Technical University of Berlin

About: Technical University of Berlin is a education organization based out in Berlin, Germany. It is known for research contribution in the topics: Quantum dot & Laser. The organization has 27292 authors who have published 59342 publications receiving 1414623 citations. The organization is also known as: Technische Universität Berlin & TU Berlin.

Verwendung von Schwingquarzen zur Wägung dünner Schichten und zur Mikrowägung

Abstract: Wird eine Fremdschicht auf eine zu Dickenscherungsschwingungen angeregte Schwingquarzplatte aufgebracht, so andert sich die Eigenfrequenz der Platte infolge Vergroserung der schwingenden Masse. Da die Frequenzanderung eines Schwingquarzes sehr genau vermessen werden kann, ergibt sich daraus eine sehr empfindliche Methode zur Wagung dunner Schichten. Massenbelegung der Fremdschicht und Frequenzanderung sind einander proportional. Die Proportionalitatskonstante last sich aus der Eigenfrequenz des Schwingquarzes berechnen, so das eine empirische Eichung bei der Schichtwagung mit Schwingquarzen entfallt. Die Genauigkeit des Schichtwageverfahrens ist in erster Linie durch die Temperaturabhangigkeit der Quarzeigenfrequenz begrenzt und betragt bei 1° C zugelassener Temperaturschwankung etwa ±4 · 10−9 g · cm−2. Das entspricht einer mittleren Dicke von 0,4 A bei der Dichte ϱ=1 g · cm−3. Das Verfahren wurde auch zur direkten Wagung einer Masse ausgenutzt (Mikrowagung). Dabei lies sich eine Genauigkeit von 10−10g erreichen.

Single Molecule Detection Using Surface-Enhanced Raman Scattering (SERS)

Abstract: By exploiting the extremely large effective cross sections ( ${10}^{\ensuremath{-}17}--{10}^{\ensuremath{-}16}{\mathrm{cm}}^{2}/\mathrm{molecule}$) available from surface-enhanced Raman scattering (SERS), we achieved the first observation of single molecule Raman scattering. Measured spectra of a single crystal violet molecule in aqueous colloidal silver solution using one second collection time and about $2\ifmmode\times\else\texttimes\fi{}{10}^{5}\mathrm{W}/{\mathrm{cm}}^{2}$ nonresonant near-infrared excitation show a clear ``fingerprint'' of its Raman features between 700 and $1700{\mathrm{cm}}^{\ensuremath{-}1}$. Spectra observed in a time sequence for an average of 0.6 dye molecule in the probed volume exhibited the expected Poisson distribution for actually measuring 0, 1, 2, or 3 molecules.

Completely Derandomized Self-Adaptation in Evolution Strategies

Abstract: This paper puts forward two useful methods for self-adaptation of the mutation distribution - the concepts of derandomization and cumulation. Principle shortcomings of the concept of mutative strategy parameter control and two levels of derandomization are reviewed. Basic demands on the self-adaptation of arbitrary (normal) mutation distributions are developed. Applying arbitrary, normal mutation distributions is equivalent to applying a general, linear problem encoding. The underlying objective of mutative strategy parameter control is roughly to favor previously selected mutation steps in the future. If this objective is pursued rigorously, a completely derandomized self-adaptation scheme results, which adapts arbitrary normal mutation distributions. This scheme, called covariance matrix adaptation (CMA), meets the previously stated demands. It can still be considerably improved by cumulation - utilizing an evolution path rather than single search steps. Simulations on various test functions reveal local and global search properties of the evolution strategy with and without covariance matrix adaptation. Their performances are comparable only on perfectly scaled functions. On badly scaled, non-separable functions usually a speed up factor of several orders of magnitude is observed. On moderately mis-scaled functions a speed up factor of three to ten can be expected.

On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation.

Abstract: Understanding and interpreting classification decisions of automated image classification systems is of high value in many applications, as it allows to verify the reasoning of the system and provides additional information to the human expert. Although machine learning methods are solving very successfully a plethora of tasks, they have in most cases the disadvantage of acting as a black box, not providing any information about what made them arrive at a particular decision. This work proposes a general solution to the problem of understanding classification decisions by pixel-wise decomposition of nonlinear classifiers. We introduce a methodology that allows to visualize the contributions of single pixels to predictions for kernel-based classifiers over Bag of Words features and for multilayered neural networks. These pixel contributions can be visualized as heatmaps and are provided to a human expert who can intuitively not only verify the validity of the classification decision, but also focus further analysis on regions of potential interest. We evaluate our method for classifiers trained on PASCAL VOC 2009 images, synthetic image data containing geometric shapes, the MNIST handwritten digits data set and for the pre-trained ImageNet model available as part of the Caffe open source package.

Silicene: Compelling Experimental Evidence for Graphenelike Two-Dimensional Silicon

Abstract: Because of its unique physical properties, graphene, a 2D honeycomb arrangement of carbon atoms, has attracted tremendous attention. Silicene, the graphene equivalent for silicon, could follow this trend, opening new perspectives for applications, especially due to its compatibility with Si-based electronics. Silicene has been theoretically predicted as a buckled honeycomb arrangement of Si atoms and having an electronic dispersion resembling that of relativistic Dirac fermions. Here we provide compelling evidence, from both structural and electronic properties, for the synthesis of epitaxial silicene sheets on a silver (111) substrate, through the combination of scanning tunneling microscopy and angular-resolved photoemission spectroscopy in conjunction with calculations based on density functional theory.