1,918 research outputs found
Utilization of Additional Highway Fund Provided by 1969 Legislature: Part II:: Comments by an Indiana State Highway Commission Member
Variational analysis for a generalized spiked harmonic oscillator
A variational analysis is presented for the generalized spiked harmonic
oscillator Hamiltonian operator H, where H = -(d/dx)^2 + Bx^2+ A/x^2 +
lambda/x^alpha, and alpha and lambda are real positive parameters. The
formalism makes use of a basis provided by exact solutions of Schroedinger's
equation for the Gol'dman and Krivchenkov Hamiltonian (alpha = 2), and the
corresponding matrix elements that were previously found. For all the discrete
eigenvalues the method provides bounds which improve as the dimension of the
basis set is increased. Extension to the N-dimensional case in arbitrary
angular-momentum subspaces is also presented. By minimizing over the free
parameter A, we are able to reduce substantially the number of basis functions
needed for a given accuracy.Comment: 15 pages, 1 figur
A Recurrent Neural Network Survival Model: Predicting Web User Return Time
The size of a website's active user base directly affects its value. Thus, it
is important to monitor and influence a user's likelihood to return to a site.
Essential to this is predicting when a user will return. Current state of the
art approaches to solve this problem come in two flavors: (1) Recurrent Neural
Network (RNN) based solutions and (2) survival analysis methods. We observe
that both techniques are severely limited when applied to this problem.
Survival models can only incorporate aggregate representations of users instead
of automatically learning a representation directly from a raw time series of
user actions. RNNs can automatically learn features, but can not be directly
trained with examples of non-returning users who have no target value for their
return time. We develop a novel RNN survival model that removes the limitations
of the state of the art methods. We demonstrate that this model can
successfully be applied to return time prediction on a large e-commerce dataset
with a superior ability to discriminate between returning and non-returning
users than either method applied in isolation.Comment: Accepted into ECML PKDD 2018; 8 figures and 1 tabl
Part of the D - dimensional Spiked harmonic oscillator spectra
The pseudoperturbative shifted - l expansion technique PSLET [5,20] is
generalized for states with arbitrary number of nodal zeros. Interdimensional
degeneracies, emerging from the isomorphism between angular momentum and
dimensionality of the central force Schrodinger equation, are used to construct
part of the D - dimensional spiked harmonic oscillator bound - states. PSLET
results are found to compare excellenly with those from direct numerical
integration and generalized variational methods [1,2].Comment: Latex file, 20 pages, to appear in J. Phys. A: Math. & Ge
Study of Digital Competence of the Students and Teachers in Ukraine
Professional fulfillment of the personality at the conditions of the digital economy requires the high level of digital competency. One of the ways to develop these competencies is education. However, to provide the implementation of digital education at a high level, the digital competency of the teachers and students is a must. This paper presents explanations on the level determination of the digital competencies for teachers and students in Ukraine according to the DigComp recommendations. We tried to identify the main factors that reflect the degree of readiness teachers and students for digital education based on their self-evaluation. We also attempted to estimate the level of digital competencies based on the analysis of Case-Studies execution results. The complex analysis let us assess the connection between respondents’ self-evaluation and their real competencies. Here we provide a methodology and a model of level competencies determination by means of a survey, expert case rating and the results of the statistical analysis. On the basis of the obtained results, this paper suggests further research prospects and recommendations on the digital competency development in educational institutions in Ukraine
Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal Cancer
With the long-term rapid increase in incidences of colorectal cancer (CRC),
there is an urgent clinical need to improve risk stratification. The
conventional pathology report is usually limited to only a few
histopathological features. However, most of the tumor microenvironments used
to describe patterns of aggressive tumor behavior are ignored. In this work, we
aim to learn histopathological patterns within cancerous tissue regions that
can be used to improve prognostic stratification for colorectal cancer. To do
so, we propose a self-supervised learning method that jointly learns a
representation of tissue regions as well as a metric of the clustering to
obtain their underlying patterns. These histopathological patterns are then
used to represent the interaction between complex tissues and predict clinical
outcomes directly. We furthermore show that the proposed approach can benefit
from linear predictors to avoid overfitting in patient outcomes predictions. To
this end, we introduce a new well-characterized clinicopathological dataset,
including a retrospective collective of 374 patients, with their survival time
and treatment information. Histomorphological clusters obtained by our method
are evaluated by training survival models. The experimental results demonstrate
statistically significant patient stratification, and our approach outperformed
the state-of-the-art deep clustering methods
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