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InGaAs/GaAs island formation during vapor phase epitaxy showed diverging behaviors when varying group V partial pressures (PP). Differences include changes in critical thicknesses for the onset of the Stranski-Krastanow (S-K) transformation, surface coverages, ratios between coherent and incoherent islands, and dissimilar morphologies upon annealing.
Complex physical systems sometimes have statistical behavior characterized by power- law dependence on the parameters of the system and spatial variability with no particular characteristic scale as the parameters approach critical values. The renormalization group (RG) approach was developed in the fields of statistical mechanics and quantum field theory to derive quantitative predictions of such behavior in cases where conventional methods of analysis fail. Techniques based on these ideas have since been extended to treat problems in many different fields, and in particular, the behavior of turbulent fluids. This lecture will describe a relatively simple but nontrivial example of the RG approach applied to the diffusion of photons out of a stellar medium when the photons have wavelengths near that of an emission line of atoms in the medium.
The fracture of carbon materials is a complex process, the understanding of which is critical to the development of next generation high performance materials. While quantum mechanical (QM) calculations are the most accurate way to model fracture, the fracture behavior of many carbon-based composite engineering materials, such as carbon nanotube (CNT) composites, is a multi-scale process that occurs on time and length scales beyond the practical limitations of QM methods. The Reax Force Field (ReaxFF) is capable of predicting mechanical properties involving strong deformation, bond breaking and bond formation in the classical molecular dynamics framework. This has been achieved by adding to the potential energy function a bond-order term that varies continuously with distance. The use of an empirical bond order potential, such as ReaxFF, enables the simulation of failure in molecular systems that are several orders of magnitude larger than would be possible in QM techniques. In this work, the fracture behavior of an amorphous carbon (AC) matrix reinforced with CNTs was modeled using molecular dynamics with the ReaxFF reactive forcefield. Care was taken to select the appropriate simulation parameters, which can be different from those required when using traditional fixed-bond force fields. The effect of CNT arrangement was investigated with three systems: a single-wall nanotube (SWNT) array, a multi-wall nanotube (MWNT) array, and a SWNT bundle system. For each arrangement, covalent bonds are added between the CNTs and AC, with crosslink fractions ranging from 0-25% of the interfacial CNT atoms. The SWNT and MWNT array systems represent ideal cases with evenly spaced CNTs; the SWNT bundle system represents a more realistic case because, in practice, van der Waals interactions lead to the agglomeration of CNTs into bundles. The simulation results will serve as guidance in setting experimental processing conditions to optimize the mechanical properties of CNT composites.
A high-performance MODFET structure grown by MBE with the incorporation of a single quantum well In(0.15)Ga(0.85)As layer for the transport of two-dimensional electron gas has been critically examined for its thermal stability at 80 K and low-frequency noise form 0.01 to 10 to the 8th Hz. Experimental results indicate that the behavior of this device in both these respects is much superior when compared with the same behavior of conventional MODFETs. A maximum low-field carrier mobility of 29,000 sq cm/s at 80 K and an average carrier saturation velocity of 2 x 10 to the 7th cm/s at 300 K in a 1-micron gate device clearly indicate that the quality of the pseudomorphic quantum well (InGaAs) layer is either comparable or better than that of the usual GaAs buffer layer. The deep level spectra, obtained through photo-FET measurements, and the low-frequency noise spectra at different temperatures obtained for the new pseudomorphic and conventional MODFET's have clearly indicated that contributions from various deep levels present in the new structure are significantly reduced.
The thermostatic properties of a high-symmetry phase of metallic hydrogen with atomic sphere radius between 0.1 and 1.5 bohr are studied, with special emphasis accorded to electronic screening and quantum proton motion. The electron-proton and proton-proton interactions receive a perturbation treatment based on the Singwi dielectric function, while the proton motion is handled by self-consistent harmonic approximation. Quantum behavior is found to be less pronounced than expected, and nuclear magnetism is absent. The phonon spectrum is, however, affected by screening and large proton motion. The zero-point vibrational energy and the superconducting critical temperature are below previous estimates. The crystalline-defect formation energies are a few times the Debye energy, which implies that defects contribute significantly to melting at the lower particle densities.
Wildfires represent a critical environmental hazard with multifaceted implications for ecosystems, communities, and public health [1]. The escalating frequency and intensity of wildfires globally have intensified the urgency for robust segmentation methodologies to facilitate effective mitigation, response, and recovery strategies [2]. Accurate wildfire segmentation is pivotal for delineating fire boundaries, assessing progression patterns, and prioritizing resource allocation during emergency scenarios. Furthermore, precise segmentation enables stakeholders, including policymakers, environmental scientists, and emergency responders, to formulate evidence-based strategies, thereby minimizing socio-economic disruptions and ecological degradation. Consequently, advancing wildfire segmentation techniques through innovative technological interventions remains a paramount research imperative. Although foundational in wildfire segmentation, traditional deterministic models exhibit inherent limitations that compromise their efficacy in dynamic and uncertain environments. These models often operate on rigid algorithms prioritizing deterministic classifications, thereby overlooking the inherent complexities and uncertainties associated with wildfire behavior and satellite data variability. Such deterministic frameworks tend to produce oversimplified representations that fail to capture the intricate nuances of evolving fire dynamics, spatial heterogeneity, and environmental interactions [1]. Consequently, the deterministic approach’s propensity for uncertainty collapsing [1, 3] hampers the accuracy, reliability, and applicability of segmentation outcomes in real-world scenarios. Contrastingly, stochastic models offer a more nuanced and adaptable framework for wildfire segmentation. By integrating probabilistic elements into the modeling paradigm, stochastic approaches, particularly probabilistic approaches such as variational auto encoders (VAEs) [4], facilitate comprehensive uncertainty assessment, enabling researchers to quantify and incorporate uncertainties into segmentation outcomes effectively. This probabilistic nature empowers stochastic models to encapsulate variability, account for data inconsistencies, and adapt to evolving environmental conditions, enhancing segmentation accuracy, reliability, and robustness. Embracing stochastic methodologies thus catalyzes advancements in wildfire science by fostering a more holistic, adaptive, and resilient segmentation framework. Despite VAEs demonstrating significant promise in various applications, they come with inherent limitations that have garnered attention within the machine learning community. One of the primary drawbacks lies in their reliance on static priors, which essentially assume a fixed distribution for latent variables, thereby limiting the model’s flexibility to capture complex data structures effectively [5]. This static nature leads to suboptimal representations, especially when dealing with complex and high-dimensional data. Additionally, VAEs often struggle with generating sharp and realistic samples, a phenomenon commonly referred to as mode collapse [5, 7, 6]. Furthermore, the optimization process in VAEs, which involves balancing the reconstruction loss and the regularization term, can sometimes be challenging to fine-tune [7]. In recent efforts to address these shortcomings, alternative approaches like Vector Quantized Variational Auto encoders(VQ-VAEs) [7], address the challenges by incorporating discrete latent variables and leveraging techniques that enhance the quality and diversity of generated samples while maintaining efficient training dynamics. VQ-VAEs propose a dynamic prior distribution generation mechanism that diverges from the static priors commonly associated with traditional VAEs. This dynamic approach allows for more adaptive and context-aware latent variable representations, thereby potentially capturing complex data structures more effectively. Unlike autoregressive prior models such as PixelCNN, which, despite their ability to model dependencies across data dimensions, suffer from significant computational inefficiencies and lack flexibility in handling diverse datasets. In our work, we propose to use a generative quantum-compatible approach to help alleviate the shortcomings of autoregressive prior model in VQ-VAEs. Restricted Boltzmann Machines (RBMs) are a viable alternative prior model that can learn prior distributions in a faster and more flexible manner. In this research endeavor, we meticulously curate a state-of-the-art dataset leveraging satellite MODIS data in conjunction with VIIRS fire masks, derived from Fire Radiative Power (FRP), thereby encapsulating diverse wildfire scenarios and environmental contexts. We developed a conditional VQ-VAE architecture with the RBM prior model that is trained in a supervised manner for segmenting wildfire masks. This innovative approach synergistically harnesses deep learning capabilities, enabling the generation of segmentation maps characterized by heightened precision, granularity, and contextual relevance. Furthermore, replacing the autoregressive prior learning method proposed by the original VQ-VAE with a prior density approximation via quantum-compatible RBM facilitates expedited inference processes, augments flexibility in prior sampling, optimizes computational efficiency and establishes a groundbreaking benchmark in wildfire segmentation methodologies.
An interdisciplinary quantum communications and sensing research effort has been underway at the NASA Glenn Research Center since the summer of 2000. Researchers in the Communications Technology, Instrumentation and Controls, and Propulsion and Turbomachinery Divisions have been working together to study and develop techniques that use the principle of quantum entanglement (QE). This work is supported principally by the Nanotechnology Base R&T program at Glenn. As applied to communications and sensing, QE is an emerging technology that holds promise as a new and innovative way to communicate faster and farther, and to sense, measure, and image environmental properties in ways that are not possible with existing technology. Quantum entangled photons are "inseparable" as described by a wave function formalism. For two entangled photons, the term "inseparable" means that one cannot describe one photon without completely describing the other. This inseparability gives rise to what appears as "spooky," or nonintuitive, behavior because of the quantum nature of the process. For example, two entangled photons of lower energy can be created simultaneously from a single photon of higher energy in a process called spontaneous parametric down-conversion. Our research is focused on the use of polarization-entangled photons generated by passing a high-energy (blue) photon through a nonlinear beta barium borate crystal to generate two red photons that have orthogonal, but entangled, polarization states. Although the actual polarization state of any one photon is not known until it is measured, the act of measuring the polarization of one photon completely determines the polarization state of its twin because of entanglement. This unique relationship between the photons provides extra information about the system. For example, entanglement makes it easy to distinguish entangled photons from other photons impinging on a detector. For many other applications, ranging from quantum computation and information to quantum sensing, the entanglement property is critical.
We think that mode lock laser pulses are generated by the summation process that take place between the monochromatic EM filed frequencies as if they interact with each other as shown in equation 1. In reality, the pulse generation is a collaborative interaction process between EM fields and various material medium. When we carry out the actual mode lock analysis, we do take into account of interpaly between all the temporal dynamics of the cavity gain medium, cavity round trip time and the response time of the intra cavity element (saturable absorber, Kerr medium, etc.). that really enforces the locking of the phase of the cavity spontaneous emissions. On a conceptual level, this simplistic representation of the mode locking by Eq.1 ignores all these critical physical processes. When we try to analyze a pulsed field, again we start by representing it very much like this equation, even though we can only detect the square modulus of this complex field and loose a lot of phase related information to the detectors quantum whims and their time constants. The key parameters for a light pulse are as follows. Foremost is the (i) carrier frequency, which cannot be described or imagined without its state of undulation expressed as its (ii) phase. Next is our imagined time finite (iii) carrier envelope that provides the temporal boundary of the field amplitude strength of the undulating E-field. The final parameter is the (iv) state of polarization or the unique plane along which the strength of the E-field gradient undulates. None of these filed characteristics are made self-evident to us by the fields themselves. We do not see light. Light does not see light. Light beams pass through each other without altering each others energy distribution unless there are interacting material molecules (dipoles) within the physical volume of superposition of the beams. In contrast, we can sense the material particles. Material particles sense each other and they cannot pass through each other without interacting with (scattering from) each other. Thus the interpretation of the superposition phenomenon of multiple fields on detectors should not be lumped under the mysterious "wave-particle duality" philosophy. The phenomenon of superposition can be understood better when we focus on the actual process experienced by the detecting dipoles when allowed by QM rules, they respond to and sum all the induced stimulations due to all the superposed fields followed by the proportionate energy absorption giving rise to the fringes we observe. We will present various experimental results to illustrate our arguments. Our position is that such detector behavior driven interpretations rather than the generally implied field-field interaction driven explanations, will help us better understand the ultimate nature of light and hence invent better and newer devices and instruments.