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1. Developing Inquiry-Based Homework Assignments with Astrobites

   https://doi.org/10.58021/S5301V  

   Hammer, Michael (January 2022, Institute for Scientist & Engineer Educators (ISEE))

   Seagroves, Scott; Barnes, Austin; Metevier, Anne; Porter, Jason; Hunter, Lisa (Ed.)

   The majority of physics and astronomy undergraduate major classes are structured around problem sets, an approach that does not typically make it possible for students to learn in an inquiry-based manner analogous to how scientists conduct research. One of the reasons professors often do not attempt an inquiry approach is the lack of educational tools needed to facilitate this method of learning. In this work, I describe how Astrobites — a website run by astronomy graduate students with the goal of making the latest research more accessible to undergraduates — is ideally suited to serve as an educational tool that can make problem sets more inquiry-based. I discuss how I designed inquiry-based problem sets that make use of Astrobites for several different astronomy classes that target physics and astronomy majors. I also present strategies for implementing such assignments based on assessment from the students, and provide example problem sets that received good student feedback. These assignments are intended to complement traditional problem sets, thereby inclusively providing an alternate way for students to take interest and engage in their homework for the class. 

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2. Estimation of the Size of Union of Delphic Sets: Achieving Independence from Stream Size

   https://doi.org/10.1145/3517804.3526222  

   Meel, Kuldeep S.; Chakraborty, Sourav; Vinodchandran, N. V. (June 2022, PODS '22: Proceedings of the 41st ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems)

   Given a family of sets (S1, S2,... SM) over a universe Ω, estimating the size of their union in the data streaming model is a fundamental computational problem with a wide variety of applications. The holy grail in the field of streaming is to seek design of algorithms that achieve (ε, δ)-approximation with poly(log |Ω|, ε-1, log δ-1) space and update time complexity. Earlier investigations achieve algorithms with desired space and update time complexity for restricted cases such as singletons (Distinct Elements problem), one-dimensional ranges, arithmetic progressions, and sub-cubes. However, techniques used in these works fail for many other simple structured sets. A prominent example is that of Klee's Measure Problem (KMP), wherein every set Si is represented by an axis-parallel rectangle in d-dimensional spaces. Despite extensive prior work, the best-known streaming algorithms for many of these cases depend on the size of the stream, and therefore the problem of whether there exists a streaming algorithm for estimations of size of the union of sets with poly(log |Ω|, ε-1, log δ-1) space and update time complexity has remained open. In this work, we focus on certain general families of sets called Delphic families (which allows efficient membership, sampling, and cardinality queries). Such families of sets capture several well-known problems, including KMP, test coverage, and hypervolume estimation. The primary contribution of our work is to resolve the above-mentioned open problem for streams over Delphic families. In particular, we design the first streaming algorithm for estimating |⋃i=1M Si| with poly(log |Ω|, ε-1, log δ-1) space and update time complexity (independent of M, the length of the stream) when each Si is a member from a Delphic family of sets. We further generalize our results to larger families of sets, called approximate-Delphic families, for which the size of a set can be known approximately but not exactly. Our results resolve two of the open problems listed in Meel, Vinodchandran, Chakraborty (PODS-21). 

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3. Domain Generalization by Marginal Transfer Learning

   Blanchard, Gilles; Deshmukh, Aniket Anand; Dogan, Urun; Lee, Gyemin; Scott, Clayton (January 2021, Journal of machine learning research)

   In the problem of domain generalization (DG), there are labeled training data sets from several related prediction problems, and the goal is to make accurate predictions on future unlabeled data sets that are not known to the learner. This problem arises in several applications where data distributions fluctuate because of environmental, technical, or other sources of variation. We introduce a formal framework for DG, and argue that it can be viewed as a kind of supervised learning problem by augmenting the original feature space with the marginal distribution of feature vectors. While our framework has several connections to conventional analysis of supervised learning algorithms, several unique aspects of DG require new methods of analysis. This work lays the learning theoretic foundations of domain generalization, building on our earlier conference paper where the problem of DG was introduced (Blanchard et al., 2011). We present two formal models of data generation, corresponding notions of risk, and distribution-free generalization error analysis. By focusing our attention on kernel methods, we also provide more quantitative results and a universally consistent algorithm. An efficient implementation is provided for this algorithm, which is experimentally compared to a pooling strategy on one synthetic and three real-world data sets. 

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4. HELLO: improved neural network architectures and methodologies for small variant calling

   https://doi.org/10.1186/s12859-021-04311-4  

   Ramachandran, Anand; Lumetta, Steven S.; Klee, Eric W.; Chen, Deming (December 2021, BMC Bioinformatics)

   null (Ed.)

   Abstract Background Modern Next Generation- and Third Generation- Sequencing methods such as Illumina and PacBio Circular Consensus Sequencing platforms provide accurate sequencing data. Parallel developments in Deep Learning have enabled the application of Deep Neural Networks to variant calling, surpassing the accuracy of classical approaches in many settings. DeepVariant, arguably the most popular among such methods, transforms the problem of variant calling into one of image recognition where a Deep Neural Network analyzes sequencing data that is formatted as images, achieving high accuracy. In this paper, we explore an alternative approach to designing Deep Neural Networks for variant calling, where we use meticulously designed Deep Neural Network architectures and customized variant inference functions that account for the underlying nature of sequencing data instead of converting the problem to one of image recognition. Results Results from 27 whole-genome variant calling experiments spanning Illumina, PacBio and hybrid Illumina-PacBio settings suggest that our method allows vastly smaller Deep Neural Networks to outperform the Inception-v3 architecture used in DeepVariant for indel and substitution-type variant calls. For example, our method reduces the number of indel call errors by up to 18%, 55% and 65% for Illumina, PacBio and hybrid Illumina-PacBio variant calling respectively, compared to a similarly trained DeepVariant pipeline. In these cases, our models are between 7 and 14 times smaller. Conclusions We believe that the improved accuracy and problem-specific customization of our models will enable more accurate pipelines and further method development in the field. HELLO is available at https://github.com/anands-repo/hello 

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5. Domain Generalization by Marginal Transfer Learning

   Blanchard, Gilles; Deshmukh, Aniket Anand; Dogan, Urun; Lee, Gyemin; Scott, Clayton (January 2021, Journal of machine learning research)

   null (Ed.)

   In the problem of domain generalization (DG), there are labeled training data sets from several related prediction problems, and the goal is to make accurate predictions on future unlabeled data sets that are not known to the learner. This problem arises in several applications where data distributions fluctuate because of environmental, technical, or other sources of variation. We introduce a formal framework for DG, and argue that it can be viewed as a kind of supervised learning problem by augmenting the original feature space with the marginal distribution of feature vectors. While our framework has several connections to conventional analysis of supervised learning algorithms, several unique aspects of DG require new methods of analysis. This work lays the learning theoretic foundations of domain generalization, building on our earlier conference paper where the problem of DG was introduced (Blanchard et al., 2011). We present two formal models of data generation, corresponding notions of risk, and distribution-free generalization error analysis. By focusing our attention on kernel methods, we also provide more quantitative results and a universally consistent algorithm. An efficient implementation is provided for this algorithm, which is experimentally compared to a pooling strategy on one synthetic and three real-world data sets. Keywords: domain generalization, generalization error bounds, Rademacher complexity, kernel methods, universal consistency, kernel approximation 

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