We merge computational mechanics' definition of causal states (predictively-equivalent histories) with reproducing-kernel Hilbert space (RKHS) representation inference. The result is a widely-applicable method that infers causal structure directly from observations of a system's behaviors whether they are over discrete or continuous events or time. A structural representation -- a finite- or infinite-state kernel $\epsilon$-machine -- is extracted by a reduced-dimension transform that gives an efficient representation of causal states and their topology. In this way, the system dynamics are represented by a stochastic (ordinary or partial) differential equation that acts on causal states. We introduce an algorithm to estimate the associated evolution operator. Paralleling the Fokker-Plank equation, it efficiently evolves causal-state distributions and makes predictions in the original data space via an RKHS functional mapping. We demonstrate these techniques, together with their predictive abilities, on discrete-time, discrete-value infinite Markov-order processes generated by finite-state hidden Markov models with (i) finite or (ii) uncountably-infinite causal states and (iii) a continuous-time, continuous-value process generated by a thermally-driven chaotic flow. The method robustly estimates causal structure in the presence of varying external and measurement noise levels.

Discrete response of structures is often a key probabilistic quantity of interest. For example, one may need to identify the probability of a binary event, such as, whether a structure has buckled or not. In this study, an adaptive domain-based decomposition and classification method, combined with sparse grid sampling, is used to develop an efficient classification surrogate modeling algorithm for such discrete outputs. An assumption of monotonic behaviour of the output with respect to all model parameters, based on the physics of the problem, helps to reduce the number of model evaluations and makes the algorithm more efficient. As an application problem, this paper deals with the development of a computational framework for generation of probabilistic penetration response of S-2 glass/SC-15 epoxy composite plates under ballistic impact. This enables the computationally feasible generation of the probabilistic velocity response (PVR) curve or the $V_0-V_{100}$ curve as a function of the impact velocity, and the ballistic limit velocity prediction as a function of the model parameters. The PVR curve incorporates the variability of the model input parameters and describes the probability of penetration of the plate as a function of impact velocity.

Predictive models with a focus on different spatial-temporal scales benefit governments and healthcare systems to combat the COVID-19 pandemic. Here we present the conditional Long Short-Term Memory networks with Quantile output (condLSTM-Q), a well-performing model for making quantile predictions on COVID-19 death tolls at the county level with a two-week forecast window. This fine geographical scale is a rare but useful feature in publicly available predictive models, which would especially benefit state-level officials to coordinate resources within the state. The quantile predictions from condLSTM-Q inform people about the distribution of the predicted death tolls, allowing better evaluation of possible trajectories of the severity. Given the scalability and generalizability of neural network models, this model could incorporate additional data sources with ease, and could be further developed to generate other useful predictions such as new cases or hospitalizations intuitively.

Overparameterization in deep learning is powerful: Very large models fit the training data perfectly and yet generalize well. This realization brought back the study of linear models for regression, including ordinary least squares (OLS), which, like deep learning, shows a "double descent" behavior. This involves two features: (1) The risk (out-of-sample prediction error) can grow arbitrarily when the number of samples $n$ approaches the number of parameters $p$, and (2) the risk decreases with $p$ at $p>n$, sometimes achieving a lower value than the lowest risk at $p

Very little is known about the cost landscape for parametrized Quantum Circuits (PQCs). Nevertheless, PQCs are employed in Quantum Neural Networks and Variational Quantum Algorithms, which may allow for near-term quantum advantage. Such applications require good optimizers to train PQCs. Recent works have focused on quantum-aware optimizers specifically tailored for PQCs. However, ignorance of the cost landscape could hinder progress towards such optimizers. In this work, we analytically prove two results for PQCs: (1) We find an exponentially large symmetry in PQCs, yielding an exponentially large degeneracy of the minima in the cost landscape. (2) We show that noise (specifically non-unital noise) can break these symmetries and lift the degeneracy of minima, making many of them local minima instead of global minima. Based on these results, we introduce an optimization method called Symmetry-based Minima Hopping (SYMH), which exploits the underlying symmetries in PQCs to hop between local minima in the cost landscape. The versatility of SYMH allows it to be combined with local optimizers (e.g., gradient descent) with minimal overhead. Our numerical simulations show that SYMH improves the overall optimizer performance.

As the pandemic reaches new heights, with nearly 12 million cases and 260,000 deaths recorded in the U.S. to date, a glimmer of hope is on the horizon. Moderna and pharmaceutical giant Pfizer, which are developing vaccines to fight the virus, have released preliminary data suggesting their vaccines are around 95% effective. Manufacturing and distribution is expected to ramp up as soon as the companies seek and receive approval from the U.S. Food and Drug Administration. Representatives from Moderna and Pfizer say the first doses could be available as early as December. But even if the majority of Americans agree to vaccination, the pandemic won't come to a sudden end.

A new machine-learning algorithm can successfully determine which specific behaviors--like walking and breathing--belong to which specific brain signal, and it has the potential to help the military maintain a more ready force. At any given time, people perform a myriad of tasks. All of the brain and behavioral signals associated with these tasks mix together to form a complicated web. Until now, this web has been difficult to untangle and translate. But researchers funded by the U.S. Army developed a machine-learning algorithm that can model and decode these signals, according to a Nov. 12 press release.

It was three months ago when my friend Kimonas asked me if I can help him with a huge secret project that he had on his mind. It was kind of a cheap shot as "huge secret project" are my trigger words. He asked me if I can join him on a Zoom at 6am. I told him that I am not going to wake up that early even if the President of Greece was on that Zoom call. It turned out that the Prime Minister and his team of scientists were on the call and I was there, 7am in Los Angeles, half awake, wearing my "A.I pays my bills" t-shirt.

The term artificial intelligence was initially revealed in 1956, yet AI has become more mainstream today on account of expanded data volumes, progressed algorithms, and enhancements in computing power and storage. During the 1960s, the US Department of Defense checked out this kind of work and started training computers to emulate fundamental human reasoning. For instance, the Defense Advanced Research Projects Agency (DARPA) finished road planning projects during the 1970s. What's more, DARPA created intelligent personal assistants in 2003, some time before Siri, Alexa or Cortana were easily recognized names. Artificial intelligence (AI), is the capacity of a digital computer or computer-controlled robot to perform activities usually connected with smart creatures.