Deep Quaternion Networks

The field of deep learning has seen significant advancement in recent years. However, much of the existing work has been focused on real-valued numbers. Recent work has shown that a deep learning system using the complex numbers can be deeper for a fixed parameter budget compared to its real-valued counterpart. In this work, we explore the benefits of generalizing one step further into the hyper-complex numbers, quaternions specifically, and provide the architecture components needed to build deep quaternion networks. We develop the theoretical basis by reviewing quaternion convolutions, developing a novel quaternion weight initialization scheme, and developing novel algorithms for quaternion batch-normalization. These pieces are tested in a classification model by end-to-end training on the CIFAR-10 and CIFAR-100 data sets and a segmentation model by end-to-end training on the KITTI Road Segmentation data set. These quaternion networks show improved convergence compared to real-valued and complex-valued networks, especially on the segmentation task, while having fewer parametersComment: IJCNN 2018, 8 pages, 1 figur

Community-level natural selection modes: A quadratic framework to link multiple functional traits with competitive ability

Research linking functional traits to competitive ability of coexisting species has largely relied on rectilinear correlations, yielding inconsistent results. Based on concepts borrowed from natural selection theory, we propose that trait?competition relationships can generally correspond to three univariate selection modes: directional (a rectilinear relationship), stabilising (an n-shaped relationship), and disruptive (a u-shaped relationship). Moreover, correlational selection occurs when two traits interact in determining competitive ability and lead to an optimum trait combination (i.e., a bivariate nonlinear selection mode). We tested our ideas using two independent datasets, each one characterising a group of species according to (a) their competitive effect on a target species in a pot experiment and (b) species-level values of well-known functional traits extracted from existing databases. The first dataset comprised 10 annual plant species frequent in a summer-rainfall desert in Argentina, while the second consisted of 37 herbaceous species from cool temperate vegetation types in Canada. Both experiments had a replacement design where the identity of neighbours was manipulated holding total plant density in pots constant. We modelled the competitive ability of neighbours (i.e., the log inverse of target plant biomass) as a function of traits using normal multiple regression. Leaf dry matter content (LDMC) was consistently subjected to negative directional selection in both experiments as well as to stabilising selection among temperate species. Leaf size was subjected to stabilising selection among desert species while among temperate species, leaf size underwent correlational selection in combination with specific leaf area (SLA): selection on SLA was negative directional for large-leaved species, while it was slightly positive for small-leaved species. Synthesis. Multiple quadratic regression adds functional flexibility to trait-based community ecology while providing a standardised basis for comparison among traits and environments. Our analyses of two datasets from contrasting environmental conditions indicate (a) that leaf dry matter content can capture an important component of plant competitive ability not accounted for by widely used competitive traits, such as specific leaf area, leaf size, and plant height and (b) that optimum relationships (either univariate or bivariate) between competitive ability and plant traits may be more common than previously realised.Fil: Rolhauser, Andrés Guillermo. Universidad Nacional de San Juan; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Nordenstahl, Marisa. Universidad Nacional de San Juan; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Aguiar, Martin Roberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Pucheta, Eduardo Raúl. Universidad Nacional de San Juan; Argentin

Empirical Legal Studies Before 1940: A Bibliographic Essay

The modern empirical legal studies movement has well-known antecedents in the law and society and law and economics traditions of the latter half of the 20th century. Less well known is the body of empirical research on legal phenomena from the period prior to World War II. This paper is an extensive bibliographic essay that surveys the English language empirical legal research from approximately 1940 and earlier. The essay is arranged around the themes in the research: criminal justice, civil justice (general studies of civil litigation, auto accident litigation and compensation, divorce, small claims, jurisdiction and procedure, civil juries), debt and bankruptcy, banking, appellate courts, legal needs, legal profession (including legal education), and judicial staffing and selection. Accompanying the essay is an extensive bibliography of research articles, books, and reports

A General Approach to the Direct Detection of Dark Matter

It is an exciting time in the direct detection of dark matter. Many experiments are showing a signal, but most of them do not overlap. Our goal was to construct a simple non-relativistic effective field theory of dark matter-nuclei scattering. This is an extension of work done on the subject, but we do not take limiting cases on mediator mass and look at a pseudo-scalar and scalar exchange. With this we looked at how the different astrophysical uncertainties and mediator mass affected the direct detection signal. Using this and dark matter to quark annihilation, we were also able to set bounds on the coupling parameters of the model to within a single order of magnitude. This information could provide insight on how dark matter and nuclei interact by matching actual signals to our data

A Conserved Protein Interaction Interface on the Type 5 G Protein β Subunit Controls Proteolytic Stability and Activity of R7 Family Regulator of G Protein Signaling Proteins*

Regulators of G protein signaling (RGS) proteins of the R7 subfamily limit signaling by neurotransmitters in the brain and by light in the retina. They form obligate complexes with the Gβ5 protein that are subject to proteolysis to control their abundance and alter signaling. The mechanisms that regulate this proteolysis, however, remain unclear. We used genetic screens to find mutations in Gβ5 that selectively destabilize one of the R7 RGS proteins in Caenorhabditis elegans. These mutations cluster at the binding interface between Gβ5 and the N terminus of R7 RGS proteins. Equivalent mutations within mammalian Gβ5 allowed the interface to still bind the N-terminal DEP domain of R7 RGS proteins, and mutant Gβ5-R7 RGS complexes initially formed in cells but were then rapidly degraded by proteolysis. Molecular dynamics simulations suggest the mutations weaken the Gβ5-DEP interface, thus promoting dynamic opening of the complex to expose determinants of proteolysis known to exist on the DEP domain. We propose that conformational rearrangements at the Gβ5-DEP interface are key to controlling the stability of R7 RGS protein complexes