Streamflow prediction is one of the key challenges in the field of hydrology
due to the complex interplay between multiple non-linear physical mechanisms
behind streamflow generation. While physically-based models are rooted in rich
understanding of the physical processes, a significant performance gap still
remains which can be potentially addressed by leveraging the recent advances in
machine learning. The goal of this work is to incorporate our understanding of
physical processes and constraints in hydrology into machine learning
algorithms, and thus bridge the performance gap while reducing the need for
large amounts of data compared to traditional data-driven approaches. In
particular, we propose an LSTM based deep learning architecture that is coupled
with SWAT (Soil and Water Assessment Tool), an hydrology model that is in wide
use today. The key idea of the approach is to model auxiliary intermediate
processes that connect weather drivers to streamflow, rather than directly
mapping runoff from weather variables which is what a deep learning
architecture without physical insight will do. The efficacy of the approach is
being analyzed on several small catchments located in the South Branch of the
Root River Watershed in southeast Minnesota. Apart from observation data on
runoff, the approach also leverages a 200-year synthetic dataset generated by
SWAT to improve the performance while reducing convergence time. In the early
phases of this study, simpler versions of the physics guided deep learning
architectures are being used to achieve a system understanding of the coupling
of physics and machine learning. As more complexity is introduced into the
present implementation, the framework will be able to generalize to more
sophisticated cases where spatial heterogeneity is present.

本文提出一种基于 LSTM 的深度学习结构，结合前沿的水文模型 SWAT，通过建模天气驱动和产流之间的中间交互过程，实现对径流预测的精确预测，同时减少了传统数据驱动方法所需的大量数据。

物理引导机器学习方法在水文学中的应用

Physics Guided Machine Learning Methods for Hydrology

Neural network training is computationally and memory intensive. Sparse
training can reduce the burden on emerging hardware platforms designed to
accelerate sparse computations, but it can affect network convergence. In this
work, we propose a novel CNN training algorithm Sparse Weight Activation
Training (SWAT). SWAT is more computation and memory-efficient than
conventional training. SWAT modifies back-propagation based on the empirical
insight that convergence during training tends to be robust to the elimination
of (i) small magnitude weights during the forward pass and (ii) both small
magnitude weights and activations during the backward pass. We evaluate SWAT on
recent CNN architectures such as ResNet, VGG, DenseNet and WideResNet using
CIFAR-10, CIFAR-100 and ImageNet datasets. For ResNet-50 on ImageNet SWAT
reduces total floating-point operations (FLOPS) during training by 80%
resulting in a 3.3$\times$ training speedup when run on a simulated sparse
learning accelerator representative of emerging platforms while incurring only
1.63% reduction in validation accuracy. Moreover, SWAT reduces memory footprint
during the backward pass by 23% to 50% for activations and 50% to 90% for
weights.

本文提出了一种名为 Sparse Weight Activation Training (SWAT) 的算法，相比传统训练更加节省计算和内存资源，在保证验证准确率的同时可以在使用最新的 CNN 架构和数据集进行训练时具有很高的速度和内存节省率。

稀疏权重激活训练

Sparse Weight Activation Training

We study the problem of entity salience by proposing the design and
implementation of SWAT, a system that identifies the salient Wikipedia entities
occurring in an input document. SWAT consists of several modules that are able
to detect and classify on-the-fly Wikipedia entities as salient or not, based
on a large number of syntactic, semantic and latent features properly extracted
via a supervised process which has been trained over millions of examples drawn
from the New York Times corpus. The validation process is performed through a
large experimental assessment, eventually showing that SWAT improves known
solutions over all publicly available datasets. We release SWAT via an API that
we describe and comment in the paper in order to ease its use in other
software.

本文介绍了一个称为 SWAT 的系统，该系统通过监督过程检测和分类维基百科中的实体，最终使用 API 的方式公开。经过大量实验对比，证明 SWAT 在提高实体关注度方面比目前已有的公开数据集更为有效。