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Register a new userSyGuS-Comp 2018: Results and Analysis
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Saswat Padhi
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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Syntax-guided synthesis (SyGuS) is the computational problem of finding an implementation $f$ that meets both a semantic constraint given by a logical formula $phi$ in a background theory $mathbb{T}$, and a syntactic constraint given by a grammar $G$, which specifies the allowed set of candidate implementations. Such a synthesis problem can be formally defined in the SyGuS input format (SyGuS-IF), a language that is built on top of SMT-LIB. The Syntax-Guided Synthesis competition (SyGuS-Comp) is an effort to facilitate, bring together and accelerate research and development of efficient solvers for SyGuS by providing a platform for evaluating different synthesis techniques on a comprehensive set of benchmarks. In the 5th SyGuS-Comp, five solvers competed on over 1600 benchmarks across various tracks. This paper presents and analyses the results of this years (2018) SyGuS competition.
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Syntax-Guided Synthesis (SyGuS) is the computational problem of finding an implementation f that meets both a semantic constraint given by a logical formula phi in a background theory T, and a syntactic constraint given by a grammar G, which specifies the allowed set of candidate implementations. Such a synthesis problem can be formally defined in SyGuS-IF, a language that is built on top of SMT-LIB. The Syntax-Guided Synthesis Competition (SyGuS-Comp) is an effort to facilitate, bring together and accelerate research and development of efficient solvers for SyGuS by providing a platform for evaluating different synthesis techniques on a comprehensive set of benchmarks. In this years competition six new solvers competed on over 1500 benchmarks. This paper presents and analyses the results of SyGuS-Comp17.
We organized a competition on Autonomous Lifelong Machine Learning with Drift that was part of the competition program of NeurIPS 2018. This data driven competition asked participants to develop computer programs capable of solving supervised learning problems where the i.i.d. assumption did not hold. Large data sets were arranged in a lifelong learning and evaluation scenario and CodaLab was used as the challenge platform. The challenge attracted more than 300 participants in its two month duration. This chapter describes the design of the challenge and summarizes its main results.
We present cosmological parameter results from the final full-mission Planck measurements of the CMB anisotropies. We find good consistency with the standard spatially-flat 6-parameter $Lambda$CDM cosmology having a power-law spectrum of adiabatic scalar perturbations (denoted base $Lambda$CDM in this paper), from polarization, temperature, and lensing, separately and in combination. A combined analysis gives dark matter density $Omega_c h^2 = 0.120pm 0.001$, baryon density $Omega_b h^2 = 0.0224pm 0.0001$, scalar spectral index $n_s = 0.965pm 0.004$, and optical depth $tau = 0.054pm 0.007$ (in this abstract we quote $68,%$ confidence regions on measured parameters and $95,%$ on upper limits). The angular acoustic scale is measured to $0.03,%$ precision, with $100theta_*=1.0411pm 0.0003$. These results are only weakly dependent on the cosmological model and remain stable, with somewhat increased errors, in many commonly considered extensions. Assuming the base-$Lambda$CDM cosmology, the inferred late-Universe parameters are: Hubble constant $H_0 = (67.4pm 0.5)$km/s/Mpc; matter density parameter $Omega_m = 0.315pm 0.007$; and matter fluctuation amplitude $sigma_8 = 0.811pm 0.006$. We find no compelling evidence for extensions to the base-$Lambda$CDM model. Combining with BAO we constrain the effective extra relativistic degrees of freedom to be $N_{rm eff} = 2.99pm 0.17$, and the neutrino mass is tightly constrained to $sum m_ u< 0.12$eV. The CMB spectra continue to prefer higher lensing amplitudes than predicted in base -$Lambda$CDM at over $2,sigma$, which pulls some parameters that affect the lensing amplitude away from the base-$Lambda$CDM model; however, this is not supported by the lensing reconstruction or (in models that also change the background geometry) BAO data. (Abridged)
We present measurements of the cosmic microwave background (CMB) lensing potential using the final $textit{Planck}$ 2018 temperature and polarization data. We increase the significance of the detection of lensing in the polarization maps from $5,sigma$ to $9,sigma$. Combined with temperature, lensing is detected at $40,sigma$. We present an extensive set of tests of the robustness of the lensing-potential power spectrum, and construct a minimum-variance estimator likelihood over lensing multipoles $8 le L le 400$. We find good consistency between lensing constraints and the results from the $textit{Planck}$ CMB power spectra within the $rm{Lambda CDM}$ model. Combined with baryon density and other weak priors, the lensing analysis alone constrains $sigma_8 Omega_{rm m}^{0.25}=0.589pm 0.020$ ($1,sigma$ errors). Also combining with baryon acoustic oscillation (BAO) data, we find tight individual parameter constraints, $sigma_8=0.811pm0.019$, $H_0=67.9_{-1.3}^{+1.2},text{km},text{s}^{-1},rm{Mpc}^{-1}$, and $Omega_{rm m}=0.303^{+0.016}_{-0.018}$. Combining with $textit{Planck}$ CMB power spectrum data, we measure $sigma_8$ to better than $1,%$ precision, finding $sigma_8=0.811pm 0.006$. We find consistency with the lensing results from the Dark Energy Survey, and give combined lensing-only parameter constraints that are tighter than joint results using galaxy clustering. Using $textit{Planck}$ cosmic infrared background (CIB) maps we make a combined estimate of the lensing potential over $60,%$ of the sky with considerably more small-scale signal. We demonstrate delensing of the $textit{Planck}$ power spectra, detecting a maximum removal of $40,%$ of the lensing-induced power in all spectra. The improvement in the sharpening of the acoustic peaks by including both CIB and the quadratic lensing reconstruction is detected at high significance (abridged).
This paper presents a review of the 2018 WIDER Challenge on Face and Pedestrian. The challenge focuses on the problem of precise localization of human faces and bodies, and accurate association of identities. It comprises of three tracks: (i) WIDER Face which aims at soliciting new approaches to advance the state-of-the-art in face detection, (ii) WIDER Pedestrian which aims to find effective and efficient approaches to address the problem of pedestrian detection in unconstrained environments, and (iii) WIDER Person Search which presents an exciting challenge of searching persons across 192 movies. In total, 73 teams made valid submissions to the challenge tracks. We summarize the winning solutions for all three tracks. and present discussions on open problems and potential research directions in these topics.
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