Was <Emphasis Type="Italic">that</Emphasis> Racist? An Experimental Study of Microaggression Ambiguity and Emotional Reactions for Racial–Ethnic Minority and White Individuals

This study examined racial–ethnic minority and white identified participants’ perceptions of racial microaggressions in four video scenarios involving a white professor and a black student. Participants (N = 261) were randomly assigned to observe a one of the four video conditions. The video involved an interaction with no microaggression, a very ambiguous microaggression, an ambiguous microaggression, or an overt microaggression. Participants rated their observations of (a) negative emotions toward the committer (white professor) and receiver (black student) of the microaggression, (b) positive emotions toward the committer and receiver of the microaggression, (c) perceptions on the degree of biasedness of committer, and (d) degree of positive perceptions of the committer. Analysis compared ratings of two groups, including racial–ethnic minority and white observers. We found a significant difference between the overt condition and other three conditions on negative emotions, positive emotions, and attributions of the professor (i.e., cultural biasedness and positive perceptions). However, there were no differences between the no microaggression, very ambiguous, and ambiguous conditions. No significant differences between racial–ethnic minority and white participants’ ratings were detected. Implications for training, practice, and future research are offered in our discussion.     

Who is More Likely to “Not See Race”? Individual Differences in Racial Colorblindness

Many Americans endorse a colorblind racial ideology, meaning they strive to “not see race” and emphasize sameness and equal distribution of resources across racial lines. Currently, there is an absence of studies examining the personality and individual difference correlates of racial colorblindness. The current study investigated the association between three different aspects of racial colorblindness (unawareness of racial privilege, unawareness of institutional discrimination, and unawareness of blatant racism) and the Big 5, empathy, and aggression in white undergraduates. Our results revealed two divergent patterns. Unawareness of racial privilege was related to lower openness and perspective taking, but more empathic concern, whereas unawareness of blatant racism and unawareness of institutional discrimination were related to lower agreeableness, perspective taking, and empathic concern. These results are discussed in relation to the broader literature on prejudice and personality.     

Managing dependency in China outsourcing relationships

Abstract

This paper examines manufacturing firms’ use of guanxi and formal control to address dependence on suppliers and achieve desired outsourcing performance in China. Using survey data collected from manufacturing firms operating in China, a structural equation model is used to test the research model. The results suggest that manufacturing firms with high dependence on suppliers build Chinese guanxi with those suppliers to overcome the dependence and enhance outsourcing performance. In addition, Chinese guanxi also appears to facilitate the use of formal control, which was also found to have a positive relationship with outsourcing performance. This paper contributes to the literature by illustrating the importance of guanxi in achieving desired outsourcing performance in the China context, particularly in the case where a manufacturer perceives high dependency on its major supplier.     

On the kidney exchange problem: cardinality constrained cycle and chain problems on directed graphs: a survey of integer programming approaches

The Kidney Exchange Problem (KEP) is a combinatorial optimization problem and has attracted the attention from the community of integer programming/combinatorial optimisation in the past few years. Defined on a directed graph, the KEP has two variations: one concerns cycles only, and the other, cycles as well as chains on the same graph. We call the former a Cardinality Constrained Multi-cycle Problem (CCMcP) and the latter a Cardinality Constrained Cycles and Chains Problem (CCCCP). The cardinality for cycles is restricted in both CCMcP and CCCCP. As for chains, some studies in the literature considered cardinality restrictions, whereas others did not. The CCMcP can be viewed as an Asymmetric Travelling Salesman Problem that does allow subtours, however these subtours are constrained by cardinality, and that it is not necessary to visit all vertices. In existing literature of the KEP, the cardinality constraint for cycles is usually considered to be small (to the best of our knowledge, no more than six). In a CCCCP, each vertex on the directed graph can be included in at most one cycle or chain, but not both. The CCMcP and the CCCCP are interesting and challenging combinatorial optimization problems in their own rights, particularly due to their similarities to some travelling salesman- and vehicle routing-family of problems. In this paper, our main focus is to review the existing mathematical programming models and solution methods in the literature, analyse the performance of these models, and identify future research directions. Further, we propose a polynomial-sized and an exponential-sized mixed-integer linear programming model, discuss a number of stronger constraints for cardinality-infeasible-cycle elimination for the latter, and present some preliminary numerical results.     

Generating a smallest binary tree by proper selection of the longest edges to bisect in a unit simplex refinement

In several areas like global optimization using branch-and-bound methods for mixture design, the unit n-simplex is refined by longest edge bisection (LEB). This process provides a binary search tree. For \(n>2\), simplices appearing during the refinement process can have more than one longest edge (LE). The size of the resulting binary tree depends on the specific sequence of bisected longest edges. The questions are how to calculate the size of one of the smallest binary trees generated by LEB and how to find the corresponding sequence of LEs to bisect, which can be represented by a set of LE indices. Algorithms answering these questions are presented here. We focus on sets of LE indices that are repeated at a level of the binary tree. A set of LEs was presented in Aparicio et al. (Informatica 26(1):17–32, 2015), for \(n=3\). An additional question is whether this set is the best one under the so-called \(m_k\)-valid condition.     

Maximum cardinality neighbourly sets in quadrilateral free graphs

Neighbourly set of a graph is a subset of edges which either share an end point or are joined by an edge of that graph. The maximum cardinality neighbourly set problem is known to be NP-complete for general graphs. Mahdian (Discret Appl Math 118:239–248, 2002) proved that it is in polynomial time for quadrilateral-free graphs and proposed an \(O(n^{11})\) algorithm for the same, here n is the number of vertices in the graph, (along with a note that by a straightforward but lengthy argument it can be proved to be solvable in \(O(n^5)\) running time). In this paper we propose an \(O(n^2)\) time algorithm for finding a maximum cardinality neighbourly set in a quadrilateral-free graph.     

A heuristic for the time constrained asymmetric linear sum assignment problem

The linear sum assignment problem is a fundamental combinatorial optimisation problem and can be broadly defined as: given an \(n \times m, m \ge n\) benefit matrix \(B = (b_{ij})\), matching each row to a different column so that the sum of entries at the row-column intersections is maximised. This paper describes the application of a new fast heuristic algorithm, Asymmetric Greedy Search, to the asymmetric version (\(n \ne m\)) of the linear sum assignment problem. Extensive computational experiments, using a range of model graphs demonstrate the effectiveness of the algorithm. The heuristic was also incorporated within an algorithm for the non-sequential protein structure matching problem where non-sequential alignment between two proteins, normally of different numbers of amino acids, needs to be maximised.     

Online MapReduce scheduling problem of minimizing the makespan

MapReduce system is a popular big data processing framework, and the performance of it is closely related to the efficiency of the centralized scheduler. In practice, the centralized scheduler often has little information in advance, which means each job may be known only after being released. In this paper, hence, we consider the online MapReduce scheduling problem of minimizing the makespan, where jobs are released over time. Both preemptive and non-preemptive version of the problem are considered. In addition, we assume that reduce tasks cannot be parallelized because they are often complex and hard to be decomposed. For the non-preemptive version, we prove the lower bound is \(\frac{m+m(\Psi (m)-\Psi (k))}{k+m(\Psi (m)-\Psi (k))}\), higher than the basic online machine scheduling problem, where k is the root of the equation \(k=\big \lfloor {\frac{m-k}{1+\Psi (m)-\Psi (k)}+1 }\big \rfloor \) and m is the quantity of machines. Then we devise an \((2-\frac{1}{m})\)-competitive online algorithm called MF-LPT (Map First-Longest Processing Time) based on the LPT. For the preemptive version, we present a 1-competitive algorithm for two machines.     

On nonlinear multi-covering problems

In this paper we define the exact k-coverage problem, and study it for the special cases of intervals and circular-arcs. Given a set system consisting of a ground set of n points with integer demands \(\{d_0,\dots ,d_{n-1}\}\) and integer rewards, subsets of points, and an integer k, select up to k subsets such that the sum of rewards of the covered points is maximized, where point i is covered if exactly \(d_i\) subsets containing it are selected. Here we study this problem and some related optimization problems. We prove that the exact k-coverage problem with unbounded demands is NP-hard even for intervals on the real line and unit rewards. Our NP-hardness proof uses instances where some of the natural parameters of the problem are unbounded (each of these parameters is linear in the number of points). We show that this property is essential, as if we restrict (at least) one of these parameters to be a constant, then the problem is polynomial time solvable. Our polynomial time algorithms are given for various generalizations of the problem (in the setting where one of the parameters is a constant).