What do innovative learning environments around the world look like? How might they be led and evaluated? What policy strategies stimulate and support them? For the past decade the OECD’s Centre for Education Research and Innovation (CERI) has addressed these and similar questions in an international study called Innovative Learning Environments.

Single-wavelength X-ray anomalous diffraction (SAD) is a frequently employed technique to solve the phase problem in X-ray crystallography. The precision and accuracy of recovered anomalous differences are crucial for determining the correct phases. Continuous rotation (CR) and inverse-beam geometry (IBG) anomalous data collection methods have been performed on tetragonal lysozyme and monoclinic survivin crystals and analysis carried out of how correlated the pairs of Friedel's reflections are after scaling. A multivariate Bayesian model for estimating anomalous differences was tested, which takes into account the correlation between pairs of intensity observations and incorporates the a priori knowledge about the positivity of intensity. The CR and IBG data collection methods resulted in positive correlation between I(+) and I(−) observations, indicating that the anomalous difference dominates between these observations, rather than different levels of radiation damage. An alternative pairing method based on near simultaneously observed Bijvoet's pairs displayed lower correlation and it was unsuccessful for recovering useful anomalous differences when using the multivariate Bayesian model. In contrast, multivariate Bayesian treatment of Friedel's pairs improved the initial phasing of the two tested crystal systems and the two data collection methods.

School's nearly back in session, but the seven young lions at the Smithsonian's National Zoo have been working hard through the summer months!

The post Lion cub summer school: Instead of learning their ABCs, the National Zoo’s lion cubs are learning behaviors that will help animal care staff evaluate their health. appeared first on Smithsonian Insider.

Each year, tens of millions of Americans, young and old, choose to learn about science in informal ways -- by visiting museums and aquariums, attending after-school programs, pursuing personal hobbies, and watching TV documentaries, for example.

A shift is needed in how science, technology, engineering, and mathematics (STEM) subjects are taught to students in grades K-12 who are learning English, says a new report from the National Academies of Sciences, Engineering, and Medicine.

Scientific research that involves nonscientists contributing to research processes – also known as ‘citizen science’ – supports participants’ learning, engages the public in science, contributes to community scientific literacy, and can serve as a valuable tool to facilitate larger scale research, says a new report from the National Academies of Sciences, Engineering, and Medicine.

DDoS attacks during the first three months of this year have seen a significant spike in attacks on educational websites.

DDoS attacks during the first three months of this year have seen a significant spike in attacks on educational websites.

As many universities are moving quickly to distance learning, it is vital for educators to think carefully about how to adapt their approach to still deliver key learning outcomes for students in an online setting. Today’s guest blogger, Ramnarayan Krishnamurthy, is at the forefront supporting universities as they transition to distance learning. In his role as a Customer Success Engineer, he partners with educators to support them in achieving their goals for teaching and learning.... read more >>

The post Aussie learning platform trains Indonesian miners appeared first on The Lead SA.

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Scientists at NTU Singapore, MIT and Brown University have developed new approaches that significantly improve the accuracy of an important material testing technique by harnessing the power of machine learning....

From video games to cell phone apps, people are making sign language easier to learn.

STIMO (STImulation Movement Overground) involves physical therapy and targeted electrical stimulation to help the brain regain control over paralyzed muscles.

NASA has released 'astonishing' results from the first up-close flyby of a Kuiper Belt space rock.

A look at an exemplary online learning institution and some key points to consider in your choice of an online or distance learning education and institution.

Online education (or 'webucation') has become incredibly popular over the past few years, and many universities are now offering part or all of their courses online. This article explores the reasons for the sudden surge in popularity and helps readers evaluate whether online education is a good option for them.

If you're uncertain about what a distance learning degree can do for you, or whether it's the way to go, this article will give you food for thought.

Simple foreign language learning can help improve business relationships.

Suite offers cost-effective, easy-to-implement access to school system resources for students and faculty

Amid the outbreak of COVID-19, EC-Council introduces flexible online learning solutions to their students, enabling them to pursue cybersecurity training and ensure learning continuity.

The COVID-19 coronavirus pandemic has created an unexpected and extremely large demand for educators to provide remote learning opportunities for students who are unable to attend school, due to mass closures to prevent the spread of the virus.

Would you like to know what your customers, users, contacts, or relatives really think? NuWave Solutions' Executive Vice President, Brian Frutchey, leads participants as they build their own sentiment analysis application with KNIME Analytics.

With many organizations having machine learning models running in production, some are discovering that inefficiencies exists in the first step of the process: feature definition and extraction. Robust feature management is now being realized as a key missing part of the ML stack, and improving it by applying standard software development practices is gaining attention.

If you find yourself quarantined and looking for free learning materials in the way of books and courses to sharpen your data science and machine learning skills, this collection of articles I have previously written curating such things is for you.

An introduction to the DBSCAN algorithm and its Implementation in Python.

Also: Should Data Scientists Model COVID19 and other Biological Events; 5 Papers on CNNs Every Data Scientist Should Read; 24 Best (and Free) Books To Understand Machine Learning; Mathematics for Machine Learning: The Free eBook; Find Your Perfect Fit: A Quick Guide for Job Roles in the Data World

Check out these 10 books that can help data scientists and aspiring data scientists learn machine learning today.

Also: Should Data Scientists Model COVID19 and other Biological Events; Learning during a crisis (Data Science 90-day learning challenge); Data Transformation: Standardization vs Normalization; DBSCAN Clustering Algorithm in Machine Learning; Find Your Perfect Fit: A Quick Guide for Job Roles in the Data World

Also: A Concise Course in Statistical Inference: The Free eBook; ML Ops: Machine Learning as an Engineering Discipline; Learning during a crisis (#DataScience 90-day learning challenge) ; Free High-Quality Machine Learning & Data Science Books & Courses: Quarantine Edition

This article demonstrates how building a smarter API serving Deep Learning models minimizes the response time.

So, you are interested in machine learning? Here is your complete learning path to start your career in the field.

Train a "blackbox" GBM model on a real dataset and make it explainable with SHAP.

What are Some 'Advanced ' #AI and #MachineLearning Online Courses?; 24 Best (and Free) Books To Understand Machine Learning; Top 5 must-have #DataScience skills for 2020

Check out this comprehensive guide to model optimization techniques.

As can be common in many technical fields, the landscape of specialized roles is evolving quickly. With more people learning at least a little machine learning, this could eventually become a common skill set for every software engineer.

Also: Introducing MIDAS: A New Baseline for Anomaly Detection in Graphs; The Super Duper NLP Repo: 100 Ready-to-Run Colab Notebooks; Five Cool Python Libraries for Data Science.

The founder of the Khan Academy talks with HBR senior editor Alison Beard.

Whitney Johnson, an executive coach, argues that on-the-job learning is the key to keeping people motivated. When managers understand that, and understand where the people they manage are on their individual learning curve — the low end, the sweet spot, or the high end — employees are engaged, productive, and innovative. Johnson is the author of the book “Build an A-Team: Play to Their Strengths and Lead Them Up the Learning Curve.”

Scott Young, who gained fame for teaching himself the four-year MIT computer science curriculum in just 12 months, says that the type of fast, focused learning he employed is possible for all of us -- whether we want to master coding, become fluent in a foreign language, or excel at public speaking. And, in a dynamic, fast-paced business environment that leaves so many of us strapped for time and struggling to keep up, he believes that the ability to quickly develop new knowledge and skills will be a tremendous asset. After researching best practices and experimenting on his own, he has developed a set of principles that any of us can follow to become "ultralearners." Young is the author of the book "Ultralearning: Master Hard Skills, Outsmart the Competition, and Accelerate Your Career."

Both AI and ML are touted to give businesses the edge they need, improve efficiencies, make sales and marketing better and even help in critical HR functions.

“Our government believes artificial intelligence, in different forms, can help us achieve the $5 trillion benchmark over the next five years, but also help us do it effectively and efficiently,” Goyal said while inaugurating the NSE Knowledge Hub here. The hub is an AI-powered learning ecosystem for the banking, financial services and insurance sector.

David Rohde points us to this new seminar series that has the following description: Machine learning is changing the world we live in at a break neck pace. From image recognition and generation, to the deployment of recommender systems, it seems to be breaking new ground constantly and influencing almost every aspect of our lives. […]

Predicting the spatial configuration of gas molecules in nanopores of shale formations is crucial for fluid flow forecasting and hydrocarbon reserves estimation. The key challenge in these tight formations is that the majority of the pore sizes are less than 50 nm. At this scale, the fluid properties are affected by nanoconfinement effects due to the increased fluid-solid interactions. For instance, gas adsorption to the pore walls could account for up to 85% of the total hydrocarbon volume in a tight reservoir. Although there are analytical solutions that describe this phenomenon for simple geometries, they are not suitable for describing realistic pores, where surface roughness and geometric anisotropy play important roles. To describe these, molecular dynamics (MD) simulations are used since they consider fluid-solid and fluid-fluid interactions at the molecular level. However, MD simulations are computationally expensive, and are not able to simulate scales larger than a few connected nanopores. We present a method for building and training physics-based deep learning surrogate models to carry out fast and accurate predictions of molecular configurations of gas inside nanopores. Since training deep learning models requires extensive databases that are computationally expensive to create, we employ active learning (AL). AL reduces the overhead of creating comprehensive sets of high-fidelity data by determining where the model uncertainty is greatest, and running simulations on the fly to minimize it. The proposed workflow enables nanoconfinement effects to be rigorously considered at the mesoscale where complex connected sets of nanopores control key applications such as hydrocarbon recovery and CO2 sequestration.

Differential machine learning (ML) extends supervised learning, with models trained on examples of not only inputs and labels, but also differentials of labels to inputs.

Differential ML is applicable in all situations where high quality first order derivatives wrt training inputs are available. In the context of financial Derivatives risk management, pathwise differentials are efficiently computed with automatic adjoint differentiation (AAD). Differential ML, combined with AAD, provides extremely effective pricing and risk approximations. We can produce fast pricing analytics in models too complex for closed form solutions, extract the risk factors of complex transactions and trading books, and effectively compute risk management metrics like reports across a large number of scenarios, backtesting and simulation of hedge strategies, or capital regulations.

The article focuses on differential deep learning (DL), arguably the strongest application. Standard DL trains neural networks (NN) on punctual examples, whereas differential DL teaches them the shape of the target function, resulting in vastly improved performance, illustrated with a number of numerical examples, both idealized and real world. In the online appendices, we apply differential learning to other ML models, like classic regression or principal component analysis (PCA), with equally remarkable results.

This paper is meant to be read in conjunction with its companion GitHub repo https://github.com/differential-machine-learning, where we posted a TensorFlow implementation, tested on Google Colab, along with examples from the article and additional ones. We also posted appendices covering many practical implementation details not covered in the paper, mathematical proofs, application to ML models besides neural networks and extensions necessary for a reliable implementation in production.

We develop a supervised machine learning algorithm that is able to learn topological phases for finite condensed matter systems from bulk data in real lattice space. The algorithm employs diagonalization in real space together with any supervised learning algorithm to learn topological phases through an eigenvector ensembling procedure. We combine our algorithm with decision trees and random forests to successfully recover topological phase diagrams of Su-Schrieffer-Heeger (SSH) models from bulk lattice data in real space and show how the Shannon information entropy of ensembles of lattice eigenvectors can be used to retrieve a signal detailing how topological information is distributed in the bulk. The discovery of Shannon information entropy signals associated with topological phase transitions from the analysis of data from several thousand SSH systems illustrates how model explainability in machine learning can advance the research of exotic quantum materials with properties that may power future technological applications such as qubit engineering for quantum computing.

Hypertension is a potentially unsafe health ailment, which can be indicated directly from the Blood pressure (BP). Hypertension always leads to other health complications. Continuous monitoring of BP is very important; however, cuff-based BP measurements are discrete and uncomfortable to the user. To address this need, a cuff-less, continuous and a non-invasive BP measurement system is proposed using Photoplethysmogram (PPG) signal and demographic features using machine learning (ML) algorithms. PPG signals were acquired from 219 subjects, which undergo pre-processing and feature extraction steps. Time, frequency and time-frequency domain features were extracted from the PPG and their derivative signals. Feature selection techniques were used to reduce the computational complexity and to decrease the chance of over-fitting the ML algorithms. The features were then used to train and evaluate ML algorithms. The best regression models were selected for Systolic BP (SBP) and Diastolic BP (DBP) estimation individually. Gaussian Process Regression (GPR) along with ReliefF feature selection algorithm outperforms other algorithms in estimating SBP and DBP with a root-mean-square error (RMSE) of 6.74 and 3.59 respectively. This ML model can be implemented in hardware systems to continuously monitor BP and avoid any critical health conditions due to sudden changes.

Segmentation is a prerequisite yet challenging task for medical image analysis. In this paper, we introduce a novel deeply supervised active learning approach for finger bones segmentation. The proposed architecture is fine-tuned in an iterative and incremental learning manner. In each step, the deep supervision mechanism guides the learning process of hidden layers and selects samples to be labeled. Extensive experiments demonstrated that our method achieves competitive segmentation results using less labeled samples as compared with full annotation.

Narrowband internet-of-things (NB-IoT) is a competitive 5G technology for massive machine-type communication scenarios, but meanwhile introduces narrowband interference (NBI) to existing broadband transmission such as the long term evolution (LTE) systems in enhanced mobile broadband (eMBB) scenarios. In order to facilitate the harmonic and fair coexistence in wireless heterogeneous networks, it is important to eliminate NB-IoT interference to LTE systems. In this paper, a novel sparse machine learning based framework and a sparse combinatorial optimization problem is formulated for accurate NBI recovery, which can be efficiently solved using the proposed iterative sparse learning algorithm called sparse cross-entropy minimization (SCEM). To further improve the recovery accuracy and convergence rate, regularization is introduced to the loss function in the enhanced algorithm called regularized SCEM. Moreover, exploiting the spatial correlation of NBI, the framework is extended to multiple-input multiple-output systems. Simulation results demonstrate that the proposed methods are effective in eliminating NB-IoT interference to LTE systems, and significantly outperform the state-of-the-art methods.