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Sample records for predictive state representations

  1. Novel Approach for the Recognition and Prediction of Multi-Function Radar Behaviours Based on Predictive State Representations

    PubMed Central

    Ou, Jian; Chen, Yongguang; Zhao, Feng; Liu, Jin; Xiao, Shunping

    2017-01-01

    The extensive applications of multi-function radars (MFRs) have presented a great challenge to the technologies of radar countermeasures (RCMs) and electronic intelligence (ELINT). The recently proposed cognitive electronic warfare (CEW) provides a good solution, whose crux is to perceive present and future MFR behaviours, including the operating modes, waveform parameters, scheduling schemes, etc. Due to the variety and complexity of MFR waveforms, the existing approaches have the drawbacks of inefficiency and weak practicability in prediction. A novel method for MFR behaviour recognition and prediction is proposed based on predictive state representation (PSR). With the proposed approach, operating modes of MFR are recognized by accumulating the predictive states, instead of using fixed transition probabilities that are unavailable in the battlefield. It helps to reduce the dependence of MFR on prior information. And MFR signals can be quickly predicted by iteratively using the predicted observation, avoiding the very large computation brought by the uncertainty of future observations. Simulations with a hypothetical MFR signal sequence in a typical scenario are presented, showing that the proposed methods perform well and efficiently, which attests to their validity. PMID:28335492

  2. State representations of ARMA-models

    NASA Astrophysics Data System (ADS)

    Lomadze, Vakhtang

    2010-10-01

    A state representation of an arbitrary ARMA-model is computed explicitly. It is shown then that every ARMA-model is homotopy equivalent to its state representation, and that two state models are homotopy equivalent if and only if they are similar.

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

    PubMed

    Furman, Wyndol; Collibee, Charlene

    2016-12-28

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

  4. On state representations of nonlinear implicit systems

    NASA Astrophysics Data System (ADS)

    Pereira da Silva, Paulo Sergio; Batista, Simone

    2010-03-01

    This work considers a semi-implicit system Δ, that is, a pair (S, y), where S is an explicit system described by a state representation ? , where x(t) ∈ ℝ n and u(t) ∈ ℝ m , which is subject to a set of algebraic constraints y(t) = h(t, x(t), u(t)) = 0, where y(t) ∈ ℝ l . An input candidate is a set of functions v = (v 1, …, v s ), which may depend on time t, on x, and on u and its derivatives up to a finite order. The problem of finding a (local) proper state representation ż = g(t, z, v) with input v for the implicit system Δ is studied in this article. The main result shows necessary and sufficient conditions for the solution of this problem, under mild assumptions on the class of admissible state representations of Δ. These solvability conditions rely on an integrability test that is computed from the explicit system S. The approach of this article is the infinite-dimensional differential geometric setting of Fliess, Lévine, Martin, and Rouchon (1999) ('A Lie-Bäcklund Approach to Equivalence and Flatness of Nonlinear Systems', IEEE Transactions on Automatic Control, 44(5), (922-937)).

  5. Network representation of dynamical systems: Connectivity patterns, information and predictability

    NASA Astrophysics Data System (ADS)

    García Cantú Ros, A.; Forti, G.; Nicolis, G.

    2013-08-01

    The present work elaborates on predictability and information aspects of dynamical systems, in connection with the connectivity features of their network representation. The basic idea underlying this work is to map the set of coarse-grained states of a dynamical system onto a set of network nodes and transitions between them onto a set of network links. Based on the vertex centrality of these nodes, we define (a) a local indicator of predictability, (b) a measure of the information that is available about the state of the system after one transition occurring within an arbitrary long time window and (c) an upper bound for the time horizon of predictability. We address the cases of the tent and the cusp maps, as representative examples of Markov and non-Markov processes. An analytical exact result for the horizon of predictability is obtained for the tent map, as well as for its higher iterates, and its connection with the corresponding network diameters is discussed. Similarly, analytical expressions are derived for the bounds of the predictability horizon in the case of the cusp map.

  6. Social state representation in prefrontal cortex.

    PubMed

    Fujii, Naotaka; Hihara, Sayaka; Nagasaka, Yasuo; Iriki, Atsushi

    2009-01-01

    One of the cardinal mental faculties of humans and other primates is social brain function, the collective name assigned to the distributed system of social cognitive processes that orchestrate our sophisticated adaptive social behavior. These must include processes for recognizing current social context and maintaining an internal representation of the current social state as a reference for decision-making. But how and where the brain processes such social-state information is unknown. To home in on the neural substrates of social-state representation, the activity of 196 prefrontal (PFC) neurons was recorded from two monkeys simultaneously during a food-grab task under varying social conditions. Of PFC neurons, 39% showed activity modulation during movement-free periods and seemed to be representing current social state. The direction of modulation was opposite between the dominant and submissive monkeys: During social engagement, PFC activity increased in the dominant monkey and was suppressed in the submissive monkey. The modulation was consistently observed in additional PFC neurons (27/72) in additional pairings with two other monkeys. Notably, PFC activity in one formerly submissive monkey switched to dominant modulation mode when he was paired with a new monkey of lower social status. These findings suggest that PFC, as part of a larger social brain network, maintains a multistate classification of social context for use as a behavioral reference for social decision-making.

  7. A new protein structure representation for efficient protein function prediction.

    PubMed

    Maghawry, Huda A; Mostafa, Mostafa G M; Gharib, Tarek F

    2014-12-01

    One of the challenging problems in bioinformatics is the prediction of protein function. Protein function is the main key that can be used to classify different proteins. Protein function can be inferred experimentally with very small throughput or computationally with very high throughput. Computational methods are sequence based or structure based. Structure-based methods produce more accurate protein function prediction. In this article, we propose a new protein structure representation for efficient protein function prediction. The representation is based on three-dimensional patterns of protein residues. In the analysis, we used protein function based on enzyme activity through six mechanistically diverse enzyme superfamilies: amidohydrolase, crotonase, haloacid dehalogenase, isoprenoid synthase type I, and vicinal oxygen chelate. We applied three different classification methods, naïve Bayes, k-nearest neighbors, and random forest, to predict the enzyme superfamily of a given protein. The prediction accuracy using the proposed representation outperforms a recently introduced representation method that is based only on the distance patterns. The results show that the proposed representation achieved prediction accuracy up to 98%, with improvement of about 10% on average.

  8. Multilevel Atomic Coherent States and Atomic Holomorphic Representation

    NASA Technical Reports Server (NTRS)

    Cao, Chang-Qi; Haake, Fritz

    1996-01-01

    The notion of atomic coherent states is extended to the case of multilevel atom collective. Based on atomic coherent states, a holomorphic representation for atom collective states and operators is defined. An example is given to illustrate its application.

  9. The Representation of Prediction Error in Auditory Cortex

    PubMed Central

    Rubin, Jonathan; Ulanovsky, Nachum; Tishby, Naftali

    2016-01-01

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

  10. Dynamic representation of time in brain states

    PubMed Central

    Bueno, Fernanda Dantas; Morita, Vanessa C.; de Camargo, Raphael Y.; Reyes, Marcelo B.; Caetano, Marcelo S.; Cravo, André M.

    2017-01-01

    The ability to process time on the scale of milliseconds and seconds is essential for behaviour. A growing number of studies have started to focus on brain dynamics as a mechanism for temporal encoding. Although there is growing evidence in favour of this view from computational and in vitro studies, there is still a lack of results from experiments in humans. We show that high-dimensional brain states revealed by multivariate pattern analysis of human EEG are correlated to temporal judgements. First, we show that, as participants estimate temporal intervals, the spatiotemporal dynamics of their brain activity are consistent across trials. Second, we present evidence that these dynamics exhibit properties of temporal perception, such as scale invariance. Lastly, we show that it is possible to predict temporal judgements based on brain states. These results show how scalp recordings can reveal the spatiotemporal dynamics of human brain activity related to temporal processing. PMID:28393850

  11. Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records

    PubMed Central

    Miotto, Riccardo; Li, Li; Kidd, Brian A.; Dudley, Joel T.

    2016-01-01

    Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. Challenges in summarizing and representing patient data prevent widespread practice of predictive modeling using EHRs. Here we present a novel unsupervised deep feature learning method to derive a general-purpose patient representation from EHR data that facilitates clinical predictive modeling. In particular, a three-layer stack of denoising autoencoders was used to capture hierarchical regularities and dependencies in the aggregated EHRs of about 700,000 patients from the Mount Sinai data warehouse. The result is a representation we name “deep patient”. We evaluated this representation as broadly predictive of health states by assessing the probability of patients to develop various diseases. We performed evaluation using 76,214 test patients comprising 78 diseases from diverse clinical domains and temporal windows. Our results significantly outperformed those achieved using representations based on raw EHR data and alternative feature learning strategies. Prediction performance for severe diabetes, schizophrenia, and various cancers were among the top performing. These findings indicate that deep learning applied to EHRs can derive patient representations that offer improved clinical predictions, and could provide a machine learning framework for augmenting clinical decision systems. PMID:27185194

  12. Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records.

    PubMed

    Miotto, Riccardo; Li, Li; Kidd, Brian A; Dudley, Joel T

    2016-05-17

    Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. Challenges in summarizing and representing patient data prevent widespread practice of predictive modeling using EHRs. Here we present a novel unsupervised deep feature learning method to derive a general-purpose patient representation from EHR data that facilitates clinical predictive modeling. In particular, a three-layer stack of denoising autoencoders was used to capture hierarchical regularities and dependencies in the aggregated EHRs of about 700,000 patients from the Mount Sinai data warehouse. The result is a representation we name "deep patient". We evaluated this representation as broadly predictive of health states by assessing the probability of patients to develop various diseases. We performed evaluation using 76,214 test patients comprising 78 diseases from diverse clinical domains and temporal windows. Our results significantly outperformed those achieved using representations based on raw EHR data and alternative feature learning strategies. Prediction performance for severe diabetes, schizophrenia, and various cancers were among the top performing. These findings indicate that deep learning applied to EHRs can derive patient representations that offer improved clinical predictions, and could provide a machine learning framework for augmenting clinical decision systems.

  13. Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records

    NASA Astrophysics Data System (ADS)

    Miotto, Riccardo; Li, Li; Kidd, Brian A.; Dudley, Joel T.

    2016-05-01

    Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. Challenges in summarizing and representing patient data prevent widespread practice of predictive modeling using EHRs. Here we present a novel unsupervised deep feature learning method to derive a general-purpose patient representation from EHR data that facilitates clinical predictive modeling. In particular, a three-layer stack of denoising autoencoders was used to capture hierarchical regularities and dependencies in the aggregated EHRs of about 700,000 patients from the Mount Sinai data warehouse. The result is a representation we name “deep patient”. We evaluated this representation as broadly predictive of health states by assessing the probability of patients to develop various diseases. We performed evaluation using 76,214 test patients comprising 78 diseases from diverse clinical domains and temporal windows. Our results significantly outperformed those achieved using representations based on raw EHR data and alternative feature learning strategies. Prediction performance for severe diabetes, schizophrenia, and various cancers were among the top performing. These findings indicate that deep learning applied to EHRs can derive patient representations that offer improved clinical predictions, and could provide a machine learning framework for augmenting clinical decision systems.

  14. 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

  15. 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

  16. 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.

  17. Gauge transformation of quantum states in probability representation

    NASA Astrophysics Data System (ADS)

    Korennoy, Ya A.; Man’ko, V. I.

    2017-04-01

    The gauge invariance of the evolution equations of tomographic probability distribution functions of quantum particles in an electromagnetic field is illustrated. Explicit expressions for the transformations of ordinary tomograms of states under a gauge transformation of electromagnetic field potentials are obtained. Gauge-independent optical and symplectic tomographic quasi-distributions and tomographic probability distributions of states of quantum system are introduced, and their evolution equations have the Liouville equation in corresponding representations as the classical limits are found.

  18. Dynamics of open bosonic quantum systems in coherent state representation

    SciTech Connect

    Dalvit, D. A. R.; Berman, G. P.; Vishik, M.

    2006-01-15

    We consider the problem of decoherence and relaxation of open bosonic quantum systems from a perspective alternative to the standard master equation or quantum trajectories approaches. Our method is based on the dynamics of expectation values of observables evaluated in a coherent state representation. We examine a model of a quantum nonlinear oscillator with a density-density interaction with a collection of environmental oscillators at finite temperature. We derive the exact solution for dynamics of observables and demonstrate a consistent perturbation approach.

  19. Representations of Canonical Commutation Relations Describing Infinite Coherent States

    NASA Astrophysics Data System (ADS)

    Joye, Alain; Merkli, Marco

    2016-10-01

    We investigate the infinite volume limit of quantized photon fields in multimode coherent states. We show that for states containing a continuum of coherent modes, it is mathematically and physically natural to consider their phases to be random and identically distributed. The infinite volume states give rise to Hilbert space representations of the canonical commutation relations which we construct concretely. In the case of random phases, the representations are random as well and can be expressed with the help of Itô stochastic integrals. We analyze the dynamics of the infinite state alone and the open system dynamics of small systems coupled to it. We show that under the free field dynamics, initial phase distributions are driven to the uniform distribution. We demonstrate that coherences in small quantum systems, interacting with the infinite coherent state, exhibit Gaussian time decay. The decoherence is qualitatively faster than the one caused by infinite thermal states, which is known to be exponentially rapid only. This emphasizes the classical character of coherent states.

  20. Predictive coding accounts of shared representations in parieto-insular networks.

    PubMed

    Ishida, Hiroaki; Suzuki, Keisuke; Grandi, Laura Clara

    2015-04-01

    The discovery of mirror neurons in the ventral premotor cortex (area F5) and inferior parietal cortex (area PFG) in the macaque monkey brain has provided the physiological evidence for direct matching of the intrinsic motor representations of the self and the visual image of the actions of others. The existence of mirror neurons implies that the brain has mechanisms reflecting shared self and other action representations. This may further imply that the neural basis self-body representations may also incorporate components that are shared with other-body representations. It is likely that such a mechanism is also involved in predicting other's touch sensations and emotions. However, the neural basis of shared body representations has remained unclear. Here, we propose a neural basis of body representation of the self and of others in both human and non-human primates. We review a series of behavioral and physiological findings which together paint a picture that the systems underlying such shared representations require integration of conscious exteroception and interoception subserved by a cortical sensory-motor network involving parieto-inner perisylvian circuits (the ventral intraparietal area [VIP]/inferior parietal area [PFG]-secondary somatosensory cortex [SII]/posterior insular cortex [pIC]/anterior insular cortex [aIC]). Based on these findings, we propose a computational mechanism of the shared body representation in the predictive coding (PC) framework. Our mechanism proposes that processes emerging from generative models embedded in these specific neuronal circuits play a pivotal role in distinguishing a self-specific body representation from a shared one. The model successfully accounts for normal and abnormal shared body phenomena such as mirror-touch synesthesia and somatoparaphrenia. In addition, it generates a set of testable experimental predictions.

  1. The generalization of attachment representations to new social situations: predicting behavior during initial interactions with strangers.

    PubMed

    Feeney, Brooke C; Cassidy, Jude; Ramos-Marcuse, Fatima

    2008-12-01

    The idea that attachment representations are generalized to new social situations and guide behavior with unfamiliar others is central to attachment theory. However, research regarding this important theoretical postulate has been lacking in adolescence and adulthood, as most research has focused on establishing the influence of attachment representations on close relationship dynamics. Thus, the goal of this investigation was to examine the extent to which attachment representations are predictive of adolescents' initial behavior when meeting and interacting with new peers. High school adolescents (N=135) participated with unfamiliar peers from another school in 2 social support interactions that were videotaped and coded by independent observers. Results indicated that attachment representations (assessed through interview and self-report measures) were predictive of behaviors exhibited during the discussions. Theoretical implications of the results and contributions to the existing literature are discussed.

  2. Prediction of nociceptive responses during sedation by time-frequency representation.

    PubMed

    Melia, Umberto; Vallverdú, Montserrat; Jospin, Mathieu; Jensen, Erik W; Valencia, Jose Fernando; Clariá, Francesc; Gambus, Pedro L; Caminal, Pere

    2013-01-01

    The level of sedation in patients undergoing medical procedures evolves continuously, such as the effect of the anesthetic and analgesic agents is counteracted by pain stimuli. The monitors of depth of anesthesia, based on the analysis of the electroencephalogram (EEG), have been progressively introduced into the daily practice to provide additional information about the state of the patient. However, the quantification of analgesia still remains an open problem. The purpose of this work is to analyze the capability of prediction of nociceptive responses based on the time-frequency representation (TFR) of EEG signal. Functions of spectral entropy, instantaneous power and instantaneous frequency were calculated in order to predict the presence or absence of the nociceptive responses to different stimuli during sedation in endoscopy procedure. Values of prediction probability of Pk above 0.75 and percentages of sensitivity and specificity above 70% and 65% respectively were achieved combining TFR functions with bispectral index (BIS) and with concentrations of propofol (CeProp) and remifentanil (CeRemi).

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

    PubMed

    Lee, Jeongmi; Geng, Joy J

    2017-02-01

    The efficiency of finding an object in a crowded environment depends largely on the similarity of nontargets to the search target. Models of attention theorize that the similarity is determined by representations stored within an "attentional template" held in working memory. However, the degree to which the contents of the attentional template are individually unique and where those idiosyncratic representations are encoded in the brain are unknown. We investigated this problem using representational similarity analysis of human fMRI data to measure the common and idiosyncratic representations of famous face morphs during an identity categorization task; data from the categorization task were then used to predict performance on a separate identity search task. We hypothesized that the idiosyncratic categorical representations of the continuous face morphs would predict their distractability when searching for each target identity. The results identified that patterns of activation in the lateral prefrontal cortex (LPFC) as well as in face-selective areas in the ventral temporal cortex were highly correlated with the patterns of behavioral categorization of face morphs and search performance that were common across subjects. However, the individually unique components of the categorization behavior were reliably decoded only in right LPFC. Moreover, the neural pattern in right LPFC successfully predicted idiosyncratic variability in search performance, such that reaction times were longer when distractors had a higher probability of being categorized as the target identity. These results suggest that the prefrontal cortex encodes individually unique components of categorical representations that are also present in attentional templates for target search.

  4. Sparse Representation for Prediction of HIV-1 Protease Drug Resistance.

    PubMed

    Yu, Xiaxia; Weber, Irene T; Harrison, Robert W

    2013-01-01

    HIV rapidly evolves drug resistance in response to antiviral drugs used in AIDS therapy. Estimating the specific resistance of a given strain of HIV to individual drugs from sequence data has important benefits for both the therapy of individual patients and the development of novel drugs. We have developed an accurate classification method based on the sparse representation theory, and demonstrate that this method is highly effective with HIV-1 protease. The protease structure is represented using our newly proposed encoding method based on Delaunay triangulation, and combined with the mutated amino acid sequences of known drug-resistant strains to train a machine-learning algorithm both for classification and regression of drug-resistant mutations. An overall cross-validated classification accuracy of 97% is obtained when trained on a publically available data base of approximately 1.5×10(4) known sequences (Stanford HIV database http://hivdb.stanford.edu/cgi-bin/GenoPhenoDS.cgi). Resistance to four FDA approved drugs is computed and comparisons with other algorithms demonstrate that our method shows significant improvements in classification accuracy.

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

    PubMed

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

    2016-01-05

    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.

  6. 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

  7. 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

  8. State-dependent representation of amplitude-modulated noise stimuli in rat auditory cortex

    PubMed Central

    Marguet, Stephan L.; Harris, Kenneth D.

    2011-01-01

    Cortical responses can vary greatly between repeated presentations of an identical stimulus. Here we report that both trial-to-trial variability and faithfulness of auditory cortical stimulus representations depend critically on brain state. A frozen amplitude-modulated white noise stimulus was repeatedly presented while recording neuronal populations and local field potentials (LFPs) in auditory cortex of urethane-anesthetized rats. An information-theoretic measure was used to predict neuronal spiking activity from either the stimulus envelope or simultaneously recorded LFP. Evoked LFPs and spiking more faithfully followed high-frequency temporal modulations when the cortex was in a “desynchronized” state. In the “synchronized” state, neural activity was poorly predictable from the stimulus envelope, but the spiking of individual neurons could still be predicted from the ongoing LFP. Our results suggest that although auditory cortical activity remains coordinated as a population in the synchronized state, the ability of continuous auditory stimuli to control this activity is greatly diminished. PMID:21525282

  9. Masking of Children's Early Understanding of the Representational Mind: Backwards Explanation versus Prediction.

    ERIC Educational Resources Information Center

    Robinson, E. J.; Mitchell, P.

    1995-01-01

    Examines the use of tasks with backward false belief explanation as an effective technique in gaining a complete picture of the development of understanding of the representational mind. Argues that traditional prediction tests of false belief cannot tell whether children's wrong answers show misunderstanding about false belief or seduction by the…

  10. Motivational states activate distinct hippocampal representations to guide goal-directed behaviors.

    PubMed

    Kennedy, Pamela J; Shapiro, Matthew L

    2009-06-30

    Adaptive behaviors are guided by motivation and memory. Motivational states specify goals, and memory can inform motivated behavior by providing detailed records of past experiences when goals were obtained. These 2 fundamental processes interact to guide animals to biologically relevant targets, but the neuronal mechanisms that integrate them remain unknown. To investigate these mechanisms, we recorded unit activity from the same population of hippocampal neurons as rats performed identical tasks while either food or water deprived. We compared the influence of motivational state (hunger and thirst), memory demand, and spatial behavior in 2 tasks: hippocampus-dependent contextual memory retrieval and hippocampus-independent random foraging. We found that: (i) hippocampal coding was most strongly influenced by motivational state during contextual memory retrieval, when motivational cues were required to select among remembered, goal-directed actions in the same places; (ii) the same neuronal populations were relatively unaffected by motivational state during random foraging, when hunger and thirst were incidental to behavior, and signals derived from deprivation states thus informed, but did not determine, hippocampal coding; and (iii) "prospective coding" in the contextual retrieval task was not influenced by allocentric spatial trajectory, but rather by the animal's deprivation state and the associated, non-spatial target, suggesting that hippocampal coding includes a wide range of predictive associations. The results show that beyond coding spatiotemporal context, hippocampal representations encode the relationships between internal states, the external environment, and action to provide a mechanism by which motivation and memory are coordinated to guide behavior.

  11. Phase-space representation of quantum state vectors: The relative-state approach and the displacement-operator approach

    NASA Astrophysics Data System (ADS)

    Ban, Masashi

    1999-08-01

    Phase-space representation of quantum state vectors has been recently formulated by means of the relative-state method developed by the present author [J. Math. Phys. 39, 1744 (1998)]. It is, however, pointed out by Mo/ller that the displacement-operator method provides another basis of phase-space representation of quantum state vectors [J. Math. Phys. (to appear)]. Hence the relation between the relative-state approach and the displacement-operator approach is discussed, both of which yield equivalent phase-space representations.

  12. Investigating Mesoscopic Non-linear Series Circuit with the Coherent Thermo State Representation

    NASA Astrophysics Data System (ADS)

    Wang, Xiu-Xia

    2017-03-01

    For the first time we considered the quantum effects of mesoscopic non-linear series circuit with the coherent thermo state representation | τ rangle . After introducing the representation |τ rangle , we derived the expression of the density matrix ρ and find that | ρ rangle T presents Gauss type with the representation | τ rangle . In addition, we derived the Wigner function and calculated the quantum fluctuation in the thermo vacuum state |0( β)>. It is shown that the circuit has the zero current fluctuation because the diode has the reverse saturation current, and the temperature affects the Wigner function of the circuit in thermo vacuum state deeply.

  13. Predicting Neural Activity Patterns Associated with Sentences Using a Neurobiologically Motivated Model of Semantic Representation.

    PubMed

    Anderson, Andrew James; Binder, Jeffrey R; Fernandino, Leonardo; Humphries, Colin J; Conant, Lisa L; Aguilar, Mario; Wang, Xixi; Doko, Donias; Raizada, Rajeev D S

    2016-08-12

    We introduce an approach that predicts neural representations of word meanings contained in sentences then superposes these to predict neural representations of new sentences. A neurobiological semantic model based on sensory, motor, social, emotional, and cognitive attributes was used as a foundation to define semantic content. Previous studies have predominantly predicted neural patterns for isolated words, using models that lack neurobiological interpretation. Fourteen participants read 240 sentences describing everyday situations while undergoing fMRI. To connect sentence-level fMRI activation patterns to the word-level semantic model, we devised methods to decompose the fMRI data into individual words. Activation patterns associated with each attribute in the model were then estimated using multiple-regression. This enabled synthesis of activation patterns for trained and new words, which were subsequently averaged to predict new sentences. Region-of-interest analyses revealed that prediction accuracy was highest using voxels in the left temporal and inferior parietal cortex, although a broad range of regions returned statistically significant results, showing that semantic information is widely distributed across the brain. The results show how a neurobiologically motivated semantic model can decompose sentence-level fMRI data into activation features for component words, which can be recombined to predict activation patterns for new sentences.

  14. 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.

  15. 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

  16. 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

  17. 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.

  18. Site of metabolism prediction based on ab initio derived atom representations.

    PubMed

    Finkelmann, Arndt R; Göller, Andreas H; Schneider, Gisbert

    2017-03-21

    Machine learning models for site of metabolism (SoM) prediction offer the ability to identify metabolic soft spots in low molecular weight drug molecules at low computational cost and enable data-based reactivity prediction. SoM prediction is an atom classification problem. Successful construction of machine learning models requires atom representations that capture the reactivity-determining features of a potential reaction site. We have developed a descriptor scheme that characterizes an atom's steric and electronic environment and its relative location in the molecular structure. The partial charge distributions were obtained from fast quantum mechanical calculations. We successfully trained machine learning classifiers on curated cytochrome p450 metabolism data. The models based on the new atom descriptors showed sustained accuracy for retrospective analyses of metabolism optimization campaigns and lead optimization projects from Bayer Pharmaceuticals. The results obtained demonstrate the practicality of quantum-chemistry-supported machine learning models for hit-to-lead optimization.

  19. An Incomplete History: Representation of American Indians in State Social Studies Standards

    ERIC Educational Resources Information Center

    Journell, Wayne

    2009-01-01

    Using an interpretive analysis, American history standards from nine states that incorporate high-stakes assessments in social studies are analyzed for their representation of American Indians. Research on high-stakes assessments shows that teachers are more likely to align their instruction with state standards due to mounting pressure to achieve…

  20. Bound states in gauge theories as the Poincare group representations

    SciTech Connect

    Cherny, A. Yu.; Dorokhov, A. E.; Han, Nguyen Suan; Pervushin, V. N. Shilin, V. I.

    2013-03-15

    The bound-state generating functional is constructed in gauge theories. This construction is based on the Dirac Hamiltonian approach to gauge theories, the Poincare group classification of fields and their nonlocal bound states, and the Markov-Yukawa constraint of irreducibility. The generating functional contains additional anomalous creations of pseudoscalar bound states: para-positronium in QED and mesons inQCDin the two-gamma processes of the type of {gamma} + {gamma} {yields} {pi}{sub 0} +para-positronium. The functional allows us to establish physically clear and transparent relations between the perturbativeQCD to its nonperturbative low-energy model by means of normal ordering and the quark and gluon condensates. In the limit of small current quark masses, the Gell-Mann-Oakes-Renner relation is derived from the Schwinger-Dyson and Bethe-Salpeter equations. The constituent quark masses can be calculated from a self-consistent nonlinear equation.

  1. 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

  2. Observables, Evolution Equation, and Stationary States Equation in the Joint Probability Representation of Quantum Mechanics

    NASA Astrophysics Data System (ADS)

    Korennoy, Ya. A.; Man'ko, V. I.

    2017-04-01

    Symplectic and optical joint probability representations of quantum mechanics are considered, in which the functions describing the states are the probability distributions with all random arguments (except the argument of time). The general formalism of quantizers and dequantizers determining the star product quantization scheme in these representations is given. Taking the Gaussian functions as the distributions of the tomographic parameters the correspondence rules for most interesting physical operators are found and the expressions of the dual symbols of operators in the form of singular and regular generalized functions are derived. Evolution equations and stationary states equations for symplectic and optical joint probability distributions are obtained.

  3. Observables, Evolution Equation, and Stationary States Equation in the Joint Probability Representation of Quantum Mechanics

    NASA Astrophysics Data System (ADS)

    Korennoy, Ya. A.; Man'ko, V. I.

    2016-12-01

    Symplectic and optical joint probability representations of quantum mechanics are considered, in which the functions describing the states are the probability distributions with all random arguments (except the argument of time). The general formalism of quantizers and dequantizers determining the star product quantization scheme in these representations is given. Taking the Gaussian functions as the distributions of the tomographic parameters the correspondence rules for most interesting physical operators are found and the expressions of the dual symbols of operators in the form of singular and regular generalized functions are derived. Evolution equations and stationary states equations for symplectic and optical joint probability distributions are obtained.

  4. Predictive decision making driven by multiple time-linked reward representations in the anterior cingulate cortex

    PubMed Central

    Wittmann, Marco K.; Kolling, Nils; Akaishi, Rei; Chau, Bolton K. H.; Brown, Joshua W.; Nelissen, Natalie; Rushworth, Matthew F. S.

    2016-01-01

    In many natural environments the value of a choice gradually gets better or worse as circumstances change. Discerning such trends makes predicting future choice values possible. We show that humans track such trends by comparing estimates of recent and past reward rates, which they are able to hold simultaneously in the dorsal anterior cingulate cortex (dACC). Comparison of recent and past reward rates with positive and negative decision weights is reflected by opposing dACC signals indexing these quantities. The relative strengths of time-linked reward representations in dACC predict whether subjects persist in their current behaviour or switch to an alternative. Computationally, trend-guided choice can be modelled by using a reinforcement-learning mechanism that computes a longer-term estimate (or expectation) of prediction errors. Using such a model, we find a relative predominance of expected prediction errors in dACC, instantaneous prediction errors in the ventral striatum and choice signals in the ventromedial prefrontal cortex. PMID:27477632

  5. State space representations of distributed fluid line dynamics

    NASA Technical Reports Server (NTRS)

    Yao, H.; Goodson, R. E.; Leonard, R. G.

    1974-01-01

    The purpose of this paper is to demonstrate the convenience of using a systematic straight forward procedure to obtain meaningful dynamic information for a class of complex distributed parameter fluid line networks. System transients in the time domain are determined by means of state space techniques. Digital computer implementation yields a simple but consistent way of obtaining overall system time solutions. A step-by-step analysis procedure flow chart is shown in Appendix I which illustrates the basic approach for modeling, approximating and selecting digital techniques for simulating the dynamic response of fluid line systems.

  6. Significant contribution of realistic vegetation representation to improved simulation and prediction of climate anomalies over land

    NASA Astrophysics Data System (ADS)

    Alessandri, Andrea; Catalano, Franco; De Felice, Matteo; Doblas-Reyes, Francisco; van den Hurk, Bart; Miller, Paul

    2015-04-01

    The EC-Earth earth system model has been recently developed to include the dynamics of vegetation through the coupling with the LPJ-Guess model. In its original formulation, the coupling between atmosphere and vegetation variability is simply operated by the vegetation Leaf Area Index (LAI), which affects climate by only changing the vegetation physiological resistance to evapotranspiration. This coupling with no implied change of the vegetation fractional coverage has been reported to have a weak effect on the surface climate modeled by EC-Earth (e.g.: also Weiss et al. 2012). The effective sub-grid vegetation fractional coverage can vary seasonally and at interannual time-scales as a function of leaf-canopy growth, phenology and senescence, and therefore affect biophysical parameters such as the surface roughness, albedo and soil field capacity. To adequately represent this effect in EC-Earth, we included an exponential dependence of the vegetation densitiy to the LAI, based on a Lambert-Beer formulation. By comparing historical 20th century simulations and retrospective forecasts performed applying the new effective fractional-coverage parameterization with the respective reference simulations using the original constant vegetation-fraction, we showed an increased effect of vegetation on the EC-Earth surface climate. The analysis shows considerable sensitivity of EC-Earth surface climate at seasonal to interannual time-scales due to the variability of vegetation effective fractional coverage. Particularly large effects are shown over boreal winter middle-to-high latitudes, where the cooling effect of the new parameterization corrects the warm biases of the control simulations over land. For boreal winter, the realistic representation of vegetation variability leads to a significant improvement of the skill in predicting surface climate over land at seasonal time-scales. A potential predictability experiment extended to longer time-scales also indicates the

  7. The New Operational Hydro-meteorological Ensemble Prediction System at Meteo-France and its representation interface for the French Service for Flood Prediction (SCHAPI)

    NASA Astrophysics Data System (ADS)

    Rousset-Regimbeau, Fabienne; Coustau, Mathieu; Martin, Eric; Thirel, Guillaume; Habets, Florence; De Saint Aubin, Céline; Ardilouze, Constantin

    2013-04-01

    The coupled physically-based hydro-meteorological model SAFRAN-ISBA-MODCOU (SIM) is developed at Meteo-France for many years. This fully distributed catchment model is used in an operationnal real-time mode since 2005 for producing mid-range ensemble streamflow forecasts based on the 51-member 10-day ECMWF EPS. New improvements have been recently implemented in this forecasting chain. First, the new version of the forecasting chain includes new atmopheric products from the ECWMF (EPS at the resolution of 0,25° over France). Then an improvement of the physics of the ISBA model (a new physical representation of the soil hydraulic conductivity) is now used. And finally, a past discharges assimilation system has been implemented in order to improve the initial states of the ensemble streamflow forecasts. These developpement were first tested in the framework of a Phd thesis, and are now evaluated in real-time conditions. This study aims to assess the improvements obtained by the new version of the forecasting chain. Several experiments were performed ton assess the effects of i) the high resolution atmospheric forcing ii) the new representation of the hydraulic conductivity iii) the data assimilation method and iv) the real-time framework. Tested on a 18-month period of reforecasts, the new chain presents significantly improved ensemble streamflow forecasts compared to the previous version. Finally, this system provides ensemble 10-day streamflow prediction to the French National Service for Flood Prediction (SCHAPI). A collaboration between Meteo-France and SCHAPI led to the development of a new website. This website shows the streamflow predictions for about 200 selected river stations over France (selected regarding their interest for flood warning) , as well as alerts for high flows (two levels of high flows corresponding to the levels of risk of the French flood warning system). It aims at providing to the French hydrological forecaters a real-time tool for mid

  8. 26 CFR 301.6361-2 - Judicial and administrative proceedings; Federal representation of State interests.

    Code of Federal Regulations, 2010 CFR

    2010-04-01

    ... representation of State interests. (a) Civil proceedings—(1) General rule. Any person shall have the same right to bring or contest a civil action, and to obtain a review thereof, with respect to a qualified tax... (including review procedures) with respect to any matter, such procedures shall replace civil...

  9. Representability of Bloch states on Projector-augmented-wave (PAW) basis sets

    NASA Astrophysics Data System (ADS)

    Agapito, Luis; Ferretti, Andrea; Curtarolo, Stefano; Buongiorno Nardelli, Marco

    2015-03-01

    Design of small, yet `complete', localized basis sets is necessary for an efficient dual representation of Bloch states on both plane-wave and localized basis. Such simultaneous dual representation permits the development of faster more accurate (beyond DFT) electronic-structure methods for atomistic materials (e.g. the ACBN0 method.) by benefiting from algorithms (real and reciprocal space) and hardware acceleration (e.g. GPUs) used in the quantum-chemistry and solid-state communities. Finding a `complete' atomic-orbital basis (partial waves) is also a requirement in the generation of robust and transferable PAW pseudopotentials. We have employed the atomic-orbital basis from available PAW data sets, which extends through most of the periodic table, and tested the representability of Bloch states on such basis. Our results show that PAW data sets allow systematic and accurate representability of the PAW Bloch states, better than with traditional quantum-chemistry double-zeta- and double-zeta-polarized-quality basis sets.

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

    SciTech Connect

    Johnson, J. D.; Oberkampf, William Louis; Helton, Jon Craig (Arizona State University, Tempe, AZ); Storlie, Curtis B. (North Carolina State University, Raleigh, NC)

    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 model 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.

  11. Prediction of the Joule-Thomson inversion curve of air from cubic equations of state

    NASA Astrophysics Data System (ADS)

    Colina, Coray M.; Olivera-Fuentes, Claudio

    A modified van der Waals equation of state recommended in the literature for improved prediction of the inversion curve of air is shown to be thermodynamically inconsistent, giving large errors in the critical and two-phase regions. An alternative procedure is presented by means of which the cohesion function of any cubic equation of state can be adjusted to give arbitrarily accurate representation of an experimental inversion curve. New versions of the van der Waals, Redlich-Kwong and Peng-Robinson equations of state are developed based on experimental inversion data of air, and are shown to give better inversion predictions than more complex, multiparameter noncubic equations of state.

  12. Geometric constraints in semiclassical initial value representation calculations in Cartesian coordinates: excited states.

    PubMed

    Issack, Bilkiss B; Roy, Pierre-Nicholas

    2007-01-14

    The authors show that a recently proposed approach [J. Chem. Phys. 123, 084103 (2005)] for the inclusion of geometric constraints in semiclassical initial value representation calculations can be used to obtain excited states of weakly bound complexes. Sample calculations are performed for free and constrained rare gas clusters. The results show that the proposed approach allows the evaluation of excited states with reasonable accuracy when compared to exact basis set calculations.

  13. Human creativity, evolutionary algorithms, and predictive representations: The mechanics of thought trials.

    PubMed

    Dietrich, Arne; Haider, Hilde

    2015-08-01

    Creative thinking is arguably the pinnacle of cerebral functionality. Like no other mental faculty, it has been omnipotent in transforming human civilizations. Probing the neural basis of this most extraordinary capacity, however, has been doggedly frustrated. Despite a flurry of activity in cognitive neuroscience, recent reviews have shown that there is no coherent picture emerging from the neuroimaging work. Based on this, we take a different route and apply two well established paradigms to the problem. First is the evolutionary framework that, despite being part and parcel of creativity research, has no informed experimental work in cognitive neuroscience. Second is the emerging prediction framework that recognizes predictive representations as an integrating principle of all cognition. We show here how the prediction imperative revealingly synthesizes a host of new insights into the way brains process variation-selection thought trials and present a new neural mechanism for the partial sightedness in human creativity. Our ability to run offline simulations of expected future environments and action outcomes can account for some of the characteristic properties of cultural evolutionary algorithms running in brains, such as degrees of sightedness, the formation of scaffolds to jump over unviable intermediate forms, or how fitness criteria are set for a selection process that is necessarily hypothetical. Prospective processing in the brain also sheds light on how human creating and designing - as opposed to biological creativity - can be accompanied by intentions and foresight. This paper raises questions about the nature of creative thought that, as far as we know, have never been asked before.

  14. 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.

  15. Predicting siRNA efficacy based on multiple selective siRNA representations and their combination at score level

    NASA Astrophysics Data System (ADS)

    He, Fei; Han, Ye; Gong, Jianting; Song, Jiazhi; Wang, Han; Li, Yanwen

    2017-03-01

    Small interfering RNAs (siRNAs) may induce to targeted gene knockdown, and the gene silencing effectiveness relies on the efficacy of the siRNA. Therefore, the task of this paper is to construct an effective siRNA prediction method. In our work, we try to describe siRNA from both quantitative and qualitative aspects. For quantitative analyses, we form four groups of effective features, including nucleotide frequencies, thermodynamic stability profile, thermodynamic of siRNA-mRNA interaction, and mRNA related features, as a new mixed representation, in which thermodynamic of siRNA-mRNA interaction is introduced to siRNA efficacy prediction for the first time to our best knowledge. And then an F-score based feature selection is employed to investigate the contribution of each feature and remove the weak relevant features. Meanwhile, we encode the siRNA sequence and existed empirical design rules as a qualitative siRNA representation. These two kinds of siRNA representations are combined to predict siRNA efficacy by supported Vector Regression (SVR) at score level. The experimental results indicate that our method may select the features with powerful discriminative ability and make the two kinds of siRNA representations work at full capacity. The prediction results also demonstrate that our method can outperform other popular siRNA efficacy prediction algorithms.

  16. Predicting siRNA efficacy based on multiple selective siRNA representations and their combination at score level

    PubMed Central

    He, Fei; Han, Ye; Gong, Jianting; Song, Jiazhi; Wang, Han; Li, Yanwen

    2017-01-01

    Small interfering RNAs (siRNAs) may induce to targeted gene knockdown, and the gene silencing effectiveness relies on the efficacy of the siRNA. Therefore, the task of this paper is to construct an effective siRNA prediction method. In our work, we try to describe siRNA from both quantitative and qualitative aspects. For quantitative analyses, we form four groups of effective features, including nucleotide frequencies, thermodynamic stability profile, thermodynamic of siRNA-mRNA interaction, and mRNA related features, as a new mixed representation, in which thermodynamic of siRNA-mRNA interaction is introduced to siRNA efficacy prediction for the first time to our best knowledge. And then an F-score based feature selection is employed to investigate the contribution of each feature and remove the weak relevant features. Meanwhile, we encode the siRNA sequence and existed empirical design rules as a qualitative siRNA representation. These two kinds of siRNA representations are combined to predict siRNA efficacy by supported Vector Regression (SVR) at score level. The experimental results indicate that our method may select the features with powerful discriminative ability and make the two kinds of siRNA representations work at full capacity. The prediction results also demonstrate that our method can outperform other popular siRNA efficacy prediction algorithms. PMID:28317874

  17. Off-Center Coherent-State Representation and an Application to Semiclassics

    NASA Astrophysics Data System (ADS)

    Parisio, F.

    2010-07-01

    By using the overcompleteness of coherent states we find an alternative form of the unit operator for which the ket and the bra appearing under the integration sign do not refer to the same phase-space point. This defines a new quantum representation in terms of Bargmann functions, whose basic features are presented. A continuous family of secondary reproducing kernels for the Bargmann functions is obtained, showing that this quantity is not unique for representations based on overcomplete sets. We illustrate the applicability of the presented results by deriving a semiclassical expression for the Feynman propagator that generalizes the well-known van Vleck formula and seems to point a way to cope with long-standing problems in semiclassical propagation of localized states.

  18. Hybrid electromagnetic transient simulation with the state variable representation of HVDC converter plant

    SciTech Connect

    Zavahir, J.M.; Arrillaga, J.; Watson, N.R. )

    1993-07-01

    The two alternative methods in current use for the transient simulation of HVdc power systems are Electromagnetic Transient Programs and State Variable Analysis. A hybrid algorithm is described in this paper which combines the two methods selecting their best features. The relative performances of conventional and hybrid algorithms are discussed. Simulation results of typical back-to back HVdc link show that the hybrid representation provides more stable, accurate and efficient solutions.

  19. 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.

  20. 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

  1. Isospin and particle representations for quasi-bound state of kaonic clusters

    NASA Astrophysics Data System (ADS)

    Filikhin, Igor; Kezerashvili, Roman; Vlahovic, Branislav

    2017-01-01

    In the framework of the method of the Faddeev equations in configuration space, the NNK (I = 0) (and KK) kaonic cluster system including two identical particles is considered. We use the formalism of isospin and particle representations to describe the systems. The treatment of I = 1 and I = 0 isospin KN channels is discussed. The presence of the Coulomb force in ppK- channel violates the isospin symmetry of the NNK (I = 0) system. According to the particle representation, NNK is a two-level system of coupled ppK- and ppnl channels with and without the Coulomb energy, respectively. The results of calculations for the bound states with the phenomenological and chiral motivated KN potentials are given for different representations. In particular, new single channel calculations for the ppK- (and K-K- p) cluster are presented. It is shown that the exchange of identical particles plays an important role in the formation of a bound state of the systems. The relation of the exchange and the three-body mass rearrangement effects is discussed. This work is supported by the National Science Foundation grant Supplement to the NSF grant HRD-1345219 and NASA (NNX09AV07A).

  2. 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.

  3. Commonality of neural representations of sentences across languages: Predicting brain activation during Portuguese sentence comprehension using an English-based model of brain function.

    PubMed

    Yang, Ying; Wang, Jing; Bailer, Cyntia; Cherkassky, Vladimir; Just, Marcel Adam

    2017-02-01

    The aim of the study was to test the cross-language generative capability of a model that predicts neural activation patterns evoked by sentence reading, based on a semantic characterization of the sentence. In a previous study on English monolingual speakers (Wang et al., submitted), a computational model performed a mapping from a set of 42 concept-level semantic features (Neurally Plausible Semantic Features, NPSFs) as well as 6 thematic role markers to neural activation patterns (assessed with fMRI), to predict activation levels in a network of brain locations. The model used two types of information gained from the English-based fMRI data to predict the activation for individual sentences in Portuguese. First, it used the mapping weights from NPSFs to voxel activation levels derived from the model for English reading. Second, the brain locations for which the activation levels were predicted were derived from a factor analysis of the brain activation patterns during English reading. These meta-language locations were defined by the clusters of voxels with high loadings on each of the four main dimensions (factors), namely people, places, actions and feelings, underlying the neural representations of the stimulus sentences. This cross-language model succeeded in predicting the brain activation patterns associated with the reading of 60 individual Portuguese sentences that were entirely new to the model, attaining accuracies reliably above chance level. The prediction accuracy was not affected by whether the Portuguese speaker was monolingual or Portuguese-English bilingual. The model's confusion errors indicated an accurate capture of the events or states described in the sentence at a conceptual level. Overall, the cross-language predictive capability of the model demonstrates the neural commonality between speakers of different languages in the representations of everyday events and states, and provides an initial characterization of the common meta

  4. Numerical Modeling of the Central Spin Problem Using the Spin-Coherent-State P Representation

    SciTech Connect

    Al Hassanieh, Khaled A; Dobrovitski, V. V.; Dagotto, Elbio R; Harmon, B. N.

    2006-01-01

    In this work, we consider decoherence of a central spin by a spin bath. In order to study the nonperturbative decoherence regimes, we develop an efficient mean-field-based method for modeling the spin-bath decoherence, based on the P representation of the central spin density matrix. The method can be applied to longitudinal and transverse relaxation at different external fields. In particular, by modeling large-size quantum systems (up to 16 000 bath spins), we make controlled predictions for the slow long-time decoherence of the central spin.

  5. Nonclassicality and Entanglement of Photon-Subtracted Two-Mode Squeezed Coherent States Studied via Entangled-States Representation

    NASA Astrophysics Data System (ADS)

    Wang, Zhen; Li, Heng-Mei; Yuan, Hong-Chun

    2016-10-01

    We theoretically introduce a kind of non-Gaussian entangled states, i.e., photon-subtracted two-mode squeezed coherent states (PSTMSCS), by successively subtracting photons from each mode of the two-mode squeezed coherent states. The normalization factor which is related to bivariate Hermite polynomials is obtained by virtue of the two-mode squeezing operator in entangled-states representation. The sub-Poissonian photon statistics, antibunching effects, and partial negative Wigner function, respectively, are observed numerically, which fully reflect the nonclassicality of the resultant states. Finally, employing the SV criteria and the EPR correlation, respectively, the entangled property of PSTMSCS is analyzed. It is shown that the photon subtraction operation can effectively enhance the inseparability between the two modes.

  6. 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…

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

    PubMed Central

    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

    2017-01-01

    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. PMID:28079189

  8. 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.

  9. Representation of Sea Ice Processes in State of the Art Earth System Models.

    NASA Astrophysics Data System (ADS)

    Bailey, D. A.; Holland, M. M.

    2015-12-01

    The majority of Earth System Models now include thermodynamic-dynamic sea ice models with a subgridscale representation of ice thickness. The current sea ice component of the Community Earth System Model is the Los Alamos sea ice model (CICE) version 5. This new version of the model includes prognostic salinity in the vertical thermodynamic calculation as well as a representation of melt pond drainage through the sea ice. The CICE5 also includes a melt pond parameterization that takes into account the deformed and non-deformed ice within a model grid cell. Snow on sea ice processes allow for an evolving effective snow grain radius as a function of temperature, which is used in the shortwave radiative transfer and surface albedo calculation. I will discuss the results from coupled climate model sensitivity simulations that consider the subgridscale representations of some of these processes. This will include analysis of mean state and feedbacks in both the Arctic and Antarctic. Additional discussion will be provided on how we have used observations to guide these efforts.

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

    SciTech Connect

    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 (two 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.

  11. PREDICTING RECIDIVISM FOR RELEASED STATE PRISON OFFENDERS

    PubMed Central

    Stahler, Gerald J.; Mennis, Jeremy; Belenko, Steven; Welsh, Wayne N.; Hiller, Matthew L.; Zajac, Gary

    2013-01-01

    We examined the influence of individual and neighborhood characteristics and spatial contagion in predicting reincarceration on a sample of 5,354 released Pennsylvania state prisoners. Independent variables included demographic characteristics, offense type, drug involvement, various neighborhood variables (e.g., concentrated disadvantage, residential mobility), and spatial contagion (i.e., proximity to others who become reincarcerated). Using geographic information systems (GIS) and logistic regression modeling, our results showed that the likelihood of reincarceration was increased with male gender, drug involvement, offense type, and living in areas with high rates of recidivism. Older offenders and those convicted of violent or drug offenses were less likely to be reincarcerated. For violent offenders, drug involvement, age, and spatial contagion were particular risk factors for reincarceration. None of the neighborhood environment variables were associated with increased risk of reincarceration. Reentry programs need to particularly address substance abuse issues of ex-offenders as well as take into consideration their residential locations. PMID:24443612

  12. Self-regulating genes. Exact steady state solution by using Poisson representation

    NASA Astrophysics Data System (ADS)

    Sugár, István P.; Simon, István

    2014-09-01

    Systems biology studies the structure and behavior of complex gene regulatory networks. One of its aims is to develop a quantitative understanding of the modular components that constitute such networks. The self-regulating gene is a type of auto regulatory genetic modules which appears in over 40% of known transcription factors in E. coli. In this work, using the technique of Poisson Representation, we are able to provide exact steady state solutions for this feedback model. By using the methods of synthetic biology (P.E.M. Purnick and Weiss, R., Nature Reviews, Molecular Cell Biology, 2009, 10: 410-422) one can build the system itself from modules like this.

  13. Representations and properties of generalized Ar statistics, coherent states and Robertson uncertainty relations

    NASA Astrophysics Data System (ADS)

    Daoud, M.

    2006-01-01

    The generalization of Ar statistics, including bosonic and fermionic sectors, is performed by means of the so-called Jacobson generators. The corresponding Fock spaces are constructed. The Bargmann representations are also considered. For the bosonic Ar statistics, two inequivalent Bargmann realizations are developed. The first (resp. second) realization induces, in a natural way, coherent states recognized as Gazeau-Klauder (resp. Klauder-Perelomov) ones. In the fermionic case, the Bargamnn realization leads to the Klauder-Perelomov coherent states. For each considered realization, the inner product of two analytic functions is defined with respect to a measure explicitly computed. The Jacobson generators are realized as differential operators. It is shown that the obtained coherent states minimize the Robertson-Schrödinger uncertainty relation.

  14. [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.

  15. Robust brain parcellation using sparse representation on resting-state fMRI.

    PubMed

    Zhang, Yu; Caspers, Svenja; Fan, Lingzhong; Fan, Yong; Song, Ming; Liu, Cirong; Mo, Yin; Roski, Christian; Eickhoff, Simon; Amunts, Katrin; Jiang, Tianzi

    2015-11-01

    Resting-state fMRI (rs-fMRI) has been widely used to segregate the brain into individual modules based on the presence of distinct connectivity patterns. Many parcellation methods have been proposed for brain parcellation using rs-fMRI, but their results have been somewhat inconsistent, potentially due to various types of noise. In this study, we provide a robust parcellation method for rs-fMRI-based brain parcellation, which constructs a sparse similarity graph based on the sparse representation coefficients of each seed voxel and then uses spectral clustering to identify distinct modules. Both the local time-varying BOLD signals and whole-brain connectivity patterns may be used as features and yield similar parcellation results. The robustness of our method was tested on both simulated and real rs-fMRI datasets. In particular, on simulated rs-fMRI data, sparse representation achieved good performance across different noise levels, including high accuracy of parcellation and high robustness to noise. On real rs-fMRI data, stable parcellation of the medial frontal cortex (MFC) and parietal operculum (OP) were achieved on three different datasets, with high reproducibility within each dataset and high consistency across these results. Besides, the parcellation of MFC was little influenced by the degrees of spatial smoothing. Furthermore, the consistent parcellation of OP was also well corresponding to cytoarchitectonic subdivisions and known somatotopic organizations. Our results demonstrate a new promising approach to robust brain parcellation using resting-state fMRI by sparse representation.

  16. Continuous multipartite entangled state in Wigner representation and violation of the Zukowski-Brukner inequality

    SciTech Connect

    Wu Chunfeng; Chen Jingling; Oh, C.H.; Kwek, L.C.; Xue Kang

    2005-02-01

    We construct an explicit Wigner function for the N-mode squeezed state. Based on a previous observation that the Wigner function describes correlations in the joint measurement of the phase-space displaced parity operator, we investigate the nonlocality of the multipartite entangled state by the violation of the Zukowski-Brukner N-qubit Bell inequality. We find that quantum predictions for such a squeezed state violate these inequalities by an amount that grows with the number N.

  17. Analytical representations of precise orbit predictions for Earth orbiting space objects

    NASA Astrophysics Data System (ADS)

    Sang, Jizhang; Li, Bin; Chen, Junyu; Zhang, Pin; Ning, Jinsheng

    2017-01-01

    Accurate orbits of Earth orbiting space objects are usually generated from an orbit determination/prediction process using numerical integrators, and presented to users in a tabulated form or a state vector including force model parameters. When dealing with hundreds of thousands of space objects such as in the space conjunction assessment, the memory required for the tabulated orbits or the computing time for propagating orbits using the state vector are both confronting to users. This paper presents two methods of analytically representing numerical orbits considering the accuracy, computing efficiency and memory. The first one is a two-step TLE-based method in which the numerical orbits are first fitted into a TLE set and then correction functions are applied to improve the position accuracy. In the second method, the orbit states are represented in equinoctial elements first, and then again correction functions are applied to reduce the position errors. Experiments using six satellite laser ranging (SLR) satellites and 12 debris objects with accurate orbits show that both methods can represent the accurate orbits over 5 days in an accuracy of a few dozens of meters for the circular orbits and several hundred meters for the eccentric orbits. The computing time is similar to that using the NORAD TLE/SGP4 algorithm, and storage for the orbit elements and function coefficients is about 3-5 KB. These features could make the two methods beneficial for the maintenance of orbit catalog of large numbers of space objects.

  18. 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

    2017-04-03

    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.

  19. 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.

  20. Spatial mental representations derived from spatial descriptions: the predicting and mediating roles of spatial preferences, strategies, and abilities.

    PubMed

    Meneghetti, Chiara; Ronconi, Lucia; Pazzaglia, Francesca; De Beni, Rossana

    2014-08-01

    The aim of this research was to investigate how spatial self-assessments and spatial cognitive abilities jointly influence the construction of mental representations derived from spatial descriptions. Two studies were conducted using the path models approach to test to what extent spatial self-assessments (Study 1, 194 participants) and the combination of the latter with spatial abilities (Study 2, 206 participants) can be modelled to predict memory for spatial descriptions. In both studies, we recorded spatial representation preferences (distinguishing between survey, route, and landmark-focused mode) and self-reported strategies used to memorize descriptions (distinguishing between survey, route, and verbal strategies); in Study 2, we also measured spatial abilities by testing mental rotation (MR) and visuo-spatial working memory (VSWM). Participants listened to spatial descriptions and then completed recall tasks. In both studies, the final path models showed that spatial preferences influenced spatial recall through the mediation of congruent strategies: that is a survey (route) preference influenced spatial recall mediated by a survey (route) strategy. MR predicted spatial recall, mediated by both VSWM and survey strategy (Study 2). Overall, these findings indicate that spatial preferences (particularly for a survey mode) in association with spatial abilities effectively concur to help form mental representations derived from spatial descriptions.

  1. Spin Coherent State Representation of the Crow-Kimura and Eigen Models of Quasispecies Theory

    NASA Astrophysics Data System (ADS)

    Ancliff, Mark; Park, Jeong-Man

    2011-05-01

    We present a spin coherent state representation of the Crow-Kimura and Eigen models of biological evolution. We deal with quasispecies models where the fitness is a function of Hamming distances from one or more reference sequences. In the limit of large sequence length N, we find exact expressions for the mean fitness and magnetization of the asymptotic quasispecies distribution in symmetric fitness landscapes. The results are obtained by constructing a path integral for the propagator on the coset SU(2)/ U(1) and taking the classical limit. The classical limit gives a Hamiltonian function on a circle for one reference sequence, and on the product of 2 m -1 circles for m reference sequences. We apply our representation to study the Schuster-Swetina phenomena, where a wide lower peak is selected over a narrow higher peak. The quadratic landscape with two reference sequences is also analyzed specifically and we present the phase diagram on the mutation-fitness parameter phase space. Furthermore, we use our method to investigate more biologically relevant system, a model of escape from adaptive conflict through gene duplication, and find three different phases for the asymptotic population distribution.

  2. Resting state functional connectivity predicts neurofeedback response

    PubMed Central

    Scheinost, Dustin; Stoica, Teodora; Wasylink, Suzanne; Gruner, Patricia; Saksa, John; Pittenger, Christopher; Hampson, Michelle

    2014-01-01

    Tailoring treatments to the specific needs and biology of individual patients—personalized medicine—requires delineation of reliable predictors of response. Unfortunately, these have been slow to emerge, especially in neuropsychiatric disorders. We have recently described a real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback protocol that can reduce contamination-related anxiety, a prominent symptom of many cases of obsessive-compulsive disorder (OCD). Individual response to this intervention is variable. Here we used patterns of brain functional connectivity, as measured by baseline resting-state fMRI (rs-fMRI), to predict improvements in contamination anxiety after neurofeedback training. Activity of a region of the orbitofrontal cortex (OFC) and anterior prefrontal cortex, Brodmann area (BA) 10, associated with contamination anxiety in each subject was measured in real time and presented as a neurofeedback signal, permitting subjects to learn to modulate this target brain region. We have previously reported both enhanced OFC/BA 10 control and improved anxiety in a group of subclinically anxious subjects after neurofeedback. Five individuals with contamination-related OCD who underwent the same protocol also showed improved clinical symptomatology. In both groups, these behavioral improvements were strongly correlated with baseline whole-brain connectivity in the OFC/BA 10, computed from rs-fMRI collected several days prior to neurofeedback training. These pilot data suggest that rs-fMRI can be used to identify individuals likely to benefit from rt-fMRI neurofeedback training to control contamination anxiety. PMID:25309375

  3. Prediction of protein-protein interactions using chaos game representation and wavelet transform via the random forest algorithm.

    PubMed

    Jia, J H; Liu, Z; Chen, X; Xiao, X; Liu, B X

    2015-10-02

    Studying the network of protein-protein interactions (PPIs) will provide valuable insights into the inner workings of cells. It is vitally important to develop an automated, high-throughput tool that efficiently predicts protein-protein interactions. This study proposes a new model for PPI prediction based on the concept of chaos game representation and the wavelet transform, which means that a considerable amount of sequence-order effects can be incorporated into a set of discrete numbers. The advantage of using chaos game representation and the wavelet transform to formulate the protein sequence is that it can more effectively reflect its overall sequence-order characteristics than the conventional correlation factors. Using such a formulation frame to represent the protein sequences means that the random forest algorithm can be used to conduct the prediction. The results for a large-scale independent test dataset show that the proposed model can achieve an excellent performance with an accuracy value of about 0.86 and a geometry mean value of about 0.85. The model is therefore a useful supplementary tool for PPI predictions. The predictor used in this article is freely available at http://www.jci-bioinfo.cn/PPI.

  4. Ab Initio Quality NMR Parameters in Solid-State Materials Using a High-Dimensional Neural-Network Representation.

    PubMed

    Cuny, Jérôme; Xie, Yu; Pickard, Chris J; Hassanali, Ali A

    2016-02-09

    Nuclear magnetic resonance (NMR) spectroscopy is one of the most powerful experimental tools to probe the local atomic order of a wide range of solid-state compounds. However, due to the complexity of the related spectra, in particular for amorphous materials, their interpretation in terms of structural information is often challenging. These difficulties can be overcome by combining molecular dynamics simulations to generate realistic structural models with an ab initio evaluation of the corresponding chemical shift and quadrupolar coupling tensors. However, due to computational constraints, this approach is limited to relatively small system sizes which, for amorphous materials, prevents an adequate statistical sampling of the distribution of the local environments that is required to quantitatively describe the system. In this work, we present an approach to efficiently and accurately predict the NMR parameters of very large systems. This is achieved by using a high-dimensional neural-network representation of NMR parameters that are calculated using an ab initio formalism. To illustrate the potential of this approach, we applied this neural-network NMR (NN-NMR) method on the (17)O and (29)Si quadrupolar coupling and chemical shift parameters of various crystalline silica polymorphs and silica glasses. This approach is, in principal, general and has the potential to be applied to predict the NMR properties of various materials.

  5. A finite element surface impedance representation for steady-state problems

    NASA Technical Reports Server (NTRS)

    Kalinowski, A. J.

    1986-01-01

    A procedure for determining the scattered pressure field resulting from a monochromatic harmonic wave that is incident upon a layer energy absorbing structure is treated. The situation where the structure is modeled with finite elements and the surrounding acoustic medium (water or air) is represented with either acoustic finite elements, or some type of boundary integral formulation, is considered. Finite element modeling problems arise when the construction of the structure, at the fluid structure interface, are nonhomogeneous and in particular, when the inhomogeneities are small relative to the acoustic wave length. An approximate procedure is presented for replacing the detailed microscopic representation of the layered surface configuration with an equivalent simple surface impedance finite element, which is especially designed to work only at limited frequencies. An example problem is presented using NASTRAN. However, the procedure is general enough to adapt to practically any finite element code having a steady state option.

  6. Newton Leibniz integration for ket bra operators in quantum mechanics and derivation of entangled state representations

    NASA Astrophysics Data System (ADS)

    Fan, Hong-yi; Lu, Hai-liang; Fan, Yue

    2006-02-01

    Newton-Leibniz integration rule only applies to commuting functions of continuum variables, while operators made of Dirac's symbols (ket versus bra, e.g., | q>< q| of continuous parameter q) in quantum mechanics are usually not commutative. Therefore, integrations over the operators of type |><| cannot be directly performed by Newton-Leibniz rule. We invented an innovative technique of integration within an ordered product (IWOP) of operators that made the integration of non-commutative operators possible. The IWOP technique thus bridges this mathematical gap between classical mechanics and quantum mechanics, and further reveals the beauty and elegance of Dirac's symbolic method and transformation theory. Various applications of the IWOP technique, including constructing the entangled state representations and their applications, are presented.

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

    PubMed

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

    2016-01-01

    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.

  8. 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

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

    SciTech Connect

    Ching, Ping Pui; Zaveri, Rahul A.; Easter, Richard C.; Riemer, Nicole; Fast, Jerome D.

    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, 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.

  10. Linear predictive control with state variable constraints

    NASA Astrophysics Data System (ADS)

    Bdirina, K.; Djoudi, D.; Lagoun, M.

    2012-11-01

    While linear model predictive control is popular since the 70s of the past century, the 90s have witnessed a steadily increasing attention from control theoretists as well as control practitioners in the area of model predictive control (MPC). The practical interest is driven by the fact that today's processes need to be operated under tighter performance specifications. At the same time more and more constraints, stemming for example from environmental and safety considerations, need to besatisfied. Often these demands can only be met when process constraints are explicitly considered in the controller. Predictive control with constraints appears to be a well suited approach for this kind of problems. In this paper the basic principle of MPC with constraints is reviewed and some of the theoretical, computational, and implementation aspects of MPC are discussed. Furthermore the MPC with constraints was applied to linear example.

  11. 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…

  12. 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.

  13. 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.

  14. 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…

  15. Learning network representations

    NASA Astrophysics Data System (ADS)

    Moyano, Luis G.

    2017-02-01

    In this review I present several representation learning methods, and discuss the latest advancements with emphasis in applications to network science. Representation learning is a set of techniques that has the goal of efficiently mapping data structures into convenient latent spaces. Either for dimensionality reduction or for gaining semantic content, this type of feature embeddings has demonstrated to be useful, for example, for node classification or link prediction tasks, among many other relevant applications to networks. I provide a description of the state-of-the-art of network representation learning as well as a detailed account of the connections with other fields of study such as continuous word embeddings and deep learning architectures. Finally, I provide a broad view of several applications of these techniques to networks in various domains.

  16. 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.…

  17. Do attachment representations predict depression and anxiety in psychiatrically hospitalized prepubertal children?

    PubMed

    Goodman, Geoff; Stroh, Martha; Valdez, Adina

    2012-01-01

    Thirty-six prepubertal inpatients were videotaped completing five stories thematically related to attachment experiences and classified by their attachment representations. Children also completed the Children's Depression Inventory and Diagnostic Interview for Children and Adolescents-Revised. Mothers completed demographic questionnaires. Percentage of secure (B) attachment was only about one tenth of the normative percentage, anxious-ambivalent (C) attachment was between two and three times the normative percentage, and disorganized (D) attachment was almost twice the normative percentage. Both D attachment and the total number of disorganized story responses were associated with negative self-esteem and clinical-range depression. Anxious-avoidant (A) attachment decreased the likelihood, while C and D attachment increased the likelihood, of separation anxiety disorder. Clinical intervention needs to focus on the meaning of parental relationships represented in the child's mind, specifically the negative self-esteem and separation anxiety associated with the lack of felt security provided by the parents.

  18. State Representation Approach for Atomistic Time-Dependent Transport Calculations in Molecular Junctions.

    PubMed

    Zelovich, Tamar; Kronik, Leeor; Hod, Oded

    2014-08-12

    We propose a new method for simulating electron dynamics in open quantum systems out of equilibrium, using a finite atomistic model. The proposed method is motivated by the intuitive and practical nature of the driven Liouville-von-Neumann equation approach of Sánchez et al. [J. Chem. Phys. 2006, 124, 214708] and Subotnik et al. [J. Chem. Phys. 2009, 130, 144105]. A key ingredient of our approach is a transformation of the Hamiltonian matrix from an atomistic to a state representation of the molecular junction. This allows us to uniquely define the bias voltage across the system while maintaining a proper thermal electronic distribution within the finite lead models. Furthermore, it allows us to investigate complex molecular junctions, including multilead configurations. A heuristic derivation of our working equation leads to explicit expressions for the damping and driving terms, which serve as appropriate electron sources and sinks that effectively "open" the finite model system. Although the method does not forbid it, in practice we find neither violation of Pauli's exclusion principles nor deviation from density matrix positivity throughout our numerical simulations of various tight-binding model systems. We believe that the new approach offers a practical and physically sound route for performing atomistic time-dependent transport calculations in realistic molecular junction models.

  19. In-silico prediction of disorder content using hybrid sequence representation

    PubMed Central

    2011-01-01

    Background Intrinsically disordered proteins play important roles in various cellular activities and their prevalence was implicated in a number of human diseases. The knowledge of the content of the intrinsic disorder in proteins is useful for a variety of studies including estimation of the abundance of disorder in protein families, classes, and complete proteomes, and for the analysis of disorder-related protein functions. The above investigations currently utilize the disorder content derived from the per-residue disorder predictions. We show that these predictions may over-or under-predict the overall amount of disorder, which motivates development of novel tools for direct and accurate sequence-based prediction of the disorder content. Results We hypothesize that sequence-level aggregation of input information may provide more accurate content prediction when compared with the content extracted from the local window-based residue-level disorder predictors. We propose a novel predictor, DisCon, that takes advantage of a small set of 29 custom-designed descriptors that aggregate and hybridize information concerning sequence, evolutionary profiles, and predicted secondary structure, solvent accessibility, flexibility, and annotation of globular domains. Using these descriptors and a ridge regression model, DisCon predicts the content with low, 0.05, mean squared error and high, 0.68, Pearson correlation. This is a statistically significant improvement over the content computed from outputs of ten modern disorder predictors on a test dataset with proteins that share low sequence identity with the training sequences. The proposed predictive model is analyzed to discuss factors related to the prediction of the disorder content. Conclusions DisCon is a high-quality alternative for high-throughput annotation of the disorder content. We also empirically demonstrate that the DisCon's predictions can be used to improve binary annotations of the disordered residues from

  20. Moon meteoritic seismic hum: Steady state prediction

    USGS Publications Warehouse

    Lognonne, P.; Feuvre, M.L.; Johnson, C.L.; Weber, R.C.

    2009-01-01

    We use three different statistical models describing the frequency of meteoroid impacts on Earth to estimate the seismic background noise due to impacts on the lunar surface. Because of diffraction, seismic events on the Moon are typically characterized by long codas, lasting 1 h or more. We find that the small but frequent impacts generate seismic signals whose codas overlap in time, resulting in a permanent seismic noise that we term the "lunar hum" by analogy with the Earth's continuous seismic background seismic hum. We find that the Apollo era impact detection rates and amplitudes are well explained by a model that parameterizes (1) the net seismic impulse due to the impactor and resulting ejecta and (2) the effects of diffraction and attenuation. The formulation permits the calculation of a composite waveform at any point on the Moon due to simulated impacts at any epicentral distance. The root-mean-square amplitude of this waveform yields a background noise level that is about 100 times lower than the resolution of the Apollo long-period seismometers. At 2 s periods, this noise level is more than 1000 times lower than the low noise model prediction for Earth's microseismic noise. Sufficiently sensitive seismometers will allow the future detection of several impacts per day at body wave frequencies. Copyright 2009 by the American Geophysical Union.

  1. 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.

  2. 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.

  3. Trait-Based Representation of Biological Nitrification: Model Development, Testing, and Predicted Community Composition

    PubMed Central

    Bouskill, Nicholas J.; Tang, Jinyun; Riley, William J.; Brodie, Eoin L.

    2012-01-01

    Trait-based microbial models show clear promise as tools to represent the diversity and activity of microorganisms across ecosystem gradients. These models parameterize specific traits that determine the relative fitness of an “organism” in a given environment, and represent the complexity of biological systems across temporal and spatial scales. In this study we introduce a microbial community trait-based modeling framework (MicroTrait) focused on nitrification (MicroTrait-N) that represents the ammonia-oxidizing bacteria (AOB) and ammonia-oxidizing archaea (AOA) and nitrite-oxidizing bacteria (NOB) using traits related to enzyme kinetics and physiological properties. We used this model to predict nitrifier diversity, ammonia (NH3) oxidation rates, and nitrous oxide (N2O) production across pH, temperature, and substrate gradients. Predicted nitrifier diversity was predominantly determined by temperature and substrate availability, the latter was strongly influenced by pH. The model predicted that transient N2O production rates are maximized by a decoupling of the AOB and NOB communities, resulting in an accumulation and detoxification of nitrite to N2O by AOB. However, cumulative N2O production (over 6 month simulations) is maximized in a system where the relationship between AOB and NOB is maintained. When the reactions uncouple, the AOB become unstable and biomass declines rapidly, resulting in decreased NH3 oxidation and N2O production. We evaluated this model against site level chemical datasets from the interior of Alaska and accurately simulated NH3 oxidation rates and the relative ratio of AOA:AOB biomass. The predicted community structure and activity indicate (a) parameterization of a small number of traits may be sufficient to broadly characterize nitrifying community structure and (b) changing decadal trends in climate and edaphic conditions could impact nitrification rates in ways that are not captured by extant biogeochemical models. PMID

  4. Dissociable neural representations of reinforcement and belief prediction errors underlie strategic learning.

    PubMed

    Zhu, Lusha; Mathewson, Kyle E; Hsu, Ming

    2012-01-31

    Decision-making in the presence of other competitive intelligent agents is fundamental for social and economic behavior. Such decisions require agents to behave strategically, where in addition to learning about the rewards and punishments available in the environment, they also need to anticipate and respond to actions of others competing for the same rewards. However, whereas we know much about strategic learning at both theoretical and behavioral levels, we know relatively little about the underlying neural mechanisms. Here, we show using a multi-strategy competitive learning paradigm that strategic choices can be characterized by extending the reinforcement learning (RL) framework to incorporate agents' beliefs about the actions of their opponents. Furthermore, using this characterization to generate putative internal values, we used model-based functional magnetic resonance imaging to investigate neural computations underlying strategic learning. We found that the distinct notions of prediction errors derived from our computational model are processed in a partially overlapping but distinct set of brain regions. Specifically, we found that the RL prediction error was correlated with activity in the ventral striatum. In contrast, activity in the ventral striatum, as well as the rostral anterior cingulate (rACC), was correlated with a previously uncharacterized belief-based prediction error. Furthermore, activity in rACC reflected individual differences in degree of engagement in belief learning. These results suggest a model of strategic behavior where learning arises from interaction of dissociable reinforcement and belief-based inputs.

  5. Applying representational state transfer (REST) architecture to archetype-based electronic health record systems

    PubMed Central

    2013-01-01

    Background The openEHR project and the closely related ISO 13606 standard have defined structures supporting the content of Electronic Health Records (EHRs). However, there is not yet any finalized openEHR specification of a service interface to aid application developers in creating, accessing, and storing the EHR content. The aim of this paper is to explore how the Representational State Transfer (REST) architectural style can be used as a basis for a platform-independent, HTTP-based openEHR service interface. Associated benefits and tradeoffs of such a design are also explored. Results The main contribution is the formalization of the openEHR storage, retrieval, and version-handling semantics and related services into an implementable HTTP-based service interface. The modular design makes it possible to prototype, test, replicate, distribute, cache, and load-balance the system using ordinary web technology. Other contributions are approaches to query and retrieval of the EHR content that takes caching, logging, and distribution into account. Triggering on EHR change events is also explored. A final contribution is an open source openEHR implementation using the above-mentioned approaches to create LiU EEE, an educational EHR environment intended to help newcomers and developers experiment with and learn about the archetype-based EHR approach and enable rapid prototyping. Conclusions Using REST addressed many architectural concerns in a successful way, but an additional messaging component was needed to address some architectural aspects. Many of our approaches are likely of value to other archetype-based EHR implementations and may contribute to associated service model specifications. PMID:23656624

  6. State-space representation of Li-ion battery porous electrode impedance model with balanced model reduction

    NASA Astrophysics Data System (ADS)

    Jun, Myungsoo; Smith, Kandler; Graf, Peter

    2015-01-01

    This paper presents an approximate time-domain solution for physics-based electrochemical lithium-ion cell battery models. The time-domain solution is represented in state-space form and can be easily used for the design of a state estimator or controller. It uses an interconnection-of-system approach to derive a state-space representation of a battery impedance model and provides a reduced order model based via the balanced truncation method. Simulation results are also provided to show the performance of the proposed model in the frequency domain.

  7. 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

  8. 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.

  9. Dynamic filtering improves attentional state prediction with fNIRS.

    PubMed

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

    2016-03-01

    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%).

  10. Construal Levels and Psychological Distance: Effects on Representation, Prediction, Evaluation, and Behavior

    PubMed Central

    Trope, Yaacov; Liberman, Nira; Wakslak, Cheryl

    2011-01-01

    Construal level theory (CLT) is an account of how psychological distance influences individuals’ thoughts and behavior. CLT assumes that people mentally construe objects that are psychologically near in terms of low-level, detailed, and contextualized features, whereas at a distance they construe the same objects or events in terms of high-level, abstract, and stable characteristics. Research has shown that different dimensions of psychological distance (time, space, social distance, and hypotheticality) affect mental construal and that these construals, in turn, guide prediction, evaluation, and behavior. The present paper reviews this research and its implications for consumer psychology. PMID:21822366

  11. 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.

  12. 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.

  13. The Invisible Link: Using State Space Representations to Investigate the Connection between Variables and Their Referents

    ERIC Educational Resources Information Center

    Pollack, Courtney

    2012-01-01

    The ability to represent numerical quantities in symbolic form is a necessary foundation for mathematical competence. Variables are particularly important symbolic representations for learning algebra and succeeding in higher mathematics, but the mechanisms of how students link a variable to what it represents are not well understood. Research…

  14. Conditions for predicting quasistationary states by rearrangement formula.

    PubMed

    Yamaguchi, Yoshiyuki Y; Ogawa, Shun

    2015-10-01

    Predicting the long-lasting quasistationary state for a given initial state is one of central issues in Hamiltonian systems having long-range interaction. A recently proposed method is based on the Vlasov description and uniformly redistributes the initial distribution along contours of the asymptotic effective Hamiltonian, which is defined by the obtained quasistationary state and is determined self-consistently. The method, to which we refer as the rearrangement formula, was suggested to give precise prediction under limited situations. Restricting initial states consisting of a spatially homogeneous part and small perturbation, we numerically reveal two conditions that the rearrangement formula prefers: One is a no Landau damping condition for the unperturbed homogeneous part, and the other comes from the Casimir invariants. Mechanisms of these conditions are discussed. Clarifying these conditions, we validate to use the rearrangement formula as the response theory for an external field, and we shed light on improving the theory as a nonequilibrium statistical mechanics.

  15. Conditions for predicting quasistationary states by rearrangement formula

    NASA Astrophysics Data System (ADS)

    Yamaguchi, Yoshiyuki Y.; Ogawa, Shun

    2015-10-01

    Predicting the long-lasting quasistationary state for a given initial state is one of central issues in Hamiltonian systems having long-range interaction. A recently proposed method is based on the Vlasov description and uniformly redistributes the initial distribution along contours of the asymptotic effective Hamiltonian, which is defined by the obtained quasistationary state and is determined self-consistently. The method, to which we refer as the rearrangement formula, was suggested to give precise prediction under limited situations. Restricting initial states consisting of a spatially homogeneous part and small perturbation, we numerically reveal two conditions that the rearrangement formula prefers: One is a no Landau damping condition for the unperturbed homogeneous part, and the other comes from the Casimir invariants. Mechanisms of these conditions are discussed. Clarifying these conditions, we validate to use the rearrangement formula as the response theory for an external field, and we shed light on improving the theory as a nonequilibrium statistical mechanics.

  16. 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.

  17. 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…

  18. Purely optical navigation with model-based state prediction

    NASA Astrophysics Data System (ADS)

    Sendobry, Alexander; Graber, Thorsten; Klingauf, Uwe

    2010-10-01

    State-of-the-art Inertial Navigation Systems (INS) based on Micro-Electro-Mechanical Systems (MEMS) have a lack of precision especially in GPS denied environments like urban canyons or in pure indoor missions. The proposed Optical Navigation System (ONS) provides bias free ego-motion estimates using triple redundant sensor information. In combination with a model based state prediction our system is able to estimate velocity, position and attitude of an arbitrary aircraft. Simulating a high performance flow-field estimator the algorithm can compete with conventional low-cost INS. By using measured velocities instead of accelerations the system states drift behavior is not as distinctive as for an INS.

  19. SEPARATE PERSONALITY TRAITS FROM STATES TO PREDICT DEPRESSION

    PubMed Central

    Vittengl, Jeffrey; Kraft, Dolores

    2005-01-01

    Results have been inconsistent regarding the ability of personality measures to predict future depression severity levels, leading some researchers to question the validity of personality assessment, especially when patients are acutely depressed. Using a combination of regression and factor analytic techniques, we separated the variance of personality measures into stable trait and variable state-affect components. Findings supported the hypotheses that depression severity measured at different time points would correlate with both stable trait and concurrent state-affect components in personality measures, whereas change in depression severity would correlate with state changes but not with stable trait scores. Thus, personality assessments tap both state affect and trait variance, with the state-affect variance masking the trait variance when patients are depressed. PMID:12755328

  20. Unacquainted callers can predict which citizens will vote over and above citizens’ stated self-predictions

    PubMed Central

    Rogers, Todd; ten Brinke, Leanne; Carney, Dana R.

    2016-01-01

    People are regularly asked to report on their likelihoods of carrying out consequential future behaviors, including complying with medical advice, completing educational assignments, and voting in upcoming elections. Despite these stated self-predictions being notoriously unreliable, they are used to inform many strategic decisions. We report two studies examining stated self-prediction about whether citizens will vote. We find that most self-predicted voters do not actually vote despite saying they will, and that campaign callers can discern which self-predicted voters will not actually vote. In study 1 (n = 4,463), self-predicted voters rated by callers as “100% likely to vote” were 2 times more likely to actually vote than those rated unlikely to vote. Study 2 (n = 3,064) replicated this finding and further demonstrated that callers’ prediction accuracy was mediated by citizens’ nonverbal signals of uncertainty and deception. Strangers can use nonverbal signals to improve predictions of follow through on self-reported intentions—an insight of potential value for politics, medicine, and education. PMID:27217566

  1. Perisaccadic Updating of Visual Representations and Attentional States: Linking Behavior and Neurophysiology

    PubMed Central

    Marino, Alexandria C.; Mazer, James A.

    2016-01-01

    During natural vision, saccadic eye movements lead to frequent retinal image changes that result in different neuronal subpopulations representing the same visual feature across fixations. Despite these potentially disruptive changes to the neural representation, our visual percept is remarkably stable. Visual receptive field remapping, characterized as an anticipatory shift in the position of a neuron’s spatial receptive field immediately before saccades, has been proposed as one possible neural substrate for visual stability. Many of the specific properties of remapping, e.g., the exact direction of remapping relative to the saccade vector and the precise mechanisms by which remapping could instantiate stability, remain a matter of debate. Recent studies have also shown that visual attention, like perception itself, can be sustained across saccades, suggesting that the attentional control system can also compensate for eye movements. Classical remapping could have an attentional component, or there could be a distinct attentional analog of visual remapping. At this time we do not yet fully understand how the stability of attentional representations relates to perisaccadic receptive field shifts. In this review, we develop a vocabulary for discussing perisaccadic shifts in receptive field location and perisaccadic shifts of attentional focus, review and synthesize behavioral and neurophysiological studies of perisaccadic perception and perisaccadic attention, and identify open questions that remain to be experimentally addressed. PMID:26903820

  2. Disproportionate Representation in Placements of Preschoolers with Disabilities in Five Southern States

    ERIC Educational Resources Information Center

    Morrier, Michael J.; Gallagher, Peggy A.

    2011-01-01

    Special education placements for more than 69,000 preschoolers with disabilities were examined within and across five southern states. Data were gathered from the 2007 December 1st Child Count reported to the U.S. Department of Education. All states examined offered state-funded prekindergarten programs. Analyses compared disproportionate…

  3. Representation, Classification and Information Fusion for Robust and Efficient Multimodal Human States Recognition

    ERIC Educational Resources Information Center

    Li, Ming

    2013-01-01

    The goal of this work is to enhance the robustness and efficiency of the multimodal human states recognition task. Human states recognition can be considered as a joint term for identifying/verifing various kinds of human related states, such as biometric identity, language spoken, age, gender, emotion, intoxication level, physical activity, vocal…

  4. 26 CFR 301.6361-2 - Judicial and administrative proceedings; Federal representation of State interests.

    Code of Federal Regulations, 2014 CFR

    2014-04-01

    ... to judicial proceedings), and under title 28 of the United States Code (relating to the judiciary and... involving the constitution of such State is unaffected by any provision of this paragraph; however, the... in a case involving the validity of a qualified tax statute under the State constitution, the...

  5. 26 CFR 301.6361-2 - Judicial and administrative proceedings; Federal representation of State interests.

    Code of Federal Regulations, 2012 CFR

    2012-04-01

    ... to judicial proceedings), and under title 28 of the United States Code (relating to the judiciary and... involving the constitution of such State is unaffected by any provision of this paragraph; however, the... in a case involving the validity of a qualified tax statute under the State constitution, the...

  6. Prediction of magnetic substorms using a state space model

    NASA Astrophysics Data System (ADS)

    Unnikrishnan, K.

    2012-02-01

    Nonlinear dynamical models of the magnetosphere derived from observational time series data using phase space reconstruction techniques have yielded new advances in the understanding of its dynamics. Considering the solar wind-magnetosphere interaction to be a natural input-output system its dynamical features can be reconstructed on the storm time scale by using the method of time delay embedding. Here, fourteen magnetic storm intervals belonging to low/moderate and high solar activity periods are considered and a suitable state space model has designed by performing training and validation tests, for which dawn to dusk electric field (VBz) is chosen as the input, and the AL time series as the output. The percentage of the output variations that is reproduced by the model is termed as fit_model and a higher number of fit_model means a better model. The number of components m used in the state space model is varied from 1-9 and the best prediction is obtained when m=4. The fit_model values of time series used for validation are 67.96, 67.2, 72.44, and 70.89, with m=4. In the present study most of the storms considered are having Dstmax in between -100 and -300 nT, and they can be predicted well with this procedure. To reveal the prediction capability of the proposed state space model the 30 steps ahead outputs for the storm events are generated, which reasonably reproduce the observed values.

  7. State Mindfulness During Meditation Predicts Enhanced Cognitive Reappraisal.

    PubMed

    Garland, Eric L; Hanley, Adam; Farb, Norman A; Froeliger, Brett E

    2015-04-01

    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.

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

    PubMed

    Ito, Makoto; Doya, Kenji

    2015-11-01

    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.

  9. Multi-Sensor Based State Prediction for Personal Mobility Vehicles

    PubMed Central

    Gupta, Pankaj; Umata, Ichiro; Watanabe, Atsushi; Even, Jani; Suyama, Takayuki; Ishii, Shin

    2016-01-01

    This paper presents a study on multi-modal human emotional state detection while riding a powered wheelchair (PMV; Personal Mobility Vehicle) in an indoor labyrinth-like environment. The study reports findings on the habituation of human stress response during self-driving. In addition, the effects of “loss of controllability”, change in the role of the driver to a passenger, are investigated via an autonomous driving modality. The multi-modal emotional state detector sensing framework consists of four sensing devices: electroencephalograph (EEG), heart inter-beat interval (IBI), galvanic skin response (GSR) and stressor level lever (in the case of autonomous riding). Physiological emotional state measurement characteristics are organized by time-scale, in terms of capturing slower changes (long-term) and quicker changes from moment-to-moment. Experimental results with fifteen participants regarding subjective emotional state reports and commercial software measurements validated the proposed emotional state detector. Short-term GSR and heart signal characterizations captured moment-to-moment emotional state during autonomous riding (Spearman correlation; ρ = 0.6, p < 0.001). Short-term GSR and EEG characterizations reliably captured moment-to-moment emotional state during self-driving (Classification accuracy; 69.7). Finally, long-term GSR and heart characterizations were confirmed to reliably capture slow changes during autonomous riding and also of emotional state during participant resting state. The purpose of this study and the exploration of various algorithms and sensors in a structured framework is to provide a comprehensive background for multi-modal emotional state prediction experiments and/or applications. Additional discussion regarding the feasibility and utility of the possibilities of these concepts are given. PMID:27732589

  10. The Operational Hydro-meteorological Ensemble Prediction System at Meteo-France and its representation interface for the French Service for Flood Prediction (SCHAPI) : description and undergoing developments.

    NASA Astrophysics Data System (ADS)

    Rousset-Regimbeau, F.; Martin, E.; Thirel, G.; Habets, F.; Coustau, M.; Roquelaure, S.; De Saint Aubin, C.; Ardilouze, C.

    2012-04-01

    The coupled physically-based hydro-meteorological model SAFRAN-ISBA-MODCOU (SIM) is developed at Meteo-France for many years. This fully distributed catchment model is used in a pre-operational mode since 2005 for producing mid-range ensemble streamflow forecasts based on the 51-member 10-day ECMWF EPS. Improvements have been made during the past few years.. First, a statistical adaptation has been performed to improve the meteorological ensemble predictions from the ECMWF. It has been developped over a 3-year archive, and assessed over a 1-year period. Its impact on the performance of the streamflow forecasts has been calculated over 8 months of predictions. Then, a past discharges assimilation system has been implemented in order to improve the initial states of these ensemble streamflow forecasts. It has been developped in the framework of a Phd thesis, and it is now evaluated in real-time conditions. Moreover, an improvement of the physics of the ISBA model (the exponential profile of the hydraulic conductivity in the soil) was implemented. Finally, this system provides ensemble 10-day streamflow prediction to the French National Service for Flood Prediction (SCHAPI). A collaboration between Meteo-France and SCHAPI led to the development of a new website. This website shows the streamflow predictions for about 200 selected river stations over France (selected regarding their interest for flood warning) , as well as alerts for high flows (two levels of high flows corresponding to the levels of risk of the French flood warning system). It aims at providing to the French hydrological forecaters a real-time tool for mid-range flood awareness.

  11. Predicting the Kinetics of RNA Oligonucleotides Using Markov State Models.

    PubMed

    Pinamonti, Giovanni; Zhao, Jianbo; Condon, David E; Paul, Fabian; Noè, Frank; Turner, Douglas H; Bussi, Giovanni

    2017-02-14

    Nowadays different experimental techniques, such as single molecule or relaxation experiments, can provide dynamic properties of biomolecular systems, but the amount of detail obtainable with these methods is often limited in terms of time or spatial resolution. Here we use state-of-the-art computational techniques, namely, atomistic molecular dynamics and Markov state models, to provide insight into the rapid dynamics of short RNA oligonucleotides, to elucidate the kinetics of stacking interactions. Analysis of multiple microsecond-long simulations indicates that the main relaxation modes of such molecules can consist of transitions between alternative folded states, rather than between random coils and native structures. After properly removing structures that are artificially stabilized by known inaccuracies of the current RNA AMBER force field, the kinetic properties predicted are consistent with the time scales of previously reported relaxation experiments.

  12. Women's Political Representation and Welfare State Spending in 12 Capitalist Democracies

    ERIC Educational Resources Information Center

    Bolzendahl, Catherine; Brooks, Clem

    2007-01-01

    One of the sharpest criticisms of welfare state research is insufficient attention to factors relating to gender relations and inequalities. Recent scholarship has begun to address welfare state effects on gender-related outcomes, but the evaluation of theories of welfare development with respect to gender factors is somewhat less developed,…

  13. Legislative Agenda Setting for In-State Resident Tuition Policies: Immigration, Representation, and Educational Access

    ERIC Educational Resources Information Center

    McLendon, Michael K.; Mokher, Christine G.; Flores, Stella M.

    2011-01-01

    Few recent issues in higher education have been as contentious as that of legislation extending in-state college tuition benefits to undocumented students, initiatives now known as in-state resident tuition (ISRT) policies. Building on several strands of literature in political science and higher education studies, we analyze the effects of…

  14. 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

    2016-11-01

    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.

  15. Dimensionless Equation of State to Predict Microemulsion Phase Behavior.

    PubMed

    Ghosh, Soumyadeep; Johns, Russell T

    2016-09-06

    Prediction of microemulsion phase behavior for changing state variables is critical to formulation design of surfactant-oil-brine (SOB) systems. SOB systems find applications in various chemical and petroleum processes, including enhanced oil recovery. A dimensional equation-of-state (EoS) was recently presented by Ghosh and Johns1 that relied on estimation of the surfactant tail length and surface area. We give an algorithm for flash calculations for estimation of three-phase Winsor regions that is more robust, simpler, and noniterative by making the equations dimensionless so that estimates of tail length and surface area are no longer needed. We predict phase behavior as a function temperature, pressure, volume, salinity, oil type, oil-water ratio, and surfactant/alcohol concentration. The dimensionless EoS is based on coupling the HLD-NAC (Hydrophilic Lipophilic Difference-Net Average Curvature) equations with new relationships between optimum salinity and solubility. An updated HLD expression that includes pressure is also used to complete the state description. A significant advantage of the dimensionless form of the EoS over the dimensional version is that salinity scans are tuned based only on one parameter, the interfacial volume ratio. Further, stability conditions are developed in a simplified way to predict whether an overall compositions lies within the single, two-, or three-phase regions. Important new microemulsion relationships are also found, the most important of which is that optimum solubilization ratio is equal to the harmonic mean of the oil and water solubilization ratios in the type III region. Thus, only one experimental measurement is needed in the three-phase zone to estimate the optimum solubilization ratio, a result which can aid experimental design and improve estimates of optimum from noisy data. Predictions with changing state variables are illustrated by comparison to experimental data using standard diagrams including a new type

  16. CFSv2 Driven Seasonal Drought Prediction over the United States

    NASA Astrophysics Data System (ADS)

    Luo, L.; Tang, W.; Tan, P.; Lin, Z.

    2012-12-01

    Skillful seasonal hydrologic predictions are useful in managing water resources, preparing for droughts and their impacts, energy planning, and many other related sectors. Earlier studies have established a VIC-based seasonal hydrological ensemble prediction system for the United States. In this study, we focused on upgrading and improving the system for predicting droughts in the US in the following ways: 1) Seasonal climate forecast from NCEP Climate Forecast System (CFS) has been replaced by the second version of CFS (CFSv2) as the major driving climate forecast for downscaling; 2) daily temperature and precipitation fields are now used instead of monthly average fields; 3) The downscaling procedure now produces posterior distributions of temperature and precipitation at multiple spatial and temporal scales. Because of these upgrades, we expect improved forecast skills and the capability to produce more frequent updates and seasonal drought predictions over the US. The performance of the new system is evaluated with both realtime drought forecast and retrospective forecast of historical events.

  17. Oscillatory Brain State Predicts Variability in Working Memory

    PubMed Central

    Stokes, Mark G.; Walther, Lena; Nobre, Anna C.

    2014-01-01

    Our capacity to remember and manipulate objects in working memory (WM) is severely limited. However, this capacity limitation is unlikely to be fixed because behavioral models indicate variability from trial to trial. We investigated whether fluctuations in neural excitability at stimulus encoding, as indexed by low-frequency oscillations (in the alpha band, 8–14 Hz), contribute to this variability. Specifically, we hypothesized that the spontaneous state of alpha band activity would correlate with trial-by-trial fluctuations in visual WM. Electroencephalography recorded from human observers during a visual WM task revealed that the prestimulus desynchronization of alpha oscillations predicts the accuracy of memory recall on a trial-by-trial basis. A model-based analysis indicated that this effect arises from a modulation in the precision of memorized items, but not the likelihood of remembering them (the recall rate). The phase of posterior alpha oscillations preceding the memorized item also predicted memory accuracy. Based on correlations between prestimulus alpha levels and stimulus-related visual evoked responses, we speculate that the prestimulus state of the visual system prefigures a cascade of state-dependent processes, ultimately affecting WM-guided behavior. Overall, our results indicate that spontaneous changes in cortical excitability can have profound consequences for higher visual cognition. PMID:24899697

  18. Anonymous predictive testing for Huntington's disease in the United States.

    PubMed

    Visintainer, C L; Matthias-Hagen, V; Nance, M A

    2001-01-01

    The widespread use of a predictive genetic test for Huntington's disease (HD) since 1993 has brought to the forefront issues regarding genetic privacy. Although the possibility of anonymous genetic testing has been discussed, its use in the United States has not been described previously. We review the experiences of 11 genetics specialists with anonymous predictive testing for HD. We found that more men than women requested anonymous testing, for reasons that more often related to personal privacy than to insurance or discrimination concerns. A number of approaches to anonymity were used, and genetics specialists varied in the degree to which they were comfortable with the process. A number of legal, medical, and practical questions are raised, which will require resolution if anonymous testing is to be performed with a greater frequency in the future.

  19. 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

  20. Digital Representation of Materials Grain Structure

    DTIC Science & Technology

    2010-01-01

    distribution unlimited. 13. SUPPLEMENTARY NOTES Technical paper submitted to Computational Methods for Microstructure--Property Prediction. PAO...property relationships. In order to achieve these goals, the development of digital microstructure models in conjunction with computational methods for...chapter to discuss the state-of-the-art methods and current limitations in the field of microstructure representation. Specific focus will be paid to

  1. Representation and evaluation of aerosol mixing state in a climate model

    NASA Astrophysics Data System (ADS)

    Bauer, S. E.; Prather, K. A.; Ault, A. P.

    2011-12-01

    Aerosol particles in the atmosphere are composed out of multiple chemical species. The aerosol mixing state is an important aerosol property that will determine the interaction of aerosols with the climate system via radiative forcings and cloud activation. Through the introduction of aerosol microphysics into climate models, aerosol mixing state is by now taken into account to a certain extend in climate models, and evaluation of mixing state is the next challenge. Here we use data from the Aerosol Time of Flight Mass Spectrometer (ATOFMS) and compare the results to the GISS-modelE-MATRIX model, a global climate model including a detailed aerosol micro-physical scheme. We use data from various field campaigns probing, urban, rural and maritime air masses and compare those to climatological and nudged simulations for the years 2005 to 2009. ATOFMS provides information about the size distributions of several mixing state classes, including the chemical components of black and organic carbon, sulfates, dust and salts. MATRIX simulates 16 aerosol populations, which definitions are based on mixing state. We have grouped ATOFMS and MATRIX data into similar mixing state classes and compare the size resolved number concentrations against each other. As a first result we find that climatological simulations are rather difficult to evaluate with field data, and that nudged simulations give a much better agreement. However this is not just caused by the better fit of natural - meteorological driven - aerosol components, but also due to the interaction between meteorology and aerosol formation. The model seems to get the right amount of mixing state of black carbon material with sulfate and organic components, but seems to always overestimate the fraction of black carbon that is externally mixed. In order to understand this bias between model and the ATOFMS data, we will look into microphysical processes near emission sources and investigate the climate relevance of these sub

  2. Finding Matrix Product State Representations of Highly Excited Eigenstates of Many-Body Localized Hamiltonians

    NASA Astrophysics Data System (ADS)

    Yu, Xiongjie; Pekker, David; Clark, Bryan K.

    2017-01-01

    A key property of many-body localized Hamiltonians is the area law entanglement of even highly excited eigenstates. Matrix product states (MPS) can be used to efficiently represent low entanglement (area law) wave functions in one dimension. An important application of MPS is the widely used density matrix renormalization group (DMRG) algorithm for finding ground states of one-dimensional Hamiltonians. Here, we develop two algorithms, the shift-and-invert MPS (SIMPS) and excited state DMRG which find highly excited eigenstates of many-body localized Hamiltonians. Excited state DMRG uses a modified sweeping procedure to identify eigenstates, whereas SIMPS applies the inverse of the shifted Hamiltonian to a MPS multiple times to project out the targeted eigenstate. To demonstrate the power of these methods, we verify the breakdown of the eigenstate thermalization hypothesis in the many-body localized phase of the random field Heisenberg model, show the saturation of entanglement in the many-body localized phase, and generate local excitations.

  3. Ethnic Labels, Latino Lives. Identity and the Politics of (Re)Presentation in the United States.

    ERIC Educational Resources Information Center

    Oboler, Suzanne

    The history and current use of the label "Hispanic" are discussed in this exploration of the myth of cultural and national homogeneity among people of Latin American descent in the United States. The historical process of labeling groups of individuals is discussed, and how ethnic labels affect the meaning of citizenship and the struggle…

  4. Conal representation of quantum states and non-trace-preserving quantum operations

    SciTech Connect

    Arrighi, Pablo; Patricot, Christophe

    2003-10-01

    We represent generalized density matrices of a d-complex dimensional quantum system as a subcone of a real pointed cone of revolution in R{sup d{sup 2}}, or indeed a Minkowskian cone in E{sup 1,d{sup 2}-1}. Generalized pure states correspond to certain future-directed lightlike vectors of E{sup 1,d{sup 2}-1}. This extension of the generalized Bloch sphere enables us to cater for non-trace-preserving quantum operations, and in particular to view the per-outcome effects of generalized measurements. We show that these consist of the product of an orthogonal transform about the axis of the cone of revolution and a positive real linear transform. We give detailed formulas for the one-qubit case and express the post-measurement states in terms of the initial-state vectors and measurement vectors. We apply these results in order to find the information gain versus disturbance trade-off in the case of two equiprobable pure states. Thus we recover Fuchs and Peres's formula in an elegant manner.

  5. Towards Integrating AI Story Controllers and Game Engines: Reconciling World State Representations

    DTIC Science & Technology

    2006-01-01

    the user. In this paper, I describe a general technique for translating between an arbi- trary game engine’s proprietary and procedural world state...story arc. That is, computer games use story to motivate action but typically have little or no branching. AI techniques have been applied to the...problem of inter- active storytelling for entertainment and training. A com- mon technique among AI research in interactive storytelling is to

  6. 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.

  7. Prediction of subjective states from psychophysiology: a multivariate approach.

    PubMed

    Fairclough, Stephen H; Venables, Louise

    2006-01-01

    Biocybernetic systems utilise real-time changes in psychophysiology in order to adapt aspects of computer control and functionality, e.g. adaptive automation. This approach to system design is based upon an assumption that psychophysiological variations represent implicit fluctuations in the subjective state of the operator, e.g. mood, motivation, cognitions. A study was performed to investigate the convergent validity between psychophysiological measurement and changes in the subjective status of the individual. Thirty-five participants performed a demanding version of the Multi-Attribute Task Battery (MATB) over four consecutive 20-min blocks. A range of psychophysiological data were collected (EEG, ECG, skin conductance level (SCL), EOG, respiratory rate) and correlated with changes in subjective state as measured by the Dundee Stress State Questionnaire (DSSQ). MATB performance was stable across time-on-task; psychophysiological activity exhibited expected changes due to sustained performance. The DSSQ was analysed in terms of three subjective meta-factors: Task Engagement, Distress and Worry. Multiple regression analyses revealed that psychophysiology predicted a substantial proportion of the variance for both Task Engagement and Distress but not for the Worry meta-factor. The consequences for the development of biocybernetic systems are discussed.

  8. Validation of Atmospheric Dynamics (VADY) - representation of circulation types/dynamical modes in the decadal-prediction model system of MPI-ESM

    NASA Astrophysics Data System (ADS)

    Lang, Benjamin; Jacobeit, Jucundus; Beck, Christoph; Philipp, Andreas

    2016-04-01

    The climate research program "Medium-range Climate Predictions" (MiKlip), funded by the Federal Ministry of Education and Research in Germany (BMBF), has the aim to improve a climate model system (MPI-ESM) in such a way that it can provide reliable decadal predictions of climate, including extreme weather events. A substantial part of the development process is a comprehensive model validation. Within MiKlip, it includes comparisons of model simulations and observations in order to allow statements about the performance of the model and to give particular recommendations for the further development of the model. The research project "Validation of Atmospheric Dynamics" (VADY), conducted by the cooperation partners "Institute of Geography at the University of Augsburg" (IGUA) and the "German Aerospace Centre" (DLR), contributes to model validation within MiKlip with a special focus on atmospheric waves (DLR) and circulation dynamics (IGUA). Within the framework of VADY, DLR validates the representation of atmospheric waves on different levels and scales based on suitable activity indices (e.g. the so-called large-scale dynamical activity index (LDAI), which is a measure for the activity of planetary waves). The focus of IGUA is on the model validation with respect to the representation of atmospheric circulation types, dynamical modes and the teleconnectivity of the atmospheric circulation. The present contribution provides results of the model validation concerning circulation types/dynamical modes. Results are shown for both the frequency of occurrence and internal characteristics (e. g. persistence or intensity), and for different classification methods (e. g. based on PCA or clustering techniques). The representation of circulation types/dynamical modes will be compared for different generations of the MPI-ESM decadal-prediction model (baseline0, baseline1, prototype) in order to clarify both advances and limitations in the development of the model. Furthermore

  9. Representation of image distortion by Moiré fringes at phase singularity state.

    PubMed

    Samavati, Katayoon; Taghi Tavassoly, M; Ghomi, Hamid

    2017-01-10

    When a grating is imaged by an optical imaging system, due to the aberrations of the system, the parameters of the image grating suffer minute gradual changes across the image. Superimposing an ideal grating image over the real grating image at the phase singularity state of the two gratings leads to phase contours, special Moiré fringes, which directly represent the distortions over the image. In this report, after a brief review of the required theoretical bases, we show when the parameters of a grating change linearly the corresponding Moiré fringes at the singularity state are represented by quadratic functions, and for nonlinear changes higher order functions are involved. Thus, by imposing desired changes on the parameters of a grating one can produce Moiré fringes satisfying functions of required orders. In the experimental part of the report we apply the technique to evaluate the image distortions imposed by a conventional camera and cameras installed in a mobile and in a tablet.

  10. Discontinuous steady-state analytical solutions of the Boussinesq equation and their numerical representation by MODFLOW.

    PubMed

    Zaidel, Jacob

    2013-01-01

    Known analytical solutions of groundwater flow equations are routinely used for verification of computer codes. However, these analytical solutions (e.g., the Dupuit solution for the steady-state unconfined unidirectional flow in a uniform aquifer with a flat bottom) represent smooth and continuous water table configurations, simulating which does not pose any significant problems for the numerical groundwater flow models, like MODFLOW. One of the most challenging numerical cases for MODFLOW arises from drying-rewetting problems often associated with abrupt changes in the elevations of impervious base of a thin unconfined aquifer. Numerical solutions of groundwater flow equations cannot be rigorously verified for such cases due to the lack of corresponding exact analytical solutions. Analytical solutions of the steady-state Boussinesq equation, associated with the discontinuous water table configurations over a stairway impervious base, are presented in this article. Conditions resulting in such configurations are analyzed and discussed. These solutions appear to be well suited for testing and verification of computer codes. Numerical solutions, obtained by the latest versions of MODFLOW (MODFLOW-2005 and MODFLOW-NWT), are compared with the presented discontinuous analytical solutions. It is shown that standard MODFLOW-2005 code (as well as MODFLOW-2000 and older versions) has significant convergence problems simulating such cases. The problems manifest themselves either in a total convergence failure or erroneous results. Alternatively, MODFLOW-NWT, providing a good match to the presented discontinuous analytical solutions, appears to be a more reliable and appropriate code for simulating abrupt changes in water table elevations.

  11. Control and Transfer of Entanglement between Two Atoms Driven by Classical Fields under Dressed-State Representation

    NASA Astrophysics Data System (ADS)

    Liao, Qing-Hong; Zhang, Qi; Xu, Juan; Yan, Qiu-Rong; Liu, Ye; Chen, An

    2016-06-01

    We have studied the dynamics and transfer of the entanglement of the two identical atoms simultaneously interacting with vacuum field by employing the dressed-state representation. The two atoms are driven by classical fields. The influence of the initial entanglement degree of two atoms, the coupling strength between the atom and the classical field and the detuning between the atomic transition frequency and the frequency of classical field on the entanglement and atomic linear entropy is discussed. The initial entanglement of the two atoms can be transferred into the entanglement between the atom and cavity field when the dissipation is neglected. The maximally entangled state between the atoms and cavity field can be obtained under some certain conditions. The time of disentanglement of two atoms can be controlled and manipulated by adjusting the detuning and classical driving fields. Moreover, the larger the cavity decay rate is, the more quickly the entanglement of the two atoms decays. Supported by National Natural Science Foundation of China under Grant Nos. 11247213, 61368002, 11304010, 11264030, 61168001, China Postdoctoral Science Foundation under Grant No. 2013M531558, Jiangxi Postdoctoral Research Project under Grant No. 2013KY33, the Natural Science Foundation of Jiangxi Province under Grant No. 20142BAB217001, the Foundation for Young Scientists of Jiangxi Province (Jinggang Star) under Grant No. 20122BCB23002, the Research Foundation of the Education Department of Jiangxi Province under Grant Nos. GJJ13051, GJJ13057, and the Graduate Innovation Special Fund of Nanchang University under Grant No. cx2015137

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

    PubMed Central

    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. PMID:24478671

  13. Improved secondary structure predictions for a nicotinic receptor subunit: incorporation of solvent accessibility and experimental data into a two-dimensional representation.

    PubMed Central

    Le Novère, N; Corringer, P J; Changeux, J P

    1999-01-01

    portion of the complete receptor were incorporated into the model. This led to a proposed two-dimensional representation of the secondary structure in which the peptide chain of the extracellular domain winds alternatively between the two interfaces of the subunit. Although this representation is not a tertiary structure and does not lead to predictions of specific beta-beta interaction, it should provide a basic framework for further mutagenesis investigations and for fold recognition (threading) searches. PMID:10233052

  14. 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

  15. Degradation of Cortical Representations during Encoding following Sleep Deprivation.

    PubMed

    Poh, Jia-Hou; Chee, Michael W L

    2017-02-01

    A night of total sleep deprivation (TSD) reduces task-related activation of fronto-parietal and higher visual cortical areas. As this reduction in activation corresponds to impaired attention and perceptual processing, it might also be associated with poorer memory encoding. Related animal work has established that cortical columns stochastically enter an 'off' state in sleep deprivation, leading to predictions that neural representations are less stable and distinctive following TSD. To test these predictions participants incidentally encoded scene images while undergoing fMRI, either during rested wakefulness (RW) or after TSD. In scene-selective PPA, TSD reduced stability of neural representations across repetition. This was accompanied by poorer subsequent memory. Greater representational stability benefitted subsequent memory in RW but not TSD. Even for items subsequently recognized, representational distinctiveness was lower in TSD, suggesting that quality of encoding is degraded. Reduced representational stability and distinctiveness are two novel mechanisms by which TSD can contribute to poorer memory formation.

  16. 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…

  17. 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…

  18. 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…

  19. Non-label immune cell state prediction using Raman spectroscopy

    PubMed Central

    Ichimura, Taro; Chiu, Liang-da; Fujita, Katsumasa; Machiyama, Hiroaki; Yamaguchi, Tomoyuki; Watanabe, Tomonobu M.; Fujita, Hideaki

    2016-01-01

    The acquired immune system, mainly composed of T and B lymphocytes, plays a key role in protecting the host from infection. It is important and technically challenging to identify cell types and their activation status in living and intact immune cells, without staining or killing the cells. Using Raman spectroscopy, we succeeded in discriminating between living T cells and B cells, and visualized the activation status of living T cells without labeling. Although the Raman spectra of T cells and B cells were similar, they could be distinguished by discriminant analysis of the principal components. Raman spectra of activated T cells with anti-CD3 and anti-CD28 antibodies largely differed compared to that of naïve T cells, enabling the prediction of T cell activation status at a single cell level. Our analysis revealed that the spectra of individual T cells gradually change from the pattern of naïve T cells to that of activated T cells during the first 24 h of activation, indicating that changes in Raman spectra reflect slow changes rather than rapid changes in cell state during activation. Our results indicate that the Raman spectrum enables the detection of dynamic changes in individual cell state scattered in a heterogeneous population. PMID:27876845

  20. Sulfur Mass Balances of Forested Catchments: Improving Predictions of Stream Sulfate Concentrations Through Better Representation of Soil Storage and Release

    NASA Astrophysics Data System (ADS)

    Scanlon, T. M.; Rice, K. C.; Riscassi, A.; Cosby, B. J., Jr.

    2015-12-01

    Sulfur dioxide (SO2) emissions in the eastern United States have declined by more than 80% since 1970, when the Clean Air Act first established limits on emissions from stationary and mobile sources. In many areas throughout the northeastern U.S., the resulting declines in sulfate (SO42-) deposition have been accompanied by declines in stream SO42- concentrations. In the southeastern U.S., however, declines in stream SO42- concentrations have not been observed on a widespread basis. In fact, SO42- concentrations continue to increase in many southeastern streams despite decades of declining deposition. This difference in behavior between northeastern and southeastern streams, owing to the distinct geological histories of their catchment soils, was anticipated by the Direct/Delayed Response Project initiated by the U.S. EPA during the early 1980s. At that time, understanding of how catchments store and release SO42- was mostly grounded in theory. Now, with the accumulation of long-term stream chemistry and hydrological datasets in forested catchments, we may develop an empirical basis for characterizing catchment storage and release of SO42-. In particular, are whole-catchment isotherms that described the partitioning between adsorbed and dissolved SO42- (1) linear or non-linear and (2) reversible or irreversible? How do these isotherms vary on a geographical basis? We apply mass balance combined with a simple theoretical framework to infer whole-catchment SO42- isotherms in Virginia and New England. Knowledge of this key soil geochemical property is needed to improve predictions of how catchments will store and export SO42- under changing levels of atmospheric deposition.

  1. Some Problems of Knowledge Representation in an Authoring Environment: Exteriorization, Anomalous State Meta-Cognition and Self-Confrontation.

    ERIC Educational Resources Information Center

    McAleese, Ray

    1985-01-01

    Summarizes findings of a collection of research studies at the University of Aberdeen (Scotland) aiding fundamental understanding of knowledge representation and its applications. Issues arising when a knowledge representation system is incorporated into an authoring language are discussed, including the problems of exteriorization, metacognition,…

  2. 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)

  3. Calculation of aerosol optical properties under different assumptions on mixing state, refractive index, density and hygroscopicity: uncertainties and importance of representation of aerosol mixing state

    NASA Astrophysics Data System (ADS)

    Curci, Gabriele

    2015-04-01

    The calculation of aerosol optical properties from aerosol mass is a process subject to uncertainty related to necessary assumptions on the treatment of the chemical species mixing state, density, refractive index, and hygroscopic growth. We used the FlexAOD post-processing tool to calculate the optical properties (aerosol optical depth (AOD), single scattering albedo (SSA) and asymmetry parameter (g)) from chemistry-transport model aerosol profiles, using a wide range of assumptions on aerosol chemical and physical properties. We calculated that the most important factor of uncertainty is the assumption about the mixing state, for which we estimate an uncertainty of 30-35% on the simulated aerosol optical depth (AOD) and single scattering albedo (SSA). The choice of the core composition in the core-shell representation is of minor importance for calculation of AOD, while it is critical for the SSA. Other factors of uncertainty tested here have a maximum average impact of 10% each on calculated AOD, and an impact of a few percent on SSA and g. We then tested simple parameterizations of the aerosol mixing state, expressed as a function of the aerosol aging, and verified that they may be helpful in reducing the uncertainty when comparing simulations with AERONET retrievals.

  4. Convolution representation of the relation between total electron density and that of s states in closed-shell atoms

    NASA Astrophysics Data System (ADS)

    Pucci, R.; March, N. H.

    1987-01-01

    Motivated by the Coulomb-field result that the derivative of the density ρ(r) for an arbitrary number of closed shells is directly proportional to the s-state density ρs(r), we have explored for closed-shell atoms a convolution relation between ρs(r) and ∂ρ/∂r. This relation is most readily expressed in K space, and we thereby establish certain relations between the scattering factors f(K) and fs(K) corresponding to total density and s density, respectively. The method is illustrated by using near-Hartree-Fock accuracy data of Clementi for closed-shell atoms Ne and Ar. For the Hartree-Fock theory, it is shown that at large r, ρs(r)~r-4ρ(r). Use is made in the convolution representation of the electron-nuclear potential energy of the closed-shell atom and the second derivative ∂2ρ/∂r2 evaluated at the nucleus.

  5. Modeling of Cancer Stem Cell State Transitions Predicts Therapeutic Response

    PubMed Central

    Sehl, Mary E.; Shimada, Miki; Landeros, Alfonso; Lange, Kenneth; Wicha, Max S.

    2015-01-01

    Cancer stem cells (CSCs) possess capacity to both self-renew and generate all cells within a tumor, and are thought to drive tumor recurrence. Targeting the stem cell niche to eradicate CSCs represents an important area of therapeutic development. The complex nature of many interacting elements of the stem cell niche, including both intracellular signals and microenvironmental growth factors and cytokines, creates a challenge in choosing which elements to target, alone or in combination. Stochastic stimulation techniques allow for the careful study of complex systems in biology and medicine and are ideal for the investigation of strategies aimed at CSC eradication. We present a mathematical model of the breast cancer stem cell (BCSC) niche to predict population dynamics during carcinogenesis and in response to treatment. Using data from cell line and mouse xenograft experiments, we estimate rates of interconversion between mesenchymal and epithelial states in BCSCs and find that EMT/MET transitions occur frequently. We examine bulk tumor growth dynamics in response to alterations in the rate of symmetric self-renewal of BCSCs and find that small changes in BCSC behavior can give rise to the Gompertzian growth pattern observed in breast tumors. Finally, we examine stochastic reaction kinetic simulations in which elements of the breast cancer stem cell niche are inhibited individually and in combination. We find that slowing self-renewal and disrupting the positive feedback loop between IL-6, Stat3 activation, and NF-κB signaling by simultaneous inhibition of IL-6 and HER2 is the most effective combination to eliminate both mesenchymal and epithelial populations of BCSCs. Predictions from our model and simulations show excellent agreement with experimental data showing the efficacy of combined HER2 and Il-6 blockade in reducing BCSC populations. Our findings will be directly examined in a planned clinical trial of combined HER2 and IL-6 targeted therapy in HER2

  6. State estimation and prediction using clustered particle filters

    PubMed Central

    Lee, Yoonsang; Majda, Andrew J.

    2016-01-01

    Particle filtering is an essential tool to improve uncertain model predictions by incorporating noisy observational data from complex systems including non-Gaussian features. A class of particle filters, clustered particle filters, is introduced for high-dimensional nonlinear systems, which uses relatively few particles compared with the standard particle filter. The clustered particle filter captures non-Gaussian features of the true signal, which are typical in complex nonlinear dynamical systems such as geophysical systems. The method is also robust in the difficult regime of high-quality sparse and infrequent observations. The key features of the clustered particle filtering are coarse-grained localization through the clustering of the state variables and particle adjustment to stabilize the method; each observation affects only neighbor state variables through clustering and particles are adjusted to prevent particle collapse due to high-quality observations. The clustered particle filter is tested for the 40-dimensional Lorenz 96 model with several dynamical regimes including strongly non-Gaussian statistics. The clustered particle filter shows robust skill in both achieving accurate filter results and capturing non-Gaussian statistics of the true signal. It is further extended to multiscale data assimilation, which provides the large-scale estimation by combining a cheap reduced-order forecast model and mixed observations of the large- and small-scale variables. This approach enables the use of a larger number of particles due to the computational savings in the forecast model. The multiscale clustered particle filter is tested for one-dimensional dispersive wave turbulence using a forecast model with model errors. PMID:27930332

  7. SU (N ) Heisenberg model with multicolumn representations

    NASA Astrophysics Data System (ADS)

    Okubo, Tsuyoshi; Harada, Kenji; Lou, Jie; Kawashima, Naoki

    2015-10-01

    The SU (N ) symmetric antiferromagnetic Heisenberg model with multicolumn representations on the two-dimensional square lattice is investigated by quantum Monte Carlo simulations. For the representation of a Young diagram with two columns, we confirm that a valence-bond solid (VBS) order appears as soon as the Néel order disappears at N =10 , indicating no intermediate phase. In the case of the representation with three columns, there is no evidence for either the Néel or the VBS ordering for N ≥15 . This is actually consistent with the large-N theory, which predicts that the VBS state immediately follows the Néel state, because the expected spontaneous order is too weak to be detected.

  8. Non-parametric representation and prediction of single- and multi-shell diffusion-weighted MRI data using Gaussian processes.

    PubMed

    Andersson, Jesper L R; Sotiropoulos, Stamatios N

    2015-11-15

    Diffusion MRI offers great potential in studying the human brain microstructure and connectivity. However, diffusion images are marred by technical problems, such as image distortions and spurious signal loss. Correcting for these problems is non-trivial and relies on having a mechanism that predicts what to expect. In this paper we describe a novel way to represent and make predictions about diffusion MRI data. It is based on a Gaussian process on one or several spheres similar to the Geostatistical method of "Kriging". We present a choice of covariance function that allows us to accurately predict the signal even from voxels with complex fibre patterns. For multi-shell data (multiple non-zero b-values) the covariance function extends across the shells which means that data from one shell is used when making predictions for another shell.

  9. Predicting standard-dose PET image from low-dose PET and multimodal MR images using mapping-based sparse representation

    NASA Astrophysics Data System (ADS)

    Wang, Yan; Zhang, Pei; An, Le; Ma, Guangkai; Kang, Jiayin; Shi, Feng; Wu, Xi; Zhou, Jiliu; Lalush, David S.; Lin, Weili; Shen, Dinggang

    2016-01-01

    Positron emission tomography (PET) has been widely used in clinical diagnosis for diseases and disorders. To obtain high-quality PET images requires a standard-dose radionuclide (tracer) injection into the human body, which inevitably increases risk of radiation exposure. One possible solution to this problem is to predict the standard-dose PET image from its low-dose counterpart and its corresponding multimodal magnetic resonance (MR) images. Inspired by the success of patch-based sparse representation (SR) in super-resolution image reconstruction, we propose a mapping-based SR (m-SR) framework for standard-dose PET image prediction. Compared with the conventional patch-based SR, our method uses a mapping strategy to ensure that the sparse coefficients, estimated from the multimodal MR images and low-dose PET image, can be applied directly to the prediction of standard-dose PET image. As the mapping between multimodal MR images (or low-dose PET image) and standard-dose PET images can be particularly complex, one step of mapping is often insufficient. To this end, an incremental refinement framework is therefore proposed. Specifically, the predicted standard-dose PET image is further mapped to the target standard-dose PET image, and then the SR is performed again to predict a new standard-dose PET image. This procedure can be repeated for prediction refinement of the iterations. Also, a patch selection based dictionary construction method is further used to speed up the prediction process. The proposed method is validated on a human brain dataset. The experimental results show that our method can outperform benchmark methods in both qualitative and quantitative measures.

  10. Transition-state theory predicts clogging at the microscale

    PubMed Central

    Laar, T. van de; Klooster, S. ten; Schroën, K.; Sprakel, J.

    2016-01-01

    Clogging is one of the main failure mechanisms encountered in industrial processes such as membrane filtration. Our understanding of the factors that govern the build-up of fouling layers and the emergence of clogs is largely incomplete, so that prevention of clogging remains an immense and costly challenge. In this paper we use a microfluidic model combined with quantitative real-time imaging to explore the influence of pore geometry and particle interactions on suspension clogging in constrictions, two crucial factors which remain relatively unexplored. We find a distinct dependence of the clogging rate on the entrance angle to a membrane pore which we explain quantitatively by deriving a model, based on transition-state theory, which describes the effect of viscous forces on the rate with which particles accumulate at the channel walls. With the same model we can also predict the effect of the particle interaction potential on the clogging rate. In both cases we find excellent agreement between our experimental data and theory. A better understanding of these clogging mechanisms and the influence of design parameters could form a stepping stone to delay or prevent clogging by rational membrane design. PMID:27328715

  11. Predicting red wolf release success in the southeastern United States

    USGS Publications Warehouse

    van Manen, Frank T.; Crawford, Barron A.; Clark, Joseph D.

    2000-01-01

    Although the red wolf (Canis rufus) was once found throughout the southeastern United States, indiscriminate killing and habitat destruction reduced its range to a small section of coastal Texas and Louisiana. Wolves trapped from 1973 to 1980 were taken to establish a captive breeding program that was used to repatriate 2 mainland and 3 island red wolf populations. We collected data from 320 red wolf releases in these areas and classified each as a success or failure based on survival and reproductive criteria, and whether recaptures were necessary to resolve conflicts with humans. We evaluated the relations between release success and conditions at the release sites, characteristics of released wolves, and release procedures. Although <44% of the variation in release success was explained, model performance based on jackknife tests indicated a 72-80% correct prediction rate for the 4 operational models we developed. The models indicated that success was associated with human influences on the landscape and the level of wolf habituation to humans prior to release. We applied the models to 31 prospective areas for wolf repatriation and calculated an index of release success for each area. Decision-makers can use these models to objectively rank prospective release areas and compare strengths and weaknesses of each.

  12. Complex multireference configuration interaction calculations employing a coupled diabatic representation for the 2Pi(g) resonance states of N2(-).

    PubMed

    Honigmann, Michael; Buenker, Robert J; Liebermann, Heinz-Peter

    2009-07-21

    Complex multireference configuration interaction calculations have been carried out for the lowest resonance states of (2)Pi(g) symmetry of the N(2)(-) molecule. It is shown that there is a forbidden crossing between the two lowest roots of this symmetry and that a satisfactory calculation of vibrational levels and cross sections therefore requires inclusion of both states and the coupling between them. A diabatic representation for the two (2)Pi(g) states was determined and vibronic calculations of the cross sections for vibrational excitation were carried out with a two-dimensional complex variational program.

  13. 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

  14. 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.

  15. 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

  16. Predictive sufficiency and the use of stored internal state

    NASA Technical Reports Server (NTRS)

    Musliner, David J.; Durfee, Edmund H.; Shin, Kang G.

    1994-01-01

    In all embedded computing systems, some delay exists between sensing and acting. By choosing an action based on sensed data, a system is essentially predicting that there will be no significant changes in the world during this delay. However, the dynamic and uncertain nature of the real world can make these predictions incorrect, and thus, a system may execute inappropriate actions. Making systems more reactive by decreasing the gap between sensing and action leaves less time for predictions to err, but still provides no principled assurance that they will be correct. Using the concept of predictive sufficiency described in this paper, a system can prove that its predictions are valid, and that it will never execute inappropriate actions. In the context of our CIRCA system, we also show how predictive sufficiency allows a system to guarantee worst-case response times to changes in its environment. Using predictive sufficiency, CIRCA is able to build real-time reactive control plans which provide a sound basis for performance guarantees that are unavailable with other reactive systems.

  17. Mixed LICORS: A Nonparametric Algorithm for Predictive State Reconstruction

    PubMed Central

    Goerg, Georg M.; Shalizi, Cosma Rohilla

    2015-01-01

    We introduce mixed LICORS, an algorithm for learning nonlinear, high-dimensional dynamics from spatio-temporal data, suitable for both prediction and simulation. Mixed LICORS extends the recent LICORS algorithm (Goerg and Shalizi, 2012) from hard clustering of predictive distributions to a non-parametric, EM-like soft clustering. This retains the asymptotic predictive optimality of LICORS, but, as we show in simulations, greatly improves out-of-sample forecasts with limited data. The new method is implemented in the publicly-available R package LICORS. PMID:26279743

  18. Text as data: using text-based features for proteins representation and for computational prediction of their characteristics.

    PubMed

    Shatkay, Hagit; Brady, Scott; Wong, Andrew

    2015-03-01

    The current era of large-scale biology is characterized by a fast-paced growth in the number of sequenced genomes and, consequently, by a multitude of identified proteins whose function has yet to be determined. Simultaneously, any known or postulated information concerning genes and proteins is part of the ever-growing published scientific literature, which is expanding at a rate of over a million new publications per year. Computational tools that attempt to automatically predict and annotate protein characteristics, such as function and localization patterns, are being developed along with systems that aim to support the process via text mining. Most work on protein characterization focuses on features derived directly from protein sequence data. Protein-related work that does aim to utilize the literature typically concentrates on extracting specific facts (e.g., protein interactions) from text. In the past few years we have taken a different route, treating the literature as a source of text-based features, which can be employed just as sequence-based protein-features were used in earlier work, for predicting protein subcellular location and possibly also function. We discuss here in detail the overall approach, along with results from work we have done in this area demonstrating the value of this method and its potential use.

  19. Cultural Representations in/as the Global Studies Curriculum: Seeing and Knowing China in the United States

    ERIC Educational Resources Information Center

    Mungur, Amy

    2014-01-01

    This study is an examination of how two popular magazines, "National Geographic" and "Life" magazine, and one educational journal, "Social Education," perform the work of representation in general, and representing China more specifically. Drawing on postcolonial theorists (Blaut, 1993; Said, 1978; Tchen, 1999; wa…

  20. Representation and Analysis of Chemistry Core Ideas in Science Education Standards between China and the United States

    ERIC Educational Resources Information Center

    Wan, Yanlan; Bi, Hualin

    2016-01-01

    Chemistry core ideas play an important role in students' chemistry learning. On the basis of the representations of chemistry core ideas about "substances" and "processes" in the Chinese Chemistry Curriculum Standards (CCCS) and the U.S. Next Generation Science Standards (NGSS), we conduct a critical comparison of chemistry…

  1. 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.

  2. 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.

  3. Basophile: Accurate Fragment Charge State Prediction Improves Peptide Identification Rates

    SciTech Connect

    Wang, Dong; Dasari, Surendra; Chambers, Matthew C.; Holman, Jerry D.; Chen, Kan; Liebler, Daniel; Orton, Daniel J.; Purvine, Samuel O.; Monroe, Matthew E.; Chung, Chang Y.; Rose, Kristie L.; Tabb, David L.

    2013-03-07

    In shotgun proteomics, database search algorithms rely on fragmentation models to predict fragment ions that should be observed for a given peptide sequence. The most widely used strategy (Naive model) is oversimplified, cleaving all peptide bonds with equal probability to produce fragments of all charges below that of the precursor ion. More accurate models, based on fragmentation simulation, are too computationally intensive for on-the-fly use in database search algorithms. We have created an ordinal-regression-based model called Basophile that takes fragment size and basic residue distribution into account when determining the charge retention during CID/higher-energy collision induced dissociation (HCD) of charged peptides. This model improves the accuracy of predictions by reducing the number of unnecessary fragments that are routinely predicted for highly-charged precursors. Basophile increased the identification rates by 26% (on average) over the Naive model, when analyzing triply-charged precursors from ion trap data. Basophile achieves simplicity and speed by solving the prediction problem with an ordinal regression equation, which can be incorporated into any database search software for shotgun proteomic identification.

  4. Basophile: Accurate Fragment Charge State Prediction Improves Peptide Identification Rates

    DOE PAGES

    Wang, Dong; Dasari, Surendra; Chambers, Matthew C.; ...

    2013-03-07

    In shotgun proteomics, database search algorithms rely on fragmentation models to predict fragment ions that should be observed for a given peptide sequence. The most widely used strategy (Naive model) is oversimplified, cleaving all peptide bonds with equal probability to produce fragments of all charges below that of the precursor ion. More accurate models, based on fragmentation simulation, are too computationally intensive for on-the-fly use in database search algorithms. We have created an ordinal-regression-based model called Basophile that takes fragment size and basic residue distribution into account when determining the charge retention during CID/higher-energy collision induced dissociation (HCD) of chargedmore » peptides. This model improves the accuracy of predictions by reducing the number of unnecessary fragments that are routinely predicted for highly-charged precursors. Basophile increased the identification rates by 26% (on average) over the Naive model, when analyzing triply-charged precursors from ion trap data. Basophile achieves simplicity and speed by solving the prediction problem with an ordinal regression equation, which can be incorporated into any database search software for shotgun proteomic identification.« less

  5. Basophile: Accurate Fragment Charge State Prediction Improves Peptide Identification Rates

    PubMed Central

    Wang, Dong; Dasari, Surendra; Chambers, Matthew C.; Holman, Jerry D.; Chen, Kan; Liebler, Daniel C.; Orton, Daniel J.; Purvine, Samuel O.; Monroe, Matthew E.; Chung, Chang Y.; Rose, Kristie L.; Tabb, David L.

    2013-01-01

    In shotgun proteomics, database search algorithms rely on fragmentation models to predict fragment ions that should be observed for a given peptide sequence. The most widely used strategy (Naive model) is oversimplified, cleaving all peptide bonds with equal probability to produce fragments of all charges below that of the precursor ion. More accurate models, based on fragmentation simulation, are too computationally intensive for on-the-fly use in database search algorithms. We have created an ordinal-regression-based model called Basophile that takes fragment size and basic residue distribution into account when determining the charge retention during CID/higher-energy collision induced dissociation (HCD) of charged peptides. This model improves the accuracy of predictions by reducing the number of unnecessary fragments that are routinely predicted for highly-charged precursors. Basophile increased the identification rates by 26% (on average) over the Naive model, when analyzing triply-charged precursors from ion trap data. Basophile achieves simplicity and speed by solving the prediction problem with an ordinal regression equation, which can be incorporated into any database search software for shotgun proteomic identification. PMID:23499924

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

    SciTech Connect

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

    2014-09-01

    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. 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

  7. The Predictive Brain State: Asynchrony in Disorders of Attention?

    PubMed Central

    Ghajar, Jamshid; Ivry, Richard B.

    2015-01-01

    It is postulated that a key function of attention in goal-oriented behavior is to reduce performance variability by generating anticipatory neural activity that can be synchronized with expected sensory information. A network encompassing the prefrontal cortex, parietal lobe, and cerebellum may be critical in the maintenance and timing of such predictive neural activity. Dysfunction of this temporal process may constitute a fundamental defect in attention, causing working memory problems, distractibility, and decreased awareness. PMID:19074688

  8. The state of the art of flood forecasting - Hydrological Ensemble Prediction Systems

    NASA Astrophysics Data System (ADS)

    Thielen-Del Pozo, J.; Pappenberger, F.; Salamon, P.; Bogner, K.; Burek, P.; de Roo, A.

    2010-09-01

    Flood forecasting systems form a key part of ‘preparedness' strategies for disastrous floods and provide hydrological services, civil protection authorities and the public with information of upcoming events. Provided the warning leadtime is sufficiently long, adequate preparatory actions can be taken to efficiently reduce the impacts of the flooding. Because of the specific characteristics of each catchment, varying data availability and end-user demands, the design of the best flood forecasting system may differ from catchment to catchment. However, despite the differences in concept and data needs, there is one underlying issue that spans across all systems. There has been an growing awareness and acceptance that uncertainty is a fundamental issue of flood forecasting and needs to be dealt with at the different spatial and temporal scales as well as the different stages of the flood generating processes. Today, operational flood forecasting centres change increasingly from single deterministic forecasts to probabilistic forecasts with various representations of the different contributions of uncertainty. The move towards these so-called Hydrological Ensemble Prediction Systems (HEPS) in flood forecasting represents the state of the art in forecasting science, following on the success of the use of ensembles for weather forecasting (Buizza et al., 2005) and paralleling the move towards ensemble forecasting in other related disciplines such as climate change predictions. The use of HEPS has been internationally fostered by initiatives such as "The Hydrologic Ensemble Prediction Experiment" (HEPEX), created with the aim to investigate how best to produce, communicate and use hydrologic ensemble forecasts in hydrological short-, medium- und long term prediction of hydrological processes. The advantages of quantifying the different contributions of uncertainty as well as the overall uncertainty to obtain reliable and useful flood forecasts also for extreme events

  9. On Belief State Representation and Its Application in Planning with Incomplete Information, Nondeterministic Actions, and Sensing Actions

    ERIC Educational Resources Information Center

    To, Son Thanh

    2012-01-01

    "Belief state" refers to the set of possible world states satisfying the agent's (usually imperfect) knowledge. The use of belief state allows the agent to reason about the world with incomplete information, by considering each possible state in the belief state individually, in the same way as if it had perfect knowledge. However, the…

  10. Investigating Marine Boundary Layer Parameterizations for Improved Off-Shore Wind Predictions by Combining Observations with Models via State Estimation

    NASA Astrophysics Data System (ADS)

    Delle Monache, Luca; Hacker, Josh; Kosovic, Branko; Lee, Jared; Vandenberghe, Francois; Wu, Yonghui; Clifton, Andrew; Hawkins, Sam; Nissen, Jesper; Rostkier-Edelstein, Dorita

    2014-05-01

    Despite advances in model representation of the spatial and temporal evolution of the atmospheric boundary layer (ABL) a fundamental understanding of the processes shaping the Marine Boundary Layer (MBL) is still lacking. As part of a project funded by the U.S. Department of Energy, we are tackling this problem by combining available atmosphere and ocean observations with advanced coupled atmosphere-wave models, and via state estimation (SE) methodologies. The over-arching goal is to achieve significant advances in the scientific understanding and prediction of the underlying physical processes of the MBL, with an emphasis on the coupling between the atmosphere and the ocean via momentum and heat fluxes. We are using the single-column model (SCM) and three-dimensional (3D) versions of the Weather Research and Forecasting (WRF) model, observations of MBL structure as provided by coastal and offshore remote sensing platforms and meteorological towers, and probabilistic SE. We are systematically investigating the errors in the treatment of the surface layer of the MBL, identifying structural model inadequacies associated with its representation. We expect one key deficiency of current model representations of the surface layer of the MBL that can have a profound effect on fluxes estimates: the use of Monin-Obukhov similarity theory (MOST). This theory was developed for continental ABLs using land-based measurements, which accounts for mechanical and thermal forcing on turbulence but neglects the influence of ocean waves. We have developed an atmosphere-wave coupled modeling system by interfacing WRF with a wave model (Wavewatch III - WWIII), which is used for evaluating errors in the representation of wave-induced forcing on the energy balance at the interface between atmosphere and ocean. The Data Assimilation Research Testbed (DART) includes the SE algorithms that provide the framework for obtaining spatial and temporal statistics of wind-error evolution (and hence

  11. The Predictive Validity of Osun State Junior Secondary Certificate Examination

    ERIC Educational Resources Information Center

    Faleye, B. A.; Afolabi, E. R. I.

    2005-01-01

    Introduction: The Junior Secondary Certificate Examination (JSCE) is a summative examination taken by candidates at the end (the third year) of Junior Secondary Education in Nigeria. The Examination is in two versions--(a) the one being conducted by the States' Ministries of Education (MOE) and (b) the Federal version being conducted by the…

  12. Predicting Enlistment Behavior from Stated Intentions and Demographic Characteristics

    DTIC Science & Technology

    1990-12-01

    researchers could determine how a respondent’s stated purchase intention is related to his actual purchase behavior. To understand the relationship...between purchase intention surveys and this thesis, consider the YATS survey discussed briefly in Chapter I. This study is a key component of the Joint

  13. Predicting SOA from organic nitrates in the southeastern United States

    EPA Science Inventory

    Organic nitrates have been identified as an important component of ambient aerosol in the Southeast United States. In this work, we use the Community Multiscale Air Quality (CMAQ) model to explore the relationship between gas-phase production of organic nitrates and their subsequ...

  14. Using General Outcome Measures to Predict Student Performance on State-Mandated Assessments: An Applied Approach for Establishing Predictive Cutscores

    ERIC Educational Resources Information Center

    Leblanc, Michael; Dufore, Emily; McDougal, James

    2012-01-01

    Cutscores for reading and math (general outcome measures) to predict passage on New York state-mandated assessments were created by using a freely available Excel workbook. The authors used linear regression to create the cutscores and diagnostic indicators were provided. A rationale and procedure for using this method is outlined. This method…

  15. A history of wind erosion prediction models in the United States Department of Agriculture: The Wind Erosion Prediction System (WEPS)

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Development of the Wind Erosion Prediction System (WEPS) was officially inaugurated in 1985 by United States Department of Agriculture-Agricultural Research Service (USDA-ARS) scientists in response to customer requests, particularly those coming from the USDA Soil Conservation Service (SCS), for im...

  16. Steady state statistical correlations predict bistability in reaction motifs.

    PubMed

    Chakravarty, Suchana; Barik, Debashis

    2017-03-01

    Various cellular decision making processes are regulated by bistable switches that take graded input signals and convert them to binary all-or-none responses. Traditionally, a bistable switch generated by a positive feedback loop is characterized either by a hysteretic signal response curve with two distinct signaling thresholds or by characterizing the bimodality of the response distribution in the bistable region. To identify the intrinsic bistability of a feedback regulated network, here we propose that bistability can be determined by correlating higher order moments and cumulants (≥2) of the joint steady state distributions of two components connected in a positive feedback loop. We performed stochastic simulations of four feedback regulated models with intrinsic bistability and we show that for a bistable switch with variation of the signal dose, the steady state variance vs. covariance adopts a signatory cusp-shaped curve. Further, we find that the (n + 1)th order cross-cumulant vs. nth order cross-cumulant adopts a closed loop structure for at least n = 3. We also propose that our method is capable of identifying systems without intrinsic bistability even though the system may show bimodality in the marginal response distribution. The proposed method can be used to analyze single cell protein data measured at steady state from experiments such as flow cytometry.

  17. Improving Hydrologic Prediction at the Basin Scale through State Updating

    NASA Astrophysics Data System (ADS)

    Weerts, A.; Kockx, A.; Schellekens, J.; Drost, N.; Tretjakova, D.; Ren, J.; Lopez Lopez, P.; Hut, R.

    2015-12-01

    Data assimilation (DA) holds considerable potential for improving hydrologic predictions. However, advances in hydrologic DA research have not been adequately or timely implemented in operational forecast systems to improve the skill of forecasts for better informed real-world decision making. Several challenges exist (see Liu et al., 2012). The objective of this paper is to highlight several recent studies on basin scale data assimilation using distributed hydrologic models that touch upon these challenges including application of streamflow data assimilation using different algorithms, combined streamflow/snow data assimilation and the development of a generic linkage of OpenDA and the open source hydrologic package Openstreams/Wflow based on the (emerging) standard Basic Model Interface (BMI) as advocated by CSDMS using cross-platform webservices (i.e. Apache Thrift). Liu et al., 2012. Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities, Hydrol. Earth Syst. Sci., 16, 3863-3887, doi:10.5194/hess-16-3863-2012.

  18. 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.

  19. Meditation-induced states predict attentional control over time.

    PubMed

    Colzato, Lorenza S; Sellaro, Roberta; Samara, Iliana; Baas, Matthijs; Hommel, Bernhard

    2015-12-01

    Meditation is becoming an increasingly popular topic for scientific research and various effects of extensive meditation practice (ranging from weeks to several years) on cognitive processes have been demonstrated. Here we show that extensive practice may not be necessary to achieve those effects. Healthy adult non-meditators underwent a brief single session of either focused attention meditation (FAM), which is assumed to increase top-down control, or open monitoring meditation (OMM), which is assumed to weaken top-down control, before performing an Attentional Blink (AB) task - which assesses the efficiency of allocating attention over time. The size of the AB was considerably smaller after OMM than after FAM, which suggests that engaging in meditation immediately creates a cognitive-control state that has a specific impact on how people allocate their attention over time.

  20. Extent And Distribution Of Montane Riparian Zone Vegetation And Representation In Protected Areas In The Sky Island Region Of The Southwestern United States

    NASA Astrophysics Data System (ADS)

    Shaw, Nicole H.

    The Sky Island region of the southwestern United States hosts some of the richest biodiversity anywhere in the world. In the mountain ranges of the Sky Islands, most vertebrate biodiversity is dependent on riparian areas for all or some of their life cycles. Riparian vegetation is threatened by human impacts and climate change. Though riparian vegetation along rivers and major perennial streams is already mapped in this region, vegetation in ephemeral and intermittent riparian areas, arguably equally important for biodiversity in the mountain ranges, has not been quantified. I developed a Random Forest classification model of riparian vegetation for all three types of riparian areas, mapped this vegetation for each of the 25 mountain ranges, described the spatial distribution and connectivity of the vegetation among and between mountain ranges, and demonstrated enhancement of regional riparian land cover classes with the new model of riparian zone vegetation. The resulting map indicates a much broader distribution of riparian zone vegetation than previous land cover mapping efforts indicate, likely due to inclusion of ephemeral and intermittent riparian types. The spatial distribution and connectivity of riparian zone vegetation varied widely within and between mountain ranges, possibly as a result of variability in environmental factors affecting aridity, temperature, water availability, landscape position, and disturbances. The model can be used with other information to augment understanding of the integrity, connectivity, and vulnerability of riparian zone vegetation in this unique and important region. To analyze the conservation status of riparian zone vegetation, I quantified its representation in protected areas. I then compared the representation relative to the overall amount of riparian zone vegetation in each mountain range. The relationships between representation of riparian zone vegetation in protected areas, degree of mountain range protection, and

  1. Systems and Methods for Automated Vessel Navigation Using Sea State Prediction

    NASA Technical Reports Server (NTRS)

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

  2. Are Droughts in the United States Great Plains Predictable on Seasonal and Longer Time Scales?

    NASA Technical Reports Server (NTRS)

    Schubert, Siegfried D.; Suarez, M.; Pegion, P.; Kistler, M.; Einaudi, Franco (Technical Monitor)

    2001-01-01

    The United States Great Plains has experienced numerous episodes of unusually dry conditions lasting anywhere from months to several years, In this presentation, we will examine the predictability of such episodes and the physical mechanisms controlling the variability of the summer climate of the continental United States. The analysis is based on ensembles of multi-year simulations and seasonal hindcasts generated with the NASA Seasonal to-Interannual Prediction Project (NSIPP-1) General Circulation Model.

  3. Representing Representation

    ERIC Educational Resources Information Center

    Kuntz, Aaron M.

    2010-01-01

    What can be known and how to render what we know are perpetual quandaries met by qualitative research, complicated further by the understanding that the everyday discourses influencing our representations are often tacit, unspoken or heard so often that they seem to warrant little reflection. In this article, I offer analytic memos as a means for…

  4. Posaconazole Plasma Concentrations on Days Three to Five Predict Steady-State Levels

    PubMed Central

    Prattes, Jürgen; Duettmann, Wiebke

    2016-01-01

    Low posaconazole plasma concentrations (PPCs) have been associated with breakthrough invasive fungal infections. We assessed the correlation between pre-steady-state PPCs (obtained between days 3 and 5) and PPCs obtained during steady state in 48 patients with underlying hematological malignancies receiving posaconazole oral-solution prophylaxis. Pre-steady-state PPCs correlated significantly with PPCs obtained at steady state (Spearman r = 0.754; P < 0.001). Receiver operating characteristic (ROC) curve analysis of pre-steady-state PPCs revealed an area under the curve (AUC) of 0.884 (95% confidence interval [CI], 0.790 to 0.977) for predicting satisfactory PPCs at steady state. PMID:27324763

  5. How to trust a perfect stranger: predicting initial trust behavior from resting-state brain-electrical connectivity

    PubMed Central

    Notebaert, Karolien; Anderl, Christine; Teckentrup, Vanessa; Kaßecker, Anja; Windmann, Sabine

    2015-01-01

    Reciprocal exchanges can be understood as the updating of an initial belief about a partner. This initial level of trust is essential when it comes to establishing cooperation with an unknown partner, as cooperation cannot arise without a minimum of trust not justified by previous successful exchanges with this partner. Here we demonstrate the existence of a representation of the initial trust level before an exchange with a partner has occurred. Specifically, we can predict the Investor’s initial investment—i.e. his initial level of trust toward the unknown trustee in Round 1 of a standard 10-round Trust Game—from resting-state functional connectivity data acquired several minutes before the start of the Trust Game. Resting-state functional connectivity is, however, not significantly associated with the level of trust in later rounds, potentially mirroring the updating of the initial belief about the partner. Our results shed light on how the initial level of trust is represented. In particular, we show that a person’s initial level of trust is, at least in part, determined by brain electrical activity acquired well before the beginning of an exchange. PMID:25274577

  6. How to trust a perfect stranger: predicting initial trust behavior from resting-state brain-electrical connectivity.

    PubMed

    Hahn, Tim; Notebaert, Karolien; Anderl, Christine; Teckentrup, Vanessa; Kaßecker, Anja; Windmann, Sabine

    2015-06-01

    Reciprocal exchanges can be understood as the updating of an initial belief about a partner. This initial level of trust is essential when it comes to establishing cooperation with an unknown partner, as cooperation cannot arise without a minimum of trust not justified by previous successful exchanges with this partner. Here we demonstrate the existence of a representation of the initial trust level before an exchange with a partner has occurred. Specifically, we can predict the Investor's initial investment--i.e. his initial level of trust toward the unknown trustee in Round 1 of a standard 10-round Trust Game-from resting-state functional connectivity data acquired several minutes before the start of the Trust Game. Resting-state functional connectivity is, however, not significantly associated with the level of trust in later rounds, potentially mirroring the updating of the initial belief about the partner. Our results shed light on how the initial level of trust is represented. In particular, we show that a person's initial level of trust is, at least in part, determined by brain electrical activity acquired well before the beginning of an exchange.

  7. Prediction of the disulfide-bonding state of cysteines in proteins based on dipeptide composition.

    PubMed

    Song, Jiang-Ning; Wang, Ming-Lei; Li, Wei-Jiang; Xu, Wen-Bo

    2004-05-21

    In this paper, a novel approach has been introduced to predict the disulfide-bonding state of cysteines in proteins by means of a linear discriminator based on their dipeptide composition. The prediction is performed with a newly enlarged dataset with 8114 cysteine-containing segments extracted from 1856 non-homologous proteins of well-resolved three-dimensional structures. The oxidation of cysteines exhibits obvious cooperativity: almost all cysteines in disulfide-bond-containing proteins are in the oxidized form. This cooperativity can be well described by protein's dipeptide composition, based on which the prediction accuracy of the oxidation form of cysteines scores as high as 89.1% and 85.2%, when measured on cysteine and protein basis using the rigorous jack-knife procedure, respectively. The result demonstrates the applicability of this new relatively simple method and provides superior prediction performance compared with existing methods for the prediction of the oxidation states of cysteines in proteins.

  8. A comparison among five equations of state in predicting the inversion curve of some fluids

    NASA Astrophysics Data System (ADS)

    Haghighi, Behzad; Laee, Mohammad Reza; Seyed Matin, Naser

    2003-07-01

    Five equations of state, modified Peng-Robinson by Danesh et al. (MPR1), modified SRK equation of state by Mathias and Copeman (MSRK), Vdw11, Harmens-Knapp (HK) and modified Peng-Robinson equation of state by Ruzy (MPR2) were compared in predicting of the inversion curve of some fluids. This enable us to judge the accuracy of the results obtained from different equations of state. MSRK and HK equations of state give good prediction of the low-temperatures branch of the inversion curve and are closely matched with the experimental inversion curve. As a corollary to the present study, we have perceived that the agreement of the MPR2 and Vdw11 equations of state with the inversion curve are inadequate. We also calculated maximum inversion temperature and maximum inversion pressure for every component used in this work.

  9. Seasonal rainfall predictions over the southeast United States using the Florida State University nested regional spectral model

    NASA Astrophysics Data System (ADS)

    Cocke, Steven; Larow, T. E.; Shin, D. W.

    2007-02-01

    Seasonal rainfall predictions over the southeast United States using the recently developed Florida State University (FSU) nested regional spectral model are presented. The regional model is nested within the FSU coupled model, which includes a version of the Max Plank Institute Hamburg Ocean Primitive Equation model. The southeast U.S. winter has a rather strong climatic signal due to teleconnections with tropical Pacific sea surface temperatures and thus provides a good test case scenario for a modeling study. Simulations were done for 12 boreal winter seasons, from 1986 to 1997. Both the regional and global models captured the basic large-scale patterns of precipitation reasonably well when compared to observed station data. The regional model was able to predict the anomaly pattern somewhat better than the global model. The regional model was particularly more skillful at predicting the frequency of significant rainfall events, in part because of the ability to produce heavier rainfall events.

  10. Prediction of Filamentous Sludge Bulking using a State-based Gaussian Processes Regression Model

    NASA Astrophysics Data System (ADS)

    Liu, Yiqi; Guo, Jianhua; Wang, Qilin; Huang, Daoping

    2016-08-01

    Activated sludge process has been widely adopted to remove pollutants in wastewater treatment plants (WWTPs). However, stable operation of activated sludge process is often compromised by the occurrence of filamentous bulking. The aim of this study is to build a proper model for timely diagnosis and prediction of filamentous sludge bulking in an activated sludge process. This study developed a state-based Gaussian Process Regression (GPR) model to monitor the filamentous sludge bulking related parameter, sludge volume index (SVI), in such a way that the evolution of SVI can be predicted over multi-step ahead. This methodology was validated with SVI data collected from one full-scale WWTP. Online diagnosis and prediction of filamentous bulking sludge with real-time SVI prediction was tested through a simulation study. The results showed that the proposed methodology was capable of predicting future SVIs with good accuracy, thus providing sufficient time for predicting and controlling filamentous sludge bulking.

  11. Prediction of Filamentous Sludge Bulking using a State-based Gaussian Processes Regression Model

    PubMed Central

    Liu, Yiqi; Guo, Jianhua; Wang, Qilin; Huang, Daoping

    2016-01-01

    Activated sludge process has been widely adopted to remove pollutants in wastewater treatment plants (WWTPs). However, stable operation of activated sludge process is often compromised by the occurrence of filamentous bulking. The aim of this study is to build a proper model for timely diagnosis and prediction of filamentous sludge bulking in an activated sludge process. This study developed a state-based Gaussian Process Regression (GPR) model to monitor the filamentous sludge bulking related parameter, sludge volume index (SVI), in such a way that the evolution of SVI can be predicted over multi-step ahead. This methodology was validated with SVI data collected from one full-scale WWTP. Online diagnosis and prediction of filamentous bulking sludge with real-time SVI prediction was tested through a simulation study. The results showed that the proposed methodology was capable of predicting future SVIs with good accuracy, thus providing sufficient time for predicting and controlling filamentous sludge bulking. PMID:27498888

  12. From a state to a trait: Trajectories of state mindfulness in meditation during intervention predict changes in trait mindfulness

    PubMed Central

    Kiken, Laura G.; Garland, Eric L.; Bluth, Karen; Palsson, Olafur S.; Gaylord, Susan A.

    2015-01-01

    Theory suggests that heightening state mindfulness in meditation practice over time increases trait mindfulness, which benefits psychological health. We prospectively examined individual trajectories of state mindfulness in meditation during a mindfulness-based intervention in relation to changes in trait mindfulness and psychological distress. Each week during the eight-week intervention, participants reported their state mindfulness in meditation after a brief mindfulness meditation. Participants also completed pre- and post-intervention measures of trait mindfulness and psychological symptoms. Tests of combined latent growth and path models suggested that individuals varied significantly in their rates of change in state mindfulness in meditation during the intervention, and that these individual trajectories predicted pre-post intervention changes in trait mindfulness and distress. These findings support that increasing state mindfulness over repeated meditation sessions may contribute to a more mindful and less distressed disposition. However, individuals’ trajectories of change may vary and warrant further investigation. PMID:25914434

  13. From a state to a trait: Trajectories of state mindfulness in meditation during intervention predict changes in trait mindfulness.

    PubMed

    Kiken, Laura G; Garland, Eric L; Bluth, Karen; Palsson, Olafur S; Gaylord, Susan A

    2015-07-01

    Theory suggests that heightening state mindfulness in meditation practice over time increases trait mindfulness, which benefits psychological health. We prospectively examined individual trajectories of state mindfulness in meditation during a mindfulness-based intervention in relation to changes in trait mindfulness and psychological distress. Each week during the eight-week intervention, participants reported their state mindfulness in meditation after a brief mindfulness meditation. Participants also completed pre- and post-intervention measures of trait mindfulness and psychological symptoms. Tests of combined latent growth and path models suggested that individuals varied significantly in their rates of change in state mindfulness in meditation during the intervention, and that these individual trajectories predicted pre-post intervention changes in trait mindfulness and distress. These findings support that increasing state mindfulness over repeated meditation sessions may contribute to a more mindful and less distressed disposition. However, individuals' trajectories of change may vary and warrant further investigation.

  14. Representation of CO{sub 2} and H{sub 2}S absorption by aqueous solutions of diethanolamine using an electrolyte equation of state

    SciTech Connect

    Vallee, G.; Fuerst, W.; Mougin, P.; Jullian, S.

    1999-09-01

    The electrolyte equation of state published in 1993 by Fuerst and Renon (AIChE J. 1993, 39, 335) has been applied to the representation of CO{sub 2} and H{sub 2}S solubility in diethaloamine (DEA) aqueous solutions. This equation of state extends the classical Redlich-Kwong-Soave equation of state associated with a Wong-Sandler mixing rule to the case of systems containing ions. The study of binary systems allowed the authors to determine the parameters of the nonelectrolyte part of the equation of state. The ionic parameters have been fitted from experimental solubility data covering a wide range of experimental conditions (temperature range, 25--100 C; amine concentration, from 0.5 to 3.5 M; loadings up to 2.34 mol{sub Co{sub 2}}/mol{sub amine}). With the assumption used in previous applications of their model to various electrolyte systems, the adjusted ionic parameters are interaction ones involving protonated amine and anions as well as molecular compounds. The resulting model represents experimental data with deviations consistent with the experimental ones and close to the deviations obtained in previous studies.

  15. Modeling and analysis of several classes of self-oscillating inverters. I - State-plane representations. II - Model extension, classification, and duality relationships

    NASA Technical Reports Server (NTRS)

    Lee, F. C. Y.; Wilson, T. G.

    1982-01-01

    The present investigation is concerned with an important class of power conditioning networks, taking into account self-oscillating dc-to-square-wave transistor inverters. The considered circuits are widely used both as the principal power converting and processing means in many systems and as low-power analog-to-discrete-time converters for controlling the switching of the output-stage semiconductors in a variety of power conditioning systems. Aspects of piecewise-linear modeling are discussed, taking into consideration component models, and an equivalent-circuit model. Questions of singular point analysis and state plane representation are also investigated, giving attention to limit cycles, starting circuits, the region of attraction, a hard oscillator, and a soft oscillator.

  16. Wavelet modeling and prediction of the stability of states: the Roman Empire and the European Union

    NASA Astrophysics Data System (ADS)

    Yaroshenko, Tatyana Y.; Krysko, Dmitri V.; Dobriyan, Vitalii; Zhigalov, Maksim V.; Vos, Hendrik; Vandenabeele, Peter; Krysko, Vadim A.

    2015-09-01

    How can the stability of a state be quantitatively determined and its future stability predicted? The rise and collapse of empires and states is very complex, and it is exceedingly difficult to understand and predict it. Existing theories are usually formulated as verbal models and, consequently, do not yield sharply defined, quantitative prediction that can be unambiguously validated with data. Here we describe a model that determines whether the state is in a stable or chaotic condition and predicts its future condition. The central model, which we test, is that growth and collapse of states is reflected by the changes of their territories, populations and budgets. The model was simulated within the historical societies of the Roman Empire (400 BC to 400 AD) and the European Union (1957-2007) by using wavelets and analysis of the sign change of the spectrum of Lyapunov exponents. The model matches well with the historical events. During wars and crises, the state becomes unstable; this is reflected in the wavelet analysis by a significant increase in the frequency ω (t) and wavelet coefficients W (ω, t) and the sign of the largest Lyapunov exponent becomes positive, indicating chaos. We successfully reconstructed and forecasted time series in the Roman Empire and the European Union by applying artificial neural network. The proposed model helps to quantitatively determine and forecast the stability of a state.

  17. 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.

  18. 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

  19. Comparison between a steady-state and a transient flow model and related radionuclide concentration predictions

    NASA Astrophysics Data System (ADS)

    Gedeon, M.; Mallants, D.

    2012-04-01

    Radionuclide concentration predictions in aquifers play an important role in estimating impact of planned surface disposal of radioactive waste in Belgium, developed by the Belgian Agency for Radioactive Waste and Enriched Fissile Materials (ONDRAF), who also coordinates and leads the corresponding research. Long-term concentration predictions are based on a steady-state flow solution obtained by a cascade of multi-scale models from the catchment to the detailed (site) scale performed in MODFLOW. To test the concept and accuracy of the groundwater flow solution and conservativeness of the concentration predictions obtained therewith, a transient model, considered more realistic, was set up in a sub-domain of the intermediate scale steady-state model. Besides the modelling domain reduction, the transient model was and exact copy of the steady-state model, having the infiltration as the only time-varying parameter. The transient model was run for a twenty-year period, whereas the results were compared to the steady-state results based on infiltration value and observations averaged over the same period. The comparison of the steady-state and transient flow solutions includes the analyses of the goodness of fit, the parameter sensitivities, relative importance of the individual observations and one-percent sensitivity maps. The steady-state and transient flow solutions were subsequently translated into a site-scale transport model, used to predict the radionuclide concentrations in a hypothetical well in the aquifers. The translation of the flow solutions between the models of distinct scales was performed using the Local grid refinement method available in MODFLOW. In the site-scale models, MT3DMS transport simulations were performed to obtain respective concentration predictions in a hypothetical well, situated at 70 meters from the disposal tumuli. The equilibrium concentrations based on a constant source flux achieved using a steady-state solution were then

  20. 5 CFR 2641.202 - Two-year restriction on any former employee's representations to United States concerning...

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... United States is a party or has a direct and substantial interest, and which such person knows or reasonably should know was actually pending under his official responsibility within the one-year period...). (4) Testifying under oath. See § 2641.301(f). (5) Acting on behalf of an international...

  1. 5 CFR 2641.202 - Two-year restriction on any former employee's representations to United States concerning...

    Code of Federal Regulations, 2011 CFR

    2011-01-01

    ... United States is a party or has a direct and substantial interest, and which such person knows or reasonably should know was actually pending under his official responsibility within the one-year period...). (4) Testifying under oath. See § 2641.301(f). (5) Acting on behalf of an international...

  2. 5 CFR 2641.202 - Two-year restriction on any former employee's representations to United States concerning...

    Code of Federal Regulations, 2012 CFR

    2012-01-01

    ... United States is a party or has a direct and substantial interest, and which such person knows or reasonably should know was actually pending under his official responsibility within the one-year period...). (4) Testifying under oath. See § 2641.301(f). (5) Acting on behalf of an international...

  3. 5 CFR 2641.202 - Two-year restriction on any former employee's representations to United States concerning...

    Code of Federal Regulations, 2013 CFR

    2013-01-01

    ... United States is a party or has a direct and substantial interest, and which such person knows or reasonably should know was actually pending under his official responsibility within the one-year period...). (4) Testifying under oath. See § 2641.301(f). (5) Acting on behalf of an international...

  4. 5 CFR 2641.202 - Two-year restriction on any former employee's representations to United States concerning...

    Code of Federal Regulations, 2014 CFR

    2014-01-01

    ... United States is a party or has a direct and substantial interest, and which such person knows or reasonably should know was actually pending under his official responsibility within the one-year period...). (4) Testifying under oath. See § 2641.301(f). (5) Acting on behalf of an international...

  5. Both trait and state mindfulness predict lower aggressiveness via anger rumination: A multilevel mediation analysis.

    PubMed

    Eisenlohr-Moul, Tory A; Peters, Jessica R; Pond, Richard S; DeWall, C Nathan

    2016-06-01

    Trait mindfulness, or the capacity for nonjudgmental, present-centered attention, predicts lower aggression in cross-sectional samples, an effect mediated by reduced anger rumination. Experimental work also implicates state mindfulness (i.e., fluctuations around one's typical mindfulness) in aggression. Despite evidence that both trait and state mindfulness predict lower aggression, their relative impact and their mechanisms remain unclear. Higher trait mindfulness and state increases in mindfulness facets may reduce aggression-related outcomes by (1) limiting the intensity of anger, or (2) limiting rumination on anger experiences. The present study tests two hypotheses: First, that both trait and state mindfulness contribute unique variance to lower aggressiveness, and second, that the impact of both trait and state mindfulness on aggressiveness will be uniquely partially mediated by both anger intensity and anger rumination. 86 participants completed trait measures of mindfulness, anger intensity, and anger rumination, then completed diaries for 35 days assessing mindfulness, anger intensity, anger rumination, anger expression, and self-reported and behavioral aggressiveness. Using multilevel zero-inflated regression, we examined unique contributions of trait and state mindfulness facets to daily anger expression and aggressiveness. We also examined the mediating roles of anger intensity and anger rumination at both trait and state levels. Mindfulness facets predicted anger expression and aggressiveness indirectly through anger rumination after controlling for indirect pathways through anger intensity. Individuals with high or fluctuating aggression may benefit from mindfulness training to reduce both intensity of and rumination on anger.

  6. 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.

  7. Prediction of conformational states of amino acids using a Ramachandran plot.

    PubMed

    Kolaskar, A S; Sawant, S

    1996-01-01

    (phi, psi) data from crystal structures of 221 proteins having high resolution and sequence similarity cut-off at the 25% level were analysed by dividing the Ramachandran plot in three regions representing three conformational states: (i) conformational state 1: conformations in the (phi, psi) range from (-140 degrees, -100 degrees) to (0 degrees, 0 degrees); (ii) conformational state 2: conformations with (phi, psi) from (-180 degrees, 80 degrees) to (0 degrees, 180 degrees); and (iii) conformational state 3: all the remaining conformations in the (phi, psi) plane which are not included in the above two conformational states. Normalized probability values of the occurrence of single amino acid residues in conformational regions 1-3 and similar values for dipeptides were calculated. Comparisons of single residue and dipeptide normalized probability values have shown that short-range interactions, although strong, destabilize conformational states of only 44 dipeptides out of the 400 x 9 possible states. However, dipeptide frequency values provide better resolving power than single-residue potentials when used to predict conformational states of residues in a protein from its primary structure. The simple approach used in the present study to predict conformational states yields an accuracy of > 70% for 14 proteins and an accuracy in the range of 50-70% for 247 proteins. Thus these studies point out yet another use of the Ramachandran plot and the role of tertiary interactions in protein folding.

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

    PubMed

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

    2016-09-06

    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.

  9. 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

  10. Precursor charge state prediction for electron transfer dissociation tandem mass spectra.

    PubMed

    Sharma, Vagisha; Eng, Jimmy K; Feldman, Sergey; von Haller, Priska D; MacCoss, Michael J; Noble, William S

    2010-10-01

    Electron-transfer dissociation (ETD) induces fragmentation along the peptide backbone by transferring an electron from a radical anion to a protonated peptide. In contrast with collision-induced dissociation, side chains and modifications such as phosphorylation are left intact through the ETD process. Because the precursor charge state is an important input to MS/MS sequence database search tools, the ability to accurately determine the precursor charge is helpful for the identification process. Furthermore, because ETD can be applied to large, highly charged peptides, the need for accurate precursor charge state determination is magnified. Otherwise, each spectrum must be searched repeatedly using a large range of possible precursor charge states. To address this problem, we have developed an ETD charge state prediction tool based on support vector machine classifiers that is demonstrated to exhibit superior classification accuracy while minimizing the overall number of predicted charge states. The tool is freely available, open source, cross platform compatible, and demonstrated to perform well when compared with an existing charge state prediction tool. The program is available from http://code.google.com/p/etdz/.

  11. Resting‐state connectivity predicts levodopa‐induced dyskinesias in Parkinson's disease

    PubMed Central

    Haagensen, Brian N.; Nielsen, Silas H.; Madsen, Kristoffer H.; Løkkegaard, Annemette; Siebner, Hartwig R.

    2016-01-01

    ABSTRACT Background Levodopa‐induced dyskinesias are a common side effect of dopaminergic therapy in PD, but their neural correlates remain poorly understood. Objectives This study examines whether dyskinesias are associated with abnormal dopaminergic modulation of resting‐state cortico‐striatal connectivity. Methods Twelve PD patients with peak‐of‐dose dyskinesias and 12 patients without dyskinesias were withdrawn from dopaminergic medication. All patients received a single dose of fast‐acting soluble levodopa and then underwent resting‐state functional magnetic resonance imaging before any dyskinesias emerged. Levodopa‐induced modulation of cortico‐striatal resting‐state connectivity was assessed between the putamen and the following 3 cortical regions of interest: supplementary motor area, primary sensorimotor cortex, and right inferior frontal gyrus. These functional connectivity measures were entered into a linear support vector classifier to predict whether an individual patient would develop dyskinesias after levodopa intake. Linear regression analysis was applied to test which connectivity measures would predict dyskinesia severity. Results Dopaminergic modulation of resting‐state connectivity between the putamen and primary sensorimotor cortex in the most affected hemisphere predicted whether patients would develop dyskinesias with a specificity of 100% and a sensitivity of 91% (P < .0001). Modulation of resting‐state connectivity between the supplementary motor area and putamen predicted interindividual differences in dyskinesia severity (R 2 = 0.627, P = .004). Resting‐state connectivity between the right inferior frontal gyrus and putamen neither predicted dyskinesia status nor dyskinesia severity. Conclusions The results corroborate the notion that altered dopaminergic modulation of cortico‐striatal connectivity plays a key role in the pathophysiology of dyskinesias in PD. © 2016 International Parkinson and Movement

  12. Predictive Validity of Early Literacy Measures for Korean English Language Learners in the United States

    ERIC Educational Resources Information Center

    Han, Jeanie Nam; Vanderwood, Michael L.; Lee, Catherine Y.

    2015-01-01

    This study examined the predictive validity of early literacy measures with first-grade Korean English language learners (ELLs) in the United States at varying levels of English proficiency. Participants were screened using Dynamic Indicators of Basic Early Literacy Skills (DIBELS) Phoneme Segmentation Fluency (PSF), DIBELS Nonsense Word Fluency…

  13. Predicting Employment Outcomes of Consumers of State-Operated Comprehensive Rehabilitation Centers

    ERIC Educational Resources Information Center

    Beach, David Thomas

    2009-01-01

    This study used records from a state-operated comprehensive rehabilitation center to investigate possible predictive factors related to completing comprehensive rehabilitation center programs and successful vocational rehabilitation (VR) case closure. An analysis of demographic data of randomly selected comprehensive rehabilitation center…

  14. Ability of Early Literacy Measures to Predict Future State Assessment Performance

    ERIC Educational Resources Information Center

    Utchell, Lynn A.; Schmitt, Ara J.; McCallum, Elizabeth; McGoey, Kara E.; Piselli, Kate

    2016-01-01

    The purpose of this study was to determine the extent to which early literacy measures administered in kindergarten and Oral Reading Fluency (ORF) measures administered in Grade 1 are related to and predict future state reading assessment performances up to 7 years later. Results indicated that early literacy and ORF performances were…

  15. Extreme Appraisals of Internal States and Bipolar Symptoms: The Hypomanic Attitudes and Positive Predictions Inventory

    ERIC Educational Resources Information Center

    Dodd, Alyson L.; Mansell, Warren; Morrison, Anthony P.; Tai, Sara

    2011-01-01

    The Hypomanic Attitudes and Positive Predictions Inventory (HAPPI; W. Mansell, 2006) was developed to assess multiple, extreme, self-relevant appraisals of internal states. The present study aimed to validate the HAPPI in a clinical sample. Participants (N = 50) with a diagnosis of bipolar disorder (confirmed by a structured clinical interview)…

  16. CURRENT STATE OF PREDICTING THE RESPIRATORY ALLERGY POTENTIAL OF CHEMICALS: WHAT ARE THE ISSUES?

    EPA Science Inventory

    Current State of Predicting the Respiratory Allergy Potential of Chemicals: What Are the Issues? M I. Gilmour1 and S. E. Loveless2, 1USEPA, Research Triangle Park, NC and 2DuPont Haskell Laboratory, Newark, DE.

    Many chemicals are clearly capable of eliciting immune respon...

  17. A Pilot Test of the Resource Requirements Prediction Model at Humboldt State College.

    ERIC Educational Resources Information Center

    California State Colleges, Inglewood. Office of the Chancellor.

    This publication presents Humboldt State College's experience with the pilot testing of the Resource Requirements Prediction Model (RRPM), an analytic computer designed to aid management decisionmaking and planning in institutions of higher education. RRPM has great potential as a planning tool that can improve resource management in higher…

  18. Dimension of discrete variable representation for mixed quantum/classical computation of three lowest vibrational states of OH stretching in liquid water.

    PubMed

    Jeon, Kiyoung; Yang, Mino

    2017-02-07

    Three low-lying vibrational states of molecular systems are responsible for the signals of linear and third-order nonlinear vibrational spectroscopies. Theoretical studies based on mixed quantum/classical calculations provide a powerful way to analyze those experiments. A statistically meaningful result can be obtained from the calculations by solving the vibrational Schrödinger equation over many numbers of molecular configurations. The discrete variable representation (DVR) method is a useful technique to calculate vibrational eigenstates subject to an arbitrary anharmonic potential surface. Considering the large number of molecular configurations over which the DVR calculations are repeated, the calculations are desired to be optimized in balance between the cost and accuracy. We determine a dimension of the DVR method which appears to be optimum for the calculations of the three states of molecular vibrations with anharmonic strengths often found in realistic molecular systems. We apply the numerical technique to calculate the local OH stretching frequencies of liquid water, which are well known to be widely distributed due to the inhomogeneity in molecular configuration, and found that the frequencies of the 0-1 and 1-2 transitions are highly correlated. An empirical relation between the two frequencies is suggested and compared with the experimental data of nonlinear IR spectroscopies.

  19. Dimension of discrete variable representation for mixed quantum/classical computation of three lowest vibrational states of OH stretching in liquid water

    NASA Astrophysics Data System (ADS)

    Jeon, Kiyoung; Yang, Mino

    2017-02-01

    Three low-lying vibrational states of molecular systems are responsible for the signals of linear and third-order nonlinear vibrational spectroscopies. Theoretical studies based on mixed quantum/classical calculations provide a powerful way to analyze those experiments. A statistically meaningful result can be obtained from the calculations by solving the vibrational Schrödinger equation over many numbers of molecular configurations. The discrete variable representation (DVR) method is a useful technique to calculate vibrational eigenstates subject to an arbitrary anharmonic potential surface. Considering the large number of molecular configurations over which the DVR calculations are repeated, the calculations are desired to be optimized in balance between the cost and accuracy. We determine a dimension of the DVR method which appears to be optimum for the calculations of the three states of molecular vibrations with anharmonic strengths often found in realistic molecular systems. We apply the numerical technique to calculate the local OH stretching frequencies of liquid water, which are well known to be widely distributed due to the inhomogeneity in molecular configuration, and found that the frequencies of the 0-1 and 1-2 transitions are highly correlated. An empirical relation between the two frequencies is suggested and compared with the experimental data of nonlinear IR spectroscopies.

  20. 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.

  1. Information integration based predictions about the conscious states of a spiking neural network.

    PubMed

    Gamez, David

    2010-03-01

    This paper describes how Tononi's information integration theory of consciousness was used to make detailed predictions about the distribution of phenomenal states in a spiking neural network. This network had approximately 18,000 neurons and 700,000 connections and it used models of emotion and imagination to control the eye movements of a virtual robot and avoid 'negative' stimuli. The first stage in the analysis was the development of a formal definition of Tononi's theory of consciousness. The network was then analysed for information integration and detailed predictions were made about the distribution of consciousness for each time step of recorded activity. This work demonstrates how an artificial system can be analysed for consciousness using a particular theory and in the future this approach could be used to make predictions about the phenomenal states associated with biological systems.

  2. Nonideal statistical rate theory formulation to predict evaporation rates from equations of state.

    PubMed

    Kapoor, Atam; Elliott, Janet A W

    2008-11-27

    A method of including nonideal effects in the statistical rate theory (SRT) formulation is presented and a generic equation-of-state based SRT model was developed for predicting evaporation rates. Further, taking the Peng-Robinson equation of state as an example, vapor phase pressures at which particular evaporation rates are expected were calculated, and the predictions were found to be in excellent agreement with the experimental observations for water and octane. A high temperature range (near the critical region) where the previously existing ideal SRT model is expected to yield inaccurate results was identified and predictions (for ethane and butane) were instead made with the Peng-Robinson based SRT model to correct for fluid nonidealities at high temperatures and pressures.

  3. Physiological state gates acquisition and expression of mesolimbic reward prediction signals

    PubMed Central

    Cone, Jackson J.; Fortin, Samantha M.; McHenry, Jenna A.; Stuber, Garret D.; McCutcheon, James E.; Roitman, Mitchell F.

    2016-01-01

    Phasic dopamine signaling participates in associative learning by reinforcing associations between outcomes (unconditioned stimulus; US) and their predictors (conditioned stimulus; CS). However, prior work has always engendered these associations with innately rewarding stimuli. Thus, whether dopamine neurons can acquire prediction signals in the absence of appetitive experience and update them when the value of the outcome changes remains unknown. Here, we used sodium depletion to reversibly manipulate the appetitive value of a hypertonic sodium solution while measuring phasic dopamine signaling in rat nucleus accumbens. Dopamine responses to the NaCl US following sodium depletion updated independent of prior experience. In contrast, prediction signals were only acquired through extensive experience with a US that had positive affective value. Once learned, dopamine prediction signals were flexibly expressed in a state-dependent manner. Our results reveal striking differences with respect to how physiological state shapes dopamine signals evoked by outcomes and their predictors. PMID:26831116

  4. Global corresponding states representation of the interfacial tension and capillary constant for the binary mixtures argon + krypton, methane + krypton, and krypton + ethane

    SciTech Connect

    Holcomb, C.D.; Zollweg, J.A. )

    1993-05-06

    Corresponding states theories for surface tension and capillary constant have been evaluated using constant liquid mole fraction and constant [open quotes]fugacity fraction[close quotes] reference fluids for three binary systems. Experimental vapor-liquid equilibria, capillary constant, and surface tension data for argon + krypton, methane + krypton, and krypton + ethane systems were measured from 125 K to the critical line. These results form a database for phase behavior of varying complexity between simple compounds. The database has been used to compare the [open quotes]fugacity fraction[close quotes] corresponding states theory for capillary constant and surface tension with the traditional corresponding states theory which uses as a reference fluid a liquid with constant mole fraction. The database was also used to test the Moldover and Rainwater prediction for the surface tension coefficient in the fugacity fraction corresponding states theory. 33 refs., 11 figs., 10 tabs.

  5. 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

    SciTech Connect

    Zhu, Xiaolei Yarkony, David R.

    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, the 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.

  6. 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.

    PubMed

    Zhu, Xiaolei; Yarkony, David R

    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(I,J), and the interstate coupling vector h(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(x)(I,J)(R), s(y)(I,J)(R), g(I,J)(R), and h(I,J)(R). As a result of the orthogonalization these parameters can be made continuous functions of R, the 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.

  7. A novel equation of state for the prediction of thermodynamic properties of fluids.

    PubMed

    Polishuk, Ilya; Vera, Juan H

    2005-03-31

    This work proposes a new equation of state (EOS) based on molecular theory for the prediction of thermodynamic properties of real fluids. The new EOS uses a novel repulsive term, which gives the correct hard sphere close packed limit and yields accurate values for hard sphere and hard chain virial coefficients. The pressure obtained from this repulsive term is corrected by a combination of van der Waals and Dieterici potentials. No empirical temperature functionality of the parameters has been introduced at this stage. The novel EOS predicts the experimental volumetric data of different compounds and their mixtures better than the successful EOS of Peng and Robinson. The prediction of vapor pressures is only slightly less accurate than the results obtained with the Peng-Robinson equation that is designed for these purposes. The theoretically based parameters of the new EOS make its predictions more reliable than those obtained from purely empirical forms.

  8. 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.

  9. Arthroplasty Utilization in the United States is Predicted by Age-Specific Population Groups.

    PubMed

    Bashinskaya, Bronislava; Zimmerman, Ryan M; Walcott, Brian P; Antoci, Valentin

    2012-01-01

    Osteoarthritis is a common indication for hip and knee arthroplasty. An accurate assessment of current trends in healthcare utilization as they relate to arthroplasty may predict the needs of a growing elderly population in the United States. First, incidence data was queried from the United States Nationwide Inpatient Sample from 1993 to 2009. Patients undergoing total knee and hip arthroplasty were identified. Then, the United States Census Bureau was queried for population data from the same study period as well as to provide future projections. Arthroplasty followed linear regression models with the population group >64 years in both hip and knee groups. Projections for procedure incidence in the year 2050 based on these models were calculated to be 1,859,553 cases (hip) and 4,174,554 cases (knee). The need for hip and knee arthroplasty is expected to grow significantly in the upcoming years, given population growth predictions.

  10. Independence and Interdependence Predict Health and Wellbeing: Divergent Patterns in the United States and Japan

    PubMed Central

    Kitayama, Shinobu; Karasawa, Mayumi; Curhan, Katherine B.; Ryff, Carol D.; Markus, Hazel Rose

    2010-01-01

    A cross-cultural survey was used to examine two hypotheses designed to link culture to wellbeing and health. The first hypothesis states that people are motivated toward prevalent cultural mandates of either independence (personal control) in the United States or interdependence (relational harmony) in Japan. As predicted, Americans with compromised personal control and Japanese with strained relationships reported high perceived constraint. The second hypothesis holds that people achieve wellbeing and health through actualizing the respective cultural mandates in their modes of being. As predicted, the strongest predictor of wellbeing and health was personal control in the United States, but the absence of relational strain in Japan. All analyses controlled for age, gender, educational attainment, and personality traits. The overall pattern of findings underscores culturally distinct pathways (independent versus interdependent) in achieving the positive life outcomes. PMID:21833228

  11. A hybrid statistical-dynamical framework for meteorological drought prediction: Application to the southwestern United States

    NASA Astrophysics Data System (ADS)

    Madadgar, Shahrbanou; AghaKouchak, Amir; Shukla, Shraddhanand; Wood, Andrew W.; Cheng, Linyin; Hsu, Kou-Lin; Svoboda, Mark

    2016-07-01

    Improving water management in water stressed-regions requires reliable seasonal precipitation predication, which remains a grand challenge. Numerous statistical and dynamical model simulations have been developed for predicting precipitation. However, both types of models offer limited seasonal predictability. This study outlines a hybrid statistical-dynamical modeling framework for predicting seasonal precipitation. The dynamical component relies on the physically based North American Multi-Model Ensemble (NMME) model simulations (99 ensemble members). The statistical component relies on a multivariate Bayesian-based model that relates precipitation to atmosphere-ocean teleconnections (also known as an analog-year statistical model). Here the Pacific Decadal Oscillation (PDO), Multivariate ENSO Index (MEI), and Atlantic Multidecadal Oscillation (AMO) are used in the statistical component. The dynamical and statistical predictions are linked using the so-called Expert Advice algorithm, which offers an ensemble response (as an alternative to the ensemble mean). The latter part leads to the best precipitation prediction based on contributing statistical and dynamical ensembles. It combines the strength of physically based dynamical simulations and the capability of an analog-year model. An application of the framework in the southwestern United States, which has suffered from major droughts over the past decade, improves seasonal precipitation predictions (3-5 month lead time) by 5-60% relative to the NMME simulations. Overall, the hybrid framework performs better in predicting negative precipitation anomalies (10-60% improvement over NMME) than positive precipitation anomalies (5-25% improvement over NMME). The results indicate that the framework would likely improve our ability to predict droughts such as the 2012-2014 event in the western United States that resulted in significant socioeconomic impacts.

  12. Prediction of SSVEP-based BCI performance by the resting-state EEG network

    NASA Astrophysics Data System (ADS)

    Zhang, Yangsong; Xu, Peng; Guo, Daqing; Yao, Dezhong

    2013-12-01

    Objective. The prediction of brain-computer interface (BCI) performance is a significant topic in the BCI field. Some researches have demonstrated that resting-state data are promising candidates to achieve the goal. However, so far the relationships between the resting-state networks and the steady-state visual evoked potential (SSVEP)-based BCI have not been investigated. In this paper, we investigate the possible relationships between the SSVEP responses, the classification accuracy of five stimulus frequencies and the closed-eye resting-state network topology. Approach. The resting-state functional connectivity networks of the corresponding five stimulus frequencies were created by coherence, and then three network topology measures—the mean functional connectivity, the clustering coefficient and the characteristic path length of each network—were calculated. In addition, canonical correlation analysis was used to perform frequency recognition with the SSVEP data. Main results. Interestingly, we found that SSVEPs of each frequency were negatively correlated with the mean functional connectivity and clustering coefficient, but positively correlated with characteristic path length. Each of the averaged network topology measures across the frequencies showed the same relationship with the SSVEPs averaged across frequencies between the subjects. Furthermore, our results also demonstrated that the classification accuracy can be predicted by three averaged network measures and their combination can further improve the prediction performance. Significance. These findings indicate that the SSVEP responses and performance are predictable using the information at the resting-state, which may be instructive in both SSVEP-aided cognition studies and SSVEP-based BCI applications.

  13. Categorical Drought Monitoring and Prediction in the United States Based on NLDAS-2

    NASA Astrophysics Data System (ADS)

    Hao, Z.; Xia, Y.; Hao, F.; Singh, V. P.

    2015-12-01

    Drought is a pervasive natural hazard and is a billion-dollar disaster in the United States, which is comparable to hurricanes and tropical storms with greater economic impacts than extratropical storms, wildfires, blizzards, and ice storms combined. Drought early warning is of critical importance for drought preparedness planning and mitigation efforts to reduce potential impacts of drought, for which drought monitoring and prediction are the essential components. The U.S. Drought Monitor (USDM) has been widely used to track droughts and their impacts. USDM is a composite product that blends quantitative drought indicators and qualitative drought information from multiple sources and classifies drought conditions into different drought categories. Due to the wide application of USDM products, drought monitoring and prediction in the categorical form would be of great importance to aid decision makers to take appropriate measures for drought managements. Based on drought indices from North American Land Data Assimilation System Phase 2 (NLDAS-2), this study proposes a statistical method for the categorical drought monitoring and prediction in the United States. The probabilities of drought conditions falling into different USDM drought categories can be estimated from the proposed method. The method is found to satisfactorily reconstruct historical USDM drought categories and predict future USDM drought categories, and has considerable potential to aid early drought warning in the United States.

  14. Evoked potentials for the prediction of vegetative state in the acute stage of coma.

    PubMed

    Fischer, Catherine; Luauté, Jacques

    2005-01-01

    For comatose patients in intensive care units, it is important to anticipate their functional outcome as soon and as reliably as possible. Among clinical variables the Glasgow Coma Score (GCS) and the patient's pupil reactivity are the strongest predictive variables. Evoked potentials help to assess objectively brain function. Over the past 20 years, numerous studies have assessed their prognostic utility in terms of awakening from coma. Fewer studies, however, have focused upon the utility of evoked potentials in predicting progression to the vegetative state. In this area evoked potentials appear to have a highly predictive value. In anoxic coma the abolition of somatosensory evoked potentials (SEPs) is related to a poor outcome, defined as death or survival in a vegetative state, with a 100% specificity. Following traumatic brain injury, the predictive value for unfavourable outcome is 98.5% when there are no focal injuries likely to abolish SEP cortical components. In contrast, the presence of event-related evoked potentials, and particularly mismatched negativity (MMN), is a strong predictor of awakening and precludes comatose patients from moving to a permanent vegetative state (PVS).

  15. Turbulence Modeling Effects on the Prediction of Equilibrium States of Buoyant Shear Flows

    NASA Technical Reports Server (NTRS)

    Zhao, C. Y.; So, R. M. C.; Gatski, T. B.

    2001-01-01

    The effects of turbulence modeling on the prediction of equilibrium states of turbulent buoyant shear flows were investigated. The velocity field models used include a two-equation closure, a Reynolds-stress closure assuming two different pressure-strain models and three different dissipation rate tensor models. As for the thermal field closure models, two different pressure-scrambling models and nine different temperature variance dissipation rate, Epsilon(0) equations were considered. The emphasis of this paper is focused on the effects of the Epsilon(0)-equation, of the dissipation rate models, of the pressure-strain models and of the pressure-scrambling models on the prediction of the approach to equilibrium turbulence. Equilibrium turbulence is defined by the time rate (if change of the scaled Reynolds stress anisotropic tensor and heat flux vector becoming zero. These conditions lead to the equilibrium state parameters. Calculations show that the Epsilon(0)-equation has a significant effect on the prediction of the approach to equilibrium turbulence. For a particular Epsilon(0)-equation, all velocity closure models considered give an equilibrium state if anisotropic dissipation is accounted for in one form or another in the dissipation rate tensor or in the Epsilon(0)-equation. It is further found that the models considered for the pressure-strain tensor and the pressure-scrambling vector have little or no effect on the prediction of the approach to equilibrium turbulence.

  16. Braid group representation on quantum computation

    SciTech Connect

    Aziz, Ryan Kasyfil; Muchtadi-Alamsyah, Intan

    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.

  17. Prediction of the disulfide-bonding state of cysteines in proteins at 88% accuracy

    PubMed Central

    Martelli, Pier Luigi; Fariselli, Piero; Malaguti, Luca; Casadio, Rita

    2002-01-01

    The task of predicting the cysteine-bonding state in proteins starting from the residue chain is addressed by implementing a new hybrid system that combines a neural network and a hidden Markov model (hidden neural network). Training is performed using 4136 cysteine-containing segments extracted from 969 nonhomologous proteins of well-resolved three-dimensional structure. After a 20-fold cross-validation procedure, the efficiency of the prediction scores as high as 88% and 84%, when measured on cysteine and protein basis, respectively. These results outperform previously described methods for the same task. PMID:12381855

  18. Predicting the activation states of the muscles governing upper esophageal sphincter relaxation and opening

    PubMed Central

    Jones, Corinne A.; Hammer, Michael J.; Cock, Charles; Dinning, Philip; Wiklendt, Lukasz; Costa, Marcello; McCulloch, Timothy M.

    2016-01-01

    The swallowing muscles that influence upper esophageal sphincter (UES) opening are centrally controlled and modulated by sensory information. Activation and deactivation of neural inputs to these muscles, including the intrinsic cricopharyngeus (CP) and extrinsic submental (SM) muscles, results in their mechanical activation or deactivation, which changes the diameter of the lumen, alters the intraluminal pressure, and ultimately reduces or promotes flow of content. By measuring the changes in diameter, using intraluminal impedance, and the concurrent changes in intraluminal pressure, it is possible to determine when the muscles are passively or actively relaxing or contracting. From these “mechanical states” of the muscle, the neural inputs driving the specific motor behaviors of the UES can be inferred. In this study we compared predictions of UES mechanical states directly with the activity measured by electromyography (EMG). In eight subjects, pharyngeal pressure and impedance were recorded in parallel with CP- and SM-EMG activity. UES pressure and impedance swallow profiles correlated with the CP-EMG and SM-EMG recordings, respectively. Eight UES muscle states were determined by using the gradient of pressure and impedance with respect to time. Guided by the level and gradient change of EMG activity, mechanical states successfully predicted the activity of the CP muscle and SM muscle independently. Mechanical state predictions revealed patterns consistent with the known neural inputs activating the different muscles during swallowing. Derivation of “activation state” maps may allow better physiological and pathophysiological interpretations of UES function. PMID:26767985

  19. The relationship between large-scale and convective states in the tropics - Towards an improved representation of convection in large-scale models

    SciTech Connect

    Jakob, Christian

    2015-02-26

    This report summarises an investigation into the relationship of tropical thunderstorms to the atmospheric conditions they are embedded in. The study is based on the use of radar observations at the Atmospheric Radiation Measurement site in Darwin run under the auspices of the DOE Atmospheric Systems Research program. Linking the larger scales of the atmosphere with the smaller scales of thunderstorms is crucial for the development of the representation of thunderstorms in weather and climate models, which is carried out by a process termed parametrisation. Through the analysis of radar and wind profiler observations the project made several fundamental discoveries about tropical storms and quantified the relationship of the occurrence and intensity of these storms to the large-scale atmosphere. We were able to show that the rainfall averaged over an area the size of a typical climate model grid-box is largely controlled by the number of storms in the area, and less so by the storm intensity. This allows us to completely rethink the way we represent such storms in climate models. We also found that storms occur in three distinct categories based on their depth and that the transition between these categories is strongly related to the larger scale dynamical features of the atmosphere more so than its thermodynamic state. Finally, we used our observational findings to test and refine a new approach to cumulus parametrisation which relies on the stochastic modelling of the area covered by different convective cloud types.

  20. An algorithm to estimate building heights from Google street-view imagery using single view metrology across a representational state transfer system

    NASA Astrophysics Data System (ADS)

    Díaz, Elkin; Arguello, Henry

    2016-05-01

    Urban ecosystem studies require monitoring, controlling and planning to analyze building density, urban density, urban planning, atmospheric modeling and land use. In urban planning, there are many methods for building height estimation using optical remote sensing images. These methods however, highly depend on sun illumination and cloud-free weather. In contrast, high resolution synthetic aperture radar provides images independent from daytime and weather conditions, although, these images rely on special hardware and expensive acquisition. Most of the biggest cities around the world have been photographed by Google street view under different conditions. Thus, thousands of images from the principal streets of a city can be accessed online. The availability of this and similar rich city imagery such as StreetSide from Microsoft, represents huge opportunities in computer vision because these images can be used as input in many applications such as 3D modeling, segmentation, recognition and stereo correspondence. This paper proposes a novel algorithm to estimate building heights using public Google Street-View imagery. The objective of this work is to obtain thousands of geo-referenced images from Google Street-View using a representational state transfer system, and estimate their average height using single view metrology. Furthermore, the resulting measurements and image metadata are used to derive a layer of heights in a Google map available online. The experimental results show that the proposed algorithm can estimate an accurate average building height map of thousands of images using Google Street-View Imagery of any city.

  1. Geographical information system and predictive risk maps of urinary schistosomiasis in Ogun State, Nigeria

    PubMed Central

    Ekpo, Uwem F; Mafiana, Chiedu F; Adeofun, Clement O; Solarin, Adewale RT; Idowu, Adewumi B

    2008-01-01

    Background The control of urinary schistosomiasis in Ogun State, Nigeria remains inert due to lack of reliable data on the geographical distribution of the disease and the population at risk. To help in developing a control programme, delineating areas of risk, geographical information system and remotely sensed environmental images were used to developed predictive risk maps of the probability of occurrence of the disease and quantify the risk for infection in Ogun State, Nigeria. Methods Infection data used were derived from carefully validated morbidity questionnaires among primary school children in 2001–2002, in which school children were asked among other questions if they have experienced "blood in urine" or urinary schistosomiasis. The infection data from 1,092 schools together with remotely sensed environmental data such as rainfall, vegetation, temperature, soil-types, altitude and land cover were analysis using binary logistic regression models to identify environmental features that influence the spatial distribution of the disease. The final regression equations were then used in Arc View 3.2a GIS software to generate predictive risk maps of the distribution of the disease and population at risk in the state. Results Logistic regression analysis shows that the only significant environmental variable in predicting the presence and absence of urinary schistosomiasis in any area of the State was Land Surface Temperature (LST) (B = 0.308, p = 0.013). While LST (B = -0.478, p = 0.035), rainfall (B = -0.006, p = 0.0005), ferric luvisols (B = 0.539, p = 0.274), dystric nitosols (B = 0.133, p = 0.769) and pellic vertisols (B = 1.386, p = 0.008) soils types were the final variables in the model for predicting the probability of an area having an infection prevalence equivalent to or more than 50%. The two predictive risk maps suggest that urinary schistosomiasis is widely distributed and occurring in all the Local Government Areas (LGAs) in State. The high

  2. Prediction of high-dimensional states subject to respiratory motion: a manifold learning approach.

    PubMed

    Liu, Wenyang; Sawant, Amit; Ruan, Dan

    2016-07-07

    The development of high-dimensional imaging systems in image-guided radiotherapy provides important pathways to the ultimate goal of real-time full volumetric motion monitoring. Effective motion management during radiation treatment usually requires prediction to account for system latency and extra signal/image processing time. It is challenging to predict high-dimensional respiratory motion due to the complexity of the motion pattern combined with the curse of dimensionality. Linear dimension reduction methods such as PCA have been used to construct a linear subspace from the high-dimensional data, followed by efficient predictions on the lower-dimensional subspace. In this study, we extend such rationale to a more general manifold and propose a framework for high-dimensional motion prediction with manifold learning, which allows one to learn more descriptive features compared to linear methods with comparable dimensions. Specifically, a kernel PCA is used to construct a proper low-dimensional feature manifold, where accurate and efficient prediction can be performed. A fixed-point iterative pre-image estimation method is used to recover the predicted value in the original state space. We evaluated and compared the proposed method with a PCA-based approach on level-set surfaces reconstructed from point clouds captured by a 3D photogrammetry system. The prediction accuracy was evaluated in terms of root-mean-squared-error. Our proposed method achieved consistent higher prediction accuracy (sub-millimeter) for both 200 ms and 600 ms lookahead lengths compared to the PCA-based approach, and the performance gain was statistically significant.

  3. Prediction of high-dimensional states subject to respiratory motion: a manifold learning approach

    NASA Astrophysics Data System (ADS)

    Liu, Wenyang; Sawant, Amit; Ruan, Dan

    2016-07-01

    The development of high-dimensional imaging systems in image-guided radiotherapy provides important pathways to the ultimate goal of real-time full volumetric motion monitoring. Effective motion management during radiation treatment usually requires prediction to account for system latency and extra signal/image processing time. It is challenging to predict high-dimensional respiratory motion due to the complexity of the motion pattern combined with the curse of dimensionality. Linear dimension reduction methods such as PCA have been used to construct a linear subspace from the high-dimensional data, followed by efficient predictions on the lower-dimensional subspace. In this study, we extend such rationale to a more general manifold and propose a framework for high-dimensional motion prediction with manifold learning, which allows one to learn more descriptive features compared to linear methods with comparable dimensions. Specifically, a kernel PCA is used to construct a proper low-dimensional feature manifold, where accurate and efficient prediction can be performed. A fixed-point iterative pre-image estimation method is used to recover the predicted value in the original state space. We evaluated and compared the proposed method with a PCA-based approach on level-set surfaces reconstructed from point clouds captured by a 3D photogrammetry system. The prediction accuracy was evaluated in terms of root-mean-squared-error. Our proposed method achieved consistent higher prediction accuracy (sub-millimeter) for both 200 ms and 600 ms lookahead lengths compared to the PCA-based approach, and the performance gain was statistically significant.

  4. Prediction of anxiety disorders using the state-trait anxiety inventory for multiethnic adolescents.

    PubMed

    Hishinuma, E S; Miyamoto, R H; Nishimura, S T; Goebert, D A; Yuen, N Y; Makini, G K; Andrade, N N; Johnson, R C; Carlton, B S

    2001-01-01

    The purpose of this study was to determine the validity of the State-Trait Anxiety Inventory (STAI) in predicting DSM-III-R anxiety disorders based on the Diagnostic Interview Schedule for Children (DISC, Version 2.3) and using Asian/Pacific Islander adolescents. An overall prevalence rate of 9.19% for generalized anxiety disorder, overanxious disorder, or social phobia was consistent with past studies. As hypothesized, STAI negatively worded (i.e., Factor 2) items were better predictors than positively stated (i.e., Factor 1) items. The STAI State mean was a better predictor of concurrent DISC anxiety disorders as compared to STAI State Factors I or 2. In contrast, the STAI Trait Factor 2 (negatively worded) composite was the best predictor for nonconcurrent DISC anxiety disorders as compared to STAI Trait Factor 1 or the overall STAI Trait subscale. Satisfactory predictive-validity values were obtained when using the STAI State mean and Trait Factor 2 composite. Implications of these findings are discussed, including using the STAI as a screening measure for ethnically diverse adolescents.

  5. Resting-state functional connectivity predicts impulsivity in economic decision-making.

    PubMed

    Li, Nan; Ma, Ning; Liu, Ying; He, Xiao-Song; Sun, De-Lin; Fu, Xian-Ming; Zhang, Xiaochu; Han, Shihui; Zhang, Da-Ren

    2013-03-13

    Increasing neuroimaging evidence suggests an association between impulsive decision-making behavior and task-related brain activity. However, the relationship between impulsivity in decision-making and resting-state brain activity remains unknown. To address this issue, we used functional MRI to record brain activity from human adults during a resting state and during a delay discounting task (DDT) that requires choosing between an immediate smaller reward and a larger delayed reward. In experiment I, we identified four DDT-related brain networks. The money network (the striatum, posterior cingulate cortex, etc.) and the time network (the medial and dorsolateral prefrontal cortices, etc.) were associated with the valuation process; the frontoparietal network and the dorsal anterior cingulate cortex-anterior insular cortex network were related to the choice process. Moreover, we found that the resting-state functional connectivity of the brain regions in these networks was significantly correlated with participants' discounting rate, a behavioral index of impulsivity during the DDT. In experiment II, we tested an independent group of subjects and demonstrated that this resting-state functional connectivity was able to predict individuals' discounting rates. Together, these findings suggest that resting-state functional organization of the human brain may be a biomarker of impulsivity and can predict economic decision-making behavior.

  6. Phenytoin pharmacokinetic analysis and steady-state level prediction using a programmable calculator.

    PubMed

    Ng, P K

    1980-07-01

    This paper describes the use of a programmable calculator (HP-97) to determine the individualized Michaelis-Menten parameters of phenytoin by utilising the linear regression technique in fitting data of multiple doses and corresponding steady-state concentrations to a linear-transformed Michaelis-Menten equation and solving for the Michaelis-Menten parameters. In addition, the calculator program can predict the corresponding steady-state concentration of phenytoin for any given dose used in an individual by employing the derived Michaelis-Menten parameters and the Michaelis-Menten equation.

  7. Prediction of Outcome after Traumatic Brain Injury: Comparison of Disease State Index and IMPACT Calculator.

    PubMed

    Liedes, Hilkka; Mattila, Jussi; Lingsma, Hester; Lötjönen, Jyrki; Menon, David; Tenovuo, Olli; van Gils, Mark

    2016-01-01

    Traumatic brain injury (TBI) is a major cause of death and disability, especially in young adults. A reliable prediction of outcome after TBI is of great importance in clinical practice and research. We aimed to compare performance of the well-established IMPACT calculator and an alternative method, Disease State Index (DSI), in the prediction of six-month outcome after TBI. Performance of the models was evaluated using 2036 patients with moderate or severe TBI from the International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) database. Prediction performance of both models was similar. The models with more variables provided better performance than the simpler models. This study showed that the DSI is a valid tool with efficient visualizations that can help clinicians with their decision making process in clinical practice.

  8. A history of Wind Erosion Prediction Models in the United States Department of Agriculture: The Wind Erosion Prediction System (WEPS)

    NASA Astrophysics Data System (ADS)

    Wagner, Larry E.

    2013-09-01

    Development of the Wind Erosion Prediction System (WEPS) was officially inaugurated in 1985 by United States Department of Agriculture-Agricultural Research Service (USDA-ARS) scientists in response to customer requests, particularly those coming from the USDA Soil Conservation Service (SCS), for improved wind erosion prediction technology. WEPS was conceived to address deficiencies in the then-20-year-old, predominately empirical Wind Erosion Equation (WEQ) widely used by SCS, and it sparked an endeavor that relied on novel laboratory wind tunnel research as well as extensive field studies to adequately uncover the physical relationships between surface properties and their susceptibility to and influence on wind erosion. The result is that WEPS incorporates many process-based features and other capabilities not available in any other wind erosion simulation model today. The USDA Natural Resource Conservation Service (NRCS) has now implemented WEPS as a replacement for WEQ within their agency. However, the road to achieve that replacement required years of close interaction between ARS and NRCS. NRCS had to ensure they had suitable national-scale WEPS databases before implementation. User input simplifications were required as well as modifications to the reports. Run-time concerns also arose during the lengthy testing and evaluation process. Many of these were strictly non-wind erosion science issues that had to be addressed before NRCS could officially implement and begin using WEPS within their agency. The history of the development of WEPS, its unique features and its solutions to selected critical issues encountered by NRCS prior to implementation are presented and discussed.

  9. Prediction of States of Discrete Systems with Unknown Input of the Model Using Compensation

    NASA Astrophysics Data System (ADS)

    Smagin, V. I.

    2017-01-01

    The problem of state prediction for linear dynamic systems with discrete time is considered in the presence of unknown input and inaccurately specified parameters in the model. An algorithm with compensation for the constant component and estimation of the unknown variable input component by the least squares method is suggested. Results of statistical simulation are presented. The algorithm can be used for solving problems of processing information obtained as a result of observations over physical processes.

  10. Ocean State Estimation and Prediction in the Intra-Americas Seas

    DTIC Science & Technology

    2016-06-07

    full parallelization for multi- processor computers. (4) Forecast Preparation and Logistics (i) The proof-of-concept sea-trials of the IAS ROMS data ...2. To compare two state-of-the-art variational data assimilation strategies (4DVAR and IOM) and gain experience using both in regional ocean models...3. To develop ensemble prediction techniques for regional ocean models; 4. Demonstrate the utility of the ROMS data assimilation framework in a real

  11. Predicting Disposal Costs for United States Air Force Aircraft (Briefing Charts)

    DTIC Science & Technology

    2015-05-01

    I N S T I T U T E F O R D E F E N S E A N A L Y S E S Predicting Disposal Costs for United States Air Force Aircraft (Presentation) Mark F. Kaye...control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. 1. REPORT DATE (DD-MM-YYYY) 2. REPORT TYPE 3. DATES COVERED (From - To) 4

  12. Predicting the likelihood of altered streamflows at ungauged rivers across the conterminous United States

    USGS Publications Warehouse

    Eng, Kenny; Carlisle, Daren M.; Wolock, David M.; Falcone, James A.

    2013-01-01

    An approach is presented in this study to aid water-resource managers in characterizing streamflow alteration at ungauged rivers. Such approaches can be used to take advantage of the substantial amounts of biological data collected at ungauged rivers to evaluate the potential ecological consequences of altered streamflows. National-scale random forest statistical models are developed to predict the likelihood that ungauged rivers have altered streamflows (relative to expected natural condition) for five hydrologic metrics (HMs) representing different aspects of the streamflow regime. The models use human disturbance variables, such as number of dams and road density, to predict the likelihood of streamflow alteration. For each HM, separate models are derived to predict the likelihood that the observed metric is greater than (‘inflated’) or less than (‘diminished’) natural conditions. The utility of these models is demonstrated by applying them to all river segments in the South Platte River in Colorado, USA, and for all 10-digit hydrologic units in the conterminous United States. In general, the models successfully predicted the likelihood of alteration to the five HMs at the national scale as well as in the South Platte River basin. However, the models predicting the likelihood of diminished HMs consistently outperformed models predicting inflated HMs, possibly because of fewer sites across the conterminous United States where HMs are inflated. The results of these analyses suggest that the primary predictors of altered streamflow regimes across the Nation are (i) the residence time of annual runoff held in storage in reservoirs, (ii) the degree of urbanization measured by road density and (iii) the extent of agricultural land cover in the river basin.

  13. Predicting drought-induced tree mortality in the western United States

    NASA Astrophysics Data System (ADS)

    Anderegg, W.; Wolf, A.; Shevliakova, E.; Pacala, S. W.

    2015-12-01

    Projected responses of forest ecosystems to warming and drying associated with 21st century climate change vary widely from resiliency to widespread dieback. A major shortcoming of current vegetation models is the inability to account for mortality of overstory trees during extreme drought due to uncertainties in mechanisms and thresholds. In this talk, I discuss two modeling efforts to predict drought-induced tree mortality in the western United States. In the first, we identify a lethal drought threshold in the loss of vascular transport capacity from xylem cavitation, which provides insight into what initiates mortality, in Populus tremuloides in the southwestern United States. We then use the hydraulic-based threshold to produce a hindcast of a drought-induced forest dieback and compare predictions against three independent regional mortality datasets. The hydraulic threshold predicted major regional patterns of tree mortality with high accuracy based on field plots and mortality maps derived from Landsat imagery. Climate model simulations project increasing drought stress in this region that exceeds the observed mortality threshold in the high emissions scenario by the 2050s, likely triggering further widespread diebacks. In the second approach, we build a dynamic plant hydraulic model into a land-surface model and compare predictions against observed mortality patterns across multiple species. These methods provide powerful and tractable approaches for incorporating tree mortality into vegetation models to resolve uncertainty over the fate of forest ecosystems in a changing climate.

  14. Quantitative Prediction of Individual Psychopathology in Trauma Survivors Using Resting-State fMRI

    PubMed Central

    Gong, Qiyong; Li, Lingjiang; Du, Mingying; Pettersson-Yeo, William; Crossley, Nicolas; Yang, Xun; Li, Jing; Huang, Xiaoqi; Mechelli, Andrea

    2014-01-01

    Neuroimaging techniques hold the promise that they may one day aid the clinical assessment of individual psychiatric patients. However, the vast majority of studies published so far have been based on average differences between groups. This study employed a multivariate approach to examine the potential of resting-state functional magnetic resonance imaging (MRI) data for making accurate predictions about psychopathology in survivors of the 2008 Sichuan earthquake at an individual level. Resting-state functional MRI data was acquired for 121 survivors of the 2008 Sichuan earthquake each of whom was assessed for symptoms of post-traumatic stress disorder (PTSD) using the 17-item PTSD Checklist (PCL). Using a multivariate analytical method known as relevance vector regression (RVR), we examined the relationship between resting-state functional MRI data and symptom scores. We found that the use of RVR allowed quantitative prediction of clinical scores with statistically significant accuracy (correlation=0.32, P=0.006; mean squared error=176.88, P=0.001). Accurate prediction was based on functional activation in a number of prefrontal, parietal, and occipital regions. This is the first evidence that neuroimaging techniques may inform the clinical assessment of trauma-exposed individuals by providing an accurate and objective quantitative estimation of psychopathology. Furthermore, the significant contribution of parietal and occipital regions to such estimation challenges the traditional view of PTSD as a disorder specific to the fronto-limbic network. PMID:24064470

  15. 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

  16. Quantitative prediction of individual psychopathology in trauma survivors using resting-state FMRI.

    PubMed

    Gong, Qiyong; Li, Lingjiang; Du, Mingying; Pettersson-Yeo, William; Crossley, Nicolas; Yang, Xun; Li, Jing; Huang, Xiaoqi; Mechelli, Andrea

    2014-02-01

    Neuroimaging techniques hold the promise that they may one day aid the clinical assessment of individual psychiatric patients. However, the vast majority of studies published so far have been based on average differences between groups. This study employed a multivariate approach to examine the potential of resting-state functional magnetic resonance imaging (MRI) data for making accurate predictions about psychopathology in survivors of the 2008 Sichuan earthquake at an individual level. Resting-state functional MRI data was acquired for 121 survivors of the 2008 Sichuan earthquake each of whom was assessed for symptoms of post-traumatic stress disorder (PTSD) using the 17-item PTSD Checklist (PCL). Using a multivariate analytical method known as relevance vector regression (RVR), we examined the relationship between resting-state functional MRI data and symptom scores. We found that the use of RVR allowed quantitative prediction of clinical scores with statistically significant accuracy (correlation=0.32, P=0.006; mean squared error=176.88, P=0.001). Accurate prediction was based on functional activation in a number of prefrontal, parietal, and occipital regions. This is the first evidence that neuroimaging techniques may inform the clinical assessment of trauma-exposed individuals by providing an accurate and objective quantitative estimation of psychopathology. Furthermore, the significant contribution of parietal and occipital regions to such estimation challenges the traditional view of PTSD as a disorder specific to the fronto-limbic network.

  17. Thermodynamic ground state of MgB{sub 6} predicted from first principles structure search methods

    SciTech Connect

    Wang, Hui; LeBlanc, K. A.; Gao, Bo; Yao, Yansun

    2014-01-28

    Crystalline structures of magnesium hexaboride, MgB{sub 6}, were investigated using unbiased structure searching methods combined with first principles density functional calculations. An orthorhombic Cmcm structure was predicted as the thermodynamic ground state of MgB{sub 6}. The energy of the Cmcm structure is significantly lower than the theoretical MgB{sub 6} models previously considered based on a primitive cubic arrangement of boron octahedra. The Cmcm structure is stable against the decomposition to elemental magnesium and boron solids at atmospheric pressure and high pressures up to 18.3 GPa. A unique feature of the predicted Cmcm structure is that the boron atoms are clustered into two forms: localized B{sub 6} octahedra and extended B{sub ∞} ribbons. Within the boron ribbons, the electrons are delocalized and this leads to a metallic ground state with vanished electric dipoles. The present prediction is in contrast to the previous proposal that the crystalline MgB{sub 6} maintains a semiconducting state with permanent dipole moments. MgB{sub 6} is estimated to have much weaker electron-phonon coupling compared with that of MgB{sub 2}, and therefore it is not expected to be able to sustain superconductivity at high temperatures.

  18. Use of Landsat data to predict the trophic state of Minnesota lakes

    NASA Technical Reports Server (NTRS)

    Lillesand, T. M.; Johnson, W. L.; Deuell, R. L.; Lindstrom, O. M.; Meisner, D. E.

    1983-01-01

    Near-concurrent Landsat Multispectral Scanner (MSS) and ground data were obtained for 60 lakes distributed in two Landsat scene areas. The ground data included measurement of secchi disk depth, chlorophyll-a, total phosphorous, turbidity, color, and total nitrogen, as well as Carlson Trophic State Index (TSI) values derived from the first three parameters. The Landsat data best correlated with the TSI values. Prediction models were developed to classify some 100 'test' lakes appearing in the two analysis scenes on the basis of TSI estimates. Clouds, wind, poor image data, small lake size, and shallow lake depth caused some problems in lake TSI prediction. Overall, however, the Landsat-predicted TSI estimates were judged to be very reliable for the secchi-derived TSI estimation, moderately reliable for prediction of the chlorophyll-a TSI, and unreliable for the phosphorous value. Numerous Landsat data extraction procedures were compared, and the success of the Landsat TSI prediction models was a strong function of the procedure employed.

  19. A novel thermodynamic model of Mg2SiO4 with a superior representation of experimental data predicts negligible layering in mantle convection

    NASA Astrophysics Data System (ADS)

    Jacobs, M. H.; de Jong, B. H.; van den Berg, A. P.

    2005-12-01

    We present a new thermodynamic database for Mg2SiO4. This novel database has three characteristics (1) thermodynamic properties are anomaly free in the complete temperature-pressure space and experimental data are represented within their experimental uncertainties in accordance with Calphad criteria (2) it discriminates between experimental data (3) it includes thermo-mechanical properties and matches them against tomographic results within experimental uncertainty. Recently [1], we showed that large differences exist between experimental data on ambient volume and between thermal expansivity data for γ-Mg2SiO4, possibly related to hydration effects. We demonstrated that a thermodynamic technique based on polynomial parameterizations of 1 bar thermodynamic properties cannot discriminate between the different ambient volume data and thermal expansivity data for γ-Mg2SiO4, hampering the accurate prediction of bulk sound velocities in the transition zone to within tomographic accuracy. We therefore developed a computational technique based on an extended form of Kieffer's [2] approach to model the vibrational density of states of a substance, a key property to derive the Helmholtz energy. This canonical thermodynamic framework, which uses input parameters from Raman and infrared spectroscopic data, constrains thermodynamic properties tighter compared to methods based on polynomial parameterizations of thermal expansivity, heat capacity and isothermal bulk modulus. We shall present recent results on the application of this approach to the Mg2SiO4 system [3]. We discovered that anharmonicity in Mg2SiO4 (α) affects the heat capacity (CP), and position and slope of the α-β phase boundary. For γ-Mg2SiO4 our thermodynamic analysis prefers the ambient volume measured by Inoue et al. [4] and thermal expansivity measured by Suzuki [5]. Our analysis reveals that experimental data for MgO and MgSiO3 are represented to within experimental uncertainty by assuming that these

  20. Prediction of a neutral noble gas compound in the triplet state.

    PubMed

    Manna, Debashree; Ghosh, Ayan; Ghanty, Tapan K

    2015-05-26

    Discovery of the HArF molecule associated with H-Ar covalent bonding [Nature, 2000, 406, 874-876] has revolutionized the field of noble gas chemistry. In general, this class of noble gas compound involving conventional chemical bonds exists as closed-shell species in a singlet electronic state. For the first time, in a bid to predict neutral noble gas chemical compounds in their triplet electronic state, we have carried out a systematic investigation of xenon inserted FN and FP species by using quantum chemical calculations with density functional theory and various post-Hartree-Fock-based correlated methods, including the multireference configuration interaction technique. The FXeP and FXeN species are predicted to be stable by all the computational methods employed in the present work, such as density functional theory (DFT), second-order Møller-Plesset perturbation theory (MP2), coupled-cluster theory (CCSD(T)), and multireference configuration interaction (MRCI). For the purpose of comparison we have also included the Kr-inserted compounds of FN and FP species. Geometrical parameters, dissociation energies, transition-state barrier heights, atomic charge distributions, vibrational frequency data, and atoms-in-molecules properties clearly indicate that it is possible to experimentally realize the most stable state of FXeP and FXeN molecules, which is triplet in nature, through the matrix isolation technique under cryogenic conditions.

  1. Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks.

    PubMed

    Wang, Yiheng; Liu, Tong; Xu, Dong; Shi, Huidong; Zhang, Chaoyang; Mo, Yin-Yuan; Wang, Zheng

    2016-01-22

    The hypo- or hyper-methylation of the human genome is one of the epigenetic features of leukemia. However, experimental approaches have only determined the methylation state of a small portion of the human genome. We developed deep learning based (stacked denoising autoencoders, or SdAs) software named "DeepMethyl" to predict the methylation state of DNA CpG dinucleotides using features inferred from three-dimensional genome topology (based on Hi-C) and DNA sequence patterns. We used the experimental data from immortalised myelogenous leukemia (K562) and healthy lymphoblastoid (GM12878) cell lines to train the learning models and assess prediction performance. We have tested various SdA architectures with different configurations of hidden layer(s) and amount of pre-training data and compared the performance of deep networks relative to support vector machines (SVMs). Using the methylation states of sequentially neighboring regions as one of the learning features, an SdA achieved a blind test accuracy of 89.7% for GM12878 and 88.6% for K562. When the methylation states of sequentially neighboring regions are unknown, the accuracies are 84.82% for GM12878 and 72.01% for K562. We also analyzed the contribution of genome topological features inferred from Hi-C. DeepMethyl can be accessed at http://dna.cs.usm.edu/deepmethyl/.

  2. Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks

    NASA Astrophysics Data System (ADS)

    Wang, Yiheng; Liu, Tong; Xu, Dong; Shi, Huidong; Zhang, Chaoyang; Mo, Yin-Yuan; Wang, Zheng

    2016-01-01

    The hypo- or hyper-methylation of the human genome is one of the epigenetic features of leukemia. However, experimental approaches have only determined the methylation state of a small portion of the human genome. We developed deep learning based (stacked denoising autoencoders, or SdAs) software named “DeepMethyl” to predict the methylation state of DNA CpG dinucleotides using features inferred from three-dimensional genome topology (based on Hi-C) and DNA sequence patterns. We used the experimental data from immortalised myelogenous leukemia (K562) and healthy lymphoblastoid (GM12878) cell lines to train the learning models and assess prediction performance. We have tested various SdA architectures with different configurations of hidden layer(s) and amount of pre-training data and compared the performance of deep networks relative to support vector machines (SVMs). Using the methylation states of sequentially neighboring regions as one of the learning features, an SdA achieved a blind test accuracy of 89.7% for GM12878 and 88.6% for K562. When the methylation states of sequentially neighboring regions are unknown, the accuracies are 84.82% for GM12878 and 72.01% for K562. We also analyzed the contribution of genome topological features inferred from Hi-C. DeepMethyl can be accessed at http://dna.cs.usm.edu/deepmethyl/.

  3. Behavior-specific changes in transcriptional modules lead to distinct and predictable neurogenomic states

    PubMed Central

    Chandrasekaran, Sriram; Ament, Seth A.; Eddy, James A.; Rodriguez-Zas, Sandra L.; Schatz, Bruce R.; Price, Nathan D.; Robinson, Gene E.

    2011-01-01

    Using brain transcriptomic profiles from 853 individual honey bees exhibiting 48 distinct behavioral phenotypes in naturalistic contexts, we report that behavior-specific neurogenomic states can be inferred from the coordinated action of transcription factors (TFs) and their predicted target genes. Unsupervised hierarchical clustering of these transcriptomic profiles showed three clusters that correspond to three ecologically important behavioral categories: aggression, maturation, and foraging. To explore the genetic influences potentially regulating these behavior-specific neurogenomic states, we reconstructed a brain transcriptional regulatory network (TRN) model. This brain TRN quantitatively predicts with high accuracy gene expression changes of more than 2,000 genes involved in behavior, even for behavioral phenotypes on which it was not trained, suggesting that there is a core set of TFs that regulates behavior-specific gene expression in the bee brain, and other TFs more specific to particular categories. TFs playing key roles in the TRN include well-known regulators of neural and behavioral plasticity, e.g., Creb, as well as TFs better known in other biological contexts, e.g., NF-κB (immunity). Our results reveal three insights concerning the relationship between genes and behavior. First, distinct behaviors are subserved by distinct neurogenomic states in the brain. Second, the neurogenomic states underlying different behaviors rely upon both shared and distinct transcriptional modules. Third, despite the complexity of the brain, simple linear relationships between TFs and their putative target genes are a surprisingly prominent feature of the networks underlying behavior. PMID:21960440

  4. 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.

  5. Forced synchronization of large-scale circulation to increase predictability of surface states

    NASA Astrophysics Data System (ADS)

    Shen, Mao-Lin; Keenlyside, Noel; Selten, Frank; Wiegerinck, Wim; Duane, Gregory

    2016-04-01

    Numerical models are key tools in the projection of the future climate change. The lack of perfect initial condition and perfect knowledge of the laws of physics, as well as inherent chaotic behavior limit predictions. Conceptually, the atmospheric variables can be decomposed into a predictable component (signal) and an unpredictable component (noise). In ensemble prediction the anomaly of ensemble mean is regarded as the signal and the ensemble spread the noise. Naturally the prediction skill will be higher if the signal-to-noise ratio (SNR) is larger in the initial conditions. We run two ensemble experiments in order to explore a way to reduce the SNR of surface winds and temperature. One ensemble experiment is AGCM with prescribing sea surface temperature (SST); the other is AGCM with both prescribing SST and nudging the high-level temperature and winds to ERA-Interim. Each ensemble has 30 members. Larger SNR is expected and found over the tropical ocean in the first experiment because the tropical circulation is associated with the convection and the associated surface wind convergence as these are to a large extent driven by the SST. However, small SNR is found over high latitude ocean and land surface due to the chaotic and non-synchronized atmosphere states. In the second experiment the higher level temperature and winds are forced to be synchronized (nudged to reanalysis) and hence a larger SNR of surface winds and temperature is expected. Furthermore, different nudging coefficients are also tested in order to understand the limitation of both synchronization of large-scale circulation and the surface states. These experiments will be useful for the developing strategies to synchronize the 3-D states of atmospheric models that can be later used to build a super model.

  6. A Methylmercury Prediction Too For Surface Waters Across The Contiguous United States (Invited)

    NASA Astrophysics Data System (ADS)

    Krabbenhoft, D. P.; Booth, N.; Lutz, M.; Fienen, M. N.; Saltman, T.

    2009-12-01

    About 20 years ago, researchers at a few locations across the globe discovered high levels of mercury in fish from remote settings lacking any obvious mercury source. We now know that for most locations atmospheric deposition is the dominant mercury source, and that mercury methylation is the key process that translates low mercury loading rates into relatively high levels in top predators of aquatic food webs. Presently, almost all US states have advisories for elevated levels of mercury in sport fish, and as a result there is considerable public awareness and concern for this nearly ubiquitous contaminant issue. In some states, “statewide” advisories have been issued because elevated fish mercury levels are so common, or the state has no effective way to monitor thousands of lakes, reservoirs, wetlands, and streams. As such, resource managers and public health officials have limited options for informing the public on of where elevated mercury concentrations in sport fish are more likely to occur than others. This project provides, for the first time, a national map of predicted (modeled) methylmercury concentrations in surface waters, which is the most toxic and bioaccumulative form of mercury in the environment. The map is the result of over two decades of research that resulted in the formulation of conceptual models of the mercury methylation process, which is strongly governed by environmental conditions - specifically hydrologic landscapes and water quality. The resulting predictive map shows clear regional trends in the distribution of methylmercury concentrations in surface waters. East of the Mississippi, the Gulf and southeastern Atlantic coast, the northeast, the lower Mississippi valley, and Great Lakes area are predicted to have generally higher environmental methylmercury levels. Higher-elevation, well-drained areas of Appalachia are predicted to have relatively lower methylmercury abundance. Other than the prairie pothole region, in the western

  7. Gender stereotype endorsement differentially predicts girls' and boys' trait-state discrepancy in math anxiety.

    PubMed

    Bieg, Madeleine; Goetz, Thomas; Wolter, Ilka; Hall, Nathan C

    2015-01-01

    Mathematics is associated with anxiety for many students; an emotion linked to lower well-being and poorer learning outcomes. While findings typically show females to report higher trait math anxiety than males, no gender differences have to date been found in state (i.e., momentary) math anxiety. The present diary study aimed to replicate previous findings in investigating whether levels of academic self-concept was related to this discrepancy in trait vs. state anxiety measures. Additionally, mathematics-related gender stereotype endorsement (mathematics is a male domain) was investigated as an additional predictor of the trait-state discrepancy. The sample included 755 German 9th and 10th graders who completed self-report measures of trait math anxiety, math self-concept, and gender stereotype endorsement, in addition to state measures of anxiety after math classes by use of a standardized diary for 2-3 weeks (N within = 6207). As expected, females reported higher trait math anxiety but no gender differences were found for state math anxiety. Also in line with our assumptions, multilevel analyses showed the discrepancy between trait and state anxiety to be negatively related to students' self-concept (i.e., a lower discrepancy for students with higher self-concepts). Furthermore, gender stereotype endorsement differentially predicted the trait-state discrepancy: When controlling for self-concept in mathematics, females who endorsed the gender stereotype of math being a male domain more strongly overestimated their trait math anxiety as compared to their state anxiety whereas this effect was not significant for males. The present findings suggest that gender stereotype endorsement plays an important role in explaining gender differences in math anxiety above and beyond academic self-concept. Implications for future research and educational practice are discussed.

  8. Gender stereotype endorsement differentially predicts girls' and boys' trait-state discrepancy in math anxiety

    PubMed Central

    Bieg, Madeleine; Goetz, Thomas; Wolter, Ilka; Hall, Nathan C.

    2015-01-01

    Mathematics is associated with anxiety for many students; an emotion linked to lower well-being and poorer learning outcomes. While findings typically show females to report higher trait math anxiety than males, no gender differences have to date been found in state (i.e., momentary) math anxiety. The present diary study aimed to replicate previous findings in investigating whether levels of academic self-concept was related to this discrepancy in trait vs. state anxiety measures. Additionally, mathematics-related gender stereotype endorsement (mathematics is a male domain) was investigated as an additional predictor of the trait-state discrepancy. The sample included 755 German 9th and 10th graders who completed self-report measures of trait math anxiety, math self-concept, and gender stereotype endorsement, in addition to state measures of anxiety after math classes by use of a standardized diary for 2–3 weeks (Nwithin = 6207). As expected, females reported higher trait math anxiety but no gender differences were found for state math anxiety. Also in line with our assumptions, multilevel analyses showed the discrepancy between trait and state anxiety to be negatively related to students' self-concept (i.e., a lower discrepancy for students with higher self-concepts). Furthermore, gender stereotype endorsement differentially predicted the trait-state discrepancy: When controlling for self-concept in mathematics, females who endorsed the gender stereotype of math being a male domain more strongly overestimated their trait math anxiety as compared to their state anxiety whereas this effect was not significant for males. The present findings suggest that gender stereotype endorsement plays an important role in explaining gender differences in math anxiety above and beyond academic self-concept. Implications for future research and educational practice are discussed. PMID:26441778

  9. Estimating the state of a geophysical system with sparse observations: time delay methods to achieve accurate initial states for prediction

    NASA Astrophysics Data System (ADS)

    An, Zhe; Rey, Daniel; Ye, Jingxin; Abarbanel, Henry D. I.

    2017-01-01

    The problem of forecasting the behavior of a complex dynamical system through analysis of observational time-series data becomes difficult when the system expresses chaotic behavior and the measurements are sparse, in both space and/or time. Despite the fact that this situation is quite typical across many fields, including numerical weather prediction, the issue of whether the available observations are "sufficient" for generating successful forecasts is still not well understood. An analysis by Whartenby et al. (2013) found that in the context of the nonlinear shallow water equations on a β plane, standard nudging techniques require observing approximately 70 % of the full set of state variables. Here we examine the same system using a method introduced by Rey et al. (2014a), which generalizes standard nudging methods to utilize time delayed measurements. We show that in certain circumstances, it provides a sizable reduction in the number of observations required to construct accurate estimates and high-quality predictions. In particular, we find that this estimate of 70 % can be reduced to about 33 % using time delays, and even further if Lagrangian drifter locations are also used as measurements.

  10. Laser Hardening Prediction Tool Based On a Solid State Transformations Numerical Model

    SciTech Connect

    Martinez, S.; Ukar, E.; Lamikiz, A.

    2011-01-17

    This paper presents a tool to predict hardening layer in selective laser hardening processes where laser beam heats the part locally while the bulk acts as a heat sink.The tool to predict accurately the temperature field in the workpiece is a numerical model that combines a three dimensional transient numerical solution for heating where is possible to introduce different laser sources. The thermal field was modeled using a kinetic model based on Johnson-Mehl-Avrami equation. Considering this equation, an experimental adjustment of transformation parameters was carried out to get the heating transformation diagrams (CHT). With the temperature field and CHT diagrams the model predicts the percentage of base material converted into austenite. These two parameters are used as first step to estimate the depth of hardened layer in the part.The model has been adjusted and validated with experimental data for DIN 1.2379, cold work tool steel typically used in mold and die making industry. This steel presents solid state diffusive transformations at relative low temperature. These transformations must be considered in order to get good accuracy of temperature field prediction during heating phase. For model validation, surface temperature measured by pyrometry, thermal field as well as the hardened layer obtained from metallographic study, were compared with the model data showing a good adjustment.

  11. Issues in Interaction Language Specification and Representation.

    DTIC Science & Technology

    1983-11-01

    A16~ REPRESENTATION(J) VIRGINIA POLYTECHNIC INST AND STATE UNIV BLACKSBURG COMPUTER S. D N JOHNSON ET AL. NOV 83 UNCLASSIFIED CSIE-83-15 NBOB14 81 K...8217, ___ 4 ~ISSUES IN INTERACTION LANGUAGE SPECIFICATION AND REPRESENTATION Deborah H. Johnson H. Rex Hartson .4 This document has been approved...ISSUES IN INTERACTION LANGUAGE SPECIFICATION AND REPRESENTATION Deborah H. Johnson H. Rex Hartson TECHNICAL REPORT Prepared for Engineering Psychology

  12. A STATE-VARIABLE APPROACH FOR PREDICTING THE TIME REQUIRED FOR 50% RECRYSTALLIZATION

    SciTech Connect

    M. STOUT; ET AL

    2000-08-01

    It is important to be able to model the recrystallization kinetics in aluminum alloys during hot deformation. The industrial relevant process of hot rolling is an example of where the knowledge of whether or not a material recrystallizes is critical to making a product with the correct properties. Classically, the equations that describe the kinetics of recrystallization predict the time to 50% recrystallization. These equations are largely empirical; they are based on the free energy for recrystallization, a Zener-Holloman parameter, and have several adjustable exponents to fit the equation to engineering data. We have modified this form of classical theory replacing the Zener-Hollomon parameter with a deformation energy increment, a free energy available to drive recrystallization. The advantage of this formulation is that the deformation energy increment is calculated based on the previously determined temperature and strain-rate sensitivity of the constitutive response. We modeled the constitutive response of the AA5182 aluminum using a state variable approach, the value of the state variable is a function of the temperature and strain-rate history of deformation. Thus, the recrystallization kinetics is a function of only the state variable and free energy for recrystallization. There are no adjustable exponents as in classical theory. Using this approach combined with engineering recrystallization data we have been able to predict the kinetics of recrystallization in AA5182 as a function of deformation strain rate and temperature.

  13. Prediction of Cognitive States During Flight Simulation Using Multimodal Psychophysiological Sensing

    NASA Technical Reports Server (NTRS)

    Harrivel, Angela R.; Stephens, Chad L.; Milletich, Robert J.; Heinich, Christina M.; Last, Mary Carolyn; Napoli, Nicholas J.; Abraham, Nijo A.; Prinzel, Lawrence J.; Motter, Mark A.; Pope, Alan T.

    2017-01-01

    The Commercial Aviation Safety Team found the majority of recent international commercial aviation accidents attributable to loss of control inflight involved flight crew loss of airplane state awareness (ASA), and distraction was involved in all of them. Research on attention-related human performance limiting states (AHPLS) such as channelized attention, diverted attention, startle/surprise, and confirmation bias, has been recommended in a Safety Enhancement (SE) entitled "Training for Attention Management." To accomplish the detection of such cognitive and psychophysiological states, a broad suite of sensors was implemented to simultaneously measure their physiological markers during a high fidelity flight simulation human subject study. Twenty-four pilot participants were asked to wear the sensors while they performed benchmark tasks and motion-based flight scenarios designed to induce AHPLS. Pattern classification was employed to predict the occurrence of AHPLS during flight simulation also designed to induce those states. Classifier training data were collected during performance of the benchmark tasks. Multimodal classification was performed, using pre-processed electroencephalography, galvanic skin response, electrocardiogram, and respiration signals as input features. A combination of one, some or all modalities were used. Extreme gradient boosting, random forest and two support vector machine classifiers were implemented. The best accuracy for each modality-classifier combination is reported. Results using a select set of features and using the full set of available features are presented. Further, results are presented for training one classifier with the combined features and for training multiple classifiers with features from each modality separately. Using the select set of features and combined training, multistate prediction accuracy averaged 0.64 +/- 0.14 across thirteen participants and was significantly higher than that for the separate training

  14. Improved prediction of RNA tertiary structure with insights into native state dynamics.

    PubMed

    Bida, John Paul; Maher, L James

    2012-03-01

    The importance of RNA tertiary structure is evident from the growing number of published high resolution NMR and X-ray crystallographic structures of RNA molecules. These structures provide insights into function and create a knowledge base that is leveraged by programs such as Assemble, ModeRNA, RNABuilder, NAST, FARNA, Mc-Sym, RNA2D3D, and iFoldRNA for tertiary structure prediction and design. While these methods sample native-like RNA structures during simulations, all struggle to capture the native RNA conformation after scoring. We propose RSIM, an improved RNA fragment assembly method that preserves RNA global secondary structure while sampling conformations. This approach enhances the quality of predicted RNA tertiary structure, provides insights into the native state dynamics, and generates a powerful visualization of the RNA conformational space. RSIM is available for download from http://www.github.com/jpbida/rsim.

  15. Predicting the spread of all invasive forest pests in the United States.

    PubMed

    Hudgins, Emma J; Liebhold, Andrew M; Leung, Brian

    2017-04-01

    We tested whether a general spread model could capture macroecological patterns across all damaging invasive forest pests in the United States. We showed that a common constant dispersal kernel model, simulated from the discovery date, explained 67.94% of the variation in range size across all pests, and had 68.00% locational accuracy between predicted and observed locational distributions. Further, by making dispersal a function of forest area and human population density, variation explained increased to 75.60%, with 74.30% accuracy. These results indicated that a single general dispersal kernel model was sufficient to predict the majority of variation in extent and locational distribution across pest species and that proxies of propagule pressure and habitat invasibility - well-studied predictors of establishment - should also be applied to the dispersal stage. This model provides a key element to forecast novel invaders and to extend pathway-level risk analyses to include spread.

  16. Testing for causality in reconstructed state spaces by an optimized mixed prediction method

    NASA Astrophysics Data System (ADS)

    Krakovská, Anna; Hanzely, Filip

    2016-11-01

    In this study, a method of causality detection was designed to reveal coupling between dynamical systems represented by time series. The method is based on the predictions in reconstructed state spaces. The results of the proposed method were compared with outcomes of two other methods, the Granger VAR test of causality and the convergent cross-mapping. We used two types of test data. The first test example is a unidirectional connection of chaotic systems of Rössler and Lorenz type. The second one, the fishery model, is an example of two correlated observables without a causal relationship. The results showed that the proposed method of optimized mixed prediction was able to reveal the presence and the direction of coupling and distinguish causality from mere correlation as well.

  17. A state-based approach to trend recognition and failure prediction for the Space Station Freedom

    NASA Technical Reports Server (NTRS)

    Nelson, Kyle S.; Hadden, George D.

    1992-01-01

    A state-based reasoning approach to trend recognition and failure prediction for the Altitude Determination, and Control System (ADCS) of the Space Station Freedom (SSF) is described. The problem domain is characterized by features (e.g., trends and impending failures) that develop over a variety of time spans, anywhere from several minutes to several years. Our state-based reasoning approach, coupled with intelligent data screening, allows features to be tracked as they develop in a time-dependent manner. That is, each state machine has the ability to encode a time frame for the feature it detects. As features are detected, they are recorded and can be used as input to other state machines, creating a hierarchical feature recognition scheme. Furthermore, each machine can operate independently of the others, allowing simultaneous tracking of features. State-based reasoning was implemented in the trend recognition and the prognostic modules of a prototype Space Station Freedom Maintenance and Diagnostic System (SSFMDS) developed at Honeywell's Systems and Research Center.

  18. A comparison of methods to predict historical daily streamflow time series in the southeastern United States

    USGS Publications Warehouse

    Farmer, William H.; Archfield, Stacey A.; Over, Thomas M.; Hay, Lauren E.; LaFontaine, Jacob H.; Kiang, Julie E.

    2015-01-01

    Effective and responsible management of water resources relies on a thorough understanding of the quantity and quality of available water. Streamgages cannot be installed at every location where streamflow information is needed. As part of its National Water Census, the U.S. Geological Survey is planning to provide streamflow predictions for ungaged locations. In order to predict streamflow at a useful spatial and temporal resolution throughout the Nation, efficient methods need to be selected. This report examines several methods used for streamflow prediction in ungaged basins to determine the best methods for regional and national implementation. A pilot area in the southeastern United States was selected to apply 19 different streamflow prediction methods and evaluate each method by a wide set of performance metrics. Through these comparisons, two methods emerged as the most generally accurate streamflow prediction methods: the nearest-neighbor implementations of nonlinear spatial interpolation using flow duration curves (NN-QPPQ) and standardizing logarithms of streamflow by monthly means and standard deviations (NN-SMS12L). It was nearly impossible to distinguish between these two methods in terms of performance. Furthermore, neither of these methods requires significantly more parameterization in order to be applied: NN-SMS12L requires 24 regional regressions—12 for monthly means and 12 for monthly standard deviations. NN-QPPQ, in the application described in this study, required 27 regressions of particular quantiles along the flow duration curve. Despite this finding, the results suggest that an optimal streamflow prediction method depends on the intended application. Some methods are stronger overall, while some methods may be better at predicting particular statistics. The methods of analysis presented here reflect a possible framework for continued analysis and comprehensive multiple comparisons of methods of prediction in ungaged basins (PUB

  19. Precise QCD Predictions for the Production of Dijet Final States in Deep Inelastic Scattering.

    PubMed

    Currie, James; Gehrmann, Thomas; Niehues, Jan

    2016-07-22

    The production of two-jet final states in deep inelastic scattering is an important QCD precision observable. We compute it for the first time to next-to-next-to-leading order (NNLO) in perturbative QCD. Our calculation is fully differential in the lepton and jet variables and allows one to impose cuts on the jets in both the laboratory and the Breit frame. We observe that the NNLO corrections are moderate in size, except at kinematical edges, and that their inclusion leads to a substantial reduction of the scale variation uncertainty on the predictions. Our results will enable the inclusion of deep inelastic dijet data in precision phenomenology studies.

  20. Can we predict seasonal changes in high impact weather in the United States?

    NASA Astrophysics Data System (ADS)

    Jung, Eunsil; Kirtman, Ben P.

    2016-07-01

    Severe convective storms cause catastrophic losses each year in the United States, suggesting that any predictive capability is of great societal benefit. While it is known that El Niño and the Southern Oscillation (ENSO) influence high impact weather events, such as a tornado activity and severe storms, in the US during early spring, this study highlights that the influence of ENSO on US severe storm characteristics is weak during May-July. Instead, warm water in the Gulf of Mexico is a potential predictor for moist instability, which is an important factor in influencing the storm characteristics in the US during May-July.

  1. DBCP: a web server for disulfide bonding connectivity pattern prediction without the prior knowledge of the bonding state of cysteines

    PubMed Central

    Lin, Hsuan-Hung; Tseng, Lin-Yu

    2010-01-01

    The proper prediction of the location of disulfide bridges is efficient in helping to solve the protein folding problem. Most of the previous works on the prediction of disulfide connectivity pattern use the prior knowledge of the bonding state of cysteines. The DBCP web server provides prediction of disulfide bonding connectivity pattern without the prior knowledge of the bonding state of cysteines. The method used in this server improves the accuracy of disulfide connectivity pattern prediction (Qp) over the previous studies reported in the literature. This DBCP server can be accessed at http://120.107.8.16/dbcp or http://140.120.14.136/dbcp. PMID:20530534

  2. Decadal temperature predictions over the continental United States: Analysis and Enhancement

    NASA Astrophysics Data System (ADS)

    Salvi, Kaustubh; Villarini, Gabriele; Vecchi, Gabriel A.; Ghosh, Subimal

    2017-02-01

    Increases in global temperature over recent decades and the projected acceleration in warming trends over the 21 century have resulted in a strong need to obtain information about future temperature conditions. Hence, skillful decadal temperature predictions (DTPs) can have profound societal and economic benefits through informed planning and response. However, skillful and actionable DTPs are extremely challenging to achieve. Even though general circulation models (GCMs) provide decadal predictions of different climate variables, the direct use of GCM data for regional-scale impacts assessment is not encouraged because of the limited skill they possibly exhibit and their coarse spatial resolution. Here, we focus on 14 GCMs and evaluate seasonally and regionally averaged skills in DTPs over the continental United States. Moreover, we address the limitations in skill and spatial resolution in the GCM outputs using two data-driven approaches: (1) quantile-based bias correction and (2) linear regression-based statistical downscaling. For both the approaches, statistical parameters/relationships, established over the calibration period (1961-1990) are applied to retrospective and near future decadal predictions by GCMs to obtain DTPs at `4 km' resolution. Predictions are assessed using different evaluation metrics, long-term statistical properties, and uncertainty in terms of the range of predictions. Both the approaches adopted here show improvements with respect to the raw GCM data, particularly in terms of the long-term statistical properties and uncertainty, irrespective of lead time. The outcome of the study is monthly DTPs from 14 GCMs with a spatial resolution of 4 km, which can be used as a key input for impacts assessments.

  3. Resting-state functional connectivity predicts longitudinal change in autistic traits and adaptive functioning in autism

    PubMed Central

    Plitt, Mark; Barnes, Kelly Anne; Wallace, Gregory L.; Kenworthy, Lauren; Martin, Alex

    2015-01-01

    Although typically identified in early childhood, the social communication symptoms and adaptive behavior deficits that are characteristic of autism spectrum disorder (ASD) persist throughout the lifespan. Despite this persistence, even individuals without cooccurring intellectual disability show substantial heterogeneity in outcomes. Previous studies have found various behavioral assessments [such as intelligence quotient (IQ), early language ability, and baseline autistic traits and adaptive behavior scores] to be predictive of outcome, but most of the variance in functioning remains unexplained by such factors. In this study, we investigated to what extent functional brain connectivity measures obtained from resting-state functional connectivity MRI (rs-fcMRI) could predict the variance left unexplained by age and behavior (follow-up latency and baseline autistic traits and adaptive behavior scores) in two measures of outcome—adaptive behaviors and autistic traits at least 1 y postscan (mean follow-up latency = 2 y, 10 mo). We found that connectivity involving the so-called salience network (SN), default-mode network (DMN), and frontoparietal task control network (FPTCN) was highly predictive of future autistic traits and the change in autistic traits and adaptive behavior over the same time period. Furthermore, functional connectivity involving the SN, which is predominantly composed of the anterior insula and the dorsal anterior cingulate, predicted reliable improvement in adaptive behaviors with 100% sensitivity and 70.59% precision. From rs-fcMRI data, our study successfully predicted heterogeneity in outcomes for individuals with ASD that was unaccounted for by simple behavioral metrics and provides unique evidence for networks underlying long-term symptom abatement. PMID:26627261

  4. Resting-state functional connectivity predicts longitudinal change in autistic traits and adaptive functioning in autism.

    PubMed

    Plitt, Mark; Barnes, Kelly Anne; Wallace, Gregory L; Kenworthy, Lauren; Martin, Alex

    2015-12-01

    Although typically identified in early childhood, the social communication symptoms and adaptive behavior deficits that are characteristic of autism spectrum disorder (ASD) persist throughout the lifespan. Despite this persistence, even individuals without cooccurring intellectual disability show substantial heterogeneity in outcomes. Previous studies have found various behavioral assessments [such as intelligence quotient (IQ), early language ability, and baseline autistic traits and adaptive behavior scores] to be predictive of outcome, but most of the variance in functioning remains unexplained by such factors. In this study, we investigated to what extent functional brain connectivity measures obtained from resting-state functional connectivity MRI (rs-fcMRI) could predict the variance left unexplained by age and behavior (follow-up latency and baseline autistic traits and adaptive behavior scores) in two measures of outcome--adaptive behaviors and autistic traits at least 1 y postscan (mean follow-up latency = 2 y, 10 mo). We found that connectivity involving the so-called salience network (SN), default-mode network (DMN), and frontoparietal task control network (FPTCN) was highly predictive of future autistic traits and the change in autistic traits and adaptive behavior over the same time period. Furthermore, functional connectivity involving the SN, which is predominantly composed of the anterior insula and the dorsal anterior cingulate, predicted reliable improvement in adaptive behaviors with 100% sensitivity and 70.59% precision. From rs-fcMRI data, our study successfully predicted heterogeneity in outcomes for individuals with ASD that was unaccounted for by simple behavioral metrics and provides unique evidence for networks underlying long-term symptom abatement.

  5. Improved MEGAN predictions of biogenic isoprene in the contiguous United States

    NASA Astrophysics Data System (ADS)

    Wang, Peng; Schade, Gunnar; Estes, Mark; Ying, Qi

    2017-01-01

    Isoprene emitted from biogenic sources significantly contributes to ozone and secondary organic aerosol formation in the troposphere. The Model of Emissions of Gases and Aerosols from Nature (MEGAN) has been widely used to estimate isoprene emissions from local to global scales. However, previous studies have shown that MEGAN significantly over-predicts isoprene emissions in the contiguous United States (US). In this study, ambient isoprene concentrations in the US were simulated by the Community Multiscale Air Quality (CMAQ) model (v5.0.1) using biogenic emissions estimated by MEGAN v2.10 with several different gridded isoprene emission factor (EF) fields. Best isoprene predictions were obtained with the EF field based on the Biogenic Emissions Landcover Database v4 (BELD4) from US EPA for its Biogenic Emission Inventory System (BEIS) model v3.61 (MEGAN-BEIS361). A seven-month simulation (April to October 2011) of isoprene emissions with MEGAN-BEIS361 and ambient concentrations using CMAQ shows that observed spatial and temporal variations (both diurnal and seasonal) of isoprene concentrations can be well predicted at most non-urban monitors using isoprene emission estimation from the MEGAN-BEIS361 without significant biases. The predicted monthly average vertical column density of formaldehyde (HCHO), a reactive volatile organic compound with significant contributions from isoprene oxidation, generally agree with the spatial distribution of HCHO column density derived using satellite data collected by the Ozone Monitoring Instrument (OMI), although summer month vertical column densities in the southeast US were overestimated, which suggests that isoprene emission might still be overestimated in that region. The agreement between observation and prediction may be further improved if more accurate PAR values, such as those derived from satellite-based observations, were used in modeling the biogenic emissions.

  6. Real-time object tracking with correlation filtering and state prediction

    NASA Astrophysics Data System (ADS)

    Contreras, Viridiana; Díaz-Ramírez, Victor H.; Kober, Vitaly; Tapia-Armenta, Juan J.

    2013-09-01

    A real-time tracking system based on adaptive correlation filtering and state prediction is proposed. The system is able to estimate at high-rate the position of multiple targets within the observed scene by taking into account information of past and present scene-frames. The position of the targets in the current frame is estimated with the help of a bank of composite correlation filters applied to several small regions taken from the observed scene. These small regions are updated in each frame according to information from a state predictor based on the motion model of targets in a twodimensional plane. The proposed system is implemented on a graphics processing unit to take advantage of massive parallelism. Computer simulation results obtained with the proposed system are presented and discussed in terms of tracking accuracy and real-time operation efficiency.

  7. Risk Recognition, Attachment Anxiety, Self-Efficacy, and State Dissociation Predict Revictimization

    PubMed Central

    Bockers, Estelle; Roepke, Stefan; Michael, Lars; Renneberg, Babette; Knaevelsrud, Christine

    2014-01-01

    Background Previous research has identified a number of variables that constitute potential risk factors for victimization and revictimization. However, it remains unclear which factors are associated not only with childhood or adolescent victimization, but specifically with revictimization. The aim of this study was to determine whether risk recognition ability and other variables previously associated with revictimization are specifically able to differentiate individuals with childhood victimization only from revictimized individuals, and thus to predict revictimization. Methods Participants were N = 85 women aged 21 to 64 years who were interpersonally victimized in childhood or adolescence only, interpersonally revictimized in another period of life, or not victimized. A logistic regression analysis was conducted to examine whether risk recognition ability, sensation seeking, self-efficacy, state dissociation, shame, guilt, assertiveness, and attachment anxiety predicted group membership. Results The logistic regression analysis revealed risk recognition ability, attachment anxiety, state dissociation, and self-efficacy as significant predictors of revictimization. The final model accurately classified 82.4% of revictimized, 59.1% of victimized and 93.1% of non-victimized women. The overall classification rate was 80%. Conclusions This study suggests that risk recognition ability, attachment anxiety, self-efficacy, and state dissociation play a key role in revictimization. Increased risk recognition ability after an interpersonal trauma may act as a protective factor against repeated victimization that revictimized individuals may lack. A lack of increased risk recognition ability in combination with higher attachment anxiety, lower self-efficacy, and higher state dissociation may increase the risk of revictimization. PMID:25238153

  8. One dimensional representations in quantum optics

    NASA Technical Reports Server (NTRS)

    Janszky, J.; Adam, P.; Foldesi, I.; Vinogradov, An. V.

    1993-01-01

    The possibility of representing the quantum states of a harmonic oscillator not on the whole alpha-plane but on its one dimensional manifolds is considered. It is shown that a simple Gaussian distribution along a straight line describes a quadrature squeezed state while a similar Gaussian distribution along a circle leads to the amplitude squeezed state. The connection between the one dimensional representations and the usual Glauber representation is discussed.

  9. An Information Retrieval Approach for Robust Prediction of Road Surface States

    PubMed Central

    Park, Jae-Hyung; Kim, Kwanho

    2017-01-01

    Recently, due to the increasing importance of reducing severe vehicle accidents on roads (especially on highways), the automatic identification of road surface conditions, and the provisioning of such information to drivers in advance, have recently been gaining significant momentum as a proactive solution to decrease the number of vehicle accidents. In this paper, we firstly propose an information retrieval approach that aims to identify road surface states by combining conventional machine-learning techniques and moving average methods. Specifically, when signal information is received from a radar system, our approach attempts to estimate the current state of the road surface based on the similar instances observed previously based on utilizing a given similarity function. Next, the estimated state is then calibrated by using the recently estimated states to yield both effective and robust prediction results. To validate the performances of the proposed approach, we established a real-world experimental setting on a section of actual highway in South Korea and conducted a comparison with the conventional approaches in terms of accuracy. The experimental results show that the proposed approach successfully outperforms the previously developed methods. PMID:28134859

  10. Predicting the crystalline and porous equations of state for secondary explosives

    NASA Astrophysics Data System (ADS)

    Wixom, Ryan; Damm, David

    2013-06-01

    Accurate simulations of energetic material response necessitate accurate unreacted equations of state at pressures much higher than even the C-J state. Unfortunately, for reactive materials, experimental data at high pressures may be unattainable, and extrapolation from low-pressure data results in unacceptable uncertainty. In addition to being low-pressure, the available data is typically limited to the porous state. The fully-dense, or crystalline, equation of state is required for building mesoscale simulations of the dynamic response of energetic materials. We have used quantum molecular dynamics to predict the Hugoniots and 300 K isotherms of crystalline PETN, HNS, CL-20 and TATB up to pressures not attainable in experiments. The porous Hugoniots for these materials were then analytically obtained and are validated by comparison with available data. Our calculations for TATB confirm the presence of a kink in the Hugoniot, and the softening of the shock response is explained in terms of a change in molecular conformation and the loss of aromaticity.

  11. Predictive Sea State Estimation for Automated Ride Control and Handling - PSSEARCH

    NASA Technical Reports Server (NTRS)

    Huntsberger, Terrance L.; Howard, Andrew B.; Aghazarian, Hrand; Rankin, Arturo L.

    2012-01-01

    PSSEARCH provides predictive sea state estimation, coupled with closed-loop feedback control for automated ride control. It enables a manned or unmanned watercraft to determine the 3D map and sea state conditions in its vicinity in real time. Adaptive path-planning/ replanning software and a control surface management system will then use this information to choose the best settings and heading relative to the seas for the watercraft. PSSEARCH looks ahead and anticipates potential impact of waves on the boat and is used in a tight control loop to adjust trim tabs, course, and throttle settings. The software uses sensory inputs including IMU (Inertial Measurement Unit), stereo, radar, etc. to determine the sea state and wave conditions (wave height, frequency, wave direction) in the vicinity of a rapidly moving boat. This information can then be used to plot a safe path through the oncoming waves. The main issues in determining a safe path for sea surface navigation are: (1) deriving a 3D map of the surrounding environment, (2) extracting hazards and sea state surface state from the imaging sensors/map, and (3) planning a path and control surface settings that avoid the hazards, accomplish the mission navigation goals, and mitigate crew injuries from excessive heave, pitch, and roll accelerations while taking into account the dynamics of the sea surface state. The first part is solved using a wide baseline stereo system, where 3D structure is determined from two calibrated pairs of visual imagers. Once the 3D map is derived, anything above the sea surface is classified as a potential hazard and a surface analysis gives a static snapshot of the waves. Dynamics of the wave features are obtained from a frequency analysis of motion vectors derived from the orientation of the waves during a sequence of inputs. Fusion of the dynamic wave patterns with the 3D maps and the IMU outputs is used for efficient safe path planning.

  12. Fine-scale predictions of distributions of Chagas disease vectors in the state of Guanajuato, Mexico.

    PubMed

    López-Cárdenas, Jorge; Gonzalez Bravo, Francisco Ernesto; Salazar Schettino, Paz Maria; Gallaga Solorzano, Juan Carlos; Ramírez Barba, Ector; Martinez Mendez, Joel; Sánchez-Cordero, V; Peterson, A Townsend; Ramsey, J M

    2005-11-01

    One of the most daunting challenges for Chagas disease surveillance and control in Mexico is the lack of community level data on vector distributions. Although many states now have assembled representative domestic triatomine collections, only two triatomine specimens had been collected and reported previously from the state of Guanajuato. Field personnel from the state's Secretaría de Salud conducted health promotion activities in 43 of the 46 counties in the state and received donations of a total of 2,522 triatomine specimens between 1998 and 2002. All specimens were identified, and live insects examined for Trypanosoma cruzi. In an effort to develop fine-scale distributional data for Guanajuato, collection localities were georeferenced and ecological niches were modeled for each species by using evolutionary-computing approaches. Five species were collected: Triatoma mexicana (Herrich-Schaeffer), Triatoma longipennis (Usinger), Triatoma pallidipennis (Stål), Triatoma barberi (Usinger), and Triatoma dimidiata (Latreille) from 201 communities located at elevations of 870-2,200 m. Based on collection success, T. mexicana had the broadest dispersion, although niche mapping indicates that T. barberi represents the greatest risk for transmission of Chagas disease in the state. T. dimidiata was represented in collections by a single adult collected from one village outside the predicted area for all species. For humans, an estimated 3,755,380 individuals are at risk for vector transmission in the state, with an incidence of 3,500 new cases per year; overall seroprevalences of 2.6% indicate that 97,640 individuals are infected with T. cruzi at present, including 29,300 chronic cases.

  13. 10 CFR 72.206 - Representation.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... 10 Energy 2 2010-01-01 2010-01-01 false Representation. 72.206 Section 72.206 Energy NUCLEAR REGULATORY COMMISSION (CONTINUED) LICENSING REQUIREMENTS FOR THE INDEPENDENT STORAGE OF SPENT NUCLEAR FUEL... Information to State Governments and Indian Tribes § 72.206 Representation. Any person who acts under...

  14. 10 CFR 72.206 - Representation.

    Code of Federal Regulations, 2011 CFR

    2011-01-01

    ... 10 Energy 2 2011-01-01 2011-01-01 false Representation. 72.206 Section 72.206 Energy NUCLEAR REGULATORY COMMISSION (CONTINUED) LICENSING REQUIREMENTS FOR THE INDEPENDENT STORAGE OF SPENT NUCLEAR FUEL... Information to State Governments and Indian Tribes § 72.206 Representation. Any person who acts under...

  15. Predicting behavioural responses to novel organisms: state-dependent detection theory.

    PubMed

    Trimmer, Pete C; Ehlman, Sean M; Sih, Andrew

    2017-01-25

    Human activity alters natural habitats for many species. Understanding variation in animals' behavioural responses to these changing environments is critical. We show how signal detection theory can be used within a wider framework of state-dependent modelling to predict behavioural responses to a major environmental change: novel, exotic species. We allow thresholds for action to be a function of reserves, and demonstrate how optimal thresholds can be calculated. We term this framework 'state-dependent detection theory' (SDDT). We focus on behavioural and fitness outcomes when animals continue to use formerly adaptive thresholds following environmental change. In a simple example, we show that exposure to novel animals which appear dangerous-but are actually safe-(e.g. ecotourists) can have catastrophic consequences for 'prey' (organisms that respond as if the new organisms are predators), significantly increasing mortality even when the novel species is not predatory. SDDT also reveals that the effect on reproduction can be greater than the effect on lifespan. We investigate factors that influence the effect of novel organisms, and address the potential for behavioural adjustments (via evolution or learning) to recover otherwise reduced fitness. Although effects of environmental change are often difficult to predict, we suggest that SDDT provides a useful route ahead.

  16. Gis predictive mapping of terrestrial gamma radiation in the Northern State, Sudan.

    PubMed

    Hamed Bashier, E; Salih, I; Khatir Sam, A

    2012-09-01

    This study presents the evaluation of absorbed dose in air due to gamma-emitting nuclides from (238)U and (232)Th series, (40)K and (137)Cs and the corresponding geographical information system (GIS) predictive mapping for the Northern State. Activity concentration of (238)U, (232)Th , (40)K and (137)Cs in soil samples collected from different locations have been measured using high-resolution gamma spectrometry. On  average, activity concentrations were 19±4 ((238)U), 47±11 ((232)Th), 317±65 ((40)K) and 2.26 Bq kg(-1) for (137)Cs. Absorbed dose rate in air at a height of 1 m above ground surface was calculated using seven sets of dose rate conversion factors (DRCFs) and the corresponding annual effective dose was estimated. On average, the values obtained fall within a narrow range of 44 and 53 nGy h(-1), indicating that the variation in absorbed dose rate is insignificant for different DRCFs. The corresponding annual effective dose ranged from 53 to 65 µSv y(-1). Using GIS, prediction maps for concentrations of (238)U, (232)Th, (40)K and (137)Cs were produced. Also, a map for absorbed dose rate in air at a height of 1 m above the ground level was produced, which showed a trend of increasing from the west towards south-east of the State.

  17. Biomarkers and the prediction of atrial fibrillation: state of the art.

    PubMed

    O'Neal, Wesley T; Venkatesh, Sanjay; Broughton, Stephen T; Griffin, William F; Soliman, Elsayed Z

    2016-01-01

    Atrial fibrillation (AF) is the most common sustained arrhythmia encountered in clinical practice, and it places a substantial burden on the health care system. Despite improvements in our understanding of AF pathophysiology, we have yet to develop targeted preventive therapies. Recently, numerous biological markers have been identified to aid in the prediction of future AF events. Subclinical markers of atrial stress, inflammation, endothelial dysfunction, kidney dysfunction, and atherosclerosis have been linked to AF. The connection between these markers and AF is the identification of subclinical states in which AF propagation is likely to occur, as these conditions are associated with abnormal atrial remodeling and fibrosis. Additionally, several risk scores have been developed to aid in the identification of at-risk patients. The practicing clinician should be aware of these subclinical markers, as several of these markers improve the predictive abilities of current AF risk scores. Knowledge of these subclinical markers also provides clinicians with a better understanding of AF risk factors, and the opportunity to reduce the occurrence of AF by incorporating well-known cardiovascular disease risk factor modification strategies. In this review, we highlight several novel biological markers that have improved our understanding of AF pathophysiology and appraise the utility of these markers to improve our ability to predict future AF events.

  18. Precedence relationship representations of mechanical assembly sequences

    NASA Technical Reports Server (NTRS)

    Homendemello, L. S.; Sanderson, A. C.

    1989-01-01

    Two types of precedence relationship representations for mechanical assembly sequences are presented: precedence relationships between the establishment of one connection between two parts and the establishment of another connection, and precedence relationships between the establishment of one connection and states of the assembly process. Precedence relationship representations have the advantage of being very compact. The problem with these representations was how to guarantee their correctness and completeness. Two theorems are presented each of which leads to the generation of one type of precedence relationship representation guaranteeing its correctness and completeness for a class of assemblies.

  19. Bayesian probabilistic model for life prediction and fault mode classification of solid state luminaires

    SciTech Connect

    Lall, Pradeep; Wei, Junchao; Sakalaukus, Peter

    2014-06-22

    A new method has been developed for assessment of the onset of degradation in solid state luminaires to classify failure mechanisms by using metrics beyond lumen degradation that are currently used for identification of failure. Luminous Flux output, Correlated Color Temperature Data on Philips LED Lamps has been gathered under 85°C/85%RH till lamp failure. Failure modes of the test population of the lamps have been studied to understand the failure mechanisms in 85°C/85%RH accelerated test. Results indicate that the dominant failure mechanism is the discoloration of the LED encapsulant inside the lamps which is the likely cause for the luminous flux degradation and the color shift. The acquired data has been used in conjunction with Bayesian Probabilistic Models to identify luminaires with onset of degradation much prior to failure through identification of decision boundaries between lamps with accrued damage and lamps beyond the failure threshold in the feature space. In addition luminaires with different failure modes have been classified separately from healthy pristine luminaires. The α-λ plots have been used to evaluate the robustness of the proposed methodology. Results show that the predicted degradation for the lamps tracks the true degradation observed during 85°C/85%RH during accelerated life test fairly closely within the ±20% confidence bounds. Correlation of model prediction with experimental results indicates that the presented methodology allows the early identification of the onset of failure much prior to development of complete failure distributions and can be used for assessing the damage state of SSLs in fairly large deployments. It is expected that, the new prediction technique will allow the development of failure distributions without testing till L70 life for the manifestation of failure.

  20. Stoichiometric Representation of Gene–Protein–Reaction Associations Leverages Constraint-Based Analysis from Reaction to Gene-Level Phenotype Prediction

    PubMed Central

    2016-01-01

    Genome-scale metabolic reconstructions are currently available for hundreds of organisms. Constraint-based modeling enables the analysis of the phenotypic landscape of these organisms, predicting the response to genetic and environmental perturbations. However, since constraint-based models can only describe the metabolic phenotype at the reaction level, understanding the mechanistic link between genotype and phenotype is still hampered by the complexity of gene-protein-reaction associations. We implement a model transformation that enables constraint-based methods to be applied at the gene level by explicitly accounting for the individual fluxes of enzymes (and subunits) encoded by each gene. We show how this can be applied to different kinds of constraint-based analysis: flux distribution prediction, gene essentiality analysis, random flux sampling, elementary mode analysis, transcriptomics data integration, and rational strain design. In each case we demonstrate how this approach can lead to improved phenotype predictions and a deeper understanding of the genotype-to-phenotype link. In particular, we show that a large fraction of reaction-based designs obtained by current strain design methods are not actually feasible, and show how our approach allows using the same methods to obtain feasible gene-based designs. We also show, by extensive comparison with experimental 13C-flux data, how simple reformulations of different simulation methods with gene-wise objective functions result in improved prediction accuracy. The model transformation proposed in this work enables existing constraint-based methods to be used at the gene level without modification. This automatically leverages phenotype analysis from reaction to gene level, improving the biological insight that can be obtained from genome-scale models. PMID:27711110

  1. B2N2O4: Prediction of a Magnetic Ground State for a Light Main-Group Molecule

    SciTech Connect

    Varga, Zoltan; Truhlar, Donald G.

    2015-09-08

    Cyclobutanetetrone, (CO)4, has a triplet ground state. Here we predict, based on electronic structure calculations, that the B2N2O4 molecule also has a triplet ground state and is therefore paramagnetic; the structure is an analogue of (CO)4 in which the carbon ring is replaced by a (BN)2 ring. Similar to (CO)4, the triplet ground-state structure of B2N2O4 is also thermodynamically unstable. Besides analysis of the molecular orbitals, we found that the partial atomic charges are good indicators for predicting magnetic ground states.

  2. The predictive state: Science, territory and the future of the Indian climate.

    PubMed

    Mahony, Martin

    2014-02-01

    Acts of scientific calculation have long been considered central to the formation of the modern nation state, yet the transnational spaces of knowledge generation and political action associated with climate change seem to challenge territorial modes of political order. This article explores the changing geographies of climate prediction through a study of the ways in which climate change is rendered knowable at the national scale in India. The recent controversy surrounding an erroneous prediction of melting Himalayan glaciers by the Intergovernmental Panel on Climate Change provides a window onto the complex and, at times, antagonistic relationship between the Panel and Indian political and scientific communities. The Indian reaction to the error, made public in 2009, drew upon a national history of contestation around climate change science and corresponded with the establishment of a scientific assessment network, the Indian Network for Climate Change Assessment, which has given the state a new platform on which to bring together knowledge about the future climate. I argue that the Indian Network for Climate Change Assessment is indicative of the growing use of regional climate models within longer traditions of national territorial knowledge-making, allowing a rescaling of climate change according to local norms and practices of linking scientific knowledge to political action. I illustrate the complex co-production of the epistemic and the normative in climate politics, but also seek to show how co-productionist understandings of science and politics can function as strategic resources in the ongoing negotiation of social order. In this case, scientific rationalities and modes of environmental governance contribute to the contested epistemic construction of territory and the evolving spatiality of the modern nation state under a changing climate.

  3. CensusPlus: A sampling and prediction approach for the 2000 census of the United States

    SciTech Connect

    Wright, T.

    1995-07-01

    For a general audience, this paper offers details of a simple proposal for estimation of the population and housing in the year 2000 for the United States. Under CensusPlus, two surveys (mass enumeration and plus sample enumeration) are made of a universe with M blocks. The mass enumeration results in an initial preliminary count for each and every block in the country. The plus sample blocks undergo a second extra high quality count which when compared with the initial count leads to observed resolved counts for the sample blocks. Under a simple model, resolved counts are predicted for the nonsample blocks. Hence an optimal estimator of N, the universe size. is obtained by adding these observed (in sample) and predicted (not in sample) resolved block counts. In fact, this sum turns out to be the classical ratio estimator. This one number census collection is additive and consistent for all levels of geography. In addition, this paper presents sample sizes for the number of blocks required by the plus sample enumeration to support reliable state level estimates of population produced by CensusPlus. In particular and using data from the 1990 Census Files and the 1990 PES Block Data File, it is shown that a nationwide deeply stratified probability sample of 22,120 blocks is needed to ensure that the housing unit population of a given state is estimated with a standard error of 40,000 persons. The 1990 PES Block Data File also provides some early empirical evidence that the model is very likely to hold.

  4. 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…

  5. Predicting later life health status and mortality using state-level socioeconomic characteristics in early life.

    PubMed

    Hamad, Rita; Rehkopf, David H; Kuan, Kai Y; Cullen, Mark R

    2016-12-01

    Studies extending across multiple life stages promote an understanding of factors influencing health across the life span. Existing work has largely focused on individual-level rather than area-level early life determinants of health. In this study, we linked multiple data sets to examine whether early life state-level characteristics were predictive of health and mortality decades later. The sample included 143,755 U.S. employees, for whom work life claims and administrative data were linked with early life state-of-residence and mortality. We first created a "state health risk score" (SHRS) and "state mortality risk score" (SMRS) by modeling state-level contextual characteristics with health status and mortality in a randomly selected 30% of the sample (the "training set"). We then examined the association of these scores with objective health status and mortality in later life in the remaining 70% of the sample (the "test set") using multivariate linear and Cox regressions, respectively. The association between the SHRS and adult health status was β=0.14 (95%CI: 0.084, 0.20), while the hazard ratio for the SMRS was 0.96 (95%CI: 0.93, 1.00). The association between the SHRS and health was not statistically significant in older age groups at a p-level of 0.05, and there was a statistically significantly different association for health status among movers compared to stayers. This study uses a life course perspective and supports the idea of "sensitive periods" in early life that have enduring impacts on health. It adds to the literature examining populations in the U.S. where large linked data sets are infrequently available.

  6. Prediction and classification of the degradation state of plastic materials used in modern and contemporary art

    NASA Astrophysics Data System (ADS)

    Manfredi, M.; Barberis, E.; Marengo, E.

    2017-01-01

    Today, artworks partially or completely made of plastic materials can be found in almost all international museums and collections. The deterioration of these objects is now becoming evident mainly because these synthetic materials are not designed for a long life and the characterization of their state of conservation can help curators and conservators. In this research we investigated the applicability of a portable attenuated total reflection (ATR) infrared spectrometer for the non-invasive characterization and for monitoring the degradation of plastics used in modern and contemporary art. Several polypropylene and polycarbonate samples were artificially aged in solar box, simulating about 200 years of museum light exposure, and they were monitored with the portable ATR, creating an infrared library of the conservation state of plastics. Through the use of chemometric techniques like principal component analysis-linear discriminant analysis and partial least square—discriminant analysis, we built a robust degradation model of each material that can be used to predict and classify the degradation state of artworks and to identify the priority of intervention in the museum collections. Portable ATR coupled to multivariate statistics can be employed for taking care of plastic artworks as it is non-invasive, the analysis is very fast and it can be performed directly in situ.

  7. Suggesting a new framework for predictive performance assessment: Trait vs State dimensions.

    NASA Astrophysics Data System (ADS)

    Pattyn, Nathalie; Neyt, Xavier; Migeotte, Pierre-François; Morais, José; Soetens, Eric; Cluydts, Raymond; Meeusen, Romain; de Schutter, Guy; Nederhof, Esther; Kolinsky, Régine

    Special Forces trainees (N=7) during their training. ResultsThe first experiment showed no relationship whatsoever between cognitive performance on the very broad array of tests and immediately subsequent performance on the evaluation flight. However, physiological results showed a trend for students who passed the test to exhibit a larger physiological reactivity. Furthermore, the medium-term outcome of SPs in their flight training showed to be related to their test performance. Results of the second experiment (still in progress) will show whether, for an individual monitoring situation, there is a potential link between performance IQ and success on the training, and whether the longitudinal assessment of both cognitive performance, physical performance and physiological reactivity relates to immediately subsequent performance. DiscussionThese results suggest that a critical distinction could be made regarding predictive performance assessment, namely trait and state dimensions. Since one of the intended uses of operational test batteries is to provide an instantaneous measure of the cognitive status of the subject to allow the immediate execution of critical tasks, our results show this would be an inappropriate application so far. However, a dimension showing promising potential is the physiological reactivity. Whereas operational priorities clearly state the need for performance evaluation tools, their application cannot guide operational choices before sufficient validation allows justifying such decisions. References(1) HUMEX: Study on the Survivability and Adaptation of Humans to Long-duration Exploratory Missions (2000). European Space Agency. (2)BPCR: Bioastronautics Critical Path Roadmap (2004). National Aeronautics and Space Administration. (3) Shephard, J. M. and Kosslyn, S. M. (2005). The MiniCog rapid assessment battery: Developing a "blood pressure cuff for the mind". Avn Space Enl Medicine, 76, B192-B197. (4) Pattyn, N.; Migeotte, P.F.; Morais, J

  8. Identification of predictive biomarkers of disease state in transition dairy cows.

    PubMed

    Hailemariam, D; Mandal, R; Saleem, F; Dunn, S M; Wishart, D S; Ametaj, B N

    2014-05-01

    In dairy cows, periparturient disease states, such as metritis, mastitis, and laminitis, are leading to increasingly significant economic losses for the dairy industry. Treatments for these pathologies are often expensive, ineffective, or not cost-efficient, leading to production losses, high veterinary bills, or early culling of the cows. Early diagnosis or detection of these conditions before they manifest themselves could lower their incidence, level of morbidity, and the associated economic losses. In an effort to identify predictive biomarkers for postpartum or periparturient disease states in dairy cows, we undertook a cross-sectional and longitudinal metabolomics study to look at plasma metabolite levels of dairy cows during the transition period, before and after becoming ill with postpartum diseases. Specifically we employed a targeted quantitative metabolomics approach that uses direct flow injection mass spectrometry to track the metabolite changes in 120 different plasma metabolites. Blood plasma samples were collected from 12 dairy cows at 4 time points during the transition period (-4 and -1 wk before and 1 and 4 wk after parturition). Out of the 12 cows studied, 6 developed multiple periparturient disorders in the postcalving period, whereas the other 6 remained healthy during the entire experimental period. Multivariate data analysis (principal component analysis and partial least squares discriminant analysis) revealed a clear separation between healthy controls and diseased cows at all 4 time points. This analysis allowed us to identify several metabolites most responsible for separating the 2 groups, especially before parturition and the start of any postpartum disease. Three metabolites, carnitine, propionyl carnitine, and lysophosphatidylcholine acyl C14:0, were significantly elevated in diseased cows as compared with healthy controls as early as 4 wk before parturition, whereas 2 metabolites, phosphatidylcholine acyl-alkyl C42:4 and

  9. Understanding Representation in Design.

    ERIC Educational Resources Information Center

    Bodker, Susanne

    1998-01-01

    Discusses the design of computer applications, focusing on understanding design representations--what makes design representations work, and how, in different contexts. Examines the place of various types of representation (e.g., formal notations, models, prototypes, scenarios, and mock-ups) in design and the role of formalisms and representations…

  10. Do PCL-R scores from state or defense experts best predict future misconduct among civilly committed sex offenders?

    PubMed

    Boccaccini, Marcus T; Turner, Darrel B; Murrie, Daniel C; Rufino, Katrina A

    2012-06-01

    In a recent study of sex offender civil commitment proceedings, Murrie et al. (Psychol Public Policy Law 15:19-53, 2009) found that state-retained experts consistently assigned higher PCL-R total scores than defense-retained experts for the same offenders (Cohen's d > .83). This finding raises an important question about the validity of these discrepant scores: Which type of score, state or defense evaluator, provides the most useful information about risk? We examined the ability of PCL-R total scores from state and defense evaluators to predict future misconduct among civilly committed sex offenders (N = 38). For comparison, we also examined predictive validity when two state experts evaluated the same offender (N = 32). Agreement between evaluators was low for cases with opposing experts (ICCA,1 = .43 to .52) and for cases with two state experts (ICCA,1 = .40). Nevertheless, scores from state and defense experts demonstrated similar levels of predictive validity (AUC values in the .70 range), although scores from different types of state evaluators (corrections-contracted vs. prosecution-retained) did not. The finding of mean differences between opposing evaluator scores, but similar levels of predictive validity, suggests that scores from opposing experts in SVP cases may need to be interpreted differently depending on who assigned them. Findings have important implications for understanding how rater disagreement may relate to predictive validity.

  11. Geostatistical prediction of stream-flow regime in southeastern United States

    NASA Astrophysics Data System (ADS)

    Pugliese, Alessio; Castellarin, Attilio; Archfield, Stacey; Farmer, William

    2015-04-01

    A Flow-Duration Curve (FDC) represents the percentage of time (duration) during which a given stream-flow is equalled or exceeded over a given period of time. In many water-engineering applications FDCs need to be predicted for ungauged sites (Prediction in Ungauged Basins, PUB problem) using the information collected in donor neighboring gauged basins. We present an application of kriging procedures which makes the procedures capable of predicting FDCs in ungauged catchments. As many of the techniques proposed in the recent literature, the curve is predicted at the target site as a weighted average of empirical dimensionless FDCs that are constructed for neighboring streamgauges and standardized by discharge Q*. Geostatistical weights are obtained by applying two different interpolation techniques, i.e. Top-kriging (TK, see e.g. Pugliese et al., 2014) and Ordinary-kriging (OK, see e.g. Castiglioni et al., 2009), for interpolating a point streamflow-index computed as the overall negative deviation of each empirical curve from Q*, which we term Total Negative Deviation (TND). Empirical TND values can be used to assess the hydrological similarity between catchments and can be interpolated using TK or OK procedures along the stream-network. We consider period-of-record/annual, and complete/seasonal FDCs standardized by two different Q* values, i.e. Mean Annual Flow (MAF) and Mean Annual Precipitation at catchment scale times the drainage area (MAP*), and we apply TK and OK in a wide study area in the Southeastern United States including 182 unregulated gauged catchments. The accuracy of the predicted FDCs is assessed comprehensively under different operational conditions through the (1) leave-one-out and (2) three-fold cross-validation procedures. The results are compared with six different methods for predicting FDCs from synthetically generated daily stream-flow series, which were recently analysed by U.S. Geological Survey. The application of OK and TK reveal

  12. Failure of glutamate dehydrogenase system to predict oxygenation state of human skeletal muscle.

    PubMed

    Katz, A; Spencer, M K; Sahlin, K

    1990-07-01

    In a recent study, the total tissue contents of glutamate (Glu), ammonium (NH+4), and 2-oxoglutarate (2-OG) were used to estimate changes in the mitochondrial redox state ([NAD+]/[NADH]) of contracting skeletal muscle with intact circulation [Am. J. Physiol. 253 (Cell Physiol. 22): C263-C268, 1987]. These metabolites participate in the glutamate dehydrogenase (GDH) reaction, which, based on a number of assumptions, theoretically enables calculation of the mitochondrial redox state as follows (brackets indicate concentrations): [NAD+]/[NADH] = ([NH+4] [2-OG])/[( Glu]Kapp), where Kapp is the apparent equilibrium constant for GDH. The purpose of this study was to determine whether changes in the total tissue contents of Glu, NH+4, and 2-OG could be used to predict a reduction of the mitochondrial redox state in anoxic skeletal muscle. Anoxia was induced in the quadriceps femoris muscle by 10 min of circulatory occlusion (low metabolic rate) and isometric contraction to fatigue (high metabolic rate). The mean (+/- SE) value for the metabolite ratio ([NH+4][2-OG]/[Glu]) at rest was 6 +/- 3 mmol/kg dry wt (x 10(-4]. No significant change occurred after circulatory occlusion (4 +/- 2 x 10(-4); P greater than 0.05), whereas an almost 60-fold increase was observed after isometric contraction (P less than 0.05). Because the muscle was anoxic under both conditions, a significant decrease in the metabolite ratio should have occurred. These data demonstrate that changes in total tissue contents of Glu, NH+4, and 2-OG cannot be used to estimate changes in the redox and oxygenation state of mitochondria in intact human skeletal muscle.

  13. Recurring Functional Interactions Predict Network Architecture of Interictal and Ictal States in Neocortical Epilepsy.

    PubMed

    Khambhati, Ankit N; Bassett, Danielle S; Oommen, Brian S; Chen, Stephanie H; Lucas, Timothy H; Davis, Kathryn A; Litt, Brian

    2017-01-01

    Human epilepsy patients suffer from spontaneous seizures, which originate in brain regions that also subserve normal function. Prior studies demonstrate focal, neocortical epilepsy is associated with dysfunction, several hours before seizures. How does the epileptic network perpetuate dysfunction during baseline periods? To address this question, we developed an unsupervised machine learning technique to disentangle patterns of functional interactions between brain regions, or subgraphs, from dynamic functional networks constructed from approximately 100 h of intracranial recordings in each of 22 neocortical epilepsy patients. Using this approach, we found: (1) subgraphs from ictal (seizure) and interictal (baseline) epochs are topologically similar, (2) interictal subgraph topology and dynamics can predict brain regions that generate seizures, and (3) subgraphs undergo slower and more coordinated fluctuations during ictal epochs compared to interictal epochs. Our observations suggest that seizures mark a critical shift away from interictal states that is driven by changes in the dynamical expression of strongly interacting components of the epileptic network.

  14. Recurring Functional Interactions Predict Network Architecture of Interictal and Ictal States in Neocortical Epilepsy

    PubMed Central

    Bassett, Danielle S.; Oommen, Brian S.; Chen, Stephanie H.; Lucas, Timothy H.

    2017-01-01

    Abstract Human epilepsy patients suffer from spontaneous seizures, which originate in brain regions that also subserve normal function. Prior studies demonstrate focal, neocortical epilepsy is associated with dysfunction, several hours before seizures. How does the epileptic network perpetuate dysfunction during baseline periods? To address this question, we developed an unsupervised machine learning technique to disentangle patterns of functional interactions between brain regions, or subgraphs, from dynamic functional networks constructed from approximately 100 h of intracranial recordings in each of 22 neocortical epilepsy patients. Using this approach, we found: (1) subgraphs from ictal (seizure) and interictal (baseline) epochs are topologically similar, (2) interictal subgraph topology and dynamics can predict brain regions that generate seizures, and (3) subgraphs undergo slower and more coordinated fluctuations during ictal epochs compared to interictal epochs. Our observations suggest that seizures mark a critical shift away from interictal states that is driven by changes in the dynamical expression of strongly interacting components of the epileptic network. PMID:28303256

  15. Prediction of explosive cylinder tests using equations of state from the PANDA code

    SciTech Connect

    Kerley, G.I.; Christian-Frear, T.L.

    1993-09-28

    The PANDA code is used to construct tabular equations of state (EOS) for the detonation products of 24 explosives having CHNO compositions. These EOS, together with a reactive burn model, are used in numerical hydrocode calculations of cylinder tests. The predicted detonation properties and cylinder wall velocities are found to give very good agreement with experimental data. Calculations of flat plate acceleration tests for the HMX-based explosive LX14 are also made and shown to agree well with the measurements. The effects of the reaction zone on both the cylinder and flat plate tests are discussed. For TATB-based explosives, the differences between experiment and theory are consistently larger than for other compositions and may be due to nonideal (finite dimameter) behavior.

  16. 48 CFR 2009.570-4 - Representation.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... ACQUISITION PLANNING CONTRACTOR QUALIFICATIONS Organizational Conflicts of Interest 2009.570-4 Representation... whether situations or relationships exist which may constitute organizational conflicts of interest with... criteria stated in the following paragraph (b) of this section. (b) The organizational conflicts...

  17. Model Predictive Control of A Matrix-Converter Based Solid State Transformer for Utility Grid Interaction

    SciTech Connect

    Xue, Yaosuo

    2016-01-01

    The matrix converter solid state transformer (MC-SST), formed from the back-to-back connection of two three-to-single-phase matrix converters, is studied for use in the interconnection of two ac grids. The matrix converter topology provides a light weight and low volume single-stage bidirectional ac-ac power conversion without the need for a dc link. Thus, the lifetime limitations of dc-bus storage capacitors are avoided. However, space vector modulation of this type of MC-SST requires to compute vectors for each of the two MCs, which must be carefully coordinated to avoid commutation failure. An additional controller is also required to control power exchange between the two ac grids. In this paper, model predictive control (MPC) is proposed for an MC-SST connecting two different ac power grids. The proposed MPC predicts the circuit variables based on the discrete model of MC-SST system and the cost function is formulated so that the optimal switch vector for the next sample period is selected, thereby generating the required grid currents for the SST. Simulation and experimental studies are carried out to demonstrate the effectiveness and simplicity of the proposed MPC for such MC-SST-based grid interfacing systems.

  18. Practical steady-state temperature prediction of active embedded chips into high density electronic board

    NASA Astrophysics Data System (ADS)

    Monier-Vinard, Eric; Rogie, Brice; Nguyen, Nhat-Minh; Laraqi, Najib; Bissuel, Valentin; Daniel, Olivier

    2016-09-01

    Printed Wiring Board die embedding technology is an innovative packaging alternative to address a very high degree of integration by stacking multiple core layers housing active chips. Nevertheless this increases the thermal management challenges by concentrating heat dissipation at the heart of the substrate and exacerbates the need of adequate cooling. In order to allow the electronic designers to early analyse the limits of the in-layer power dissipation, depending on the chip location inside the board, various analytical thermal modelling approaches were investigated. Therefore the buried active chips can be represented using surface or volumetric heating sources according with the expected accuracy. Moreover the current work describes the comparison of the volumetric heating source analytical model with the state-of-art numerical detailed models of several embedded chips configurations, and debates about the need or not to simulate in full details the embedded chips as well as the surrounding layers and micro-via structures of the substrate. The results highlight that the thermal behaviour predictions of the analytical model are found to be within ±5% of relative error and so demonstrate their relevance to model an embedded chip and its neighbouring heating chips or components. Further this predictive model proves to be in good agreement with an experimental characterization performed on a thermal test vehicle. To summarize, the developed analytical approach promotes several practical solutions to achieve a more efficient design and to early identify the potential issues of board cooling.

  19. A simple modelling approach for prediction of standard state real gas entropy of pure materials.

    PubMed

    Bagheri, M; Borhani, T N G; Gandomi, A H; Manan, Z A

    2014-01-01

    The performance of an energy conversion system depends on exergy analysis and entropy generation minimisation. A new simple four-parameter equation is presented in this paper to predict the standard state absolute entropy of real gases (SSTD). The model development and validation were accomplished using the Linear Genetic Programming (LGP) method and a comprehensive dataset of 1727 widely used materials. The proposed model was compared with the results obtained using a three-layer feed forward neural network model (FFNN model). The root-mean-square error (RMSE) and the coefficient of determination (r(2)) of all data obtained for the LGP model were 52.24 J/(mol K) and 0.885, respectively. Several statistical assessments were used to evaluate the predictive power of the model. In addition, this study provides an appropriate understanding of the most important molecular variables for exergy analysis. Compared with the LGP based model, the application of FFNN improved the r(2) to 0.914. The developed model is useful in the design of materials to achieve a desired entropy value.

  20. Statistical analysis of clinical prediction rules for rehabilitation interventions: current state of the literature.

    PubMed

    Lubetzky-Vilnai, Anat; Ciol, Marcia; McCoy, Sarah Westcott

    2014-01-01

    Deriving clinical prediction rules (CPRs) to identify specific characteristics of patients who would likely respond to certain interventions has become a research priority in physical rehabilitation. Understanding the appropriate statistical principles and methods of analyses underlying the derivation of CPRs is important for future rehabilitation research and clinical applications. In this article, we aimed to provide an overview of statistical techniques used for the derivation of CPRs to predict success following physical therapy interventions and to generate recommendations for improvements in CPR derivation research and statistical analysis in rehabilitation. We have summarized the current state of CPR intervention-related research by reviewing 26 studies. A common technique was found in most studies and included univariate association of factors with treatment success, stepwise logistic regression to determine the most parsimonious set of predictors for success, and calculation of accuracy statistics (focusing on positive likelihood ratios). We identified several shortcomings related to inadequate ratio of events by number of predictors, lack of standardization regarding acceptable interobserver reliability of predictors, questionable handling of predictors including reliance on univariate analysis and early categorization, and not accounting for dependence and collinearity of predictors in multivariable model construction. Interpretation of the derived CPRs was found to be difficult due to lack of precision of estimates and paradoxical findings when a subset of the predictors yielded a larger positive likelihood ratio than did the full set of predictors. Finally, we make recommendations regarding how to strengthen the use of statistical principles and methods to create consistency across rehabilitation research for CPR derivations.

  1. Dynamic Baysesian state-space model with a neural network for an online river flow prediction

    NASA Astrophysics Data System (ADS)

    Ham, Jonghwa; Hong, Yoon-Seok

    2013-04-01

    The usefulness of artificial neural networks in complex hydrological modeling has been demonstrated by successful applications. Several different types of neural network have been used for the hydrological modeling task but the multi-layer perceptron (MLP) neural network (also known as the feed-forward neural network) has enjoyed a predominant position because of its simplicity and its ability to provide good approximations. In many hydrological applications of MLP neural networks, the gradient descent-based batch learning algorithm such as back-propagation, quasi-Newton, Levenburg-Marquardt, and conjugate gradient algorithms has been used to optimize the cost function (usually by minimizing the error function in the prediction) by updating the parameters and structure in a neural network defined using a set of input-output training examples. Hydrological systems are highly with time-varying inputs and outputs, and are characterized by data that arrive sequentially. The gradient descent-based batch learning approaches that are implemented in MLP neural networks have significant disadvantages for online dynamic hydrological modeling because they could not update the model structure and parameter when a new set of hydrological measurement data becomes available. In addition, a large amount of training data is always required off-line with a long model training time. In this work, a dynamic nonlinear Bayesian state-space model with a multi-layer perceptron (MLP) neural network via a sequential Monte Carlo (SMC) learning algorithm is proposed for an online dynamic hydrological modeling. This proposed new method of modeling is herein known as MLP-SMC. The sequential Monte Carlo learning algorithm in the MLP-SMC is designed to evolve and adapt the weight of a MLP neural network sequentially in time on the arrival of each new item of hydrological data. The weight of a MLP neural network is treated as the unknown dynamic state variable in the dynamic Bayesian state

  2. TOPICAL REVIEW: Analytic representations in quantum mechanics

    NASA Astrophysics Data System (ADS)

    Vourdas, A.

    2006-02-01

    Various Euclidean, hyperbolic and elliptic analytic representations are introduced and relations among them are discussed. The Bargmann analytic representation in the complex plane is considered and its relation to other phase-space methods for the harmonic oscillator is reviewed. The general theory that relates the growth of analytic functions with the density of their zeros is applied to Bargmann functions and it leads to theorems on the completeness of sequences of Glauber coherent states. Two hyperbolic analytic representations in the unit disc, based on SU(1, 1) coherent states and also on phase states are introduced. A third analytic representation in the complex plane based on Barut-Girardello states is also considered and transformations which relate it to the other ones are studied. In the case of systems with finite-dimensional Hilbert space, an elliptic analytic representation in the extended complex plane and also another analytic representation based on theta functions are introduced. The Berezin formalism in the Euclidean, hyperbolic and elliptic cases is discussed. Contour analytic representations in these three cases are also presented.

  3. State dependent model predictive control for orbital rendezvous using pulse-width pulse-frequency modulated thrusters

    NASA Astrophysics Data System (ADS)

    Li, Peng; Zhu, Zheng H.; Meguid, S. A.

    2016-07-01

    This paper studies the pulse-width pulse-frequency modulation based trajectory planning for orbital rendezvous and proximity maneuvering near a non-cooperative spacecraft in an elliptical orbit. The problem is formulated by converting the continuous control input, output from the state dependent model predictive control, into a sequence of pulses of constant magnitude by controlling firing frequency and duration of constant-magnitude thrusters. The state dependent model predictive control is derived by minimizing the control error of states and control roughness of control input for a safe, smooth and fuel efficient approaching trajectory. The resulting nonlinear programming problem is converted into a series of quadratic programming problem and solved by numerical iteration using the receding horizon strategy. The numerical results show that the proposed state dependent model predictive control with the pulse-width pulse-frequency modulation is able to effectively generate optimized trajectories using equivalent control pulses for the proximity maneuvering with less energy consumption.

  4. Prediction and validation of protein intermediate states from structurally rich ensembles and coarse-grained simulations.

    PubMed

    Orellana, Laura; Yoluk, Ozge; Carrillo, Oliver; Orozco, Modesto; Lindahl, Erik

    2016-08-31

    Protein conformational changes are at the heart of cell functions, from signalling to ion transport. However, the transient nature of the intermediates along transition pathways hampers their experimental detection, making the underlying mechanisms elusive. Here we retrieve dynamic information on the actual transition routes from principal component analysis (PCA) of structurally-rich ensembles and, in combination with coarse-grained simulations, explore the conformational landscapes of five well-studied proteins. Modelling them as elastic networks in a hybrid elastic-network Brownian dynamics simulation (eBDIMS), we generate trajectories connecting stable end-states that spontaneously sample the crystallographic motions, predicting the structures of known intermediates along the paths. We also show that the explored non-linear routes can delimit the lowest energy passages between end-states sampled by atomistic molecular dynamics. The integrative methodology presented here provides a powerful framework to extract and expand dynamic pathway information from the Protein Data Bank, as well as to validate sampling methods in general.

  5. Prediction and validation of protein intermediate states from structurally rich ensembles and coarse-grained simulations

    PubMed Central

    Orellana, Laura; Yoluk, Ozge; Carrillo, Oliver; Orozco, Modesto; Lindahl, Erik

    2016-01-01

    Protein conformational changes are at the heart of cell functions, from signalling to ion transport. However, the transient nature of the intermediates along transition pathways hampers their experimental detection, making the underlying mechanisms elusive. Here we retrieve dynamic information on the actual transition routes from principal component analysis (PCA) of structurally-rich ensembles and, in combination with coarse-grained simulations, explore the conformational landscapes of five well-studied proteins. Modelling them as elastic networks in a hybrid elastic-network Brownian dynamics simulation (eBDIMS), we generate trajectories connecting stable end-states that spontaneously sample the crystallographic motions, predicting the structures of known intermediates along the paths. We also show that the explored non-linear routes can delimit the lowest energy passages between end-states sampled by atomistic molecular dynamics. The integrative methodology presented here provides a powerful framework to extract and expand dynamic pathway information from the Protein Data Bank, as well as to validate sampling methods in general. PMID:27578633

  6. Predicting freakish sea state with an operational third-generation wave model

    NASA Astrophysics Data System (ADS)

    Waseda, T.; In, K.; Kiyomatsu, K.; Tamura, H.; Miyazawa, Y.; Iyama, K.

    2014-04-01

    The understanding of freak wave generation mechanisms has advanced and the community has reached a consensus that spectral geometry plays an important role. Numerous marine accident cases were studied and revealed that the narrowing of the directional spectrum is a good indicator of dangerous sea. However, the estimation of the directional spectrum depends on the performance of the third-generation wave model. In this work, a well-studied marine accident case in Japan in 1980 (Onomichi-Maru incident) is revisited and the sea states are hindcasted using both the DIA (discrete interaction approximation) and SRIAM (Simplified Research Institute of Applied Mechanics) nonlinear source terms. The result indicates that the temporal evolution of the basic parameters (directional spreading and frequency bandwidth) agree reasonably well between the two schemes and therefore the most commonly used DIA method is qualitatively sufficient to predict freakish sea state. The analyses revealed that in the case of Onomichi-Maru, a moving gale system caused the spectrum to grow in energy with limited downshifting at the accident's site. This conclusion contradicts the marine inquiry report speculating that the two swell systems crossed at the accident's site. The unimodal wave system grew under strong influence of local wind with a peculiar energy transfer.

  7. Prediction and validation of protein intermediate states from structurally rich ensembles and coarse-grained simulations

    NASA Astrophysics Data System (ADS)

    Orellana, Laura; Yoluk, Ozge; Carrillo, Oliver; Orozco, Modesto; Lindahl, Erik

    2016-08-01

    Protein conformational changes are at the heart of cell functions, from signalling to ion transport. However, the transient nature of the intermediates along transition pathways hampers their experimental detection, making the underlying mechanisms elusive. Here we retrieve dynamic information on the actual transition routes from principal component analysis (PCA) of structurally-rich ensembles and, in combination with coarse-grained simulations, explore the conformational landscapes of five well-studied proteins. Modelling them as elastic networks in a hybrid elastic-network Brownian dynamics simulation (eBDIMS), we generate trajectories connecting stable end-states that spontaneously sample the crystallographic motions, predicting the structures of known intermediates along the paths. We also show that the explored non-linear routes can delimit the lowest energy passages between end-states sampled by atomistic molecular dynamics. The integrative methodology presented here provides a powerful framework to extract and expand dynamic pathway information from the Protein Data Bank, as well as to validate sampling methods in general.

  8. Hydrological states and fluxes in terrestrial systems: from observation to prediction (John Dalton Medal Lecture)

    NASA Astrophysics Data System (ADS)

    Vereecken, Harry

    2016-04-01

    Quantification and prediction of hydrological processes requires information on the spatial and temporal distribution of soil water fluxes and soil water content. The access to spatially and temporally highly resolved soil water content and fluxes is needed to adequately test hydrological hypotheses and to validate hydrological models. In this presentation we will discuss new developments for the determination of soil water content and quantification and prediction of hydrological fluxes based on hydrogeophysical measurement techniques and novel ground- and satellite based sensing platforms. At the field scale, ground penetrating radar and passive microwave methods are presently being developed which provide the possibility to map soil water content with a high spatial and temporal resolution, also in the subsurface environment. Recent developments show that the application of full wave form inversion methods is a unique technique to derive soil water and soil hydraulic parameters from on- and off-ground systems with high spatial resolution. At the small catchment scale, wireless sensor networks are presently being developed providing soil moisture content values with a high spatial and temporal resolution. Stochastic theories have been used to interpret the relationship between average soil water content and its standard deviation. Cosmic ray sensors are presently being deployed within the TERENO observatories. These sensors provide soil moisture content values with a high temporal resolution at a scale of one to two hundred meters, thereby bridging the gap between local scale measurements and remote sensing platforms. Cosmic ray probes are extremely valuable for the determination of soil water content in agriculturally managed soils. Data assimilation methods provide a unique approach to fully exploit the value of spatially and temporally highly resolved soil water content measurements and states of the terrestrial system for the prediction of hydrological fluxes

  9. Time to Decide? Dynamical Analysis Predicts Partial Tip/Stalk Patterning States Arise during Angiogenesis.

    PubMed

    Venkatraman, Lakshmi; Regan, Erzsébet Ravasz; Bentley, Katie

    2016-01-01

    Angiogenesis is a highly dynamic morphogenesis process; however, surprisingly little is known about the timing of the different molecular processes involved. Although the role of the VEGF-notch-DLL4 signaling pathway has been established as essential for tip/stalk cell competition during sprouting, the speed and dynamic properties of the underlying process at the individual cell level has not been fully elucidated. In this study, using mathematical modeling we investigate how specific, biologically meaningful, local conditions around and within an individual cell can influence their unique tip/stalk phenotype switching kinetics. To this end we constructed an ordinary differential equation model of VEGF-notch-DLL4 signaling in a system of two, coupled endothelial cells (EC). Our studies reveal that at any given point in an angiogenic vessel the time it takes a cell to decide to take on a tip or stalk phenotype may be drastically different, and this asynchrony of tip/stalk cell decisions along vessels itself acts to speed up later competitions. We unexpectedly uncover intermediate "partial" yet stable states lying between the tip and stalk cell fates, and identify that internal cellular factors, such as NAD-dependent deacetylase sirtuin-1 (Sirt1) and Lunatic fringe 1 (Lfng1), can specifically determine the length of time a cell spends in these newly identified partial tip/stalk states. Importantly, the model predicts that these partial EC states can arise during normal angiogenesis, in particular during cell rearrangement in sprouts, providing a novel two-stage mechanism for rapid adaptive behavior to the cells highly dynamic environment. Overall, this study demonstrates that different factors (both internal and external to EC) can be used to modulate the speed of tip/stalk decisions, opening up new opportunities and challenges for future biological experiments and therapeutic targeting to manipulate vascular network topology, and our basic understanding of

  10. Time to Decide? Dynamical Analysis Predicts Partial Tip/Stalk Patterning States Arise during Angiogenesis

    PubMed Central

    Regan, Erzsébet Ravasz; Bentley, Katie

    2016-01-01

    Angiogenesis is a highly dynamic morphogenesis process; however, surprisingly little is known about the timing of the different molecular processes involved. Although the role of the VEGF-notch-DLL4 signaling pathway has been established as essential for tip/stalk cell competition during sprouting, the speed and dynamic properties of the underlying process at the individual cell level has not been fully elucidated. In this study, using mathematical modeling we investigate how specific, biologically meaningful, local conditions around and within an individual cell can influence their unique tip/stalk phenotype switching kinetics. To this end we constructed an ordinary differential equation model of VEGF-notch-DLL4 signaling in a system of two, coupled endothelial cells (EC). Our studies reveal that at any given point in an angiogenic vessel the time it takes a cell to decide to take on a tip or stalk phenotype may be drastically different, and this asynchrony of tip/stalk cell decisions along vessels itself acts to speed up later competitions. We unexpectedly uncover intermediate “partial” yet stable states lying between the tip and stalk cell fates, and identify that internal cellular factors, such as NAD-dependent deacetylase sirtuin-1 (Sirt1) and Lunatic fringe 1 (Lfng1), can specifically determine the length of time a cell spends in these newly identified partial tip/stalk states. Importantly, the model predicts that these partial EC states can arise during normal angiogenesis, in particular during cell rearrangement in sprouts, providing a novel two-stage mechanism for rapid adaptive behavior to the cells highly dynamic environment. Overall, this study demonstrates that different factors (both internal and external to EC) can be used to modulate the speed of tip/stalk decisions, opening up new opportunities and challenges for future biological experiments and therapeutic targeting to manipulate vascular network topology, and our basic understanding of

  11. Adenosine 5'-triphosphate consumption by smooth muscle as predicted by the coupled four-state crossbridge model.

    PubMed Central

    Hai, C M; Murphy, R A

    1992-01-01

    We have proposed a four-state crossbridge model to explain contraction and the latch state in arterial smooth muscle. Ca(2+)-dependent crossbridge phosphorylation was the only postulated regulatory mechanism and the latchbridge (a dephosphorylated, attached crossbridge) was the only novel element in the model. In this study, we used the model to predict rates of ATP consumption by crossbridge phosphorylation (JPhos) and cycling (JCycle) during isometric and isotonic contractions in arterial smooth muscle; then we compared model predictions with experimental data. The model predicted that JPhos and JCycle were similar in magnitude in isometric contractions, and both increased almost linearly with myosin phosphorylation. The predicted relationship between isometric stress and ATP consumption was quasihyperbolic, but approximately linear when myosin phosphorylation was below 35%, in agreement with most of the available data. Muscle shortening increased the predicted values of JCycle up to 3.7-fold depending on shortening velocity and the level of myosin phosphorylation. The predicted maximum work output per ATP was 7.4-7.8 kJ/mol ATP and was relatively insensitive to changes in myosin phosphorylation. The predicted increase in JCycle with shortening was in agreement with available data, but the model prediction that work output per ATP was insensitive to changes in myosin phosphorylation was unexpected and remains to be tested in future experiments. PMID:1547336

  12. Computer aided surface representation

    SciTech Connect

    Barnhill, R.E.

    1991-04-02

    Modern computing resources permit the generation of large amounts of numerical data. These large data sets, if left in numerical form, can be overwhelming. Such large data sets are usually discrete points from some underlying physical phenomenon. Because we need to evaluate the phenomenon at places where we don't have data, a continuous representation (a surface'') is required. A simple example is a weather map obtained from a discrete set of weather stations. (For more examples including multi-dimensional ones, see the article by Dr. Rosemary Chang in the enclosed IRIS Universe). In order to create a scientific structure encompassing the data, we construct an interpolating mathematical surface which can evaluate at arbitrary locations. We can also display and analyze the results via interactive computer graphics. In our research we construct a very wide variety of surfaces for applied geometry problems that have sound theoretical foundations. However, our surfaces have the distinguishing feature that they are constructed to solve short or long term practical problems. This DOE-funded project has developed the premiere research team in the subject of constructing surfaces (3D and higher dimensional) that provide smooth representations of real scientific and engineering information, including state of the art computer graphics visualizations. However, our main contribution is in the development of fundamental constructive mathematical methods and visualization techniques which can be incorporated into a wide variety of applications. This project combines constructive mathematics, algorithms, and computer graphics, all applied to real problems. The project is a unique resource, considered by our peers to be a de facto national center for this type of research.

  13. Predicting Michael-acceptor reactivity and toxicity through quantum chemical transition-state calculations.

    PubMed

    Mulliner, Denis; Wondrousch, Dominik; Schüürmann, Gerrit

    2011-12-21

    The electrophilic reactivity of Michael acceptors is an important determinant of their toxicity. For a set of 35 α,β-unsaturated aldehydes, ketones and esters with experimental rate constants of their reaction with glutathione (GSH), k(GSH), quantum chemical transition-state calculations of the corresponding Michael addition of the model nucleophile methane thiol (CH(3)SH) have been performed at the B3LYP/6-31G** level, focusing on the 1,2-olefin addition pathway without and with initial protonation. Inclusion of Boltzmann-weighting of conformational flexibility yields intrinsic reaction barriers ΔE(‡) that for the case of initial protonation correctly reflect the structural variation of k(GSH) across all three compound classes, except that they fail to account for a systematic (essentially incremental) decrease in reactivity upon α-substitution. By contrast, the reduction in k(GSH) through β-substitution is well captured by ΔE(‡). Empirical correction for the α-substitution effect yields a high squared correlation coefficient (r(2) = 0.96) for the quantum chemical prediction of log k(GSH), thus enabling an in silico screening of the toxicity-relevant electrophilicity of α,β-unsaturated carbonyls. The latter is demonstrated through application of the calculation scheme for a larger set of 46 Michael-acceptor aldehydes, ketones and esters with experimental values for their toxicity toward the ciliates Tetrahymena pyriformis in terms of 50% growth inhibition values after 48 h exposure (EC(50)). The developed approach may add in the predictive hazard evaluation of α,β-unsaturated carbonyls such as for the European REACH (Registration, Evaluation, Authorization and Restriction of Chemicals) Directive, enabling in particular an early identification of toxicity-relevant Michael-acceptor reactivity.

  14. Seasonal Variability of Aragonite Saturation State in the North Pacific Ocean Predicted by Multiple Linear Regression

    NASA Astrophysics Data System (ADS)

    Kim, T. W.; Park, G. H.

    2014-12-01

    Seasonal variation of aragonite saturation state (Ωarag) in the North Pacific Ocean (NPO) was investigated, using multiple linear regression (MLR) models produced from the PACIFICA (Pacific Ocean interior carbon) dataset. Data within depth ranges of 50-1200m were used to derive MLR models, and three parameters (potential temperature, nitrate, and apparent oxygen utilization (AOU)) were chosen as predictor variables because these parameters are associated with vertical mixing, DIC (dissolved inorganic carbon) removal and release which all affect Ωarag in water column directly or indirectly. The PACIFICA dataset was divided into 5° × 5° grids, and a MLR model was produced in each grid, giving total 145 independent MLR models over the NPO. Mean RMSE (root mean square error) and r2 (coefficient of determination) of all derived MLR models were approximately 0.09 and 0.96, respectively. Then the obtained MLR coefficients for each of predictor variables and an intercept were interpolated over the study area, thereby making possible to allocate MLR coefficients to data-sparse ocean regions. Predictability from the interpolated coefficients was evaluated using Hawaiian time-series data, and as a result mean residual between measured and predicted Ωarag values was approximately 0.08, which is less than the mean RMSE of our MLR models. The interpolated MLR coefficients were combined with seasonal climatology of World Ocean Atlas 2013 (1° × 1°) to produce seasonal Ωarag distributions over various depths. Large seasonal variability in Ωarag was manifested in the mid-latitude Western NPO (24-40°N, 130-180°E) and low-latitude Eastern NPO (0-12°N, 115-150°W). In the Western NPO, seasonal fluctuations of water column stratification appeared to be responsible for the seasonal variation in Ωarag (~ 0.5 at 50 m) because it closely followed temperature variations in a layer of 0-75 m. In contrast, remineralization of organic matter was the main cause for the seasonal

  15. Prediction of the homogeneous droplet nucleation by the density gradient theory and PC-SAFT equation of state

    NASA Astrophysics Data System (ADS)

    Planková, Barbora; Hrubý, Jan; Vinš, Václav

    2013-05-01

    We combined the density gradient theory (DGT) with the PC-SAFT and Peng-Robinson equations of state to model the homogeneous droplet nucleation and compared it to the classical nucleation theory (CNT) and experimental data. We also consider the effect of capillary waves on the surface tension. DGT predicts nucleation rates smaller than the CNT and slightly improves the temperature-dependent deviation of the predicted and experimental nucleation rates.

  16. Converting Boundary Representation Solid Models to Half-Space Representation Models for Monte Carlo Analysis

    SciTech Connect

    Davis JE, Eddy MJ, Sutton TM, Altomari TJ

    2007-03-01

    Solid modeling computer software systems provide for the design of three-dimensional solid models used in the design and analysis of physical components. The current state-of-the-art in solid modeling representation uses a boundary representation format in which geometry and topology are used to form three-dimensional boundaries of the solid. The geometry representation used in these systems is cubic B-spline curves and surfaces--a network of cubic B-spline functions in three-dimensional Cartesian coordinate space. Many Monte Carlo codes, however, use a geometry representation in which geometry units are specified by intersections and unions of half-spaces. This paper describes an algorithm for converting from a boundary representation to a half-space representation.

  17. XML-BASED REPRESENTATION

    SciTech Connect

    R. KELSEY

    2001-02-01

    For focused applications with limited user and use application communities, XML can be the right choice for representation. It is easy to use, maintain, and extend and enjoys wide support in commercial and research sectors. When the knowledge and information to be represented is object-based and use of that knowledge and information is a high priority, then XML-based representation should be considered. This paper discusses some of the issues involved in using XML-based representation and presents an example application that successfully uses an XML-based representation.

  18. Prediction of recrystallization behavior of troglitazone/polyvinylpyrrolidone solid dispersion by solid-state NMR.

    PubMed

    Ito, Atsutoshi; Watanabe, Tomoyuki; Yada, Shuichi; Hamaura, Takeshi; Nakagami, Hiroaki; Higashi, Kenjirou; Moribe, Kunikazu; Yamamoto, Keiji

    2010-01-04

    The purpose of this study was to elaborate the relationship between the (13)C CP/MAS NMR spectra and the recrystallization behavior during the storage of troglitazone solid dispersions. The solid dispersions were prepared by either the solvent method or by co-grinding. The recrystallization behavior under storage conditions at 40 degrees C/94% RH was evaluated by the Kolmogorov-Johnson-Mehl-Avrami (KJMA) equation. Solid dispersions prepared by the solvent method or by prolonged grinding brought about inhibition of the nucleation and the nuclei growth at the same time. No differences in the PXRD profiles were found in the samples prepared by the co-grinding and solvent methods, however, (13)C CP/MAS NMR showed significant differences in the spectra. The correlation coefficients using partial least square regression analysis between the PXRD profiles and the apparent nuclei-growth constant or induction period to nucleation were 0.1305 or 0.6350, respectively. In contrast, those between the (13)C CP/MAS NMR spectra and the constant or the period were 0.9916 or 0.9838, respectively. The (13)C CP/MAS NMR spectra had good correlation with the recrystallization kinetic parameters evaluated by the KJMA equation. Consequently, solid-state NMR was judged to be a useful tool for the prediction of the recrystallization behavior of solid dispersions.

  19. Predicting onset and duration of airborne allergenic pollen season in the United States

    NASA Astrophysics Data System (ADS)

    Zhang, Yong; Bielory, Leonard; Cai, Ting; Mi, Zhongyuan; Georgopoulos, Panos

    2015-02-01

    Allergenic pollen is one of the main triggers of Allergic Airway Disease (AAD) affecting 5%-30% of the population in industrialized countries. A modeling framework has been developed using correlation and collinearity analyses, simulated annealing, and stepwise regression based on nationwide observations of airborne pollen counts and climatic factors to predict the onsets and durations of allergenic pollen seasons of representative trees, weeds and grass in the contiguous United States. Main factors considered are monthly, seasonal and annual mean temperatures and accumulative precipitations, latitude, elevation, Growing Degree Day (GDD), Frost Free Day (FFD), Start Date (SD) and Season Length (SL) in the previous year. The estimated mean SD and SL for birch (Betula), oak (Quercus), ragweed (Ambrosia), mugwort (Artemisia) and grass (Poaceae) pollen season in 1994-2010 are mostly within 0-6 days of the corresponding observations for the majority of the National Allergy Bureau (NAB) monitoring stations across the contiguous US. The simulated spatially resolved maps for onset and duration of allergenic pollen season in the contiguous US are consistent with the long term observations.

  20. On the robustness of near term climate predictability regarding initial state uncertainties

    NASA Astrophysics Data System (ADS)

    Germe, Agathe; Sévellec, Florian; Mignot, Juliette; Swingedouw, Didier; Nguyen, Sebastien

    2017-01-01

    A set of four ensemble simulations has been designed to assess the relative importance of atmospheric, oceanic, and deep ocean initial state uncertainties, as represented by spatial white noise perturbations, on seasonal to decadal prediction skills in a perfect model framework. It is found that a perturbation mimicking random oceanic uncertainties have the same impact as an atmospheric-only perturbation on the future evolution of the ensemble after the first 3 months, even if they are initially only located in the deep ocean. This is due to the fast (1 month) perturbation of the atmospheric component regardless of the initial ensemble generation strategy. The divergence of the ensemble upper-ocean characteristics is then mainly induced by ocean-atmosphere interactions. While the seasonally varying mixed layer depth allows the penetration of the different signals in the thermocline in the mid-high latitudes, the rapid adjustment of the thermocline to wind anomalies followed by Kelvin and Rossby waves adjustment dominates the growth of the ensemble spread in the tropics. These mechanisms result in similar ensemble distribution characteristics for the four ensembles design strategy at the interannual timescale.

  1. Neural correlates of envy: Regional homogeneity of resting-state brain activity predicts dispositional envy.

    PubMed

    Xiang, Yanhui; Kong, Feng; Wen, Xue; Wu, Qihan; Mo, Lei

    2016-11-15

    Envy differs from common negative emotions across cultures. Although previous studies have explored the neural basis of episodic envy via functional magnetic resonance imaging (fMRI), little is known about the neural processes associated with dispositional envy. In the present study, we used regional homogeneity (ReHo) as an index in resting-state fMRI (rs-fMRI) to identify brain regions involved in individual differences in dispositional envy, as measured by the Dispositional Envy Scale (DES). Results showed that ReHo in the inferior/middle frontal gyrus (IFG/MFG) and dorsomedial prefrontal cortex (DMPFC) positively predicted dispositional envy. Moreover, of all the personality traits measured by the Revised NEO Personality Inventory (NEO-PI-R), only neuroticism was significantly associated with dispositional envy. Furthermore, neuroticism mediated the underlying association between the ReHo of the IFG/MFG and dispositional envy. Hence, to the best of our knowledge, this study provides the first evidence that spontaneous brain activity in multiple regions related to self-evaluation, social perception, and social emotion contributes to dispositional envy. In addition, our findings reveal that neuroticism may play an important role in the cognitive processing of dispositional envy.

  2. Combined discrete particle and continuum model predicting solid-state fermentation in a drum fermentor.

    PubMed

    Schutyser, M A I; Briels, W J; Boom, R M; Rinzema, A

    2004-05-20

    The development of mathematical models facilitates industrial (large-scale) application of solid-state fermentation (SSF). In this study, a two-phase model of a drum fermentor is developed that consists of a discrete particle model (solid phase) and a continuum model (gas phase). The continuum model describes the distribution of air in the bed injected via an aeration pipe. The discrete particle model describes the solid phase. In previous work, mixing during SSF was predicted with the discrete particle model, although mixing simulations were not carried out in the current work. Heat and mass transfer between the two phases and biomass growth were implemented in the two-phase model. Validation experiments were conducted in a 28-dm3 drum fermentor. In this fermentor, sufficient aeration was provided to control the temperatures near the optimum value for growth during the first 45-50 hours. Several simulations were also conducted for different fermentor scales. Forced aeration via a single pipe in the drum fermentors did not provide homogeneous cooling in the substrate bed. Due to large temperature gradients, biomass yield decreased severely with increasing size of the fermentor. Improvement of air distribution would be required to avoid the need for frequent mixing events, during which growth is hampered. From these results, it was concluded that the two-phase model developed is a powerful tool to investigate design and scale-up of aerated (mixed) SSF fermentors.

  3. Solubility prediction of carbon dioxide in water by an iterative equation of state/excess Gibbs energy model

    NASA Astrophysics Data System (ADS)

    Suleman, H.; Maulud, A. S.; Man, Z.

    2016-06-01

    The solubility of carbon dioxide in water has been predicted extensively by various models, owing to their vast applications in process industry. Henry's law has been widely utilized for solubility prediction with good results at low pressure. However, the law shows large deviations at high pressure, even when adjusted to pressure correction and improved conditions. Contrarily, equations of state/excess Gibbs energy models are a promising addition to thermodynamic models for prediction at high pressure non-ideal equilibria. These models can efficiently predict solubilities at high pressures, even when the experimental solubilities are not corroborated. Hence, these models work iteratively, utilizing the mathematical redundancy of local composition excess Gibbs energy models. In this study, an iterative form of Linear Combination of Vidal and Michelsen (LCVM) mixing rule has been used for prediction of carbon dioxide solubility in water, in conjunction with UNIFAC and translated modified Peng- Robinson equation of state. The proposed model, termed iterative LCVM (i-LCVM), predicts carbon dioxide solubility in water for a wide range of temperature (273 to 453 K) and pressure (101.3 to 7380 kPa). The i-LCVM shows good agreement with experimental values and predicts better than Henry's law (53% improvement).

  4. Discrete-state representation of ion permeation coupled to fast gating in a model of CLC-chloride channels: analytic estimation of the state-to-state rate constants.

    PubMed

    Coalson, Rob D; Cheng, Mary Hongying

    2011-09-01

    Analytical estimation of state-to-state rate constants is carried out for a recently developed discrete state model of chloride ion motion in a CLC chloride channel (Coalson and Cheng, J. Phys. Chem. B 2010, 114, 1424). In the original presentation of this model, the same rate constants were evaluated via three-dimensional Brownian dynamics simulations. The underlying dynamical theory is an appropriate single- or multiparticle three-dimensional Smoluchowski equation. Taking advantage of approximate geometric symmetries (based on the details of the model channel geometry), well-known formulas for state-to-state transition rates are appealed to herein and adapted as necessary to the problem at hand. Rates of ionic influx from a bulk electrolyte reservoir to the nearest binding site within the channel pore are particularly challenging to compute analytically because they reflect multi-ion interactions (as opposed to single-ion dynamics). A simple empirical correction factor is added to the single-ion rate constant formula in this case to account for the saturation of influx rate constants with increasing bulk Cl(-) concentration. Overall, the agreement between all analytically estimated rate constants is within a factor of 2 of those computed via three-dimensional Brownian dynamics simulations, and often better than this. Current-concentration curves obtained using rate constants derived from these two different computational approaches agree to within 25%.

  5. Randomness in a Galton board from the viewpoint of predictability: sensitivity and statistical bias of output states.

    PubMed

    Arai, Kenichi; Harayama, Takahisa; Sunada, Satoshi; Davis, Peter

    2012-11-01

    The Galton board is a classic example of the appearance of randomness and stochasticity. In the dynamical model of the Galton board, the macroscopic motion is governed by deterministic equations of motion, and predictability depends on uncertainty in the initial conditions and its evolution by the dynamics. In this sense the Galton board is similar to coin tossing. In this paper, we analyze a simple dynamical model which is inspired by the Galton board. Especially, we focus on the predictability, considering the relation between the uncertainty of initial states and the structure of basins of initial states that result in the same exit state. The model has basins with fractal basin structure, unlike the basins in coin tossing models which have only finite structure. Arbitrarily small uncertainty of initial conditions can cause unpredictability of final states if the initial conditions are chosen in fractal regions. In this sense, our model is in a different category from the coin tossing model. We examine the predictability of a small Galton board model from the viewpoint of the sensitivity and the statistical bias of final states. We show that it is possible to determine the radii of scatterers corresponding to a given predictability criterion, specified as a statistical bias, and a given uncertainty of initial conditions.

  6. Contribution of Sequence Motif, Chromatin State, and DNA Structure Features to Predictive Models of Transcription Factor Binding in Yeast.

    PubMed

    Tsai, Zing Tsung-Yeh; Shiu, Shin-Han; Tsai, Huai-Kuang

    2015-08-01

    Transcription factor (TF) binding is determined by the presence of specific sequence motifs (SM) and chromatin accessibility, where the latter is influenced by both chromatin state (CS) and DNA structure (DS) properties. Although SM, CS, and DS have been used to predict TF binding sites, a predictive model that jointly considers CS and DS has not been developed to predict either TF-specific binding or general binding properties of TFs. Using budding yeast as model, we found that machine learning classifiers trained with either CS or DS features alone perform better in predicting TF-specific binding compared to SM-based classifiers. In addition, simultaneously considering CS and DS further improves the accuracy of the TF binding predictions, indicating the highly complementary nature of these two properties. The contributions of SM, CS, and DS features to binding site predictions differ greatly between TFs, allowing TF-specific predictions and potentially reflecting different TF binding mechanisms. In addition, a "TF-agnostic" predictive model based on three DNA "intrinsic properties" (in silico predicted nucleosome occupancy, major groove geometry, and dinucleotide free energy) that can be calculated from genomic sequences alone has performance that rivals the model incorporating experiment-derived data. This intrinsic property model allows prediction of binding regions not only across TFs, but also across DNA-binding domain families with distinct structural folds. Furthermore, these predicted binding regions can help identify TF binding sites that have a significant impact on target gene expression. Because the intrinsic property model allows prediction of binding regions across DNA-binding domain families, it is TF agnostic and likely describes general binding potential of TFs. Thus, our findings suggest that it is feasible to establish a TF agnostic model for identifying functional regulatory regions in potentially any sequenced genome.

  7. Contribution of Sequence Motif, Chromatin State, and DNA Structure Features to Predictive Models of Transcription Factor Binding in Yeast

    PubMed Central

    Tsai, Zing Tsung-Yeh; Shiu, Shin-Han; Tsai, Huai-Kuang

    2015-01-01

    Transcription factor (TF) binding is determined by the presence of specific sequence motifs (SM) and chromatin accessibility, where the latter is influenced by both chromatin state (CS) and DNA structure (DS) properties. Although SM, CS, and DS have been used to predict TF binding sites, a predictive model that jointly considers CS and DS has not been developed to predict either TF-specific binding or general binding properties of TFs. Using budding yeast as model, we found that machine learning classifiers trained with either CS or DS features alone perform better in predicting TF-specific binding compared to SM-based classifiers. In addition, simultaneously considering CS and DS further improves the accuracy of the TF binding predictions, indicating the highly complementary nature of these two properties. The contributions of SM, CS, and DS features to binding site predictions differ greatly between TFs, allowing TF-specific predictions and potentially reflecting different TF binding mechanisms. In addition, a "TF-agnostic" predictive model based on three DNA “intrinsic properties” (in silico predicted nucleosome occupancy, major groove geometry, and dinucleotide free energy) that can be calculated from genomic sequences alone has performance that rivals the model incorporating experiment-derived data. This intrinsic property model allows prediction of binding regions not only across TFs, but also across DNA-binding domain families with distinct structural folds. Furthermore, these predicted binding regions can help identify TF binding sites that have a significant impact on target gene expression. Because the intrinsic property model allows prediction of binding regions across DNA-binding domain families, it is TF agnostic and likely describes general binding potential of TFs. Thus, our findings suggest that it is feasible to establish a TF agnostic model for identifying functional regulatory regions in potentially any sequenced genome. PMID:26291518

  8. 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 (Mage=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.

  9. Real Time Numerical Weather Prediction by The Florida State University Superensemble

    NASA Astrophysics Data System (ADS)

    Ross, R. S.; Krishnamurti, T. N.

    2005-05-01

    The Florida State University (FSU) Superensemble technique as applied to real-time numerical weather prediction will be described. An evaluation of the skill of the Superensemble forecasts will be presented in comparison to the skills of the seven global numerical weather prediction models that comprise the Superensemble. Forecast variables that will be examined include lower and upper tropospheric wind fields, mean sea level pressure, mid-tropospheric geopotential height, and precipitation. Forecast skill will be evaluated globally, as well as for a number of sub-regions, such as the Indian monsoon region, North and South America, and the tropical North Atlantic Ocean. Statistical measures of forecast skill will include root mean square error, anomaly correlation, and systematic error for most variables. Forecast precipitation will also be evaluated by use of correlation, bias, and equitable threat scores. The skill scores will be presented the years 2000, 2001, and 2004. The FSU Superensemble technique uses multiple linear regression to derive coefficients from a comparison of member model forecasts to a benchmark analysis during a training period of 120 days. This procedure removes the bias of each individual forecast model and allows for an optimal linear combination of the individual model forecasts, which takes into account the relative skill of each model. The result is a forecast that has greater skill than the individual model forecasts and the ensemble mean forecast. The real-time FSU Superensemble forecasts are available on a website that shows the forecasts for the entire globe, as well as for ten sub-regions of the world. The website has links to the skill scores that are routinely updated, as well as to a number of journal articles that describe the FSU Superensemble technique in detail. Overall, the FSU Superensemble has been shown to be a valuable tool for significantly improving upon the numerical model forecasts emanating from the world

  10. Inscriptions Becoming Representations in Representational Practices

    ERIC Educational Resources Information Center

    Medina, Richard; Suthers, Daniel

    2013-01-01

    We analyze the interaction of 3 students working on mathematics problems over several days in a virtual math team. Our analysis traces out how successful collaboration in a later session is contingent upon the work of prior sessions and shows how the development of representational practices is an important aspect of these participants' problem…

  11. How the state vector configuration matters in multivariate data assimilation for streamflow predictions of snow-fed rivers

    NASA Astrophysics Data System (ADS)

    Bergeron, J.; Trudel, M.; Leconte, R.

    2014-12-01

    Hydrological modelling and streamflow prediction for watersheds over which multiple data sets are available can benefit from data assimilation. For example, updating modelled upstream flows and snow water equivalent (SWE) via existing correlations with downstream flow and SWE observations can positively impact short-term (days) and mid-term (weeks) streamflow forecast, respectively. Other variables can be updated indirectly if they are included in the state vector, which will further affect results. In order to fully benefit from existing correlations between variables, one may be tempted to augment the state vector to include all related variables and parameters, or choose to include a very limited number of variables in order to prevent erroneous correlations from deteriorating other model states. Localizing the correlations on the spatial level or between variables can also affect results. This makes it unclear as to how to configure the state vector, especially when multivariate observations are assimilated. This study presents a sensitivity analysis of the state vector configuration for synthetic multivariate data assimilation using an Ensemble Kalman filter. A spatially distributed hydrological model is used to simulate streamflow predictions for the mountainous Nechako River located in British-Columbia, Canada. Synthetic data includes daily snow cover extent, daily measurements of snow water equivalent (SWE) at three different locations and daily streamflow data at the watershed outlet. Results show a large variability of the Nash-Sutcliffe efficiency and streamflow bias over a wide range of prediction horizons (days to weeks) depending on the state vector configuration and the type of observations assimilated. Some configurations are shown to improve the accuracy of streamflow predictions while others offer worse results than the open loop simulation. These results serve as a first step toward comparing streamflow prediction performance of various real

  12. 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.

  13. Quasiprobability Representations of Quantum Mechanics with Minimal Negativity.

    PubMed

    Zhu, Huangjun

    2016-09-16

    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.

  14. Spectral Approaches to Learning Predictive Representations

    DTIC Science & Technology

    2012-09-01

    Dean P. Foster, and Lyle H. Ungar . Spectral learning of latent-variable pcfgs. In ACL (1), pages 223–231, 2012. 1.1 [26] Paramveer S. Dhillon, Jordan...Rodu, Michael Collins, Dean P. Foster, and Lyle H. Ungar . Spectral dependency parsing with latent variables. In EMNLP-CoNLL, pages 205–213, 2012. 1.1...H. Ungar . Spectral dimensionality reduction for hmms. CoRR, abs/1203.6130, 2012. 1.1 [34] Kenji Fukumizu, Le Song, and Arthur Gretton. Kernel bayes

  15. Prediction of the Chapman-Jouguet chemical equilibrium state in a detonation wave from first principles based reactive molecular dynamics.

    PubMed

    Guo, Dezhou; Zybin, Sergey V; An, Qi; Goddard, William A; Huang, Fenglei

    2016-01-21

    The combustion or detonation of reacting materials at high temperature and pressure can be characterized by the Chapman-Jouguet (CJ) state that describes the chemical equilibrium of the products at the end of the reaction zone of the detonation wave for sustained detonation. This provides the critical properties and product kinetics for input to macroscale continuum simulations of energetic materials. We propose the ReaxFF Reactive Dynamics to CJ point protocol (Rx2CJ) for predicting the CJ state parameters, providing the means to predict the performance of new materials prior to synthesis and characterization, allowing the simulation based design to be done in silico. Our Rx2CJ method is based on atomistic reactive molecular dynamics (RMD) using the QM-derived ReaxFF force field. We validate this method here by predicting the CJ point and detonation products for three typical energetic materials. We find good agreement between the predicted and experimental detonation velocities, indicating that this method can reliably predict the CJ state using modest levels of computation.

  16. Risk analysis and prediction of visceral leishmaniasis dispersion in São Paulo State, Brazil

    PubMed Central

    Mao, Liang; Galvis-Ovallos, Fredy; Tucker Lima, Joanna Marie; Valle, Denis

    2017-01-01

    Visceral leishmaniasis (VL) is an important neglected disease caused by a protozoan parasite, and represents a serious public health problem in many parts of the world. It is zoonotic in Europe and Latin America, where infected dogs constitute the main domestic reservoir for the parasite and play a key role in VL transmission to humans. In Brazil this disease is caused by the protozoan Leishmania infantum chagasi, and is transmitted by the sand fly Lutzomyia longipalpis. Despite programs aimed at eliminating infection sources, the disease continues to spread throughout the Country. VL in São Paulo State, Brazil, first appeared in the northwestern region, spreading in a southeasterly direction over time. We integrate data on the VL vector, infected dogs and infected human dispersion from 1999 to 2013 through an innovative spatial temporal Bayesian model in conjunction with geographic information system. This model is used to infer the drivers of the invasion process and predict the future progression of VL through the State. We found that vector dispersion was influenced by vector presence in nearby municipalities at the previous time step, proximity to the Bolívia-Brazil gas pipeline, and high temperatures (i.e., annual average between 20 and 23°C). Key factors affecting infected dog dispersion included proximity to the Marechal Rondon Highway, high temperatures, and presence of the competent vector within the same municipality. Finally, vector presence, presence of infected dogs, and rainfall (approx. 270 to 540mm/year) drove the dispersion of human VL cases. Surprisingly, economic factors exhibited no noticeable influence on disease dispersion. Based on these drivers and stochastic simulations, we identified which municipalities are most likely to be invaded by vectors and infected hosts in the future. Prioritizing prevention and control strategies within the identified municipalities may help halt the spread of VL while reducing monitoring costs. Our results

  17. State-of-the-Science Report on Predictive Models and Modeling Approaches for Characterizing and Evaluating Exposure to Nanomaterials

    EPA Science Inventory

    This state-of-the-science review was undertaken to identify fate and transport models and alternative modeling approaches that could be used to predict exposure to engineered nanomaterials (ENMs) released into the environment, specifically, for aquatic systems. The development of...

  18. Data worth and prediction uncertainty for pesticide transport and fate models in Nebraska and Maryland, United States

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Few studies have attempted to quantify mass balances of both pesticides and degradates in multiple agricultural settings of the United States. We used inverse modeling to calibrate the Root Zone Water Quality Model (RZWQM) for predicting the unsaturated-zone transport and fate of metolachlor, metola...

  19. Predictive Factors in Undergraduates' Involvement in Campus Secret Cults in Public Universities in Edo State of Nigeria

    ERIC Educational Resources Information Center

    Azetta Arhedo, Philip; Aluede, Oyaziwo; Adomeh, Ilu O. C.

    2011-01-01

    This study examined the predictive factors in undergraduates' involvement in campus secret cults in public universities in Edo State of Nigeria. The study employed the descriptive method, specifically the survey format. A random sample of three hundred and eighty (380) undergraduates was drawn from the two public universities. Data were elicited…

  20. The modulatory influence of a predictive cue on the auditory steady-state response.

    PubMed

    Weisz, Nathan; Lecaignard, Françoise; Müller, Nadia; Bertrand, Olivier

    2012-06-01

    Whether attention exerts its impact already on primary sensory levels is still a matter of debate. Particularly in the auditory domain the amount of empirical evidence is scarce. Recently noninvasive and invasive studies have shown attentional modulations of the auditory Steady-State Response (aSSR). This evoked oscillatory brain response is of importance to the issue, because the main generators have been shown to be located in primary auditory cortex. So far, the issue whether the aSSR is sensitive to the predictive value of a cue preceding a target has not been investigated. Participants in the present study had to indicate on which ear the faster amplitude modulated (AM) sound of a compound sound (42 and 19 Hz AM frequencies) was presented. A preceding auditory cue was either informative (75%) or uninformative (50%) with regards to the location of the target. Behaviorally we could confirm that typical attentional modulations of performance were present in case of a preceding informative cue. With regards to the aSSR we found differences between the informative and uninformative condition only when the cue/target combination was presented to the right ear. Source analysis indicated this difference to be generated by a reduced 42 Hz aSSR in right primary auditory cortex. Our and previous data by others show a default tendency of "40 Hz" AM sounds to be processed by the right auditory cortex. We interpret our results as active suppression of this automatic response pattern, when attention needs to be allocated to right ear input.

  1. Improving the Prediction of Baseflows in the Driftless Area of the Upper Midwestern United States

    NASA Astrophysics Data System (ADS)

    Schuster, Z.; Potter, K. W.

    2014-12-01

    The Driftless Area of the Upper Midwestern United States is a unique region that was not glaciated during the Quaternary Period. Groundwater discharges in the hilly landscape support over 4,000 miles of coldwater, high-class trout streams that are a destination for anglers across the Midwest. Temperature increases due to anthropogenic climate change are predicted to have a negative impact on the cold water thermal regimes that support species such as brook and brown trout. Previous work has concluded that the hillslopes in the region play an important role in producing the recharge that supports cool groundwater discharges concentrated in the headwaters of these streams. In this study, we used a set of baseflow measurements recorded by Potter and Gaffield (2001) in the headwaters of a Driftless Area stream and a simple Geographic Information Systems (GIS) analysis to assess the relationship between the percentage of hillslope in a watershed and average unit baseflow. We found that there is a strong correlation between the hillslope percentage and the unit baseflow values from the Potter and Gaffield (2001) study. Further work is needed to verify the findings of this study and to assess the impacts of the underlying geological layers on the production of baseflow, but these results provide a first step in understanding the conditions that produce spatial variability in baseflow conditions in the Driftless Area. Agencies such as the Wisconsin Department of Natural Resources are beginning to plan for climate change adaptation in the region to protect the coldwater fisheries, so understanding the hydrology of headwater streams will be important in helping to identify areas with high, coldwater baseflow discharges that can provided refugia for coldwater species.

  2. Human Leg Model Predicts Muscle Forces, States, and Energetics during Walking

    PubMed Central

    Markowitz, Jared; Herr, Hugh

    2016-01-01

    Humans employ a high degree of redundancy in joint actuation, with different combinations of muscle and tendon action providing the same net joint torque. Both the resolution of these redundancies and the energetics of such systems depend on the dynamic properties of muscles and tendons, particularly their force-length relations. Current walking models that use stock parameters when simulating muscle-tendon dynamics tend to significantly overestimate metabolic consumption, perhaps because they do not adequately consider the role of elasticity. As an alternative, we posit that the muscle-tendon morphology of the human leg has evolved to maximize the metabolic efficiency of walking at self-selected speed. We use a data-driven approach to evaluate this hypothesis, utilizing kinematic, kinetic, electromyographic (EMG), and metabolic data taken from five participants walking at self-selected speed. The kinematic and kinetic data are used to estimate muscle-tendon lengths, muscle moment arms, and joint moments while the EMG data are used to estimate muscle activations. For each subject we perform an optimization using prescribed skeletal kinematics, varying the parameters that govern the force-length curve of each tendon as well as the strength and optimal fiber length of each muscle while seeking to simultaneously minimize metabolic cost and maximize agreement with the estimated joint moments. We find that the metabolic cost of transport (MCOT) values of our participants may be correctly matched (on average 0.36±0.02 predicted, 0.35±0.02 measured) with acceptable joint torque fidelity through application of a single constraint to the muscle metabolic budget. The associated optimal muscle-tendon parameter sets allow us to estimate the forces and states of individual muscles, resolving redundancies in joint actuation and lending insight into the potential roles and control objectives of the muscles of the leg throughout the gait cycle. PMID:27175486

  3. Analytical predictions of the temperature profile within semiconductor nanostructures for solid-state laser refrigeration

    NASA Astrophysics Data System (ADS)

    Smith, Bennett E.; Zhou, Xuezhe; Davis, E. James; Pauzauskie, Peter J.

    2016-03-01

    The laser refrigeration of solid-state materials with nanoscale dimensions has been demonstrated for both semi- conducting (cadmium sulfide, CdS) and insulating dielectrics (Yb:YLiF4, YLF) in recent years. During laser refrigeration it is possible to observe morphology dependent resonances (MDRs), analogous to what is well- known in classical (Mie) light scattering theory, when the characteristic dimensions of the nanostructure are comparable to the wavelength of light used to initiate the laser cooling process. Mie resonances can create substantial increases for internal optical fields within a given nanostructure with the potential to enhance the absorption efficiency at the beginning of the cooling cycle. Recent breakthroughs in the laser refrigeration of semiconductor nanostructures have relied on materials that exhibit rectangular symmetry (nanoribbons). Here, we will present recent analytical, closed-form solutions to the energy partial differential equation that can be used to calculate the internal spatial temperature profile with a given semiconductor nanoribbon during irradiation by a continuous-wave laser. First, the energy equation is made dimensionless through the substitution of variables before being solved using the classical separation-of-variables approach. In particular, calculations will be presented for chalcogenide (CdS) nanoribbons using a pump wavelength of 1064 nm. For nanostructures with lower symmetry (such as YLF truncated tetragonal bipyramids) it is also possible to observe MDRs through numerical simulations using either the discrete dipole approximation or finite-difference time-domain simulations, and the resulting temperature profile can be calculated using the finite element method. Theoretical predictions are presented using parameters that will allow comparison with experimental data in the near future.

  4. Normalized Neural Representations of Complex Odors

    PubMed Central

    2016-01-01

    The olfactory system removes correlations in natural odors using a network of inhibitory neurons in the olfactory bulb. It has been proposed that this network integrates the response from all olfactory receptors and inhibits them equally. However, how such global inhibition influences the neural representations of odors is unclear. Here, we study a simple statistical model of the processing in the olfactory bulb, which leads to concentration-invariant, sparse representations of the odor composition. We show that the inhibition strength can be tuned to obtain sparse representations that are still useful to discriminate odors that vary in relative concentration, size, and composition. The model reveals two generic consequences of global inhibition: (i) odors with many molecular species are more difficult to discriminate and (ii) receptor arrays with heterogeneous sensitivities perform badly. Comparing these predictions to experiments will help us to understand the role of global inhibition in shaping normalized odor representations in the olfactory bulb. PMID:27835696

  5. Predicting Electrocardiogram and Arterial Blood Pressure Waveforms with Different Echo State Network Architectures

    DTIC Science & Technology

    2014-11-01

    networks were trained to predict an individual’s electrocardiogram ( ECG ) and arterial blood pressure (ABP) waveform data, which can potentially help...various ESN architectures for prediction tasks, and establishes the benefits of using ESN architecture designs for predicting ECG and ABP waveforms...alarms into true alarms and false alarms. These authors then developed an algorithm that classified alarms based on both electrocardiogram ( ECG ) and

  6. 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.

  7. Predicting the redox state and secondary structure of cysteine residues using multi-dimensional classification analysis of NMR chemical shifts.

    PubMed

    Wang, Ching-Cheng; Lai, Wen-Chung; Chuang, Woei-Jer

    2016-09-01

    A tool for predicting the redox state and secondary structure of cysteine residues using multi-dimensional analyses of different combinations of nuclear magnetic resonance (NMR) chemical shifts has been developed. A data set of cysteine [Formula: see text], (13)C(α), (13)C(β), (1)H(α), (1)H(N), and (15)N(H) chemical shifts was created, classified according to redox state and secondary structure, using a library of 540 re-referenced BioMagResBank (BMRB) entries. Multi-dimensional analyses of three, four, five, and six chemical shifts were used to derive rules for predicting the structural states of cysteine residues. The results from 60 BMRB entries containing 122 cysteines showed that four-dimensional analysis of the C(α), C(β), H(α), and N(H) chemical shifts had the highest prediction accuracy of 100 and 95.9 % for the redox state and secondary structure, respectively. The prediction of secondary structure using 3D, 5D, and 6D analyses had the accuracy of ~90 %, suggesting that H(N) and [Formula: see text] chemical shifts may be noisy and made the discrimination worse. A web server (6DCSi) was established to enable users to submit NMR chemical shifts, either in BMRB or key-in formats, for prediction. 6DCSi displays predictions using sets of 3, 4, 5, and 6 chemical shifts, which shows their consistency and allows users to draw their own conclusions. This web-based tool can be used to rapidly obtain structural information regarding cysteine residues directly from experimental NMR data.

  8. Ground states of the SU(N) Heisenberg model.

    PubMed

    Kawashima, Naoki; Tanabe, Yuta

    2007-02-02

    The SU(N) Heisenberg model with various single-row representations is investigated by quantum Monte Carlo simulations. While the zero-temperature phase boundary agrees qualitatively with the theoretical predictions based on the 1/N expansion, some unexpected features are also observed. For N> or =5 with the fundamental representation, for example, it is suggested that the ground states possess exact or approximate U(1) degeneracy. In addition, for the representation of Young tableau with more than one column, the ground state shows no valence-bond-solid order even at N greater than the threshold value.

  9. States versus Rewards: Dissociable neural prediction error signals underlying model-based and model-free reinforcement learning

    PubMed Central

    Gläscher, Jan; Daw, Nathaniel; Dayan, Peter; O’Doherty, John P.

    2010-01-01

    Reinforcement learning (RL) uses sequential experience with situations (“states”) and outcomes to assess actions. Whereas model-free RL uses this experience directly, in the form of a reward prediction error (RPE), model-based RL uses it indirectly, building a model of the state transition and outcome structure of the environment, and evaluating actions by searching this model. A state prediction error (SPE) plays a central role, reporting discrepancies between the current model and the observed state transitions. Using functional magnetic resonance imaging in humans solving a probabilistic Markov decision task we found the neural signature of an SPE in the intraparietal sulcus and lateral prefrontal cortex, in addition to the previously well-characterized RPE in the ventral striatum. This finding supports the existence of two unique forms of learning signal in humans, which may form the basis of distinct computational strategies for guiding behavior. PMID:20510862

  10. Prediction of transport properties of dense gases and liquids by the Peng-Robinson (PR) equation of state

    NASA Astrophysics Data System (ADS)

    Sheng, W.; Chen, G.-J.; Lu, H.-C.

    1989-01-01

    An attempt is made in this work to combine the Enskog theory of transport properties with the simple cubic Peng-Robinson (PR) equation of state. The PR equation of state provides the density dependence of the equilibrium radial distribution function. A slight empirical modification of the Enskog equation is proposed to improve the accuracy of correlation of thermal conductivity and viscosity coefficient for dense gases and liquids. Extensive comparisons with experimental data of pure fluids are made for a wide range of fluid states with temperatures from 90 to 500 K and pressures from 1 to 740 atm. The total average absolute deviations are 2.67% and 2.02% for viscosity and thermal conductivity predictions, respectively. The proposed procedure for predicting viscosity and thermal conductivity is simple and straightforward. It requires only critical parameters and acentric factors for the fluids.

  11. Computer aided surface representation

    SciTech Connect

    Barnhill, R.E.

    1989-02-09

    The central research problem of this project is the effective representation and display of surfaces, interpolating to given information, in three or more dimensions. In a typical problem, we wish to create a surface from some discrete information. If this information is itself on another surface, the problem is to determine a surface defined on a surface,'' which is discussed below. Often, properties of an already constructed surface are desired: such geometry processing'' is described below. The Summary of Proposed Research from our original proposal describes the aims of this research project. This Summary and the Table of Contents from the original proposal are enclosed as an Appendix to this Progress Report. The broad sweep from constructive mathematics through algorithms and computer graphics displays is utilized in the research. The wide range of activity, directed in both theory and applications, makes this project unique. Last month in the first Ardent Titan delivered in the State of Arizona came to our group, funded by the DOE and Arizona State University. Although the Titan is a commercial product, its newness requires our close collaboration with Ardent to maximize results. During the past year, four faculty members and several graduate research assistants have worked on this DOE project. The gaining of new professionals is an important aspect of this project. A listing of the students and their topics is given in the Appendix. The most significant publication during the past year is the book, Curves and Surfaces for Computer Aided Geometric Design, by Dr. Gerald Farin. This 300 page volume helps fill a considerable gap in the subject and includes many new results on Bernstein-Bezier curves and surfaces.

  12. Ensemble Canonical Correlation Prediction of Seasonal Precipitation Over the United States: Raising the Bar for Dynamical Model Forecasts

    NASA Technical Reports Server (NTRS)

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

    2001-01-01

    This paper presents preliminary results of an ensemble canonical correlation (ECC) prediction scheme developed at the Climate and Radiation Branch, NASA/Goddard Space Flight Center for determining the potential predictability of regional precipitation, and for climate downscaling studies. The scheme is tested on seasonal hindcasts of anomalous precipitation over the continental United States using global sea surface temperature (SST) for 1951-2000. To maximize the forecast skill derived from SST, the world ocean is divided into non-overlapping sectors. The canonical SST modes for each sector are used as the predictor for the ensemble hindcasts. Results show that the ECC yields a substantial (10-25%) increase in prediction skills for all the regions of the US in every season compared to traditional CCA prediction schemes. For the boreal winter, the tropical Pacific contributes the largest potential predictability to precipitation in the southwestern and southeastern regions, while the North Pacific and the North Atlantic are responsible to the enhanced forecast skills in the Pacific Northwest, the northern Great Plains and Ohio Valley. Most importantly, the ECC increases skill for summertime precipitation prediction and substantially reduces the spring predictability barrier over all the regions of the US continent. Besides SST, the ECC is designed with the flexibility to include any number of predictor fields, such as soil moisture, snow cover and additional local observations. The enhanced ECC forecast skill provides a new benchmark for evaluating dynamical model forecasts.

  13. How Are You Feeling?: A Personalized Methodology for Predicting Mental States from Temporally Observable Physical and Behavioral Information.

    PubMed

    Tuarob, Suppawong; Tucker, Conrad S; Kumara, Soundar; Lee Giles, C; Pincus, Aaron L; Conroy, David E; Ram, Nilam

    2017-02-14

    It is believed that anomalous mental states such as stress and anxiety not only cause suffering for the individuals, but also lead to tragedies in some extreme cases. The ability to predict the mental state of an individual at both current and future time periods could prove critical to healthcare practitioners. Currently, the practical way to predict an individual's mental state is through mental examinations that involve psychological experts performing the evaluations. However, such methods can be time and resource consuming, mitigating their broad applicability to a wide population. Furthermore, some individuals may also be unaware of their mental states or may feel uncomfortable to express themselves during the evaluations. Hence, their anomalous mental states could remain undetected for a prolonged period of time. The objective of this work is to demonstrate the ability of using advanced machine learning based approaches to generate mathematical models that predict current and future mental states of an individual. The problem of mental state prediction is transformed into the time series forecasting problem, where an individual is represented as a multivariate time series stream of monitored physical and behavioral attributes. A personalized mathematical model is then automatically generated to capture the dependencies among these attributes, which is used for prediction of mental states for each individual. In particular, we first illustrate the drawbacks of traditional multivariate time series forecasting methodologies such as vector autoregression. Then, we show that such issues could be mitigated by using machine learning regression techniques which are modified for capturing temporal dependencies in time series data. A case study using the data from 150 human participants illustrates that the proposed machine learning based forecasting methods are more suitable for high-dimensional psychological data than the traditional vector autoregressive model in

  14. Efficient visual tracking via low-complexity sparse representation

    NASA Astrophysics Data System (ADS)

    Lu, Weizhi; Zhang, Jinglin; Kpalma, Kidiyo; Ronsin, Joseph

    2015-12-01

    Thanks to its good performance on object recognition, sparse representation has recently been widely studied in the area of visual object tracking. Up to now, little attention has been paid to the complexity of sparse representation, while most works are focused on the performance improvement. By reducing the computation load related to sparse representation hundreds of times, this paper proposes by far the most computationally efficient tracking approach based on sparse representation. The proposal simply consists of two stages of sparse representation, one is for object detection and the other for object validation. Experimentally, it achieves better performance than some state-of-the-art methods in both accuracy and speed.

  15. Comparative modeling: the state of the art and protein drug target structure prediction.

    PubMed

    Liu, Tianyun; Tang, Grace W; Capriotti, Emidio

    2011-07-01

    The goal of computational protein structure prediction is to provide three-dimensional (3D) structures with resolution comparable to experimental results. Comparative modeling, which predicts the 3D structure of a protein based on its sequence similarity to homologous structures, is the most accurate computational method for structure prediction. In the last two decades, significant progress has been made on comparative modeling methods. Using the large number of protein structures deposited in the Protein Data Bank (~65,000), automatic prediction pipelines are generating a tremendous number of models (~1.9 million) for sequences whose structures have not been experimentally determined. Accurate models are suitable for a wide range of applications, such as prediction of protein binding sites, prediction of the effect of protein mutations, and structure-guided virtual screening. In particular, comparative modeling has enabled structure-based drug design against protein targets with unknown structures. In this review, we describe the theoretical basis of comparative modeling, the available automatic methods and databases, and the algorithms to evaluate the accuracy of predicted structures. Finally, we discuss relevant applications in the prediction of important drug target proteins, focusing on the G protein-coupled receptor (GPCR) and protein kinase families.

  16. Comparison of Local Information Indices Applied in Resting State Functional Brain Network Connectivity Prediction

    PubMed Central

    Cheng, Chen; Chen, Junjie; Cao, Xiaohua; Guo, Hao

    2016-01-01

    Anatomical distance has been widely used to predict functional connectivity because of the potential relationship between structural connectivity and functional connectivity. The basic implicit assumption of this method is “distance penalization.” But studies have shown that one-parameter model (anatomical distance) cannot account for the small-worldness, modularity, and degree distribution of normal human brain functional networks. Two local information indices–common neighbor (CN) and preferential attachment index (PA), are introduced into the prediction model as another parameter to emulate many key topological of brain functional networks in the previous study. In addition to these two indices, many other local information indices can be chosen for investigation. Different indices evaluate local similarity from different perspectives. Currently, we still have no idea about how to select local information indices to achieve higher predicted accuracy of functional connectivity. Here, seven local information indices are chosen, including CN, hub depressed index (HDI), hub promoted index (HPI), Leicht-Holme-Newman index (LHN-I), Sørensen index (SI), PA, and resource allocation index (RA). Statistical analyses were performed on eight network topological properties to evaluate the predictions. Analysis shows that different prediction models have different performances in terms of simulating topological properties and most of the predicted network properties are close to the real data. There are four topological properties whose average relative error is less than 5%, including characteristic path length, clustering coefficient, global efficiency, and local efficiency. CN model shows the most accurate predictions. Statistical analysis reveals that five properties within the CN-predicted network do not differ significantly from the real data (P > 0.05, false-discovery rate method corrected for seven comparisons). PA model shows the worst prediction performance

  17. State of the art and challenges in sequence based T-cell epitope prediction

    PubMed Central

    2010-01-01

    Sequence based T-cell epitope predictions have improved immensely in the last decade. From predictions of peptide binding to major histocompatibility complex molecules with moderate accuracy, limited allele coverage, and no good estimates of the other events in the antigen-processing pathway, the field has evolved significantly. Methods have now been developed that produce highly accurate binding predictions for many alleles and integrate both proteasomal cleavage and transport events. Moreover have so-called pan-specific methods been developed, which allow for prediction of peptide binding to MHC alleles characterized by limited or no peptide binding data. Most of the developed methods are publicly available, and have proven to be very useful as a shortcut in epitope discovery. Here, we will go through some of the history of sequence-based predictions of helper as well as cytotoxic T cell epitopes. We will focus on some of the most accurate methods and their basic background. PMID:21067545

  18. Toward an evolutionary-predictive foundation for creativity : Commentary on "Human creativity, evolutionary algorithms, and predictive representations: The mechanics of thought trials" by Arne Dietrich and Hilde Haider, 2014 (Accepted pending minor revisions for publication in Psychonomic Bulletin & Review).

    PubMed

    Gabora, Liane; Kauffman, Stuart

    2016-04-01

    Dietrich and Haider (Psychonomic Bulletin & Review, 21 (5), 897-915, 2014) justify their integrative framework for creativity founded on evolutionary theory and prediction research on the grounds that "theories and approaches guiding empirical research on creativity have not been supported by the neuroimaging evidence." Although this justification is controversial, the general direction holds promise. This commentary clarifies points of disagreement and unresolved issues, and addresses mis-applications of evolutionary theory that lead the authors to adopt a Darwinian (versus Lamarckian) approach. To say that creativity is Darwinian is not to say that it consists of variation plus selection - in the everyday sense of the term - as the authors imply; it is to say that evolution is occurring because selection is affecting the distribution of randomly generated heritable variation across generations. In creative thought the distribution of variants is not key, i.e., one is not inclined toward idea A because 60 % of one's candidate ideas are variants of A while only 40 % are variants of B; one is inclined toward whichever seems best. The authors concede that creative variation is partly directed; however, the greater the extent to which variants are generated non-randomly, the greater the extent to which the distribution of variants can reflect not selection but the initial generation bias. Since each thought in a creative process can alter the selective criteria against which the next is evaluated, there is no demarcation into generations as assumed in a Darwinian model. We address the authors' claim that reduced variability and individuality are more characteristic of Lamarckism than Darwinian evolution, and note that a Lamarckian approach to creativity has addressed the challenge of modeling the emergent features associated with insight.

  19. Toward Improved Off-Shore Wind Predictions by Combining Observations with Models through State Estimation - An Analysis of Marine Boundary Layer Parameterizations

    NASA Astrophysics Data System (ADS)

    Kosovic, B.; Delle Monache, L.; Hacker, J.; Lee, J. A.; Vandenberghe, F. C.; Wu, Y.; Clifton, A.; Hawkins, S.; Nissen, J.; Rostkier-Edelstein, D.

    2014-12-01

    In recent years, significant advances have been achieved in model representation of atmospheric boundary layers (ABL). However, fundamental understanding of the processes governing the evolution of the Marine Boundary Layer (MBL) is still incomplete. We address this problem by combining available atmosphere and ocean observations with advanced coupled atmosphere-wave models, via state estimation (SE) methodologies. The goal is to improve wind prediction for off-shore wind energy applications through advances in understanding and parameterization of underlying physical processes, with an emphasis on the coupling between the atmosphere and the ocean via momentum and heat fluxes. We systematically investigate the errors in the treatment of the surface layer of the MBL in the Weather Research and Forecasting (WRF) model and identify structural model inadequacies associated with the MBL parameterization. For this purpose we are using both the single-column model (SCM) and three-dimensional (3D) versions of the WRF model, observations of MBL structure provided by offshore observational platform FINO1, and probabilistic SE. We have also developed an atmosphere-wave coupled modeling system by interfacing WRF with a wave model (WaveWatch III - WWIII). This modeling system is used for evaluating errors in the representation of wave-induced forcing on the energy balance at the interface between atmosphere and ocean. Probabilistic SE is based on the Data Assimilation Research Testbed (DART). DART is the framework for obtaining spatial and temporal statistics of wind-error evolution (and hence the surface-layer fluxes), along with objective tuning of model parameters. We explore one of the potential sources of MBL model errors associated with roughness length parameterized using Charnock's relation. Charnock's roughness length parameterization assumes wind-driven waves are in equilibrium. However, it has been shown that swells propagating at different speeds and angles with

  20. Failure Mode Classification for Life Prediction Modeling of Solid-State Lighting

    SciTech Connect

    Sakalaukus, Peter Joseph

    2015-08-01

    Since the passing of the Energy Independence and Security Act of 2007, the U.S. government has mandated greater energy independence which has acted as a catalyst for accelerating and facilitating research efforts toward the development and deployment of market-driven solutions for energy-saving homes, buildings and manufacturing, as well as sustainable transportation and renewable electricity generation. As part of this effort, an emphasis toward advancing solid-state lighting technology through research, development, demonstration, and commercial applications is assisting in the phase out of the common incandescent light bulb, as well as developing a more economical lighting source that is less toxic than compact fluorescent lighting. This has led lighting manufacturers to pursue SSL technologies for a wide range of consumer lighting applications. An SSL luminaire’s lifetime can be characterized in terms of lumen maintenance life. Lumen maintenance or lumen depreciation is the percentage decrease in the relative luminous flux from that of the original, pristine luminous flux value. Lumen maintenance life is the estimated operating time, in hours, when the desired failure threshold is projected to be reached at normal operating conditions. One accepted failure threshold of SSL luminaires is lumen maintenance of 70% -- a 30% reduction in the light output of the luminaire. Currently, the only approved lighting standard that puts forth a recommendation for long-term luminous flux maintenance projections towards a specified failure threshold of an SSL luminaire is the IES TM-28-14 (TM28) standard. iii TM28 was derived as a means to compare luminaires that have been tested at different facilities, research labs or companies. TM28 recommends the use of the Arrhenius equation to determine SSL device specific reaction rates from thermally driven failure mechanisms used to characterize a single failure mode – the relative change in the luminous flux output or

  1. Ground Motion Prediction Equations for the Central and Eastern United States

    NASA Astrophysics Data System (ADS)

    Seber, D.; Graizer, V.

    2015-12-01

    New ground motion prediction equations (GMPE) G15 model for the Central and Eastern United States (CEUS) is presented. It is based on the modular filter based approach developed by Graizer and Kalkan (2007, 2009) for active tectonic environment in the Western US (WUS). The G15 model is based on the NGA-East database for the horizontal peak ground acceleration and 5%-damped pseudo spectral acceleration RotD50 component (Goulet et al., 2014). In contrast to active tectonic environment the database for the CEUS is not sufficient for creating purely empirical GMPE covering the range of magnitudes and distances required for seismic hazard assessments. Recordings in NGA-East database are sparse and cover mostly range of M<6.0 with limited amount of near-fault recordings. The functional forms of the G15 GMPEs are derived from filters—each filter represents a particular physical phenomenon affecting the seismic wave radiation from the source. Main changes in the functional forms for the CEUS relative to the WUS model (Graizer and Kalkan, 2015) are a shift of maximum frequency of the acceleration response spectrum toward higher frequencies and an increase in the response spectrum amplitudes at high frequencies. Developed site correction is based on multiple runs of representative VS30 profiles through SHAKE-type equivalent-linear programs using time histories and random vibration theory approaches. Site amplification functions are calculated for different VS30 relative to hard rock definition used in nuclear industry (Vs=2800 m/s). The number of model predictors is limited to a few measurable parameters: moment magnitude M, closest distance to fault rupture plane R, average shear-wave velocity in the upper 30 m of the geological profile VS30, and anelastic attenuation factor Q0. Incorporating anelastic attenuation Q0 as an input parameter allows adjustments based on the regional crustal properties. The model covers the range of magnitudes 4.0

  2. Prospective evaluation of the effects of anxiety sensitivity and state anxiety in predicting acute nicotine withdrawal symptoms during smoking cessation.

    PubMed

    Johnson, Kirsten A; Stewart, Sherry; Rosenfield, David; Steeves, Dan; Zvolensky, Michael J

    2012-06-01

    The current investigation explored the main and interactive effects of anxiety sensitivity (AS) and state anxiety in predicting acute nicotine withdrawal symptoms experienced during the initial 14 days of smoking cessation. Participants included 123 adult daily smokers (84 women; Mage = 45.93 years, SD = 10.34) undergoing psychosocial-pharmacological cessation treatment. Results indicated that after controlling for the effects of participant sex and nicotine dependence, state anxiety but not AS significantly predicted initial levels of nicotine withdrawal symptoms. Results also demonstrated that both state anxiety and AS were significantly related to the change in nicotine withdrawal symptoms over time. Finally, our results revealed a significant interaction between AS and state anxiety. Specifically, higher levels of AS were associated with a stronger relation between state anxiety and nicotine withdrawal symptoms experienced during the cessation attempt. Results suggest that among high AS persons, state anxiety may be more relevant, compared to those low in AS, in regard to experiencing withdrawal symptoms as more intense during the early phases of quitting.

  3. Predicting an Individual’s Physiologic State without a Crystal Ball

    DTIC Science & Technology

    2008-04-05

    RT) CGM # of Diabetes Sampling Frequency (min)Device* Subjects Type iSense 9 1 1 DexCom 7 2 5 Guardian RT 18 1 5 Glucose Prediction for Type 1 & 2...40.5 41 Prediction 95% Prediction interval Measurement Time, hh:mm *Data provided by Ken Ward (iSense), Robert Vigersky ( DexCom ), Direcnet (Guardian...Diabetes - three studies using distinct continuous glucose monitoring ( CGM ) devices - Collection Time (days) 5 56 6 Time (min) G l u c o s e ( m g / d

  4. Evaluation of snow data assimilation using the ensemble Kalman filter for seasonal streamflow prediction in the western United States

    NASA Astrophysics Data System (ADS)

    Huang, Chengcheng; Newman, Andrew J.; Clark, Martyn P.; Wood, Andrew W.; Zheng, Xiaogu

    2017-01-01

    In this study, we examine the potential of snow water equivalent data assimilation (DA) using the ensemble Kalman filter (EnKF) to improve seasonal streamflow predictions. There are several goals of this study. First, we aim to examine some empirical aspects of the EnKF, namely the observational uncertainty estimates and the observation transformation operator. Second, we use a newly created ensemble forcing dataset to develop ensemble model states that provide an estimate of model state uncertainty. Third, we examine the impact of varying the observation and model state uncertainty on forecast skill. We use basins from the Pacific Northwest, Rocky Mountains, and California in the western United States with the coupled Snow-17 and Sacramento Soil Moisture Accounting (SAC-SMA) models. We find that most EnKF implementation variations result in improved streamflow prediction, but the methodological choices in the examined components impact predictive performance in a non-uniform way across the basins. Finally, basins with relatively higher calibrated model performance (> 0.80 NSE) without DA generally have lesser improvement with DA, while basins with poorer historical model performance show greater improvements.

  5. Predictive Models for Carcinogenicity and Mutagenicity: Frameworks, State-of-the-Art, and Perspectives

    EPA Science Inventory

    Mutagenicity and carcinogenicity are endpoints of major environmental and regulatory concern. These endpoints are also important targets for development of alternative methods for screening and prediction due to the large number of chemicals of potential concern and the tremendou...

  6. Predictive Models for Carcinogenicity and Mutagenicity: Frameworks,State-of-the-Art, and Perspectives

    EPA Science Inventory

    Mutagenicity and carcinogenicity are endpoints of major environmental and regulatory concern. These endpoints are also important targets for development of alternative methods for screening and prediction due to the large number of chemicals of potential concern and the tremendou...

  7. Function, anticipation, representation

    NASA Astrophysics Data System (ADS)

    Bickhard, Mark. H.

    2001-06-01

    Function emerges in certain kinds of far-from-equilibrium systems. One important kind of function is that of interactive anticipation, an adaptedness to temporal complexity. Interactive anticipation is the locus of the emergence of normative representational content, and, thus, of representation in general: interactive anticipation is the naturalistic core of the evolution of cognition. Higher forms of such anticipation are involved in the subsequent macro-evolutionary sequence of learning, emotions, and reflexive consciousness.

  8. Topographic NMF for data representation.

    PubMed

    Xiao, Yanhui; Zhu, Zhenfeng; Zhao, Yao; Wei, Yunchao; Wei, Shikui; Li, Xuelong

    2014-10-01

    Nonnegative matrix factorization (NMF) is a useful technique to explore a parts-based representation by decomposing the original data matrix into a few parts-based basis vectors and encodings with nonnegative constraints. It has been widely used in image processing and pattern recognition tasks due to its psychological and physiological interpretation of natural data whose representation may be parts-based in human brain. However, the nonnegative constraint for matrix factorization is generally not sufficient to produce representations that are robust to local transformations. To overcome this problem, in this paper, we proposed a topographic NMF (TNMF), which imposes a topographic constraint on the encoding factor as a regularizer during matrix factorization. In essence, the topographic constraint is a two-layered network, which contains the square nonlinearity in the first layer and the square-root nonlinearity in the second layer. By pooling together the structure-correlated features belonging to the same hidden topic, the TNMF will force the encodings to be organized in a topographical map. Thus, the feature invariance can be promoted. Some experiments carried out on three standard datasets validate the effectiveness of our method in comparison to the state-of-the-art approaches.

  9. Symbol Systems and Pictorial Representations

    NASA Astrophysics Data System (ADS)

    Diederich, Joachim; Wright, Susan

    All problem-solvers are subject to the same ultimate constraints -- limitations on space, time, and materials (Minsky, 1985). He introduces two principles: (1) Economics: Every intelligence must develop symbol-systems for representing objects, causes and goals, and (2) Sparseness: Every evolving intelligence will eventually encounter certain very special ideas -- e.g., about arithmetic, causal reasoning, and economics -- because these particular ideas are very much simpler than other ideas with similar uses. An extra-terrestrial intelligence (ETI) would have developed symbol systems to express these ideas and would have the capacity of multi-modal processing. Vakoch (1998) states that ...``ETI may rely significantly on other sensory modalities (than vision). Particularly useful representations would be ones that may be intelligible through more than one sensory modality. For instance, the information used to create a three-dimensional representation of an object might be intelligible to ETI heavily reliant on either visual or tactile sensory processes.'' The cross-modal representations Vakoch (1998) describes and the symbol systems Minsky (1985) proposes are called ``metaphors'' when combined. Metaphors allow for highly efficient communication. Metaphors are compact, condensed ways of expressing an idea: words, sounds, gestures or images are used in novel ways to refer to something they do not literally denote. Due to the importance of Minsky's ``economics'' principle, it is therefore possible that a message heavily relies on metaphors.

  10. High Speed Displacement Vessels Parametric Studies and Calm Water Resistance Predictions - State of the Art,

    DTIC Science & Technology

    1986-04-01

    Hull Form Parameters for Powering T-7 Predictions -- High Speed Displacement Hull Series - V %-, V 5.0-1 Hull Form Series Applications T-8 iv...1.049 - - - - - - T-6- A ’. .. - ~% ". Jb TABLE 4.0-1 Basic Hull Form Parameters for Powering Predictions -- High Speed Displacement Hull ...8217 developed by the author based on some of the high - speed round bilge

  11. State-of-the-Art Climate Predictions for Energy Climate Services

    NASA Astrophysics Data System (ADS)

    Torralba-Fernandez, Veronica; Davis, Melanie; Doblas-Reyes, Francisco J.; Gonzalez-Reviriego, Nube

    2015-04-01

    Climate predictions tailored to the energy sector represent the cutting edge in climate sciences to forecast wind power generation. At seasonal time scales, current energy practices use a deterministic approach based on retrospective climatology, but climate predictions have recently been shown to provide additional value. For this reason, probabilistic climate predictions of near surface winds can allow end users to take calculated, precautionary action with a potential cost savings to their operations. As every variable predicted in a coupled model forecast system, the prediction of wind speed is affected by biases. To overcome this, two different techniques for the post-processing of ensemble forecasts are considered: a simple bias correction and a calibration method. The former is based on the assumption that the reference and predicted distributions are well approximated by a normal distribution. The latter is a calibration technique which inflates the model variance, and the inflation of the ensemble is required in order to obtain a reliable outcome. Both methods use the "one-year out" cross-validated mode, and they provide corrected forecasts with improved statistical properties. The impact of these bias corrections on the quality of the ECMWF S4 predictions of near surface wind speed during winter is explored. To offer a comprehensive picture of the post-processing effect on the forecast quality of the system, it is necessary to use several scoring measures: rank histograms, reliability diagrams and skill maps. These tools are essential to assess different aspects of the forecasts, and to observe changes in their properties when the two methods are applied. This study reveals that the different techniques to correct the predictions produce a statistically consistent ensemble. However, the operations performed on the forecasts decrease their skill which correspond to an increase in the uncertainty. Therefore, even though the bias correction is fundamental

  12. A history of wind erosion prediction models in the United States Department of Agriculture prior to the Wind Erosion Prediction System

    NASA Astrophysics Data System (ADS)

    Tatarko, John; Sporcic, Michael A.; Skidmore, Edward L.

    2013-09-01

    The Great Plains experienced an influx of settlers in the late 1850s-1900. Periodic drought was hard on both settlers and the soil and caused severe wind erosion. The period known as the Dirty Thirties, 1931-1939, produced many severe windstorms, and the resulting dusty sky over Washington, DC helped Hugh Hammond Bennett gain political support for the Soil Conservation Act of 1937 that started the USDA Soil Conservation Service (SCS). Austin W. Zingg and William S. Chepil began wind erosion studies at a USDA laboratory at Kansas State University in 1947. Neil P. Woodruff and Francis H. Siddoway published the first widely used model for wind erosion in 1965, called the Wind Erosion Equation (WEQ). The WEQ was solved using a series of charts and lookup tables. Subsequent improvements to WEQ included monthly magnitudes of the total wind, a computer version of WEQ programmed in FORTRAN, small-grain equivalents for range grasses, tillage systems, effects of residue management, crop row direction, cloddiness, monthly climate factors, and the weather. The SCS and the Natural Resources Conservation Service (NRCS) produced several computer versions of WEQ with the goal of standardizing and simplifying it for field personnel including a standalone version of WEQ was developed in the late 1990s using Microsoft Excel. Although WEQ was a great advancement to the science of prediction and control of wind erosion on cropland, it had many limitations that prevented its use on many lands throughout the United States and the world. In response to these limitations, the USDA developed a process-based model know as the Wind Erosion Prediction System (WEPS). The USDA Agricultural Research Service has taken the lead in developing science and technology for wind erosion prediction.

  13. Long-lead predictions of eastern United States hot days from Pacific sea surface temperatures

    NASA Astrophysics Data System (ADS)

    McKinnon, K. A.; Rhines, A.; Tingley, M. P.; Huybers, P.

    2016-05-01

    Seasonal forecast models exhibit only modest skill in predicting extreme summer temperatures across the eastern US. Anomalies in sea surface temperature and monthly-resolution rainfall have, however, been correlated with hot days in the US, and seasonal persistence of these anomalies suggests potential for long-lead predictability. Here we present a clustering analysis of daily maximum summer temperatures from US weather stations between 1982-2015 and identify a region spanning most of the eastern US where hot weather events tend to occur synchronously. We then show that an evolving pattern of sea surface temperature anomalies, termed the Pacific Extreme Pattern, provides for skillful prediction of hot weather within this region as much as 50 days in advance. Skill is demonstrated using out-of-sample predictions between 1950 and 2015. Rainfall deficits over the eastern US are also associated with the occurrence of the Pacific Extreme Pattern and are demonstrated to offer complementary skill in predicting high temperatures. The Pacific Extreme Pattern appears to provide a cohesive framework for improving seasonal prediction of summer precipitation deficits and high temperature anomalies in the eastern US.

  14. 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…

  15. 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

  16. 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…

  17. On the Use of Statistical Downscaling to Improve the Skill in Decadal Predictions of Temperature over the Continental United States

    NASA Astrophysics Data System (ADS)

    Salvi, K. A.; Villarini, G.; Vecchi, G. A.

    2015-12-01

    Increases in global temperature over recent decades and projected acceleration in warming trends over the 21st century are established consequences of climate change. Possible manifestations of altered climate (e.g., more frequent, intense, and persistent temperature extremes) have resulted in a strong need to obtain information about future conditions. Under these circumstances, skillful decadal temperature predictions (DTPs) can have profound societal and economic benefits through informed planning and response. However, skillful and actionable DTPs are extremely challenging to achieve. Even though General Circulation Models (GCMs) provide decadal predictions of a number of climate variables, the direct use of GCM simulations is not encouraged because of the limited skill they exhibit. To address these shortcomings, we apply a statistical downscaling methodology to increase the GCMs' skill in predicting decadal temperature over the continental United States. Here, we use kernel regression to establish statistical relationships between coarse resolution climate variables (predictors) and the fine resolution climate variable of interest (predictand). The climate variables used as predictors are obtained from National Centers for Environmental Prediction and the National Center for Atmospheric Research (NCEP/NCAR) reanalysis data, and 'Parameter-elevation Relationships on Independent Slopes Model' (PRISM) temperature data are used as predictand. Statistical relationships established over calibration period (1961-1990) are applied to retrospective decadal predictions by GCMs. The skill is quantified using a diagnostic skill score that allows the evaluation of the potential skill and conditional and unconditional biases associated with these predictions. Our approach has led to significant improvements in the prediction skill and to the removal of the biases. Results are discussed for predictions at different spatial and temporal scales.

  18. Including Thermal Fluctuations in Actomyosin Stable States Increases the Predicted Force per Motor and Macroscopic Efficiency in Muscle Modelling.

    PubMed

    Marcucci, Lorenzo; Washio, Takumi; Yanagida, Toshio

    2016-09-01

    Muscle contractions are generated by cyclical interactions of myosin heads with actin filaments to form the actomyosin complex. To simulate actomyosin complex stable states, mathematical models usually define an energy landscape with a corresponding number of wells. The jumps between these wells are defined through rate constants. Almost all previous models assign these wells an infinite sharpness by imposing a relatively simple expression for the detailed balance, i.e., the ratio of the rate constants depends exponentially on the sole myosin elastic energy. Physically, this assumption corresponds to neglecting thermal fluctuations in the actomyosin complex stable states. By comparing three mathematical models, we examine the extent to which this hypothesis affects muscle model predictions at the single cross-bridge, single fiber, and organ levels in a ceteris paribus analysis. We show that including fluctuations in stable states allows the lever arm of the myosin to easily and dynamically explore all possible minima in the energy landscape, generating several backward and forward jumps between states during the lifetime of the actomyosin complex, whereas the infinitely sharp minima case is characterized by fewer jumps between states. Moreover, the analysis predicts that thermal fluctuations enable a more efficient contraction mechanism, in which a higher force is sustained by fewer attached cross-bridges.

  19. Including Thermal Fluctuations in Actomyosin Stable States Increases the Predicted Force per Motor and Macroscopic Efficiency in Muscle Modelling

    PubMed Central

    2016-01-01

    Muscle contractions are generated by cyclical interactions of myosin heads with actin filaments to form the actomyosin complex. To simulate actomyosin complex stable states, mathematical models usually define an energy landscape with a corresponding number of wells. The jumps between these wells are defined through rate constants. Almost all previous models assign these wells an infinite sharpness by imposing a relatively simple expression for the detailed balance, i.e., the ratio of the rate constants depends exponentially on the sole myosin elastic energy. Physically, this assumption corresponds to neglecting thermal fluctuations in the actomyosin complex stable states. By comparing three mathematical models, we examine the extent to which this hypothesis affects muscle model predictions at the single cross-bridge, single fiber, and organ levels in a ceteris paribus analysis. We show that including fluctuations in stable states allows the lever arm of the myosin to easily and dynamically explore all possible minima in the energy landscape, generating several backward and forward jumps between states during the lifetime of the actomyosin complex, whereas the infinitely sharp minima case is characterized by fewer jumps between states. Moreover, the analysis predicts that thermal fluctuations enable a more efficient contraction mechanism, in which a higher force is sustained by fewer attached cross-bridges. PMID:27626630

  20. Pupil diameter tracks changes in control state predicted by the adaptive gain theory of locus coeruleus function.

    PubMed

    Gilzenrat, Mark S; Nieuwenhuis, Sander; Jepma, Marieke; Cohen, Jonathan D

    2010-05-01

    An important dimension of cognitive control is the adaptive regulation of the balance between exploitation (pursuing known sources of reward) and exploration (seeking new ones) in response to changes in task utility. Recent studies have suggested that the locus coeruleus-norepinephrine system may play an important role in this function and that pupil diameter can be used to index locus coeruleus activity. On the basis of this, we reasoned that pupil diameter may correlate closely with control state and associated changes in behavior. Specifically, we predicted that increases in baseline pupil diameter would be associated with decreases in task utility and disengagement from the task (exploration), whereas reduced baseline diameter (but increases in task-evoked dilations) would be associated with task engagement (exploitation). Findings in three experiments were consistent with these predictions, suggesting that pupillometry may be useful as an index of both control state and, indirectly, locus coeruleus function.

  1. 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

  2. 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.

  3. Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli

    PubMed Central

    Kim, Minseung; Rai, Navneet; Zorraquino, Violeta; Tagkopoulos, Ilias

    2016-01-01

    A significant obstacle in training predictive cell models is the lack of integrated data sources. We develop semi-supervised normalization pipelines and perform experimental characterization (growth, transcriptional, proteome) to create Ecomics, a consistent, quality-controlled multi-omics compendium for Escherichia coli with cohesive meta-data information. We then use this resource to train a multi-scale model that integrates four omics layers to predict genome-wide concentrations and growth dynamics. The genetic and environmental ontology reconstructed from the omics data is substantially different and complementary to the genetic and chemical ontologies. The integration of different layers confers an incremental increase in the prediction performance, as does the information about the known gene regulatory and protein-protein interactions. The predictive performance of the model ranges from 0.54 to 0.87 for the various omics layers, which far exceeds various baselines. This work provides an integrative framework of omics-driven predictive modelling that is broadly applicable to guide biological discovery. PMID:27713404

  4. 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

  5. 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.

  6. 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.

  7. Low-dimensional representations of exact coherent states of the Navier-Stokes equations from the resolvent model of wall turbulence.

    PubMed

    Sharma, Ati S; Moarref, Rashad; McKeon, Beverley J; Park, Jae Sung; Graham, Michael D; Willis, Ashley P

    2016-02-01

    We report that many exact invariant solutions of the Navier-Stokes equations for both pipe and channel flows are well represented by just a few modes of the model of McKeon and Sharma [J. Fluid Mech. 658, 336 (2010)]. This model provides modes that act as a basis to decompose the velocity field, ordered by their amplitude of response to forcing arising from the interaction between scales. The model was originally derived from the Navier-Stokes equations to represent turbulent flows and has been used to explain coherent structure and to predict turbulent statistics. This establishes a surprising new link between the two distinct approaches to understanding turbulence.

  8. Time Perspectives Predict Mood States and Satisfaction with Life over and above Personality.

    PubMed

    Stolarski, Maciej; Matthews, Gerald

    2016-01-01

    The present study aimed to test the incremental validity of Time Perspective (TP) scales in predicting satisfaction with life and mood, over and above the Big Five personality traits. It also investigated whether the new TP construct of Future Negative perspective contributed to prediction of these outcomes. Participants (N = 265) completed four measures: Satisfaction With Life Scale (SWLS), UWIST Mood Adjective Checklist (UMACL), a modified Zimbardo Time Perspective Inventory (ZTPI), and NEO-Five Factor Inventory (NEO-FFI). Results confirmed the incremental validity of TP, although Big Five dimensions were independently predictive of life satisfaction and certain mood scales. Past Negative TP was the strongest single predictor of life satisfaction. However, Future Negative TP was be the strongest mood predictor from the TP universe, after controlling for the Big Five and remaining TP dimensions. Findings suggest that TP is an important aspect of personality for understanding individual differences in well-being.

  9. Using state diagrams for predicting colloidal stability of whey protein beverages.

    PubMed

    Wagoner, Ty B; Ward, Loren; Foegeding, E Allen

    2015-05-06

    A method for evaluating aspects of colloidal stability of whey protein beverages after thermal treatment was established. Three state diagrams for beverages (pH 3-7) were developed representing protein solubility, turbidity, and macroscopic state after two ultrahigh-temperature (UHT) treatments. Key transitions of stability in the state diagrams were explored using electrophoresis and chromatography to determine aggregation propensities of β-lactoglobulin, α-lactalbumin, bovine serum albumin, and glycomacropeptide. The state diagrams present an overlapping view of high colloidal stability at pH 3 accompanied by high solubility of individual whey proteins. At pH 5, beverages were characterized by poor solubility, high turbidity, and aggregation/gelation of whey proteins with the exception of glycomacropeptide. Stability increased at pH 6, due to increased solubility of α-lactalbumin. The results indicate that combinations of state diagrams can be used to identify key regions of stability for whey protein containing beverages.

  10. Predictive models for carcinogenicity and mutagenicity: frameworks, state-of-the-art, and perspectives.

    PubMed

    Benfenati, E; Benigni, R; Demarini, D M; Helma, C; Kirkland, D; Martin, T M; Mazzatorta, P; Ouédraogo-Arras, G; Richard, A M; Schilter, B; Schoonen, W G E J; Snyder, R D; Yang, C

    2009-04-01

    Mutagenicity and carcinogenicity are endpoints of major environmental and regulatory concern. These endpoints are also important targets for development of alternative methods for screening and prediction due to the large number of chemicals of potential concern and the tremendous cost (in time, money, animals) of rodent carcinogenicity bioassays. Both mutagenicity and carcinogenicity involve complex, cellular processes that are only partially understood. Advances in technologies and generation of new data will permit a much deeper understanding. In silico methods for predicting mutagenicity and rodent carcinogenicity based on chemical structural features, along with current mutagenicity and carcinogenicity data sets, have performed well for local prediction (i.e., within specific chemical classes), but are less successful for global prediction (i.e., for a broad range of chemicals). The predictivity of in silico methods can be improved by improving the quality of the data base and endpoints used for modelling. In particular, in vitro assays for clastogenicity need to be improved to reduce false positives (relative to rodent carcinogenicity) and to detect compounds that do not interact directly with DNA or have epigenetic activities. New assays emerging to complement or replace some of the standard assays include Vitotox, GreenScreenGC, and RadarScreen. The needs of industry and regulators to assess thousands of compounds necessitate the development of high-throughput assays combined with innovative data-mining and in silico methods. Various initiatives in this regard have begun, including CAESAR, OSIRIS, CHEMOMENTUM, CHEMPREDICT, OpenTox, EPAA, and ToxCast. In silico methods can be used for priority setting, mechanistic studies, and to estimate potency. Ultimately, such efforts should lead to improvements in application of in silico methods for predicting carcinogenicity to assist industry and regulators and to enhance protection of public health.

  11. Negative Ion Photoelectron Spectroscopy Confirms the Prediction that (CO)5 and (CO)6 Each Has a Singlet Ground State

    SciTech Connect

    Bao, Xiaoguang; Hrovat, David; Borden, Weston; Wang, Xue B.

    2013-03-20

    Cyclobutane-1,2,3,4-tetraone has been both predicted and found to have a triplet ground state, in which a b2g MO and an a2u MO is each singly occupied. In contrast, (CO)5 and (CO)6 have each been predicted to have a singlet ground state. This prediction has been tested by generating the (CO)5 - and (CO)6 - anions in the gas-phase by electrospray vaporization of solutions of, respectively, the croconate (CO)52- and rhodizonate (CO)62- dianions. The negative ion photoelectron (NIPE) spectra of the (CO)5•- radical anion give electron affinity (EA) = 3.830 eV and a singlet ground state for (CO)5, with the triplet higher in energy by 0.850 eV (19.6 kcal/mol). The NIPE spectra of the (CO)6•- radical anion give EA = 3.785 eV and a singlet ground state for (CO)6, with the triplet higher in energy by 0.915 eV (21.1 kcal/mol). (RO)CCSD(T)/aug-cc-pVTZ//(U)B3LYP/6-311+G(2df) calculations give EA values that are only ca. 1 kcal/mol lower than those measured and EST values that are only 2 - 3 kcal/mol higher than those obtained from the NIPE spectra. Thus, the calculations support the interpretations of the NIPE spectra and the finding, based on the spectra, that (CO)5 and (CO)6 both have a singlet ground state.

  12. Testing Predictive Models of Technology Integration in Mexico and the United States

    ERIC Educational Resources Information Center

    Velazquez, Cesareo Morales

    2008-01-01

    Data from Mexico City, Mexico (N = 978) and from Texas, USA (N = 932) were used to test the predictive validity of the teacher professional development component of the Will, Skill, Tool Model of Technology Integration in a cross-cultural context. Structural equation modeling (SEM) was used to test the model. Analyses of these data yielded…

  13. An investigation of gear mesh failure prediction techniques. M.S. Thesis - Cleveland State Univ.

    NASA Technical Reports Server (NTRS)

    Zakrajsek, James J.

    1989-01-01

    A study was performed in which several gear failure prediction methods were investigated and applied to experimental data from a gear fatigue test apparatus. The primary objective was to provide a baseline understanding of the prediction methods and to evaluate their diagnostic capabilities. The methods investigated use the signal average in both the time and frequency domain to detect gear failure. Data from eleven gear fatigue tests were recorded at periodic time intervals as the gears were run from initiation to failure. Four major failure modes, consisting of heavy wear, tooth breakage, single pits, and distributed pitting were observed among the failed gears. Results show that the prediction methods were able to detect only those gear failures which involved heavy wear or distributed pitting. None of the methods could predict fatigue cracks, which resulted in tooth breakage, or single pits. It is suspected that the fatigue cracks were not detected because of limitations in data acquisition rather than in methodology. Additionally, the frequency response between the gear shaft and the transducer was found to significantly affect the vibration signal. The specific frequencies affected were filtered out of the signal average prior to application of the methods.

  14. Predicting geogenic arsenic contamination in shallow groundwater of south Louisiana, United States.

    PubMed

    Yang, Ningfang; Winkel, Lenny H E; Johannesson, Karen H

    2014-05-20

    Groundwater contaminated with arsenic (As) threatens the health of more than 140 million people worldwide. Previous studies indicate that geology and sedimentary depositional environments are important factors controlling groundwater As contamination. The Mississippi River delta has broadly similar geology and sedimentary depositional environments to the large deltas in South and Southeast Asia, which are severely affected by geogenic As contamination and therefore may also be vulnerable to groundwater As contamination. In this study, logistic regression is used to develop a probability model based on surface hydrology, soil properties, geology, and sedimentary depositional environments. The model is calibrated using 3286 aggregated and binary-coded groundwater As concentration measurements from Bangladesh and verified using 78 As measurements from south Louisiana. The model's predictions are in good agreement with the known spatial distribution of groundwater As contamination of Bangladesh, and the predictions also indicate high risk of As contamination in shallow groundwater from Holocene sediments of south Louisiana. Furthermore, the model correctly predicted 79% of the existing shallow groundwater As measurements in the study region, indicating good performance of the model in predicting groundwater As contamination in shallow aquifers of south Louisiana.

  15. NIR models for predicting total sugar in tobacco for samples with different physical states

    NASA Astrophysics Data System (ADS)

    Qin, Yuhua; Gong, Huili

    2016-07-01

    Due to the spectra variation of the inhomogeneous tobacco flakes results the inaccuracy and instability of the near infrared model. This paper presented the strategies of calibration transfer and hybrid modeling for determining total sugar content in tobacco based on the homogeneous powder model. The necessity judgments and acceptance criteria of the calibration transfer were also proposed. Calibration transfer methods include Slope/Bias Correction (S/B), Piecewise Direct Standardization (PDS), double window piecewise direct standardization (DWPDS), and Shenk's were adopted, a transfer set of 15 samples were chosen for each methods, and the results showed that Shenk's is the adequate transfer method as only one indicator did not fulfill the acceptance criteria of the transfer. Other methods were all dissatisfied with the acceptance criteria and cannot be applied to the calibration transfer between the tobacco flake and powder. While the hybrid model of adding some flake samples to the powder model achieved preferred prediction ability. The study showed that adding around 10% variation samples caused the average prediction error of total sugar content (range 12.1-37.2%) in flake samples from 7.25% (predicted by a flake model) significantly dropping to 4.98%, even close to the prediction of the same powder samples (4.21%) by the powder model. It will valuable for the promotion of the NIR network and online analysis.

  16. Progress on implementation of process-based erosion prediction technology in the United States

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Use of soil erosion prediction models is becoming increasingly important due to growing environmental concerns and impacts of runoff and sediment on off-site water quality. The USDA Natural Resources Conservation Service (NRCS) has implemented and is now using the process-based Wind Erosion Predicti...

  17. Grassmannian sparse representations

    NASA Astrophysics Data System (ADS)

    Azary, Sherif; Savakis, Andreas

    2015-05-01

    We present Grassmannian sparse representations (GSR), a sparse representation Grassmann learning framework for efficient classification. Sparse representation classification offers a powerful approach for recognition in a variety of contexts. However, a major drawback of sparse representation methods is their computational performance and memory utilization for high-dimensional data. A Grassmann manifold is a space that promotes smooth surfaces where points represent subspaces and the relationship between points is defined by the mapping of an orthogonal matrix. Grassmann manifolds are well suited for computer vision problems because they promote high between-class discrimination and within-class clustering, while offering computational advantages by mapping each subspace onto a single point. The GSR framework combines Grassmannian kernels and sparse representations, including regularized least squares and least angle regression, to improve high accuracy recognition while overcoming the drawbacks of performance and dependencies on high dimensional data distributions. The effectiveness of GSR is demonstrated on computationally intensive multiview action sequences, three-dimensional action sequences, and face recognition datasets.

  18. Resting-State fMRI Activity Predicts Unsupervised Learning and Memory in an Immersive Virtual Reality Environment

    PubMed Central

    Wong, Chi Wah; Olafsson, Valur; Plank, Markus; Snider, Joseph; Halgren, Eric; Poizner, Howard; Liu, Thomas T.

    2014-01-01

    In the real world, learning often proceeds in an unsupervised manner without explicit instructions or feedback. In this study, we employed an experimental paradigm in which subjects explored an immersive virtual reality environment on each of two days. On day 1, subjects implicitly learned the location of 39 objects in an unsupervised fashion. On day 2, the locations of some of the objects were changed, and object location recall performance was assessed and found to vary across subjects. As prior work had shown that functional magnetic resonance imaging (fMRI) measures of resting-state brain activity can predict various measures of brain performance across individuals, we examined whether resting-state fMRI measures could be used to predict object location recall performance. We found a significant correlation between performance and the variability of the resting-state fMRI signal in the basal ganglia, hippocampus, amygdala, thalamus, insula, and regions in the frontal and temporal lobes, regions important for spatial exploration, learning, memory, and decision making. In addition, performance was significantly correlated with resting-state fMRI connectivity between the left caudate and the right fusiform gyrus, lateral occipital complex, and superior temporal gyrus. Given the basal ganglia's role in exploration, these findings suggest that tighter integration of the brain systems responsible for exploration and visuospatial processing may be critical for learning in a complex environment. PMID:25286145

  19. Combining robust state estimation with nonlinear model predictive control to regulate the acute inflammatory response to pathogen.

    PubMed

    Zitelli, Gregory; Djouadi, Seddik M; Day, Judy D

    2015-10-01

    The inflammatory response aims to restore homeostasis by means of removing a biological stress, such as an invading bacterial pathogen. In cases of acute systemic inflammation, the possibility of collateral tissue damage arises, which leads to a necessary down-regulation of the response. A reduced ordinary differential equations (ODE) model of acute inflammation was presented and investigated in [10]. That system contains multiple positive and negative feedback loops and is a highly coupled and nonlinear ODE. The implementation of nonlinear model predictive control (NMPC) as a methodology for determining proper therapeutic intervention for in silico patients displaying complex inflammatory states was initially explored in [5]. Since direct measurements of the bacterial population and the magnitude of tissue damage/dysfunction are not readily available or biologically feasible, the need for robust state estimation was evident. In this present work, we present results on the nonlinear reachability of the underlying model, and then focus our attention on improving the predictability of the underlying model by coupling the NMPC with a particle filter. The results, though comparable to the initial exploratory study, show that robust state estimation of this highly nonlinear model can provide an alternative to prior updating strategies used when only partial access to the unmeasurable states of the system are available.

  20. Developing a Predictive Model for Facility Repair Costs on United States Air Force Installations

    DTIC Science & Technology

    2011-06-01

    Department of Defense, or the United States Government . This material is declared a work of the U.S. Government and is not subject to copyright...is investing 9%; and the United States is spending only 2.4% (American Society of Civil Engineers, 2009). According to the 2009 Repot Card for...2007. This delta between the obligation amounts and the FSM represents what the Government Accountability Office (GAO) refers to as “deferred

  1. Communication: Theoretical prediction of the importance of the (3)B2 state in the dynamics of sulfur dioxide.

    PubMed

    Lévêque, Camille; Taïeb, Richard; Köppel, Horst

    2014-03-07

    Even though the sulfur dioxide molecule has been extensively studied over the last decades, its photo-excitation dynamics is still unclear, due to its complexity, combining conical intersections, and spin-orbit coupling between a manifold of states. We present a comprehensive ab initio study of the intersystem crossing of the molecule in the low energy domain, based on a wave-packet propagation on the manifold of the lowest singlet and triplet states. Furthermore, spin-orbit couplings are evaluated on a geometry-dependent grid, and diabatized along with the different conical intersections. Our results show for the first time the primordial role of the triplet (3)B2 state and furthermore predict novel interference patterns due to the different intersystem crossing channels induced by the spin-orbit couplings and the shapes of the different potential energy surfaces. These give new insight into the coupled singlet-triplet dynamics of SO2.

  2. Next-to-leading order predictions for Z gamma+jet and Z gamma gamma final states at the LHC

    SciTech Connect

    Campbell, John M.; Hartanto, Heribertus B.; Williams, Ciaran

    2012-11-01

    We present next-to-leading order predictions for final states containing leptons produced through the decay of a Z boson in association with either a photon and a jet, or a pair of photons. The effect of photon radiation from the final state leptons is included and we also allow for contributions arising from fragmentation processes. Phenomenological studies are presented for the LHC in the case of final states containing charged leptons and in the case of neutrinos. We also use the procedure introduced by Stewart and Tackmann to provide a reliable estimate of the scale uncertainty inherent in our theoretical calculations of jet-binned Z gamma cross sections. These computations have been implemented in the public code MCFM.

  3. Spacecraft Attitude Representations

    NASA Technical Reports Server (NTRS)

    Markley, F. Landis

    1999-01-01

    The direction cosine matrix or attitude matrix is the most fundamental representation of the attitude, but it is very inefficient: It has six redundant parameters, it is difficult to enforce the six (orthogonality) constraints. the four-component quaternion representation is very convenient: it has only one redundant parameter, it is easy to enforce the normalization constraint, the attitude matrix is a homogeneous quadratic function of q, quaternion kinematics are bilinear in q and m. Euler angles are extensively used: they often have a physical interpretation, they provide a natural description of some spacecraft motions (COBE, MAP), but kinematics and attitude matrix involve trigonometric functions, "gimbal lock" for certain values of the angles. Other minimum (three-parameter) representations: Gibbs vector is infinite for 180 deg rotations, but useful for analysis, Modified Rodrigues Parameters are nonsingular, no trig functions, Rotation vector phi is nonsingular, but requires trig functions.

  4. 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.

  5. Gene expression in local stroma reflects breast tumor states and predicts patient outcome

    PubMed Central

    Bainer, Russell; Frankenberger, Casey; Rabe, Daniel; An, Gary; Gilad, Yoav; Rosner, Marsha Rich

    2016-01-01

    The surrounding microenvironment has been implicated in the progression of breast tumors to metastasis. However, the degree to which metastatic breast tumors locally reprogram stromal cells as they disrupt tissue boundaries is not well understood. We used species-specific RNA sequencing in a mouse xenograft model to determine how the metastasis suppressor RKIP influences transcription in a panel of paired tumor and stroma tissues. We find that gene expression in metastatic breast tumors is pervasively correlated with gene expression in local stroma of both mouse xenografts and human patients. Changes in stromal gene expression elicited by tumors better predicts subtype and patient survival than tumor gene expression, and genes with coordinated expression in both tissues predict metastasis-free survival. These observations support the use of stroma-based strategies for the diagnosis and prognosis of breast cancer. PMID:27982086

  6. Prediction of spatially explicit rainfall intensity-duration thresholds for post-fire debris-flow generation in the western United States

    NASA Astrophysics Data System (ADS)

    Staley, Dennis; Negri, Jacquelyn; Kean, Jason

    2016-04-01

    burned areas. Our approach synthesizes the two methods by incorporating measured rainfall intensity into each model variable (based on measures of topographic steepness, burn severity and surface properties) within the logistic regression equation. This approach provides a more realistic representation of the relation between rainfall intensity and debris-flow likelihood, as likelihood values asymptotically approach zero when rainfall intensity approaches 0 mm/h, and increase with more intense rainfall. Model performance was evaluated by comparing predictions to several existing regional thresholds. The model, based upon training data collected in southern California, USA, has proven to accurately predict rainfall intensity-duration thresholds for other areas in the western United States not included in the original training dataset. In addition, the improved logistic regression model shows promise for emergency planning purposes and real-time, site-specific early warning. With further validation, this model may permit the prediction of spatially-explicit intensity-duration thresholds for debris-flow generation in areas where empirically derived regional thresholds do not exist. This improvement would permit the expansion of the early-warning system into other regions susceptible to post-fire debris flow.

  7. Estimation of the Aral Sea state predictability based on the open data sources and the unique field observations

    NASA Astrophysics Data System (ADS)

    Izhitskiy, Alexander; Ayzel, Georgy; Zavialov, Peter; Kurbaniyazov, Abilgazi

    2016-04-01

    The Aral Sea, formerly one of the four largest lakes in the world, has lost over 90% of its volume during the dramatical dessication mainly caused by the severe alteration of water budget of the basin. Shrinkage of the Aral Sea resulted in profound changes of the lake's ecosystem, that became a subject for a number of publications based on a wide range of methods such as field observations, remote sensing data analysis and numerical modeling. However, by the early 21th century, the number of field studies decreased significantly due to almost complete cessation of navigation and displacement of the Aral's shoreline far away from roads and other infrastructure. Thus, only a small amount of field data (salinity, temperature, etc.) for different regions of the lake is available for the last two decades. On the other hand, a set of the open data sources (sea level variability, atmospheric reanalysis) were developed for the region. The main idea of the presented study is to estimate the possibility of prediction of the Aral Sea state using coupled system of basic geoanalysis tools, numerical modeling of hydrological cycle (both for sea and land-surface interactions with atmosphere) and state-of-art machine learning techniques. Firstly, available in situ data, obtained in the Aral Sea by Shirshov Institute and other researchers, are concerned as the "base points of state" for each year within the studied period. Secondly, consistent patterns in the interannual variability of all other available parameters, taken from the open data sources and numerical modeling predictions, are founded out. As a result, such an approach allows predicting the future state of sea basing on the possible climatic scenario.

  8. Investigating the potential of SST assimilation for ocean state estimation and climate prediction

    NASA Astrophysics Data System (ADS)

    Keenlyside, Noel; Counillon, Francois; Bethke, Ingo; Wang, Yiguo; Billeau, Sebastien; Shen, Mao-Lin; Bentsen, Mats

    2016-04-01

    The Norwegian Climate Prediction Model (NorCPM) assimilates the stochastic HadISST2 product with the ensemble Kalman Filter data assimilation method into the ocean part the Norwegian Earth System model. We document a pilot stochastic reanalysis for the period 1950-2010 and use it to perform seasonal-to-decadal (s2d) predictions. The accuracy, reliability and drift is investigated using both assimilated and independent observations. NorCPM is found slightly over-dispersive against assimilated observations but shows stable performance through the analysis period (˜0.4K). It demonstrates skill against independent measurements: SSH, heat and salt content, in particular in the ENSO, the North Pacific, the North Atlantic subpolar gyre (SPG) regions and the Nordic Seas. Furthermore, NorCPM provides a reliable monitoring of the SPG index and represents the variability of the temperature vertical structure there in good agreement with observations. The monitoring of the Atlantic meridional overturning circulation is also encouraging. The benefit of using flow dependent assimilation method and constructing the covariance in isopycnal coordinate are investigated in the SPG region. Isopycnal coordinate discretisation is found to better captures the vertical structure than standard depth-coordinate discretisation, which can deepen the influence of assimilation when assimilating surface observations. The vertical covariance shows a pronounced seasonal and decadal variability, which highlights the benefit of flow dependent data assimilation method. This study demonstrates the potential of NorCPM for providing a long reanalysis for the 19-20 century when SST observations are available. The results of s2d predictions carried out will be presented, and the potential to use this method to assess decadal predictability over the historical period will be discussed.

  9. Predicting Brook Trout occurrence in stream reaches throughout their native range in the eastern United States

    USGS Publications Warehouse

    DeWeber, Jefferson Tyrell; Wagner, Tyler

    2015-01-01

    The Brook Trout Salvelinus fontinalis is an important species of conservation concern in the eastern USA. We developed a model to predict Brook Trout population status within individual stream reaches throughout the species’ native range in the eastern USA. We utilized hierarchical logistic regression with Bayesian estimation to predict Brook Trout occurrence probability, and we allowed slopes and intercepts to vary among ecological drainage units (EDUs). Model performance was similar for 7,327 training samples and 1,832 validation samples based on the area under the receiver operating curve (∼0.78) and Cohen's kappa statistic (0.44). Predicted water temperature had a strong negative effect on Brook Trout occurrence probability at the stream reach scale and was also negatively associated with the EDU average probability of Brook Trout occurrence (i.e., EDU-specific intercepts). The effect of soil permeability was positive but decreased as EDU mean soil permeability increased. Brook Trout were less likely to occur in stream reaches surrounded by agricultural or developed land cover, and an interaction suggested that agricultural land cover also resulted in an increased sensitivity to water temperature. Our model provides a further understanding of how Brook Trout are shaped by habitat characteristics in the region and yields maps of stream-reach-scale predictions, which together can be used to support ongoing conservation and management efforts. These decision support tools can be used to identify the extent of potentially suitable habitat, estimate historic habitat losses, and prioritize conservation efforts by selecting suitable stream reaches for a given action. Future work could extend the model to account for additional landscape or habitat characteristics, include biotic interactions, or estimate potential Brook Trout responses to climate and land use changes.

  10. Umbra's system representation.

    SciTech Connect

    McDonald, Michael James

    2005-07-01

    This document describes the Umbra System representation. Umbra System representation, initially developed in the spring of 2003, is implemented in Incr/Tcl using concepts borrowed from Carnegie Mellon University's Architecture Description Language (ADL) called Acme. In the spring of 2004 through January 2005, System was converted to Umbra 4, extended slightly, and adopted as the underlying software system for a variety of Umbra applications that support Complex Systems Engineering (CSE) and Complex Adaptive Systems Engineering (CASE). System is now a standard part Of Umbra 4. While Umbra 4 also includes an XML parser for System, the XML parser and Schema are not described in this document.

  11. Numerical prediction of the Mid-Atlantic states cyclone of 18-19 February 1979

    NASA Technical Reports Server (NTRS)

    Atlas, R.; Rosenberg, R.

    1982-01-01

    A series of forecast experiments was conducted to assess the accuracy of the GLAS model, and to determine the importance of large scale dynamical processes and diabatic heating to the cyclogenesis. The GLAS model correctly predicted intense coastal cyclogenesis and heavy precipitation. Repeated without surface heat and moisture fluxes, the model failed to predict any cyclone development. An extended range forecast, a forecast from the NMC analysis interpolated to the GLAS grid, and a forecast from the GLAS analysis with the surface moisture flux excluded predicted weak coastal low development. Diabatic heating resulting from oceanic fluxes significantly contributed to the generation of low level cyclonic vorticity and the intensification and slow rate of movement of an upper level ridge over the western Atlantic. As an upper level short wave trough approached this ridge, diabatic heating associated with the release of latent heat intensified, and the gradient of vorticity, vorticity advection and upper level divergence in advance of the trough were greatly increased, providing strong large scale forcing for the surface cyclogenesis.

  12. Prediction of a Mobile Solid State in Dense Hydrogen under High Pressures.

    PubMed

    Geng, Hua Y; Wu, Q; Sun, Y

    2017-01-05

    Solid rigidity and liquid-scale mobility are thought to be incompatible in elemental substances. One cannot have an elemental solid that is long-range positionally ordered wherein the atoms flow like in a liquid simultaneously. The only exception might be the hypothetical supersolid state of (4)He. In this work, we demonstrate that such exotic state could exist even in the classical regime. Using ab initio molecular dynamics (AIMD) and ab initio path integral molecular dynamics (AI-PIMD), a novel state of dense hydrogen that simultaneously has both long-range spatial ordering and liquid-scale atomic mobility is discovered at 1 to 1.5 TPa (1 TPa ≈ 10 000 000 atmospheric pressures). The features distinct from a normal solid and liquid are carefully characterized, and the stability and melting behavior are investigated. Extensive AI-PIMD simulations further revealed that this state might be (meta-)stable even at ultralow temperatures, suggesting an emerging candidate for an alternative type of supersolid state in dense metallic hydrogen.

  13. Validation of a Previously Developed Geospatial Model That Predicts the Prevalence of Listeria monocytogenes in New York State Produce Fields.

    PubMed

    Weller, Daniel; Shiwakoti, Suvash; Bergholz, Peter; Grohn, Yrjo; Wiedmann, Martin; Strawn, Laura K

    2015-11-20

    Technological advancements, particularly in the field of geographic information systems (GIS), have made it possible to predict the likelihood of foodborne pathogen contamination in produce production environments using geospatial models. Yet, few studies have examined the validity and robustness of such models. This study was performed to test and refine the rules associated with a previously developed geospatial model that predicts the prevalence of Listeria monocytogenes in produce farms in New York State (NYS). Produce fields for each of four enrolled produce farms were categorized into areas of high or low predicted L. monocytogenes prevalence using rules based on a field's available water storage (AWS) and its proximity to water, impervious cover, and pastures. Drag swabs (n = 1,056) were collected from plots assigned to each risk category. Logistic regression, which tested the ability of each rule to accurately predict the prevalence of L. monocytogenes, validated the rules based on water and pasture. Samples collected near water (odds ratio [OR], 3.0) and pasture (OR, 2.9) showed a significantly increased likelihood of L. monocytogenes isolation compared to that for samples collected far from water and pasture. Generalized linear mixed models identified additional land cover factors associated with an increased likelihood of L. monocytogenes isolation, such as proximity to wetlands. These findings validated a subset of previously developed rules that predict L. monocytogenes prevalence in produce production environments. This suggests that GIS and geospatial models can be used to accurately predict L. monocytogenes prevalence on farms and can be used prospectively to minimize the risk of preharvest contamination of produce.

  14. Validation of a Previously Developed Geospatial Model That Predicts the Prevalence of Listeria monocytogenes in New York State Produce Fields

    PubMed Central

    Weller, Daniel; Shiwakoti, Suvash; Bergholz, Peter; Grohn, Yrjo; Wiedmann, Martin

    2015-01-01

    Technological advancements, particularly in the field of geographic information systems (GIS), have made it possible to predict the likelihood of foodborne pathogen contamination in produce production environments using geospatial models. Yet, few studies have examined the validity and robustness of such models. This study was performed to test and refine the rules associated with a previously developed geospatial model that predicts the prevalence of Listeria monocytogenes in produce farms in New York State (NYS). Produce fields for each of four enrolled produce farms were categorized into areas of high or low predicted L. monocytogenes prevalence using rules based on a field's available water storage (AWS) and its proximity to water, impervious cover, and pastures. Drag swabs (n = 1,056) were collected from plots assigned to each risk category. Logistic regression, which tested the ability of each rule to accurately predict the prevalence of L. monocytogenes, validated the rules based on water and pasture. Samples collected near water (odds ratio [OR], 3.0) and pasture (OR, 2.9) showed a significantly increased likelihood of L. monocytogenes isolation compared to that for samples collected far from water and pasture. Generalized linear mixed models identified additional land cover factors associated with an increased likelihood of L. monocytogenes isolation, such as proximity to wetlands. These findings validated a subset of previously developed rules that predict L. monocytogenes prevalence in produce production environments. This suggests that GIS and geospatial models can be used to accurately predict L. monocytogenes prevalence on farms and can be used prospectively to minimize the risk of preharvest contamination of produce. PMID:26590280

  15. A new equation of state for better liquid density prediction of natural gas systems

    NASA Astrophysics Data System (ADS)

    Nwankwo, Princess C.

    Equations of state formulations, modifications and applications have remained active research areas since the success of van der Waal's equation in 1873. The need for better reservoir fluid modeling and characterization is of great importance to petroleum engineers who deal with thermodynamic related properties of petroleum fluids at every stage of the petroleum "life span" from its drilling, to production through the wellbore, to transportation, metering and storage. Equations of state methods are far less expensive (in terms of material cost and time) than laboratory or experimental forages and the results are interestingly not too far removed from the limits of acceptable accuracy. In most cases, the degree of accuracy obtained, by using various EOS's, though not appreciable, have been acceptable when considering the gain in time. The possibility of obtaining an equation of state which though simple in form and in use, could have the potential of further narrowing the present existing bias between experimentally determined and popular EOS estimated results spurred the interest that resulted in this study. This research study had as its chief objective, to develop a new equation of state that would more efficiently capture the thermodynamic properties of gas condensate fluids, especially the liquid phase density, which is the major weakness of other established and popular cubic equations of state. The set objective was satisfied by a new semi analytical cubic three parameter equation of state, derived by the modification of the attraction term contribution to pressure of the van der Waal EOS without compromising either structural simplicity or accuracy of estimating other vapor liquid equilibria properties. The application of new EOS to single and multi-component light hydrocarbon fluids recorded far lower error values than does the popular two parameter, Peng-Robinson's (PR) and three parameter Patel-Teja's (PT) equations of state. Furthermore, this research

  16. Climate downscaling effects on predictive ecological models: a case study for threatened and endangered vertebrates in the southeastern United States

    USGS Publications Warehouse

    Bucklin, David N.; Watling, James I.; Speroterra, Carolina; Brandt, Laura A.; Mazzotti, Frank J.; Romañach, Stephanie S.

    2013-01-01

    High-resolution (downscaled) projections of future climate conditions are critical inputs to a wide variety of ecological and socioeconomic models and are created using numerous different approaches. Here, we conduct a sensitivity analysis of spatial predictions from climate envelope models for threatened and endangered vertebrates in the southeastern United States to determine whether two different downscaling approaches (with and without the use of a regional climate model) affect climate envelope model predictions when all other sources of variation are held constant. We found that prediction maps differed spatially between downscaling approaches and that the variation attributable to downscaling technique was comparable to variation between maps generated using different general circulation models (GCMs). Precipitation variables tended to show greater discrepancies between downscaling techniques than temperature variables, and for one GCM, there was evidence that more poorly resolved precipitation variables contributed relatively more to model uncertainty than more well-resolved variables. Our work suggests that ecological modelers requiring high-resolution climate projections should carefully consider the type of downscaling applied to the climate projections prior to their use in predictive ecological modeling. The uncertainty associated with alternative downscaling methods may rival that of other, more widely appreciated sources of variation, such as the general circulation model or emissions scenario with which future climate projections are created.

  17. Computer program simplifies transient and steady-state temperature prediction for complex body shapes

    NASA Technical Reports Server (NTRS)

    Giebler, K. N.

    1966-01-01

    Computer program evaluates heat transfer modes and calculates either the transient or steady-state temperature distributions throughout an object of complex shape when heat sources are applied to specified points on the object. It uses an electrothermal model to simulate the conductance, heat capacity, and temperature potential of the object.

  18. A Predictive Model for Migrant Farmworker Movement in the United States.

    ERIC Educational Resources Information Center

    Davis, Benjamin G.

    Since migration is strongly influenced by economic variables, an economic model was developed to identify, locate, and track migrant and seasonal farmworkers as they move throughout the United States. Focusing on the Florida-based migrant agricultural workers who migrated at least once during the past five years, the model included the following…

  19. The Effectiveness of Six Personality Variables in Predicting Success on the Nursing State Board Examination.

    ERIC Educational Resources Information Center

    Cusick, Patricia; Harckham, Laura D.

    A study was conducted to determine whether six personality variables, presently used in admissions decisions by a nursing school, were effective predictors of success on the State Board Examination (SBE), the nursing licensing examination. The personality variables were measured by subtests of the Personal Preference Schedule of the Psychological…

  20. Prediction of an I=1 DD¯* state and relationship to the claimed Zc(3900), Zc(3885)

    NASA Astrophysics Data System (ADS)

    Aceti, F.; Bayar, M.; Oset, E.; Torres, A. Martínez; Khemchandani, K. P.; Dias, J. M.; Navarra, F. S.; Nielsen, M.

    2014-07-01

    We study here the interaction of DD¯* in the isospin I=1 channel in light of recent theoretical advances that allow us to combine elements of the local hidden gauge approach with heavy quark spin symmetry. We find that the exchange of light qq¯ is Okubo-Zweig-Iizuka (OZI) suppressed and thus we concentrate on the exchange of heavy vectors and of two pion exchange. The latter is found to be small compared to the exchange of heavy vectors, which then determines the strength of the interaction. A barely DD¯* bound state decaying into ηcρ and πJ/ψ is found. At the same time we reanalyze the data of the BESIII experiment on e+e-→π±(DD¯*)∓, from where a Zc(3885) state was claimed, associated to a peak in the (DD¯*)∓ invariant mass distribution close to threshold, and we find the data compatible with a resonance with mass around 3875 MeV and width around 30 MeV. We discuss the possibility that this and the Zc(3900) state found at BESIII, reconfirmed at 3894 MeV at Belle, or 3885 MeV at CLEO, could all be the same state and correspond to the one that we find theoretically.

  1. Updated Graizer-Kalkan Ground Motion Prediction Equations for Western United States

    NASA Astrophysics Data System (ADS)

    Graizer, V.

    2013-12-01

    Ground motion prediction equations (GMPEs) for peak-ground acceleration (PGA) and 5-percent damped pseudo spectral accelerations (SA) of horizontal component ground motions were developed by Graizer and Kalkan (2007, 2009) using the extended ground motion database of the Next Generation of Attenuation project for shallow crustal earthquakes in active tectonic regions. The main features of these GMPEs (GK09) are: (1) only most essential measureable parameters [moment magnitude (M), closest distance to the fault rupture, style of faulting and average shear-wave velocity in the upper 30 meters of profile under the site (Vs30)] are used; (2) predictive model for SA is a continuous function of spectral period (T), which eliminates the standard matrix of estimator coefficients, and allows for calculation of SA at any period of interest within the model range of 0.01 to 10 sec; (3) mathematical form of GMPEs constitutes a series of filters--each filter represents a certain physical phenomenon affecting the radiation of seismic waves from the source. In contrast to the existing GMPEs, GK09 predictive model allows PGA to reach its maximum value at some distance from the fault effectively capturing the phenomenon observed in earthquakes with large number of near-source recordings such as the 1979 (M6.5) Imperial Valley and the 2004 (M6.0) Parkfield earthquakes. The GK09 GMPEs are shown to provide accuracy (expected median prediction without significant bias) and efficiency (relatively small standard error of predictions) as compared to recorded data at distances of up to 250 km during recent shallow-crustal earthquakes with 5.0≤M≤7.9 including the 2008 (M7.9) Wenchuan (China), 2010 (M7.2) El-Mayor Cucapah (Mexico), 2011 (M6.3) Christchurch (New Zealand) and other earthquakes. The GK09 GMPEs are updated here by adding an anelastic attenuation filter as a function of quality-factor (Q), and by improving the existing basin-effect filter, which is now a function of depth

  2. 18 CFR 1308.22 - Representation.

    Code of Federal Regulations, 2012 CFR

    2012-04-01

    ... 18 Conservation of Power and Water Resources 2 2012-04-01 2012-04-01 false Representation. 1308.22 Section 1308.22 Conservation of Power and Water Resources TENNESSEE VALLEY AUTHORITY CONTRACT DISPUTES... represented by an attorney at law duly licensed by any state, commonwealth, territory, or the District...

  3. 18 CFR 1308.22 - Representation.

    Code of Federal Regulations, 2014 CFR

    2014-04-01

    ... 18 Conservation of Power and Water Resources 2 2014-04-01 2014-04-01 false Representation. 1308.22 Section 1308.22 Conservation of Power and Water Resources TENNESSEE VALLEY AUTHORITY CONTRACT DISPUTES... represented by an attorney at law duly licensed by any state, commonwealth, territory, or the District...

  4. 18 CFR 1308.22 - Representation.

    Code of Federal Regulations, 2013 CFR

    2013-04-01

    ... 18 Conservation of Power and Water Resources 2 2013-04-01 2012-04-01 true Representation. 1308.22 Section 1308.22 Conservation of Power and Water Resources TENNESSEE VALLEY AUTHORITY CONTRACT DISPUTES... represented by an attorney at law duly licensed by any state, commonwealth, territory, or the District...

  5. 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…

  6. 15 CFR 280.205 - Representation.

    Code of Federal Regulations, 2011 CFR

    2011-01-01

    ... OF STANDARDS AND TECHNOLOGY, DEPARTMENT OF COMMERCE ACCREDITATION AND ASSESSMENT PROGRAMS FASTENER QUALITY Enforcement § 280.205 Representation. A respondent individual may appear and participate in person..., Commonwealth or Territory of the United States, or of the District of Columbia, or be licensed to practice...

  7. Monitoring of the stress state variations of the Southern California for the purpose of earthquake prediction

    NASA Astrophysics Data System (ADS)

    Gokhberg, M.; Garagash, I.; Bondur, V.; Steblov, G. M.

    2014-12-01

    The three-dimensional geomechanical model of Southern California was developed, including a mountain relief, fault tectonics and characteristic internal features such as the roof of the consolidated crust and Moho surface. The initial stress state of the model is governed by the gravitational forces and horizontal tectonic motions estimated from GPS observations. The analysis shows that the three-dimensional geomechanical models allows monitoring of the changes in the stress state during the seismic process in order to constrain the distribution of the future places with increasing seismic activity. This investigation demonstrates one of possible approach to monitor upcoming seismicity for the periods of days - weeks - months. Continuous analysis of the stress state was carried out during 2009-2014. Each new earthquake with М~1 and above from USGS catalog was considered as the new defect of the Earth crust which has some definite size and causes redistribution of the stress state. Overall calculation technique was based on the single function of the Earth crust damage, recalculated each half month. As a result each half month in the upper crust layers and partially in the middle layers we revealed locations of the maximal values of the stress state parameters: elastic energy density, shear stress, proximity of the earth crust layers to their strength limit. All these parameters exhibit similar spatial and temporal distribution. How follows from observations all four strongest events with М ~ 5.5-7.2 occurred in South California during the analyzed period were prefaced by the parameters anomalies in peculiar advance time of weeks-months in the vicinity of 10-50 km from the upcoming earthquake. After the event the stress state source disappeared. The figure shows migration of the maximums of the stress state variations gradients (parameter D) in the vicinity of the epicenter of the earthquake 04.04.2010 with М=7.2 in the period of 01.01.2010-01.05.2010. Grey lines

  8. A toolbox for representational similarity analysis.

    PubMed

    Nili, Hamed; Wingfield, Cai; Walther, Alexander; Su, Li; Marslen-Wilson, William; Kriegeskorte, Nikolaus

    2014-04-01

    Neuronal population codes are increasingly being investigated with multivariate pattern-information analyses. A key challenge is to use measured brain-activity patterns to test computational models of brain information processing. One approach to this problem is representational similarity analysis (RSA), which characterizes a representation in a brain or computational model by the distance matrix of the response patterns elicited by a set of stimuli. The representational distance matrix encapsulates what distinctions between stimuli are emphasized and what distinctions are de-emphasized in the representation. A model is tested by comparing the representational distance matrix it predicts to that of a measured brain region. RSA also enables us to compare representations between stages of processing within a given brain or model, between brain and behavioral data, and between individuals and species. Here, we introduce a Matlab toolbox for RSA. The toolbox supports an analysis approach that is simultaneously data- and hypothesis-driven. It is designed to help integrate a wide range of computational models into the analysis of multichannel brain-activity measurements as provided by modern functional imaging and neuronal recording techniques. Tools for visualization and inference enable the user to relate sets of models to sets of brain regions and to statistically test and compare the models using nonparametric inference methods. The toolbox supports searchlight-based RSA, to continuously map a measured brain volume in search of a neuronal population code with a specific geometry. Finally, we introduce the linear-discriminant t value as a measure of representational discriminability that bridges the gap between linear decoding analyses and RSA. In order to demonstrate the capabilities of the toolbox, we apply it to both simulated and real fMRI data. The key functions are equally applicable to other modalities of brain-activity measurement. The toolbox is freely

  9. Habitat and Vegetation Variables Are Not Enough When Predicting Tick Populations in the Southeastern United States.

    PubMed

    Trout Fryxell, R T; Moore, J E; Collins, M D; Kwon, Y; Jean-Philippe, S R; Schaeffer, S M; Odoi, A; Kennedy, M; Houston, A E

    2015-01-01

    Two tick-borne diseases with expanding case and vector distributions are ehrlichiosis (transmitted by Amblyomma americanum) and rickettiosis (transmitted by A. maculatum and Dermacentor variabilis). There is a critical need to identify the specific habitats where each of these species is likely to be encountered to classify and pinpoint risk areas. Consequently, an in-depth tick prevalence study was conducted on the dominant ticks in the southeast. Vegetation, soil, and remote sensing data were used to test the hypothesis that habitat and vegetation variables can predict tick abundances. No variables were significant predictors of A. americanum adult and nymph tick abundance, and no clustering was evident because this species was found throughout the study area. For A. maculatum adult tick abundance was predicted by NDVI and by the interaction between habitat type and plant diversity; two significant population clusters were identified in a heterogeneous area suitable for quail habitat. For D. variabilis no environmental variables were significant predictors of adult abundance; however, D. variabilis collections clustered in three significant areas best described as agriculture areas with defined edges. This study identified few landscape and vegetation variables associated with tick presence. While some variables were significantly associated with tick populations, the amount of explained variation was not useful for predicting reliably where ticks occur; consequently, additional research that includes multiple sampling seasons and locations throughout the southeast are warranted. This low amount of explained variation may also be due to the use of hosts for dispersal, and potentially to other abiotic and biotic variables. Host species play a large role in the establishment, maintenance, and dispersal of a tick species, as well as the maintenance of disease cycles, dispersal to new areas, and identification of risk areas.

  10. Habitat and Vegetation Variables Are Not Enough When Predicting Tick Populations in the Southeastern United States

    PubMed Central

    Trout Fryxell, R. T.; Moore, J. E.; Collins, M. D.; Kwon, Y.; Jean-Philippe, S. R.; Schaeffer, S. M.; Odoi, A.; Kennedy, M.; Houston, A. E.

    2015-01-01

    Two tick-borne diseases with expanding case and vector distributions are ehrlichiosis (transmitted by Amblyomma americanum) and rickettiosis (transmitted by A. maculatum and Dermacentor variabilis). There is a critical need to identify the specific habitats where each of these species is likely to be encountered to classify and pinpoint risk areas. Consequently, an in-depth tick prevalence study was conducted on the dominant ticks in the southeast. Vegetation, soil, and remote sensing data were used to test the hypothesis that habitat and vegetation variables can predict tick abundances. No variables were significant predictors of A. americanum adult and nymph tick abundance, and no clustering was evident because this species was found throughout the study area. For A. maculatum adult tick abundance was predicted by NDVI and by the interaction between habitat type and plant diversity; two significant population clusters were identified in a heterogeneous area suitable for quail habitat. For D. variabilis no environmental variables were significant predictors of adult abundance; however, D. variabilis collections clustered in three significant areas best described as agriculture areas with defined edges. This study identified few landscape and vegetation variables associated with tick presence. While some variables were significantly associated with tick populations, the amount of explained variation was not useful for predicting reliably where ticks occur; consequently, additional research that includes multiple sampling seasons and locations throughout the southeast are warranted. This low amount of explained variation may also be due to the use of hosts for dispersal, and potentially to other abiotic and biotic variables. Host species play a large role in the establishment, maintenance, and dispersal of a tick species, as well as the maintenance of disease cycles, dispersal to new areas, and identification of risk areas. PMID:26656122

  11. Prediction of the Functional Performance of Machined Components Based on Surface Topography: State of the Art

    NASA Astrophysics Data System (ADS)

    Grzesik, Wit

    2016-10-01

    This survey overviews the functional performance of manufactured components produced by typical finishing machining operations in terms of their topographical characteristics. Surface topographies were characterized using both profile (2D) and 3D (areal) surface roughness parameters. The prediction of typical functional properties such as fatigue, friction, wear, bonding and corrosion is discussed based on appropriate surface roughness parameters. Some examples of real 3D surface topographies produced with desired functional characteristics are provided. This survey highlights technological possibilities of producing surfaces with enhanced functional properties by machining processes.

  12. Knowledge Representation in PARKA

    DTIC Science & Technology

    1990-02-01

    the color of Poodle could be restricted to being just black or white, while the color of Irish-Setter could be set to red. Note that this would allow a...sub- field of knowledge representation with considerable subtlety and a history of interesting, difficult problems (see, e.g. [10]). Winston et. al

  13. Reading Students' Representations

    ERIC Educational Resources Information Center

    Diezmann, Carmel M.; McCosker, Natalie T.

    2011-01-01

    Representations play a key role in mathematical thinking: They offer "a medium" to express mathematical knowledge or organize mathematical information and to discern mathematical relationships (e.g., relative household expenditures on a pie chart) using text, symbols, or graphics. They also furnish "tools" for mathematical processes (e.g., use of…

  14. The Problem of Representation

    ERIC Educational Resources Information Center

    Tervo, Juuso

    2012-01-01

    In "Postphysical Vision: Art Education's Challenge in an Age of Globalized Aesthetics (AMondofesto)" (2008) and "Beyond Aesthetics: Returning Force and Truth to Art and Its Education" (2009), jan jagodzinski argued for politics that go "beyond" representation--a project that radically questions visual culture…

  15. V-Notched Bar Creep Life Prediction: GH3536 Ni-Based Superalloy Under Multiaxial Stress State

    NASA Astrophysics Data System (ADS)

    Zhang, D. X.; Wang, J. P.; Wen, Z. X.; Liu, D. S.; Yue, Z. F.

    2016-07-01

    In this study, creep experiments on smooth and circumferential V-type notched round bars were conducted in GH3536 Ni-based superalloy at 750 °C to identify notch strengthening effect in notched specimens. FE analysis was carried out, coupled with continuum damage mechanics (CDM), to analyze stress distribution and damage evolution under multiaxial stress state. The creep deformation of smooth specimens and the rupture life of both smooth and notched specimens showed good agreement between experimental results and FE analysis predictions; the creep rupture life for the notched specimen was successfully predicted via the "skeletal point" concept. Both creep damage analysis and the observed fracture morphology suggest that creep rupture started first at the root in the V-type notched specimens, and shifted to the region close to the notch root when the notch was relatively shallow compared to U-type notched specimens.

  16. The reliability and predictive validity of a sixth-semester OSPE in conservative dentistry regarding performance on the state examination

    PubMed Central

    Petko, Petkov; Knuth-Herzig, Katja; Hoefer, Sebastian; Stehle, Sebastian; Scherer, Sonja; Steffen, Björn; Scherzer, Stephan; Ochsendorf, Falk; Horz, Holger; Sader, Robert; Gerhardt-Szép, Susanne

    2017-01-01

    Introduction: The aim of this study was to ascertain whether the testing format of an OSPE (Objective Structured Practical Examination) in conservative dentistry (sixth semester) predicts the scores on the practical section of the state examination (11th semester) in the same subject. Taking general student profiles into consideration (score on the school-leaving exam [Abitur], score on the preliminary exam in dental medicine [Physikum], length of university study, cohorts, and sex), we also investigated if any correlations or differences exist in regard to the total and partial scores on the OSPE and the corresponding state examination. Methods: Within the scope of this longitudinal retrospective study, exam-specific data spanning 11 semesters for dental students (N=223) in Frankfurt am Main were collected and analyzed. Statistical analysis was carried out by calculating Spearman rank correlations, partial correlations, Pearson’s correlation coefficients, and multiple regressions (SPSS Statistics 21, IBM Corporation, New York). Results: The results show that the OSPE (Cronbach’s α=.87) correlates with level of success on the practical section of the state exam in conservative dentistry (p=.01, r=.17). Length of university study also emerged to correlate significantly with the state exam score (p=.001, r=.23). Together, these two variables contribute significantly to predicting the state exam score (p=.001, R2=.076). This was seen extensively among female students. It was also discovered that these female students had higher school-leaving exam scores than male students (F=6.09, p=.01, η2=.027), and that a significant correlation between scores on the Physikum (preliminary exam in dental medicine) and OSPE scores existed only for male students (r=.17, p=.01). Conclusion: This study was able to demonstrate the predictive effect of a clinical OSPE regarding scores achieved on the state exam. Taking the limitations of this study into account, we are able to

  17. Negative emotions predict elevated interleukin-6 in the United States but not in Japan.

    PubMed

    Miyamoto, Yuri; Boylan, Jennifer Morozink; Coe, Christopher L; Curhan, Katherine B; Levine, Cynthia S; Markus, Hazel Rose; Park, Jiyoung; Kitayama, Shinobu; Kawakami, Norito; Karasawa, Mayumi; Love, Gayle D; Ryff, Carol D

    2013-11-01

    Previous studies conducted in Western cultures have shown that negative emotions predict higher levels of pro-inflammatory biomarkers, specifically interleukin-6 (IL-6). This link between negative emotions and IL-6 may be specific to Western cultures where negative emotions are perceived to be problematic and thus may not extend to Eastern cultures where negative emotions are seen as acceptable and normal. Using samples of 1044 American and 382 Japanese middle-aged and older adults, we investigated whether the relationship between negative emotions and IL-6 varies by cultural context. Negative emotions predicted higher IL-6 among American adults, whereas no association was evident among Japanese adults. Furthermore, the interaction between culture and negative emotions remained even after controlling for demographic variables, psychological factors (positive emotions, neuroticism, extraversion), health behaviors (smoking status, alcohol consumption), and health status (chronic conditions, BMI). These findings highlight the role of cultural context in shaping how negative emotions affect inflammatory physiology and underscore the importance of cultural ideas and practices relevant to negative emotions for understanding of the interplay between psychology, physiology, and health.

  18. Model-based predictions of solid state intermetallic compound layer growth in hybrid microelectronic circuits

    SciTech Connect

    Vianco, P.T.; Erickson, K.L.; Hopkins, P.L.

    1997-12-31

    A mathematical model was developed to quantitatively describe the intermetallic compound (IMC) layer growth that takes place between a Sn-based solder and a noble metal thick film conductor material used in hybrid microcircuit (HMC) assemblies. The model combined the reaction kinetics of the solder/substrate interaction, as determined from ancillary isothermal aging experiments, with a 2-D finite element mesh that took account of the porous morphology of the thick film coating. The effect of the porous morphology on the IMC layer growth when compared to the traditional 1-D computations was significant. The previous 1-D calculations under-predicted the nominal IMC layer thickness relative to the 2-D case. The 2-D model showed greater substrate consumption by IMC growth and lesser solder consumption that was determined with the 1-D computation. The new 2-D model allows the design engineer to better predict circuit aging and hence, the reliability of HMC hardware that is placed in the field.

  19. 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…

  20. Improved Separability Criteria Based on Bloch Representation of Density Matrices

    PubMed Central

    Shen, Shu-Qian; Yu, Juan; Li, Ming; Fei, Shao-Ming

    2016-01-01

    The correlation matrices or tensors in the Bloch representation of density matrices are encoded with entanglement properties. In this paper, based on the Bloch representation of density matrices, we give some new separability criteria for bipartite and multipartite quantum states. Theoretical analysis and some examples show that the proposed criteria can be more efficient than the previous related criteria. PMID:27350031

  1. 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…

  2. Online state of health estimation on NMC cells based on predictive analytics

    NASA Astrophysics Data System (ADS)

    Berecibar, Maitane; Devriendt, Floris; Dubarry, Matthieu; Villarreal, Igor; Omar, Noshin; Verbeke, Wouter; Van Mierlo, Joeri

    2016-07-01

    Accurate on board state of health estimation is a key battery management system function to provide optimal management of the battery system under control. In this regard, this paper presents an extensive study and comparison of three of commonly used supervised learning methods for state of health estimation in Graphite/Nickel Manganese Cobalt oxide cells. The three methods were based from the study of both incremental capacity and differential voltage curves. According to the ageing evolution of both curves, features were extracted and used as inputs for the estimation techniques. Ordinary Least Squares, Multilayer Perceptron and Support Vector Machine were used as the estimation techniques and accurate results were obtained while requiring a low computational effort. Moreover, this work allows a deep comparison of the different estimation techniques in terms of accuracy, online estimation and BMS applicability. In addition, estimation can be developed by partial charging and/or partial discharging, reducing the required maintenance time.

  3. Predicting Sediment and Nutrient Loads for Selected Agricultural Watersheds in the Midwestern United States

    NASA Astrophysics Data System (ADS)

    Ren, J.; Campbell, J. B.; Shao, Y.

    2015-12-01

    Changing agricultural land use and land management practices are regarded as one of the main factors driving water quality degradation. Landscapes of the Midwestern United States have experienced significant changes in expansion of corn production in response to the growing demand for corn-based ethanol. This study integrated remote sensing-derived products and the Soil and Water Assessment Tool (SWAT) within a geographic information system (GIS) modeling environment to estimate sediment and nutrient loads associated with land use change and land management practices within three selected watersheds in the Midwestern United States. The SWAT models were calibrated during a 6-year period (2000-2005) to forecast, and then validate, estimated stream flows. Then, our SWAT models were applied to estimate sediment and nutrient loadings for several future agricultural and climate scenarios.

  4. An assessment of the state of the art in predicting the failure of ceramics: Final report

    SciTech Connect

    Boulet, J.A.M.

    1988-03-01

    The greatest weakness in existing design strategies for brittle fracture is in the narrow range of conditions for which the strategies are adequate. The primary reason for this weakness is the use of simplistic mechanical models of fracture processes and unverified statistical models of materials. To improve the design methodology, the models must first be improved. Specifically recommended research goals are: to develop models of cracks with realistic geometry under arbitrary stress states; to identify and model the most important relationships between fracture processes and microstructural features; to assess the technology available for acquiring statistical data on microstructure and flaw populations, and to establish the amount of data required for verification of statistical models; and to establish a computer-based fracture simulation that can incorporate a wide variety of mechanical and statistical models and crack geometries, as well as arbitrary stress states. 204 refs., 2 tabs.

  5. Mass Shootings in the United States: Common Characteristics and Predictive Behaviors

    DTIC Science & Technology

    2013-06-14

    in age, prior to his self-inflicted suicide (Bratu 2013). In total, at least 16 mass shootings occurred within the United States during 2012...problems – either a hospitalization, a presription for psychiatric drugs, a suicide attempt or evidence of psychosis” prior to the incident...Further, social marginalization is defined as a combination of bullying and loner status (Newman et al. 2004). Bullying included instances when the

  6. Recent and predicted changes in atmospheric composition over the United States from climate, emissions, and pine beetles

    NASA Astrophysics Data System (ADS)

    Heald, C. L.; Berg, A.; Val Martin, M.; Meddens, A. J.; Hicke, J. A.; Huff Hartz, K. E.; Lamarque, J.; Tilmes, S.; Emmons, L. K.

    2012-12-01

    Changes in emissions, climate and land use all play a key role in modulating the composition of the troposphere. In this talk I will cover two topics related to this theme. First, to examine the relative impacts of these effects, I will discuss predicted changes in air quality (PM and ozone) by 2050 over the United States following the latest RCP scenarios in the Community Earth System Model. Second, as an example of climate-biosphere-atmosphere interactions, I will discuss the impact of the recent mountain pine beetle outbreak on VOC emissions and organic aerosol concentrations in Western North America over the last decade.

  7. 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.

  8. Mind games: the mental representation of conflict.

    PubMed

    Halevy, Nir; Chou, Eileen Y; Murnighan, J Keith

    2012-01-01

    Perception and misperception play a pivotal role in conflict and negotiation. We introduce a framework that explains how people think about their outcome interdependence in conflict and negotiation and how their views shape their behavior. Seven studies show that people's mental representations of conflict are predictably constrained to a small set of possibilities with important behavioral and social consequences. Studies 1 and 2 found that, when prompted to represent a conflict in matrix form, more than 70% of the people created 1 of 4 archetypal mixed-motive games (out of 576 possibilities): Maximizing Difference, Assurance, Chicken, and Prisoner's Dilemma. Study 3 demonstrated that these mental representations relate in predictable ways to negotiators' fixed-pie perceptions. Studies 4-6 showed that these mental representations shape individuals' behavior and interactions with others, including cooperation, perspective taking, and use of deception in negotiation, and through them, conflict's outcomes. Study 7 found that the games that people think they are playing influence how their counterparts see them, as well as their counterparts' negotiation expectations. Overall, the findings document noteworthy regularities in people's mental representations of outcome interdependence in conflict and illustrate that 4 archetypal games can encapsulate fundamental psychological processes that emerge repeatedly in conflict and negotiation.

  9. Multisensory body representation in autoimmune diseases.

    PubMed

    Finotti, Gianluca; Costantini, Marcello

    2016-02-12

    Body representation has been linked to the processing and integration of multisensory signals. An outstanding example of the pivotal role played by multisensory mechanisms in body representation is the Rubber Hand Illusion (RHI). In this paradigm, multisensory stimulation induces a sense of ownership over a fake limb. Previous work has shown high interindividual differences in the susceptibility to the RHI. The origin of this variability remains largely unknown. Given the tight and bidirectional communication between the brain and the immune system, we predicted that the origin of this variability could be traced, in part, to the immune system's functioning, which is altered by several clinical conditions, including Coeliac Disease (CD). Consistent with this prediction, we found that the Rubber Hand Illusion is stronger in CD patients as compared to healthy controls. We propose a biochemical mechanism accounting for the dependency of multisensory body representation upon the Immune system. Our finding has direct implications for a range of neurological, psychiatric and immunological conditions where alterations of multisensory integration, body representation and dysfunction of the immune system co-exist.

  10. Multisensory body representation in autoimmune diseases

    PubMed Central

    Finotti, Gianluca; Costantini, Marcello

    2016-01-01

    Body representation has been linked to the processing and integration of multisensory signals. An outstanding example of the pivotal role played by multisensory mechanisms in body representation is the Rubber Hand Illusion (RHI). In this paradigm, multisensory stimulation induces a sense of ownership over a fake limb. Previous work has shown high interindividual differences in the susceptibility to the RHI. The origin of this variability remains largely unknown. Given the tight and bidirectional communication between the brain and the immune system, we predicted that the origin of this variability could be traced, in part, to the immune system’s functioning, which is altered by several clinical conditions, including Coeliac Disease (CD). Consistent with this prediction, we found that the Rubber Hand Illusion is stronger in CD patients as compared to healthy controls. We propose a biochemical mechanism accounting for the dependency of multisensory body representation upon the Immune system. Our finding has direct implications for a range of neurological, psychiatric and immunological conditions where alterations of multisensory integration, body representation and dysfunction of the immune system co-exist. PMID:26867786

  11. Issues in Interaction Language Specification and Representation.

    DTIC Science & Technology

    1983-11-01

    JOHNSON ET AL. NOV 83 UNCLASSIFIED CSIE-83-15 N@014-8 1K -143 F/G 5/8 N mEEEomhEEEmhiI EEEEEEEEmhEEEE mEEEEEEEmhEEEE EEEEEEEEohEohEmhE~~hEEEEEE 1.8...IN - INTERACTION LANGUAGE SPECIFICATION CAND REPRESENTATION Deborah H. Johnson H. Rex Hartson N nT1C Virginia Polytechnic Institute and State...oISSUES IN INTERACTION LANGUAGE SPECIFICATION N. AND REPRESENTATION Deborah H. Johnson I. H. Rex Hartson - I TECHNICAL REPORT Prepared for Engineering

  12. Molecular self-organization: Predicting the pattern diversity and lowest energy state of competing ordering motifs

    NASA Astrophysics Data System (ADS)

    Hermann, B. A.; Rohr, C.; Balbás Gambra, M.; Malecki, A.; Malarek, M. S.; Frey, E.; Franosch, T.

    2010-10-01

    Self-organized monolayers of highly flexible Fréchet dendrons were deposited on graphite surfaces by solution casting. Scanning tunneling microscopy (STM) reveals an unprecedented variety of patterns with up to seven stable hierarchical ordering motifs allowing us to use these molecules as a versatile model system. The essential molecular properties determined by molecular mechanics simulations are condensed to a coarse grained interaction-site model of various chain configurations. In a Monte Carlo approach with random starting configurations, the experimental pattern diversity can be reproduced in all facets of the local and global ordering. Based on an energy analysis of the Monte Carlo and molecular mechanics modeling, the thermodynamically most stable pattern is predicted and shown to coincide with the pattern which dominates the STM images after several hours or upon moderate heating.

  13. A State-Space Approach to Optimal Level-Crossing Prediction for Linear Gaussian Processes

    NASA Technical Reports Server (NTRS)

    Martin, Rodney Alexander

    2009-01-01

    In many complex engineered systems, the ability to give an alarm prior to impending critical events is of great importance. These critical events may have varying degrees of severity, and in fact they may occur during normal system operation. In this article, we investigate approximations to theoretically optimal methods of designing alarm systems for the prediction of level-crossings by a zero-mean stationary linear dynamic system driven by Gaussian noise. An optimal alarm system is designed to elicit the fewest false alarms for a fixed detection probability. This work introduces the use of Kalman filtering in tandem with the optimal level-crossing problem. It is shown that there is a negligible loss in overall accuracy when using approximations to the theoretically optimal predictor, at the advantage of greatly reduced computational complexity. I

  14. Predicting bond strength from a single Hartree-Fock ground state using the localized pair model.

    PubMed

    Hennessey, Dylan C; Sheppard, Brendan J H; Mackenzie, Dalton E C K; Pearson, Jason K

    2014-12-14

    We present an application of the recently introduced Localized Pair Model (LPM) [Z. A. Zielinksi and J. K. Pearson, Comput. Theor. Chem., 2013, 1003, 7990] to characterize and quantify properties of the chemical bond in a series of substituted benzoic acid molecules. By computing interelectronic distribution functions for doubly-occupied Edmiston-Ruedenberg localized molecular orbitals (LMOs), we show that chemically intuitive electron pairs may be uniquely classified and bond strength may be predicted with remarkable accuracy. Specifically, the HF/u6-311G(d,p) level (where u denotes a complete uncontraction of the basis set) is used to generate the relevant LMOs and their respective interelectronic distribution functions can be linearly correlated to the well-known Hammett σp or σm parameters with near-unity correlation coefficients.

  15. Remote sensing of rainfall for flash flood prediction in the United States

    NASA Astrophysics Data System (ADS)

    Gourley, J. J.; Flamig, Z.; Vergara, H. J.; Clark, R. A.; Kirstetter, P.; Terti, G.; Hong, Y.; Howard, K.

    2015-12-01

    This presentation will briefly describe the Multi-Radar Multi-Sensor (MRMS) system that ingests all NEXRAD and Canadian weather radar data and produces accurate rainfall estimates at 1-km resolution every 2 min. This real-time system, which was recently transitioned for operational use in the National Weather Service, provides forcing to a suite of flash flood prediction tools. The Flooded Locations and Simulated Hydrographs (FLASH) project provides 6-hr forecasts of impending flash flooding across the US at the same 1-km grid cell resolution as the MRMS rainfall forcing. This presentation will describe the ensemble hydrologic modeling framework, provide an evaluation at gauged basins over a 10-year period, and show the FLASH tools' performance during the record-setting floods in Oklahoma and Texas in May and June 2015.

  16. 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…

  17. Cubical equations of state for predicting the phase equilibria of poorly studied substances

    NASA Astrophysics Data System (ADS)

    Shestova, T. D.; Markvart, A. S.; Lozovskii, T. L.; Zheleznyi, V. P.

    2013-06-01

    A new procedure for determining the coefficients of the Peng-Robinson equation of state is proposed, for which a minimum of information is required. It is shown that using the Morachevskii complexity factor of molecular interaction in the algorithm for calculating the saturation vapor pressure of substances enables us to study the parameters of the vapor-liquid equilibria of substances with various polarities. Based on our validation of the procedure for determining the coefficients of the Ping-Robinson equation, it is concluded that the values for the saturation vapor pressure of halide derivatives of hydrocarbons calculated from tabular reference data agree satisfactorily in practice.

  18. 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.

  19. Predicting post-event processing in social anxiety disorder following two prototypical social situations: state variables and dispositional determinants.

    PubMed

    Kiko, Sonja; Stevens, Stephan; Mall, Anna Katharina; Steil, Regina; Bohus, Martin; Hermann, Christiane

    2012-10-01

    This study investigated self-reported state (anxiety, physical symptoms, cognitions, internally focused attention, safety behaviors, social performance) and trait (social anxiety, depressive symptoms, dysfunctional self-consciousness) predictors of post-event processing (PEP) subsequent to two social situations (interaction, speech) in participants with a primary diagnosis of social anxiety disorder (SAD) and healthy controls (HC). The speech triggered significantly more intense PEP, especially in SAD. Regardless of the type of social situation, PEP was best predicted by situational anxiety and dysfunctional cognitions among the state variables. If only trait variables were considered, PEP following both situations was accounted for by trait social anxiety. In addition, dysfunctional self-consciousness contributed to PEP-speech. If state and trait variables were jointly considered, for both situations, situational anxiety and dysfunctional cognitions were confirmed as the most powerful PEP predictors above and beyond trait social anxiety (interaction) and dysfunctional self-consciousness (speech). Hence, PEP as assessed on the day after a social situation seems to be mainly determined by state variables. Trait social anxiety and dysfunctional self-consciousness also significantly contribute to PEP depending on the type of social situation. The present findings support dysfunctional cognitions as a core cognitive mechanism for the maintenance of SAD. Implications for treatment are discussed.

  20. Major Source of Error in QSPR Prediction of Intrinsic Thermodynamic Solubility of Drugs: Solid vs Nonsolid State Contributions?

    PubMed

    Abramov, Yuriy A

    2015-06-01

    The main purpose of this study is to define the major limiting factor in the accuracy of the quantitative structure-property relationship (QSPR) models of the thermodynamic intrinsic aqueous solubility of the drug-like compounds. For doing this, the thermodynamic intrinsic aqueous solubility property was suggested to be indirectly "measured" from the contributions of solid state, ΔGfus, and nonsolid state, ΔGmix, properties, which are estimated by the corresponding QSPR models. The QSPR models of ΔGfus and ΔGmix properties were built based on a set of drug-like compounds with available accurate measurements of fusion and thermodynamic solubility properties. For consistency ΔGfus and ΔGmix models were developed using similar algorithms and descriptor sets, and validated against the similar test compounds. Analysis of the relative performances of these two QSPR models clearly demonstrates that it is the solid state contribution which is the limiting factor in the accuracy and predictive power of the QSPR models of the thermodynamic intrinsic solubility. The performed analysis outlines a necessity of development of new descriptor sets for an accurate description of the long-range order (periodicity) phenomenon in the crystalline state. The proposed approach to the analysis of limitations and suggestions for improvement of QSPR-type models may be generalized to other applications in the pharmaceutical industry.