Autonomous systems will play an essential role in many applications across diverse domains including space, marine, air, field, road, and service robotics. They will assist us in our daily routines and perform dangerous, dirty and dull tasks. However, enabling robotic systems to perform autonomously in complex, real-world scenarios over extended time periods (i.e. weeks, months, or years) poses many challenges. Some of these have been investigated by sub-disciplines of Artificial Intelligence (AI) including navigation & mapping, perception, knowledge representation & reasoning, planning, interaction, and learning. The different sub-disciplines have developed techniques that, when re-integrated within an autonomous system, can enable robots to operate effectively in complex, long-term scenarios. In this paper, we survey and discuss AI techniques as ‘enablers’ for long-term robot autonomy, current progress in integrating these techniques within long-running robotic systems, and the future challenges and opportunities for AI in long-term autonomy. Artificial Intelligence for Long-Term Robot Autonomy: A Survey

R is designed to make working with statistical models fast, succinct, and reliable.

CONvergence of iterated CORrelations (CONCOR) Given an adjacency matrix, or a set of adjacency matrices for different relations, a correlation matrix can be formed by the following procedure. Form a profile vector for a vertex i by concatenating the ith row in every adjacency matrix; the i,jth element of the correlation matrix is the Pearson correlation coefficient of the profile vectors of i and j. This (square, symmetric) matrix is called the first correlation matrix. The procedure can be performed iteratively on the correlation matrix until convergence. Each entry is now 1 or -1. This matrix is used to split the data into two blocks such that members of the same block are positively correlated, members of different blocks are negatively correlated. CONCOR uses the above technique to split the initial data into two blocks. Successive splits are then applied to the separate blocks. At each iteration all blocks are submitted for analysis, however blocks containing two vertices are not split. Consequently n-partitions of the binary tree can produce up to 2n blocks. Note that any similarity matrix can be used as input. http://…/concor-in-r …

Since the proposal of big data analysis and Graphic Processing Unit (GPU), the deep learning technology has received a great deal of attention and has been widely applied in the field of imaging processing. In this paper, we have an aim to completely review and summarize the deep learning technologies for image denoising proposed in recent years. Morever, we systematically analyze the conventional machine learning methods for image denoising. Finally, we point out some research directions for the deep learning technologies in image denoising. Deep Learning for Image Denoising: A Survey

CONvergence of iterated CORrelations (CONCOR) Given an adjacency matrix, or a set of adjacency matrices for different relations, a correlation matrix can be formed by the following procedure. Form a profile vector for a vertex i by concatenating the ith row in every adjacency matrix; the i,jth element of the correlation matrix is the Pearson correlation coefficient of the profile vectors of i and j. This (square, symmetric) matrix is called the first correlation matrix. The procedure can be performed iteratively on the correlation matrix until convergence. Each entry is now 1 or -1. This matrix is used to split the data into two blocks such that members of the same block are positively correlated, members of different blocks are negatively correlated. CONCOR uses the above technique to split the initial data into two blocks. Successive splits are then applied to the separate blocks. At each iteration all blocks are submitted for analysis, however blocks containing two vertices are not split. Consequently n-partitions of the binary tree can produce up to 2n blocks. Note that any similarity matrix can be used as input. http://…/concor-in-r …

R is designed to make working with statistical models fast, succinct, and reliable.

From the university webpage:

At some point, I ended up with a model like this one:

Programming Exercises for the Analysis of Knowledge Graphs