Accuracy vs. Simplicity: A Complex Trade-Off

Inductive learning aims at finding general rules that hold true in a database. Targeted learning seeks rules for the predictions of the value of a variable based on the values of others, as in the case of linear or non-parametric regression analysis. Non-targeted learning finds regularities without a specific prediction goal. We model the product of non-targeted learning as rules that state that a certain phenomenon never happens, or that certain conditions necessitate another. For all types of rules, there is a trade-off between the rule's accuracy and its simplicity. Thus rule selection can be viewed as a choice problem, among pairs of degree of accuracy and degree of complexity. However, one cannot in general tell what is the feasible set in the accuracy-complexity space. Formally, we show that finding out whether a point belongs to this set is computationally hard. In particular, in the context of linear regression, finding a small set of variables that obtain a certain value of R2 is computationally hard. Computational complexity may explain why a person is not always aware of rules that, if asked, she would find valid. This, in turn, may explain why one can change other people's minds (opinions, beliefs) without providing new information.

Fact-Free Learning

People may be surprised by noticing certain regularities that hold in existing knowledge they have had for some time. That is, they may learn without getting new factual information. We argue that this can be partly explained by computational complexity. We show that, given a database, finding a small set of variables that obtain a certain value of R2 is computationally hard, in the sense that this term is used in computer science. We discuss some of the implications of this result and of fact-free learning in general.Learning, Behavioral Economics

Fact-Free Learning

People may be surprised by noticing certain regularities that hold in existing knowledge they have had for some time. That is, they may learn without getting new factual information. We argue that this can be partly explained by computational complexity. We show that, given a database, finding a small set of variables that obtain a certain value of R^2 is computationally hard, in the sense that this term is used in computer science. We discuss some of the implications of this result and of fact-free learning in general.Computational complexity, Linear regression, Rule-based reasoning

Fact-Free Learning

People may be surprised by noticing certain regularities that hold in existing knowledge they have had for some time. That is, they may learn without getting new factual information. We argue that this can be partly explained by computational complexity. We show that, given a knowledge base, finding a small set of variables that obtain a certain value of R2 is computationally hard, in the sense that this term is used in computer science. We discuss some of the implications of this result and of fact-free learning in general.Learning, Behavioral Economics

Fact-Free Learning

People may be surprised by noticing certain regularities that hold in existing knowledge they have had for some time. That is, they may learn without getting new factual information. We argue that this can be partly explained by computational complexity. We show that, given a database, finding a small set of variables that obtain a certain value of R 2 is computationally hard, in the sense that this term is used in computer science. We discuss some of the implications of this result and of fact-free learning in general

Fact-Free Learning

People may be surprised by noticing certain regularities that hold in existing knowledge they have had for some time. That is, they may learn without getting new factual information. We argue that this can be partly explained by computational complexity. We show that, given a database, finding a small set of variables that obtain a certain value of R 2 is computationally hard, in the sense that this term is used in computer science. We discuss some of the implications of this result and of fact-free learning in general

Investigating Membrane Material Alternatives for Air Revitalization in Space

Recently, NASA’s ultimate goal has been to launch a crewed Mars mission. However, the current system used for carbon dioxide (CO2) removal in air revitalization in the International Space Station (ISS) is not equipped to handle beyond low-earth-orbit missions. The Carbon Dioxide Removal Assembly (CDRA) is a complex system that relies heavily on sorbent materials and faces challenges in reliability, energy efficiency, and material degradation. Although the CDRA has operated well in the ISS for the past two decades, health effects from high CO2 levels are amongst the most common complaints from and challenges for astronauts. Recent developments in membrane technology prove to be a promising alternative to sorbent-based systems for CO2 removal. Maintaining high selectivity for CO2 with a reasonable permeability, at such low partial pressures and in the presence of water, is among the main challenges of using membranes in this application. In this work, we have created a membrane-based model with appropriate conditions to identify the membrane technology for this application. We expect to determine a working range of critical parameters such as permeability, selectivity, and membrane area for successful CO2 separation. We will also be comparing the thermodynamic efficiency of a membrane-based process to that of the CDRA to pin-point areas of improvement

A Thermodynamics Analysis for Improvement of Carbon Dioxide Removal Technologies for Space

The carbon dioxide removal assembly (CDRA) has been used for the past two decades to continually remove carbon dioxide (CO2) as part of the air revitalization system onboard the international space station (ISS). The CDRA is an adsorption-based system that relies on sorbent materials that require a significant energy input to be thermally regenerated. Additionally, the system faces challenges in reliability and size/weight, so it is being re-evaluated for viability beyond-lowearth-orbit missions. The CDRA removes CO2 from the cabin air through a cyclical adsorption-desorption process that uses four molecular sieve beds. The main components include two desiccant beds to remove H2O, two CO2 zeolite sorbent beds, an air blower, two resistive heaters, and a cooling heat exchanger. Past studies on the CDRA primarily focus on predictive physics-based modeling of the sorbent beds to understand reliability, performance, and sorbent kinetics, with very few performing a thermodynamic analysis of the entire system. This study aims to improve the understanding of component-level losses of the CDRA using exergy destruction analysis and to quantify the losses. We developed a thermodynamics black-box model using a first and second law balances over each individual component over one operational cycle. The results indicate that the molecular sieve sorbent beds are major contributors to lost work within the CDRA. However, the total exergy destruction in the desiccant beds is greater than the sorbent beds. This indicates that the desiccant beds are the largest contributor of losses. Removing water prior to the removal of CO2 from the flow stream is a necessary step because the zeolite sorbent will preferentially adsorb water. Our findings motivate the use of alternative components that may offer direct separation of water at higher efficiencies

Modulation of Pore Shape and Adsorption Selectivity by Ligand Functionalization in a Series of “rob”-like Flexible Metal-Organic Frameworks

We report the synthesis of a new family of four new isoreticular metal–organic frameworks (MOFs) based on Cu–Cu paddle-wheel building units. The four MOFs contain 1D microchannels modulated by chemical functionalisation of a dicarboxylate ligand or the use of different bis-4,40-pyridyl-like connectors behaving as ancillary linkers. A deep analysis of their CO2, H2 and CH4 adsorption properties, combining both experimental and grand canonical Monte Carlo isotherms as well as in situ synchrotron X-ray diffraction, shows variable adsorption behaviour towards the studied gases, with some materials acting as molecular sieves with virtually infinite selectivity.This work was supported by the Junta de Andalucía (FQM-1484), Red Guipuzcoana de Ciencia, Tecnolgía e Innovación (OF188/2017) and University of the Basque Country (GIU14/01, GIU17/013, EHUA16/32). The authors acknowledge technical and human support provided by SGIker of UPV/EHU and European funding (ERDF and ESF). D. F.-J. thanks the Royal Society for funding through a University Research Fellowship and the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (NanoMOFdeli), ERC-2016-COG 726380