FORTRAN programming - A self-taught course

Comprehensive programming course begins with numerical systems and basic concepts, proceeds systematically through FORTRAN language elements, and concludes with discussion of programming techniques. Course is suitable either for individual study or for group study on informal basis

How Policy Variables Influence the Timing of Social Security Disability Insurance Applications - Policy Brief

While the onset of a health based work limitation will affect a worker’s ability to remain on the job, it does not necessarily result in a swift and certain job exit and transition onto the disability rolls. The decision to leave the workforce and apply for SSDI benefits can be influenced both positively and negatively by policy variables. These two policy thrusts—SSDI transfers to replace lost earnings and accommodation to increase duration on the job—can send mixed signals to workers who experience the onset of a disability. Hence, understanding how such policies influence behavior for those who experience a disability is critical in developing policies that fully integrate people with disabilities into the workforce

Evaluation of the HARDMAN comparability methodology for manpower, personnel and training

The methodology evaluation and recommendation are part of an effort to improve Hardware versus Manpower (HARDMAN) methodology for projecting manpower, personnel, and training (MPT) to support new acquisition. Several different validity tests are employed to evaluate the methodology. The methodology conforms fairly well with both the MPT user needs and other accepted manpower modeling techniques. Audits of three completed HARDMAN applications reveal only a small number of potential problem areas compared to the total number of issues investigated. The reliability study results conform well with the problem areas uncovered through the audits. The results of the accuracy studies suggest that the manpower life-cycle cost component is only marginally sensitive to changes in other related cost variables. Even with some minor problems, the methodology seem sound and has good near term utility to the Army. Recommendations are provided to firm up the problem areas revealed through the evaluation

Cancer cell lines show high heritability for motility but not generation time

Tumour evolution depends on heritable differences between cells in traits affecting cell survival or replication. It is well established that cancer cells are genetically and phenotypically heterogeneous; however, the extent to which this phenotypic variation is heritable is far less well explored. Here, we estimate the broad-sense heritability (H2) of two cell traits related to cancer hallmarks––cell motility and generation time––within populations of four cancer cell lines in vitro and find that motility is strongly heritable. This heritability is stable across multiple cell generations, with heritability values at the high end of those measured for a range of traits in natural populations of animals or plants. These findings confirm a central assumption of cancer evolution, provide a first quantification of the evolvability of key traits in cancer cells and indicate that there is ample raw material for experimental evolution in cancer cell lines. Generation time, a trait directly affecting cell fitness, shows substantially lower values of heritability than cell speed, consistent with its having been under directional selection removing heritable variation

Multidimensional Inverse Scattering of Integrable Lattice Equations

We present a discrete inverse scattering transform for all ABS equations excluding Q4. The nonlinear partial difference equations presented in the ABS hierarchy represent a comprehensive class of scalar affine-linear lattice equations which possess the multidimensional consistency property. Due to this property it is natural to consider these equations living in an N-dimensional lattice, where the solutions depend on N distinct independent variables and associated parameters. The direct scattering procedure, which is one-dimensional, is carried out along a staircase within this multidimensional lattice. The solutions obtained are dependent on all N lattice variables and parameters. We further show that the soliton solutions derived from the Cauchy matrix approach are exactly the solutions obtained from reflectionless potentials, and we give a short discussion on inverse scattering solutions of some previously known lattice equations, such as the lattice KdV equation.Comment: 18 page

The Stabilized Poincare-Heisenberg algebra: a Clifford algebra viewpoint

The stabilized Poincare-Heisenberg algebra (SPHA) is the Lie algebra of quantum relativistic kinematics generated by fifteen generators. It is obtained from imposing stability conditions after attempting to combine the Lie algebras of quantum mechanics and relativity which by themselves are stable, however not when combined. In this paper we show how the sixteen dimensional Clifford algebra CL(1,3) can be used to generate the SPHA. The Clifford algebra path to the SPHA avoids the traditional stability considerations, relying instead on the fact that CL(1,3) is a semi-simple algebra and therefore stable. It is therefore conceptually easier and more straightforward to work with a Clifford algebra. The Clifford algebra path suggests the next evolutionary step toward a theory of physics at the interface of GR and QM might be to depart from working in space-time and instead to work in space-time-momentum.Comment: 14 page

Scalable Full Flow with Learned Binary Descriptors

We propose a method for large displacement optical flow in which local matching costs are learned by a convolutional neural network (CNN) and a smoothness prior is imposed by a conditional random field (CRF). We tackle the computation- and memory-intensive operations on the 4D cost volume by a min-projection which reduces memory complexity from quadratic to linear and binary descriptors for efficient matching. This enables evaluation of the cost on the fly and allows to perform learning and CRF inference on high resolution images without ever storing the 4D cost volume. To address the problem of learning binary descriptors we propose a new hybrid learning scheme. In contrast to current state of the art approaches for learning binary CNNs we can compute the exact non-zero gradient within our model. We compare several methods for training binary descriptors and show results on public available benchmarks.Comment: GCPR 201