'"Safe and well" outlines the actions the Government of South Australia is taking to support families at risk of entering the child protection system, protect children from harm when they come into care, and invest in young people to leave care with opportunities for a bright future.

This paper is intended to provide a technical support resource for the reader in relation to the treatment of superannuation under family law.

Graphic novels -- History and criticism.

School districts have "unconscionably and impermissibly shifted operating costs of the classrooms directly on the financial backs of our teachers," the lawsuit alleges.

Casey Smitherman, the superintendent of the Elwood school district, cited the "negative attention" from the incident, according to the Indianapolis Star.

A state leader shares how research helped raise important considerations for increasing college success and completion in Indiana.

The state's courts said the additional $548 million the legislature provided its schools this year is still not enough to ensure a constitutionally adequate education.

While the court said the state is moving in the right direction, it also said it doesn't feel comfortable closing a funding lawsuit and wants to make sure the state keeps up with the costs of inflation.

Kansas' highest court has declared that the state finally is spending enough money on its public schools under a new education funding law but refused to end a lawsuit filed nearly a decade ago because it wants to monitor future funding by the legislature.

Many are whipsawed by shutdown recommendations aimed at stemming the coronavirus, and the logistical and financial consequences of those actions.

Dijon : Darantiere, 1893.

London : Layton, 1903.

Edinburgh : Young J. Pentland, 1887.

London : printed for Knight and Lacey, 1826.

Paris : O. Doin, 1879.

Edinburgh : Young J. Pentland, 1887.

Many are whipsawed by shutdown recommendations aimed at stemming the coronavirus, and the logistical and financial consequences of those actions.

In a case implicating the use of schools as voting locations as well as free speech in education, the justices said Minnesota went too far.

Nearly 45 percent of children in rural McDowell County, West Virginia do not live with their parents and many are being raised by grandparents.

Michael Martirano led the state's schools through dramatic budget cuts, academic challenges, and a state-versus-local battle over school construction.

State lawmakers have made it illegal for school districts to spend any public money on the Mississippi Association of School Superintendents, saying district leaders personally attacked state officials while they were seeking votes for a school funding initiative last year.

The justices agreed to decide whether the Eighth Amendment requires a trial court to find that a juvenile is permanently incorrigible before imposing a sentence of life without parole.

London (No. 4, Hanover Street) : Published ... for the proprietor by T.G. March ; [London] (Threadneedle Street) : Sold also by F. G. Moon ; [London] (Strand) : [Sold also by] Ackermann & Co., April 10, 1842 ([London?] : Printed by R. Lloyd)

[Rome?]

[Paris], [1807]

Brian Whiston since 2015 has led the state's education department while it put together its plan under the Every Student Succeeds Act and took control of the state's school takeover district.

Nathel Burtley, 79, was a lifelong educator who led the Flint, Mich., school system, paving the way for other African-American educators to become the heads of districts. He died earlier this month from the coronavirus.

David Schuler, the superintendent of Township High School District 214 in Arlington Heights, Ill., has been named 2018 National Superintendent of the Year.

Controversy over an intiative aimed a reducing inequities in Lee's Summit, Mo., schools led the police department to provide security protection for the district's first African-American superintendent. Now the school board has reversed course.

Rockville, Maryland : National Institute on Drug Abuse, 1979.

Rockville, Maryland : National Institute on Drug Abuse, 1978.

New York : Experimental Manpower Laboratory at Mobilization for Youth, Inc., [1971]

Washington, D.C. : Department of Transportation, Federal Railroad Administration, 1976.

Pac-12 Networks' Mike Yam interviews former Oregon State guard Sydney Wiese to hear how she's recovering from contracting COVID-19. Wiese recounts her recent travel and how she's been lifted up by steadfast support from friends, family and fellow WNBA players. See more from Wiese during "Pac-12 Playlist" on Monday, April 6 at 7 p.m. PT/ 8 p.m. MT on Pac-12 Network.

On the day Kobe Bryant suddenly passed away, the Beavers embraced their rivals at midcourt in a moment of strength to support the Ducks, many of whom had personal connections to Bryant and his daughter, Gigi. For this, Oregon State is the 2020 recipient of the Pac-12 Sportsmanship Award.

Heiko Werner, Hajo Holzmann, Pierre Vandekerkhove.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 1816--1871.

Abstract:
We investigate a flexible two-component semiparametric mixture of regressions model, in which one of the conditional component distributions of the response given the covariate is unknown but assumed symmetric about a location parameter, while the other is specified up to a scale parameter. The location and scale parameters together with the proportion are allowed to depend nonparametrically on covariates. After settling identifiability, we provide local M-estimators for these parameters which converge in the sup-norm at the optimal rates over Hölder-smoothness classes. We also introduce an adaptive version of the estimators based on the Lepski-method. Sup-norm bounds show that the local M-estimator properly estimates the functions globally, and are the first step in the construction of useful inferential tools such as confidence bands. In our analysis we develop general results about rates of convergence in the sup-norm as well as adaptive estimation of local M-estimators which might be of some independent interest, and which can also be applied in various other settings. We investigate the finite-sample behaviour of our method in a simulation study, and give an illustration to a real data set from bioinformatics.

In this paper, we extend the methodology developed for Support Vector Machines (SVM) using the $ell_2$-norm ($ell_2$-SVM) to the more general case of $ell_p$-norms with $p>1$ ($ell_p$-SVM). We derive second order cone formulations for the resulting dual and primal problems. The concept of kernel function, widely applied in $ell_2$-SVM, is extended to the more general case of $ell_p$-norms with $p>1$ by defining a new operator called multidimensional kernel. This object gives rise to reformulations of dual problems, in a transformed space of the original data, where the dependence on the original data always appear as homogeneous polynomials. We adapt known solution algorithms to efficiently solve the primal and dual resulting problems and some computational experiments on real-world datasets are presented showing rather good behavior in terms of the accuracy of $ell_p$-SVM with $p>1$.

This paper considers a Bayesian approach to graph-based semi-supervised learning. We show that if the graph parameters are suitably scaled, the graph-posteriors converge to a continuum limit as the size of the unlabeled data set grows. This consistency result has profound algorithmic implications: we prove that when consistency holds, carefully designed Markov chain Monte Carlo algorithms have a uniform spectral gap, independent of the number of unlabeled inputs. Numerical experiments illustrate and complement the theory.

Application and use of deep learning algorithms for different healthcare applications is gaining interest at a steady pace. However, use of such algorithms can prove to be challenging as they require large amounts of training data that capture different possible variations. This makes it difficult to use them in a clinical setting since in most health applications researchers often have to work with limited data. Less data can cause the deep learning model to over-fit. In this paper, we ask how can we use data from a different environment, different use-case, with widely differing data distributions. We exemplify this use case by using single-sensor accelerometer data from healthy subjects performing activities of daily living - ADLs (source dataset), to extract features relevant to multi-sensor accelerometer gait data (target dataset) for Parkinson's disease classification. We train the pre-trained model using the source dataset and use it as a feature extractor. We show that the features extracted for the target dataset can be used to train an effective classification model. Our pre-trained source model consists of a convolutional autoencoder, and the target classification model is a simple multi-layer perceptron model. We explore two different pre-trained source models, trained using different activity groups, and analyze the influence the choice of pre-trained model has over the task of Parkinson's disease classification.

Supervised machine learning algorithms have seen spectacular advances and surpassed human level performance in a wide range of specific applications. However, using complex ensemble or deep learning algorithms typically results in black box models, where the path leading to individual predictions cannot be followed in detail. In order to address this issue, we propose the novel "Cyclic Boosting" machine learning algorithm, which allows to efficiently perform accurate regression and classification tasks while at the same time allowing a detailed understanding of how each individual prediction was made.

In this paper we introduce plan2vec, an unsupervised representation learning approach that is inspired by reinforcement learning. Plan2vec constructs a weighted graph on an image dataset using near-neighbor distances, and then extrapolates this local metric to a global embedding by distilling path-integral over planned path. When applied to control, plan2vec offers a way to learn goal-conditioned value estimates that are accurate over long horizons that is both compute and sample efficient. We demonstrate the effectiveness of plan2vec on one simulated and two challenging real-world image datasets. Experimental results show that plan2vec successfully amortizes the planning cost, enabling reactive planning that is linear in memory and computation complexity rather than exhaustive over the entire state space.