Efficient tool segmentation for endoscopic videos in the wild

In recent years, deep learning methods have become the most effective approach for tool segmentation in endoscopic images, achieving the state of the art on the available public benchmarks. However, these methods present some challenges that hinder their direct deployment in real world scenarios. This work explores how to solve two of the most common challenges: real-time and memory restrictions and false positives in frames with no tools. To cope with the first case, we show how to adapt an efficient general purpose semantic segmentation model. Then, we study how to cope with the common issue of only training on images with at least one tool. Then, when images of endoscopic procedures without tools are processed, there are a lot of false positives. To solve this, we propose to add an extra classification head that performs binary frame classification, to identify frames with no tools present. Finally, we present a thorough comparison of this approach with current state of the art on different benchmarks, including real medical practice recordings, demonstrating similar accuracy with much lower computational requirements

Building an enhanced vocabulary of the robot environment with a ceiling pointing camera

Mobile robots are of great help for automatic monitoring tasks in different environments. One of the first tasks that needs to be addressed when creating these kinds of robotic systems is modeling the robot environment. This work proposes a pipeline to build an enhanced visual model of a robot environment indoors. Vision based recognition approaches frequently use quantized feature spaces, commonly known as Bag of Words (BoW) or vocabulary representations. A drawback using standard BoW approaches is that semantic information is not considered as a criteria to create the visual words. To solve this challenging task, this paper studies how to leverage the standard vocabulary construction process to obtain a more meaningful visual vocabulary of the robot work environment using image sequences. We take advantage of spatio-temporal constraints and prior knowledge about the position of the camera. The key contribution of our work is the definition of a new pipeline to create a model of the environment. This pipeline incorporates (1) tracking information to the process of vocabulary construction and (2) geometric cues to the appearance descriptors. Motivated by long term robotic applications, such as the aforementioned monitoring tasks, we focus on a configuration where the robot camera points to the ceiling, which captures more stable regions of the environment. The experimental validation shows how our vocabulary models the environment in more detail than standard vocabulary approaches, without loss of recognition performance. We show different robotic tasks that could benefit of the use of our visual vocabulary approach, such as place recognition or object discovery. For this validation, we use our publicly available data-set

Fine grained pointing recognition for natural drone guidance

Human action recognition systems are typically focused on identifying different actions, rather than fine grained variations of the same action. This work explores strategies to identify different pointing directions in order to build a natural interaction system to guide autonomous systems such as drones. Commanding a drone with hand-held panels or tablets is common practice but intuitive user-drone interfaces might have significant benefits. The system proposed in this work just requires the user to provide occasional high-level navigation commands by pointing the drone towards the desired motion direction. Due to the lack of data on these settings, we present a new benchmarking video dataset to validate our framework and facilitate future research on the area. Our results show good accuracy for pointing direction recognition, while running at interactive rates and exhibiting robustness to variability in user appearance, viewpoint, camera distance and scenery

One-shot action recognition in challenging therapy scenarios

One-shot action recognition aims to recognize new action categories from a single reference example, typically referred to as the anchor example. This work presents a novel approach for one-shot action recognition in the wild that computes motion representations robust to variable kinematic conditions. One-shot action recognition is then performed by evaluating anchor and target motion representations. We also develop a set of complementary steps that boost the action recognition performance in the most challenging scenarios. Our approach is evaluated on the public NTU-120 one-shot action recognition benchmark, outperforming previous action recognition models. Besides, we evaluate our framework on a real use-case of therapy with autistic people. These recordings are particularly challenging due to high-level artifacts from the patient motion. Our results provide not only quantitative but also online qualitative measures, essential for the patient evaluation and monitoring during the actual therapy. © 2021 IEEE

Repeatable semantic reef-mapping through photogrammetry and label-augmentation

In an endeavor to study natural systems at multiple spatial and taxonomic resolutions, there is an urgent need for automated, high-throughput frameworks that can handle plethora of information. The coalescence of remote-sensing, computer-vision, and deep-learning elicits a new era in ecological research. However, in complex systems, such as marine-benthic habitats, key ecological processes still remain enigmatic due to the lack of cross-scale automated approaches (mms to kms) for community structure analysis. We address this gap by working towards scalable and comprehensive photogrammetric surveys, tackling the profound challenges of full semantic segmentation and 3D grid definition. Full semantic segmentation (where every pixel is classified) is extremely labour-intensive and difficult to achieve using manual labeling. We propose using label-augmentation, i.e., propagation of sparse manual labels, to accelerate the task of full segmentation of photomosaics. Photomosaics are synthetic images generated from a projected point-of-view of a 3D model. In the lack of navigation sensors (e.g., a diver-held camera), it is difficult to repeatably determine the slope-angle of a 3D map. We show this is especially important in complex topographical settings, prevalent in coral-reefs. Specifically, we evaluate our approach on benthic habitats, in three different environments in the challenging underwater domain. Our approach for label-augmentation shows human-level accuracy in full segmentation of photomosaics using labeling as sparse as 0.1%, evaluated on several ecological measures. Moreover, we found that grid definition using a leveler improves the consistency in community-metrics obtained due to occlusions and topology (angle and distance between objects), and that we were able to standardise the 3D transformation with two percent error in size measurements. By significantly easing the annotation process for full segmentation and standardizing the 3D grid definition we present a semantic mapping methodology enabling change-detection, which is practical, swift, and cost-effective. Our workflow enables repeatable surveys without permanent markers and specialized mapping gear, useful for research and monitoring, and our code is available online. Additionally, we release the Benthos data-set, fully manually labeled photomosaics from three oceanic environments with over 4500 segmented objects useful for research in computer-vision and marine ecology

Integrating an autonomous robot on a dance and new technologies festival

This paper presents the results of a project to integrate an autonomous mobile robot into a modern dance performance at a dance and new technologies festival. The main goal is to integrate a simple low cost mobile robot into the dance performance, in order to study the possibilities that this kind of platforms can offer to the artists. First, this work explains the process and design to embed the robotic platform into the choreography theme. Another contribution described in this work is the system architecture proposed and built to make the robot behaviours match the artists requirements: precise, synchronized and robust robot movements. Finally, we discuss the main issues and lessons learned for this kind of robotics and arts applications and summarize the results obtained, including the successful final live performance results

Gender gap in STEM: a cross-sectional study of primary school students’ self-perception and test anxiety in mathematics

Contribution: Significant gender differences are observed on primary school students’ perception of self-efficacy and test anxiety in mathematics. Girls perceive themselves to be significantly worse than boys in mathematics and report higher test anxiety toward mathematics exams. Gender differences in self-efficacy become more pronounced as students grow up, and test anxiety increases for all students. However, the present study shows that teachers’ do not perceive differences in self-efficacy in mathematics between boys and girls. Background: The low presence of women in science, technology, engineering, and mathematics (STEM) might be explained by the attitude of young students toward mathematics. Different studies show that girls are less interested in STEM areas than boys during secondary school. A study on the reasons for this fact pointed out that the early years of education can provide a relevant insight to reverse the situation. Research Questions: Is there any age-dependent gender difference in primary school students in aspects related to mathematics? Are teachers aware of students’ perceptions? Methodology: This work presents a study of over 2000 primary school students (6–12 years old) and 200 teachers in Aragón (Spain). The study consists of a survey on aspects that influence the experience of female and male students with mathematics and Spanish language for comparison purposes and teacher’s awareness of students’ perception. Findings: The present study shows that during primary school, girls are more likely to experiment a negative attitude toward mathematics than boys as they grow up, and teachers may not perceive girls’ situation. La baja presencia de mujeres en ciencia, tecnología, la ingeniería y las matemáticas (STEM) podrían explicarse por la actitud de las niños y niñas hacia las matemáticas. Diferentes estudios muestran que las niñas están menos interesadas en las áreas STEM que niños cuando cursan educación secundaria. Además, un estudio sobre los motivos para este hecho señaló que los primeros años de educación podrían proporcionar una visión relevante para revertir la situación. Por ello, este trabajo parte de las siguientes preguntas de investigación, ¿Existe alguna diferencia de género que sea dependiente de la edad en estudiantes de educación primaria en aspectos relacionados con las matemáticas? ¿Conoce el profesorado la autopercepción de sus estudiantes? Las principales contribuciones de este trabajo son que las diferencias significativas de género se observan en la percepción de autoeficacia de los estudiantes de primaria y ansiedad ante los exámenes en matemáticas. Las niñas se perciben a sí mismas significativamente peor que los niños en matemáticas e indican mayor ansiedad ante los exámenes de matemáticas. Las diferencias de género en la autoeficacia se vuelven más pronunciada a medida que los estudiantes crecen, mientras que la ansiedad ante los exámenes aumenta para todos los estudiantes. Pese a estos resultados, el presente estudio muestra que los profesores no perciben diferencias en la autoeficacia en matemáticas entre niños y niñas. Este estudio se basa en las encuestas realizadas a más de 2000 escolares (6-12 años) y 200 profesores en Aragón (España). El estudio consiste en una encuesta a los estudiantes sobre aspectos que pueden influir en la experiencia de los niños y niñas con las matemáticas, así como con la lengua española para disponer de una materia que permita establecer comparaciones y una encuesta al profesor que incluye cuestiones sobre su percepción de los estudiantes. El principal hallazgo del estudio es que, durante la escuela primaria, es más probable que las niñas experimenten una actitud negativa hacia matemáticas que los niños a medida que crecen, y que los maestros pueden no ser conscientes de la situación de las niñas

Enzyme-Powered Gated Mesoporous Silica Nanomotors for On-Command Intracellular Payload Delivery

[EN] The introduction of stimuli-responsive cargo release capabilities on self-propelled micro- and nano- motors holds enormous potential in a number of applications in the biomedical field. Herein, we report the preparation of mesoporous silica nano-particles gated with pH-responsive supramolecular nanovalves and equipped with urease enzymes which act as chemical engines to power the nanomotors. The nanoparticles are loaded with different cargo molecules ([Ru(bpy)(3)]Cl-2 (bpy = 2,2'-bipyridine) or doxorubicin), grafted with benzimidazole groups on the outer surface, and capped by the formation of inclusion complexes between benzimidazole and cyclodextrin-modified urease. The nanomotor exhibits enhanced Brownian motion in the presence of urea. Moreover, no cargo is released at neutral pH, even in the presence of the biofuel urea, due to the blockage of the pores by the bulky benzimidazole:cyclodextrin-urease caps. Cargo delivery is only triggered on-command at acidic pH due to the protonation of benzimidazole groups, the dethreading of the supramolecular nanovalves, and the subsequent uncapping of the nanoparticles. Studies with HeLa cells indicate that the presence of biofuel urea enhances nanoparticle internalization and both [Ru(bpy)(3)]Cl-2 or doxorubicin intracellular release due to the acidity of lysosomal compartments. Gated enzyme-powered nanomotors shown here display some of the requirements for ideal drug delivery carriers such as the capacity to self-propel and the ability to "sense" the environment and deliver the payload on demand in response to predefined stimuli.A.L.-L. is grateful to La Caixa Banking Foundation for his Ph.D. grant. A.G.-F. thanks the Spanish government for her FPU fellowship. The authors are grateful to the Spanish Government (MINECO Projects MAT2015-64139-C4-1, CTQ2014-58989- PCTQ2015-71936-REDT, CTQ2015-68879-R (MICRODIA) and CTQ2015-72471-EXP (Enzwim)), the BBVA foundation (MEDIROBOTS), the CERCA Programme by the Generalitat de Catalunya, and the Generalitat Valenciana (Project PROMETEO/2018/024 and PROMETEOII/2014/061) for support. T.P. thanks MINECO for the Juan de la Cierva postdoctoral fellowship and the European Union's Horizon 2020 research and innovation program, under the Marie Sk¿odowska-Curie Individual Fellowship (H2020-MSCA-IF2018, DNA-bots). A.C.H. thanks MINECO for the Severo Ochoa fellowship. The authors would like to thank A. Miguel Lopez for the development of the python code for motion analysis.Llopis-Lorente, A.; García-Fernández, A.; Murillo-Cremaes, N.; Hortelao, A.; Patiño, T.; Villalonga, R.; Sancenón Galarza, F.... (2019). Enzyme-Powered Gated Mesoporous Silica Nanomotors for On-Command Intracellular Payload Delivery. ACS Nano. 13(10):12171-12183. https://doi.org/10.1021/acsnano.9b067061217112183131

Measurement of the polarisation of W bosons produced with large transverse momentum in pp collisions at sqrt(s) = 7 TeV with the ATLAS experiment

This paper describes an analysis of the angular distribution of W->enu and W->munu decays, using data from pp collisions at sqrt(s) = 7 TeV recorded with the ATLAS detector at the LHC in 2010, corresponding to an integrated luminosity of about 35 pb^-1. Using the decay lepton transverse momentum and the missing transverse energy, the W decay angular distribution projected onto the transverse plane is obtained and analysed in terms of helicity fractions f0, fL and fR over two ranges of W transverse momentum (ptw): 35 < ptw < 50 GeV and ptw > 50 GeV. Good agreement is found with theoretical predictions. For ptw > 50 GeV, the values of f0 and fL-fR, averaged over charge and lepton flavour, are measured to be : f0 = 0.127 +/- 0.030 +/- 0.108 and fL-fR = 0.252 +/- 0.017 +/- 0.030, where the first uncertainties are statistical, and the second include all systematic effects.Comment: 19 pages plus author list (34 pages total), 9 figures, 11 tables, revised author list, matches European Journal of Physics C versio