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Crowdsourcing has been a prompt and cost-effective way of obtaining labels in many machine learning applications. In the literature, a number of algorithms have been developed to infer the ground truth based on the collected labels. However, most existing studies assume workers to be independent and are vulnerable to worker collusion. This paper aims at detecting the collusive behaviors of workers in labeling tasks. Specifically, we consider collusion in a pairwise manner and propose a penalized pairwise profile likelihood method based on the adaptive LASSO penalty for collusion detection. Many models that describe the behavior of independent workers can be incorporated into our proposed framework as the baseline model. We further investigate the theoretical properties of the proposed method that guarantee the asymptotic performance. An algorithm based on expectationmaximization algorithm and coordinate descent is proposed to numerically maximize the penalized pairwise profile likelihood function for parameter estimation. To the best of our knowledge, this is the first statistical model that simultaneously detects collusion, learns workers' capabilities, and infers the ground true labels. Numerical studies using synthetic and real data sets are also conducted to verify the performance of the method. [ABSTRACT FROM AUTHOR]

As many manufacturing and service processes nowadays involve multiple categorical quality characteristics, statistical surveillance for multivariate categorical processes has attracted increasing attention recently. However, in the literature there are only a few research papers that focus on the monitoring and diagnosis of such processes. This may be partly due to the challenges and limitations in describing the correlation relationships among categorical variables. In many applications, causal relationships may exist among categorical variables, in which the shifts at upstream, or cause, variables will propagate to their downstream, or effect, variables based on the causal structure. In such cases, a causation-based rather than correlationbased descriptionwould better account for the relationship among multiple categorical variables. This provides a new opportunity to establish improved monitoring and diagnosis schemes. In this article, we employ a Bayesian network to characterize such causal relationships and integrate it with a statistical process control technique. We propose two control charts for detecting shifts in the conditional probabilities of the multiple categorical variables that are embedded in the Bayesian network. The first chart provides a general tool, and the second chart integrates directional information, which also leads to a diagnostic prescription of shift locations. Both simulation and real case studies are used to demonstrate the effectiveness of the proposed monitoring and diagnostic schemes. [ABSTRACT FROM AUTHOR]

The constant emergence of COVID-19 variants reduces the effectiveness of existing vaccines and test kits. Therefore, it is critical to identify conserved structures in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes as potential targets for variant-proof diagnostics and therapeutics. However, the algorithms to predict these conserved structures, which simultaneously fold and alignmultiple RNA homologs, scale at best cubically with sequence length and are thus infeasible for coronaviruses, which possess the longest genomes (~30,000 nt) among RNA viruses. As a result, existing efforts on modeling SARS-CoV-2 structures resort to single-sequence folding as well as local folding methods with short window sizes, which inevitably neglect longrange interactions that are crucial in RNA functions. Here we present LinearTurboFold, an efficient algorithm for folding RNA homologs that scales linearly with sequence length, enabling unprecedented global structural analysis on SARS-CoV-2. Surprisingly, on a group of SARS-CoV-2 and SARS-related genomes, LinearTurboFold's purely in silico prediction not only is close to experimentally guided models for local structures, but also goes far beyond them by capturing the end-to-end pairs between 5' and 3' untranslated regions (UTRs) (~29,800 nt apart) that match perfectly with a purely experimental work. Furthermore, LinearTurboFold identifies undiscovered conserved structures and conserved accessible regions as potential targets for designing efficient andmutation-insensitive small-molecule drugs, antisense oligonucleotides, small interfering RNAs (siRNAs), CRISPR-Cas13 guide RNAs, and RT-PCR primers. LinearTurboFold is a general technique that can also be applied to other RNA viruses and fulllength genome studies and will be a useful tool in fighting the current and future pandemics. [ABSTRACT FROM AUTHOR]