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): S17–S30, 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·ha−1·yr−1. 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