Trajectory Prediction refers to the task of forecasting the future path or course of an object or entity in motion based on historical data, observations, or current information. This task is particularly important in fields such as robotics, autonomous vehicles, surveillance, and sports analysis, where predicting the future movements of objects or individuals is crucial for decision-making and planning.

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Identifying Combinatorial Regulatory Genes for Cell Fate Decision via Reparameterizable Subset Explanations

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Cell fate decisions are highly coordinated processes governed bycomplex interactions among numerous regulatory genes, whiledisruptions in these mechanisms can lead to developmental abnormalitiesand disease. Traditional methods often fail to capture suchcombinatorial interactions, limiting their ability to fully model cellfate dynamics. Here, we introduce MetaVelo, a global feature explanationframework for identifying key regulatory gene sets influencingcell fate transitions. MetaVelo models these transitions as ablack-box function and employs a differentiable neural ordinary differentialequation (ODE) surrogate to enable efficient optimization.By reparameterizing the problem as a controllable data generationprocess, MetaVelo overcomes the challenges posed by the nondifferentiablenature of cell fate dynamics. Benchmarking acrossdiverse stand-alone and longitudinal single-cell RNA-seq datasetsand three black-box cell fate models demonstrates its superiorityover 12 baseline methods in predicting developmental trajectoriesand identifying combinatorial regulatory gene sets. MetaVelo furtherdistinguishes independent from synergistic regulatory genes,offering novel insights into the gene interactions governing cellfate. With the growing availability of high-resolution single-celldata, MetaVelo provides a scalable and effective framework fo

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Divide-and-Conquer for Lane-Aware Diverse Trajectory Prediction

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Trajectory prediction is a safety-critical tool for autonomous vehicles to plan and execute actions. Our work addresses two key challenges in trajectory prediction, learning multimodal outputs, and better predictions by imposing constraints using driving knowledge. Recent methods have achieved strong performances using Multi-Choice Learning objectives like winner-takes-all (WTA) or best-of-many. But the impact of those methods in learning diverse hypotheses is under-studied as such objectives highly depend on their initialization for diversity. As our first contribution, we propose a novel Divide-And-Conquer (DAC) approach that acts as a better initialization technique to WTA objective, resulting in diverse outputs without any spurious modes. Our second contribution is a novel trajectory prediction framework called ALAN that uses existing lane centerlines as anchors to provide trajectories constrained to the input lanes. Our framework provides multi-agent trajectory outputs in a forward pass by capturing interactions through hypercolumn descriptors and incorporating scene information in the form of rasterized images and per-agent lane anchors. Experiments on synthetic and real data show that the proposed DAC captures the data distribution better compare to other WTA family of objectives. Further, we show that our ALAN approach provides on par or better performance with SOTA methods evaluated on Nuscenes urban driving benchmark.

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