Machine learning challenges in theoretical HEP

In these proceedings we perform a brief review of machine learning (ML) applications in theoretical High Energy Physics (HEP-TH). We start the discussion by defining and then classifying machine learning tasks in theoretical HEP. We then discuss some of the most popular and recent published approaches with focus on a relevant case study topic: the determination of parton distribution functions (PDFs) and related tools. Finally, we provide an outlook about future applications and developments due to the synergy between ML and HEP-TH.Comment: 7 pages, 3 figures, in proceedings of the 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017

Situating multimodal learning analytics

The digital age has introduced a host of new challenges and opportunities for the learning sciences community. These challenges and opportunities are particularly abundant in multimodal learning analytics (MMLA), a research methodology that aims to extend work from Educational Data Mining (EDM) and Learning Analytics (LA) to multimodal learning environments by treating multimodal data. Recognizing the short-term opportunities and longterm challenges will help develop proof cases and identify grand challenges that will help propel the field forward. To support the field's growth, we use this paper to describe several ways that MMLA can potentially advance learning sciences research and touch upon key challenges that researchers who utilize MMLA have encountered over the past few years

E-learning as a Vehicle for Knowledge Management

Nowadays, companies want to learn from their own experiences and to be able to enhance that experience with best principles and lessons learned from other companies. Companies emphasise the importance of knowledge management, particularly the relationship between knowledge and learning within an organisation. We feel that an e-learning environment may contribute to knowledge management on the one hand and to the learning need in companies on the other hand. In this paper, we report on the challenges in designing and implementing an e-learning environment. We identify the properties from a pedagogical view that should be supported by an e-learning environment. Then, we discuss the challenges in developing a system that includes these properties

Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

The context, influences and challenges for undergraduate nurse clinical education: Continuing the dialogue

Introduction – Approaches to clinical education are highly diverse and becoming increasingly complex to sustain in complex milieu Objective – To identify the influences and challenges of providing nurse clinical education in the undergraduate setting and to illustrate emerging solutions. Method: A discursive exploration into the broad and varied body of evidence including peer reviewed and grey literature. Discussion - Internationally, enabling undergraduate clinical learning opportunities faces a range of challenges. These can be illustrated under two broad themes: (1) Legacies from the past and the inherent features of nurse education and (2) Challenges of the present, including, population changes, workforce changes, and the disconnection between the health and education sectors. Responses to these challenges are triggering the emergence of novel approaches, such as collaborative models. Conclusion(s) – Ongoing challenges in providing accessible, effective and quality clinical learning experiences are apparent

Build your Responsive Open Learning Environment

The European research project ROLE (Responsive Open Learning Environments) is aiming at empowering learners for self-regulated learning within a personalised learning environment. Towards this goal, ROLE has developed a number of learning technologies, addressing a variety of learning requirements. The purpose of this roundtable is to discuss the applications of these technologies in different learning contexts, as well as the challenges associated with enabling and supporting self-regulated learning

Focusing on the Big Picture: Insights into a Systems Approach to Deep Learning for Satellite Imagery

Deep learning tasks are often complicated and require a variety of components working together efficiently to perform well. Due to the often large scale of these tasks, there is a necessity to iterate quickly in order to attempt a variety of methods and to find and fix bugs. While participating in IARPA's Functional Map of the World challenge, we identified challenges along the entire deep learning pipeline and found various solutions to these challenges. In this paper, we present the performance, engineering, and deep learning considerations with processing and modeling data, as well as underlying infrastructure considerations that support large-scale deep learning tasks. We also discuss insights and observations with regard to satellite imagery and deep learning for image classification.Comment: Accepted to IEEE Big Data 201