AI techniques have been leveraged that combine known biophysical, machine learning, and reinforcement learning models to effectively predict the constructs' impact on the host and vice versa, but there is much room for improvement.

Anxiety about automation is prevalent in this era of rapid technological advances, especially in artificial intelligence (AI), machine learning (ML), and robotics. Accordingly, how human labor competes, or cooperates, with machines in performing a range of tasks (what we term "the race between human labor and machines") has attracted a great deal of attention among the public, policymakers, and researchers.14,15,18 While there have been persistent concerns about new technology and automation replacing human tasks at least since the Industrial Revolution,8 recent technological advances in executing sophisticated and complex tasks--enabled by a combinatorial innovation of new techniques and algorithms, advances in computational power, and exponential increases in data--differentiate the 21st century from previous ones.14 For instance, recent advances in autonomous self-driving cars demonstrate the way a wide range of human tasks that have been considered least susceptible to automation may no longer be safe from automation and computerization. Another case in point is human competition against machines, such as IBM's Watson on the TV game show "Jeopardy!" Both cases imply that some tasks, such as pattern recognition and information processing, are being rapidly computerized. Furthermore, recent studies suggest that robotics also plays a role in automating manual tasks and decreasing employment of low-wage workers.3,22

People with disabilities often cannot count on modern digital devices, software, and services to be accessible. Will streaming video platforms include closed captions for viewers who are deaf or hard of hearing? How will virtual assistants work for users with speech disabilities? Can websites be read aloud by text-to-speech engines for readers who are blind or visually impaired? How will smartphones be accessed by people with physical and mobility disabilities?

The ACM constitution provides that our Association hold a general election in the even-numbered years for the positions of President, Vice President, Secretary/Treasurer, and Members-at-Large. Biographical information and statements of the candidates appear on the following pages (candidates' names appear in random order). In addition to the election of ACM's officers--President, Vice President, Secretary/Treasurer--two Members-at-Large will be elected to serve on ACM Council. The 2022 candidates for ACM President, Yannis Ioannidis and Joseph A. Konstan, are working together to solicit and answer questions from the computing community! Please refer to the instructions posted at https://vote.escvote.com/acm. Please note the election email will be addressed from acmhelp@mg.electionservicescorp.com. Please return your ballot in the enclosed envelope, which must be signed by you on the outside in the space provided. The signed ballot envelope may be inserted into a separate envelope for mailing if you prefer this method. All ballots must be received by no later than 16:00 UTC on 23 May 2022. Validation by the Elections Committee will take place at 14:00 UTC on 25 May 2022. Yannis Ioannidis is Professor of Informatics & Telecom at the U. of Athens, Greece (since 1997). Prior to that, he was a professor of Computer Sciences at the U. of Wisconsin-Madison (1986-1997).

We introduce a data-driven approach to building reduced dynamical models through manifold learning; the reduced latent space is discovered using Diffusion Maps (a manifold learning technique) on time series data. A second round of Diffusion Maps on those latent coordinates allows the approximation of the reduced dynamical models. This second round enables mapping the latent space coordinates back to the full ambient space (what is called lifting); it also enables the approximation of full state functions of interest in terms of the reduced coordinates. In our work, we develop and test three different reduced numerical simulation methodologies, either through pre-tabulation in the latent space and integration on the fly or by going back and forth between the ambient space and the latent space. The data-driven latent space simulation results, based on the three different approaches, are validated through (a) the latent space observation of the full simulation through the Nystr\"om Extension formula, or through (b) lifting the reduced trajectory back to the full ambient space, via Latent Harmonics. Latent space modeling often involves additional regularization to favor certain properties of the space over others, and the mapping back to the ambient space is then constructed mostly independently from these properties; here, we use the same data-driven approach to construct the latent space and then map back to the ambient space.

We propose a novel federated learning paradigm to model data variability among heterogeneous clients in multi-centric studies. Our method is expressed through a hierarchical Bayesian latent variable model, where client-specific parameters are assumed to be realization from a global distribution at the master level, which is in turn estimated to account for data bias and variability across clients. We show that our framework can be effectively optimized through expectation maximization (EM) over latent master's distribution and clients' parameters. We also introduce formal differential privacy (DP) guarantees compatibly with our EM optimization scheme. We tested our method on the analysis of multi-modal medical imaging data and clinical scores from distributed clinical datasets of patients affected by Alzheimer's disease. We demonstrate that our method is robust when data is distributed either in iid and non-iid manners, even when local parameters perturbation is included to provide DP guarantees. Moreover, the variability of data, views and centers can be quantified in an interpretable manner, while guaranteeing high-quality data reconstruction as compared to state-of-the-art autoencoding models and federated learning schemes.

The push by the United States to send artillery to Ukraine aims to degrade Russian forces -- not only on the immediate battlefield but over the longer term, according to US Defense Secretary Lloyd Austin and military experts. The United States, France, Czech Republic and other allies are sending scores of the long-range howitzers to help Ukraine blunt Russia's mounting offensive in the eastern Donbas region. Backed by better air defense, attack drones and Western intelligence, the allies hope that Kyiv will be able to destroy a large amount of Russia's firepower in the looming showdown. After returning from Kyiv, where he met Ukraine defense chiefs and President Volodymyr Zelensky, Austin told journalists in Poland early Monday that Washington's hopes are larger than that. Russia "has already lost a lot of military capability, and a lot of its troops, quite frankly. And we want to see them not have the capability to very quickly reproduce that capability," Austin said.

U.S. Northern Command and North American Aerospace Defense Command don't have sufficient artificial intelligence and machine learning capabilities, the dual-hatted chief of both organizations warned Monday. The Pentagon is pursuing new space-based sensors, communications systems and other capabilities to improve situational awareness. But it needs AI to better crunch and share the data it collects. "This year's budget, I think, moves the ball down the field with regards to domain awareness. We'll be able to hopefully field over-the-horizon capabilities, which will give us more standoff distance than what we currently have today. But we also need to take that domain awareness -- the sensors that we have today and any potential new sensors -- and share that data and information, and utilize artificial intelligence and machine learning to make that data and information available sooner than we have in the past to decision-makers," Gen. Glen VanHerck, commander of U.S. Northern Command (Northcom) and North American Aerospace Defense Command (NORAD), told the Defense Writers Group.

Dr. Craig Martell will serve as the Defense Department's new chief digital and artificial intelligence officer. Martell, who most recently served as the head of machine learning at Lyft after AI and machine learning-related positions with LinkedIn and Dropbox, will now serve as the Pentagon's senior official responsible for the "adoption of data, analytics, digital solutions and AI functions," according to a Pentagon press statement. "Advances in AI and machine learning are critical to delivering the capabilities we need to address key challenges both today and into the future," Deputy Secretary of Defense Kathleen H. Hicks said in a statement. "With Craig's appointment, we hope to see the department increase the speed at which we develop and field advances in AI, data analytics, and machine-learning technology. He brings cutting-edge industry experience to apply to our unique mission set."

The Pentagon is still new to wielding artificial intelligence, and it's looking to an outsider for help. Breaking Defense has learned Lyft machine learning head Craig Martell is joining the Defense Department as its Chief Digital and Artificial Intelligence Officer (CDAO). He'll lead the American military's strategies for AI, analytics and data, and should play a key part in a Joint All-Domain Command and Control initiative to improve multi-force combat awareness through technology. Martell is a partial outsider. While he directed the Naval Postgraduate School's AI-driven Natural Language Processing Lab for 11 years, he hasn't served in military leadership.