Strategi Pembelajaran IPA di Masa New Normal : Suatu Studi Kegiatan Belajar Mengajar Siswa

New Normal is a term to describe human life to coexist with the Covid-19 outbreak, including in learning activities at school. With all the restrictions at this time, the subject of Natural Sciences (IPA) certainly becomes more difficult to implement, because Natural Science is a complex subject that tends to be understood quickly for students. The findings and discussion of this research are presented using a qualitative method through a descriptive approach. Data collection related to the teaching and learning process of students was carried out through a series of in-depth interviews with classroom teachers and students who were considered qualified as subjects and objects of research, which also used the other available documents. The results of the research revealed that in order to adapt the government direction for keeping the community around in a stable health condition, the MTs Kayu Aro School through its policy set two technical solutions related to teaching and learning activities during the new normal period. First, by imposing a narrowing of the range of school activities for a week with the one-day, no-day method. Second, school policy also imposes a reduction in the duration of teaching and learning activities per day. In addition, the school also implements dual process as a learning method, offline and online by utilizing some learning media, such as WhatsApp application or other learning websites.New Normal is a term to describe human life to coexist with the Covid-19 outbreak, including in learning activities at school. With all the restrictions at this time, the subject of Natural Sciences (IPA) certainly becomes more difficult to implement, because Natural Science is a complex subject that tends to be understood quickly for students. The findings and discussion of this research are presented using a qualitative method through a descriptive approach. Data collection related to the teaching and learning process of students was carried out through a series of in-depth interviews with classroom teachers and students who were considered qualified as subjects and objects of research, which also used the other available documents. The results of the research revealed that in order to adapt the government direction for keeping the community around in a stable health condition, the MTs Kayu Aro School through its policy set two technical solutions related to teaching and learning activities during the new normal period. First, by imposing a narrowing of the range of school activities for a week with the one-day, no-day method. Second, school policy also imposes a reduction in the duration of teaching and learning activities per day. In addition, the school also implements dual process as a learning method, offline and online by utilizing some learning media, such as WhatsApp application or other learning websites

A deep implicit-explicit minimizing movement method for option pricing in jump-diffusion models

We develop a novel deep learning approach for pricing European basket options written on assets that follow jump-diffusion dynamics. The option pricing problem is formulated as a partial integro-differential equation, which is approximated via a new implicit-explicit minimizing movement time-stepping approach, involving approximation by deep, residual-type Artificial Neural Networks (ANNs) for each time step. The integral operator is discretized via two different approaches: a) a sparse-grid Gauss--Hermite approximation following localised coordinate axes arising from singular value decompositions, and b) an ANN-based high-dimensional special-purpose quadrature rule. Crucially, the proposed ANN is constructed to ensure the asymptotic behavior of the solution for large values of the underlyings and also leads to consistent outputs with respect to a priori known qualitative properties of the solution. The performance and robustness with respect to the dimension of the methods are assessed in a series of numerical experiments involving the Merton jump-diffusion model.Comment: 16 pages, 11 figure

Degenerative adversarial neuroimage nets for brain scan simulations: Application in ageing and dementia

Accurate and realistic simulation of high-dimensional medical images has become an important research area relevant to many AI-enabled healthcare applications. However, current state-of-the-art approaches lack the ability to produce satisfactory high-resolution and accurate subject-specific images. In this work, we present a deep learning framework, namely 4D-Degenerative Adversarial NeuroImage Net (4D-DANI-Net), to generate high-resolution, longitudinal MRI scans that mimic subject-specific neurodegeneration in ageing and dementia. 4D-DANI-Net is a modular framework based on adversarial training and a set of novel spatiotemporal, biologically-informed constraints. To ensure efficient training and overcome memory limitations affecting such high-dimensional problems, we rely on three key technological advances: i) a new 3D training consistency mechanism called Profile Weight Functions (PWFs), ii) a 3D super-resolution module and iii) a transfer learning strategy to fine-tune the system for a given individual. To evaluate our approach, we trained the framework on 9852 T1-weighted MRI scans from 876 participants in the Alzheimer's Disease Neuroimaging Initiative dataset and held out a separate test set of 1283 MRI scans from 170 participants for quantitative and qualitative assessment of the personalised time series of synthetic images. We performed three evaluations: i) image quality assessment; ii) quantifying the accuracy of regional brain volumes over and above benchmark models; and iii) quantifying visual perception of the synthetic images by medical experts. Overall, both quantitative and qualitative results show that 4D-DANI-Net produces realistic, low-artefact, personalised time series of synthetic T1 MRI that outperforms benchmark models

Learning process' analysis system: Learning Analytics

As a consequence of nowadays’ intensive data production, companies are setting its exploitation as a cornerstone for their growth, with new disciplines emerging with the intention of guiding this force of technological development, as it is the case of data intensive processes related with formative scenarios (academical or not). Guidelines have been provided with the purpose of tackling current and upcoming challenges identified for the advancement of Learning Analytics. With special attention on its development and analytics facets, this project aims to take a step towards its feasible adoption. With this purpose, an assessment of current literature’s approach to this discipline’s objectives has been conducted, concluding that, in order to capture a broader and effective picture of students’ engagement to learning processes, a wide variety of information sources need to be considered, including qualitative ones. Additionally, a set of scalable predictive models (involving regression and time series forecasting) related to students’ interaction and outcomes have been developed with favourable results. Finally, viability of the further development of these tasks and its inclusion in a real-world application are discussed.Grado en Ingeniería Informátic

Degenerative adversarial neuroimage nets for brain scan simulations: Application in ageing and dementia

© 2021 The Author(s). Published by Elsevier B.V. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Accurate and realistic simulation of high-dimensional medical images has become an important research area relevant to many AI-enabled healthcare applications. However, current state-of-the-art approaches lack the ability to produce satisfactory high-resolution and accurate subject-specific images. In this work, we present a deep learning framework, namely 4D-Degenerative Adversarial NeuroImage Net (4D-DANI-Net), to generate high-resolution, longitudinal MRI scans that mimic subject-specific neurodegeneration in ageing and dementia. 4D-DANI-Net is a modular framework based on adversarial training and a set of novel spatiotemporal, biologically-informed constraints. To ensure efficient training and overcome memory limitations affecting such high-dimensional problems, we rely on three key technological advances: i) a new 3D training consistency mechanism called Profile Weight Functions (PWFs), ii) a 3D super-resolution module and iii) a transfer learning strategy to fine-tune the system for a given individual. To evaluate our approach, we trained the framework on 9852 T1-weighted MRI scans from 876 participants in the Alzheimer's Disease Neuroimaging Initiative dataset and held out a separate test set of 1283 MRI scans from 170 participants for quantitative and qualitative assessment of the personalised time series of synthetic images. We performed three evaluations: i) image quality assessment; ii) quantifying the accuracy of regional brain volumes over and above benchmark models; and iii) quantifying visual perception of the synthetic images by medical experts. Overall, both quantitative and qualitative results show that 4D-DANI-Net produces realistic, low-artefact, personalised time series of synthetic T1 MRI that outperforms benchmark models.Peer reviewe

Introducing an online community into a clinical education setting: a pilot study of student and staff engagement and outcomes using blended learning

<p>Abstract</p> <p>Background</p> <p>There are growing reasons to use both information and communication functions of learning technologies as part of clinical education, but the literature offers few accounts of such implementations or evaluations of their impact. This paper details the process of implementing a blend of online and face-to-face learning and teaching in a clinical education setting and it reports on the educational impact of this innovation.</p> <p>Methods</p> <p>This study designed an online community to complement a series of on-site workshops and monitored its use over a semester. Quantitative and qualitative data recording 43 final-year medical students' and 13 clinical educators' experiences with this blended approach to learning and teaching were analysed using access, adoption and quality criteria as measures of impact.</p> <p>Results</p> <p>The introduction of the online community produced high student ratings of the quality of learning and teaching and it produced student academic results that were equivalent to those from face-to-face-only learning and teaching. Staff had mixed views about using blended learning.</p> <p>Conclusions</p> <p>Projects such as this take skilled effort and time. Strong incentives are required to encourage clinical staff and students to use a new mode of communication. A more synchronous or multi-channel communication feedback system might stimulate increased adoption. Cultural change in clinical teaching is also required before clinical education can benefit more widely from initiatives such as this.</p

Learning and inferencing state-space models through GRU cells and Bayesian principles

학위논문(석사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2023. 2. 최완.State-space models (SSMs) perform predictions by learning the underlying dynamics of observed sequence. We start with a throughout literature review on Gaussian Process (GP) models and time series models based on GPs. Then, we elaborate more on the Gaussian Process State-Space Model (GP-SSM): a Bayesian nonparametric generalisation of discrete time nonlinear state-space models. We provide a formulation of the GP-SSM that offers different insight into its properties. Then, we propose a new SSM approach in both high and low dimensional observation space, which utilizes Bayesian filtering-smoothing to model systems dynamics more accurately than RNN-based SSMs and can be learned in an end-to-end manner. The designed architecture, which we call the Gated Inference Network (GIN), is able to integrate the uncertainty estimates and learn the complicated dynamics of the system that enables us to perform estimation and imputation tasks in both data presence and absence. The proposed model uses the GRU cells into its structure to complete the data flow, while avoids expensive computations and potentially unstable matrix inversions. The GIN is able to deal with any time-series data and gives us a strong robustness to handle the observational noise. Finally, in the numerical experiments, we show that the GIN reduces the uncertainty of estimates and outperforms its counterparts , LSTMs, GRUs and variational approaches. Several SOTA approaches are taken into account for the sake of comparison in order to show the out-performance of the proposed algorithm.1 Introduction 4 1.1 Time Series Modeling 4 1.2 Bayesian inference for time series models 5 1.2.1 Bayesian methods 5 1.3 Nonparametric Models 7 1.4 Contributions 8 2 Background and Literature Review 10 2.1 Introduction 10 2.2 Gaussian Process 12 2.2.1 Gaussian Process For Regression 13 2.2.2 Linear Gaussian state space models 16 2.2.3 Filtering and Smoothing Parameterization for LGSSMs 17 2.3 Related Works 20 2.3.1 Qualitative comparison of the GIN to recent related work 21 3 Gated Inference Network 23 3.1 Gated Inference Network For System Identification 23 3.2 Parameterization 25 3.3 Learning The Process Noise 28 3.4 Prediction Step 30 3.5 Filtering Step 30 3.6 Smoothing Step 31 3.7 Learning Dynamics 31 3.8 Fitting 33 4 Evaluation And Experiments 36 4.1 High Dimensional Observation with Lack of Dynamics 39 4.1.1 Single Pendulum and Double Pendulum 40 4.1.2 Visual Odometry of KITTI Dataset 57 4.2 Low Dimensional Observation with Presence of Dynamics 59 4.2.1 Lorenz Atrractor 59 4.2.2 Real World Dynamics: Michigan NCLT dataset 61 5 Conclusion 63 Bibliography 65석