One of the significant barriers to the training of statistical models on
knowledge graphs is the difficulty that scientists have in finding the best
input data to address their prediction goal. In addition to this, a key
challenge is to determine how to manipulate these relational data, which are
often in the form of particular triples (i.e., subject, predicate, object), to
enable the learning process. Currently, many high-quality catalogs of knowledge
graphs, are available. However, their primary goal is the re-usability of these
resources, and their interconnection, in the context of the Semantic Web. This
paper describes the LiveSchema initiative, namely, a first version of a gateway
that has the main scope of leveraging the gold mine of data collected by many
existing catalogs collecting relational data like ontologies and knowledge
graphs. At the current state, LiveSchema contains - 1000 datasets from 4 main
sources and offers some key facilities, which allow to: i) evolving LiveSchema,
by aggregating other source catalogs and repositories as input sources; ii)
querying all the collected resources; iii) transforming each given dataset into
formal concept analysis matrices that enable analysis and visualization
services; iv) generating models and tensors from each given dataset.

LiveSchema 是一个旨在利用现有的本体论和知识图谱等关系数据资源的网关，并提供了聚合其他源目录和仓库、查询、转换为概念分析矩阵以及生成模型和张量的功能。

迈向知识图谱模式收集、分析和嵌入的网关

Towards a Gateway for Knowledge Graph Schemas Collection, Analysis, and  Embedding

We performed a search for strong lens galaxy-scale systems in the first data
release of the Dark Energy Survey (DES), from a color-selected parent sample of
18~745~029 Luminous Red Galaxies (LRGs). Our search was based on a
Convolutional Neural Network (CNN) to grade our LRG selection with values
between 0 (non-lens) and 1 (lens). Our training set was data-driven, i.e. using
lensed sources taken from HST COSMOS images and where the light distribution of
the lens plane was taken directly from DES images of our LRGs. A total of
76~582 cutouts obtained a score above 0.9. These were visually inspected and
resulted in two catalogs. The first one contains 405 lens candidates, where 90
present clear lensing features and counterparts, while the others 315 require
more evidence, such as higher resolution images or spectra to be conclusive. A
total of 186 candidates were totally new identified in this search. The second
catalog includes 539 ring galaxy candidates that will be useful to train CNNs
against this type of false positives. For the 90 best lens candidates we
carried out color-based deblending of the lens and source light without fitting
any analytical profile to the data. The method turned out to be very efficient
in the deblending, even for very compact objects and for objects with very
complex morphology. Finally, from the 90 best lens candidates we selected 52
systems having one single deflector, to test an automated modeling pipeline
which successfully modeled 79\% of the sample within an acceptable amount of
computing time.

使用卷积神经网络从第一批 Dark Energy Survey（DES）数据中筛选出强引力透镜系统的候选物，包括 405 个透镜候选物和 539 个可用于训练 CNNs 的环状星系候选物，并构建自动化建模管道来对 52 个有单个偏转器的系统进行建模。

利用卷积神经网络在暗能量调查中搜索强透镜系统

Strong lens systems search in the Dark Energy Survey using Convolutional  Neural Networks

We present a methodology to discover outliers in catalogs of periodic
light-curves. We use cross-correlation as measure of ``similarity'' between two
individual light-curves and then classify light-curves with lowest average
``similarity'' as outliers. We performed the analysis on catalogs of variable
stars of known type from the MACHO and OGLE projects and established that our
method correctly identifies light-curves that do not belong to those catalogs
as outliers. We show how our method can scale to large datasets that will be
available in the near future such as those anticipated from Pan-STARRS and
LSST.

提出了一种在定期光曲线目录中发现异常值的方法，使用交叉相关作为两个单独的光曲线之间 “相似性” 的度量，然后将平均 “相似性” 最低的光曲线分类为异常值，用 MACHO 和 OGLE 计划的已知类型的变量星目录进行了分析，证明该方法正确地将不属于这些目录的光曲线识别为异常值 ，展示了如何将该方法扩展到未来可用的大型数据集，例如来自 Pan-STARRS 和 LSST 的数据集。