1,693 research outputs found

    Signatures of Gate-Tunable Superconductivity in Trilayer Graphene/Boron Nitride Moir\'e Superlattice

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    Understanding the mechanism of high temperature (high Tc) superconductivity is a central problem in condensed matter physics. It is often speculated that high Tc superconductivity arises from a doped Mott insulator as described by the Hubbard model. An exact solution of the Hubbard model, however, is extremely challenging due to the strong electron-electron correlation. Therefore, it is highly desirable to experimentally study a model Hubbard system in which the unconventional superconductivity can be continuously tuned by varying the Hubbard parameters. Here we report signatures of tunable superconductivity in ABC-trilayer graphene (TLG) / boron nitride (hBN) moir\'e superlattice. Unlike "magic angle" twisted bilayer graphene, theoretical calculations show that under a vertical displacement field the ABC-TLG/hBN heterostructure features an isolated flat valence miniband associated with a Hubbard model on a triangular superlattice. Upon applying such a displacement field we find experimentally that the ABC-TLG/hBN superlattice displays Mott insulating states below 20 Kelvin at 1/4 and 1/2 fillings, corresponding to 1 and 2 holes per unit cell, respectively. Upon further cooling, signatures of superconducting domes emerge below 1 kelvin for the electron- and hole-doped sides of the 1/4 filling Mott state. The electronic behavior in the TLG/hBN superlattice is expected to depend sensitively on the interplay between the electron-electron interaction and the miniband bandwidth, which can be tuned continuously with the displacement field D. By simply varying the D field, we demonstrate transitions from the candidate superconductor to Mott insulator and metallic phases. Our study shows that TLG/hBN heterostructures offer an attractive model system to explore rich correlated behavior emerging in the tunable triangular Hubbard model.Comment: 14 pages, 4 figure

    "Alexa, Can I Program You?": Student Perceptions of Conversational Artificial Intelligence Before and After Programming Alexa

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    Growing up in an artificial intelligence-filled world, with Siri and Amazon Alexa often within arm's - or speech's - reach, could have significant impact on children. Conversational agents could influence how students anthropomorphize computer systems or develop a theory of mind. Previous research has explored how conversational agents are used and perceived by children within and outside of learning contexts. This study investigates how middle and high school students' perceptions of Alexa change through programming their own conversational agents in week-long AI education workshops. Specifically, we investigate the workshops' influence on student perceptions of Alexa's intelligence, friendliness, aliveness, safeness, trustworthiness, human-likeness, and feelings of closeness. We found that students felt Alexa was more intelligent and felt closer to Alexa after the workshops. We also found strong correlations between students' perceptions of Alexa's friendliness and trustworthiness, and safeness and trustworthiness. Finally, we explored how students tended to more frequently use computer science-related diction and ideas after the workshops. Based on our findings, we recommend designers carefully consider personification, transparency, playfulness and utility when designing CAs for learning contexts.Comment: 16 pages, 6 figure

    High quality factor manganese-doped aluminum lumped-element kinetic inductance detectors sensitive to frequencies below 100 GHz

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    Aluminum lumped-element kinetic inductance detectors (LEKIDs) sensitive to millimeter-wave photons have been shown to exhibit high quality factors, making them highly sensitive and multiplexable. The superconducting gap of aluminum limits aluminum LEKIDs to photon frequencies above 100 GHz. Manganese-doped aluminum (Al-Mn) has a tunable critical temperature and could therefore be an attractive material for LEKIDs sensitive to frequencies below 100 GHz if the internal quality factor remains sufficiently high when manganese is added to the film. To investigate, we measured some of the key properties of Al-Mn LEKIDs. A prototype eight-element LEKID array was fabricated using a 40 nm thick film of Al-Mn deposited on a 500 µm thick high-resistivity, float-zone silicon substrate. The manganese content was 900 ppm, the measured Tc = 694 ± 1mK, and the resonance frequencies were near 150 MHz. Using measurements of the forward scattering parameter S21 at various bath temperatures between 65 and 250 mK, we determined that the Al-Mn LEKIDs we fabricated have internal quality factors greater than 2 × 105 , which is high enough for millimeter-wave astrophysical observations. In the dark conditions under which these devices were measured, the fractional frequency noise spectrum shows a shallow slope that depends on bath temperature and probe tone amplitude, which could be two-level system noise. The anticipated white photon noise should dominate this level of low-frequency noise when the detectors are illuminated with millimeter-waves in future measurements. The LEKIDs responded to light pulses from a 1550 nm light-emitting diode, and we used these light pulses to determine that the quasiparticle lifetime is 60 µs

    A unified mechanism for intron and exon definition and back-splicing.

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    The molecular mechanisms of exon definition and back-splicing are fundamental unanswered questions in pre-mRNA splicing. Here we report cryo-electron microscopy structures of the yeast spliceosomal E complex assembled on introns, providing a view of the earliest event in the splicing cycle that commits pre-mRNAs to splicing. The E complex architecture suggests that the same spliceosome can assemble across an exon, and that it either remodels to span an intron for canonical linear splicing (typically on short exons) or catalyses back-splicing to generate circular RNA (on long exons). The model is supported by our experiments, which show that an E complex assembled on the middle exon of yeast EFM5 or HMRA1 can be chased into circular RNA when the exon is sufficiently long. This simple model unifies intron definition, exon definition, and back-splicing through the same spliceosome in all eukaryotes and should inspire experiments in many other systems to understand the mechanism and regulation of these processes

    The molecular basis of breast cancer pathological phenotypes

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    The histopathological evaluation of morphological features in breast tumours provides prognostic information to guide therapy. Adjunct molecular analyses provide further diagnostic, prognostic and predictive information. However, there is limited knowledge of the molecular basis of morphological phenotypes in invasive breast cancer. This study integrated genomic, transcriptomic and protein data to provide a comprehensive molecular profiling of morphological features in breast cancer. Fifteen pathologists assessed 850 invasive breast cancer cases from The Cancer Genome Atlas (TCGA). Morphological features were significantly associated with genomic alteration, DNA methylation subtype, PAM50 and microRNA subtypes, proliferation scores, gene expression and/or RPPA subtype. Marked nuclear pleomorphism, necrosis, inflammation and high mitotic count were associated with Basal-like subtype and have similar molecular basis. Omics-based signatures were constructed to predict morphological features. The association of morphology transcriptome signatures with overall survival in oestrogen receptor (ER)-positive and ER-negative breast cancer was first assessed using the METABRIC dataset; signatures that remained prognostic in the METABRIC multivariate analysis were further evaluated in five additional datasets. The transcriptomic signature of epithelial tubule formation was prognostic in ER-positive breast cancer. No signature was prognostic in ER-negative. This study provided new insights into the molecular basis of breast cancer morphological phenotypes. The integration of morphological with molecular data has potential to refine breast cancer classification, predict response to therapy, enhance our understanding of breast cancer biology and improve clinical management. This work is publicly accessible at www.dx.ai/tcga_breast
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