624 research outputs found
RIDI: Robust IMU Double Integration
This paper proposes a novel data-driven approach for inertial navigation,
which learns to estimate trajectories of natural human motions just from an
inertial measurement unit (IMU) in every smartphone. The key observation is
that human motions are repetitive and consist of a few major modes (e.g.,
standing, walking, or turning). Our algorithm regresses a velocity vector from
the history of linear accelerations and angular velocities, then corrects
low-frequency bias in the linear accelerations, which are integrated twice to
estimate positions. We have acquired training data with ground-truth motions
across multiple human subjects and multiple phone placements (e.g., in a bag or
a hand). The qualitatively and quantitatively evaluations have demonstrated
that our algorithm has surprisingly shown comparable results to full Visual
Inertial navigation. To our knowledge, this paper is the first to integrate
sophisticated machine learning techniques with inertial navigation, potentially
opening up a new line of research in the domain of data-driven inertial
navigation. We will publicly share our code and data to facilitate further
research
On landmark selection and sampling in high-dimensional data analysis
In recent years, the spectral analysis of appropriately defined kernel
matrices has emerged as a principled way to extract the low-dimensional
structure often prevalent in high-dimensional data. Here we provide an
introduction to spectral methods for linear and nonlinear dimension reduction,
emphasizing ways to overcome the computational limitations currently faced by
practitioners with massive datasets. In particular, a data subsampling or
landmark selection process is often employed to construct a kernel based on
partial information, followed by an approximate spectral analysis termed the
Nystrom extension. We provide a quantitative framework to analyse this
procedure, and use it to demonstrate algorithmic performance bounds on a range
of practical approaches designed to optimize the landmark selection process. We
compare the practical implications of these bounds by way of real-world
examples drawn from the field of computer vision, whereby low-dimensional
manifold structure is shown to emerge from high-dimensional video data streams.Comment: 18 pages, 6 figures, submitted for publicatio
Deep Learning for Forecasting Stock Returns in the Cross-Section
Many studies have been undertaken by using machine learning techniques,
including neural networks, to predict stock returns. Recently, a method known
as deep learning, which achieves high performance mainly in image recognition
and speech recognition, has attracted attention in the machine learning field.
This paper implements deep learning to predict one-month-ahead stock returns in
the cross-section in the Japanese stock market and investigates the performance
of the method. Our results show that deep neural networks generally outperform
shallow neural networks, and the best networks also outperform representative
machine learning models. These results indicate that deep learning shows
promise as a skillful machine learning method to predict stock returns in the
cross-section.Comment: 12 pages, 2 figures, 8 tables, accepted at PAKDD 201
Data drift correction via time-varying importance weight estimator
Real-world deployment of machine learning models is challenging when data
evolves over time. And data does evolve over time. While no model can work when
data evolves in an arbitrary fashion, if there is some pattern to these
changes, we might be able to design methods to address it. This paper addresses
situations when data evolves gradually. We introduce a novel time-varying
importance weight estimator that can detect gradual shifts in the distribution
of data. Such an importance weight estimator allows the training method to
selectively sample past data -- not just similar data from the past like a
standard importance weight estimator would but also data that evolved in a
similar fashion in the past. Our time-varying importance weight is quite
general. We demonstrate different ways of implementing it that exploit some
known structure in the evolution of data. We demonstrate and evaluate this
approach on a variety of problems ranging from supervised learning tasks
(multiple image classification datasets) where the data undergoes a sequence of
gradual shifts of our design to reinforcement learning tasks (robotic
manipulation and continuous control) where data undergoes a shift organically
as the policy or the task changes
Pre-endoscopy SARS-CoV-2 testing strategy during COVID-19 pandemic: the care must go on
Background: In response to the COVID-19 pandemic, endoscopic societies initially recommended reduction of
endoscopic procedures. In particular non-urgent endoscopies should be postponed. However, this might lead to
unnecessary delay in diagnosing gastrointestinal conditions.
Methods: Retrospectively we analysed the gastrointestinal endoscopies performed at the Central Endoscopy Unit
of Saarland University Medical Center during seven weeks from 23 March to 10 May 2020 and present our real-world
single-centre experience with an individualized rtPCR-based pre-endoscopy SARS-CoV-2 testing strategy. We also
present our experience with this strategy in 2021.
Results: Altogether 359 gastrointestinal endoscopies were performed in the initial period. The testing strategy enabled us to conservatively handle endoscopy programme reduction (44% reduction as compared 2019) during the frst
wave of the COVID-19 pandemic. The results of COVID-19 rtPCR from nasopharyngeal swabs were available in 89%
of patients prior to endoscopies. Apart from six patients with known COVID-19, all other tested patients were negative. The frequencies of endoscopic therapies and clinically signifcant fndings did not difer between patients with
or without SARS-CoV-2 tests. In 2021 we were able to unrestrictedly perform all requested endoscopic procedures
(>5000 procedures) by applying the rtPCR-based pre-endoscopy SARS-CoV-2 testing strategy, regardless of next
waves of COVID-19. Only two out-patients (1893 out-patient procedures) were tested positive in the year 2021.
Conclusion: A structured pre-endoscopy SARS-CoV-2 testing strategy is feasible in the clinical routine of an endoscopy unit. rtPCR-based pre-endoscopy SARS-CoV-2 testing safely allowed unrestricted continuation of endoscopic
procedures even in the presence of high incidence rates of COVID-19. Given the low frequency of positive tests, the
absolute efect of pre-endoscopy testing on viral transmission may be low when FFP-2 masks are regularly used
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