9,763 research outputs found

    The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions

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    The Metaverse offers a second world beyond reality, where boundaries are non-existent, and possibilities are endless through engagement and immersive experiences using the virtual reality (VR) technology. Many disciplines can benefit from the advancement of the Metaverse when accurately developed, including the fields of technology, gaming, education, art, and culture. Nevertheless, developing the Metaverse environment to its full potential is an ambiguous task that needs proper guidance and directions. Existing surveys on the Metaverse focus only on a specific aspect and discipline of the Metaverse and lack a holistic view of the entire process. To this end, a more holistic, multi-disciplinary, in-depth, and academic and industry-oriented review is required to provide a thorough study of the Metaverse development pipeline. To address these issues, we present in this survey a novel multi-layered pipeline ecosystem composed of (1) the Metaverse computing, networking, communications and hardware infrastructure, (2) environment digitization, and (3) user interactions. For every layer, we discuss the components that detail the steps of its development. Also, for each of these components, we examine the impact of a set of enabling technologies and empowering domains (e.g., Artificial Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on its advancement. In addition, we explain the importance of these technologies to support decentralization, interoperability, user experiences, interactions, and monetization. Our presented study highlights the existing challenges for each component, followed by research directions and potential solutions. To the best of our knowledge, this survey is the most comprehensive and allows users, scholars, and entrepreneurs to get an in-depth understanding of the Metaverse ecosystem to find their opportunities and potentials for contribution

    TransFusionOdom: Interpretable Transformer-based LiDAR-Inertial Fusion Odometry Estimation

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    Multi-modal fusion of sensors is a commonly used approach to enhance the performance of odometry estimation, which is also a fundamental module for mobile robots. However, the question of \textit{how to perform fusion among different modalities in a supervised sensor fusion odometry estimation task?} is still one of challenging issues remains. Some simple operations, such as element-wise summation and concatenation, are not capable of assigning adaptive attentional weights to incorporate different modalities efficiently, which make it difficult to achieve competitive odometry results. Recently, the Transformer architecture has shown potential for multi-modal fusion tasks, particularly in the domains of vision with language. In this work, we propose an end-to-end supervised Transformer-based LiDAR-Inertial fusion framework (namely TransFusionOdom) for odometry estimation. The multi-attention fusion module demonstrates different fusion approaches for homogeneous and heterogeneous modalities to address the overfitting problem that can arise from blindly increasing the complexity of the model. Additionally, to interpret the learning process of the Transformer-based multi-modal interactions, a general visualization approach is introduced to illustrate the interactions between modalities. Moreover, exhaustive ablation studies evaluate different multi-modal fusion strategies to verify the performance of the proposed fusion strategy. A synthetic multi-modal dataset is made public to validate the generalization ability of the proposed fusion strategy, which also works for other combinations of different modalities. The quantitative and qualitative odometry evaluations on the KITTI dataset verify the proposed TransFusionOdom could achieve superior performance compared with other related works.Comment: Submitted to IEEE Sensors Journal with some modifications. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

    Vegetation responses to variations in climate: A combined ordinary differential equation and sequential Monte Carlo estimation approach

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    Vegetation responses to variation in climate are a current research priority in the context of accelerated shifts generated by climate change. However, the interactions between environmental and biological factors still represent one of the largest uncertainties in projections of future scenarios, since the relationship between drivers and ecosystem responses has a complex and nonlinear nature. We aimed to develop a model to study the vegetation’s primary productivity dynamic response to temporal variations in climatic conditions as measured by rainfall, temperature and radiation. Thus, we propose a new way to estimate the vegetation response to climate via a non-autonomous version of a classical growth curve, with a time-varying growth rate and carrying capacity parameters according to climate variables. With a Sequential Monte Carlo Estimation to account for complexities in the climate-vegetation relationship to minimize the number of parameters. The model was applied to six key sites identified in a previous study, consisting of different arid and semiarid rangelands from North Patagonia, Argentina. For each site, we selected the time series of MODIS NDVI, and climate data from ERA5 Copernicus hourly reanalysis from 2000 to 2021. After calculating the time series of the a posteriori distribution of parameters, we analyzed the explained capacity of the model in terms of the linear coefficient of determination and the parameters distribution variation. Results showed that most rangelands recorded changes in their sensitivity over time to climatic factors, but vegetation responses were heterogeneous and influenced by different drivers. Differences in this climate-vegetation relationship were recorded among different cases: (1) a marginal and decreasing sensitivity to temperature and radiation, respectively, but a high sensitivity to water availability; (2) high and increasing sensitivity to temperature and water availability, respectively; and (3) a case with an abrupt shift in vegetation dynamics driven by a progressively decreasing sensitivity to water availability, without any changes in the sensitivity either to temperature or radiation. Finally, we also found that the time scale, in which the ecosystem integrated the rainfall phenomenon in terms of the width of the window function used to convolve the rainfall series into a water availability variable, was also variable in time. This approach allows us to estimate the connection degree between ecosystem productivity and climatic variables. The capacity of the model to identify changes over time in the vegetation-climate relationship might inform decision-makers about ecological transitions and the differential impact of climatic drivers on ecosystems.Estación Experimental Agropecuaria BarilocheFil: Bruzzone, Octavio Augusto. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche; ArgentinaFil: Bruzzone, Octavio Augusto. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Perri, Daiana Vanesa. Instituto Nacional de Tecnologia Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Área de Recursos Naturales; ArgentinaFil: Perri, Daiana Vanesa. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Easdale, Marcos Horacio. Instituto Nacional de Tecnologia Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Área de Recursos Naturales; ArgentinaFil: Easdale, Marcos Horacio. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentin

    Deep Transfer Learning Applications in Intrusion Detection Systems: A Comprehensive Review

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    Globally, the external Internet is increasingly being connected to the contemporary industrial control system. As a result, there is an immediate need to protect the network from several threats. The key infrastructure of industrial activity may be protected from harm by using an intrusion detection system (IDS), a preventive measure mechanism, to recognize new kinds of dangerous threats and hostile activities. The most recent artificial intelligence (AI) techniques used to create IDS in many kinds of industrial control networks are examined in this study, with a particular emphasis on IDS-based deep transfer learning (DTL). This latter can be seen as a type of information fusion that merge, and/or adapt knowledge from multiple domains to enhance the performance of the target task, particularly when the labeled data in the target domain is scarce. Publications issued after 2015 were taken into account. These selected publications were divided into three categories: DTL-only and IDS-only are involved in the introduction and background, and DTL-based IDS papers are involved in the core papers of this review. Researchers will be able to have a better grasp of the current state of DTL approaches used in IDS in many different types of networks by reading this review paper. Other useful information, such as the datasets used, the sort of DTL employed, the pre-trained network, IDS techniques, the evaluation metrics including accuracy/F-score and false alarm rate (FAR), and the improvement gained, were also covered. The algorithms, and methods used in several studies, or illustrate deeply and clearly the principle in any DTL-based IDS subcategory are presented to the reader

    Learning disentangled speech representations

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    A variety of informational factors are contained within the speech signal and a single short recording of speech reveals much more than the spoken words. The best method to extract and represent informational factors from the speech signal ultimately depends on which informational factors are desired and how they will be used. In addition, sometimes methods will capture more than one informational factor at the same time such as speaker identity, spoken content, and speaker prosody. The goal of this dissertation is to explore different ways to deconstruct the speech signal into abstract representations that can be learned and later reused in various speech technology tasks. This task of deconstructing, also known as disentanglement, is a form of distributed representation learning. As a general approach to disentanglement, there are some guiding principles that elaborate what a learned representation should contain as well as how it should function. In particular, learned representations should contain all of the requisite information in a more compact manner, be interpretable, remove nuisance factors of irrelevant information, be useful in downstream tasks, and independent of the task at hand. The learned representations should also be able to answer counter-factual questions. In some cases, learned speech representations can be re-assembled in different ways according to the requirements of downstream applications. For example, in a voice conversion task, the speech content is retained while the speaker identity is changed. And in a content-privacy task, some targeted content may be concealed without affecting how surrounding words sound. While there is no single-best method to disentangle all types of factors, some end-to-end approaches demonstrate a promising degree of generalization to diverse speech tasks. This thesis explores a variety of use-cases for disentangled representations including phone recognition, speaker diarization, linguistic code-switching, voice conversion, and content-based privacy masking. Speech representations can also be utilised for automatically assessing the quality and authenticity of speech, such as automatic MOS ratings or detecting deep fakes. The meaning of the term "disentanglement" is not well defined in previous work, and it has acquired several meanings depending on the domain (e.g. image vs. speech). Sometimes the term "disentanglement" is used interchangeably with the term "factorization". This thesis proposes that disentanglement of speech is distinct, and offers a viewpoint of disentanglement that can be considered both theoretically and practically

    Predictive Maintenance of Critical Equipment for Floating Liquefied Natural Gas Liquefaction Process

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    Predictive Maintenance of Critical Equipment for Liquefied Natural Gas Liquefaction Process Meeting global energy demand is a massive challenge, especially with the quest of more affinity towards sustainable and cleaner energy. Natural gas is viewed as a bridge fuel to a renewable energy. LNG as a processed form of natural gas is the fastest growing and cleanest form of fossil fuel. Recently, the unprecedented increased in LNG demand, pushes its exploration and processing into offshore as Floating LNG (FLNG). The offshore topsides gas processes and liquefaction has been identified as one of the great challenges of FLNG. Maintaining topside liquefaction process asset such as gas turbine is critical to profitability and reliability, availability of the process facilities. With the setbacks of widely used reactive and preventive time-based maintenances approaches, to meet the optimal reliability and availability requirements of oil and gas operators, this thesis presents a framework driven by AI-based learning approaches for predictive maintenance. The framework is aimed at leveraging the value of condition-based maintenance to minimises the failures and downtimes of critical FLNG equipment (Aeroderivative gas turbine). In this study, gas turbine thermodynamics were introduced, as well as some factors affecting gas turbine modelling. Some important considerations whilst modelling gas turbine system such as modelling objectives, modelling methods, as well as approaches in modelling gas turbines were investigated. These give basis and mathematical background to develop a gas turbine simulated model. The behaviour of simple cycle HDGT was simulated using thermodynamic laws and operational data based on Rowen model. Simulink model is created using experimental data based on Rowen’s model, which is aimed at exploring transient behaviour of an industrial gas turbine. The results show the capability of Simulink model in capture nonlinear dynamics of the gas turbine system, although constraint to be applied for further condition monitoring studies, due to lack of some suitable relevant correlated features required by the model. AI-based models were found to perform well in predicting gas turbines failures. These capabilities were investigated by this thesis and validated using an experimental data obtained from gas turbine engine facility. The dynamic behaviours gas turbines changes when exposed to different varieties of fuel. A diagnostics-based AI models were developed to diagnose different gas turbine engine’s failures associated with exposure to various types of fuels. The capabilities of Principal Component Analysis (PCA) technique have been harnessed to reduce the dimensionality of the dataset and extract good features for the diagnostics model development. Signal processing-based (time-domain, frequency domain, time-frequency domain) techniques have also been used as feature extraction tools, and significantly added more correlations to the dataset and influences the prediction results obtained. Signal processing played a vital role in extracting good features for the diagnostic models when compared PCA. The overall results obtained from both PCA, and signal processing-based models demonstrated the capabilities of neural network-based models in predicting gas turbine’s failures. Further, deep learning-based LSTM model have been developed, which extract features from the time series dataset directly, and hence does not require any feature extraction tool. The LSTM model achieved the highest performance and prediction accuracy, compared to both PCA-based and signal processing-based the models. In summary, it is concluded from this thesis that despite some challenges related to gas turbines Simulink Model for not being integrated fully for gas turbine condition monitoring studies, yet data-driven models have proven strong potentials and excellent performances on gas turbine’s CBM diagnostics. The models developed in this thesis can be used for design and manufacturing purposes on gas turbines applied to FLNG, especially on condition monitoring and fault detection of gas turbines. The result obtained would provide valuable understanding and helpful guidance for researchers and practitioners to implement robust predictive maintenance models that will enhance the reliability and availability of FLNG critical equipment.Petroleum Technology Development Funds (PTDF) Nigeri

    Annals [...].

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    Pedometrics: innovation in tropics; Legacy data: how turn it useful?; Advances in soil sensing; Pedometric guidelines to systematic soil surveys.Evento online. Coordenado por: Waldir de Carvalho Junior, Helena Saraiva Koenow Pinheiro, Ricardo Simão Diniz Dalmolin

    The mechanisms of antibody generation in the llama

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    The llama is able to generate a unique class of antibody. The heavy chain immunoglobulins consist only of two heavy chain polypeptides and bind antigen specifically through single protein domains. Although the mechanisms by which such an antibody interacts with antigen has been studied at some length the manner in which the heavy chain antibody is generated within the llama is unknown. In this study a number of components of the llama immune system have been characterised. The isolation of genes encoding the variable domain of the heavy chain antibody indicates that specific genetic elements within the llama genome are responsible for the generation of the heavy chain antibody. The discovery of constant region genes that encode the heavy chain antibody provides an explanation for the absence of a major immunoglobulin domain from the final, secreted gene product. The lack of this domain within the expressed antibody is believed to be the result of a single nucleotide splice site mutation. In order to investigate the process of llama antibody generation further additional components of the llama immune system, the recombination activating genes (rag) were isolated. One such llama rag gene (rag-i) was cloned, expressed and utilised in an in vitro assay system to investigate recombination events taking place during antibody generation. This assay involved the use of specific signal sequences derived from variable domain gene sequence data and represents, to our knowledge, the first examination of non-murine RAG activity. Through the use of this system distinct differences between llama and mouse recombination signal sequences (RSSs) were uncovered. These differences, located within a specific region of the RSS known as the coding flank, may play an important role in llama antibody generation. These results have led to the proposal of a number of models for the mechanisms involved in llama antibody generation

    Cis-Regulation of Gremlin1 Expression during Mouse Limb Bud Development and its Diversification during Vertebrate Evolution

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    Embryonic development and organogenesis rely on tightly controlled gene expression, which is achieved by cis-regulatory modules (CRMs) interacting with distinct transcription factors (TFs) that control spatio-temporal and tissue-specific gene expression. During organogenesis, gene regulatory networks (GRNs) with selfregulatory feedback properties coordinately control growth and patterning and provide systemic robustness against genetic and/or environmental perturbations. During limb bud development, various interlinked GRNs control outgrowth and patterning along all three limb axes. A paradigm network is the epithelial-mesenchymal (e-m) SHH/GREM1/AER-FGF feedback signaling system which controls limb bud outgrowth and digit patterning. The BMP antagonist GREMLIN1 (GREM1) is central to this e-m interactions as its antagonism of BMP activity is essential to maintain both AER-Fgf and Shh expression. In turn, SHH signaling upregulates Grem1 expression, which results in establishment of a self-regulatory signaling network. One previous study provided evidence that several CRMs could regulate Grem1 expression during limb bud development. However, the cis-regulatory logics underlying the spatio-temporal regulation of the Grem1 expression dynamics remained obscure. From an evolutionary point of view, diversification of CRMs can result in diversification of gene regulation which can drive the establishment of morphological novelties and adaptions. This was evidenced by the observed differences in Grem1 expression in different species that correlates with the evolutionary plasticity of tetrapod digit patterning. Hence, a better understanding of spatio-temporal regulation of the Grem1 expression dynamics and underlying cis-regulatory logic is of interest from both adevelopmental and an evolutionary perspective. Recently, multiple candidate CRMs have been identified that might be functionally relevant for Grem1 expression during mouse limb bud development. For my PhD project, I genetically analyzed which of these CRMs are involved in the regulation of the spatial-temporal Grem1 expression dynamics in limb buds. Therefore, we generated various single and compound CRM mutant alleles using CRISPR/Cas9. Our CRMs allelic series revealed a complex Grem1 cis-regulation among a minimum of six CRMs, where a subset of CRMs regulates Grem1 transcript levels in an additive manner. Surprisingly, phenotypic robustness depends not on threshold transcript levels but the spatial integrity of the Grem1 expression domain. In particular, interactions among five CRMs control the characteristic asymmetrical and posteriorly biased Grem1 expression in mouse limb buds. Our results provide an example of how multiple seemingly redundant limb-specific CRMs provide phenotypical robustness by cooperative/synergistic regulation of the spatial Grem1 expression dynamics. Three CRMs are conserved along the phylogeny of extant vertebrates with paired appendages. Of those, the activities of two CRMs recapitulate the major spatiotemporal aspects of Grem1 expression in mouse limb buds. In order to study their functions in species-specific regulation of Grem1 expression and their functional diversification in tetrapods, I tested the orthologous of both CRMs from representative species using LacZ reporter assays in transgenic mice, in comparison to the endogenous Grem1 expression in limb buds of the species of origin. Surprisingly, the activities of CRM orthologues display high evolutionary plasticity, which correlates better with the Grem1 expression pattern in limb buds of the species of origin than its mouse orthologue. This differential responsiveness to the GRNs in mouse suggests that TF binding site alterations in CRMs could underlie the spatial diversification of Grem1 in limb buds during tetrapod evolution. While the fish fin and tetrapod limb share some homologies of proximal bones, the autopod is a neomorphic feature of tetrapods. The Grem1 requirement for digit patterning and conserved expression in fin buds prompted us to assess the enhancer activity of fish CRM orthologues in transgenic mice. Surprisingly, all tested fish CRMs are active in the mouse autopod primordia providing strong evidence that Grem1 CRMs are active in fin buds and that they predate the fin-to-limb transition. Our results corroborate increasing evidence that CRMs governing autopodial gene expression have been co-opted during the emergence of tetrapod autopod. Furthermore, as part of a collaboration with Dr. S. Jhanwar, I contributed to the study of shared and species-specific epigenomic and genomic variations during mouse and chicken limb bud development. In this analysis, Dr. S. Jhanwar identified putative enhancers that show higher chicken-specific sequence turnover rates in comparison to their mouse orthologues, which defines them as so-called chicken accelerated regions (CARs). Here, I analyzed the CAR activities in comparison to their mouse orthologues by transgenic LacZ reporter assays, which was complemented by analysis of the endogenous gene expression in limb buds of both species. This analysis indicates that diversified activity of CARs and their mouse orthologues could be linked to the differential gene expression patterns in limb buds of both species
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