Hybrid SAFE-GMM approach for predictive modeling of guided wave propagation in layered media
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An improved gravity model for Mars: Goddard Mars Model-1 (GMM-1)
Doppler tracking data of three orbiting spacecraft have been reanalyzed to develop a new gravitational field model for the planet Mars, GMM-1 (Goddard Mars Model-1). This model employs nearly all available data, consisting of approximately 1100 days of S-bank tracking data collected by NASA's Deep Space Network from the Mariner 9, and Viking 1 and Viking 2 spacecraft, in seven different orbits, between 1971 and 1979. GMM-1 is complete to spherical harmonic degree and order 50, which corresponds to a half-wavelength spatial resolution of 200-300 km where the data permit. GMM-1 represents satellite orbits with considerably better accuracy than previous Mars gravity models and shows greater resolution of identifiable geological structures. The notable improvement in GMM-1 over previous models is a consequence of several factors: improved computational capabilities, the use of optimum weighting and least-squares collocation solution techniques which stabilized the behavior of the solution at high degree and order, and the use of longer satellite arcs than employed in previous solutions that were made possible by improved force and measurement models. The inclusion of X-band tracking data from the 379-km altitude, near-polar orbiting Mars Observer spacecraft should provide a significant improvement over GMM-1, particularly at high latitudes where current data poorly resolves the gravitational signature of the planet.
Smith, D. E.↗
High degree gravitational sensitivity from Mars orbiters for the GMM-1 gravity model
Orbital sensitivity of the gravity field for high degree terms (greater than 30) is analyzed on satellites employed in a Goddard Mars Model GMM-1, complete in spherical harmonics through degree and order 50. The model is obtained from S-band Doppler data on Mariner 9 (M9), Viking Orbiter 1 (VO1), and Viking Orbiter 2 (VO2) spacecraft, which were tracked by the NASA Deep Space Network on seven different highly eccentric orbits. The main sensitivity of the high degree terms is obtained from the VO1 and VO2 low orbits (300 km periapsis altitude), where significant spectral sensitivity is seen for all degrees out through degree 50. The velocity perturbations show a dominant effect at periapsis and significant effects out beyond the semi-latus rectum covering over 180 degrees of the orbital groundtrack for the low altitude orbits. Because of the wideband of periapsis motion covering nearly 180 degrees in w and +39 degrees in latitude coverage, the VO1 300 km periapsis altitude orbit with inclination of 39 degrees gave the dominant sensitivity in the GMM-1 solution for the high degree terms. Although the VO2 low periapsis orbit has a smaller band of periapsis mapping coverage, it strongly complements the VO1 orbit sensitivity for the GMM-1 solution with Doppler tracking coverage over a different inclination of 80 degrees.
Lerch, F. J.↗
GMM-1: A 50 degree and order gravitational field model for Mars
Knowledge of the gravitational field, in combination with surface topography, provides one of the principal means of inferring the internal structure of a planetary body. The highest resolution gravitational field for Mars published thus far was derived from Doppler tracking data from the Mariner 9 and Viking 1 and 2 spacecraft and is complete to degree and order 18 corresponding to a half wavelength resolution of approximately 600 km. This field, which is characterized by a spatial resolution that is slightly better than that of the highest resolution (16x16) topographic model, has been utilized extensively in analyses of the state of stress and isostatic compensation of the Martian lithosphere. However, the resolution and quality of current gravity and topographic fields are such that the origin and evolution of even the major physiographic features on Mars, such as the hemispheric dichotomy and Tharsis rise, are not well understood. We have re-analyzed the Viking and Mariner data sets and have derived a new gravitational field, which we designated GMM-1 (Goddard Mars Model-1). This model is complete to spherical harmonic degree and order 50 with a corresponding (half wavelength) spatial resolution of 200-300 km where the data permit. In contrast to previous models, GMM-1 was solved to as high degree and order as necessary to nearly exhaust the attenuated gravitational signal contained in the tracking data.
Smith, D. E.↗
Progress in the development of the GMM-2 gravity field model for Mars
Last year we published the GMM-1 (Goddard Mars Model-1) gravity model for Mars. We have completely re-analyzed the Viking and Mariner 9 tracking data in the development of the new field, designated GMM-2. The model is complete to degree and order 70. Various aspects of the model are discussed.
Lemoine, F. G.↗
PSD GMM Trainer
Code takes a data file of input features of a set of digitized pulses and converts the file to three vectors describing a two-component Gaussian mixture model of the input pulses. These three vectors can then be applied mathematically to a set of input feature data of digitized pulses to generate soft-scores describing the likelihood of each pulse's membership in each of the two Gaussian modeled components. The operation of the code also displays graphics to the user for interpreting the output vectors and distribution of the soft-scores of the input data.
Blair, BrentonS↗
Th1-skewed tissue responses to a mycolyl glycolipid in mycobacteria-infected rhesus macaques
Highlights: •Glucose monomycolate (GMM) is a marker glycolipid for active tuberculosis. •Tissue responses to GMM involved up-regulation of Th1-attracting chemokines. •Th1-skewed local responses were mounted at the GMM-injected tissue. -- Abstract: Trehalose 6,6′-dimycolate (TDM) is a major glycolipid of the cell wall of mycobacteria with remarkable adjuvant functions. To avoid detection by the host innate immune system, invading mycobacteria down-regulate the expression of TDM by utilizing host-derived glucose as a competitive substrate for their mycolyltransferases; however, this enzymatic reaction results in the concomitant biosynthesis of glucose monomycolate (GMM) which is recognized by the acquired immune system. GMM-specific, CD1-restricted T cell responses have been detected in the peripheral blood of infected human subjects and monkeys as well as in secondary lymphoid organs of small animals, such as guinea pigs and human CD1-transgenic mice. Nevertheless, it remains to be determined how tissues respond at the site where GMM is produced. Here we found that rhesus macaques vaccinated with Mycobacterium bovis bacillus Calmette–Guerin mounted a chemokine response in GMM-challenged skin that was favorable for recruiting T helper (Th)1 T cells. Indeed, the expression of interferon-γ, but not Th2 or Th17 cytokines, was prominent in the GMM-injected tissue. The GMM-elicited tissue response was also associated with the expression of monocyte/macrophage-attracting CC chemokines, such as CCL2, CCL4 and CCL8. Furthermore, the skin response to GMM involved the up-regulated expression of granulysin and perforin. Given that GMM is produced primarily by pathogenic mycobacteria proliferating within the host, the Th1-skewed tissue response to GMM may function efficiently at the site of infection.
60 APPLIED LIFE SCIENCES↗
Efficient Speaker Verification Using Gaussian Mixture Model Component Clustering
In speaker verification (SV) systems that employ a support vector machine (SVM) classifier to make decisions on a supervector derived from Gaussian mixture model (GMM) component mean vectors, a significant portion of the computational load is involved in the calculation of the a posteriori probability of the feature vectors of the speaker under test with respect to the individual component densities of the universal background model (UBM). Further, the calculation of the sufficient statistics for the weight, mean, and covariance parameters derived from these same feature vectors also contribute a substantial amount of processing load to the SV system. In this paper, we propose a method that utilizes clusters of GMM-UBM mixture component densities in order to reduce the computational load required. In the adaptation step we score the feature vectors against the clusters and calculate the a posteriori probabilities and update the statistics exclusively for mixture components belonging to appropriate clusters. Each cluster is a grouping of multivariate normal distributions and is modeled by a single multivariate distribution. As such, the set of multivariate normal distributions representing the different clusters also form a GMM. This GMM is referred to as a hash GMM which can be considered to a lower resolution representation of the GMM-UBM. The mapping that associates the components of the hash GMM with components of the original GMM-UBM is referred to as a shortlist. This research investigates various methods of clustering the components of the GMM-UBM and forming hash GMMs. Of five different methods that are presented one method, Gaussian mixture reduction as proposed by Runnall's, easily outperformed the other methods. This method of Gaussian reduction iteratively reduces the size of a GMM by successively merging pairs of component densities. Pairs are selected for merger by using a Kullback-Leibler based metric. Using Runnal's method of reduction, we were able to achieve a factor of 2.77 reduction in a posteriori probability calculations with no loss in accuracy when the original UBM consisted of 256 component densities. When clustering was implemented with a 1024 component UBM, we achieved a computation reduction of 5 with no loss in accuracy and a reduction by a factor of 10 with less than 2.4% relative loss in accuracy.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Scattering and Diffraction of Electromagnetic Radiation: An Effective Probe to Material Structure
Scattered electromagnetic waves from material bodies of different forms contain, in an intricate way, precise information on the intrinsic, geometrical and physical properties of the objects. Scattering theories, ever deepening, aim to provide dependable interpretation and prediction to the complicated interaction of electromagnetic radiation with matter. There are well-established multiple-scattering formulations based on classical electromagnetic theories. An example is the Generalized Multi-particle Mie-solution (GMM), which has recently been extended to a special version ̶ the GMM-PA approach, applicable to finite periodic arrays consisting of a huge number (e.g., >>106) of identical scattering centers [1]. The framework of the GMM-PA is nearly complete. When the size of the constituent unit scatterers becomes considerably small in comparison with incident wavelength, an appropriate array of such small element volumes may well be a satisfactory representation of a material entity having an arbitrary structure. X-ray diffraction is a powerful characterization tool used in a variety of scientific and technical fields, including material science. A diffraction pattern is nothing more than the spatial distribution of scattered intensity, determined by the distribution of scattering matter by way of its Fourier transform [1]. Since all linear dimensions entered into Maxwell's equations are normalized by wavelength, an analogy exists between optical and X-ray diffraction patterns. A large set of optical diffraction patterns experimentally obtained can be found in the literature [e.g., 2,3]. Theoretical results from the GMM-PA have been scrutinized using a large collection of publically accessible, experimentally obtained Fraunhofer diffraction patterns. As far as characteristic structures of the patterns are concerned, theoretical and experimental results are in uniform agreement; no exception has been found so far. Closely connected with the spatial distribution of scattered intensities are cross sections, such as for extinction, scattering, absorption, and radiation pressure, as a critical type of key quantity addressed in most theoretical and experimental studies of radiative scattering. Cross sections predicted from different scattering theories are supposed to be in general agreement. For objects of irregular shape, the GMM-PA solutions can be compared with the highly flexible Discrete Dipole Approximation (DDA) [4,5] when dividing a target to no more than ~106 unit cells. Also, there are different ways to calculate the cross sections in the GMM-PA, providing an additional means to examine the accuracy of the numerical solutions and to unveil potential issues concerning the theoretical formulations and numerical aspects. To solve multiple scattering by an assembly of material volumes through classical theories such as the GMM-PA, the radiative properties of the component scatterers, the complex refractive index in particular, must be provided as input parameters. When using a PA to characterize a material body, this involves the use of an adequate theoretical tool, an effective medium theory, to connect Maxwell's phenomenogical theory with the atomistic theory of matter. In the atomic theory, one regards matter as composed of interacting particles (atoms and molecules) embedded in the vacuum [6]. However, the radiative properties of atomic-scaled particles are known to be substantially different from bulk materials. Intensive research efforts in the fields of cluster science and nanoscience attempt to bridge the gap between bulk and atom and to understand the transition from classical to quantum physics. The GMM-PA calculations, which place virtually no restriction on the component-particle size, might help to gain certain insight into the transition.
Xu, Yu-Lin↗
Background based Gaussian mixture model lesion segmentation in PET
Purpose: Quantitative {sup 18}F-fluorodeoxyglucose positron emission tomography is limited by the uncertainty in lesion delineation due to poor SNR, low resolution, and partial volume effects, subsequently impacting oncological assessment, treatment planning, and follow-up. The present work develops and validates a segmentation algorithm based on statistical clustering. The introduction of constraints based on background features and contiguity priors is expected to improve robustness vs clinical image characteristics such as lesion dimension, noise, and contrast level. Methods: An eight-class Gaussian mixture model (GMM) clustering algorithm was modified by constraining the mean and variance parameters of four background classes according to the previous analysis of a lesion-free background volume of interest (background modeling). Hence, expectation maximization operated only on the four classes dedicated to lesion detection. To favor the segmentation of connected objects, a further variant was introduced by inserting priors relevant to the classification of neighbors. The algorithm was applied to simulated datasets and acquired phantom data. Feasibility and robustness toward initialization were assessed on a clinical dataset manually contoured by two expert clinicians. Comparisons were performed with respect to a standard eight-class GMM algorithm and to four different state-of-the-art methods in terms of volume error (VE), Dice index, classification error (CE), and Hausdorff distance (HD). Results: The proposed GMM segmentation with background modeling outperformed standard GMM and all the other tested methods. Medians of accuracy indexes were VE <3%, Dice >0.88, CE <0.25, and HD <1.2 in simulations; VE <23%, Dice >0.74, CE <0.43, and HD <1.77 in phantom data. Robustness toward image statistic changes (±15%) was shown by the low index changes: <26% for VE, <17% for Dice, and <15% for CE. Finally, robustness toward the user-dependent volume initialization was demonstrated. The inclusion of the spatial prior improved segmentation accuracy only for lesions surrounded by heterogeneous background: in the relevant simulation subset, the median VE significantly decreased from 13% to 7%. Results on clinical data were found in accordance with simulations, with absolute VE <7%, Dice >0.85, CE <0.30, and HD <0.81. Conclusions: The sole introduction of constraints based on background modeling outperformed standard GMM and the other tested algorithms. Insertion of a spatial prior improved the accuracy for realistic cases of objects in heterogeneous backgrounds. Moreover, robustness against initialization supports the applicability in a clinical setting. In conclusion, application-driven constraints can generally improve the capabilities of GMM and statistical clustering algorithms.
60 APPLIED LIFE SCIENCES↗
An optimized method to identify RR Lyrae stars in the SDSS×Pan-STARRS1 overlapping area using a bayesian generative technique
We present a method for selecting RR Lyrae (RRL) stars (or other types of variable stars) in the absence of a large number of multi-epoch data and light curve analyses. Our method uses color and variability selection cuts that are defined by applying a Gaussian Mixture Bayesian Generative Method (GMM) on 636 pre-identified RRL stars instead of applying the commonly used rectangular cuts. Specifically, our method selects 8115 RRL candidates (heliocentric distances < 70 kpc) using GMM color cuts from the Sloan Digital Sky Survey (SDSS) and GMM variability cuts from the Panoramic Survey Telescope and Rapid Response System 1 3π survey (PS1). Comparing our method with the Stripe 82 catalog of RRL stars shows that the efficiency and completeness levels of our method are ∼77% and ∼52%, respectively. Most contaminants are either non-variable main-sequence stars or stars in eclipsing systems. The method described here efficiently recovers known stellar halo substructures. It is expected that the current completeness and efficiency levels will further improve with the additional PS1 epochs (∼3 epochs per filter) that will be observed before the conclusion of the survey. A comparison between our efficiency and completeness levels using the GMM method to the efficiency and completeness levels using rectangular cuts that are commonly used yielded a significant increase in the efficiency level from ∼13% to ∼77% and an insignificant change in the completeness levels. Hence, we favor using the GMM technique in future studies. Although we develop it over the SDSS×PS1 footprint, the technique presented here would work well on any multi-band, multi-epoch survey for which the number of epochs is limited.
79 ASTRONOMY AND ASTROPHYSICS↗
A robust estimator of mutual information for deep learning interpretability
Abstract We develop the use of mutual information (MI), a well-established metric in information theory, to interpret the inner workings of deep learning (DL) models. To accurately estimate MI from a finite number of samples, we present GMM-MI (pronounced ‘Jimmie’), an algorithm based on Gaussian mixture models that can be applied to both discrete and continuous settings. GMM-MI is computationally efficient, robust to the choice of hyperparameters and provides the uncertainty on the MI estimate due to the finite sample size. We extensively validate GMM-MI on toy data for which the ground truth MI is known, comparing its performance against established MI estimators. We then demonstrate the use of our MI estimator in the context of representation learning, working with synthetic data and physical datasets describing highly non-linear processes. We train DL models to encode high-dimensional data within a meaningful compressed (latent) representation, and use GMM-MI to quantify both the level of disentanglement between the latent variables, and their association with relevant physical quantities, thus unlocking the interpretability of the latent representation. We make GMM-MI publicly available in this GitHub repository.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
An improved gravity model for Mars: Goddard Mars Model 1
Doppler tracking data of three orbiting spacecraft have been reanalyzed to develop a new gravitational field model for the planet Mars, Goddard Mars Model 1 (GMM-1). This model employs nearly all available data, consisting of approximately 1100 days of S band tracking data collected by NASA's Deep Space Network from the Mariner 9 and Viking 1 and Viking 2 spacecraft, in seven different orbits, between 1971 and 1979. GMM-1 is complete to spherical harmonic degree and order 50, which corresponds to a half-wavelength spatial resolution of 200-300 km where the data permit. GMM-1 represents satellite orbits with considerably better accuracy than previous Mars gravity models and shows greater resolution of identifiable geological structures. The notable improvement in GMM-1 over previous models is a consequence of several factors: improved computational capabilities, the use of otpimum weighting and least squares collocation solution techniques which stabilized the behavior of the solution at high degree and order, and the use of longer satellite arcs than employed in previous solutions that were made possible by improved force and measurement models. The inclusion of X band tracking data from the 379-km altitude, nnear-polar orbiting Mars Observer spacecraft should provide a significant improvement over GMM-1, particularly at high latitudes where current data poorly resolve the gravitational signature of the planet.
Smith, D. E.↗
Green micromachining of ceramics using tungsten carbide micro-endmills
This paper presents an experimental analysis on micromachining of green-state silicon carbide (SiC) and aluminum nitride (AlN) using uncoated tungsten carbide (WC) micro-endmills. Although the unique properties of ceramics make them ideal materials for many applications involving micro-scale features, the industrial adaptation of ceramics at the micro-scale has been hindered due to the lack of viable ceramic micro-manufacturing techniques. Green micromachining (GMM), where micro-scale features are created by micromachining green-state ceramics that contain ceramic particles and a polymer binder, offers a potential solution for this problem by affording tremendous improvements in micromachinability with respect to sintered ceramics. After the completion of GMM, the green parts are debound and sintered to obtain the final component with the desired microscale features. In this work, we evaluated GMM characteristics of powder-injection molded green-state SiC and AlN to correlate process conditions and green-material compositions with the micromachining forces, quality, and tool wear. An experimental study that involves full immersion micromilling tests with 254 μm diameter micro-endmills at different cutting speeds and feed rates is conducted. For each of the two materials, two binder states (with and without wax) and two powder states (with micro particles only, or with both micro- and nanoparticles) are investigated. Green micromachining forces, specific energies and the resulting surface roughness are analyzed quantitatively. A qualitative analysis of burr formation and channel quality, as well as a preliminary study on micro-tool wear are also performed. Both the machining conditions and the material compositions are seen to have a strong effect on micro-machinability of green-state ceramics. Overall, we conclude that GMM can be a viable ceramic micro-manufacturing strategy if favorable (or optimal) micromachining and material parameters are identified through the presented (or a similar) micromachinability study.
36 MATERIALS SCIENCE↗
Host-region parameters for an adjustable model for crustal earthquakes to facilitate the implementation of the backbone approach to building ground-motion logic trees in probabilistic seismic hazard analysis
The backbone approach to constructing a ground-motion logic tree for probabilistic seismic hazard analysis (PSHA) can address shortcomings in the traditional approach of populating the branches with multiple existing, or potentially modified, ground-motion models (GMMs) by rendering more transparent the relationship between branch weights and the resulting distribution of predicted accelerations. To capture epistemic uncertainty in a tractable manner, there are benefits in building the logic tree through the application of successive adjustments for differences in source, path, and site characteristics between the host region of the selected backbone GMM and the target region for which the PSHA is being conducted. The implementation of this approach is facilitated by selecting a backbone GMM that is amenable to such host-to-target adjustments for individual source, path, and site characteristics. The NGA-West2 GMM of Chiou and Youngs (CY14) has been identified as a highly adaptable model for crustal seismicity that is well suited to such adjustments. Rather than using generic source, path, and site characteristics assumed appropriate for the host region, the final suite of adjusted GMMs for the target region will be better constrained if the host-region parameters are defined specifically on the basis of their compatibility with the CY14 backbone GMM. To this end, making use of a recently developed crustal shear-wave velocity profile consistent with CY14, we present an inversion of the model to estimate the key source and path parameters, namely the stress parameter and the anelastic attenuation. With these outputs, the effort in constructing a ground-motion logic tree for any PSHA dealing with crustal seismicity can be focused primarily on the estimation of the target-region characteristics and their associated uncertainties. The inversion procedure can also be adapted for any application in which different constraints might be relevant.
Stafford, Peter J.↗
Identification and Control of Aircrafts using Multiple Models and Adaptive Critics
We compared two possible implementations of local linear models for control: one approach is based on a self-organizing map (SOM) to cluster the dynamics followed by a set of linear models operating at each cluster. Therefore the gating function is hard (a single local model will represent the regional dynamics). This simplifies the controller design since there is a one to one mapping between controllers and local models. The second approach uses a soft gate using a probabilistic framework based on a Gaussian Mixture Model (also called a dynamic mixture of experts). In this approach several models may be active at a given time, we can expect a smaller number of models, but the controller design is more involved, with potentially better noise rejection characteristics. Our experiments showed that the SOM provides overall best performance in high SNRs, but the performance degrades faster than with the GMM for the same noise conditions. The SOM approach required about an order of magnitude more models than the GMM, so in terms of implementation cost, the GMM is preferable. The design of the SOM is straight forward, while the design of the GMM controllers, although still reasonable, is more involved and needs more care in the selection of the parameters. Either one of these locally linear approaches outperform global nonlinear controllers based on neural networks, such as the time delay neural network (TDNN). Therefore, in essence the local model approach warrants practical implementations. In order to call the attention of the control community for this design methodology we extended successfully the multiple model approach to PID controllers (still today the most widely used control scheme in the industry), and wrote a paper on this subject. The echo state network (ESN) is a recurrent neural network with the special characteristics that only the output parameters are trained. The recurrent connections are preset according to the problem domain and are fixed. In a nutshell, the states of the reservoir of recurrent processing elements implement a projection space, where the desired response is optimally projected. This architecture trades training efficiency by a large increase in the dimension of the recurrent layer. However, the power of the recurrent neural networks can be brought to bear on practical difficult problems. Our goal was to implement an adaptive critic architecture implementing Bellman s approach to optimal control. However, we could only characterize the ESN performance as a critic in value function evaluation, which is just one of the pieces of the overall adaptive critic controller. The results were very convincing, and the simplicity of the implementation was unparalleled.
Principe, Jose C.↗
Tailoring Microbial Fitness Through Computational Steering and CRISPRi-Driven Robustness Regulation
The widespread application of genetically modified microorganisms (GMMs) across diverse sectors underscores the pressing need for robust strategies to mitigate the risks associated with their potential uncontrolled escape. This study merges computational modeling with CRISPR interference (CRISPRi) to refine GMM metabolic robustness. Utilizing ensemble modeling, we achieved high-throughput in silico screening for enzymatic targets susceptible to expression alterations. Translating these insights, we developed functional CRISPRi, boosting fitness control via multiplexed gene knockdown. Our method, enhanced by an insulator-improved gRNA structure and an off-switch circuit controlling a compact Cas12m, resulted in rationally engineered strains with escape frequencies below National Institutes of Health standards. The effectiveness of this approach was confirmed under various conditions, showcasing its ability for secure GMM management. This research underscores the resilience of microbial metabolism, strategically modifying key nodes to halt growth without provoking significant resistance, thereby enabling more reliable and precise GMM control. A record of this paper's transparent peer review process is included in the supplemental information.
59 BASIC BIOLOGICAL SCIENCES↗
Pulse discrimination with a Gaussian mixture model on an FPGA
A Gaussian Mixture Model (GMM) based machine learning algorithm has been applied to the problem of gamma/neutron pulse shape discrimination (PSD). The algorithm has been successfully implemented on a standard PC as well as a field programmable gate array (FPGA). We describe the GMM classifier and its implementation on these two different types of hardware. We compare the performance of the algorithm on these two platforms against each other, along with other standard techniques applied in PSD. Our results show that the FPGA-based GMM classifier outperforms the standard PSD techniques in terms of classification accuracy at low particle energy and executes more quickly than its CPU-based counterpart.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗