Solar filaments are well-known tracers of polarity inversion lines that
separate two opposite magnetic polarities on the solar photosphere. Because
observations of filaments began long before the systematic observations of
solar magnetic fields, historical filament catalogs can facilitate the
reconstruction of magnetic polarity maps at times when direct magnetic
observations were not yet available. In practice, this reconstruction is often
ambiguous and typically performed manually. We propose an automatic approach
based on a machine-learning model that generates a variety of magnetic polarity
maps consistent with filament observations. To evaluate the model and discuss
the results we use the catalog of solar filaments and polarity maps compiled by
McIntosh. We realize that the process of manual compilation of polarity maps
includes not only information on filaments, but also a large amount of prior
information, which is difficult to formalize. In order to compensate for the
lack of prior knowledge for the machine-learning model, we provide it with
polarity information at several reference points. We demonstrate that this
process, which can be considered as the user-guided reconstruction or
super-resolution, leads to polarity maps that are reasonably close to
hand-drawn ones, and additionally allows for uncertainty estimation.

通过机器学习模型基于太阳丝状物观测生成变化的磁极性图，使用 McIntosh 编制的太阳丝状物和极性地图目录进行评估和结果讨论，展示了用户引导下的重建或超分辨过程可以得到与手绘极性地图相似且能进行不确定性评估的结果。

用于太阳丝的极性倒转线重建的机器学习

Machine learning for reconstruction of polarity inversion lines from  solar filaments

Context: An increasing demand is observed in various domains to employ
Machine Learning (ML) for solving complex problems. ML models are implemented
as software components and deployed in Machine Learning Software Systems
(MLSSs). Problem: There is a strong need for ensuring the serving quality of
MLSSs. False or poor decisions of such systems can lead to malfunction of other
systems, significant financial losses, or even threats to human life. The
quality assurance of MLSSs is considered a challenging task and currently is a
hot research topic. Objective: This paper aims to investigate the
characteristics of real quality issues in MLSSs from the viewpoint of
practitioners. This empirical study aims to identify a catalog of quality
issues in MLSSs. Method: We conduct a set of interviews with
practitioners/experts, to gather insights about their experience and practices
when dealing with quality issues. We validate the identified quality issues via
a survey with ML practitioners. Results: Based on the content of 37 interviews,
we identified 18 recurring quality issues and 24 strategies to mitigate them.
For each identified issue, we describe the causes and consequences according to
the practitioners' experience. Conclusion: We believe the catalog of issues
developed in this study will allow the community to develop efficient quality
assurance tools for ML models and MLSSs. A replication package of our study is
available on our public GitHub repository.

本文旨在从实践者的角度研究机器学习软件系统中的质量问题，通过一系列采访和调研，我们创建了一个包含 18 个质量问题和 24 个解决策略的目录，这将有助于开发高效的质量保证工具。

机器学习软件系统中的质量问题

Quality Issues in Machine Learning Software Systems

This paper presents prompt design techniques for software engineering, in the
form of patterns, to solve common problems when using large language models
(LLMs), such as ChatGPT to automate common software engineering activities,
such as ensuring code is decoupled from third-party libraries and simulating a
web application API before it is implemented. This paper provides two
contributions to research on using LLMs for software engineering. First, it
provides a catalog of patterns for software engineering that classifies
patterns according to the types of problems they solve. Second, it explores
several prompt patterns that have been applied to improve requirements
elicitation, rapid prototyping, code quality, refactoring, and system design.

本文介绍了利用大型语言模型 (ChatGPT) 自动化软件工程活动，如确保代码与第三方库解耦和模拟 Web 应用程序 API 的快速设计技术。该文提供了解决软件工程中常见问题的模式目录，并探讨了几个应用模式以改善需求概述、快速原型、代码质量、重构和系统设计。

利用 ChatGPT 提示模式改善代码质量、重构、需求引出和软件设计

ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design

Prompt engineering is an increasingly important skill set needed to converse
effectively with large language models (LLMs), such as ChatGPT. Prompts are
instructions given to an LLM to enforce rules, automate processes, and ensure
specific qualities (and quantities) of generated output. Prompts are also a
form of programming that can customize the outputs and interactions with an
LLM. This paper describes a catalog of prompt engineering techniques presented
in pattern form that have been applied to solve common problems when conversing
with LLMs. Prompt patterns are a knowledge transfer method analogous to
software patterns since they provide reusable solutions to common problems
faced in a particular context, i.e., output generation and interaction when
working with LLMs. This paper provides the following contributions to research
on prompt engineering that apply LLMs to automate software development tasks.
First, it provides a framework for documenting patterns for structuring prompts
to solve a range of problems so that they can be adapted to different domains.
Second, it presents a catalog of patterns that have been applied successfully
to improve the outputs of LLM conversations. Third, it explains how prompts can
be built from multiple patterns and illustrates prompt patterns that benefit
from combination with other prompt patterns.

这篇文章介绍了 Prompt 工程技术的目录，包含适用于大型语言模型的多个生产输出和互动方面的工作中的问题的重复使用解决方案。

一个提示模式目录：用于增强 ChatGPT 的提示工程

A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT

In the past few years we have seen the meteoric appearance of dozens of
models of the Transformer family, all of which have funny, but not
self-explanatory, names. The goal of this paper is to offer a somewhat
comprehensive but simple catalog and classification of the most popular
Transformer models. The paper also includes an introduction to the most
important aspects and innovation in Transformer models.

本文介绍了最受欢迎的 Transformer 模型的综合分类目录，并介绍了 Transformer 模型中最重要的方面和创新。

Transformer 模型：简介和目录

Transformer models: an introduction and catalog

We tackle the problem of producing realistic simulations of LiDAR point
clouds, the sensor of preference for most self-driving vehicles. We argue that,
by leveraging real data, we can simulate the complex world more realistically
compared to employing virtual worlds built from CAD/procedural models. Towards
this goal, we first build a large catalog of 3D static maps and 3D dynamic
objects by driving around several cities with our self-driving fleet. We can
then generate scenarios by selecting a scene from our catalog and "virtually"
placing the self-driving vehicle (SDV) and a set of dynamic objects from the
catalog in plausible locations in the scene. To produce realistic simulations,
we develop a novel simulator that captures both the power of physics-based and
learning-based simulation. We first utilize ray casting over the 3D scene and
then use a deep neural network to produce deviations from the physics-based
simulation, producing realistic LiDAR point clouds. We showcase LiDARsim's
usefulness for perception algorithms-testing on long-tail events and end-to-end
closed-loop evaluation on safety-critical scenarios.

本文提出了一种名为 LiDARsim 的模拟器，利用真实数据生成 3D 场景，通过物理模拟和深度神经网络相结合来生成 LiDAR 点云，用于测试感知算法和评估安全场景。

LiDARsim: 通过利用现实世界，实现逼真的激光雷达模拟

LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World

The DESI Legacy Imaging Surveys are a combination of three public projects
(the Dark Energy Camera Legacy Survey, the Beijing-Arizona Sky Survey, and the
Mayall z-band Legacy Survey) that will jointly image approximately 14,000 deg^2
of the extragalactic sky visible from the northern hemisphere in three optical
bands (g, r, and z) using telescopes at the Kitt Peak National Observatory and
the Cerro Tololo Inter-American Observatory. The combined survey footprint is
split into two contiguous areas by the Galactic plane. The optical imaging is
conducted using a unique strategy of dynamically adjusting the exposure times
and pointing selection during observing that results in a survey of nearly
uniform depth. In addition to calibrated images, the project is delivering a
catalog, constructed by using a probabilistic inference-based approach to
estimate source shapes and brightnesses. The catalog includes photometry from
the grz optical bands and from four mid-infrared bands (at 3.4, 4.6, 12 and 22
micorons) observed by the Wide-field Infrared Survey Explorer (WISE) satellite
during its full operational lifetime. The project plans two public data
releases each year. All the software used to generate the catalogs is also
released with the data. This paper provides an overview of the Legacy Surveys
project.

该论文概述了 DESI 遗产成像调查项目，该项目使用光学成像和概率推断方法构建星系目录，并计划每年推出两次公共数据发布。

DESI 遗产成像调查概述

Overview of the DESI Legacy Imaging Surveys

The All-Sky Automated Survey for Supernovae (ASAS-SN) is the first optical
survey to routinely monitor the whole sky with a cadence of $\sim2-3$ days down
to V$\lesssim17$ mag. ASAS-SN has monitored the whole sky since 2014,
collecting $\sim100-500$ epochs of observations per field. The V-band light
curves for candidate variables identified during the search for supernovae are
classified using a random forest classifier and visually verified. We present a
catalog of 66,179 bright, new variable stars discovered during our search for
supernovae, including 27,479 periodic variables and 38,700 irregular variables.
V-band light curves for the ASAS-SN variables are available through the ASAS-SN
variable stars database (this https URL). The database will
begin to include the light curves of known variable stars in the near future
along with the results for a systematic, all-sky variability survey.

ASAS-SN is a survey that monitors the whole sky for supernovae using V-band light curves and presents a catalog of 66,179 variable stars, including periodic and irregular ones.

ASAS-SN 可变星星表 I：意外调查

The ASAS-SN Catalog of Variable Stars I: The Serendipitous Survey

We report on discovery results from a quasar lens search in the ATLAS public
footprint, extending quasar lens searches to a regime without $u-$band or
fiber-spectroscopic information, using a combination of data mining techniques
on multi-band catalog magnitudes and image-cutout modelling. Spectroscopic
follow-up campaigns, conducted at the 2.6m Nordic Optical Telescope (La Palma)
and 3.6m New Technology Telescope (La Silla) in 2016, yielded seven pairs of
quasars exhibiting the same lines at the same redshift and monotonic
flux-ratios with wavelength (hereafter NIQs, Nearly Identical Quasar pairs).
The quasar redshifts range between $\approx1.2$ and $\approx 2.7;$ contaminants
are typically pairs of bright blue stars, quasar-star alignments along the line
of sight, and narrow-line galaxies at $0.3<z<0.7.$ Magellan data of A0140-1152
(01$^h$40$^m$03.0$^s$-11$^d$52$^m$19.0$^s$, $z_{s}=1.807$) confirm it as a lens
with deflector at $z_{l}=0.277$ and Einstein radius $\theta_{\rm
E}=(0.73\pm0.02)^\ase$. We show the use of spatial resolution from the Gaia
mission to select lenses and list additional systems from a WISE-Gaia-ATLAS
search, yielding three additional lenses
(02$^h$35$^m$27.4$^s$-24$^d$33$^m$13.2$^s$,
02$^h$59$^m$33.$^s$-23$^d$38$^m$01.8$^s$,
01$^h$46$^m$32.9$^s$-11$^d$33$^m$39.0$^s$). The overall sample consists of 11
lenses/NIQs, plus three lenses known before 2016, over the ATLAS-DR3 footprint
($\approx3500$~deg$^2$). Finally, we discuss future prospects for objective
classification of pair/NIQ/contaminant spectra.

在 ATLAS 公共中心区域，通过对多波段目录幅度和图像切割模型的数据挖掘技术相结合且不需要 $u-$band 或光纤光谱信息，以及在 2016 年的 2.6m 北欧光学望远镜（La Palma）和 3.6m 新技术望远镜（La Silla）上进行的光谱后续运动，发现了 NIQs，包括 7 对具有相同线以及波长单调流量比的类星体，其中污染物通常为亮蓝色恒星对，沿视线方向的类星体 - 恒星对齐和 0.3<z<0.7 的窄线星系。我们还展示了来自盖亚使命的空间分辨率的使用来选择透镜，并从 WISE-Gaia-ATLAS 搜索中列出了其他系统产生了三个额外的透镜 (02$^h$35$^m$27.4$^s$-24$^d$33$^m$13.2$^s$，02$^h$59$^m$33.$^s$-23$^d$38$^m$01.8$^s$，01$^h$46$^m$32.9$^s$-11$^d$33$^m$39.0$^s$)。总体样本包括 11 个镜头 / NIQs，以及 2016 年之前已知的三个透镜，在 ATLAS-DR3 垫脚处（约 3500 平方度）。最后，我们还讨论了关于对组 / NIQ / 污染物光谱的客观分类的未来展望。

VST-ATLAS 和 Gaia 中的类星体透镜和双星

Quasar lenses and pairs in the VST-ATLAS and Gaia

We present the fifth edition of the Sloan Digital Sky Survey (SDSS) Quasar
Catalog, which is based upon the SDSS Seventh Data Release. The catalog, which
contains 105,783 spectroscopically confirmed quasars, represents the conclusion
of the SDSS-I and SDSS-II quasar survey. The catalog consists of the SDSS
objects that have luminosities larger than M_i = -22.0 (in a cosmology with H_0
= 70 km/s/Mpc Omega_M = 0.3, and Omega_Lambda = 0.7) have at least one emission
line with FWHM larger than 1000 km/s or have interesting/complex absorption
features, are fainter than i > 15.0 and have highly reliable redshifts. The
catalog covers an area of 9380 deg^2. The quasar redshifts range from 0.065 to
5.46, with a median value of 1.49; the catalog includes 1248 quasars at
redshifts greater than four, of which 56 are at redshifts greater than five.
The catalog contains 9210 quasars with i < 18; slightly over half of the
entries have i< 19. For each object the catalog presents positions accurate to
better than 0.1" rms per coordinate, five-band (ugriz) CCD-based photometry
with typical accuracy of 0.03 mag, and information on the morphology and
selection method. The catalog also contains radio, near-infrared, and X-ray
emission properties of the quasars, when available, from other large-area
surveys. The calibrated digital spectra cover the wavelength region 3800-9200
Ang. at a spectral resolution R = 2000 the spectra can be retrieved from the
SDSS public database using the information provided in the catalog. Over 96% of
the objects in the catalog were discovered by the SDSS. We also include a
supplemental list of an additional 207 quasars with SDSS spectra whose archive
photometric information is incomplete.

该文介绍了基于 Sloan Digital Sky Survey 的第七次数据发布的 SDSS 准星目录，包含了 105,783 个光谱确认的准星，涵盖了 9380 平方度的面积，其中红移范围为 0.065 到 5.46，中位值为 1.49，并提供了他们的位置、光度学和选取方法的信息。