70 research outputs found
Assisting Language Learners: Automated Trans-Lingual Definition Generation via Contrastive Prompt Learning
The standard definition generation task requires to automatically produce
mono-lingual definitions (e.g., English definitions for English words), but
ignores that the generated definitions may also consist of unfamiliar words for
language learners. In this work, we propose a novel task of Trans-Lingual
Definition Generation (TLDG), which aims to generate definitions in another
language, i.e., the native speaker's language. Initially, we explore the
unsupervised manner of this task and build up a simple implementation of
fine-tuning the multi-lingual machine translation model. Then, we develop two
novel methods, Prompt Combination and Contrastive Prompt Learning, for further
enhancing the quality of the generation. Our methods are evaluated against the
baseline Pipeline method in both rich- and low-resource settings, and we
empirically establish its superiority in generating higher-quality
trans-lingual definitions.Comment: Accepted by ACL-BEA worksho
Explicit gain equations for hybrid graphene-quantum-dot photodetectors
Graphene is an attractive material for broadband photodetection but suffers
from weak light absorption. Coating graphene with quantum dots can
significantly enhance light absorption and create extraordinarily high photo
gain. This high gain is often explained by the classical gain theory which is
unfortunately an implicit function and may even be questionable. In this work,
we managed to derive explicit gain equations for hybrid graphene-quantum-dot
photodetectors. Due to the work function mismatch, lead sulfide (PbS) quantum
dots coated on graphene will form a surface depletion region near the interface
of quantum dots and graphene. Light illumination narrows down the surface
depletion region, creating a photovoltage that gates the graphene. As a result,
high photo gain in graphene is observed. The explicit gain equations are
derived from the theoretical gate transfer characteristics of graphene and the
correlation of the photovoltage with the light illumination intensity. The
derived explicit gain equations fit well with the experimental data, from which
physical parameters are extracted.Comment: 14 pages, 6 figure
Multi-Level Variational Spectroscopy using a Programmable Quantum Simulator
Energy spectroscopy is a powerful tool with diverse applications across
various disciplines. The advent of programmable digital quantum simulators
opens new possibilities for conducting spectroscopy on various models using a
single device. Variational quantum-classical algorithms have emerged as a
promising approach for achieving such tasks on near-term quantum simulators,
despite facing significant quantum and classical resource overheads. Here, we
experimentally demonstrate multi-level variational spectroscopy for fundamental
many-body Hamiltonians using a superconducting programmable digital quantum
simulator. By exploiting symmetries, we effectively reduce circuit depth and
optimization parameters allowing us to go beyond the ground state. Combined
with the subspace search method, we achieve full spectroscopy for a 4-qubit
Heisenberg spin chain, yielding an average deviation of 0.13 between
experimental and theoretical energies, assuming unity coupling strength. Our
method, when extended to 8-qubit Heisenberg and transverse-field Ising
Hamiltonians, successfully determines the three lowest energy levels. In
achieving the above, we introduce a circuit-agnostic waveform compilation
method that enhances the robustness of our simulator against signal crosstalk.
Our study highlights symmetry-assisted resource efficiency in variational
quantum algorithms and lays the foundation for practical spectroscopy on
near-term quantum simulators, with potential applications in quantum chemistry
and condensed matter physics
Advances in ultrashallow doping of silicon
Ultrashallow doping is required for both classical field-effect transistors in integrated circuits and revolutionary quantum devices in quantum computing. In this review, we give a brief overview on recent research advances in three technologies to form ultrashallow doping, namely molecular monolayer doping, molecular beam epitaxy, and low energy ion implantation. A research perspective will be provided at the end of this review
Decoding Cancer Evolution: Integrating Genetic and Non-Genetic Insights
The development of cancer begins with cells transitioning from their multicellular nature to a state akin to unicellular organisms. This shift leads to a breakdown in the crucial regulators inherent to multicellularity, resulting in the emergence of diverse cancer cell subpopulations that have enhanced adaptability. The presence of different cell subpopulations within a tumour, known as intratumoural heterogeneity (ITH), poses challenges for cancer treatment. In this review, we delve into the dynamics of the shift from multicellularity to unicellularity during cancer onset and progression. We highlight the role of genetic and non-genetic factors, as well as tumour microenvironment, in promoting ITH and cancer evolution. Additionally, we shed light on the latest advancements in omics technologies that allow for in-depth analysis of tumours at the single-cell level and their spatial organization within the tissue. Obtaining such detailed information is crucial for deepening our understanding of the diverse evolutionary paths of cancer, allowing for the development of effective therapies targeting the key drivers of cancer evolution
Visualization 4: High dynamic range real-time 3D shape measurement
Visualization 4 Originally published in Optics Express on 04 April 2016 (oe-24-7-7337
Visualization 2: High dynamic range real-time 3D shape measurement
Visualization 2 Originally published in Optics Express on 04 April 2016 (oe-24-7-7337
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