Canonical Model Research Articles

Debt and demand regimes in simplified growth models: a comparison of neo-Kaleckian and Supermultiplier models

Abstract The paper compares stock-flow consistent (SFC) versions of extended canonical neo-Kaleckian and Supermultiplier models that deal with either households’ or firms’ debt accumulation. This comparison aim is twofold: (i) to evaluate the differences of a debt accumulation process in these models due to their specific closures; (ii) to provide a pedagogical tool for understanding the basic features of each model when dealing with similar issues. In the short run, we find that the relation of debt ratios, demand and growth is similar for both models. In the long run of the Supemultiplier model, only the Minskyan debt regime is economically viable for the firms’ sector. As for the household sector, the paradox of debt is indeed a feature of the canonical Supermultiplier model, yet there may be episodes of rising debt-toincome ratios. As for the neo-Kaleckian model, firms’ leverage ratio can be either pro- or anti-cyclical; in the household sector, the absence of the paradox of debt seems to be the most likely scenario.

Tensors and Algebras: An Algebraic Spacetime Interpretation for Tensor Models

The quest for a consistent theory for quantum gravity is one of the most challenging problems in theoretical high-energy physics. An often-used approach is to describe the gravitational degrees of freedom by the metric tensor or related variables, and finding a way to quantise this. In the canonical tensor model, the gravitational degrees of freedom are encoded in a tensorial quantity $P_{abc}$, and this quantity is subsequently quantised. This makes the quantisation much more straightforward mathematically, but the interpretation of this tensor as a spacetime is less evident. In this work we take a first step towards fully understanding the relationship to spacetime. By considering $P_{abc}$ as the generator of an algebra of functions, we first describe how we can recover the topology and the measure of a compact Riemannian manifold. Using the tensor rank decomposition, we then generalise this principle in order to have a well-defined notion of the topology and geometry for a large class of tensors $P_{abc}$. We provide some examples of the emergence of a topology and measure of both exact and perturbed Riemannian manifolds, and of a purely algebraically-defined space called the semi-local circle.

Quality-related process monitoring scheme based on neighborhood embedding canonical correlation analysis model

BackgroundAccompanied by the development of sensor technology and the scale and integration of industrial processes, the safety and quality of the operating process is widely concerned. Process monitoring is an important technology for modern enterprises to ensure product quality and improve comprehensive economic efficiency. MethodsCanonical correlation analysis (CCA) is a useful algorithm for exploring the correlation between two sets of variables and has been successfully employed in quality-related process monitoring. Nevertheless, CCA neglects the neighborhood structure information while capturing the global maximum correlation feature. To ensure a more comprehensive feature representation, considering the effectiveness of neighborhood preserving embedding (NPE) algorithm in extracting local structure, this work proposes a quality-related process monitoring model named Neighborhood Embedding Canonical Correlation Analysis (NECCA). Firstly, the neighborhood information extracted through the improved NPE algorithm is incorporated into CCA. This model not only possesses analogous expression and analytical solution with CCA but also integrates the local structural feature. Secondly, a regression model is established, then the coefficient matrix is decomposed to distinguish quality-related and quality-unrelated subspaces. Finally, the proposed model is evaluated in a typical test case to demonstrate its rationality and effectiveness.

The Topological Mu-Calculus: Completeness and Decidability

We study the topological μ-calculus, based on both Cantor derivative and closure modalities, proving completeness, decidability, and finite model property over general topological spaces, as well as over T 0 and T D spaces. We also investigate the relational μ-calculus, providing general completeness results for all natural fragments of the μ-calculus over many different classes of relational frames. Unlike most other such proofs for μ-calculi, ours is model theoretic, making an innovative use of a known method from modal logic (the ‘final’ submodel of the canonical model), which has the twin advantages of great generality and essential simplicity.

Marshall Lecture 2023: Behavioral Macroeconomics via Sparse Dynamic Programming

Abstract This paper proposes a tractable way to model boundedly rational dynamic programming. The agent uses an endogenously simplified, or “sparse,” model of the world and the consequences of his actions and acts according to a behavioral Bellman equation. The framework yields a behavioral version of some of the canonical models in macroeconomics and finance. In the life-cycle model, the agent initially does not pay much attention to retirement and undersaves; late in life, he progressively saves more, generating realistic dynamics. In the consumption-savings model, the consumer decides to pay little or no attention to the interest rate and more attention to his income. Ricardian equivalence and the Lucas critique partially fail because the consumer may not pay full attention to taxes and policy changes. In a Merton-style dynamic portfolio choice problem, the agent endogenously pays limited or no attention to the varying equity premium and hedging demand terms. Finally, in the neoclassical growth model, agents act on a simplified model of the macroeconomy; in equilibrium, fluctuations are larger and more persistent.

Dynamic Canonical Data Model: An Architecture Proposal for the External and Data Loose Coupling for the Integration of Software Units

Integrating third-party and legacy systems has become a critical necessity for companies, driven by the need to exchange information with various entities such as banks, suppliers, customers, and partners. Ensuring data integrity, keeping integrations up-to-date, reducing transaction risks, and preventing data loss are all vital aspects of this complex task. Achieving success in this endeavor, which involves both technological and business challenges, necessitates the implementation of a well-suited architecture. This article introduces an architecture known as the Dynamic Canonical Data Model through Agnostic Messages. The proposal addresses the integration of loosely coupled software units, mainly when dealing with internal and external data integration. To illustrate the architecture’s components, a case study from the Mexican Logistics Company Paquetexpress is presented. This organization manages integrations across several platforms, including SalesForce and Oracle ERP, with clients like Amazon, Mercado Libre, Grainger, and Afull. Each of these incurs costs ranging from USD 30,000 to USD 36,000, with consultants from firms such as Quanam, K&F, TSOL, and TekSi playing a crucial role in their execution. This consumes much time, making maintenance costs considerably high when clients request data transmission or type changes, particularly when utilizing tools like Oracle Integration Cloud (OIC) or Oracle Service Bus (OSB). The article provides insights into the architecture’s design and implementation in a real-world scenario within the delivery company. The proposed architecture significantly reduces integration and maintenance times and costs while maximizing scalability and encouraging the reuse of components. The source code for this implementation has been registered in the National Registry of Copyrights in Mexico.

Estimation, Comparison, and Projection of Multifactor Age–Cohort Affine Mortality Models

Affine mortality models, developed in continuous time, are well suited to longevity risk applications including pricing and capital management. A major advantage of this mortality modeling approach is the availability of closed-form cohort survival curves, consistent with the assumed time dynamics of mortality rates. This article makes new contributions to the estimation of multifactor continuous-time affine models including the canonical Blackburn-Sherris, the arbitrage-free Nelson-Siegel (AFNS), and the Cox-Ingersoll-Ross (CIR) mortality models. We discuss and address numerical issues with model estimation. We apply the estimation methods to age–cohort mortality data from five different countries, providing insights into the dynamics of mortality rates and the fitting performance of the models. We show how the use of maximum likelihood with the univariate Kalman filter turns out to be faster and more robust compared to traditional estimation methods that heavily use large matrix multiplication and inversion. We present graphical and numerical goodness-of-fit results and assess model robustness. We project cohort survival curves and assess the out-of-sample performance of the models for the five countries. We confirm previous results by showing that, across these countries, although the CIR mortality model fits the historical mortality data well, particularly at older ages, the canonical and AFNS affine mortality models provide better out-of-sample performance. We also show how these affine mortality models are robust with respect to the set of age–cohort data used for parameter estimation. R code is provided.

A universal workflow for creation, validation, and generalization of detailed neuronal models

Detailed single-neuron modeling is widely used to study neuronal functions. While cellular and functional diversity across the mammalian cortex is vast, most of the available computational tools focus on a limited set of specific features characteristic of a single neuron. Here, we present a generalized automated workflow for the creation of robust electrical models and illustrate its performance by building cell models for the rat somatosensory cortex. Each model is based on a 3D morphological reconstruction and a set of ionic mechanisms. We use an evolutionary algorithm to optimize neuronal parameters to match the electrophysiological features extracted from experimental data. Then we validate the optimized models against additional stimuli and assess their generalizability on a population of similar morphologies. Compared to the state-of-the-art canonical models, our models show 5-fold improved generalizability. This versatile approach can be used to build robust models of any neuronal type.

Physically meaningful parameters of viscoelastic models based on the general functional form of the transfer functions

Viscoelastic models aim to characterize material properties by extracting the parameters that best describe observed deformations. However, due to the equivalence of various model structures, the conventional model parameters as constants assigned to each model element cannot be uniquely determined for a given deformation and, therefore, are unfit to represent material properties. To resolve this problem, we formulate the framework of viscoelastic models based on their transfer functions. We explicitly derive the functional form of the transfer functions of viscoelastic models and show that a set of parameters is uniquely determined for each equivalent set of viscoelastic models. The newly identified parameters correspond to the instantaneous modulus and the distribution of the characteristic time scales of the system, and based on this perspective, we show that the differentiation order of a fractional viscoelastic element, spring-pot, is determined solely by the recursive nature of the characteristic time scales of the system. Furthermore, we show that all viscoelastic models are equivalent to four canonical model structures.

Vascular Disease Is Associated With Differences in Brain Structure and Lower Cognitive Functioning in Inflammatory Bowel Disease: A Cross-Sectional Study.

Vascular disease and cognitive impairment have been increasingly documented in inflammatory bowel disease (IBD), and both have been individually correlated with changes in brain structure. This study aimed to determine if both macro- and microstructural brain changes are prevalent in IBD and whether alterations in brain structure mediate the relationship between vascular disease and cognitive functioning. Eighty-four IBD participants underwent multimodal magnetic resonance imaging. Volumetric and mean diffusivity measures of the thalamus, hippocampus, normal-appearing white matter, and white matter lesions were converted to age- and sex-adjusted z scores. Vascular comorbidity was assessed using a modified Framingham Risk Score and cognition was assessed using a battery of neuropsychological tests. Test scores were standardized using local regression-based norms. We generated summary statistics for the magnetic resonance imaging metrics and cognitive tests, and these were examined using canonical correlation analysis and linear regression modeling. Greater vascular comorbidity was negatively correlated with thalamic, normal-appearing white matter, and white matter lesion volumes. Higher Framingham Risk Score were also correlated with lower processing speed, learning and memory, and verbal fluency. Increased vascular comorbidity was predictive of poorer cognitive functioning, and this effect was almost entirely mediated (94.76%) by differences in brain structure. Vascular comorbidity is associated with deleterious effects on brain structure and lower cognitive functioning in IBD. These findings suggest that proper identification and treatment of vascular disease is essential to the overall management of IBD, and that certain brain areas may serve as critical targets for predicting the response to therapeutic interventions.

Unimodal singularities and boundary divisors in the KSBA moduli of a class of Horikawa surfaces

AbstractSmooth minimal surfaces of general type with , , and constitute a fundamental example in the geography of algebraic surfaces, and the 28‐dimensional moduli space of their canonical models admits a modular compactification via the minimal model program. We describe eight new irreducible boundary divisors in such compactification parameterizing reducible stable surfaces. Additionally, we study the relation with the GIT compactification of and the Hodge theory of the degenerate surfaces that the eight divisors parameterize.

A review of misfit functions for adjoint full waveform inversion in seismology

SUMMARY In seismological full waveform inversion, the choice of misfit functions plays a critical role in quantifying the discrepancy between observed and synthetic data, affecting convergence rate and also final results. We revisit and compare six commonly used misfit functions, including cross-correlation time-shift (CC), least-square waveform difference (L2), multitaper time-shift (MT), exponentiated phase shift (EP), time–frequency phase shift (TF) and zero-lag cross-correlation coefficient (CCC), with respect to their definitions, adjoint sources and misfit kernels for velocity perturbations. Synthetic tests are performed for several canonical models. First, we simulated wave propagation in a model with a single rectangular anomaly with sharp boundaries and a smoothed variant of that model. We analysed the resulting misfit kernels first for the P-wave phase, which is highly distorted in the sharp model due to strong heterogeneities, and mostly experiences traveltime perturbations in the smooth model. Second, we considered a model where a laterally limited region is subject to layered anomalies (low velocity in the middle crust and high velocity in the lower crust) and determine misfit kernels for S and surface waves in this model. Based on these two simplified seismological scenarios, we further perform iterative test inversions using different misfit functions. Combining the features of misfit kernels and synthetic inversion results, we find that CCC, L2 and EP are the most effective at identifying the sharpness of velocity anomalies from the direct body waves and their scattered phases. Consequently, inversion based on those misfit measures yielded the best recovery in the inversion test. For surface and S waves from crustal sources, TF appears to be the most effective in constraining the heterogeneous structure in the crust but needs more iterations for convergence than other misfit functions.

The China trade shock and the European Union's employment

ABSTRACT This paper analyzes the effect of trade with China on the European Union's industrial and regional employment. Built on a canonical multi-country Ricardian model, we identify and estimate the employment reallocation effect across fifteen EU countries with specific discussions on three channels: import penetration, direct export, and third-market competition vis-à-vis China. Data suggest that the adverse effects stemming from the import penetration and the third-market competition quantitatively dominate the positive effect from the export channel for the effect on the EU. In particular, the third-market competition channel is a key channel for understanding the impact of China trade shock on the EU market, accounting for about 46% and 36% of the total negative effect at the industry level and region level, respectively. Moreover, the third-market competition channel particularly influenced the relative employment growth of the worst-affected industries in the European market.

F0(980) production in inelastic pp collisions at [formula omitted] = 5.02 TeV

The measurement of the production of f0(980) in inelastic pp collisions at s=5.02 TeV is presented. This is the first reported measurement of inclusive f0(980) yield at LHC energies. The production is measured at midrapidity, |y|<0.5, in a wide transverse momentum range, 0<pT<16 GeV/c, by reconstructing the resonance in the f0(980)→π+π− hadronic decay channel using the ALICE detector. The pT-differential yields are compared to those of pions, protons and ϕ mesons as well as to predictions from the HERWIG 7.2 QCD-inspired Monte Carlo event generator and calculations from a coalescence model that uses the AMPT model as an input. The ratio of the pT-integrated yield of f0(980) relative to pions is compared to measurements in e+e− and pp collisions at lower energies and predictions from statistical hadronisation models and HERWIG 7.2. A mild collision energy dependence of the f0(980) to pion production is observed in pp collisions from SPS to LHC energies. All considered models underpredict the pT-integrated 2f0(980)/(π+ +π−) ratio. The prediction from the canonical statistical hadronisation model assuming a zero total strangeness content of f0(980) is consistent with the data within 1.9σ and is the closest to the data. The results provide an essential reference for future measurements of the particle yield and nuclear modification in p–Pb and Pb–Pb collisions, which have been proposed to be instrumental to probe the elusive nature and quark composition of the f0(980) scalar meson.

Large-Signal Stability of the Quadratic Boost Converter Using a Disturbance Observer-Based Sliding-Mode Control

The quadratic boost (QB) converter is a fourth-order system with a dc gain that is higher than the traditional second-order step-up configuration. The modern controllers that control these high-order dc–dc converters often only guarantee local stability around a steady-state equilibrium point, which is one of their primary drawbacks. In this article, a non-linear robust control law design to attain large-signal stability in this single switch QB converter is presented. In the presence of an unpredictable load, the control objective is to maintain the regulation of an output voltage. The Brunovsky canonical model of the converter was derived first, and the non-linear disturbance observer-based sliding-mode (SM) control law is designed based on it. An observer variable precisely estimates the output disturbances. The detailed process for deriving the control signal is described in this paper and the large-signal stability of the closed-loop converter system is ensured via the Lyapunov function. Finally, some simulation results are shown to validate the usefulness of the given controller.

Polyrate 2023: A computer program for the calculation of chemical reaction rates for polyatomics. New version announcement

Polyrate 2023: A computer program for the calculation of chemical reaction rates for polyatomics. New version announcement

Replicating superspreader dynamics with compartmental models

Infectious disease outbreaks often exhibit superspreader dynamics, where most infected people generate no, or few secondary cases, and only a small fraction of individuals are responsible for a large proportion of transmission. Although capturing this heterogeneity is critical for estimating outbreak risk and the effectiveness of group-specific interventions, it is typically neglected in compartmental models of infectious disease transmission—which constitute the most common transmission dynamic modeling framework. In this study we propose different classes of compartmental epidemic models that incorporate transmission heterogeneity, fit them to a number of real outbreak datasets, and benchmark their performance against the canonical superspreader model (i.e., the negative binomial branching process model). We find that properly constructed compartmental models can capably reproduce observed superspreader dynamics and we provide the pathogen-specific parameter settings required to do so. As a consequence, we also show that compartmental models parameterized according to a binary clinical classification have limited support.

Measurement of the production of (anti)nuclei in p–Pb collisions at [formula omitted

Measurements of (anti)proton, (anti)deuteron, and (anti)3He production in the rapidity range −1<y<0 as a function of the transverse momentum and event multiplicity in p–Pb collisions at a center-of-mass energy per nucleon–nucleon pair sNN=8.16TeV are presented. The coalescence parameters B2 and B3, measured as a function of the transverse momentum per nucleon and of the mean charged-particle multiplicity density, confirm a smooth evolution from low to high multiplicity across different collision systems and energies. The ratios between (anti)deuteron and (anti)3He yields and those of (anti)protons are also reported as a function of the mean charged-particle multiplicity density. A comparison with the predictions of the statistical hadronization and coalescence models for different collision systems and center-of-mass energies favors the coalescence description for the deuteron-to-proton yield ratio with respect to the canonical statistical model.

Do firms that state they are financially constrained tend to reinvest more of their profits?

Firms in global surveys often state that access to finance is their primary obstacle to growth. However, merely stating that access to finance is an obstacle does not necessarily imply the firm is financially constrained. We examine the idea that firms facing external finance constraints should in theory reinvest profits more heavily, as some canonical models in financial economics predict. We test this prediction using global firm surveys from 2006 to 2019. We do not find such higher profit reinvestment among firms that state finance is their most important obstacle. This puzzling null finding is robust to alternative measures of profit reinvestment and many alternative confounders. In contrast to stated constraints, profit reinvestment is in fact higher for non-stated measures of financial constraints (e.g. younger or smaller firms, as well as firms in sectors with fewer tangible assets). Overall, our findings suggest that these self-assessments of a main obstacle might be due to other reasons, beyond access to funds that finance capital investment.

Test-retest reliability of reinforcement learning parameters.

It has recently been suggested that parameter estimates of computational models can be used to understand individual differences at the process level. One area of research in which this approach, called computational phenotyping, has taken hold is computational psychiatry. One requirement for successful computational phenotyping is that behavior and parameters are stable over time. Surprisingly, the test-retest reliability of behavior and model parameters remains unknown for most experimental tasks and models. The present study seeks to close this gap by investigating the test-retest reliability of canonical reinforcement learning models in the context of two often-used learning paradigms: a two-armed bandit and a reversal learning task. We tested independent cohorts for the two tasks (N = 69 and N = 47) via an online testing platform with a between-test interval of five weeks. Whereas reliability was high for personality and cognitive measures (with ICCs ranging from .67 to .93), it was generally poor for the parameter estimates of the reinforcement learning models (with ICCs ranging from .02 to .52 for the bandit task and from .01 to .71 for the reversal learning task). Given that simulations indicated that our procedures could detect high test-retest reliability, this suggests that a significant proportion of the variability must be ascribed to the participants themselves. In support of that hypothesis, we show that mood (stress and happiness) can partly explain within-participant variability. Taken together, these results are critical for current practices in computational phenotyping and suggest that individual variability should be taken into account in the future development of the field.