A systematic review of pedagogical approaches that can effectively include children with special educational needs in mainstream classrooms with a particular focus on peer group interactive approaches

The broad background to this review is a long history of concepts of special pupils and special education, and a faith in special pedagogical approaches. The rise of inclusive schools and some important critiques of special pedagogy (e.g. Hart, 1996; Norwich and Lewis, 2001; Thomas and Loxley, 2001) have raised the profile of teaching approaches that ordinary teachers can and do use to include children with special educational needs in mainstream classrooms. Inclusive education itself is increasingly conceived as being about the quality of learning and participation that goes on in inclusive schools rather than simplistic matters of where children are place

Task adapted reconstruction for inverse problems

The paper considers the problem of performing a task defined on a model parameter that is only observed indirectly through noisy data in an ill-posed inverse problem. A key aspect is to formalize the steps of reconstruction and task as appropriate estimators (non-randomized decision rules) in statistical estimation problems. The implementation makes use of (deep) neural networks to provide a differentiable parametrization of the family of estimators for both steps. These networks are combined and jointly trained against suitable supervised training data in order to minimize a joint differentiable loss function, resulting in an end-to-end task adapted reconstruction method. The suggested framework is generic, yet adaptable, with a plug-and-play structure for adjusting both the inverse problem and the task at hand. More precisely, the data model (forward operator and statistical model of the noise) associated with the inverse problem is exchangeable, e.g., by using neural network architecture given by a learned iterative method. Furthermore, any task that is encodable as a trainable neural network can be used. The approach is demonstrated on joint tomographic image reconstruction, classification and joint tomographic image reconstruction segmentation

Learning Bayesian Networks Using Fast Heuristics

This thesis addresses score-based learning of Bayesian networks from data using a few fast heuristics. The algorithmic implementation of the heuristics is able to learn size 30-40 networks in seconds and size 1000-2000 networks in hours. Two algorithms, which are devised by Scanagatta et al. and dubbed Independence Selection and Acyclic Selection OBS have the capacity of learning very large Bayesian networks without the liabilities of the traditional heuristics that require maximum in-degree or ordering constraints. The two algorithms are respectively called Insightful Searching and Acyclic Selection Obeying Boolean-matrix Sanctioning (acronym ASOBS) in this thesis. This thesis also serves as an expansion of the work of Scanagatta et al. by revealing a computationally simple ordering strategy called Randomised Pairing Greedy Weight (acronym RPGw) that works well as an adjunct along with ASOBS with corresponding experiment results, which show that ASOBS was able to score higher and faster with the help of RPGw. Insightful Searching, ASOBS, and RPGw together form a system that learns Bayesian networks from data very fast

Computer Aided Verification

This open access two-volume set LNCS 13371 and 13372 constitutes the refereed proceedings of the 34rd International Conference on Computer Aided Verification, CAV 2022, which was held in Haifa, Israel, in August 2022. The 40 full papers presented together with 9 tool papers and 2 case studies were carefully reviewed and selected from 209 submissions. The papers were organized in the following topical sections: Part I: Invited papers; formal methods for probabilistic programs; formal methods for neural networks; software Verification and model checking; hyperproperties and security; formal methods for hardware, cyber-physical, and hybrid systems. Part II: Probabilistic techniques; automata and logic; deductive verification and decision procedures; machine learning; synthesis and concurrency. This is an open access book

Qualitative exploration of recovery following first episode psychosis: a grounded theory approach & Clinical research portfolio

Specialist Early Intervention (E.I.) services are becoming increasingly accepted as the most effective way of working with people experiencing first episode psychosis. Although there have been a wealth of studies looking at the clinical outcomes of E.I. services, there remains relatively little research on the subjective experiences of recovery following a first episode of psychosis (FEP). This study explored the individual’s experiences of recovery following a first episode of psychosis, in the context of a specialist E. I. Service. Grounded Theory methodology was employed to explore the individual experiences of people involved with an E.I. service. A total of 9 individuals selected from a group of E.I. service users approaching the end of their care were interviewed. Data from the transcribed interviews were analysed using Grounded Theory methodology, where themes emerged from the coding process. The study identified 7 key themes: Build Up of experiences, Awareness, Acceptance, Shame, Family and Friends, Loss and Re-building. Themes were often expressed as continuums and all appeared to overlap. This study provided an initial understanding of the predominant experiences of recovery following FEP for E.I. service-users. Future research might consider the experiences of those in other service settings