335,358 research outputs found

    How to Identify Scientifc Revolutions?

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    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

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    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

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    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

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    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

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    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

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    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

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    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

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    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 Îł\gamma-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 Îł\gamma-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

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    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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