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"Woods, John"
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Could a Computer Learn to Be an Appeals Court Judge? The Place of the Unspeakable and Unwriteable in All-Purpose Intelligent Systems
I will take it that general intelligence is intelligence of the kind that a typical human being—Fred, say—manifests in his role as a cognitive agent, that is, as an acquirer, receiver and circulator of knowledge in his cognitive economy. Framed in these terms, the word “general” underserves our ends. Hereafter our questions will bear upon the all-purpose intelligence of beings like Fred. Frederika appears as Fred’s AI-counterpart, not as a fully programmed and engineered being, but as a presently unrealized theoretical construct. Our basic question is whether it is in principle possible to equip Frederika to do what Fred does as an all-purpose participant in his own cognitive economy. Can she achieve a sufficiency of relevant similarity to him to allow us to say that she herself can do what Fred can do, perhaps even better? One of the things that Fred can do—or at least could learn from experience to do—is discharge the duties of an Appeals Court judge. As set down in the ancient doctrine of lex non scripta, Fred must be able to detect, understand and correctly apply certain tacit and implicit rules of law which defy express propositional formulation and linguistic articulation. Fred has an even more widespread capacity for the epistemically tacit and implicit, clearly one of his most cost-saving kinds of intelligence. Indeed, most by far of what Fred will ever know he will know tacitly and implicitly. So we must ask: how tightly bound to the peculiarities of Fred’s cognitive enablement conditions is the character of the intelligence that he manifests? And how far down Fred’s causal make-up does intelligence actually go?
Journal Article
Elementary excitations of single-photon emitters in hexagonal boron nitride
Single-photon emitters serve as building blocks for many emerging concepts in quantum photonics. The recent identification of bright, tunable and stable emitters in hexagonal boron nitride (hBN) has opened the door to quantum platforms operating across the infrared to ultraviolet spectrum. Although it is widely acknowledged that defects are responsible for single-photon emitters in hBN, crucial details regarding their origin, electronic levels and orbital involvement remain unknown. Here we employ a combination of resonant inelastic X-ray scattering and photoluminescence spectroscopy in defective hBN, unveiling an elementary excitation at 285 meV that gives rise to a plethora of harmonics correlated with single-photon emitters. We discuss the importance of N π* anti-bonding orbitals in shaping the electronic states of the emitters. The discovery of elementary excitations in hBN provides fundamental insights into quantum emission in low-dimensional materials, paving the way for future investigations in other platforms.
Combining resonant inelastic X-ray scattering and photoluminescence spectroscopy, an elementary excitation in hexagonal-boron-nitride-based single-photon emitters has been demonstrated, giving rise to multiple regular harmonics that can explain the wide frequency range of these emitters.
Journal Article
Measuring for primary prevention: An online survey of local community perspectives on family and domestic violence in regional Australia
Family and domestic violence, encompassing diverse behaviours including physical, sexual, emotional and financial abuse, is endemic worldwide and has multiple adverse health and social consequences. Principal drivers include traditional gender values that disempower women. Changing these is a key prevention strategy. In Australia, high-quality national surveys provide data on public perspectives concerning family and domestic violence but may not capture community-level diversity. As part of a project for primary prevention family and domestic violence in outer regional Australia, our aims were to develop and administer a questionnaire-based survey suitable for the local community encompassing knowledge about, attitudes towards, and personal experiences of family and domestic violence, to describe and to investigate the theoretical (factor) structure and local socio-demographic predictors of responses, and to determine the extent to which the survey findings are locally distinctive.
The online community survey for local residents (≥15 years), comprised items on respondents' sociodemographic characteristics plus questions abridged from pre-existing national instruments on knowledge about, attitudes towards, and personal experiences of family and domestic violence. Responses were rake-weighted to correct census-ascertained sample imbalance and investigated using exploratory factor analysis, with sociodemographic predictors determined using multiple linear regression and dominance analysis.
Among 914 respondents, males (27.0%), those from age-group extremes, and less-educated persons were underrepresented. Familiarity with diverse family and domestic violence behaviours was high among all subgroups. Poorer knowledge of the FDV behaviour continuum and attitudes supporting traditional gender roles and FDV were disproportionately evident among males, older respondents and those with lower education levels. Both the factor structure of extracted composite measures reflecting community perspectives and sociodemographic predictors of responses generally aligned with patterns evident in national data.
Local reinforcement of existing nationwide findings on community understanding of and attitudes towards family and domestic violence provides salience for targeted interventions.
Journal Article
Prediction and Validation of Gene-Disease Associations Using Methods Inspired by Social Network Analyses
Correctly identifying associations of genes with diseases has long been a goal in biology. With the emergence of large-scale gene-phenotype association datasets in biology, we can leverage statistical and machine learning methods to help us achieve this goal. In this paper, we present two methods for predicting gene-disease associations based on functional gene associations and gene-phenotype associations in model organisms. The first method, the Katz measure, is motivated from its success in social network link prediction, and is very closely related to some of the recent methods proposed for gene-disease association inference. The second method, called Catapult (Combining dATa Across species using Positive-Unlabeled Learning Techniques), is a supervised machine learning method that uses a biased support vector machine where the features are derived from walks in a heterogeneous gene-trait network. We study the performance of the proposed methods and related state-of-the-art methods using two different evaluation strategies, on two distinct data sets, namely OMIM phenotypes and drug-target interactions. Finally, by measuring the performance of the methods using two different evaluation strategies, we show that even though both methods perform very well, the Katz measure is better at identifying associations between traits and poorly studied genes, whereas Catapult is better suited to correctly identifying gene-trait associations overall [corrected].
Journal Article
PDGFRα signaling regulates cartilage and fibrous tissue differentiation during synovial joint development
Synovial joints develop from mesenchymal structures called interzones, with progenitor cells differentiating into specialized cartilaginous and fibrous tissues of the joint. Platelet-derived growth factor receptor-α (PDGFRα) is a tyrosine kinase expressed by cells of the limb bud, but its role in limb development is unknown. To investigate PDGFRα function, we generated mice expressing mutant PDGFRα with a point mutation (D842V) that increases receptor signaling. Mutant hindlimbs are immobile with knee joints fused by cartilage and lacking ligaments and menisci. The interzone marker
Gdf5
is initially expressed at E12.5 but is downregulated thereafter, suggesting a defect in interzone maintenance. Omics analysis of the joint tissues identifies ectopic cartilage matrix expressing genes for cartilage and fibrotic tissue. Thus, elevated PDGFRα signaling corrupts joint development by downregulating
Gdf5
and redirecting interzone progenitors into a fibrocartilage fate. This suggests that tight regulation of tyrosine kinase activity is necessary for the development of the mouse knee joint.
While the embryonic limb bud expresses PDGFRα, its role during limb development is unknown. Here, the authors show that elevated PDGFRα signaling causes joint fusion in mice, as joint progenitors form fibrocartilage instead of synovial joint tissues.
Journal Article