6,345 research outputs found

    Phenomenological Implications of the Topflavor Model

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    We explore phenomenologies of the topflavour model for the LEP experiment at mZm_{_Z} scale and the atomic parity violation (APV) experiment in the CsC_s atoms at low energies. Implications of the model on the ZZ peak data are studied in terms of the precision variables Ļµi\epsilon_i's. We find that the LEP data give more stringent constraints on the model parameters than the APV data.Comment: 23 pages (including 5 .eps figs), ReVTeX, the 1st revised version, to appear in Phys. Lett.

    Heating-compensated constant-temperature tunneling measurements on stacks of Bi2_2Sr2_2CaCu2_2O8+x_{8+x} intrinsic junctions

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    In highly anisotropic layered cuprates such as Bi2_2Sr2_2CaCu2_2O8+x_{8+x} tunneling measurements on a stack of intrinsic junctions in a high-bias range are often susceptible to self-heating. In this study we monitored the temperature variation of a stack ("sample stack") of intrinsic junctions by measuring the resistance change of a nearby stack ("thermometer stack") of intrinsic junctions, which was strongly thermal-coupled to the sample stack through a common Au electrode. We then adopted a proportional-integral-derivative scheme incorporated with a substrate-holder heater to compensate the temperature variation. This in-situ temperature monitoring and controlling technique allows one to get rid of spurious tunneling effects arising from the self-heating in a high bias range.Comment: 3 pages, 3 figure

    Collective Josephson vortex dynamics in a finite number of intrinsic Josephson junctions

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    We report the experimental confirmation of the collective transverse plasma modes excited by the Josephson vortex lattice in stacks of intrinsic Josephson junctions in Bi2_{2}Sr2_{2}CaCu2_{2}O8+x_{8+x} single crystals. The excitation was confirmed by analyzing the temperature (TT) and magnetic field (HH) dependencies of the multiple sub-branches in the Josephson-vortex-flow region of the current-voltage characteristics of the system. In the near-static Josephson vortex state for a low tunneling bias current, pronounced magnetoresistance oscillations were observed, which represented a triangular-lattice vortex configuration along the c axis. In the dynamic vortex state in a sufficiently high magnetic field and for a high bias current, splitting of a single Josephson vortex-flow branch into multiple sub-branches was observed. Detailed examination of the sub-branches for varying HH field reveals that sub-branches represent the different modes of the Josephson-vortex lattice along the c axis, with varied configuration from a triangular to a rectangular lattices. These multiple sub-branches merge to a single curve at a characteristic temperature, above which no dynamical structural transitions of the Josephson vortex lattice is expected

    Analysis on Effects of Fault Elements in Memristive Neuromorphic Systems

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    Nowadays, neuromorphic systems based on Spiking Neural Networks (SNNs) attract attentions of many researchers. There are many studies to improve performances of neuromorphic systems. These studies have been showing satisfactory results. To magnify performances of neuromorphic systems, developing actual neuromorphic systems is essential. For developing them, memristors play key role due to their useful characteristics. Although memristors are essential for actual neuromorphic systems, they are vulnerable to faults. However, there are few studies analyzing effects of fault elements in neuromorphic systems using memristors. To solve this problem, we analyze performance of a memristive neuromorphic system with fault elements changing fault ratios, types, and positions. We choose neurons and synapses to inject faults. We inject two types of faults to synapses: SA0 and SA1 faults. The fault synapses appear in random and important positions. Through our analysis, we discover the following four interesting points. First, memristive characteristics increase vulnerability of neuromorphic systems to fault elements. Second, fault neuron ratios reducing performance sharply exist. Third, performance degradation by fault synapses depends on fault types. Finally, SA1 fault synapses improve performance when they appear in important positions.Comment: 8 pages, 7 figures, 5 tables, IJCAI 2023 GLOW workshop(https://sites.google.com/view/glow-ijcai-23/home

    k-Space Deep Learning for Reference-free EPI Ghost Correction

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    Nyquist ghost artifacts in EPI are originated from phase mismatch between the even and odd echoes. However, conventional correction methods using reference scans often produce erroneous results especially in high-field MRI due to the non-linear and time-varying local magnetic field changes. Recently, it was shown that the problem of ghost correction can be reformulated as k-space interpolation problem that can be solved using structured low-rank Hankel matrix approaches. Another recent work showed that data driven Hankel matrix decomposition can be reformulated to exhibit similar structures as deep convolutional neural network. By synergistically combining these findings, we propose a k-space deep learning approach that immediately corrects the phase mismatch without a reference scan in both accelerated and non-accelerated EPI acquisitions. To take advantage of the even and odd-phase directional redundancy, the k-space data is divided into two channels configured with even and odd phase encodings. The redundancies between coils are also exploited by stacking the multi-coil k-space data into additional input channels. Then, our k-space ghost correction network is trained to learn the interpolation kernel to estimate the missing virtual k-space data. For the accelerated EPI data, the same neural network is trained to directly estimate the interpolation kernels for missing k-space data from both ghost and subsampling. Reconstruction results using 3T and 7T in-vivo data showed that the proposed method outperformed the image quality compared to the existing methods, and the computing time is much faster.The proposed k-space deep learning for EPI ghost correction is highly robust and fast, and can be combined with acceleration, so that it can be used as a promising correction tool for high-field MRI without changing the current acquisition protocol.Comment: To appear in Magnetic Resonance in Medicin
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