3,743 research outputs found
Effective field theory with resonant P-wave interaction
A new effective field theory has been developed to describe shallow -wave
resonances using nonlocal, momentum-dependent two-body potentials. This
approach is expected to facilitate many-body calculations and has been
demonstrated to converge and to be renormalizable in perturbative calculations
at subleading orders. The theory has been applied to the neutron-alpha system,
with good agreement found between its predictions and a phase-shift analysis of
neutron-alpha elastic scattering. In the three-body system consisting of two
neutrons and an alpha particle, the nonlocal potential in this framework has
been found to recover the same qualitative features as previously shown with
energy-dependent formulations.Comment: 17 pages, 4 figure
QAScore -- An Unsupervised Unreferenced Metric for the Question Generation Evaluation
Question Generation (QG) aims to automate the task of composing questions for
a passage with a set of chosen answers found within the passage. In recent
years, the introduction of neural generation models has resulted in substantial
improvements of automatically generated questions in terms of quality,
especially compared to traditional approaches that employ manually crafted
heuristics. However, the metrics commonly applied in QG evaluations have been
criticized for their low agreement with human judgement. We therefore propose a
new reference-free evaluation metric that has the potential to provide a better
mechanism for evaluating QG systems, called QAScore. Instead of fine-tuning a
language model to maximize its correlation with human judgements, QAScore
evaluates a question by computing the cross entropy according to the
probability that the language model can correctly generate the masked words in
the answer to that question. Furthermore, we conduct a new crowd-sourcing human
evaluation experiment for the QG evaluation to investigate how QAScore and
other metrics can correlate with human judgements. Experiments show that
QAScore obtains a stronger correlation with the results of our proposed human
evaluation method compared to existing traditional word-overlap-based metrics
such as BLEU and ROUGE, as well as the existing pretrained-model-based metric
BERTScore.Comment: 19 pages, 5 figures, 7 table
BaySize: Bayesian Sample Size Planning for Phase I Dose-Finding Trials
We propose BaySize, a sample size calculator for phase I clinical trials
using Bayesian models. BaySize applies the concept of effect size in dose
finding, assuming the MTD is defined based on an equivalence interval.
Leveraging a decision framework that involves composite hypotheses, BaySize
utilizes two prior distributions, the fitting prior (for model fitting) and
sampling prior (for data generation), to conduct sample size calculation under
desirable statistical power. Look-up tables are generated to facilitate
practical applications. To our knowledge, BaySize is the first sample size tool
that can be applied to a broad range of phase I trial designs
Accelerated Federated Learning with Decoupled Adaptive Optimization
The federated learning (FL) framework enables edge clients to collaboratively
learn a shared inference model while keeping privacy of training data on
clients. Recently, many heuristics efforts have been made to generalize
centralized adaptive optimization methods, such as SGDM, Adam, AdaGrad, etc.,
to federated settings for improving convergence and accuracy. However, there is
still a paucity of theoretical principles on where to and how to design and
utilize adaptive optimization methods in federated settings. This work aims to
develop novel adaptive optimization methods for FL from the perspective of
dynamics of ordinary differential equations (ODEs). First, an analytic
framework is established to build a connection between federated optimization
methods and decompositions of ODEs of corresponding centralized optimizers.
Second, based on this analytic framework, a momentum decoupling adaptive
optimization method, FedDA, is developed to fully utilize the global momentum
on each local iteration and accelerate the training convergence. Last but not
least, full batch gradients are utilized to mimic centralized optimization in
the end of the training process to ensure the convergence and overcome the
possible inconsistency caused by adaptive optimization methods
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