Cognitive Supervision for Robot-Assisted Minimally Invasive Laser Surgery

Biomedical Engineering, Robotics and Automation, User Interfaces and Human Computer Interaction, Minimally Invasive Surger

Mean Field Analysis of Neural Networks: A Law of Large Numbers

Machine learning, and in particular neural network models, have revolutionized fields such as image, text, and speech recognition. Today, many important real-world applications in these areas are driven by neural networks. There are also growing applications in engineering, robotics, medicine, and finance. Despite their immense success in practice, there is limited mathematical understanding of neural networks. This paper illustrates how neural networks can be studied via stochastic analysis, and develops approaches for addressing some of the technical challenges which arise. We analyze one-layer neural networks in the asymptotic regime of simultaneously (A) large network sizes and (B) large numbers of stochastic gradient descent training iterations. We rigorously prove that the empirical distribution of the neural network parameters converges to the solution of a nonlinear partial differential equation. This result can be considered a law of large numbers for neural networks. In addition, a consequence of our analysis is that the trained parameters of the neural network asymptotically become independent, a property which is commonly called "propagation of chaos"

Disposable collection kit for rapid and reliable collection of saliva.

ObjectivesTo describe and evaluate disposable saliva collection kit for rapid, reliable, and reproducible collection of saliva samples.MethodsThe saliva collection kit comprised of a saliva absorbent swab and an extractor unit was used to retrieve whole saliva samples from 10 subjects. The accuracy and precision of the extracted volumes (3, 10, and 30 μl) were compared to similar volumes drawn from control samples obtained by passive drool. Additionally, the impact of kit collection method on subsequent immunoassay results was verified by assessing salivary cortisol levels in the samples and comparing them to controls.ResultsThe recovered volumes for the whole saliva samples were 3.85 ± 0.28, 10.79 ± 0.95, and 31.18 ± 1.72 μl, respectively (CV = 8.76%) and 2.91 ± 0.19, 9.75 ± 0.43, and 29.64 ± 0.91 μl, respectively, (CV = 6.36%) for the controls. There was a close correspondence between the salivary cortisol levels from the saliva samples obtained by the collection kit and the controls (R(2)  > 0.96).ConclusionsThe disposable saliva collection kit allows accurate and repeatable collection of fixed amounts of whole saliva and does not interfere with subsequent measurements of salivary cortisol. The simple collection process, lack of elaborate specimen recovery steps, and the short turnaround time (<3 min) should render the kit attractive to test subjects and researchers alike

ALADIN-α\alpha -- An open-source MATLAB toolbox for distributed non-convex optimization

This paper introduces an open-source software for distributed and decentralized non-convex optimization named ALADIN-$\alpha$. ALADIN-$\alpha$ is a MATLAB implementation of the Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN) algorithm, which is tailored towards rapid prototyping for non-convex distributed optimization. An improved version of the recently proposed bi-level variant of ALADIN is included enabling decentralized non-convex optimization. A collection of application examples from different applications fields including chemical engineering, robotics, and power systems underpins the application potential of ALADIN-$\alpha$

ALADIN-α—An open-source MATLAB toolbox for distributed non-convex optimization

This article introduces an open-source software for distributed and decentralized non-convex optimization named ALADIN-α. ALADIN-α is a MATLAB implementation of tailored variants of the Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN) algorithm. It is user interface is convenient for rapid prototyping of non-convex distributed optimization algorithms. An improved version of the recently proposed bi-level variant of ALADIN is included enabling decentralized non-convex optimization with reduced information exchange. A collection of examples from different applications fields including chemical engineering, robotics, and power systems underpins the potential of ALADIN-α

Experiences in teaching Hydrogen Technologies in the framework of the International Campus of Excellence Andalucia TECH

The increasing importance of hydrogen technologies is demanding prepared professionals in the numerous topics related to this energy vector. Apart from some basic and advanced courses given by actual experts in these topics, universities must play an important role in the general formation of future professionals. In this way, the International Campus of Excellence (ICE) Andalucía TECH has created several degrees taught jointly by its participating universities of Seville (US) and Málaga (UMA). Among those degrees, Energy Engineering is the best suited to the field of this conference because this degree provide future engineers with specialized training in energy generation, transformation and management. In relation to hydrogen technologies this degree includes a cross sectional optional unit named Hydrogen Based Systems (4.5 ECTS/112.5 h), which is placed in the last term of the fourth year and sharing space with work placement and mobility courses. This paper will present some author’s experiences as teachers of that unit during its short history of only three years. In spite of being a joint degree, which is coordinated between both universities (US and UMA) trying to give similar contents, it is interesting to highlight the differential experiences coming from the teachers of both universities in relation to the teaching methodologies and academic results.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech