1,331 research outputs found
Tailoring excitonic states of van der Waals bilayers through stacking configuration, band alignment and valley-spin
Excitons in monolayer semiconductors have large optical transition dipole for
strong coupling with light field. Interlayer excitons in heterobilayers, with
layer separation of electron and hole components, feature large electric dipole
that enables strong coupling with electric field and exciton-exciton
interaction, at the cost that the optical dipole is substantially quenched (by
several orders of magnitude). In this letter, we demonstrate the ability to
create a new class of excitons in transition metal dichalcogenide (TMD) hetero-
and homo-bilayers that combines the advantages of monolayer- and
interlayer-excitons, i.e. featuring both large optical dipole and large
electric dipole. These excitons consist of an electron that is well confined in
an individual layer, and a hole that is well extended in both layers, realized
here through the carrier-species specific layer-hybridization controlled
through the interplay of rotational, translational, band offset, and
valley-spin degrees of freedom. We observe different species of such
layer-hybridized valley excitons in different heterobilayer and homobilayer
systems, which can be utilized for realizing strongly interacting
excitonic/polaritonic gases, as well as optical quantum coherent controls of
bidirectional interlayer carrier transfer either with upper conversion or down
conversion in energy
A contact formulation based on a volumetric potential: Application to isogeometric simulations of atrioventricular valves
This work formulates frictionless contact between solid bodies in terms of a repulsive potential energy term and illustrates how numerical integration of the resulting forces is computationally similar to the “pinball algorithm” proposed and studied by Belytschko and collaborators in the 1990s. We thereby arrive at a numerical approach that has both the theoretical advantages of a potential-based formulation and the algorithmic simplicity, computational efficiency, and geometrical versatility of pinball contact. The singular nature of the contact potential requires a specialized nonlinear solver and an adaptive time stepping scheme to ensure reliable convergence of implicit dynamic calculations. We illustrate the effectiveness of this numerical method by simulating several benchmark problems and the structural mechanics of the right atrioventricular (tricuspid) heart valve. Atrioventricular valve closure involves contact between every combination of shell surfaces, edges of shells, and cables, but our formulation handles all contact scenarios in a unified manner. We take advantage of this versatility to demonstrate the effects of chordal rupture on tricuspid valve coaptation behavior
An Exploration of In-Context Learning for Speech Language Model
Ever since the development of GPT-3 in the natural language processing (NLP)
field, in-context learning (ICL) has played an important role in utilizing
large language models (LLMs). By presenting the LM utterance-label
demonstrations at the input, the LM can accomplish few-shot learning without
relying on gradient descent or requiring explicit modification of its
parameters. This enables the LM to learn and adapt in a black-box manner.
Despite the success of ICL in NLP, little work is exploring the possibility of
ICL in speech processing. This study proposes the first exploration of ICL with
a speech LM without text supervision. We first show that the current speech LM
does not have the ICL capability. With the proposed warmup training, the speech
LM can, therefore, perform ICL on unseen tasks. In this work, we verify the
feasibility of ICL for speech LM on speech classification tasks.Comment: The first two authors contributed equall
Quantum correlation generation capability of experimental processes
Einstein-Podolsky-Rosen (EPR) steering and Bell nonlocality illustrate two
different kinds of correlations predicted by quantum mechanics. They not only
motivate the exploration of the foundation of quantum mechanics, but also serve
as important resources for quantum-information processing in the presence of
untrusted measurement apparatuses. Herein, we introduce a method for
characterizing the creation of EPR steering and Bell nonlocality for dynamical
processes in experiments. We show that the capability of an experimental
process to create quantum correlations can be quantified and identified simply
by preparing separable states as test inputs of the process and then performing
local measurements on single qubits of the corresponding outputs. This finding
enables the construction of objective benchmarks for the two-qubit controlled
operations used to perform universal quantum computation. We demonstrate this
utility by examining the experimental capability of creating quantum
correlations with the controlled-phase operations on the IBM Quantum Experience
and Amazon Braket Rigetti superconducting quantum computers. The results show
that our method provides a useful diagnostic tool for evaluating the primitive
operations of nonclassical correlation creation in noisy intermediate scale
quantum devices.Comment: 5 figures, 3 appendice
Disordered Fe vacancies and superconductivity in potassium-intercalated iron selenide (K2-xFe4+ySe5)
The parent compound of an unconventional superconductor must contain unusual
correlated electronic and magnetic properties of its own. In the high-Tc
potassium intercalated FeSe, there has been significant debate regarding what
the exact parent compound is. Our studies unambiguously show that the
Fe-vacancy ordered K2Fe4Se5 is the magnetic, Mott insulating parent compound of
the superconducting state. Non-superconducting K2Fe4Se5 becomes a
superconductor after high temperature annealing, and the overall picture
indicates that superconductivity in K2-xFe4+ySe5 originates from the Fe-vacancy
order to disorder transition. Thus, the long pending question whether magnetic
and superconducting state are competing or cooperating for cuprate
superconductors may also apply to the Fe-chalcogenide superconductors. It is
believed that the iron selenides and related compounds will provide essential
information to understand the origin of superconductivity in the iron-based
superconductors, and possibly to the superconducting cuprates
Diffusion Model-Augmented Behavioral Cloning
Imitation learning addresses the challenge of learning by observing an
expert's demonstrations without access to reward signals from environments.
Most existing imitation learning methods that do not require interacting with
environments either model the expert distribution as the conditional
probability p(a|s) (e.g., behavioral cloning, BC) or the joint probability p(s,
a) (e.g., implicit behavioral cloning). Despite its simplicity, modeling the
conditional probability with BC usually struggles with generalization. While
modeling the joint probability can lead to improved generalization performance,
the inference procedure can be time-consuming and it often suffers from
manifold overfitting. This work proposes an imitation learning framework that
benefits from modeling both the conditional and joint probability of the expert
distribution. Our proposed diffusion model-augmented behavioral cloning (DBC)
employs a diffusion model trained to model expert behaviors and learns a policy
to optimize both the BC loss (conditional) and our proposed diffusion model
loss (joint). DBC outperforms baselines in various continuous control tasks in
navigation, robot arm manipulation, dexterous manipulation, and locomotion. We
design additional experiments to verify the limitations of modeling either the
conditional probability or the joint probability of the expert distribution as
well as compare different generative models
Comparison of the clinical features and outcomes of infective endocarditis between hemodialysis and non-hemodialysis patients
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