Child Welfare: An Overview of Federal Programs and Their Current Funding

Child welfare services are intended to prevent the abuse or neglect of children; ensure that children have safe, permanent homes; and promote the well-being of children and their families. As the U.S. Constitution has been interpreted, states have the primary obligation to ensure the welfare of children and their families. At the state level, the child welfare “system” consists of public child protection and child welfare workers, private child welfare and social service workers, state and local judges, prosecutors, and law enforcement personnel. These representatives of various state and local entities assume interrelated roles while carrying out child welfare activities, including investigating allegations of child abuse and neglect, providing services to families to ensure children’s safety in the home, removing children from their homes when that is necessary for their safety, supervising and administering payments for children placed in foster care, and ensuring permanency planning and regular case review for children in foster care. Most federal dollars dedicated to child welfare purposes are provided to state child welfare agencies, and federal involvement in child welfare is primarily tied to this financial assistance. In recent years, Congress has appropriated just above or below $8 billion in federal support dedicated to child welfare purposes. As a condition of receiving these foster care and other child welfare program funds, states must typically provide nonfederal funds of between 20Apart from the federal funding provided for programs described in this report, state child welfare agencies tap significant program resources—as much as$5.6 billion—from other federal funding streams, including the Temporary Assistance for Needy Families (TANF) block grant, the Social Services Block Grant (SSBG), and Medicaid. These federal funding streams have federal statutory goals, or support activities, that overlap with child welfare purposes. However, they are not solely dedicated to child welfare purposes and states are not necessarily required to use them for those specific purposes. Neither do states need to meet federal requirements specific to the conduct of their child welfare programs as a condition of receiving this “nondedicated” funding. This report begins with a review of federal appropriations activity in FY2014 as it relates to child welfare programs, including the effect of the automatic spending cuts, known as sequestration. The bulk of the report provides a short description of each federal child welfare program, including its purpose and recent (FY2012-FY2014) funding levels

Pattern Research Project: An Investigation of The Pattern And Printing Process - Shippo Tsunagi

2017 Pattern Research Project Emilie Krysa - Shippo Tsunagi Pattern The Pattern Research Project involves research and analysis of contemporary patterns found in the textiles and wallcoverings of the built interior environment. Patterns use motif, repetition, color, geometry, craft, technology, and space to communicate place, time, and concept. Through this research and analysis, built environments - their designers, occupants, construction, and context - can be better understood. Emilie Krysa, VCU Interior Design BFA 2020, selected the Shippo Tsunagi pattern for the 2017 Pattern Research Project. The text below is excerpted from the student’s work: “[The] Shippo pattern originates from Japan and dates to the Heian period (794-1185 AD)... The pattern is called ‘shippo’ in Japanese, which means ‘cloisonne,’ which is an ancient form of enameling… The pattern was traditionally embroidered on by hand or it was hand dyed/painted in a very long and tedious process by professionals. ‘Shashiko,’ which is a basic running stitch, is one style of embroidery that Shippo is often depicted. Today Shippo can be applied to nearly every surface imaginable through digital printing.”https://scholarscompass.vcu.edu/prp/1006/thumbnail.jp

Domain adaptation of weighted majority votes via perturbed variation-based self-labeling

In machine learning, the domain adaptation problem arrives when the test (target) and the train (source) data are generated from different distributions. A key applied issue is thus the design of algorithms able to generalize on a new distribution, for which we have no label information. We focus on learning classification models defined as a weighted majority vote over a set of real-val ued functions. In this context, Germain et al. (2013) have shown that a measure of disagreement between these functions is crucial to control. The core of this measure is a theoretical bound--the C-bound (Lacasse et al., 2007)--which involves the disagreement and leads to a well performing majority vote learning algorithm in usual non-adaptative supervised setting: MinCq. In this work, we propose a framework to extend MinCq to a domain adaptation scenario. This procedure takes advantage of the recent perturbed variation divergence between distributions proposed by Harel and Mannor (2012). Justified by a theoretical bound on the target risk of the vote, we provide to MinCq a target sample labeled thanks to a perturbed variation-based self-labeling focused on the regions where the source and target marginals appear similar. We also study the influence of our self-labeling, from which we deduce an original process for tuning the hyperparameters. Finally, our framework called PV-MinCq shows very promising results on a rotation and translation synthetic problem

N-block presentations and decidability of direct conjugacy between Subshifts of Finite Type

We consider the problem of inverting the transformation which consists in replacing a word by the sequence of its blocks of length N, i.e. its so-called N-block presentation. It was previously shown that among all the possible preimages of an N-block presentation, there exists a particular one which is maximal in the sense that all the other preimages can be obtained from it by letter to letter applications. We give here a combinatorial characterization of the maximal preimages of N-block presentations. Using this characterization, we show that, being given two subshifts of finite type X and Y, the existence of two numbers N and M such that the N-block presentation of X is similar to the M-block presentation of Y, which implies that X and Y are conjugate, is decidable.Comment: 14 pages, 2 figure

Separating algebras and finite reflection groups

A separating algebra is, roughly speaking, a subalgebra of the ring of invariants whose elements distinguish between any two orbits that can be distinguished using invariants. In this paper, we introduce a geometric notion of separating algebra. This allows us to prove that only groups generated by reflections may have polynomial separating algebras, and only groups generated by bireflections may have complete intersection separating algebras.Comment: 12 pages, corrected yet another typ

Finite mixture regression: A sparse variable selection by model selection for clustering

We consider a finite mixture of Gaussian regression model for high- dimensional data, where the number of covariates may be much larger than the sample size. We propose to estimate the unknown conditional mixture density by a maximum likelihood estimator, restricted on relevant variables selected by an 1-penalized maximum likelihood estimator. We get an oracle inequality satisfied by this estimator with a Jensen-Kullback-Leibler type loss. Our oracle inequality is deduced from a general model selection theorem for maximum likelihood estimators with a random model collection. We can derive the penalty shape of the criterion, which depends on the complexity of the random model collection.Comment: 20 pages. arXiv admin note: text overlap with arXiv:1103.2021 by other author