Life history of the rare red alga Tsengia bairdii (=Platoma bairdii) (Nemastomataceae, Rhodophyta) from Scotland

A single thallus of the rare red seaweed Tsengia bairdii (Farlow) K. Fan et Y.Fan( Platoma bairdii (Farlow) Kuckuck) (Nemastomataceae) was collected on a subtidal pebble on the west coast of Scotland. The terete gelatinous axes, which were only 7 mm high, were monoecious. They bore numerous cystocarps and a few spermatangia, which represent the first observation of male structures in this genus. Released carpospores grew into expanded basal discs that gave rise to erect axes bearing irregularly cruciate tetrasporangia. irregularly cruciate to zonate tetrasporangia were also formed on these basal discs. Karyological studies on dividing tetrasporocytes showed about 25 bodies, identified as paired meiotic chromosomes on the basis of their size in comparison to mitotic and meiotic chromosomes in other red algal species. These observations confirm the isomorphic life history inferred from early field collections and show that this species is monoecious. Tsengia bairdii is an extremely rare seaweed in Europe - it seems to be confined to sublittoral cobbles and has a temporally patchy distribution.

A 64-point fourier transform chip for video motion compensation using phase correlation

Details of a new low power fast Fourier transform (FFT) processor for use in digital television applications are presented. This has been fabricated using a 0.6-µm CMOS technology and can perform a 64 point complex forward or inverse FFT on real-time video at up to 18 Megasamples per second. It comprises 0.5 million transistors in a die area of 7.8 × 8 mm and dissipates 1 W. The chip design is based on a novel VLSI architecture which has been derived from a first principles factorization of the discrete Fourier transform (DFT) matrix and tailored to a direct silicon implementation.

Autonomy and Control of State Agencies:Comparing States and Agencies

Do public organizations with similar tasks or structures differ across states with respect to their autonomy and control? If so, why? By comparing the autonomy, control and internal management of state agencies, this book shows how New Public Management doctrines actually work in three small European states with different politico-administrative regimes. Using a unique set of similar survey data on 226 state agencies in Norway, Ireland and Flanders, this study explains differences in agency autonomy, control and management by referring to international isomorphic pressures, state-specific politico-administrative regimes and characteristics of agencies. Therefore, organization theory and neo-institutional schools are used to formulate four competing theoretical perspectives and hypotheses are tested through simple and advanced statistical techniques. By comparing practices between states and between types of agencies, this study substantially enhances scientific knowledge about why public organizations are granted autonomy, why they are controlled in specific ways, and how autonomy affects internal management.

Isomorphism between ice and silica

Both ice and silica crystallize into solid-state structures composed of tetrahedral building units that are joined together to form an infinite four-connected net. Mathematical considerations suggest that there is a vast number of such nets and thus potential crystal structures. It is therefore perhaps surprising to discover that, despite the differences in the nature of interatomic interactions in these materials, a fair number of commonly observed ice and silica phases are based on common nets. Here we use computer simulation to investigate the origin of this symmetry between the structures formed for ice and silica and to attempt to understand why it is not complete. We start from a comparison of the dense phases and then move to the relationship between the different open (zeolitic and clathratic) structures formed for both materials. We show that there is a remarkably strong correlation between the energetics of isomorphic silica and water ice structures and that this correlation arises because of the strong link between the total energy of a material and its local geometric features. Finally, we discuss a number of as yet unsynthesized low-energy structures which include a phase of ice based on quartz, a silica based on the structure of ice VI, and an ice clathrate that is isomorphic to the silicate structure nonasil.

An isomorphism between group C*-algebras of ax + b-like groups

We give a necessary and sufficient condition for two ax+b-like groups to have isomorphic C*-algebras. In particular, we show that there are many non-isomorphic ax+b -like Lie groups having isomorphic group C*-algebras.

Learning Effective and Interpretable Semantic Models using Non-Negative Sparse Embedding

In this paper, we introduce an application of matrix factorization to produce corpus-derived, distributional
models of semantics that demonstrate cognitive plausibility. We find that word representations
learned by Non-Negative Sparse Embedding (NNSE), a variant of matrix factorization, are sparse,
effective, and highly interpretable. To the best of our knowledge, this is the first approach which
yields semantic representation of words satisfying these three desirable properties. Though extensive
experimental evaluations on multiple real-world tasks and datasets, we demonstrate the superiority
of semantic models learned by NNSE over other state-of-the-art baselines.

Turbo-smt: Accelerating coupled sparse matrix-tensor factorizations by 200x

How can we correlate the neural activity in the human brain as it responds to typed words, with properties of these terms (like ‘edible’, ‘fits in hand’)? In short, we want to find latent variables, that jointly explain both the brain activity, as well as the behavioral responses. This is one of many settings of the Coupled Matrix-Tensor Factorization (CMTF) problem.

Can we accelerate any CMTF solver, so that it runs within a few minutes instead of tens of hours to a day, while maintaining good accuracy? We introduce Turbo-SMT, a meta-method capable of doing exactly that: it boosts the performance of any CMTF algorithm, by up to 200x, along with an up to 65 fold increase in sparsity, with comparable accuracy to the baseline.

We apply Turbo-SMT to BrainQ, a dataset consisting of a (nouns, brain voxels, human subjects) tensor and a (nouns, properties) matrix, with coupling along the nouns dimension. Turbo-SMT is able to find meaningful latent variables, as well as to predict brain activity with competitive accuracy.

Discovering subgroups of patients from DNA copy number data using NMF on compacted matrices

In the study of complex genetic diseases, the identification of subgroups of patients sharing similar genetic characteristics represents a challenging task, for example, to improve treatment decision. One type of genetic lesion, frequently investigated in such disorders, is the change of the DNA copy number (CN) at specific genomic traits. Non-negative Matrix Factorization (NMF) is a standard technique to reduce the dimensionality of a data set and to cluster data samples, while keeping its most relevant information in meaningful components. Thus, it can be used to discover subgroups of patients from CN profiles. It is however computationally impractical for very high dimensional data, such as CN microarray data. Deciding the most suitable number of subgroups is also a challenging problem. The aim of this work is to derive a procedure to compact high dimensional data, in order to improve NMF applicability without compromising the quality of the clustering. This is particularly important for analyzing high-resolution microarray data. Many commonly used quality measures, as well as our own measures, are employed to decide the number of subgroups and to assess the quality of the results. Our measures are based on the idea of identifying robust subgroups, inspired by biologically/clinically relevance instead of simply aiming at well-separated clusters. We evaluate our procedure using four real independent data sets. In these data sets, our method was able to find accurate subgroups with individual molecular and clinical features and outperformed the standard NMF in terms of accuracy in the factorization fitness function. Hence, it can be useful for the discovery of subgroups of patients with similar CN profiles in the study of heterogeneous diseases.

Complexity of Inferences in Polytree-shaped Semi-Qualitative Probabilistic Networks

Semi-qualitative probabilistic networks (SQPNs) merge two important graphical model formalisms: Bayesian networks and qualitative probabilistic networks. They provide a very general modeling framework by allowing the combination of numeric and qualitative assessments over a discrete domain, and can be compactly encoded by exploiting the same factorization of joint probability distributions that are behind the Bayesian networks. This paper explores the computational complexity of semi-qualitative probabilistic networks, and takes the polytree-shaped networks as its main target. We show that the inference problem is coNP-Complete for binary polytrees with multiple observed nodes. We also show that inferences can be performed in linear time if there is a single observed node, which is a relevant practical case. Because our proof is constructive, we obtain an efficient linear time algorithm for SQPNs under such assumptions. To the best of our knowledge, this is the first exact polynomial-time algorithm for SQPNs. Together these results provide a clear picture of the inferential complexity in polytree-shaped SQPNs.

Schur multipliers of Cartan pairs

We define the Schur multipliers of a separable von Neumann algebra M with Cartan masa A, generalising the classical Schur multipliers of B(` 2 ). We characterise these as the normal A-bimodule maps on M. If M contains a direct summand isomorphic to the hyper- finite II1 factor, then we show that the Schur multipliers arising from the extended Haagerup tensor product A ⊗eh A are strictly contained in the algebra of all Schur multipliers.

Orbital and strongly orbital spaces

We say that a (countably dimensional) topological vector space X is orbital if there is T∈L(X) and a vector x∈X such that X is the linear span of the orbit {Tnx:n=0,1,…}. We say that X is strongly orbital if, additionally, x can be chosen to be a hypercyclic vector for T. Of course, X can be orbital only if the algebraic dimension of X is finite or infinite countable. We characterize orbital and strongly orbital metrizable locally convex spaces. We also show that every countably dimensional metrizable locally convex space X does not have the invariant subset property. That is, there is T∈L(X) such that every non-zero x∈X is a hypercyclic vector for T. Finally, assuming the Continuum Hypothesis, we construct a complete strongly orbital locally convex space.

As a byproduct of our constructions, we determine the number of isomorphism classes in the set of dense countably dimensional subspaces of any given separable infinite dimensional Fréchet space X. For instance, in X=ℓ2×ω, there are exactly 3 pairwise non-isomorphic (as topological vector spaces) dense countably dimensional subspaces.