14,622 research outputs found

    Adaptive Kernel Kalman Filter

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    Likelihood Asymptotics in Nonregular Settings: A Review with Emphasis on the Likelihood Ratio

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    This paper reviews the most common situations where one or more regularity conditions which underlie classical likelihood-based parametric inference fail. We identify three main classes of problems: boundary problems, indeterminate parameter problems -- which include non-identifiable parameters and singular information matrices -- and change-point problems. The review focuses on the large-sample properties of the likelihood ratio statistic. We emphasize analytical solutions and acknowledge software implementations where available. We furthermore give summary insight about the possible tools to derivate the key results. Other approaches to hypothesis testing and connections to estimation are listed in the annotated bibliography of the Supplementary Material

    τ±νγγ\tau^\pm \nu \gamma\gamma and ±±γγ/ETX\ell^\pm \ell^\pm \gamma \gamma {\rlap{\,/}{E}_T} X to probe the fermiophobic Higgs boson with high cutoff scales

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    The light fermiophobic Higgs boson hfh_{\rm f} in the type-I two-Higgs-doublet model can evade the current search programs at the LHC since its production through the quark-antiquark annihilation and gluon fusion is not feasible. The particle can be more elusive if the model retains stability up to the Planck scale because the efficient discovery channels are missing from the existing search chart. Through the comprehensive scanning, we show that all the viable parameter points with the Planck cutoff scale require mhf[80,120]  GeV m_{h_{\rm f}} \in[80,\, 120]{\;{\rm GeV}} and MA/H±[90,150]  GeVM_{A/H^\pm} \in [90,\,150]{\;{\rm GeV}}. Since hfhfγγW+Wh_{\rm f}h_{\rm f}\to \gamma\gamma W^+ W^- and H±τ±ν/hfW±H^\pm \to \tau^\pm \nu/h_{\rm f}W^\pm are dominant in this case, two final states are more efficient to probe hfh_{\rm f} than the conventional search mode of 4γ+W±/Z4\gamma+W^\pm/Z. One is τ±νγγ\tau^\pm\nu \gamma\gamma from ppH±(τ±ν)hf(γγ)pp \to H^\pm(\to\tau^\pm\nu) h_{\rm f}(\to \gamma\gamma) and the other is ±±γγ/ETX\ell^\pm \ell^\pm \gamma\gamma {\rlap{\,/}{E}_T} X (±=e±,μ±\ell^\pm=e^\pm,\mu^\pm) from ppH±(hfW±)hfγγW+WW±pp \to H^\pm(\to h_{\rm f}W^\pm) h_{\rm f} \to \gamma\gamma W^+ W^-W^\pm , ppH±(hfW±)A(hfZ)γγW+WW±Zpp \to H^\pm(\to h_{\rm f} W^\pm) A(\to h_{\rm f} Z) \to \gamma\gamma W^+ W^- W^\pm Z , and ppH+(hfW+)H(hfW)γγW+WW+Wpp \to H^+(\to h_{\rm f} W^+)H^-(\to h_{\rm f} W^-)\to \gamma\gamma W^+ W^- W^+ W^-. The inclusive ±±γγ/ETX\ell^\pm \ell^\pm \gamma\gamma {\rlap{\,/}{E}_T} X consists of a same-sign dilepton, two prompt photons, and missing transverse energy. We perform the signal-background analysis at the detector level. With the total integrated luminosity of 300  fb1300\;{\rm fb}^{-1} and the 5\% background uncertainty, two proposed channels at the 14 TeV LHC yield signal significances above five in the entire viable parameter space of the fermiophobic type-I with a high cutoff scale.Comment: Final version to appear in JHE

    A framework for experimental-data-driven assessment of Magnetized Liner Inertial Fusion stagnation image metrics

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    A variety of spherical crystal x-ray imager (SCXI) diagnostics have been developed and fielded on Magnetized Liner Inertial Fusion (MagLIF) experiments at the Sandia National Laboratories Z-facility. These different imaging modalities provide detailed insight into different physical phenomena such as mix of liner material into the hot fuel, cold liner emission, or reduce impact of liner opacity. However, several practical considerations ranging from the lack of a consistent spatial fiducial for registration to different point-spread-functions and tuning crystals or using filters to highlight specific spectral regions make it difficult to develop broadly applicable metrics to compare experiments across our stagnation image database without making significant unverified assumptions. We leverage experimental data for a model-free assessment of sensitivities to instrumentation-based features for any specified image metric. In particular, we utilize a database of historical and recent MagLIF data including Nscans=139N_{\text{scans}} = 139 image plate scans gathered across Nexp=67N_{\text{exp}} = 67 different experiments to assess the impact of a variety of features in the experimental observations arising from uncertainties in registration as well as discrepancies in signal-to-noise ratio and instrument resolution. We choose a wavelet-based image metric known as the Mallat Scattering Transform for the study and highlight how alternate metric choices could also be studied. In particular, we demonstrate a capability to understand and mitigate the impact of signal-to-noise, image registration, and resolution difference between images. This is achieved by utilizing multiple scans of the same image plate, sampling random translations and rotations, and applying instrument specific point-spread-functions found by ray tracing to high-resolution datasets, augmenting our data in an effectively model-free fashion.Comment: 17 pages, 14 figure

    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

    Guess the cheese flavour by the size of its holes: A cosmological test using the abundance of Popcorn voids

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    We present a new definition of cosmic void and a publicly available code with the algorithm that implements it. Underdense regions are defined as free-form objects, called popcorn voids, made from the union of spheres of maximum volume with a given joint integrated underdensity contrast.The method is inspired by the excursion-set theory and consequently no rescaling processing is needed, the removal of overlapping voids and objects with sizes below the shot noise threshold is inherent in the algorithm. The abundance of popcorn voids in the matter field can be fitted using the excursion-set theory provided the relationship between the linear density contrast of the barrier and the threshold used in void identification is modified relative to the spherical evolution model. We also analysed the abundance of voids in biased tracer samples in redshift space. We show how the void abundance can be used to measure the geometric distortions due to the assumed fiducial cosmology, in a test similar to an Alcock-Paczy\'nski test. Using the formalism derived from previous works, we show how to correct the abundance of popcorn voids for redshift-space distortion effects. Using this treatment, in combination with the excursion-set theory, we demonstrate the feasibility of void abundance measurements as cosmological probes. We obtain unbiased estimates of the target parameters, albeit with large degeneracies in the parameter space. Therefore, we conclude that the proposed test in combination with other cosmological probes has potential to improve current cosmological parameter constraints.Comment: Updated manuscript sent to the MNRAS after referee report: 16 pages, 8 figures. Corrections were made to Fig. 4, some related conclusions were modified. The main conclusions remain unchange

    Single Image Depth Prediction Made Better: A Multivariate Gaussian Take

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    Neural-network-based single image depth prediction (SIDP) is a challenging task where the goal is to predict the scene's per-pixel depth at test time. Since the problem, by definition, is ill-posed, the fundamental goal is to come up with an approach that can reliably model the scene depth from a set of training examples. In the pursuit of perfect depth estimation, most existing state-of-the-art learning techniques predict a single scalar depth value per-pixel. Yet, it is well-known that the trained model has accuracy limits and can predict imprecise depth. Therefore, an SIDP approach must be mindful of the expected depth variations in the model's prediction at test time. Accordingly, we introduce an approach that performs continuous modeling of per-pixel depth, where we can predict and reason about the per-pixel depth and its distribution. To this end, we model per-pixel scene depth using a multivariate Gaussian distribution. Moreover, contrary to the existing uncertainty modeling methods -- in the same spirit, where per-pixel depth is assumed to be independent, we introduce per-pixel covariance modeling that encodes its depth dependency w.r.t all the scene points. Unfortunately, per-pixel depth covariance modeling leads to a computationally expensive continuous loss function, which we solve efficiently using the learned low-rank approximation of the overall covariance matrix. Notably, when tested on benchmark datasets such as KITTI, NYU, and SUN-RGB-D, the SIDP model obtained by optimizing our loss function shows state-of-the-art results. Our method's accuracy (named MG) is among the top on the KITTI depth-prediction benchmark leaderboard.Comment: Accepted to IEEE/CVF CVPR 2023. Draft info: 17 pages, 13 Figures, 9 Table

    Dietary plasticity linked to divergent growth trajectories in a critically endangered sea turtle

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    Foraging habitat selection and diet quality are key factors that influence individual fitness and meta-population dynamics through effects on demographic rates. There is growing evidence that sea turtles exhibit regional differences in somatic growth linked to alternative dispersal patterns during the oceanic life stage. Yet, the role of habitat quality and diet in shaping somatic growth rates is poorly understood. Here, we evaluate whether diet variation is linked to regional growth variation in hawksbill sea turtles (Eretmochelys imbricata), which grow significantly slower in Texas, United States versus Florida, United States, through novel integrations of skeletal growth, gastrointestinal content (GI), and bulk tissue and amino acid (AA)-specific stable nitrogen (δ15N) and carbon (δ13C) isotope analyses. We also used AA δ15N ΣV values (heterotrophic bacterial re-synthesis index) and δ13C essential AA (δ13CEAA) fingerprinting to test assumptions about the energy sources fueling hawksbill food webs regionally. GI content analyses, framed within a global synthesis of hawksbill dietary plasticity, revealed that relatively fast-growing hawksbills stranded in Florida conformed with assumptions of extensive spongivory for this species. In contrast, relatively slow-growing hawksbills stranded in Texas consumed considerable amounts of non-sponge invertebrate prey and appear to forage higher in the food web as indicated by isotopic niche metrics and higher AA δ15N-based trophic position estimates internally indexed to baseline nitrogen isotope variation. However, regional differences in estimated trophic position may also be driven by unique isotope dynamics of sponge food webs. AA δ15N ΣV values and δ13CEAA fingerprinting indicated minimal bacterial re-synthesis of organic matter (ΣV < 2) and that eukaryotic microalgae were the primary energy source supporting hawksbill food webs. These findings run contrary to assumptions that hawksbill diets predominantly comprise high microbial abundance sponges expected to primarily derive energy from bacterial symbionts. Our findings suggest alternative foraging patterns could underlie regional variation in hawksbill growth rates, as divergence from typical sponge prey might correspond with increased energy expenditure and reduced foraging success or diet quality. As a result, differential dispersal patterns may infer substantial individual and population fitness costs and represent a previously unrecognized challenge to the persistence and recovery of this critically endangered species

    A Decision Support System for Economic Viability and Environmental Impact Assessment of Vertical Farms

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    Vertical farming (VF) is the practice of growing crops or animals using the vertical dimension via multi-tier racks or vertically inclined surfaces. In this thesis, I focus on the emerging industry of plant-specific VF. Vertical plant farming (VPF) is a promising and relatively novel practice that can be conducted in buildings with environmental control and artificial lighting. However, the nascent sector has experienced challenges in economic viability, standardisation, and environmental sustainability. Practitioners and academics call for a comprehensive financial analysis of VPF, but efforts are stifled by a lack of valid and available data. A review of economic estimation and horticultural software identifies a need for a decision support system (DSS) that facilitates risk-empowered business planning for vertical farmers. This thesis proposes an open-source DSS framework to evaluate business sustainability through financial risk and environmental impact assessments. Data from the literature, alongside lessons learned from industry practitioners, would be centralised in the proposed DSS using imprecise data techniques. These techniques have been applied in engineering but are seldom used in financial forecasting. This could benefit complex sectors which only have scarce data to predict business viability. To begin the execution of the DSS framework, VPF practitioners were interviewed using a mixed-methods approach. Learnings from over 19 shuttered and operational VPF projects provide insights into the barriers inhibiting scalability and identifying risks to form a risk taxonomy. Labour was the most commonly reported top challenge. Therefore, research was conducted to explore lean principles to improve productivity. A probabilistic model representing a spectrum of variables and their associated uncertainty was built according to the DSS framework to evaluate the financial risk for VF projects. This enabled flexible computation without precise production or financial data to improve economic estimation accuracy. The model assessed two VPF cases (one in the UK and another in Japan), demonstrating the first risk and uncertainty quantification of VPF business models in the literature. The results highlighted measures to improve economic viability and the viability of the UK and Japan case. The environmental impact assessment model was developed, allowing VPF operators to evaluate their carbon footprint compared to traditional agriculture using life-cycle assessment. I explore strategies for net-zero carbon production through sensitivity analysis. Renewable energies, especially solar, geothermal, and tidal power, show promise for reducing the carbon emissions of indoor VPF. Results show that renewably-powered VPF can reduce carbon emissions compared to field-based agriculture when considering the land-use change. The drivers for DSS adoption have been researched, showing a pathway of compliance and design thinking to overcome the ‘problem of implementation’ and enable commercialisation. Further work is suggested to standardise VF equipment, collect benchmarking data, and characterise risks. This work will reduce risk and uncertainty and accelerate the sector’s emergence

    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
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