This manuscript describes the development of a resources module that is part of a learning platform named ‘NIGMS Sandbox for Cloud-based Learning’ https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on implementing deep learning algorithms for biomedical image data in an interactive format that uses appropriate cloud resources for data access and analyses. Biomedical-related datasets are widely used in both research and clinical settings, but the ability for professionally trained clinicians and researchers to interpret datasets becomes difficult as the size and breadth of these datasets increases. Artificial intelligence, and specifically deep learning neural networks, have recently become an important tool in novel biomedical research. However, use is limited due to their computational requirements and confusion regarding different neural network architectures. The goal of this learning module is to introduce types of deep learning neural networks and cover practices that are commonly used in biomedical research. This module is subdivided into four submodules that cover classification, augmentation, segmentation and regression. Each complementary submodule was written on the Google Cloud Platform and contains detailed code and explanations, as well as quizzes and challenges to facilitate user training. Overall, the goal of this learning module is to enable users to identify and integrate the correct type of neural network with their data while highlighting the ease-of-use of cloud computing for implementing neural networks. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

The Tsetlin Machine (TM) is a novel machine learning approach that implements propositional logic to perform various tasks such as classification and regression. The TM not only achieves competitive accuracy in these tasks but also provides results that are explainable and easy to implement using simple hardware. The TM learns using clauses based on the features of the data, and final classification is done using a combination of these clauses. In this paper, we propose the novel idea of adding regularizers to the TM, referred to as Regularized TM (RegTM), to improve generalization. Regularizers have been widely used in machine learning to enhance accuracy. We explore different regularization strategies and their influence on performance. We demonstrate the feasibility of our methodology through various experiments on benchmark datasets.

In this paper, we propose a novel algorithm called adaptive learning & momentum sharpness-aware minimization MAML (AS-MAML) that builds upon the concept of sharpness-aware minimization and adaptive learning and momentum to improve generalization. We prove theoretically by performing convergence analysis and PAC-Bayes analysis that AS-MAML performs better than the state-of-the-art algorithms in model-agnostic meta-learning. We draw the same conclusion through extensive experimental analysis using benchmark datasets.

In this paper, we develop a novel approach called Domain Adapted Weighted Trilateration (DAWT). We use the idea of there are reference coordinates at known locations that scrape the microblog (tweet) counts in time and space (circular regions around the reference coordinate). The change in counts of tweets would be indicative of an event pattern. We propose the use of DAWT to combine the information from multiple reference points to find the exact location of the event. We propse the use of domain knowledge in the form of counts of tweet or spatial relationship between reference points to calculate the weights for trilateration. We use microblogging data collected from Twitter to evaluate our model and compare it with other baseline methods.

Sentiment Analysis (SA) or opinion mining is analysis of emotions and opinions from any kind of text. SA helps in tracking peoples viewpoints and it is an important factor when it comes to social media monitoring product and brand recognition customer satisfaction customer loyalty advertising and promotions success and product acceptance. That is why SA is one of the active research areas in Natural Language Processing (NLP). SA is applied on data sourced from various media platforms to mine sentiment knowledge from them. Various approaches have been deployed in the literature to solve the problem. Most techniques devise complex and sophisticated frameworks in order to attain optimal accuracy. This work aims to finetune Bidirectional Encoder Representations from Transformers (BERT) with Bidirectional Long Short-Term Memory (BiLSTM) for movie reviews sentiment analysis and still provide better accuracy than the State-of-The-Art (SOTA) methods. The paper also shows how sentiment analysis can be applied if someone wants to recommend a certain movie for example by computing overall polarity of its sentiments predicted by the model. That is our proposed method serves as an upper-bound baseline in prediction of a predominant reaction to a movie. To compute overall polarity a heuristic algorithm is applied to BERTBiLSTM output vector. Our model can be extended to three-class four-class or any fine-grained classification and apply overall polarity computation again. This is intended to be exploited in future work.