Different Types Of Constraints Research Articles

Diffusion-Based Influence Maximization in GOLAP

Influence analysis is one of the most important research in social network. Specifically, more and more researchers and advertisers are interested in the area of influence maximization (IM). The concept of influence among people or organizations has been the core basis for making business decisions as well as performing everyday social activities.In this research, we begin by extending a new influence diffusion model information diffusion model (IDM) using various constraints. We incorporate colors and additional nodes constraints. By adding colors and constraints for different types of nodes in a graph, we would be able to answer complex queries on multi-dimensional graphs such as ‘find at most two most important genes that are related to lung disease and heart disease’. More specifically, we discuss the following variations of IM-IDM; Colorblind IM-IDM, Colored IM-IDM and Colored IM-IDM with constraints. We also present our experiment results to prove the effectiveness of our model and algorithms.

Even–odd partition identities of Rogers–Ramanujan type

We prove a theorem which adds a new companion to the Rogers–Ramanujan identities. This new companion counts partitions with different type of constraints on even and odd parts. Generalizing this theorem, we obtain two families of partition identities.

Effects of leisure constraints and negotiation on activity enjoyment: a forgotten part of the leisure constraints theory

ABSTRACT Although identified in the definition of leisure constraints, leisure enjoyment has been rarely studied as an outcome of constraints and constraint negotiation. The purpose of this paper is, therefore, to examine the associations among leisure constraints, constraint negotiation, and enjoyment, within the context of leisure-time physical activity (LTPA). Cross-sectional online survey data from 618 Japanese and Euro-Canadian adults were used. Regression results suggested that across different levels of LTPA, enjoyment was negatively associated with constraints and positively with constraint negotiation. Follow-up regression analyses at sub-category level identified specific types of leisure constraints and negotiation strategies particularly pertinent to enjoyment. We conclude that leisure enjoyment is a direct outcome of constraints and constraint negotiation, which supports the call to extend the leisure constraints theory beyond participation as the outcome. Moreover, we suggest that facilitating leisure enjoyment requires awareness of different types of constraints and negotiation strategies depending on activity contexts.

Solving hybrid Boolean constraints in continuous space via multilinear Fourier expansions

The Boolean SATisfiability problem (SAT) is of central importance in computer science. Although SAT is known to be NP-complete, progress on the engineering side—especially that of Conflict-Driven Clause Learning (CDCL) and Local Search SAT solvers—has been remarkable. Yet, while SAT solvers, aimed at solving industrial-scale benchmarks in Conjunctive Normal Form (CNF), have become quite mature, SAT solvers that are effective on other types of constraints (e.g., cardinality constraints and XORs) are less well-studied; a general approach to handling non-CNF constraints is still lacking.To address the issue above, we design FourierSAT,1 an incomplete SAT solver based on Fourier Analysis (also known as Walsh-Fourier Transform) of Boolean functions, a technique to represent Boolean functions by multilinear polynomials. By such a reduction to continuous optimization, we propose an algebraic framework for solving systems consisting of different types of constraints. The idea is to leverage gradient information to guide the search process in the direction of local improvements. We show this reduction enjoys interesting theoretical properties. Empirical results demonstrate that FourierSAT can be a useful complement to other solvers on certain classes of benchmarks.

Time-Based Corporate-Social-Responsibility Evaluation Model Taking Chinese Listed Forestry Companies as an Example

With the rapid development of the economy, corporate social responsibility (CSR) is receiving increasing attention from companies themselves, but also increasing attention from society as a whole. How to reasonably evaluate the performance of CSR is a current research hotspot. Existing corporate-social-responsibility evaluation methods mostly focus on the static evaluation of enterprises in the industry, and do not take the time factor into account, which cannot reflect the performance of long-term CSR. On this basis, this article proposes a time-based entropy method that can evaluate long-term changes in CSR. Studies have shown that the completion of CSR in a static state does not necessarily reflect the dynamic and increasing trend of CSR in the long term. Therefore, the assessment of CSR should consider both the static and dynamic aspects of a company. In addition, the research provides the focus of different types of forestry enterprises in fulfilling CSR in the long term, and provides a clearer information path for the standard identification and normative constraints of different types of forestry enterprises CSR.

Constraints on subject-verb agreement marking in Turkish-German bilingual speakers

Abstract Turkish 3rd person plural subjects normally appear with verbs that are unmarked for number. Following earlier findings which indicate that Turkish heritage speakers (HS) accept overt plural marking more readily compared to monolingually raised Turkish speakers, the present study investigates to what extent bilingual speakers are sensitive to grammatical, surface-level and semantic constraints on Turkish plural agreement marking. A scalar acceptability judgement task was carried out with non-bilingual Turkish speakers residing in Turkey and Turkish-German bilinguals residing in Germany. Our experimental design involved manipulating both subject animacy and subject position. Participants’ judgement patterns confirmed Turkish speakers’ general preference for unmarked verb forms, which was modulated both by subject animacy and by subject position. Significant differences were observed between lower proficiency HS on the one hand, and monolinguals and advanced proficiency HS on the other, suggesting that the relatively subtle interplay between different types of constraint on number agreement marking is affected by heritage language conditions. We found no evidence for simplification or optionality reduction in the lower proficiency HS’ judgements, however. We innovate on previous research by using Gradient Symbolic Computation modelling to capture between-group differences in the relative weightings of the constraints under investigation.

Simulation analysis of regional surface mass anomalies inversion based on different types of constraints

Surface mass anomalies estimated by mass concentration (mascon) approach using Gravity Recovery and Climate Experiment (GRACE) observations with regularization constraints generally present higher spatial resolution than the spheric harmonic (SH) solutions. To analyze the influence of different types of constraints on the estimation of mascon solutions, we carried out a closed-loop simulation experiment to estimate surface mass anomalies over South America based on simulated GRACE intersatellite geopotential differences. Tikhonov regularization with spatial constraint (SC), uniform weighting constraint (UWC), and a prior information constraint (APC) were employed to stabilize the mascon solutions, and the corresponding optimal regularization parameters were determined based on the minimum residual root-mean-square (RMS) criterion. The results show that mascon solutions estimated under different types of constraints are consistent and equivalent when the optimal regularization parameters are selected. The spatial distributions and main characteristics of regional surface mass anomalies estimated by the three types of constraints agree well, and the values of residual RMS with different constraints are very close. But due to the smoothing effect of regularization, the signal strength of mascon solutions is a bit weaker than that of original true signal, especially in the regions with strong signals. In addition, due to the ill-conditioned problem is more serious for higher grid resolution, the relative contribution of the three types of constraints to the final mascon solutions would be stronger. The results show that the averages of relative contribution percentages of these constraints for 2°×2° mascon grids are 80% -90%, while the corresponding values for 4°×4° mascon grids are 30% -60%. However, based on the minimum residual RMS criterion, the accuracy of estimation results is not affected by the type of constraints and their relative contribution to the final mascon solutions.

Constrained Feedback Control for Spacecraft Reorientation With an Optimal Gain

This article addresses the problem of spacecraft reorientation subject to attitude pointing, angular velocity, and actuator constraints. In order to tackle the attitude pointing constraints, a potential function is proposed, which is parameterized to allow for a wider range of optimal repointing solutions with attitude pointing constraints. Based on the potential function, a feedback control law is then designed for ensuring the system stability. The controller gains are optimized using a gradient-based optimal parameter tuning method. Furthermore, by imposing the angular velocity constraints and the actuator constraints into the formulation, the optimal parameter tuning method also ensures the satisfaction of these constraints. Therefore, stability, optimality, and feasibility are achieved simultaneously. Numerical simulations demonstrate accurate repointing maneuvering under different types of constraints.

Improving Continuous-time Conflict Based Search

Conflict-Based Search (CBS) is a powerful algorithmic framework for optimally solving classical multi-agent path finding (MAPF) problems, where time is discretized into the time steps. Continuous-time CBS (CCBS) is a recently proposed version of CBS that guarantees optimal solutions without the need to discretize time. However, the scalability of CCBS is limited because it does not include any known improvements of CBS. In this paper, we begin to close this gap and explore how to adapt successful CBS improvements, namely, prioritizing conflicts (PC), disjoint splitting (DS), and high-level heuristics, to the continuous time setting of CCBS. These adaptions are not trivial, and require careful handling of different types of constraints, applying a generalized version of the Safe interval path planning (SIPP) algorithm, and extending the notion of cardinal conflicts. We evaluate the effect of the suggested enhancements by running experiments both on general graphs and 2^k-neighborhood grids. CCBS with these improvements significantly outperforms vanilla CCBS, solving problems with almost twice as many agents in some cases and pushing the limits of multi-agent path finding in continuous-time domains.

An innovative operation‐time‐space network for solving different logistic problems with capacity and time constraints

AbstractThis work describes an innovative operation‐time‐space network that can be easily adapted to model different logistic problems involving time and space decisions. In the recent literature, different time‐space networks have been proposed to deal with specific requirements of problems arising in the logistic and transportation fields. The proposed network is able to deal with different types of capacity and time constraints that characterized the most part of logistic problems. The article explains how to construct the operation‐time‐space network, how to model time and capacity constraints of different types; moreover, it presents an application of this network to solve a logistic problem arising in the rail‐sea exchange node, that is the scheduling of port rail shunting operations. Other applications are briefly introduced. Finally, the well‐known quay crane scheduling problem is modeled, thanks to the operation‐time‐space network; the related flow model is tested by using a benchmark suite available in the literature.

Graph-Based Non-Linear Least Squares Optimization for Visual Place Recognition in Changing Environments

Visual place recognition is an important subproblem of mobile robot localization. Since it is a special case of image retrieval, the basic source of information is the pairwise similarity of image descriptors. However, the embedding of the image retrieval problem in this robotic task provides additional structure that can be exploited, e.g., spatio-temporal consistency. Several algorithms exist to exploit this structure, e.g., sequence processing approaches or descriptor standardization approaches for changing environments. In this letter, we propose a graph-based framework to systematically exploit different types of additional structure and information. The graphical model is used to formulate a non-linear least squares problem that can be optimized with standard tools. Beyond sequences and standardization, we propose the usage of intra-set similarities within the database and/or the query image set as additional source of information. If available, our approach also allows to seamlessly integrate additional knowledge about poses of database images. We evaluate the system on a variety of standard place recognition datasets and demonstrate performance improvements for a large number of different configurations including different sources of information, different types of constraints, and online or offline place recognition setups.

Constraint-Based Thinking: A Structured Approach for Developing Frugal Innovations

While thinking in constraints is a key element of frugal innovation, it remains underexplored in the context of developing frugal innovations. Based on insights from the literature and two cases from the medical device industry, this article introduces the concept of constraint-based thinking to drive the frugal product development process based on constraints. This approach is operationalized through a systematic and iterative process to investigate the visible and underlying symptoms of different types of constraints, which are then mapped to specific product features and requirements. It consists of a problem space and a solution space, which are based on the idea that constraints are opportunities to develop innovative solutions. The main components of the constraint-based thinking approach are as follows: first, the identification of constraints or existing limitations, hindrances behind the current status quo that need to be changed; second, a root-cause analysis to understand the underlying causes for each of the identified constraints; third, a mapping of each of those causes to specific product characteristics or requirements; and, fourth, development of a minimal viable product or a prototype. This approach can be used both by companies to develop frugal innovations and by researchers to further develop the link between constraints and new products and services.

Polytope-based tolerance analysis with consideration of form defects and surface deformations

ABSTRACT Tolerance analysis aims to evaluate the functional requirement of mechanical assemblies in the design stage. As one of the typical tolerance analysis methods, the polytope model has shown great potential in Computer-Aided Tolerancing (CAT) field based on a series of constraints. However, existing research outcomes on this topic rarely consider form defects and the combined effects of form defects and surface deformations. This limitation may cause inaccuracy of tolerance analysis results. Hence, this paper integrates form defects and surface deformations in polytope-based tolerance analysis. Specifically, form defects are considered based on the concept of Skin Model Shapes and surface deformations are calculated based on a Conjugate Gradient-Fast Fourier Transform (CG-FFT) method. Then, the modeling method of the new polytope model is elaborated based on surface types and different types of constraints. Moreover, a case study is conducted to explain the procedure of polytope-based tolerance analysis. Through operations of the polytopes, tolerance analysis results are obtained, and the comparison with conventional polytope analysis results shows the considerable effects of form defects and surface deformations. It is expected that the proposed method can have a more realistic and accurate analysis result because of its consideration of real surface contact status.

Statistics for N = 1

Research in education is often associated with comparing group averages and linear relations in sufficiently large samples and evidence-based practice is about using the outcomes of that research in the practice of education. However, there are questions that are important for the practice of education that cannot really be addressed by comparisons of group averages and linear relations, no matter how large the samples. Besides, different types of constraints including logistic, financial, and ethical ones may make larger-sample research unfeasible or at least questionable. What has remained less known in many fields is that there are study designs and statistical methods for research involving small samples or even individuals that allow us to address questions of importance for the practice of education. This article discusses one type of such situations and provides a simple coherent statistical approach that provides point and interval estimates of differences of interest regardless of the type of the outcome variable and that is of use in other types of studies involving large samples, small samples, and single individuals.

Answer Set Programming for Computing Constraints-Based Elementary Flux Modes: Application to Escherichia coli Core Metabolism

Elementary Flux Modes (EFMs) provide a rigorous basis to systematically characterize the steady state, cellular phenotypes, as well as metabolic network robustness and fragility. However, the number of EFMs typically grows exponentially with the size of the metabolic network, leading to excessive computational demands, and unfortunately, a large fraction of these EFMs are not biologically feasible due to system constraints. This combinatorial explosion often prevents the complete analysis of genome-scale metabolic models. Traditionally, EFMs are computed by the double description method, an efficient algorithm based on matrix calculation; however, only a few constraints can be integrated into this computation. They must be monotonic with regard to the set inclusion of the supports; otherwise, they must be treated in post-processing and thus do not save computational time. We present aspefm, a hybrid computational tool based on Answer Set Programming (ASP) and Linear Programming (LP) that permits the computation of EFMs while implementing many different types of constraints. We apply our methodology to the Escherichia coli core model, which contains 226×106 EFMs. In considering transcriptional and environmental regulation, thermodynamic constraints, and resource usage considerations, the solution space is reduced to 1118 EFMs that can be computed directly with aspefm. The solution set, for E. coli growth on O2 gradients spanning fully aerobic to anaerobic, can be further reduced to four optimal EFMs using post-processing and Pareto front analysis.

Solving fully fuzzy multi-objective linear programming problem with fuzzy dominant degrees

In this paper, we investigate all relative relationships between two fuzzy numbers. Then, we introduce new relative measures to compare two fuzzy numbers instead of using absolute value to represent the fuzzy number. These measures address the dominant level that one fuzzy number is better than the other in terms of its position and shape. The so-called absolute fuzzy dominant degree and relative fuzzy dominant degree are developed to measure the differences between two fuzzy numbers applying for different types of constraint. These measures could capture all the shape’s characteristics and relative positions of fuzzy numbers. Finally, the fully fuzzy multi-objective decision making (FFMODM) problem is solved by using these fuzzy dominant degrees. For validation, we compare our approach to the fuzzy ranking method of the linear ranking function. Our obtained results show better performance.

A 101-line MATLAB code for topology optimization using binary variables and integer programming

This paper presents a MATLAB code with the implementation of the Topology Optimization of Binary Structures (TOBS) method first published by Sivapuram and Picelli (Finite Elem Anal Des 139: pp. 49–61, 2018). The TOBS is a gradient-based topology optimization method that employs binary design variables and formal mathematical programming. Besides its educational purposes, the 101-line code is provided to show that topology optimization with integer linear programming can be efficiently carried out, contrary to the previous reports in the literature. Compliance minimization subject to a volume constraint is first solved to highlight the main features of the TOBS method. The optimization parameters are discussed. Then, volume minimization subject to a compliance constraint is solved to illustrate that the method can efficiently deal with different types of constraints. Finally, simultaneous volume and displacement constraints are investigated in order to expose the capabilities of the optimizer and to serve as a tutorial of multiple constraints. The 101-line MATLAB code and some simple enhancements are elucidated, keeping only the integer programming solver unmodified so that it can be tested and extended to other numerical examples of interest.

Dynamic competition in electricity markets under uncertainty

Abstract We study discrete-time infinite-horizon imperfect competition between asymmetric firms producing from different technologies. Specifically, one firm produces from hydroelectric units and the others operate thermal generators. This type of structure is common in some electricity markets. What makes this research interesting is that firms have different types of constraints, face different kinds of uncertainties, need to allocate their resources over time, and yet produce strategically. For the renewable energy holder, the key issue is how to allocate water between current and future electricity generation given the thermal firms' strategic actions along with demand and/or water inflow uncertainties. We analyze equilibrium outcomes (e.g., the price distribution) and market inefficiencies stemming from both production constraints and imperfect competition. We show that equilibrium price volatility and skewness are generally lower than optimal, although average price is higher than optimal. The hydro producer under-utilizes the available water, which leads to more water being available to smooth price fluctuations. However, in the extreme case of water inflows so plentiful that the hydro firm is never constrained, prices can be more volatile than optimal. We also demonstrate that the lack of social optimality of the market outcome is tempered by the capacity constraints: the welfare loss under the oligopoly market structure is much less than would occur in the absence of water and capacity constraints. These results are demonstrated using numerical simulations of the infinite horizon game, one of which is calibrated to match the characteristics of the Ontario wholesale electricity market.

The Role of the Body in Instrumental and Vocal Music Pedagogy: A Dynamical Systems Theory Perspective on the Music Teacher's Bodily Engagement in Teaching and Learning

Instrumental and vocal teachers often employ their body in teaching to facilitate sensorimotor engagement with the voice or an instrument. Yet, teacher’s bodily engagement in instrumental and vocal education is scarcely addressed in music educational research studies. In our view, this scarcity is related to the lack of a framework about the role of the music teacher’s body in instrumental and vocal education. In this article, we will adopt a dynamical systems theory perspective to set first steps in conceptualizing the role of the instrumental and vocal teacher’s body in teaching and learning music. From this perspective, learning processes are viewed as emerging from the learner’s goal-oriented, situated, adaptive actions in the learning environment. Teachers play a significant role in that environment, due to the different types of constraints (e.g. environmental and task constraints) they can introduce to aid learners in finding a solution for a musical task. In this article, we argue that different types of teacher’s bodily engagement can act as constraints in instrumental and vocal music learning, thereby facilitating the learning process in non-verbal ways. To demonstrate this, we describe four types of bodily involvement: physical modelling, action demonstration, pedagogical gestures and touch. In summary, based on existing theoretical and empirical research, the article will present a first conceptualisation of the role of the music teacher in instrumental and vocal education viewed from a dynamical systems approach.

FourierSAT: A Fourier Expansion-Based Algebraic Framework for Solving Hybrid Boolean Constraints

The Boolean SATisfiability problem (SAT) is of central importance in computer science. Although SAT is known to be NP-complete, progress on the engineering side—especially that of Conflict-Driven Clause Learning (CDCL) and Local Search SAT solvers—has been remarkable. Yet, while SAT solvers, aimed at solving industrial-scale benchmarks in Conjunctive Normal Form (CNF), have become quite mature, SAT solvers that are effective on other types of constraints (e.g., cardinality constraints and XORs) are less well-studied; a general approach to handling non-CNF constraints is still lacking. In addition, previous work indicated that for specific classes of benchmarks, the running time of extant SAT solvers depends heavily on properties of the formula and details of encoding, instead of the scale of the benchmarks, which adds uncertainty to expectations of running time.To address the issues above, we design FourierSAT, an incomplete SAT solver based on Fourier analysis of Boolean functions, a technique to represent Boolean functions by multilinear polynomials. By such a reduction to continuous optimization, we propose an algebraic framework for solving systems consisting of different types of constraints. The idea is to leverage gradient information to guide the search process in the direction of local improvements. Empirical results demonstrate that FourierSAT is more robust than other solvers on certain classes of benchmarks.