141 research outputs found
Convergence Rates for Localized Actor-Critic in Networked Markov Potential Games
We introduce a class of networked Markov potential games in which agents are
associated with nodes in a network. Each agent has its own local potential
function, and the reward of each agent depends only on the states and actions
of the agents within a neighborhood. In this context, we propose a localized
actor-critic algorithm. The algorithm is scalable since each agent uses only
local information and does not need access to the global state. Further, the
algorithm overcomes the curse of dimensionality through the use of function
approximation. Our main results provide finite-sample guarantees up to a
localization error and a function approximation error. Specifically, we achieve
an sample complexity measured by
the averaged Nash regret. This is the first finite-sample bound for multi-agent
competitive games that does not depend on the number of agents
TextScanner: Reading Characters in Order for Robust Scene Text Recognition
Driven by deep learning and the large volume of data, scene text recognition
has evolved rapidly in recent years. Formerly, RNN-attention based methods have
dominated this field, but suffer from the problem of \textit{attention drift}
in certain situations. Lately, semantic segmentation based algorithms have
proven effective at recognizing text of different forms (horizontal, oriented
and curved). However, these methods may produce spurious characters or miss
genuine characters, as they rely heavily on a thresholding procedure operated
on segmentation maps. To tackle these challenges, we propose in this paper an
alternative approach, called TextScanner, for scene text recognition.
TextScanner bears three characteristics: (1) Basically, it belongs to the
semantic segmentation family, as it generates pixel-wise, multi-channel
segmentation maps for character class, position and order; (2) Meanwhile, akin
to RNN-attention based methods, it also adopts RNN for context modeling; (3)
Moreover, it performs paralleled prediction for character position and class,
and ensures that characters are transcripted in correct order. The experiments
on standard benchmark datasets demonstrate that TextScanner outperforms the
state-of-the-art methods. Moreover, TextScanner shows its superiority in
recognizing more difficult text such Chinese transcripts and aligning with
target characters.Comment: Accepted by AAAI-202
Real-time Scene Text Detection with Differentiable Binarization
Recently, segmentation-based methods are quite popular in scene text
detection, as the segmentation results can more accurately describe scene text
of various shapes such as curve text. However, the post-processing of
binarization is essential for segmentation-based detection, which converts
probability maps produced by a segmentation method into bounding boxes/regions
of text. In this paper, we propose a module named Differentiable Binarization
(DB), which can perform the binarization process in a segmentation network.
Optimized along with a DB module, a segmentation network can adaptively set the
thresholds for binarization, which not only simplifies the post-processing but
also enhances the performance of text detection. Based on a simple segmentation
network, we validate the performance improvements of DB on five benchmark
datasets, which consistently achieves state-of-the-art results, in terms of
both detection accuracy and speed. In particular, with a light-weight backbone,
the performance improvements by DB are significant so that we can look for an
ideal tradeoff between detection accuracy and efficiency. Specifically, with a
backbone of ResNet-18, our detector achieves an F-measure of 82.8, running at
62 FPS, on the MSRA-TD500 dataset. Code is available at:
https://github.com/MhLiao/DBComment: Accepted to AAAI 202
Natural products in drug discovery and development: Synthesis and medicinal perspective of leonurine
Natural products, those molecules derived from nature, have been used by humans for thousands of years to treat ailments and diseases. More recently, these compounds have inspired chemists to use natural products as structural templates in the development of new drug molecules. One such compound is leonurine, a molecule isolated and characterized in the tissues of Herb leonuri. This molecule has received attention from scientists in recent years due to its potent anti-oxidant, anti-apoptotic, and anti-inflammatory properties. More recently researchers have shown leonurine to be useful in the treatment of cardiovascular and nervous system diseases. Like other natural products such as paclitaxel and artemisinin, the historical development of leonurine as a therapeutic is very interesting. Therefore, this review provided an overview of natural product discovery, through to the development of a potential new drug. Content will summarize known plant sources, the pathway used in the synthesis of leonurine, and descriptions of leonurine’s pharmacological properties in mammalian systems
Simultaneous Monitoring of Multiple People's Vital Sign Leveraging a Single Phased-MIMO Radar
Vital sign monitoring plays a critical role in tracking the physiological
state of people and enabling various health-related applications (e.g.,
recommending a change of lifestyle, examining the risk of diseases).
Traditional approaches rely on hospitalization or body-attached instruments,
which are costly and intrusive. Therefore, researchers have been exploring
contact-less vital sign monitoring with radio frequency signals in recent
years. Early studies with continuous wave radars/WiFi devices work on detecting
vital signs of a single individual, but it still remains challenging to
simultaneously monitor vital signs of multiple subjects, especially those who
locate in proximity. In this paper, we design and implement a time-division
multiplexing (TDM) phased-MIMO radar sensing scheme for high-precision vital
sign monitoring of multiple people. Our phased-MIMO radar can steer the mmWave
beam towards different directions with a micro-second delay, which enables
capturing the vital signs of multiple individuals at the same radial distance
to the radar. Furthermore, we develop a TDM-MIMO technique to fully utilize all
transmitting antenna (TX)-receiving antenna (RX) pairs, thereby significantly
boosting the signal-to-noise ratio. Based on the designed TDM phased-MIMO
radar, we develop a system to automatically localize multiple human subjects
and estimate their vital signs. Extensive evaluations show that under
two-subject scenarios, our system can achieve an error of less than 1 beat per
minute (BPM) and 3 BPM for breathing rate (BR) and heartbeat rate (HR)
estimations, respectively, at a subject-to-radar distance of . The
minimal subject-to-subject angle separation is , corresponding to a
close distance of between two subjects, which outperforms the
state-of-the-art
The impact of electronic health records (EHR) data continuity on prediction model fairness and racial-ethnic disparities
Electronic health records (EHR) data have considerable variability in data
completeness across sites and patients. Lack of "EHR data-continuity" or "EHR
data-discontinuity", defined as "having medical information recorded outside
the reach of an EHR system" can lead to a substantial amount of information
bias. The objective of this study was to comprehensively evaluate (1) how EHR
data-discontinuity introduces data bias, (2) case finding algorithms affect
downstream prediction models, and (3) how algorithmic fairness is associated
with racial-ethnic disparities. We leveraged our EHRs linked with Medicaid and
Medicare claims data in the OneFlorida+ network and used a validated measure
(i.e., Mean Proportions of Encounters Captured [MPEC]) to estimate patients'
EHR data continuity. We developed a machine learning model for predicting type
2 diabetes (T2D) diagnosis as the use case for this work. We found that using
cohorts selected by different levels of EHR data-continuity affects utilities
in disease prediction tasks. The prediction models trained on high continuity
data will have a worse fit on low continuity data. We also found variations in
racial and ethnic disparities in model performances and model fairness in
models developed using different degrees of data continuity. Our results
suggest that careful evaluation of data continuity is critical to improving the
validity of real-world evidence generated by EHR data and health equity
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