The influence of longwave ultraviolet radiation (u.v.-A) on the photosynthetic activity (14C-assimilation) of phytoplankton

The impact of u.v.-A (315-400 nm) on phytoplanktonic C-assimilation has been studied in situ and in the laboratory under artificial light. Water samples from Lake Lucerne were placed in DURAN-glass bottles and incubated either covered or uncovered with u.v. absorbing transparent tubes. Exposure to u.v.-A clearly inhibited 14C-assimilation in the uncovered samples both in situ and in the laboratory. Variations in visible light intensity and filtering of u.v.-B selectively demonstrated small inhibition of 14C-assimilation. U.v.-A inhibition of productivity is the major factor in the well known depression in productivity for surface water

Toward a unified theory of sparse dimensionality reduction in Euclidean space

Let $\Phi\in\mathbb{R}^{m\times n}$ be a sparse Johnson-Lindenstrauss transform [KN14] with $s$ non-zeroes per column. For a subset $T$ of the unit sphere, $\varepsilon\in(0,1/2)$ given, we study settings for $m,s$ required to ensure $$\mathop{\mathbb{E}}_\Phi \sup_{x\in T} \left|\|\Phi x\|_2^2 - 1 \right| < \varepsilon ,$$ i.e. so that $\Phi$ preserves the norm of every $x\in T$ simultaneously and multiplicatively up to $1+\varepsilon$. We introduce a new complexity parameter, which depends on the geometry of $T$, and show that it suffices to choose $s$ and $m$ such that this parameter is small. Our result is a sparse analog of Gordon's theorem, which was concerned with a dense $\Phi$ having i.i.d. Gaussian entries. We qualitatively unify several results related to the Johnson-Lindenstrauss lemma, subspace embeddings, and Fourier-based restricted isometries. Our work also implies new results in using the sparse Johnson-Lindenstrauss transform in numerical linear algebra, classical and model-based compressed sensing, manifold learning, and constrained least squares problems such as the Lasso

P-values for high-dimensional regression

Assigning significance in high-dimensional regression is challenging. Most computationally efficient selection algorithms cannot guard against inclusion of noise variables. Asymptotically valid p-values are not available. An exception is a recent proposal by Wasserman and Roeder (2008) which splits the data into two parts. The number of variables is then reduced to a manageable size using the first split, while classical variable selection techniques can be applied to the remaining variables, using the data from the second split. This yields asymptotic error control under minimal conditions. It involves, however, a one-time random split of the data. Results are sensitive to this arbitrary choice: it amounts to a `p-value lottery' and makes it difficult to reproduce results. Here, we show that inference across multiple random splits can be aggregated, while keeping asymptotic control over the inclusion of noise variables. We show that the resulting p-values can be used for control of both family-wise error (FWER) and false discovery rate (FDR). In addition, the proposed aggregation is shown to improve power while reducing the number of falsely selected variables substantially.Comment: 25 pages, 4 figure

De la sociologie de l'innovation à l'imagination sociologique : la théorie des champs à l'épreuve de la profession infirmière

Si l'on suit la perspective de Saussure pour qui le point de vue crée l'objet, la « nouveauté » d'un objet sociologique suppose au moins autant le renouvellement de son approche que la « nouveauté intrinsèque » de celui-ci. Dans cet article, nous privilégions la voie d'un tel renouvellement en mettant à l'épreuve un « vieil objet » par une « approche ancienne ». Nous inscrivant à contre-courant d'une tendance à la parcellisation de la discipline en sociologies thématiques, nous montrons la valeur heuristique qu'il y a à saisir une profession comme un espace social de positions différenciées qui ne prend sens qu'une fois réinscrit dans le champ au sein duquel il s'insère. Largement féminisée et partiellement dominée, la profession infirmière est soumise à une théorie traditionnellement mobilisée pour l'étude de groupes masculins et dominants : la théorie des champs de Pierre Bourdieu

Pivotal estimation in high-dimensional regression via linear programming

We propose a new method of estimation in high-dimensional linear regression model. It allows for very weak distributional assumptions including heteroscedasticity, and does not require the knowledge of the variance of random errors. The method is based on linear programming only, so that its numerical implementation is faster than for previously known techniques using conic programs, and it allows one to deal with higher dimensional models. We provide upper bounds for estimation and prediction errors of the proposed estimator showing that it achieves the same rate as in the more restrictive situation of fixed design and i.i.d. Gaussian errors with known variance. Following Gautier and Tsybakov (2011), we obtain the results under weaker sensitivity assumptions than the restricted eigenvalue or assimilated conditions

Goodness-of-fit tests for high dimensional linear models

We propose a framework for constructing goodness-of-fit tests in both low and high dimensional linear models. We advocate applying regression methods to the scaled residuals following either an ordinary least squares or lasso fit to the data, and using some proxy for prediction error as the final test statistic. We call this family residual prediction tests. We show that simulation can be used to obtain the critical values for such tests in the low dimensional setting and demonstrate using both theoretical results and extensive numerical studies that some form of the parametric bootstrap can do the same when the high dimensional linear model is under consideration.We show that residual prediction tests can be used to test for significance of groups or individual variables as special cases, and here they compare favourably with state of the art methods, but we also argue that they can be designed to test for as diverse model misspecifications as heteroscedasticity and non-linearity.Rajen Shah was supported in part by the Forschungsinstitut fur Mathematik at the Eidgenössiche Technische Hochschule Zürich

Goodness-of-fit tests for high dimensional linear models

We propose a framework for constructing goodness-of-fit tests in both low and high dimensional linear models. We advocate applying regression methods to the scaled residuals following either an ordinary least squares or lasso fit to the data, and using some proxy for prediction error as the final test statistic. We call this family residual prediction tests. We show that simulation can be used to obtain the critical values for such tests in the low dimensional setting and demonstrate using both theoretical results and extensive numerical studies that some form of the parametric bootstrap can do the same when the high dimensional linear model is under consideration.We show that residual prediction tests can be used to test for significance of groups or individual variables as special cases, and here they compare favourably with state of the art methods, but we also argue that they can be designed to test for as diverse model misspecifications as heteroscedasticity and non-linearity.Rajen Shah was supported in part by the Forschungsinstitut fur Mathematik at the Eidgenössiche Technische Hochschule Zürich

Identifiability of Gaussian structural equation models with equal error variances

We consider structural equation models in which variables can be written as a function of their parents and noise terms, which are assumed to be jointly independent. Corresponding to each structural equation model, there is a directed acyclic graph describing the relationships between the variables. In Gaussian structural equation models with linear functions, the graph can be identified from the joint distribution only up to Markov equivalence classes, assuming faithfulness. In this work, we prove full identifiability if all noise variables have the same variances: the directed acyclic graph can be recovered from the joint Gaussian distribution. Our result has direct implications for causal inference: if the data follow a Gaussian structural equation model with equal error variances and assuming that all variables are observed, the causal structure can be inferred from observational data only. We propose a statistical method and an algorithm that exploit our theoretical findings

ACS Applied Materials & Interfaces

Key parameters that influence the specific energy of electrochemical double-layer capacitors (EDLCs) are the double-layer capacitance and the operating potential of the cell. The operating potential of the cell is generally limited by the electrochemical window of the electrolyte solution, that is, the range of applied voltages within which the electrolyte or solvent is not reduced or oxidized. Ionic liquids are of interest as electrolytes for EDLCs because they offer relatively wide potential windows. Here, we provide a systematic study of the influence of the physical properties of ionic liquid electrolytes on the electrochemical stability and electrochemical performance (double-layer capacitance, specific energy) of EDLCs that employ a mesoporous carbon model electrode with uniform, highly interconnected mesopores (3DOm carbon). Several ionic liquids with structurally diverse anions (tetrafluoroborate, trifluoromethanesulfonate, trifluoromethanesulfonimide) and cations (imidazolium, ammonium, pyridinium, piperidinium, and pyrrolidinium) were investigated. We show that the cation size has a significant effect on the electrolyte viscosity and conductivity, as well as the capacitance of EDLCs. Imidazolium- and pyridinium-based ionic liquids provide the highest cell capacitance, and ammonium-based ionic liquids offer potential windows much larger than imidazolium and pyridinium ionic liquids. Increasing the chain length of the alkyl substituents in 1-alkyl-3-methylimidazolium trifluoromethanesulfonimide does not widen the potential window of the ionic liquid. We identified the ionic liquids that maximize the specific energies of EDLCs through the combined effects of their potential windows and the double-layer capacitance. The highest specific energies are obtained with ionic liquid electrolytes that possess moderate electrochemical stability, small ionic volumes, low viscosity, and hence high conductivity, the best performing ionic liquid tested being 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide

Handwritten digit recognition by bio-inspired hierarchical networks

The human brain processes information showing learning and prediction abilities but the underlying neuronal mechanisms still remain unknown. Recently, many studies prove that neuronal networks are able of both generalizations and associations of sensory inputs. In this paper, following a set of neurophysiological evidences, we propose a learning framework with a strong biological plausibility that mimics prominent functions of cortical circuitries. We developed the Inductive Conceptual Network (ICN), that is a hierarchical bio-inspired network, able to learn invariant patterns by Variable-order Markov Models implemented in its nodes. The outputs of the top-most node of ICN hierarchy, representing the highest input generalization, allow for automatic classification of inputs. We found that the ICN clusterized MNIST images with an error of 5.73% and USPS images with an error of 12.56%