First-order Representation Research Articles

Disentangling residence time and temperature sensitivity of microbial decomposition in a global soil carbon model

Abstract. Recent studies have identified the first-order representation of microbial decomposition as a major source of uncertainty in simulations and projections of the terrestrial carbon balance. Here, we use a reduced complexity model representative of current state-of-the-art models of soil organic carbon decomposition. We undertake a systematic sensitivity analysis to disentangle the effect of the time-invariant baseline residence time (k) and the sensitivity of microbial decomposition to temperature (Q10) on soil carbon dynamics at regional and global scales. Our simulations produce a range in total soil carbon at equilibrium of ~ 592 to 2745 Pg C, which is similar to the ~ 561 to 2938 Pg C range in pre-industrial soil carbon in models used in the fifth phase of the Coupled Model Intercomparison Project (CMIP5). This range depends primarily on the value of k, although the impact of Q10 is not trivial at regional scales. As climate changes through the historical period, and into the future, k is primarily responsible for the magnitude of the response in soil carbon, whereas Q10 determines whether the soil remains a sink, or becomes a source in the future mostly by its effect on mid-latitude carbon balance. If we restrict our simulations to those simulating total soil carbon stocks consistent with observations of current stocks, the projected range in total soil carbon change is reduced by 42% for the historical simulations and 45% for the future projections. However, while this observation-based selection dismisses outliers, it does not increase confidence in the future sign of the soil carbon feedback. We conclude that despite this result, future estimates of soil carbon and how soil carbon responds to climate change should be more constrained by available data sets of carbon stocks.

Comparing microbial and chemical kinetics for modelling soil organic carbon decomposition using the DecoChem v1.0 and DecoBio v1.0 models

Abstract. Soil organic matter is a vast store of carbon, with a critical role in the global carbon cycle. Despite its importance, the dynamics of soil organic carbon decomposition, under the impact of climate change or changing litter inputs, are poorly understood. Current biogeochemical models usually lack microbial processes and thus miss an important feedback when considering the fate of carbon. Here we use a series of modelling experiments to evaluate two different model structures: one with a standard first-order kinetic representation of soil decomposition (DecoChem v1.0, hereafter chemical model) and one with control of soil decomposition through microbial activity (DecoBio v1.0, hereafter biological model). The biological model includes cycling of organic matter into and out of microbial biomass, and simulates the decay rate as a functional of microbial activity. We tested two hypotheses. First, we hypothesized different responses in the two models to increased litter inputs and glucose additions. In the microbial model we hypothesized that this perturbation would prime microbial activity and reduce soil carbon stocks; in the chemical model we expected this perturbation to increase C stocks. In the biological model, responses to changed litter quantity were more rapid, but with the residence time of soil C altering such that soil C stocks were buffered. However, in the biological model there was a strong response to increased glucose additions (i.e. changes in litter quality), with significant losses to soil C stocks over time, driven by priming. Secondly, we hypothesized that warming will stimulate decomposition in the chemical model and loss of C, but in the biological model soil C will be less sensitive to warming, due to complex microbial feedbacks. The numerical experiments supported this hypothesis, with the chemical model soil C residence times and steady-state C stocks adjusting strongly with temperature changes, extending over decades. On the other hand, the biological model showed a rapid response to temperature that subsided after a few years, with total soil C stocks largely unchanged. The microbial model shows qualitative agreement with experimental warming studies that found transient increases in soil respiration that decline within a few years. In conclusion, the biological model is largely buffered against bulk changes in litter inputs and climate, unlike the chemical model, while the biological model displays a strong priming response to additions of labile litter. Our results have therefore highlighted significantly different sensitivities between chemical and biological modelling approaches for soil decomposition.

Financial crises, Asian stock indices, and current accounts: An Asian-U.S. comparative study

This paper investigates the effects of financial crises-based exchange rate, real interest rate, and personal consumption expenditure on stock market indices and balances of current account in four Asian countries/areas, and the U.S. from 1997 to 2010. Results obtained from Sims's first-order DSGE representation suggest that two policy variables – changes in the exchange rate and changes in the real interest rate lagged by one quarter – act as stabilizers for contemporaneous changes in stock indices for Thailand, Malaysia, and the U.S., but as destabilizers for Taiwan and Hong Kong. However, changes in personal consumption expenditure lagged by one quarter only play a destabilizing role in Hong Kong. For contemporaneous changes in the current account balance, all three policy variables become destabilizers for all five countries except the one-quarter lagged change in real interest rate, which acts as a stabilizer in Malaysia.

Solving Einstein’s equation numerically on manifolds with arbitrary spatial topologies

This paper develops a method for solving Einstein's equation numerically on multicube representations of manifolds with arbitrary spatial topologies. This method is designed to provide a set of flexible, easy to use computational procedures that make it possible to explore the never before studied properties of solutions to Einstein's equation on manifolds with arbitrary toplogical structures. A new covariant, first-order symmetric-hyperbolic representation of Einstein's equation is developed for this purpose, along with the needed boundary conditions at the interfaces between adjoining cubic regions. Numerical tests are presented that demonstrate the long-term numerical stability of this method for evolutions of a complicated, time-dependent solution of Einstein's equation coupled to a complex scalar field on a manifold with spatial topology ${S}^{3}$. The accuracy of these numerical test solutions is evaluated by performing convergence studies and by comparing the full nonlinear numerical results to the analytical perturbation solutions, which are also derived here.

The Currency of Fantasy: Discourses of Popular Culture in International Relations

The “facts” of international politics constitute the first-order representations of political life and can be reflected in popular entertainment as second-order or fictional representations. This article demonstrates that discourses of popular culture are powerful and implicated in International Relations (IR) studies. The article makes two correlated claims: the first is that the humanist and anthropological methodology often used to analyse pop culture could also be used to analyse international issues, if appropriately contextualized; the second claim is that a nation can manifest its ‘discourse’ in international politics via its popular culture, as soft power.

Prediction and early diagnosis of complex diseases by edge-network

In this article, we develop a novel edge-based network i.e. edge-network, to detect early signals of diseases by identifying the corresponding edge-biomarkers with their dynamical network biomarker score from dynamical network biomarkers. Specifically, we derive an edge-network based on the second-order statistics representation of gene expression profiles, which is able to accurately represent the stochastic dynamics of the original biological system (with Gaussian distribution assumption) by combining with the traditional node-network, which is based only on the first-order statistics representation of the noisy data. In other words, we show that the stochastic network of a biological system can be described by the integration of its node-network and its edge-network in an accurate manner. By applying edge-network analysis to gene expressions of healthy adults within live influenza experiment sampling at time points before the appearance of infection symptoms, we identified the edge-biomarkers (80 edges with 22 densely connected genes) discovered in edge-networks corresponding to symptomatic adults, which were used to predict the subsequent outcomes of influenza infection. In particular, we not only correctly predict the final infection outcome of each individual at an early time point before his/her clinic symptom but also reveal the key molecules during the disease progression. The prediction accuracy achieves ~90% under the leave-one-out cross-validation. Furthermore, we demonstrate the superiority of our method on disease classification and predication by comparing with the conventional node-biomarkers. Our edge-network analysis not only opens a new way to understand pathogenesis at a network level due to the new representation for a stochastic network, but also provides a powerful tool to make the early diagnosis of diseases. lnchen@sibs.ac.cn Supplementary data are available at Bioinformatics online.

Modeling particle aggregation using size class and size spectrum approaches

[1] Particle aggregation plays an important role in many marine biogeochemical processes such as determining the vertical flux of particulate material and trace metal scavenging. Models of particle aggregation vary in complexity and in this paper I compare the behavior of a detailed size spectrum model with that of a series of simple, two size class models with different representations of aggregation, all of which have appeared in the literature. The simplest model uses a first-order representation of aggregation kinetics, while two other models have nonlinear representations of aggregation. The simplest model is unable to reproduce the dynamic or steady state behavior of the size spectrum model. Results from the two nonlinear size class models show better agreement with the behavior of the size spectrum model. I find that the mode of aggregation and the dependence of aggregation on particle size are crucial for understanding the differences between the models.

A First-Order Formalization of Commitments and Goals for Planning

Commitments help model interactions in multiagent systems in a computationally realizable yet high-level manner without compromising the autonomy and heterogeneity of the member agents. Recent work shows how to combine commitments with goals and apply planning methods to enable agents to determine their actions. However, previous approaches to modeling commitments are confined to propositional representations, which limits their applicability in practical cases. We propose a first-order representation and reasoning technique that accommodates templatic commitments and goals that may be applied repeatedly with differing bindings for domain objects. Doing so not only leads to a more perspicuous modeling, but also supports many practical patterns.

Mean Flow Near Edges and Within Cavities Situated Inside Dense Canopies

A streamfunction-vorticity formulation is used to explore the extent to which turbulent and turbulently inviscid solutions to the mean momentum balance explain the mean flow across forest edges and within cavities situated inside dense forested canopies. The turbulent solution is based on the mean momentum balance where first-order closure principles are used to model turbulent stresses. The turbulently inviscid solution retains all the key terms in the mean momentum balance but for the turbulent stress gradients. Both exit and entry versions of the forest edge problem are explored. The turbulent solution is found to describe sufficiently the bulk spatial patterns of the mean flow near the edge including signatures of different length scales reported in canopy transition studies. Next, the 'clearing inside canopy' or the so-called 'cavity' problem is solved for the inviscid and turbulent solutions and then compared against flume experiments. The inviscid solution describes the bulk flow dynamics in much of the zones within the cavity. In particular, the solution can capture the correct position of the bulk recirculation zone within the cavity, although with a weaker magnitude. The inviscid solution cannot capture the large vertical heterogeneity in the mean velocity above the canopy, as expected. These features are better captured via the first-order closure representation of the turbulent solution. Given the ability of this vorticity formulation to capture the mean pressure variations and the mean advective

First-order representation of the Faddeev formulation of gravity

We study the Faddeev formulation of gravity in which the metric is composed of a ten-dimensional tetrad . Here, A = 1, …, 10 refers to an Euclidean (or Minkowsky) ten-dimensional spacetime and λ = 1, 2, 3, 4 is the usual world index. A remarkable unique feature of the Faddeev formulation is that the action remains finite for the discontinuous tetrad. If the spacetime is composed of flat microblocks (in the discrete version of gravity), this means that these microblocks in quantum theory do not coincide on their common faces, that is, they are independent. We propose a representation of the Faddeev formulation analogous to the Cartan–Weyl formalism. It is based on extending the set of variables by introducing an infinitesimal SO(10) connection. Excluding this connection via equations of motion we reproduce the original Faddeev action. A peculiar feature of this representation is the occurrence of a local SO(10) symmetry violating condition so that the SO(10) symmetry is only global one in full correspondence with the fact that the original Faddeev formulation possesses just the global SO(10) symmetry. We also consider an analogue of the Barbero–Immirzi parameter which can be naturally introduced in the considered representation.

Reasoning about delegation and revocation schemes in answer set programming

In this article we show how to model a range of notions in the context of delegation and revocation applied to security scenarios. We demonstrate how a range of delegation–revocation models and policies may be represented in pictorial form and formally represented in terms of reactive Kripke models and a first-order policy specification language. We translate first-order representations of our reactive Kripke models into an equivalent Answer Set Programming form that enables users to apply flexibly well-defined definitions of predicates to represent their requirements in terms of delegation–revocation policy specification.

Reward Prediction Error Signals are Meta-Representational.

Reward Prediction Error Signals are Meta-Representational.

Anatomy of the Andean subduction zone: three-dimensional density model upgraded and compared against global-scale models

We present an upgraded version of a previously published 3-D density model of the Andean subduction zone between 18°S and 45°S. This model consists of 3-D bodies of constant density, which geometry is constrained by independent seismic data and is triangulated from vertical cross-sections. These bodies define the first-order morphology and internal structure of the subducted Nazca slab and South American Plate. The new version of the density model results after forward modelling the Bouguer anomaly as computed from the most recent version of the Earth Gravitational Model (EGM2008). The 3-D density model incorporates new seismic information to better constrain the geometry of the subducted slab and continental Moho (CMH) and has a trench-parallel resolution doubling the resolution of the previous model. As an example of the potential utility of our model, we compare the geometry of the subducted slab and CMH against the corresponding global models Slab1.0 and Crust2.0, respectively. This exercise demonstrates that, although global models provide a good first-order representation of the slab and upper-plate crustal geometries, they show large discrepancies (up to ±40 km) with our upgraded model for some well-constrained areas. The geometries of the slab, lithosphere-asthenosphere boundary below the continent, CMH and intracrustal density discontinuity that we present here as Supporting Information can be used to study Andean geodynamic processes from a wide range of quantitative approaches.

GPU-Based Interactive Cut-Surface Extraction From High-Order Finite Element Fields

We present a GPU-based ray-tracing system for the accurate and interactive visualization of cut-surfaces through 3D simulations of physical processes created from spectral/hp high-order finite element methods. When used by the numerical analyst to debug the solver, the ability for the imagery to precisely reflect the data is critical. In practice, the investigator interactively selects from a palette of visualization tools to construct a scene that can answer a query of the data. This is effective as long as the implicit contract of image quality between the individual and the visualization system is upheld. OpenGL rendering of scientific visualizations has worked remarkably well for exploratory visualization for most solver results. This is due to the consistency between the use of first-order representations in the simulation and the linear assumptions inherent in OpenGL (planar fragments and color-space interpolation). Unfortunately, the contract is broken when the solver discretization is of higher-order. There have been attempts to mitigate this through the use of spatial adaptation and/or texture mapping. These methods do a better job of approximating what the imagery should be but are not exact and tend to be view-dependent. This paper introduces new rendering mechanisms that specifically deal with the kinds of native data generated by high-order finite element solvers. The exploratory visualization tools are reassessed and cast in this system with the focus on image accuracy. This is accomplished in a GPU setting to ensure interactivity.

Lyapunov stability of n-D strongly autonomous systems

In this article we look into stability properties of strongly autonomous n-D systems, i.e. systems having finite-dimensional behaviour. These systems are known to have a first-order representation akin to 1-D state-space representation; we consider our systems to be already in this form throughout. We first define restriction of an n-D system to a 1-D subspace. Using this we define stability with respect to a given half-line, and then stability with respect to collections of such half-lines: proper cones. Then we show how stability with respect to a half-line, for the strongly autonomous case, reduces to a linear combination of the state representation matrices being Hurwitz. We first relate the eigenvalues of this linear combination with those of the individual matrices. With this we give an equivalent geometric criterion in terms of the real part of the characteristic variety of the system for half-line stability. Then we extend this geometric criterion to the case of stability with respect to a proper cone. Finally, we look into a Lyapunov theory of stability with respect to a proper cone for strongly autonomous systems. Each non-zero vector in the given proper cone gives rise to a linear combination of the system matrices. Each of these linear combinations gives a corresponding Lyapunov inequality. We show that the system is stable with respect to the proper cone if and only if there exists a common solution to all of these Lyapunov inequalities.

Prediction of spatiotemporal patterns of neural activity using a higher-order Markov representation of instantaneous pairwise maximum entropy model

Maximum entropy (MaxEnt) models have been suggested to fit the spatiotemporal patterns of simultaneously observed neuronal spike trains. To build such models, the distribution, px(X), needs to be determined, where X is a sequence of neural patterns formed from the vector X1,…,Xn for n time samples, where Xi is a sample of the spike train of a population at time i . Each neural pattern itself is a binary sequence, for which one represents a spike, and zero otherwise. MaxEnt models are constructed by matching the moments between neuronal constituents with the analogous empirical estimates from the distribution px(X). MaxEnt models that incorporate the first and second order interactions among neurons, also referred to as the instantaneous pairwise MaxEnt model or the Ising model, are expressed as where the individual, hi, and coupling, Jij, terms are parameters associated with the first and second order interactions, respectively, and Z is a normalization factor to insure . This model was shown to be effective in representing almost 90 percent of the information in small populations (less than 20 neurons) [1,2]. The main disadvantage of this model, however, is that it ignores the temporal correlation that represents the effect of the history of spiking activity on the current population state. Extending the width of neural patterns has been proposed as a method to incorporate spiking history. It destroys, however, the information in precise spike timings [3]. To incorporate the history term without compromising information in spike times, a higher-order Markov model is constructed in which patterns from different time samples are added. An example first-order Markov representation is expressed as where and are the individual, and , and are the coupling terms among the neuronal elements. This spatiotemporal model is more general than the representation introduced in Marre et al [4] to account for history terms , but at the expense of increasing the number of parameters. We evaluated the performance of the extended MaxEnt model in predicting the activity pattern of cat V1 neurons in response to drifting grating stimuli. A population of 21 neurons was recorded with 5 tetrodes. We compared the prediction power of this model with the instantaneous pairwise MaxEnt model, as well as the independent model. We show that the iterative scaling algorithm makes the extended model converge faster compared to [4], and also reduces the Kullback-Leibler divergence between the estimated and true distributions by 20 percent. Taken together, these results suggest the importance of accounting for temporal correlations in predicting the spatiotemporal patterns.

A discriminative method for family-based protein remote homology detection that combines inductive logic programming and propositional models.

BackgroundRemote homology detection is a hard computational problem. Most approaches have trained computational models by using either full protein sequences or multiple sequence alignments (MSA), including all positions. However, when we deal with proteins in the "twilight zone" we can observe that only some segments of sequences (motifs) are conserved. We introduce a novel logical representation that allows us to represent physico-chemical properties of sequences, conserved amino acid positions and conserved physico-chemical positions in the MSA. From this, Inductive Logic Programming (ILP) finds the most frequent patterns (motifs) and uses them to train propositional models, such as decision trees and support vector machines (SVM).ResultsWe use the SCOP database to perform our experiments by evaluating protein recognition within the same superfamily. Our results show that our methodology when using SVM performs significantly better than some of the state of the art methods, and comparable to other. However, our method provides a comprehensible set of logical rules that can help to understand what determines a protein function.ConclusionsThe strategy of selecting only the most frequent patterns is effective for the remote homology detection. This is possible through a suitable first-order logical representation of homologous properties, and through a set of frequent patterns, found by an ILP system, that summarizes essential features of protein functions.

Directed Search for Generalized Plans Using Classical Planners

We consider the problem of finding generalized plans for situations where the number of objects may be unknown and unbounded during planning. The input is a domain specification, a goal condition, and a class of concrete problem instances or initial states to be solved, expressed in an abstract first-order representation. Starting with an empty generalized plan, our overall approach is to incrementally increase the applicability of the plan by identifying a problem instance that it cannot solve, invoking a classical planner to solve that problem, generalizing the obtained solution and merging it back into the generalized plan. The main contributions of this paper are methods for (a) generating and solving small problem instances not yet covered by an existing generalized plan, (b) translating between concrete classical plans and abstract plan representations, and (c) extending partial generalized plans and increasing their applicability. We analyze the theoretical properties of these methods, prove their correctness, and illustrate experimentally their scalability. The resulting hybrid approach shows that solving only a few, small, classical planning problems can be sufficient to produce a generalized plan that applies to infinitely many problems with unknown numbers of objects.

Critical form and feedbacks in mountain-belt dynamics: Role of rheology as a tectonic governor

[1] The rheology that governs deformation within a convergent orogen also controls its topographic form, or in other words, the covaration of its height (or thickness) and its width. Under conditions pertaining to small orogens and for rheologies ranging from linear viscous to Coulomb plastic, we show that this topographic form is insensitive to the distributions of fluxes into and out of the wedge and is thus a fundamental property of the system. It can therefore be thought of as a “critical” topographic form, directly analogous to the well-studied case of a Coulomb plastic rheology, which predicts a constant critical taper angle, independent of its size. The tendency of the system to evolve toward a critical topographic form can be regarded as a “tectonic governor” that strongly damps the response of the orogen to changes in climate or tectonic forcing. Scaling relationships can be derived for the variation of orogen width, height, and exhumation rate as a function of accretionary flux and precipitation rate. This study explores how sensitive these scaling relationships are to the assumed rheology. It is found that the scaling relationships vary by less than a factor of 2 across the range of typical geological rheologies, from Coulomb friction, to power law viscous, to linear viscous. These scaling relationships provide a first-order representation of the behavior of convergent wedges and are relatively insensitive to the underlying assumptions.

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

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