Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.
2021-01-19 00:00:00 +0000
Surgery
Following the success of machine vision systems for on-line automated quality
control and inspection processes, an object recognition solution is presented
in this work for two different specific applications, i.e., the detection of
quality control items in surgery toolboxes prepared for sterilizing in a
hospital, as well as the detection of defects in vessel hulls to prevent
potential structural failures. The solution has two stages. First, a feature
pyramid architecture based on Single Shot MultiBox Detector (SSD) is used to
improve the detection performance, and a statistical analysis based on ground
truth is employed to select parameters of a range of default boxes. Second, a
lightweight neural network is exploited to achieve oriented detection results
using a regression method. The first stage of the proposed method is capable of
detecting the small targets considered in the two scenarios. In the second
stage, despite the simplicity, it is efficient to detect elongated targets
while maintaining high running efficiency.
2021-01-19 00:00:00 +0000
Surgery
Following the success of machine vision systems for on-line automated quality
control and inspection processes, an object recognition solution is presented
in this work for two different specific applications, i.e., the detection of
quality control items in surgery toolboxes prepared for sterilizing in a
hospital, as well as the detection of defects in vessel hulls to prevent
potential structural failures. The solution has two stages. First, a feature
pyramid architecture based on Single Shot MultiBox Detector (SSD) is used to
improve the detection performance, and a statistical analysis based on ground
truth is employed to select parameters of a range of default boxes. Second, a
lightweight neural network is exploited to achieve oriented detection results
using a regression method. The first stage of the proposed method is capable of
detecting the small targets considered in the two scenarios. In the second
stage, despite the simplicity, it is efficient to detect elongated targets
while maintaining high running efficiency.
2021-01-19 00:00:00 +0000
General
Wind farms are a crucial driver toward the generation of ecological and
renewable energy. Due to their rapid increase in capacity, contemporary wind
farms need to adhere to strict constraints on power output to ensure stability
of the electricity grid. Specifically, a wind farm controller is required to
match the farm's power production with a power demand imposed by the grid
operator. This is a non-trivial optimization problem, as complex dependencies
exist between the wind turbines. State-of-the-art wind farm control typically
relies on physics-based heuristics that fail to capture the full load spectrum
that defines a turbine's health status. When this is not taken into account,
the long-term viability of the farm's turbines is put at risk. Given the
complex dependencies that determine a turbine's lifetime, learning a flexible
and optimal control strategy requires a data-driven approach. However, as wind
farms are large-scale multi-agent systems, optimizing control strategies over
the full joint action space is intractable. We propose a new learning method
for wind farm control that leverages the sparse wind farm structure to
factorize the optimization problem. Using a Bayesian approach, based on
multi-agent Thompson sampling, we explore the factored joint action space for
configurations that match the demand, while considering the lifetime of
turbines. We apply our method to a grid-like wind farm layout, and evaluate
configurations using a state-of-the-art wind flow simulator. Our results are
competitive with a physics-based heuristic approach in terms of demand error,
while, contrary to the heuristic, our method prolongs the lifetime of high-risk
turbines.
2021-01-19 00:00:00 +0000
General
Online peer-to-peer support platforms enable conversations between millions
of people who seek and provide mental health support. If successful, web-based
mental health conversations could improve access to treatment and reduce the
global disease burden. Psychologists have repeatedly demonstrated that empathy,
the ability to understand and feel the emotions and experiences of others, is a
key component leading to positive outcomes in supportive conversations.
However, recent studies have shown that highly empathic conversations are rare
in online mental health platforms.
In this paper, we work towards improving empathy in online mental health
support conversations. We introduce a new task of empathic rewriting which aims
to transform low-empathy conversational posts to higher empathy. Learning such
transformations is challenging and requires a deep understanding of empathy
while maintaining conversation quality through text fluency and specificity to
the conversational context. Here we propose PARTNER, a deep reinforcement
learning agent that learns to make sentence-level edits to posts in order to
increase the expressed level of empathy while maintaining conversation quality.
Our RL agent leverages a policy network, based on a transformer language model
adapted from GPT-2, which performs the dual task of generating candidate
empathic sentences and adding those sentences at appropriate positions. During
training, we reward transformations that increase empathy in posts while
maintaining text fluency, context specificity and diversity. Through a
combination of automatic and human evaluation, we demonstrate that PARTNER
successfully generates more empathic, specific, and diverse responses and
outperforms NLP methods from related tasks like style transfer and empathic
dialogue generation. Our work has direct implications for facilitating empathic
conversations on web-based platforms.
2021-01-19 00:00:00 +0000
General
Online peer-to-peer support platforms enable conversations between millions
of people who seek and provide mental health support. If successful, web-based
mental health conversations could improve access to treatment and reduce the
global disease burden. Psychologists have repeatedly demonstrated that empathy,
the ability to understand and feel the emotions and experiences of others, is a
key component leading to positive outcomes in supportive conversations.
However, recent studies have shown that highly empathic conversations are rare
in online mental health platforms.
In this paper, we work towards improving empathy in online mental health
support conversations. We introduce a new task of empathic rewriting which aims
to transform low-empathy conversational posts to higher empathy. Learning such
transformations is challenging and requires a deep understanding of empathy
while maintaining conversation quality through text fluency and specificity to
the conversational context. Here we propose PARTNER, a deep reinforcement
learning agent that learns to make sentence-level edits to posts in order to
increase the expressed level of empathy while maintaining conversation quality.
Our RL agent leverages a policy network, based on a transformer language model
adapted from GPT-2, which performs the dual task of generating candidate
empathic sentences and adding those sentences at appropriate positions. During
training, we reward transformations that increase empathy in posts while
maintaining text fluency, context specificity and diversity. Through a
combination of automatic and human evaluation, we demonstrate that PARTNER
successfully generates more empathic, specific, and diverse responses and
outperforms NLP methods from related tasks like style transfer and empathic
dialogue generation. Our work has direct implications for facilitating empathic
conversations on web-based platforms.
2021-01-19 00:00:00 +0000
General
This study analyzes the impact of the COVID-19 pandemic on the subjective
well-being as measured through Twitter data indicators for Japan and Italy. It
turns out that, overall, the subjective well-being dropped by 11.7% for Italy
and 8.3% for Japan in the first nine months of 2020 compared to the last two
months of 2019 and even more compared to the historical mean of the indexes.
Through a data science approach we try to identify the possible causes of this
drop down by considering several explanatory variables including, climate and
air quality data, number of COVID-19 cases and deaths, Facebook Covid and flu
symptoms global survey, Google Trends data and coronavirus-related searches,
Google mobility data, policy intervention measures, economic variables and
their Google Trends proxies, as well as health and stress proxy variables based
on big data. We show that a simple static regression model is not able to
capture the complexity of well-being and therefore we propose a dynamic elastic
net approach to show how different group of factors may impact the well-being
in different periods, even over a short time length, and showing further
country-specific aspects. Finally, a structural equation modeling analysis
tries to address the causal relationships among the COVID-19 factors and
subjective well-being showing that, overall, prolonged mobility
restrictions,flu and Covid-like symptoms, economic uncertainty, social
distancing and news about the pandemic have negative effects on the subjective
well-being.
Surgery
A DCNN-based Arbitrarily-Oriented Object Detector for Quality Control and Inspection Application
ArXiv Preprint
Kai Yao, Alberto Ortiz, Francisco Bonnin-Pascual
2021-01-19
Surgery
A DCNN-based Arbitrarily-Oriented Object Detector for Quality Control and Inspection Application
ArXiv Preprint
Kai Yao, Alberto Ortiz, Francisco Bonnin-Pascual
2021-01-19
General
Scalable Optimization for Wind Farm Control using Coordination Graphs
ArXiv Preprint
Timothy Verstraeten, Pieter-Jan Daems, Eugenio Bargiacchi, Diederik M. Roijers, Pieter J. K. Libin, Jan Helsen
2021-01-19
General
Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach
ArXiv Preprint
Ashish Sharma, Inna W. Lin, Adam S. Miner, David C. Atkins, Tim Althoff
2021-01-19
General
Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach
ArXiv Preprint
Ashish Sharma, Inna W. Lin, Adam S. Miner, David C. Atkins, Tim Althoff
2021-01-19
General
Twitter Subjective Well-Being Indicator During COVID-19 Pandemic: A Cross-Country Comparative Study
ArXiv Preprint
Tiziana Carpi, Airo Hino, Stefano Maria Iacus, Giuseppe Porro
2021-01-19
Weekly Summary
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