Computers Math

An international team of researchers, writing in the journal Nature Medicine, advises that strong care needs to be taken not to misuse or overuse machine learning (ML) in healthcare research.
“I absolutely believe in the power of ML but it has to be a relevant addition,” said neurosurgeon-in-training and statistics editor Dr Victor Volovici, first author of the comment, from Erasmus MC University Medical Center, The Netherlands. “Sometimes ML algorithms do not perform better than traditional statistical methods, leading to the publication of papers that lack clinical or scientific value.”
Real world examples have shown that the misuse of algorithms in healthcare could perpetuate human prejudices or inadvertently cause harm when the machines are trained on biased datasets.
“Many believe ML will revolutionise healthcare because machines make choices more objectively than humans. But without proper oversight, ML models may do more harm than good,” said Associate Professor Nan Liu, senior author of the comment, from the Centre for Quantitative Medicine and Health Services & Systems Research Programme at Duke-NUS Medical School, Singapore.
“If, through ML, we uncover patterns that we otherwise would not see — like in radiology and pathology images — we should be able to explain how the algorithms got there, to allow for checks and balances.”
Together with a group of scientists from the UK and Singapore, the researchers highlight that although guidelines have been formulated to regulate the use of ML in clinical research, these guidelines are only applicable once a decision to use ML has been made and do not ask whether or when its use is appropriate in the first place.
For example, companies have successfully trained ML algorithms to recognise faces and road objects using billions of images and videos. But when it comes to their use in healthcare settings, they are often trained on data in the tens, hundreds or thousands. “This underscores the relative poverty of big data in healthcare and the importance of working towards achieving sample sizes that have been attained in other industries, as well as the importance of a concerted, international big data sharing effort for health data,” the researchers write.
Another issue is that most ML and deep learning algorithms (that do not receive explicit instructions regarding the outcome) are often still regarded as a ‘black box’. For example, at the start of the COVID-19 pandemic, scientists published an algorithm that could predict coronavirus infections from lung photos. Afterwards, it turned out that the algorithm had drawn conclusions based on the imprint of the letter ‘R’ (for ‘Right Lung’) in the photos, which was always found in a slightly different spot on the scans.
“We have to get rid of the idea that ML can discover patterns in data that we cannot understand,” said Dr Volovici about the incident. “ML can very well discover patterns that we cannot see directly, but then you have to be able to explain how you came to that conclusion. In order to do that, the algorithm has to be able to show what steps it took, and that requires innovation.”
The researchers advise that ML algorithms should be evaluated against traditional statistical approaches (when applicable) before they are used in clinical research. And when deemed appropriate, they should complement clinician decision-making, rather than replace it. “ML researchers should recognise the limits of their algorithms and models in order to prevent their overuse and misuse, which could otherwise sow distrust and cause patient harm,” the researchers write.
The team is working on organising an international effort to provide guidance on the use of ML and traditional statistics, and also to set up a large database of anonymised clinical data that can harness the power of ML algorithms.
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Materials provided by Duke-NUS Medical School. Note: Content may be edited for style and length. More

Symmetries in nature make things beautiful; symmetries in data make data handling efficient. However, the complexity of identifying such patterns in data has always bedeviled researchers. Scientists from Osaka Metropolitan University and their colleagues have taken a major step towards detecting symmetries in multi-dimensional data by utilizing Bayesian statistics. Their findings were published in The Annals of Statistics.
Bayesian statistics has been in the spotlight in recent years due to improvements in computer performance and its potential applications in artificial intelligence. Bayesian statistics is a statistical approach that, even when data are insufficient, derives the probability of an event occurring by first setting a prior probability and then, whenever new information is obtained, calculating a posterior probability — an update to the prior probability — that the event will occur. The calculation of posterior probabilities requires complex calculations of integrals and therefore is often considered an approximation only.
The international team including Professor Hideyuki Ishi from Osaka Metropolitan University, Professor Piotr Graczyk from the University of Angers, Professor Bartosz Kołodziejek from Warsaw University of Technology, and the late Professor Hélène Massam from York University (Toronto) has succeeded in deriving new exact integral formulas, and in developing a method to search for symmetries in multi-dimensional data using Bayesian statistical techniques.
When the amount of data to be handled increases, the optimal pattern must be selected from a vast number of patterns, making it difficult to solve the problem precisely. Addressing this challenge, the team has also developed an efficient algorithm for obtaining an approximate solution even in such cases.
In the words of Professor Ishi, “Symmetries in data are ubiquitous in a wide variety of models. Once symmetries are identified, the number of parameters required to display the structure of the data, and the number of samples required to determine the parameters, can be significantly reduced. In the future, the results of this research are expected to contribute to genetic analysis, discovering chromosomes that have the same function in different locations.”
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Materials provided by Osaka Metropolitan University. Note: Content may be edited for style and length. More

If you’re going to break a rule with style, make sure everybody sees it. That’s the goal of engineers at Rice University who hope to improve screens for virtual reality, 3D displays and optical technologies in general.
Gururaj Naik, an associate professor of electrical and computer engineering at Rice’s George R. Brown School of Engineering, and Applied Physics Graduate Program alumna Chloe Doiron found a way to manipulate light at the nanoscale that breaks the Moss rule, which describes a trade-off between a material’s optical absorption and how it refracts light.
Apparently, it’s more like a guideline than an actual rule, because a number of “super-Mossian” semiconductors do exist. Fool’s gold, aka iron pyrite, is one of them.
For their study in Advanced Optical Materials, Naik, Doiron and co-author Jacob Khurgin, a professor of electrical and computer engineering at Johns Hopkins University, find iron pyrite works particularly well as a nanophotonic material and could lead to better and thinner displays for wearable devices.
More important is that they’ve established a method for finding materials that surpass the Moss rule and offer useful light-handling properties for displays and sensing applications.
“In optics, we’re still limited to a very few materials,” Naik said. “Our periodic table is really small. But there are so many materials that are simply unknown, just because we haven’t developed any insight on how to find them. More

Revealing the scientific laws that govern our world is often considered the ‘holy grail’ by scientists, as such discoveries have wide-ranging implications. In an exciting development from Japan, scientists have shown how to use geometric representations to encode the laws of thermodynamics, and apply these representations to obtain generalized predictions. This work may significantly improve our understanding of the theoretical limits that apply within chemistry and biology.
While living systems are bound by the laws of physics, they often find creative ways to take advantage of these rules in ways that non-living physical systems rarely can. For example, every living organism finds a way to reproduce itself. At a fundamental level, this relies on autocatalytic cycles in which a certain molecule can spur the production of identical molecules, or a set of molecules produce each other. As part of this, the compartment in which the molecules exist grows in volume. However, scientific knowledge lacks a complete thermodynamic representation of such self-replicating processes, which would enable scientists to understand how living systems can emerge from non-living objects.
Now, in two related articles published in Physical Review Research, researchers from the Institute of Industrial Science at The University of Tokyo used a geometric technique to characterize the conditions that correspond with the growth of a self-reproducing system. The guiding principle is the famous second law of thermodynamics, which requires that entropy — generally understood to mean disorder — can only increase. However, an increase in order may be possible, such as a bacterium absorbing nutrients to enable it to divide into two bacteria, but at the cost of increased entropy somewhere else. “Self-replication is a hallmark of living systems, and our theory helps explain the environmental conditions to determine their fate, whether growing, shrinking, or equilibration,” says senior author Tetsuya J. Kobayashi.
The main insight was to represent the thermodynamic relationships as hypersurfaces in a multidimensional space. Then, the researchers could study what happens as various operations are performed, in this case, using the Legendre transformation. This transformation describes how a surface to be mapped into a different geometric object with a significant thermodynamic meaning.
“The results were obtained solely on the basis of the second law of thermodynamics that the total entropy must increase. Because of this, assumptions of an ideal gas or other simplifications about the types of interactions in the system were not required,” says first author Yuki Sughiyama. Being able to calculate the rate of entropy production can be vital for evaluating biophysical systems. This research can help put the study of the thermodynamics of living systems on a more solid theoretical footing, which may improve our understanding of biological reproduction.
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Not too sport heavy, not too sleep deprived — finding the ‘just right’ balance in a child’s busy day can be a challenge. But while parents may struggle to squeeze in homework amid extracurricular commitments and downtime, a world-first app could provide a much-needed solution.
Developed by University of South Australia in partnership with the Murdoch Children’s Research Institute, the Healthy-Day-App is helping parents understand which combination of activities can best help their child’s mental, physical, and academic outcomes.
The study found that shifting 60 minutes of screen time to 60 minutes of physical activity resulted in 4.2 per cent lower body fat, 2.5 per cent improved wellbeing and 0.9 per cent higher academic performance.
Lead researcher, UniSA’s Dr Dot Dumuid says that the app will help parents and health professionals better understand the relationships between children’s time use, health, and academic outcomes.
“How children use their time can have a big impact on their health, wellbeing, and productivity,” Dr Dumuid says.
“We know that screens are not great for children’s wellbeing, so if they’re choosing to play video games at the expense of playing sport, it’s easy to guess the negative impacts effects on their health. More

Wildfires are a growing threat in a world shaped by climate change. Now, researchers at Aalto University have developed a neural network model that can accurately predict the occurrence of fires in peatlands. They used the new model to assess the effect of different strategies for managing fire risk and identified a suite of interventions that would reduce fire incidence by 50-76%.
The study focused on the Central Kalimantan province of Borneo in Indonesia, which has the highest density of peatland fires in Southeast Asia. Drainage to support agriculture or residential expansion has made peatlands increasingly vulnerable to recurring fires. In addition to threatening lives and livelihoods, peatland fires release significant amounts of carbon dioxide. However, prevention strategies have faced difficulties because of the lack of clear, quantified links between proposed interventions and fire risk.
The new model uses measurements taken before each fire season in 2002-2019 to predict the distribution of peatland fires. While the findings can be broadly applied to peatlands elsewhere, a new analysis would have to be done for other contexts. ‘Our methodology could be used for other contexts, but this specific model would have to be re-trained on the new data,’ says Alexander Horton, the postdoctoral researcher who carried out study.
The researchers used a convolutional neural network to analyse 31 variables, such as the type of land cover and pre-fire indices of vegetation and drought. Once trained, the network predicted the likelihood of a peatland fire at each spot on the map, producing an expected distribution of fires for the year.
Overall, the neural network’s predictions were correct 80-95% of the time. However, while the model was usually right in predicting a fire, it also missed many fires that actually occurred. About half of the observed fires weren’t predicted by the model, meaning that it isn’t suitable as an early-warning predictive system. Larger groupings of fires tended to be predicted well, while isolated fires were often missed by the network. With further work, the researchers hope to improve the network’s performance so it can also serve as an early-warning system.
The team took advantage of the fact that fire predictions were usually correct to test the effect of different land management strategies. By simulating different interventions, they found that the most effective plausible strategy would be to convert shrubland and scrubland into swamp forests, which would reduce fire incidence by 50%. If this were combined with blocking all of the drainage canals except the major ones, fires would decrease by 70% in total.
However, such a strategy would have clear economic drawbacks. ‘The local community is in desperate need of long-term, stable cultivation to booster the local economy,’ says Horton.
An alternative strategy would be to establish more plantations, since well-managed dramatically reduce the likelihood of fire. However, the plantations are among the key drivers of forest loss, and Horton points out ‘the plantations are mostly owned by larger corporations, often based outside Borneo, which means the profits aren’t directly fed back into the local economy beyond the provision of labour for the local workforce.’
Ultimately, fire prevention strategies have to balance risks, benefits, and costs, and this research provides the information to do that, explains Professor Matti Kummu, who led the study team. ‘We tried to quantify how the different strategies would work. It’s more about informing policy-makers than providing direct solutions.’
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Training a machine-learning model to effectively perform a task, such as image classification, involves showing the model thousands, millions, or even billions of example images. Gathering such enormous datasets can be especially challenging when privacy is a concern, such as with medical images. Researchers from MIT and the MIT-born startup DynamoFL have now taken one popular solution to this problem, known as federated learning, and made it faster and more accurate.
Federated learning is a collaborative method for training a machine-learning model that keeps sensitive user data private. Hundreds or thousands of users each train their own model using their own data on their own device. Then users transfer their models to a central server, which combines them to come up with a better model that it sends back to all users.
A collection of hospitals located around the world, for example, could use this method to train a machine-learning model that identifies brain tumors in medical images, while keeping patient data secure on their local servers.
But federated learning has some drawbacks. Transferring a large machine-learning model to and from a central server involves moving a lot of data, which has high communication costs, especially since the model must be sent back and forth dozens or even hundreds of times. Plus, each user gathers their own data, so those data don’t necessarily follow the same statistical patterns, which hampers the performance of the combined model. And that combined model is made by taking an average — it is not personalized for each user.
The researchers developed a technique that can simultaneously address these three problems of federated learning. Their method boosts the accuracy of the combined machine-learning model while significantly reducing its size, which speeds up communication between users and the central server. It also ensures that each user receives a model that is more personalized for their environment, which improves performance.
The researchers were able to reduce the model size by nearly an order of magnitude when compared to other techniques, which led to communication costs that were between four and six times lower for individual users. Their technique was also able to increase the model’s overall accuracy by about 10 percent. More

A household microwave oven modified by a Cornell engineering professor is helping to cook up the next generation of cellphones, computers and other electronics after the invention was shown to overcome a major challenge faced by the semiconductor industry.
The research is detailed in a paper published in Applied Physics Letters. The lead author is James Hwang, a research professor in the department of materials science and engineering.
As microchips continue to shrink, silicon must be doped, or mixed, with higher concentrations of phosphorus to produce the desired current. Semiconductor manufacturers are now approaching a critical limit in which heating the highly doped materials using traditional methods no longer produces consistently functional semiconductors.
The Taiwan Semiconductor Manufacturing Company (TSMC) theorized that microwaves could be used to activate the excess dopants, but just like with household microwave ovens that sometimes heat food unevenly, previous microwave annealers produced “standing waves” that prevented consistent dopant activation.
TSMC partnered with Hwang, who modified a microwave oven to selectively control where the standing waves occur. Such precision allows for the proper activation of the dopants without excessive heating or damage of the silicon crystal.
This discovery could be used to produce semiconductor materials and electronics appearing around the year 2025, said Hwang, who has filed two patents for the prototype.
“A few manufacturers are currently producing semiconductor materials that are 3 nanometers,” Hwang said. “This new microwave approach can potentially enable leading manufacturers such as TSMC and Samsung to scale down to just 2 nanometers.”
The breakthrough could change the geometry of transistors used in microchips. For more than 20 years, transistors have been made to stand up like dorsal fins so that more can be packed on each microchip, but manufacturers have recently begun to experiment with a new architecture in which transistors are stacked horizontally. The excessively doped materials enabled by microwave annealing would be key to the new architecture.
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Materials provided by Cornell University. Original written by Syl Kacapyr, courtesy of the Cornell Chronicle. Note: Content may be edited for style and length. More