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Thursday, August 27, 2026

 

Families bear hidden costs of finding safety



Study reveals the cost of becoming safe after violence




La Trobe University






Australian parents navigating the family courts amid family violence face substantial legal, health and security costs, with new research finding the average expenditure per parent is more than $41,000 a year. 

Led by La Trobe University, the research sheds light on a largely overlooked question in response to family violence: what does it cost families to become safe? 

Published in the Journal of Family Violence, the study followed 416 parents during their first year of involvement with the Australian family courts, examining expenditure on legal services, health care, counselling, wellbeing and physical security. 

Across these areas, parents spent an average of $41,041 on out-of-pocket expenses over the year. While some families incurred relatively modest expenses, others faced very high costs, particularly for legal services.  

Funded by an Australian Research Council grant, the research found the financial burden associated with family violence in the context of family separation extended well beyond legal costs.  

Professor Jennifer McIntosh, lead researcher from La Trobe’s Bouverie Centre, said the findings highlighted an aspect of family violence that is often missing from discussions about its economic impact. 

“We often talk about the enormous economic costs of family violence. What has received far less attention is the cost to families of trying to become safe,” Professor McIntosh said. 

“For parents navigating separation, family violence and the family law system at the same time, safety can require significant private expenditure such as legal assistance, health care, counselling and practical security measures.” 

The study also found important differences in where families' money was being spent. 

Mothers carried the greatest security and healthcare costs, while fathers incurred higher expenditure on legal and mediation services. 

One of the study's most significant findings concerned counselling, with expenditure on counselling for parents and children strongly associated with improved safety during the study period. 

Professor McIntosh said the findings warranted close attention and reinforced the need to prioritise therapeutic support in investments designed to help families move towards safety. 

“Children say it like it is, and when listened to, the path to change is rapid. For parents, being validated and helped to think clearly in the face of strong emotion are important tools in re-building safety after violence,” she said. 

“Our study shifts the economic question from simply asking, ‘what does family violence cost?’ to asking, ‘what investments help create safety, and who currently bears those costs?’” 

The researchers say the findings have implications across family law, family violence, health and social policy. 

In particular, the study raises questions about whether families experiencing violence have access to counselling services and support they need to recover and stay safe, and whether too much of the financial burden of achieving safety is currently being shouldered by affected parents. 

“Safety is not simply what remains when violence stops,” Professor McIntosh said.  

“Creating and sustaining safety requires resources. Understanding which resources make the greatest difference and ensuring families can access them, should be an important part of our response to family violence.” 

The researchers say further work, already underway, is needed to understand which forms of expenditure most effectively sustain safety over time and how public investment could reduce the financial burden on families experiencing violence. 

 

A novel framework to enhance high-resolution images taken in poor lighting conditions



Researchers devise a multistage approach that uses coarse enhancement to guide the recovery of fine details, outperforming state-of-the-art techniques




Chinese Association of Automation

Enhancing images taken in low-light conditions is challenging 

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Systems that can restore proper lighting in a low-light, high-resolution photograph often struggle to preserve finer details and structures, calling for new techniques.

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Credit: salmanghaffar15 from Flickr Image source link: https://www.flickr.com/photos/50717189@N05/22664940938






Modern cameras can capture an extraordinary amount of detail, producing images made up of millions of pixels that reveal textures, edges, and colors invisible to earlier generations of digital photography. In poor lighting conditions, however, much of that information becomes difficult to recover, with pictures coming out grainy and details getting lost in dark patches of the frame. Now that cameras are being increasingly used as inputs for computer-vision systems in domains such as surveillance, healthcare, and autonomous driving, the issues introduced by poor lighting extend beyond aesthetics.  

Interestingly, fixing this problem only gets harder as image resolution goes up. An ultra-high-definition (UHD) image contains both broad, scene-level information and extremely fine details, so machine learning-based enhancement systems must handle these scales carefully; they need to preserve overall illumination, color, and scene structure while properly recovering small features. On top of this, the huge number of pixels in a UHD image makes it difficult to use sophisticated neural networks on consumer-grade hardware. How can we use machine learning to enhance UHD low-light images, preserving global appearance and fine details, without excessive computational demands? 

To address this problem, a research team led by Professor Jiayi Ma and Dr. Hao Zhang from Wuhan University, China, has developed a new image enhancement method called LL-Refiner. Their study, published in Volume 13, Issue 6, of the IEEE/CAA Journal of Automatica Sinica on July 3, 2026, presents a framework designed specifically for the efficient enhancement of low-light UHD images.  

Rather than relying on a direct, one-step enhancement of the heavy high-resolution image, LL-Refiner operates in two coordinated stages. First, an enhanced coarse version of the image is produced at a lower resolution using a Transformer-based neural network, which efficiently handles global lighting, color distribution, and overall scene structure. This coarse result is then injected into an adaptive refinement network via cross-attention modules, progressively guiding the network to sharpen edges, textures, and fine text across hierarchical scales up to full resolution. 

The team tested LL-Refiner against several leading enhancement methods using real-world low-light datasets, including images captured with a smartphone camera under conditions different from those used in training. The results consistently favored the new approach, as Prof. Ma remarks: “Our method successfully preserves both the clarity of textual regions and the fine structure of patterns, demonstrating a balanced enhancement in both global consistency and local detail.” 

Beyond visual quality, the team also tested whether their enhanced images could improve performance in a separate computer-vision task, namely depth estimation, which is used in applications like robotics and autonomous navigation. Images enhanced with LL-Refiner led to more accurate depth predictions than images processed with other methods, indicating the improvements are not just cosmetic. “LL-Refiner was the only method to yield reasonably accurate background depth estimation,” highlights Prof. Ma. “The other approaches failed to capture background structures, indicating their limited effectiveness in supporting downstream tasks under low-light conditions.” 

Overall, the results suggest that this coarse-to-fine approach could inform future systems designed to process high-resolution images efficiently on consumer-grade hardware. In turn, this could serve as the foundation for various applications in photography, surveillance, and many computer-vision technologies that depend on images captured in difficult lighting. 

 

*** 

 

Reference
DOI: 10.1109/JAS.2026.125939 

 

About Wuhan University 
Wuhan University (WHU) is a comprehensive and key national university directly under the administration of the Ministry of Education. Founded in 1893 by Zhang Zhidong, it is one of the “211 Project” and “985 Project” universities that received full support in construction and development from the central and local governments of China. The university currently has over 53,000 students and 3,700 teachers and is recognized as one of China’s leading institutions for education and research, with a strong international presence and broad academic strengths spanning the sciences, engineering, medicine, humanities, and social sciences. 
Website: https://en.whu.edu.cn/ 

 

About Professor Jiayi Ma from Wuhan University 
Dr. Jiayi Ma received a B.S. degree in information and computing science and a Ph.D. degree in control science and engineering from Huazhong University of Science and Technology in 2008 and 2014, respectively. He is currently a Professor at both the Electronic Information School and the School of Robotics at Wuhan University. He has coauthored more than 400 refereed journal and conference papers, with publications in Cell, IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Computer Vision, and other prestigious journals.  

 

About Dr. Hao Zhang from Wuhan University 
Dr. Hao Zhang received a B.E. degree from the School of Mechanical Engineering and Electronic Information at the China University of Geosciences in 2019, as well as M.S. and Ph.D. degrees from Wuhan University in 2021 and 2024, respectively. He is currently a Postdoctoral Researcher with the Electronic Information School at Wuhan University. He has first-authored over 10 refereed journal and conference papers, with publications in IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Computer Vision, Conference on Computer Vision and Pattern Recognition (CVPR), NeurIPS, and AAAI, among others. His research interests include computer-vision, machine learning, and pattern recognition. 

 

Funding information 
This work was supported by the National Natural Science Foundation of China (625B2135, 62506268, and 62276192). 

 

World-first: AI guides live brain surgery in the UK to save patient's sight

Hani Marcus, neurosurgeon (left) and Rhys Hibbert (right).
Copyright UCLH

By Marta Iraola Iribarren
Published on


A British man has become the first patient in the world to undergo brain surgery guided live by artificial intelligence, in an operation that saved his sight.

A man in England has become the first patient to undergo live AI-assisted brain surgery, which protected his sight during the removal of a brain tumour, the University College London Hospitals announced on Thursday.

Rhys Hibbert, a 48-year-old from Bedfordshire, was diagnosed in December 2024 when he collapsed during a walk and suffered a seizure.

As his symptoms worsened, Hibbert opted for surgery and volunteered to take part in research.

“If patients are not prepared to join research how can doctors ever learn and how can medicine ever progress?” he said.

“Asked if I would be prepared to be the world's first patient to have this technology used live… I said, yes, of course.”

According to Hibbert’s doctors, the tumour would have continued to threaten his sight and could ultimately have led to blindness. The operation successfully removed the growth and protected his vision.

Hibbert said that upon waking from surgery, his vision had dramatically improved: “When I came round… I could see everything in the room clearly.”

The operation was carried out at the National Hospital for Neurology and Neurosurgery (NHNN), part of University College London Hospitals (UCLH), as part of a clinical trial using AI technology developed in-house at University College London and funded by the National Institute for Health and Care Research (NIHR).

During the surgery, the AI analysed the live surgical video feed in real time, rather than using pre-surgery scans, helping the surgical team make more precise decisions by highlighting critical structures at the base of the brain, UCLH explained.

In this part of the brain, the pituitary gland — which is the size of a marble — sits tightly blood vessels and nerves controlling vision. A margin of error of just a millimetre can prove critical, potentially leading to death, blindness or stroke.

The doctors explained that AI supported the surgical team by helping identify risky areas to avoid while removing as much of the tumour as safely possible.

It also has the potential to track surgical instruments and instrument–tissue interactions, with the aim of supporting surgeons during complex procedures and providing feedback.

“This is an example of AI at its best: patients getting care previously deemed unimaginable thanks to the latest groundbreaking technology,” said James Frith, the UK’s Health Innovation Minister.

He added that AI needs proper safeguards and we will always ensure that safety is taken seriously.


Major security weaknesses found in leading open AI models



International research team finds all 21 leading open-weight AI models tested could be modified to bypass safety protections, raising concerns about misuse at scale




University of Waterloo






Safety protections built into some of the world's most widely used artificial intelligence (AI) models can be stripped away with alarming ease, according to a new international study. 

The research team, led by the University of Waterloo and FAR.AI, a non-profit AI security research group, rigorously tested 21 of the most popular open-weight large language models (LLMs) and found they could all be tampered with despite their built-in safeguards. 

The holes in even the best protections currently available raise concerns open-weight models could be used to wage mass disinformation campaigns, create sophisticated email scams or produce step-by-step instructions to make hazardous chemicals. 

“When the safety guardrails are stripped out of a capable model, it can be used at scale for harm in ways a single person could never manage manually,” said Dr. Sirisha Rambhatla, a professor of management science and engineering at Waterloo. 

LLMs are advanced AI systems that can essentially understand and generate human language to perform tasks such as drafting emails, writing computer code and conversing with users. 

Unlike closed proprietary models such as ChatGPT and Gemini, open-weight LLMs are publicly available to be downloaded and fine-tuned for use by everybody from individual software developers to private companies and public organizations like hospitals. 

Rambhatla said the “sobering” results of testing by the team – which included members in Canada, the United States and Switzerland – should serve as a wake-up call to global researchers on the need to develop stronger security systems. 

“The leading open-weight models are often not too far behind the best closed models,” said Rambhatla, director of the Critical Machine Learning Lab at Waterloo. “As they grow more powerful, the potential consequences of someone stripping out their safety features grow with them.” 
 
While the study identified significant vulnerabilities, Rambhatla noted that the weaknesses may not be unique to open models. “Open-weight models remain essential to AI research and accountability,” Rambhatla said. "This openness is part of how we make sure the models people use work for everyone.” 

To test a cross-section of open-weight AI models, the research team first built an open-source tool called TamperBench, a standardized way to simulate a variety of different attacks. The hope is that other researchers will now help refine and improve it. 

“The defences available today don’t yet appear strong enough to guarantee that a publicly released model will remain safe once it’s in the hands of anyone who chooses to modify it,” said Saad Hossain, a researcher in the lab who led the study. 

“And as governments increasingly rely on AI in healthcare, fraud detection, education and other public services, the assessment of models and their procurement must be more rigorous and grounded in evidence.” 

The research team also included members from the Massachusetts Institute of Technology, ETH Zurich and the University of Toronto. 

A paper on its work, TamperBench: Systematically Stress-Testing LLM Safety Under Fine-Tuning and Tampering, was recently presented at the ACM Conference on Knowledge Discovery and Data Mining in South Korea. 

 

Turning scientific evidence into action for airborne disease control



Animation most effective in motivating preventive health behaviors, according to a new study published in Humanities and Social Sciences Communications, a Nature Portfolio journal




National Sun Yat-sen University

Turning Scientific Evidence into Action for Airborne Disease Control and Prevention 

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An international study involving 3,217 participants, including healthcare professionals, educators, and university students across Taiwan, Indonesia, Malaysia, and the Philippines found that animation was the most effective science communication format for improving understanding, strengthening empathy, and motivating disease-prevention behavior change. The study, led by the Aerosol Science Research Center at National Sun Yat-sen University, Taiwan, was published in Humanities and Social Sciences Communications, a Nature Portfolio journal.

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Credit: Aerosol Science Rersearch Center, National Sun Yat-sen University





NSYSU, TAIWAN — Even as the scientific community has reached broad consensus that infectious pathogens, such as viruses, can spread through airborne transmission in virus-laden aerosols, one fundamental challenge remains:

How can scientific evidence be transformed into public understanding—and ultimately into meaningful action for effective airborne disease control and prevention?

A new international study led by researchers at Aerosol Science Research Center, National Sun Yat-sen University (NSYSU), Taiwan, offers an evidence-based answer. Published in Humanities and Social Sciences Communications, a Nature Portfolio journal, the study demonstrates that animation is significantly more effective than text alone in motivating people to adopt preventive health behaviors.

The findings suggest that effective science communication is not simply about delivering accurate information. Rather, it is about helping people understand complex science, emotionally connect with its relevance, and translate that understanding into action.

 

From Knowledge to Action

The interdisciplinary research team evaluated three science communication formats—plain text, explainer comics, and animation — using "The Quest of the Virosols", an original educational series developed by the team of Aerosol Science Research Center, NSYSU following publication of the team's Science review article on airborne transmission of respiratory viruses (Wang et al, Science 373, eabd9149 (2021)).

The cross-national study involved 3,217 participants from Taiwan, Indonesia, Malaysia, and the Philippines, including healthcare professionals, educators, and university students. Researchers examined how different communication formats influenced scientific understanding and willingness to adopt preventive measures against airborne infectious diseases.

While all three formats improved participants' knowledge, animation consistently produced the strongest behavioral impact. Compared with plain text, animation and explainer comics made complex concepts surrounding airborne virus transmission substantially easier to understand. Animation, however, went one step further—significantly increasing participants' willingness to implement preventive measures.

 

Why Animation Works Better Than Comics and Plain Text

Professor Paichi Pat Shein, one of the study's corresponding authors, noted that animation's effectiveness lies not only in its visual appeal but also in its ability to create emotional engagement.

By combining narrative, imagery, and character-driven storytelling, animation allows viewers to better understand how airborne diseases affect themselves, their families, and society. This emotional connection strengthens risk perception, increases empathy, and ultimately encourages behavioral change.

The study also found that animation had an especially strong impact among female participants, possibly reflecting stronger concern for environmental issues, public health, and the well-being of others. These findings suggest that empathy may play an important role in translating scientific understanding into preventive action.

 

Science Communication as a Public Health Strategy

"The COVID-19 pandemic taught us that scientific evidence alone is not enough to change the world," said Professor Chia C. Wang, Director of the Aerosol Science Research Center, and Professor of Department of Chemistry at National Sun Yat-sen University and one of the study's corresponding authors.

"Science communication is not merely about transferring knowledge. It is about transforming knowledge into understanding, understanding into empathy, and empathy into action."

Professor Wang noted that the implications of the research extend well beyond aerosol science.

"Effective science communication should be recognized as an essential component of public health preparedness. Our findings demonstrate that helping people understand science is just as important as generating scientific evidence."

 

Beyond COVID-19

Originally developed during the COVID-19 pandemic, "The Quest of the Virosols" was designed to explain the science of airborne virus transmission through engaging comics and animation. The educational materials have since been translated into 20 languages, reaching audiences around the world.

The new study closes an important loop: educational resources created during the pandemic have now become the foundation of an international evidence-based evaluation demonstrating how science communication can influence human behavior.

The researchers argue that these findings have implications extending beyond infectious disease prevention. Effective science communication can strengthen public engagement with complex societal challenges, including climate change, environmental health, sustainability, and future global emergencies.

 

Supporting Global Sustainable Development from Inside Out

The study further highlights that effective science communication helps cultivate essential human capacities—including openness to learning, systems thinking, critical thinking, empathy, connectedness, communication, courage, and responsible resource use—closely aligning with the Inner Development Goals (IDGs).

By strengthening these capacities, science communication may accelerate progress toward multiple UN Sustainable Development Goals (SDGs) while fostering more resilient and informed societies.

"In our increasingly interconnected world," the researchers conclude, "we breathe the same air and share a common destiny. The challenge ahead is no longer simply generating more scientific knowledge— it is ensuring that knowledge becomes understanding, empathy, and collective action." The findings demonstrate that effective science communication should be recognized as an essential component of public health preparedness and evidence-informed policymaking. More broadly, the study demonstrates that effective science communication can serve as a bridge between scientific discovery and societal transformation.

This work contributes not only to better future pandemic preparedness, but also to broader efforts in public health communication, disease prevention, sustainability, and global resilience.

 

Link to the origainl explainer comics and animation of "The Quest of Virosols" developed by ASRC, NSYSU: https://aerosol.nsysu.edu.tw/en/scopes/108