Spiking neural networks (SNNs) process time-series data via internal
event-driven neural dynamics whose energy consumption depends on the number of
spikes exchanged between neurons over the course of the input presentation. In
typical implementations of an SNN classifier, decisions are produced after the
entire input sequence has been processed, resulting in latency and energy
consumption levels that are fairly uniform across inputs. Recently introduced
delay-adaptive SNNs tailor the inference latency -- and, with it, the energy
consumption -- to the difficulty of each example, by producing an early
decision when the SNN model is sufficiently ``confident''. In this paper, we
start by observing that, as an SNN processes input samples, its classification
decisions tend to be first under-confident and then over-confident with respect
to the decision's ground-truth, unknown, test accuracy. This makes it difficult
to determine a stopping time that ensures a desired level of accuracy. To
address this problem, we introduce a novel delay-adaptive SNN-based inference
methodology that, wrapping around any pre-trained SNN classifier, provides
guaranteed reliability for the decisions produced at input-dependent stopping
times. The approach entails minimal added complexity as compared to the
underlying SNN, requiring only thresholding and counting operations at run
time, and it leverages tools from conformal prediction (CP).

本文提出一种基于延迟自适应脉冲神经网络的推理方法，旨在通过阈值分类和计数操作结合符合预测的工具实现输入相关的停止时间下的决策可靠性，从而进一步降低脉冲神经网络的能耗。

SpikeCP：通过合规预测实现延迟自适应的可靠脉冲神经网络

SpikeCP: Delay-Adaptive Reliable Spiking Neural Networks via Conformal  Prediction

While the presence of clustering in crime and security event data is well
established, the mechanism(s) by which clustering arises is not fully
understood. Both contagion models and history independent correlation models
are applied, but not simultaneously. In an attempt to disentangle contagion
from other types of correlation, we consider a Hawkes process with background
rate driven by a log Gaussian Cox process. Our inference methodology is an
efficient Metropolis adjusted Langevin algorithm for filtering of the intensity
and estimation of the model parameters. We apply the methodology to property
and violent crime data from Chicago, terrorist attack data from Northern
Ireland and Israel, and civilian casualty data from Iraq. For each data set we
quantify the uncertainty in the levels of contagion vs. history independent
correlation.

本文探讨了犯罪事件和安全事件数据的聚类机制，通过使用 Hawkes 过程和背景速率为高斯 Cox 进程的滤波算法，对芝加哥的财产犯罪数据、北爱尔兰和以色列的恐怖袭击数据以及伊拉克的平民伤亡数据进行了分析，量化了传染程度与独立历史相关性水平之间的不确定性。