OBJECTIVE

To construct and internally validate prediction models to estimate the risk of long-term end-organ complications and mortality in patients with type 2 diabetes and obesity that can be used to inform treatment decisions for patients and practitioners who are considering metabolic surgery.

Chief technology officers are facing an unprecedented test of digital preparedness due to the coronavirus pandemic, struggling with shortfalls of available learning devices and huge Wi-Fi access challenges.

In this fifth installment on the growth in dual-language learning, the executive director of the BUENO Center for Multicultural Education at the University of Colorado, Boulder., says districts should focus on the what students and their families need, not what educators want.

In this fourth installment on the growth in dual-language learning, the director of dual-language education in Portland, Ore., says schools must have a clear reason for why they are offering dual-language instruction.

Chief technology officers are facing an unprecedented test of digital preparedness due to the coronavirus pandemic, struggling with shortfalls of available learning devices and huge Wi-Fi access challenges.

In this seventh installment on the growth in dual-language learning, two experts from Delaware explore how state education leaders can build capacity to support both students and educators.

We asked parents, students, and educators to share what their home learning environments look like as nearly all schools are shut down for extended periods because of the coronavirus pandemic.                          

Chief technology officers are facing an unprecedented test of digital preparedness due to the coronavirus pandemic, struggling with shortfalls of available learning devices and huge Wi-Fi access challenges.

We present a theoretical analysis framework for relational ensemble models. We show that ensembles of collective classifiers can improve predictions for graph data by reducing errors due to variance in both learning and inference. In addition, we propose a relational ensemble framework that combines a relational ensemble learning approach with a relational ensemble inference approach for collective classification. The proposed ensemble techniques are applicable for both single and multiple graph settings. Experiments on both synthetic and real-world data demonstrate the effectiveness of the proposed framework. Finally, our experimental results support the theoretical analysis and confirm that ensemble algorithms that explicitly focus on both learning and inference processes and aim at reducing errors associated with both, are the best performers.

When the data is distributed across multiple servers, lowering the communication cost between the servers (or workers) while solving the distributed learning problem is an important problem and is the focus of this paper. In particular, we propose a fast, and communication-efficient decentralized framework to solve the distributed machine learning (DML) problem. The proposed algorithm, Group Alternating Direction Method of Multipliers (GADMM) is based on the Alternating Direction Method of Multipliers (ADMM) framework. The key novelty in GADMM is that it solves the problem in a decentralized topology where at most half of the workers are competing for the limited communication resources at any given time. Moreover, each worker exchanges the locally trained model only with two neighboring workers, thereby training a global model with a lower amount of communication overhead in each exchange. We prove that GADMM converges to the optimal solution for convex loss functions, and numerically show that it converges faster and more communication-efficient than the state-of-the-art communication-efficient algorithms such as the Lazily Aggregated Gradient (LAG) and dual averaging, in linear and logistic regression tasks on synthetic and real datasets. Furthermore, we propose Dynamic GADMM (D-GADMM), a variant of GADMM, and prove its convergence under the time-varying network topology of the workers.

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.

Online multiple kernel learning (OMKL) has provided an attractive performance in nonlinear function learning tasks. Leveraging a random feature approximation, the major drawback of OMKL, known as the curse of dimensionality, has been recently alleviated. In this paper, we introduce a new research problem, termed (stream-based) active multiple kernel learning (AMKL), in which a learner is allowed to label selected data from an oracle according to a selection criterion. This is necessary in many real-world applications as acquiring true labels is costly or time-consuming. We prove that AMKL achieves an optimal sublinear regret, implying that the proposed selection criterion indeed avoids unuseful label-requests. Furthermore, we propose AMKL with an adaptive kernel selection (AMKL-AKS) in which irrelevant kernels can be excluded from a kernel dictionary 'on the fly'. This approach can improve the efficiency of active learning as well as the accuracy of a function approximation. Via numerical tests with various real datasets, it is demonstrated that AMKL-AKS yields a similar or better performance than the best-known OMKL, with a smaller number of labeled data.

Aryeh Kontorovich, Iosif Pinelis.

Source: The Annals of Statistics, Volume 47, Number 5, 2822--2854.

Abstract:
We provide an exact nonasymptotic lower bound on the minimax expected excess risk (EER) in the agnostic probably-approximately-correct (PAC) machine learning classification model and identify minimax learning algorithms as certain maximally symmetric and minimally randomized “voting” procedures. Based on this result, an exact asymptotic lower bound on the minimax EER is provided. This bound is of the simple form $c_{infty}/sqrt{ u}$ as $ u oinfty$, where $c_{infty}=0.16997dots$ is a universal constant, $ u=m/d$, $m$ is the size of the training sample and $d$ is the Vapnik–Chervonenkis dimension of the hypothesis class. It is shown that the differences between these asymptotic and nonasymptotic bounds, as well as the differences between these two bounds and the maximum EER of any learning algorithms that minimize the empirical risk, are asymptotically negligible, and all these differences are due to ties in the mentioned “voting” procedures. A few easy to compute nonasymptotic lower bounds on the minimax EER are also obtained, which are shown to be close to the exact asymptotic lower bound $c_{infty}/sqrt{ u}$ even for rather small values of the ratio $ u=m/d$. As an application of these results, we substantially improve existing lower bounds on the tail probability of the excess risk. Among the tools used are Bayes estimation and apparently new identities and inequalities for binomial distributions.

ACT and the American Federation of Teachers are partnering to provide free resources as educators increasingly switch to distance learning amid the COVID-19 pandemic.

The post ACT and Teachers’ Union Partner to Provide Remote Learning Resources Amid Pandemic appeared first on Market Brief.

Ioannis Ntzoufras, Claudia Tarantola, Monia Lupparelli.

Source: Bayesian Analysis, Volume 14, Number 3, 797--823.

Abstract:
We introduce a novel Bayesian approach for quantitative learning for graphical log-linear marginal models. These models belong to curved exponential families that are difficult to handle from a Bayesian perspective. The likelihood cannot be analytically expressed as a function of the marginal log-linear interactions, but only in terms of cell counts or probabilities. Posterior distributions cannot be directly obtained, and Markov Chain Monte Carlo (MCMC) methods are needed. Finally, a well-defined model requires parameter values that lead to compatible marginal probabilities. Hence, any MCMC should account for this important restriction. We construct a fully automatic and efficient MCMC strategy for quantitative learning for such models that handles these problems. While the prior is expressed in terms of the marginal log-linear interactions, we build an MCMC algorithm that employs a proposal on the probability parameter space. The corresponding proposal on the marginal log-linear interactions is obtained via parameter transformation. We exploit a conditional conjugate setup to build an efficient proposal on probability parameters. The proposed methodology is illustrated by a simulation study and a real dataset.

Awareness for surprising sensory events is shaped by prior belief inferred from past experience. Here, we combined hierarchical Bayesian modeling with fMRI on an associative learning task in 28 male human participants to characterize the effect of the prior belief of tactile events on connections mediating the outcome of perceptual decisions. Activity in anterior insular cortex (AIC), premotor cortex (PMd), and inferior parietal lobule (IPL) were modulated by prior belief on unexpected targets compared with expected targets. On expected targets, prior belief decreased the connection strength from AIC to IPL, whereas it increased the connection strength from AIC to PMd when targets were unexpected. Individual differences in the modulatory strength of prior belief on insular projections correlated with the precision that increases the influence of prediction errors on belief updating. These results suggest complementary effects of prior belief on insular-frontoparietal projections mediating the precision of prediction during probabilistic tactile learning.

SIGNIFICANCE STATEMENT In a probabilistic environment, the prior belief of sensory events can be inferred from past experiences. How this prior belief modulates effective brain connectivity for updating expectations for future decision-making remains unexplored. Combining hierarchical Bayesian modeling with fMRI, we show that during tactile associative learning, prior expectations modulate connections originating in the anterior insula cortex and targeting salience-related and attention-related frontoparietal areas (i.e., parietal and premotor cortex). These connections seem to be involved in updating evidence based on the precision of ascending inputs to guide future decision-making.

The post Remote Learning with Apps for Kids Classroom appeared first on SOLIDWORKS Education Blog.

One of the biggest pieces of unfinished business for education groups when it comes to federal help with the coronavirus is connectivity and online learning. But what's the state of play?

Chief technology officers are facing an unprecedented test of digital preparedness due to the coronavirus pandemic, struggling with shortfalls of available learning devices and huge Wi-Fi access challenges.

E-learning may help some schools keep instruction flowing but major gaps in access and resources mean not all schools are ready to offer virtual classes, and not all students are equipped to learn online.

The Federal Communications Commission is engaging Congress to expand funding for in-home connectivity and devices for teachers and students grappling with the coronavirus crisis.

The top technology official for Virginia's Fairfax County schools resigned after the district struggled to handle some major technical glitches in its e-learning platforms.

Northshore School District teachers, parents, and students practiced remote learning in advance of the district's closure for two weeks.

Schools and tech companies in the U.S. and abroad have experience deploying virtual learning should a coronavirus emergency arise.

K-12 teachers are faced with a question many likely thought they'd never have to ask: How often during the school day do my students need to see me and when?

It will take more than online tools to activate student learning during a school closure. Kate Ehrenfeld Gardoqui offers five sample assignments.

If class sizes aren't going to be addressed because of bottom lines, either because of a lack of teacher resources or school funding, then we are going to have to find a way to function better inside of these undesirable situations.

Chris Knutson, an 8th grade history teacher at Horning Middle School in Waukesha, Wis., shares how he incorporates learning conferences into his lessons.

Demand for bilingual, biliterate graduates is high. Experts in dual-language learning explain how schools can start programs and strengthen existing ones.

In this episode of the podcast, Tom chats with Tim Stuart about his new book, Personalized Learning in a PLC at Work: Student Agency Through the Four Critical Questions.

Two things are true as I sort through my reflections on transformative learning experiences: We need intensive, immersive opportunities for learning (such as a trip to Kenya) and we also need to build in mini-opportunities for transformative learning every day.

Penn State Campus Recreation is now offering a library of more than 50 workouts online through YouTube as well as live classes that are held every business day on the Penn State Campus Recreation Instagram. Group fitness instructor Alexis "Lexi" Neimeyer talked about her experience on the transition to virtual fitness classes.

The Education Department says federal law should not be used to prevent schools from offering online learning to all students, including those with disabilities.

Nearly 30 disability rights and education advocacy organizations have launched a new resource hub and online network designed to help special educators during the coronavirus crisis.

Penn State Harrisburg faculty share their experiences – the challenges, triumphs and innovations – as they are adapting lesson plans and teaching processes during this time of social distancing.

We talk a lot about blended learning opportunities in my district, asking ourselves whether we are offering the most beneficial learning opportunities for both staff and students. We're looking to provide quality online learning resources to students when they are outside of our classrooms, as well

Given the continuing challenge and uncertainty of the coronavirus pandemic and to protect the health of students, faculty and staff, Penn State has made the decision to extend virtual delivery of courses into the summer. Further, the University will adjust tuition for the summer sessions in light of the ongoing pandemic and the persistent fiscal strain it is causing across Pennsylvania and the country.

In order to ensure business continuity during these challenging times, the German enterprise software maker SAP is actively engaged with its workforce and customers to help navigate this challenging business environment.

As the stakes for winning server segment market share grow ever higher an increasing number of companies are seeking to grasp the latest Holy Grail of multi-chip coherence. The approach promises to better enable applications such as machine learning...(read more)

This week Sempra Energy subsidiary PXiSE Energy Solutions announced that Sempra-owned development company Infraestructura Energetica Nova (IEnova) would be using its software at the 110-MW Pima Solar facility located in Mexico to help manage the integration of renewable power to the electric grid.

This week global engineering company Emerson announced that it had formed a 3-year alliance with Vayu to combine Emerson’s Ovation automation platform with Vayu’s cloud-computing wind energy optimization technology. The new technology will optimize wind farms in the Americas, Caribbean and Europe.

In an interview with Natasa Meli, SACAP’s Head of Online Campus, she discusses how resistance to online learning has changed.

The surge in online learning in the People’s Republic of China during the coronavirus outbreak highlights the importance of infrastructure, platforms and the preparedness of teachers, students and parents.

Title: College Kids May Be Learning, Even When Checking Smartphones
Category: Health News
Created: 4/27/2018 12:00:00 AM
Last Editorial Review: 4/30/2018 12:00:00 AM

Background

Transient loss of consciousness (TLOC) is a common reason for presentation to primary/emergency care; over 90% are because of epilepsy, syncope, or psychogenic non-epileptic seizures (PNES). Misdiagnoses are common, and there are currently no validated decision rules to aid diagnosis and management. We seek to explore the utility of machine-learning techniques to develop a short diagnostic instrument by extracting features with optimal discriminatory values from responses to detailed questionnaires about TLOC manifestations and comorbidities (86 questions to patients, 31 to TLOC witnesses).