255 research outputs found
Analysis of a high-resolution hand-written digits data set with writer characteristics
The contributions in this article are two-fold. First, we introduce a new
hand-written digit data set that we collected. It contains high-resolution
images of hand-written digits together with various writer characteristics
which are not available in the well-known MNIST database. The data set is
publicly available and is designed to create new research opportunities.
Second, we perform a first analysis of this new data set. We begin with simple
supervised tasks. We assess the predictability of the writer characteristics
gathered, the effect of using some of those characteristics as predictors in
classification task and the effect of higher resolution images on
classification accuracy. We also explore semi-supervised applications; we can
leverage the high quantity of hand-written digits data sets already existing
online to improve the accuracy of various classifications task with noticeable
success. Finally, we also demonstrate the generative perspective offered by
this new data set; we are able to generate images that mimics the writing style
of specific writers. The data set provides new research opportunities and our
analysis establishes benchmarks and showcases some of the new opportunities
made possible with this new data set.Comment: Data set available here :
https://drive.google.com/drive/folders/1f2o1kjXLvcxRgtmMMuDkA2PQ5Zato4Or?usp=sharin
An evaluation of machine learning techniques to predict the outcome of children treated for Hodgkin-Lymphoma on the AHOD0031 trial: A report from the Children's Oncology Group
In this manuscript we analyze a data set containing information on children
with Hodgkin Lymphoma (HL) enrolled on a clinical trial. Treatments received
and survival status were collected together with other covariates such as
demographics and clinical measurements. Our main task is to explore the
potential of machine learning (ML) algorithms in a survival analysis context in
order to improve over the Cox Proportional Hazard (CoxPH) model. We discuss the
weaknesses of the CoxPH model we would like to improve upon and then we
introduce multiple algorithms, from well-established ones to state-of-the-art
models, that solve these issues. We then compare every model according to the
concordance index and the brier score. Finally, we produce a series of
recommendations, based on our experience, for practitioners that would like to
benefit from the recent advances in artificial intelligence
Spectral and Fermi surface properties from Wannier interpolation
We present an efficient first-principles approach for calculating Fermi
surface averages and spectral properties of solids, and use it to compute the
low-field Hall coefficient of several cubic metals and the magnetic circular
dichroism of iron. The first step is to perform a conventional first-principles
calculation and store the low-lying Bloch functions evaluated on a uniform grid
of k-points in the Brillouin zone. We then map those states onto a set of
maximally-localized Wannier functions, and evaluate the matrix elements of the
Hamiltonian and the other needed operators between the Wannier orbitals, thus
setting up an ``exact tight-binding model.'' In this compact representation the
k-space quantities are evaluated inexpensively using a generalized
Slater-Koster interpolation. Because of the strong localization of the Wannier
orbitals in real space, the smoothness and accuracy of the k-space
interpolation increases rapidly with the number of grid points originally used
to construct the Wannier functions. This allows k-space integrals to be
performed with ab-initio accuracy at low cost. In the Wannier representation,
band gradients, effective masses, and other k-derivatives needed for transport
and optical coefficients can be evaluated analytically, producing numerically
stable results even at band crossings and near weak avoided crossings.Comment: 12 pages, 7 figure
Neuroimaging Feature Extraction using a Neural Network Classifier for Imaging Genetics
A major issue in the association of genes to neuroimaging phenotypes is the
high dimension of both genetic data and neuroimaging data. In this article, we
tackle the latter problem with an eye toward developing solutions that are
relevant for disease prediction. Supported by a vast literature on the
predictive power of neural networks, our proposed solution uses neural networks
to extract from neuroimaging data features that are relevant for predicting
Alzheimer's Disease (AD) for subsequent relation to genetics. Our
neuroimaging-genetic pipeline is comprised of image processing, neuroimaging
feature extraction and genetic association steps. We propose a neural network
classifier for extracting neuroimaging features that are related with disease
and a multivariate Bayesian group sparse regression model for genetic
association. We compare the predictive power of these features to expert
selected features and take a closer look at the SNPs identified with the new
neuroimaging features.Comment: Under revie
NLOAD : an interactive, web-based modeling tool for nitrogen management in estuaries
Author Posting. © Ecological Society of America, 2007. This article is posted here by permission of Ecological Society of America for personal use, not for redistribution. The definitive version was published in Ecological Applications 17, Supple. (2007): S17âS30, doi:10.1890/05-1460.1.Eutrophication of estuaries is an increasing global concern that requires development of new tools to identify causes, quantify conditions, and propose management options that address this environmental problem. Since eutrophication is often associated with increased inputs of land-derived nitrogen to estuaries, we developed NLOAD, a user-friendly, web-based tool that brings together six different published models that predict nitrogen loading to estuaries and two models that estimate nitrogen concentrations in coastal waters. Here we describe each of the models, demonstrate how NLOAD is designed to function, and then use the models in NLOAD to predict nitrogen loads to Barnegat Bay, New Jersey (USA). The four models that we used to estimate nitrogen loads to Barnegat Bay, when adjusted, all had similar results that matched well with measured values and indicated that Barnegat Bay receives roughly 26 kg N·haâ1·yrâ1. Atmospheric deposition was the dominant source of nitrogen to Barnegat Bay, followed by fertilizer nitrogen. Wastewater in Barnegat Bay is diverted to an offshore outfall and contributes no nitrogen to the system. The NLOAD tool has an additional feature that allows managers to assess the effectiveness of a variety of management options to reduce nitrogen loads. We demonstrate this feature of NLOAD through simulations in which fertilizer inputs to the Barnegat Bay watershed are reduced. Even modest cutbacks in the use of fertilizers on agricultural fields and lawns can be shown to reduce the amount of N entering Barnegat Bay.Support for the NLOAD tool came from the Cooperative
Institute for Coastal and Estuarine Environmental Technologies
(CICEET, CICEET-UNH grants #02-610 and #04-833).
Additional funding was received from Environmental Defense
Tunable magnetic exchange interactions in manganese-doped inverted core/shell ZnSe/CdSe nanocrystals
Magnetic doping of semiconductor nanostructures is actively pursued for
applications in magnetic memory and spin-based electronics. Central to these
efforts is a drive to control the interaction strength between carriers
(electrons and holes) and the embedded magnetic atoms. In this respect,
colloidal nanocrystal heterostructures provide great flexibility via
growth-controlled `engineering' of electron and hole wavefunctions within
individual nanocrystals. Here we demonstrate a widely tunable magnetic sp-d
exchange interaction between electron-hole excitations (excitons) and
paramagnetic manganese ions using `inverted' core-shell nanocrystals composed
of Mn-doped ZnSe cores overcoated with undoped shells of narrower-gap CdSe.
Magnetic circular dichroism studies reveal giant Zeeman spin splittings of the
band-edge exciton that, surprisingly, are tunable in both magnitude and sign.
Effective exciton g-factors are controllably tuned from -200 to +30 solely by
increasing the CdSe shell thickness, demonstrating that strong quantum
confinement and wavefunction engineering in heterostructured nanocrystal
materials can be utilized to manipulate carrier-Mn wavefunction overlap and the
sp-d exchange parameters themselves.Comment: To appear in Nature Materials; 18 pages, 4 figures + Supp. Inf
Coherent multi-flavour spin dynamics in a fermionic quantum gas
Microscopic spin interaction processes are fundamental for global static and
dynamical magnetic properties of many-body systems. Quantum gases as pure and
well isolated systems offer intriguing possibilities to study basic magnetic
processes including non-equilibrium dynamics. Here, we report on the
realization of a well-controlled fermionic spinor gas in an optical lattice
with tunable effective spin ranging from 1/2 to 9/2. We observe long-lived
intrinsic spin oscillations and investigate the transition from two-body to
many-body dynamics. The latter results in a spin-interaction driven melting of
a band insulator. Via an external magnetic field we control the system's
dimensionality and tune the spin oscillations in and out of resonance. Our
results open new routes to study quantum magnetism of fermionic particles
beyond conventional spin 1/2 systems.Comment: 9 pages, 5 figure
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