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.

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.

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 .

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

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…

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

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.

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

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

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

Belief state representation in the dopamine system.

PubMed

Babayan, Benedicte M; Uchida, Naoshige; Gershman, Samuel J

2018-05-14

Learning to predict future outcomes is critical for driving appropriate behaviors. Reinforcement learning (RL) models have successfully accounted for such learning, relying on reward prediction errors (RPEs) signaled by midbrain dopamine neurons. It has been proposed that when sensory data provide only ambiguous information about which state an animal is in, it can predict reward based on a set of probabilities assigned to hypothetical states (called the belief state). Here we examine how dopamine RPEs and subsequent learning are regulated under state uncertainty. Mice are first trained in a task with two potential states defined by different reward amounts. During testing, intermediate-sized rewards are given in rare trials. Dopamine activity is a non-monotonic function of reward size, consistent with RL models operating on belief states. Furthermore, the magnitude of dopamine responses quantitatively predicts changes in behavior. These results establish the critical role of state inference in RL.

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

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

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.

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.

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.

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.

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

Predicting RNA folding thermodynamics with a reduced chain representation model

PubMed Central

CAO, SONG; CHEN, SHI-JIE

2005-01-01

Based on the virtual bond representation for the nucleotide backbone, we develop a reduced conformational model for RNA. We use the experimentally measured atomic coordinates to model the helices and use the self-avoiding walks in a diamond lattice to model the loop conformations. The atomic coordinates of the helices and the lattice representation for the loops are matched at the loop–helix junction, where steric viability is accounted for. Unlike the previous simplified lattice-based models, the present virtual bond model can account for the atomic details of realistic three-dimensional RNA structures. Based on the model, we develop a statistical mechanical theory for RNA folding energy landscapes and folding thermodynamics. Tests against experiments show that the theory can give much more improved predictions for the native structures, the thermal denaturation curves, and the equilibrium folding/unfolding pathways than the previous models. The application of the model to the P5abc region of Tetrahymena group I ribozyme reveals the misfolded intermediates as well as the native-like intermediates in the equilibrium folding process. Moreover, based on the free energy landscape analysis for each and every loop mutation, the model predicts five lethal mutations that can completely alter the free energy landscape and the folding stability of the molecule. PMID:16251382

Basic state lower-tropospheric humidity distribution: key to successful simulation and prediction of the Madden-Julian oscillation

NASA Astrophysics Data System (ADS)

Kim, D.; Ahn, M. S.; DeMott, C. A.; Jiang, X.; Klingaman, N. P.; Kim, H. M.; Lee, J. H.; Lim, Y.; Xavier, P. K.

2017-12-01

The Madden-Julian Oscillation (MJO) influences the global weather-climate system, thereby providing the source of predictability on the intraseasonal timescales worldwide. An accurate representation of the MJO, however, is still one of the most challenging tasks for many contemporary global climate models (GCMs). Identifying aspects of the GCMs that are tightly linked to GCMs' MJO simulation capability is a step toward improving the GCM representation of the MJO. This study surveys recent modeling work that collectively evidence that the horizontal distribution of the basic state low-tropospheric humidity is crucial to a successful simulation and prediction of the MJO. Specifically, the simulated horizontal and meridional gradients of the mean low-tropospheric humidity determine the magnitude of the moistening (drying) to the east (west) of the enhance MJO, thereby enabling or disabling the eastward propagation of the MJO. Supporting this argument, many MJO-incompetent GCMs also exhibit biases in the mean humidity that weaken the horizontal moisture gradient. Also, MJO prediction skill of the S2S models is tightly related to the biases in the mean moisture gradient. Implications of the robust relationship between the MJO and the mean state on MJO modeling and prediction will be discussed.

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

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.

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

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.

The felt presence of other minds: Predictive processing, counterfactual predictions, and mentalising in autism.

PubMed

Palmer, Colin J; Seth, Anil K; Hohwy, Jakob

2015-11-01

The mental states of other people are components of the external world that modulate the activity of our sensory epithelia. Recent probabilistic frameworks that cast perception as unconscious inference on the external causes of sensory input can thus be expanded to enfold the brain's representation of others' mental states. This paper examines this subject in the context of the debate concerning the extent to which we have perceptual awareness of other minds. In particular, we suggest that the notion of perceptual presence helps to refine this debate: are others' mental states experienced as veridical qualities of the perceptual world around us? This experiential aspect of social cognition may be central to conditions such as autism spectrum disorder, where representations of others' mental states seem to be selectively compromised. Importantly, recent work ties perceptual presence to the counterfactual predictions of hierarchical generative models that are suggested to perform unconscious inference in the brain. This enables a characterisation of mental state representations in terms of their associated counterfactual predictions, allowing a distinction between spontaneous and explicit forms of mentalising within the framework of predictive processing. This leads to a hypothesis that social cognition in autism spectrum disorder is characterised by a diminished set of counterfactual predictions and the reduced perceptual presence of others' mental states. Copyright © 2015 Elsevier Inc. 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

Semiclassical approximations in the coherent-state representation

NASA Technical Reports Server (NTRS)

Kurchan, J.; Leboeuf, P.; Saraceno, M.

1989-01-01

The semiclassical limit of the stationary Schroedinger equation in the coherent-state representation is analyzed simultaneously for the groups W1, SU(2), and SU(1,1). A simple expression for the first two orders for the wave function and the associated semiclassical quantization rule is obtained if a definite choice for the classical Hamiltonian and expansion parameter is made. The behavior of the modulus of the wave function, which is a distribution function in a curved phase space, is studied for the three groups. The results are applied to the quantum triaxial rotor.

Maintenance and Representation of Mind Wandering during Resting-State fMRI.

PubMed

Chou, Ying-Hui; Sundman, Mark; Whitson, Heather E; Gaur, Pooja; Chu, Mei-Lan; Weingarten, Carol P; Madden, David J; Wang, Lihong; Kirste, Imke; Joliot, Marc; Diaz, Michele T; Li, Yi-Ju; Song, Allen W; Chen, Nan-Kuei

2017-01-12

Major advances in resting-state functional magnetic resonance imaging (fMRI) techniques in the last two decades have provided a tool to better understand the functional organization of the brain both in health and illness. Despite such developments, characterizing regulation and cerebral representation of mind wandering, which occurs unavoidably during resting-state fMRI scans and may induce variability of the acquired data, remains a work in progress. Here, we demonstrate that a decrease or decoupling in functional connectivity involving the caudate nucleus, insula, medial prefrontal cortex and other domain-specific regions was associated with more sustained mind wandering in particular thought domains during resting-state fMRI. Importantly, our findings suggest that temporal and between-subject variations in functional connectivity of above-mentioned regions might be linked with the continuity of mind wandering. Our study not only provides a preliminary framework for characterizing the maintenance and cerebral representation of different types of mind wandering, but also highlights the importance of taking mind wandering into consideration when studying brain organization with resting-state fMRI in the future.

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

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.

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

Signal Sampling for Efficient Sparse Representation of Resting State FMRI Data

PubMed Central

Ge, Bao; Makkie, Milad; Wang, Jin; Zhao, Shijie; Jiang, Xi; Li, Xiang; Lv, Jinglei; Zhang, Shu; Zhang, Wei; Han, Junwei; Guo, Lei; Liu, Tianming

2015-01-01

As the size of brain imaging data such as fMRI grows explosively, it provides us with unprecedented and abundant information about the brain. How to reduce the size of fMRI data but not lose much information becomes a more and more pressing issue. Recent literature studies tried to deal with it by dictionary learning and sparse representation methods, however, their computation complexities are still high, which hampers the wider application of sparse representation method to large scale fMRI datasets. To effectively address this problem, this work proposes to represent resting state fMRI (rs-fMRI) signals of a whole brain via a statistical sampling based sparse representation. First we sampled the whole brain’s signals via different sampling methods, then the sampled signals were aggregate into an input data matrix to learn a dictionary, finally this dictionary was used to sparsely represent the whole brain’s signals and identify the resting state networks. Comparative experiments demonstrate that the proposed signal sampling framework can speed-up by ten times in reconstructing concurrent brain networks without losing much information. The experiments on the 1000 Functional Connectomes Project further demonstrate its effectiveness and superiority. PMID:26646924

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.

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

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.

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.

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.

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

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.

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

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.

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.

Gaussian Process Regression (GPR) Representation in Predictive Model Markup Language (PMML)

PubMed Central

Lechevalier, D.; Ak, R.; Ferguson, M.; Law, K. H.; Lee, Y.-T. T.; Rachuri, S.

2017-01-01

This paper describes Gaussian process regression (GPR) models presented in predictive model markup language (PMML). PMML is an extensible-markup-language (XML) -based standard language used to represent data-mining and predictive analytic models, as well as pre- and post-processed data. The previous PMML version, PMML 4.2, did not provide capabilities for representing probabilistic (stochastic) machine-learning algorithms that are widely used for constructing predictive models taking the associated uncertainties into consideration. The newly released PMML version 4.3, which includes the GPR model, provides new features: confidence bounds and distribution for the predictive estimations. Both features are needed to establish the foundation for uncertainty quantification analysis. Among various probabilistic machine-learning algorithms, GPR has been widely used for approximating a target function because of its capability of representing complex input and output relationships without predefining a set of basis functions, and predicting a target output with uncertainty quantification. GPR is being employed to various manufacturing data-analytics applications, which necessitates representing this model in a standardized form for easy and rapid employment. In this paper, we present a GPR model and its representation in PMML. Furthermore, we demonstrate a prototype using a real data set in the manufacturing domain. PMID:29202125

Gaussian Process Regression (GPR) Representation in Predictive Model Markup Language (PMML).

PubMed

Park, J; Lechevalier, D; Ak, R; Ferguson, M; Law, K H; Lee, Y-T T; Rachuri, S

2017-01-01

This paper describes Gaussian process regression (GPR) models presented in predictive model markup language (PMML). PMML is an extensible-markup-language (XML) -based standard language used to represent data-mining and predictive analytic models, as well as pre- and post-processed data. The previous PMML version, PMML 4.2, did not provide capabilities for representing probabilistic (stochastic) machine-learning algorithms that are widely used for constructing predictive models taking the associated uncertainties into consideration. The newly released PMML version 4.3, which includes the GPR model, provides new features: confidence bounds and distribution for the predictive estimations. Both features are needed to establish the foundation for uncertainty quantification analysis. Among various probabilistic machine-learning algorithms, GPR has been widely used for approximating a target function because of its capability of representing complex input and output relationships without predefining a set of basis functions, and predicting a target output with uncertainty quantification. GPR is being employed to various manufacturing data-analytics applications, which necessitates representing this model in a standardized form for easy and rapid employment. In this paper, we present a GPR model and its representation in PMML. Furthermore, we demonstrate a prototype using a real data set in the manufacturing domain.

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 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.

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.

Phase-space representations of symmetric informationally complete positive-operator-valued-measure fiducial states

NASA Astrophysics Data System (ADS)

Saraceno, Marcos; Ermann, Leonardo; Cormick, Cecilia

2017-03-01

The problem of finding symmetric informationally complete positive-operator-valued-measures (SIC-POVMs) has been solved numerically for all dimensions d up to 67 [A. J. Scott and M. Grassl, J. Math. Phys. 51, 042203 (2010), 10.1063/1.3374022], but a general proof of existence is still lacking. For each dimension, it was shown that it is possible to find a SIC-POVM that is generated from a fiducial state upon application of the operators of the Heisenberg-Weyl group. We draw on the numerically determined fiducial states to study their phase-space features, as displayed by the characteristic function and the Wigner, Bargmann, and Husimi representations, adapted to a Hilbert space of finite dimension. We analyze the phase-space localization of fiducial states, and observe that the SIC-POVM condition is equivalent to a maximal delocalization property. Finally, we explore the consequences in phase space of the conjectured Zauner symmetry. In particular, we construct a Hermitian operator commuting with this symmetry that leads to a representation of fiducial states in terms of eigenfunctions with definite semiclassical features.

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.

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.

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.

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.

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.

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

NASA Astrophysics Data System (ADS)

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

2017-09-01

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

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.

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

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.

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.

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...

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.

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

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.

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…

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…

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...

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.

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.

Geometric multiaxial representation of N -qubit mixed symmetric separable states

NASA Astrophysics Data System (ADS)

SP, Suma; Sirsi, Swarnamala; Hegde, Subramanya; Bharath, Karthik

2017-08-01

The study of N -qubit mixed symmetric separable states is a longstanding challenging problem as no unique separability criterion exists. In this regard, we take up the N -qubit mixed symmetric separable states for a detailed study as these states are of experimental importance and offer an elegant mathematical analysis since the dimension of the Hilbert space is reduced from 2N to N +1 . Since there exists a one-to-one correspondence between the spin-j system and an N -qubit symmetric state, we employ Fano statistical tensor parameters for the parametrization of the spin-density matrix. Further, we use a geometric multiaxial representation (MAR) of the density matrix to characterize the mixed symmetric separable states. Since the separability problem is NP-hard, we choose to study it in the continuum limit where mixed symmetric separable states are characterized by the P -distribution function λ (θ ,ϕ ) . We show that the N -qubit mixed symmetric separable states can be visualized as a uniaxial system if the distribution function is independent of θ and ϕ . We further choose a distribution function to be the most general positive function on a sphere and observe that the statistical tensor parameters characterizing the N -qubit symmetric system are the expansion coefficients of the distribution function. As an example for the discrete case, we investigate the MAR of a uniformly weighted two-qubit mixed symmetric separable state. We also observe that there exists a correspondence between the separability and classicality of states.

The Problem State: A Cognitive Bottleneck in Multitasking

ERIC Educational Resources Information Center

Borst, Jelmer P.; Taatgen, Niels A.; van Rijn, Hedderik

2010-01-01

The main challenge for theories of multitasking is to predict when and how tasks interfere. Here, we focus on interference related to the problem state, a directly accessible intermediate representation of the current state of a task. On the basis of Salvucci and Taatgen's (2008) threaded cognition theory, we predict interference if 2 or more…

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.

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

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

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.

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.

Body representation disorders predict left right orientation impairments after stroke: A voxel-based lesion symptom mapping study.

PubMed

van Stralen, Haike E; Dijkerman, H Chris; Biesbroek, J Matthijs; Kuijf, Hugo J; van Gemert, H Maarten A; Sluiter, David; Kappelle, L Jaap; van Zandvoort, Martine J E

2017-06-07

Deficits in the ability to distinguish between the left and right side of the body can severely impair daily life functioning. The current study examined the relation between left right orientation (LRO) impairments and somatosensory related deficits, ranging from primary somatosensory impairments to body representation impairments, in patients who suffered a recent stroke. We also examined which areas in the brain are associated with LRO impairments using a Voxel-based Lesion Symptom Mapping (VLSM) analysis. We tested 47 first-ever stroke patients and 48 age-matched healthy controls. LRO was assessed with the Bergen Right Left Discrimination Test (BRLD). Impairments on primary somatosensory function (touch perception, proprioception), higher order somatosensory function (finger gnosis, subjective sense of body ownership) and other cognitive functions (language, attention & working memory, visuospatial neglect) were entered as predictors in a logistic regression analyses. Outcome measures consisted of the BRLD-total performance which was further subdivided in performance for 1) first person perspective stimuli, 2) third person perspective stimuli, 3) alternating between first- and third person perspective. Impairments on BRLD-total performance was predicted by impairments in finger gnosis and visuospatial neglect. For items placed in third person perspective, performance was predicted by body representation impairments; finger agnosia and the subjective sense of body ownership. VLSM analysis showed a significant association between LRO impairments and damage to the right insula. The current study suggests that the somatosensory system is important for LRO. Furthermore, the results indicate that an affected body representation may hinder adopting a third person perspective that may subsequently also lead to LRO impairments. The right insular cortex appeared crucially involved in these processes. Copyright © 2017 Elsevier Ltd. All rights reserved.

The seasonal predictability of blocking frequency in two seasonal prediction systems (CMCC, Met-Office) and the associated representation of low-frequency variability.

NASA Astrophysics Data System (ADS)

Athanasiadis, Panos; Gualdi, Silvio; Scaife, Adam A.; Bellucci, Alessio; Hermanson, Leon; MacLachlan, Craig; Arribas, Alberto; Materia, Stefano; Borelli, Andrea

2014-05-01

Low-frequency variability is a fundamental component of the atmospheric circulation. Extratropical teleconnections, the occurrence of blocking and the slow modulation of the jet streams and storm tracks are all different aspects of low-frequency variability. Part of the latter is attributed to the chaotic nature of the atmosphere and is inherently unpredictable. On the other hand, primarily as a response to boundary forcings, tropospheric low-frequency variability includes components that are potentially predictable. Seasonal forecasting faces the difficult task of predicting these components. Particularly referring to the extratropics, the current generation of seasonal forecasting systems seem to be approaching this target by realistically initializing most components of the climate system, using higher resolution and utilizing large ensemble sizes. Two seasonal prediction systems (Met-Office GloSea and CMCC-SPS-v1.5) are analyzed in terms of their representation of different aspects of extratropical low-frequency variability. The current operational Met-Office system achieves unprecedented high scores in predicting the winter-mean phase of the North Atlantic Oscillation (NAO, corr. 0.74 at 500 hPa) and the Pacific-N. American pattern (PNA, corr. 0.82). The CMCC system, considering its small ensemble size and course resolution, also achieves good scores (0.42 for NAO, 0.51 for PNA). Despite these positive features, both models suffer from biases in low-frequency variance, particularly in the N. Atlantic. Consequently, it is found that their intrinsic variability patterns (sectoral EOFs) differ significantly from the observed, and the known teleconnections are underrepresented. Regarding the representation of N. hemisphere blocking, after bias correction both systems exhibit a realistic climatology of blocking frequency. In this assessment, instantaneous blocking and large-scale persistent blocking events are identified using daily geopotential height fields at

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.

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.

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.

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.

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.…

Modeling alpine grasslands with two integrated hydrologic models: a comparison of the different process representation in CATHY and GEOtop

NASA Astrophysics Data System (ADS)

Camporese, M.; Bertoldi, G.; Bortoli, E.; Wohlfahrt, G.

2017-12-01

Integrated hydrologic surface-subsurface models (IHSSMs) are increasingly used as prediction tools to solve simultaneously states and fluxes in and between multiple terrestrial compartments (e.g., snow cover, surface water, groundwater), in an attempt to tackle environmental problems in a holistic approach. Two such models, CATHY and GEOtop, are used in this study to investigate their capabilities to reproduce hydrological processes in alpine grasslands. The two models differ significantly in the complexity of the representation of the surface energy balance and the solution of Richards equation for water flow in the variably saturated subsurface. The main goal of this research is to show how these differences in process representation can lead to different predictions of hydrologic states and fluxes, in the simulation of an experimental site located in the Venosta Valley (South Tyrol, Italy). Here, a large set of relevant hydrological data (e.g., evapotranspiration, soil moisture) has been collected, with ground and remote sensing observations. The area of interest is part of a Long-Term Ecological Research (LTER) site, a mountain steep, heterogeneous slope, where the predominant land use types are meadow, pasture, and forest. The comparison between data and model predictions, as well as between simulations with the two IHSSMs, contributes to advance our understanding of the tradeoffs between different complexities in modeĺs process representation, model accuracy, and the ability to explain observed hydrological dynamics in alpine environments.

Psychology of knowledge representation.

PubMed

Grimm, Lisa R

2014-05-01

Every cognitive enterprise involves some form of knowledge representation. Humans represent information about the external world and internal mental states, like beliefs and desires, and use this information to meet goals (e.g., classification or problem solving). Unfortunately, researchers do not have direct access to mental representations. Instead, cognitive scientists design experiments and implement computational models to develop theories about the mental representations present during task performance. There are several main types of mental representation and corresponding processes that have been posited: spatial, feature, network, and structured. Each type has a particular structure and a set of processes that are capable of accessing and manipulating information within the representation. The structure and processes determine what information can be used during task performance and what information has not been represented at all. As such, the different types of representation are likely used to solve different kinds of tasks. For example, structured representations are more complex and computationally demanding, but are good at representing relational information. Researchers interested in human psychology would benefit from considering how knowledge is represented in their domain of inquiry. For further resources related to this article, please visit the WIREs website. The author has declared no conflicts of interest for this article. © 2014 John Wiley & Sons, Ltd.

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.

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.

Representation in incremental learning

NASA Technical Reports Server (NTRS)

1993-01-01

Work focused on two areas in machine learning: representation for inductive learning and how to apply concept learning techniques to learning state preferences, which can represent search control knowledge for problem solving. Specifically, in the first area the issues of the effect of representation on learning, on how learning formalisms are biased, and how concept learning can benefit from the use of a hybrid formalism are addressed. In the second area, the issues of developing an agent to learn search control knowledge from the relative values of states, of the source of that qualitative information, and of the ability to use both quantitative and qualitative information in order to develop an effective problem-solving policy are examined.

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.

The role of physical digit representation and numerical magnitude representation in children's multiplication fact retrieval.

PubMed

De Visscher, Alice; Noël, Marie-Pascale; De Smedt, Bert

2016-12-01

Arithmetic facts, in particular multiplication tables, are thought to be stored in long-term memory and to be interference prone. At least two representations underpinning these arithmetic facts have been suggested: a physical representation of the digits and a numerical magnitude representation. We hypothesized that both representations are possible sources of interference that could explain individual differences in multiplication fact performance and/or in strategy use. We investigated the specificity of these interferences on arithmetic fact retrieval and explored the relation between interference and performance on the different arithmetic operations and on general mathematics achievement. Participants were 79 fourth-grade children (M age =9.6 years) who completed a products comparison and a multiplication production task with verbal strategy reports. Performances on a speeded calculation test including the four operations and on a general mathematics achievement test were also collected. Only the interference coming from physical representations was a significant predictor of the performance across multiplications. However, both the magnitude and physical representations were unique predictors of individual differences in multiplication. The frequency of the retrieval strategy across multiplication problems and across individuals was determined only by the physical representation, which therefore is suggested as being responsible for memory storage issues. Interestingly, this impact of physical representation was not observed when predicting performance on subtraction or on general mathematical achievement. In contrast, the impact of the numerical magnitude representation was more general in that it was observed across all arithmetic operations and in general mathematics achievement. Copyright © 2016 Elsevier Inc. All rights reserved.

Representational and Executive Selection Resources in "Theory of Mind": Evidence from Compromised Belief-Desire Reasoning in Old Age

ERIC Educational Resources Information Center

German, Tim P.; Hehman, Jessica A.

2006-01-01

Effective belief-desire reasoning requires both specialized representational capacities--the capacity to represent the mental states as such--as well as executive selection processes for accurate performance on tasks requiring the prediction and explanation of the actions of social agents. Compromised belief-desire reasoning in a given population…

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…

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.

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.

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

Nonequilibrium, steady-state electron transport with N-representable density matrices from the anti-Hermitian contracted Schrödinger equation

NASA Astrophysics Data System (ADS)

Rothman, Adam E.; Mazziotti, David A.

2010-03-01

We study molecular conductivity for a one-electron, bath-molecule-bath model Hamiltonian. The primary quantum-mechanical variable is the one-electron reduced density matrix (1-RDM). By identifying similarities between the steady-state Liouville equation and the anti-Hermitian contracted Schrödinger equation (ACSE) [D. A. Mazziotti, Phys. Rev. A 75, 022505 (2007)], we develop a way of enforcing nonequilibrium, steady-state behavior in a time-independent theory. Our results illustrate the relationship between current and voltage in molecular junctions assuming that the total number of electrons under consideration can be fixed across all driving potentials. The impetus for this work is a recent study by Subotnik et al. that also uses the 1-RDM to study molecular conductivity under different assumptions regarding the total number of electrons [J. E. Subotnik et al., J. Chem. Phys. 130, 144105 (2009)]. Unlike calculations in the previous study, our calculations result in 1-RDMs that are fully N-representable. The present work maintains N-representability through a bath-bath mixing that is related to a time-independent relaxation of the baths in the absence of the molecule, as governed by the ACSE. A lack of N-representability can be important since it corresponds to occupying energy states in the molecule or baths with more than one electron or hole (the absence of an electron) in violation of the Pauli principle. For this reason the present work may serve as an important, albeit preliminary, step in designing a 2-RDM/ACSE method for studying steady-state molecular conductivity with an explicit treatment of electron correlation.

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

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

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…

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

Optimal spatiotemporal representation of multichannel EEG for recognition of brain states associated with distinct visual stimulus

NASA Astrophysics Data System (ADS)

Hramov, Alexander; Musatov, Vyacheslav Yu.; Runnova, Anastasija E.; Efremova, Tatiana Yu.; Koronovskii, Alexey A.; Pisarchik, Alexander N.

2018-04-01

In the paper we propose an approach based on artificial neural networks for recognition of different human brain states associated with distinct visual stimulus. Based on the developed numerical technique and the analysis of obtained experimental multichannel EEG data, we optimize the spatiotemporal representation of multichannel EEG to provide close to 97% accuracy in recognition of the EEG brain states during visual perception. Different interpretations of an ambiguous image produce different oscillatory patterns in the human EEG with similar features for every interpretation. Since these features are inherent to all subjects, a single artificial network can classify with high quality the associated brain states of other subjects.

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

DeepText2GO: Improving large-scale protein function prediction with deep semantic text representation.

PubMed

You, Ronghui; Huang, Xiaodi; Zhu, Shanfeng

2018-06-06

As of April 2018, UniProtKB has collected more than 115 million protein sequences. Less than 0.15% of these proteins, however, have been associated with experimental GO annotations. As such, the use of automatic protein function prediction (AFP) to reduce this huge gap becomes increasingly important. The previous studies conclude that sequence homology based methods are highly effective in AFP. In addition, mining motif, domain, and functional information from protein sequences has been found very helpful for AFP. Other than sequences, alternative information sources such as text, however, may be useful for AFP as well. Instead of using BOW (bag of words) representation in traditional text-based AFP, we propose a new method called DeepText2GO that relies on deep semantic text representation, together with different kinds of available protein information such as sequence homology, families, domains, and motifs, to improve large-scale AFP. Furthermore, DeepText2GO integrates text-based methods with sequence-based ones by means of a consensus approach. Extensive experiments on the benchmark dataset extracted from UniProt/SwissProt have demonstrated that DeepText2GO significantly outperformed both text-based and sequence-based methods, validating its superiority. Copyright © 2018 Elsevier Inc. All rights reserved.

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…

Exploring the Structure of Spatial Representations

PubMed Central

Madl, Tamas; Franklin, Stan; Chen, Ke; Trappl, Robert; Montaldi, Daniela

2016-01-01

It has been suggested that the map-like representations that support human spatial memory are fragmented into sub-maps with local reference frames, rather than being unitary and global. However, the principles underlying the structure of these ‘cognitive maps’ are not well understood. We propose that the structure of the representations of navigation space arises from clustering within individual psychological spaces, i.e. from a process that groups together objects that are close in these spaces. Building on the ideas of representational geometry and similarity-based representations in cognitive science, we formulate methods for learning dissimilarity functions (metrics) characterizing participants’ psychological spaces. We show that these learned metrics, together with a probabilistic model of clustering based on the Bayesian cognition paradigm, allow prediction of participants’ cognitive map structures in advance. Apart from insights into spatial representation learning in human cognition, these methods could facilitate novel computational tools capable of using human-like spatial concepts. We also compare several features influencing spatial memory structure, including spatial distance, visual similarity and functional similarity, and report strong correlations between these dimensions and the grouping probability in participants’ spatial representations, providing further support for clustering in spatial memory. PMID:27347681

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.

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.

Thirst and the state-dependent representation of incentive stimulus value in human motive circuitry.

PubMed

Becker, Christoph A; Schmälzle, Ralf; Flaisch, Tobias; Renner, Britta; Schupp, Harald T

2015-12-01

Depletion imposes both need and desire to drink, and potentiates the response to need-relevant cues in the environment. The present fMRI study aimed to determine which neural structures selectively increase the incentive value of need-relevant stimuli in a thirst state. Towards this end, participants were scanned twice--either in a thirst or no-thirst state--while viewing pictures of beverages and chairs. As expected, thirst led to a selective increase in self-reported pleasantness and arousal by beverages. Increased responses to beverage when compared with chair stimuli were observed in the cingulate cortex, insular cortex and the amygdala in the thirst state, which were absent in the no-thirst condition. Enhancing the incentive value of need-relevant cues in a thirst state is a key mechanism for motivating drinking behavior. Overall, distributed regions of the motive circuitry, which are also implicated in salience processing, craving and interoception, provide a dynamic body-state dependent representation of stimulus value. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

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}}

Neurofeedback and the Neural Representation of Self: Lessons From Awake State and Sleep.

PubMed

Ioannides, Andreas A

2018-01-01

Neurofeedback has been around for half a century, but despite some promising results it is not yet widely appreciated. Recently, some of the concerns about neurofeedback have been addressed with functional magnetic resonance imaging and magnetoencephalography adding their contributions to the long history of neurofeedback with electroencephalography. Attempts to address other concerns related to methodological issues with new experiments and meta-analysis of earlier studies, have opened up new questions about its efficacy. A key concern about neurofeedback is the missing framework to explain how improvements in very different and apparently unrelated conditions are achieved. Recent advances in neuroscience begin to address this concern. A particularly promising approach is the analysis of resting state of fMRI data, which has revealed robust covariations in brain networks that maintain their integrity in sleep and even anesthesia. Aberrant activity in three brain wide networks (i.e., the default mode, central executive and salience networks) has been associated with a number of psychiatric disorders. Recent publications have also suggested that neurofeedback guides the restoration of "normal" activity in these three networks. Using very recent results from our analysis of whole night MEG sleep data together with key concepts from developmental psychology, cloaked in modern neuroscience terms, a theoretical framework is proposed for a neural representation of the self, located at the core of a double onion-like structure of the default mode network. This framework fits a number of old and recent neuroscientific findings, and unites the way attention and memory operate in awake state and during sleep. In the process, safeguards are uncovered, put in place by evolution, before any interference with the core representation of self can proceed. Within this framework, neurofeedback is seen as set of methods for restoration of aberrant activity in large scale networks

Neurofeedback and the Neural Representation of Self: Lessons From Awake State and Sleep

PubMed Central

Ioannides, Andreas A.

2018-01-01

Neurofeedback has been around for half a century, but despite some promising results it is not yet widely appreciated. Recently, some of the concerns about neurofeedback have been addressed with functional magnetic resonance imaging and magnetoencephalography adding their contributions to the long history of neurofeedback with electroencephalography. Attempts to address other concerns related to methodological issues with new experiments and meta-analysis of earlier studies, have opened up new questions about its efficacy. A key concern about neurofeedback is the missing framework to explain how improvements in very different and apparently unrelated conditions are achieved. Recent advances in neuroscience begin to address this concern. A particularly promising approach is the analysis of resting state of fMRI data, which has revealed robust covariations in brain networks that maintain their integrity in sleep and even anesthesia. Aberrant activity in three brain wide networks (i.e., the default mode, central executive and salience networks) has been associated with a number of psychiatric disorders. Recent publications have also suggested that neurofeedback guides the restoration of “normal” activity in these three networks. Using very recent results from our analysis of whole night MEG sleep data together with key concepts from developmental psychology, cloaked in modern neuroscience terms, a theoretical framework is proposed for a neural representation of the self, located at the core of a double onion-like structure of the default mode network. This framework fits a number of old and recent neuroscientific findings, and unites the way attention and memory operate in awake state and during sleep. In the process, safeguards are uncovered, put in place by evolution, before any interference with the core representation of self can proceed. Within this framework, neurofeedback is seen as set of methods for restoration of aberrant activity in large scale networks

Representations of mechanical assembly sequences

NASA Technical Reports Server (NTRS)

Homem De Mello, Luiz S.; Sanderson, Arthur C.

1991-01-01

Five types of representations for assembly sequences are reviewed: the directed graph of feasible assembly sequences, the AND/OR graph of feasible assembly sequences, the set of establishment conditions, and two types of sets of precedence relationships. (precedence relationships between the establishment of one connection between parts and the establishment of another connection, and precedence relationships between the establishment of one connection and states of the assembly process). The mappings of one representation into the others are established. The correctness and completeness of these representations are established. The results presented are needed in the proof of correctness and completeness of algorithms for the generation of mechanical assembly sequences.

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.

On the nonlocal predictions of quantum optics

NASA Technical Reports Server (NTRS)

Marshall, Trevor W.; Santos, Emilio; Vidiella-Barranco, Antonio

1994-01-01

We give a definition of locality in quantum optics based upon Bell's work, and show that locality has been violated in no experiment performed up to now. We argue that the interpretation of the Wigner function as a probability density gives a very attractive local realistic picture of quantum optics provided that this function is nonnegative. We conjecture that this is the case for all states which can be realized in the laboratory. In particular, we believe that the usual representation of 'single photon states' by a Fock state of the Hilbert space is not correct and that a more physical, although less simple mathematically, representation involves density matrices. We study in some detail the experiment showing anticorrelation after a beam splitter and prove that it naturally involves a positive Wigner function. Our (quantum) predictions for this experiment disagree with the ones reported in the literature.

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.

United States Temperature and Precipitation Extremes: Phenomenology, Large-Scale Organization, Physical Mechanisms and Model Representation

NASA Astrophysics Data System (ADS)

Black, R. X.

2017-12-01

We summarize results from a project focusing on regional temperature and precipitation extremes over the continental United States. Our project introduces a new framework for evaluating these extremes emphasizing their (a) large-scale organization, (b) underlying physical sources (including remote-excitation and scale-interaction) and (c) representation in climate models. Results to be reported include the synoptic-dynamic behavior, seasonality and secular variability of cold waves, dry spells and heavy rainfall events in the observational record. We also study how the characteristics of such extremes are systematically related to Northern Hemisphere planetary wave structures and thus planetary- and hemispheric-scale forcing (e.g., those associated with major El Nino events and Arctic sea ice change). The underlying physics of event onset are diagnostically quantified for different categories of events. Finally, the representation of these extremes in historical coupled climate model simulations is studied and the origins of model biases are traced using new metrics designed to assess the large-scale atmospheric forcing of local extremes.

Matrix product representation of the stationary state of the open zero range process

NASA Astrophysics Data System (ADS)

Bertin, Eric; Vanicat, Matthieu

2018-06-01

Many one-dimensional lattice particle models with open boundaries, like the paradigmatic asymmetric simple exclusion process (ASEP), have their stationary states represented in the form of a matrix product, with matrices that do not explicitly depend on the lattice site. In contrast, the stationary state of the open 1D zero-range process (ZRP) takes an inhomogeneous factorized form, with site-dependent probability weights. We show that in spite of the absence of correlations, the stationary state of the open ZRP can also be represented in a matrix product form, where the matrices are site-independent, non-commuting and determined from algebraic relations resulting from the master equation. We recover the known distribution of the open ZRP in two different ways: first, using an explicit representation of the matrices and boundary vectors; second, from the sole knowledge of the algebraic relations satisfied by these matrices and vectors. Finally, an interpretation of the relation between the matrix product form and the inhomogeneous factorized form is proposed within the framework of hidden Markov chains.

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.

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.

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.

Predictability sieve, pointer states, and the classicality of quantum trajectories

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

Dalvit, D. A. R.; Zurek, W. H.; Dziarmaga, J.

2005-12-15

We study various measures of classicality of the states of open quantum systems subject to decoherence. Classical states are expected to be stable in spite of decoherence, and are thought to leave conspicuous imprints on the environment. Here these expected features of environment-induced superselection are quantified using four different criteria: predictability sieve (which selects states that produce least entropy), purification time (which looks for states that are the easiest to find out from the imprint they leave on the environment), efficiency threshold (which finds states that can be deduced from measurements on a smallest fraction of the environment), and puritymore » loss time (that looks for states for which it takes the longest to lose a set fraction of their initial purity). We show that when pointer states--the most predictable states of an open quantum system selected by the predictability sieve--are well defined, all four criteria agree that they are indeed the most classical states. We illustrate this with two examples: an underdamped harmonic oscillator, for which coherent states are unanimously chosen by all criteria, and a free particle undergoing quantum Brownian motion, for which most criteria select almost identical Gaussian states (although, in this case, the predictability sieve does not select well defined pointer states)« 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

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

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.

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

Understanding Karma Police: The Perceived Plausibility of Noun Compounds as Predicted by Distributional Models of Semantic Representation

PubMed Central

Günther, Fritz; Marelli, Marco

2016-01-01

Noun compounds, consisting of two nouns (the head and the modifier) that are combined into a single concept, differ in terms of their plausibility: school bus is a more plausible compound than saddle olive. The present study investigates which factors influence the plausibility of attested and novel noun compounds. Distributional Semantic Models (DSMs) are used to obtain formal (vector) representations of word meanings, and compositional methods in DSMs are employed to obtain such representations for noun compounds. From these representations, different plausibility measures are computed. Three of those measures contribute in predicting the plausibility of noun compounds: The relatedness between the meaning of the head noun and the compound (Head Proximity), the relatedness between the meaning of modifier noun and the compound (Modifier Proximity), and the similarity between the head noun and the modifier noun (Constituent Similarity). We find non-linear interactions between Head Proximity and Modifier Proximity, as well as between Modifier Proximity and Constituent Similarity. Furthermore, Constituent Similarity interacts non-linearly with the familiarity with the compound. These results suggest that a compound is perceived as more plausible if it can be categorized as an instance of the category denoted by the head noun, if the contribution of the modifier to the compound meaning is clear but not redundant, and if the constituents are sufficiently similar in cases where this contribution is not clear. Furthermore, compounds are perceived to be more plausible if they are more familiar, but mostly for cases where the relation between the constituents is less clear. PMID:27732599

Predictability and prediction of persistent cool states of the Tropical Pacific Ocean

NASA Astrophysics Data System (ADS)

Ramesh, Nandini; Cane, Mark A.; Seager, Richard; Lee, Dong Eun

2017-10-01

The Tropical Pacific Ocean displays persistently cool sea surface temperature (SST) anomalies that last several years to a decade, with either no El Niño events or a few weak El Niño events. These cause large-scale droughts in the extratropics, including major North American droughts such as the 1930s Dust Bowl, and also modulate the global mean surface temperature. Here we show that two models with different levels of complexity—the Zebiak-Cane intermediate model and the Geophysical Fluid Dynamics Laboratory Coupled Model version 2.1—are able to produce such periods in a realistic manner. We then test the predictability of these periods in the Zebiak-Cane model using an ensemble of experiments with perturbed initial states. Our results show that in most cases the cool mean state is predictable. We then apply this method to make retrospective forecasts of shifts in the decadal mean state and to forecast the mean state of the Tropical Pacific Ocean for the upcoming decade. Our results suggest that the Pacific will undergo a shift to a warmer mean state after the 2015-2016 El Niño. This could imply the cessation of the drier than normal conditions that have generally afflicted southwest North America since the 1997-1998 El Niño, as well as the twenty-first-century pause in global warming. Implications for our understanding of the origins of such persistent cool states and the possibility of improving predictions of large-scale droughts are discussed.

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

Varied representation of the West Pacific pattern in multiple dynamical seasonal predictions of APCC-MME

NASA Astrophysics Data System (ADS)

Lee, Yun-Young

2017-04-01

phenomena: East Asian winter monsoon (EAWM), Atlantic dipole, Pacific/Atlantic jets and Pacific/Atlantic Hadley circulations. Changes in structure and amplitude of them are diagnosed in terms of root mean square error, pattern correlation, intensity bias, zonal displacement and/or downstream extension. There is consistent strengthening/downstream extension of Atlantic jet and absence of southern divergence cell of Atlantic Hadley in most seasonal prediction models. It is demonstrated that WP representation has something to do with bias of Atlantic winter climatology (Atlantic dipole and Atlantic jet) from scatter plot and regression analysis. This implies the importance of realistic simulation of winter climatology further upstream for better WP representation. A fundamental conclusion of this study is that the representation of primary WP features varies among individual models of APCC-MME and it is significantly dependent on the deficiencies of some winter mean climatological patterns.

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 scale-invariant internal representation of time.

PubMed

Shankar, Karthik H; Howard, Marc W

2012-01-01

We propose a principled way to construct an internal representation of the temporal stimulus history leading up to the present moment. A set of leaky integrators performs a Laplace transform on the stimulus function, and a linear operator approximates the inversion of the Laplace transform. The result is a representation of stimulus history that retains information about the temporal sequence of stimuli. This procedure naturally represents more recent stimuli more accurately than less recent stimuli; the decrement in accuracy is precisely scale invariant. This procedure also yields time cells that fire at specific latencies following the stimulus with a scale-invariant temporal spread. Combined with a simple associative memory, this representation gives rise to a moment-to-moment prediction that is also scale invariant in time. We propose that this scale-invariant representation of temporal stimulus history could serve as an underlying representation accessible to higher-level behavioral and cognitive mechanisms. In order to illustrate the potential utility of this scale-invariant representation in a variety of fields, we sketch applications using minimal performance functions to problems in classical conditioning, interval timing, scale-invariant learning in autoshaping, and the persistence of the recency effect in episodic memory across timescales.

Characterization of normality of chaotic systems including prediction and detection of anomalies

NASA Astrophysics Data System (ADS)

Engler, Joseph John

Accurate prediction and control pervades domains such as engineering, physics, chemistry, and biology. Often, it is discovered that the systems under consideration cannot be well represented by linear, periodic nor random data. It has been shown that these systems exhibit deterministic chaos behavior. Deterministic chaos describes systems which are governed by deterministic rules but whose data appear to be random or quasi-periodic distributions. Deterministically chaotic systems characteristically exhibit sensitive dependence upon initial conditions manifested through rapid divergence of states initially close to one another. Due to this characterization, it has been deemed impossible to accurately predict future states of these systems for longer time scales. Fortunately, the deterministic nature of these systems allows for accurate short term predictions, given the dynamics of the system are well understood. This fact has been exploited in the research community and has resulted in various algorithms for short term predictions. Detection of normality in deterministically chaotic systems is critical in understanding the system sufficiently to able to predict future states. Due to the sensitivity to initial conditions, the detection of normal operational states for a deterministically chaotic system can be challenging. The addition of small perturbations to the system, which may result in bifurcation of the normal states, further complicates the problem. The detection of anomalies and prediction of future states of the chaotic system allows for greater understanding of these systems. The goal of this research is to produce methodologies for determining states of normality for deterministically chaotic systems, detection of anomalous behavior, and the more accurate prediction of future states of the system. Additionally, the ability to detect subtle system state changes is discussed. The dissertation addresses these goals by proposing new representational

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

State-space prediction model for chaotic time series

NASA Astrophysics Data System (ADS)

Alparslan, A. K.; Sayar, M.; Atilgan, A. R.

1998-08-01

A simple method for predicting the continuation of scalar chaotic time series ahead in time is proposed. The false nearest neighbors technique in connection with the time-delayed embedding is employed so as to reconstruct the state space. A local forecasting model based upon the time evolution of the topological neighboring in the reconstructed phase space is suggested. A moving root-mean-square error is utilized in order to monitor the error along the prediction horizon. The model is tested for the convection amplitude of the Lorenz model. The results indicate that for approximately 100 cycles of the training data, the prediction follows the actual continuation very closely about six cycles. The proposed model, like other state-space forecasting models, captures the long-term behavior of the system due to the use of spatial neighbors in the state space.

Part I: Steady States in Two-Species Particle Aggregation. Part II: Sparse Representations for Multiscale PDE

DTIC Science & Technology

2015-03-01

University of California Los Angeles Part I: Steady States in Two-Species Particle Aggregation Part II: Sparse Representations for Multiscale PDE A ...Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a ...penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. 1. REPORT DATE MAR 2015

Does the uncertainty in the representation of terrestrial water flows affect precipitation predictability? A WRF-Hydro ensemble analysis for Central Europe

NASA Astrophysics Data System (ADS)

Arnault, Joel; Rummler, Thomas; Baur, Florian; Lerch, Sebastian; Wagner, Sven; Fersch, Benjamin; Zhang, Zhenyu; Kerandi, Noah; Keil, Christian; Kunstmann, Harald

2017-04-01

Precipitation predictability can be assessed by the spread within an ensemble of atmospheric simulations being perturbed in the initial, lateral boundary conditions and/or modeled processes within a range of uncertainty. Surface-related processes are more likely to change precipitation when synoptic forcing is weak. This study investigates the effect of uncertainty in the representation of terrestrial water flows on precipitation predictability. The tools used for this investigation are the Weather Research and Forecasting (WRF) model and its hydrologically-enhanced version WRF-Hydro, applied over Central Europe during April-October 2008. The WRF grid is that of COSMO-DE, with a resolution of 2.8 km. In WRF-Hydro, the WRF grid is coupled with a sub-grid at 280 m resolution to resolve lateral terrestrial water flows. Vertical flow uncertainty is considered by modifying the parameter controlling the partitioning between surface runoff and infiltration in WRF, and horizontal flow uncertainty is considered by comparing WRF with WRF-Hydro. Precipitation predictability is deduced from the spread of an ensemble based on three turbulence parameterizations. Model results are validated with E-OBS precipitation and surface temperature, ESA-CCI soil moisture, FLUXNET-MTE surface evaporation and GRDC discharge. It is found that the uncertainty in the representation of terrestrial water flows is more likely to significantly affect precipitation predictability when surface flux spatial variability is high. In comparison to the WRF ensemble, WRF-Hydro slightly improves the adjusted continuous ranked probability score of daily precipitation. The reproduction of observed daily discharge with Nash-Sutcliffe model efficiency coefficients up to 0.91 demonstrates the potential of WRF-Hydro for flood forecasting.

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.…

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

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

The semantic representation of prejudice and stereotypes.

PubMed

Bhatia, Sudeep

2017-07-01

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

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…

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

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

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.

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

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…

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

Pain, dissociation and subliminal self-representations.

PubMed

Bob, Petr

2008-03-01

According to recent evidence, neurophysiological processes coupled to pain are closely related to the mechanisms of consciousness. This evidence is in accordance with findings that changes in states of consciousness during hypnosis or traumatic dissociation strongly affect conscious perception and experience of pain, and markedly influence brain functions. Past research indicates that painful experience may induce dissociated state and information about the experience may be stored or processed unconsciously. Reported findings suggest common neurophysiological mechanisms of pain and dissociation and point to a hypothesis of dissociation as a defense mechanism against psychological and physical pain that substantially influences functions of consciousness. The hypothesis is also supported by findings that information can be represented in the mind/brain without the subject's awareness. The findings of unconsciously present information suggest possible binding between conscious contents and self-functions that constitute self-representational dimensions of consciousness. The self-representation means that certain inner states of own body are interpreted as mental and somatic identity, while other bodily signals, currently not accessible to the dominant interpreter's access are dissociated and may be defined as subliminal self-representations. In conclusion, the neurophysiological aspects of consciousness and its integrative role in the therapy of painful traumatic memories are discussed.

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

The Interaction between Semantic Representation and Episodic Memory.

PubMed

Fang, Jing; Rüther, Naima; Bellebaum, Christian; Wiskott, Laurenz; Cheng, Sen

2018-02-01

The experimental evidence on the interrelation between episodic memory and semantic memory is inconclusive. Are they independent systems, different aspects of a single system, or separate but strongly interacting systems? Here, we propose a computational role for the interaction between the semantic and episodic systems that might help resolve this debate. We hypothesize that episodic memories are represented as sequences of activation patterns. These patterns are the output of a semantic representational network that compresses the high-dimensional sensory input. We show quantitatively that the accuracy of episodic memory crucially depends on the quality of the semantic representation. We compare two types of semantic representations: appropriate representations, which means that the representation is used to store input sequences that are of the same type as those that it was trained on, and inappropriate representations, which means that stored inputs differ from the training data. Retrieval accuracy is higher for appropriate representations because the encoded sequences are less divergent than those encoded with inappropriate representations. Consistent with our model prediction, we found that human subjects remember some aspects of episodes significantly more accurately if they had previously been familiarized with the objects occurring in the episode, as compared to episodes involving unfamiliar objects. We thus conclude that the interaction with the semantic system plays an important role for episodic memory.

Toward the Computational Representation of Individual Cultural, Cognitive, and Physiological State: The Sensor Shooter Simulation

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

RAYBOURN,ELAINE M.; FORSYTHE,JAMES C.

2001-08-01

This report documents an exploratory FY 00 LDRD project that sought to demonstrate the first steps toward a realistic computational representation of the variability encountered in individual human behavior. Realism, as conceptualized in this project, required that the human representation address the underlying psychological, cultural, physiological, and environmental stressors. The present report outlines the researchers' approach to representing cognitive, cultural, and physiological variability of an individual in an ambiguous situation while faced with a high-consequence decision that would greatly impact subsequent events. The present project was framed around a sensor-shooter scenario as a soldier interacts with an unexpected target (twomore » young Iraqi girls). A software model of the ''Sensor Shooter'' scenario from Desert Storm was developed in which the framework consisted of a computational instantiation of Recognition Primed Decision Making in the context of a Naturalistic Decision Making model [1]. Recognition Primed Decision Making was augmented with an underlying foundation based on our current understanding of human neurophysiology and its relationship to human cognitive processes. While the Gulf War scenario that constitutes the framework for the Sensor Shooter prototype is highly specific, the human decision architecture and the subsequent simulation are applicable to other problems similar in concept, intensity, and degree of uncertainty. The goal was to provide initial steps toward a computational representation of human variability in cultural, cognitive, and physiological state in order to attain a better understanding of the full depth of human decision-making processes in the context of ambiguity, novelty, and heightened arousal.« less

Hemoglobin state-flux: A finite-state model representation of the hemoglobin signal for evaluation of the resting state and the influence of disease

PubMed Central

Barbour, Randall L.; Barbour, San-Lian S.

2018-01-01

Summary In this report we introduce a weak-model approach for examination of the intrinsic time-varying properties of the hemoglobin signal, with the aim of advancing the application of functional near infrared spectroscopy (fNIRS) for the detection of breast cancer, among other potential uses. The developed methodology integrates concepts from stochastic network theory with known modulatory features of the vascular bed, and in doing so provides access to a previously unrecognized dense feature space that is shown to have promising diagnostic potential. Notable features of the methodology include access to this information solely from measures acquired in the resting state, and analysis of these by treating the various components of the hemoglobin (Hb) signal as a co-varying interacting system. Approach The principal data-transform kernel projects Hb state-space trajectories onto a coordinate system that constitutes a finite-state representation of covariations among the principal elements of the Hb signal (i.e., its oxygenated (ΔoxyHb) and deoxygenated (ΔdeoxyHb) forms and the associated dependent quantities: total hemoglobin (ΔtotalHb = ΔoxyHb + ΔdeoxyHb), hemoglobin oxygen saturation (ΔHbO2Sat = 100Δ(oxyHb/totalHb)), and tissue-hemoglobin oxygen exchange (ΔHbO2Exc = ΔdeoxyHb—ΔoxyHb)). The resulting ten-state representation treats the evolution of this signal as a one-space, spatiotemporal network that undergoes transitions from one state to another. States of the network are defined by the algebraic signs of the amplitudes of the time-varying components of the Hb signal relative to their temporal mean values. This assignment produces several classes of coefficient arrays, most with a dimension of 10×10. Biological motivation Motivating our approach is the understanding that effector mechanisms that modulate blood delivery to tissue operate on macroscopic scales, in a spatially and temporally varying manner. Also recognized is that this behavior is

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.

Dynamic Filtering Improves Attentional State Prediction with fNIRS

NASA Technical Reports Server (NTRS)

Harrivel, Angela R.; Weissman, Daniel H.; Noll, Douglas C.; Huppert, Theodore; Peltier, Scott J.

2016-01-01

Brain activity can predict a person's level of engagement in an attentional task. However, estimates of brain activity are often confounded by measurement artifacts and systemic physiological noise. The optimal method for filtering this noise - thereby increasing such state prediction accuracy - remains unclear. To investigate this, we asked study participants to perform an attentional task while we monitored their brain activity with functional near infrared spectroscopy (fNIRS). We observed higher state prediction accuracy when noise in the fNIRS hemoglobin [Hb] signals was filtered with a non-stationary (adaptive) model as compared to static regression (84% +/- 6% versus 72% +/- 15%).

Reformulation of Density Functional Theory for N-Representable Densities and the Resolution of the v-Representability Problem

DOE PAGES

Gonis, A.; Zhang, X. G.; Stocks, G. M.; ...

2015-10-23

Density functional theory for the case of general, N-representable densities is reformulated in terms of density functional derivatives of expectation values of operators evaluated with wave functions leading to a density, making no reference to the concept of potential. The developments provide a complete solution of the v-representability problem by establishing a mathematical procedure that determines whether a density is v-representable and in the case of an affirmative answer determines the potential (within an additive constant) as a derivative with respect to the density of a constrained search functional. It also establishes the existence of an energy functional of themore » density that, for v-representable densities, assumes its minimum value at the density describing the ground state of an interacting many-particle system. The theorems of Hohenberg and Kohn emerge as special cases of the formalism.« less

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.

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…

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…

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.

Identifying Representational Competence with Multi-Representational Displays

ERIC Educational Resources Information Center

Stieff, Mike; Hegarty, Mary; Deslongchamps, Ghislain

2011-01-01

Increasingly, multi-representational educational technologies are being deployed in science classrooms to support science learning and the development of representational competence. Several studies have indicated that students experience significant challenges working with these multi-representational displays and prefer to use only one…

The Current State of Predicting Furrow Irrigation Erosion

USDA-ARS?s Scientific Manuscript database

There continues to be a need to predict furrow irrigation erosion to estimate on- and off-site impacts of irrigation management. The objective of this paper is to review the current state of furrow erosion prediction technology considering four models: SISL, WEPP, WinSRFR and APEX. SISL is an empiri...

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…

Early Numeracy Indicators: Examining Predictive Utility Across Years and States

ERIC Educational Resources Information Center

Conoyer, Sarah J.; Foegen, Anne; Lembke, Erica S.

2016-01-01

Two studies using similar methods in two states investigated the long-term predictive utility of two single-skill early numeracy Curriculum Based Measures (CBMs) and the degree to which they can adequately predict high-stakes test scores. Data were drawn from kindergarten and first-grade students. State standardized assessment data from the…

Dynamic filtering improves attentional state prediction with fNIRS

PubMed Central

Harrivel, Angela R.; Weissman, Daniel H.; Noll, Douglas C.; Huppert, Theodore; Peltier, Scott J.

2016-01-01

Brain activity can predict a person’s level of engagement in an attentional task. However, estimates of brain activity are often confounded by measurement artifacts and systemic physiological noise. The optimal method for filtering this noise – thereby increasing such state prediction accuracy – remains unclear. To investigate this, we asked study participants to perform an attentional task while we monitored their brain activity with functional near infrared spectroscopy (fNIRS). We observed higher state prediction accuracy when noise in the fNIRS hemoglobin [Hb] signals was filtered with a non-stationary (adaptive) model as compared to static regression (84% ± 6% versus 72% ± 15%). PMID:27231602

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.

Body representation in patients after vascular brain injuries.

PubMed

Razmus, Magdalena

2017-11-01

Neuropsychological literature suggests that body representation is a multidimensional concept consisting of various types of representations. Previous studies have demonstrated dissociations between three types of body representation specified by the kind of data and processes, i.e. body schema, body structural description, and body semantics. The aim of the study was to describe the state of body representation in patients after vascular brain injuries and to provide evidence for the different types of body representation. The question about correlations between body representation deficits and neuropsychological dysfunctions was also investigated. Fifty patients after strokes and 50 control individuals participated in the study. They were examined with tasks referring to dynamic representation of body parts positions, topological body map, and lexical and semantic knowledge about the body. Data analysis showed that vascular brain injuries result in deficits of body representation, which may co-occur with cognitive dysfunctions, but the latter are a possible risk factor for body representation deficits rather than sufficient or imperative requisites for them. The study suggests that types of body representation may be separated on the basis not only of their content, but also of their relation with self. Principal component analysis revealed three factors, which explained over 66% of results variance. The factors, which may be interpreted as types or dimensions of mental model of a body, represent different degrees of connection with self. The results indicate another possibility of body representation types classification, which should be verified in future research.

State of Jet Noise Prediction-NASA Perspective

NASA Technical Reports Server (NTRS)

Bridges, James E.

2008-01-01

This presentation covers work primarily done under the Airport Noise Technical Challenge portion of the Supersonics Project in the Fundamental Aeronautics Program. To provide motivation and context, the presentation starts with a brief overview of the Airport Noise Technical Challenge. It then covers the state of NASA s jet noise prediction tools in empirical, RANS-based, and time-resolved categories. The empirical tools, requires seconds to provide a prediction of noise spectral directivity with an accuracy of a few dB, but only for axisymmetric configurations. The RANS-based tools are able to discern the impact of three-dimensional features, but are currently deficient in predicting noise from heated jets and jets with high speed and require hours to produce their prediction. The time-resolved codes are capable of predicting resonances and other time-dependent phenomena, but are very immature, requiring months to deliver predictions without unknown accuracies and dependabilities. In toto, however, when one considers the progress being made it appears that aeroacoustic prediction tools are soon to approach the level of sophistication and accuracy of aerodynamic engineering tools.

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.

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

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.

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.

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.

Automatic speech recognition using a predictive echo state network classifier.

PubMed

Skowronski, Mark D; Harris, John G

2007-04-01

We have combined an echo state network (ESN) with a competitive state machine framework to create a classification engine called the predictive ESN classifier. We derive the expressions for training the predictive ESN classifier and show that the model was significantly more noise robust compared to a hidden Markov model in noisy speech classification experiments by 8+/-1 dB signal-to-noise ratio. The simple training algorithm and noise robustness of the predictive ESN classifier make it an attractive classification engine for automatic speech recognition.

With age comes representational wisdom in social signals.

PubMed

van Rijsbergen, Nicola; Jaworska, Katarzyna; Rousselet, Guillaume A; Schyns, Philippe G

2014-12-01

In an increasingly aging society, age has become a foundational dimension of social grouping broadly targeted by advertising and governmental policies. However, perception of old age induces mainly strong negative social biases. To characterize their cognitive and perceptual foundations, we modeled the mental representations of faces associated with three age groups (young age, middle age, and old age), in younger and older participants. We then validated the accuracy of each mental representation of age with independent validators. Using statistical image processing, we identified the features of mental representations that predict perceived age. Here, we show that whereas younger people mentally dichotomize aging into two groups, themselves (younger) and others (older), older participants faithfully represent the features of young age, middle age, and old age, with richer representations of all considered ages. Our results demonstrate that, contrary to popular public belief, older minds depict socially relevant information more accurately than their younger counterparts. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

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.

Linked-cluster formulation of electron-hole interaction kernel in real-space representation without using unoccupied states.

PubMed

Bayne, Michael G; Scher, Jeremy A; Ellis, Benjamin H; Chakraborty, Arindam

2018-05-21

Electron-hole or quasiparticle representation plays a central role in describing electronic excitations in many-electron systems. For charge-neutral excitation, the electron-hole interaction kernel is the quantity of interest for calculating important excitation properties such as optical gap, optical spectra, electron-hole recombination and electron-hole binding energies. The electron-hole interaction kernel can be formally derived from the density-density correlation function using both Green's function and TDDFT formalism. The accurate determination of the electron-hole interaction kernel remains a significant challenge for precise calculations of optical properties in the GW+BSE formalism. From the TDDFT perspective, the electron-hole interaction kernel has been viewed as a path to systematic development of frequency-dependent exchange-correlation functionals. Traditional approaches, such as MBPT formalism, use unoccupied states (which are defined with respect to Fermi vacuum) to construct the electron-hole interaction kernel. However, the inclusion of unoccupied states has long been recognized as the leading computational bottleneck that limits the application of this approach for larger finite systems. In this work, an alternative derivation that avoids using unoccupied states to construct the electron-hole interaction kernel is presented. The central idea of this approach is to use explicitly correlated geminal functions for treating electron-electron correlation for both ground and excited state wave functions. Using this ansatz, it is derived using both diagrammatic and algebraic techniques that the electron-hole interaction kernel can be expressed only in terms of linked closed-loop diagrams. It is proved that the cancellation of unlinked diagrams is a consequence of linked-cluster theorem in real-space representation. The electron-hole interaction kernel derived in this work was used to calculate excitation energies in many-electron systems and results

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…

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…

Contacts de langues et representations (Language Contacts and Representations).

ERIC Educational Resources Information Center

Matthey, Marinette, Ed.

1997-01-01

Essays on language contact and the image of language, entirely in French, include: "Representations 'du' contexte et representations 'en' contexte? Eleves et enseignants face a l'apprentissage de la langue" ("Representations 'of' Context or Representations 'in' Context? Students and Teachers Facing Language Learning" (Laurent…

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.

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

Medico-Artistic Complicities on Swedish Stages: The Boys in the Band and the Regulation of Gay Male Representation in the Welfare State.

PubMed

Gindt, Dirk

2016-01-01

Seeking to understand the highly unfavorable conditions for the development of gay male theater in Sweden, this essay engages in a historical study of the national opening of Mart Crowley's The Boys in the Band at Malmö City Theatre in 1970. Propelled by a Foucauldian-inspired theoretical approach, it identifies the subtle, yet highly effective, measures of control that the, at the time, social democratic welfare state exercised over representations of homosexuality on stage. State representatives, who complied with the official political and medical doctrine that homosexuality was a mental illness and posed a potential threat to social stability, interfered at various levels of the production, including the rehearsal process and post-performance talks between cast members and audiences. This alliance between Swedish theaters and members of the medical, psychological, and sexological professions constituted a medico-artistic complicity that supervised and regulated early attempts of gay representation on stage.

The Representation of Other Cultures in Award-Winning Picture Books from the United States, Australia, and Great Britain (1960-2009)

ERIC Educational Resources Information Center

Hall, Virginia

2011-01-01

The purpose of this study was to investigate the representation of other cultures in award-winning picture books from the United States, Australia, and Great Britain between 1960 and 2009. Not only was the cultural content of children's literature over the past fifty years investigated, but the protocol created to evaluate the books was a newly…

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.

Understanding Deep Representations Learned in Modeling Users Likes.

PubMed

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

2016-08-01

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

Similarity preserving low-rank representation for enhanced data representation and effective subspace learning.

PubMed

Zhang, Zhao; Yan, Shuicheng; Zhao, Mingbo

2014-05-01

Latent Low-Rank Representation (LatLRR) delivers robust and promising results for subspace recovery and feature extraction through mining the so-called hidden effects, but the locality of both similar principal and salient features cannot be preserved in the optimizations. To solve this issue for achieving enhanced performance, a boosted version of LatLRR, referred to as Regularized Low-Rank Representation (rLRR), is proposed through explicitly including an appropriate Laplacian regularization that can maximally preserve the similarity among local features. Resembling LatLRR, rLRR decomposes given data matrix from two directions by seeking a pair of low-rank matrices. But the similarities of principal and salient features can be effectively preserved by rLRR. As a result, the correlated features are well grouped and the robustness of representations is also enhanced. Based on the outputted bi-directional low-rank codes by rLRR, an unsupervised subspace learning framework termed Low-rank Similarity Preserving Projections (LSPP) is also derived for feature learning. The supervised extension of LSPP is also discussed for discriminant subspace learning. The validity of rLRR is examined by robust representation and decomposition of real images. Results demonstrated the superiority of our rLRR and LSPP in comparison to other related state-of-the-art algorithms. Copyright © 2014 Elsevier Ltd. All rights reserved.

The Frequency of "Brilliant" and "Genius" in Teaching Evaluations Predicts the Representation of Women and African Americans across Fields.

PubMed

Storage, Daniel; Horne, Zachary; Cimpian, Andrei; Leslie, Sarah-Jane

2016-01-01

Women and African Americans-groups targeted by negative stereotypes about their intellectual abilities-may be underrepresented in careers that prize brilliance and genius. A recent nationwide survey of academics provided initial support for this possibility. Fields whose practitioners believed that natural talent is crucial for success had fewer female and African American PhDs. The present study seeks to replicate this initial finding with a different, and arguably more naturalistic, measure of the extent to which brilliance and genius are prized within a field. Specifically, we measured field-by-field variability in the emphasis on these intellectual qualities by tallying-with the use of a recently released online tool-the frequency of the words "brilliant" and "genius" in over 14 million reviews on RateMyProfessors.com, a popular website where students can write anonymous evaluations of their instructors. This simple word count predicted both women's and African Americans' representation across the academic spectrum. That is, we found that fields in which the words "brilliant" and "genius" were used more frequently on RateMyProfessors.com also had fewer female and African American PhDs. Looking at an earlier stage in students' educational careers, we found that brilliance-focused fields also had fewer women and African Americans obtaining bachelor's degrees. These relationships held even when accounting for field-specific averages on standardized mathematics assessments, as well as several competing hypotheses concerning group differences in representation. The fact that this naturalistic measure of a field's focus on brilliance predicted the magnitude of its gender and race gaps speaks to the tight link between ability beliefs and diversity.

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…

Systems and Methods for Automated Vessel Navigation Using Sea State Prediction

NASA Technical Reports Server (NTRS)

Huntsberger, Terrance L. (Inventor); Howard, Andrew B. (Inventor); Reinhart, Rene Felix (Inventor); Aghazarian, Hrand (Inventor); Rankin, Arturo (Inventor)

2017-01-01

Systems and methods for sea state prediction and autonomous navigation in accordance with embodiments of the invention are disclosed. One embodiment of the invention includes a method of predicting a future sea state including generating a sequence of at least two 3D images of a sea surface using at least two image sensors, detecting peaks and troughs in the 3D images using a processor, identifying at least one wavefront in each 3D image based upon the detected peaks and troughs using the processor, characterizing at least one propagating wave based upon the propagation of wavefronts detected in the sequence of 3D images using the processor, and predicting a future sea state using at least one propagating wave characterizing the propagation of wavefronts in the sequence of 3D images using the processor. Another embodiment includes a method of autonomous vessel navigation based upon a predicted sea state and target location.

Systems and Methods for Automated Vessel Navigation Using Sea State Prediction

NASA Technical Reports Server (NTRS)

Aghazarian, Hrand (Inventor); Reinhart, Rene Felix (Inventor); Huntsberger, Terrance L. (Inventor); Rankin, Arturo (Inventor); Howard, Andrew B. (Inventor)

2015-01-01

Systems and methods for sea state prediction and autonomous navigation in accordance with embodiments of the invention are disclosed. One embodiment of the invention includes a method of predicting a future sea state including generating a sequence of at least two 3D images of a sea surface using at least two image sensors, detecting peaks and troughs in the 3D images using a processor, identifying at least one wavefront in each 3D image based upon the detected peaks and troughs using the processor, characterizing at least one propagating wave based upon the propagation of wavefronts detected in the sequence of 3D images using the processor, and predicting a future sea state using at least one propagating wave characterizing the propagation of wavefronts in the sequence of 3D images using the processor. Another embodiment includes a method of autonomous vessel navigation based upon a predicted sea state and target location.

33 CFR 20.301 - Representation.

Code of Federal Regulations, 2010 CFR

2010-07-01

... representative, shall file a notice of appearance. The notice must indicate— (1) The name of the case, including... authorized representative shall also file a notice, including the items listed in paragraph (a) of this... United States. A personal representation of membership is sufficient proof, unless the ALJ orders more...

33 CFR 20.301 - Representation.

Code of Federal Regulations, 2011 CFR

2011-07-01

... representative, shall file a notice of appearance. The notice must indicate— (1) The name of the case, including... authorized representative shall also file a notice, including the items listed in paragraph (a) of this... United States. A personal representation of membership is sufficient proof, unless the ALJ orders more...

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.

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.

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.

Prediction Error Representation in Individuals with Generalized Anxiety Disorder During Passive Avoidance

PubMed Central

White, Stuart F.; Geraci, Marilla; Lewis, Elizabeth; Leshin, Joseph; Teng, Cindy; Averbeck, Bruno; Meffert, Harma; Ernst, Monique; Blair, James R.; Grillon, Christian; Blair, Karina S.

2017-01-01

Objective Deficits in reinforcement-based decision-making have been reported in Generalized Anxiety Disorder. However, the pathophysiology of these deficits is largely unknown, extant studies have mainly examined youth and the integrity of core functional processes underpinning decision-making remain undetermined. In particular, it is unclear whether the representation of reinforcement prediction error (PE: the difference between received and expected reinforcement) is disrupted in Generalized Anxiety Disorder. The current study addresses these issues in adults with the disorder. Methods Forty-six un-medicated individuals with Generalized Anxiety Disorder and 32 healthy controls group-matched on IQ, gender and age, completed a passive avoidance task while undergoing functional MRI. Results Behaviorally, individuals with Generalized Anxiety Disorder showed impaired reinforcement-based decision-making. Imaging results revealed that during feedback, individuals with Generalized Anxiety Disorder relative to healthy controls showed a reduced correlation between PE and activity within ventromedial prefrontal cortex, ventral striatum and other structures implicated in decision-making. In addition, individuals with Generalized Anxiety Disorder relative to healthy participants showed a reduced correlation between punishment, but not reward, PEs and activity within bilateral lentiform nucleus/putamen. Conclusions This is the first study to identify computational impairments during decision-making in Generalized Anxiety Disorder. PE signaling is significantly disrupted in individuals with the disorder and may underpin the decision-making deficits observed in patients with GAD. PMID:27631963

The Representation of Motor (Inter)action, States of Action, and Learning: Three Perspectives on Motor Learning by Way of Imagery and Execution

PubMed Central

Frank, Cornelia; Schack, Thomas

2017-01-01

Learning in intelligent systems is a result of direct and indirect interaction with the environment. While humans can learn by way of different states of (inter)action such as the execution or the imagery of an action, their unique potential to induce brain- and mind-related changes in the motor action system is still being debated. The systematic repetition of different states of action (e.g., physical and/or mental practice) and their contribution to the learning of complex motor actions has traditionally been approached by way of performance improvements. More recently, approaches highlighting the role of action representation in the learning of complex motor actions have evolved and may provide additional insight into the learning process. In the present perspective paper, we build on brain-related findings and sketch recent research on learning by way of imagery and execution from a hierarchical, perceptual-cognitive approach to motor control and learning. These findings provide insights into the learning of intelligent systems from a perceptual-cognitive, representation-based perspective and as such add to our current understanding of action representation in memory and its changes with practice. Future research should build bridges between approaches in order to more thoroughly understand functional changes throughout the learning process and to facilitate motor learning, which may have particular importance for cognitive systems research in robotics, rehabilitation, and sports. PMID:28588510

Fidelity of the representation of value in decision-making

PubMed Central

Dowding, Ben A.

2017-01-01

The ability to make optimal decisions depends on evaluating the expected rewards associated with different potential actions. This process is critically dependent on the fidelity with which reward value information can be maintained in the nervous system. Here we directly probe the fidelity of value representation following a standard reinforcement learning task. The results demonstrate a previously-unrecognized bias in the representation of value: extreme reward values, both low and high, are stored significantly more accurately and precisely than intermediate rewards. The symmetry between low and high rewards pertained despite substantially higher frequency of exposure to high rewards, resulting from preferential exploitation of more rewarding options. The observed variation in fidelity of value representation retrospectively predicted performance on the reinforcement learning task, demonstrating that the bias in representation has an impact on decision-making. A second experiment in which one or other extreme-valued option was omitted from the learning sequence showed that representational fidelity is primarily determined by the relative position of an encoded value on the scale of rewards experienced during learning. Both variability and guessing decreased with the reduction in the number of options, consistent with allocation of a limited representational resource. These findings have implications for existing models of reward-based learning, which typically assume defectless representation of reward value. PMID:28248958

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.

Representation learning via Dual-Autoencoder for recommendation.

PubMed

Zhuang, Fuzhen; Zhang, Zhiqiang; Qian, Mingda; Shi, Chuan; Xie, Xing; He, Qing

2017-06-01

Recommendation has provoked vast amount of attention and research in recent decades. Most previous works employ matrix factorization techniques to learn the latent factors of users and items. And many subsequent works consider external information, e.g., social relationships of users and items' attributions, to improve the recommendation performance under the matrix factorization framework. However, matrix factorization methods may not make full use of the limited information from rating or check-in matrices, and achieve unsatisfying results. Recently, deep learning has proven able to learn good representation in natural language processing, image classification, and so on. Along this line, we propose a new representation learning framework called Recommendation via Dual-Autoencoder (ReDa). In this framework, we simultaneously learn the new hidden representations of users and items using autoencoders, and minimize the deviations of training data by the learnt representations of users and items. Based on this framework, we develop a gradient descent method to learn hidden representations. Extensive experiments conducted on several real-world data sets demonstrate the effectiveness of our proposed method compared with state-of-the-art matrix factorization based methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Predicting epileptic seizures from scalp EEG based on attractor state analysis.

PubMed

Chu, Hyunho; Chung, Chun Kee; Jeong, Woorim; Cho, Kwang-Hyun

2017-05-01

Epilepsy is the second most common disease of the brain. Epilepsy makes it difficult for patients to live a normal life because it is difficult to predict when seizures will occur. In this regard, if seizures could be predicted a reasonable period of time before their occurrence, epilepsy patients could take precautions against them and improve their safety and quality of life. In this paper, we investigate a novel seizure precursor based on attractor state analysis for seizure prediction. We analyze the transition process from normal to seizure attractor state and investigate a precursor phenomenon seen before reaching the seizure attractor state. From the result of an analysis, we define a quantified spectral measure in scalp EEG for seizure prediction. From scalp EEG recordings, the Fourier coefficients of six EEG frequency bands are extracted, and the defined spectral measure is computed based on the coefficients for each half-overlapped 20-second-long window. The computed spectral measure is applied to seizure prediction using a low-complexity methodology. Within scalp EEG, we identified an early-warning indicator before an epileptic seizure occurs. Getting closer to the bifurcation point that triggers the transition from normal to seizure state, the power spectral density of low frequency bands of the perturbation of an attractor in the EEG, showed a relative increase. A low-complexity seizure prediction algorithm using this feature was evaluated, using ∼583h of scalp EEG in which 143 seizures in 16 patients were recorded. With the test dataset, the proposed method showed high sensitivity (86.67%) with a false prediction rate of 0.367h -1 and average prediction time of 45.3min. A novel seizure prediction method using scalp EEG, based on attractor state analysis, shows potential for application with real epilepsy patients. This is the first study in which the seizure-precursor phenomenon of an epileptic seizure is investigated based on attractor

Representation control increases task efficiency in complex graphical representations.

PubMed

Moritz, Julia; Meyerhoff, Hauke S; Meyer-Dernbecher, Claudia; Schwan, Stephan

2018-01-01

In complex graphical representations, the relevant information for a specific task is often distributed across multiple spatial locations. In such situations, understanding the representation requires internal transformation processes in order to extract the relevant information. However, digital technology enables observers to alter the spatial arrangement of depicted information and therefore to offload the transformation processes. The objective of this study was to investigate the use of such a representation control (i.e. the users' option to decide how information should be displayed) in order to accomplish an information extraction task in terms of solution time and accuracy. In the representation control condition, the participants were allowed to reorganize the graphical representation and reduce information density. In the control condition, no interactive features were offered. We observed that participants in the representation control condition solved tasks that required reorganization of the maps faster and more accurate than participants without representation control. The present findings demonstrate how processes of cognitive offloading, spatial contiguity, and information coherence interact in knowledge media intended for broad and diverse groups of recipients.

Representation control increases task efficiency in complex graphical representations

PubMed Central

Meyerhoff, Hauke S.; Meyer-Dernbecher, Claudia; Schwan, Stephan

2018-01-01

In complex graphical representations, the relevant information for a specific task is often distributed across multiple spatial locations. In such situations, understanding the representation requires internal transformation processes in order to extract the relevant information. However, digital technology enables observers to alter the spatial arrangement of depicted information and therefore to offload the transformation processes. The objective of this study was to investigate the use of such a representation control (i.e. the users' option to decide how information should be displayed) in order to accomplish an information extraction task in terms of solution time and accuracy. In the representation control condition, the participants were allowed to reorganize the graphical representation and reduce information density. In the control condition, no interactive features were offered. We observed that participants in the representation control condition solved tasks that required reorganization of the maps faster and more accurate than participants without representation control. The present findings demonstrate how processes of cognitive offloading, spatial contiguity, and information coherence interact in knowledge media intended for broad and diverse groups of recipients. PMID:29698443

Dynamic updating of hippocampal object representations reflects new conceptual knowledge

PubMed Central

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

2016-01-01

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

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

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…

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

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

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.

(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…

Berry phase in Heisenberg representation

NASA Technical Reports Server (NTRS)

Andreev, V. A.; Klimov, Andrei B.; Lerner, Peter B.

1994-01-01

We define the Berry phase for the Heisenberg operators. This definition is motivated by the calculation of the phase shifts by different techniques. These techniques are: the solution of the Heisenberg equations of motion, the solution of the Schrodinger equation in coherent-state representation, and the direct computation of the evolution operator. Our definition of the Berry phase in the Heisenberg representation is consistent with the underlying supersymmetry of the model in the following sense. The structural blocks of the Hamiltonians of supersymmetrical quantum mechanics ('superpairs') are connected by transformations which conserve the similarity in structure of the energy levels of superpairs. These transformations include transformation of phase of the creation-annihilation operators, which are generated by adiabatic cyclic evolution of the parameters of the system.

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.

Knowledge representation in space flight operations

NASA Technical Reports Server (NTRS)

Busse, Carl

1989-01-01

In space flight operations rapid understanding of the state of the space vehicle is essential. Representation of knowledge depicting space vehicle status in a dynamic environment presents a difficult challenge. The NASA Jet Propulsion Laboratory has pursued areas of technology associated with the advancement of spacecraft operations environment. This has led to the development of several advanced mission systems which incorporate enhanced graphics capabilities. These systems include: (1) Spacecraft Health Automated Reasoning Prototype (SHARP); (2) Spacecraft Monitoring Environment (SME); (3) Electrical Power Data Monitor (EPDM); (4) Generic Payload Operations Control Center (GPOCC); and (5) Telemetry System Monitor Prototype (TSM). Knowledge representation in these systems provides a direct representation of the intrinsic images associated with the instrument and satellite telemetry and telecommunications systems. The man-machine interface includes easily interpreted contextual graphic displays. These interactive video displays contain multiple display screens with pop-up windows and intelligent, high resolution graphics linked through context and mouse-sensitive icons and text.

Integrating conventional and inverse representation for face recognition.

PubMed

Xu, Yong; Li, Xuelong; Yang, Jian; Lai, Zhihui; Zhang, David

2014-10-01

Representation-based classification methods are all constructed on the basis of the conventional representation, which first expresses the test sample as a linear combination of the training samples and then exploits the deviation between the test sample and the expression result of every class to perform classification. However, this deviation does not always well reflect the difference between the test sample and each class. With this paper, we propose a novel representation-based classification method for face recognition. This method integrates conventional and the inverse representation-based classification for better recognizing the face. It first produces conventional representation of the test sample, i.e., uses a linear combination of the training samples to represent the test sample. Then it obtains the inverse representation, i.e., provides an approximation representation of each training sample of a subject by exploiting the test sample and training samples of the other subjects. Finally, the proposed method exploits the conventional and inverse representation to generate two kinds of scores of the test sample with respect to each class and combines them to recognize the face. The paper shows the theoretical foundation and rationale of the proposed method. Moreover, this paper for the first time shows that a basic nature of the human face, i.e., the symmetry of the face can be exploited to generate new training and test samples. As these new samples really reflect some possible appearance of the face, the use of them will enable us to obtain higher accuracy. The experiments show that the proposed conventional and inverse representation-based linear regression classification (CIRLRC), an improvement to linear regression classification (LRC), can obtain very high accuracy and greatly outperforms the naive LRC and other state-of-the-art conventional representation based face recognition methods. The accuracy of CIRLRC can be 10% greater than that of LRC.

Representation of Ecological Systems within the Protected Areas Network of the Continental United States

PubMed Central

Aycrigg, Jocelyn L.; Davidson, Anne; Svancara, Leona K.; Gergely, Kevin J.; McKerrow, Alexa; Scott, J. Michael

2013-01-01

If conservation of biodiversity is the goal, then the protected areas network of the continental US may be one of our best conservation tools for safeguarding ecological systems (i.e., vegetation communities). We evaluated representation of ecological systems in the current protected areas network and found insufficient representation at three vegetation community levels within lower elevations and moderate to high productivity soils. We used national-level data for ecological systems and a protected areas database to explore alternative ways we might be able to increase representation of ecological systems within the continental US. By following one or more of these alternatives it may be possible to increase the representation of ecological systems in the protected areas network both quantitatively (from 10% up to 39%) and geographically and come closer to meeting the suggested Convention on Biological Diversity target of 17% for terrestrial areas. We used the Landscape Conservation Cooperative framework for regional analysis and found that increased conservation on some private and public lands may be important to the conservation of ecological systems in Western US, while increased public-private partnerships may be important in the conservation of ecological systems in Eastern US. We have not assessed the pros and cons of following the national or regional alternatives, but rather present them as possibilities that may be considered and evaluated as decisions are made to increase the representation of ecological systems in the protected areas network across their range of ecological, geographical, and geophysical occurrence in the continental US into the future. PMID:23372754

Representation of ecological systems within the protected areas network of the continental United States

USGS Publications Warehouse

Aycrigg, Jocelyn L.; Davidson, Anne; Svancara, Leona K.; Gergely, Kevin J.; McKerrow, Alexa; Scott, J. Michael

2013-01-01

If conservation of biodiversity is the goal, then the protected areas network of the continental US may be one of our best conservation tools for safeguarding ecological systems (i.e., vegetation communities). We evaluated representation of ecological systems in the current protected areas network and found insufficient representation at three vegetation community levels within lower elevations and moderate to high productivity soils. We used national-level data for ecological systems and a protected areas database to explore alternative ways we might be able to increase representation of ecological systems within the continental US. By following one or more of these alternatives it may be possible to increase the representation of ecological systems in the protected areas network both quantitatively (from 10% up to 39%) and geographically and come closer to meeting the suggested Convention on Biological Diversity target of 17% for terrestrial areas. We used the Landscape Conservation Cooperative framework for regional analysis and found that increased conservation on some private and public lands may be important to the conservation of ecological systems in Western US, while increased public-private partnerships may be important in the conservation of ecological systems in Eastern US. We have not assessed the pros and cons of following the national or regional alternatives, but rather present them as possibilities that may be considered and evaluated as decisions are made to increase the representation of ecological systems in the protected areas network across their range of ecological, geographical, and geophysical occurrence in the continental US into the future.

Quantifying confidence in density functional theory predictions of magnetic ground states

NASA Astrophysics Data System (ADS)

Houchins, Gregory; Viswanathan, Venkatasubramanian

2017-10-01

Density functional theory (DFT) simulations, at the generalized gradient approximation (GGA) level, are being routinely used for material discovery based on high-throughput descriptor-based searches. The success of descriptor-based material design relies on eliminating bad candidates and keeping good candidates for further investigation. While DFT has been widely successfully for the former, oftentimes good candidates are lost due to the uncertainty associated with the DFT-predicted material properties. Uncertainty associated with DFT predictions has gained prominence and has led to the development of exchange correlation functionals that have built-in error estimation capability. In this work, we demonstrate the use of built-in error estimation capabilities within the BEEF-vdW exchange correlation functional for quantifying the uncertainty associated with the magnetic ground state of solids. We demonstrate this approach by calculating the uncertainty estimate for the energy difference between the different magnetic states of solids and compare them against a range of GGA exchange correlation functionals as is done in many first-principles calculations of materials. We show that this estimate reasonably bounds the range of values obtained with the different GGA functionals. The estimate is determined as a postprocessing step and thus provides a computationally robust and systematic approach to estimating uncertainty associated with predictions of magnetic ground states. We define a confidence value (c-value) that incorporates all calculated magnetic states in order to quantify the concurrence of the prediction at the GGA level and argue that predictions of magnetic ground states from GGA level DFT is incomplete without an accompanying c-value. We demonstrate the utility of this method using a case study of Li-ion and Na-ion cathode materials and the c-value metric correctly identifies that GGA-level DFT will have low predictability for NaFePO4F . Further, there

Graphical Representations of Electronic Search Patterns.

ERIC Educational Resources Information Center

Lin, Xia; And Others

1991-01-01

Discussion of search behavior in electronic environments focuses on the development of GRIP (Graphic Representor of Interaction Patterns), a graphing tool based on HyperCard that produces graphic representations of search patterns. Search state spaces are explained, and forms of data available from electronic searches are described. (34…

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

Multiple neural states of representation in short-term memory? It's a matter of attention.

PubMed

Larocque, Joshua J; Lewis-Peacock, Jarrod A; Postle, Bradley R

2014-01-01

Short-term memory (STM) refers to the capacity-limited retention of information over a brief period of time, and working memory (WM) refers to the manipulation and use of that information to guide behavior. In recent years it has become apparent that STM and WM interact and overlap with other cognitive processes, including attention (the selection of a subset of information for further processing) and long-term memory (LTM-the encoding and retention of an effectively unlimited amount of information for a much longer period of time). Broadly speaking, there have been two classes of memory models: systems models, which posit distinct stores for STM and LTM (Atkinson and Shiffrin, 1968; Baddeley and Hitch, 1974); and state-based models, which posit a common store with different activation states corresponding to STM and LTM (Cowan, 1995; McElree, 1996; Oberauer, 2002). In this paper, we will focus on state-based accounts of STM. First, we will consider several theoretical models that postulate, based on considerable behavioral evidence, that information in STM can exist in multiple representational states. We will then consider how neural data from recent studies of STM can inform and constrain these theoretical models. In the process we will highlight the inferential advantage of multivariate, information-based analyses of neuroimaging data (fMRI and electroencephalography (EEG)) over conventional activation-based analysis approaches (Postle, in press). We will conclude by addressing lingering questions regarding the fractionation of STM, highlighting differences between the attention to information vs. the retention of information during brief memory delays.

Adjusted Clinical Groups: Predictive Accuracy for Medicaid Enrollees in Three States

PubMed Central

Adams, E. Kathleen; Bronstein, Janet M.; Raskind-Hood, Cheryl

2002-01-01

Actuarial split-sample methods were used to assess predictive accuracy of adjusted clinical groups (ACGs) for Medicaid enrollees in Georgia, Mississippi (lagging in managed care penetration), and California. Accuracy for two non-random groups—high-cost and located in urban poor areas—was assessed. Measures for random groups were derived with and without short-term enrollees to assess the effect of turnover on predictive accuracy. ACGs improved predictive accuracy for high-cost conditions in all States, but did so only for those in Georgia's poorest urban areas. Higher and more unpredictable expenses of short-term enrollees moderated the predictive power of ACGs. This limitation was significant in Mississippi due in part, to that State's very high proportion of short-term enrollees. PMID:12545598

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.

Representation of Vegetation and Other Nonerodible Elements in Aeolian Shear Stress Partitioning Models for Predicting Transport Threshold

NASA Technical Reports Server (NTRS)

King, James; Nickling, William G.; Gillies, John A.

2005-01-01

The presence of nonerodible elements is well understood to be a reducing factor for soil erosion by wind, but the limits of its protection of the surface and erosion threshold prediction are complicated by the varying geometry, spatial organization, and density of the elements. The predictive capabilities of the most recent models for estimating wind driven particle fluxes are reduced because of the poor representation of the effectiveness of vegetation to reduce wind erosion. Two approaches have been taken to account for roughness effects on sediment transport thresholds. Marticorena and Bergametti (1995) in their dust emission model parameterize the effect of roughness on threshold with the assumption that there is a relationship between roughness density and the aerodynamic roughness length of a surface. Raupach et al. (1993) offer a different approach based on physical modeling of wake development behind individual roughness elements and the partition of the surface stress and the total stress over a roughened surface. A comparison between the models shows the partitioning approach to be a good framework to explain the effect of roughness on entrainment of sediment by wind. Both models provided very good agreement for wind tunnel experiments using solid objects on a nonerodible surface. However, the Marticorena and Bergametti (1995) approach displays a scaling dependency when the difference between the roughness length of the surface and the overall roughness length is too great, while the Raupach et al. (1993) model's predictions perform better owing to the incorporation of the roughness geometry and the alterations to the flow they can cause.

Studying action representation in children via motor imagery.

PubMed

Gabbard, Carl

2009-12-01

The use of motor imagery is a widely used experimental paradigm for the study of cognitive aspects of action planning and control in adults. Furthermore, there are indications that motor imagery provides a window into the process of action representation. These notions complement internal model theory suggesting that such representations allow predictions (estimates) about the mapping of the self to parameters of the external world; processes that enable successful planning and execution of action. The ability to mentally represent action is important to the development of motor control. This paper presents a critical review of motor imagery research conducted with children (typically developing and special populations) with focus on its merits and possible shortcomings in studying action representation. Included in the review are age-related findings, possible brain structures involved, experimental paradigms, and recommendations for future work. The merits of this review are associated with the apparent increasing attraction for using and studying motor imagery to understand the developmental aspects of action processing in children.

[Social representations of illness among people with chronic kidney disease].

PubMed

Campos, Caroline Gonçalves Pustiglione; Mantovani, Maria de Fátima; Nascimento, Maria Elisa Brum do; Cassi, Cristiam Carla

2015-06-01

To describe the social representations of illness among people with chronic kidney disease undergoing haemodialysis. Descriptive, qualitative research, anchored on the social representations theory. This study was conducted in the municipality of Ponta Grossa, Paraná State, Brazil, with 23 adults with chronic kidney disease. Data were collection between February and November 2012 by means of a semi-structured interview, and analyzed using Content Analysis. The interviews led to the categories "the meaning of kidney disease": awareness of finitude, and "survival": the visible with chronic kidney disease. The representation of illness unveiled a difference and interruption in life projects, and haemodialysis meant loss of freedom, imprisonment and stigma. Family ties and the individuals´ social role are determining representations for healthcare.

Constrained model predictive control, state estimation and coordination

NASA Astrophysics Data System (ADS)

Yan, Jun

In this dissertation, we study the interaction between the control performance and the quality of the state estimation in a constrained Model Predictive Control (MPC) framework for systems with stochastic disturbances. This consists of three parts: (i) the development of a constrained MPC formulation that adapts to the quality of the state estimation via constraints; (ii) the application of such a control law in a multi-vehicle formation coordinated control problem in which each vehicle operates subject to a no-collision constraint posed by others' imperfect prediction computed from finite bit-rate, communicated data; (iii) the design of the predictors and the communication resource assignment problem that satisfy the performance requirement from Part (ii). Model Predictive Control (MPC) is of interest because it is one of the few control design methods which preserves standard design variables and yet handles constraints. MPC is normally posed as a full-state feedback control and is implemented in a certainty-equivalence fashion with best estimates of the states being used in place of the exact state. However, if the state constraints were handled in the same certainty-equivalence fashion, the resulting control law could drive the real state to violate the constraints frequently. Part (i) focuses on exploring the inclusion of state estimates into the constraints. It does this by applying constrained MPC to a system with stochastic disturbances. The stochastic nature of the problem requires re-posing the constraints in a probabilistic form. In Part (ii), we consider applying constrained MPC as a local control law in a coordinated control problem of a group of distributed autonomous systems. Interactions between the systems are captured via constraints. First, we inspect the application of constrained MPC to a completely deterministic case. Formation stability theorems are derived for the subsystems and conditions on the local constraint set are derived in order to

Distinguishing Representations as Origin and Representations as Input: Roles for Individual Neurons.

PubMed

Edwards, Jonathan C W

2016-01-01

It is widely perceived that there is a problem in giving a naturalistic account of mental representation that deals adequately with the issue of meaning, interpretation, or significance (semantic content). It is suggested here that this problem may arise partly from the conflation of two vernacular senses of representation: representation-as-origin and representation-as-input. The flash of a neon sign may in one sense represent a popular drink, but to function as a representation it must provide an input to a 'consumer' in the street. The arguments presented draw on two principles - the neuron doctrine and the need for a venue for 'presentation' or 'reception' of a representation at a specified site, consistent with the locality principle. It is also argued that domains of representation cannot be defined by signal traffic, since they can be expected to include 'null' elements based on non-firing cells. In this analysis, mental representations-as-origin are distributed patterns of cell firing. Each firing cell is given semantic value in its own right - some form of atomic propositional significance - since different axonal branches may contribute to integration with different populations of signals at different downstream sites. Representations-as-input are patterns of local co-arrival of signals in the form of synaptic potentials in dendrites. Meaning then draws on the relationships between active and null inputs, forming 'scenarios' comprising a molecular combination of 'premises' from which a new output with atomic propositional significance is generated. In both types of representation, meaning, interpretation or significance pivots on events in an individual cell. (This analysis only applies to 'occurrent' representations based on current neural activity.) The concept of representations-as-input emphasizes the need for an internal 'consumer' of a representation and the dependence of meaning on the co-relationships involved in an input interaction between

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…

Switch-Independent Task Representations in Frontal and Parietal Cortex.

PubMed

Loose, Lasse S; Wisniewski, David; Rusconi, Marco; Goschke, Thomas; Haynes, John-Dylan

2017-08-16

Alternating between two tasks is effortful and impairs performance. Previous fMRI studies have found increased activity in frontoparietal cortex when task switching is required. One possibility is that the additional control demands for switch trials are met by strengthening task representations in the human brain. Alternatively, on switch trials, the residual representation of the previous task might impede the buildup of a neural task representation. This would predict weaker task representations on switch trials, thus also explaining the performance costs. To test this, male and female participants were cued to perform one of two similar tasks, with the task being repeated or switched between successive trials. Multivoxel pattern analysis was used to test which regions encode the tasks and whether this encoding differs between switch and repeat trials. As expected, we found information about task representations in frontal and parietal cortex, but there was no difference in the decoding accuracy of task-related information between switch and repeat trials. Using cross-classification, we found that the frontoparietal cortex encodes tasks using a generalizable spatial pattern in switch and repeat trials. Therefore, task representations in frontal and parietal cortex are largely switch independent. We found no evidence that neural information about task representations in these regions can explain behavioral costs usually associated with task switching. SIGNIFICANCE STATEMENT Alternating between two tasks is effortful and slows down performance. One possible explanation is that the representations in the human brain need time to build up and are thus weaker on switch trials, explaining performance costs. Alternatively, task representations might even be enhanced to overcome the previous task. Here, we used a combination of fMRI and a brain classifier to test whether the additional control demands under switching conditions lead to an increased or decreased strength

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...

The Frequency of “Brilliant” and “Genius” in Teaching Evaluations Predicts the Representation of Women and African Americans across Fields

PubMed Central

Storage, Daniel; Horne, Zachary; Cimpian, Andrei; Leslie, Sarah-Jane

2016-01-01

Women and African Americans—groups targeted by negative stereotypes about their intellectual abilities—may be underrepresented in careers that prize brilliance and genius. A recent nationwide survey of academics provided initial support for this possibility. Fields whose practitioners believed that natural talent is crucial for success had fewer female and African American PhDs. The present study seeks to replicate this initial finding with a different, and arguably more naturalistic, measure of the extent to which brilliance and genius are prized within a field. Specifically, we measured field-by-field variability in the emphasis on these intellectual qualities by tallying—with the use of a recently released online tool—the frequency of the words “brilliant” and “genius” in over 14 million reviews on RateMyProfessors.com, a popular website where students can write anonymous evaluations of their instructors. This simple word count predicted both women’s and African Americans’ representation across the academic spectrum. That is, we found that fields in which the words “brilliant” and “genius” were used more frequently on RateMyProfessors.com also had fewer female and African American PhDs. Looking at an earlier stage in students’ educational careers, we found that brilliance-focused fields also had fewer women and African Americans obtaining bachelor’s degrees. These relationships held even when accounting for field-specific averages on standardized mathematics assessments, as well as several competing hypotheses concerning group differences in representation. The fact that this naturalistic measure of a field’s focus on brilliance predicted the magnitude of its gender and race gaps speaks to the tight link between ability beliefs and diversity. PMID:26938242

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.

Predicting switched-bias response from steady-state irradiations

NASA Astrophysics Data System (ADS)

Fleetwood, D. M.; Winokur, P. S.; Riewe, L. C.

1990-12-01

A novel semiempirical model of radiation-induced charge neutralization is presented. This model is combined with 12 heuristic guidelines derived from studies of oxide- and interface-trap charge (Delta Vot and Delta Vit) buildup and annealing to develop a method to predict MOS switched-bias response from steady-state irradiations, with no free parameters. For n-channel MOS devices, predictions of Delta Vot, Delta Vit, and mobility degradation differ from experimental values through irradiation by less than 30 percent in all cases considered. This is demonstrated for gate oxides with widely varying Delta Vot and Delta Vit and for parasitic field oxides. Preliminary results suggest that n-channel MOS Delta Vot annealing and Delta Vit buildup following switched-bias irradiation and through switched-bias annealing also may be predicted with less than 30 percent error. The p-channel MOS response at high frequencies is more difficult to predict.

Prediction Error Representation in Individuals With Generalized Anxiety Disorder During Passive Avoidance.

PubMed

White, Stuart F; Geraci, Marilla; Lewis, Elizabeth; Leshin, Joseph; Teng, Cindy; Averbeck, Bruno; Meffert, Harma; Ernst, Monique; Blair, James R; Grillon, Christian; Blair, Karina S

2017-02-01

Deficits in reinforcement-based decision making have been reported in generalized anxiety disorder. However, the pathophysiology of these deficits is largely unknown; published studies have mainly examined adolescents, and the integrity of core functional processes underpinning decision making remains undetermined. In particular, it is unclear whether the representation of reinforcement prediction error (PE) (the difference between received and expected reinforcement) is disrupted in generalized anxiety disorder. This study addresses these issues in adults with the disorder. Forty-six unmedicated individuals with generalized anxiety disorder and 32 healthy comparison subjects group-matched on IQ, gender, and age performed a passive avoidance task while undergoing functional MRI. Data analyses were performed using a computational modeling approach. Behaviorally, individuals with generalized anxiety disorder showed impaired reinforcement-based decision making. Imaging results revealed that during feedback, individuals with generalized anxiety disorder relative to healthy subjects showed a reduced correlation between PE and activity within the ventromedial prefrontal cortex, ventral striatum, and other structures implicated in decision making. In addition, individuals with generalized anxiety disorder relative to healthy participants showed a reduced correlation between punishment PEs, but not reward PEs, and activity within the left and right lentiform nucleus/putamen. This is the first study to identify computational impairments during decision making in generalized anxiety disorder. PE signaling is significantly disrupted in individuals with the disorder and may lead to their decision-making deficits and excessive worry about everyday problems by disrupting the online updating ("reality check") of the current relationship between the expected values of current response options and the actual received rewards and punishments.

Distinguishing Representations as Origin and Representations as Input: Roles for Individual Neurons

PubMed Central

Edwards, Jonathan C. W.

2016-01-01

It is widely perceived that there is a problem in giving a naturalistic account of mental representation that deals adequately with the issue of meaning, interpretation, or significance (semantic content). It is suggested here that this problem may arise partly from the conflation of two vernacular senses of representation: representation-as-origin and representation-as-input. The flash of a neon sign may in one sense represent a popular drink, but to function as a representation it must provide an input to a ‘consumer’ in the street. The arguments presented draw on two principles – the neuron doctrine and the need for a venue for ‘presentation’ or ‘reception’ of a representation at a specified site, consistent with the locality principle. It is also argued that domains of representation cannot be defined by signal traffic, since they can be expected to include ‘null’ elements based on non-firing cells. In this analysis, mental representations-as-origin are distributed patterns of cell firing. Each firing cell is given semantic value in its own right – some form of atomic propositional significance – since different axonal branches may contribute to integration with different populations of signals at different downstream sites. Representations-as-input are patterns of local co-arrival of signals in the form of synaptic potentials in dendrites. Meaning then draws on the relationships between active and null inputs, forming ‘scenarios’ comprising a molecular combination of ‘premises’ from which a new output with atomic propositional significance is generated. In both types of representation, meaning, interpretation or significance pivots on events in an individual cell. (This analysis only applies to ‘occurrent’ representations based on current neural activity.) The concept of representations-as-input emphasizes the need for an internal ‘consumer’ of a representation and the dependence of meaning on the co-relationships involved

Embedded Data Representations.

PubMed

Willett, Wesley; Jansen, Yvonne; Dragicevic, Pierre

2017-01-01

We introduce embedded data representations, the use of visual and physical representations of data that are deeply integrated with the physical spaces, objects, and entities to which the data refers. Technologies like lightweight wireless displays, mixed reality hardware, and autonomous vehicles are making it increasingly easier to display data in-context. While researchers and artists have already begun to create embedded data representations, the benefits, trade-offs, and even the language necessary to describe and compare these approaches remain unexplored. In this paper, we formalize the notion of physical data referents - the real-world entities and spaces to which data corresponds - and examine the relationship between referents and the visual and physical representations of their data. We differentiate situated representations, which display data in proximity to data referents, and embedded representations, which display data so that it spatially coincides with data referents. Drawing on examples from visualization, ubiquitous computing, and art, we explore the role of spatial indirection, scale, and interaction for embedded representations. We also examine the tradeoffs between non-situated, situated, and embedded data displays, including both visualizations and physicalizations. Based on our observations, we identify a variety of design challenges for embedded data representation, and suggest opportunities for future research and applications.

The Koslowski-Sahlmann representation: quantum configuration space

NASA Astrophysics Data System (ADS)

Campiglia, Miguel; Varadarajan, Madhavan

2014-09-01

The Koslowski-Sahlmann (KS) representation is a generalization of the representation underlying the discrete spatial geometry of loop quantum gravity (LQG), to accommodate states labelled by smooth spatial geometries. As shown recently, the KS representation supports, in addition to the action of the holonomy and flux operators, the action of operators which are the quantum counterparts of certain connection dependent functions known as ‘background exponentials’. Here we show that the KS representation displays the following properties which are the exact counterparts of LQG ones: (i) the abelian * algebra of SU(2) holonomies and ‘U(1)’ background exponentials can be completed to a C* algebra, (ii) the space of semianalytic SU(2) connections is topologically dense in the spectrum of this algebra, (iii) there exists a measure on this spectrum for which the KS Hilbert space is realized as the space of square integrable functions on the spectrum, (iv) the spectrum admits a characterization as a projective limit of finite numbers of copies of SU(2) and U(1), (v) the algebra underlying the KS representation is constructed from cylindrical functions and their derivations in exactly the same way as the LQG (holonomy-flux) algebra except that the KS cylindrical functions depend on the holonomies and the background exponentials, this extra dependence being responsible for the differences between the KS and LQG algebras. While these results are obtained for compact spaces, they are expected to be of use for the construction of the KS representation in the asymptotically flat case.

Finite-Dimensional Representations for Controlled Diffusions with Delay

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

Federico, Salvatore, E-mail: salvatore.federico@unimi.it; Tankov, Peter, E-mail: tankov@math.univ-paris-diderot.fr

2015-02-15

We study stochastic delay differential equations (SDDE) where the coefficients depend on the moving averages of the state process. As a first contribution, we provide sufficient conditions under which the solution of the SDDE and a linear path functional of it admit a finite-dimensional Markovian representation. As a second contribution, we show how approximate finite-dimensional Markovian representations may be constructed when these conditions are not satisfied, and provide an estimate of the error corresponding to these approximations. These results are applied to optimal control and optimal stopping problems for stochastic systems with delay.

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 ...

Optical coherence tomography retinal image reconstruction via nonlocal weighted sparse representation

NASA Astrophysics Data System (ADS)

Abbasi, Ashkan; Monadjemi, Amirhassan; Fang, Leyuan; Rabbani, Hossein

2018-03-01

We present a nonlocal weighted sparse representation (NWSR) method for reconstruction of retinal optical coherence tomography (OCT) images. To reconstruct a high signal-to-noise ratio and high-resolution OCT images, utilization of efficient denoising and interpolation algorithms are necessary, especially when the original data were subsampled during acquisition. However, the OCT images suffer from the presence of a high level of noise, which makes the estimation of sparse representations a difficult task. Thus, the proposed NWSR method merges sparse representations of multiple similar noisy and denoised patches to better estimate a sparse representation for each patch. First, the sparse representation of each patch is independently computed over an overcomplete dictionary, and then a nonlocal weighted sparse coefficient is computed by averaging representations of similar patches. Since the sparsity can reveal relevant information from noisy patches, combining noisy and denoised patches' representations is beneficial to obtain a more robust estimate of the unknown sparse representation. The denoised patches are obtained by applying an off-the-shelf image denoising method and our method provides an efficient way to exploit information from noisy and denoised patches' representations. The experimental results on denoising and interpolation of spectral domain OCT images demonstrated the effectiveness of the proposed NWSR method over existing state-of-the-art methods.

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

Knowledge representation in metabolic pathway databases.

PubMed

Stobbe, Miranda D; Jansen, Gerbert A; Moerland, Perry D; van Kampen, Antoine H C

2014-05-01

The accurate representation of all aspects of a metabolic network in a structured format, such that it can be used for a wide variety of computational analyses, is a challenge faced by a growing number of researchers. Analysis of five major metabolic pathway databases reveals that each database has made widely different choices to address this challenge, including how to deal with knowledge that is uncertain or missing. In concise overviews, we show how concepts such as compartments, enzymatic complexes and the direction of reactions are represented in each database. Importantly, also concepts which a database does not represent are described. Which aspects of the metabolic network need to be available in a structured format and to what detail differs per application. For example, for in silico phenotype prediction, a detailed representation of gene-protein-reaction relations and the compartmentalization of the network is essential. Our analysis also shows that current databases are still limited in capturing all details of the biology of the metabolic network, further illustrated with a detailed analysis of three metabolic processes. Finally, we conclude that the conceptual differences between the databases, which make knowledge exchange and integration a challenge, have not been resolved, so far, by the exchange formats in which knowledge representation is standardized.

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...

Mechanistic Representation of Soil C Dynamics: for Arctic Ecosystem

NASA Astrophysics Data System (ADS)

Dwivedi, D.; Riley, W. J.; Bisht, G.

2013-12-01

Arctic and sub-Arctic soils store vast amounts of carbon, approximately 1700 billion metric tones of frozen organic carbon. This carbon is susceptible to release to the atmosphere due to environmental changes (e.g., rapidly evolving landscape, warming); however, the mechanisms responsible for this susceptibility of soil organic matter (SOM) are not well understood, and uncertainties exist in terms of their representation in Earth System models. The representation of SOM dynamics in Earth System Models is critical for future climate prediction. To investigate the impacts of various physical (e.g., multi-phase transport, sorption, desorption, temperature), chemical (e.g., pH), and biological (e.g., microbial activity, enzyme dynamics) factors on SOM stability, we have developed CENTURY-like (describing labile and recalcitrant pools) and complex (describing multiple archetypal polymers and monomers C substrate groups) reaction networks. These reaction networks are integrated in a three-dimensional, multi-phase reactive transport solver (PFLOTRAN) and include representations of bacterial and fungal activity as well as population dynamics, gaseous and aqueous advection, and adsorption and desorption. We test and compare these reaction networks in PFLOTRAN to accurately predict depth-resolved soil organic matter (SOM) in the subsurface. We present results showing impacts of abiotic controls (e.g., surface interactions and temperature) on the long-term stabilization of SOM under permafrost conditions.

Identified state-space prediction model for aero-optical wavefronts

NASA Astrophysics Data System (ADS)

Faghihi, Azin; Tesch, Jonathan; Gibson, Steve

2013-07-01

A state-space disturbance model and associated prediction filter for aero-optical wavefronts are described. The model is computed by system identification from a sequence of wavefronts measured in an airborne laboratory. Estimates of the statistics and flow velocity of the wavefront data are shown and can be computed from the matrices in the state-space model without returning to the original data. Numerical results compare velocity values and power spectra computed from the identified state-space model with those computed from the aero-optical data.

Mental Health and Immigrant Detainees in the United States: Competency and Self-Representation.

PubMed

Korngold, Caleb; Ochoa, Kristen; Inlender, Talia; McNiel, Dale; Binder, Renée

2015-09-01

Most immigrant detainees held in U.S. Immigration and Customs Enforcement (ICE) facilities do not have legal representation, because immigration proceedings are a matter of civil, not criminal, law. In 2005, Mr. Franco, an immigrant from Mexico with an IQ between 35 and 55, was found incompetent to stand trial, but was not appointed an attorney for his immigration proceedings. This failure led to a class action lawsuit, known as the Franco litigation, and in April 2013, a federal judge ordered the U. S. government to provide legal representation for immigrant detainees in California, Arizona, and Washington who are incompetent to represent themselves due to a mental disorder or defect. This development has implications for forensic evaluators, because there is likely to be an increase in the number of competency examinations requested by courts for immigrant detainees. Furthermore, forensic evaluators must understand that an evaluation for competency of an immigrant detainee includes both the Dusky criteria and capacity for self-representation. In this article, we explore the legal context and ethics concerns related to the Franco litigation. © 2015 American Academy of Psychiatry and the Law.

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

Identification of DNA-Binding Proteins Using Mixed Feature Representation Methods.

PubMed

Qu, Kaiyang; Han, Ke; Wu, Song; Wang, Guohua; Wei, Leyi

2017-09-22

DNA-binding proteins play vital roles in cellular processes, such as DNA packaging, replication, transcription, regulation, and other DNA-associated activities. The current main prediction method is based on machine learning, and its accuracy mainly depends on the features extraction method. Therefore, using an efficient feature representation method is important to enhance the classification accuracy. However, existing feature representation methods cannot efficiently distinguish DNA-binding proteins from non-DNA-binding proteins. In this paper, a multi-feature representation method, which combines three feature representation methods, namely, K-Skip-N-Grams, Information theory, and Sequential and structural features (SSF), is used to represent the protein sequences and improve feature representation ability. In addition, the classifier is a support vector machine. The mixed-feature representation method is evaluated using 10-fold cross-validation and a test set. Feature vectors, which are obtained from a combination of three feature extractions, show the best performance in 10-fold cross-validation both under non-dimensional reduction and dimensional reduction by max-relevance-max-distance. Moreover, the reduced mixed feature method performs better than the non-reduced mixed feature technique. The feature vectors, which are a combination of SSF and K-Skip-N-Grams, show the best performance in the test set. Among these methods, mixed features exhibit superiority over the single features.

Spectral Approaches to Learning Predictive Representations

DTIC Science & Technology

2012-09-01

conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed...to the mean to form an initial prediction of x̂(ht). Similarly, Equation 2.3b can be interpreted as using the dynamics matrix A and error covarianceQ...in the sense of Lyapunov if its dynamics matrix A is. Thus, the Lyapunov criterion can be interpreted as holding for an LDS if, for a given covariance

Multi-representation based on scientific investigation for enhancing students’ representation skills

NASA Astrophysics Data System (ADS)

Siswanto, J.; Susantini, E.; Jatmiko, B.

2018-03-01

This research aims to implementation learning physics with multi-representation based on the scientific investigation for enhancing students’ representation skills, especially on the magnetic field subject. The research design is one group pretest-posttest. This research was conducted in the department of mathematics education, Universitas PGRI Semarang, with the sample is students of class 2F who take basic physics courses. The data were obtained by representation skills test and documentation of multi-representation worksheet. The Results show gain analysis value of .64 which means some medium improvements. The result of t-test (α = .05) is shows p-value = .001. This learning significantly improves students representation skills.

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

Why Representations?

ERIC Educational Resources Information Center

Schultz, James E.; Waters, Michael S.

2000-01-01

Discusses representations in the context of solving a system of linear equations. Views representations (concrete, tables, graphs, algebraic, matrices) from perspectives of understanding, technology, generalization, exact versus approximate solution, and learning style. (KHR)

Representation in Memory.

ERIC Educational Resources Information Center

Rumelhart, David E.; Norman, Donald A.

This paper reviews work on the representation of knowledge from within psychology and artificial intelligence. The work covers the nature of representation, the distinction between the represented world and the representing world, and significant issues concerned with propositional, analogical, and superpositional representations. Specific topics…

Robust and efficient anomaly detection using heterogeneous representations

NASA Astrophysics Data System (ADS)

Hu, Xing; Hu, Shiqiang; Xie, Jinhua; Zheng, Shiyou

2015-05-01

Various approaches have been proposed for video anomaly detection. Yet these approaches typically suffer from one or more limitations: they often characterize the pattern using its internal information, but ignore its external relationship which is important for local anomaly detection. Moreover, the high-dimensionality and the lack of robustness of pattern representation may lead to problems, including overfitting, increased computational cost and memory requirements, and high false alarm rate. We propose a video anomaly detection framework which relies on a heterogeneous representation to account for both the pattern's internal information and external relationship. The internal information is characterized by slow features learned by slow feature analysis from low-level representations, and the external relationship is characterized by the spatial contextual distances. The heterogeneous representation is compact, robust, efficient, and discriminative for anomaly detection. Moreover, both the pattern's internal information and external relationship can be taken into account in the proposed framework. Extensive experiments demonstrate the robustness and efficiency of our approach by comparison with the state-of-the-art approaches on the widely used benchmark datasets.

7 CFR 1221.100 - Establishment and representation.

Code of Federal Regulations, 2013 CFR

2013-01-01

... allow representation from a broad geographical area. The Board shall initially be composed of 13... 4 sorghum producers to serve as at-large national representatives with at least two representatives... State, there shall be one importer to serve as a representative plus an additional at-large national...

7 CFR 1221.100 - Establishment and representation.

Code of Federal Regulations, 2014 CFR

2014-01-01

... allow representation from a broad geographical area. The Board shall initially be composed of 13... 4 sorghum producers to serve as at-large national representatives with at least two representatives... State, there shall be one importer to serve as a representative plus an additional at-large national...

7 CFR 1221.100 - Establishment and representation.

Code of Federal Regulations, 2012 CFR

2012-01-01

... allow representation from a broad geographical area. The Board shall initially be composed of 13... 4 sorghum producers to serve as at-large national representatives with at least two representatives... State, there shall be one importer to serve as a representative plus an additional at-large national...

7 CFR 1221.100 - Establishment and representation.

Code of Federal Regulations, 2011 CFR

2011-01-01

... allow representation from a broad geographical area. The Board shall initially be composed of 13... 4 sorghum producers to serve as at-large national representatives with at least two representatives... State, there shall be one importer to serve as a representative plus an additional at-large national...

22 CFR 145.17 - Certifications and representations.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Certifications and representations. 145.17 Section 145.17 Foreign Relations DEPARTMENT OF STATE CIVIL RIGHTS GRANTS AND AGREEMENTS WITH INSTITUTIONS OF HIGHER EDUCATION, HOSPITALS, AND OTHER NON-PROFIT ORGANIZATIONS Pre-Award Requirements § 145.17...

Increased experience amplifies the activation of task-irrelevant category representations.

PubMed

Wu, Rachel; Pruitt, Zoe; Zinszer, Benjamin D; Cheung, Olivia S

2017-02-01

Prior research has demonstrated the benefits (i.e., task-relevant attentional selection) and costs (i.e., task-irrelevant attentional capture) of prior knowledge on search for an individual target or multiple targets from a category. This study investigated whether the level of experience with particular categories predicts the degree of task-relevant and task-irrelevant activation of item and category representations. Adults with varying levels of dieting experience (measured via 3 subscales of Disinhibition, Restraint, Hunger; Stunkard & Messick, Journal of Psychosomatic Research, 29(1), 71-83, 1985) searched for targets defined as either a specific food item (e.g., carrots), or a category (i.e., any healthy or unhealthy food item). Apart from the target-present trials, in the target-absent "foil" trials, when searching for a specific item (e.g., carrots), irrelevant items from the target's category (e.g., squash) were presented. The ERP (N2pc) results revealed that the activation of task-relevant representations (measured via Exemplar and Category N2pc amplitudes) did not differ based on the degree of experience. Critically, however, increased dieting experience, as revealed by lower Disinhibition scores, predicted activation of task-irrelevant representations (i.e., attentional capture of foils from the target item category). Our results suggest that increased experience with particular categories encourages the rapid activation of category representations even when category information is task irrelevant, and that the N2pc in foil trials could potentially serve as an indication of experience level in future studies on categorization.

Solid-state lighting life prediction using extended Kalman filter

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

Lall, Pradeep; Wei, Junchao; Davis, Lynn

2013-07-16

Solid-state lighting (SSL) luminaires containing light emitting diodes (LEDs) have the potential of seeing excessive temperatures when being transported across country or being stored in non-climate controlled warehouses. They are also being used in outdoor applications in desert environments that see little or no humidity but will experience extremely high temperatures during the day. This makes it important to increase our understanding of what effects high temperature exposure for a prolonged period of time will have on the usability and survivability of these devices. The U.S. Department of Energy has made a long term commitment to advance the efficiency, understandingmore » and development of solid-state lighting (SSL) and is making a strong push for the acceptance and use of SSL products to reduce overall energy consumption attributable to lighting. Traditional light sources “burn out” at end-of-life. For an incandescent bulb, the lamp life is defined by B50 life. However, the LEDs have no filament to “burn”. The LEDs continually degrade and the light output decreases eventually below useful levels causing failure. Presently, the TM-21 test standard is used to predict the L70 life of SSL Luminaires from LM-80 test data. The TM-21 model uses an Arrhenius Equation with an Activation Energy, Pre-decay factor and Decay Rates. Several failure mechanisms may be active in a luminaire at a single time causing lumen depreciation. The underlying TM-21 Arrhenius Model may not capture the failure physics in presence of multiple failure mechanisms. Correlation of lumen maintenance with underlying physics of degradation at system-level is needed. In this paper, a Kalman Filter and Extended Kalman Filters have been used to develop a 70% Lumen Maintenance Life Prediction Model for a LEDs used in SSL luminaires. This model can be used to calculate acceleration factors, evaluate failure-probability and identify ALT methodologies for reducing test time. Ten

Statistical Mining of Predictability of Seasonal Precipitation over the United States

NASA Technical Reports Server (NTRS)

Lau, William K. M.; Kim, Kyu-Myong; Shen, S. P.

2001-01-01

Results from a new ensemble canonical correlation (ECC) prediction model yield a remarkable (10-20%) increases in baseline prediction skills for seasonal precipitation over the US for all seasons, compared to traditional statistical predictions. While the tropical Pacific, i.e., El Nino, contributes to the largest share of potential predictability in the southern tier States during boreal winter, the North Pacific and the North Atlantic are responsible for enhanced predictability in the northern Great Plains, Midwest and the southwest US during boreal summer. Most importantly, ECC significantly reduces the spring predictability barrier over the conterminous US, thereby raising the skill bar for dynamical predictions.

Predicting flow at work: investigating the activities and job characteristics that predict flow states at work.

PubMed

Nielsen, Karina; Cleal, Bryan

2010-04-01

Flow (a state of consciousness where people become totally immersed in an activity and enjoy it intensely) has been identified as a desirable state with positive effects for employee well-being and innovation at work. Flow has been studied using both questionnaires and Experience Sampling Method (ESM). In this study, we used a newly developed 9-item flow scale in an ESM study combined with a questionnaire to examine the predictors of flow at two levels: the activities (brainstorming, planning, problem solving and evaluation) associated with transient flow states and the more stable job characteristics (role clarity, influence and cognitive demands). Participants were 58 line managers from two companies in Denmark; a private accountancy firm and a public elder care organization. We found that line managers in elder care experienced flow more often than accountancy line managers, and activities such as planning, problem solving, and evaluation predicted transient flow states. The more stable job characteristics included in this study were not, however, found to predict flow at work. Copyright 2010 APA, all rights reserved.

Predictability of state-level flood damage in the conterminous United States: the role of hazard, exposure and vulnerability

DOE PAGES

Zhou, Qianqian; Leng, Guoyong; Feng, Leyang

2017-07-13

Understanding historical changes in flood damage and the underlying mechanisms is critical for predicting future changes for better adaptations. In this study, a detailed assessment of flood damage for 1950–1999 is conducted at the state level in the conterminous United States (CONUS). Geospatial datasets on possible influencing factors are then developed by synthesizing natural hazards, population, wealth, cropland and urban area to explore the relations with flood damage. A considerable increase in flood damage in CONUS is recorded for the study period which is well correlated with hazards. Comparably, runoff indexed hazards simulated by the Variable Infiltration Capacity (VIC) modelmore » can explain a larger portion of flood damage variations than precipitation in 84% of the states. Cropland is identified as an important factor contributing to increased flood damage in central US while urbanland exhibits positive and negative relations with total flood damage and damage per unit wealth in 20 and 16 states, respectively. Altogether, flood damage in 34 out of 48 investigated states can be predicted at the 90% confidence level. In extreme cases, ~76% of flood damage variations can be explained in some states, highlighting the potential of future flood damage prediction based on climate change and socioeconomic scenarios.« less

Predictability of state-level flood damage in the conterminous United States: the role of hazard, exposure and vulnerability

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

Zhou, Qianqian; Leng, Guoyong; Feng, Leyang

Understanding historical changes in flood damage and the underlying mechanisms is critical for predicting future changes for better adaptations. In this study, a detailed assessment of flood damage for 1950–1999 is conducted at the state level in the conterminous United States (CONUS). Geospatial datasets on possible influencing factors are then developed by synthesizing natural hazards, population, wealth, cropland and urban area to explore the relations with flood damage. A considerable increase in flood damage in CONUS is recorded for the study period which is well correlated with hazards. Comparably, runoff indexed hazards simulated by the Variable Infiltration Capacity (VIC) modelmore » can explain a larger portion of flood damage variations than precipitation in 84% of the states. Cropland is identified as an important factor contributing to increased flood damage in central US while urbanland exhibits positive and negative relations with total flood damage and damage per unit wealth in 20 and 16 states, respectively. Altogether, flood damage in 34 out of 48 investigated states can be predicted at the 90% confidence level. In extreme cases, ~76% of flood damage variations can be explained in some states, highlighting the potential of future flood damage prediction based on climate change and socioeconomic scenarios.« less

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.

Resting-state connectivity predicts visuo-motor skill learning.

PubMed

Manuel, Aurélie L; Guggisberg, Adrian G; Thézé, Raphaël; Turri, Francesco; Schnider, Armin

2018-08-01

Spontaneous brain activity at rest is highly organized even when the brain is not explicitly engaged in a task. Functional connectivity (FC) in the alpha frequency band (α, 8-12 Hz) during rest is associated with improved performance on various cognitive and motor tasks. In this study we explored how FC is associated with visuo-motor skill learning and offline consolidation. We tested two hypotheses by which resting-state FC might achieve its impact on behavior: preparing the brain for an upcoming task or consolidating training gains. Twenty-four healthy participants were assigned to one of two groups: The experimental group (n = 12) performed a computerized mirror-drawing task. The control group (n = 12) performed a similar task but with concordant cursor direction. High-density 156-channel resting-state EEG was recorded before and after learning. Subjects were tested for offline consolidation 24h later. The Experimental group improved during training and showed offline consolidation. Increased α-FC between the left superior parietal cortex and the rest of the brain before training and decreased α-FC in the same region after training predicted learning. Resting-state FC following training did not predict offline consolidation and none of these effects were present in controls. These findings indicate that resting-state alpha-band FC is primarily implicated in providing optimal neural resources for upcoming tasks. Copyright © 2018 Elsevier Inc. All rights reserved.

Spatiotemporal dynamics of similarity-based neural representations of facial identity.

PubMed

Vida, Mark D; Nestor, Adrian; Plaut, David C; Behrmann, Marlene

2017-01-10

Humans' remarkable ability to quickly and accurately discriminate among thousands of highly similar complex objects demands rapid and precise neural computations. To elucidate the process by which this is achieved, we used magnetoencephalography to measure spatiotemporal patterns of neural activity with high temporal resolution during visual discrimination among a large and carefully controlled set of faces. We also compared these neural data to lower level "image-based" and higher level "identity-based" model-based representations of our stimuli and to behavioral similarity judgments of our stimuli. Between ∼50 and 400 ms after stimulus onset, face-selective sources in right lateral occipital cortex and right fusiform gyrus and sources in a control region (left V1) yielded successful classification of facial identity. In all regions, early responses were more similar to the image-based representation than to the identity-based representation. In the face-selective regions only, responses were more similar to the identity-based representation at several time points after 200 ms. Behavioral responses were more similar to the identity-based representation than to the image-based representation, and their structure was predicted by responses in the face-selective regions. These results provide a temporally precise description of the transformation from low- to high-level representations of facial identity in human face-selective cortex and demonstrate that face-selective cortical regions represent multiple distinct types of information about face identity at different times over the first 500 ms after stimulus onset. These results have important implications for understanding the rapid emergence of fine-grained, high-level representations of object identity, a computation essential to human visual expertise.

Spatiotemporal dynamics of similarity-based neural representations of facial identity

PubMed Central

Vida, Mark D.; Nestor, Adrian; Plaut, David C.; Behrmann, Marlene

2017-01-01

Humans’ remarkable ability to quickly and accurately discriminate among thousands of highly similar complex objects demands rapid and precise neural computations. To elucidate the process by which this is achieved, we used magnetoencephalography to measure spatiotemporal patterns of neural activity with high temporal resolution during visual discrimination among a large and carefully controlled set of faces. We also compared these neural data to lower level “image-based” and higher level “identity-based” model-based representations of our stimuli and to behavioral similarity judgments of our stimuli. Between ∼50 and 400 ms after stimulus onset, face-selective sources in right lateral occipital cortex and right fusiform gyrus and sources in a control region (left V1) yielded successful classification of facial identity. In all regions, early responses were more similar to the image-based representation than to the identity-based representation. In the face-selective regions only, responses were more similar to the identity-based representation at several time points after 200 ms. Behavioral responses were more similar to the identity-based representation than to the image-based representation, and their structure was predicted by responses in the face-selective regions. These results provide a temporally precise description of the transformation from low- to high-level representations of facial identity in human face-selective cortex and demonstrate that face-selective cortical regions represent multiple distinct types of information about face identity at different times over the first 500 ms after stimulus onset. These results have important implications for understanding the rapid emergence of fine-grained, high-level representations of object identity, a computation essential to human visual expertise. PMID:28028220

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.

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.

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.

Predicting landscape vegetation dynamics using state-and-transition simulation models

Treesearch

Colin J. Daniel; Leonardo Frid

2012-01-01

This paper outlines how state-and-transition simulation models (STSMs) can be used to project changes in vegetation over time across a landscape. STSMs are stochastic, empirical simulation models that use an adapted Markov chain approach to predict how vegetation will transition between states over time, typically in response to interactions between succession,...

Web of Objects Based Ambient Assisted Living Framework for Emergency Psychiatric State Prediction

PubMed Central

Alam, Md Golam Rabiul; Abedin, Sarder Fakhrul; Al Ameen, Moshaddique; Hong, Choong Seon

2016-01-01

Ambient assisted living can facilitate optimum health and wellness by aiding physical, mental and social well-being. In this paper, patients’ psychiatric symptoms are collected through lightweight biosensors and web-based psychiatric screening scales in a smart home environment and then analyzed through machine learning algorithms to provide ambient intelligence in a psychiatric emergency. The psychiatric states are modeled through a Hidden Markov Model (HMM), and the model parameters are estimated using a Viterbi path counting and scalable Stochastic Variational Inference (SVI)-based training algorithm. The most likely psychiatric state sequence of the corresponding observation sequence is determined, and an emergency psychiatric state is predicted through the proposed algorithm. Moreover, to enable personalized psychiatric emergency care, a service a web of objects-based framework is proposed for a smart-home environment. In this framework, the biosensor observations and the psychiatric rating scales are objectified and virtualized in the web space. Then, the web of objects of sensor observations and psychiatric rating scores are used to assess the dweller’s mental health status and to predict an emergency psychiatric state. The proposed psychiatric state prediction algorithm reported 83.03 percent prediction accuracy in an empirical performance study. PMID:27608023

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

Distinct state anxiety after predictable and unpredictable fear training in mice.

PubMed

Seidenbecher, Thomas; Remmes, Jasmin; Daldrup, Thiemo; Lesting, Jörg; Pape, Hans-Christian

2016-05-01

Sustained fear paradigms in rodents have been developed to monitor states of anxious apprehension and to model situations in patients suffering from long-lasting anxiety disorders. A recent report describes a fear conditioning paradigm, allowing distinction between phasic and sustained states of conditioned fear in non-restrained mice. However, so far no prospective studies have yet been conducted to elucidate whether induction of phasic or sustained fear can affect states of anxiety. Here, we used CS (conditioned stimulus) and US (unconditioned stimulus) pairing with predictable and unpredictable timing to induce phasic and sustained fear in mice. State anxiety during various fear response components was assessed using the elevated plus-maze test. Training with unpredictable CS-US timing resulted in CS-evoked sustained components of fear (freezing), while predictable CS-US timing resulted in rapid decline. Data suggested the influence of training procedure on state anxiety which is dependent on progression of conditioned fear during fear memory retrieval. Animals trained with unpredictable CS-US timing showed an unchanged high anxiety state throughout behavioral observation. In contrast, mice trained with predictable CS-US timing showed anxiolytic-like behavior 3 min after CS onset, which was accompanied by a fast decline of the fear conditioned response (freezing). Further systematic studies are needed to validate the phasic/sustained fear model in rodents as translational model for anxiety disorders in humans. Copyright © 2016 Elsevier B.V. All rights reserved.

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

Acoustic Prediction State of the Art Assessment

NASA Technical Reports Server (NTRS)

Dahl, Milo D.

2007-01-01

The acoustic assessment task for both the Subsonic Fixed Wing and the Supersonic projects under NASA s Fundamental Aeronautics Program was designed to assess the current state-of-the-art in noise prediction capability and to establish baselines for gauging future progress. The documentation of our current capabilities included quantifying the differences between predictions of noise from computer codes and measurements of noise from experimental tests. Quantifying the accuracy of both the computed and experimental results further enhanced the credibility of the assessment. This presentation gives sample results from codes representative of NASA s capabilities in aircraft noise prediction both for systems and components. These include semi-empirical, statistical, analytical, and numerical codes. System level results are shown for both aircraft and engines. Component level results are shown for a landing gear prototype, for fan broadband noise, for jet noise from a subsonic round nozzle, and for propulsion airframe aeroacoustic interactions. Additional results are shown for modeling of the acoustic behavior of duct acoustic lining and the attenuation of sound in lined ducts with flow.

Representations of the veterinary profession in nonfiction children's books.

PubMed

Amass, Sandra F

2011-05-01

To evaluate how the veterinary profession is represented in nonfiction children's books and determine whether representations reflect the current veterinary profession or the demographics of the United States. Survey. Covers of 46 nonfiction children's books and contents of 45 nonfiction children's books. Book covers and book contents (images and text) were evaluated for representations of veterinarians and to identify settings, clients, technology and equipment, and animals portrayed. Book contents were additionally evaluated to identify specialties and career opportunities specifically mentioned in the text. Book covers predominantly portrayed veterinarians as Caucasian women who wore examination coats, worked alone in veterinary clinics, and cared for dogs without a client present. Book contents predominantly portrayed veterinarians as a Caucasian man or woman who wore an examination coat, worked as part of a team in a veterinary clinic, and helped clients care for dogs, cats, and exotic animals. Specialties and career opportunities in the veterinary profession were mentioned in the text of 29 of 45 (64.4%) books. Nonfiction children's book covers that focused on the veterinary profession portrayed a greater percentage of women than is currently found in the profession. Similarly, books portrayed a greater percentage of Caucasians than in the current or predicted US population. With the exception of Asians, books collectively represented lower or similar percentages of underrepresented minorities, compared with the US population. Veterinarians are encouraged to select books for individual children that portray veterinarians with whom the children can identify.

Developing young adults' representational competence through infographic-based science news reporting

NASA Astrophysics Data System (ADS)

Gebre, Engida H.; Polman, Joseph L.

2016-12-01

This study presents descriptive analysis of young adults' use of multiple representations in the context of science news reporting. Across one semester, 71 high school students, in a socioeconomically diverse suburban secondary school in Midwestern United States, participated in activities of researching science topics of their choice and producing infographic-based science news for possible online publication. An external editor reviewed their draft infographics and provided comments for subsequent revision. Students also provided peer feedback to the draft version of infographics using an online commentary tool. We analysed the nature of representations students used as well as the comments from peer and the editor feedback. Results showed both students' capabilities and challenges in learning with representations in this context. Students frequently rely on using certain kinds of representations that are depictive in nature, and supporting their progress towards using more abstract representations requires special attention and identifying learning gaps. Results also showed that students were able to determine representational adequacy in the context of providing peer feedback. The study has implication for research and instruction using infographics as expressive tools to support learning.

Characterizing and differentiating task-based and resting state fMRI signals via two-stage sparse representations.

PubMed

Zhang, Shu; Li, Xiang; Lv, Jinglei; Jiang, Xi; Guo, Lei; Liu, Tianming

2016-03-01

A relatively underexplored question in fMRI is whether there are intrinsic differences in terms of signal composition patterns that can effectively characterize and differentiate task-based or resting state fMRI (tfMRI or rsfMRI) signals. In this paper, we propose a novel two-stage sparse representation framework to examine the fundamental difference between tfMRI and rsfMRI signals. Specifically, in the first stage, the whole-brain tfMRI or rsfMRI signals of each subject were composed into a big data matrix, which was then factorized into a subject-specific dictionary matrix and a weight coefficient matrix for sparse representation. In the second stage, all of the dictionary matrices from both tfMRI/rsfMRI data across multiple subjects were composed into another big data-matrix, which was further sparsely represented by a cross-subjects common dictionary and a weight matrix. This framework has been applied on the recently publicly released Human Connectome Project (HCP) fMRI data and experimental results revealed that there are distinctive and descriptive atoms in the cross-subjects common dictionary that can effectively characterize and differentiate tfMRI and rsfMRI signals, achieving 100% classification accuracy. Moreover, our methods and results can be meaningfully interpreted, e.g., the well-known default mode network (DMN) activities can be recovered from the very noisy and heterogeneous aggregated big-data of tfMRI and rsfMRI signals across all subjects in HCP Q1 release.

On the importance of an accurate representation of the initial state of the system in classical dynamics simulations

NASA Astrophysics Data System (ADS)

García-Vela, A.

2000-05-01

A definition of a quantum-type phase-space distribution is proposed in order to represent the initial state of the system in a classical dynamics simulation. The central idea is to define an initial quantum phase-space state of the system as the direct product of the coordinate and momentum representations of the quantum initial state. The phase-space distribution is then obtained as the square modulus of this phase-space state. The resulting phase-space distribution closely resembles the quantum nature of the system initial state. The initial conditions are sampled with the distribution, using a grid technique in phase space. With this type of sampling the distribution of initial conditions reproduces more faithfully the shape of the original phase-space distribution. The method is applied to generate initial conditions describing the three-dimensional state of the Ar-HCl cluster prepared by ultraviolet excitation. The photodissociation dynamics is simulated by classical trajectories, and the results are compared with those of a wave packet calculation. The classical and quantum descriptions are found in good agreement for those dynamical events less subject to quantum effects. The classical result fails to reproduce the quantum mechanical one for the more strongly quantum features of the dynamics. The properties and applicability of the phase-space distribution and the sampling technique proposed are discussed.

Digital Art Making as a Representational Process

ERIC Educational Resources Information Center

Halverson, Erica Rosenfeld

2013-01-01

In this article I bring artistic production into the learning sciences conversation by using the production of representations as a bridging concept between art making and the new literacies. Through case studies with 4 youth media arts organizations across the United States I ask how organizations structure the process of producing…

Representation Elements of Spatial Thinking

NASA Astrophysics Data System (ADS)

Fiantika, F. R.

2017-04-01

This paper aims to add a reference in revealing spatial thinking. There several definitions of spatial thinking but it is not easy to defining it. We can start to discuss the concept, its basic a forming representation. Initially, the five sense catch the natural phenomenon and forward it to memory for processing. Abstraction plays a role in processing information into a concept. There are two types of representation, namely internal representation and external representation. The internal representation is also known as mental representation; this representation is in the human mind. The external representation may include images, auditory and kinesthetic which can be used to describe, explain and communicate the structure, operation, the function of the object as well as relationships. There are two main elements, representations properties and object relationships. These elements play a role in forming a representation.

Phase Shadows: An Enhanced Representation of Nonlinear Dynamic Systems

NASA Astrophysics Data System (ADS)

Luque, Amalia; Barbancho, Julio; Cañete, Javier Fernández; Córdoba, Antonio

2017-12-01

Many nonlinear dynamic systems have a rotating behavior where an angle defining its state may extend to more than 360∘. In these cases the use of the phase portrait does not properly depict the system’s evolution. Normalized phase portraits or cylindrical phase portraits have been extensively used to overcome the original phase portrait’s disadvantages. In this research a new graphic representation is introduced: the phase shadow. Its use clearly reveals the system behavior while overcoming the drawback of the existing plots. Through the paper the method to obtain the graphic is stated. Additionally, to show the phase shadow’s expressiveness, a rotating pendulum is considered. The work exposes that the new graph is an enhanced representational tool for systems having equilibrium points, limit cycles, chaotic attractors and/or bifurcations.

Use of the Wigner representation in scattering problems

NASA Technical Reports Server (NTRS)

Bemler, E. A.

1975-01-01

The basic equations of quantum scattering were translated into the Wigner representation, putting quantum mechanics in the form of a stochastic process in phase space, with real valued probability distributions and source functions. The interpretative picture associated with this representation is developed and stressed and results used in applications published elsewhere are derived. The form of the integral equation for scattering as well as its multiple scattering expansion in this representation are derived. Quantum corrections to classical propagators are briefly discussed. The basic approximation used in the Monte-Carlo method is derived in a fashion which allows for future refinement and which includes bound state production. Finally, as a simple illustration of some of the formalism, scattering is treated by a bound two body problem. Simple expressions for single and double scattering contributions to total and differential cross-sections as well as for all necessary shadow corrections are obtained.

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

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

Bryan, Frank; Dennis, John; MacCready, Parker

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

The Impact of Immigration on Congressional Representation.

ERIC Educational Resources Information Center

Bouvier, Leon

Explanation of shifts in U.S. Congressional representation among states have often overlooked the effects of international migration on the size and distribution of the U.S. population. Seventy percent of recent U.S. immigrants have settled in California, New York, Texas, Florida, New Jersey, and Illinois. Estimates of the distribution of…

Semantic representation in the white matter pathway

PubMed Central

Fang, Yuxing; Wang, Xiaosha; Zhong, Suyu; Song, Luping; Han, Zaizhu; Gong, Gaolang

2018-01-01

Object conceptual processing has been localized to distributed cortical regions that represent specific attributes. A challenging question is how object semantic space is formed. We tested a novel framework of representing semantic space in the pattern of white matter (WM) connections by extending the representational similarity analysis (RSA) to structural lesion pattern and behavioral data in 80 brain-damaged patients. For each WM connection, a neural representational dissimilarity matrix (RDM) was computed by first building machine-learning models with the voxel-wise WM lesion patterns as features to predict naming performance of a particular item and then computing the correlation between the predicted naming score and the actual naming score of another item in the testing patients. This correlation was used to build the neural RDM based on the assumption that if the connection pattern contains certain aspects of information shared by the naming processes of these two items, models trained with one item should also predict naming accuracy of the other. Correlating the neural RDM with various cognitive RDMs revealed that neural patterns in several WM connections that connect left occipital/middle temporal regions and anterior temporal regions associated with the object semantic space. Such associations were not attributable to modality-specific attributes (shape, manipulation, color, and motion), to peripheral picture-naming processes (picture visual similarity, phonological similarity), to broad semantic categories, or to the properties of the cortical regions that they connected, which tended to represent multiple modality-specific attributes. That is, the semantic space could be represented through WM connection patterns across cortical regions representing modality-specific attributes. PMID:29624578

Students' Development of Representational Competence Through the Sense of Touch

NASA Astrophysics Data System (ADS)

Magana, Alejandra J.; Balachandran, Sadhana

2017-06-01

Electromagnetism is an umbrella encapsulating several different concepts like electric current, electric fields and forces, and magnetic fields and forces, among other topics. However, a number of studies in the past have highlighted the poor conceptual understanding of electromagnetism concepts by students even after instruction. This study aims to identify novel forms of "hands-on" instruction that can result in representational competence and conceptual gain. Specifically, this study aimed to identify if the use of visuohaptic simulations can have an effect on student representations of electromagnetic-related concepts. The guiding questions is How do visuohaptic simulations influence undergraduate students' representations of electric forces? Participants included nine undergraduate students from science, technology, or engineering backgrounds who participated in a think-aloud procedure while interacting with a visuohaptic simulation. The think-aloud procedure was divided in three stages, a prediction stage, a minimally visual haptic stage, and a visually enhanced haptic stage. The results of this study suggest that students' accurately characterized and represented the forces felt around a particle, line, and ring charges either in the prediction stage, a minimally visual haptic stage or the visually enhanced haptic stage. Also, some students accurately depicted the three-dimensional nature of the field for each configuration in the two stages that included a tactile mode, where the point charge was the most challenging one.

37 CFR 1.455 - Representation in international applications.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 37 Patents, Trademarks, and Copyrights 1 2010-07-01 2010-07-01 false Representation in international applications. 1.455 Section 1.455 Patents, Trademarks, and Copyrights UNITED STATES PATENT AND TRADEMARK OFFICE, DEPARTMENT OF COMMERCE GENERAL RULES OF PRACTICE IN PATENT CASES International Processing...

37 CFR 1.455 - Representation in international applications.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 37 Patents, Trademarks, and Copyrights 1 2014-07-01 2014-07-01 false Representation in international applications. 1.455 Section 1.455 Patents, Trademarks, and Copyrights UNITED STATES PATENT AND TRADEMARK OFFICE, DEPARTMENT OF COMMERCE GENERAL RULES OF PRACTICE IN PATENT CASES International Processing...

37 CFR 1.455 - Representation in international applications.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 37 Patents, Trademarks, and Copyrights 1 2012-07-01 2012-07-01 false Representation in international applications. 1.455 Section 1.455 Patents, Trademarks, and Copyrights UNITED STATES PATENT AND TRADEMARK OFFICE, DEPARTMENT OF COMMERCE GENERAL RULES OF PRACTICE IN PATENT CASES International Processing...

37 CFR 1.455 - Representation in international applications.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 37 Patents, Trademarks, and Copyrights 1 2011-07-01 2011-07-01 false Representation in international applications. 1.455 Section 1.455 Patents, Trademarks, and Copyrights UNITED STATES PATENT AND TRADEMARK OFFICE, DEPARTMENT OF COMMERCE GENERAL RULES OF PRACTICE IN PATENT CASES International Processing...

37 CFR 1.455 - Representation in international applications.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 37 Patents, Trademarks, and Copyrights 1 2013-07-01 2013-07-01 false Representation in international applications. 1.455 Section 1.455 Patents, Trademarks, and Copyrights UNITED STATES PATENT AND TRADEMARK OFFICE, DEPARTMENT OF COMMERCE GENERAL RULES OF PRACTICE IN PATENT CASES International Processing...

Evaluating and improving the representation of heteroscedastic errors in hydrological models

NASA Astrophysics Data System (ADS)

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

2013-12-01

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

Two States Mapping Based Time Series Neural Network Model for Compensation Prediction Residual Error

NASA Astrophysics Data System (ADS)

Jung, Insung; Koo, Lockjo; Wang, Gi-Nam

2008-11-01

The objective of this paper was to design a model of human bio signal data prediction system for decreasing of prediction error using two states mapping based time series neural network BP (back-propagation) model. Normally, a lot of the industry has been applied neural network model by training them in a supervised manner with the error back-propagation algorithm for time series prediction systems. However, it still has got a residual error between real value and prediction result. Therefore, we designed two states of neural network model for compensation residual error which is possible to use in the prevention of sudden death and metabolic syndrome disease such as hypertension disease and obesity. We determined that most of the simulation cases were satisfied by the two states mapping based time series prediction model. In particular, small sample size of times series were more accurate than the standard MLP model.

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

On volume-source representations based on the representation theorem

NASA Astrophysics Data System (ADS)

Ichihara, Mie; Kusakabe, Tetsuya; Kame, Nobuki; Kumagai, Hiroyuki

2016-01-01

We discuss different ways to characterize a moment tensor associated with an actual volume change of ΔV C , which has been represented in terms of either the stress glut or the corresponding stress-free volume change ΔV T . Eshelby's virtual operation provides a conceptual model relating ΔV C to ΔV T and the stress glut, where non-elastic processes such as phase transitions allow ΔV T to be introduced and subsequent elastic deformation of - ΔV T is assumed to produce the stress glut. While it is true that ΔV T correctly represents the moment tensor of an actual volume source with volume change ΔV C , an explanation as to why such an operation relating ΔV C to ΔV T exists has not previously been given. This study presents a comprehensive explanation of the relationship between ΔV C and ΔV T based on the representation theorem. The displacement field is represented using Green's function, which consists of two integrals over the source surface: one for displacement and the other for traction. Both integrals are necessary for representing volumetric sources, whereas the representation of seismic faults includes only the first term, as the second integral over the two adjacent fault surfaces, across which the traction balances, always vanishes. Therefore, in a seismological framework, the contribution from the second term should be included as an additional surface displacement. We show that the seismic moment tensor of a volume source is directly obtained from the actual state of the displacement and stress at the source without considering any virtual non-elastic operations. A purely mathematical procedure based on the representation theorem enables us to specify the additional imaginary displacement necessary for representing a volume source only by the displacement term, which links ΔV C to ΔV T . It also specifies the additional imaginary stress necessary for representing a moment tensor solely by the traction term, which gives the "stress glut." The

Students' Representational Fluency at University: A Cross-Sectional Measure of How Multiple Representations Are Used by Physics Students Using the Representational Fluency Survey

ERIC Educational Resources Information Center

Hill, Matthew; Sharma, Manjula Devi

2015-01-01

To succeed within scientific disciplines, using representations, including those based on words, graphs, equations, and diagrams, is important. Research indicates that the use of discipline specific representations (sometimes referred to as expert generated representations), as well as multi-representational use, is critical for problem solving…

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

PubMed

Pezzulo, Giovanni; Dindo, Haris

2011-06-01

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

Hierarchical Representation Learning for Kinship Verification.

PubMed

Kohli, Naman; Vatsa, Mayank; Singh, Richa; Noore, Afzel; Majumdar, Angshul

2017-01-01

Kinship verification has a number of applications such as organizing large collections of images and recognizing resemblances among humans. In this paper, first, a human study is conducted to understand the capabilities of human mind and to identify the discriminatory areas of a face that facilitate kinship-cues. The visual stimuli presented to the participants determine their ability to recognize kin relationship using the whole face as well as specific facial regions. The effect of participant gender and age and kin-relation pair of the stimulus is analyzed using quantitative measures such as accuracy, discriminability index d' , and perceptual information entropy. Utilizing the information obtained from the human study, a hierarchical kinship verification via representation learning (KVRL) framework is utilized to learn the representation of different face regions in an unsupervised manner. We propose a novel approach for feature representation termed as filtered contractive deep belief networks (fcDBN). The proposed feature representation encodes relational information present in images using filters and contractive regularization penalty. A compact representation of facial images of kin is extracted as an output from the learned model and a multi-layer neural network is utilized to verify the kin accurately. A new WVU kinship database is created, which consists of multiple images per subject to facilitate kinship verification. The results show that the proposed deep learning framework (KVRL-fcDBN) yields the state-of-the-art kinship verification accuracy on the WVU kinship database and on four existing benchmark data sets. Furthermore, kinship information is used as a soft biometric modality to boost the performance of face verification via product of likelihood ratio and support vector machine based approaches. Using the proposed KVRL-fcDBN framework, an improvement of over 20% is observed in the performance of face verification.

40 CFR 96.213 - Certificate of representation.

Code of Federal Regulations, 2010 CFR

2010-07-01

... (CONTINUED) NOX BUDGET TRADING PROGRAM AND CAIR NOX AND SO2 TRADING PROGRAMS FOR STATE IMPLEMENTATION PLANS CAIR Designated Representative for CAIR SO2 Sources § 96.213 Certificate of representation. (a) A...) Identification of the CAIR SO2 source, and each CAIR SO2 unit at the source, for which the certificate of...

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.

State institutions and social identity: National representation in soldiers' and civilians' interview talk concerning military service.

PubMed

Gibson, Stephen; Condor, Susan

2009-06-01

Theory and research deriving from social identity or self-categorization perspectives often starts out with the presumption that social actors necessarily view societal objects such as nations or states as human categories. However, recent work suggests that this may be only one of a number of forms that societal representation may take. For example, nations may be understood variously as peoples, places, or institutions. This paper presents findings from a qualitative interview study conducted in England, in which soldiers and civilians talked about nationhood in relation to military service. Analysis indicated that, in this context, speakers were often inclined to use the terms 'Britain', 'nation', and 'country' as references to a political institution as opposed to a category of people. In addition, there were systematic differences between the ways in which the two samples construed their nation in institutional terms. The civilians were inclined to treat military service as a matter of obedience to the dictates of the Government of the day. In contrast, the soldiers were more inclined to frame military service as a matter of loyalty to state as symbolically instantiated in the body of the sovereign. Implications for work adopting a social identity perspective are discussed.

MATERNAL REPRESENTATIONS AND INFANT ATTACHMENT: AN EXAMINATION OF THE PROTOTYPE HYPOTHESIS.

PubMed

Madigan, Sheri; Hawkins, Erinn; Plamondon, Andre; Moran, Greg; Benoit, Diane

2015-01-01

The prototype hypothesis suggests that attachment representations derived in infancy continue to influence subsequent relationships over the life span, including those formed with one's own children. In the current study, we test the prototype hypothesis by exploring (a) whether child-specific representations following actual experience in interaction with a specific child impacts caregiver-child attachment over and above the prenatal forecast of that representation and (b) whether maternal attachment representations exert their influence on infant attachment via the more child-specific representation of that relationship. In a longitudinal study of 84 mother-infant dyads, mothers' representations of their attachment history were obtained prenatally with the Adult Attachment Interview (AAI; M. Main, R. Goldwyn, & E. Hesse, 2002), representations of relationship with a specific child were assessed with the Working Model of the Child Interview (WMCI; C.H. Zeanah, D. Benoit, & L. Barton, 1986), collected both prenatally and again at infant age 11 months, and infant attachment was assessed in the Strange Situation Procedure (M.D.S. Ainsworth, M.C. Blehar, E. Walters, & S. Wall, 1978) when infants were 11 months of age. Consistent with the prototype hypothesis, considerable correspondence was found between mothers' AAI and WMCI classifications. A mediation analysis showed that WMCI fully accounted for the association between AAI and infant attachment. Postnatal WMCI measured at 11 months' postpartum did not add to the prediction of infant attachment, over and above that explained by the prenatal WMCI. Implications for these findings are discussed. © 2015 Michigan Association for Infant Mental Health.

Physics instruction induces changes in neural knowledge representation during successive stages of learning.

PubMed

Mason, Robert A; Just, Marcel Adam

2015-05-01

Incremental instruction on the workings of a set of mechanical systems induced a progression of changes in the neural representations of the systems. The neural representations of four mechanical systems were assessed before, during, and after three phases of incremental instruction (which first provided information about the system components, then provided partial causal information, and finally provided full functional information). In 14 participants, the neural representations of four systems (a bathroom scale, a fire extinguisher, an automobile braking system, and a trumpet) were assessed using three recently developed techniques: (1) machine learning and classification of multi-voxel patterns; (2) localization of consistently responding voxels; and (3) representational similarity analysis (RSA). The neural representations of the systems progressed through four stages, or states, involving spatially and temporally distinct multi-voxel patterns: (1) initially, the representation was primarily visual (occipital cortex); (2) it subsequently included a large parietal component; (3) it eventually became cortically diverse (frontal, parietal, temporal, and medial frontal regions); and (4) at the end, it demonstrated a strong frontal cortex weighting (frontal and motor regions). At each stage of knowledge, it was possible for a classifier to identify which one of four mechanical systems a participant was thinking about, based on their brain activation patterns. The progression of representational states was suggestive of progressive stages of learning: (1) encoding information from the display; (2) mental animation, possibly involving imagining the components moving; (3) generating causal hypotheses associated with mental animation; and finally (4) determining how a person (probably oneself) would interact with the system. This interpretation yields an initial, cortically-grounded, theory of learning of physical systems that potentially can be related to cognitive

Intelligent automated control of life support systems using proportional representations.

PubMed

Wu, Annie S; Garibay, Ivan I

2004-06-01

Effective automatic control of Advanced Life Support Systems (ALSS) is a crucial component of space exploration. An ALSS is a coupled dynamical system which can be extremely sensitive and difficult to predict. As a result, such systems can be difficult to control using deliberative and deterministic methods. We investigate the performance of two machine learning algorithms, a genetic algorithm (GA) and a stochastic hill-climber (SH), on the problem of learning how to control an ALSS, and compare the impact of two different types of problem representations on the performance of both algorithms. We perform experiments on three ALSS optimization problems using five strategies with multiple variations of a proportional representation for a total of 120 experiments. Results indicate that although a proportional representation can effectively boost GA performance, it does not necessarily have the same effect on other algorithms such as SH. Results also support previous conclusions that multivector control strategies are an effective method for control of coupled dynamical systems.

Preschool Children's Participation in Representational and Non-Representational Activities

ERIC Educational Resources Information Center

Braswell, Gregory S.

2017-01-01

The present study examined representational and non-representational activities in which children in a Head Start classroom participated. This was an investigation from the perspective of cultural-historical activity theory of how components (e.g. artifacts and division of labour) of classroom activities vary across and within types of activities.…

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.

On the v-representability of ensemble densities of electron systems

NASA Astrophysics Data System (ADS)

Gonis, A.; Däne, M.

2018-05-01

Analogously to the case at zero temperature, where the density of the ground state of an interacting many-particle system determines uniquely (within an arbitrary additive constant) the external potential acting on the system, the thermal average of the density over an ensemble defined by the Boltzmann distribution at the minimum of the thermodynamic potential, or the free energy, determines the external potential uniquely (and not just modulo a constant) acting on a system described by this thermodynamic potential or free energy. The paper describes a formal procedure that generates the domain of a constrained search over general ensembles (at zero or elevated temperatures) that lead to a given density, including as a special case a density thermally averaged at a given temperature, and in the case of a v-representable density determines the external potential leading to the ensemble density. As an immediate consequence of the general formalism, the concept of v-representability is extended beyond the hitherto discussed case of ground state densities to encompass excited states as well. Specific application to thermally averaged densities solves the v-representability problem in connection with the Mermin functional in a manner analogous to that in which this problem was recently settled with respect to the Hohenberg and Kohn functional. The main formalism is illustrated with numerical results for ensembles of one-dimensional, non-interacting systems of particles under a harmonic potential.

State Mindfulness During Meditation Predicts Enhanced Cognitive Reappraisal

PubMed Central

Hanley, Adam; Farb, Norman A.; Froeliger, Brett E.

2013-01-01

Putatively, mindfulness meditation involves generation of a state of “nonappraisal”, yet, little is known about how mindfulness may influence appraisal processes. We investigated whether the state and practice of mindfulness could enhance cognitive reappraisal. Participants (N = 44; M age = 24.44, SD = 4.00, range 19 – 38, 82.2% female) were randomized to either 1) mindfulness, 2) suppression, or 3) mind-wandering induction training conditions. Cognitive reappraisal was assessed with the Emotion Regulation Questionnaire (ERQ) prior to experimental induction, and state mindfulness was assessed immediately following induction using the Toronto Mindfulness Scale (TMS). Participants practiced their assigned strategy for one week and then were reassessed with the ERQ reappraisal subscale. Participants receiving mindfulness training reported significantly higher levels of state mindfulness than participants in the thought suppression and mind wandering conditions. Although brief mindfulness training did not lead to significantly greater increases in reappraisal than the other two conditions, state mindfulness during mindfulness meditation was prospectively associated with increases in reappraisal. Path analysis revealed that the indirect effect between mindfulness training and reappraisal was significant through state mindfulness. Degree of state mindfulness achieved during the act of mindfulness meditation significantly predicted increases in reappraisal over time, suggesting that mindfulness may promote emotion regulation by enhancing cognitive reappraisal. PMID:26085851

Secure Base Representations in Middle Childhood Across Two Western Cultures: Associations with Parental Attachment Representations and Maternal Reports of Behavior Problems

PubMed Central

Waters, Theodore E. A.; Bosmans, Guy; Vandevivere, Eva; Dujardin, Adinda; Waters, Harriet S.

2015-01-01

Recent work examining the content and organization of attachment representations suggests that one way in which we represent the attachment relationship is in the form of a cognitive script. That said, this work has largely focused on early childhood or adolescence/adulthood, leaving a large gap in our understanding of script-like attachment representations in the middle childhood period. We present two studies and provide three critical pieces of evidence regarding the presence of a script-like representation of the attachment relationship in middle childhood. We present evidence that a middle childhood attachment script assessment tapped a stable underlying script using samples drawn from two western cultures, the United States (Study 1) and Belgium (Study 2). We also found evidence suggestive of the intergenerational transmission of secure base script knowledge (Study 1) and relations between secure base script knowledge and symptoms of psychopathology in middle childhood (Study 2). The results from this investigation represent an important downward extension of the secure base script construct. PMID:26147774

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.

Standard model of knowledge representation

NASA Astrophysics Data System (ADS)

Yin, Wensheng

2016-09-01

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

Negotiated Representational Mediators: How Young Children Decide What to Include in Their Science Representations

ERIC Educational Resources Information Center

Danish, Joshua A.; Enyedy, Noel

2007-01-01

In this paper, we synthesize two bodies of work related to students' representational activities: the notions of meta-representational competence and representation as a form of practice. We report on video analyses of kindergarten and first-grade students as they create representations of pollination in a science classroom, as well as summarize…

What is rate? Does context or representation matter?

NASA Astrophysics Data System (ADS)

Herbert, Sandra; Pierce, Robyn

2011-12-01

Rate is an important, but difficult, mathematical concept. Despite more than 20 years of research, especially with calculus students, difficulties are reported with this concept. This paper reports the results from analysis of data from 20 Australian Grade 10 students. Interviews targeted students' conceptions of rate, focussing on the influence of representation and context on their expression of their understanding of rate. This analysis shows that different representations of functions provide varying levels of rate-related information for individual students. Understandings of rate in one representation or context are not necessarily transferred to another representation or context. Rate is an important, but commonly misunderstood, mathematical concept with many everyday applications (Swedosh, Dowsey, Caruso, Flynn, & Tynan, 2007). It is a complicated concept comprising many interwoven ideas such as the ratio of two numeric, measurable quantities but in a context where both quantities are changing. In mathematics classes, this is commonly expressed as change in the dependent variable resulting from a unit change in the independent variable, and variously described as constant or variable rate; average or instantaneous rate. In addition, rate may be seen as a purely abstract mathematical notion or embedded in the understanding of real-world applications. This paper explores the research question: Are students' expressions of their conceptions of rate affected by either context or mathematical representation? This question was part of a larger study (Herbert, 2010) conducted with Grade 10 students from the Australian state of Victoria.

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

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.

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

PubMed

Karimi, Hossein; Ferreira, Fernanda

2016-01-01

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

Origins of Secure Base Script Knowledge and the Developmental Construction of Attachment Representations

PubMed Central

Waters, Theodore E. A.; Ruiz, Sarah K.; Roisman, Glenn I.

2016-01-01

Increasing evidence suggests that attachment representations take at least two forms—a secure base script and an autobiographical narrative of childhood caregiving experiences. This study presents data from the first 26 years of the Minnesota Longitudinal Study of Risk and Adaptation (N = 169), examining the developmental origins of secure base script knowledge in a high-risk sample, and testing alternative models of the developmental sequencing of the construction of attachment representations. Results demonstrated that secure base script knowledge was predicted by observations of maternal sensitivity across childhood and adolescence. Further, findings suggest that the construction of a secure base script supports the development of a coherent autobiographical representation of childhood attachment experiences with primary caregivers by early adulthood. PMID:27302650

Structure-reactivity modeling using mixture-based representation of chemical reactions.

PubMed

Polishchuk, Pavel; Madzhidov, Timur; Gimadiev, Timur; Bodrov, Andrey; Nugmanov, Ramil; Varnek, Alexandre

2017-09-01

We describe a novel approach of reaction representation as a combination of two mixtures: a mixture of reactants and a mixture of products. In turn, each mixture can be encoded using an earlier reported approach involving simplex descriptors (SiRMS). The feature vector representing these two mixtures results from either concatenated product and reactant descriptors or the difference between descriptors of products and reactants. This reaction representation doesn't need an explicit labeling of a reaction center. The rigorous "product-out" cross-validation (CV) strategy has been suggested. Unlike the naïve "reaction-out" CV approach based on a random selection of items, the proposed one provides with more realistic estimation of prediction accuracy for reactions resulting in novel products. The new methodology has been applied to model rate constants of E2 reactions. It has been demonstrated that the use of the fragment control domain applicability approach significantly increases prediction accuracy of the models. The models obtained with new "mixture" approach performed better than those required either explicit (Condensed Graph of Reaction) or implicit (reaction fingerprints) reaction center labeling.

Age Differences in Symbolic Representation: Fluidity in Representational Construction.

ERIC Educational Resources Information Center

Reifel, Stuart

This paper reports a cross-sectional, developmental study of the fluidity of children's mental functioning (representational skills) in contexts involving the representational use of blocks. Data were collected from a sample of 40 children from a laboratory school: 20 four-year-olds and 20 seven-year-olds, with an equal number of boys and girls in…

Slug sizing/slug volume prediction, state of the art review and simulation

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

Burke, N.E.; Kashou, S.F.

1995-12-01

Slug flow is a flow pattern commonly encountered in offshore multiphase flowlines. It is characterized by an alternate flow of liquid slugs and gas pockets, resulting in an unsteady hydrodynamic behavior. All important design variables, such as slug length and slug frequency, liquid holdup, and pressure drop, vary with time and this makes the prediction of slug flow characteristics both difficult and challenging. This paper reviews the state of the art methods in slug catcher sizing and slug volume predictions. In addition, history matching of measured slug flow data is performed using the OLGA transient simulator. This paper reviews themore » design factors that impact slug catcher sizing during steady state, during transient, during pigging, and during operations under a process control system. The slug tracking option of the OLGA simulator is applied to predict the slug length and the slug volume during a field operation. This paper will also comment on the performance of common empirical slug prediction correlations.« less

Slug-sizing/slug-volume prediction: State of the art review and simulation

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

Burke, N.E.; Kashou, S.F.

1996-08-01

Slug flow is a flow pattern commonly encountered in offshore multiphase flowlines. It is characterized by an alternate flow of liquid slugs and gas pockets, resulting in an unsteady hydrodynamic behavior. All important design variables, such as slug length and slug frequency, liquid holdup, and pressure drop, vary with time and this makes the prediction of slug flow characteristics both difficult and challenging. This paper reviews the state of the art methods in slug-catcher sizing and slug-volume predictions. In addition, history matching of measured slug flow data is performed using the OLGA transient simulator. This paper reviews the design factorsmore » that impact slug-catcher sizing during steady state, during transient, during pigging, and during operations under a process-control system. The slug-tracking option of the simulator is applied to predict the slug length and the slug volume during a field operation. This paper will also comment on the performance of common empirical slug-prediction correlations.« less

Human Object-Similarity Judgments Reflect and Transcend the Primate-IT Object Representation

PubMed Central

Mur, Marieke; Meys, Mirjam; Bodurka, Jerzy; Goebel, Rainer; Bandettini, Peter A.; Kriegeskorte, Nikolaus

2013-01-01

Primate inferior temporal (IT) cortex is thought to contain a high-level representation of objects at the interface between vision and semantics. This suggests that the perceived similarity of real-world objects might be predicted from the IT representation. Here we show that objects that elicit similar activity patterns in human IT (hIT) tend to be judged as similar by humans. The IT representation explained the human judgments better than early visual cortex, other ventral-stream regions, and a range of computational models. Human similarity judgments exhibited category clusters that reflected several categorical divisions that are prevalent in the IT representation of both human and monkey, including the animate/inanimate and the face/body division. Human judgments also reflected the within-category representation of IT. However, the judgments transcended the IT representation in that they introduced additional categorical divisions. In particular, human judgments emphasized human-related additional divisions between human and non-human animals and between man-made and natural objects. hIT was more similar to monkey IT than to human judgments. One interpretation is that IT has evolved visual-feature detectors that distinguish between animates and inanimates and between faces and bodies because these divisions are fundamental to survival and reproduction for all primate species, and that other brain systems serve to more flexibly introduce species-dependent and evolutionarily more recent divisions. PMID:23525516

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.

Does improvement in maternal attachment representations predict greater maternal sensitivity, child attachment security and lower rates of relapse to substance use? A second test of Mothering from the Inside Out treatment mechanisms.

PubMed

Suchman, Nancy E; DeCoste, Cindy; Borelli, Jessica L; McMahon, Thomas J

2018-02-01

In this study, we replicated a rigorous test of the proposed mechanisms of change associated with Mothering from the Inside out (MIO), an evidence-based parenting therapy that aims to enhance maternal reflective functioning and mental representations of caregiving in mothers enrolled in addiction treatment and caring for young children. First, using data from 84 mothers who enrolled in our second randomized controlled trial, we examined whether therapist fidelity to core MIO treatment components predicted improvement in maternal reflective functioning and mental representations of caregiving, even after taking fidelity to non-MIO components into account. Next, we examined whether improvement in directly targeted outcomes (e.g., maternal mentalizing and mental representations of caregiving) led to improvements in the indirectly targeted outcome of maternal caregiving sensitivity, even after controlling for other plausible competing mechanisms (e.g., improvement in maternal psychiatric distress and substance use). Third, we examined whether improvement in targeted parenting outcomes (e.g., maternal mentalizing, mental representations of caregiving and caregiving sensitivity) was associated in improvement in child attachment status, even after controlling for competing mechanisms (e.g., improvement in maternal psychiatric distress and substance use). Finally, we examined whether improvement in maternal mentalizing and caregiving representations was associated with a reduction in relapse to substance use. Support was found for the first three tests of mechanisms but not the fourth. Implications for future research and intervention development are discussed. Copyright © 2017 Elsevier Inc. All rights reserved.

Empirical algorithms to predict aragonite saturation state

NASA Astrophysics Data System (ADS)

Turk, Daniela; Dowd, Michael

2017-04-01

Novel sensor packages deployed on autonomous platforms (Profiling Floats, Gliders, Moorings, SeaCycler) and biogeochemical models have a potential to increase the coverage of a key water chemistry variable, aragonite saturation state (ΩAr) in time and space, in particular in the under sampled regions of global ocean. However, these do not provide the set of inorganic carbon measurements commonly used to derive ΩAr. There is therefore a need to develop regional predictive models to determine ΩAr from measurements of commonly observed or/and non carbonate oceanic variables. Here, we investigate predictive skill of several commonly observed oceanographic variables (temperature, salinity, oxygen, nitrate, phosphate and silicate) in determining ΩAr using climatology and shipboard data. This will allow us to assess potential for autonomous sensors and biogeochemical models to monitor ΩAr regionally and globally. We apply the regression models to several time series data sets and discuss regional differences and their implications for global estimates of ΩAr.

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.

Hidden state prediction: a modification of classic ancestral state reconstruction algorithms helps unravel complex symbioses

PubMed Central

Zaneveld, Jesse R. R.; Thurber, Rebecca L. V.

2014-01-01

Complex symbioses between animal or plant hosts and their associated microbiotas can involve thousands of species and millions of genes. Because of the number of interacting partners, it is often impractical to study all organisms or genes in these host-microbe symbioses individually. Yet new phylogenetic predictive methods can use the wealth of accumulated data on diverse model organisms to make inferences into the properties of less well-studied species and gene families. Predictive functional profiling methods use evolutionary models based on the properties of studied relatives to put bounds on the likely characteristics of an organism or gene that has not yet been studied in detail. These techniques have been applied to predict diverse features of host-associated microbial communities ranging from the enzymatic function of uncharacterized genes to the gene content of uncultured microorganisms. We consider these phylogenetically informed predictive techniques from disparate fields as examples of a general class of algorithms for Hidden State Prediction (HSP), and argue that HSP methods have broad value in predicting organismal traits in a variety of contexts, including the study of complex host-microbe symbioses. PMID:25202302

Hidden state prediction: a modification of classic ancestral state reconstruction algorithms helps unravel complex symbioses.

PubMed

Zaneveld, Jesse R R; Thurber, Rebecca L V

2014-01-01

Complex symbioses between animal or plant hosts and their associated microbiotas can involve thousands of species and millions of genes. Because of the number of interacting partners, it is often impractical to study all organisms or genes in these host-microbe symbioses individually. Yet new phylogenetic predictive methods can use the wealth of accumulated data on diverse model organisms to make inferences into the properties of less well-studied species and gene families. Predictive functional profiling methods use evolutionary models based on the properties of studied relatives to put bounds on the likely characteristics of an organism or gene that has not yet been studied in detail. These techniques have been applied to predict diverse features of host-associated microbial communities ranging from the enzymatic function of uncharacterized genes to the gene content of uncultured microorganisms. We consider these phylogenetically informed predictive techniques from disparate fields as examples of a general class of algorithms for Hidden State Prediction (HSP), and argue that HSP methods have broad value in predicting organismal traits in a variety of contexts, including the study of complex host-microbe symbioses.

Volta-Based Cells Materials Chemical Multiple Representation to Improve Ability of Student Representation

NASA Astrophysics Data System (ADS)

Helsy, I.; Maryamah; Farida, I.; Ramdhani, M. A.

2017-09-01

This study aimed to describe the application of teaching materials, analyze the increase in the ability of students to connect the three levels of representation and student responses after application of multiple representations based teaching materials chemistry. The method used quasi one-group pretest-posttest design to 71 students. The results showed the application of teaching materials carried 88% with very good category. A significant increase ability to connect the three levels of representation of students after the application of multiple representations based teaching materials chemistry with t-value > t-crit (11.402 > 1.991). Recapitulation N-gain pretest and posttest showed relatively similar for all groups is 0.6 criterion being achievement. Students gave a positive response to the application of multiple representations based teaching materials chemistry. Students agree teaching materials used in teaching chemistry (88%), and agrees teaching materials to provide convenience in connecting the three levels of representation (95%).

Predictable internal brain dynamics in EEG and its relation to conscious states

PubMed Central

Yoo, Jaewook; Kwon, Jaerock; Choe, Yoonsuck

2014-01-01

Consciousness is a complex and multi-faceted phenomenon defying scientific explanation. Part of the reason why this is the case is due to its subjective nature. In our previous computational experiments, to avoid such a subjective trap, we took a strategy to investigate objective necessary conditions of consciousness. Our basic hypothesis was that predictive internal dynamics serves as such a condition. This is in line with theories of consciousness that treat retention (memory), protention (anticipation), and primary impression as the tripartite temporal structure of consciousness. To test our hypothesis, we analyzed publicly available sleep and awake electroencephalogram (EEG) data. Our results show that EEG signals from awake or rapid eye movement (REM) sleep states have more predictable dynamics compared to those from slow-wave sleep (SWS). Since awakeness and REM sleep are associated with conscious states and SWS with unconscious or less consciousness states, these results support our hypothesis. The results suggest an intricate relationship among prediction, consciousness, and time, with potential applications to time perception and neurorobotics. PMID:24917813

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

Detailed 3D representations for object recognition and modeling.

PubMed

Zia, M Zeeshan; Stark, Michael; Schiele, Bernt; Schindler, Konrad

2013-11-01

Geometric 3D reasoning at the level of objects has received renewed attention recently in the context of visual scene understanding. The level of geometric detail, however, is typically limited to qualitative representations or coarse boxes. This is linked to the fact that today's object class detectors are tuned toward robust 2D matching rather than accurate 3D geometry, encouraged by bounding-box-based benchmarks such as Pascal VOC. In this paper, we revisit ideas from the early days of computer vision, namely, detailed, 3D geometric object class representations for recognition. These representations can recover geometrically far more accurate object hypotheses than just bounding boxes, including continuous estimates of object pose and 3D wireframes with relative 3D positions of object parts. In combination with robust techniques for shape description and inference, we outperform state-of-the-art results in monocular 3D pose estimation. In a series of experiments, we analyze our approach in detail and demonstrate novel applications enabled by such an object class representation, such as fine-grained categorization of cars and bicycles, according to their 3D geometry, and ultrawide baseline matching.

Representational momentum in perception and grasping: translating versus transforming objects.

PubMed

Brouwer, Anne-Marie; Franz, Volker H; Thornton, Ian M

2004-07-14

Representational momentum is the tendency to misremember the stopping point of a moving object as further forward in the direction of movement. Results of several studies suggest that this effect is typical for changes in position (e.g., translation) and not for changes in object shape (transformation). Additionally, the effect seems to be stronger in motor tasks than in perceptual tasks. Here, participants judged the final distance between two spheres after this distance had been increasing or decreasing. The spheres were two separately translating objects or were connected to form a single transforming object (a dumbbell). Participants also performed a motor task in which they grasped virtual versions of the final objects. We found representational momentum for the visual judgment task for both stimulus types. As predicted, it was stronger for the spheres than for the dumbbells. In contrast, for grasping, only the dumbbells produced representational momentum (larger maximum grip aperture when the dumbbells had been growing compared to when they had been shrinking). Because type of stimulus change had these different effects on representational momentum for perception and action, we conclude that different sources of information are used in the two tasks or that they are governed by different mechanisms.

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).

Early prediction of extreme stratospheric polar vortex states based on causal precursors

NASA Astrophysics Data System (ADS)

Kretschmer, Marlene; Runge, Jakob; Coumou, Dim

2017-08-01

Variability in the stratospheric polar vortex (SPV) can influence the tropospheric circulation and thereby winter weather. Early predictions of extreme SPV states are thus important to improve forecasts of winter weather including cold spells. However, dynamical models are usually restricted in lead time because they poorly capture low-frequency processes. Empirical models often suffer from overfitting problems as the relevant physical processes and time lags are often not well understood. Here we introduce a novel empirical prediction method by uniting a response-guided community detection scheme with a causal discovery algorithm. This way, we objectively identify causal precursors of the SPV at subseasonal lead times and find them to be in good agreement with known physical drivers. A linear regression prediction model based on the causal precursors can explain most SPV variability (r2 = 0.58), and our scheme correctly predicts 58% (46%) of extremely weak SPV states for lead times of 1-15 (16-30) days with false-alarm rates of only approximately 5%. Our method can be applied to any variable relevant for (sub)seasonal weather forecasts and could thus help improving long-lead predictions.

osFP: a web server for predicting the oligomeric states of fluorescent proteins.

PubMed

Simeon, Saw; Shoombuatong, Watshara; Anuwongcharoen, Nuttapat; Preeyanon, Likit; Prachayasittikul, Virapong; Wikberg, Jarl E S; Nantasenamat, Chanin

2016-01-01

Currently, monomeric fluorescent proteins (FP) are ideal markers for protein tagging. The prediction of oligomeric states is helpful for enhancing live biomedical imaging. Computational prediction of FP oligomeric states can accelerate the effort of protein engineering efforts of creating monomeric FPs. To the best of our knowledge, this study represents the first computational model for predicting and analyzing FP oligomerization directly from the amino acid sequence. After data curation, an exhaustive data set consisting of 397 non-redundant FP oligomeric states was compiled from the literature. Results from benchmarking of the protein descriptors revealed that the model built with amino acid composition descriptors was the top performing model with accuracy, sensitivity and specificity in excess of 80% and MCC greater than 0.6 for all three data subsets (e.g. training, tenfold cross-validation and external sets). The model provided insights on the important residues governing the oligomerization of FP. To maximize the benefit of the generated predictive model, it was implemented as a web server under the R programming environment. osFP affords a user-friendly interface that can be used to predict the oligomeric state of FP using the protein sequence. The advantage of osFP is that it is platform-independent meaning that it can be accessed via a web browser on any operating system and device. osFP is freely accessible at http://codes.bio/osfp/ while the source code and data set is provided on GitHub at https://github.com/chaninn/osFP/.Graphical Abstract.

Intelligence with representation.

PubMed

Steels, Luc

2003-10-15

Behaviour-based robotics has always been inspired by earlier cybernetics work such as that of W. Grey Walter. It emphasizes that intelligence can be achieved without the kinds of representations common in symbolic AI systems. The paper argues that such representations might indeed not be needed for many aspects of sensory-motor intelligence but become a crucial issue when bootstrapping to higher levels of cognition. It proposes a scenario in the form of evolutionary language games by which embodied agents develop situated grounded representations adapted to their needs and the conventions emerging in the population.

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

On the v-representability of ensemble densities of electron systems

DOE PAGES

Gonis, A.; Dane, M.

2017-12-30

Analogously to the case at zero temperature, where the density of the ground state of an interacting many-particle system determines uniquely (within an arbitrary additive constant) the external potential acting on the system, the thermal average of the density over an ensemble defined by the Boltzmann distribution at the minimum of the thermodynamic potential, or the free energy, determines the external potential uniquely (and not just modulo a constant) acting on a system described by this thermodynamic potential or free energy. The study describes a formal procedure that generates the domain of a constrained search over general ensembles (at zeromore » or elevated temperatures) that lead to a given density, including as a special case a density thermally averaged at a given temperature, and in the case of a v-representable density determines the external potential leading to the ensemble density. As an immediate consequence of the general formalism, the concept of v-representability is extended beyond the hitherto discussed case of ground state densities to encompass excited states as well. Specific application to thermally averaged densities solves the v-representability problem in connection with the Mermin functional in a manner analogous to that in which this problem was recently settled with respect to the Hohenberg and Kohn functional. Finally, the main formalism is illustrated with numerical results for ensembles of one-dimensional, non-interacting systems of particles under a harmonic potential.« less

Medical Image Fusion Based on Feature Extraction and Sparse Representation

PubMed Central

Wei, Gao; Zongxi, Song

2017-01-01

As a novel multiscale geometric analysis tool, sparse representation has shown many advantages over the conventional image representation methods. However, the standard sparse representation does not take intrinsic structure and its time complexity into consideration. In this paper, a new fusion mechanism for multimodal medical images based on sparse representation and decision map is proposed to deal with these problems simultaneously. Three decision maps are designed including structure information map (SM) and energy information map (EM) as well as structure and energy map (SEM) to make the results reserve more energy and edge information. SM contains the local structure feature captured by the Laplacian of a Gaussian (LOG) and EM contains the energy and energy distribution feature detected by the mean square deviation. The decision map is added to the normal sparse representation based method to improve the speed of the algorithm. Proposed approach also improves the quality of the fused results by enhancing the contrast and reserving more structure and energy information from the source images. The experiment results of 36 groups of CT/MR, MR-T1/MR-T2, and CT/PET images demonstrate that the method based on SR and SEM outperforms five state-of-the-art methods. PMID:28321246

Predicting electrocardiogram and arterial blood pressure waveforms with different Echo State Network architectures.

PubMed

Fong, Allan; Mittu, Ranjeev; Ratwani, Raj; Reggia, James

2014-01-01

Alarm fatigue caused by false alarms and alerts is an extremely important issue for the medical staff in Intensive Care Units. The ability to predict electrocardiogram and arterial blood pressure waveforms can potentially help the staff and hospital systems better classify a patient's waveforms and subsequent alarms. This paper explores the use of Echo State Networks, a specific type of neural network for mining, understanding, and predicting electrocardiogram and arterial blood pressure waveforms. Several network architectures are designed and evaluated. The results show the utility of these echo state networks, particularly ones with larger integrated reservoirs, for predicting electrocardiogram waveforms and the adaptability of such models across individuals. The work presented here offers a unique approach for understanding and predicting a patient's waveforms in order to potentially improve alarm generation. We conclude with a brief discussion of future extensions of this research.

Predicting Periodontitis at State and Local Levels in the United States.

PubMed

Eke, P I; Zhang, X; Lu, H; Wei, L; Thornton-Evans, G; Greenlund, K J; Holt, J B; Croft, J B

2016-05-01

The objective of the study was to estimate the prevalence of periodontitis at state and local levels across the United States by using a novel, small area estimation (SAE) method. Extended multilevel regression and poststratification analyses were used to estimate the prevalence of periodontitis among adults aged 30 to 79 y at state, county, congressional district, and census tract levels by using periodontal data from the National Health and Nutrition Examination Survey (NHANES) 2009-2012, population counts from the 2010 US census, and smoking status estimates from the Behavioral Risk Factor Surveillance System in 2012. The SAE method used age, race, gender, smoking, and poverty variables to estimate the prevalence of periodontitis as defined by the Centers for Disease Control and Prevention/American Academy of Periodontology case definitions at the census block levels and aggregated to larger administrative and geographic areas of interest. Model-based SAEs were validated against national estimates directly from NHANES 2009-2012. Estimated prevalence of periodontitis ranged from 37.7% in Utah to 52.8% in New Mexico among the states (mean, 45.1%; median, 44.9%) and from 33.7% to 68% among counties (mean, 46.6%; median, 45.9%). Severe periodontitis ranged from 7.27% in New Hampshire to 10.26% in Louisiana among the states (mean, 8.9%; median, 8.8%) and from 5.2% to 17.9% among counties (mean, 9.2%; median, 8.8%). Overall, the predicted prevalence of periodontitis was highest for southeastern and southwestern states and for geographic areas in the Southeast along the Mississippi Delta, as well as along the US and Mexico border. Aggregated model-based SAEs were consistent with national prevalence estimates from NHANES 2009-2012. This study is the first-ever estimation of periodontitis prevalence at state and local levels in the United States, and this modeling approach complements public health surveillance efforts to identify areas with a high burden of

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 Representation of Black People in History Textbooks

ERIC Educational Resources Information Center

de Souza Santos, Kátia Silva; de Almeida, Mahatma Lenin Avelino; Amaral, Daniel Ferreira; Santos, Carlos Alberto Batista

2017-01-01

The objective of this study is based on the sense of rethinking the representation of the black population within history textbooks. The research was carried out in a public school in the countryside of the municipality of Dormentes (Pernambuco State, Brazil) through the application of questionnaires. The way a black person is represented within…

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.

Predicting county-level cancer incidence rates and counts in the United States

PubMed Central

Yu, Binbing

2018-01-01

Many countries, including the United States, publish predicted numbers of cancer incidence and death in current and future years for the whole country. These predictions provide important information on the cancer burden for cancer control planners, policymakers and the general public. Based on evidence from several empirical studies, the joinpoint (segmented-line linear regression) model has been adopted by the American Cancer Society to estimate the number of new cancer cases in the United States and in individual states since 2007. Recently, cancer incidence in smaller geographic regions such as counties and FIPS code regions is of increasing interest by local policymakers. The natural extension is to directly apply the joinpoint model to county-level cancer incidence data. The direct application has several drawbacks and its performance has not been evaluated. To address the concerns, we developed a spatial random-effects joinpoint model for county-level cancer incidence data. The proposed model was used to predict both cancer incidence rates and counts at the county level. The standard joinpoint model and the proposed method were compared through a validation study. The proposed method out-performed the standard joinpoint model for almost all cancer sites, especially for moderate or rare cancer sites and for counties with small population sizes. As an application, we predicted county-level prostate cancer incidence rates and counts for the year 2011 in Connecticut. PMID:23670947

Origins of Secure Base Script Knowledge and the Developmental Construction of Attachment Representations.

PubMed

Waters, Theodore E A; Ruiz, Sarah K; Roisman, Glenn I

2017-01-01

Increasing evidence suggests that attachment representations take at least two forms: a secure base script and an autobiographical narrative of childhood caregiving experiences. This study presents data from the first 26 years of the Minnesota Longitudinal Study of Risk and Adaptation (N = 169), examining the developmental origins of secure base script knowledge in a high-risk sample and testing alternative models of the developmental sequencing of the construction of attachment representations. Results demonstrated that secure base script knowledge was predicted by observations of maternal sensitivity across childhood and adolescence. Furthermore, findings suggest that the construction of a secure base script supports the development of a coherent autobiographical representation of childhood attachment experiences with primary caregivers by early adulthood. © 2016 The Authors. Child Development © 2016 Society for Research in Child Development, Inc.

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

Resting-state qEEG predicts rate of second language learning in adults.

PubMed

Prat, Chantel S; Yamasaki, Brianna L; Kluender, Reina A; Stocco, Andrea

2016-01-01

Understanding the neurobiological basis of individual differences in second language acquisition (SLA) is important for research on bilingualism, learning, and neural plasticity. The current study used quantitative electroencephalography (qEEG) to predict SLA in college-aged individuals. Baseline, eyes-closed resting-state qEEG was used to predict language learning rate during eight weeks of French exposure using an immersive, virtual scenario software. Individual qEEG indices predicted up to 60% of the variability in SLA, whereas behavioral indices of fluid intelligence, executive functioning, and working-memory capacity were not correlated with learning rate. Specifically, power in beta and low-gamma frequency ranges over right temporoparietal regions were strongly positively correlated with SLA. These results highlight the utility of resting-state EEG for studying the neurobiological basis of SLA in a relatively construct-free, paradigm-independent manner. Published by Elsevier Inc.

Prediction of elemental creep. [steady state and cyclic data from regression analysis

NASA Technical Reports Server (NTRS)

Davis, J. W.; Rummler, D. R.

1975-01-01

Cyclic and steady-state creep tests were performed to provide data which were used to develop predictive equations. These equations, describing creep as a function of stress, temperature, and time, were developed through the use of a least squares regression analyses computer program for both the steady-state and cyclic data sets. Comparison of the data from the two types of tests, revealed that there was no significant difference between the cyclic and steady-state creep strains for the L-605 sheet under the experimental conditions investigated (for the same total time at load). Attempts to develop a single linear equation describing the combined steady-state and cyclic creep data resulted in standard errors of estimates higher than obtained for the individual data sets. A proposed approach to predict elemental creep in metals uses the cyclic creep equation and a computer program which applies strain and time hardening theories of creep accumulation.

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…

Mental disability and discriminatory practices: effects of social representations of the Mexican population.

PubMed

Mariana, Espinola-Nadurille; Guadalupe, Delgado

2009-05-01

The prevalence of mental disorders in Mexico is 26.1%. This shows that an important percentage of the population suffers from mental disability. Despite this the country's healthcare system does not provide the least acceptable standard of care for the mentally disabled. The aim of this study was to describe the general population's social representations of the disabled and analyze their relationship with the discriminatory practices from the state towards the mentally ill with respect to their right to health. This study was a secondary analysis of the First National Survey on Discrimination in Mexico. In the survey 1,437 effective interviews that comprised a representative sample, were obtained from people aged 18 to 60 living in rural and urban settings. The response rate was 76.5%. The assessment tool was a self-administered questionnaire that yielded perceptions, attitudes, values and social representations about discrimination towards groups of people that supposedly were targets of discrimination by the general population. In the survey the mentally ill were included under disability. As a secondary analysis of the survey for the purpose of this study, we selected a subset of questions that provided important information about social representations of the general Mexican population towards persons with disabilities. The general population's social representations of the disabled were analyzed. The disabled are the second group after the elderly perceived as the most discriminated and neglected and bearing more suffering. A whole set of negative representations concerning the disabled, such as lack of acceptance and respect, low self-confidence, mistreatment, incomprehension, isolation, intolerance, indifference and bad attitudes from others, were elicited. Social representations are social correspondents of the discriminatory practices that the state exerts toward the mentally ill with respect to their right to health. These representations serve to

Autoscopic phenomena and one's own body representation in dreams.

PubMed

Occhionero, Miranda; Cicogna, Piera Carla

2011-12-01

Autoscopic phenomena (AP) are complex experiences that include the visual illusory reduplication of one's own body. From a phenomenological point of view, we can distinguish three conditions: autoscopic hallucinations, heautoscopy, and out-of-body experiences. The dysfunctional pattern involves multisensory disintegration of personal and extrapersonal space perception. The etiology, generally either neurological or psychiatric, is different. Also, the hallucination of Self and own body image is present during dreams and differs according to sleep stage. Specifically, the representation of the Self in REM dreams is frequently similar to the perception of Self in wakefulness, whereas in NREM dreams, a greater polymorphism of Self and own body representation is observed. The parallels between autoscopic phenomena in pathological cases and the Self-hallucination in dreams will be discussed to further the understanding of the particular states of self awareness, especially the complex integration of different memory sources in Self and body representation. Copyright © 2011 Elsevier Inc. All rights reserved.

[Municipal Health Councils: activity and representation of grassroots communities].

PubMed

Gerschman, Silvia

2004-01-01

This article was based on the results of research concerning health policy in municipalities that achieved the most extensive development of decentralization and innovation in the State of Rio de Janeiro, Brazil. The study applied a questionnaire for health system users' representatives in Municipal Health Councils. The central issues were: the Councils' political role; social control by the Councils, viewed as surveillance by organized society over government actions; the nature of social representation exercised by the Council members; and the type of mandate they serve. Community representatives in the Councils reinforce aspects pertaining to the exercise of representation in unequal societies. There is a predominance of a differentiated elite consisting of older males with more schooling and higher income than the community average. The notion of "social control" as the basis for the Councils is difficult for the members to grasp. Exercise of representation is diffuse, occurring by way of designation by community associations, election in assemblies, or designation by institutional health policy agencies.

From innervation density to tactile acuity: 1. Spatial representation.

PubMed

Brown, Paul B; Koerber, H Richard; Millecchia, Ronald

2004-06-11

We tested the hypothesis that the population receptive field representation (a superposition of the excitatory receptive field areas of cells responding to a tactile stimulus) provides spatial information sufficient to mediate one measure of static tactile acuity. In psychophysical tests, two-point discrimination thresholds on the hindlimbs of adult cats varied as a function of stimulus location and orientation, as they do in humans. A statistical model of the excitatory low threshold mechanoreceptive fields of spinocervical, postsynaptic dorsal column and spinothalamic tract neurons was used to simulate the population receptive field representations in this neural population of the one- and two-point stimuli used in the psychophysical experiments. The simulated and observed thresholds were highly correlated. Simulated and observed thresholds' relations to physiological and anatomical variables such as stimulus location and orientation, receptive field size and shape, map scale, and innervation density were strikingly similar. Simulated and observed threshold variations with receptive field size and map scale obeyed simple relationships predicted by the signal detection model, and were statistically indistinguishable from each other. The population receptive field representation therefore contains information sufficient for this discrimination.

Secure base representations in middle childhood across two Western cultures: Associations with parental attachment representations and maternal reports of behavior problems.

PubMed

Waters, Theodore E A; Bosmans, Guy; Vandevivere, Eva; Dujardin, Adinda; Waters, Harriet S

2015-08-01

Recent work examining the content and organization of attachment representations suggests that 1 way in which we represent the attachment relationship is in the form of a cognitive script. This work has largely focused on early childhood or adolescence/adulthood, leaving a large gap in our understanding of script-like attachment representations in the middle childhood period. We present 2 studies and provide 3 critical pieces of evidence regarding the presence of a script-like representation of the attachment relationship in middle childhood. We present evidence that a middle childhood attachment script assessment tapped a stable underlying script using samples drawn from 2 western cultures, the United States (Study 1) and Belgium (Study 2). We also found evidence suggestive of the intergenerational transmission of secure base script knowledge (Study 1) and relations between secure base script knowledge and symptoms of psychopathology in middle childhood (Study 2). The results from this investigation represent an important downward extension of the secure base script construct. (c) 2015 APA, all rights reserved).

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...

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...

Semiclassical propagation: Hilbert space vs. Wigner representation

NASA Astrophysics Data System (ADS)

Gottwald, Fabian; Ivanov, Sergei D.

2018-03-01

A unified viewpoint on the van Vleck and Herman-Kluk propagators in Hilbert space and their recently developed counterparts in Wigner representation is presented. Based on this viewpoint, the Wigner Herman-Kluk propagator is conceptually the most general one. Nonetheless, the respective semiclassical expressions for expectation values in terms of the density matrix and the Wigner function are mathematically proven here to coincide. The only remaining difference is a mere technical flexibility of the Wigner version in choosing the Gaussians' width for the underlying coherent states beyond minimal uncertainty. This flexibility is investigated numerically on prototypical potentials and it turns out to provide neither qualitative nor quantitative improvements. Given the aforementioned generality, utilizing the Wigner representation for semiclassical propagation thus leads to the same performance as employing the respective most-developed (Hilbert-space) methods for the density matrix.

Effect of antacids on predicted steady-state cimetidine concentrations.

PubMed

Russell, W L; Lopez, L M; Normann, S A; Doering, P L; Guild, R T

1984-05-01

The purpose of this study was to evaluate effects of antacids on predicted steady-state concentrations of cimetidine. Ten healthy volunteers received in random order one week apart, cimetidine and cimetidine and antacid suspension. Blood was obtained at specified times and analyzed for cimetidine. Bioavailability was assessed by comparison of peak concentration, time to peak concentration, area under the curve, and time spent over 0.5 micrograms/ml. Single-dose data were extrapolated to steady-state using computer simulation. Concurrent administration of antacid suspension reduced parameters of bioavailability approximately 30%. When steady-state conditions were simulated, concentrations of cimetidine greater than or equal to 0.5 micrograms/ml were maintained for the entire dosing interval in seven of 10 subjects. These data suggest that temporal separation of cimetidine and antacid suspension may be unnecessary.

The effects of learner-generated representations versus computer-generated representations on physics problem solving

NASA Astrophysics Data System (ADS)

Price, Gwyneth A.

In this study, multiple external representations and Generative Learning Theory were used to design instruction that would facilitate physics learning. Specifically, the study looks at the learning differences that may occur when students are engaged in generating a graphical representation as compared to being presented with a computer-generated graph. It is hypothesized that by generating the graphical representation students will be able to overcome obstacles to integration and determine the relationships involved within a representation. In doing so, students will build a more complete mental model of the situation and be able to more readily use this information in transfer situations, thus improving their problem solving ability. Though the results of this study do not lend strong support for the hypothesis, the results are still informative and encouraging. Though several of the obstacles associated with learning from multiple representations such as cognitive load were cause for concern, those students with appropriate prior knowledge and familiarity with graphical representations were able to benefit from the generative activity. This finding indicates that if the issues are directly addressed within instruction, it may be that all students may be able to benefit from being actively engaged in generating representations.

Reading Visual Representations

ERIC Educational Resources Information Center

Rubenstein, Rheta N.; Thompson, Denisse R.

2012-01-01

Mathematics is rich in visual representations. Such visual representations are the means by which mathematical patterns "are recorded and analyzed." With respect to "vocabulary" and "symbols," numerous educators have focused on issues inherent in the language of mathematics that influence students' success with mathematics communication.…

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.

Multi-agent cooperation rescue algorithm based on influence degree and state prediction

NASA Astrophysics Data System (ADS)

Zheng, Yanbin; Ma, Guangfu; Wang, Linlin; Xi, Pengxue

2018-04-01

Aiming at the multi-agent cooperative rescue in disaster, a multi-agent cooperative rescue algorithm based on impact degree and state prediction is proposed. Firstly, based on the influence of the information in the scene on the collaborative task, the influence degree function is used to filter the information. Secondly, using the selected information to predict the state of the system and Agent behavior. Finally, according to the result of the forecast, the cooperative behavior of Agent is guided and improved the efficiency of individual collaboration. The simulation results show that this algorithm can effectively solve the cooperative rescue problem of multi-agent and ensure the efficient completion of the task.

On the description of conical intersections—A continuous representation of the local topography of seams of conical intersection of three or more electronic states: A generalization of the two state result

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

Zhu, Xiaolei, E-mail: virtualzx@gmail.com; Yarkony, David R., E-mail: yarkony@jhu.edu

2014-11-07

For conical intersections of two states (I,J = I + 1) the vectors defining the branching or g-h plane, the energy difference gradient vector g{sup I,J}, and the interstate coupling vector h{sup I,J}, can be made orthogonal by a one parameter rotation of the degenerate electronic eigenstates. The representation obtained from this rotation is used to construct the parameters that describe the vicinity of the conical intersection seam, the conical parameters, s{sup I,J}{sub x} (R), s{sup I,J}{sub y} (R), g{sup I,J}(R), and h{sup I,J}(R). As a result of the orthogonalization these parameters can be made continuous functions of R, themore » internuclear coordinates. In this work we generalize this notion to construct continuous parametrizations of conical intersection seams of three or more states. The generalization derives from a recently introduced procedure for using non-degenerate electronic states to construct coupled diabatic states that represent adiabatic states coupled by conical intersections. The procedure is illustrated using the seam of conical intersections of three states in parazolyl as an example.« less

Semiclassical initial value representation for the quantum propagator in the Heisenberg interaction representation

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

Petersen, Jakob; Pollak, Eli, E-mail: eli.pollak@weizmann.ac.il

2015-12-14

One of the challenges facing on-the-fly ab initio semiclassical time evolution is the large expense needed to converge the computation. In this paper, we suggest that a significant saving in computational effort may be achieved by employing a semiclassical initial value representation (SCIVR) of the quantum propagator based on the Heisenberg interaction representation. We formulate and test numerically a modification and simplification of the previous semiclassical interaction representation of Shao and Makri [J. Chem. Phys. 113, 3681 (2000)]. The formulation is based on the wavefunction form of the semiclassical propagation instead of the operator form, and so is simpler andmore » cheaper to implement. The semiclassical interaction representation has the advantage that the phase and prefactor vary relatively slowly as compared to the “standard” SCIVR methods. This improves its convergence properties significantly. Using a one-dimensional model system, the approximation is compared with Herman-Kluk’s frozen Gaussian and Heller’s thawed Gaussian approximations. The convergence properties of the interaction representation approach are shown to be favorable and indicate that the interaction representation is a viable way of incorporating on-the-fly force field information within a semiclassical framework.« less

Social representations of biosecurity in nursing: occupational health and preventive care.

PubMed

Sousa, Álvaro Francisco Lopes de; Queiroz, Artur Acelino Francisco Luz Nunes; Oliveira, Layze Braz de; Moura, Maria Eliete Batista; Batista, Odinéa Maria Amorim; Andrade, Denise de

2016-01-01

to understand the biosecurity social representations by primary care nursing professionals and analyze how they articulate with quality of care. exploratory and qualitative research based on social representation theory. The study participants were 36 nursing workers from primary health care in a state capital in the Northeast region of Brazil. The data were analyzed by descending hierarchical classification. five classes were obtained: occupational accidents suffered by professionals; occupational exposure to biological agents; biosecurity management in primary health care; the importance of personal protective equipment; and infection control and biosecurity. the different positions taken by the professionals seem to be based on a field of social representations related to the concept of biosecurity, namely exposure to accidents and risks to which they are exposed. However, occupational accidents are reported as inherent to the practice.

Statistical representation of multiphase flow

NASA Astrophysics Data System (ADS)

Subramaniam

2000-11-01

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

Robust kernel collaborative representation for face recognition

NASA Astrophysics Data System (ADS)

Huang, Wei; Wang, Xiaohui; Ma, Yanbo; Jiang, Yuzheng; Zhu, Yinghui; Jin, Zhong

2015-05-01

One of the greatest challenges of representation-based face recognition is that the training samples are usually insufficient. In other words, the training set usually does not include enough samples to show varieties of high-dimensional face images caused by illuminations, facial expressions, and postures. When the test sample is significantly different from the training samples of the same subject, the recognition performance will be sharply reduced. We propose a robust kernel collaborative representation based on virtual samples for face recognition. We think that the virtual training set conveys some reasonable and possible variations of the original training samples. Hence, we design a new object function to more closely match the representation coefficients generated from the original and virtual training sets. In order to further improve the robustness, we implement the corresponding representation-based face recognition in kernel space. It is noteworthy that any kind of virtual training samples can be used in our method. We use noised face images to obtain virtual face samples. The noise can be approximately viewed as a reflection of the varieties of illuminations, facial expressions, and postures. Our work is a simple and feasible way to obtain virtual face samples to impose Gaussian noise (and other types of noise) specifically to the original training samples to obtain possible variations of the original samples. Experimental results on the FERET, Georgia Tech, and ORL face databases show that the proposed method is more robust than two state-of-the-art face recognition methods, such as CRC and Kernel CRC.

A ganglion-cell-based primary image representation method and its contribution to object recognition

NASA Astrophysics Data System (ADS)

Wei, Hui; Dai, Zhi-Long; Zuo, Qing-Song

2016-10-01

A visual stimulus is represented by the biological visual system at several levels: in the order from low to high levels, they are: photoreceptor cells, ganglion cells (GCs), lateral geniculate nucleus cells and visual cortical neurons. Retinal GCs at the early level need to represent raw data only once, but meet a wide number of diverse requests from different vision-based tasks. This means the information representation at this level is general and not task-specific. Neurobiological findings have attributed this universal adaptation to GCs' receptive field (RF) mechanisms. For the purposes of developing a highly efficient image representation method that can facilitate information processing and interpretation at later stages, here we design a computational model to simulate the GC's non-classical RF. This new image presentation method can extract major structural features from raw data, and is consistent with other statistical measures of the image. Based on the new representation, the performances of other state-of-the-art algorithms in contour detection and segmentation can be upgraded remarkably. This work concludes that applying sophisticated representation schema at early state is an efficient and promising strategy in visual information processing.

A Complex Prime Numerical Representation of Amino Acids for Protein Function Comparison.

PubMed

Chen, Duo; Wang, Jiasong; Yan, Ming; Bao, Forrest Sheng

2016-08-01

Computationally assessing the functional similarity between proteins is an important task of bioinformatics research. It can help molecular biologists transfer knowledge on certain proteins to others and hence reduce the amount of tedious and costly benchwork. Representation of amino acids, the building blocks of proteins, plays an important role in achieving this goal. Compared with symbolic representation, representing amino acids numerically can expand our ability to analyze proteins, including comparing the functional similarity of them. Among the state-of-the-art methods, electro-ion interaction pseudopotential (EIIP) is widely adopted for the numerical representation of amino acids. However, it could suffer from degeneracy that two different amino acid sequences have the same numerical representation, due to the design of EIIP. In light of this challenge, we propose a complex prime numerical representation (CPNR) of amino acids, inspired by the similarity between a pattern among prime numbers and the number of codons of amino acids. To empirically assess the effectiveness of the proposed method, we compare CPNR against EIIP. Experimental results demonstrate that the proposed method CPNR always achieves better performance than EIIP. We also develop a framework to combine the advantages of CPNR and EIIP, which enables us to improve the performance and study the unique characteristics of different representations.

Representations and Rafts

ERIC Educational Resources Information Center

Hartweg, Kimberly Sipes

2011-01-01

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

Consistent maximum entropy representations of pipe flow networks

NASA Astrophysics Data System (ADS)

Waldrip, Steven H.; Niven, Robert K.; Abel, Markus; Schlegel, Michael

2017-06-01

The maximum entropy method is used to predict flows on water distribution networks. This analysis extends the water distribution network formulation of Waldrip et al. (2016) Journal of Hydraulic Engineering (ASCE), by the use of a continuous relative entropy defined on a reduced parameter set. This reduction in the parameters that the entropy is defined over ensures consistency between different representations of the same network. The performance of the proposed reduced parameter method is demonstrated with a one-loop network case study.

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

NASA Technical Reports Server (NTRS)

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

2012-01-01

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

Three-dimensional visual feature representation in the primary visual cortex

PubMed Central

Tanaka, Shigeru; Moon, Chan-Hong; Fukuda, Mitsuhiro; Kim, Seong-Gi

2011-01-01

In the cat primary visual cortex, it is accepted that neurons optimally responding to similar stimulus orientations are clustered in a column extending from the superficial to deep layers. The cerebral cortex is, however, folded inside a skull, which makes gyri and fundi. The primary visual area of cats, area 17, is located on the fold of the cortex called the lateral gyrus. These facts raise the question of how to reconcile the tangential arrangement of the orientation columns with the curvature of the gyrus. In the present study, we show a possible configuration of feature representation in the visual cortex using a three-dimensional (3D) self-organization model. We took into account preferred orientation, preferred direction, ocular dominance and retinotopy, assuming isotropic interaction. We performed computer simulation only in the middle layer at the beginning and expanded the range of simulation gradually to other layers, which was found to be a unique method in the present model for obtaining orientation columns spanning all the layers in the flat cortex. Vertical columns of preferred orientations were found in the flat parts of the model cortex. On the other hand, in the curved parts, preferred orientations were represented in wedge-like columns rather than straight columns, and preferred directions were frequently reversed in the deeper layers. Singularities associated with orientation representation appeared as warped lines in the 3D model cortex. Direction reversal appeared on the sheets that were delimited by orientation-singularity lines. These structures emerged from the balance between periodic arrangements of preferred orientations and vertical alignment of same orientations. Our theoretical predictions about orientation representation were confirmed by multi-slice, high-resolution functional MRI in the cat visual cortex. We obtained a close agreement between theoretical predictions and experimental observations. The present study throws a doubt

Three-dimensional visual feature representation in the primary visual cortex.

PubMed

Tanaka, Shigeru; Moon, Chan-Hong; Fukuda, Mitsuhiro; Kim, Seong-Gi

2011-12-01

In the cat primary visual cortex, it is accepted that neurons optimally responding to similar stimulus orientations are clustered in a column extending from the superficial to deep layers. The cerebral cortex is, however, folded inside a skull, which makes gyri and fundi. The primary visual area of cats, area 17, is located on the fold of the cortex called the lateral gyrus. These facts raise the question of how to reconcile the tangential arrangement of the orientation columns with the curvature of the gyrus. In the present study, we show a possible configuration of feature representation in the visual cortex using a three-dimensional (3D) self-organization model. We took into account preferred orientation, preferred direction, ocular dominance and retinotopy, assuming isotropic interaction. We performed computer simulation only in the middle layer at the beginning and expanded the range of simulation gradually to other layers, which was found to be a unique method in the present model for obtaining orientation columns spanning all the layers in the flat cortex. Vertical columns of preferred orientations were found in the flat parts of the model cortex. On the other hand, in the curved parts, preferred orientations were represented in wedge-like columns rather than straight columns, and preferred directions were frequently reversed in the deeper layers. Singularities associated with orientation representation appeared as warped lines in the 3D model cortex. Direction reversal appeared on the sheets that were delimited by orientation-singularity lines. These structures emerged from the balance between periodic arrangements of preferred orientations and vertical alignment of the same orientations. Our theoretical predictions about orientation representation were confirmed by multi-slice, high-resolution functional MRI in the cat visual cortex. We obtained a close agreement between theoretical predictions and experimental observations. The present study throws a

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

Completing the Physical Representation of Quantum Algorithms Provides a Quantitative Explanation of Their Computational Speedup

NASA Astrophysics Data System (ADS)

Castagnoli, Giuseppe

2018-03-01

The usual representation of quantum algorithms, limited to the process of solving the problem, is physically incomplete. We complete it in three steps: (i) extending the representation to the process of setting the problem, (ii) relativizing the extended representation to the problem solver to whom the problem setting must be concealed, and (iii) symmetrizing the relativized representation for time reversal to represent the reversibility of the underlying physical process. The third steps projects the input state of the representation, where the problem solver is completely ignorant of the setting and thus the solution of the problem, on one where she knows half solution (half of the information specifying it when the solution is an unstructured bit string). Completing the physical representation shows that the number of computation steps (oracle queries) required to solve any oracle problem in an optimal quantum way should be that of a classical algorithm endowed with the advanced knowledge of half solution.

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.

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

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

NASA Technical Reports Server (NTRS)

Thomas, R. H.

1971-01-01

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

State public policy issues involved with the Parkfield prediction experiment.

USGS Publications Warehouse

Andrews, R.; Goltz, J.

1988-01-01

The earthquake-prediction experiment at Parkfield may well be the most important such experiment currently underway worldwide. Its importance, however, extends beyond the scientific data that will be gathered and whether those data that will be gathered and whether those data can provide reliable prediction methods. Important public policy lessons are being learned (and are yet to be learned), and these lessons may be transferable to other parts of California and the nation. Indeed, the Parkfield experiment has captured the interest of numerous Californians, including State officials, emergency managers, the news media, and at least some of the public.

Dopamine reward prediction errors reflect hidden-state inference across time.

PubMed

Starkweather, Clara Kwon; Babayan, Benedicte M; Uchida, Naoshige; Gershman, Samuel J

2017-04-01

Midbrain dopamine neurons signal reward prediction error (RPE), or actual minus expected reward. The temporal difference (TD) learning model has been a cornerstone in understanding how dopamine RPEs could drive associative learning. Classically, TD learning imparts value to features that serially track elapsed time relative to observable stimuli. In the real world, however, sensory stimuli provide ambiguous information about the hidden state of the environment, leading to the proposal that TD learning might instead compute a value signal based on an inferred distribution of hidden states (a 'belief state'). Here we asked whether dopaminergic signaling supports a TD learning framework that operates over hidden states. We found that dopamine signaling showed a notable difference between two tasks that differed only with respect to whether reward was delivered in a deterministic manner. Our results favor an associative learning rule that combines cached values with hidden-state inference.

Dynamic Trial-by-Trial Recoding of Task-Set Representations in the Frontoparietal Cortex Mediates Behavioral Flexibility

PubMed Central

Qiao, Lei; Zhang, Lijie

2017-01-01

Cognitive flexibility forms the core of the extraordinary ability of humans to adapt, but the precise neural mechanisms underlying our ability to nimbly shift between task sets remain poorly understood. Recent functional magnetic resonance imaging (fMRI) studies employing multivoxel pattern analysis (MVPA) have shown that a currently relevant task set can be decoded from activity patterns in the frontoparietal cortex, but whether these regions support the dynamic transformation of task sets from trial to trial is not clear. Here, we combined a cued task-switching protocol with human (both sexes) fMRI, and harnessed representational similarity analysis (RSA) to facilitate a novel assessment of trial-by-trial changes in neural task-set representations. We first used MVPA to define task-sensitive frontoparietal and visual regions and found that neural task-set representations on switch trials are less stably encoded than on repeat trials. We then exploited RSA to show that the neural representational pattern dissimilarity across consecutive trials is greater for switch trials than for repeat trials, and that the degree of this pattern dissimilarity predicts behavior. Moreover, the overall neural pattern of representational dissimilarities followed from the assumption that repeating sets, compared with switching sets, results in stronger neural task representations. Finally, when moving from cue to target phase within a trial, pattern dissimilarities tracked the transformation from previous-trial task representations to the currently relevant set. These results provide neural evidence for the longstanding assumptions of an effortful task-set reconfiguration process hampered by task-set inertia, and they demonstrate that frontoparietal and stimulus processing regions support “dynamic adaptive coding,” flexibly representing changing task sets in a trial-by-trial fashion. SIGNIFICANCE STATEMENT Humans can fluently switch between different tasks, reflecting an ability

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

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.

Participation without representation? Senior opinion, legislative behavior, and federal health reform.

PubMed

Bradley, Katharine W V; Chen, Jowei

2014-04-01

Why do legislators sometimes engage in behavior that deviates from the expressed policy preferences of constituents who participate in politics at high rates? We examine this puzzle in the context of Democratic legislators' representation of their senior citizen constituents on the Patient Protection and Affordable Care Act of 2010 (ACA). We find that legislators' roll-call votes on the ACA did not reflect the stated preferences of their respective senior constituents; by contrast, these roll-call votes did reflect the preferences of nonsenior adults. We draw upon a theoretical framework developed by Mansbridge to explain this apparent nonresponsiveness to seniors on the ACA. This framework distinguishes between promissory representation, whereby legislators merely respond to constituents' preferences, and anticipatory representation, whereby legislators respond to constituents' underlying policy interests, even when such interests conflict with expressed preferences. By considering the Medicare provisions in the ACA and analyzing Democratic legislators' floor speeches on health reform, we provide preliminary evidence that members of Congress engaged in anticipatory representation of their senior constituents by attempting to educate seniors about how the ACA serves their policy interests.

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.

Representation of natural numbers in quantum mechanics

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

Benioff, Paul

2001-03-01

This paper represents one approach to making explicit some of the assumptions and conditions implied in the widespread representation of numbers by composite quantum systems. Any nonempty set and associated operations is a set of natural numbers or a model of arithmetic if the set and operations satisfy the axioms of number theory or arithmetic. This paper is limited to k-ary representations of length L and to the axioms for arithmetic modulo k{sup L}. A model of the axioms is described based on an abstract L-fold tensor product Hilbert space H{sup arith}. Unitary maps of this space onto a physicalmore » parameter based product space H{sup phy} are then described. Each of these maps makes states in H{sup phy}, and the induced operators, a model of the axioms. Consequences of the existence of many of these maps are discussed along with the dependence of Grover's and Shor's algorithms on these maps. The importance of the main physical requirement, that the basic arithmetic operations are efficiently implementable, is discussed. This condition states that there exist physically realizable Hamiltonians that can implement the basic arithmetic operations and that the space-time and thermodynamic resources required are polynomial in L.« less

AND/OR graph representation of assembly plans

NASA Astrophysics Data System (ADS)

Homem de Mello, Luiz S.; Sanderson, Arthur C.

1990-04-01

A compact representation of all possible assembly plans of a product using AND/OR graphs is presented as a basis for efficient planning algorithms that allow an intelligent robot to pick a course of action according to instantaneous conditions. The AND/OR graph is equivalent to a state transition graph but requires fewer nodes and simplifies the search for feasible plans. Three applications are discussed: (1) the preselection of the best assembly plan, (2) the recovery from execution errors, and (3) the opportunistic scheduling of tasks. An example of an assembly with four parts illustrates the use of the AND/OR graph representation in assembly-plan preselection, based on the weighting of operations according to complexity of manipulation and stability of subassemblies. A hypothetical error situation is discussed to show how a bottom-up search of the AND/OR graph leads to an efficient recovery.

AND/OR graph representation of assembly plans

NASA Technical Reports Server (NTRS)

Homem De Mello, Luiz S.; Sanderson, Arthur C.

1990-01-01

A compact representation of all possible assembly plans of a product using AND/OR graphs is presented as a basis for efficient planning algorithms that allow an intelligent robot to pick a course of action according to instantaneous conditions. The AND/OR graph is equivalent to a state transition graph but requires fewer nodes and simplifies the search for feasible plans. Three applications are discussed: (1) the preselection of the best assembly plan, (2) the recovery from execution errors, and (3) the opportunistic scheduling of tasks. An example of an assembly with four parts illustrates the use of the AND/OR graph representation in assembly-plan preselection, based on the weighting of operations according to complexity of manipulation and stability of subassemblies. A hypothetical error situation is discussed to show how a bottom-up search of the AND/OR graph leads to an efficient recovery.