations of self and other in the narratives of neglected, physically abused, and sexually abused preschoolers.

PubMed

Toth, S L; Cicchetti, D; Macfie, J; Emde, R N

1997-01-01

The MacArthur Story Stem Battery was used to examine maternal and self-representations in neglected, physically abused, sexually abused, and nonmaltreated comparison preschool children. The narratives of maltreated children contained more negative maternal representations and more negative self-representations than did the narratives of nonmaltreated children. Maltreated children also were more controlling with and less responsive to the examiner. In examining the differential impact of maltreatment subtype differences on maternal and self-representations, physically abused children evidenced the most negative maternal representations; they also had more negative self-representations than nonmaltreated children. Sexually abused children manifested more positive self-representations than neglected children. Despite these differences in the nature of maternal and self-representations, physically and sexually abused children both were more controlling and less responsive to the examiner. The investigation adds to the corpus of knowledge regarding disturbances in the self-system functioning of maltreated children and provides support for relations between representational models of self and other and the self-organizing function that these models exert on children's lives.

Acute effects of alcohol on intrusive memory development and viewpoint dependence in spatial memory support a dual representation model.

PubMed

Bisby, James A; King, John A; Brewin, Chris R; Burgess, Neil; Curran, H Valerie

2010-08-01

A dual representation model of intrusive memory proposes that personally experienced events give rise to two types of representation: an image-based, egocentric representation based on sensory-perceptual features; and a more abstract, allocentric representation that incorporates spatiotemporal context. The model proposes that intrusions reflect involuntary reactivation of egocentric representations in the absence of a corresponding allocentric representation. We tested the model by investigating the effect of alcohol on intrusive memories and, concurrently, on egocentric and allocentric spatial memory. With a double-blind independent group design participants were administered alcohol (.4 or .8 g/kg) or placebo. A virtual environment was used to present objects and test recognition memory from the same viewpoint as presentation (tapping egocentric memory) or a shifted viewpoint (tapping allocentric memory). Participants were also exposed to a trauma video and required to detail intrusive memories for 7 days, after which explicit memory was assessed. There was a selective impairment of shifted-view recognition after the low dose of alcohol, whereas the high dose induced a global impairment in same-view and shifted-view conditions. Alcohol showed a dose-dependent inverted "U"-shaped effect on intrusions, with only the low dose increasing the number of intrusions, replicating previous work. When same-view recognition was intact, decrements in shifted-view recognition were associated with increases in intrusions. The differential effect of alcohol on intrusive memories and on same/shifted-view recognition support a dual representation model in which intrusions might reflect an imbalance between two types of memory representation. These findings highlight important clinical implications, given alcohol's involvement in real-life trauma. Copyright 2010 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Acoustic and Lexical Representations for Affect Prediction in Spontaneous Conversations.

PubMed

Cao, Houwei; Savran, Arman; Verma, Ragini; Nenkova, Ani

2015-01-01

In this article we investigate what representations of acoustics and word usage are most suitable for predicting dimensions of affect|AROUSAL, VALANCE, POWER and EXPECTANCY|in spontaneous interactions. Our experiments are based on the AVEC 2012 challenge dataset. For lexical representations, we compare corpus-independent features based on psychological word norms of emotional dimensions, as well as corpus-dependent representations. We find that corpus-dependent bag of words approach with mutual information between word and emotion dimensions is by far the best representation. For the analysis of acoustics, we zero in on the question of granularity. We confirm on our corpus that utterance-level features are more predictive than word-level features. Further, we study more detailed representations in which the utterance is divided into regions of interest (ROI), each with separate representation. We introduce two ROI representations, which significantly outperform less informed approaches. In addition we show that acoustic models of emotion can be improved considerably by taking into account annotator agreement and training the model on smaller but reliable dataset. Finally we discuss the potential for improving prediction by combining the lexical and acoustic modalities. Simple fusion methods do not lead to consistent improvements over lexical classifiers alone but improve over acoustic models.

To What Degree Does Handling Concrete Molecular Models Promote the Ability to Translate and Coordinate between 2D and 3D Molecular Structure Representations? A Case Study with Algerian Students

ERIC Educational Resources Information Center

Mohamed-Salah, Boukhechem; Alain, Dumon

2016-01-01

This study aims to assess whether the handling of concrete ball-and-stick molecular models promotes translation between diagrammatic representations and a concrete model (or vice versa) and the coordination of the different types of structural representations of a given molecular structure. Forty-one Algerian undergraduate students were requested…

Applying the Common Sense Model to Understand Representations of Arsenic Contaminated Well Water

PubMed Central

Severtson, Dolores J.; Baumann, Linda C.; Brown, Roger L.

2015-01-01

Theory-based research is needed to understand how people respond to environmental health risk information. The common sense model of self-regulation and the mental models approach propose that information shapes individual’s personal understandings that influence their decisions and actions. We compare these frameworks and explain how the common sense model (CSM) was applied to describe and measure mental representations of arsenic contaminated well water. Educational information, key informant interviews, and environmental risk literature were used to develop survey items to measure dimensions of cognitive representations (identity, cause, timeline, consequences, control) and emotional representations. Surveys mailed to 1067 private well users with moderate and elevated arsenic levels yielded an 84% response rate (n=897). Exploratory and confirmatory factor analyses of data from the elevated arsenic group identified a factor structure that retained the CSM representational structure and was consistent across moderate and elevated arsenic groups. The CSM has utility for describing and measuring representations of environmental health risks thus supporting its application to environmental health risk communication research. PMID:18726811

Flexible Coding of Visual Working Memory Representations during Distraction.

PubMed

Lorenc, Elizabeth S; Sreenivasan, Kartik K; Nee, Derek E; Vandenbroucke, Annelinde R E; D'Esposito, Mark

2018-06-06

Visual working memory (VWM) recruits a broad network of brain regions, including prefrontal, parietal, and visual cortices. Recent evidence supports a "sensory recruitment" model of VWM, whereby precise visual details are maintained in the same stimulus-selective regions responsible for perception. A key question in evaluating the sensory recruitment model is how VWM representations persist through distracting visual input, given that the early visual areas that putatively represent VWM content are susceptible to interference from visual stimulation.To address this question, we used a functional magnetic resonance imaging inverted encoding model approach to quantitatively assess the effect of distractors on VWM representations in early visual cortex and the intraparietal sulcus (IPS), another region previously implicated in the storage of VWM information. This approach allowed us to reconstruct VWM representations for orientation, both before and after visual interference, and to examine whether oriented distractors systematically biased these representations. In our human participants (both male and female), we found that orientation information was maintained simultaneously in early visual areas and IPS in anticipation of possible distraction, and these representations persisted in the absence of distraction. Importantly, early visual representations were susceptible to interference; VWM orientations reconstructed from visual cortex were significantly biased toward distractors, corresponding to a small attractive bias in behavior. In contrast, IPS representations did not show such a bias. These results provide quantitative insight into the effect of interference on VWM representations, and they suggest a dynamic tradeoff between visual and parietal regions that allows flexible adaptation to task demands in service of VWM. SIGNIFICANCE STATEMENT Despite considerable evidence that stimulus-selective visual regions maintain precise visual information in working memory, it remains unclear how these representations persist through subsequent input. Here, we used quantitative model-based fMRI analyses to reconstruct the contents of working memory and examine the effects of distracting input. Although representations in the early visual areas were systematically biased by distractors, those in the intraparietal sulcus appeared distractor-resistant. In contrast, early visual representations were most reliable in the absence of distraction. These results demonstrate the dynamic, adaptive nature of visual working memory processes, and provide quantitative insight into the ways in which representations can be affected by interference. Further, they suggest that current models of working memory should be revised to incorporate this flexibility. Copyright © 2018 the authors 0270-6474/18/385267-10$15.00/0.

Representational Distance Learning for Deep Neural Networks

PubMed Central

McClure, Patrick; Kriegeskorte, Nikolaus

2016-01-01

Deep neural networks (DNNs) provide useful models of visual representational transformations. We present a method that enables a DNN (student) to learn from the internal representational spaces of a reference model (teacher), which could be another DNN or, in the future, a biological brain. Representational spaces of the student and the teacher are characterized by representational distance matrices (RDMs). We propose representational distance learning (RDL), a stochastic gradient descent method that drives the RDMs of the student to approximate the RDMs of the teacher. We demonstrate that RDL is competitive with other transfer learning techniques for two publicly available benchmark computer vision datasets (MNIST and CIFAR-100), while allowing for architectural differences between student and teacher. By pulling the student's RDMs toward those of the teacher, RDL significantly improved visual classification performance when compared to baseline networks that did not use transfer learning. In the future, RDL may enable combined supervised training of deep neural networks using task constraints (e.g., images and category labels) and constraints from brain-activity measurements, so as to build models that replicate the internal representational spaces of biological brains. PMID:28082889

Representational Distance Learning for Deep Neural Networks.

PubMed

McClure, Patrick; Kriegeskorte, Nikolaus

2016-01-01

Deep neural networks (DNNs) provide useful models of visual representational transformations. We present a method that enables a DNN (student) to learn from the internal representational spaces of a reference model (teacher), which could be another DNN or, in the future, a biological brain. Representational spaces of the student and the teacher are characterized by representational distance matrices (RDMs). We propose representational distance learning (RDL), a stochastic gradient descent method that drives the RDMs of the student to approximate the RDMs of the teacher. We demonstrate that RDL is competitive with other transfer learning techniques for two publicly available benchmark computer vision datasets (MNIST and CIFAR-100), while allowing for architectural differences between student and teacher. By pulling the student's RDMs toward those of the teacher, RDL significantly improved visual classification performance when compared to baseline networks that did not use transfer learning. In the future, RDL may enable combined supervised training of deep neural networks using task constraints (e.g., images and category labels) and constraints from brain-activity measurements, so as to build models that replicate the internal representational spaces of biological brains.

Transactional processes in the development of adult personality disorder symptoms.

PubMed

Carlson, Elizabeth A; Ruiz, Sarah K

2016-08-01

The development of adult personality disorder symptoms, including transactional processes of relationship representational and behavioral experience from infancy to early adolescence, was examined using longitudinal data from a risk sample (N = 162). Significant preliminary correlations were found between early caregiving experience and adult personality disorder symptoms and between representational and behavioral indices across time and adult symptomatology. Significant correlations were also found among diverse representational assessments (e.g., interview, drawing, and projective narrative) and between concurrent representational and observational measures of relationship functioning. Path models were analyzed to investigate the combined relations of caregiving experience in infancy; relationship representation and experience in early childhood, middle childhood, and early adolescence; and personality disorder symptoms in adulthood. The hypothesized model representing interactive contributions of representational and behavioral experience represented the data significantly better than competing models representing noninteractive contributions. Representational and behavioral indicators mediated the link between early caregiving quality and personality disorder symptoms. The findings extend previous studies of normative development and support an organizational developmental view that early relationship experiences contribute to socioemotional maladaptation as well as adaptation through the progressive transaction of mutually informing expectations and experience.

Bio-SimVerb and Bio-SimLex: wide-coverage evaluation sets of word similarity in biomedicine.

PubMed

Chiu, Billy; Pyysalo, Sampo; Vulić, Ivan; Korhonen, Anna

2018-02-05

Word representations support a variety of Natural Language Processing (NLP) tasks. The quality of these representations is typically assessed by comparing the distances in the induced vector spaces against human similarity judgements. Whereas comprehensive evaluation resources have recently been developed for the general domain, similar resources for biomedicine currently suffer from the lack of coverage, both in terms of word types included and with respect to the semantic distinctions. Notably, verbs have been excluded, although they are essential for the interpretation of biomedical language. Further, current resources do not discern between semantic similarity and semantic relatedness, although this has been proven as an important predictor of the usefulness of word representations and their performance in downstream applications. We present two novel comprehensive resources targeting the evaluation of word representations in biomedicine. These resources, Bio-SimVerb and Bio-SimLex, address the previously mentioned problems, and can be used for evaluations of verb and noun representations respectively. In our experiments, we have computed the Pearson's correlation between performances on intrinsic and extrinsic tasks using twelve popular state-of-the-art representation models (e.g. word2vec models). The intrinsic-extrinsic correlations using our datasets are notably higher than with previous intrinsic evaluation benchmarks such as UMNSRS and MayoSRS. In addition, when evaluating representation models for their abilities to capture verb and noun semantics individually, we show a considerable variation between performances across all models. Bio-SimVerb and Bio-SimLex enable intrinsic evaluation of word representations. This evaluation can serve as a predictor of performance on various downstream tasks in the biomedical domain. The results on Bio-SimVerb and Bio-SimLex using standard word representation models highlight the importance of developing dedicated evaluation resources for NLP in biomedicine for particular word classes (e.g. verbs). These are needed to identify the most accurate methods for learning class-specific representations. Bio-SimVerb and Bio-SimLex are publicly available.

Color Sparse Representations for Image Processing: Review, Models, and Prospects.

PubMed

Barthélemy, Quentin; Larue, Anthony; Mars, Jérôme I

2015-11-01

Sparse representations have been extended to deal with color images composed of three channels. A review of dictionary-learning-based sparse representations for color images is made here, detailing the differences between the models, and comparing their results on the real and simulated data. These models are considered in a unifying framework that is based on the degrees of freedom of the linear filtering/transformation of the color channels. Moreover, this allows it to be shown that the scalar quaternionic linear model is equivalent to constrained matrix-based color filtering, which highlights the filtering implicitly applied through this model. Based on this reformulation, the new color filtering model is introduced, using unconstrained filters. In this model, spatial morphologies of color images are encoded by atoms, and colors are encoded by color filters. Color variability is no longer captured in increasing the dictionary size, but with color filters, this gives an efficient color representation.

Continuous state-space representation of a bucket-type rainfall-runoff model: a case study with the GR4 model using state-space GR4 (version 1.0)

NASA Astrophysics Data System (ADS)

Santos, Léonard; Thirel, Guillaume; Perrin, Charles

2018-04-01

In many conceptual rainfall-runoff models, the water balance differential equations are not explicitly formulated. These differential equations are solved sequentially by splitting the equations into terms that can be solved analytically with a technique called operator splitting. As a result, only the solutions of the split equations are used to present the different models. This article provides a methodology to make the governing water balance equations of a bucket-type rainfall-runoff model explicit and to solve them continuously. This is done by setting up a comprehensive state-space representation of the model. By representing it in this way, the operator splitting, which makes the structural analysis of the model more complex, could be removed. In this state-space representation, the lag functions (unit hydrographs), which are frequent in rainfall-runoff models and make the resolution of the representation difficult, are first replaced by a so-called Nash cascade and then solved with a robust numerical integration technique. To illustrate this methodology, the GR4J model is taken as an example. The substitution of the unit hydrographs with a Nash cascade, even if it modifies the model behaviour when solved using operator splitting, does not modify it when the state-space representation is solved using an implicit integration technique. Indeed, the flow time series simulated by the new representation of the model are very similar to those simulated by the classic model. The use of a robust numerical technique that approximates a continuous-time model also improves the lag parameter consistency across time steps and provides a more time-consistent model with time-independent parameters.

Promoting Decimal Number Sense and Representational Fluency

ERIC Educational Resources Information Center

Suh, Jennifer M.; Johnston, Chris; Jamieson, Spencer; Mills, Michelle

2008-01-01

The abstract nature of mathematics requires the communication of mathematical ideas through multiple representations, such as words, symbols, pictures, objects, or actions. Building representational fluency involves using mathematical representations flexibly and being able to interpret and translate among these different models and mathematical…

Hologram representation of design data in an expert system knowledge base

NASA Technical Reports Server (NTRS)

Shiva, S. G.; Klon, Peter F.

1988-01-01

A novel representational scheme for design object descriptions is presented. An abstract notion of modules and signals is developed as a conceptual foundation for the scheme. This abstraction relates the objects to the meaning of system descriptions. Anchored on this abstraction, a representational model which incorporates dynamic semantics for these objects is presented. This representational model is called a hologram scheme since it represents dual level information, namely, structural and semantic. The benefits of this scheme are presented.

Formalizing nursing knowledge: from theories and models to ontologies.

PubMed

Peace, Jane; Brennan, Patricia Flatley

2009-01-01

Knowledge representation in nursing is poised to address the depth of nursing knowledge about the specific phenomena of importance to nursing. Nursing theories and models may provide a starting point for making this knowledge explicit in representations. We combined knowledge building methods from nursing and ontology design methods from biomedical informatics to create a nursing representation of family health history. Our experience provides an example of how knowledge representations may be created to facilitate electronic support for nursing practice and knowledge development.

Illness representations, coping, and illness outcomes in people with cancer: a systematic review and meta-analysis.

PubMed

Richardson, Emma M; Schüz, Natalie; Sanderson, Kristy; Scott, Jennifer L; Schüz, Benjamin

2017-06-01

Cancer is associated with negative health and emotional outcomes in those affected by it, suggesting the need to better understand the psychosocial determinants of illness outcomes and coping. The common sense model is the leading psychological model of self-regulation in the face of illness and assumes that subjective illness representations explain how people attempt to cope with illness. This systematic review and meta-analysis examines the associations of the common sense model's illness representation dimensions with health and coping outcomes in people with cancer. A systematic literature search located 54 studies fulfilling the inclusion criteria, with 38 providing sufficient data for meta-analysis. A narrative review of the remaining studies was also conducted. Random-effects models revealed small to moderate effect sizes (Fisher Z) for the relations between illness representations and coping behaviors (in particular between control perceptions, problem-focused coping, and cognitive reappraisal) and moderate to large effect sizes between illness representations and illness outcomes (in particular between identity, consequences, emotional representations, and psychological distress). The narrative review of studies with insufficient data provided similar results. The results indicate how illness representations relate to illness outcomes in people with cancer. However, more high-quality studies are needed to examine causal effects of illness representations on coping and outcomes. High heterogeneity indicates potential moderators of the relationships between illness representations and health and coping outcomes, including diagnostic, prognostic, and treatment-related variables. This review can inform the design of interventions to improve coping strategies and mental health outcomes in people with cancer. Copyright © 2016 John Wiley & Sons, Ltd.

Properties of heuristic search strategies

NASA Technical Reports Server (NTRS)

Vanderbrug, G. J.

1973-01-01

A directed graph is used to model the search space of a state space representation with single input operators, an AND/OR is used for problem reduction representations, and a theorem proving graph is used for state space representations with multiple input operators. These three graph models and heuristic strategies for searching them are surveyed. The completeness, admissibility, and optimality properties of search strategies which use the evaluation function f = (1 - omega)g = omega(h) are presented and interpreted using a representation of the search process in the plane. The use of multiple output operators to imply dependent successors, and thus obtain a formalism which includes all three types of representations, is discussed.

Implementation multi representation and oral communication skills in Department of Physics Education on Elementary Physics II

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kusumawati, Intan, E-mail: intankusumawati10@gmail.com; Marwoto, Putut, E-mail: pmarwoto@yahoo.com; Linuwih, Suharto, E-mail: suhartolinuwih@gmail.com

The ability of multi representation has been widely studied, but there has been no implementation through a model of learning. This study aimed to determine the ability of the students multi representation, relationships multi representation capabilities and oral communication skills, as well as the application of the relations between the two capabilities through learning model Presentatif Based on Multi representation (PBM) in solving optical geometric (Elementary Physics II). A concurrent mixed methods research methods with qualitative–quantitative weights. Means of collecting data in the form of the pre-test and post-test with essay form, observation sheets oral communication skills, and assessment ofmore » learning by observation sheet PBM–learning models all have a high degree of respectively validity category is 3.91; 4.22; 4.13; 3.88. Test reliability with Alpha Cronbach technique, reliability coefficient of 0.494. The students are department of Physics Education Unnes as a research subject. Sequence multi representation tendency of students from high to low in sequence, representation of M, D, G, V; whereas the order of accuracy, the group representation V, D, G, M. Relationship multi representation ability and oral communication skills, comparable/proportional. Implementation conjunction generate grounded theory. This study should be applied to the physics of matter, or any other university for comparison.« less

What Can Biochemistry Students Learn about Protein Translation? Using Variation Theory to Explore the Space of Learning Created by Some Common External Representations

ERIC Educational Resources Information Center

Bussey, Thomas J.

2013-01-01

Biochemistry education relies heavily on students' ability to visualize abstract cellular and molecular processes, mechanisms, and components. As such, biochemistry educators often turn to external representations to provide tangible, working models from which students' internal representations (mental models) can be constructed, evaluated, and…

(Op)posing Representations: Disentangling the Model Minority and the Foreigner.

ERIC Educational Resources Information Center

Lei, Joy L.

This paper examines how the representations of Asian Americans as the model minority and as perpetual foreigners play off one another to shape the positioning and experiences of Asian American students in U.S. schools and maintain the dominant racial order in the United States. Although the representation of Asian Americans as a high-achieving and…

Dynamics of scene representations in the human brain revealed by magnetoencephalography and deep neural networks

PubMed Central

Cichy, Radoslaw Martin; Khosla, Aditya; Pantazis, Dimitrios; Oliva, Aude

2017-01-01

Human scene recognition is a rapid multistep process evolving over time from single scene image to spatial layout processing. We used multivariate pattern analyses on magnetoencephalography (MEG) data to unravel the time course of this cortical process. Following an early signal for lower-level visual analysis of single scenes at ~100 ms, we found a marker of real-world scene size, i.e. spatial layout processing, at ~250 ms indexing neural representations robust to changes in unrelated scene properties and viewing conditions. For a quantitative model of how scene size representations may arise in the brain, we compared MEG data to a deep neural network model trained on scene classification. Representations of scene size emerged intrinsically in the model, and resolved emerging neural scene size representation. Together our data provide a first description of an electrophysiological signal for layout processing in humans, and suggest that deep neural networks are a promising framework to investigate how spatial layout representations emerge in the human brain. PMID:27039703

A depictive neural model for the representation of motion verbs.

PubMed

Rao, Sunil; Aleksander, Igor

2011-11-01

In this paper, we present a depictive neural model for the representation of motion verb semantics in neural models of visual awareness. The problem of modelling motion verb representation is shown to be one of function application, mapping a set of given input variables defining the moving object and the path of motion to a defined output outcome in the motion recognition context. The particular function-applicative implementation and consequent recognition model design presented are seen as arising from a noun-adjective recognition model enabling the recognition of colour adjectives as applied to a set of shapes representing objects to be recognised. The presence of such a function application scheme and a separately implemented position identification and path labelling scheme are accordingly shown to be the primitives required to enable the design and construction of a composite depictive motion verb recognition scheme. Extensions to the presented design to enable the representation of transitive verbs are also discussed.

Parents’ Education Shapes, but Does Not Originate, the Disability Representations of Their Children

PubMed Central

Meloni, Fabio; Federici, Stefano; Dennis, John Lawrence

2015-01-01

The present research tested whether children’s disability representations are influenced by cultural variables (e.g., social activities, parent education, custom complex variables) or by cognitive constraints. Four questionnaires were administered to a sample of 76 primary school aged children and one of their parents (n = 152). Questionnaires included both open-ended and closed-ended questions. The open-ended questions were created to collect uncensored personal explanations of disability, whereas the closed-ended questions were designed to elicit a response of agreement for statements built on the basis of the three most widespread disability models: individual, social, and biopsychosocial. For youngest children (6–8 years old), people with disabilities are thought of as being sick. This early disability representation of children is consistent with the individual model of disability and independent from parents’ disability explanations and representations. As children grow older (9–11 years old), knowledge regarding disability increases and stereotypical beliefs about disability decrease, by tending to espouse their parents representations. The individual model remains in the background for the adults too, emerging when the respondents rely on their most immediately available mental representation of disability such as when they respond to an open-ended question. These findings support that the youngest children are not completely permeable to social representations of disability likely due to cognitive constraints. Nevertheless, as the age grows, children appear educable on perspectives of disability adhering to a model of disability representation integral with social context and parent perspective. PMID:26053585

Maximum entropy perception-action space: a Bayesian model of eye movement selection

NASA Astrophysics Data System (ADS)

Colas, Francis; Bessière, Pierre; Girard, Benoît

2011-03-01

In this article, we investigate the issue of the selection of eye movements in a free-eye Multiple Object Tracking task. We propose a Bayesian model of retinotopic maps with a complex logarithmic mapping. This model is structured in two parts: a representation of the visual scene, and a decision model based on the representation. We compare different decision models based on different features of the representation and we show that taking into account uncertainty helps predict the eye movements of subjects recorded in a psychophysics experiment. Finally, based on experimental data, we postulate that the complex logarithmic mapping has a functional relevance, as the density of objects in this space in more uniform than expected. This may indicate that the representation space and control strategies are such that the object density is of maximum entropy.

The effect of training methodology on knowledge representation in categorization.

PubMed

Hélie, Sébastien; Shamloo, Farzin; Ell, Shawn W

2017-01-01

Category representations can be broadly classified as containing within-category information or between-category information. Although such representational differences can have a profound impact on decision-making, relatively little is known about the factors contributing to the development and generalizability of different types of category representations. These issues are addressed by investigating the impact of training methodology and category structures using a traditional empirical approach as well as the novel adaptation of computational modeling techniques from the machine learning literature. Experiment 1 focused on rule-based (RB) category structures thought to promote between-category representations. Participants learned two sets of two categories during training and were subsequently tested on a novel categorization problem using the training categories. Classification training resulted in a bias toward between-category representations whereas concept training resulted in a bias toward within-category representations. Experiment 2 focused on information-integration (II) category structures thought to promote within-category representations. With II structures, there was a bias toward within-category representations regardless of training methodology. Furthermore, in both experiments, computational modeling suggests that only within-category representations could support generalization during the test phase. These data suggest that within-category representations may be dominant and more robust for supporting the reconfiguration of current knowledge to support generalization.

The effect of training methodology on knowledge representation in categorization

PubMed Central

Shamloo, Farzin; Ell, Shawn W.

2017-01-01

Category representations can be broadly classified as containing within–category information or between–category information. Although such representational differences can have a profound impact on decision–making, relatively little is known about the factors contributing to the development and generalizability of different types of category representations. These issues are addressed by investigating the impact of training methodology and category structures using a traditional empirical approach as well as the novel adaptation of computational modeling techniques from the machine learning literature. Experiment 1 focused on rule–based (RB) category structures thought to promote between–category representations. Participants learned two sets of two categories during training and were subsequently tested on a novel categorization problem using the training categories. Classification training resulted in a bias toward between–category representations whereas concept training resulted in a bias toward within–category representations. Experiment 2 focused on information-integration (II) category structures thought to promote within–category representations. With II structures, there was a bias toward within–category representations regardless of training methodology. Furthermore, in both experiments, computational modeling suggests that only within–category representations could support generalization during the test phase. These data suggest that within–category representations may be dominant and more robust for supporting the reconfiguration of current knowledge to support generalization. PMID:28846732

Prediction task guided representation learning of medical codes in EHR.

PubMed

Cui, Liwen; Xie, Xiaolei; Shen, Zuojun

2018-06-18

There have been rapidly growing applications using machine learning models for predictive analytics in Electronic Health Records (EHR) to improve the quality of hospital services and the efficiency of healthcare resource utilization. A fundamental and crucial step in developing such models is to convert medical codes in EHR to feature vectors. These medical codes are used to represent diagnoses or procedures. Their vector representations have a tremendous impact on the performance of machine learning models. Recently, some researchers have utilized representation learning methods from Natural Language Processing (NLP) to learn vector representations of medical codes. However, most previous approaches are unsupervised, i.e. the generation of medical code vectors is independent from prediction tasks. Thus, the obtained feature vectors may be inappropriate for a specific prediction task. Moreover, unsupervised methods often require a lot of samples to obtain reliable results, but most practical problems have very limited patient samples. In this paper, we develop a new method called Prediction Task Guided Health Record Aggregation (PTGHRA), which aggregates health records guided by prediction tasks, to construct training corpus for various representation learning models. Compared with unsupervised approaches, representation learning models integrated with PTGHRA yield a significant improvement in predictive capability of generated medical code vectors, especially for limited training samples. Copyright © 2018. Published by Elsevier Inc.

ART-ML: a new markup language for modelling and representation of biological processes in cardiovascular diseases.

PubMed

Karvounis, E C; Exarchos, T P; Fotiou, E; Sakellarios, A I; Iliopoulou, D; Koutsouris, D; Fotiadis, D I

2013-01-01

With an ever increasing number of biological models available on the internet, a standardized modelling framework is required to allow information to be accessed and visualized. In this paper we propose a novel Extensible Markup Language (XML) based format called ART-ML that aims at supporting the interoperability and the reuse of models of geometry, blood flow, plaque progression and stent modelling, exported by any cardiovascular disease modelling software. ART-ML has been developed and tested using ARTool. ARTool is a platform for the automatic processing of various image modalities of coronary and carotid arteries. The images and their content are fused to develop morphological models of the arteries in 3D representations. All the above described procedures integrate disparate data formats, protocols and tools. ART-ML proposes a representation way, expanding ARTool, for interpretability of the individual resources, creating a standard unified model for the description of data and, consequently, a format for their exchange and representation that is machine independent. More specifically, ARTool platform incorporates efficient algorithms which are able to perform blood flow simulations and atherosclerotic plaque evolution modelling. Integration of data layers between different modules within ARTool are based upon the interchange of information included in the ART-ML model repository. ART-ML provides a markup representation that enables the representation and management of embedded models within the cardiovascular disease modelling platform, the storage and interchange of well-defined information. The corresponding ART-ML model incorporates all relevant information regarding geometry, blood flow, plaque progression and stent modelling procedures. All created models are stored in a model repository database which is accessible to the research community using efficient web interfaces, enabling the interoperability of any cardiovascular disease modelling software models. ART-ML can be used as a reference ML model in multiscale simulations of plaque formation and progression, incorporating all scales of the biological processes.

Collaborative Project: Improving the Representation of Coastal and Estuarine Processes in Earth System Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bryan, Frank; Dennis, John; MacCready, Parker

This project aimed to improve long term global climate simulations by resolving and enhancing the representation of the processes involved in the cycling of freshwater through estuaries and coastal regions. This was a collaborative multi-institution project consisting of physical oceanographers, climate model developers, and computational scientists. It specifically targeted the DOE objectives of advancing simulation and predictive capability of climate models through improvements in resolution and physical process representation.

Final Report Collaborative Project: Improving the Representation of Coastal and Estuarine Processes in Earth System Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bryan, Frank; Dennis, John; MacCready, Parker

This project aimed to improve long term global climate simulations by resolving and enhancing the representation of the processes involved in the cycling of freshwater through estuaries and coastal regions. This was a collaborative multi-institution project consisting of physical oceanographers, climate model developers, and computational scientists. It specifically targeted the DOE objectives of advancing simulation and predictive capability of climate models through improvements in resolution and physical process representation.

Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives.

PubMed

Zhong, Junpei; Cangelosi, Angelo; Wermter, Stefan

2014-01-01

The acquisition of symbolic and linguistic representations of sensorimotor behavior is a cognitive process performed by an agent when it is executing and/or observing own and others' actions. According to Piaget's theory of cognitive development, these representations develop during the sensorimotor stage and the pre-operational stage. We propose a model that relates the conceptualization of the higher-level information from visual stimuli to the development of ventral/dorsal visual streams. This model employs neural network architecture incorporating a predictive sensory module based on an RNNPB (Recurrent Neural Network with Parametric Biases) and a horizontal product model. We exemplify this model through a robot passively observing an object to learn its features and movements. During the learning process of observing sensorimotor primitives, i.e., observing a set of trajectories of arm movements and its oriented object features, the pre-symbolic representation is self-organized in the parametric units. These representational units act as bifurcation parameters, guiding the robot to recognize and predict various learned sensorimotor primitives. The pre-symbolic representation also accounts for the learning of sensorimotor primitives in a latent learning context.

Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives

PubMed Central

Zhong, Junpei; Cangelosi, Angelo; Wermter, Stefan

2014-01-01

The acquisition of symbolic and linguistic representations of sensorimotor behavior is a cognitive process performed by an agent when it is executing and/or observing own and others' actions. According to Piaget's theory of cognitive development, these representations develop during the sensorimotor stage and the pre-operational stage. We propose a model that relates the conceptualization of the higher-level information from visual stimuli to the development of ventral/dorsal visual streams. This model employs neural network architecture incorporating a predictive sensory module based on an RNNPB (Recurrent Neural Network with Parametric Biases) and a horizontal product model. We exemplify this model through a robot passively observing an object to learn its features and movements. During the learning process of observing sensorimotor primitives, i.e., observing a set of trajectories of arm movements and its oriented object features, the pre-symbolic representation is self-organized in the parametric units. These representational units act as bifurcation parameters, guiding the robot to recognize and predict various learned sensorimotor primitives. The pre-symbolic representation also accounts for the learning of sensorimotor primitives in a latent learning context. PMID:24550798

A study of different modeling choices for simulating platelets within the immersed boundary method

PubMed Central

Shankar, Varun; Wright, Grady B.; Fogelson, Aaron L.; Kirby, Robert M.

2012-01-01

The Immersed Boundary (IB) method is a widely-used numerical methodology for the simulation of fluid–structure interaction problems. The IB method utilizes an Eulerian discretization for the fluid equations of motion while maintaining a Lagrangian representation of structural objects. Operators are defined for transmitting information (forces and velocities) between these two representations. Most IB simulations represent their structures with piecewise linear approximations and utilize Hookean spring models to approximate structural forces. Our specific motivation is the modeling of platelets in hemodynamic flows. In this paper, we study two alternative representations – radial basis functions (RBFs) and Fourier-based (trigonometric polynomials and spherical harmonics) representations – for the modeling of platelets in two and three dimensions within the IB framework, and compare our results with the traditional piecewise linear approximation methodology. For different representative shapes, we examine the geometric modeling errors (position and normal vectors), force computation errors, and computational cost and provide an engineering trade-off strategy for when and why one might select to employ these different representations. PMID:23585704

Evaluation, Use, and Refinement of Knowledge Representations through Acquisition Modeling

ERIC Educational Resources Information Center

Pearl, Lisa

2017-01-01

Generative approaches to language have long recognized the natural link between theories of knowledge representation and theories of knowledge acquisition. The basic idea is that the knowledge representations provided by Universal Grammar enable children to acquire language as reliably as they do because these representations highlight the…

The Array Representation and Primary Children's Understanding and Reasoning in Multiplication

ERIC Educational Resources Information Center

Barmby, Patrick; Harries, Tony; Higgins, Steve; Suggate, Jennifer

2009-01-01

We examine whether the array representation can support children's understanding and reasoning in multiplication. To begin, we define what we mean by understanding and reasoning. We adopt a "representational-reasoning" model of understanding, where understanding is seen as connections being made between mental representations of concepts, with…

A Rational Analysis of the Acquisition of Multisensory Representations

ERIC Educational Resources Information Center

Yildirim, Ilker; Jacobs, Robert A.

2012-01-01

How do people learn multisensory, or amodal, representations, and what consequences do these representations have for perceptual performance? We address this question by performing a rational analysis of the problem of learning multisensory representations. This analysis makes use of a Bayesian nonparametric model that acquires latent multisensory…

Texture-Based Correspondence Display

NASA Technical Reports Server (NTRS)

Gerald-Yamasaki, Michael

2004-01-01

Texture-based correspondence display is a methodology to display corresponding data elements in visual representations of complex multidimensional, multivariate data. Texture is utilized as a persistent medium to contain a visual representation model and as a means to create multiple renditions of data where color is used to identify correspondence. Corresponding data elements are displayed over a variety of visual metaphors in a normal rendering process without adding extraneous linking metadata creation and maintenance. The effectiveness of visual representation for understanding data is extended to the expression of the visual representation model in texture.

Representational geometry: integrating cognition, computation, and the brain

PubMed Central

Kriegeskorte, Nikolaus; Kievit, Rogier A.

2013-01-01

The cognitive concept of representation plays a key role in theories of brain information processing. However, linking neuronal activity to representational content and cognitive theory remains challenging. Recent studies have characterized the representational geometry of neural population codes by means of representational distance matrices, enabling researchers to compare representations across stages of processing and to test cognitive and computational theories. Representational geometry provides a useful intermediate level of description, capturing both the information represented in a neuronal population code and the format in which it is represented. We review recent insights gained with this approach in perception, memory, cognition, and action. Analyses of representational geometry can compare representations between models and the brain, and promise to explain brain computation as transformation of representational similarity structure. PMID:23876494

Using topographic networks to build a representation of consciousness.

PubMed

Tinsley, Chris J

2008-04-01

The subject of consciousness has intrigued both psychologists and neuroscientists for many years. Recently, following many recent advances in the emerging field of cognitive neuroscience, there is the possibility that this fundamental process may soon be explained. In particular, there have been dramatic insights gained into the mechanisms of attention, cognition and perception in recent decades. Here, simple network models are proposed which are used to create a representation of consciousness. The models are inspired by the structure of the thalamus and all incorporate topographic layers in their structure. Operation of the models allows filtering of the information reaching the representation according to (1) modality and/or (2) sub-modality, in addition several of the models allowing filtering at the topographic level. The models presented have different structures and employ different integrative mechanisms to produce gating or amplification at different levels; the resultant representations of consciousness are discussed.

Implications of Neuroscientific Evidence for the Cognitive Models of Post-Traumatic Stress Disorder

ERIC Educational Resources Information Center

Cruwys, Tegan; O'Kearney, Richard

2008-01-01

Brewin's dual representation theory, Ehlers and Clark's cognitive appraisal model, and Dalgleish's schematic, propositional, analogue and associative representational systems model are considered in the light of recent evidence on the neural substrates of post-traumatic stress disorder (PTSD). The models' proposals about the cognitive mechanism of…

Visualization and Rule Validation in Human-Behavior Representation

ERIC Educational Resources Information Center

Moya, Lisa Jean; McKenzie, Frederic D.; Nguyen, Quynh-Anh H.

2008-01-01

Human behavior representation (HBR) models simulate human behaviors and responses. The Joint Crowd Federate [TM] cognitive model developed by the Virginia Modeling, Analysis, and Simulation Center (VMASC) and licensed by WernerAnderson, Inc., models the cognitive behavior of crowds to provide credible crowd behavior in support of military…

Plot Scale Factor Models for Standard Orthographic Views

ERIC Educational Resources Information Center

Osakue, Edward E.

2007-01-01

Geometric modeling provides graphic representations of real or abstract objects. Realistic representation requires three dimensional (3D) attributes since natural objects have three principal dimensions. CAD software gives the user the ability to construct realistic 3D models of objects, but often prints of these models must be generated on two…

Images of Animals: Interpreting Three-Dimensional, Life-Sized 'Representations' of Animals--Zoo, Museum and Robotic Animals.

ERIC Educational Resources Information Center

Tunnicliffe, Sue Dale

A visit to the natural history museum is part of many pupils' educational program. One way of investigating what children learn about animals is to examine the mental models they reveal through their talk when they come face to face with animal representations. In this study, representations were provided by: (1) robotic models in a museum; (2)…

Towards a category theory approach to analogy: Analyzing re-representation and acquisition of numerical knowledge.

PubMed

Navarrete, Jairo A; Dartnell, Pablo

2017-08-01

Category Theory, a branch of mathematics, has shown promise as a modeling framework for higher-level cognition. We introduce an algebraic model for analogy that uses the language of category theory to explore analogy-related cognitive phenomena. To illustrate the potential of this approach, we use this model to explore three objects of study in cognitive literature. First, (a) we use commutative diagrams to analyze an effect of playing particular educational board games on the learning of numbers. Second, (b) we employ a notion called coequalizer as a formal model of re-representation that explains a property of computational models of analogy called "flexibility" whereby non-similar representational elements are considered matches and placed in structural correspondence. Finally, (c) we build a formal learning model which shows that re-representation, language processing and analogy making can explain the acquisition of knowledge of rational numbers. These objects of study provide a picture of acquisition of numerical knowledge that is compatible with empirical evidence and offers insights on possible connections between notions such as relational knowledge, analogy, learning, conceptual knowledge, re-representation and procedural knowledge. This suggests that the approach presented here facilitates mathematical modeling of cognition and provides novel ways to think about analogy-related cognitive phenomena.

Towards a category theory approach to analogy: Analyzing re-representation and acquisition of numerical knowledge

PubMed Central

2017-01-01

Category Theory, a branch of mathematics, has shown promise as a modeling framework for higher-level cognition. We introduce an algebraic model for analogy that uses the language of category theory to explore analogy-related cognitive phenomena. To illustrate the potential of this approach, we use this model to explore three objects of study in cognitive literature. First, (a) we use commutative diagrams to analyze an effect of playing particular educational board games on the learning of numbers. Second, (b) we employ a notion called coequalizer as a formal model of re-representation that explains a property of computational models of analogy called “flexibility” whereby non-similar representational elements are considered matches and placed in structural correspondence. Finally, (c) we build a formal learning model which shows that re-representation, language processing and analogy making can explain the acquisition of knowledge of rational numbers. These objects of study provide a picture of acquisition of numerical knowledge that is compatible with empirical evidence and offers insights on possible connections between notions such as relational knowledge, analogy, learning, conceptual knowledge, re-representation and procedural knowledge. This suggests that the approach presented here facilitates mathematical modeling of cognition and provides novel ways to think about analogy-related cognitive phenomena. PMID:28841643

The Differential Contributions of Conceptual Representation Format and Language Structure to Levels of Semantic Abstraction Capacity.

PubMed

Gainotti, Guido

2017-06-01

This paper reviews some controversies concerning the original and revised versions of the 'hub-and-spoke' model of conceptual representations and their implication for abstraction capacity levels. The 'hub-and-spoke' model, which is based on data gathered in patients with semantic dementia (SD), is the most authoritative model of conceptual knowledge. Patterson et al.'s (Nature Reviews Neuroscience, 8(12), 976-987, 2007) classical version of this model maintained that conceptual representations are stored in a unitary 'amodal' format in the right and left anterior temporal lobes (ATLs), because in SD the semantic disorder cuts across modalities and categories. Several authors questioned the unitary nature of these representations. They showed that the semantic impairment is 'multi-modal'only in the advanced stages of SD, when atrophy affects the ATLs bilaterally, but that impariments can be modality-specific in lateralised (early) stages of the disease. In these cases, SD mainly affects lexical-semantic knowledge when atrophy predominates on the left side and pictorial representations when atrophy prevails on the right side. Some aspects of the model (i.e. the importance of spokes, the multimodal format of representations and the graded convergence of modalities within the ATLs), which had already been outlined by Rogers et al. (Psychological Review, 111(1), 205-235, 2004) in a computational model of SD, were strengthened by these results. The relevance of these theoretical problems and of empirical data concerning the neural substrate of concrete and abstract words is discussed critically. The conclusion of the review is that the highest levels of abstraction are due more to the structuring influence of language than to the format of representations.

Population Coding of Visual Space: Modeling

PubMed Central

Lehky, Sidney R.; Sereno, Anne B.

2011-01-01

We examine how the representation of space is affected by receptive field (RF) characteristics of the encoding population. Spatial responses were defined by overlapping Gaussian RFs. These responses were analyzed using multidimensional scaling to extract the representation of global space implicit in population activity. Spatial representations were based purely on firing rates, which were not labeled with RF characteristics (tuning curve peak location, for example), differentiating this approach from many other population coding models. Because responses were unlabeled, this model represents space using intrinsic coding, extracting relative positions amongst stimuli, rather than extrinsic coding where known RF characteristics provide a reference frame for extracting absolute positions. Two parameters were particularly important: RF diameter and RF dispersion, where dispersion indicates how broadly RF centers are spread out from the fovea. For large RFs, the model was able to form metrically accurate representations of physical space on low-dimensional manifolds embedded within the high-dimensional neural population response space, suggesting that in some cases the neural representation of space may be dimensionally isomorphic with 3D physical space. Smaller RF sizes degraded and distorted the spatial representation, with the smallest RF sizes (present in early visual areas) being unable to recover even a topologically consistent rendition of space on low-dimensional manifolds. Finally, although positional invariance of stimulus responses has long been associated with large RFs in object recognition models, we found RF dispersion rather than RF diameter to be the critical parameter. In fact, at a population level, the modeling suggests that higher ventral stream areas with highly restricted RF dispersion would be unable to achieve positionally-invariant representations beyond this narrow region around fixation. PMID:21344012

Internal Representational Models of Attachment Relationships.

ERIC Educational Resources Information Center

Crittenden, Patricia M.

This paper outlines several properties of internal representational models (IRMs) and offers terminology that may help to differentiate the models. Properties of IRMs include focus, memory systems, content, cognitive function, "metastructure," quality of attachment, behavioral strategies, and attitude toward attachment. An IRM focuses on…

Dynamics of scene representations in the human brain revealed by magnetoencephalography and deep neural networks.

PubMed

Martin Cichy, Radoslaw; Khosla, Aditya; Pantazis, Dimitrios; Oliva, Aude

2017-06-01

Human scene recognition is a rapid multistep process evolving over time from single scene image to spatial layout processing. We used multivariate pattern analyses on magnetoencephalography (MEG) data to unravel the time course of this cortical process. Following an early signal for lower-level visual analysis of single scenes at ~100ms, we found a marker of real-world scene size, i.e. spatial layout processing, at ~250ms indexing neural representations robust to changes in unrelated scene properties and viewing conditions. For a quantitative model of how scene size representations may arise in the brain, we compared MEG data to a deep neural network model trained on scene classification. Representations of scene size emerged intrinsically in the model, and resolved emerging neural scene size representation. Together our data provide a first description of an electrophysiological signal for layout processing in humans, and suggest that deep neural networks are a promising framework to investigate how spatial layout representations emerge in the human brain. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Knowledge-based vision and simple visual machines.

PubMed Central

Cliff, D; Noble, J

1997-01-01

The vast majority of work in machine vision emphasizes the representation of perceived objects and events: it is these internal representations that incorporate the 'knowledge' in knowledge-based vision or form the 'models' in model-based vision. In this paper, we discuss simple machine vision systems developed by artificial evolution rather than traditional engineering design techniques, and note that the task of identifying internal representations within such systems is made difficult by the lack of an operational definition of representation at the causal mechanistic level. Consequently, we question the nature and indeed the existence of representations posited to be used within natural vision systems (i.e. animals). We conclude that representations argued for on a priori grounds by external observers of a particular vision system may well be illusory, and are at best place-holders for yet-to-be-identified causal mechanistic interactions. That is, applying the knowledge-based vision approach in the understanding of evolved systems (machines or animals) may well lead to theories and models that are internally consistent, computationally plausible, and entirely wrong. PMID:9304684

The semantic representation of prejudice and stereotypes.

PubMed

Bhatia, Sudeep

2017-07-01

We use a theory of semantic representation to study prejudice and stereotyping. Particularly, we consider large datasets of newspaper articles published in the United States, and apply latent semantic analysis (LSA), a prominent model of human semantic memory, to these datasets to learn representations for common male and female, White, African American, and Latino names. LSA performs a singular value decomposition on word distribution statistics in order to recover word vector representations, and we find that our recovered representations display the types of biases observed in human participants using tasks such as the implicit association test. Importantly, these biases are strongest for vector representations with moderate dimensionality, and weaken or disappear for representations with very high or very low dimensionality. Moderate dimensional LSA models are also the best at learning race, ethnicity, and gender-based categories, suggesting that social category knowledge, acquired through dimensionality reduction on word distribution statistics, can facilitate prejudiced and stereotyped associations. Copyright © 2017 Elsevier B.V. All rights reserved.

Understanding Deep Representations Learned in Modeling Users Likes.

PubMed

Guntuku, Sharath Chandra; Zhou, Joey Tianyi; Roy, Sujoy; Lin, Weisi; Tsang, Ivor W

2016-08-01

Automatically understanding and discriminating different users' liking for an image is a challenging problem. This is because the relationship between image features (even semantic ones extracted by existing tools, viz., faces, objects, and so on) and users' likes is non-linear, influenced by several subtle factors. This paper presents a deep bi-modal knowledge representation of images based on their visual content and associated tags (text). A mapping step between the different levels of visual and textual representations allows for the transfer of semantic knowledge between the two modalities. Feature selection is applied before learning deep representation to identify the important features for a user to like an image. The proposed representation is shown to be effective in discriminating users based on images they like and also in recommending images that a given user likes, outperforming the state-of-the-art feature representations by  ∼ 15 %-20%. Beyond this test-set performance, an attempt is made to qualitatively understand the representations learned by the deep architecture used to model user likes.

Learning multisensory representations for auditory-visual transfer of sequence category knowledge: a probabilistic language of thought approach.

PubMed

Yildirim, Ilker; Jacobs, Robert A

2015-06-01

If a person is trained to recognize or categorize objects or events using one sensory modality, the person can often recognize or categorize those same (or similar) objects and events via a novel modality. This phenomenon is an instance of cross-modal transfer of knowledge. Here, we study the Multisensory Hypothesis which states that people extract the intrinsic, modality-independent properties of objects and events, and represent these properties in multisensory representations. These representations underlie cross-modal transfer of knowledge. We conducted an experiment evaluating whether people transfer sequence category knowledge across auditory and visual domains. Our experimental data clearly indicate that we do. We also developed a computational model accounting for our experimental results. Consistent with the probabilistic language of thought approach to cognitive modeling, our model formalizes multisensory representations as symbolic "computer programs" and uses Bayesian inference to learn these representations. Because the model demonstrates how the acquisition and use of amodal, multisensory representations can underlie cross-modal transfer of knowledge, and because the model accounts for subjects' experimental performances, our work lends credence to the Multisensory Hypothesis. Overall, our work suggests that people automatically extract and represent objects' and events' intrinsic properties, and use these properties to process and understand the same (and similar) objects and events when they are perceived through novel sensory modalities.

Overcoming the Subject-Object Dichotomy in Urban Modeling: Axial Maps as Geometric Representations of Affordances in the Built Environment.

PubMed

Marcus, Lars

2018-01-01

The world is witnessing unprecedented urbanization, bringing extreme challenges to contemporary practices in urban planning and design. This calls for improved urban models that can generate new knowledge and enhance practical skill. Importantly, any urban model embodies a conception of the relation between humans and the physical environment. In urban modeling this is typically conceived of as a relation between human subjects and an environmental object, thereby reproducing a humans-environment dichotomy. Alternative modeling traditions, such as space syntax that originates in architecture rather than geography, have tried to overcome this dichotomy. Central in this effort is the development of new representations of urban space, such as in the case of space syntax, the axial map. This form of representation aims to integrate both human behavior and the physical environment into one and the same description. Interestingly, models based on these representations have proved to better capture pedestrian movement than regular models. Pedestrian movement, as well as other kinds of human flows in urban space, is essential for urban modeling, since increasingly flows of this kind are understood as the driver in urban processes. Critical for a full understanding of space syntax modeling is the ontology of its' representations, such as the axial map. Space syntax theory here often refers to James Gibson's "Theory of affordances," where the concept of affordances, in a manner similar to axial maps, aims to bridge the subject-object dichotomy by neither constituting physical properties of the environment or human behavior, but rather what emerges in the meeting between the two. In extension of this, the axial map can be interpreted as a representation of how the physical form of the environment affords human accessibility and visibility in urban space. This paper presents a close examination of the form of representations developed in space syntax methodology, in particular in the light of Gibson's "theory of affordances." The overarching aim is to contribute to a theoretical framework for urban models based on affordances, which may support the overcoming of the subject-object dichotomy in such models, here deemed essential for a greater social-ecological sustainability of cities.

How do agents represent?

NASA Astrophysics Data System (ADS)

Ryan, Alex

Representation is inherent to the concept of an agent, but its importance in complex systems has not yet been widely recognised. In this paper I introduce Peirce's theory of signs, which facilitates a definition of representation in general. In summary, representation means that for some agent, a model is used to stand in for another entity in a way that shapes the behaviour of the agent with respect to that entity. Representation in general is then related to the theories of representation that have developed within different disciplines. I compare theories of representation from metaphysics, military theory and systems theory. Additional complications arise in explaining the special case of mental representations, which is the focus of cognitive science. I consider the dominant theory of cognition — that the brain is a representational device — as well as the sceptical anti-representational response. Finally, I argue that representation distinguishes agents from non-representational objects: agents are objects capable of representation.

Representational geometry: integrating cognition, computation, and the brain.

PubMed

Kriegeskorte, Nikolaus; Kievit, Rogier A

2013-08-01

The cognitive concept of representation plays a key role in theories of brain information processing. However, linking neuronal activity to representational content and cognitive theory remains challenging. Recent studies have characterized the representational geometry of neural population codes by means of representational distance matrices, enabling researchers to compare representations across stages of processing and to test cognitive and computational theories. Representational geometry provides a useful intermediate level of description, capturing both the information represented in a neuronal population code and the format in which it is represented. We review recent insights gained with this approach in perception, memory, cognition, and action. Analyses of representational geometry can compare representations between models and the brain, and promise to explain brain computation as transformation of representational similarity structure. Copyright © 2013 Elsevier Ltd. All rights reserved.

Stereo-tomography in triangulated models

NASA Astrophysics Data System (ADS)

Yang, Kai; Shao, Wei-Dong; Xing, Feng-yuan; Xiong, Kai

2018-04-01

Stereo-tomography is a distinctive tomographic method. It is capable of estimating the scatterer position, the local dip of scatterer and the background velocity simultaneously. Building a geologically consistent velocity model is always appealing for applied and earthquake seismologists. Differing from the previous work to incorporate various regularization techniques into the cost function of stereo-tomography, we think extending stereo-tomography to the triangulated model will be the most straightforward way to achieve this goal. In this paper, we provided all the Fréchet derivatives of stereo-tomographic data components with respect to model components for slowness-squared triangulated model (or sloth model) in 2D Cartesian coordinate based on the ray perturbation theory for interfaces. A sloth model representation means a sparser model representation when compared with conventional B-spline model representation. A sparser model representation leads to a smaller scale of stereo-tomographic (Fréchet) matrix, a higher-accuracy solution when solving linear equations, a faster convergence rate and a lower requirement for quantity of data space. Moreover, a quantitative representation of interface strengthens the relationships among different model components, which makes the cross regularizations among these model components, such as node coordinates, scatterer coordinates and scattering angles, etc., more straightforward and easier to be implemented. The sensitivity analysis, the model resolution matrix analysis and a series of synthetic data examples demonstrate the correctness of the Fréchet derivatives, the applicability of the regularization terms and the robustness of the stereo-tomography in triangulated model. It provides a solid theoretical foundation for the real applications in the future.

Formal Representations of Eligibility Criteria: A Literature Review

PubMed Central

Weng, Chunhua; Tu, Samson W.; Sim, Ida; Richesson, Rachel

2010-01-01

Standards-based, computable knowledge representations for eligibility criteria are increasingly needed to provide computer-based decision support for automated research participant screening, clinical evidence application, and clinical research knowledge management. We surveyed the literature and identified five aspects of eligibility criteria knowledge representations that contribute to the various research and clinical applications: the intended use of computable eligibility criteria, the classification of eligibility criteria, the expression language for representing eligibility rules, the encoding of eligibility concepts, and the modeling of patient data. We consider three of them (expression language, codification of eligibility concepts, and patient data modeling), to be essential constructs of a formal knowledge representation for eligibility criteria. The requirements for each of the three knowledge constructs vary for different use cases, which therefore should inform the development and choice of the constructs toward cost-effective knowledge representation efforts. We discuss the implications of our findings for standardization efforts toward sharable knowledge representation of eligibility criteria. PMID:20034594

A Model of Female Sexual Desire: Internalized Working Models of Parent-Child Relationships and Sexual Body Self-Representations.

PubMed

Cherkasskaya, Eugenia; Rosario, Margaret

2017-11-01

The etiology of low female sexual desire, the most prevalent sexual complaint in women, is multi-determined, implicating biological and psychological factors, including women's early parent-child relationships and bodily self-representations. The current study evaluated a model that hypothesized that sexual body self-representations (sexual subjectivity, self-objectification, genital self-image) explain (i.e., mediate) the relation between internalized working models of parent-child relationships (attachment, separation-individuation, parental identification) and sexual desire in heterosexual women. We recruited 614 young, heterosexual women (M = 25.5 years, SD = 4.63) through social media. The women completed an online survey. Structural equation modeling was used. The hypotheses were supported in that the relation between internalized working models of parent-child relationships (attachment and separation-individuation) and sexual desire was mediated by sexual body self-representations (sexual body esteem, self-objectification, genital self-image). However, parental identification was not related significantly to sexual body self-representations or sexual desire in the model. Current findings demonstrated that understanding female sexual desire necessitates considering women's internalized working models of early parent-child relationships and their experiences of their bodies in a sexual context. Treatment of low or absent desire in women would benefit from modalities that emphasize early parent-child relationships as well as interventions that foster mind-body integration.

Interactive Structure (EUCLID) For Static And Dynamic Representation Of Human Body

NASA Astrophysics Data System (ADS)

Renaud, Ch.; Steck, R.

1983-07-01

A specific software (EUCLID) for static and dynamic representation of human models is described. The data processing system is connected with ERGODATA and used in interactive mode by intrinsic or specific functions. More or less complex representations in 3-D view of models of the human body are developed. Biostereometric and conventional anthropometric raw data from the data bank are processed for different applications in ergonomy.

Development of the Bonding Representations Inventory to Identify Student Misconceptions about Covalent and Ionic Bonding Representations

ERIC Educational Resources Information Center

Luxford, Cynthia J.; Bretz, Stacey Lowery

2014-01-01

Teachers use multiple representations to communicate the concepts of bonding, including Lewis structures, formulas, space-filling models, and 3D manipulatives. As students learn to interpret these multiple representations, they may develop misconceptions that can create problems in further learning of chemistry. Interviews were conducted with 28…

Competition and Cooperation among Similar Representations: Toward a Unified Account of Facilitative and Inhibitory Effects of Lexical Neighbors

ERIC Educational Resources Information Center

Chen, Qi; Mirman, Daniel

2012-01-01

One of the core principles of how the mind works is the graded, parallel activation of multiple related or similar representations. Parallel activation of multiple representations has been particularly important in the development of theories and models of language processing, where coactivated representations ("neighbors") have been shown to…

What should I do next? Using shared representations to solve interaction problems.

PubMed

Pezzulo, Giovanni; Dindo, Haris

2011-06-01

Studies on how "the social mind" works reveal that cognitive agents engaged in joint actions actively estimate and influence another's cognitive variables and form shared representations with them. (How) do shared representations enhance coordination? In this paper, we provide a probabilistic model of joint action that emphasizes how shared representations help solving interaction problems. We focus on two aspects of the model. First, we discuss how shared representations permit to coordinate at the level of cognitive variables (beliefs, intentions, and actions) and determine a coherent unfolding of action execution and predictive processes in the brains of two agents. Second, we discuss the importance of signaling actions as part of a strategy for sharing representations and the active guidance of another's actions toward the achievement of a joint goal. Furthermore, we present data from a human-computer experiment (the Tower Game) in which two agents (human and computer) have to build together a tower made of colored blocks, but only the human knows the constellation of the tower to be built (e.g., red-blue-red-blue-…). We report evidence that humans use signaling strategies that take another's uncertainty into consideration, and that in turn our model is able to use humans' actions as cues to "align" its representations and to select complementary actions.

Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition

PubMed Central

Cadieu, Charles F.; Hong, Ha; Yamins, Daniel L. K.; Pinto, Nicolas; Ardila, Diego; Solomon, Ethan A.; Majaj, Najib J.; DiCarlo, James J.

2014-01-01

The primate visual system achieves remarkable visual object recognition performance even in brief presentations, and under changes to object exemplar, geometric transformations, and background variation (a.k.a. core visual object recognition). This remarkable performance is mediated by the representation formed in inferior temporal (IT) cortex. In parallel, recent advances in machine learning have led to ever higher performing models of object recognition using artificial deep neural networks (DNNs). It remains unclear, however, whether the representational performance of DNNs rivals that of the brain. To accurately produce such a comparison, a major difficulty has been a unifying metric that accounts for experimental limitations, such as the amount of noise, the number of neural recording sites, and the number of trials, and computational limitations, such as the complexity of the decoding classifier and the number of classifier training examples. In this work, we perform a direct comparison that corrects for these experimental limitations and computational considerations. As part of our methodology, we propose an extension of “kernel analysis” that measures the generalization accuracy as a function of representational complexity. Our evaluations show that, unlike previous bio-inspired models, the latest DNNs rival the representational performance of IT cortex on this visual object recognition task. Furthermore, we show that models that perform well on measures of representational performance also perform well on measures of representational similarity to IT, and on measures of predicting individual IT multi-unit responses. Whether these DNNs rely on computational mechanisms similar to the primate visual system is yet to be determined, but, unlike all previous bio-inspired models, that possibility cannot be ruled out merely on representational performance grounds. PMID:25521294

Model representations of kerogen structures: An insight from density functional theory calculations and spectroscopic measurements

DOE PAGES

Weck, Philippe F.; Kim, Eunja; Wang, Yifeng; ...

2017-08-01

Molecular structures of kerogen control hydrocarbon production in unconventional reservoirs. Significant progress has been made in developing model representations of various kerogen structures. These models have been widely used for the prediction of gas adsorption and migration in shale matrix. However, using density functional perturbation theory (DFPT) calculations and vibrational spectroscopic measurements, we here show that a large gap may still remain between the existing model representations and actual kerogen structures, therefore calling for new model development. Using DFPT, we calculated Fourier transform infrared (FTIR) spectra for six most widely used kerogen structure models. The computed spectra were then systematicallymore » compared to the FTIR absorption spectra collected for kerogen samples isolated from Mancos, Woodford and Marcellus formations representing a wide range of kerogen origin and maturation conditions. Limited agreement between the model predictions and the measurements highlights that the existing kerogen models may still miss some key features in structural representation. A combination of DFPT calculations with spectroscopic measurements may provide a useful diagnostic tool for assessing the adequacy of a proposed structural model as well as for future model development. This approach may eventually help develop comprehensive infrared (IR)-fingerprints for tracing kerogen evolution.« less

Model representations of kerogen structures: An insight from density functional theory calculations and spectroscopic measurements.

PubMed

Weck, Philippe F; Kim, Eunja; Wang, Yifeng; Kruichak, Jessica N; Mills, Melissa M; Matteo, Edward N; Pellenq, Roland J-M

2017-08-01

Molecular structures of kerogen control hydrocarbon production in unconventional reservoirs. Significant progress has been made in developing model representations of various kerogen structures. These models have been widely used for the prediction of gas adsorption and migration in shale matrix. However, using density functional perturbation theory (DFPT) calculations and vibrational spectroscopic measurements, we here show that a large gap may still remain between the existing model representations and actual kerogen structures, therefore calling for new model development. Using DFPT, we calculated Fourier transform infrared (FTIR) spectra for six most widely used kerogen structure models. The computed spectra were then systematically compared to the FTIR absorption spectra collected for kerogen samples isolated from Mancos, Woodford and Marcellus formations representing a wide range of kerogen origin and maturation conditions. Limited agreement between the model predictions and the measurements highlights that the existing kerogen models may still miss some key features in structural representation. A combination of DFPT calculations with spectroscopic measurements may provide a useful diagnostic tool for assessing the adequacy of a proposed structural model as well as for future model development. This approach may eventually help develop comprehensive infrared (IR)-fingerprints for tracing kerogen evolution.

Model representations of kerogen structures: An insight from density functional theory calculations and spectroscopic measurements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Weck, Philippe F.; Kim, Eunja; Wang, Yifeng

Molecular structures of kerogen control hydrocarbon production in unconventional reservoirs. Significant progress has been made in developing model representations of various kerogen structures. These models have been widely used for the prediction of gas adsorption and migration in shale matrix. However, using density functional perturbation theory (DFPT) calculations and vibrational spectroscopic measurements, we here show that a large gap may still remain between the existing model representations and actual kerogen structures, therefore calling for new model development. Using DFPT, we calculated Fourier transform infrared (FTIR) spectra for six most widely used kerogen structure models. The computed spectra were then systematicallymore » compared to the FTIR absorption spectra collected for kerogen samples isolated from Mancos, Woodford and Marcellus formations representing a wide range of kerogen origin and maturation conditions. Limited agreement between the model predictions and the measurements highlights that the existing kerogen models may still miss some key features in structural representation. A combination of DFPT calculations with spectroscopic measurements may provide a useful diagnostic tool for assessing the adequacy of a proposed structural model as well as for future model development. This approach may eventually help develop comprehensive infrared (IR)-fingerprints for tracing kerogen evolution.« less

Alternative spatial configurations to reflect landscape structure in a hydrological model: SUMMA applications to the Reynolds Creek Watershed and the Columbia River Basin

NASA Astrophysics Data System (ADS)

Nijssen, Bart; Clark, Martyn; Mizukami, Naoki; Chegwidden, Oriana

2016-04-01

Most existing hydrological models use a fixed representation of landscape structure. For example, high-resolution, spatially-distributed models may use grid cells that exchange moisture through the saturated subsurface or may divide the landscape into hydrologic response units that only exchange moisture through surface channels. Alternatively, many regional models represent the landscape through coarse elements that do not model any moisture exchange between these model elements. These spatial organizations are often represented at a low-level in the model code and its data structures, which makes it difficult to evaluate different landscape representations using the same hydrological model. Instead, such experimentation requires the use of multiple, different hydrological models, which in turn complicates the analysis, because differences in model outcomes are no longer constrained by differing spatial representations. This inflexibility in the representation of landscape structure also limits a model's capability for scaling local processes to regional outcomes. In this study, we used the Structure for Unifying Multiple Modeling Alternatives (SUMMA) to evaluate different model spatial configurations to represent landscape structure and to evaluate scaling behavior. SUMMA can represent the moisture exchange between arbitrarily shaped landscape elements in a number of different ways, while using the same model parameterizations for vertical fluxes. This allows us to isolate the effects of changes in landscape representations on modeled hydrological fluxes and states. We examine the effects of spatial configuration in Reynolds Creek, Idaho, USA, which is a research watershed with gaged areas from 1-20 km2. We then use the same modeling system to evaluate scaling behavior in simulated hydrological fluxes in the Columbia River Basin, Pacific Northwest, USA. This basin drains more than 500,000 km2 and includes the Reynolds Creek Watershed.

The influence of ligament modelling strategies on the predictive capability of finite element models of the human knee joint.

PubMed

Naghibi Beidokhti, Hamid; Janssen, Dennis; van de Groes, Sebastiaan; Hazrati, Javad; Van den Boogaard, Ton; Verdonschot, Nico

2017-12-08

In finite element (FE) models knee ligaments can represented either by a group of one-dimensional springs, or by three-dimensional continuum elements based on segmentations. Continuum models closer approximate the anatomy, and facilitate ligament wrapping, while spring models are computationally less expensive. The mechanical properties of ligaments can be based on literature, or adjusted specifically for the subject. In the current study we investigated the effect of ligament modelling strategy on the predictive capability of FE models of the human knee joint. The effect of literature-based versus specimen-specific optimized material parameters was evaluated. Experiments were performed on three human cadaver knees, which were modelled in FE models with ligaments represented either using springs, or using continuum representations. In spring representation collateral ligaments were each modelled with three and cruciate ligaments with two single-element bundles. Stiffness parameters and pre-strains were optimized based on laxity tests for both approaches. Validation experiments were conducted to evaluate the outcomes of the FE models. Models (both spring and continuum) with subject-specific properties improved the predicted kinematics and contact outcome parameters. Models incorporating literature-based parameters, and particularly the spring models (with the representations implemented in this study), led to relatively high errors in kinematics and contact pressures. Using a continuum modelling approach resulted in more accurate contact outcome variables than the spring representation with two (cruciate ligaments) and three (collateral ligaments) single-element-bundle representations. However, when the prediction of joint kinematics is of main interest, spring ligament models provide a faster option with acceptable outcome. Copyright © 2017 Elsevier Ltd. All rights reserved.

Exploiting salient semantic analysis for information retrieval

NASA Astrophysics Data System (ADS)

Luo, Jing; Meng, Bo; Quan, Changqin; Tu, Xinhui

2016-11-01

Recently, many Wikipedia-based methods have been proposed to improve the performance of different natural language processing (NLP) tasks, such as semantic relatedness computation, text classification and information retrieval. Among these methods, salient semantic analysis (SSA) has been proven to be an effective way to generate conceptual representation for words or documents. However, its feasibility and effectiveness in information retrieval is mostly unknown. In this paper, we study how to efficiently use SSA to improve the information retrieval performance, and propose a SSA-based retrieval method under the language model framework. First, SSA model is adopted to build conceptual representations for documents and queries. Then, these conceptual representations and the bag-of-words (BOW) representations can be used in combination to estimate the language models of queries and documents. The proposed method is evaluated on several standard text retrieval conference (TREC) collections. Experiment results on standard TREC collections show the proposed models consistently outperform the existing Wikipedia-based retrieval methods.

Statistical representation of multiphase flow

NASA Astrophysics Data System (ADS)

Subramaniam

2000-11-01

The relationship between two common statistical representations of multiphase flow, namely, the single--point Eulerian statistical representation of two--phase flow (D. A. Drew, Ann. Rev. Fluid Mech. (15), 1983), and the Lagrangian statistical representation of a spray using the dropet distribution function (F. A. Williams, Phys. Fluids 1 (6), 1958) is established for spherical dispersed--phase elements. This relationship is based on recent work which relates the droplet distribution function to single--droplet pdfs starting from a Liouville description of a spray (Subramaniam, Phys. Fluids 10 (12), 2000). The Eulerian representation, which is based on a random--field model of the flow, is shown to contain different statistical information from the Lagrangian representation, which is based on a point--process model. The two descriptions are shown to be simply related for spherical, monodisperse elements in statistically homogeneous two--phase flow, whereas such a simple relationship is precluded by the inclusion of polydispersity and statistical inhomogeneity. The common origin of these two representations is traced to a more fundamental statistical representation of a multiphase flow, whose concepts derive from a theory for dense sprays recently proposed by Edwards (Atomization and Sprays 10 (3--5), 2000). The issue of what constitutes a minimally complete statistical representation of a multiphase flow is resolved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ng, B

This survey gives an overview of popular generative models used in the modeling of stochastic temporal systems. In particular, this survey is organized into two parts. The first part discusses the discrete-time representations of dynamic Bayesian networks and dynamic relational probabilistic models, while the second part discusses the continuous-time representation of continuous-time Bayesian networks.

Method and apparatus for modeling interactions

DOEpatents

Xavier, Patrick G.

2002-01-01

The present invention provides a method and apparatus for modeling interactions that overcomes drawbacks. The method of the present invention comprises representing two bodies undergoing translations by two swept volume representations. Interactions such as nearest approach and collision can be modeled based on the swept body representations. The present invention is more robust and allows faster modeling than previous methods.

Attachment change processes in the early years of marriage.

PubMed

Davila, J; Karney, B R; Bradbury, T N

1999-05-01

The authors examined 4 models of attachment change: a contextual model, a social-cognitive model, an individual-difference model, and a diathesis-stress model. Models were examined in a sample of newlyweds over the first 2 years of marriage, using growth curve analyses. Reciprocal processes, whereby attachment representations and interpersonal life circumstances affect one another over time, also were studied. On average, newlyweds became more secure over time. However, there was significant within-subject variability on attachment change that was predicted by intra- and interpersonal factors. Attachment representations changed in response to contextual, social-cognitive, and individual-difference factors. Reciprocal processes between attachment representations and marital variables emerged, suggesting that these factors influence one another in an ongoing way.

Reliability in the location of hindlimb motor representations in Fischer-344 rats: laboratory investigation.

PubMed

Frost, Shawn B; Iliakova, Maria; Dunham, Caleb; Barbay, Scott; Arnold, Paul; Nudo, Randolph J

2013-08-01

The purpose of the present study was to determine the feasibility of using a common laboratory rat strain for reliably locating cortical motor representations of the hindlimb. Intracortical microstimulation techniques were used to derive detailed maps of the hindlimb motor representations in 6 adult Fischer-344 rats. The organization of the hindlimb movement representation, while variable across individual rats in topographic detail, displayed several commonalities. The hindlimb representation was positioned posterior to the forelimb motor representation and posterolateral to the motor trunk representation. The areal extent of the hindlimb representation across the cortical surface averaged 2.00 ± 0.50 mm(2). Superimposing individual maps revealed an overlapping area measuring 0.35 mm(2), indicating that the location of the hindlimb representation can be predicted reliably based on stereotactic coordinates. Across the sample of rats, the hindlimb representation was found 1.25-3.75 mm posterior to the bregma, with an average center location approximately 2.6 mm posterior to the bregma. Likewise, the hindlimb representation was found 1-3.25 mm lateral to the midline, with an average center location approximately 2 mm lateral to the midline. The location of the cortical hindlimb motor representation in Fischer-344 rats can be reliably located based on its stereotactic position posterior to the bregma and lateral to the longitudinal skull suture at midline. The ability to accurately predict the cortical localization of functional hindlimb territories in a rodent model is important, as such animal models are being increasingly used in the development of brain-computer interfaces for restoration of function after spinal cord injury.

Reliability in the Location of Hindlimb Motor Representations in Fischer-344 Rats

PubMed Central

Frost, Shawn B.; Iliakova, Maria; Dunham, Caleb; Barbay, Scott; Arnold, Paul; Nudo, Randolph J.

2014-01-01

Object The purpose of the present study was to determine the feasibility of using a common laboratory rat strain for locating cortical motor representations of the hindlimb reliably. Methods Intracortical Microstimulation (ICMS) techniques were used to derive detailed maps of the hindlimb motor representations in six adult Fischer-344 rats. Results The organization of the hindlimb movement representation, while variable across individuals in topographic detail, displayed several commonalities. The hindlimb representation was positioned posterior to the forelimb motor representation and postero-lateral to the motor trunk representation. The areal extent of the hindlimb representation across the cortical surface averaged 2.00 +/− 0.50 mm2. Superimposing individual maps revealed an overlapping area measuring 0.35 mm2, indicating that the location of the hindlimb representation can be predicted reliably based on stereotactic coordinates. Across the sample of rats, the hindlimb representation was found 1.25–3.75 mm posterior to Bregma, with an average center location ~ 2.6 mm posterior to Bregma. Likewise, the hindlimb representation was found 1–3.25 mm lateral to the midline, with an average center location ~ 2 mm lateral to midline. Conclusions The location of the cortical hindlimb motor representation in Fischer-344 rats can be reliably located based on its stereotactic position posterior to Bregma and lateral to the longitudinal skull suture at midline. The ability to accurately predict the cortical localization of functional hindlimb territories in a rodent model is important, as such animal models are being used increasingly in the development of brain-computer interfaces for restoration of function after spinal cord injury. PMID:23725395

Impossibility Theorem in Proportional Representation Problem

NASA Astrophysics Data System (ADS)

Karpov, Alexander

2010-09-01

The study examines general axiomatics of Balinski and Young and analyzes existed proportional representation methods using this approach. The second part of the paper provides new axiomatics based on rational choice models. New system of axioms is applied to study known proportional representation systems. It is shown that there is no proportional representation method satisfying a minimal set of the axioms (monotonicity and neutrality).

Mathematical formalisms based on approximated kinetic representations for modeling genetic and metabolic pathways.

PubMed

Alves, Rui; Vilaprinyo, Ester; Hernádez-Bermejo, Benito; Sorribas, Albert

2008-01-01

There is a renewed interest in obtaining a systemic understanding of metabolism, gene expression and signal transduction processes, driven by the recent research focus on Systems Biology. From a biotechnological point of view, such a systemic understanding of how a biological system is designed to work can facilitate the rational manipulation of specific pathways in different cell types to achieve specific goals. Due to the intrinsic complexity of biological systems, mathematical models are a central tool for understanding and predicting the integrative behavior of those systems. Particularly, models are essential for a rational development of biotechnological applications and in understanding system's design from an evolutionary point of view. Mathematical models can be obtained using many different strategies. In each case, their utility will depend upon the properties of the mathematical representation and on the possibility of obtaining meaningful parameters from available data. In practice, there are several issues at stake when one has to decide which mathematical model is more appropriate for the study of a given problem. First, one needs a model that can represent the aspects of the system one wishes to study. Second, one must choose a mathematical representation that allows an accurate analysis of the system with respect to different aspects of interest (for example, robustness of the system, dynamical behavior, optimization of the system with respect to some production goal, parameter value determination, etc). Third, before choosing between alternative and equally appropriate mathematical representations for the system, one should compare representations with respect to easiness of automation for model set-up, simulation, and analysis of results. Fourth, one should also consider how to facilitate model transference and re-usability by other researchers and for distinct purposes. Finally, one factor that is important for all four aspects is the regularity in the mathematical structure of the equations because it facilitates computational manipulation. This regularity is a mark of kinetic representations based on approximation theory. The use of approximation theory to derive mathematical representations with regular structure for modeling purposes has a long tradition in science. In most applied fields, such as engineering and physics, those approximations are often required to obtain practical solutions to complex problems. In this paper we review some of the more popular mathematical representations that have been derived using approximation theory and are used for modeling in molecular systems biology. We will focus on formalisms that are theoretically supported by the Taylor Theorem. These include the Power-law formalism, the recently proposed (log)linear and Lin-log formalisms as well as some closely related alternatives. We will analyze the similarities and differences between these formalisms, discuss the advantages and limitations of each representation, and provide a tentative "road map" for their potential utilization for different problems.

Feature-Selective Attentional Modulations in Human Frontoparietal Cortex.

PubMed

Ester, Edward F; Sutterer, David W; Serences, John T; Awh, Edward

2016-08-03

Control over visual selection has long been framed in terms of a dichotomy between "source" and "site," where top-down feedback signals originating in frontoparietal cortical areas modulate or bias sensory processing in posterior visual areas. This distinction is motivated in part by observations that frontoparietal cortical areas encode task-level variables (e.g., what stimulus is currently relevant or what motor outputs are appropriate), while posterior sensory areas encode continuous or analog feature representations. Here, we present evidence that challenges this distinction. We used fMRI, a roving searchlight analysis, and an inverted encoding model to examine representations of an elementary feature property (orientation) across the entire human cortical sheet while participants attended either the orientation or luminance of a peripheral grating. Orientation-selective representations were present in a multitude of visual, parietal, and prefrontal cortical areas, including portions of the medial occipital cortex, the lateral parietal cortex, and the superior precentral sulcus (thought to contain the human homolog of the macaque frontal eye fields). Additionally, representations in many-but not all-of these regions were stronger when participants were instructed to attend orientation relative to luminance. Collectively, these findings challenge models that posit a strict segregation between sources and sites of attentional control on the basis of representational properties by demonstrating that simple feature values are encoded by cortical regions throughout the visual processing hierarchy, and that representations in many of these areas are modulated by attention. Influential models of visual attention posit a distinction between top-down control and bottom-up sensory processing networks. These models are motivated in part by demonstrations showing that frontoparietal cortical areas associated with top-down control represent abstract or categorical stimulus information, while visual areas encode parametric feature information. Here, we show that multivariate activity in human visual, parietal, and frontal cortical areas encode representations of a simple feature property (orientation). Moreover, representations in several (though not all) of these areas were modulated by feature-based attention in a similar fashion. These results provide an important challenge to models that posit dissociable top-down control and sensory processing networks on the basis of representational properties. Copyright © 2016 the authors 0270-6474/16/368188-12$15.00/0.

A redefinition of the representation of mammary cells and enzyme activities in a lactating dairy cow model.

PubMed

Hanigan, M D; Rius, A G; Kolver, E S; Palliser, C C

2007-08-01

The Molly model predicts various aspects of digestion and metabolism in the cow, including nutrient partitioning between milk and body stores. It has been observed previously that the model underpredicts milk component yield responses to nutrition and consequently overpredicts body energy store responses. In Molly, mammary enzyme activity is represented as an aggregate of mammary cell numbers and activity per cell with minimal endocrine regulation. Work by others suggests that mammary cells can cycle between active and quiescent states in response to various stimuli. Simple models of milk production have demonstrated the utility of this representation when using the model to simulate variable milking and nutrient restriction. It was hypothesized that replacing the current representation of mammary cells and enzyme activity in Molly with a representation of active and quiescent cells and improving the representation of endocrine control of cell activity would improve predictions of milk component yield. The static representation of cell numbers was replaced with a representation of cell growth during gestation and early lactation periods and first-order cell death. Enzyme capacity for fat and protein synthesis was assumed to be proportional to cell numbers. Enzyme capacity for lactose synthesis was represented with the same equation form as for cell numbers. Data used for parameter estimation were collected as part of an extended lactation trial. Cows with North American or New Zealand genotypes were fed 0, 3, or 6 kg of concentrate dry matter daily during a 600-d lactation. The original model had root mean square prediction errors of 17.7, 22.3, and 19.8% for lactose, protein, and fat yield, respectively, as compared with values of 8.3, 9.4, and 11.7% for the revised model, respectively. The original model predicted body weight with an error of 19.7% vs. 5.7% for the revised model. Based on these observations, it was concluded that representing mammary synthetic capacity as a function of active cell numbers and revisions to endocrine control of cell activity was meritorious.

Ontology-Driven Business Modelling: Improving the Conceptual Representation of the REA Ontology

NASA Astrophysics Data System (ADS)

Gailly, Frederik; Poels, Geert

Business modelling research is increasingly interested in exploring how domain ontologies can be used as reference models for business models. The Resource Event Agent (REA) ontology is a primary candidate for ontology-driven modelling of business processes because the REA point of view on business reality is close to the conceptual modelling perspective on business models. In this paper Ontology Engineering principles are employed to reengineer REA in order to make it more suitable for ontology-driven business modelling. The new conceptual representation of REA that we propose uses a single representation formalism, includes a more complete domain axiomatizat-ion (containing definitions of concepts, concept relations and ontological axioms), and is proposed as a generic model that can be instantiated to create valid business models. The effects of these proposed improvements on REA-driven business modelling are demonstrated using a business modelling example.

Invariant Visual Object and Face Recognition: Neural and Computational Bases, and a Model, VisNet

PubMed Central

Rolls, Edmund T.

2012-01-01

Neurophysiological evidence for invariant representations of objects and faces in the primate inferior temporal visual cortex is described. Then a computational approach to how invariant representations are formed in the brain is described that builds on the neurophysiology. A feature hierarchy model in which invariant representations can be built by self-organizing learning based on the temporal and spatial statistics of the visual input produced by objects as they transform in the world is described. VisNet can use temporal continuity in an associative synaptic learning rule with a short-term memory trace, and/or it can use spatial continuity in continuous spatial transformation learning which does not require a temporal trace. The model of visual processing in the ventral cortical stream can build representations of objects that are invariant with respect to translation, view, size, and also lighting. The model has been extended to provide an account of invariant representations in the dorsal visual system of the global motion produced by objects such as looming, rotation, and object-based movement. The model has been extended to incorporate top-down feedback connections to model the control of attention by biased competition in, for example, spatial and object search tasks. The approach has also been extended to account for how the visual system can select single objects in complex visual scenes, and how multiple objects can be represented in a scene. The approach has also been extended to provide, with an additional layer, for the development of representations of spatial scenes of the type found in the hippocampus. PMID:22723777

Invariant Visual Object and Face Recognition: Neural and Computational Bases, and a Model, VisNet.

PubMed

Rolls, Edmund T

2012-01-01

Neurophysiological evidence for invariant representations of objects and faces in the primate inferior temporal visual cortex is described. Then a computational approach to how invariant representations are formed in the brain is described that builds on the neurophysiology. A feature hierarchy model in which invariant representations can be built by self-organizing learning based on the temporal and spatial statistics of the visual input produced by objects as they transform in the world is described. VisNet can use temporal continuity in an associative synaptic learning rule with a short-term memory trace, and/or it can use spatial continuity in continuous spatial transformation learning which does not require a temporal trace. The model of visual processing in the ventral cortical stream can build representations of objects that are invariant with respect to translation, view, size, and also lighting. The model has been extended to provide an account of invariant representations in the dorsal visual system of the global motion produced by objects such as looming, rotation, and object-based movement. The model has been extended to incorporate top-down feedback connections to model the control of attention by biased competition in, for example, spatial and object search tasks. The approach has also been extended to account for how the visual system can select single objects in complex visual scenes, and how multiple objects can be represented in a scene. The approach has also been extended to provide, with an additional layer, for the development of representations of spatial scenes of the type found in the hippocampus.

Concepts, Control, and Context: A Connectionist Account of Normal and Disordered Semantic Cognition

PubMed Central

2018-01-01

Semantic cognition requires conceptual representations shaped by verbal and nonverbal experience and executive control processes that regulate activation of knowledge to meet current situational demands. A complete model must also account for the representation of concrete and abstract words, of taxonomic and associative relationships, and for the role of context in shaping meaning. We present the first major attempt to assimilate all of these elements within a unified, implemented computational framework. Our model combines a hub-and-spoke architecture with a buffer that allows its state to be influenced by prior context. This hybrid structure integrates the view, from cognitive neuroscience, that concepts are grounded in sensory-motor representation with the view, from computational linguistics, that knowledge is shaped by patterns of lexical co-occurrence. The model successfully codes knowledge for abstract and concrete words, associative and taxonomic relationships, and the multiple meanings of homonyms, within a single representational space. Knowledge of abstract words is acquired through (a) their patterns of co-occurrence with other words and (b) acquired embodiment, whereby they become indirectly associated with the perceptual features of co-occurring concrete words. The model accounts for executive influences on semantics by including a controlled retrieval mechanism that provides top-down input to amplify weak semantic relationships. The representational and control elements of the model can be damaged independently, and the consequences of such damage closely replicate effects seen in neuropsychological patients with loss of semantic representation versus control processes. Thus, the model provides a wide-ranging and neurally plausible account of normal and impaired semantic cognition. PMID:29733663

Multiscale geometric modeling of macromolecules II: Lagrangian representation

PubMed Central

Feng, Xin; Xia, Kelin; Chen, Zhan; Tong, Yiying; Wei, Guo-Wei

2013-01-01

Geometric modeling of biomolecules plays an essential role in the conceptualization of biolmolecular structure, function, dynamics and transport. Qualitatively, geometric modeling offers a basis for molecular visualization, which is crucial for the understanding of molecular structure and interactions. Quantitatively, geometric modeling bridges the gap between molecular information, such as that from X-ray, NMR and cryo-EM, and theoretical/mathematical models, such as molecular dynamics, the Poisson-Boltzmann equation and the Nernst-Planck equation. In this work, we present a family of variational multiscale geometric models for macromolecular systems. Our models are able to combine multiresolution geometric modeling with multiscale electrostatic modeling in a unified variational framework. We discuss a suite of techniques for molecular surface generation, molecular surface meshing, molecular volumetric meshing, and the estimation of Hadwiger’s functionals. Emphasis is given to the multiresolution representations of biomolecules and the associated multiscale electrostatic analyses as well as multiresolution curvature characterizations. The resulting fine resolution representations of a biomolecular system enable the detailed analysis of solvent-solute interaction, and ion channel dynamics, while our coarse resolution representations highlight the compatibility of protein-ligand bindings and possibility of protein-protein interactions. PMID:23813599

Groundwater modelling in conceptual hydrological models - introducing space

NASA Astrophysics Data System (ADS)

Boje, Søren; Skaugen, Thomas; Møen, Knut; Myrabø, Steinar

2017-04-01

The tiny Sæternbekken Minifelt (Muren) catchment (7500 m2) in Bærumsmarka, Norway, was during the 1990s, densely instrumented with more than a 100 observation points for measuring groundwater levels. The aim was to investigate the link between shallow groundwater dynamics and runoff. The DDD (Distance Distribution Dynamics) model is a newly developed rainfall-runoff model used operationally by the Norwegian Flood-Forecasting service at NVE. The model estimates the capacity of the subsurface reservoir at different levels of saturation and predicts overland flow. The subsurface in the DDD model has a 2-D representation that calculates the saturated and unsaturated soil moisture along a hillslope representing the entire catchment in question. The groundwater observations from more than two decades ago are used to verify assumptions of the subsurface reservoir in the DDD model and to validate its spatial representation of the subsurface reservoir. The Muren catchment will, during 2017, be re-instrumented in order to continue the work to bridge the gap between conceptual hydrological models, with typically single value or 0-dimension representation of the subsurface, and models with more realistic 2- or 3-dimension representation of the subsurface.

On a categorial aspect of knowledge representation

NASA Astrophysics Data System (ADS)

Tataj, Emanuel; Mulawka, Jan; Nieznański, Edward

Adequate representation of data is crucial for modeling any type of data. To faithfully present and describe the relevant section of the world it is necessary to select the method that can easily be implemented on a computer system which will help in further description allowing reasoning. The main objective of this contribution is to present methods of knowledge representation using categorial approach. Next to identify the main advantages for computer implementation. Categorical aspect of knowledge representation is considered in semantic networks realisation. Such method borrows already known metaphysics properties for data modeling process. The potential topics of further development of categorical semantic networks implementations are also underlined.

Higher Dimensional Spacetimes for Visualizing and Modeling Subluminal, Luminal and Superluminal Flight

DOE Office of Scientific and Technical Information (OSTI.GOV)

Froning, H. David; Meholic, Gregory V.

2010-01-28

This paper briefly explores higher dimensional spacetimes that extend Meholic's visualizable, fluidic views of: subluminal-luminal-superluminal flight; gravity, inertia, light quanta, and electromagnetism from 2-D to 3-D representations. Although 3-D representations have the potential to better model features of Meholic's most fundamental entities (Transluminal Energy Quantum) and of the zero-point quantum vacuum that pervades all space, the more complex 3-D representations loose some of the clarity of Meholic's 2-D representations of subluminal and superlumimal realms. So, much new work would be needed to replace Meholic's 2-D views of reality with 3-D ones.

Teacher change in implementing a research-developed representation construction pedagogy

NASA Astrophysics Data System (ADS)

Hubber, Peter; Chittleborough, Gail

2016-05-01

The Representations in Learning Science (RiLS) project developed a representation construction approach to teaching and learning in science, which has successfully demonstrated enhanced student learning through sustained engagement with ideas, and enhancement of teachers' pedagogical knowledge and understandings of how knowledge in science is developed and communicated. The current Constructing Representations in Science Pedagogy (CRISP) project aims at wider scale implementation of the representation construction approach. This paper explores a range of issues that confronted four Year-8 teachers in implementing this research-developed approach, such as: preparedness of the teacher in terms of epistemological positioning and positioning as a learner, significant support for planning and modelling by the university expert, and a team ethos where teachers share ideas and plan jointly. The Year-8 teachers implemented a representation construction approach to the teaching of the topic of astronomy. The Interconnected Model of Teacher Growth (IMTG) (Clarke and Hollingworth, Teach. Educ., 18 (2001) 947) was used to analyse the teachers' experience in planning and delivering the teaching sequence. This model was found to be flexible in identifying the experiences of teachers in different situations and useful in identifying issues for implementation of a research-developed pedagogy.

Model Representation of Secondary Organic Aerosol in CMAQ v4.7

EPA Science Inventory

Numerous scientific upgrades to the representation of secondary organic aerosol (SOA) are incorporated into the Community Multiscale Air Quality (CMAQ) modeling system. Additions include several recently identified SOA precursors: benzene, isoprene, and sesquiterpenes; and pathwa...

Highest weight representation for Sklyanin algebra sl(3)(u) with application to the Gaudin model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Burdik, C., E-mail: burdik@kmlinux.fjfi.cvut.cz; Navratil, O.

2011-06-15

We study the infinite-dimensional Sklyanin algebra sl(3)(u). Specifically we construct the highest weight representation for this algebra in an explicit form. Its application to the Gaudin model is mentioned.

Abstract memory representations in the ventromedial prefrontal cortex and hippocampus support concept generalization.

PubMed

Bowman, Caitlin R; Zeithamova, Dagmar

2018-02-07

Memory function involves both the ability to remember details of individual experiences and the ability to link information across events to create new knowledge. Prior research has identified the ventromedial prefrontal cortex (VMPFC) and the hippocampus as important for integrating across events in service of generalization in episodic memory. The degree to which these memory integration mechanisms contribute to other forms of generalization, such as concept learning, is unclear. The present study used a concept-learning task in humans (both sexes) coupled with model-based fMRI to test whether VMPFC and hippocampus contribute to concept generalization, and whether they do so by maintaining specific category exemplars or abstract category representations. Two formal categorization models were fit to individual subject data: a prototype model that posits abstract category representations and an exemplar model that posits category representations based on individual category members. Latent variables from each of these models were entered into neuroimaging analyses to determine whether VMPFC and the hippocampus track prototype or exemplar information during concept generalization. Behavioral model fits indicated that almost three quarters of the subjects relied on prototype information when making judgments about new category members. Paralleling prototype dominance in behavior, correlates of the prototype model were identified in VMPFC and the anterior hippocampus with no significant exemplar correlates. These results indicate that the VMPFC and portions of the hippocampus play a broad role in memory generalization and that they do so by representing abstract information integrated from multiple events. SIGNIFICANCE STATEMENT Whether people represent concepts as a set of individual category members or by deriving generalized concept representations abstracted across exemplars has been debated. In episodic memory, generalized memory representations have been shown to arise through integration across events supported by the ventromedial prefrontal cortex (VMPFC) and hippocampus. The current study combined formal categorization models with fMRI data analysis to show that the VMPFC and anterior hippocampus represent abstract prototype information during concept generalization, contributing novel evidence of generalized concept representations in the brain. Results indicate that VMPFC-hippocampal memory integration mechanisms contribute to knowledge generalization across multiple cognitive domains, with the degree of abstraction of memory representations varying along the long axis of the hippocampus. Copyright © 2018 the authors.

A combined model of sensory and cognitive representations underlying tonal expectations in music: from audio signals to behavior.

PubMed

Collins, Tom; Tillmann, Barbara; Barrett, Frederick S; Delbé, Charles; Janata, Petr

2014-01-01

Listeners' expectations for melodies and harmonies in tonal music are perhaps the most studied aspect of music cognition. Long debated has been whether faster response times (RTs) to more strongly primed events (in a music theoretic sense) are driven by sensory or cognitive mechanisms, such as repetition of sensory information or activation of cognitive schemata that reflect learned tonal knowledge, respectively. We analyzed over 300 stimuli from 7 priming experiments comprising a broad range of musical material, using a model that transforms raw audio signals through a series of plausible physiological and psychological representations spanning a sensory-cognitive continuum. We show that RTs are modeled, in part, by information in periodicity pitch distributions, chroma vectors, and activations of tonal space--a representation on a toroidal surface of the major/minor key relationships in Western tonal music. We show that in tonal space, melodies are grouped by their tonal rather than timbral properties, whereas the reverse is true for the periodicity pitch representation. While tonal space variables explained more of the variation in RTs than did periodicity pitch variables, suggesting a greater contribution of cognitive influences to tonal expectation, a stepwise selection model contained variables from both representations and successfully explained the pattern of RTs across stimulus categories in 4 of the 7 experiments. The addition of closure--a cognitive representation of a specific syntactic relationship--succeeded in explaining results from all 7 experiments. We conclude that multiple representational stages along a sensory-cognitive continuum combine to shape tonal expectations in music. (PsycINFO Database Record (c) 2014 APA, all rights reserved).

Models of Acetylcholine and Dopamine Signals Differentially Improve Neural Representations

PubMed Central

Holca-Lamarre, Raphaël; Lücke, Jörg; Obermayer, Klaus

2017-01-01

Biological and artificial neural networks (ANNs) represent input signals as patterns of neural activity. In biology, neuromodulators can trigger important reorganizations of these neural representations. For instance, pairing a stimulus with the release of either acetylcholine (ACh) or dopamine (DA) evokes long lasting increases in the responses of neurons to the paired stimulus. The functional roles of ACh and DA in rearranging representations remain largely unknown. Here, we address this question using a Hebbian-learning neural network model. Our aim is both to gain a functional understanding of ACh and DA transmission in shaping biological representations and to explore neuromodulator-inspired learning rules for ANNs. We model the effects of ACh and DA on synaptic plasticity and confirm that stimuli coinciding with greater neuromodulator activation are over represented in the network. We then simulate the physiological release schedules of ACh and DA. We measure the impact of neuromodulator release on the network's representation and on its performance on a classification task. We find that ACh and DA trigger distinct changes in neural representations that both improve performance. The putative ACh signal redistributes neural preferences so that more neurons encode stimulus classes that are challenging for the network. The putative DA signal adapts synaptic weights so that they better match the classes of the task at hand. Our model thus offers a functional explanation for the effects of ACh and DA on cortical representations. Additionally, our learning algorithm yields performances comparable to those of state-of-the-art optimisation methods in multi-layer perceptrons while requiring weaker supervision signals and interacting with synaptically-local weight updates. PMID:28690509

Emergent Spatial Patterns of Excitatory and Inhibitory Synaptic Strengths Drive Somatotopic Representational Discontinuities and their Plasticity in a Computational Model of Primary Sensory Cortical Area 3b

PubMed Central

Grajski, Kamil A.

2016-01-01

Mechanisms underlying the emergence and plasticity of representational discontinuities in the mammalian primary somatosensory cortical representation of the hand are investigated in a computational model. The model consists of an input lattice organized as a three-digit hand forward-connected to a lattice of cortical columns each of which contains a paired excitatory and inhibitory cell. Excitatory and inhibitory synaptic plasticity of feedforward and lateral connection weights is implemented as a simple covariance rule and competitive normalization. Receptive field properties are computed independently for excitatory and inhibitory cells and compared within and across columns. Within digit representational zones intracolumnar excitatory and inhibitory receptive field extents are concentric, single-digit, small, and unimodal. Exclusively in representational boundary-adjacent zones, intracolumnar excitatory and inhibitory receptive field properties diverge: excitatory cell receptive fields are single-digit, small, and unimodal; and the paired inhibitory cell receptive fields are bimodal, double-digit, and large. In simulated syndactyly (webbed fingers), boundary-adjacent intracolumnar receptive field properties reorganize to within-representation type; divergent properties are reacquired following syndactyly release. This study generates testable hypotheses for assessment of cortical laminar-dependent receptive field properties and plasticity within and between cortical representational zones. For computational studies, present results suggest that concurrent excitatory and inhibitory plasticity may underlie novel emergent properties. PMID:27504086

Investigating the Representational Fluency of Pre-Service Mathematics Teachers in a Modelling Process

ERIC Educational Resources Information Center

Delice, Ali; Kertil, Mahmut

2015-01-01

This article reports the results of a study that investigated pre-service mathematics teachers' modelling processes in terms of representational fluency in a modelling activity related to a cassette player. A qualitative approach was used in the data collection process. Students' individual and group written responses to the mathematical modelling…

Spreading Activation in an Attractor Network with Latching Dynamics: Automatic Semantic Priming Revisited

ERIC Educational Resources Information Center

Lerner, Itamar; Bentin, Shlomo; Shriki, Oren

2012-01-01

Localist models of spreading activation (SA) and models assuming distributed representations offer very different takes on semantic priming, a widely investigated paradigm in word recognition and semantic memory research. In this study, we implemented SA in an attractor neural network model with distributed representations and created a unified…

Invariant visual object recognition: a model, with lighting invariance.

PubMed

Rolls, Edmund T; Stringer, Simon M

2006-01-01

How are invariant representations of objects formed in the visual cortex? We describe a neurophysiological and computational approach which focusses on a feature hierarchy model in which invariant representations can be built by self-organizing learning based on the statistics of the visual input. The model can use temporal continuity in an associative synaptic learning rule with a short term memory trace, and/or it can use spatial continuity in Continuous Transformation learning. The model of visual processing in the ventral cortical stream can build representations of objects that are invariant with respect to translation, view, size, and in this paper we show also lighting. The model has been extended to provide an account of invariant representations in the dorsal visual system of the global motion produced by objects such as looming, rotation, and object-based movement. The model has been extended to incorporate top-down feedback connections to model the control of attention by biased competition in for example spatial and object search tasks. The model has also been extended to account for how the visual system can select single objects in complex visual scenes, and how multiple objects can be represented in a scene.

Uncertainty Representation and Interpretation in Model-Based Prognostics Algorithms Based on Kalman Filter Estimation

NASA Technical Reports Server (NTRS)

Galvan, Jose Ramon; Saxena, Abhinav; Goebel, Kai Frank

2012-01-01

This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process, and how it relates to uncertainty representation, management and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for two while considering prognostics in making critical decisions.

Overcoming the Subject-Object Dichotomy in Urban Modeling: Axial Maps as Geometric Representations of Affordances in the Built Environment

PubMed Central

Marcus, Lars

2018-01-01

The world is witnessing unprecedented urbanization, bringing extreme challenges to contemporary practices in urban planning and design. This calls for improved urban models that can generate new knowledge and enhance practical skill. Importantly, any urban model embodies a conception of the relation between humans and the physical environment. In urban modeling this is typically conceived of as a relation between human subjects and an environmental object, thereby reproducing a humans-environment dichotomy. Alternative modeling traditions, such as space syntax that originates in architecture rather than geography, have tried to overcome this dichotomy. Central in this effort is the development of new representations of urban space, such as in the case of space syntax, the axial map. This form of representation aims to integrate both human behavior and the physical environment into one and the same description. Interestingly, models based on these representations have proved to better capture pedestrian movement than regular models. Pedestrian movement, as well as other kinds of human flows in urban space, is essential for urban modeling, since increasingly flows of this kind are understood as the driver in urban processes. Critical for a full understanding of space syntax modeling is the ontology of its' representations, such as the axial map. Space syntax theory here often refers to James Gibson's “Theory of affordances,” where the concept of affordances, in a manner similar to axial maps, aims to bridge the subject-object dichotomy by neither constituting physical properties of the environment or human behavior, but rather what emerges in the meeting between the two. In extension of this, the axial map can be interpreted as a representation of how the physical form of the environment affords human accessibility and visibility in urban space. This paper presents a close examination of the form of representations developed in space syntax methodology, in particular in the light of Gibson's “theory of affordances.“ The overarching aim is to contribute to a theoretical framework for urban models based on affordances, which may support the overcoming of the subject-object dichotomy in such models, here deemed essential for a greater social-ecological sustainability of cities. PMID:29731726

Representations and Rafts

ERIC Educational Resources Information Center

Hartweg, Kimberly Sipes

2011-01-01

To build on prior knowledge and mathematical understanding, middle school students need to be given the opportunity to make connections among a variety of representations. Graphs, tables, algebraic formulas, and models are just a few examples of representations that can help students explore quantitative relationships. As a mathematics educator,…

Knowledge Representation: A Brief Review.

ERIC Educational Resources Information Center

Vickery, B. C.

1986-01-01

Reviews different structures and techniques of knowledge representation: structure of database records and files, data structures in computer programming, syntatic and semantic structure of natural language, knowledge representation in artificial intelligence, and models of human memory. A prototype expert system that makes use of some of these…

A Review on Human Activity Recognition Using Vision-Based Method.

PubMed

Zhang, Shugang; Wei, Zhiqiang; Nie, Jie; Huang, Lei; Wang, Shuang; Li, Zhen

2017-01-01

Human activity recognition (HAR) aims to recognize activities from a series of observations on the actions of subjects and the environmental conditions. The vision-based HAR research is the basis of many applications including video surveillance, health care, and human-computer interaction (HCI). This review highlights the advances of state-of-the-art activity recognition approaches, especially for the activity representation and classification methods. For the representation methods, we sort out a chronological research trajectory from global representations to local representations, and recent depth-based representations. For the classification methods, we conform to the categorization of template-based methods, discriminative models, and generative models and review several prevalent methods. Next, representative and available datasets are introduced. Aiming to provide an overview of those methods and a convenient way of comparing them, we classify existing literatures with a detailed taxonomy including representation and classification methods, as well as the datasets they used. Finally, we investigate the directions for future research.

A Review on Human Activity Recognition Using Vision-Based Method

PubMed Central

Nie, Jie

2017-01-01

Human activity recognition (HAR) aims to recognize activities from a series of observations on the actions of subjects and the environmental conditions. The vision-based HAR research is the basis of many applications including video surveillance, health care, and human-computer interaction (HCI). This review highlights the advances of state-of-the-art activity recognition approaches, especially for the activity representation and classification methods. For the representation methods, we sort out a chronological research trajectory from global representations to local representations, and recent depth-based representations. For the classification methods, we conform to the categorization of template-based methods, discriminative models, and generative models and review several prevalent methods. Next, representative and available datasets are introduced. Aiming to provide an overview of those methods and a convenient way of comparing them, we classify existing literatures with a detailed taxonomy including representation and classification methods, as well as the datasets they used. Finally, we investigate the directions for future research. PMID:29065585

Tracing Growth of Teachers' Classroom Interactions with Representations of Functions in the Connected Classroom

NASA Astrophysics Data System (ADS)

Morton, Brian Lee

The purpose of this study is to create an empirically based theoretic model of change of the use and treatment of representations of functions with the use of Connected Classroom Technology (CCT) using data previously collected for the Classroom Connectivity in Promoting Mathematics and Science Achievement (CCMS) project. Qualitative analysis of videotapes of three algebra teachers' instruction focused on different categories thought to influence teaching representations with technology: representations, discourse, technology, and decisions. Models for rating teachers low, medium, or high for each of these categories were created using a priori codes and grounded methodology. A cross case analysis was conducted after the completion of the case studies by comparing and contrasting the three cases. Data revealed that teachers' decisions shifted to incorporate the difference in student ideas/representations made visible by the CCT into their instruction and ultimately altered their orientation to mathematics teaching. The shift in orientation seemed to lead to the teachers' growth with regards to representations, discourse, and technology.

A rapid-pressure correlation representation consistent with the Taylor-Proudman theorem materially-frame-indifferent in the 2D limit

NASA Technical Reports Server (NTRS)

Ristorcelli, J. R.; Lumley, J. L.; Abid, R.

1994-01-01

A nonlinear representation for the rapid-pressure correlation appearing in the Reynolds stress equations, consistent with the Taylor-Proudman theorem, is presented. The representation insures that the modeled second-order equations are frame-invariant with respect to rotation when the flow is two-dimensional in planes perpendicular to the axis of rotation. The representation satisfies realizability in a new way: a special ansatz is used to obtain analytically, the values of coefficients valid away from the realizability limit: the model coefficients are functions of the state of the turbulence that are valid for all states of the mechanical turbulence attaining their constant limiting values only when the limit state is achieved. Utilization of all the mathematical constraints are not enough to specify all the coefficients in the model. The unspecified coefficients appear as free parameters which are used to insure that the representation is asymptotically consistent with the known equilibrium states of a homogeneous sheared turbulence. This is done by insuring that the modeled evolution equations have the same fixed points as those obtained from computer and laboratory experiments for the homogeneous shear. Results of computations of the homogeneous shear, with and without rotation, and with stabilizing and destabilizing curvature, are shown. Results are consistently better, in a wide class of flows which the model not been calibrated, than those obtained with other nonlinear models.

Visualizing the engram: learning stabilizes odor representations in the olfactory network.

PubMed

Shakhawat, Amin M D; Gheidi, Ali; Hou, Qinlong; Dhillon, Sandeep K; Marrone, Diano F; Harley, Carolyn W; Yuan, Qi

2014-11-12

The nature of memory is a central issue in neuroscience. How does our representation of the world change with learning and experience? Here we use the transcription of Arc mRNA, which permits probing the neural representations of temporally separated events, to address this in a well characterized odor learning model. Rat pups readily associate odor with maternal care. In pups, the lateralized olfactory networks are independent, permitting separate training and within-subject control. We use multiday training to create an enduring memory of peppermint odor. Training stabilized rewarded, but not nonrewarded, odor representations in both mitral cells and associated granule cells of the olfactory bulb and in the pyramidal cells of the anterior piriform cortex. An enlarged core of stable, likely highly active neurons represent rewarded odor at both stages of the olfactory network. Odor representations in anterior piriform cortex were sparser than typical in adult rat and did not enlarge with learning. This sparser representation of odor is congruent with the maturation of lateral olfactory tract input in rat pups. Cortical representations elsewhere have been shown to be highly variable in electrophysiological experiments, suggesting brains operate normally using dynamic and network-modulated representations. The olfactory cortical representations here are consistent with the generalized associative model of sparse variable cortical representation, as normal responses to repeated odors were highly variable (∼70% of the cells change as indexed by Arc). Learning and memory modified rewarded odor ensembles to increase stability in a core representational component. Copyright © 2014 the authors 0270-6474/14/3415394-08$15.00/0.

Reading as Active Sensing: A Computational Model of Gaze Planning in Word Recognition

PubMed Central

Ferro, Marcello; Ognibene, Dimitri; Pezzulo, Giovanni; Pirrelli, Vito

2010-01-01

We offer a computational model of gaze planning during reading that consists of two main components: a lexical representation network, acquiring lexical representations from input texts (a subset of the Italian CHILDES database), and a gaze planner, designed to recognize written words by mapping strings of characters onto lexical representations. The model implements an active sensing strategy that selects which characters of the input string are to be fixated, depending on the predictions dynamically made by the lexical representation network. We analyze the developmental trajectory of the system in performing the word recognition task as a function of both increasing lexical competence, and correspondingly increasing lexical prediction ability. We conclude by discussing how our approach can be scaled up in the context of an active sensing strategy applied to a robotic setting. PMID:20577589

Reading as active sensing: a computational model of gaze planning in word recognition.

PubMed

Ferro, Marcello; Ognibene, Dimitri; Pezzulo, Giovanni; Pirrelli, Vito

2010-01-01

WE OFFER A COMPUTATIONAL MODEL OF GAZE PLANNING DURING READING THAT CONSISTS OF TWO MAIN COMPONENTS: a lexical representation network, acquiring lexical representations from input texts (a subset of the Italian CHILDES database), and a gaze planner, designed to recognize written words by mapping strings of characters onto lexical representations. The model implements an active sensing strategy that selects which characters of the input string are to be fixated, depending on the predictions dynamically made by the lexical representation network. We analyze the developmental trajectory of the system in performing the word recognition task as a function of both increasing lexical competence, and correspondingly increasing lexical prediction ability. We conclude by discussing how our approach can be scaled up in the context of an active sensing strategy applied to a robotic setting.

A geometric modeler based on a dual-geometry representation polyhedra and rational b-splines

NASA Technical Reports Server (NTRS)

Klosterman, A. L.

1984-01-01

For speed and data base reasons, solid geometric modeling of large complex practical systems is usually approximated by a polyhedra representation. Precise parametric surface and implicit algebraic modelers are available but it is not yet practical to model the same level of system complexity with these precise modelers. In response to this contrast the GEOMOD geometric modeling system was built so that a polyhedra abstraction of the geometry would be available for interactive modeling without losing the precise definition of the geometry. Part of the reason that polyhedra modelers are effective is that all bounded surfaces can be represented in a single canonical format (i.e., sets of planar polygons). This permits a very simple and compact data structure. Nonuniform rational B-splines are currently the best representation to describe a very large class of geometry precisely with one canonical format. The specific capabilities of the modeler are described.

The temporal evolution of conceptual object representations revealed through models of behavior, semantics and deep neural networks.

PubMed

Bankson, B B; Hebart, M N; Groen, I I A; Baker, C I

2018-05-17

Visual object representations are commonly thought to emerge rapidly, yet it has remained unclear to what extent early brain responses reflect purely low-level visual features of these objects and how strongly those features contribute to later categorical or conceptual representations. Here, we aimed to estimate a lower temporal bound for the emergence of conceptual representations by defining two criteria that characterize such representations: 1) conceptual object representations should generalize across different exemplars of the same object, and 2) these representations should reflect high-level behavioral judgments. To test these criteria, we compared magnetoencephalography (MEG) recordings between two groups of participants (n = 16 per group) exposed to different exemplar images of the same object concepts. Further, we disentangled low-level from high-level MEG responses by estimating the unique and shared contribution of models of behavioral judgments, semantics, and different layers of deep neural networks of visual object processing. We find that 1) both generalization across exemplars as well as generalization of object-related signals across time increase after 150 ms, peaking around 230 ms; 2) representations specific to behavioral judgments emerged rapidly, peaking around 160 ms. Collectively, these results suggest a lower bound for the emergence of conceptual object representations around 150 ms following stimulus onset. Copyright © 2018 Elsevier Inc. All rights reserved.

Predictive representations can link model-based reinforcement learning to model-free mechanisms.

PubMed

Russek, Evan M; Momennejad, Ida; Botvinick, Matthew M; Gershman, Samuel J; Daw, Nathaniel D

2017-09-01

Humans and animals are capable of evaluating actions by considering their long-run future rewards through a process described using model-based reinforcement learning (RL) algorithms. The mechanisms by which neural circuits perform the computations prescribed by model-based RL remain largely unknown; however, multiple lines of evidence suggest that neural circuits supporting model-based behavior are structurally homologous to and overlapping with those thought to carry out model-free temporal difference (TD) learning. Here, we lay out a family of approaches by which model-based computation may be built upon a core of TD learning. The foundation of this framework is the successor representation, a predictive state representation that, when combined with TD learning of value predictions, can produce a subset of the behaviors associated with model-based learning, while requiring less decision-time computation than dynamic programming. Using simulations, we delineate the precise behavioral capabilities enabled by evaluating actions using this approach, and compare them to those demonstrated by biological organisms. We then introduce two new algorithms that build upon the successor representation while progressively mitigating its limitations. Because this framework can account for the full range of observed putatively model-based behaviors while still utilizing a core TD framework, we suggest that it represents a neurally plausible family of mechanisms for model-based evaluation.

Predictive representations can link model-based reinforcement learning to model-free mechanisms

PubMed Central

Botvinick, Matthew M.

2017-01-01

Humans and animals are capable of evaluating actions by considering their long-run future rewards through a process described using model-based reinforcement learning (RL) algorithms. The mechanisms by which neural circuits perform the computations prescribed by model-based RL remain largely unknown; however, multiple lines of evidence suggest that neural circuits supporting model-based behavior are structurally homologous to and overlapping with those thought to carry out model-free temporal difference (TD) learning. Here, we lay out a family of approaches by which model-based computation may be built upon a core of TD learning. The foundation of this framework is the successor representation, a predictive state representation that, when combined with TD learning of value predictions, can produce a subset of the behaviors associated with model-based learning, while requiring less decision-time computation than dynamic programming. Using simulations, we delineate the precise behavioral capabilities enabled by evaluating actions using this approach, and compare them to those demonstrated by biological organisms. We then introduce two new algorithms that build upon the successor representation while progressively mitigating its limitations. Because this framework can account for the full range of observed putatively model-based behaviors while still utilizing a core TD framework, we suggest that it represents a neurally plausible family of mechanisms for model-based evaluation. PMID:28945743

Word Length and Lexical Activation: Longer Is Better

ERIC Educational Resources Information Center

Pitt, Mark A.; Samuel, Arthur G.

2006-01-01

Many models of spoken word recognition posit the existence of lexical and sublexical representations, with excitatory and inhibitory mechanisms used to affect the activation levels of such representations. Bottom-up evidence provides excitatory input, and inhibition from phonetically similar representations leads to lexical competition. In such a…

New Actions Upon Old Objects: A New Ontological Perspective on Functions.

ERIC Educational Resources Information Center

Schwarz, Baruch; Dreyfus, Tommy

1995-01-01

A computer microworld called Triple Representation Model uses graphical, tabular, and algebraic representations to influence conceptions of function. A majority of students were able to cope with partial data, recognize invariants while coordinating actions among representations, and recognize invariants while creating and comparing different…

Higher rank ABJM Wilson loops from matrix models

DOE PAGES

Cookmeyer, Jonathan; Liu, James T.; Pando Zayas, Leopoldo A.

2016-11-21

We compute the vacuum expectation values of 1/6 supersymmetric Wilson loops in higher dimensional representations of the gauge group in ABJM theory. We then present results for the m-symmetric and m-antisymmetric representations by exploiting standard matrix model techniques. At leading order, in the saddle point approximation, our expressions reproduce holographic results from both D6 and D2 branes corresponding to the antisymmetric and symmetric representations, respectively. We also compute 1/N corrections to the leading saddle point results.

Speeded Probed Recall Is Affected by Grouping.

PubMed

Morra, Sergio; Epidendio, Valentina

2015-01-01

Most of the evidence from previous studies on speeded probed recall supported primacy-gradient models of serial order representation. Two experiments investigated the effect of grouping on speeded probed recall. Six-word lists, followed by a number between 1 and 6, were presented for speeded recall of the word in the position indicated by the number. Grouping was manipulated through interstimulus intervals. In both experiments, a significant Position × Grouping interaction was found in RT. It is concluded that the results are not consistent with models of order representation only based on a primacy gradient. Possible alternative representations of serial order are also discussed; a case is made for a holistic order representation.

Good-enough linguistic representations and online cognitive equilibrium in language processing.

PubMed

Karimi, Hossein; Ferreira, Fernanda

2016-01-01

We review previous research showing that representations formed during language processing are sometimes just "good enough" for the task at hand and propose the "online cognitive equilibrium" hypothesis as the driving force behind the formation of good-enough representations in language processing. Based on this view, we assume that the language comprehension system by default prefers to achieve as early as possible and remain as long as possible in a state of cognitive equilibrium where linguistic representations are successfully incorporated with existing knowledge structures (i.e., schemata) so that a meaningful and coherent overall representation is formed, and uncertainty is resolved or at least minimized. We also argue that the online equilibrium hypothesis is consistent with current theories of language processing, which maintain that linguistic representations are formed through a complex interplay between simple heuristics and deep syntactic algorithms and also theories that hold that linguistic representations are often incomplete and lacking in detail. We also propose a model of language processing that makes use of both heuristic and algorithmic processing, is sensitive to online cognitive equilibrium, and, we argue, is capable of explaining the formation of underspecified representations. We review previous findings providing evidence for underspecification in relation to this hypothesis and the associated language processing model and argue that most of these findings are compatible with them.

Natural language generation of surgical procedures.

PubMed

Wagner, J C; Rogers, J E; Baud, R H; Scherrer, J R

1999-01-01

A number of compositional Medical Concept Representation systems are being developed. Although these provide for a detailed conceptual representation of the underlying information, they have to be translated back to natural language for used by end-users and applications. The GALEN programme has been developing one such representation and we report here on a tool developed to generate natural language phrases from the GALEN conceptual representations. This tool can be adapted to different source modelling schemes and to different destination languages or sublanguages of a domain. It is based on a multilingual approach to natural language generation, realised through a clean separation of the domain model from the linguistic model and their link by well defined structures. Specific knowledge structures and operations have been developed for bridging between the modelling 'style' of the conceptual representation and natural language. Using the example of the scheme developed for modelling surgical operative procedures within the GALEN-IN-USE project, we show how the generator is adapted to such a scheme. The basic characteristics of the surgical procedures scheme are presented together with the basic principles of the generation tool. Using worked examples, we discuss the transformation operations which change the initial source representation into a form which can more directly be translated to a given natural language. In particular, the linguistic knowledge which has to be introduced--such as definitions of concepts and relationships is described. We explain the overall generator strategy and how particular transformation operations are triggered by language-dependent and conceptual parameters. Results are shown for generated French phrases corresponding to surgical procedures from the urology domain.

Visual features as stepping stones toward semantics: Explaining object similarity in IT and perception with non-negative least squares

PubMed Central

Jozwik, Kamila M.; Kriegeskorte, Nikolaus; Mur, Marieke

2016-01-01

Object similarity, in brain representations and conscious perception, must reflect a combination of the visual appearance of the objects on the one hand and the categories the objects belong to on the other. Indeed, visual object features and category membership have each been shown to contribute to the object representation in human inferior temporal (IT) cortex, as well as to object-similarity judgments. However, the explanatory power of features and categories has not been directly compared. Here, we investigate whether the IT object representation and similarity judgments are best explained by a categorical or a feature-based model. We use rich models (>100 dimensions) generated by human observers for a set of 96 real-world object images. The categorical model consists of a hierarchically nested set of category labels (such as “human”, “mammal”, and “animal”). The feature-based model includes both object parts (such as “eye”, “tail”, and “handle”) and other descriptive features (such as “circular”, “green”, and “stubbly”). We used non-negative least squares to fit the models to the brain representations (estimated from functional magnetic resonance imaging data) and to similarity judgments. Model performance was estimated on held-out images not used in fitting. Both models explained significant variance in IT and the amounts explained were not significantly different. The combined model did not explain significant additional IT variance, suggesting that it is the shared model variance (features correlated with categories, categories correlated with features) that best explains IT. The similarity judgments were almost fully explained by the categorical model, which explained significantly more variance than the feature-based model. The combined model did not explain significant additional variance in the similarity judgments. Our findings suggest that IT uses features that help to distinguish categories as stepping stones toward a semantic representation. Similarity judgments contain additional categorical variance that is not explained by visual features, reflecting a higher-level more purely semantic representation. PMID:26493748

Visual features as stepping stones toward semantics: Explaining object similarity in IT and perception with non-negative least squares.

PubMed

Jozwik, Kamila M; Kriegeskorte, Nikolaus; Mur, Marieke

2016-03-01

Object similarity, in brain representations and conscious perception, must reflect a combination of the visual appearance of the objects on the one hand and the categories the objects belong to on the other. Indeed, visual object features and category membership have each been shown to contribute to the object representation in human inferior temporal (IT) cortex, as well as to object-similarity judgments. However, the explanatory power of features and categories has not been directly compared. Here, we investigate whether the IT object representation and similarity judgments are best explained by a categorical or a feature-based model. We use rich models (>100 dimensions) generated by human observers for a set of 96 real-world object images. The categorical model consists of a hierarchically nested set of category labels (such as "human", "mammal", and "animal"). The feature-based model includes both object parts (such as "eye", "tail", and "handle") and other descriptive features (such as "circular", "green", and "stubbly"). We used non-negative least squares to fit the models to the brain representations (estimated from functional magnetic resonance imaging data) and to similarity judgments. Model performance was estimated on held-out images not used in fitting. Both models explained significant variance in IT and the amounts explained were not significantly different. The combined model did not explain significant additional IT variance, suggesting that it is the shared model variance (features correlated with categories, categories correlated with features) that best explains IT. The similarity judgments were almost fully explained by the categorical model, which explained significantly more variance than the feature-based model. The combined model did not explain significant additional variance in the similarity judgments. Our findings suggest that IT uses features that help to distinguish categories as stepping stones toward a semantic representation. Similarity judgments contain additional categorical variance that is not explained by visual features, reflecting a higher-level more purely semantic representation. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Model Manipulation and Learning: Fostering Representational Competence with Virtual and Concrete Models

ERIC Educational Resources Information Center

Stull, Andrew T.; Hegarty, Mary

2016-01-01

This study investigated the development of representational competence among organic chemistry students by using 3D (concrete and virtual) models as aids for teaching students to translate between multiple 2D diagrams. In 2 experiments, students translated between different diagrams of molecules and received verbal feedback in 1 of the following 3…

A Model of Factors Determining Students' Ability to Interpret External Representations in Biochemistry

ERIC Educational Resources Information Center

Schonborn, Konrad J.; Anderson, Trevor R.

2009-01-01

The aim of this research was to develop a model of factors affecting students' ability to interpret external representations (ERs) in biochemistry. The study was qualitative in design and was guided by the modelling framework of Justi and Gilbert. Application of the process outlined by the framework, and consultation with relevant literature, led…

Associating Animations with Concrete Models to Enhance Students' Comprehension of Different Visual Representations in Organic Chemistry

ERIC Educational Resources Information Center

Al-Balushi, Sulaiman M.; Al-Hajri, Sheikha H.

2014-01-01

The purpose of the current study is to explore the impact of associating animations with concrete models on eleventh-grade students' comprehension of different visual representations in organic chemistry. The study used a post-test control group quasi-experimental design. The experimental group (N = 28) used concrete models, submicroscopic…

Teachers and Students' Conceptions of Computer-Based Models in the Context of High School Chemistry: Elicitations at the Pre-Intervention Stage

ERIC Educational Resources Information Center

Waight, Noemi; Gillmeister, Kristina

2014-01-01

This study examined teachers' and students' initial conceptions of computer-based models--Flash and NetLogo models--and documented how teachers and students reconciled notions of multiple representations featuring macroscopic, submicroscopic and symbolic representations prior to actual intervention in eight high school chemistry…

Use of Words and Visuals in Modelling Context of Annual Plant

ERIC Educational Resources Information Center

Park, Jungeun; DiNapoli, Joseph; Mixell, Robert A.; Flores, Alfinio

2017-01-01

This study looks at the various verbal and non-verbal representations used in a process of modelling the number of annual plants over time. Analysis focuses on how various representations such as words, diagrams, letters and mathematical equations evolve in the mathematization process of the modelling context. Our results show that (1) visual…

Children's representations of multiple family relationships: organizational structure and development in early childhood.

PubMed

Schermerhorn, Alice C; Cummings, E Mark; Davies, Patrick T

2008-02-01

The authors examine mutual family influence processes at the level of children's representations of multiple family relationships, as well as the structure of those representations. From a community sample with 3 waves, each spaced 1 year apart, kindergarten-age children (105 boys and 127 girls) completed a story-stem completion task, tapping representations of multiple family relationships. Structural equation modeling with autoregressive controls indicated that representational processes involving different family relationships were interrelated over time, including links between children's representations of marital conflict and reactions to conflict, between representations of security about marital conflict and parent-child relationships, and between representations of security in father-child and mother-child relationships. Mixed support was found for notions of increasing stability in representations during this developmental period. Results are discussed in terms of notions of transactional family dynamics, including family-wide perspectives on mutual influence processes attributable to multiple family relationships.

Representational Approach: A Conceptual Framework to Guide Patient Education Research and Practice.

PubMed

Arida, Janet A; Sherwood, Paula R; Flannery, Marie; Donovan, Heidi S

2016-11-01

Illness representations are cognitive structures that individuals rely on to understand and explain their illnesses and associated symptoms. The Representational Approach (RA) to patient education offers a theoretically based, clinically useful model that can support oncology nurses to develop a shared understanding of patients' illness representations to collaboratively develop highly personalized plans for symptom management and other important self-management behaviors. This article discusses theoretical underpinnings, practical applications, challenges, and future directions for incorporating illness representations and the RA in clinical and research endeavors.

Assessing representation errors of IAGOS CO2, CO and CH4 profile observations: the impact of spatial variations in near-field emissions

NASA Astrophysics Data System (ADS)

Boschetti, Fabio; Thouret, Valerie; Nedelec, Philippe; Chen, Huilin; Gerbig, Christoph

2015-04-01

Airborne platforms have their main strength in the ability of collecting mixing ratio and meteorological data at different heights across a vertical profile, allowing an insight in the internal structure of the atmosphere. However, rental airborne platforms are usually expensive, limiting the number of flights that can be afforded and hence on the amount of data that can be collected. To avoid this disadvantage, the MOZAIC/IAGOS (Measurements of Ozone and water vapor by Airbus In-service airCraft/In-service Aircraft for a Global Observing System) program makes use of commercial airliners, providing data on a regular basis. It is therefore considered an important tool in atmospheric investigations. However, due to the nature of said platforms, MOZAIC/IAGOS's profiles are located near international airports, which are usually significant emission sources, and are in most cases close to major urban settlements, characterized by higher anthropogenic emissions compared to rural areas. When running transport models at finite resolution, these local emissions can heavily affect measurements resulting in biases in model/observation mismatch. Model/observation mismatch can include different aspects in both horizontal and vertical direction, for example spatial and temporal resolution of the modeled fluxes, or poorly represented convective transport or turbulent mixing in the boundary layer. In the framework of the IGAS (IAGOS for GMES Atmospheric Service) project, whose aim is to improve connections between data collected by MOZAIC/IAGOS and Copernicus Atmospheric Service, the present study is focused on the effect of the spatial resolution of emission fluxes, referred to here as representation error. To investigate this, the Lagrangian transport model STILT (Stochastic Time Inverted Lagrangian Transport) was coupled with EDGAR (Emission Database for Global Atmospheric Research) version-4.3 emission inventory at European regional scale. EDGAR's simulated fluxes for CO, CO2 and CH4 with a spatial resolution of 10x10 km for the time frame 2006-2011 was be aggregated into coarser and coarser grid cells in order to evaluate the representation error at different spatial scales. The dependence of representation error from wind direction and month of the year was evaluated for different location in the European domain, for both random and bias component. The representation error was then validated against the model-data mismatch derived from the comparison of MACC (Monitoring Atmospheric Composition and Climate) reanalysis with IAGOS observations for CO to investigate its suitability for modeling applications. We found that the random and bias components of the representation error show a similar pattern dependent on wind direction. In addition, we found a clear linear relationship between the representation error and the model-data mismatch for both (random and bias) components, indicating that about 50% of the model-data mismatch is related to the representation error. This suggests that the representation error derived using STILT provides useful information for better understanding causes for model-data mismatch.

Rubber airplane: Constraint-based component-modeling for knowledge representation in computer-aided conceptual design

NASA Technical Reports Server (NTRS)

Kolb, Mark A.

1990-01-01

Viewgraphs on Rubber Airplane: Constraint-based Component-Modeling for Knowledge Representation in Computer Aided Conceptual Design are presented. Topics covered include: computer aided design; object oriented programming; airfoil design; surveillance aircraft; commercial aircraft; aircraft design; and launch vehicles.

Thinking Egyptian: Active Models for Understanding Spatial Representation.

ERIC Educational Resources Information Center

Schiferl, Ellen

This paper highlights how introductory textbooks on Egyptian art inhibit understanding by reinforcing student preconceptions, and demonstrates another approach to discussing space with a classroom exercise and software. The alternative approach, an active model for spatial representation, introduced here was developed by adapting classroom…

Redistribution, Recognition and Representation: Working against Pedagogies of Indifference

ERIC Educational Resources Information Center

Lingard, Bob; Keddie, Amanda

2013-01-01

This paper reports on an Australian government-commissioned research study that documented classroom pedagogies in 24 Queensland schools. The research created the model of "productive pedagogies", which conjoined what Nancy Fraser calls a politics of redistribution, recognition and representation. In this model pedagogies are…

Teachers and Students' Conceptions of Computer-Based Models in the Context of High School Chemistry: Elicitations at the Pre-intervention Stage

NASA Astrophysics Data System (ADS)

Waight, Noemi; Gillmeister, Kristina

2014-04-01

This study examined teachers' and students' initial conceptions of computer-based models—Flash and NetLogo models—and documented how teachers and students reconciled notions of multiple representations featuring macroscopic, submicroscopic and symbolic representations prior to actual intervention in eight high school chemistry classrooms. Individual in-depth interviews were conducted with 32 students and 6 teachers. Findings revealed an interplay of complex factors that functioned as opportunities and obstacles in the implementation of technologies in science classrooms. Students revealed preferences for the Flash models as opposed to the open-ended NetLogo models. Altogether, due to lack of content and modeling background knowledge, students experienced difficulties articulating coherent and blended understandings of multiple representations. Concurrently, while the aesthetic and interactive features of the models were of great value, they did not sustain students' initial curiosity and opportunities to improve understandings about chemistry phenomena. Most teachers recognized direct alignment of the Flash model with their existing curriculum; however, the benefits were relegated to existing procedural and passive classroom practices. The findings have implications for pedagogical approaches that address the implementation of computer-based models, function of models, models as multiple representations and the role of background knowledge and cognitive load, and the role of teacher vision and classroom practices.

A Methodology for Multiple Rule System Integration and Resolution Within a Singular Knowledge Base

NASA Technical Reports Server (NTRS)

Kautzmann, Frank N., III

1988-01-01

Expert Systems which support knowledge representation by qualitative modeling techniques experience problems, when called upon to support integrated views embodying description and explanation, especially when other factors such as multiple causality, competing rule model resolution, and multiple uses of knowledge representation are included. A series of prototypes are being developed to demonstrate the feasibility of automating the process of systems engineering, design and configuration, and diagnosis and fault management. A study involves not only a generic knowledge representation; it must also support multiple views at varying levels of description and interaction between physical elements, systems, and subsystems. Moreover, it will involve models of description and explanation for each level. This multiple model feature requires the development of control methods between rule systems and heuristics on a meta-level for each expert system involved in an integrated and larger class of expert system. The broadest possible category of interacting expert systems is described along with a general methodology for the knowledge representation and control of mutually exclusive rule systems.

When intelligence is in control

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bellman, K.L.

Each time a discipline redefines itself, I look at it as a sign of growth, because often such redefinition means that there is new theory, new methods, or new {open_quotes}disciples{close_quote} from other disciplines who are stretching, enlarging, and deepening the field. Such is the case with semiotics. Deeply entwined with the concepts of {open_quotes}intelligent systems{close_quotes}, {open_quotes}intelligent control{close_quotes}, and complex systems theory, semiotics struggles to develop representations, notations (systems of representations), and models (functionally-oriented sets of related representations) to study systems that may or may not be usefully described as employing representations, notations, and models themselves. That last, of course, ismore » the main problem that semiotics faces. Semiotics, like psychology, philosophy, or any other self-referential discipline, is burdened by the eye attempting to study the eye or the mind studying the mind, or more to the point here, the modeler studying the modeling acts of others.« less

A nucleobase-centered coarse-grained representation for structure prediction of RNA motifs.

PubMed

Poblete, Simón; Bottaro, Sandro; Bussi, Giovanni

2018-02-28

We introduce the SPlit-and-conQueR (SPQR) model, a coarse-grained (CG) representation of RNA designed for structure prediction and refinement. In our approach, the representation of a nucleotide consists of a point particle for the phosphate group and an anisotropic particle for the nucleoside. The interactions are, in principle, knowledge-based potentials inspired by the $\\mathcal {E}$SCORE function, a base-centered scoring function. However, a special treatment is given to base-pairing interactions and certain geometrical conformations which are lost in a raw knowledge-based model. This results in a representation able to describe planar canonical and non-canonical base pairs and base-phosphate interactions and to distinguish sugar puckers and glycosidic torsion conformations. The model is applied to the folding of several structures, including duplexes with internal loops of non-canonical base pairs, tetraloops, junctions and a pseudoknot. For the majority of these systems, experimental structures are correctly predicted at the level of individual contacts. We also propose a method for efficiently reintroducing atomistic detail from the CG representation.

Representations in Dynamical Embodied Agents: Re-Analyzing a Minimally Cognitive Model Agent

ERIC Educational Resources Information Center

Mirolli, Marco

2012-01-01

Understanding the role of "representations" in cognitive science is a fundamental problem facing the emerging framework of embodied, situated, dynamical cognition. To make progress, I follow the approach proposed by an influential representational skeptic, Randall Beer: building artificial agents capable of minimally cognitive behaviors and…

Representing Energy. II. Energy Tracking Representations

ERIC Educational Resources Information Center

Scherr, Rachel E.; Close, Hunter G.; Close, Eleanor W.; Vokos, Stamatis

2012-01-01

The Energy Project at Seattle Pacific University has developed representations that embody the substance metaphor and support learners in conserving and tracking energy as it flows from object to object and changes form. Such representations enable detailed modeling of energy dynamics in complex physical processes. We assess student learning by…

Studying Action Representation in Children via Motor Imagery

ERIC Educational Resources Information Center

Gabbard, Carl

2009-01-01

The use of motor imagery is a widely used experimental paradigm for the study of cognitive aspects of action planning and control in adults. Furthermore, there are indications that motor imagery provides a window into the process of action representation. These notions complement internal model theory suggesting that such representations allow…

Building Cognition: The Construction of Computational Representations for Scientific Discovery

ERIC Educational Resources Information Center

Chandrasekharan, Sanjay; Nersessian, Nancy J.

2015-01-01

Novel computational representations, such as simulation models of complex systems and video games for scientific discovery (Foldit, EteRNA etc.), are dramatically changing the way discoveries emerge in science and engineering. The cognitive roles played by such computational representations in discovery are not well understood. We present a…

Parallel updating and weighting of multiple spatial maps for visual stability during whole body motion

PubMed Central

Medendorp, W. P.

2015-01-01

It is known that the brain uses multiple reference frames to code spatial information, including eye-centered and body-centered frames. When we move our body in space, these internal representations are no longer in register with external space, unless they are actively updated. Whether the brain updates multiple spatial representations in parallel, or whether it restricts its updating mechanisms to a single reference frame from which other representations are constructed, remains an open question. We developed an optimal integration model to simulate the updating of visual space across body motion in multiple or single reference frames. To test this model, we designed an experiment in which participants had to remember the location of a briefly presented target while being translated sideways. The behavioral responses were in agreement with a model that uses a combination of eye- and body-centered representations, weighted according to the reliability in which the target location is stored and updated in each reference frame. Our findings suggest that the brain simultaneously updates multiple spatial representations across body motion. Because both representations are kept in sync, they can be optimally combined to provide a more precise estimate of visual locations in space than based on single-frame updating mechanisms. PMID:26490289

DOE Office of Scientific and Technical Information (OSTI.GOV)

Smith, Curtis L.; Prescott, Steven; Coleman, Justin

This report describes the current progress and status related to the Industry Application #2 focusing on External Hazards. For this industry application within the Light Water Reactor Sustainability (LWRS) Program Risk-Informed Safety Margin Characterization (RISMC) R&D Pathway, we will create the Risk-Informed Margin Management (RIMM) approach to represent meaningful (i.e., realistic facility representation) event scenarios and consequences by using an advanced 3D facility representation that will evaluate external hazards such as flooding and earthquakes in order to identify, model and analyze the appropriate physics that needs to be included to determine plant vulnerabilities related to external events; manage the communicationmore » and interactions between different physics modeling and analysis technologies; and develop the computational infrastructure through tools related to plant representation, scenario depiction, and physics prediction. One of the unique aspects of the RISMC approach is how it couples probabilistic approaches (the scenario) with mechanistic phenomena representation (the physics) through simulation. This simulation-based modeling allows decision makers to focus on a variety of safety, performance, or economic metrics. In this report, we describe the evaluation of various physics toolkits related to flooding representation. Ultimately, we will be coupling the flooding representation with other events such as earthquakes in order to provide coupled physics analysis for scenarios where interactions exist.« less

Remembering the past and imagining the future

PubMed Central

Byrne, Patrick; Becker, Suzanna; Burgess, Neil

2009-01-01

The neural mechanisms underlying spatial cognition are modelled, integrating neuronal, systems and behavioural data, and addressing the relationships between long-term memory, short-term memory and imagery, and between egocentric and allocentric and visual and idiothetic representations. Long-term spatial memory is modeled as attractor dynamics within medial-temporal allocentric representations, and short-term memory as egocentric parietal representations driven by perception, retrieval and imagery, and modulated by directed attention. Both encoding and retrieval/ imagery require translation between egocentric and allocentric representations, mediated by posterior parietal and retrosplenial areas and utilizing head direction representations in Papez’s circuit. Thus hippocampus effectively indexes information by real or imagined location, while Papez’s circuit translates to imagery or from perception according to the direction of view. Modulation of this translation by motor efference allows “spatial updating” of representations, while prefrontal simulated motor efference allows mental exploration. The alternating temporo-parietal flows of information are organized by the theta rhythm. Simulations demonstrate the retrieval and updating of familiar spatial scenes, hemispatial neglect in memory, and the effects on hippocampal place cell firing of lesioned head direction representations and of conflicting visual and ideothetic inputs. PMID:17500630

Dynamic updating of hippocampal object representations reflects new conceptual knowledge

PubMed Central

Mack, Michael L.; Love, Bradley C.; Preston, Alison R.

2016-01-01

Concepts organize the relationship among individual stimuli or events by highlighting shared features. Often, new goals require updating conceptual knowledge to reflect relationships based on different goal-relevant features. Here, our aim is to determine how hippocampal (HPC) object representations are organized and updated to reflect changing conceptual knowledge. Participants learned two classification tasks in which successful learning required attention to different stimulus features, thus providing a means to index how representations of individual stimuli are reorganized according to changing task goals. We used a computational learning model to capture how people attended to goal-relevant features and organized object representations based on those features during learning. Using representational similarity analyses of functional magnetic resonance imaging data, we demonstrate that neural representations in left anterior HPC correspond with model predictions of concept organization. Moreover, we show that during early learning, when concept updating is most consequential, HPC is functionally coupled with prefrontal regions. Based on these findings, we propose that when task goals change, object representations in HPC can be organized in new ways, resulting in updated concepts that highlight the features most critical to the new goal. PMID:27803320

[Citizen constitution and social representations: reflecting about health care models].

PubMed

da Silva, Sílvio Eder Dias; Ramos, Flávia Regina Souza; Martins, Cleusa Rios; Padilha, Maria Itayra; Vasconcelos, Esleane Vilela

2010-12-01

This article presents a reflection on the meaning of the terms citizenship and health, addressing the Theory of Social Representations as a strategy for implementing and evaluating health care models in Brazil. First, a brief history about the concept of citizenship is presented; then the article addresses the principles of freedom and equality according to Kant; the third section of the article shows that health is as a right of the citizen and a duty of the state. Finally, the Theory of Social Representations is emphasized as a strategy to evaluate and implement the health services provided to citizens by the current health care models in Brazil.

Squeezing and its graphical representations in the anharmonic oscillator model

NASA Astrophysics Data System (ADS)

Tanaś, R.; Miranowicz, A.; Kielich, S.

1991-04-01

The problem of squeezing and its graphical representations in the anharmonic oscillator model is considered. Explicit formulas for squeezing, principal squeezing, and the quasiprobability distribution (QPD) function are given and illustrated graphically. Approximate analytical formulas for the variances, extremal variances, and QPD are obtained for the case of small nonlinearities and large numbers of photons. The possibility of almost perfect squeezing in the model is demonstrated and its graphical representations in the form of variance lemniscates and QPD contours are plotted. For large numbers of photons the crescent shape of the QPD contours is hardly visible and quite regular ellipses are obtained.

The impact of the parental illness representation on disease management in childhood asthma.

PubMed

Yoos, H Lorrie; Kitzman, Harriet; Henderson, Charles; McMullen, Ann; Sidora-Arcoleo, Kimberly; Halterman, Jill S; Anson, Elizabeth

2007-01-01

Despite significant advances in treatment modalities, morbidity due to childhood asthma has continued to increase, particularly for poor and minority children. To describe the parental illness representation of asthma in juxtaposition to the professional model of asthma and to evaluate the impact of that illness representation on the adequacy of the child's medication regimen. Parents (n = 228) of children with asthma were interviewed regarding illness beliefs using a semistructured interview. The impact of background characteristics, parental beliefs, the child's symptom interpretation, and the parent-healthcare provider (HCP) relationship on the adequacy of the child's medication regimen were evaluated. The parental and professional models of asthma differ markedly. Demographic risk factors (p = .005), low parental education (p < .0001), inaccurate symptom evaluation by the child (p = .02), and a poor parent-HCP relationship (p < .0001) had a negative effect on the parental illness representation. A parental illness representation concordant with the professional model of asthma (p = .05) and more formal asthma education (p = .02) had a direct positive effect on the medication regimen. Demographic risk factors (p = .006) and informal advice-seeking (p = .0003) had a negative impact on the regimen. The parental illness representation mediated the impact of demographic risk factors (p = .10), parental education (p =.07), and the parent-HCP relationship (p = .06) on the regimen. Parents and HCPs may come to the clinical encounter with markedly different illness representations. Establishing a partnership with parents by eliciting and acknowledging parental beliefs is an important component of improving disease management.

Advances in visual representation of molecular potentials.

PubMed

Du, Qi-Shi; Huang, Ri-Bo; Chou, Kuo-Chen

2010-06-01

The recent advances in visual representations of molecular properties in 3D space are summarized, and their applications in molecular modeling study and rational drug design are introduced. The visual representation methods provide us with detailed insights into protein-ligand interactions, and hence can play a major role in elucidating the structure or reactivity of a biomolecular system. Three newly developed computation and visualization methods for studying the physical and chemical properties of molecules are introduced, including their electrostatic potential, lipophilicity potential and excess chemical potential. The newest application examples of visual representations in structure-based rational drug are presented. The 3D electrostatic potentials, calculated using the empirical method (EM-ESP), in which the classical Coulomb equation and traditional atomic partial changes are discarded, are highly consistent with the results by the higher level quantum chemical method. The 3D lipophilicity potentials, computed by the heuristic molecular lipophilicity potential method based on the principles of quantum mechanics and statistical mechanics, are more accurate and reliable than those by using the traditional empirical methods. The 3D excess chemical potentials, derived by the reference interaction site model-hypernetted chain theory, provide a new tool for computational chemistry and molecular modeling. For structure-based drug design, the visual representations of molecular properties will play a significant role in practical applications. It is anticipated that the new advances in computational chemistry will stimulate the development of molecular modeling methods, further enriching the visual representation techniques for rational drug design, as well as other relevant fields in life science.

Sampling-free Bayesian inversion with adaptive hierarchical tensor representations

NASA Astrophysics Data System (ADS)

Eigel, Martin; Marschall, Manuel; Schneider, Reinhold

2018-03-01

A sampling-free approach to Bayesian inversion with an explicit polynomial representation of the parameter densities is developed, based on an affine-parametric representation of a linear forward model. This becomes feasible due to the complete treatment in function spaces, which requires an efficient model reduction technique for numerical computations. The advocated perspective yields the crucial benefit that error bounds can be derived for all occuring approximations, leading to provable convergence subject to the discretization parameters. Moreover, it enables a fully adaptive a posteriori control with automatic problem-dependent adjustments of the employed discretizations. The method is discussed in the context of modern hierarchical tensor representations, which are used for the evaluation of a random PDE (the forward model) and the subsequent high-dimensional quadrature of the log-likelihood, alleviating the ‘curse of dimensionality’. Numerical experiments demonstrate the performance and confirm the theoretical results.

Implicit kernel sparse shape representation: a sparse-neighbors-based objection segmentation framework.

PubMed

Yao, Jincao; Yu, Huimin; Hu, Roland

2017-01-01

This paper introduces a new implicit-kernel-sparse-shape-representation-based object segmentation framework. Given an input object whose shape is similar to some of the elements in the training set, the proposed model can automatically find a cluster of implicit kernel sparse neighbors to approximately represent the input shape and guide the segmentation. A distance-constrained probabilistic definition together with a dualization energy term is developed to connect high-level shape representation and low-level image information. We theoretically prove that our model not only derives from two projected convex sets but is also equivalent to a sparse-reconstruction-error-based representation in the Hilbert space. Finally, a "wake-sleep"-based segmentation framework is applied to drive the evolutionary curve to recover the original shape of the object. We test our model on two public datasets. Numerical experiments on both synthetic images and real applications show the superior capabilities of the proposed framework.

An atom is known by the company it keeps: Content, representation and pedagogy within the epistemic revolution of the complexity sciences

NASA Astrophysics Data System (ADS)

Blikstein, Paulo

The goal of this dissertation is to explore relations between content, representation, and pedagogy, so as to understand the impact of the nascent field of complexity sciences on science, technology, engineering and mathematics (STEM) learning. Wilensky & Papert coined the term "structurations" to express the relationship between knowledge and its representational infrastructure. A change from one representational infrastructure to another they call a "restructuration." The complexity sciences have introduced a novel and powerful structuration: agent-based modeling. In contradistinction to traditional mathematical modeling, which relies on equational descriptions of macroscopic properties of systems, agent-based modeling focuses on a few archetypical micro-behaviors of "agents" to explain emergent macro-behaviors of the agent collective. Specifically, this dissertation is about a series of studies of undergraduate students' learning of materials science, in which two structurations are compared (equational and agent-based), consisting of both design research and empirical evaluation. I have designed MaterialSim, a constructionist suite of computer models, supporting materials and learning activities designed within the approach of agent-based modeling, and over four years conducted an empirical inves3 tigation of an undergraduate materials science course. The dissertation is comprised of three studies: Study 1 - diagnosis . I investigate current representational and pedagogical practices in engineering classrooms. Study 2 - laboratory studies. I investigate the cognition of students engaging in scientific inquiry through programming their own scientific models. Study 3 - classroom implementation. I investigate the characteristics, advantages, and trajectories of scientific content knowledge that is articulated in epistemic forms and representational infrastructures unique to complexity sciences, as well as the feasibility of the integration of constructionist, agent-based learning environments in engineering classrooms. Data sources include classroom observations, interviews, videotaped sessions of model-building, questionnaires, analysis of computer-generated logfiles, and quantitative and qualitative analysis of artifacts. Results shows that (1) current representational and pedagogical practices in engineering classrooms were not up to the challenge of the complex content being taught, (2) by building their own scientific models, students developed a deeper understanding of core scientific concepts, and learned how to better identify unifying principles and behaviors in materials science, and (3) programming computer models was feasible within a regular engineering classroom.

Length-Two Representations of Quantum Affine Superalgebras and Baxter Operators

NASA Astrophysics Data System (ADS)

Zhang, Huafeng

2018-03-01

Associated to quantum affine general linear Lie superalgebras are two families of short exact sequences of representations whose first and third terms are irreducible: the Baxter TQ relations involving infinite-dimensional representations; the extended T-systems of Kirillov-Reshetikhin modules. We make use of these representations over the full quantum affine superalgebra to define Baxter operators as transfer matrices for the quantum integrable model and to deduce Bethe Ansatz Equations, under genericity conditions.

On Productive Knowledge and Levels of Questions.

ERIC Educational Resources Information Center

Andre, Thomas

A model is proposed for memory that stresses a distinction between episodic memory for encoded personal experience and semantic memory for abstractors and generalizations. Basically, the model holds that questions influence the nature of memory representations formed during instruction, and that memory representation controls the way in which…

Learned Vector-Space Models for Document Retrieval.

ERIC Educational Resources Information Center

Caid, William R.; And Others

1995-01-01

The Latent Semantic Indexing and MatchPlus systems examine similar contexts in which words appear and create representational models that capture the similarity of meaning of terms and then use the representation for retrieval. Text Retrieval Conference experiments using these systems demonstrate the computational feasibility of using…

ART-ML - a novel XML format for the biological procedures modeling and the representation of blood flow simulation.

PubMed

Karvounis, E C; Tsakanikas, V D; Fotiou, E; Fotiadis, D I

2010-01-01

The paper proposes a novel Extensible Markup Language (XML) based format called ART-ML that aims at supporting the interoperability and the reuse of models of blood flow, mass transport and plaque formation, exported by ARTool. ARTool is a platform for the automatic processing of various image modalities of coronary and carotid arteries. The images and their content are fused to develop morphological models of the arteries in easy to handle 3D representations. The platform incorporates efficient algorithms which are able to perform blood flow simulation. In addition atherosclerotic plaque development is estimated taking into account morphological, flow and genetic factors. ART-ML provides a XML format that enables the representation and management of embedded models within the ARTool platform and the storage and interchange of well-defined information. This approach influences in the model creation, model exchange, model reuse and result evaluation.

Action and perception in literacy: A common-code for spelling and reading.

PubMed

Houghton, George

2018-01-01

There is strong evidence that reading and spelling in alphabetical scripts depend on a shared representation (common-coding). However, computational models usually treat the two skills separately, producing a wide variety of proposals as to how the identity and position of letters is represented. This article treats reading and spelling in terms of the common-coding hypothesis for perception-action coupling. Empirical evidence for common representations in spelling-reading is reviewed. A novel version of the Start-End Competitive Queuing (SE-CQ) spelling model is introduced, and tested against the distribution of positional errors in Letter Position Dysgraphia, data from intralist intrusion errors in spelling to dictation, and dysgraphia because of nonperipheral neglect. It is argued that no other current model is equally capable of explaining this range of data. To pursue the common-coding hypothesis, the representation used in SE-CQ is applied, without modification, to the coding of letter identity and position for reading and lexical access, and a lexical matching rule for the representation is proposed (Start End Position Code model, SE-PC). Simulations show the model's compatibility with benchmark findings from form priming, its ability to account for positional effects in letter identification priming and the positional distribution of perseverative intrusion errors. The model supports the view that spelling and reading use a common orthographic description, providing a well-defined account of the major features of this representation. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Supporting Students in Learning with Multiple Representation to Improve Student Mental Models on Atomic Structure Concepts

ERIC Educational Resources Information Center

Sunyono; Yuanita, L.; Ibrahim, M.

2015-01-01

The aim of this research is identify the effectiveness of a multiple representation-based learning model, which builds a mental model within the concept of atomic structure. The research sample of 108 students in 3 classes is obtained randomly from among students of Mathematics and Science Education Studies using a stratified random sampling…

Risk-assessment of post-wildfire hydrological response in semi-arid basins: The effects of varying rainfall representations in the KINEROS2/AGWA model

USDA-ARS?s Scientific Manuscript database

Representation of precipitation is one of the most difficult aspects of modeling post-fire runoff and erosion and also one of the most sensitive input parameters to rainfall-runoff models. The impact of post-fire convective rainstorms, especially in semi-arid watersheds, depends on the overlap betwe...

Hyperspectral Image Classification via Kernel Sparse Representation

DTIC Science & Technology

2013-01-01

classification algorithms. Moreover, the spatial coherency across neighboring pixels is also incorporated through a kernelized joint sparsity model , where...joint sparsity model , where all of the pixels within a small neighborhood are jointly represented in the feature space by selecting a few common training...hyperspectral imagery, joint spar- sity model , kernel methods, sparse representation. I. INTRODUCTION HYPERSPECTRAL imaging sensors capture images

Bayesian inference on multiscale models for poisson intensity estimation: applications to photon-limited image denoising.

PubMed

Lefkimmiatis, Stamatios; Maragos, Petros; Papandreou, George

2009-08-01

We present an improved statistical model for analyzing Poisson processes, with applications to photon-limited imaging. We build on previous work, adopting a multiscale representation of the Poisson process in which the ratios of the underlying Poisson intensities (rates) in adjacent scales are modeled as mixtures of conjugate parametric distributions. Our main contributions include: 1) a rigorous and robust regularized expectation-maximization (EM) algorithm for maximum-likelihood estimation of the rate-ratio density parameters directly from the noisy observed Poisson data (counts); 2) extension of the method to work under a multiscale hidden Markov tree model (HMT) which couples the mixture label assignments in consecutive scales, thus modeling interscale coefficient dependencies in the vicinity of image edges; 3) exploration of a 2-D recursive quad-tree image representation, involving Dirichlet-mixture rate-ratio densities, instead of the conventional separable binary-tree image representation involving beta-mixture rate-ratio densities; and 4) a novel multiscale image representation, which we term Poisson-Haar decomposition, that better models the image edge structure, thus yielding improved performance. Experimental results on standard images with artificially simulated Poisson noise and on real photon-limited images demonstrate the effectiveness of the proposed techniques.

The relevance of different trust models for representation in patient organizations: conceptual considerations.

PubMed

Gerhards, Helene; Jongsma, Karin; Schicktanz, Silke

2017-07-11

Trust within organizations is important for ensuring members' acceptance of the organization's activities and to expand their scope of action. Remarkably, Patient Organizations (POs) that often both function as a forum for self-help and represent patients on the health-political level, have been understudied in this respect. This paper analyzes the relation between trust and representation in POs. We distinguish between two models of representation originating from political theory: the trustee and delegate model and between two types of trust: horizontal and vertical trust. Our theoretical approach is illustrated with an analysis of 13 interviews with representatives of German POs. We have found that the delegate model requires horizontal trust and the trustee model vertical trust. Both models: horizontal/delegate and vertical/trustee exist within single POs. The representation process within POs demands a balancing act between inclusion of affected persons and strategically aggregating a clear-cut political claim. Trust plays in that process of coming from individual wishes to collective and political standpoints a major role both in terms of horizontal as well as vertical trust. Horizontal trust serves the communication between affected members, and vertical trust allows representatives to be decisive.

Self-organized Evaluation of Dynamic Hand Gestures for Sign Language Recognition

NASA Astrophysics Data System (ADS)

Buciu, Ioan; Pitas, Ioannis

Two main theories exist with respect to face encoding and representation in the human visual system (HVS). The first one refers to the dense (holistic) representation of the face, where faces have "holon"-like appearance. The second one claims that a more appropriate face representation is given by a sparse code, where only a small fraction of the neural cells corresponding to face encoding is activated. Theoretical and experimental evidence suggest that the HVS performs face analysis (encoding, storing, face recognition, facial expression recognition) in a structured and hierarchical way, where both representations have their own contribution and goal. According to neuropsychological experiments, it seems that encoding for face recognition, relies on holistic image representation, while a sparse image representation is used for facial expression analysis and classification. From the computer vision perspective, the techniques developed for automatic face and facial expression recognition fall into the same two representation types. Like in Neuroscience, the techniques which perform better for face recognition yield a holistic image representation, while those techniques suitable for facial expression recognition use a sparse or local image representation. The proposed mathematical models of image formation and encoding try to simulate the efficient storing, organization and coding of data in the human cortex. This is equivalent with embedding constraints in the model design regarding dimensionality reduction, redundant information minimization, mutual information minimization, non-negativity constraints, class information, etc. The presented techniques are applied as a feature extraction step followed by a classification method, which also heavily influences the recognition results.

The Representation of Abstract Words: Why Emotion Matters

ERIC Educational Resources Information Center

Kousta, Stavroula-Thaleia; Vigliocco, Gabriella; Vinson, David P.; Andrews, Mark; Del Campo, Elena

2011-01-01

Although much is known about the representation and processing of concrete concepts, knowledge of what abstract semantics might be is severely limited. In this article we first address the adequacy of the 2 dominant accounts (dual coding theory and the context availability model) put forward in order to explain representation and processing…

Numerical Ordering Ability Mediates the Relation between Number-Sense and Arithmetic Competence

ERIC Educational Resources Information Center

Lyons, Ian M.; Beilock, Sian L.

2011-01-01

What predicts human mathematical competence? While detailed models of number representation in the brain have been developed, it remains to be seen exactly how basic number representations link to higher math abilities. We propose that representation of ordinal associations between numerical symbols is one important factor that underpins this…

Gender Difference in the Use of Thought Representation--A Corpus-Based Study

ERIC Educational Resources Information Center

Riissanen, Anne; Watson, Greg

2014-01-01

This study (Note 1) investigates potential differences in language use between genders, by applying a modified model of thought representation. Our hypothesis is that women use more direct forms of thought representation than men in modern spoken British English. Women are said to favour "private speech" that creates intimacy and…

Several Characteristic Features of Children's Representations

ERIC Educational Resources Information Center

Dulama, Maria Eliza; Ilovan, Oana-Ramona; Vanea, Cornelia

2009-01-01

The purpose of our research was to test the following hypothesis: 6 and 7 years old children's representations were strongly influenced by the environment they lived in. Representations are interiorised models of objects, phenomena and events, independent of present use of our senses and of the presence or absence of objects. We realised our…

Large-Scale Modeling of Wordform Learning and Representation

ERIC Educational Resources Information Center

Sibley, Daragh E.; Kello, Christopher T.; Plaut, David C.; Elman, Jeffrey L.

2008-01-01

The forms of words as they appear in text and speech are central to theories and models of lexical processing. Nonetheless, current methods for simulating their learning and representation fail to approach the scale and heterogeneity of real wordform lexicons. A connectionist architecture termed the "sequence encoder" is used to learn…

Children's Orthographic Knowledge and Their Word Reading Skill: Testing Bidirectional Relations

ERIC Educational Resources Information Center

Conrad, Nicole J.; Deacon, S. Hélène

2016-01-01

Prominent models of word reading concur that the development of efficient word reading depends on the establishment of lexical orthographic representations in memory. In turn, word reading skills are conceptualised as supporting the development of these orthographic representations. As such, models of word reading development make clear…

Teachers' Practices and Mental Models: Transformation through Reflection on Action

ERIC Educational Resources Information Center

Manrique, María Soledad; Sánchez Abchi, Verónica

2015-01-01

This contribution explores the relationship between teaching practices, teaching discourses and teachers' implicit representations and mental models and the way these dimensions change through teacher education (T.E). In order to study these relationships, and based on the assumptions that representations underlie teaching practices and that T.E…

Cortical dynamics of three-dimensional figure-ground perception of two-dimensional pictures.

PubMed

Grossberg, S

1997-07-01

This article develops the FACADE theory of 3-dimensional (3-D) vision and figure-ground separation to explain data concerning how 2-dimensional pictures give rise to 3-D percepts of occluding and occluded objects. The model describes how geometrical and contrastive properties of a picture can either cooperate or compete when forming the boundaries and surface representation that subserve conscious percepts. Spatially long-range cooperation and spatially short-range competition work together to separate the boundaries of occluding figures from their occluded neighbors. This boundary ownership process is sensitive to image T junctions at which occluded figures contact occluding figures. These boundaries control the filling-in of color within multiple depth-sensitive surface representations. Feedback between surface and boundary representations strengthens consistent boundaries while inhibiting inconsistent ones. Both the boundary and the surface representations of occluded objects may be amodally completed, while the surface representations of unoccluded objects become visible through modal completion. Functional roles for conscious modal and amodal representations in object recognition, spatial attention, and reaching behaviors are discussed. Model interactions are interpreted in terms of visual, temporal, and parietal cortices.

Decoding the dynamic representation of musical pitch from human brain activity.

PubMed

Sankaran, N; Thompson, W F; Carlile, S; Carlson, T A

2018-01-16

In music, the perception of pitch is governed largely by its tonal function given the preceding harmonic structure of the music. While behavioral research has advanced our understanding of the perceptual representation of musical pitch, relatively little is known about its representational structure in the brain. Using Magnetoencephalography (MEG), we recorded evoked neural responses to different tones presented within a tonal context. Multivariate Pattern Analysis (MVPA) was applied to "decode" the stimulus that listeners heard based on the underlying neural activity. We then characterized the structure of the brain's representation using decoding accuracy as a proxy for representational distance, and compared this structure to several well established perceptual and acoustic models. The observed neural representation was best accounted for by a model based on the Standard Tonal Hierarchy, whereby differences in the neural encoding of musical pitches correspond to their differences in perceived stability. By confirming that perceptual differences honor those in the underlying neuronal population coding, our results provide a crucial link in understanding the cognitive foundations of musical pitch across psychological and neural domains.

Multi-representation ability of students on the problem solving physics

NASA Astrophysics Data System (ADS)

Theasy, Y.; Wiyanto; Sujarwata

2018-03-01

Accuracy in representing knowledge possessed by students will show how the level of student understanding. The multi-representation ability of students on the problem solving of physics has been done through qualitative method of grounded theory model and implemented on physics education student of Unnes academic year 2016/2017. Multiforms of representation used are verbal (V), images/diagrams (D), graph (G), and mathematically (M). High and low category students have an accurate use of graphical representation (G) of 83% and 77.78%, and medium category has accurate use of image representation (D) equal to 66%.

Cortical processing of pitch: Model-based encoding and decoding of auditory fMRI responses to real-life sounds.

PubMed

De Angelis, Vittoria; De Martino, Federico; Moerel, Michelle; Santoro, Roberta; Hausfeld, Lars; Formisano, Elia

2017-11-13

Pitch is a perceptual attribute related to the fundamental frequency (or periodicity) of a sound. So far, the cortical processing of pitch has been investigated mostly using synthetic sounds. However, the complex harmonic structure of natural sounds may require different mechanisms for the extraction and analysis of pitch. This study investigated the neural representation of pitch in human auditory cortex using model-based encoding and decoding analyses of high field (7 T) functional magnetic resonance imaging (fMRI) data collected while participants listened to a wide range of real-life sounds. Specifically, we modeled the fMRI responses as a function of the sounds' perceived pitch height and salience (related to the fundamental frequency and the harmonic structure respectively), which we estimated with a computational algorithm of pitch extraction (de Cheveigné and Kawahara, 2002). First, using single-voxel fMRI encoding, we identified a pitch-coding region in the antero-lateral Heschl's gyrus (HG) and adjacent superior temporal gyrus (STG). In these regions, the pitch representation model combining height and salience predicted the fMRI responses comparatively better than other models of acoustic processing and, in the right hemisphere, better than pitch representations based on height/salience alone. Second, we assessed with model-based decoding that multi-voxel response patterns of the identified regions are more informative of perceived pitch than the remainder of the auditory cortex. Further multivariate analyses showed that complementing a multi-resolution spectro-temporal sound representation with pitch produces a small but significant improvement to the decoding of complex sounds from fMRI response patterns. In sum, this work extends model-based fMRI encoding and decoding methods - previously employed to examine the representation and processing of acoustic sound features in the human auditory system - to the representation and processing of a relevant perceptual attribute such as pitch. Taken together, the results of our model-based encoding and decoding analyses indicated that the pitch of complex real life sounds is extracted and processed in lateral HG/STG regions, at locations consistent with those indicated in several previous fMRI studies using synthetic sounds. Within these regions, pitch-related sound representations reflect the modulatory combination of height and the salience of the pitch percept. Copyright © 2017 Elsevier Inc. All rights reserved.

Improving the representation of photosynthesis in Earth system models

NASA Astrophysics Data System (ADS)

Rogers, A.; Medlyn, B. E.; Dukes, J.; Bonan, G. B.; von Caemmerer, S.; Dietze, M.; Kattge, J.; Leakey, A. D.; Mercado, L. M.; Niinemets, U.; Prentice, I. C. C.; Serbin, S.; Sitch, S.; Way, D. A.; Zaehle, S.

2015-12-01

Continued use of fossil fuel drives an accelerating increase in atmospheric CO2 concentration ([CO2]) and is the principal cause of global climate change. Many of the observed and projected impacts of rising [CO2] portend increasing environmental and economic risk, yet the uncertainty surrounding the projection of our future climate by Earth System Models (ESMs) is unacceptably high. Improving confidence in our estimation of future [CO2] is essential if we seek to project global change with greater confidence. There are critical uncertainties over the long term response of terrestrial CO2 uptake to global change, more specifically, over the size of the terrestrial carbon sink and over its sensitivity to rising [CO2] and temperature. Reducing the uncertainty associated with model representation of the largest CO2 flux on the planet is therefore an essential part of improving confidence in projections of global change. Here we have examined model representation of photosynthesis in seven process models including several global models that underlie the representation of photosynthesis in the land surface model component of ESMs that were part of the recent Fifth Assessment Report from the IPCC. Our approach was to focus on how physiological responses are represented by these models, and to better understand how structural and parametric differences drive variation in model responses to light, CO2, nutrients, temperature, vapor pressure deficit and soil moisture. We challenged each model to produce leaf and canopy responses to these factors to help us identify areas in which current process knowledge and emerging data sets could be used to improve model skill, and also identify knowledge gaps in current understanding that directly impact model outputs. We hope this work will provide a roadmap for the scientific activity that is necessary to advance process representation, parameterization and scaling of photosynthesis in the next generation of Earth System Models.

The representation of order information in auditory-verbal short-term memory.

PubMed

Kalm, Kristjan; Norris, Dennis

2014-05-14

Here we investigate how order information is represented in auditory-verbal short-term memory (STM). We used fMRI and a serial recall task to dissociate neural activity patterns representing the phonological properties of the items stored in STM from the patterns representing their order. For this purpose, we analyzed fMRI activity patterns elicited by different item sets and different orderings of those items. These fMRI activity patterns were compared with the predictions made by positional and chaining models of serial order. The positional models encode associations between items and their positions in a sequence, whereas the chaining models encode associations between successive items and retain no position information. We show that a set of brain areas in the postero-dorsal stream of auditory processing store associations between items and order as predicted by a positional model. The chaining model of order representation generates a different pattern similarity prediction, which was shown to be inconsistent with the fMRI data. Our results thus favor a neural model of order representation that stores item codes, position codes, and the mapping between them. This study provides the first fMRI evidence for a specific model of order representation in the human brain. Copyright © 2014 the authors 0270-6474/14/346879-08$15.00/0.

The Graphical Representation of the Digital Astronaut Physiology Backbone

NASA Technical Reports Server (NTRS)

Briers, Demarcus

2010-01-01

This report summarizes my internship project with the NASA Digital Astronaut Project to analyze the Digital Astronaut (DA) physiology backbone model. The Digital Astronaut Project (DAP) applies integrated physiology models to support space biomedical operations, and to assist NASA researchers in closing knowledge gaps related to human physiologic responses to space flight. The DA physiology backbone is a set of integrated physiological equations and functions that model the interacting systems of the human body. The current release of the model is HumMod (Human Model) version 1.5 and was developed over forty years at the University of Mississippi Medical Center (UMMC). The physiology equations and functions are scripted in an XML schema specifically designed for physiology modeling by Dr. Thomas G. Coleman at UMMC. Currently it is difficult to examine the physiology backbone without being knowledgeable of the XML schema. While investigating and documenting the tags and algorithms used in the XML schema, I proposed a standard methodology for a graphical representation. This standard methodology may be used to transcribe graphical representations from the DA physiology backbone. In turn, the graphical representations can allow examination of the physiological functions and equations without the need to be familiar with the computer programming languages or markup languages used by DA modeling software.

Thalamocortical dynamics of the McCollough effect: boundary-surface alignment through perceptual learning.

PubMed

Grossberg, Stephen; Hwang, Seungwoo; Mingolla, Ennio

2002-05-01

This article further develops the FACADE neural model of 3-D vision and figure-ground perception to quantitatively explain properties of the McCollough effect (ME). The model proposes that many ME data result from visual system mechanisms whose primary function is to adaptively align, through learning, boundary and surface representations that are positionally shifted due to the process of binocular fusion. For example, binocular boundary representations are shifted by binocular fusion relative to monocular surface representations, yet the boundaries must become positionally aligned with the surfaces to control binocular surface capture and filling-in. The model also includes perceptual reset mechanisms that use habituative transmitters in opponent processing circuits. Thus the model shows how ME data may arise from a combination of mechanisms that have a clear functional role in biological vision. Simulation results with a single set of parameters quantitatively fit data from 13 experiments that probe the nature of achromatic/chromatic and monocular/binocular interactions during induction of the ME. The model proposes how perceptual learning, opponent processing, and habituation at both monocular and binocular surface representations are involved, including early thalamocortical sites. In particular, it explains the anomalous ME utilizing these multiple processing sites. Alternative models of the ME are also summarized and compared with the present model.

Unification with vector-like fermions and signals at LHC

NASA Astrophysics Data System (ADS)

Bhattacherjee, Biplob; Byakti, Pritibhajan; Kushwaha, Ashwani; Vempati, Sudhir K.

2018-05-01

We look for minimal extensions of Standard Model with vector like fermions leading to precision unification of gauge couplings. Constraints from proton decay, Higgs stability and perturbativity are considered. The simplest models contain several copies of vector fermions in two different (incomplete) representations. Some of these models encompass Type III seesaw mechanism for neutrino masses whereas some others have a dark matter candidate. In all the models, at least one of the candidates has non-trivial representation under SU(3)color. In the limit of vanishing Yukawa couplings, new QCD bound states are formed, which can be probed at LHC. The present limits based on results from 13 TeV already probe these particles for masses around a TeV. Similar models can be constructed with three or four vector representations, examples of which are presented.

Dynamic knowledge representation using agent-based modeling: ontology instantiation and verification of conceptual models.

PubMed

An, Gary

2009-01-01

The sheer volume of biomedical research threatens to overwhelm the capacity of individuals to effectively process this information. Adding to this challenge is the multiscale nature of both biological systems and the research community as a whole. Given this volume and rate of generation of biomedical information, the research community must develop methods for robust representation of knowledge in order for individuals, and the community as a whole, to "know what they know." Despite increasing emphasis on "data-driven" research, the fact remains that researchers guide their research using intuitively constructed conceptual models derived from knowledge extracted from publications, knowledge that is generally qualitatively expressed using natural language. Agent-based modeling (ABM) is a computational modeling method that is suited to translating the knowledge expressed in biomedical texts into dynamic representations of the conceptual models generated by researchers. The hierarchical object-class orientation of ABM maps well to biomedical ontological structures, facilitating the translation of ontologies into instantiated models. Furthermore, ABM is suited to producing the nonintuitive behaviors that often "break" conceptual models. Verification in this context is focused at determining the plausibility of a particular conceptual model, and qualitative knowledge representation is often sufficient for this goal. Thus, utilized in this fashion, ABM can provide a powerful adjunct to other computational methods within the research process, as well as providing a metamodeling framework to enhance the evolution of biomedical ontologies.

Calibrating Bayesian Network Representations of Social-Behavioral Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Whitney, Paul D.; Walsh, Stephen J.

2010-04-08

While human behavior has long been studied, recent and ongoing advances in computational modeling present opportunities for recasting research outcomes in human behavior. In this paper we describe how Bayesian networks can represent outcomes of human behavior research. We demonstrate a Bayesian network that represents political radicalization research – and show a corresponding visual representation of aspects of this research outcome. Since Bayesian networks can be quantitatively compared with external observations, the representation can also be used for empirical assessments of the research which the network summarizes. For a political radicalization model based on published research, we show this empiricalmore » comparison with data taken from the Minorities at Risk Organizational Behaviors database.« less

A Discussion on Uncertainty Representation and Interpretation in Model-Based Prognostics Algorithms based on Kalman Filter Estimation Applied to Prognostics of Electronics Components

NASA Technical Reports Server (NTRS)

Celaya, Jose R.; Saxen, Abhinav; Goebel, Kai

2012-01-01

This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process and how it relates to uncertainty representation, management, and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function and the true remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for the two while considering prognostics in making critical decisions.

Opaque models: Using drugs and dreams to explore the neurobiological basis of mental phenomena.

PubMed

Langlitz, Nicolas

2017-01-01

On the basis of four historical and ethnographic case studies of modeling in neuroscience laboratories, this chapter introduces a distinction between transparent and opaque models. A transparent model is a simplified representation of a real world phenomenon. If it is not patently clear, it is at least much better comprehended than its objects of representation. An opaque model, by contrast, looks at one only partially understood phenomenon to stand in for another partially understood phenomenon. Here, the model is often just as complex as its target. Examples of such opaque models discussed in this chapter are the use of hallucinogen intoxication in humans and animals as well as the dreaming brain as models of psychosis as well as the dreaming brain as a model of consciousness in general. Several functions of opaque models are discussed, ranging from the generation of funding to the formulation of new research questions. While science studies scholars have often emphasized the epistemic fertility of failures of representation, the opacity of hallucinogen intoxications and dreams seems to have diminished the potential to produce positive knowledge from the representational relationship between the supposed models and their targets. Bidirectional comparisons between inebriation, dreaming, and psychosis, however, proved to be generative on the level of basic science. Moreover, the opaque models discussed in this chapter implicated cosmologies that steered research endeavors into certain directions rather than others. © 2017 Elsevier B.V. All rights reserved.

Visual influence on path integration in darkness indicates a multimodal representation of large-scale space

PubMed Central

Tcheang, Lili; Bülthoff, Heinrich H.; Burgess, Neil

2011-01-01

Our ability to return to the start of a route recently performed in darkness is thought to reflect path integration of motion-related information. Here we provide evidence that motion-related interoceptive representations (proprioceptive, vestibular, and motor efference copy) combine with visual representations to form a single multimodal representation guiding navigation. We used immersive virtual reality to decouple visual input from motion-related interoception by manipulating the rotation or translation gain of the visual projection. First, participants walked an outbound path with both visual and interoceptive input, and returned to the start in darkness, demonstrating the influences of both visual and interoceptive information in a virtual reality environment. Next, participants adapted to visual rotation gains in the virtual environment, and then performed the path integration task entirely in darkness. Our findings were accurately predicted by a quantitative model in which visual and interoceptive inputs combine into a single multimodal representation guiding navigation, and are incompatible with a model of separate visual and interoceptive influences on action (in which path integration in darkness must rely solely on interoceptive representations). Overall, our findings suggest that a combined multimodal representation guides large-scale navigation, consistent with a role for visual imagery or a cognitive map. PMID:21199934

On push-forward representations in the standard gyrokinetic model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Miyato, N., E-mail: miyato.naoaki@jaea.go.jp; Yagi, M.; Scott, B. D.

2015-01-15

Two representations of fluid moments in terms of a gyro-center distribution function and gyro-center coordinates, which are called push-forward representations, are compared in the standard electrostatic gyrokinetic model. In the representation conventionally used to derive the gyrokinetic Poisson equation, the pull-back transformation of the gyro-center distribution function contains effects of the gyro-center transformation and therefore electrostatic potential fluctuations, which is described by the Poisson brackets between the distribution function and scalar functions generating the gyro-center transformation. Usually, only the lowest order solution of the generating function at first order is considered to explicitly derive the gyrokinetic Poisson equation. This ismore » true in explicitly deriving representations of scalar fluid moments with polarization terms. One also recovers the particle diamagnetic flux at this order because it is associated with the guiding-center transformation. However, higher-order solutions are needed to derive finite Larmor radius terms of particle flux including the polarization drift flux from the conventional representation. On the other hand, the lowest order solution is sufficient for the other representation, in which the gyro-center transformation part is combined with the guiding-center one and the pull-back transformation of the distribution function does not appear.« less

Self-organization of globally continuous and locally distributed information representation.

PubMed

Wada, Koji; Kurata, Koji; Okada, Masato

2004-01-01

A number of findings suggest that the preferences of neighboring neurons in the inferior temporal (IT) cortex of macaque monkeys tend to be similar. However, a recent study reports convincingly that the preferences of neighboring neurons actually differ. These findings seem contradictory. To explain this conflict, we propose a new view of information representation in the IT cortex. This view takes into account sparse and local neuronal excitation. Since the excitation is sparse, information regarding visual objects seems to be encoded in a distributed manner. The local excitation of neurons coincides with the classical notion of a column structure. Our model consists of input layer and output layer. The main difference from conventional models is that the output layer has local and random intra-layer connections. In this paper, we adopt two rings embedded in three-dimensional space as an input signal space, and examine how resultant information representation depends on the distance between two rings that is denoted as D. We show that there exists critical value for the distance Dc. When D > Dc the output layer becomes able to form the column structure, this model can obtain the distributed representation within the column. While the output layer acquires the conventional information representation observed in the V1 cortex when D < Dc. Moreover, we consider the origin of the difference between information representation of the V1 cortex and that of the IT cortex. Our finding suggests that the difference in the information representations between the V1 and the IT cortices could be caused by difference between the input space structures.

A nucleobase-centered coarse-grained representation for structure prediction of RNA motifs

PubMed Central

Poblete, Simón; Bottaro, Sandro; Bussi, Giovanni

2018-01-01

Abstract We introduce the SPlit-and-conQueR (SPQR) model, a coarse-grained (CG) representation of RNA designed for structure prediction and refinement. In our approach, the representation of a nucleotide consists of a point particle for the phosphate group and an anisotropic particle for the nucleoside. The interactions are, in principle, knowledge-based potentials inspired by the \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$\\mathcal {E}$\\end{document}SCORE function, a base-centered scoring function. However, a special treatment is given to base-pairing interactions and certain geometrical conformations which are lost in a raw knowledge-based model. This results in a representation able to describe planar canonical and non-canonical base pairs and base–phosphate interactions and to distinguish sugar puckers and glycosidic torsion conformations. The model is applied to the folding of several structures, including duplexes with internal loops of non-canonical base pairs, tetraloops, junctions and a pseudoknot. For the majority of these systems, experimental structures are correctly predicted at the level of individual contacts. We also propose a method for efficiently reintroducing atomistic detail from the CG representation. PMID:29272539

Topic segmentation via community detection in complex networks

NASA Astrophysics Data System (ADS)

de Arruda, Henrique F.; Costa, Luciano da F.; Amancio, Diego R.

2016-06-01

Many real systems have been modeled in terms of network concepts, and written texts are a particular example of information networks. In recent years, the use of network methods to analyze language has allowed the discovery of several interesting effects, including the proposition of novel models to explain the emergence of fundamental universal patterns. While syntactical networks, one of the most prevalent networked models of written texts, display both scale-free and small-world properties, such a representation fails in capturing other textual features, such as the organization in topics or subjects. We propose a novel network representation whose main purpose is to capture the semantical relationships of words in a simple way. To do so, we link all words co-occurring in the same semantic context, which is defined in a threefold way. We show that the proposed representations favor the emergence of communities of semantically related words, and this feature may be used to identify relevant topics. The proposed methodology to detect topics was applied to segment selected Wikipedia articles. We found that, in general, our methods outperform traditional bag-of-words representations, which suggests that a high-level textual representation may be useful to study the semantical features of texts.

Topic segmentation via community detection in complex networks.

PubMed

de Arruda, Henrique F; Costa, Luciano da F; Amancio, Diego R

2016-06-01

Many real systems have been modeled in terms of network concepts, and written texts are a particular example of information networks. In recent years, the use of network methods to analyze language has allowed the discovery of several interesting effects, including the proposition of novel models to explain the emergence of fundamental universal patterns. While syntactical networks, one of the most prevalent networked models of written texts, display both scale-free and small-world properties, such a representation fails in capturing other textual features, such as the organization in topics or subjects. We propose a novel network representation whose main purpose is to capture the semantical relationships of words in a simple way. To do so, we link all words co-occurring in the same semantic context, which is defined in a threefold way. We show that the proposed representations favor the emergence of communities of semantically related words, and this feature may be used to identify relevant topics. The proposed methodology to detect topics was applied to segment selected Wikipedia articles. We found that, in general, our methods outperform traditional bag-of-words representations, which suggests that a high-level textual representation may be useful to study the semantical features of texts.

Speaking two "Languages" in America: A semantic space analysis of how presidential candidates and their supporters represent abstract political concepts differently.

PubMed

Li, Ping; Schloss, Benjamin; Follmer, D Jake

2017-10-01

In this article we report a computational semantic analysis of the presidential candidates' speeches in the two major political parties in the USA. In Study One, we modeled the political semantic spaces as a function of party, candidate, and time of election, and findings revealed patterns of differences in the semantic representation of key political concepts and the changing landscapes in which the presidential candidates align or misalign with their parties in terms of the representation and organization of politically central concepts. Our models further showed that the 2016 US presidential nominees had distinct conceptual representations from those of previous election years, and these patterns did not necessarily align with their respective political parties' average representation of the key political concepts. In Study Two, structural equation modeling demonstrated that reported political engagement among voters differentially predicted reported likelihoods of voting for Clinton versus Trump in the 2016 presidential election. Study Three indicated that Republicans and Democrats showed distinct, systematic word association patterns for the same concepts/terms, which could be reliably distinguished using machine learning methods. These studies suggest that given an individual's political beliefs, we can make reliable predictions about how they understand words, and given how an individual understands those same words, we can also predict an individual's political beliefs. Our study provides a bridge between semantic space models and abstract representations of political concepts on the one hand, and the representations of political concepts and citizens' voting behavior on the other.

Students and Teacher Academic Evaluation Perceptions: Methodology to Construct a Representation Based on Actionable Knowledge Discovery Framework

ERIC Educational Resources Information Center

Molina, Otilia Alejandro; Ratté, Sylvie

2017-01-01

This research introduces a method to construct a unified representation of teachers and students perspectives based on the actionable knowledge discovery (AKD) and delivery framework. The representation is constructed using two models: one obtained from student evaluations and the other obtained from teachers' reflections about their teaching…

Linking Passive and Active Representation: A Review of the Literature and a Model for Testing the Linkage for Women Representatives.

ERIC Educational Resources Information Center

Robinson, Ted P.; And Others

Most research efforts concerning minority politics have focused on descriptive representation, which emphasizes (1) counting the minority or female persons in office, and (2) explaining representative levels on the basis of political, social and economic determinants. Descriptive representation, however, is passive and focuses on "being something"…

The Effect of Content Representation Design Principles on Users' Intuitive Beliefs and Use of E-Learning Systems

ERIC Educational Resources Information Center

Al-Samarraie, Hosam; Selim, Hassan; Zaqout, Fahed

2016-01-01

A model is proposed to assess the effect of different content representation design principles on learners' intuitive beliefs about using e-learning. We hypothesized that the impact of the representation of course contents is mediated by the design principles of alignment, quantity, clarity, simplicity, and affordance, which influence the…

Covariation between Variables in a Modelling Process: The ACODESA (Collaborative Learning, Scientific Debate and Self-Reflection) Method

ERIC Educational Resources Information Center

Hitt, Fernando; González-Martín, Alejandro S.

2015-01-01

Semiotic representations have been an important topic of study in mathematics education. Previous research implicitly placed more importance on the development of institutional representations of mathematical concepts in students rather than other types of representations. In the context of an extensive research project, in progress since 2005,…

Social Representations of the Development of Intelligence, Parental Values and Parenting Styles: A Theoretical Model for Analysis

ERIC Educational Resources Information Center

Miguel, Isabel; Valentim, Joaquim Pires; Carugati, Felice

2013-01-01

Within the theoretical framework of social representations theory, a substantial body of literature has advocated and shown that, as interpretative systems and forms of knowledge concurring in the construction of a social reality, social representations are guides for action, influencing behaviours and social relations. Based on this assumption,…

Teachers' Reasoning: Classroom Visual Representational Practices in the Context of Introductory Chemical Bonding

ERIC Educational Resources Information Center

Patron, Emelie; Wikman, Susanne; Edfors, Inger; Johansson-Cederblad, Brita; Linder, Cedric

2017-01-01

Visual representations are essential for communication and meaning-making in chemistry, and thus the representational practices play a vital role in the teaching and learning of chemistry. One powerful contemporary model of classroom learning, the variation theory of learning, posits that the way an object of learning gets handled is another vital…

Changing Mental Representations Using Related Physical Models: The Effects of Analyzing Number Lines on Learner Internal Scale of Numerical Magnitude

ERIC Educational Resources Information Center

Bengtson, Barbara J.

2013-01-01

Understanding the linear relationship of numbers is essential for doing practical and abstract mathematics throughout education and everyday life. There is evidence that number line activities increase learners' number sense, improving the linearity of mental number line representations (Siegler & Ramani, 2009). Mental representations of…

A Wavelet Polarization Decomposition Net Model for Polarimetric SAR Image Classification

NASA Astrophysics Data System (ADS)

He, Chu; Ou, Dan; Yang, Teng; Wu, Kun; Liao, Mingsheng; Chen, Erxue

2014-11-01

In this paper, a deep model based on wavelet texture has been proposed for Polarimetric Synthetic Aperture Radar (PolSAR) image classification inspired by recent successful deep learning method. Our model is supposed to learn powerful and informative representations to improve the generalization ability for the complex scene classification tasks. Given the influence of speckle noise in Polarimetric SAR image, wavelet polarization decomposition is applied first to obtain basic and discriminative texture features which are then embedded into a Deep Neural Network (DNN) in order to compose multi-layer higher representations. We demonstrate that the model can produce a powerful representation which can capture some untraceable information from Polarimetric SAR images and show a promising achievement in comparison with other traditional SAR image classification methods for the SAR image dataset.

Representation and presentation of requirements knowledge

NASA Technical Reports Server (NTRS)

Johnson, W. L.; Feather, Martin S.; Harris, David R.

1992-01-01

An approach to representation and presentation of knowledge used in the ARIES, an experimental requirements/specification environment, is described. The approach applies the notion of a representation architecture to the domain of software engineering and incorporates a strong coupling to a transformation system. It is characterized by a single highly expressive underlying representation, interfaced simultaneously to multiple presentations, each with notations of differing degrees of expressivity. This enables analysts to use multiple languages for describing systems and have these descriptions yield a single consistent model of the system.

Influence of the Digital Anatomist Foundational Model on traditional representations of anatomical concepts.

PubMed

Agoncillo, A V; Mejino, J L; Rosse, C

1999-01-01

A principled and logical representation of the structure of the human body has led to conflicts with traditional representations of the same knowledge by anatomy textbooks. The examples which illustrate resolution of these conflicts suggest that stricter requirements must be met for semantic consistency, expressivity and specificity by knowledge sources intended to support inference than by textbooks and term lists. These next-generation resources should influence traditional concept representation, rather than be constrained by convention.

What Happens when Representations Fail to Represent? Graduate Students' Mental Models of Organic Chemistry Diagrams

ERIC Educational Resources Information Center

Strickland, Amanda M.; Kraft, Adam; Bhattacharyya, Gautam

2010-01-01

As part of our investigations into the development of representational competence, we report results from a study in which we elicited sixteen graduate students' expressed mental models of commonly-used terms for describing organic reactions--functional group, nucleophile/electrophile, acid/base--and for diagrams of transformations and their…

Effects of Long-Term Representations on Free Recall of Unrelated Words

ERIC Educational Resources Information Center

Katkov, Mikhail; Romani, Sandro; Tsodyks, Misha

2015-01-01

Human memory stores vast amounts of information. Yet recalling this information is often challenging when specific cues are lacking. Here we consider an associative model of retrieval where each recalled item triggers the recall of the next item based on the similarity between their long-term neuronal representations. The model predicts that…

Using Bar Representations as a Model for Connecting Concepts of Rational Number.

ERIC Educational Resources Information Center

Middleton, James A.; van den Heuvel-Panhuizen, Marja; Shew, Julia A.

1998-01-01

Examines bar models as graphical representations of rational numbers and presents related real life problems. Concludes that, through pairing the fraction bars with ratio tables and other ways of teaching numbers, numeric strategies become connected with visual strategies that allow students with diverse ways of thinking to share their…

Bilingual Lexical Interactions in an Unsupervised Neural Network Model

ERIC Educational Resources Information Center

Zhao, Xiaowei; Li, Ping

2010-01-01

In this paper we present an unsupervised neural network model of bilingual lexical development and interaction. We focus on how the representational structures of the bilingual lexicons can emerge, develop, and interact with each other as a function of the learning history. The results show that: (1) distinct representations for the two lexicons…

Developing Explanations and Developing Understanding: Students Explain the Phases of the Moon Using Visual Representations

ERIC Educational Resources Information Center

Parnafes, Orit

2012-01-01

This article presents a theoretical model of the process by which students construct and elaborate explanations of scientific phenomena using visual representations. The model describes progress in the underlying conceptual processes in students' explanations as a reorganization of fine-grained knowledge elements based on the Knowledge in Pieces…

Inadequacy representation of flamelet-based RANS model for turbulent non-premixed flame

NASA Astrophysics Data System (ADS)

Lee, Myoungkyu; Oliver, Todd; Moser, Robert

2017-11-01

Stochastic representations for model inadequacy in RANS-based models of non-premixed jet flames are developed and explored. Flamelet-based RANS models are attractive for engineering applications relative to higher-fidelity methods because of their low computational costs. However, the various assumptions inherent in such models introduce errors that can significantly affect the accuracy of computed quantities of interest. In this work, we develop an approach to represent the model inadequacy of the flamelet-based RANS model. In particular, we pose a physics-based, stochastic PDE for the triple correlation of the mixture fraction. This additional uncertain state variable is then used to construct perturbations of the PDF for the instantaneous mixture fraction, which is used to obtain an uncertain perturbation of the flame temperature. A hydrogen-air non-premixed jet flame is used to demonstrate the representation of the inadequacy of the flamelet-based RANS model. This work was supported by DARPA-EQUiPS(Enabling Quantification of Uncertainty in Physical Systems) program.

Workshop on Aircraft Surface Representation for Aerodynamic Computation

NASA Technical Reports Server (NTRS)

Gregory, T. J. (Editor); Ashbaugh, J. (Editor)

1980-01-01

Papers and discussions on surface representation and its integration with aerodynamics, computers, graphics, wind tunnel model fabrication, and flow field grid generation are presented. Surface definition is emphasized.

Target recognition for ladar range image using slice image

NASA Astrophysics Data System (ADS)

Xia, Wenze; Han, Shaokun; Wang, Liang

2015-12-01

A shape descriptor and a complete shape-based recognition system using slice images as geometric feature descriptor for ladar range images are introduced. A slice image is a two-dimensional image generated by three-dimensional Hough transform and the corresponding mathematical transformation. The system consists of two processes, the model library construction and recognition. In the model library construction process, a series of range images are obtained after the model object is sampled at preset attitude angles. Then, all the range images are converted into slice images. The number of slice images is reduced by clustering analysis and finding a representation to reduce the size of the model library. In the recognition process, the slice image of the scene is compared with the slice image in the model library. The recognition results depend on the comparison. Simulated ladar range images are used to analyze the recognition and misjudgment rates, and comparison between the slice image representation method and moment invariants representation method is performed. The experimental results show that whether in conditions without noise or with ladar noise, the system has a high recognition rate and low misjudgment rate. The comparison experiment demonstrates that the slice image has better representation ability than moment invariants.

Towards a multilevel cognitive probabilistic representation of space

NASA Astrophysics Data System (ADS)

Tapus, Adriana; Vasudevan, Shrihari; Siegwart, Roland

2005-03-01

This paper addresses the problem of perception and representation of space for a mobile agent. A probabilistic hierarchical framework is suggested as a solution to this problem. The method proposed is a combination of probabilistic belief with "Object Graph Models" (OGM). The world is viewed from a topological optic, in terms of objects and relationships between them. The hierarchical representation that we propose permits an efficient and reliable modeling of the information that the mobile agent would perceive from its environment. The integration of both navigational and interactional capabilities through efficient representation is also addressed. Experiments on a set of images taken from the real world that validate the approach are reported. This framework draws on the general understanding of human cognition and perception and contributes towards the overall efforts to build cognitive robot companions.

A logical foundation for representation of clinical data.

PubMed Central

Campbell, K E; Das, A K; Musen, M A

1994-01-01

OBJECTIVE: A general framework for representation of clinical data that provides a declarative semantics of terms and that allows developers to define explicitly the relationships among both terms and combinations of terms. DESIGN: Use of conceptual graphs as a standard representation of logic and of an existing standardized vocabulary, the Systematized Nomenclature of Medicine (SNOMED International), for lexical elements. Concepts such as time, anatomy, and uncertainty must be modeled explicitly in a way that allows relation of these foundational concepts to surface-level clinical descriptions in a uniform manner. RESULTS: The proposed framework was used to model a simple radiology report, which included temporal references. CONCLUSION: Formal logic provides a framework for formalizing the representation of medical concepts. Actual implementations will be required to evaluate the practicality of this approach. PMID:7719805

Calibration strategies for a groundwater model in a highly dynamic alpine floodplain

USGS Publications Warehouse

Foglia, L.; Burlando, P.; Hill, Mary C.; Mehl, S.

2004-01-01

Most surface flows to the 20-km-long Maggia Valley in Southern Switzerland are impounded and the valley is being investigated to determine environmental flow requirements. The aim of the investigation is the devel-opment of a modelling framework that simulates the dynamics of the ground-water, hydrologic, and ecologic systems. Because of the multi-scale nature of the modelling framework, large-scale models are first developed to provide the boundary conditions for more detailed models of reaches that are of eco-logical importance. We describe here the initial (large-scale) groundwa-ter/surface water model and its calibration in relation to initial and boundary conditions. A MODFLOW-2000 model was constructed to simulate the inter-action of groundwater and surface water and was developed parsimoniously to avoid modelling artefacts and parameter inconsistencies. Model calibration includes two steady-state conditions, with and without recharge to the aquifer from the adjoining hillslopes. Parameters are defined to represent areal re-charge, hydraulic conductivity of the aquifer (up to 5 classes), and streambed hydraulic conductivity. Model performance was investigated following two system representation. The first representation assumed unknown flow input at the northern end of the groundwater domain and unknown lateral inflow. The second representation used simulations of the lateral flow obtained by means of a raster-based, physically oriented and continuous in time rainfall-runoff (R-R) model. Results based on these two representations are compared and discussed.

Comparing visual representations across human fMRI and computational vision

PubMed Central

Leeds, Daniel D.; Seibert, Darren A.; Pyles, John A.; Tarr, Michael J.

2013-01-01

Feedforward visual object perception recruits a cortical network that is assumed to be hierarchical, progressing from basic visual features to complete object representations. However, the nature of the intermediate features related to this transformation remains poorly understood. Here, we explore how well different computer vision recognition models account for neural object encoding across the human cortical visual pathway as measured using fMRI. These neural data, collected during the viewing of 60 images of real-world objects, were analyzed with a searchlight procedure as in Kriegeskorte, Goebel, and Bandettini (2006): Within each searchlight sphere, the obtained patterns of neural activity for all 60 objects were compared to model responses for each computer recognition algorithm using representational dissimilarity analysis (Kriegeskorte et al., 2008). Although each of the computer vision methods significantly accounted for some of the neural data, among the different models, the scale invariant feature transform (Lowe, 2004), encoding local visual properties gathered from “interest points,” was best able to accurately and consistently account for stimulus representations within the ventral pathway. More generally, when present, significance was observed in regions of the ventral-temporal cortex associated with intermediate-level object perception. Differences in model effectiveness and the neural location of significant matches may be attributable to the fact that each model implements a different featural basis for representing objects (e.g., more holistic or more parts-based). Overall, we conclude that well-known computer vision recognition systems may serve as viable proxies for theories of intermediate visual object representation. PMID:24273227

Becoming a patient-illness representations of depression of Anglo-Australian and Sri Lankan patients through the lens of Leventhal's illness representational model.

PubMed

Antoniades, Josefine; Mazza, Danielle; Brijnath, Bianca

2017-11-01

Depression is prevalent globally. While the uptake of mental health services is poor in the general community, the lack of service engagement is particularly profound in migrant and refugee communities. To understand why there is under-utilisation cross-cultural comparisons of how people make sense of mental illnesses such as depression are essential. To verify how differing cultural aetiologies about depression influence mental health service use, this study investigated illness representational models of depression held by Sri Lankan migrants and Anglo-Australians living with depression. In-depth interviews ( n = 48) were conducted with Sri Lankan migrants and Anglo-Australians living with depression to explore their illness beliefs. Data were analysed using Leventhal's illness representational model. Significant overlaps in illness representational models were noted but distinctive differences were found between causal and chronicity beliefs; Sri Lankan migrants more frequently endorsed depression as a time-limited condition underpinned by situational factors, whereas Anglo-Australians endorsed a chronic, biopsychosocial model of depression. Findings highlight the importance of forging a shared understanding of patient beliefs in the clinical encounter to ensure that interventions are coherent with illness beliefs or at least work towards improving mental health literacy. Differences in illness beliefs also provide insights into possible interventions. For example, psychosocial interventions that align with their illness beliefs may be more suited to Sri Lankan migrants than pharmaceutical or psychological ones.

A CAD Approach to Developing Mass Distribution and Composition Models for Spaceflight Radiation Risk Analyses

NASA Astrophysics Data System (ADS)

Zapp, E.; Shelfer, T.; Semones, E.; Johnson, A.; Weyland, M.; Golightly, M.; Smith, G.; Dardano, C.

For roughly the past three decades, combinatorial geometries have been the predominant mode for the development of mass distribution models associated with the estimation of radiological risk for manned space flight. Examples of these are the MEVDP (Modified Elemental Volume Dose Program) vehicle representation of Liley and Hamilton, and the quadratic functional representation of the CAM/CAF (Computerized Anatomical Male/Female) human body models as modified by Billings and Yucker. These geometries, have the advantageous characteristics of being simple for a familiarized user to maintain, and because of the relative lack of any operating system or run-time library dependence, they are also easy to transfer from one computing platform to another. Unfortunately they are also limited in the amount of modeling detail possible, owing to the abstract geometric representation. In addition, combinatorial representations are also known to be error-prone in practice, since there is no convenient method for error identification (i.e. overlap, etc.), and extensive calculation and/or manual comparison may is often necessary to demonstrate that the geometry is adequately represented. We present an alternate approach linking materials -specific, CAD-based mass models directly to geometric analysis tools requiring no approximation with respect to materials , nor any meshing (i.e. tessellation) of the representative geometry. A new approach to ray tracing is presented which makes use of the fundamentals of the CAD representation to perform geometric analysis directly on the NURBS (Non-Uniform Rational BSpline) surfaces themselves. In this way we achieve a framework for- the rapid, precise development and analysis of materials-specific mass distribution models.

When this means that: the role of working memory and inhibitory control in children's understanding of representations.

PubMed

Astle, Andrea; Kamawar, Deepthi; Vendetti, Corrie; Podjarny, Gal

2013-10-01

We investigated cognitive skills that contribute to 4-year-olds' understanding of representations. In our main task, children used representations on a perspective line drawing to find stickers hidden in a model room. To compare the contributions made by various cognitive skills with children's understanding of different types of representations, we manipulated the resemblance between the representations and their referents. Our results indicate that when representations are iconic (i.e., look like their referents), children have very little difficulty with the task. Controlling for performance on this baseline version of the task, we found that specific cognitive skills are differentially predictive of performance when using arbitrary and conflicting representations (i.e., symbols). When the representation was arbitrarily linked to the sticker, performance was related to phonological and visuospatial working memory. When the representation matched the color of an alternate sticker (thereby conflicting with the desired sticker), performance was related to phonological working memory and inhibitory control. We discuss the role that different cognitive skills play in representational understanding as a function of the nature of the representation-referent relation. Copyright © 2013 Elsevier Inc. All rights reserved.

Two spatial memories are not better than one: evidence of exclusivity in memory for object location.

PubMed

Baguley, Thom; Lansdale, Mark W; Lines, Lorna K; Parkin, Jennifer K

2006-05-01

This paper studies the dynamics of attempting to access two spatial memories simultaneously and its implications for the accuracy of recall. Experiment 1 demonstrates in a range of conditions that two cues pointing to different experiences of the same object location produce little or no higher recall than that observed with a single cue. Experiment 2 confirms this finding in a within-subject design where both cues have previously elicited recall. Experiment 3 shows that these findings are only consistent with a model in which two representations of the same object location are mutually exclusive at both encoding and retrieval, and inconsistent with models that assume information from both representations is available. We propose that these representations quantify directionally specific judgments of location relative to specific anchor points in the stimulus; a format that precludes the parallel processing of like representations. Finally, we consider the apparent paradox of how such representations might contribute to the acquisition of spatial knowledge from multiple experiences of the same stimuli.

Human inferior colliculus activity relates to individual differences in spoken language learning.

PubMed

Chandrasekaran, Bharath; Kraus, Nina; Wong, Patrick C M

2012-03-01

A challenge to learning words of a foreign language is encoding nonnative phonemes, a process typically attributed to cortical circuitry. Using multimodal imaging methods [functional magnetic resonance imaging-adaptation (fMRI-A) and auditory brain stem responses (ABR)], we examined the extent to which pretraining pitch encoding in the inferior colliculus (IC), a primary midbrain structure, related to individual variability in learning to successfully use nonnative pitch patterns to distinguish words in American English-speaking adults. fMRI-A indexed the efficiency of pitch representation localized to the IC, whereas ABR quantified midbrain pitch-related activity with millisecond precision. In line with neural "sharpening" models, we found that efficient IC pitch pattern representation (indexed by fMRI) related to superior neural representation of pitch patterns (indexed by ABR), and consequently more successful word learning following sound-to-meaning training. Our results establish a critical role for the IC in speech-sound representation, consistent with the established role for the IC in the representation of communication signals in other animal models.

Manifold decoding for neural representations of face viewpoint and gaze direction using magnetoencephalographic data.

PubMed

Kuo, Po-Chih; Chen, Yong-Sheng; Chen, Li-Fen

2018-05-01

The main challenge in decoding neural representations lies in linking neural activity to representational content or abstract concepts. The transformation from a neural-based to a low-dimensional representation may hold the key to encoding perceptual processes in the human brain. In this study, we developed a novel model by which to represent two changeable features of faces: face viewpoint and gaze direction. These features are embedded in spatiotemporal brain activity derived from magnetoencephalographic data. Our decoding results demonstrate that face viewpoint and gaze direction can be represented by manifold structures constructed from brain responses in the bilateral occipital face area and right superior temporal sulcus, respectively. Our results also show that the superposition of brain activity in the manifold space reveals the viewpoints of faces as well as directions of gazes as perceived by the subject. The proposed manifold representation model provides a novel opportunity to gain further insight into the processing of information in the human brain. © 2018 Wiley Periodicals, Inc.

AIC identifies optimal representation of longitudinal dietary variables.

PubMed

VanBuren, John; Cavanaugh, Joseph; Marshall, Teresa; Warren, John; Levy, Steven M

2017-09-01

The Akaike Information Criterion (AIC) is a well-known tool for variable selection in multivariable modeling as well as a tool to help identify the optimal representation of explanatory variables. However, it has been discussed infrequently in the dental literature. The purpose of this paper is to demonstrate the use of AIC in determining the optimal representation of dietary variables in a longitudinal dental study. The Iowa Fluoride Study enrolled children at birth and dental examinations were conducted at ages 5, 9, 13, and 17. Decayed or filled surfaces (DFS) trend clusters were created based on age 13 DFS counts and age 13-17 DFS increments. Dietary intake data (water, milk, 100 percent-juice, and sugar sweetened beverages) were collected semiannually using a food frequency questionnaire. Multinomial logistic regression models were fit to predict DFS cluster membership (n=344). Multiple approaches could be used to represent the dietary data including averaging across all collected surveys or over different shorter time periods to capture age-specific trends or using the individual time points of dietary data. AIC helped identify the optimal representation. Averaging data for all four dietary variables for the whole period from age 9.0 to 17.0 provided a better representation in the multivariable full model (AIC=745.0) compared to other methods assessed in full models (AICs=750.6 for age 9 and 9-13 increment dietary measurements and AIC=762.3 for age 9, 13, and 17 individual measurements). The results illustrate that AIC can help researchers identify the optimal way to summarize information for inclusion in a statistical model. The method presented here can be used by researchers performing statistical modeling in dental research. This method provides an alternative approach for assessing the propriety of variable representation to significance-based procedures, which could potentially lead to improved research in the dental community. © 2017 American Association of Public Health Dentistry.

Addressing Common Cloud-Radiation Errors from 4-hour to 4-week Model Prediction

NASA Astrophysics Data System (ADS)

Benjamin, S.; Sun, S.; Grell, G. A.; Green, B.; Olson, J.; Kenyon, J.; James, E.; Smirnova, T. G.; Brown, J. M.

2017-12-01

Cloud-radiation representation in models for subgrid-scale clouds is a known gap from subseasonal-to-seasonal models down to storm-scale models applied for forecast duration of only a few hours. NOAA/ESRL has been applying common physical parameterizations for scale-aware deep/shallow convection and boundary-layer mixing over this wide range of time and spatial scales, with some progress to be reported in this presentation. The Grell-Freitas scheme (2014, Atmos. Chem. Phys.) and MYNN boundary-layer EDMF scheme (Olson / Benjamin et al. 2016 Mon. Wea. Rev.) have been applied and tested extensively for the NOAA hourly updated 3-km High-Resolution Rapid Refresh (HRRR) and 13-km Rapid Refresh (RAP) model/assimilation systems over the United States and North America, with targeting toward improvement to boundary-layer evolution and cloud-radiation representation in all seasons. This representation is critical for both warm-season severe convective storm forecasting and for winter-storm prediction of snow and mixed precipitation. At the same time the Grell-Freitas scheme has been applied also as an option for subseasonal forecasting toward improved US week 3-4 prediction with the FIM-HYCOM coupled model (Green et al 2017, MWR). Cloud/radiation evaluation using CERES satellite-based estimates have been applied to both 12-h RAP (13km) and also during Weeks 1-4 from 32-day FIM-HYCOM (60km) forecasts. Initial results reveal that improved cloud representation is needed for both resolutions and now is guiding further refinement for cloud representation including with the Grell-Freitas scheme and with the updated MYNN-EDMF scheme (both now also in global testing as well as with the 3km HRRR and 13km RAP models).

A Viscoelastic Constitutive Model Can Accurately Represent Entire Creep Indentation Tests of Human Patella Cartilage

PubMed Central

Pal, Saikat; Lindsey, Derek P.; Besier, Thor F.; Beaupre, Gary S.

2013-01-01

Cartilage material properties provide important insights into joint health, and cartilage material models are used in whole-joint finite element models. Although the biphasic model representing experimental creep indentation tests is commonly used to characterize cartilage, cartilage short-term response to loading is generally not characterized using the biphasic model. The purpose of this study was to determine the short-term and equilibrium material properties of human patella cartilage using a viscoelastic model representation of creep indentation tests. We performed 24 experimental creep indentation tests from 14 human patellar specimens ranging in age from 20 to 90 years (median age 61 years). We used a finite element model to reproduce the experimental tests and determined cartilage material properties from viscoelastic and biphasic representations of cartilage. The viscoelastic model consistently provided excellent representation of the short-term and equilibrium creep displacements. We determined initial elastic modulus, equilibrium elastic modulus, and equilibrium Poisson’s ratio using the viscoelastic model. The viscoelastic model can represent the short-term and equilibrium response of cartilage and may easily be implemented in whole-joint finite element models. PMID:23027200

Visual shape perception as Bayesian inference of 3D object-centered shape representations.

PubMed

Erdogan, Goker; Jacobs, Robert A

2017-11-01

Despite decades of research, little is known about how people visually perceive object shape. We hypothesize that a promising approach to shape perception is provided by a "visual perception as Bayesian inference" framework which augments an emphasis on visual representation with an emphasis on the idea that shape perception is a form of statistical inference. Our hypothesis claims that shape perception of unfamiliar objects can be characterized as statistical inference of 3D shape in an object-centered coordinate system. We describe a computational model based on our theoretical framework, and provide evidence for the model along two lines. First, we show that, counterintuitively, the model accounts for viewpoint-dependency of object recognition, traditionally regarded as evidence against people's use of 3D object-centered shape representations. Second, we report the results of an experiment using a shape similarity task, and present an extensive evaluation of existing models' abilities to account for the experimental data. We find that our shape inference model captures subjects' behaviors better than competing models. Taken as a whole, our experimental and computational results illustrate the promise of our approach and suggest that people's shape representations of unfamiliar objects are probabilistic, 3D, and object-centered. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

The field representation language.

PubMed

Tsafnat, Guy

2008-02-01

The complexity of quantitative biomedical models, and the rate at which they are published, is increasing to a point where managing the information has become all but impossible without automation. International efforts are underway to standardise representation languages for a number of mathematical entities that represent a wide variety of physiological systems. This paper presents the Field Representation Language (FRL), a portable representation of values that change over space and/or time. FRL is an extensible mark-up language (XML) derivative with support for large numeric data sets in Hierarchical Data Format version 5 (HDF5). Components of FRL can be reused through unified resource identifiers (URI) that point to external resources such as custom basis functions, boundary geometries and numerical data. To demonstrate the use of FRL as an interchange we present three models that study hyperthermia cancer treatment: a fractal model of liver tumour microvasculature; a probabilistic model simulating the deposition of magnetic microspheres throughout it; and a finite element model of hyperthermic treatment. The microsphere distribution field was used to compute the heat generation rate field around the tumour. We used FRL to convey results from the microsphere simulation to the treatment model. FRL facilitated the conversion of the coordinate systems and approximated the integral over regions of the microsphere deposition field.

The Role of Inhibition in a Computational Model of an Auditory Cortical Neuron during the Encoding of Temporal Information

PubMed Central

Bendor, Daniel

2015-01-01

In auditory cortex, temporal information within a sound is represented by two complementary neural codes: a temporal representation based on stimulus-locked firing and a rate representation, where discharge rate co-varies with the timing between acoustic events but lacks a stimulus-synchronized response. Using a computational neuronal model, we find that stimulus-locked responses are generated when sound-evoked excitation is combined with strong, delayed inhibition. In contrast to this, a non-synchronized rate representation is generated when the net excitation evoked by the sound is weak, which occurs when excitation is coincident and balanced with inhibition. Using single-unit recordings from awake marmosets (Callithrix jacchus), we validate several model predictions, including differences in the temporal fidelity, discharge rates and temporal dynamics of stimulus-evoked responses between neurons with rate and temporal representations. Together these data suggest that feedforward inhibition provides a parsimonious explanation of the neural coding dichotomy observed in auditory cortex. PMID:25879843

Distinct contributions of functional and deep neural network features to representational similarity of scenes in human brain and behavior

PubMed Central

Greene, Michelle R; Baldassano, Christopher; Fei-Fei, Li; Beck, Diane M; Baker, Chris I

2018-01-01

Inherent correlations between visual and semantic features in real-world scenes make it difficult to determine how different scene properties contribute to neural representations. Here, we assessed the contributions of multiple properties to scene representation by partitioning the variance explained in human behavioral and brain measurements by three feature models whose inter-correlations were minimized a priori through stimulus preselection. Behavioral assessments of scene similarity reflected unique contributions from a functional feature model indicating potential actions in scenes as well as high-level visual features from a deep neural network (DNN). In contrast, similarity of cortical responses in scene-selective areas was uniquely explained by mid- and high-level DNN features only, while an object label model did not contribute uniquely to either domain. The striking dissociation between functional and DNN features in their contribution to behavioral and brain representations of scenes indicates that scene-selective cortex represents only a subset of behaviorally relevant scene information. PMID:29513219

Developing PFC representations using reinforcement learning

PubMed Central

Reynolds, Jeremy R.; O'Reilly, Randall C.

2009-01-01

From both functional and biological considerations, it is widely believed that action production, planning, and goal-oriented behaviors supported by the frontal cortex are organized hierarchically (Fuster, 1990, Koechlin, Ody, & Kouneiher, 2003, & Miller, Galanter, & Pribram, 1960) However, the nature of the different levels of the hierarchy remains unclear, and little attention has been paid to the origins of such a hierarchy. We address these issues through biologically-inspired computational models that develop representations through reinforcement learning. We explore several different factors in these models that might plausibly give rise to a hierarchical organization of representations within the PFC, including an initial connectivity hierarchy within PFC, a hierarchical set of connections between PFC and subcortical structures controlling it, and differential synaptic plasticity schedules. Simulation results indicate that architectural constraints contribute to the segregation of different types of representations, and that this segregation facilitates learning. These findings are consistent with the idea that there is a functional hierarchy in PFC, as captured in our earlier computational models of PFC function and a growing body of empirical data. PMID:19591977

Computer-Based Learning: Graphical Integration of Whole and Sectional Neuroanatomy Improves Long-Term Retention

PubMed Central

Naaz, Farah; Chariker, Julia H.; Pani, John R.

2013-01-01

A study was conducted to test the hypothesis that instruction with graphically integrated representations of whole and sectional neuroanatomy is especially effective for learning to recognize neural structures in sectional imagery (such as MRI images). Neuroanatomy was taught to two groups of participants using computer graphical models of the human brain. Both groups learned whole anatomy first with a three-dimensional model of the brain. One group then learned sectional anatomy using two-dimensional sectional representations, with the expectation that there would be transfer of learning from whole to sectional anatomy. The second group learned sectional anatomy by moving a virtual cutting plane through the three-dimensional model. In tests of long-term retention of sectional neuroanatomy, the group with graphically integrated representation recognized more neural structures that were known to be challenging to learn. This study demonstrates the use of graphical representation to facilitate a more elaborated (deeper) understanding of complex spatial relations. PMID:24563579

Distinct contributions of functional and deep neural network features to representational similarity of scenes in human brain and behavior.

PubMed

Groen, Iris Ia; Greene, Michelle R; Baldassano, Christopher; Fei-Fei, Li; Beck, Diane M; Baker, Chris I

2018-03-07

Inherent correlations between visual and semantic features in real-world scenes make it difficult to determine how different scene properties contribute to neural representations. Here, we assessed the contributions of multiple properties to scene representation by partitioning the variance explained in human behavioral and brain measurements by three feature models whose inter-correlations were minimized a priori through stimulus preselection. Behavioral assessments of scene similarity reflected unique contributions from a functional feature model indicating potential actions in scenes as well as high-level visual features from a deep neural network (DNN). In contrast, similarity of cortical responses in scene-selective areas was uniquely explained by mid- and high-level DNN features only, while an object label model did not contribute uniquely to either domain. The striking dissociation between functional and DNN features in their contribution to behavioral and brain representations of scenes indicates that scene-selective cortex represents only a subset of behaviorally relevant scene information.

From scenarios to domain models: processes and representations

NASA Astrophysics Data System (ADS)

Haddock, Gail; Harbison, Karan

1994-03-01

The domain specific software architectures (DSSA) community has defined a philosophy for the development of complex systems. This philosophy improves productivity and efficiency by increasing the user's role in the definition of requirements, increasing the systems engineer's role in the reuse of components, and decreasing the software engineer's role to the development of new components and component modifications only. The scenario-based engineering process (SEP), the first instantiation of the DSSA philosophy, has been adopted by the next generation controller project. It is also the chosen methodology of the trauma care information management system project, and the surrogate semi-autonomous vehicle project. SEP uses scenarios from the user to create domain models and define the system's requirements. Domain knowledge is obtained from a variety of sources including experts, documents, and videos. This knowledge is analyzed using three techniques: scenario analysis, task analysis, and object-oriented analysis. Scenario analysis results in formal representations of selected scenarios. Task analysis of the scenario representations results in descriptions of tasks necessary for object-oriented analysis and also subtasks necessary for functional system analysis. Object-oriented analysis of task descriptions produces domain models and system requirements. This paper examines the representations that support the DSSA philosophy, including reference requirements, reference architectures, and domain models. The processes used to create and use the representations are explained through use of the scenario-based engineering process. Selected examples are taken from the next generation controller project.

Cortico-hippocampal representations in simultaneous odor discrimination: a computational interpretation of Eichenbaum, Mathews, and Cohen (1989).

PubMed

Myers, C E; Gluck, M A

1996-08-01

A previous model of hippocampal region function in classical conditioning is generalized to H. Eichenbaum, A. Fagan, P. Mathews, and N.J. Cohen's (1989) and H. Eichenbaum, A. Fagan, and N.J. Cohen's (1989) simultaneous odor discrimination studies in rats. The model assumes that the hippocampal region forms new stimulus representations that compress redundant information while differentiating predictie information; the piriform (olfactory) cortex meanwhile clusters similar and co-occurring odors. Hippocampal damage interrupts the ability to differentiate odor representations, while leaving piriform-mediated odor clustering unchecked. The result is a net tendency to overcompress in the lesioned model. Behavior in the model is very similar to that of the rats, including lesion deficits, facilitation of successively learned tasks, and transfer performance. The computational mechanisms underlying model performance are consistent with the qualitative interpretations suggested by Eichen baum et al. to explain their empirical data.

Predicting perceptual quality of images in realistic scenario using deep filter banks

NASA Astrophysics Data System (ADS)

Zhang, Weixia; Yan, Jia; Hu, Shiyong; Ma, Yang; Deng, Dexiang

2018-03-01

Classical image perceptual quality assessment models usually resort to natural scene statistic methods, which are based on an assumption that certain reliable statistical regularities hold on undistorted images and will be corrupted by introduced distortions. However, these models usually fail to accurately predict degradation severity of images in realistic scenarios since complex, multiple, and interactive authentic distortions usually appear on them. We propose a quality prediction model based on convolutional neural network. Quality-aware features extracted from filter banks of multiple convolutional layers are aggregated into the image representation. Furthermore, an easy-to-implement and effective feature selection strategy is used to further refine the image representation and finally a linear support vector regression model is trained to map image representation into images' subjective perceptual quality scores. The experimental results on benchmark databases present the effectiveness and generalizability of the proposed model.

Matter in transition

DOE PAGES

Anderson, Lara B.; Gray, James; Raghuram, Nikhil; ...

2016-04-13

In this study, we explore a novel type of transition in certain 6D and 4D quantum field theories, in which the matter content of the theory changes while the gauge group and other parts of the spectrum remain invariant. Such transitions can occur, for example, for SU(6) and SU(7) gauge groups, where matter fields in a three-index antisymmetric representation and the fundamental representation are exchanged in the transition for matter in the two-index antisymmetric representation. These matter transitions are realized by passing through superconformal theories at the transition point. We explore these transitions in dual F-theory and heterotic descriptions, wheremore » a number of novel features arise. For example, in the heterotic description the relevant 6D SU(7) theories are described by bundles on K3 surfaces where the geometry of the K3 is constrained in addition to the bundle structure. On the F-theory side, non-standard representations such as the three-index antisymmetric representation of SU(N) require Weierstrass models that cannot be realized from the standard SU(N) Tate form. We also briefly describe some other situations, with groups such as Sp(3), SO(12), and SU(3), where analogous matter transitions can occur between different representations. For SU(3), in particular, we find a matter transition between adjoint matter and matter in the symmetric representation, giving an explicit Weierstrass model for the F-theory description of the symmetric representation that complements another recent analogous construction.« less

Maternal and paternal infant representations: a comparison between parents of term and preterm infants.

PubMed

Tooten, Anneke; Hall, Ruby A S; Hoffenkamp, Hannah N; Braeken, Johan; Vingerhoets, Ad J J M; van Bakel, Hedwig J A

2014-08-01

Research on parental attachment representations after preterm birth is limited and inconclusive. The present study is the first in which maternal and paternal attachment representations after term, moderately and very preterm birth are compared. In addition, special attention was directed toward disrupted attachment representations. Mothers and fathers of term infants (≥ 37 weeks of gestational age, n=71), moderately preterm infants (≥ 32-37 weeks of gestational age, n=62) and very preterm infants (<32 weeks of gestational age, n=56) participated in the present study. Attachment representations (balanced, disengaged, distorted) about their infants were evaluated with the Working Model of the Child Interview (WMCI). To asses disrupted representations the coding of the WMCI was extended with the disrupted scale (WMCI-D). The three main classifications of attachment representations were not affected by preterm birth. In addition, there were no gender differences in the rate of balanced representations. In case of non-balanced representations however, maternal representations were more often distorted, whereas fathers showed more often disengaged representations. Results further revealed that maternal disrupted attachment representations were marked by role/boundary confusion or disorientation, whereas paternal disrupted attachment representations were characterized by withdrawal. Given the gender differences it is essential to tailor interventions according to the attachment representations of the parent, in order to be able to alter their non-balanced and/or disrupted attachment representations. Copyright © 2014 Elsevier Inc. All rights reserved.

Probabilistic representation in syllogistic reasoning: A theory to integrate mental models and heuristics.

PubMed

Hattori, Masasi

2016-12-01

This paper presents a new theory of syllogistic reasoning. The proposed model assumes there are probabilistic representations of given signature situations. Instead of conducting an exhaustive search, the model constructs an individual-based "logical" mental representation that expresses the most probable state of affairs, and derives a necessary conclusion that is not inconsistent with the model using heuristics based on informativeness. The model is a unification of previous influential models. Its descriptive validity has been evaluated against existing empirical data and two new experiments, and by qualitative analyses based on previous empirical findings, all of which supported the theory. The model's behavior is also consistent with findings in other areas, including working memory capacity. The results indicate that people assume the probabilities of all target events mentioned in a syllogism to be almost equal, which suggests links between syllogistic reasoning and other areas of cognition. Copyright © 2016 The Author(s). Published by Elsevier B.V. All rights reserved.

Computational representation of the aponeuroses as NURBS surfaces in 3D musculoskeletal models.

PubMed

Wu, Florence T H; Ng-Thow-Hing, Victor; Singh, Karan; Agur, Anne M; McKee, Nancy H

2007-11-01

Computational musculoskeletal (MSK) models - 3D graphics-based models that accurately simulate the anatomical architecture and/or the biomechanical behaviour of organ systems consisting of skeletal muscles, tendons, ligaments, cartilage and bones - are valued biomedical tools, with applications ranging from pathological diagnosis to surgical planning. However, current MSK models are often limited by their oversimplifications in anatomical geometries, sometimes lacking discrete representations of connective tissue components entirely, which ultimately affect their accuracy in biomechanical simulation. In particular, the aponeuroses - the flattened fibrous connective sheets connecting muscle fibres to tendons - have never been geometrically modeled. The initiative was thus to extend Anatomy3D - a previously developed software bundle for reconstructing muscle fibre architecture - to incorporate aponeurosis-modeling capacity. Two different algorithms for aponeurosis reconstruction were written in the MEL scripting language of the animation software Maya 6.0, using its NURBS (non-uniform rational B-splines) modeling functionality for aponeurosis surface representation. Both algorithms were validated qualitatively against anatomical and functional criteria.

How to Make a Good Animation: A Grounded Cognition Model of How Visual Representation Design Affects the Construction of Abstract Physics Knowledge

ERIC Educational Resources Information Center

Chen, Zhongzhou; Gladding, Gary

2014-01-01

Visual representations play a critical role in teaching physics. However, since we do not have a satisfactory understanding of how visual perception impacts the construction of abstract knowledge, most visual representations used in instructions are either created based on existing conventions or designed according to the instructor's intuition,…

Mathematics Teacher Candidates' Skills of Using Multiple Representations for Division of Fractions

ERIC Educational Resources Information Center

Biber, Abdullah Çagri

2014-01-01

The aim of this study is to reveal teacher candidates' preference regarding uses of verbal, symbolic, number line, and/or model representations of fraction divisions, and to investigate their skill of transferring from one representation type to the others. Case study was used as the research method in this study. The case that is examined within…

Role Models without Guarantees: Corrective Representations and the Cultural Politics of a Latino Male Teacher in the Borderlands

ERIC Educational Resources Information Center

Singh, Michael V.

2018-01-01

In recent years mentorship has become a popular 'solution' for struggling boys of color and has led to the recruitment of more male of color teachers. While not arguing against the merits of mentorship, this article critiques what the author deems 'corrective representations.' Corrective representations are the imagined embodiment of proper and…

Dealing with Multiple Documents on the WWW: The Role of Metacognition in the Formation of Documents Models

ERIC Educational Resources Information Center

Stadtler, Marc; Bromme, Rainer

2007-01-01

Drawing on the theory of documents representation (Perfetti et al., Toward a theory of documents representation. In: H. v. Oostendorp & S. R. Goldman (Eds.), "The construction of mental representations during reading." Mahwah, NJ: Erlbaum, 1999), we argue that successfully dealing with multiple documents on the World Wide Web requires readers to…

Investigating the Implementation of Knowledge Representation in the COMBATXXI System

DTIC Science & Technology

2015-06-01

mechanism. Finally, follow-on research can work towards more cognitive modeling in order to distinguish between manned systems and unmanned systems in...Approved for public release; distribution is unlimited INVESTIGATING THE IMPLEMENTATION OF KNOWLEDGE REPRESENTATION IN THE COMBATXXI SYSTEM by Mongi...INVESTIGATING THE IMPLEMENTATION OF KNOWLEDGE REPRESENTATION IN THE COMBATXXI SYSTEM 5. FUNDING NUMBERS GM10331601, National Institute of General

Progress in knowledge representation research

NASA Technical Reports Server (NTRS)

Lum, Henry

1985-01-01

Brief descriptions are given of research being carried out in the field of knowledge representation. Dynamic simulation and modelling of planning systems with real-time sensor inputs; development of domain-independent knowledge representation tools which can be used in the development of application-specific expert and planning systems; and development of a space-borne very high speed integrated circuit processor are among the projects discussed.

Biologically Plausible, Human-Scale Knowledge Representation.

PubMed

Crawford, Eric; Gingerich, Matthew; Eliasmith, Chris

2016-05-01

Several approaches to implementing symbol-like representations in neurally plausible models have been proposed. These approaches include binding through synchrony (Shastri & Ajjanagadde, ), "mesh" binding (van der Velde & de Kamps, ), and conjunctive binding (Smolensky, ). Recent theoretical work has suggested that most of these methods will not scale well, that is, that they cannot encode structured representations using any of the tens of thousands of terms in the adult lexicon without making implausible resource assumptions. Here, we empirically demonstrate that the biologically plausible structured representations employed in the Semantic Pointer Architecture (SPA) approach to modeling cognition (Eliasmith, ) do scale appropriately. Specifically, we construct a spiking neural network of about 2.5 million neurons that employs semantic pointers to successfully encode and decode the main lexical relations in WordNet, which has over 100,000 terms. In addition, we show that the same representations can be employed to construct recursively structured sentences consisting of arbitrary WordNet concepts, while preserving the original lexical structure. We argue that these results suggest that semantic pointers are uniquely well-suited to providing a biologically plausible account of the structured representations that underwrite human cognition. Copyright © 2015 Cognitive Science Society, Inc.

Dissociations of the number and precision of visual short-term memory representations in change detection.

PubMed

Xie, Weizhen; Zhang, Weiwei

2017-11-01

The present study dissociated the number (i.e., quantity) and precision (i.e., quality) of visual short-term memory (STM) representations in change detection using receiver operating characteristic (ROC) and experimental manipulations. Across three experiments, participants performed both recognition and recall tests of visual STM using the change-detection task and the continuous color-wheel recall task, respectively. Experiment 1 demonstrated that the estimates of the number and precision of visual STM representations based on the ROC model of change-detection performance were robustly correlated with the corresponding estimates based on the mixture model of continuous-recall performance. Experiments 2 and 3 showed that the experimental manipulation of mnemonic precision using white-noise masking and the experimental manipulation of the number of encoded STM representations using consolidation masking produced selective effects on the corresponding measures of mnemonic precision and the number of encoded STM representations, respectively, in both change-detection and continuous-recall tasks. Altogether, using the individual-differences (Experiment 1) and experimental dissociation (Experiment 2 and 3) approaches, the present study demonstrated the some-or-none nature of visual STM representations across recall and recognition.

Using field observations to inform thermal hydrology models of permafrost dynamics with ATS (v0.83)

DOE PAGES

Atchley, A. L.; Painter, S. L.; Harp, D. R.; ...

2015-04-14

Climate change is profoundly transforming the carbon-rich Arctic tundra landscape, potentially moving it from a carbon sink to a carbon source by increasing the thickness of soil that thaws on a seasonal basis. However, the modeling capability and precise parameterizations of the physical characteristics needed to estimate projected active layer thickness (ALT) are limited in Earth System Models (ESMs). In particular, discrepancies in spatial scale between field measurements and Earth System Models challenge validation and parameterization of hydrothermal models. A recently developed surface/subsurface model for permafrost thermal hydrology, the Advanced Terrestrial Simulator (ATS), is used in combination with field measurementsmore » to calibrate and identify fine scale controls of ALT in ice wedge polygon tundra in Barrow, Alaska. An iterative model refinement procedure that cycles between borehole temperature and snow cover measurements and simulations functions to evaluate and parameterize different model processes necessary to simulate freeze/thaw processes and ALT formation. After model refinement and calibration, reasonable matches between simulated and measured soil temperatures are obtained, with the largest errors occurring during early summer above ice wedges (e.g. troughs). The results suggest that properly constructed and calibrated one-dimensional thermal hydrology models have the potential to provide reasonable representation of the subsurface thermal response and can be used to infer model input parameters and process representations. The models for soil thermal conductivity and snow distribution were found to be the most sensitive process representations. However, information on lateral flow and snowpack evolution might be needed to constrain model representations of surface hydrology and snow depth.« less

Teaching Subtraction and Multiplication with Regrouping Using the Concrete-Representational-Abstract Sequence and Strategic Instruction Model

ERIC Educational Resources Information Center

Flores, Margaret M.; Hinton, Vanessa; Strozier, Shaunita D.

2014-01-01

Based on Common Core Standards (2010), mathematics interventions should emphasize conceptual understanding of numbers and operations as well as fluency. For students at risk for failure, the concrete-representational-abstract (CRA) sequence and the Strategic Instruction Model (SIM) have been shown effective in teaching computation with an emphasis…

Learning with Technology: Video Modeling with Concrete-Representational-Abstract Sequencing for Students with Autism Spectrum Disorder

ERIC Educational Resources Information Center

Yakubova, Gulnoza; Hughes, Elizabeth M.; Shinaberry, Megan

2016-01-01

The purpose of this study was to determine the effectiveness of a video modeling intervention with concrete-representational-abstract instructional sequence in teaching mathematics concepts to students with autism spectrum disorder (ASD). A multiple baseline across skills design of single-case experimental methodology was used to determine the…

Process and representation in graphical displays

NASA Technical Reports Server (NTRS)

Gillan, Douglas J.; Lewis, Robert; Rudisill, Marianne

1993-01-01

Our initial model of graphic comprehension has focused on statistical graphs. Like other models of human-computer interaction, models of graphical comprehension can be used by human-computer interface designers and developers to create interfaces that present information in an efficient and usable manner. Our investigation of graph comprehension addresses two primary questions: how do people represent the information contained in a data graph?; and how do they process information from the graph? The topics of focus for graphic representation concern the features into which people decompose a graph and the representations of the graph in memory. The issue of processing can be further analyzed as two questions: what overall processing strategies do people use?; and what are the specific processing skills required?

Using representations in geometry: a model of students' cognitive and affective performance

NASA Astrophysics Data System (ADS)

Panaoura, Areti

2014-05-01

Self-efficacy beliefs in mathematics, as a dimension of the affective domain, are related with students' performance on solving tasks and mainly on overcoming cognitive obstacles. The present study investigated the interrelations of cognitive performance on geometry and young students' self-efficacy beliefs about using representations for solving geometrical tasks. The emphasis was on confirming a theoretical model for the primary-school and secondary-school students and identifying the differences and similarities for the two ages. A quantitative study was developed and data were collected from 1086 students in Grades 5-8. Confirmatory factor analysis affirmed the existence of a coherent model of affective dimensions about the use of representations for understanding the geometrical concepts, which becomes more stable across the educational levels.

Scalar products of Bethe vectors in models with {\\mathfrak{gl}}(2| 1) symmetry 1. Super-analog of Reshetikhin formula

NASA Astrophysics Data System (ADS)

Hutsalyuk, A.; Liashyk, A.; Pakuliak, S. Z.; Ragoucy, E.; Slavnov, N. A.

2016-11-01

We study the scalar products of Bethe vectors in integrable models solvable by the nested algebraic Bethe ansatz and possessing {gl}(2| 1) symmetry. Using explicit formulas of the monodromy matrix entries’ multiple actions onto Bethe vectors we obtain a representation for the scalar product in the most general case. This explicit representation appears to be a sum over partitions of the Bethe parameters. It can be used for the analysis of scalar products involving on-shell Bethe vectors. As a by-product, we obtain a determinant representation for the scalar products of generic Bethe vectors in integrable models with {gl}(1| 1) symmetry. Dedicated to the memory of Petr Petrovich Kulish.

Increasing situation awareness of the CBRNE robot operators

NASA Astrophysics Data System (ADS)

Jasiobedzki, Piotr; Ng, Ho-Kong; Bondy, Michel; McDiarmid, Carl H.

2010-04-01

Situational awareness of CBRN robot operators is quite limited, as they rely on images and measurements from on-board detectors. This paper describes a novel framework that enables a uniform and intuitive access to live and recent data via 2D and 3D representations of visited sites. These representations are created automatically and augmented with images, models and CBRNE measurements. This framework has been developed for CBRNE Crime Scene Modeler (C2SM), a mobile CBRNE mapping system. The system creates representations (2D floor plans and 3D photorealistic models) of the visited sites, which are then automatically augmented with CBRNE detector measurements. The data stored in a database is accessed using a variety of user interfaces providing different perspectives and increasing operators' situational awareness.

Advancing Clouds Lifecycle Representation in Numerical Models Using Innovative Analysis Methods that Bridge ARM Observations and Models Over a Breadth of Scales

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kollias, Pavlos

2016-09-06

This the final report for the DE-SC0007096 - Advancing Clouds Lifecycle Representation in Numerical Models Using Innovative Analysis Methods that Bridge ARM Observations and Models Over a Breadth of Scales - PI: Pavlos Kollias. The final report outline the main findings of the research conducted using the aforementioned award in the area of cloud research from the cloud scale (10-100 m) to the mesoscale (20-50 km).

Dynamic Model and Control of a Photovoltaic Generation System using Energetic Macroscopic Representation

NASA Astrophysics Data System (ADS)

Solano, Javier; Duarte, José; Vargas, Erwin; Cabrera, Jhon; Jácome, Andrés; Botero, Mónica; Rey, Juan

2016-10-01

This paper addresses the Energetic Macroscopic Representation EMR, the modelling and the control of photovoltaic panel PVP generation systems for simulation purposes. The model of the PVP considers the variations on irradiance and temperature. A maximum power point tracking MPPT algorithm is considered to control the power converter. A novel EMR is proposed to consider the dynamic model of the PVP with variations in the irradiance and the temperature. The EMR is evaluated through simulations of a PVP generation system.

PiTS-1: Carbon Partitioning in Loblolly Pine after 13C Labeling and Shade Treatments

DOE Data Explorer

Warren, J. M.; Iversen, C. M.; Garten, Jr., C. T.; Norby, R. J.; Childs, J.; Brice, D.; Evans, R. M.; Gu, L.; Thornton, P.; Weston, D. J.

2013-01-01

The PiTS task was established with the objective of improving the C partitioning routines in existing ecosystem models by exploring mechanistic model representations of partitioning tested against field observations. We used short-term field manipulations of C flow, through 13CO2 labeling, canopy shading and stem girdling, to dramatically alter C partitioning, and resultant data are being used to test model representation of C partitioning processes in the Community Land Model (CLM4 or CLM4.5).

Expert systems and simulation models; Proceedings of the Seminar, Tucson, AZ, November 18, 19, 1985

NASA Technical Reports Server (NTRS)

1986-01-01

The seminar presents papers on modeling and simulation methodology, artificial intelligence and expert systems, environments for simulation/expert system development, and methodology for simulation/expert system development. Particular attention is given to simulation modeling concepts and their representation, modular hierarchical model specification, knowledge representation, and rule-based diagnostic expert system development. Other topics include the combination of symbolic and discrete event simulation, real time inferencing, and the management of large knowledge-based simulation projects.

Dual coding: a cognitive model for psychoanalytic research.

PubMed

Bucci, W

1985-01-01

Four theories of mental representation derived from current experimental work in cognitive psychology have been discussed in relation to psychoanalytic theory. These are: verbal mediation theory, in which language determines or mediates thought; perceptual dominance theory, in which imagistic structures are dominant; common code or propositional models, in which all information, perceptual or linguistic, is represented in an abstract, amodal code; and dual coding, in which nonverbal and verbal information are each encoded, in symbolic form, in separate systems specialized for such representation, and connected by a complex system of referential relations. The weight of current empirical evidence supports the dual code theory. However, psychoanalysis has implicitly accepted a mixed model-perceptual dominance theory applying to unconscious representation, and verbal mediation characterizing mature conscious waking thought. The characterization of psychoanalysis, by Schafer, Spence, and others, as a domain in which reality is constructed rather than discovered, reflects the application of this incomplete mixed model. The representations of experience in the patient's mind are seen as without structure of their own, needing to be organized by words, thus vulnerable to distortion or dissolution by the language of the analyst or the patient himself. In these terms, hypothesis testing becomes a meaningless pursuit; the propositions of the theory are no longer falsifiable; the analyst is always more or less "right." This paper suggests that the integrated dual code formulation provides a more coherent theoretical framework for psychoanalysis than the mixed model, with important implications for theory and technique. In terms of dual coding, the problem is not that the nonverbal representations are vulnerable to distortion by words, but that the words that pass back and forth between analyst and patient will not affect the nonverbal schemata at all. Using the dual code formulation, and applying an investigative methodology derived from experimental cognitive psychology, a new approach to the verification of interpretations is possible. Some constructions of a patient's story may be seen as more accurate than others, by virtue of their linkage to stored perceptual representations in long-term memory. We can demonstrate that such linking has occurred in functional or operational terms--through evaluating the representation of imagistic content in the patient's speech.

Compensatory Root Water Uptake of Overlapping Root Systems

NASA Astrophysics Data System (ADS)

Agee, E.; Ivanov, V. Y.; He, L.; Bisht, G.; Shahbaz, P.; Fatichi, S.; Gough, C. M.; Couvreur, V.; Matheny, A. M.; Bohrer, G.

2015-12-01

Land-surface models use simplified representations of root water uptake based on biomass distributions and empirical functions that constrain water uptake during unfavorable soil moisture conditions. These models fail to capture the observed hydraulic plasticity that allows plants to regulate root hydraulic conductivity and zones of active uptake based on local gradients. Recent developments in root water uptake modeling have sought to increase its mechanistic representation by bridging the gap between physically based microscopic models and computationally feasible macroscopic approaches. It remains to be demonstrated whether bulk parameterization of microscale characteristics (e.g., root system morphology and root conductivity) can improve process representation at the ecosystem scale. We employ the Couvreur method of microscopic uptake to yield macroscopic representation in a coupled soil-root model. Using a modified version of the PFLOTRAN model, which represents the 3-D physics of variably saturated soil, we model a one-hectare temperate forest stand under natural and synthetic climatic forcing. Our results show that as shallow soil layers dry, uptake at the tree and stand level shift to deeper soil layers, allowing the transpiration stream demanded by the atmosphere. We assess the potential capacity of the model to capture compensatory root water uptake. Further, the hydraulic plasticity of the root system is demonstrated by the quick response of uptake to rainfall pulses. These initial results indicate a promising direction for land surface models in which significant three-dimensional information from large root systems can be feasibly integrated into the forest scale simulations of root water uptake.

Sexual orientation and education politics: gay and lesbian representation in American schools.

PubMed

Wald, Kenneth D; Rienzo, Barbara A; Button, James W

2002-01-01

In what has sometimes provoked a "culture war" over America's schools, gays and lesbians have sought an expanded voice in the making of education policy. This paper explores the factors that promote gay representation on school boards, how this variable in turn influences gay representation in both administrative and teaching positions, and how all three forms of gay representation relate to school board policies regarding sexual orientation education. Three of the four models drawn from the social movement literature help to explain gay school board representation. In a manner similar to other minority groups, gay representation on school boards directly or indirectly promotes the appointment of gays to administrative and teaching positions and the adoption of policies that address the problems faced by gay and lesbian students in the public schools.

Invariant recognition drives neural representations of action sequences

PubMed Central

Poggio, Tomaso

2017-01-01

Recognizing the actions of others from visual stimuli is a crucial aspect of human perception that allows individuals to respond to social cues. Humans are able to discriminate between similar actions despite transformations, like changes in viewpoint or actor, that substantially alter the visual appearance of a scene. This ability to generalize across complex transformations is a hallmark of human visual intelligence. Advances in understanding action recognition at the neural level have not always translated into precise accounts of the computational principles underlying what representations of action sequences are constructed by human visual cortex. Here we test the hypothesis that invariant action discrimination might fill this gap. Recently, the study of artificial systems for static object perception has produced models, Convolutional Neural Networks (CNNs), that achieve human level performance in complex discriminative tasks. Within this class, architectures that better support invariant object recognition also produce image representations that better match those implied by human and primate neural data. However, whether these models produce representations of action sequences that support recognition across complex transformations and closely follow neural representations of actions remains unknown. Here we show that spatiotemporal CNNs accurately categorize video stimuli into action classes, and that deliberate model modifications that improve performance on an invariant action recognition task lead to data representations that better match human neural recordings. Our results support our hypothesis that performance on invariant discrimination dictates the neural representations of actions computed in the brain. These results broaden the scope of the invariant recognition framework for understanding visual intelligence from perception of inanimate objects and faces in static images to the study of human perception of action sequences. PMID:29253864

Attachment-related mental representations: introduction to the special issue.

PubMed

Thompson, Ross A

2008-12-01

Bowlby's concept of mental working models of self, attachment figures, and the social world has been theoretically generative as a bridge between early relational experience and the beliefs and expectations that color later relationships. Contemporary attachment researchers, following his example, are applying new knowledge of children's conceptual development to their study of attachment-related mental representations in children and adults. The contributors to this special issue highlight recent advances in how the mental representations arising from attachment security should be conceptualized and studied, and identify a number of important directions for future work. This paper introduces the special issue by summarizing the major ideas of Bowlby and his followers concerning the nature and development of mental working models, points of theoretical clarity and uncertainty, and challenges in assessing these representations, as well as profiling each of the contributions to this issue.

Beyond Natural Numbers: Negative Number Representation in Parietal Cortex

PubMed Central

Blair, Kristen P.; Rosenberg-Lee, Miriam; Tsang, Jessica M.; Schwartz, Daniel L.; Menon, Vinod

2012-01-01

Unlike natural numbers, negative numbers do not have natural physical referents. How does the brain represent such abstract mathematical concepts? Two competing hypotheses regarding representational systems for negative numbers are a rule-based model, in which symbolic rules are applied to negative numbers to translate them into positive numbers when assessing magnitudes, and an expanded magnitude model, in which negative numbers have a distinct magnitude representation. Using an event-related functional magnetic resonance imaging design, we examined brain responses in 22 adults while they performed magnitude comparisons of negative and positive numbers that were quantitatively near (difference <4) or far apart (difference >6). Reaction times (RTs) for negative numbers were slower than positive numbers, and both showed a distance effect whereby near pairs took longer to compare. A network of parietal, frontal, and occipital regions were differentially engaged by negative numbers. Specifically, compared to positive numbers, negative number processing resulted in greater activation bilaterally in intraparietal sulcus (IPS), middle frontal gyrus, and inferior lateral occipital cortex. Representational similarity analysis revealed that neural responses in the IPS were more differentiated among positive numbers than among negative numbers, and greater differentiation among negative numbers was associated with faster RTs. Our findings indicate that despite negative numbers engaging the IPS more strongly, the underlying neural representation are less distinct than that of positive numbers. We discuss our findings in the context of the two theoretical models of negative number processing and demonstrate how multivariate approaches can provide novel insights into abstract number representation. PMID:22363276

Beyond natural numbers: negative number representation in parietal cortex.

PubMed

Blair, Kristen P; Rosenberg-Lee, Miriam; Tsang, Jessica M; Schwartz, Daniel L; Menon, Vinod

2012-01-01

Unlike natural numbers, negative numbers do not have natural physical referents. How does the brain represent such abstract mathematical concepts? Two competing hypotheses regarding representational systems for negative numbers are a rule-based model, in which symbolic rules are applied to negative numbers to translate them into positive numbers when assessing magnitudes, and an expanded magnitude model, in which negative numbers have a distinct magnitude representation. Using an event-related functional magnetic resonance imaging design, we examined brain responses in 22 adults while they performed magnitude comparisons of negative and positive numbers that were quantitatively near (difference <4) or far apart (difference >6). Reaction times (RTs) for negative numbers were slower than positive numbers, and both showed a distance effect whereby near pairs took longer to compare. A network of parietal, frontal, and occipital regions were differentially engaged by negative numbers. Specifically, compared to positive numbers, negative number processing resulted in greater activation bilaterally in intraparietal sulcus (IPS), middle frontal gyrus, and inferior lateral occipital cortex. Representational similarity analysis revealed that neural responses in the IPS were more differentiated among positive numbers than among negative numbers, and greater differentiation among negative numbers was associated with faster RTs. Our findings indicate that despite negative numbers engaging the IPS more strongly, the underlying neural representation are less distinct than that of positive numbers. We discuss our findings in the context of the two theoretical models of negative number processing and demonstrate how multivariate approaches can provide novel insights into abstract number representation.

Hierarchical representation of shapes in visual cortex—from localized features to figural shape segregation

PubMed Central

Tschechne, Stephan; Neumann, Heiko

2014-01-01

Visual structures in the environment are segmented into image regions and those combined to a representation of surfaces and prototypical objects. Such a perceptual organization is performed by complex neural mechanisms in the visual cortex of primates. Multiple mutually connected areas in the ventral cortical pathway receive visual input and extract local form features that are subsequently grouped into increasingly complex, more meaningful image elements. Such a distributed network of processing must be capable to make accessible highly articulated changes in shape boundary as well as very subtle curvature changes that contribute to the perception of an object. We propose a recurrent computational network architecture that utilizes hierarchical distributed representations of shape features to encode surface and object boundary over different scales of resolution. Our model makes use of neural mechanisms that model the processing capabilities of early and intermediate stages in visual cortex, namely areas V1–V4 and IT. We suggest that multiple specialized component representations interact by feedforward hierarchical processing that is combined with feedback signals driven by representations generated at higher stages. Based on this, global configurational as well as local information is made available to distinguish changes in the object's contour. Once the outline of a shape has been established, contextual contour configurations are used to assign border ownership directions and thus achieve segregation of figure and ground. The model, thus, proposes how separate mechanisms contribute to distributed hierarchical cortical shape representation and combine with processes of figure-ground segregation. Our model is probed with a selection of stimuli to illustrate processing results at different processing stages. We especially highlight how modulatory feedback connections contribute to the processing of visual input at various stages in the processing hierarchy. PMID:25157228

Hierarchical representation of shapes in visual cortex-from localized features to figural shape segregation.

PubMed

Tschechne, Stephan; Neumann, Heiko

2014-01-01

Visual structures in the environment are segmented into image regions and those combined to a representation of surfaces and prototypical objects. Such a perceptual organization is performed by complex neural mechanisms in the visual cortex of primates. Multiple mutually connected areas in the ventral cortical pathway receive visual input and extract local form features that are subsequently grouped into increasingly complex, more meaningful image elements. Such a distributed network of processing must be capable to make accessible highly articulated changes in shape boundary as well as very subtle curvature changes that contribute to the perception of an object. We propose a recurrent computational network architecture that utilizes hierarchical distributed representations of shape features to encode surface and object boundary over different scales of resolution. Our model makes use of neural mechanisms that model the processing capabilities of early and intermediate stages in visual cortex, namely areas V1-V4 and IT. We suggest that multiple specialized component representations interact by feedforward hierarchical processing that is combined with feedback signals driven by representations generated at higher stages. Based on this, global configurational as well as local information is made available to distinguish changes in the object's contour. Once the outline of a shape has been established, contextual contour configurations are used to assign border ownership directions and thus achieve segregation of figure and ground. The model, thus, proposes how separate mechanisms contribute to distributed hierarchical cortical shape representation and combine with processes of figure-ground segregation. Our model is probed with a selection of stimuli to illustrate processing results at different processing stages. We especially highlight how modulatory feedback connections contribute to the processing of visual input at various stages in the processing hierarchy.

[Representation and mathematical analysis of human crystalline lens].

PubMed

Tălu, Stefan; Giovanzana, Stefano; Tălu, Mihai

2011-01-01

The surface of human crystalline lens can be described and analyzed using mathematical models based on parametric representations, used in biomechanical studies and 3D solid modeling of the lens. The mathematical models used in lens biomechanics allow the study and the behavior of crystalline lens on variables and complex dynamic loads. Also, the lens biomechanics has the potential to improve the results in the development of intraocular lenses and cataract surgery. The paper presents the most representative mathematical models currently used for the modeling of human crystalline lens, both optically and biomechanically.

Covariant spinor representation of iosp(d,2/2) and quantization of the spinning relativistic particle

NASA Astrophysics Data System (ADS)

Jarvis, P. D.; Corney, S. P.; Tsohantjis, I.

1999-12-01

A covariant spinor representation of iosp(d,2/2) is constructed for the quantization of the spinning relativistic particle. It is found that, with appropriately defined wavefunctions, this representation can be identified with the state space arising from the canonical extended BFV-BRST quantization of the spinning particle with admissible gauge fixing conditions after a contraction procedure. For this model, the cohomological determination of physical states can thus be obtained purely from the representation theory of the iosp(d,2/2) algebra.

Medical Named Entity Recognition for Indonesian Language Using Word Representations

NASA Astrophysics Data System (ADS)

Rahman, Arief

2018-03-01

Nowadays, Named Entity Recognition (NER) system is used in medical texts to obtain important medical information, like diseases, symptoms, and drugs. While most NER systems are applied to formal medical texts, informal ones like those from social media (also called semi-formal texts) are starting to get recognition as a gold mine for medical information. We propose a theoretical Named Entity Recognition (NER) model for semi-formal medical texts in our medical knowledge management system by comparing two kinds of word representations: cluster-based word representation and distributed representation.

Improving predictions of large scale soil carbon dynamics: Integration of fine-scale hydrological and biogeochemical processes, scaling, and benchmarking

NASA Astrophysics Data System (ADS)

Riley, W. J.; Dwivedi, D.; Ghimire, B.; Hoffman, F. M.; Pau, G. S. H.; Randerson, J. T.; Shen, C.; Tang, J.; Zhu, Q.

2015-12-01

Numerical model representations of decadal- to centennial-scale soil-carbon dynamics are a dominant cause of uncertainty in climate change predictions. Recent attempts by some Earth System Model (ESM) teams to integrate previously unrepresented soil processes (e.g., explicit microbial processes, abiotic interactions with mineral surfaces, vertical transport), poor performance of many ESM land models against large-scale and experimental manipulation observations, and complexities associated with spatial heterogeneity highlight the nascent nature of our community's ability to accurately predict future soil carbon dynamics. I will present recent work from our group to develop a modeling framework to integrate pore-, column-, watershed-, and global-scale soil process representations into an ESM (ACME), and apply the International Land Model Benchmarking (ILAMB) package for evaluation. At the column scale and across a wide range of sites, observed depth-resolved carbon stocks and their 14C derived turnover times can be explained by a model with explicit representation of two microbial populations, a simple representation of mineralogy, and vertical transport. Integrating soil and plant dynamics requires a 'process-scaling' approach, since all aspects of the multi-nutrient system cannot be explicitly resolved at ESM scales. I will show that one approach, the Equilibrium Chemistry Approximation, improves predictions of forest nitrogen and phosphorus experimental manipulations and leads to very different global soil carbon predictions. Translating model representations from the site- to ESM-scale requires a spatial scaling approach that either explicitly resolves the relevant processes, or more practically, accounts for fine-resolution dynamics at coarser scales. To that end, I will present recent watershed-scale modeling work that applies reduced order model methods to accurately scale fine-resolution soil carbon dynamics to coarse-resolution simulations. Finally, we contend that creating believable soil carbon predictions requires a robust, transparent, and community-available benchmarking framework. I will present an ILAMB evaluation of several of the above-mentioned approaches in ACME, and attempt to motivate community adoption of this evaluation approach.

A critical review of the allocentric spatial representation and its neural underpinnings: toward a network-based perspective

PubMed Central

Ekstrom, Arne D.; Arnold, Aiden E. G. F.; Iaria, Giuseppe

2014-01-01

While the widely studied allocentric spatial representation holds a special status in neuroscience research, its exact nature and neural underpinnings continue to be the topic of debate, particularly in humans. Here, based on a review of human behavioral research, we argue that allocentric representations do not provide the kind of map-like, metric representation one might expect based on past theoretical work. Instead, we suggest that almost all tasks used in past studies involve a combination of egocentric and allocentric representation, complicating both the investigation of the cognitive basis of an allocentric representation and the task of identifying a brain region specifically dedicated to it. Indeed, as we discuss in detail, past studies suggest numerous brain regions important to allocentric spatial memory in addition to the hippocampus, including parahippocampal, retrosplenial, and prefrontal cortices. We thus argue that although allocentric computations will often require the hippocampus, particularly those involving extracting details across temporally specific routes, the hippocampus is not necessary for all allocentric computations. We instead suggest that a non-aggregate network process involving multiple interacting brain areas, including hippocampus and extra-hippocampal areas such as parahippocampal, retrosplenial, prefrontal, and parietal cortices, better characterizes the neural basis of spatial representation during navigation. According to this model, an allocentric representation does not emerge from the computations of a single brain region (i.e., hippocampus) nor is it readily decomposable into additive computations performed by separate brain regions. Instead, an allocentric representation emerges from computations partially shared across numerous interacting brain regions. We discuss our non-aggregate network model in light of existing data and provide several key predictions for future experiments. PMID:25346679

Putting the psychology back into psychological models: mechanistic versus rational approaches.

PubMed

Sakamoto, Yasuaki; Jones, Mattr; Love, Bradley C

2008-09-01

Two basic approaches to explaining the nature of the mind are the rational and the mechanistic approaches. Rational analyses attempt to characterize the environment and the behavioral outcomes that humans seek to optimize, whereas mechanistic models attempt to simulate human behavior using processes and representations analogous to those used by humans. We compared these approaches with regard to their accounts of how humans learn the variability of categories. The mechanistic model departs in subtle ways from rational principles. In particular, the mechanistic model incrementally updates its estimates of category means and variances through error-driven learning, based on discrepancies between new category members and the current representation of each category. The model yields a prediction, which we verify, regarding the effects of order manipulations that the rational approach does not anticipate. Although both rational and mechanistic models can successfully postdict known findings, we suggest that psychological advances are driven primarily by consideration of process and representation and that rational accounts trail these breakthroughs.

The Role of Visual Representations in Scientific Practices: From Conceptual Understanding and Knowledge Generation to 'Seeing' How Science Works

ERIC Educational Resources Information Center

Evagorou, Maria; Erduran, Sibel; Mäntylä, Terhi

2015-01-01

Background: The use of visual representations (i.e., photographs, diagrams, models) has been part of science, and their use makes it possible for scientists to interact with and represent complex phenomena, not observable in other ways. Despite a wealth of research in science education on visual representations, the emphasis of such research has…

Toward a human-centered hyperlipidemia management system: the interaction between internal and external information on relational data search.

PubMed

Gong, Yang; Zhang, Jiajie

2011-04-01

In a distributed information search task, data representation and cognitive distribution jointly affect user search performance in terms of response time and accuracy. Guided by UFuRT (User, Function, Representation, Task), a human-centered framework, we proposed a search model and task taxonomy. The model defines its application in the context of healthcare setting. The taxonomy clarifies the legitimate operations for each type of search task of relational data. We then developed experimental prototypes of hyperlipidemia data displays. Based on the displays, we tested the search tasks performance through two experiments. The experiments are of a within-subject design with a random sample of 24 participants. The results support our hypotheses and validate the prediction of the model and task taxonomy. In this study, representation dimensions, data scales, and search task types are the main factors in determining search efficiency and effectiveness. Specifically, the more external representations provided on the interface the better search task performance of users. The results also suggest the ideal search performance occurs when the question type and its corresponding data scale representation match. The implications of the study lie in contributing to the effective design of search interface for relational data, especially laboratory results, which could be more effectively designed in electronic medical records.

Enhanced representations of lithium-ion batteries in power systems models and their effect on the valuation of energy arbitrage applications

NASA Astrophysics Data System (ADS)

Sakti, Apurba; Gallagher, Kevin G.; Sepulveda, Nestor; Uckun, Canan; Vergara, Claudio; de Sisternes, Fernando J.; Dees, Dennis W.; Botterud, Audun

2017-02-01

We develop three novel enhanced mixed integer-linear representations of the power limit of the battery and its efficiency as a function of the charge and discharge power and the state of charge of the battery, which can be directly implemented in large-scale power systems models and solved with commercial optimization solvers. Using these battery representations, we conduct a techno-economic analysis of the performance of a 10 MWh lithium-ion battery system testing the effect of a 5-min vs. a 60-min price signal on profits using real time prices from a selected node in the MISO electricity market. Results show that models of lithium-ion batteries where the power limits and efficiency are held constant overestimate profits by 10% compared to those obtained from an enhanced representation that more closely matches the real behavior of the battery. When the battery system is exposed to a 5-min price signal, the energy arbitrage profitability improves by 60% compared to that from hourly price exposure. These results indicate that a more accurate representation of li-ion batteries as well as the market rules that govern the frequency of electricity prices can play a major role on the estimation of the value of battery technologies for power grid applications.

Top-down attention based on object representation and incremental memory for knowledge building and inference.

PubMed

Kim, Bumhwi; Ban, Sang-Woo; Lee, Minho

2013-10-01

Humans can efficiently perceive arbitrary visual objects based on an incremental learning mechanism with selective attention. This paper proposes a new task specific top-down attention model to locate a target object based on its form and color representation along with a bottom-up saliency based on relativity of primitive visual features and some memory modules. In the proposed model top-down bias signals corresponding to the target form and color features are generated, which draw the preferential attention to the desired object by the proposed selective attention model in concomitance with the bottom-up saliency process. The object form and color representation and memory modules have an incremental learning mechanism together with a proper object feature representation scheme. The proposed model includes a Growing Fuzzy Topology Adaptive Resonance Theory (GFTART) network which plays two important roles in object color and form biased attention; one is to incrementally learn and memorize color and form features of various objects, and the other is to generate a top-down bias signal to localize a target object by focusing on the candidate local areas. Moreover, the GFTART network can be utilized for knowledge inference which enables the perception of new unknown objects on the basis of the object form and color features stored in the memory during training. Experimental results show that the proposed model is successful in focusing on the specified target objects, in addition to the incremental representation and memorization of various objects in natural scenes. In addition, the proposed model properly infers new unknown objects based on the form and color features of previously trained objects. Copyright © 2013 Elsevier Ltd. All rights reserved.

Bayesian analogy with relational transformations.

PubMed

Lu, Hongjing; Chen, Dawn; Holyoak, Keith J

2012-07-01

How can humans acquire relational representations that enable analogical inference and other forms of high-level reasoning? Using comparative relations as a model domain, we explore the possibility that bottom-up learning mechanisms applied to objects coded as feature vectors can yield representations of relations sufficient to solve analogy problems. We introduce Bayesian analogy with relational transformations (BART) and apply the model to the task of learning first-order comparative relations (e.g., larger, smaller, fiercer, meeker) from a set of animal pairs. Inputs are coded by vectors of continuous-valued features, based either on human magnitude ratings, normed feature ratings (De Deyne et al., 2008), or outputs of the topics model (Griffiths, Steyvers, & Tenenbaum, 2007). Bootstrapping from empirical priors, the model is able to induce first-order relations represented as probabilistic weight distributions, even when given positive examples only. These learned representations allow classification of novel instantiations of the relations and yield a symbolic distance effect of the sort obtained with both humans and other primates. BART then transforms its learned weight distributions by importance-guided mapping, thereby placing distinct dimensions into correspondence. These transformed representations allow BART to reliably solve 4-term analogies (e.g., larger:smaller::fiercer:meeker), a type of reasoning that is arguably specific to humans. Our results provide a proof-of-concept that structured analogies can be solved with representations induced from unstructured feature vectors by mechanisms that operate in a largely bottom-up fashion. We discuss potential implications for algorithmic and neural models of relational thinking, as well as for the evolution of abstract thought. Copyright 2012 APA, all rights reserved.

Using field observations to inform thermal hydrology models of permafrost dynamics with ATS (v0.83)

DOE PAGES

Atchley, Adam L.; Painter, Scott L.; Harp, Dylan R.; ...

2015-09-01

Climate change is profoundly transforming the carbon-rich Arctic tundra landscape, potentially moving it from a carbon sink to a carbon source by increasing the thickness of soil that thaws on a seasonal basis. Thus, the modeling capability and precise parameterizations of the physical characteristics needed to estimate projected active layer thickness (ALT) are limited in Earth system models (ESMs). In particular, discrepancies in spatial scale between field measurements and Earth system models challenge validation and parameterization of hydrothermal models. A recently developed surface–subsurface model for permafrost thermal hydrology, the Advanced Terrestrial Simulator (ATS), is used in combination with field measurementsmore » to achieve the goals of constructing a process-rich model based on plausible parameters and to identify fine-scale controls of ALT in ice-wedge polygon tundra in Barrow, Alaska. An iterative model refinement procedure that cycles between borehole temperature and snow cover measurements and simulations functions to evaluate and parameterize different model processes necessary to simulate freeze–thaw processes and ALT formation. After model refinement and calibration, reasonable matches between simulated and measured soil temperatures are obtained, with the largest errors occurring during early summer above ice wedges (e.g., troughs). The results suggest that properly constructed and calibrated one-dimensional thermal hydrology models have the potential to provide reasonable representation of the subsurface thermal response and can be used to infer model input parameters and process representations. The models for soil thermal conductivity and snow distribution were found to be the most sensitive process representations. However, information on lateral flow and snowpack evolution might be needed to constrain model representations of surface hydrology and snow depth.« less

A computational model of the human visual cortex

NASA Astrophysics Data System (ADS)

Albus, James S.

2008-04-01

The brain is first and foremost a control system that is capable of building an internal representation of the external world, and using this representation to make decisions, set goals and priorities, formulate plans, and control behavior with intent to achieve its goals. The computational model proposed here assumes that this internal representation resides in arrays of cortical columns. More specifically, it models each cortical hypercolumn together with its underlying thalamic nuclei as a Fundamental Computational Unit (FCU) consisting of a frame-like data structure (containing attributes and pointers) plus the computational processes and mechanisms required to maintain it. In sensory-processing areas of the brain, FCUs enable segmentation, grouping, and classification. Pointers stored in FCU frames link pixels and signals to objects and events in situations and episodes that are overlaid with meaning and emotional values. In behavior-generating areas of the brain, FCUs make decisions, set goals and priorities, generate plans, and control behavior. Pointers are used to define rules, grammars, procedures, plans, and behaviors. It is suggested that it may be possible to reverse engineer the human brain at the FCU level of fidelity using nextgeneration massively parallel computer hardware and software. Key Words: computational modeling, human cortex, brain modeling, reverse engineering the brain, image processing, perception, segmentation, knowledge representation

Computational Models of the Representation of Bangla Compound Words in the Mental Lexicon.

PubMed

Dasgupta, Tirthankar; Sinha, Manjira; Basu, Anupam

2016-08-01

In this paper we aim to model the organization and processing of Bangla compound words in the mental lexicon. Our objective is to determine whether the mental lexicon access a Bangla compound word as a whole or decomposes the whole word into its constituent morphemes and then recognize them accordingly. To address this issue, we adopted two different strategies. First, we conduct a cross-modal priming experiment over a number of native speakers. Analysis of reaction time (RT) and error rates indicates that in general, Bangla compound words are accessed via partial decomposition process. That is some word follows full-listing mode of representation and some words follow the decomposition route of representation. Next, based on the collected RT data we have developed a computational model that can explain the processing phenomena of the access and representation of Bangla compound words. In order to achieve this, we first explored the individual roles of head word position, morphological complexity, orthographic transparency and semantic compositionality between the constituents and the whole compound word. Accordingly, we have developed a complexity based model by combining these features together. To a large extent we have successfully explained the possible processing phenomena of most of the Bangla compound words. Our proposed model shows an accuracy of around 83 %.

Biologically Inspired Model for Inference of 3D Shape from Texture

PubMed Central

Gomez, Olman; Neumann, Heiko

2016-01-01

A biologically inspired model architecture for inferring 3D shape from texture is proposed. The model is hierarchically organized into modules roughly corresponding to visual cortical areas in the ventral stream. Initial orientation selective filtering decomposes the input into low-level orientation and spatial frequency representations. Grouping of spatially anisotropic orientation responses builds sketch-like representations of surface shape. Gradients in orientation fields and subsequent integration infers local surface geometry and globally consistent 3D depth. From the distributions in orientation responses summed in frequency, an estimate of the tilt and slant of the local surface can be obtained. The model suggests how 3D shape can be inferred from texture patterns and their image appearance in a hierarchically organized processing cascade along the cortical ventral stream. The proposed model integrates oriented texture gradient information that is encoded in distributed maps of orientation-frequency representations. The texture energy gradient information is defined by changes in the grouped summed normalized orientation-frequency response activity extracted from the textured object image. This activity is integrated by directed fields to generate a 3D shape representation of a complex object with depth ordering proportional to the fields output, with higher activity denoting larger distance in relative depth away from the viewer. PMID:27649387

Measuring the Performance and Intelligence of Systems: Proceedings of the 2002 PerMIS Workshop

NASA Technical Reports Server (NTRS)

Messina, E. R.; Meystel, A. M.

2002-01-01

Contents include the following: Performance Metrics; Performance of Multiple Agents; Performance of Mobility Systems; Performance of Planning Systems; General Discussion Panel 1; Uncertainty of Representation I; Performance of Robots in Hazardous Domains; Modeling Intelligence; Modeling of Mind; Measuring Intelligence; Grouping: A Core Procedure of Intelligence; Uncertainty in Representation II; Towards Universal Planning/Control Systems.

Graphical Representations and Odds Ratios in a Distance-Association Model for the Analysis of Cross-Classified Data

ERIC Educational Resources Information Center

de Rooij, Mark; Heiser, Willem J.

2005-01-01

Although RC(M)-association models have become a generally useful tool for the analysis of cross-classified data, the graphical representation resulting from such an analysis can at times be misleading. The relationships present between row category points and column category points cannot be interpreted by inter point distances but only through…

School and Modernity Representations as Pedagogical Models: A Study on Their Circulation and Usages in Brazil (1889-1940)

ERIC Educational Resources Information Center

de Carvalho, Marta Maria Chagas

2005-01-01

This article addresses the issue of school and modernity representations that circulated in Brazil as from the end of the nineteenth century until the middle of the twentieth century and determined the configuration process of the Republican school. First, the article examines the pedagogical models that guided the process of school…

A model of olfactory associative learning

NASA Astrophysics Data System (ADS)

Tavoni, Gaia; Balasubramanian, Vijay

We propose a mechanism, rooted in the known anatomy and physiology of the vertebrate olfactory system, by which presentations of rewarded and unrewarded odors lead to formation of odor-valence associations between piriform cortex (PC) and anterior olfactory nucleus (AON) which, in concert with neuromodulators release in the bulb, entrains a direct feedback from the AON representation of valence to a group of mitral cells (MCs). The model makes several predictions concerning MC activity during and after associative learning: (a) AON feedback produces synchronous divergent responses in a localized subset of MCs; (b) such divergence propagates to other MCs by lateral inhibition; (c) after learning, MC responses reconverge; (d) recall of the newly formed associations in the PC increases feedback inhibition in the MCs. These predictions have been confirmed in disparate experiments which we now explain in a unified framework. For cortex, our model further predicts that the response divergence developed during learning reshapes odor representations in the PC, with the effects of (a) decorrelating PC representations of odors with different valences, (b) increasing the size and reliability of those representations, and enabling recall correction and redundancy reduction after learning. Simons Foundation for Mathematical Modeling of Living Systems.

Evaluating and improving the representation of heteroscedastic errors in hydrological models

NASA Astrophysics Data System (ADS)

McInerney, D. J.; Thyer, M. A.; Kavetski, D.; Kuczera, G. A.

2013-12-01

Appropriate representation of residual errors in hydrological modelling is essential for accurate and reliable probabilistic predictions. In particular, residual errors of hydrological models are often heteroscedastic, with large errors associated with high rainfall and runoff events. Recent studies have shown that using a weighted least squares (WLS) approach - where the magnitude of residuals are assumed to be linearly proportional to the magnitude of the flow - captures some of this heteroscedasticity. In this study we explore a range of Bayesian approaches for improving the representation of heteroscedasticity in residual errors. We compare several improved formulations of the WLS approach, the well-known Box-Cox transformation and the more recent log-sinh transformation. Our results confirm that these approaches are able to stabilize the residual error variance, and that it is possible to improve the representation of heteroscedasticity compared with the linear WLS approach. We also find generally good performance of the Box-Cox and log-sinh transformations, although as indicated in earlier publications, the Box-Cox transform sometimes produces unrealistically large prediction limits. Our work explores the trade-offs between these different uncertainty characterization approaches, investigates how their performance varies across diverse catchments and models, and recommends practical approaches suitable for large-scale applications.

On the biological plausibility of grandmother cells: implications for neural network theories in psychology and neuroscience.

PubMed

Bowers, Jeffrey S

2009-01-01

A fundamental claim associated with parallel distributed processing (PDP) theories of cognition is that knowledge is coded in a distributed manner in mind and brain. This approach rejects the claim that knowledge is coded in a localist fashion, with words, objects, and simple concepts (e.g. "dog"), that is, coded with their own dedicated representations. One of the putative advantages of this approach is that the theories are biologically plausible. Indeed, advocates of the PDP approach often highlight the close parallels between distributed representations learned in connectionist models and neural coding in brain and often dismiss localist (grandmother cell) theories as biologically implausible. The author reviews a range a data that strongly challenge this claim and shows that localist models provide a better account of single-cell recording studies. The author also contrast local and alternative distributed coding schemes (sparse and coarse coding) and argues that common rejection of grandmother cell theories in neuroscience is due to a misunderstanding about how localist models behave. The author concludes that the localist representations embedded in theories of perception and cognition are consistent with neuroscience; biology only calls into question the distributed representations often learned in PDP models.

Do we have an internal model of the outside world?

PubMed Central

Land, Michael F.

2014-01-01

Our phenomenal world remains stationary in spite of movements of the eyes, head and body. In addition, we can point or turn to objects in the surroundings whether or not they are in the field of view. In this review, I argue that these two features of experience and behaviour are related. The ability to interact with objects we cannot see implies an internal memory model of the surroundings, available to the motor system. And, because we maintain this ability when we move around, the model must be updated, so that the locations of object memories change continuously to provide accurate directional information. The model thus contains an internal representation of both the surroundings and the motions of the head and body: in other words, a stable representation of space. Recent functional MRI studies have provided strong evidence that this egocentric representation has a location in the precuneus, on the medial surface of the superior parietal cortex. This is a region previously identified with ‘self-centred mental imagery’, so it seems likely that the stable egocentric representation, required by the motor system, is also the source of our conscious percept of a stable world. PMID:24395972

Extending peripersonal space representation without tool-use: evidence from a combined behavioral-computational approach

PubMed Central

Serino, Andrea; Canzoneri, Elisa; Marzolla, Marilena; di Pellegrino, Giuseppe; Magosso, Elisa

2015-01-01

Stimuli from different sensory modalities occurring on or close to the body are integrated in a multisensory representation of the space surrounding the body, i.e., peripersonal space (PPS). PPS dynamically modifies depending on experience, e.g., it extends after using a tool to reach far objects. However, the neural mechanism underlying PPS plasticity after tool use is largely unknown. Here we use a combined computational-behavioral approach to propose and test a possible mechanism accounting for PPS extension. We first present a neural network model simulating audio-tactile representation in the PPS around one hand. Simulation experiments showed that our model reproduced the main property of PPS neurons, i.e., selective multisensory response for stimuli occurring close to the hand. We used the neural network model to simulate the effects of a tool-use training. In terms of sensory inputs, tool use was conceptualized as a concurrent tactile stimulation from the hand, due to holding the tool, and an auditory stimulation from the far space, due to tool-mediated action. Results showed that after exposure to those inputs, PPS neurons responded also to multisensory stimuli far from the hand. The model thus suggests that synchronous pairing of tactile hand stimulation and auditory stimulation from the far space is sufficient to extend PPS, such as after tool-use. Such prediction was confirmed by a behavioral experiment, where we used an audio-tactile interaction paradigm to measure the boundaries of PPS representation. We found that PPS extended after synchronous tactile-hand stimulation and auditory-far stimulation in a group of healthy volunteers. Control experiments both in simulation and behavioral settings showed that the same amount of tactile and auditory inputs administered out of synchrony did not change PPS representation. We conclude by proposing a simple, biological-plausible model to explain plasticity in PPS representation after tool-use, which is supported by computational and behavioral data. PMID:25698947

Extending peripersonal space representation without tool-use: evidence from a combined behavioral-computational approach.

PubMed

Serino, Andrea; Canzoneri, Elisa; Marzolla, Marilena; di Pellegrino, Giuseppe; Magosso, Elisa

2015-01-01

Stimuli from different sensory modalities occurring on or close to the body are integrated in a multisensory representation of the space surrounding the body, i.e., peripersonal space (PPS). PPS dynamically modifies depending on experience, e.g., it extends after using a tool to reach far objects. However, the neural mechanism underlying PPS plasticity after tool use is largely unknown. Here we use a combined computational-behavioral approach to propose and test a possible mechanism accounting for PPS extension. We first present a neural network model simulating audio-tactile representation in the PPS around one hand. Simulation experiments showed that our model reproduced the main property of PPS neurons, i.e., selective multisensory response for stimuli occurring close to the hand. We used the neural network model to simulate the effects of a tool-use training. In terms of sensory inputs, tool use was conceptualized as a concurrent tactile stimulation from the hand, due to holding the tool, and an auditory stimulation from the far space, due to tool-mediated action. Results showed that after exposure to those inputs, PPS neurons responded also to multisensory stimuli far from the hand. The model thus suggests that synchronous pairing of tactile hand stimulation and auditory stimulation from the far space is sufficient to extend PPS, such as after tool-use. Such prediction was confirmed by a behavioral experiment, where we used an audio-tactile interaction paradigm to measure the boundaries of PPS representation. We found that PPS extended after synchronous tactile-hand stimulation and auditory-far stimulation in a group of healthy volunteers. Control experiments both in simulation and behavioral settings showed that the same amount of tactile and auditory inputs administered out of synchrony did not change PPS representation. We conclude by proposing a simple, biological-plausible model to explain plasticity in PPS representation after tool-use, which is supported by computational and behavioral data.

A semantic-web oriented representation of the clinical element model for secondary use of electronic health records data.

PubMed

Tao, Cui; Jiang, Guoqian; Oniki, Thomas A; Freimuth, Robert R; Zhu, Qian; Sharma, Deepak; Pathak, Jyotishman; Huff, Stanley M; Chute, Christopher G

2013-05-01

The clinical element model (CEM) is an information model designed for representing clinical information in electronic health records (EHR) systems across organizations. The current representation of CEMs does not support formal semantic definitions and therefore it is not possible to perform reasoning and consistency checking on derived models. This paper introduces our efforts to represent the CEM specification using the Web Ontology Language (OWL). The CEM-OWL representation connects the CEM content with the Semantic Web environment, which provides authoring, reasoning, and querying tools. This work may also facilitate the harmonization of the CEMs with domain knowledge represented in terminology models as well as other clinical information models such as the openEHR archetype model. We have created the CEM-OWL meta ontology based on the CEM specification. A convertor has been implemented in Java to automatically translate detailed CEMs from XML to OWL. A panel evaluation has been conducted, and the results show that the OWL modeling can faithfully represent the CEM specification and represent patient data.

A semantic-web oriented representation of the clinical element model for secondary use of electronic health records data

PubMed Central

Tao, Cui; Jiang, Guoqian; Oniki, Thomas A; Freimuth, Robert R; Zhu, Qian; Sharma, Deepak; Pathak, Jyotishman; Huff, Stanley M; Chute, Christopher G

2013-01-01

The clinical element model (CEM) is an information model designed for representing clinical information in electronic health records (EHR) systems across organizations. The current representation of CEMs does not support formal semantic definitions and therefore it is not possible to perform reasoning and consistency checking on derived models. This paper introduces our efforts to represent the CEM specification using the Web Ontology Language (OWL). The CEM-OWL representation connects the CEM content with the Semantic Web environment, which provides authoring, reasoning, and querying tools. This work may also facilitate the harmonization of the CEMs with domain knowledge represented in terminology models as well as other clinical information models such as the openEHR archetype model. We have created the CEM-OWL meta ontology based on the CEM specification. A convertor has been implemented in Java to automatically translate detailed CEMs from XML to OWL. A panel evaluation has been conducted, and the results show that the OWL modeling can faithfully represent the CEM specification and represent patient data. PMID:23268487

Digital and optical shape representation and pattern recognition; Proceedings of the Meeting, Orlando, FL, Apr. 4-6, 1988

NASA Technical Reports Server (NTRS)

Juday, Richard D. (Editor)

1988-01-01

The present conference discusses topics in pattern-recognition correlator architectures, digital stereo systems, geometric image transformations and their applications, topics in pattern recognition, filter algorithms, object detection and classification, shape representation techniques, and model-based object recognition methods. Attention is given to edge-enhancement preprocessing using liquid crystal TVs, massively-parallel optical data base management, three-dimensional sensing with polar exponential sensor arrays, the optical processing of imaging spectrometer data, hybrid associative memories and metric data models, the representation of shape primitives in neural networks, and the Monte Carlo estimation of moment invariants for pattern recognition.

Drug related webpages classification using images and text information based on multi-kernel learning

NASA Astrophysics Data System (ADS)

Hu, Ruiguang; Xiao, Liping; Zheng, Wenjuan

2015-12-01

In this paper, multi-kernel learning(MKL) is used for drug-related webpages classification. First, body text and image-label text are extracted through HTML parsing, and valid images are chosen by the FOCARSS algorithm. Second, text based BOW model is used to generate text representation, and image-based BOW model is used to generate images representation. Last, text and images representation are fused with a few methods. Experimental results demonstrate that the classification accuracy of MKL is higher than those of all other fusion methods in decision level and feature level, and much higher than the accuracy of single-modal classification.

Tripartite Governance: Enabling Successful Implementations with Vulnerable Populations.

PubMed

Kennedy, Margaret Ann

2016-01-01

Vulnerable populations are often at a distinct disadvantage when it comes to the implementation of health information systems in an equitable, appropriate, and timely manner. The disadvantages experienced by vulnerable populations are innumerable and include lack of representation, lack of appropriate levels of funding, lack of resources and capacity, and lack of representation. Increasingly, models of representation for complex implementations involve a tripartite project governance model. This tripartite partnership distributes accountability across all partners, and ensures that vulnerable populations have an equitable contribution to the direction of implementation according to their needs. This article shares lessons learned and best practices from complex tripartite partnerships supporting implementations with vulnerable populations in Canada.

Comparison of Ionospheric Vertical Total Electron Content modelling approaches using spline based representations

NASA Astrophysics Data System (ADS)

Krypiak-Gregorczyk, Anna; Wielgosz, Pawel; Borkowski, Andrzej; Schmidt, Michael; Erdogan, Eren; Goss, Andreas

2017-04-01

Since electromagnetic measurements show dispersive characteristics, accurate modelling of the ionospheric electron content plays an important role for positioning and navigation applications to mitigate the effect of the ionospheric disturbances. Knowledge about the ionosphere contributes to a better understanding of space weather events as well as to forecast these events to enable protective measures in advance for electronic systems and satellite missions. In the last decades, advances in satellite technologies, data analysis techniques and models together with a rapidly growing number of analysis centres allow modelling the ionospheric electron content with an unprecedented accuracy in (near) real-time. In this sense, the representation of electron content variations in time and space with spline basis functions has gained practical importance in global and regional ionosphere modelling. This is due to their compact support and their flexibility to handle unevenly distributed observations and data gaps. In this contribution, the performances of two ionosphere models from UWM and DGFI-TUM, which are developed using spline functions are evaluated. The VTEC model of DGFI-TUM is based on tensor products of trigonometric B-spline functions in longitude and polynomial B-spline functions in latitude for a global representation. The UWM model uses two dimensional planar thin plate spline (TPS) with the Universal Transverse Mercator representation of ellipsoidal coordinates. In order to provide a smooth VTEC model, the TPS minimizes both, the squared norm of the Hessian matrix and deviations between data points and the model. In the evaluations, the differenced STEC analysis method and Jason-2 altimetry comparisons are applied.

Computation of wind tunnel model deflections. [for transport type solid wing

NASA Technical Reports Server (NTRS)

Mehrotra, S. C.; Gloss, B. B.

1981-01-01

The experimental deflections for a transport type solid wing model were measured for several single point load conditions. These deflections were compared with those obtained by structural modeling of the wing by using plate and solid elements of Structural Performance Analysis and Redesign (SPAR) program. The solid element representation of the wing showed better agreement with the experimental deflections than the plate representation. The difference between the measured and calculated deflections is about 5 percent.

Generative models for discovering sparse distributed representations.

PubMed Central

Hinton, G E; Ghahramani, Z

1997-01-01

We describe a hierarchical, generative model that can be viewed as a nonlinear generalization of factor analysis and can be implemented in a neural network. The model uses bottom-up, top-down and lateral connections to perform Bayesian perceptual inference correctly. Once perceptual inference has been performed the connection strengths can be updated using a very simple learning rule that only requires locally available information. We demonstrate that the network learns to extract sparse, distributed, hierarchical representations. PMID:9304685

Baryon spectrum of SU(4) composite Higgs theory with two distinct fermion representations

NASA Astrophysics Data System (ADS)

Ayyar, Venkitesh; DeGrand, Thomas; Hackett, Daniel C.; Jay, William I.; Neil, Ethan T.; Shamir, Yigal; Svetitsky, Benjamin

2018-06-01

We use lattice simulations to compute the baryon spectrum of SU(4) lattice gauge theory coupled to dynamical fermions in the fundamental and two-index antisymmetric (sextet) representations simultaneously. This model is closely related to a composite Higgs model in which the chimera baryon made up of fermions from both representations plays the role of a composite top-quark partner. The dependence of the baryon masses on each underlying fermion mass is found to be generally consistent with a quark-model description and large-Nc scaling. We combine our numerical results with experimental bounds on the scale of the new strong sector to estimate a lower bound on the mass of the top-quark partner. We discuss some theoretical uncertainties associated with this estimate.

Computational effects of inlet representation on powered hypersonic, airbreathing models

NASA Technical Reports Server (NTRS)

Huebner, Lawrence D.; Tatum, Kenneth E.

1993-01-01

Computational results are presented to illustrate the powered aftbody effects of representing the scramjet inlet on a generic hypersonic vehicle with a fairing, to divert the external flow, as compared to an operating flow-through scramjet inlet. This study is pertinent to the ground testing of hypersonic, airbreathing models employing scramjet exhaust flow simulation in typical small-scale hypersonic wind tunnels. The comparison of aftbody effects due to inlet representation is well-suited for computational study, since small model size typically precludes the ability to ingest flow into the inlet and perform exhaust simulation at the same time. Two-dimensional analysis indicates that, although flowfield differences exist for the two types of inlet representations, little, if any, difference in surface aftbody characteristics is caused by fairing over the inlet.

Local air temperature tolerance: a sensible basis for estimating climate variability

NASA Astrophysics Data System (ADS)

Kärner, Olavi; Post, Piia

2016-11-01

The customary representation of climate using sample moments is generally biased due to the noticeably nonstationary behaviour of many climate series. In this study, we introduce a moment-free climate representation based on a statistical model fitted to a long-term daily air temperature anomaly series. This model allows us to separate the climate and weather scale variability in the series. As a result, the climate scale can be characterized using the mean annual cycle of series and local air temperature tolerance, where the latter is computed using the fitted model. The representation of weather scale variability is specified using the frequency and the range of outliers based on the tolerance. The scheme is illustrated using five long-term air temperature records observed by different European meteorological stations.

Effects of long-term representations on free recall of unrelated words

PubMed Central

Katkov, Mikhail; Romani, Sandro

2015-01-01

Human memory stores vast amounts of information. Yet recalling this information is often challenging when specific cues are lacking. Here we consider an associative model of retrieval where each recalled item triggers the recall of the next item based on the similarity between their long-term neuronal representations. The model predicts that different items stored in memory have different probability to be recalled depending on the size of their representation. Moreover, items with high recall probability tend to be recalled earlier and suppress other items. We performed an analysis of a large data set on free recall and found a highly specific pattern of statistical dependencies predicted by the model, in particular negative correlations between the number of words recalled and their average recall probability. Taken together, experimental and modeling results presented here reveal complex interactions between memory items during recall that severely constrain recall capacity. PMID:25593296

Hierarchical Boltzmann simulations and model error estimation

NASA Astrophysics Data System (ADS)

Torrilhon, Manuel; Sarna, Neeraj

2017-08-01

A hierarchical simulation approach for Boltzmann's equation should provide a single numerical framework in which a coarse representation can be used to compute gas flows as accurately and efficiently as in computational fluid dynamics, but a subsequent refinement allows to successively improve the result to the complete Boltzmann result. We use Hermite discretization, or moment equations, for the steady linearized Boltzmann equation for a proof-of-concept of such a framework. All representations of the hierarchy are rotationally invariant and the numerical method is formulated on fully unstructured triangular and quadrilateral meshes using a implicit discontinuous Galerkin formulation. We demonstrate the performance of the numerical method on model problems which in particular highlights the relevance of stability of boundary conditions on curved domains. The hierarchical nature of the method allows also to provide model error estimates by comparing subsequent representations. We present various model errors for a flow through a curved channel with obstacles.

A quasi-current representation for information needs inspired by Two-State Vector Formalism

NASA Astrophysics Data System (ADS)

Wang, Panpan; Hou, Yuexian; Li, Jingfei; Zhang, Yazhou; Song, Dawei; Li, Wenjie

2017-09-01

Recently, a number of quantum theory (QT)-based information retrieval (IR) models have been proposed for modeling session search task that users issue queries continuously in order to describe their evolving information needs (IN). However, the standard formalism of QT cannot provide a complete description for users' current IN in a sense that it does not take the 'future' information into consideration. Therefore, to seek a more proper and complete representation for users' IN, we construct a representation of quasi-current IN inspired by an emerging Two-State Vector Formalism (TSVF). With the enlightenment of the completeness of TSVF, a "two-state vector" derived from the 'future' (the current query) and the 'history' (the previous query) is employed to describe users' quasi-current IN in a more complete way. Extensive experiments are conducted on the session tracks of TREC 2013 & 2014, and show that our model outperforms a series of compared IR models.

Enabling large-scale viscoelastic calculations via neural network acceleration

NASA Astrophysics Data System (ADS)

Robinson DeVries, P.; Thompson, T. B.; Meade, B. J.

2017-12-01

One of the most significant challenges involved in efforts to understand the effects of repeated earthquake cycle activity are the computational costs of large-scale viscoelastic earthquake cycle models. Deep artificial neural networks (ANNs) can be used to discover new, compact, and accurate computational representations of viscoelastic physics. Once found, these efficient ANN representations may replace computationally intensive viscoelastic codes and accelerate large-scale viscoelastic calculations by more than 50,000%. This magnitude of acceleration enables the modeling of geometrically complex faults over thousands of earthquake cycles across wider ranges of model parameters and at larger spatial and temporal scales than have been previously possible. Perhaps most interestingly from a scientific perspective, ANN representations of viscoelastic physics may lead to basic advances in the understanding of the underlying model phenomenology. We demonstrate the potential of artificial neural networks to illuminate fundamental physical insights with specific examples.

Network representations of angular regions for electromagnetic scattering

PubMed Central

2017-01-01

Network modeling in electromagnetics is an effective technique in treating scattering problems by canonical and complex structures. Geometries constituted of angular regions (wedges) together with planar layers can now be approached with the Generalized Wiener-Hopf Technique supported by network representation in spectral domain. Even if the network representations in spectral planes are of great importance by themselves, the aim of this paper is to present a theoretical base and a general procedure for the formulation of complex scattering problems using network representation for the Generalized Wiener Hopf Technique starting basically from the wave equation. In particular while the spectral network representations are relatively well known for planar layers, the network modelling for an angular region requires a new theory that will be developed in this paper. With this theory we complete the formulation of a network methodology whose effectiveness is demonstrated by the application to a complex scattering problem with practical solutions given in terms of GTD/UTD diffraction coefficients and total far fields for engineering applications. The methodology can be applied to other physics fields. PMID:28817573

Cognitive representations of breast cancer, emotional distress and preventive health behaviour: a theoretical perspective.

PubMed

Decruyenaere, M; Evers-Kiebooms, G; Welkenhuysen, M; Denayer, L; Claes, E

2000-01-01

Individuals at high risk for developing breast and/or ovarian cancer are faced with difficult decisions regarding genetic testing, cancer prevention and/or intensive surveillance. Large interindividual differences exist in the uptake of these health-related services. This paper is aimed at understanding and predicting how people emotionally and behaviourally react to information concerning genetic predisposition to breast/ovarian cancer. For this purpose, the self-regulation model of illness representations is elaborated. This model suggests that health-related behaviour is influenced by a person's cognitive and emotional representation of the health threat. These representations generate coping behaviour aimed at resolving the objective health problems (problem-focussed coping) and at reducing the emotional distress induced by the health threat (emotion-focussed coping). Based on theoretical considerations and empirical studies, four interrelated attributes of the cognitive illness representation of hereditary breast/ovarian cancer are described: causal beliefs concerning the disease, perceived severity, perceived susceptibility to the disease and perceived controllability. The paper also addresses the complex interactions between these cognitive attributes, emotional distress and preventive health behaviour.

Whiteboard Confessionals: Investigating a New Model Using Student Representations in Teaching Astro 101

NASA Astrophysics Data System (ADS)

Prather, Edward

2018-01-01

Astronomy education researchers in the Department of Astronomy at the University of Arizona have been investigating a new framework for getting students to engage in discussions about fundamental astronomy topics. This framework is intended to also provide students with explicit feedback on the correctness and coherency of their mental models on these topics. This framework builds upon our prior efforts to create productive Pedagogical Discipline Representations (PDR). Students are asked to work collaboratively to generate their own representations (drawings, graphs, data tables, etc.) that reflect important characteristics of astrophysical scenarios presented in class. We have found these representation tasks offer tremendous insight into the broad range of ideas and knowledge students possess after instruction that includes both traditional lecture and actively learning strategies. In particular, we find that some of our students are able to correctly answer challenging multiple-choice questions on topics, however, they struggle to accurately create representations of these same topics themselves. Our work illustrates that some of our students are not developing a robust level of discipline fluency with many core ideas in astronomy, even after engaging with active learning strategies.

To bind or not to bind, that's the wrong question: Features and objects coexist in visual short-term memory.

PubMed

Geigerman, Shriradha; Verhaeghen, Paul; Cerella, John

2016-06-01

In three experiments, we investigated whether features and whole-objects can be represented simultaneously in visual short-term memory (VSTM). Participants were presented with a memory set of colored shapes; we probed either for the constituent features or for the whole object, and analyzed retrieval dynamics (cumulative response time distributions). In our first experiment, we used whole-object probes that recombined features from the memory display; we found that subjects' data conformed to a kitchen-line model, showing that they used whole-object representations for the matching process. In the second experiment, we encouraged independent-feature representations by using probes that used features not present in the memory display; subjects' data conformed to the race-model inequality, showing that they used independent-feature representations for the matching process. In a final experiment, we used both types of probes; subjects now used both types of representations, depending on the nature of the probe. Combined, our three experiments suggest that both feature and whole-object representations can coexist in VSTM. Copyright © 2016 Elsevier B.V. All rights reserved.

Human inferior colliculus activity relates to individual differences in spoken language learning

PubMed Central

Chandrasekaran, Bharath; Kraus, Nina

2012-01-01

A challenge to learning words of a foreign language is encoding nonnative phonemes, a process typically attributed to cortical circuitry. Using multimodal imaging methods [functional magnetic resonance imaging-adaptation (fMRI-A) and auditory brain stem responses (ABR)], we examined the extent to which pretraining pitch encoding in the inferior colliculus (IC), a primary midbrain structure, related to individual variability in learning to successfully use nonnative pitch patterns to distinguish words in American English-speaking adults. fMRI-A indexed the efficiency of pitch representation localized to the IC, whereas ABR quantified midbrain pitch-related activity with millisecond precision. In line with neural “sharpening” models, we found that efficient IC pitch pattern representation (indexed by fMRI) related to superior neural representation of pitch patterns (indexed by ABR), and consequently more successful word learning following sound-to-meaning training. Our results establish a critical role for the IC in speech-sound representation, consistent with the established role for the IC in the representation of communication signals in other animal models. PMID:22131377

Volterra representation enables modeling of complex synaptic nonlinear dynamics in large-scale simulations.

PubMed

Hu, Eric Y; Bouteiller, Jean-Marie C; Song, Dong; Baudry, Michel; Berger, Theodore W

2015-01-01

Chemical synapses are comprised of a wide collection of intricate signaling pathways involving complex dynamics. These mechanisms are often reduced to simple spikes or exponential representations in order to enable computer simulations at higher spatial levels of complexity. However, these representations cannot capture important nonlinear dynamics found in synaptic transmission. Here, we propose an input-output (IO) synapse model capable of generating complex nonlinear dynamics while maintaining low computational complexity. This IO synapse model is an extension of a detailed mechanistic glutamatergic synapse model capable of capturing the input-output relationships of the mechanistic model using the Volterra functional power series. We demonstrate that the IO synapse model is able to successfully track the nonlinear dynamics of the synapse up to the third order with high accuracy. We also evaluate the accuracy of the IO synapse model at different input frequencies and compared its performance with that of kinetic models in compartmental neuron models. Our results demonstrate that the IO synapse model is capable of efficiently replicating complex nonlinear dynamics that were represented in the original mechanistic model and provide a method to replicate complex and diverse synaptic transmission within neuron network simulations.

Volterra representation enables modeling of complex synaptic nonlinear dynamics in large-scale simulations

PubMed Central

Hu, Eric Y.; Bouteiller, Jean-Marie C.; Song, Dong; Baudry, Michel; Berger, Theodore W.

2015-01-01

Chemical synapses are comprised of a wide collection of intricate signaling pathways involving complex dynamics. These mechanisms are often reduced to simple spikes or exponential representations in order to enable computer simulations at higher spatial levels of complexity. However, these representations cannot capture important nonlinear dynamics found in synaptic transmission. Here, we propose an input-output (IO) synapse model capable of generating complex nonlinear dynamics while maintaining low computational complexity. This IO synapse model is an extension of a detailed mechanistic glutamatergic synapse model capable of capturing the input-output relationships of the mechanistic model using the Volterra functional power series. We demonstrate that the IO synapse model is able to successfully track the nonlinear dynamics of the synapse up to the third order with high accuracy. We also evaluate the accuracy of the IO synapse model at different input frequencies and compared its performance with that of kinetic models in compartmental neuron models. Our results demonstrate that the IO synapse model is capable of efficiently replicating complex nonlinear dynamics that were represented in the original mechanistic model and provide a method to replicate complex and diverse synaptic transmission within neuron network simulations. PMID:26441622

Exploring Middle School Students' Representational Competence in Science: Development and Verification of a Framework for Learning with Visual Representations

NASA Astrophysics Data System (ADS)

Tippett, Christine Diane

Scientific knowledge is constructed and communicated through a range of forms in addition to verbal language. Maps, graphs, charts, diagrams, formulae, models, and drawings are just some of the ways in which science concepts can be represented. Representational competence---an aspect of visual literacy that focuses on the ability to interpret, transform, and produce visual representations---is a key component of science literacy and an essential part of science reading and writing. To date, however, most research has examined learning from representations rather than learning with representations. This dissertation consisted of three distinct projects that were related by a common focus on learning from visual representations as an important aspect of scientific literacy. The first project was the development of an exploratory framework that is proposed for use in investigations of students constructing and interpreting multimedia texts. The exploratory framework, which integrates cognition, metacognition, semiotics, and systemic functional linguistics, could eventually result in a model that might be used to guide classroom practice, leading to improved visual literacy, better comprehension of science concepts, and enhanced science literacy because it emphasizes distinct aspects of learning with representations that can be addressed though explicit instruction. The second project was a metasynthesis of the research that was previously conducted as part of the Explicit Literacy Instruction Embedded in Middle School Science project (Pacific CRYSTAL, http://www.educ.uvic.ca/pacificcrystal). Five overarching themes emerged from this case-to-case synthesis: the engaging and effective nature of multimedia genres, opportunities for differentiated instruction using multimodal strategies, opportunities for assessment, an emphasis on visual representations, and the robustness of some multimodal literacy strategies across content areas. The third project was a mixed-methods verification study that was conducted to refine and validate the theoretical framework. This study examined middle school students' representational competence and focused on students' creation of visual representations such as labelled diagrams, a form of representation commonly found in science information texts and textbooks. An analysis of the 31 Grade 6 participants' representations and semistructured interviews revealed five themes, each of which supports one or more dimensions of the exploratory framework: participants' use of color, participants' choice of representation (form and function), participants' method of planning for representing, participants' knowledge of conventions, and participants' selection of information to represent. Together, the results of these three projects highlight the need for further research on learning with rather than learning from representations.

Archetypal dynamics, emergent situations, and the reality game.

PubMed

Sulis, William

2010-07-01

The classical approach to the modeling of reality is founded upon its objectification. Although successful dealing with inanimate matter, objectification has proven to be much less successful elsewhere, sometimes to the point of paradox. This paper discusses an approach to the modeling of reality based upon the concept of process as formulated within the framework of archetypal dynamics. Reality is conceptualized as an intermingling of information-transducing systems, together with the semantic frames that effectively describe and ascribe meaning to each system, along with particular formal representations of same which constitute the archetypes. Archetypal dynamics is the study of the relationships between systems, frames and their representations and the flow of information among these different entities. In this paper a specific formal representation of archetypal dynamics using tapestries is given, and a dynamics is founded upon this representation in the form of a combinatorial game called a reality game. Some simple examples are presented.

Communication: Multiple-property-based diabatization for open-shell van der Waals molecules

DOE Office of Scientific and Technical Information (OSTI.GOV)

Karman, Tijs; Avoird, Ad van der; Groenenboom, Gerrit C., E-mail: gerritg@theochem.ru.nl

2016-03-28

We derive a new multiple-property-based diabatization algorithm. The transformation between adiabatic and diabatic representations is determined by requiring a set of properties in both representations to be related by a similarity transformation. This set of properties is determined in the adiabatic representation by rigorous electronic structure calculations. In the diabatic representation, the same properties are determined using model diabatic states defined as products of undistorted monomer wave functions. This diabatic model is generally applicable to van der Waals molecules in arbitrary electronic states. Application to locating seams of conical intersections and collisional transfer of electronic excitation energy is demonstrated formore » O{sub 2} − O{sub 2} in low-lying excited states. Property-based diabatization for this test system included all components of the electric quadrupole tensor, orbital angular momentum, and spin-orbit coupling.« less

The Interplay Among Children's Negative Family Representations, Visual Processing of Negative Emotions, and Externalizing Symptoms.

PubMed

Davies, Patrick T; Coe, Jesse L; Hentges, Rochelle F; Sturge-Apple, Melissa L; van der Kloet, Erika

2018-03-01

This study examined the transactional interplay among children's negative family representations, visual processing of negative emotions, and externalizing symptoms in a sample of 243 preschool children (M age  = 4.60 years). Children participated in three annual measurement occasions. Cross-lagged autoregressive models were conducted with multimethod, multi-informant data to identify mediational pathways. Consistent with schema-based top-down models, negative family representations were associated with attention to negative faces in an eye-tracking task and their externalizing symptoms. Children's negative representations of family relationships specifically predicted decreases in their attention to negative emotions, which, in turn, was associated with subsequent increases in their externalizing symptoms. Follow-up analyses indicated that the mediational role of diminished attention to negative emotions was particularly pronounced for angry faces. © 2017 The Authors. Child Development © 2017 Society for Research in Child Development, Inc.

Theoretical foundations for information representation and constraint specification

NASA Technical Reports Server (NTRS)

Menzel, Christopher P.; Mayer, Richard J.

1991-01-01

Research accomplished at the Knowledge Based Systems Laboratory of the Department of Industrial Engineering at Texas A&M University is described. Outlined here are the theoretical foundations necessary to construct a Neutral Information Representation Scheme (NIRS), which will allow for automated data transfer and translation between model languages, procedural programming languages, database languages, transaction and process languages, and knowledge representation and reasoning control languages for information system specification.

Computer Vision Research and Its Applications to Automated Cartography

DTIC Science & Technology

1984-09-01

reflecting from scene surfaces, and the film and digitization processes that result in the computer representation of the image. These models, when...alone. Specifically, intepretations that are in some sense "orthogonal" are preferred. A method for finding such interpretations for right-angle...saturated colors are not precisely representable and the colors recorded with different films or cameras may differ, but the tricomponent representation is t

Representation and Exchange of Knowledge as a Basis of Information Processes. Proceedings of the International Research Forum in Information Science (5th, Heidelberg, West Germany, September 5-7, 1983).

ERIC Educational Resources Information Center

Dietschmann, Hans, Ed.

This 22-paper collection addresses a variety of issues related to representation and transfer of knowledge. Individual papers include an explanation of the usefulness of general scientific models versus case-specific approaches and a discussion of different empirical approaches to the general problem of knowledge representation for information…

Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records

PubMed Central

Miotto, Riccardo; Li, Li; Kidd, Brian A.; Dudley, Joel T.

2016-01-01

Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. Challenges in summarizing and representing patient data prevent widespread practice of predictive modeling using EHRs. Here we present a novel unsupervised deep feature learning method to derive a general-purpose patient representation from EHR data that facilitates clinical predictive modeling. In particular, a three-layer stack of denoising autoencoders was used to capture hierarchical regularities and dependencies in the aggregated EHRs of about 700,000 patients from the Mount Sinai data warehouse. The result is a representation we name “deep patient”. We evaluated this representation as broadly predictive of health states by assessing the probability of patients to develop various diseases. We performed evaluation using 76,214 test patients comprising 78 diseases from diverse clinical domains and temporal windows. Our results significantly outperformed those achieved using representations based on raw EHR data and alternative feature learning strategies. Prediction performance for severe diabetes, schizophrenia, and various cancers were among the top performing. These findings indicate that deep learning applied to EHRs can derive patient representations that offer improved clinical predictions, and could provide a machine learning framework for augmenting clinical decision systems. PMID:27185194

Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records

NASA Astrophysics Data System (ADS)

Miotto, Riccardo; Li, Li; Kidd, Brian A.; Dudley, Joel T.

2016-05-01

Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. Challenges in summarizing and representing patient data prevent widespread practice of predictive modeling using EHRs. Here we present a novel unsupervised deep feature learning method to derive a general-purpose patient representation from EHR data that facilitates clinical predictive modeling. In particular, a three-layer stack of denoising autoencoders was used to capture hierarchical regularities and dependencies in the aggregated EHRs of about 700,000 patients from the Mount Sinai data warehouse. The result is a representation we name “deep patient”. We evaluated this representation as broadly predictive of health states by assessing the probability of patients to develop various diseases. We performed evaluation using 76,214 test patients comprising 78 diseases from diverse clinical domains and temporal windows. Our results significantly outperformed those achieved using representations based on raw EHR data and alternative feature learning strategies. Prediction performance for severe diabetes, schizophrenia, and various cancers were among the top performing. These findings indicate that deep learning applied to EHRs can derive patient representations that offer improved clinical predictions, and could provide a machine learning framework for augmenting clinical decision systems.

Can representational trajectory reveal the nature of an internal model of gravity?

PubMed

De Sá Teixeira, Nuno; Hecht, Heiko

2014-05-01

The memory for the vanishing location of a horizontally moving target is usually displaced forward in the direction of motion (representational momentum) and downward in the direction of gravity (representational gravity). Moreover, this downward displacement has been shown to increase with time (representational trajectory). However, the degree to which different kinematic events change the temporal profile of these displacements remains to be determined. The present article attempts to fill this gap. In the first experiment, we replicate the finding that representational momentum for downward-moving targets is bigger than for upward motions, showing, moreover, that it increases rapidly during the first 300 ms, stabilizing afterward. This temporal profile, but not the increased error for descending targets, is shown to be disrupted when eye movements are not allowed. In the second experiment, we show that the downward drift with time emerges even for static targets. Finally, in the third experiment, we report an increased error for upward-moving targets, as compared with downward movements, when the display is compatible with a downward ego-motion by including vection cues. Thus, the errors in the direction of gravity are compatible with the perceived event and do not merely reflect a retinotopic bias. Overall, these results provide further evidence for an internal model of gravity in the visual representational system.

Differential geometry based solvation model II: Lagrangian formulation.

PubMed

Chen, Zhan; Baker, Nathan A; Wei, G W

2011-12-01

Solvation is an elementary process in nature and is of paramount importance to more sophisticated chemical, biological and biomolecular processes. The understanding of solvation is an essential prerequisite for the quantitative description and analysis of biomolecular systems. This work presents a Lagrangian formulation of our differential geometry based solvation models. The Lagrangian representation of biomolecular surfaces has a few utilities/advantages. First, it provides an essential basis for biomolecular visualization, surface electrostatic potential map and visual perception of biomolecules. Additionally, it is consistent with the conventional setting of implicit solvent theories and thus, many existing theoretical algorithms and computational software packages can be directly employed. Finally, the Lagrangian representation does not need to resort to artificially enlarged van der Waals radii as often required by the Eulerian representation in solvation analysis. The main goal of the present work is to analyze the connection, similarity and difference between the Eulerian and Lagrangian formalisms of the solvation model. Such analysis is important to the understanding of the differential geometry based solvation model. The present model extends the scaled particle theory of nonpolar solvation model with a solvent-solute interaction potential. The nonpolar solvation model is completed with a Poisson-Boltzmann (PB) theory based polar solvation model. The differential geometry theory of surfaces is employed to provide a natural description of solvent-solute interfaces. The optimization of the total free energy functional, which encompasses the polar and nonpolar contributions, leads to coupled potential driven geometric flow and PB equations. Due to the development of singularities and nonsmooth manifolds in the Lagrangian representation, the resulting potential-driven geometric flow equation is embedded into the Eulerian representation for the purpose of computation, thanks to the equivalence of the Laplace-Beltrami operator in the two representations. The coupled partial differential equations (PDEs) are solved with an iterative procedure to reach a steady state, which delivers desired solvent-solute interface and electrostatic potential for problems of interest. These quantities are utilized to evaluate the solvation free energies and protein-protein binding affinities. A number of computational methods and algorithms are described for the interconversion of Lagrangian and Eulerian representations, and for the solution of the coupled PDE system. The proposed approaches have been extensively validated. We also verify that the mean curvature flow indeed gives rise to the minimal molecular surface and the proposed variational procedure indeed offers minimal total free energy. Solvation analysis and applications are considered for a set of 17 small compounds and a set of 23 proteins. The salt effect on protein-protein binding affinity is investigated with two protein complexes by using the present model. Numerical results are compared to the experimental measurements and to those obtained by using other theoretical methods in the literature. © Springer-Verlag 2011

Differential geometry based solvation model II: Lagrangian formulation

PubMed Central

Chen, Zhan; Baker, Nathan A.; Wei, G. W.

2010-01-01

Solvation is an elementary process in nature and is of paramount importance to more sophisticated chemical, biological and biomolecular processes. The understanding of solvation is an essential prerequisite for the quantitative description and analysis of biomolecular systems. This work presents a Lagrangian formulation of our differential geometry based solvation model. The Lagrangian representation of biomolecular surfaces has a few utilities/advantages. First, it provides an essential basis for biomolecular visualization, surface electrostatic potential map and visual perception of biomolecules. Additionally, it is consistent with the conventional setting of implicit solvent theories and thus, many existing theoretical algorithms and computational software packages can be directly employed. Finally, the Lagrangian representation does not need to resort to artificially enlarged van der Waals radii as often required by the Eulerian representation in solvation analysis. The main goal of the present work is to analyze the connection, similarity and difference between the Eulerian and Lagrangian formalisms of the solvation model. Such analysis is important to the understanding of the differential geometry based solvation model. The present model extends the scaled particle theory (SPT) of nonpolar solvation model with a solvent-solute interaction potential. The nonpolar solvation model is completed with a Poisson-Boltzmann (PB) theory based polar solvation model. The differential geometry theory of surfaces is employed to provide a natural description of solvent-solute interfaces. The minimization of the total free energy functional, which encompasses the polar and nonpolar contributions, leads to coupled potential driven geometric flow and Poisson-Boltzmann equations. Due to the development of singularities and nonsmooth manifolds in the Lagrangian representation, the resulting potential-driven geometric flow equation is embedded into the Eulerian representation for the purpose of computation, thanks to the equivalence of the Laplace-Beltrami operator in the two representations. The coupled partial differential equations (PDEs) are solved with an iterative procedure to reach a steady state, which delivers desired solvent-solute interface and electrostatic potential for problems of interest. These quantities are utilized to evaluate the solvation free energies and protein-protein binding affinities. A number of computational methods and algorithms are described for the interconversion of Lagrangian and Eulerian representations, and for the solution of the coupled PDE system. The proposed approaches have been extensively validated. We also verify that the mean curvature flow indeed gives rise to the minimal molecular surface (MMS) and the proposed variational procedure indeed offers minimal total free energy. Solvation analysis and applications are considered for a set of 17 small compounds and a set of 23 proteins. The salt effect on protein-protein binding affinity is investigated with two protein complexes by using the present model. Numerical results are compared to the experimental measurements and to those obtained by using other theoretical methods in the literature. PMID:21279359

Competition and cooperation among similar representations: toward a unified account of facilitative and inhibitory effects of lexical neighbors.

PubMed

Chen, Qi; Mirman, Daniel

2012-04-01

One of the core principles of how the mind works is the graded, parallel activation of multiple related or similar representations. Parallel activation of multiple representations has been particularly important in the development of theories and models of language processing, where coactivated representations (neighbors) have been shown to exhibit both facilitative and inhibitory effects on word recognition and production. Researchers generally ascribe these effects to interactive activation and competition, but there is no unified explanation for why the effects are facilitative in some cases and inhibitory in others. We present a series of simulations of a simple domain-general interactive activation and competition model that is broadly consistent with more specialized domain-specific models of lexical processing. The results showed that interactive activation and competition can indeed account for the complex pattern of reversals. Critically, the simulations revealed a core computational principle that determines whether neighbor effects are facilitative or inhibitory: strongly active neighbors exert a net inhibitory effect, and weakly active neighbors exert a net facilitative effect.

Evaluation of improved land use data and canopy representation in BEIS with biogenic VOC measurements in California

EPA Pesticide Factsheets

The link provided access to all the datasets and metadata used in this manuscript for the model development and evaluation per Geoscientific Model Development's publication guidelines with the exception of the model output due to its size. This dataset is associated with the following publication:Bash , J., K. Baker , and M. Beaver. Evaluation of improved land use and canopy representation in BEIS v3.61 with biogenic VOC measurements in California. Geoscientific Model Development. Copernicus Publications, Katlenburg-Lindau, GERMANY, 9: 2191-2207, (2016).

Reverse engineering of aircraft wing data using a partial differential equation surface model

NASA Astrophysics Data System (ADS)

Huband, Jacalyn Mann

Reverse engineering is a multi-step process used in industry to determine a production representation of an existing physical object. This representation is in the form of mathematical equations that are compatible with computer-aided design and computer-aided manufacturing (CAD/CAM) equipment. The four basic steps to the reverse engineering process are data acquisition, data separation, surface or curve fitting, and CAD/CAM production. The surface fitting step determines the design representation of the object, and thus is critical to the success or failure of the reverse engineering process. Although surface fitting methods described in the literature are used to model a variety of surfaces, they are not suitable for reversing aircraft wings. In this dissertation, we develop and demonstrate a new strategy for reversing a mathematical representation of an aircraft wing. The basis of our strategy is to take an aircraft design model and determine if an inverse model can be derived. A candidate design model for this research is the partial differential equation (PDE) surface model, proposed by Bloor and Wilson and used in the Rapid Airplane Parameter Input Design (RAPID) tool at the NASA-LaRC Geolab. There are several basic mathematical problems involved in reversing the PDE surface model: (i) deriving a computational approximation of the surface function; (ii) determining a radial parametrization of the wing; (iii) choosing mathematical models or classes of functions for representation of the boundary functions; (iv) fitting the boundary data points by the chosen boundary functions; and (v) simultaneously solving for the axial parameterization and the derivative boundary functions. The study of the techniques to solve the above mathematical problems has culminated in a reverse PDE surface model and two reverse PDE surface algorithms. One reverse PDE surface algorithm recovers engineering design parameters for the RAPID tool from aircraft wing data and the other generates a PDE surface model with spline boundary functions from an arbitrary set of grid points. Our numerical tests show that the reverse PDE surface model and the reverse PDE surface algorithms can be used for the reverse engineering of aircraft wing data.

The effects of learner-generated representations versus computer-generated representations on physics problem solving

NASA Astrophysics Data System (ADS)

Price, Gwyneth A.

In this study, multiple external representations and Generative Learning Theory were used to design instruction that would facilitate physics learning. Specifically, the study looks at the learning differences that may occur when students are engaged in generating a graphical representation as compared to being presented with a computer-generated graph. It is hypothesized that by generating the graphical representation students will be able to overcome obstacles to integration and determine the relationships involved within a representation. In doing so, students will build a more complete mental model of the situation and be able to more readily use this information in transfer situations, thus improving their problem solving ability. Though the results of this study do not lend strong support for the hypothesis, the results are still informative and encouraging. Though several of the obstacles associated with learning from multiple representations such as cognitive load were cause for concern, those students with appropriate prior knowledge and familiarity with graphical representations were able to benefit from the generative activity. This finding indicates that if the issues are directly addressed within instruction, it may be that all students may be able to benefit from being actively engaged in generating representations.

OBJECT REPRESENTATION, IDENTITY, AND THE PARADOX OF EARLY PERMANENCE: Steps Toward a New Framework.

PubMed

Meltzoff, Andrew N; Moore, M Keith

1998-01-01

The sensorimotor theory of infancy has been overthrown, but there is little consensus on a replacement. We hypothesize that a capacity for representation is the starting point for infant development, not its culmination. Logical distinctions are drawn between object representation, identity, and permanence. Modern experiments on early object permanence and deferred imitation suggest: (a) even for young infants, representations persist over breaks in sensory contact, (b) numerical identity of objects ( O s) is initially specified by spatiotemporal criteria (place and trajectory), (c) featural and functional identity criteria develop, (d) events are analyzed by comparing representations to current perception, and (e) representation operates both prospectively, anticipating future contacts with an O , and retrospectively, reidentifying an O as the "same one again." A model of the architecture and functioning of the early representational system is proposed. It accounts for young infants' behavior toward absent people and things in terms of their efforts to determine the identity of objects. Our proposal is developmental without denying innate structure and elevates the power of perception and representation while being cautious about attributing complex concepts to young infants.

OBJECT REPRESENTATION, IDENTITY, AND THE PARADOX OF EARLY PERMANENCE: Steps Toward a New Framework

PubMed Central

Meltzoff, Andrew N.; Moore, M. Keith

2013-01-01

The sensorimotor theory of infancy has been overthrown, but there is little consensus on a replacement. We hypothesize that a capacity for representation is the starting point for infant development, not its culmination. Logical distinctions are drawn between object representation, identity, and permanence. Modern experiments on early object permanence and deferred imitation suggest: (a) even for young infants, representations persist over breaks in sensory contact, (b) numerical identity of objects (Os) is initially specified by spatiotemporal criteria (place and trajectory), (c) featural and functional identity criteria develop, (d) events are analyzed by comparing representations to current perception, and (e) representation operates both prospectively, anticipating future contacts with an O, and retrospectively, reidentifying an O as the “same one again.” A model of the architecture and functioning of the early representational system is proposed. It accounts for young infants’ behavior toward absent people and things in terms of their efforts to determine the identity of objects. Our proposal is developmental without denying innate structure and elevates the power of perception and representation while being cautious about attributing complex concepts to young infants. PMID:25147418

Anthropomorphic Coding of Speech and Audio: A Model Inversion Approach

NASA Astrophysics Data System (ADS)

Feldbauer, Christian; Kubin, Gernot; Kleijn, W. Bastiaan

2005-12-01

Auditory modeling is a well-established methodology that provides insight into human perception and that facilitates the extraction of signal features that are most relevant to the listener. The aim of this paper is to provide a tutorial on perceptual speech and audio coding using an invertible auditory model. In this approach, the audio signal is converted into an auditory representation using an invertible auditory model. The auditory representation is quantized and coded. Upon decoding, it is then transformed back into the acoustic domain. This transformation converts a complex distortion criterion into a simple one, thus facilitating quantization with low complexity. We briefly review past work on auditory models and describe in more detail the components of our invertible model and its inversion procedure, that is, the method to reconstruct the signal from the output of the auditory model. We summarize attempts to use the auditory representation for low-bit-rate coding. Our approach also allows the exploitation of the inherent redundancy of the human auditory system for the purpose of multiple description (joint source-channel) coding.

Robust Real-Time Music Transcription with a Compositional Hierarchical Model.

PubMed

Pesek, Matevž; Leonardis, Aleš; Marolt, Matija

2017-01-01

The paper presents a new compositional hierarchical model for robust music transcription. Its main features are unsupervised learning of a hierarchical representation of input data, transparency, which enables insights into the learned representation, as well as robustness and speed which make it suitable for real-world and real-time use. The model consists of multiple layers, each composed of a number of parts. The hierarchical nature of the model corresponds well to hierarchical structures in music. The parts in lower layers correspond to low-level concepts (e.g. tone partials), while the parts in higher layers combine lower-level representations into more complex concepts (tones, chords). The layers are learned in an unsupervised manner from music signals. Parts in each layer are compositions of parts from previous layers based on statistical co-occurrences as the driving force of the learning process. In the paper, we present the model's structure and compare it to other hierarchical approaches in the field of music information retrieval. We evaluate the model's performance for the multiple fundamental frequency estimation. Finally, we elaborate on extensions of the model towards other music information retrieval tasks.

A Representation for Gaining Insight into Clinical Decision Models

PubMed Central

Jimison, Holly B.

1988-01-01

For many medical domains uncertainty and patient preferences are important components of decision making. Decision theory is useful as a representation for such medical models in computer decision aids, but the methodology has typically had poor performance in the areas of explanation and user interface. The additional representation of probabilities and utilities as random variables serves to provide a framework for graphical and text insight into complicated decision models. The approach allows for efficient customization of a generic model that describes the general patient population of interest to a patient- specific model. Monte Carlo simulation is used to calculate the expected value of information and sensitivity for each model variable, thus providing a metric for deciding what to emphasize in the graphics and text summary. The computer-generated explanation includes variables that are sensitive with respect to the decision or that deviate significantly from what is typically observed. These techniques serve to keep the assessment and explanation of the patient's decision model concise, allowing the user to focus on the most important aspects for that patient.

Tensor and Spin Representations of SO(4) and Discrete Quantum Gravity

NASA Astrophysics Data System (ADS)

Lorente, M.; Kramer, P.

Starting from the defining transformations of complex matrices for the SO(4) group, we construct the fundamental representation and the tensor and spinor representations of the group SO(4). Given the commutation relations for the corresponding algebra, the unitary representations of the group in terms of the generalized Euler angles are constructed. These mathematical results help us to a more complete description of the Barret-Crane model in Quantum Gravity. In particular a complete realization of the weight function for the partition function is given and a new geometrical interpretation of the asymptotic limit for the Regge action is presented.

Multiplicative Versus Additive Filtering for Spacecraft Attitude Determination

NASA Technical Reports Server (NTRS)

Markley, F. Landis

2003-01-01

The absence of a globally nonsingular three-parameter representation of rotations forces attitude Kalman filters to estimate either a singular or a redundant attitude representation. We compare two filtering strategies using simplified kinematics and measurement models. Our favored strategy estimates a three-parameter representation of attitude deviations from a reference attitude specified by a higher- dimensional nonsingular parameterization. The deviations from the reference are assumed to be small enough to avoid any singularity or discontinuity of the three-dimensional parameterization. We point out some disadvantages of the other strategy, which directly estimates the four-parameter quaternion representation.

Covariant scalar representation of ? and quantization of the scalar relativistic particle

NASA Astrophysics Data System (ADS)

Jarvis, P. D.; Tsohantjis, I.

1996-03-01

A covariant scalar representation of iosp(d,2/2) is constructed and analysed in comparison with existing BFV-BRST methods for the quantization of the scalar relativistic particle. It is found that, with appropriately defined wavefunctions, this iosp(d,2/2) produced representation can be identified with the state space arising from the canonical BFV-BRST quantization of the modular-invariant, unoriented scalar particle (or antiparticle) with admissible gauge-fixing conditions. For this model, the cohomological determination of physical states can thus be obtained purely from the representation theory of the iosp(d,2/2) algebra.

Modeling alpine grasslands with two integrated hydrologic models: a comparison of the different process representation in CATHY and GEOtop

NASA Astrophysics Data System (ADS)

Camporese, M.; Bertoldi, G.; Bortoli, E.; Wohlfahrt, G.

2017-12-01

Integrated hydrologic surface-subsurface models (IHSSMs) are increasingly used as prediction tools to solve simultaneously states and fluxes in and between multiple terrestrial compartments (e.g., snow cover, surface water, groundwater), in an attempt to tackle environmental problems in a holistic approach. Two such models, CATHY and GEOtop, are used in this study to investigate their capabilities to reproduce hydrological processes in alpine grasslands. The two models differ significantly in the complexity of the representation of the surface energy balance and the solution of Richards equation for water flow in the variably saturated subsurface. The main goal of this research is to show how these differences in process representation can lead to different predictions of hydrologic states and fluxes, in the simulation of an experimental site located in the Venosta Valley (South Tyrol, Italy). Here, a large set of relevant hydrological data (e.g., evapotranspiration, soil moisture) has been collected, with ground and remote sensing observations. The area of interest is part of a Long-Term Ecological Research (LTER) site, a mountain steep, heterogeneous slope, where the predominant land use types are meadow, pasture, and forest. The comparison between data and model predictions, as well as between simulations with the two IHSSMs, contributes to advance our understanding of the tradeoffs between different complexities in modeĺs process representation, model accuracy, and the ability to explain observed hydrological dynamics in alpine environments.

Dual deep modeling: multi-level modeling with dual potencies and its formalization in F-Logic.

PubMed

Neumayr, Bernd; Schuetz, Christoph G; Jeusfeld, Manfred A; Schrefl, Michael

2018-01-01

An enterprise database contains a global, integrated, and consistent representation of a company's data. Multi-level modeling facilitates the definition and maintenance of such an integrated conceptual data model in a dynamic environment of changing data requirements of diverse applications. Multi-level models transcend the traditional separation of class and object with clabjects as the central modeling primitive, which allows for a more flexible and natural representation of many real-world use cases. In deep instantiation, the number of instantiation levels of a clabject or property is indicated by a single potency. Dual deep modeling (DDM) differentiates between source potency and target potency of a property or association and supports the flexible instantiation and refinement of the property by statements connecting clabjects at different modeling levels. DDM comes with multiple generalization of clabjects, subsetting/specialization of properties, and multi-level cardinality constraints. Examples are presented using a UML-style notation for DDM together with UML class and object diagrams for the representation of two-level user views derived from the multi-level model. Syntax and semantics of DDM are formalized and implemented in F-Logic, supporting the modeler with integrity checks and rich query facilities.

Representation and visualization of variability in a 3D anatomical atlas using the kidney as an example

NASA Astrophysics Data System (ADS)

Hacker, Silke; Handels, Heinz

2006-03-01

Computer-based 3D atlases allow an interactive exploration of the human body. However, in most cases such 3D atlases are derived from one single individual, and therefore do not regard the variability of anatomical structures concerning their shape and size. Since the geometric variability across humans plays an important role in many medical applications, our goal is to develop a framework of an anatomical atlas for representation and visualization of the variability of selected anatomical structures. The basis of the project presented is the VOXEL-MAN atlas of inner organs that was created from the Visible Human data set. For modeling anatomical shapes and their variability we utilize "m-reps" which allow a compact representation of anatomical objects on the basis of their skeletons. As an example we used a statistical model of the kidney that is based on 48 different variants. With the integration of a shape description into the VOXEL-MAN atlas it is now possible to query and visualize different shape variations of an organ, e.g. by specifying a person's age or gender. In addition to the representation of individual shape variants, the average shape of a population can be displayed. Besides a surface representation, a volume-based representation of the kidney's shape variants is also possible. It results from the deformation of the reference kidney of the volume-based model using the m-rep shape description. In this way a realistic visualization of the shape variants becomes possible, as well as the visualization of the organ's internal structures.

Dispersive approaches for three-particle final state interaction

DOE PAGES

Guo, Peng; Danilkin, Igor V.; Szczepaniak, Adam P.

2015-10-30

In this work, we presented different representations of Khuri-Treiman equation, the advantage and disadvantage of each representations are discussed. With a scattering amplitude toy model, we also studied the sensitivity of solution of KT equation to left-hand cut of toy model and to the different approximate methods. At last, we give a brief discussion of Watson's theorem when three particles in final states are involved.

Representational Flexibility and Problem-Solving Ability in Fraction and Decimal Number Addition: A Structural Model

ERIC Educational Resources Information Center

Deliyianni, Eleni; Gagatsis, Athanasios; Elia, Iliada; Panaoura, Areti

2016-01-01

The aim of this study was to propose and validate a structural model in fraction and decimal number addition, which is founded primarily on a synthesis of major theoretical approaches in the field of representations in Mathematics and also on previous research on the learning of fractions and decimals. The study was conducted among 1,701 primary…

Representation of Renormalization Group Functions By Nonsingular Integrals in a Model of the Critical Dynamics of Ferromagnets: The Fourth Order of The ɛ-Expansion

NASA Astrophysics Data System (ADS)

Adzhemyan, L. Ts.; Vorob'eva, S. E.; Ivanova, E. V.; Kompaniets, M. V.

2018-04-01

Using the representation for renormalization group functions in terms of nonsingular integrals, we calculate the dynamical critical exponents in the model of critical dynamics of ferromagnets in the fourth order of the ɛ-expansion. We calculate the Feynman diagrams using the sector decomposition technique generalized to critical dynamics problems.

Mathematical Modeling Of The Terrain Around A Robot

NASA Technical Reports Server (NTRS)

Slack, Marc G.

1992-01-01

In conceptual system for modeling of terrain around autonomous mobile robot, representation of terrain used for control separated from representation provided by sensors. Concept takes motion-planning system out from under constraints imposed by discrete spatial intervals of square terrain grid(s). Separation allows sensing and motion-controlling systems to operate asynchronously; facilitating integration of new map and sensor data into planning of motions.

Using graph approach for managing connectivity in integrative landscape modelling

NASA Astrophysics Data System (ADS)

Rabotin, Michael; Fabre, Jean-Christophe; Libres, Aline; Lagacherie, Philippe; Crevoisier, David; Moussa, Roger

2013-04-01

In cultivated landscapes, a lot of landscape elements such as field boundaries, ditches or banks strongly impact water flows, mass and energy fluxes. At the watershed scale, these impacts are strongly conditionned by the connectivity of these landscape elements. An accurate representation of these elements and of their complex spatial arrangements is therefore of great importance for modelling and predicting these impacts.We developped in the framework of the OpenFLUID platform (Software Environment for Modelling Fluxes in Landscapes) a digital landscape representation that takes into account the spatial variabilities and connectivities of diverse landscape elements through the application of the graph theory concepts. The proposed landscape representation consider spatial units connected together to represent the flux exchanges or any other information exchanges. Each spatial unit of the landscape is represented as a node of a graph and relations between units as graph connections. The connections are of two types - parent-child connection and up/downstream connection - which allows OpenFLUID to handle hierarchical graphs. Connections can also carry informations and graph evolution during simulation is possible (connections or elements modifications). This graph approach allows a better genericity on landscape representation, a management of complex connections and facilitate development of new landscape representation algorithms. Graph management is fully operational in OpenFLUID for developers or modelers ; and several graph tools are available such as graph traversal algorithms or graph displays. Graph representation can be managed i) manually by the user (for example in simple catchments) through XML-based files in easily editable and readable format or ii) by using methods of the OpenFLUID-landr library which is an OpenFLUID library relying on common open-source spatial libraries (ogr vector, geos topologic vector and gdal raster libraries). OpenFLUID-landr library has been developed in order i) to be used with no GIS expert skills needed (common gis formats can be read and simplified spatial management is provided), ii) to easily develop adapted rules of landscape discretization and graph creation to follow spatialized model requirements and iii) to allow model developers to manage dynamic and complex spatial topology. Graph management in OpenFLUID are shown with i) examples of hydrological modelizations on complex farmed landscapes and ii) the new implementation of Geo-MHYDAS tool based on the OpenFLUID-landr library, which allows to discretize a landscape and create graph structure for the MHYDAS model requirements.

Insight and search in Katona's five-square problem.

PubMed

Ollinger, Michael; Jones, Gary; Knoblich, Günther

2014-01-01

Insights are often productive outcomes of human thinking. We provide a cognitive model that explains insight problem solving by the interplay of problem space search and representational change, whereby the problem space is constrained or relaxed based on the problem representation. By introducing different experimental conditions that either constrained the initial search space or helped solvers to initiate a representational change, we investigated the interplay of problem space search and representational change in Katona's five-square problem. Testing 168 participants, we demonstrated that independent hints relating to the initial search space and to representational change had little effect on solution rates. However, providing both hints caused a significant increase in solution rates. Our results show the interplay between problem space search and representational change in insight problem solving: The initial problem space can be so large that people fail to encounter impasse, but even when representational change is achieved the resulting problem space can still provide a major obstacle to finding the solution.

Part-based deep representation for product tagging and search

NASA Astrophysics Data System (ADS)

Chen, Keqing

2017-06-01

Despite previous studies, tagging and indexing the product images remain challenging due to the large inner-class variation of the products. In the traditional methods, the quantized hand-crafted features such as SIFTs are extracted as the representation of the product images, which are not discriminative enough to handle the inner-class variation. For discriminative image representation, this paper firstly presents a novel deep convolutional neural networks (DCNNs) architect true pre-trained on a large-scale general image dataset. Compared to the traditional features, our DCNNs representation is of more discriminative power with fewer dimensions. Moreover, we incorporate the part-based model into the framework to overcome the negative effect of bad alignment and cluttered background and hence the descriptive ability of the deep representation is further enhanced. Finally, we collect and contribute a well-labeled shoe image database, i.e., the TBShoes, on which we apply the part-based deep representation for product image tagging and search, respectively. The experimental results highlight the advantages of the proposed part-based deep representation.

Identifying network representation issues with the network trip.

DOT National Transportation Integrated Search

2012-04-23

The purpose of this study was to evaluate the effects of road-network representation on the application of the Network Robustness Index (NRI), using the Chittenden County Regional Transportation Model. The results are expected to improve the requirem...

Representations of control and psychological symptoms in couples dealing with cancer: a dyadic-regulation approach.

PubMed

Karademas, Evangelos C; Giannousi, Zoe

2013-01-01

The aim of this study was to examine the relation between illness representations of personal and treatment control and psychological symptoms (i.e. symptoms of anxiety and depression) in 72 married couples dealing with a recently diagnosed cancer. Patients were first-diagnosed with early stage (45.83%) or metastatic cancer (54.17%). Dyadic responses were examined with the actor-partner interdependence model. Also, in order to examine whether patients and spouses' representations of control moderate the relation of their partners' corresponding representations to psychological symptoms, we used the relevant bootstrapping framework developed by Hayes and Matthes [(2009). Computational procedures for probing interactions in OLS and logistic regression: SPSS and SAS implementations. Behavior Research Methods, 41, 924-936]. Patients' symptoms of anxiety and depression were associated with both partners' representations of control. Chi-square difference tests indicated that actor and partner effects were equal. Spouses' symptoms of anxiety and depression were related only to their own representations. Moreover, spouses' representations of personal control moderated the relation of patients' corresponding representations to depressive symptoms, whereas patients' representations of treatment control moderated the relation of their spouses' corresponding representations to both anxiety and depression. Findings suggest that both partners' representations of control are important for adaptation to illness. Moreover, they indicate that dyadic regulation may be equally important to self-regulation as far as adaptation to illness is concerned.

Getting Mental Models and Computer Models to Cooperate

NASA Technical Reports Server (NTRS)

Sheridan, T. B.; Roseborough, J.; Charney, L.; Mendel, M.

1984-01-01

A qualitative theory of supervisory control is outlined wherein the mental models of one or more human operators are related to the knowledge representations within automatic controllers (observers, estimators) and operator decision aids (expert systems, advice-givers). Methods of quantifying knowledge and the calibration of one knowledge representation to another (human, computer, or objective truth) are discussed. Ongoing experiments in the use of decision aids for exploring one's own objective function or exploring system constraints and control strategies are described.

Continuous versus discontinuous albedo representations in a simple diffusive climate model

NASA Astrophysics Data System (ADS)

Simmons, P. A.; Griffel, D. H.

1988-07-01

A one-dimensional annually and zonally averaged energy-balance model, with diffusive meridional heat transport and including icealbedo feedback, is considered. This type of model is found to be very sensitive to the form of albedo used. The solutions for a discontinuous step-function albedo are compared to those for a more realistic smoothly varying albedo. The smooth albedo gives a closer fit to present conditions, but the discontinuous form gives a better representation of climates in earlier epochs.

The evaluative imaging of mental models - Visual representations of complexity

NASA Technical Reports Server (NTRS)

Dede, Christopher

1989-01-01

The paper deals with some design issues involved in building a system that could visually represent the semantic structures of training materials and their underlying mental models. In particular, hypermedia-based semantic networks that instantiate classification problem solving strategies are thought to be a useful formalism for such representations; the complexity of these web structures can be best managed through visual depictions. It is also noted that a useful approach to implement in these hypermedia models would be some metrics of conceptual distance.

A bio-behavioral model of addiction treatment: applying dual representation theory to craving management and relapse prevention.

PubMed

Matto, Holly

2005-01-01

A bio-behavioral approach to drug addiction treatment is outlined. The presented treatment model uses dual representation theory as a guiding framework for understanding the bio-behavioral processes activated during the application of expressive therapeutic methods. Specifically, the treatment model explains how visual processing techniques can supplement traditional relapse prevention therapy protocols, to help clients better manage cravings and control triggers in hard-to-treat populations such as chronic substance-dependent persons.

Reliability models for dataflow computer systems

NASA Technical Reports Server (NTRS)

Kavi, K. M.; Buckles, B. P.

1985-01-01

The demands for concurrent operation within a computer system and the representation of parallelism in programming languages have yielded a new form of program representation known as data flow (DENN 74, DENN 75, TREL 82a). A new model based on data flow principles for parallel computations and parallel computer systems is presented. Necessary conditions for liveness and deadlock freeness in data flow graphs are derived. The data flow graph is used as a model to represent asynchronous concurrent computer architectures including data flow computers.

Qualitative aspects of representational competence among college chemistry students: Multiple representations and their role in the understanding of ideal gases

NASA Astrophysics Data System (ADS)

Madden, Sean Patrick

This study examined the role of multiple representations of chemical phenomena, specifically, the temperature-pressure relationship of ideal gases, in the problem solving strategies of college chemistry students. Volunteers included students enrolled in a first semester general chemistry course at a western university. Two additional volunteers from the same university were asked to participate and serve as models of greater sophistication. One was a senior chemistry major; another was a junior science writing major. Volunteers completed an initial screening task involving multiple representations of concentration and dilution concepts. Based on the results of this screening instrument a smaller set of subjects were asked to complete a think aloud session involving multiple representations of the temperature-pressure relationship. Data consisted of the written work of the volunteers and transcripts from videotaped think aloud sessions. The data were evaluated by the researcher and two other graduate students in chemical education using a coding scheme (Kozma, Schank, Coppola, Michalchik, and Allen. 2000). This coding scheme was designed to identify essential features of representational competence and differences in uses of multiple representations. The results indicate that students tend to have a strong preference for one type of representation. Students scoring low on representational competence, as measured by the rubric, ignored important features of some representations or acknowledged them only superficially. Students scoring higher on representational competence made meaningful connections among representations. The more advanced students, those who rated highly on representational competence, tended to use their preferred representation in a heuristic manner to establish meaning for other representations. The more advanced students also reflected upon the problem at greater length before beginning work. Molecular level sketches seemed to be the most difficult type of representation for students to interpret. Most subjects scored higher on representational competence when engaged in creating graphs and sketches than when evaluating provided representations. This study suggests that students may benefit from an instruction that emphasizes heuristic use of multiple representations in chemistry problem solving. An instructional strategy that makes use of a variety of representations and requires students to create their own representations may have measurable benefits to chemistry students.

On the Representation of Subgrid Microtopography Effects in Process-based Hydrologic Models

NASA Astrophysics Data System (ADS)

Jan, A.; Painter, S. L.; Coon, E. T.

2017-12-01

Increased availability of high-resolution digital elevation are enabling process-based hydrologic modeling on finer and finer scales. However, spatial variability in surface elevation (microtopography) exists below the scale of a typical hyper-resolution grid cell and has the potential to play a significant role in water retention, runoff, and surface/subsurface interactions. Though the concept of microtopographic features (depressions, obstructions) and the associated implications on flow and discharge are well established, representing those effects in watershed-scale integrated surface/subsurface hydrology models remains a challenge. Using the complex and coupled hydrologic environment of the Arctic polygonal tundra as an example, we study the effects of submeter topography and present a subgrid model parameterized by small-scale spatial heterogeneities for use in hyper-resolution models with polygons at a scale of 15-20 meters forming the surface cells. The subgrid model alters the flow and storage terms in the diffusion wave equation for surface flow. We compare our results against sub-meter scale simulations (acts as a benchmark for our simulations) and hyper-resolution models without the subgrid representation. The initiation of runoff in the fine-scale simulations is delayed and the recession curve is slowed relative to simulated runoff using the hyper-resolution model with no subgrid representation. Our subgrid modeling approach improves the representation of runoff and water retention relative to models that ignore subgrid topography. We evaluate different strategies for parameterizing subgrid model and present a classification-based method to efficiently move forward to larger landscapes. This work was supported by the Interoperable Design of Extreme-scale Application Software (IDEAS) project and the Next-Generation Ecosystem Experiments-Arctic (NGEE Arctic) project. NGEE-Arctic is supported by the Office of Biological and Environmental Research in the DOE Office of Science.

Body representation in patients after vascular brain injuries.

PubMed

Razmus, Magdalena

2017-11-01

Neuropsychological literature suggests that body representation is a multidimensional concept consisting of various types of representations. Previous studies have demonstrated dissociations between three types of body representation specified by the kind of data and processes, i.e. body schema, body structural description, and body semantics. The aim of the study was to describe the state of body representation in patients after vascular brain injuries and to provide evidence for the different types of body representation. The question about correlations between body representation deficits and neuropsychological dysfunctions was also investigated. Fifty patients after strokes and 50 control individuals participated in the study. They were examined with tasks referring to dynamic representation of body parts positions, topological body map, and lexical and semantic knowledge about the body. Data analysis showed that vascular brain injuries result in deficits of body representation, which may co-occur with cognitive dysfunctions, but the latter are a possible risk factor for body representation deficits rather than sufficient or imperative requisites for them. The study suggests that types of body representation may be separated on the basis not only of their content, but also of their relation with self. Principal component analysis revealed three factors, which explained over 66% of results variance. The factors, which may be interpreted as types or dimensions of mental model of a body, represent different degrees of connection with self. The results indicate another possibility of body representation types classification, which should be verified in future research.

Representational Similarity Analysis – Connecting the Branches of Systems Neuroscience

PubMed Central

Kriegeskorte, Nikolaus; Mur, Marieke; Bandettini, Peter

2008-01-01

A fundamental challenge for systems neuroscience is to quantitatively relate its three major branches of research: brain-activity measurement, behavioral measurement, and computational modeling. Using measured brain-activity patterns to evaluate computational network models is complicated by the need to define the correspondency between the units of the model and the channels of the brain-activity data, e.g., single-cell recordings or voxels from functional magnetic resonance imaging (fMRI). Similar correspondency problems complicate relating activity patterns between different modalities of brain-activity measurement (e.g., fMRI and invasive or scalp electrophysiology), and between subjects and species. In order to bridge these divides, we suggest abstracting from the activity patterns themselves and computing representational dissimilarity matrices (RDMs), which characterize the information carried by a given representation in a brain or model. Building on a rich psychological and mathematical literature on similarity analysis, we propose a new experimental and data-analytical framework called representational similarity analysis (RSA), in which multi-channel measures of neural activity are quantitatively related to each other and to computational theory and behavior by comparing RDMs. We demonstrate RSA by relating representations of visual objects as measured with fMRI in early visual cortex and the fusiform face area to computational models spanning a wide range of complexities. The RDMs are simultaneously related via second-level application of multidimensional scaling and tested using randomization and bootstrap techniques. We discuss the broad potential of RSA, including novel approaches to experimental design, and argue that these ideas, which have deep roots in psychology and neuroscience, will allow the integrated quantitative analysis of data from all three branches, thus contributing to a more unified systems neuroscience. PMID:19104670

Quality of motion considerations in numerical analysis of motion restoring implants of the spine.

PubMed

Bowden, Anton E; Guerin, Heather L; Villarraga, Marta L; Patwardhan, Avinash G; Ochoa, Jorge A

2008-06-01

Motion restoring implants function in a dynamic environment that encompasses the full range of spinal kinematics. Accurate assessment of the in situ performance of these devices using numerical techniques requires model verification and validation against the well-established nonlinear quality of motion of the spine, as opposed to the previous norm of matching kinematic endpoint metrics such as range of motion and intervertebral disc pressure measurements at a single kinematic reference point. Experimental data was obtained during cadaveric testing of nine three-functional spinal unit (L3-S1) lumbar spine segments. Each specimen was tested from 8 Nm of applied flexion moment to 6 Nm of applied extension moment with an applied 400 N compressive follower preload. A nonlinear kinematic curve representing the spinal quality of motion (applied moment versus angular rotation) for the index finite element model was constructed and compared to the kinematic responses of the experimental specimens. The effect of spinal soft tissue structure mechanical behaviors on the fidelity of the model's quality of motion to experimental data was assessed by iteratively modifying the material representations of annulus fibrosus, nucleus pulposus, and ligaments. The present work demonstrated that for this model, the annulus fibrosus played a small role in the nonlinear quality of motion of the model, whereas changes in ligament representations had a large effect, as validated against the full kinematic range of motion. An anisotropic continuum representation of the annulus fibrosus was used, along with nonlinear fabric representations of the ligaments and a hyperelastic representation of the nucleus pulposus. Our results suggest that improvements in current methodologies broadly used in numerical simulations of the lumbar spine are needed to fully describe the highly nonlinear motion of the spine.

Single-footprint retrievals for AIRS using a fast TwoSlab cloud-representation model and the SARTA all-sky infrared radiative transfer algorithm

NASA Astrophysics Data System (ADS)

DeSouza-Machado, Sergio; Larrabee Strow, L.; Tangborn, Andrew; Huang, Xianglei; Chen, Xiuhong; Liu, Xu; Wu, Wan; Yang, Qiguang

2018-01-01

One-dimensional variational retrievals of temperature and moisture fields from hyperspectral infrared (IR) satellite sounders use cloud-cleared radiances (CCRs) as their observation. These derived observations allow the use of clear-sky-only radiative transfer in the inversion for geophysical variables but at reduced spatial resolution compared to the native sounder observations. Cloud clearing can introduce various errors, although scenes with large errors can be identified and ignored. Information content studies show that, when using multilayer cloud liquid and ice profiles in infrared hyperspectral radiative transfer codes, there are typically only 2-4 degrees of freedom (DOFs) of cloud signal. This implies a simplified cloud representation is sufficient for some applications which need accurate radiative transfer. Here we describe a single-footprint retrieval approach for clear and cloudy conditions, which uses the thermodynamic and cloud fields from numerical weather prediction (NWP) models as a first guess, together with a simple cloud-representation model coupled to a fast scattering radiative transfer algorithm (RTA). The NWP model thermodynamic and cloud profiles are first co-located to the observations, after which the N-level cloud profiles are converted to two slab clouds (TwoSlab; typically one for ice and one for water clouds). From these, one run of our fast cloud-representation model allows an improvement of the a priori cloud state by comparing the observed and model-simulated radiances in the thermal window channels. The retrieval yield is over 90 %, while the degrees of freedom correlate with the observed window channel brightness temperature (BT) which itself depends on the cloud optical depth. The cloud-representation and scattering package is benchmarked against radiances computed using a maximum random overlap (RMO) cloud scheme. All-sky infrared radiances measured by NASA's Atmospheric Infrared Sounder (AIRS) and NWP thermodynamic and cloud profiles from the European Centre for Medium-Range Weather Forecasts (ECMWF) forecast model are used in this paper.

Parental representations and dimensions of personality: empirical relations and assessment implications.

PubMed

Pincus, A L; Ruiz, M A

1997-04-01

Research on the relations between parental representations, personality traits, and psychopathology was discussed with reference to their integration for clinical personality assessment. Empirical results linking parental representations assessed by the Structural Analysis of Social Behavior and the Five-Factor Model of personality traits in a young adult population supported the position that parental representations significantly relate to adult personality. Individuals whose parental representations were generally affiliative described themselves as less prone to emotional distress (lower neuroticism); more interpersonally oriented and experiencing of positive emotions (higher extraversion); more peaceable and trustworthy (higher agreeableness); and more dutiful, resourceful, and dependable (higher conscientiousness). Parental representations colored by autonomy granting and autonomy taking were related to higher levels of openness to experience but lower levels of conscientiousness and extraversion in self-descriptions. Assessment implications and an integrative assessment strategy were presented along with a clinical case example.

Effects of an ontology display with history representation on organizational memory information systems.

PubMed

Hwang, Wonil; Salvendy, Gavriel

2005-06-10

Ontologies, as a possible element of organizational memory information systems, appear to support organizational learning. Ontology tools can be used to share knowledge among the members of an organization. However, current ontology-viewing user interfaces of ontology tools do not fully support organizational learning, because most of them lack proper history representation in their display. In this study, a conceptual model was developed that emphasized the role of ontology in the organizational learning cycle and explored the integration of history representation in the ontology display. Based on the experimental results from a split-plot design with 30 participants, two conclusions were derived: first, appropriately selected history representations in the ontology display help users to identify changes in the ontologies; and second, compatibility between types of ontology display and history representation is more important than ontology display and history representation in themselves.

Deep Unfolding for Topic Models.

PubMed

Chien, Jen-Tzung; Lee, Chao-Hsi

2018-02-01

Deep unfolding provides an approach to integrate the probabilistic generative models and the deterministic neural networks. Such an approach is benefited by deep representation, easy interpretation, flexible learning and stochastic modeling. This study develops the unsupervised and supervised learning of deep unfolded topic models for document representation and classification. Conventionally, the unsupervised and supervised topic models are inferred via the variational inference algorithm where the model parameters are estimated by maximizing the lower bound of logarithm of marginal likelihood using input documents without and with class labels, respectively. The representation capability or classification accuracy is constrained by the variational lower bound and the tied model parameters across inference procedure. This paper aims to relax these constraints by directly maximizing the end performance criterion and continuously untying the parameters in learning process via deep unfolding inference (DUI). The inference procedure is treated as the layer-wise learning in a deep neural network. The end performance is iteratively improved by using the estimated topic parameters according to the exponentiated updates. Deep learning of topic models is therefore implemented through a back-propagation procedure. Experimental results show the merits of DUI with increasing number of layers compared with variational inference in unsupervised as well as supervised topic models.

Research of Uncertainty Reasoning in Pineapple Disease Identification System

NASA Astrophysics Data System (ADS)

Liu, Liqun; Fan, Haifeng

In order to deal with the uncertainty of evidences mostly existing in pineapple disease identification system, a reasoning model based on evidence credibility factor was established. The uncertainty reasoning method is discussed,including: uncertain representation of knowledge, uncertain representation of rules, uncertain representation of multi-evidences and update of reasoning rules. The reasoning can fully reflect the uncertainty in disease identification and reduce the influence of subjective factors on the accuracy of the system.

Occupant behavior models: A critical review of implementation and representation approaches in building performance simulation programs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hong, Tianzhen; Chen, Yixing; Belafi, Zsofia

Occupant behavior (OB) in buildings is a leading factor influencing energy use in buildings. Quantifying this influence requires the integration of OB models with building performance simulation (BPS). This study reviews approaches to representing and implementing OB models in today’s popular BPS programs, and discusses weaknesses and strengths of these approaches and key issues in integrating of OB models with BPS programs. Two of the key findings are: (1) a common data model is needed to standardize the representation of OB models, enabling their flexibility and exchange among BPS programs and user applications; the data model can be implemented usingmore » a standard syntax (e.g., in the form of XML schema), and (2) a modular software implementation of OB models, such as functional mock-up units for co-simulation, adopting the common data model, has advantages in providing a robust and interoperable integration with multiple BPS programs. Such common OB model representation and implementation approaches help standardize the input structures of OB models, enable collaborative development of a shared library of OB models, and allow for rapid and widespread integration of OB models with BPS programs to improve the simulation of occupant behavior and quantification of their impact on building performance.« less

Occupant behavior models: A critical review of implementation and representation approaches in building performance simulation programs

DOE PAGES

Hong, Tianzhen; Chen, Yixing; Belafi, Zsofia; ...

2017-07-27

Occupant behavior (OB) in buildings is a leading factor influencing energy use in buildings. Quantifying this influence requires the integration of OB models with building performance simulation (BPS). This study reviews approaches to representing and implementing OB models in today’s popular BPS programs, and discusses weaknesses and strengths of these approaches and key issues in integrating of OB models with BPS programs. Two of the key findings are: (1) a common data model is needed to standardize the representation of OB models, enabling their flexibility and exchange among BPS programs and user applications; the data model can be implemented usingmore » a standard syntax (e.g., in the form of XML schema), and (2) a modular software implementation of OB models, such as functional mock-up units for co-simulation, adopting the common data model, has advantages in providing a robust and interoperable integration with multiple BPS programs. Such common OB model representation and implementation approaches help standardize the input structures of OB models, enable collaborative development of a shared library of OB models, and allow for rapid and widespread integration of OB models with BPS programs to improve the simulation of occupant behavior and quantification of their impact on building performance.« less

A Review On Accuracy and Uncertainty of Spatial Data and Analyses with special reference to Urban and Hydrological Modelling

NASA Astrophysics Data System (ADS)

Devendran, A. A.; Lakshmanan, G.

2014-11-01

Data quality for GIS processing and analysis is becoming an increased concern due to the accelerated application of GIS technology for problem solving and decision making roles. Uncertainty in the geographic representation of the real world arises as these representations are incomplete. Identification of the sources of these uncertainties and the ways in which they operate in GIS based representations become crucial in any spatial data representation and geospatial analysis applied to any field of application. This paper reviews the articles on the various components of spatial data quality and various uncertainties inherent in them and special focus is paid to two fields of application such as Urban Simulation and Hydrological Modelling. Urban growth is a complicated process involving the spatio-temporal changes of all socio-economic and physical components at different scales. Cellular Automata (CA) model is one of the simulation models, which randomly selects potential cells for urbanisation and the transition rules evaluate the properties of the cell and its neighbour. Uncertainty arising from CA modelling is assessed mainly using sensitivity analysis including Monte Carlo simulation method. Likewise, the importance of hydrological uncertainty analysis has been emphasized in recent years and there is an urgent need to incorporate uncertainty estimation into water resources assessment procedures. The Soil and Water Assessment Tool (SWAT) is a continuous time watershed model to evaluate various impacts of land use management and climate on hydrology and water quality. Hydrological model uncertainties using SWAT model are dealt primarily by Generalized Likelihood Uncertainty Estimation (GLUE) method.

True Numerical Cognition in the Wild.

PubMed

Piantadosi, Steven T; Cantlon, Jessica F

2017-04-01

Cognitive and neural research over the past few decades has produced sophisticated models of the representations and algorithms underlying numerical reasoning in humans and other animals. These models make precise predictions for how humans and other animals should behave when faced with quantitative decisions, yet primarily have been tested only in laboratory tasks. We used data from wild baboons' troop movements recently reported by Strandburg-Peshkin, Farine, Couzin, and Crofoot (2015) to compare a variety of models of quantitative decision making. We found that the decisions made by these naturally behaving wild animals rely specifically on numerical representations that have key homologies with the psychophysics of human number representations. These findings provide important new data on the types of problems human numerical cognition was designed to solve and constitute the first robust evidence of true numerical reasoning in wild animals.

SEE: structured representation of scientific evidence in the biomedical domain using Semantic Web techniques

PubMed Central

2014-01-01

Background Accounts of evidence are vital to evaluate and reproduce scientific findings and integrate data on an informed basis. Currently, such accounts are often inadequate, unstandardized and inaccessible for computational knowledge engineering even though computational technologies, among them those of the semantic web, are ever more employed to represent, disseminate and integrate biomedical data and knowledge. Results We present SEE (Semantic EvidencE), an RDF/OWL based approach for detailed representation of evidence in terms of the argumentative structure of the supporting background for claims even in complex settings. We derive design principles and identify minimal components for the representation of evidence. We specify the Reasoning and Discourse Ontology (RDO), an OWL representation of the model of scientific claims, their subjects, their provenance and their argumentative relations underlying the SEE approach. We demonstrate the application of SEE and illustrate its design patterns in a case study by providing an expressive account of the evidence for certain claims regarding the isolation of the enzyme glutamine synthetase. Conclusions SEE is suited to provide coherent and computationally accessible representations of evidence-related information such as the materials, methods, assumptions, reasoning and information sources used to establish a scientific finding by adopting a consistently claim-based perspective on scientific results and their evidence. SEE allows for extensible evidence representations, in which the level of detail can be adjusted and which can be extended as needed. It supports representation of arbitrary many consecutive layers of interpretation and attribution and different evaluations of the same data. SEE and its underlying model could be a valuable component in a variety of use cases that require careful representation or examination of evidence for data presented on the semantic web or in other formats. PMID:25093070

SEE: structured representation of scientific evidence in the biomedical domain using Semantic Web techniques.

PubMed

Bölling, Christian; Weidlich, Michael; Holzhütter, Hermann-Georg

2014-01-01

Accounts of evidence are vital to evaluate and reproduce scientific findings and integrate data on an informed basis. Currently, such accounts are often inadequate, unstandardized and inaccessible for computational knowledge engineering even though computational technologies, among them those of the semantic web, are ever more employed to represent, disseminate and integrate biomedical data and knowledge. We present SEE (Semantic EvidencE), an RDF/OWL based approach for detailed representation of evidence in terms of the argumentative structure of the supporting background for claims even in complex settings. We derive design principles and identify minimal components for the representation of evidence. We specify the Reasoning and Discourse Ontology (RDO), an OWL representation of the model of scientific claims, their subjects, their provenance and their argumentative relations underlying the SEE approach. We demonstrate the application of SEE and illustrate its design patterns in a case study by providing an expressive account of the evidence for certain claims regarding the isolation of the enzyme glutamine synthetase. SEE is suited to provide coherent and computationally accessible representations of evidence-related information such as the materials, methods, assumptions, reasoning and information sources used to establish a scientific finding by adopting a consistently claim-based perspective on scientific results and their evidence. SEE allows for extensible evidence representations, in which the level of detail can be adjusted and which can be extended as needed. It supports representation of arbitrary many consecutive layers of interpretation and attribution and different evaluations of the same data. SEE and its underlying model could be a valuable component in a variety of use cases that require careful representation or examination of evidence for data presented on the semantic web or in other formats.

Representations of the Extended Poincare Superalgebras in Four Dimensions

NASA Astrophysics Data System (ADS)

Griffis, John D.

Eugene Wigner used the Poincare group to induce representations from the fundamental internal space-time symmetries of (special) relativistic quantum particles. Wigner's students spent considerable amount of time translating passages of this paper into more detailed and accessible papers and books. In 1975, R. Haag et al. investigated the possible extensions of the symmetries of relativistic quantum particles. They showed that the only consistent (super)symmetric extensions to the standard model of physics are obtained by using super charges to generate the odd part of a Lie superalgebra whose even part is generated by the Poincare group; this theory has become known as supersymmetry. In this paper, R. Haag et al. used a notation called supermultiplets to give the dimension of a representation and its multiplicity; this notation is described mathematically in chapter 5 of this thesis. By 1980 S. Ferrara et al. began classifying the representations of these algebras for dimensions greater than four, and in 1986 Strathdee published considerable work listing some representations for the Poincare superalgebra in any finite dimension. This work has been continued to date. We found the work of S. Ferrara et al. to be essential to our understanding extended supersymmetries. However, this paper was written using imprecise language meant for physicists, so it was far from trivial to understand the mathematical interpretation of this work. In this thesis, we provide a "translation" of the previous results (along with some other literature on the Extended Poincare Superalgebras) into a rigorous mathematical setting, which makes the subject more accessible to a larger audience. Having a mathematical model allows us to give explicit results and detailed proofs. Further, this model allows us to see beyond just the physical interpretation and it allows investigation by a purely mathematically adept audience. Our work was motivated by a paper written in 2012 by M. Chaichian et al, which classified all of the unitary, irreducible representations of the extended Poincare superalgebra in three dimensions. We consider only the four dimensional case, which is of interest to physicists working on quantum supergravity models without cosmological constant, and we provide explicit branching rules for the invariant subgroups corresponding to the most physically relevant symmetries of the irreducible representations of the Extended Poincare Superalgebra in four dimensions. However, it is possible to further generalize this work into any finite dimension. Such work would classify all possible finitely extended supersymmetric models.

Biologically Plausible, Human-scale Knowledge Representation

ERIC Educational Resources Information Center

Crawford, Eric; Gingerich, Matthew; Eliasmith, Chris

2016-01-01

Several approaches to implementing symbol-like representations in neurally plausible models have been proposed. These approaches include binding through synchrony (Shastri & Ajjanagadde, 1993), "mesh" binding (van der Velde & de Kamps, 2006), and conjunctive binding (Smolensky, 1990). Recent theoretical work has suggested that…

Evaluation of multisectional and two-section particulate matter photochemical grid models in the Western United States.

PubMed

Morris, Ralph; Koo, Bonyoung; Yarwood, Greg

2005-11-01

Version 4.10s of the comprehensive air-quality model with extensions (CAMx) photochemical grid model has been developed, which includes two options for representing particulate matter (PM) size distribution: (1) a two-section representation that consists of fine (PM2.5) and coarse (PM2.5-10) modes that has no interactions between the sections and assumes all of the secondary PM is fine; and (2) a multisectional representation that divides the PM size distribution into N sections (e.g., N = 10) and simulates the mass transfer between sections because of coagulation, accumulation, evaporation, and other processes. The model was applied to Southern California using the two-section and multisection representation of PM size distribution, and we found that allowing secondary PM to grow into the coarse mode had a substantial effect on PM concentration estimates. CAMx was then applied to the Western United States for the 1996 annual period with a 36-km grid resolution using both the two-section and multisection PM representation. The Community Multiscale Air Quality (CMAQ) and Regional Modeling for Aerosol and Deposition (REMSAD) models were also applied to the 1996 annual period. Similar model performance was exhibited by the four models across the Interagency Monitoring of Protected Visual Environments (IMPROVE) and Clean Air Status and Trends Network monitoring networks. All four of the models exhibited fairly low annual bias for secondary PM sulfate and nitrate but with a winter overestimation and summer underestimation bias. The CAMx multisectional model estimated that coarse mode secondary sulfate and nitrate typically contribute <10% of the total sulfate and nitrate when averaged across the more rural IMPROVE monitoring network.

The neural dynamics of task context in free recall.

PubMed

Polyn, Sean M; Kragel, James E; Morton, Neal W; McCluey, Joshua D; Cohen, Zachary D

2012-03-01

Multivariate pattern analysis (MVPA) is a powerful tool for relating theories of cognitive function to the neural dynamics observed while people engage in cognitive tasks. Here, we use the Context Maintenance and Retrieval model of free recall (CMR; Polyn et al., 2009a) to interpret variability in the strength of task-specific patterns of distributed neural activity as participants study and recall lists of words. The CMR model describes how temporal and source-related (here, encoding task) information combine in a contextual representation that is responsible for guiding memory search. Each studied word in the free-recall paradigm is associated with one of two encoding tasks (size and animacy) that have distinct neural representations during encoding. We find evidence for the context retrieval hypothesis central to the CMR model: Task-specific patterns of neural activity are reactivated during memory search, as the participant recalls an item previously associated with a particular task. Furthermore, we find that the fidelity of these task representations during study is related to task-shifting, the serial position of the studied item, and variability in the magnitude of the recency effect across participants. The CMR model suggests that these effects may be related to a central parameter of the model that controls the rate that an internal contextual representation integrates information from the surrounding environment. Copyright © 2011 Elsevier Ltd. All rights reserved.

Final Report Collaborative Project. Improving the Representation of Coastal and Estuarine Processes in Earth System Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bryan, Frank; Dennis, John; MacCready, Parker

This project aimed to improve long term global climate simulations by resolving and enhancing the representation of the processes involved in the cycling of freshwater through estuaries and coastal regions. This was a collaborative multi-institution project consisting of physical oceanographers, climate model developers, and computational scientists. It specifically targeted the DOE objectives of advancing simulation and predictive capability of climate models through improvements in resolution and physical process representation. The main computational objectives were: 1. To develop computationally efficient, but physically based, parameterizations of estuary and continental shelf mixing processes for use in an Earth System Model (CESM). 2. Tomore » develop a two-way nested regional modeling framework in order to dynamically downscale the climate response of particular coastal ocean regions and to upscale the impact of the regional coastal processes to the global climate in an Earth System Model (CESM). 3. To develop computational infrastructure to enhance the efficiency of data transfer between specific sources and destinations, i.e., a point-to-point communication capability, (used in objective 1) within POP, the ocean component of CESM.« less

Extended spin symmetry and the standard model

NASA Astrophysics Data System (ADS)

Besprosvany, J.; Romero, R.

2010-12-01

We review unification ideas and explain the spin-extended model in this context. Its consideration is also motivated by the standard-model puzzles. With the aim of constructing a common description of discrete degrees of freedom, as spin and gauge quantum numbers, the model departs from q-bits and generalized Hilbert spaces. Physical requirements reduce the space to one that is represented by matrices. The classification of the representations is performed through Clifford algebras, with its generators associated with Lorentz and scalar symmetries. We study a reduced space with up to two spinor elements within a matrix direct product. At given dimension, the demand that Lorentz symmetry be maintained, determines the scalar symmetries, which connect to vector-and-chiral gauge-interacting fields; we review the standard-model information in each dimension. We obtain fermions and bosons, with matter fields in the fundamental representation, radiation fields in the adjoint, and scalar particles with the Higgs quantum numbers. We relate the fields' representation in such spaces to the quantum-field-theory one, and the Lagrangian. The model provides a coupling-constant definition.

Virtual terrain: a security-based representation of a computer network

NASA Astrophysics Data System (ADS)

Holsopple, Jared; Yang, Shanchieh; Argauer, Brian

2008-03-01

Much research has been put forth towards detection, correlating, and prediction of cyber attacks in recent years. As this set of research progresses, there is an increasing need for contextual information of a computer network to provide an accurate situational assessment. Typical approaches adopt contextual information as needed; yet such ad hoc effort may lead to unnecessary or even conflicting features. The concept of virtual terrain is, therefore, developed and investigated in this work. Virtual terrain is a common representation of crucial information about network vulnerabilities, accessibilities, and criticalities. A virtual terrain model encompasses operating systems, firewall rules, running services, missions, user accounts, and network connectivity. It is defined as connected graphs with arc attributes defining dynamic relationships among vertices modeling network entities, such as services, users, and machines. The virtual terrain representation is designed to allow feasible development and maintenance of the model, as well as efficacy in terms of the use of the model. This paper will describe the considerations in developing the virtual terrain schema, exemplary virtual terrain models, and algorithms utilizing the virtual terrain model for situation and threat assessment.

Design Models and Model Based Design in Fluid Flow With Application to Micro Air Vehicles

DTIC Science & Technology

2009-03-12

system is dynamically essential for the dynamic representation of transients. Initial validation, in [2], used the laminar cylinder wake as a...conceptually equivalnt harmonic balancing representations (e.g., for Helicopter blades ). A by-product of [J6] is a first systematic framework for...both rapid prototyping and implementation. Wake attenuation is achieved by symmetrizing the two shear layers, using a single pressure gauge: Pulsed

Representation, Modeling and Recognition of Outdoor Scenes

DTIC Science & Technology

1994-04-01

B. C. Vemuri and R . Malladi . Deformable models: Canonical parameters for surface representation and multiple view integration. In Conference on...or a high disparity gradient. If both L- R and R -L disparity images are made available, then mirror images of this pattern may be sought in the two...et at., 1991, Terzopoulos and Vasilescu, 1991, Vemuri and Malladi , 1991], parameterized surfaces [Stokely and Wu, 1992, Lowe, 1991], local surfaces

Derivation of Rigid Body Analysis Models from Vehicle Architecture Abstractions

DTIC Science & Technology

2011-06-17

models of every type have their basis in some type of physical representation of the design domain. Rather than describing three-dimensional continua of...arrangement, while capturing just enough physical detail to be used as the basis for a meaningful representation of the design , and eventually, analyses that...permit architecture assessment. The design information captured by the abstractions is available at the very earliest stages of the vehicle

Representation and Structure in Connectionist Models

DTIC Science & Technology

1989-08-01

among those who are actively exploring the to wonder how these models might differ topic (cf. Dolan & Dyer, 1987; Dolan & from traditional theories , and...because one of the critical ways in which cognitive theories may differ is in the Elman Representation & Structure some of the specific questions raised...that whereas Classi- atomistic or can they possess internal struc- cal theories (e.g., the Language of Thought, ture? Can that structure be used to

A neural network model of semantic memory linking feature-based object representation and words.

PubMed

Cuppini, C; Magosso, E; Ursino, M

2009-06-01

Recent theories in cognitive neuroscience suggest that semantic memory is a distributed process, which involves many cortical areas and is based on a multimodal representation of objects. The aim of this work is to extend a previous model of object representation to realize a semantic memory, in which sensory-motor representations of objects are linked with words. The model assumes that each object is described as a collection of features, coded in different cortical areas via a topological organization. Features in different objects are segmented via gamma-band synchronization of neural oscillators. The feature areas are further connected with a lexical area, devoted to the representation of words. Synapses among the feature areas, and among the lexical area and the feature areas are trained via a time-dependent Hebbian rule, during a period in which individual objects are presented together with the corresponding words. Simulation results demonstrate that, during the retrieval phase, the network can deal with the simultaneous presence of objects (from sensory-motor inputs) and words (from acoustic inputs), can correctly associate objects with words and segment objects even in the presence of incomplete information. Moreover, the network can realize some semantic links among words representing objects with shared features. These results support the idea that semantic memory can be described as an integrated process, whose content is retrieved by the co-activation of different multimodal regions. In perspective, extended versions of this model may be used to test conceptual theories, and to provide a quantitative assessment of existing data (for instance concerning patients with neural deficits).

How to Measure Qualitative Understanding of DC-Circuit Phenomena - Taking a Closer Look at the External Representations of 9-Year-Olds

NASA Astrophysics Data System (ADS)

Kallunki, Veera

2013-04-01

Pupils' qualitative understanding of DC-circuit phenomena is reported to be weak. In numerous research reports lists of problems in understanding the functioning of simple DC-circuits have been presented. So-called mental model surveys have uncovered difficulties in different age groups, and in different phases of instruction. In this study, the concept of qualitative understanding, and the content or position of reported mental models of DC-circuit phenomena are discussed. On the grounds of this review, new tools for investigating qualitative understanding and analysing external representations of DC-circuit phenomena are presented. According to this approach, the external representations of DC-circuit phenomena that describe pupils' expressed conceptions of the topic should include both empirical-based models and theoretical explanations. In the empirical part of this study , third-graders (9-year-olds) learning DC-circuit phenomena in a comprehensive school in a small group were scrutinised. The focus of the study is the external representations manifested in the talk of the small group. The study challenges earlier studies, which claim that children exhibit a wide range of qualitative difficulties when learning DC-circuit phenomena. In this study it will be shown that even in the case of abstract subject matter like DC-circuit phenomena, small groups that highlight empirical-based modelling and activate talk can be a fruitful learning environment, where pupils' qualitative understanding really develops. Thus, the study proposes taking a closer look at pupils' external representations concerning DC-circuit phenomena.

Simulating Complex, Cold-region Process Interactions Using a Multi-scale, Variable-complexity Hydrological Model

NASA Astrophysics Data System (ADS)

Marsh, C.; Pomeroy, J. W.; Wheater, H. S.

2017-12-01

Accurate management of water resources is necessary for social, economic, and environmental sustainability worldwide. In locations with seasonal snowcovers, the accurate prediction of these water resources is further complicated due to frozen soils, solid-phase precipitation, blowing snow transport, and snowcover-vegetation-atmosphere interactions. Complex process interactions and feedbacks are a key feature of hydrological systems and may result in emergent phenomena, i.e., the arising of novel and unexpected properties within a complex system. One example is the feedback associated with blowing snow redistribution, which can lead to drifts that cause locally-increased soil moisture, thus increasing plant growth that in turn subsequently impacts snow redistribution, creating larger drifts. Attempting to simulate these emergent behaviours is a significant challenge, however, and there is concern that process conceptualizations within current models are too incomplete to represent the needed interactions. An improved understanding of the role of emergence in hydrological systems often requires high resolution distributed numerical hydrological models that incorporate the relevant process dynamics. The Canadian Hydrological Model (CHM) provides a novel tool for examining cold region hydrological systems. Key features include efficient terrain representation, allowing simulations at various spatial scales, reduced computational overhead, and a modular process representation allowing for an alternative-hypothesis framework. Using both physics-based and conceptual process representations sourced from long term process studies and the current cold regions literature allows for comparison of process representations and importantly, their ability to produce emergent behaviours. Examining the system in a holistic, process-based manner can hopefully derive important insights and aid in development of improved process representations.

Inequality across consonantal contrasts in speech perception: evidence from mismatch negativity.

PubMed

Cornell, Sonia A; Lahiri, Aditi; Eulitz, Carsten

2013-06-01

The precise structure of speech sound representations is still a matter of debate. In the present neurobiological study, we compared predictions about differential sensitivity to speech contrasts between models that assume full specification of all phonological information in the mental lexicon with those assuming sparse representations (only contrastive or otherwise not predictable information is stored). In a passive oddball paradigm, we studied the contrast sensitivity as reflected in the mismatch negativity (MMN) response to changes in the manner of articulation, as well as place of articulation of consonants in intervocalic positions of nonwords (manner of articulation: [edi ~ eni], [ezi ~ eni]; place of articulation: [edi ~ egi]). Models that assume full specification of all phonological information in the mental lexicon posit equal MMNs within each contrast (symmetric MMNs), that is, changes from standard [edi] to deviant [eni] elicit a similar MMN response as changes from standard [eni] to deviant [edi]. In contrast, models that assume sparse representations predict that only the [ezi] ~ [eni] reversals will evoke symmetric MMNs because of their conflicting fully specified manner features. Asymmetric MMNs are predicted, however, for the reversals of [edi] ~ [eni] and [edi] ~ [egi] because either a manner or place property in each pair is not fully specified in the mental lexicon. Our results show a pattern of symmetric and asymmetric MMNs that is in line with predictions of the featurally underspecified lexicon model that assumes sparse phonological representations. We conclude that the brain refers to underspecified phonological representations during speech perception. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

Study on Information Management for the Conservation of Traditional Chinese Architectural Heritage - 3d Modelling and Metadata Representation

NASA Astrophysics Data System (ADS)

Yen, Y. N.; Weng, K. H.; Huang, H. Y.

2013-07-01

After over 30 years of practise and development, Taiwan's architectural conservation field is moving rapidly into digitalization and its applications. Compared to modern buildings, traditional Chinese architecture has considerably more complex elements and forms. To document and digitize these unique heritages in their conservation lifecycle is a new and important issue. This article takes the caisson ceiling of the Taipei Confucius Temple, octagonal with 333 elements in 8 types, as a case study for digitization practise. The application of metadata representation and 3D modelling are the two key issues to discuss. Both Revit and SketchUp were appliedin this research to compare its effectiveness to metadata representation. Due to limitation of the Revit database, the final 3D models wasbuilt with SketchUp. The research found that, firstly, cultural heritage databasesmustconvey that while many elements are similar in appearance, they are unique in value; although 3D simulations help the general understanding of architectural heritage, software such as Revit and SketchUp, at this stage, could onlybe used tomodel basic visual representations, and is ineffective indocumenting additional critical data ofindividually unique elements. Secondly, when establishing conservation lifecycle information for application in management systems, a full and detailed presentation of the metadata must also be implemented; the existing applications of BIM in managing conservation lifecycles are still insufficient. Results of the research recommends SketchUp as a tool for present modelling needs, and BIM for sharing data between users, but the implementation of metadata representation is of the utmost importance.

Sparse representation based image interpolation with nonlocal autoregressive modeling.

PubMed

Dong, Weisheng; Zhang, Lei; Lukac, Rastislav; Shi, Guangming

2013-04-01

Sparse representation is proven to be a promising approach to image super-resolution, where the low-resolution (LR) image is usually modeled as the down-sampled version of its high-resolution (HR) counterpart after blurring. When the blurring kernel is the Dirac delta function, i.e., the LR image is directly down-sampled from its HR counterpart without blurring, the super-resolution problem becomes an image interpolation problem. In such cases, however, the conventional sparse representation models (SRM) become less effective, because the data fidelity term fails to constrain the image local structures. In natural images, fortunately, many nonlocal similar patches to a given patch could provide nonlocal constraint to the local structure. In this paper, we incorporate the image nonlocal self-similarity into SRM for image interpolation. More specifically, a nonlocal autoregressive model (NARM) is proposed and taken as the data fidelity term in SRM. We show that the NARM-induced sampling matrix is less coherent with the representation dictionary, and consequently makes SRM more effective for image interpolation. Our extensive experimental results demonstrate that the proposed NARM-based image interpolation method can effectively reconstruct the edge structures and suppress the jaggy/ringing artifacts, achieving the best image interpolation results so far in terms of PSNR as well as perceptual quality metrics such as SSIM and FSIM.

Understanding the psychosocial experiences of adults with mild-moderate hearing loss: An application of Leventhal’s self-regulatory model

PubMed Central

Heffernan, Eithne; Coulson, Neil S.; Henshaw, Helen; Barry, Johanna G.; Ferguson, Melanie A

2017-01-01

Objective This study explored the psychosocial experiences of adults with hearing loss using the self-regulatory model as a theoretical framework. The primary components of the model, namely cognitive representations, emotional representations, and coping responses, were examined. Design Individual semi-structured interviews were conducted. The data were analysed using an established thematic analysis procedure. Study sample Twenty-five adults with mild-moderate hearing loss from the UK and nine hearing healthcare professionals from the UK, USA, and Canada were recruited via maximum variation sampling. Results Cognitive representations: Most participants described their hearing loss as having negative connotations and consequences, although they were not particularly concerned about the progression or controllability/curability of the condition. Opinions differed regarding the benefits of understanding the causes of one’s hearing loss in detail. Emotional representations: negative emotions dominated, although some experienced positive emotions or muted emotions. Coping responses: engaged coping (e.g. hearing aids, communication tactics) and disengaged coping (e.g. withdrawal from situations, withdrawal within situations): both had perceived advantages and disadvantages. Conclusions This novel application of the self-regulatory model demonstrates that it can be used to capture the key psychosocial experiences (i.e. perceptions, emotions, and coping responses) of adults with mild-moderate hearing loss within a single, unifying framework. PMID:26754550

Neural representations and mechanisms for the performance of simple speech sequences

PubMed Central

Bohland, Jason W.; Bullock, Daniel; Guenther, Frank H.

2010-01-01

Speakers plan the phonological content of their utterances prior to their release as speech motor acts. Using a finite alphabet of learned phonemes and a relatively small number of syllable structures, speakers are able to rapidly plan and produce arbitrary syllable sequences that fall within the rules of their language. The class of computational models of sequence planning and performance termed competitive queuing (CQ) models have followed Lashley (1951) in assuming that inherently parallel neural representations underlie serial action, and this idea is increasingly supported by experimental evidence. In this paper we develop a neural model that extends the existing DIVA model of speech production in two complementary ways. The new model includes paired structure and content subsystems (cf. MacNeilage, 1998) that provide parallel representations of a forthcoming speech plan, as well as mechanisms for interfacing these phonological planning representations with learned sensorimotor programs to enable stepping through multi-syllabic speech plans. On the basis of previous reports, the model’s components are hypothesized to be localized to specific cortical and subcortical structures, including the left inferior frontal sulcus, the medial premotor cortex, the basal ganglia and thalamus. The new model, called GODIVA (Gradient Order DIVA), thus fills a void in current speech research by providing formal mechanistic hypotheses about both phonological and phonetic processes that are grounded by neuroanatomy and physiology. This framework also generates predictions that can be tested in future neuroimaging and clinical case studies. PMID:19583476

Cloud Model Bat Algorithm

PubMed Central

Zhou, Yongquan; Xie, Jian; Li, Liangliang; Ma, Mingzhi

2014-01-01

Bat algorithm (BA) is a novel stochastic global optimization algorithm. Cloud model is an effective tool in transforming between qualitative concepts and their quantitative representation. Based on the bat echolocation mechanism and excellent characteristics of cloud model on uncertainty knowledge representation, a new cloud model bat algorithm (CBA) is proposed. This paper focuses on remodeling echolocation model based on living and preying characteristics of bats, utilizing the transformation theory of cloud model to depict the qualitative concept: “bats approach their prey.” Furthermore, Lévy flight mode and population information communication mechanism of bats are introduced to balance the advantage between exploration and exploitation. The simulation results show that the cloud model bat algorithm has good performance on functions optimization. PMID:24967425

Impact of GPS-Integrated Water Vapour assimilation on Regional Climate Model simulations of heavy precipitation events in the western Mediterranean

NASA Astrophysics Data System (ADS)

Caldas-Alvarez, Alberto; Khodayar, Samiro

2017-04-01

An accurate representation of the devastating heavy precipitation events, that typically strike the western Mediterranean regions by autumn, is still a challenge for current weather prediction models. The misrepresentation of the atmospheric moisture distribution and the convective processes where it plays a role have been pointed out as sources of error in their prediction. Provided the fast variability of water vapour in the atmosphere, an improved representation of its distribution is expected from the Data Assimilation (DA) of very frequent measurements, such is the case of Global Positioning System derived Integrated Water Vapour (GPS-IWV). Moreover, an improved representation of the model physics is expected from the application of the DA on fine-scale model grids. The presented research work aims at assessing the impact of the selective assimilation of GPS-IWV retrievals on the representation of the atmospheric moisture distribution in relation to heavy precipitation in seasonal simulations over the western Mediterranean. COSMO simulations in CLimate Mode (CCLM) are run with two different horizontal resolutions (2.8 km and 7 km) to reproduce the period September 2012 to March 2013, encompassing the Special Observation Period 1 (SOP1) of the Hydrological Cycle in the Mediterranean Experiment (HyMeX). A state-of-art GPS-IWV data set, specially homogenized for the western Mediterranean countries spanning the aforementioned seven month period is selectively assimilated into the model runs with a high frequency (10 minutes). The impact of such assimilation combined with the grid refinement of the model is assessed in the representation of the atmospheric moisture distribution and its influence in the processes leading to deep moist convection and heavy rain. Observational data sets of precipitation obtained with the Climate Prediction Centre MORPHing technique (CMORPH), from the HyMeX rain gauge network as well as the GPS-IWV retrievals are employed to validate our model results and support the process studies. Results show remarkable discrepancies in the representation of the temporal evolution of IWV by CCLM well corrected by the assimilation. This rectification of the amount of water vapour in the atmosphere influences the intensity and location of extreme precipitation, albeit the sign and extent of this influence was shown to be event-dependent.

The Secondary Organic Aerosol Processor (SOAP v1.0) model: a unified model with different ranges of complexity based on the molecular surrogate approach

NASA Astrophysics Data System (ADS)

Couvidat, F.; Sartelet, K.

2015-04-01

In this paper the Secondary Organic Aerosol Processor (SOAP v1.0) model is presented. This model determines the partitioning of organic compounds between the gas and particle phases. It is designed to be modular with different user options depending on the computation time and the complexity required by the user. This model is based on the molecular surrogate approach, in which each surrogate compound is associated with a molecular structure to estimate some properties and parameters (hygroscopicity, absorption into the aqueous phase of particles, activity coefficients and phase separation). Each surrogate can be hydrophilic (condenses only into the aqueous phase of particles), hydrophobic (condenses only into the organic phases of particles) or both (condenses into both the aqueous and the organic phases of particles). Activity coefficients are computed with the UNIFAC (UNIversal Functional group Activity Coefficient; Fredenslund et al., 1975) thermodynamic model for short-range interactions and with the Aerosol Inorganic-Organic Mixtures Functional groups Activity Coefficients (AIOMFAC) parameterization for medium- and long-range interactions between electrolytes and organic compounds. Phase separation is determined by Gibbs energy minimization. The user can choose between an equilibrium representation and a dynamic representation of organic aerosols (OAs). In the equilibrium representation, compounds in the particle phase are assumed to be at equilibrium with the gas phase. However, recent studies show that the organic aerosol is not at equilibrium with the gas phase because the organic phases could be semi-solid (very viscous liquid phase). The condensation-evaporation of organic compounds could then be limited by the diffusion in the organic phases due to the high viscosity. An implicit dynamic representation of secondary organic aerosols (SOAs) is available in SOAP with OAs divided into layers, the first layer being at the center of the particle (slowly reaches equilibrium) and the final layer being near the interface with the gas phase (quickly reaches equilibrium). Although this dynamic implicit representation is a simplified approach to model condensation-evaporation with a low number of layers and short CPU (central processing unit) time, it shows good agreements with an explicit representation of condensation-evaporation (no significant differences after a few hours of condensation).

A Model-based Approach to Reactive Self-Configuring Systems

NASA Technical Reports Server (NTRS)

Williams, Brian C.; Nayak, P. Pandurang

1996-01-01

This paper describes Livingstone, an implemented kernel for a self-reconfiguring autonomous system, that is reactive and uses component-based declarative models. The paper presents a formal characterization of the representation formalism used in Livingstone, and reports on our experience with the implementation in a variety of domains. Livingstone's representation formalism achieves broad coverage of hybrid software/hardware systems by coupling the concurrent transition system models underlying concurrent reactive languages with the discrete qualitative representations developed in model-based reasoning. We achieve a reactive system that performs significant deductions in the sense/response loop by drawing on our past experience at building fast prepositional conflict-based algorithms for model-based diagnosis, and by framing a model-based configuration manager as a prepositional, conflict-based feedback controller that generates focused, optimal responses. Livingstone automates all these tasks using a single model and a single core deductive engine, thus making significant progress towards achieving a central goal of model-based reasoning. Livingstone, together with the HSTS planning and scheduling engine and the RAPS executive, has been selected as the core autonomy architecture for Deep Space One, the first spacecraft for NASA's New Millennium program.

Categorical Working Memory Representations are used in Delayed Estimation of Continuous Colors

PubMed Central

Hardman, Kyle O; Vergauwe, Evie; Ricker, Timothy J

2016-01-01

In the last decade, major strides have been made in understanding visual working memory through mathematical modeling of color production responses. In the delayed color estimation task (Wilken & Ma, 2004), participants are given a set of colored squares to remember and a few seconds later asked to reproduce those colors by clicking on a color wheel. The degree of error in these responses is characterized with mathematical models that estimate working memory precision and the proportion of items remembered by participants. A standard mathematical model of color memory assumes that items maintained in memory are remembered through memory for precise details about the particular studied shade of color. We contend that this model is incomplete in its present form because no mechanism is provided for remembering the coarse category of a studied color. In the present work we remedy this omission and present a model of visual working memory that includes both continuous and categorical memory representations. In two experiments we show that our new model outperforms this standard modeling approach, which demonstrates that categorical representations should be accounted for by mathematical models of visual working memory. PMID:27797548

Categorical working memory representations are used in delayed estimation of continuous colors.

PubMed

Hardman, Kyle O; Vergauwe, Evie; Ricker, Timothy J

2017-01-01

In the last decade, major strides have been made in understanding visual working memory through mathematical modeling of color production responses. In the delayed color estimation task (Wilken & Ma, 2004), participants are given a set of colored squares to remember, and a few seconds later asked to reproduce those colors by clicking on a color wheel. The degree of error in these responses is characterized with mathematical models that estimate working memory precision and the proportion of items remembered by participants. A standard mathematical model of color memory assumes that items maintained in memory are remembered through memory for precise details about the particular studied shade of color. We contend that this model is incomplete in its present form because no mechanism is provided for remembering the coarse category of a studied color. In the present work, we remedy this omission and present a model of visual working memory that includes both continuous and categorical memory representations. In 2 experiments, we show that our new model outperforms this standard modeling approach, which demonstrates that categorical representations should be accounted for by mathematical models of visual working memory. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Deep Convolutional Neural Networks Outperform Feature-Based But Not Categorical Models in Explaining Object Similarity Judgments

PubMed Central

Jozwik, Kamila M.; Kriegeskorte, Nikolaus; Storrs, Katherine R.; Mur, Marieke

2017-01-01

Recent advances in Deep convolutional Neural Networks (DNNs) have enabled unprecedentedly accurate computational models of brain representations, and present an exciting opportunity to model diverse cognitive functions. State-of-the-art DNNs achieve human-level performance on object categorisation, but it is unclear how well they capture human behavior on complex cognitive tasks. Recent reports suggest that DNNs can explain significant variance in one such task, judging object similarity. Here, we extend these findings by replicating them for a rich set of object images, comparing performance across layers within two DNNs of different depths, and examining how the DNNs’ performance compares to that of non-computational “conceptual” models. Human observers performed similarity judgments for a set of 92 images of real-world objects. Representations of the same images were obtained in each of the layers of two DNNs of different depths (8-layer AlexNet and 16-layer VGG-16). To create conceptual models, other human observers generated visual-feature labels (e.g., “eye”) and category labels (e.g., “animal”) for the same image set. Feature labels were divided into parts, colors, textures and contours, while category labels were divided into subordinate, basic, and superordinate categories. We fitted models derived from the features, categories, and from each layer of each DNN to the similarity judgments, using representational similarity analysis to evaluate model performance. In both DNNs, similarity within the last layer explains most of the explainable variance in human similarity judgments. The last layer outperforms almost all feature-based models. Late and mid-level layers outperform some but not all feature-based models. Importantly, categorical models predict similarity judgments significantly better than any DNN layer. Our results provide further evidence for commonalities between DNNs and brain representations. Models derived from visual features other than object parts perform relatively poorly, perhaps because DNNs more comprehensively capture the colors, textures and contours which matter to human object perception. However, categorical models outperform DNNs, suggesting that further work may be needed to bring high-level semantic representations in DNNs closer to those extracted by humans. Modern DNNs explain similarity judgments remarkably well considering they were not trained on this task, and are promising models for many aspects of human cognition. PMID:29062291

Asymmetries in the Processing of Vowel Height

ERIC Educational Resources Information Center

Scharinger, Mathias; Monahan, Philip J.; Idsardi, William J.

2012-01-01

Purpose: Speech perception can be described as the transformation of continuous acoustic information into discrete memory representations. Therefore, research on neural representations of speech sounds is particularly important for a better understanding of this transformation. Speech perception models make specific assumptions regarding the…

Computational Models of Human Performance: Validation of Memory and Procedural Representation in Advanced Air/Ground Simulation

NASA Technical Reports Server (NTRS)

Corker, Kevin M.; Labacqz, J. Victor (Technical Monitor)

1997-01-01

The Man-Machine Interaction Design and Analysis System (MIDAS) under joint U.S. Army and NASA cooperative is intended to assist designers of complex human/automation systems in successfully incorporating human performance capabilities and limitations into decision and action support systems. MIDAS is a computational representation of multiple human operators, selected perceptual, cognitive, and physical functions of those operators, and the physical/functional representation of the equipment with which they operate. MIDAS has been used as an integrated predictive framework for the investigation of human/machine systems, particularly in situations with high demands on the operators. We have extended the human performance models to include representation of both human operators and intelligent aiding systems in flight management, and air traffic service. The focus of this development is to predict human performance in response to aiding system developed to identify aircraft conflict and to assist in the shared authority for resolution. The demands of this application requires representation of many intelligent agents sharing world-models, coordinating action/intention, and cooperative scheduling of goals and action in an somewhat unpredictable world of operations. In recent applications to airborne systems development, MIDAS has demonstrated an ability to predict flight crew decision-making and procedural behavior when interacting with automated flight management systems and Air Traffic Control. In this paper, we describe two enhancements to MIDAS. The first involves the addition of working memory in the form of an articulatory buffer for verbal communication protocols and a visuo-spatial buffer for communications via digital datalink. The second enhancement is a representation of multiple operators working as a team. This enhanced model was used to predict the performance of human flight crews and their level of compliance with commercial aviation communication procedures. We show how the data produced by MIDAS compares with flight crew performance data from full mission simulations. Finally, we discuss the use of these features to study communication issues connected with aircraft-based separation assurance.

Reservoir Computing Properties of Neural Dynamics in Prefrontal Cortex

PubMed Central

Procyk, Emmanuel; Dominey, Peter Ford

2016-01-01

Primates display a remarkable ability to adapt to novel situations. Determining what is most pertinent in these situations is not always possible based only on the current sensory inputs, and often also depends on recent inputs and behavioral outputs that contribute to internal states. Thus, one can ask how cortical dynamics generate representations of these complex situations. It has been observed that mixed selectivity in cortical neurons contributes to represent diverse situations defined by a combination of the current stimuli, and that mixed selectivity is readily obtained in randomly connected recurrent networks. In this context, these reservoir networks reproduce the highly recurrent nature of local cortical connectivity. Recombining present and past inputs, random recurrent networks from the reservoir computing framework generate mixed selectivity which provides pre-coded representations of an essentially universal set of contexts. These representations can then be selectively amplified through learning to solve the task at hand. We thus explored their representational power and dynamical properties after training a reservoir to perform a complex cognitive task initially developed for monkeys. The reservoir model inherently displayed a dynamic form of mixed selectivity, key to the representation of the behavioral context over time. The pre-coded representation of context was amplified by training a feedback neuron to explicitly represent this context, thereby reproducing the effect of learning and allowing the model to perform more robustly. This second version of the model demonstrates how a hybrid dynamical regime combining spatio-temporal processing of reservoirs, and input driven attracting dynamics generated by the feedback neuron, can be used to solve a complex cognitive task. We compared reservoir activity to neural activity of dorsal anterior cingulate cortex of monkeys which revealed similar network dynamics. We argue that reservoir computing is a pertinent framework to model local cortical dynamics and their contribution to higher cognitive function. PMID:27286251

Expression-invariant representations of faces.

PubMed

Bronstein, Alexander M; Bronstein, Michael M; Kimmel, Ron

2007-01-01

Addressed here is the problem of constructing and analyzing expression-invariant representations of human faces. We demonstrate and justify experimentally a simple geometric model that allows to describe facial expressions as isometric deformations of the facial surface. The main step in the construction of expression-invariant representation of a face involves embedding of the facial intrinsic geometric structure into some low-dimensional space. We study the influence of the embedding space geometry and dimensionality choice on the representation accuracy and argue that compared to its Euclidean counterpart, spherical embedding leads to notably smaller metric distortions. We experimentally support our claim showing that a smaller embedding error leads to better recognition.

Moving beyond the priming of single-language sentences: A proposal for a comprehensive model to account for linguistic representation in bilinguals.

PubMed

Kootstra, Gerrit Jan; Rossi, Eleonora

2017-01-01

In their target article, Branigan & Pickering (B&P) briefly discuss bilingual language representation, focusing primarily on cross-language priming between single-language sentences. We follow up on this discussion by showing how structural priming drives real-life phenomena of bilingual language use beyond the priming of unilingual sentences and by arguing that B&P's account should be extended with a representation for language membership.

Language Networks as Models of Cognition: Understanding Cognition through Language

NASA Astrophysics Data System (ADS)

Beckage, Nicole M.; Colunga, Eliana

Language is inherently cognitive and distinctly human. Separating the object of language from the human mind that processes and creates language fails to capture the full language system. Linguistics traditionally has focused on the study of language as a static representation, removed from the human mind. Network analysis has traditionally been focused on the properties and structure that emerge from network representations. Both disciplines could gain from looking at language as a cognitive process. In contrast, psycholinguistic research has focused on the process of language without committing to a representation. However, by considering language networks as approximations of the cognitive system we can take the strength of each of these approaches to study human performance and cognition as related to language. This paper reviews research showcasing the contributions of network science to the study of language. Specifically, we focus on the interplay of cognition and language as captured by a network representation. To this end, we review different types of language network representations before considering the influence of global level network features. We continue by considering human performance in relation to network structure and conclude with theoretical network models that offer potential and testable explanations of cognitive and linguistic phenomena.

An Algebraic Implicitization and Specialization of Minimum KL-Divergence Models

NASA Astrophysics Data System (ADS)

Dukkipati, Ambedkar; Manathara, Joel George

In this paper we study representation of KL-divergence minimization, in the cases where integer sufficient statistics exists, using tools from polynomial algebra. We show that the estimation of parametric statistical models in this case can be transformed to solving a system of polynomial equations. In particular, we also study the case of Kullback-Csisźar iteration scheme. We present implicit descriptions of these models and show that implicitization preserves specialization of prior distribution. This result leads us to a Gröbner bases method to compute an implicit representation of minimum KL-divergence models.

Improving the Representation of Land in Climate Models by Application of EOS Observations

NASA Technical Reports Server (NTRS)

2004-01-01

The PI's IDS current and previous investigation has focused on the applications of the land data toward the improvement of climate models. The previous IDS research identified the key factors limiting the accuracy of climate models to be the representation of albedos, land cover, fraction of landscape covered by vegetation, roughness lengths, surface skin temperature and canopy properties such as leaf area index (LAI) and average stomatal conductance. Therefore, we assembled a team uniquely situated to focus on these key variables and incorporate the remotely sensed measures of these variables into the next generation of climate models.

Varied representation of the West Pacific pattern in multiple dynamical seasonal predictions of APCC-MME

NASA Astrophysics Data System (ADS)

Lee, Yun-Young

2017-04-01

West Pacific (WP) teleconnection pattern is one of the well-known primary modes of boreal winter low-frequency variability (LFV) resolved in 500 hPa geopotential height and its phase and amplitude strongly influence regional weather conditions including temperature and rainfall extremes [Baxter and Nigam, 2015; Hsu and Wallace, 1985; Linkin and Nigam, 2008; Mo and Livezey, 1986; Thompson and Wallace, 1998; Wallace and Gutzler, 1981]. This study primary aims to evaluate individual 11 GCMs seasonal hindcasts employed as members of multi-model ensemble (MME) produced in APEC Climate Center (APCC) in representing WP. For the extensive and comprehensive evaluation, this study applied seven verification metrics in three scopes: (a) temporal representation of observed indices, (b) spatial mode separation in the Northern Hemisphere (NH), and (c) regional mode isolated in the preset longitudinal domain. Verification results display quite large inter-model spread. Some models mimic observed index variability while others display large bias of index variability compared to climatology. Basic north-south dipole pattern is mostly well reproduced in both rotated and unrotated loading modes. However, each individual seasonal forecast model exhibits slightly different behavior (e.g. amplification/weakening, zonal and meridional shift, downstream extension and so forth) in representing spatial structure of WP. When taking all 7 metrics into account, one Europe (CMCC) model, one Oceania (POAMA) model and two North America (NASA and NCEP) models are classified as relatively good performers while PNU is classified as a matchless poor performer out of 11. Least WP representing skill of PNU is sort of consistent with the largest bias of NH total variability. This study further tries to examine winter mean biases of individual models and figure out how mean bias is linked to WP representation in model world. Model bias of winter climatology is investigated focusing on six large scale phenomena: East Asian winter monsoon (EAWM), Atlantic dipole, Pacific/Atlantic jets and Pacific/Atlantic Hadley circulations. Changes in structure and amplitude of them are diagnosed in terms of root mean square error, pattern correlation, intensity bias, zonal displacement and/or downstream extension. There is consistent strengthening/downstream extension of Atlantic jet and absence of southern divergence cell of Atlantic Hadley in most seasonal prediction models. It is demonstrated that WP representation has something to do with bias of Atlantic winter climatology (Atlantic dipole and Atlantic jet) from scatter plot and regression analysis. This implies the importance of realistic simulation of winter climatology further upstream for better WP representation. A fundamental conclusion of this study is that the representation of primary WP features varies among individual models of APCC-MME and it is significantly dependent on the deficiencies of some winter mean climatological patterns.

The Interaction between Semantic Representation and Episodic Memory.

PubMed

Fang, Jing; Rüther, Naima; Bellebaum, Christian; Wiskott, Laurenz; Cheng, Sen

2018-02-01

The experimental evidence on the interrelation between episodic memory and semantic memory is inconclusive. Are they independent systems, different aspects of a single system, or separate but strongly interacting systems? Here, we propose a computational role for the interaction between the semantic and episodic systems that might help resolve this debate. We hypothesize that episodic memories are represented as sequences of activation patterns. These patterns are the output of a semantic representational network that compresses the high-dimensional sensory input. We show quantitatively that the accuracy of episodic memory crucially depends on the quality of the semantic representation. We compare two types of semantic representations: appropriate representations, which means that the representation is used to store input sequences that are of the same type as those that it was trained on, and inappropriate representations, which means that stored inputs differ from the training data. Retrieval accuracy is higher for appropriate representations because the encoded sequences are less divergent than those encoded with inappropriate representations. Consistent with our model prediction, we found that human subjects remember some aspects of episodes significantly more accurately if they had previously been familiarized with the objects occurring in the episode, as compared to episodes involving unfamiliar objects. We thus conclude that the interaction with the semantic system plays an important role for episodic memory.

Unpacking Exoplanet Detection Using Pedagogical Discipline Representations (PDRs)

NASA Astrophysics Data System (ADS)

Prather, Edward E.; Chambers, Timothy G.; Wallace, Colin Scott; Brissenden, Gina

2017-01-01

Successful educators know the importance of using multiple representations to teach the content of their disciplines. We have all seen the moments of epiphany that can be inspired when engaging with just the right representation of a difficult concept. The formal study of the cognitive impact of different representations on learners is now an active area of education research. The affordances of a particular representation are defined as the elements of disciplinary knowledge that students are able to access and reason about using that representation. Instructors with expert pedagogical content knowledge teach each topic using representations with complementary affordances, maximizing their students’ opportunity to develop fluency with all aspects of the topic. The work presented here examines how we have applied the theory of affordances to the development of pedagogical discipline representation (PDR) in an effort to provide access to, and help non-science-majors engage in expert-like reasoning about, general relativity as applied to detection of exoplanets. We define a pedagogical discipline representation (PDR) as a representation that has been uniquely tailored for the purpose of teaching a specific topic within a discipline. PDRs can be simplified versions of expert representations or can be highly contextualized with features that purposefully help unpack specific reasoning or concepts, and engage learners’ pre-existing mental models while promoting and enabling critical discourse. Examples of PDRs used for instruction and assessment will be provided along with preliminary results documenting the effectiveness of their use in the classroom.

Readmission prediction via deep contextual embedding of clinical concepts.

PubMed

Xiao, Cao; Ma, Tengfei; Dieng, Adji B; Blei, David M; Wang, Fei

2018-01-01

Hospital readmission costs a lot of money every year. Many hospital readmissions are avoidable, and excessive hospital readmissions could also be harmful to the patients. Accurate prediction of hospital readmission can effectively help reduce the readmission risk. However, the complex relationship between readmission and potential risk factors makes readmission prediction a difficult task. The main goal of this paper is to explore deep learning models to distill such complex relationships and make accurate predictions. We propose CONTENT, a deep model that predicts hospital readmissions via learning interpretable patient representations by capturing both local and global contexts from patient Electronic Health Records (EHR) through a hybrid Topic Recurrent Neural Network (TopicRNN) model. The experiment was conducted using the EHR of a real world Congestive Heart Failure (CHF) cohort of 5,393 patients. The proposed model outperforms state-of-the-art methods in readmission prediction (e.g. 0.6103 ± 0.0130 vs. second best 0.5998 ± 0.0124 in terms of ROC-AUC). The derived patient representations were further utilized for patient phenotyping. The learned phenotypes provide more precise understanding of readmission risks. Embedding both local and global context in patient representation not only improves prediction performance, but also brings interpretable insights of understanding readmission risks for heterogeneous chronic clinical conditions. This is the first of its kind model that integrates the power of both conventional deep neural network and the probabilistic generative models for highly interpretable deep patient representation learning. Experimental results and case studies demonstrate the improved performance and interpretability of the model.

Effects of spatially distributed sectoral water management on the redistribution of water resources in an integrated water model

NASA Astrophysics Data System (ADS)

Voisin, Nathalie; Hejazi, Mohamad I.; Leung, L. Ruby; Liu, Lu; Huang, Maoyi; Li, Hong-Yi; Tesfa, Teklu

2017-05-01

Realistic representations of sectoral water withdrawals and consumptive demands and their allocation to surface and groundwater sources are important for improving modeling of the integrated water cycle. To inform future model development, we enhance the representation of water management in a regional Earth system (ES) model with a spatially distributed allocation of sectoral water demands simulated by a regional integrated assessment (IA) model to surface and groundwater systems. The integrated modeling framework (IA-ES) is evaluated by analyzing the simulated regulated flow and sectoral supply deficit in major hydrologic regions of the conterminous U.S, which differ from ES studies looking at water storage variations. Decreases in historical supply deficit are used as metrics to evaluate IA-ES model improvement in representating the complex sectoral human activities for assessing future adaptation and mitigation strategies. We also assess the spatial changes in both regulated flow and unmet demands, for irrigation and nonirrigation sectors, resulting from the individual and combined additions of groundwater and return flow modules. Results show that groundwater use has a pronounced regional and sectoral effect by reducing water supply deficit. The effects of sectoral return flow exhibit a clear east-west contrast in the hydrologic patterns, so the return flow component combined with the IA sectoral demands is a major driver for spatial redistribution of water resources and water deficits in the US. Our analysis highlights the need for spatially distributed sectoral representation of water management to capture the regional differences in interbasin redistribution of water resources and deficits.

The impact of 14nm photomask variability and uncertainty on computational lithography solutions

NASA Astrophysics Data System (ADS)

Sturtevant, John; Tejnil, Edita; Buck, Peter D.; Schulze, Steffen; Kalk, Franklin; Nakagawa, Kent; Ning, Guoxiang; Ackmann, Paul; Gans, Fritz; Buergel, Christian

2013-09-01

Computational lithography solutions rely upon accurate process models to faithfully represent the imaging system output for a defined set of process and design inputs. These models rely upon the accurate representation of multiple parameters associated with the scanner and the photomask. Many input variables for simulation are based upon designed or recipe-requested values or independent measurements. It is known, however, that certain measurement methodologies, while precise, can have significant inaccuracies. Additionally, there are known errors associated with the representation of certain system parameters. With shrinking total CD control budgets, appropriate accounting for all sources of error becomes more important, and the cumulative consequence of input errors to the computational lithography model can become significant. In this work, we examine via simulation, the impact of errors in the representation of photomask properties including CD bias, corner rounding, refractive index, thickness, and sidewall angle. The factors that are most critical to be accurately represented in the model are cataloged. CD bias values are based on state of the art mask manufacturing data and other variables changes are speculated, highlighting the need for improved metrology and communication between mask and OPC model experts. The simulations are done by ignoring the wafer photoresist model, and show the sensitivity of predictions to various model inputs associated with the mask. It is shown that the wafer simulations are very dependent upon the 1D/2D representation of the mask and for 3D, that the mask sidewall angle is a very sensitive factor influencing simulated wafer CD results.

Constraining composite Higgs models using LHC data

NASA Astrophysics Data System (ADS)

Banerjee, Avik; Bhattacharyya, Gautam; Kumar, Nilanjana; Ray, Tirtha Sankar

2018-03-01

We systematically study the modifications in the couplings of the Higgs boson, when identified as a pseudo Nambu-Goldstone boson of a strong sector, in the light of LHC Run 1 and Run 2 data. For the minimal coset SO(5)/SO(4) of the strong sector, we focus on scenarios where the standard model left- and right-handed fermions (specifically, the top and bottom quarks) are either in 5 or in the symmetric 14 representation of SO(5). Going beyond the minimal 5 L - 5 R representation, to what we call here the `extended' models, we observe that it is possible to construct more than one invariant in the Yukawa sector. In such models, the Yukawa couplings of the 125 GeV Higgs boson undergo nontrivial modifications. The pattern of such modifications can be encoded in a generic phenomenological Lagrangian which applies to a wide class of such models. We show that the presence of more than one Yukawa invariant allows the gauge and Yukawa coupling modifiers to be decorrelated in the `extended' models, and this decorrelation leads to a relaxation of the bound on the compositeness scale ( f ≥ 640 GeV at 95% CL, as compared to f ≥ 1 TeV for the minimal 5 L - 5 R representation model). We also study the Yukawa coupling modifications in the context of the next-to-minimal strong sector coset SO(6)/SO(5) for fermion-embedding up to representations of dimension 20. While quantifying our observations, we have performed a detailed χ 2 fit using the ATLAS and CMS combined Run 1 and available Run 2 data.

Semiclassical initial value representation for the quantum propagator in the Heisenberg interaction representation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Petersen, Jakob; Pollak, Eli, E-mail: eli.pollak@weizmann.ac.il

2015-12-14

One of the challenges facing on-the-fly ab initio semiclassical time evolution is the large expense needed to converge the computation. In this paper, we suggest that a significant saving in computational effort may be achieved by employing a semiclassical initial value representation (SCIVR) of the quantum propagator based on the Heisenberg interaction representation. We formulate and test numerically a modification and simplification of the previous semiclassical interaction representation of Shao and Makri [J. Chem. Phys. 113, 3681 (2000)]. The formulation is based on the wavefunction form of the semiclassical propagation instead of the operator form, and so is simpler andmore » cheaper to implement. The semiclassical interaction representation has the advantage that the phase and prefactor vary relatively slowly as compared to the “standard” SCIVR methods. This improves its convergence properties significantly. Using a one-dimensional model system, the approximation is compared with Herman-Kluk’s frozen Gaussian and Heller’s thawed Gaussian approximations. The convergence properties of the interaction representation approach are shown to be favorable and indicate that the interaction representation is a viable way of incorporating on-the-fly force field information within a semiclassical framework.« less

Attachment in families with Huntington's disease. A paradigm in clinical genetics.

PubMed

Van der Meer, Lucienne; Timman, Reinier; Trijsburg, Wim; Duisterhof, Marleen; Erdman, Ruud; Van Elderen, Thérèse; Tibben, Aad

2006-10-01

Based on the premise that attachment experiences lead to a working model for social relationships throughout life, this study investigates if there is a difference between adult attachment representations in individuals who were brought up by a parent with Huntington's disease (HD), compared to a non-clinical population. Specific events in the parents' disease process, especially those leading to trauma and loss will receive attention. Using the Adult Attachment Interview, adult attachment representations were investigated in 32 unaffected adults at 50% risk for HD who were raised by an affected parent. We found a lower percentage of secure attachment representations, a higher percentage of preoccupied representations, and a higher percentage of unresolved/disorganized representations in our sample, compared to a non-clinical population. A relatively late start of the parent's HD career was associated with a secure adult attachment representation. Death of the HD parent before the child's 18th birthday was associated with an unresolved/disorganized adult attachment representation. Growing up in a family where one of the parents has Huntington's disease appears to affect the offspring's adult attachment representation. This study can be of relevance for genetic counselling, as well as for counselling and intervention in childrearing matters.

[The Relationship Between Attachment Representations of Foster Parents and Foster Children and the Role of the Child's Sex].

PubMed

Nowacki, Katja; Kliewer-Neumann, Josephine; Bovenschen, Ina; Lang, Katrin; Zimmermann, Janin; Spangler, Gottfried

2015-01-01

Children who have been placed in foster care after having experienced difficult family situations need to experience secure relationships. The development of a secure attachment model is regarded as a key protective factor for a healthy development. The present study examines predictors of attachment representations in a sample of 37 foster children aged three to eight years. Children's attachment representations were assessed using the Attachment Story Completion Task, and foster parents' attachment representations with the Adult Attachment Interview. Female foster children scored higher in secure attachment representations than males. Attachment representations of male foster children were positively influenced by a secure attachment representation of their primary foster parent and slightly by the duration of placement in the foster family as well as their age of placement but differently than expected. These results suggest that male foster children may be more vulnerable in their development of attachment representations and that foster parents' state of mind regarding attachment as well as the duration of the placement seem to have an impact on the development of attachment patterns in their foster children. This should be considered in the choice and counseling of foster parents.

Psychology of knowledge representation.

PubMed

Grimm, Lisa R

2014-05-01

Every cognitive enterprise involves some form of knowledge representation. Humans represent information about the external world and internal mental states, like beliefs and desires, and use this information to meet goals (e.g., classification or problem solving). Unfortunately, researchers do not have direct access to mental representations. Instead, cognitive scientists design experiments and implement computational models to develop theories about the mental representations present during task performance. There are several main types of mental representation and corresponding processes that have been posited: spatial, feature, network, and structured. Each type has a particular structure and a set of processes that are capable of accessing and manipulating information within the representation. The structure and processes determine what information can be used during task performance and what information has not been represented at all. As such, the different types of representation are likely used to solve different kinds of tasks. For example, structured representations are more complex and computationally demanding, but are good at representing relational information. Researchers interested in human psychology would benefit from considering how knowledge is represented in their domain of inquiry. For further resources related to this article, please visit the WIREs website. The author has declared no conflicts of interest for this article. © 2014 John Wiley & Sons, Ltd.

Color perception involves color representations firstly at a semantic level and then at a lexical level.

PubMed

Heurley, Loïc P; Brouillet, Thibaut; Chesnoy, Gabrielle; Brouillet, Denis

2013-03-01

Studies and models have suggested that color perception first involves access to semantic representations of color. This result leads to two questions: (1) is knowledge able to influence the perception of color when associated with a color? and (2) can the perception of color really involve only semantic representations? We developed an experiment where participants have to discriminate the color of a patch (yellow vs. green). The target patch is preceded either by a black-and-white line drawing or by a word representing a natural object associated with the same or a different color (banana vs. frog). We expected a priming effect for pictures because, with a 350-ms SOA, they only involve access to semantic representations of color, whereas words seem only elicit an access to lexical representations. As expected, we found a priming effect for pictures, but also for words. Moreover, we found a general slowdown of response times in the word-prime-condition suggesting the need of an additional processing step to produce priming. In a second experiment, we manipulated the SOA in order to preclude a semantic access in the word-prime-condition that could explain the additional step of processing. We also found a priming effect, suggesting that interaction with perception occurs at a lexical level and the additional step occurs at a color perception level. In the discussion, we develop a new model of color perception assuming that color perception involves access to semantic representations and then access to lexical representations.

Speech perception at the interface of neurobiology and linguistics.

PubMed

Poeppel, David; Idsardi, William J; van Wassenhove, Virginie

2008-03-12

Speech perception consists of a set of computations that take continuously varying acoustic waveforms as input and generate discrete representations that make contact with the lexical representations stored in long-term memory as output. Because the perceptual objects that are recognized by the speech perception enter into subsequent linguistic computation, the format that is used for lexical representation and processing fundamentally constrains the speech perceptual processes. Consequently, theories of speech perception must, at some level, be tightly linked to theories of lexical representation. Minimally, speech perception must yield representations that smoothly and rapidly interface with stored lexical items. Adopting the perspective of Marr, we argue and provide neurobiological and psychophysical evidence for the following research programme. First, at the implementational level, speech perception is a multi-time resolution process, with perceptual analyses occurring concurrently on at least two time scales (approx. 20-80 ms, approx. 150-300 ms), commensurate with (sub)segmental and syllabic analyses, respectively. Second, at the algorithmic level, we suggest that perception proceeds on the basis of internal forward models, or uses an 'analysis-by-synthesis' approach. Third, at the computational level (in the sense of Marr), the theory of lexical representation that we adopt is principally informed by phonological research and assumes that words are represented in the mental lexicon in terms of sequences of discrete segments composed of distinctive features. One important goal of the research programme is to develop linking hypotheses between putative neurobiological primitives (e.g. temporal primitives) and those primitives derived from linguistic inquiry, to arrive ultimately at a biologically sensible and theoretically satisfying model of representation and computation in speech.

What can biochemistry students learn about protein translation? Using variation theory to explore the space of learning created by some common external representations

NASA Astrophysics Data System (ADS)

Bussey, Thomas J.

Biochemistry education relies heavily on students' ability to visualize abstract cellular and molecular processes, mechanisms, and components. As such, biochemistry educators often turn to external representations to provide tangible, working models from which students' internal representations (mental models) can be constructed, evaluated, and revised. However, prior research has shown that, while potentially beneficial, external representations can also lead to alternative student conceptions. Considering the breadth of biochemical phenomena, protein translation has been identified as an essential biochemical process and can subsequently be considered a fundamental concept for biochemistry students to learn. External representations of translation range from static diagrams to dynamic animations, from simplistic, stylized illustrations to more complex, realistic presentations. In order to explore the potential for student learning about protein translation from some common external representations of translation, I used variation theory. Variation theory offers a theoretical framework from which to explore what is intended for students to learn, what is possible for students to learn, and what students actually learn about an object of learning, e.g., protein translation. The goals of this project were threefold. First, I wanted to identify instructors' intentions for student learning about protein translation. From a phenomenographic analysis of instructor interviews, I was able to determine the critical features instructors felt their students should be learning. Second, I wanted to determine which features of protein translation were possible for students to learn from some common external representations of the process. From a variation analysis of the three representations shown to students, I was able to describe the possible combinations of features enacted by the sequential viewing of pairs of representations. Third, I wanted to identify what students actually learned about protein translation by viewing these external representations. From a phenomenographic analysis of student interviews, I was able to describe changes between students prior lived object of learning and their post lived object of learning. Based on the findings from this project, I can conclude that variation can be used to cue students to notice particular features of an external representation. Additionally, students' prior knowledge and, potentially, the intended objects of learning from previous instructors can also affect what students can learn from a representation. Finally, further study is needed to identify the extent to which mode and level of abstraction of an external representation affect student learning outcomes.