DeepEverest: Accelerating Declarative Top-K Queries for Deep Neural Network Interpretation [Technical Report]

We design, implement, and evaluate DeepEverest, a system for the efficient execution of interpretation by example queries over the activation values of a deep neural network. DeepEverest consists of an efficient indexing technique and a query execution algorithm with various optimizations. We prove that the proposed query execution algorithm is instance optimal. Experiments with our prototype show that DeepEverest, using less than 20% of the storage of full materialization, significantly accelerates individual queries by up to 63x and consistently outperforms other methods on multi-query workloads that simulate DNN interpretation processes

Tea: A High-level Language and Runtime System for Automating Statistical Analysis

Though statistical analyses are centered on research questions and hypotheses, current statistical analysis tools are not. Users must first translate their hypotheses into specific statistical tests and then perform API calls with functions and parameters. To do so accurately requires that users have statistical expertise. To lower this barrier to valid, replicable statistical analysis, we introduce Tea, a high-level declarative language and runtime system. In Tea, users express their study design, any parametric assumptions, and their hypotheses. Tea compiles these high-level specifications into a constraint satisfaction problem that determines the set of valid statistical tests, and then executes them to test the hypothesis. We evaluate Tea using a suite of statistical analyses drawn from popular tutorials. We show that Tea generally matches the choices of experts while automatically switching to non-parametric tests when parametric assumptions are not met. We simulate the effect of mistakes made by non-expert users and show that Tea automatically avoids both false negatives and false positives that could be produced by the application of incorrect statistical tests.Comment: 11 page

EQUI-VOCAL: Synthesizing Queries for Compositional Video Events from Limited User Interactions [Technical Report]

We introduce EQUI-VOCAL: a new system that automatically synthesizes queries over videos from limited user interactions. The user only provides a handful of positive and negative examples of what they are looking for. EQUI-VOCAL utilizes these initial examples and additional ones collected through active learning to efficiently synthesize complex user queries. Our approach enables users to find events without database expertise, with limited labeling effort, and without declarative specifications or sketches. Core to EQUI-VOCAL's design is the use of spatio-temporal scene graphs in its data model and query language and a novel query synthesis approach that works on large and noisy video data. Our system outperforms two baseline systems -- in terms of F1 score, synthesis time, and robustness to noise -- and can flexibly synthesize complex queries that the baselines do not support.Comment: This is an extended technical report for the following paper: "Enhao Zhang, Maureen Daum, Dong He, Brandon Haynes, Ranjay Krishna, and Magdalena Balazinska. EQUI-VOCAL: Synthesizing Queries for Compositional Video Events from Limited User Interactions. PVLDB, 16(11): 2714-2727, 2023. doi:10.14778/3611479.3611482

VOCALExplore: Pay-as-You-Go Video Data Exploration and Model Building [Technical Report]

We introduce VOCALExplore, a system designed to support users in building domain-specific models over video datasets. VOCALExplore supports interactive labeling sessions and trains models using user-supplied labels. VOCALExplore maximizes model quality by automatically deciding how to select samples based on observed skew in the collected labels. It also selects the optimal video representations to use when training models by casting feature selection as a rising bandit problem. Finally, VOCALExplore implements optimizations to achieve low latency without sacrificing model performance. We demonstrate that VOCALExplore achieves close to the best possible model quality given candidate acquisition functions and feature extractors, and it does so with low visible latency (~1 second per iteration) and no expensive preprocessing

Cardiolipin Mediates Cross-Talk between Mitochondria and the Vacuole

Cardiolipin (CL) is an anionic phospholipid with a dimeric structure predominantly localized in the mitochondrial inner membrane, where it is closely associated with mitochondrial function, biogenesis, and genome stability (Daum, 1985; Janitor and Subik, 1993; Jiang et al., 2000; Schlame et al., 2000; Zhong et al., 2004). Previous studies have shown that yeast mutant cells lacking CL due to a disruption in CRD1, the structural gene encoding CL synthase, exhibit defective colony formation at elevated temperature even on glucose medium (Jiang et al., 1999; Zhong et al., 2004), suggesting a role for CL in cellular processes apart from mitochondrial bioenergetics. In the current study, we present evidence that the crd1Δ mutant exhibits severe vacuolar defects, including swollen vacuole morphology and loss of vacuolar acidification, at 37°C. Moreover, vacuoles from crd1Δ show decreased vacuolar H+-ATPase activity and proton pumping, which may contribute to loss of vacuolar acidification. Deletion mutants in RTG2 and NHX1, which mediate vacuolar pH and ion homeostasis, rescue the defective colony formation phenotype of crd1Δ, strongly suggesting that the temperature sensitivity of crd1Δ is a consequence of the vacuolar defects. Our results demonstrate the existence of a novel mitochondria-vacuole signaling pathway mediated by CL synthesis