3,859,690 research outputs found

    On Probability and Cosmology: Inference Beyond Data?

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    Modern scientific cosmology pushes the boundaries of knowledge and the knowable. This is prompting questions on the nature of scientific knowledge. A central issue is what defines a 'good' model. When addressing global properties of the Universe or its initial state this becomes a particularly pressing issue. How to assess the probability of the Universe as a whole is empirically ambiguous, since we can examine only part of a single realisation of the system under investigation: at some point, data will run out. We review the basics of applying Bayesian statistical explanation to the Universe as a whole. We argue that a conventional Bayesian approach to model inference generally fails in such circumstances, and cannot resolve, e.g., the so-called 'measure problem' in inflationary cosmology. Implicit and non-empirical valuations inevitably enter model assessment in these cases. This undermines the possibility to perform Bayesian model comparison. One must therefore either stay silent, or pursue a more general form of systematic and rational model assessment. We outline a generalised axiological Bayesian model inference framework, based on mathematical lattices. This extends inference based on empirical data (evidence) to additionally consider the properties of model structure (elegance) and model possibility space (beneficence). We propose this as a natural and theoretically well-motivated framework for introducing an explicit, rational approach to theoretical model prejudice and inference beyond data

    First implications of LHCb data on models beyond the Standard Model

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    We discuss the theoretical and experimental details of two of the main results obtained by LHCb with the 2011 data, namely the measurement of the mixing-induced CP-violation in the decay B_s -> J/psi phi and the upper limits on the decays B_(s) -> mu+ mu-. Then we describe the possible strategies to obtain new constraints on two different New Physics models in the light of these results.Comment: 5 pages, Proceedings of "QCD@Work 2012" - June 18-21, 2012 - Lecce (Italy

    Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

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    From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

    Scorch marks from the sky

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    Daily sunshine duration is commonly reported at weather stations. Beyond the basic duration report, more information is available from scorched cards of Campbell-Stokes sunshine recorders, such as the estimation of direct-beam solar irradiance. Sunshine cards therefore potentially provide information on sky state, as inferred from solar-radiation data. Some sites have been operational since the late 19th century, hence sunshine cards potentially provide underexploited historical data on sky state. Sunshine cards provide an example of an archive source yielding data beyond the measurements originally sought

    Searches for new phenomena at CMS and ATLAS

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    The prospects of the ATLAS and CMS experiments at LHC for beyond standard model searches are depicted in this document. The presented studies concentrate on the search plans for supersymmetry (SUSY) and beyond in the first few years of data taking.Comment: 4 pages, 4 figures, Presented at Moriond/QCD: XLIVth Rencontres de Moriond on QCD and High Energy Interaction

    On Single Index Models beyond Gaussian Data

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    Sparse high-dimensional functions have arisen as a rich framework to study the behavior of gradient-descent methods using shallow neural networks, showcasing their ability to perform feature learning beyond linear models. Amongst those functions, the simplest are single-index models f(x)=ϕ(xθ)f(x) = \phi( x \cdot \theta^*), where the labels are generated by an arbitrary non-linear scalar link function ϕ\phi applied to an unknown one-dimensional projection θ\theta^* of the input data. By focusing on Gaussian data, several recent works have built a remarkable picture, where the so-called information exponent (related to the regularity of the link function) controls the required sample complexity. In essence, these tools exploit the stability and spherical symmetry of Gaussian distributions. In this work, building from the framework of \cite{arous2020online}, we explore extensions of this picture beyond the Gaussian setting, where both stability or symmetry might be violated. Focusing on the planted setting where ϕ\phi is known, our main results establish that Stochastic Gradient Descent can efficiently recover the unknown direction θ\theta^* in the high-dimensional regime, under assumptions that extend previous works \cite{yehudai2020learning,wu2022learning}

    Time and Financial Transfers Within and Beyond the Family: Results From the Health and Retirement Study

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    Research on time and financial transfers is often conducted along two distinct lines—transfers within the family and transfers beyond the family—without considering the fact that the two types of transfers are actually interrelated. Using longitudinal data from the Health and Retirement Study (HRS), this article investigates the links between the time and financial transfers within and beyond the family. The concepts of within and beyond the family transfers are discussed. Several data quality problems with the transfer measures in the HRS are corrected. Focusing on the interrelationships among the four types of transfers, the study finds that the transfers within and beyond the family are complements in the sense that households that are more willing to make within-family transfers are also more willing to make beyond-family transfers, and vice versa. Income and wealth are strong predictors of financial transfers. Black and Hispanic households lag systematically in the generosity to help the people both within and beyond their families.time and financial transfers; transfers within the family; transfers beyond the family; philanthropy and volunteerism; HRS

    Analysis of Data Relevant to Establishing Outer Limits of a Continental Shelf under Law of the Sea Article 76

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    Coastal states may extend the limits of their juridically defined continental shelf beyond 200 nautical miles from their baselines under the provisions set forth in Article 76 of the United Nations Convention on the Law of the Sea (UNCLOS). In a preparatory desktop study, the University of New Hampshire’s Center for Coastal and Ocean Mapping/Joint Hydrographic Center analysed existing U.S. bathymetric and geophysical data holdings, identified data adequacy, and survey requirements to prepare a U.S. claim beyond the Exclusive Economical Zone (EEZ). In this paper we describe the methodology for our desktop study with particular emphasis on how we assembled and evaluated the existing data around the shelf areas of the United States, and estimated where additional surveys may be required
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