Maximal Rules Research Articles

WELFARE ANALYSIS VIA MARGINAL TREATMENT EFFECTS

We consider a causal structure with endogeneity, i.e., unobserved confoundedness, where an instrumental variable is available. In this setting, we show that the mean social welfare function can be identified and represented via the marginal treatment effect as the operator kernel. This representation result can be applied to a variety of statistical decision rules for treatment choice, including plug-in rules, Bayes rules, and empirical welfare maximization rules. Focusing on the application of the empirical welfare maximization framework, we provide convergence rates of the worst-case average welfare loss (regret).

Iterated variable neighborhood search for integrated scheduling of additive manufacturing and multi-trip vehicle routing problem

This study addresses a novel problem structure for integrated scheduling of additive manufacturing and delivery processes. In the manufacturing process, we consider batch production of parts, simulating Selective Laser Melting technology in parallel additive manufacturing machines with heterogeneous specifications. A batch production, referred to as a build, must consist of parts with homogenous material. During the production run, sequence-dependent material setup times occur in between builds. In the delivery process, parts are delivered in batches using a limited number of homogenous capacitated vehicles. Each vehicle might perform multi-trips. Thus, the route for each trip and departure schedule among trips must be decided. In this regard, batch delivery is independent to batch production. The problem objective function is to minimize the total completion time of all parts. For optimally solving the problem, a mixed-integer linear programming model is devised. To solve larger problems sizes under a lower computation time, an iterated variable neighborhood search algorithm with embedded rule-based heuristics is proposed. The computational experiment denotes that the proposed algorithm with route minimization rule effectively solves small-scale problems, while capacity usage maximization rule performs the best in large-scale problems and outperforms other existing metaheuristics. Further result analysis is also performed to provide managerial insights.

The Path Innovation of Regional Economic Growth: Data Analysis Based on China

AbstractInnovative development to some extent overcomes the path-dependent characteristics of knowledge proximity, and the diversity and complexity of regional knowledge and technology foster regional economic growth. This study constructs a dynamic mechanism for maximizing the enterprise profit to explore the conditions enabling regions that can achieve knowledge accumulation and technological leapfrogging. Utilizing the fitness landscape methodology based on customs data from 30 provinces in China and employing genetic algorithms, we analyze two modes of enterprises with similar and different initial conditions under varying leapfrogging scenarios. By applying profit maximization rules within the fitness landscape of the product space, we examine how to promote regional industrial transformation and upgrade. The results indicate that a larger leap range in the production of products by enterprises, reflecting stronger innovative capabilities, significantly enhances the region’s ability for industrial transformation and upgrade. There is a strong positive relationship between the initial industrial foundation and regional economic development. When regions share a similar industrial base, the complexity of regional knowledge becomes more critical. Therefore, an increase in the foundational development of industries and technology-intensive products can enhance the potential for regional path innovation. Past research has primarily been based on the path dependence theory, emphasizing that the most crucial aspect for underdeveloped economies is the accumulation and development of foundational capabilities. However, our study contends that, for emerging economies, it is crucial to enhance the diversity and complexity of knowledge and technology. Particularly, it is essential to, based on a comprehensive understanding of their own strengths, open up new areas and new arenas in development, continually shaping new growth drivers and new strengths. This emerges as a significant factor in regional growth and leapfrog development.

Particle swarm optimization for integrated scheduling problem with batch additive manufacturing and batch direct-shipping delivery

This study addresses a novel proRblem for the integrated scheduling of additive manufacturing and delivery processes with the objective of minimizing the total weighted completion time of the ordered parts. In the manufacturing process, batch production on non-identical parallel additive manufacturing machines is considered. A single batch of production, which is referred to as a build, consists of parts requiring the same material. Sequence-dependent setup times occur between two adjacent builds. Once the production run is completed, the parts in the builds are regrouped into delivery batches within the same customer. In the delivery process, parts are delivered by homogeneous vehicles to the corresponding customers through direct-shipping method. To optimally solve the small problem, a mixed-integer linear programming model is formulated. For solving large-scale problems, three rule-based heuristics that are embedded in particle swarm optimization (PSO) are proposed. A tight lower bound is developed to evaluate the performance of the proposed algorithms. The computational experiment shows that the proposed PSO with delivery waiting time minimization rule is effective to solve small-scale problems, while in large-scale problems, capacity usage maximization rule performs the best and both outperform another existing metaheuristic. Several sensitivity analyses are also performed to provide additional managerial insights.

MCoR-Miner: Maximal Co-Occurrence Nonoverlapping Sequential Rule Mining

The aim of sequential pattern mining (SPM) is to discover potentially useful information from a given sequence. Although various SPM methods have been investigated, most of these focus on mining all of the patterns. However, users sometimes want to mine patterns with the same specific prefix pattern, called co-occurrence pattern. Since sequential rule mining can make better use of the results of SPM, and obtain better recommendation performance, this paper addresses the issue of maximal co-occurrence nonoverlapping sequential rule (MCoR) mining and proposes the MCoR-Miner algorithm. To improve the efficiency of support calculation, MCoR-Miner employs depth-first search and backtracking strategies equipped with an indexing mechanism to avoid the use of sequential searching. To obviate useless support calculations for some sequences, MCoR-Miner adopts a filtering strategy to prune the sequences without the prefix pattern. To reduce the number of candidate patterns, MCoR-Miner applies the frequent item and binomial enumeration tree strategies. To avoid searching for the maximal rules through brute force, MCoR-Miner uses a screening strategy. To validate the performance of MCoR-Miner, eleven competitive algorithms were conducted on eight sequences. Our experimental results showed that MCoR-Miner outperformed other competitive algorithms, and yielded better recommendation performance than frequent co-occurrence pattern mining. All algorithms and datasets can be downloaded from <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/wuc567/Pattern-Mining/tree/master/MCoR-Miner</uri> .

Values of Travel Time for Recreational Trips under Different Behavioural Rules

In this study, discrete choice models that combine different behavioural rules are estimated to study the visitors’ preferences in relation to their travel mode choices to access a national park. Using a revealed preference survey conducted on visitors of Teide National Park (Tenerife, Spain), we present a hybrid model specification—with random parameters—in which we assume that some attributes are evaluated by the individuals under conventional random utility maximization (RUM) rules, whereas others are evaluated under random regret minimization (RRM) rules. We then compare the results obtained using exclusively a conventional RUM approach to those obtained using both RUM and RRM approaches, derive monetary valuations of the different components of travel time and calculate direct elasticity measures. Our results provide useful instruments to evaluate policies that promote the use of more sustainable modes of transport in natural sites. Such policies should be considered as priorities in many national parks, where negative transport externalities such as traffic congestion, pollution, noise and accidents are causing problems that jeopardize not only the sustainability of the sites, but also the quality of the visit.

Skin detection in video under uncontrolled illumination

Many vision-based human-computer interaction (HCI) applications require skin detection. However, their performance relies on accuracy in detecting skin regions in video, which is difficult under uncontrolled illumination. The chromatic appearance of skin changes because of shading, often caused by body movement. To address this, we propose a dynamic adaptation method to detect skin regions affected by local color deformations. Static and dynamic skin regions are detected by a corresponding module. The static module includes a facial skin distribution model (FSDM) and a fusion-based background distribution model (FBDM). The FBDM is obtained from a local background distribution model (LBDM) and a global background distribution model (GBDM). The LBDM is obtained by comparing a frame pixel distribution model with the FSDM and GBDM. Next, the FBDM is derived from the LBDM and the GBDM. The dynamic module includes a moving skin distribution model (MSDM), derived from a set of moving skin samples. Initially, moving skin regions are detected using a modified double frame-difference method and then modeled using a Gaussian mixture model. To avoid misidentifying background regions as skin, the final MSDM is obtained by comparing the initial moving skin model to the FSDM and FBDM. Finally, the static and the dynamic models are fused by applying a maximization rule. Experimental results shows that the proposed method can detect skin regions more accurately than state-of-the-art methods.

Model Selection for Treatment Choice: Penalized Welfare Maximization

This paper studies a penalized statistical decision rule for the treatment assignment problem. Consider the setting of a utilitarian policy maker who must use sample data to allocate a binary treatment to members of a population, based on their observable characteristics. We model this problem as a statistical decision problem where the policy maker must choose a subset of the covariate space to assign to treatment, out of a class of potential subsets. We focus on settings in which the policy maker may want to select amongst a collection of constrained subset classes: examples include choosing the number of covariates over which to perform best‐subset selection, and model selection when approximating a complicated class via a sieve. We adapt and extend results from statistical learning to develop the Penalized Welfare Maximization (PWM) rule. We establish an oracle inequality for the regret of the PWM rule which shows that it is able to perform model selection over the collection of available classes. We then use this oracle inequality to derive relevant bounds on maximum regret for PWM. An important consequence of our results is that we are able to formalize model‐selection using a “holdout” procedure, where the policy maker would first estimate various policies using half of the data, and then select the policy which performs the best when evaluated on the other half of the data.

Intrapersonal heterogeneity in car-sharing decision-making processes by activity-travel contexts: A context-dependent latent class random utility – random regret model

This paper investigates stated preferences for car-sharing in the context of travel mode choice under conditions of uncertain travel times. In particular, the study focuses on heterogeneity in the decision-making process due to different activity-travel contexts such as time pressure, activity duration, and uncertain travel times. It is assumed that the contextual factors influence the decision rules that potential users adopt regarding the use of a shared car for a specific trip purpose. A context-dependent latent class model allowing for the random panel effect is estimated, in which the latent classes respectively represent utility maximization and regret minimization decision rules. The model employs the contextual factors as covariates of class membership probability. A stated choice experiment, in which car-sharing, own car and public transport serve as the choice alternatives, is used to estimate the model. Results indicate that the activity-travel contexts play an important role in accounting for heterogeneity in decision rules.

Noise-robust image fusion with low-rank sparse decomposition guided by external patch prior

It is challenging to simultaneously achieve noise suppression and fine detail preservation in noisy image fusion. To address this challenge, we propose a novel strategy for noisy image fusion. Assuming that an image can be modeled as a superposition of low-rank and sparse (LR-and-S) components, we develop a novel discriminative dictionary learning algorithm to construct two dictionaries so as to decompose the input image into LR-and-S components. Specifically, to make dictionary possess discriminative power, we enforce spatial morphology constraint on each dictionary. Furthermore, we develop within-class consistency constraint by exploiting the similarity of low-rank components and impose it on the coding coefficients to further improve the discriminative power of the learned dictionary. In image decomposition, external patch prior and internal self-similarity prior of an image are exploited to build image decomposition model, based on which the latent subspace for fusion and recovery is estimated by minimizing rank-regularization of the subspace learned via clustering of similar patches. To construct different components of fused result, we use l1-norm maximization rule to fuse the decomposed components. Finally, the fused image is obtained by adding the fused components together. Experiments demonstrate that our method outperforms several state-of-the-art methods in terms of both objective quality assessment and subjective visual perception.

The wealth of nations and economic governance, international evidence

The paper suggests that wealth maximization and sustainability model (WMS) conforms to the basic maximization rule advocated by finance theory and they can therefore be dealt with within the same theoretical framework. A nation can develop and create wealth to the maximum of its potential only when it efficiently manages its development, i.e. when it observes the rule of national wealth maximization. Wealth is increasing unevenly around the world, as is the quality of living conditions. This prompts the following questions: What drives wealth maximization and sustainability (WMS), and how can it be made to last? To answer these two questions thepaper suspects economic governance to impact tremendously wealth creation and accumulation, and tests its effect using a sample of 40 rich countries for the year 2019. It reveals important findings that make it possible to sketch a model of wealth maximization and sustainability.

Nonlinear Endmember Identification for Hyperspectral Imagery via Hyperpath-Based Simplex Growing and Fuzzy Assessment

Nonlinear geometric manifold of hyperspectral data usually makes great trouble for accurate endmember extraction in literature. To address this issue, we propose a novel nonlinear endmember extraction algorithm by building a hypergraph and a fuzzy assessment strategy. The global change of nonlinear data manifold is first measured in a hypergraph whose hyperedges correspond to different local pixel subgroups. In contrast to edges in a simple graph, every hyperpath connected by multiple hyperedges instead of individual pixels effectively facilitates the determination of the simplex spanned by endmembers on complex data manifolds interfered by noises and outliers. Furthermore, in the hypergraph-based manifold system, a reliable fuzzy assessment mechanism for extracting the final endmembers is established by combining the classic simplex volume maximization rule with the inherent properties of hyperspectral data and hypergraph. In this procedure, the impact of outliers and the complex geometric structures of data can be further effectively reduced, leading to better unmixing results. Experimental results of various types of simulated data and real hyperspectral imagery demonstrate that the proposed algorithm (hypergraph and fuzzy-assessment-based nonlinear endmember extraction) can extract more accurate endmembers from complex data manifolds, and it is more robust to noises and outliers compared with state-of-the-art algorithms.

A User-Oriented Pricing Design for Demand Response in Smart Grid

Demand response (DR) programs are designed to affect the energy consumption behavior of end-users in smart grid. However, most existing pricing designs for DR programs ignore the influence of end-users’s diversity and personal preference. Thus, in this paper, we investigate an incentive pricing design based on the utility maximization rule with consideration of end-users’ preference and appliances’ operational patterns. In particular, the utility company determines the pricing policy by trading off the budget revenue and social obligation, while each end-user aims to maximize their own utility profits with high satisfaction level by scheduling multiclass appliances. We formulate the conflict and cooperative relationship between the utility company and end-users as a Stackelberg game, and the equilibrium points are obtained by the backward induction method, which exists and is unique. At the equilibrium, the utility company adopts real-time pricing (RTP) scheme to coordinate end-users to fulfill the benefit of themselves, i.e., under such price, end-users automatically maximize overall utility profits of the overall system. We propose a distributed algorithm and an adaptive pricing scheme for the utility company and end-users to jointly achieve the best performance of the entire system. Finally, extensive simulation results based on real operation data show the effectiveness of the proposed scheme.

Automatic Emotion Perception Using Eye Movement Information for E-Healthcare Systems.

Facing the adolescents and detecting their emotional state is vital for promoting rehabilitation therapy within an E-Healthcare system. Focusing on a novel approach for a sensor-based E-Healthcare system, we propose an eye movement information-based emotion perception algorithm by collecting and analyzing electrooculography (EOG) signals and eye movement video synchronously. Specifically, we extract the time-frequency eye movement features by firstly applying the short-time Fourier transform (STFT) to raw multi-channel EOG signals. Subsequently, in order to integrate time domain eye movement features (i.e., saccade duration, fixation duration, and pupil diameter), we investigate two feature fusion strategies: feature level fusion (FLF) and decision level fusion (DLF). Recognition experiments have been also performed according to three emotional states: positive, neutral, and negative. The average accuracies are 88.64% (the FLF method) and 88.35% (the DLF with maximal rule method), respectively. Experimental results reveal that eye movement information can effectively reflect the emotional state of the adolescences, which provides a promising tool to improve the performance of the E-Healthcare system.

Multimodal medical image fusion using multi-resolution transform

Multimodal image fusion is used to enhance the quality of fused image in medical diagnosis by combining two images 'same scene' from different techniques, such as Computed Tomography (CT) scan or Magnetic Resonance Imaging (MRI) with Positron Emission Tomography (PET). In this paper, we present a hybrid technique using non-subsampled contourlet for contrast-enhancement to balance the requirement of local and global contrast enhancements of each input image appearance and biorthogonal CDF9/7 wavelet based on lifting scheme. Then the low frequency sub-bands are combined by a maximal selection rule and the high frequency sub-bands are combined by a maximal spatial frequency. In this technique, the proposed scheme is free from the common shortcomings of the state-of-the-art MIF techniques: contrast reduction, loss of image fine details and unwanted image degradations etc. Subjective and objective evaluations show better performance of this new approach compared to existing techniques. This shows that the quality of fused image was better compared with existing methods in case of hybrid method.

Rate maximization and hyperbolic discounting in human experiential intertemporal decision making

Decisions between differently timed outcomes are a well-studied topic in as diverse academic disciplines as economics, psychology, and behavioral ecology. Humans and other animals have been shown to make these intertemporal choices by hyperbolically devaluing rewards as a function of their delays (“delay discounting”), thus often deemed to behave myopically. In behavioral ecology, however, intertemporal choices are assumed to meet optimization principles, that is, the maximization of energy or reward rate. Thus far, it is unclear how different approaches assuming these 2 currencies, reward devaluation and reward rate maximization, could be reconciled. Here, we investigated the degree at which humans (N = 81) discount reward value and maximize reward rate when making intertemporal decisions. We found that both hyperbolic discounting and rate maximization well approximated the choices made in a range of different intertemporal choice design conditions. Notably, rate maximization rules provided even better fits to the choice data than hyperbolic discounting models in all conditions. Interestingly, in contrast to previous findings, rate maximization was universally observed in all choice frames, and not confined to foraging settings. Moreover, rate maximization correlated with the degree of hyperbolic discounting in all conditions. This finding is in line with the possibility that evolution has favored hyperbolic discounting because it subserves reward rate maximization by allowing for flexible adjustment of preference for smaller, sooner or larger, later rewards. Thus, rate maximization may be a universal principle that has shaped intertemporal decision making in general and across a wide range of choice problems.

Small asynchronous P systems with inhibitors defining non-semilinear sets

The objective of this work is to present concrete membrane systems generating non-semilinear sets that are small in the following sense: Attention is paid to such parameters of descriptional complexity as the alphabet size, the number of rules, the total number of inhibitors used, and the maximal rule size. A total of 54 systems is described, depending on the exact goal; the presented systems for the same goal are incomparable.

New Precision Metrics for Contrast Sensitivity Testing.

Visual sensitivity is comprehensively described by the contrast sensitivity function (CSF), but current routine clinical care does not include its assessment because of the time-consuming need to estimate thresholds for a large number of spatial frequencies. The quick CSF method, however, dramatically reduces testing times by using a Bayesian information maximization rule. We evaluate the test-retest variability of a tablet-based quick CSF implementation in a study with 100 subjects who repeatedly assessed their vision with and without optical correction. We first discuss two commonly used measures of repeatability, intraclass correlation and the Bland-Altman Coefficient of Repeatability, and show that they are vulnerable to artifacts. Instead, we propose to formulate precision as an information retrieval task: from all repeat test scores, can we retrieve a certain individual based on their first test score? We then use rank-based analyses such as mean average precision as a better measure to compare different test metrics, and show that the highest test-retest precision is achieved using a summary statistic, the area under the log CSF (AULCSF). This demonstrates the benefit of assessment of the whole CSF compared to sensitivity at individual spatial frequencies only. AULCSF also yields best discrimination performance (99.2%) between measurements that were taken with and without glasses, respectively, even better than CSF Acuity. The tablet-based quick CSF thus enables the rapid and reliable home monitoring of visual function, which has the potential to improve early diagnosis and treatment of ophthalmic pathologies such as diabetic retinopathy or age-related macular degeneration.

Classification rules based on distribution functions of functional depth

In ordering multivariate objects, the use of data depth provides a centre-outward ranking. The notion of data depth has been extended to functional data setting and applied in classifying functional data, for example maximal depth classification rules. In this paper, we explore notions of functional depth and propose a classification method based on distribution functions of data depth for functional data. The performance of this method is examined by using simulations and real data sets and the results are compared with the results from existing methods.

An introductory microeconomics in-class experiment to reinforce the marginal utility/price maximization rule and the integration of modern theory

This paper presents an in-class experiment used as a teaching tool in an introductory microeconomics class at the undergraduate college level. It is directed at a critical but challenging concept for principles students—constrained utility maximization and a methodology to intuit preferences. The experimental project is nested in the literature pertaining to the current transition in microeconomic theory motivated by contributions from behavioral economics and transactions-cost economics, among other elements; modern pedagogical models; experimental economics; and experiments as in-classroom teaching tools. While not dispositive as to the general efficacy of in-class experiments, the paper provides an example of an alternative instructional approach which is helpful to principles students under strictly defined protocols. The benefits to students include heightened understanding of the core subject topic, greater interest in the subject matter, a closer connection to real-world economics, and enhanced critical thinking capabilities.