Differentially Private Publication of Sparse Data

The problem of privately releasing data is to provide a version of a dataset without revealing sensitive information about the individuals who contribute to the data. The model of differential privacy allows such private release while providing strong guarantees on the output. A basic mechanism achieves differential privacy by adding noise to the frequency counts in the contingency tables (or, a subset of the count data cube) derived from the dataset. However, when the dataset is sparse in its underlying space, as is the case for most multi-attribute relations, then the effect of adding noise is to vastly increase the size of the published data: it implicitly creates a huge number of dummy data points to mask the true data, making it almost impossible to work with. We present techniques to overcome this roadblock and allow efficient private release of sparse data, while maintaining the guarantees of differential privacy. Our approach is to release a compact summary of the noisy data. Generating the noisy data and then summarizing it would still be very costly, so we show how to shortcut this step, and instead directly generate the summary from the input data, without materializing the vast intermediate noisy data. We instantiate this outline for a variety of sampling and filtering methods, and show how to use the resulting summary for approximate, private, query answering. Our experimental study shows that this is an effective, practical solution, with comparable and occasionally improved utility over the costly materialization approach

Thermal reaction of Al/Ti bilayers with contaminated interface

We have studied some new aspects of thermal reactions in Al/Ti bilayers in which the interface is purposely contaminated with oxygen. After annealing at a temperature of 460 °C, an Al_3Ti compound forms at the interface, moreover some Al diffuses through the Ti to form a compound at the free surface. The amount of aluminum at the free surface can be as large as at the interface. Nucleation and lateral growth of Al_3Ti at the interface are locally unfavorable. This results in a competition between the lateral growth of Al_3Ti at the Al/Ti interface and the diffusion of Al to the free surface. Once full coverage by Al_3Ti is obtained at the Al/Ti interface, the diffusion of Al to the surface becomes negligible

Controlled cortical impact traumatic brain injury in 3xTg-AD mice causes acute intra-axonal amyloid-β accumulation and independently accelerates the development of tau abnormalities

Alzheimer\u27s disease (AD) is a neurodegenerative disorder characterized pathologically by progressive neuronal loss, extracellular plaques containing the amyloid-β (Aβ) peptides, and neurofibrillary tangles composed of hyperphosphorylated tau proteins. Aβ is thought to act upstream of tau, affecting its phosphorylation and therefore aggregation state. One of the major risk factors for AD is traumatic brain injury (TBI). Acute intra-axonal Aβ and diffuse extracellular plaques occur in ∼30% of human subjects after severe TBI. Intra-axonal accumulations of tau but not tangle-like pathologies have also been found in these patients. Whether and how these acute accumulations contribute to subsequent AD development is not known, and the interaction between Aβ and tau in the setting of TBI has not been investigated. Here, we report that controlled cortical impact TBI in 3xTg-AD mice resulted in intra-axonal Aβ accumulations and increased phospho-tau immunoreactivity at 24 h and up to 7 d after TBI. Given these findings, we investigated the relationship between Aβ and tau pathologies after trauma in this model by systemic treatment of Compound E to inhibit γ-secretase activity, a proteolytic process required for Aβ production. Compound E treatment successfully blocked posttraumatic Aβ accumulation in these injured mice at both time points. However, tau pathology was not affected. Our data support a causal role for TBI in acceleration of AD-related pathologies and suggest that TBI may independently affect Aβ and tau abnormalities. Future studies will be required to assess the behavioral and long-term neurodegenerative consequences of these pathologies

ProfPPIdb: Pairs of physical protein-protein interactions predicted for entire proteomes

Motivation Protein-protein interactions (PPIs) play a key role in many cellular processes. Most annotations of PPIs mix experimental and computational data. The mix optimizes coverage, but obfuscates the annotation origin. Some resources excel at focusing on reliable experimental data. Here, we focused on new pairs of interacting proteins for several model organisms based solely on sequence-based prediction methods. Results We extracted reliable experimental data about which proteins interact (binary) for eight diverse model organisms from public databases, namely from Escherichia coli, Schizosaccharomyces pombe, Plasmodium falciparum, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, Rattus norvegicus, Arabidopsis thaliana, and for the previously used Homo sapiens and Saccharomyces cerevisiae. Those data were the base to develop a PPI prediction method for each model organism. The method used evolutionary information through a profile-kernel Support Vector Machine (SVM). With the resulting eight models, we predicted all possible protein pairs in each organism and made the top predictions available through a web application. Almost all of the PPIs made available were predicted between proteins that have not been observed in any interaction, in particular for less well-studied organisms. Thus, our work complements existing resources and is particularly helpful for designing experiments because of its uniqueness. Experimental annotations and computational predictions are strongly influenced by the fact that some proteins have many partners and others few. To optimize machine learning, recent methods explicitly ignored such a network-structure and rely either on domain knowledge or sequence-only methods. Our approach is independent of domain-knowledge and leverages evolutionary information. The database interface representing our results is accessible from https://rostlab.org/services/ppipair/. The data can also be downloaded from https://figshare.com/collections/ProfPPI-DB/4141784

The Outstanding Decisions of the United States Supreme Court in 1954

We perform a kinematic and morphological analysis of 44 star-forming galaxies at z ̃ 2 in the COSMOS legacy field using near-infrared spectroscopy from Keck/MOSFIRE and F160W imaging from CANDELS/3D-HST as part of the ZFIRE survey. Our sample consists of cluster and field galaxies from 2.0 < z < 2.5 with K-band multi-object slit spectroscopic measurements of their Hα emission lines. Hα rotational velocities and gas velocity dispersions are measured using the Heidelberg Emission Line Algorithm (HELA), which compares directly to simulated 3D data cubes. Using a suite of simulated emission lines, we determine that HELA reliably recovers input S 0.5 and angular momentum at small offsets, but V 2.2/σ g values are offset and highly scattered. We examine the role of regular and irregular morphology in the stellar mass kinematic scaling relations, deriving the kinematic measurement S 0.5, and finding {log}({S}0.5)=(0.38+/- 0.07){log}(M/{M}☉ -10)+(2.04+/- 0.03) with no significant offset between morphological populations and similar levels of scatter (̃0.16 dex). Additionally, we identify a correlation between M ⋆ and V 2.2/σ g for the total sample, showing an increasing level of rotation dominance with increasing M ⋆, and a high level of scatter for both regular and irregular galaxies. We estimate the specific angular momenta (j disk) of these galaxies and find a slope of 0.36 ± 0.12, shallower than predicted without mass-dependent disk growth, but this result is possibly due to measurement uncertainty at M ⋆ < 9.5 However, through a Kolmogorov-Smirnov test we find irregular galaxies to have marginally higher j disk values than regular galaxies, and high scatter at low masses in both populations

Supporting User-Defined Functions on Uncertain Data

Uncertain data management has become crucial in many sensing and scientific applications. As user-defined functions (UDFs) become widely used in these applications, an important task is to capture result uncertainty for queries that evaluate UDFs on uncertain data. In this work, we provide a general framework for supporting UDFs on uncertain data. Specifically, we propose a learning approach based on Gaussian processes (GPs) to compute approximate output distributions of a UDF when evaluated on uncertain input, with guaranteed error bounds. We also devise an online algorithm to compute such output distributions, which employs a suite of optimizations to improve accuracy and performance. Our evaluation using both real-world and synthetic functions shows that our proposed GP approach can outperform the state-of-the-art sampling approach with up to two orders of magnitude improvement for a variety of UDFs. 1

Computer assisted rigid contact lens fitting training program

Training eye care professionals and students to fit Rigid Gas Permeable contact lenses is a detailed process with many variables. Reliable, consistent, quality fitting comes only after considerable patient contact. This thesis proposes a method of increasing trainee fitting experience without increasing the number of patient encounters. This can potentially reduce fitting errors and excessive number of trial fittings. This method involves using the program SUPERFIT , in combination with worksheets that instruct the trainee to move through the program using different fitting variables, such as keratometry readings, lens power, and peripheral K readings. Thus the trainee can directly observe different fitting relationships including tear volume, projected flourescein patterns and power, diameter, and base curve relationships