Showing papers by "Instituto Politécnico Nacional published in 2019"

Amphibian fungal panzootic causes catastrophic and ongoing loss of biodiversity

Abstract: Anthropogenic trade and development have broken down dispersal barriers, facilitating the spread of diseases that threaten Earth's biodiversity. We present a global, quantitative assessment of the amphibian chytridiomycosis panzootic, one of the most impactful examples of disease spread, and demonstrate its role in the decline of at least 501 amphibian species over the past half-century, including 90 presumed extinctions. The effects of chytridiomycosis have been greatest in large-bodied, range-restricted anurans in wet climates in the Americas and Australia. Declines peaked in the 1980s, and only 12% of declined species show signs of recovery, whereas 39% are experiencing ongoing decline. There is risk of further chytridiomycosis outbreaks in new areas. The chytridiomycosis panzootic represents the greatest recorded loss of biodiversity attributable to a disease.

MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations

Abstract: Emotion recognition in conversations is a challenging task that has recently gained popularity due to its potential applications. Until now, however, a large-scale multimodal multi-party emotional conversational database containing more than two speakers per dialogue was missing. Thus, we propose the Multimodal EmotionLines Dataset (MELD), an extension and enhancement of EmotionLines. MELD contains about 13,000 utterances from 1,433 dialogues from the TV-series Friends. Each utterance is annotated with emotion and sentiment labels, and encompasses audio, visual and textual modalities. We propose several strong multimodal baselines and show the importance of contextual and multimodal information for emotion recognition in conversations. The full dataset is available for use at http://affective-meld.github.io.

DialogueRNN: An Attentive RNN for Emotion Detection in Conversations.

Abstract: Emotion detection in conversations is a necessary step for a number of applications, including opinion mining over chat history, social media threads, debates, argumentation mining, understanding consumer feedback in live conversations, and so on. Currently systems do not treat the parties in the conversation individually by adapting to the speaker of each utterance. In this paper, we describe a new method based on recurrent neural networks that keeps track of the individual party states throughout the conversation and uses this information for emotion classification. Our model outperforms the state-of-the-art by a significant margin on two different datasets.

Mobile robot path planning using membrane evolutionary artificial potential field

Abstract: In this paper, a membrane evolutionary artificial potential field (memEAPF) approach for solving the mobile robot path planning problem is proposed, which combines membrane computing with a genetic algorithm (membrane-inspired evolutionary algorithm with one-level membrane structure) and the artificial potential field method to find the parameters to generate a feasible and safe path. The memEAPF proposal consists of delimited compartments where multisets of parameters evolve according to rules of biochemical inspiration to minimize the path length. The proposed approach is compared with artificial potential field based path planning methods concerning to their planning performance on a set of twelve benchmark test environments, and it exhibits a better performance regarding path length. Experiments to demonstrate the statistical significance of the improvements achieved by the proposed approach in static and dynamic environments are shown. Moreover, the implementation results using parallel architectures proved the effectiveness and practicality of the proposal to obtain solutions in considerably less time.

Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances

Abstract: Emotion is intrinsic to humans and consequently, emotion understanding is a key part of human-like artificial intelligence (AI). Emotion recognition in conversation (ERC) is becoming increasingly popular as a new research frontier in natural language processing (NLP) due to its ability to mine opinions from the plethora of publicly available conversational data on platforms such as Facebook, Youtube, Reddit, Twitter, and others. Moreover, it has potential applications in health-care systems (as a tool for psychological analysis), education (understanding student frustration), and more. In Addition, ERC is also extremely important for generating emotion-aware dialogues that require an understanding of the user's emotions. Catering to these needs calls for effective and scalable conversational emotion-recognition algorithms. However, it is a difficult problem to solve because of several research challenges. In this paper, we discuss these challenges and shed light on recent research in this field. We also describe the drawbacks of these approaches and discuss the reasons why they fail to successfully overcome the research challenges in ERC.

Notes, outline and divergence times of Basidiomycota

Abstract: The Basidiomycota constitutes a major phylum of the kingdom Fungi and is second in species numbers to the Ascomycota. The present work provides an overview of all validly published, currently used basidiomycete genera to date in a single document. An outline of all genera of Basidiomycota is provided, which includes 1928 currently used genera names, with 1263 synonyms, which are distributed in 241 families, 68 orders, 18 classes and four subphyla. We provide brief notes for each accepted genus including information on classification, number of accepted species, type species, life mode, habitat, distribution, and sequence information. Furthermore, three phylogenetic analyses with combined LSU, SSU, 5.8s, rpb1, rpb2, and ef1 datasets for the subphyla Agaricomycotina, Pucciniomycotina and Ustilaginomycotina are conducted, respectively. Divergence time estimates are provided to the family level with 632 species from 62 orders, 168 families and 605 genera. Our study indicates that the divergence times of the subphyla in Basidiomycota are 406–430 Mya, classes are 211–383 Mya, and orders are 99–323 Mya, which are largely consistent with previous studies. In this study, all phylogenetically supported families were dated, with the families of Agaricomycotina diverging from 27–178 Mya, Pucciniomycotina from 85–222 Mya, and Ustilaginomycotina from 79–177 Mya. Divergence times as additional criterion in ranking provide additional evidence to resolve taxonomic problems in the Basidiomycota taxonomic system, and also provide a better understanding of their phylogeny and evolution.

Entrepreneurship research: mapping intellectual structures and research trends

Abstract: Using bibliometric techniques to identify connections between existing entrepreneurship studies, this article seeks to understand the scientific structure of entrepreneurship research and how entrepreneurship scholarship is organized. Co-citation data and quantitative approach were used to identify scientific publications, intellectual structures, and research trends interrelated with theories of innovative entrepreneurship. Data were gathered from the citations and co-citations found in the Science Citation Index Expanded, Social Science Citation Index, and Arts and Humanities Citation Index. Our research reveals six groups of underlying entrepreneurship theories: (1) knowledge spillover theory of entrepreneurship, (2) creation and networks theory, (3) decision-making theory, (4) specialization of labor, (5) social entrepreneurship theory, and (6) entrepreneurship in the informal economy.

Global spatial risk assessment of sharks under the footprint of fisheries

Abstract: Effective ocean management and the conservation of highly migratory species depend on resolving the overlap between animal movements and distributions, and fishing effort. However, this information is lacking at a global scale. Here we show, using a big-data approach that combines satellite-tracked movements of pelagic sharks and global fishing fleets, that 24% of the mean monthly space used by sharks falls under the footprint of pelagic longline fisheries. Space-use hotspots of commercially valuable sharks and of internationally protected species had the highest overlap with longlines (up to 76% and 64%, respectively), and were also associated with significant increases in fishing effort. We conclude that pelagic sharks have limited spatial refuge from current levels of fishing effort in marine areas beyond national jurisdictions (the high seas). Our results demonstrate an urgent need for conservation and management measures at high-seas hotspots of shark space use, and highlight the potential of simultaneous satellite surveillance of megafauna and fishers as a tool for near-real-time, dynamic management.

DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation

Abstract: Emotion recognition in conversation (ERC) has received much attention, lately, from researchers due to its potential widespread applications in diverse areas, such as health-care, education, and human resources. In this paper, we present Dialogue Graph Convolutional Network (DialogueGCN), a graph neural network based approach to ERC. We leverage self and inter-speaker dependency of the interlocutors to model conversational context for emotion recognition. Through the graph network, DialogueGCN addresses context propagation issues present in the current RNN-based methods. We empirically show that this method alleviates such issues, while outperforming the current state of the art on a number of benchmark emotion classification datasets.

Sentiment and Sarcasm Classification With Multitask Learning

Abstract: Sentiment classification and sarcasm detection are both important natural language processing tasks. Sentiment is always coupled with sarcasm where intensive emotion is expressed. Nevertheless, most literature considers them as two separate tasks. We argue that knowledge in sarcasm detection can also be beneficial to sentiment classification and vice versa. We show that these two tasks are correlated, and present a multitask learning-based framework using a deep neural network that models this correlation to improve the performance of both tasks in a multitask learning setting. Our method outperforms the state of the art by 3–4% in the benchmark dataset.

A Decade of Gamma-Ray Bursts Observed by Fermi-LAT: The Second GRB Catalog

Abstract: The Large Area Telescope (LAT) aboard the Fermi spacecraft routinely observes high-energy emission from gamma-ray bursts (GRBs). Here we present the second catalog of LAT-detected GRBs, covering the first 10 yr of operations, from 2008 to 2018 August 4. A total of 186 GRBs are found; of these, 91 show emission in the range 30–100 MeV (17 of which are seen only in this band) and 169 are detected above 100 MeV. Most of these sources were discovered by other instruments (Fermi/GBM, Swift/BAT, AGILE, INTEGRAL) or reported by the Interplanetary Network (IPN); the LAT has independently triggered on four GRBs. This catalog presents the results for all 186 GRBs. We study onset, duration, and temporal properties of each GRB, as well as spectral characteristics in the 100 MeV–100 GeV energy range. Particular attention is given to the photons with the highest energy. Compared with the first LAT GRB catalog, our rate of detection is significantly improved. The results generally confirm the main findings of the first catalog: the LAT primarily detects the brightest GBM bursts, and the high-energy emission shows delayed onset as well as longer duration. However, in this work we find delays exceeding 1 ks and several GRBs with durations over 10 ks. Furthermore, the larger number of LAT detections shows that these GRBs not only cover the high-fluence range of GBM-detected GRBs but also sample lower fluences. In addition, the greater number of detected GRBs with redshift estimates allows us to study their properties in both the observer and rest frames. Comparison of the observational results with theoretical predictions reveals that no model is currently able to explain all results, highlighting the role of LAT observations in driving theoretical models.

Standard methods for Apis mellifera propolis research

Abstract: Propolis is one of the most fascinating honey bee (Apis mellifera L) products It is a plant derived product that bees produce from resins that they collect from different plant organs and with which they mix beeswax Propolis is a building material and a protective agent in the bee hive It also plays an important role in honey bee social immunity, and is widely used by humans as an ingredient of nutraceuticals, over-the-counter preparations and cosmetics Its chemical composition varies by geographic location, climatic zone and local flora The understanding of the chemical diversity of propolis is very important in propolis research In this manuscript, we give an overview of the available methods for studying propolis in different aspects: propolis in the bee colony; chemical composition and plant sources of propolis; biological activity of propolis with respect to bees and humans; and approaches for standardization and quality control for the purposes of industrial application

Combination of Searches for Higgs Boson Pair Production in Proton-Proton Collisions at √s = 13 TeV

Abstract: This Letter describes a search for Higgs boson pair production using the combined results from four final states: bbγγ, bbττ, bbbb, and bbVV, where V represents a W or Z boson. The search is performed using data collected in 2016 by the CMS experiment from LHC proton-proton collisions at s=13 TeV, corresponding to an integrated luminosity of 35.9 fb-1. Limits are set on the Higgs boson pair production cross section. A 95% confidence level observed (expected) upper limit on the nonresonant production cross section is set at 22.2 (12.8) times the standard model value. A search for narrow resonances decaying to Higgs boson pairs is also performed in the mass range 250–3000 GeV. No evidence for a signal is observed, and upper limits are set on the resonance production cross section.

A very-high-energy component deep in the γ-ray burst afterglow

Abstract: Gamma-ray bursts (GRBs) are brief flashes of γ-rays and are considered to be the most energetic explosive phenomena in the Universe1. The emission from GRBs comprises a short (typically tens of seconds) and bright prompt emission, followed by a much longer afterglow phase. During the afterglow phase, the shocked outflow—produced by the interaction between the ejected matter and the circumburst medium—slows down, and a gradual decrease in brightness is observed2. GRBs typically emit most of their energy via γ-rays with energies in the kiloelectronvolt-to-megaelectronvolt range, but a few photons with energies of tens of gigaelectronvolts have been detected by space-based instruments3. However, the origins of such high-energy (above one gigaelectronvolt) photons and the presence of very-high-energy (more than 100 gigaelectronvolts) emission have remained elusive4. Here we report observations of very-high-energy emission in the bright GRB 180720B deep in the GRB afterglow—ten hours after the end of the prompt emission phase, when the X-ray flux had already decayed by four orders of magnitude. Two possible explanations exist for the observed radiation: inverse Compton emission and synchrotron emission of ultrarelativistic electrons. Our observations show that the energy fluxes in the X-ray and γ-ray range and their photon indices remain comparable to each other throughout the afterglow. This discovery places distinct constraints on the GRB environment for both emission mechanisms, with the inverse Compton explanation alleviating the particle energy requirements for the emission observed at late times. The late timing of this detection has consequences for the future observations of GRBs at the highest energies.

Online Vehicle Routing With Neural Combinatorial Optimization and Deep Reinforcement Learning

Abstract: Online vehicle routing is an important task of the modern transportation service provider. Contributed by the ever-increasing real-time demand on the transportation system, especially small-parcel last-mile delivery requests, vehicle route generation is becoming more computationally complex than before. The existing routing algorithms are mostly based on mathematical programming, which requires huge computation time in city-size transportation networks. To develop routes with minimal time, in this paper, we propose a novel deep reinforcement learning-based neural combinatorial optimization strategy. Specifically, we transform the online routing problem to a vehicle tour generation problem, and propose a structural graph embedded pointer network to develop these tours iteratively. Furthermore, since constructing supervised training data for the neural network is impractical due to the high computation complexity, we propose a deep reinforcement learning mechanism with an unsupervised auxiliary network to train the model parameters. A multisampling scheme is also devised to further improve the system performance. Since the parameter training process is offline, the proposed strategy can achieve a superior online route generation speed. To assess the proposed strategy, we conduct comprehensive case studies with a real-world transportation network. The simulation results show that the proposed strategy can significantly outperform conventional strategies with limited computation time in both static and dynamic logistic systems. In addition, the influence of control parameters on the system performance is investigated.

Waveguide-coupled single collective excitation of atomic arrays

Abstract: Considerable efforts have been recently devoted to combining ultracold atoms and nanophotonic devices1-4 to obtain not only better scalability and figures of merit than in free-space implementations, but also new paradigms for atom-photon interactions5. Dielectric waveguides offer a promising platform for such integration because they enable tight transverse confinement of the propagating light, strong photon-atom coupling in single-pass configurations and potentially long-range atom-atom interactions mediated by the guided photons. However, the preparation of non-classical quantum states in such atom-waveguide interfaces has not yet been realized. Here, by using arrays of individual caesium atoms trapped along an optical nanofibre6,7, we observe a single collective atomic excitation8,9 coupled to a nanoscale waveguide. The stored collective entangled state can be efficiently read out with an external laser pulse, leading to on-demand emission of a single photon into the guided mode. We characterize the emitted single photon via the suppression of the two-photon component and confirm the single character of the atomic excitation, which can be retrieved with an efficiency of about 25%. Our results demonstrate a capability that is essential for the emerging field of waveguide quantum electrodynamics, with applications to quantum networking, quantum nonlinear optics and quantum many-body physics10,11.

Pulses and food security: dietary protein, digestibility, bioactive and functional properties

Abstract: Background Food security is a current major concern. Protein malnutrition, in particular, must be overcome urgently. Simultaneously, food manufacturers are increasingly interested proteins from plant sources. Hence, pulse crops could be exploited as sustainable, climate change resistant and high quality protein sources. Scope and approach Pulses, such as peas, common beans, cowpea, fava beans, chickpea, lentil, or lupin, contain high levels of globulins and albumins, providing around 33% of dietary needs. Nevertheless, there are different compositional and technological problems (e.g., digestibility, allergenicity, or antinutritional factors: ANF) that need to be overcome to consider pulse proteins as feasible alternative sources of protein. Key findings and conclusions The ANF might be highly decreased by adequate processing conditions, increasing nutrients bioavailability and protein digestibility. Curiously, ANF might also be associated with the positive physiologic effects commonly attributed to pulses. In turn, the technological and functional properties (water and fat absorption, solubility, gel forming, emulsifying activity, foaming capacity and foam stability) of pulse proteins should be considered in food formulation and processing, particularly in the development of innovative food products, as already validated at industrial level. In either case, the potential allergenicity of pulse proteins (such as exemplified by lupin), or cross reactivity, should not be neglected throughout the complete production chain, including downstream concerns such as safe labelling. Despite the highlighted limitations, it seems evident that pulse proteins will have a major role in fulfilling food protein demands, contributing to food security worldwide, besides being achievable in more sustainable exploration processes.

Towards Augmented Reality manuals for Industry 4.0: a methodology

Abstract: Augmented Reality (AR), is one of the most promising technology for technical manuals in the context of Industry 4.0. However, the implementation of AR documentation in industry is still challenging because specific standards and guidelines are missing. In this work, we propose a novel methodology for the conversion of existing “traditional” documentation, and for the authoring of new manuals in AR in compliance to Industry 4.0 principles. The methodology is based on the optimization of text usage with the ASD Simplified Technical English, the conversion of text instructions into 2D graphic symbols, and the structuring of the content through the combination of Darwin Information Typing Architecture (DITA) and Information Mapping (IM). We tested the proposed approach with a case study of a maintenance manual of hydraulic breakers. We validated it with a user test collecting subjective feedbacks of 22 users. The results of this experiment confirm that the manual obtained using our methodology is clearer than other templates.

Observation of inverse Compton emission from a long γ-ray burst

Abstract: Long-duration γ-ray bursts (GRBs) originate from ultra-relativistic jets launched from the collapsing cores of dying massive stars. They are characterized by an initial phase of bright and highly variable radiation in the kiloelectronvolt-to-megaelectronvolt band, which is probably produced within the jet and lasts from milliseconds to minutes, known as the prompt emission1,2. Subsequently, the interaction of the jet with the surrounding medium generates shock waves that are responsible for the afterglow emission, which lasts from days to months and occurs over a broad energy range from the radio to the gigaelectronvolt bands1-6. The afterglow emission is generally well explained as synchrotron radiation emitted by electrons accelerated by the external shock7-9. Recently, intense long-lasting emission between 0.2 and 1 teraelectronvolts was observed from GRB 190114C10,11. Here we report multi-frequency observations of GRB 190114C, and study the evolution in time of the GRB emission across 17 orders of magnitude in energy, from 5 × 10-6 to 1012 electronvolts. We find that the broadband spectral energy distribution is double-peaked, with the teraelectronvolt emission constituting a distinct spectral component with power comparable to the synchrotron component. This component is associated with the afterglow and is satisfactorily explained by inverse Compton up-scattering of synchrotron photons by high-energy electrons. We find that the conditions required to account for the observed teraelectronvolt component are typical for GRBs, supporting the possibility that inverse Compton emission is commonly produced in GRBs.

Measurement of the cosmic-ray proton spectrum from 40 GeV to 100 TeV with the DAMPE satellite

Abstract: The precise measurement of the spectrum of protons, the most abundant component of the cosmic radiation, is necessary to understand the source and acceleration of cosmic rays in the Milky Way. This work reports the measurement of the cosmic ray proton fluxes with kinetic energies from 40 GeV to 100 TeV, with 2 1/2 years of data recorded by the DArk Matter Particle Explorer (DAMPE). This is the first time that an experiment directly measures the cosmic ray protons up to ~100 TeV with high statistics. The measured spectrum confirms the spectral hardening at ~300 GeV found by previous experiments and reveals a softening at ~13.6 TeV, with the spectral index changing from ~2.60 to ~2.85. Our result suggests the existence of a new spectral feature of cosmic rays at energies lower than the so-called knee and sheds new light on the origin of Galactic cosmic rays.

Search for supersymmetry in proton-proton collisions at 13 TeV in final states with jets and missing transverse momentum

Abstract: Results are reported from a search for supersymmetric particles in the final state with multiple jets and large missing transverse momentum. The search uses a sample of proton-proton collisions at $ \sqrt{s} $ = 13 TeV collected with the CMS detector in 2016–2018, corresponding to an integrated luminosity of 137 fb$^{−1}$, representing essentially the full LHC Run 2 data sample. The analysis is performed in a four-dimensional search region defined in terms of the number of jets, the number of tagged bottom quark jets, the scalar sum of jet transverse momenta, and the magnitude of the vector sum of jet transverse momenta. No significant excess in the event yield is observed relative to the expected background contributions from standard model processes. Limits on the pair production of gluinos and squarks are obtained in the framework of simplified models for supersymmetric particle production and decay processes. Assuming the lightest supersymmetric particle to be a neutralino, lower limits on the gluino mass as large as 2000 to 2310 GeV are obtained at 95% confidence level, while lower limits on the squark mass as large as 1190 to 1630 GeV are obtained, depending on the production scenario.

Dietary Compounds as Epigenetic Modulating Agents in Cancer.

Abstract: Epigenetic mechanisms control gene expression during normal development and their aberrant regulation may lead to human diseases including cancer. Natural phytochemicals can largely modulate mammalian epigenome through regulation of mechanisms and proteins responsible for chromatin remodeling. Phytochemicals are mainly contained in fruits, seeds, and vegetables as well as in foods supplements. These compounds act as powerful cellular antioxidants and anti-carcinogens agents. Several dietary compounds such as catechins, curcumin, genistein, quercetin and resveratrol, among others, exhibit potent anti-tumor activities through the reversion of epigenetic alterations associated to oncogenes activation and inactivation of tumor suppressor genes. In this review, we summarized the actual knowledge about the role of dietary phytochemicals in the restoration of aberrant epigenetic alterations found in cancer cells with a particular focus on DNA methylation and histone modifications. Furthermore, we discussed the mechanisms by which these natural compounds modulate gene expression at epigenetic level and described their molecular targets in diverse types of cancer. Modulation of epigenetic activities by phytochemicals will allow the discovery of novel biomarkers for cancer prevention, and highlights its potential as an alternative therapeutic approach in cancer.