4,786 research outputs found

    Searching for Ξcc+\Xi_{cc}^+ in Relativistic Heavy Ion Collisions

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    We study the doubly charmed baryon Ξcc+\Xi_{cc}^+ in high energy nuclear collisions. We solve the three-body Schroedinger equation with relativistic correction and calculate the Ξcc+\Xi_{cc}^+ yield and transverse momentum distribution via coalescence mechanism. For Ξcc+\Xi_{cc}^+ production in central Pb+Pb collisions at LHC energy, the yield is extremely enhanced, and the production cross section per binary collision is one order of magnitude larger than that in p+p collisions. This indicates that, it is most probable to discover Ξcc+\Xi_{cc}^+ in heavy ion collisions and its discovery can be considered as a probe of the quark-luon plasma formation.Comment: 5 pages and 4 figure

    Automorphisms of 3-folds of general type acting trivially on cohomology

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    Let XX be a minimal projecitve complex 33-fold of general type with canonical Gorenstein singularities and Aut0(X)\mathrm{Aut}_0(X) be the subgroup of automorphisms acting trivially on Hβˆ—(X,Q)H^*(X,\mathbb{Q}). We prove that if XX has maximal Albanese dimension, ∣Aut0(X)βˆ£β‰€6|\mathrm{Aut}_0(X)|\leq 6. Then we concern the class of complex varieties X=(C1Γ—C2Γ—β‹―Γ—Cd)/GX=(C_1\times C_2\times\dots\times C_d)/G (dβ‰₯2d\geq2) isogenous to a (higher) product of unmixed type with each q(Ci/G)β‰₯1q(C_i/G)\geq 1. The main result of this paper is that if the irregularity q(X)β‰₯d+1q(X)\geq d+1, Aut0(X)\mathrm{Aut}_0(X) is trivial. Moreover, in the case d=3d=3, let (C1Γ—C2Γ—C3)/G(C_1\times C_2\times C_3)/G itself be the minimal realization of XX and KiK_i be the subgroup of GG acting identity on CiC_i, we show that if GG is abelian, every KiK_i is cyclic and q(X)=3q(X) = 3, then Aut0(X)β‰…Z2k\mathrm{Aut}_0(X)\cong \mathbb{Z}_2^k with k=0,1,2k=0,1,2. In the end some examples of complex 33-folds with Aut0(X)β‰…Z2\mathrm{Aut}_0(X)\cong \mathbb{Z}_2 and Z22\mathbb{Z}_2^2 are provided

    Floquet engineering of long-range p-wave superconductivity: Beyond the high-frequency limit

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    It has been shown that long-range {\it p}-wave superconductivity in a Kitaev chain can be engineered via an ac field with a high frequency [Benito et al., Phys. Rev. B 90, 205127 (2014)]. For its experimental realization, however, theoretical understanding of Floquet engineering with a broader range of driving frequencies becomes important. In this work, focusing on the ac-driven tunneling interactions of a Kitaev chain, we investigate effects from the leading correction to the high-frequency limit on the emergent {\it p}-wave superconductivity. Importantly, we find new engineered long-range {\it p}-wave pairing interactions that can significantly alter the ones in the high-frequency limit at long interaction ranges. We also find that the leading correction additionally generates nearest-neighbor {\it p}-wave pairing interactions with a renormalized pairing energy, long-range tunneling interactions, and in particular multiple pairs of Floquet Majorana edge states that are destroyed in the high- frequency limit.Comment: 13 pages, 8 figure

    Capillary-based multiplexed isothermal nucleic acid-based test for sexually transmitted diseases in patients

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    We demonstrate a multiplexed loop mediated isothermal amplification (LAMP) assay for infectious disease diagnostics, where the analytical process flow of target pathogens genomic DNA is performed manually by moving magnetic beads through a series of plugs in a capillary. Heat is provided by a water bath and the results read by the naked eye, enabling applications in low resource settings

    Self-Supervised Audio-Visual Co-Segmentation

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    Segmenting objects in images and separating sound sources in audio are challenging tasks, in part because traditional approaches require large amounts of labeled data. In this paper we develop a neural network model for visual object segmentation and sound source separation that learns from natural videos through self-supervision. The model is an extension of recently proposed work that maps image pixels to sounds. Here, we introduce a learning approach to disentangle concepts in the neural networks, and assign semantic categories to network feature channels to enable independent image segmentation and sound source separation after audio-visual training on videos. Our evaluations show that the disentangled model outperforms several baselines in semantic segmentation and sound source separation.Comment: Accepted to ICASSP 201
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