Man-machine interface analysis of the flight design system

The objective of the current effort was to perform a broad analysis of the human factors issues involved in the design of the Flight Design System (FDS). The analysis was intended to include characteristics of the system itself, such as: (1) basic structure and functional capabilities of FDS; (2) user backgrounds, capabilities, and possible modes of use; (3) FDS interactive dialogue, problem solving aids; (4) system data management capabilities; and to include, as well, such system related matters as: (1) flight design team structure; (2) roles of technicians; (3) user training; and (4) methods of evaluating system performance. Wherever possible, specific recommendations are made. In other cases, the issues which seem most important are identified. In some cases, additional analyses or experiments which might provide resolution are suggested

Application of expert systems in project management decision aiding

The feasibility of developing an expert systems-based project management decision aid to enhance the performance of NASA project managers was assessed. The research effort included extensive literature reviews in the areas of project management, project management decision aiding, expert systems technology, and human-computer interface engineering. Literature reviews were augmented by focused interviews with NASA managers. Time estimation for project scheduling was identified as the target activity for decision augmentation, and a design was developed for an Integrated NASA System for Intelligent Time Estimation (INSITE). The proposed INSITE design was judged feasible with a low level of risk. A partial proof-of-concept experiment was performed and was successful. Specific conclusions drawn from the research and analyses are included. The INSITE concept is potentially applicable in any management sphere, commercial or government, where time estimation is required for project scheduling. As project scheduling is a nearly universal management activity, the range of possibilities is considerable. The INSITE concept also holds potential for enhancing other management tasks, especially in areas such as cost estimation, where estimation-by-analogy is already a proven method

Quantum cryptography: key distribution and beyond

Uniquely among the sciences, quantum cryptography has driven both foundational research as well as practical real-life applications. We review the progress of quantum cryptography in the last decade, covering quantum key distribution and other applications.Comment: It's a review on quantum cryptography and it is not restricted to QK

The On-board Operative Advisory Expert Systems for Anthropocentric Object

A class of intelligent systems located on anthropocentric objects that provide a crew with recommendations on the anthropocentric object's rational behavior in typical situations of operation is considered. We refer to this class of intelligent systems as onboard real-time advisory expert systems. Here, we present a formal model of the object domain, procedures for obtaining knowledge about the object domain, and a semantic structure of basic functional units of the onboard real-time advisory expert systems of typical situations. The stages of the development and improvement of knowledge bases for onboard real-time advisory expert systems of typical situations that are important in practice are considered

On the group theoretic structure of a class of quantum dialogue protocols

Intrinsic symmetry of the existing protocols of quantum dialogue are explored. It is shown that if we have a set of mutually orthogonal $n$-qubit states {\normalsize $\{|\phi_{0}>,|\phi_{1}>,....,|\phi_{i}\}$ and a set of $m-qubit$ ($m\leq n$) unitary operators $\{U_{0},U_{2},...,U_{2^{n}-1}\}:U_{i}|\phi_{0}>=|\phi_{i}>$ and $\{U_{0},U_{2},...,U_{2^{n}-1}\}$ forms a group under multiplication then it would be sufficient to construct a quantum dialogue protocol using this set of quantum states and this group of unitary operators}. The sufficiency condition is used to provide a generalized protocol of quantum dialogue. Further the basic concepts of group theory and quantum mechanics are used here to systematically generate several examples of possible groups of unitary operators that may be used for implementation of quantum dialogue. A large number of examples of quantum states that may be used to implement the generalized quantum dialogue protocol using these groups of unitary operators are also obtained. For example, it is shown that GHZ state, GHZ-like state, W state, 4 and 5 qubit Cluster states, Omega state, Brown state, $Q_{4}$ state and $Q_{5}$ state can be used for implementation of quantum dialogue protocol. The security and efficiency of the proposed protocol is appropriately analyzed. It is also shown that if a group of unitary operators and a set of mutually orthogonal states are found to be suitable for quantum dialogue then they can be used to provide solutions of socialist millionaire problem.Comment: 15 page

From Quantum Optics to Quantum Technologies

Quantum optics is the study of the intrinsically quantum properties of light. During the second part of the 20th century experimental and theoretical progress developed together; nowadays quantum optics provides a testbed of many fundamental aspects of quantum mechanics such as coherence and quantum entanglement. Quantum optics helped trigger, both directly and indirectly, the birth of quantum technologies, whose aim is to harness non-classical quantum effects in applications from quantum key distribution to quantum computing. Quantum light remains at the heart of many of the most promising and potentially transformative quantum technologies. In this review, we celebrate the work of Sir Peter Knight and present an overview of the development of quantum optics and its impact on quantum technologies research. We describe the core theoretical tools developed to express and study the quantum properties of light, the key experimental approaches used to control, manipulate and measure such properties and their application in quantum simulation, and quantum computing.Comment: 20 pages, 3 figures, Accepted, Prog. Quant. Ele

Certified Reinforcement Learning with Logic Guidance

This paper proposes the first model-free Reinforcement Learning (RL) framework to synthesise policies for unknown, and continuous-state Markov Decision Processes (MDPs), such that a given linear temporal property is satisfied. We convert the given property into a Limit Deterministic Buchi Automaton (LDBA), namely a finite-state machine expressing the property. Exploiting the structure of the LDBA, we shape a synchronous reward function on-the-fly, so that an RL algorithm can synthesise a policy resulting in traces that probabilistically satisfy the linear temporal property. This probability (certificate) is also calculated in parallel with policy learning when the state space of the MDP is finite: as such, the RL algorithm produces a policy that is certified with respect to the property. Under the assumption of finite state space, theoretical guarantees are provided on the convergence of the RL algorithm to an optimal policy, maximising the above probability. We also show that our method produces ''best available'' control policies when the logical property cannot be satisfied. In the general case of a continuous state space, we propose a neural network architecture for RL and we empirically show that the algorithm finds satisfying policies, if there exist such policies. The performance of the proposed framework is evaluated via a set of numerical examples and benchmarks, where we observe an improvement of one order of magnitude in the number of iterations required for the policy synthesis, compared to existing approaches whenever available.Comment: This article draws from arXiv:1801.08099, arXiv:1809.0782

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues