9,903 research outputs found

    An iterative warping and clustering algorithm to estimate multiple wave-shape functions from a nonstationary oscillatory signal

    Full text link
    Nonsinusoidal oscillatory signals are everywhere. In practice, the nonsinusoidal oscillatory pattern, modeled as a 1-periodic wave-shape function (WSF), might vary from cycle to cycle. When there are finite different WSFs, s1,,sKs_1,\ldots,s_K, so that the WSF jumps from one to another suddenly, the different WSFs and jumps encode useful information. We present an iterative warping and clustering algorithm to estimate s1,,sKs_1,\ldots,s_K from a nonstationary oscillatory signal with time-varying amplitude and frequency, and hence the change points of the WSFs. The algorithm is a novel combination of time-frequency analysis, singular value decomposition entropy and vector spectral clustering. We demonstrate the efficiency of the proposed algorithm with simulated and real signals, including the voice signal, arterial blood pressure, electrocardiogram and accelerometer signal. Moreover, we provide a mathematical justification of the algorithm under the assumption that the amplitude and frequency of the signal are slowly time-varying and there are finite change points that model sudden changes from one wave-shape function to another one.Comment: 39 pages, 11 figure

    Rational-approximation-based model order reduction of Helmholtz frequency response problems with adaptive finite element snapshots

    Get PDF
    We introduce several spatially adaptive model order reduction approaches tailored to non-coercive elliptic boundary value problems, specifically, parametric-in-frequency Helmholtz problems. The offline information is computed by means of adaptive finite elements, so that each snapshot lives in a different discrete space that resolves the local singularities of the analytical solution and is adjusted to the considered frequency value. A rational surrogate is then assembled adopting either a least squares or an interpolatory approach, yielding a function-valued version of the standard rational interpolation method (V-SRI) and the minimal rational interpolation method (MRI). In the context of building an approximation for linear or quadratic functionals of the Helmholtz solution, we perform several numerical experiments to compare the proposed methodologies. Our simulations show that, for interior resonant problems (whose singularities are encoded by poles on the V-SRI and MRI work comparably well. Instead, when dealing with exterior scattering problems, whose frequency response is mostly smooth, the V-SRI method seems to be the best performing one

    Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review

    Full text link
    In this paper, a critical bibliometric analysis study is conducted, coupled with an extensive literature survey on recent developments and associated applications in machine learning research with a perspective on Africa. The presented bibliometric analysis study consists of 2761 machine learning-related documents, of which 98% were articles with at least 482 citations published in 903 journals during the past 30 years. Furthermore, the collated documents were retrieved from the Science Citation Index EXPANDED, comprising research publications from 54 African countries between 1993 and 2021. The bibliometric study shows the visualization of the current landscape and future trends in machine learning research and its application to facilitate future collaborative research and knowledge exchange among authors from different research institutions scattered across the African continent

    Statistical Learning for Gene Expression Biomarker Detection in Neurodegenerative Diseases

    Get PDF
    In this work, statistical learning approaches are used to detect biomarkers for neurodegenerative diseases (NDs). NDs are becoming increasingly prevalent as populations age, making understanding of disease and identification of biomarkers progressively important for facilitating early diagnosis and the screening of individuals for clinical trials. Advancements in gene expression profiling has enabled the exploration of disease biomarkers at an unprecedented scale. The work presented here demonstrates the value of gene expression data in understanding the underlying processes and detection of biomarkers of NDs. The value of novel approaches to previously collected -omics data is shown and it is demonstrated that new therapeutic targets can be identified. Additionally, the importance of meta-analysis to improve power of multiple small studies is demonstrated. The value of blood transcriptomics data is shown in applications to researching NDs to understand underlying processes using network analysis and a novel hub detection method. Finally, after demonstrating the value of blood gene expression data for investigating NDs, a combination of feature selection and classification algorithms were used to identify novel accurate biomarker signatures for the diagnosis and prognosis of Parkinson’s disease (PD) and Alzheimer’s disease (AD). Additionally, the use of feature pools based on previous knowledge of disease and the viability of neural networks in dimensionality reduction and biomarker detection is demonstrated and discussed. In summary, gene expression data is shown to be valuable for the investigation of ND and novel gene biomarker signatures for the diagnosis and prognosis of PD and AD

    The Role of Transient Vibration of the Skull on Concussion

    Get PDF
    Concussion is a traumatic brain injury usually caused by a direct or indirect blow to the head that affects brain function. The maximum mechanical impedance of the brain tissue occurs at 450±50 Hz and may be affected by the skull resonant frequencies. After an impact to the head, vibration resonance of the skull damages the underlying cortex. The skull deforms and vibrates, like a bell for 3 to 5 milliseconds, bruising the cortex. Furthermore, the deceleration forces the frontal and temporal cortex against the skull, eliminating a layer of cerebrospinal fluid. When the skull vibrates, the force spreads directly to the cortex, with no layer of cerebrospinal fluid to reflect the wave or cushion its force. To date, there is few researches investigating the effect of transient vibration of the skull. Therefore, the overall goal of the proposed research is to gain better understanding of the role of transient vibration of the skull on concussion. This goal will be achieved by addressing three research objectives. First, a MRI skull and brain segmentation automatic technique is developed. Due to bones’ weak magnetic resonance signal, MRI scans struggle with differentiating bone tissue from other structures. One of the most important components for a successful segmentation is high-quality ground truth labels. Therefore, we introduce a deep learning framework for skull segmentation purpose where the ground truth labels are created from CT imaging using the standard tessellation language (STL). Furthermore, the brain region will be important for a future work, thus, we explore a new initialization concept of the convolutional neural network (CNN) by orthogonal moments to improve brain segmentation in MRI. Second, the creation of a novel 2D and 3D Automatic Method to Align the Facial Skeleton is introduced. An important aspect for further impact analysis is the ability to precisely simulate the same point of impact on multiple bone models. To perform this task, the skull must be precisely aligned in all anatomical planes. Therefore, we introduce a 2D/3D technique to align the facial skeleton that was initially developed for automatically calculating the craniofacial symmetry midline. In the 2D version, the entire concept of using cephalometric landmarks and manual image grid alignment to construct the training dataset was introduced. Then, this concept was extended to a 3D version where coronal and transverse planes are aligned using CNN approach. As the alignment in the sagittal plane is still undefined, a new alignment based on these techniques will be created to align the sagittal plane using Frankfort plane as a framework. Finally, the resonant frequencies of multiple skulls are assessed to determine how the skull resonant frequency vibrations propagate into the brain tissue. After applying material properties and mesh to the skull, modal analysis is performed to assess the skull natural frequencies. Finally, theories will be raised regarding the relation between the skull geometry, such as shape and thickness, and vibration with brain tissue injury, which may result in concussive injury

    Neural Natural Language Generation: A Survey on Multilinguality, Multimodality, Controllability and Learning

    Get PDF
    Developing artificial learning systems that can understand and generate natural language has been one of the long-standing goals of artificial intelligence. Recent decades have witnessed an impressive progress on both of these problems, giving rise to a new family of approaches. Especially, the advances in deep learning over the past couple of years have led to neural approaches to natural language generation (NLG). These methods combine generative language learning techniques with neural-networks based frameworks. With a wide range of applications in natural language processing, neural NLG (NNLG) is a new and fast growing field of research. In this state-of-the-art report, we investigate the recent developments and applications of NNLG in its full extent from a multidimensional view, covering critical perspectives such as multimodality, multilinguality, controllability and learning strategies. We summarize the fundamental building blocks of NNLG approaches from these aspects and provide detailed reviews of commonly used preprocessing steps and basic neural architectures. This report also focuses on the seminal applications of these NNLG models such as machine translation, description generation, automatic speech recognition, abstractive summarization, text simplification, question answering and generation, and dialogue generation. Finally, we conclude with a thorough discussion of the described frameworks by pointing out some open research directions.This work has been partially supported by the European Commission ICT COST Action “Multi-task, Multilingual, Multi-modal Language Generation” (CA18231). AE was supported by BAGEP 2021 Award of the Science Academy. EE was supported in part by TUBA GEBIP 2018 Award. BP is in in part funded by Independent Research Fund Denmark (DFF) grant 9063-00077B. IC has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 838188. EL is partly funded by Generalitat Valenciana and the Spanish Government throught projects PROMETEU/2018/089 and RTI2018-094649-B-I00, respectively. SMI is partly funded by UNIRI project uniri-drustv-18-20. GB is partly supported by the Ministry of Innovation and the National Research, Development and Innovation Office within the framework of the Hungarian Artificial Intelligence National Laboratory Programme. COT is partially funded by the Romanian Ministry of European Investments and Projects through the Competitiveness Operational Program (POC) project “HOLOTRAIN” (grant no. 29/221 ap2/07.04.2020, SMIS code: 129077) and by the German Academic Exchange Service (DAAD) through the project “AWAKEN: content-Aware and netWork-Aware faKE News mitigation” (grant no. 91809005). ESA is partially funded by the German Academic Exchange Service (DAAD) through the project “Deep-Learning Anomaly Detection for Human and Automated Users Behavior” (grant no. 91809358)

    Stochastic maximum principle with control-dependent terminal time and applications

    Get PDF
    In this thesis we study stochastic control problems with control-dependent stopping terminal time. We assess what are the methods and theorems from standard control optimization settings that can be applied to this framework and we introduce new statements where necessary. In the first part of the thesis we study a general optimal liquidation problem with a control-dependent stopping time which is the first time the stock holding becomes zero or a fixed terminal time, whichever comes first. We prove a stochastic maximum principle (SMP) which is markedly different in its Hamiltonian condition from that of the standard SMP with fixed terminal time. The new version of the SMP involves an innovative definition of the FBSDE associated to the problem and a new type of Hamiltonian. We present several examples in which the optimal solution satisfies the SMP in this thesis but fails the standard SMP in the literature. The generalised version of the SMP Theorem can also be applied to any problem in physics and engineering in which the terminal time of the optimization depends on the control, such as optimal planning problems. In the second part of thesis, we introduce an optimal liquidation problem with control-dependent stopping time as before. We analyze the case when an agent is trading on a market with two financial assets correlated with each other. The agent’s task is to liquidate via market orders an initial position of shares of one of the two financial assets, without having the possi- bility of trading the other stock. The main results of this part consist in proving a verification theorem and a comparison principle for the viscosity solution to the HJB equation and finding an approximation of the classical solution of the Hamilton-Jacobi-Bellman (HJB) equation associated to this problem.Open Acces

    Behavior prediction of traffic actors for intelligent vehicle using artificial intelligence techniques: A review

    Full text link
    Intelligent vehicle technology has made tremendous progress due to Artificial Intelligence (AI) techniques. Accurate behavior prediction of surrounding traffic actors is essential for the safe and secure navigation of the intelligent vehicle. Minor misbehavior of these vehicles on the busy roads may lead to an accident. Due to this, there is a need for vehicle behavior research work in today's era. This research article reviews traffic actors' behavior prediction techniques for intelligent vehicles to perceive, infer, and anticipate other vehicles' intentions and future actions. It identifies the key strategies and methods for AI, emerging trends, datasets, and ongoing research issues in these fields. As per the authors' knowledge, this is the first systematic literature review dedicated to the vehicle behavior study examining existing academic literature published by peer review venues between 2011 and 2021. A systematic review was undertaken to examine these papers, and five primary research questions have been addressed. The findings show that using sophisticated input representation that includes traffic rules and road geometry, artificial intelligence-based solutions applied to behavior prediction of traffic actors for intelligent vehicles have shown promising success, particularly in complex driving scenarios. Finally, the paper summarizes the most widely used approaches in behavior prediction of traffic actors for intelligent vehicles, which the authors believe serves as a foundation for future research in behavior prediction of surrounding traffic actors for secure and accurate intelligent vehicle navigation

    Conscience and Consciousness: British Theatre and Human Rights.

    Get PDF
    This research project investigates a paradigm of human rights theatre. Through the lens of performance and theatre-making, this thesis explores how we came to represent, speak about, discuss, and own human rights in Britain. My framework of ‘human rights theatre’ proposes three distinctive features: firstly, such works dramatise real-world issues and highlights the role of the state in endangering its citizens; secondly, ethical ruptures are encountered within and without the drama, and finally, these performances characteristically aspire to produce an activist effect on the collective behaviours of the audience. This thesis interrogates the strategies theatre-makers use to articulate human rights concerns or to animate human rights intent. The selected case-studies for this investigation are ice&fire’s testimonial project, Actors for Human Rights; Badac Theatre; Jonathan Holmes’ work as director of Jericho House; Cardboard Citizens’ youth participation programme, ACT NOW; and Tony Cealy’s Black Men’s Consortium. Deliberately selecting companies and performance events that have received limited critical attention, my methodology constellates case-studies through original interviews, durational observation of creative working methods and proximate descriptions of practice. The thesis is interested in the experience of coming to ‘consciousness’ through human rights theatre, an awakening to the impacts of rights infringements and rights claiming. I explore consciousness as a processual, procedural, and durational happening in these performance events. I explore the ‘æffect’ of activist art and examine the ways in which makers of human rights theatre aim to amplify both affective and effective qualities in their work. My thesis also considers the articulation of activist purpose and the campaigning intent of the selected theatre-makers and explores how their activism is animated in their productions. Through the rich seam of discussion generated by the identification and exploration of the traits of a distinctive human rights theatre, I affirm the generative value of this typological enquiry

    Response of saline reservoir to different phaseCO₂-brine: experimental tests and image-based modelling

    Get PDF
    Geological CO₂ storage in saline rocks is a promising method for meeting the target of net zero emission and minimizing the anthropogenic CO₂ emitted into the earth’s atmosphere. Storage of CO₂ in saline rocks triggers CO₂-brine-rock interaction that alters the properties of the rock. Properties of rocks are very crucial for the integrity and efficiency of the storage process. Changes in properties of the reservoir rocks due to CO₂-brine-rock interaction must be well predicted, as some changes can reduce the storage integrity of the reservoir. Considering the thermodynamics, phase behavior, solubility of CO₂ in brine, and the variable pressure-temperature conditions of the reservoir, there will be undissolved CO₂ in a CO₂ storage reservoir alongside the brine for a long time, and there is a potential for phase evolution of the undissolved CO₂. The phase of CO₂ influence the CO₂-brine-rock interaction, different phaseCO₂-brine have a unique effect on the properties of the reservoir rocks, Therefore, this study evaluates the effect of four different phaseCO₂-brine reservoir states on the properties of reservoir rocks using experimental and image-based approach. Samples were saturated with the different phaseCO₂-brine, then subjected to reservoir conditions in a triaxial compression test. The representative element volume (REV)/representative element area (REA) for the rock samples was determined from processed digital images, and rock properties were evaluated using digital rock physics and rock image analysis techniques. This research has evaluated the effect of different phaseCO₂-brine on deformation rate and deformation behavior, bulk modulus, compressibility, strength, and stiffness as well as porosity and permeability of sample reservoir rocks. Changes in pore geometry properties, porosity, and permeability of the rocks in CO₂ storage conditions with different phaseCO₂-brine have been evaluated using digital rock physics techniques. Microscopic rock image analysis has been applied to provide evidence of changes in micro-fabric, the topology of minerals, and elemental composition of minerals in saline rocks resulting from different phaseCO₂-br that can exist in a saline CO₂ storage reservoir. It was seen that the properties of the reservoir that are most affected by the scCO₂-br state of the reservoir include secondary fatigue rate, bulk modulus, shear strength, change in the topology of minerals after saturation as well as change in shape and flatness of pore surfaces. The properties of the reservoir that is most affected by the gCO₂-br state of the reservoir include primary fatigue rate, change in permeability due to stress, change in porosity due to stress, and change topology of minerals due to stress. For all samples, the roundness and smoothness of grains as well as smoothness of pores increased after compression while the roundness of pores decreased. Change in elemental composition in rock minerals in CO₂-brine-rock interaction was seen to depend on the reactivity of the mineral with CO₂ and/or brine and the presence of brine accelerates such change. Carbon, oxygen, and silicon can be used as index minerals for elemental changes in a CO₂-brine-rock system. The result of this work can be applied to predicting the effect the different possible phases of CO₂ will have on the deformation, geomechanics indices, and storage integrity of giant CO₂ storage fields such as Sleipner, In Salah, etc
    corecore