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Building Ramps and Pathways to Data science in K-12, Minority-Serving Institutions, and Two-Year Colleges

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Scatterplot 2024年第1期1卷

作者： Rachel Saidi Helen Burn Ben Baumer Jason Black Debzani Deb Zarek Drozda a Math Statistics and Data Science Department Montgomery College – Rockville Campus Rockville MD USA b Mathematics Department Highline College Des Moines Washington Des Moines WA USA c Statistical and Data Sciences Department Smith College Northampton MA USA d School of Business and Interdisciplinary Center for Creativity and Innovation (ICCI) Florida A&M University Tallahassee FL USA e Computer Science Department Center for Applied Data Science (CADS) Winston-Salem State University Winston-Salem NC USA f Data Science 4 Everyone (DS4E) Program University of Chicago Chicago IL USA

Global demand for data scientists and analysts is on a trajectory of sustained growth, underscoring the need to expand enrollment of students in data science and data analytics courses and programs. This article explores diverse pathways to data science for various groups, including high school students, those in two-year colleges, and students in small colleges and minority-serving institutions. This article identifies opportunities and challenges associated with pursuing data science for students in these sectors, including factors such as building partnerships and awareness of the field and difficulties in establishing articulation agreements between institutions. The authors, representing various organizations participating in a panel discussion at the 2023 Data science Leadership Summit of the Academic Data science Alliance (ADSA), share insights into efforts to develop nontraditional pathways, serving as a resource for schools, colleges, and organizations contemplating creation of data science programs. The panelists’ combined experience spans work with a broad range of organizations including ADSA, American math.matics Association of Two-Year Colleges, American Statistical Association, Data science for Everyone, the department of Education, HBCU Data science Consortium, math.matical Association of America, National Academies of science, Engineering, and Medicine, HBCU Data science Consortium, National science Foundation, and StatPREP.

关键词： Data science pathways access K-12 minority-serving institution two-year college

KLF2 maintains lineage fidelity and suppresses CD8 T cell exhaustion during acute LCMV infection

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science (New York, N.Y.) 2025年第6735期387卷 eadn2337页

作者： Fagerberg, Eric Attanasio, John Dien, Christine Singh, Jaiveer Kessler, Emily A. Abdullah, Leena Shen, Jian Hunt, Brian G. Connolly, Kelli A. De Brouwer, Edward He, Jiaming Iyer, Nivedita R. Buck, Jessica Borr, Emily R. Damo, Martina Foster, Gena G. Giles, Josephine R. Huang, Yina H. Tsang, John S. Krishnaswamy, Smita Cui, Weiguo Joshi, Nikhil S. Department of Immunobiology Yale University School of Medicine New Haven CT United States Program in Computational Biology & Bioinformatics Yale University New Haven CT United States Department of Microbiology and Immunology Geisel School of Medicine at Dartmouth NH United States Department of Pathology Feinberg School of Medicine at Northwestern University Chicago IL United States Department of Genetics and Computer Science Yale University School of Medicine New Haven CT United States Department of Medicine University of Chicago Chicago IL United States Section of Hematology Department of Internal Medicine Yale University School of Medicine New Haven CT United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania Philadelphia PA United States Institute for Immunology Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Parker Institute for Cancer Immunotherapy Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Yale Center for Systems and Engineering Immunology Yale University School of Medicine New Haven CT United States New Haven CT United States Department of Biomedical Engineering Yale University New Haven CT United States Computational Biology and Bioinformatics Program Yale University CT USA New Haven United States Applied Math Program Yale University New Haven CT United States

Naïve CD8 T cells have the potential to differentiate into a spectrum of functional states during an immune response. How these developmental decisions are made and what mechanisms exist to suppress differentiation toward alternative fates remains unclear. We employed in vivo CRISPR-Cas9-based perturbation sequencing to assess the role of ~40 transcription factors (TFs) and epigenetic modulators in T cell fate decisions. Unexpectedly, we found that knockout of the TF Klf2 resulted in aberrant differentiation to exhausted-like CD8 T cells during acute infection. KLF2 was required to suppress the exhaustion-promoting TF TOX and to enable the TF TBET to drive effector differentiation. KLF2 was also necessary to maintain a polyfunctional tumor-specific progenitor state. Thus, KLF2 provides effector CD8 T cell lineage fidelity and suppresses the exhaustion program.

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Natural and adversarial error detection using invariance to image transformations

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arXiv 2019年

作者： Bahat, Yuval Irani, Michal Shakhnarovich, Gregory Department of Applied Math and Computer Science Weizmann Institute of Science Rehovot Israel Toyoyta Technological Institute at Chicago ChicagoIL United States

We propose an approach to distinguish between correct and incorrect image classifications. Our approach can detect misclassifications which either occur unintentionally ("natural errors"), or due to intentional adversarial attacks ("adversarial errors"), both in a single unified framework. Our approach is based on the observation that correctly classified images tend to exhibit robust and consistent classifications under certain image transformations (e.g., horizontal flip, small image translation, etc.). In contrast, incorrectly classified images (whether due to adversarial errors or natural errors) tend to exhibit large variations in classification results under such transformations. Our approach does not require any modifications or retraining of the classifier, hence can be applied to any pre-trained classifier. We further use state of the art targeted adversarial attacks to demonstrate that even when the adversary has full knowledge of our method, the adversarial distortion needed for bypassing our detector is no longer imperceptible to the human eye. Our approach obtains state-of-the-art results compared to previous adversarial detection methods, surpassing them by a large margin. Copyright © 2019, The Authors. All rights reserved.

关键词： Errors

On null-homology and stationary sequences

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arXiv 2019年

作者： Alsmeyer, Gerold Mukherjee, Chiranjib Inst. Math. Stochastics Department of Mathematics and Computer Science University of Münster Orléans-Ring 10 MünsterD-48149 Germany

The concept of homology, originally developed as a useful tool in algebraic topology, has by now become pervasive in quite different branches of math.matics. The notion particularly appears quite naturally in ergodic theory in the study of measure-preserving transformations arising from various group actions or, equivalently, the study of stationary sequences when adopting a probabilistic perspective as in this paper. Our purpose is to give a new and relatively short proof of the coboundary theorem due to Schmidt [35] which provides a sharp criterion that determines (and rules out) when two stationary processes belong to the same null-homology equivalence class. We also discuss various aspects of null-homology within the class of Markov random walks, compare null-homology with a formally stronger notion which we call strict-sense null-homology. Finally, we also discuss some concrete cases where the notion of null-homology turns up in a relevant *** Codes 28D05 (Primary) 60G10 (Secondary) Copyright © 2019, The Authors. All rights reserved.

关键词： Topology

Trees whose even-degree vertices induce a path are antimagic

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arXiv 2019年

作者： Lozano, Antoni Mora, Mercè Seara, Carlos Tey, Joaquín Computer Science Department Universitat Politècnica de Catalunya Spain Mathematics Department Universitat Politècnica de Catalunya Spain Math. Department Universidad Autónoma Metropolitana-Iztapalapa Mexico

An antimagic labeling of a connected graph G is a bijection from the set of edges E(G) to {1, 2, ⋯, |E(G)|} such that all vertex sums are pairwise distinct, where the vertex sum at vertex v is the sum of the labels assigned to edges incident to v. A graph is called antimagic if it has an antimagic labeling. In 1990, Hartsfield and Ringel conjectured that every simple connected graph other than K2is antimagic;however the conjecture remains open, even for trees. In this note we prove that trees whose vertices of even degree induce a path are antimagic, extending a result given by Liang, Wong, and Zhu [Discrete math. 331 (2014) 9-14].MSC Codes 05C78, 05C05 Copyright © 2019, The Authors. All rights reserved.

关键词： Forestry

Application of L -fuzzy sets in m -ary semigroups

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Application of L -fuzzy sets in m -ary semigroups

作者： Kuka, Shkelqim Hila, Kostaq Naka, Krisanthi Department of Mathematics Faculty of Math. Engineering and Physics Engineering Polytechnic University of Tirana Tirana Albania Department of Mathematics and Computer Science Faculty of NaturalSciences University of Gjirokastra Gjirokastra Albania

In this paper, applying the theory of L-fuzzy sets, we introduce the concept of an L-fuzzy ideal (L-fuzzy k-ideal) of an m-ary semigroup. Some properties of them are investigated and some structural theorems for L-fuzzy ideals (L-fuzzy k-ideals) of m-ary semigroups are proved. In this direction the concept of image and preimage of an L-fuzzy set under m-ary semigroup homomorphism are discussed. Also, the notions of normal L-fuzzy ideal (L-fuzzy k-ideal) and maximal L-fuzzy ideal (L-fuzzy k-ideal) of an m-ary semigroup are introduced and some properties of them are studied. Further, we introduce the notion of L-fuzzy congruence on an m-ary semigroup and make a study of L-fuzzy quotient m-ary semigroup using an L-fuzzy congruence, an L-fuzzy quotient m-ary semigroup induced by L-fuzzy ideals. We also investigate some properties of homomorphisms between them. © 2018 - IOS Press and the authors. All rights reserved.

关键词： Algebra

Classification of two-dimensional left(right) unital algebras over algebraically closed fields and

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Journal of Physics: Conference Series 2020年第1期1489卷

作者： H Ahmed U Bekbaev I Rakhimov Department of Math. Faculty of Science UPM Selangor Malaysia & Depart. of Math. Faculty of Science Taiz University Taiz Yemen Department of Science in Engineering Faculty of Engineering IIUM Malaysia Department of Mathematics Faculty of Computer and Mathematical Sciences UiTM Malaysia & Institute for Mathematical Research (INSPEM) UPM Serdang Selangor Malaysia

In this paper we describe all left, right unital and unital algebra structures on two-dimensional vector space over any algebraically closed field and . We tabulate the algebras and provide their unit elements.

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POS-tags features for Protein-Protein Interaction Extraction from Biomedical Articles 25

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POS-tags features for Protein-Protein Interaction Extraction...

25th IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2018

作者： Shiguihara-Juarez, Pedro Murrugarra-Llerena, Nils Andrade Lopes, Alneu De Department of Computer Science Universidad Peruana de Ciencias Aplicadas Lima Peru Department of Computer Science Univesity of Pittsburgh PittsburghPA United States Institute of Math. Sciences and Comp. University of São Paulo São Carlos Brazil

ISBN: (纸本)9781538654903

Protein-Protein Interaction (PPI) extraction from biomedical articles consists on extracting sentences were two or more proteins interact. Traditional articles tackle this problem creating more sophisticated classifiers. In contrast to them, we focus on discriminative features that can be exploited by traditional classifiers. Our method exploits information from POS-tags features and are combined with a bag-of-words approach. We used five standard corpora of PPI: Aimed, Bioinfer, HPRD50, IEPA and LLL. Our method is simple and achieves high results compared with other approaches. We achieve an improvement of 11% with our best competitor. © 2018 IEEE.

关键词： Proteins

Certified and fast computations with shallow covariance kernels

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arXiv 2020年

作者： Kressner, Daniel Latz, Jonas Massei, Stefano Ullmann, Elisabeth MATH-ANCHP École Polytechnique fédérale de Lausanne Lausanne1015 Switzerland Department of Applied Mathematics and Theoretical Physics University of Cambridge CambridgeCB3 0WA United Kingdom Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven5612 AZ Netherlands Department of Mathematics Technical University of Munich Garching85748 Germany

Many techniques for data science and uncertainty quantification demand efficient tools to handle Gaussian random fields, which are defined in terms of their mean functions and covariance operators. Recently, parameterized Gaussian random fields have gained increased attention, due to their higher degree of flexibility. However, especially if the random field is parameterized through its covariance operator, classical random field discretization techniques fail or become inefficient. In this work we introduce and analyze a new and certified algorithm for the low-rank approximation of a parameterized family of covariance operators which represents an extension of the adaptive cross approximation method for symmetric positive definite matrices. The algorithm relies on an affine linear expansion of the covariance operator with respect to the parameters, which needs to be computed in a preprocessing step using, e.g., the empirical interpolation method. We discuss and test our new approach for isotropic covariance kernels, such as Matérn kernels. The numerical results demonstrate the advantages of our approach in terms of computational time and confirm that the proposed algorithm provides the basis of a fast sampling procedure for parameter dependent Gaussian random *** Codes 62M40, 65R20, 65C20, 65D15, 65G20 Copyright © 2020, The Authors. All rights reserved.

关键词： Gaussian distribution