We introduce a semi-parametric model for the primary mass distribution of
binary black holes (BBHs) observed with gravitational waves (GWs) that applies
a cubic-spline perturbation to a power law. We apply this model to the 46 BBHs
included in the second gravitational wave transient catalog (GWTC-2). The
spline perturbation model recovers a consistent primary mass distribution with
previous results, corroborating the existence of a peak at $35\,M_\odot$
($>97\%$ credibility) found with the \textsc{Powerlaw+Peak} model. The peak
could be the result pulsational pair-instability supernovae (PPISNe). The
spline perturbation model finds potential signs of additional features in the
primary mass distribution at lower masses similar to those previously reported
by Tiwari and Fairhurst (2021). However, with fluctuations due to small number
statistics, the simpler \textsc{Powerlaw+Peak} and \textsc{BrokenPowerlaw}
models are both still perfectly consistent with observations. Our
semi-parametric approach serves as a way to bridge the gap between parametric
and non-parametric models to more accurately measure the BBH mass distribution.
With larger catalogs we will be able to use this model to resolve possible
additional features that could be used to perform cosmological measurements,
and will build on our understanding of BBH formation, stellar evolution and
nuclear astrophysics.

本文介绍了一种半参数模型，它利用立方样条曲线对幂律进行微扰来建模观测到的双黑洞引力波的初级质量分布，并将其应用于第二次引力波临时目录中包括的 46 组双黑洞数据，发现与之前使用 Powerlaw + Peak 模型得到的结果相当一致，验证了 35 太阳质量的峰值存在的可能性，发现了一些可能的低质量附加特征，但需要更大的样本来验证我们的结论。

LIGO-Virgos 双黑洞质量分布的半参数建模

Ain't No Mountain High Enough: Semi-Parametric Modeling of LIGO-Virgos  Binary Black Hole Mass Distribution

Volumetric (4D) performance capture is fundamental for AR/VR content
generation. Whereas previous work in 4D performance capture has shown
impressive results in studio settings, the technology is still far from being
accessible to a typical consumer who, at best, might own a single RGBD sensor.
Thus, in this work, we propose a method to synthesize free viewpoint renderings
using a single RGBD camera. The key insight is to leverage previously seen
"calibration" images of a given user to extrapolate what should be rendered in
a novel viewpoint from the data available in the sensor. Given these past
observations from multiple viewpoints, and the current RGBD image from a fixed
view, we propose an end-to-end framework that fuses both these data sources to
generate novel renderings of the performer. We demonstrate that the method can
produce high fidelity images, and handle extreme changes in subject pose and
camera viewpoints. We also show that the system generalizes to performers not
seen in the training data. We run exhaustive experiments demonstrating the
effectiveness of the proposed semi-parametric model (i.e. calibration images
available to the neural network) compared to other state of the art machine
learned solutions. Further, we compare the method with more traditional
pipelines that employ multi-view capture. We show that our framework is able to
achieve compelling results, with substantially less infrastructure than
previously required.

提出使用一台 RGBD 相机综合已获取的图像数据生成自由视角视频的方法。该方法通过半参数模型结合深度神经网络的技术实现 4D 表演捕捉，从而为 AR / VR 内容生成提供高逼真度的渲染效果。

利用半参数学习单个 RGBD 相机对人体进行体积捕捉

Volumetric Capture of Humans with a Single RGBD Camera via  Semi-Parametric Learning

Motivated by the application of real-time pricing in e-commerce platforms, we
consider the problem of revenue-maximization in a setting where the seller can
leverage contextual information describing the customer's history and the
product's type to predict her valuation of the product. However, her true
valuation is unobservable to the seller, only binary outcome in the form of
success-failure of a transaction is observed. Unlike in usual contextual bandit
settings, the optimal price/arm given a covariate in our setting is sensitive
to the detailed characteristics of the residual uncertainty distribution. We
develop a semi-parametric model in which the residual distribution is
non-parametric and provide the first algorithm which learns both regression
parameters and residual distribution with $\tilde O(\sqrt{n})$ regret. We
empirically test a scalable implementation of our algorithm and observe good
performance.

基于实时定价与上下文信息，本文提出了一种半参数模型，能够通过回归参数和残差分布来最大化收益，考虑到了细节特征。