Computers Math

A new statistical tool can help researchers get meaningful results when a randomized experiment, considered the gold standard, is not possible.
Randomized experiments split participants into groups by chance, with one undergoing an intervention and the other not. But in real-world situations, they can’t always be done. Companies might not want to use the method, or such experiments might be against the law.
Developed by a researcher at The University of Texas at Austin, the new tool called two-step synthetic control adapts an existing research workaround, known as the synthetic control method.
The traditional synthetic control method creates synthetic control groups from the data, in place of real ones. The groups are weighted statistically and compared with a group undergoing an intervention.
But the synthetic control method does not perfectly apply to all situations, especially ones in which the intervention group is different from control groups, according to Kathleen Li, an assistant professor of marketing at the McCombs School of Business. In these scenarios, the method’s lack of flexibility could lead to less accurate results.
“Our framework allows managers and policymakers to estimate effects they previously weren’t able to estimate accurately,” said Li, who developed the tool along with Venkatesh Shankar of Texas A&M University. “They get a more precise estimate that can help them make more informed decisions.”
The study, published in advance online in the journal Management Science, offers a two-step synthetic control approach: First, it determines whether the traditional synthetic control method applies to a given case. If it does not, the second step uses a more flexible framework that allows weighted controls to differ from 100% or to shift the control group up and down.The researchers tested the new method on a real-world situation by looking at sales of tampons: how they responded in 2016, when New York repealed a sales tax on them. More

An international research team from the Max Planck Institute of Microstructure Physics, Halle (Saale), Germany, the University of Cambridge, UK and the University of Pennsylvania, USA reported the first realization of single-crystalline T-Nb2O5 thin films having two-dimensional (2D) vertical ionic transport channels, which results in a fast and colossal insulator-metal transition via Li ion intercalation through the 2D channels.
Since the 1940s, scientists have been exploring the use of niobium oxide, specifically a form of niobium oxide known as T-Nb2O5, to create more efficient batteries. This unique material is known for its ability to allow lithium ions, the tiny charged particles that make batteries work, to move quickly within it. The faster these lithium ions can move, the faster a battery can be charged.
The challenge, however, has always been to grow this niobium oxide material into thin, flat layers, or ‘films’ that are of high enough quality to be used in practical applications. This problem stems from the complex structure of T-Nb2O5 and the existence of many similar forms, or polymorphs, of niobium oxide.
Now, in a paper published in Nature Materials, researchers from the Max Planck Institute of Microstructure Physics, University of Cambridge and the University of Pennsylvania have successfully demonstrated the growth of high-quality, single-crystal thin films of T-Nb2O5, aligned in such a way that the lithium ions can move even faster along vertical ionic transport channels.
The T-Nb2O5 films undergo a significant electrical change at an early stage of Li insertion into the initially insulating films. This is a dramatic shift — the resistivity of the material decreases by a factor of 100 billion. The research team further demonstrate tunable and low voltage operation of thin film devices by altering the chemical composition of the ‘gate’ electrode, a component that controls the flow of ions in a device, further extending the potential applications.
The Max Planck Institute of Microstructure Physics group realized the growth of the single-crystalline T-Nb2O5 thin films, and showed how Li-ion intercalation can dramatically increase their electrical conductivity. Together with the University of Cambridge group multiple previously unknown transitions in the material’s structure were discovered as the concentration of lithium ions was changed. These transitions change the electronic properties of the material, allowing it to switch from being an insulator to a metal, meaning that it goes from blocking electric current to conducting it. Researchers from the University of Pennsylvania rationalized the multiple phase transitions they observed, as well as, how these phases might be related to the concentration of lithium ions and their arrangement within the crystal structure.
These results could only have been successful through synergies between the three international groups with diverse specialties: thin films from the Max Planck Institute of Microstructure Physics, batteries from the University of Cambridge, and theory from the University of Pennsylvania. More

Mammography screening supported by artificial intelligence (AI) is a safe alternative to today’s conventional double reading by radiologists and can reduce heavy workloads for doctors. This has now been shown in an interim analysis of a prospective, randomised controlled trial, which addressed the clinical safety of using AI in mammography screening. The trial, led by researchers from Lund University in Sweden, has been published in The Lancet Oncology.
Each year around one million women in Sweden are called to mammography screening. Each screening examination is reviewed by two breast radiologists to ensure a high sensitivity, so called double reading. There is however a workforce shortage of breast radiologists, in Sweden and elsewhere, which can put the screening service at risk. Lately, the potential of AI to support mammography screening has attracted much attention, but how this is to be optimally conducted and what the clinical consequences will be, remains unclear.
To know with certainty what happens when radiologists work with the support of AI requires studies in which women are randomly allocated to AI-supported screening or to standard screening. The Mammography Screening with Artificial Intelligence (MASAI) trial is the first randomised controlled trial evaluating the effect of AI-supported screening.
“In our trial, we used AI to identify screening examinations with a high risk of breast cancer, which underwent double reading by radiologists. The remaining examinations were classified as low risk and were read only by one radiologist. In the screen reading, radiologists used AI as detection support, in which it highlighted suspicious findings on the images,” says Kristina Lång, researcher and associate professor in diagnostic radiology at Lund University and consultant at Skåne University Hospital, who led the study.
The 80,033 women included in the safety analysis were randomly allocated into two groups: 40,003 women in the intervention group that underwent AI-supported screening and 40,030 in the control group that underwent standard double reading without AI support.
“We found that using AI resulted in the detection of 20 % (41) more cancers compared with standard screening, without affecting false positives. A false positive in screening occurs when a woman is recalled but cleared of suspicion of cancer after workup,” says Kristina Lång.
At the same time, the screen-reading workload for radiologists was reduced by 44 %. The number of screen readings with AI-supported screening was 46,345 compared with 83,231 with standard screening. More

A proposed machine learning framework and expanded use of blockchain technology could help counter the spread of fake news by allowing content creators to focus on areas where the misinformation is likely to do the most public harm, according to new research from Binghamton University, State University of New York.
The research led by Thi Tran, assistant professor of management information systems at Binghamton University’s School of Management, expands on existing studies by offering tools for recognizing patterns in misinformation and helping content creators zero in the worst offenders.
“I hope this research helps us educate more people about being aware of the patterns,” Tran said, “so they know when to verify something before sharing it and are more alert to mismatches between the headline and the content itself, which would keep the misinformation from spreading unintentionally.”
Tran’s research proposed machine learning systems — a branch of artificial intelligence (AI) and computer science that uses data and algorithms to imitate the way humans learn while gradually improving its accuracy — to help determine the scale to which content could cause the most harm to its audience.
Examples could include stories that circulated during the height of the COVID-19 pandemic touting false alternate treatments to the vaccine.
The framework would use data and algorithms to spot indicators of misinformation and use those examples to inform and improve the detection process. It would also consider user characteristics from people with prior experience or knowledge about fake news to help piece together a harm index. The index would reflect the severity of possible harm to a person in certain contexts if they were exposed and victimized by the misinformation.
“We’re most likely to care about fake news if it causes a harm that impacts readers or audiences. If people perceive there’s no harm, they’re more likely to share the misinformation,” Tran said. “The harms come from whether audiences act according to claims from the misinformation, or if they refuse the proper action because of it. If we have a systematic way of identifying where misinformation will do the most harm, that will help us know where to focus on mitigation.”
Based on the information gathered, Tran said, the machine learning system could help fake news mitigators discern which messages are likely to be the most damaging if allowed to spread unchallenged. More

Researchers at Purdue University are advancing the world of robotics and autonomy with their patent-pending method that improves on traditional machine vision and perception.
Zubin Jacob, the Elmore Associate Professor of Electrical and Computer Engineering in the Elmore Family School of Electrical and Computer Engineering, and research scientist Fanglin Bao have developed HADAR, or heat-assisted detection and ranging. Their research was featured on the cover of the July 26 issue of the peer-reviewed journal Nature. A video about HADAR is available on YouTube. Nature also has released a podcast episode that includes an interview with Jacob.
Jacob said it is expected that one in 10 vehicles will be automated and that there will be 20 million robot helpers that serve people by 2030.
“Each of these agents will collect information about its surrounding scene through advanced sensors to make decisions without human intervention,” Jacob said. “However, simultaneous perception of the scene by numerous agents is fundamentally prohibitive.”
Traditional active sensors like LiDAR, or light detection and ranging, radar and sonar emit signals and subsequently receive them to collect 3D information about a scene. These methods have drawbacks that increase as they are scaled up, including signal interference and risks to people’s eye safety. In comparison, video cameras that work based on sunlight or other sources of illumination are advantageous, but low-light conditions such as nighttime, fog or rain present a serious impediment.
Traditional thermal imaging is a fully passive sensing method that collects invisible heat radiation originating from all objects in a scene. It can sense through darkness, inclement weather and solar glare. But Jacob said fundamental challenges hinder its use today.
“Objects and their environment constantly emit and scatter thermal radiation, leading to textureless images famously known as the ‘ghosting effect,'” Bao said. “Thermal pictures of a person’s face show only contours and some temperature contrast; there are no features, making it seem like you have seen a ghost. This loss of information, texture and features is a roadblock for machine perception using heat radiation.”
HADAR combines thermal physics, infrared imaging and machine learning to pave the way to fully passive and physics-aware machine perception. More

An interdisciplinary team of mathematicians, engineers, physicists, and medical scientists has uncovered an unexpected link between pure mathematics and genetics, that reveals key insights into the structure of neutral mutations and the evolution of organisms.
Number theory, the study of the properties of positive integers, is perhaps the purest form of mathematics. At first sight, it may seem far too abstract to apply to the natural world. In fact, the influential American number theorist Leonard Dickson wrote ‘Thank God that number theory is unsullied by any application.’ And yet, again and again, number theory finds unexpected applications in science and engineering, from leaf angles that (almost) universally follow the Fibonacci sequence, to modern encryption techniques based on factoring prime numbers. Now, researchers have demonstrated an unexpected link between number theory and evolutionary genetics.
Specifically, the team of researchers (from Oxford, Harvard, Cambridge, GUST, MIT, Imperial, and the Alan Turing Institute) have discovered a deep connection between the sums-of-digits function from number theory and a key quantity in genetics, the phenotype mutational robustness. This quality is defined as the average probability that a point mutation does not change a phenotype (a characteristic of an organism).
The discovery may have important implications for evolutionary genetics. Many genetic mutations are neutral, meaning that they can slowly accumulate over time without affecting the viability of the phenotype. These neutral mutations cause genome sequences to change at a steady rate over time. Because this rate is known, scientists can compare the percentage difference in the sequence between two organisms and infer when their latest common ancestor lived.
But the existence of these neutral mutations posed an important question: what fraction of mutations to a sequence are neutral? This property, called the phenotype mutational robustness, defines the average amount of mutations that can occur across all sequences without affecting the phenotype.
Professor Ard Louis from the University of Oxford, who led the study, said: ‘We have known for some time that many biological systems exhibit remarkably high phenotype robustness, without which evolution would not be possible. But we didn’t know what the absolute maximal robustness possible would be, or if there even was a maximum.’
It is precisely this question that the team has answered. They proved that the maximum robustness is proportional to the logarithm of the fraction of all possible sequences that map to a phenotype, with a correction which is given by the sums of digits function sk(n), defined as the sum of the digits of a natural number n in base k. For example, for n = 123 in base 10, the digit sum would be s10(123) = 1 + 2 + 3 = 6.
Another surprise was that the maximum robustness also turns out to be related to the famous Tagaki function, a bizarre function that is continuous everywhere, but differentiable nowhere. This fractal function is also called the blancmange curve, because it looks like the French dessert.
First author Dr. Vaibhav Mohanty (Harvard Medical School) added: ‘What is most surprising is that we found clear evidence in the mapping from sequences to RNA secondary structures that nature in some cases achieves the exact maximum robustness bound. It’s as if biology knows about the fractal sums-of-digits function.’
Professor Ard Louis added: ‘The beauty of number theory lies not only in the abstract relationships it uncovers between integers, but also in the deep mathematical structures it illuminates in our natural world. We believe that many intriguing new links between number theory and genetics will be found in the future.’ More

Around the world, political violence increased by 27 percent last year, affecting 1.7 billion people. The numbers come from the Armed Conflict Location & Event Data Project (ACLED), which collects real-time data on conflict events worldwide.
Some armed conflicts occur between states, such as Russia’s invasion of Ukraine. There are, however, many more that take place within the borders of a single state. In Nigeria, violence, particularly from Boko Haram, has escalated in the past few years. In Somalia, populations remain at risk amidst conflict and attacks perpetrated by armed groups, particularly Al-Shabaab.
To address the challenge of understanding how violent events spread, a team at the Complexity Science Hub (CSH) created a mathematical method that transforms raw data on armed conflicts into meaningful clusters by detecting causal links.
“Our main question was: what is a conflict? How can we define it?,” says CSH scientist Niraj Kushwaha, one of the coauthors of the study published in the latest issue of PNAS Nexus. “It was important for us to find a quantitative and bias-free way to see if there were any correlations between different violent events, just by looking at the data.”
Inspiration
“We often tell multiple narratives about a single conflict, which depend on whether we zoom in on it as an example of local tension or zoom out from it and consider it as part of a geopolitical plot; these are not necessarily incompatible,” explains coauthor Eddie Lee, a postdoctoral fellow at CSH. “Our technique allows us to titrate between them and fill out a multiscale portrait of conflict.”
In order to investigate the many scales of political violence, the researchers turned to physics and biophysics for inspiration. The approach they developed is inspired by studies of stress propagation in collapsing materials and of neural cascades in the brain. More

When you need accurate information about a serious illness, should you go to Google or ChatGPT?
An interdisciplinary study led by University of California, Riverside, computer scientists found that both internet information gathering services have strengths and weaknesses for people seeking information about Alzheimer’s disease and other forms of dementia. The team included clinical scientists from the University of Alabama and Florida International University.
Google provides the most current information, but query results are skewed by service and product providers seeking customers, the researchers found. ChatGPT, meanwhile, provides more objective information, but it can be outdated and lacks the sources of its information in its narrative responses.
“If you pick the best features of both, you can build a better system, and I think that this is what will happen in the next couple of years,” said Vagelis Hristidis, a professor of computer science and engineering in UCR’s Bourns College of Engineering.
In their study, Hristidis and his co-authors submitted 60 queries to both Google and ChatGPT that would be typical submissions from people living with dementia and their families.
The researchers focused on dementia because more than 6 million Americans are impacted by Alzheimer’s disease or a related condition, said study co-author Nicole Ruggiano, a professor of social work at the University of Alabama.
“Research also shows that caregivers of people living with dementia are among the most engaged stakeholders in pursuing health information, since they often are tasked with making decisions for their loved one’s care,” Ruggiano said. More