Knowledge-Based Stable Roommates Problem: A Real-World Application

The Stable Roommates problem with Ties and Incomplete lists (SRTI) is a matching problem characterized by the preferences of agents over other agents as roommates, where the preferences may have ties or be incomplete. SRTI asks for a matching that is stable and, sometimes, optimizes a domain-independent fairness criterion (e.g. Egalitarian). However, in real-world applications (e.g. assigning students as roommates at a dormitory), we usually consider a variety of domain-specific criteria depending on preferences over the habits and desires of the agents. With this motivation, we introduce a knowledge-based method to SRTI considering domain-specific knowledge and investigate its real-world application for assigning students as roommates at a university dormitory.

Challenges in Defining the Functional, Non-Coding, Expressed Genome of Pathogenic Mycobacteria

A definitive transcriptome atlas for the non-coding expressed elements of pathogenic mycobacteria does not exist. Incomplete lists of non-coding transcripts can be obtained for some of the reference genomes (e.g. Mycobacterium tuberculosis H37Rv) but to what extent these transcripts have homologues in closely related species or even strains is not clear. This has implications for the analysis of transcriptomic data; non-coding parts of the transcriptome are often ignored in the absence of formal, reliable annotation. Here, we review the state of our knowledge of non-coding RNAs in pathogenic mycobacteria, emphasising the disparities in the information included in commonly used databases. We then proceed to review ways of combining computational solutions for predicting the non- coding transcriptome with experiments that can help refine and confirm these predictions.

A Method for Aggregating Ranked Services for Personal Preference Based Selection

Typically, users' service requests, which are similar with varying preferences on non-functional attributes, may result in ranked lists of services that partially meet their needs due to conflicting non-functional attributes. The resultant multiple ranked lists of services that partially satisfies the user's request makes it challenging for the user to choose an optimal service, based on his/her preference. This work proposes a method that aggregates multiple ranked lists of services into a single aggregated ranked list, where top ranked services are selected for the user. Two algorithms are proposed; 1) Rank Aggregation for Complete Lists (RACoL), that aggregates complete ranked lists and 2) Rank Aggregation for Incomplete Lists (RAIL) to aggregate incomplete ranked lists. Examples using real-world airline services to evaluate both algorithms show that the results from both proposed algorithms closely represent the sets of ranked lists better than using alternative approaches. Experiments were also carried out to validate their performance.

A Method for Aggregating Ranked Services for Personal Preference Based Selection

Typically, users' service requests, which are similar with varying preferences on non-functional attributes, may result in ranked lists of services that partially meet their needs due to conflicting non-functional attributes. The resultant multiple ranked lists of services that partially satisfies the user's request makes it challenging for the user to choose an optimal service, based on his/her preference. This work proposes a method that aggregates multiple ranked lists of services into a single aggregated ranked list, where top ranked services are selected for the user. Two algorithms are proposed; 1) Rank Aggregation for Complete Lists (RACoL), that aggregates complete ranked lists and 2) Rank Aggregation for Incomplete Lists (RAIL) to aggregate incomplete ranked lists. Examples using real-world airline services to evaluate both algorithms show that the results from both proposed algorithms closely represent the sets of ranked lists better than using alternative approaches. Experiments were also carried out to validate their performance.

Vector Generation of Quantum Contextual Sets in Even Dimensional Hilbert Spaces

Recently, quantum contextuality has been proved to be the source of quantum computation’s power. That, together with multiple recent contextual experiments, prompts improving the methods of generation of contextual sets and finding their features. The most elaborated contextual sets, which offer blueprints for contextual experiments and computational gates, are the Kochen–Specker (KS) sets. In this paper, we show a method of vector generation that supersedes previous methods. It is implemented by means of algorithms and programs that generate hypergraphs embodying the Kochen–Specker property and that are designed to be carried out on supercomputers. We show that vector component generation of KS hypergraphs exhausts all possible vectors that can be constructed from chosen vector components, in contrast to previous studies that used incomplete lists of vectors and therefore missed a majority of hypergraphs. Consequently, this unified method is far more efficient for generations of KS sets and their implementation in quantum computation and quantum communication. Several new KS classes and their features have been found and are elaborated on in the paper. Greechie diagrams are discussed.

Population Size Estimation Using Multiple Incomplete Lists with Overcoverage

The quantity and quality of administrative information available to National Statistical Institutes have been constantly increasing over the past several years. However, different sources of administrative data are not expected to each have the same population coverage, so that estimating the true population size from the collective set of data poses several methodological challenges that set the problem apart from a classical capture-recapture setting. In this article, we consider two specific aspects of this problem: (1) misclassification of the units, leading to lists with both overcoverage and undercoverage; and (2) lists focusing on a specific subpopulation, leaving a proportion of the population with null probability of being captured. We propose an approach to this problem that employs a class of capturerecapture methods based on Latent Class models. We assess the proposed approach via a simulation study, then apply the method to five sources of empirical data to estimate the number of active local units of Italian enterprises in 2011.