APPLICATION DEADLINE OCTOBER 15, 2019 The Operations, Information and Decisions Department at the Wharton School is seeking applicants for a full-time, tenure track, Assistant Professor faculty position in the area of decision-making. Our decision-making faculty is comprised of scholars with a diverse set of backgrounds and interests, and we encourage applicants with degrees in economics, […]

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Utah Valley University offers career and technical education, associate degrees, bachelor's degrees, and master's degrees under one roof, all for the average out-of-pocket tuition of $1,700.

The university’s Center for Vaccine Research is working to create a coronavirus vaccine.

The Chinese Communist Party (CCP) is building links between China’s civilian universities, military and security agencies. Those efforts, carried out under a policy of leveraging the civilian sector to maximise military power (known as ‘military–civil fusion’), have accelerated in the past decade. Research for the China Defence Universities Tracker has determined that greater numbers of Chinese universities are engaged in defence research, training defence scientists, collaborating with the military and cooperating with defence industry conglomerates and are involved in classified research.1 At least 15 civilian universities have been implicated in cyberattacks, illegal exports or espionage. China’s defence industry conglomerates are supervising agencies of nine universities and have sent thousands of their employees to train abroad.

"It is timely that the higher education sector examines paths forward to address and indeed engage in the new environment in which it will work in the future. This fellowship explored two approaches to engage students more deeply in their education. The first approach is generally termed 'interdisciplinary studies' where students define their own program of study; the second approach focuses on topic structure where students are given a wide range of choice and in effect can build a topic that suits their interests."--Page iv.

This paper assesses whether the Newstart Allowance for unemployed people should be increased. It investigates the relationship of the Newstart Allowance to Average Weekly Earnings, the Federal Minimum Wage, and Australian poverty lines, and considers the typical the duration of unemployment and long term unemployment, duration of time on income support, overseas allowances, cost of living, and a comparison with policy makers' remuneration. It finds that the value of the allowance has eroded since it was first set and that unemployment for many is not a transitional situation, and concludes that an increase is warranted to bridge the gap to the poverty line.

Australian fiction.

Intellectual property -- History.

Lyricists -- Australia -- Biography.

Microorganisms -- History.

This is the delivery platform that provides online access to the full text of individual Edinburgh Univeristy Press journals. The Wellcome Library offers access to selected titles from this publisher.

Delivery platform that provides online access to the full text of individual University of Texas Press journals. The Wellcome Library offers access to selected titles from this publisher.

Edinburgh : William Whyte, 1829.

Edinburgh : printed by Morrison & Gibb, 1884.

London : J. Churchill, 1845.

Paris : J.-B. Baillière, 1829-1846.

Leipzig : Veit, 1888.

Leipzig : Veit, 1879.

Leipzig : S. Hirzel, 1892.

Jena : G. Fischer, 1897.

Berlin : Hirschwald, 1896.

Stuttgart : F. Enke, 1884.

Leipzig : Druck und Verlag von Breitkopf und Hartel, 1876.

Heidelberg : J.C.B. Mohr, 1858.

Leipzig : Duncker & Humblot, 1898.

Berlin : A. Hirschwald, 1879.

Argentorati : Sumtibus Amandi Koenig, 1786-1787.

Viennæ : Typis Christiani Friderici Wappler, 1788-92.

Berlin : Urban & Schwarzenberg, 1899.

[United Kingdom] : [Darcy Leigh], 2019.

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

[Indiana] : Integrated Research Services, Inc., 1986.

Daren Wang, Yi Yu, Alessandro Rinaldo.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 1917--1961.

Abstract:
The problem of univariate mean change point detection and localization based on a sequence of $n$ independent observations with piecewise constant means has been intensively studied for more than half century, and serves as a blueprint for change point problems in more complex settings. We provide a complete characterization of this classical problem in a general framework in which the upper bound $sigma ^{2}$ on the noise variance, the minimal spacing $Delta $ between two consecutive change points and the minimal magnitude $kappa $ of the changes, are allowed to vary with $n$. We first show that consistent localization of the change points is impossible in the low signal-to-noise ratio regime $frac{kappa sqrt{Delta }}{sigma }preceq sqrt{log (n)}$. In contrast, when $frac{kappa sqrt{Delta }}{sigma }$ diverges with $n$ at the rate of at least $sqrt{log (n)}$, we demonstrate that two computationally-efficient change point estimators, one based on the solution to an $ell _{0}$-penalized least squares problem and the other on the popular wild binary segmentation algorithm, are both consistent and achieve a localization rate of the order $frac{sigma ^{2}}{kappa ^{2}}log (n)$. We further show that such rate is minimax optimal, up to a $log (n)$ term.

Latent space models are effective tools for statistical modeling and visualization of network data. Due to their close connection to generalized linear models, it is also natural to incorporate covariate information in them. The current paper presents two universal fitting algorithms for networks with edge covariates: one based on nuclear norm penalization and the other based on projected gradient descent. Both algorithms are motivated by maximizing the likelihood function for an existing class of inner-product models, and we establish their statistical rates of convergence for these models. In addition, the theory informs us that both methods work simultaneously for a wide range of different latent space models that allow latent positions to affect edge formation in flexible ways, such as distance models. Furthermore, the effectiveness of the methods is demonstrated on a number of real world network data sets for different statistical tasks, including community detection with and without edge covariates, and network assisted learning.

The accuracy and complexity of kernel learning algorithms is determined by the set of kernels over which it is able to optimize. An ideal set of kernels should: admit a linear parameterization (tractability); be dense in the set of all kernels (accuracy); and every member should be universal so that the hypothesis space is infinite-dimensional (scalability). Currently, there is no class of kernel that meets all three criteria - e.g. Gaussians are not tractable or accurate; polynomials are not scalable. We propose a new class that meet all three criteria - the Tessellated Kernel (TK) class. Specifically, the TK class: admits a linear parameterization using positive matrices; is dense in all kernels; and every element in the class is universal. This implies that the use of TK kernels for learning the kernel can obviate the need for selecting candidate kernels in algorithms such as SimpleMKL and parameters such as the bandwidth. Numerical testing on soft margin Support Vector Machine (SVM) problems show that algorithms using TK kernels outperform other kernel learning algorithms and neural networks. Furthermore, our results show that when the ratio of the number of training data to features is high, the improvement of TK over MKL increases significantly.

Umberto Amato, Anestis Antoniadis, Italia De Feis.

Source: Statistics Surveys, Volume 14, 32--70.

Abstract:
We present and compare some nonparametric estimation methods (wavelet and/or spline-based) designed to recover a one-dimensional piecewise-smooth regression function in both a fixed equidistant or not equidistant design regression model and a random design model. Wavelet methods are known to be very competitive in terms of denoising and compression, due to the simultaneous localization property of a function in time and frequency. However, boundary assumptions, such as periodicity or symmetry, generate bias and artificial wiggles which degrade overall accuracy. Simple methods have been proposed in the literature for reducing the bias at the boundaries. We introduce new ones based on adaptive combinations of two estimators. The underlying idea is to combine a highly accurate method for non-regular functions, e.g., wavelets, with one well behaved at boundaries, e.g., Splines or Local Polynomial. We provide some asymptotic optimal results supporting our approach. All the methods can handle data with a random design. We also sketch some generalization to the multidimensional setting. To study the performance of the proposed approaches we have conducted an extensive set of simulations on synthetic data. An interesting regression analysis of two real data applications using these procedures unambiguously demonstrates their effectiveness.

Adityanand Guntuboyina, Donovan Lieu, Sabyasachi Chatterjee, Bodhisattva Sen.

Source: The Annals of Statistics, Volume 48, Number 1, 205--229.

Abstract:
We study trend filtering, a relatively recent method for univariate nonparametric regression. For a given integer $rgeq1$, the $r$th order trend filtering estimator is defined as the minimizer of the sum of squared errors when we constrain (or penalize) the sum of the absolute $r$th order discrete derivatives of the fitted function at the design points. For $r=1$, the estimator reduces to total variation regularization which has received much attention in the statistics and image processing literature. In this paper, we study the performance of the trend filtering estimator for every $rgeq1$, both in the constrained and penalized forms. Our main results show that in the strong sparsity setting when the underlying function is a (discrete) spline with few “knots,” the risk (under the global squared error loss) of the trend filtering estimator (with an appropriate choice of the tuning parameter) achieves the parametric $n^{-1}$-rate, up to a logarithmic (multiplicative) factor. Our results therefore provide support for the use of trend filtering, for every $rgeq1$, in the strong sparsity setting.

Many teens have taken on the role, but not every Robin was a "boy" wonder

The institution took decades to come to grips with the trauma of the killing of four students 50 years ago

Before COVID-19 hit, Hemliss Eloïse Konan had plans for how she'd spend her summer in Sudbury. After finishing her first year at Laurentian University, Konan planned to stay in residence, and get a job for the summer.

Speech by Mr Mugur Isărescu, Governor of the National Bank of Romania, Bucharest, 29 April 2020.