1,286 research outputs found
Answering SPARQL queries modulo RDF Schema with paths
SPARQL is the standard query language for RDF graphs. In its strict
instantiation, it only offers querying according to the RDF semantics and would
thus ignore the semantics of data expressed with respect to (RDF) schemas or
(OWL) ontologies. Several extensions to SPARQL have been proposed to query RDF
data modulo RDFS, i.e., interpreting the query with RDFS semantics and/or
considering external ontologies. We introduce a general framework which allows
for expressing query answering modulo a particular semantics in an homogeneous
way. In this paper, we discuss extensions of SPARQL that use regular
expressions to navigate RDF graphs and may be used to answer queries
considering RDFS semantics. We also consider their embedding as extensions of
SPARQL. These SPARQL extensions are interpreted within the proposed framework
and their drawbacks are presented. In particular, we show that the PSPARQL
query language, a strict extension of SPARQL offering transitive closure,
allows for answering SPARQL queries modulo RDFS graphs with the same complexity
as SPARQL through a simple transformation of the queries. We also consider
languages which, in addition to paths, provide constraints. In particular, we
present and compare nSPARQL and our proposal CPSPARQL. We show that CPSPARQL is
expressive enough to answer full SPARQL queries modulo RDFS. Finally, we
compare the expressiveness and complexity of both nSPARQL and the corresponding
fragment of CPSPARQL, that we call cpSPARQL. We show that both languages have
the same complexity through cpSPARQL, being a proper extension of SPARQL graph
patterns, is more expressive than nSPARQL.Comment: RR-8394; alkhateeb2003
Generative Adversarial Estimation of Channel Covariance in Vehicular Millimeter Wave Systems
Enabling highly-mobile millimeter wave (mmWave) systems is challenging
because of the huge training overhead associated with acquiring the channel
knowledge or designing the narrow beams. Current mmWave beam training and
channel estimation techniques do not normally make use of the prior beam
training or channel estimation observations. Intuitively, though, the channel
matrices are functions of the various elements of the environment. Learning
these functions can dramatically reduce the training overhead needed to obtain
the channel knowledge. In this paper, a novel solution that exploits machine
learning tools, namely conditional generative adversarial networks (GAN), is
developed to learn these functions between the environment and the channel
covariance matrices. More specifically, the proposed machine learning model
treats the covariance matrices as 2D images and learns the mapping function
relating the uplink received pilots, which act as RF signatures of the
environment, and these images. Simulation results show that the developed
strategy efficiently predicts the covariance matrices of the large-dimensional
mmWave channels with negligible training overhead.Comment: to appear in Asilomar Conference on Signals, Systems, and Computers,
Oct. 201
Parametric studies on pattern recognition mechanisms in human vision
Imperial Users onl
Achievable Rates of Multi-User Millimeter Wave Systems with Hybrid Precoding
Millimeter wave (mmWave) systems will likely employ large antenna arrays at
both the transmitters and receivers. A natural application of antenna arrays is
simultaneous transmission to multiple users, which requires multi-user
precoding at the transmitter. Hardware constraints, however, make it difficult
to apply conventional lower frequency MIMO precoding techniques at mmWave. This
paper proposes and analyzes a low complexity hybrid analog/digital beamforming
algorithm for downlink multi-user mmWave systems. Hybrid precoding involves a
combination of analog and digital processing that is motivated by the
requirement to reduce the power consumption of the complete radio frequency and
mixed signal hardware. The proposed algorithm configures hybrid precoders at
the transmitter and analog combiners at multiple receivers with a small
training and feedback overhead. For this algorithm, we derive a lower bound on
the achievable rate for the case of single-path channels, show its asymptotic
optimality at large numbers of antennas, and make useful insights for more
general cases. Simulation results show that the proposed algorithm offers
higher sum rates compared with analog-only beamforming, and approaches the
performance of the unconstrained digital precoding solutions.Comment: to be presented in IEEE ICC 2015 - Workshop on 5G & Beyond - Enabling
Technologies and Application
Science Teaching Efficacy Beliefs of Palestinian Elementary Education Students
To assess elementary education students’ self-efficacy beliefs in science teaching, the Science Teaching Efficacy Belief Instrument developed by Enochs and Riggs was used. The instrument consisted of two scales, Personal Science Teaching Efficacy Belief Scale and the Outcome Expectancy Scale. It was administered to 90 undergraduate university students majoring in elementary education at a large public Palestinian university. Results indicated that students’ self-efficacy beliefs in science teaching were comparable to those reported in the original study by Enochs and Riggs. Overall, the participants presented moderate to high self-efficacy in science teaching
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