3,648 research outputs found
MIMO-aided near-capacity turbo transceivers: taxonomy and performance versus complexity
In this treatise, we firstly review the associated Multiple-Input Multiple-Output (MIMO) system theory and review the family of hard-decision and soft-decision based detection algorithms in the context of Spatial Division Multiplexing (SDM) systems. Our discussions culminate in the introduction of a range of powerful novel MIMO detectors, such as for example Markov Chain assisted Minimum Bit-Error Rate (MC-MBER) detectors, which are capable of reliably operating in the challenging high-importance rank-deficient scenarios, where there are more transmitters than receivers and hence the resultant channel-matrix becomes non-invertible. As a result, conventional detectors would exhibit a high residual error floor. We then invoke the Soft-Input Soft-Output (SISO) MIMO detectors for creating turbo-detected two- or three-stage concatenated SDM schemes and investigate their attainable performance in the light of their computational complexity. Finally, we introduce the powerful design tools of EXtrinsic Information Transfer (EXIT)-charts and characterize the achievable performance of the diverse near- capacity SISO detectors with the aid of EXIT charts
Convergence of economic growth in Russian megacities
Purpose: The article presents the results of an empirical analysis of the economic growth of Russian cities with a population of over 1 million people (megacities). Design/Methodology/Approach: The analyzed indicator is the city product calculated according to the UN methodology for the period from 2010 to 2016. The paper analyses the process of β- and σ-convergence across Russian megacities using methods of spatial econometrics in addition to the traditional β-convergence techniques from the neoclassical theoretical framework. Findings: The dynamics of the coefficient of variation confirmed the presence of σ-convergence in city product. Empirically, positive spatial autocorrelation has been confirmed. Beta-convergence for Russian megacities is found to be significant and the spatial location of megacities significantly affects β-convergence. Control factors such as fixed capital investment per capita in 2010, average retail volume per capita in 2010, average annual number of employees of enterprises and organizations in 2010 and the dummy variable introduced for “federal cities” Moscow and St. Petersburg are all found to have positive and statistically significant impact on economic growth. Practical Implications: Policymakers may take the results into account under the planning of economical strategies for megacities and regions in Russia in order to facilitate the regional economic growth and the speed of convergence. Originality/Value: The main contribution of the study is the consideration of the economical growth for the megacities and not for the regions as it often used to be the case in similar studies. The important finding is that megacities‘ economies do converge and the influence of control factors is pronounced.peer-reviewe
Signed Distance-based Deep Memory Recommender
Personalized recommendation algorithms learn a user's preference for an item
by measuring a distance/similarity between them. However, some of the existing
recommendation models (e.g., matrix factorization) assume a linear relationship
between the user and item. This approach limits the capacity of recommender
systems, since the interactions between users and items in real-world
applications are much more complex than the linear relationship. To overcome
this limitation, in this paper, we design and propose a deep learning framework
called Signed Distance-based Deep Memory Recommender, which captures non-linear
relationships between users and items explicitly and implicitly, and work well
in both general recommendation task and shopping basket-based recommendation
task. Through an extensive empirical study on six real-world datasets in the
two recommendation tasks, our proposed approach achieved significant
improvement over ten state-of-the-art recommendation models
Cerebellar models of associative memory: Three papers from IEEE COMPCON spring 1989
Three papers are presented on the following topics: (1) a cerebellar-model associative memory as a generalized random-access memory; (2) theories of the cerebellum - two early models of associative memory; and (3) intelligent network management and functional cerebellum synthesis
Word-Entity Duet Representations for Document Ranking
This paper presents a word-entity duet framework for utilizing knowledge
bases in ad-hoc retrieval. In this work, the query and documents are modeled by
word-based representations and entity-based representations. Ranking features
are generated by the interactions between the two representations,
incorporating information from the word space, the entity space, and the
cross-space connections through the knowledge graph. To handle the
uncertainties from the automatically constructed entity representations, an
attention-based ranking model AttR-Duet is developed. With back-propagation
from ranking labels, the model learns simultaneously how to demote noisy
entities and how to rank documents with the word-entity duet. Evaluation
results on TREC Web Track ad-hoc task demonstrate that all of the four-way
interactions in the duet are useful, the attention mechanism successfully
steers the model away from noisy entities, and together they significantly
outperform both word-based and entity-based learning to rank systems
The Child is Father of the Man: Foresee the Success at the Early Stage
Understanding the dynamic mechanisms that drive the high-impact scientific
work (e.g., research papers, patents) is a long-debated research topic and has
many important implications, ranging from personal career development and
recruitment search, to the jurisdiction of research resources. Recent advances
in characterizing and modeling scientific success have made it possible to
forecast the long-term impact of scientific work, where data mining techniques,
supervised learning in particular, play an essential role. Despite much
progress, several key algorithmic challenges in relation to predicting
long-term scientific impact have largely remained open. In this paper, we
propose a joint predictive model to forecast the long-term scientific impact at
the early stage, which simultaneously addresses a number of these open
challenges, including the scholarly feature design, the non-linearity, the
domain-heterogeneity and dynamics. In particular, we formulate it as a
regularized optimization problem and propose effective and scalable algorithms
to solve it. We perform extensive empirical evaluations on large, real
scholarly data sets to validate the effectiveness and the efficiency of our
method.Comment: Correct some typos in our KDD pape
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