Surface climatological information - Twenty selected stations for space shuttle studies

Surface climatological data for twenty selected launch, landing, and alternate landing sites for space shuttle syste

On the predictability of domain-independent temporal planners

Temporal planning is a research discipline that addresses the problem of generating a totally or a partially ordered sequence of actions that transform the environment from some initial state to a desired goal state, while taking into account time constraints and actions' duration. For its ability to describe and address temporal constraints, temporal planning is of critical importance for a wide range of real-world applications. Predicting the performance of temporal planners can lead to significant improvements in the area, as planners can then be combined in order to boost the performance on a given set of problem instances. This paper investigates the predictability of the state-of-the-art temporal planners by introducing a new set of temporal-specific features and exploiting them for generating classification and regression empirical performance models (EPMs) of considered planners. EPMs are also tested with regard to their ability to select the most promising planner for efficiently solving a given temporal planning problem. Our extensive empirical analysis indicates that the introduced set of features allows to generate EPMs that can effectively perform algorithm selection, and the use of EPMs is therefore a promising direction for improving the state of the art of temporal planning, hence fostering the use of planning in real-world applications.</p

Inducing Probabilistic Grammars by Bayesian Model Merging

We describe a framework for inducing probabilistic grammars from corpora of positive samples. First, samples are {\em incorporated} by adding ad-hoc rules to a working grammar; subsequently, elements of the model (such as states or nonterminals) are {\em merged} to achieve generalization and a more compact representation. The choice of what to merge and when to stop is governed by the Bayesian posterior probability of the grammar given the data, which formalizes a trade-off between a close fit to the data and a default preference for simpler models (`Occam's Razor'). The general scheme is illustrated using three types of probabilistic grammars: Hidden Markov models, class-based $n$-grams, and stochastic context-free grammars.Comment: To appear in Grammatical Inference and Applications, Second International Colloquium on Grammatical Inference; Springer Verlag, 1994. 13 page

Randomized Reference Classifier with Gaussian Distribution and Soft Confusion Matrix Applied to the Improving Weak Classifiers

In this paper, an issue of building the RRC model using probability distributions other than beta distribution is addressed. More precisely, in this paper, we propose to build the RRR model using the truncated normal distribution. Heuristic procedures for expected value and the variance of the truncated-normal distribution are also proposed. The proposed approach is tested using SCM-based model for testing the consequences of applying the truncated normal distribution in the RRC model. The experimental evaluation is performed using four different base classifiers and seven quality measures. The results showed that the proposed approach is comparable to the RRC model built using beta distribution. What is more, for some base classifiers, the truncated-normal-based SCM algorithm turned out to be better at discovering objects coming from minority classes.Comment: arXiv admin note: text overlap with arXiv:1901.0882

Incommensurate Charge and Spin Fluctuations in d-wave Superconductors

We show analytic results for the irreducible charge and spin susceptibilities, $\chi_0 (\omega, {\bf Q})$, where ${\bf Q}$ is the momentum transfer between the nodes in d-wave superconductors. Using the BCS theory and a circular Fermi surface, we find that the singular behavior of the irreducible charge susceptibility leads to the dynamic incommensurate charge collective modes. The peaks in the charge structure factor occur at a set of wave vectors which form an ellipse around ${\bf Q}_{\pi}=(\pi,\pi)$ and ${\bf Q}_0=(0,0)$ in momentum space with momentum dependent spectral weight. It is also found that, due to the non-singular irreducible spin susceptibility, an extremely strong interaction via random phase approximation is required to support the magnetic peaks near ${\bf Q}_{\pi}$. Under certain conditions, the peaks in the magnetic structure factor occur near ${\bf Q}=(\pi,\pi (1 \pm \delta))$ and $(\pi (1 \pm \delta),\pi)$.Comment: 5 pages, 3 figure

A Decision Tree Approach to Predicting Recidivism in Domestic Violence

Domestic violence (DV) is a global social and public health issue that is highly gendered. Being able to accurately predict DV recidivism, i.e., re-offending of a previously convicted offender, can speed up and improve risk assessment procedures for police and front-line agencies, better protect victims of DV, and potentially prevent future re-occurrences of DV. Previous work in DV recidivism has employed different classification techniques, including decision tree (DT) induction and logistic regression, where the main focus was on achieving high prediction accuracy. As a result, even the diagrams of trained DTs were often too difficult to interpret due to their size and complexity, making decision-making challenging. Given there is often a trade-off between model accuracy and interpretability, in this work our aim is to employ DT induction to obtain both interpretable trees as well as high prediction accuracy. Specifically, we implement and evaluate different approaches to deal with class imbalance as well as feature selection. Compared to previous work in DV recidivism prediction that employed logistic regression, our approach can achieve comparable area under the ROC curve results by using only 3 of 11 available features and generating understandable decision trees that contain only 4 leaf nodes.Comment: 12 pages; Accepted at The 2018 Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD

Spin effects in gravitational radiation backreaction III. Compact binaries with two spinning components

The secular evolution of a spinning, massive binary system in eccentric orbit is analyzed, expanding and generalizing our previous treatments of the Lense-Thirring motion and the one-spin limit. The spin-orbit and spin-spin effects up to the 3/2 post-Newtonian order are considered, both in the equations of motion and in the radiative losses. The description of the orbit in terms of the true anomaly parametrization provides a simple averaging technique, based on the residue theorem, over eccentric orbits. The evolution equations of the angle variables characterizing the relative orientation of the spin and orbital angular momenta reveal a speed-up effect due to the eccentricity. The dissipative evolutions of the relevant dynamical and angular variables is presented in the form of a closed system of differential equations.Comment: 10 pages, 1 figur

Collisional Dark Matter and the Origin of Massive Black Holes

If the cosmological dark matter is primarily in the form of an elementary particle which has cross section and mass for self-interaction having a ratio similar to that of ordinary nuclear matter, then seed black holes (formed in stellar collapse) will grow in a Hubble time, due to accretion of the dark matter, to a mass range 10^6 - 10^9 solar masses. Furthermore, the dependence of the final black hole mass on the galaxy velocity dispersion will be approximately as observed and the growth rate will show a time dependence consistent with observations. Other astrophysical consequences of collisional dark matter and tests of the idea are noted.Comment: 7 pages, no figures, LaTeX2e, Accepted for publication in Phys. Rev. Lett. Changed conten