Tuesday, February 27, 2024

 

Carnegie Mellon University researchers learn much from in-home test of adaptive robot interface


Head-worn assistive device impresses expert evaluator Henry Evans during trial


Peer-Reviewed Publication

CARNEGIE MELLON UNIVERSITY

HAT_Henry_talking_kitchen 

IMAGE: 

HENRY EVANS IN HIS KITCHEN TALKING WITH AKHIL PADMANABHA AND JANAVI GUPTA, RESEARCHERS FROM CARNEGIE MELLON UNIVERSITY'S SCHOOL OF COMPUTER SCIENCE WHO CONDUCTED AN IN-HOME TEST OF Head-Worn Assistive Teleoperation (HAT) — AN EXPERIMENTAL INTERFACE TO CONTROL A MOBILE ROBOT. EVANS, A PERSON WITH QUADRIPLEGIA, USES A LETTERBOARD, A TOOL THAT ALLOWS HIM TO SPELL OUT WORDS BY LOOKING AT OR POINTING A LASER AT EACH LETTER, TOM COMMUNICATE.

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CREDIT: CARNEGIE MELLON UNIVERSITY





No one could blame Carnegie Mellon University students Akhil Padmanabha and Janavi Gupta if they were a bit anxious this past August as they traveled to the Bay Area home of Henry and Jane Evans.

The students were about to live with strangers for the next seven days. On top of that, Henry, a person with quadriplegia, would spend the week putting their Head-Worn Assistive Teleoperation (HAT) — an experimental interface to control a mobile robot — to the test.

HAT requires fewer fine motor skills than other interfaces to help people with some form of paralysis or similar motor impairments control a mobile robot and manipulator. It allows users to control a mobile robot via head motion and speech recognition, and versions of the device have featured a hands-free microphone and head-worn sensor.

Padmanabha and Gupta quickly realized that any trepidation they may have felt was misplaced. Henry, who lost the ability to move his limbs and talk after a brain-stem stroke two decades ago, enjoyed using HAT to control the robot by moving his head and in some situations preferred HAT to the computer screen he normally uses.

"We were excited to see it work well in the real world," said Padmanabha, a Ph.D. student in robotics who leads the HAT research team. "Henry became increasingly proficient in using HAT over the week and gave us lots of valuable feedback."

During the home trial, the researchers had Henry perform predefined tasks, such as fetching a drink, feeding himself and scratching an itch. Henry directed a robot — Stretch, a commercially available mobile robot outfitted with a pincer-like gripper on its single arm — using HAT to control it.

Daily, Henry performed the so-called blanket+tissue+trash task, which involved moving a blanket off his body, grabbing a tissue and wiping his face with it, and then throwing the tissue away. As the week progressed, Henry could do it faster and faster and with fewer errors.

Henry said he preferred using HAT with a robot for certain tasks rather than depending on a caregiver.

"Definitely scratching itches," he said. "I would be happy to have it stand next to me all day, ready to do that or hold a towel to my mouth. Also, feeding me soft foods, operating the blinds and doing odd jobs around the room."

One innovation in particular, software called Driver Assistance that helps align the robot's gripper with an object the user wants to pick up, was "awesome," Henry said. Driver Assistance leaves the user in control while it makes the fine adjustments and corrections that can make controlling a robot both tedious and demanding.

"That's better than anything I have tried for grasping," Henry said, adding that he would like to see Driver Assistance used for every interface that controls Stretch robots.

Praise from Henry, as well as his suggestions for improving HAT, is no small thing. He has collaborated in multiple research projects, including the development of Stretch, and his expertise is widely admired within the assistive robotics community. He's even been featured by the Washington Post and last year appeared on the cover of IEEE Spectrum.

Via email, Henry said his incentive for participating in research is simple. "Without technology I would spend each day staring at the ceiling waiting to die," he said. "To be able to manipulate my environment again according to my will is motivation enough."

Padmanabha said user-centered or participatory design is important within the assistive device community and requires getting feedback from potential users at every step. Henry's feedback proved extremely helpful and gave the team new ideas to think about as they move forward.

The HAT researchers will present the results of their study at the ACM/IEEE International Conference on Human-Robot Interaction March 11–15 in Boulder, Colorado.

HAT originated more than two years ago in a project course taught by Zackory Erickson, an assistant professor in the Robotics Institute. The students contacted Henry as part of their customer discovery process. Even then, he was excited about the possibility of using a prototype.

The project showed promise and later was spun out of the class. An early version of HAT was developed and tested in the lab by participants both with and without motor impairments. When it came time to do an in-home case study, Henry seemed the logical person to start with.

During the weeklong study, Padmanabha and Gupta lived in the Evans home around the clock, both for travel convenience and to be able to perform testing whenever Henry was ready. Having strangers in the house 24/7 is typical of the studies Henry's been involved in and is no big deal for him or Jane.

"We're both from large families," he said.

Padmanabha and Gupta, a computer science major, likewise adjusted quickly to the new surroundings and got used to communicating with Henry using a letterboard, a tool that allows Henry to spell out words by looking at or pointing a laser at each letter. The pair even played poker with Henry and Jane, with Henry using Stretch to manipulate his cards.

In the earlier tests, HAT used head movements and voice commands to control a robot. Henry can't speak, but he can move his left thumb just enough to click a computer mouse. So the team reconfigured HAT for the Evans trial, substituting computer clicks for voice commands as a way to shift between modes that include controlling the movement of the robot base, arm or wrist, or pausing the robot.

"Among people with motor impairments, everyone has different levels of motor function," Padmanabha said. "Some may have head movement, others may only have speech, others just have clicking capabilities. So it's important that you allow for customization of your interface."

Head motions are key to using HAT, which detects head movement using a sensor in a cap, headband or — in Henry's case — a chin strap.

"People use head gesturing as a way to communicate with each other and I think it's a natural way of controlling or gesturing to a robot," Padmanabha said.

A graphical user interface — a computer screen — is more typical for controlling robots. But Gupta said users don't like using a computer screen to control a robot that is operating around their body.

"It can be scary to have a robot close to your face, trying to feed you or wipe your face," she said. Many user studies therefore shy away from attempting tasks that come close to the face. But once Henry got used to HAT, he didn't hesitate to perform such tasks, she added.

A computer screen is available to control Stretch in tasks that are out of the user's line of sight, such as sending the robot to fetch something from another room. At Henry's suggestion, the researchers made it possible to use HAT to control a computer cursor with head movements.

In addition to Gupta, Padmanabha and Erickson, the research team includes CMU's Carmel Majidi, the Clarence H. Adamson Professor of Mechanical Engineering; Douglas Weber, the Akhtar and Bhutta Professor of Mechanical Engineering; and Jehan Yang, a Ph.D. student in mechanical engineering. Also included are Vy Nguyen of Hello Robot Inc, maker of Stretch; and Chen Chen, an undergraduate at Tsinghua University in Beijing, who implemented the Driver Assistance software.

Though Stretch is commercially available, it is still primarily used by researchers and CMU has 10–15 of them. It's a simple robot with limited capabilities, but Padmanabha said its approximate $25,000 price tag inspires hope for expanded use of mobile robots.

"We're getting to the price point where we think robots could be in the home in the near future," he said.

Henry said Stretch/HAT still needs systemwide debugging and added features before it is more widely adopted. He thinks that might occur in as little as five years, though that will depend not only on price and features, but the choice of market.

"I believe the market for elderly people is larger and more affluent and will therefore develop faster than the market for people with disabilities," he said.


Henry Evans plays cards with his wife, Jane. Evans, a person with quadriplegia, tested Head-Worn Assistive Teleoperation (HAT) — an experimental interface to control a mobile robot — in his home, using the interface to control a robot to accomplish daily tasks. Akhil Padmanabha and Janavi Gupta, researchers from Carnegie Mellon University's School of Computer Science, developed HAT and stayed with the Evans during the test. 

Henry Evans uses Head-Worn Assistive Teleoperation (HAT) to control a robot to manipulate a blanket. HAT is an experimental interface to control a mobile robot developed by Akhil Padmanabha and Janavi Gupta, researchers from Carnegie Mellon University's School of Computer Science. Evans, a person with quadriplegia, tested HAT in his home and used it to complete daily tasks.

Henry Evans used a chin strap to interact with the Head-Worn Assistive Teleoperation (HAT) interface he tested in his home. HAT is an experimental interface to control a mobile robot developed by Akhil Padmanabha and Janavi Gupta, researchers from Carnegie Mellon University's School of Computer Science. Evans, a person with quadriplegia, tested HAT in his home and used it to complete daily tasks.

Henry Evans, a person with quadriplegia, tested the Head-Worn Assistive Teleoperation (HAT) interface in his home and used it to complete daily tasks.

CREDIT

Carnegie Mellon University


 

Poor sleep health associated with muscle dysmorphia in Canadian young adults


New research finds that individuals who experienced symptoms of muscle dysmorphia were more likely to have shorter sleep duration and greater difficulty falling or staying asleep


Peer-Reviewed Publication

UNIVERSITY OF TORONTO





Toronto, ON – Getting enough sleep is crucial for our body to maintain vital health functions and is especially important for the growth and development of adolescents and young adults. But a new study from the University of Toronto’s Factor-Inwentash Faculty of Social Work found an association between poor sleep and symptoms of muscle dysmorphia, the pathological pursuit of muscularity that is increasing in prevalence among young people.

Published in the journal Sleep Health, the study surveyed over 900 adolescents and young adults. Participants who reported experiencing greater symptoms of muscle dysmorphia reported fewer hours of sleep and greater difficulty when falling asleep or staying asleep more than half the time over a two-week period.

“Poor sleep can have significant negative impacts for adolescents and young adults, including increased negative mental health symptoms,” says lead author Kyle T. Ganson, PhD, MSW, assistant professor at the University of Toronto’s Factor-Inwentash Faculty of Social Work. “Poor sleep among those who experience muscle dysmorphia symptoms is concerning as it may exacerbate the functional and social impairment these individuals commonly report, as well as increase suicidal thoughts and behaviors.”

Prior research supports this cause for concern. Past studies indicate that on average adolescents and young adults are sleeping less than the recommended 7 to 10 hours per night. A plethora of research has also found that poor sleep is a marker of mental health diagnoses and associated with symptoms of anxiety, depression, and psychosis. Ganson and his colleague’s study is the first to investigate the relationship between sleep and muscle dysmorphia.

The mechanisms connecting greater muscle dysmorphia symptomatology and poor sleep may be multifaceted, say the study’s authors. For example, those who have greater intolerance for their appearance, who engage in obsessive thinking and who experience anxiety related to one’s body and muscularity may experience impaired sleep. As well, for some, sleep may be displaced by physical activity, as an individual engages in muscle-building exercise during the evening hours so as to not interfere with occupational responsibilities.

“Individuals experiencing symptoms of muscle dysmorphia may be more likely to use and consume dietary supplements that are marketed for improving workouts, increasing muscle mass, and accelerating muscle recovery,” says Ganson. “These products tend to have high levels of caffeine or other stimulants which may negatively impact sleep. In addition, anabolic-androgenic steroids, which are commonly used among people with muscle dysmorphia, have also been shown to negatively impact sleep.”

The authors underscore the need for healthcare professionals to be alerted to these findings given the continued emphasis of the muscular body ideal and pursuits of muscularity among adolescents and young adults. Additionally, future research is needed to continue to explore the connection between muscle dysmorphia symptoms poor sleep to ensure a holistic care approach.

 

Researchers look at environmental impacts of AI tools


Peer-Reviewed Publication

RADIOLOGICAL SOCIETY OF NORTH AMERICA






OAK BROOK, Ill. – As artificial intelligence (AI) is increasingly used in radiology, researchers caution that it’s essential to consider the environmental impact of AI tools, according to a focus article published today in Radiology, a journal of the Radiological Society of North America (RSNA).

Health care and medical imaging significantly contribute to the greenhouse gas (GHG) emissions fueling global climate change. AI tools can improve both the practice of and sustainability in radiology through optimized imaging protocols resulting in shorter scan times, improved scheduling efficiency to reduce patient travel, and the integration of decision-support tools to reduce low-value imaging. But there is a downside to AI utilization.

“Medical imaging generates a lot of greenhouse gas emissions, but we often don’t think about the environmental impact of associated data storage and AI tools,” said Kate Hanneman, M.D., M.P.H., vice chair of research and associate professor at the University of Toronto and deputy lead of sustainability at the Joint Department of Medical Imaging, Toronto General Hospital. “The development and deployment of AI models consume large amounts of energy, and the data storage needs in medical imaging and AI are growing exponentially.”

Dr. Hanneman and a team of researchers looked at the benefits and downsides of incorporating AI tools into radiology. AI offers the potential to improve workflows, accelerate image acquisition, reduce costs and improve the patient experience. However, the energy required to develop AI tools and store the associated data significantly contributes to GHG.

“We need to do a balancing act, bridging to the positive effects while minimizing the negative impacts,” Dr. Hanneman said. “Improving patient outcomes is our ultimate goal, but we want to do that while using less energy and generating less waste.”

Developing AI models requires large amounts of training data that health care institutions must store along with the billions of medical images generated annually. Many health systems use cloud storage, meaning the data is stored off-site and accessed electronically when needed.

“Even though we call it cloud storage, data are physically housed in centers that typically require large amounts of energy to power and cool,” Dr. Hanneman said. “Recent estimates suggest that the total global GHG emissions from all data centers is greater than the airline industry, which is absolutely staggering.”

The location of a data center has a massive impact on its sustainability, especially if it’s in a cooler climate or in an area where renewable energy sources are available.

To minimize the overall environmental impact of data storage, the researchers recommended sharing resources and, where possible, collaborating with other providers and partners to distribute the expended energy more broadly.

To decrease GHG emissions from data storage and the AI model development process, the researchers also offered other suggestions. These included exploring computationally efficient AI algorithms, selecting hardware that requires less energy, using data compression techniques, removing redundant data, implementing tiered storage systems and partnering with providers that use renewable energy.

“Departments that manage their cloud storage can take immediate action by choosing a sustainable partner,” she said.

Dr. Hanneman said although challenges and knowledge gaps remain, including limited data on radiology specific GHG emissions, resource constraints and complex regulations, she hopes sustainability will become a quality metric in the decision-making process around AI and radiology.

“Environmental costs should be considered along with financial costs in health care and medical imaging,” she said. “I believe AI can help us improve sustainability if we apply the tools judiciously. We just need to be mindful and aware of its energy usage and GHG emissions.”

###

“Environmental Sustainability and AI in Radiology: A Double-Edged Sword.” Collaborating with Dr. Hanneman were Florence X. Doo, M.D., M.A., Jan Vosshenrich, M.D., Tessa S. Cook, M.D., Ph.D., Linda Moy, M.D., Eduardo P.R.P. Almeida, M.D., Sean A. Woolen, M.D., M.Sc., Judy Wawira Gichoya, M.D., M.S., and Tobias Heye, M.D.

Radiology is edited by Linda Moy, M.D., New York University, New York, N.Y., and owned and published by the Radiological Society of North America, Inc. (https://pubs.rsna.org/journal/radiology)

RSNA is an association of radiologists, radiation oncologists, medical physicists and related scientists promoting excellence in patient care and health care delivery through education, research and technologic innovation. The Society is based in Oak Brook, Illinois. (RSNA.org)

For patient-friendly information on medical imaging, visit RadiologyInfo.org.

 

Gardeners can help identify potentially invasive plants


Peer-Reviewed Publication

PENSOFT PUBLISHERS

Mexican fleabane (Erigeron karvinskianus) 

IMAGE: 

MEXICAN FLEABANE (ERIGERON KARVINSKIANUS)

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CREDIT: KENPEI VIA WIKIMEDIA COMMONS




The critical role of gardeners in identifying 'future invaders' - ornamental plants that could become invasive species – has been revealed by researchers from the University of Reading and the Royal Horticultural Society.

Looking to draw from the experience of Britain's millions of gardeners, the team created an online survey where gardeners reported ornamentals that showed ‘invasive behaviour’ in their gardens.

Based on reports from 558 gardeners, 251 different plants were identified as potential invaders, reflecting the extensive variety and potential risks in domestic gardens. The team analysed the results, considering both domestic and global invasive status, and prioritised ornamental plants of concern. The result was a shortlist of plants which need their invasive potential in Britain and Ireland assessed.

The shortlisted plants include, for example: Mexican fleabane (Erigeron karvinskianus); cypress spurge (Euphorbia cyparissias); chameleon plant (Houttuynia cordata); Himalayan honeysuckle (Leycesteria formosa); and purple top (Verbena bonariensis).

The results, published in the open-access journal NeoBiota, highlight the critical role of gardeners in the early detection of invasive species, a key factor in the global nature crisis. Such proactive identification could prove invaluable for future risk assessments and prevention strategies.

Lead author Tomos Jones said: “The simple yet structured scheme we developed was used to prioritise which of the around 70,000 ornamental plants available to buy in the UK could be future invaders. This is crucial for focusing research efforts and resources, such as conducting formal risk assessments to explore the invasive potential of those shortlisted.”

John David, RHS Head of Horticultural Taxonomy, said: “It’s important to remember that these shortlisted plants are not yet officially invasive, and that many non-native plants that occur in the wild present no threat to our native biodiversity.”

Gardeners are encouraged to get involved in helping identify future invaders. Ornamental plants showing ‘invasive behaviour’ can be reported by gardeners through an ongoing project called Plant Alert. This is run by the Botanical Society of Britain and Ireland and Coventry University.

Original source:

Jones TS, Culham A, Pickles BJ, David J (2024) Can gardeners identify ‘future invaders’? NeoBiota 91: 125–144. https://doi.org/10.3897/neobiota.91.110560


Cypress spurge (Euphorbia cyparissias)

CREDIT

AnRo0002 via Wikimedia Commons

chameleon plant (Houttuynia cordata) (IMAGE)

PENSOFT PUBLISHERS

 

Female psychopaths ‘more common than we think’


Talk at Cambridge Festival will explain how current thinking may be misguided

Meeting Announcement

ANGLIA RUSKIN UNIVERSITY




Female psychopaths are up to five times more common than previously thought, according to an expert who will present his work at the Cambridge Festival later this month.
 
Current scientific evidence suggests that male psychopaths outnumber females by around 6:1. However, expert in corporate psychopathy, Dr Clive Boddy of Anglia Ruskin University (ARU), argues that studies may be failing to identify female psychopaths because they are largely based around profiles of criminal and male psychopaths.
 
During his talk at ARU’s Cambridge campus on Saturday, 16 March, Dr Boddy will argue that the characteristics of female psychopaths differ from males and that gender bias plays a role in the under-reporting, with society ignoring what people perceive to be male traits when they are displayed by women.
 
Dr Boddy will present his own research which shows that using measures of primary psychopathy, which exclude psychopathy’s antisocial behavioural characteristics and concentrate on its core elements, the real ratio of male female psychopathy may be about 1.2:1 – up to five times higher than previously suggested.
 
Referencing research into corporate psychopaths and how they operate in high-achieving roles in the workplace, Dr Boddy will explain how female psychopaths are more manipulative than males, use different techniques to create a good impression, and utilise deceit and sexually seductive behaviour to gain social and financial advantage more than male psychopaths do.
 
Dr Boddy, Deputy Head of the School of Management at Anglia Ruskin University (ARU), said:

“People generally attribute psychopathic characteristics to males rather than to females. So even when females display some of the key traits associated with psychopathy – such as being insincere, deceitful, antagonistic, unempathetic and lacking in emotional depth – because these are seen as male characteristics they may not be labelled as such, even when they should be.
 
“Also, female psychopaths tend to use words, rather than violence, to achieve their aims, differing from how male psychopaths tend to operate. If female psychopathy expresses differently, then measures designed to capture and identify male, criminal, psychopaths may be inadequate at identifying female non-criminal, psychopaths.
 
“Female psychopaths, while not as severely psychopathic or as psychopathic as often as males are, have nevertheless been underestimated in their incidence levels and are therefore more of a potential threat to business and society than anyone previously suspected.
 
“This has implications for the criminal justice system because current risk management decisions involving partners and children may be faulty. It also has implications for organisational leadership selection decisions because female leaders cannot automatically be assumed to be more honest, caring and concerned with issues such as corporate social responsibility.”

 
Dr Clive Boddy has been researching the effects of having psychopaths in the workplace since 2005 and has published more on corporate psychopaths than any other academic. His research interests include toxic leadership and particularly the effects of corporate psychopaths on employees, organisations and society.
 
Dr Boddy’s talk at 6pm on Saturday, 16 March will take place at ARU’s campus in East Road, Cambridge, and will also be available to attend virtually. Places are free but must be booked in advance here for the in-person event, and here for the virtual event.

 

New AI model could streamline operations in a robotic warehouse


By breaking an intractable problem into smaller chunks, a deep-learning technique identifies the optimal areas for thinning out traffic in a warehouse.


Reports and Proceedings

MASSACHUSETTS INSTITUTE OF TECHNOLOGY




CAMBRIDGE, MA -- Hundreds of robots zip back and forth across the floor of a colossal robotic warehouse, grabbing items and delivering them to human workers for packing and shipping. Such warehouses are increasingly becoming part of the supply chain in many industries, from e-commerce to automotive production.

However, getting 800 robots to and from their destinations efficiently while keeping them from crashing into each other is no easy task. It is such a complex problem that even the best path-finding algorithms struggle to keep up with the breakneck pace of e-commerce or manufacturing. 

In a sense, these robots are like cars trying to navigate a crowded city center. So, a group of MIT researchers who use AI to mitigate traffic congestion applied ideas from that domain to tackle this problem.

They built a deep-learning model that encodes important information about the warehouse, including the robots, planned paths, tasks, and obstacles, and uses it to predict the best areas of the warehouse to decongest to improve overall efficiency.

Their technique divides the warehouse robots into groups, so these smaller groups of robots can be decongested faster with traditional algorithms used to coordinate robots. In the end, their method decongests the robots nearly four times faster than a strong random search method.

In addition to streamlining warehouse operations, this deep learning approach could be used in other complex planning tasks, like computer chip design or pipe routing in large buildings.

“We devised a new neural network architecture that is actually suitable for real-time operations at the scale and complexity of these warehouses. It can encode hundreds of robots in terms of their trajectories, origins, destinations, and relationships with other robots, and it can do this in an efficient manner that reuses computation across groups of robots,” says Cathy Wu, the Gilbert W. Winslow Career Development Assistant Professor in Civil and Environmental Engineering (CEE), and a member of a member of the Laboratory for Information and Decision Systems (LIDS) and the Institute for Data, Systems, and Society (IDSS).

Wu, senior author of a paper on this technique, is joined by lead author Zhongxia Yan, a graduate student in electrical engineering and computer science. The work will be presented at the International Conference on Learning Representations.

Robotic Tetris

From a bird’s eye view, the floor of a robotic e-commerce warehouse looks a bit like a fast-paced game of “Tetris.”

When a customer order comes in, a robot travels to an area of the warehouse, grabs the shelf that holds the requested item, and delivers it to a human operator who picks and packs the item. Hundreds of robots do this simultaneously, and if two robots’ paths conflict as they cross the massive warehouse, they might crash.

Traditional search-based algorithms avoid potential crashes by keeping one robot on its course and replanning a trajectory for the other. But with so many robots and potential collisions, the problem quickly grows exponentially.

“Because the warehouse is operating online, the robots are replanned about every 100 milliseconds. That means that every second, a robot is replanned 10 times. So, these operations need to be very fast,” Wu says.

Because time is so critical during replanning, the MIT researchers use machine learning to focus the replanning on the most actionable areas of congestion — where there exists the most potential to reduce the total travel time of robots.

Wu and Yan built a neural network architecture that considers smaller groups of robots at the same time. For instance, in a warehouse with 800 robots, the network might cut the warehouse floor into smaller groups that contain 40 robots each.

Then, it predicts which group has the most potential to improve the overall solution if a search-based solver were used to coordinate trajectories of robots in that group.

An iterative process, the overall algorithm picks the most promising robot group with the neural network, decongests the group with the search-based solver, then picks the next most promising group with the neural network, and so on.

Considering relationships

The neural network can reason about groups of robots efficiently because it captures complicated relationships that exist between individual robots. For example, even though one robot may be far away from another initially, their paths could still cross during their trips.

The technique also streamlines computation by encoding constraints only once, rather than repeating the process for each subproblem. For instance, in a warehouse with 800 robots, decongesting a group of 40 robots requires holding the other 760 robots as constraints. Other approaches require reasoning about all 800 robots once per group in each iteration.

Instead, the researchers’ approach only requires reasoning about the 800 robots once across all groups in each iteration.

“The warehouse is one big setting, so a lot of these robot groups will have some shared aspects of the larger problem. We designed our architecture to make use of this common information,” she adds.

They tested their technique in several simulated environments, including some set up like warehouses, some with random obstacles, and even maze-like settings that emulate building interiors.

By identifying more effective groups to decongest, their learning-based approach decongests the warehouse up to four times faster than strong, non-learning-based approaches. Even when they factored in the additional computational overhead of running the neural network, their approach still solved the problem 3.5 times faster.

In the future, the researchers want to derive simple, rule-based insights from their neural model, since the decisions of the neural network can be opaque and difficult to interpret. Simpler, rule-based methods could also be easier to implement and maintain in actual robotic warehouse settings.

###

This work was supported by Amazon and the MIT Amazon Science Hub.

 

 JAMA

Changes in health care workers’ economic outcomes following Medicaid expansion

JAMA

Peer-Reviewed Publication

JAMA NETWORK




About The Study: In this study, only health care workers in higher-earning occupations (e.g., registered nurses, physicians, and managers) experienced increases in annual income after state-level Medicaid expansion, which has been shown to improve health care organization finances. These findings suggest that improvements in health care sector finances may increase economic inequality among health care workers, with implications for worker health and well-being. 

Authors: Sasmira Matta, M.H.S., of the University of Pennsylvania in Philadelphia, is the corresponding author.

To access the embargoed study: Visit our For The Media website at this link https://media.jamanetwork.com/

(doi:10.1001/jama.2023.27014)

Editor’s Note: Please see the article for additional information, including other authors, author contributions and affiliations, conflict of interest and financial disclosures, and funding and support.

#  #  #

Embed this link to provide your readers free access to the full-text article This link will be live at the embargo time https://jamanetwork.com/journals/jama/fullarticle/10.1001/jama.2023.27014?guestAccessKey=9da27dd1-d4fe-4b45-9664-a400d8e1be8b&utm_source=For_The_Media&utm_medium=referral&utm_campaign=ftm_links&utm_content=tfl&utm_term=022724



Socioeconomic status, palliative care, and death at home among patients with cancer before and during covid-19

JAMA Network Open

Peer-Reviewed Publication

JAMA NETWORK




About The Study: The findings of this study of 173,000 adult patients who died with cancer suggest that the COVID-19 pandemic was associated with amplified socioeconomic disparities in death at home and specialized palliative care delivery at the end of life. Future research should focus on the mechanisms of these disparities and on developing interventions to ensure equitable and consistent specialized palliative care access. 

Authors: Camilla Zimmermann, M.D., M.P.H., Ph.D., of the Princess Margaret Cancer Centre in Toronto, is the corresponding author. 

To access the embargoed study: Visit our For The Media website at this link https://media.jamanetwork.com/ 

(doi:10.1001/jamanetworkopen.2024.0503)

Editor’s Note: Please see the article for additional information, including other authors, author contributions and affiliations, conflict of interest and financial disclosures, and funding and support.

#  #  #

Embed this link to provide your readers free access to the full-text article This link will be live at the embargo time http://jamanetwork.com/journals/jamanetworkopen/fullarticle/10.1001/jamanetworkopen.2024.0503?utm_source=For_The_Media&utm_medium=referral&utm_campaign=ftm_links&utm_term=022724

About JAMA Network Open: JAMA Network Open is an online-only open access general medical journal from the JAMA Network. On weekdays, the journal publishes peer-reviewed clinical research and commentary in more than 40 medical and health subject areas. Every article is free online from the day of publication.