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.

Several states already offer a state-sanctioned LMS option to their schools, with some encouraging results in their efforts to cut costs and improve technical capabilities.

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.

An increasing number of schools are replacing snow days with remote learning, but some plan to stick with the snow day tradition for now.

Data suggest low-income communities are hit much harder than affluent ones, writes researcher Heather C. Hill.

Acellus Learning Accelerator, used by 6,000 schools nationwide, is under fire for lessons that parents and educators have decried as racist, sexist, and age-inappropriate.

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.

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 third installment on the growth in dual-language learning, one expert says broad access to programs is important, but that students need an early start to reap the benefits.

Linda M. Gojak and Sara Delano Moore, two of the co-authors of "Visible Learning For Mathematics: What Works Best to Optimize Student Learning", agreed to answer a few questions about the book.

When it comes to parenting students with learning differences, every family's experience is unique. And that reality has never been more true than it is now as millions of students are out of school due to the coronavirus pandemic.

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.

A new digital guide aims to identify students missing out on special education services and supports during distance learning.

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

To avoid the frustrations and mistakes of last spring, see our tips, checklists, best practices, and expert advice on how to make teaching and learning at home engaging, productive, and equitable.

Four educators share their experiences of blended learning. They suggest elements needed to make it work in remote teaching such as emphasizing relationship-building and minimizing the number of online tools.

These four evidence-based suggestions can help educators offset learning loss for young English learners, write Leslie M. Babinski, Steven J. Amendum, Steven E. Knotek, and Marta Sánchez.

The new SAS Certified Specialist: Statistics for Machine Learning credential is designed to help you showcase your expertise and commitment to staying ahead in the industry.

Earn this SAS certification to validate your skills and training in machine learning was published on SAS Users.

Artificial intelligence (AI) is everywhere. Machine learning (ML) and its associated inference abilities promise to revolutionize everything from driving your car to making your breakfast. Verification is never truly complete; it is over when you run...(read more)

Title: One Benefit of Online Learning: Better Sleep for Kids
Category: Health News
Created: 8/27/2021 12:00:00 AM
Last Editorial Review: 8/27/2021 12:00:00 AM

The pursuit of harnessing data for knowledge creation has been an enduring quest, with the advent of machine learning (ML) and artificial intelligence (AI) marking significant milestones in this journey. ML, a subset of AI, emerged as the practice of employing mathematical models to enable computers to learn and improve autonomously based on their experiences. In the pharmaceutical and biopharmaceutical sectors, a significant portion of manufacturing data remains untapped or insufficient for practical use. Recognizing the potential advantages of leveraging the available data for process design and optimization, manufacturers face the daunting challenge of data utilization. Diverse proprietary data formats and parallel data generation systems compound the complexity. The transition to Pharma 4.0 necessitates a paradigm shift in data capture, storage, and accessibility for manufacturing and process operations. This paper highlights the pivotal role of AI in converting process data into actionable knowledge to support critical functions throughout the whole product life cycle. Furthermore, it underscores the importance of maintaining compliance with data integrity guidelines, as mandated by regulatory bodies globally. Embracing AI-driven transformations is a crucial step toward shaping the future of the pharmaceutical industry, ensuring its competitiveness and resilience in an evolving landscape.

Massively parallel reporter assays (MPRAs) are powerful tools for quantifying the impacts of sequence variation on gene expression. Reading out molecular phenotypes with sequencing enables interrogating the impact of sequence variation beyond genome scale. Machine learning models integrate and codify information learned from MPRAs and enable generalization by predicting sequences outside the training data set. Models can provide a quantitative understanding of cis-regulatory codes controlling gene expression, enable variant stratification, and guide the design of synthetic regulatory elements for applications from synthetic biology to mRNA and gene therapy. This review focuses on cis-regulatory MPRAs, particularly those that interrogate cotranscriptional and post-transcriptional processes: alternative splicing, cleavage and polyadenylation, translation, and mRNA decay.

Two pioneers of artificial intelligence have won the Nobel Prize in physics for discoveries and inventions that formed the building blocks of machine learning.

Finished chips coming in from the foundry are subject to a battery of tests. For those destined for critical systems in cars, those tests are particularly extensive and can add 5 to 10 percent to the cost of a chip. But do you really need to do every single test?

Engineers at NXP have developed a machine-learning algorithm that learns the patterns of test results and figures out the subset of tests that are really needed and those that they could safely do without. The NXP engineers described the process at the IEEE International Test Conference in San Diego last week.

NXP makes a wide variety of chips with complex circuitry and advanced chip-making technology, including inverters for EV motors, audio chips for consumer electronics, and key-fob transponders to secure your car. These chips are tested with different signals at different voltages and at different temperatures in a test process called continue-on-fail. In that process, chips are tested in groups and are all subjected to the complete battery, even if some parts fail some of the tests along the way.

Chips were subject to between 41 and 164 tests, and the algorithm was able to recommend removing 42 to 74 percent of those tests.

“We have to ensure stringent quality requirements in the field, so we have to do a lot of testing,” says Mehul Shroff, an NXP Fellow who led the research. But with much of the actual production and packaging of chips outsourced to other companies, testing is one of the few knobs most chip companies can turn to control costs. “What we were trying to do here is come up with a way to reduce test cost in a way that was statistically rigorous and gave us good results without compromising field quality.”

A Test Recommender System

Shroff says the problem has certain similarities to the machine learning-based recommender systems used in e-commerce. “We took the concept from the retail world, where a data analyst can look at receipts and see what items people are buying together,” he says. “Instead of a transaction receipt, we have a unique part identifier and instead of the items that a consumer would purchase, we have a list of failing tests.”

The NXP algorithm then discovered which tests fail together. Of course, what’s at stake for whether a purchaser of bread will want to buy butter is quite different from whether a test of an automotive part at a particular temperature means other tests don’t need to be done. “We need to have 100 percent or near 100 percent certainty,” Shroff says. “We operate in a different space with respect to statistical rigor compared to the retail world, but it’s borrowing the same concept.”

As rigorous as the results are, Shroff says that they shouldn’t be relied upon on their own. You have to “make sure it makes sense from engineering perspective and that you can understand it in technical terms,” he says. “Only then, remove the test.”

Shroff and his colleagues analyzed data obtained from testing seven microcontrollers and applications processors built using advanced chipmaking processes. Depending on which chip was involved, they were subject to between 41 and 164 tests, and the algorithm was able to recommend removing 42 to 74 percent of those tests. Extending the analysis to data from other types of chips led to an even wider range of opportunities to trim testing.

The algorithm is a pilot project for now, and the NXP team is looking to expand it to a broader set of parts, reduce the computational overhead, and make it easier to use.

Bayer MSMS �Say TkU� Campaign

We recently launched a completely reimagined version of Machine Learning Crash Course.

AutMedAI, a new medlinkmachine learning/medlink (ML) model, enhances early detection of medlinkAutism Spectrum Disorder/medlink (ASD) with minimal medical and background data.

Distance learning solutions and online educational tools are rapidly growing in popularity and effectiveness with teachers, colleges and university-level programs worldwide. One recent survey estimated that...

Everybody knows that education play important role in every human life. Every people learn from education more things that we use in our general and professional life. But it's only a fundamental education that teaches us how to...

Ravi Kiran Magham highlights the transformative role of Machine Learning Feature Stores in modern data infrastructures.

About a year and a half ago, quantum control startup Quantum Machines and Nvidia announced a deep partnership that would bring together Nvidia’s DGX Quantum computing platform and Quantum Machine’s advanced quantum control hardware. We didn’t hear much about the results of this partnership for a while, but it’s now starting to bear fruit and […]

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Educational apps have become indispensable tools for children's learning. These apps cater to various age groups, offering tailored content that enhances cognitive skills and creativity. For tech enthusiasts in India, understanding the best educational apps by age group can help in

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