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We consider probabilistic models for sequential observations which exhibit gradual transitions among a finite number of states. We are particularly motivated by applications such as human activity analysis where observed accelerometer time series contains segments representing distinct activities, which we call pure states, as well as periods characterized by continuous transition among these pure states. To capture this transitory behavior, the dynamical Wasserstein barycenter (DWB) model of (Cheng et al., 2021) associates with each pure state a data-generating distribution and models the continuous transitions among these states as a Wasserstein barycenter of these distributions with dynamically evolving weights. Focusing on the univariate case where Wasserstein distances and barycenters can be computed in closed form, we extend (Cheng et al., 2021) specifically relaxing the parameterization of the pure states as Gaussian distributions. We highlight issues related to the uniqueness in identifying the model parameters as well as uncertainties induced when estimating a dynamically evolving distribution from a limited number of samples. To ameliorate non-uniqueness, we introduce regularization that imposes temporal smoothness on the dynamics of the barycentric weights. A quantile-based approximation of the pure state distributions yields a finite dimensional estimation problem which we numerically solve using cyclic descent alternating between updates to the pure-state quantile functions and the barycentric weights. We demonstrate the utility of the proposed algorithm in segmenting both simulated and real world human activity time series.

Abstract

Quorum sensing (QS), where bacteria secrete and respond to chemical signals to coordinate population-wide behaviors, has revealed that bacteria are highly social. Here, we investigate how diversity in QS signals and receptors can modify social interactions controlled by the QS system regulating bacteriocin secretion in Streptococcus pneumoniae, encoded by the blpoperon (bacteriocin-like peptide). Analysis of 4096 pneumococcal genomes detected nine blpQS signals (BlpC) and five QS receptor groups (BlpH). Imperfect concordance between signals and receptors suggested widespread social interactions between cells, specifically eavesdropping (where cells respond to signals that they do not produce) and crosstalk (where cells produce signals that non-clones detect). This was confirmed in vitro by measuring the response of reporter strains containing six different blpQS receptors to cognate and non-cognate peptides. Assays between pneumococcal colonies grown adjacent to one another provided further evidence that crosstalk and eavesdropping occur at endogenous levels of signal secretion. Finally, simulations of QS strains producing bacteriocins revealed that eavesdropping can be evolutionarily beneficial even when the affinity for non-cognate signals is very weak. Our results highlight that social interactions can mediate intraspecific competition among bacteria and reveal that competitive interactions can be modified by polymorphic QS systems.

The HydroGEN (https://www.h2awsm.org/) energy materials network (EMN) aims to accelerate the research and development (R&D) of advanced water splitting (AWS) technologies for clean, sustainable hydrogen production. Announced in October 2016, the HydroGEN EMN comprises six core National Laboratories and focuses on four AWS pathways: low- and high-temperature electrolysis, photoelectrochemical, and solar thermochemical water splitting. The HydroGEN consortium offers an extensive collection of materials research capabilities for addressing R&D challenges in discovery and design, efficacy and efficiency, durability and cost. Leveraging the HydroGEN Consortium's technical experts and broad collection of unique resource capabilities is expected to advance the maturity and technology readiness levels in each advanced water splitting technology pathway.

Affordable, reliable, high performance materials and manufacturing technologies are key for transformational technology advancements in critical domestic energy applications. However, many materials discoveries in the laboratory either never reach widespread market deployment or spend decades in the development cycle at an extremely high cost. In response, the U.S. Department of Energy has established the Energy Materials Network (EMN) to accelerate the materials-to-market process by better integrating the stages from materials discovery through qualification. The EMN is a network of National Lab-led consortia that support both industry-guided and government-led research on specific energy-related material challenges. The EMN framework facilitates industry stakeholder engagement to ensure that research is immediately relevant, with high-impact commercial potential. To date, EMN consortia have been set up in seven technology areas. This paper describes the vision of the EMN as an enduring national resource, and discusses recent accomplishments in the current network of EMN consortia.

The emergence of hydrogen and fuel cell technologies offers the world important and potentially transformative environmental and energy security benefits. In recent years, research sponsored by the US Department of Energy's (DOE) Fuel Cell Technologies Office (FCTO) has made significant contributions to the development of these technologies. With fuel cell electric vehicles beginning to be introduced to the market, enabling technologies for the widespread production of affordable hydrogen are becoming increasingly important. One of the more versatile hydrogen production pathways is based on splitting water via either electrolytic, photoelectrochemical, or thermochemical routes. For these advanced water splitting (AWS) technologies, there are tradeoffs amongst efficiency, durability, and cost at the materials, device, and system levels that need to be balanced for low cost hydrogen production. Recent advances have been made in catalytic materials for AWS to improve these attributes; however, further developments, from new materials to scale-up of state-of-the-art (SOA) materials, are required to reach large scale technoeconomic viability. Research innovations to advance the SOA in AWS materials are being facilitated by the DOE "HydroGEN" Energy Materials Network (EMN) consortium. An overview of FCTO's Hydrogen Production Program activities with a focus on catalysts for advanced water splitting technologies and the HydroGEN consortium will be provided.

The emergence of hydrogen and fuel cell technologies offers important and potentially transformative environmental and energy security benefits. In recent years, research supported by the U.S. Department of Energy's Fuel Cell Technologies Office has contributed substantially to the development and advancement of these technologies. The research investments to reduce costs in fuel cell stacks, for example, are clearly paying off, as commercial fuel-cell electric vehicles are being rolled out by major car manufacturers today. With increasing market penetration of FCEVs, enabling technologies for the affordable and widespread production of clean, renewable hydrogen is becoming increasingly important. Long term commercial viability of renewable hydrogen in the commercial marketplace will rely on continued materials research on several important fronts. Examples include the discovery and development of efficient, durable, and cost effective materials, devices and systems for hydrogen production based on: (1) low and high temperature advanced electrolysis powered by renewable electricity sources; (2) direct low-temperature photoelectrochemical water splitting powered by sunlight; and (3) direct high-temperature solar thermochemical water splitting. Research innovations in macro-, meso- and nano-scale materials are all needed for pushing forward the state-of-the-art in these areas. Such innovations are being facilitated by the DOE Energy Materials Network (EMN) initiative which aims to accelerate materials research and development (R&D) for clean energy applications through the streamlined utilization of advanced scientific resources in theory, computation, experimentation, analysis, and data informatics. The "HydroGEN" EMN consortium on Advanced Water Splitting Materials for renewable hydrogen production will be described and discussed.

We explore the use of diffuse optical tomography (DOT) for the recovery of 3D tubular shapes representing vascular structures in breast tissue. Using a parametric level set method (PaLS) our method incorporates the connectedness of vascular structures in breast tissue to reconstruct shape and absorption values from severely limited data sets. The approach is based on a decomposition of the unknown structure into a series of two dimensional slices. Using a simplified physical model that ignores 3D effects of the complete structure, we develop a novel inter-slice regularization strategy to obtain global regularity. We report on simulated and experimental reconstructions using realistic optical contrasts where our method provides a more accurate estimate compared to an unregularized approach and a pixel based reconstruction.

A parametric level set method (PaLS) is implemented for image reconstruction for hyperspectral diffuse optical tomography (DOT). Chromophore concentrations and diffusion amplitude are recovered using a linearized Born approximation model and employing data from over 100 wavelengths. The images to be recovered are taken to be piecewise constant and a newly introduced, shape-based model is used as the foundation for reconstruction. The PaLS method significantly reduces the number of unknowns relative to more traditional level-set reconstruction methods and has been show to be particularly well suited for ill-posed inverse problems such as the one of interest here. We report on reconstructions for multiple chromophores from simulated and experimental data where the PaLS method provides a more accurate estimation of chromophore concentrations compared to a pixel-based method.

We explore the development and performance of algorithms for hyperspectral diffuse optical tomography (DOT) for which data from hundreds of wavelengths are collected and used to determine the concentration distribution of chromophores in the medium under investigation. An efficient method is detailed for forming the images using iterative algorithms applied to a linearized Born approximation model assuming the scattering coefficient is spatially constant and known. The L-surface framework is employed to select optimal regularization parameters for the inverse problem. We report image reconstructions using 126 wavelengths with estimation error in simulations as low as 0.05 and mean square error of experimental data of 0.18 and 0.29 for ink and dye concentrations, respectively, an improvement over reconstructions using fewer specifically chosen wavelengths.

The accuracy of the Born Approximation as a forward model of elastic wave scattering in the context of simulating Impact Echo tests of reinforced of concrete is investigated in this paper. The ability of a forward model to realistically simulate the physics of a system can be important when such a model is used as part of an inverse solution. Synthetic data of scattering by air void defects that are typically present in damaged civil engineering structures is generated by a two-dimensional Finite Difference in Time Domain (FDTD) model for elastic wave propagation in an infinite, homogeneous and isotropic concrete medium. Horizontal elongated cracks and air voids with compact shapes are considered in this study for comparison between the synthetic and the Born approximated data. It is observed that the Born Approximation simulates a compact air void better than a horizontal elongated one. This knowledge provides insight on Born Approximation as part of an inverse solution towards imaging of air voids of various shapes in a damaged civil engineering structure.

A general framework for incorporating single and multiple priors in diffuse optical tomography is described. We explore the use of this framework for simultaneously utilizing spatial and spectral priors in the context of imaging breast cancer. The utilization of magnetic resonance images of water and lipid content as a statistical spatial prior for the diffuse optical image reconstructions is also discussed. Simulations are performed to demonstrate the significant improvement in image quality afforded by combining spatial and spectral priors.

We introduce a modified Tikhonov regularization method to include three-dimensional x-ray mammography as a prior in the diffuse optical tomography reconstruction. With simulations we show that the optical image reconstruction resolution and contrast are improved by implementing this x-ray-guided spatial constraint. We suggest an approach to find the optimal regularization parameters. The presented preliminary clinical result indicates the utility of the method.

We present a shape-based approach to three-dimensional image reconstruction from diffuse optical data. Our approach differs from others in the literature in that we jointly reconstruct object and background characterization and localization simultaneously, rather than sequentially process for optical properties and postprocess for edges. The key to the efficiency and robustness of our algorithm is in the model we propose for the optical properties of the background and anomaly: We use a low-order parameterization of the background and another for the interior of the anomaly, and we use an ellipsoid to describe the boundary of the anomaly. This model has the effect of regularizing the inversion problem and provides a natural means of including additional physical properties if they are known a priori. A Gauss-Newton-type algorithm with line search is implemented to solve the underlying nonlinear least-squares problem and thereby determine the coefficients of the parameterizations and the descriptors of the ellipsoid. Numerical results show the effectiveness of this method.

Twenty-two laboratories participated in a collaborative test to determine the iodine value (IV) of eight samples of fish oil (four with IV<150, four with IV>150) with either carbon tetrachloride (AOCS Official Method Cd 1–25) or cyclohexane (AOCS Recommended Practice Cd 1b-87) as solvent and either 1 or 2 h of reaction time. Laboratories received coded duplicate samples (hidden duplicates) and carried out duplicate determinations on each oil by each solvent-time combination (open duplicates). Replacing carbon tetrachloride with cyclohexane resulted in a lower IV (P<0.001). The decrease averaged 1.6 IV units for low-IV oils and 3.8 IV units for high-IV oils; this difference in response of 2.2 IV units between low- and high-IV oils was significant (P<0.001). Increasing the reaction time had a relatively small effect (0.34±0.18). There was no interaction of reaction time with solvent or oil type. Cyclohexane caused emulsions, which made it difficult to titrate residual iodine and thus increased the variability of the determination. The repeatability standard deviations (sr), based on hidden duplicates, for 1-h reaction time with carbon tetrachloride and cyclohexane were 2.17 and 3.35, respectively. The corresponding reproducibility standard deviations were 2.73 and 4.53.

Nine laboratories participated in a collaborative test to determine the iodine value (IV) of eight samples of fish oil (four with IV<150; four with IV>150) with either carbon tetrachloride (AOCS Official Method Cd 1–25) or cyclohexane/acetic acid (AOCS Recommended Practice Cd 1d-92) as solvent and 1 h of reaction time. Laboratories received coded duplicate samples (hidden duplicates) and carried out duplicate determinations on each oil by each method (open duplicates). Replacing carbon tetrachloride with cyclohexane/acetic acid resulted in similar mean values for both low- and high-IV oils and similar estimates of repeatability and reproducibility. The repeatability standard deviation (sr), based on hidden duplicates, with carbon tetrachloride and cyclohexane/acetic acid were 1.71 and 1.55, respectively. The corresponding reproducibility standard deviations were 1.81 and 1.98.

A wavelet domain, nonlinear inverse scattering approach is presented for imaging subsurface defects in a material sample, given observations of scattered thermal waves. Unlike methods using the Born linearization, our inversion scheme is based on the full wave-field model describing the propagation of thermal waves. Multiresolution techniques are employed to regularize and to lower the computational burden of this ill-posed imaging problem. We use newly developed wavelet-based regularization methods to resolve better the edge structures of defects relative to reconstructions obtained with smoothness-type regularizers. A nonlinear approximation to the exact forward-scattering model is introduced to simplify the inversion with little loss in accuracy. We demonstrate this approach on cross-section imaging problems by using synthetically generated scattering data from transmission and backprojection geometries. S0740-3232(98)00906-5

Nine laboratories participated in a collaborative test to determine the iodine value (IV) of eight samples of fish oil (four with IV<150; four with IV>150) with either carbon tetrachloride (AOCS Official Method Cd 1–25) or cyclohexane/acetic acid (AOCS Recommended Practice Cd 1d-92) as solvent and 1 h of reaction time. Laboratories received coded duplicate samples (hidden duplicates) and carried out duplicate determinations on each oil by each method (open duplicates). Replacing carbon tetrachloride with cyclohexane/acetic acid resulted in similar mean values for both low- and high-IV oils and similar estimates of repeatability and reproducibility. The repeatability standard deviation (sr), based on hidden duplicates, with carbon tetrachloride and cyclohexane/acetic acid were 1.71 and 1.55, respectively. The corresponding reproducibility standard deviations were 1.81 and 1.98.

Twenty-two laboratories participated in a collaborative test to determine the iodine value (IV) of eight samples of fish oil (four with IV<150, four with IV>150) with either carbon tetrachloride (AOCS Official Method Cd 1–25) or cyclohexane (AOCS Recommended Practice Cd 1b-87) as solvent and either 1 or 2 h of reaction time. Laboratories received coded duplicate samples (hidden duplicates) and carried out duplicate determinations on each oil by each solvent-time combination (open duplicates). Replacing carbon tetrachloride with cyclohexane resulted in a lower IV (P<0.001). The decrease averaged 1.6 IV units for low-IV oils and 3.8 IV units for high-IV oils; this difference in response of 2.2 IV units between low- and high-IV oils was significant (P<0.001). Increasing the reaction time had a relatively small effect (0.34±0.18). There was no interaction of reaction time with solvent or oil type. Cyclohexane caused emulsions, which made it difficult to titrate residual iodine and thus increased the variability of the determination. The repeatability standard deviations (sr), based on hidden duplicates, for 1-h reaction time with carbon tetrachloride and cyclohexane were 2.17 and 3.35, respectively. The corresponding reproducibility standard deviations were 2.73 and 4.53.

A wavelet domain, nonlinear inverse scattering approach is presented for imaging subsurface defects in a material sample, given observations of scattered thermal waves. Unlike methods using the Born linearization, our inversion scheme is based on the full wave-field model describing the propagation of thermal waves. Multiresolution techniques are employed to regularize and to lower the computational burden of this ill-posed imaging problem. We use newly developed wavelet-based regularization methods to resolve better the edge structures of defects relative to reconstructions obtained with smoothness-type regularizers. A nonlinear approximation to the exact forward-scattering model is introduced to simplify the inversion with little loss in accuracy. We demonstrate this approach on cross-section imaging problems by using synthetically generated scattering data from transmission and backprojection geometries.

To facilitate the research in photocatalysts and solar fuel production, a symposium on "Renewable Fuels from Sunlight and Electricity" was held at the 222nd ECS Meeting in Honolulu, in October 2012. In this article the highlights from the symposium are summarized.

We present an algorithm to reconstruct chromosphere concentration images directly rather than following the traditional two-step process of reconstructing wavelength-dependent absorption coefficient images and then calculating chromosphere concentration images. This procedure imposes prior spectral information into the image reconstruction that results in a dramatic improvement in the image contrast-to-noise ratio of better than 100%. We demonstrate this improvement with simulations and a dynamic blood phantom experiment.

The HydroGEN (https://www.h2awsm.org/) energy materials network (EMN) aims to accelerate the research and development (R&D) of advanced water splitting (AWS) technologies for clean, sustainable hydrogen production. Announced in October 2016, the HydroGEN EMN comprises six core National Laboratories and focuses on four AWS pathways: low- and high-temperature electrolysis, photoelectrochemical, and solar thermochemical water splitting. The HydroGEN consortium offers an extensive collection of materials research capabilities for addressing R&D challenges in discovery and design, efficacy and efficiency, durability and cost. Leveraging the HydroGEN Consortium’s technical experts and broad collection of unique resource capabilities is expected to advance the maturity and technology readiness levels in each advanced water splitting technology pathway.

Affordable, reliable, high performance materials and manufacturing technologies are key for transformational technology advancements in critical domestic energy applications. However, many materials discoveries in the laboratory either never reach widespread market deployment or spend decades in the development cycle at an extremely high cost. In response, the U.S. Department of Energy has established the Energy Materials Network (EMN) to accelerate the materials-to-market process by better integrating the stages from materials discovery through qualification. The EMN is a network of National Lab-led consortia that support both industry-guided and government-led research on specific energy-related material challenges. The EMN framework facilitates industry stakeholder engagement to ensure that research is immediately relevant, with high-impact commercial potential. To date, EMN consortia have been set up in seven technology areas. This paper describes the vision of the EMN as an enduring national resource, and discusses recent accomplishments in the current network of EMN consortia.

The emergence of hydrogen and fuel cell technologies offers the world important and potentially transformative environmental and energy security benefits. In recent years, research sponsored by the US Department of Energy’s (DOE) Fuel Cell Technologies Office (FCTO) has made significant contributions to the development of these technologies. With fuel cell electric vehicles beginning to be introduced to the market, enabling technologies for the widespread production of affordable hydrogen are becoming increasingly important. One of the more versatile hydrogen production pathways is based on splitting water via either electrolytic, photoelectrochemical, or thermochemical routes. For these advanced water splitting (AWS) technologies, there are tradeoffs amongst efficiency, durability, and cost at the materials, device, and system levels that need to be balanced for low cost hydrogen production. Recent advances have been made in catalytic materials for AWS to improve these attributes; however, further developments, from new materials to scale-up of state-of-the-art (SOA) materials, are required to reach large scale technoeconomic viability. Research innovations to advance the SOA in AWS materials are being facilitated by the DOE “HydroGEN” Energy Materials Network (EMN) consortium. An overview of FCTO’s Hydrogen Production Program activities with a focus on catalysts for advanced water splitting technologies and the HydroGEN consortium will be provided.

The emergence of hydrogen and fuel cell technologies offers important and potentially transformative environmental and energy security benefits. In recent years, research supported by the U.S. Department of Energy’s Fuel Cell Technologies Office has contributed substantially to the development and advancement of these technologies. The research investments to reduce costs in fuel cell stacks, for example, are clearly paying off, as commercial fuel-cell electric vehicles are being rolled out by major car manufacturers today. With increasing market penetration of FCEVs, enabling technologies for the affordable and widespread production of clean, renewable hydrogen is becoming increasingly important. Long term commercial viability of renewable hydrogen in the commercial marketplace will rely on continued materials research on several important fronts. Examples include the discovery and development of efficient, durable, and cost effective materials, devices and systems for hydrogen production based on: (1) low and high temperature advanced electrolysis powered by renewable electricity sources; (2) direct low-temperature photoelectrochemical water splitting powered by sunlight; and (3) direct high-temperature solar thermochemical water splitting. Research innovations in macro-, meso- and nano-scale materials are all needed for pushing forward the state-of-the-art in these areas. Such innovations are being facilitated by the DOE Energy Materials Network (EMN) initiative which aims to accelerate materials research and development (R&D) for clean energy applications through the streamlined utilization of advanced scientific resources in theory, computation, experimentation, analysis, and data informatics. The “HydroGEN” EMN consortium on Advanced Water Splitting Materials for renewable hydrogen production will be described and discussed.

To facilitate the research in photocatalysts and solar fuel production, a symposium on “Renewable Fuels from Sunlight and Electricity” was held at the 222nd ECS Meeting in Honolulu, in October 2012. In this article the highlights from the symposium are summarized.

Tungsten oxide semiconductor films for photoelectrochemical water-splitting applications, reactively sputtered at low temperatures (<300°C), have demonstrated photocurrents of 3.4mA∕cm2under AM 1.5 illumination in 0.33M H3PO4, exceeding published results for material pyrolytically fabricated at higher temperature, and approaching the theoretical maximum for tungsten trioxide based on optical absorption limits. The microstructural form of the reactively sputtered films, which is sensitive to the sputter process parameters, critically affects the photoconversion efficiency. It has also been determined that the sputtered WO3films form true compact photoelectrodes in solution, with active charge generation and separation over the material’s optical width.