Johns Hopkins University Applied Physics Laboratory

This paper proposes an extension of generalized linear models to the analysis of longitudinal data. We introduce a class of estimating equations that give consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence. The estimating equations are derived without specifying the joint distribution of a subject's observations yet they reduce to the score equations for multivariate Gaussian outcomes. Asymptotic theory is presented for the general class of estimators. Specific cases in which we assume independence, m-dependence and exchangeable correlation structures from each subject are discussed. Efficiency of the proposed estimators in two simple situations is considered. The approach is closely related to quasi-likelihood.

A nonlinear differential equation of the Riccati type is derived for the covariance matrix of the optimal filtering error. The solution of this “variance equation” completely specifies the optimal filter for either finite or infinite smoothing intervals and stationary or nonstationary statistics. The variance equation is closely related to the Hamiltonian (canonical) differential equations of the calculus of variations. Analytic solutions are available in some cases. The significance of the variance equation is illustrated by examples which duplicate, simplify, or extend earlier results in this field. The Duality Principle relating stochastic estimation and deterministic control problems plays an important role in the proof of theoretical results. In several examples, the estimation problem and its dual are discussed side-by-side. Properties of the variance equation are of great interest in the theory of adaptive systems. Some aspects of this are considered briefly.

Full list of authors: Price-Whelan, Adrian M.; Lim, Pey Lian; Earl, Nicholas; Starkman, Nathaniel; Bradley, Larry; Shupe, David L.; Patil, Aarya A.; Corrales, Lia; Brasseur, C. E.; Noethe, Maximilian; Donath, Axel; Tollerud, Erik; Morris, Brett M.; Ginsburg, Adam; Vaher, Eero; Weaver, Benjamin A.; Tocknell, James; Jamieson, William; van Kerkwijk, Marten H.; Robitaille, Thomas P.; Merry, Bruce; Bachetti, Matteo; Gunther, H. Moritz; Aldcroft, Thomas L.; Alvarado-Montes, Jaime A.; Archibald, Anne M.; Bodi, Attila; Bapat, Shreyas; Barentsen, Geert; Bazan, Juanjo; Biswas, Manish; Boquien, Mederic; Burke, D. J.; Cara, Daria; Cara, Mihai; Conroy, Kyle E.; Conseil, Simon; Craig, Matthew W.; Cross, Robert M.; Cruz, Kelle L.; D'Eugenio, Francesco; Dencheva, Nadia; Devillepoix, Hadrien A. R.; Dietrich, Jorg P.; Eigenbrot, Arthur Davis; Erben, Thomas; Ferreira, Leonardo; Foreman-Mackey, Daniel; Fox, Ryan; Freij, Nabil; Garg, Suyog; Geda, Robel; Glattly, Lauren; Gondhalekar, Yash; Gordon, Karl D.; Grant, David; Greenfield, Perry; Groener, Austen M.; Guest, Steve; Gurovich, Sebastian; Handberg, Rasmus; Hart, Akeem; Hatfield-Dodds, Zac; Homeier, Derek; Hosseinzadeh, Griffin; Jenness, Tim; Jones, Craig K.; Joseph, Prajwel; Kalmbach, J. Bryce; Karamehmetoglu, Emir; Kaluszynski, Mikolaj; Kelley, Michael S. P.; Kern, Nicholas; Kerzendorf, Wolfgang E.; Koch, Eric W.; Kulumani, Shankar; Lee, Antony; Ly, Chun; Ma, Zhiyuan; MacBride, Conor; Maljaars, Jakob M.; Muna, Demitri; Murphy, N. A.; Norman, Henrik; O'Steen, Richard; Oman, Kyle A.; Pacifici, Camilla; Pascual, Sergio; Pascual-Granado, J.; Patil, Rohit R.; Perren, Gabriel, I; Pickering, Timothy E.; Rastogi, Tanuj; Roulston, Benjamin R.; Ryan, Daniel F.; Rykoff, Eli S.; Sabater, Jose; Sakurikar, Parikshit; Salgado, Jesus; Sanghi, Aniket; Saunders, Nicholas; Savchenko, Volodymyr; Schwardt, Ludwig; Seifert-Eckert, Michael; Shih, Albert Y.; Jain, Anany Shrey; Shukla, Gyanendra; Sick, Jonathan; Simpson, Chris; Singanamalla, Sudheesh; Singer, Leo P.; Singhal, Jaladh; Sinha, Manodeep; Sipocz, Brigitta M.; Spitler, Lee R.; Stansby, David; Streicher, Ole; Sumak, Jani; Swinbank, John D.; Taranu, Dan S.; Tewary, Nikita; Tremblay, Grant R.; De Val-Borro, Miguel; Vasovic, Zlatan; Van Kooten, Samuel J.; Verma, Shresth; Cardoso, Jose Vinicius de Miranda; Williams, Peter K. G.; Wilson, Tom J.; Winkel, Benjamin; Wood-Vasey, W. M.; Xue, Rui; Yoachim, Peter; Zhang, Chen; Zonca, Andrea; Astropy Project Contributors; TARDIS Collaboration; Astropy Coordination Comm.--This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

This survey paper describes a focused literature survey of machine learning (ML) and data mining (DM) methods for cyber analytics in support of intrusion detection. Short tutorial descriptions of each ML/DM method are provided. Based on the number of citations or the relevance of an emerging method, papers representing each method were identified, read, and summarized. Because data are so important in ML/DM approaches, some well-known cyber data sets used in ML/DM are described. The complexity of ML/DM algorithms is addressed, discussion of challenges for using ML/DM for cyber security is presented, and some recommendations on when to use a given method are provided.

The problem of finding a root of the multivariate gradient equation that arises in function minimization is considered. When only noisy measurements of the function are available, a stochastic approximation (SA) algorithm for the general Kiefer-Wolfowitz type is appropriate for estimating the root. The paper presents an SA algorithm that is based on a simultaneous perturbation gradient approximation instead of the standard finite-difference approximation of Keifer-Wolfowitz type procedures. Theory and numerical experience indicate that the algorithm can be significantly more efficient than the standard algorithms in large-dimensional problems.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Metagenomic experiments attempt to characterize microbial communities using high-throughput DNA sequencing. Identification of the microorganisms in a sample provides information about the genetic profile, population structure, and role of microorganisms within an environment. Until recently, most metagenomics studies focused on high-level characterization at the level of phyla, or alternatively sequenced the 16S ribosomal RNA gene that is present in bacterial species. As the cost of sequencing has fallen, though, metagenomics experiments have increasingly used unbiased shotgun sequencing to capture all the organisms in a sample. This approach requires a method for estimating abundance directly from the raw read data. Here we describe a fast, accurate new method that computes the abundance at the species level using the reads collected in a metagenomics experiment. Bracken (Bayesian Reestimation of Abundance after Classification with KrakEN) uses the taxonomic assignments made by Kraken, a very fast read-level classifier, along with information about the genomes themselves to estimate abundance at the species level, the genus level, or above. We demonstrate that Bracken can produce accurate species- and genus-level abundance estimates even when a sample contains multiple near-identical species.

Warming and Melting Mass loss from the ice sheets of Greenland and Antarctica account for a large fraction of global sea-level rise. Part of this loss is because of the effects of warmer air temperatures, and another because of the rising ocean temperatures to which they are being exposed. Joughin et al. (p. 1172 ) review how ocean-ice interactions are impacting ice sheets and discuss the possible ways that exposure of floating ice shelves and grounded ice margins are subject to the influences of warming ocean currents. Estimates of the mass balance of the ice sheets of Greenland and Antarctica have differed greatly—in some cases, not even agreeing about whether there is a net loss or a net gain—making it more difficult to project accurately future sea-level change. Shepherd et al. (p. 1183 ) combined data sets produced by satellite altimetry, interferometry, and gravimetry to construct a more robust ice-sheet mass balance for the period between 1992 and 2011. All major regions of the two ice sheets appear to be losing mass, except for East Antarctica. All told, mass loss from the polar ice sheets is contributing about 0.6 millimeters per year (roughly 20% of the total) to the current rate of global sea-level rise.

The Fast Plasma Investigation (FPI) was developed for flight on the Magnetospheric Multiscale (MMS) mission to measure the differential directional flux of magnetospheric electrons and ions with unprecedented time resolution to resolve kinetic-scale plasma dynamics. This increased resolution has been accomplished by placing four dual 180-degree top hat spectrometers for electrons and four dual 180-degree top hat spectrometers for ions around the periphery of each of four MMS spacecraft. Using electrostatic field-of-view deflection, the eight spectrometers for each species together provide 4pi-sr field-of-view with, at worst, 11.25-degree sample spacing. Energy/charge sampling is provided by swept electrostatic energy/charge selection over the range from 10 eV/q to 30000 eV/q. The eight dual spectrometers on each spacecraft are controlled and interrogated by a single block redundant Instrument Data Processing Unit, which in turn interfaces to the observatory’s Instrument Suite Central Instrument Data Processor. This paper describes the design of FPI, its ground and in-flight calibration, its operational concept, and its data products.

Abstract A new test of verbal learning and memory, the Hopkins Verbal Learning Test, was developed. The test consists of three trials of free-recall of a 12-item, semantically categorized list, followed by yes/no recognition. Six parallel forms yielded equivalent results in normals. The performance of patients with Alzheimer's disease and chronic amnesia is described. The test is likely to be useful in patients too impaired for more comprehensive memory assessments and where repeated testing is necessary.

Solar Probe Plus (SPP) will be the first spacecraft to fly into the low solar corona. SPP’s main science goal is to determine the structure and dynamics of the Sun’s coronal magnetic field, understand how the solar corona and wind are heated and accelerated, and determine what processes accelerate energetic particles. Understanding these fundamental phenomena has been a top-priority science goal for over five decades, dating back to the 1958 Simpson Committee Report. The scale and concept of such a mission has been revised at intervals since that time, yet the core has always been a close encounter with the Sun. The mission design and the technology and engineering developments enable SPP to meet its science objectives to: (1) Trace the flow of energy that heats and accelerates the solar corona and solar wind; (2) Determine the structure and dynamics of the plasma and magnetic fields at the sources of the solar wind; and (3) Explore mechanisms that accelerate and transport energetic particles. The SPP mission was confirmed in March 2014 and is under development as a part of NASA’s Living with a Star (LWS) Program. SPP is scheduled for launch in mid-2018, and will perform 24 orbits over a 7-year nominal mission duration. Seven Venus gravity assists gradually reduce SPP’s perihelion from 35 solar radii ( $R_{S}$ ) for the first orbit to ${<}10~R_{S}$ for the final three orbits. In this paper we present the science, mission concept and the baseline vehicle for SPP, and examine how the mission will address the key science questions

This paper proposes an extension of generalized linear models to the analysis of longitudinal data. We introduce a class of estimating equations that give consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence. The estimating equations are derived without specifying the joint distribution of a subject's observations yet they reduce to the score equations for niultivariate Gaussian outcomes. Asymptotic theory is presented for the general class of estimators. Specific cases in which we assume independence, m-dependence and exchangeable correlation structures from each subject are discussed. Efficiency of the pioposecl estimators in two simple situations is considered. The approach is closely related to quasi-likelihood.

The success of the Magnetospheric Multiscale mission depends on the accurate measurement of the magnetic field on all four spacecraft. To ensure this success, two independently designed and built fluxgate magnetometers were developed, avoiding single-point failures. The magnetometers were dubbed the digital

This paper describes an approach to harvesting electrical energy from a mechanically excited piezoelectric element. A vibrating piezoelectric device differs from a typical electrical power source in that it has a capacitive rather than inductive source impedance, and may be driven by mechanical vibrations of varying amplitude. An analytical expression for the optimal power flow from a rectified piezoelectric device is derived, and an "energy harvesting" circuit is proposed which can achieve this optimal power flow. The harvesting circuit consists of an AC-DC rectifier with an output capacitor, an electrochemical battery, and a switch-mode DC-DC converter that controls the energy flow into the battery. An adaptive control technique for the DC-DC converter is used to continuously implement the optimal power transfer theory and maximize the power stored by the battery. Experimental results reveal that use of the adaptive DC-DC converter increases power transfer by over 400% as compared to when the DC-DC converter is not used.

The International Bathymetric Chart of the Arctic Ocean (IBCAO) released its first gridded bathymetric compilation in 1999. The IBCAO bathymetric portrayals have since supported a wide range of Arctic science activities, for example, by providing constraint for ocean circulation models and the means to define and formulate hypotheses about the geologic origin of Arctic undersea features. IBCAO Version 3.0 represents the largest improvement since 1999 taking advantage of new data sets collected by the circum‐Arctic nations, opportunistic data collected from fishing vessels, data acquired from US Navy submarines and from research ships of various nations. Built using an improved gridding algorithm, this new grid is on a 500 meter spacing, revealing much greater details of the Arctic seafloor than IBCAO Version 1.0 (2.5 km) and Version 2.0 (2.0 km). The area covered by multibeam surveys has increased from ∼6% in Version 2.0 to ∼11% in Version 3.0.

The quantum-mechanical uncertainty in the position of a particle or the time of its emission is shown to produce observable effects that are inconsistent with any local hidden-variable theory. A new experimental test of local hidden-variable theories based on optical interference is proposed.

Extraction of three-dimensional structure of a scene from stereo images is a problem that has been studied by the computer vision community for decades. Early work focused on the fundamentals of image correspondence and stereo geometry. Stereo research has matured significantly throughout the years and many advances in computational stereo continue to be made, allowing stereo to be applied to new and more demanding problems. We review recent advances in computational stereo, focusing primarily on three important topics: correspondence methods, methods for occlusion, and real-time implementations. Throughout, we present tables that summarize and draw distinctions among key ideas and approaches. Where available, we provide comparative analyses and we make suggestions for analyses yet to be done.

The Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) is a hyperspectral imager on the Mars Reconnaissance Orbiter (MRO) spacecraft. CRISM consists of three subassemblies, a gimbaled Optical Sensor Unit (OSU), a Data Processing Unit (DPU), and the Gimbal Motor Electronics (GME). CRISM's objectives are (1) to map the entire surface using a subset of bands to characterize crustal mineralogy, (2) to map the mineralogy of key areas at high spectral and spatial resolution, and (3) to measure spatial and seasonal variations in the atmosphere. These objectives are addressed using three major types of observations. In multispectral mapping mode, with the OSU pointed at planet nadir, data are collected at a subset of 72 wavelengths covering key mineralogic absorptions and binned to pixel footprints of 100 or 200 m/pixel. Nearly the entire planet can be mapped in this fashion. In targeted mode the OSU is scanned to remove most along‐track motion, and a region of interest is mapped at full spatial and spectral resolution (15–19 m/pixel, 362–3920 nm at 6.55 nm/channel). Ten additional abbreviated, spatially binned images are taken before and after the main image, providing an emission phase function (EPF) of the site for atmospheric study and correction of surface spectra for atmospheric effects. In atmospheric mode, only the EPF is acquired. Global grids of the resulting lower data volume observations are taken repeatedly throughout the Martian year to measure seasonal variations in atmospheric properties. Raw, calibrated, and map‐projected data are delivered to the community with a spectral library to aid in interpretation.

The NASA Radiation Belt Storm Probes (RBSP) mission addresses how populations of high energy charged particles are created, vary, and evolve in space environments, and specifically within Earth’s magnetically trapped radiation belts. RBSP, with a nominal launch date of August 2012, comprises two spacecraft making in situ measurements for at least 2 years in nearly the same highly elliptical, low inclination orbits (1.1×5.8 RE, 10∘). The orbits are slightly different so that 1 spacecraft laps the other spacecraft about every 2.5 months, allowing separation of spatial from temporal effects over spatial scales ranging from ∼0.1 to 5 RE. The uniquely comprehensive suite of instruments, identical on the two spacecraft, measures all of the particle (electrons, ions, ion composition), fields (E and B), and wave distributions (d E and d B) that are needed to resolve the most critical science questions. Here we summarize the high level science objectives for the RBSP mission, provide historical background on studies of Earth and planetary radiation belts, present examples of the most compelling scientific mysteries of the radiation belts, present the mission design of the RBSP mission that targets these mysteries and objectives, present the observation and measurement requirements for the mission, and introduce the instrumentation that will deliver these measurements. This paper references and is followed by a number of companion papers that describe the details of the RBSP mission, spacecraft, and instruments.

Resting atop a deep marine basin, the West Antarctic Ice Sheet has long been considered prone to instability. Using a numerical model, we investigated the sensitivity of Thwaites Glacier to ocean melt and whether its unstable retreat is already under way. Our model reproduces observed losses when forced with ocean melt comparable to estimates. Simulated losses are moderate (<0.25 mm per year at sea level) over the 21st century but generally increase thereafter. Except possibly for the lowest-melt scenario, the simulations indicate that early-stage collapse has begun. Less certain is the time scale, with the onset of rapid (>1 mm per year of sea-level rise) collapse in the different simulations within the range of 200 to 900 years.

Many datasets of interest today are best described as a linked collection of interrelated objects. These may represent homogeneous networks, in which there is a single-object type and link type, or richer, heterogeneous networks, in which there may be multiple object and link types (and possibly other semantic information). Examples of homogeneous networks include single mode social networks, such as people connected by friendship links, or the WWW, a collection of linked web pages. Examples of heterogeneous networks include those in medical domains describing patients, diseases, treatments and contacts, or in bibliographic domains describing publications, authors, and venues. Link mining refers to data mining techniques that explicitly consider these links when building predictive or descriptive models of the linked data. Commonly addressed link mining tasks include object ranking, group detection, collective classification, link prediction and subgraph discovery. While network analysis has been studied in depth in particular areas such as social network analysis, hypertext mining, and web analysis, only recently has there been a cross-fertilization of ideas among these different communities. This is an exciting, rapidly expanding area. In this article, we review some of the common emerging themes.