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The upgrade of the ALICE TPC will allow the experiment to cope with the high interaction rates foreseen for the forthcoming Run 3 and Run 4 at the CERN LHC. In this article, we describe the design of new readout chambers and front-end electronics, which are driven by the goals of the experiment. Gas Electron Multiplier (GEM) detectors arranged in stacks containing four GEMs each, and continuous readout electronics based on the SAMPA chip, an ALICE development, are replacing the previous elements. The construction of these new elements, together with their associated quality control procedures, is explained in detail. Finally, the readout chamber and front-end electronics cards replacement, together with the commissioning of the detector prior to installation in the experimental cavern, are presented. After a nine-year period of R&D, construction, and assembly, the upgrade of the TPC was completed in 2020., Comment: 88 pages, 60 figures

Landmark recognition and matching is a critical step in many Image Navigation and Registration (INR) models for geostationary satellite services, as well as to maintain the geometric quality assessment (GQA) in the instrument data processing chain of Earth observation satellites. Matching the landmark accurately is of paramount relevance, and the process can be strongly impacted by the cloud contamination of a given landmark. This paper introduces a complete pattern recognition methodology able to detect the presence of clouds over landmarks using Meteosat Second Generation (MSG) data. The methodology is based on the ensemble combination of dedicated support vector machines (SVMs) dependent on the particular landmark and illumination conditions. This divide-and-conquer strategy is motivated by the data complexity and follows a physically-based strategy that considers variability both in seasonality and illumination conditions along the day to split observations. In addition, it allows training the classification scheme with millions of samples at an affordable computational costs. The image archive was composed of 200 landmark test sites with near 7 million multispectral images that correspond to MSG acquisitions during 2010. Results are analyzed in terms of cloud detection accuracy and computational cost. We provide illustrative source code and a portion of the huge training data to the community.

Euler solved the problem of the collapse of tall thin columns under unexpectedly small loads in 1744. The analogous problem of the collapse of circular elastic rings or tubes under external pressure was mathematically intractable and only fully solved recently. In the context of carbon nanotubes, an additional phenomenon was found experimentally and in atomistic simulations but not explained: the collapse pressure of smaller diameter tubes deviates below the continuum mechanics solution [Torres-Dias et al., Carbon 123, 145 (2017)]. Here, this deviation is shown to occur in discretized straight columns and it is fully explained in terms of the phonon dispersion curve. This reveals an unexpected link between the static mechanical properties of discrete systems and their dynamics described through dispersion curves., Comment: 4 pages, 3 Figures, draft in preparation

Aims: We investigate the orbital and phase space properties of loss cone stars that interact strongly with a hard, high-redshift binary supermassive black hole (SMBH) system formed in a cosmological scenario. Methods: We use a novel hybrid integration approach that combines the direct N-body code $\varphi$-GRAPE with ETICS, a collisionless code that employs the self-consistent field method for force calculation. The hybrid approach shows considerable speed-up over direct summation for particle numbers $> 10^6$, while retaining accuracy of direct N-body for a subset of particles. During the SMBH binary evolution we monitor individual stellar interactions with the binary in order to identify stars that noticeably contribute to the SMBH binary hardening. Results: We successfully identify and analyze in detail the properties of stars that extract energy from the binary. We find that the summed energy changes seen in these stars match very well with the overall binary energy change, demonstrating that stellar interactions are the primary drivers of SMBH binary hardening in triaxial, gas-poor systems. The slight triaxiality of our system results in efficient loss cone refilling, avoiding the final parsec problem. We distinguish three different populations of interactions based on their apocenter. We find a clear prevalence of interactions co-rotating with the binary. Nevertheless, retrograde interactions are the most energetic, contributing only slightly less than the prograde population to the overall energy exchange. The most energetic interactions are also likely to result in a change of sign in the angular momentum of the star. We estimate the merger timescale of the binary to be $\approx 20$ $\mathrm{Myr}$, a value larger by a factor of two than the timescale reported in a previous study., Comment: 17 pages, 13 figures, 2 tables; accepted for publication in Astronomy & Astrophysics

Mixer services purportedly remove all connections between the input (deposited) Bitcoins and the output (withdrawn) mixed Bitcoins, seemingly rendering taint analysis tracking ineffectual. In this paper, we introduce and explore a novel tracking strategy, called \emph{Address Taint Analysis}, that adapts from existing transaction-based taint analysis techniques for tracking Bitcoins that have passed through a mixer service. We also investigate the potential of combining address taint analysis with address clustering and backward tainting. We further introduce a set of filtering criteria that reduce the number of false-positive results based on the characteristics of withdrawn transactions and evaluate our solution with verifiable mixing transactions of nine mixer services from previous reverse-engineering studies. Our finding shows that it is possible to track the mixed Bitcoins from the deposited Bitcoins using address taint analysis and the number of potential transaction outputs can be significantly reduced with the filtering criteria., Comment: 17 pages, 3 figures, CBT 2020

Given the closeness of the two open clusters Cr 135 and UBC 7 on the sky, we investigate the possibility of the two clusters to be physically related. We aim to recover the present-day stellar membership in the open clusters Collinder 135 and UBC 7 (300 pc from the Sun), to constrain their kinematic parameters, ages and masses, and to restore their primordial phase space configuration. The most reliable cluster members are selected with our traditional method modified for the use of Gaia DR2 data. Numerical simulations use the integration of cluster trajectories backwards in time with our original high order Hermite4 code \PGRAPE. We constrain the age, spatial coordinates and velocities, radii and masses of the clusters. We estimate the actual separation of the cluster centres equal to 24 pc. The orbital integration shows that the clusters were much closer in the past if their current line-of-sight velocities are very similar and the total mass is more than 7 times larger the mass of the determined most reliable members. We conclude that the two clusters Cr 135 and UBC 7 might very well have formed a physial pair, based on the observational evidence as well as numerical simulations. The probability of a chance coincidence is only about $2\%$., Comment: Accepted for publication as a Letter in Astronomy and Astrophysics. 5 pages, 2 figures

Yield monitors on harvesters are a key component of precision agriculture. Mass flow estimation is the critical factor to measure, and having this allows for field productivity analysis, adjustments to machine efficiency, and cost minimization by ensuring trucks are filled maximally without exceeding weight limits. Several common technologies used on grain harvesters, including impact plate sensors, are accurate enough on combines to be valuable but suffer from issues such as drift. Sugarcane is composed of a mixture of billets and trash, which is a very dispersed material with much less consistency than grains. In this study, a 3d point cloud approach is used to estimate volume, from which a calibration factor is derived [density] to translate to mass. The system was proved in concept in a controlled environment using bamboo, achieving an R2 of 97.4% when fitting average volume flow per test against average mass flow after correcting for bulk density changes with volume. The system was also tested on field data, which was collected from nearly 1700 wagon loads from the southern U.S. and Brazil over the course of 3 seasons in both green and burnt cane. Results indicated that the concept is very robust with good accuracy, having seasonal CVs for density values ranging from 6.9% to 16.2%. The camera concept proves relatively robust to environmental conditions. The same approach could be used in sugar beets, potatoes or other sparse/non-flowing crops with highly varying material properties, where traditional mass flow sensors do not work., Comment: 14 pages, 6 figures, computers and electronics in agriculture journal

We study the distribution of the Milky Way satellites stellar and dark matter debris. For the first time we address the question of the tidal disruption of satellites in simulations by utilising simultaneously a) a realistic set of orbits extracted from cosmological simulations, b) a three component host galaxy with live halo, disc and bulge components, and c) satellites from hydrodynamical simulations. We analyse the statistical properties of the satellite debris of all massive galaxies reaching the inner Milky Way on a timescale of 2 Gyr. Up to 80$\%$ of the dark matter is stripped from the satellites, while this happens for up to 30$\%$ of their stars. The stellar debris ends mostly in the inner Milky Way halo, whereas the dark matter debris shows a flat mass distribution over the full main halo. The dark matter debris follows a density profile with inner power law index $\alpha_{\rm DM}=-0.66$ and outer index $\beta_{\rm DM}=2.94$, while for stars $\alpha_{*}=-0.44$ and $\beta_{*}=6.17$. In the inner 25 kpc, the distribution of the stellar debris is flatter than that of the dark matter debris and the orientations of their short axes differ significantly. Changing the orientation of the stellar disc by 90$^{\rm{o}}$ has only a minor impact on the distribution of the satellite debris. Our results indicate that the dark matter is more easily stripped than stars from the Milky Way satellites. The structure of the debris is dominated by the satellite orbital properties. The radial profiles, the flattening and the orientation of the stellar and dark matter debris are significantly different, which prevents the prediction of the dark matter distribution from the observed stellar component., Comment: 13 pages, 12 figures, 3 tables, accepted for publication by Astronomy & Astrophysics

Mass flow estimation is of great importance to several industries, and it can be quite challenging to obtain accurate estimates due to limitation in expense or general infeasibility. In the context of agricultural applications, yield monitoring is a key component to precision agriculture and mass flow is the critical factor to measure. Measuring mass flow allows for field productivity analysis, cost minimization, and adjustments to machine efficiency. Methods such as volume or force-impact have been used to measure mass flow; however, these methods are limited in application and accuracy. In this work, we use deep learning to develop and test a vision system that can accurately estimate the mass of sugarcane while running in real-time on a sugarcane harvester during operation. The deep learning algorithm that is used to estimate mass flow is trained using very sparsely annotated images (semi-supervised) using only final load weights (aggregated weights over a certain period of time). The deep neural network (DNN) succeeds in capturing the mass of sugarcane accurately and surpasses older volumetric-based methods, despite highly varying lighting and material colors in the images. The deep neural network is initially trained to predict mass on laboratory data (bamboo) and then transfer learning is utilized to apply the same methods to estimate mass of sugarcane. Using a vision system with a relatively lightweight deep neural network we are able to estimate mass of bamboo with an average error of 4.5% and 5.9% for a select season of sugarcane., Comment: 22 pages, 21 figures, Computers and Electronics in Agriculture. arXiv admin note: text overlap with arXiv:1908.04387

We present part 2 of the 6th and final Data Release (DR6 or FDR) of the Radial Velocity Experiment (RAVE), a magnitude-limited (9

The Radial Velocity Experiment (RAVE) is a magnitude-limited (9

We review the contributions of surface science methods to discover and improve 3D topological insulator materials, while illustrating with examples from our own work. In particular, we demonstrate that spin-polarized angular-resolved photoelectron spectroscopy is instrumental to evidence the spin-helical surface Dirac cone, to tune its Dirac point energy towards the Fermi level, and to discover novel types of topological insulators such as dual ones or switchable ones in phase change materials. Moreover, we introduce procedures to spatially map potential fluctuations by scanning tunneling spectroscopy and to identify topological edge states in weak topological insulators., Comment: Review article, 37 pages, 8 figures

An approach to utilize recent advances in deep generative models for anomaly detection in a granular (continuous) sense on a real-world image dataset with quality issues is detailed using recent normalizing flow models, with implications in many other applications/domains/data types. The approach is completely unsupervised (no annotations available) but qualitatively shown to provide accurate semantic labeling for images via heatmaps of the scaled log-likelihood overlaid on the images. When sorted based on the median values per image, clear trends in quality are observed. Furthermore, downstream classification is shown to be possible and effective via a weakly supervised approach using the log-likelihood output from a normalizing flow model as a training signal for a feature-extracting convolutional neural network. The pre-linear dense layer outputs on the CNN are shown to disentangle high level representations and efficiently cluster various quality issues. Thus, an entirely non-annotated (fully unsupervised) approach is shown possible for accurate estimation and classification of quality issues..

A new so-called `gravitational loss-cone instability' in stellar systems has recently been investigated theoretically in the framework of linear perturbation theory and proved to be potentially important in understanding the physical processes in centres of galaxies, star clusters, and the Oort comet cloud. Using N-body simulations, we confirm previous findings and go beyond the linear theory. Unlike the well-known instabilities, the new one shows no notable change in spherical geometry of the cluster, but it significantly accelerates the speed of diffusion of particles in phase space leading to a repopulation of the loss cone and early instability saturation., Comment: 7 pages, 4 figures, MNRAS submitted

A new so-called `gravitational loss-cone instability' in stellar systems has recently been investigated theoretically in the framework of linear perturbation theory and proved to be potentially important in understanding the physical processes in centres of galaxies, star clusters, and the Oort comet cloud. Using N-body simulations of a toy model, we confirm previous findings for the harmonic dominating potential and go beyond the linear theory. Unlike the well-known instabilities, the new one shows no notable change in the spherical geometry of the cluster, but it significantly accelerates the speed of diffusion of particles in phase space leading to an early instability saturation., Comment: 6 pages, 9 figures, MNRAS accepted

Advances in deep generative and density models have shown impressive capacity to model complex probability density functions in lower-dimensional space. Also, applying such models to high-dimensional image data to model the PDF has shown poor generalization, with out-of-distribution data being assigned equal or higher likelihood than in-sample data. Methods to deal with this have been proposed that deviate from a fully unsupervised approach, requiring large ensembles or additional knowledge about the data, not commonly available in the real-world. In this work, the previously offered reasoning behind these issues is challenged empirically, and it is shown that data-sets such as MNIST fashion/digits and CIFAR10/SVHN are trivially separable and have no overlap on their respective data manifolds that explains the higher OoD likelihood. Models like masked autoregressive flows and block neural autoregressive flows are shown to not suffer from OoD likelihood issues to the extent of GLOW, PixelCNN++, and real NVP. A new avenue is also explored which involves a change of basis to a new space of the same dimension with an orthonormal unitary basis of eigenvectors before modeling. In the test data-sets and models, this aids in pushing down the relative likelihood of the contrastive OoD data set and improve discrimination results. The significance of the density of the original space is maintained, while invertibility remains tractable. Finally, a look to the previous generation of generative models in the form of probabilistic principal component analysis is inspired, and revisited for the same data-sets and shown to work really well for discriminating anomalies based on likelihood in a fully unsupervised fashion compared with pixelCNN++, GLOW, and real NVP with less complexity and faster training. Also, dimensionality reduction using PCA is shown to improve anomaly detection in generative models.

The computation of wave-energy distributions in the mid-to-high frequency regime can be reduced to ray-tracing calculations. Solving the ray-tracing problem in terms of an operator equation for the energy density leads to an inhomogeneous equation which involves a Perron-Frobenius operator defined on a suitable Sobolev space. Even for fairly simple geometries, let alone realistic scenarios such as typical boundary value problems in room acoustics or for mechanical vibrations, numerical approximations are necessary. Here we study the convergence of approximation schemes by rigorous methods. For circular billiards we prove that convergence of finite-rank approximations using a Fourier basis follows a power law where the power depends on the smoothness of the source distribution driving the system. The relevance of our studies for more general geometries is illustrated by numerical examples., Comment: 19 pages, 4 figures

Thin films of topological insulators (TI) usually exhibit multiple parallel conduction channels for the transport of electrical current. Beside the topologically protected surface states (TSS), parallel channels may exist, namely the interior of the not-ideally insulating TI film, the interface layer to the substrate, and the substrate itself. To be able to take advantage of the auspicious transport properties of the TSS, the influence of the parasitic parallel channels on the total current transport has to be minimized. Because the conductivity of the interior (bulk) of the thin TI film is difficult to access by measurements, we propose here an approach for calculating the mobile charge carrier concentration in the TI film. To this end, we calculate the near-surface band bending using parameters obtained experimentally from surface-sensitive measurements, namely (gate-dependent) four-point resistance measurements and angle-resolved photoelectron spectroscopy (ARPES). While in most cases another parameter in the calculations, i.e. the concentration of unintentional dopants inside the thin TI film, is unknown, it turns out that in the thin-film limit the band bending is largely independent of the dopant concentration in the film. Thus, a well-founded estimate of the total mobile charge carrier concentration and the conductivity of the interior of the thin TI film proves possible. Since the interface and substrate conductivities can be measured by a four-probe conductance measurement prior to the deposition of the TI film, the total contribution of all parasitic channels, and therefore also the contribution of the vitally important TSS, can be determined reliably., Comment: 18 pages, 10 color figures

Supervised learning is the workhorse for regression and classification tasks, but the standard approach presumes ground truth for every measurement. In real world applications, limitations due to expense or general in-feasibility due to the specific application are common. In the context of agriculture applications, yield monitoring is one such example where simple-physics based measurements such as volume or force-impact have been used to quantify mass flow, which incur error due to sensor calibration. By utilizing semi-supervised deep learning with gradient aggregation and a sequence of images, in this work we can accurately estimate a physical quantity (mass) with complex data structures and sparse ground truth. Using a vision system capturing images of a sugarcane elevator and running bamboo under controlled testing as a surrogate material to harvesting sugarcane, mass is accurately predicted from images by training a DNN using only final load weights. The DNN succeeds in capturing the complex density physics of random stacking of slender rods internally as part of the mass prediction model, and surpasses older volumetric-based methods for mass prediction. Furthermore, by incorporating knowledge about the system physics through the DNN architecture and penalty terms, improvements in prediction accuracy and stability, as well as faster learning are obtained. It is shown that the classic nonlinear regression optimization can be reformulated with an aggregation term with some independence assumptions to achieve this feat. Since the number of images for any given run are too large to fit on typical GPU vRAM, an implementation is shown that compensates for the limited memory but still achieve fast training times. The same approach presented herein could be applied to other applications like yield monitoring on grain combines or other harvesters using vision or other instrumentation., Comment: 9 pages, 19 figures, pre-print NIPS2019

We study the evolution of star clusters in the Galactic tidal field starting from their birth in molecular clumps. Our model clusters form according to the local-density-driven cluster formation model in which the stellar density profile is steeper than that of gas. As a result, clusters resist the gas expulsion better than predicted by earlier models. We vary the impact of the Galactic tidal field {\lambda}, considering different Galactocentric distances (3-18 kpc), as well as different cluster sizes. Our model clusters survive the gas expulsion independent of {\lambda}. We investigated the relation between the cluster mass at the onset of secular evolution and their dissolution time. The model clusters formed with a high star-formation efficiency (SFE) follow a tight mass-dependent dissolution relation, in agreement with previous theoretical studies. However, the low-SFE models present a shallower mass-dependent relation than high-SFE clusters, and most dissolve before reaching 1 Gyr (cluster teenage mortality)., Comment: 4 pages, 2 figures. Submitted for publication in the Proceedings of IAU Symposium 351

We derive the circular velocity curves of the gaseous and stellar discs of UGC 4056, a giant barred galaxy with an active galactic nucleus (AGN). We analyse UGC 4056 using the 2D spectroscopy obtained within the framework of the Mapping Nearby Galaxies at APO (MaNGA) survey. Using images and the colour index g-r from the Sloan Digital Sky Survey (SDSS), we determined the tilt of the galaxy, which allows us to conclude that the galaxy rotates clockwise with trailing spiral arms. We found that the gas motion at the central part of the UGC 4056 shows peculiar features. The rotation velocity of the gaseous disc shows a bump within around three kiloparsecs while the rotation velocity of the stellar disc falls smoothly to zero with decreasing galactocentric distance. We demonstrate that the peculiar radial velocities in the central part of the galaxy may be caused by the inflow of the gas towards the nucleus of the galaxy. The unusual motion of the gas takes place at the region with the AGN-like radiation and can be explained by the gas response to the bar potential., Comment: 11 pages, 10 figures, 1 table, accepted for publication in A&A

Since the creation of Bitcoin, transaction tracking is one of the prominent means for following the movement of Bitcoins involved in illegal activities. Although every Bitcoin transaction is recorded in the blockchain database, which is transparent for anyone to observe and analyse, Bitcoin's pseudonymity system and transaction obscuring techniques still allow criminals to disguise their transaction trail. While there have been a few attempts to develop tracking methods, there is no accepted evaluation method to measure their accuracy. Therefore, this paper investigates strategies for transaction tracking by introducing two new tainting methods, and proposes an address profiling approach with a metrics-based evaluation framework. We use our approach and framework to compare the accuracy of our new tainting methods with the previous tainting techniques, using data from two real Bitcoin theft transactions and several related control transactions., Comment: 13 pages 12 figures, 3 tables

Extended dynamic mode decomposition (EDMD) provides a class of algorithms to identify patterns and effective degrees of freedom in complex dynamical systems. We show that the modes identified by EDMD correspond to those of compact Perron-Frobenius and Koopman operators defined on suitable Hardy-Hilbert spaces when the method is applied to classes of analytic maps. Our findings elucidate the interpretation of the spectra obtained by EDMD for complex dynamical systems. We illustrate our results by numerical simulations for analytic maps., Comment: 14 pages. 3 figures

We prepare qudits based on angular multimode biphoton states by modulating the pump angular spectrum. The modes are prepared in the Schmidt basis and their intensity distributions do not overlap in space. This allows one to get rid of filtering losses while addressing single modes and to realize a single-shot qudit readout.

Though statistical analyses are centered on research questions and hypotheses, current statistical analysis tools are not. Users must first translate their hypotheses into specific statistical tests and then perform API calls with functions and parameters. To do so accurately requires that users have statistical expertise. To lower this barrier to valid, replicable statistical analysis, we introduce Tea, a high-level declarative language and runtime system. In Tea, users express their study design, any parametric assumptions, and their hypotheses. Tea compiles these high-level specifications into a constraint satisfaction problem that determines the set of valid statistical tests, and then executes them to test the hypothesis. We evaluate Tea using a suite of statistical analyses drawn from popular tutorials. We show that Tea generally matches the choices of experts while automatically switching to non-parametric tests when parametric assumptions are not met. We simulate the effect of mistakes made by non-expert users and show that Tea automatically avoids both false negatives and false positives that could be produced by the application of incorrect statistical tests., Comment: 11 pages

We describe a multi-messenger interpretation of GW170817, which yields a robust lower limit on NS radii. This excludes NSs with radii smaller than about 10.7 km and thus rules out very soft nuclear matter. We stress the potential of this type of constraints when future detections become available. A very similar argumentation may yield an upper bound on the maximum mass of nonrotating NSs. We also discuss simulations of NS mergers, which undergo a first-order phase transition to quark matter. We point out a different dynamical behavior. Considering the gravitational-wave signal, we identify an unambiguous signature of the QCD phase transition in NS mergers. The occurrence of quark matter through a strong first-order phase transition during merging leads to a characteristic shift of the dominant postmerger frequency. The frequency shift is indicative for a phase transition if it is compared to the postmerger frequency which is expected for purely hadronic EoS models. A very strong deviation of several 100 Hz is observed for hybrid EoSs in an otherwise tight relation between the tidal deformability and the postmerger frequency. We address the potential impact of a first-order phase transition on the electromagnetic counterpart of NS mergers. Our simulations suggest that there would be no significant qualitative differences between a system undergoing a phase transition to quark matter and purely hadronic mergers. The quantitative differences are within the spread which is found between different hadronic EoS models. This implies on the one hand that GW170817 is compatible with a possible transition to quark matter. On the other hand these considerations show that it may not be easy to identify quantitative differences between purely hadronic mergers and events in which quark matter occurs considering solely their electromagnetic counterpart or their nucleosynthesis products. (abridged), Comment: 15 pages, 11 figures, submitted to the AIP Conference Proceedings of the Xiamen-CUSTIPEN Workshop on the EOS of Dense Neutron-Rich Matter in the Era of Gravitational Wave Astronomy (January 3 - 7, 2019, Xiamen, China)

We study the impact of the tidal field on the survivability of star clusters following instantaneous gas expulsion. Our model clusters are formed with a centrally-peaked star-formation efficiency profile as a result of star-formation taking place with a constant efficiency per free-fall time. We define the impact of the tidal field as the ratio of the cluster half-mass radius to its Jacobi radius immediately after gas expulsion, $\lambda = r_{h}/R_{J}$. We vary $\lambda$ by varying either the Galactocentric distance, or the size (hence volume density) of star clusters. We propose a new method to measure the violent relaxation duration, in which we compare the total mass-loss rate of star clusters with their stellar evolutionary mass-loss rate. That way, we can robustly estimate the bound mass fraction of our model clusters at the end of violent relaxation. The duration of violent relaxation correlates linearly with the Jacobi radius, when considering identical clusters at different Galactocentric distances. In contrast, it is nearly constant for the solar neighbourhood clusters, slightly decreasing with $\lambda$. The violent relaxation does not last longer than 50 Myr in our simulations. Identical model clusters placed at different Galactocentric distances have the same final bound fraction, despite experiencing different impacts of the tidal field. The solar neighbourhood clusters with different densities experience only limited variations of their final bound fraction. In general, we conclude that the cluster survivability after instantaneous gas expulsion, as measured by their bound mass fraction at the end of violent relaxation, $F_{bound}$, is independent of the impact of the tidal field, $\lambda$., Comment: accepted for publication in MNRAS, 8 pages, 5 figures,3 tables

Studying the effects of air-pollution on health is a key area in environmental epidemiology. An accurate estimation of air-pollution effects requires spatio-temporally resolved datasets of air-pollution, especially, Fine Particulate Matter (PM). Satellite-based technology has greatly enhanced the ability to provide PM assessments in locations where direct measurement is impossible. Indirect PM measurement is a statistical prediction problem. The spatio-temporal statistical literature offer various predictive models: Gaussian Random Fields (GRF) and Linear Mixed Models (LMM), in particular. GRF emphasize the spatio-temporal structure in the data, but are computationally demanding to fit. LMMs are computationally easier to fit, but require some tampering to deal with space and time. Recent advances in the spatio-temporal statistical literature propose to alleviate the computation burden of GRFs by approximating them with Gaussian Markov Random Fields (GMRFs). Since LMMs and GMRFs are both computationally feasible, the question arises: which is statistically better? We show that despite the great popularity of LMMs in environmental monitoring and pollution assessment, LMMs are statistically inferior to GMRF for measuring PM in the Northeastern USA.