10,245 research outputs found
Deep Learning for Classifying and Characterizing Atmospheric Ducting within the Maritime Setting
Real-time characterization of refractivity within the marine atmospheric
boundary layer can provide valuable information that can potentially be used to
mitigate the effects of atmospheric ducting on radar performance. Many duct
characterization models are successful at predicting parameters from a specific
refractivity profile associated with a given type of duct; however, the ability
to classify, and then subsequently characterize, various duct types is an
important step towards a more comprehensive prediction model. We introduce a
two-step approach using deep learning to differentiate sparsely sampled
propagation factor measurements collected under evaporation ducting conditions
with those collected under surface-based duct conditions in order to
subsequently estimate the appropriate refractivity parameters based on that
differentiation. We show that this approach is not only accurate, but also
efficient; thus providing a suitable method for real-time applications.Comment: 13 pages, 3 figure
Data-driven Predictive Energy Optimization in a Wastewater Pumping Station
Urban wastewater sector is being pushed to optimize processes in order to
reduce energy consumption without compromising its quality standards. Energy
costs can represent a significant share of the global operational costs
(between 50% and 60%) in an intensive energy consumer. Pumping is the largest
consumer of electrical energy in a wastewater treatment plant. Thus, the
optimal control of pump units can help the utilities to decrease operational
costs. This work describes an innovative predictive control policy for
wastewater variable-frequency pumps that minimize electrical energy
consumption, considering uncertainty forecasts for wastewater intake rate and
information collected by sensors accessible through the Supervisory Control and
Data Acquisition system. The proposed control method combines statistical
learning (regression and predictive models) and deep reinforcement learning
(Proximal Policy Optimization). The following main original contributions are
produced: i) model-free and data-driven predictive control; ii) control
philosophy focused on operating the tank with a variable wastewater set-point
level; iii) use of supervised learning to generate synthetic data for
pre-training the reinforcement learning policy, without the need to physically
interact with the system. The results for a real case-study during 90 days show
a 16.7% decrease in electrical energy consumption while still achieving a 97%
reduction in the number of alarms (tank level above 7.2 meters) when compared
with the current operating scenario (operating with a fixed set-point level).
The numerical analysis showed that the proposed data-driven method is able to
explore the trade-off between number of alarms and consumption minimization,
offering different options to decision-makers
Machine Learning Methods Economists Should Know About
We discuss the relevance of the recent Machine Learning (ML) literature for
economics and econometrics. First we discuss the differences in goals, methods
and settings between the ML literature and the traditional econometrics and
statistics literatures. Then we discuss some specific methods from the machine
learning literature that we view as important for empirical researchers in
economics. These include supervised learning methods for regression and
classification, unsupervised learning methods, as well as matrix completion
methods. Finally, we highlight newly developed methods at the intersection of
ML and econometrics, methods that typically perform better than either
off-the-shelf ML or more traditional econometric methods when applied to
particular classes of problems, problems that include causal inference for
average treatment effects, optimal policy estimation, and estimation of the
counterfactual effect of price changes in consumer choice models
Closed-Form Training of Conditional Random Fields for Large Scale Image Segmentation
We present LS-CRF, a new method for very efficient large-scale training of
Conditional Random Fields (CRFs). It is inspired by existing closed-form
expressions for the maximum likelihood parameters of a generative graphical
model with tree topology. LS-CRF training requires only solving a set of
independent regression problems, for which closed-form expression as well as
efficient iterative solvers are available. This makes it orders of magnitude
faster than conventional maximum likelihood learning for CRFs that require
repeated runs of probabilistic inference. At the same time, the models learned
by our method still allow for joint inference at test time. We apply LS-CRF to
the task of semantic image segmentation, showing that it is highly efficient,
even for loopy models where probabilistic inference is problematic. It allows
the training of image segmentation models from significantly larger training
sets than had been used previously. We demonstrate this on two new datasets
that form a second contribution of this paper. They consist of over 180,000
images with figure-ground segmentation annotations. Our large-scale experiments
show that the possibilities of CRF-based image segmentation are far from
exhausted, indicating, for example, that semi-supervised learning and the use
of non-linear predictors are promising directions for achieving higher
segmentation accuracy in the future
Persistent-Homology-based Machine Learning and its Applications -- A Survey
A suitable feature representation that can both preserve the data intrinsic
information and reduce data complexity and dimensionality is key to the
performance of machine learning models. Deeply rooted in algebraic topology,
persistent homology (PH) provides a delicate balance between data
simplification and intrinsic structure characterization, and has been applied
to various areas successfully. However, the combination of PH and machine
learning has been hindered greatly by three challenges, namely topological
representation of data, PH-based distance measurements or metrics, and PH-based
feature representation. With the development of topological data analysis,
progresses have been made on all these three problems, but widely scattered in
different literatures. In this paper, we provide a systematical review of PH
and PH-based supervised and unsupervised models from a computational
perspective. Our emphasizes are the recent development of mathematical models
and tools, including PH softwares and PH-based functions, feature
representations, kernels, and similarity models. Essentially, this paper can
work as a roadmap for the practical application of PH-based machine learning
tools. Further, we consider different topological feature representations in
different machine learning models, and investigate their impacts on the protein
secondary structure classification.Comment: 42 pages; 6 figures; 9 table
Machine learning in acoustics: theory and applications
Acoustic data provide scientific and engineering insights in fields ranging
from biology and communications to ocean and Earth science. We survey the
recent advances and transformative potential of machine learning (ML),
including deep learning, in the field of acoustics. ML is a broad family of
techniques, which are often based in statistics, for automatically detecting
and utilizing patterns in data. Relative to conventional acoustics and signal
processing, ML is data-driven. Given sufficient training data, ML can discover
complex relationships between features and desired labels or actions, or
between features themselves. With large volumes of training data, ML can
discover models describing complex acoustic phenomena such as human speech and
reverberation. ML in acoustics is rapidly developing with compelling results
and significant future promise. We first introduce ML, then highlight ML
developments in four acoustics research areas: source localization in speech
processing, source localization in ocean acoustics, bioacoustics, and
environmental sounds in everyday scenes.Comment: Published with free access in Journal of the Acoustical Society of
America, 27 Nov. 201
Machine Learning Analysis of Heterogeneity in the Effect of Student Mindset Interventions
We study heterogeneity in the effect of a mindset intervention on
student-level performance through an observational dataset from the National
Study of Learning Mindsets (NSLM). Our analysis uses machine learning (ML) to
address the following associated problems: assessing treatment group overlap
and covariate balance, imputing conditional average treatment effects, and
interpreting imputed effects. By comparing several different model families we
illustrate the flexibility of both off-the-shelf and purpose-built estimators.
We find that the mindset intervention has a positive average effect of 0.26,
95%-CI [0.22, 0.30], and that heterogeneity in the range of [0.1, 0.4] is
moderated by school-level achievement level, poverty concentration, urbanicity,
and student prior expectations
A Scalable Machine Learning Approach for Inferring Probabilistic US-LI-RADS Categorization
We propose a scalable computerized approach for large-scale inference of
Liver Imaging Reporting and Data System (LI-RADS) final assessment categories
in narrative ultrasound (US) reports. Although our model was trained on reports
created using a LI-RADS template, it was also able to infer LI-RADS scoring for
unstructured reports that were created before the LI-RADS guidelines were
established. No human-labelled data was required in any step of this study; for
training, LI-RADS scores were automatically extracted from those reports that
contained structured LI-RADS scores, and it translated the derived knowledge to
reasoning on unstructured radiology reports. By providing automated LI-RADS
categorization, our approach may enable standardizing screening recommendations
and treatment planning of patients at risk for hepatocellular carcinoma, and it
may facilitate AI-based healthcare research with US images by offering large
scale text mining and data gathering opportunities from standard hospital
clinical data repositories.Comment: AMIA Annual Symposium 2018 (accepted
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Machine learning for cognitive networks : technology assessment and research challenges
The field of machine learning has made major strides over the last 20 years. This document summarizes the major problem formulations that the discipline has studied, then reviews three tasks in cognitive networking and briefly discusses how aspects of those tasks fit these formulations. After this, it discusses challenges for machine learning research raised by Knowledge Plane applications and closes with proposals for the evaluation of learning systems developed for these problems
A Primal-Dual Method for Training Recurrent Neural Networks Constrained by the Echo-State Property
We present an architecture of a recurrent neural network (RNN) with a
fully-connected deep neural network (DNN) as its feature extractor. The RNN is
equipped with both causal temporal prediction and non-causal look-ahead, via
auto-regression (AR) and moving-average (MA), respectively. The focus of this
paper is a primal-dual training method that formulates the learning of the RNN
as a formal optimization problem with an inequality constraint that provides a
sufficient condition for the stability of the network dynamics. Experimental
results demonstrate the effectiveness of this new method, which achieves 18.86%
phone recognition error on the TIMIT benchmark for the core test set. The
result approaches the best result of 17.7%, which was obtained by using RNN
with long short-term memory (LSTM). The results also show that the proposed
primal-dual training method produces lower recognition errors than the popular
RNN methods developed earlier based on the carefully tuned threshold parameter
that heuristically prevents the gradient from exploding
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