Cognitive theory development as we know it: specificity, explanatory power and the brain

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Consistency measures individuate dissociating semantic modulations in priming paradigms: A new look on semantics in the processing of (complex) words

In human language the mapping between form and meaning is arbitrary, as there is no direct connection between words and the objects that they represent. However, within a given language, it is possible to recognize systematic associations that support productivity and comprehension. In this work, we focus on the consistency between orthographic forms and meaning, and we investigate how the cognitive system may exploit it to process words. We take morphology as our case study, since it arguably represents one of the most notable examples of systematicity in form-meaning mapping. In a series of three experiments, we investigate the impact of form-meaning mapping in word processing by testing new consistency metrics as predictors of priming magnitude in primed lexical decision. In Experiment 1, we re-analyse data from five masked morphological priming studies and show that Orthography-Semantics-Consistency explains independent variance in priming magnitude, suggesting that word semantics is accessed already at early stages of word processing and that crucially semantic access is constrained by word orthography. In Experiment 2 and 3, we investigate whether this pattern is replicated when looking at semantic priming. In Experiment 2, we show that Orthography-Semantics-Consistency is not a viable predictor of priming magnitude with longer SOA. However, in Experiment 3, we develop a new semantic consistency measure based on the semantic density of target neighbourhoods. This measure is shown to significantly predict independent variance in semantic priming effect. Overall our results indicate that consistency measures provide crucial information for the understanding of word processing. Specifically, the dissociation between measures and priming paradigms shows that different priming conditions are associated with the activation of different semantic cohorts

Managing the Socially Marginalized: Attitudes Towards Welfare, Punishment and Race

Welfare and incarceration policies have converged to form a system of governance over socially marginalized groups, particularly racial minorities. In both of these policy areas, rehabilitative and social support objectives have been replaced with a more punitive and restrictive system. The authors examine the convergence in individual-level attitudes concerning welfare and criminal punishment, using national survey data. The authors\u27 analysis indicates a statistically significant relationship between punitive attitudes toward welfare and punishment. Furthermore, accounting for the respondents\u27 racial attitudes explains the bivariate relationship between welfare and punishment. Thus, racial attitudes seemingly link support for punitive approaches to opposition to welfare expenditures. The authors discuss the implications of this study for welfare and crime control policies by way of the conclusion

Constructing Delaunay triangulations along space-filling curves

Incremental construction con BRIO using a space-filling curve order for insertion is a popular algorithm for constructing Delaunay triangulations. So far, it has only been analyzed for the case that a worst-case optimal point location data structure is used which is often avoided in implementations. In this paper, we analyze its running time for the more typical case that points are located by walking. We show that in the worst-case the algorithm needs quadratic time, but that this can only happen in degenerate cases. We show that the algorithm runs in O(n logn) time under realistic assumptions. Furthermore, we show that it runs in expected linear time for many random point distributions. This research was supported by the Deutsche Forschungsgemeinschaft within the European graduate program ’Combinatorics, Geometry, and Computation’ (No. GRK 588/2) and by the Netherlands’ Organisation for Scientific Research (NWO) under BRICKS/FOCUS grant number 642.065.503 and project no. 639.022.707

The ℓs\ell^s-boundedness of a family of integral operators on UMD Banach function spaces

We prove the $\ell^s$-boundedness of a family of integral operators with an operator-valued kernel on UMD Banach function spaces. This generalizes and simplifies earlier work by Gallarati, Veraar and the author, where the $\ell^s$-boundedness of this family of integral operators was shown on Lebesgue spaces. The proof is based on a characterization of $\ell^s$-boundedness as weighted boundedness by Rubio de Francia.Comment: 13 pages. Generalization of arXiv:1410.665

On the use of cartographic projections in visualizing phylo-genetic tree space

Phylogenetic analysis is becoming an increasingly important tool for biological research. Applications include epidemiological studies, drug development, and evolutionary analysis. Phylogenetic search is a known NP-Hard problem. The size of the data sets which can be analyzed is limited by the exponential growth in the number of trees that must be considered as the problem size increases. A better understanding of the problem space could lead to better methods, which in turn could lead to the feasible analysis of more data sets. We present a definition of phylogenetic tree space and a visualization of this space that shows significant exploitable structure. This structure can be used to develop search methods capable of handling much larger data sets

Mapping the internal recognition surface of an octanuclear coordination cage using guest libraries

Size and shape criteria for guest binding inside the cavity of an octanuclear cubic coordination cage in water have been established using a new fluorescence displacement assay to quantify guest binding. For aliphatic cyclic ketones of increasing size (from C5 to C11), there is a linear relationship between ΔG for guest binding and the guest’s surface area: the change in ΔG for binding is 0.3 kJ mol–1 Å–2, corresponding to 5 kJ mol–1 for each additional CH2 group in the guest, in good agreement with expectations based on hydrophobic desolvation. The highest association constant is K = 1.2 × 106 M–1 for cycloundecanone, whose volume is approximately 50% of the cavity volume; for larger C12 and C13 cyclic ketones, the association constant progressively decreases as the guests become too large. For a series of C10 aliphatic ketones differing in shape but not size, ΔG for guest binding showed no correlation with surface area. These guests are close to the volume limit of the cavity (cf. Rebek’s 55% rule), so the association constant is sensitive to shape complementarity, with small changes in guest structure resulting in large changes in binding affinity. The most flexible members of this series (linear aliphatic ketones) did not bind, whereas the more preorganized cyclic ketones all have association constants of 104–105 M–1. A crystal structure of the cage·cycloundecanone complex shows that the guest carbonyl oxygen is directed into a binding pocket defined by a convergent set of CH groups, which act as weak hydrogen-bond donors, and also shows close contacts between the exterior surface of the disc-shaped guest and the interior surface of the pseudospherical cage cavity despite the slight mismatch in shape

Constructing majority-rule supertrees

<p>Abstract</p> <p>Background</p> <p>Supertree methods combine the phylogenetic information from multiple partially-overlapping trees into a larger phylogenetic tree called a supertree. Several supertree construction methods have been proposed to date, but most of these are not designed with any specific properties in mind. Recently, Cotton and Wilkinson proposed extensions of the majority-rule consensus tree method to the supertree setting that inherit many of the appealing properties of the former.</p> <p>Results</p> <p>We study a variant of one of Cotton and Wilkinson's methods, called majority-rule (+) supertrees. After proving that a key underlying problem for constructing majority-rule (+) supertrees is NP-hard, we develop a polynomial-size exact integer linear programming formulation of the problem. We then present a data reduction heuristic that identifies smaller subproblems that can be solved independently. While this technique is not guaranteed to produce optimal solutions, it can achieve substantial problem-size reduction. Finally, we report on a computational study of our approach on various real data sets, including the 121-taxon, 7-tree Seabirds data set of Kennedy and Page.</p> <p>Conclusions</p> <p>The results indicate that our exact method is computationally feasible for moderately large inputs. For larger inputs, our data reduction heuristic makes it feasible to tackle problems that are well beyond the range of the basic integer programming approach. Comparisons between the results obtained by our heuristic and exact solutions indicate that the heuristic produces good answers. Our results also suggest that the majority-rule (+) approach, in both its basic form and with data reduction, yields biologically meaningful phylogenies.</p

Robust Poisson Surface Reconstruction

Abstract. We propose a method to reconstruct surfaces from oriented point clouds with non-uniform sampling and noise by formulating the problem as a convex minimization that reconstructs the indicator func-tion of the surface’s interior. Compared to previous models, our recon-struction is robust to noise and outliers because it substitutes the least-squares fidelity term by a robust Huber penalty; this allows to recover sharp corners and avoids the shrinking bias of least squares. We choose an implicit parametrization to reconstruct surfaces of unknown topology and close large gaps in the point cloud. For an efficient representation, we approximate the implicit function by a hierarchy of locally supported basis elements adapted to the geometry of the surface. Unlike ad-hoc bases over an octree, our hierarchical B-splines from isogeometric analysis locally adapt the mesh and degree of the splines during reconstruction. The hi-erarchical structure of the basis speeds-up the minimization and efficiently represents clustered data. We also advocate for convex optimization, in-stead isogeometric finite-element techniques, to efficiently solve the min-imization and allow for non-differentiable functionals. Experiments show state-of-the-art performance within a more flexible framework.