Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

Data Mining Practical Machine Learning Tools and Techniques

Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way, the dependence on a large number of target-domain data can be reduced for constructing target learners. Due to the wide application prospects, transfer learning has become a popular and promising area in machine learning. Although there are already some valuable and impressive surveys on transfer learning, these surveys introduce approaches in a relatively isolated way and lack the recent advances in transfer learning. Due to the rapid expansion of the transfer learning area, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing transfer learning research studies, as well as to summarize and interpret the mechanisms and the strategies of transfer learning in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. Unlike previous surveys, this survey article reviews more than 40 representative transfer learning approaches, especially homogeneous transfer learning approaches, from the perspectives of data and model. The applications of transfer learning are also briefly introduced. In order to show the performance of different transfer learning models, over 20 representative transfer learning models are used for experiments. The models are performed on three different data sets, that is, Amazon Reviews, Reuters-21578, and Office-31, and the experimental results demonstrate the importance of selecting appropriate transfer learning models for different applications in practice.

A Comprehensive Survey on Transfer Learning

The cultural origins of human cognition

Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to leverage useful information contained in multiple related tasks to help improve the generalization performance of all the tasks. In this paper, we give a survey for MTL from the perspective of algorithmic modeling, applications and theoretical analyses. For algorithmic modeling, we give a definition of MTL and then classify different MTL algorithms into five categories, including feature learning approach, low-rank approach, task clustering approach, task relation learning approach and decomposition approach as well as discussing the characteristics of each approach. In order to improve the performance of learning tasks further, MTL can be combined with other learning paradigms including semi-supervised learning, active learning, unsupervised learning, reinforcement learning, multi-view learning and graphical models. When the number of tasks is large or the data dimensionality is high, we review online, parallel and distributed MTL models as well as dimensionality reduction and feature hashing to reveal their computational and storage advantages. Many real-world applications use MTL to boost their performance and we review representative works in this paper. Finally, we present theoretical analyses and discuss several future directions for MTL.

A Survey on Multi-Task Learning

Trajectory planning and replanning in complex environments often reuses very little information from the previous solutions. This is particularly evident when the motion is repeated multiple times with only a limited amount of variation between each run. To address this issue, we propose the DRM-connect algorithm, a combination of dynamic reachability maps (DRM) with lazy collision checking and a fallback strategy based on the RRT-connect algorithm which is used to repair the roadmap through further exploration. This fallback allows us to use much sparser roadmaps. Furthermore, we investigate using an approximate Reeb graph to capture the topology-persistent features of the past solutions of the problem utilising this sparsity. We evaluate DRM-connect with a Reeb graph on reaching tasks, and we compare it to state-of-the-art methods. We show that the proposed method outperforms both RRT-connect and BKPIECE algorithms in the number of collision checks required and we show that our method has the potential to scale to systems with higher number degrees of freedom.

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Real-Time Motion Planning in Changing Environments Using Topology-Based Encoding of Past Knowledge

Dexterous multi-fingered robotic hands represent a promising solution for robotic manipulators to perform a wide range of complex tasks, through acquiring more general purpose skills. Nevertheless, developing complex behaviours for a robot needs sophisticated control strategies, which requires domain expertise with a good understanding of mathematics, and underlying physics, this will be very difficult for such complex robots. Learning algorithms like deep reinforcement learning provide a general framework for robots to learn complex behaviours directly from data. But, even for learning algorithms, dexterous manipulators represent a major challenge due to their high dimensionality and contact rich under-actuated object manipulation. To overcome these challenges, learning algorithms demand extensive data generation, which is often expensive or hard to obtain. So, the need for more efficient and more effective algorithms -that extract more useful information from the least amount of available data- has increased. This paper presents a contribution which is the development of the algorithm Model Predictive Control-Soft Actor Critic (MPC-SAC), an algorithm that combines offline learning with online planning to develop a control policy. The algorithm is benchmarked on two challenging complex dexterous manipulation tasks, against other state of the art model free reinforcement learning algorithms. Finally, it was found that the algorithm achieves asymptotic performance with state of the art data efficiency in both tasks.

Model Predictive-Actor Critic Reinforcement Learning for Dexterous Manipulation

In most countries throughout the world, heavy vehicle use on public roads are governed by prescriptive rules, typically by imposing stringent mass and dimension limits in an attempt to control vehicle safety. A recent alternative framework is a performance-based standards approach which specifies on-road vehicle performance measures. One such standard is the low-speed swept path, which is a measure of road width required by a vehicle to complete a prescribed turning manoeuvre. This is typically determined by physical testing or detailed vehicle simulations, both of which are costly and time consuming processes. This paper presents a data driven, detailed model to predict the low-speed performance of an articulated vehicle, given only the vehicle geometry. The development of a lightweight tool to predict the swept path of an articulated heavy vehicle, without the need for detailed simulation or testing, is discussed.

Autonomous prediction of performance-based standards for heavy vehicles

Autonomous Learning of Object-Centric Abstractions for High-Level Planning

This paper addresses the problem of acquiring a hierarchically structured robotic skill in a nonstationary environment. This is achieved through a combination of learning primitive strategies from observation of an expert, and autonomously synthesising composite strategies from that basis. Both aspects of this problem are approached from a game theoretic viewpoint, building on prior work in the area of multiplicative weights learning algorithms. The utility of this procedure is demonstrated through simulation experiments motivated by the problem of autonomous driving. We show that this procedure allows the agent to come to terms with two forms of uncertainty in the world - continually varying goals (due to oncoming traffic) and nonstationarity of optimisation criteria (e.g., driven by changing navigability of the road). We argue that this type of factored task specification and learning is a necessary ingredient for robust autonomous behaviour in a “large-world” setting.

/pdf/a-game-theoretic-procedure-for-learning-hierarchically-5cyu061zfp.pdf

Benjamin Rosman

Papers

Real-Time Motion Planning in Changing Environments Using Topology-Based Encoding of Past Knowledge

Model Predictive-Actor Critic Reinforcement Learning for Dexterous Manipulation

Autonomous prediction of performance-based standards for heavy vehicles

Autonomous Learning of Object-Centric Abstractions for High-Level Planning

A game-theoretic procedure for learning hierarchically structured strategies