Review of Two-Photon Interactions

Presented are recent results of two-photon interactions. Topics inlcude photon structure functions, inclusive hadron production, differential cross sections derived from tagged 2-photon fusion events and results in exclusive hadron production, particularly the observations of the eta_c prime.Comment: 5 pages, Latex, To be published in proceedings to CIPANP held in NY city May 200

Exploring the Charm Sector with CLEO-c

The CLEO collaboration proposes to explore the charm sector starting early 2003. It is foreseen to collect on the order of 6 million D D-bar pairs, 300000 Ds Ds-bar pairs at threshold and one billion J/psi decays. High precision charm data will enable us to validate upcoming Lattice QCD calculations that are expected to produce 1-3% errors for some non-perturbative QCD quantities. These can then be used to improve the accuracy of CKM elements. The radiative J/psi decays will be the first high statistics data set well suited for meson spectroscopy between 1600 and 3000 MeV.Comment: 10 pages, Latex, to be published in proceedings of Hadron 2001 Protvino Added Reference to Detector Section. Corrected typo in table

Measurement of HQET Parameters and CKM Matrix Elements

The determination of CKM matrix elements in the b-sector is discussed, emphasizing the new measurements of Vub and Vcb by the CLEO collaboration.Comment: 4 pages, Latex, to be published in proceedings of Hadron 2001 at Protvin

Active Nearest-Neighbor Learning in Metric Spaces

We propose a pool-based non-parametric active learning algorithm for general metric spaces, called MArgin Regularized Metric Active Nearest Neighbor (MARMANN), which outputs a nearest-neighbor classifier. We give prediction error guarantees that depend on the noisy-margin properties of the input sample, and are competitive with those obtained by previously proposed passive learners. We prove that the label complexity of MARMANN is significantly lower than that of any passive learner with similar error guarantees. MARMANN is based on a generalized sample compression scheme, and a new label-efficient active model-selection procedure

Oligoglycerol Detergents for Native Mass Spectrometry of Membrane Proteins

Membrane proteins are associated with biological membranes and fulfill functions that are vital for all living organisms. They mediate many physiological processes and are therefore important targets for the development of new pharmaceutical drugs. Numerous biophysical approaches have been developed that address the elucidation of their three-dimensional structure upon isolation. In this context, detergents are important tools, because they can isolate and stabilize membrane proteins apart from their native host. In recent years there has been a growing interest in studying the structure and dynamics of isolated membrane protein complexes by native mass spectrometry (MS). This technique can provide information about the mass and composition of a membrane protein complex and allows one to study interactions with structurally relevant ligands, such as drugs, nucleotides, or lipids. Detergents that are suitable for native MS do not only require solution conditions appropriate for membrane protein isolation but also gas-phase properties that help to retain native protein structures and their non-covalent interactions in the vacuum of a MS instrument. However, detergent families that are currently available can facilitate either the isolation of membrane proteins or their analysis by native MS. This thesis addresses the question, how one can optimize the molecular structure of a detergent family for membrane protein isolation and individual applications in native MS. With this regard, the utility of dendritic oligoglycerol detergents (OGDs) was evaluated, which have not yet been used in membrane protein research. For this purpose, a highly systematic OGD library was constructed by systematically changing the structure of these detergents. In order to prove their general utility for protein MS, mixtures between soluble proteins and OGDs were analyzed by native MS. Data obtained from the soluble protein ß-lactoglobulin (BLG) indicated that protein charge states can be manipulated by tuning the basicity of these detergents. A comparative study between BLG and the membrane protein OmpF revealed the validity of this design criterion and demonstrated for the first time the utility of OGDs for native MS of membrane proteins. Moreover, with a set of five different OGDs and four membrane proteins it was examined, how crucial aspects, such as membrane protein isolation, charge states, and the ability to detect binding with structurally relevant membrane lipids could be independently optimized by altering the OGDs’ molecular structure. Furthermore, these detergents enabled the easy MS analysis of a G protein-coupled receptor protein (GPCR), which is one of the most challenging and most important protein families in pharmacology. The obtained results open new avenues for the future development of detergents that cover optimal properties for membrane protein isolation and native MS applications.Membranproteine sind ein essentieller Bestandteil von biologischen Membranen. Sie tragen maßgeblich zur Regulierung von physiologischen Prozessen bei und bilden deshalb einen wichtigen Anhaltspunkt bei der Entwicklung von neuen Medikamenten. Verschiedene biophysikalische Methoden wurden entwickelt, um die dreidimensionale Struktur von Membranproteinen zu untersuchen. In diesem Zusammenhang haben Detergenzien eine besondere Bedeutung, denn sie können dabei helfen Membranproteine unter Erhalt ihrer nativen Struktur zu isolieren. In den vergangenen Jahren wuchs das Interesse an der Strukturuntersuchung von isolierten Membranproteinen mittels nativer Massenspektrometrie (MS). Diese Technik liefert Informationen über Masse und Zusammensetzung von komplexen Membranproteinen und ermöglicht die Untersuchung von Wechselwirkungen zu wichtigen Liganden, wie z. B. Medikamenten, Nukleotiden oder Lipiden. Die in diesem Zusammenhang bisher untersuchten Detergenzien bieten entweder vorteilhafte Eigenschaften für die Isolierung von Membranproteinen oder für deren Untersuchungen mittels nativer MS. In der vorliegenden Arbeit wurde die Fragestellung adressiert, ob es möglich ist die molekulare Struktur von Detergenzien für beide Anwendungsgebiete zu optimieren. Dafür wurde erstmalig das Potential von dendritischen Oligoglycerol-Detergenzien (OGDs) evaluiert. Für diesen Zweck wurde eine Substanzbibliothek aufgebaut, indem einzelne Strukturparameter der OGDs systematisch variiert wurden. Um die generelle Anwendbarkeit von OGDs für die MS-Analyse von Proteinen zu evaluieren, wurden im nächsten Schritt Mischungen von löslichen Proteinen und OGDs mittels MS untersucht. Beobachtungen führten zu der Hypothese, dass sich Ladungszustände von Proteinen durch die Basizität der OGDs steuern lassen. Weiterführende Studien mit dem trimeren Membranprotein OmpF untermauerten diese Hypothese und bestätigten darüber hinaus die Anwendbarkeit von OGDs für die Analyse von Membranproteinen mittels nativer MS. In einer weiteren Studie wurde mit Hilfe von fünf verschiedenen OGDs und vier verschiedenen Membranproteinen gezeigt, wie sich durch Variationen in der OGD-Struktur wichtige Aspekte individuell steuern lassen, z. B. die Isolierung von Membran- proteinen, Ladungsreduktion und die Möglichkeit Komplexe zwischen Membranproteinen und Lipiden zu detektieren. Darüber hinaus ermöglichten OGDs die einfache MS-Analyse von einem G-Protein- gekoppelten Rezeptor, welche derzeit eine der am schwierigsten zu untersuchenden Proteinfamilien ist und dessen Struktur von außerordentlicher pharmakologischer Bedeutung ist. Die vorliegenden Ergebnisse zeigen, wie sich die molekulare Struktur von OGDs für die Isolierung von Membranproteinen und nativer MS-Anwendungen optimieren lässt. Darauf aufbauend lassen sich neue Detergenzien entwickeln, welche in Zukunft die Untersuchungen von Membranproteinen erleichtern können

Efficient Learning of Linear Separators under Bounded Noise

We study the learnability of linear separators in $\Re^d$ in the presence of bounded (a.k.a Massart) noise. This is a realistic generalization of the random classification noise model, where the adversary can flip each example $x$ with probability $\eta(x) \leq \eta$. We provide the first polynomial time algorithm that can learn linear separators to arbitrarily small excess error in this noise model under the uniform distribution over the unit ball in $\Re^d$, for some constant value of $\eta$. While widely studied in the statistical learning theory community in the context of getting faster convergence rates, computationally efficient algorithms in this model had remained elusive. Our work provides the first evidence that one can indeed design algorithms achieving arbitrarily small excess error in polynomial time under this realistic noise model and thus opens up a new and exciting line of research. We additionally provide lower bounds showing that popular algorithms such as hinge loss minimization and averaging cannot lead to arbitrarily small excess error under Massart noise, even under the uniform distribution. Our work instead, makes use of a margin based technique developed in the context of active learning. As a result, our algorithm is also an active learning algorithm with label complexity that is only a logarithmic the desired excess error $\epsilon$

Secondary stresses in thin-walled beams with closed cross sections

An accurate method of determining secondary stresses in thin-walled, uniform beams of closed cross-section is herein presented. The cross-sections are assumed to be preserved by closely spaced rigid diaphragms. In part I the integro-differential equation governing axial displacements is formulated and solved for a beam without longitudinal stiffeners. In Part II the corresponding summation-difference equation is developed and solved for a beam with stiffeners (flanges and stringers). The cross-section, loading distribution and end conditions are assumed to be arbitrary. By introducing generalized difference equations the mathematical analysis for the stiffened beam may be performed in a manner exactly analogous to the process used for the unstiffened beam. A separation of variables in the homogeneous equation leads to the natural stress or displacement modes for a cross-section. The solution of the non-homogeneous equation is then expressed as an expansion in terms of the natural stress modes. Particular attention is given to cross-sections with single symmetry and double symmetry

Advances in Neural Information Processing Systems

Better understanding of the potential benefits of information transfer and representation learning is an important step towards the goal of building intelligent systems that are able to persist in the world and learn over time. In this work, we consider a setting where the learner encounters a stream of tasks but is able to retain only limited information from each encountered task, such as a learned predictor. In contrast to most previous works analyzing this scenario, we do not make any distributional assumptions on the task generating process. Instead, we formulate a complexity measure that captures the diversity of the observed tasks. We provide a lifelong learning algorithm with error guarantees for every observed task (rather than on average). We show sample complexity reductions in comparison to solving every task in isolation in terms of our task complexity measure. Further, our algorithmic framework can naturally be viewed as learning a representation from encountered tasks with a neural network