In this work, we explore how to classify asteroids in co-orbital motion with
a given planet using Machine Learning. We consider four different kinds of
motion in mean motion resonance with the planet, nominally Tadpole, Horseshoe
and Quasi-satellite, building 3 datasets defined as Real (taking the
ephemerides of real asteroids from the JPL Horizons system), Ideal and
Perturbed (both simulated, obtained by propagating initial conditions
considering two different dynamical systems) for training and testing the
Machine Learning algorithms in different conditions.
The time series of the variable theta (angle related to the resonance) are
studied with a data analysis pipeline defined ad hoc for the problem and
composed by: data creation and annotation, time series features extraction
thanks to the tsfresh package (potentially followed by selection and
standardization) and the application of Machine Learning algorithms for
Dimensionality Reduction and Classification. Such approach, based on features
extracted from the time series, allows to work with a smaller number of data
with respect to Deep Learning algorithms, also allowing to define a ranking of
the importance of the features. Physical Interpretability of the features is
another key point of this approach. In addition, we introduce the SHapley
Additive exPlanations for Explainability technique.
Different training and test sets are used, in order to understand the power
and the limits of our approach. The results show how the algorithms are able to
identify and classify correctly the time series, with a high degree of
performance.

本文研究了如何使用机器学习对共轨行星中的小行星进行分类。通过考虑不同种类的运动以及时间序列特征提取等方法，我们展示了机器学习算法在识别和分类时间序列方面的高性能。

基于机器学习的小行星共轨运动分类

Asteroids co-orbital motion classification based on Machine Learning

Quantum state engineering, the cornerstone of quantum photonic technologies,
mainly relies on spontaneous parametric down-conversion and four-wave mixing,
where one or two pump photons decay into a photon pair. Both these nonlinear
effects require momentum conservation (i.e., phase-matching) for the
participating photons, which strongly limits the versatility of the resulting
quantum states. Nonlinear metasurfaces, due to their subwavelength thickness,
relax this constraint and extend the boundaries of quantum state engineering.
Here, we generate entangled photons via spontaneous parametric down-conversion
in semiconductor metasurfaces with high-quality resonances. By enhancing the
quantum vacuum field, our metasurfaces boost the emission of photon pairs
within narrow resonance bands at multiple selected wavelengths. Due to the
relaxed momentum conservation, the same resonances support photon pair
generation from pump photons of practically any energy. This enables the
generation of complex frequency-multiplexed quantum states, in particular
cluster states. Our results demonstrate the multifunctional use of metasurfaces
for quantum state engineering.

在半导体超表面中通过自发参量下转换产生了纠缠光子，由于相位匹配条件被破坏，这些非线性超表面扩展了量子态工程的边界，为复杂的频率多路复用量子状态如团簇态的产生提供了可能性。

产生复杂量子态的共振半导体超表面

Resonant Semiconductor Metasurfaces for Generating Complex Quantum  States

Social media generates a prodigious wealth of real-time content at an
incessant rate. From all the content that people create and share, only a few
topics manage to attract enough attention to rise to the top and become
temporal trends which are displayed to users. The question of what factors
cause the formation and persistence of trends is an important one that has not
been answered yet. In this paper, we conduct an intensive study of trending
topics on Twitter and provide a theoretical basis for the formation,
persistence and decay of trends. We also demonstrate empirically how factors
such as user activity and number of followers do not contribute strongly to
trend creation and its propagation. In fact, we find that the resonance of the
content with the users of the social network plays a major role in causing
trends.

本文通过对 Twitter 上热门话题的深入研究，提供了形成、持久性和衰退趋势的理论基础，并从实证角度证明用户活动和关注者数量不是趋势创造和传播的强因素，事实上，内容与社交网络用户的共鸣在推动趋势中起着重要作用。

社交媒体趋势：持续性和衰退

Trends in Social Media : Persistence and Decay

When searching for a new resonance somewhere in a possible mass range, the
significance of observing a local excess of events must take into account the
probability of observing such an excess anywhere in the range. This is the so
called "look elsewhere effect". The effect can be quantified in terms of a
trial factor, which is the ratio between the probability of observing the
excess at some fixed mass point, to the probability of observing it anywhere in
the range. We propose a simple and fast procedure for estimating the trial
factor, based on earlier results by Davies. We show that asymptotically, the
trial factor grows linearly with the (fixed mass) significance.

提出了一种快速估算试验因子的方法，该因子可以在搜索新共振体时考虑观察到局部事件超额的概率，并可以用来量化查找异常的效率。