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Abstract MotivationHigh-throughput sequencing (HTS) is a modern sequencing technology used to profile microbiomes by sequencing thousands of short genomic fragments from the microorganisms within a given sample. This technology presents a unique opportunity for artificial intelligence to comprehend the underlying functional relationships of microbial communities. However, due to the unstructured nature of HTS data, nearly all computational models are limited to processing DNA sequences individually. This limitation causes them to miss out on key interactions between microorganisms, significantly hindering our understanding of how these interactions influence the microbial communities as a whole. Furthermore, most computational methods rely on post-processing of samples which could inadvertently introduce unintentional protocol-specific bias. ResultsAddressing these concerns, we present SetBERT, a robust pre-training methodology for creating generalized deep learning models for processing HTS data to produce contextualized embeddings and be fine-tuned for downstream tasks with explainable predictions. By leveraging sequence interactions, we show that SetBERT significantly outperforms other models in taxonomic classification with genus-level classification accuracy of 95%. Furthermore, we demonstrate that SetBERT is able to accurately explain its predictions autonomously by confirming the biological-relevance of taxa identified by the model. Availability and implementationAll source code is available at https://github.com/DLii-Research/setbert. SetBERT may be used through the q2-deepdna QIIME 2 plugin whose source code is available at https://github.com/DLii-Research/q2-deepdna. 

Current deep-learning techniques for processing sets are limited to a fixed cardinality, causing a steep increase in computational complexity when the set is large. To address this, we have taken techniques used to model long-term dependencies from natural language processing and combined them with the permutation equivariant architecture, Set Transformer (STr). The result is Set Transformer XL (STrXL), a novel deep learning model capable of extending to sets of arbitrary cardinality given fixed computing resources. STrXL's extension capability lies in its recurrent architecture. Rather than processing the entire set at once, STrXL processes only a portion of the set at a time and uses a memory mechanism to provide additional input from the past. STrXL is particularly applicable to processing sets of high-throughput sequencing (HTS) samples of DNA sequences as their set sizes can range into hundreds of thousands. When tasked with classifying HTS prairie soil samples and MNIST digits, results show that STrXL exhibits an expected memory size-accuracy trade-off that scales proportionally with the complexity of downstream tasks, but, unlike STr, is capable of generalizing to sets of arbitrary cardinality. 

The human ability to generalize beyond interpolation, often called extrapolation or symbol-binding, is challenging to recreate with computational models. Biologically plausible models incorporating indirection mechanisms have demonstrated strong performance in this regard. Deep learning approaches such as Long Short-Term Memory (LSTM) and Transformers have shown varying degrees of success, but recent work has suggested that Transformers are capable of extrapolation as well. We evaluate the capabilities of the above approaches on a series of increasingly complex sentence-processing tasks to infer the capacity of each individual architecture to extrapolate sentential roles across novel word fillers. We confirm that the Transformer does possess superior abstraction capabilities compared to LSTM. However, what it does not possess is extrapolation capabilities, as evidenced by clear performance disparities on novel filler tasks as compared to working memory-based indirection models. 

In this Work in Progress, we present a progress report from the first two years of a five-year Scholarships in STEM program. The number of graduates with computing related degrees from colleges and universities, especially female and underrepresented minorities (URM), is too small to keep up with the fast-growing demand for IT professionals across nation and Tennessee specifically. To reduce the gap in the Tennessee region, our university launched a 5-year S-STEM Scholarship program in 2018 to recruit and graduate more computer science students, especially female and URM. The scholarship program supports about 20 qualified Pell-eligible students every year. Each recipient receives an annual stipend of up to $6000 for no more than three years. In order to increase their interest in computer science and to improve retention of CS majors, a pipeline of well-proven activities were integrated into the program to inspire exploration of the CS discipline and computing careers at an early stage and help students gain work experience before graduation. These activities include, but are not limited to: summer research program that provides opportunities for students to conduct research in different computer science areas, peer-mentoring program that leverages experience and expertise of the group of CS majors who work in the computing field to better prepare scholarship recipients for their careers, and professional conference attendance program that sends students to professional conferences to explore computer science careers and build their own networks. The preliminary data suggest that these activities had a positive effect on our students. We find that the financial support allows students to focus on both academics and searching for computing-related employment. Early analysis of institutional data shows that scholars take more CS credit hours and achieve a higher GPA than other Pell-eligible and non-Pell eligible students, thus making faster progress toward their degree. The support to attend in-person conferences and summer research opportunities had a transformative impact on many participating scholars. The original mentoring program was less effective and has been redesigned to include higher expectations for mentors and mentees and increased faculty involvement. This paper will describe the program elements and explain the effects of these activities on our students with preliminary outcome data and formative evaluation results about the program 

This paper presents the background and methodologies used in programming and teaching the humanoid robot NAO to play the game of "Simon Says" with human players. Choreographe programming was used to provide the overall game logic and incorporate NAO's sensory capabilities. OpenPose pose detection and OpenCV APIs were used to develop the image processing components to convert the raw images captured by NAO for pose classification, and the Keras APIs were used to build the Convolutional Neural Network to classify and recognize the player poses.