Londres : Paris, 1860.

Paris : Delahaye, 1873.

Paris : A. Delahaye, 1872.

Paris : O. Doin, 1887.

Paris : G. Steinheil, 1895.

Paris : Librarie des sciences médicales de Just Rouvier et E. Le Bouvier, 1836.

[Edinburgh] : [publisher not identified], [1890]

London : Swan Sonnenschein, 1898.

[Paris], [1807]

London : [Published not identified], 1871.

London : [Published not identified], 1872.

London : [Published not identified], 1873.

London : [Published not identified], 1874.

London : [Published not identified], 1875.

London : [Published not identified], 1876.

Since the day she stepped on campus, Satou Sabally's game has turned heads — and for good reason. She's had many memorable moments in a Duck uniform, including a standout performance against the USA Women in Nov. 2019, a monster game against Cal in Jan. 2020 and a career performance in Pullman in Jan. 2019.

Baylor has signed a transfer point guard for the third year in a row, and this one can play multiple seasons with the Lady Bears. Jaden Owens is transferring from UCLA after signing a national letter of intent with Baylor, which had graduate transfers at point guard each of the past two seasons. The Texas native just completed her freshman season with the Bruins and has three seasons of eligibility remaining.

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.

Assaf Rabinowicz, Saharon Rosset.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 272--301.

Abstract:
Common model selection criteria, such as $AIC$ and its variants, are based on in-sample prediction error estimators. However, in many applications involving predicting at interpolation and extrapolation points, in-sample error does not represent the relevant prediction error. In this paper new prediction error estimators, $tAI$ and $Loss(w_{t})$ are introduced. These estimators generalize previous error estimators, however are also applicable for assessing prediction error in cases involving interpolation and extrapolation. Based on these prediction error estimators, two model selection criteria with the same spirit as $AIC$ and Mallow’s $C_{p}$ are suggested. The advantages of our suggested methods are demonstrated in a simulation and a real data analysis of studies involving interpolation and extrapolation in linear mixed model and Gaussian process regression.

Aurélie Fischer, Dominique Picard.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 1648--1689.

Abstract:
In this paper, we consider the estimation of a change-point for possibly high-dimensional data in a Gaussian model, using a maximum likelihood method. We are interested in how dimension reduction can affect the performance of the method. We provide an estimator of the change-point that has a minimax rate of convergence, up to a logarithmic factor. The minimax rate is in fact composed of a fast rate —dimension-invariant— and a slow rate —increasing with the dimension. Moreover, it is proved that considering the case of sparse data, with a Sobolev regularity, there is a bound on the separation of the regimes above which there exists an optimal choice of dimension reduction, leading to the fast rate of estimation. We propose an adaptive dimension reduction procedure based on Lepski’s method and show that the resulting estimator attains the fast rate of convergence. Our results are then illustrated by a simulation study. In particular, practical strategies are suggested to perform dimension reduction.

Christophe A. N. Biscio, Nicolas Chenavier, Christian Hirsch, Anne Marie Svane.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 1024--1074.

Abstract:
We introduce tests for the goodness of fit of point patterns via methods from topological data analysis. More precisely, the persistent Betti numbers give rise to a bivariate functional summary statistic for observed point patterns that is asymptotically Gaussian in large observation windows. We analyze the power of tests derived from this statistic on simulated point patterns and compare its performance with global envelope tests. Finally, we apply the tests to a point pattern from an application context in neuroscience. As the main methodological contribution, we derive sufficient conditions for a functional central limit theorem on bounded persistent Betti numbers of point processes with exponential decay of correlations.

Stochastic gradient Langevin dynamics (SGLD) is a fundamental algorithm in stochastic optimization. Recent work by Zhang et al. (2017) presents an analysis for the hitting time of SGLD for the first and second order stationary points. The proof in Zhang et al. (2017) is a two-stage procedure through bounding the Cheeger's constant, which is rather complicated and leads to loose bounds. In this paper, using intuitions from stochastic differential equations, we provide a direct analysis for the hitting times of SGLD to the first and second order stationary points. Our analysis is straightforward. It only relies on basic linear algebra and probability theory tools. Our direct analysis also leads to tighter bounds comparing to Zhang et al. (2017) and shows the explicit dependence of the hitting time on different factors, including dimensionality, smoothness, noise strength, and step size effects. Under suitable conditions, we show that the hitting time of SGLD to first-order stationary points can be dimension-independent. Moreover, we apply our analysis to study several important online estimation problems in machine learning, including linear regression, matrix factorization, and online PCA.

Dani Gamerman.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 4, 686--705.

Abstract:
Point processes are one of the most commonly encountered observation processes in Spatial Statistics. Model-based inference for them depends on the likelihood function. In the most standard setting of Poisson processes, the likelihood depends on the intensity function, and can not be computed analytically. A number of approximating techniques have been proposed to handle this difficulty. In this paper, we review recent work on exact solutions that solve this problem without resorting to approximations. The presentation concentrates more heavily on discrete time but also considers continuous time. The solutions are based on model specifications that impose smoothness constraints on the intensity function. We also review approaches to include a regression component and different ways to accommodate it while accounting for additional heterogeneity. Applications are provided to illustrate the results. Finally, we discuss possible extensions to account for discontinuities and/or jumps in the intensity function.

Davide Gabrielli, Ida Germana Minelli.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 3, 558--585.

Abstract:
Stochastic monotonicity is a well-known partial order relation between probability measures defined on the same partially ordered set. Strassen theorem establishes equivalence between stochastic monotonicity and the existence of a coupling compatible with respect to the partial order. We consider the case of a countable set and introduce the class of finitely decomposable flows on a directed acyclic graph associated to the partial order. We show that a probability measure stochastically dominates another probability measure if and only if there exists a finitely decomposable flow having divergence given by the difference of the two measures. We illustrate the result with some examples.

Emilia Athayde, Assis Azevedo, Michelli Barros, Víctor Leiva.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 2, 301--328.

Abstract:
The Birnbaum–Saunders (BS) distribution has been largely studied and applied. A random variable with BS distribution is a transformation of another random variable with standard normal distribution. Generalized BS distributions are obtained when the normally distributed random variable is replaced by another symmetrically distributed random variable. This allows us to obtain a wide class of positively skewed models with lighter and heavier tails than the BS model. Its failure rate admits several shapes, including the unimodal case, with its change-point being able to be used for different purposes. For example, to establish the reduction in a dose, and then in the cost of the medical treatment. We analyze the failure rates of generalized BS distributions obtained by the logistic, normal and Student-t distributions, considering their shape and change-point, estimating them, evaluating their robustness, assessing their performance by simulations, and applying the results to real data from different areas.

The paper focuses on a novel approach for false-positive reduction (FPR) of nodule candidates in Computer-aided detection (CADe) system after suspicious lesions proposing stage. Unlike common decisions in medical image analysis, the proposed approach considers input data not as 2d or 3d image, but as a point cloud and uses deep learning models for point clouds. We found out that models for point clouds require less memory and are faster on both training and inference than traditional CNN 3D, achieves better performance and does not impose restrictions on the size of the input image, thereby the size of the nodule candidate. We propose an algorithm for transforming 3d CT scan data to point cloud. In some cases, the volume of the nodule candidate can be much smaller than the surrounding context, for example, in the case of subpleural localization of the nodule. Therefore, we developed an algorithm for sampling points from a point cloud constructed from a 3D image of the candidate region. The algorithm guarantees to capture both context and candidate information as part of the point cloud of the nodule candidate. An experiment with creating a dataset from an open LIDC-IDRI database for a feature of the FPR task was accurately designed, set up and described in detail. The data augmentation technique was applied to avoid overfitting and as an upsampling method. Experiments are conducted with PointNet, PointNet++ and DGCNN. We show that the proposed approach outperforms baseline CNN 3D models and demonstrates 85.98 FROC versus 77.26 FROC for baseline models.

Changliang Zou, Guanghui Wang, Runze Li.

Source: The Annals of Statistics, Volume 48, Number 1, 413--439.

Abstract:
In multiple change-point analysis, one of the major challenges is to estimate the number of change-points. Most existing approaches attempt to minimize a Schwarz information criterion which balances a term quantifying model fit with a penalization term accounting for model complexity that increases with the number of change-points and limits overfitting. However, different penalization terms are required to adapt to different contexts of multiple change-point problems and the optimal penalization magnitude usually varies from the model and error distribution. We propose a data-driven selection criterion that is applicable to most kinds of popular change-point detection methods, including binary segmentation and optimal partitioning algorithms. The key idea is to select the number of change-points that minimizes the squared prediction error, which measures the fit of a specified model for a new sample. We develop a cross-validation estimation scheme based on an order-preserved sample-splitting strategy, and establish its asymptotic selection consistency under some mild conditions. Effectiveness of the proposed selection criterion is demonstrated on a variety of numerical experiments and real-data examples.

Bo Zhou, Ramon van den Akker, Bas J. M. Werker.

Source: The Annals of Statistics, Volume 47, Number 5, 2601--2638.

Abstract:
We propose a new class of unit root tests that exploits invariance properties in the Locally Asymptotically Brownian Functional limit experiment associated to the unit root model. The invariance structures naturally suggest tests that are based on the ranks of the increments of the observations, their average and an assumed reference density for the innovations. The tests are semiparametric in the sense that they are valid, that is, have the correct (asymptotic) size, irrespective of the true innovation density. For a correctly specified reference density, our test is point-optimal and nearly efficient. For arbitrary reference densities, we establish a Chernoff–Savage-type result, that is, our test performs as well as commonly used tests under Gaussian innovations but has improved power under other, for example, fat-tailed or skewed, innovation distributions. To avoid nonparametric estimation, we propose a simplified version of our test that exhibits the same asymptotic properties, except for the Chernoff–Savage result that we are only able to demonstrate by means of simulations.

Where a service connects to the network. In a service oriented architecture, any single network interaction involves two endpoints: one to provide a service, and the other to consume it. In web services, an endpoint is specified by a URI.

Medha Uppala, Mark S. Handcock.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 339--356.

Abstract:
This paper focuses on spatial and temporal modeling of point processes on linear networks. Point processes on linear networks can simply be defined as point events occurring on or near line segment network structures embedded in a certain space. A separable modeling framework is introduced that posits separate formation and dissolution models of point processes on linear networks over time. While the model was inspired by spider web building activity in brick mortar lines, the focus is on modeling wildfire ignition origins near road networks over a span of 14 years. As most wildfires in California have human-related origins, modeling the origin locations with respect to the road network provides insight into how human, vehicular and structural densities affect ignition occurrence. Model results show that roads that traverse different types of regions such as residential, interface and wildland regions have higher ignition intensities compared to roads that only exist in each of the mentioned region types.

Zhenguo Gao, Pang Du, Ran Jin, John L. Robertson.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 143--159.

Abstract:
Liver procurement experiments with surface-temperature monitoring motivated Gao et al. ( J. Amer. Statist. Assoc. 114 (2019) 773–781) to develop a variance change-point detection method under a smoothly-changing mean trend. However, the spotwise change points yielded from their method do not offer immediate information to surgeons since an organ is often transplanted as a whole or in part. We develop a new practical method that can analyze a defined portion of the organ surface at a time. It also provides a novel addition to the developing field of functional data monitoring. Furthermore, numerical challenge emerges for simultaneously modeling the variance functions of 2D locations and the mean function of location and time. The respective sample sizes in the scales of 10,000 and 1,000,000 for modeling these functions make standard spline estimation too costly to be useful. We introduce a multistage subsampling strategy with steps educated by quickly-computable preliminary statistical measures. Extensive simulations show that the new method can efficiently reduce the computational cost and provide reasonable parameter estimates. Application of the new method to our liver surface temperature monitoring data shows its effectiveness in providing accurate status change information for a selected portion of the organ in the experiment.

Sarat B. Moka, Dirk P. Kroese.

Source: Bernoulli, Volume 26, Number 3, 2082--2104.

Abstract:
We present a perfect sampling algorithm for Gibbs point processes, based on the partial rejection sampling of Guo, Jerrum and Liu (In STOC’17 – Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing (2017) 342–355 ACM). Our particular focus is on pairwise interaction processes, penetrable spheres mixture models and area-interaction processes, with a finite interaction range. For an interaction range $2r$ of the target process, the proposed algorithm can generate a perfect sample with $O(log(1/r))$ expected running time complexity, provided that the intensity of the points is not too high and $Theta(1/r^{d})$ parallel processor units are available.

Ilaria Bianchini, Alessandra Guglielmi, Fernando A. Quintana.

Source: Bayesian Analysis, Volume 15, Number 1, 187--214.

Abstract:
We consider mixture models where location parameters are a priori encouraged to be well separated. We explore a class of determinantal point process (DPP) mixture models, which provide the desired notion of separation or repulsion. Instead of using the rather restrictive case where analytical results are partially available, we adopt a spectral representation from which approximations to the DPP density functions can be readily computed. For the sake of concreteness the presentation focuses on a power exponential spectral density, but the proposed approach is in fact quite general. We later extend our model to incorporate covariate information in the likelihood and also in the assignment to mixture components, yielding a trade-off between repulsiveness of locations in the mixtures and attraction among subjects with similar covariates. We develop full Bayesian inference, and explore model properties and posterior behavior using several simulation scenarios and data illustrations. Supplementary materials for this article are available online (Bianchini et al., 2019).

Stefano Peluso, Siddhartha Chib, Antonietta Mira.

Source: Bayesian Analysis, Volume 14, Number 3, 727--751.

Abstract:
We develop a general Bayesian semiparametric change-point model in which separate groups of structural parameters (for example, location and dispersion parameters) can each follow a separate multiple change-point process, driven by time-dependent transition matrices among the latent regimes. The distribution of the observations within regimes is unknown and given by a Dirichlet process mixture prior. The properties of the proposed model are studied theoretically through the analysis of inter-arrival times and of the number of change-points in a given time interval. The prior-posterior analysis by Markov chain Monte Carlo techniques is developed on a forward-backward algorithm for sampling the various regime indicators. Analysis with simulated data under various scenarios and an application to short-term interest rates are used to show the generality and usefulness of the proposed model.

Christophe Leterrier
Feb 28, 2018; 38:2135-2145
Viewpoints

Customer experience as a boardroom topic is more relevant than ever. Enterprises are investing significant digital transformation budgets and commissioning large projects to elevate CX. Yet more than 70 percent of digital transformation projects fail to move the needle at scale. One topic that has a substantial impact on CX is the transformation of customer service operations using digital tools.

Harjo, a member of the Muskogee Creek Nation, says the appointment "honors the place of Native people in this country, the place of Native people’s poetry"

The Bank for International Settlements (BIS) today announced two key appointments to the BIS Innovation Hub, a new initiative designed to support central bank collaboration on new financial technology. (Press release, 19 February 2020)

Green Lake’s mayor says he wants to see further measures introduced to prevent people who aren't from Saskatchewan from travelling through his community as the region deals with the COVID-19 pandemic. 

Vancouver police are investigating an incident where a man attempted to lure a child to his car in the city’s West Point Grey neighbourhood on Wednesday.

The post SOLIDWORKS Electrical Formula SAE Tutorial: Creating Electrical Connection Points inside a SOLIDWORKS part appeared first on SOLIDWORKS Education Blog.

One of the main mechanisms of azole resistance of Aspergillus fumigatus is thought to be a reduction in the drug’s affinity for the target molecule, Cyp51A, due to its amino acid mutation(s). It is known that the azole resistance pattern is closely related to the mutation site(s) of the molecule. In this study, we tried to develop a simple and rapid detection method for cyp51A mutations using the endonuclease Surveyor nuclease. The Surveyor nuclease assay was verified using several azole-resistant strains of A. fumigatus that possess point mutations in Cyp51A. For validation of the Surveyor nuclease assay, blind tests were conducted using 48 strains of A. fumigatus (17 azole-resistant and 31 azole-susceptible strains). The Surveyor nuclease assay could rapidly detect cyp51A mutations with one primer set. Also, all the tested strains harboring different cyp51A single point mutations could be clearly distinguished from the wild type. The Surveyor nuclease assay is a simple method that can detect cyp51A mutations rapidly.

CNA Staff, May 8, 2020 / 06:00 am (CNA).- EWTN Global Catholic Network, the world’s largest Catholic media organization, announced Friday that Andrew Walther – an experienced Catholic journalist, media executive, and advocate for persecuted Christians – has been named president and chief operating officer of EWTN News, Inc. The appointment is effective June 1.

Walther began his Catholic media career as a journalist writing for the National Catholic Register two decades ago. Most recently he has served as vice president for communications and strategic planning at the Knights of Columbus.

In his role as president of the news division, Walther will oversee EWTN’s vast news media platforms, which create content in English, Spanish, German, French, Portuguese and Italian. Its holdings include Catholic News Agency, the National Catholic Register, the ACI Group, ChurchPop and EWTN’s lineup of television and radio news programming.

“As well as being an accomplished Catholic journalist and media executive, Andrew Walther brings to this role unique expertise in the global Church,” said EWTN Chairman and CEO Michael P. Warsaw.

“His leadership experience with a global Catholic communications and media operation – and his previous work with the National Catholic Register and EWTN News Nightly – gives him the added advantage of already knowing the Catholic media world and many of the people within the EWTN family. We look forward to having him lead and strengthen our news division,” Warsaw said.

Since 2005, Walther has worked in senior roles at the Knights of Columbus. During his tenure at the Knights, Walther helped launch the organization’s modern communications department, overseeing work with Catholic and secular media outlets, the launch of social media channels and video production, and the organization’s global media work, especially in Europe and the Middle East. He was also heavily involved in the organization’s charitable work and disaster relief initiatives.

Walther also organized and led the Knights’ work on behalf of persecuted Christians in the Middle East, traveling to Iraq several times and successfully leading a public effort to have ISIS’ campaign of persecution declared  a genocide by Secretary of State John Kerry.

His advocacy for persecuted Christians and other religious minorities in the Middle East helped shape policy in both the Obama and Trump administrations, and he also helped play a role in forging a bipartisan legislative consensus on behalf of persecuted Christians and other victims of ISIS in the Middle East. Walther’s efforts included working with other governments and the UN as well as with Catholic, Orthodox, Protestant, Jewish and Muslim leaders to end violence and persecution and bring relief to persecuted Christian communities.

“Andrew Walther has been a good friend and a trusted colleague for many years,” said Archbishop José H. Gomez of Los Angeles, who is president of the U.S bishop’s conference and a longtime member of EWTN’s board of governors.

“Andrew is one of the Church’s finest strategic thinkers and a highly respected advocate for international religious liberty. All of this will serve him well as head of the world’s largest Catholic news organization. I wish him great success.”

While working closely over the years with many bishops, dioceses and Catholic organizations in North America, Walther also worked closely with the Vatican on several projects under both Pope Benedict XVI and Pope Francis.

“Mother Angelica created a network dedicated to ‘the advancement of truth’ and Catholic news is a vital part of this mission,” Walther said Friday.

“I look forward to working with the talented and dedicated team of journalists at EWTN News to provide news from a Catholic perspective and to highlight important stories that might otherwise be overlooked.”

In addition to his roles in media and religious freedom advocacy, Walther also oversaw the Knights’ polling and book publishing operations, which included several New York Times bestsellers. Together with his wife, Maureen, he co-authored “The Knights of Columbus: An Illustrated History,” a book released this year.

CNA Staff, May 8, 2020 / 06:00 am (CNA).- EWTN Global Catholic Network, the world’s largest Catholic media organization, announced Friday that Andrew Walther – an experienced Catholic journalist, media executive, and advocate for persecuted Christians – has been named president and chief operating officer of EWTN News, Inc. The appointment is effective June 1.

Walther began his Catholic media career as a journalist writing for the National Catholic Register two decades ago. Most recently he has served as vice president for communications and strategic planning at the Knights of Columbus.

In his role as president of the news division, Walther will oversee EWTN’s vast news media platforms, which create content in English, Spanish, German, French, Portuguese and Italian. Its holdings include Catholic News Agency, the National Catholic Register, the ACI Group, ChurchPop and EWTN’s lineup of television and radio news programming.

“As well as being an accomplished Catholic journalist and media executive, Andrew Walther brings to this role unique expertise in the global Church,” said EWTN Chairman and CEO Michael P. Warsaw.

“His leadership experience with a global Catholic communications and media operation – and his previous work with the National Catholic Register and EWTN News Nightly – gives him the added advantage of already knowing the Catholic media world and many of the people within the EWTN family. We look forward to having him lead and strengthen our news division,” Warsaw said.

Since 2005, Walther has worked in senior roles at the Knights of Columbus. During his tenure at the Knights, Walther helped launch the organization’s modern communications department, overseeing work with Catholic and secular media outlets, the launch of social media channels and video production, and the organization’s global media work, especially in Europe and the Middle East. He was also heavily involved in the organization’s charitable work and disaster relief initiatives.

Walther also organized and led the Knights’ work on behalf of persecuted Christians in the Middle East, traveling to Iraq several times and successfully leading a public effort to have ISIS’ campaign of persecution declared  a genocide by Secretary of State John Kerry.

His advocacy for persecuted Christians and other religious minorities in the Middle East helped shape policy in both the Obama and Trump administrations, and he also helped play a role in forging a bipartisan legislative consensus on behalf of persecuted Christians and other victims of ISIS in the Middle East. Walther’s efforts included working with other governments and the UN as well as with Catholic, Orthodox, Protestant, Jewish and Muslim leaders to end violence and persecution and bring relief to persecuted Christian communities.

“Andrew Walther has been a good friend and a trusted colleague for many years,” said Archbishop José H. Gomez of Los Angeles, who is president of the U.S bishop’s conference and a longtime member of EWTN’s board of governors.

“Andrew is one of the Church’s finest strategic thinkers and a highly respected advocate for international religious liberty. All of this will serve him well as head of the world’s largest Catholic news organization. I wish him great success.”

While working closely over the years with many bishops, dioceses and Catholic organizations in North America, Walther also worked closely with the Vatican on several projects under both Pope Benedict XVI and Pope Francis.

“Mother Angelica created a network dedicated to ‘the advancement of truth’ and Catholic news is a vital part of this mission,” Walther said Friday.

“I look forward to working with the talented and dedicated team of journalists at EWTN News to provide news from a Catholic perspective and to highlight important stories that might otherwise be overlooked.”

In addition to his roles in media and religious freedom advocacy, Walther also oversaw the Knights’ polling and book publishing operations, which included several New York Times bestsellers. Together with his wife, Maureen, he co-authored “The Knights of Columbus: An Illustrated History,” a book released this year.