Group decision-making (GDM) is a crucial approach to ensuring the scientific nature and impartiality of decisions. However, strategic manipulative behaviors driven by self-interested motives often undermine the fairness and effectiveness of decision outcomes, leading to results that deviate from expectations. While most prior studies have focused on theoretical analysis, there remains a significant gap in effective measures to prevent such manipulative behaviors. Moreover, current consensus models predominantly emphasize cost optimization, with less attention paid to the acceptability of feedback. To address these challenges, this study introduces an optimal consensus adjustment mechanism based on the Nash bargaining (NB) solution, aiming to prevent manipulation and self-interested behaviors in GDM. Specifically, we first analyze the opinion manipulation problem within the framework of the minimum adjustment consensus model (MACM). We then construct the Nash product to mitigate the risk of weight manipulation. Subsequently, we examine the nonuniqueness issue in the allocation of minimal total consensus adjustments from the perspective of cooperative game theory. Building on this, we incorporate regret theory to characterize the risk aversion and loss sensitivity of decision-makers (DMs) and propose a consensus adjustment mechanism based on the NB game. Finally, we establish three novel optimization methods to allocate optimal individual consensus adjustments. Case studies and comparative experiments demonstrate the superiority of these methods.

Database autonomy service (DAS) is a platform that provides assistance to database maintainers or administrators in managing a large number of database instances in major internet companies. An important task of DAS is to find missing indexes to improve the performance of slow queries reported from its managed online database instances. Traditional database systems provide the “what-if” function or the hypothetical index technique. Index metadata is modified to simulate the benefits of indexes for queries without creating physical index files. Decades of research have led to plenty of ideas for index recommendation through the use of the “what-if” function and different search strategies. However, the popular open-source database system MySQL, used by most internet companies, has not provided the “what-if” function. In Meituan, tens of thousands of MySQL instances have been deployed across many business lines. Consequently, the DAS platform has accumulated lots of index creation samples. In this paper, we introduce index learner (IdxL), designed to learn index creation knowledge from these informative index data. IdxL resolves the problem of index recommendation by formulating it into an end-to-end supervised learning problem. Given a slow query, IdxL uses learned index creation knowledge to directly predict the missing indexes. Experimental results demonstrate: (1) IdxL is superior to the state-of-the-art index recommendation methods, especially when the error in cost estimation was propagated to the search in candidate index space, and (2) in particular, IdxL achieves up to 97% performance gain over a state-of-the-art method relying on the optimizer's cost estimation in the Meituan-specific index recommendation scenario. Finally, we present the applied results of IdxL in the Meituan DAS platform, demonstrating its ability to transfer index creation knowledge from certain databases to others.

The Database Autonomy Service (DAS) is a platform designed to assist database administrators in managing a large number of database instances within major internet companies. One of the key tasks in DAS is to find missing indexes to improve the slow query execution. In Meituan, a vast array of business lines deploy tens of thousands of MySQL database instances. Consequently, a great number of human-generated index cases are accumulated in the DAS platform. This motivates us to build a data-driven index recommendation system, referred to as idxLearner, which can learn index creation knowledge from human-generated index cases. In this demonstration, users can interact with idxLearner by choosing source databases to construct the training data, training the recommendation model, inputting slow queries for various target databases, and observing the recommended indexes and their evaluation results.

Accurate variant pathogenicity predictions are important in genetic studies of human diseases. Inframe insertion and deletion variants (indels) alter protein sequence and length, but not as deleterious as frameshift indels. Inframe indel Interpretation is challenging due to limitations in the available number of known pathogenic variants for training. Existing prediction methods largely use manually encoded features including conservation, protein structure and function, and allele frequency to infer variant pathogenicity. Recent advances in deep learning modeling of protein sequences and structures provide an opportunity to improve the representation of salient features based on large numbers of protein sequences. We developed a new pathogenicity predictor for SHort Inframe iNsertion and dEletion (SHINE). SHINE uses pretrained protein language models to construct a latent representation of an indel and its protein context from protein sequences and multiple protein sequence alignments, and feeds the latent representation into supervised machine learning models for pathogenicity prediction. We curated training data from ClinVar and gnomAD, and created two test datasets from different sources. SHINE achieved better prediction performance than existing methods for both deletion and insertion variants in these two test datasets. Our work suggests that unsupervised protein language models can provide valuable information about proteins, and new methods based on these models can improve variant interpretation in genetic analyses.

MOTIVATION: Whole-genome sequencing (WGS) allows direct interrogation of previously undetected uncommon or rare variants, which potentially contribute to the missing heritability of human disease. However, cost of sequencing large numbers of samples limits its application in case-control association studies. Here, we describe theoretical and empirical design considerations for such sequencing studies, aimed at maximizing the power of detecting association under the constraint of study-wide cost. RESULTS: We consider two cost regimes. First, assuming cost is proportional to the total amount of base pairs to be sequenced across all samples, which is a practical model for whole-genome sequencing, we explored the tradeoff in terms of study power between increasing the number of subjects and increasing depth coverage. We demonstrate that the optimal power of detecting association is achieved at medium depth coverage under a wide range of realistic conditions for case-only sequencing designs. Second, if cost is fixed per sample, which is approximately the case in exome sequencing, we show that in a simple case+control sequencing study, the optimal design should include cases totaling 1/e of all subjects. AVAILABILITY: A web tool implementing the methods is available at http://www.cs.columbia.edu/~itsik/OPERA/.

MOTIVATION: Copy Number Variants (CNVs) are important genetic factors for studying human diseases. While high-throughput whole genome re-sequencing provides multiple lines of evidence for detecting CNVs, computational algorithms need to be tailored for different type or size of CNVs under different experimental designs. RESULTS: To achieve optimal power and resolution of detecting CNVs at low depth of coverage, we implemented a Hidden Markov Model that integrates both depth of coverage and mate-pair relationship. The novelty of our algorithm is that we infer the likelihood of carrying a deletion jointly from multiple mate pairs in a region without the requirement of a single mate pairs being obvious outliers. By integrating all useful information in a comprehensive model, our method is able to detect medium-size deletions (200-2000bp) at low depth (<10× per sample). We applied the method to simulated data and demonstrate the power of detecting medium-size deletions is close to theoretical values. AVAILABILITY: A program implemented in Java, Zinfandel, is available at http://www.cs.columbia.edu/~itsik/zinfandel/

In this paper we develop a novel data description kernel based on one-class SVM (OCSVM-DD kernel) used for text-independent SVM speaker verification. The basic idea of the new kernel is to combine the data description model OCSVM with SVM discriminant classifier. Utterances are firstly mapped to the normal vector of the separating hyperplane in OCSVM model. Then a SVM classifier with linear kernel is applied on those mapped vectors. Experiments results on NIST 2001 SRE database show that the performance of our new kernel is superior to generalized linear discriminative sequence (GLDS) kernel and comparative with UBM-MAP-GMM method.