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Random quantities from continuous time stochastic process with application to reliability and probability
A television tube is a useful way to record a many-element picture such as is obtained from an echelle spectrograph. The signal on the tube target is scanned and relayed to the data-processing center where it must be reconstituted into the scene that was present in the focal plane of the spectrograph camera. A typical television frame will contain four million bits of information which must be processed to remove transmission errors and random noise pulses, to remove periodic noise, to correct for optical and electrical distortion, to remove sensitivity variations across the face of the tube, to calibrate from the photoelectrons back to incident energy, and to provide a wavelength scale. Results of a study of these problems is presented.
Recent technological advances have made it possible to develop an advanced OMEGA position location experiment for a global search and rescue application. This application generated some new problem areas such as the OMEGA lane ambiguity, random access, location accuracy, real time processing, and size and weight of the Search and Rescue Communication (SARCOM). This experiment will demonstrate the feasibility of instantaneous alarm and position location by using a relatively inexpensive, battery operated, three-pound package. This package can transmit the alarm and position through a synchronous satellite to a search and rescue station in less than three minutes.
The interest on the part of selected U.S. corporation in using the research facilities and capabilities for materials processing in space utilizing the Space Shuttle was assessed. The effectiveness of the interview techniques as a method for gaining insight into the complex array of issues related to materials processing in space was evaluated. The survey, conducted was intended as a random sample of individuals, representing industry, who were currently involved in materials processing in space.
A program for calculating the radiation patterns of analytic reflector antennas, using geometric optics and aperture plane integration techniques, was modified and extended to include nonanalytic surfaces. The original program, while capable of predicting patterns for a variety of reflectors, both smooth and multipanelled, was subject to the restriction that the surface had to be expressed analytically, i.e., described by a set of mathematical equations. This restriction was removed by adapting a technique which allows the reflector configuration to be interpolated from a finite set of points measured on the surface. Criteria for choosing both the number and distribution of these measured surface points were developed by comparing the predicted antenna radiation patterns with measured laboratory results. Test cases involved relatively smooth as well as grossly distorted arbitrary surfaces. Finally, surface point accuracies were established by applying random error analysis to the measurement process.
Partition of biological cells in two phase aqueous polymer systems is recognized as a powerful separation technique which is limited by gravity. The synthesis of new, selective polymer ligand conjugates to be used in affinity partition separations is of interest. The two most commonly used polymers in two phase partitioning are dextran and polyethylene glycol. A thorough review of the chemistry of these polymers was begun, particularly in the area of protein attachment. Preliminary studies indicate the importance in affinity partitioning of minimizing gravity induced randomizing forces in the phase separation process. The PEG-protein conjugates that were prepared appear to be ideally suited for achieving high quality purifications in a microgravity environment. An interesting spin-off of this synthetic work was the observation of catalytic activity for certain of our polymer derivatives.
A study of differences between the morning and evening monthly rainfall for 5 x 5 deg cells over the oceans from the SSM/I data has been conducted. The monthly rainfalls are estimated from the technique given by Wilheit et al. The difference between the morning and evening monthly rainfall arises due to the various random errors involved in the retrieval process, the sampling error in the observations, and the diurnal component of oceanic rainfall. The diurnal component is weak but clearly visible when averaged over large areas and for long time periods. The analysis shows that morning rainfall is consistently greater than evening rainfall. The Northern Hemisphere seem to have a larger diurnal variation than does the Southern Hemisphere. The maximum ratio between the morning and evening monthly rainfall is 1.7 while 1.2 is the more typical value.
Improved algorithm reduces computation needed to estimate dynamics affecting trajectory of satellite in orbit around Earth with approximately repeating ground track. Underlying concepts presented in "Computing Gravitational Bumps From Repeating-Orbit Data", (NPO-17925). Satellite presumed to undergo same gravitational perturbation upon each near-repetition. Tracking data from multiple nearly repeating arcs collected into segments called "bins" and processed together to reduce effects of random errors, and thereby obtain more-accurate estimates of dynamical parameters. Bumps in gravitational field of Earth estimated with less computation.
The synthesis of Newtonian concept of corpuscles during emission with Huygens’ concept of secondary-wavelets during propagation implies that all EM radiations from quantized atoms and molecules are released as discrete amount of energies. However, they propagate out as time-finite Maxwellian light pulses. Huygens also underscored that his secondary wavelets keep propagating as independent pulses in the absence of any interacting medium, or until intercepted by an interface with a medium or a detector. Then we use the Superposition Principle and the coherence theory to derive Einstein’s photoelectric equation by summing innumerable random time-finite pulses. This process driven approach should yield the characteristic statistical variations of photoelectron current pulses, as generated by photodetectors for different kinds of light sources. Lamb & Scully originally proposed this semiclassical approach without assuming that light actually consists of time finite pulses. The quantumness remains confined within the excitation and de-excitations processes in the material particles.
Niobium's native surface oxide affects SRF cavity and superconducting qubit performance, motivating interest in controlling its crystalline structure. We combine a literature-derived machine-learning analysis with temperature-dependent XRD to study crystalline ordering in Nb2O5. Random Forest models, trained on 74 processing conditions from 17 papers and validated by leave-one-group-out cross-validation, predicted broad crystallinity outcomes well (balanced accuracy 0.809), but struggled with specific polymorph identity (0.577). Annealing temperature was the dominant predictor across all targets; oxygen partial pressure showed negligible importance, reflecting narrow literature coverage rather than physical irrelevance. Temperature-dependent XRD on anodized and H2O2-treated Niobium showed structural evolution consistent with the machine learning predictions. Our model and overall approach provide a data-driven framework for identifying and optimizing conditions that promote crystallization in initially amorphous oxides. This framework can guide the selection of growth and post-annealing conditions for Nb surfaces by narrowing the experimental parameter space, thereby reducing trial-and-error efforts in developing oxide structures relevant to SRF applications.
The OMEGA Position Location Experiment (OPLE) was performed in 1967 by the Goddard Space Flight Center in order to demonstrate a position location and data collection system. OMEGA navigation signals were received at a remote site and retransmitted via a synchronous satellite to a ground processing center where data collecting and position determination were performed. Recent technological advances have made it possible to develop an Advanced OPLE System towards a global search and rescue application. This application generated some new problem areas such as the OMEGA lane ambiguity, random access, location accuracy, real-time processing, and size and weight of the Search and Rescue Communications (SARCOM). This experiment demonstrates the feasibility of instantaneous alarm and position location by using a relatively inexpensive, battery operated, three-pound package.
Advanced simulations and calculations on quantum computers require high-fidelity implementations of quantum operations. The universal gateset approach builds complex unitaries from a small set of primitive gates, often resulting in a long gate sequence, which is typically a leading factor in the total accumulated error. Compiling a complex unitary for processors with higher-dimensional logical elements, such as qutrits, exacerbates the accumulated error per unitary, since an even longer gate sequence is required. Optimal control methods promise time- and resource-efficient compact gate sequences and, therefore, higher fidelity. These methods generate pulses that can directly implement any complex unitary on a quantum device. In this work, we demonstrate that any arbitrary qubit and qutrit gate can be realized with high fidelity, which can significantly reduce the length of a gate sequence. We generate and test pulses for a large set of randomly selected arbitrary unitaries on several quantum processing units (QPUs): the Lawrence Livermore National Laboratory Quantum Device and Integration Testbed’s (QuDIT’s) standard QPU and three of Rigetti’s QPUs: Ankaa-2, Ankaa-9Q-1, and Aspen-M-3. On the QuDIT platform’s standard QPU, the average fidelity of random qutrit gates is 97.9 ± 0.5% measured with conventional QPT and 98.8 ± 0.6% from QPT with gate folding. Rigetti’s Ankaa-2 achieves random qubit gates with an average fidelity of 98.4 ± 0.5% (conventional QPT) and 99.7 ± 0.1% (QPT with gate folding). On Ankaa-9Q-1 and Aspen-M-3, the average fidelities with conventional qubit QPT measurements were higher than 99% (see Appendix). Here we show that optimal control gates are robust to drift for at least 3 h and that the same calibration parameters can be used for all implemented gates. Our work promises that the calibration overheads for optimal control gates can be made small enough to enable efficient quantum circuits based on this technique.
Adopting an effective top coal caving method is the key to enhancing coal recovery and reducing gangue content for the fully mechanized top coal caving working face with extra‐thick coal seams. In this study, the movement of coal particles generated during top coal caving is considered to follow a normal distribution. Then, the caving body and coal‐rock settlement along the working face during the caving process are studied based on both the random media theory and probability theory. Accordingly, the optimal caving interval and caving sequences are determined, and a novel interval symmetrical coal caving method is proposed. The proposed method is systematically verified with results from physical similarity tests, and different caving methods are assessed by field tests. The results show the following: (1) The coal‐rock settlement and the caving body demonstrate clear axial symmetrical features along the working face; the size of the caving body increases as the caving height grows and its shape turns progressively from semicircular to semielliptical with a lower foot of the coal‐rock settlement. (2) The caving interval is derived using the sum of the radii of the coal‐rock settlement curves formed by the two largest caving bodies. (3) The symmetrical caving approach provides a symmetrical space for the subsequent movement of the broken top coal, which enables a uniform development of the caving body. (4) Compared with the traditional sequential coal caving method with the same number of supports, the interval symmetrical caving method results in a 21.7% of coal production increase, 17% caving rate promotion, and a shortened caving time by 23.4%. (5) The interval symmetrical caving method is found to improve the controllability of the caving process at the fully mechanized top coal caving working face. In general, this work presents a theoretical approach to select the optimal caving methods for the fully mechanized caving working face in extra‐thick coal seams for an improved production efficiency of the work face. The results of this study can also provide theoretical significance and referencing value for quantitative analyses of the coal caving methods for work faces with similar geological conditions.
A Monte Carlo fluence estimator has been designed to take advantage of the computational power of graphical processing units (GPUs). This new estimator, termed the volumetric-ray-casting estimator, is an extension of the expectation estimator. It can be used as a replacement of the track-length estimator for the estimation of global fluence. Calculations for this estimator are performed on the GPU while the Monte Carlo random walk is performed on the central processing unit (CPU). This method lowers the implementation cost for GPU acceleration of existing Monte Carlo particle transport codes as there is little modification of the particle history logic flow. Three test problems have been evaluated to assess the performance of the volumetric-ray-casting estimator for neutron transport on GPU hardware in comparison to the standard track-length estimator on CPU hardware. Evaluation of neutron transport through air in a criticality accident scenario showed that the volumetric-ray-casting estimator achieved 23 times the performance of the track-length estimator using a single core CPU paired with a GPU and 15 times the performance of the track-length estimator using an eight core CPU paired with a GPU. Finally, simulation of a pressurized water reactor fuel assembly showed that the performance improvement was 6 times within the fuel and 7 times within the control rods using an eight core CPU paired with a single GPU.
Abstract—Phasor Measurement Units (PMUs) provide time synchronized measurements across the power grid, enabling data driven event detection and classification for enhanced system monitoring and situational awareness. However, variations in event duration, spatial extent, and severity, along with coincident events, pose challenges for conventional classification models that require fixed-size inputs. This paper presents a feature-based framework that aggregates diverse attributes from all available PMUs for each event into a fixed-length vector, facilitating the application of standard machine learning classifiers, including Random Forest, XGBoost, and Multilayer Perceptron. A probabilistic post-processing scheme is further introduced to enable multi-label classification in the presence of overlapping events. Experiments using real-world PMU data demonstrate that the Random Forest model achieves 95% accuracy, while the proposed post-processing method yields an additional 3% improvement.
The SEU/SRAM is a 4-kbit Static Random Access Memory (SRAM) designed to detect Single-Event Upsets (SEUs) produced by high energy particles. This device was used to determine the distribution in the memory cell spontaneous flip potential.
Discovering pragmatic and efficient approaches to construct ε-approximations of quantum operators such as real (imaginary) time-evolution propagators in terms of the basic quantum operations (gates) is challenging. Prior ε-approximations are invaluable, in that they enable the compilation of classical and quantum algorithm modeling of, e.g., dynamical and thermodynamic quantum properties. In parallel, symmetries are powerful tools concisely describing the fundamental laws of nature; the symmetric underpinnings of physical laws have consistently provided profound insights and substantially increased predictive power. In this work, we consider the interplay between the ε-approximate processes and the exact symmetries in a semicoherent context—where measurements occur at each logical clock cycle. Here we draw inspiration from Pascual Jordan's groundbreaking formulation of nonassociative, but commutative, symmetric algebraic form. Our symmetrized formalism is then applied in various domains such as quantum random walks, real-time evolutions, variational algorithm ansatzes, and efficient entanglement verification. Our work paves the way for a deeper understanding and greater appreciation of how symmetries can be used to control quantum dynamics in settings where coherence is a limited resource.
Scalable data management is essential for processing large scientific dataset on HPC platforms for distributed deep learning. In-memory distributed storage is preferred for its speed, enabling rapid, random, and frequent data access required by stochastic optimizers. Processes use one-sided or collective communication to fetch remote data, with optimal performance depending on (i) dataset characteristics, (ii) training scale, and (iii) interconnection network. Empirical analysis shows collective communication excels with larger mini-batch sizes and/or fewer processes, whereas one-sided communication outperforms at larger scales. We propose MDLoader, a hybrid in-memory data loader for distributed graph neural network training. MDLoader features a model-driven performance estimator that dynamically selects between one-sided and collective communication at the beginning of training using Tree of Parzen Estimators (TPE). Evaluations on NERSC Perlmutter and OLCF Summit show MDLoader outperforms single-backend loaders by up to 2.83 × and predicts the suitable communication method with 96.3% (Perlmutter) and 94.3% (Summit) success rate.