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DOE OSTI2017

Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification

We address the problem of multi-class classification in the case where the number of classes is very large. We propose a double sampling strategy on top of a multi-class to binary reduction strategy, which transforms the original multi-class problem into a binary classification problem over pairs of examples. The aim of the sampling strategy is to overcome the curse of long-tailed class distributions exhibited in majority of large-scale multi-class classification problems and to reduce the number of pairs of examples in the expanded data. We show that this strategy does not alter the consistency of the empirical risk minimization principle defined over the double sample reduction. As a result, experiments are carried out on DMOZ and Wikipedia collections with 10,000 to 100,000 classes where we show the efficiency of the proposed approach in terms of training and prediction time, memory consumption, and predictive performance with respect to state-of-the-art approaches.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Biosequence-based approach to analyzing binaries

In a dynamic computing environment, it is a nontrivial task to verify code running in the environment because most approaches to software similarity require extensive and time-consuming analysis of a binary, or the approaches fail to recognize executables that are similar but nonidentical. A biosequence-based method for quantifying similarity of executable binaries is used to identify allowed codes in a real-world multi-user environment.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Biosequence-based approach to analyzing binaries

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Binary classification with noisy quantum circuits and noisy quantum data

We study the effects of single-qubit noises in the quantum circuit and the corruption in the quantum training data to the performance of binary classification problem. We find that under the presence of errors, the measurement at a qubit is affected only by the noise in the corresponding qubit, and that the errors on other qubits do not affect this outcome. Furthermore, for the task where we fit a binary classifier using a quantum training data, we show that the noise in the data can work as a regularizer, implying that we can benefit from the noise in certain cases. We support our findings with simulations.

Lee, Yonghoon↗

DOE OSTI2021

Uniformly Ordered Binary Decision Algorithm for Benchmark Experiment Correlations in Whisper Validation

When performing a validation exercise for determining the upper subcritical limit of a nuclear criticality safety application, an analyst should select and perform a statistical analysis on a population of benchmark experiments that are neutronically similar to the application. The size of this population should be sufficiently large such that the statistical analysis has a high degree of confidence that the bias plus bias uncertainty (calculational margin) has been accurately quantified. A complication arises because many benchmark experiments share common components, leading to correlations in their measured effective multiplication factors. Correlations between benchmark experiments within the population reduces its predictive power. This motivates the need for methods that consider benchmark experiment correlations and ensure adequate statistical significance of results. The Whisper code is a statistical analysis pack- age that incorporates nuclear data sensitivity coefficients from MCNP to assess benchmark experiment similarity and then performs an extreme-value analysis to estimate the bias plus bias uncertainty. The original methodology in Whisper does not consider the effect of benchmark experiment correlations when making this estimation, and this summary proposes the uniformly ordered binary decision algorithm to address this shortcoming. The original methodology in Whisper computes similarity coefficients ck for an application compared to all benchmark experiments in its library and develops weighting factors for a selected population proportional to the ck values. The methodology can be interpreted as statistically emulating a validation exercise for a particular application where the weighting factors may be viewed as the likelihood that an analyst would include a particular benchmark experiment within the population. The effective sample size of the population is the expected or mean number of benchmark experiments in the population. The uniformly ordered binary decision algorithm identifies clusters of correlated benchmark experiments within the population and then computes adjusted weighting factors based on the magnitude of the correlation coefficients within the cluster to compute a reduced effective sample size accounting for the lower information content because of correlations. Benchmark experiments within the cluster are ordered randomly with equal probability and probabilistic decisions are made as to whether a benchmark. experiment within the cluster should treated as redundant with a previous one; if two redundant benchmark experiments are included, then the conservative worst case bias plus bias uncertainty is used and the pair is counted as a single benchmark experiment in the population. Results are provided for HEU solutions in a research version of the Whisper software using benchmark experiment correlations provided by DICE, the Database for the International Criticality Safety Benchmark Evaluation Project (ICSBEP). These show that there can be a significant increase in the bias plus bias uncertainty because the effective sample size is reduced, and therefore the algorithm, needing to meet sample size requirements, expands the benchmark experiment population by accepting less similar benchmark experiments that would have otherwise not been included.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Extending Power of Nature from Binary Problems to Real-Valued Graph Learning in Real World

Nature performs complex computations constantly at clearly lower cost and higher performance than digital computers. It is crucial to understand how to harness the unique computational power of nature in Machine Learning (ML). In the past decade, besides the development of Neural Networks (NNs), the community has also relentlessly explored nature-powered ML paradigms. Although most of them are still predominantly theoretical, a new practical paradigm enabled by the recent advent of CMOS-compatible room-temperature nature-based computers has emerged. By harnessing the nature's power of entropy increase, this paradigm can solve binary learning problems delivering immense speedup and energy savings compared with NNs, while maintaining comparable accuracy. Regrettably, its values to the real world are highly constrained by its binary nature. A clear pathway to its extension to real-valued problems remains elusive. This paper aims to unleash this pathway by proposing a novel end-to-end Nature-Powered Graph Learning (NP-GL) framework. Specifically, through a three-dimensional co-design, NP-GL can leverage the nature's power of entropy increase to efficiently solve real-valued graph learning problems. Experimental results across 4 real-world applications with 6 datasets demonstrate that NP-GL delivers, on average, 6970X speedup and 10^5x energy consumption reduction with comparable or even higher accuracy than Graph Neural Networks (GNNs).

artificial intelligence↗

DOE OSTI2014

January 2014 Dixie Valley Binary Cycle Production Data

Dixie Valley production data for January 2014, for a DOE Report. Used to demonstrate the techno-economic feasibility of utilizing the available unused heat to generate additional electric power from a binary power plant. *Note - This data is incomplete. See link below "Monthly Production Data September 2014" for more complete data set.

15 GEOTHERMAL ENERGY↗

DOE OSTI2013

Production and Injection data for NV Binary facilities

Excel files are provided with well production and injection data for binary facilities in Nevada. The files contain the data that reported montly to the Nevada Bureau of Mines and Geology (NBMG) by the facility operators. this data has been complied into Excel spreadsheets for each of the facilities given on the NBMG web site.

15 GEOTHERMAL ENERGY↗

DOE OSTI2013

2013 YTD Dixie Valley Binary Cycle Production Data

Sensor data proving the technical and economic feasibility of utilizing the available unused heat to generate additional electric power from a binary power plant from the low-temperature brine at the Dixie Valley Geothermal Power Plant. Monthly data for Jan 2013 - October 7 2013

15 GEOTHERMAL ENERGY↗

NASA NTRS2022

Elevated Hot Gas and High-mass X-Ray Binary Emission in Low-metallicity Galaxies: Implications for Nebular Ionization and Intergalactic Medium Heating in the Early Universe

High-energy emission associated with star formation has been proposed as a significant source of interstellar medium (ISM) ionization in low-metallicity starbursts and an important contributor to the heating of the intergalactic medium (IGM) in the high-redshift (z ≳ 8) universe. Using Chandra observations of a sample of 30 galaxies at D ≈ 200–450 Mpc that have high specific star formation rates of 3–9 Gyr^(−1) and metallicities near Z ≈ 0.3Z_⊙, we provide new measurements of the average 0.5–8 keV spectral shape and normalization per unit star formation rate (SFR). We model the sample-combined X-ray spectrum as a combination of hot gas and high-mass X-ray binary (HMXB) populations and constrain their relative contributions. We derive scaling relations of log L^(HMXB)_(0.5-8keV) / SFR = 40.19 ± 0.06 and log L^(gas)_(0.5-2keV) / SFR = 39.58 ^(+0.17)_(-0.28), significantly elevated compared to local relations. The HMXB scaling is also somewhat higher than L^(HMXB)_(0.5-8keV) -SFR-Z relations presented in the literature, potentially due to our galaxies having relatively low HMXB obscuration and young and X-ray luminous stellar populations. The elevation of the hot gas scaling relation is at the level expected for diminished attenuation due to a reduction of metals; however, we cannot conclude that an L^(gas)_(0.5-2keV) -SFR-Z relation is driven solely by changes in ISM metal content. Finally, we present SFR-scaled spectral models (both emergent and intrinsic) that span the X-ray-to-IR band, providing new benchmarks for studies of the impact of ISM ionization and IGM heating in the early universe.

high-mass x-ray binary stars↗

NASA NTRS2023

JWST Observations of the Enigmatic Y Dwarf WISE 1828+2650: I. Limits to a Binary Companion

The Y-dwarf WISE 1828+2650 is one of the coldest known Brown Dwarfs with an effective temperature of ~300 K. Located at a distance of just 10 pc, previous model-based estimates suggest WISE 1828+2650 has a mass of ~5-10 M J , making it a valuable laboratory for understanding the formation, evolution and physical characteristics of gas giant planets. However, previous photometry and spectroscopy have presented a puzzle with the near-impossibility of simultaneously fitting both the short (0.9-2.0 μm) and long wavelength (3-5 μm) data. A potential solution to this problem has been the suggestion that WISE 1828+2650 is a binary system whose composite spectrum might provide a better match to the data. Alternatively, new models being developed to fit JWST/NIRSpec and MIRI spectroscopy might provide new insights. This article describes JWST/NIRCam observations of WISE 1828+2650 in 6 filters to address the binarity question and to provide new photometry to be used in model fitting. We also report Adaptive Optics imaging with the Keck 10 m telescope. We find no evidence for multiplicity for a companion beyond 0.5 AU with either JWST or Keck. Companion articles will present low and high resolution spectra of WISE 1828 obtained with both NIRSpec and MIRI.

Infrared spectroscopy↗

DOE OSTI2022

Versatile Sequential Casting Processing for Highly Efficient and Stable Binary Organic Photovoltaics

Forming an ideal bulk heterojunction (BHJ) morphology is a critical issue governing the photon to electron process in organic solar cells (OSCs). Complementary to the widely-used blend casting (BC) method for BHJ construction, sequential casting (SC) can also enable similar or even better morphology and device performance for OSCs. Here, BC and SC methods on three representative donor:acceptor (D:A) blends are utilized, that is, PM6:PC 71 BM, PM6:IT-4F and PM6:L8-BO. Higher power conversion efficiencies (PCEs) in all cases by taking advantage of beneficial morphology from SC processing are achieved, and a champion PCE of 18.86% (certified as 18.44%) based on the PM6:L8-BO blend is reached, representing the record value among binary OSCs. The observations on phase separation and vertical distribution inspire the proposal of the swelling–intercalation phase-separation model to interpret the morphology evolution during SC processing. Further, the vertical phase segregation is found to deliver an improvement of device performance via affecting the charge transport and collection processes, as evidenced by the D:A-ratio-dependent photovoltaic properties. Besides, OSCs based on SC processing show advantages on device photostability and upscale fabrication. Finally, this work demonstrates the versatility and efficacy of the SC method for BHJ-based OSCs.

14 SOLAR ENERGY↗

DOE OSTI2024

Quantifying Epistemic Uncertainty in Binary Classification via Accuracy Gain

ABSTRACT Recently, a surge of interest has been given to quantifying epistemic uncertainty (EU), the reducible portion of uncertainty due to lack of data. We propose a novel EU estimator in the binary classification setting, as the posterior expected value of the empirical gain in accuracy between the current prediction and the optimal prediction. In order to validate the performance of our EU estimator, we introduce an experimental procedure where we take an existing dataset, remove a set of points, and compare the estimated EU with the observed change in accuracy. Through real and simulated data experiments, we demonstrate the effectiveness of our proposed EU estimator.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Binary optimal control by trust-region steepest descent

Abstract We present a trust-region steepest descent method for dynamic optimal control problems with binary-valued integrable control functions. Our method interprets the control function as an indicator function of a measurable set and makes set-valued adjustments derived from the sublevel sets of a topological gradient function. By combining this type of update with a trust-region framework, we are able to show by theoretical argument that our method achieves asymptotic stationarity despite possible discretization errors and truncation errors during step determination. To demonstrate the practical applicability of our method, we solve two optimal control problems constrained by ordinary and partial differential equations, respectively, and one topological optimization problem.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Characterizations of Binary FeCr (AISI 430) Thin Films Deposited from a Single Magnetron Sputtering Under Easy Controllable Deposition Parameters

A series of 50-nm binary FeCr martensitic thin films were sputtered from a single source made of commercial AISI 430 ferritic stainless steel under the deposition rates gradually increased from 0.03 to 0.11 nm/s with 0.02 nm/s steps at stationary condition. And, under 0.09 nm/s deposition rate, a second series of the films were also deposited under the rotation speed of their substrates which was chosen at 0, 25, and 45 rpm. As far as we are concerned, this study is the first investigation of properties of the thin films produced from AISI 430. The atomic Fe content in the films increased from 79.3 to 98.6% while atomic Cr content decreased from 20.5 to 1.2% with the increase of deposition rate from 0.03 to 0.11 nm/s. According to compositional analysis, the Fe content increased while Cr content decreased with increasing deposition rate. The reason for this may be attributed to the relatively different bond energy/melting point of metals which have different contents sputtered from source material since this physical parameter is very significant for the sputtering process. And, the Fe content in the films decreased from 84.9 to 79.2 at. % while the Cr content increased from 14.9 to 20.6 at. % when the increase of rotation speed of substrate. The crystal structure of all films was observed to have a body-centered tetragonal phase and the intensity of (110) peak varied with the atomic Fe content. The surface observations of films performed by a scanning electron microscope exposed that the number of surface grains increased with the increase of deposition rate and decreased with the increase of rotation speed. According to surface roughness analysis done by an atomic force microscope, the roughness of the film surfaces increased as the deposition rate increased. And, the roughness of the film surfaces decreased as the rotation speed increased. This has been consistent with the grain size and roughness parameters. Thus, increasing deposition rate and decreasing rotation speed of the substrate caused an increase in grain size and roughness parameters. The magnetic measurements of the films achieved, by a vibrating sample magnetometer at room temperature, displayed that the saturation magnetization, M{sub s}, values increased from 820.1 to 1700.4 emu/cm{sup 3}, the remanence magnetization, M{sub r}, values increased from 293 to 817 emu/cm{sup 3}, and the coercivity, H{sub c}, value also increased from 38 to 107 Oe with the increasing of deposition rate. It is also seen that the magnetic easy axis are in the film plane due to the shape anisotropy. With the increase rotation speed, the values of M{sub s} and M{sub r} increased and the H{sub c} decreased. It was seen that variation of Fe content in the films influences the M{sub s} values and the H{sub c} values are consistent with the surface properties. It was concluded that the deposition rate and the rotation speed of the substrate play a considerable role on the structural and related magnetic properties of the sputtered FeCr thin films, and the properties of the films can be easily controlled by changing production parameters.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Thermodynamic Properties of Magnesium-Boron Binary Alloys Determined Using Solid-State Electrochemical Measurements

This study provides the thermodynamic properties of the Mg-B binary alloys. Experiments were performed to measure the electromotive force (emf) as a function of temperature (773 K to 873 K) using a solid-state electrochemical cells that can be represented as (−)Pt,Ar/{Mg+CaF_2⋅MgF_2}∥CaF{sub 2}∥{Mg(alloy)+CaF_2⋅MgF_2}/Ar,Pt(+)The activities of Mg in Mg-B alloys were calculated from the experimental emf data. The activity coefficients of Mg in Mg-B alloys were also determined. The integral Gibbs energies of formation (∆G{sub f}{sup °}) of alloys were calculated from the activities of the Mg using tangent rule. The integral Gibbs energies of formation (∆G{sub f}{sup °}) of MgB{sub 2}, MgB{sub 4}, and MgB{sub 7} are − 15.48, − 22.03, and − 15.89 kJ/mol-atoms at 873 K.

36 MATERIALS SCIENCE↗

DOE OSTI2023

High dimensional binary classification under label shift: phase transition and regularization

Label Shift has been widely believed to be harmful to the generalization performance of machine learning models. Researchers have proposed many approaches to mitigate the impact of the label shift, e.g., balancing the training data. However, these methods often consider the underparametrized regime, where the sample size is much larger than the data dimension. The research under the overparametrized regime is very limited. Here, to bridge this gap, we propose a new asymptotic analysis of the Fisher Linear Discriminant classifier for binary classification with label shift. Specifically, we prove that there exists a phase transition phenomenon: Under certain overparametrized regime, the classifier trained using imbalanced data outperforms the counterpart with reduced balanced data. Moreover, we investigate the impact of regularization to the label shift: The aforementioned phase transition vanishes as the regularization becomes strong.

binary classification↗

DOE OSTI2023

Barrier-free predictions of short-range ordering/clustering kinetics in binary FCC solid solutions

We present comparisons of kinetic Monte Carlo (kMC) simulations of isothermal short-range ordering (SRO) and clustering (SRC) kinetics in binary FCC alloys with a mean-field concentration wave (CW) model. We find that the CW model is able to give order-of-magnitude agreement with kMC simulations for ordering/clustering relaxation times over a wide range of temperatures and compositions. The advantage of the CW model is that it does not require parameterization of vacancy hopping energy barriers, which, for a concentrated alloy, becomes prohibitive. We assess limits in the accuracy of the model and discuss the effect of cooling rates as well as the extension to multi-component systems. Ultimately, the simplicity and performance of the CW model compared to kMC simulations suggests that it is a useful tool to connect with models of properties dependent on SRO/SRC as well as for designing thermal treatments to control formation of SRO/SRC.

36 MATERIALS SCIENCE↗