8,934 research outputs found

    Indistinguishability of quantum states and rotation counting

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    We propose a quantum system in which the winding number of rotations of a particle around a ring can be monitored and emerges as a physical observable. We explicitly analyze the situation when, as a result of the monitoring of the winding number, the period of the orbital motion of the particle is extended to n>1n>1 full rotations, which leads to changes in the energy spectrum and in all observable properties. In particular, we show that in this case, the usual magnetic flux period Ί0=h/q\Phi_0=h/q of the Aharonov-Bohm effect is reduced to Ί0/n\Phi_0/n.Comment: 5 pages, 3 embedded figure

    Experience-driven formation of parts-based representations in a model of layered visual memory

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    Growing neuropsychological and neurophysiological evidence suggests that the visual cortex uses parts-based representations to encode, store and retrieve relevant objects. In such a scheme, objects are represented as a set of spatially distributed local features, or parts, arranged in stereotypical fashion. To encode the local appearance and to represent the relations between the constituent parts, there has to be an appropriate memory structure formed by previous experience with visual objects. Here, we propose a model how a hierarchical memory structure supporting efficient storage and rapid recall of parts-based representations can be established by an experience-driven process of self-organization. The process is based on the collaboration of slow bidirectional synaptic plasticity and homeostatic unit activity regulation, both running at the top of fast activity dynamics with winner-take-all character modulated by an oscillatory rhythm. These neural mechanisms lay down the basis for cooperation and competition between the distributed units and their synaptic connections. Choosing human face recognition as a test task, we show that, under the condition of open-ended, unsupervised incremental learning, the system is able to form memory traces for individual faces in a parts-based fashion. On a lower memory layer the synaptic structure is developed to represent local facial features and their interrelations, while the identities of different persons are captured explicitly on a higher layer. An additional property of the resulting representations is the sparseness of both the activity during the recall and the synaptic patterns comprising the memory traces.Comment: 34 pages, 12 Figures, 1 Table, published in Frontiers in Computational Neuroscience (Special Issue on Complex Systems Science and Brain Dynamics), http://www.frontiersin.org/neuroscience/computationalneuroscience/paper/10.3389/neuro.10/015.2009

    A Generative Model of People in Clothing

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    We present the first image-based generative model of people in clothing for the full body. We sidestep the commonly used complex graphics rendering pipeline and the need for high-quality 3D scans of dressed people. Instead, we learn generative models from a large image database. The main challenge is to cope with the high variance in human pose, shape and appearance. For this reason, pure image-based approaches have not been considered so far. We show that this challenge can be overcome by splitting the generating process in two parts. First, we learn to generate a semantic segmentation of the body and clothing. Second, we learn a conditional model on the resulting segments that creates realistic images. The full model is differentiable and can be conditioned on pose, shape or color. The result are samples of people in different clothing items and styles. The proposed model can generate entirely new people with realistic clothing. In several experiments we present encouraging results that suggest an entirely data-driven approach to people generation is possible

    Heavy Majorana neutrinos in e^-e^- collisions

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    We discuss the process e−e−→W−W−e^-e^- \to W^- W^- mediated by heavy Majorana neutrino exchange in the t- and u channel. In our model the cross section for this reaction is a function of the masses (m_N) of the heavy Majorana neutrinos and mixing parameters (U_{eN}) originating from mixing between the ordinary left-handed standard model neutrinos and additional singlet right-handed neutrino fields. Taking into account the standard model background and contraints from low energy measurements, we present discovery limits in the (m_N,U_{eN}^2) plane. We also discuss how to measure in principle the CP violating phases, i.e., the relative phases between the mixing parameters.Comment: 18 pages with 7 postscript figures included, uses epsfig.st

    Scalable and fast heterogeneous molecular simulation with predictive parallelization schemes

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    Multiscale and inhomogeneous molecular systems are challenging topics in the field of molecular simulation. In particular, modeling biological systems in the context of multiscale simulations and exploring material properties are driving a permanent development of new simulation methods and optimization algorithms. In computational terms, those methods require parallelization schemes that make a productive use of computational resources for each simulation and from its genesis. Here, we introduce the heterogeneous domain decomposition approach which is a combination of an heterogeneity sensitive spatial domain decomposition with an \textit{a priori} rearrangement of subdomain-walls. Within this approach, the theoretical modeling and scaling-laws for the force computation time are proposed and studied as a function of the number of particles and the spatial resolution ratio. We also show the new approach capabilities, by comparing it to both static domain decomposition algorithms and dynamic load balancing schemes. Specifically, two representative molecular systems have been simulated and compared to the heterogeneous domain decomposition proposed in this work. These two systems comprise an adaptive resolution simulation of a biomolecule solvated in water and a phase separated binary Lennard-Jones fluid.Comment: 14 pages, 12 figure

    Low B Field Magneto-Phonon Resonances in Single-Layer and Bilayer Graphene

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    Many-body effects resulting from strong electron-electron and electron-phonon interactions play a significant role in graphene physics. We report on their manifestation in low B field magneto-phonon resonances in high quality exfoliated single-layer and bilayer graphene encapsulated in hexagonal boron nitride. These resonances allow us to extract characteristic effective Fermi velocities, as high as 1.20×1061.20 \times 10^6 m/s, for the observed "dressed" Landau level transitions, as well as the broadening of the resonances, which increases with Landau level index
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