Visualization of Computational Processes of Procedural Abstraction Optimization Passes

2011 ◽  
Author(s):  
Stefan Schaeckler ◽  
Nityananda Jayadevaprakash
Keyword(s):  
Procedural Abstraction ◽  
Computational Processes

scholarly journals Neither mindful nor mindless, but minded: habits, ecological psychology, and skilled performance

Synthese ◽  
2021 ◽  
Author(s):  
Miguel Segundo-Ortin ◽  
Manuel Heras-Escribano
Keyword(s):  
Cognitive Processes ◽  
Motor Behavior ◽  
Mental Representations ◽  
Ecological Psychology ◽  
Context Sensitive ◽  
Skilled Performance ◽  
New Information ◽  
Skilled Action ◽  
Conscious Attention ◽  
Computational Processes

AbstractA widely shared assumption in the literature about skilled motor behavior is that any action that is not blindly automatic and mechanical must be the product of computational processes upon mental representations. To counter this assumption, in this paper we offer a radical embodied (non-representational) account of skilled action that combines ecological psychology and the Deweyan theory of habits. According to our proposal, skilful performance can be understood as composed of sequences of mutually coherent, task-specific perceptual-motor habits. Such habits play a crucial role in simplifying both our exploration of the perceptual environment and our decision-making. However, we argue that what keeps habits situated, precluding them from becoming rote and automatic, are not mental representations but the agent's conscious attention to the affordances of the environment. It is because the agent is not acting on autopilot but constantly searching for new information for affordances that she can control her behavior, adapting previously learned habits to the current circumstances. We defend that our account provides the resources needed to understand how skilled action can be intelligent (flexible, adaptive, context-sensitive) without having any representational cognitive processes built into them.

scholarly journals Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI

2021 ◽  
pp. medethics-2020-106820 ◽  
Author(s):  
Juan Manuel Durán ◽  
Karin Rolanda Jongsma
Keyword(s):  
Artificial Intelligence ◽  
Medical Informatics ◽  
Patient Autonomy ◽  
Black Box ◽  
Epistemic Reasons ◽  
Potential Bias ◽  
Ethical Concerns ◽  
Normative Issues ◽  
Ethical Basis ◽  
Computational Processes

The use of black box algorithms in medicine has raised scholarly concerns due to their opaqueness and lack of trustworthiness. Concerns about potential bias, accountability and responsibility, patient autonomy and compromised trust transpire with black box algorithms. These worries connect epistemic concerns with normative issues. In this paper, we outline that black box algorithms are less problematic for epistemic reasons than many scholars seem to believe. By outlining that more transparency in algorithms is not always necessary, and by explaining that computational processes are indeed methodologically opaque to humans, we argue that the reliability of algorithms provides reasons for trusting the outcomes of medical artificial intelligence (AI). To this end, we explain how computational reliabilism, which does not require transparency and supports the reliability of algorithms, justifies the belief that results of medical AI are to be trusted. We also argue that several ethical concerns remain with black box algorithms, even when the results are trustworthy. Having justified knowledge from reliable indicators is, therefore, necessary but not sufficient for normatively justifying physicians to act. This means that deliberation about the results of reliable algorithms is required to find out what is a desirable action. Thus understood, we argue that such challenges should not dismiss the use of black box algorithms altogether but should inform the way in which these algorithms are designed and implemented. When physicians are trained to acquire the necessary skills and expertise, and collaborate with medical informatics and data scientists, black box algorithms can contribute to improving medical care.

scholarly journals Multi-Scale Analyses and Modeling of Metallic Nano-Layers

Materials ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 450
Author(s):  
Zara Moleinia ◽  
David Bahr
Keyword(s):  
Experimental Data ◽  
Constitutive Model ◽  
Crystal Plasticity ◽  
Single Layer ◽  
Adaptive Boosting ◽  
Multi Scale ◽  
Nano Scale ◽  
Boosting Technique ◽  
Constitutive Parameters ◽  
Computational Processes

The current work centers on multi-scale approaches to simulate and predict metallic nano-layers’ thermomechanical responses in crystal plasticity large deformation finite element platforms. The study is divided into two major scales: nano- and homogenized levels where Cu/Nb nano-layers are designated as case studies. At the nano-scale, a size-dependent constitutive model based on entropic kinetics is developed. A deep-learning adaptive boosting technique named single layer calibration is established to acquire associated constitutive parameters through a single process applicable to a broad range of setups entirely different from those of the calibration. The model is validated through experimental data with solid agreement followed by the behavioral predictions of multiple cases regarding size, loading pattern, layer type, and geometrical combination effects for which the performances are discussed. At the homogenized scale, founded on statistical analyses of microcanonical ensembles, a homogenized crystal plasticity-based constitutive model is developed with the aim of expediting while retaining the accuracy of computational processes. Accordingly, effective constitutive functionals are realized where the associated constants are obtained via metaheuristic genetic algorithms. The model is favorably verified with nano-scale data while accelerating the computational processes by several orders of magnitude. Ultimately, a temperature-dependent homogenized constitutive model is developed where the effective constitutive functionals along with the associated constants are determined. The model is validated by experimental data with which multiple demonstrations of temperature effects are assessed and analyzed.

Experimental and numerical study of an overlay composite absorber plate material for a solar air heater

2021 ◽  
Vol 63 (7) ◽  
pp. 681-686
Author(s):  
Duraisamy Jagadeesh ◽  
Ramasamy Venkatachalam ◽  
Gurusamy Nallakumarasamy
Keyword(s):  
Numerical Study ◽  
The Other ◽  
Solar Air Heater ◽  
Outlet Temperature ◽  
Cfd Analysis ◽  
Plate Material ◽  
Absorber Plate ◽  
Air Heater ◽  
Margin Of Error ◽  
Computational Processes

Abstract The research in this paper is a sequel of an earlier work by the author in which experimental and CFD results were compared for an absorber plate made of iron with and without fins for two flow rates. The research yielded a good comparative result between the experimental and computational process for an optimized flow rate and the effect of the fins. The objective of this paper is to verify the effect of the overlay composite absorber plate material on a solar air heater through experimental and computational fluid dynamics. The experimental setup consists of an absorber plate as an overlay composite of aluminum and copper for enhanced heat transfer. Experiments and CFD analysis were done in three configurations. In configuration one, only the aluminum absorber plate with fins was considered. In configuration two, the overlay composite was considered with copper on the top and aluminum at the bottom as fins, and in configuration three, the overlay composite was considered with aluminum at the top and copper at the bottom as fins. A transient 8 hours CFD analysis was carried out using these configurations. While validating the results it was found that the overlay absorber plate Cu-Al was capable of generating a high outlet temperature Max of 88 °C and capable of generating 83 °C air for 5 hours and had good thermal efficiency when compared to the other materials in the other two configuration. It was found that experimental and computational analysis were in very close agreement, and the margin of error between the experimental and computational processes was less than 8 %.

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