Geometric Bias Research Articles

Comparison of two air quality models in complex terrain near sea shore

Air pollution is the most important environmental problem in Zonguldak, Turkey due to excessive coal combustion and thermal power plant emissions. The city center is located on a complex terrain near the Black Sea shore. There exist some previous studies about PM10 pollution in the study area, but none of them is related to the spatial distribution of the pollutant. This air quality modeling study aims to fill this gap in the literature. Firstly, PM10 emission inventory has been prepared for point, line, and area sources for the year 2011. For that period, bituminous coal was the principal fuel for domestic heating in houses and generating electricity in thermal power plants, therefore particulate matter (PM10) was the leading air pollutant. Emission inventory calculations revealed that 2710.2 tons of PM10 have been emitted to the atmosphere from all sources in the study area. Then, the air quality modeling has been performed for PM10 by using two air quality models: AERMOD and CALPUFF. According to the modeling results, the PM10 pollution levels may pose a health threat to the inhabitants of Zonguldak. The maximum PM10 concentrations predicted by the CALPUFF model were higher than that of the AERMOD model. The model predicted values plus background concentration were validated against the PM10 measurements by using fractional bias, index of agreement, geometric mean bias, and geometric mean-variance. According to the model performance analysis, CALPUFF showed slightly better performance as compared to AERMOD.

Edge-of-the-Multis: Evidence for a Transition in the Outer Architectures of Compact Multiplanet Systems

Although the architectures of compact multiple-planet systems are well characterized, there has been little examination of their “outer edges,” or the locations of their outermost planets. Here we present evidence that the observed high-multiplicity Kepler systems truncate at smaller orbital periods than can be explained by geometric and detection biases alone. To show this, we considered the existence of hypothetical planets orbiting beyond the observed transiting planets with properties dictated by the “peas-in-a-pod” patterns of intrasystem radius and period ratio uniformity. We evaluated the detectability of these hypothetical planets using (1) a novel approach for estimating the mutual inclination dispersion of multitransiting systems based on transit chord length ratios, and (2) a model of transit probability and detection efficiency that accounts for the impacts of planet multiplicity on completeness. Under the assumption that the “peas-in-a-pod” patterns continue to larger orbital separations than observed, we find that ≳35% of Kepler compact multis should possess additional detected planets beyond the known planets, constituting a ∼7σ discrepancy with the lack of such detections. These results indicate that the outer (∼100–300 days) regions of compact multis experience a truncation (i.e., an “edge-of-the-multis”) or a significant breakdown of the “peas-in-a-pod” patterns, in the form of systematically smaller radii or larger period ratios. We outline future observations that can distinguish these possibilities, and we discuss implications for planet formation theories.

SIMILARITY LIMITS OF ORIENTATION CHANGES IN AFFINE TRANSFORMATIONS WITH APPLICATIONS TO PLANAR PATTERN MATCHING

Besides descriptor designing, Factoring out the categorical effects of affine transformations is also an effective way to match features. This paper proposed and demonstrated a hypothesis that any coplanar orientations have limited geometrical changes when observed in the camera screen under given ranges of 3D affine transformations.Confirmatory experiments are accomplished by Matlab simulations focusing on specific influences of 3D affine transformations on similarity biases between concerned orientation changes. Statistical analyses and results show that rotations about plane axes mainly affect the geometrical biases of orientation changes. A set of fit limits standing for the biggest influences of most affine transformations in specified ranges are numerically approximated by total sample space distribution and experimental extreme values. We use these Similarity Limits of Orientation Changes (SLOC) as optimization constraints in matching problems, and an application example of planar pattern matching is given. The effectiveness and efficiency of SLOC are proved by the experimental results in Mikolajczyk and HPatches testbed.

Manifold embedding data-driven mechanics

This article introduces a manifold embedding data-driven paradigm to solve small- and finite-strain elasticity problems without a conventional constitutive law. This formulation follows the classical data-driven paradigm by seeking the solution that obeys the balance of linear momentum and compatibility conditions while remaining consistent with the material data by minimizing a distance measure. Our key point of departure is the introduction of a global manifold embedding as a means to learn the geometrical trend of the constitutive data mathematically represented by a smooth manifold. By training an invertible neural network to embed the data of an underlying constitutive manifold onto a Euclidean space, we reformulate the local distance-minimization problem that requires a computationally intensive combinatorial search to identify the optimal data points closest to the conservation law with a cost-efficient projection step. Meanwhile, numerical experiments performed on path-independent elastic materials of different material symmetries suggest that the geometrical inductive bias learned by the neural network is helpful to ensure more consistent predictions when dealing with data sets of limited sizes or those with missing data.

Numerical study of premixed gas explosion in a 1-m channel partly filled with 18650 cell-like cylinders with experiments

Abused and defective Li-ion cells can cause a catastrophic failure of a Li-ion battery (LIB), leading to severe fires and explosions. In recent years, several numerical and experimental studies have been conducted on the explosion hazard related to the vented combustible gases from failed Li-ion cells. Experimentally quantifying fundamental properties for failing LIBs is essential for understanding safety issues; however, it can be costly, time-consuming, and can be partly incomplete. Computational fluid dynamic (CFD) simulations have been an essential tool for studying the risk and consequences in the process industry. In this study, the prediction accuracy of the open-source CFD combustion model/solver XiFoam was evaluated by comparing numerical simulations and experiments of premixed gas explosions in a 1-m explosion channel partly filled with 18650 cell-like cylinders. The prediction accuracy was determined by calculating the mean geometric bias and variance for the temporal pressure evolution, maximum pressure peak, positive impulse, spatial flame front velocity for two different channel geometries, in addition to two gas compositions at several fuel-air equivalence ratios. From this method, the XiFoam model/solver gave an overall acceptable model performance for both geometries and gas composition.

The role of spatial embedding in mouse brain networks constructed from diffusion tractography and tracer injections

Diffusion MRI tractography is the only noninvasive method to measure the structural connectome in humans. However, recent validation studies have revealed limitations of modern tractography approaches, which lead to significant mistracking caused in part by local uncertainties in fiber orientations that accumulate to produce larger errors for longer streamlines. Characterizing the role of this length bias in tractography is complicated by the true underlying contribution of spatial embedding to brain topology. In this work, we compare graphs constructed with ex vivo tractography data in mice and neural tracer data from the Allen Mouse Brain Connectivity Atlas to random geometric surrogate graphs which preserve the low-order distance effects from each modality in order to quantify the role of geometry in various network properties. We find that geometry plays a substantially larger role in determining the topology of graphs produced by tractography than graphs produced by tracers. Tractography underestimates weights at long distances compared to neural tracers, which leads tractography to place network hubs close to the geometric center of the brain, as do corresponding tractography-derived random geometric surrogates, while tracer graphs place hubs further into peripheral areas of the cortex. We also explore the role of spatial embedding in modular structure, network efficiency and other topological measures in both modalities. Throughout, we compare the use of two different tractography streamline node assignment strategies and find that the overall differences between tractography approaches are small relative to the differences between tractography- and tracer-derived graphs. These analyses help quantify geometric biases inherent to tractography and promote the use of geometric benchmarking in future tractography validation efforts.

Computationally Tractable Riemannian Manifolds for Graph Embeddings

Representing graphs as sets of node embeddings in certain curved Riemannian manifolds has recently gained momentum in machine learning due to their desirable geometric inductive biases (e.g., hierarchical structures benefit from hyperbolic geometry). However, going beyond embedding spaces of constant sectional curvature, while potentially more representationally powerful, proves to be challenging as one can easily lose the appeal of computationally tractable tools such as geodesic distances or Riemannian gradients. Here, we explore two computationally efficient matrix manifolds, showcasing how to learn and optimize graph embeddings in these Riemannian spaces. Empirically, we demonstrate consistent improvements over Euclidean geometry while often outperforming hyperbolic and elliptical embeddings based on various metrics that capture different graph properties. Our results serve as new evidence for the benefits of non-Euclidean embeddings in machine learning pipelines.

Dethroning Aristocracy in Graphs via Adversarial Perturbations (Student Abstract)

Learning low-dimensional embeddings of graph data in curved Riemannian manifolds has gained traction due to their desirable property of acting as effective geometrical inductive biases. More specifically, models of Hyperbolic geometry such as Poincar\'{e} Ball and Lorentz/Hyperboloid Model have found applications for learning data with hierarchical anatomy. Gromov's hyperbolicity measures whether a graph can be isometrically embedded in hyperbolic space. This paper shows that adversarial attacks that perturb the network structure also affect the hyperbolicity of graphs rendering hyperbolic space less effective for learning low-dimensional node embeddings of the graph. To circumvent this problem, we introduce learning embeddings in pseudo-Riemannian manifolds such as Lorentzian manifolds and show empirically that they are robust to adversarial perturbations. Despite the recent proliferation of adversarial robustness methods in the graph data, this is the first work exploring the relationship between adversarial attacks and hyperbolicity while also providing resolution to navigate such vulnerabilities.

Microtubule pivoting enables mitotic spindle assembly in S. cerevisiae.

To assemble a bipolar spindle, microtubules emanating from two poles must bundle into an antiparallel midzone, where plus end-directed motors generate outward pushing forces to drive pole separation. Midzone cross-linkers and motors display only modest preferences for antiparallel filaments, and duplicated poles are initially tethered together, an arrangement that instead favors parallel interactions. Pivoting of microtubules around spindle poles might help overcome this geometric bias, but the intrinsic pivoting flexibility of the microtubule-pole interface has not been directly measured, nor has its importance during early spindle assembly been tested. By measuring the pivoting of microtubules around isolated yeast spindle poles, we show that pivoting flexibility can be modified by mutating a microtubule-anchoring pole component, Spc110. By engineering mutants with different flexibilities, we establish the importance of pivoting in vivo for timely pole separation. Our results suggest that passive thermal pivoting can bring microtubules from side-by-side poles into initial contact, but active minus end-directed force generation will be needed to achieve antiparallel alignment.

Shear wave cardiovascular MR elastography using intrinsic cardiac motion for transducer-free non-invasive evaluation of myocardial shear wave velocity

Changes in myocardial stiffness may represent a valuable biomarker for early tissue injury or adverse remodeling. In this study, we developed and validated a novel transducer-free magnetic resonance elastography (MRE) approach for quantifying myocardial biomechanics using aortic valve closure-induced shear waves. Using motion-sensitized two-dimensional pencil beams, septal shear waves were imaged at high temporal resolution. Shear wave speed was measured using time-of-flight of waves travelling between two pencil beams and corrected for geometrical biases. After validation in phantoms, results from twelve healthy volunteers and five cardiac patients (two left ventricular hypertrophy, two myocardial infarcts, and one without confirmed pathology) were obtained. Torsional shear wave speed in the phantom was 3.0 ± 0.1 m/s, corresponding with reference speeds of 2.8 ± 0.1 m/s. Geometrically-biased flexural shear wave speed was 1.9 ± 0.1 m/s, corresponding with simulation values of 2.0 m/s. Corrected septal shear wave speeds were significantly higher in patients than healthy volunteers [14.1 (11.0–15.8) m/s versus 3.6 (2.7–4.3) m/s, p = 0.001]. The interobserver 95%-limits-of-agreement in healthy volunteers were ± 1.3 m/s and interstudy 95%-limits-of-agreement − 0.7 to 1.2 m/s. In conclusion, myocardial shear wave speed can be measured using aortic valve closure-induced shear waves, with cardiac patients showing significantly higher shear wave speeds than healthy volunteers. This non-invasive measure may provide valuable insights into the pathophysiology of heart failure.

Three Birds, One Stone: Unified Laser-Based 3-D Reconstruction Across Different Media

The laser-based 3-D reconstruction is crucial for autonomous manipulation and resource exploration in both the air and underwater scenarios, due to its accurate precision and robustness to disturbances. However, most current laser-based 3-D reconstruction sensors cannot be applied across different media without recalibration directly, e.g., air, air$\rightarrow $ glass$\rightarrow $ air, and air$\rightarrow $ glass$\rightarrow $ water, which is called killing three birds with one stone. This is because the variation of the medium density could change the sensor calibration parameters and further make the sensor suffer from a systematic geometric bias. To address these challenges, a unified laser-based 3-D reconstruction method is proposed in order that the laser-based scanner could be used across different media without recalibration, where we first explicitly model the refraction of the underwater vision systems and transform it across different media into a unified sensor reference frame. More specifically, an underwater refractive camera calibration model is designed to calibrate the orientation and position of the refractive interface, which can improve the accuracy of underwater reconstruction for the laser-based scanner, and we then present a refractive pose estimation model with a unified sensor reference frame, which can help the sensor to be applied across different scenarios directly. For the experiments, we validate the performance of our method on our underwater 3-D scanner prototype. Several reconstruction results on different objects and scenarios demonstrate the effectiveness of our proposed method and the practicality of our designed sensor.

Evaluation of vehicular pollution levels using line source model for hot spots in Muscat, Oman.

A detailed investigation was carried out to assess the concentration of near-road traffic-related air pollution (TRAP) using a dispersion model in Muscat. Two ambient air quality monitoring (AQM) stations were utilized separately at six locations near major roadways (each location for 2months) to monitor carbon monoxide (CO) and nitrogen oxides (NOx). The study aimed to measure the concentration of near-road TRAP in a city hot spots and develop a validated dispersion model via performance measures. The US Environmental Protection Agency (US EPA) Line Source Model was implemented in which the pollutant emission factors were obtained through Comprehensive Modal Emission Model (CMEM) and COmputer Programme to calculate Emissions from Road Transport (COPERT) model. Traffic data of all vehicle categories under normal driving conditions including average vehicle speed limits and local meteorological conditions were included in the modeling study. The analysis of monitoring data showed that hourly (00:00 to 23:00) concentrations of CO were within the US EPA limits, while NOx concentration was exceeded in most locations. Also, the measured pollutant levels were consistent with hourly peak and off-peak traffic volumes. The overall primary statistical performance measures showed that COPERT model was better than CMEM due to the high sensitivity of CMEM to the local meteorological factors. The best fractional bias (0.47 and 0.39), normalized mean square error (0.44 and 0.50), correlation coefficient (0.64 and 0.70), geometric mean bias (1.07 and 1.57), and geometric variance (2.00 and 2.32) were obtained for CO and NOx, respectively. However, the bootstrap 95% CI estimates over normalized mean square error, fractional bias, and correlation coefficient for COPERT and CMEM were found to be statistically significant from 0 in the case of combined model comparison across all the traffic locations for both CO and NOx. In overall, certain roads showed weak performance mainly due to the terrain features and the lack of reliable background concentrations, which need to be considered in the future study.

Refractive Two-View Reconstruction for Underwater 3D Vision

Recovering 3D geometry from cameras in underwater applications involves the Refractive Structure-from-Motion problem where the non-linear distortion of light induced by a change of medium density invalidates the single viewpoint assumption. The pinhole-plus-distortion camera projection model suffers from a systematic geometric bias since refractive distortion depends on object distance. This leads to inaccurate camera pose and 3D shape estimation. To account for refraction, it is possible to use the axial camera model or to explicitly consider one or multiple parallel refractive interfaces whose orientations and positions with respect to the camera can be calibrated. Although it has been demonstrated that the refractive camera model is well-suited for underwater imaging, Refractive Structure-from-Motion remains particularly difficult to use in practice when considering the seldom studied case of a camera with a flat refractive interface. Our method applies to the case of underwater imaging systems whose entrance lens is in direct contact with the external medium. By adopting the refractive camera model, we provide a succinct derivation and expression for the refractive fundamental matrix and use this as the basis for a novel two-view reconstruction method for underwater imaging. For validation we use synthetic data to show the numerical properties of our method and we provide results on real data to demonstrate its practical application within laboratory settings and for medical applications in fluid-immersed endoscopy. We demonstrate our approach outperforms classic two-view Structure-from-Motion method relying on the pinhole-plus-distortion camera model.

Metal−π Interactions in Light of Geometry-Corrected Statistical Analysis

Statistical analysis on metal−π interactions has been performed, and a geometrical correction, named as offset correction, has been incorporated into the analysis to nullify the geometrical bias to understand the geometrical preference for this interaction. The population distributions of these interactions before and after offset correction with respect to the offset from the centroid of the benzenoid ring are completely different. In the cases of alkali metal−π interactions, the population is maximum at offset zero or near zero. However, in the case of transition metal−π interactions, the offset for the maximum population is found near the carbon atom of the benzenoid ring. Interestingly, alkaline earth metals, which lie in between the alkali and transition metals in the periodic table, behave in between these two metals when metal−π interaction geometry is concerned.

A statistical study on lognormal central tendency estimation in probabilistic seismic assessments

Lognormal distribution is one of the most applicable distributions in many different probabilistic seismic assessments such as seismic hazard analysis, fragility analyses, and reliability evaluations. This study compares the sample median (SM), sample geometric mean (SGM) and Sample Bias Corrected Geometric Mean (SBCGM) as three different estimators of lognormal central estimator from different aspects. To this end, the efficiency of each estimator is investigated by calculating their bias and minimum squared error (MSE) using Monte Carlo Simulations. Further, probabilistic performance of four Steel Moment Resisting Frames (SMRF) is investigated using Incremental Dynamic Analysis (IDA), and the stochastic seismic performance of one of them (5 story frame) is investigated considering the aleatoric and epistemic uncertainties. Additionally, the probabilistic seismic performance of a sample jacket type offshore platform based on the mentioned statistical estimators of lognormal central tendency is investigated implementing the IDA procedure. The results show that the efficiency of SM is significantly lower than the other two estimators. In general, SBCGM is the most efficient one for small sample sizes. However, SBCGM and SGM show the same efficiency as the sample size increases. Also, noticeable differences can be observed in the seismic performance of the structures based on each estimator. According to the results of goodness of fit tests and the likelihood ratio evaluations, SBCGM is recommended for achieving more reliable seismic assessments when using small number of records. As another marginal conclusion, the conducted statistical study revealed that the lognormal assumption is approved for collapse fragility curves considering both aleatoric and epistemic sources of uncertainty.

A comprehensive approach to study stack emissions from a research building in a small urban setting

Gaseous emissions from research facilities within an urban environment have not been fully studied. The present study investigates the impacts of stack emissions from a research building in Hong Kong on nearby urban areas. Fifteen chemicals emitted from the laboratories of the building were selected for a year-long air monitoring. Among the 15 chemicals, the levels of NO2, acetonitrile, and total volatile organic compounds (TVOC) exceeded the predetermined exposure levels. A tracer gas study was performed to identify the dilution factor of the environment and to validate two turbulence models, namely, Renormalized Group (RNG) and Realizable (RLZ) k-ε models. Statistical tests, including geometric mean bias (MG) and factor of two (FAC2), demonstrated that the RNG (MG: 0.88–1.5; FAC2: 0.67–1.14) was better than the RLZ k-ε model (MG: 0.26-0.68; FAC2: 1.47–3.80) for the prediction of pollutant dispersion and concentration distribution but still could not achieve statically sound results. Despite the RNG k-ε model is a popular and economical choice in numerical simulations, careful interpretation of data would be required, and it should also be used and read in conjunction with air monitoring and tracer gas assessment data as a comprehensive approach when assessing the impact of stack emissions in an urban setting.

Performance evaluation by computational fluid dynamics modelling of the heavy gas dispersion with a low Froude number in a built environment

To evaluate the dispersion of a heavy gas, such as sulphur hexafluoride, with a low Froude number in a built environment, an experimental and numerical simulation study was conducted. The experiment was carried out using seven different injection inlet configurations in an experimental chamber. The release rate was found to have a great effect on the concentration in the lower part of the chamber. The obstacle in the middle of the chamber could cause a non-uniform distribution of concentration, particularly due to variations in locations and angles of the release outlets. Additionally, numerical simulations were carried out to evaluate four turbulence models: the standard k- ε model, the realizable k- ε model, the re-normalization group (RNG) k- ε model and the shear stress transport (SST) k-ω model. Four indicators were used to evaluate the turbulent model performance. In general, the SST k-ω model performed the best, with geometric mean bias ( MG) = 0.968 and geometric variance ( VG) = 1.09 at 0.055 m height, and with MG = 0.384 and VG = 2.80 at 0.6 m height. The standard k- ε model was the next best in performance, followed by the realizable k- ε and the RNG k- ε model.

Towards Sub-Pixel Automatic Geometric Corrections of Very-High Resolution Panchromatic Satellite Data of Urban Areas

This paper presents an automatic procedure for the geometric corrections of very-high resolution (VHR) optical panchromatic satellite images. The procedure is composed of three steps: an automatic ground control point (GCP) extraction algorithm that matches the linear features that were extracted from the satellite image and reference data; a geometric model that applies a rational function model; and, the orthorectification procedure. Accurate geometric corrections can only be achieved if GCPs are employed to precisely correct the geometric biases of images. Due to the high resolution and the varied acquisition geometry of images, we propose a fast, segmentation based method for feature extraction. The research focuses on densely populated urban areas, which are very challenging in terms of feature extraction and matching. The proposed algorithm is capable of achieving results with a root mean square error of approximately one pixel or better, on a test set of 14 panchromatic Pléiades images. The procedure is robust and it performs well in urban areas, even for images with high off-nadir angles.

Performance Analysis of Double Gate Junctionless Tunnel Field Effect Transistor: RF Stability Perspective

This paper investigates the RF Stability performance of the Double Gate Junctionless Tunnel Field Effect Transistor (DGJL-TFET). The impact of the geometrical parameter, material and bias conditions on the key figure of merit (FoM) like Transconductance (gm), Gate capacitance (Cgg) and RF parameters like Stern Stability Factor (K), Critical Frequency (fk) are investigated. The analytical model provides the relation between fk and small signal parameters which provide guidelines for optimizing the device parameter. The results show improvement in ON current, gm, ft and fk for the optimized device structure. The optimized device parameters provide guidelines to operate DGJL-TFET for RF applications.

Planets, candidates, and binaries from the CoRoT/Exoplanet programme

The CoRoT space mission observed 163 665 stars over 26 stellar fields in the faint star channel. The exoplanet teams detected a total of 4123 transit-like features in the 177 454 light curves. We present the complete re-analysis of all these detections carried out with the same softwares so that to ensure their homogeneous analysis. Although the vetting process involves some human evaluation, it also involves a simple binary flag system over basic tests: detection significance, presence of a secondary, difference between odd and even depths, colour dependence, V-shape transit, and duration of the transit. We also gathered the information from the large accompanying ground-based programme carried out on the planet candidates and checked how useful the flag system could have been at the vetting stage of the candidates. From the initial list of transit-like features, we identified and separated 824 false alarms of various kind, 2269 eclipsing binaries among which 616 are contact binaries and 1653 are detached ones, 37 planets and brown dwarfs, and 557 planet candidates. We provide the catalogue of all these transit-like features, including false alarms. For the planet candidates, the catalogue gives not only their transit parameters but also the products of their light curve modelling: reduced radius, reduced semi-major axis, and impact parameter, together with a summary of the outcome of follow-up observations when carried out and their current status. For the detached eclipsing binaries, the catalogue provides, in addition to their transit parameters, a simple visual classification. Among the planet candidates whose nature remains unresolved, we estimate that eight (within an error of three) planets are still to be identified. After correcting for geometric and sensitivity biases, we derived planet and brown dwarf occurrences and confirm disagreements with Kepler estimates, as previously reported by other authors from the analysis of the first runs: small-size planets with orbital period less than ten days are underabundant by a factor of three in the CoRoT fields whereas giant planets are overabundant by a factor of two. These preliminary results would however deserve further investigations using the recently released CoRoT light curves that are corrected of the various instrumental effects and a homogeneous analysis of the stellar populations observed by the two missions.