Non-Linear Representation of the Profit Impacts of Local Government Tax and Expenditure Decisions

1986 ◽  
pp. 152-164
Author(s):  
R. J. Bennett
Keyword(s):  
Local Government ◽  
Linear Representation ◽  
Non Linear

scholarly journals Psychophysical evidence for a non-linear representation of facial identity

2009 ◽  
Vol 49 (18) ◽  
pp. 2285-2296 ◽  
Author(s):  
Steven C. Dakin ◽  
Diana Omigie
Keyword(s):  
Linear Representation ◽  
Facial Identity ◽  
Non Linear

scholarly journals Towards non-linear representation

2011 ◽  
Vol 15 (2) ◽  
pp. 93-95
Author(s):  
Paul Richens
Keyword(s):  
Linear Representation ◽  
Non Linear

Reconstructing Sea Surface Dynamics Using a Linear Koopman Kalman Filter

2021 ◽  
Author(s):  
Said Ouala ◽  
Ronan Fablet ◽  
Ananda Pascual Pascual ◽  
Bertrand Chapron ◽  
Fabrice Collard ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Linear Representation ◽  
Sea Surface ◽  
Dynamical Model ◽  
Optimal Interpolation ◽  
Model Based ◽  
Trade Offs ◽  
Non Linear ◽  
Spatio Temporal

<p>Spatio-temporal interpolation applications are important in the context of ocean surface modeling. Current state-of-the-art techniques typically rely either on optimal interpolation or on model-based approaches which explicitly exploit a dynamical model. While the optimal interpolation suffers from smoothing issues making it unreliable in retrieving fine-scale variability, the selection and parametrization of a dynamical model, when considering model-based data assimilation strategies, remains a complex issue since several trade-offs between the model's complexity and its applicability in sea surface data assimilation need to be carefully addressed. For these reasons, deriving new data assimilation architectures that can perfectly exploit the observations and the current advances in signal processing, modeling and artificial intelligence is crucial.</p><p>In this work, we explore new advances in data-driven data assimilation to exploit the classical Kalman filter in the interpolation of spatio-temporal fields. The proposed algorithm is written in an end-to-end differentiable setting in order to allow for the learning of the linear dynamical model from a data assimilation cost. Furthermore, the linear model is formulated on a space of observables, rather than the space of observations, which allows for perfect replication of non-linear dynamics when considering periodic and quasi-periodic limit sets and providing a decent (short-term) forecast of chaotic ones. One of the main advantages of the proposed architecture is its simplicity since it utilises a linear representation coupled with a Kalman filter. Interestingly, our experiments show that exploiting such a linear representation leads to better data assimilation when compared to non-linear filtering techniques, on numerous applications, including the sea level anomaly reconstruction from satellite remote sensing observations.</p>

Graphical Non-Linear Representation of Multi-Dimensional Laboratory Measurements in their Clinical Context

1991 ◽  
Vol 30 (02) ◽  
pp. 138-144 ◽  
Author(s):  
J. O. O. Hoeke ◽  
E. S. Gelsema ◽  
R. W. WuIkan ◽  
B. Leijnse
Keyword(s):  
Information Transfer ◽  
Graphical Representation ◽  
Clinical Chemistry ◽  
Linear Representation ◽  
Clinical Decision ◽  
Chemistry Laboratory ◽  
Test Results ◽  
Laboratory Test Results ◽  
Non Linear ◽  
Clinical Chemistry Laboratory

AbstractA polygon-based graphical representation of laboratory test results using non-linear scaling is described. It is argued that the non-linearity of the scale and the use of colors in the representation facilitates interpretation of the test result in its relationship to the standard reference range and critical clinical decision levels. Preliminary results suggest that this representation may be fruitfully used to enhance the efficiency of the information transfer from the clinical chemistry laboratory to clinicians. Other applications, inside as well as outside the medical field, may easily be imagined.

scholarly journals Deep Embedded Non-Redundant Clustering

2020 ◽  
Vol 34 (04) ◽  
pp. 5174-5181
Author(s):  
Lukas Miklautz ◽  
Dominik Mautz ◽  
Muzaffer Can Altinigneli ◽  
Christian Böhm ◽  
Claudia Plant
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Image Data ◽  
Linear Representation ◽  
Representation Learning ◽  
Complex Data ◽  
Data Types ◽  
Data Set ◽  
Non Linear ◽  
Representational Power

Complex data types like images can be clustered in multiple valid ways. Non-redundant clustering aims at extracting those meaningful groupings by discouraging redundancy between clusterings. Unfortunately, clustering images in pixel space directly has been shown to work unsatisfactory. This has increased interest in combining the high representational power of deep learning with clustering, termed deep clustering. Algorithms of this type combine the non-linear embedding of an autoencoder with a clustering objective and optimize both simultaneously. None of these algorithms try to find multiple non-redundant clusterings. In this paper, we propose the novel Embedded Non-Redundant Clustering algorithm (ENRC). It is the first algorithm that combines neural-network-based representation learning with non-redundant clustering. ENRC can find multiple highly non-redundant clusterings of different dimensionalities within a data set. This is achieved by (softly) assigning each dimension of the embedded space to the different clusterings. For instance, in image data sets it can group the objects by color, material and shape, without the need for explicit feature engineering. We show the viability of ENRC in extensive experiments and empirically demonstrate the advantage of combining non-linear representation learning with non-redundant clustering.

Segmental aspects of Kɨbwanɉi Phonology: A Non-Linear Representation

Utafiti ◽  
2017 ◽  
Vol 12 (1-2) ◽  
pp. 25-50
Author(s):  
Okoa Simile ◽  
Rose Acen Upor
Keyword(s):  
Preliminary Analysis ◽  
Native Speakers ◽  
Linear Representation ◽  
Syllable Structure ◽  
Phonological Processes ◽  
Linear Approach ◽  
Autosegmental Phonology ◽  
Non Linear ◽  
Structural Descriptions ◽  
Phonological Rules

This paper attempts a preliminary analysis of the phonological processes that affect vowels and consonants in Kɨβwanɉi language. Specifically, the paper examines the role played by these phonological processes in preserving the configuration of the phonologically possible word or morpheme in Kɨβwanɉi by using a Non-linear Approach (Autosegmental Phonology Theory). The findings reveal that the distribution of consonants is restricted in Kɨβwanɉi and the canonical syllable structure of Kɨβwanɉi is CV but not limited to $V$, $C$, $CV$ and $CGV$. Syllables are conditioned by phonological sequential constraints (PSCs) that govern the sequence of segments in the language. These constraints serve as the mechanism through which the native speakers are able to recognize words by applying phonological rules that are in conspiracy. It is also revealed that the rules are ordered with respect to the satisfaction of the structural descriptions that allow more than one rule to apply.

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