Adaptive Design Scheme Research Articles

Achieving Human Computer Symbiosis: □A Practitioners Perspective and Recommendations on Achieving Effective Human-Systems Integration by Augmenting Cognition

Event Abstract Back to Event Achieving Human Computer Symbiosis: □A Practitioners Perspective and Recommendations on Achieving Effective Human-Systems Integration by Augmenting Cognition Dylan Schmorrow1* 1 SoarTech (United States), United States This talk will discuss how practitioners applied basic knowledge from augmented cognition, brain-computer interface, neuroergonomics, and cognitive neuroscience to achieve more effective human-systems integration in the tools and systems that they developed at the turn of the century. This talk will walk participants through the needs for augmented cognition, the historical programs that addressed similar issues, the scientific developments that helped to create optimal systems, the initial sensors necessary, and lessons learned regarding integration into existing and new applications. The talk will provide those working in the broad domains of Human Factors, Neuroergonomics, Industrial Engineering, System Design, and Human-Computer-Interface with a synopsis of key findings and theoretical advances from augmented cognition, neuroerogonmics, brain-computer interface, cognitive neuroscience and related fields that have direct bearing upon their profession. Augmented cognition based systems (that sense a multitude of brain states, combined with other behavior and modeling techniques) adapt to users in real time, providing a true symbiosis between the human and computational systems. This tutorial will assist the participate in understanding how to better select and integrate brain sensors, design adaptive strategies to enhance human performance and it will provide an introduction to the field of augmented cognition Topics discussed in the talk will include: brain sensors and measures, sensor integration to characterize cognitive state, mitigations for improving performance, evaluation methodology, performance metrics and practical considerations for developing applications. The talk will address factors contributing to effective engineering systems design and management and it will summarize developments in operational neuroscience, neuroergonomics, and brain-computer interface that contributed significantly to human computer symbiosis during the first decade of the 21st century. Keywords: Augmented Cognition, Human Computer Interaction (HCI), human factors, Human System Interaction, Neuroergonomcis Conference: 2nd International Neuroergonomics Conference, Philadelphia, PA, United States, 27 Jun - 29 Jun, 2018. Presentation Type: Oral Presentation Topic: Neuroergonomics Citation: Schmorrow D (2019). Achieving Human Computer Symbiosis: □A Practitioners Perspective and Recommendations on Achieving Effective Human-Systems Integration by Augmenting Cognition. Conference Abstract: 2nd International Neuroergonomics Conference. doi: 10.3389/conf.fnhum.2018.227.00120 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 03 Apr 2018; Published Online: 27 Sep 2019. * Correspondence: PhD. Dylan Schmorrow, SoarTech (United States), Ann Arbor, United States, schmorrow@yahoo.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Dylan Schmorrow Google Dylan Schmorrow Google Scholar Dylan Schmorrow PubMed Dylan Schmorrow Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

A Taylor Expansion‐Based Adaptive Design Strategy for Global Surrogate Modeling With Applications in Groundwater Modeling

AbstractGlobal sensitivity analysis (GSA) and uncertainty quantification (UQ) for groundwater modeling are challenging because of the model complexity and significant computational requirements. To reduce the massive computational cost, a cheap‐to‐evaluate surrogate model is usually constructed to approximate and replace the expensive groundwater models in the GSA and UQ. Constructing an accurate surrogate requires actual model simulations on a number of parameter samples. Thus, a robust experimental design strategy is desired to locate informative samples so as to reduce the computational cost in surrogate construction and consequently to improve the efficiency in the GSA and UQ. In this study, we develop a Taylor expansion‐based adaptive design (TEAD) that aims to build an accurate global surrogate model with a small training sample size. TEAD defines a novel hybrid score function to search informative samples, and a robust stopping criterion to terminate the sample search that guarantees the resulted approximation errors satisfy the desired accuracy. The good performance of TEAD in building global surrogate models is demonstrated in seven analytical functions with different dimensionality and complexity in comparison to two widely used experimental design methods. The application of the TEAD‐based surrogate method in two groundwater models shows that the TEAD design can effectively improve the computational efficiency of GSA and UQ for groundwater modeling.

“Adaptive radiation” optimization for climate adaptive building facade design strategy

This paper introduces an evolutionary algorithm methodology to solve facade optimization problems in different climates. The algorithm is based on the improvement of simple Genetic Algorithm (GA). The concept of Adaptive Radiation (AR) is derived from the biological process of adaptation where specific species are evolutionarily adapted to their immediate ecological niches. This algorithm obtains near global optimal solutions in significantly less computation time than simple GA. AR is implemented in three different climates in the United States to demonstrate its robustness and efficiency. Climate adaptive facade design strategies for these climates are illustrated based on the optimization results.

Population-Enrichment Adaptive Design Strategy for an Event-Driven Vaccine Efficacy Trial

A population-enrichment adaptive design allows a prospective use for study population selection. It has the flexibility allowing pre-specified modifications to an ongoing trial to mitigate the potential risk associated with the assumptions made at design stage. In this way, the trial can potentially encompass a broader target patient population, and move forward only with the subpopulations that appear to be benefiting from the treatment. Our work is motivated by a Phase III event-driven vaccine efficacy trial. Two target patient subpopulations were enrolled with the assumption that vaccine efficacy can be demonstrated based on the combined population. It is recognized due to the nature of patients’ underlying conditions, one subpopulation might respond to the treatment better than the other. To maximize the probability of demonstrating vaccine efficacy in at least one patient population while taking advantage of combining two subpopulations in one single trial, an adaptive design strategy with potential population enrichment is developed. Specifically, if the observed vaccine efficacy at interim for one subpopulation is not promising to warrant carrying forward, the population may be enriched with the other subpopulation with better performance. Simulations were conducted to evaluate the operational characteristics from a selection of interim analysis plans. This population-enrichment design provides a more efficient way as compared to the conventional approaches when targeting multiple subpopulations. If executed and planned with caution, this strategy can provide a greater chance of success of the trial and help maintain scientific and regulatory rigors.

The Batture Effect

On a narrow strip of land between the Mississippi River and the U.S. Army Corps of Engineers’ flood control levees in New Orleans, a squatters’ community called the Batture exhibits an unusual inversion of conventional assumptions regarding hazard awareness and development security. For a New Orleanian to willingly take up residence on the “wrong side” of the levee is to break the most fundamental rule of the city’s spatial economy and collective psychology. This article proposes the existence of a Batture Effect , a mode of insurgent citizenship whereby hidden environmental threats are made visible and small-scale architectural, urban, and psychological adaptation measures are tested. As an adaptive design strategy, organic in its development over time and responsive to the immediate needs of residents, the occupation of the Batture demonstrates a homesteader’s approach to land appropriation. In calling into question land-use and ownership, the urban planning process and its associated spatial agency, and the role of the individual in disaster preparedness, this guerilla urbanism suggests relevant lessons to the practice of landscape architecture and urban planning.

Adaptive Architecture and the Prevention of Infections in Hospitals

Abstract Researches has shown that climate change may spark global epidemics. The objectives of hospital design consistent with a high standard of sustainable architecture must not only be the tropicalization of buildings but also a system to confront the impact of infectious diseases which arise from climate change. Infection control is the discipline concerned with preventing nosocomial or healthcare-associated infection. Infection control addresses factors related to the spread of infections within the hospital building, including prevention, monitoring and management measures. As the application of new technologies such as the Heating, ventilation and air conditioning system (HVAC) with high-efficiency particulate arrestance (HEPA) has application range within stamina, the study suggests the need to adopt an integrated adaptive hospital design strategy to prevent infection.

Accelerated search for materials with targeted properties by adaptive design.

Finding new materials with targeted properties has traditionally been guided by intuition, and trial and error. With increasing chemical complexity, the combinatorial possibilities are too large for an Edisonian approach to be practical. Here we show how an adaptive design strategy, tightly coupled with experiments, can accelerate the discovery process by sequentially identifying the next experiments or calculations, to effectively navigate the complex search space. Our strategy uses inference and global optimization to balance the trade-off between exploitation and exploration of the search space. We demonstrate this by finding very low thermal hysteresis (ΔT) NiTi-based shape memory alloys, with Ti50.0Ni46.7Cu0.8Fe2.3Pd0.2 possessing the smallest ΔT (1.84 K). We synthesize and characterize 36 predicted compositions (9 feedback loops) from a potential space of ∼800,000 compositions. Of these, 14 had smaller ΔT than any of the 22 in the original data set.

Adaptive Designs for Two Candidate Primary Time-to-Event Endpoints

ABSTRACTIn clinical trials, the choice of an adequate primary endpoint is often difficult. Besides its clinical relevance, the endpoint must be measurable within reasonable time and must allow differentiating between the treatments. Often, the most relevant endpoint is ‘’time-to-death,” but if the overall survival prognosis is good, only a few deaths are observed during the study duration. A possible solution is to use surrogate endpoints instead. However, various examples from the literature demonstrate that surrogates do not always perform as intended. Sometimes, the surrogate effect is smaller than for the original endpoint, or the latter shows a higher effect than anticipated so using the surrogate is not reasonable. In this work, different adaptive design strategies for two candidate endpoints are proposed to solve these problems. The idea is to base the efficacy proof on the significance of at least one endpoint. At an interim analysis, both candidates are evaluated. If it is not possible to stop the study early, the sample size is recalculated based on the more promising endpoint. The new methods are illustrated by a clinical study example and compared in terms of power and sample size using Monte Carlo simulations. The software code is provided as supplementary material.

Adaptive Strategies for Materials Design using Uncertainties.

We compare several adaptive design strategies using a data set of 223 M2AX family of compounds for which the elastic properties [bulk (B), shear (G), and Young’s (E) modulus] have been computed using density functional theory. The design strategies are decomposed into an iterative loop with two main steps: machine learning is used to train a regressor that predicts elastic properties in terms of elementary orbital radii of the individual components of the materials; and a selector uses these predictions and their uncertainties to choose the next material to investigate. The ultimate goal is to obtain a material with desired elastic properties in as few iterations as possible. We examine how the choice of data set size, regressor and selector impact the design. We find that selectors that use information about the prediction uncertainty outperform those that don’t. Our work is a step in illustrating how adaptive design tools can guide the search for new materials with desired properties.

Experimental adaptive quantum tomography of two-qubit states

We report an experimental realization of adaptive Bayesian quantum state tomography for two-qubit states. Our implementation is based on the adaptive experimental design strategy proposed in [F.Husz\'ar and N.M.T.Houlsby, Phys.Rev.A 85, 052120 (2012)] and provides an optimal measurement approach in terms of the information gain. We address the practical questions, which one faces in any experimental application: the influence of technical noise, and behavior of the tomographic algorithm for an easy to implement class of factorized measurements. In an experiment with polarization states of entangled photon pairs we observe a lower instrumental noise floor and superior reconstruction accuracy for nearly-pure states of the adaptive protocol compared to a non-adaptive. At the same time we show, that for the mixed states the restriction to factorized measurements results in no advantage for adaptive measurements, so general measurements have to be used.

Rational Design and Adaptive Management of Combination Therapies for Hepatitis C Virus Infection.

Recent discoveries of direct acting antivirals against Hepatitis C virus (HCV) have raised hopes of effective treatment via combination therapies. Yet rapid evolution and high diversity of HCV populations, combined with the reality of suboptimal treatment adherence, make drug resistance a clinical and public health concern. We develop a general model incorporating viral dynamics and pharmacokinetics/ pharmacodynamics to assess how suboptimal adherence affects resistance development and clinical outcomes. We derive design principles and adaptive treatment strategies, identifying a high-risk period when missing doses is particularly risky for de novo resistance, and quantifying the number of additional doses needed to compensate when doses are missed. Using data from large-scale resistance assays, we demonstrate that the risk of resistance can be reduced substantially by applying these principles to a combination therapy of daclatasvir and asunaprevir. By providing a mechanistic framework to link patient characteristics to the risk of resistance, these findings show the potential of rational treatment design.

Protecting the Coastline from the Effects of Climate Change: Adaptive Design for the Coastal Areas of Gangneung, Korea

The purpose of this research is to present design strategies to enable coastal areas to adapt to climate change and maintain the coastlines by addressing the environmental and urban issues. Gangneung is a tourist attraction situated on South Korea’s east coast, and there is an urgent need for integrated research on strategies to prevent the loss of sandy beaches and the damage caused by storm surges and high swell. This research has two objectives: The first is to offer an overview and describe the characteristics of exemplary projects carried out to manage the storm damage while maintaining the coastlines. The second is to propose a design model that can be applied to coastal areas susceptible to climate change by analyzing the design strategies and the current conditions of the Gangneung coastal area. In the case of Gangneung, the damage caused by the storm surges and high swells are more severe compared to inundation caused by sea level rise because of the steep slope and deep water. Therefore, adaptive design strategies are mainly focused on accommodation and retreat strategies that consider these characteristics by moving the coastal roads behind the pine forest and raising the coastal buildings to connect the coast to the forest and to prevent coastal erosion. This research has the potential to be used as an exemplary design adaptation for coastal erosion as well as a basis for regulating the land use policy in areas susceptible to flood by establishing guidelines for publicly funded developments, and preparing long-term relocation plans for the existing coastal developments to create a sustainable and resilient future for the coastal areas.

Adaptive control design under structured model information limitation: A cost-biased maximum-likelihood approach

Networked control strategies based on limited information about the plant model usually result in worse closed-loop performance than optimal centralized control with full plant model information. Recently, this fact has been established by utilizing the concept of competitive ratio, which is defined as the worst-case ratio of the cost of a control design with limited model information to the cost of the optimal control design with full model information. We show that an adaptive controller, inspired by a controller proposed by Campi and Kumar, with limited plant model information, asymptotically achieves the closed-loop performance of the optimal centralized controller with full model information for almost any plant. Therefore, there exists, at least, one adaptive control design strategy with limited plant model information that can achieve a competitive ratio equal to one. The plant model considered in the paper belongs to a compact set of stochastic linear time-invariant systems and the closed-loop performance measure is the ergodic mean of a quadratic function of the state and control input.

Analysing the cost efficiency of parcel distribution networks with changes in demand

Demand for parcel delivery services has rapidly increased recently mainly due to the tremendous growth of e-commerce worldwide. Responding to changes in parcel volume with small-sized frequent shipments poses a great challenge to parcel delivery companies, as they need to rationalize the cost of operating and maintaining their physical distribution networks, while achieving acceptable level of on-time delivery rate throughout their large market areas. The objective of this paper is to analyse the impact of increasing demand on the parcel distribution network structures in terms of minimizing transportation and sortation costs. Based on the case of South Korean parcel delivery industry, this study simulated various types of networks including point-to-point, hub-and-spoke, and multiple hub structure with parcel demand changes. This study can provide well-prepared adaptive network design strategies to parcel delivery service providers for various possible demand changes over the long term.

Adaptive optimal designs for dose-finding studies based on sigmoid Emax models

The adaptive design strategy is a feasible approach for optimal designs in dose-finding studies, where the information matrices usually depend on the unknown parameters. Focusing on three pharmacodynamics sigmoid Emax models, we derive the corresponding simple formats of the adaptive optimal designs regardless of the optimality criteria or parameters of interest. An algorithm for deriving a specific adaptive optimal design is developed. A simulation study comparing the adaptive optimal designs and the uniform designs is also performed.

Adaptive façade design for the daylighting performance in an office building: the investigation of an opening design strategy with cellular automata

Daylighting has an important role in sustainable architecture as it affects the energy consumption in a building. Especially for an office building, daylight helps not only to reduce the load on artificial lighting but also to promote work efficiency through promoting good human health, well-being and user comfort. The objective of this research is to develop an innovative facade design strategy that comes from the development of digital technology and dynamic daylight performance measuring methods. Thus, the various parameters are studied through the computational process of cellular automata (CA) to generate the several alternative opening patterns on the building facade. Each CA design value was tested under static and dynamic sky condition to analyze the quality and quantity of daylight and visual comfort throughout the year. The results were compared to find an optimum alternative design in terms of the daylighting design criteria, from building code, standard and design guidelines for the office building. Finally, research of an adaptive facade design strategy was concluded with the results from the above hybridization of generative and performative design methodology. This study discovers the architectural design approach from the CA and it will make not only progress in building facade esthetics, but also human comfort with building sustainability.

Comparing the Response Rates for Superiority, Noninferiority and Equivalence Testing with Multiple-to-One Matched Binary Data

Paired and multiple-to-one matched data have often been collected in clinical trials and epidemiological safety studies. When the response is binary, the sample size and power are determined by the discordant pairs or matched sets. Fixed sample size determination in assessing response rate difference of superiority, noninferiority, and equivalence tests of paired binary data has been discussed by Lu and Bean (1995)Nam (1997), and Liu et al. (2002). We extend the results of Lu and Bean (1995)Nam (1997), and Liu et al. (2002) to more general cases of multiple-to-one matched binary data. We further examine two issues regarding such tests. First, we examine the issue of simultaneous test and two-stage test for both superiority and noninferiority/equivalence hypotheses. Although, as discussed in Nam (1997) and Liu et al. (2002), the standard errors restricted to null hypothesis are different between superiority and noninferiority test, the monotonic property of the two tests makes the simultaneous testing and switching between the hypotheses valid. Second, in practice, the joint distribution of matched responses is often unknown, and thus determining the sample size using only the background information of the control group could be inefficient. Furthermore, for noninferiority or equivalence tests, the sample sizes are often determined using the unrealistic alternative hypothesis that the response rates of both treatments are identical. We propose to use a two-stage adaptive design strategy for sample size reestimation that uses the interim information to improve the efficiency.

Prediction under Uncertainty on a Mature Field

Reservoir engineering studies involve a large number of parameters with great uncertainties. To ensure correct future production, a comparison of possible scenarios in managing related uncertainties is needed. Comparisons can be performed with more information than only a single mean case for each scenario. The Bayesian formalism is well tailored to address the key problem of making predictions under uncertainty, especially in mature fields. It enables to define the reservoir uncertainty taking into account static and dynamic data. This posterior uncertainty can then be propagated to compute probabilistic production forecasts for each scenario, while honoring static and dynamic knowledge of the reservoir. But obtaining posterior uncertainty, as well as propagating it on production forecasts, entails a prohibitive number of reservoir simulations.In this paper, we propose an application of several advanced statistical techniques to perform prediction under uncertainty on a mature field using a reasonable number of simulations. The considered mature field is the PUNQS reservoir model which has been previously used in several comparison studies on uncertainty quantification and history-matching. A workflow based on three steps has been applied. First, a screening and a sensitivity analysis were performed to find the most influential parameters. Then, a probabilistic inversion method was used to reduce uncertainty on the parameters by estimating their posterior uncertainty. Finally, probabilistic predictions are computed by propagating the reduced uncertainty of parameters. In the first step of the workflow, two different sensitivity techniques are discussed and compared. One, more qualitative, based on the Morris method and another, more quantitative, based on Sobol’ indices. In the second step, a probabilistic history-matching procedure is applied to reduce the uncertainty. It is based on both a non parametric response surface approach which uses Gaussian process modeling and an adaptive design strategy. In the final step of the workflow, parametric response surfaces are used to approximate the reservoir production forecasts and obtain their probabilistic distribution by propagating the remaining posterior uncertainty of input parameters.

Evaluation of the Pre-Posterior Distribution of Optimized Sampling Times for the Design of Pharmacokinetic Studies

Information theoretic methods are often used to design studies that aim to learn about pharmacokinetic and linked pharmacokinetic–pharmacodynamic systems. These design techniques, such as D-optimality, provide the optimum experimental conditions. The performance of the optimum design will depend on the ability of the investigator to comply with the proposed study conditions. However, in clinical settings it is not possible to comply exactly with the optimum design and hence some degree of unplanned suboptimality occurs due to error in the execution of the study. In addition, due to the nonlinear relationship of the parameters of these models to the data, the designs are also locally dependent on an arbitrary choice of a nominal set of parameter values. A design that is robust to both study conditions and uncertainty in the nominal set of parameter values is likely to be of use clinically. We propose an adaptive design strategy to account for both execution error and uncertainty in the parameter values. In this study we investigate designs for a one-compartment first-order pharmacokinetic model. We do this in a Bayesian framework using Markov-chain Monte Carlo (MCMC) methods. We consider log-normal prior distributions on the parameters and investigate several prior distributions on the sampling times. An adaptive design was used to find the sampling window for the current sampling time conditional on the actual times of all previous samples.

Novel design of adaptive neural network controller for a class of non-affine nonlinear systems

The tracking control problem is studied for a class of uncertain non-affine systems. Based on the principle of sliding mode control (SMC), using the neural networks (NNs) and the property of the basis function, a novel adaptive design scheme is proposed. A novel Lyapunov function, which depends on both system states and control input variable, is used for the development of the control law and the adaptive law. The approach overcomes the drawback in the literature. In addition, the lumped disturbances are taken in account. By theoretical analysis, it is proved that tracking errors asymptotically converge to zero. Finally, simulation results demonstrate the effectiveness of the proposed approach.