In an era of stepped-up immigration enforcement, speakers at this event present their research on the impact of enforcement policies on children from immigrant families and U.S. public schools. 

This MPI webinar brought together public health and migration experts to analyze the impact that COVID-19 preventative measures will have on vulnerable immigrants and refugees in Latin America, with a particular look at Colombia as a case study. Speakers also discussed how policymakers and international organizations can include migrant populations in their emergency response plans.

OBJECTIVE

To evaluate whether high glucose levels in the normoglycemic range and higher have a causal genetic effect on risk of retinopathy, neuropathy, nephropathy, chronic kidney disease (CKD), peripheral arterial disease (PAD), and myocardial infarction (MI; positive control) in the general population.

This MPI webinar brought together public health and migration experts to analyze the impact that COVID-19 preventative measures will have on vulnerable immigrants and refugees in Colombia and Latin America. Speakers also discussed how policymakers and international organizations can include migrant populations in their emergency response plans.

According to leaked drafts, the Trump administration is considering a rule that could have sweeping effects on both legal immigration to the United States and the use of public benefits by legal immigrants and their families. This report examines the potential scale of the expected rule’s impact, including at national and state levels and among children, as well as Hispanic and Asian American/Pacific Islander immigrants.

This webinar highlights findings from an MPI report examining the potential impacts of expected changes to the public charge rule by the Trump administration. Leaked draft versions suggest the rule could sharply expand the number of legally present noncitizens facing difficulty getting a green card or extending a visa as a result of their family's use of public benefits. The rule likely would discourage millions from accessing health, nutrition, and social services for which they or their U.S.-citizen dependents are eligible.

While the Trump administration public-charge rule is likely to vastly reshape legal immigration based on its test to assess if a person might ever use public benefits in the future, the universe of noncitizens who could be denied a green card based on current benefits use is quite small. That's because very few benefit programs are open to noncitizens who do not hold a green card. This commentary offers estimates of who might be affected.

Edward A. Chow
Jul 1, 2012; 30:130-133
Diabetes Advocacy

The coronavirus pandemic put India's population of 1.3 billion into an extreme and sudden lockdown. Social entrepreneur Gayathri Vasudevan explains how the situation is impacting the country's migrant workers, who are stuck far from home with limited access to food and shelter, and calls for an overhaul of India's social infrastructure in order to get people the essentials they need right now. (This virtual conversation is part of the TED Connects series, hosted by current affairs curator Whitney Pennington Rodgers. Recorded April 9, 2020)

Research findings on the implementation of a new teacher candidate performance assessment in Massachusetts inform the development of additional supports for educator preparation programs.

Massachusetts ranks high in laying the groundwork for moving through its educational system, but officials are keenly aware that children in poverty, those with special needs, and English-learners can fall short.

A Smarter Balanced testing vendor has released completion rates in three states that had serious challenges giving the common-core aligned exam.

Policy Analysis for California Education has been a premier ed policy organization for three decades. As Daisy Gonzales writes, it is in the forefront of shaping current reforms and interpreting them for audiences such as 'On California.'

Five of the 12 schools in the Pac-12 expect to reopen their campuses this fall, a key step to the return of college sports. The football season begins Aug. 29 with a slate of games that include three Pac-12 schools. Both Arizona schools, both Washington schools and Oregon anticipate holding in-person classes in the fall, but that leaves seven others still mulling whether to follow suit or continue holding online classes.

Download the Pac-12 Now app to watch an epic, triple-overtime 2003 battle between USC and California this Saturday at 1:30 p.m. PT/ 2:30 p.m. MT. The game will be available to all fans.

"The objective of the audit was to assess whether the award of funding under the RJIP program was informed by appropriate departmental advice and that processes complied with the grants administration framework."--Page 8.

This evaluation report identifies experiences, motivations, supporting mechanisms, barriers and impacts of fee-for-service commercial activities undertaken by Indigenous Land and Water Management (ILWM) organisations. The report draws on a literature review, interviews with ILWM organisations, administrative data, in-depth case studies, and online survey data.

This paper sets out three challenges to the creation of a future for Indo-Pacific states and peoples consistent with the visions of a ‘free and open Indo-Pacific’ (FOIP) expressed by Japan, India, the US and Australia, and now by the ASEAN outlook on the Indo-Pacific. It also describes a path for states to operate in an environment of coercive Chinese state power that seeks to influence how states relate and how they operate within their domestic boundaries.

The Regional Recycling Transport Assistance Package provides funding to support resource recovery and recycling in regional Queensland, helping fund the costs of transporting recyclable material from regional Queensland to facilities where it can be recovered or processed and turned into new products. Details regarding eligible applicants, projects and costs are provided in these guidelines.

"The HIV resilience framework will be of relevance to a wide range of people involved at all levels of the HIV response. We hope it provides encouragement for government decision makers and policy developers to pursue consistent and enabling policy and legislative environments and work cooperatively between jurisdictions and across departments to achieve best practice. Our consultation suggests that consistent legislation with the goal of supporting strong health outcomes, making disclosure safe, and eliminating stigmatising laws is crucial to creating environments in which people with HIV feel safe to access testing, treatment, and care. The HIV resilience framework also contains valuable insights for those who have responsibility for HIV health program management and service delivery, providing guidance to help ensure that programs, services, interventions or strategies supporting people living with HIV are developed and delivered in a way that is mindful of the interlinked twin goals of building personal resilience and eliminating stigma and discrimination." --President's message (page 6).

Virago Press -- History.

Pokémon Go (Game) -- Social aspects.

London : Macmillan, 1893.

Springfield, Virginia : National Technical Information Service, 1973.

[Manchester] : School of Nursing Studies, University of Manchester, [1995?]

Athletic forward Satou Sabally is preparing to take the leap to the WNBA level following three productive seasons at Oregon. As a junior, she averaged 16.2 points and 6.9 rebounds per game while helping the Ducks sweep the Pac-12 regular season and tournament titles. At 6-foot-4, she also drained 45 3-pointers for Oregon in 2019-20 while notching a career-best average of 2.3 assists per game.

Pac-12 Networks' Ashley Adamson speaks with former Arizona State women's basketball player Michelle Tom, who is now a doctor treating COVID-19 patients in Winslow, Arizona.

Pac-12 Networks' Yogi Roth and Ashley Adamson talk with Hall of Fame player and Pac-12 Networks talent Bill Walton during Thursday's Pac-12 Perspective podcast.

The Pac-12 Student-Athlete Advisory Committee voted to award the Oregon State women’s basketball team with the Pac-12 Sportsmanship Award for the 2019-20 season, honoring their character and sportsmanship before a rivalry game against Oregon in Jan. 2020 -- the day Kobe Bryant, his daughter, Gigi, and seven others passed away in a helicopter crash in Southern California. In the above video, Aleah Goodman and Madison Washington share how the teams came together as one in a circle of prayer before the game.

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.

Pac-12 student-athletes give shout-outs to their moms ahead of Mother's Day on May 10th, 2020 including UCLA's Michaela Onyenwere, Oregon's Sabrina Ionescu and Satou Sabally, Arizona's Aari McDonald, Cate Reese, and Lacie Hull, Stanford's Kiana Williams, USC's Endyia Rogers, and Aliyah Jeune, and Utah's Brynna Maxwell.

Mario Teixeira Parente, Jonas Wallin, Barbara Wohlmuth.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 917--943.

Abstract:
In this article, we consider scenarios in which traditional estimates for the active subspace method based on probabilistic Poincaré inequalities are not valid due to unbounded Poincaré constants. Consequently, we propose a framework that allows to derive generalized estimates in the sense that it enables to control the trade-off between the size of the Poincaré constant and a weaker order of the final error bound. In particular, we investigate independently exponentially distributed random variables in dimension two or larger and give explicit expressions for corresponding Poincaré constants showing their dependence on the dimension of the problem. Finally, we suggest possibilities for future work that aim for extending the class of distributions applicable to the active subspace method as we regard this as an opportunity to enlarge its usability.

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.

Sufficient dimension reduction (SDR) is a very useful concept for exploratory analysis and data visualization in regression, especially when the number of covariates is large. Many SDR methods have been proposed for regression with a continuous response, where the central subspace (CS) is the target of estimation. Various conditions, such as the linearity condition and the constant covariance condition, are imposed so that these methods can estimate at least a portion of the CS. In this paper we study SDR for regression and discriminant analysis with categorical response. Motivated by the exploratory analysis and data visualization aspects of SDR, we propose a new geometric framework to reformulate the SDR problem in terms of manifold optimization and introduce a new concept called Maximum Separation Subspace (MASES). The MASES naturally preserves the “sufficiency” in SDR without imposing additional conditions on the predictor distribution, and directly inspires a semi-parametric estimator. Numerical studies show MASES exhibits superior performance as compared with competing SDR methods in specific settings.

Parametrised state space models in the form of recurrent networks are often used in machine learning to learn from data streams exhibiting temporal dependencies. To break the black box nature of such models it is important to understand the dynamical features of the input-driving time series that are formed in the state space. We propose a framework for rigorous analysis of such state representations in vanishing memory state space models such as echo state networks (ESN). In particular, we consider the state space a temporal feature space and the readout mapping from the state space a kernel machine operating in that feature space. We show that: (1) The usual ESN strategy of randomly generating input-to-state, as well as state coupling leads to shallow memory time series representations, corresponding to cross-correlation operator with fast exponentially decaying coefficients; (2) Imposing symmetry on dynamic coupling yields a constrained dynamic kernel matching the input time series with straightforward exponentially decaying motifs or exponentially decaying motifs of the highest frequency; (3) Simple ring (cycle) high-dimensional reservoir topology specified only through two free parameters can implement deep memory dynamic kernels with a rich variety of matching motifs. We quantify richness of feature representations imposed by dynamic kernels and demonstrate that for dynamic kernel associated with cycle reservoir topology, the kernel richness undergoes a phase transition close to the edge of stability.

pyts is an open-source Python package for time series classification. This versatile toolbox provides implementations of many algorithms published in the literature, preprocessing functionalities, and data set loading utilities. pyts relies on the standard scientific Python packages numpy, scipy, scikit-learn, joblib, and numba, and is distributed under the BSD-3-Clause license. Documentation contains installation instructions, a detailed user guide, a full API description, and concrete self-contained examples.

Co-training is a well-known semi-supervised learning approach which trains classifiers on two or more different views and exchanges pseudo labels of unlabeled instances in an iterative way. During the co-training process, pseudo labels of unlabeled instances are very likely to be false especially in the initial training, while the standard co-training algorithm adopts a 'draw without replacement' strategy and does not remove these wrongly labeled instances from training stages. Besides, most of the traditional co-training approaches are implemented for two-view cases, and their extensions in multi-view scenarios are not intuitive. These issues not only degenerate their performance as well as available application range but also hamper their fundamental theory. Moreover, there is no optimization model to explain the objective a co-training process manages to optimize. To address these issues, in this study we design a unified self-paced multi-view co-training (SPamCo) framework which draws unlabeled instances with replacement. Two specified co-regularization terms are formulated to develop different strategies for selecting pseudo-labeled instances during training. Both forms share the same optimization strategy which is consistent with the iteration process in co-training and can be naturally extended to multi-view scenarios. A distributed optimization strategy is also introduced to train the classifier of each view in parallel to further improve the efficiency of the algorithm. Furthermore, the SPamCo algorithm is proved to be PAC learnable, supporting its theoretical soundness. Experiments conducted on synthetic, text categorization, person re-identification, image recognition and object detection data sets substantiate the superiority of the proposed method.

We consider the problem of clustering and completing a set of tensors with missing data that are drawn from a union of low-rank tensor spaces. In the clustering problem, given a partially sampled tensor data that is composed of a number of subtensors, each chosen from one of a certain number of unknown tensor spaces, we need to group the subtensors that belong to the same tensor space. We provide a geometrical analysis on the sampling pattern and subsequently derive the sampling rate that guarantees the correct clustering under some assumptions with high probability. Moreover, we investigate the fundamental conditions for finite/unique completability for the union of tensor spaces completion problem. Both deterministic and probabilistic conditions on the sampling pattern to ensure finite/unique completability are obtained. For both the clustering and completion problems, our tensor analysis provides significantly better bound than the bound given by the matrix analysis applied to any unfolding of the tensor data.

Stanislav Nagy, Carsten Schütt, Elisabeth M. Werner.

Source: Statistics Surveys, Volume 13, 52--118.

Abstract:
Little known relations of the renown concept of the halfspace depth for multivariate data with notions from convex and affine geometry are discussed. Maximum halfspace depth may be regarded as a measure of symmetry for random vectors. As such, the maximum depth stands as a generalization of a measure of symmetry for convex sets, well studied in geometry. Under a mild assumption, the upper level sets of the halfspace depth coincide with the convex floating bodies of measures used in the definition of the affine surface area for convex bodies in Euclidean spaces. These connections enable us to partially resolve some persistent open problems regarding theoretical properties of the depth.