209 research outputs found
4-D Computational Modeling of Cardiac Outflow Tract Hemodynamics over Looping Developmental Stages in Chicken Embryos
Cardiogenesis is interdependent with blood flow within the embryonic system. Recently, a number of studies have begun to elucidate the effects of hemodynamic forces acting upon and within cells as the cardiovascular system begins to develop. Changes in flow are picked up by mechanosensors in endocardial cells exposed to wall shear stress (the tangential force exerted by blood flow) and by myocardial and mesenchymal cells exposed to cyclic strain (deformation). Mechanosensors stimulate a variety of mechanotransduction pathways which elicit functional cellular responses in order to coordinate the structural development of the heart and cardiovascular system. The looping stages of heart development are critical to normal cardiac morphogenesis and have previously been shown to be extremely sensitive to experimental perturbations in flow, with transient exposure to altered flow dynamics causing severe late stage cardiac defects in animal models. This paper seeks to expand on past research and to begin establishing a detailed baseline for normal hemodynamic conditions in the chick outflow tract during these critical looping stages. Specifically, we will use 4-D (3-D over time) optical coherence tomography to create in vivo geometries for computational fluid dynamics simulations of the cardiac cycle, enabling us to study in great detail 4-D velocity patterns and heterogeneous wall shear stress distributions on the outflow tract endocardium. This information will be useful in determining the normal variation of hemodynamic patterns as well as in mapping hemodynamics to developmental processes such as morphological changes and signaling events during and after the looping stages examined here
Semantic Modelling of Citation Contexts for Context-Aware Citation Recommendation
Contents
The four CSV files are the data used for the evaluation in:
Saier T., Färber M. (2020) Semantic Modelling of Citation Contexts for Context-Aware Citation Recommendation. In: Advances in Information Retrieval. ECIR 2020. Lecture Notes in Computer Science, vol 12035.
DOI: 10.1007/978-3-030-45439-5_15
Code: github.com/IllDepence/ecir2020
The evaluation was conducted in a citation re-prediction setting.
CSV Format
7 columns divided by \u241E
cited document ID
for *_nomarker.csv: citation marker position ambiguous
for *_withmarker.csv: citation marker position at 'MAINCIT' in citation context
adjacent cited document IDs
only given in citrec_unarxive_*.csv
divided by \u241F
order matches 'CIT' markers in citation context
citing document ID
citation context
MAG field of study IDs
divided by \u241F
predicate:argument tuples generated based on PredPatt
JSON
noun phrases
for *_nomarker.csv: divided by \u241F
for *_withmarker.csv:
divided by \u241D into
noun phrases
noun phrase directly preceding citation marker
Data Sources
citrec_unarxive_cs_withmarker.csv
data set
unarXive
Paper DOI: 10.1007/s11192-020-03382-z
Data DOI: 10.5281/zenodo.2553522
filter
citing doc from computer science
cited doc is cited at least 5 times
citrec_mag_cs_en.csv
data set
Microsoft Academic Graph (MAG)
Paper DOI: 10.1145/2740908.2742839
filter
citing doc from computer science and in English
citing doc abstract in MAG given
cited doc is cited at least 50 times
citrec_refseer.csv
data set
RefSeer
Paper URL: ojs.aaai.org/index.php/AAAI/article/view/9528
Data URL: psu.app.box.com/v/refseer
filter
for citing and cited docs title, venue, venuetype, abstract, and year not NULL
citrec_acl-arc_withmarker.csv
data set
ACL ARC
Paper URL: aclanthology.org/L08-1005
Data URL: acl-arc.comp.nus.edu.sg/
filter
cited doc has a DBLP ID
Paper Citation
@inproceedings{Saier2020ECIR,
author = {Tarek Saier and
Michael F{\"{a}}rber},
title = {{Semantic Modelling of Citation Contexts for Context-aware Citation Recommendation}},
booktitle = {Proceedings of the 42nd European Conference on Information Retrieval},
pages = {220--233},
year = {2020},
month = apr,
doi = {10.1007/978-3-030-45439-5_15},
Research Paper Recommender System with Serendipity Using Tweets vs. Diversification
21st International Conference on Asia-Pacific Digital Libraries, ICADL 2019, Kuala Lumpur, Malaysia, November 4–7, 2019. Part of the Lecture Notes in Computer Science book series (LNCS, volume 11853), also part of the Information Systems and Applications, incl. Internet/Web, and HCI book sub series (LNISA, volume 11853).So far, a lot of works have studied research paper recommender systems. However, most of them have focused only on the accuracy and ignored the serendipity, which is an important aspect for user satisfaction. The serendipity is concerned with the novelty of recommendations and to which extent recommendations positively surprise users. In this paper, we investigate a research paper recommender system focusing on serendipity. In particular, we examine (1) whether a user’s tweets lead to a generation of serendipitous recommendations and (2) whether the use of diversification on a recommendation list improves serendipity. We have conducted an online experiment with 22 subjects in the domain of computer science. The result of our experiment shows that tweets do not improve the serendipity, despite their heterogeneous nature. However, diversification delivers serendipitous research papers that cannot be generated by a traditional strategy
Surprisal Driven -NN for Robust and Interpretable Nonparametric Learning
Nonparametric learning is a fundamental concept in machine learning that aims
to capture complex patterns and relationships in data without making strong
assumptions about the underlying data distribution. Owing to simplicity and
familiarity, one of the most well-known algorithms under this paradigm is the
-nearest neighbors (-NN) algorithm. Driven by the usage of machine
learning in safety-critical applications, in this work, we shed new light on
the traditional nearest neighbors algorithm from the perspective of information
theory and propose a robust and interpretable framework for tasks such as
classification, regression, density estimation, and anomaly detection using a
single model. We can determine data point weights as well as feature
contributions by calculating the conditional entropy for adding a feature
without the need for explicit model training. This allows us to compute feature
contributions by providing detailed data point influence weights with perfect
attribution and can be used to query counterfactuals. Instead of using a
traditional distance measure which needs to be scaled and contextualized, we
use a novel formulation of (amount of information required
to explain the difference between the observed and expected result). Finally,
our work showcases the architecture's versatility by achieving state-of-the-art
results in classification and anomaly detection, while also attaining
competitive results for regression across a statistically significant number of
datasets
Domain-independent Extraction of Scientific Concepts from Research Articles
We examine the novel task of domain-independent scientific concept extraction
from abstracts of scholarly articles and present two contributions. First, we
suggest a set of generic scientific concepts that have been identified in a
systematic annotation process. This set of concepts is utilised to annotate a
corpus of scientific abstracts from 10 domains of Science, Technology and
Medicine at the phrasal level in a joint effort with domain experts. The
resulting dataset is used in a set of benchmark experiments to (a) provide
baseline performance for this task, (b) examine the transferability of concepts
between domains. Second, we present two deep learning systems as baselines. In
particular, we propose active learning to deal with different domains in our
task. The experimental results show that (1) a substantial agreement is
achievable by non-experts after consultation with domain experts, (2) the
baseline system achieves a fairly high F1 score, (3) active learning enables us
to nearly halve the amount of required training data.Comment: Accepted for publishing in 42nd European Conference on IR Research,
ECIR 202
Reproducibility of experiments in recommender systems evaluation
© IFIP International Federation for Information Processing 2018 Published by Springer International Publishing AG 2018. All Rights Reserved. Recommender systems evaluation is usually based on predictive accuracy metrics with better scores meaning recommendations of higher quality. However, the comparison of results is becoming increasingly difficult, since there are different recommendation frameworks and different settings in the design and implementation of the experiments. Furthermore, there might be minor differences on algorithm implementation among the different frameworks. In this paper, we compare well known recommendation algorithms, using the same dataset, metrics and overall settings, the results of which point to result differences across frameworks with the exact same settings. Hence, we propose the use of standards that should be followed as guidelines to ensure the replication of experiments and the reproducibility of the results
The Rural Digital Economy
Retailers in remote rural areas face competition from online retailers that can offer superior product availability and variety. This paper explores the issues stores in Scottish small island communities face due to the residents’ increased opportunities for ‘virtual mobility’, and highlights strategies for their economically sustainable operation. Eight semi-structured interviews were conducted with shop owners on seven islands in the council areas of Orkney, Argyll and Bute, and Highland. The research has found that while online retailers are frequently used by the islanders, the small shops on the islands are vital for the communities, particularly for elderly residents. Their close connection with the community enables shop owners to flexibly respond to demand, but elevated transport cost and lack of economies of scale lead to high prices. Shops stay competitive by offering additional services to the community, for example, they frequently incorporate the post office. Local produce is available in many shops, but is not distributed beyond the island community, as none of the retailers sell via the Internet. There is very little evidence of cooperation with other businesses, despite an acknowledged opportunity to create valuable economies of scale to cut transport cost. Any attempt to tackle this issue will have to focus primarily on the creation of trust amongst local businesses
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