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A new report from the Centers for Disease Control and Prevention (CDC) found that an estimated 1 in 31 U.S. children has autism; that's about a 15% increase from a 2020 report, which estimated 1 in 36. The latest numbers come from the CDC’s Autism and Developmental Disabilities Monitoring (ADDM) Network, which tracked diagnoses in 2022 among 8-year-old children.
Autism spectrum disorder (ASD) is a neurological disorder that refers to a broad range of conditions affecting social interaction. People with autism may experience challenges with social skills, repetitive behaviors, speech and nonverbal communication.
The news has experts like Florida Tech's Kimberly Sloman, Ph.D, weighing in on the matter. She noted that the definition of autism was expanded to include mild cases, which could explain the increase.
“Research shows that increased rates are largely due to increased awareness and changes to diagnostic criteria. Much of the increase reflects individuals who have fewer support needs, women and girls and others who may have been misdiagnosed previously," said Sloman. Her insight follows federal health secretary Robert F. Kennedy Jr.'s recent declaration, vowing to conduct further studies to identify environmental factors that could cause the disorder. In his remarks, he also miscategorized autism as a "preventable disease," prompting scrutiny from experts and media attention.
“Autism destroys families,” Kennedy said. “More importantly, it destroys our greatest resource, which is our children. These are children who should not be suffering like this.” Kennedy described autism as a “preventable disease,” although researchers and scientists have identified genetic factors that are associated with it. Autism is not considered a disease, but a complex disorder that affects the brain. Cases range widely in severity, with symptoms that can include delays in language, learning, and social or emotional skills. Some autistic traits can go unnoticed well into adulthood. Those who have spent decades researching autism have found no single cause. Besides genetics, scientists have identified various possible factors, including the age of a child’s father, the mother’s weight, and whether she had diabetes or was exposed to certain chemicals. Kennedy said his wide-ranging plan to determine the cause of autism will look at all of those environmental factors, and others. He had previously set a September deadline for determining what causes autism, but said Wednesday that by then, his department will determine at least “some” of the answers. The effort will involve issuing grants to universities and researchers, Kennedy said. He said the researchers will be encouraged to “follow the science, no matter what it says.” April 17 - Associated Press Sloman emphasized that experts are confident that autism has a strong genetic component, meaning there's an element of the disorder that may not be preventable. However, scientists are still working to understand the full scope of the disorder, and much is still unknown.
“We know that there’s a strong genetic component for autism, but environmental factors may interact with genetic susceptibility," Sloman said. "This is still not well understood.” Kimberly Sloman’s research interests include best practices for treating individuals with autism spectrum disorder (ASD). She studies the assessment and treatment of problem behavior with methods such as stereotypy, individualized skill assessments and generalization of treatment effects. Are you covering this story or looking to know more about autism and the research behind the disorder? Let us help.
Kimberly is available to speak with media about this subject. Simply click on her icon now to arrange an interview today.
When Florida Today columnist Tim Walters wanted to 'clear the air' about a popular conspiracy theory, he connected with Michael Splitt, an assistant professor at Florida Institute of Technology's College of Aeronautics with a focus on meteorology.
The "chemtrail" conspiracy follows the erroneous belief that condensation trails (contrails) that trail behind jets are actually being used on a large scale to manage radiation and combat global warming. In the column, Splitt argued against the conspiracy by explaining what might happen if that level of "climate engineering" was actually going on.
I recently wrote a column about the “chemtrail” conspiracy theory, and to say it caused quite a stir would be a serious understatement. My motivation for writing the piece came because there is a bill being looked at by the Florida legislature to address concerns of people who think the skies are being seeded by commercial airplanes with poisonous, weather-manipulating substances. Some of those raising concerns claim there are vague amorphous operatives in the federal government leading this charge. I decided I’d try to find answers, and I did so by asking someone credible in the field of weather sciences. Answers from climate expert Can the climate be altered by humans? The idea of trying to manipulate weather is called “climate engineering.” There is a form of this called solar geoengineering. “We've been doing things like this for decades in terms of, for example, fog management products. People have used this kind of methodology of adding things to the air to help get rid of fog, like the ice fog problem in Salt Lake City. So, there are places where people try to manage a local cloud layer,” Splitt said. However, it’s not done to a scale that would impact the country or globe. That’s where conspiracy theorists take climate engineering a step too far. There are those who say commercial airliners are spraying other substances like aluminum and barium (and other metallic) nano particles to reflect the sun's heat to reduce global warming. Splitt said if this were real, it might have the opposite effect. “When you have more contrails, it actually ends up warming the planet. The cirrus clouds created by aircraft and their reflective power isn't as much as let's say, the warming impact from below, from infrared radiation, so they end up being warmer." March 20 - Florida Today In the full column, Splitt also takes on other common misconceptions such as, "Why do some contrails last longer than others?" And, "Are ‘chemtrails’ steering, strengthening storms?" It's a worthwhile read for those interested in meteorology or conspiracy theories.
Are you curious or looking to know more about those chasing clouds?
Michael Splitt is available to speak with media. Contact Adam Lowenstein, Director of Media Communications at Florida Institute of Technology, at adam@fit.edu to arrange an interview today.
Conducting research at 5:30 a.m. may not be everybody’s first choice. But for Siddhartha Bhattacharyya and Ph.D. students Mohammed Abdul, Hafeez Khan and Parth Ganeriwala, it’s an essential part of the process for their latest endeavor.
Bhattacharyya and his students are developing a more efficient framework for creating and evaluating image-based machine learning classification models for autonomous systems, such as those guiding cars and aircraft. That process involves creating new datasets with taxiway and runway images for vision-based autonomous aircraft.
Just as humans need textbooks to fuel their learning, some machines are taught using thousands of photographs and images of the environment where their autonomous pupil will eventually operate. To help ensure their trained models can identify the correct course to take in a hyper-specific environment – with indicators such as centerline markings and side stripes on a runway at dawn – Bhattacharyya and his Ph.D. students chose a December morning to rise with the sun, board one of Florida Tech’s Piper Archer aircraft and photograph the views from above.
Bhattacharyya, an associate professor of computer science and software engineering, is exploring the boundaries of operation of efficient and effective machine-learning approaches for vision-based classification in autonomous systems. In this case, these machine learning systems are trained on video or image data collected from environments including runways, taxiways or roadways.
With this kind of model, it can take more than 100,000 images to help the algorithm learn and adapt to an environment. Today’s technology demands a pronounced human effort to manually label and classify each image.
This can be an overwhelming process.
To combat that, Bhattacharyya was awarded funding from NASA Langley Research Center to advance existing machine learning/computer vision-based systems, such as his lab’s “Advanced Line Identification and Notation Algorithm” (ALINA), by exploring automated labeling that would enable the model to learn and classify data itself – with humans intervening only as necessary. This measure would ease the overwhelming human demand, he said.
ALINA is an annotation framework that Hafeez and Parth developed under Bhattacharyya’s guidance to detect and label data for algorithms, such as taxiway line markings for autonomous aircraft.
Bhattacharyya will use NASA’s funding to explore transfer learning-based approaches, led by Parth, and few-shot learning (FSL) approaches, led by Hafeez. The researchers are collecting images via GoPro of runways and taxiways at airports in Melbourne and Grant-Valkaria with help from Florida Tech’s College of Aeronautics.
Bhattacharyya’s students will take the data they collect from the airports and train their models to, in theory, drive an aircraft autonomously. They are working to collect diverse images of the runways – those of different angles and weather and lighting conditions – so that the model learns to identify patterns that determine the most accurate course regardless of environment or conditions. That includes the daybreak images captured on that December flight.
“We went at sunrise, where there is glare on the camera. Now we need to see if it’s able to identify the lines at night because that’s when there are lights embedded on the taxiways,” Bhattacharyya said. “We want to collect diverse datasets and see what methods work, what methods fail and what else do we need to do to build that reliable software.” Transfer learning is a machine learning technique in which a model trained to do one task can generalize information and reuse it to complete another task. For example, a model trained to drive autonomous cars could transfer its intelligence to drive autonomous aircraft. This transfer helps explore generalization of knowledge. It also improves efficiency by eliminating the need for new models that complete different but related tasks. For example, a car trained to operate autonomously in California could retain generalized knowledge when learning how to drive in Florida, despite different landscapes.
“This model already knows lines and lanes, and we are going to train it on certain other types of lines hoping it generalizes and keeps the previous knowledge,” Bhattacharyya explained. “That model could do both tasks, as humans do.” FSL is a technique that teaches a model to generalize information with just a few data samples instead of the massive datasets used in transfer learning. With this type of training, a model should be able to identify an environment based on just four or five images.
“That would help us reduce the time and cost of data collection as well as time spent labeling the data that we typically go through for several thousands of datasets,” Bhattacharyya said. Learning when results may or may not be reliable is a key part of this research. Bhattacharyya said identifying degradation in the autonomous system’s performance will help guide the development of online monitors that can catch errors and alert human operators to take corrective action.
Ultimately, he hopes that this research can help create a future where we utilize the benefits of machine learning without fear of it failing before notifying the operator, driver or user.
“That’s the end goal,” Bhattacharyya said. “It motivates me to learn how the context relates to assumptions associated with these images, that helps in understanding when the autonomous system is not confident in its decision, thus sending an alert to the user. This could apply to a future generation of autonomous systems where we don’t need to fear the unknown – when the system could fail.” Siddhartha (Sid) Bhattacharyya’s primary area of research expertise/interest is in model based engineering, formal methods, machine learning engineering, and explainable AI applied to intelligent autonomous systems, cyber security, human factors, healthcare, explainable AI, and avionics. His research lab ASSIST (Assured Safety, Security, and Intent with Systematic Tactics) focuses on the research in the design of innovative formal methods to assure performance of intelligent systems, machine learning engineering to characterize intelligent systems for safety and model based engineering to analyze system behavior.
Siddhartha Bhattacharyya is available to speak with media. Contact Adam Lowenstein, Director of Media Communications at Florida Institute of Technology at adam@fit.edu to arrange an interview today.
