Adjoints and Low-rank Covariance Representation

NASA Technical Reports Server (NTRS)

Tippett, Michael K.; Cohn, Stephen E.

2000-01-01

Quantitative measures of the uncertainty of Earth System estimates can be as important as the estimates themselves. Second moments of estimation errors are described by the covariance matrix, whose direct calculation is impractical when the number of degrees of freedom of the system state is large. Ensemble and reduced-state approaches to prediction and data assimilation replace full estimation error covariance matrices by low-rank approximations. The appropriateness of such approximations depends on the spectrum of the full error covariance matrix, whose calculation is also often impractical. Here we examine the situation where the error covariance is a linear transformation of a forcing error covariance. We use operator norms and adjoints to relate the appropriateness of low-rank representations to the conditioning of this transformation. The analysis is used to investigate low-rank representations of the steady-state response to random forcing of an idealized discrete-time dynamical system.

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.

Prediction of protein secondary structure content for the twilight zone sequences.

PubMed

Homaeian, Leila; Kurgan, Lukasz A; Ruan, Jishou; Cios, Krzysztof J; Chen, Ke

2007-11-15

Secondary protein structure carries information about local structural arrangements, which include three major conformations: alpha-helices, beta-strands, and coils. Significant majority of successful methods for prediction of the secondary structure is based on multiple sequence alignment. However, multiple alignment fails to provide accurate results when a sequence comes from the twilight zone, that is, it is characterized by low (<30%) homology. To this end, we propose a novel method for prediction of secondary structure content through comprehensive sequence representation, called PSSC-core. The method uses a multiple linear regression model and introduces a comprehensive feature-based sequence representation to predict amount of helices and strands for sequences from the twilight zone. The PSSC-core method was tested and compared with two other state-of-the-art prediction methods on a set of 2187 twilight zone sequences. The results indicate that our method provides better predictions for both helix and strand content. The PSSC-core is shown to provide statistically significantly better results when compared with the competing methods, reducing the prediction error by 5-7% for helix and 7-9% for strand content predictions. The proposed feature-based sequence representation uses a comprehensive set of physicochemical properties that are custom-designed for each of the helix and strand content predictions. It includes composition and composition moment vectors, frequency of tetra-peptides associated with helical and strand conformations, various property-based groups like exchange groups, chemical groups of the side chains and hydrophobic group, auto-correlations based on hydrophobicity, side-chain masses, hydropathy, and conformational patterns for beta-sheets. The PSSC-core method provides an alternative for predicting the secondary structure content that can be used to validate and constrain results of other structure prediction methods. At the same time, it also provides useful insight into design of successful protein sequence representations that can be used in developing new methods related to prediction of different aspects of the secondary protein structure. (c) 2007 Wiley-Liss, Inc.

Decision support systems and methods for complex networks

DOEpatents

Huang, Zhenyu [Richland, WA; Wong, Pak Chung [Richland, WA; Ma, Jian [Richland, WA; Mackey, Patrick S [Richland, WA; Chen, Yousu [Richland, WA; Schneider, Kevin P [Seattle, WA

2012-02-28

Methods and systems for automated decision support in analyzing operation data from a complex network. Embodiments of the present invention utilize these algorithms and techniques not only to characterize the past and present condition of a complex network, but also to predict future conditions to help operators anticipate deteriorating and/or problem situations. In particular, embodiments of the present invention characterize network conditions from operation data using a state estimator. Contingency scenarios can then be generated based on those network conditions. For at least a portion of all of the contingency scenarios, risk indices are determined that describe the potential impact of each of those scenarios. Contingency scenarios with risk indices are presented visually as graphical representations in the context of a visual representation of the complex network. Analysis of the historical risk indices based on the graphical representations can then provide trends that allow for prediction of future network conditions.

NOAA's National Air Quality Predictions and Development of Aerosol and Atmospheric Composition Prediction Components for the Next Generation Global Prediction System

NASA Astrophysics Data System (ADS)

Stajner, I.; Hou, Y. T.; McQueen, J.; Lee, P.; Stein, A. F.; Tong, D.; Pan, L.; Huang, J.; Huang, H. C.; Upadhayay, S.

2016-12-01

NOAA provides operational air quality predictions using the National Air Quality Forecast Capability (NAQFC): ozone and wildfire smoke for the United States and airborne dust for the contiguous 48 states at http://airquality.weather.gov. NOAA's predictions of fine particulate matter (PM2.5) became publicly available in February 2016. Ozone and PM2.5 predictions are produced using a system that operationally links the Community Multiscale Air Quality (CMAQ) model with meteorological inputs from the North American mesoscale forecast Model (NAM). Smoke and dust predictions are provided using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Current NAQFC focus is on updating CMAQ to version 5.0.2, improving PM2.5 predictions, and updating emissions estimates, especially for NOx using recently observed trends. Wildfire smoke emissions from a newer version of the USFS BlueSky system are being included in a new configuration of the NAQFC NAM-CMAQ system, which is re-run for the previous 24 hours when the wildfires were observed from satellites, to better represent wildfire emissions prior to initiating predictions for the next 48 hours. In addition, NOAA is developing the Next Generation Global Prediction System (NGGPS) to represent the earth system for extended weather prediction. NGGPS will include a representation of atmospheric dynamics, physics, aerosols and atmospheric composition as well as coupling with ocean, wave, ice and land components. NGGPS is being developed with a broad community involvement, including community developed components and academic research to develop and test potential improvements for potentially inclusion in NGGPS. Several investigators at NOAA's research laboratories and in academia are working to improve the aerosol and gaseous chemistry representation for NGGPS, to develop and evaluate the representation of atmospheric composition, and to establish and improve the coupling with radiation and microphysics. Additional efforts may include the improved use of predicted atmospheric composition in assimilation of observations and the linkage of full global atmospheric composition predictions with national air quality predictions.

Combining Physicochemical and Evolutionary Information for Protein Contact Prediction

PubMed Central

Schneider, Michael; Brock, Oliver

2014-01-01

We introduce a novel contact prediction method that achieves high prediction accuracy by combining evolutionary and physicochemical information about native contacts. We obtain evolutionary information from multiple-sequence alignments and physicochemical information from predicted ab initio protein structures. These structures represent low-energy states in an energy landscape and thus capture the physicochemical information encoded in the energy function. Such low-energy structures are likely to contain native contacts, even if their overall fold is not native. To differentiate native from non-native contacts in those structures, we develop a graph-based representation of the structural context of contacts. We then use this representation to train an support vector machine classifier to identify most likely native contacts in otherwise non-native structures. The resulting contact predictions are highly accurate. As a result of combining two sources of information—evolutionary and physicochemical—we maintain prediction accuracy even when only few sequence homologs are present. We show that the predicted contacts help to improve ab initio structure prediction. A web service is available at http://compbio.robotics.tu-berlin.de/epc-map/. PMID:25338092

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

Intrinsic states in the sdg interacting boson model

NASA Astrophysics Data System (ADS)

Yoshinaga, N.

1986-08-01

We give the intrinsic states explicitly in the boson representation in the framework of the sdg interacting boson model. Although they are only valid in the large- N limit, they are useful to estimate various physical quantities in well deformed nuclei. One can compare these results with those predicted in the IBM1 or in the IBM2.

Distributions in the error space: goal-directed movements described in time and state-space representations.

PubMed

Fisher, Moria E; Huang, Felix C; Wright, Zachary A; Patton, James L

2014-01-01

Manipulation of error feedback has been of great interest to recent studies in motor control and rehabilitation. Typically, motor adaptation is shown as a change in performance with a single scalar metric for each trial, yet such an approach might overlook details about how error evolves through the movement. We believe that statistical distributions of movement error through the extent of the trajectory can reveal unique patterns of adaption and possibly reveal clues to how the motor system processes information about error. This paper describes different possible ordinate domains, focusing on representations in time and state-space, used to quantify reaching errors. We hypothesized that the domain with the lowest amount of variability would lead to a predictive model of reaching error with the highest accuracy. Here we showed that errors represented in a time domain demonstrate the least variance and allow for the highest predictive model of reaching errors. These predictive models will give rise to more specialized methods of robotic feedback and improve previous techniques of error augmentation.

A Generalized Timeline Representation, Services, and Interface for Automating Space Mission Operations

NASA Technical Reports Server (NTRS)

Chien, Steve A.; Johnston, Mark; Frank, Jeremy; Giuliano, Mark; Kavelaars, Alicia; Lenzen, Christoph; Policella, Nicola

2012-01-01

Numerous automated and semi-automated planning & scheduling systems have been developed for space applications. Most of these systems are model-based in that they encode domain knowledge necessary to predict spacecraft state and resources based on initial conditions and a proposed activity plan. The spacecraft state and resources as often modeled as a series of timelines, with a timeline or set of timelines to represent a state or resource key in the operations of the spacecraft. In this paper, we first describe a basic timeline representation that can represent a set of state, resource, timing, and transition constraints. We describe a number of planning and scheduling systems designed for space applications (and in many cases deployed for use of ongoing missions) and describe how they do and do not map onto this timeline model.

Mentalizing about emotion and its relationship to empathy.

PubMed

Hooker, Christine I; Verosky, Sara C; Germine, Laura T; Knight, Robert T; D'Esposito, Mark

2008-09-01

Mentalizing involves the ability to predict someone else's behavior based on their belief state. More advanced mentalizing skills involve integrating knowledge about beliefs with knowledge about the emotional impact of those beliefs. Recent research indicates that advanced mentalizing skills may be related to the capacity to empathize with others. However, it is not clear what aspect of mentalizing is most related to empathy. In this study, we used a novel, advanced mentalizing task to identify neural mechanisms involved in predicting a future emotional response based on a belief state. Subjects viewed social scenes in which one character had a False Belief and one character had a True Belief. In the primary condition, subjects were asked to predict what emotion the False Belief Character would feel if they had a full understanding about the situation. We found that neural regions related to both mentalizing and emotion were involved when predicting a future emotional response, including the superior temporal sulcus, medial prefrontal cortex, temporal poles, somatosensory related cortices (SRC), inferior frontal gyrus and thalamus. In addition, greater neural activity in primarily emotion-related regions, including right SRC and bilateral thalamus, when predicting emotional response was significantly correlated with more self-reported empathy. The findings suggest that predicting emotional response involves generating and using internal affective representations and that greater use of these affective representations when trying to understand the emotional experience of others is related to more empathy.

Multiple time-scales and the developmental dynamics of social systems

PubMed Central

Flack, Jessica C.

2012-01-01

To build a theory of social complexity, we need to understand how aggregate social properties arise from individual interaction rules. Here, I review a body of work on the developmental dynamics of pigtailed macaque social organization and conflict management that provides insight into the mechanistic causes of multi-scale social systems. In this model system coarse-grained, statistical representations of collective dynamics are more predictive of the future state of the system than the constantly in-flux behavioural patterns at the individual level. The data suggest that individuals can perceive and use these representations for strategical decision-making. As an interaction history accumulates the coarse-grained representations consolidate. This constrains individual behaviour and provides the foundations for new levels of organization. The time-scales on which these representations change impact whether the consolidating higher-levels can be modified by individuals and collectively. The time-scales appear to be a function of the ‘coarseness’ of the representations and the character of the collective dynamics over which they are averages. The data suggest that an advantage of multiple timescales is that they allow social systems to balance tradeoffs between predictability and adaptability. I briefly discuss the implications of these findings for cognition, social niche construction and the evolution of new levels of organization in biological systems. PMID:22641819

Multiple time-scales and the developmental dynamics of social systems.

PubMed

Flack, Jessica C

2012-07-05

To build a theory of social complexity, we need to understand how aggregate social properties arise from individual interaction rules. Here, I review a body of work on the developmental dynamics of pigtailed macaque social organization and conflict management that provides insight into the mechanistic causes of multi-scale social systems. In this model system coarse-grained, statistical representations of collective dynamics are more predictive of the future state of the system than the constantly in-flux behavioural patterns at the individual level. The data suggest that individuals can perceive and use these representations for strategical decision-making. As an interaction history accumulates the coarse-grained representations consolidate. This constrains individual behaviour and provides the foundations for new levels of organization. The time-scales on which these representations change impact whether the consolidating higher-levels can be modified by individuals and collectively. The time-scales appear to be a function of the 'coarseness' of the representations and the character of the collective dynamics over which they are averages. The data suggest that an advantage of multiple timescales is that they allow social systems to balance tradeoffs between predictability and adaptability. I briefly discuss the implications of these findings for cognition, social niche construction and the evolution of new levels of organization in biological systems.

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.

What do we mean by prediction in language comprehension?

PubMed Central

Kuperberg, Gina R.; Jaeger, T. Florian

2016-01-01

We consider several key aspects of prediction in language comprehension: its computational nature, the representational level(s) at which we predict, whether we use higher level representations to predictively pre-activate lower level representations, and whether we ‘commit’ in any way to our predictions, beyond pre-activation. We argue that the bulk of behavioral and neural evidence suggests that we predict probabilistically and at multiple levels and grains of representation. We also argue that we can, in principle, use higher level inferences to predictively pre-activate information at multiple lower representational levels. We also suggest that the degree and level of predictive pre-activation might be a function of the expected utility of prediction, which, in turn, may depend on comprehenders’ goals and their estimates of the relative reliability of their prior knowledge and the bottom-up input. Finally, we argue that all these properties of language understanding can be naturally explained and productively explored within a multi-representational hierarchical actively generative architecture whose goal is to infer the message intended by the producer, and in which predictions play a crucial role in explaining the bottom-up input. PMID:27135040

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.

Neural evidence that three dimensions organize mental state representation: Rationality, social impact, and valence

PubMed Central

Tamir, Diana I.; Thornton, Mark A.; Contreras, Juan Manuel; Mitchell, Jason P.

2016-01-01

How do people understand the minds of others? Existing psychological theories have suggested a number of dimensions that perceivers could use to make sense of others’ internal mental states. However, it remains unclear which of these dimensions, if any, the brain spontaneously uses when we think about others. The present study used multivoxel pattern analysis (MVPA) of neuroimaging data to identify the primary organizing principles of social cognition. We derived four unique dimensions of mental state representation from existing psychological theories and used functional magnetic resonance imaging to test whether these dimensions organize the neural encoding of others’ mental states. MVPA revealed that three such dimensions could predict neural patterns within the medial prefrontal and parietal cortices, temporoparietal junction, and anterior temporal lobes during social thought: rationality, social impact, and valence. These results suggest that these dimensions serve as organizing principles for our understanding of other people. PMID:26621704

A controlled study of Hostile-Helpless states of mind among borderline and dysthymic women

PubMed Central

LYONS-RUTH, KARLEN; MELNICK, SHARON; PATRICK, MATTHEW; HOBSON, R. PETER

2008-01-01

The aim of this study was to determine whether women with borderline personality disorder (BPD) are more likely than those with dysthymia to manifest contradictory Hostile-Helpless (HH) states of mind. A reliable rater blind to diagnosis evaluated features of such mental representations in transcripts of Adult Attachment Interviews from 12 women with BPD and 11 women with dysthymia of similar socioeconomic status (SES), all awaiting psychotherapy. In keeping with three hierarchical (non-independent) a priori predictions regarding the mental representations of women with BPD, the results were that (a) all those with BPD, compared with half the group with dysthymia, displayed HH states of mind; (b) those with BPD manifested a significantly higher frequency of globally devaluing representations; and (c) they exhibited a strong trend toward identifying with the devalued hostile caregiver (58% BPD vs. 18% dysthymic). In addition, significantly more BPD than dysthymic patients made reference to controlling behavior towards attachment figures in childhood. These findings offer fresh insights into the nature of BPD and extend previous evidence concerning affected individuals’ patterns of thinking and feeling about childhood attachment figures. PMID:17364479

Risk Taking Under the Influence: A Fuzzy-Trace Theory of Emotion in Adolescence

PubMed Central

Rivers, Susan E.; Reyna, Valerie F.; Mills, Britain

2008-01-01

Fuzzy-trace theory explains risky decision making in children, adolescents, and adults, incorporating social and cultural factors as well as differences in impulsivity. Here, we provide an overview of the theory, including support for counterintuitive predictions (e.g., when adolescents “rationally” weigh costs and benefits, risk taking increases, but it decreases when the core gist of a decision is processed). Then, we delineate how emotion shapes adolescent risk taking—from encoding of representations of options, to retrieval of values/principles, to application of those values/principles to representations of options. Our review indicates that: (i) Gist representations often incorporate emotion including valence, arousal, feeling states, and discrete emotions; and (ii) Emotion determines whether gist or verbatim representations are processed. We recommend interventions to reduce unhealthy risk-taking that inculcate stable gist representations, enabling adolescents to identify quickly and automatically danger even when experiencing emotion, which differs sharply from traditional approaches emphasizing deliberation and precise analysis. PMID:19255597

Active Sampling State Dynamically Enhances Olfactory Bulb Odor Representation.

PubMed

Jordan, Rebecca; Fukunaga, Izumi; Kollo, Mihaly; Schaefer, Andreas T

2018-06-27

The olfactory bulb (OB) is the first site of synaptic odor information processing, yet a wealth of contextual and learned information has been described in its activity. To investigate the mechanistic basis of contextual modulation, we use whole-cell recordings to measure odor responses across rapid learning episodes in identified mitral/tufted cells (MTCs). Across these learning episodes, diverse response changes occur already during the first sniff cycle. Motivated mice develop active sniffing strategies across learning that robustly correspond to the odor response changes, resulting in enhanced odor representation. Evoking fast sniffing in different behavioral states demonstrates that response changes during active sampling exceed those predicted from feedforward input alone. Finally, response changes are highly correlated in tufted cells, but not mitral cells, indicating there are cell-type-specific effects on odor representation during active sampling. Altogether, we show that active sampling is strongly associated with enhanced OB responsiveness on rapid timescales. Copyright © 2018 The Francis Crick Institute. Published by Elsevier Inc. All rights reserved.

Maternal Narratives Contribute to Foundations of the Child's Inner World. Commentary on: "Maternal Adult Attachment Interview (AAI) Collected During Pregnancy Predicts Reflective Functioning in AAIs from their First-Born Children 17 Years Later"

ERIC Educational Resources Information Center

Soares, Isabel; Baptista, Joana

2016-01-01

In this commentary, Soares and Baptista state that the Steele, Perez, Segal, and Steele (2016) article contributed with an informative study that adolescents' reflective functioning (RF) is predicted by maternal attachment representation, which was assessed even before the youth were born by using the Adult Attachment Interview. The authors assert…

Dopamine Receptor D4 Gene Variation Predicts Preschoolers' Developing Theory of Mind

ERIC Educational Resources Information Center

Lackner, Christine; Sabbagh, Mark A.; Hallinan, Elizabeth; Liu, Xudong; Holden, Jeanette J. A.

2012-01-01

Individual differences in preschoolers' understanding that human action is caused by internal mental states, or representational theory of mind (RTM), are heritable, as are developmental disorders such as autism in which RTM is particularly impaired. We investigated whether polymorphisms of genes affecting dopamine (DA) utilization and metabolism…

Spatial representation of organic carbon and active-layer thickness of high latitude soils in CMIP5 earth system models

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

Mishra, Umakant; Drewniak, Beth; Jastrow, Julie D.

Soil properties such as soil organic carbon (SOC) stocks and active-layer thickness are used in earth system models (F.SMs) to predict anthropogenic and climatic impacts on soil carbon dynamics, future changes in atmospheric greenhouse gas concentrations, and associated climate changes in the permafrost regions. Accurate representation of spatial and vertical distribution of these soil properties in ESMs is a prerequisite for redudng existing uncertainty in predicting carbon-climate feedbacks. We compared the spatial representation of SOC stocks and active-layer thicknesses predicted by the coupled Modellntercomparison Project Phase 5 { CMIP5) ESMs with those predicted from geospatial predictions, based on observation datamore » for the state of Alaska, USA. For the geospatial modeling. we used soil profile observations {585 for SOC stocks and 153 for active-layer thickness) and environmental variables (climate, topography, land cover, and surficial geology types) and generated fine-resolution (50-m spatial resolution) predictions of SOC stocks (to 1-m depth) and active-layer thickness across Alaska. We found large inter-quartile range (2.5-5.5 m) in predicted active-layer thickness of CMIP5 modeled results and small inter-quartile range (11.5-22 kg m-2) in predicted SOC stocks. The spatial coefficient of variability of active-layer thickness and SOC stocks were lower in CMIP5 predictions compared to our geospatial estimates when gridded at similar spatial resolutions (24.7 compared to 30% and 29 compared to 38%, respectively). However, prediction errors. when calculated for independent validation sites, were several times larger in ESM predictions compared to geospatial predictions. Primaly factors leading to observed differences were ( 1) lack of spatial heterogeneity in ESM predictions, (2) differences in assumptions concerning environmental controls, and (3) the absence of pedogenic processes in ESM model structures. Our results suggest that efforts to incorporate these factors in F.SMs should reduce current uncertainties associated with ESM predictions of carbon-climate feedbacks.« less

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).

Learning-Induced Plasticity in Medial Prefrontal Cortex Predicts Preference Malleability

PubMed Central

Garvert, Mona M.; Moutoussis, Michael; Kurth-Nelson, Zeb; Behrens, Timothy E.J.; Dolan, Raymond J.

2015-01-01

Summary Learning induces plasticity in neuronal networks. As neuronal populations contribute to multiple representations, we reasoned plasticity in one representation might influence others. We used human fMRI repetition suppression to show that plasticity induced by learning another individual’s values impacts upon a value representation for oneself in medial prefrontal cortex (mPFC), a plasticity also evident behaviorally in a preference shift. We show this plasticity is driven by a striatal “prediction error,” signaling the discrepancy between the other’s choice and a subject’s own preferences. Thus, our data highlight that mPFC encodes agent-independent representations of subjective value, such that prediction errors simultaneously update multiple agents’ value representations. As the resulting change in representational similarity predicts interindividual differences in the malleability of subjective preferences, our findings shed mechanistic light on complex human processes such as the powerful influence of social interaction on beliefs and preferences. PMID:25611512

Potential impact of remote sensing data on sea-state analysis and prediction

NASA Technical Reports Server (NTRS)

Cardone, V. J.

1983-01-01

The severe North Atlantic storm which damaged the ocean liner Queen Elizabeth 2 (QE2) was studied to assess the impact of remotely sensed marine surface wind data obtained by SEASAT-A, on sea state specifications and forecasts. Alternate representations of the surface wind field in the QE2 storm were produced from the SEASAT enhanced data base, and from operational analyses based upon conventional data. The wind fields were used to drive a high resolution spectral ocean surface wave prediction model. Results show that sea state analyses would have been vastly improved during the period of storm formation and explosive development had remote sensing wind data been available in real time. A modest improvement in operational 12 to 24 hour wave forecasts would have followed automatically from the improved initial state specification made possible by the remote sensing data in both numerical and sea state prediction models. Significantly improved 24 to 48 hour wave forecasts require in addition to remote sensing data, refinement in the numerical and physical aspects of weather prediction models.

Memory consolidation by replay of stimulus-specific neural activity.

PubMed

Deuker, Lorena; Olligs, Jan; Fell, Juergen; Kranz, Thorsten A; Mormann, Florian; Montag, Christian; Reuter, Martin; Elger, Christian E; Axmacher, Nikolai

2013-12-04

Memory consolidation transforms initially labile memory traces into more stable representations. One putative mechanism for consolidation is the reactivation of memory traces after their initial encoding during subsequent sleep or waking state. However, it is still unknown whether consolidation of individual memory contents relies on reactivation of stimulus-specific neural representations in humans. Investigating stimulus-specific representations in humans is particularly difficult, but potentially feasible using multivariate pattern classification analysis (MVPA). Here, we show in healthy human participants that stimulus-specific activation patterns can indeed be identified with MVPA, that these patterns reoccur spontaneously during postlearning resting periods and sleep, and that the frequency of reactivation predicts subsequent memory for individual items. We conducted a paired-associate learning task with items and spatial positions and extracted stimulus-specific activity patterns by MVPA in a simultaneous electroencephalography and functional magnetic resonance imaging (fMRI) study. As a first step, we investigated the amount of fMRI volumes during rest that resembled either one of the items shown before or one of the items shown as a control after the resting period. Reactivations during both awake resting state and sleep predicted subsequent memory. These data are first evidence that spontaneous reactivation of stimulus-specific activity patterns during resting state can be investigated using MVPA. They show that reactivation occurs in humans and is behaviorally relevant for stabilizing memory traces against interference. They move beyond previous studies because replay was investigated on the level of individual stimuli and because reactivations were not evoked by sensory cues but occurred spontaneously.

Evaluating the Community Land Model (CLM4.5) at a coniferous forest site in northwestern United States using flux and carbon-isotope measurements

Treesearch

Henrique F. Duarte; Brett M. Raczka; Daniel M. Ricciuto; John C. Lin; Charles D. Koven; Peter E. Thornton; David R. Bowling; Chun-Ta Lai; Kenneth J. Bible; James R. Ehleringer

2017-01-01

Droughts in the western United States are expected to intensify with climate change. Thus, an adequate representation of ecosystem response to water stress in land models is critical for predicting carbon dynamics. The goal of this study was to evaluate the performance of the Community Land Model (CLM) version 4.5 against observations at an old-growth coniferous forest...

Disruption of Attention by Irrelevant Stimuli in Serial Recall

ERIC Educational Resources Information Center

Lange, Elke B.

2005-01-01

In four experiments the behavioral consequences of an involuntary attentional distraction concerning memory performance was investigated. The working memory model of Cowan (1995) predicts a performance deficit for memory representations that are held in an active state when the focus of attention is distracted by a change in physical properties.…

On the Relation between Maternal State of Mind and Sensitivity in the Prediction of Infant Attachment Security

ERIC Educational Resources Information Center

Atkinson, Leslie; Goldberg, Susan; Raval, Vaishali; Pederson, David; Benoit, Diane; Moran, Greg; Poulton, Lori; Myhal, Natalie; Zwiers, Michael; Leung, Eman

2005-01-01

Attachment theorists assume that maternal mental representations influence responsivity, which influences infant attachment security. However, primary studies do not support this mediation model. The authors tested mediation using 2 mother-infant samples and found no evidence of mediation. Therefore, the authors explored sensitivity as a…

Predictive genetic testing for hereditary breast and ovarian cancer: psychological distress and illness representations 1 year following disclosure.

PubMed

Claes, E; Evers-Kiebooms, G; Denayer, L; Decruyenaere, M; Boogaerts, A; Philippe, K; Legius, E

2005-10-01

This prospective study evaluates emotional functioning and illness representations in 68 unaffected women (34 carriers/34 noncarriers) 1 year after predictive testing for BRCA1/2 mutations when offered within a multidisciplinary approach. Carriers had higher subjective risk perception of breast cancer than noncarriers. Carriers who did not have prophylactic oophorectomy had the highest risk perception of ovarian cancer. No differences were found between carriers and noncarriers regarding perceived seriousness and perceived control of breast and ovarian cancer. Mean levels of distress were within normal ranges. Only few women showed an overall pattern of clinically elevated distress. Cancer-specific distress and state-anxiety significantly decreased in noncarriers from pre- to posttest while general distress remained about the same. There were no significant changes in distress in the group of carriers except for ovarian cancer distress which significantly decreased from pre- to posttest. Our study did not reveal adverse effects of predictive testing when offered in the context of a multidisciplinary approach.

A Sensitive and Specific Neural Signature for Picture-Induced Negative Affect

PubMed Central

Chang, Luke J.; Gianaros, Peter J.; Manuck, Stephen B.; Krishnan, Anjali; Wager, Tor D.

2015-01-01

Neuroimaging has identified many correlates of emotion but has not yet yielded brain representations predictive of the intensity of emotional experiences in individuals. We used machine learning to identify a sensitive and specific signature of emotional responses to aversive images. This signature predicted the intensity of negative emotion in individual participants in cross validation (n =121) and test (n = 61) samples (high–low emotion = 93.5% accuracy). It was unresponsive to physical pain (emotion–pain = 92% discriminative accuracy), demonstrating that it is not a representation of generalized arousal or salience. The signature was comprised of mesoscale patterns spanning multiple cortical and subcortical systems, with no single system necessary or sufficient for predicting experience. Furthermore, it was not reducible to activity in traditional “emotion-related” regions (e.g., amygdala, insula) or resting-state networks (e.g., “salience,” “default mode”). Overall, this work identifies differentiable neural components of negative emotion and pain, providing a basis for new, brain-based taxonomies of affective processes. PMID:26098873

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.

The role of national identity representation in the relation between in-group identification and out-group derogation: ethnic versus civic representation.

PubMed

Meeus, Joke; Duriez, Bart; Vanbeselaere, Norbert; Boen, Filip

2010-06-01

Two studies investigated whether the content of in-group identity affects the relation between in-group identification and ethnic prejudice. The first study among university students, tested whether national identity representations (i.e., ethnic vs. civic) moderate or mediate the relation between Flemish in-group identification and ethnic prejudice. A moderation hypothesis is supported when those higher in identification who subscribe to a more ethnic representation display higher ethnic prejudice levels than those higher in identification who subscribe to a more civic representation. A mediation hypothesis is supported when those higher in identification tend towards one specific representation, which in turn, should predict ethnic prejudice. Results supported a mediation hypothesis and showed that the more respondents identified with the Flemish in-group, the more ethnic their identity representation, and the more they were inclined to display ethnic prejudice. The second study tested this mediation from a longitudinal perspective in a two-wave study among high school students. In-group identification at Time 1 predicted over-time changes in identity representation, which in turn, predicted changes in ethnic prejudice. In addition to this, changes in identity representation were predicted by initial ethnic prejudice levels. The implications of these findings are discussed.

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.

Statistical Orbit Determination using the Particle Filter for Incorporating Non-Gaussian Uncertainties

NASA Technical Reports Server (NTRS)

Mashiku, Alinda; Garrison, James L.; Carpenter, J. Russell

2012-01-01

The tracking of space objects requires frequent and accurate monitoring for collision avoidance. As even collision events with very low probability are important, accurate prediction of collisions require the representation of the full probability density function (PDF) of the random orbit state. Through representing the full PDF of the orbit state for orbit maintenance and collision avoidance, we can take advantage of the statistical information present in the heavy tailed distributions, more accurately representing the orbit states with low probability. The classical methods of orbit determination (i.e. Kalman Filter and its derivatives) provide state estimates based on only the second moments of the state and measurement errors that are captured by assuming a Gaussian distribution. Although the measurement errors can be accurately assumed to have a Gaussian distribution, errors with a non-Gaussian distribution could arise during propagation between observations. Moreover, unmodeled dynamics in the orbit model could introduce non-Gaussian errors into the process noise. A Particle Filter (PF) is proposed as a nonlinear filtering technique that is capable of propagating and estimating a more complete representation of the state distribution as an accurate approximation of a full PDF. The PF uses Monte Carlo runs to generate particles that approximate the full PDF representation. The PF is applied in the estimation and propagation of a highly eccentric orbit and the results are compared to the Extended Kalman Filter and Splitting Gaussian Mixture algorithms to demonstrate its proficiency.

Why trace and delay conditioning are sometimes (but not always) hippocampal dependent: A computational model

PubMed Central

Moustafa, Ahmed A.; Wufong, Ella; Servatius, Richard J.; Pang, Kevin C. H.; Gluck, Mark A.; Myers, Catherine E.

2013-01-01

A recurrent-network model provides a unified account of the hippocampal region in mediating the representation of temporal information in classical eyeblink conditioning. Much empirical research is consistent with a general conclusion that delay conditioning (in which the conditioned stimulus CS and unconditioned stimulus US overlap and co-terminate) is independent of the hippocampal system, while trace conditioning (in which the CS terminates before US onset) depends on the hippocampus. However, recent studies show that, under some circumstances, delay conditioning can be hippocampal-dependent and trace conditioning can be spared following hippocampal lesion. Here, we present an extension of our prior trial-level models of hippocampal function and stimulus representation that can explain these findings within a unified framework. Specifically, the current model includes adaptive recurrent collateral connections that aid in the representation of intra-trial temporal information. With this model, as in our prior models, we argue that the hippocampus is not specialized for conditioned response timing, but rather is a general-purpose system that learns to predict the next state of all stimuli given the current state of variables encoded by activity in recurrent collaterals. As such, the model correctly predicts that hippocampal involvement in classical conditioning should be critical not only when there is an intervening trace interval, but also when there is a long delay between CS onset and US onset. Our model simulates empirical data from many variants of classical conditioning, including delay and trace paradigms in which the length of the CS, the inter-stimulus interval, or the trace interval is varied. Finally, we discuss model limitations, future directions, and several novel empirical predictions of this temporal processing model of hippocampal function and learning. PMID:23178699

Theory of cortical function

PubMed Central

Heeger, David J.

2017-01-01

Most models of sensory processing in the brain have a feedforward architecture in which each stage comprises simple linear filtering operations and nonlinearities. Models of this form have been used to explain a wide range of neurophysiological and psychophysical data, and many recent successes in artificial intelligence (with deep convolutional neural nets) are based on this architecture. However, neocortex is not a feedforward architecture. This paper proposes a first step toward an alternative computational framework in which neural activity in each brain area depends on a combination of feedforward drive (bottom-up from the previous processing stage), feedback drive (top-down context from the next stage), and prior drive (expectation). The relative contributions of feedforward drive, feedback drive, and prior drive are controlled by a handful of state parameters, which I hypothesize correspond to neuromodulators and oscillatory activity. In some states, neural responses are dominated by the feedforward drive and the theory is identical to a conventional feedforward model, thereby preserving all of the desirable features of those models. In other states, the theory is a generative model that constructs a sensory representation from an abstract representation, like memory recall. In still other states, the theory combines prior expectation with sensory input, explores different possible perceptual interpretations of ambiguous sensory inputs, and predicts forward in time. The theory, therefore, offers an empirically testable framework for understanding how the cortex accomplishes inference, exploration, and prediction. PMID:28167793

Episodic Memory Does Not Add Up: Verbatim-Gist Superposition Predicts Violations of the Additive Law of Probability

PubMed Central

Brainerd, C. J.; Wang, Zheng; Reyna, Valerie. F.; Nakamura, K.

2015-01-01

Fuzzy-trace theory’s assumptions about memory representation are cognitive examples of the familiar superposition property of physical quantum systems. When those assumptions are implemented in a formal quantum model (QEMc), they predict that episodic memory will violate the additive law of probability: If memory is tested for a partition of an item’s possible episodic states, the individual probabilities of remembering the item as belonging to each state must sum to more than 1. We detected this phenomenon using two standard designs, item false memory and source false memory. The quantum implementation of fuzzy-trace theory also predicts that violations of the additive law will vary in strength as a function of reliance on gist memory. That prediction, too, was confirmed via a series of manipulations (e.g., semantic relatedness, testing delay) that are thought to increase gist reliance. Surprisingly, an analysis of the underlying structure of violations of the additive law revealed that as a general rule, increases in remembering correct episodic states do not produce commensurate reductions in remembering incorrect states. PMID:26236091

The Representation of Prediction Error in Auditory Cortex

PubMed Central

Rubin, Jonathan; Ulanovsky, Nachum; Tishby, Naftali

2016-01-01

To survive, organisms must extract information from the past that is relevant for their future. How this process is expressed at the neural level remains unclear. We address this problem by developing a novel approach from first principles. We show here how to generate low-complexity representations of the past that produce optimal predictions of future events. We then illustrate this framework by studying the coding of ‘oddball’ sequences in auditory cortex. We find that for many neurons in primary auditory cortex, trial-by-trial fluctuations of neuronal responses correlate with the theoretical prediction error calculated from the short-term past of the stimulation sequence, under constraints on the complexity of the representation of this past sequence. In some neurons, the effect of prediction error accounted for more than 50% of response variability. Reliable predictions often depended on a representation of the sequence of the last ten or more stimuli, although the representation kept only few details of that sequence. PMID:27490251

Word-Decoding Skill Interacts with Working Memory Capacity to Influence Inference Generation during Reading

ERIC Educational Resources Information Center

Hamilton, Stephen; Freed, Erin; Long, Debra L.

2016-01-01

The aim of this study was to examine predictions derived from a proposal about the relation between word-decoding skill and working memory capacity, called verbal efficiency theory. The theory states that poor word representations and slow decoding processes consume resources in working memory that would otherwise be used to execute high-level…

Inverse Thermal Analysis of Titanium GTA Welds Using Multiple Constraints

NASA Astrophysics Data System (ADS)

Lambrakos, S. G.; Shabaev, A.; Huang, L.

2015-06-01

Inverse thermal analysis of titanium gas-tungsten-arc welds using multiple constraint conditions is presented. This analysis employs a methodology that is in terms of numerical-analytical basis functions for inverse thermal analysis of steady-state energy deposition in plate structures. The results of this type of analysis provide parametric representations of weld temperature histories that can be adopted as input data to various types of computational procedures, such as those for prediction of solid-state phase transformations. In addition, these temperature histories can be used to construct parametric function representations for inverse thermal analysis of welds corresponding to other process parameters or welding processes whose process conditions are within similar regimes. The present study applies an inverse thermal analysis procedure that provides for the inclusion of constraint conditions associated with both solidification and phase transformation boundaries.

Prioritizing Information during Working Memory: Beyond Sustained Internal Attention.

PubMed

Myers, Nicholas E; Stokes, Mark G; Nobre, Anna C

2017-06-01

Working memory (WM) has limited capacity. This leaves attention with the important role of allowing into storage only the most relevant information. It is increasingly evident that attention is equally crucial for prioritizing representations within WM as the importance of individual items changes. Retrospective prioritization has been proposed to result from a focus of internal attention highlighting one of several representations. Here, we suggest an updated model, in which prioritization acts in multiple steps: first orienting towards and selecting a memory, and then reconfiguring its representational state in the service of upcoming task demands. Reconfiguration sets up an optimized perception-action mapping, obviating the need for sustained attention. This view is consistent with recent literature, makes testable predictions, and links WM with task switching and action preparation. Copyright © 2017 Elsevier Ltd. All rights reserved.

Time Alignment as a Necessary Step in the Analysis of Sleep Probabilistic Curves

NASA Astrophysics Data System (ADS)

Rošt'áková, Zuzana; Rosipal, Roman

2018-02-01

Sleep can be characterised as a dynamic process that has a finite set of sleep stages during the night. The standard Rechtschaffen and Kales sleep model produces discrete representation of sleep and does not take into account its dynamic structure. In contrast, the continuous sleep representation provided by the probabilistic sleep model accounts for the dynamics of the sleep process. However, analysis of the sleep probabilistic curves is problematic when time misalignment is present. In this study, we highlight the necessity of curve synchronisation before further analysis. Original and in time aligned sleep probabilistic curves were transformed into a finite dimensional vector space, and their ability to predict subjects' age or daily measures is evaluated. We conclude that curve alignment significantly improves the prediction of the daily measures, especially in the case of the S2-related sleep states or slow wave sleep.

Regional Ocean Data Assimilation

NASA Astrophysics Data System (ADS)

Edwards, Christopher A.; Moore, Andrew M.; Hoteit, Ibrahim; Cornuelle, Bruce D.

2015-01-01

This article reviews the past 15 years of developments in regional ocean data assimilation. A variety of scientific, management, and safety-related objectives motivate marine scientists to characterize many ocean environments, including coastal regions. As in weather prediction, the accurate representation of physical, chemical, and/or biological properties in the ocean is challenging. Models and observations alone provide imperfect representations of the ocean state, but together they can offer improved estimates. Variational and sequential methods are among the most widely used in regional ocean systems, and there have been exciting recent advances in ensemble and four-dimensional variational approaches. These techniques are increasingly being tested and adapted for biogeochemical applications.

Regional ocean data assimilation.

PubMed

Edwards, Christopher A; Moore, Andrew M; Hoteit, Ibrahim; Cornuelle, Bruce D

2015-01-01

This article reviews the past 15 years of developments in regional ocean data assimilation. A variety of scientific, management, and safety-related objectives motivate marine scientists to characterize many ocean environments, including coastal regions. As in weather prediction, the accurate representation of physical, chemical, and/or biological properties in the ocean is challenging. Models and observations alone provide imperfect representations of the ocean state, but together they can offer improved estimates. Variational and sequential methods are among the most widely used in regional ocean systems, and there have been exciting recent advances in ensemble and four-dimensional variational approaches. These techniques are increasingly being tested and adapted for biogeochemical applications.

Increasingly complex representations of natural movies across the dorsal stream are shared between subjects.

PubMed

Güçlü, Umut; van Gerven, Marcel A J

2017-01-15

Recently, deep neural networks (DNNs) have been shown to provide accurate predictions of neural responses across the ventral visual pathway. We here explore whether they also provide accurate predictions of neural responses across the dorsal visual pathway, which is thought to be devoted to motion processing and action recognition. This is achieved by training deep neural networks to recognize actions in videos and subsequently using them to predict neural responses while subjects are watching natural movies. Moreover, we explore whether dorsal stream representations are shared between subjects. In order to address this question, we examine if individual subject predictions can be made in a common representational space estimated via hyperalignment. Results show that a DNN trained for action recognition can be used to accurately predict how dorsal stream responds to natural movies, revealing a correspondence in representations of DNN layers and dorsal stream areas. It is also demonstrated that models operating in a common representational space can generalize to responses of multiple or even unseen individual subjects to novel spatio-temporal stimuli in both encoding and decoding settings, suggesting that a common representational space underlies dorsal stream responses across multiple subjects. Copyright © 2015 Elsevier Inc. All rights reserved.

Prediction of clinical depression scores and detection of changes in whole-brain using resting-state functional MRI data with partial least squares regression

PubMed Central

Shimizu, Yu; Yoshimoto, Junichiro; Takamura, Masahiro; Okada, Go; Okamoto, Yasumasa; Yamawaki, Shigeto; Doya, Kenji

2017-01-01

In diagnostic applications of statistical machine learning methods to brain imaging data, common problems include data high-dimensionality and co-linearity, which often cause over-fitting and instability. To overcome these problems, we applied partial least squares (PLS) regression to resting-state functional magnetic resonance imaging (rs-fMRI) data, creating a low-dimensional representation that relates symptoms to brain activity and that predicts clinical measures. Our experimental results, based upon data from clinically depressed patients and healthy controls, demonstrated that PLS and its kernel variants provided significantly better prediction of clinical measures than ordinary linear regression. Subsequent classification using predicted clinical scores distinguished depressed patients from healthy controls with 80% accuracy. Moreover, loading vectors for latent variables enabled us to identify brain regions relevant to depression, including the default mode network, the right superior frontal gyrus, and the superior motor area. PMID:28700672

The Past Is Present: Representations of Parents, Friends, and Romantic Partners Predict Subsequent Romantic Representations.

PubMed

Furman, Wyndol; Collibee, Charlene

2018-01-01

This study examined how representations of parent-child relationships, friendships, and past romantic relationships are related to subsequent romantic representations. Two-hundred 10th graders (100 female; M age  = 15.87 years) from diverse neighborhoods in a Western U.S. city were administered questionnaires and were interviewed to assess avoidant and anxious representations of their relationships with parents, friends, and romantic partners. Participants then completed similar questionnaires and interviews about their romantic representations six more times over the next 7.5 years. Growth curve analyses revealed that representations of relationships with parents, friends, and romantic partners each uniquely predicted subsequent romantic representations across development. Consistent with attachment and behavioral systems theory, representations of romantic relationships are revised by representations and experiences in other relationships. © 2016 The Authors. Child Development © 2016 Society for Research in Child Development, Inc.

Predicting the integration of overlapping memories by decoding mnemonic processing states during learning

PubMed Central

Richter, Franziska R.; Chanales, Avi J. H.; Kuhl, Brice A.

2015-01-01

The hippocampal memory system is thought to alternate between two opposing processing states: encoding and retrieval. When present experience overlaps with past experience, this creates a potential tradeoff between encoding the present and retrieving the past. This tradeoff may be resolved by memory integration—that is, by forming a mnemonic representation that links present experience with overlapping past experience. Here, we used fMRI decoding analyses to predict when—and establish how—past and present experiences become integrated in memory. In an initial experiment, we alternately instructed subjects to adopt encoding, retrieval or integration states during overlapping learning. We then trained across-subject pattern classifiers to ‘read out’ the instructed processing states from fMRI activity patterns. We show that an integration state was clearly dissociable from encoding or retrieval states. Moreover, trial-by-trial fluctuations in decoded evidence for an integration state during learning reliably predicted behavioral expressions of successful memory integration. Strikingly, the decoding algorithm also successfully predicted specific instances of spontaneous memory integration in an entirely independent sample of subjects for whom processing state instructions were not administered. Finally, we show that medial prefrontal cortex and hippocampus differentially contribute to encoding, retrieval, and integration states: whereas hippocampus signals the tradeoff between encoding vs. retrieval states, medial prefrontal cortex actively represents past experience in relation to new learning. PMID:26327243

A state-based probabilistic model for tumor respiratory motion prediction

NASA Astrophysics Data System (ADS)

Kalet, Alan; Sandison, George; Wu, Huanmei; Schmitz, Ruth

2010-12-01

This work proposes a new probabilistic mathematical model for predicting tumor motion and position based on a finite state representation using the natural breathing states of exhale, inhale and end of exhale. Tumor motion was broken down into linear breathing states and sequences of states. Breathing state sequences and the observables representing those sequences were analyzed using a hidden Markov model (HMM) to predict the future sequences and new observables. Velocities and other parameters were clustered using a k-means clustering algorithm to associate each state with a set of observables such that a prediction of state also enables a prediction of tumor velocity. A time average model with predictions based on average past state lengths was also computed. State sequences which are known a priori to fit the data were fed into the HMM algorithm to set a theoretical limit of the predictive power of the model. The effectiveness of the presented probabilistic model has been evaluated for gated radiation therapy based on previously tracked tumor motion in four lung cancer patients. Positional prediction accuracy is compared with actual position in terms of the overall RMS errors. Various system delays, ranging from 33 to 1000 ms, were tested. Previous studies have shown duty cycles for latencies of 33 and 200 ms at around 90% and 80%, respectively, for linear, no prediction, Kalman filter and ANN methods as averaged over multiple patients. At 1000 ms, the previously reported duty cycles range from approximately 62% (ANN) down to 34% (no prediction). Average duty cycle for the HMM method was found to be 100% and 91 ± 3% for 33 and 200 ms latency and around 40% for 1000 ms latency in three out of four breathing motion traces. RMS errors were found to be lower than linear and no prediction methods at latencies of 1000 ms. The results show that for system latencies longer than 400 ms, the time average HMM prediction outperforms linear, no prediction, and the more general HMM-type predictive models. RMS errors for the time average model approach the theoretical limit of the HMM, and predicted state sequences are well correlated with sequences known to fit the data.

Effective chiral restoration in the ρ' meson in lattice QCD

NASA Astrophysics Data System (ADS)

Glozman, L. Ya.; Lang, C. B.; Limmer, Markus

2010-11-01

In simulations with dynamical quarks it has been established that the ground state ρ in the infrared is a strong mixture of the two chiral representations (0,1)+(1,0) and (1/2,1/2)b. Its angular momentum content is approximately the S13 partial wave. Effective chiral restoration in an excited ρ-meson would require that in the infrared this meson couples predominantly to one of the two representations. The variational method allows one to study the mixing of interpolators with different chiral transformation properties in the nonperturbatively determined excited state at different resolution scales. We present results for the first excited state of the ρ-meson using simulations with nf=2 dynamical quarks. We point out, that in the infrared a leading contribution to ρ'=ρ(1450) comes from (1/2,1/2)b, in contrast to the ρ. The ρ' wave function contains a significant contribution of the D13 wave which is not consistent with the quark model prediction.

The Wave Logic of Consciousness: A Hypothesis

NASA Astrophysics Data System (ADS)

Orlov, Yuri F.

1982-01-01

A physical model is proposed for volitional decision making. It is postulated that consciousness reduces doubt states of the brain into labels by a quantum-mechanical measurement act of free choice. Elementary doubt states illustrate analogical encodement of information having “insufficient resolution” from a classical viewpoint. Measures of certitude (inner conviction) and doubt are formulated. “Adequate propositions” for nonclassical statements, e.g., Hamlet's soliloquy, are constructed. A role is proposed for the superposition principle in imagination and creativity. Experimental predictions are offered for positive and negative interference of doubts. Necessary criteria are made explicit for doubting sense information. Wholeness of perception is illustrated using irreducible, unitary representations of n-valued logics. The interpreted formalism includes nonclassical features of doubt, e.g., scalor representations for imprecise propositions and state changes due to self-reflection. The “liar paradox” is resolved. An internal origin is suggested for spinor dichotomies, e.g., “true-false” and “good-bad,” analogous to particle production.

Active inference and cognitive-emotional interactions in the brain.

PubMed

Pezzulo, Giovanni; Barca, Laura; Friston, Karl J

2015-01-01

All organisms must integrate cognition, emotion, and motivation to guide action toward valuable (goal) states, as described by active inference. Within this framework, cognition, emotion, and motivation interact through the (Bayesian) fusion of exteroceptive, proprioceptive, and interoceptive signals, the precision-weighting of prediction errors, and the "affective tuning" of neuronal representations. Crucially, misregulation of these processes may have profound psychopathological consequences.

Examining the Pipeline: People of Color's Pathway to Headship

ERIC Educational Resources Information Center

Brown, Ara Carlos

2016-01-01

Predictions indicate that a large majority of the heads of independent schools in the United States will retire within the next decade. Although there is a push for a greater representation of people of color in this position, in order for people of color to be considered, it seems that they are held to a higher standard than their white…

How Equivalent Are the Action Execution, Imagery, and Observation of Intransitive Movements? Revisiting the Concept of Somatotopy during Action Simulation

ERIC Educational Resources Information Center

Lorey, Britta; Naumann, Tim; Pilgramm, Sebastian; Petermann, Carmen; Bischoff, Matthias; Zentgraf, Karen; Stark, Rudolf; Vaitl, Dieter; Munzert, Jorn

2013-01-01

Jeannerod (2001) hypothesized that action execution, imagery, and observation are functionally equivalent. This led to the major prediction that these motor states are based on the same action-specific and even effector-specific motor representations. The present study examined whether hand and foot movements are represented in a somatotopic…

In silico toxicity prediction by support vector machine and SMILES representation-based string kernel.

PubMed

Cao, D-S; Zhao, J-C; Yang, Y-N; Zhao, C-X; Yan, J; Liu, S; Hu, Q-N; Xu, Q-S; Liang, Y-Z

2012-01-01

There is a great need to assess the harmful effects or toxicities of chemicals to which man is exposed. In the present paper, the simplified molecular input line entry specification (SMILES) representation-based string kernel, together with the state-of-the-art support vector machine (SVM) algorithm, were used to classify the toxicity of chemicals from the US Environmental Protection Agency Distributed Structure-Searchable Toxicity (DSSTox) database network. In this method, the molecular structure can be directly encoded by a series of SMILES substrings that represent the presence of some chemical elements and different kinds of chemical bonds (double, triple and stereochemistry) in the molecules. Thus, SMILES string kernel can accurately and directly measure the similarities of molecules by a series of local information hidden in the molecules. Two model validation approaches, five-fold cross-validation and independent validation set, were used for assessing the predictive capability of our developed models. The results obtained indicate that SVM based on the SMILES string kernel can be regarded as a very promising and alternative modelling approach for potential toxicity prediction of chemicals.

Dynamic Data-Driven Reduced-Order Models of Macroscale Quantities for the Prediction of Equilibrium System State for Multiphase Porous Medium Systems

NASA Astrophysics Data System (ADS)

Talbot, C.; McClure, J. E.; Armstrong, R. T.; Mostaghimi, P.; Hu, Y.; Miller, C. T.

2017-12-01

Microscale simulation of multiphase flow in realistic, highly-resolved porous medium systems of a sufficient size to support macroscale evaluation is computationally demanding. Such approaches can, however, reveal the dynamic, steady, and equilibrium states of a system. We evaluate methods to utilize dynamic data to reduce the cost associated with modeling a steady or equilibrium state. We construct data-driven models using extensions to dynamic mode decomposition (DMD) and its connections to Koopman Operator Theory. DMD and its variants comprise a class of equation-free methods for dimensionality reduction of time-dependent nonlinear dynamical systems. DMD furnishes an explicit reduced representation of system states in terms of spatiotemporally varying modes with time-dependent oscillation frequencies and amplitudes. We use DMD to predict the steady and equilibrium macroscale state of a realistic two-fluid porous medium system imaged using micro-computed tomography (µCT) and simulated using the lattice Boltzmann method (LBM). We apply Koopman DMD to direct numerical simulation data resulting from simulations of multiphase fluid flow through a 1440x1440x4320 section of a full 1600x1600x5280 realization of imaged sandstone. We determine a representative set of system observables via dimensionality reduction techniques including linear and kernel principal component analysis. We demonstrate how this subset of macroscale quantities furnishes a representation of the time-evolution of the system in terms of dynamic modes, and discuss the selection of a subset of DMD modes yielding the optimal reduced model, as well as the time-dependence of the error in the predicted equilibrium value of each macroscale quantity. Finally, we describe how the above procedure, modified to incorporate methods from compressed sensing and random projection techniques, may be used in an online fashion to facilitate adaptive time-stepping and parsimonious storage of system states over time.

Diverse Region-Based CNN for Hyperspectral Image Classification.

PubMed

Zhang, Mengmeng; Li, Wei; Du, Qian

2018-06-01

Convolutional neural network (CNN) is of great interest in machine learning and has demonstrated excellent performance in hyperspectral image classification. In this paper, we propose a classification framework, called diverse region-based CNN, which can encode semantic context-aware representation to obtain promising features. With merging a diverse set of discriminative appearance factors, the resulting CNN-based representation exhibits spatial-spectral context sensitivity that is essential for accurate pixel classification. The proposed method exploiting diverse region-based inputs to learn contextual interactional features is expected to have more discriminative power. The joint representation containing rich spectral and spatial information is then fed to a fully connected network and the label of each pixel vector is predicted by a softmax layer. Experimental results with widely used hyperspectral image data sets demonstrate that the proposed method can surpass any other conventional deep learning-based classifiers and other state-of-the-art classifiers.

Interdisciplinary Data Fusion for Diachronic 3d Reconstruction of Historic Sites

NASA Astrophysics Data System (ADS)

Micoli, L. L.; Gonizzi Barsanti, S.; Guidi, G.

2017-02-01

In recent decades, 3D reconstruction has progressively become a tool to show archaeological and architectural monuments in their current state, presumed past aspect and to predict their future evolution. The 3D representations trough time can be useful in order to study and preserve the memory of Cultural Heritage and to plan maintenance and promotion of the historical sites. This paper represent a case study, at architectonic and urbanistic scale, based on methodological approach for CH time-varying representations proposed by JPI-CH European Project called Cultural Heritage Through Time (CHT2). The work is focused on the area of Milan Roman circus, relatively to which was conducted both a thorough philological research based on several sources and a 3D survey campaign of still accessible remains, aiming at obtaining the monumental representation of the area in 3 different ages.

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

Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art.

PubMed

Walia, Rasna R; Caragea, Cornelia; Lewis, Benjamin A; Towfic, Fadi; Terribilini, Michael; El-Manzalawy, Yasser; Dobbs, Drena; Honavar, Vasant

2012-05-10

RNA molecules play diverse functional and structural roles in cells. They function as messengers for transferring genetic information from DNA to proteins, as the primary genetic material in many viruses, as catalysts (ribozymes) important for protein synthesis and RNA processing, and as essential and ubiquitous regulators of gene expression in living organisms. Many of these functions depend on precisely orchestrated interactions between RNA molecules and specific proteins in cells. Understanding the molecular mechanisms by which proteins recognize and bind RNA is essential for comprehending the functional implications of these interactions, but the recognition 'code' that mediates interactions between proteins and RNA is not yet understood. Success in deciphering this code would dramatically impact the development of new therapeutic strategies for intervening in devastating diseases such as AIDS and cancer. Because of the high cost of experimental determination of protein-RNA interfaces, there is an increasing reliance on statistical machine learning methods for training predictors of RNA-binding residues in proteins. However, because of differences in the choice of datasets, performance measures, and data representations used, it has been difficult to obtain an accurate assessment of the current state of the art in protein-RNA interface prediction. We provide a review of published approaches for predicting RNA-binding residues in proteins and a systematic comparison and critical assessment of protein-RNA interface residue predictors trained using these approaches on three carefully curated non-redundant datasets. We directly compare two widely used machine learning algorithms (Naïve Bayes (NB) and Support Vector Machine (SVM)) using three different data representations in which features are encoded using either sequence- or structure-based windows. Our results show that (i) Sequence-based classifiers that use a position-specific scoring matrix (PSSM)-based representation (PSSMSeq) outperform those that use an amino acid identity based representation (IDSeq) or a smoothed PSSM (SmoPSSMSeq); (ii) Structure-based classifiers that use smoothed PSSM representation (SmoPSSMStr) outperform those that use PSSM (PSSMStr) as well as sequence identity based representation (IDStr). PSSMSeq classifiers, when tested on an independent test set of 44 proteins, achieve performance that is comparable to that of three state-of-the-art structure-based predictors (including those that exploit geometric features) in terms of Matthews Correlation Coefficient (MCC), although the structure-based methods achieve substantially higher Specificity (albeit at the expense of Sensitivity) compared to sequence-based methods. We also find that the expected performance of the classifiers on a residue level can be markedly different from that on a protein level. Our experiments show that the classifiers trained on three different non-redundant protein-RNA interface datasets achieve comparable cross-validation performance. However, we find that the results are significantly affected by differences in the distance threshold used to define interface residues. Our results demonstrate that protein-RNA interface residue predictors that use a PSSM-based encoding of sequence windows outperform classifiers that use other encodings of sequence windows. While structure-based methods that exploit geometric features can yield significant increases in the Specificity of protein-RNA interface residue predictions, such increases are offset by decreases in Sensitivity. These results underscore the importance of comparing alternative methods using rigorous statistical procedures, multiple performance measures, and datasets that are constructed based on several alternative definitions of interface residues and redundancy cutoffs as well as including evaluations on independent test sets into the comparisons.

Mothers' and fathers' attachment and caregiving representations during transition to parenthood: an actor-partner approach.

PubMed

Fonseca, Ana; Nazaré, Bárbara; Canavarro, Maria Cristina

2018-07-01

This study aimed to investigate the effect of one's attachment representations on one's and the partner's caregiving representations. According to attachment theory, individual differences in parenting and caregiving behaviours may be a function of parents' caregiving representations of the self as caregiver, and of others as worthy of care, which are rooted on parents' attachment representations. Furthermore, the care-seeking and caregiving interactions that occur within the couple relationship may also shape individuals' caregiving representations. The sample comprised 286 cohabiting couples who were assessed during pregnancy (attachment representations) and one month post-birth (caregiving representations). Path analyses were used to examine effects among variables. Results showed that for mothers and fathers, their own more insecure attachment representations predicted their less positive caregiving representations of the self as caregiver and of others as worthy of help and more self-focused motivations for caregiving. Moreover, fathers' attachment representations were found to predict mothers' caregiving representations of themselves as caregivers. Secure attachment representations of both members of the couple seem to be an inner resource promoting parents' positive representations of caregiving, and should be assessed and fostered during the transition to parenthood in both members of the couple.

Pseudo-steady-state non-Gaussian Einstein-Podolsky-Rosen steering of massive particles in pumped and damped Bose-Hubbard dimers

NASA Astrophysics Data System (ADS)

Olsen, M. K.

2017-02-01

We propose and analyze a pumped and damped Bose-Hubbard dimer as a source of continuous-variable Einstein-Podolsky-Rosen (EPR) steering with non-Gaussian statistics. We use and compare the results of the approximate truncated Wigner and the exact positive-P representation to calculate and compare the predictions for intensities, second-order quantum correlations, and third- and fourth-order cumulants. We find agreement for intensities and the products of inferred quadrature variances, which indicate that states demonstrating the EPR paradox are present. We find clear signals of non-Gaussianity in the quantum states of the modes from both the approximate and exact techniques, with quantitative differences in their predictions. Our proposed experimental configuration is extrapolated from current experimental techniques and adds another apparatus to the current toolbox of quantum atom optics.

Self-consistent quantum kinetic theory of diatomic molecule formation

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

Forrey, Robert C.

2015-07-14

A quantum kinetic theory of molecule formation is presented which includes three-body recombination and radiative association for a thermodynamically closed system which may or may not exchange energy with its surrounding at a constant temperature. The theory uses a Sturmian representation of a two-body continuum to achieve a steady-state solution of a governing master equation which is self-consistent in the sense that detailed balance between all bound and unbound states is rigorously enforced. The role of quasibound states in catalyzing the molecule formation is analyzed in complete detail. The theory is used to make three predictions which differ from conventionalmore » kinetic models. These predictions suggest significant modifications may be needed to phenomenological rate constants which are currently in wide use. Implications for models of low and high density systems are discussed.« less

New Three-Mode Squeezing Operators Gained via Tripartite Entangled State Representation

NASA Astrophysics Data System (ADS)

Jiang, Nian-Quan; Fan, Hong-Yi

2008-01-01

We show that the Agarwal Simon representation of single-mode squeezed states can be generalized to find new form of three-mode squeezed states. We use the tripartite entangled state representations |p,y,z> and |x,u,v> to realize this goal.

Identity-Specific Reward Representations in Orbitofrontal Cortex Are Modulated by Selective Devaluation

PubMed Central

Howard, James D.

2017-01-01

Goal-directed behavior is sensitive to the current value of expected outcomes. This requires independent representations of specific rewards, which have been linked to orbitofrontal cortex (OFC) function. However, the mechanisms by which the human brain updates specific goals on the fly, and translates those updates into choices, have remained unknown. Here we implemented selective devaluation of appetizing food odors in combination with pattern-based neuroimaging and a decision-making task. We found that in a hungry state, participants chose to smell high-intensity versions of two value-matched food odor rewards. After eating a meal corresponding to one of the two odors, participants switched choices toward the low intensity of the sated odor but continued to choose the high intensity of the nonsated odor. This sensory-specific behavioral effect was mirrored by pattern-based changes in fMRI signal in lateral posterior OFC, where specific reward identity representations were altered after the meal for the sated food odor but retained for the nonsated counterpart. In addition, changes in functional connectivity between the OFC and general value coding in ventromedial prefrontal cortex (vmPFC) predicted individual differences in satiety-related choice behavior. These findings demonstrate how flexible representations of specific rewards in the OFC are updated by devaluation, and how functional connections to vmPFC reflect the current value of outcomes and guide goal-directed behavior. SIGNIFICANCE STATEMENT The orbitofrontal cortex (OFC) is critical for goal-directed behavior. A recent proposal is that OFC fulfills this function by representing a variety of state and task variables (“cognitive maps”), including a conjunction of expected reward identity and value. Here we tested how identity-specific representations of food odor reward are updated by satiety. We found that fMRI pattern-based signatures of reward identity in lateral posterior OFC were modulated after selective devaluation, and that connectivity between this region and general value coding ventromedial prefrontal cortex (vmPFC) predicted choice behavior. These results provide evidence for a mechanism by which devaluation modulates a cognitive map of expected reward in OFC and thereby alters general value signals in vmPFC to guide goal-directed behavior. PMID:28159906

Hierarchical modeling of molecular energies using a deep neural network

NASA Astrophysics Data System (ADS)

Lubbers, Nicholas; Smith, Justin S.; Barros, Kipton

2018-06-01

We introduce the Hierarchically Interacting Particle Neural Network (HIP-NN) to model molecular properties from datasets of quantum calculations. Inspired by a many-body expansion, HIP-NN decomposes properties, such as energy, as a sum over hierarchical terms. These terms are generated from a neural network—a composition of many nonlinear transformations—acting on a representation of the molecule. HIP-NN achieves the state-of-the-art performance on a dataset of 131k ground state organic molecules and predicts energies with 0.26 kcal/mol mean absolute error. With minimal tuning, our model is also competitive on a dataset of molecular dynamics trajectories. In addition to enabling accurate energy predictions, the hierarchical structure of HIP-NN helps to identify regions of model uncertainty.

Gender in Science and Engineering Faculties: Demographic Inertia Revisited.

PubMed

Thomas, Nicole R; Poole, Daniel J; Herbers, Joan M

2015-01-01

The under-representation of women on faculties of science and engineering is ascribed in part to demographic inertia, which is the lag between retirement of current faculty and future hires. The assumption of demographic inertia implies that, given enough time, gender parity will be achieved. We examine that assumption via a semi-Markov model to predict the future faculty, with simulations that predict the convergence demographic state. Our model shows that existing practices that produce gender gaps in recruitment, retention, and career progression preclude eventual gender parity. Further, we examine sensitivity of the convergence state to current gender gaps to show that all sources of disparity across the entire faculty career must be erased to produce parity: we cannot blame demographic inertia.

Field-theory representation of gauge-gravity symmetry-protected topological invariants, group cohomology, and beyond.

PubMed

Wang, Juven C; Gu, Zheng-Cheng; Wen, Xiao-Gang

2015-01-23

The challenge of identifying symmetry-protected topological states (SPTs) is due to their lack of symmetry-breaking order parameters and intrinsic topological orders. For this reason, it is impossible to formulate SPTs under Ginzburg-Landau theory or probe SPTs via fractionalized bulk excitations and topology-dependent ground state degeneracy. However, the partition functions from path integrals with various symmetry twists are universal SPT invariants, fully characterizing SPTs. In this work, we use gauge fields to represent those symmetry twists in closed spacetimes of any dimensionality and arbitrary topology. This allows us to express the SPT invariants in terms of continuum field theory. We show that SPT invariants of pure gauge actions describe the SPTs predicted by group cohomology, while the mixed gauge-gravity actions describe the beyond-group-cohomology SPTs. We find new examples of mixed gauge-gravity actions for U(1) SPTs in (4+1)D via the gravitational Chern-Simons term. Field theory representations of SPT invariants not only serve as tools for classifying SPTs, but also guide us in designing physical probes for them. In addition, our field theory representations are independently powerful for studying group cohomology within the mathematical context.

The Effect of Secure Attachment State and Infant Facial Expressions on Childless Adults' Parental Motivation.

PubMed

Ding, Fangyuan; Zhang, Dajun; Cheng, Gang

2016-01-01

This study examined the association between infant facial expressions and parental motivation as well as the interaction between attachment state and expressions. Two-hundred eighteen childless adults (M age = 19.22, 118 males, 100 females) were recruited. Participants completed the Chinese version of the State Adult Attachment Measure and the E-prime test, which comprised three components (a) liking, the specific hedonic experience in reaction to laughing, neutral, and crying infant faces; (b) representational responding, actively seeking infant faces with specific expressions; and (c) evoked responding, actively retaining images of three different infant facial expressions. While the first component refers to the "liking" of infants, the second and third components entail the "wanting" of an infant. Random intercepts multilevel models with emotion nested within participants revealed a significant interaction between secure attachment state and emotion on both liking and representational response. A hierarchical regression analysis was conducted to examine the unique contributions of secure attachment state. Findings demonstrated that, after controlling for sex, anxious, and avoidant, secure attachment state positively predicted parental motivations (liking and wanting) in the neutral and crying conditions, but not the laughing condition. These findings demonstrate the significant role of secure attachment state in parental motivation, specifically when infants display uncertain and negative emotions.

Two takes on the social brain: a comparison of theory of mind tasks.

PubMed

Gobbini, Maria Ida; Koralek, Aaron C; Bryan, Ronald E; Montgomery, Kimberly J; Haxby, James V

2007-11-01

We compared two tasks that are widely used in research on mentalizing--false belief stories and animations of rigid geometric shapes that depict social interactions--to investigate whether the neural systems that mediate the representation of others' mental states are consistent across these tasks. Whereas false belief stories activated primarily the anterior paracingulate cortex (APC), the posterior cingulate cortex/precuneus (PCC/PC), and the temporo-parietal junction (TPJ)--components of the distributed neural system for theory of mind (ToM)--the social animations activated an extensive region along nearly the full extent of the superior temporal sulcus, including a locus in the posterior superior temporal sulcus (pSTS), as well as the frontal operculum and inferior parietal lobule (IPL)--components of the distributed neural system for action understanding--and the fusiform gyrus. These results suggest that the representation of covert mental states that may predict behavior and the representation of intentions that are implied by perceived actions involve distinct neural systems. These results show that the TPJ and the pSTS play dissociable roles in mentalizing and are parts of different distributed neural systems. Because the social animations do not depict articulated body movements, these results also highlight that the perception of the kinematics of actions is not necessary to activate the mirror neuron system, suggesting that this system plays a general role in the representation of intentions and goals of actions. Furthermore, these results suggest that the fusiform gyrus plays a general role in the representation of visual stimuli that signify agency, independent of visual form.

Steady-state, lumped-parameter model for capacitor-run, single-phase induction motors

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

Umans, S.D.

1996-01-01

This paper documents a technique for deriving a steady-state, lumped-parameter model for capacitor-run, single-phase induction motors. The objective of this model is to predict motor performance parameters such as torque, loss distribution, and efficiency as a function of applied voltage and motor speed as well as the temperatures of the stator windings and of the rotor. The model includes representations of both the main and auxiliary windings (including arbitrary external impedances) and also the effects of core and rotational losses. The technique can be easily implemented and the resultant model can be used in a wide variety of analyses tomore » investigate motor performance as a function of load, speed, and winding and rotor temperatures. The technique is based upon a coupled-circuit representation of the induction motor. A notable feature of the model is the technique used for representing core loss. In equivalent-circuit representations of transformers and induction motors, core loss is typically represented by a core-loss resistance in shunt with the magnetizing inductance. In order to maintain the coupled-circuit viewpoint adopted in this paper, this technique was modified slightly; core loss is represented by a set of core-loss resistances connected to the ``secondaries`` of a set of windings which perfectly couple to the air-gap flux of the motor. An example of the technique is presented based upon a 3.5 kW, single-phase, capacitor-run motor and the validity of the technique is demonstrated by comparing predicted and measured motor performance.« less

Attractor concretion as a mechanism for the formation of context representations

PubMed Central

Rigotti, Mattia; Ben Dayan Rubin, Daniel; Morrison, Sara E.; Salzman, C. Daniel; Fusi, Stefano

2010-01-01

Complex tasks often require the memory of recent events, the knowledge about the context in which they occur, and the goals we intend to reach. All this information is stored in our mental states. Given a set of mental states, reinforcement learning (RL) algorithms predict the optimal policy that maximizes future reward. RL algorithms assign a value to each already-known state so that discovering the optimal policy reduces to selecting the action leading to the state with the highest value. But how does the brain create representations of these mental states in the first place? We propose a mechanism for the creation of mental states that contain information about the temporal statistics of the events in a particular context. We suggest that the mental states are represented by stable patterns of reverberating activity, which are attractors of the neural dynamics. These representations are built from neurons that are selective to specific combinations of external events (e.g. sensory stimuli) and pre-existent mental states. Consistent with this notion, we find that neurons in the amygdala and in orbito-frontal cortex (OFC) often exhibit this form of mixed selectivity. We propose that activating different mixed selectivity neurons in a fixed temporal order modifies synaptic connections so that conjunctions of events and mental states merge into a single pattern of reverberating activity. This process corresponds to the birth of a new different mental state that encodes a different temporal context. The concretion process depends on temporal contiguity, i.e. on the probability that a combination of an event and mental states follows or precedes the events and states that define a certain context. The information contained in the context thereby allows an animal to assign unambiguously a value to the events that initially appeared in different situations with different meanings. PMID:20100580

Baryon-baryon interactions and spin-flavor symmetry from lattice quantum chromodynamics

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

Wagman, Michael L.; Winter, Frank; Chang, Emmanuel

Lattice quantum chromodynamics is used to constrain the interactions of two octet baryons at the SU(3) flavor-symmetric point, with quark masses that are heavier than those in nature (equal to that of the physical strange quark mass and corresponding to a pion mass ofmore » $$\\approx 806~\\tt{MeV}$$). Specifically, the S-wave scattering phase shifts of two-baryon systems at low energies are obtained with the application of L\\"uscher's formalism, mapping the energy eigenvalues of two interacting baryons in a finite volume to the two-particle scattering amplitudes below the relevant inelastic thresholds. The values of the leading-order low-energy scattering parameters in the irreducible representations of SU(3) are consistent with an approximate SU(6) spin-flavor symmetry in the nuclear and hypernuclear forces that is predicted in the large-$$N_c$$ limit of QCD. The two distinct SU(6)-invariant interactions between two baryons are constrained at this value of the quark masses, and their values indicate an approximate accidental SU(16) symmetry. The SU(3) irreducible representations containing the $$NN~({^1}S_0)$$, $$NN~({^3}S_1)$$ and $$\\frac{1}{\\sqrt{2}}(\\Xi^0n+\\Xi^-p)~({^3}S_1)$$ channels unambiguously exhibit a single bound state, while the irreducible representation containing the $$\\Sigma^+ p~({^3}S_1)$$ channel exhibits a state that is consistent with either a bound state or a scattering state close to threshold. These results are in agreement with the previous conclusions of the NPLQCD collaboration regarding the existence of two-nucleon bound states at this value of the quark masses.« less

Baryon-baryon interactions and spin-flavor symmetry from lattice quantum chromodynamics

DOE PAGES

Wagman, Michael L.; Winter, Frank; Chang, Emmanuel; ...

2017-12-28

Lattice quantum chromodynamics is used to constrain the interactions of two octet baryons at the SU(3) flavor-symmetric point, with quark masses that are heavier than those in nature (equal to that of the physical strange quark mass and corresponding to a pion mass ofmore » $$\\approx 806~\\tt{MeV}$$). Specifically, the S-wave scattering phase shifts of two-baryon systems at low energies are obtained with the application of L\\"uscher's formalism, mapping the energy eigenvalues of two interacting baryons in a finite volume to the two-particle scattering amplitudes below the relevant inelastic thresholds. The values of the leading-order low-energy scattering parameters in the irreducible representations of SU(3) are consistent with an approximate SU(6) spin-flavor symmetry in the nuclear and hypernuclear forces that is predicted in the large-$$N_c$$ limit of QCD. The two distinct SU(6)-invariant interactions between two baryons are constrained at this value of the quark masses, and their values indicate an approximate accidental SU(16) symmetry. The SU(3) irreducible representations containing the $$NN~({^1}S_0)$$, $$NN~({^3}S_1)$$ and $$\\frac{1}{\\sqrt{2}}(\\Xi^0n+\\Xi^-p)~({^3}S_1)$$ channels unambiguously exhibit a single bound state, while the irreducible representation containing the $$\\Sigma^+ p~({^3}S_1)$$ channel exhibits a state that is consistent with either a bound state or a scattering state close to threshold. These results are in agreement with the previous conclusions of the NPLQCD collaboration regarding the existence of two-nucleon bound states at this value of the quark masses.« less

Abstract Linguistic Structure Correlates with Temporal Activity during Naturalistic Comprehension

PubMed Central

Brennan, Jonathan R.; Stabler, Edward P.; Van Wagenen, Sarah E.; Luh, Wen-Ming; Hale, John T.

2016-01-01

Neurolinguistic accounts of sentence comprehension identify a network of relevant brain regions, but do not detail the information flowing through them. We investigate syntactic information. Does brain activity implicate a computation over hierarchical grammars or does it simply reflect linear order, as in a Markov chain? To address this question, we quantify the cognitive states implied by alternative parsing models. We compare processing-complexity predictions from these states against fMRI timecourses from regions that have been implicated in sentence comprehension. We find that hierarchical grammars independently predict timecourses from left anterior and posterior temporal lobe. Markov models are predictive in these regions and across a broader network that includes the inferior frontal gyrus. These results suggest that while linear effects are wide-spread across the language network, certain areas in the left temporal lobe deal with abstract, hierarchical syntactic representations. PMID:27208858

Interpersonal Problems and Their Relationship to Depression, Self-Esteem, and Malignant Self-Regard.

PubMed

Huprich, Steven K; Lengu, Ketrin; Evich, Carly

2016-12-01

DSM-5 Section III recommends that level of personality functioning be assessed. This requires an assessment of self and other representations. Malignant self-regard (MSR) is a way of assessing the level of functioning of those with a masochistic, self-defeating, depressive, or vulnerably narcissistic personality. In Study 1, 840 undergraduates were assessed for MSR, depressive symptoms, self-esteem, anaclitic and introjective depression, and interpersonal problems. MSR, self-esteem, depressive symptoms, and anaclitic and introjective depression were correlated with multiple dimensions of interpersonal problems, and MSR predicted the most variance in interpersonal scales measuring social inhibition, nonassertion, over-accommodation, and excessive self-sacrifice. MSR, anaclitic, and introjective depression predicted unique variance in six of the eight domains of interpersonal problems assessed. In Study 2, 68 undergraduates were provided positive or negative feedback. Consistent with theory, MSR predicted unique variance in state anxiety but not state anger. Results support the validity of the MSR construct.

Unique semantic space in the brain of each beholder predicts perceived similarity

PubMed Central

Charest, Ian; Kievit, Rogier A.; Schmitz, Taylor W.; Deca, Diana; Kriegeskorte, Nikolaus

2014-01-01

The unique way in which each of us perceives the world must arise from our brain representations. If brain imaging could reveal an individual’s unique mental representation, it could help us understand the biological substrate of our individual experiential worlds in mental health and disease. However, imaging studies of object vision have focused on commonalities between individuals rather than individual differences and on category averages rather than representations of particular objects. Here we investigate the individually unique component of brain representations of particular objects with functional MRI (fMRI). Subjects were presented with unfamiliar and personally meaningful object images while we measured their brain activity on two separate days. We characterized the representational geometry by the dissimilarity matrix of activity patterns elicited by particular object images. The representational geometry remained stable across scanning days and was unique in each individual in early visual cortex and human inferior temporal cortex (hIT). The hIT representation predicted perceived similarity as reflected in dissimilarity judgments. Importantly, hIT predicted the individually unique component of the judgments when the objects were personally meaningful. Our results suggest that hIT brain representational idiosyncrasies accessible to fMRI are expressed in an individual's perceptual judgments. The unique way each of us perceives the world thus might reflect the individually unique representation in high-level visual areas. PMID:25246586

A hierarchy of time-scales and the brain.

PubMed

Kiebel, Stefan J; Daunizeau, Jean; Friston, Karl J

2008-11-01

In this paper, we suggest that cortical anatomy recapitulates the temporal hierarchy that is inherent in the dynamics of environmental states. Many aspects of brain function can be understood in terms of a hierarchy of temporal scales at which representations of the environment evolve. The lowest level of this hierarchy corresponds to fast fluctuations associated with sensory processing, whereas the highest levels encode slow contextual changes in the environment, under which faster representations unfold. First, we describe a mathematical model that exploits the temporal structure of fast sensory input to track the slower trajectories of their underlying causes. This model of sensory encoding or perceptual inference establishes a proof of concept that slowly changing neuronal states can encode the paths or trajectories of faster sensory states. We then review empirical evidence that suggests that a temporal hierarchy is recapitulated in the macroscopic organization of the cortex. This anatomic-temporal hierarchy provides a comprehensive framework for understanding cortical function: the specific time-scale that engages a cortical area can be inferred by its location along a rostro-caudal gradient, which reflects the anatomical distance from primary sensory areas. This is most evident in the prefrontal cortex, where complex functions can be explained as operations on representations of the environment that change slowly. The framework provides predictions about, and principled constraints on, cortical structure-function relationships, which can be tested by manipulating the time-scales of sensory input.

Measuring, Predicting and Visualizing Short-Term Change in Word Representation and Usage in VKontakte Social Network

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

Stewart, Ian B.; Arendt, Dustin L.; Bell, Eric B.

Language in social media is extremely dynamic: new words emerge, trend and disappear, while the meaning of existing words can fluctuate over time. This work addresses several important tasks of visualizing and predicting short term text representation shift, i.e. the change in a word’s contextual semantics. We study the relationship between short-term concept drift and representation shift on a large social media corpus – VKontakte collected during the Russia-Ukraine crisis in 2014 – 2015. We visualize short-term representation shift for example keywords and build predictive models to forecast short-term shifts in meaning from previous meaning as well as from conceptmore » drift. We show that short-term representation shift can be accurately predicted up to several weeks in advance and that visualization provides insight into meaning change. Our approach can be used to explore and characterize specific aspects of the streaming corpus during crisis events and potentially improve other downstream classification tasks including real-time event forecasting in social media.« less

Deep supervised dictionary learning for no-reference image quality assessment

NASA Astrophysics Data System (ADS)

Huang, Yuge; Liu, Xuesong; Tian, Xiang; Zhou, Fan; Chen, Yaowu; Jiang, Rongxin

2018-03-01

We propose a deep convolutional neural network (CNN) for general no-reference image quality assessment (NR-IQA), i.e., accurate prediction of image quality without a reference image. The proposed model consists of three components such as a local feature extractor that is a fully CNN, an encoding module with an inherent dictionary that aggregates local features to output a fixed-length global quality-aware image representation, and a regression module that maps the representation to an image quality score. Our model can be trained in an end-to-end manner, and all of the parameters, including the weights of the convolutional layers, the dictionary, and the regression weights, are simultaneously learned from the loss function. In addition, the model can predict quality scores for input images of arbitrary sizes in a single step. We tested our method on commonly used image quality databases and showed that its performance is comparable with that of state-of-the-art general-purpose NR-IQA algorithms.

Improved protein-protein interactions prediction via weighted sparse representation model combining continuous wavelet descriptor and PseAA composition.

PubMed

Huang, Yu-An; You, Zhu-Hong; Chen, Xing; Yan, Gui-Ying

2016-12-23

Protein-protein interactions (PPIs) are essential to most biological processes. Since bioscience has entered into the era of genome and proteome, there is a growing demand for the knowledge about PPI network. High-throughput biological technologies can be used to identify new PPIs, but they are expensive, time-consuming, and tedious. Therefore, computational methods for predicting PPIs have an important role. For the past years, an increasing number of computational methods such as protein structure-based approaches have been proposed for predicting PPIs. The major limitation in principle of these methods lies in the prior information of the protein to infer PPIs. Therefore, it is of much significance to develop computational methods which only use the information of protein amino acids sequence. Here, we report a highly efficient approach for predicting PPIs. The main improvements come from the use of a novel protein sequence representation by combining continuous wavelet descriptor and Chou's pseudo amino acid composition (PseAAC), and from adopting weighted sparse representation based classifier (WSRC). This method, cross-validated on the PPIs datasets of Saccharomyces cerevisiae, Human and H. pylori, achieves an excellent results with accuracies as high as 92.50%, 95.54% and 84.28% respectively, significantly better than previously proposed methods. Extensive experiments are performed to compare the proposed method with state-of-the-art Support Vector Machine (SVM) classifier. The outstanding results yield by our model that the proposed feature extraction method combing two kinds of descriptors have strong expression ability and are expected to provide comprehensive and effective information for machine learning-based classification models. In addition, the prediction performance in the comparison experiments shows the well cooperation between the combined feature and WSRC. Thus, the proposed method is a very efficient method to predict PPIs and may be a useful supplementary tool for future proteomics studies.

Alchemical and structural distribution based representation for universal quantum machine learning

NASA Astrophysics Data System (ADS)

Faber, Felix A.; Christensen, Anders S.; Huang, Bing; von Lilienfeld, O. Anatole

2018-06-01

We introduce a representation of any atom in any chemical environment for the automatized generation of universal kernel ridge regression-based quantum machine learning (QML) models of electronic properties, trained throughout chemical compound space. The representation is based on Gaussian distribution functions, scaled by power laws and explicitly accounting for structural as well as elemental degrees of freedom. The elemental components help us to lower the QML model's learning curve, and, through interpolation across the periodic table, even enable "alchemical extrapolation" to covalent bonding between elements not part of training. This point is demonstrated for the prediction of covalent binding in single, double, and triple bonds among main-group elements as well as for atomization energies in organic molecules. We present numerical evidence that resulting QML energy models, after training on a few thousand random training instances, reach chemical accuracy for out-of-sample compounds. Compound datasets studied include thousands of structurally and compositionally diverse organic molecules, non-covalently bonded protein side-chains, (H2O)40-clusters, and crystalline solids. Learning curves for QML models also indicate competitive predictive power for various other electronic ground state properties of organic molecules, calculated with hybrid density functional theory, including polarizability, heat-capacity, HOMO-LUMO eigenvalues and gap, zero point vibrational energy, dipole moment, and highest vibrational fundamental frequency.

The orbitofrontal cortex and beyond: from affect to decision-making.

PubMed

Rolls, Edmund T; Grabenhorst, Fabian

2008-11-01

The orbitofrontal cortex represents the reward or affective value of primary reinforcers including taste, touch, texture, and face expression. It learns to associate other stimuli with these to produce representations of the expected reward value for visual, auditory, and abstract stimuli including monetary reward value. The orbitofrontal cortex thus plays a key role in emotion, by representing the goals for action. The learning process is stimulus-reinforcer association learning. Negative reward prediction error neurons are related to this affective learning. Activations in the orbitofrontal cortex correlate with the subjective emotional experience of affective stimuli, and damage to the orbitofrontal cortex impairs emotion-related learning, emotional behaviour, and subjective affective state. With an origin from beyond the orbitofrontal cortex, top-down attention to affect modulates orbitofrontal cortex representations, and attention to intensity modulates representations in earlier cortical areas of the physical properties of stimuli. Top-down word-level cognitive inputs can bias affective representations in the orbitofrontal cortex, providing a mechanism for cognition to influence emotion. Whereas the orbitofrontal cortex provides a representation of reward or affective value on a continuous scale, areas beyond the orbitofrontal cortex such as the medial prefrontal cortex area 10 are involved in binary decision-making when a choice must be made. For this decision-making, the orbitofrontal cortex provides a representation of each specific reward in a common currency.

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.

Classification of forensic autopsy reports through conceptual graph-based document representation model.

PubMed

Mujtaba, Ghulam; Shuib, Liyana; Raj, Ram Gopal; Rajandram, Retnagowri; Shaikh, Khairunisa; Al-Garadi, Mohammed Ali

2018-06-01

Text categorization has been used extensively in recent years to classify plain-text clinical reports. This study employs text categorization techniques for the classification of open narrative forensic autopsy reports. One of the key steps in text classification is document representation. In document representation, a clinical report is transformed into a format that is suitable for classification. The traditional document representation technique for text categorization is the bag-of-words (BoW) technique. In this study, the traditional BoW technique is ineffective in classifying forensic autopsy reports because it merely extracts frequent but discriminative features from clinical reports. Moreover, this technique fails to capture word inversion, as well as word-level synonymy and polysemy, when classifying autopsy reports. Hence, the BoW technique suffers from low accuracy and low robustness unless it is improved with contextual and application-specific information. To overcome the aforementioned limitations of the BoW technique, this research aims to develop an effective conceptual graph-based document representation (CGDR) technique to classify 1500 forensic autopsy reports from four (4) manners of death (MoD) and sixteen (16) causes of death (CoD). Term-based and Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) based conceptual features were extracted and represented through graphs. These features were then used to train a two-level text classifier. The first level classifier was responsible for predicting MoD. In addition, the second level classifier was responsible for predicting CoD using the proposed conceptual graph-based document representation technique. To demonstrate the significance of the proposed technique, its results were compared with those of six (6) state-of-the-art document representation techniques. Lastly, this study compared the effects of one-level classification and two-level classification on the experimental results. The experimental results indicated that the CGDR technique achieved 12% to 15% improvement in accuracy compared with fully automated document representation baseline techniques. Moreover, two-level classification obtained better results compared with one-level classification. The promising results of the proposed conceptual graph-based document representation technique suggest that pathologists can adopt the proposed system as their basis for second opinion, thereby supporting them in effectively determining CoD. Copyright © 2018 Elsevier Inc. All rights reserved.

Comparison of chlorine and ammonia concentration field trial data with calculated results from a Gaussian atmospheric transport and dispersion model.

PubMed

Bauer, Timothy J

2013-06-15

The Jack Rabbit Test Program was sponsored in April and May 2010 by the Department of Homeland Security Transportation Security Administration to generate source data for large releases of chlorine and ammonia from transport tanks. In addition to a variety of data types measured at the release location, concentration versus time data was measured using sensors at distances up to 500 m from the tank. Release data were used to create accurate representations of the vapor flux versus time for the ten releases. This study was conducted to determine the importance of source terms and meteorological conditions in predicting downwind concentrations and the accuracy that can be obtained in those predictions. Each source representation was entered into an atmospheric transport and dispersion model using simplifying assumptions regarding the source characterization and meteorological conditions, and statistics for cloud duration and concentration at the sensor locations were calculated. A detailed characterization for one of the chlorine releases predicted 37% of concentration values within a factor of two, but cannot be considered representative of all the trials. Predictions of toxic effects at 200 m are relevant to incidents involving 1-ton chlorine tanks commonly used in parts of the United States and internationally. Published by Elsevier B.V.

A framework for qualitative reasoning about solid objects

NASA Technical Reports Server (NTRS)

Davis, E.

1987-01-01

Predicting the behavior of a qualitatively described system of solid objects requires a combination of geometrical, temporal, and physical reasoning. Methods based upon formulating and solving differential equations are not adequate for robust prediction, since the behavior of a system over extended time may be much simpler than its behavior over local time. A first-order logic, in which one can state simple physical problems and derive their solution deductively, without recourse to solving the differential equations, is discussed. This logic is substantially more expressive and powerful than any previous AI representational system in this domain.

Comparisons of several aerodynamic methods for application to dynamic loads analyses

NASA Technical Reports Server (NTRS)

Kroll, R. I.; Miller, R. D.

1976-01-01

The results of a study are presented in which the applicability at subsonic speeds of several aerodynamic methods for predicting dynamic gust loads on aircraft, including active control systems, was examined and compared. These aerodynamic methods varied from steady state to an advanced unsteady aerodynamic formulation. Brief descriptions of the structural and aerodynamic representations and of the motion and load equations are presented. Comparisons of numerical results achieved using the various aerodynamic methods are shown in detail. From these results, aerodynamic representations for dynamic gust analyses are identified. It was concluded that several aerodynamic methods are satisfactory for dynamic gust analyses of configurations having either controls fixed or active control systems that primarily affect the low frequency rigid body aircraft response.

Low-dimensional Representation of Error Covariance

NASA Technical Reports Server (NTRS)

Tippett, Michael K.; Cohn, Stephen E.; Todling, Ricardo; Marchesin, Dan

2000-01-01

Ensemble and reduced-rank approaches to prediction and assimilation rely on low-dimensional approximations of the estimation error covariances. Here stability properties of the forecast/analysis cycle for linear, time-independent systems are used to identify factors that cause the steady-state analysis error covariance to admit a low-dimensional representation. A useful measure of forecast/analysis cycle stability is the bound matrix, a function of the dynamics, observation operator and assimilation method. Upper and lower estimates for the steady-state analysis error covariance matrix eigenvalues are derived from the bound matrix. The estimates generalize to time-dependent systems. If much of the steady-state analysis error variance is due to a few dominant modes, the leading eigenvectors of the bound matrix approximate those of the steady-state analysis error covariance matrix. The analytical results are illustrated in two numerical examples where the Kalman filter is carried to steady state. The first example uses the dynamics of a generalized advection equation exhibiting nonmodal transient growth. Failure to observe growing modes leads to increased steady-state analysis error variances. Leading eigenvectors of the steady-state analysis error covariance matrix are well approximated by leading eigenvectors of the bound matrix. The second example uses the dynamics of a damped baroclinic wave model. The leading eigenvectors of a lowest-order approximation of the bound matrix are shown to approximate well the leading eigenvectors of the steady-state analysis error covariance matrix.

Application of principal component analysis (PCA) and improved joint probability distributions to the inverse first-order reliability method (I-FORM) for predicting extreme sea states

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

Eckert-Gallup, Aubrey C.; Sallaberry, Cédric J.; Dallman, Ann R.

Environmental contours describing extreme sea states are generated as the input for numerical or physical model simulations as a part of the standard current practice for designing marine structures to survive extreme sea states. These environmental contours are characterized by combinations of significant wave height (H s) and either energy period (T e) or peak period (T p) values calculated for a given recurrence interval using a set of data based on hindcast simulations or buoy observations over a sufficient period of record. The use of the inverse first-order reliability method (I-FORM) is a standard design practice for generating environmentalmore » contours. This paper develops enhanced methodologies for data analysis prior to the application of the I-FORM, including the use of principal component analysis (PCA) to create an uncorrelated representation of the variables under consideration as well as new distribution and parameter fitting techniques. As a result, these modifications better represent the measured data and, therefore, should contribute to the development of more realistic representations of environmental contours of extreme sea states for determining design loads for marine structures.« less

Application of principal component analysis (PCA) and improved joint probability distributions to the inverse first-order reliability method (I-FORM) for predicting extreme sea states

DOE PAGES

Eckert-Gallup, Aubrey C.; Sallaberry, Cédric J.; Dallman, Ann R.; ...

2016-01-06

Environmental contours describing extreme sea states are generated as the input for numerical or physical model simulations as a part of the standard current practice for designing marine structures to survive extreme sea states. These environmental contours are characterized by combinations of significant wave height (H s) and either energy period (T e) or peak period (T p) values calculated for a given recurrence interval using a set of data based on hindcast simulations or buoy observations over a sufficient period of record. The use of the inverse first-order reliability method (I-FORM) is a standard design practice for generating environmentalmore » contours. This paper develops enhanced methodologies for data analysis prior to the application of the I-FORM, including the use of principal component analysis (PCA) to create an uncorrelated representation of the variables under consideration as well as new distribution and parameter fitting techniques. As a result, these modifications better represent the measured data and, therefore, should contribute to the development of more realistic representations of environmental contours of extreme sea states for determining design loads for marine structures.« less

Orbit Determination and Maneuver Detection Using Event Representation with Thrust-Fourier-Coefficients

NASA Astrophysics Data System (ADS)

Lubey, D.; Ko, H.; Scheeres, D.

The classical orbit determination (OD) method of dealing with unknown maneuvers is to restart the OD process with post-maneuver observations. However, it is also possible to continue the OD process through such unknown maneuvers by representing those unknown maneuvers with an appropriate event representation. It has been shown in previous work (Ko & Scheeres, JGCD 2014) that any maneuver performed by a satellite transitioning between two arbitrary orbital states can be represented as an equivalent maneuver connecting those two states using Thrust-Fourier-Coefficients (TFCs). Event representation using TFCs rigorously provides a unique control law that can generate the desired secular behavior for a given unknown maneuver. This paper presents applications of this representation approach to orbit prediction and maneuver detection problem across unknown maneuvers. The TFCs are appended to a sequential filter as an adjoint state to compensate unknown perturbing accelerations and the modified filter estimates the satellite state and thrust coefficients by processing OD across the time of an unknown maneuver. This modified sequential filter with TFCs is capable of fitting tracking data and maintaining an OD solution in the presence of unknown maneuvers. Also, the modified filter is found effective in detecting a sudden change in TFC values which indicates a maneuver. In order to illustrate that the event representation approach with TFCs is robust and sufficiently general to be easily adjustable, different types of measurement data are processed with the filter in a realistic LEO setting. Further, cases with mis-modeling of non-gravitational force are included in our study to verify the versatility and efficiency of our presented algorithm. Simulation results show that the modified sequential filter with TFCs can detect and estimate the orbit and thrust parameters in the presence of unknown maneuvers with or without measurement data during maneuvers. With no measurement data during maneuvers, the modified filter with TFCs uses an existing pre-maneuver orbit solution to compute a post-maneuver orbit solution by forcing TFCs to compensate for an unknown maneuver. With observation data available during maneuvers, maneuver start time and stop time is determined

Categorical and dimensional approaches in the evaluation of the relationship between attachment and personality disorders: an empirical study.

PubMed

Chiesa, Marco; Cirasola, Antonella; Williams, Riccardo; Nassisi, Valentina; Fonagy, Peter

2017-04-01

Although several studies have highlighted the relationship between attachment states of mind and personality disorders, their findings have not been consistent, possibly due to the application of the traditional taxonomic classification model of attachment. A more recently developed dimensional classification of attachment representations, including more specific aspects of trauma-related representations, may have advantages. In this study, we compare specific associations and predictive power of the categorical attachment and dimensional models applied to 230 Adult Attachment Interview transcripts obtained from personality disordered and nonpsychiatric subjects. We also investigate the role that current levels of psychiatric distress may have in the prediction of PD. The results showed that both models predict the presence of PD, with the dimensional approach doing better in discriminating overall diagnosis of PD. However, both models are less helpful in discriminating specific PD diagnostic subtypes. Current psychiatric distress was found to be the most consistent predictor of PD capturing a large share of the variance and obscuring the role played by attachment variables. The results suggest that attachment parameters correlate with the presence of PD alone and have no specific associations with particular PD subtypes when current psychiatric distress is taken into account.

Does Mother's Rather than Father's Attachment Representation Contribute to the Adolescent's Attachment Representation? Commentary on: "Maternal Adult Attachment Interview (AAI) Collected During Pregnancy Predicts Reflective Functioning in AAIs from their First-Born Children 17 Years Later"

ERIC Educational Resources Information Center

Spangler, Gottfried

2016-01-01

In this commentary, Spangler evaluates the Steele, Perez, Segal, and Steele report that arguede that reflective functioning in adolescence could not be predicted by quality of early infant attachment, but was associated with maternal (but not paternal) attachment representation, assessed before the adolescents' birth. Assuming that parental…

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.

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.

Mental models accurately predict emotion transitions.

PubMed

Thornton, Mark A; Tamir, Diana I

2017-06-06

Successful social interactions depend on people's ability to predict others' future actions and emotions. People possess many mechanisms for perceiving others' current emotional states, but how might they use this information to predict others' future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others' emotional dynamics. People could then use these mental models of emotion transitions to predict others' future emotions from currently observable emotions. To test this hypothesis, studies 1-3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants' ratings of emotion transitions predicted others' experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation-valence, social impact, rationality, and human mind-inform participants' mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants' accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone.

Changing predictions, stable recognition: Children's representations of downward incline motion.

PubMed

Hast, Michael; Howe, Christine

2017-11-01

Various studies to-date have demonstrated children hold ill-conceived expressed beliefs about the physical world such as that one ball will fall faster than another because it is heavier. At the same time, they also demonstrate accurate recognition of dynamic events. How these representations relate is still unresolved. This study examined 5- to 11-year-olds' (N = 130) predictions and recognition of motion down inclines. Predictions were typically in error, matching previous work, but children largely recognized correct events as correct and rejected incorrect ones. The results also demonstrate while predictions change with increasing age, recognition shows signs of stability. The findings provide further support for a hybrid model of object representations and argue in favour of stable core cognition existing alongside developmental changes. Statement of contribution What is already known on this subject? Children's predictions of physical events show limitations in accuracy Their recognition of such events suggests children may use different knowledge sources in their reasoning What the present study adds? Predictions fluctuate more strongly than recognition, suggesting stable core cognition But recognition also shows some fluctuation, arguing for a hybrid model of knowledge representation. © 2017 The British Psychological Society.

On the performance of metrics to predict quality in point cloud representations

NASA Astrophysics Data System (ADS)

Alexiou, Evangelos; Ebrahimi, Touradj

2017-09-01

Point clouds are a promising alternative for immersive representation of visual contents. Recently, an increased interest has been observed in the acquisition, processing and rendering of this modality. Although subjective and objective evaluations are critical in order to assess the visual quality of media content, they still remain open problems for point cloud representation. In this paper we focus our efforts on subjective quality assessment of point cloud geometry, subject to typical types of impairments such as noise corruption and compression-like distortions. In particular, we propose a subjective methodology that is closer to real-life scenarios of point cloud visualization. The performance of the state-of-the-art objective metrics is assessed by considering the subjective scores as the ground truth. Moreover, we investigate the impact of adopting different test methodologies by comparing them. Advantages and drawbacks of every approach are reported, based on statistical analysis. The results and conclusions of this work provide useful insights that could be considered in future experimentation.

Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies.

PubMed

Hansen, Katja; Montavon, Grégoire; Biegler, Franziska; Fazli, Siamac; Rupp, Matthias; Scheffler, Matthias; von Lilienfeld, O Anatole; Tkatchenko, Alexandre; Müller, Klaus-Robert

2013-08-13

The accurate and reliable prediction of properties of molecules typically requires computationally intensive quantum-chemical calculations. Recently, machine learning techniques applied to ab initio calculations have been proposed as an efficient approach for describing the energies of molecules in their given ground-state structure throughout chemical compound space (Rupp et al. Phys. Rev. Lett. 2012, 108, 058301). In this paper we outline a number of established machine learning techniques and investigate the influence of the molecular representation on the methods performance. The best methods achieve prediction errors of 3 kcal/mol for the atomization energies of a wide variety of molecules. Rationales for this performance improvement are given together with pitfalls and challenges when applying machine learning approaches to the prediction of quantum-mechanical observables.

Concepts in context: Processing mental state concepts with internal or external focus involves different neural systems

PubMed Central

Oosterwijk, Suzanne; Mackey, Scott; Wilson-Mendenhall, Christine; Winkielman, Piotr; Paulus, Martin P.

2015-01-01

According to embodied cognition theories concepts are contextually-situated and grounded in neural systems that produce experiential states. This view predicts that processing mental state concepts recruits neural regions associated with different aspects of experience depending on the context in which people understand a concept. This neuroimaging study tested this prediction using a set of sentences that described emotional (e.g., fear, joy) and non-emotional (e.g., thinking, hunger) mental states with internal focus (i.e. focusing on bodily sensations and introspection) or external focus (i.e. focusing on expression and action). Consistent with our predictions, data suggested that the inferior frontal gyrus, a region associated with action representation, was engaged more by external than internal sentences. By contrast, the ventromedial prefrontal cortex, a region associated with the generation of internal states, was engaged more by internal emotion sentences than external sentence categories. Similar patterns emerged when we examined the relationship between neural activity and independent ratings of sentence focus. Furthermore, ratings of emotion were associated with activation in the medial prefrontal cortex, whereas ratings of activity were associated with activation in the inferior frontal gyrus. These results suggest that mental state concepts are represented in a dynamic way, using context-relevant interoceptive and sensorimotor resources. PMID:25748274

Idiosyncratic Patterns of Representational Similarity in Prefrontal Cortex Predict Attentional Performance.

PubMed

Lee, Jeongmi; Geng, Joy J

2017-02-01

The efficiency of finding an object in a crowded environment depends largely on the similarity of nontargets to the search target. Models of attention theorize that the similarity is determined by representations stored within an "attentional template" held in working memory. However, the degree to which the contents of the attentional template are individually unique and where those idiosyncratic representations are encoded in the brain are unknown. We investigated this problem using representational similarity analysis of human fMRI data to measure the common and idiosyncratic representations of famous face morphs during an identity categorization task; data from the categorization task were then used to predict performance on a separate identity search task. We hypothesized that the idiosyncratic categorical representations of the continuous face morphs would predict their distractability when searching for each target identity. The results identified that patterns of activation in the lateral prefrontal cortex (LPFC) as well as in face-selective areas in the ventral temporal cortex were highly correlated with the patterns of behavioral categorization of face morphs and search performance that were common across subjects. However, the individually unique components of the categorization behavior were reliably decoded only in right LPFC. Moreover, the neural pattern in right LPFC successfully predicted idiosyncratic variability in search performance, such that reaction times were longer when distractors had a higher probability of being categorized as the target identity. These results suggest that the prefrontal cortex encodes individually unique components of categorical representations that are also present in attentional templates for target search. Everyone's perception of the world is uniquely shaped by personal experiences and preferences. Using functional MRI, we show that individual differences in the categorization of face morphs between two identities could be decoded from the prefrontal cortex and the ventral temporal cortex. Moreover, the individually unique representations in prefrontal cortex predicted idiosyncratic variability in attentional performance when looking for each identity in the "crowd" of another morphed face in a separate search task. Our results reveal that the representation of task-related information in prefrontal cortex is individually unique and preserved across categorization and search performance. This demonstrates the possibility of predicting individual behaviors across tasks with patterns of brain activity. Copyright © 2017 the authors 0270-6474/17/371257-12$15.00/0.

Specialized mechanisms for theory of mind: are mental representations special because they are mental or because they are representations?

PubMed

Cohen, Adam S; Sasaki, Joni Y; German, Tamsin C

2015-03-01

Does theory of mind depend on a capacity to reason about representations generally or on mechanisms selective for the processing of mental state representations? In four experiments, participants reasoned about beliefs (mental representations) and notes (non-mental, linguistic representations), which according to two prominent theories are closely matched representations because both are represented propositionally. Reaction times were faster and accuracies higher when participants endorsed or rejected statements about false beliefs than about false notes (Experiment 1), even when statements emphasized representational format (Experiment 2), which should have favored the activation of representation concepts. Experiments 3 and 4 ruled out a counterhypothesis that differences in task demands were responsible for the advantage in belief processing. These results demonstrate for the first time that understanding of mental and linguistic representations can be dissociated even though both may carry propositional content, supporting the theory that mechanisms governing theory of mind reasoning are narrowly specialized to process mental states, not representations more broadly. Extending this theory, we discuss whether less efficient processing of non-mental representations may be a by-product of mechanisms specialized for processing mental states. Crown Copyright © 2014. Published by Elsevier B.V. All rights reserved.

Understanding marital conflict 7 years later from prenatal representations of marriage.

PubMed

Curran, Melissa; Ogolsky, Brian; Hazen, Nancy; Bosch, Leslie

2011-06-01

We examine how representations of marriage, assessed prenatally, predict different types of marital conflict (cooperation, avoidance/capitulation, stonewalling, and child involvement in parental conflict) at 7 years postpartum (N=132 individuals). We assessed representations of marriage prenatally by interviewing spouses about their own parents' marriage, and then rated the content and insightfulness of their memories. Results show that marital representations characterized by higher insight predict higher cooperation and lower child involvement in parental conflict, whereas content of marital representations was not a significant predictor of marital conflict. Further, individuals who remember negative memories from their parents' marriage with high insight were lowest on child involvement in parental conflict, whereas those who remember negative memories with low insight were highest on child involvement in parental conflict. Finally, women who remember negative content with high insight report the highest cooperation, whereas women who remember negative content with low insight report the lowest cooperation. For men, however, marital representations were less effective in predicting later cooperation. We conclude that marital representations, even when assessed prenatally, influence certain types of marital conflict 7 years later. Using such findings, therapists could help spouses gain insight into how the memories of their parents' marriage relate to the use of specific conflict strategies in their marriage. 2011 © FPI, Inc.

Modified Inverse First Order Reliability Method (I-FORM) for Predicting Extreme Sea States.

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

Eckert-Gallup, Aubrey Celia; Sallaberry, Cedric Jean-Marie; Dallman, Ann Renee

Environmental contours describing extreme sea states are generated as the input for numerical or physical model simulation s as a part of the stand ard current practice for designing marine structure s to survive extreme sea states. Such environmental contours are characterized by combinations of significant wave height ( ) and energy period ( ) values calculated for a given recurrence interval using a set of data based on hindcast simulations or buoy observations over a sufficient period of record. The use of the inverse first - order reliability method (IFORM) i s standard design practice for generating environmental contours.more » In this paper, the traditional appli cation of the IFORM to generating environmental contours representing extreme sea states is described in detail and its merits and drawbacks are assessed. The application of additional methods for analyzing sea state data including the use of principal component analysis (PCA) to create an uncorrelated representation of the data under consideration is proposed. A reexamination of the components of the IFORM application to the problem at hand including the use of new distribution fitting techniques are shown to contribute to the development of more accurate a nd reasonable representations of extreme sea states for use in survivability analysis for marine struc tures. Keywords: In verse FORM, Principal Component Analysis , Environmental Contours, Extreme Sea State Characteri zation, Wave Energy Converters« less

Representation of grasp postures and anticipatory motor planning in children.

PubMed

Stöckel, Tino; Hughes, Charmayne M L; Schack, Thomas

2012-11-01

In this study, we investigated anticipatory motor planning and the development of cognitive representation of grasp postures in children aged 7, 8, and 9 years. Overall, 9-year-old children were more likely to plan their movements to end in comfortable postures, and have distinct representational structures of certain grasp postures, compared to the 7- and 8-year old children. Additionally, the sensitivity toward comfortable end-states (end-state comfort) was related to the mental representation of certain grasp postures. Children with grasp comfort related and functionally well-structured representations were more likely to have satisfied end-state comfort in both the simple and the advanced planning condition. In contrast, end-state comfort satisfaction for the advanced planning condition was much lower for children whose cognitive representations were not structured by grasp comfort. The results of the present study support the notion that cognitive action representation plays an important role in the planning and control of grasp postures.

Visual Representations on High School Biology, Chemistry, Earth Science, and Physics Assessments

ERIC Educational Resources Information Center

LaDue, Nicole D.; Libarkin, Julie C.; Thomas, Stephen R.

2015-01-01

The pervasive use of visual representations in textbooks, curricula, and assessments underscores their importance in K-12 science education. For example, visual representations figure prominently in the recent publication of the Next Generation Science Standards (NGSS Lead States in Next generation science standards: for states, by states.…

Detection and Prediction of Hail Storms in Satellite Imagery using Deep Learning

NASA Astrophysics Data System (ADS)

Pullman, M.; Gurung, I.; Ramachandran, R.; Maskey, M.

2017-12-01

Natural hazards, such as damaging hail storms, dramatically disrupt both industry and agriculture, having significant socio-economic impacts in the United States. In 2016, hail was responsible for 3.5 billion and 23 million dollars in damage to property and crops, respectively, making it the second costliest 2016 weather phenomenon in the United States. The destructive nature and high cost of hail storms has driven research into the development of more accurate hail-prediction algorithms in an effort to mitigate societal impacts. Recently, weather forecasting efforts have turned to deep learning neural networks because neural networks can more effectively model complex, nonlinear, dynamical phenomenon that exist in large datasets through multiple stages of transformation and representation. In an effort to improve hail-prediction techniques, we propose a deep learning technique that leverages satellite imagery to detect and predict the occurrence of hail storms. The technique is applied to satellite imagery from 2006 to 2016 for the contiguous United States and incorporates hail reports obtained from the National Center for Environmental Information Storm Events Database for training and validation purposes. In this presentation, we describe a novel approach to predicting hail via a neural network model that creates a large labeled dataset of hail storms, the accuracy and results of the model, and its applications for improving hail forecasting.

On the suitability of different representations of solid catalysts for combinatorial library design by genetic algorithms.

PubMed

Gobin, Oliver C; Schüth, Ferdi

2008-01-01

Genetic algorithms are widely used to solve and optimize combinatorial problems and are more often applied for library design in combinatorial chemistry. Because of their flexibility, however, their implementation can be challenging. In this study, the influence of the representation of solid catalysts on the performance of genetic algorithms was systematically investigated on the basis of a new, constrained, multiobjective, combinatorial test problem with properties common to problems in combinatorial materials science. Constraints were satisfied by penalty functions, repair algorithms, or special representations. The tests were performed using three state-of-the-art evolutionary multiobjective algorithms by performing 100 optimization runs for each algorithm and test case. Experimental data obtained during the optimization of a noble metal-free solid catalyst system active in the selective catalytic reduction of nitric oxide with propene was used to build up a predictive model to validate the results of the theoretical test problem. A significant influence of the representation on the optimization performance was observed. Binary encodings were found to be the preferred encoding in most of the cases, and depending on the experimental test unit, repair algorithms or penalty functions performed best.

Hyperresolution Global Land Surface Modeling: Meeting a Grand Challenge for Monitoring Earth's Terrestrial Water

NASA Technical Reports Server (NTRS)

Wood, Eric F.; Roundy, Joshua K.; Troy, Tara J.; van Beek, L. P. H.; Bierkens, Marc F. P.; 4 Blyth, Eleanor; de Roo, Ad; Doell. Petra; Ek, Mike; Famiglietti, James;

Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water

NASA Astrophysics Data System (ADS)

Wood, Eric F.; Roundy, Joshua K.; Troy, Tara J.; van Beek, L. P. H.; Bierkens, Marc F. P.; Blyth, Eleanor; de Roo, Ad; DöLl, Petra; Ek, Mike; Famiglietti, James; Gochis, David; van de Giesen, Nick; Houser, Paul; Jaffé, Peter R.; Kollet, Stefan; Lehner, Bernhard; Lettenmaier, Dennis P.; Peters-Lidard, Christa; Sivapalan, Murugesu; Sheffield, Justin; Wade, Andrew; Whitehead, Paul

2011-05-01

Monitoring Earth's terrestrial water conditions is critically important to many hydrological applications such as global food production; assessing water resources sustainability; and flood, drought, and climate change prediction. These needs have motivated the development of pilot monitoring and prediction systems for terrestrial hydrologic and vegetative states, but to date only at the rather coarse spatial resolutions (˜10-100 km) over continental to global domains. Adequately addressing critical water cycle science questions and applications requires systems that are implemented globally at much higher resolutions, on the order of 1 km, resolutions referred to as hyperresolution in the context of global land surface models. This opinion paper sets forth the needs and benefits for a system that would monitor and predict the Earth's terrestrial water, energy, and biogeochemical cycles. We discuss six major challenges in developing a system: improved representation of surface-subsurface interactions due to fine-scale topography and vegetation; improved representation of land-atmospheric interactions and resulting spatial information on soil moisture and evapotranspiration; inclusion of water quality as part of the biogeochemical cycle; representation of human impacts from water management; utilizing massively parallel computer systems and recent computational advances in solving hyperresolution models that will have up to 109 unknowns; and developing the required in situ and remote sensing global data sets. We deem the development of a global hyperresolution model for monitoring the terrestrial water, energy, and biogeochemical cycles a "grand challenge" to the community, and we call upon the international hydrologic community and the hydrological science support infrastructure to endorse the effort.

The use of ERTS imagery for lake classification. [turbidity due to phytoplankton

NASA Technical Reports Server (NTRS)

Scarpace, F. L.; Wade, R. E.; Fisher, L. T.

1975-01-01

The feasibility of using photographic representations of the ERTS imagery to classify lakes in the State of Wisconsin as to their trophic level was studied. Densitometric readings in band 5 of ERTS 70 mm imagery were taken for all the lakes in Wisconsin greater than 100 acres (approximately 1000 lakes). An algorithm has been developed from ground truth measurements to predict from satellite imagery an indicator of trophic status.

Classification and Realizations of Type III Factor Representations of Cuntz-Krieger Algebras Associated with Quasi-Free States

NASA Astrophysics Data System (ADS)

Kawamura, Katsunori

2009-03-01

We completely classify type III factor representations of Cuntz-Krieger algebras associated with quasi-free states up to unitary equivalence. Furthermore, we realize these representations on concrete Hilbert spaces without using GNS construction. Free groups and their type II1 factor representations are used in these realizations.

Illness representation after acute myocardial infarction: impact on in-hospital recovery.

PubMed

Cherrington, Candace C; Moser, Debra K; Lennie, Terry A; Kennedy, Carol W

2004-03-01

Despite significant progress in the treatment of coronary artery disease, myocardial infarction is still the leading cause of death in the United States. As suggested by Leventhal's Self-Regulation Model of Illness, the continued high morbidity and mortality may be due to a failure to address the role of psychosocial factors such as illness representation, depression, and anxiety in recovery. To determine the relationship between illness representation of myocardial infarction and the occurrence of in-hospital complications and if anxiety and depression mediate this relationship. A prospective correlational design was used to measure illness representation, depression, and anxiety 24 to 48 hours after admission for myocardial infarction in 49 patients and the frequency of complications during the acute event. Logistic regression was used to determine the likelihood of experiencing a complication. When demographic and clinical variables were controlled for, the more negative the representation of illness, the greater were the odds of experiencing a complication (chi2 = 16.9, df = 6, P =.01). The odds of experiencing a complication increased 5.1% for each 1 unit increase in the score on the Illness Preparation Questionnaire (B = 0.05, Wald = 4.442, Exp(B) = 1.051, 95% CI = 1.003-1.1010). Neither anxiety (chi2 = 3.0, df = 1, P =. 09) nor depression (chi2 = 2.5, df = 1, P = .11) were significant predictors of the occurrence of complications. In these patients, illness representation was predictive of the likelihood of experiencing a complication. Thus, illness representation appears to be an important psychosocial factor in acute recovery from myocardial infarction.

A link prediction approach to cancer drug sensitivity prediction.

PubMed

Turki, Turki; Wei, Zhi

2017-10-03

Predicting the response to a drug for cancer disease patients based on genomic information is an important problem in modern clinical oncology. This problem occurs in part because many available drug sensitivity prediction algorithms do not consider better quality cancer cell lines and the adoption of new feature representations; both lead to the accurate prediction of drug responses. By predicting accurate drug responses to cancer, oncologists gain a more complete understanding of the effective treatments for each patient, which is a core goal in precision medicine. In this paper, we model cancer drug sensitivity as a link prediction, which is shown to be an effective technique. We evaluate our proposed link prediction algorithms and compare them with an existing drug sensitivity prediction approach based on clinical trial data. The experimental results based on the clinical trial data show the stability of our link prediction algorithms, which yield the highest area under the ROC curve (AUC) and are statistically significant. We propose a link prediction approach to obtain new feature representation. Compared with an existing approach, the results show that incorporating the new feature representation to the link prediction algorithms has significantly improved the performance.

SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition

PubMed Central

Melvin, Iain; Ie, Eugene; Kuang, Rui; Weston, Jason; Stafford, William Noble; Leslie, Christina

2007-01-01

Background Predicting a protein's structural class from its amino acid sequence is a fundamental problem in computational biology. Much recent work has focused on developing new representations for protein sequences, called string kernels, for use with support vector machine (SVM) classifiers. However, while some of these approaches exhibit state-of-the-art performance at the binary protein classification problem, i.e. discriminating between a particular protein class and all other classes, few of these studies have addressed the real problem of multi-class superfamily or fold recognition. Moreover, there are only limited software tools and systems for SVM-based protein classification available to the bioinformatics community. Results We present a new multi-class SVM-based protein fold and superfamily recognition system and web server called SVM-Fold, which can be found at . Our system uses an efficient implementation of a state-of-the-art string kernel for sequence profiles, called the profile kernel, where the underlying feature representation is a histogram of inexact matching k-mer frequencies. We also employ a novel machine learning approach to solve the difficult multi-class problem of classifying a sequence of amino acids into one of many known protein structural classes. Binary one-vs-the-rest SVM classifiers that are trained to recognize individual structural classes yield prediction scores that are not comparable, so that standard "one-vs-all" classification fails to perform well. Moreover, SVMs for classes at different levels of the protein structural hierarchy may make useful predictions, but one-vs-all does not try to combine these multiple predictions. To deal with these problems, our method learns relative weights between one-vs-the-rest classifiers and encodes information about the protein structural hierarchy for multi-class prediction. In large-scale benchmark results based on the SCOP database, our code weighting approach significantly improves on the standard one-vs-all method for both the superfamily and fold prediction in the remote homology setting and on the fold recognition problem. Moreover, our code weight learning algorithm strongly outperforms nearest-neighbor methods based on PSI-BLAST in terms of prediction accuracy on every structure classification problem we consider. Conclusion By combining state-of-the-art SVM kernel methods with a novel multi-class algorithm, the SVM-Fold system delivers efficient and accurate protein fold and superfamily recognition. PMID:17570145

Identity-Specific Reward Representations in Orbitofrontal Cortex Are Modulated by Selective Devaluation.

PubMed

Howard, James D; Kahnt, Thorsten

2017-03-08

Goal-directed behavior is sensitive to the current value of expected outcomes. This requires independent representations of specific rewards, which have been linked to orbitofrontal cortex (OFC) function. However, the mechanisms by which the human brain updates specific goals on the fly, and translates those updates into choices, have remained unknown. Here we implemented selective devaluation of appetizing food odors in combination with pattern-based neuroimaging and a decision-making task. We found that in a hungry state, participants chose to smell high-intensity versions of two value-matched food odor rewards. After eating a meal corresponding to one of the two odors, participants switched choices toward the low intensity of the sated odor but continued to choose the high intensity of the nonsated odor. This sensory-specific behavioral effect was mirrored by pattern-based changes in fMRI signal in lateral posterior OFC, where specific reward identity representations were altered after the meal for the sated food odor but retained for the nonsated counterpart. In addition, changes in functional connectivity between the OFC and general value coding in ventromedial prefrontal cortex (vmPFC) predicted individual differences in satiety-related choice behavior. These findings demonstrate how flexible representations of specific rewards in the OFC are updated by devaluation, and how functional connections to vmPFC reflect the current value of outcomes and guide goal-directed behavior. SIGNIFICANCE STATEMENT The orbitofrontal cortex (OFC) is critical for goal-directed behavior. A recent proposal is that OFC fulfills this function by representing a variety of state and task variables ("cognitive maps"), including a conjunction of expected reward identity and value. Here we tested how identity-specific representations of food odor reward are updated by satiety. We found that fMRI pattern-based signatures of reward identity in lateral posterior OFC were modulated after selective devaluation, and that connectivity between this region and general value coding ventromedial prefrontal cortex (vmPFC) predicted choice behavior. These results provide evidence for a mechanism by which devaluation modulates a cognitive map of expected reward in OFC and thereby alters general value signals in vmPFC to guide goal-directed behavior. Copyright © 2017 the authors 0270-6474/17/372627-12$15.00/0.

A novel collaborative representation and SCAD based classification method for fibrosis and inflammatory activity analysis of chronic hepatitis C

NASA Astrophysics Data System (ADS)

Cai, Jiaxin; Chen, Tingting; Li, Yan; Zhu, Nenghui; Qiu, Xuan

2018-03-01

In order to analysis the fibrosis stage and inflammatory activity grade of chronic hepatitis C, a novel classification method based on collaborative representation (CR) with smoothly clipped absolute deviation penalty (SCAD) penalty term, called CR-SCAD classifier, is proposed for pattern recognition. After that, an auto-grading system based on CR-SCAD classifier is introduced for the prediction of fibrosis stage and inflammatory activity grade of chronic hepatitis C. The proposed method has been tested on 123 clinical cases of chronic hepatitis C based on serological indexes. Experimental results show that the performance of the proposed method outperforms the state-of-the-art baselines for the classification of fibrosis stage and inflammatory activity grade of chronic hepatitis C.

Braid group representation on quantum computation

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

Aziz, Ryan Kasyfil, E-mail: kasyfilryan@gmail.com; Muchtadi-Alamsyah, Intan, E-mail: ntan@math.itb.ac.id

2015-09-30

There are many studies about topological representation of quantum computation recently. One of diagram representation of quantum computation is by using ZX-Calculus. In this paper we will make a diagrammatical scheme of Dense Coding. We also proved that ZX-Calculus diagram of maximally entangle state satisfies Yang-Baxter Equation and therefore, we can construct a Braid Group representation of set of maximally entangle state.

Kalman filters for fractional discrete-time stochastic systems along with time-delay in the observation signal

NASA Astrophysics Data System (ADS)

Torabi, H.; Pariz, N.; Karimpour, A.

2016-02-01

This paper investigates fractional Kalman filters when time-delay is entered in the observation signal in the discrete-time stochastic fractional order state-space representation. After investigating the common fractional Kalman filter, we try to derive a fractional Kalman filter for time-delay fractional systems. A detailed derivation is given. Fractional Kalman filters will be used to estimate recursively the states of fractional order state-space systems based on minimizing the cost function when there is a constant time delay (d) in the observation signal. The problem will be solved by converting the filtering problem to a usual d-step prediction problem for delay-free fractional systems.

Bringing simulation to engineers in the field: a Web 2.0 approach.

PubMed

Haines, Robert; Khan, Kashif; Brooke, John

2009-07-13

Field engineers working on water distribution systems have to implement day-to-day operational decisions. Since pipe networks are highly interconnected, the effects of such decisions are correlated with hydraulic and water quality conditions elsewhere in the network. This makes the provision of predictive decision support tools (DSTs) for field engineers critical to optimizing the engineering work on the network. We describe how we created DSTs to run on lightweight mobile devices by using the Web 2.0 technique known as Software as a Service. We designed our system following the architectural style of representational state transfer. The system not only displays static geographical information system data for pipe networks, but also dynamic information and prediction of network state, by invoking and displaying the results of simulations running on more powerful remote resources.

deepNF: Deep network fusion for protein function prediction.

PubMed

Gligorijevic, Vladimir; Barot, Meet; Bonneau, Richard

2018-06-01

The prevalence of high-throughput experimental methods has resulted in an abundance of large-scale molecular and functional interaction networks. The connectivity of these networks provides a rich source of information for inferring functional annotations for genes and proteins. An important challenge has been to develop methods for combining these heterogeneous networks to extract useful protein feature representations for function prediction. Most of the existing approaches for network integration use shallow models that encounter difficulty in capturing complex and highly-nonlinear network structures. Thus, we propose deepNF, a network fusion method based on Multimodal Deep Autoencoders to extract high-level features of proteins from multiple heterogeneous interaction networks. We apply this method to combine STRING networks to construct a common low-dimensional representation containing high-level protein features. We use separate layers for different network types in the early stages of the multimodal autoencoder, later connecting all the layers into a single bottleneck layer from which we extract features to predict protein function. We compare the cross-validation and temporal holdout predictive performance of our method with state-of-the-art methods, including the recently proposed method Mashup. Our results show that our method outperforms previous methods for both human and yeast STRING networks. We also show substantial improvement in the performance of our method in predicting GO terms of varying type and specificity. deepNF is freely available at: https://github.com/VGligorijevic/deepNF. vgligorijevic@flatironinstitute.org, rb133@nyu.edu. Supplementary data are available at Bioinformatics online.

Agent-specific learning signals for self–other distinction during mentalising

PubMed Central

Dolan, Raymond J.; Kurth-Nelson, Zeb

2018-01-01

Humans have a remarkable ability to simulate the minds of others. How the brain distinguishes between mental states attributed to self and mental states attributed to someone else is unknown. Here, we investigated how fundamental neural learning signals are selectively attributed to different agents. Specifically, we asked whether learning signals are encoded in agent-specific neural patterns or whether a self–other distinction depends on encoding agent identity separately from this learning signal. To examine this, we tasked subjects to learn continuously 2 models of the same environment, such that one was selectively attributed to self and the other was selectively attributed to another agent. Combining computational modelling with magnetoencephalography (MEG) enabled us to track neural representations of prediction errors (PEs) and beliefs attributed to self, and of simulated PEs and beliefs attributed to another agent. We found that the representational pattern of a PE reliably predicts the identity of the agent to whom the signal is attributed, consistent with a neural self–other distinction implemented via agent-specific learning signals. Strikingly, subjects exhibiting a weaker neural self–other distinction also had a reduced behavioural capacity for self–other distinction and displayed more marked subclinical psychopathological traits. The neural self–other distinction was also modulated by social context, evidenced in a significantly reduced decoding of agent identity in a nonsocial control task. Thus, we show that self–other distinction is realised through an encoding of agent identity intrinsic to fundamental learning signals. The observation that the fidelity of this encoding predicts psychopathological traits is of interest as a potential neurocomputational psychiatric biomarker. PMID:29689053

Agent-specific learning signals for self-other distinction during mentalising.

PubMed

Ereira, Sam; Dolan, Raymond J; Kurth-Nelson, Zeb

2018-04-01

Humans have a remarkable ability to simulate the minds of others. How the brain distinguishes between mental states attributed to self and mental states attributed to someone else is unknown. Here, we investigated how fundamental neural learning signals are selectively attributed to different agents. Specifically, we asked whether learning signals are encoded in agent-specific neural patterns or whether a self-other distinction depends on encoding agent identity separately from this learning signal. To examine this, we tasked subjects to learn continuously 2 models of the same environment, such that one was selectively attributed to self and the other was selectively attributed to another agent. Combining computational modelling with magnetoencephalography (MEG) enabled us to track neural representations of prediction errors (PEs) and beliefs attributed to self, and of simulated PEs and beliefs attributed to another agent. We found that the representational pattern of a PE reliably predicts the identity of the agent to whom the signal is attributed, consistent with a neural self-other distinction implemented via agent-specific learning signals. Strikingly, subjects exhibiting a weaker neural self-other distinction also had a reduced behavioural capacity for self-other distinction and displayed more marked subclinical psychopathological traits. The neural self-other distinction was also modulated by social context, evidenced in a significantly reduced decoding of agent identity in a nonsocial control task. Thus, we show that self-other distinction is realised through an encoding of agent identity intrinsic to fundamental learning signals. The observation that the fidelity of this encoding predicts psychopathological traits is of interest as a potential neurocomputational psychiatric biomarker.

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.

Inequivalent coherent state representations in group field theory

NASA Astrophysics Data System (ADS)

Kegeles, Alexander; Oriti, Daniele; Tomlin, Casey

2018-06-01

In this paper we propose an algebraic formulation of group field theory and consider non-Fock representations based on coherent states. We show that we can construct representations with an infinite number of degrees of freedom on compact manifolds. We also show that these representations break translation symmetry. Since such representations can be regarded as quantum gravitational systems with an infinite number of fundamental pre-geometric building blocks, they may be more suitable for the description of effective geometrical phases of the theory.

BMS symmetry, soft particles and memory

NASA Astrophysics Data System (ADS)

Chatterjee, Atreya; Lowe, David A.

2018-05-01

In this work, we revisit unitary irreducible representations of the Bondi–Metzner–Sachs (BMS) group discovered by McCarthy. Representations are labelled by an infinite number of supermomenta in addition to 4-momentum. Tensor products of these irreducible representations lead to particle-like states dressed by soft gravitational modes. Conservation of 4-momentum and supermomentum in the scattering of such states leads to a memory effect encoded in the outgoing soft modes. We note there exist irreducible representations corresponding to soft states with strictly vanishing 4-momentum, which may nevertheless be produced by scattering of particle-like states. This fact has interesting implications for the S-matrix in gravitational theories.

Predicting phase behavior of mixtures of reservoir fluids with carbon dioxide

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

Grigg, R.B.; Lingane, P.J.

1983-10-01

The use of an equation of state to predict phase behavior during carbon dioxide flooding is well established. There is consensus that the characterization of the C fraction, the grouping of this fraction into ''pseudo components'', and the selection of interaction parameters are the most important variables. However, the literature is vague as to how to best select the pseudo components, especially when aiming for a few-component representation as for a field scale compositional simulation. Single-contact phase behavior is presented for mixtures of Ford Geraldine (Delaware), Maljamar (Grayburg), West Sussex (Shannon), and Reservoir D reservoir fluids, and of a syntheticmore » oil C/C/C, with carbon dioxide. One can reproduce the phase behavior of these mixtures using 3-5 pseudo components and common interaction parameters. The critical properties of the pseudo components are calculated from detailed oil characterizations. Because the parameters are not further adjusted, this approach reduces the empiricism in fitting phase data and may result in a more accurate representation of the system as the composition of the oil changes during the approach to miscibility.« less

Mapping the cortical representation of speech sounds in a syllable repetition task.

PubMed

Markiewicz, Christopher J; Bohland, Jason W

2016-11-01

Speech repetition relies on a series of distributed cortical representations and functional pathways. A speaker must map auditory representations of incoming sounds onto learned speech items, maintain an accurate representation of those items in short-term memory, interface that representation with the motor output system, and fluently articulate the target sequence. A "dorsal stream" consisting of posterior temporal, inferior parietal and premotor regions is thought to mediate auditory-motor representations and transformations, but the nature and activation of these representations for different portions of speech repetition tasks remains unclear. Here we mapped the correlates of phonetic and/or phonological information related to the specific phonemes and syllables that were heard, remembered, and produced using a series of cortical searchlight multi-voxel pattern analyses trained on estimates of BOLD responses from individual trials. Based on responses linked to input events (auditory syllable presentation), predictive vowel-level information was found in the left inferior frontal sulcus, while syllable prediction revealed significant clusters in the left ventral premotor cortex and central sulcus and the left mid superior temporal sulcus. Responses linked to output events (the GO signal cueing overt production) revealed strong clusters of vowel-related information bilaterally in the mid to posterior superior temporal sulcus. For the prediction of onset and coda consonants, input-linked responses yielded distributed clusters in the superior temporal cortices, which were further informative for classifiers trained on output-linked responses. Output-linked responses in the Rolandic cortex made strong predictions for the syllables and consonants produced, but their predictive power was reduced for vowels. The results of this study provide a systematic survey of how cortical response patterns covary with the identity of speech sounds, which will help to constrain and guide theoretical models of speech perception, speech production, and phonological working memory. Copyright © 2016 Elsevier Inc. All rights reserved.

Visual search for object categories is predicted by the representational architecture of high-level visual cortex

PubMed Central

Alvarez, George A.; Nakayama, Ken; Konkle, Talia

2016-01-01

Visual search is a ubiquitous visual behavior, and efficient search is essential for survival. Different cognitive models have explained the speed and accuracy of search based either on the dynamics of attention or on similarity of item representations. Here, we examined the extent to which performance on a visual search task can be predicted from the stable representational architecture of the visual system, independent of attentional dynamics. Participants performed a visual search task with 28 conditions reflecting different pairs of categories (e.g., searching for a face among cars, body among hammers, etc.). The time it took participants to find the target item varied as a function of category combination. In a separate group of participants, we measured the neural responses to these object categories when items were presented in isolation. Using representational similarity analysis, we then examined whether the similarity of neural responses across different subdivisions of the visual system had the requisite structure needed to predict visual search performance. Overall, we found strong brain/behavior correlations across most of the higher-level visual system, including both the ventral and dorsal pathways when considering both macroscale sectors as well as smaller mesoscale regions. These results suggest that visual search for real-world object categories is well predicted by the stable, task-independent architecture of the visual system. NEW & NOTEWORTHY Here, we ask which neural regions have neural response patterns that correlate with behavioral performance in a visual processing task. We found that the representational structure across all of high-level visual cortex has the requisite structure to predict behavior. Furthermore, when directly comparing different neural regions, we found that they all had highly similar category-level representational structures. These results point to a ubiquitous and uniform representational structure in high-level visual cortex underlying visual object processing. PMID:27832600

Parallel Representation of Value-Based and Finite State-Based Strategies in the Ventral and Dorsal Striatum

PubMed Central

Ito, Makoto; Doya, Kenji

2015-01-01

Previous theoretical studies of animal and human behavioral learning have focused on the dichotomy of the value-based strategy using action value functions to predict rewards and the model-based strategy using internal models to predict environmental states. However, animals and humans often take simple procedural behaviors, such as the “win-stay, lose-switch” strategy without explicit prediction of rewards or states. Here we consider another strategy, the finite state-based strategy, in which a subject selects an action depending on its discrete internal state and updates the state depending on the action chosen and the reward outcome. By analyzing choice behavior of rats in a free-choice task, we found that the finite state-based strategy fitted their behavioral choices more accurately than value-based and model-based strategies did. When fitted models were run autonomously with the same task, only the finite state-based strategy could reproduce the key feature of choice sequences. Analyses of neural activity recorded from the dorsolateral striatum (DLS), the dorsomedial striatum (DMS), and the ventral striatum (VS) identified significant fractions of neurons in all three subareas for which activities were correlated with individual states of the finite state-based strategy. The signal of internal states at the time of choice was found in DMS, and for clusters of states was found in VS. In addition, action values and state values of the value-based strategy were encoded in DMS and VS, respectively. These results suggest that both the value-based strategy and the finite state-based strategy are implemented in the striatum. PMID:26529522

Development of a global aerosol model using a two-dimensional sectional method: 1. Model design

NASA Astrophysics Data System (ADS)

Matsui, H.

2017-08-01

This study develops an aerosol module, the Aerosol Two-dimensional bin module for foRmation and Aging Simulation version 2 (ATRAS2), and implements the module into a global climate model, Community Atmosphere Model. The ATRAS2 module uses a two-dimensional (2-D) sectional representation with 12 size bins for particles from 1 nm to 10 μm in dry diameter and 8 black carbon (BC) mixing state bins. The module can explicitly calculate the enhancement of absorption and cloud condensation nuclei activity of BC-containing particles by aging processes. The ATRAS2 module is an extension of a 2-D sectional aerosol module ATRAS used in our previous studies within a framework of a regional three-dimensional model. Compared with ATRAS, the computational cost of the aerosol module is reduced by more than a factor of 10 by simplifying the treatment of aerosol processes and 2-D sectional representation, while maintaining good accuracy of aerosol parameters in the simulations. Aerosol processes are simplified for condensation of sulfate, ammonium, and nitrate, organic aerosol formation, coagulation, and new particle formation processes, and box model simulations show that these simplifications do not substantially change the predicted aerosol number and mass concentrations and their mixing states. The 2-D sectional representation is simplified (the number of advected species is reduced) primarily by the treatment of chemical compositions using two interactive bin representations. The simplifications do not change the accuracy of global aerosol simulations. In part 2, comparisons with measurements and the results focused on aerosol processes such as BC aging processes are shown.

Representational Momentum for the Human Body: Awkwardness Matters, Experience Does Not

ERIC Educational Resources Information Center

Wilson, Margaret; Lancaster, Jessy; Emmorey, Karen

2010-01-01

Perception of the human body appears to involve predictive simulations that project forward to track unfolding body-motion events. Here we use representational momentum (RM) to investigate whether implicit knowledge of a learned arbitrary system of body movement such as sign language influences this prediction process, and how this compares to…

Representation of DNA sequences in genetic codon context with applications in exon and intron prediction.

PubMed

Yin, Changchuan

2015-04-01

To apply digital signal processing (DSP) methods to analyze DNA sequences, the sequences first must be specially mapped into numerical sequences. Thus, effective numerical mappings of DNA sequences play key roles in the effectiveness of DSP-based methods such as exon prediction. Despite numerous mappings of symbolic DNA sequences to numerical series, the existing mapping methods do not include the genetic coding features of DNA sequences. We present a novel numerical representation of DNA sequences using genetic codon context (GCC) in which the numerical values are optimized by simulation annealing to maximize the 3-periodicity signal to noise ratio (SNR). The optimized GCC representation is then applied in exon and intron prediction by Short-Time Fourier Transform (STFT) approach. The results show the GCC method enhances the SNR values of exon sequences and thus increases the accuracy of predicting protein coding regions in genomes compared with the commonly used 4D binary representation. In addition, this study offers a novel way to reveal specific features of DNA sequences by optimizing numerical mappings of symbolic DNA sequences.

An animated landscape representation of CD4+ T-cell differentiation, variability, and plasticity: Insights into the behavior of populations versus cells

PubMed Central

Rebhahn, Jonathan A; Deng, Nan; Sharma, Gaurav; Livingstone, Alexandra M; Huang, Sui; Mosmann, Tim R

2014-01-01

Recent advances in understanding CD4+ T-cell differentiation suggest that previous models of a few distinct, stable effector phenotypes were too simplistic. Although several well-characterized phenotypes are still recognized, some states display plasticity, and intermediate phenotypes exist. As a framework for reexamining these concepts, we use Waddington's landscape paradigm, augmented with explicit consideration of stochastic variations. Our animation program “LAVA” visualizes T-cell differentiation as cells moving across a landscape of hills and valleys, leading to attractor basins representing stable or semistable differentiation states. The model illustrates several principles, including: (i) cell populations may behave more predictably than individual cells; (ii) analogous to reticulate evolution, differentiation may proceed through a network of interconnected states, rather than a single well-defined pathway; (iii) relatively minor changes in the barriers between attractor basins can change the stability or plasticity of a population; (iv) intrapopulation variability of gene expression may be an important regulator of differentiation, rather than inconsequential noise; (v) the behavior of some populations may be defined mainly by the behavior of outlier cells. While not a quantitative representation of actual differentiation, our model is intended to provoke discussion of T-cell differentiation pathways, particularly highlighting a probabilistic view of transitions between states. PMID:24945794

Thinking about Seeing: perceptual sources of knowledge are encoded in the theory of mind brain regions of sighted and blind adults

PubMed Central

Koster-Hale, Jorie; Bedny, Marina; Saxe, Rebecca

2014-01-01

Blind people's inferences about how other people see provide a window into fundamental questions about the human capacity to think about one another's thoughts. By working with blind individuals, we can ask both what kinds of representations people form about others’ minds, and how much these representations depend on the observer having had similar mental states themselves. Thinking about others’ mental states depends on a specific group of brain regions, including the right temporo-parietal junction (RTPJ). We investigated the representations of others’ mental states in these brain regions, using multivoxel pattern analyses (MVPA). We found that, first, in the RTPJ of sighted adults, the pattern of neural response distinguished the source of the mental state (did the protagonist see or hear something?) but not the valence (did the protagonist feel good or bad?). Second, these neural representations were preserved in congenitally blind adults. These results suggest that the temporo-parietal junction contains explicit, abstract representations of features of others’ mental states, including the perceptual source. The persistence of these representations in congenitally blind adults, who have no first-person experience with sight, provides evidence that these representations emerge even in the absence of first-person perceptual experiences. PMID:24960530

Thinking about seeing: perceptual sources of knowledge are encoded in the theory of mind brain regions of sighted and blind adults.

PubMed

Koster-Hale, Jorie; Bedny, Marina; Saxe, Rebecca

2014-10-01

Blind people's inferences about how other people see provide a window into fundamental questions about the human capacity to think about one another's thoughts. By working with blind individuals, we can ask both what kinds of representations people form about others' minds, and how much these representations depend on the observer having had similar mental states themselves. Thinking about others' mental states depends on a specific group of brain regions, including the right temporo-parietal junction (RTPJ). We investigated the representations of others' mental states in these brain regions, using multivoxel pattern analyses (MVPA). We found that, first, in the RTPJ of sighted adults, the pattern of neural response distinguished the source of the mental state (did the protagonist see or hear something?) but not the valence (did the protagonist feel good or bad?). Second, these neural representations were preserved in congenitally blind adults. These results suggest that the temporo-parietal junction contains explicit, abstract representations of features of others' mental states, including the perceptual source. The persistence of these representations in congenitally blind adults, who have no first-person experience with sight, provides evidence that these representations emerge even in the absence of relevant first-person perceptual experiences. Copyright © 2014 Elsevier B.V. All rights reserved.

Neural Differentiation of Incorrectly Predicted Memories.

PubMed

Kim, Ghootae; Norman, Kenneth A; Turk-Browne, Nicholas B

2017-02-22

When an item is predicted in a particular context but the prediction is violated, memory for that item is weakened (Kim et al., 2014). Here, we explore what happens when such previously mispredicted items are later reencountered. According to prior neural network simulations, this sequence of events-misprediction and subsequent restudy-should lead to differentiation of the item's neural representation from the previous context (on which the misprediction was based). Specifically, misprediction weakens connections in the representation to features shared with the previous context and restudy allows new features to be incorporated into the representation that are not shared with the previous context. This cycle of misprediction and restudy should have the net effect of moving the item's neural representation away from the neural representation of the previous context. We tested this hypothesis using human fMRI by tracking changes in item-specific BOLD activity patterns in the hippocampus, a key structure for representing memories and generating predictions. In left CA2/3/DG, we found greater neural differentiation for items that were repeatedly mispredicted and restudied compared with items from a control condition that was identical except without misprediction. We also measured prediction strength in a trial-by-trial fashion and found that greater misprediction for an item led to more differentiation, further supporting our hypothesis. Therefore, the consequences of prediction error go beyond memory weakening. If the mispredicted item is restudied, the brain adaptively differentiates its memory representation to improve the accuracy of subsequent predictions and to shield it from further weakening. SIGNIFICANCE STATEMENT Competition between overlapping memories leads to weakening of nontarget memories over time, making it easier to access target memories. However, a nontarget memory in one context might become a target memory in another context. How do such memories get restrengthened without increasing competition again? Computational models suggest that the brain handles this by reducing neural connections to the previous context and adding connections to new features that were not part of the previous context. The result is neural differentiation away from the previous context. Here, we provide support for this theory, using fMRI to track neural representations of individual memories in the hippocampus and how they change based on learning. Copyright © 2017 the authors 0270-6474/17/372022-10$15.00/0.

Decoding the future from past experience: learning shapes predictions in early visual cortex.

PubMed

Luft, Caroline D B; Meeson, Alan; Welchman, Andrew E; Kourtzi, Zoe

2015-05-01

Learning the structure of the environment is critical for interpreting the current scene and predicting upcoming events. However, the brain mechanisms that support our ability to translate knowledge about scene statistics to sensory predictions remain largely unknown. Here we provide evidence that learning of temporal regularities shapes representations in early visual cortex that relate to our ability to predict sensory events. We tested the participants' ability to predict the orientation of a test stimulus after exposure to sequences of leftward- or rightward-oriented gratings. Using fMRI decoding, we identified brain patterns related to the observers' visual predictions rather than stimulus-driven activity. Decoding of predicted orientations following structured sequences was enhanced after training, while decoding of cued orientations following exposure to random sequences did not change. These predictive representations appear to be driven by the same large-scale neural populations that encode actual stimulus orientation and to be specific to the learned sequence structure. Thus our findings provide evidence that learning temporal structures supports our ability to predict future events by reactivating selective sensory representations as early as in primary visual cortex. Copyright © 2015 the American Physiological Society.

On inducing finite dimensional physical field representations for massless particles in even dimensions

NASA Technical Reports Server (NTRS)

Bhansali, Vineer

1993-01-01

Assuming trivial action of Euclidean translations, the method of induced representations is used to derive a correspondence between massless field representations transforming under the full generalized even dimensional Lorentz group, and highest weight states of the relevant little group. This gives a connection between 'helicity' and 'chirality' in all dimensions. Restrictions on 'gauge independent' representations for physical particles that this induction imposes are also stated.

Squeezed states: A geometric framework

NASA Technical Reports Server (NTRS)

Ali, S. T.; Brooke, J. A.; Gazeau, J.-P.

1992-01-01

A general definition of squeezed states is proposed and its main features are illustrated through a discussion of the standard optical coherent states represented by 'Gaussian pure states'. The set-up involves representations of groups on Hilbert spaces over homogeneous spaces of the group, and relies on the construction of a square integrable (coherent state) group representation modulo a subgroup. This construction depends upon a choice of a Borel section which has a certain permissible arbitrariness in its selection; this freedom is attributable to a squeezing of the defining coherent states of the representation, and corresponds in this way to a sort of gauging.

Improved pan-specific MHC class I peptide-binding predictions using a novel representation of the MHC-binding cleft environment.

PubMed

Carrasco Pro, S; Zimic, M; Nielsen, M

2014-02-01

Major histocompatibility complex (MHC) molecules play a key role in cell-mediated immune responses presenting bounded peptides for recognition by the immune system cells. Several in silico methods have been developed to predict the binding affinity of a given peptide to a specific MHC molecule. One of the current state-of-the-art methods for MHC class I is NetMHCpan, which has a core ingredient for the representation of the MHC class I molecule using a pseudo-sequence representation of the binding cleft amino acid environment. New and large MHC-peptide-binding data sets are constantly being made available, and also new structures of MHC class I molecules with a bound peptide have been published. In order to test if the NetMHCpan method can be improved by integrating this novel information, we created new pseudo-sequence definitions for the MHC-binding cleft environment from sequence and structural analyses of different MHC data sets including human leukocyte antigen (HLA), non-human primates (chimpanzee, macaque and gorilla) and other animal alleles (cattle, mouse and swine). From these constructs, we showed that by focusing on MHC sequence positions found to be polymorphic across the MHC molecules used to train the method, the NetMHCpan method achieved a significant increase in the predictive performance, in particular, of non-human MHCs. This study hence showed that an improved performance of MHC-binding methods can be achieved not only by the accumulation of more MHC-peptide-binding data but also by a refined definition of the MHC-binding environment including information from non-human species. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Prediction of cause of death from forensic autopsy reports using text classification techniques: A comparative study.

PubMed

Mujtaba, Ghulam; Shuib, Liyana; Raj, Ram Gopal; Rajandram, Retnagowri; Shaikh, Khairunisa

2018-07-01

Automatic text classification techniques are useful for classifying plaintext medical documents. This study aims to automatically predict the cause of death from free text forensic autopsy reports by comparing various schemes for feature extraction, term weighing or feature value representation, text classification, and feature reduction. For experiments, the autopsy reports belonging to eight different causes of death were collected, preprocessed and converted into 43 master feature vectors using various schemes for feature extraction, representation, and reduction. The six different text classification techniques were applied on these 43 master feature vectors to construct a classification model that can predict the cause of death. Finally, classification model performance was evaluated using four performance measures i.e. overall accuracy, macro precision, macro-F-measure, and macro recall. From experiments, it was found that that unigram features obtained the highest performance compared to bigram, trigram, and hybrid-gram features. Furthermore, in feature representation schemes, term frequency, and term frequency with inverse document frequency obtained similar and better results when compared with binary frequency, and normalized term frequency with inverse document frequency. Furthermore, the chi-square feature reduction approach outperformed Pearson correlation, and information gain approaches. Finally, in text classification algorithms, support vector machine classifier outperforms random forest, Naive Bayes, k-nearest neighbor, decision tree, and ensemble-voted classifier. Our results and comparisons hold practical importance and serve as references for future works. Moreover, the comparison outputs will act as state-of-art techniques to compare future proposals with existing automated text classification techniques. Copyright © 2017 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Summary of Cumulus Parameterization Workshop

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Starr, David OC.; Hou, Arthur; Newman, Paul; Sud, Yogesh

2002-01-01

A workshop on cumulus parameterization took place at the NASA Goddard Space Flight Center from December 3-5, 2001. The major objectives of this workshop were (1) to review the problem of representation of moist processes in large-scale models (mesoscale models, Numerical Weather Prediction models and Atmospheric General Circulation Models), (2) to review the state-of-the-art in cumulus parameterization schemes, and (3) to discuss the need for future research and applications. There were a total of 31 presentations and about 100 participants from the United States, Japan, the United Kingdom, France and South Korea. The specific presentations and discussions during the workshop are summarized in this paper.

A review of nonlinear constitutive models for metals

NASA Technical Reports Server (NTRS)

Allen, David H.; Harris, Charles E.

1990-01-01

Over the past two decades a number of thermomechanical constitutive theories have been proposed for viscoplastic metals. These models are in most cases similar in that they utilize a set of internal state variables which provide locally averaged representations of microphysical phenomena such as dislocation rearrangement and grain boundary sliding. The state of development of several of these models is now at the point where accurate theoretical solutions can be obtained for a wide variety of structural problems at elevated temperatures. The fundamentals of viscoplasticity are briefly reviewed and a general framework is outlined. Several of the more prominent models are reviewed, and predictions from models are compared to experimental results.

[The legitimacy of representation in forums with social participation: the case of the Bahia State Health Council, Brazil].

PubMed

Bispo Júnior, José Patrício; Gerschman, Sílvia

2015-01-01

The electoral representation model is insufficient and inadequate for new participatory roles such as those played by members of health councils. This article analyzes representation and representativeness in the Bahia State Health Council, Brazil. The study included interviews with 20 current or former members of the State Health Council, analysis of the council minutes and bylaws, and observation of plenary meetings. Discourse analysis technique was used to analyze interventions by members. The article discusses the results in four analytical lines: the process by which various organizations name representatives to the Council; the relationship between Council members and their constituencies; interest representation in the Council; and criteria used by the plenary to take positions. The study reveals various problems with the representativeness of the Bahia State Health Council and discusses the peculiarities of representation in social participation forums and the characteristics that give legitimacy to representatives.

Efficient alignment-free DNA barcode analytics.

PubMed

Kuksa, Pavel; Pavlovic, Vladimir

2009-11-10

In this work we consider barcode DNA analysis problems and address them using alternative, alignment-free methods and representations which model sequences as collections of short sequence fragments (features). The methods use fixed-length representations (spectrum) for barcode sequences to measure similarities or dissimilarities between sequences coming from the same or different species. The spectrum-based representation not only allows for accurate and computationally efficient species classification, but also opens possibility for accurate clustering analysis of putative species barcodes and identification of critical within-barcode loci distinguishing barcodes of different sample groups. New alignment-free methods provide highly accurate and fast DNA barcode-based identification and classification of species with substantial improvements in accuracy and speed over state-of-the-art barcode analysis methods. We evaluate our methods on problems of species classification and identification using barcodes, important and relevant analytical tasks in many practical applications (adverse species movement monitoring, sampling surveys for unknown or pathogenic species identification, biodiversity assessment, etc.) On several benchmark barcode datasets, including ACG, Astraptes, Hesperiidae, Fish larvae, and Birds of North America, proposed alignment-free methods considerably improve prediction accuracy compared to prior results. We also observe significant running time improvements over the state-of-the-art methods. Our results show that newly developed alignment-free methods for DNA barcoding can efficiently and with high accuracy identify specimens by examining only few barcode features, resulting in increased scalability and interpretability of current computational approaches to barcoding.

Signifying Zika: heterogeneity in the representations of the virus by history of infection.

PubMed

Guedes, Gilvan Ramalho; Coutinho, Raquel Zanatta; Marteleto, Leticia; Pereira, Wesley Henrique Silva; Duarte, Denise

2018-06-07

Despite having been broadly advertised by the mass media, many negative consequences of the Zika virus have been less significant than originally predicted. It is likely that after a few months from the epidemic's onset, personal experience with the virus has altered the person's way to deal with the disease. This study explores the relation between exposure to Zika virus and the social representation of the epidemic. More specifically, one analyzes if increased exposure to the risk of Zika infection changes the characteristics of the web of meanings surrounding the epidemic. Between August and November of 2016, 150 interviews were conducted in the municipality of Governador Valadares, Minas Gerais State, Brazil. Based on the Free Words Association Technique, data on evocations related to the Zika virus were modeled by social network analysis, allowing the characterization of the web of meanings by level of exposure to the risk of Zika infection. The analysis performed here suggests that those never infected by any disease transmitted by the Aedes aegypti mosquito have a lesser representation, incorporating information from the media through lay thinking. In contrast to those with low levels of exposure, the social representation of people infected by Zika is associated with meanings related to the most common symptoms, such as pain, rash, and itching. Personal experience seems to shape the social representation of the disease, increasing the focus on its proximate consequences. Public campaigns designed to foster protective behavior should take into consideration the heterogeneity in the representations of this epidemic to improve adherence to preventive behavior.

Public Higher Education Performance Accountability Framework Report: Goal--Access and Affordability. Measure: Percentage of Racial Representation in Systems of Higher Education Compared to Racial Representation in the State. Commission Report 07-20

ERIC Educational Resources Information Center

California Postsecondary Education Commission, 2007

2007-01-01

Despite segmental efforts to increase diversity in higher education, African American and Latino students are not achieving levels of representation in California public universities that are equivalent to their levels of representation in the overall State population. Using data for the years 1997 through 2006, the California Postsecondary…

New representation of n-mode squeezed state gained via n-partite entangled state [rapid communication

NASA Astrophysics Data System (ADS)

Jiang, Nian-Quan

2005-10-01

By virtue of the n-partite entangled state, we extend the way of Agarwal-Simon's presenting single-mode squeezed state to n-mode case and find a new representation of the n-mode squeezed state. This n-mode squeezed state is also an entangled state and can be a superposition of n-mode coherent states.

Theory of quantized systems: formal basis for DEVS/HLA distributed simulation environment

NASA Astrophysics Data System (ADS)

Zeigler, Bernard P.; Lee, J. S.

1998-08-01

In the context of a DARPA ASTT project, we are developing an HLA-compliant distributed simulation environment based on the DEVS formalism. This environment will provide a user- friendly, high-level tool-set for developing interoperable discrete and continuous simulation models. One application is the study of contract-based predictive filtering. This paper presents a new approach to predictive filtering based on a process called 'quantization' to reduce state update transmission. Quantization, which generates state updates only at quantum level crossings, abstracts a sender model into a DEVS representation. This affords an alternative, efficient approach to embedding continuous models within distributed discrete event simulations. Applications of quantization to message traffic reduction are discussed. The theory has been validated by DEVSJAVA simulations of test cases. It will be subject to further test in actual distributed simulations using the DEVS/HLA modeling and simulation environment.

Structured Semantic Knowledge Can Emerge Automatically from Predicting Word Sequences in Child-Directed Speech

PubMed Central

Huebner, Philip A.; Willits, Jon A.

2018-01-01

Previous research has suggested that distributional learning mechanisms may contribute to the acquisition of semantic knowledge. However, distributional learning mechanisms, statistical learning, and contemporary “deep learning” approaches have been criticized for being incapable of learning the kind of abstract and structured knowledge that many think is required for acquisition of semantic knowledge. In this paper, we show that recurrent neural networks, trained on noisy naturalistic speech to children, do in fact learn what appears to be abstract and structured knowledge. We trained two types of recurrent neural networks (Simple Recurrent Network, and Long Short-Term Memory) to predict word sequences in a 5-million-word corpus of speech directed to children ages 0–3 years old, and assessed what semantic knowledge they acquired. We found that learned internal representations are encoding various abstract grammatical and semantic features that are useful for predicting word sequences. Assessing the organization of semantic knowledge in terms of the similarity structure, we found evidence of emergent categorical and hierarchical structure in both models. We found that the Long Short-term Memory (LSTM) and SRN are both learning very similar kinds of representations, but the LSTM achieved higher levels of performance on a quantitative evaluation. We also trained a non-recurrent neural network, Skip-gram, on the same input to compare our results to the state-of-the-art in machine learning. We found that Skip-gram achieves relatively similar performance to the LSTM, but is representing words more in terms of thematic compared to taxonomic relations, and we provide reasons why this might be the case. Our findings show that a learning system that derives abstract, distributed representations for the purpose of predicting sequential dependencies in naturalistic language may provide insight into emergence of many properties of the developing semantic system. PMID:29520243

Prediction of the Arctic Oscillation in Boreal Winter by Dynamical Seasonal Forecasting Systems

NASA Technical Reports Server (NTRS)

Kang, Daehyun; Lee, Myong-In; Im, Jungho; Kim, Daehyun; Kim, Hye-Mi; Kang, Hyun-Suk; Shubert, Siegfried D.; Arriba, Albertom; MacLachlan, Craig

2013-01-01

This study assesses the prediction skill of the boreal winter Arctic Oscillation (AO) in the state-of-the-art dynamical ensemble prediction systems (EPSs): the UKMO GloSea4, the NCEP CFSv2, and the NASA GEOS-5. Long-term reforecasts made with the EPSs are used to evaluate representations of the AO, and to examine skill scores for the deterministic and probabilistic forecast of the AO index. The reforecasts reproduce the observed changes in the large-scale patterns of the Northern Hemispheric surface temperature, upper-level wind, and precipitation according to the AO phase. Results demonstrate that all EPSs have better prediction skill than the persistence prediction for lead times up to 3-month, suggesting a great potential for skillful prediction of the AO and the associated climate anomalies in seasonal time scale. It is also found that the deterministic and probabilistic forecast skill of the AO in the recent period (1997-2010) is higher than that in the earlier period (1983-1996).

Deadbeat Predictive Controllers

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan; Phan, Minh

1997-01-01

Several new computational algorithms are presented to compute the deadbeat predictive control law. The first algorithm makes use of a multi-step-ahead output prediction to compute the control law without explicitly calculating the controllability matrix. The system identification must be performed first and then the predictive control law is designed. The second algorithm uses the input and output data directly to compute the feedback law. It combines the system identification and the predictive control law into one formulation. The third algorithm uses an observable-canonical form realization to design the predictive controller. The relationship between all three algorithms is established through the use of the state-space representation. All algorithms are applicable to multi-input, multi-output systems with disturbance inputs. In addition to the feedback terms, feed forward terms may also be added for disturbance inputs if they are measurable. Although the feedforward terms do not influence the stability of the closed-loop feedback law, they enhance the performance of the controlled system.

Family cumulative risk and at-risk kindergarteners' social competence: the mediating role of parent representations of the attachment relationship.

PubMed

Sparks, Lauren A; Trentacosta, Christopher J; Owusu, Erika; McLear, Caitlin; Smith-Darden, Joanne

2018-08-01

Secure attachment relationships have been linked to social competence in at-risk children. In the current study, we examined the role of parent secure base scripts in predicting at-risk kindergarteners' social competence. Parent representations of secure attachment were hypothesized to mediate the relationship between lower family cumulative risk and children's social competence. Participants included 106 kindergarteners and their primary caregivers recruited from three urban charter schools serving low-income families as a part of a longitudinal study. Lower levels of cumulative risk predicted greater secure attachment representations in parents, and scores on the secure base script assessment predicted children's social competence. An indirect relationship between lower cumulative risk and kindergarteners' social competence via parent secure base script scores was also supported. Parent script-based representations of the attachment relationship appear to be an important link between lower levels of cumulative risk and low-income kindergarteners' social competence. Implications of these findings for future interventions are discussed.

A novel representation for apoptosis protein subcellular localization prediction using support vector machine.

PubMed

Zhang, Li; Liao, Bo; Li, Dachao; Zhu, Wen

2009-07-21

Apoptosis, or programmed cell death, plays an important role in development of an organism. Obtaining information on subcellular location of apoptosis proteins is very helpful to understand the apoptosis mechanism. In this paper, based on the concept that the position distribution information of amino acids is closely related with the structure and function of proteins, we introduce the concept of distance frequency [Matsuda, S., Vert, J.P., Ueda, N., Toh, H., Akutsu, T., 2005. A novel representation of protein sequences for prediction of subcellular location using support vector machines. Protein Sci. 14, 2804-2813] and propose a novel way to calculate distance frequencies. In order to calculate the local features, each protein sequence is separated into p parts with the same length in our paper. Then we use the novel representation of protein sequences and adopt support vector machine to predict subcellular location. The overall prediction accuracy is significantly improved by jackknife test.

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

Sonic Boom Pressure Signature Uncertainty Calculation and Propagation to Ground Noise

NASA Technical Reports Server (NTRS)

West, Thomas K., IV; Bretl, Katherine N.; Walker, Eric L.; Pinier, Jeremy T.

2015-01-01

The objective of this study was to outline an approach for the quantification of uncertainty in sonic boom measurements and to investigate the effect of various near-field uncertainty representation approaches on ground noise predictions. These approaches included a symmetric versus asymmetric uncertainty band representation and a dispersion technique based on a partial sum Fourier series that allows for the inclusion of random error sources in the uncertainty. The near-field uncertainty was propagated to the ground level, along with additional uncertainty in the propagation modeling. Estimates of perceived loudness were obtained for the various types of uncertainty representation in the near-field. Analyses were performed on three configurations of interest to the sonic boom community: the SEEB-ALR, the 69o DeltaWing, and the LM 1021-01. Results showed that representation of the near-field uncertainty plays a key role in ground noise predictions. Using a Fourier series based dispersion approach can double the amount of uncertainty in the ground noise compared to a pure bias representation. Compared to previous computational fluid dynamics results, uncertainty in ground noise predictions were greater when considering the near-field experimental uncertainty.

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

Reward Selectively Modulates the Lingering Neural Representation of Recently Attended Objects in Natural Scenes.

PubMed

Hickey, Clayton; Peelen, Marius V

2017-08-02

Theories of reinforcement learning and approach behavior suggest that reward can increase the perceptual salience of environmental stimuli, ensuring that potential predictors of outcome are noticed in the future. However, outcome commonly follows visual processing of the environment, occurring even when potential reward cues have long disappeared. How can reward feedback retroactively cause now-absent stimuli to become attention-drawing in the future? One possibility is that reward and attention interact to prime lingering visual representations of attended stimuli that sustain through the interval separating stimulus and outcome. Here, we test this idea using multivariate pattern analysis of fMRI data collected from male and female humans. While in the scanner, participants searched for examples of target categories in briefly presented pictures of cityscapes and landscapes. Correct task performance was followed by reward feedback that could randomly have either high or low magnitude. Analysis showed that high-magnitude reward feedback boosted the lingering representation of target categories while reducing the representation of nontarget categories. The magnitude of this effect in each participant predicted the behavioral impact of reward on search performance in subsequent trials. Other analyses show that sensitivity to reward-as expressed in a personality questionnaire and in reactivity to reward feedback in the dopaminergic midbrain-predicted reward-elicited variance in lingering target and nontarget representations. Credit for rewarding outcome thus appears to be assigned to the target representation, causing the visual system to become sensitized for similar objects in the future. SIGNIFICANCE STATEMENT How do reward-predictive visual stimuli become salient and attention-drawing? In the real world, reward cues precede outcome and reward is commonly received long after potential predictors have disappeared. How can the representation of environmental stimuli be affected by outcome that occurs later in time? Here, we show that reward acts on lingering representations of environmental stimuli that sustain through the interval between stimulus and outcome. Using naturalistic scene stimuli and multivariate pattern analysis of fMRI data, we show that reward boosts the representation of attended objects and reduces the representation of unattended objects. This interaction of attention and reward processing acts to prime vision for stimuli that may serve to predict outcome. Copyright © 2017 the authors 0270-6474/17/377297-08$15.00/0.

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).

Noisy bases in Hilbert space: A new class of thermal coherent states and their properties

NASA Technical Reports Server (NTRS)

Vourdas, A.; Bishop, R. F.

1995-01-01

Coherent mixed states (or thermal coherent states) associated with the displaced harmonic oscillator at finite temperature, are introduced as a 'random' (or 'thermal' or 'noisy') basis in Hilbert space. A resolution of the identity for these states is proved and used to generalize the usual coherent state formalism for the finite temperature case. The Bargmann representation of an operator is introduced and its relation to the P and Q representations is studied. Generalized P and Q representations for the finite temperature case are also considered and several interesting relations among them are derived.

37 CFR 11.101 - Competence.

Code of Federal Regulations, 2013 CFR

2013-07-01

... competent representation to a client. Competent representation requires the legal, scientific, and technical knowledge, skill, thoroughness and preparation reasonably necessary for the representation. ... COMMERCE REPRESENTATION OF OTHERS BEFORE THE UNITED STATES PATENT AND TRADEMARK OFFICE USPTO Rules of...

37 CFR 11.101 - Competence.

Code of Federal Regulations, 2014 CFR

2014-07-01

... competent representation to a client. Competent representation requires the legal, scientific, and technical knowledge, skill, thoroughness and preparation reasonably necessary for the representation. ... COMMERCE REPRESENTATION OF OTHERS BEFORE THE UNITED STATES PATENT AND TRADEMARK OFFICE USPTO Rules of...

Dynamics of Entropy in Quantum-like Model of Decision Making

NASA Astrophysics Data System (ADS)

Basieva, Irina; Khrennikov, Andrei; Asano, Masanari; Ohya, Masanori; Tanaka, Yoshiharu

2011-03-01

We present a quantum-like model of decision making in games of the Prisoner's Dilemma type. By this model the brain processes information by using representation of mental states in complex Hilbert space. Driven by the master equation the mental state of a player, say Alice, approaches an equilibrium point in the space of density matrices. By using this equilibrium point Alice determines her mixed (i.e., probabilistic) strategy with respect to Bob. Thus our model is a model of thinking through decoherence of initially pure mental state. Decoherence is induced by interaction with memory and external environment. In this paper we study (numerically) dynamics of quantum entropy of Alice's state in the process of decision making. Our analysis demonstrates that this dynamics depends nontrivially on the initial state of Alice's mind on her own actions and her prediction state (for possible actions of Bob.)

Spatial Data Structures for Robotic Vehicle Route Planning

DTIC Science & Technology

1988-12-01

goal will be realized in an intelligent Spatial Data Structure Development System (SDSDS) intended for use by Terrain Analysis applications...from the user the details of representation and to permit the infrastructure itself to decide which representations will be most efficient or effective ...to intelligently predict performance of algorithmic sequences and thereby optimize the application (within the accuracy of the prediction models). The

Experiments with Interaction between the National Water Model and the Reservoir System Simulation Model: A Case Study of Russian River Basin

NASA Astrophysics Data System (ADS)

Kim, J.; Johnson, L.; Cifelli, R.; Chandra, C. V.; Gochis, D.; McCreight, J. L.; Yates, D. N.; Read, L.; Flowers, T.; Cosgrove, B.

2017-12-01

NOAA National Water Center (NWC) in partnership with the National Centers for Environmental Prediction (NCEP), the National Center for Atmospheric Research (NCAR) and other academic partners have produced operational hydrologic predictions for the nation using a new National Water Model (NWM) that is based on the community WRF-Hydro modeling system since the summer of 2016 (Gochis et al., 2015). The NWM produces a variety of hydrologic analysis and prediction products, including gridded fields of soil moisture, snowpack, shallow groundwater levels, inundated area depths, evapotranspiration as well as estimates of river flow and velocity for approximately 2.7 million river reaches. Also included in the NWM are representations for more than 1,200 reservoirs which are linked into the national channel network defined by the USGS NHDPlusv2.0 hydrography dataset. Despite the unprecedented spatial and temporal coverage of the NWM, many known deficiencies exist, including the representation of lakes and reservoirs. This study addresses the implementation of a reservoir assimilation scheme through coupling of a reservoir simulation model to represent the influence of managed flows. We examine the use of the reservoir operations to dynamically update lake/reservoir storage volume states, characterize flow characteristics of river reaches flowing into and out of lakes and reservoirs, and incorporate enhanced reservoir operating rules for the reservoir model options within the NWM. Model experiments focus on a pilot reservoir domain-Lake Mendocino, CA, and its contributing watershed, the East Fork Russian River. This reservoir is modeled using United States Army Corps of Engineers (USACE) HEC-ResSim developed for application to examine forecast informed reservoir operations (FIRO) in the Russian River basin.

Attachment as an organizer of behavior: implications for substance abuse problems and willingness to seek treatment

PubMed Central

Caspers, Kristin M; Yucuis, Rebecca; Troutman, Beth; Spinks, Ruth

2006-01-01

Background Attachment theory allows specific predictions about the role of attachment representations in organizing behavior. Insecure attachment is hypothesized to predict maladaptive emotional regulation whereas secure attachment is hypothesized to predict adaptive emotional regulation. In this paper, we test specific hypotheses about the role of attachment representations in substance abuse/dependence and treatment participation. Based on theory, we expect divergence between levels of maladaptive functioning and adaptive methods of regulating negative emotions. Methods Participants for this study consist of a sample of adoptees participating in an ongoing longitudinal adoption study (n = 208). The Semi-Structured Assessment of the Genetics of Alcohol-II [41] was used to determine lifetime substance abuse/dependence and treatment participation. Attachment representations were derived by the Adult Attachment Interview [AAI; [16

An Exploration of Secondary Students' Mental States When Learning about Acids and Bases

ERIC Educational Resources Information Center

Liu, Chia-Ju; Hou, I-Lin; Chiu, Houn-Lin; Treagust, David F.

2014-01-01

This study explored factors of students' mental states, including emotion, intention, internal mental representation, and external mental representation, which can affect their learning performance. In evaluating students' mental states during the science learning process and the relationship between mental states and learning…

A Systematic Prediction of Drug-Target Interactions Using Molecular Fingerprints and Protein Sequences.

PubMed

Huang, Yu-An; You, Zhu-Hong; Chen, Xing

2018-01-01

Drug-Target Interactions (DTI) play a crucial role in discovering new drug candidates and finding new proteins to target for drug development. Although the number of detected DTI obtained by high-throughput techniques has been increasing, the number of known DTI is still limited. On the other hand, the experimental methods for detecting the interactions among drugs and proteins are costly and inefficient. Therefore, computational approaches for predicting DTI are drawing increasing attention in recent years. In this paper, we report a novel computational model for predicting the DTI using extremely randomized trees model and protein amino acids information. More specifically, the protein sequence is represented as a Pseudo Substitution Matrix Representation (Pseudo-SMR) descriptor in which the influence of biological evolutionary information is retained. For the representation of drug molecules, a novel fingerprint feature vector is utilized to describe its substructure information. Then the DTI pair is characterized by concatenating the two vector spaces of protein sequence and drug substructure. Finally, the proposed method is explored for predicting the DTI on four benchmark datasets: Enzyme, Ion Channel, GPCRs and Nuclear Receptor. The experimental results demonstrate that this method achieves promising prediction accuracies of 89.85%, 87.87%, 82.99% and 81.67%, respectively. For further evaluation, we compared the performance of Extremely Randomized Trees model with that of the state-of-the-art Support Vector Machine classifier. And we also compared the proposed model with existing computational models, and confirmed 15 potential drug-target interactions by looking for existing databases. The experiment results show that the proposed method is feasible and promising for predicting drug-target interactions for new drug candidate screening based on sizeable features. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Automatic prediction of facial trait judgments: appearance vs. structural models.

PubMed

Rojas, Mario; Masip, David; Todorov, Alexander; Vitria, Jordi

2011-01-01

Evaluating other individuals with respect to personality characteristics plays a crucial role in human relations and it is the focus of attention for research in diverse fields such as psychology and interactive computer systems. In psychology, face perception has been recognized as a key component of this evaluation system. Multiple studies suggest that observers use face information to infer personality characteristics. Interactive computer systems are trying to take advantage of these findings and apply them to increase the natural aspect of interaction and to improve the performance of interactive computer systems. Here, we experimentally test whether the automatic prediction of facial trait judgments (e.g. dominance) can be made by using the full appearance information of the face and whether a reduced representation of its structure is sufficient. We evaluate two separate approaches: a holistic representation model using the facial appearance information and a structural model constructed from the relations among facial salient points. State of the art machine learning methods are applied to a) derive a facial trait judgment model from training data and b) predict a facial trait value for any face. Furthermore, we address the issue of whether there are specific structural relations among facial points that predict perception of facial traits. Experimental results over a set of labeled data (9 different trait evaluations) and classification rules (4 rules) suggest that a) prediction of perception of facial traits is learnable by both holistic and structural approaches; b) the most reliable prediction of facial trait judgments is obtained by certain type of holistic descriptions of the face appearance; and c) for some traits such as attractiveness and extroversion, there are relationships between specific structural features and social perceptions.

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…

Age-Related Declines in the Fidelity of Newly Acquired Category Representations

ERIC Educational Resources Information Center

Davis, Tyler; Love, Bradley C.; Maddox, W. Todd

2012-01-01

We present a theory suggesting that the ability to build category representations that reflect the nuances of category structures in the environment depends upon clustering mechanisms instantiated in an MTL-PFC-based circuit. Because function in this circuit declines with age, we predict that the ability to build category representations will be…

Observations and Student Perceptions of the Quality of Preservice Teachers' Teaching Behaviour: Construct Representation and Predictive Quality

ERIC Educational Resources Information Center

Maulana, Ridwan; Helms-Lorenz, Michelle

2016-01-01

Observations and student perceptions are recognised as important tools for examining teaching behaviour, but little is known about whether both perspectives share similar construct representations and how both perspectives link with student academic outcomes. The present study compared the construct representation of preservice teachers' teaching…

Parenting and Children's Representations of Family Predict Disruptive and Callous-Unemotional Behaviors

ERIC Educational Resources Information Center

Wagner, Nicholas J.; Mills-Koonce, W. Roger; Willoughby, Michael T.; Zvara, Bharathi; Cox, Martha J.

2015-01-01

Data from a large prospective longitudinal study (n = 1,239) was used to investigate the association between observed sensitive parenting in early childhood and children's representations of family relationships as measured by the Family Drawing Paradigm (FDP) in first grade as well as the extent to which these representations partially mediate…

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.

Narrative representations of caregivers and emotion dysregulation as predictors of maltreated children's rejection by peers.

PubMed

Shields, A; Ryan, R M; Cicchetti, D

2001-05-01

This study examined whether maltreated children were more likely than nonmaltreated children to develop poor-quality representations of caregivers and whether these representations predicted children's rejection by peers. A narrative task assessing representations of mothers and fathers was administered to 76 maltreated and 45 nonmaltreated boys and girls (8-12 years old). Maltreated children's representations were more negative/constricted and less positive/coherent than those of nonmaltreated children. Maladaptive representations were associated with emotion dysregulation, aggression, and peer rejection, whereas positive/coherent representations were related to prosocial behavior and peer preference. Representations mediated maltreatment's effects on peer rejection in part by undermining emotion regulation. Findings suggest that representations of caregivers serve an important regulatory function in the peer relationships of at-risk children.

A Predictive Model for Medical Events Based on Contextual Embedding of Temporal Sequences

PubMed Central

Wang, Zhimu; Huang, Yingxiang; Wang, Shuang; Wang, Fei; Jiang, Xiaoqian

2016-01-01

Background Medical concepts are inherently ambiguous and error-prone due to human fallibility, which makes it hard for them to be fully used by classical machine learning methods (eg, for tasks like early stage disease prediction). Objective Our work was to create a new machine-friendly representation that resembles the semantics of medical concepts. We then developed a sequential predictive model for medical events based on this new representation. Methods We developed novel contextual embedding techniques to combine different medical events (eg, diagnoses, prescriptions, and labs tests). Each medical event is converted into a numerical vector that resembles its “semantics,” via which the similarity between medical events can be easily measured. We developed simple and effective predictive models based on these vectors to predict novel diagnoses. Results We evaluated our sequential prediction model (and standard learning methods) in estimating the risk of potential diseases based on our contextual embedding representation. Our model achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.79 on chronic systolic heart failure and an average AUC of 0.67 (over the 80 most common diagnoses) using the Medical Information Mart for Intensive Care III (MIMIC-III) dataset. Conclusions We propose a general early prognosis predictor for 80 different diagnoses. Our method computes numeric representation for each medical event to uncover the potential meaning of those events. Our results demonstrate the efficiency of the proposed method, which will benefit patients and physicians by offering more accurate diagnosis. PMID:27888170

Theory of Mind in the Wild: Toward Tackling the Challenges of Everyday Mental State Reasoning

PubMed Central

Wertz, Annie E.; German, Tamsin C.

2013-01-01

A complete understanding of the cognitive systems underwriting theory of mind (ToM) abilities requires articulating how mental state representations are generated and processed in everyday situations. Individuals rarely announce their intentions prior to acting, and actions are often consistent with multiple mental states. In order for ToM to operate effectively in such situations, mental state representations should be generated in response to certain actions, even when those actions occur in the presence of mental state content derived from other aspects of the situation. Results from three experiments with preschool children and adults demonstrate that mental state information is indeed generated based on an approach action cue in situations that contain competing mental state information. Further, the frequency with which participants produced or endorsed explanations that include mental states about an approached object decreased when the competing mental state information about a different object was made explicit. This set of experiments provides some of the first steps toward identifying the observable action cues that are used to generate mental state representations in everyday situations and offers insight into how both young children and adults processes multiple mental state representations. PMID:24069160

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.

Childhood abuse and neglect, attachment states of mind, and non-suicidal self-injury.

PubMed

Martin, Jodi; Raby, K Lee; Labella, Madelyn H; Roisman, Glenn I

2017-10-01

This investigation examined preoccupied attachment states of mind as both a risk factor for non-suicidal self-injury (NSSI) and as a mechanism by which prospectively assessed childhood experiences of abuse and neglect predicted the frequency/severity of NSSI behavior up to age 26 years in 164 individuals (83 females) who were followed from birth in the Minnesota Longitudinal Study of Risk and Adaptation. Preoccupied (but not dismissing) states of mind regarding both childhood caregivers and adult romantic partners were correlated with more frequent/severe NSSI. Furthermore, preoccupied states of mind regarding caregivers partially accounted for the association between childhood abuse/neglect and NSSI. This work represents a rare prospective test of a developmental psychopathology framework for understanding NSSI behavior, in which atypical caregiving experiences are carried forward through attachment representations of caregivers that reflect behavioral risk.

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.

Coupling internal cerebellar models enhances online adaptation and supports offline consolidation in sensorimotor tasks

PubMed Central

Passot, Jean-Baptiste; Luque, Niceto R.; Arleo, Angelo

2013-01-01

The cerebellum is thought to mediate sensorimotor adaptation through the acquisition of internal models of the body-environment interaction. These representations can be of two types, identified as forward and inverse models. The first predicts the sensory consequences of actions, while the second provides the correct commands to achieve desired state transitions. In this paper, we propose a composite architecture consisting of multiple cerebellar internal models to account for the adaptation performance of humans during sensorimotor learning. The proposed model takes inspiration from the cerebellar microcomplex circuit, and employs spiking neurons to process information. We investigate the intrinsic properties of the cerebellar circuitry subserving efficient adaptation properties, and we assess the complementary contributions of internal representations by simulating our model in a procedural adaptation task. Our simulation results suggest that the coupling of internal models enhances learning performance significantly (compared with independent forward and inverse models), and it allows for the reproduction of human adaptation capabilities. Furthermore, we provide a computational explanation for the performance improvement observed after one night of sleep in a wide range of sensorimotor tasks. We predict that internal model coupling is a necessary condition for the offline consolidation of procedural memories. PMID:23874289

Vegetation Demographics in Earth System Models: a review of progress and priorities

DOE PAGES

Fisher, Rosie A.; Koven, Charles D.; Anderegg, William R. L.; ...

2017-09-18

Numerous current efforts seek to improve the representation of ecosystem ecology and vegetation demographic processes within Earth System Models (ESMs). Furthermore, these developments are widely viewed as an important step in developing greater realism in predictions of future ecosystem states and fluxes. Increased realism, however, leads to increased model complexity, with new features raising a suite of ecological questions that require empirical constraints. We review the developments that permit the representation of plant demographics in ESMs, and identify issues raised by these developments that highlight important gaps in ecological understanding. These issues inevitably translate into uncertainty in model projections butmore » also allow models to be applied to new processes and questions concerning the dynamics of real-world ecosystems. We also argue that stronger and more innovative connections to data, across the range of scales considered, are required to address these gaps in understanding. The development of first-generation land surface models as a unifying framework for ecophysiological understanding stimulated much research into plant physiological traits and gas exchange. Constraining predictions at ecologically relevant spatial and temporal scales will require a similar investment of effort and intensified inter-disciplinary communication.« less

Vegetation Demographics in Earth System Models: a review of progress and priorities

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

Fisher, Rosie A.; Koven, Charles D.; Anderegg, William R. L.

Numerous current efforts seek to improve the representation of ecosystem ecology and vegetation demographic processes within Earth System Models (ESMs). Furthermore, these developments are widely viewed as an important step in developing greater realism in predictions of future ecosystem states and fluxes. Increased realism, however, leads to increased model complexity, with new features raising a suite of ecological questions that require empirical constraints. We review the developments that permit the representation of plant demographics in ESMs, and identify issues raised by these developments that highlight important gaps in ecological understanding. These issues inevitably translate into uncertainty in model projections butmore » also allow models to be applied to new processes and questions concerning the dynamics of real-world ecosystems. We also argue that stronger and more innovative connections to data, across the range of scales considered, are required to address these gaps in understanding. The development of first-generation land surface models as a unifying framework for ecophysiological understanding stimulated much research into plant physiological traits and gas exchange. Constraining predictions at ecologically relevant spatial and temporal scales will require a similar investment of effort and intensified inter-disciplinary communication.« less

Cloud Condensation Nuclei Prediction Error from Application of Kohler Theory: Importance for the Aerosol Indirect Effect

NASA Technical Reports Server (NTRS)

Sotiropoulou, Rafaella-Eleni P.; Nenes, Athanasios; Adams, Peter J.; Seinfeld, John H.

2007-01-01

In situ observations of aerosol and cloud condensation nuclei (CCN) and the GISS GCM Model II' with an online aerosol simulation and explicit aerosol-cloud interactions are used to quantify the uncertainty in radiative forcing and autoconversion rate from application of Kohler theory. Simulations suggest that application of Koehler theory introduces a 10-20% uncertainty in global average indirect forcing and 2-11% uncertainty in autoconversion. Regionally, the uncertainty in indirect forcing ranges between 10-20%, and 5-50% for autoconversion. These results are insensitive to the range of updraft velocity and water vapor uptake coefficient considered. This study suggests that Koehler theory (as implemented in climate models) is not a significant source of uncertainty for aerosol indirect forcing but can be substantial for assessments of aerosol effects on the hydrological cycle in climatically sensitive regions of the globe. This implies that improvements in the representation of GCM subgrid processes and aerosol size distribution will mostly benefit indirect forcing assessments. Predictions of autoconversion, by nature, will be subject to considerable uncertainty; its reduction may require explicit representation of size-resolved aerosol composition and mixing state.

A Battery Health Monitoring Framework for Planetary Rovers

NASA Technical Reports Server (NTRS)

Daigle, Matthew J.; Kulkarni, Chetan Shrikant

2014-01-01

Batteries have seen an increased use in electric ground and air vehicles for commercial, military, and space applications as the primary energy source. An important aspect of using batteries in such contexts is battery health monitoring. Batteries must be carefully monitored such that the battery health can be determined, and end of discharge and end of usable life events may be accurately predicted. For planetary rovers, battery health estimation and prediction is critical to mission planning and decision-making. We develop a model-based approach utilizing computaitonally efficient and accurate electrochemistry models of batteries. An unscented Kalman filter yields state estimates, which are then used to predict the future behavior of the batteries and, specifically, end of discharge. The prediction algorithm accounts for possible future power demands on the rover batteries in order to provide meaningful results and an accurate representation of prediction uncertainty. The framework is demonstrated on a set of lithium-ion batteries powering a rover at NASA.

Transient excitation and mechanical admittance test techniques for prediction of payload vibration environments

NASA Technical Reports Server (NTRS)

Kana, D. D.; Vargas, L. M.

1977-01-01

Transient excitation forces were applied separately to simple beam-and-mass launch vehicle and payload models to develop complex admittance functions for the interface and other appropriate points on the structures. These measured admittances were then analytically combined by a matrix representation to obtain a description of the coupled system dynamic characteristics. Response of the payload model to excitation of the launch vehicle model was predicted and compared with results measured on the combined models. These results are also compared with results of earlier work in which a similar procedure was employed except that steady-state sinusoidal excitation techniques were included. It is found that the method employing transient tests produces results that are better overall than the steady state methods. Furthermore, the transient method requires far less time to implement, and provides far better resolution in the data. However, the data acquisition and handling problem is more complex for this method. It is concluded that the transient test and admittance matrix prediction method can be a valuable tool for development of payload vibration tests.

Rethinking Social Cognition in Light of Psychosis: Reciprocal Implications for Cognition and Psychopathology

PubMed Central

Bell, Vaughan; Mills, Kathryn L.; Modinos, Gemma; Wilkinson, Sam

2017-01-01

The positive symptoms of psychosis largely involve the experience of illusory social actors, and yet our current measures of social cognition, at best, only weakly predict their presence. We review evidence to suggest that the range of current approaches in social cognition is not sufficient to explain the fundamentally social nature of these experiences. We argue that social agent representation is an important organizing principle for understanding social cognition and that alterations in social agent representation may be a factor in the formation of delusions and hallucination in psychosis. We evaluate the feasibility of this approach in light of clinical and nonclinical studies, developmental research, cognitive anthropology, and comparative psychology. We conclude with recommendations for empirical testing of specific hypotheses and how studies of social cognition could more fully capture the extent of social reasoning and experience in both psychosis and more prosaic mental states. PMID:28533946

A hybrid probabilistic/spectral model of scalar mixing

NASA Astrophysics Data System (ADS)

Vaithianathan, T.; Collins, Lance

2002-11-01

In the probability density function (PDF) description of a turbulent reacting flow, the local temperature and species concentration are replaced by a high-dimensional joint probability that describes the distribution of states in the fluid. The PDF has the great advantage of rendering the chemical reaction source terms closed, independent of their complexity. However, molecular mixing, which involves two-point information, must be modeled. Indeed, the qualitative shape of the PDF is sensitive to this modeling, hence the reliability of the model to predict even the closed chemical source terms rests heavily on the mixing model. We will present a new closure to the mixing based on a spectral representation of the scalar field. The model is implemented as an ensemble of stochastic particles, each carrying scalar concentrations at different wavenumbers. Scalar exchanges within a given particle represent ``transfer'' while scalar exchanges between particles represent ``mixing.'' The equations governing the scalar concentrations at each wavenumber are derived from the eddy damped quasi-normal Markovian (or EDQNM) theory. The model correctly predicts the evolution of an initial double delta function PDF into a Gaussian as seen in the numerical study by Eswaran & Pope (1988). Furthermore, the model predicts the scalar gradient distribution (which is available in this representation) approaches log normal at long times. Comparisons of the model with data derived from direct numerical simulations will be shown.

Parenting and children's representations of family predict disruptive and callous-unemotional behaviors

PubMed Central

Wagner, Nicholas J.; Mills-Koonce, W. Roger; Willoughby, Michael T.; Zvara, Bharathi; Cox, Martha J.

2015-01-01

Data from a large prospective longitudinal study (n = 1,239) was used to investigate the association between observed sensitive parenting in early childhood and children's representations of family relationships as measured by the Family Drawing Paradigm (FDP) in first grade as well as the extent to which these representations partially mediate the influences of early caregiving experiences on later conduct problems and callous-unemotional behaviors. A structural equation modeling approach revealed that less sensitive parenting at 24, 36, and 58 months predicts higher levels of conduct problems (CP) and callous-unemotional (CU) behaviors in first grade controlling for earlier measures of CP and CU behaviors. Results also indicated that greater dysfunctional family representations, as assessed with the FDP, are significantly associated with higher CU behaviors in the first grade, but not CP. Finally, a test of the indirect pathway suggests that children's dysfunctional family representations may, in part, account for the association between sensitive parenting and CU behaviors. PMID:26010385

Computational neural networks in chemistry: Model free mapping devices for predicting chemical reactivity from molecular structure

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

Elrod, D.W.

1992-01-01

Computational neural networks (CNNs) are a computational paradigm inspired by the brain's massively parallel network of highly interconnected neurons. The power of computational neural networks derives not so much from their ability to model the brain as from their ability to learn by example and to map highly complex, nonlinear functions, without the need to explicitly specify the functional relationship. Two central questions about CNNs were investigated in the context of predicting chemical reactions: (1) the mapping properties of neural networks and (2) the representation of chemical information for use in CNNs. Chemical reactivity is here considered an example ofmore » a complex, nonlinear function of molecular structure. CNN's were trained using modifications of the back propagation learning rule to map a three dimensional response surface similar to those typically observed in quantitative structure-activity and structure-property relationships. The computational neural network's mapping of the response surface was found to be robust to the effects of training sample size, noisy data and intercorrelated input variables. The investigation of chemical structure representation led to the development of a molecular structure-based connection-table representation suitable for neural network training. An extension of this work led to a BE-matrix structure representation that was found to be general for several classes of reactions. The CNN prediction of chemical reactivity and regiochemistry was investigated for electrophilic aromatic substitution reactions, Markovnikov addition to alkenes, Saytzeff elimination from haloalkanes, Diels-Alder cycloaddition, and retro Diels-Alder ring opening reactions using these connectivity-matrix derived representations. The reaction predictions made by the CNNs were more accurate than those of an expert system and were comparable to predictions made by chemists.« less

Prediction of Indian Summer-Monsoon Onset Variability: A Season in Advance.

PubMed

Pradhan, Maheswar; Rao, A Suryachandra; Srivastava, Ankur; Dakate, Ashish; Salunke, Kiran; Shameera, K S

2017-10-27

Monsoon onset is an inherent transient phenomenon of Indian Summer Monsoon and it was never envisaged that this transience can be predicted at long lead times. Though onset is precipitous, its variability exhibits strong teleconnections with large scale forcing such as ENSO and IOD and hence may be predictable. Despite of the tremendous skill achieved by the state-of-the-art models in predicting such large scale processes, the prediction of monsoon onset variability by the models is still limited to just 2-3 weeks in advance. Using an objective definition of onset in a global coupled ocean-atmosphere model, it is shown that the skillful prediction of onset variability is feasible under seasonal prediction framework. The better representations/simulations of not only the large scale processes but also the synoptic and intraseasonal features during the evolution of monsoon onset are the comprehensions behind skillful simulation of monsoon onset variability. The changes observed in convection, tropospheric circulation and moisture availability prior to and after the onset are evidenced in model simulations, which resulted in high hit rate of early/delay in monsoon onset in the high resolution model.

Evidence for the Role of Shape in Mental Representations of Similes

ERIC Educational Resources Information Center

Weelden, Lisanne; Schilperoord, Joost; Maes, Alfons

2014-01-01

People mentally represent the shapes of objects. For instance, the mental representation of an eagle is different when one thinks about a flying or resting eagle. This study examined the role of shape in mental representations of "similes" (i.e., metaphoric comparisons). We tested the prediction that when people process a simile they…

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.

On the continuity of the stationary state distribution of DPCM

NASA Astrophysics Data System (ADS)

Naraghi-Pour, Morteza; Neuhoff, David L.

1990-03-01

Continuity and singularity properties of the stationary state distribution of differential pulse code modulation (DPCM) are explored. Two-level DPCM (i.e., delta modulation) operating on a first-order autoregressive source is considered, and it is shown that, when the magnitude of the DPCM prediciton coefficient is between zero and one-half, the stationary state distribution is singularly continuous; i.e., it is not discrete but concentrates on an uncountable set with a Lebesgue measure of zero. Consequently, it cannot be represented with a probability density function. For prediction coefficients with magnitude greater than or equal to one-half, the distribution is pure, i.e., either absolutely continuous and representable with a density function, or singular. This problem is compared to the well-known and still substantially unsolved problem of symmetric Bernoulli convolutions.

Political Gender Inequality and Infant Mortality in the United States, 1990–2012

PubMed Central

Homan, Patricia

2017-01-01

Although gender inequality has been recognized as a crucial factor influencing population health in the developing world, research has not yet thoroughly documented the role it may play in shaping U.S. infant mortality rates (IMRs). This study uses administrative data with fixed-effects and random-effects models to (1) investigate the relationship between political gender inequality in state legislatures and state infant mortality rates in the United States from 1990 to 2012, and (2) project the population level costs associated with women’s underrepresentation in 2012. Results indicate that higher percentages of women in state legislatures are associated with reduced IMRs, both between states and within-states over time. According to model predictions, if women were at parity with men in state legislatures, the expected number of infant deaths in the U.S. in 2012 would have been lower by approximately 14.6% (3,478 infant deaths). These findings underscore the importance of women’s political representation for population health. PMID:28458098

Political gender inequality and infant mortality in the United States, 1990-2012.

PubMed

Homan, Patricia

2017-06-01

Although gender inequality has been recognized as a crucial factor influencing population health in the developing world, research has not yet thoroughly documented the role it may play in shaping U.S. infant mortality rates (IMRs). This study uses administrative data with fixed-effects and random-effects models to (1) investigate the relationship between political gender inequality in state legislatures and state infant mortality rates in the United States from 1990 to 2012, and (2) project the population level costs associated with women's underrepresentation in 2012. Results indicate that higher percentages of women in state legislatures are associated with reduced IMRs, both between states and within-states over time. According to model predictions, if women were at parity with men in state legislatures, the expected number of infant deaths in the U.S. in 2012 would have been lower by approximately 14.6% (3,478 infant deaths). These findings underscore the importance of women's political representation for population health. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

Statistical Aspects of Coherent States of the Higgs Algebra

NASA Astrophysics Data System (ADS)

Shreecharan, T.; Kumar, M. Naveen

2018-04-01

We construct and study various aspects of coherent states of a polynomial angular momentum algebra. The coherent states are constructed using a new unitary representation of the nonlinear algebra. The new representation involves a parameter γ that shifts the eigenvalues of the diagonal operator J 0.

Predicting Visual Consciousness Electrophysiologically from Intermittent Binocular Rivalry

PubMed Central

O’Shea, Robert P.; Kornmeier, Jürgen; Roeber, Urte

2013-01-01

Purpose We sought brain activity that predicts visual consciousness. Methods We used electroencephalography (EEG) to measure brain activity to a 1000-ms display of sine-wave gratings, oriented vertically in one eye and horizontally in the other. This display yields binocular rivalry: irregular alternations in visual consciousness between the images viewed by the eyes. We replaced both gratings with 200 ms of darkness, the gap, before showing a second display of the same rival gratings for another 1000 ms. We followed this by a 1000-ms mask then a 2000-ms inter-trial interval (ITI). Eleven participants pressed keys after the second display in numerous trials to say whether the orientation of the visible grating changed from before to after the gap or not. Each participant also responded to numerous non-rivalry trials in which the gratings had identical orientations for the two eyes and for which the orientation of both either changed physically after the gap or did not. Results We found that greater activity from lateral occipital-parietal-temporal areas about 180 ms after initial onset of rival stimuli predicted a change in visual consciousness more than 1000 ms later, on re-presentation of the rival stimuli. We also found that less activity from parietal, central, and frontal electrodes about 400 ms after initial onset of rival stimuli predicted a change in visual consciousness about 800 ms later, on re-presentation of the rival stimuli. There was no such predictive activity when the change in visual consciousness occurred because the stimuli changed physically. Conclusion We found early EEG activity that predicted later visual consciousness. Predictive activity 180 ms after onset of the first display may reflect adaption of the neurons mediating visual consciousness in our displays. Predictive activity 400 ms after onset of the first display may reflect a less-reliable brain state mediating visual consciousness. PMID:24124536

Efficient alignment-free DNA barcode analytics

PubMed Central

Kuksa, Pavel; Pavlovic, Vladimir

2009-01-01

Background In this work we consider barcode DNA analysis problems and address them using alternative, alignment-free methods and representations which model sequences as collections of short sequence fragments (features). The methods use fixed-length representations (spectrum) for barcode sequences to measure similarities or dissimilarities between sequences coming from the same or different species. The spectrum-based representation not only allows for accurate and computationally efficient species classification, but also opens possibility for accurate clustering analysis of putative species barcodes and identification of critical within-barcode loci distinguishing barcodes of different sample groups. Results New alignment-free methods provide highly accurate and fast DNA barcode-based identification and classification of species with substantial improvements in accuracy and speed over state-of-the-art barcode analysis methods. We evaluate our methods on problems of species classification and identification using barcodes, important and relevant analytical tasks in many practical applications (adverse species movement monitoring, sampling surveys for unknown or pathogenic species identification, biodiversity assessment, etc.) On several benchmark barcode datasets, including ACG, Astraptes, Hesperiidae, Fish larvae, and Birds of North America, proposed alignment-free methods considerably improve prediction accuracy compared to prior results. We also observe significant running time improvements over the state-of-the-art methods. Conclusion Our results show that newly developed alignment-free methods for DNA barcoding can efficiently and with high accuracy identify specimens by examining only few barcode features, resulting in increased scalability and interpretability of current computational approaches to barcoding. PMID:19900305

Mentalizing regions represent distributed, continuous, and abstract dimensions of others' beliefs.

PubMed

Koster-Hale, Jorie; Richardson, Hilary; Velez, Natalia; Asaba, Mika; Young, Liane; Saxe, Rebecca

2017-11-01

The human capacity to reason about others' minds includes making causal inferences about intentions, beliefs, values, and goals. Previous fMRI research has suggested that a network of brain regions, including bilateral temporo-parietal junction (TPJ), superior temporal sulcus (STS), and medial prefrontal-cortex (MPFC), are reliably recruited for mental state reasoning. Here, in two fMRI experiments, we investigate the representational content of these regions. Building on existing computational and neural evidence, we hypothesized that social brain regions contain at least two functionally and spatially distinct components: one that represents information related to others' motivations and values, and another that represents information about others' beliefs and knowledge. Using multi-voxel pattern analysis, we find evidence that motivational versus epistemic features are independently represented by theory of mind (ToM) regions: RTPJ contains information about the justification of the belief, bilateral TPJ represents the modality of the source of knowledge, and VMPFC represents the valence of the resulting emotion. These representations are found only in regions implicated in social cognition and predict behavioral responses at the level of single items. We argue that cortical regions implicated in mental state inference contain complementary, but distinct, representations of epistemic and motivational features of others' beliefs, and that, mirroring the processes observed in sensory systems, social stimuli are represented in distinct and distributed formats across the human brain. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

32 CFR 776.29 - Imputed disqualification: General rule.

Code of Federal Regulations, 2012 CFR

2012-07-01

... their federal, state, and local bar rules governing the representation of multiple or adverse clients within the same office before such representation is initiated, as such representation may expose them to... military (or Government) service may require representation of opposing sides by covered USG attorneys...

32 CFR 776.29 - Imputed disqualification: General rule.

Code of Federal Regulations, 2014 CFR

2014-07-01

... their federal, state, and local bar rules governing the representation of multiple or adverse clients within the same office before such representation is initiated, as such representation may expose them to... military (or Government) service may require representation of opposing sides by covered USG attorneys...

32 CFR 776.29 - Imputed disqualification: General rule.

Code of Federal Regulations, 2013 CFR

2013-07-01

... their federal, state, and local bar rules governing the representation of multiple or adverse clients within the same office before such representation is initiated, as such representation may expose them to... military (or Government) service may require representation of opposing sides by covered USG attorneys...

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.

Coherent States for Kronecker Products of Non Compact Groups: Formulation and Applications

NASA Technical Reports Server (NTRS)

Bambah, Bindu A.; Agarwal, Girish S.

1996-01-01

We introduce and study the properties of a class of coherent states for the group SU(1,1) X SU(1,1) and derive explicit expressions for these using the Clebsch-Gordan algebra for the SU(1,1) group. We restrict ourselves to the discrete series representations of SU(1,1). These are the generalization of the 'Barut Girardello' coherent states to the Kronecker Product of two non-compact groups. The resolution of the identity and the analytic phase space representation of these states is presented. This phase space representation is based on the basis of products of 'pair coherent states' rather than the standard number state canonical basis. We discuss the utility of the resulting 'bi-pair coherent states' in the context of four-mode interactions in quantum optics.

Modeling, Simulation, and Forecasting of Subseasonal Variability

NASA Technical Reports Server (NTRS)

Waliser, Duane; Schubert, Siegfried; Kumar, Arun; Weickmann, Klaus; Dole, Randall

2003-01-01

A planning workshop on "Modeling, Simulation and Forecasting of Subseasonal Variability" was held in June 2003. This workshop was the first of a number of meetings planned to follow the NASA-sponsored workshop entitled "Prospects For Improved Forecasts Of Weather And Short-Term Climate Variability On Sub-Seasonal Time Scales" that was held April 2002. The 2002 workshop highlighted a number of key sources of unrealized predictability on subseasonal time scales including tropical heating, soil wetness, the Madden Julian Oscillation (MJO) [a.k.a Intraseasonal Oscillation (ISO)], the Arctic Oscillation (AO) and the Pacific/North American (PNA) pattern. The overarching objective of the 2003 follow-up workshop was to proceed with a number of recommendations made from the 2002 workshop, as well as to set an agenda and collate efforts in the areas of modeling, simulation and forecasting intraseasonal and short-term climate variability. More specifically, the aims of the 2003 workshop were to: 1) develop a baseline of the "state of the art" in subseasonal prediction capabilities, 2) implement a program to carry out experimental subseasonal forecasts, and 3) develop strategies for tapping the above sources of predictability by focusing research, model development, and the development/acquisition of new observations on the subseasonal problem. The workshop was held over two days and was attended by over 80 scientists, modelers, forecasters and agency personnel. The agenda of the workshop focused on issues related to the MJO and tropicalextratropical interactions as they relate to the subseasonal simulation and prediction problem. This included the development of plans for a coordinated set of GCM hindcast experiments to assess current model subseasonal prediction capabilities and shortcomings, an emphasis on developing a strategy to rectify shortcomings associated with tropical intraseasonal variability, namely diabatic processes, and continuing the implementation of an experimental forecast and model development program that focuses on one of the key sources of untapped predictability, namely the MJO. The tangible outcomes of the meeting included: 1) the development of a recommended framework for a set of multi-year ensembles of 45-day hindcasts to be carried out by a number of GCMs so that they can be analyzed in regards to their representations of subseasonal variability, predictability and forecast skill, 2) an assessment of the present status of GCM representations of the MJO and recommendations for future steps to take in order to remedy the remaining shortcomings in these representations, and 3) a final implementation plan for a multi-institute/multi-nation Experimental MJO Prediction Program.

Representation and the Removal of State Capitals, 1776-1812.

ERIC Educational Resources Information Center

Zagarri, Rosemarie

1988-01-01

Discusses the process of moving state capitals (between 1776 and 1812) to achieve equal representation through geographic centrality. Presents contemporary arguments for the process including the belief that central location of the capital promoted better attendance by all state representatives. Describes how the system was replaced by numerical…

With or without you: predictive coding and Bayesian inference in the brain

PubMed Central

Aitchison, Laurence; Lengyel, Máté

2018-01-01

Two theoretical ideas have emerged recently with the ambition to provide a unifying functional explanation of neural population coding and dynamics: predictive coding and Bayesian inference. Here, we describe the two theories and their combination into a single framework: Bayesian predictive coding. We clarify how the two theories can be distinguished, despite sharing core computational concepts and addressing an overlapping set of empirical phenomena. We argue that predictive coding is an algorithmic / representational motif that can serve several different computational goals of which Bayesian inference is but one. Conversely, while Bayesian inference can utilize predictive coding, it can also be realized by a variety of other representations. We critically evaluate the experimental evidence supporting Bayesian predictive coding and discuss how to test it more directly. PMID:28942084

Linear and non-linear perturbations in dark energy models

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

Escamilla-Rivera, Celia; Casarini, Luciano; Fabris, Júlio C.

2016-11-01

In this work we discuss observational aspects of three time-dependent parameterisations of the dark energy equation of state w ( z ). In order to determine the dynamics associated with these models, we calculate their background evolution and perturbations in a scalar field representation. After performing a complete treatment of linear perturbations, we also show that the non-linear contribution of the selected w ( z ) parameterisations to the matter power spectra is almost the same for all scales, with no significant difference from the predictions of the standard ΛCDM model.

Progressive Label Fusion Framework for Multi-atlas Segmentation by Dictionary Evolution

PubMed Central

Song, Yantao; Wu, Guorong; Sun, Quansen; Bahrami, Khosro; Li, Chunming; Shen, Dinggang

2015-01-01

Accurate segmentation of anatomical structures in medical images is very important in neuroscience studies. Recently, multi-atlas patch-based label fusion methods have achieved many successes, which generally represent each target patch from an atlas patch dictionary in the image domain and then predict the latent label by directly applying the estimated representation coefficients in the label domain. However, due to the large gap between these two domains, the estimated representation coefficients in the image domain may not stay optimal for the label fusion. To overcome this dilemma, we propose a novel label fusion framework to make the weighting coefficients eventually to be optimal for the label fusion by progressively constructing a dynamic dictionary in a layer-by-layer manner, where a sequence of intermediate patch dictionaries gradually encode the transition from the patch representation coefficients in image domain to the optimal weights for label fusion. Our proposed framework is general to augment the label fusion performance of the current state-of-the-art methods. In our experiments, we apply our proposed method to hippocampus segmentation on ADNI dataset and achieve more accurate labeling results, compared to the counterpart methods with single-layer dictionary. PMID:26942233

Progressive Label Fusion Framework for Multi-atlas Segmentation by Dictionary Evolution.

PubMed

Song, Yantao; Wu, Guorong; Sun, Quansen; Bahrami, Khosro; Li, Chunming; Shen, Dinggang

2015-10-01

Accurate segmentation of anatomical structures in medical images is very important in neuroscience studies. Recently, multi-atlas patch-based label fusion methods have achieved many successes, which generally represent each target patch from an atlas patch dictionary in the image domain and then predict the latent label by directly applying the estimated representation coefficients in the label domain. However, due to the large gap between these two domains, the estimated representation coefficients in the image domain may not stay optimal for the label fusion. To overcome this dilemma, we propose a novel label fusion framework to make the weighting coefficients eventually to be optimal for the label fusion by progressively constructing a dynamic dictionary in a layer-by-layer manner, where a sequence of intermediate patch dictionaries gradually encode the transition from the patch representation coefficients in image domain to the optimal weights for label fusion. Our proposed framework is general to augment the label fusion performance of the current state-of-the-art methods. In our experiments, we apply our proposed method to hippocampus segmentation on ADNI dataset and achieve more accurate labeling results, compared to the counterpart methods with single-layer dictionary.

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.

Dopamine D2-receptor blockade enhances decoding of prefrontal signals in humans.

PubMed

Kahnt, Thorsten; Weber, Susanna C; Haker, Helene; Robbins, Trevor W; Tobler, Philippe N

2015-03-04

The prefrontal cortex houses representations critical for ongoing and future behavior expressed in the form of patterns of neural activity. Dopamine has long been suggested to play a key role in the integrity of such representations, with D2-receptor activation rendering them flexible but weak. However, it is currently unknown whether and how D2-receptor activation affects prefrontal representations in humans. In the current study, we use dopamine receptor-specific pharmacology and multivoxel pattern-based functional magnetic resonance imaging to test the hypothesis that blocking D2-receptor activation enhances prefrontal representations. Human subjects performed a simple reward prediction task after double-blind and placebo controlled administration of the D2-receptor antagonist amisulpride. Using a whole-brain searchlight decoding approach we show that D2-receptor blockade enhances decoding of reward signals in the medial orbitofrontal cortex. Examination of activity patterns suggests that amisulpride increases the separation of activity patterns related to reward versus no reward. Moreover, consistent with the cortical distribution of D2 receptors, post hoc analyses showed enhanced decoding of motor signals in motor cortex, but not of visual signals in visual cortex. These results suggest that D2-receptor blockade enhances content-specific representations in frontal cortex, presumably by a dopamine-mediated increase in pattern separation. These findings are in line with a dual-state model of prefrontal dopamine, and provide new insights into the potential mechanism of action of dopaminergic drugs. Copyright © 2015 the authors 0270-6474/15/354104-08$15.00/0.

Representations of the language recognition problem for a theorem prover

NASA Technical Reports Server (NTRS)

Minker, J.; Vanderbrug, G. J.

1972-01-01

Two representations of the language recognition problem for a theorem prover in first order logic are presented and contrasted. One of the representations is based on the familiar method of generating sentential forms of the language, and the other is based on the Cocke parsing algorithm. An augmented theorem prover is described which permits recognition of recursive languages. The state-transformation method developed by Cordell Green to construct problem solutions in resolution-based systems can be used to obtain the parse tree. In particular, the end-order traversal of the parse tree is derived in one of the representations. An inference system, termed the cycle inference system, is defined which makes it possible for the theorem prover to model the method on which the representation is based. The general applicability of the cycle inference system to state space problems is discussed. Given an unsatisfiable set S, where each clause has at most one positive literal, it is shown that there exists an input proof. The clauses for the two representations satisfy these conditions, as do many state space problems.

A single-rate context-dependent learning process underlies rapid adaptation to familiar object dynamics.

PubMed

Ingram, James N; Howard, Ian S; Flanagan, J Randall; Wolpert, Daniel M

2011-09-01

Motor learning has been extensively studied using dynamic (force-field) perturbations. These induce movement errors that result in adaptive changes to the motor commands. Several state-space models have been developed to explain how trial-by-trial errors drive the progressive adaptation observed in such studies. These models have been applied to adaptation involving novel dynamics, which typically occurs over tens to hundreds of trials, and which appears to be mediated by a dual-rate adaptation process. In contrast, when manipulating objects with familiar dynamics, subjects adapt rapidly within a few trials. Here, we apply state-space models to familiar dynamics, asking whether adaptation is mediated by a single-rate or dual-rate process. Previously, we reported a task in which subjects rotate an object with known dynamics. By presenting the object at different visual orientations, adaptation was shown to be context-specific, with limited generalization to novel orientations. Here we show that a multiple-context state-space model, with a generalization function tuned to visual object orientation, can reproduce the time-course of adaptation and de-adaptation as well as the observed context-dependent behavior. In contrast to the dual-rate process associated with novel dynamics, we show that a single-rate process mediates adaptation to familiar object dynamics. The model predicts that during exposure to the object across multiple orientations, there will be a degree of independence for adaptation and de-adaptation within each context, and that the states associated with all contexts will slowly de-adapt during exposure in one particular context. We confirm these predictions in two new experiments. Results of the current study thus highlight similarities and differences in the processes engaged during exposure to novel versus familiar dynamics. In both cases, adaptation is mediated by multiple context-specific representations. In the case of familiar object dynamics, however, the representations can be engaged based on visual context, and are updated by a single-rate process.

Strategies to reduce the complexity of hydrologic data assimilation for high-dimensional models

NASA Astrophysics Data System (ADS)

Hernandez, F.; Liang, X.

2017-12-01

Probabilistic forecasts in the geosciences offer invaluable information by allowing to estimate the uncertainty of predicted conditions (including threats like floods and droughts). However, while forecast systems based on modern data assimilation algorithms are capable of producing multi-variate probability distributions of future conditions, the computational resources required to fully characterize the dependencies between the model's state variables render their applicability impractical for high-resolution cases. This occurs because of the quadratic space complexity of storing the covariance matrices that encode these dependencies and the cubic time complexity of performing inference operations with them. In this work we introduce two complementary strategies to reduce the size of the covariance matrices that are at the heart of Bayesian assimilation methods—like some variants of (ensemble) Kalman filters and of particle filters—and variational methods. The first strategy involves the optimized grouping of state variables by clustering individual cells of the model into "super-cells." A dynamic fuzzy clustering approach is used to take into account the states (e.g., soil moisture) and forcings (e.g., precipitation) of each cell at each time step. The second strategy consists in finding a compressed representation of the covariance matrix that still encodes the most relevant information but that can be more efficiently stored and processed. A learning and a belief-propagation inference algorithm are developed to take advantage of this modified low-rank representation. The two proposed strategies are incorporated into OPTIMISTS, a state-of-the-art hybrid Bayesian/variational data assimilation algorithm, and comparative streamflow forecasting tests are performed using two watersheds modeled with the Distributed Hydrology Soil Vegetation Model (DHSVM). Contrasts are made between the efficiency gains and forecast accuracy losses of each strategy used in isolation, and of those achieved through their coupling. We expect these developments to help catalyze improvements in the predictive accuracy of large-scale forecasting operations by lowering the costs of deploying advanced data assimilation techniques.

Neural Prediction Errors Distinguish Perception and Misperception of Speech.

PubMed

Blank, Helen; Spangenberg, Marlene; Davis, Matthew H

2018-06-11

Humans use prior expectations to improve perception, especially of sensory signals that are degraded or ambiguous. However, if sensory input deviates from prior expectations, correct perception depends on adjusting or rejecting prior expectations. Failure to adjust or reject the prior leads to perceptual illusions especially if there is partial overlap (hence partial mismatch) between expectations and input. With speech, "Slips of the ear" occur when expectations lead to misperception. For instance, a entomologist, might be more susceptible to hear "The ants are my friends" for "The answer, my friend" (in the Bob Dylan song "Blowing in the Wind"). Here, we contrast two mechanisms by which prior expectations may lead to misperception of degraded speech. Firstly, clear representations of the common sounds in the prior and input (i.e., expected sounds) may lead to incorrect confirmation of the prior. Secondly, insufficient representations of sounds that deviate between prior and input (i.e., prediction errors) could lead to deception. We used cross-modal predictions from written words that partially match degraded speech to compare neural responses when male and female human listeners were deceived into accepting the prior or correctly reject it. Combined behavioural and multivariate representational similarity analysis of functional magnetic resonance imaging data shows that veridical perception of degraded speech is signalled by representations of prediction error in the left superior temporal sulcus. Instead of using top-down processes to support perception of expected sensory input, our findings suggest that the strength of neural prediction error representations distinguishes correct perception and misperception. SIGNIFICANCE STATEMENT Misperceiving spoken words is an everyday experience with outcomes that range from shared amusement to serious miscommunication. For hearing-impaired individuals, frequent misperception can lead to social withdrawal and isolation with severe consequences for well-being. In this work, we specify the neural mechanisms by which prior expectations - which are so often helpful for perception - can lead to misperception of degraded sensory signals. Most descriptive theories of illusory perception explain misperception as arising from a clear sensory representation of features or sounds that are in common between prior expectations and sensory input. Our work instead provides support for a complementary proposal; namely that misperception occurs when there is an insufficient sensory representations of the deviation between expectations and sensory signals. Copyright © 2018 the authors.

The Index Offence Representation Scales; a predictive clinical tool in the management of dangerous, violent patients with personality disorder?

PubMed

McGauley, Gill; Ferris, Scott; Marin-Avellan, Luisa; Fonagy, Peter

2013-10-01

Forensic mental health professionals attach considerable importance to their patient's description of his or her index offence. Despite this, there is no systematic approach to examining and formulating the patient's offence narrative. To use the index offence narratives and capacity to mentalize of violent offender-patients with personality disorder to develop a tool to predict their progress and to evaluate that tool. In a prospective, cohort study, the index offence narratives of 66 violent high security hospital patients with personality disorder were obtained from a semi-structured interview and used to generate the Index Offence Representational Scales (IORS). The predictive validity of these scales was investigated across a range of outcome variables, controlling for the association between initial and final value of the dependent variable. The degree to which patients held internal representations of interpersonal violence and malevolence, as measured by the IORS, predicted subsequent violent behaviour. In contrast to their actual aggressive behaviour, these patients rated themselves as having fewer symptoms on the Symptom Checklist-90-R (SCL-90-R) and fewer problems in interpersonal relationships on the Inventory of Interpersonal Problems. A more empathic victim representation on the IORS predicted better engagement with treatment. The IORS show promise for helping clinicians formulate the early institutional pathway of seriously violent people with personality disorder, particularly with respect to their overt aggression and prosocial engagement. Replication studies are, however, indicated. Copyright © 2013 John Wiley & Sons, Ltd.

Deep-Learning-Based Drug-Target Interaction Prediction.

PubMed

Wen, Ming; Zhang, Zhimin; Niu, Shaoyu; Sha, Haozhi; Yang, Ruihan; Yun, Yonghuan; Lu, Hongmei

2017-04-07

Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug-target interaction (DTI) can speed up the expensive and time-consuming experimental work by providing the most potent DTIs. In silico prediction of DTI can also provide insights about the potential drug-drug interaction and promote the exploration of drug side effects. Traditionally, the performance of DTI prediction depends heavily on the descriptors used to represent the drugs and the target proteins. In this paper, to accurately predict new DTIs between approved drugs and targets without separating the targets into different classes, we developed a deep-learning-based algorithmic framework named DeepDTIs. It first abstracts representations from raw input descriptors using unsupervised pretraining and then applies known label pairs of interaction to build a classification model. Compared with other methods, it is found that DeepDTIs reaches or outperforms other state-of-the-art methods. The DeepDTIs can be further used to predict whether a new drug targets to some existing targets or whether a new target interacts with some existing drugs.

The best of both Reps—Diabatized Gaussians on adiabatic surfaces

NASA Astrophysics Data System (ADS)

Meek, Garrett A.; Levine, Benjamin G.

2016-11-01

When simulating nonadiabatic molecular dynamics, choosing an electronic representation requires consideration of well-known trade-offs. The uniqueness and spatially local couplings of the adiabatic representation come at the expense of an electronic wave function that changes discontinuously with nuclear motion and associated singularities in the nonadiabatic coupling matrix elements. The quasi-diabatic representation offers a smoothly varying wave function and finite couplings, but identification of a globally well-behaved quasi-diabatic representation is a system-specific challenge. In this work, we introduce the diabatized Gaussians on adiabatic surfaces (DGAS) approximation, a variant of the ab initio multiple spawning (AIMS) method that preserves the advantages of both electronic representations while avoiding their respective pitfalls. The DGAS wave function is expanded in a basis of vibronic functions that are continuous in both electronic and nuclear coordinates, but potentially discontinuous in time. Because the time-dependent Schrödinger equation contains only first-order derivatives with respect to time, singularities in the second-derivative nonadiabatic coupling terms (i.e., diagonal Born-Oppenheimer correction; DBOC) at conical intersections are rigorously absent, though singular time-derivative couplings remain. Interpolation of the electronic wave function allows the accurate prediction of population transfer probabilities even in the presence of the remaining singularities. We compare DGAS calculations of the dynamics of photoexcited ethene to AIMS calculations performed in the adiabatic representation, including the DBOC. The 28 fs excited state lifetime observed in DGAS simulations is considerably shorter than the 50 fs lifetime observed in the adiabatic simulations. The slower decay in the adiabatic representation is attributable to the large, repulsive DBOC in the neighborhood of conical intersections. These repulsive DBOC terms are artifacts of the discontinuities in the individual adiabatic vibronic basis functions and therefore cannot reflect the behavior of the exact molecular wave function, which must be continuous.

The relationship between visual working memory and attention: retention of precise colour information in the absence of effects on perceptual selection.

PubMed

Hollingworth, Andrew; Hwang, Seongmin

2013-10-19

We examined the conditions under which a feature value in visual working memory (VWM) recruits visual attention to matching stimuli. Previous work has suggested that VWM supports two qualitatively different states of representation: an active state that interacts with perceptual selection and a passive (or accessory) state that does not. An alternative hypothesis is that VWM supports a single form of representation, with the precision of feature memory controlling whether or not the representation interacts with perceptual selection. The results of three experiments supported the dual-state hypothesis. We established conditions under which participants retained a relatively precise representation of a parcticular colour. If the colour was immediately task relevant, it reliably recruited attention to matching stimuli. However, if the colour was not immediately task relevant, it failed to interact with perceptual selection. Feature maintenance in VWM is not necessarily equivalent with feature-based attentional selection.

Emotion, Cognition, and Mental State Representation in Amygdala and Prefrontal Cortex

PubMed Central

Salzman, C. Daniel; Fusi, Stefano

2011-01-01

Neuroscientists have often described cognition and emotion as separable processes implemented by different regions of the brain, such as the amygdala for emotion and the prefrontal cortex for cognition. In this framework, functional interactions between the amygdala and prefrontal cortex mediate emotional influences on cognitive processes such as decision-making, as well as the cognitive regulation of emotion. However, neurons in these structures often have entangled representations, whereby single neurons encode multiple cognitive and emotional variables. Here we review studies using anatomical, lesion, and neurophysiological approaches to investigate the representation and utilization of cognitive and emotional parameters. We propose that these mental state parameters are inextricably linked and represented in dynamic neural networks composed of interconnected prefrontal and limbic brain structures. Future theoretical and experimental work is required to understand how these mental state representations form and how shifts between mental states occur, a critical feature of adaptive cognitive and emotional behavior. PMID:20331363

Goal-directed EEG activity evoked by discriminative stimuli in reinforcement learning.

PubMed

Luque, David; Morís, Joaquín; Rushby, Jacqueline A; Le Pelley, Mike E

2015-02-01

In reinforcement learning (RL), discriminative stimuli (S) allow agents to anticipate the value of a future outcome, and the response that will produce that outcome. We examined this processing by recording EEG locked to S during RL. Incentive value of outcomes and predictive value of S were manipulated, allowing us to discriminate between outcome-related and response-related activity. S predicting the correct response differed from nonpredictive S in the P2. S paired with high-value outcomes differed from those paired with low-value outcomes in a frontocentral positivity and in the P3b. A slow negativity then distinguished between predictive and nonpredictive S. These results suggest that, first, attention prioritizes detection of informative S. Activation of mental representations of these informative S then retrieves representations of outcomes, which in turn retrieve representations of responses that previously produced those outcomes. © 2014 Society for Psychophysiological Research.

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.

Representational constraints on children's suggestibility.

PubMed

Ceci, Stephen J; Papierno, Paul B; Kulkofsky, Sarah

2007-06-01

In a multistage experiment, twelve 4- and 9-year-old children participated in a triad rating task. Their ratings were mapped with multidimensional scaling, from which euclidean distances were computed to operationalize semantic distance between items in target pairs. These children and age-mates then participated in an experiment that employed these target pairs in a story, which was followed by a misinformation manipulation. Analyses linked individual and developmental differences in suggestibility to children's representations of the target items. Semantic proximity was a strong predictor of differences in suggestibility: The closer a suggested distractor was to the original item's representation, the greater was the distractor's suggestive influence. The triad participants' semantic proximity subsequently served as the basis for correctly predicting memory performance in the larger group. Semantic proximity enabled a priori counterintuitive predictions of reverse age-related trends to be confirmed whenever the distance between representations of items in a target pair was greater for younger than for older children.

Verification of predicted specimen-specific natural and implanted patellofemoral kinematics during simulated deep knee bend.

PubMed

Baldwin, Mark A; Clary, Chadd; Maletsky, Lorin P; Rullkoetter, Paul J

2009-10-16

Verified computational models represent an efficient method for studying the relationship between articular geometry, soft-tissue constraint, and patellofemoral (PF) mechanics. The current study was performed to evaluate an explicit finite element (FE) modeling approach for predicting PF kinematics in the natural and implanted knee. Experimental three-dimensional kinematic data were collected on four healthy cadaver specimens in their natural state and after total knee replacement in the Kansas knee simulator during a simulated deep knee bend activity. Specimen-specific FE models were created from medical images and CAD implant geometry, and included soft-tissue structures representing medial-lateral PF ligaments and the quadriceps tendon. Measured quadriceps loads and prescribed tibiofemoral kinematics were used to predict dynamic kinematics of an isolated PF joint between 10 degrees and 110 degrees femoral flexion. Model sensitivity analyses were performed to determine the effect of rigid or deformable patellar representations and perturbed PF ligament mechanical properties (pre-tension and stiffness) on model predictions and computational efficiency. Predicted PF kinematics from the deformable analyses showed average root mean square (RMS) differences for the natural and implanted states of less than 3.1 degrees and 1.7 mm for all rotations and translations. Kinematic predictions with rigid bodies increased average RMS values slightly to 3.7 degrees and 1.9 mm with a five-fold decrease in computational time. Two-fold increases and decreases in PF ligament initial strain and linear stiffness were found to most adversely affect kinematic predictions for flexion, internal-external tilt and inferior-superior translation in both natural and implanted states. The verified models could be used to further investigate the effects of component alignment or soft-tissue variability on natural and implant PF mechanics.

Structural damage detection using deep learning of ultrasonic guided waves

NASA Astrophysics Data System (ADS)

Melville, Joseph; Alguri, K. Supreet; Deemer, Chris; Harley, Joel B.

2018-04-01

Structural health monitoring using ultrasonic guided waves relies on accurate interpretation of guided wave propagation to distinguish damage state indicators. However, traditional physics based models do not provide an accurate representation, and classic data driven techniques, such as a support vector machine, are too simplistic to capture the complex nature of ultrasonic guide waves. To address this challenge, this paper uses a deep learning interpretation of ultrasonic guided waves to achieve fast, accurate, and automated structural damaged detection. To achieve this, full wavefield scans of thin metal plates are used, half from the undamaged state and half from the damaged state. This data is used to train our deep network to predict the damage state of a plate with 99.98% accuracy given signals from just 10 spatial locations on the plate, as compared to that of a support vector machine (SVM), which achieved a 62% accuracy.

High Prevalence of Insecure Attachment in Patients with Primary Hypertension

PubMed Central

Balint, Elisabeth M.; Gander, Manuela; Pokorny, Dan; Funk, Alexandra; Waller, Christiane; Buchheim, Anna

2016-01-01

Hypertension is a major cardiovascular (CV) risk factor and is predicted by heightened CV reactivity to stress in healthy individuals. Patients with hypertension also show an altered stress response, while insecure attachment is linked to a heightened stress reactivity as well. This is the first study aiming to assess attachment representations in patients with primary hypertension and to investigate their CV responses when their attachment system is activated. We studied 50 patients (38 men, 12 women) with primary hypertension. The Adult Attachment Projective Picture System (AAP), a widely used and validated interview, was performed to measure the patients' attachment representations, and to activate their attachment system. Blood pressure and heart rate were measured after 10 min at rest prior to and directly after the AAP interview. Mood and state anxiety were assessed using the Multidimensional Mood State Questionnaire (MDBF) and the State Trait Anxiety Inventory-State (STAI-S) before and after the experiment. The prevalence of insecure attachment (dismissing, preoccupied, unresolved) in hypertensive patients was predominant (88%), while in non-clinical populations, only about 50% of individuals had insecure attachment patterns. Blood pressure (p < 0.001), heart rate (p = 0.016), and rate pressure product (p < 0.001) significantly increased in response to the attachment interview. Secure attached patients showed the highest rise in systolic blood pressure (p = 0.020) and the lowest heart rate compared to the other attachment groups (p = 0.043). However, attachment representation showed no significant group or interaction effects on diastolic blood pressure (DBP) and rate pressure product. Insecure attachment was highly over-represented in our sample of patients with primary hypertension. Additionally, a robust CV response to the attachment-activating stimulus was observed. Our data suggest that insecure attachment is significantly linked to primary hypertension, which implies the need for further investigations to evaluate attachment insecurity as a possible risk factor for hypertension. PMID:27536255

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.

76 FR 39974 - Delegation by the Deputy Secretary of State Regarding Department Representation on the Investment...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-07-07

... DEPARTMENT OF STATE [Delegation of Authority No. 337] Delegation by the Deputy Secretary of State Regarding Department Representation on the Investment Working Group Established by the SelectUSA Executive... Group established by the SelectUSA Executive Order of June 15, 2011. The Under Secretary or Assistant...

The Development of Shared Liking of Representational but not Abstract Art in Primary School Children and Their Justifications for Liking

PubMed Central

Rodway, Paul; Kirkham, Julie; Schepman, Astrid; Lambert, Jordana; Locke, Anastasia

2016-01-01

Understanding how aesthetic preferences are shared among individuals, and its developmental time course, is a fundamental question in aesthetics. It has been shown that semantic associations, in response to representational artworks, overlap more strongly among individuals than those generated by abstract artworks and that the emotional valence of the associations also overlaps more for representational artworks. This valence response may be a key driver in aesthetic appreciation. The current study tested predictions derived from the semantic association account in a developmental context. Twenty 4-, 6-, 8- and 10-year-old children (n = 80) were shown 20 artworks (10 representational, 10 abstract) and were asked to rate each artwork and to explain their decision. Cross-observer agreement in aesthetic preferences increased with age from 4–8 years for both abstract and representational art. However, after age 6 the level of shared appreciation for representational and abstract artworks diverged, with significantly higher levels of agreement for representational than abstract artworks at age 8 and 10. The most common justifications for representational artworks involved subject matter, while for abstract artworks formal artistic properties and color were the most commonly used justifications. Representational artwork also showed a significantly higher proportion of associations and emotional responses than abstract artworks. In line with predictions from developmental cognitive neuroscience, references to the artist as an agent increased between ages 4 and 6 and again between ages 6 and 8, following the development of Theory of Mind. The findings support the view that increased experience with representational content during the life span reduces inter-individual variation in aesthetic appreciation and increases shared preferences. In addition, brain and cognitive development appear to impact on art appreciation at milestone ages. PMID:26903834

The Development of Shared Liking of Representational but not Abstract Art in Primary School Children and Their Justifications for Liking.

PubMed

Rodway, Paul; Kirkham, Julie; Schepman, Astrid; Lambert, Jordana; Locke, Anastasia

2016-01-01

Understanding how aesthetic preferences are shared among individuals, and its developmental time course, is a fundamental question in aesthetics. It has been shown that semantic associations, in response to representational artworks, overlap more strongly among individuals than those generated by abstract artworks and that the emotional valence of the associations also overlaps more for representational artworks. This valence response may be a key driver in aesthetic appreciation. The current study tested predictions derived from the semantic association account in a developmental context. Twenty 4-, 6-, 8- and 10-year-old children (n = 80) were shown 20 artworks (10 representational, 10 abstract) and were asked to rate each artwork and to explain their decision. Cross-observer agreement in aesthetic preferences increased with age from 4-8 years for both abstract and representational art. However, after age 6 the level of shared appreciation for representational and abstract artworks diverged, with significantly higher levels of agreement for representational than abstract artworks at age 8 and 10. The most common justifications for representational artworks involved subject matter, while for abstract artworks formal artistic properties and color were the most commonly used justifications. Representational artwork also showed a significantly higher proportion of associations and emotional responses than abstract artworks. In line with predictions from developmental cognitive neuroscience, references to the artist as an agent increased between ages 4 and 6 and again between ages 6 and 8, following the development of Theory of Mind. The findings support the view that increased experience with representational content during the life span reduces inter-individual variation in aesthetic appreciation and increases shared preferences. In addition, brain and cognitive development appear to impact on art appreciation at milestone ages.

State-Based Delay Representation and Its Transfer from a Game of Pong to Reaching and Tracking

PubMed Central

Leib, Raz; Pressman, Assaf; Simo, Lucia S.; Karniel, Amir

2017-01-01

Abstract To accurately estimate the state of the body, the nervous system needs to account for delays between signals from different sensory modalities. To investigate how such delays may be represented in the sensorimotor system, we asked human participants to play a virtual pong game in which the movement of the virtual paddle was delayed with respect to their hand movement. We tested the representation of this new mapping between the hand and the delayed paddle by examining transfer of adaptation to blind reaching and blind tracking tasks. These blind tasks enabled to capture the representation in feedforward mechanisms of movement control. A Time Representation of the delay is an estimation of the actual time lag between hand and paddle movements. A State Representation is a representation of delay using current state variables: the distance between the paddle and the ball originating from the delay may be considered as a spatial shift; the low sensitivity in the response of the paddle may be interpreted as a minifying gain; and the lag may be attributed to a mechanical resistance that influences paddle’s movement. We found that the effects of prolonged exposure to the delayed feedback transferred to blind reaching and tracking tasks and caused participants to exhibit hypermetric movements. These results, together with simulations of our representation models, suggest that delay is not represented based on time, but rather as a spatial gain change in visuomotor mapping. PMID:29379875

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.

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.

The Existence of Smooth Densities for the Prediction, Filtering and Smoothing Problems

DTIC Science & Technology

1990-12-20

128 - 139. [14] With D. COLWELL and P.E. KOPP, Martingale representation and hedging policies. Stochastic Processes and Applications. (Accepted) [5j...Martingale Representation and Hedging Policies David B. COLWELL Robert J. ELLIOTT P. Ekkehard KOPP* Department of Statistics and Applied Probability...is determined by elementary methods in the Markov situation. Applications to hedging portfolios in finance are described. martingale 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

Cognitive, perceptual and action-oriented representations of falling objects.

PubMed

Zago, Myrka; Lacquaniti, Francesco

2005-01-01

We interact daily with moving objects. How accurate are our predictions about objects' motions? What sources of information do we use? These questions have received wide attention from a variety of different viewpoints. On one end of the spectrum are the ecological approaches assuming that all the information about the visual environment is present in the optic array, with no need to postulate conscious or unconscious representations. On the other end of the spectrum are the constructivist approaches assuming that a more or less accurate representation of the external world is built in the brain using explicit or implicit knowledge or memory besides sensory inputs. Representations can be related to naive physics or to context cue-heuristics or to the construction of internal copies of environmental invariants. We address the issue of prediction of objects' fall at different levels. Cognitive understanding and perceptual judgment of simple Newtonian dynamics can be surprisingly inaccurate. By contrast, motor interactions with falling objects are often very accurate. We argue that the pragmatic action-oriented behaviour and the perception-oriented behaviour may use different modes of operation and different levels of representation.

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.

Boosting compound-protein interaction prediction by deep learning.

PubMed

Tian, Kai; Shao, Mingyu; Wang, Yang; Guan, Jihong; Zhou, Shuigeng

2016-11-01

The identification of interactions between compounds and proteins plays an important role in network pharmacology and drug discovery. However, experimentally identifying compound-protein interactions (CPIs) is generally expensive and time-consuming, computational approaches are thus introduced. Among these, machine-learning based methods have achieved a considerable success. However, due to the nonlinear and imbalanced nature of biological data, many machine learning approaches have their own limitations. Recently, deep learning techniques show advantages over many state-of-the-art machine learning methods in some applications. In this study, we aim at improving the performance of CPI prediction based on deep learning, and propose a method called DL-CPI (the abbreviation of Deep Learning for Compound-Protein Interactions prediction), which employs deep neural network (DNN) to effectively learn the representations of compound-protein pairs. Extensive experiments show that DL-CPI can learn useful features of compound-protein pairs by a layerwise abstraction, and thus achieves better prediction performance than existing methods on both balanced and imbalanced datasets. Copyright © 2016 Elsevier Inc. All rights reserved.

DIY EOS: Experimentally Validated Equations of State for Planetary Fluids to GPa Pressures, Tools for Understanding Planetary Processes and Habitability

NASA Astrophysics Data System (ADS)

Vance, Steven; Brown, J. Michael; Bollengier, Olivier

2016-10-01

Sound speeds are fundamental to seismology, and provide a path allowing the accurate determination of thermodynamic potentials. Prior equations of state (EOS) for pure ammonia (Harr and Gallagher 1978, Tillner-Roth et al. 1993) are based primarily on measured densities and heat capacities. Sound speeds, not included in the fitting, are poorly predicted.We couple recent high pressure sound speed data with prior densities and heat capacities to generate a new equation of state. Our representation fits both the earlier lower pressure work as well as measured sound speeds to 4 GPa and 700 K and the Hugoniot to 70 GPa and 6000 K.In contrast to the damped polynomial representation previously used, our equation of state is based on local basis functions in the form of tensor b-splines. Regularization allows the thermodynamic surface to be continued into regimes poorly sampled by experiments. We discuss application of this framework for aqueous equations of state validated by experimental measurements. Preliminary equations of state have been prepared applying the local basis function methodology to aqueous NH3, Mg2SO4, NaCl, and Na2SO4. We describe its use for developing new equations of state, and provide some applications of the new thermodynamic data to the interior structures of gas giant planets and ocean worlds.References:L. Haar and J. S. Gallagher. Thermodynamic properties of ammonia. American Chemical Society and the American Institute of Physics for the National Bureau of Standards, 1978.R. Tillner-Roth, F. Harms-Watzenberg, and H. Baehr. Eine neue fundamentalgleichung fuer ammoniak. DKV TAGUNGSBERICHT, 20:67-67, 1993.

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.

Prediction of dendrite arm spacings in unsteady-and steady-state heat flow of unidirectionally solidified binary alloys

NASA Astrophysics Data System (ADS)

Bouchard, Dominique; Kirkaldy, John S.

1997-08-01

Various theoretical dendrite and cell spacing formulas have been tested against experimental data obtained in unsteady- and steady-state heat flow conditions. An iterative assessment strategy satisfactorily overcomes the circumstances that certain constitutive parameters are inadequately established and/or highly variable and that many of the data sets, in terms of gradients, velocities, and/or cooling rates, are unreliable. The accessed unsteady- and steady-state observations on near-terminal binary alloys for primary and secondary spacings were first examined within conventional power law representations, the deduced exponents and confidence limits for each alloy being tabularly recorded. Through this analysis, it became clear that to achieve predictive generality the many constitutive parameters must be included in a rational way, this being achievable only through extant or new theoretical formulations. However, in the case of primary spacings, all formulas, including our own, failed within the unsteady heat flow algorithm while performing adequately within their steady-state context. An earlier untested, heuristically derived steady-state formula after modification, λ _1 = 120 ( {{16X_0^{{1/2}} G_0 (\\varepsilon σ )T_M D}/{(1 - k)mΔ H G R}} )^{{1/2}} ultimately proved its utility in the unsteady regime, and so it is recommended for purposes of predictions for general terminal alloys. For secondary spacings, a Mullins and Sekerka type formula proved from the start to be adequate in both unsteady- and steady-state heat flows, and so it recommends itself in calibrated form, λ _2 = 12π ( {{4σ }/{X_0 (1 - k)^2 Δ H}( {D/R} )^2 } )^{{1/3}}

Back in the USSR: Path Dependence Effects in Student Representation in Russia

ERIC Educational Resources Information Center

Chirikov, Igor; Gruzdev, Ivan

2014-01-01

This paper analyses the current state of student representation in Russia as deeply rooted in the institutional structure of the Soviet higher education system. The study traces the origins of existing institutional arrangements for student representation at the level of university governance and analyses how representation practices have been…

V4 activity predicts the strength of visual short-term memory representations.

PubMed

Sligte, Ilja G; Scholte, H Steven; Lamme, Victor A F

2009-06-10

Recent studies have shown the existence of a form of visual memory that lies intermediate of iconic memory and visual short-term memory (VSTM), in terms of both capacity (up to 15 items) and the duration of the memory trace (up to 4 s). Because new visual objects readily overwrite this intermediate visual store, we believe that it reflects a weak form of VSTM with high capacity that exists alongside a strong but capacity-limited form of VSTM. In the present study, we isolated brain activity related to weak and strong VSTM representations using functional magnetic resonance imaging. We found that activity in visual cortical area V4 predicted the strength of VSTM representations; activity was low when there was no VSTM, medium when there was a weak VSTM representation regardless of whether this weak representation was available for report or not, and high when there was a strong VSTM representation. Altogether, this study suggests that the high capacity yet weak VSTM store is represented in visual parts of the brain. Allegedly, only some of these VSTM traces are amplified by parietal and frontal regions and as a consequence reside in traditional or strong VSTM. The additional weak VSTM representations remain available for conscious access and report when attention is redirected to them yet are overwritten as soon as new visual stimuli hit the eyes.

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.

Limits on the prediction of helicopter rotor noise using thickness and loading sources: Validation of helicopter noise prediction techniques

NASA Technical Reports Server (NTRS)

Succi, G. P.

1983-01-01

The techniques of helicopter rotor noise prediction attempt to describe precisely the details of the noise field and remove the empiricisms and restrictions inherent in previous methods. These techniques require detailed inputs of the rotor geometry, operating conditions, and blade surface pressure distribution. The Farassat noise prediction techniques was studied, and high speed helicopter noise prediction using more detailed representations of the thickness and loading noise sources was investigated. These predictions were based on the measured blade surface pressures on an AH-1G rotor and compared to the measured sound field. Although refinements in the representation of the thickness and loading noise sources improve the calculation, there are still discrepancies between the measured and predicted sound field. Analysis of the blade surface pressure data indicates shocks on the blades, which are probably responsible for these discrepancies.

Predicting and interpreting identification errors in military vehicle training using multidimensional scaling.

PubMed

Bohil, Corey J; Higgins, Nicholas A; Keebler, Joseph R

2014-01-01

We compared methods for predicting and understanding the source of confusion errors during military vehicle identification training. Participants completed training to identify main battle tanks. They also completed card-sorting and similarity-rating tasks to express their mental representation of resemblance across the set of training items. We expected participants to selectively attend to a subset of vehicle features during these tasks, and we hypothesised that we could predict identification confusion errors based on the outcomes of the card-sort and similarity-rating tasks. Based on card-sorting results, we were able to predict about 45% of observed identification confusions. Based on multidimensional scaling of the similarity-rating data, we could predict more than 80% of identification confusions. These methods also enabled us to infer the dimensions receiving significant attention from each participant. This understanding of mental representation may be crucial in creating personalised training that directs attention to features that are critical for accurate identification. Participants completed military vehicle identification training and testing, along with card-sorting and similarity-rating tasks. The data enabled us to predict up to 84% of identification confusion errors and to understand the mental representation underlying these errors. These methods have potential to improve training and reduce identification errors leading to fratricide.

Graph-Theoretic Properties of Networks Based on Word Association Norms: Implications for Models of Lexical Semantic Memory.

PubMed

Gruenenfelder, Thomas M; Recchia, Gabriel; Rubin, Tim; Jones, Michael N

2016-08-01

We compared the ability of three different contextual models of lexical semantic memory (BEAGLE, Latent Semantic Analysis, and the Topic model) and of a simple associative model (POC) to predict the properties of semantic networks derived from word association norms. None of the semantic models were able to accurately predict all of the network properties. All three contextual models over-predicted clustering in the norms, whereas the associative model under-predicted clustering. Only a hybrid model that assumed that some of the responses were based on a contextual model and others on an associative network (POC) successfully predicted all of the network properties and predicted a word's top five associates as well as or better than the better of the two constituent models. The results suggest that participants switch between a contextual representation and an associative network when generating free associations. We discuss the role that each of these representations may play in lexical semantic memory. Concordant with recent multicomponent theories of semantic memory, the associative network may encode coordinate relations between concepts (e.g., the relation between pea and bean, or between sparrow and robin), and contextual representations may be used to process information about more abstract concepts. Copyright © 2015 Cognitive Science Society, Inc.

Distributed Neural Processing Predictors of Multi-dimensional Properties of Affect

PubMed Central

Bush, Keith A.; Inman, Cory S.; Hamann, Stephan; Kilts, Clinton D.; James, G. Andrew

2017-01-01

Recent evidence suggests that emotions have a distributed neural representation, which has significant implications for our understanding of the mechanisms underlying emotion regulation and dysregulation as well as the potential targets available for neuromodulation-based emotion therapeutics. This work adds to this evidence by testing the distribution of neural representations underlying the affective dimensions of valence and arousal using representational models that vary in both the degree and the nature of their distribution. We used multi-voxel pattern classification (MVPC) to identify whole-brain patterns of functional magnetic resonance imaging (fMRI)-derived neural activations that reliably predicted dimensional properties of affect (valence and arousal) for visual stimuli viewed by a normative sample (n = 32) of demographically diverse, healthy adults. Inter-subject leave-one-out cross-validation showed whole-brain MVPC significantly predicted (p < 0.001) binarized normative ratings of valence (positive vs. negative, 59% accuracy) and arousal (high vs. low, 56% accuracy). We also conducted group-level univariate general linear modeling (GLM) analyses to identify brain regions whose response significantly differed for the contrasts of positive versus negative valence or high versus low arousal. Multivoxel pattern classifiers using voxels drawn from all identified regions of interest (all-ROIs) exhibited mixed performance; arousal was predicted significantly better than chance but worse than the whole-brain classifier, whereas valence was not predicted significantly better than chance. Multivoxel classifiers derived using individual ROIs generally performed no better than chance. Although performance of the all-ROI classifier improved with larger ROIs (generated by relaxing the clustering threshold), performance was still poorer than the whole-brain classifier. These findings support a highly distributed model of neural processing for the affective dimensions of valence and arousal. Finally, joint error analyses of the MVPC hyperplanes encoding valence and arousal identified regions within the dimensional affect space where multivoxel classifiers exhibited the greatest difficulty encoding brain states – specifically, stimuli of moderate arousal and high or low valence. In conclusion, we highlight new directions for characterizing affective processing for mechanistic and therapeutic applications in affective neuroscience. PMID:28959198

Understanding Human Original Actions Directed at Real-World Goals: The Role of the Lateral Prefrontal Cortex

PubMed Central

Sitnikova, Tatiana; Rosen, Bruce R.; Lord, Louis-David; West, W. Caroline

2014-01-01

Adaptive, original actions, which can succeed in multiple contextual situations, require understanding of what is relevant to a goal. Recognizing what is relevant may also help in predicting kinematics of observed, original actions. During action observation, comparisons between sensory input and expected action kinematics have been argued critical to accurate goal inference. Experimental studies with laboratory tasks, both in humans and nonhuman primates, demonstrated that the lateral prefrontal cortex (LPFC) can learn, hierarchically organize, and use goal-relevant information. To determine whether this LPFC capacity is generalizable to real-world cognition, we recorded functional magnetic resonance imaging (fMRI) data in the human brain during comprehension of original and usual object-directed actions embedded in video-depictions of real-life behaviors. We hypothesized that LPFC will contribute to forming goal-relevant representations necessary for kinematic predictions of original actions. Additionally, resting-state fMRI was employed to examine functional connectivity between the brain regions delineated in the video fMRI experiment. According to behavioral data, original videos could be understood by identifying elements relevant to real-life goals at different levels of abstraction. Patterns of enhanced activity in four regions in the left LPFC, evoked by original, relative to usual, video scenes, were consistent with previous neuroimaging findings on representing abstract and concrete stimuli dimensions relevant to laboratory goals. In the anterior left LPFC, the activity increased selectively when representations of broad classes of objects and actions, which could achieve the perceived overall behavioral goal, were likely to bias kinematic predictions of original actions. In contrast, in the more posterior regions, the activity increased even when concrete properties of the target object were more likely to bias the kinematic prediction. Functional connectivity was observed between contiguous regions along the rostro-caudal LPFC axis, but not between the regions that were not immediately adjacent. These findings generalize the representational hierarchy account of LPFC function to diverse core principles that can govern both production and comprehension of flexible real-life behavior. PMID:25224997

Representation and redistribution in federations.

PubMed

Dragu, Tiberiu; Rodden, Jonathan

2011-05-24

Many of the world's most populous democracies are political unions composed of states or provinces that are unequally represented in the national legislature. Scattered empirical studies, most of them focusing on the United States, have discovered that overrepresented states appear to receive larger shares of the national budget. Although this relationship is typically attributed to bargaining advantages associated with greater legislative representation, an important threat to empirical identification stems from the fact that the representation scheme was chosen by the provinces. Thus, it is possible that representation and fiscal transfers are both determined by other characteristics of the provinces in a specific country. To obtain an improved estimate of the relationship between representation and redistribution, we collect and analyze provincial-level data from nine federations over several decades, taking advantage of the historical process through which federations formed and expanded. Controlling for a variety of country- and province-level factors and using a variety of estimation techniques, we show that overrepresented provinces in political unions around the world are rather dramatically favored in the distribution of resources.

v-representability and density functional theory. [for nonrelativistic electrons in nondegenerate ground state

NASA Technical Reports Server (NTRS)

Kohn, W.

1983-01-01

It is shown that if n(r) is the discrete density on a lattice (enclosed in a finite box) associated with a nondegenerate ground state in an external potential v(r) (i.e., is 'v-representable'), then the density n(r) + mu(r), with m(r) arbitrary (apart from trivial constraints) and mu small enough, is also associated with a nondegenerate ground state in an external potential v'(r) near v(r); i.e., n(r) + m(r) is also v-representable. Implications for the Hohenberg-Kohn variational principle and the Kohn-Sham equations are discussed.

The Application of Satellite-Derived, High-Resolution Land Use/Land Cover Data to Improve Urban Air Quality Model Forecasts

NASA Technical Reports Server (NTRS)

Quattrochi, D. A.; Lapenta, W. M.; Crosson, W. L.; Estes, M. G., Jr.; Limaye, A.; Kahn, M.

2006-01-01

Local and state agencies are responsible for developing state implementation plans to meet National Ambient Air Quality Standards. Numerical models used for this purpose simulate the transport and transformation of criteria pollutants and their precursors. The specification of land use/land cover (LULC) plays an important role in controlling modeled surface meteorology and emissions. NASA researchers have worked with partners and Atlanta stakeholders to incorporate an improved high-resolution LULC dataset for the Atlanta area within their modeling system and to assess meteorological and air quality impacts of Urban Heat Island (UHI) mitigation strategies. The new LULC dataset provides a more accurate representation of land use, has the potential to improve model accuracy, and facilitates prediction of LULC changes. Use of the new LULC dataset for two summertime episodes improved meteorological forecasts, with an existing daytime cold bias of approx. equal to 3 C reduced by 30%. Model performance for ozone prediction did not show improvement. In addition, LULC changes due to Atlanta area urbanization were predicted through 2030, for which model simulations predict higher urban air temperatures. The incorporation of UHI mitigation strategies partially offset this warming trend. The data and modeling methods used are generally applicable to other U.S. cities.

NoGOA: predicting noisy GO annotations using evidences and sparse representation.

PubMed

Yu, Guoxian; Lu, Chang; Wang, Jun

2017-07-21

Gene Ontology (GO) is a community effort to represent functional features of gene products. GO annotations (GOA) provide functional associations between GO terms and gene products. Due to resources limitation, only a small portion of annotations are manually checked by curators, and the others are electronically inferred. Although quality control techniques have been applied to ensure the quality of annotations, the community consistently report that there are still considerable noisy (or incorrect) annotations. Given the wide application of annotations, however, how to identify noisy annotations is an important but yet seldom studied open problem. We introduce a novel approach called NoGOA to predict noisy annotations. NoGOA applies sparse representation on the gene-term association matrix to reduce the impact of noisy annotations, and takes advantage of sparse representation coefficients to measure the semantic similarity between genes. Secondly, it preliminarily predicts noisy annotations of a gene based on aggregated votes from semantic neighborhood genes of that gene. Next, NoGOA estimates the ratio of noisy annotations for each evidence code based on direct annotations in GOA files archived on different periods, and then weights entries of the association matrix via estimated ratios and propagates weights to ancestors of direct annotations using GO hierarchy. Finally, it integrates evidence-weighted association matrix and aggregated votes to predict noisy annotations. Experiments on archived GOA files of six model species (H. sapiens, A. thaliana, S. cerevisiae, G. gallus, B. Taurus and M. musculus) demonstrate that NoGOA achieves significantly better results than other related methods and removing noisy annotations improves the performance of gene function prediction. The comparative study justifies the effectiveness of integrating evidence codes with sparse representation for predicting noisy GO annotations. Codes and datasets are available at http://mlda.swu.edu.cn/codes.php?name=NoGOA .

Phonological Representations and Early Literacy in Chinese

ERIC Educational Resources Information Center

Kidd, Joanna C.; Shum, Kathy Kar-Man; Ho, Connie Suk-Han; Au, Terry Kit-fong

2015-01-01

Phonological processing skills predict early reading development, but what underlies developing phonological processing skills? Phonological representations of 140 native Cantonese-speaking Chinese children (age 4-10) were assessed with speech gating, mispronunciation detection, and nonword repetition tasks; their nonverbal IQ, reading, and…

Advanced propeller aerodynamic analysis

NASA Technical Reports Server (NTRS)

Bober, L. J.

1980-01-01

The analytical approaches as well as the capabilities of three advanced analyses for predicting propeller aerodynamic performance are presented. It is shown that two of these analyses use a lifting line representation for the propeller blades, and the third uses a lifting surface representation.

Triple representation of language, working memory, social and emotion processing in the cerebellum: convergent evidence from task and seed-based resting-state fMRI analyses in a single large cohort.

PubMed

Guell, Xavier; Gabrieli, John D E; Schmahmann, Jeremy D

2018-05-15

Delineation of functional topography is critical to the evolving understanding of the cerebellum's role in a wide range of nervous system functions. We used data from the Human Connectome Project (n = 787) to analyze cerebellar fMRI task activation (motor, working memory, language, social and emotion processing) and resting-state functional connectivity calculated from cerebral cortical seeds corresponding to the peak Cohen's d of each task contrast. The combination of exceptional statistical power, activation from both motor and multiple non-motor tasks in the same participants, and convergent resting-state networks in the same participants revealed novel aspects of the functional topography of the human cerebellum. Consistent with prior studies there were two distinct representations of motor activation. Newly revealed were three distinct representations each for working memory, language, social, and emotional task processing that were largely separate for these four cognitive and affective domains. In most cases, the task-based activations and the corresponding resting-network correlations were congruent in identifying the two motor representations and the three non-motor representations that were unique to working memory, language, social cognition, and emotion. The definitive localization and characterization of distinct triple representations for cognition and emotion task processing in the cerebellum opens up new basic science questions as to why there are triple representations (what different functions are enabled by the different representations?) and new clinical questions (what are the differing consequences of lesions to the different representations?). Copyright © 2018 Elsevier Inc. All rights reserved.

NMRDSP: an accurate prediction of protein shape strings from NMR chemical shifts and sequence data.

PubMed

Mao, Wusong; Cong, Peisheng; Wang, Zhiheng; Lu, Longjian; Zhu, Zhongliang; Li, Tonghua

2013-01-01

Shape string is structural sequence and is an extremely important structure representation of protein backbone conformations. Nuclear magnetic resonance chemical shifts give a strong correlation with the local protein structure, and are exploited to predict protein structures in conjunction with computational approaches. Here we demonstrate a novel approach, NMRDSP, which can accurately predict the protein shape string based on nuclear magnetic resonance chemical shifts and structural profiles obtained from sequence data. The NMRDSP uses six chemical shifts (HA, H, N, CA, CB and C) and eight elements of structure profiles as features, a non-redundant set (1,003 entries) as the training set, and a conditional random field as a classification algorithm. For an independent testing set (203 entries), we achieved an accuracy of 75.8% for S8 (the eight states accuracy) and 87.8% for S3 (the three states accuracy). This is higher than only using chemical shifts or sequence data, and confirms that the chemical shift and the structure profile are significant features for shape string prediction and their combination prominently improves the accuracy of the predictor. We have constructed the NMRDSP web server and believe it could be employed to provide a solid platform to predict other protein structures and functions. The NMRDSP web server is freely available at http://cal.tongji.edu.cn/NMRDSP/index.jsp.

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.

Compressive residual strength of graphite/epoxy laminates after impact

NASA Technical Reports Server (NTRS)

Guy, Teresa A.; Lagace, Paul A.

1992-01-01

The issue of damage tolerance after impact, in terms of the compressive residual strength, was experimentally examined in graphite/epoxy laminates using Hercules AS4/3501-6 in a (+ or - 45/0)(sub 2S) configuration. Three different impactor masses were used at various velocities and the resultant damage measured via a number of nondestructive and destructive techniques. Specimens were then tested to failure under uniaxial compression. The results clearly show that a minimum compressive residual strength exists which is below the open hole strength for a hole of the same diameter as the impactor. Increases in velocity beyond the point of minimum strength cause a difference in the damage produced and cause a resultant increase in the compressive residual strength which asymptotes to the open hole strength value. Furthermore, the results show that this minimum compressive residual strength value is independent of the impactor mass used and is only dependent upon the damage present in the impacted specimen which is the same for the three impactor mass cases. A full 3-D representation of the damage is obtained through the various techniques. Only this 3-D representation can properly characterize the damage state that causes the resultant residual strength. Assessment of the state-of-the-art in predictive analysis capabilities shows a need to further develop techniques based on the 3-D damage state that exists. In addition, the need for damage 'metrics' is clearly indicated.

Antecedent reactivation by surface and deep anaphora in Norwegian

PubMed Central

HESTVIK, ARILD; NORDBY, HELGE; KARLSEN, GEIR

2005-01-01

Anaphora are expressions in language that depend on other linguistic entities for their full meaning. They can furthermore be divided into two types according to the level of representation where they find their antecedents: Surface anaphora, which resolve their reference at the sentence representation level, and deep anaphora, which resolve their reference at the non-grammatical level of discourse representation. The linguistic theory of these two anaphor types, and recent findings about processing differences at these two levels, combine to predict that surface anaphora should show fast and immediate reactivation of their antecedents, whereas deep anaphora should have a slower time course of antecedent reaccess. These predictions were confirmed with two lexical decision task experiments with Norwegian stimuli. PMID:15842413

Neural evidence for description dependent reward processing in the framing effect.

PubMed

Yu, Rongjun; Zhang, Ping

2014-01-01

Human decision making can be influenced by emotionally valenced contexts, known as the framing effect. We used event-related brain potentials to investigate how framing influences the encoding of reward. We found that the feedback related negativity (FRN), which indexes the "worse than expected" negative prediction error in the anterior cingulate cortex (ACC), was more negative for the negative frame than for the positive frame in the win domain. Consistent with previous findings that the FRN is not sensitive to "better than expected" positive prediction error, the FRN did not differentiate the positive and negative frame in the loss domain. Our results provide neural evidence that the description invariance principle which states that reward representation and decision making are not influenced by how options are presented is violated in the framing effect.

Ab initio study of the ground and excited electronic states of the methyl radical

PubMed Central

Zanchet, A.; Bañares, L.; Senent, M. L.; García-Vela, A.

2016-01-01

The ground and some excited electronic states of the methyl radical have been characterized by means of highly correlated ab intio techniques. The specific excited states investigated are those involved in the dissociation of the radical, namely the 3s and 3pz Rydberg states, and the A1 and B1 valence states crossing them, respectively. The C-H dissociative coordinate and the HCH bending angle were considered in order to generate the first two-dimensional ab initio representation of the potential surfaces of the above electronic states of CH3, along with the nonadiabatic couplings between them. Spectroscopic constants and frequencies calculated for the ground and bound excited states agree well with most of the available experimental data. Implications of the shape of the excited potential surfaces and couplings for the dissociation pathways of CH3 are discussed in the light of recent experimental results for dissociation from low-lying vibrational states of CH3. Based on the ab initio data some predictions are made regarding methyl photodissociation from higher initial vibrational states. PMID:27892569

The role of language in the experience and perception of emotion: a neuroimaging meta-analysis

PubMed Central

Brooks, Jeffrey A.; Shablack, Holly; Gendron, Maria; Satpute, Ajay B.; Parrish, Michael H.

2017-01-01

Abstract Recent behavioral and neuroimaging studies demonstrate that labeling one’s emotional experiences and perceptions alters those states. Here, we used a comprehensive meta-analysis of the neuroimaging literature to systematically explore whether the presence of emotion words in experimental tasks has an impact on the neural representation of emotional experiences and perceptions across studies. Using a database of 386 studies, we assessed brain activity when emotion words (e.g. ‘anger’, ‘disgust’) and more general affect words (e.g. ‘pleasant’, ‘unpleasant’) were present in experimental tasks vs not present. As predicted, when emotion words were present, we observed more frequent activations in regions related to semantic processing. When emotion words were not present, we observed more frequent activations in the amygdala and parahippocampal gyrus, bilaterally. The presence of affect words did not have the same effect on the neural representation of emotional experiences and perceptions, suggesting that our observed effects are specific to emotion words. These findings are consistent with the psychological constructionist prediction that in the absence of accessible emotion concepts, the meaning of affective experiences and perceptions are ambiguous. Findings are also consistent with the regulatory role of ‘affect labeling’. Implications of the role of language in emotion construction and regulation are discussed. PMID:27539864

Resting-state brain activity in the motor cortex reflects task-induced activity: A multi-voxel pattern analysis.

PubMed

Kusano, Toshiki; Kurashige, Hiroki; Nambu, Isao; Moriguchi, Yoshiya; Hanakawa, Takashi; Wada, Yasuhiro; Osu, Rieko

2015-08-01

It has been suggested that resting-state brain activity reflects task-induced brain activity patterns. In this study, we examined whether neural representations of specific movements can be observed in the resting-state brain activity patterns of motor areas. First, we defined two regions of interest (ROIs) to examine brain activity associated with two different behavioral tasks. Using multi-voxel pattern analysis with regularized logistic regression, we designed a decoder to detect voxel-level neural representations corresponding to the tasks in each ROI. Next, we applied the decoder to resting-state brain activity. We found that the decoder discriminated resting-state neural activity with accuracy comparable to that associated with task-induced neural activity. The distribution of learned weighted parameters for each ROI was similar for resting-state and task-induced activities. Large weighted parameters were mainly located on conjunctive areas. Moreover, the accuracy of detection was higher than that for a decoder whose weights were randomly shuffled, indicating that the resting-state brain activity includes multi-voxel patterns similar to the neural representation for the tasks. Therefore, these results suggest that the neural representation of resting-state brain activity is more finely organized and more complex than conventionally considered.

Large eddy simulation of turbulent premixed combustion using tabulated detailed chemistry and presumed probability density function

NASA Astrophysics Data System (ADS)

Zhang, Hongda; Han, Chao; Ye, Taohong; Ren, Zhuyin

2016-03-01

A method of chemistry tabulation combined with presumed probability density function (PDF) is applied to simulate piloted premixed jet burner flames with high Karlovitz number using large eddy simulation. Thermo-chemistry states are tabulated by the combination of auto-ignition and extended auto-ignition model. To evaluate the predictive capability of the proposed tabulation method to represent the thermo-chemistry states under the condition of different fresh gases temperature, a-priori study is conducted by performing idealised transient one-dimensional premixed flame simulations. Presumed PDF is used to involve the interaction of turbulence and flame with beta PDF to model the reaction progress variable distribution. Two presumed PDF models, Dirichlet distribution and independent beta distribution, respectively, are applied for representing the interaction between two mixture fractions that are associated with three inlet streams. Comparisons of statistical results show that two presumed PDF models for the two mixture fractions are both capable of predicting temperature and major species profiles, however, they are shown to have a significant effect on the predictions for intermediate species. An analysis of the thermo-chemical state-space representation of the sub-grid scale (SGS) combustion model is performed by comparing correlations between the carbon monoxide mass fraction and temperature. The SGS combustion model based on the proposed chemistry tabulation can reasonably capture the peak value and change trend of intermediate species. Aspects regarding model extensions to adequately predict the peak location of intermediate species are discussed.

Characterization of uncertainty in ETMS flight events predictions and its effect on traffic demand predictions

DOT National Transportation Integrated Search

2008-07-11

This report presents the results of analysis and characterization of uncertainty in traffic demand predictions using ETMS data and probabilistic representation of the predictions. Our previous research, described in two prior reports, was focused on ...

Hierarchical levels of representation in language prediction: The influence of first language acquisition in highly proficient bilinguals.

PubMed

Molinaro, Nicola; Giannelli, Francesco; Caffarra, Sendy; Martin, Clara

2017-07-01

Language comprehension is largely supported by predictive mechanisms that account for the ease and speed with which communication unfolds. Both native and proficient non-native speakers can efficiently handle contextual cues to generate reliable linguistic expectations. However, the link between the variability of the linguistic background of the speaker and the hierarchical format of the representations predicted is still not clear. We here investigate whether native language exposure to typologically highly diverse languages (Spanish and Basque) affects the way early balanced bilingual speakers carry out language predictions. During Spanish sentence comprehension, participants developed predictions of words the form of which (noun ending) could be either diagnostic of grammatical gender values (transparent) or totally ambiguous (opaque). We measured electrophysiological prediction effects time-locked both to the target word and to its determiner, with the former being expected or unexpected. Event-related (N200-N400) and oscillatory activity in the low beta-band (15-17Hz) frequency channel showed that both Spanish and Basque natives optimally carry out lexical predictions independently of word transparency. Crucially, in contrast to Spanish natives, Basque natives displayed visual word form predictions for transparent words, in consistency with the relevance that noun endings (post-nominal suffixes) play in their native language. We conclude that early language exposure largely shapes prediction mechanisms, so that bilinguals reading in their second language rely on the distributional regularities that are highly relevant in their first language. More importantly, we show that individual linguistic experience hierarchically modulates the format of the predicted representation. Copyright © 2017 Elsevier B.V. All rights reserved.

Spatiotemporal Context Awareness for Urban Traffic Modeling and Prediction: Sparse Representation Based Variable Selection.

PubMed

Yang, Su; Shi, Shixiong; Hu, Xiaobing; Wang, Minjie

2015-01-01

Spatial-temporal correlations among the data play an important role in traffic flow prediction. Correspondingly, traffic modeling and prediction based on big data analytics emerges due to the city-scale interactions among traffic flows. A new methodology based on sparse representation is proposed to reveal the spatial-temporal dependencies among traffic flows so as to simplify the correlations among traffic data for the prediction task at a given sensor. Three important findings are observed in the experiments: (1) Only traffic flows immediately prior to the present time affect the formation of current traffic flows, which implies the possibility to reduce the traditional high-order predictors into an 1-order model. (2) The spatial context relevant to a given prediction task is more complex than what is assumed to exist locally and can spread out to the whole city. (3) The spatial context varies with the target sensor undergoing prediction and enlarges with the increment of time lag for prediction. Because the scope of human mobility is subject to travel time, identifying the varying spatial context against time lag is crucial for prediction. Since sparse representation can capture the varying spatial context to adapt to the prediction task, it outperforms the traditional methods the inputs of which are confined as the data from a fixed number of nearby sensors. As the spatial-temporal context for any prediction task is fully detected from the traffic data in an automated manner, where no additional information regarding network topology is needed, it has good scalability to be applicable to large-scale networks.

Spatiotemporal Context Awareness for Urban Traffic Modeling and Prediction: Sparse Representation Based Variable Selection

PubMed Central

Yang, Su; Shi, Shixiong; Hu, Xiaobing; Wang, Minjie

2015-01-01

Spatial-temporal correlations among the data play an important role in traffic flow prediction. Correspondingly, traffic modeling and prediction based on big data analytics emerges due to the city-scale interactions among traffic flows. A new methodology based on sparse representation is proposed to reveal the spatial-temporal dependencies among traffic flows so as to simplify the correlations among traffic data for the prediction task at a given sensor. Three important findings are observed in the experiments: (1) Only traffic flows immediately prior to the present time affect the formation of current traffic flows, which implies the possibility to reduce the traditional high-order predictors into an 1-order model. (2) The spatial context relevant to a given prediction task is more complex than what is assumed to exist locally and can spread out to the whole city. (3) The spatial context varies with the target sensor undergoing prediction and enlarges with the increment of time lag for prediction. Because the scope of human mobility is subject to travel time, identifying the varying spatial context against time lag is crucial for prediction. Since sparse representation can capture the varying spatial context to adapt to the prediction task, it outperforms the traditional methods the inputs of which are confined as the data from a fixed number of nearby sensors. As the spatial-temporal context for any prediction task is fully detected from the traffic data in an automated manner, where no additional information regarding network topology is needed, it has good scalability to be applicable to large-scale networks. PMID:26496370

Real-Time Ligand Binding Pocket Database Search Using Local Surface Descriptors

PubMed Central

Chikhi, Rayan; Sael, Lee; Kihara, Daisuke

2010-01-01

Due to the increasing number of structures of unknown function accumulated by ongoing structural genomics projects, there is an urgent need for computational methods for characterizing protein tertiary structures. As functions of many of these proteins are not easily predicted by conventional sequence database searches, a legitimate strategy is to utilize structure information in function characterization. Of a particular interest is prediction of ligand binding to a protein, as ligand molecule recognition is a major part of molecular function of proteins. Predicting whether a ligand molecule binds a protein is a complex problem due to the physical nature of protein-ligand interactions and the flexibility of both binding sites and ligand molecules. However, geometric and physicochemical complementarity is observed between the ligand and its binding site in many cases. Therefore, ligand molecules which bind to a local surface site in a protein can be predicted by finding similar local pockets of known binding ligands in the structure database. Here, we present two representations of ligand binding pockets and utilize them for ligand binding prediction by pocket shape comparison. These representations are based on mapping of surface properties of binding pockets, which are compactly described either by the two dimensional pseudo-Zernike moments or the 3D Zernike descriptors. These compact representations allow a fast real-time pocket searching against a database. Thorough benchmark study employing two different datasets show that our representations are competitive with the other existing methods. Limitations and potentials of the shape-based methods as well as possible improvements are discussed. PMID:20455259

Real-time ligand binding pocket database search using local surface descriptors.

PubMed

Chikhi, Rayan; Sael, Lee; Kihara, Daisuke

2010-07-01

Because of the increasing number of structures of unknown function accumulated by ongoing structural genomics projects, there is an urgent need for computational methods for characterizing protein tertiary structures. As functions of many of these proteins are not easily predicted by conventional sequence database searches, a legitimate strategy is to utilize structure information in function characterization. Of particular interest is prediction of ligand binding to a protein, as ligand molecule recognition is a major part of molecular function of proteins. Predicting whether a ligand molecule binds a protein is a complex problem due to the physical nature of protein-ligand interactions and the flexibility of both binding sites and ligand molecules. However, geometric and physicochemical complementarity is observed between the ligand and its binding site in many cases. Therefore, ligand molecules which bind to a local surface site in a protein can be predicted by finding similar local pockets of known binding ligands in the structure database. Here, we present two representations of ligand binding pockets and utilize them for ligand binding prediction by pocket shape comparison. These representations are based on mapping of surface properties of binding pockets, which are compactly described either by the two-dimensional pseudo-Zernike moments or the three-dimensional Zernike descriptors. These compact representations allow a fast real-time pocket searching against a database. Thorough benchmark studies employing two different datasets show that our representations are competitive with the other existing methods. Limitations and potentials of the shape-based methods as well as possible improvements are discussed.

Birman—Wenzl—Murakami Algebra and Topological Basis

NASA Astrophysics Data System (ADS)

Zhou, Cheng-Cheng; Xue, Kang; Wang, Gang-Cheng; Sun, Chun-Fang; Du, Gui-Jiao

2012-02-01

In this paper, we use entangled states to construct 9 × 9-matrix representations of Temperley—Lieb algebra (TLA), then a family of 9 × 9-matrix representations of Birman—Wenzl—Murakami algebra (BWMA) have been presented. Based on which, three topological basis states have been found. And we apply topological basis states to recast nine-dimensional BWMA into its three-dimensional counterpart. Finally, we find the topological basis states are spin singlet states in special case.

The Predictive Brain State: Timing Deficiency in Traumatic Brain Injury?

PubMed Central

Ghajar, Jamshid; Ivry, Richard B.

2015-01-01

Attention and memory deficits observed in traumatic brain injury (TBI) are postulated to result from the shearing of white matter connections between the prefrontal cortex, parietal lobe, and cerebellum that are critical in the generation, maintenance, and precise timing of anticipatory neural activity. These fiber tracts are part of a neural network that generates predictions of future states and events, processes that are required for optimal performance on attention and working memory tasks. The authors discuss the role of this anticipatory neural system for understanding the varied symptoms and potential rehabilitation interventions for TBI. Preparatory neural activity normally allows the efficient integration of sensory information with goal-based representations. It is postulated that an impairment in the generation of this activity in traumatic brain injury (TBI) leads to performance variability as the brain shifts from a predictive to reactive mode. This dysfunction may constitute a fundamental defect in TBI as well as other attention disorders, causing working memory deficits, distractibility, a loss of goal-oriented behavior, and decreased awareness. “The future is not what is coming to meet us, but what we are moving forward to meet.” —Jean-Marie Guyau1 PMID:18460693

Recurrent Neural Networks for Multivariate Time Series with Missing Values.

PubMed

Che, Zhengping; Purushotham, Sanjay; Cho, Kyunghyun; Sontag, David; Liu, Yan

2018-04-17

Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values. In time series prediction and other related tasks, it has been noted that missing values and their missing patterns are often correlated with the target labels, a.k.a., informative missingness. There is very limited work on exploiting the missing patterns for effective imputation and improving prediction performance. In this paper, we develop novel deep learning models, namely GRU-D, as one of the early attempts. GRU-D is based on Gated Recurrent Unit (GRU), a state-of-the-art recurrent neural network. It takes two representations of missing patterns, i.e., masking and time interval, and effectively incorporates them into a deep model architecture so that it not only captures the long-term temporal dependencies in time series, but also utilizes the missing patterns to achieve better prediction results. Experiments of time series classification tasks on real-world clinical datasets (MIMIC-III, PhysioNet) and synthetic datasets demonstrate that our models achieve state-of-the-art performance and provide useful insights for better understanding and utilization of missing values in time series analysis.

Model-Based and Model-Free Pavlovian Reward Learning: Revaluation, Revision and Revelation

PubMed Central

Dayan, Peter; Berridge, Kent C.

2014-01-01

Evidence supports at least two methods for learning about reward and punishment and making predictions for guiding actions. One method, called model-free, progressively acquires cached estimates of the long-run values of circumstances and actions from retrospective experience. The other method, called model-based, uses representations of the environment, expectations and prospective calculations to make cognitive predictions of future value. Extensive attention has been paid to both methods in computational analyses of instrumental learning. By contrast, although a full computational analysis has been lacking, Pavlovian learning and prediction has typically been presumed to be solely model-free. Here, we revise that presumption and review compelling evidence from Pavlovian revaluation experiments showing that Pavlovian predictions can involve their own form of model-based evaluation. In model-based Pavlovian evaluation, prevailing states of the body and brain influence value computations, and thereby produce powerful incentive motivations that can sometimes be quite new. We consider the consequences of this revised Pavlovian view for the computational landscape of prediction, response and choice. We also revisit differences between Pavlovian and instrumental learning in the control of incentive motivation. PMID:24647659

Model-based and model-free Pavlovian reward learning: revaluation, revision, and revelation.

PubMed

Dayan, Peter; Berridge, Kent C

2014-06-01

Evidence supports at least two methods for learning about reward and punishment and making predictions for guiding actions. One method, called model-free, progressively acquires cached estimates of the long-run values of circumstances and actions from retrospective experience. The other method, called model-based, uses representations of the environment, expectations, and prospective calculations to make cognitive predictions of future value. Extensive attention has been paid to both methods in computational analyses of instrumental learning. By contrast, although a full computational analysis has been lacking, Pavlovian learning and prediction has typically been presumed to be solely model-free. Here, we revise that presumption and review compelling evidence from Pavlovian revaluation experiments showing that Pavlovian predictions can involve their own form of model-based evaluation. In model-based Pavlovian evaluation, prevailing states of the body and brain influence value computations, and thereby produce powerful incentive motivations that can sometimes be quite new. We consider the consequences of this revised Pavlovian view for the computational landscape of prediction, response, and choice. We also revisit differences between Pavlovian and instrumental learning in the control of incentive motivation.

Predicting nonstationary flood frequencies: Evidence supports an updated stationarity thesis in the United States

NASA Astrophysics Data System (ADS)

Luke, Adam; Vrugt, Jasper A.; AghaKouchak, Amir; Matthew, Richard; Sanders, Brett F.

2017-07-01

Nonstationary extreme value analysis (NEVA) can improve the statistical representation of observed flood peak distributions compared to stationary (ST) analysis, but management of flood risk relies on predictions of out-of-sample distributions for which NEVA has not been comprehensively evaluated. In this study, we apply split-sample testing to 1250 annual maximum discharge records in the United States and compare the predictive capabilities of NEVA relative to ST extreme value analysis using a log-Pearson Type III (LPIII) distribution. The parameters of the LPIII distribution in the ST and nonstationary (NS) models are estimated from the first half of each record using Bayesian inference. The second half of each record is reserved to evaluate the predictions under the ST and NS models. The NS model is applied for prediction by (1) extrapolating the trend of the NS model parameters throughout the evaluation period and (2) using the NS model parameter values at the end of the fitting period to predict with an updated ST model (uST). Our analysis shows that the ST predictions are preferred, overall. NS model parameter extrapolation is rarely preferred. However, if fitting period discharges are influenced by physical changes in the watershed, for example from anthropogenic activity, the uST model is strongly preferred relative to ST and NS predictions. The uST model is therefore recommended for evaluation of current flood risk in watersheds that have undergone physical changes. Supporting information includes a MATLAB® program that estimates the (ST/NS/uST) LPIII parameters from annual peak discharge data through Bayesian inference.

48 CFR 52.241-1 - Electric Service Territory Compliance Representation.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 48 Federal Acquisition Regulations System 2 2010-10-01 2010-10-01 false Electric Service Territory Compliance Representation. 52.241-1 Section 52.241-1 Federal Acquisition Regulations System FEDERAL... utility franchises or service territories established pursuant to State statute, State regulation, or...

The relationship between visual working memory and attention: retention of precise colour information in the absence of effects on perceptual selection

PubMed Central

Hollingworth, Andrew; Hwang, Seongmin

2013-01-01

We examined the conditions under which a feature value in visual working memory (VWM) recruits visual attention to matching stimuli. Previous work has suggested that VWM supports two qualitatively different states of representation: an active state that interacts with perceptual selection and a passive (or accessory) state that does not. An alternative hypothesis is that VWM supports a single form of representation, with the precision of feature memory controlling whether or not the representation interacts with perceptual selection. The results of three experiments supported the dual-state hypothesis. We established conditions under which participants retained a relatively precise representation of a parcticular colour. If the colour was immediately task relevant, it reliably recruited attention to matching stimuli. However, if the colour was not immediately task relevant, it failed to interact with perceptual selection. Feature maintenance in VWM is not necessarily equivalent with feature-based attentional selection. PMID:24018723

Real-time prediction of respiratory motion based on a local dynamic model in an augmented space

NASA Astrophysics Data System (ADS)

Hong, S.-M.; Jung, B.-H.; Ruan, D.

2011-03-01

Motion-adaptive radiotherapy aims to deliver ablative radiation dose to the tumor target with minimal normal tissue exposure, by accounting for real-time target movement. In practice, prediction is usually necessary to compensate for system latency induced by measurement, communication and control. This work focuses on predicting respiratory motion, which is most dominant for thoracic and abdominal tumors. We develop and investigate the use of a local dynamic model in an augmented space, motivated by the observation that respiratory movement exhibits a locally circular pattern in a plane augmented with a delayed axis. By including the angular velocity as part of the system state, the proposed dynamic model effectively captures the natural evolution of respiratory motion. The first-order extended Kalman filter is used to propagate and update the state estimate. The target location is predicted by evaluating the local dynamic model equations at the required prediction length. This method is complementary to existing work in that (1) the local circular motion model characterizes 'turning', overcoming the limitation of linear motion models; (2) it uses a natural state representation including the local angular velocity and updates the state estimate systematically, offering explicit physical interpretations; (3) it relies on a parametric model and is much less data-satiate than the typical adaptive semiparametric or nonparametric method. We tested the performance of the proposed method with ten RPM traces, using the normalized root mean squared difference between the predicted value and the retrospective observation as the error metric. Its performance was compared with predictors based on the linear model, the interacting multiple linear models and the kernel density estimator for various combinations of prediction lengths and observation rates. The local dynamic model based approach provides the best performance for short to medium prediction lengths under relatively low observation rate. Sensitivity analysis indicates its robustness toward the choice of parameters. Its simplicity, robustness and low computation cost makes the proposed local dynamic model an attractive tool for real-time prediction with system latencies below 0.4 s.

Real-time prediction of respiratory motion based on a local dynamic model in an augmented space.

PubMed

Hong, S-M; Jung, B-H; Ruan, D

2011-03-21

Motion-adaptive radiotherapy aims to deliver ablative radiation dose to the tumor target with minimal normal tissue exposure, by accounting for real-time target movement. In practice, prediction is usually necessary to compensate for system latency induced by measurement, communication and control. This work focuses on predicting respiratory motion, which is most dominant for thoracic and abdominal tumors. We develop and investigate the use of a local dynamic model in an augmented space, motivated by the observation that respiratory movement exhibits a locally circular pattern in a plane augmented with a delayed axis. By including the angular velocity as part of the system state, the proposed dynamic model effectively captures the natural evolution of respiratory motion. The first-order extended Kalman filter is used to propagate and update the state estimate. The target location is predicted by evaluating the local dynamic model equations at the required prediction length. This method is complementary to existing work in that (1) the local circular motion model characterizes 'turning', overcoming the limitation of linear motion models; (2) it uses a natural state representation including the local angular velocity and updates the state estimate systematically, offering explicit physical interpretations; (3) it relies on a parametric model and is much less data-satiate than the typical adaptive semiparametric or nonparametric method. We tested the performance of the proposed method with ten RPM traces, using the normalized root mean squared difference between the predicted value and the retrospective observation as the error metric. Its performance was compared with predictors based on the linear model, the interacting multiple linear models and the kernel density estimator for various combinations of prediction lengths and observation rates. The local dynamic model based approach provides the best performance for short to medium prediction lengths under relatively low observation rate. Sensitivity analysis indicates its robustness toward the choice of parameters. Its simplicity, robustness and low computation cost makes the proposed local dynamic model an attractive tool for real-time prediction with system latencies below 0.4 s.

Quantum correlations are weaved by the spinors of the Euclidean primitives

PubMed Central

2018-01-01

The exceptional Lie group E8 plays a prominent role in both mathematics and theoretical physics. It is the largest symmetry group associated with the most general possible normed division algebra, namely, that of the non-associative real octonions, which—thanks to their non-associativity—form the only possible closed set of spinors (or rotors) that can parallelize the 7-sphere. By contrast, here we show how a similar 7-sphere also arises naturally from the algebraic interplay of the graded Euclidean primitives, such as points, lines, planes and volumes, which characterize the three-dimensional conformal geometry of the ambient physical space, set within its eight-dimensional Clifford-algebraic representation. Remarkably, the resulting algebra remains associative, and allows us to understand the origins and strengths of all quantum correlations locally, in terms of the geometry of the compactified physical space, namely, that of a quaternionic 3-sphere, S3, with S7 being its algebraic representation space. Every quantum correlation can thus be understood as a correlation among a set of points of this S7, computed using manifestly local spinors within S3, thereby extending the stringent bounds of ±2 set by Bell inequalities to the bounds of ±22 on the strengths of all possible strong correlations, in the same quantitatively precise manner as that predicted within quantum mechanics. The resulting geometrical framework thus overcomes Bell’s theorem by producing a strictly deterministic and realistic framework that allows a locally causal understanding of all quantum correlations, without requiring either remote contextuality or backward causation. We demonstrate this by first proving a general theorem concerning the geometrical origins of the correlations predicted by arbitrarily entangled quantum states, and then reproducing the correlations predicted by the EPR-Bohm and the GHZ states. The raison d’être of strong correlations turns out to be the Möbius-like twists in the Hopf bundles of S3 and S7. PMID:29893385

Can home-monitoring of sleep predict depressive episodes in bipolar patients?

PubMed

Migliorini, M; Mariani, S; Bertschy, G; Kosel, M; Bianchi, A M

2015-08-01

The aim of this study is the evaluation of the autonomic regulations during depressive stages in bipolar patients in order to test new quantitative and objective measures to detect such events. A sensorized T-shirt was used to record ECG signal and body movements during the night, from which HRV data and sleep macrostructure were estimated and analyzed. 9 out of 20 features extracted resulted to be significant (p<;0.05) in discriminating among depressive and non-depressive states. Such features are representation of HRV dynamics in both linear and non-linear domain and parameters linked to sleep modulations.

A map of abstract relational knowledge in the human hippocampal-entorhinal cortex.

PubMed

Garvert, Mona M; Dolan, Raymond J; Behrens, Timothy Ej

2017-04-27

The hippocampal-entorhinal system encodes a map of space that guides spatial navigation. Goal-directed behaviour outside of spatial navigation similarly requires a representation of abstract forms of relational knowledge. This information relies on the same neural system, but it is not known whether the organisational principles governing continuous maps may extend to the implicit encoding of discrete, non-spatial graphs. Here, we show that the human hippocampal-entorhinal system can represent relationships between objects using a metric that depends on associative strength. We reconstruct a map-like knowledge structure directly from a hippocampal-entorhinal functional magnetic resonance imaging adaptation signal in a situation where relationships are non-spatial rather than spatial, discrete rather than continuous, and unavailable to conscious awareness. Notably, the measure that best predicted a behavioural signature of implicit knowledge and blood oxygen level-dependent adaptation was a weighted sum of future states, akin to the successor representation that has been proposed to account for place and grid-cell firing patterns.

Information content of contact-pattern representations and predictability of epidemic outbreaks

PubMed Central

Holme, Petter

2015-01-01

To understand the contact patterns of a population—who is in contact with whom, and when the contacts happen—is crucial for modeling outbreaks of infectious disease. Traditional theoretical epidemiology assumes that any individual can meet any with equal probability. A more modern approach, network epidemiology, assumes people are connected into a static network over which the disease spreads. Newer yet, temporal network epidemiology, includes the time in the contact representations. In this paper, we investigate the effect of these successive inclusions of more information. Using empirical proximity data, we study both outbreak sizes from unknown sources, and from known states of ongoing outbreaks. In the first case, there are large differences going from a fully mixed simulation to a network, and from a network to a temporal network. In the second case, differences are smaller. We interpret these observations in terms of the temporal network structure of the data sets. For example, a fast overturn of nodes and links seem to make the temporal information more important. PMID:26403504

Predicted Errors In Children's Early Sentence Comprehension

PubMed Central

Gertner, Yael; Fisher, Cynthia

2012-01-01

Children use syntax to interpret sentences and learn verbs; this is syntactic bootstrapping. The structure-mapping account of early syntactic bootstrapping proposes that a partial representation of sentence structure, the set of nouns occurring with the verb, guides initial interpretation and provides an abstract format for new learning. This account predicts early successes, but also telltale errors: Toddlers should be unable to tell transitive sentences from other sentences containing two nouns. In testing this prediction, we capitalized on evidence that 21-month-olds use what they have learned about noun order in English sentences to understand new transitive verbs. In two experiments, 21-month-olds applied this noun-order knowledge to two-noun intransitive sentences, mistakenly assigning different interpretations to “The boy and the girl are gorping!” and “The girl and the boy are gorping!”. This suggests that toddlers exploit partial representations of sentence structure to guide sentence interpretation; these sparse representations are useful, but error-prone. PMID:22525312

Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity

NASA Astrophysics Data System (ADS)

Huang, Bing; von Lilienfeld, O. Anatole

2016-10-01

The predictive accuracy of Machine Learning (ML) models of molecular properties depends on the choice of the molecular representation. Inspired by the postulates of quantum mechanics, we introduce a hierarchy of representations which meet uniqueness and target similarity criteria. To systematically control target similarity, we simply rely on interatomic many body expansions, as implemented in universal force-fields, including Bonding, Angular (BA), and higher order terms. Addition of higher order contributions systematically increases similarity to the true potential energy and predictive accuracy of the resulting ML models. We report numerical evidence for the performance of BAML models trained on molecular properties pre-calculated at electron-correlated and density functional theory level of theory for thousands of small organic molecules. Properties studied include enthalpies and free energies of atomization, heat capacity, zero-point vibrational energies, dipole-moment, polarizability, HOMO/LUMO energies and gap, ionization potential, electron affinity, and electronic excitations. After training, BAML predicts energies or electronic properties of out-of-sample molecules with unprecedented accuracy and speed.

Influence of Microstructure Representation on Flow Stress and Grain Size Prediction in Through-Process Modeling of AA5182 Sheet Production

NASA Astrophysics Data System (ADS)

Lohmar, Johannes; Bambach, Markus; Karhausen, Kai F.

2013-01-01

Integrated computational materials engineering is an up to date method for developing new materials and optimizing complete process chains. In the simulation of a process chain, material models play a central role as they capture the response of the material to external process conditions. While much effort is put into their development and improvement, less attention is paid to their implementation, which is problematic because the representation of microstructure in the model has a decisive influence on modeling accuracy and calculation speed. The aim of this article is to analyze the influence of different microstructure representation concepts on the prediction of flow stress and microstructure evolution when using the same set of material equations. Scalar, tree-based and cluster-based concepts are compared for a multi-stage rolling process of an AA5182 alloy. It was found that implementation influences the predicted flow stress and grain size, in particular in the regime of coupled hardening and softening.

Positive Interactions and Avoidant and Anxious Representations in Relationships with Parents, Friends, and Romantic Partners

PubMed Central

Furman, Wyndol; Stephenson, J. Claire; Rhoades, Galena K.

2013-01-01

We examined associations between positive interactions and avoidant and anxious representations in relationships with parents, friends, and romantic partners. Two hundred adolescents completed questionnaires, observations, and attachment interviews. From a between-person perspective, those adolescents with more positive interactions overall had less avoidant representations. Within persons, more positive interactions were relative to one’s own average level in relationships, the less avoidant representations were for that type of relationship. Adolescents were less anxious about a particular type of relationship if they have positive interactions in their other types of relationships. Finally, representations were primarily predicted by interactions in the same type of relationship; interactions in other relationships contributed little. The findings underscore the importance of examining representations of particular types of relationships. PMID:26346530

Open Knee: Open Source Modeling & Simulation to Enable Scientific Discovery and Clinical Care in Knee Biomechanics

PubMed Central

Erdemir, Ahmet

2016-01-01

Virtual representations of the knee joint can provide clinicians, scientists, and engineers the tools to explore mechanical function of the knee and its tissue structures in health and disease. Modeling and simulation approaches such as finite element analysis also provide the possibility to understand the influence of surgical procedures and implants on joint stresses and tissue deformations. A large number of knee joint models are described in the biomechanics literature. However, freely accessible, customizable, and easy-to-use models are scarce. Availability of such models can accelerate clinical translation of simulations, where labor intensive reproduction of model development steps can be avoided. The interested parties can immediately utilize readily available models for scientific discovery and for clinical care. Motivated by this gap, this study aims to describe an open source and freely available finite element representation of the tibiofemoral joint, namely Open Knee, which includes detailed anatomical representation of the joint's major tissue structures, their nonlinear mechanical properties and interactions. Three use cases illustrate customization potential of the model, its predictive capacity, and its scientific and clinical utility: prediction of joint movements during passive flexion, examining the role of meniscectomy on contact mechanics and joint movements, and understanding anterior cruciate ligament mechanics. A summary of scientific and clinically directed studies conducted by other investigators are also provided. The utilization of this open source model by groups other than its developers emphasizes the premise of model sharing as an accelerator of simulation-based medicine. Finally, the imminent need to develop next generation knee models are noted. These are anticipated to incorporate individualized anatomy and tissue properties supported by specimen-specific joint mechanics data for evaluation, all acquired in vitro from varying age groups and pathological states. PMID:26444849

Open Knee: Open Source Modeling and Simulation in Knee Biomechanics.

PubMed

Erdemir, Ahmet

2016-02-01

Virtual representations of the knee joint can provide clinicians, scientists, and engineers the tools to explore mechanical functions of the knee and its tissue structures in health and disease. Modeling and simulation approaches such as finite element analysis also provide the possibility to understand the influence of surgical procedures and implants on joint stresses and tissue deformations. A large number of knee joint models are described in the biomechanics literature. However, freely accessible, customizable, and easy-to-use models are scarce. Availability of such models can accelerate clinical translation of simulations, where labor-intensive reproduction of model development steps can be avoided. Interested parties can immediately utilize readily available models for scientific discovery and clinical care. Motivated by this gap, this study aims to describe an open source and freely available finite element representation of the tibiofemoral joint, namely Open Knee, which includes the detailed anatomical representation of the joint's major tissue structures and their nonlinear mechanical properties and interactions. Three use cases illustrate customization potential of the model, its predictive capacity, and its scientific and clinical utility: prediction of joint movements during passive flexion, examining the role of meniscectomy on contact mechanics and joint movements, and understanding anterior cruciate ligament mechanics. A summary of scientific and clinically directed studies conducted by other investigators are also provided. The utilization of this open source model by groups other than its developers emphasizes the premise of model sharing as an accelerator of simulation-based medicine. Finally, the imminent need to develop next-generation knee models is noted. These are anticipated to incorporate individualized anatomy and tissue properties supported by specimen-specific joint mechanics data for evaluation, all acquired in vitro from varying age groups and pathological states. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Human Orbitofrontal Cortex Represents a Cognitive Map of State Space.

PubMed

Schuck, Nicolas W; Cai, Ming Bo; Wilson, Robert C; Niv, Yael

2016-09-21

Although the orbitofrontal cortex (OFC) has been studied intensely for decades, its precise functions have remained elusive. We recently hypothesized that the OFC contains a "cognitive map" of task space in which the current state of the task is represented, and this representation is especially critical for behavior when states are unobservable from sensory input. To test this idea, we apply pattern-classification techniques to neuroimaging data from humans performing a decision-making task with 16 states. We show that unobservable task states can be decoded from activity in OFC, and decoding accuracy is related to task performance and the occurrence of individual behavioral errors. Moreover, similarity between the neural representations of consecutive states correlates with behavioral accuracy in corresponding state transitions. These results support the idea that OFC represents a cognitive map of task space and establish the feasibility of decoding state representations in humans using non-invasive neuroimaging. Copyright © 2016 Elsevier Inc. All rights reserved.

Using abiotic variables to predict importance of sites for species representation.

PubMed

Albuquerque, Fabio; Beier, Paul

2015-10-01

In systematic conservation planning, species distribution data for all sites in a planning area are used to prioritize each site in terms of the site's importance toward meeting the goal of species representation. But comprehensive species data are not available in most planning areas and would be expensive to acquire. As a shortcut, ecologists use surrogates, such as occurrences of birds or another well-surveyed taxon, or land types defined from remotely sensed data, in the hope that sites that represent the surrogates also represent biodiversity. Unfortunately, surrogates have not performed reliably. We propose a new type of surrogate, predicted importance, that can be developed from species data for a q% subset of sites. With species data from this subset of sites, importance can be modeled as a function of abiotic variables available at no charge for all terrestrial areas on Earth. Predicted importance can then be used as a surrogate to prioritize all sites. We tested this surrogate with 8 sets of species data. For each data set, we used a q% subset of sites to model importance as a function of abiotic variables, used the resulting function to predict importance for all sites, and evaluated the number of species in the sites with highest predicted importance. Sites with the highest predicted importance represented species efficiently for all data sets when q = 25% and for 7 of 8 data sets when q = 20%. Predicted importance requires less survey effort than direct selection for species representation and meets representation goals well compared with other surrogates currently in use. This less expensive surrogate may be useful in those areas of the world that need it most, namely tropical regions with the highest biodiversity, greatest biodiversity loss, most severe lack of inventory data, and poorly developed protected area networks. © 2015 Society for Conservation Biology.

Intention understanding over T: a neuroimaging study on shared representations and tennis return predictions

PubMed Central

Cacioppo, Stephanie; Fontang, Frederic; Patel, Nisa; Decety, Jean; Monteleone, George; Cacioppo, John T.

2014-01-01

Studying the way athletes predict actions of their peers during fast-ball sports, such as a tennis, has proved to be a valuable tool for increasing our knowledge of intention understanding. The working model in this area is that the anticipatory representations of others' behaviors require internal predictive models of actions formed from pre-established and shared representations between the observer and the actor. This model also predicts that observers would not be able to read accurately the intentions of a competitor if the competitor were to perform the action without prior knowledge of their intention until moments before the action. To test this hypothesis, we recorded brain activity from 25 male tennis players while they performed a novel behavioral tennis intention inference task, which included two conditions: (i) one condition in which they viewed video clips of a tennis athlete who knew in advance where he was about to act/serve (initially intended serves) and (ii) one condition in which they viewed video clips of that same athlete when he did not know where he was to act/serve until the target was specified after he had tossed the ball into the air to complete his serve (non-initially intended serves). Our results demonstrated that (i) tennis expertise is related to the accuracy in predicting where another server intends to serve when that server knows where he intends to serve before (but not after) he tosses the ball in the air; and (ii) accurate predictions are characterized by the recruitment of both cortical areas within the human mirror neuron system (that is known to be involved in higher-order (top-down) processes of embodied cognition and shared representation) and subcortical areas within brain regions involved in procedural memory (caudate nucleus). Interestingly, inaccurate predictions instead recruit areas known to be involved in low-level (bottom-up) computational processes associated with the sense of agency and self-other distinction. PMID:25339886

Multi-voxel patterns of visual category representation during episodic encoding are predictive of subsequent memory

PubMed Central

Kuhl, Brice A.; Rissman, Jesse; Wagner, Anthony D.

2012-01-01

Successful encoding of episodic memories is thought to depend on contributions from prefrontal and temporal lobe structures. Neural processes that contribute to successful encoding have been extensively explored through univariate analyses of neuroimaging data that compare mean activity levels elicited during the encoding of events that are subsequently remembered vs. those subsequently forgotten. Here, we applied pattern classification to fMRI data to assess the degree to which distributed patterns of activity within prefrontal and temporal lobe structures elicited during the encoding of word-image pairs were diagnostic of the visual category (Face or Scene) of the encoded image. We then assessed whether representation of category information was predictive of subsequent memory. Classification analyses indicated that temporal lobe structures contained information robustly diagnostic of visual category. Information in prefrontal cortex was less diagnostic of visual category, but was nonetheless associated with highly reliable classifier-based evidence for category representation. Critically, trials associated with greater classifier-based estimates of category representation in temporal and prefrontal regions were associated with a higher probability of subsequent remembering. Finally, consideration of trial-by-trial variance in classifier-based measures of category representation revealed positive correlations between prefrontal and temporal lobe representations, with the strength of these correlations varying as a function of the category of image being encoded. Together, these results indicate that multi-voxel representations of encoded information can provide unique insights into how visual experiences are transformed into episodic memories. PMID:21925190

The Attachment Doll Play Assessment: Predictive Validity with Concurrent Mother-Child Interaction and Maternal Caregiving Representations

PubMed Central

George, Carol; Solomon, Judith

2016-01-01

Attachment is central to the development of children’s regulatory processes. It has been associated with developmental and psychiatric health across the life span, especially emotional and behavioral regulation of negative affect when stressed (Schore, 2001; Schore and Schore, 2008). Assessment of attachment patterns provides a critical frame for understanding emerging developmental competencies and formulating treatment and intervention. Play-based attachment assessments provide access to representational models of attachment, which are regarded in attachment theory as the central organizing mechanisms associated with stability or change (Bowlby, 1969/1982; Bretherton and Munholland, 2008). The Attachment Doll Play Assessment (ADPA, George and Solomon, 1990–2016; Solomon et al., 1995) is a prominent established representational attachment measure for children aged early latency through childhood. This study examines the predictive validity of the ADPA to caregiving accessibility and responsiveness assessed from mother-child interaction and maternal representation. Sixty nine mothers and their 5–7-year-old children participated in this study. Mother-child interaction was observed during a pre-separation dyadic interaction task. Caregiving representations were rated from the Caregiving Interview (George and Solomon, 1988/1993/2005/2007). Child security with mother was associated with positive dyadic interaction and flexibly integrated maternal caregiving representations. Child controlling/disorganized attachments were significantly associated with problematic dyadic interaction and dysregulated-helpless maternal caregiving representations. The clinical implications and the use of the ADPA in clinical and educational settings are discussed. PMID:27803683

The Attachment Doll Play Assessment: Predictive Validity with Concurrent Mother-Child Interaction and Maternal Caregiving Representations.

PubMed

George, Carol; Solomon, Judith

2016-01-01

Attachment is central to the development of children's regulatory processes. It has been associated with developmental and psychiatric health across the life span, especially emotional and behavioral regulation of negative affect when stressed (Schore, 2001; Schore and Schore, 2008). Assessment of attachment patterns provides a critical frame for understanding emerging developmental competencies and formulating treatment and intervention. Play-based attachment assessments provide access to representational models of attachment, which are regarded in attachment theory as the central organizing mechanisms associated with stability or change (Bowlby, 1969/1982; Bretherton and Munholland, 2008). The Attachment Doll Play Assessment (ADPA, George and Solomon, 1990-2016; Solomon et al., 1995) is a prominent established representational attachment measure for children aged early latency through childhood. This study examines the predictive validity of the ADPA to caregiving accessibility and responsiveness assessed from mother-child interaction and maternal representation. Sixty nine mothers and their 5-7-year-old children participated in this study. Mother-child interaction was observed during a pre-separation dyadic interaction task. Caregiving representations were rated from the Caregiving Interview (George and Solomon, 1988/1993/2005/2007). Child security with mother was associated with positive dyadic interaction and flexibly integrated maternal caregiving representations. Child controlling/disorganized attachments were significantly associated with problematic dyadic interaction and dysregulated-helpless maternal caregiving representations. The clinical implications and the use of the ADPA in clinical and educational settings are discussed.

New analytical model for the ozone electronic ground state potential surface and accurate ab initio vibrational predictions at high energy range.

PubMed

Tyuterev, Vladimir G; Kochanov, Roman V; Tashkun, Sergey A; Holka, Filip; Szalay, Péter G

2013-10-07

An accurate description of the complicated shape of the potential energy surface (PES) and that of the highly excited vibration states is of crucial importance for various unsolved issues in the spectroscopy and dynamics of ozone and remains a challenge for the theory. In this work a new analytical representation is proposed for the PES of the ground electronic state of the ozone molecule in the range covering the main potential well and the transition state towards the dissociation. This model accounts for particular features specific to the ozone PES for large variations of nuclear displacements along the minimum energy path. The impact of the shape of the PES near the transition state (existence of the "reef structure") on vibration energy levels was studied for the first time. The major purpose of this work was to provide accurate theoretical predictions for ozone vibrational band centres at the energy range near the dissociation threshold, which would be helpful for understanding the very complicated high-resolution spectra and its analyses currently in progress. Extended ab initio electronic structure calculations were carried out enabling the determination of the parameters of a minimum energy path PES model resulting in a new set of theoretical vibrational levels of ozone. A comparison with recent high-resolution spectroscopic data on the vibrational levels gives the root-mean-square deviations below 1 cm(-1) for ozone band centres up to 90% of the dissociation energy. New ab initio vibrational predictions represent a significant improvement with respect to all previously available calculations.

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

Koenig, Robert; Institute for Quantum Information, California Institute of Technology, Pasadena, California 91125; Mitchison, Graeme

In its most basic form, the finite quantum de Finetti theorem states that the reduced k-partite density operator of an n-partite symmetric state can be approximated by a convex combination of k-fold product states. Variations of this result include Renner's 'exponential' approximation by 'almost-product' states, a theorem which deals with certain triples of representations of the unitary group, and the result of D'Cruz et al. [e-print quant-ph/0606139;Phys. Rev. Lett. 98, 160406 (2007)] for infinite-dimensional systems. We show how these theorems follow from a single, general de Finetti theorem for representations of symmetry groups, each instance corresponding to a particular choicemore » of symmetry group and representation of that group. This gives some insight into the nature of the set of approximating states and leads to some new results, including an exponential theorem for infinite-dimensional systems.« less

Improving Permafrost Hydrology Prediction Through Data-Model Integration

NASA Astrophysics Data System (ADS)

Wilson, C. J.; Andresen, C. G.; Atchley, A. L.; Bolton, W. R.; Busey, R.; Coon, E.; Charsley-Groffman, L.

2017-12-01

The CMIP5 Earth System Models were unable to adequately predict the fate of the 16GT of permafrost carbon in a warming climate due to poor representation of Arctic ecosystem processes. The DOE Office of Science Next Generation Ecosystem Experiment, NGEE-Arctic project aims to reduce uncertainty in the Arctic carbon cycle and its impact on the Earth's climate system by improved representation of the coupled physical, chemical and biological processes that drive how much buried carbon will be converted to CO2 and CH4, how fast this will happen, which form will dominate, and the degree to which increased plant productivity will offset increased soil carbon emissions. These processes fundamentally depend on permafrost thaw rate and its influence on surface and subsurface hydrology through thermal erosion, land subsidence and changes to groundwater flow pathways as soil, bedrock and alluvial pore ice and massive ground ice melts. LANL and its NGEE colleagues are co-developing data and models to better understand controls on permafrost degradation and improve prediction of the evolution of permafrost and its impact on Arctic hydrology. The LANL Advanced Terrestrial Simulator was built using a state of the art HPC software framework to enable the first fully coupled 3-dimensional surface-subsurface thermal-hydrology and land surface deformation simulations to simulate the evolution of the physical Arctic environment. Here we show how field data including hydrology, snow, vegetation, geochemistry and soil properties, are informing the development and application of the ATS to improve understanding of controls on permafrost stability and permafrost hydrology. The ATS is being used to inform parameterizations of complex coupled physical, ecological and biogeochemical processes for implementation in the DOE ACME land model, to better predict the role of changing Arctic hydrology on the global climate system. LA-UR-17-26566.

Female political representation and child health: Evidence from a multilevel analysis.

PubMed

Quamruzzaman, Amm; Lange, Matthew

2016-10-24

This article explores the impact of female political representation in national parliaments on child health through a multilevel analysis. Using available Demographic and Health Surveys, we employ both cross-sectional data for 51 low- and middle-income countries and longitudinal data for 20 countries with multiple surveys. For both the cross-sectional and longitudinal analyses, female representation is negatively related to infant mortality and positively related to measles vaccination status. To explore potential mechanisms, we control for state spending on health and analyze whether the impact of female representation depends on a critical mass of female representatives. The analysis offers evidence that state spending accounts for some of the mediation effect and that the impact of female representation on infant death depends on a critical mass. Copyright © 2016 Elsevier Ltd. All rights reserved.

Multiple Domains of Parental Secure Base Support During Childhood and Adolescence Contribute to Adolescents’ Representations of Attachment as a Secure Base Script

PubMed Central

Vaughn, Brian E.; Waters, Theodore E. A.; Steele, Ryan D.; Roisman, Glenn I.; Bost, Kelly K.; Truitt, Warren; Waters, Harriet S.; Booth-LaForce, Cathryn

2016-01-01

Although attachment theory claims that early attachment representations reflecting the quality of the child’s “lived experiences” are maintained across developmental transitions, evidence that has emerged over the last decade suggests that the association between early relationship quality and adolescents’ attachment representations is fairly modest in magnitude. We used aspects of parenting beyond sensitivity over childhood and adolescence and early security to predict adolescents’ scripted attachment representations. At age 18 years, 673 participants from the NICHD Study of Early Child Care and Youth Development (SECCYD) completed the Attachment Script Assessment (ASA) from which we derived an assessment of secure base script knowledge. Measures of secure base support from childhood through age 15 years (e.g., parental monitoring of child activity, father presence in the home) were selected as predictors and accounted for an additional 8% of the variance in secure base script knowledge scores above and beyond direct observations of sensitivity and early attachment status alone, suggesting that adolescents’ scripted attachment representations reflect multiple domains of parenting. Cognitive and demographic variables also significantly increased predicted variance in secure base script knowledge by 2% each. PMID:27032953

Prediction of protein structural classes by recurrence quantification analysis based on chaos game representation.

PubMed

Yang, Jian-Yi; Peng, Zhen-Ling; Yu, Zu-Guo; Zhang, Rui-Jie; Anh, Vo; Wang, Desheng

2009-04-21

In this paper, we intend to predict protein structural classes (alpha, beta, alpha+beta, or alpha/beta) for low-homology data sets. Two data sets were used widely, 1189 (containing 1092 proteins) and 25PDB (containing 1673 proteins) with sequence homology being 40% and 25%, respectively. We propose to decompose the chaos game representation of proteins into two kinds of time series. Then, a novel and powerful nonlinear analysis technique, recurrence quantification analysis (RQA), is applied to analyze these time series. For a given protein sequence, a total of 16 characteristic parameters can be calculated with RQA, which are treated as feature representation of protein sequences. Based on such feature representation, the structural class for each protein is predicted with Fisher's linear discriminant algorithm. The jackknife test is used to test and compare our method with other existing methods. The overall accuracies with step-by-step procedure are 65.8% and 64.2% for 1189 and 25PDB data sets, respectively. With one-against-others procedure used widely, we compare our method with five other existing methods. Especially, the overall accuracies of our method are 6.3% and 4.1% higher for the two data sets, respectively. Furthermore, only 16 parameters are used in our method, which is less than that used by other methods. This suggests that the current method may play a complementary role to the existing methods and is promising to perform the prediction of protein structural classes.

The effects of sexual orientation on state legislators' behavior and priorities.

PubMed

Herrick, Rebekah

2009-01-01

This article explores whether sexual orientation, surrogate representation, and political factors affect legislators' work on gay, lesbian, and bisexual (GLB) interests, and whether the latter explains away the influence of sexual orientation. A survey of openly GLB state legislators and their colleagues was conducted to measure legislators' campaign issues, legislative priorities, surrogate representation, and ambition. This information is supplemented with bill introduction and district data. The results indicate that legislators' sexual orientation strongly influences their work on GLB issues and although surrogate representation and electoral considerations also affect GLB work, they do not explain away the importance of sexual orientation. The implications of this for the relationship between descriptive and substantive representation are explored.

Rotational KMS States and Type I Conformal Nets

NASA Astrophysics Data System (ADS)

Longo, Roberto; Tanimoto, Yoh

2018-01-01

We consider KMS states on a local conformal net on S 1 with respect to rotations. We prove that, if the conformal net is of type I, namely if it admits only type I DHR representations, then the extremal KMS states are the Gibbs states in an irreducible representation. Completely rational nets, the U(1)-current net, the Virasoro nets and their finite tensor products are shown to be of type I. In the completely rational case, we also give a direct proof that all factorial KMS states are Gibbs states.

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

Representation and redistribution in federations

PubMed Central

Dragu, Tiberiu; Rodden, Jonathan

2011-01-01

Many of the world's most populous democracies are political unions composed of states or provinces that are unequally represented in the national legislature. Scattered empirical studies, most of them focusing on the United States, have discovered that overrepresented states appear to receive larger shares of the national budget. Although this relationship is typically attributed to bargaining advantages associated with greater legislative representation, an important threat to empirical identification stems from the fact that the representation scheme was chosen by the provinces. Thus, it is possible that representation and fiscal transfers are both determined by other characteristics of the provinces in a specific country. To obtain an improved estimate of the relationship between representation and redistribution, we collect and analyze provincial-level data from nine federations over several decades, taking advantage of the historical process through which federations formed and expanded. Controlling for a variety of country- and province-level factors and using a variety of estimation techniques, we show that overrepresented provinces in political unions around the world are rather dramatically favored in the distribution of resources. PMID:21555553

A group matrix representation relevant to scales of measurement of clinical disease states via stratified vectors.

PubMed

Sawamura, Jitsuki; Morishita, Shigeru; Ishigooka, Jun

2016-02-09

Previously, we applied basic group theory and related concepts to scales of measurement of clinical disease states and clinical findings (including laboratory data). To gain a more concrete comprehension, we here apply the concept of matrix representation, which was not explicitly exploited in our previous work. Starting with a set of orthonormal vectors, called the basis, an operator Rj (an N-tuple patient disease state at the j-th session) was expressed as a set of stratified vectors representing plural operations on individual components, so as to satisfy the group matrix representation. The stratified vectors containing individual unit operations were combined into one-dimensional square matrices [Rj]s. The [Rj]s meet the matrix representation of a group (ring) as a K-algebra. Using the same-sized matrix of stratified vectors, we can also express changes in the plural set of [Rj]s. The method is demonstrated on simple examples. Despite the incompleteness of our model, the group matrix representation of stratified vectors offers a formal mathematical approach to clinical medicine, aligning it with other branches of natural science.

Contour Tracking in Echocardiographic Sequences via Sparse Representation and Dictionary Learning

PubMed Central

Huang, Xiaojie; Dione, Donald P.; Compas, Colin B.; Papademetris, Xenophon; Lin, Ben A.; Bregasi, Alda; Sinusas, Albert J.; Staib, Lawrence H.; Duncan, James S.

2013-01-01

This paper presents a dynamical appearance model based on sparse representation and dictionary learning for tracking both endocardial and epicardial contours of the left ventricle in echocardiographic sequences. Instead of learning offline spatiotemporal priors from databases, we exploit the inherent spatiotemporal coherence of individual data to constraint cardiac contour estimation. The contour tracker is initialized with a manual tracing of the first frame. It employs multiscale sparse representation of local image appearance and learns online multiscale appearance dictionaries in a boosting framework as the image sequence is segmented frame-by-frame sequentially. The weights of multiscale appearance dictionaries are optimized automatically. Our region-based level set segmentation integrates a spectrum of complementary multilevel information including intensity, multiscale local appearance, and dynamical shape prediction. The approach is validated on twenty-six 4D canine echocardiographic images acquired from both healthy and post-infarct canines. The segmentation results agree well with expert manual tracings. The ejection fraction estimates also show good agreement with manual results. Advantages of our approach are demonstrated by comparisons with a conventional pure intensity model, a registration-based contour tracker, and a state-of-the-art database-dependent offline dynamical shape model. We also demonstrate the feasibility of clinical application by applying the method to four 4D human data sets. PMID:24292554

Sensory motor remapping of space in human–machine interfaces

PubMed Central

Mussa-Ivaldi, Ferdinando A.; Casadio, Maura; Danziger, Zachary C.; Mosier, Kristine M.; Scheidt, Robert A.

2012-01-01

Studies of adaptation to patterns of deterministic forces have revealed the ability of the motor control system to form and use predictive representations of the environment. These studies have also pointed out that adaptation to novel dynamics is aimed at preserving the trajectories of a controlled endpoint, either the hand of a subject or a transported object. We review some of these experiments and present more recent studies aimed at understanding how the motor system forms representations of the physical space in which actions take place. An extensive line of investigations in visual information processing has dealt with the issue of how the Euclidean properties of space are recovered from visual signals that do not appear to possess these properties. The same question is addressed here in the context of motor behavior and motor learning by observing how people remap hand gestures and body motions that control the state of an external device. We present some theoretical considerations and experimental evidence about the ability of the nervous system to create novel patterns of coordination that are consistent with the representation of extrapersonal space. We also discuss the perspective of endowing human–machine interfaces with learning algorithms that, combined with human learning, may facilitate the control of powered wheelchairs and other assistive devices. PMID:21741543

Classical and quantum cosmology of minimal massive bigravity

NASA Astrophysics Data System (ADS)

Darabi, F.; Mousavi, M.

2016-10-01

In a Friedmann-Robertson-Walker (FRW) space-time background we study the classical cosmological models in the context of recently proposed theory of nonlinear minimal massive bigravity. We show that in the presence of perfect fluid the classical field equations acquire contribution from the massive graviton as a cosmological term which is positive or negative depending on the dynamical competition between two scale factors of bigravity metrics. We obtain the classical field equations for flat and open universes in the ordinary and Schutz representation of perfect fluid. Focusing on the Schutz representation for flat universe, we find classical solutions exhibiting singularities at early universe with vacuum equation of state. Then, in the Schutz representation, we study the quantum cosmology for flat universe and derive the Schrodinger-Wheeler-DeWitt equation. We find its exact and wave packet solutions and discuss on their properties to show that the initial singularity in the classical solutions can be avoided by quantum cosmology. Similar to the study of Hartle-Hawking no-boundary proposal in the quantum cosmology of de Rham, Gabadadze and Tolley (dRGT) massive gravity, it turns out that the mass of graviton predicted by quantum cosmology of the minimal massive bigravity is large at early universe. This is in agreement with the fact that at early universe the cosmological constant should be large.

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.

Distant failure prediction for early stage NSCLC by analyzing PET with sparse representation

NASA Astrophysics Data System (ADS)

Hao, Hongxia; Zhou, Zhiguo; Wang, Jing

2017-03-01

Positron emission tomography (PET) imaging has been widely explored for treatment outcome prediction. Radiomicsdriven methods provide a new insight to quantitatively explore underlying information from PET images. However, it is still a challenging problem to automatically extract clinically meaningful features for prognosis. In this work, we develop a PET-guided distant failure predictive model for early stage non-small cell lung cancer (NSCLC) patients after stereotactic ablative radiotherapy (SABR) by using sparse representation. The proposed method does not need precalculated features and can learn intrinsically distinctive features contributing to classification of patients with distant failure. The proposed framework includes two main parts: 1) intra-tumor heterogeneity description; and 2) dictionary pair learning based sparse representation. Tumor heterogeneity is initially captured through anisotropic kernel and represented as a set of concatenated vectors, which forms the sample gallery. Then, given a test tumor image, its identity (i.e., distant failure or not) is classified by applying the dictionary pair learning based sparse representation. We evaluate the proposed approach on 48 NSCLC patients treated by SABR at our institute. Experimental results show that the proposed approach can achieve an area under the characteristic curve (AUC) of 0.70 with a sensitivity of 69.87% and a specificity of 69.51% using a five-fold cross validation.

Error Tolerant Plan Recognition: An Empirical Investigation

DTIC Science & Technology

2015-05-01

structure can differ drastically in semantics. For instance, a plan to travel to a grocery store to buy milk might coincidentally be structurally...algorithm for its ability to tolerate input errors, and that storing and leveraging state information in its plan representation substantially...proposed a novel representation for storing and organizing plans in a plan library, based on action-state pairs and abstract states. It counts the

Laughlin states on the Poincaré half-plane and their quantum group symmetry

NASA Astrophysics Data System (ADS)

Alimohammadi, M.; Mohseni Sadjadi, H.

1996-09-01

We find the Laughlin states of the electrons on the Poincaré half-plane in different representations. In each case we show that a quantum group 0305-4470/29/17/025/img5 symmetry exists such that the Laughlin states are a representation of it. We calculate the corresponding filling factor by using the plasma analogy of the fractional quantum Hall effect.

Linking Language with Embodied and Teleological Representations of Action for Humanoid Cognition

PubMed Central

Lallee, Stephane; Madden, Carol; Hoen, Michel; Dominey, Peter Ford

2010-01-01

The current research extends our framework for embodied language and action comprehension to include a teleological representation that allows goal-based reasoning for novel actions. The objective of this work is to implement and demonstrate the advantages of a hybrid, embodied-teleological approach to action–language interaction, both from a theoretical perspective, and via results from human–robot interaction experiments with the iCub robot. We first demonstrate how a framework for embodied language comprehension allows the system to develop a baseline set of representations for processing goal-directed actions such as “take,” “cover,” and “give.” Spoken language and visual perception are input modes for these representations, and the generation of spoken language is the output mode. Moving toward a teleological (goal-based reasoning) approach, a crucial component of the new system is the representation of the subcomponents of these actions, which includes relations between initial enabling states, and final resulting states for these actions. We demonstrate how grammatical categories including causal connectives (e.g., because, if–then) can allow spoken language to enrich the learned set of state-action-state (SAS) representations. We then examine how this enriched SAS inventory enhances the robot's ability to represent perceived actions in which the environment inhibits goal achievement. The paper addresses how language comes to reflect the structure of action, and how it can subsequently be used as an input and output vector for embodied and teleological aspects of action. PMID:20577629

AMBIT RESTful web services: an implementation of the OpenTox application programming interface.

PubMed

Jeliazkova, Nina; Jeliazkov, Vedrin

2011-05-16

The AMBIT web services package is one of the several existing independent implementations of the OpenTox Application Programming Interface and is built according to the principles of the Representational State Transfer (REST) architecture. The Open Source Predictive Toxicology Framework, developed by the partners in the EC FP7 OpenTox project, aims at providing a unified access to toxicity data and predictive models, as well as validation procedures. This is achieved by i) an information model, based on a common OWL-DL ontology ii) links to related ontologies; iii) data and algorithms, available through a standardized REST web services interface, where every compound, data set or predictive method has a unique web address, used to retrieve its Resource Description Framework (RDF) representation, or initiate the associated calculations.The AMBIT web services package has been developed as an extension of AMBIT modules, adding the ability to create (Quantitative) Structure-Activity Relationship (QSAR) models and providing an OpenTox API compliant interface. The representation of data and processing resources in W3C Resource Description Framework facilitates integrating the resources as Linked Data. By uploading datasets with chemical structures and arbitrary set of properties, they become automatically available online in several formats. The services provide unified interfaces to several descriptor calculation, machine learning and similarity searching algorithms, as well as to applicability domain and toxicity prediction models. All Toxtree modules for predicting the toxicological hazard of chemical compounds are also integrated within this package. The complexity and diversity of the processing is reduced to the simple paradigm "read data from a web address, perform processing, write to a web address". The online service allows to easily run predictions, without installing any software, as well to share online datasets and models. The downloadable web application allows researchers to setup an arbitrary number of service instances for specific purposes and at suitable locations. These services could be used as a distributed framework for processing of resource-intensive tasks and data sharing or in a fully independent way, according to the specific needs. The advantage of exposing the functionality via the OpenTox API is seamless interoperability, not only within a single web application, but also in a network of distributed services. Last, but not least, the services provide a basis for building web mashups, end user applications with friendly GUIs, as well as embedding the functionalities in existing workflow systems.

AMBIT RESTful web services: an implementation of the OpenTox application programming interface

PubMed Central

2011-01-01

The AMBIT web services package is one of the several existing independent implementations of the OpenTox Application Programming Interface and is built according to the principles of the Representational State Transfer (REST) architecture. The Open Source Predictive Toxicology Framework, developed by the partners in the EC FP7 OpenTox project, aims at providing a unified access to toxicity data and predictive models, as well as validation procedures. This is achieved by i) an information model, based on a common OWL-DL ontology ii) links to related ontologies; iii) data and algorithms, available through a standardized REST web services interface, where every compound, data set or predictive method has a unique web address, used to retrieve its Resource Description Framework (RDF) representation, or initiate the associated calculations. The AMBIT web services package has been developed as an extension of AMBIT modules, adding the ability to create (Quantitative) Structure-Activity Relationship (QSAR) models and providing an OpenTox API compliant interface. The representation of data and processing resources in W3C Resource Description Framework facilitates integrating the resources as Linked Data. By uploading datasets with chemical structures and arbitrary set of properties, they become automatically available online in several formats. The services provide unified interfaces to several descriptor calculation, machine learning and similarity searching algorithms, as well as to applicability domain and toxicity prediction models. All Toxtree modules for predicting the toxicological hazard of chemical compounds are also integrated within this package. The complexity and diversity of the processing is reduced to the simple paradigm "read data from a web address, perform processing, write to a web address". The online service allows to easily run predictions, without installing any software, as well to share online datasets and models. The downloadable web application allows researchers to setup an arbitrary number of service instances for specific purposes and at suitable locations. These services could be used as a distributed framework for processing of resource-intensive tasks and data sharing or in a fully independent way, according to the specific needs. The advantage of exposing the functionality via the OpenTox API is seamless interoperability, not only within a single web application, but also in a network of distributed services. Last, but not least, the services provide a basis for building web mashups, end user applications with friendly GUIs, as well as embedding the functionalities in existing workflow systems. PMID:21575202

Constructing networks with correlation maximization methods.

PubMed

Mellor, Joseph C; Wu, Jie; Delisi, Charles

2004-01-01

Problems of inference in systems biology are ideally reduced to formulations which can efficiently represent the features of interest. In the case of predicting gene regulation and pathway networks, an important feature which describes connected genes and proteins is the relationship between active and inactive forms, i.e. between the "on" and "off" states of the components. While not optimal at the limits of resolution, these logical relationships between discrete states can often yield good approximations of the behavior in larger complex systems, where exact representation of measurement relationships may be intractable. We explore techniques for extracting binary state variables from measurement of gene expression, and go on to describe robust measures for statistical significance and information that can be applied to many such types of data. We show how statistical strength and information are equivalent criteria in limiting cases, and demonstrate the application of these measures to simple systems of gene regulation.

An experimental comparison of online object-tracking algorithms

NASA Astrophysics Data System (ADS)

Wang, Qing; Chen, Feng; Xu, Wenli; Yang, Ming-Hsuan

2011-09-01

This paper reviews and evaluates several state-of-the-art online object tracking algorithms. Notwithstanding decades of efforts, object tracking remains a challenging problem due to factors such as illumination, pose, scale, deformation, motion blur, noise, and occlusion. To account for appearance change, most recent tracking algorithms focus on robust object representations and effective state prediction. In this paper, we analyze the components of each tracking method and identify their key roles in dealing with specific challenges, thereby shedding light on how to choose and design algorithms for different situations. We compare state-of-the-art online tracking methods including the IVT,1 VRT,2 FragT,3 BoostT,4 SemiT,5 BeSemiT,6 L1T,7 MILT,8 VTD9 and TLD10 algorithms on numerous challenging sequences, and evaluate them with different performance metrics. The qualitative and quantitative comparative results demonstrate the strength and weakness of these algorithms.

[Dangerous states and mental health disorders: perceptions and reality].

PubMed

Tassone-Monchicourt, C; Daumerie, N; Caria, A; Benradia, I; Roelandt, J-L

2010-01-01

Image of Madness was always strongly linked with the notion of "dangerousness", provoking fear and social exclusion, despite the evolution of psychiatric practices and organisation, and the emphasis on user's rights respect. Mediatization and politicization of this issue through news item combining crime and mental illness, reinforce and spread out this perception. This paper presents a review of the litterature on social perceptions associating "dangerousness", "Insanity" and "mental illness", available data about the link between "dangerous states" and "psychiatric disorders", as well as the notion of "dangerousness" and the assessment of "dangerous state" of people suffering or not from psychiatric disorders. MAPPING OF SOCIAL REPRESENTATIONS: The French Survey "Mental Health in General Population: Images and Realities (MHGP)" was carried out between 1999 and 2003, on a representative sample of 36.000 individuals over 18 years old. It aims at describing the social representations of the population about "insanity/insane" and "mental illness/mentally ill". The results show that about 75% of the people interviewed link "insanity" or "mental illness" with "criminal or violent acts". Young people and those with a high level of education more frequently categorize violent and dangerous behaviours in the field of Mental illness rather than in that of madness. CORRELATION BETWEEN DANGEROUS STATE AND PSYCHIATRIC DISORDERS: in the scientific literature, all experts reject the hypothesis of a direct link between violence and mental disorder. Besides, 2 tendencies appear in their conclusions: on one hand, some studies establish a significative link between violence and severe mental illness, compared with the general population. On the other hand, results show that 87 to 97% of des aggressors are not mentally ills. Therefore, the absence of scientific consensus feeds the confusion and reinforce the link of causality between psychiatric disorders and violence. OFFICIAL FIGURES BY THE MINISTRY OF JUSTICE: according to the French Ministry of Justice, there is a lack of significative data in general population, that would allow the accurate evaluation of the proportion of authors of crimes and offences presenting a "dangerous state", either of criminological order or related to a psychiatric disorder. FROM "DANGEROUSNESS" TO "DANGEROUS STATE": the vagueness of the notion of "dangerousness" aggravates the confusion and reinforce the negative social representations attached to subjects labelled as "mentally ills". A way to alleviate this stigmatisation would be to stop using the word "dangerous", and rather use those of "dangerous states". Assessment of dangerous states is complex and needs to take into account several heterogeneous factors (circumstances of acting, social and family environment...). Besides, it is not a linear process for a given individual. Those risk factors of "dangerous state" lead to the construction of evaluation or prediction scales, which limits lay in the biaises of over or under predictive value. The overestimation of dangerousness is harmful, not only to individuals wrongly considered as "dangerous", but also to the society which, driven by safety concerns, agrees on the implementation of inaccurate measures. A FEW TRACKS FOR REMEDIATION: the representations linking "mental illness" and "dangerousness" are the major vectors of stigma, and deeply anchored in the collective popular imagination. They are shared by all population categories, with no distinction of age, gender, professional status or level of education. To overcome those prejudices, one has to carefully study their basis, their criteria, document them with statistical data, look for consistency and scientific rigour, in the terminology as well as in the methodology. Moreover, one has to encourage exchanges about this topic, between users, relatives, carers, local elected, politicians, media and health professional. Copyright 2010 L’Encéphale. Published by Elsevier Masson SAS.. All rights reserved.

Continuum Damage Mechanics Models for the Analysis of Progressive Failure in Open-Hole Tension Laminates

NASA Technical Reports Server (NTRS)

Song, Kyonchan; Li, Yingyong; Rose, Cheryl A.

2011-01-01

The performance of a state-of-the-art continuum damage mechanics model for interlaminar damage, coupled with a cohesive zone model for delamination is examined for failure prediction of quasi-isotropic open-hole tension laminates. Limitations of continuum representations of intra-ply damage and the effect of mesh orientation on the analysis predictions are discussed. It is shown that accurate prediction of matrix crack paths and stress redistribution after cracking requires a mesh aligned with the fiber orientation. Based on these results, an aligned mesh is proposed for analysis of the open-hole tension specimens consisting of different meshes within the individual plies, such that the element edges are aligned with the ply fiber direction. The modeling approach is assessed by comparison of analysis predictions to experimental data for specimen configurations in which failure is dominated by complex interactions between matrix cracks and delaminations. It is shown that the different failure mechanisms observed in the tests are well predicted. In addition, the modeling approach is demonstrated to predict proper trends in the effect of scaling on strength and failure mechanisms of quasi-isotropic open-hole tension laminates.

Efficient visual coding and the predictability of eye movements on natural movies.

PubMed

Vig, Eleonora; Dorr, Michael; Barth, Erhardt

2009-01-01

We deal with the analysis of eye movements made on natural movies in free-viewing conditions. Saccades are detected and used to label two classes of movie patches as attended and non-attended. Machine learning techniques are then used to determine how well the two classes can be separated, i.e., how predictable saccade targets are. Although very simple saliency measures are used and then averaged to obtain just one average value per scale, the two classes can be separated with an ROC score of around 0.7, which is higher than previously reported results. Moreover, predictability is analysed for different representations to obtain indirect evidence for the likelihood of a particular representation. It is shown that the predictability correlates with the local intrinsic dimension in a movie.

Use of model calibration to achieve high accuracy in analysis of computer networks

DOEpatents

Frogner, Bjorn; Guarro, Sergio; Scharf, Guy

2004-05-11

A system and method are provided for creating a network performance prediction model, and calibrating the prediction model, through application of network load statistical analyses. The method includes characterizing the measured load on the network, which may include background load data obtained over time, and may further include directed load data representative of a transaction-level event. Probabilistic representations of load data are derived to characterize the statistical persistence of the network performance variability and to determine delays throughout the network. The probabilistic representations are applied to the network performance prediction model to adapt the model for accurate prediction of network performance. Certain embodiments of the method and system may be used for analysis of the performance of a distributed application characterized as data packet streams.

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.

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.

Action simulation: time course and representational mechanisms

PubMed Central

Springer, Anne; Parkinson, Jim; Prinz, Wolfgang

2013-01-01

The notion of action simulation refers to the ability to re-enact foreign actions (i.e., actions observed in other individuals). Simulating others' actions implies a mirroring of their activities, based on one's own sensorimotor competencies. Here, we discuss theoretical and experimental approaches to action simulation and the study of its representational underpinnings. One focus of our discussion is on the timing of internal simulation and its relation to the timing of external action, and a paradigm that requires participants to predict the future course of actions that are temporarily occluded from view. We address transitions between perceptual mechanisms (referring to action representation before and after occlusion) and simulation mechanisms (referring to action representation during occlusion). Findings suggest that action simulation runs in real-time; acting on newly created action representations rather than relying on continuous visual extrapolations. A further focus of our discussion pertains to the functional characteristics of the mechanisms involved in predicting other people's actions. We propose that two processes are engaged, dynamic updating and static matching, which may draw on both semantic and motor information. In a concluding section, we discuss these findings in the context of broader theoretical issues related to action and event representation, arguing that a detailed functional analysis of action simulation in cognitive, neural, and computational terms may help to further advance our understanding of action cognition and motor control. PMID:23847563

A study of how the particle spectra of SU(N) gauge theories with a fundamental Higgs emerge

NASA Astrophysics Data System (ADS)

Törek, Pascal; Maas, Axel; Sondenheimer, René

2018-03-01

In gauge theories, the physical, experimentally observable spectrum consists only of gauge-invariant states. In the standard model the Fröhlich-Morchio-Strocchi mechanism shows that these states can be adequately mapped to the gauge-dependent elementary W, Z, Higgs, and fermions. In theories with a more general gauge group and Higgs sector, appearing in various extensions of the standard model, this has not to be the case. In this work we determine analytically the physical spectrum of SU(N > 2) gauge theories with a Higgs field in the fundamental representation. We show that discrepancies between the spectrum predicted by perturbation theory and the observable physical spectrum arise. We confirm these analytic findings with lattice simulations for N = 3.

Anharmonic frequencies of CX2Y2 (X, Y = O, N, F, H, D) isomers and related systems obtained from vibrational multiconfiguration self-consistent field theory.

PubMed

Pfeiffer, Florian; Rauhut, Guntram

2011-10-13

Accurate anharmonic frequencies are provided for molecules of current research, i.e., diazirines, diazomethane, the corresponding fluorinated and deuterated compounds, their dioxygen analogs, and others. Vibrational-state energies were obtained from state-specific vibrational multiconfiguration self-consistent field theory (VMCSCF) based on multilevel potential energy surfaces (PES) generated from explicitly correlated coupled cluster, CCSD(T)-F12a, and double-hybrid density functional calculations, B2PLYP. To accelerate the vibrational structure calculations, a configuration selection scheme as well as a polynomial representation of the PES have been exploited. Because experimental data are scarce for these systems, many calculated frequencies of this study are predictions and may guide experiments to come.

Representing delayed force feedback as a combination of current and delayed states.

PubMed

Avraham, Guy; Mawase, Firas; Karniel, Amir; Shmuelof, Lior; Donchin, Opher; Mussa-Ivaldi, Ferdinando A; Nisky, Ilana

2017-10-01

To adapt to deterministic force perturbations that depend on the current state of the hand, internal representations are formed to capture the relationships between forces experienced and motion. However, information from multiple modalities travels at different rates, resulting in intermodal delays that require compensation for these internal representations to develop. To understand how these delays are represented by the brain, we presented participants with delayed velocity-dependent force fields, i.e., forces that depend on hand velocity either 70 or 100 ms beforehand. We probed the internal representation of these delayed forces by examining the forces the participants applied to cope with the perturbations. The findings showed that for both delayed forces, the best model of internal representation consisted of a delayed velocity and current position and velocity. We show that participants relied initially on the current state, but with adaptation, the contribution of the delayed representation to adaptation increased. After adaptation, when the participants were asked to make movements with a higher velocity for which they had not previously experienced with the delayed force field, they applied forces that were consistent with current position and velocity as well as delayed velocity representations. This suggests that the sensorimotor system represents delayed force feedback using current and delayed state information and that it uses this representation when generalizing to faster movements. NEW & NOTEWORTHY The brain compensates for forces in the body and the environment to control movements, but it is unclear how it does so given the inherent delays in information transmission and processing. We examined how participants cope with delayed forces that depend on their arm velocity 70 or 100 ms beforehand. After adaptation, participants applied opposing forces that revealed a partially correct representation of the perturbation using the current and the delayed information. Copyright © 2017 the American Physiological Society.

Matrix Representation of Symmetry Operators in Elementary Crystallography

ERIC Educational Resources Information Center

Cody, R. D.

1972-01-01

Presents the derivation of rotation and reflection matrix representation of symmetry operators as used in the initial discussion of crystal symmetry in elementary mineralogy at Iowa State University. Includes references and an appended list of matrix representations of the important crystallographic symmetry operators, excluding the trigonal and…

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.

See it with feeling: affective predictions during object perception

PubMed Central

Barrett, L.F.; Bar, Moshe

2009-01-01

People see with feeling. We ‘gaze’, ‘behold’, ‘stare’, ‘gape’ and ‘glare’. In this paper, we develop the hypothesis that the brain's ability to see in the present incorporates a representation of the affective impact of those visual sensations in the past. This representation makes up part of the brain's prediction of what the visual sensations stand for in the present, including how to act on them in the near future. The affective prediction hypothesis implies that responses signalling an object's salience, relevance or value do not occur as a separate step after the object is identified. Instead, affective responses support vision from the very moment that visual stimulation begins. PMID:19528014

Jack Polynomials as Fractional Quantum Hall States and the Betti Numbers of the ( k + 1)-Equals Ideal

NASA Astrophysics Data System (ADS)

Zamaere, Christine Berkesch; Griffeth, Stephen; Sam, Steven V.

2014-08-01

We show that for Jack parameter α = -( k + 1)/( r - 1), certain Jack polynomials studied by Feigin-Jimbo-Miwa-Mukhin vanish to order r when k + 1 of the coordinates coincide. This result was conjectured by Bernevig and Haldane, who proposed that these Jack polynomials are model wavefunctions for fractional quantum Hall states. Special cases of these Jack polynomials include the wavefunctions of Laughlin and Read-Rezayi. In fact, along these lines we prove several vanishing theorems known as clustering properties for Jack polynomials in the mathematical physics literature, special cases of which had previously been conjectured by Bernevig and Haldane. Motivated by the method of proof, which in the case r = 2 identifies the span of the relevant Jack polynomials with the S n -invariant part of a unitary representation of the rational Cherednik algebra, we conjecture that unitary representations of the type A Cherednik algebra have graded minimal free resolutions of Bernstein-Gelfand-Gelfand type; we prove this for the ideal of the ( k + 1)-equals arrangement in the case when the number of coordinates n is at most 2 k + 1. In general, our conjecture predicts the graded S n -equivariant Betti numbers of the ideal of the ( k + 1)-equals arrangement with no restriction on the number of ambient dimensions.

Inequality across Consonantal Contrasts in Speech Perception: Evidence from Mismatch Negativity

ERIC Educational Resources Information Center

Cornell, Sonia A.; Lahiri, Aditi; Eulitz, Carsten

2013-01-01

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…

Young Children's Understanding of Conflicting Mental Representation Predicts Suggestibility.

ERIC Educational Resources Information Center

Welch-Ross, Melissa K.; And Others

1997-01-01

Examined the relation between developmental suggestibility effects and preschoolers' emerging ability to reason about conflicting mental representations. Subjects were 42 three- to five-year-olds. Found in the children significant initial encoding and ability to retrieve event details. Also found an integration between children's theory of mind…

Teaching Representation Translations with Magnetic Field Experiments

ERIC Educational Resources Information Center

Tillotson, Wilson Andrew; McCaskey, Timothy; Nasser, Luis

2017-01-01

We have developed a laboratory exercise designed to help students translate between different field representations. It starts with students qualitatively mapping field lines for various bar magnet configurations and continues with a Hall probe experiment in which students execute a series of scaffolded tasks, culminating in the prediction and…

Three Strategies for the Critical Use of Statistical Methods in Psychological Research

ERIC Educational Resources Information Center

Campitelli, Guillermo; Macbeth, Guillermo; Ospina, Raydonal; Marmolejo-Ramos, Fernando

2017-01-01

We present three strategies to replace the null hypothesis statistical significance testing approach in psychological research: (1) visual representation of cognitive processes and predictions, (2) visual representation of data distributions and choice of the appropriate distribution for analysis, and (3) model comparison. The three strategies…

REM Dreaming and Cognitive Skills at Ages 5-8: A Cross-Sectional Study.

ERIC Educational Resources Information Center

Foulkes, David; And Others

1990-01-01

Describes laboratory research on REM (rapid eye movement) sleep in children ages five to eight. Image quality, self-representation, and narrative complexity of dreams all develop as age progresses. Children's representational intelligence predicts their rate of dream production, but language skills do not. (GH)

Emerging Object Representations in the Visual System Predict Reaction Times for Categorization

PubMed Central

Ritchie, J. Brendan; Tovar, David A.; Carlson, Thomas A.

2015-01-01

Recognizing an object takes just a fraction of a second, less than the blink of an eye. Applying multivariate pattern analysis, or “brain decoding”, methods to magnetoencephalography (MEG) data has allowed researchers to characterize, in high temporal resolution, the emerging representation of object categories that underlie our capacity for rapid recognition. Shortly after stimulus onset, object exemplars cluster by category in a high-dimensional activation space in the brain. In this emerging activation space, the decodability of exemplar category varies over time, reflecting the brain’s transformation of visual inputs into coherent category representations. How do these emerging representations relate to categorization behavior? Recently it has been proposed that the distance of an exemplar representation from a categorical boundary in an activation space is critical for perceptual decision-making, and that reaction times should therefore correlate with distance from the boundary. The predictions of this distance hypothesis have been born out in human inferior temporal cortex (IT), an area of the brain crucial for the representation of object categories. When viewed in the context of a time varying neural signal, the optimal time to “read out” category information is when category representations in the brain are most decodable. Here, we show that the distance from a decision boundary through activation space, as measured using MEG decoding methods, correlates with reaction times for visual categorization during the period of peak decodability. Our results suggest that the brain begins to read out information about exemplar category at the optimal time for use in choice behaviour, and support the hypothesis that the structure of the representation for objects in the visual system is partially constitutive of the decision process in recognition. PMID:26107634

Predicting behavior change from persuasive messages using neural representational similarity and social network analyses.

PubMed

Pegors, Teresa K; Tompson, Steven; O'Donnell, Matthew Brook; Falk, Emily B

2017-08-15

Neural activity in medial prefrontal cortex (MPFC), identified as engaging in self-related processing, predicts later health behavior change. However, it is unknown to what extent individual differences in neural representation of content and lived experience influence this brain-behavior relationship. We examined whether the strength of content-specific representations during persuasive messaging relates to later behavior change, and whether these relationships change as a function of individuals' social network composition. In our study, smokers viewed anti-smoking messages while undergoing fMRI and we measured changes in their smoking behavior one month later. Using representational similarity analyses, we found that the degree to which message content (i.e. health, social, or valence information) was represented in a self-related processing MPFC region was associated with later smoking behavior, with increased representations of negatively valenced (risk) information corresponding to greater message-consistent behavior change. Furthermore, the relationship between representations and behavior change depended on social network composition: smokers who had proportionally fewer smokers in their network showed increases in smoking behavior when social or health content was strongly represented in MPFC, whereas message-consistent behavior (i.e., less smoking) was more likely for those with proportionally more smokers in their social network who represented social or health consequences more strongly. These results highlight the dynamic relationship between representations in MPFC and key outcomes such as health behavior change; a complete understanding of the role of MPFC in motivation and action should take into account individual differences in neural representation of stimulus attributes and social context variables such as social network composition. Copyright © 2017 Elsevier Inc. All rights reserved.

Identifying different mechanisms in the control of a nitrogen-vacancy center system

NASA Astrophysics Data System (ADS)

Li, Shouzhi; Yang, Ling; Cao, Dewen; Wang, Yaoxiong; Shuang, Feng; Gao, Fang

2017-10-01

The nitrogen-vacancy (NV) center system has shown great potential in quantum computing due to its long decoherence time at room temperature by encoding the qubit in dressed states [28]. The corresponding control mechanisms, which is expressed by the pathways linking the initial and target states, can be naturally investigated with the Hamiltonian-encoding and observable-decoding (HE-OD) method in the interaction adiabatic representation. This is proved by the fact that the mechanisms change slightly with different detunings, magnetic and driving field intensities, and the dominant pathway is always | g 〉 → | d 〉 → | g 〉 , with | g 〉 and | d 〉 as the first two lowest dressed states. Cases are different in the diabatic representation. The orders of dominant pathways increase the driving field intensities. Tendencies of quantum pathway amplitudes with driving fields, magnetic fields and detunings change at different conditions, which can be analyzed from the Dyson series. HE-OD analysis show that the two states | g 〉 and | d 〉 in the interaction adiabatic representation are preferable to be employed as a qubit than the state pair |0〉 and | - 1 〉 in the diabatic representation under the current Hamiltonian and parameters.

Multivariate Patterns in the Human Object-Processing Pathway Reveal a Shift from Retinotopic to Shape Curvature Representations in Lateral Occipital Areas, LO-1 and LO-2.

PubMed

Vernon, Richard J W; Gouws, André D; Lawrence, Samuel J D; Wade, Alex R; Morland, Antony B

2016-05-25

Representations in early visual areas are organized on the basis of retinotopy, but this organizational principle appears to lose prominence in the extrastriate cortex. Nevertheless, an extrastriate region, such as the shape-selective lateral occipital cortex (LO), must still base its activation on the responses from earlier retinotopic visual areas, implying that a transition from retinotopic to "functional" organizations should exist. We hypothesized that such a transition may lie in LO-1 or LO-2, two visual areas lying between retinotopically defined V3d and functionally defined LO. Using a rapid event-related fMRI paradigm, we measured neural similarity in 12 human participants between pairs of stimuli differing along dimensions of shape exemplar and shape complexity within both retinotopically and functionally defined visual areas. These neural similarity measures were then compared with low-level and more abstract (curvature-based) measures of stimulus similarity. We found that low-level, but not abstract, stimulus measures predicted V1-V3 responses, whereas the converse was true for LO, a double dissociation. Critically, abstract stimulus measures were most predictive of responses within LO-2, akin to LO, whereas both low-level and abstract measures were predictive for responses within LO-1, perhaps indicating a transitional point between those two organizational principles. Similar transitions to abstract representations were not observed in the more ventral stream passing through V4 and VO-1/2. The transition we observed in LO-1 and LO-2 demonstrates that a more "abstracted" representation, typically considered the preserve of "category-selective" extrastriate cortex, can nevertheless emerge in retinotopic regions. Visual areas are typically identified either through retinotopy (e.g., V1-V3) or from functional selectivity [e.g., shape-selective lateral occipital complex (LOC)]. We combined these approaches to explore the nature of shape representations through the visual hierarchy. Two different representations emerged: the first reflected low-level shape properties (dependent on the spatial layout of the shape outline), whereas the second captured more abstract curvature-related shape features. Critically, early visual cortex represented low-level information but this diminished in the extrastriate cortex (LO-1/LO-2/LOC), in which the abstract representation emerged. Therefore, this work further elucidates the nature of shape representations in the LOC, provides insight into how those representations emerge from early retinotopic cortex, and crucially demonstrates that retinotopically tuned regions (LO-1/LO-2) are not necessarily constrained to retinotopic representations. Copyright © 2016 Vernon et al.

Multivariate Patterns in the Human Object-Processing Pathway Reveal a Shift from Retinotopic to Shape Curvature Representations in Lateral Occipital Areas, LO-1 and LO-2

PubMed Central

Vernon, Richard J. W.; Gouws, André D.; Lawrence, Samuel J. D.; Wade, Alex R.

2016-01-01

Representations in early visual areas are organized on the basis of retinotopy, but this organizational principle appears to lose prominence in the extrastriate cortex. Nevertheless, an extrastriate region, such as the shape-selective lateral occipital cortex (LO), must still base its activation on the responses from earlier retinotopic visual areas, implying that a transition from retinotopic to “functional” organizations should exist. We hypothesized that such a transition may lie in LO-1 or LO-2, two visual areas lying between retinotopically defined V3d and functionally defined LO. Using a rapid event-related fMRI paradigm, we measured neural similarity in 12 human participants between pairs of stimuli differing along dimensions of shape exemplar and shape complexity within both retinotopically and functionally defined visual areas. These neural similarity measures were then compared with low-level and more abstract (curvature-based) measures of stimulus similarity. We found that low-level, but not abstract, stimulus measures predicted V1–V3 responses, whereas the converse was true for LO, a double dissociation. Critically, abstract stimulus measures were most predictive of responses within LO-2, akin to LO, whereas both low-level and abstract measures were predictive for responses within LO-1, perhaps indicating a transitional point between those two organizational principles. Similar transitions to abstract representations were not observed in the more ventral stream passing through V4 and VO-1/2. The transition we observed in LO-1 and LO-2 demonstrates that a more “abstracted” representation, typically considered the preserve of “category-selective” extrastriate cortex, can nevertheless emerge in retinotopic regions. SIGNIFICANCE STATEMENT Visual areas are typically identified either through retinotopy (e.g., V1–V3) or from functional selectivity [e.g., shape-selective lateral occipital complex (LOC)]. We combined these approaches to explore the nature of shape representations through the visual hierarchy. Two different representations emerged: the first reflected low-level shape properties (dependent on the spatial layout of the shape outline), whereas the second captured more abstract curvature-related shape features. Critically, early visual cortex represented low-level information but this diminished in the extrastriate cortex (LO-1/LO-2/LOC), in which the abstract representation emerged. Therefore, this work further elucidates the nature of shape representations in the LOC, provides insight into how those representations emerge from early retinotopic cortex, and crucially demonstrates that retinotopically tuned regions (LO-1/LO-2) are not necessarily constrained to retinotopic representations. PMID:27225766

The proactive brain: memory for predictions

PubMed Central

Bar, Moshe

2009-01-01

It is proposed that the human brain is proactive in that it continuously generates predictions that anticipate the relevant future. In this proposal, analogies are derived from elementary information that is extracted rapidly from the input, to link that input with the representations that exist in memory. Finding an analogical link results in the generation of focused predictions via associative activation of representations that are relevant to this analogy, in the given context. Predictions in complex circumstances, such as social interactions, combine multiple analogies. Such predictions need not be created afresh in new situations, but rather rely on existing scripts in memory, which are the result of real as well as of previously imagined experiences. This cognitive neuroscience framework provides a new hypothesis with which to consider the purpose of memory, and can help explain a variety of phenomena, ranging from recognition to first impressions, and from the brain's ‘default mode’ to a host of mental disorders. PMID:19528004

Neural evidence for description dependent reward processing in the framing effect

PubMed Central

Yu, Rongjun; Zhang, Ping

2014-01-01

Human decision making can be influenced by emotionally valenced contexts, known as the framing effect. We used event-related brain potentials to investigate how framing influences the encoding of reward. We found that the feedback related negativity (FRN), which indexes the “worse than expected” negative prediction error in the anterior cingulate cortex (ACC), was more negative for the negative frame than for the positive frame in the win domain. Consistent with previous findings that the FRN is not sensitive to “better than expected” positive prediction error, the FRN did not differentiate the positive and negative frame in the loss domain. Our results provide neural evidence that the description invariance principle which states that reward representation and decision making are not influenced by how options are presented is violated in the framing effect. PMID:24733998

Predictability and hierarchy in Drosophila behavior.

PubMed

Berman, Gordon J; Bialek, William; Shaevitz, Joshua W

2016-10-18

Even the simplest of animals exhibit behavioral sequences with complex temporal dynamics. Prominent among the proposed organizing principles for these dynamics has been the idea of a hierarchy, wherein the movements an animal makes can be understood as a set of nested subclusters. Although this type of organization holds potential advantages in terms of motion control and neural circuitry, measurements demonstrating this for an animal's entire behavioral repertoire have been limited in scope and temporal complexity. Here, we use a recently developed unsupervised technique to discover and track the occurrence of all stereotyped behaviors performed by fruit flies moving in a shallow arena. Calculating the optimally predictive representation of the fly's future behaviors, we show that fly behavior exhibits multiple time scales and is organized into a hierarchical structure that is indicative of its underlying behavioral programs and its changing internal states.

Correlations of nucleotide substitution rates and base composition of mammalian coding sequences with protein structure.

PubMed

Chiusano, M L; D'Onofrio, G; Alvarez-Valin, F; Jabbari, K; Colonna, G; Bernardi, G

1999-09-30

We investigated the relationships between the nucleotide substitution rates and the predicted secondary structures in the three states representation (alpha-helix, beta-sheet, and coil). The analysis was carried out on 34 alignments, each of which comprised sequences belonging to at least four different mammalian orders. The rates of synonymous substitution were found to be significantly different in regions predicted to be alpha-helix, beta-sheet, or coil. Likewise, the nonsynonymous rates also differ, although expectedly at a lower extent, in the three types of secondary structure, suggesting that different selective constraints associated with the different structures are affecting in a similar way the synonymous and nonsynonymous rates. Moreover, the base composition of the third codon positions is different in coding sequence regions corresponding to different secondary structures of proteins.

Protein Sub-Nuclear Localization Based on Effective Fusion Representations and Dimension Reduction Algorithm LDA.

PubMed

Wang, Shunfang; Liu, Shuhui

2015-12-19

An effective representation of a protein sequence plays a crucial role in protein sub-nuclear localization. The existing representations, such as dipeptide composition (DipC), pseudo-amino acid composition (PseAAC) and position specific scoring matrix (PSSM), are insufficient to represent protein sequence due to their single perspectives. Thus, this paper proposes two fusion feature representations of DipPSSM and PseAAPSSM to integrate PSSM with DipC and PseAAC, respectively. When constructing each fusion representation, we introduce the balance factors to value the importance of its components. The optimal values of the balance factors are sought by genetic algorithm. Due to the high dimensionality of the proposed representations, linear discriminant analysis (LDA) is used to find its important low dimensional structure, which is essential for classification and location prediction. The numerical experiments on two public datasets with KNN classifier and cross-validation tests showed that in terms of the common indexes of sensitivity, specificity, accuracy and MCC, the proposed fusing representations outperform the traditional representations in protein sub-nuclear localization, and the representation treated by LDA outperforms the untreated one.

Interparental conflict and adolescents' self-representations: The role of emotional insecurity.

PubMed

Silva, Carla Sofia; Calheiros, Maria Manuela; Carvalho, Helena

2016-10-01

Adolescents' signs of emotional insecurity in the context of interparental conflict (IC) - emotional reactivity, internal representations (i.e., constructive/destructive; spillover) and behavioral responses (i.e., withdrawal; inhibition; involvement) - were examined as mediators in the relation between IC and adolescents' self-representations. Self-reported measures were filled out by 221 Portuguese adolescents (59.3% girls; Mage = 12.91), attending public elementary and secondary schools. IC predicted less favorable self-representations. Adolescents' emotional reactivity and withdrawal mediated the relation between IC and emotional and physical appearance self-representations, while conflict spillover representations and constructive family representations mediated associations between IC and instrumental self-representations. This study emphasizes the importance of interparental conflict and adolescent emotional insecurity in the construction of their self-representations, having important theoretical and practical implications. It highlights the value of analyzing the specific role of several emotional insecurity dimensions, and informs practitioners' work aimed at promoting constructive conflict and adaptive emotional regulation skills. Copyright © 2016 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Protein Sub-Nuclear Localization Based on Effective Fusion Representations and Dimension Reduction Algorithm LDA

PubMed Central

Wang, Shunfang; Liu, Shuhui

2015-01-01

An effective representation of a protein sequence plays a crucial role in protein sub-nuclear localization. The existing representations, such as dipeptide composition (DipC), pseudo-amino acid composition (PseAAC) and position specific scoring matrix (PSSM), are insufficient to represent protein sequence due to their single perspectives. Thus, this paper proposes two fusion feature representations of DipPSSM and PseAAPSSM to integrate PSSM with DipC and PseAAC, respectively. When constructing each fusion representation, we introduce the balance factors to value the importance of its components. The optimal values of the balance factors are sought by genetic algorithm. Due to the high dimensionality of the proposed representations, linear discriminant analysis (LDA) is used to find its important low dimensional structure, which is essential for classification and location prediction. The numerical experiments on two public datasets with KNN classifier and cross-validation tests showed that in terms of the common indexes of sensitivity, specificity, accuracy and MCC, the proposed fusing representations outperform the traditional representations in protein sub-nuclear localization, and the representation treated by LDA outperforms the untreated one. PMID:26703574

Nonlinear Model Predictive Control for Cooperative Control and Estimation

NASA Astrophysics Data System (ADS)

Ru, Pengkai

Recent advances in computational power have made it possible to do expensive online computations for control systems. It is becoming more realistic to perform computationally intensive optimization schemes online on systems that are not intrinsically stable and/or have very small time constants. Being one of the most important optimization based control approaches, model predictive control (MPC) has attracted a lot of interest from the research community due to its natural ability to incorporate constraints into its control formulation. Linear MPC has been well researched and its stability can be guaranteed in the majority of its application scenarios. However, one issue that still remains with linear MPC is that it completely ignores the system's inherent nonlinearities thus giving a sub-optimal solution. On the other hand, if achievable, nonlinear MPC, would naturally yield a globally optimal solution and take into account all the innate nonlinear characteristics. While an exact solution to a nonlinear MPC problem remains extremely computationally intensive, if not impossible, one might wonder if there is a middle ground between the two. We tried to strike a balance in this dissertation by employing a state representation technique, namely, the state dependent coefficient (SDC) representation. This new technique would render an improved performance in terms of optimality compared to linear MPC while still keeping the problem tractable. In fact, the computational power required is bounded only by a constant factor of the completely linearized MPC. The purpose of this research is to provide a theoretical framework for the design of a specific kind of nonlinear MPC controller and its extension into a general cooperative scheme. The controller is designed and implemented on quadcopter systems.

Conflation and aggregation of spatial data improve predictive models for species with limited habitats: a case of the threatened yellow-billed cuckoo in Arizona, USA

USGS Publications Warehouse

Villarreal, Miguel L.; van Riper, Charles; Petrakis, Roy E.

2013-01-01

Riparian vegetation provides important wildlife habitat in the Southwestern United States, but limited distributions and spatial complexity often leads to inaccurate representation in maps used to guide conservation. We test the use of data conflation and aggregation on multiple vegetation/land-cover maps to improve the accuracy of habitat models for the threatened western yellow-billed cuckoo (Coccyzus americanus occidentalis). We used species observations (n = 479) from a state-wide survey to develop habitat models from 1) three vegetation/land-cover maps produced at different geographic scales ranging from state to national, and 2) new aggregate maps defined by the spatial agreement of cover types, which were defined as high (agreement = all data sets), moderate (agreement ≥ 2), and low (no agreement required). Model accuracies, predicted habitat locations, and total area of predicted habitat varied considerably, illustrating the effects of input data quality on habitat predictions and resulting potential impacts on conservation planning. Habitat models based on aggregated and conflated data were more accurate and had higher model sensitivity than original vegetation/land-cover, but this accuracy came at the cost of reduced geographic extent of predicted habitat. Using the highest performing models, we assessed cuckoo habitat preference and distribution in Arizona and found that major watersheds containing high-probably habitat are fragmented by a wide swath of low-probability habitat. Focus on riparian restoration in these areas could provide more breeding habitat for the threatened cuckoo, offset potential future habitat losses in adjacent watershed, and increase regional connectivity for other threatened vertebrates that also use riparian corridors.

26 CFR 301.6361-2 - Judicial and administrative proceedings; Federal representation of State interests.

Code of Federal Regulations, 2010 CFR

2010-04-01

...; Federal representation of State interests. 301.6361-2 Section 301.6361-2 Internal Revenue INTERNAL REVENUE... Seizure of Property for Collection of Taxes § 301.6361-2 Judicial and administrative proceedings; Federal... purposes of this section, the term “person” includes the Federal Government. Except as provided in...

26 CFR 301.6361-2 - Judicial and administrative proceedings; Federal representation of State interests.

Code of Federal Regulations, 2012 CFR

2012-04-01

...; Federal representation of State interests. 301.6361-2 Section 301.6361-2 Internal Revenue INTERNAL REVENUE... Seizure of Property for Collection of Taxes § 301.6361-2 Judicial and administrative proceedings; Federal... purposes of this section, the term “person” includes the Federal Government. Except as provided in...

26 CFR 301.6361-2 - Judicial and administrative proceedings; Federal representation of State interests.

Code of Federal Regulations, 2014 CFR

2014-04-01

...; Federal representation of State interests. 301.6361-2 Section 301.6361-2 Internal Revenue INTERNAL REVENUE... Seizure of Property for Collection of Taxes § 301.6361-2 Judicial and administrative proceedings; Federal... purposes of this section, the term “person” includes the Federal Government. Except as provided in...

On coherent states for the simplest quantum groups

NASA Astrophysics Data System (ADS)

Jurčo, Branislav

1991-01-01

The coherent states for the simplest quantum groups ( q-Heisenberg-Weyl, SU q (2) and the discrete series of representations of SU q (1, 1)) are introduced and their properties investigated. The corresponding analytic representations, path integrals, and q-deformation of Berezin's quantization on ℂ, a sphere, and the Lobatchevsky plane are discussed.

Public Conceptions of Algorithms and Representations in the Common Core State Standards for Mathematics

ERIC Educational Resources Information Center

Nanna, Robert J.

2016-01-01

Algorithms and representations have been an important aspect of the work of mathematics, especially for understanding concepts and communicating ideas about concepts and mathematical relationships. They have played a key role in various mathematics standards documents, including the Common Core State Standards for Mathematics. However, there have…

The surface water register: an empirically improved sample frame for monitoring the rivers and streams of Kansas

EPA Science Inventory

State-wide monitoring based on probability survey designs requires a spatially explicit representation of all streams and rivers of interest within a state, i.e., a sample frame. The sample frame should be the best available map representation of the resource. Many stream progr...

37 CFR 11.10 - Restrictions on practice in patent matters.

Code of Federal Regulations, 2012 CFR

2012-07-01

... TRADEMARK OFFICE, DEPARTMENT OF COMMERCE REPRESENTATION OF OTHERS BEFORE THE UNITED STATES PATENT AND... otherwise represent, or assist in any manner the representation of, any other person: (i) Before the Office... assist in any manner the representation of any other person: (i) Before the Office, (ii) In connection...

7 CFR 28.304 - Original representation of American Pima cotton staple lengths.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 7 Agriculture 2 2013-01-01 2013-01-01 false Original representation of American Pima cotton staple... STANDARDS AND STANDARD CONTAINER REGULATIONS COTTON CLASSING, TESTING, AND STANDARDS Standards Official Cotton Standards of the United States for Length of Staple § 28.304 Original representation of American...

7 CFR 28.304 - Original representation of American Pima cotton staple lengths.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 7 Agriculture 2 2012-01-01 2012-01-01 false Original representation of American Pima cotton staple... STANDARDS AND STANDARD CONTAINER REGULATIONS COTTON CLASSING, TESTING, AND STANDARDS Standards Official Cotton Standards of the United States for Length of Staple § 28.304 Original representation of American...

7 CFR 28.304 - Original representation of American Pima cotton staple lengths.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 7 Agriculture 2 2014-01-01 2014-01-01 false Original representation of American Pima cotton staple... STANDARDS AND STANDARD CONTAINER REGULATIONS COTTON CLASSING, TESTING, AND STANDARDS Standards Official Cotton Standards of the United States for Length of Staple § 28.304 Original representation of American...

7 CFR 28.304 - Original representation of American Pima cotton staple lengths.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 2 2011-01-01 2011-01-01 false Original representation of American Pima cotton staple... STANDARDS AND STANDARD CONTAINER REGULATIONS COTTON CLASSING, TESTING, AND STANDARDS Standards Official Cotton Standards of the United States for Length of Staple § 28.304 Original representation of American...

7 CFR 28.304 - Original representation of American Pima cotton staple lengths.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Original representation of American Pima cotton staple... STANDARDS AND STANDARD CONTAINER REGULATIONS COTTON CLASSING, TESTING, AND STANDARDS Standards Official Cotton Standards of the United States for Length of Staple § 28.304 Original representation of American...

Parent Trigger Policies, Representation, and the Public Good

ERIC Educational Resources Information Center

Allen, Ann; Saultz, Andrew

2015-01-01

Using theories of representation and democratic education, this article examines the impetus of parent trigger policies in the United States and their potential effects on public good goals for public education. The article also uses theories of representation and responsible democratic governance to assess the parent trigger policies, or what are…

A three-dimensional sectional representation of aerosol mixing state for simulating optical properties and cloud condensation nuclei

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

Ching, Ping Pui; Zaveri, Rahul A.; Easter, Richard C.

2016-05-27

Light absorption by black carbon (BC) particles emitted from fossil fuel combustion depends on the how thickly they are coated with non-refractory species such as ammonium, sulfate, nitrate, organics, and water. The cloud condensation nuclei (CCN) activation property of a particle depends on its dry size and the hygroscopicities of all the individual species mixed together. It is therefore necessary to represent both size and mixing state of aerosols to reliably predict their climate-relevant properties in atmospheric models. Here we describe and evaluate a novel sectional framework in the Model for Simulating Aerosol Interactions and Chemistry, referred to as MOSAIC-mix,more » that represents the mixing state by resolving aerosol dry size (Ddry), BC dry mass fraction (wBC), and hygroscopicity (κ). Using ten idealized urban plume scenarios in which different types of aerosols evolve over 24 hours under a range of atmospherically relevant environmental conditions, we examine errors in CCN concentrations and optical properties with respect to a more explicit aerosol mixing state representation. We find that only a small number of wBC and κ bins are needed to achieve significant reductions in the errors, and propose a configuration consisting of 24 Ddry bins, 2 wBC bins, and 2 κ bins that gives 24-hour average errors of about 5% or less in CCN concentrations and optical properties, 3-4 times lower than those from size-only-resolved simulations. These results show that MOSAIC-mix is suitable for use in regional and global models to examine the effects of evolving aerosol mixing states on aerosol-radiation-cloud feedbacks.« less

Minimized state complexity of quantum-encoded cryptic processes

NASA Astrophysics Data System (ADS)

Riechers, Paul M.; Mahoney, John R.; Aghamohammadi, Cina; Crutchfield, James P.

2016-05-01

The predictive information required for proper trajectory sampling of a stochastic process can be more efficiently transmitted via a quantum channel than a classical one. This recent discovery allows quantum information processing to drastically reduce the memory necessary to simulate complex classical stochastic processes. It also points to a new perspective on the intrinsic complexity that nature must employ in generating the processes we observe. The quantum advantage increases with codeword length: the length of process sequences used in constructing the quantum communication scheme. In analogy with the classical complexity measure, statistical complexity, we use this reduced communication cost as an entropic measure of state complexity in the quantum representation. Previously difficult to compute, the quantum advantage is expressed here in closed form using spectral decomposition. This allows for efficient numerical computation of the quantum-reduced state complexity at all encoding lengths, including infinite. Additionally, it makes clear how finite-codeword reduction in state complexity is controlled by the classical process's cryptic order, and it allows asymptotic analysis of infinite-cryptic-order processes.

Physical states and finite-size effects in Kitaev's honeycomb model: Bond disorder, spin excitations, and NMR line shape

NASA Astrophysics Data System (ADS)

Zschocke, Fabian; Vojta, Matthias

2015-07-01

Kitaev's compass model on the honeycomb lattice realizes a spin liquid whose emergent excitations are dispersive Majorana fermions and static Z2 gauge fluxes. We discuss the proper selection of physical states for finite-size simulations in the Majorana representation, based on a recent paper by F. L. Pedrocchi, S. Chesi, and D. Loss [Phys. Rev. B 84, 165414 (2011), 10.1103/PhysRevB.84.165414]. Certain physical observables acquire large finite-size effects, in particular if the ground state is not fermion-free, which we prove to generally apply to the system in the gapless phase and with periodic boundary conditions. To illustrate our findings, we compute the static and dynamic spin susceptibilities for finite-size systems. Specifically, we consider random-bond disorder (which preserves the solubility of the model), calculate the distribution of local flux gaps, and extract the NMR line shape. We also predict a transition to a random-flux state with increasing disorder.

Dynamic belief state representations.

PubMed

Lee, Daniel D; Ortega, Pedro A; Stocker, Alan A

2014-04-01

Perceptual and control systems are tasked with the challenge of accurately and efficiently estimating the dynamic states of objects in the environment. To properly account for uncertainty, it is necessary to maintain a dynamical belief state representation rather than a single state vector. In this review, canonical algorithms for computing and updating belief states in robotic applications are delineated, and connections to biological systems are highlighted. A navigation example is used to illustrate the importance of properly accounting for correlations between belief state components, and to motivate the need for further investigations in psychophysics and neurobiology. Copyright © 2014 Elsevier Ltd. All rights reserved.

Mean-field scaling of the superfluid to Mott insulator transition in a 2D optical superlattice.

NASA Astrophysics Data System (ADS)

Okano, Masayuki; Thomas, Claire; Barter, Thomas; Leung, Tsz-Him; Jo, Gyu-Boong; Guzman, Jennie; Kimchi, Itamar; Vishwanath, Ashvin; Stamper-Kurn, Dan

2017-04-01

Quantum gases within optical lattices provide a nearly ideal experimental representation of the Bose-Hubbard model. The mean-field treatment of this model predicts properties of non-zero temperature lattice-trapped gasses to be insensitive to the specific lattice geometry once system energies are scaled by the lattice coordination number z. We examine an ultracold Bose gas of rubidium atoms prepared within a two-dimensional lattice whose geometry can be tuned between two configurations, triangular and kagome, for which z varies from six to four, respectively. Measurements of the coherent fraction of the gas thereby provide a quantitative test of the mean-field scaling prediction. We observe the suppression of superfluidity upon decreasing z, and find our results to be consistent with the predicted mean-field scaling. These optical lattice systems can offer a way to study paradigmatic solid-state phenomena in highly controlled crystal structures. This work was supported by the NSF and by the Army Research Office with funding from the DARPA OLE program.

FragBag, an accurate representation of protein structure, retrieves structural neighbors from the entire PDB quickly and accurately.

PubMed

Budowski-Tal, Inbal; Nov, Yuval; Kolodny, Rachel

2010-02-23

Fast identification of protein structures that are similar to a specified query structure in the entire Protein Data Bank (PDB) is fundamental in structure and function prediction. We present FragBag: An ultrafast and accurate method for comparing protein structures. We describe a protein structure by the collection of its overlapping short contiguous backbone segments, and discretize this set using a library of fragments. Then, we succinctly represent the protein as a "bags-of-fragments"-a vector that counts the number of occurrences of each fragment-and measure the similarity between two structures by the similarity between their vectors. Our representation has two additional benefits: (i) it can be used to construct an inverted index, for implementing a fast structural search engine of the entire PDB, and (ii) one can specify a structure as a collection of substructures, without combining them into a single structure; this is valuable for structure prediction, when there are reliable predictions only of parts of the protein. We use receiver operating characteristic curve analysis to quantify the success of FragBag in identifying neighbor candidate sets in a dataset of over 2,900 structures. The gold standard is the set of neighbors found by six state of the art structural aligners. Our best FragBag library finds more accurate candidate sets than the three other filter methods: The SGM, PRIDE, and a method by Zotenko et al. More interestingly, FragBag performs on a par with the computationally expensive, yet highly trusted structural aligners STRUCTAL and CE.

Multi-perspective analysis and spatiotemporal mapping of air pollution monitoring data.

PubMed

Kolovos, Alexander; Skupin, André; Jerrett, Michael; Christakos, George

2010-09-01

Space-time data analysis and assimilation techniques in atmospheric sciences typically consider input from monitoring measurements. The input is often processed in a manner that acknowledges characteristics of the measurements (e.g., underlying patterns, fluctuation features) under conditions of uncertainty; it also leads to the derivation of secondary information that serves study-oriented goals, and provides input to space-time prediction techniques. We present a novel approach that blends a rigorous space-time prediction model (Bayesian maximum entropy, BME) with a cognitively informed visualization of high-dimensional data (spatialization). The combined BME and spatialization approach (BME-S) is used to study monthly averaged NO2 and mean annual SO4 measurements in California over the 15-year period 1988-2002. Using the original scattered measurements of these two pollutants BME generates spatiotemporal predictions on a regular grid across the state. Subsequently, the prediction network undergoes the spatialization transformation into a lower-dimensional geometric representation, aimed at revealing patterns and relationships that exist within the input data. The proposed BME-S provides a powerful spatiotemporal framework to study a variety of air pollution data sources.

Structure of the low-lying positive parity states in the proton-neutron symplectic model

NASA Astrophysics Data System (ADS)

Ganev, H. G.

2018-05-01

The proton-neutron symplectic model with Sp(12, R) dynamical symmetry is applied for the simultaneous description of the microscopic structure of the low-lying states of the ground state, γ and β bands in 166 Er. For this purpose, the model Hamiltonian is diagonalized in the space of stretched states by exploiting the SUp (3) ⊗ SUn (3) symmetry-adapted basis. The theoretical predictions are compared with experiment and some other microscopic collective models, like the one-component Sp(6, R) symplectic and pseudo-SU(3) models. A good description of the energy levels of the three bands under consideration, as well as the enhanced intraband B(E2) transition strengths between the states of the ground and γ bands is obtained without the use of effective charges. The results show the presence of a good SU(3) dynamical symmetry. It is also shown that, in contrast to the Sp(6, R) case, the lowest excited bands, e.g., the β and γ bands, naturally appear together with the ground state band within a single Sp(12, R) irreducible representation.

Qualitative and temporal reasoning in engine behavior analysis

NASA Technical Reports Server (NTRS)

Dietz, W. E.; Stamps, M. E.; Ali, M.

1987-01-01

Numerical simulation models, engine experts, and experimental data are used to generate qualitative and temporal representations of abnormal engine behavior. Engine parameters monitored during operation are used to generate qualitative and temporal representations of actual engine behavior. Similarities between the representations of failure scenarios and the actual engine behavior are used to diagnose fault conditions which have already occurred, or are about to occur; to increase the surveillance by the monitoring system of relevant engine parameters; and to predict likely future engine behavior.

Computational Account of Spontaneous Activity as a Signature of Predictive Coding

PubMed Central

Koren, Veronika

2017-01-01

Spontaneous activity is commonly observed in a variety of cortical states. Experimental evidence suggested that neural assemblies undergo slow oscillations with Up ad Down states even when the network is isolated from the rest of the brain. Here we show that these spontaneous events can be generated by the recurrent connections within the network and understood as signatures of neural circuits that are correcting their internal representation. A noiseless spiking neural network can represent its input signals most accurately when excitatory and inhibitory currents are as strong and as tightly balanced as possible. However, in the presence of realistic neural noise and synaptic delays, this may result in prohibitively large spike counts. An optimal working regime can be found by considering terms that control firing rates in the objective function from which the network is derived and then minimizing simultaneously the coding error and the cost of neural activity. In biological terms, this is equivalent to tuning neural thresholds and after-spike hyperpolarization. In suboptimal working regimes, we observe spontaneous activity even in the absence of feed-forward inputs. In an all-to-all randomly connected network, the entire population is involved in Up states. In spatially organized networks with local connectivity, Up states spread through local connections between neurons of similar selectivity and take the form of a traveling wave. Up states are observed for a wide range of parameters and have similar statistical properties in both active and quiescent state. In the optimal working regime, Up states are vanishing, leaving place to asynchronous activity, suggesting that this working regime is a signature of maximally efficient coding. Although they result in a massive increase in the firing activity, the read-out of spontaneous Up states is in fact orthogonal to the stimulus representation, therefore interfering minimally with the network function. PMID:28114353

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.

Density functional theory and chromium: Insights from the dimers

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

Würdemann, Rolf; Kristoffersen, Henrik H.; Moseler, Michael

2015-03-28

The binding in small Cr clusters is re-investigated, where the correct description of the dimer in three charge states is used as criterion to assign the most suitable density functional theory approximation. The difficulty in chromium arises from the subtle interplay between energy gain from hybridization and energetic cost due to exchange between s and d based molecular orbitals. Variations in published bond lengths and binding energies are shown to arise from insufficient numerical representation of electron density and Kohn-Sham wave-functions. The best functional performance is found for gradient corrected (GGA) functionals and meta-GGAs, where we find severe differences betweenmore » functionals from the same family due to the importance of exchange. Only the “best fit” from Bayesian error estimation is able to predict the correct energetics for all three charge states unambiguously. With this knowledge, we predict small bond-lengths to be exclusively present in Cr{sub 2} and Cr{sub 2}{sup −}. Already for the dimer cation, solely long bond-lengths appear, similar to what is found in the trimer and in chromium bulk.« less

Modeling late rectal toxicities based on a parameterized representation of the 3D dose distribution

NASA Astrophysics Data System (ADS)

Buettner, Florian; Gulliford, Sarah L.; Webb, Steve; Partridge, Mike

2011-04-01

Many models exist for predicting toxicities based on dose-volume histograms (DVHs) or dose-surface histograms (DSHs). This approach has several drawbacks as firstly the reduction of the dose distribution to a histogram results in the loss of spatial information and secondly the bins of the histograms are highly correlated with each other. Furthermore, some of the complex nonlinear models proposed in the past lack a direct physical interpretation and the ability to predict probabilities rather than binary outcomes. We propose a parameterized representation of the 3D distribution of the dose to the rectal wall which explicitly includes geometrical information in the form of the eccentricity of the dose distribution as well as its lateral and longitudinal extent. We use a nonlinear kernel-based probabilistic model to predict late rectal toxicity based on the parameterized dose distribution and assessed its predictive power using data from the MRC RT01 trial (ISCTRN 47772397). The endpoints under consideration were rectal bleeding, loose stools, and a global toxicity score. We extract simple rules identifying 3D dose patterns related to a specifically low risk of complication. Normal tissue complication probability (NTCP) models based on parameterized representations of geometrical and volumetric measures resulted in areas under the curve (AUCs) of 0.66, 0.63 and 0.67 for predicting rectal bleeding, loose stools and global toxicity, respectively. In comparison, NTCP models based on standard DVHs performed worse and resulted in AUCs of 0.59 for all three endpoints. In conclusion, we have presented low-dimensional, interpretable and nonlinear NTCP models based on the parameterized representation of the dose to the rectal wall. These models had a higher predictive power than models based on standard DVHs and their low dimensionality allowed for the identification of 3D dose patterns related to a low risk of complication.

The Radical Plasticity Thesis: How the Brain Learns to be Conscious

PubMed Central

Cleeremans, Axel

2011-01-01

In this paper, I explore the idea that consciousness is something that the brain learns to do rather than an intrinsic property of certain neural states and not others. Starting from the idea that neural activity is inherently unconscious, the question thus becomes: How does the brain learn to be conscious? I suggest that consciousness arises as a result of the brain's continuous attempts at predicting not only the consequences of its actions on the world and on other agents, but also the consequences of activity in one cerebral region on activity in other regions. By this account, the brain continuously and unconsciously learns to redescribe its own activity to itself, so developing systems of meta-representations that characterize and qualify the target first-order representations. Such learned redescriptions, enriched by the emotional value associated with them, form the basis of conscious experience. Learning and plasticity are thus central to consciousness, to the extent that experiences only occur in experiencers that have learned to know they possess certain first-order states and that have learned to care more about certain states than about others. This is what I call the “Radical Plasticity Thesis.” In a sense thus, this is the enactive perspective, but turned both inwards and (further) outwards. Consciousness involves “signal detection on the mind”; the conscious mind is the brain's (non-conceptual, implicit) theory about itself. I illustrate these ideas through neural network models that simulate the relationships between performance and awareness in different tasks. PMID:21687455

Acceleration of saddle-point searches with machine learning.

PubMed

Peterson, Andrew A

2016-08-21

In atomistic simulations, the location of the saddle point on the potential-energy surface (PES) gives important information on transitions between local minima, for example, via transition-state theory. However, the search for saddle points often involves hundreds or thousands of ab initio force calls, which are typically all done at full accuracy. This results in the vast majority of the computational effort being spent calculating the electronic structure of states not important to the researcher, and very little time performing the calculation of the saddle point state itself. In this work, we describe how machine learning (ML) can reduce the number of intermediate ab initio calculations needed to locate saddle points. Since machine-learning models can learn from, and thus mimic, atomistic simulations, the saddle-point search can be conducted rapidly in the machine-learning representation. The saddle-point prediction can then be verified by an ab initio calculation; if it is incorrect, this strategically has identified regions of the PES where the machine-learning representation has insufficient training data. When these training data are used to improve the machine-learning model, the estimates greatly improve. This approach can be systematized, and in two simple example problems we demonstrate a dramatic reduction in the number of ab initio force calls. We expect that this approach and future refinements will greatly accelerate searches for saddle points, as well as other searches on the potential energy surface, as machine-learning methods see greater adoption by the atomistics community.

Acceleration of saddle-point searches with machine learning

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

Peterson, Andrew A., E-mail: andrew-peterson@brown.edu

In atomistic simulations, the location of the saddle point on the potential-energy surface (PES) gives important information on transitions between local minima, for example, via transition-state theory. However, the search for saddle points often involves hundreds or thousands of ab initio force calls, which are typically all done at full accuracy. This results in the vast majority of the computational effort being spent calculating the electronic structure of states not important to the researcher, and very little time performing the calculation of the saddle point state itself. In this work, we describe how machine learning (ML) can reduce the numbermore » of intermediate ab initio calculations needed to locate saddle points. Since machine-learning models can learn from, and thus mimic, atomistic simulations, the saddle-point search can be conducted rapidly in the machine-learning representation. The saddle-point prediction can then be verified by an ab initio calculation; if it is incorrect, this strategically has identified regions of the PES where the machine-learning representation has insufficient training data. When these training data are used to improve the machine-learning model, the estimates greatly improve. This approach can be systematized, and in two simple example problems we demonstrate a dramatic reduction in the number of ab initio force calls. We expect that this approach and future refinements will greatly accelerate searches for saddle points, as well as other searches on the potential energy surface, as machine-learning methods see greater adoption by the atomistics community.« less

The Radical Plasticity Thesis: How the Brain Learns to be Conscious.

PubMed

Cleeremans, Axel

2011-01-01

In this paper, I explore the idea that consciousness is something that the brain learns to do rather than an intrinsic property of certain neural states and not others. Starting from the idea that neural activity is inherently unconscious, the question thus becomes: How does the brain learn to be conscious? I suggest that consciousness arises as a result of the brain's continuous attempts at predicting not only the consequences of its actions on the world and on other agents, but also the consequences of activity in one cerebral region on activity in other regions. By this account, the brain continuously and unconsciously learns to redescribe its own activity to itself, so developing systems of meta-representations that characterize and qualify the target first-order representations. Such learned redescriptions, enriched by the emotional value associated with them, form the basis of conscious experience. Learning and plasticity are thus central to consciousness, to the extent that experiences only occur in experiencers that have learned to know they possess certain first-order states and that have learned to care more about certain states than about others. This is what I call the "Radical Plasticity Thesis." In a sense thus, this is the enactive perspective, but turned both inwards and (further) outwards. Consciousness involves "signal detection on the mind"; the conscious mind is the brain's (non-conceptual, implicit) theory about itself. I illustrate these ideas through neural network models that simulate the relationships between performance and awareness in different tasks.

NOAA's National Air Quality Prediction and Development of Aerosol and Atmospheric Composition Prediction Components for NGGPS

NASA Astrophysics Data System (ADS)

Stajner, I.; McQueen, J.; Lee, P.; Stein, A. F.; Wilczak, J. M.; Upadhayay, S.; daSilva, A.; Lu, C. H.; Grell, G. A.; Pierce, R. B.

2017-12-01

NOAA's operational air quality predictions of ozone, fine particulate matter (PM2.5) and wildfire smoke over the United States and airborne dust over the contiguous 48 states are distributed at http://airquality.weather.gov. The National Air Quality Forecast Capability (NAQFC) providing these predictions was updated in June 2017. Ozone and PM2.5 predictions are now produced using the system linking the Community Multiscale Air Quality model (CMAQ) version 5.0.2 with meteorological inputs from the North American Mesoscale Forecast System (NAM) version 4. Predictions of PM2.5 include intermittent dust emissions and wildfire emissions from an updated version of BlueSky system. For the latter, the CMAQ system is initialized by rerunning it over the previous 24 hours to include wildfire emissions at the time when they were observed from the satellites. Post processing to reduce the bias in PM2.5 prediction was updated using the Kalman filter analog (KFAN) technique. Dust related aerosol species at the CMAQ domain lateral boundaries now come from the NEMS Global Aerosol Component (NGAC) v2 predictions. Further development of NAQFC includes testing of CMAQ predictions to 72 hours, Canadian fire emissions data from Environment and Climate Change Canada (ECCC) and the KFAN technique to reduce bias in ozone predictions. NOAA is developing the Next Generation Global Predictions System (NGGPS) with an aerosol and gaseous atmospheric composition component to improve and integrate aerosol and ozone predictions and evaluate their impacts on physics, data assimilation and weather prediction. Efforts are underway to improve cloud microphysics, investigate aerosol effects and include representations of atmospheric composition of varying complexity into NGGPS: from the operational ozone parameterization, GOCART aerosols, with simplified ozone chemistry, to CMAQ chemistry with aerosol modules. We will present progress on community building, planning and development of NGGPS.

Plant physiological models of heat, water and photoinhibition stress for climate change modelling and agricultural prediction

NASA Astrophysics Data System (ADS)

Nicolas, B.; Gilbert, M. E.; Paw U, K. T.

2015-12-01

Soil-Vegetation-Atmosphere Transfer (SVAT) models are based upon well understood steady state photosynthetic physiology - the Farquhar-von Caemmerer-Berry model (FvCB). However, representations of physiological stress and damage have not been successfully integrated into SVAT models. Generally, it has been assumed that plants will strive to conserve water at higher temperatures by reducing stomatal conductance or adjusting osmotic balance, until potentially damaging temperatures and the need for evaporative cooling become more important than water conservation. A key point is that damage is the result of combined stresses: drought leads to stomatal closure, less evaporative cooling, high leaf temperature, less photosynthetic dissipation of absorbed energy, all coupled with high light (photosynthetic photon flux density; PPFD). This leads to excess absorbed energy by Photosystem II (PSII) and results in photoinhibition and damage, neither are included in SVAT models. Current representations of photoinhibition are treated as a function of PPFD, not as a function of constrained photosynthesis under heat or water. Thus, it seems unlikely that current models can predict responses of vegetation to climate variability and change. We propose a dynamic model of damage to Rubisco and RuBP-regeneration that accounts, mechanistically, for the interactions between high temperature, light, and constrained photosynthesis under drought. Further, these predictions are illustrated by key experiments allowing model validation. We also integrated this new framework within the Advanced Canopy-Atmosphere-Soil Algorithm (ACASA). Preliminary results show that our approach can be used to predict reasonable photosynthetic dynamics. For instances, a leaf undergoing one day of drought stress will quickly decrease its maximum quantum yield of PSII (Fv/Fm), but it won't recover to unstressed levels for several days. Consequently, cumulative effect of photoinhibition on photosynthesis can cause a decrease of about 35% of CO2 uptake. As a result, the incorporation of stress and damage into SVAT models could considerably improve our ability to predict global responses to climate change.

Maternal secure-base scripts and children's attachment security in an adopted sample.

PubMed

Veríssimo, Manuela; Salvaterra, Fernanda

2006-09-01

Studies of families with adopted children are of special interest to attachment theorists because they afford opportunities to probe assumptions of attachment theory with regard to the developmental timing of interactions necessary to form primary attachments and also with regard to effects of shared genes on child attachment quality. In Bowlby's model, attachment-relevant behaviors and interactions are observable from the moment of birth, but for adoptive families, these interactions cannot begin until the child enters the family, sometimes several months or even years post-partum. Furthermore, because adoptive parents and adopted children do not usually share genes by common descent, any correspondence between attachment representations of the parent and secure base behavior of the child must arise as a consequence of dyadic interaction histories. The objectives of this study were to evaluate whether the child's age at the time of adoption or at the time of attachment assessment predicted child attachment security in adoptive families and also whether the adoptive mother's internal attachment representation predicted the child's attachment security. The participants were 106 mother - child dyads selected from the 406 adoptions carried out through the Lisbon Department of Adoption Services over a period of 3 years. The Attachment Behavior Q-Set (AQS; Waters, 1995) was used to assess secure base behavior and an attachment script representation task was used to assess the maternal attachment representations. Neither child's age at the time of adoption, nor age of the child at assessment significantly predicted the AQS security score; however, scores reflecting the presence and quality of maternal secure base scripts did predict AQS security. These findings support the notion that the transmission of attachment security across generations involves mutual exchanges and learning by the child and that the exchanges leading to secure attachment need not begin at birth. These results complement the findings and conceptual arguments offered by Bowlby and Ainsworth concerning the critical influence of maternal representations of attachment to the quality of attachment security in children.

A canonical state-space representation for SISO systems using multipoint Jordan CFE. [Continued-Fraction Expansion

NASA Technical Reports Server (NTRS)

Hwang, Chyi; Guo, Tong-Yi; Shieh, Leang-San

1991-01-01

A canonical state-space realization based on the multipoint Jordan continued-fraction expansion (CFE) is presented for single-input-single-output (SISO) systems. The similarity transformation matrix which relates the new canonical form to the phase-variable canonical form is also derived. The presented canonical state-space representation is particularly attractive for the application of SISO system theory in which a reduced-dimensional time-domain model is necessary.

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.

Intrinsic dimensionality predicts the saliency of natural dynamic scenes.

PubMed

Vig, Eleonora; Dorr, Michael; Martinetz, Thomas; Barth, Erhardt

2012-06-01

Since visual attention-based computer vision applications have gained popularity, ever more complex, biologically inspired models seem to be needed to predict salient locations (or interest points) in naturalistic scenes. In this paper, we explore how far one can go in predicting eye movements by using only basic signal processing, such as image representations derived from efficient coding principles, and machine learning. To this end, we gradually increase the complexity of a model from simple single-scale saliency maps computed on grayscale videos to spatiotemporal multiscale and multispectral representations. Using a large collection of eye movements on high-resolution videos, supervised learning techniques fine-tune the free parameters whose addition is inevitable with increasing complexity. The proposed model, although very simple, demonstrates significant improvement in predicting salient locations in naturalistic videos over four selected baseline models and two distinct data labeling scenarios.

An Experimental Study in Determining Energy Expenditure from Treadmill Walking using Hip-Worn Inertial Sensors

PubMed Central

Vathsangam, Harshvardhan; Emken, Adar; Schroeder, E. Todd; Spruijt-Metz, Donna; Sukhatme, Gaurav S.

2011-01-01

This paper describes an experimental study in estimating energy expenditure from treadmill walking using a single hip-mounted triaxial inertial sensor comprised of a triaxial accelerometer and a triaxial gyroscope. Typical physical activity characterization using accelerometer generated counts suffers from two drawbacks - imprecison (due to proprietary counts) and incompleteness (due to incomplete movement description). We address these problems in the context of steady state walking by directly estimating energy expenditure with data from a hip-mounted inertial sensor. We represent the cyclic nature of walking with a Fourier transform of sensor streams and show how one can map this representation to energy expenditure (as measured by V O2 consumption, mL/min) using three regression techniques - Least Squares Regression (LSR), Bayesian Linear Regression (BLR) and Gaussian Process Regression (GPR). We perform a comparative analysis of the accuracy of sensor streams in predicting energy expenditure (measured by RMS prediction accuracy). Triaxial information is more accurate than uniaxial information. LSR based approaches are prone to outlier sensitivity and overfitting. Gyroscopic information showed equivalent if not better prediction accuracy as compared to accelerometers. Combining accelerometer and gyroscopic information provided better accuracy than using either sensor alone. We also analyze the best algorithmic approach among linear and nonlinear methods as measured by RMS prediction accuracy and run time. Nonlinear regression methods showed better prediction accuracy but required an order of magnitude of run time. This paper emphasizes the role of probabilistic techniques in conjunction with joint modeling of triaxial accelerations and rotational rates to improve energy expenditure prediction for steady-state treadmill walking. PMID:21690001

The role of organo-mineral interactions on the capacity of soils to store carbon

NASA Astrophysics Data System (ADS)

Georgiou, K.; Abramoff, R. Z.; Riley, W. J.; Torn, M. S.

2017-12-01

Observed patterns of soil organic carbon (SOC) content across geochemical regimes are signatures of process and provide opportunities to understand the underlying decomposition and stabilization mechanisms that can guide their representation in models. The type of sorption equation used in soil decomposition models has large implications for both SOC stock and its temperature sensitivity. Here we compared different model formulations of SOC sorption to mineral surfaces, motivated by the myriad of chemical associations between organic and mineral surfaces, and used laboratory and field incubations to inform model parameters. We explored linear, Langmuir, and Freundlich adsorption models, where the latter emerges from heterogeneous compositions of substrate and surface components. We show the effect of model representations on predicted trends of SOC as a function of mineralogy and discuss the role of soil C saturation on emergent patterns. Specifically, our results highlight that the response of mineral-associated (`protected') SOC to changes in plant C inputs depends greatly on the C saturation deficit of the soil and thus, the representation of organo-mineral interactions in models can lead to nonlinear steady-state responses in protected SOC. We also find that, consistent with field experiments, the trend in protected SOC and mineral C saturation capacity is linear, but, interestingly, the slope depends on the degree of C saturation. We contend that this latter finding is an important consideration for field studies that did not find a universal slope and interpreted this as an inability of mineralogy to explain observed patterns. Our results also suggest that warming affects this slope, with higher temperatures causing a decrease in the amount of protected C for a given saturation capacity and C input rate. This means that more C inputs will be needed to keep the same amount of protected C at higher temperatures. Organo-mineral interactions play a key role in governing soil C stabilization and long-term storage, and thus, improving their representation for inclusion in Earth system models is crucial for understanding and predicting feedbacks under global change.

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…

Order Short-Term Memory Capacity Predicts Nonword Reading and Spelling in First and Second Grade

ERIC Educational Resources Information Center

Binamé, Florence; Poncelet, Martine

2016-01-01

Recent theories of short-term memory (STM) distinguish between item information, which reflects the temporary activation of long-term representations stored in the language system, and serial-order information, which is encoded in a specific representational system that is independent of the language network. Some studies examining the…

Evidence for Holistic Representations of Ignored Images and Analytic Representations of Attended Images

ERIC Educational Resources Information Center

Thoma, Volker; Hummel, John E.; Davidoff, Jules

2004-01-01

According to the hybrid theory of object recognition (J. E. Hummel, 2001), ignored object images are represented holistically, and attended images are represented both holistically and analytically. This account correctly predicts patterns of visual priming as a function of translation, scale (B. J. Stankiewicz & J. E. Hummel, 2002), and…

UltraPse: A Universal and Extensible Software Platform for Representing Biological Sequences.

PubMed

Du, Pu-Feng; Zhao, Wei; Miao, Yang-Yang; Wei, Le-Yi; Wang, Likun

2017-11-14

With the avalanche of biological sequences in public databases, one of the most challenging problems in computational biology is to predict their biological functions and cellular attributes. Most of the existing prediction algorithms can only handle fixed-length numerical vectors. Therefore, it is important to be able to represent biological sequences with various lengths using fixed-length numerical vectors. Although several algorithms, as well as software implementations, have been developed to address this problem, these existing programs can only provide a fixed number of representation modes. Every time a new sequence representation mode is developed, a new program will be needed. In this paper, we propose the UltraPse as a universal software platform for this problem. The function of the UltraPse is not only to generate various existing sequence representation modes, but also to simplify all future programming works in developing novel representation modes. The extensibility of UltraPse is particularly enhanced. It allows the users to define their own representation mode, their own physicochemical properties, or even their own types of biological sequences. Moreover, UltraPse is also the fastest software of its kind. The source code package, as well as the executables for both Linux and Windows platforms, can be downloaded from the GitHub repository.

Task-irrelevant distractors in the delay period interfere selectively with visual short-term memory for spatial locations.

PubMed

Marini, Francesco; Scott, Jerry; Aron, Adam R; Ester, Edward F

2017-07-01

Visual short-term memory (VSTM) enables the representation of information in a readily accessible state. VSTM is typically conceptualized as a form of "active" storage that is resistant to interference or disruption, yet several recent studies have shown that under some circumstances task-irrelevant distractors may indeed disrupt performance. Here, we investigated how task-irrelevant visual distractors affected VSTM by asking whether distractors induce a general loss of remembered information or selectively interfere with memory representations. In a VSTM task, participants recalled the spatial location of a target visual stimulus after a delay in which distractors were presented on 75% of trials. Notably, the distractor's eccentricity always matched the eccentricity of the target, while in the critical conditions the distractor's angular position was shifted either clockwise or counterclockwise relative to the target. We then computed estimates of recall error for both eccentricity and polar angle. A general interference model would predict an effect of distractors on both polar angle and eccentricity errors, while a selective interference model would predict effects of distractors on angle but not on eccentricity errors. Results showed that for stimulus angle there was an increase in the magnitude and variability of recall errors. However, distractors had no effect on estimates of stimulus eccentricity. Our results suggest that distractors selectively interfere with VSTM for spatial locations.

Molecular cancer classification using a meta-sample-based regularized robust coding method.

PubMed

Wang, Shu-Lin; Sun, Liuchao; Fang, Jianwen

2014-01-01

Previous studies have demonstrated that machine learning based molecular cancer classification using gene expression profiling (GEP) data is promising for the clinic diagnosis and treatment of cancer. Novel classification methods with high efficiency and prediction accuracy are still needed to deal with high dimensionality and small sample size of typical GEP data. Recently the sparse representation (SR) method has been successfully applied to the cancer classification. Nevertheless, its efficiency needs to be improved when analyzing large-scale GEP data. In this paper we present the meta-sample-based regularized robust coding classification (MRRCC), a novel effective cancer classification technique that combines the idea of meta-sample-based cluster method with regularized robust coding (RRC) method. It assumes that the coding residual and the coding coefficient are respectively independent and identically distributed. Similar to meta-sample-based SR classification (MSRC), MRRCC extracts a set of meta-samples from the training samples, and then encodes a testing sample as the sparse linear combination of these meta-samples. The representation fidelity is measured by the l2-norm or l1-norm of the coding residual. Extensive experiments on publicly available GEP datasets demonstrate that the proposed method is more efficient while its prediction accuracy is equivalent to existing MSRC-based methods and better than other state-of-the-art dimension reduction based methods.

The Feeling of Action Tendencies: On the Emotional Regulation of Goal-Directed Behavior

PubMed Central

Lowe, Robert; Ziemke, Tom

2011-01-01

In this article, we review the nature of the functional and causal relationship between neurophysiologically/psychologically generated states of emotional feeling and action tendencies and extrapolate a novel perspective. Emotion theory, over the past century and beyond, has tended to regard feeling and action tendency as independent phenomena: attempts to outline the functional and causal relationship that exists between them have been framed therein. Classically, such relationships have been viewed as unidirectional, but an argument for bidirectionality rooted in a dynamic systems perspective has gained strength in recent years whereby the feeling–action tendency relationship is viewed as a composite whole. On the basis of our review of somatic–visceral theories of feelings, we argue that feelings are grounded upon neural-dynamic representations (elevated and stable activation patterns) of action tendency. Such representations amount to predictions updated by cognitive and bodily feedback. Specifically, we view emotional feelings as minimalist predictions of the action tendency (what the agent is physiologically and cognitively primed to do) in a given situation. The essence of this point is captured by our exposition of action tendency prediction–feedback loops which we consider, above all, in the context of emotion regulation, and in particular, of emotional regulation of goal-directed behavior. The perspective outlined may be of use to emotion theorists, computational modelers, and roboticists. PMID:22207854

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.

Talking about Internal States in Mother-Child Reminiscing Influences Children's Self-Representations: A Cross-Cultural Study

ERIC Educational Resources Information Center

Wang, Qi; Doan, Stacey N.; Song, Qingfang

2010-01-01

This study examined the relation of mother-child discussions of internal states during reminiscing to the development of trait and evaluative self-representations in 131 European American and Chinese immigrant 3-year olds. Mothers and children discussed one positive and one negative event, and children were interviewed for self-descriptions.…

Neural representations of the concepts in simple sentences: Concept activation prediction and context effects.

PubMed

Just, Marcel Adam; Wang, Jing; Cherkassky, Vladimir L

2017-08-15

Although it has been possible to identify individual concepts from a concept's brain activation pattern, there have been significant obstacles to identifying a proposition from its fMRI signature. Here we demonstrate the ability to decode individual prototype sentences from readers' brain activation patterns, by using theory-driven regions of interest and semantic properties. It is possible to predict the fMRI brain activation patterns evoked by propositions and words which are entirely new to the model with reliably above-chance rank accuracy. The two core components implemented in the model that reflect the theory were the choice of intermediate semantic features and the brain regions associated with the neurosemantic dimensions. This approach also predicts the neural representation of object nouns across participants, studies, and sentence contexts. Moreover, we find that the neural representation of an agent-verb-object proto-sentence is more accurately characterized by the neural signatures of its components as they occur in a similar context than by the neural signatures of these components as they occur in isolation. Copyright © 2017 Elsevier Ltd. All rights reserved.

Striatum-medial prefrontal cortex connectivity predicts developmental changes in reinforcement learning.

PubMed

van den Bos, Wouter; Cohen, Michael X; Kahnt, Thorsten; Crone, Eveline A

2012-06-01

During development, children improve in learning from feedback to adapt their behavior. However, it is still unclear which neural mechanisms might underlie these developmental changes. In the current study, we used a reinforcement learning model to investigate neurodevelopmental changes in the representation and processing of learning signals. Sixty-seven healthy volunteers between ages 8 and 22 (children: 8-11 years, adolescents: 13-16 years, and adults: 18-22 years) performed a probabilistic learning task while in a magnetic resonance imaging scanner. The behavioral data demonstrated age differences in learning parameters with a stronger impact of negative feedback on expected value in children. Imaging data revealed that the neural representation of prediction errors was similar across age groups, but functional connectivity between the ventral striatum and the medial prefrontal cortex changed as a function of age. Furthermore, the connectivity strength predicted the tendency to alter expectations after receiving negative feedback. These findings suggest that the underlying mechanisms of developmental changes in learning are not related to differences in the neural representation of learning signals per se but rather in how learning signals are used to guide behavior and expectations.

Two Anatomically and Computationally Distinct Learning Signals Predict Changes to Stimulus-Outcome Associations in Hippocampus.

PubMed

Boorman, Erie D; Rajendran, Vani G; O'Reilly, Jill X; Behrens, Tim E

2016-03-16

Complex cognitive processes require sophisticated local processing but also interactions between distant brain regions. It is therefore critical to be able to study distant interactions between local computations and the neural representations they act on. Here we report two anatomically and computationally distinct learning signals in lateral orbitofrontal cortex (lOFC) and the dopaminergic ventral midbrain (VM) that predict trial-by-trial changes to a basic internal model in hippocampus. To measure local computations during learning and their interaction with neural representations, we coupled computational fMRI with trial-by-trial fMRI suppression. We find that suppression in a medial temporal lobe network changes trial-by-trial in proportion to stimulus-outcome associations. During interleaved choice trials, we identify learning signals that relate to outcome type in lOFC and to reward value in VM. These intervening choice feedback signals predicted the subsequent change to hippocampal suppression, suggesting a convergence of signals that update the flexible representation of stimulus-outcome associations. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Intrinsic and Extrinsic Evaluation of Spatiotemporal Text Representations in Twitter Streams

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

Phillips, Lawrence A.; Shaffer, Kyle J.; Arendt, Dustin L.

Language in social media is a dynamic system, constantly evolving and adapting, with words and concepts rapidly emerging, disappearing, and changing their meaning. These changes can be estimated using word representations in context, over time and across locations. A number of methods have been proposed to track these spatiotemporal changes but no general method exists to evaluate the quality of these representations. Previous work largely focused on qualitative evaluation, which we improve by proposing a set of visualizations that highlight changes in text representation over both space and time. We demonstrate usefulness of novel spatiotemporal representations to explore and characterizemore » specific aspects of the corpus of tweets collected from European countries over a two-week period centered around the terrorist attacks in Brussels in March 2016. In addition, we quantitatively evaluate spatiotemporal representations by feeding them into a downstream classification task – event type prediction. Thus, our work is the first to provide both intrinsic (qualitative) and extrinsic (quantitative) evaluation of text representations for spatiotemporal trends.« less

Predicting solvatochromic shifts and colours of a solvated organic dye: The example of nile red

NASA Astrophysics Data System (ADS)

Zuehlsdorff, T. J.; Haynes, P. D.; Payne, M. C.; Hine, N. D. M.

2017-03-01

The solvatochromic shift, as well as the change in colour of the simple organic dye nile red, is studied in two polar and two non-polar solvents in the context of large-scale time-dependent density-functional theory (TDDFT) calculations treating large parts of the solvent environment from first principles. We show that an explicit solvent representation is vital to resolve absorption peak shifts between nile red in n-hexane and toluene, as well as acetone and ethanol. The origin of the failure of implicit solvent models for these solvents is identified as being due to the strong solute-solvent interactions in form of π-stacking and hydrogen bonding in the case of toluene and ethanol. We furthermore demonstrate that the failures of the computationally inexpensive Perdew-Burke-Ernzerhof (PBE) functional in describing some features of the excited state potential energy surface of the S1 state of nile red can be corrected for in a straightforward fashion, relying only on a small number of calculations making use of more sophisticated range-separated hybrid functionals. The resulting solvatochromic shifts and predicted colours are in excellent agreement with experiment, showing the computational approach outlined in this work to yield very robust predictions of optical properties of dyes in solution.

On-board ephemeris representation for Topex/Poseidon

NASA Technical Reports Server (NTRS)

Salama, Ahmed H.

1990-01-01

The Topex/Poseidon satellite requires real-time on-board knowledge of the satellite and TDRS ephemeris for attitude determination and control and High-Gain Antenna (HGA) pointing. The ephemeris representation concept for the MMS (Multimission Modular Spacecraft) satellites has shown that compressing the predicted ephemeris in a Fourier Power Series (FPS) before uplinking in conjunction with the On-Board Computer (OBC) ephemeris reconstruction algorithms is an efficient technique for ephemeris representation. As an MMS-based satellite, Topex/Poseidon has inherited the Landsat ephemeris representation concept including a daily FPS upload. This paper presents the Topex/Poseidon concept, analysis, and results including the conclusion that the ephemeris representation duration could be extended to 10 days or more and convenient weekly uploading is adopted without an increase in OBC memory requirements.

Shared and Distinctive Origins and Correlates of Adult Attachment Representations: The Developmental Organization of Romantic Functioning

PubMed Central

Haydon, Katherine C.; Collins, W. Andrew; Salvatore, Jessica E.; Simpson, Jeffry A.; Roisman, Glenn I.

2012-01-01

To test proposals regarding the hierarchical organization of adult attachment, this study examined developmental origins of generalized and romantic attachment representations and their concurrent associations with romantic functioning. Participants (N = 112) in a 35-year prospective study completed the Adult Attachment Interview (AAI) and Current Relationship Interview (CRI). Two-way ANOVAs tested interactive associations of AAI and CRI security with infant attachment, early parenting quality, preschool ego resiliency, adolescent friendship quality, and adult romantic functioning. Both representations were associated with earlier parenting and core attachment-related romantic behavior, but romantic representations had distinctive links to ego resiliency and relationship-specific romantic behaviors. Attachment representations were independent and did not interactively predict romantic functioning, suggesting that they confer somewhat distinctive benefits for romantic functioning. PMID:22694197

A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks

PubMed Central

Yin, Junming; Ho, Qirong; Xing, Eric P.

2014-01-01

We propose a scalable approach for making inference about latent spaces of large networks. With a succinct representation of networks as a bag of triangular motifs, a parsimonious statistical model, and an efficient stochastic variational inference algorithm, we are able to analyze real networks with over a million vertices and hundreds of latent roles on a single machine in a matter of hours, a setting that is out of reach for many existing methods. When compared to the state-of-the-art probabilistic approaches, our method is several orders of magnitude faster, with competitive or improved accuracy for latent space recovery and link prediction. PMID:25400487

Wentzel-Kramers-Brillouin method in the Bargmann representation. [of quantum mechanics

NASA Technical Reports Server (NTRS)

Voros, A.

1989-01-01

It is demonstrated that the Bargmann representation of quantum mechanics is ideally suited for semiclassical analysis, using as an example the WKB method applied to the bound-state problem in a single well of one degree of freedom. For the harmonic oscillator, this WKB method trivially gives the exact eigenfunctions in addition to the exact eigenvalues. For an anharmonic well, a self-consistent variational choice of the representation greatly improves the accuracy of the semiclassical ground state. Also, a simple change of scale illuminates the relationship of semiclassical versus linear perturbative expansions, allowing a variety of multidimensional extensions.

General and specific consciousness: a first-order representationalist approach

PubMed Central

Mehta, Neil; Mashour, George A.

2013-01-01

It is widely acknowledged that a complete theory of consciousness should explain general consciousness (what makes a state conscious at all) and specific consciousness (what gives a conscious state its particular phenomenal quality). We defend first-order representationalism, which argues that consciousness consists of sensory representations directly available to the subject for action selection, belief formation, planning, etc. We provide a neuroscientific framework for this primarily philosophical theory, according to which neural correlates of general consciousness include prefrontal cortex, posterior parietal cortex, and non-specific thalamic nuclei, while neural correlates of specific consciousness include sensory cortex and specific thalamic nuclei. We suggest that recent data support first-order representationalism over biological theory, higher-order representationalism, recurrent processing theory, information integration theory, and global workspace theory. PMID:23882231

Predictive coding of visual object position ahead of moving objects revealed by time-resolved EEG decoding.

PubMed

Hogendoorn, Hinze; Burkitt, Anthony N

2018-05-01

Due to the delays inherent in neuronal transmission, our awareness of sensory events necessarily lags behind the occurrence of those events in the world. If the visual system did not compensate for these delays, we would consistently mislocalize moving objects behind their actual position. Anticipatory mechanisms that might compensate for these delays have been reported in animals, and such mechanisms have also been hypothesized to underlie perceptual effects in humans such as the Flash-Lag Effect. However, to date no direct physiological evidence for anticipatory mechanisms has been found in humans. Here, we apply multivariate pattern classification to time-resolved EEG data to investigate anticipatory coding of object position in humans. By comparing the time-course of neural position representation for objects in both random and predictable apparent motion, we isolated anticipatory mechanisms that could compensate for neural delays when motion trajectories were predictable. As well as revealing an early neural position representation (lag 80-90 ms) that was unaffected by the predictability of the object's trajectory, we demonstrate a second neural position representation at 140-150 ms that was distinct from the first, and that was pre-activated ahead of the moving object when it moved on a predictable trajectory. The latency advantage for predictable motion was approximately 16 ± 2 ms. To our knowledge, this provides the first direct experimental neurophysiological evidence of anticipatory coding in human vision, revealing the time-course of predictive mechanisms without using a spatial proxy for time. The results are numerically consistent with earlier animal work, and suggest that current models of spatial predictive coding in visual cortex can be effectively extended into the temporal domain. Copyright © 2018 Elsevier Inc. All rights reserved.

Impact of the basic state and MJO representation on MJO Pacific teleconnections in GCMs

NASA Astrophysics Data System (ADS)

Henderson, S. A.; Maloney, E. D.; Son, S. W.

2017-12-01

Teleconnection patterns induced by the Madden-Julian Oscillation (MJO) are known to significantly alter extratropical weather and climate patterns. However, accurate MJO representation has been difficult for many General Circulation Models (GCMs). Furthermore, many GCMs contain large basic state biases. These issues present challenges to the simulation of MJO teleconnections and, in turn, their associated extratropical impacts. This study examines the impacts of basic state quality and MJO representation on the quality of MJO teleconnection patterns in GCMs from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Results suggest that GCMs assessed to have a good MJO but with large basic state biases have similarly low skill in reproducing MJO teleconnections as GCMs with poor MJO representation. In the good MJO models examined, poor teleconnection quality is associated with large errors in the zonal extent of the Pacific subtropical jet. Whereas the horizontal structure of MJO heating in the Indo-Pacific region is found to have modest impacts on the teleconnection patterns, results suggest that MJO heating east of the dateline can alter the teleconnection pattern characteristics over North America. These findings suggest that in order to accurately simulate the MJO teleconnection patterns and associated extratropical impacts, both the MJO and the basic state must be well represented.

BPS States, Crystals, and Matrices

DOE PAGES

Sułkowski, Piotr

2011-01-01

We review free fermion, melting crystal, and matrix model representations of wall-crossing phenomena on local, toric Calabi-Yau manifolds. We consider both unrefined and refined BPS counting of closed BPS states involving D2- and D0-branes bound to a D6-brane, as well as open BPS states involving open D2-branes ending on an additional D4-brane. Appropriate limit of these constructions provides, among the others, matrix model representation of refined and unrefined topological string amplitudes.

The open XXX spin chain in the SoV framework: scalar product of separate states

NASA Astrophysics Data System (ADS)

Kitanine, N.; Maillet, J. M.; Niccoli, G.; Terras, V.

2017-06-01

We consider the XXX open spin-1/2 chain with the most general non-diagonal boundary terms, that we solve by means of the quantum separation of variables (SoV) approach. We compute the scalar products of separate states, a class of states which notably contains all the eigenstates of the model. As usual for models solved by SoV, these scalar products can be expressed as some determinants with a non-trivial dependance in terms of the inhomogeneity parameters that have to be introduced for the method to be applicable. We show that these determinants can be transformed into alternative ones in which the homogeneous limit can easily be taken. These new representations can be considered as generalizations of the well-known determinant representation for the scalar products of the Bethe states of the periodic chain. In the particular case where a constraint is applied on the boundary parameters, such that the transfer matrix spectrum and eigenstates can be characterized in terms of polynomial solutions of a usual T-Q equation, the scalar product that we compute here corresponds to the scalar product between two off-shell Bethe-type states. If in addition one of the states is an eigenstate, the determinant representation can be simplified, hence leading in this boundary case to direct analogues of algebraic Bethe ansatz determinant representations of the scalar products for the periodic chain.

An Explosives Products Thermodynamic Equation of State Appropriate for Material Acceleration and Overdriven Detonation: Theoretical Background and Formulation

DTIC Science & Technology

1991-07-01

provide poor representations of overdriven detonation. The Jones-Wilkens- Lee-Baker ( JWLB ) has been formulated to provide a more accurate representation...Chapman-Jouguet state. The resulting equation of state form, named Jones-Wilkens-Lee-Baker ( JWLB ), is P. A,[-+ e-R-iV -t-V-4- C(1 V(wl 1 where, ,=L(AAi...is the specific internal energy. The JWLB equation of state form is based on a first order expansion around the principal isentrope: A, .’ie’R iV + CV

Basolateral amygdala rapid glutamate release encodes an outcome-specific representation vital for reward-predictive cues to selectively invigorate reward-seeking actions

PubMed Central

Malvaez, Melissa; Greenfield, Venuz Y.; Wang, Alice S.; Yorita, Allison M.; Feng, Lili; Linker, Kay E.; Monbouquette, Harold G.; Wassum, Kate M.

2015-01-01

Environmental stimuli have the ability to generate specific representations of the rewards they predict and in so doing alter the selection and performance of reward-seeking actions. The basolateral amygdala participates in this process, but precisely how is unknown. To rectify this, we monitored, in near-real time, basolateral amygdala glutamate concentration changes during a test of the ability of reward-predictive cues to influence reward-seeking actions (Pavlovian-instrumental transfer). Glutamate concentration was found to be transiently elevated around instrumental reward seeking. During the Pavlovian-instrumental transfer test these glutamate transients were time-locked to and correlated with only those actions invigorated by outcome-specific motivational information provided by the reward-predictive stimulus (i.e., actions earning the same specific outcome as predicted by the presented CS). In addition, basolateral amygdala AMPA, but not NMDA glutamate receptor inactivation abolished the selective excitatory influence of reward-predictive cues over reward seeking. These data the hypothesis that transient glutamate release in the BLA can encode the outcome-specific motivational information provided by reward-predictive stimuli. PMID:26212790

Designing hydrologic monitoring networks to maximize predictability of hydrologic conditions in a data assimilation system: a case study from South Florida, U.S.A

NASA Astrophysics Data System (ADS)

Flores, A. N.; Pathak, C. S.; Senarath, S. U.; Bras, R. L.

2009-12-01

Robust hydrologic monitoring networks represent a critical element of decision support systems for effective water resource planning and management. Moreover, process representation within hydrologic simulation models is steadily improving, while at the same time computational costs are decreasing due to, for instance, readily available high performance computing resources. The ability to leverage these increasingly complex models together with the data from these monitoring networks to provide accurate and timely estimates of relevant hydrologic variables within a multiple-use, managed water resources system would substantially enhance the information available to resource decision makers. Numerical data assimilation techniques provide mathematical frameworks through which uncertain model predictions can be constrained to observational data to compensate for uncertainties in the model forcings and parameters. In ensemble-based data assimilation techniques such as the ensemble Kalman Filter (EnKF), information in observed variables such as canal, marsh and groundwater stages are propagated back to the model states in a manner related to: (1) the degree of certainty in the model state estimates and observations, and (2) the cross-correlation between the model states and the observable outputs of the model. However, the ultimate degree to which hydrologic conditions can be accurately predicted in an area of interest is controlled, in part, by the configuration of the monitoring network itself. In this proof-of-concept study we developed an approach by which the design of an existing hydrologic monitoring network is adapted to iteratively improve the predictions of hydrologic conditions within an area of the South Florida Water Management District (SFWMD). The objective of the network design is to minimize prediction errors of key hydrologic states and fluxes produced by the spatially distributed Regional Simulation Model (RSM), developed specifically to simulate the hydrologic conditions in several intensively managed and hydrologically complex watersheds within the SFWMD system. In a series of synthetic experiments RSM is used to generate the notionally true hydrologic state and the relevant observational data. The EnKF is then used as the mechanism to fuse RSM hydrologic estimates with data from the candidate network. The performance of the candidate network is measured by the prediction errors of the EnKF estimates of hydrologic states, relative to the notionally true scenario. The candidate network is then adapted by relocating existing observational sites to unobserved areas where predictions of local hydrologic conditions are most uncertain and the EnKF procedure repeated. Iteration of the monitoring network continues until further improvements in EnKF-based predictions of hydrologic conditions are negligible.

Quasiprobability Representations of Quantum Mechanics with Minimal Negativity

NASA Astrophysics Data System (ADS)

Zhu, Huangjun

2016-09-01

Quasiprobability representations, such as the Wigner function, play an important role in various research areas. The inevitable appearance of negativity in such representations is often regarded as a signature of nonclassicality, which has profound implications for quantum computation. However, little is known about the minimal negativity that is necessary in general quasiprobability representations. Here we focus on a natural class of quasiprobability representations that is distinguished by simplicity and economy. We introduce three measures of negativity concerning the representations of quantum states, unitary transformations, and quantum channels, respectively. Quite surprisingly, all three measures lead to the same representations with minimal negativity, which are in one-to-one correspondence with the elusive symmetric informationally complete measurements. In addition, most representations with minimal negativity are automatically covariant with respect to the Heisenberg-Weyl groups. Furthermore, our study reveals an interesting tradeoff between negativity and symmetry in quasiprobability representations.

Neural correlates of learning and trajectory planning in the posterior parietal cortex

PubMed Central

Torres, Elizabeth B.; Quian Quiroga, Rodrigo; Cui, He; Buneo, Christopher A.

2013-01-01

The posterior parietal cortex (PPC) is thought to play an important role in the planning of visually-guided reaching movements. However, the relative roles of the various subdivisions of the PPC in this function are still poorly understood. For example, studies of dorsal area 5 point to a representation of reaches in both extrinsic (endpoint) and intrinsic (joint or muscle) coordinates, as evidenced by partial changes in preferred directions and positional discharge with changes in arm posture. In contrast, recent findings suggest that the adjacent medial intraparietal area (MIP) is involved in more abstract representations, e.g., encoding reach target in visual coordinates. Such a representation is suitable for planning reach trajectories involving shortest distance paths to targets straight ahead. However, it is currently unclear how MIP contributes to the planning of other types of trajectories, including those with various degrees of curvature. Such curved trajectories recruit different joint excursions and might help us address whether their representation in the PPC is purely in extrinsic coordinates or in intrinsic ones as well. Here we investigated the role of the PPC in these processes during an obstacle avoidance task for which the animals had not been explicitly trained. We found that PPC planning activity was predictive of both the spatial and temporal aspects of upcoming trajectories. The same PPC neurons predicted the upcoming trajectory in both endpoint and joint coordinates. The predictive power of these neurons remained stable and accurate despite concomitant motor learning across task conditions. These findings suggest the role of the PPC can be extended from specifying abstract movement goals to expressing these plans as corresponding trajectories in both endpoint and joint coordinates. Thus, the PPC appears to contribute to reach planning and approach-avoidance arm motions at multiple levels of representation. PMID:23730275

Generalized Pauli constraints in reduced density matrix functional theory.

PubMed

Theophilou, Iris; Lathiotakis, Nektarios N; Marques, Miguel A L; Helbig, Nicole

2015-04-21

Functionals of the one-body reduced density matrix (1-RDM) are routinely minimized under Coleman's ensemble N-representability conditions. Recently, the topic of pure-state N-representability conditions, also known as generalized Pauli constraints, received increased attention following the discovery of a systematic way to derive them for any number of electrons and any finite dimensionality of the Hilbert space. The target of this work is to assess the potential impact of the enforcement of the pure-state conditions on the results of reduced density-matrix functional theory calculations. In particular, we examine whether the standard minimization of typical 1-RDM functionals under the ensemble N-representability conditions violates the pure-state conditions for prototype 3-electron systems. We also enforce the pure-state conditions, in addition to the ensemble ones, for the same systems and functionals and compare the correlation energies and optimal occupation numbers with those obtained by the enforcement of the ensemble conditions alone.

Coherent states for quantum compact groups

NASA Astrophysics Data System (ADS)

Jurĉo, B.; Ŝťovíĉek, P.

1996-12-01

Coherent states are introduced and their properties are discussed for simple quantum compact groups A l, Bl, Cl and D l. The multiplicative form of the canonical element for the quantum double is used to introduce the holomorphic coordinates on a general quantum dressing orbit. The coherent state is interpreted as a holomorphic function on this orbit with values in the carrier Hilbert space of an irreducible representation of the corresponding quantized enveloping algebra. Using Gauss decomposition, the commutation relations for the holomorphic coordinates on the dressing orbit are derived explicitly and given in a compact R-matrix formulation (generalizing this way the q-deformed Grassmann and flag manifolds). The antiholomorphic realization of the irreducible representations of a compact quantum group (the analogue of the Borel-Weil construction) is described using the concept of coherent state. The relation between representation theory and non-commutative differential geometry is suggested.

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

Theophilou, Iris; Helbig, Nicole; Lathiotakis, Nektarios N.

Functionals of the one-body reduced density matrix (1-RDM) are routinely minimized under Coleman’s ensemble N-representability conditions. Recently, the topic of pure-state N-representability conditions, also known as generalized Pauli constraints, received increased attention following the discovery of a systematic way to derive them for any number of electrons and any finite dimensionality of the Hilbert space. The target of this work is to assess the potential impact of the enforcement of the pure-state conditions on the results of reduced density-matrix functional theory calculations. In particular, we examine whether the standard minimization of typical 1-RDM functionals under the ensemble N-representability conditions violatesmore » the pure-state conditions for prototype 3-electron systems. We also enforce the pure-state conditions, in addition to the ensemble ones, for the same systems and functionals and compare the correlation energies and optimal occupation numbers with those obtained by the enforcement of the ensemble conditions alone.« less

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

Dependence of the Radiation Pressure on the Background Refractive Index

NASA Astrophysics Data System (ADS)

Webb, Kevin J.

2013-07-01

The 1978 experiments by Jones and Leslie showing that the radiation pressure on a mirror depends on the background medium refractive index have yet to be adequately explained using a force model and have provided a leading challenge to the Abraham form of the electromagnetic momentum. Those experimental results are predicted for the first time using a force representation that incorporates the Abraham momentum by utilizing the power calibration method employed in the Jones and Leslie experiments. With an extension of the same procedure, the polarization and angle independence of the experimental data are also explained by this model. Prospects are good for this general form of the electromagnetic force density to be effective in predicting other experiments with macroscopic materials. Furthermore, the rigorous representation of material dispersion makes the representation important for metamaterials that operate in the vicinity of homogenized material resonances.

The Role of Visuospatial Resources in Generating Predictive and Bridging Inferences

ERIC Educational Resources Information Center

Fincher-Kiefer, Rebecca; D'Agostino, Paul R.

2004-01-01

It has been suggested that predictive and bridging inferences are generated at different levels of text representation: predictive inferences at a reader's situation model and bridging inferences at a reader's propositional textbase (Fincher-Kiefer, 1993, 1996; McDaniel, Schmalhofer, & Keefe, 2001; Schmalhofer, McDaniel, & Keefe, 2002). Recently,…

(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…

39 CFR 966.6 - Filing, docketing and serving documents; computation of time; representation of parties.

Code of Federal Regulations, 2013 CFR

2013-07-01

... are between 8:45 a.m. and 4:45 p.m., eastern standard or daylight saving time as appropriate during...; computation of time; representation of parties. 966.6 Section 966.6 Postal Service UNITED STATES POSTAL... time; representation of parties. (a) Filing. All documents required under this part must be filed by...

39 CFR 966.6 - Filing, docketing and serving documents; computation of time; representation of parties.

Code of Federal Regulations, 2014 CFR

2014-07-01

... are between 8:45 a.m. and 4:45 p.m., eastern standard or daylight saving time as appropriate during...; computation of time; representation of parties. 966.6 Section 966.6 Postal Service UNITED STATES POSTAL... time; representation of parties. (a) Filing. All documents required under this part must be filed by...

Specificity of Emotion Inferences as a Function of Emotional Contextual Support

ERIC Educational Resources Information Center

Gillioz, Christelle; Gygax, Pascal M.

2017-01-01

Research on emotion inferences has shown that readers include a representation of the main character's emotional state in their mental representations of the text. We examined the specificity of emotion representations as a function of the emotion content of short narratives, in terms of the quantity and quality of emotion components included in…

Unitary Transformations in 3 D Vector Representation of Qutrit States

DTIC Science & Technology

2018-03-12

Representation of Qutrit States Vinod K Mishra Computational and Information Sciences Directorate, ARL Approved for public... information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and...maintaining the data needed, and completing and reviewing the collection information . Send comments regarding this burden estimate or any other aspect

Assigning the low lying vibronic states of CH3O and CD3O

NASA Astrophysics Data System (ADS)

Johnson, Britta A.; Sibert, Edwin L.

2017-05-01

The assignment of lines in vibrational spectra in strongly mixing systems is considered. Several low lying vibrational states of the ground electronic X˜ 2E state of the CH3O and CD3O radicals are assigned. Jahn-Teller, spin-orbit, and Fermi couplings mix the normal mode states. The mixing complicates the assignment of the infrared spectra using a zero-order normal mode representation. Alternative zero-order representations, which include specific Jahn-Teller couplings, are explored. These representations allow for definitive assignments. In many instances it is possible to plot the wavefunctions on which the assignments are based. The plots, which are shown in the adiabatic representation, allow one to visualize the effects of various higher order couplings. The plots also enable one to visualize the conical seam and its effect on the wavefunctions. The first and the second order Jahn-Teller couplings in the rocking motion dominate the spectral features in CH3O, while first order and modulated first order couplings dominate the spectral features in CD3O. The methods described here are general and can be applied to other Jahn-Teller systems.

Optimal Prediction in the Retina and Natural Motion Statistics

NASA Astrophysics Data System (ADS)

Salisbury, Jared M.; Palmer, Stephanie E.

2016-03-01

Almost all behaviors involve making predictions. Whether an organism is trying to catch prey, avoid predators, or simply move through a complex environment, the organism uses the data it collects through its senses to guide its actions by extracting from these data information about the future state of the world. A key aspect of the prediction problem is that not all features of the past sensory input have predictive power, and representing all features of the external sensory world is prohibitively costly both due to space and metabolic constraints. This leads to the hypothesis that neural systems are optimized for prediction. Here we describe theoretical and computational efforts to define and quantify the efficient representation of the predictive information by the brain. Another important feature of the prediction problem is that the physics of the world is diverse enough to contain a wide range of possible statistical ensembles, yet not all inputs are probable. Thus, the brain might not be a generalized predictive machine; it might have evolved to specifically solve the prediction problems most common in the natural environment. This paper summarizes recent results on predictive coding and optimal predictive information in the retina and suggests approaches for quantifying prediction in response to natural motion. Basic statistics of natural movies reveal that general patterns of spatiotemporal correlation are present across a wide range of scenes, though individual differences in motion type may be important for optimal processing of motion in a given ecological niche.

Predicting Word Maturity from Frequency and Semantic Diversity: A Computational Study

ERIC Educational Resources Information Center

Jorge-Botana, Guillermo; Olmos, Ricardo; Sanjosé, Vicente

2017-01-01

Semantic word representation changes over different ages of childhood until it reaches its adult form. One method to formally model this change is the word maturity paradigm. This method uses a text sample for each age, including adult age, and transforms the samples into a semantic space by means of Latent Semantic Analysis. The representation of…

Qualitative Differences in the Representation of Abstract versus Concrete Words: Evidence from the Visual-World Paradigm

ERIC Educational Resources Information Center

Dunabeitia, Jon Andoni; Aviles, Alberto; Afonso, Olivia; Scheepers, Christoph; Carreiras, Manuel

2009-01-01

In the present visual-world experiment, participants were presented with visual displays that included a target item that was a semantic associate of an abstract or a concrete word. This manipulation allowed us to test a basic prediction derived from the qualitatively different representational framework that supports the view of different…

Spirituality, Self-Representations, and Attachment to Parents: A Longitudinal Study of Roman Catholic College Seminarians

ERIC Educational Resources Information Center

Reinert, Duane F.

2005-01-01

The author used an attachment theory framework to explore relationships between early attachment to parents and seminarians' later self-representations and relationship with God. Attachment to mother was a key variable in predicting seminarians' level of self-esteem and internalized shame as well as the quality of their relationship with God. This…

Forecasting wildland fire behavior using high-resolution large-eddy simulations

NASA Astrophysics Data System (ADS)

Munoz-Esparza, D.; Kosovic, B.; Jimenez, P. A.; Anderson, A.; DeCastro, A.; Brown, B.

2016-12-01

Wildland fires are responsible for large socio-economic impacts. Fires affect the environment, damage structures, threaten lives, cause health issues, and involve large suppression costs. These impacts can be mitigated via accurate fire spread forecast to inform the incident management team. To this end, the state of Colorado is funding the development of the Colorado Fire Prediction System (CO-FPS). The system is based on the Weather Research and Forecasting (WRF) model enhanced with a fire behavior module (WRF-Fire). Realistic representation of wildland fire behavior requires explicit representation of small scale weather phenomena to properly account for coupled atmosphere-wildfire interactions. Moreover, transport and dispersion of biomass burning emissions from wildfires is controlled by turbulent processes in the atmospheric boundary layer, which are difficult to parameterize and typically lead to large errors when simplified source estimation and injection height methods are used. Therefore, we utilize turbulence-resolving large-eddy simulations at a resolution of 111 m to forecast fire spread and smoke distribution using a coupled atmosphere-wildfire model. This presentation will describe our improvements to the level-set based fire-spread algorithm in WRF-Fire and an evaluation of the operational system using 12 wildfire events that occurred in Colorado in 2016, as well as other historical fires. In addition, the benefits of explicit representation of turbulence for smoke transport and dispersion will be demonstrated.

Forecasting wildland fire behavior using high-resolution large-eddy simulations

NASA Astrophysics Data System (ADS)

Munoz-Esparza, D.; Kosovic, B.; Jimenez, P. A.; Anderson, A.; DeCastro, A.; Brown, B.

2017-12-01

Wildland fires are responsible for large socio-economic impacts. Fires affect the environment, damage structures, threaten lives, cause health issues, and involve large suppression costs. These impacts can be mitigated via accurate fire spread forecast to inform the incident management team. To this end, the state of Colorado is funding the development of the Colorado Fire Prediction System (CO-FPS). The system is based on the Weather Research and Forecasting (WRF) model enhanced with a fire behavior module (WRF-Fire). Realistic representation of wildland fire behavior requires explicit representation of small scale weather phenomena to properly account for coupled atmosphere-wildfire interactions. Moreover, transport and dispersion of biomass burning emissions from wildfires is controlled by turbulent processes in the atmospheric boundary layer, which are difficult to parameterize and typically lead to large errors when simplified source estimation and injection height methods are used. Therefore, we utilize turbulence-resolving large-eddy simulations at a resolution of 111 m to forecast fire spread and smoke distribution using a coupled atmosphere-wildfire model. This presentation will describe our improvements to the level-set based fire-spread algorithm in WRF-Fire and an evaluation of the operational system using 12 wildfire events that occurred in Colorado in 2016, as well as other historical fires. In addition, the benefits of explicit representation of turbulence for smoke transport and dispersion will be demonstrated.

Is there a doctor in the house? . . . Or the Senate? Physicians in US Congress, 1960-2004.

PubMed

Kraus, Chadd K; Suarez, Thomas A

2004-11-03

The legislative and fiscal influences of Congress, as well as the continuing overall growth in health care spending as a portion of the gross domestic product, make congressional representation by physicians important because physicians have unique expertise in the impact of legislation on patient care and medical practice. To describe physician representation in the US Congress between 1960 and 2004 and relate the results to past representation of physicians in Congress. A retrospective observational study of members of the US Congress from all 50 states and all represented territories, who served from January 1960 to April 2004 (including 108th Congress), using data available in public access databases and congressional biographical records. Physician representation in Congress, including occupation before taking office, state/territory of representation, sex, party affiliation, and time served. During the past 44 years, 25 (1.1%) of 2196 members of Congress were physicians. Physicians in Congress were more likely to be members of the Republican Party (60% vs 45.1% of all members, P = .007) and were similar to other members of Congress in mean years of service (9.2 years for physicians vs 12.3 years for all members, P = .09) and in sex distribution (4.0% female physicians vs 6.8% all female members, P = .57). Physicians in Congress represented 17 states, the Virgin Islands, and Puerto Rico. Physician representation in Congress is low and is in stark contrast with physician roles during the first century of the United States. However, the 8 physicians currently serving in Congress may be indicative of a shift toward more direct influence of physicians in national politics.

Robustness of atomistic Gō models in predicting native-like folding intermediates

NASA Astrophysics Data System (ADS)

Estácio, S. G.; Fernandes, C. S.; Krobath, H.; Faísca, P. F. N.; Shakhnovich, E. I.

2012-08-01

Gō models are exceedingly popular tools in computer simulations of protein folding. These models are native-centric, i.e., they are directly constructed from the protein's native structure. Therefore, it is important to understand up to which extent the atomistic details of the native structure dictate the folding behavior exhibited by Gō models. Here we address this challenge by performing exhaustive discrete molecular dynamics simulations of a Gō potential combined with a full atomistic protein representation. In particular, we investigate the robustness of this particular type of Gō models in predicting the existence of intermediate states in protein folding. We focus on the N47G mutational form of the Spc-SH3 folding domain (x-ray structure) and compare its folding pathway with that of alternative native structures produced in silico. Our methodological strategy comprises equilibrium folding simulations, structural clustering, and principal component analysis.

Predicting phase behavior of mixtures of reservoir fluids with carbon dioxide

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

Grigg, R.B.; Lingane, P.J.

1983-01-01

The use of an equation of state to predict phase behavior during carbon dioxide flooding is well established. The characterization of the C/sub 7/ fraction and the selection of interaction parameters are the most important variables. Single-contact phase behavior is presented for mixtures of Ford Geraldine (Delaware), Maljamar (Grayburg), West Sussex (Shannon), and Reservoir D reservoir fluids, and of a synthetic oil with carbon dioxide. The phase behavior of these mixtures can be reproduced using 3 to 5 pseudo components and common interaction parameters. The critical properties of the pseudo components are calculated from detailed oil characterizations. Because the parametersmore » are not further adjusted, this approach reduces the empiricism in fitting phase data and may result in a more accurate representation of the system as the composition of the oil changes during the approach to miscibility. 21 references.« less

Prediction of Enzyme Mutant Activity Using Computational Mutagenesis and Incremental Transduction

PubMed Central

Basit, Nada; Wechsler, Harry

2011-01-01

Wet laboratory mutagenesis to determine enzyme activity changes is expensive and time consuming. This paper expands on standard one-shot learning by proposing an incremental transductive method (T2bRF) for the prediction of enzyme mutant activity during mutagenesis using Delaunay tessellation and 4-body statistical potentials for representation. Incremental learning is in tune with both eScience and actual experimentation, as it accounts for cumulative annotation effects of enzyme mutant activity over time. The experimental results reported, using cross-validation, show that overall the incremental transductive method proposed, using random forest as base classifier, yields better results compared to one-shot learning methods. T2bRF is shown to yield 90% on T4 and LAC (and 86% on HIV-1). This is significantly better than state-of-the-art competing methods, whose performance yield is at 80% or less using the same datasets. PMID:22007208

The orbitofrontal oracle: cortical mechanisms for the prediction and evaluation of specific behavioral outcomes.

PubMed

Rudebeck, Peter H; Murray, Elisabeth A

2014-12-17

The orbitofrontal cortex (OFC) has long been associated with the flexible control of behavior and concepts such as behavioral inhibition, self-control, and emotional regulation. These ideas emphasize the suppression of behaviors and emotions, but OFC's affirmative functions have remained enigmatic. Here we review recent work that has advanced our understanding of this prefrontal area and how its functions are shaped through interaction with subcortical structures such as the amygdala. Recent findings have overturned theories emphasizing behavioral inhibition as OFC's fundamental function. Instead, new findings indicate that OFC provides predictions about specific outcomes associated with stimuli, choices, and actions, especially their moment-to-moment value based on current internal states. OFC function thereby encompasses a broad representation or model of an individual's sensory milieu and potential actions, along with their relationship to likely behavioral outcomes. Copyright © 2014 Elsevier Inc. All rights reserved.

Fatigue damage mechanics of notched graphite-epoxy laminates

NASA Astrophysics Data System (ADS)

Spearing, Mark; Beaumont, Peter W. R.; Ashby, Michael F.

A modeling approach is presented that recognizes that the residual properties of composite laminates after any form of loading depend on the damage state. Therefore, in the case of cyclic loading, it is necessary to first derive a damage growth law and then relate the residual properties to the accumulated damage. The propagation of fatigue damage in notched laminates is investigated. A power law relationship between damage growth and the strain energy release rate is developed. The material constants used in the model have been determined in independent experiments and are invariant for all the layups investigated. The strain energy release rates are calculated using a simple finite element representation of the damaged specimen. The model is used to predict the effect of tension-tension cyclic loading on laminates of the T300/914C carbon-fiber epoxy system. The extent of damage propagation is successfully predicted in a number of cross-ply laminates.

Dissociating mental states related to doing nothing by means of fMRI pattern classification.

PubMed

Kühn, Simone; Bodammer, Nils Christian; Brass, Marcel

2010-12-01

Most juridical systems recognize intentional non-actions - the failure to render assistance - as intentional acts by regarding them as in principle culpable. This raises the fundamental question whether intentional non-actions can be distinguished from simply not doing anything. Classical GLM analysis on functional magnetic resonance imaging (fMRI) data reveals that not doing anything is associated with resting state brain areas whereas intentionally non-acting is associated with brain activity in left inferior parietal lobe and left dorsal premotor cortex. By means of pattern classification we quantify the accuracy with which we can distinguish these two mental states on the basis of brain activity. In order to identify brain regions that harbour a distributed, overlapping representation of voluntary non-actions and the decision not to act we performed pattern classification on brain areas that did not appear in the GLM contrasts. The prediction rate is not reduced and we show that the prediction relies mostly on brain areas that have been associated with action production and motor imagery as supplementary motor area, right inferior frontal gyrus and right middle temporal area (V5/MT). Hence our data support the implicit assumption of legal practice that voluntary non-action shares important features with overt voluntary action. Copyright © 2010 Elsevier Inc. All rights reserved.

Resting-state functional connectivity and motor imagery brain activation

PubMed Central

Saiote, Catarina; Tacchino, Andrea; Brichetto, Giampaolo; Roccatagliata, Luca; Bommarito, Giulia; Cordano, Christian; Battaglia, Mario; Mancardi, Giovanni Luigi; Inglese, Matilde

2016-01-01

Motor imagery (MI) relies on the mental simulation of an action without any overt motor execution (ME), and can facilitate motor learning and enhance the effect of rehabilitation in patients with neurological conditions. While functional magnetic resonance imaging (fMRI) during MI and ME reveals shared cortical representations, the role and functional relevance of the resting-state functional connectivity (RSFC) of brain regions involved in MI is yet unknown. Here, we performed resting-state fMRI followed by fMRI during ME and MI with the dominant hand. We used a behavioral chronometry test to measure ME and MI movement duration and compute an index of performance (IP). Then, we analyzed the voxel-matched correlation between the individual MI parameter estimates and seed-based RSFC maps in the MI network to measure the correspondence between RSFC and MI fMRI activation. We found that inter-individual differences in intrinsic connectivity in the MI network predicted several clusters of activation. Taken together, present findings provide first evidence that RSFC within the MI network is predictive of the activation of MI brain regions, including those associated with behavioral performance, thus suggesting a role for RSFC in obtaining a deeper understanding of neural substrates of MI and of MI ability. PMID:27273577

Unitary irreducible representations of SL(2,C) in discrete and continuous SU(1,1) bases

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

Conrady, Florian; Hnybida, Jeff; Department of Physics, University of Waterloo, Waterloo, Ontario

2011-01-15

We derive the matrix elements of generators of unitary irreducible representations of SL(2,C) with respect to basis states arising from a decomposition into irreducible representations of SU(1,1). This is done with regard to a discrete basis diagonalized by J{sup 3} and a continuous basis diagonalized by K{sup 1}, and for both the discrete and continuous series of SU(1,1). For completeness, we also treat the more conventional SU(2) decomposition as a fifth case. The derivation proceeds in a functional/differential framework and exploits the fact that state functions and differential operators have a similar structure in all five cases. The states aremore » defined explicitly and related to SU(1,1) and SU(2) matrix elements.« less

Decoherence and Noise in Spin-based Solid State Quantum Computers. Approximation-Free Numerical Simulations

DTIC Science & Technology

2007-07-21

the spin coherent states P-representation", Conference on Quantum Computations and Many- Body Systems, February 2006, Key West, FL 9. B. N. Harmon...solid-state spin-based qubit systems was the focus of our project. Since decoherence is a complex many- body non-equilibrium process, and its...representation of the density matrix, see Sec. 3 below). This work prompted J. Taylor from the experimental group of C. Marcus and M. Lukin (funded by

A map of abstract relational knowledge in the human hippocampal–entorhinal cortex

PubMed Central

Garvert, Mona M; Dolan, Raymond J; Behrens, Timothy EJ

2017-01-01

The hippocampal–entorhinal system encodes a map of space that guides spatial navigation. Goal-directed behaviour outside of spatial navigation similarly requires a representation of abstract forms of relational knowledge. This information relies on the same neural system, but it is not known whether the organisational principles governing continuous maps may extend to the implicit encoding of discrete, non-spatial graphs. Here, we show that the human hippocampal–entorhinal system can represent relationships between objects using a metric that depends on associative strength. We reconstruct a map-like knowledge structure directly from a hippocampal–entorhinal functional magnetic resonance imaging adaptation signal in a situation where relationships are non-spatial rather than spatial, discrete rather than continuous, and unavailable to conscious awareness. Notably, the measure that best predicted a behavioural signature of implicit knowledge and blood oxygen level-dependent adaptation was a weighted sum of future states, akin to the successor representation that has been proposed to account for place and grid-cell firing patterns. DOI: http://dx.doi.org/10.7554/eLife.17086.001 PMID:28448253

Modelling the effect of religion on human empathy based on an adaptive temporal-causal network model.

PubMed

van Ments, Laila; Roelofsma, Peter; Treur, Jan

2018-01-01

Religion is a central aspect of many individuals' lives around the world, and its influence on human behaviour has been extensively studied from many different perspectives. The current study integrates a number of these perspectives into one adaptive temporal-causal network model describing the mental states involved, their mutual relations, and the adaptation of some of these relations over time due to learning. By first developing a conceptual representation of a network model based on the literature, and then formalizing this model into a numerical representation, simulations can be done for almost any kind of religion and person, showing different behaviours for persons with different religious backgrounds and characters. The focus was mainly on the influence of religion on human empathy and dis-empathy, a topic very relevant today. The developed model could be valuable for many uses, involving support for a better understanding, and even prediction, of the behaviour of religious individuals. It is illustrated for a number of different scenarios based on different characteristics of the persons and of the religion.

End-to-End ASR-Free Keyword Search From Speech

NASA Astrophysics Data System (ADS)

Audhkhasi, Kartik; Rosenberg, Andrew; Sethy, Abhinav; Ramabhadran, Bhuvana; Kingsbury, Brian

2017-12-01

End-to-end (E2E) systems have achieved competitive results compared to conventional hybrid hidden Markov model (HMM)-deep neural network based automatic speech recognition (ASR) systems. Such E2E systems are attractive due to the lack of dependence on alignments between input acoustic and output grapheme or HMM state sequence during training. This paper explores the design of an ASR-free end-to-end system for text query-based keyword search (KWS) from speech trained with minimal supervision. Our E2E KWS system consists of three sub-systems. The first sub-system is a recurrent neural network (RNN)-based acoustic auto-encoder trained to reconstruct the audio through a finite-dimensional representation. The second sub-system is a character-level RNN language model using embeddings learned from a convolutional neural network. Since the acoustic and text query embeddings occupy different representation spaces, they are input to a third feed-forward neural network that predicts whether the query occurs in the acoustic utterance or not. This E2E ASR-free KWS system performs respectably despite lacking a conventional ASR system and trains much faster.

One wouldn't expect an expert bowler to hit only two pins: Hierarchical predictive processing of agent-caused events.

PubMed

Heil, Lieke; Kwisthout, Johan; van Pelt, Stan; van Rooij, Iris; Bekkering, Harold

2018-01-01

Evidence is accumulating that our brains process incoming information using top-down predictions. If lower level representations are correctly predicted by higher level representations, this enhances processing. However, if they are incorrectly predicted, additional processing is required at higher levels to "explain away" prediction errors. Here, we explored the potential nature of the models generating such predictions. More specifically, we investigated whether a predictive processing model with a hierarchical structure and causal relations between its levels is able to account for the processing of agent-caused events. In Experiment 1, participants watched animated movies of "experienced" and "novice" bowlers. The results are in line with the idea that prediction errors at a lower level of the hierarchy (i.e., the outcome of how many pins fell down) slow down reporting of information at a higher level (i.e., which agent was throwing the ball). Experiments 2 and 3 suggest that this effect is specific to situations in which the predictor is causally related to the outcome. Overall, the study supports the idea that a hierarchical predictive processing model can account for the processing of observed action outcomes and that the predictions involved are specific to cases where action outcomes can be predicted based on causal knowledge.

Year of the Woman, Decade of the Man: trajectories of growth in women's state legislative representation.

PubMed

Paxton, Pamela; Painter, Matthew A; Hughes, Melanie M

2009-03-01

The expansion of women's political representation ranks among the most significant trends in American politics of the last 100 years. In this paper, we develop two longitudinal theories to explain patterns of growth and change in women's state legislative representation over time. Gender salience suggests that years in which women's absence from politics is problematized (e.g., 1992-the Year of the Woman) will demonstrate higher levels of growth. Political climate suggests that periods in which domestic issues are stressed (e.g., the 1990s) will produce higher levels of growth than periods in which international issues are stressed (e.g., post 9/11). Combinations of these two theories create four possible trajectories of growth in women's representation that may be observed over time. We use latent growth curve models to assess the four theoretical trajectories, using data on women's state legislative representation from 1982 to 2006. We find that while women achieved fleeting success in the Year of the Woman, further gains were limited in the remainder of the 1990s and average growth stalled completely after 2001. Our results show futher that gender salience and, to a lesser extent political, climate matter to growth and change in women's political power over time.

Modulation of Emotional Appraisal by False Physiological Feedback during fMRI

PubMed Central

Gray, Marcus A.; Harrison, Neil A.; Wiens, Stefan; Critchley, Hugo D.

2007-01-01

Background James and Lange proposed that emotions are the perception of physiological reactions. Two-level theories of emotion extend this model to suggest that cognitive interpretations of physiological changes shape self-reported emotions. Correspondingly false physiological feedback of evoked or tonic bodily responses can alter emotional attributions. Moreover, anxiety states are proposed to arise from detection of mismatch between actual and anticipated states of physiological arousal. However, the neural underpinnings of these phenomena previously have not been examined. Methodology/Principal Findings We undertook a functional brain imaging (fMRI) experiment to investigate how both primary and second-order levels of physiological (viscerosensory) representation impact on the processing of external emotional cues. 12 participants were scanned while judging face stimuli during both exercise and non-exercise conditions in the context of true and false auditory feedback of tonic heart rate. We observed that the perceived emotional intensity/salience of neutral faces was enhanced by false feedback of increased heart rate. Regional changes in neural activity corresponding to this behavioural interaction were observed within included right anterior insula, bilateral mid insula, and amygdala. In addition, right anterior insula activity was enhanced during by asynchronous relative to synchronous cardiac feedback even with no change in perceived or actual heart rate suggesting this region serves as a comparator to detect physiological mismatches. Finally, BOLD activity within right anterior insula and amygdala predicted the corresponding changes in perceived intensity ratings at both a group and an individual level. Conclusions/Significance Our findings identify the neural substrates supporting behavioural effects of false physiological feedback, and highlight mechanisms that underlie subjective anxiety states, including the importance of the right anterior insula in guiding second-order “cognitive” representations of bodily arousal state. PMID:17579718

Evaluation of land surface model representation of phenology: an analysis of model runs submitted to the NACP Interim Site Synthesis

NASA Astrophysics Data System (ADS)

Richardson, A. D.; Nacp Interim Site Synthesis Participants

2010-12-01

Phenology represents a critical intersection point between organisms and their growth environment. It is for this reason that phenology is a sensitive and robust integrator of the biological impacts of year-to-year climate variability and longer-term climate change on natural systems. However, it is perhaps equally important that phenology, by controlling the seasonal activity of vegetation on the land surface, plays a fundamental role in regulating ecosystem processes, competitive interactions, and feedbacks to the climate system. Unfortunately, the phenological sub-models implemented in most state-of-the-art ecosystem models and land surface schemes are overly simplified. We quantified model errors in the representation of the seasonal cycles of leaf area index (LAI), gross ecosystem photosynthesis (GEP), and net ecosystem exchange of CO2. Our analysis was based on site-level model runs (14 different models) submitted to the North American Carbon Program (NACP) Interim Synthesis, and long-term measurements from 10 forested (5 evergreen conifer, 5 deciduous broadleaf) sites within the AmeriFlux and Fluxnet-Canada networks. Model predictions of the seasonality of LAI and GEP were unacceptable, particularly in spring, and especially for deciduous forests. This is despite an historical emphasis on deciduous forest phenology, and the perception that controls on spring phenology are better understood than autumn phenology. Errors of up to 25 days in predicting “spring onset” transition dates were common, and errors of up to 50 days were observed. For deciduous sites, virtually every model was biased towards spring onset being too early, and autumn senescence being too late. Thus, models predicted growing seasons that were far too long for deciduous forests. For most models, errors in the seasonal representation of deciduous forest LAI were highly correlated with errors in the seasonality of both GPP and NEE, indicating the importance of getting the underlying canopy dynamics correct. Most of the models in this comparison were unable to successfully predict the observed interannual variability in either spring or autumn transition dates. And, perhaps surprisingly, the seasonal cycles of models using phenology prescribed by remote sensing observations was, in general, no better than that that predicted by models with prognostic phenology. Reasons for the poor performance of both approaches will be discussed. These results highlight the need for improved understanding of the environmental controls on vegetation phenology. Existing models are unlikely to accurately predict future responses of phenology to climate change, and therefore will misrepresent the seasonality of key biosphere-atmosphere feedbacks and interactions in coupled model runs. New data sets, as for example from webcam-based monitoring networks (e.g. PhenoCam) or citizen science efforts (USA National Phenology Network) should prove valuable in this regard.

39 CFR 966.6 - Filing, docketing and serving documents; computation of time; representation of parties.

Code of Federal Regulations, 2010 CFR

2010-07-01

... business hours are between 8:15 a.m. and 4:45 p.m., eastern standard or daylight saving time as appropriate...; computation of time; representation of parties. 966.6 Section 966.6 Postal Service UNITED STATES POSTAL... time; representation of parties. (a) Filing. All documents required under this part must be filed by...

39 CFR 966.6 - Filing, docketing and serving documents; computation of time; representation of parties.

Code of Federal Regulations, 2011 CFR

2011-07-01

... business hours are between 8:15 a.m. and 4:45 p.m., eastern standard or daylight saving time as appropriate...; computation of time; representation of parties. 966.6 Section 966.6 Postal Service UNITED STATES POSTAL... time; representation of parties. (a) Filing. All documents required under this part must be filed by...

39 CFR 966.6 - Filing, docketing and serving documents; computation of time; representation of parties.

Code of Federal Regulations, 2012 CFR

2012-07-01

... business hours are between 8:15 a.m. and 4:45 p.m., eastern standard or daylight saving time as appropriate...; computation of time; representation of parties. 966.6 Section 966.6 Postal Service UNITED STATES POSTAL... time; representation of parties. (a) Filing. All documents required under this part must be filed by...

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.

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.

Slavery, the Civil War Era, and African American Representation in U.S. History: An Analysis of Four States' Academic Standards

ERIC Educational Resources Information Center

Anderson, Carl B.; Metzger, Scott Alan

2011-01-01

This study is a mixed-methods text analysis of African American representation within K-12 U.S. History content standards treating the revolutionary era, the early U.S. republic, the Civil War era, and Reconstruction. The states included in the analysis are Michigan, New Jersey, South Carolina, and Virginia. The analysis finds that the reviewed…

High Stakes Principalship--Sleepless Nights, Heart Attacks and Sudden Death Accountabilities: Reading Media Representations of the United States Principal Shortage.

ERIC Educational Resources Information Center

Thomson, Pat; Blackmore, Jill; Sachs, Judyth; Tregenza, Karen

2003-01-01

Subjects a corpus of predominantly United States news articles to deconstructive narrative analysis and finds that the dominant media representation of principals' work is one of long hours, low salary, high stress, and sudden death from high stakes accountabilities. Notes that the media picture may perpetuate the problem, and that it is at odds…

Single-digit Arabic numbers do not automatically activate magnitude representations in adults or in children: Evidence from the symbolic same–different task☆

PubMed Central

Wong, Becky; Szücs, Dénes

2013-01-01

We investigated whether the mere presentation of single-digit Arabic numbers activates their magnitude representations using a visually-presented symbolic same–different task for 20 adults and 15 children. Participants saw two single-digit Arabic numbers on a screen and judged whether the numbers were the same or different. We examined whether reaction time in this task was primarily driven by (objective or subjective) perceptual similarity, or by the numerical difference between the two digits. We reasoned that, if Arabic numbers automatically activate magnitude representations, a numerical function would best predict reaction time; but if Arabic numbers do not automatically activate magnitude representations, a perceptual function would best predict reaction time. Linear regressions revealed that a perceptual function, specifically, subjective visual similarity, was the best and only significant predictor of reaction time in adults and in children. These data strongly suggest that, in this task, single-digit Arabic numbers do not necessarily automatically activate magnitude representations in adults or in children. As the first study to date to explicitly study the developmental importance of perceptual factors in the symbolic same–different task, we found no significant differences between adults and children in their reliance on perceptual information in this task. Based on our findings, we propose that visual properties may play a key role in symbolic number judgements. PMID:24076332

Nekrasov and Argyres-Douglas theories in spherical Hecke algebra representation

NASA Astrophysics Data System (ADS)

Rim, Chaiho; Zhang, Hong

2017-06-01

AGT conjecture connects Nekrasov instanton partition function of 4D quiver gauge theory with 2D Liouville conformal blocks. We re-investigate this connection using the central extension of spherical Hecke algebra in q-coordinate representation, q being the instanton expansion parameter. Based on AFLT basis together with intertwiners we construct gauge conformal state and demonstrate its equivalence to the Liouville conformal state, with careful attention to the proper scaling behavior of the state. Using the colliding limit of regular states, we obtain the formal expression of irregular conformal states corresponding to Argyres-Douglas theory, which involves summation of functions over Young diagrams.

Power-law modeling based on least-squares minimization criteria.

PubMed

Hernández-Bermejo, B; Fairén, V; Sorribas, A

1999-10-01

The power-law formalism has been successfully used as a modeling tool in many applications. The resulting models, either as Generalized Mass Action or as S-systems models, allow one to characterize the target system and to simulate its dynamical behavior in response to external perturbations and parameter changes. The power-law formalism was first derived as a Taylor series approximation in logarithmic space for kinetic rate-laws. The especial characteristics of this approximation produce an extremely useful systemic representation that allows a complete system characterization. Furthermore, their parameters have a precise interpretation as local sensitivities of each of the individual processes and as rate-constants. This facilitates a qualitative discussion and a quantitative estimation of their possible values in relation to the kinetic properties. Following this interpretation, parameter estimation is also possible by relating the systemic behavior to the underlying processes. Without leaving the general formalism, in this paper we suggest deriving the power-law representation in an alternative way that uses least-squares minimization. The resulting power-law mimics the target rate-law in a wider range of concentration values than the classical power-law. Although the implications of this alternative approach remain to be established, our results show that the predicted steady-state using the least-squares power-law is closest to the actual steady-state of the target system.

Semantic representations in the temporal pole predict false memories

PubMed Central

Chadwick, Martin J.; Anjum, Raeesa S.; Kumaran, Dharshan; Schacter, Daniel L.; Spiers, Hugo J.; Hassabis, Demis

2016-01-01

Recent advances in neuroscience have given us unprecedented insight into the neural mechanisms of false memory, showing that artificial memories can be inserted into the memory cells of the hippocampus in a way that is indistinguishable from true memories. However, this alone is not enough to explain how false memories can arise naturally in the course of our daily lives. Cognitive psychology has demonstrated that many instances of false memory, both in the laboratory and the real world, can be attributed to semantic interference. Whereas previous studies have found that a diverse set of regions show some involvement in semantic false memory, none have revealed the nature of the semantic representations underpinning the phenomenon. Here we use fMRI with representational similarity analysis to search for a neural code consistent with semantic false memory. We find clear evidence that false memories emerge from a similarity-based neural code in the temporal pole, a region that has been called the “semantic hub” of the brain. We further show that each individual has a partially unique semantic code within the temporal pole, and this unique code can predict idiosyncratic patterns of memory errors. Finally, we show that the same neural code can also predict variation in true-memory performance, consistent with an adaptive perspective on false memory. Taken together, our findings reveal the underlying structure of neural representations of semantic knowledge, and how this semantic structure can both enhance and distort our memories. PMID:27551087

Semantic representations in the temporal pole predict false memories.

PubMed

Chadwick, Martin J; Anjum, Raeesa S; Kumaran, Dharshan; Schacter, Daniel L; Spiers, Hugo J; Hassabis, Demis

2016-09-06

Recent advances in neuroscience have given us unprecedented insight into the neural mechanisms of false memory, showing that artificial memories can be inserted into the memory cells of the hippocampus in a way that is indistinguishable from true memories. However, this alone is not enough to explain how false memories can arise naturally in the course of our daily lives. Cognitive psychology has demonstrated that many instances of false memory, both in the laboratory and the real world, can be attributed to semantic interference. Whereas previous studies have found that a diverse set of regions show some involvement in semantic false memory, none have revealed the nature of the semantic representations underpinning the phenomenon. Here we use fMRI with representational similarity analysis to search for a neural code consistent with semantic false memory. We find clear evidence that false memories emerge from a similarity-based neural code in the temporal pole, a region that has been called the "semantic hub" of the brain. We further show that each individual has a partially unique semantic code within the temporal pole, and this unique code can predict idiosyncratic patterns of memory errors. Finally, we show that the same neural code can also predict variation in true-memory performance, consistent with an adaptive perspective on false memory. Taken together, our findings reveal the underlying structure of neural representations of semantic knowledge, and how this semantic structure can both enhance and distort our memories.

Weighted Discriminative Dictionary Learning based on Low-rank Representation

NASA Astrophysics Data System (ADS)

Chang, Heyou; Zheng, Hao

2017-01-01

Low-rank representation has been widely used in the field of pattern classification, especially when both training and testing images are corrupted with large noise. Dictionary plays an important role in low-rank representation. With respect to the semantic dictionary, the optimal representation matrix should be block-diagonal. However, traditional low-rank representation based dictionary learning methods cannot effectively exploit the discriminative information between data and dictionary. To address this problem, this paper proposed weighted discriminative dictionary learning based on low-rank representation, where a weighted representation regularization term is constructed. The regularization associates label information of both training samples and dictionary atoms, and encourages to generate a discriminative representation with class-wise block-diagonal structure, which can further improve the classification performance where both training and testing images are corrupted with large noise. Experimental results demonstrate advantages of the proposed method over the state-of-the-art methods.