14,622 research outputs found
Likelihood Asymptotics in Nonregular Settings: A Review with Emphasis on the Likelihood Ratio
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
and to probe the fermiophobic Higgs boson with high cutoff scales
The light fermiophobic Higgs boson 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 and .
Since and are dominant in this case, two final states are more
efficient to probe than the conventional search mode of
. One is from and the other is () from , , and . The inclusive 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
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
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 image plate scans
gathered across 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
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
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
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
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
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 [...].
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
- …