Tel Aviv University

About: Tel Aviv University is a education organization based out in Tel Aviv, Israel. It is known for research contribution in the topics: Population & Medicine. The organization has 47791 authors who have published 115959 publications receiving 3904391 citations. The organization is also known as: TAU & Universiṭat Tel-Aviv.

Monoclonal antibodies inhibit in vitro fibrillar aggregation of the Alzheimer beta-amyloid peptide.

Abstract: The beta-amyloid peptide, the hallmark of Alzheimer disease, forms fibrillar toxic aggregates in brain tissue that can be dissolved only by strong denaturing agents. To study beta-amyloid formation and its inhibition, we prepared immune complexes with two monoclonal antibodies (mAbs), AMY-33 and 6F/3D, raised against beta-amyloid fragments spanning amino acid residues 1-28 and 8-17 of the beta-amyloid peptide chain, respectively. In vitro aggregation of beta-amyloid peptide was induced by incubation for 3 h at 37 degrees C and monitored by ELISA, negative staining electron microscopy, and fluorimetric studies. We found that the mAs prevent the aggregation of beta-amyloid peptide and that the inhibitory effect appears to be related to the localization of the antibody-binding sites and the nature of the aggregating agents. Preparation of mAbs against "aggregating epitopes," defined as sequences related to the sites where protein aggregation is initiated, may lead to the understanding and prevention of protein aggregation. The results of this study may provide a foundation for using mAbs in vivo to prevent the beta-amyloid peptide aggregation that is associated with Alzheimer disease.

Performance of the ATLAS Trigger System in 2010

Abstract: Proton-proton collisions at root s = 7 TeV and heavy ion collisions at root(NN)-N-s = 2.76 TeV were produced by the LHC and recorded using the ATLAS experiment's trigger system in 2010. The LHC is designed with a maximum bunch crossing rate of 40 MHz and the ATLAS trigger system is designed to record approximately 200 of these per second. The trigger system selects events by rapidly identifying signatures of muon, electron, photon, tau lepton, jet, and B meson candidates, as well as using global event signatures, such as missing transverse energy. An overview of the ATLAS trigger system, the evolution of the system during 2010 and the performance of the trigger system components and selections based on the 2010 collision data are shown. A brief outline of plans for the trigger system in 2011 is presented.

A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes

Abstract: A critical issue for the application of Markov decision processes (MDPs) to realistic problems is how the complexity of planning scales with the size of the MDP In stochastic environments with very large or infinite state spaces, traditional planning and reinforcement learning algorithms may be inapplicable, since their running time typically grows linearly with the state space size in the worst case In this paper we present a new algorithm that, given only a generative model (a natural and common type of simulator) for an arbitrary MDP, performs on-line, near-optimal planning with a per-state running time that has no dependence on the number of states The running time is exponential in the horizon time (which depends only on the discount factor γ and the desired degree of approximation to the optimal policy) Our algorithm thus provides a different complexity trade-off than classical algorithms such as value iteration—rather than scaling linearly in both horizon time and state space size, our running time trades an exponential dependence on the former in exchange for no dependence on the latter Our algorithm is based on the idea of sparse sampling We prove that a randomly sampled look-ahead tree that covers only a vanishing fraction of the full look-ahead tree nevertheless suffices to compute near-optimal actions from any state of an MDP Practical implementations of the algorithm are discussed, and we draw ties to our related recent results on finding a near-best strategy from a given class of strategies in very large partially observable MDPs (Kearns, Mansour, & Ng Neural information processing systems 13, to appear)

Macroeconomic Consequences of Terror: Theory and the Case of Israel

Abstract: This Paper analyzes the effect of terror on the economy. Terror endangers life such that the value of the future relative to the present is reduced. Hence, due to a rise in terror activity, investment goes down, and in the long run, income and consumption go down as well. Governments can offset terror by putting tax revenues into the production of security. Facing a tide of terror, a government that acts optimally increases the proportion of output spent on defense, but does not fully offset the tide. Thus, when terror peaks, the long-run equilibrium with an optimizing government is of lower output and welfare. Next, we show that this theory of terror and the economy helps to understand changes in trend and business cycle of the Israeli economy. The estimates show that terror has a large impact on the aggregate economy. Continued terror, at the level of the death toll by about the same size as due to car accidents, is expected to decrease annual consumption per capita by about 5% in 2004. Had Israel not suffered from terror during the last three years, we estimate that output per capita would have been about 10% higher than it is today.