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Pittsburgh self-driving safety firm Edge Case announces $7 million in funding

A Pittsburgh company working to make self-driving cars see better and ultimately be safer announced Thursday it raised $7 million in its first round of investment funding.

Edge Case Research, based in Lawrenceville, said the money will be used to expand its Hologram software that helps identify bugs in the software used by autonomous vehicles and will allow it to grow its staff of about 40 employees to about 60.

The investment was led by Chris Urmson, co-founder and CEO of the self-driving car company Aurora Innovation, which has offices in Pittsburgh and California, and ANSYS, a Pittsburgh-based simulation software company.

Edge Case CEO Michael Wagner said the investors are part of an “ecosystem” that together will work to advance technologies to make self-driving cars a reality.

”We’ve built an investor group that shares our mission to empower innovators to bring safe, trustworthy technologies to market. This ambitious vision is behind everything we do,” Wagner said in a statement.

Urmson said developing safe, self-driving technology is paramount. Matt Zack, vice president of corporate marketing and business development at ANSYS, said Edge Case technology will help the company move toward fully autonomous vehicles.

The investment into Edge Case comes at a time when investors appear to be returning to self-driving technology and companies are announcing a flurry of deals.

Aurora announced that it’s partnering with Fiat Chrysler Automobiles on Monday to build self-driving vehicles and shared news later in the week that it had expanded its partnership with Hyundai and Kia. The deal expands an agreement from last year. Aurora wouldn’t say how much Hyundai is putting in, but it announced in February that it had raised $530 million. So that means Hyundai-Kia and some other investors are kicking in around $70 million.

The company already has partnerships with Hyundai and Volkswagen. Volkswagen, however, confirmed that it has cut ties with Aurora. The German automaker is working on a tie-up with Ford that includes a partnership with Argo AI.

Argo AI, an autonomous vehicle company partly owned by Ford, announced Wednesday that it would expand testing to Detroit. The company already is testing vehicles in Pittsburgh, Miami, Washington, D.C., Dearborn, Michigan, and Palo Alto, California.

On Wednesday, Uber announced that it would work with Volvo Cars to build a vehicle that comes off the assembly line capable of driving autonomously. The ride-hailing company’s self-driving system will be installed in production versions of the Volvo XC90 SUV. Various sensors will allow Uber’s self-driving system to safely operate and maneuver in urban areas, the company said.

Uber and Volvo Cars partnered in September 2016. This is the third car they’ve developed together. Volvo says it plans to use a similar vehicle to introduce its own self-driving cars in the early 2020s.

Uber, Toyota and others announced a $1 billion deal in April.

The Associated Press contributed to this report.

Tom Davidson is a TribLive news editor. He has been a journalist in Western Pennsylvania for more than 25 years. He can be reached at [email protected] .

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Edge Case Research

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About Edge Case Research

Edge Case Research is a risk-management company focusing on the autonomous vehicle industry. The company provides services to help developers and operators of self-driving vehicles measure, manage, and insure the risks posed by software driving in real-world conditions. Their primary customers are companies in the autonomous trucking, ride-hailing, passenger cars, and delivery and logistics sectors. It was founded in 2018 and is based in Pittsburgh, Pennsylvania.

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Edge Case Research 's Products & Differentiators

nLoop is a platform to build, manage, measure, and communicate a live safety case. The platform is composed of two pieces. First, a safety case template that provides a basis to create a credible, evidence-based safety case. The second component is the cloud-based software to manage it, connect to sources of data/evidence, and provide an up-to-date status to internal and external stakeholders. nLoop architecture begins with artifact and evidence providers that connect to safety engineering work products hazard analyses, development artifacts, design documents, test results, and operational data.

Expert Collections containing Edge Case Research

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Edge Case Research is included in 2 Expert Collections, including Auto Tech .

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Companies working on automotive technology, which includes vehicle connectivity, autonomous driving technology, and electric vehicle technology. This includes EV manufacturers, autonomous driving developers, and companies supporting the rise of the software-defined vehicles.

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Edge Case Research Patents

Edge Case Research has filed 4 patents.

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• artificial neural networks
• automotive safety technologies

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Latest Edge Case Research News

Jul 2, 2021

Making Sense of the Vehicle Automation Levels Article By : Colin Barnden The sequential numbering standard has been widely misinterpreted to mean one level leads to the next. It does not. At the beginning of May, SAE International once again updated its levels of driving automation as described in its J3016 standard (shown below). No taxonomy is better known or more widely referenced than J3016 in mapping the journey from human- to machine-driven vehicles. (Click on image to enlarge.) I see two distinctly different roles for technology to improve safety on public roads: To make humans safer drivers, and to replace humans as drivers. However, these are two entirely independent development tracks which do not intersect. They are parallel, not convergent. The use of a sequential numbering system (from 0 to 5) has been widely misinterpreted to mean one level of J3016 leads on to the next. It does not. Broadly speaking, using technology to make human drivers into safer drivers roughly covers developments from Level 0 to Level 2; using technology to replace humans as drivers covers Levels 4 and 5. The illusion of a continuum occurs at Level 3, where the machine drives until it doesn’t, then the human is expected to assume both the driving task and legal liability. Practical experience tells us this is a nonsensical idea, backed up by countless videos of Tesla drivers seeking to trick Autopilot and decades of human factors research proving humans cannot fulfil this responsibility safely. Intentionally or not, J3016 has been misappropriated by startups and disruptors into a “race to Level 5,” resulting in a widely held belief that technology for “low levels” of driving automation becomes obsolete with the development of “high levels” of driving automation. This conclusion is erroneous. We can see evidence for this narrative in the absence of even a single company valued at more than $1 billion that is developing proven safety technology such as automatic emergency braking (AEB) or vision-based driver monitoring systems (DMS). Meanwhile  Waymo  recently raised another $2.5 billion, while investors salivate over almost any  lidar announcement . In reality, Level 5 makes  no practical sense  and Level 3 is unsafe at speeds over 25 mph, meaning the haughty promises to “save lives” using high levels of driving automation now rest squarely on the shoulders of Level 4 developers such as Waymo. However Waymo has recently been making news for all the wrong reasons, with the departures of  CEO John Krafcik  and  CFO Ger Dwyer , followed by video of a  driverless Waymo  unable to navigate traffic cones in a construction zone. Which begs the question, if behind the confident façade of the main suppliers, are the real-world challenges of developing autonomous driving technology to safely transport humans proving much harder to address than we are being led to believe? Let’s take a closer look. Introducing Waymore Waymo doesn’t really have a problem recognizing traffic cones, but it does appear to have a problem recognizing that public roads are an example of a complex, not a complicated, system. I have  written previously  about the unpredictability described by complexity theory, and it remains unclear how machine learning software trained using brute force road testing can realistically lead to a machine that navigates uncertainty on public roads more safely than an attentive and engaged human. Unknown, extreme, and rare events are known as “edge cases.” Complexity theory teaches us there are an infinite number of possible combinations of events. The machine learned perception system in an autonomous vehicle must be trained to understand all possible scenarios within its operational design domain. It will fail in unfamiliar situations, just as the Waymo failed with cones. Phil Koopman , co-founder of Edge Case Research and an engineering professor at Carnegie Mellon University discusses these issues in a  video  on heavy tail distribution in the real world (see below). Note Koopman’s conclusion that “Humans are good at heavy tail.” (Click on image to enlarge.) In  another video , Koopman estimates the quantity of brute force road testing necessary to validate the safety case for autonomous driving technology, of which the Waymo Driver is one example. See below, showing Koopman’s analysis that the answer might be around 2 billion miles. (Click on image to enlarge.) If  Waymo Driver  accumulates “over 20 million miles on real-world roads since 2009” then that equates to only 1 percent of the necessary distance estimated by Koopman. For all the over Level 4 autonomous driving, these calculations imply the technology requires way more money, time and testing before the suppliers are anywhere close to proving the safety case. In which case, please allow me to introduce Waymore. This analysis suggests many more testing miles to go, prompting me to question whether investors in the Level 4 suppliers truly grasp the scale of the challenge ahead and have the money, patience and nerve to complete the journey? Or, did the comfort blanket of J3016 and the promises of autonomous driving technology inadvertently create the longest, most expensive cul-de-sac in history? Scooters, NHTSA and NCAP In February, Waymo announced expanded testing to include San Francisco. In the accompanying  blog post , the company observed: “When asked to name factors making it hard to get around the city, 63 percent of respondents pointed to dangerous drivers, 74 percent to parking and 57 percent to stressful commutes. Worryingly, nearly a quarter didn’t feel safe on San Francisco’s roads at all.” San Franciscans’ worries probably weren’t soothed when a Waymo test vehicle then promptly  collided with a pedestrian riding a scooter . In a statement, Waymo said: “The autonomous specialist had recently disengaged the vehicle from autonomous mode and was driving in manual mode when the vehicle entered the intersection and made the left turn. After turning, and while still in manual mode, the vehicle came into contact with an individual on a motorized scooter.” Pause and consider that every Waymo test vehicle is outfitted with more sensors and processors than nearly all privately-owned passenger vehicles, and yet when operating in manual (human driven) mode the collision avoidance technology was insufficient to even prevent a collision with a scooter. How worried are San Franciscans now, I wonder? AEB Vulnerable Road User (AEB-VRU) has for several years been specified by the European New Car Assessment Program (Euro NCAP) for passenger vehicles. However, the San Francisco incident implies Waymo is deploying test vehicles on public roads fitted with collision avoidance technology for manual mode that does not even meet historical Euro NCAP standards, let alone exceed the current one. Why are there no guidelines published by the  U.S. National Highway Transportation Safety Administration  (NHTSA) specifying minimum AEB performance standards for test-level AVs when operating in manual mode on public roads? Also, why are there no guidelines specifying minimum performance standards for  driver monitoring systems  to assess distraction and fatigue in human backup drivers? These are two obvious safety cases which have been overlooked. While little can be expected of  AV foxes guarding the public hen house , the regulatory environment shifted dramatically this week when NHTSA required operators of test-level AVs to report all crashes, with the publication of  Standing General Order . This is likely to be the first of several significant changes to the regulatory environment for AV testing and development, with lawmakers now increasingly questioning the promises of the AV industry while listening carefully to safety advocacy groups such as Consumer Reports and the  Center for Auto Safety , which are pushing instead for proven vehicle safety technology such as AEB, DMS and lane-departure warning systems to become mandatory. Keeping the human driver attentive and engaged is the primary role of DMS. AEB and lane-keeping systems that provide longitudinal speed assist and lateral lane support, respectively. These proven vehicle safety technologies look set to save many more lives in the decades ahead than anything “self-driving” at Levels 3, 4 or 5. Although well-known and widely referenced, perhaps the J3016 spec just isn’t that helpful after all. This article was originally published on  EE Times . Colin Barnden is principal analyst at Semicast Research and has over 25 years of experience as an industry analyst. He is considered a world expert on market trends for automotive vision-based driver monitoring systems (DMS). He holds a B.Eng. (Hons) in Electrical & Electronic Engineering from Aston University in England and has covered the automotive electronics market since 1999.

Edge Case Research Frequently Asked Questions (FAQ)

When was Edge Case Research founded?

Edge Case Research was founded in 2013.

Where is Edge Case Research's headquarters?

Edge Case Research's headquarters is located at 2555 Smallman Street, Pittsburgh.

What is Edge Case Research's latest funding round?

Edge Case Research's latest funding round is Unattributed VC.

How much did Edge Case Research raise?

Edge Case Research raised a total of $28M.

Who are the investors of Edge Case Research?

Investors of Edge Case Research include Monozukuri Ventures, Ansys, Liberty Mutual Strategic Ventures, BlueTree Allied Angels, Lockheed Martin Ventures and 8 more.

Who are Edge Case Research's competitors?

Competitors of Edge Case Research include Kognic and 8 more.

What products does Edge Case Research offer?

Edge Case Research's products include nLoop and 4 more.

Who are Edge Case Research's customers?

Customers of Edge Case Research include Locomation, BMW and Robotic Research.

Compare Edge Case Research to Competitors

Foretellix specializes in the verification and validation (V&V) of automated driving systems (ADS) and advanced driver-assistance systems (ADAS) within the automotive industry. The company offers a platform that orchestrates and manages large-scale simulations to ensure the safety and compliance of ADS and ADAS, utilizing methodologies and technologies such as hyper-automation, big data analytics, and AI. Foretellix's solutions are designed to integrate with various commercial and proprietary simulators, supporting the development and deployment of safe autonomous vehicles in accordance with the latest safety standards. It was founded in 2017 and is based in Ramat Gan, Israel.

Parallel Domain is a synthetic data platform specializing in the generation of data for AI development in the autonomous systems industry. The company offers an API that allows users to generate and access sensor data, including camera, LiDAR, and radar, to train and test perception models for various applications. Parallel Domain primarily serves companies developing autonomous vehicles and other autonomous systems that require machine learning and perception capabilities. It was founded in 2017 and is based in San Francisco, California.

DiffuseDrive focuses on the development of autonomous driving technology, operating within the computer vision and machine learning sectors. The company's main service involves the generation of high-fidelity synthetic data, which is used to enhance computer vision perception and train machine learning models, particularly for autonomous vehicles. DiffuseDrive primarily caters to the autonomous driving development industry. It was founded in 2023 and is based in San Francisco, California.

Alpha Drive is a company focused on quality assurance in the artificial intelligence sector. It provides cloud-based data and tools for testing and validation of AI algorithms. The company primarily serves the automotive industry. It was founded in 2017 and is based in Brooklyn, New York.

Imagry is an autonomous driving software provider specializing in HD-mapless AI-based driving systems for the automotive industry. The company offers a software solution that enables vehicles to navigate roads autonomously without relying on high-definition maps, using real-time vision-based perception and deep neural networks to imitate human driving behavior. Imagry's technology is hardware agnostic, self-sufficient, and can be adapted to various locations and applications, including passenger vehicles, buses, and shuttles. It was founded in 2015 and is based in Haifa, Israel.

Wayve develops embodied intelligence for autonomous vehicles within the artificial intelligence and automotive industries. The company specializes in a next-generation approach to self-driving technology, known as AV2.0, which is designed to help fleet operators implement autonomous vehicle technology at scale. Wayve's technology is notable for being the first to deploy autonomous vehicles on public roads using end-to-end deep learning. It was founded in 2017 and is based in London, United Kingdom.

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ANSYS and Edge Case Research Transform Autonomous Vehicle Artificial Intelligence

Collaboration significantly advances autonomous vehicles safety, solves complex AI reliability challenges

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Sep 03, 2019, 07:01 ET

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PITTSBURGH , Sept. 3, 2019 /PRNewswire/ --   ANSYS  (NASDAQ: ANSS ) is collaborating with Edge Case Research to engineer the next generation of autonomous vehicles (AV) with unmatched state-of-the-art hazard detection capabilities. Through a new OEM agreement, Edge Case Research integrates its powerful AV artificial intelligence (AI) perception stress testing and risk analysis system, Hologram , within ANSYS' comprehensive AV simulation solution — delivering a solution to maximize the safety of AVs. 

Today's AVs rely on AI perception algorithms that are trained to make safety-critical driving decisions. Though highly advanced, an AV may fail to detect hazardous driving scenarios known as "edge cases" — because its algorithmic training has not prepared it for the many unusual road situations it will encounter in the real world. To ensure the highest safety of an AV — and make fully autonomous vehicles a reality, developers need tools to automatically identify these challenging edge cases in a way that is far more scalable than manual data labeling.  

Through this collaboration, Edge Case Research, a global leader in autonomy safety assessment software, will integrate Hologram with ANSYS' highly sophisticated AV open simulation solution. This unrivalled end-to-end capability analyzes AV algorithms to detect edge cases to advance the development of and help to validate perception algorithms in the most advanced AV systems. Hologram can be scaled to other industries such as aerospace and defense, mining, agriculture, industrial robotics, and any other domain that relies on AI-based vision and perception software.

"We're excited to join the ANSYS AV ecosystem. We chose to partner with ANSYS because of their deep expertise in safety, which is critical to understanding how products like Hologram, VRXPERIENCE and SCADE can be used together in support of safety cases for autonomous products," said Mike Wagner , CEO at Edge Case Research. "ANSYS and Edge Case Research will deliver an unprecedented comprehensive capability for safeguarding the next generation of autonomous driving systems."

"Edge Case delivers a powerful data testing and analytics platform that unlocks the value of petabytes of AV's recorded road data to find edge cases, significantly accelerating the development of safer, AI-driven perception software. Underlying capabilities have been incorporated into our recently announced collaboration with BMW," said Eric Bantegnie, vice president and general manager at ANSYS. "Together we will usher in a new era of AI and shape the future of safe autonomous driving."

About ANSYS, Inc.

If you've ever seen a rocket launch, flown on an airplane, driven a car, used a computer, touched a mobile device, crossed a bridge or put on wearable technology, chances are you've used a product where ANSYS software played a critical role in its creation. ANSYS is the global leader in engineering simulation. Through our strategy of Pervasive Engineering Simulation, we help the world's most innovative companies deliver radically better products to their customers. By offering the best and broadest portfolio of engineering simulation software, we help them solve the most complex design challenges and create products limited only by imagination. Founded in 1970, ANSYS is headquartered south of Pittsburgh, Pennsylvania , U.S.A., Visit  www.ansys.com  for more information.

ANSYS and any and all ANSYS, Inc. brand, product, service and feature names, logos and slogans are registered trademarks or trademarks of ANSYS, Inc. or its subsidiaries in the United States or other countries

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The Real Value of Synthetic Data in Clinical Research: 3 Use Cases

Synthetic data has many applications in clinical research. In this article, we’ll dive into three use cases:

• Protecting patient privacy
• Training AI models
• Filling gaps in RWD

In the pharmaceutical industry today, there’s a lot of buzz around real-world data (RWD). What’s flying under the radar, despite its value in clinical research, is synthetic data.

Life sciences isn’t the only industry benefiting from the use of synthetic data. In the banking industry, artificial data is bolstering artificial intelligence (AI) models used to detect fraud. Because synthetic data can simulate a wide range of scenarios, it can improve testing and validation in manufacturing. The retail industry has applied the use of synthetic data to training and automation. In fact, Gartner estimates that by 2030, the majority of the data used to build AI models will be synthetic data.

Not all AI is created alike. Synthetic data is a form of generative AI. In contrast with traditional AI, which focuses on analyzing and interpreting data, generative AI focuses on creating new content. Diving a little deeper, one type of generative AI is generative adversarial networks (GANs), which uses two competing AI models to produce synthetic data. 1

Synthetic data should not be confused with synthetic controls, cohorts of patients from external data and adjusted by employing statistical methodologies, used in clinical trials. In lieu of recruiting patients assigned to a control arm, a synthetic control arm repurposes historical clinical data or RWD to accurately match those patients.

In clinical research, synthetic data also can be used to enhance datasets and increase diversity. Where datasets are imbalanced and not representative of the population they aim to serve, generative AI in the form of synthetic minority oversampling technique (SMOTE) may be used to selectively augment the representation of minority datapoints. 1

Another advantage of synthetic data is in relation to safeguarding privacy. A Gartner article expands on this, quoting Alys Woodward, senior director analyst: “Synthetic data can bridge information silos by acting as a substitute for real data and not revealing sensitive information, such as personal details and intellectual property.”

“Synthetic data is a relatively new kind of space, in the past five to seven years or so,” says Brad Davis, a principal in consulting and analytics at Norstella, Citeline’s parent company. “It is a relatively new concept, but one that’s incredibly powerful.” He cites a trio of use cases for clinical research:

Use case #1: protecting patient privacy

Because synthetic datasets have all the same statistical characteristics as the real-world dataset, the same analyses and calculations can be applied. However, synthetic data can randomly generate patient-identifying information which can further protect against patient reidentification. For example, simulated data will randomly generate 9-digit numbers to blind and protect Social Security numbers.

Even with a deidentified and HIPAA-compliant dataset, patients with a rare disease who have received a particular therapy are more easily reidentifiable simply due to the few number of patients with those same characteristics. With synthetic data, the same medical history data can be replicated but with randomly generated other data fields allowing for the same analysis but with reduced risk of patient reidentification.

While rare and ultra-rare diseases are a perfect example of this use case of synthetic data, the downstream effects are broader and more impactful. “If we can reduce risk without compromising the outcome by using synthetic data, it ultimately leads to increased data sharing — both internally as well as with external data partners,” says Davis. The value to the pharmaceutical industry as a whole, he notes, is efficiency. “If data sharing becomes more commonplace it becomes the fuel for true innovation.”

Use case #2: training AI models

Within the context of innovation, Davis says, is the most rapidly evolving technology of all, artificial intelligence. “If we can cheaply and reliably generate synthetic data, this data can most easily be applied to the training of AI models, which creates not only efficiencies but potentially increased accuracy of those models as well,” he says.

Because synthetic data has randomly generated data points but maintains the structural and mathematical integrity of the original dataset, it can actually remove potential biases when training an AI model. Furthermore, AI models can be “trained harder” with more diverse datasets rife with outliers. Ultimately, the use of synthetic data within AI model training will lead to increased accuracy as well as speed and efficiency of the training itself.

Use case #3: filling gaps in RWD

When choosing the proper datasets to analyze for a specific business question, there are almost always pros and cons that must be weighed. One dataset may provide the longitudinality required, another may possess the depth of acute care data necessary, but yet another dataset may be needed to address pharmacy claims data. To procure three separate databases and link them together takes significant time and investment, which are not always readily available.

By introducing synthetic data, existing real-world data can be augmented theoretically quicker and cheaper while still maintaining the statistical properties of true RWD. “In these instances,” Davis says, “we can potentially throw gasoline on the fire of RWD analyses and learn much more about our markets and patient populations, including deep root-cause analyses which can, in theory, revolutionize our approach to medicine.”

Sponsors may want to learn how a disease progresses within a specific patient population, how many times patients see a physician, exhibit a certain behavior, or what percentage develop a particular morbidity. For post-market studies, sponsors may seek to gather additional safety data on the marketed product. They want to understand how the product is being utilized in the real world but may lack a robust enough dataset to enable the statistical analyses required.

In terms of HEOR, sponsors want to determine the potential economic impact. In a non-interventional study, for example, how many hospitals were included, what did it cost the system, what drugs were used?

“In all of these cases,” Davis says, “we can significantly increase the efficiency of data procurement and analytics.” When it comes to statistical significance, it’s more about the trends than the data points, he adds.

“We are building additional tools for making this data much more accessible than it has been in the past. We’re at our infancy in this space, which affords us some flexibility and advantages.”

To see how Citeline is harnessing the power of AI for clinical planning and , visit Citeline.com .

1 Arora A, Arora A. Generative adversarial networks and synthetic patient data: current challenges and future perspectives. Future Healthc J. 2022 Jul;9(2):190-193. doi: 10.7861/fhj.2022-0013. PMID: 35928184; PMCID: PMC9345230. Available from: Generative adversarial networks and synthetic patient data: current challenges and future perspectives - PMC (nih.gov) [Accessed Aug. 21, 2024].

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Typically, electrons are free agents that can move through most metals in any direction. When they encounter an obstacle, the charged particles experience friction and scatter randomly like colliding billiard balls.

But in certain exotic materials, electrons can appear to flow with single-minded purpose. In these materials, electrons may become locked to the material’s edge and flow in one direction, like ants marching single-file along a blanket’s boundary. In this rare “edge state,” electrons can flow without friction, gliding effortlessly around obstacles as they stick to their perimeter-focused flow. Unlike in a superconductor, where all electrons in a material flow without resistance, the current carried by edge modes occurs only at a material’s boundary.

Now MIT physicists have directly observed edge states in a cloud of ultracold atoms. For the first time, the team has captured images of atoms flowing along a boundary without resistance, even as obstacles are placed in their path. The results, which appear today in Nature Physics , could help physicists manipulate electrons to flow without friction in materials that could enable super-efficient, lossless transmission of energy and data.

“You could imagine making little pieces of a suitable material and putting it inside future devices, so electrons could shuttle along the edges and between different parts of your circuit without any loss,” says study co-author Richard Fletcher, assistant professor of physics at MIT. “I would stress though that, for us, the beauty is seeing with your own eyes physics which is absolutely incredible but usually hidden away in materials and unable to be viewed directly.”

The study’s co-authors at MIT include graduate students Ruixiao Yao and Sungjae Chi, former graduate students Biswaroop Mukherjee PhD ’20 and Airlia Shaffer PhD ’23, along with Martin Zwierlein, the Thomas A. Frank Professor of Physics. The co-authors are all members of MIT’s Research Laboratory of Electronics and the MIT-Harvard Center for Ultracold Atoms.

Forever on the edge

Physicists first invoked the idea of edge states to explain a curious phenomenon, known today as the Quantum Hall effect, which scientists first observed in 1980, in experiments with layered materials, where electrons were confined to two dimensions. These experiments were performed in ultracold conditions, and under a magnetic field. When scientists tried to send a current through these materials, they observed that electrons did not flow straight through the material, but instead accumulated on one side, in precise quantum portions.

To try and explain this strange phenomenon, physicists came up with the idea that these Hall currents are carried by edge states. They proposed that, under a magnetic field, electrons in an applied current could be deflected to the edges of a material, where they would flow and accumulate in a way that might explain the initial observations. “The way charge flows under a magnetic field suggests there must be edge modes,” Fletcher says. “But to actually see them is quite a special thing because these states occur over femtoseconds, and across fractions of a nanometer, which is incredibly difficult to capture.”

Rather than try and catch electrons in an edge state, Fletcher and his colleagues realized they might be able to recreate the same physics in a larger and more observable system. The team has been studying the behavior of ultracold atoms in a carefully designed setup that mimics the physics of electrons under a magnetic field.

“In our setup, the same physics occurs in atoms, but over milliseconds and microns,” Zwierlein explains. “That means that we can take images and watch the atoms crawl essentially forever along the edge of the system.”

A spinning world

In their new study, the team worked with a cloud of about 1 million sodium atoms, which they corralled in a laser-controlled trap, and cooled to nanokelvin temperatures. They then manipulated the trap to spin the atoms around, much like riders on an amusement park Gravitron.

“The trap is trying to pull the atoms inward, but there’s centrifugal force that tries to pull them outward,” Fletcher explains. “The two forces balance each other, so if you’re an atom, you think you’re living in a flat space, even though your world is spinning. There’s also a third force, the Coriolis effect, such that if they try to move in a line, they get deflected. So these massive atoms now behave as if they were electrons living in a magnetic field.”

Into this manufactured reality, the researchers then introduced an “edge,” in the form of a ring of laser light, which formed a circular wall around the spinning atoms. As the team took images of the system, they observed that when the atoms encountered the ring of light, they flowed along its edge, in just one direction.

“You can imagine these are like marbles that you’ve spun up really fast in a bowl, and they just keep going around and around the rim of the bowl,” Zwierlein offers. “There is no friction. There is no slowing down, and no atoms leaking or scattering into the rest of the system. There is just beautiful, coherent flow.”

“These atoms are flowing, free of friction, for hundreds of microns,” Fletcher adds. “To flow that long, without any scattering, is a type of physics you don’t normally see in ultracold atom systems.”

This effortless flow held up even when the researchers placed an obstacle in the atoms’ path, like a speed bump, in the form of a point of light, which they shone along the edge of the original laser ring. Even as they came upon this new obstacle, the atoms didn’t slow their flow or scatter away, but instead glided right past without feeling friction as they normally would.

“We intentionally send in this big, repulsive green blob, and the atoms should bounce off it,” Fletcher says. “But instead what you see is that they magically find their way around it, go back to the wall, and continue on their merry way.”

The team’s observations in atoms document the same behavior that has been predicted to occur in electrons. Their results show that the setup of atoms is a reliable stand-in for studying how electrons would behave in edge states.

“It’s a very clean realization of a very beautiful piece of physics, and we can directly demonstrate the importance and reality of this edge,” Fletcher says. “A natural direction is to now introduce more obstacles and interactions into the system, where things become more unclear as to what to expect.”

This research was supported, in part, by the National Science Foundation.

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Ernest Courant Traineeship in Accelerator Science & Engineering

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Stony Brook University in collaboration with Brookhaven National Laboratory (BNL), Cornell University (CU) and FERMI National Accelerator Laboratory (FNAL) is establishing the Ernest Courant Traineeship in Accelerator Science & Engineering supported by a 5-year grant from the High Energy Office of the US Department of Energy. This novel program is named after eminent accelerator physicist, Ernest Courant, who lay the foundation of modern accelerator science. At Stony Brook, the traineeship is a part of the Center for Accelerator Physics and Education (CASE) .

The main goal of the program is to train scientists and engineers in the field of accelerator sciences with a focus in the four areas identified as the Department of Energy (DOE) Mission Critical Workforce Needs in Accelerator Science and Engineering: (a) Physics of large accelerators and systems engineering; (b) Superconducting radiofrequency accelerator physics and engineering; (c) Radiofrequency power system engineering and (d) Cryogenic systems engineering (especially liquid helium systems).

The graduate level curriculum consists of courses and practical training at accelerator facilities of the collaborating institutions, and thesis requirements. Each of participant will have a supervisor to guide the training. Every graduate student – PhD, MS/MSI, ME - successfully completing the traineeship program will be issued a Certificate in Accelerator Science and Engineering with specializations including the four areas listed above. The expectation is that the traineeship can be completed in two years and students can pursue their research interest beyond the program (for example, complete their PhD). Undergraduate students can enter the program via a dedicated summer internship program at BNL.

If you are interested in this unique traineeship in 21st century accelerator sciences and want to know details, please contact one of professors involved in the program:

• Thomas Hemmick [email protected]
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The Traineeship Certificate is offered as part of a graduate program at Stony Brook University. Students must apply and be accepted in a MS/MSI/PhD program, which will allow them to complete the Certificate as part of their degree program. Students who are already part of a graduate program at Stony Brook will need to follow these  instructions to enroll in the CASE Certificate in Accelerator Science Program to receive your certificate. 

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Integrated deployment and operation system for pulmonary CT image analysis model in ARDS patients on a server

• Lin, Peijun
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This paper introduces an integrated deployment and operation system for a pulmonary CT image analysis model aimed at patients with Acute Respiratory Distress Syndrome (ARDS). The system is based on Inspur Cloud servers and Cambricon MLU220 edge computing boxes, designed to assist in the clinical examination and targeted treatment of ARDS. The research team uses Xshell and Xmanager for remote connection to cloud servers without a graphical interface to deploy the model. They have developed an interactive interface for deployment and operation on mobile servers or edge computing boxes with graphical interfaces using a pseudo-encapsulation method. The paper details the preparation of the system and hardware, the installation of the 3D Slicer software and plugins upon which the model relies, as well as the deployment process based on Inspur Cloud servers and Cambricon MLU220 edge computing boxes. Experimental results show that edge computing boxes with acceleration cards have a significant advantage in terms of running time, with error control within 0.5%.The integrated deployment and operation system is simple to operate, with a user-friendly interface, capable of real-time stable transmission of remote data, while ensuring the security of user data. However, the study also points out the shortcomings of the system, such as a limited number of experimental cases and the complexity of cloud serverside deployment, and suggests directions for future improvements.