Showing papers by "Polytechnic University of Catalonia published in 2020"

TRY plant trait database : Enhanced coverage and open access

Abstract: Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.

The physical oceanography of the transport of floating marine debris

Abstract: Marine plastic debris floating on the ocean surface is a major environmental problem. However, its distribution in the ocean is poorly mapped, and most of the plastic waste estimated to have entered the ocean from land is unaccounted for. Better understanding of how plastic debris is transported from coastal and marine sources is crucial to quantify and close the global inventory of marine plastics, which in turn represents critical information for mitigation or policy strategies. At the same time, plastic is a unique tracer that provides an opportunity to learn more about the physics and dynamics of our ocean across multiple scales, from the Ekman convergence in basin-scale gyres to individual waves in the surfzone. In this review, we comprehensively discuss what is known about the different processes that govern the transport of floating marine plastic debris in both the open ocean and the coastal zones, based on the published literature and referring to insights from neighbouring fields such as oil spill dispersion, marine safety recovery, plankton connectivity, and others. We discuss how measurements of marine plastics (both in situ and in the laboratory), remote sensing, and numerical simulations can elucidate these processes and their interactions across spatio-temporal scales.

Cell type–specific genetic regulation of gene expression across human tissues

Abstract: The Genotype-Tissue Expression (GTEx) project has identified expression and splicing quantitative trait loci in cis (QTLs) for the majority of genes across a wide range of human tissues. However, the functional characterization of these QTLs has been limited by the heterogeneous cellular composition of GTEx tissue samples. We mapped interactions between computational estimates of cell type abundance and genotype to identify cell type-interaction QTLs for seven cell types and show that cell type-interaction expression QTLs (eQTLs) provide finer resolution to tissue specificity than bulk tissue cis-eQTLs. Analyses of genetic associations with 87 complex traits show a contribution from cell type-interaction QTLs and enables the discovery of hundreds of previously unidentified colocalized loci that are masked in bulk tissue.

The impact of sex on gene expression across human tissues

Abstract: Many complex human phenotypes exhibit sex-differentiated characteristics. However, the molecular mechanisms underlying these differences remain largely unknown. We generated a catalog of sex differences in gene expression and in the genetic regulation of gene expression across 44 human tissue sources surveyed by the Genotype-Tissue Expression project (GTEx, v8 release). We demonstrate that sex influences gene expression levels and cellular composition of tissue samples across the human body. A total of 37% of all genes exhibit sex-biased expression in at least one tissue. We identify cis expression quantitative trait loci (eQTLs) with sex-differentiated effects and characterize their cellular origin. By integrating sex-biased eQTLs with genome-wide association study data, we identify 58 gene-trait associations that are driven by genetic regulation of gene expression in a single sex. These findings provide an extensive characterization of sex differences in the human transcriptome and its genetic regulation.

The importance of interpretability and visualization in machine learning for applications in medicine and health care

Abstract: In a short period of time, many areas of science have made a sharp transition towards data-dependent methods. In some cases, this process has been enabled by simultaneous advances in data acquisition and the development of networked system technologies. This new situation is particularly clear in the life sciences, where data overabundance has sparked a flurry of new methodologies for data management and analysis. This can be seen as a perfect scenario for the use of machine learning and computational intelligence techniques to address problems in which more traditional data analysis approaches might struggle. But, this scenario also poses some serious challenges. One of them is model interpretability and explainability, especially for complex nonlinear models. In some areas such as medicine and health care, not addressing such challenge might seriously limit the chances of adoption, in real practice, of computer-based systems that rely on machine learning and computational intelligence methods for data analysis. In this paper, we reflect on recent investigations about the interpretability and explainability of machine learning methods and discuss their impact on medicine and health care. We pay specific attention to one of the ways in which interpretability and explainability in this context can be addressed, which is through data and model visualization. We argue that, beyond improving model interpretability as a goal in itself, we need to integrate the medical experts in the design of data analysis interpretation strategies. Otherwise, machine learning is unlikely to become a part of routine clinical and health care practice.

The 2020 motile active matter roadmap

Abstract: Activity and autonomous motion are fundamental in living and engineering systems. This has stimulated the new field of 'active matter' in recent years, which focuses on the physical aspects of propulsion mechanisms, and on motility-induced emergent collective behavior of a larger number of identical agents. The scale of agents ranges from nanomotors and microswimmers, to cells, fish, birds, and people. Inspired by biological microswimmers, various designs of autonomous synthetic nano- and micromachines have been proposed. Such machines provide the basis for multifunctional, highly responsive, intelligent (artificial) active materials, which exhibit emergent behavior and the ability to perform tasks in response to external stimuli. A major challenge for understanding and designing active matter is their inherent nonequilibrium nature due to persistent energy consumption, which invalidates equilibrium concepts such as free energy, detailed balance, and time-reversal symmetry. Unraveling, predicting, and controlling the behavior of active matter is a truly interdisciplinary endeavor at the interface of biology, chemistry, ecology, engineering, mathematics, and physics. The vast complexity of phenomena and mechanisms involved in the self-organization and dynamics of motile active matter comprises a major challenge. Hence, to advance, and eventually reach a comprehensive understanding, this important research area requires a concerted, synergetic approach of the various disciplines. The 2020 motile active matter roadmap of Journal of Physics: Condensed Matter addresses the current state of the art of the field and provides guidance for both students as well as established scientists in their efforts to advance this fascinating area.

Multi-Task Self-Supervised Learning for Robust Speech Recognition

Abstract: Despite the growing interest in unsupervised learning, extracting meaningful knowledge from unlabelled audio remains an open challenge. To take a step in this direction, we recently proposed a problem-agnostic speech encoder (PASE), that combines a convolutional encoder followed by multiple neural networks, called workers, tasked to solve self-supervised problems (i.e., ones that do not require manual annotations as ground truth). PASE was shown to capture relevant speech information, including speaker voice-print and phonemes. This paper proposes PASE+, an improved version of PASE for robust speech recognition in noisy and reverberant environments. To this end, we employ an online speech distortion module, that contaminates the input signals with a variety of random disturbances. We then propose a revised encoder that better learns short- and long-term speech dynamics with an efficient combination of recurrent and convolutional networks. Finally, we refine the set of workers used in self-supervision to encourage better cooperation.Results on TIMIT, DIRHA and CHiME-5 show that PASE+ significantly outperforms both the previous version of PASE as well as common acoustic features. Interestingly, PASE+ learns transferable representations suitable for highly mismatched acoustic conditions.

A critical review of resource recovery from municipal wastewater treatment plants – market supply potentials, technologies and bottlenecks

Abstract: In recent decades, academia has elaborated a wide range of technological solutions to recover water, energy, fertiliser and other products from municipal wastewater treatment plants. Drivers for this work range from low resource recovery potential and cost effectiveness, to the high energy demands and large environmental footprints of current treatment-plant designs. However, only a few technologies have been implemented and a shift from wastewater treatment plants towards water resource facilities still seems far away. This critical review aims to inform decision-makers in water management utilities about the vast technical possibilities and market supply potentials, as well as the bottlenecks, related to the design or redesign of a municipal wastewater treatment process from a resource recovery perspective. Information and data have been extracted from literature to provide a holistic overview of this growing research field. First, reviewed data is used to calculate the potential of 11 resources recoverable from municipal wastewater treatment plants to supply national resource consumption. Depending on the resource, the supply potential may vary greatly. Second, resource recovery technologies investigated in academia are reviewed comprehensively and critically. The third section of the review identifies nine non-technical bottlenecks mentioned in literature that have to be overcome to successfully implement these technologies into wastewater treatment process designs. The bottlenecks are related to economics and value chain development, environment and health, and society and policy issues. Considering market potentials, technological innovations, and addressing potential bottlenecks early in the planning and process design phase, may facilitate the design and integration of water resource facilities and contribute to more circular urban water management practices.

Localization and delocalization of light in photonic moiré lattices

Abstract: Moire lattices consist of two superimposed identical periodic structures with a relative rotation angle. Moire lattices have several applications in everyday life, including artistic design, the textile industry, architecture, image processing, metrology and interferometry. For scientific studies, they have been produced using coupled graphene-hexagonal boron nitride monolayers1,2, graphene-graphene layers3,4 and graphene quasicrystals on a silicon carbide surface5. The recent surge of interest in moire lattices arises from the possibility of exploring many salient physical phenomena in such systems; examples include commensurable-incommensurable transitions and topological defects2, the emergence of insulating states owing to band flattening3,6, unconventional superconductivity4 controlled by the rotation angle7,8, the quantum Hall effect9, the realization of non-Abelian gauge potentials10 and the appearance of quasicrystals at special rotation angles11. A fundamental question that remains unexplored concerns the evolution of waves in the potentials defined by moire lattices. Here we experimentally create two-dimensional photonic moire lattices, which-unlike their material counterparts-have readily controllable parameters and symmetry, allowing us to explore transitions between structures with fundamentally different geometries (periodic, general aperiodic and quasicrystal). We observe localization of light in deterministic linear lattices that is based on flat-band physics6, in contrast to previous schemes based on light diffusion in optical quasicrystals12, where disorder is required13 for the onset of Anderson localization14 (that is, wave localization in random media). Using commensurable and incommensurable moire patterns, we experimentally demonstrate the two-dimensional localization-delocalization transition of light. Moire lattices may feature an almost arbitrary geometry that is consistent with the crystallographic symmetry groups of the sublattices, and therefore afford a powerful tool for controlling the properties of light patterns and exploring the physics of periodic-aperiodic phase transitions and two-dimensional wavepacket phenomena relevant to several areas of science, including optics, acoustics, condensed matter and atomic physics.

CO and CO2 methanation over Ni catalysts supported on CeO2, Al2O3 and Y2O3 oxides

Abstract: Methanation of carbon oxides (CO and CO2) was studied over Ni-based catalysts supported on CeO2, Al2O3, and Y2O3 oxides. Catalysts were synthesized by solution combustion synthesis and characterized by N2-physisorption, XRD, H2-TPR, TEM, CO-chemisorption, UV–vis DRS, XPS, and CO2-TPD. The effect of reaction temperature (250−500 °C) was investigated under atmospheric pressure, space velocity (GHSV) of 10,000 h−1, and stoichiometric reactants ratio of (H2-CO2)/(CO + CO2) = 3. It can be concluded that the nature of Ni-support interactions played a crucial role in enhancing CO and CO2 hydrogenation at low reaction temperature. Ni/CeO2 catalyst deactivated rapidly due to coke deposition, while the formation of NiAl2O4 spinel explained the lower activity of the Al2O3-supported system. Activity data for Ni/Y2O3 catalysts were closely related to the degree of Ni dispersion as well as to the medium-strength basicity. Good anti-coking and anti-sintering ability were observed after 200 h of lifetime test.

Toward Intelligent Metasurfaces: The Progress from Globally Tunable Metasurfaces to Software-Defined Metasurfaces with an Embedded Network of Controllers

Abstract: This work was supported by the European Union’s Horizon 2020 research and innovation programme-Future Emerging Topics (FETOPEN) under grant agreement No 736876 (VISORSURF). Financial support by the National Priorities Research Program grant No. NPRP9-383-1-083 from the Qatar National Research Fund is also acknowledged. O.T. acknowledges the financial support of the Stavros Niarchos Foundation within the framework of the project ARCHERS (“Advancing Young Researchers’ Human Capital in Cutting Edge Technologies in the Preservation of Cultural Heritage and the Tackling of Societal Challenges”).

Nonlinearity-induced photonic topological insulator.

Abstract: A hallmark feature of topological insulators is robust edge transport that is impervious to scattering at defects and lattice disorder. We demonstrate a topological system, using a photonic platform, in which the existence of the topological phase is brought about by optical nonlinearity. The lattice structure remains topologically trivial in the linear regime, but as the optical power is increased above a certain power threshold, the system is driven into the topologically nontrivial regime. This transition is marked by the transient emergence of a protected unidirectional transport channel along the edge of the structure. Our work studies topological properties of matter in the nonlinear regime, providing a possible route for the development of compact devices that harness topological features in an on-demand fashion.

RouteNet: leveraging graph neural networks for network modeling and optimization in SDN

Abstract: Network modeling is a key enabler to achieve efficient network operation in future self-driving Software-Defined Networks. However, we still lack functional network models able to produce accurate predictions of Key Performance Indicators (KPI) such as delay, jitter or loss at limited cost. In this paper we propose RouteNet, a novel network model based on Graph Neural Network (GNN) that is able to understand the complex relationship between topology, routing, and input traffic to produce accurate estimates of the per-source/destination per-packet delay distribution and loss. RouteNet leverages the ability of GNNs to learn and model graph-structured information and as a result, our model is able to generalize over arbitrary topologies, routing schemes and traffic intensity. In our evaluation, we show that RouteNet is able to predict accurately the delay distribution (mean delay and jitter) and loss even in topologies, routing and traffic unseen in the training (worst case MRE = 15.4%). Also, we present several use cases where we leverage the KPI predictions of our GNN model to achieve efficient routing optimization and network planning.

Localization and delocalization of light in photonic moire lattices

Abstract: Moire lattices consist of two identical periodic structures overlaid with a relative rotation angle. Present even in everyday life, moire lattices have been also produced, e.g., with coupled graphene-hexagonal boron nitride monolayers, graphene-graphene layers, and layers on a silicon carbide surface.A fundamental question that remains unexplored is the evolution of waves in the potentials defined by the moire lattices. Here we experimentally create two-dimensional photonic moire lattices, which, unlike their material predecessors, have readily controllable parameters and symmetry allowing to explore transitions between structures with fundamentally different geometries: periodic, general aperiodic and quasi-crystal ones. Equipped with such realization, we observe localization of light in deterministic linear lattices. Such localization is based on at band physics, in contrast to previous schemes based on light difusion in optical quasicrystals,where disorder is required for the onset of Anderson localization. Using commensurable and incommensurable moire patterns, we report the first experimental demonstration of two-dimensional localization-delocalization-transition (LDT) of light. Moire lattices may feature almost arbitrary geometry that is consistent with the crystallographic symmetry groups of the sublattices, and therefore afford a powerful tool to control the properties of light patterns, to explore the physics of transitions between periodic and aperiodic phases, and two-dimensional wavepacket phenomena relevant to several areas of science.

A survey on Kornia: an Open Source Differentiable Computer Vision Library for PyTorch

Abstract: This work presents Kornia, an open source computer vision library built upon a set of differentiable routines and modules that aims to solve generic computer vision problems. The package uses PyTorch as its main backend, not only for efficiency but also to take advantage of the reverse auto-differentiation engine to define and compute the gradient of complex functions. Inspired by OpenCV, Kornia is composed of a set of modules containing operators that can be integrated into neural networks to train models to perform a wide range of operations including image transformations,camera calibration, epipolar geometry, and low level image processing techniques, such as filtering and edge detection that operate directly on high dimensional tensor representations on graphical processing units, generating faster systems. Examples of classical vision problems implemented using our framework are provided including a benchmark comparing to existing vision libraries.

3D Printing in Medicine for Preoperative Surgical Planning: A Review

Abstract: The aim of this paper is to review the recent evolution of additive manufacturing (AM) within the medical field of preoperative surgical planning. The discussion begins with an overview of the different techniques, pointing out their advantages and disadvantages as well as an in-depth comparison of different characteristics of the printed parts. Then, the state-of-the-art with respect to preoperative surgical planning is presented. On the one hand, different surgical planning prototypes manufactured by several AM technologies are described. On the other hand, materials used for mimicking different living tissues are explored by focusing on the material properties: elastic modulus, hardness, etc. As a result, doctors can practice before performing surgery and thereby reduce the time needed for the operation. The subject of patient education is also introduced. A thorough review of the process that is required to obtain 3D printed surgical planning prototypes, which is based on different stages, is then carried out. Finally, the ethical issues associated with 3D printing in medicine are discussed, along with its future perspectives. Overall, this is important for improving the outcome of the surgery, since doctors will be able to visualize the affected organs and even to practice surgery before performing it.

Enhanced Chiral Sensing with Dielectric Nanoresonators

Abstract: Chiro-sensitive molecular detection is highly relevant as many biochemical compounds, the building blocks of life, are chiral. Optical chirality is conventionally detected through circular dichroism (CD) in the UV range, where molecules naturally absorb. Recently, plasmonics has been proposed as a way to boost the otherwise very weak CD signal and translate it to the visible/NIR range, where technology is friendlier. Here, we explore how dielectric nanoresonators can contribute to efficiently differentiate molecular enantiomers. We study the influence of the detuning between electric (ED) and magnetic dipole (MD) resonances in silicon nanocylinders on the quality of the CD signal. While our experimental data, supported by numerical simulations, demonstrate that dielectric nanoresonators can perform even better than their plasmonic counterpart, exhibiting larger CD enhancements, we do not observe any significant influence of the optical chirality.

ZnSe/N-doped carbon nanoreactor with multiple adsorption sites for stable lithium–sulfur batteries

Abstract: To commercially realize the enormous potential of lithium-sulfur batteries (LSBs) several challenges remain to be overcome. At the cathode, the lithium polysulfide (LiPS) shuttle effect must be inhibited and the redox reaction kinetics need to be substantially promoted. In this direction, this work proposes a cathode material based on a transition-metal selenide (TMSe) as both adsorber and catalyst and a hollow nanoreactor architecture: ZnSe/N-doped hollow carbon (ZnSe/NHC). It is here demonstrated both experimentally and by means of density functional theory that this composite provides three key benefits to the LSBs cathode: (i) A highly effective trapping of LiPS due to the combination of sulfiphilic sites of ZnSe, lithiophilic sites of NHC, and the confinement effect of the cage-based structure; (ii) a redox kinetic improvement in part associated with the multiple adsorption sites that facilitate the Li+ diffusion; and (iii) an easier accommodation of the volume expansion preventing the cathode damage due to the hollow design. As a result, LSB cathodes based on S@ZnSe/NHC are characterized by high initial capacities, superior rate capability, and an excellent stability. Overall, this work not only demonstrates the large potential of TMSe as cathode materials in LSBs but also probes the nanoreactor design to be a highly suitable architecture to enhance cycle stability.

Continual Lifelong Learning in Natural Language Processing: A Survey

Abstract: Continual learning (CL) aims to enable information systems to learn from a continuous data stream across time. However, it is difficult for existing deep learning architectures to learn a new task without largely forgetting previously acquired knowledge. Furthermore, CL is particularly challenging for language learning, as natural language is ambiguous: it is discrete, compositional, and its meaning is context-dependent. In this work, we look at the problem of CL through the lens of various NLP tasks. Our survey discusses major challenges in CL and current methods applied in neural network models. We also provide a critical review of the existing CL evaluation methods and datasets in NLP. Finally, we present our outlook on future research directions.

Understanding the carbonation of concrete with supplementary cementitious materials: a critical review by RILEM TC 281-CCC

Abstract: Blended cements, where Portland cement clinker is partially replaced by supplementary cementitious materials (SCMs), provide the most feasible route for reducing carbon dioxide emissions associated with concrete production. However, lowering the clinker content can lead to an increasing risk of neutralisation of the concrete pore solution and potential reinforcement corrosion due to carbonation. carbonation of concrete with SCMs differs from carbonation of concrete solely based on Portland cement (PC). This is a consequence of the differences in the hydrate phase assemblage and pore solution chemistry, as well as the pore structure and transport properties, when varying the binder composition, age and curing conditions of the concretes. The carbonation mechanism and kinetics also depend on the saturation degree of the concrete and CO2 partial pressure which in turn depends on exposure conditions (e.g. relative humidity, volume, and duration of water in contact with the concrete surface and temperature conditions). This in turn influence the microstructural changes identified upon carbonation. This literature review, prepared by members of RILEM technical committee 281-CCC carbonation of concrete with supplementary cementitious materials, working groups 1 and 2, elucidates the effect of numerous SCM characteristics, exposure environments and curing conditions on the carbonation mechanism, kinetics and structural alterations in cementitious systems containing SCMs.

Findings of the 2020 Conference on Machine Translation (WMT20)

Abstract: This paper presents the results of the news translation task and the similar language translation task, both organised alongside the Conference on Machine Translation (WMT) 2020. In the news task, participants were asked to build machine translation systems for any of 11 language pairs, to be evaluated on test sets consisting mainly of news stories. The task was also opened up to additional test suites to probe specific aspects of translation. In the similar language translation task, participants built machine translation systems for translating between closely related pairs of languages.

The Rocketbox library and the utility of freely available rigged avatars

Abstract: As part of the open sourcing of the Microsoft Rocketbox avatar library for research and academic purposes, here we discuss the importance of rigged avatars for the Virtual and Augmented Reality (VR, AR) research community. Avatars, virtual representations of humans, are widely used in VR applications. Furthermore many research areas ranging from crowd simulation to neuroscience, psychology or sociology have used avatars to investigate new theories or to demonstrate how they influence human performance and interactions. We divide this paper in two main parts: the first one gives an overview of the different methods available to create and animate avatars. We cover the current main alternatives for face and body animation as well introduce upcoming capture methods. The second part presents the scientific evidence of the utility of using rigged avatars for embodiment but also for applications such as crowd simulation and entertainment. All in all this paper attempts to convey why rigged avatars will be key to the future of VR and its wide adoption.

Chondroinductive alginate-based hydrogels having graphene oxide for 3D printed scaffold fabrication

Abstract: Scaffolds based on bioconjugated hydrogels are attractive for tissue engineering because they can partly mimic human tissue characteristics. For example, they can further increase their bioactivity with cells. However, most of the hydrogels present problems related to their processability, consequently limiting their use in 3D printing to produce tailor-made scaffolds. The goal of this work is to develop bioconjugated hydrogel nanocomposite inks for 3D printed scaffold fabrication through a micro-extrusion process having improved both biocompatibility and processability. The hydrogel is based on a photocrosslinkable alginate bioconjugated with both gelatin and chondroitin sulfate in order to mimic the cartilage extracellular matrix, while the nanofiller is based on graphene oxide to enhance the printability and cell proliferation. Our results show that the incorporation of graphene oxide into the hydrogel inks considerably improved the shape fidelity and resolution of 3D printed scaffolds because of a faster viscosity recovery post extrusion of the ink. Moreover, the nanocomposite inks produce anisotropic threads after the 3D printing process because of the templating of the graphene oxide liquid crystal. The in vitro proliferation assay of human adipose tissue-derived mesenchymal stem cells (hADMSCs) shows that bioconjugated scaffolds present higher cell proliferation than pure alginate, with the nanocomposites presenting the highest values at long times. Live/Dead assay otherwise displays full viability of hADMSCs adhered on the different scaffolds at day 7. Notably, the scaffolds produced with nanocomposite hydrogel inks were able to guide the cell proliferation following the direction of the 3D printed threads. In addition, the bioconjugated alginate hydrogel matrix induced chondrogenic differentiation without exogenous pro-chondrogenesis factors as concluded from immunostaining after 28 days of culture. This high cytocompatibility and chondroinductive effect toward hADMSCs, together with the improved printability and anisotropic structures, makes these nanocomposite hydrogel inks a promising candidate for cartilage tissue engineering based on 3D printing.

Green Synthesis of Biomass Phytic Acid-Functionalized UiO-66-NH2 Hierarchical Hybrids toward Fire Safety of Epoxy Resin

Abstract: A phosphorus abundant biomass phytic acid (PA)-functionalized metal–organic framework (MOF) UiO-66-NH2 (PA-UiO66-NH2) was synthesized successfully as a novel fire retardant (FR) to reduce fire haza...