Towards Optimal Variance Reduction in Online Controlled Experiments

We study optimal variance reduction solutions for count and ratio metrics in online controlled experiments. Our methods leverage flexible machine learning tools to incorporate covariates that are independent from the treatment but have predictive power for the outcomes, and employ the cross-fitting technique to remove the bias in complex machine learning models. We establish CLT-type asymptotic inference based on our estimators under mild convergence conditions. Our procedures are optimal (efficient) for the corresponding targets as long as the machine learning estimators are consistent, without any requirement for their convergence rates. In complement to the general optimal procedure, we also derive a linear adjustment method for ratio metrics as a special case that is computationally efficient and can flexibly incorporate any pre-treatment covariates. We evaluate the proposed variance reduction procedures with comprehensive simulation studies and provide practical suggestions regarding commonly adopted assumptions in computing ratio metrics. When tested on real online experiment data from LinkedIn, the proposed optimal procedure for ratio metrics can reduce up to 80\% of variance compared to the standard difference-in-mean estimator and also further reduce up to 30\% of variance compared to the CUPED approach by going beyond linearity and incorporating a large number of extra covariates

3-Ethyl-4-[(E)-2-methyl­benzyl­idene­amino]-1H-1,2,4-triazole-5(4H)-thione

Crystals of the title compound, C12H14N4S, were obtained from a condensation reaction of 4-amino-3-ethyl-1H-1,2,4-triazole-5(4H)-thione and 2-methyl­benzaldehyde. In the mol­ecular structure, there is a short N=C double bond [1.255 (2) Å], and the benzene and triazole rings are located on opposite sites of this double bond. The two rings are approximately parallel to each other, the dihedral angle being 1.75 (11)°. A partially overlapped arrangement is observed between the nearly parallel triazole and benzene rings of adjacent mol­ecules; the perpendicular distance of the centroid of the triazole ring from the benzene ring is 3.482 Å, indicating the existence of π–π stacking in the crystal structure

2-Methyl­benzaldehyde 2-methyl­benzyl­idenehydrazone

The mol­ecule of the title compound, C16H16N2, is centrosymmetric and the dihedral angle between the benzene ring and the dimethyl­hydrazine mean plane is 16.11 (15)°

3-Methyl-1-(3-nitro­phen­yl)-5-phenyl-4,5-dihydro-1H-pyrazole

In the title compound, C16H15N3O2, the planar [maximum deviation 0.156 (2) Å] pyrazoline ring is nearly coplanar with the 3-nitro­phenyl group and is approximately perpendicular to the phenyl ring, making dihedral angles of 3.80 (8) and 80.58 (10)°, respectively. Weak inter­molecular C—H⋯O hydrogen bonding is present in the crystal structure

5-(2-Fur­yl)-3-methyl-1-(3-nitro­phen­yl)-4,5-dihydro-1H-pyrazole

In the title compound, C14H13N3O3, the pyrazoline ring assumes an envelope conformation with the furanyl-bearing C atom at the flap position. The dihedral angle between the furan and nitrobenzene rings is 84.40 (9)°. Weak inter­molecular C—H⋯O hydrogen bonding is present in the crystal structure

On the Universal Adversarial Perturbations for Efficient Data-free Adversarial Detection

Detecting adversarial samples that are carefully crafted to fool the model is a critical step to socially-secure applications. However, existing adversarial detection methods require access to sufficient training data, which brings noteworthy concerns regarding privacy leakage and generalizability. In this work, we validate that the adversarial sample generated by attack algorithms is strongly related to a specific vector in the high-dimensional inputs. Such vectors, namely UAPs (Universal Adversarial Perturbations), can be calculated without original training data. Based on this discovery, we propose a data-agnostic adversarial detection framework, which induces different responses between normal and adversarial samples to UAPs. Experimental results show that our method achieves competitive detection performance on various text classification tasks, and maintains an equivalent time consumption to normal inference.Comment: Accepted by ACL2023 (Short Paper

Morphological and genetic characteristics of Nicotiana langsdorffii, N. glauca and its hybrid

Plant tumors, including genetic tumors, are disorganized and proliferate in an uncontrolled fashion. In this report we describe the morphological, physiological and genetic properties of Nicotiana. langsdorffii and Nicotiana. glauca and their hybrids (Nicotiana. langsdorffii x Nicotiana. Glauca). Nicotiana. langsdorffii leaves are oblate and pubescent with winged petioles, while Nicotiana. glauca leaf-blades are rubbery and oval-to-heart-shaped. The hybrid plants are intermediate in leaf shape, with anisocytic stomata and well-developed trichomes. In addition, they produce tumors in the absence of bacteria and exogenous hormones. Tumor growth in the hybrid plants was not affected by indole-3-acetic acid or kinetin. Genetic polymorphism was analyzed by the randomly amplified polymorphic DNA technique in the parents (Nicotiana. langsdorffii and Nicotiana. glauca) and in the genetic tumors produced by the hybrids. A total of 128 randomly amplified polymorphic DNA fragments were scored from fifteen random primers, and pronounced differences were found between the genetic tumors and their parents. These observations show that randomly amplified polymorphic DNA markers are might informative about genetic similarities and dissimilarities