The Naval Research Laboratory (NRL) Network Pump, or Pump, is a standard for mitigating covert channels that arise in a multilevel secure (MLS) system when a high user (HU) sends acknowledgements to a low user (LU). The issue here is that HU can encode information in the "timings" of the acknowledgements. The Pump aims at mitigating the covert timing channel by introducing buffering between HU and LU, as well as adding noise to the acknowledgment timings. We model the working of the Pump in certain situations, as a communication system with feedback and use then this perspective to derive an upper bound on the capacity of the covert channel between HU and LU in the Pump. This upper bound is presented in terms of a directed information flow over the dynamics of the system. We also present an achievable scheme that can transmit information over this channel. When the support of the noise added by Pump to acknowledgment timings is finite, the achievable rate is nonzero, i.e., infinite number of bits can be reliably communicated. If the support of the noise is infinite, the achievable rate is zero and hence a finite number of bits can be communicated.

In this review, our goal is to design and test quantum-like algorithms for Artificial Intelligence (AI) in open systems to structure a human–machine team to be able to reach its maximum performance. Unlike the laboratory, in open systems, teams face complexity, uncertainty and conflict. All task domains have complexity levels—some low, and others high. Complexity in this new domain is affected by the environment and the task, which are both affected by uncertainty and conflict. We contrast individual and interdependence approaches to teams. The traditional and individual approach focuses on building teams and systems by aggregating the best available information for individuals, their thoughts, behaviors and skills. Its concepts are characterized chiefly by one-to-one relations between mind and body, a summation of disembodied individual mental and physical attributes, and degrees of freedom corresponding to the number of members in a team; however, this approach is characterized by the many researchers who have invested in it for almost a century with few results that can be generalized to human–machine interactions; by the replication crisis of today (e.g., the invalid scale for self-esteem); and by its many disembodied concepts. In contrast, our approach is based on the quantum-like nature of interdependence. It allows us theorization about the bistability of mind and body, but it poses a measurement problem and a non-factorable nature. Bistability addresses team structure and performance; the measurement problem solves the replication crisis; and the non-factorable aspect of teams reduces the degrees of freedom and the information derivable from teammates to match findings by the National Academies of Science. We review the science of teams and human–machine team research in the laboratory versus in the open field; justifications for rejecting traditional social science while supporting our approach; a fuller understanding of the complexity of teams and tasks; the mathematics involved; a review of results from our quantum-like model in the open field (e.g., tradeoffs between team structure and performance); and the path forward to advance the science of interdependence and autonomy.

We use a Riemannian metric as a cost metric when it comes to the optimal decisions that should be made in a multi-agent/Team scenario. The two parameters of interest to us are Team skill and Team interdependence, which are modeled as Wiener process drift and the inverse of Wiener process diffusion, respectively. The underlying mathematics is presented, along with some approximating rules of thumb. It is noteworthy that the mathematics points to, what seems at first, counter-intuitive paradigms for Team performance. However, in reality the mathematics shows a subtle interplay between the factors affecting Team performance.

A modular laboratory curriculum with exercises for students and lesson plans for teachers is presented. Fundamentals of basic integrated photonic (IP) devices can be taught, first as a lecture-in-the-lab followed by “hands-on” laboratory measurements. This comprehensive curriculum utilizes data collected from the “AIM Photonics Institute PIC education chip” that was designed specifically for the purpose of education, and was fabricated at AIM SUNY Poly. Training using this modular curriculum will be performed through the AIM Photonics Academy network in New York (NY) and Massachusetts (MA), either as a full semester course or as a condensed boot-camp. A synergistic development and delivery of this curriculum will coherently leverage multiple resources across the network and can serve as a model for education and workforce development in other Manufacturing USA institutes, as well as for overseas partners.

The NRL Network Pump ^® , or Pump, is a standard for mitigating covert channels that arise in a multi-level secure (MLS) system when a high user (HU) sends acknowledgements to a low user (LU). The issue here is that HU can encode information in the “timings” of the acknowledgements. The Pump aims at mitigating the covert timing channel by introducing buffering between HU and LU, as well as adding noise to the acknowledgment timings. Here, for the first time, we model the workings of the Pump in certain situations, as a communication system with feedback and use then this novel perspective to derive a upper bound on the rate of the covert channel between HU and LU in the Pump, in specific situations. This upper bound is presented in terms of a directed information flow over the dynamics of the system.

We consider the use of Shannon information theory, and its various entropic terms to aid in reaching optimal decisions that should be made in a multi-agent/Team scenario. The methods that we use are to model how various agents interact, including power allocation. Our metric for agents passing information are classical Shannon channel capacity. Our results are the mathematical theorems showing how combining agents influences the channel capacity.