56,373 research outputs found
Similarity, Retrieval, and Classification of Motion Capture Data
Three-dimensional motion capture data is a digital representation of the complex spatio-temporal structure of human motion. Mocap data is widely used for the synthesis of realistic computer-generated characters in data-driven computer animation and also plays an important role in motion analysis tasks such as activity recognition. Both for efficiency and cost reasons, methods for the reuse of large collections of motion clips are gaining in importance in the field of computer animation. Here, an active field of research is the application of morphing and blending techniques for the creation of new, realistic motions from prerecorded motion clips. This requires the identification and extraction of logically related motions scattered within some data set. Such content-based retrieval of motion capture data, which is a central topic of this thesis, constitutes a difficult problem due to possible spatio-temporal deformations between logically related motions. Recent approaches to motion retrieval apply techniques such as dynamic time warping, which, however, are not applicable to large data sets due to their quadratic space and time complexity. In our approach, we introduce various kinds of relational features describing boolean geometric relations between specified body points and show how these features induce a temporal segmentation of motion capture data streams. By incorporating spatio-temporal invariance into the relational features and induced segments, we are able to adopt indexing methods allowing for flexible and efficient content-based retrieval in large motion capture databases. As a further application of relational motion features, a new method for fully automatic motion classification and retrieval is presented. We introduce the concept of motion templates (MTs), by which the spatio-temporal characteristics of an entire motion class can be learned from training data, yielding an explicit, compact matrix representation. The resulting class MT has a direct, semantic interpretation, and it can be manually edited, mixed, combined with other MTs, extended, and restricted. Furthermore, a class MT exhibits the characteristic as well as the variational aspects of the underlying motion class at a semantically high level. Classification is then performed by comparing a set of precomputed class MTs with unknown motion data and labeling matching portions with the respective motion class label. Here, the crucial point is that the variational (hence uncharacteristic) motion aspects encoded in the class MT are automatically masked out in the comparison, which can be thought of as locally adaptive feature selection
Linked Data for the Natural Sciences. Two Use Cases in Chemistry and Biology
Wiljes C, Cimiano P. Linked Data for the Natural Sciences. Two Use Cases in Chemistry and Biology. In: Proceedings of the Workshop on the Semantic Publishing (SePublica 2012). 2012: 48-59.The Web was designed to improve the way people work together. The Semantic Web extends the Web with a layer of Linked Data that offers new paths for scientific publishing and co-operation. Experimental raw data, released as Linked Data, could be discovered automatically, fostering its reuse and validation by scientists in different contexts and across the boundaries of disciplines. However, the technological barrier for scientists who want to publish and share their research data as Linked Data remains rather high. We present two real-life use cases in the fields of chemistry and biology and outline a general methodology for transforming research data into Linked Data. A key element of our methodology is the role of a scientific data curator, who is proficient in Linked Data technologies and works in close co-operation with the scientist
Core Lexicon and Contagious Words
We present the new empirical parameter , the most probable usage
frequency of a word in a language, computed via the distribution of documents
over frequency of the word. This parameter allows for filtering the core
lexicon of a language from the content words, which tend to be extremely
frequent in some texts written in specific genres or by certain authors.
Distributions of documents over frequencies for such words display long tails
as representing a bunch of documents in which such words are used in
abundance. Collections of such documents exhibit a percolation like phase
transition as the coarse grain of frequency (flattening out the
strongly irregular frequency data series) approaches the critical value .Comment: RevTex, 4 pages, 2 figure
Context Based Visual Content Verification
In this paper the intermediary visual content verification method based on
multi-level co-occurrences is studied. The co-occurrence statistics are in
general used to determine relational properties between objects based on
information collected from data. As such these measures are heavily subject to
relative number of occurrences and give only limited amount of accuracy when
predicting objects in real world. In order to improve the accuracy of this
method in the verification task, we include the context information such as
location, type of environment etc. In order to train our model we provide new
annotated dataset the Advanced Attribute VOC (AAVOC) that contains additional
properties of the image. We show that the usage of context greatly improve the
accuracy of verification with up to 16% improvement.Comment: 6 pages, 6 Figures, Published in Proceedings of the Information and
Digital Technology Conference, 201
Joint Modeling of Topics, Citations, and Topical Authority in Academic Corpora
Much of scientific progress stems from previously published findings, but
searching through the vast sea of scientific publications is difficult. We
often rely on metrics of scholarly authority to find the prominent authors but
these authority indices do not differentiate authority based on research
topics. We present Latent Topical-Authority Indexing (LTAI) for jointly
modeling the topics, citations, and topical authority in a corpus of academic
papers. Compared to previous models, LTAI differs in two main aspects. First,
it explicitly models the generative process of the citations, rather than
treating the citations as given. Second, it models each author's influence on
citations of a paper based on the topics of the cited papers, as well as the
citing papers. We fit LTAI to four academic corpora: CORA, Arxiv Physics, PNAS,
and Citeseer. We compare the performance of LTAI against various baselines,
starting with the latent Dirichlet allocation, to the more advanced models
including author-link topic model and dynamic author citation topic model. The
results show that LTAI achieves improved accuracy over other similar models
when predicting words, citations and authors of publications.Comment: Accepted by Transactions of the Association for Computational
Linguistics (TACL); to appea
Group field theory condensate cosmology: An appetizer
This contribution is an appetizer to the relatively young and fast evolving
approach to quantum cosmology based on group field theory condensate states. We
summarize the main assumptions and pillars of this approach which has revealed
new perspectives on the long-standing question of how to recover the continuum
from discrete geometric building blocks. Among others, we give a snapshot of
recent work on isotropic cosmological solutions exhibiting an accelerated
expansion, a bounce where anisotropies are shown to be under control and
inhomogeneities with an approximately scale-invariant power spectrum. Finally,
we point to open issues in the condensate cosmology approach.Comment: Review article as an invited contribution for the special issue
"Progress in Group Field Theory and Related Quantum Gravity Formalisms",
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