The objective of our study is to observe dynamics of multiple substances in
vivo with high temporal resolution from multi-spectral magnetic resonance
spectroscopic imaging (MRSI) data. The multi-spectral MRSI can effectively
separate spectral peaks of multiple substances and is useful to measure spatial
distributions of substances. However it is difficult to measure time-varying
substance distributions directly by ordinary full sampling because the
measurement requires a significantly long time. In this study, we propose a
novel method to reconstruct the spatio-temporal distributions of substances
from randomly undersampled multi-spectral MRSI data on the basis of compressed
sensing (CS) and the partially separable function model with base spectra of
substances. In our method, we have employed spatio-temporal sparsity and
temporal smoothness of the substance distributions as prior knowledge to
perform CS. The effectiveness of our method has been evaluated using phantom
data sets of glass tubes filled with glucose or lactate solution in increasing
amounts over time and animal data sets of a tumor-bearing mouse to observe the
metabolic dynamics involved in the Warburg effect in vivo. The reconstructed
results are consistent with the expected behaviors, showing that our method can
reconstruct the spatio-temporal distribution of substances with a temporal
resolution of four seconds which is extremely short time scale compared with
that of full sampling. Since this method utilizes only prior knowledge
naturally assumed for the spatio-temporal distributions of substances and is
independent of the number of the spectral and spatial dimensions or the
acquisition sequence of MRSI, it is expected to contribute to revealing the
underlying substance dynamics in MRSI data already acquired or to be acquired
in the future.

通过压缩感知（CS）和基于物质基本光谱的部分可分模型，我们提出了一种新方法来从随机欠采样的多光谱磁共振波谱成像（MRSI）数据中重建物质的时空分布。我们利用空时稀疏性和时间平滑性作为先验知识执行 CS，实验结果表明我们的方法能够以极短的时间分辨率重建物质的时空分布，预期对揭示磁共振波谱成像数据中的物质动力学起到重要作用。

多光谱磁共振波谱成像中基于压缩感知的物质动态时空重建

Spatio-temporal reconstruction of substance dynamics using compressed  sensing in multi-spectral magnetic resonance spectroscopic imaging

Ubiquitous mobile computing have enabled ride-hailing services to collect
vast amounts of behavioral data of riders and drivers and optimize supply and
demand matching in real time. While these mobility service providers have some
degree of control over the market by assigning vehicles to requests, they need
to deal with the uncertainty arising from self-interested driver behavior since
workers are usually free to drive when they are not assigned tasks. In this
work, we formulate the problem of passenger-vehicle matching in a sparsely
connected graph and proposed an algorithm to derive an equilibrium policy in a
multi-agent environment. Our framework combines value iteration methods to
estimate the optimal policy given expected state visitation and policy
propagation to compute multi-agent state visitation frequencies. Furthermore,
we developed a method to learn the driver's reward function transferable to an
environment with significantly different dynamics from training data. We
evaluated the robustness to changes in spatio-temporal supply-demand
distributions and deterioration in data quality using a real-world taxi
trajectory dataset; our approach significantly outperforms several baselines in
terms of imitation accuracy. The computational time required to obtain an
equilibrium policy shared by all vehicles does not depend on the number of
agents, and even on the scale of real-world services, it takes only a few
seconds on a single CPU.

该研究提出了一种算法，能够在具有自主性的司机行为中，通过利用值迭代方法和策略传播计算所有乘客和司机的均衡策略，在 spatio-temporal distributions 与税车轨迹数据集等多个方面表现出优异的性能。