On Objective Measures of Rule Surprisingness

Most of the literature argues that surprisingness is an inherently subjective aspect of the discovered knowledge, which cannot be measured in objective terms. This paper departs from this view, and it has a twofold goal: (1) showing that it is indeed possible to define objective (rather than subjective) measures of discovered rule surprisingness; (2) proposing new ideas and methods for defining objective rule surprisingness measures

El Pla Territorial de l'illa de Menorca

El Pla Territorial de Menorca és un exemple d’aplicació dels criteris de desenvolupament sostenible a la realitat territorial de l’Illa. Aquests criteris condueixen tot el procés planificador i troben un bon suport en la societat menorquina, que és ben conscient de la qualitat, i també de la fragilitat, del seu territori. Menorca opta així per un model territorial diferenciat que pretén fer compatible economia i respecte als recursos naturals i al paisatge. El Pla introdueix mesures de protecció sobre la totalitat del sòl rústic, per tal de preservar la diversitat d’àmbits rurals, tant naturals com humanitzats, tots ells amb valor paisatgístic remarcable. Amplia els espais naturals protegits, de tal manera que es passa de tenir una sèrie inconnexa d’espais naturals protegits a establir un vertader sistema d’espais naturals protegits. Limita el creixement de les zones turístiques, a fi que aquestes puguin ser reconduïdes a paràmetres de més qualitat i preveu un desenvolupament harmònic dins els nuclis tradicionals de població

Learning, Social Intelligence and the Turing Test - why an "out-of-the-box" Turing Machine will not pass the Turing Test

The Turing Test (TT) checks for human intelligence, rather than any putative general intelligence. It involves repeated interaction requiring learning in the form of adaption to the human conversation partner. It is a macro-level post-hoc test in contrast to the definition of a Turing Machine (TM), which is a prior micro-level definition. This raises the question of whether learning is just another computational process, i.e. can be implemented as a TM. Here we argue that learning or adaption is fundamentally different from computation, though it does involve processes that can be seen as computations. To illustrate this difference we compare (a) designing a TM and (b) learning a TM, defining them for the purpose of the argument. We show that there is a well-defined sequence of problems which are not effectively designable but are learnable, in the form of the bounded halting problem. Some characteristics of human intelligence are reviewed including it's: interactive nature, learning abilities, imitative tendencies, linguistic ability and context-dependency. A story that explains some of these is the Social Intelligence Hypothesis. If this is broadly correct, this points to the necessity of a considerable period of acculturation (social learning in context) if an artificial intelligence is to pass the TT. Whilst it is always possible to 'compile' the results of learning into a TM, this would not be a designed TM and would not be able to continually adapt (pass future TTs). We conclude three things, namely that: a purely "designed" TM will never pass the TT; that there is no such thing as a general intelligence since it necessary involves learning; and that learning/adaption and computation should be clearly distinguished.Comment: 10 pages, invited talk at Turing Centenary Conference CiE 2012, special session on "The Turing Test and Thinking Machines

Comparison of existing methods for algorithmic classification of dementia in the Health and Retirement Study

Background: Dementia ascertainment is difficult and costly, hindering the use of large, representative studies such as the Health and Retirement Study (HRS) to monitor trends or disparities in dementia. To address this issue, multiple groups of researchers have developed algorithms to classify dementia status in HRS participants using data from HRS and the Aging, Demographics, and Memory Study (ADAMS), an HRS sub-study that systematically ascertained dementia status. However, the relative performance of each algorithm has not been systematically evaluated. Objective: To compare the performance of five existing algorithms, overall and by sociodemographic subgroups. Methods: We created two standardized datasets: (a) training data (N=786, i.e. ADAMS Wave A and corresponding HRS data, which was used previously to create the algorithms) and (b) validation data (N=530, i.e. ADAMS Waves B, C, and D and corresponding HRS data which was not used previously to create the algorithms). In both, we used each algorithm to classify HRS participants as demented or not demented and compared the algorithmic diagnoses to the ADAMS diagnoses. Results: In the training data, overall classification accuracies ranged from 80% to 87%, sensitivity ranged from 53% to 90%, and specificity ranged from 79% to 96% across the five algorithms. Though overall classification accuracy was similar in the validation data (range: 79% to 88%), sensitivity was much lower (range: 17% to 61%), while specificity was higher (range: 82% to 98%) compared to the training data. Classification accuracy was generally worse in non-Hispanic blacks (range: 68% to 85%) and Hispanics (range: 65% to 88%), compared to non-Hispanic whites (range: 79% to 88%). Across datasets, sensitivity was generally higher for proxy-respondents, while specificity (and overall accuracy) was higher for self-respondents. Conclusions: Worse sensitivity in the validation dataset may suggest either overfitting or that the algorithms are better at identifying prevalent versus incident dementia, while differences in performance across algorithms suggest that the usefulness of each will vary depending on the user’s purpose. Further planned work will evaluate algorithm performance in external validation datasets

Content & Watkins's account of natural axiomatizations

This paper briefly recounts the importance of the notion of natural axiomatizations for explicating hypothetico-deductivism, empirical significance, theoretical reduction, and organic fertility. Problems for the account of natural axiomatizations developed by John Watkins in Science and Scepticism and the revised account developed by Elie Zahar are demonstrated. It is then shown that Watkins's account can be salvaged from various counter-examples in a principled way by adding the demand that every axiom of a natural axiomatization should be part of the content of the theory being axiomatized. The crucial point here is that content cannot simply be identified with the set of logical consequences of a theory, but must be restricted to a proper subset of the consequence set. It is concluded that the revised Watkins account has certain advantages over the account of natural axiomatizations offered in Gemes (1993)

Domain adaptation with conditional transferable components

© 2016 by the author(s). Domain adaptation arises in supervised learning when the training (source domain) and test (target domain) data have different distribution- s. Let X and Y denote the features and target, respectively, previous work on domain adaptation mainly considers the covariate shift situation where the distribution of the features P(X) changes across domains while the conditional distribution P(Y\X) stays the same. To reduce domain discrepancy, recent methods try to find invariant components T(X) that have similar P(T(X)) on different domains by explicitly minimizing a distribution discrepancy measure. However, it is not clear if P(Y\T(X)) in different domains is also similar when P(Y/X)changes. Furthermore, transferable components do not necessarily have to be invariant. If the change in some components is identifiable, we can make use of such components for prediction in the target domain. In this paper, we focus on the case where P{X ,Y) and P(Y') both change in a causal system in which Y is the cause for X. Under appropriate assumptions, we aim to extract conditional transferable components whose conditional distribution P(T{X)\Y) is invariant after proper location-scale (LS) transformations, and identify how P{Y) changes between domains simultaneously. We provide theoretical analysis and empirical evaluation on both synthetic and real-world data to show the effectiveness of our method

Coordinated analysis of age, sex, and education effects on change in MMSE scores

Objectives. We describe and compare the expected performance trajectories of older adults on the Mini-Mental Status Examination (MMSE) across six independent studies from four countries in the context of a collaborative network of longitudinal studies of aging. A coordinated analysis approach is used to compare patterns of change conditional on sample composition differences related to age, sex, and education. Such coordination accelerates evaluation of particular hypotheses. In particular, we focus on the effect of educational attainment on cognitive decline.Method. Regular and Tobit mixed models were fit to MMSE scores from each study separately. The effects of age, sex, and education were examined based on more than one centering point.Results. Findings were relatively consistent across studies. On average, MMSE scores were lower for older individuals and declined over time. Education predicted MMSE score, but, with two exceptions, was not associated with decline in MMSE over time.Conclusion. A straightforward association between educational attainment and rate of cognitive decline was not supported. Thoughtful consideration is needed when synthesizing evidence across studies, as methodologies adopted and sample characteristics, such as educational attainment, invariably differ. © 2012 The Author