221 research outputs found
ASIR: Robust Agent-based Representation Of SIR Model
Compartmental models (written as ) and agent-based models (written as
) are dominant methods in the field of epidemic simulation. But in the
literature there lacks discussion on how to build the \textbf{quantitative
relationship} between them. In this paper, we propose an agent-based
model: . can robustly reproduce the infection curve predicted by a
given SIR model (the simplest .) Notably, one can deduce any parameter of
from parameters of without manual tuning. offers
epidemiologists a method to transform a calibrated model into an
agent-based model that inherit 's performance without another round of
calibration. The design is inspirational for building a general
quantitative relationship between and
TAG : Type Auxiliary Guiding for Code Comment Generation
Existing leading code comment generation approaches with the
structure-to-sequence framework ignores the type information of the
interpretation of the code, e.g., operator, string, etc. However, introducing
the type information into the existing framework is non-trivial due to the
hierarchical dependence among the type information. In order to address the
issues above, we propose a Type Auxiliary Guiding encoder-decoder framework for
the code comment generation task which considers the source code as an N-ary
tree with type information associated with each node. Specifically, our
framework is featured with a Type-associated Encoder and a Type-restricted
Decoder which enables adaptive summarization of the source code. We further
propose a hierarchical reinforcement learning method to resolve the training
difficulties of our proposed framework. Extensive evaluations demonstrate the
state-of-the-art performance of our framework with both the auto-evaluated
metrics and case studies.Comment: ACL 2020, Accepte
Limited Information Aggregation and Externalities - A Simple Model of Metastable Market
We analyze a model in which agents’ decisions to enter or exit investments are influenced from their individual and external parties’ transaction histories. Actual investment outcomes are unknown to all participants until the end of decision periods, but outcomes do change depending on the number of participating players in the market and the market’s current state of condition. In this particular
model, agents have access to external parties’ information from those who are within their specific social network. Our study of limited information aggregation mainly focuses on market responses to investors’ decisions of exiting the investment. With social structures complicating investment outcomes, we present a model that describes how markets can enter relatively stable statuses long enough for exiting participants to return, which brings the investment back to normal conditions. Our model also supports previous studies that limited information aggregation can cause the exogenous shock effect of global collapse
Limited Information Aggregation and Externalities - A Simple Model of Metastable Market
We analyze a model in which agents’ decisions to enter or exit investments are influenced from their individual and external parties’ transaction histories. Actual investment outcomes are unknown to all participants until the end of decision periods, but outcomes do change depending on the number of participating players in the market and the market’s current state of condition. In this particular
model, agents have access to external parties’ information from those who are within their specific social network. Our study of limited information aggregation mainly focuses on market responses to investors’ decisions of exiting the investment. With social structures complicating investment outcomes, we present a model that describes how markets can enter relatively stable statuses long enough for exiting participants to return, which brings the investment back to normal conditions. Our model also supports previous studies that limited information aggregation can cause the exogenous shock effect of global collapse
Data Driven Chiller Plant Energy Optimization with Domain Knowledge
Refrigeration and chiller optimization is an important and well studied topic
in mechanical engineering, mostly taking advantage of physical models, designed
on top of over-simplified assumptions, over the equipments. Conventional
optimization techniques using physical models make decisions of online
parameter tuning, based on very limited information of hardware specifications
and external conditions, e.g., outdoor weather. In recent years, new generation
of sensors is becoming essential part of new chiller plants, for the first time
allowing the system administrators to continuously monitor the running status
of all equipments in a timely and accurate way. The explosive growth of data
flowing to databases, driven by the increasing analytical power by machine
learning and data mining, unveils new possibilities of data-driven approaches
for real-time chiller plant optimization. This paper presents our research and
industrial experience on the adoption of data models and optimizations on
chiller plant and discusses the lessons learnt from our practice on real world
plants. Instead of employing complex machine learning models, we emphasize the
incorporation of appropriate domain knowledge into data analysis tools, which
turns out to be the key performance improver over state-of-the-art deep
learning techniques by a significant margin. Our empirical evaluation on a real
world chiller plant achieves savings by more than 7% on daily power
consumption.Comment: CIKM2017. Proceedings of the 26th ACM International Conference on
Information and Knowledge Management. 201
SPI1-induced downregulation of FTO promotes GBM progression by regulating pri-miR-10a processing in an m6A-dependent manner
As one of the most common post-transcriptional modifications of mRNAs and noncoding RNAs, N6-methyladenosine (m6A) modification regulates almost every aspect of RNA metabolism. Evidence indicates that dysregulation of m6A modification and associated proteins contributes to glioblastoma (GBM) progression. However, the function of fat mass and obesity-associated protein (FTO), an m6A demethylase, has not been systematically and comprehensively explored in GBM. Here, we found that decreased FTO expression in clinical specimens correlated with higher glioma grades and poorer clinical outcomes. Functionally, FTO inhibited growth and invasion in GBM cells in vitro and in vivo. Mechanistically, FTO regulated the m6A modification of primary microRNA-10a (pri-miR-10a), which could be recognized by reader HNRNPA2B1, recruiting the microRNA microprocessor complex protein DGCR8 and mediating pri-miR-10a processing. Furthermore, the transcriptional activity of FTO was inhibited by the transcription factor SPI1, which could be specifically disrupted by the SPI1 inhibitor DB2313. Treatment with this inhibitor restored endogenous FTO expression and decreased GBM tumor burden, suggesting that FTO may serve as a novel prognostic indicator and therapeutic molecular target of GBM.publishedVersio
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