245 research outputs found
Critical behavior of the Random-Field Ising model at and beyond the Upper Critical Dimension
The disorder-driven phase transition of the RFIM is observed using exact
ground-state computer simulations for hyper cubic lattices in d=5,6,7
dimensions. Finite-size scaling analyses are used to calculate the critical
point and the critical exponents of the specific heat, magnetization,
susceptibility and of the correlation length. For dimensions d=6,7 which are
larger or equal to the assumed upper critical dimension, d_u=6, mean-field
behaviour is found, i.e. alpha=0, beta=1/2, gamma=1, nu=1/2. For the analysis
of the numerical data, it appears to be necessary to include recently proposed
corrections to scaling at and beyond the upper critical dimension.Comment: 8 pages and 13 figures; A consise summary of this work can be found
in the papercore database at http://www.papercore.org/Ahrens201
Critical behavior of the Random-Field Ising Magnet with long range correlated disorder
We study the correlated-disorder driven zero-temperature phase transition of
the Random-Field Ising Magnet using exact numerical ground-state calculations
for cubic lattices. We consider correlations of the quenched disorder decaying
proportional to r^a, where r is the distance between two lattice sites and a<0.
To obtain exact ground states, we use a well established mapping to the
graph-theoretical maximum-flow problem, which allows us to study large system
sizes of more than two million spins. We use finite-size scaling analyses for
values a={-1,-2,-3,-7} to calculate the critical point and the critical
exponents characterizing the behavior of the specific heat, magnetization,
susceptibility and of the correlation length close to the critical point. We
find basically the same critical behavior as for the RFIM with delta-correlated
disorder, except for the finite-size exponent of the susceptibility and for the
case a=-1, where the results are also compatible with a phase transition at
infinitesimal disorder strength.
A summary of this work can be found at the papercore database at
www.papercore.org.Comment: 9 pages, 13 figure
OmniFill: Domain-Agnostic Form Filling Suggestions Using Multi-Faceted Context
Predictive suggestion systems offer contextually-relevant text entry
completions. Existing approaches, like autofill, often excel in
narrowly-defined domains but fail to generalize to arbitrary workflows. We
introduce a conceptual framework to analyze the compound demands of a
particular suggestion context, yielding unique opportunities for large language
models (LLMs) to infer suggestions for a wide range of domain-agnostic
form-filling tasks that were out of reach with prior approaches. We explore
these opportunities in OmniFill, a prototype that collects multi-faceted
context including browsing and text entry activity to construct an LLM prompt
that offers suggestions in situ for arbitrary structured text entry interfaces.
Through a user study with 18 participants, we found that OmniFill offered
valuable suggestions and we identified four themes that characterize users'
behavior and attitudes: an "opportunistic scrapbooking" approach; a trust
placed in the system; value in partial success; and a need for visibility into
prompt context.Comment: 14 pages, 5 figure
Strategisches Verhalten in Systemen mit Interaktions- und Kontaktwahl
Verteilte Systeme haben große gesellschaftliche und wirtschaftliche Bedeutung. Bisher war unklar, ob sich ihre Teilnehmer effizient verhalten. Theoretische Analysen und verhaltensökonomische Experimente zeigen, dass kooperatives Verhalten entstehen kann. Die gebildeten Netzwerke sind fast effizient und können durch in dieser Arbeit entwickelte Strategien in ihrer Effizienz weiter erhöht werden
From environmental exploration to clearance measurement – developing mobile robot systems for decommissioning of nuclear power plants = Von der Umgebungsexploration bis zur Freimessung – Entwicklung mobiler Robotersysteme für den Rückbau kerntechnischer Anlagen
Understanding the Predictability of Gesture Parameters from Speech and their Perceptual Importance
Gesture behavior is a natural part of human conversation. Much work has
focused on removing the need for tedious hand-animation to create embodied
conversational agents by designing speech-driven gesture generators. However,
these generators often work in a black-box manner, assuming a general
relationship between input speech and output motion. As their success remains
limited, we investigate in more detail how speech may relate to different
aspects of gesture motion. We determine a number of parameters characterizing
gesture, such as speed and gesture size, and explore their relationship to the
speech signal in a two-fold manner. First, we train multiple recurrent networks
to predict the gesture parameters from speech to understand how well gesture
attributes can be modeled from speech alone. We find that gesture parameters
can be partially predicted from speech, and some parameters, such as path
length, being predicted more accurately than others, like velocity. Second, we
design a perceptual study to assess the importance of each gesture parameter
for producing motion that people perceive as appropriate for the speech.
Results show that a degradation in any parameter was viewed negatively, but
some changes, such as hand shape, are more impactful than others. A video
summarization can be found at https://youtu.be/aw6-_5kmLjY.Comment: To be published in the Proceedings of the 20th ACM International
Conference on Intelligent Virtual Agents (IVA 20
History Assisted View Authoring for 3D Models
3D modelers often wish to showcase their models for sharing or review purposes. This may consist of generating static viewpoints of the model or authoring animated flythroughs. Manually creating such views is often tedious and few automatic methods are designed to interactively assist the modelers with the view authoring process. We present a view authoring assistance system that supports the creation of informative view points, view paths, and view surfaces, allowing modelers to author the interactive navigation experience of a model. The key concept of our implementation is to analyze the model’s workflow history, to infer important regions of the model and representative viewpoints of those areas. An evaluation indicated that the viewpoints generated by our algorithm are comparable to those manually selected by the modeler. In addition, participants of a user study found our system easy to use and effective for authoring viewpoint summaries. Author Keywords 3D model; editing history; viewpoint authorin
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