The privileged sensing framework: A principled approach to improved human-autonomy integration

ABSTRACT

A primary goal for human-autonomy integration (HAI) is to balance the strengths of human and autonomy in order to achieve performance objectives more efficiently and robustly than either the human or autonomous agents would independently. This paper proposes the Privileged Sensing Framework (PSF) as a novel approach to HAI. This approach is based on the concept of dynamically ‘privileging’ information during the process of integration by dynamically bestowing special rights based on the characteristics of each individual agent, the task context, and the performance goals. The proposed framework is tested through a series of simulation experiments that provide a clear demonstration of increased accuracy and throughput of human-autonomy performance. These proof-of-concept simulations provide initial evidence of the utility of the PSF. Continued development of this approach has the potential to revolutionise capabilities of multi-agent cooperative teams across a broad range of applications.     

Design of synthetic materials for intracellular delivery of RNAs: From siRNA-mediated gene silencing to CRISPR/Cas gene editing

Ribonucleic acids (RNAs) possess great therapeutic potential and can be used to treat a variety of diseases. The unique biophysical properties of RNAs, such as high molecular weight, negative charge, hydrophilicity, low stability, and potential immunogenicity, require chemical modification and development of carriers to enable intracellular delivery of RNAs for clinical use. A variety of nanomaterials have been developed for the effective in vivo delivery of short/ small RNAs, messenger RNAs, and RNAs required for gene editing technologies including clustered regularly interspaced palindromic repeat (CRISPR)/Cas. This review outlines the challenges of delivering RNA therapeutics, explores the chemical synthesis of RNA modifications and carriers, and describes the efforts to design nanomaterials that can be used for a variety of clinical indications.     

Adaptive discrete neural observer design for nonlinear systems with unknown time‐delay

This paper focuses on the adaptive observer design for nonlinear discrete‐time MIMO systems with unknown time‐delay and nonlinear dynamics. The delayed states involved in the system are arguments of a nonlinear function and only the estimated delay is utilized. By constructing an appropriate Lyapunov–Krasovskii function, the delay estimation error is considered in the observer parameter design. The proposed method is then extended to the system with a nonlinear output measurement equation and the delayed dynamics. With the help of a high‐order neural network (HONN), the requirement for a precise system model, the linear‐in‐the‐parameters (LIP) assumption of the delayed states, the Lipschitz or norm‐boundedness assumption of unknown nonlinearities are removed. A novel converse Lyapunov technical lemma is also developed and used to prove the uniform ultimate boundedness of the proposed observer. The effectiveness of the proposed results is verified by simulations. Copyright © 2010 John Wiley & Sons, Ltd.     

Review Paper: A critical review of the present and future prospects for electronic paper

Abstract— The commercial success of monochrome electronic paper (e‐Paper) is now propelling the development of next‐generation flexible, video, and color e‐Paper products. Unlike the early battles in the 1980s and 1990s between transmissive and emissive display technologies, there is a extraordinary diversity of technologies vying to become the next generation of e‐Paper. A critical review of all major e‐Paper technologies, including a technical breakdown of the performance limitations based on device physics and commentary on possible future breakthroughs, is presented. In addition, the visual requirements for color e‐Paper are provided and compared to standards used in conventional print. It is concluded that researchers have much work remaining in order to bridge the significant gap between reflective electronic displays and print‐on‐paper.     

A fully integrated 1‐in. AMOLED display using current feedback based on a five‐mask LTPS CMOS process

Abstract— In the past, a five‐mask LTPS CMOS process requiring only one single ion‐doping step was used. Based on that process, all necessary components for the realization of a fully integrated AMOLED display using a 3T1C current‐feedback pixel circuit has recently been developed. The integrated data driver is based on a newly developed LTPS operational amplifier, which does not require any compensation for Vth or mobility variations. Only one operational amplifier per column is used to perform digital‐to‐analog conversion as well as current control. In order to achieve high‐precision analog behavior, the operational amplifier is embedded in a switched capacitor network. In addition to circuit verification by simulation and analytic analysis, a 1‐in. fully integrated AMOLED demonstrator was successfully built. To the best of the authors' knowledge, this is the first implementation of a fully integrated AMOLED display with current feedback.     

Feature-based statistical analysis of combustion simulation data

We present a new framework for feature-based statistical analysis of large-scale scientific data and demonstrate its effectiveness by analyzing features from Direct Numerical Simulations (DNS) of turbulent combustion. Turbulent flows are ubiquitous and account for transport and mixing processes in combustion, astrophysics, fusion, and climate modeling among other disciplines. They are also characterized by coherent structure or organized motion, i.e. nonlocal entities whose geometrical features can directly impact molecular mixing and reactive processes. While traditional multi-point statistics provide correlative information, they lack nonlocal structural information, and hence, fail to provide mechanistic causality information between organized fluid motion and mixing and reactive processes. Hence, it is of great interest to capture and track flow features and their statistics together with their correlation with relevant scalar quantities, e.g. temperature or species concentrations. In our approach we encode the set of all possible flow features by pre-computing merge trees augmented with attributes, such as statistical moments of various scalar fields, e.g. temperature, as well as length-scales computed via spectral analysis. The computation is performed in an efficient streaming manner in a pre-processing step and results in a collection of meta-data that is orders of magnitude smaller than the original simulation data. This meta-data is sufficient to support a fully flexible and interactive analysis of the features, allowing for arbitrary thresholds, providing per-feature statistics, and creating various global diagnostics such as Cumulative Density Functions (CDFs), histograms, or time-series. We combine the analysis with a rendering of the features in a linked-view browser that enables scientists to interactively explore, visualize, and analyze the equivalent of one terabyte of simulation data. We highlight the utility of this new framework for combustion science; however, it is applicable to many other science domains.     

BallotMaps: detecting name bias in alphabetically ordered ballot papers

The relationship between candidates' position on a ballot paper and vote rank is explored in the case of 5000 candidates for the UK 2010 local government elections in the Greater London area. This design study uses hierarchical spatially arranged graphics to represent two locations that affect candidates at very different scales: the geographical areas for which they seek election and the spatial location of their names on the ballot paper. This approach allows the effect of position bias to be assessed; that is, the degree to which the position of a candidate's name on the ballot paper influences the number of votes received by the candidate, and whether this varies geographically. Results show that position bias was significant enough to influence rank order of candidates, and in the case of many marginal electoral wards, to influence who was elected to government. Position bias was observed most strongly for Liberal Democrat candidates but present for all major political parties. Visual analysis of classification of candidate names by ethnicity suggests that this too had an effect on votes received by candidates, in some cases overcoming alphabetic name bias. The results found contradict some earlier research suggesting that alphabetic name bias was not sufficiently significant to affect electoral outcome and add new evidence for the geographic and ethnicity influences on voting behaviour. The visual approach proposed here can be applied to a wider range of electoral data and the patterns identified and hypotheses derived from them could have significant implications for the design of ballot papers and the conduct of fair elections.     

Development of quantitative structure-activity relationships for explanatory modeling of fast reacting (meth)acrylate monomers bearing novel functionality

The photoinitiated polymerization of (meth)acrylate monomers bearing novel carbamate functionality exhibits significantly greater reaction rate when compared to more traditional acrylate monomers undergoing similar polymerization. This unusually fast reactivity has been the subject of much investigation. In order to suggest an explanatory mechanism for the enhanced polymerization rates we have conducted quantitative structure-activity relationship investigations of these novel monomers. These studies have resulted in statistically sound models with coefficients of multiple determination of R(2)>0.93. Principal component and k nearest neighbor similarity analysis were also conducted on the multiple regression models. These results are discussed in light of published experimental investigations of the photopolymerization reactivity of the novel monomers.