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Selecting the best items in a dataset is a common task in data exploration. However, the concept of “best” lies in the eyes of the beholder: different users may consider different attributes more important and, hence, arrive at different rankings. Nevertheless, one can remove “dominated” items and create a “representative” subset of the data, comprising the “best items” in it. A Pareto-optimal representative is guaranteed to contain the best item of each possible ranking, but it can be a large portion of data. A much smaller representative can be found if we relax the requirement of including the best item for each user and, instead, just limit the users’ “regret”. Existing work defines regret as the loss in score by limiting consideration to the representative instead of the full dataset, for any chosen ranking function. However, the score is often not a meaningful number, and users may not understand its absolute value. Sometimes small ranges in score can include large fractions of the dataset. In contrast, users do understand the notion of rank ordering. Therefore, we consider items’ positions in the ranked list in defining the regret and propose the rank-regret representative as the minimal subset of the data containing at least one of the top-k of any possible ranking function. This problem is polynomial time solvable in 2D space but is NP-hard on 3 or more dimensions. We design a suite of algorithms to fulfill different purposes, such as whether relaxation is permitted on k, the result size, or both, whether a distribution is known, whether theoretical guarantees or practical efficiency is important, etc. Experiments on real datasets demonstrate that we can efficiently find small subsets with small rank-regrets. 

Brain development in adolescence is synthetically influenced by various factors such as age, education, and socioeconomic conditions. To identify an independent effect from a variable of interest (e.g., socioeconomic conditions), statistical models such as General Linear Model (GLM) are typically adopted to account for covariates (e.g., age and gender). However, statistical models may be vulnerable with insufficient sample size and outliers, and multiple tests for a whole brain analysis lead to inevitable false-positives without sufficient sensitivity. Hence, it is necessary to develop a unified framework for multiple tests that robustly fits the observation and increases sensitivity. We therefore propose a unified flexible neural network that optimizes on the contribution from the main variable of interest as introduced in original GLM, which leads to improved statistical outcomes. The results on group analysis with fractional anisotropy (FA) from Diffusion Tensor Images from Adolescent Brain Cognitive Development (ABCD) study demonstrate that the proposed method provides much more selective and meaningful detection of ROIs related to socioeconomic status over conventional methods. 

Tremendous recent literature show that associations between different brain regions, i.e., brain connectivity, provide early symptoms of neurological disorders. Despite significant efforts made for graph neural network (GNN) techniques, their focus on graph nodes makes the state-of-the-art GNN methods not suitable for classifying brain connectivity as graphs where the objective is to characterize disease-relevant network dysfunction patterns on graph links. To address this issue, we propose Multi-resolution Edge Network (MENET) to detect disease-specific connectomic benchmarks with high discrimination power across diagnostic categories. The core of MENET is a novel graph edge-wise transform that we propose, which allows us to capture multi-resolution “connectomic” features. Using a rich set of the connectomic features, we devise a graph learning framework to jointly select discriminative edges and assign diagnostic labels for graphs. Experiments on two real datasets show that MENET accurately predicts diagnostic labels and identify brain connectivities highly associated with neurological disorders such as Alzheimer’s Disease and Attention-Deficit/Hyperactivity Disorder. 

Accurate selectivity estimation for string predicates is a long-standing research challenge in databases. Supporting pattern matching on strings (such as prefix, substring, and suffix) makes this problem much more challenging, thereby necessitating a dedicated study. Traditional approaches often build pruned summary data structures such as tries followed by selectivity estimation using statistical correlations. However, this produces insufficiently accurate cardinality estimates resulting in the selection of sub-optimal plans by the query optimizer. Recently proposed deep learning based approaches leverage techniques from natural language processing such as embeddings to encode the strings and use it to train a model. While this is an improvement over traditional approaches, there is a large scope for improvement. We propose Astrid, a framework for string selectivity estimation that synthesizes ideas from traditional and deep learning based approaches. We make two complementary contributions. First, we propose an embedding algorithm that is query-type (prefix, substring, and suffix) and selectivity aware. Consider three strings 'ab', 'abc' and 'abd' whose prefix frequencies are 1000, 800 and 100 respectively. Our approach would ensure that the embedding for 'ab' is closer to 'abc' than 'abd'. Second, we describe how neural language models could be used for selectivity estimation. While they work well for prefix queries, their performance for substring queries is sub-optimal. We modify the objective function of the neural language model so that it could be used for estimating selectivities of pattern matching queries. We also propose a novel and efficient algorithm for optimizing the new objective function. We conduct extensive experiments over benchmark datasets and show that our proposed approaches achieve state-of-the-art results.