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Identifying the subset of events that influence events of interest from continuous time datasets is of great interest in various applications. Existing methods however often fail to produce accurate and interpretable results in a time-efficient manner. In this paper, we propose a neural model – Influence-Aware Attention for Multivariate Temporal Point Processes (IAA-MTPPs) – which leverages the powerful attention mechanism in transformers to capture temporal dynamics between event types, which is different from existing instance-to-instance attentions, using variational inference while maintaining interpretability. Given event sequences and a prior influence matrix, IAA-MTPP efficiently learns an approximate posterior by an Attention-to-Influence mechanism, and subsequently models the conditional likelihood of the sequences given a sampled influence through an Influence-to-Attention formulation. Both steps are completed efficiently inside a Bblock multi-head self-attention layer, thus our end-to-end training with parallelizable transformer architecture enables faster training compared to sequential models such as RNNs. We demonstrate strong empirical performance compared to existing baselines on multiple synthetic and real benchmarks, including qualitative analysis for an application in decentralized finance. 

We report a new measurement of transverse single-spin asymmetries for dijet production in collisions of polarized protons at $\sqrt{s}=200\mathrm{GeV}$. Correlations between the proton spin and the transverse momenta of its partons, each perpendicular to the proton momentum direction, are probed at high $Q^2\approx 160{\mathrm{GeV}}^2$. Evidence for nonzero Sivers effects is measured for the first time in dijets from proton-proton collisions, but only when the jets are sorted by their net charge, which enhances the otherwise canceling opposite-sign $u$- or $d$-quark contributions to separate data samples. The resulting asymmetries are compared to recent theoretical calculations. Separately, the associated Sivers observable $⟨k_T⟩$, the average parton transverse momentum, is extracted using a simple kinematics approach which further enables a determination of the individual partonic contributions to the observed asymmetries.