Quantification of statistical type i and ii errors in correlation analysis of simulated functional magnetic resonance imaging data

The potential of statistical analyses of functional magnetic resonance images using various threshold strategies in combination with correlation analysis was studied by simulating brain activation. Differences in statistical Type I () and II () errors are substantial for the various thresholds. Absolute thresholds and individualized thresholds based on the assumption of a gaussian noise distribution are producing constant -errors and thus do not sufficiently improve discrimination of truly activated pixels even for very high contrast-to-noise ratios (CNR). Only relative threshold strategies related to the maximum correlation coefficient and thus the individual data quality and activation level, i.e., a data-driven approach, can perfectly discriminate true positives, at least for CNR>2.5. To further improve discrimination of activated and non-activated pixel in studies with lower CNR, additional prior knowledge would be necessary. From the data presented, one would also expect that the best performing threshold strategy in this simulation study would perform best underin vivo conditions.Preliminary results presented in abstract form at the 13th Annual ESMRMB Meeting, Prague, Czech Republic, September 12–15, 1996.     

Pressure dependence of superconductivity in Ba4C60

In Ba_xC₆₀ superconductivity (sc) at T_c=(6.8±0.2) K is associated with the x=4 phase. Here we present pressure studies of T_c of a Ba_xC₆₀ sample (dT_c/dp= –1.9 K/GPa) which exhibits a large volume fraction of the x=4 phase. In the alkali-fullerides K₆₀C₆₀ and Rb₃C₆₀ T_c is governed by the electronic density of states N(E_F) (T_c exp{–1/N(E_F)V}). The dependence of N(E_F) on volume can be approximated by N(E_F)=N₀ d _eff ³, where d_eff is the surface to surface distance of neighboured C₆₀-molecules. This relationship is corroborated by (T_C,d)-data of various compounds of the form A_3–xB_xC₆₀ (A: K, Rb; B: K, Rb, Cs; x2). Strong deviation from this behavior is found for the alkaline-fulleride superconductor Ba₄C₆₀. This deviation is attributed to the hybridization of alkaline-metal s-, d-states and C₆₀ -states.     

Combination problems for commutative/monoidal theories or how algebra can help in equational unification

We study the class of theories for which solving unification problems is equivalent to solving systems of linear equations over a semiring. It encompasses important examples like the theories of Abelian monoids, idempotent Abelian monoids, and Abelian groups. This class has been introduced by the authors independently of each other as commutative theories (Baader) and monoidal theories (Nutt).We show that commutative theories and monoidal theories indeed define the same class (modulo a translation of the signature), and we prove that it is undecidable whether a given theory belongs to it. In the remainder of the paper we investigate combinations of commutative/monoidal theories with other theories. We show that finitary commutative/monoidal theories always satisfy the requirements for applying general methods developed for the combination of unification algorithms for disjoint equational theories.Then we study the adjunction of monoids of homomorphisms to commutative/monoidal theories. This is a special case of a non-disjoint combination, which has an algebraic counterpart in the corresponding semiring. By studying equations over this semiring, we identify a large subclass of commutative/monoidal theories that are of unification type zero. We also show with methods from linear algebra that unitary and finitary commutative/monoidal theories do not change their unification type when they are augmented by a finite monoid of homomor-phisms, and how algorithms for the extended theory can be obtained from algorithms for the basic theory.     

Associating visual textures with human perceptions using genetic algorithms

This paper deals with an approach allowing to associate visual textures with given human perceptions. Hereby, based on a forward model associating human perceptions for given visual textures, the deduction of an reverse process is presented which is able to associate and characterize visual textures for given human perceptions. For doing so, we propose a constraint-based genetic algorithm approach, which is able to minimize a specific optimization problem containing constraints in form of band-widths for valid individuals (low level features extracted from textures) in a population. The constraints are determined by relationships between (low level) features characterizing textures in form of high-dimensional approximation models. Additionally, in each iteration step checking for valid individuals is carried out with a texture/non-texture classifier or by using a convex hull over a set of valid textures. The whole approach is evaluated based on a real-world texture set used as a start population in the genetic algorithm and by defining various kinds of human perceptions (for which textures are sought) represented by adjective vectors in the aesthetic space. The generated individuals (low level feature vectors) have a high level of fitness (they are quite close to the pre-defined adjective vectors) and a small distance to the initial population. The textures synthesized based on the generated individuals are visualized and compared with textures synthesized by a time-intensive direct texture mixing and re-combination method based on a real-world texture data base.     

Parallel machine problems with equal processing times: a survey

The basic scheduling problem we are dealing with is the following. There are n jobs, each requiring an identical execution time. All jobs have to be processed on a set of parallel machines. Preemptions can be either allowed or forbidden. The aim is to construct a feasible schedule such that a given criterion is minimized. In this paper, we survey existing approaches for the problem class considered.     

Bug localization using latent Dirichlet allocation

ContextSome recent static techniques for automatic bug localization have been built around modern information retrieval (IR) models such as latent semantic indexing (LSI). Latent Dirichlet allocation (LDA) is a generative statistical model that has significant advantages, in modularity and extensibility, over both LSI and probabilistic LSI (pLSI). Moreover, LDA has been shown effective in topic model based information retrieval. In this paper, we present a static LDA-based technique for automatic bug localization and evaluate its effectiveness.ObjectiveWe evaluate the accuracy and scalability of the LDA-based technique and investigate whether it is suitable for use with open-source software systems of varying size, including those developed using agile methods.MethodWe present five case studies designed to determine the accuracy and scalability of the LDA-based technique, as well as its relationships to software system size and to source code stability. The studies examine over 300 bugs across more than 25 iterations of three software systems.ResultsThe results of the studies show that the LDA-based technique maintains sufficient accuracy across all bugs in a single iteration of a software system and is scalable to a large number of bugs across multiple revisions of two software systems. The results of the studies also indicate that the accuracy of the LDA-based technique is not affected by the size of the subject software system or by the stability of its source code base.ConclusionWe conclude that an effective static technique for automatic bug localization can be built around LDA. We also conclude that there is no significant relationship between the accuracy of the LDA-based technique and the size of the subject software system or the stability of its source code base. Thus, the LDA-based technique is widely applicable.     

Study on TSV isolation liners for a Via Last approach with the use in 3D-WLP for MEMS

This paper discusses approaches for the isolation of deep high aspect ratio through silicon vias (TSV) with respect to a Via Last approach for micro-electro-mechanical systems (MEMS). Selected TSV samples have depths in the range of 170…270 µm and a diameter of 50 µm. The investigations comprise the deposition of different layer stacks by means of subatmospheric and plasma enhanced chemical vapour deposition (PECVD) of tetraethyl orthosilicate; Si(OC₂H₅)₄ (TEOS). Moreover, an etch-back approach and the selective deposition on SiN were also included in the investigations. With respect to the Via Last approach, the contact opening at the TSV bottom by means of a specific spacer-etching method have been addressed within this paper. Step coverage values of up to 74 % were achieved for the best of those approaches. As an alternative to the SiO₂-isolation liners a polymer coating based on the CVD of Parylene F was investigated, which yields even higher step coverage in the range of 80 % at the lower TSV sidewall for a surface film thickness of about 1000 nm. Leakage current measurements were performed and values below 0.1 nA/cm² at 10 kV/cm were determined for the Parylene F films which represents a promising result for the aspired application to Via Last MEMS-TSV.     

Self-consistent gradient flow for shape optimization

We present a model for image segmentation and describe a gradient-descent method for level-set based shape optimization. It is commonly known that gradient-descent methods converge slowly due to zig–zag movement. This can also be observed for our problem, especially when sharp edges are present in the image. We interpret this in our specific context to gain a better understanding of the involved difficulties. One way to overcome slow convergence is the use of second-order methods. For our situation, they require derivatives of the potentially noisy image data and are thus undesirable. Hence, we propose a new method that can be interpreted as a self-consistent gradient flow and does not need any derivatives of the image data. It works very well in practice and leads to a far more efficient optimization algorithm. A related idea can also be used to describe the mean-curvature flow of a mean-convex surface. For this, we formulate a mean-curvature Eikonal equation, which allows a numerical propagation of the mean-curvature flow of a surface without explicit time stepping.     

Continuous-time quantum Monte Carlo impurity solvers

Continuous-time quantum Monte Carlo impurity solvers are algorithms that sample the partition function of an impurity model using diagrammatic Monte Carlo techniques. The present paper describes codes that implement the interaction expansion algorithm originally developed by Rubtsov, Savkin, and Lichtenstein, as well as the hybridization expansion method developed by Werner, Millis, Troyer, et al. These impurity solvers are part of the ALPS-DMFT application package and are accompanied by an implementation of dynamical mean-field self-consistency equations for (single orbital single site) dynamical mean-field problems with arbitrary densities of states.

Program summary

Program title: dmftCatalogue identifier: AEIL_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEIL_v1_0.htmlProgram obtainable from: CPC Program Library, Queen's University, Belfast, N. IrelandLicensing provisions: ALPS LIBRARY LICENSE version 1.1No. of lines in distributed program, including test data, etc.: 899 806No. of bytes in distributed program, including test data, etc.: 32 153 916Distribution format: tar.gzProgramming language: C++Operating system: The ALPS libraries have been tested on the following platforms and compilers:

• • 

  Linux with GNU Compiler Collection (g++ version 3.1 and higher), and Intel C++ Compiler (icc version 7.0 and higher)

• • 

  MacOS X with GNU Compiler (g++ Apple-version 3.1, 3.3 and 4.0)

• • 

  IBM AIX with Visual Age C++ (xlC version 6.0) and GNU (g++ version 3.1 and higher) compilers

• • 

  Compaq Tru64 UNIX with Compq C++ Compiler (cxx)

• • 

  SGI IRIX with MIPSpro C++ Compiler (CC)

• • 

  HP-UX with HP C++ Compiler (aCC)

• • 

  Windows with Cygwin or coLinux platforms and GNU Compiler Collection (g++ version 3.1 and higher)

RAM: 10 MB–1 GBClassification: 7.3External routines: ALPS [1], BLAS/LAPACK, HDF5Nature of problem: (See [2].) Quantum impurity models describe an atom or molecule embedded in a host material with which it can exchange electrons. They are basic to nanoscience as representations of quantum dots and molecular conductors and play an increasingly important role in the theory of “correlated electron” materials as auxiliary problems whose solution gives the “dynamical mean field” approximation to the self-energy and local correlation functions.Solution method: Quantum impurity models require a method of solution which provides access to both high and low energy scales and is effective for wide classes of physically realistic models. The continuous-time quantum Monte Carlo algorithms for which we present implementations here meet this challenge. Continuous-time quantum impurity methods are based on partition function expansions of quantum impurity models that are stochastically sampled to all orders using diagrammatic quantum Monte Carlo techniques. For a review of quantum impurity models and their applications and of continuous-time quantum Monte Carlo methods for impurity models we refer the reader to [2].Additional comments: Use of dmft requires citation of this paper. Use of any ALPS program requires citation of the ALPS [1] paper.Running time: 60 s–8 h per iteration.References:

• [1] 

  A. Albuquerque, F. Alet, P. Corboz, et al., J. Magn. Magn. Mater. 310 (2007) 1187.

• [2] 

  http://arxiv.org/abs/1012.4474, Rev. Mod. Phys., in press.

     

Relating the X-band opacity of a tropical tree canopy to sapflow, rain interception and dew formation

During summer and autumn 2007, a 11 GHz microwave radiometer was deployed in an experimental tree plantation in Sardinilla, Panama. The opacity of the tree canopy was derived from incoming brightness temperatures received on the ground. A collocated eddy-covariance flux tower measured water vapor fluxes and meteorological variables above the canopy. In addition, xylem sapflow of trees was measured within the flux tower footprint. We observed considerable diurnal differences between measured canopy opacities and modeled theoretical opacities that were closely linked to xylem sapflow. It is speculated that dielectric changes in the leaves induced by the sapflow are causing the observed diurnal changes. In addition, canopy intercepted rain and dew formation also modulated the diurnal opacity cycle. With an enhanced canopy opacity model accounting for water deposited on the leaves, we quantified the influence of canopy stored water (i.e. intercepted water and dew) on the opacity. A time series of dew formation and rain interception was directly monitored during a period of two weeks. We found that during light rainfall up to 60% of the rain amount is intercepted by the canopy whereas during periods of intense rainfall only 4% were intercepted. On average, 0.17 mm of dew was formed during the night. Dew evaporation contributed 5% to the total water vapor flux measured above the canopy.