False Discovery Rate Controlled Heterogeneous Treatment Effect Detection for Online Controlled Experiments

Online controlled experiments (a.k.a. A/B testing) have been used as the mantra for data-driven decision making on feature changing and product shipping in many Internet companies. However, it is still a great challenge to systematically measure how every code or feature change impacts millions of users with great heterogeneity (e.g. countries, ages, devices). The most commonly used A/B testing framework in many companies is based on Average Treatment Effect (ATE), which cannot detect the heterogeneity of treatment effect on users with different characteristics. In this paper, we propose statistical methods that can systematically and accurately identify Heterogeneous Treatment Effect (HTE) of any user cohort of interest (e.g. mobile device type, country), and determine which factors (e.g. age, gender) of users contribute to the heterogeneity of the treatment effect in an A/B test. By applying these methods on both simulation data and real-world experimentation data, we show how they work robustly with controlled low False Discover Rate (FDR), and at the same time, provides us with useful insights about the heterogeneity of identified user groups. We have deployed a toolkit based on these methods, and have used it to measure the Heterogeneous Treatment Effect of many A/B tests at Snap

Sales Channel Optimization via Simulations Based on Observational Data with Delayed Rewards: A Case Study at LinkedIn

Training models on data obtained from randomized experiments is ideal for making good decisions. However, randomized experiments are often time-consuming, expensive, risky, infeasible or unethical to perform, leaving decision makers little choice but to rely on observational data collected under historical policies when training models. This opens questions regarding not only which decision-making policies would perform best in practice, but also regarding the impact of different data collection protocols on the performance of various policies trained on the data, or the robustness of policy performance with respect to changes in problem characteristics such as action- or reward- specific delays in observing outcomes. We aim to answer such questions for the problem of optimizing sales channel allocations at LinkedIn, where sales accounts (leads) need to be allocated to one of three channels, with the goal of maximizing the number of successful conversions over a period of time. A key problem feature constitutes the presence of stochastic delays in observing allocation outcomes, whose distribution is both channel- and outcome- dependent. We built a discrete-time simulation that can handle our problem features and used it to evaluate: a) a historical rule-based policy; b) a supervised machine learning policy (XGBoost); and c) multi-armed bandit (MAB) policies, under different scenarios involving: i) data collection used for training (observational vs randomized); ii) lead conversion scenarios; iii) delay distributions. Our simulation results indicate that LinUCB, a simple MAB policy, consistently outperforms the other policies, achieving a 18-47% lift relative to a rule-based policyComment: Accepted at REVEAL'22 Workshop (16th ACM Conference on Recommender Systems - RecSys 2022

Dynamically-Driven Inactivation of the Catalytic Machinery of the SARS 3C-Like Protease by the N214A Mutation on the Extra Domain

Despite utilizing the same chymotrypsin fold to host the catalytic machinery, coronavirus 3C-like proteases (3CLpro) noticeably differ from picornavirus 3C proteases in acquiring an extra helical domain in evolution. Previously, the extra domain was demonstrated to regulate the catalysis of the SARS-CoV 3CLpro by controlling its dimerization. Here, we studied N214A, another mutant with only a doubled dissociation constant but significantly abolished activity. Unexpectedly, N214A still adopts the dimeric structure almost identical to that of the wild-type (WT) enzyme. Thus, we conducted 30-ns molecular dynamics (MD) simulations for N214A, WT, and R298A which we previously characterized to be a monomer with the collapsed catalytic machinery. Remarkably, three proteases display distinctive dynamical behaviors. While in WT, the catalytic machinery stably retains in the activated state; in R298A it remains largely collapsed in the inactivated state, thus implying that two states are not only structurally very distinguishable but also dynamically well separated. Surprisingly, in N214A the catalytic dyad becomes dynamically unstable and many residues constituting the catalytic machinery jump to sample the conformations highly resembling those of R298A. Therefore, the N214A mutation appears to trigger the dramatic change of the enzyme dynamics in the context of the dimeric form which ultimately inactivates the catalytic machinery. The present MD simulations represent the longest reported so far for the SARS-CoV 3CLpro, unveiling that its catalysis is critically dependent on the dynamics, which can be amazingly modulated by the extra domain. Consequently, mediating the dynamics may offer a potential avenue to inhibit the SARS-CoV 3CLpro

Nonlinear Control of a Single Tail Tilt Servomotor Tri-Rotor Ducted VTOL-UAV

This paper explores a novel single tail tilt servomotor tri-rotor ducted vertical takeoff and landing unmanned aerial vehicle system (VTOL-UAV), and proposes a compound control method combining disturbance observer, model predictive control (MPC) and sliding mode nonlinear dynamic inversion (NDI), and realizes the robust tracking control of the VTOL-UAV trajectory under compound disturbance. Specifically, the inner loop adopts nonlinear dynamic inverse which improved by sliding mode to realize the pseudo linearization of the system. The outer loop adopts the model predictive control based on the E-SSPC (State Space Predictive Controller based on the Error model) method, on this basis, the sliding mode disturbance observer based on fast Super-twisting algorithm is introduced into the position loop to observe and compensate the disturbance in real time, which improves the robustness of the outer loop system. Numerical simulation experiments verify the effectiveness and robustness of the control method. Finally, the flight test of the VTOL-UAV is carried out

Nonlinear Control of a Single Tail Tilt Servomotor Tri-Rotor Ducted VTOL-UAV

This paper explores a novel single tail tilt servomotor tri-rotor ducted vertical takeoff and landing unmanned aerial vehicle system (VTOL-UAV), and proposes a compound control method combining disturbance observer, model predictive control (MPC) and sliding mode nonlinear dynamic inversion (NDI), and realizes the robust tracking control of the VTOL-UAV trajectory under compound disturbance. Specifically, the inner loop adopts nonlinear dynamic inverse which improved by sliding mode to realize the pseudo linearization of the system. The outer loop adopts the model predictive control based on the E-SSPC (State Space Predictive Controller based on the Error model) method, on this basis, the sliding mode disturbance observer based on fast Super-twisting algorithm is introduced into the position loop to observe and compensate the disturbance in real time, which improves the robustness of the outer loop system. Numerical simulation experiments verify the effectiveness and robustness of the control method. Finally, the flight test of the VTOL-UAV is carried out

Accurate detection of SNPs using base-specific cleavage and mass spectrometry

Accurate detection of single-nucleotide polymorphisms (SNPs) is crucial for the success of many downstream analyses such as clinical diagnosis, virus identification, genetic mapping and association studies. Among many others, one valuable approach for SNP detection is based on the base-specific cleavage of single-stranded nucleic acids followed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) analysis. In this paper, we present a new SNP detection algorithm, which in particular permits an efficient and effective integration of the information in four complementary base-specific mass spectra. The new algorithm was implemented in a program called SnpMs. Comparative evaluation has been carried out on both simulated and real biological datasets, where experimental results clearly demonstrated the high ability of SnpMs as a tool to accurately detect SNPs

Application of coronarin enhances maize drought tolerance by affecting interactions between rhizosphere fungal community and metabolites

Coronarin (COR), an analog of jasmonic acid, has been shown to enhance the tolerance of plants to drought. However, the effects of COR on the interactions among microorganisms associated with plant roots and their implications for enhancing the drought tolerance of plants remain unclear. Here, we studied the effects of applying COR on the microorganisms associated with plant roots and the rhizosphere metabolome. Treatment with COR affected the fungal community of the rhizosphere by inducing changes in the rhizosphere metabolome, which enhanced the drought tolerance of plants. However, treatment with COR had no significant effect on root microorganisms or rhizosphere bacteria. Specifically, the application of COR resulted in a significant reduction in the relative abundance of metabolites, such as mucic acid, 1,4-cyclohexanedione, 4-acetylbutyric acid, Ribonic acid, palmitic acid, and stearic acid, in maize roots under drought conditions; COR application also led to increases in the abundance of drought-resistant fungal microorganisms, including Rhizopus, and the assembly of a highly drought-resistant rhizosphere fungal network, which enhanced the drought tolerance of plants. Overall, the results of our study indicate that COR application positively regulates interactions between plants and microbes and increases the drought tolerance of plants