3,859,690 research outputs found
On Probability and Cosmology: Inference Beyond Data?
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
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
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
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
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
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 , where the labels are generated by an arbitrary non-linear
scalar link function applied to an unknown one-dimensional projection
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 is known, our main results establish that
Stochastic Gradient Descent can efficiently recover the unknown direction
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
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
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
- …