30,419 research outputs found
An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval
Machine learning is now used in many areas of astrophysics, from detecting
exoplanets in Kepler transit signals to removing telescope systematics. Recent
work demonstrated the potential of using machine learning algorithms for
atmospheric retrieval by implementing a random forest to perform retrievals in
seconds that are consistent with the traditional, computationally-expensive
nested-sampling retrieval method. We expand upon their approach by presenting a
new machine learning model, \texttt{plan-net}, based on an ensemble of Bayesian
neural networks that yields more accurate inferences than the random forest for
the same data set of synthetic transmission spectra. We demonstrate that an
ensemble provides greater accuracy and more robust uncertainties than a single
model. In addition to being the first to use Bayesian neural networks for
atmospheric retrieval, we also introduce a new loss function for Bayesian
neural networks that learns correlations between the model outputs.
Importantly, we show that designing machine learning models to explicitly
incorporate domain-specific knowledge both improves performance and provides
additional insight by inferring the covariance of the retrieved atmospheric
parameters. We apply \texttt{plan-net} to the Hubble Space Telescope Wide Field
Camera 3 transmission spectrum for WASP-12b and retrieve an isothermal
temperature and water abundance consistent with the literature. We highlight
that our method is flexible and can be expanded to higher-resolution spectra
and a larger number of atmospheric parameters
The relationship between IR and multimedia databases
Modern extensible database systems support multimedia data through ADTs. However, because of the problems with multimedia query formulation, this support is not sufficient.\ud
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Multimedia querying requires an iterative search process involving many different representations of the objects in the database. The support that is needed is very similar to the processes in information retrieval.\ud
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Based on this observation, we develop the miRRor architecture for multimedia query processing. We design a layered framework based on information retrieval techniques, to provide a usable query interface to the multimedia database.\ud
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First, we introduce a concept layer to enable reasoning over low-level concepts in the database.\ud
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Second, we add an evidential reasoning layer as an intermediate between the user and the concept layer.\ud
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Third, we add the functionality to process the users' relevance feedback.\ud
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We then adapt the inference network model from text retrieval to an evidential reasoning model for multimedia query processing.\ud
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We conclude with an outline for implementation of miRRor on top of the Monet extensible database system
Vision systems with the human in the loop
The emerging cognitive vision paradigm deals with vision systems that apply machine learning and automatic reasoning in order to learn from what they perceive. Cognitive vision systems can rate the relevance and consistency of newly acquired knowledge, they can adapt to their environment and thus will exhibit high robustness. This contribution presents vision systems that aim at flexibility and robustness. One is tailored for content-based image retrieval, the others are cognitive vision systems that constitute prototypes of visual active memories which evaluate, gather, and integrate contextual knowledge for visual analysis. All three systems are designed to interact with human users. After we will have discussed adaptive content-based image retrieval and object and action recognition in an office environment, the issue of assessing cognitive systems will be raised. Experiences from psychologically evaluated human-machine interactions will be reported and the promising potential of psychologically-based usability experiments will be stressed
Hierarchical Attention Network for Visually-aware Food Recommendation
Food recommender systems play an important role in assisting users to
identify the desired food to eat. Deciding what food to eat is a complex and
multi-faceted process, which is influenced by many factors such as the
ingredients, appearance of the recipe, the user's personal preference on food,
and various contexts like what had been eaten in the past meals. In this work,
we formulate the food recommendation problem as predicting user preference on
recipes based on three key factors that determine a user's choice on food,
namely, 1) the user's (and other users') history; 2) the ingredients of a
recipe; and 3) the descriptive image of a recipe. To address this challenging
problem, we develop a dedicated neural network based solution Hierarchical
Attention based Food Recommendation (HAFR) which is capable of: 1) capturing
the collaborative filtering effect like what similar users tend to eat; 2)
inferring a user's preference at the ingredient level; and 3) learning user
preference from the recipe's visual images. To evaluate our proposed method, we
construct a large-scale dataset consisting of millions of ratings from
AllRecipes.com. Extensive experiments show that our method outperforms several
competing recommender solutions like Factorization Machine and Visual Bayesian
Personalized Ranking with an average improvement of 12%, offering promising
results in predicting user preference for food. Codes and dataset will be
released upon acceptance
Measuring the similarity of PML documents with RFID-based sensors
The Electronic Product Code (EPC) Network is an important part of the
Internet of Things. The Physical Mark-Up Language (PML) is to represent and
de-scribe data related to objects in EPC Network. The PML documents of each
component to exchange data in EPC Network system are XML documents based on PML
Core schema. For managing theses huge amount of PML documents of tags captured
by Radio frequency identification (RFID) readers, it is inevitable to develop
the high-performance technol-ogy, such as filtering and integrating these tag
data. So in this paper, we propose an approach for meas-uring the similarity of
PML documents based on Bayesian Network of several sensors. With respect to the
features of PML, while measuring the similarity, we firstly reduce the
redundancy data except information of EPC. On the basis of this, the Bayesian
Network model derived from the structure of the PML documents being compared is
constructed.Comment: International Journal of Ad Hoc and Ubiquitous Computin
Evaluation of a Bayesian inference network for ligand-based virtual screening
Background
Bayesian inference networks enable the computation of the probability that an event will occur. They have been used previously to rank textual documents in order of decreasing relevance to a user-defined query. Here, we modify the approach to enable a Bayesian inference network to be used for chemical similarity searching, where a database is ranked in order of decreasing probability of bioactivity.
Results
Bayesian inference networks were implemented using two different types of network and four different types of belief function. Experiments with the MDDR and WOMBAT databases show that a Bayesian inference network can be used to provide effective ligand-based screening, especially when the active molecules being sought have a high degree of structural homogeneity; in such cases, the network substantially out-performs a conventional, Tanimoto-based similarity searching system. However, the effectiveness of the network is much less when structurally heterogeneous sets of actives are being sought.
Conclusion
A Bayesian inference network provides an interesting alternative to existing tools for ligand-based virtual screening
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