Most machine learning models are static, but the world is dynamic, and
increasing online deployment of learned models gives increasing urgency to the
development of efficient and effective mechanisms to address learning in the
context of non-stationary distributions, or as it is commonly called concept
drift. However, the key issue of characterizing the different types of drift
that can occur has not previously been subjected to rigorous definition and
analysis. In particular, while some qualitative drift categorizations have been
proposed, few have been formally defined, and the quantitative descriptions
required for precise and objective understanding of learner performance have
not existed. We present the first comprehensive framework for quantitative
analysis of drift. This supports the development of the first comprehensive set
of formal definitions of types of concept drift. The formal definitions clarify
ambiguities and identify gaps in previous definitions, giving rise to a new
comprehensive taxonomy of concept drift types and a solid foundation for
research into mechanisms to detect and address concept drift.

本研究提出了一个全面的概念漂移类型的分类法并支持定量分析，从而为解决这一问题提供了基础。

描述概念漂移

Characterizing Concept Drift

Noise is always presents in digital images during image acquisition, coding,
transmission, and processing steps. Noise is very difficult to remove it from
the digital images without the prior knowledge of noise model. That is why,
review of noise models are essential in the study of image denoising
techniques. In this paper, we express a brief overview of various noise models.
These noise models can be selected by analysis of their origin. In this way, we
present a complete and quantitative analysis of noise models available in
digital images.

本文简要介绍了各种噪声模型的概述，这些噪声模型可以通过其来源的分析进行选择，从而提供了数字图像中现有噪声模型的完整和定量分析。

数字图像处理中的噪声模型综述

A Review Paper: Noise Models in Digital Image Processing

Instagram is a relatively new form of communication where users can instantly
share their current status by taking pictures and tweaking them using filters.
It has seen a rapid growth in the number of users as well as uploads since it
was launched in October 2010. Inspite of the fact that it is the most popular
photo sharing application, it has attracted relatively less attention from the
web and social media research community. In this paper, we present a
large-scale quantitative analysis on millions of users and pictures we crawled
over 1 month from Instagram. Our analysis reveals several insights on Instagram
which were never studied before: 1) its social network properties are quite
different from other popular social media like Twitter and Flickr, 2) people
typically post once a week, and 3) people like to share their locations with
friends. To the best of our knowledge, this is the first in-depth analysis of
user activities, demographics, social network structure and user-generated
content on Instagram.

通过对 Instagram user-generated content 进行大规模定量分析，我们揭示了 Instagram 的社交网络结构特性、用户行为、个人资料以及用户生成的内容等多方面洞见，这是第一篇深入分析 Instagram 的首个研究。

研究 Instagram 上用户活动、人口统计、社交网络结构和用户生成内容

Analyzing User Activities, Demographics, Social Network Structure and  User-Generated Content on Instagram

This paper presents a method for retinal vasculature extraction based on
biologically inspired multi-orientation analysis. We apply multi-orientation
analysis via so-called invertible orientation scores, modeling the cortical
columns in the visual system of higher mammals. This allows us to generically
deal with many hitherto complex problems inherent to vessel tracking, such as
crossings, bifurcations, parallel vessels, vessels of varying widths and
vessels with high curvature. Our approach applies tracking in invertible
orientation scores via a novel geometrical principle for curve optimization in
the Euclidean motion group SE(2). The method runs fully automatically and
provides a detailed model of the retinal vasculature, which is crucial as a
sound basis for further quantitative analysis of the retina, especially in
screening applications.

本文提出了一种基于生物启发的多方向分析的视网膜血管提取方法，应用可逆向分数进行多方向分析，通过 SE (2) 中的曲线优化几何学原理来跟踪血管并提取视网膜血管模型，该方法可自动执行，提供视网膜血管的详细模型，是进一步定量分析视网膜的有力基础。