Machine learning model like Lasso regression is not sufficient for multi-output Fischer Tropsch synthesis prediction. Artificial Neural Network regression able to capture complex non-linearity in Fischer Tropsch synthesis kinetic data. Shap interpretation technique finds process variable ranking in model prediction. Predicting the impact of input process variables on chemical processes is key to assess their performance of the latter. Models explaining this impact through a mechanistic approach are rarely readily available, complex in nature and/or require long development time.

In this paper, we propose a weak approximation of the reflection coupling (RC) for stochastic differential equations (SDEs), and prove it converges weakly to the desired coupling. In contrast to the RC, the proposed approximate reflection coupling (ARC) need not take the hitting time of processes to the diagonal set into consideration and can be defined as the solution of some SDEs on the whole time interval. Therefore, ARC can work effectively against SDEs with different drift terms. As an application of ARC, an evaluation on the effectiveness of the stochastic gradient descent in a non-convex setting is also described. For the sample size n, the step size η, and the batch size B, we derive uniform evaluations on the time with orders n -1, η 1/2, and ((n - B) / B (n - 1)), respectively.

This paper studies the utility of using data analytics and machine learning techniques for identifying, classifying, and characterizing the dynamics of large-scale parallel (MPI) programs. To this end, we run microbenchmarks and realistic proxy applications with the regular compute-communicate structure on two different supercomputing platforms and choose the per-process performance and MPI time per time step as relevant observables. Using principal component analysis, clustering techniques, correlation functions, and a new "phase space plot," we show how desynchronization patterns (or lack thereof) can be readily identified from a data set that is much smaller than a full MPI trace. Our methods also lead the way towards a more general classification of parallel program dynamics.

The medical field has seen a rapid increase in the development of artificial intelligence (AI)-based prediction models. With the introduction of such AI-based prediction model tools and software in cardiovascular patient care, the cardiovascular researcher and healthcare professional are challenged to understand the opportunities as well as the limitations of the AI-based predictions. In this article, we present 12 critical questions for cardiovascular health professionals to ask when confronted with an AI-based prediction model. We aim to support medical professionals to distinguish the AI-based prediction models that can add value to patient care from the AI that does not. Listen to the audio abstract of this contribution. Artificial intelligence (AI) and its subdiscipline machine learning are receiving increasing attention throughout medicine, including cardiovascular medicine.1,2 Proponents promise AI will change the way medicine and healthcare is practiced, by making use of technological advancements that allow for collection of increasingly detailed and diverse data and the ever-increasing computational ability to analyse and combine such data. An important part of these promises is the development and implementation of more accurate clinical prediction models (algorithms, tools, or rules, from here onwards simply referred to as prediction models) to improve--or according to some advocates, even revolutionize--screening, diagnosis, and prognostication of diseases.

Regularization is a popular technique to solve the overfitting problem of machine learning algorithms. Most regularization technique relies on parameter selection of the regularization coefficient. Plug-in method and cross-validation approach are two most common parameter selection approaches for regression methods such as Ridge Regression, Lasso Regression and Kernel Regression. Matrix factorization based recommendation system also has heavy reliance on the regularization technique. In this paper, we prove that such approach of selecting regularization coefficient is invalid, and we provide a theoretically accurate method that outperforms the most widely used approach in both accuracy and fairness metrics.

With recent advances in communication networks and machine learning (ML), healthcare is one of the key application domains which stands to benefit from many opportunities, including remote global healthcare, hospital services on cloud, remote diagnosis or surgeries, among others. One of those advances is network slicing, making it possible to provide high-bandwidth, low-latency and personalized healthcare services for individual users. This is important for patients using healthcare monitoring devices that capture various biological signals (biosignals) such as from the heart (ECG), muscles (EMG), brain (EEG), or activities from other parts of the body. In this blog, we discuss the challenges to building a scalable delivery platform for such connected healthcare services, and how technological advances can help to transform this landscape significantly for the benefit of both users and healthcare service providers. Our specific focus is on assistive technology devices which are increasingly being used by many individuals.

Some interviewers ask hard questions while others ask relatively easy questions. As an interviewee, it is your choice to go prepared. And when it comes to a domain like Machine Learning, preparations might fall short. You have to be prepared for everything. While preparing, you might have stuck at a point where you wonder what more shall I read. Well, based on almost 15-17 data science interviews that I have attended, here I have put 15, very commonly asked, as well as important Data Science and Machine Learning related questions that were asked to me in almost all of them and I recommend you must study these thoroughly.

Clustering is an unsupervised algorithm that is used for determining the intrinsic groups present in unlabelled data. For instance, a B2C business might be interested in finding segments in its customer base. Clustering is hence used commonly for different use-cases like customer segmentation, market segmentation, pattern recognition, search result clustering etc. Some standard clustering techniques are K-means, DBSCAN, Hierarchical clustering amongst other methods. Clusters created using techniques like Kmeans are often not easy to decipher because it is difficult to determine why a particular row of data is classified in a particular bucket.

Graph Neural Network (GNN) models typically assume a full feature vector for each node. The two inputs to this model are the (normalised) adjacency matrix A encoding the graph structure and the feature matrix X containing as rows the node feature vectors and outputs the node embeddings Z. Each layer of GCN performs node-wise feature transformation (parametrised by the learnable matrices W₁ and W₂) and then propagates the transformed feature vectors to the neighboring nodes. Importantly, GCN assumes that all the entries in X are observed. In real-world scenarios, we often see situations where some node features are missing (Fig 1).

Survival analysis appears in various fields such as medicine, economics, engineering, and business. Recent studies showed that the Ordinary Differential Equation (ODE) modeling framework unifies many existing survival models while the framework is flexible and widely applicable. However, naively applying the ODE framework to survival analysis problems may model fiercely changing density function which may worsen the model's performance. Though we can apply L1 or L2 regularizers to the ODE model, their effect on the ODE modeling framework is barely known. In this paper, we propose hazard gradient penalty (HGP) to enhance the performance of a survival analysis model. Our method imposes constraints on local data points by regularizing the gradient of hazard function with respect to the data point. Our method applies to any survival analysis model including the ODE modeling framework and is easy to implement. We theoretically show that our method is related to minimizing the KL divergence between the density function at a data point and that of the neighborhood points. Experimental results on three public benchmarks show that our approach outperforms other regularization methods.