114 research outputs found
Content-Based Image Retrial Based on Hadoop
Generally, time complexity of algorithms for content-based image retrial is extremely high. In order to retrieve images on large-scale databases efficiently, a new way for retrieving based on Hadoop distributed framework is proposed. Firstly, a database of images features is built by using Speeded Up Robust Features algorithm and Locality-Sensitive Hashing and then perform the search on Hadoop platform in a parallel way specially designed. Considerable experimental results show that it is able to retrieve images based on content on large-scale cluster and image sets effectively
Vegetation Changes in Alberta Oil Sands, Canada, Based on Remotely Sensed Data from 1995 to 2020
There are rich oil and gas resources in Alberta oil sand mining area in Canada. Since the 1960s, the Canadian government decided to increase the mining intensity. However, the exploitation will bring many adverse effects. In recent years, more people pay attention to the environmental protection and ecological restoration of mining area, such as issues related with changes of vegetated lands. Thus, the authors used the Landsat-5 TM and Landsat-8 OLI remote sensing images as the basic data sources, and obtained the land cover classification maps from 1995 to 2020 by ENVI. Based on the NDVI, NDMI and RVI, three images in each period are processed and output to explore the long-term impact of exploitation. The results show that from 1995 to 2020, the proportion of vegetation around mining areas decreased sharply, the scale of construction land in the mining area increased, and the vegetated land was changed to land types such as tailings pond, oil sand mine and other land types. In addition, three vegetation indexes decreased from 1995 to 2020. Although the exploitation of oil sand mining area brings great economic benefits, the environmental protection (especially vegetation) in oil sand mining areas should be paid more attention
Local search for efficient causal effect estimation
Causal effect estimation from observational data is an important but
challenging problem. Causal effect estimation with unobserved variables in data
is even more difficult. The challenges lie in (1) whether the causal effect can
be estimated from observational data (identifiability); (2) accuracy of
estimation (unbiasedness), and (3) fast data-driven algorithm for the
estimation (efficiency). Each of the above problems by its own, is challenging.
There does not exist many data-driven methods for causal effect estimation so
far, and they solve one or two of the above problems, but not all. In this
paper, we present an algorithm that is fast, unbiased and is able to confirm if
a causal effect is identifiable or not under a very practical and commonly seen
problem setting. To achieve high efficiency, we approach the causal effect
estimation problem as a local search for the minimal adjustment variable sets
in data. We have shown that identifiability and unbiased estimation can be both
resolved using data in our problem setting, and we have developed theorems to
support the local search for searching for adjustment variable sets to achieve
unbiased causal effect estimation. We make use of frequent pattern mining
strategy to further speed up the search process. Experiments performed on an
extensive collection of synthetic and real-world datasets demonstrate that the
proposed algorithm outperforms the state-of-the-art causal effect estimation
methods in both accuracy and time-efficiency.Comment: 30 page
Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Survey
In many fields of scientific research and real-world applications, unbiased
estimation of causal effects from non-experimental data is crucial for
understanding the mechanism underlying the data and for decision-making on
effective responses or interventions. A great deal of research has been
conducted to address this challenging problem from different angles. For
estimating causal effect in observational data, assumptions such as Markov
condition, faithfulness and causal sufficiency are always made. Under the
assumptions, full knowledge such as, a set of covariates or an underlying
causal graph, is typically required. A practical challenge is that in many
applications, no such full knowledge or only some partial knowledge is
available. In recent years, research has emerged to use search strategies based
on graphical causal modelling to discover useful knowledge from data for causal
effect estimation, with some mild assumptions, and has shown promise in
tackling the practical challenge. In this survey, we review these data-driven
methods on causal effect estimation for a single treatment with a single
outcome of interest and focus on the challenges faced by data-driven causal
effect estimation. We concisely summarise the basic concepts and theories that
are essential for data-driven causal effect estimation using graphical causal
modelling but are scattered around the literature. We identify and discuss the
challenges faced by data-driven causal effect estimation and characterise the
existing methods by their assumptions and the approaches to tackling the
challenges. We analyse the strengths and limitations of the different types of
methods and present an empirical evaluation to support the discussions. We hope
this review will motivate more researchers to design better data-driven methods
based on graphical causal modelling for the challenging problem of causal
effect estimation.Comment: 35 pages, 10 figures and 2 table, Accepted by ACM Computing Survey
Disentangled Representation with Causal Constraints for Counterfactual Fairness
Much research has been devoted to the problem of learning fair
representations; however, they do not explicitly the relationship between
latent representations. In many real-world applications, there may be causal
relationships between latent representations. Furthermore, most fair
representation learning methods focus on group-level fairness and are based on
correlations, ignoring the causal relationships underlying the data. In this
work, we theoretically demonstrate that using the structured representations
enable downstream predictive models to achieve counterfactual fairness, and
then we propose the Counterfactual Fairness Variational AutoEncoder (CF-VAE) to
obtain structured representations with respect to domain knowledge. The
experimental results show that the proposed method achieves better fairness and
accuracy performance than the benchmark fairness methods.Comment: This paper has been accepted by PAKDD 2023. Please check:
https://doi.org/10.1007/978-3-031-33374-3_3
Instrumental Variable Estimation for Causal Inference in Longitudinal Data with Time-Dependent Latent Confounders
Causal inference from longitudinal observational data is a challenging
problem due to the difficulty in correctly identifying the time-dependent
confounders, especially in the presence of latent time-dependent confounders.
Instrumental variable (IV) is a powerful tool for addressing the latent
confounders issue, but the traditional IV technique cannot deal with latent
time-dependent confounders in longitudinal studies. In this work, we propose a
novel Time-dependent Instrumental Factor Model (TIFM) for time-varying causal
effect estimation from data with latent time-dependent confounders. At each
time-step, the proposed TIFM method employs the Recurrent Neural Network (RNN)
architecture to infer latent IV, and then uses the inferred latent IV factor
for addressing the confounding bias caused by the latent time-dependent
confounders. We provide a theoretical analysis for the proposed TIFM method
regarding causal effect estimation in longitudinal data. Extensive evaluation
with synthetic datasets demonstrates the effectiveness of TIFM in addressing
causal effect estimation over time. We further apply TIFM to a climate dataset
to showcase the potential of the proposed method in tackling real-world
problems.Comment: 13 pages, 7 figures and 3 table
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