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Machine learning method identifies precancerous colon polyps
A machine learning algorithm helps accurately differentiate benign and premalignant colorectal polyps on CT colonography scans, according to a new study. (2021-02-23)

Artificial pancreas system upgraded with AI algorithm
POSTECH professor Sung-Min Park's research team is developing a fully automated glucose management system that goes beyond the limits. (2021-02-22)

Deep learning may help doctors choose better lung cancer treatments
Researchers have developed a deep learning model that, in certain conditions, is more than 71 percent accurate in predicting survival expectancy of lung cancer patients, significantly better than traditional machine learning models that the team tested. The other machine learning models the team tested had about a 61 percent accuracy rate. (2021-02-18)

Study: Including videos in college teaching may improve student learning
As higher education institutions worldwide transition to new methods of instruction, including the use of more pre-recorded videos, in response to the COVID-19 pandemic, many observers are concerned that student learning is suffering as a result. However, a new comprehensive review of research offers some positive news for college students. The authors found that, in many cases, replacing teaching methods with pre-recorded videos leads to small improvements in learning and that supplementing existing content with videos results in strong learning benefits. (2021-02-17)

Foreign language learners should be exposed to slang in the classroom and here's why....
Experts say English slang and regional dialect should not be banned from classrooms but when you're getting to grips with a second language how helpful is it to learn non-standard lingo? Very, says Sascha Stollhans, of the Department of Languages and Cultures at Lancaster University, who argues that standardised language norms are artificial and language learners should learn about all aspects of language, even the controversial ones. (2021-02-17)

Pigs show potential for 'remarkable' level of behavioral, mental flexibility in new study
A study involving two different pig species demonstrated that the animals are capable of remarkable behavioral and mental flexibility. The pigs learned to play a simple video game, connecting the movement of the cursor on the computer screen to the joystick they manipulated using their snouts. The researchers say understanding the depth of an animal's intelligence can provide insight into its evolution, how it compares with humans and other species, and how cognition impacts its welfare. (2021-02-11)

Learning by observation reduces cognitive bias, research suggests
Research suggests that observing others' decision-making can teach people to make better decisions themselves. The research, co-authored by Professor Irene Scopelliti, Professor of Marketing and Behavioural Science, tested the effectiveness of a new debiasing training strategy and reports first evidence that watching others make decisions can improve our own decision making. (2021-02-11)

Compounds from apples may boost brain function
Natural compounds found in apples and other fruits may help stimulate the production of new brain cells, which may have implications for learning and memory, according to a new study in mice published in Stem Cell Reports. (2021-02-11)

Insilico announces MolGrow -- a new generative model for hierarchical molecular generation
Insilico Presented Its New Molecular Generation Model at the 35th AAAI Conference on Artificial Intelligence (2021-02-11)

Learn what you live? Study finds watching others can reduce decision bias
New research finds first evidence that watching and learning from others can help reduce bias and improve decision-making. In business, the results could help improve hiring practices or increase cost savings. (2021-02-11)

Machine learning could aid mental health diagnoses
A way of using machine learning to more accurately identify patients with a mix of psychotic and depressive symptoms has been developed by researchers at the University of Birmingham. (2021-02-08)

Geisinger researchers find AI can predict death risk
Researchers at Geisinger have found that a computer algorithm developed using echocardiogram videos of the heart can predict mortality within a year. The algorithm--an example of what is known as machine learning, or artificial intelligence (AI)--outperformed other clinically used predictors, including pooled cohort equations and the Seattle Heart Failure score. (2021-02-08)

Machine learning generates realistic genomes for imaginary humans
Machines, thanks to novel algorithms and advances in computer technology, can now learn complex models and even generate high-quality synthetic data such as photo-realistic images or even resumes of imaginary humans. A study recently published in the international journal PLOS Genetics uses machine learning to mine existing biobanks and generate chunks of human genomes which do not belong to real humans but have the characteristics of real genomes. (2021-02-05)

State-funded pre-K may enhance math achievement
Students who attend the Georgia Prekindergarten Program are more likely to achieve in mathematics than those who do not attend pre-K, according to a new study by the University of Georgia. (2021-02-03)

A computational approach to understanding how infants perceive language
A multi-institutional team of cognitive scientists and computational linguists have developed computationally-based modeling approach that opens the path toward a much deeper understanding of early language acquisition. (2021-01-29)

Automated AI algorithm uses routine imaging to predict cardiovascular risk
Investigators teamed up to develop and evaluate a deep learning system that may help change this. The system automatically measures coronary artery calcium from CT scans to help physicians and patients make more informed decisions about cardiovascular prevention. (2021-01-29)

New AI-severity score COVID-19 integrating CT images published to Nature Communications
Owkin, a French-American startup pioneering AI and Federated Learning in medical research, has been focusing it's COVID-19 research efforts on aspects of the pandemic that still require much public health attention, despite the arrival of an effective vaccine. Efforts to support frontline health systems as they devote their resources to the influx of COVID-19 related hospitalizations, have resulted in the AI-Severity Score, published in Nature Communications this week . (2021-01-28)

Mammogram-based breast cancer risk model could lead to better screening guidelines
A new machine learning algorithm based on mammograms can estimate the risk of breast cancer in women more accurately than current risk models, according to a study from Adam Yala and colleagues. (2021-01-27)

Smart algorithm cleans up images by searching for clues buried in noise
In a new study published in Nature Machine Intelligence, researchers at Texas A&M University have unveiled a machine learning-based algorithm that can reduce graininess in low-resolution images and reveal new details that were otherwise buried within the noise. (2021-01-26)

Drink and drug risk is lower among optimistic pupils with 'happy' memories, says study
Teenagers with happy childhood memories are likely to drink less, take fewer drugs and enjoy learning, according to research published in the peer-reviewed journal Addiction Research & Theory. (2021-01-25)

Experts call for more pragmatic approach to higher education teaching
Millions of students around the world could benefit if their educators adopted a more flexible and practical approach, say Swansea University experts. After analysing the techniques current being used in higher education, the researchers have released a new paper calling for a pragmatic and evidence-based approach instead. (2021-01-22)

How the brain learns that earmuffs are not valuable at the beach
A collaboration between the University of Tsukuba and the NEI in the US has discovered that fast-spiking neurons in the basal ganglia allow monkeys to associate different values with the same objects based on the surrounding environment. Blocking input from these cells inhibited learning of new scene-based values, but did not erase already learned associations. This could help understand clinical conditions such as Tourette syndrome, which is characterized by reduced input from these cells. (2021-01-21)

Angstrom multilayer metrology by combining spectral measurements and machine learning
The 3D-NAND is the most commercially successful 3D memory device today, and its demand is growing exponentially. As each layer thickness corresponds to the effective channel length, accurate characterization and control of layer-by-layer thickness is critical. Engineers in South Korea invented a nondestructive thickness characterization method of each layer in semiconductor multilayer stacks with more than 200 layers used for 3D-NAND. The method will provide new ways for total inspection in 3D semiconductor device manufacturing. (2021-01-20)

Online courses reinforce inequalities
With the global student community taking online courses, a study (UNIGE) reveals that online courses deepen inequalities between gifted and less gifted students by 5%. The results of the study, which was based on data collected in 2016-2017 prior to the anti-Covid lockdown initiatives. They indicate that this learning gap between different student profiles is mainly due to their behaviour and motivation. (2021-01-19)

Appearance, social norms keep students off Zoom cameras
University researchers surveyed the 312 students found that while some students had concerns about the lack of privacy or their home environment, 41% of the 276 respondents cited their appearance, as their reason not to switch on their cameras on zoom. (2021-01-19)

Mount Sinai researchers build models using machine learning technique to enhance predictions of COVID-19 outcomes
Mount Sinai researchers have published one of the first studies using federated learning to examine electronic health records to better predict how COVID-19 patients will progress. (2021-01-18)

Robot learns fast but safe navigation strategy
A research group from the Active Intelligent System Laboratory (AISL) at Toyohashi University of Technology (TUT) has proposed a new framework for training mobile robots to quickly navigate while maintaining low collision rates. The framework combines deep reinforcement learning (DRL) and curriculum learning in the training process for robots to learn a fast but safe navigation policy. (2021-01-18)

Deep learning outperforms standard machine learning in biomedical research applications
Compared to standard machine learning models, deep learning models are largely superior at discerning patterns and discriminative features in brain imaging, despite being more complex in their architecture. (2021-01-14)

Families' remote learning experience during lockdown more positive than widely believed
The remote learning experience of parents who had their children at home in Spring 2020, as schools across the US closed during the United States' COVID-19 lockdown, was more positive than widely believed. (2021-01-13)

Diffractive networks light the way for optical image classification
There is renewed interest in optical computing due to its potential advantages, including parallelization, power-efficiency, and computation speed. Diffractive networks utilize deep learning-based design of successive diffractive layers to all-optically process information as the light is transmitted from the input to the output plane. UCLA researchers significantly improved the statistical inference performance of diffractive optical networks using feature engineering and ensemble learning, which is important for applications including all-optical object classification and computational imaging. (2021-01-13)

When AI is used to set prices, can inadvertent collusion be a result?
CATONSVILLE, MD, January 12, 2021 - Machine learning and artificial intelligence (AI) are perfectly suited to help companies and marketers monitor and set prices based on real-time dynamic pricing. But new research has identified some possible unintended consequences of AI in this area. (2021-01-12)

UCI researchers use deep learning to identify gene regulation at single-cell level
In a Science Advances study, UCI researchers describe how they developed a deep-learning framework to observe gene regulation at the cellular level. (2021-01-12)

Scientists identify workflow algorithm to predict psychosis
Cleverly combining artificial and human intelligence leads to improved prevention of psychosis in young patients (2021-01-09)

Accelerating AI computing to the speed of light
A University of Washington-led team has come up with a system that could help speed up AI performance and find ways to reduce its energy consumption: an optical computing core prototype that uses phase-change material. (2021-01-08)

Deep learning helps predicting occult peritoneal metastasis in stomach cancer
A new study led by the Shenzhen Institutes of Advanced Technology (SIAT) of the Chinese Academy of Sciences shows that deep learning can help predicting the occult peritoneal metastasis in stomach cancer. It provides a novel and noninvasive approach for stomach cancer patients and may inform individualized surgical management of stomach cancer. (2021-01-07)

New review says the ineffective 'learning styles' theory persists in education
A new review by Swansea University reveals there is widespread belief, around the world, in a teaching method that is not only ineffective but may actually be harmful to learners. For decades educators have been advised to match their teaching to the supposed 'learning styles' of students. However, a new paper by Professor Phil Newton, of Swansea University Medical School, highlights that this ineffective approach is still believed by teachers and calls for a more evidence-based approach to teacher-training. (2021-01-06)

Guinea baboons grunt with an accent
Vocal learning leads to modification of call structure in a multi-level baboon society (2021-01-06)

DeepTFactor predicts transcription factors
A joint research team from KAIST and UCSD has developed a deep neural network named DeepTFactor that predicts transcription factors from protein sequences. DeepTFactor will serve as a useful tool for understanding the regulatory systems of organisms, accelerating the use of deep learning for solving biological problems. (2021-01-05)

Advanced materials in a snap
A research team at Sandia National Laboratories has successfully used machine learning -- computer algorithms that improve themselves by learning patterns in data -- to complete cumbersome materials science calculations more than 40,000 times faster than normal. (2021-01-05)

DUAL takes AI to the next level
Scientists at DGIST in Korea, and UC Irvine and UC San Diego in the US, have developed a computer architecture that processes unsupervised machine learning algorithms faster, while consuming significantly less energy than state-of-the-art graphics processing units. The key is processing data where it is stored in computer memory and in an all-digital format. The researchers presented the new architecture, called DUAL, at the 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture. (2020-12-30)

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