Consistent Modeling of Velocity Statistics and Redshift-Space Distortions in One-Loop Perturbation Theory

The peculiar velocities of biased tracers of the cosmic density field contain important information about the growth of large scale structure and generate anisotropy in the observed clustering of galaxies. Using N-body data, we show that velocity expansions for halo redshift-space power spectra are converged at the percent-level at perturbative scales for most line-of-sight angles $\mu$ when the first three pairwise velocity moments are included, and that the third moment is well-approximated by a counterterm-like contribution. We compute these pairwise-velocity statistics in Fourier space using both Eulerian and Lagrangian one-loop perturbation theory using a cubic bias scheme and a complete set of counterterms and stochastic contributions. We compare the models and show that our models fit both real-space velocity statistics and redshift-space power spectra for both halos and a mock sample of galaxies at sub-percent level on perturbative scales using consistent sets of parameters, making them appealing choices for the upcoming era of spectroscopic, peculiar-velocity and kSZ surveys.Comment: 63 pages, 11 figures, updated to match version accepted by JCA

Synergies between radio, optical and microwave observations at high redshift

We study synergies between three promising methods to measure $2<z<5$ large-scale structure in the next decade. Optical spectroscopic surveys are the most mature, but become increasingly difficult at $z>2$ and suffer from interloper problems even for spectroscopic surveys. Intensity mapping of the 21-cm signal can cover large volumes with exquisite fidelity, but is limited both by loss of information to foreground cleaning and by lack of knowledge of the mean signal. Cosmic microwave background (CMB) lensing is theoretically very clean, but ultimately measures just the projected variations in density. We find that cross-correlation between optical and radio can significantly improve the measurement of growth rate. Combining these with the CMB provides a promising avenue to detecting modified gravity at high redshifts, in particular by independently probing the Weyl and Newtonian potentials and by strengthening control of systematics. We find that cross-correlating a Stage {\sc ii} 21-cm survey with DESI quasars with a reasonable brightness temperature prior could enable measurements of the growth rate $f\sigma_8$ at sub 3\% and sub 8\% levels at $z = 3, 4$, representing a factor of 4 and 8 improvement over constraints obtainable from DESI quasars alone. Similarly, cross-correlating 21-cm data with a futuristic LBG survey to $m_{UV}<24.5$ over 1000 square degrees will make possible $f\sigma_8$ measurements at close to 1\% at $z = 3$ and 3\% at $z = 4$, and improve similar constraints at $z = 5$ by close to a factor of 3 to sub-10\% precision. Combining the above with CMB lensing from a Stage 4 CMB survey and LSST data can additionally constrain the gravitational slip $\gamma$ parameter to similar precision at these redshifts, enabling us to test the predictions of general relativity at large scales.Comment: Updated to match version accepted by JCAP. Fixed typo in Equation 3.2, updated 21-cm experiment specifications and added Figure

Multilingual Unsupervised Sentence Simplification

Progress in Sentence Simplification has been hindered by the lack of supervised data, particularly in languages other than English. Previous work has aligned sentences from original and simplified corpora such as English Wikipedia and Simple English Wikipedia, but this limits corpus size, domain, and language. In this work, we propose using unsupervised mining techniques to automatically create training corpora for simplification in multiple languages from raw Common Crawl web data. When coupled with a controllable generation mechanism that can flexibly adjust attributes such as length and lexical complexity, these mined paraphrase corpora can be used to train simplification systems in any language. We further incorporate multilingual unsupervised pretraining methods to create even stronger models and show that by training on mined data rather than supervised corpora, we outperform the previous best results. We evaluate our approach on English, French, and Spanish simplification benchmarks and reach state-of-the-art performance with a totally unsupervised approach. We will release our models and code to mine the data in any language included in Common Crawl

Staged Program Repair in SPR

We present SPR, a new program repair system that uses condition synthesis to instantiate transformation schemas to repair program defects. SPR s staged repair strategy combines a rich space of potential repairs with a targeted search algorithm that makes this space viably searchable in practice. This strategy enables SPR to successfully find correct program repairs within a space that contains many meaningful and useful patches. The majority of these correct repairs are not within the search spaces of previous automatic program repair systems

Prophet: Automatic Patch Generation via Learning from Successful Patches

We present Prophet, a novel patch generation system that learns a probabilistic model over candidate patches from a database of past successful patches. Prophet defines the probabilistic model as the combination of a distribution over program points based on defect localization algorithms and a parametrized log-linear distribution over modification operations. It then learns the model parameters via maximum log-likelihood, which identifies important characteristics of the previous successful patches in the database. For a new defect, Prophet generates a search space that contains many candidate patches, applies the learned model to prioritize those potentially correct patches that are consistent with the identified successful patch characteristics, and then validates the candidate patches with a user supplied test suite. The experimental results indicate that these techniques enable Prophet to generate correct patches for 15 out of 69 real-world defects in eight open source projects. The previous state of the art generate and validate system, which uses a set of hand-code heuristics to prioritize the search, generates correct patches for 11 of these same 69 defects

Prophet: Automatic Patch Generation via Learning from Successful Human Patches

We present Prophet, a novel patch generation system that learns a probabilistic model over candidate patches from a large code database that contains many past successful human patches. It defines the probabilistic model as the combination of a distribution over program points based on error localization algorithms and a parameterized log-linear distribution over modification operations. It then learns the model parameters via maximum log-likelihood, which identifies important characteristics of the successful human patches. For a new defect, Prophet generates a search space that contains many candidate patches, applies the learned model to prioritize those potentially correct patches that are consistent with the identified successful patch characteristics, and then validates the candidate patches with a user supplied test suite