Embedding OWL ontologies with OWL2Vec

In this paper, we present a preliminary study to compute embeddings for OWL 2 ontologies by projecting the ontology axioms into a graph and performing (random) walks over the ontology graph to create a corpus of sentences. This corpus is then given to a neural language model to create concept embeddings. The conducted preliminary evaluation shows promising results

Disorder-to-order transition in the magnetic and electronic properties of URh_2Ge_2

We present a study of annealing effects on the physical properties of tetragonal single--crystalline URh_2Ge_2. This system, which in as-grown form was recently established as the first metallic 3D random-bond heavy-fermion spin glass, is transformed by an annealing treatment into a long-range antiferromagnetically (AFM) ordered heavy-fermion compound. The transport properties, which in the as-grown material were dominated by the structural disorder, exhibit in the annealed material signs of typical metallic behavior along the crystallographic a axis. From our study URh_2Ge_2 emerges as exemplary material highlighting the role and relevance of structural disorder for the properties of strongly correlated electron systems. We discuss the link between the magnetic and electronic behavior and how they are affected by the structural disorder.Comment: Phys. Rev. B, in print (scheduled 1 Mar 2000

Optimal In Silico Target Gene Deletion through Nonlinear Programming for Genetic Engineering

Optimal selection of multiple regulatory genes, known as targets, for deletion to enhance or suppress the activities of downstream genes or metabolites is an important problem in genetic engineering. Such problems become more feasible to address in silico due to the availability of more realistic dynamical system models of gene regulatory and metabolic networks. The goal of the computational problem is to search for a subset of genes to knock out so that the activity of a downstream gene or a metabolite is optimized.Based on discrete dynamical system modeling of gene regulatory networks, an integer programming problem is formulated for the optimal in silico target gene deletion problem. In the first result, the integer programming problem is proved to be NP-hard and equivalent to a nonlinear programming problem. In the second result, a heuristic algorithm, called GKONP, is designed to approximate the optimal solution, involving an approach to prune insignificant terms in the objective function, and the parallel differential evolution algorithm. In the third result, the effectiveness of the GKONP algorithm is demonstrated by applying it to a discrete dynamical system model of the yeast pheromone pathways. The empirical accuracy and time efficiency are assessed in comparison to an optimal, but exhaustive search strategy.Although the in silico target gene deletion problem has enormous potential applications in genetic engineering, one must overcome the computational challenge due to its NP-hardness. The presented solution, which has been demonstrated to approximate the optimal solution in a practical amount of time, is among the few that address the computational challenge. In the experiment on the yeast pheromone pathways, the identified best subset of genes for deletion showed advantage over genes that were selected empirically. Once validated in vivo, the optimal target genes are expected to achieve higher genetic engineering effectiveness than a trial-and-error procedure

Six Human-Centered Artificial Intelligence Grand Challenges

Widespread adoption of artificial intelligence (AI) technologies is substantially affecting the human condition in ways that are not yet well understood. Negative unintended consequences abound including the perpetuation and exacerbation of societal inequalities and divisions via algorithmic decision making. We present six grand challenges for the scientific community to create AI technologies that are human-centered, that is, ethical, fair, and enhance the human condition. These grand challenges are the result of an international collaboration across academia, industry and government and represent the consensus views of a group of 26 experts in the field of human-centered artificial intelligence (HCAI). In essence, these challenges advocate for a human-centered approach to AI that (1) is centered in human well-being, (2) is designed responsibly, (3) respects privacy, (4) follows human-centered design principles, (5) is subject to appropriate governance and oversight, and (6) interacts with individuals while respecting human’s cognitive capacities. We hope that these challenges and their associated research directions serve as a call for action to conduct research and development in AI that serves as a force multiplier towards more fair, equitable and sustainable societies

Systems thinking and efficiency under emissions constraints: Addressing rebound effects in digital innovation and policy

Innovations and efficiencies in digital technology have lately been depicted as paramount in the green transition to enable the reduction of greenhouse gas emissions, both in the information and communication technology (ICT) sector and the wider economy. This, however, fails to adequately account for rebound effects that can offset emission savings and, in the worst case, increase emissions. In this perspective, we draw on a transdisciplinary workshop with 19 experts from carbon accounting, digital sustainability research, ethics, sociology, public policy, and sustainable business to expose the challenges of addressing rebound effects in digital innovation processes and associated policy. We utilize a responsible innovation approach to uncover potential ways forward for incorporating rebound effects in these domains, concluding that addressing ICT-related rebound effects ultimately requires a shift from an ICT efficiency-centered perspective to a “systems thinking” model, which aims to understand efficiency as one solution among others that requires constraints on emissions for ICT environmental savings to be realized

Agreement, reliability and validity in 3 shoulder questionnaires in patients with rotator cuff disease

Background Self-report questionnaires play an important role as outcome measures in shoulder research. Having an estimate of the measurement error of these questionnaires is of importance when assessing follow-up results after treatment and when planning intervention studies. The aim of this study was to cross-culturally adapt the Norwegian version of the OSS and WORC questionnaire and examine and compare agreement, reliability and construct validity of the disease-specific shoulder questionnaire WORC with two commonly used shoulder questionnaires, SPADI and OSS, in patients with rotator cuff disease. Methods 74 patients with rotator cuff disease were recruited from the outpatient clinic of the Physical Medicine and Rehabilitation Department at Ullevaal University Hospital in Oslo, Norway. A test-retest design was used, and the questionnaires were filled out by the patients at the clinic, with a one week interval between test administrations. Agreement (repeatability coefficient), reliability (ICC) and construct validity were examined and compared for WORC, SPADI and OSS. Results Reliability analysis was restricted to the 55 patients (51 ± 10 yrs) who reported no change between test administrations according to scoring on a global scale. The agreement, reliability and construct validity was moderate for all three questionnaires with ICC ranging from 0.83 to 0.85, repeatability coefficient from 16.1 to 19.7 and Spearman rank correlations between total scores from r = 0.57 to 0.69. There was a lower degree of floor and ceiling effects in SPADI compared to WORC and OSS. Conclusion We conclude that the agreement and reliability of the three shoulder questionnaires examined, WORC index, SPADI and OSS are acceptable and that differences between scores were small. The Norwegian version of the questionnaires is acceptable for assessing Norwegian-speaking patients with rotator cuff disease. The moderate agreement and construct validity should be taken into consideration when assessing follow-up results after treatment and in the planning of prospective studies

Factor structure of the Shoulder Pain and Disability Index in patients with adhesive capsulitis

<p>Abstract</p> <p>Background</p> <p>The Shoulder Pain and Disability Index (SPADI) is a self-administered questionnaire that aims to measure pain and disability associated with shoulder disease. It consists of a pain section and a disability section with 13 items being responded to on visual analogue scales. Few researchers have investigated SPADI validity in specified diagnostic groups, although the selection of an evaluative instrument should be based on evidence of validity in the target patient group. The aim of the present study was to investigate factor structure of the SPADI in a study population of patients with adhesive capsulitis.</p> <p>Methods</p> <p>The questionnaire was administered to 191 patients with adhesive capsulitis. Descriptive statistics for items and a comparison of scores for the two subscales were produced. Internal consistency was analyzed by use of the Cronbach alpha and a principal components analysis with varimax rotation was conducted. Study design was cross-sectional.</p> <p>Results</p> <p>Two factors were extracted, but the factor structure failed to support the original division of items into separate pain and disability sections.</p> <p>Conclusion</p> <p>We found minimal evidence to justify the use of separate subscales for pain and disability. It is our impression that the SPADI should be viewed as essentially unidimensional in patients with adhesive capsulitis.</p