COMPENDIUM: A text summarization system for generating abstracts of research papers

This article analyzes the appropriateness of a text summarization system, COMPENDIUM, for generating abstracts of biomedical papers. Two approaches are suggested: an extractive (COMPENDIUM E), which only selects and extracts the most relevant sentences of the documents, and an abstractive-oriented one (COMPENDIUM E–A), thus facing also the challenge of abstractive summarization. This novel strategy combines extractive information, with some pieces of information of the article that have been previously compressed or fused. Specifically, in this article, we want to study: i) whether COMPENDIUM produces good summaries in the biomedical domain; ii) which summarization approach is more suitable; and iii) the opinion of real users towards automatic summaries. Therefore, two types of evaluation were performed: quantitative and qualitative, for evaluating both the information contained in the summaries, as well as the user satisfaction. Results show that extractive and abstractive-oriented summaries perform similarly as far as the information they contain, so both approaches are able to keep the relevant information of the source documents, but the latter is more appropriate from a human perspective, when a user satisfaction assessment is carried out. This also confirms the suitability of our suggested approach for generating summaries following an abstractive-oriented paradigm.

NO adsorption on Pt (111)/Bi surfaces

The adsorption of nitric oxide (NO) on a Pt (111) surface modified with irreversible adsorbed bismuth adatoms is reported. While the voltammetric results reveal a close interaction between the two co-adsorbed compounds. In-situ infrared spectroscopy and scanning tunnelling microscopy indicate the formation of segregated adlayers. Formation of compressed Bi adlayers with modified redox properties is proposed to reconcile both results. This agrees with the observation of Bi islands in the STM images when NO is coadsorbed, not observed in the absence of NO.

Adjustable compression method for still JPEG images

There are a large number of image processing applications that work with different performance requirements and available resources. Recent advances in image compression focus on reducing image size and processing time, but offer no real-time solutions for providing time/quality flexibility of the resulting image, such as using them to transmit the image contents of web pages. In this paper we propose a method for encoding still images based on the JPEG standard that allows the compression/decompression time cost and image quality to be adjusted to the needs of each application and to the bandwidth conditions of the network. The real-time control is based on a collection of adjustable parameters relating both to aspects of implementation and to the hardware with which the algorithm is processed. The proposed encoding system is evaluated in terms of compression ratio, processing delay and quality of the compressed image when compared with the standard method.

High-Pressure Methane Storage in Porous Materials: Are Carbon Materials in the Pole Position?

Natural gas storage on porous materials (ANG) is a promising alternative to conventional on-board compressed (CNG) or liquefied natural gas (LNG). To date, Metal–organic framework (MOF) materials have apparently been the only system published in the literature that is able to reach the new Department of Energy (DOE) value of 263 cm3 (STP: 273.15 K, 1 atm)/cm3; however, this value was obtained by using the ideal single-crystal density to calculate the volumetric capacity. Here, we prove experimentally, and for the first time, that properly designed activated carbon materials can really achieve the new DOE value while avoiding the additional drawback usually associated with MOF materials (i.e., the low mechanical stability under pressure (conforming), which is required for any practical application).

Controlled rippling of graphene via irradiation and applied strain modify its mechanical properties: a nanoindentation simulation study

Ripples, present in free standing graphene, have an important influence in the mechanical behavior of this two-dimensional material. In this work we show through nanoindentation simulations, how out-of-plane displacements can be modified by strain resulting in softening of the membrane under compression and stiffening under tension. Irradiation also induces changes in the mechanical properties of graphene. Interestingly, compressed samples, irradiated at low doses are stiffened by the irradiation while samples under tensile strain do not show significant changes in their mechanical properties. These simulations indicate that vacancies, produced by the energetic ions, cannot be the ones directly responsible for this behavior. However, changes in roughness induced by the momentum transferred from the energetic ions to the membrane, can explain these differences. These results provide an alternative explanation to recent experimental observations of stiffening of graphene under low dose irradiation, as well as paths to tailor the mechanical properties of this material via applied strain and irradiation.

Contributions to 3D Data Registration and Representation

Nowadays, new computers generation provides a high performance that enables to build computationally expensive computer vision applications applied to mobile robotics. Building a map of the environment is a common task of a robot and is an essential part to allow the robots to move through these environments. Traditionally, mobile robots used a combination of several sensors from different technologies. Lasers, sonars and contact sensors have been typically used in any mobile robotic architecture, however color cameras are an important sensor due to we want the robots to use the same information that humans to sense and move through the different environments. Color cameras are cheap and flexible but a lot of work need to be done to give robots enough visual understanding of the scenes. Computer vision algorithms are computational complex problems but nowadays robots have access to different and powerful architectures that can be used for mobile robotics purposes. The advent of low-cost RGB-D sensors like Microsoft Kinect which provide 3D colored point clouds at high frame rates made the computer vision even more relevant in the mobile robotics field. The combination of visual and 3D data allows the systems to use both computer vision and 3D processing and therefore to be aware of more details of the surrounding environment. The research described in this thesis was motivated by the need of scene mapping. Being aware of the surrounding environment is a key feature in many mobile robotics applications from simple robotic navigation to complex surveillance applications. In addition, the acquisition of a 3D model of the scenes is useful in many areas as video games scene modeling where well-known places are reconstructed and added to game systems or advertising where once you get the 3D model of one room the system can add furniture pieces using augmented reality techniques. In this thesis we perform an experimental study of the state-of-the-art registration methods to find which one fits better to our scene mapping purposes. Different methods are tested and analyzed on different scene distributions of visual and geometry appearance. In addition, this thesis proposes two methods for 3d data compression and representation of 3D maps. Our 3D representation proposal is based on the use of Growing Neural Gas (GNG) method. This Self-Organizing Maps (SOMs) has been successfully used for clustering, pattern recognition and topology representation of various kind of data. Until now, Self-Organizing Maps have been primarily computed offline and their application in 3D data has mainly focused on free noise models without considering time constraints. Self-organising neural models have the ability to provide a good representation of the input space. In particular, the Growing Neural Gas (GNG) is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation. However, this type of learning is time consuming, specially for high-dimensional input data. Since real applications often work under time constraints, it is necessary to adapt the learning process in order to complete it in a predefined time. This thesis proposes a hardware implementation leveraging the computing power of modern GPUs which takes advantage of a new paradigm coined as General-Purpose Computing on Graphics Processing Units (GPGPU). Our proposed geometrical 3D compression method seeks to reduce the 3D information using plane detection as basic structure to compress the data. This is due to our target environments are man-made and therefore there are a lot of points that belong to a plane surface. Our proposed method is able to get good compression results in those man-made scenarios. The detected and compressed planes can be also used in other applications as surface reconstruction or plane-based registration algorithms. Finally, we have also demonstrated the goodness of the GPU technologies getting a high performance implementation of a CAD/CAM common technique called Virtual Digitizing.

Applications for CO2-Activated Carbon Monoliths: I. Gas Storage

Carbon monoliths with high densities are studied as adsorbents for the storage of H2, CH4, and CO2 at ambient temperature and high pressures. The starting monolith A3 (produced by ATMI Co.) was activated under a CO2 flow at 1073 K, applying different activation times up to 48 h. Micropore volumes and apparent surface areas were deduced from N2 and CO2 adsorption isotherms at 77 K and 273 K, respectively. CO2 and CH4 isotherms were measured up to 3 MPa and H2 up to 20 MPa. The BET surface area of the starting monolith (941 m2/g) could be significantly increased up to 1586 m2/g, and the developed porosity is almost exclusively comprised of micropores <1 nm. Total storage amounts take into account the compressed gas in the void space of the material, in addition to the adsorbed gas. Remarkably, high total storage amounts are reached for CO2 (482 g/L), CH4 (123 g/L), and H2 (18 g/L). These values are much higher than for other sorbents with similar surface areas, due to the high density of the starting monolith and of the activated ones, for which the density decreases only slightly (from 1.0 g/cm3 to 0.8 g /cm3 upon CO2 activation). The findings reveal the suitability of high density activated carbon monoliths for gas storage application. Thus, the amounts of stored gas can be increased by more than a 70 % in the case of H2 at 20 MPa, almost 5.5 times in the case of CH4 at 3 MPa, and more than 7.5 times in the case of CO2 at 3 MPa when adsorbents are used for gas storage under the investigated conditions rather than simple compression. Furthermore, the obtained results have been recently confirmed by a scale-up study in which 2.64 kg of high density monolith adsorbent was filled a tank cylinder of 2.5 L (Carbon, 76, 2014, 123).