Abstract:
Let X be a locally symmetric variety. Let EBS(X) and TorE(X) denote its excentric Borel-Serre and excentric toroidal compactifications, resp. We determine their least common modification and use it to prove a conjecture of Goresky and Tai concerning canonical extensions of homogeneous vector bundles. In the process, we see that EBS(X) and TorE(X) are homotopy equivalent.

Abstract:
The $L^2$-cohomology of a locally symmetric variety is known to have the topological interpretation as the intersection homology of its Baily-Borel Satake compactification. In this article, we observe that even without the Hermitian hypothesis, the $L^p$-cohomology of an arithmetic quotient, for $p$ finite and sufficiently large, is isomorphic to the ordinary cohomology of its reductive Borel-Serre compactification. We use this to generalize a theorem of Mumford concerning homogeneous vector bundles, their invariant Chern forms and the canonical extensions of the bundles; here, though, we are referring to canonical extensions to the reductive Borel-Serre compactification of any arithmetic quotient. To achieve that, we give a systematic discussion of vector bundles and Chern classes on stratified

Abstract:
We introduce the notion of Artin motives and cohomological motives over a scheme X. Given a cohomological motive M over X, we construct its punctual weight zero part $\omega^0_X(M)$ as the universal Artin motive mapping to M. We use this to define a motive E_X over X which is an invariant of the singularities of X. The first half of the paper is devoted to the study of the functors $\omega^0_X$ and the computation of the motives E_X. In the second half of the paper, we develop the application to locally symmetric varieties. Specifically, let Y be a locally symmetric variety and denote by p:W-->Z the projection of its reductive Borel-Serre compactification W onto its Baily-Borel Satake compactification Z. We show that $Rp_*(\Q_W)$ is naturally isomorphic to the Betti realization of the motive E_Z, where Z is viewed as a scheme. In particular, the direct image of E_Z along the projection of Z to Spec(C) gives a motive whose Betti realization is naturally isomorphic to the cohomology of W.

Abstract:
We develop a biologically-plausible learning rule called Triplet BCM that provably converges to the class means of general mixture models. This rule generalizes the classical BCM neural rule, and provides a novel interpretation of classical BCM as performing a kind of tensor decomposition. It achieves a substantial generalization over classical BCM by incorporating triplets of samples from the mixtures, which provides a novel information processing interpretation to spike-timing-dependent plasticity. We provide complete proofs of convergence of this learning rule, and an extended discussion of the connection between BCM and tensor learning.

Abstract:
We develop an algorithm to learn Bernoulli Mixture Models based on the principle that some variables are more informative than others. Working from an information-theoretic perspective, we propose both backward and forward schemes for selecting the informative 'active' variables and using them to guide EM. The result is a stagewise EM algorithm, analogous to stagewise approaches to linear regression, that should be applicable to neuroscience (and other) datasets with confounding (or irrelevant) variables. Results on synthetic and MNIST datasets illustrate the approach.

Abstract:
Association field models have attempted to explain human contour grouping performance, and to explain the mean frequency of long-range horizontal connections across cortical columns in V1. However, association fields only depend on the pairwise statistics of edges in natural scenes. We develop a spectral test of the sufficiency of pairwise statistics and show there is significant higher order structure. An analysis using a probabilistic spectral embedding reveals curvature-dependent components.

Abstract:
Shape from shading is a classical inverse problem in computer vision. This shape reconstruction problem is inherently ill-defined; it depends on the assumed light source direction. We introduce a novel mathematical formulation for calculating local surface shape based on covariant derivatives of the shading flow field, rather than the customary integral minimization or P.D.E approaches. On smooth surfaces, we show second derivatives of brightness are independent of the light sources and can be directly related to surface properties. We use these measurements to define the matching local family of surfaces that can result from any given shading patch, changing the emphasis to characterizing ambiguity in the problem. We give an example of how these local surface ambiguities collapse along certain image contours and how this can be used for the reconstruction problem.

Abstract:
Experimental data regarding auxin and venation formation exist at both macroscopic and molecular scales, and we attempt to unify them into a comprehensive model for venation formation. We begin with a set of principles to guide an abstract model of venation formation, from which we show how patterns in plant development are related to the representation of global distance information locally as cellular-level signals. Venation formation, in particular, is a function of distances between cells and their locations. The first principle, that auxin is produced at a constant rate in all cells, leads to a (Poisson) reaction-diffusion equation. Equilibrium solutions uniquely codify information about distances, thereby providing cells with the signal to begin differentiation from ground to vascular. A uniform destruction hypothesis and scaling by cell size leads to a more biologically-relevant (Helmholtz) model, and simulations demonstrate its capability to predict leaf and root auxin distributions and venation patterns. The mathematical development is centered on properties of the distance map, and provides a mechanism by which global information about shape can be presented locally to individual cells. The principles provide the foundation for an elaboration of these models in a companion paper \cite{plos-paper2}, and together they provide a framework for understanding organ- and plant-scale organization.

Abstract:
The principles underlying plant development are extended to allow a more molecular mechanism to elaborate the schema by which ground cells differentiate into vascular cells. Biophysical considerations dictate that linear dynamics are not sufficent to capture facilitated auxin transport (e.g., through PIN). We group these transport facilitators into a non-linear model under the assumption that they attempt to minimize certain {\em differences} of auxin concentration. This Constant Gradient Hypothesis greatly increases the descriptive power of our model to include complex dynamical behaviour. Specifically, we show how the early pattern of PIN1 expression appears in the embryo, how the leaf primordium emerges, how convergence points arise on the leaf margin, how the first loop is formed, and how the intricate pattern of PIN shifts during the early establishment of vein patterns in incipient leaves of Arabidopsis. Given our results, we submit that the model provides evidence that many of the salient structural characteristics that have been described at various stages of plant development can arise from the uniform application of a small number of abstract principles.

Abstract:
This is a substantial revision of the older version of this paper. The main result of the old version (the equality, up to a factor of 2 of the Beilinson and Borel regulators) is now a conjecture. The main results give equality of Beilinson chern classes and Cheeger-Simons-Chern classes in various situations such as for flat bundles over quasi projective varieties. We also prove the equality (up to a factor of two) of the Borel regulator element and the universal Cheeger-Simons Chern class.