Classification of protein interaction sentences via gaussian processes

The increase in the availability of protein interaction studies in textual format coupled with the demand for easier access to the key results has lead to a need for text mining solutions. In the text processing pipeline, classification is a key step for extraction of small sections of relevant text. Consequently, for the task of locating protein-protein interaction sentences, we examine the use of a classifier which has rarely been applied to text, the Gaussian processes (GPs). GPs are a non-parametric probabilistic analogue to the more popular support vector machines (SVMs). We find that GPs outperform the SVM and na\"ive Bayes classifiers on binary sentence data, whilst showing equivalent performance on abstract and multiclass sentence corpora. In addition, the lack of the margin parameter, which requires costly tuning, along with the principled multiclass extensions enabled by the probabilistic framework make GPs an appealing alternative worth of further adoption

인공신경망을 이용한 아격자스케일 응력 모델링과 난류 채널 및 후향 계단 유동에의 적용

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계항공공학부(멀티스케일 기계설계전공), 2021.8. 최해천.A fully-connected neural network (NN) is used to develop a subgrid-scale model which maps the relation between the subgrid-scale stress and filtered flow variable in a turbulent channel (Part I) and backward-facing-step (Part II) flows. For turbulent channel flow, DNS (direct numerical simulation) database of Reτ = 178 is used to develop an NN-based subgrid-scale (SGS) model, and a priori and a posteriori tests are performed to investigate its prediction performance. In a priori test, an NN-based SGS model with the input of filtered velocity gradient or strain rate tensor at multiple grid points provides high correlation coefficients between the true and predicted SGS stresses. However, this model provides an unstable solution in a posteriori test, as the model produces a non-negligible backscatter which is known to induce numerical instability in large eddy simulation (LES). To ensure a stable LES solution with this model, a special treatment like backscatter clipping is required. On the other hand, an NN-based SGS model with the input of filtered strain rate tensor at a single grid point shows an excellent prediction performance for the mean velocity and Reynolds shear stress in a posteriori test, although it gives low correlation coefficients between the true and predicted SGS stresses in a priori test. This NN-based SGS model trained at Reτ = 178 is applied to a turbulent channel flow at Reτ = 723 using the same grid resolution in wall units, providing fairly good agreements of the solutions with the filtered DNS data. When the grid resolution in wall units is different from that of trained data, this NN-based SGS model does not perform well. This is overcome by training an NN with the datasets having two filters whose sizes are larger and smaller than the grid size in large eddy simulation. For turbulent flow over a backward-facing step (BFS), an NN-based SGS model is developed with the filtered DNS data at Reh = 5100. Two input variables, the filtered strain rate and velocity gradient tensors at a single grid point, respectively, are adopted, where the NN-based SGS models with these inputs provide a stable LES solution in the turbulent channel flow without any special treatment. In the LES at Reh = 5100, those NN-based SGS models show similar performance, and provide good predictions for the reattachment length and root-mean-square velocity fluctuations. Then, we assess the performance of the NN-based SGS model with the input of filtered strain rate tensor for the LES at Reh = 24000, and this model provides fairly good results, compared to those from the LES with dynamic Smagorinsky model (DSM). Finally, we apply this model for LES of controlled BFS flow with multiple taps installed at the step edge. LES with this NN-based SGS model predicts the amount of reduction in the reattachment length better than by LES with DSM, showing that the NN-based model trained with uncontrolled BFS flow maintains its prediction performance in LES of controlled BFS flow.본 연구에서는 난류 채널 유동과 후향 계단 주위 난류 유동에 대해, 필터링 된 유동변수를 입력변수로 하여 아격자스케일 (subgrid scale, SGS) 응력을 예측하는 인공신경망 기반 SGS 모델을 개발하였다. 난류 채널 유동의 경우, 필터링 된 직접수치모사 데이터베이스(Reτ = 178)를 사용하여 인공신경망 기반 SGS 모델을 개발하고, 본 모델의 성능을 평가하기 위해 사전 및 사후 테스트를 수행하였다. 사전 테스트에서, 여러 격자점에 있는 필터링 된 속도기울기 또는 속도변형률 텐서를 입력변수로 하는 인공신경망 모델은 실제 SGS 응력과 높은 상관계수를 보이는 SGS 응력을 예측하였다. 그러나 이 모델은 사후 테스트에서 불안정한 수치계산 결과를 보여주었고, LES 솔루션을 얻기 위해서는 후방 산란(backscatter)을 강제적으로 없애는 등의 임의적인 처리를 필요로 했다. 반면, 단일 격자점에서의 필터링 된 속도변형률 텐서를 입력변수로 한 인공신경망 모델은, 사전 테스트에서 실제값과 낮은 상관계수를 보이는 SGS 응력을 예측하였으나, 사후 테스트에서 평균 속도 프로파일과 Reynolds 전단 응력에 대해 우수한 예측 성능을 보였다. 더 높은 Reynolds 수에서 인공신경망 모델의 성능을 확인하기 위하여, Reτ = 178에서 훈련된 인공신경망 기반 SGS 모델(입력변수: 단일 격자점의 필터링 된 속도변형률 텐서)을, Reτ = 723의 큰 에디 모사에 적용하였다. 벽 단위 격자 크기를 학습데이터의 것과 같도록 설정한 경우, 인공신경망 모델의 큰 에디 모사는 필터링 된 직접수치모사의 솔루션과 상당히 잘 일치하였다. 한편, 벽 단위 격자 크기가 훈련데이터의 것과 다른 경우, 인공신경망 모델의 성능이 저하되었으나, 큰 에디 모사의 격자 크기보다 격자 크기가 큰 그리고 작은 학습데이터를 한꺼번에 인공신경망 학습에 사용하게 되면 좋은 성능을 보여주었다. 후향 계단 주위 난류 유동의 경우, Reh = 5100의 필터링 된 직접수치모사 데이터베이스를 사용하여 인공신경망 기반 SGS 모델을 개발하였다. 인공신경망의 입력변수로는, 난류 채널 유동의 큰 에디 모사에서 안정적이고 좋은 성능을 보여준, 단일 격자점의 필터링 된 속도기울기 그리고 속도변형률을 각각 시험해보았다. Reh = 5100에서 큰 에디 모사를 수행한 결과, 두 개의 입력변수로 각각 학습된 인공신경망 모델 모두 재부착 길이 및 난류 섭동량에 대해서, 가장 널리 사용되는 동적 Smagorinksy 모델(DSM)과 비교하여 좋은 예측 성능을 보였다. 속도변형률을 입력변수로 하는 인공신경망 모델을 Reh = 24000의 큰 에디 모사에 적용한 결과, DSM을 사용한 큰 에디 모사와 비교하여 여전히 좋은 결과를 보여주었다. 마지막으로, 후향 계단 모서리에 탭이 설치된 유동에 대한 큰 에디 모사를 수행하였다. 그 결과, SGS 모델을 사용하지 않은 경우에는, 평균속도와 난류섭동량에 대해 기존 실험결과 및 DSM을 사용한 LES와 매우 큰 차이를 보였으나, 인공신경망 모델은 DSM과 유사한 유동예측 성능을 보여주었으며, 재부착 길이 감소량의 경우, 인공신경망 모델이 DSM보다 실험값과 더 잘 일치하였다.Part I Modeling of the subgrid-scale stress with a neural network: application to turbulent channel flow 1 1 Introduction 2 2 Numerical details 9 2.1. Neural-network-based SGS model 9 2.2. Details of DNS and input and output variables 14 3 Results 23 3.1. A priori test 24 3.2. A posteriori test 30 3.3. LES with a grid resolution different from that of trained data 46 4 Conclusions 54 Part II Modeling of the subgrid-scale stress with a neural network: application to turbulent flow over a backward-facing step 56 1 Introduction 57 2 Computational details 62 2.1. Outline of the NN-based SGS model 62 2.2. Details of DNS for training data 64 2.2.1. Computational domain and grid spacing 64 2.2.2. Boundary conditions and numerical methods 65 2.2.3. Filtered DNS flow fields 66 2.3. Training details and hyperparameter optimization 72 3 LES of flow over a backward-facing step at Reh = 5100 79 3.1. Computational details 79 3.2. Results and discussions 81 3.2.1. LES51GR case 81 3.2.2. LES51GC case 90 4 LES of controlled backward-facing-step flow by multiple taps 96 4.1. Computational details 97 4.2. Results 103 5 Concluding remarks 118 References 122 Appendix 133 A Parametric study on the neural-network-based SGS model in turbulent channel flow 133 B Normalization method based on a resolved-scale dissipation toward a universal NN-based SGS model 158 C Computational details for DNS of a forced homogeneous isotropic turbulence 164 D SGS stress from NN model in laminar shear flow 166 Abstract (in Korean) 168박

Learning design approaches for personalised and non-personalised e-learling systems

Recognizing the powerful role that technology plays in the lives of people, researchers are increasingly focusing on the most effective uses of technology to support learning and teaching. Technology enhanced learning (TEL) has the potential to support and transform students’ learning and allows them to choose when, where and how to learn. This paper describes two different approaches for the design of personalised and non-personalised online learning environments, which have been developed to investigate whether personalised e-learning is more efficient than non-personalised e-learning, and discuss some of the student’s experiences and assessment test results based on experiments conducted so far

Effective Spoken Language Labeling with Deep Recurrent Neural Networks

Understanding spoken language is a highly complex problem, which can be decomposed into several simpler tasks. In this paper, we focus on Spoken Language Understanding (SLU), the module of spoken dialog systems responsible for extracting a semantic interpretation from the user utterance. The task is treated as a labeling problem. In the past, SLU has been performed with a wide variety of probabilistic models. The rise of neural networks, in the last couple of years, has opened new interesting research directions in this domain. Recurrent Neural Networks (RNNs) in particular are able not only to represent several pieces of information as embeddings but also, thanks to their recurrent architecture, to encode as embeddings relatively long contexts. Such long contexts are in general out of reach for models previously used for SLU. In this paper we propose novel RNNs architectures for SLU which outperform previous ones. Starting from a published idea as base block, we design new deep RNNs achieving state-of-the-art results on two widely used corpora for SLU: ATIS (Air Traveling Information System), in English, and MEDIA (Hotel information and reservation in France), in French.Comment: 8 pages. Rejected from IJCAI 2017, good remarks overall, but slightly off-topic as from global meta-reviews. Recommendations: 8, 6, 6, 4. arXiv admin note: text overlap with arXiv:1706.0174

An adaptive sampling method for global sensitivity analysis based on least-squares support vector regression

In the field of engineering, surrogate models are commonly used for approximating the behavior of a physical phenomenon in order to reduce the computational costs. Generally, a surrogate model is created based on a set of training data, where a typical method for the statistical design is the Latin hypercube sampling (LHS). Even though a space filling distribution of the training data is reached, the sampling process takes no information on the underlying behavior of the physical phenomenon into account and new data cannot be sampled in the same distribution if the approximation quality is not sufficient. Therefore, in this study we present a novel adaptive sampling method based on a specific surrogate model, the least-squares support vector regresson. The adaptive sampling method generates training data based on the uncertainty in local prognosis capabilities of the surrogate model - areas of higher uncertainty require more sample data. The approach offers a cost efficient calculation due to the properties of the least-squares support vector regression. The opportunities of the adaptive sampling method are proven in comparison with the LHS on different analytical examples. Furthermore, the adaptive sampling method is applied to the calculation of global sensitivity values according to Sobol, where it shows faster convergence than the LHS method. With the applications in this paper it is shown that the presented adaptive sampling method improves the estimation of global sensitivity values, hence reducing the overall computational costs visibly

M-learning in higher education in Bahrain: the educators' view

Universities in the oil-rich Gulf Cooperation Countries (GCC) have shown particular interest in m-learning which currently is treated as fashion, but at the same time is considered by corporations and educational institutions to be very promising. This papers investigates the adoption of m-learning at universities in the Kingdom of Bahrain, and explores the educators' views and perceptions of m-learning, as well as its future potential in higher education. A survey questionnaire was distributed to instructors in four universities in the Kingdom of Bahrain, both private and public. This papers presents the pilot study, which includes the results of 45 responses. The findings suggest that although most of the educators understand the concept and they use M-Learning tools to some limited extent, there is a long way until we reach full integration with curriculum and the blended learning approach. In addition, despite the fact that most educators understand the necessity and role of M-Learning in content delivery, they do not seem to embrace at its full potential, as it is mainly used for communication purposes and navigation. The paper proposes that m-learning provides opportunities for more creativity in designing and delivering the course with further enhancement of the student experience, but it will be utilized in its full potential in the area within the next 5 years. This study provides guidance to instructors on the potential of m-learning and the need to change the teaching and learning culture to student-oriented for more effective and appropriate use of m-learning. TI highlights the need for institutions to invest in faculty and staff training, and in technology as well as provides suggestions to other stakeholders on the need to incorporate m-learning in decision-making for further development in the region

IST Austria Thesis

Because of the increasing popularity of machine learning methods, it is becoming important to understand the impact of learned components on automated decision-making systems and to guarantee that their consequences are beneficial to society. In other words, it is necessary to ensure that machine learning is sufficiently trustworthy to be used in real-world applications. This thesis studies two properties of machine learning models that are highly desirable for the sake of reliability: robustness and fairness. In the first part of the thesis we study the robustness of learning algorithms to training data corruption. Previous work has shown that machine learning models are vulnerable to a range of training set issues, varying from label noise through systematic biases to worst-case data manipulations. This is an especially relevant problem from a present perspective, since modern machine learning methods are particularly data hungry and therefore practitioners often have to rely on data collected from various external sources, e.g. from the Internet, from app users or via crowdsourcing. Naturally, such sources vary greatly in the quality and reliability of the data they provide. With these considerations in mind, we study the problem of designing machine learning algorithms that are robust to corruptions in data coming from multiple sources. We show that, in contrast to the case of a single dataset with outliers, successful learning within this model is possible both theoretically and practically, even under worst-case data corruptions. The second part of this thesis deals with fairness-aware machine learning. There are multiple areas where machine learning models have shown promising results, but where careful considerations are required, in order to avoid discrimanative decisions taken by such learned components. Ensuring fairness can be particularly challenging, because real-world training datasets are expected to contain various forms of historical bias that may affect the learning process. In this thesis we show that data corruption can indeed render the problem of achieving fairness impossible, by tightly characterizing the theoretical limits of fair learning under worst-case data manipulations. However, assuming access to clean data, we also show how fairness-aware learning can be made practical in contexts beyond binary classification, in particular in the challenging learning to rank setting