Interatomic potentials for ionic systems with density functional accuracy based on charge densities obtained by a neural network

Based on an analysis of the short range chemical environment of each atom in a system, standard machine learning based approaches to the construction of interatomic potentials aim at determining directly the central quantity which is the total energy. This prevents for instance an accurate description of the energetics of systems where long range charge transfer is important as well as of ionized systems. We propose therefore not to target directly with machine learning methods the total energy but an intermediate physical quantity namely the charge density, which then in turn allows to determine the total energy. By allowing the electronic charge to distribute itself in an optimal way over the system, we can describe not only neutral but also ionized systems with unprecedented accuracy. We demonstrate the power of our approach for both neutral and ionized NaCl clusters where charge redistribution plays a decisive role for the energetics. We are able to obtain chemical accuracy, i.e. errors of less than a milli Hartree per atom compared to the reference density functional results. The introduction of physically motivated quantities which are determined by the short range atomic environment via a neural network leads also to an increased stability of the machine learning process and transferability of the potential.Comment: 4 figure

Feature discrimination learning transfers to noisy displays in complex stimuli

Introduction: Perception under noisy conditions requires not only feature identification but also a process whereby target features are selected and noise is filtered out (e.g., when identifying an animal hiding in the savannah). Interestingly, previous perceptual learning studies demonstrated the utility of training feature representation (without noise) for improving discrimination under noisy conditions. Furthermore, learning to filter out noise also appears to transfer to other perceptual task under similar noisy conditions. However, such learning transfer effects were thus far demonstrated predominantly in simple stimuli. Here we sought to explore whether similar learning transfer can be observed with complex real-world stimuli.Methods: We assessed the feature-to-noise transfer effect by using complex stimuli of human faces. We first examined participants' performance on a face-noise task following either training in the same task, or in a different face-feature task. Second, we assessed the transfer effect across different noise tasks defined by stimulus complexity, simple stimuli (Gabor) and complex stimuli (faces).Results: We found a clear learning transfer effect in the face-noise task following learning of face features. In contrast, we did not find transfer effect across the different noise tasks (from Gabor-noise to face-noise).Conclusion: These results extend previous findings regarding transfer of feature learning to noisy conditions using real-life stimuli

The Role of Transfer in Learning (extended abstract)

Introduction Virtually all of today's approaches to artificial neural network learning generalize considerably well if sufficiently many training examples are available. However, they often work poorly when training data is scarce. Various psychological studies have illustrated that humans are able to generalize accurately even when training data is extremely scarce. Often, we generalize correctly from just a single training instance. In order to do so, we appear to massively re-use knowledge acquired in our previous lifetime. Lifelong learning is a framework that addresses the issue of knowledge re-use and inductive transfer in learning. In lifelong learning, it is assumed that the learner faces an entire family of learning tasks, not just a single one. When facing a new learning task, the learner may transfer knowledge acquired in previous learning tasks to boost generalization. Three questions are of fundamental importance for any approach to lifelong learn

Geometry as Transfer

It is generally accepted that intelligent action involves considerable use of transfer. For example, Carbonell [1] has argued that learning proceeds by analogical reasoning; Rosch [12] has argued that categorization proceeds by seeing objects in terms of prototypes; and Leyton [9] has argued that the human perceptual system is organized as a hierarchy of transfer. The role of geometry is also seen as fundamental to the representations produced by the cognitive system. For example, Gallistel [2] has elaborated the powerful role of geometry in animal learning and navigation; Lakoff [3] has emphasized the role of geometry in semantics; and Leyton [9] has proposed an extensive role for geometry in causal explanation. We bring together the two above factors, transfer and geometry, in the book, Leyton [10], by developing a generative theory of shape in which transfer is a fundamental organizing principle. In this approach, transfer is basic to the very meaning of geometry. The purpose of the present paper is to give an introduction to this transfer-based theory of geometry

Europeanization in VET policy as a process of reshaping the educational space

The EU represents a transforming educational space, where national and supranational boundaries in educational governance are becoming blurred. The EU has become an important actor in educational governance and an important arena for policy learning and transfer. This paper explores how the process of reshaping the educational space manifests itself in the process of the Europeanization of VET policy in the case of Estonia. In Estonia, this process was followed by the growth of executive VET institutions and has developed from rather uncritical initial policy transfer to more active learning from the EU, although conformism can still be seen in cases of the introduction of standardizing policy tools. (author's abstract

Optimizing Brain Tumor Classification: A Comprehensive Study on Transfer Learning and Imbalance Handling in Deep Learning Models

Deep learning has emerged as a prominent field in recent literature, showcasing the introduction of models that utilize transfer learning to achieve remarkable accuracies in the classification of brain tumor MRI images. However, the majority of these proposals primarily focus on balanced datasets, neglecting the inherent data imbalance present in real-world scenarios. Consequently, there is a pressing need for approaches that not only address the data imbalance but also prioritize precise classification of brain cancer. In this work, we present a novel deep learning-based approach, called Transfer Learning-CNN, for brain tumor classification using MRI data. The proposed model leverages the predictive capabilities of existing publicly available models by utilizing their pre-trained weights and transferring those weights to the CNN. By leveraging a publicly available Brain MRI dataset, the experiment evaluated various transfer learning models for classifying different tumor types, including meningioma, glioma, and pituitary tumors. We investigate the impact of different loss functions, including focal loss, and oversampling methods, such as SMOTE and ADASYN, in addressing the data imbalance issue. Notably, the proposed strategy, which combines VGG-16 and CNN, achieved an impressive accuracy rate of 96%, surpassing alternative approaches significantly.Comment: Our code is available at https://github.com/Razaimam45/AI701-Project-Transfer-Learning-approach-for-imbalance-classification-of-Brain-Tumor-MRI

Enhancing students’ confidence, competence and knowledge with Integrated Skills Challenge

Introduction/background: In today's complex healthcare environment, new nursing graduates are expected to master nursing skills in a timely manner and become critical thinkers with the capacity of solving complex healthcare problems efficiently. The increased complexity of the clinical setting requires competence-building begin in introductory courses, establishing foundational skills for critical thinking and prioritisation. In the healthcare professions, teaching and learning methods are focused on integration of clinical knowledge and skills. However, traditional teaching and learning methodologies do not always facilitate the development of a requisite level of these clinical skills. For the Master of Nursing Studies (MNSt) students whose program is shortened this means the acquisition of these skills must be achieved more rapidly. Aim/objectives: The purpose of this study is to investigate the feasibility of developing simulation scenarios (Integrated Skill Challenge [ISC]) as a supplemental teaching-learning strategy to enhance the transfer of student self-confidence and competence to the clinical nursing environment. Methods To examine potential effects of ISC on the MNSt students, a pilot study was conducted including 52 participants. Data were collected weekly over 11 week period by using pre and post-test design. Results: Analysis showed a significant increase in the confidence, competence and knowledge. Confidence, competence and knowledge scores increased when students were pre-loaded with knowledge prior to performing in the ISC. Results generally indicated that the ISC had the anticipated effects. Conclusions: This study reveals a high feasibility of developing simulation scenarios as an active learning methodology and that it should be developed further and piloted on a larger sample