Researchers at America's Pacific Northwest National Laboratory (PNNL) are developing machine learning techniques to help the Feds crack down on potentially rogue nuclear weapons. Suffice to say, it's generally illegal for any individual or group to own a nuclear weapon, certainly in the United States. Yes, there are the five officially recognized nuclear-armed nations – France, Russia, China, the UK, and the US – whose governments have a stash of these devices. And there are countries that have signed the United Nations' Treaty on the Prohibition of Nuclear Weapons, meaning they've promised not to "develop, test, produce, acquire, possess, stockpile, use or threaten to use" these gadgets. So if anyone has a nuke in their possession, it's because they are a country in the official nuclear-armed club, they are a government that's produced its own nukes, a terrorist who stole, bought, or somehow built one themselves, or some other sketchy scenario, in America's eyes at least.

Despite the remarkable technological advances that fill our lives today, the ways we work with the metals that underlie these developments haven't changed significantly in thousands of years. This is true of everything from the metal rods, tubes, and cubes that provide cars and trucks with their shape, strength, and fuel economy, to wires that move electrical energy in everything from motors to undersea cables. But things are changing rapidly: The materials manufacturing industry is using new and innovative technologies, processes, and methods to improve existing products and create new ones. Pacific Northwest National Laboratory (PNNL) is a leader in this space, known as advanced manufacturing. For example, scientists working in PNNL's Mathematics for Artificial Reasoning in Science initiative are pioneering approaches in the branch of artificial intelligence known as machine learning to design and train computer software programs that guide the development of new manufacturing processes.

Despite the remarkable technological advances that fill our lives today, the ways we work with the metals that underlie these developments haven't changed significantly in thousands of years. This is true of everything from the metal rods, tubes, and cubes that provide cars and trucks with their shape, strength, and fuel economy, to wires that move electrical energy in everything from motors to undersea cables. But things are changing rapidly: The materials manufacturing industry is using new and innovative technologies, processes, and methods to improve existing products and create new ones. Pacific Northwest National Laboratory (PNNL) is a leader in this space, known as advanced manufacturing. For example, scientists working in PNNL's Mathematics for Artificial Reasoning in Science initiative are pioneering approaches in the branch of artificial intelligence known as machine learning to design and train computer software programs that guide the development of new manufacturing processes.

A hallmark of artificial intelligence is its ability to learn from the past. As researchers advance and refine AI applications, it could increasingly become part of routine research, too--the type of work that supported the advances toward tackling this pandemic and can support the response to a future one, too. Finding meaning in a sea of messy or incomplete data is precisely what data scientists at Pacific Northwest National Laboratory (PNNL) do. With expertise in applying graph-based machine learning, detailed molecular modeling, and explainable AI to questions of national security and basic science, PNNL researchers are now turning their artificial intelligence tools to the study of fundamental questions about treatments for COVID. What they are learning sharpens the tools available in the computational toolbox for responding quickly to a future pandemic.

When the novel coronavirus led to a global pandemic last year, doctors and researchers rushed to learn as much as possible about the virus and how our bodies respond to it. They needed a lot of information, and they needed it fast. Doctors studied whether available medicines could effectively treat the symptoms of COVID-19. Virologists, biologists, and chemists scrambled to understand how the virus affects the molecular workings of cells, information key to designing medicine to treat infection and resulting disease. Medical and biological data flowed fast and furiously.

Over the past decade, artificial intelligence (AI) has experienced a renaissance. AI enables machines to learn and make decisions without being explicitly programmed. AI has enabled a new generation of applications, opening the door to breakthroughs in many aspects of daily life. From situational awareness to threat detection, online signals to system assurance, PNNL is advancing the frontiers of scientific research and national security by applying AI to scientific problems. For machine learning models, domain-specific knowledge can enhance domain-agnostic data in terms of accuracy, interpretability, and defensibility. PNNL's AI research has been applied across a variety of domain areas from national security, to the electric grid and Earth systems.

We present a novel variant of Domain Adversarial Networks with impactful improvements to the loss functions, training paradigm, and hyperparameter optimization. New loss functions are defined for both forks of the DANN network, the label predictor and domain classifier, in order to facilitate more rapid gradient descent, provide more seamless integration into modern neural networking frameworks, and allow previously unavailable inferences into network behavior. Using these loss functions, it is possible to extend the concept of 'domain' to include arbitrary user defined labels applicable to subsets of the training data, the test data, or both. As such, the network can be operated in either 'On the Fly' mode where features provided by the feature extractor indicative of differences between 'domain' labels in the training data are removed or in 'Test Collection Informed' mode where features indicative of difference between 'domain' labels in the combined training and test data are removed (without needing to know or provide test activity labels to the network). This work also draws heavily from previous works on Robust Training which draws training examples from a L_inf ball around the training data in order to remove fragile features induced by random fluctuations in the data. On these networks we explore the process of hyperparameter optimization for both the domain adversarial and robust hyperparameters. Finally, this network is applied to the construction of a binary classifier used to identify the presence of EM signal emitted by a turbopump. For this example, the effect of the robust and domain adversarial training is to remove features indicative of the difference in background between instances of operation of the device - providing highly discriminative features on which to construct the classifier.

If a person in the developing world severely fractures a limb, they face an impossible choice. An improperly healed fracture could mean a lifetime of pain, but lengthy healing time in traction or a bulky cast results in immediate financial hardship. That's why Pacific Northwest National Laboratory (PNNL) machine learning scientists leaped into action when they learned they could help a local charity enable patients in the developing world to walk within one week of surgery--even when fractures are severe. For more than 20 years, the Richland, Washington-based charity SIGN Fracture Care has pioneered orthopedic care, including training and innovatively designed implants that speed healing without real-time operating room X-ray machines. During those 20 years, they've built a database of 500,000 procedure images and outcomes that serves as a learning hub for doctors around the world.

Do you want to create a legacy of meaningful research for the greater good? Do you want to lead and contribute to work in support of an organization that addresses some of today's most challenging problems that face our Nation? Then join us in the Data Sciences and Analytics Group at the Pacific Northwest National Laboratory (PNNL)! For more than 50 years, PNNL has advanced the frontiers of science and engineering in the service of our nation and the world in the areas of energy, the environment and national security. PNNL is committed to advancing the state-of-the-art in artificial intelligence through applied machine learning and deep learning to support scientific discovery and our sponsors' missions.

PNNL scientists working with Stanford researchers have put forth a new approach to incorporate medical knowledge into AI systems, improving the accuracy of patient diagnosis dramatically. Artificial intelligence will never replace a doctor. However, researchers at the Department of Energy's Pacific Northwest National Laboratory have taken a big step toward the day when AI can help physicians predict medical events. A new approach developed by PNNL scientists improves the accuracy of patient diagnosis up to 20 percent when compared to other embedding approaches. The PNNL approach seeks to capture and recreate the types of connections physicians do naturally when they apply a lifetime of learning and knowledge to the patient standing in front of them in the exam room.