Getting good performance out of numerical equation solvers requires that the
user has provided stable and efficient functions representing their model.
However, users should not be trusted to write good code. In this manuscript we
describe ModelingToolkit (MTK), a symbolic equation-based modeling system which
allows for composable transformations to generate stable, efficient, and
parallelized model implementations. MTK blurs the lines of traditional symbolic
computing by acting directly on a user's numerical code. We show the ability to
apply graph algorithms for automatically parallelizing and performing index
reduction on code written for differential-algebraic equation (DAE) solvers,
"fixing" the performance and stability of the model without requiring any
changes to on the user's part. We demonstrate how composable model
transformations can be combined with automated data-driven surrogate generation
techniques, allowing machine learning methods to generate accelerated
approximate models within an acausal modeling framework. These reduced models
are shown to outperform the Dymola Modelica compiler on an HVAC model by 590x
at 3\% error. Together, this demonstrates MTK as a system for bringing the
latest research in graph transformations directly to modeling applications.

这篇论文介绍了 ModelingToolkit (MTK) 这一符号方程为基础的建模工具，该工具可以通过组合变换来生成稳定、高效和并行的模型实现。MTK 通过对用户的数值代码进行处理来提高性能和稳定性，并演示了如何将机器学习方法应用于减少模型复杂性并加速近似建模。

ModelingToolkit: 用于方程式建模的可组合图转换系统

ModelingToolkit: A Composable Graph Transformation System For  Equation-Based Modeling

Contact tracing is a well-established and effective approach for the
containment of the spread of infectious diseases. While Bluetooth-based contact
tracing method using phones has become popular recently, these approaches
suffer from the need for a critical mass adoption to be effective. In this
paper, we present WiFiTrace, a network-centric approach for contact tracing
that relies on passive WiFi sensing with no client-side involvement. Our
approach exploits WiFi network logs gathered by enterprise networks for
performance and security monitoring, and utilizes them for reconstructing
device trajectories for contact tracing. Our approach is specifically designed
to enhance the efficacy of traditional methods, rather than to supplant them
with new technology. We designed an efficient graph algorithm to scale our
approach to large networks with tens of thousands of users. The graph-based
approach outperforms an indexed PostgresSQL in memory by at least 4.5X without
any index update overheads or blocking. We have implemented a full prototype of
our system and deployed it on two large university campuses. We validated our
approach and demonstrate its efficacy using case studies and detailed
experiments using real-world WiFi datasets.

通过 WiFi 传感器实现无需客户端参与的网络中心式接触者追踪，使用图算法提升传统方法的效率，基于实验验证其可行性和有效性。

WiFiTrace: 使用被动 WiFi 传感进行基于网络的传染病接触追踪

WiFiTrace: Network-based Contact Tracing for Infectious Diseases Using  Passive WiFi Sensing

Transportation cost is an attractive similarity measure between probability
distributions due to its many useful theoretical properties. However, solving
optimal transport exactly can be prohibitively expensive. Therefore, there has
been significant effort towards the design of scalable approximation
algorithms. Previous combinatorial results [Sharathkumar, Agarwal STOC '12,
Agarwal, Sharathkumar STOC '14] have focused primarily on the design of
near-linear time multiplicative approximation algorithms. There has also been
an effort to design approximate solutions with additive errors [Cuturi NIPS
'13, Altschuler \etal\ NIPS '17, Dvurechensky \etal\, ICML '18, Quanrud, SOSA
'19] within a time bound that is linear in the size of the cost matrix and
polynomial in $C/\delta$; here $C$ is the largest value in the cost matrix and
$\delta$ is the additive error. We present an adaptation of the classical graph
algorithm of Gabow and Tarjan and provide a novel analysis of this algorithm
that bounds its execution time by $O(\frac{n^2 C}{\delta}+
\frac{nC^2}{\delta^2})$. Our algorithm is extremely simple and executes, for an
arbitrarily small constant $\varepsilon$, only $\lfloor
\frac{2C}{(1-\varepsilon)\delta}\rfloor + 1$ iterations, where each iteration
consists only of a Dijkstra-type search followed by a depth-first search. We
also provide empirical results that suggest our algorithm is competitive with
respect to a sequential implementation of the Sinkhorn algorithm in execution
time. Moreover, our algorithm quickly computes a solution for very small values
of $\delta$ whereas Sinkhorn algorithm slows down due to numerical instability.

本文提出了一种基于 Gabow 和 Tarjan 的图算法的新颖分析，并提供了一个简单的贪婪式迭代算法，其执行时间为 O (n^2*C/δ + nC^2/δ^2)，用于求解概率分布的最优运输问题，在数值不稳定的情况下具有竞争力的执行时间。