Computers Math

Strong precipitation may cause natural disasters, such as flooding or landslides. Global climate models are required to forecast the frequency of these extreme events, which is expected to change as a result of climate change. Researchers of Karlsruhe Institute of Technology (KIT) have now developed a first method based on artificial intelligence (AI), by means of which the precision of coarse precipitation fields generated by global climate models can be increased. The researchers succeeded in improving spatial resolution of precipitation fields from 32 to two kilometers and temporal resolution from one hour to ten minutes. This higher resolution is required to better forecast the more frequent occurrence of heavy local precipitation and the resulting natural disasters in future.
Many natural disasters, such as flooding or landslides, are directly caused by extreme precipitation. Researchers expect that increasing average temperatures will cause extreme precipitation events to further increase. To adapt to a changing climate and prepare for disasters at an early stage, precise local and global data on the current and future water cycle are indispensable. “Precipitation is highly variable in space and time and, hence, difficult to forecast, in particular on the local level,” says Dr. Christian Chwala from the Atmospheric Environmental Research Division of KIT’s Institute of Meteorology and Climate Research (IMK-IFU), KIT’s Campus Alpine in Garmisch-Partenkirchen.” For this reason, we want to enhance the resolution of precipitation fields generated e.g. by global climate models and improve their classification as regards possible threats, such as floodings.”
Higher Resolution for More Precise Regional Climate Models
Currently used global climate models are based on a grid that is not fine enough to precisely present the variability of precipitation. Highly resolved precipitation maps can only be produced with computationally expensive and, hence, spatially or temporally limited models. “For this reason, we have developed an AI-based generative neural network, called GAN, and trained it with high-resolution radar precipitation fields. In this way, the GAN learns how to generate realistic precipitation fields and derive their temporal sequence from coarsely resolved data,” says Luca Glawion from IMK-IFU. “The network is able to generate highly resolved radar precipitation films from very coarsely resolved maps.” These refined radar maps not only show how rain cells develop and move, but precisely reconstruct local rain statistics and the corresponding extreme value distribution.
“Our method serves as a basis to increase the resolution of coarsely grained precipitation fields, such that the high spatial and temporal variability of precipitation can be reproduced adequately and local effects can be studied,” says Julius Polz from IMK-IFU. “Our deep learning method is quicker by several orders of magnitude than the calculation of such highly resolved precipitation fields with numerical weather models usually applied to regionally refine data of global climate models.” The researchers point out that their method also generates an ensemble of different potential precipitation fields. This is important, as a multitude of physically plausible highly resolved solutions exists for each coarsely resolved precipitation field. Similar to a weather forecast, an ensemble allows for a more precise determination of the associated uncertainty.
Higher Resolution for Better Forecasts under Climate Change
The results show that the AI model and methodology developed by the researchers will enable future use of neural networks to improve the spatial and temporal resolution of precipitation calculated by climate models. This will allow for a more precise analysis of the impacts and developments of precipitation in a changing climate.
“In a next step, we will apply the method to global climate simulations that transfer specific large-scale weather situations to a future world with a changed climate, e.g. to the year of 2100. The higher resolution of precipitation events simulated with our method will allow for a better estimation of the impacts the weather conditions that caused the flooding of the river Ahr in 2021 would have had in a world warmer by 2 degrees,” Glawion explains. Such information is of decisive importance to develop climate adaptation methods. More

Manatees are endangered species volatile to the environment. Because of their voracious appetites, they often spend up to eight hours a day grazing for food within shallow waters, making them vulnerable to environmental changes and other risks.
Accurately counting manatee aggregations within a region is not only biologically meaningful in observing their habit, but also crucial for designing safety rules for boaters and divers as well as scheduling nursing, intervention, and other plans. Nevertheless, counting manatees is challenging.
Because manatees tend to live in herds, they often block each other when viewed from the surface. As a result, small manatees are likely to be partially or completely blocked from view. In addition, water reflections tend to make manatees invisible, and they also can be mistaken for other objects such as rocks and branches.
While aerial survey data are used in some regions to count manatees, this method is time-consuming and costly, and the accuracy depends on factors such as observer bias, weather conditions and time of day. Moreover, it is crucial to have a low-cost method that provides a real-time count to alert ecologists of threats early to enable them to act proactively to protect manatees.
Artificial intelligence is used in a wide spectrum of fields, and now, researchers from Florida Atlantic University’s College of Engineering and Computer Science have harnessed its powers to help save the beloved manatee. They are among the first to use a deep learning-based crowd counting approach to automatically count the number of manatees in a designated region, using images captured from CCTV cameras, which are readily available, as input.
This pioneering study, published Scientific Reports, not only addresses the technical challenges of counting in complex outdoor environments but also offers potential ways to aid endangered species.
To determine manatee densities and calculate their numbers, researchers used generic images captured from surveillance videos from the water surface. They then used a unique design matching to manatees’ shape — Anisotropic Gaussian Kernel (AGK) — to transform the images into manatee customized density maps, representing manatees’ unique body shapes.

Although many methods exist for counting, most of the existing counting methods are applied to crowds to count the number of people, due to their relevance to important applications such as urban planning and public safety.
To save labeling costs, researchers used line-label based annotation with a single straight line to mark each manatee. The goal of the study was to learn to count the number of objects within a scene and obtain labels to support counting.
Results of the study reveal that the FAU-developed method outperformed other baselines, including the traditional Gaussian kernel-based approach. Transitioning from dot to line labeling also improved wheat head counting accuracy, an important role in crop yield estimation, suggesting broader applications for convex-shaped objects in diverse contexts. This approach worked particularly well when the image had a high density of manatees in a complicated background.
By formatting manatee counting as a deep neural network density estimation learning task, this approach balanced the labeling costs vs. counting efficiency. As a result, this method delivers a simple and high throughput solution for manatee counting that requires very little labeling efforts. A direct impact is that state parks can leverage this method to understand the number of manatees in different regions, by using their existing CCTV cameras, in real time.
“There are many ways to use computational methods to help save endangered species, such as detecting the presence of the species and counting them to collect information about numbers and density,” said Xingquan (Hill) Zhu, Ph.D., senior author, an IEEE Fellow and a professor in FAU’s Department of Electrical Engineering and Computer Science. “Our method considered distortions caused by the perspective between the water space and the image plane. Since the shape of the manatee is closer to an ellipse than a circle, we used AGK to best represent the manatee contour and estimate manatee density in the scene. This allows density map to be more accurate, in terms of mean absolute errors and root mean square error, than other alternatives in estimating manatees’ numbers.”
To validate their method and facilitate further research in this domain, the researchers developed a comprehensive manatee counting dataset, along with their source code, published through GitHub for public access at github.com/yeyimilk/deep-learning-for-manatee-counting.

“Manatees are one of the wildlife species being affected by human-related threats. Therefore, calculating their numbers and gathering patterns in real time is vital for understanding their population dynamics,” said Stella Batalama, Ph.D., dean, FAU College of Engineering and Computer Science. “The methodology developed by professor Zhu and our graduate students provides a promising trajectory for broader applications, especially for convex-shaped objects, to improve counting techniques that may foretell better ecological results from management decisions.”
Manatees can be found from Brazil to Florida and all the way around the Caribbean islands. Some species including the Florida Manatee are considered endangered by the International Union for Conservation of Nature.
Study co-authors are FAU graduate students Zhiqiang Wang; Yiran Pang; and Cihan Ulus, also a teaching assistant, all within the Department of Electrical Engineering and Computer Science.
The research was sponsored by the United States National Science Foundation. More

Much of the discussion around implementing artificial intelligence systems focuses on whether an AI application is “trustworthy”: Does it produce useful, reliable results, free of bias, while ensuring data privacy? But a new paper published Dec. 7 in Frontiers in Artificial Intelligence poses a different question: What if an AI is just too good?
Carrie Alexander, a postdoctoral researcher at the AI Institute for Next Generation Food Systems, or AIFS, at the University of California, Davis, interviewed a wide range of food industry stakeholders, including business leaders and academic and legal experts, on the attitudes of the food industry toward adopting AI. A notable issue was whether gaining extensive new knowledge about their operations might inadvertently create new liability risks and other costs.
For example, an AI system in a food business might reveal potential contamination with pathogens. Having that information could be a public benefit but also open the firm to future legal liability, even if the risk is very small.
“The technology most likely to benefit society as a whole may be the least likely to be adopted, unless new legal and economic structures are adopted,” Alexander said.
An on-ramp for AI
Alexander and co-authors Professor Aaron Smith of the UC Davis Department of Agricultural and Resource Economics and Professor Renata Ivanek of Cornell University, argue for a temporary “on-ramp” that would allow companies to begin using AI, while exploring the benefits, risks and ways to mitigate them. This would also give the courts, legislators and government agencies time to catch up and consider how best to use the information generated by AI systems in legal, political and regulatory decisions.
“We need ways for businesses to opt in and try out AI technology,” Alexander said. Subsidies, for example for digitizing existing records, might be helpful especially for small companies.
“We’re really hoping to generate more research and discussion on what could be a significant issue,” Alexander said. “It’s going to take all of us to figure it out.”
The work was supported in part by a grant from the USDA National Institute of Food and Agriculture. The AI Institute for Next Generation Food Systems is funded by a grant from USDA-NIFA and is one of 25 AI institutes established by the National Science Foundation in partnership with other agencies. More

Artificial intelligence (AI) systems are often depicted as sentient agents poised to overshadow the human mind. But AI lacks the crucial human ability of innovation, researchers at the University of California, Berkeley have found.
While children and adults alike can solve problems by finding novel uses for everyday objects, AI systems often lack the ability to view tools in a new way, according to findings published according to findings published in Perspectives on Psychological Science, a journal of the Association for Psychological Science.
AI language models like ChatGPT are passively trained on data sets containing billions of words and images produced by humans. This allows AI systems to function as a “cultural technology” similar to writing that can summarize existing knowledge, Eunice Yiu, a co-author of the article, explained in an interview. But unlike humans, they struggle when it comes to innovating on these ideas, she said.
“Even young human children can produce intelligent responses to certain questions that [language learning models] cannot,” Yiu said. “Instead of viewing these AI systems as intelligent agents like ourselves, we can think of them as a new form of library or search engine. They effectively summarize and communicate the existing culture and knowledge base to us.”
Yiu and Eliza Kosoy, along with their doctoral advisor and senior author on the paper, developmental psychologist Alison Gopnik, tested how the AI systems’ ability to imitate and innovate differs from that of children and adults. They presented 42 children ages 3 to 7 and 30 adults with text descriptions of everyday objects. In the first part of the experiment, 88% of children and 84% of adults were able to correctly identify which objects would “go best” with another. For example, they paired a compass with a ruler instead of a teapot.
In the next stage of the experiment, 85% of children and 95% of adults were also able to innovate on the expected use of everyday objects to solve problems. In one task, for example, participants were asked how they could draw a circle without using a typical tool such as a compass. Given the choice between a similar tool like a ruler, a dissimilar tool such as a teapot with a round bottom, and an irrelevant tool such as a stove, the majority of participants chose the teapot, a conceptually dissimilar tool that could nonetheless fulfill the same function as the compass by allowing them to trace the shape of a circle.
When Yiu and colleagues provided the same text descriptions to five large language models, the models performed similarly to humans on the imitation task, with scores ranging from 59% for the worst-performing model to 83% for the best-performing model. The AIs’ answers to the innovation task were far less accurate, however. Effective tools were selected anywhere from 8% of the time by the worst-performing model to 75% by the best-performing model.

“Children can imagine completely novel uses for objects that they have not witnessed or heard of before, such as using the bottom of a teapot to draw a circle,” Yiu said. “Large models have a much harder time generating such responses.”
In a related experiment, the researchers noted, children were able to discover how a new machine worked just by experimenting and exploring. But when the researchers gave several large language models text descriptions of the evidence that the children produced, they struggled to make the same inferences, likely because the answers were not explicitly included in their training data, Yiu and colleagues wrote.
These experiments demonstrate that AI’s reliance on statistically predicting linguistic patterns is not enough to discover new information about the world, Yiu and colleagues wrote.
“AI can help transmit information that is already known, but it is not an innovator,” Yiu said. “These models can summarize conventional wisdom but they cannot expand, create, change, abandon, evaluate, and improve on conventional wisdom in the way a young human can.” The development of AI is still in its early days, though, and much remains to be learned about how to expand the learning capacity of AI, Yiu said. Taking inspiration from children’s curious, active, and intrinsically motivated approach to learning could help researchers design new AI systems that are better prepared to explore the real world, she said. More

McGill University researchers have made a breakthrough in diagnostic technology, inventing a ‘lab on a chip’ that can be 3D-printed in just 30 minutes. The chip has the potential to make on-the-spot testing widely accessible.
As part of a recent study, the results of which were published in the journal Advanced Materials, the McGill team developed capillaric chips that act as miniature laboratories. Unlike other computer microprocessors, these chips are single-use and require no external power source — a simple paper strip suffices. They function through capillary action — the very phenomena by which a spilled liquid on the kitchen table spontaneously wicks into the paper towel used to wipe it up.
“Traditional diagnostics require peripherals, while ours can circumvent them. Our diagnostics are a bit what the cell phone was to traditional desktop computers that required a separate monitor, keyboard and power supply to operate,” explains Prof. David Juncker, Chair of the Department of Biomedical Engineering at McGill and senior author on the study.
At-home testing became crucial during the COVID-19 pandemic. But rapid tests have limited availability and can only drive one liquid across the strip, meaning most diagnostics are still done in central labs. Notably, the capillaric chips can be 3D-printed for various tests, including COVID-19 antibody quantification.
The study brings 3D-printed home diagnostics one step closer to reality, though some challenges remain, such as regulatory approvals and securing necessary test materials. The team is actively working to make their technology more accessible, adapting it for use with affordable 3D printers. The innovation aims to speed up diagnoses, enhance patient care, and usher in a new era of accessible testing.
“This advancement has the capacity to empower individuals, researchers, and industries to explore new possibilities and applications in a more cost-effective and user-friendly manner,” says Prof. Juncker. “This innovation also holds the potential to eventually empower health professionals with the ability to rapidly create tailored solutions for specific needs right at the point-of-care.” More

A single strand of fiber developed at Washington State University has the flexibility of cotton and the electric conductivity of a polymer, called polyaniline.
The newly developed material showed good potential for wearable e-textiles. The WSU researchers tested the fibers with a system that powered an LED light and another that sensed ammonia gas, detailing their findings in the journal Carbohydrate Polymers.
“We have one fiber in two sections: one section is the conventional cotton: flexible and strong enough for everyday use, and the other side is the conductive material,” said Hang Liu, WSU textile researcher and the study’s corresponding author. “The cotton can support the conductive material which can provide the needed function.”
While more development is needed, the idea is to integrate fibers like these into apparel as sensor patches with flexible circuits. These patches could be part of uniforms for firefighters, soldiers or workers who handle chemicals to detect for hazardous exposures. Other applications include health monitoring or exercise shirts that can do more than current fitness monitors.
“We have some smart wearables, like smart watches, that can track your movement and human vital signs, but we hope that in the future your everyday clothing can do these functions as well,” said Liu. “Fashion is not just color and style, as a lot of people think about it: fashion is science.”
In this study, the WSU team worked to overcome the challenges of mixing the conductive polymer with cotton cellulose. Polymers are substances with very large molecules that have repeating patterns. In this case, the researchers used polyaniline, also known as PANI, a synthetic polymer with conductive properties already used in applications such as printed circuit board manufacturing.
While intrinsically conductive, polyaniline is brittle and by itself, cannot be made into a fiber for textiles. To solve this, the WSU researchers dissolved cotton cellulose from recycled t-shirts into a solution and the conductive polymer into another separate solution. These two solutions were then merged together side-by-side, and the material was extruded to make one fiber.

The result showed good interfacial bonding, meaning the molecules from the different materials would stay together through stretching and bending.
Achieving the right mixture at the interface of cotton cellulose and polyaniline was a delicate balance, Liu said.
“We wanted these two solutions to work so that when the cotton and the conductive polymer contact each other they mix to a certain degree to kind of glue together, but we didn’t want them to mix too much, otherwise the conductivity would be reduced,” she said.
Additional WSU authors on this study included first author Wangcheng Liu as well as Zihui Zhao, Dan Liang, Wei-Hong Zhong and Jinwen Zhang. This research received support from the National Science Foundation and the Walmart Foundation Project. More

Physician-investigators at Beth Israel Deaconess Medical Center (BIDMC) compared a chatbot’s probabilistic reasoning to that of human clinicians. The findings, published in JAMA Network Open, suggest that artificial intelligence could serve as useful clinical decision support tools for physicians.
“Humans struggle with probabilistic reasoning, the practice of making decisions based on calculating odds,” said the study’s corresponding author Adam Rodman, MD, an internal medicine physician and investigator in the department of Medicine at BIDMC. “Probabilistic reasoning is one of several components of making a diagnosis, which is an incredibly complex process that uses a variety of different cognitive strategies. We chose to evaluate probabilistic reasoning in isolation because it is a well-known area where humans could use support.”
Basing their study on a previously published national survey of more than 550 practitioners performing probabilistic reasoning on five medical cases, Rodman and colleagues fed the publicly available Large Language Model (LLM), Chat GPT-4, the same series of cases and ran an identical prompt 100 times to generate a range of responses.
The chatbot — just like the practitioners before them — was tasked with estimating the likelihood of a given diagnosis based on patients’ presentation. Then, given test results such as chest radiography for pneumonia, mammography for breast cancer, stress test for coronary artery disease and a urine culture for urinary tract infection, the chatbot program updated its estimates.
When test results were positive, it was something of a draw; the chatbot was more accurate in making diagnoses than the humans in two cases, similarly accurate in two cases and less accurate in one case. But when tests came back negative, the chatbot shone, demonstrating more accuracy in making diagnoses than humans in all five cases.
“Humans sometimes feel the risk is higher than it is after a negative test result, which can lead to overtreatment, more tests and too many medications,” said Rodman.
But Rodman is less interested in how chatbots and humans perform toe-to-toe than in how highly skilled physicians’ performance might change in response to having these new supportive technologies available to them in the clinic, added Rodman. He and colleagues are looking into it.
“LLMs can’t access the outside world — they aren’t calculating probabilities the way that epidemiologists, or even poker players, do. What they’re doing has a lot more in common with how humans make spot probabilistic decisions,” he said. “But that’s what is exciting. Even if imperfect, their ease of use and ability to be integrated into clinical workflows could theoretically make humans make better decisions,” he said. “Future research into collective human and artificial intelligence is sorely needed.”
Co-authors included Thomas A. Buckley, University of Massachusetts Amherst; Arun K. Manrai, PhD, Harvard Medical School; Daniel J. Morgan, MD, MS, University of Maryland School of Medicine.
Rodman reported receiving grants from the Gordon and Betty Moore Foundation. Morgan reported receiving grants from the Department of Veterans Affairs, the Agency for Healthcare Research and Quality, the Centers for Disease Control and Prevention, and the National Institutes of Health, and receiving travel reimbursement from the Infectious Diseases Society of America, the Society for Healthcare Epidemiology of America. The American College of Physicians and the World Heart Health Organization outside the submitted work. More

The use of generative AI in literature search suggests the possibility of efficiently collecting a vast amount of medical information, provided that users are well aware that the performance of generative AI is still in its infancy and that not all information presented is necessarily reliable. It is advised to use different generative AIs depending on the type of information needed.
Can AI save us from the arduous and time-consuming task of academic research collection? An international team of researchers investigated the credibility and efficiency of generative AI as an information-gathering tool in the medical field.
The research team, led by Professor Masaru Enomoto of the Graduate School of Medicine at Osaka Metropolitan University, fed identical clinical questions and literature selection criteria to two generative AIs; ChatGPT and Elicit. The results showed that while ChatGPT suggested fictitious articles, Elicit was efficient, suggesting multiple references within a few minutes with the same level of accuracy as the researchers.
“This research was conceived out of our experience with managing vast amounts of medical literature over long periods of time. Access to information using generative AI is still in its infancy, so we need to exercise caution as the current information is not accurate or up-to-date.” Said Dr. Enomoto. “However, ChatGPT and other generative AIs are constantly evolving and are expected to revolutionize the field of medical research in the future.”
Their findings were published in Hepatology Communications. More