121,062 research outputs found
Towards retrieving force feedback in robotic-assisted surgery: a supervised neuro-recurrent-vision approach
Robotic-assisted minimally invasive surgeries have gained a lot of popularity over conventional procedures as they offer many benefits to both surgeons and patients. Nonetheless, they still suffer from some limitations that affect their outcome. One of them is the lack of force feedback which restricts the surgeon's sense of touch and might reduce precision during a procedure. To overcome this limitation, we propose a novel force estimation approach that combines a vision based solution with supervised learning to estimate the applied force and provide the surgeon with a suitable representation of it. The proposed solution starts with extracting the geometry of motion of the heart's surface by minimizing an energy functional to recover its 3D deformable structure. A deep network, based on a LSTM-RNN architecture, is then used to learn the relationship between the extracted visual-geometric information and the applied force, and to find accurate mapping between the two. Our proposed force estimation solution avoids the drawbacks usually associated with force sensing devices, such as biocompatibility and integration issues. We evaluate our approach on phantom and realistic tissues in which we report an average root-mean square error of 0.02 N.Peer ReviewedPostprint (author's final draft
The Smooth-Lasso and other -penalized methods
We consider a linear regression problem in a high dimensional setting where
the number of covariates can be much larger than the sample size . In
such a situation, one often assumes sparsity of the regression vector, \textit
i.e., the regression vector contains many zero components. We propose a
Lasso-type estimator (where '' stands for quadratic)
which is based on two penalty terms. The first one is the norm of the
regression coefficients used to exploit the sparsity of the regression as done
by the Lasso estimator, whereas the second is a quadratic penalty term
introduced to capture some additional information on the setting of the
problem. We detail two special cases: the Elastic-Net , which
deals with sparse problems where correlations between variables may exist; and
the Smooth-Lasso , which responds to sparse problems where
successive regression coefficients are known to vary slowly (in some
situations, this can also be interpreted in terms of correlations between
successive variables). From a theoretical point of view, we establish variable
selection consistency results and show that achieves a
Sparsity Inequality, \textit i.e., a bound in terms of the number of non-zero
components of the 'true' regression vector. These results are provided under a
weaker assumption on the Gram matrix than the one used by the Lasso. In some
situations this guarantees a significant improvement over the Lasso.
Furthermore, a simulation study is conducted and shows that the S-Lasso
performs better than known methods as the Lasso, the
Elastic-Net , and the Fused-Lasso with respect to the
estimation accuracy. This is especially the case when the regression vector is
'smooth', \textit i.e., when the variations between successive coefficients of
the unknown parameter of the regression are small. The study also reveals that
the theoretical calibration of the tuning parameters and the one based on 10
fold cross validation imply two S-Lasso solutions with close performance
Statistical and Computational Tradeoff in Genetic Algorithm-Based Estimation
When a Genetic Algorithm (GA), or a stochastic algorithm in general, is
employed in a statistical problem, the obtained result is affected by both
variability due to sampling, that refers to the fact that only a sample is
observed, and variability due to the stochastic elements of the algorithm. This
topic can be easily set in a framework of statistical and computational
tradeoff question, crucial in recent problems, for which statisticians must
carefully set statistical and computational part of the analysis, taking
account of some resource or time constraints. In the present work we analyze
estimation problems tackled by GAs, for which variability of estimates can be
decomposed in the two sources of variability, considering some constraints in
the form of cost functions, related to both data acquisition and runtime of the
algorithm. Simulation studies will be presented to discuss the statistical and
computational tradeoff question.Comment: 17 pages, 5 figure
Probabilistic Numerics and Uncertainty in Computations
We deliver a call to arms for probabilistic numerical methods: algorithms for
numerical tasks, including linear algebra, integration, optimization and
solving differential equations, that return uncertainties in their
calculations. Such uncertainties, arising from the loss of precision induced by
numerical calculation with limited time or hardware, are important for much
contemporary science and industry. Within applications such as climate science
and astrophysics, the need to make decisions on the basis of computations with
large and complex data has led to a renewed focus on the management of
numerical uncertainty. We describe how several seminal classic numerical
methods can be interpreted naturally as probabilistic inference. We then show
that the probabilistic view suggests new algorithms that can flexibly be
adapted to suit application specifics, while delivering improved empirical
performance. We provide concrete illustrations of the benefits of probabilistic
numeric algorithms on real scientific problems from astrometry and astronomical
imaging, while highlighting open problems with these new algorithms. Finally,
we describe how probabilistic numerical methods provide a coherent framework
for identifying the uncertainty in calculations performed with a combination of
numerical algorithms (e.g. both numerical optimisers and differential equation
solvers), potentially allowing the diagnosis (and control) of error sources in
computations.Comment: Author Generated Postprint. 17 pages, 4 Figures, 1 Tabl
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