18,056 research outputs found
A Survey on Usage and Diffusion of Project Risk Management Techniques and Software Tools in the Construction Industry
The area of Project Risk Management (PRM) has been extensively researched, and the utilization of various tools and techniques for managing risk in several industries has been sufficiently reported. Formal and systematic PRM practices have been made available for the construction industry. Based on such body of knowledge, this paper tries to find out the global picture of PRM practices and approaches with the help of a survey to look into the usage of PRM techniques and diffusion of software tools, their level of maturity, and their usefulness in the construction sector. Results show that, despite existing techniques and tools, their usage is limited: software tools are used only by a minority of respondents and their cost is one of the largest hurdles in adoption. Finally, the paper provides some important guidelines for future research regarding quantitative risk analysis techniques and suggestions for PRM software tools development and improvemen
Tensor Analysis and Fusion of Multimodal Brain Images
Current high-throughput data acquisition technologies probe dynamical systems
with different imaging modalities, generating massive data sets at different
spatial and temporal resolutions posing challenging problems in multimodal data
fusion. A case in point is the attempt to parse out the brain structures and
networks that underpin human cognitive processes by analysis of different
neuroimaging modalities (functional MRI, EEG, NIRS etc.). We emphasize that the
multimodal, multi-scale nature of neuroimaging data is well reflected by a
multi-way (tensor) structure where the underlying processes can be summarized
by a relatively small number of components or "atoms". We introduce
Markov-Penrose diagrams - an integration of Bayesian DAG and tensor network
notation in order to analyze these models. These diagrams not only clarify
matrix and tensor EEG and fMRI time/frequency analysis and inverse problems,
but also help understand multimodal fusion via Multiway Partial Least Squares
and Coupled Matrix-Tensor Factorization. We show here, for the first time, that
Granger causal analysis of brain networks is a tensor regression problem, thus
allowing the atomic decomposition of brain networks. Analysis of EEG and fMRI
recordings shows the potential of the methods and suggests their use in other
scientific domains.Comment: 23 pages, 15 figures, submitted to Proceedings of the IEE
Use of a Bayesian belief network to predict the impacts of commercializing non-timber forest products on livelihoods
Commercialization of non-timber forest products (NTFPs) has been widely promoted as a means of sustainably developing tropical forest resources, in a way that promotes forest conservation while supporting rural livelihoods. However, in practice, NTFP commercialization has often failed to deliver the expected benefits. Progress in analyzing the causes of such failure has been hindered by the lack of a
suitable framework for the analysis of NTFP case studies, and by the lack of predictive theory. We address
these needs by developing a probabilistic model based on a livelihood framework, enabling the impact of
NTFP commercialization on livelihoods to be predicted. The framework considers five types of capital
asset needed to support livelihoods: natural, human, social, physical, and financial. Commercialization of
NTFPs is represented in the model as the conversion of one form of capital asset into another, which is
influenced by a variety of socio-economic, environmental, and political factors. Impacts on livelihoods are
determined by the availability of the five types of assets following commercialization. The model,
implemented as a Bayesian Belief Network, was tested using data from participatory research into 19 NTFP
case studies undertaken in Mexico and Bolivia. The model provides a novel tool for diagnosing the causes
of success and failure in NTFP commercialization, and can be used to explore the potential impacts of
policy options and other interventions on livelihoods. The potential value of this approach for the
development of NTFP theory is discussed
A Factor-Graph Representation of Probabilities in Quantum Mechanics
A factor-graph representation of quantum-mechanical probabilities is
proposed. Unlike standard statistical models, the proposed representation uses
auxiliary variables (state variables) that are not random variables.Comment: Proc. IEEE International Symposium on Information Theory (ISIT),
Cambridge, MA, July 1-6, 201
Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures
Probabilistic graphical models are a central tool in AI; however, they are
generally not as expressive as deep neural models, and inference is notoriously
hard and slow. In contrast, deep probabilistic models such as sum-product
networks (SPNs) capture joint distributions in a tractable fashion, but still
lack the expressive power of intractable models based on deep neural networks.
Therefore, we introduce conditional SPNs (CSPNs), conditional density
estimators for multivariate and potentially hybrid domains which allow
harnessing the expressive power of neural networks while still maintaining
tractability guarantees. One way to implement CSPNs is to use an existing SPN
structure and condition its parameters on the input, e.g., via a deep neural
network. This approach, however, might misrepresent the conditional
independence structure present in data. Consequently, we also develop a
structure-learning approach that derives both the structure and parameters of
CSPNs from data. Our experimental evidence demonstrates that CSPNs are
competitive with other probabilistic models and yield superior performance on
multilabel image classification compared to mean field and mixture density
networks. Furthermore, they can successfully be employed as building blocks for
structured probabilistic models, such as autoregressive image models.Comment: 13 pages, 6 figure
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