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Aug 16, 2021

‘Fingerprints’ of extreme weather revealed by new statistical approach

Posted by in categories: climatology, mathematics, physics, sustainability

Determining if particular extreme hot or cold spells were caused by climate change could be made easier by a new mathematical method.

The , developed by physicists at the University of Reading and Uppsala University in Sweden, looks at the characteristics, or “fingerprints,” of a specific extreme weather event of interest, like a , in order to ascertain whether it can be attributed to natural variability of the climate or is a unique product of global warming.

The method also allows predictions to be made about how likely extreme climate events will be in the future.

Aug 14, 2021

A poorer father-child relationship predicts increased math anxiety in children one year later

Posted by in categories: education, mathematics

According to findings published in Learning and Individual Differences, a secure bond between father and child is particularly important for children’s development of coping skills related to mathematics. The longitudinal study found that the father-child bond predicted children’s math anxiety one year later, while the mother-child bond did not.

The term “math anxiety” is used to describe fear and apprehension surrounding math and can occur in children and adults alike. Math anxiety can arise in response to any situation that requires mathematics — from solving a math problem at school to calculating the tip at a restaurant.

Previous studies have uncovered parental factors that play a role in the development of math anxiety among children — for example, parents’ use of math at home with their children. There is also evidence that that the quality of the parent-child relationship influences math anxiety among children, but until now, no study had teased apart the specific roles of the mother-child versus father-child bond.

Continue reading “A poorer father-child relationship predicts increased math anxiety in children one year later” »

Aug 13, 2021

ThirdAI raises $6M to democratize AI to any hardware

Posted by in categories: mathematics, robotics/AI

Houston-based ThirdAI, a company building tools to speed up deep learning technology without the need for specialized hardware like graphics processing units, brought in $6 million in seed funding.

Neotribe Ventures, Cervin Ventures and Firebolt Ventures co-led the investment, which will be used to hire additional employees and invest in computing resources, Anshumali Shrivastava, Third AI co-founder and CEO, told TechCrunch.

Shrivastava, who has a mathematics background, was always interested in artificial intelligence and machine learning, especially rethinking how AI could be developed in a more efficient manner. It was when he was at Rice University that he looked into how to make that work for deep learning. He started ThirdAI in April with some Rice graduate students.

Aug 7, 2021

Mathematicians Solve Decades-Old Classification Problem

Posted by in category: mathematics

A pair of researchers has shown that trying to classify groups of numbers called “torsion-free abelian groups” is as hard as it can possibly be.

Aug 4, 2021

New Shape Opens ‘Wormhole’ Between Numbers and Geometry

Posted by in categories: cosmology, mathematics

Mathematicians have proved that a geometric object called the Fargues-Fontaine curve can connect arithmetic and geometry. The work is a major advance in one of the most ambitious projects in mathematics.


The grandest project in mathematics has received a rare gift, in the form of a mammoth 350-page paper posted in February that will change the way researchers around the world investigate some of the field’s deepest questions. The work fashions a new geometric object that fulfills a bold, once fanciful dream about the relationship between geometry and numbers.

Continue reading “New Shape Opens ‘Wormhole’ Between Numbers and Geometry” »

Aug 3, 2021

The Creation of Abstract Thoughts in the Brain

Posted by in categories: mathematics, robotics/AI

Summary: Combining artificial intelligence, mathematical modeling, and brain imaging data, researchers shed light on the neural processes that occur when people use mental abstraction.

Source: UCL

By using a combination of mathematical modeling, machine learning and brain imaging technology, researchers have discovered what happens in the brain when people use mental abstractions.

Jul 28, 2021

Berkeley Lab’s CAMERA leads international effort on autonomous scientific discoveries

Posted by in categories: information science, mathematics, robotics/AI

Experimental facilities around the globe are facing a challenge: their instruments are becoming increasingly powerful, leading to a steady increase in the volume and complexity of the scientific data they collect. At the same time, these tools demand new, advanced algorithms to take advantage of these capabilities and enable ever-more intricate scientific questions to be asked—and answered. For example, the ALS-U project to upgrade the Advanced Light Source facility at Lawrence Berkeley National Laboratory (Berkeley Lab) will result in 100 times brighter soft X-ray light and feature superfast detectors that will lead to a vast increase in data-collection rates.

To make full use of modern instruments and facilities, researchers need new ways to decrease the amount of data required for and address data acquisition rates humans can no longer keep pace with. A promising route lies in an emerging field known as autonomous discovery, where algorithms learn from a comparatively little amount of input data and decide themselves on the next steps to take, allowing multi-dimensional parameter spaces to be explored more quickly, efficiently, and with minimal human intervention.

“More and more experimental fields are taking advantage of this new optimal and autonomous data acquisition because, when it comes down to it, it’s always about approximating some function, given noisy data,” said Marcus Noack, a research scientist in the Center for Advanced Mathematics for Energy Research Applications (CAMERA) at Berkeley Lab and lead author on a new paper on Gaussian processes for autonomous data acquisition published July 28 in Nature Reviews Physics. The paper is the culmination of a multi-year, multinational effort led by CAMERA to introduce innovative autonomous discovery techniques across a broad scientific community.

Jul 23, 2021

Neurotransmitter Levels Predict Math Ability

Posted by in categories: mathematics, neuroscience

Summary: A new study found a person’s math ability was linked to levels of GABA and glutamate in the brain. In children, greater math fluency was associated with higher GABA levels in the left intraparietal sulcus, while lower levels of GABA were linked to math ability in adults. The reverse was true for glutamate in both children and adults.

Source: PLOS

The neurotransmitters GABA and glutamate have complementary roles — GABA inhibits neurons, while glutamate makes them more active.

Jul 21, 2021

Nvidia releases TensorRT 8 for faster AI inference

Posted by in categories: mathematics, robotics/AI

Nvidia today announced the release of TensorRT 8, the latest version of its software development kit (SDK) designed for AI and machine learning inference. Built for deploying AI models that can power search engines, ad recommendations, chatbots, and more, Nvidia claims that TensorRT 8 cuts inference time in half for language queries compared with the previous release of TensorRT.

Models are growing increasingly complex, and demand is on the rise for real-time deep learning applications. According to a recent O’Reilly survey, 86.7% of organizations are now considering, evaluating, or putting into production AI products. And Deloitte reports that 53% of enterprises adopting AI spent more than $20 million in 2019 and 2020 on technology and talent.

TensorRT essentially dials a model’s mathematical coordinates to a balance of the smallest model size with the highest accuracy for the system it’ll run on. Nvidia claims that TensorRT-based apps perform up to 40 times faster than CPU-only platforms during inference, and that TensorRT 8-specific optimizations allow BERT-Large — one of the most popular Transformer-based models — to run in 1.2 milliseconds.

Jul 16, 2021

We Now Have Precise Math to Describe How Black Holes Reflect The Universe

Posted by in categories: cosmology, information science, mathematics, physics

A new set of equations can precisely describe the reflections of the Universe that appear in the warped light around a black hole.

The proximity of each reflection is dependent on the angle of observation with respect to the black hole, and the rate of the black hole’s spin, according to a mathematical solution worked out by physics student Albert Sneppen of the Niels Bohr Institute in Denmark.

This is really cool, absolutely, but it’s not just really cool. It also potentially gives us a new tool for probing the gravitational environment around these extreme objects.

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