3,096 research outputs found
Citations v/s Altmetric Attention Score: A Comparison of Top 10 Highly Cited Papers in Nature
This study aims to analyze the correlation between citations and altmetric score of top 10 highly cited papers in Nature by extracting the data from Google metrics. It tries to investigate whether a highly cited paper has high altmetric score or not by using correlation method and the result show that there exists a high correlation. The study found that Mendeley is the main medium through which scientific papers are being disseminated more and contributing to the altmetric score intensely. The country wise tweeting data show that U.S and U.K holds the first and second position in tweeting with 1143 &14 tweets respectively. As the altmetric values the online attention, it prompts the entire research community to opt for social media for publication for getting good attentions and there by promoting open access. Even though, altmetrics is not at all a replacement of traditional metrics but acts as supplement to it
Flow-based Intrinsic Curiosity Module
In this paper, we focus on a prediction-based novelty estimation strategy
upon the deep reinforcement learning (DRL) framework, and present a flow-based
intrinsic curiosity module (FICM) to exploit the prediction errors from optical
flow estimation as exploration bonuses. We propose the concept of leveraging
motion features captured between consecutive observations to evaluate the
novelty of observations in an environment. FICM encourages a DRL agent to
explore observations with unfamiliar motion features, and requires only two
consecutive frames to obtain sufficient information when estimating the
novelty. We evaluate our method and compare it with a number of existing
methods on multiple benchmark environments, including Atari games, Super Mario
Bros., and ViZDoom. We demonstrate that FICM is favorable to tasks or
environments featuring moving objects, which allow FICM to utilize the motion
features between consecutive observations. We further ablatively analyze the
encoding efficiency of FICM, and discuss its applicable domains
comprehensively.Comment: The SOLE copyright holder is IJCAI (International Joint Conferences
on Artificial Intelligence), all rights reserved. The link is provided as
follows: https://www.ijcai.org/Proceedings/2020/28
Can Machines Think in Radio Language?
People can think in auditory, visual and tactile forms of language, so can
machines principally. But is it possible for them to think in radio language?
According to a first principle presented for general intelligence, i.e. the
principle of language's relativity, the answer may give an exceptional solution
for robot astronauts to talk with each other in space exploration.Comment: 4 pages, 1 figur
Interpretable Deep Learning applied to Plant Stress Phenotyping
Availability of an explainable deep learning model that can be applied to
practical real world scenarios and in turn, can consistently, rapidly and
accurately identify specific and minute traits in applicable fields of
biological sciences, is scarce. Here we consider one such real world example
viz., accurate identification, classification and quantification of biotic and
abiotic stresses in crop research and production. Up until now, this has been
predominantly done manually by visual inspection and require specialized
training. However, such techniques are hindered by subjectivity resulting from
inter- and intra-rater cognitive variability. Here, we demonstrate the ability
of a machine learning framework to identify and classify a diverse set of
foliar stresses in the soybean plant with remarkable accuracy. We also present
an explanation mechanism using gradient-weighted class activation mapping that
isolates the visual symptoms used by the model to make predictions. This
unsupervised identification of unique visual symptoms for each stress provides
a quantitative measure of stress severity, allowing for identification,
classification and quantification in one framework. The learnt model appears to
be agnostic to species and make good predictions for other (non-soybean)
species, demonstrating an ability of transfer learning
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