scholarly journals A Technology-Based Classification of Firms: Can We Learn Something Looking Beyond Industry Classifications?

Entropy ◽  
2018 ◽  
Vol 20 (11) ◽  
pp. 887 ◽  
Author(s):  
Petros Gkotsis ◽  
Emanuele Pugliese ◽  
Antonio Vezzani
Keyword(s):  
Technological Development ◽  
Data Driven ◽  
Patent Data ◽  
Amount Of Information ◽  
Industrial Sectors ◽  
Attractive Option ◽  
Data Driven Approach ◽  
Fast Adaptation ◽  
Technological Space

In this work we use clustering techniques to identify groups of firms competing in similar technological markets. Our clustering properly highlights technological similarities grouping together firms normally classified in different industrial sectors. Technological development leads to a continuous changing structure of industries and firms. For this reason, we propose a data driven approach to classify firms together allowing for fast adaptation of the classification to the changing technological landscape. In this respect we differentiate from previous taxonomic exercises of industries and innovation which are based on more general common features. In our empirical application, we use patent data as a proxy for the firms’ capabilities of developing new solutions in different technological fields. On this basis, we extract what we define a Technologically Driven Classification (TDC). In order to validate the result of our exercise we use information theory to look at the amount of information explained by our clustering and the amount of information shared with an industrial classification. All-in-all, our approach provides a good grouping of firms on the basis of their technological capabilities and represents an attractive option to compare firms in the technological space and better characterise competition in technological markets.

A study of English blends: From structure to meaning and back again

2014 ◽  
Vol 7 (1) ◽  
pp. 29-54 ◽  
Author(s):  
Natalia Beliaeva
Keyword(s):  
Data Driven ◽  
Morphological Classification ◽  
Multifactorial Analysis ◽  
Structural Differences ◽  
Data Driven Approach ◽  
Semantic Properties

This article presents an approach to the resolution of the much discussed problem of morphological classification of blend words and their distinction from such neighbouring morphological categories as clipping compounds. The research focuses on novel coinages and takes a data-driven approach to study the interaction between the form and the meaning of blends/clipping compounds. A multifactorial analysis of formal and semantic properties of these words is undertaken, as a result of which phonological and structural differences between blends and clipping compounds are explained using formal and semantic factors.

scholarly journals Classification of visual cortex plasticity phenotypes following treatment for amblyopia

2019 ◽  
Author(s):  
Justin L. Balsor ◽  
David G. Jones ◽  
Kathryn M. Murphy
Keyword(s):  
Visual Cortex ◽  
Critical Period ◽  
Monocular Deprivation ◽  
Data Driven ◽  
Adaptive Plasticity ◽  
Gabaergic Receptors ◽  
Data Driven Approach ◽  
Binocular Deprivation ◽  
Induced Changes

AbstractMonocular deprivation (MD) during the critical period (CP) has enduring effects on visual acuity and the functioning of the visual cortex (V1). This experience-dependent plasticity has become a model for studying the mechanisms, especially glutamatergic and GABAergic receptors, that regulate amblyopia. Less is known, however, about treatment-induced changes to those receptors and if those changes differentiate treatments that support the recovery of acuity versus persistent acuity deficits. Here we use an animal model to explore the effects of 3 visual treatments started during the CP (n=24, 10 male and 14 female); binocular vision (BV) that promotes good acuity versus reverse occlusion (RO) and binocular deprivation (BD) that causes persistent acuity deficits. We measured recovery of a collection of glutamatergic and GABAergic receptor subunits in V1 and modeled recovery of kinetics for NMDAR and GABAAR. There was a complex pattern of protein changes that prompted us to develop an unbiased data-driven approach for these high-dimensional data analyses to identify plasticity features and construct plasticity phenotypes. Cluster analysis of the plasticity phenotypes suggests that BV supports adaptive plasticity while RO and BD promote a maladaptive pattern. The RO plasticity phenotype appeared more similar to adults with high expression of GluA2 and the BD phenotypes were dominated by GABAAα1, highlighting that multiple plasticity phenotypes can underlie persistent poor acuity. After 2-4 days of BV the plasticity phenotypes resembled normals, but only one feature, the GluN2A:GluA2 balance, returned to normal levels. Perhaps, balancing Hebbian (GluN2A) and homeostatic (GluA2) mechanisms is necessary for the recovery of vision.

scholarly journals The Cohesion-Based Communities of Symptoms of the Largest Component of the DSM-IV Network

2019 ◽  
Vol 19 (01) ◽  
pp. 1940002
Author(s):  
MOHAMMAD NAZMUL HAQUE ◽  
PABLO MOSCATO
Keyword(s):  
Mental Disorders ◽  
Optimization Method ◽  
Data Driven ◽  
Dsm 5 ◽  
Network Analytics ◽  
Classification Of Diseases ◽  
Data Driven Approach ◽  
New Type ◽  
Dsm Iv

Modern methods for network analytics provide an opportunity to revisit preconceived notions in the classification of diseases as “clusters of symptoms”. Curated collections which were subsequently modified, like the Diagnostic and Statistical Manuals of Mental Disorders “DSM-IV” and the most recent addition, DSM-5 allow us to introspect, using the solution provided by modern algorithms, if there exists a consensus between the clusters obtained via a data-driven approach, with the current classifications. In the case of mental disorders, the availability of a follow-up consensus collection (e.g. in this case the DSM-5), potentially allows investigating if the classification of disorders has moved closer (or away) to what a data-driven analytic approach would have unveiled by objectively inferring it from the data of DSM-IV. In this contribution, we present a new type of mathematical approach based on a global cohesion score which we introduce for the first time for the identification of communities of symptoms. Different from other approaches, this combinatorial optimization method is based on the identification of “triangles” in the network; these triads are the building block of feedback loops that can exist between groups of symptoms. We used a memetic algorithm to obtain a collection of highly connected-cohesive sets of symptoms and we compare the resulting community structure with the classification of disorders present in the DSM-IV.

scholarly journals Transient-optimised real-bogus classification with Bayesian Convolutional Neural Networks — sifting the GOTO candidate stream

2021 ◽  
Author(s):  
T L Killestein ◽  
J Lyman ◽  
D Steeghs ◽  
K Ackley ◽  
M J Dyer ◽  
...  
Keyword(s):  
Large Scale ◽  
Data Driven ◽  
Wide Field ◽  
Data Generation ◽  
Training Set ◽  
Transient Sources ◽  
Data Driven Approach ◽  
Model Training ◽  
Difference Imaging

Abstract Large-scale sky surveys have played a transformative role in our understanding of astrophysical transients, only made possible by increasingly powerful machine learning-based filtering to accurately sift through the vast quantities of incoming data generated. In this paper, we present a new real-bogus classifier based on a Bayesian convolutional neural network that provides nuanced, uncertainty-aware classification of transient candidates in difference imaging, and demonstrate its application to the datastream from the GOTO wide-field optical survey. Not only are candidates assigned a well-calibrated probability of being real, but also an associated confidence that can be used to prioritise human vetting efforts and inform future model optimisation via active learning. To fully realise the potential of this architecture, we present a fully-automated training set generation method which requires no human labelling, incorporating a novel data-driven augmentation method to significantly improve the recovery of faint and nuclear transient sources. We achieve competitive classification accuracy (FPR and FNR both below 1%) compared against classifiers trained with fully human-labelled datasets, whilst being significantly quicker and less labour-intensive to build. This data-driven approach is uniquely scalable to the upcoming challenges and data needs of next-generation transient surveys. We make our data generation and model training codes available to the community.

scholarly journals The cohesion-based communities of symptoms of the largest component of the DSM-IV network

2018 ◽  
Author(s):  
Mohammad Nazmul Haque ◽  
Pablo Moscato
Keyword(s):  
Mental Disorders ◽  
Optimization Method ◽  
Data Driven ◽  
Dsm 5 ◽  
Network Analytics ◽  
Classification Of Diseases ◽  
Data Driven Approach ◽  
New Type ◽  
Dsm Iv

Modern methods for network analytics provide an opportunity to revisit preconceived notions in the classification of diseases as "clusters of symptoms''. Curated collections which were subsequently modified, like the Diagnostic and Statistical Manuals of Mental Disorders (DSM-IV and the most recent addition, DSM 5) allow us to introspect, using the solution provided by modern algorithms, if there exists a consensus between the clusters obtained via a data-driven approach, with the current classifications. In the case of mental disorders, the availability of a follow-up consensus collection (e.g. in this case the DSM 5), potentially allows to investigate if the classification of disorders has moved closer (or away) to what a data-driven analytic approach would have unveiled by objectively inferring it from the data of DSM-IV. In this contribution we present a new type of mathematical approach based on a global cohesion score which we introduce for the first time for the identification of communities of symptoms. Different from other approaches, this combinatorial optimization method is basedon the identification of "triangles'' in the network; these triads are the building block of feedback loops that can exist between groups of symptoms.We used a memetic algorithm to obtain a collection of highly connected-cohesive sets of symptoms and we compare the resulting community structure with the classification of disorders present in the DSM-IV.

scholarly journals ASigNTF: Agnostic Signature using NTF – a universal agnostic strategy to estimate cell-types abundance from transcriptomic datasets

2021 ◽  
Author(s):  
Paul Fogel ◽  
Galina Boldina ◽  
Corinne Rocher ◽  
Charles Bettembourg ◽  
George Luta ◽  
...  
Keyword(s):  
Biological Material ◽  
Ad Hoc ◽  
Immune Cell ◽  
Cell Types ◽  
Data Driven ◽  
Molecular Signatures ◽  
Tensor Factorization ◽  
Rna Seq ◽  
Data Driven Approach

AbstractBackgroundMolecular signatures for deconvolution of immune cell types have been proposed, based on a methodology that relies on the biological classification of the cell types being studied. When working with less known biological material, a data-driven approach is needed to uncover the underlying classes and construct ad hoc signatures.ResultsWe introduce a new approach, ASigNTF: Agnostic Signature using Non-negative Tensor Factorization, to perform the deconvolution of cell types from transcriptomics data (RNAseq and microarray). ASigNTF, which is based on two complementary statistical/mathematical tools: non-negative tensor factorization (for dimensionality reduction) and the Herfindahl-Hirschman index (for signature selection), can be applied to any type of tissue as long as transcriptomic data on isolated cells is available. As a direct result of the new method, we propose two new signatures for the deconvolution of immune cell types, one consisting of a relatively small set of 415 genes, which is more compatible with microarray platforms, and a larger set of 915 genes. Using external datasets, our two signatures outperform the CIBERSORT LM22 signature in deconvolution of RNA-seq data. Our signature with 415 genes allows to recognize a larger number of cell types compared to the ABIS microarray signature.ConclusionsThe paper proposes a new method, ASigNTF; applies the method, and also provides a software implementation that allows to identify molecular signatures for deconvolution of complex tissues and specifically up to 16 immune cell types from micro-array or RNA-seq data.HighlightsSeveral signatures of immune cell types have been proposed, which follow a methodology deeply rooted in the known biological classification of the investigated cell types.When working with less known biological material, a more agnostic, data-driven approach is required to uncover the underlying classes and construct ad hoc signatures.We present ASigNTF, a new agnostic approach to cell type classification and signature selection supported by an application software.We discuss the results of benchmarking our proposed signatures, ABIS-seq and CIBERSORT on external datasets.

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