A novel electromyographic signal simulator for muscle contraction studies

Mathematical simulation has been widely used in biomedical and biological sciences. In the case of the surface electromyographic (SEMG) activity, some models have been proposed aiming to study muscle contraction strategies that are used during different tasks and conditions. Most of SEMG simulators are based on energy modulation of a Gaussian noise. This work proposes a novel simulator in which the user-defined parameters are associated with the motor units (MUs) recruitment and their firing rate. Comparison between the mean spectrum of real SEMG signals collected in isometric contraction of the muscle biceps brachii and the mean spectrum obtained from simulated SEMG signals showed a good agreement, pointing the proposed simulator seems to be capable to generate consistent electromyographic signals in time and frequency domains and that can be used in many studies, in particular in the evaluation of automatic methods aimed to detect muscular contraction.     

Studying properties of Lagrangian bounds for many-to-many assignment problems

Classical and modified Lagrangian bounds for the optimal value of optimization problems with a double decomposable structure are studied. For the class of many-to-many assignment problems, this property of constraints is used to design a subgradient algorithm for solving the modified dual problem. Numerical results are presented to compare the quality of classical and modified bounds, as well as the properties of the corresponding Lagrangian solutions.     

Performing Efficient NURBS Modeling Operations on the GPU

We present algorithms for evaluating and performing modeling operations on NURBS surfaces using the programmable fragment processor on the Graphics Processing Unit (GPU). We extend our GPU-based NURBS evaluator that evaluates NURBS surfaces to compute exact normals for either standard or rational B-spline surfaces for use in rendering and geometric modeling. We build on these calculations in our new GPU algorithms to perform standard modeling operations such as inverse evaluations, ray intersections, and surface-surface intersections on the GPU. Our modeling algorithms run in real time, enabling the user to sketch on the actual surface to create new features. In addition, the designer can edit the surface by interactively trimming it without the need for retessellation. Our GPU-accelerated algorithm to perform surface-surface intersection operations with NURBS surfaces can output intersection curves in the model space as well as in the parametric spaces of both the intersecting surfaces at interactive rates. We also extend our surface-surface intersection algorithm to evaluate self-intersections in NURBS surfaces.     

A Genre-Aware Approach to Focused Crawling

Focused crawlers have as their main goal to crawl Web pages that are relevant to a specific topic or user interest, playing an important role for a great variety of applications. In general, they work by trying to find and crawl all kinds of pages deemed as related to an implicitly declared topic. However, users are often not simply interested in any document about a topic, but instead they may want only documents of a given type or genre on that topic to be retrieved. In this article, we describe an approach to focused crawling that exploits not only content-related information but also genre information present in Web pages to guide the crawling process. This approach has been designed to address situations in which the specific topic of interest can be expressed by specifying two sets of terms, the first describing genre aspects of the desired pages and the second related to the subject or content of these pages, thus requiring no training or any kind of preprocessing. The effectiveness, efficiency and scalability of the proposed approach are demonstrated by a set of experiments involving the crawling of pages related to syllabi of computer science courses, job offers in the computer science field and sale offers of computer equipments. These experiments show that focused crawlers constructed according to our genre-aware approach achieve levels of F1 superior to 88%, requiring the analysis of no more than 65% of the visited pages in order to find 90% of the relevant pages. In addition, we experimentally analyze the impact of term selection on our approach and evaluate a proposed strategy for semi-automatic generation of such terms. This analysis shows that a small set of terms selected by an expert or a set of terms specified by a typical user familiar with the topic is usually enough to produce good results and that such a semi-automatic strategy is very effective in supporting the task of selecting the sets of terms required to guide a crawling process.     

Simple tests for multi-criteria optimality

In this paper we present two tests which can decide whether a given pointx ₀ ^N is locally efficient or not with respect to a given finite set of real valued continuously differentiable functions defined on ^N . Examples indicate that the tests may fail on a nowhere dense set.     

Developed profile of holographically exposed photoresist gratings

A simulation of the profile of holographically recorded structures in photoresists is performed. In addition to its simplicity this simulation can be used to take into account the effects that arise from exposure, photosensitization, development, and resolution of positive photoresists. We analyzed the effects of isotropy of wet development, nonlinearity of the photoresist response curve, background light, and standing waves produced by reflection at the film-substrate interface by using this simulation, and the results agree with the experimentally recorded profiles.     

Tracking social provenance in chains of retweets

Knowledge and Information Systems - In the era of massive sharing of information, the term social provenance is used to denote the ownership, source or origin of a piece of information which has...     

Tough Transient Ionic Junctions Printed with Ionic Microgels

Emerging soft ionotronics better match the human body mechanically and electrically compared to conventional rigid electronics. They hold great potential for human-machine interfaces, wearable and implantable devices, and soft machines. Among various ionotronic devices, ionic junctions play critical roles in rectifying currents as electrical p–n junctions. Existing ionic junctions, however, are limited in electrical and mechanical performance, and are difficult to fabricate and degrade. Herein, the design, fabrication, and characterization of tough transient ionic junctions fabricated via 3D ionic microgel printing is reported. The 3D printing method demonstrates excellent printability and allows one to fabricate ionic junctions of various configurations with high fidelity. By combining ionic microgels, degradable networks, and highly charged biopolymers, the ionic junctions feature high stretchability (stretch limit 27), high fracture energy (>1000 Jm⁻²), excellent electrical performance (current rectification ratio >100), and transient stability (degrade in 1 week). A variety of ionotronic devices, including ionic diodes, ionic bipolar junction transistors, ionic full-wave rectifiers, and ionic touchpads are further demonstrated. This study merges ionotronics, 3D printing, and degradable hydrogels, and will motivate the future development of high-performance transient ionotronics.     

Honest confidence regions and optimality in high-dimensional precision matrix estimation

We propose methodology for estimation of sparse precision matrices and statistical inference for their low-dimensional parameters in a high-dimensional setting where the number of parameters p can be much larger than the sample size. We show that the novel estimator achieves minimax rates in supremum norm and the low-dimensional components of the estimator have a Gaussian limiting distribution. These results hold uniformly over the class of precision matrices with row sparsity of small order \(\sqrt{n}/\log p\) and spectrum uniformly bounded, under a sub-Gaussian tail assumption on the margins of the true underlying distribution. Consequently, our results lead to uniformly valid confidence regions for low-dimensional parameters of the precision matrix. Thresholding the estimator leads to variable selection without imposing irrepresentability conditions. The performance of the method is demonstrated in a simulation study and on real data.