24,389 research outputs found
Reasoning about Cardinal Directions between Extended Objects
Direction relations between extended spatial objects are important
commonsense knowledge. Recently, Goyal and Egenhofer proposed a formal model,
known as Cardinal Direction Calculus (CDC), for representing direction
relations between connected plane regions. CDC is perhaps the most expressive
qualitative calculus for directional information, and has attracted increasing
interest from areas such as artificial intelligence, geographical information
science, and image retrieval. Given a network of CDC constraints, the
consistency problem is deciding if the network is realizable by connected
regions in the real plane. This paper provides a cubic algorithm for checking
consistency of basic CDC constraint networks, and proves that reasoning with
CDC is in general an NP-Complete problem. For a consistent network of basic CDC
constraints, our algorithm also returns a 'canonical' solution in cubic time.
This cubic algorithm is also adapted to cope with cardinal directions between
possibly disconnected regions, in which case currently the best algorithm is of
time complexity O(n^5)
Accelerating Innovation Through Analogy Mining
The availability of large idea repositories (e.g., the U.S. patent database)
could significantly accelerate innovation and discovery by providing people
with inspiration from solutions to analogous problems. However, finding useful
analogies in these large, messy, real-world repositories remains a persistent
challenge for either human or automated methods. Previous approaches include
costly hand-created databases that have high relational structure (e.g.,
predicate calculus representations) but are very sparse. Simpler
machine-learning/information-retrieval similarity metrics can scale to large,
natural-language datasets, but struggle to account for structural similarity,
which is central to analogy. In this paper we explore the viability and value
of learning simpler structural representations, specifically, "problem
schemas", which specify the purpose of a product and the mechanisms by which it
achieves that purpose. Our approach combines crowdsourcing and recurrent neural
networks to extract purpose and mechanism vector representations from product
descriptions. We demonstrate that these learned vectors allow us to find
analogies with higher precision and recall than traditional
information-retrieval methods. In an ideation experiment, analogies retrieved
by our models significantly increased people's likelihood of generating
creative ideas compared to analogies retrieved by traditional methods. Our
results suggest a promising approach to enabling computational analogy at scale
is to learn and leverage weaker structural representations.Comment: KDD 201
Phase retrieval via non-rigid image registration
Phase retrieval is the numerical procedure of recovering a complex-valued
signal from knowledge about its amplitude and some additional information.
Here, an indirect registration procedure, based on the large deformation
diffeomorphic metric mapping (LDDMM) formalism, is investigated as a phase
retrieval method for coherent diffractive imaging. The method attempts to find
a deformation which transforms an initial, template image to match an unknown
target image by comparing the diffraction pattern to the data. The exterior
calculus framework is used to treat different types of deformations in a
unified and coordinate-free way. The algorithm performance with respect to
measurement noise, image topology, and particular action are explored through
numerical examples
User Centered and Ontology Based InformationRetrieval System for Life Sciences
Because of the increasing number of electronic data, designing efficient tools to retrieve and exploit documents is a major challenge. Current search engines suffer from two main drawbacks: there is limited interaction with the list of retrieved documents and no explanation for their adequacy to the query. Users may thus be confused by the selection and have no idea how to adapt their query so that the results match their expectations. 
This talk describes a request method and an environment based on aggregating models to assess the relevance of documents annotated by concepts of ontology. The selection of documents is then displayed in a semantic map to provide graphical indications that make explicit to what extent they match the user’s query; this man/machine interface favors a more interactive exploration of data corpus.

Towards Conceptual and Logical Modelling of NoSQL Databases
NoSQL databases support the ability to handle large volumes of data in the absence of an explicit data schema. On the other hand, schema information is sometimes essential for applications during data retrieval. Consequently, there are approaches to schema construction in, e.g., the JSON DB and graph DB communities. The difference between a conceptual and database schema is often vague in this case. We use functional constructs – typed attributes for a conceptual view of DB that provide a sufficiently structured approach for expressing semantics of document and graph data. Attribute names are natural language expressions. Such typed functional data objects can be manipulated by terms of a typed λ-calculus, providing powerful nonprocedural query features for considered data structures. The calculus is extendible. Logical, arithmetic, and aggregation functions can be included there. Conceptual and database modelling merge in this case
Possibility of information encoding/decoding using the memory effect in fractional-order capacitive devices
In this study, we show that the discharge voltage pattern of a supercapacitor exhibiting fractional-order behavior from the same initial steady-state voltage into a constant resistor is dependent on the past charging voltage profile. The charging voltage was designed to follow a power-law function, i.e. [Formula: see text], in which [Formula: see text] (charging time duration between zero voltage to the terminal voltage [Formula: see text]) and p ([Formula: see text]) act as two variable parameters. We used this history-dependence of the dynamic behavior of the device to uniquely retrieve information pre-coded in the charging waveform pattern. Furthermore, we provide an analytical model based on fractional calculus that explains phenomenologically the information storage mechanism. The use of this intrinsic material memory effect may lead to new types of methods for information storage and retrieval
A Linked Data representation of the Nomenclature of Territorial Units for Statistics
The recent publication of public sector information (PSI) data sets has brought to the attention of the scientific community the redundant presence of location based context. At the same time it stresses the inadequacy of current Linked Data services for exploiting the semantics of such contextual dimensions for easing entity retrieval and browsing. In this paper describes our approach for supporting the publication of geographical subdivisions in Linked Data format for supporting the e-government and public sector in publishing their data sets. The topological knowledge published can be reused in order to enrich the geographical context of other data sets, in particular we propose an exploitation scenario using statistical data sets described with the SCOVO ontology. The topological knowledge is then exploited within a service that supports the navigation and retrieval of statistical geographical entities for the EU territory. Geographical entities, in the extent of this paper, are linked data resources that describe objects that have a geographical extension. The data and services presented in this paper allows the discovery of resources that contain or are contained by a given entity URI and their representation within map widgets. We present an approach for a geography based service that helps in querying qualitative spatial relations for the EU statistical geography (proper containment so far). We also provide a rationale for publishing geographical information in Linked Data format based on our experience, within the EnAKTing project, in publishing UK PSI data
Low rank matrix recovery from rank one measurements
We study the recovery of Hermitian low rank matrices from undersampled measurements via nuclear norm minimization. We
consider the particular scenario where the measurements are Frobenius inner
products with random rank-one matrices of the form for some
measurement vectors , i.e., the measurements are given by . The case where the matrix to be recovered
is of rank one reduces to the problem of phaseless estimation (from
measurements, via the PhaseLift approach,
which has been introduced recently. We derive bounds for the number of
measurements that guarantee successful uniform recovery of Hermitian rank
matrices, either for the vectors , , being chosen independently
at random according to a standard Gaussian distribution, or being sampled
independently from an (approximate) complex projective -design with .
In the Gaussian case, we require measurements, while in the case
of -designs we need . Our results are uniform in the
sense that one random choice of the measurement vectors guarantees
recovery of all rank -matrices simultaneously with high probability.
Moreover, we prove robustness of recovery under perturbation of the
measurements by noise. The result for approximate -designs generalizes and
improves a recent bound on phase retrieval due to Gross, Kueng and Krahmer. In
addition, it has applications in quantum state tomography. Our proofs employ
the so-called bowling scheme which is based on recent ideas by Mendelson and
Koltchinskii.Comment: 24 page
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