University of Massachusetts Amherst

About: University of Massachusetts Amherst is a education organization based out in Amherst Center, Massachusetts, United States. It is known for research contribution in the topics: Population & Galaxy. The organization has 37274 authors who have published 83965 publications receiving 3834996 citations. The organization is also known as: UMass Amherst & Massachusetts State College.

Shape quantization and recognition with randomized trees

Abstract: We explore a new approach to shape recognition based on a virtually infinite family of binary features (queries) of the image data, designed to accommodate prior information about shape invariance and regularity. Each query corresponds to a spatial arrangement of several local topographic codes (or tags), which are in themselves too primitive and common to be informative about shape. All the discriminating power derives from relative angles and distances among the tags. The important attributes of the queries are a natural partial ordering corresponding to increasing structure and complexity; semi-invariance, meaning that most shapes of a given class will answer the same way to two queries that are successive in the ordering; and stability, since the queries are not based on distinguished points and substructures. No classifier based on the full feature set can be evaluated, and it is impossible to determine a priori which arrangements are informative. Our approach is to select informative features and build tree classifiers at the same time by inductive learning. In effect, each tree provides an approximation to the full posterior where the features chosen depend on the branch that is traversed. Due to the number and nature of the queries, standard decision tree construction based on a fixed-length feature vector is not feasible. Instead we entertain only a small random sample of queries at each node, constrain their complexity to increase with tree depth, and grow multiple trees. The terminal nodes are labeled by estimates of the corresponding posterior distribution over shape classes. An image is classified by sending it down every tree and aggregating the resulting distributions. The method is applied to classifying handwritten digits and synthetic linear and nonlinear deformations of three hundred L AT E X symbols. Stateof-the-art error rates are achieved on the National Institute of Standards and Technology database of digits. The principal goal of the experiments on L AT E X symbols is to analyze invariance, generalization error and related issues, and a comparison with artificial neural networks methods is presented in this context.

Evidence-Based Practices for Children, Youth, and Young Adults with Autism Spectrum Disorder: A Comprehensive Review.

Abstract: The purpose of this study was to identify evidenced-based, focused intervention practices for children and youth with autism spectrum disorder. This study was an extension and elaboration of a previous evidence-based practice review reported by Odom et al. (Prev Sch Fail 54:275–282, 2010b, doi: 10.1080/10459881003785506 ). In the current study, a computer search initially yielded 29,105 articles, and the subsequent screening and evaluation process found 456 studies to meet inclusion and methodological criteria. From this set of research studies, the authors found 27 focused intervention practices that met the criteria for evidence-based practice (EBP). Six new EBPs were identified in this review, and one EBP from the previous review was removed. The authors discuss implications for current practices and future research.

Undoing Appropriateness: Raciolinguistic Ideologies and Language Diversity in Education

Abstract: In this article, Nelson Flores and Jonathan Rosa critique appropriateness-based approaches to language diversity in education. Those who subscribe to these approaches conceptualize standardized linguistic practices as an objective set of linguistic forms that are appropriate for an academic setting. In contrast, Flores and Rosa highlight the raciolinguistic ideologies through which racialized bodies come to be constructed as engaging in appropriately academic linguistic practices. Drawing on theories of language ideologies and racialization, they offer a perspective from which students classified as long-term English learners, heritage language learners, and Standard English learners can be understood to inhabit a shared racial positioning that frames their linguistic practices as deficient regardless of how closely they follow supposed rules of appropriateness. The authors illustrate how appropriateness-based approaches to language education are implicated in the reproduction of racial normativity by exp...