2,040 research outputs found
CommAID: Visual Analytics for Communication Analysis through Interactive Dynamics Modeling
Communication consists of both meta-information as well as content.
Currently, the automated analysis of such data often focuses either on the
network aspects via social network analysis or on the content, utilizing
methods from text-mining. However, the first category of approaches does not
leverage the rich content information, while the latter ignores the
conversation environment and the temporal evolution, as evident in the
meta-information. In contradiction to communication research, which stresses
the importance of a holistic approach, both aspects are rarely applied
simultaneously, and consequently, their combination has not yet received enough
attention in automated analysis systems. In this work, we aim to address this
challenge by discussing the difficulties and design decisions of such a path as
well as contribute CommAID, a blueprint for a holistic strategy to
communication analysis. It features an integrated visual analytics design to
analyze communication networks through dynamics modeling, semantic pattern
retrieval, and a user-adaptable and problem-specific machine learning-based
retrieval system. An interactive multi-level matrix-based visualization
facilitates a focused analysis of both network and content using inline visuals
supporting cross-checks and reducing context switches. We evaluate our approach
in both a case study and through formative evaluation with eight law
enforcement experts using a real-world communication corpus. Results show that
our solution surpasses existing techniques in terms of integration level and
applicability. With this contribution, we aim to pave the path for a more
holistic approach to communication analysis.Comment: 12 pages, 7 figures, Computer Graphics Forum 2021 (pre-peer reviewed
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Visual Analytics for Temporal Hypergraph Model Exploration
Many processes, from gene interaction in biology to computer networks to
social media, can be modeled more precisely as temporal hypergraphs than by
regular graphs. This is because hypergraphs generalize graphs by extending
edges to connect any number of vertices, allowing complex relationships to be
described more accurately and predict their behavior over time. However, the
interactive exploration and seamless refinement of such hypergraph-based
prediction models still pose a major challenge. We contribute Hyper-Matrix, a
novel visual analytics technique that addresses this challenge through a tight
coupling between machine-learning and interactive visualizations. In
particular, the technique incorporates a geometric deep learning model as a
blueprint for problem-specific models while integrating visualizations for
graph-based and category-based data with a novel combination of interactions
for an effective user-driven exploration of hypergraph models. To eliminate
demanding context switches and ensure scalability, our matrix-based
visualization provides drill-down capabilities across multiple levels of
semantic zoom, from an overview of model predictions down to the content. We
facilitate a focused analysis of relevant connections and groups based on
interactive user-steering for filtering and search tasks, a dynamically
modifiable partition hierarchy, various matrix reordering techniques, and
interactive model feedback. We evaluate our technique in a case study and
through formative evaluation with law enforcement experts using real-world
internet forum communication data. The results show that our approach surpasses
existing solutions in terms of scalability and applicability, enables the
incorporation of domain knowledge, and allows for fast search-space traversal.
With the technique, we pave the way for the visual analytics of temporal
hypergraphs in a wide variety of domains.Comment: 11 pages, 6 figures, IEEE VIS VAST 2020 - Proceedings of IEEE
Conference on Visual Analytics Science and Technology (VAST), 202
Communication Analysis through Visual Analytics: Current Practices, Challenges, and New Frontiers
The automated analysis of digital human communication data often focuses on
specific aspects such as content or network structure in isolation. This can
provide limited perspectives while making cross-methodological analyses,
occurring in domains like investigative journalism, difficult. Communication
research in psychology and the digital humanities instead stresses the
importance of a holistic approach to overcome these limiting factors. In this
work, we conduct an extensive survey on the properties of over forty
semi-automated communication analysis systems and investigate how they cover
concepts described in theoretical communication research. From these
investigations, we derive a design space and contribute a conceptual framework
based on communication research, technical considerations, and the surveyed
approaches. The framework describes the systems' properties, capabilities, and
composition through a wide range of criteria organized in the dimensions (1)
Data, (2) Processing and Models, (3) Visual Interface, and (4) Knowledge
Generation. These criteria enable a formalization of digital communication
analysis through visual analytics, which, we argue, is uniquely suited for this
task by tackling automation complexity while leveraging domain knowledge. With
our framework, we identify shortcomings and research challenges, such as group
communication dynamics, trust and privacy considerations, and holistic
approaches. Simultaneously, our framework supports the evaluation of systems
and promotes the mutual exchange between researchers through a structured
common language, laying the foundations for future research on communication
analysis.Comment: 11 pages, 2 tables, 1 figur
Ultrafast Photo-Induced Charge Transfer Unveiled by Two-Dimensional Electronic Spectroscopy
The interaction of exciton and charge transfer (CT) states plays a central
role in photo-induced CT processes in chemistry, biology and physics. In this
work, we use a combination of two-dimensional electronic spectroscopy (2D-ES),
pump-probe measurements and quantum chemistry to investigate the ultrafast CT
dynamics in a lutetium bisphthalocyanine dimer in different oxidation states.
It is found that in the anionic form, the combination of strong CT-exciton
interaction and electronic asymmetry induced by a counter-ion enables CT
between the two macrocycles of the complex on a 30 fs timescale. Following
optical excitation, a chain of electron and hole transfer steps gives rise to
characteristic cross-peak dynamics in the electronic 2D spectra, and we monitor
how the excited state charge density ultimately localizes on the macrocycle
closest to the counter-ion within 100 fs. A comparison with the dynamics in the
radical species further elucidates how CT states modulate the electronic
structure and tune fs-reaction dynamics. Our experiments demonstrate the unique
capability of 2D-ES in combination with other methods to decipher ultrafast CT
dynamics.Comment: 14 pages, 11 figures, and Supporting informatio
Recognizing Members of the Tournament Equilibrium Set is NP-hard
A recurring theme in the mathematical social sciences is how to select the
"most desirable" elements given a binary dominance relation on a set of
alternatives. Schwartz's tournament equilibrium set (TEQ) ranks among the most
intriguing, but also among the most enigmatic, tournament solutions that have
been proposed so far in this context. Due to its unwieldy recursive definition,
little is known about TEQ. In particular, its monotonicity remains an open
problem up to date. Yet, if TEQ were to satisfy monotonicity, it would be a
very attractive tournament solution concept refining both the Banks set and
Dutta's minimal covering set. We show that the problem of deciding whether a
given alternative is contained in TEQ is NP-hard.Comment: 9 pages, 3 figure
PEDIA: prioritization of exome data by image analysis.
PURPOSE: Phenotype information is crucial for the interpretation of genomic variants. So far it has only been accessible for bioinformatics workflows after encoding into clinical terms by expert dysmorphologists.
METHODS: Here, we introduce an approach driven by artificial intelligence that uses portrait photographs for the interpretation of clinical exome data. We measured the value added by computer-assisted image analysis to the diagnostic yield on a cohort consisting of 679 individuals with 105 different monogenic disorders. For each case in the cohort we compiled frontal photos, clinical features, and the disease-causing variants, and simulated multiple exomes of different ethnic backgrounds.
RESULTS: The additional use of similarity scores from computer-assisted analysis of frontal photos improved the top 1 accuracy rate by more than 20-89% and the top 10 accuracy rate by more than 5-99% for the disease-causing gene.
CONCLUSION: Image analysis by deep-learning algorithms can be used to quantify the phenotypic similarity (PP4 criterion of the American College of Medical Genetics and Genomics guidelines) and to advance the performance of bioinformatics pipelines for exome analysis
Measurement of the high-energy all-flavor neutrino-nucleon cross section with IceCube
The flux of high-energy neutrinos passing through the Earth is attenuated due to their interactions with matter. The interaction rate is determined by the neutrino interaction cross section and affects the flux arriving at the IceCube Neutrino Observatory, a cubic-kilometer neutrino detector embedded in the Antarctic ice sheet. We present a measurement of the neutrino cross section between 60 TeV and 10 PeV using the high-energy starting event (HESE) sample from IceCube with 7.5 years of data. The result is binned in neutrino energy and obtained using both Bayesian and frequentist statistics. We find it compatible with predictions from the Standard Model. While the cross section is expected to be flavor independent above 1 TeV, additional constraints on the measurement are included through updated experimental particle identification (PID) classifiers, proxies for the three neutrino flavors. This is the first such measurement to use a ternary PID observable and the first to account for neutrinos from tau decay
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