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We propose an interactive visual analytics tool, Vis-SPLIT, for partitioning a population of individuals into groups with similar gene signatures. Vis-SPLIT allows users to interactively explore a dataset and exploit visual separations to build a classification model for specific cancers. The visualization components reveal gene expression and correlation to assist specific partitioning decisions, while also providing overviews for the decision model and clustered genetic signatures. We demonstrate the effectiveness of our framework through a case study and evaluate its usability with domain experts. Our results show that Vis-SPLIT can classify patients based on their genetic signatures to effectively gain insights into RNA sequencing data, as compared to an existing classification system., Comment: To be published in IEEE Visualization and Visual Analytics (VIS), 2023

In this paper, we study the various ways 3rd-5th grade educators in Wisconsin utilized Jo Wilder and the Capitol Case, a historical inquiry game, as part of their classroom instruction. The 15 educators involved in the study were all grade school teachers in Wisconsin who took part in the "Doing History Fellowship" program, a professional development opportunity offered by the authors, designed to increase their understanding of historical inquiry instruction and game-based learning. As part of the program, the educators planned and implemented the game within their own classroom context and reported their results back to the authors and other educators. Through their reports, surveys and semi-structured interviews we discovered the educators were motivated by five distinct instructional purposes, which influenced how the game was integrated into their curriculum. In this paper, we name and describe these five purposes. We see these findings as useful insights into how educators think about games and how educational video games and corresponding professional development activities may be designed in the future., Comment: Paper presented at Meaningful Play 2022. East Lansing, MI

Unsupervised clustering of feature matrix data is an indispensible technique for exploratory data analysis and quality control of experimental data. However, clusters are difficult to assess for statistical significance in an objective way. We prove a formula for the distribution of the size of the set of samples, out of a population of fixed size, which display a given signature, conditional on the marginals (frequencies) of each individual feature comprising the signature. The resulting "exact test for coincidence" is widely applicable to objective assessment of clusters in any binary data. We also present a software package implementing the test, a suite of computational verifications of the main theorems, and a supplemental tool for cluster discovery using Formal Concept Analysis., Comment: 9 pages, 5 figures (typo correction and additional reference)

Tumorigenesis is a complex process that is heterogeneous and affected by numerous sources of variability. This study presents a stochastic extension of a biologically grounded tumor growth model, referred to as the Norton-Simon-Massagu\'e (NSM) tumor growth model. We first study the uncontrolled version of the model where the effect of chemotherapeutic drug agent is absent. Conditions on the model's parameters are derived to guarantee the positivity of the tumor volume and hence the validity of the proposed stochastic NSM model. To calibrate the proposed model we utilize a maximum likelihood-based estimation algorithm and population mixed-effect modeling formulation. The algorithm is tested by fitting previously published tumor volume mice data. Then, we study the controlled version of the model which includes the effect of chemotherapy treatment. Analysis of the influence of adding the control drug agent into the model and how sensitive it is to the stochastic parameters is performed both in open-loop and closed-loop viewpoints through different numerical simulations.

We generalize the notion of coisotropic hypersurfaces to subvarieties of Grassmannians having arbitrary codimension. To every projective variety X, Gel'fand, Kapranov and Zelevinsky associate a series of coisotropic hypersurfaces in different Grassmannians. These include the Chow form and the Hurwitz form of X. Gel'fand, Kapranov and Zelevinsky characterized coisotropic hypersurfaces by a rank one condition on conormal spaces, which we use as the starting point for our generalization. We also study the dual notion of isotropic varieties by imposing rank one conditions on tangent spaces instead of conormal spaces.

The variance, higher order moments, covariance, and joint moments or cumulants are shown to be special cases of a certain tensor in $V^{\otimes n}$ defined in terms of a collection $X_1,...,X_n$ of $V$-valued random variables, for an appropriate finite-dimensional real vector space $V$. A statistical transform is proposed from such collections--finite spatial stochastic processes--to numerical tuples using the Schur-Weyl decomposition of $V^{\otimes n}$. It is analogous to the Fourier transform, replacing the periodicity group $\mathbb{Z}$, $\mathbb{R}$, or $U(1)$ with the permutation group $S_{n}$. As a test case, we apply the transform to one of the datasets used for benchmarking the Continuous Registration Challenge, the thoracic 4D Computed Tomography (CT) scans from the M.D. Anderson Cancer Center available for download from DIR-Lab. Further applications to morphometry and statistical shape analysis are suggested., Comment: 9 pages, 1 figure

In the present work, we study the properties of biological networks by applying analogous notions of fundamental concepts in Riemannian geometry and optimal mass transport to discrete networks described by weighted graphs. Specifically, we employ possible generalizations of the notion of Ricci curvature on Riemannian manifold to discrete spaces in order to infer certain robustness properties of the networks of interest. We compare three possible discrete notions of Ricci curvature (Olivier Ricci curvature, Bakry-\'Emery Ricci curvature, and Forman Ricci curvature) on some model and biological networks. While the exact relationship of each of the three definitions of curvature to one another is still not known, they do yield similar results on our biological networks of interest. These notions are initially defined on positively weighted graphs; however, Forman-Ricci curvature can also be defined on directed positively weighted networks. We will generalize this notion of directed Forman Ricci curvature on the network to a form that also considers the signs of the directions (e. g., activator and repressor in transcription networks). We call this notion the \emph{signed-control Ricci curvature}. Given real biological networks are almost always directed and possess positive and negative controls, the notion of signed-control curvature can elucidate the network-based study of these complex networks. Finally, we compare the results of these notions of Ricci curvature on networks to some known network measures, namely, betweenness centrality and clustering coefficients on graphs., Comment: 23 pages

For each relative $\operatorname{GL}(V)$-invariant tensor $I\in \Lambda^{p_1+1}V^{\vee}\otimes .. \otimes \Lambda^{p_n+1}V^{\vee}$ we construct a $\operatorname{GL}(V)$-invariant weighted differential form $\eta$ on $(\mathbb{P} V)^{n}$. Then $\eta$ is expressed explicitly with respect to $n$-tuples of frames for tangent spaces at points of $\mathbb{P} V$ to obtain elements of a different tensor space. For certain invariants $I$, the resulting elements are shown to be the multi-focal tensors appearing in the machine vision literature (Demazure 1988, Longuet-Higgins 1981, Luong 1992, Faugeras 1993, Faugeras and Luong 2001, Hartley and Zisserman 2003). This generalizes the 3 multi-focal varieties known in dimension $\dim V = 4$ to an infinite collection of special tensor subvarieties. We use this framework to exhibit a new system of degree 4 polynomial equations, reminiscent of the braid relation, satisfied by the Euclidean trifocal variety., Comment: 41 pages, 1 figure

This article is a description of elasticity theory for readers with mathematical background. The first sections are an abridgment of parts of the book by Marsden and Hughes, including a compact identification of the equations of motion as the Euler-Lagrange equations for the lagrangian density. The other sections describe the basic first-order classification of materials, from the point of view of representation theory as opposed to index calculus. It includes a computation of the axes of symmetry, when they exist, for most of the irreducible components of the elasticity tensor. When the two components of the 5-dimensional type $V_5$ have axes of symmetry, some invariants appear: 2 angles in $S^{1}$ that measure the deviation of an associated decomposition $V_5\otimes \mathbb{R}^2=V_5\oplus V_5$ from the standard one. See also the classification appearing for example in (Chadwick, Vianello, and Cowin) and (Bona, Bucataru, and Slawinski) by symmetry group in $SO(3)$. A somewhat more representation-theoretic approach can be found in (Itin and Hehl), and a complete list of polynomial invariants for generic elasticity tensors can be found in (Boehler, Kirillov Jr, and Onat).