A New Method for Protecting Interrelated Time Series with Bayesian Prior Distributions and Synthetic Data

Organizations disseminate statistical summaries of administrative data via the Web for unrestricted public use. They balance the trade-off between confidentiality protection and inference quality. Recent developments in disclosure avoidance techniques include the incorporation of synthetic data, which capture the essential features of underlying data by releasing altered data generated from a posterior predictive distribution. The United States Census Bureau collects millions of interrelated time series micro-data that are hierarchical and contain many zeros and suppressions. Rule-based disclosure avoidance techniques often require the suppression of count data for small magnitudes and the modification of data based on a small number of entities. Motivated by this problem, we use zero-inflated extensions of Bayesian Generalized Linear Mixed Models (BGLMM) with privacy-preserving prior distributions to develop methods for protecting and releasing synthetic data from time series about thousands of small groups of entities without suppression based on the of magnitudes or number of entities. We find that as the prior distributions of the variance components in the BGLMM become more precise toward zero, confidentiality protection increases and inference quality deteriorates. We evaluate our methodology using a strict privacy measure, empirical differential privacy, and a newly defined risk measure, Probability of Range Identification (PoRI), which directly measures attribute disclosure risk. We illustrate our results with the U.S. Census Bureau’s Quarterly Workforce Indicators

ROC-Based Model Estimation for Forecasting Large Changes in Demand

Forecasting for large changes in demand should benefit from different estimation than that used for estimating mean behavior. We develop a multivariate forecasting model designed for detecting the largest changes across many time series. The model is fit based upon a penalty function that maximizes true positive rates along a relevant false positive rate range and can be used by managers wishing to take action on a small percentage of products likely to change the most in the next time period. We apply the model to a crime dataset and compare results to OLS as the basis for comparisons as well as models that are promising for exceptional demand forecasting such as quantile regression, synthetic data from a Bayesian model, and a power loss model. Using the Partial Area Under the Curve (PAUC) metric, our results show statistical significance, a 35 percent improvement over OLS, and at least a 20 percent improvement over competing methods. We suggest management with an increasing number of products to use our method for forecasting large changes in conjunction with typical magnitude-based methods for forecasting expected demand

Differential Privacy Applications to Bayesian and Linear Mixed Model Estimation

We consider a particular maximum likelihood estimator (MLE) and a computationally-intensive Bayesian method for differentially private estimation of the linear mixed-effects model (LMM) with normal random errors. The LMM is important because it is used in small area estimation and detailed industry tabulations that present significant challenges for confidentiality protection of the underlying data. The differentially private MLE performs well compared to the regular MLE, and deteriorates as the protection increases for a problem in which the small-area variation is at the county level. More dimensions of random effects are needed to adequately represent the time- dimension of the data, and for these cases the differentially private MLE cannot be computed. The direct Bayesian approach for the same model uses an informative, but reasonably diffuse, prior to compute the posterior predictive distribution for the random effects. The differential privacy of this approach is estimated by direct computation of the relevant odds ratios after deleting influential observations according to various criteria

Finite temperature superfluid density in very underdoped cuprates

The combination of a large superconducting gap, low transition temperature, and quasi two-dimensionality in strongly underdoped high temperature superconductors severely constrains the behavior of the ab-plane superfluid density \rho with temperature T. In particular, we argue that the contribution of nodal quasiparticles to \rho(T) is essential to account both for the amplitude of, and the recently observed deviations from, the Uemura scaling. A relation between T_c and \rho(0) which combines the effects of quasiparticle excitations at low temperatures and of vortex fluctuations near the critical temperature is proposed and discussed in light of recent experiments.Comment: 5 RevTex pages, 4 figures (one new); more discussion and comparison with experiment; version to appear in Phys. Rev.

Protecting Time Series Data with Minimal Forecast Loss

Forecasting could be negatively impacted due to anonymization requirements in data protection legislation. To measure the potential severity of this problem, we derive theoretical bounds for the loss to forecasts from additive exponential smoothing models using protected data. Following the guidelines of anonymization from the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA), we develop the $k$-nearest Time Series ($k$-nTS) Swapping and $k$-means Time Series ($k$-mTS) Shuffling methods to create protected time series data that minimizes the loss to forecasts while preventing a data intruder from detecting privacy issues. For efficient and effective decision making, we formally model an integer programming problem for a perfect matching for simultaneous data swapping in each cluster. We call it a two-party data privacy framework since our optimization model includes the utilities of a data provider and data intruder. We apply our data protection methods to thousands of time series and find that it maintains the forecasts and patterns (level, trend, and seasonality) of time series well compared to standard data protection methods suggested in legislation. Substantively, our paper addresses the challenge of protecting time series data when used for forecasting. Our findings suggest the managerial importance of incorporating the concerns of forecasters into the data protection itself

Development and Testing of a Methane/Oxygen Catalytic Microtube Ignition System for Rocket Propulsion

This study sought to develop a catalytic ignition advanced torch system with a unique catalyst microtube design that could serve as a low energy alternative or redundant system for the ignition of methane and oxygen rockets. Development and testing of iterations of hardware was carried out to create a system that could operate at altitude and produce a torch. A unique design was created that initiated ignition via the catalyst and then propagated into external staged ignition. This system was able to meet the goals of operating across a range of atmospheric and altitude conditions with power inputs on the order of 20 to 30 watts with chamber pressures and mass flow rates typical of comparable ignition systems for a 100 Ibf engine

Catalytic Microtube Rocket Igniter

Devices that generate both high energy and high temperature are required to ignite reliably the propellant mixtures in combustion chambers like those present in rockets and other combustion systems. This catalytic microtube rocket igniter generates these conditions with a small, catalysis-based torch. While traditional spark plug systems can require anywhere from 50 W to multiple kW of power in different applications, this system has demonstrated ignition at less than 25 W. Reactants are fed to the igniter from the same tanks that feed the reactants to the rest of the rocket or combustion system. While this specific igniter was originally designed for liquid methane and liquid oxygen rockets, it can be easily operated with gaseous propellants or modified for hydrogen use in commercial combustion devices. For the present cryogenic propellant rocket case, the main propellant tanks liquid oxygen and liquid methane, respectively are regulated and split into different systems for the individual stages of the rocket and igniter. As the catalyst requires a gas phase for reaction, either the stored boil-off of the tanks can be used directly or one stream each of fuel and oxidizer can go through a heat exchanger/vaporizer that turns the liquid propellants into a gaseous form. For commercial applications, where the reactants are stored as gases, the system is simplified. The resulting gas-phase streams of fuel and oxidizer are then further divided for the individual components of the igniter. One stream each of the fuel and oxidizer is introduced to a mixing bottle/apparatus where they are mixed to a fuel-rich composition with an O/F mass-based mixture ratio of under 1.0. This premixed flow then feeds into the catalytic microtube device. The total flow is on the order of 0.01 g/s. The microtube device is composed of a pair of sub-millimeter diameter platinum tubes connected only at the outlet so that the two outlet flows are parallel to each other. The tubes are each approximately 10 cm long and are heated via direct electric resistive heating. This heating brings the gasses to their minimum required ignition temperature, which is lower than the auto-thermal ignition temperature, and causes the onset of both surface and gas phase ignition producing hot temperatures and a highly reacting flame. The combustion products from the catalytic tubes, which are below the melting point of platinum, are injected into the center of another combustion stage, called the primary augmenter. The reactants for this combustion stage come from the same source but the flows of non-premixed methane and oxygen gas are split off to a secondary mixing apparatus and can be mixed in a near-stoichiometric to highly lean mixture ratio. The primary augmenter is a component that has channels venting this mixed gas to impinge on each other in the center of the augmenter, perpendicular to the flow from the catalyst. The total crosssectional area of these channels is on a similar order as that of the catalyst. The augmenter has internal channels that act as a manifold to distribute equally the gas to the inward-venting channels. This stage creates a stable flame kernel as its flows, which are on the order of 0.01 g/s, are ignited by the combustion products of the catalyst. This stage is designed to produce combustion products in the flame kernel that exceed the autothermal ignition temperature of oxygen and methane

Non-innocent role of fluorine as an electron donor in oxides

Engineering of reducible oxides is generally focused on the cation sites. As such, anion doping remains an underutilized tool despite its unique potential in altering the defect chemistry and steering redox processes. In this contribution, we explore the possibilities offered by substitution on the anion site on the case of a prototypical reducible oxide, namely cerium oxide, doped with fluorine. The choice of fluorine is motivated by the general stability of fluorine in oxide lattices and the fact that it can be readily incorporated in these up to very high concentration with minimal structural distortion [1]. Utilizing photoemission spectroscopy in combination with density functional theory [2], we show that the general notion of fluorine acting as a straightforward ionic donor fails to capture the intricacies of electronic interactions at play. Specifically, we provide evidence for covalent hybridization in the nominally ionic fluorine-cerium interaction that allows for altering the anion derived electron density in cerium oxide beyond the oxygen 2p band (see Figure 1), contrary to the simplified picture of solely introducing a deeper-laying fluorine 2p band [3]. The emergent electronic configuration can be further coupled to standard valence band engineering methods, such as strain manipulation, to provide an unprecedented playground for designing the oxide properties. Our results also demonstrate the practicality of interatomic resonant photoemission spectroscopy as a gauge of non-trivial electronic effects of ligand origin, allowing to efficiently probe the above-mentioned effects. We note that fluorine doping represents a complement to oxygen vacancy engineering and highlight the fact that, unlike oxygen vacancies, the electronic effects generated by fluorine can persist in an oxidizing environment. The latter represents an important contribution the electronic modification of mixed-anion oxides can provide to a breadth of fields, ranging from superoxide stabilization to resistive switching. Please click Additional Files below to see the full abstract

Induction Mapping of the 3D-Modulated Spin Texture of Skyrmions in Thin Helimagnets

Envisaged applications of skyrmions in magnetic memory and logic devices crucially depend on the stability and mobility of these topologically non-trivial magnetic textures in thin films. We present for the first time quantitative maps of the magnetic induction that provide evidence for a 3D modulation of the skyrmionic spin texture. The projected in-plane magnetic induction maps as determined from in-line and off-axis electron holography carry the clear signature of Bloch skyrmions. However, the magnitude of this induction is much smaller than the values expected for homogeneous Bloch skyrmions that extend throughout the thickness of the film. This finding can only be understood, if the underlying spin textures are modulated along the out-of-plane z direction. The projection of (the in-plane magnetic induction of) helices is further found to exhibit thickness-dependent lateral shifts, which show that this z modulation is accompanied by an (in-plane) modulation along the x and y directions

KIBRA: A New Gateway to Learning and Memory?

The genetic locus encoding KIBRA, a member of the WWC family of proteins, has recently been shown to be associated with human memory performance through genome-wide single nucleotide polymorphism screening. Gene expression analysis and a variety of functional studies have further indicated that such a role is biologically plausible for KIBRA. Here, we review the existing literature, illustrate connections between the different lines of evidence, and derive models based on KIBRA's function(s) in the brain that can be further tested experimentally