Restrictive Assumptions Research Articles

On The Assembly Line Balancing Problem: A Simplified Perspective With The Precedence Matrix

The assembly line balancing problem (ALBP) has been an attractive research area for decades; however, the industrial application of the research findings remains limited. This can be attributed to the complexity of solution methods, restrictive assumptions, and the numerous variants of the problem in the real-world settings. This article describes using the precedence matrix as the basis for developing simplified, more practical analysis frameworks for the ALBP. We introduce algorithms to construct the precedence matrix from basic assembly problem data and implement it within a spreadsheet model, and to utilize this matrix in assigning the assembly tasks to workstations while ensuring assignment feasibility. The structure and potential uses of the precedence matrix-based framework are presented in detail. Furthermore, we compare selected line balancing priority rules using the proposed framework to demonstrate its effectiveness. The results suggest that the precedence matrix model is a straightforward, efficient tool for line balancing analysis, that can offer substantial support to both industry practitioners and academic researchers.

Stochastic First-Order Methods for Average-Reward Markov Decision Processes

We study average-reward Markov decision processes (AMDPs) and develop novel first-order methods with strong theoretical guarantees for both policy optimization and policy evaluation. Compared with intensive research efforts in finite sample analysis of policy gradient methods for discounted MDPs, existing studies on policy gradient methods for AMDPs mostly focus on regret bounds under restrictive assumptions, and they often lack guarantees on the overall sample complexities. Toward this end, we develop an average-reward stochastic policy mirror descent method for solving AMDPs with and without regularizers and provide convergence guarantees in terms of the long-term average reward. For policy evaluation, existing on-policy methods suffer from suboptimal convergence rates as well as failure in handling insufficiently random policies due to the lack of exploration in the action space. To remedy these issues, we develop a variance-reduced temporal difference (VRTD) method with linear function approximation for randomized policies along with optimal convergence guarantees, and design an exploratory VRTD method that resolves the exploration issue and provides comparable convergence guarantees. By combining the policy evaluation and policy optimization parts, we establish sample complexity results for solving AMDPs under both generative and Markovian noise models. It is worth noting that when linear function approximation is utilized, our algorithm only needs to update in the low-dimensional parameter space, and thus can handle MDPs with large state and action spaces. Funding: T. Li and G. Lan were supported in part by the National Science Foundation [Grant CCF-1909298] and the Office of Navel Research [Grant N00014-20-1-2089].

Handling Distinct Correlated Effects with CCE

AbstractThe common correlated effects (CCE) approach by Pesaran is a popular method for estimating panel data models with interactive effects. Due to its simplicity, i.e., unobserved common factors are approximated with cross‐section averages of the observables, the estimator is highly flexible and lends itself to a wide range of applications. Despite such flexibility, however, the properties of CCE estimators are typically only examined under the restrictive assumption that all the observed variables load on the same set of factors, which ensures joint identification of the factor space. In this article, we take a different perspective, and explore the empirically relevant case where the dependent and explanatory variables are driven by distinct but correlated factors. Hence, we consider the case of Distinct Correlated Effects. Such settings can be argued to be relevant for practice, for instance in studies linking economic growth to climatic variables. In so doing, we consider panel dimensions such that as , which is known to induce an asymptotic bias for the pooled CCE estimator even under the usual common factor assumption. We subsequently develop a robust bootstrap‐based toolbox that enables asymptotically valid inference in both homogeneous and heterogeneous panels, without requiring knowledge about whether factors are distinct or common.

VSS-Hi-C: variance-stabilized signals for chromatin contacts.

The genome-wide chromosome conformation capture assay Hi-C is widely used to study chromatin 3D structures and their functional implications. Read counts from Hi-C indicate the strength of chromatin contact between each pair of genomic loci. These read counts are heteroskedastic: that is, a difference between the interaction frequency of 0 and 100 is much more significant than a difference between the interaction frequency of 1000 and 1100. This property impedes visualization and downstream analysis because it violates the Gaussian variable assumption of many computational tools. Thus heuristic transformations aimed at stabilizing the variance of signals like the shifted-log transformation are typically applied to data before its visualization and inputting to models with Gaussian assumption. However, such heuristic transformations cannot fully stabilize the variance because of their restrictive assumptions about the mean-variance relationship in the data. Here, we present VSS-Hi-C, a data-driven variance stabilization method for Hi-C data. We show that VSS-Hi-C signals have a unit variance improving visualization of Hi-C, for example in heatmap contact maps. VSS-Hi-C signals also improve the performance of subcompartment callers relying on Gaussian observations. VSS-Hi-C is implemented as an R package and can be used for variance stabilization of different genomic and epigenomic data types with two replicates available. https://github.com/nedashokraneh/vssHiC.

Hybrid metaheuristic algorithm methods and econometric models in prediction of dogecoin price

Purpose This paper aims to predict Dogecoin price by using artificial intelligence (AI) methods and comparing the results with the econometrics models. Design/methodology/approach An artificial neural network (ANN) was applied as a prediction method without any optimization techniques. Additionally, the genetic algorithm (GA) is used to select the most appropriate input variables. Additionally, based on the literature review and the relationships between crypto-price and global indices, 20 economic indicators, such as Coinbase Bitcoin, Coinbase Litecoin and US dollars, along with main global stock indices such as FTSE100 and NIFTY50, are identified as input variables for the model. Lichtenberg algorithm (LA) and aquila optimization (AO) algorithm are used to make the ANN more robust. To validate our algorithms, they have been implemented on daily data for the last three years. To demonstrate the superiority of the models over traditional methods such as econometrics, regression analysis and curve fitting techniques are used. The effectiveness of these models is then evaluated and compared using criteria such as recall, accuracy and precision. Findings The results indicate that AI-based algorithms not only enhance the accuracy, recall and precision of calculations but also expedite the process without requiring the numerous and restrictive assumptions associated with time series and econometric models. Originality/value The main contribution of this paper is the application of novel approaches such as AO and LA to improve the predictive capabilities of the ANN method for various cryptocurrencies’ prices. It demonstrates the superiority of the proposed algorithms over traditional econometric models using real-life data.

Improved Hille-Type and Ohriska-Type Criteria for Half-Linear Third-Order Dynamic Equations

In this paper, we examine the oscillatory behavior of solutions to a class of half-linear third-order dynamic equations with deviating arguments α2(η)ϕδ2α1ηϕδ1uΔ(η)ΔΔ+p(η)ϕδu(g(η))=0, on an arbitrary unbounded-above time scale T, where η∈[η0,∞)T:=[η0,∞)∩T, η0≥0, η0∈T and ϕζ(w):=wζsgnw, ζ>0. Using the integral mean approach and the known Riccati transform methodology, several improved Hille-type and Ohriska-type oscillation criteria have been derived that do not require some restrictive assumptions in the relevant results. Illustrative examples and conclusions show that these criteria are sharp for all third-order dynamic equations compared to the previous results in the literature.

Machine Learning Enabled Operando Optical Microscopy for Determination of Lithium Transport in Battery Electrodes

Diffusion coefficients of electrode materials are often determined using galvanostatic (GITT) or potentiostatic intermittent titration technique (PITT), electrochemical impedance spectroscopy (EIS) or cyclic voltammetry (CV). However, these methods require special care, as each of their formal derivations use quite restrictive assumptions. As an alternative, a machine learning model is presented to extend a previously proposed optical method of studying lithium transport by operando microscopy. The herein reported model enables the measurement of concentration-dependent diffusion coefficients in a wide solubility range. For this purpose, a python code is developed that determines concentration profiles from RGB images using support vector regression (SVR), a flexible machine learning tool. To evaluate the diffusion coefficient, an inverse Boltzmann-Matano concept is applied. Representing the diffusion coefficient with generalized Redlich-Kister polynomials, concentration profiles are predicted and fit to the measured data. The method is demonstrated here on the example of delithiation of LiMn2O4, but it can, in-principle, be extended to any other battery material showing significant optical response on lithiation, which most of them do.

On Rosenbaum’s Rank-based Matching Estimator

Summary In two influential contributions, Rosenbaum (2005, 2020a) advocated for using the distances between component-wise ranks, instead of the original data values, to measure covariate similarity when constructing matching estimators of average treatment effects. While the intuitive benefits of using covariate ranks for matching estimation are apparent, there is no theoretical understanding of such procedures in the literature. We fill this gap by demonstrating that Rosenbaum’s rank-based matching estimator, when coupled with a regression adjustment, enjoys the properties of double robustness and semiparametric efficiency without the need to enforce restrictive covariate moment assumptions. Our theoretical findings further emphasize the statistical virtues of employing ranks for estimation and inference, more broadly aligning with the insights put forth by Peter Bickel in his 2004 Rietz lecture (Bickel, 2004).

Measuring the evolution of n‐dimensional environmental niches

The study of species' environmental niches underpins numerous questions in ecology and evolution and has increasing relevance in a rapidly changing world. Environmental niches, characterized by observations of organisms, inform about a species' specialization in multivariate environment space and help assess their exposure and sensitivity to changing conditions. Environmental niches are also the central concept behind species distribution models (SDMs), which quantify and predict the geographic variation in environmental suitability. Despite the clear role of past evolutionary processes in shaping contemporary biodiversity distribution, the assessment of multivariate or n‐dimensional (where n is the number of environmental axes) niches in a phylogenetic framework has remained limited and constrained by restrictive assumptions. This hampers important existing and emerging applications, such as assessments of niche conservatism, estimates of species' adaptive potential under changing climates, and prediction of niches in less‐studied parts of the tree of life. Here, we introduce a framework that extends SDMs to estimate n‐dimensional environmental niches jointly with underlying evolutionary processes. Specifically, we fit the relationship between niche similarity and phylogenetic distance as a latent Gaussian process across all species in a clade. We demonstrate mathematically how the parameters of the Gaussian process can be linked to existing traditional evolutionary models. Simulations indicate that the approach successfully recovers niche and evolutionary parameters. Applied to two clades of hummingbirds, the presented joint framework uncovers the relationships among species' niches in phylogenetic space and supports the quantification and hypothesis testing of niche evolution. A key advantage of the presented framework is its joint estimation of the evolutionary process alongside niches directly from species observations with uncertainty propagated to evolutionary model parameters. The proposed approach has the potential to increase the robustness of inference about niche evolution and improve understanding of how the processes of niche formation and evolution interact.

On the input‐output decoupling of Boolean control networks by state feedback

AbstractWe propose a new state feedback‐based input‐output‐decoupling approach for Boolean control networks. A constructive necessary and sufficient condition, based on some appropriately constructed input‐output‐decoupling matrices, is established for the design of the input‐output‐decoupling state feedback gain. The proposed approach does not rely on the restrictive assumption of the existence of the input‐output decomposed form which is instrumental in the very few existing state‐feedback‐based input‐output‐decoupling approaches in the literature. Finally, a numerical example is provided to illustrate the effectiveness of the proposed approach.

Scaling Terrain-Aware Spatial Machine Learning for Flood Mapping on Large Scale Earth Imagery Data

The accurate and prompt mapping of flood-affected regions is important for effective disaster management, including damage assessment and relief efforts. While high-resolution optical imagery from satellites during disasters presents an opportunity for automated flood inundation mapping, existing segmentation models face challenges due to noises like cloud cover and tree canopies. Thanks to the digital elevation model (DEM) data readily available from sources such as United States Geological Survey (USGS), terrain guidance was utilized by recent graphical models such as hidden Markov trees (HMTs) to improve segmentation quality. Unfortunately, these methods either can only handle a small area where water levels at different locations are assumed to be consistent, or require restricted assumptions such as there is only one river channel. This paper presents an algorithm for flood extent mapping on large-scale Earth imagery, applicable to a large geographic area with multiple river channels. Since water level can vary a lot from upstream to downstream, we propose to detect river pixels in order to partition the remaining pixels into localized zones, each with a unique water level. In each zone, water at all locations flow to the same river entry point. Pixels in each zone are organized by an HMT to capture water flow directions guided by elevations. Moreover, a novel regularization scheme is designed to enforce inter-zone consistency by penalizing pixel-pairs of adjacent zones that violate terrain guidance. Efficient parallelization is made possible by coloring the zone adjacency graph to identify zones and zone-pairs that have no dependency and hence can be processed in parallel, and incremental one-pass terrain-guided scanning is conducted wherever applicable to reuse computations. Experiments demonstrate that our solution is more accurate than existing solutions, and can efficiently and accurately map out flooding pixels in a giant area of size 24,805 × 40,129. Despite the imbalanced workloads caused by a few large zonal HMTs dominating the serial computing time, our parallelization approach is effective and manages to achieve up to 14.3 × speedup on a machine with Intel Xeon Gold 6126 CPU @ 2.60GHz (24 cores, 48 threads) using 32 threads.

Existence and stability of a weakly damped laminated beam with a nonlinear delay

AbstractA weakly damped laminated beam system with nonlinear time delay is studied. The existence and uniqueness are proved by Faedo–Galerkin approach. We prove that the system is stable under some specific conditions on the weight of the delay and the equal wave speeds of propagation. The general energy decay rate is established by using multiplier method and some properties of convex functions. This decay result is obtained without imposing any restrictive growth assumption on the damping term at the origin. In addition, our result improves and develops some existing results in the literature.

GLS under monotone heteroskedasticity

GLS under monotone heteroskedasticity

The Microeconomics of Agricultural Development: Risk, Institutions, and Agricultural Policy

Assertions of pervasive inefficiency in the behavior and organization of developing agriculture are found to be based on unsound methodologies. Models apparently based on expected utility theory are theoretically flawed and use highly restrictive assumptions that make them largely irrelevant for explaining actual decisions. When a more appropriate model is applied to the case of the green revolution in the Philippines, the hypothesis that loss aversion impedes adoption of new technology is rejected. Common assertions about the inefficiency of agricultural institutions are also found wanting. The risk-bearing theory of share-tenancy, which is thought to imply inefficiency, cannot explain observed tenant shares. Once the disadvantages of fixed-lease contracts are recognized, sharing is plausibly second-best efficient. The purported inefficiency implied by the inverse relationship between farm size and yield per hectare also dissipates once the endogeneity of farm size is accounted for.

Robust approximation of the conditional mean for applications of Machine Learning

Robust approximation of the conditional mean for applications of Machine Learning

Estimating Time-Varying Exposure Effects Through Continuous-Time Modelling in Mendelian Randomization.

Mendelian randomization is an instrumental variable method that utilizes genetic information to investigate the causal effect of a modifiable exposure on an outcome. In most cases, the exposure changes over time. Understanding the time-varying causal effect of the exposure can yield detailed insights into mechanistic effects and the potential impact of public health interventions. Recently, a growing number of Mendelian randomization studies have attempted to explore time-varying causal effects. However, the proposed approaches oversimplify temporal information and rely on overly restrictive structural assumptions, limiting their reliability in addressing time-varying causal problems. This article considers a novel approach to estimate time-varying effects through continuous-time modelling by combining functional principal component analysis and weak-instrument-robust techniques. Our method effectively utilizes available data without making strong structural assumptions and can be applied in general settings where the exposure measurements occur at different timepoints for different individuals. We demonstrate through simulations that our proposed method performs well in estimating time-varying effects and provides reliable inference when the time-varying effect form is correctly specified. The method could theoretically be used to estimate arbitrarily complex time-varying effects. However, there is a trade-off between model complexity and instrument strength. Estimating complex time-varying effects requires instruments that are unrealistically strong. We illustrate the application of this method in a case study examining the time-varying effects of systolic blood pressure on urea levels.

Advantages of Accounting for Stochasticity in the Premium Process

In this paper, we study a risk model with stochastic premium income and its impact on solvency risk management. It is assumed that both the premium arrival process and the claim arrival process are modelled by homogeneous Poisson processes, and that the premium amounts are modelled by independent and identically distributed random variables. While this model has been studied in the existing literature and certain explicit results are known under more restrictive assumptions, these results are relatively difficult to apply in practice. In this paper, we investigate the factors that differentiate this model and the classical risk model. After reviewing various known results of this model, we derive a simulation approach for obtaining the probability of ultimate ruin based on importance sampling, which does not require specific distributions for the premium and the claim. We demonstrate this approach first with examples where the distribution of the sampling random variable can be identified. We then provide additional examples where we use the fast Fourier transform to obtain an approximation of the sampling random variable. The simulated results are compared with the known results for the probability of ruin. Using the simulation approach, we apply this model to a real-life auto-insurance data set. Differences with the classical model are then discussed. Finally, we comment on the suitability and impact of using this model in the context of solvency risk management.

Temporal generative models for learning heterogeneous group dynamics of ecological momentary assessment data.

One of the goals of precision psychiatry is to characterize mental disorders in an individualized manner, taking into account the underlying dynamic processes. Recent advances in mobile technologies have enabled the collection of ecological momentary assessments that capture multiple responses in real-time at high frequency. However, ecological momentary assessment data are often multi-dimensional, correlated, and hierarchical. Mixed-effect models are commonly used but may require restrictive assumptions about the fixed and random effects and the correlation structure. The recurrent temporal restricted Boltzmann machine (RTRBM) is a generative neural network that can be used to model temporal data, but most existing RTRBM approaches do not account for the potential heterogeneity of group dynamics within a population based on available covariates. In this paper, we propose a new temporal generative model, the HDRBM, to learn the heterogeneous group dynamics and demonstrate the effectiveness of this approach on simulated and real-world ecological momentary assessment datasets. We show that by incorporating covariates, HDRBM can improve accuracy and interpretability, explore the underlying drivers of the group dynamics of participants, and serve as a generative model for ecological momentary assessment studies.

Sensitive periods and other timing hypotheses in developmental psychopathology: A tutorial

Abstract Researchers often aim to assess whether repeated measures of an exposure are associated with repeated measures of an outcome. A question of particular interest is how associations between exposures and outcomes may differ over time. In other words, researchers may seek the best form of a temporal model. While several models are possible, researchers often consider a few key models. For example, researchers may hypothesize that an exposure measured during a sensitive period may be associated with repeated measures of the outcome over time. Alternatively, they may hypothesize that the exposure measured immediately before the current time period may be most strongly associated with the outcome at the current time. Finally, they may hypothesize that all prior exposures are important. Many analytic methods cannot compare and evaluate these alternative temporal models, perhaps because they make the restrictive assumption that the associations between exposures and outcomes remains constant over time. Instead, we provide a tutorial describing four temporal models that allow the associations between repeated measures of exposures and outcomes to vary, and showing how to test which temporal model is best supported by the data. By finding the best temporal model, developmental psychopathology researchers can find optimal windows for intervention.

On a subclass of alpha-quasi convex mappings

On a subclass of alpha-quasi convex mappings