Spatial Judgments in Impossible Spaces Preserve Important Relative Information

2021 ◽  
Author(s):  
Andrew Robb ◽  
Catherine Barwulor
Keyword(s):  
Relative Information

Individual categorisation of glucose profiles using compositional data analysis

2018 ◽  
Vol 28 (12) ◽  
pp. 3550-3567 ◽  
Author(s):  
Lyvia Biagi ◽  
Arthur Bertachi ◽  
Marga Giménez ◽  
Ignacio Conget ◽  
Jorge Bondia ◽  
...  
Keyword(s):  
Data Analysis ◽  
Compositional Data ◽  
Glucose Monitoring ◽  
Glucose Variability ◽  
Compositional Data Analysis ◽  
Relative Information ◽  
Glucose Profiles ◽  
Patient Groups ◽  
The Times

The aim of this study was to apply a methodology based on compositional data analysis (CoDA) to categorise glucose profiles obtained from continuous glucose monitoring systems. The methodology proposed considers complete daily glucose profiles obtained from six patients with type 1 diabetes (T1D) who had their glucose monitored for eight weeks. The glucose profiles were distributed into the time spent in six different ranges. The time in one day is finite and limited to 24 h, and the times spent in each of these different ranges are co-dependent and carry only relative information; therefore, CoDA is applied to these profiles. A K-means algorithm was applied to the coordinates obtained from the CoDA to obtain different patterns of days for each patient. Groups of days with relatively high time in the hypo and/or hyperglycaemic ranges and with different glucose variability were observed. Using CoDA of time in different ranges, individual glucose profiles were categorised into groups of days, which can be used by physicians to detect the different conditions of patients and personalise patient's insulin therapy according to each group. This approach can be useful to assist physicians and patients in managing the day-to-day variability that hinders glycaemic control.

scholarly journals Representation Lost: The Case for a Relational Interpretation of Quantum Mechanics

Entropy ◽  
2018 ◽  
Vol 20 (12) ◽  
pp. 975 ◽  
Author(s):  
Raffael Krismer
Keyword(s):  
Thought Experiment ◽  
Dynamical Evolution ◽  
Bayesian Probability ◽  
Special Focus ◽  
Probability Models ◽  
Classical Probability ◽  
Unitary Evolution ◽  
Relative Information ◽  
Mechanical Analogue ◽  
Argument Proceeds

Contemporary non-representationalist interpretations of the quantum state (especially QBism, neo-Copenhagen views, and the relational interpretation) maintain that quantum states codify observer-relative information. This paper provides an extensive defense of such views, while emphasizing the advantages of, specifically, the relational interpretation. The argument proceeds in three steps: (1) I present a classical example (which exemplifies the spirit of the relational interpretation) to illustrate why some of the most persistent charges against non-representationalism have been misguided. (2) The special focus is placed on dynamical evolution. Non-representationalists often motivate their views by interpreting the collapse postulate as the quantum mechanical analogue of Bayesian probability updating. However, it is not clear whether one can also interpret the Schrödinger equation as a form of rational opinion updating. Using results due to Hughes & van Fraassen as well as Lisi, I argue that unitary evolution has a counterpart in classical probability theory: in both cases (quantum and classical) probabilities relative to a non-participating observer evolve according to an entropy maximizing principle (and can be interpreted as rational opinion updating). (3) Relying on a thought-experiment by Frauchiger and Renner, I discuss the differences between quantum and classical probability models.

The relative information content of cash flows and earnings affected by their extremity

2014 ◽  
Vol 40 (7) ◽  
pp. 646-661 ◽  
Author(s):  
Wael Mostafa
Keyword(s):  
Stock Returns ◽  
Information Content ◽  
Statistical Significance ◽  
Explanatory Power ◽  
Cash Flows ◽  
Content Type ◽  
Nested Models ◽  
Relative Information ◽  
Security Returns ◽  
Relative Information Content

Purpose – Many studies examine the relative information content of earnings and cash flows from operations. Most studies find that earnings have higher information content than cash flows. An interesting question that follows is whether these findings hold after controlling the extremity of earnings and cash flows. The purpose of this paper is to examine the relative information content of earnings and cash flows in the following four different cases: first, moderate earnings vs moderate cash flows, second, extreme earnings vs moderate cash flows, third, moderate earnings vs extreme cash flows, and fourth, extreme earnings vs extreme cash flows. Design/methodology/approach – To assess the relative information content of earnings and cash flows for each of the four cases mentioned above, the authors compare the explanatory power for regression of returns on unexpected earnings relative to regression of returns on unexpected cash flows. Therefore, the author compares the adjusted R2 of the model with earnings variables and the model with cash flows variables using Vuong's test, that examines the statistical significance of the difference between adjusted R2s of the rival (non-nested) models, and interpret a statistically higher adjusted R2 as an indicator for higher relative information content. Findings – The results show that: first, when both earnings and cash flows are moderate, earnings are more highly associated with stock market price changes than cash flows, second, when both earnings and cash flows are extreme, earnings also have greater relative information content than cash flows, third, when the extremity differs between earnings and cash flows, the moderate variable is superior to the other extreme variable in explaining security returns. These results suggest that earnings are definitely more value relevant than cash flows. However, only in cases when cash flows from operations are moderate and earnings are extreme, cash flows possess higher information content than earnings. Practical implications – The explanatory power for stock returns will be higher for earnings or cash flows depending on which is more highly persistent. This result reverses the conventional finding of the superiority of earnings over cash flows in explaining security returns. Originality/value – In contrast to previous studies, the authors control for the extremity of earnings and cash flows when evaluating the relative information content of earnings and cash flows from operations.

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