The neural Hawkes process (Mei & Eisner, 2017) is a generative model of
irregularly spaced sequences of discrete events. To handle complex domains with
many event types, Mei et al. (2020a) further consider a setting in which each
event in the sequence updates a deductive database of facts (via
domain-specific pattern-matching rules); future events are then conditioned on
the database contents. They show how to convert such a symbolic system into a
neuro-symbolic continuous-time generative model, in which each database fact
and the possible event has a time-varying embedding that is derived from its
symbolic provenance.
In this paper, we modify both models, replacing their recurrent LSTM-based
architectures with flatter attention-based architectures (Vaswani et al.,
2017), which are simpler and more parallelizable. This does not appear to hurt
our accuracy, which is comparable to or better than that of the original models
as well as (where applicable) previous attention-based methods (Zuo et al.,
2020; Zhang et al., 2020a).

本文在神经 Hawkes 过程、演绎式数据库、神经符号化模型、注意力机制等关键技术的基础上，采用注意力机制的扁平化结构替换其原来的 LSTM 架构，并展示了相对较高的精度。

不规则间隔事件及其参与者的变形器嵌入

Transformer Embeddings of Irregularly Spaced Events and Their Participants

When developing a (web) interface for a deductive database, functionality
required by the client is provided by means of HTTP handlers that wrap the
logical data access predicates. These handlers are responsible for converting
between client and server data representations and typically include options
for paginating results. Designing the web accessible API is difficult because
it is hard to predict the exact requirements of clients. Pengines changes this
picture. The client provides a Prolog program that selects the required data by
accessing the logical API of the server. The pengine infrastructure provides
general mechanisms for converting Prolog data and handling Prolog
non-determinism. The Pengines library is small (2000 lines Prolog, 150 lines
JavaScript). It greatly simplifies defining an AJAX based client for a Prolog
program and provides non-deterministic RPC between Prolog processes as well as
interaction with Prolog engines similar to Paul Tarau's engines. Pengines are
available as a standard package for SWI-Prolog 7.

本文介绍了如何使用 Pengines 库，以简化开发 AJAX 式基于 Prolog 程序的 Web 客户端的流程，同时提供了一些端到端的处理方法，例如 HTTP 处理程序和查询内容序列化。