101 research outputs found
Bilayer Kagome Borophene with Multiple van Hove Singularities
The appearance of van Hove singularities near the Fermi level leads to
prominent phenomena, including superconductivity, charge density wave, and
ferromagnetism. Here a bilayer Kagome lattice with multiple van Hove
singularities is designed and a novel borophene with such lattice
(BK-borophene) is proposed by the first-principles calculations. BK-borophene,
which is formed via three-center two-electron (3c-2e) sigma-type bonds, is
predicted to be energetically, dynamically, thermodynamically, and mechanically
stable. The electronic structure hosts both conventional and high-order van
Hove singularities in one band. The conventional van Hove singularity resulting
from the horse saddle is 0.065 eV lower than the Fermi level, while the
high-order one resulting from the monkey saddle is 0.385 eV below the Fermi
level. Both the singularities lead to the divergence of electronic density of
states. Besides, the high-order singularity is just intersected to a Dirac-like
cone, where the Fermi velocity can reach 1340000 m/s. The interaction between
the two Kagome lattices is critical for the appearance of high-order van Hove
singularities. The novel bilayer Kagome borophene with rich and intriguing
electronic structure offers an unprecedented platform for studying correlation
phenomena in quantum material systems and beyond
HC3 Plus: A Semantic-Invariant Human ChatGPT Comparison Corpus
ChatGPT has gained significant interest due to its impressive performance,
but people are increasingly concerned about its potential risks, particularly
around the detection of AI-generated content (AIGC), which is often difficult
for untrained humans to identify. Current datasets utilized for detecting
ChatGPT-generated text primarily center around question-answering, yet they
tend to disregard tasks that possess semantic-invariant properties, such as
summarization, translation, and paraphrasing. Our primary studies demonstrate
that detecting model-generated text on semantic-invariant tasks is more
difficult. To fill this gap, we introduce a more extensive and comprehensive
dataset that considers more types of tasks than previous work, including
semantic-invariant tasks. In addition, the model after a large number of task
instruction fine-tuning shows a strong powerful performance. Owing to its
previous success, we further instruct fine-tuning Tk-instruct and built a more
powerful detection system. Experimental results show that our proposed detector
outperforms the previous state-of-the-art RoBERTa-based detector
InfoEntropy Loss to Mitigate Bias of Learning Difficulties for Generative Language Models
Generative language models are usually pretrained on large text corpus via
predicting the next token (i.e., sub-word/word/phrase) given the previous ones.
Recent works have demonstrated the impressive performance of large generative
language models on downstream tasks. However, existing generative language
models generally neglect an inherent challenge in text corpus during training,
i.e., the imbalance between frequent tokens and infrequent ones. It can lead a
language model to be dominated by common and easy-to-learn tokens, thereby
overlooking the infrequent and difficult-to-learn ones. To alleviate that, we
propose an Information Entropy Loss (InfoEntropy Loss) function. During
training, it can dynamically assess the learning difficulty of a to-be-learned
token, according to the information entropy of the corresponding predicted
probability distribution over the vocabulary. Then it scales the training loss
adaptively, trying to lead the model to focus more on the difficult-to-learn
tokens. On the Pile dataset, we train generative language models at different
scales of 468M, 1.2B, and 6.7B parameters. Experiments reveal that models
incorporating the proposed InfoEntropy Loss can gain consistent performance
improvement on downstream benchmarks
Exotic single-photon and enhanced deep-level emissions in hBN strain superlattice
The peculiar defect-related photon emission processes in 2D hexagonal boron
nitride (hBN) have become a topic of intense research due to their potential
applications in quantum information and sensing technologies. Recent efforts
have focused on activating and modulating the defect energy levels in hBN by
methods that can be integrated on a chip, and understanding the underlying
physical mechanism. Here, we report on exotic single photon and enhanced
deep-level emissions in 2D hBN strain superlattice, which is fabricated by
transferring multilayer hBN onto hexagonal close-packed silica spheres on
silica substrate. We realize effective activation of the single photon
emissions (SPEs) in the multilayer hBN at the positions that are in contact
with the apex of the SiO2 spheres. At these points, the local tensile strain
induced blue-shift of the SPE is found to be up to 12 nm. Furthermore, high
spatial resolution cathodoluminescence measurments show remarkable
strain-enhanced deep-level (DL) emissions in the multilayer hBN with the
emission intensity distribution following the periodic hexagonal pattern of the
strain superlattice. The maximum DL emission enhancement is up to 350% with a
energy redshift of 6 nm. Our results provide a simple on-chip compatible method
for activating and tuning the defect-related photon emissions in multilayer
hBN, demonstrating the potential of hBN strain superlattice as a building block
for future on-chip quantum nanophotonic devices
Correlation-driven eightfold magnetic anisotropy in a two-dimensional oxide monolayer.
Engineering magnetic anisotropy in two-dimensional systems has enormous scientific and technological implications. The uniaxial anisotropy universally exhibited by two-dimensional magnets has only two stable spin directions, demanding 180° spin switching between states. We demonstrate a previously unobserved eightfold anisotropy in magnetic SrRuO3 monolayers by inducing a spin reorientation in (SrRuO3)1/(SrTiO3) N superlattices, in which the magnetic easy axis of Ru spins is transformed from uniaxial 〈001〉 direction (N < 3) to eightfold 〈111〉 directions (N ≥ 3). This eightfold anisotropy enables 71° and 109° spin switching in SrRuO3 monolayers, analogous to 71° and 109° polarization switching in ferroelectric BiFeO3. First-principle calculations reveal that increasing the SrTiO3 layer thickness induces an emergent correlation-driven orbital ordering, tuning spin-orbit interactions and reorienting the SrRuO3 monolayer easy axis. Our work demonstrates that correlation effects can be exploited to substantially change spin-orbit interactions, stabilizing unprecedented properties in two-dimensional magnets and opening rich opportunities for low-power, multistate device applications
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