Test and Measure for Partial Mean Dependence Based on Deep Neural Networks

It is of great importance to investigate the significance of a subset of covariates W for the response Y given covariates Z in regression modeling. To this end, we propose a new significance test for the partial mean independence problem based on deep neural networks and data splitting. The test statistic converges to the standard chi-squared distribution under the null hypothesis while it converges to a normal distribution under the alternative hypothesis. We also suggest a powerful ensemble algorithm based on multiple data splitting to enhance the testing power. If the null hypothesis is rejected, we propose a new partial Generalized Measure of Correlation (pGMC) to measure the partial mean dependence of Y given W after controlling for the nonlinear effect of Z, which is an interesting extension of the GMC proposed by Zheng et al. (2012). We present the appealing theoretical properties of the pGMC and establish the asymptotic normality of its estimator with the optimal root-N converge rate. Furthermore, the valid confidence interval for the pGMC is also derived. As an important special case when there is no conditional covariates Z, we also consider a new test of overall significance of covariates for the response in a model-free setting. We also introduce new estimator of GMC and derive its asymptotic normality. Numerical studies and real data analysis are also conducted to compare with existing approaches and to illustrate the validity and flexibility of our proposed procedures

SpreadCluster: Recovering Versioned Spreadsheets through Similarity-Based Clustering

Version information plays an important role in spreadsheet understanding, maintaining and quality improving. However, end users rarely use version control tools to document spreadsheet version information. Thus, the spreadsheet version information is missing, and different versions of a spreadsheet coexist as individual and similar spreadsheets. Existing approaches try to recover spreadsheet version information through clustering these similar spreadsheets based on spreadsheet filenames or related email conversation. However, the applicability and accuracy of existing clustering approaches are limited due to the necessary information (e.g., filenames and email conversation) is usually missing. We inspected the versioned spreadsheets in VEnron, which is extracted from the Enron Corporation. In VEnron, the different versions of a spreadsheet are clustered into an evolution group. We observed that the versioned spreadsheets in each evolution group exhibit certain common features (e.g., similar table headers and worksheet names). Based on this observation, we proposed an automatic clustering algorithm, SpreadCluster. SpreadCluster learns the criteria of features from the versioned spreadsheets in VEnron, and then automatically clusters spreadsheets with the similar features into the same evolution group. We applied SpreadCluster on all spreadsheets in the Enron corpus. The evaluation result shows that SpreadCluster could cluster spreadsheets with higher precision and recall rate than the filename-based approach used by VEnron. Based on the clustering result by SpreadCluster, we further created a new versioned spreadsheet corpus VEnron2, which is much bigger than VEnron. We also applied SpreadCluster on the other two spreadsheet corpora FUSE and EUSES. The results show that SpreadCluster can cluster the versioned spreadsheets in these two corpora with high precision.Comment: 12 pages, MSR 201

Is the late near-infrared bump in short-hard GRB 130603B due to the Li-Paczynski kilonova?

Short-hard gamma-ray bursts (GRBs) are widely believed to be produced by the merger of two binary compact objects, specifically by two neutron stars or by a neutron star orbiting a black hole. According to the Li-Paczynski kilonova model, the merger would launch sub-relativistic ejecta and a near-infrared/optical transient would then occur, lasting up to days, which is powered by the radioactive decay of heavy elements synthesized in the ejecta. The detection of a late bump using the {\em Hubble Space Telescope} ({\em HST}) in the near-infrared afterglow light curve of the short-hard GRB 130603B is indeed consistent with such a model. However, as shown in this Letter, the limited {\em HST} near-infrared lightcurve behavior can also be interpreted as the synchrotron radiation of the external shock driven by a wide mildly relativistic outflow. In such a scenario, the radio emission is expected to peak with a flux of $\sim 100 \mu$Jy, which is detectable for current radio arrays. Hence, the radio afterglow data can provide complementary evidence on the nature of the bump in GRB 130603B. It is worth noting that good spectroscopy during the bump phase in short-hard bursts can test validity of either model above, analogous to spectroscopy of broad-lined Type Ic supernova in long-soft GRBs.Comment: 4 pages, 2 figures, published in ApJ Lette

A supra-massive magnetar central engine for short GRB 130603B

We show that the peculiar early optical and in particular X-ray afterglow emission of the short duration burst GRB 130603B can be explained by continuous energy injection into the blastwave from a supra-massive magnetar central engine. The observed energetics and temporal/spectral properties of the late infrared bump (i.e., the "kilonova") are also found consistent with emission from the ejecta launched during an NS-NS merger and powered by a magnetar central engine. The isotropic-equivalent kinetic energies of both the GRB blastwave and the kilonova are about $E_{\rm k}\sim 10^{51}$ erg, consistent with being powered by a near-isotropic magnetar wind. However, this relatively small value demands that most of the initial rotational energy of the magnetar $(\sim {\rm a~ few \times 10^{52}~ erg})$ is carried away by gravitational wave radiation. Our results suggest that (i) the progenitor of GRB 130603B would be a NS-NS binary system, whose merger product would be a supra-massive neutron star that lasted for about $\sim 1000$ seconds; (ii) the equation-of-state of nuclear matter would be stiff enough to allow survival of a long-lived supra-massive neutron star, so that it is promising to detect bright electromagnetic counterparts of gravitational wave triggers without short GRB associations in the upcoming Advanced LIGO/Virgo era.Comment: Five pages including 1 Figure, to appear in ApJ

2-[1-(1-Phenyl­eth­yl)imidazolidin-2-yl­idene]malononitrile

In the title compound, C14H14N4, the imidazolidine moiety is nearly planar, having an N—C—N—C torsion angle of 4.43 (3)°. The crystal structure is characterized by classical N—H⋯N hydrogen bonds, which form inversion dimers

Quantum Correction in Exact Quantization Rules

An exact quantization rule for the Schr\"{o}dinger equation is presented. In the exact quantization rule, in addition to $N\pi$, there is an integral term, called the quantum correction. For the exactly solvable systems we find that the quantum correction is an invariant, independent of the number of nodes in the wave function. In those systems, the energy levels of all the bound states can be easily calculated from the exact quantization rule and the solution for the ground state, which can be obtained by solving the Riccati equation. With this new method, we re-calculate the energy levels for the one-dimensional systems with a finite square well, with the Morse potential, with the symmetric and asymmetric Rosen-Morse potentials, and with the first and the second P\"{o}schl-Teller potentials, for the harmonic oscillators both in one dimension and in three dimensions, and for the hydrogen atom.Comment: 10 pages, no figure, Revte

Discourse Level Factors for Sentence Deletion in Text Simplification

This paper presents a data-driven study focusing on analyzing and predicting sentence deletion -- a prevalent but understudied phenomenon in document simplification -- on a large English text simplification corpus. We inspect various document and discourse factors associated with sentence deletion, using a new manually annotated sentence alignment corpus we collected. We reveal that professional editors utilize different strategies to meet readability standards of elementary and middle schools. To predict whether a sentence will be deleted during simplification to a certain level, we harness automatically aligned data to train a classification model. Evaluated on our manually annotated data, our best models reached F1 scores of 65.2 and 59.7 for this task at the levels of elementary and middle school, respectively. We find that discourse level factors contribute to the challenging task of predicting sentence deletion for simplification.Comment: Accepted in AAAI 2020. Adding more details on manual data annotatio