Compatible Model Research Articles

Flexible neural color compatibility model for efficient color extraction from image

AbstractColor choice is an essential aspect of many applications, including graphic design, web design and fashion design. The selection of colors can have a significant impact on the overall aesthetic and appeal of a design, as well as its effectiveness in conveying a particular message or mood. This paper introduces new and simple tools for choosing colors. First, we introduce a convolutional neural network that scores the quality of a set of five colors, called a color theme. Such a network can be used to rate the quality of a new color theme. Second, we propose a method to extract a variable‐size palette from an image. The size of the extracted palette can vary depending on the color richness of the image. Third, we demonstrate simple prototypes that apply the trained neural network and the palette extraction method to tasks in graphic design, such as improving existing themes. Our proposed network has the advantage of being significantly simpler than other state‐of‐the‐art methods with better performance.

In Vitro Study of the Compatibility of Four Species of Trichoderma with Three Fungicides and Their Antagonistic Activity against Fusarium solani

Strawberry wilt is a disease caused by Fusarium solani, which it provokes the death of the plant. Farmers mainly use chemical methods for its control, which has a negative impact on the environment and human health. Given the growing demand for organic agricultural products, compatible alternatives must be sought for disease management that can reduce the doses of fungicides. A combination of pesticides and biological control agents could be an alternative for the management of F. solani. Consequently, investigations on fungicide compatibility and synergistic effects are recommended in relation to the biological control of strawberry wilt. In this study, potential antagonism was calculated according to the class of mycoparasitism and the percentage inhibition of radial growth in order to later design a compatibility model of the different species of Trichoderma with three protective fungicides at different concentrations. The potential antagonism showed that Trichoderma asperellum presented high compatibility with the fungicides Captan and Mancozeb added in concentrations of 450, 900, and 1350 mg L−1. The use of antagonistic strains together with the fungicide Chlorothalonil in its three concentrations showed a negative effect on the growth of Trichoderma species, which caused low and null compatibility against the MA-FC120 strain of F. solani in vitro.

A Simplified Method for Predicting Bond-Slip Behaviour of Ribbed Bars and Threaded Rods Glued in Glulam along the Grain.

The bond between a steel reinforcement/rod and glulam plays a crucial role in the resistance and deformation capacity of timbers joints. Existing studies provide different bond-slip models for reinforcements and rods with different anchorage lengths, in which the relationship between local bond stress and global bond behaviour cannot not be established. This study presents a unified analytical method for predicting the bond-slip behaviour of ribbed bars and threaded rods along the grain using a local bond-slip model of reinforcement at the elastic and post-yield stages. In the analytical method, equilibrium, compatibility, and constitutive models for reinforcement and rods are considered. The method is verified using test data of rebars and rods with different anchorage lengths. Comparisons between the experimental and calculated results suggest that the analytical method yields reasonably good predictions of the load-slip relationship and failure mode. Furthermore, the embedment lengths required for yield and the ultimate strengths of the reinforcement and rods along the grain are determined by assuming uniform bond stress distributions over the elastic and post-yield steel segment. The average bond stress over the entire anchorage length is calculated and compared with existing equations. Design recommendations for anchorage lengths are proposed for ribbed bars and threaded rods glued in glulam.

Harmonization of ICF Body Structures and ICD-11 Anatomic Detail: One foundation for multiple classifications.

The Family of International Classifications of the World Health Organization (WHO-FIC) currently includes three reference classifications, namely International Classification of Diseases (ICD), International Classification of Functioning, Disability, and Health (ICF), and International Classification of Health Interventions (ICHI). Recently, the three classifications have been incorporated into a single WHO-FIC Foundation that serves as the repository of all concepts in the classifications. Each classification serves a specific classification need. However, they share some common concepts that are present, in different forms, in two or all of them. For the WHO-FIC Foundation to be a logically consistent repository without duplicates, these common concepts must be reconciled. One important set of shared concepts is the representation of human anatomy entities, which are not always modeled in the same way and with the same level of detail. To understand the relationships among the three anatomical representations, an effort is needed to compare them, identifying common areas, gaps, and compatible and incompatible modeling. The work presented here contributes to this effort, focusing on the anatomy representations in ICF and ICD-11. For this aim, three experts were asked to identify, for each entity in the ICF Body Structures, one or more entities in the ICD-11 Anatomic Detail that could be considered identical, broader or narrower. To do this, they used a specifically developed web application, which also automatically identified the most obvious equivalences. A total of 631 maps were independently identified by the three mappers for 218 ICF Body Structures, with an interobserver agreement of 93.5%. Together with 113 maps identified by the software, they were then consolidated into 434 relations. The results highlight some differences between the two classifications: in general, ICF is less detailed than ICD-11; ICF favors lumping of structures; in very few cases, the two classifications follow different anatomic models. For these issues, solutions have to be found that are compliant with the WHO approach to classification modeling and maintenance.

Regionally Compatible Individual Tree Growth Model under the Combined Influence of Environment and Competition.

To explore the effects of competition, site, and climate on the growth of Chinese fir individual tree diameter at breast height (DBH) and tree height (H), a regionally compatible individual tree growth model under the combined influence of environment and competition was constructed. Using continuous forest inventory (CFI) sample plot data from Fujian Province between 1993 and 2018, we constructed an individual tree DBH model and an H model based on re-parameterization (RP), BP neural network (BP), and random forest (RF), which compared the accuracy of the different modeling methods. The results showed that the inclusion of competition and environmental factors could improve the prediction accuracy of the model. Among the site factors, slope position (PW) had the most significant effect, followed by elevation (HB) and slope aspect (PX). Among the climate factors, the highest contribution was made by degree-days above 18 °C (DD18), followed by mean annual precipitation (MAP) and Hargreaves reference evaporation (Eref). The comparison results of the three modeling methods show that the RF model has the best fitting effect. The R2 of the individual DBH model based on RF is 0.849, RMSE is 1.691 cm, and MAE is 1.267 cm. The R2 of the individual H model based on RF is 0.845, RMSE is 1.267 m, and MAE is 1.153 m. The model constructed in this study has the advantages of environmental sensitivity, statistical reliability, and prediction efficiency. The results can provide theoretical support for management decision-making and harvest prediction of mixed uneven-aged forest.

Comparing estimation algorithms for compatible biomass models of Moso Bamboo

Comparing estimation algorithms for compatible biomass models of Moso Bamboo

A Fuzzy-Random Extension of Jamshidian’s Bond Option Pricing Model and Compatible One-Factor Term Structure Models

The primary objective of this paper is to expand Jamshidian’s bond option formula and compatible one-factor term structure models by incorporating the existence of uncertainty in the parameters governing interest-rate fluctuations. Specifically, we consider imprecision in the parameters related to the speed of reversion, equilibrium short-term interest rate, and volatility. To model this uncertainty, we utilize fuzzy numbers, which, in this context, are interpreted as epistemic fuzzy sets. The second objective of this study is to propose a methodology for estimating these parameters based on historical data. To do so, we use the possibility distribution functions capability to quantify imprecise probability distributions. Furthermore, this paper presents an application to the term structure of fixed-income bonds with the highest credit rating in the Euro area. This empirical application allows for evaluating the effectiveness of the fuzzy extension in fitting the dynamics of interest rates and assessing the suitability of the proposed extension.

Assessing the Architectural Compatibility of Urban Buildings With Passive Defense Principles in Eslamshahr City

Background and objective Urban security is one of the important challenges for urban managers and planners. In this regard, the passive defense approach in urbanization and residential constructions is considered by urban management as a way to cope with natural and man-made disasters and manage the crisis. The present study aims to develop a conceptual model for the architectural compatibility of buildings in Eslamshahr County, Tehran, Iran, with passive defense principles by assessing the compatible and non-compatible elements to provide essential strategies. Method This is a quantitative-qualitative study. The participants were 30 experts in the field of urban security in Eslamshahr. The data analysis was done using statistical tests such as binominal test, one sample T-test, exploratory factor analysis, and strengths, weaknesses, opportunities, and threats (SWOT) technique. Results The results indicated that the constructions in Eslamshahr are not so compatible with the passive defense principles; the level of compatibility was low to moderate. Conclusion The conceptual model was based on four main categories, including stimulating factors, developing factors, leading factors, and supportive factors. The relations between these categories were set according to the structure of a car wheel, which can be the basis for using stable and safe architecture in constructions.

Investigation on the Flow Behaviour of Agar Solution and Rheological Modelling

In this study, the effect of shear rate, agar concentration (15-35 kg/m3) and temperature (30-60°C) on the apparent viscosity of the agar solution was investigated. Apparent viscosities at different shear rates were determined using a rotational viscometer. The findings show that the apparent viscosity decreasing with a rise of shear rate increased with increasing concentration. An increase in the tempearture, on the other hand, led to a decrease in the apparent viscosity. To illustrate both temperature and concentration changes together, the Bingham, power law, and Casson models were utilized in choosing the most suitable model. When the models were compared using statistical tests, the most compatible model was found to be the power-law model. The consistency coefficient and flow behaviour index calculated using the power law model showed that agar solutions exhibited shear-thinning flow behavior (pseudoplastic). This study suggests that power law, within the ranges of the temperature and concentration studied could be used to estimate the viscosity of the agar solutions in the applications requiring the knowledge of flow behavior.

MCCP: multi-modal fashion compatibility and conditional preference model for personalized clothing recommendation

MCCP: multi-modal fashion compatibility and conditional preference model for personalized clothing recommendation

Personalized fashion outfit generation with user coordination preference learning

This paper focuses on personalized outfit generation, aiming to generate compatible fashion outfits catering to given users. Personalized recommendation by generating outfits of compatible items is an emerging task in the recommendation community with great commercial value but less explored. The task requires to explore both user-outfit personalization and outfit compatibility, any of which is challenging due to the huge learning space resulted from large number of items, users, and possible outfit options. To specify the user preference on outfits and regulate the outfit compatibility modeling, we propose to incorporate coordination knowledge in fashion. Inspired by the fact that users might have coordination preference in terms of category combination, we first define category combinations as templates and propose to model user-template relationship to capture users’ coordination preferences. Moreover, since a small number of templates can cover the majority of fashion outfits, leveraging templates is also promising to guide the outfit generation process. In this paper, we propose Template-guided Outfit Generation (TOG) framework, which unifies the learning of user-template interaction, user–item interaction and outfit compatibility modeling. The personal preference modeling and outfit generation are organically blended together in our problem formulation, and therefore can be achieved simultaneously. Furthermore, we propose new evaluation protocols to evaluate different models from both the personalization and compatibility perspectives. Extensive experiments on two public datasets have demonstrated that the proposed TOG can achieve preferable performance in both evaluation perspectives, namely outperforming the most competitive baseline BGN by 7.8% and 10.3% in terms of personalization precision on iFashion and Polyvore datasets, respectively, and improving the compatibility of the generated outfits by over 2%.

Multiple imputation of missing data under missing at random: compatible imputation models are not sufficient to avoid bias if they are mis-specified

ObjectivesEpidemiological studies often have missing data, which are commonly handled by multiple imputation (MI). Standard (default) MI procedures use simple linear covariate functions in the imputation model. We examine the bias that may be caused by acceptance of this default option and evaluate methods to identify problematic imputation models, providing practical guidance for researchers. Study Design and SettingUsing simulation and real data analysis, we investigated how imputation model mis-specification affected MI performance, comparing results with complete records analysis (CRA). We considered scenarios in which imputation model mis-specification occurred because (i) the analysis model was mis-specified or (ii) the relationship between exposure and confounder was mis-specified. ResultsMis-specification of the relationship between outcome and exposure, or between exposure and confounder, can cause biased CRA and MI estimates (in addition to any bias in the full-data estimate due to analysis model mis-specification). MI by predictive mean matching can mitigate model mis-specification. Methods for examining model mis-specification were effective in identifying mis-specified relationships. ConclusionWhen using MI methods that assume data are MAR, compatibility between the analysis and imputation models is necessary, but not sufficient to avoid bias. We propose a step-by-step procedure for identifying and correcting mis-specification of imputation models.

Safe learning-based model predictive control using the compatible models approach

In this paper, we introduce a novel approach to safe learning-based Model Predictive Control (MPC) for nonlinear systems. This approach, which we call the “compatible model approach”, relies on computing two models of the given unknown system using data generated from the system. The first model is a set-valued over-approximation guaranteed to contain the system’s dynamics. This model is used to find a set of provably safe controller actions at every state. The second model is a single-valued estimation of the system’s dynamics used to find a controller that minimises a cost function. If the two models are compatible, in the sense that the estimation is included in the over-approximation, we show that we can use the set of safe controller actions to constrain the minimisation problem and guarantee the feasibility and safety of the learning-based MPC controller at all times. We present a method to build an over-approximation for nonlinear systems with bounded derivatives on a partition of the states and inputs spaces. Then, we use piecewise multi-affine functions (defined on the same partition) to calculate a system’s dynamics estimation that is compatible with the previous over-approximation. Finally, we show the effectiveness of the approach by considering a path-planning problem with obstacle avoidance.

Physical implications of the extrapolation and statistical bootstrap of nucleon structure function ratios F2nF2p for mirror nuclei He3 and H3

A nuclear physics example of statistical bootstrap is used on the MARATHON nucleon structure function ratio data in the quark momentum fraction regions ${x}_{B}\ensuremath{\rightarrow}0$ and ${x}_{B}\ensuremath{\rightarrow}1$. The extrapolated ${F}_{2}$ ratio as quark momentum fraction ${x}_{B}\ensuremath{\rightarrow}1$ is $\frac{{F}_{2}^{n}}{{F}_{2}^{p}}\ensuremath{\rightarrow}0.4\ifmmode\pm\else\textpm\fi{}0.05$ and this value is compared to theoretical predictions. The extrapolated ratio when ${x}_{B}\ensuremath{\rightarrow}0$ favors the simple model of isospin symmetry with the complete dominance of sea quarks at low momentum fraction. At high-${x}_{B}$, the proton quark distribution function ratio $d/u$ is derived from the ${F}_{2}$ ratio and found to be $d/u\ensuremath{\rightarrow}1/6$. Our extrapolated values for both the $\frac{{F}_{2}^{n}}{{F}_{2}^{p}}$ ratio and the $d/u$ parton distribution function ratio are within uncertainties of perturbative QCD values from quark counting, helicity conservation arguments, and a Dyson-Schwinger equation with a contact interaction model. In addition, it is possible to match the statistical bootstrap value to theoretical predictions by allowing two compatible models to act simultaneously in the nucleon wave function. One such example is nucleon wave functions composed of a linear combination of a quark-diquark state and a three-valence quark correlated state with coefficients that combine to give the extrapolated ${F}_{2}$ ratio at ${x}_{B}=1$.

Simplified geodata models for integrated urban and public transport planning

Abstract. The current division between urban and transport planning is a significant obstacle to achieving sustainable urban development. To transform cities towards sustainability, both fields must adopt shared or at least compatible models of the urban systems, namely transport, street and public space networks for all users and urban activities. Although several models and tools have emerged in recent years to facilitate this integration, there are still usability gaps that hinder their wider adoption. One of the gaps is a lack of flexibility to operate at different stages of integrated planning. To address this gap, the study aims to develop a set of aligned and flexible multimodal urban network models and tools to support different stages of planning. This paper focuses on the public transport geodata models, which were built by aggregating a General Transit Feed Specification (GTFS) dataset at various spatial and temporal levels. The aggregation levels range from a baseline data model that is useful for detailed planning stages, up to a topological data model that is suitable for macro scale and strategic planning. By using this unified set of models, the dialogue between the two fields at different integrated planning phases can be facilitated, and decision-making can be enhanced.

Calibration strategy of the PROSPECT-II detector with external and intrinsic sources

This paper presents an energy calibration scheme for an upgraded reactor antineutrino detector for the Precision Reactor Oscillation and Spectrum Experiment (PROSPECT). The PROSPECT collaboration is preparing an upgraded detector, PROSPECT-II (P-II), to advance capabilities for the investigation of fundamental neutrino physics, fission processes and associated reactor neutrino flux, and nuclear security applications. P-II will expand the statistical power of the original PROSPECT (P-I) dataset by at least an order of magnitude. The new design builds uponprevious P-I design and focuses on improving the detector robustness and long-term stability to enable multi-year operation at one or more sites. The new design optimizes the fiducial volume by elimination of dead space previously occupied by internal calibration channels, which in turn necessitates the external deployment. In this paper, we describe a calibration strategy for P-II. The expected performance of externally deployed calibration sources is evaluated using P-I data and a well-benchmarked simulation package by varying detector segmentation configurations in the analysis. The proposed external calibration scheme delivers a compatible energy scale model and achieves comparable performance with the inclusion of an additional AmBe neutron source, in comparison to the previous internal arrangement. Most importantly, the estimated uncertainty contribution from the external energy scale calibration model meets the precision requirements of the P-II experiment.

Bayesian learning of coupled biogeochemical–physical models

Predictive dynamical models for marine ecosystems are used for a variety of needs. Due to the sparse measurements and limited understanding of the myriad of ocean processes, there is however significant uncertainty. There is model uncertainty in the parameter values, functional forms with diverse parameterizations, and level of complexity needed, and thus in the state variable fields. We develop a Bayesian model learning methodology that allows interpolation in the space of candidate dynamical models and discovery of new models from noisy, sparse, and indirect observations, all while estimating state variable fields and parameter values, as well as the joint probability distributions of all learned quantities. We address the challenges of high-dimensional and multidisciplinary dynamics governed by partial differential equations (PDEs) by using state augmentation and the computationally efficient Gaussian Mixture Model — Dynamically Orthogonal filter. Our innovations include stochastic formulation parameters and stochastic complexity parameters to unify candidate models into a single general model as well as stochastic expansion parameters within piecewise function approximations to generate dense candidate model spaces. These innovations allow handling many compatible and embedded candidate models, possibly none of which are accurate, and learning elusive unknown functional forms that augment these models. Our new Bayesian methodology is generalizable and interpretable. It seamlessly and rigorously discriminates among existing models, but also extrapolates out of the space of models to discover new ones. We perform a series of twin experiments based on flows past a ridge coupled with three-to-five component ecosystem models, including flows with chaotic advection. We quantify the learning skill, and evaluate convergence and the sensitivity to hyper-parameters. Our PDE framework successfully discriminates among functional forms and model complexities, and learns in the absence of prior knowledge by searching in dense function spaces. The probabilities of known, uncertain, and unknown model formulations, and of biogeochemical–physical fields and parameters, are updated jointly using Bayes’ law. Non-Gaussian statistics, ambiguity, and biases are captured. The parameter values and the model formulations that best explain the noisy, sparse, and indirect data are identified. When observations are sufficiently informative, model complexity and model functions are discovered.

Compatible Truss-Arch Model for Predicting the Shear Strength of Steel Shape-Reinforced Concrete (SRC) Beams

A steel shape-reinforced concrete (SRC) beam, in which a steel profile is encased in an R.C. section, is an essential configuration of steel-concrete composite members. Nevertheless, the precise estimation of shear strength for SRC elements is currently being explored due to the challenges associated with incorporating steel-concrete interaction. This paper establishes a compatible truss-arch model to simulate the shear behavior of SRC beams and predict their maximum shear strength. In the established model, the shear contribution of the R.C. encasement is evaluated using the traditional truss-arch model, and a stress decomposition based on von Mises yielding criterion and strain compatibility is conducted within the steel shape to decouple its shear contribution. Finally, the validity of the proposed model is confirmed using a comprehensive database. The comparison between the experimental and calculated results demonstrates that the established model can effectively and reliably calculate the SRC beams’ shear strength.

ScAnno: a deconvolution strategy-based automatic cell type annotation tool for single-cell RNA-sequencing data sets.

Undoubtedly, single-cell RNA sequencing (scRNA-seq) has changed the research landscape by providing insights into heterogeneous, complex and rare cell populations. Given that more such data sets will become available in the near future, their accurate assessment with compatible and robust models for cell type annotation is a prerequisite. Considering this, herein, we developed scAnno (scRNA-seq data annotation), an automated annotation tool for scRNA-seq data sets primarily based on the single-cell cluster levels, using a joint deconvolution strategy and logistic regression. We explicitly constructed a reference profile for human (30 cell types and 50 human tissues) and a reference profile for mouse (26 cell types and 50 mouse tissues) to support this novel methodology (scAnno). scAnno offers a possibility to obtain genes with high expression and specificity in a given cell type as cell type-specific genes (marker genes) by combining co-expression genes with seed genes as a core. Of importance, scAnno can accurately identify cell type-specific genes based on cell type reference expression profiles without any prior information. Particularly, in the peripheral blood mononuclear cell data set, the marker genes identified by scAnno showed cell type-specific expression, and the majority of marker genes matched exactly with those included in the CellMarker database. Besides validating the flexibility and interpretability of scAnno in identifying marker genes, we also proved its superiority in cell type annotation over other cell type annotation tools (SingleR, scPred, CHETAH and scmap-cluster) through internal validation of data sets (average annotation accuracy: 99.05%) and cross-platform data sets (average annotation accuracy: 95.56%). Taken together, we established the first novel methodology that utilizes a deconvolution strategy for automated cell typing and is capable of being a significant application in broader scRNA-seq analysis. scAnno is available at https://github.com/liuhong-jia/scAnno.

AC Grid Model Order Reduction Based on Interaction Modes Identification in Converter-Based Power Systems

Increasing application of power electronics devices (inverter-based generation and storage, HVDC lines, FACTS devices), facilitated by growing renewable energy integration, has also changed the ac grid modeling features. Namely, the incorporation of fast-acting control devices can lead to possible detrimental interactions that appear at much higher frequencies compared to synchronous generator-based power systems. In this regard, the phasor-based ac grid model is no longer adequate, putting forward the ac line models with extended validity in the frequency range. However, modeling all the lines in a system in high-frequency resolution requires high computational power and is often not necessary either. To overcome this challenge, this paper proposes the methodology for model order reduction (MOR) of an ac grid that enables a systematic identification of more significant lines in the system from the stability perspective and the necessary level of their model complexity. The methodology is established on subsystem participation calculation and the analysis of interaction modes between power electronics devices and a particular line. Thereat, the vector fitting algorithm is employed to obtain the high bandwidth state-space compatible ac line models. The functionalities and effectiveness of the proposed MOR methodology are demonstrated on a 9-bus test system.