692 research outputs found
Neural networks classifying symbolic data
No full textHammer B. Neural networks classifying symbolic data. In: de Raedt L, Kramer S, eds. ICML workshop on attribute-value and relational learning: crossing the boundaries. 2000: 61-65
Comment on “A local realist model for correlations of the singlet state” by K. De Raedt, K. Keimpema, H. De Raedt, K. Michielsen and S. Miyashita
No full textDe Raedt et al. [Eur. Phys. J. B 53, 139 (2006)] have claimed to provide a local realist model for correlations of the singlet state in the familiar Einstein-Podolsky-Rosen-Bohm (EPRB) experiment when time-coincidence is used to decide which detection events should count in the analysis, and furthermore that this suggests that it is possible to construct local realistic models that can reproduce the quantum mechanical expectation values. In this letter we show that these conclusions cannot be upheld since their model exploits the so-called coincidence-time loophole. When this is properly taken into account no startling conclusions can be drawn about local realist modelling of quantum mechanics. Copyright EDP Sciences/Società Italiana di Fisica/Springer-Verlag 200703.65.Ud Entanglement and quantum nonlocality, 03.65.Ta Foundations of quantum mechanics,
Rest-related dynamics of risk and protective factors for depression: A behavioral study
No full textResting state provides an important condition to study depressogenic cognition because neuropsychological studies have indicated that rest is associated with heightened self-related processing. We examined relationships between rest-related internal focus, cognitive reactivity (vs. mindfulness), rumination, and negative mood outside the functional magnetic resonance imaging scanner in an undergraduate sample (N = 80). We tested a theory-based effect by which, in the presence of cognitive risk (vs. protective) factors, internal focus lowers mood via ruminative self-focus. Such an effect was detected in individuals with high cognitive reactivity, whereas brooding showed only an incremental effect. However, this dynamic was not significant in individuals with low cognitive reactivity, despite the level of brooding, or high mindfulness. These results provide an important window on risk for depressogenic thought during resting state. © The Author(s) 2013
Towards a corpuscular model of optical phenomena
No full textThis thesis presents a collection of event-by-event models that simulate fundamental optical experiments.
The simulation approach is completely based on the experimental facts.
Each component in the model corresponds to one kind of optical device, such as a beam splitter, a wave plate, a detector and so on.
Networks of such components build computational experiments which are one-to-one copies of real experiments.
As all components share the same mechanism (leaning machine) as in the previous work, our event-by-event simulation models are systematic and consistent with each other.
As the model provides a description of interference and other wave phenomena on the level of individual event,
it goes beyond the description of quantum theory. All the results presented in this thesis demonstrate that it is possible to simulate quantum phenomena by classical, non-Hamiltonian, local, causal and dynamical models.
Inductive queries for a drug designing robot scientist
No full textIt is increasingly clear that machine learning algorithms need to be integrated in an iterative scientific discovery loop, in which data is queried repeatedly by means of inductive queries and where the computer provides guidance to the experiments that are being performed. In this chapter, we summarise several key challenges in achieving this integration of machine learning and data mining algorithms in methods for the discovery of Quantitative Structure Activity Relationships (QSARs). We introduce the concept of a robot scientist, in which all steps of the discovery process are automated; we discuss the representation of molecular data such that knowledge discovery tools can analyse it, and we discuss the adaptation of machine learning and data mining algorithms to guide QSAR experiments
From Statistical Relational to Neural Symbolic Artificial Intelligence: a Survey
No full textsponsorship: This work has received funding from the Research Foundation-Flanders (FWO) (G. Marra: 1239422N, S. Dumancic: 12ZE520N, R. Manhaeve: 1S61718N). Luc De Raedt has received funding from the Flemish Government (AI Research Program), from the FWO, from the KU Leuven Research Fund (C1418062), from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 694980 SYNTH: Synthesising Inductive Data Models) and the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation. This work was also supported by TAILOR, a project funded by EU Horizon 2020 research and innovation programme under GA No 952215. (Research Foundation-Flanders (FWO)|1239422N, Research Foundation-Flanders (FWO)|C1418062, Flemish Government (AI Research Program), FWO, KU Leuven Research Fund|694980, European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme|952215, Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation, TAILOR - EU Horizon 2020 research and innovation programme|12ZE520N, 1S61718N)status: Published onlin
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