335,358 research outputs found
How to Identify Scientifc Revolutions?
Conceptualizing scientific revolutions by means of explicating their causes, their underlying structure and implications has been an important part of Kuhn's philosophy of science and belongs to its legacy. In this paper we show that such “explanatory concepts” of revolutions should be distinguished from a concept based on the identification criteria of scientific revolutions. The aim of this paper is to offer such a concept, and to show that it can be fruitfully used for a further elaboration of the explanatory conceptions of revolutions. On the one hand, our concept can be used to test the preciseness and accuracy of these conceptions, by examining to what extent their criteria fit revolutions as they are defined by our concept. On the other hand, our concept can serve as the basis on which these conceptions can be further specified. We will present four different explanatory concepts of revolutions – Kuhn's, Thagard's, Chen's and Barker's, and Laudan's – and point to the ways in which each of them can be further specified in view of our concept
Hierarchical Neyman-Pearson Classification for Prioritizing Severe Disease Categories in COVID-19 Patient Data
COVID-19 has a spectrum of disease severity, ranging from asymptomatic to
requiring hospitalization. Understanding the mechanisms driving disease
severity is crucial for developing effective treatments and reducing mortality
rates. One way to gain such understanding is using a multi-class classification
framework, in which patients' biological features are used to predict patients'
severity classes. In this severity classification problem, it is beneficial to
prioritize the identification of more severe classes and control the
"under-classification" errors, in which patients are misclassified into less
severe categories. The Neyman-Pearson (NP) classification paradigm has been
developed to prioritize the designated type of error. However, current NP
procedures are either for binary classification or do not provide high
probability controls on the prioritized errors in multi-class classification.
Here, we propose a hierarchical NP (H-NP) framework and an umbrella algorithm
that generally adapts to popular classification methods and controls the
under-classification errors with high probability. On an integrated collection
of single-cell RNA-seq (scRNA-seq) datasets for 864 patients, we explore ways
of featurization and demonstrate the efficacy of the H-NP algorithm in
controlling the under-classification errors regardless of featurization. Beyond
COVID-19 severity classification, the H-NP algorithm generally applies to
multi-class classification problems, where classes have a priority order
Classification without labels: Learning from mixed samples in high energy physics
Modern machine learning techniques can be used to construct powerful models
for difficult collider physics problems. In many applications, however, these
models are trained on imperfect simulations due to a lack of truth-level
information in the data, which risks the model learning artifacts of the
simulation. In this paper, we introduce the paradigm of classification without
labels (CWoLa) in which a classifier is trained to distinguish statistical
mixtures of classes, which are common in collider physics. Crucially, neither
individual labels nor class proportions are required, yet we prove that the
optimal classifier in the CWoLa paradigm is also the optimal classifier in the
traditional fully-supervised case where all label information is available.
After demonstrating the power of this method in an analytical toy example, we
consider a realistic benchmark for collider physics: distinguishing quark-
versus gluon-initiated jets using mixed quark/gluon training samples. More
generally, CWoLa can be applied to any classification problem where labels or
class proportions are unknown or simulations are unreliable, but statistical
mixtures of the classes are available.Comment: 18 pages, 5 figures; v2: intro extended and references added; v3:
additional discussion to match JHEP versio
ARTMAP-FTR: A Neural Network For Fusion Target Recognition, With Application To Sonar Classification
ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. Applications include automatic mapping from satellite remote sensing data, machine tool monitoring, medical prediction, digital circuit design, chemical analysis, and robot vision. Supervised ART architectures, called ARTMAP systems, feature internal control mechanisms that create stable recognition categories of optimal size by maximizing code compression while minimizing predictive error in an on-line setting. Special-purpose requirements of various application domains have led to a number of ARTMAP variants, including fuzzy ARTMAP, ART-EMAP, ARTMAP-IC, Gaussian ARTMAP, and distributed ARTMAP. A new ARTMAP variant, called ARTMAP-FTR (fusion target recognition), has been developed for the problem of multi-ping sonar target classification. The development data set, which lists sonar returns from underwater objects, was provided by the Naval Surface Warfare Center (NSWC) Coastal Systems Station (CSS), Dahlgren Division. The ARTMAP-FTR network has proven to be an effective tool for classifying objects from sonar returns. The system also provides a procedure for solving more general sensor fusion problems.Office of Naval Research (N00014-95-I-0409, N00014-95-I-0657
From Method Fragments to Method Services
In Method Engineering (ME) science, the key issue is the consideration of
information system development methods as fragments. Numerous ME approaches
have produced several definitions of method parts. Different in nature, these
fragments have nevertheless some common disadvantages: lack of implementation
tools, insufficient standardization effort, and so on. On the whole, the
observed drawbacks are related to the shortage of usage orientation. We have
proceeded to an in-depth analysis of existing method fragments within a
comparison framework in order to identify their drawbacks. We suggest
overcoming them by an improvement of the ?method service? concept. In this
paper, the method service is defined through the service paradigm applied to a
specific method fragment ? chunk. A discussion on the possibility to develop a
unique representation of method fragment completes our contribution
ARTMAP-FTR: A Neural Network for Object Recognition Through Sonar on a Mobile Robot
ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. Applications include automatic mapping from satellite remote sensing data, machine tool monitoring, medical prediction, digital circuit design, chemical analysis, and robot vision. Supervised ART architectures, called ARTMAP systems, feature internal control mechanisms that create stable recognition categories of optimal size by maximizing code compression while minimizing predictive error in an on-line setting. Special-purpose requirements of various application domains have led to a number of ARTMAP variants, including fuzzy ARTMAP, ART-EMAP, ARTMAP-IC, Gaussian ARTMAP, and distributed ARTMAP. A new ARTMAP variant, called ARTMAP-FTR (fusion target recognition), has been developed for the problem of multi-ping sonar target classification. The development data set, which lists sonar returns from underwater objects, was provided by the Naval Surface Warfare Center (NSWC) Coastal Systems Station (CSS), Dahlgren Division. The ARTMAP-FTR network has proven to be an effective tool for classifying objects from sonar returns. The system also provides a procedure for solving more general sensor fusion problems.Office of Naval Research (N00014-95-I-0409, N00014-95-I-0657
Model-Free Data-Driven Methods in Mechanics: Material Data Identification and Solvers
This paper presents an integrated model-free data-driven approach to solid
mechanics, allowing to perform numerical simulations on structures on the basis
of measures of displacement fields on representative samples, without
postulating a specific constitutive model. A material data identification
procedure, allowing to infer strain-stress pairs from displacement fields and
boundary conditions, is used to build a material database from a set of
mutiaxial tests on a non-conventional sample. This database is in turn used by
a data-driven solver, based on an algorithm minimizing the distance between
manifolds of compatible and balanced mechanical states and the given database,
to predict the response of structures of the same material, with arbitrary
geometry and boundary conditions. Examples illustrate this modelling cycle and
demonstrate how the data-driven identification method allows importance
sampling of the material state space, yielding faster convergence of simulation
results with increasing database size, when compared to synthetic material
databases with regular sampling patterns.Comment: Revised versio
Systematic and quantitative approach for the identification of high energy gamma-ray source populations
A large fraction of the detections to be made by the Gamma-ray Large Area
Space Telescope (GLAST) will initially be unidentified. We argue that
traditional methodological approaches to identify individuals and/or
populations of -ray sources will encounter procedural limitations.
These limitations will hamper our ability to classify source populations lying
in the anticipated dataset with the required degree of confidence, particularly
those for which no member has yet been convincingly detected in the predecessor
experiment EGRET. Here we suggest a new paradigm for achieving the
classification of -ray source populations based on the implementation
of an a priori protocol to search for theoretically-motivated candidate
sources. In order to protect the discovery potential of the sample, it is
essential that such paradigm will be defined before the data is unblinded. Key
to the new procedure is a statistical assessment by which the discovery of a
new population can be claimed. Although we explicitly refer here to the case of
GLAST, the scheme we present may be adapted to other experiments confronted
with a similar problematic.Comment: In press in The Astrophysical Journal Letters. Accepted on July 12,
200
Learning probability distributions generated by finite-state machines
We review methods for inference of probability distributions generated by probabilistic automata and related models for sequence generation. We focus on methods that can be proved to learn in the inference
in the limit and PAC formal models. The methods we review are state merging and state splitting methods for probabilistic deterministic automata and the recently developed spectral method for nondeterministic probabilistic automata. In both cases, we derive them from a high-level algorithm described in terms of the Hankel matrix of the distribution to be learned, given as an oracle, and then describe how to adapt that algorithm to account for the error introduced by a finite sample.Peer ReviewedPostprint (author's final draft
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