Geometric deep learning: going beyond Euclidean data

Many scientific fields study data with an underlying structure that is a non-Euclidean space. Some examples include social networks in computational social sciences, sensor networks in communications, functional networks in brain imaging, regulatory networks in genetics, and meshed surfaces in computer graphics. In many applications, such geometric data are large and complex (in the case of social networks, on the scale of billions), and are natural targets for machine learning techniques. In particular, we would like to use deep neural networks, which have recently proven to be powerful tools for a broad range of problems from computer vision, natural language processing, and audio analysis. However, these tools have been most successful on data with an underlying Euclidean or grid-like structure, and in cases where the invariances of these structures are built into networks used to model them. Geometric deep learning is an umbrella term for emerging techniques attempting to generalize (structured) deep neural models to non-Euclidean domains such as graphs and manifolds. The purpose of this paper is to overview different examples of geometric deep learning problems and present available solutions, key difficulties, applications, and future research directions in this nascent field

English Speaking and Listening Assessment Project - Baseline. Bangladesh

This study seeks to understand the current practices of English Language Teaching (ELT) and assessment at the secondary school level in Bangladesh, with specific focus on speaking and listening skills. The study draws upon prior research on general ELT practices, English language proficiencies and exploration of assessment practices, in Bangladesh. The study aims to provide some baseline evidence about the way speaking and listening are taught currently, whether these skills are assessed informally, and if so, how this is done. The study addresses two research questions: 1. How ready are English Language Teachers in government-funded secondary schools in Bangladesh to implement continuous assessment of speaking and listening skills? 2. Are there identifiable contextual factors that promote or inhibit the development of effective assessment of listening and speaking in English? These were assessed with a mixed-methods design, drawing upon prior quantitative research and new qualitative fieldwork in 22 secondary schools across three divisions (Dhaka, Sylhet and Chittagong). At the suggestion of DESHE, the sample also included 2 of the ‘highest performing’ schools from Dhaka city. There are some signs of readiness for effective school-based assessment of speaking and listening skills: teachers, students and community members alike are enthusiastic for a greater emphasis on speaking and listening skills, which are highly valued. Teachers and students are now speaking mostly in English and most teachers also attempt to organise some student talk in pairs or groups, at least briefly. Yet several factors limit students’ opportunities to develop skills at the level of CEFR A1 or A2. Firstly, teachers generally do not yet have sufficient confidence, understanding or competence to introduce effective teaching or assessment practices at CEFR A1-A2. In English lessons, students generally make short, predictable utterances or recite texts. No lessons were observed in which students had an opportunity to develop or demonstrate language functions at CEFR A1-A2. Secondly, teachers acknowledge a washback effect from final examinations, agreeing that inclusion of marks for speaking and listening would ensure teachers and students took these skills more seriously during lesson time. Thirdly, almost two thirds of secondary students achieve no CEFR level, suggesting many enter and some leave secondary education with limited communicative English language skills. One possible contributor to this may be that almost half (43%) of the ELT population are only at the target level for students (CEFR A2) themselves, whilst approximately one in ten teachers (12%) do not achieve the student target (being at A1 or below). Fourthly, the Bangladesh curriculum student competency statements are generic and broad, providing little support to the development of teaching or assessment practices. The introduction and development of effective teaching and assessment strategies at CEFR A1-A2 requires a profound shift in teachers’ understanding and practice. We recommend that: 1. Future sector wide programmes provide sustained support to the develop teachers' competence in teaching and assessment of speaking and listening skills at CEFR A1-A2 2. Options are explored for introducing assessment of these skills in terminal examinations 3. Mechanisms are identified for improving teachers own speaking and listening skills 4. Student competency statements within the Bangladesh curriculum are revised to provide more guidance to teachers and students

Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning

Computational study of molecules and materials from first principles is a cornerstone of physics, chemistry and materials science, but limited by the cost of accurate and precise simulations. In settings involving many simulations, machine learning can reduce these costs, sometimes by orders of magnitude, by interpolating between reference simulations. This requires representations that describe any molecule or material and support interpolation. We review, discuss and benchmark state-of-the-art representations and relations between them, including smooth overlap of atomic positions, many-body tensor representation, and symmetry functions. For this, we use a unified mathematical framework based on many-body functions, group averaging and tensor products, and compare energy predictions for organic molecules, binary alloys and Al-Ga-In sesquioxides in numerical experiments controlled for data distribution, regression method and hyper-parameter optimization

How Feedback Can Improve Managerial Evaluations of Model-based Marketing Decision Support Systems

Marketing managers often provide much poorer evaluations of model-based marketing decision support systems (MDSSs) than are warranted by the objective performance of those systems. We show that a reason for this discrepant evaluation may be that MDSSs are often not designed to help users understand and internalize the underlying factors driving the MDSS results and related recommendations. Thus, there is likely to be a gap between a marketing manager’s mental model and the decision model embedded in the MDSS. We suggest that this gap is an important reason for the poor subjective evaluations of MDSSs, even when the MDSSs are of high objective quality, ultimately resulting in unreasonably low levels of MDSS adoption and use. We propose that to have impact, an MDSS should not only be of high objective quality, but should also help reduce any mental model-MDSS model gap. We evaluate two design characteristics that together lead model-users to update their mental models and reduce the mental model-MDSS gap, resulting in better MDSS evaluations: providing feedback on the upside potential for performance improvement and providing specific suggestions for corrective actions to better align the user's mental model with the MDSS. We hypothesize that, in tandem, these two types of MDSS feedback induce marketing managers to update their mental models, a process we call deep learning, whereas individually, these two types of feedback will have much smaller effects on deep learning. We validate our framework in an experimental setting, using a realistic MDSS in the context of a direct marketing decision problem. We then discuss how our findings can lead to design improvements and better returns on investments in MDSSs such as CRM systems, Revenue Management systems, pricing decision support systems, and the like.Learning;Feedback;Marketing Decision Models;Marketing Decision Support Systems;Marketing Information Systems

Active and Passive Causal Inference Learning

This paper serves as a starting point for machine learning researchers, engineers and students who are interested in but not yet familiar with causal inference. We start by laying out an important set of assumptions that are collectively needed for causal identification, such as exchangeability, positivity, consistency and the absence of interference. From these assumptions, we build out a set of important causal inference techniques, which we do so by categorizing them into two buckets; active and passive approaches. We describe and discuss randomized controlled trials and bandit-based approaches from the active category. We then describe classical approaches, such as matching and inverse probability weighting, in the passive category, followed by more recent deep learning based algorithms. By finishing the paper with some of the missing aspects of causal inference from this paper, such as collider biases, we expect this paper to provide readers with a diverse set of starting points for further reading and research in causal inference and discovery