Adult attachment style across individuals and role-relationships: Avoidance is relationship-specific, but anxiety shows greater generalizability

A generalisability study examined the hypotheses that avoidant attachment, reflecting the representation of others, should be more relationship-specific (vary across relationships more than across individuals), while attachment anxiety, reflecting self-representation, should be more generalisable across a person’s relationships. College students responded to 6-item questionnaire measures of these variables for 5 relationships (mother, father, best same-gender friend, romantic partner or best opposite-gender friend, other close person), on 3 (N = 120) or 2 (N = 77) occasions separated by a few weeks. Results supported the hypotheses, with the person variance component being larger than the relationship-specific component for anxiety, and the opposite happening for avoidance. Anxiety therefore seems not to be as relationship-specific as previous research suggested. Possible reasons for discrepancies between the current and previous studies are discussed

The maximum clique enumeration problem: algorithms, applications, and implementations

Background The maximum clique enumeration (MCE) problem asks that we identify all maximum cliques in a finite, simple graph. MCE is closely related to two other well-known and widely-studied problems: the maximum clique optimization problem, which asks us to determine the size of a largest clique, and the maximal clique enumeration problem, which asks that we compile a listing of all maximal cliques. Naturally, these three problems are View MathML /\u3e-hard, given that they subsume the classic version of the View MathML /\u3e-complete clique decision problem. MCE can be solved in principle with standard enumeration methods due to Bron, Kerbosch, Kose and others. Unfortunately, these techniques are ill-suited to graphs encountered in our applications. We must solve MCE on instances deeply seeded in data mining and computational biology, where high-throughput data capture often creates graphs of extreme size and density. MCE can also be solved in principle using more modern algorithms based in part on vertex cover and the theory of fixed-parameter tractability (FPT). While FPT is an improvement, these algorithms too can fail to scale sufficiently well as the sizes and densities of our datasets grow. Results An extensive testbed of benchmark graphs are created using publicly available transcriptomic datasets from the Gene Expression Omnibus (GEO). Empirical testing reveals crucial but latent features of such high-throughput biological data. In turn, it is shown that these features distinguish real data from random data intended to reproduce salient topological features. In particular, with real data there tends to be an unusually high degree of maximum clique overlap. Armed with this knowledge, novel decomposition strategies are tuned to the data and coupled with the best FPT MCE implementations. Conclusions Several algorithmic improvements to MCE are made which progressively decrease the run time on graphs in the testbed. Frequently the final runtime improvement is several orders of magnitude. As a result, instances which were once prohibitively time-consuming to solve are brought into the domain of realistic feasibility

Two Types of Planning in Neighborhoods

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68338/2/10.1177_0739456X8400300209.pd