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The term network surveillance is defined in general terms and illustrated with many examples. Statistical methodologies that can be used as tools for network surveillance are discussed. Details for 3 illustrative examples that address network security, surveillance for data network failures, and surveillance of email traffic flows are presented. Some open areas of research are identified.
Polyvinyl toluene (PVT) scintillator plastic may degrade in the field due to inward water diffusion at elevated temperatures, which exceeds the saturation limit at lower temperatures, and after long periods of time (e.g., many years), leads to the formation of disk-like defects that attenuate scintillation light, leading to detector degradation. In this work, using fractography and high-magnification optical and electron microscopy to characterize the water-induced defects, a model of the fogging process is hypothesized as follows: excess water present at low temperatures diffuses to spheroids to minimize contact with the hydrophobic polymer. Through the hydrophobic effect, the entropy of water increases by forming nanoclusters which minimizes the contact between the water and the PVT. As the water nanoclusters grow, they break and fold the polymer into densely-packed crystalline regions creating more space for water within the spheroids. The polymer outside the spheroid resists the shrinkage which builds up tension within the spheroid. Once the tensile stress exceeds the yield strength of the plastic, the spheroid is torn in half resulting in a defect. Excess water then drains into the cavities along the disk thereby further increasing its entropy. Slower cooling (over 1 day) leads to larger spheroids and hence, larger “permanent” defects. Freezing causes some defects to further grow due to the expansion of water to ice. These findings imply that the remaining lifetime of scintillator plastic in the field could be predicted using temperature and humidity data thereby mitigating security risks of degraded radiation portal monitors.
The analysis of graphs has become increasingly important to a wide range of applications. Graph analysis presents a number of unique challenges in the areas of (1) software complexity, (2) data complexity, (3) security, (4) mathematical complexity, (5) theoretical analysis, (6) serial performance, and (7) parallel performance. Implementing graph algorithms using matrix-based approaches provides a number of promising solutions to these challenges. The GraphBLAS standard (istcbigdata.org/GraphBlas) is being developed to bring the potential of matrix based graph algorithms to the broadest possible audience. The GraphBLAS mathematically defines a core set of matrix-based graph operations that can be used to implement a wide class of graph algorithms in a wide range of programming environments. This paper provides an introduction to the GraphBLAS and describes how the GraphBLAS can be used to address many of the challenges associated with analysis of graphs.
Mid-infrared images from the Spitzer Space Telescope Galactic Legacy Infrared MidPlane Survey Extraordinaire program reveal that the infrared source IRAS 03063+5735 is a bowshock nebula produced by an early B star, 2MASS 03101044+5747035. We present new optical spectra of this star, classify it as a B1.5 V, and determine a probable association with a molecular cloud complex at V{sub LSR} = -38 to -42 km s{sup -1} in the outer Galaxy near l = 140.{sup 0}59, b = -0.{sup 0}250. On the basis of spectroscopic parallax, we estimate a distance of 4.0 {+-} 1 kpc to both the bowshock nebula and the molecular complex. One plausible scenario is that this is a high-velocity runaway star impinging upon a molecular cloud. We identify the H II region and stellar cluster associated with IRAS 03064+5638 at a projected distance of 64 pc as one plausible birth site. The spectrophotometric distance and linkage to a molecular feature provides another piece of data helping to secure the ill-determined rotation curve in the outer Galaxy. As a by-product of spectral typing this star, we present empirical spectral diagnostic diagrams suitable for approximate spectral classification of O and B stars using He lines in the little-used yellow-red portion of the optical spectrum.
Using the Atacama Large Millimeter/submillimeter Array, we have conducted a blind redshift survey in the 3 mm atmospheric transmission window for 26 strongly lensed dusty star-forming galaxies (DSFGs) selected with the South Pole Telescope. The sources were selected to have S{sub 1.4{sub mm}} > 20 mJy and a dust-like spectrum and, to remove low-z sources, not have bright radio (S{sub 843{sub MHz}} < 6 mJy) or far-infrared counterparts (S{sub 100{sub {mu}m}} < 1 Jy, S{sub 60{sub {mu}m}} < 200 mJy). We robustly detect 44 line features in our survey, which we identify as redshifted emission lines of {sup 12}CO, {sup 13}CO, C I, H{sub 2}O, and H{sub 2}O{sup +}. We find one or more spectral features in 23 sources yielding a {approx}90% detection rate for this survey; in 12 of these sources we detect multiple lines, while in 11 sources we detect only a single line. For the sources with only one detected line, we break the redshift degeneracy with additional spectroscopic observations if available, or infer the most likely line identification based on photometric data. This yields secure redshifts for {approx}70% of the sample. The three sources with no lines detected are tentatively placed in the redshift desert between 1.7 < z < 2.0. The resulting mean redshift of our sample is z-bar = 3.5. This finding is in contrast to the redshift distribution of radio-identified DSFGs, which have a significantly lower mean redshift of z-bar = 2.3 and for which only 10%-15% of the population is expected to be at z > 3. We discuss the effect of gravitational lensing on the redshift distribution and compare our measured redshift distribution to that of models in the literature.
We investigate the coevolution of galaxies and hosted supermassive black holes (BHs) throughout the history of the universe by a statistical approach based on the continuity equation and the abundance matching technique. Specifically, we present analytical solutions of the continuity equation without source terms to reconstruct the supermassive BH mass function from the active galactic nucleus (AGN) luminosity functions. Such an approach includes physically motivated AGN light curves tested on independent data sets, which describe the evolution of the Eddington ratio and radiative efficiency from slim- to thin-disk conditions. We nicely reproduce the local estimates of the BH mass function, the AGN duty cycle as a function of mass and redshift, along with the Eddington ratio function and the fraction of galaxies with given stellar mass hosting an AGN with given Eddington ratio. We exploit the same approach to reconstruct the observed stellar mass function at different redshift from the ultraviolet and far-IR luminosity functions associated with star formation in galaxies. These results imply that the build-up of stars and BHs in galaxies occurs via in situ processes, with dry mergers playing a marginal role at least for stellar masses ≲3×10{sup 11} M{sub ⊙} and BH masses ≲10{sup 9} M{sub ⊙}, where the statistical data are more secure and less biased by systematic errors. In addition, we develop an improved abundance matching technique to link the stellar and BH content of galaxies to the gravitationally dominant dark matter (DM) component. The resulting relationships constitute a testbed for galaxy evolution models, highlighting the complementary role of stellar and AGN feedback in the star formation process. In addition, they may be operationally implemented in numerical simulations to populate DM halos or to gauge subgrid physics. Moreover, they may be exploited to investigate the galaxy/AGN clustering as a function of redshift, mass, and/or luminosity. In fact, the clustering properties of BHs and galaxies are found to be in full agreement with current observations, thus further validating our results from the continuity equation. Finally, our analysis highlights that (i) the fraction of AGNs observed in the slim-disk regime, where most of the BH mass is accreted, increases with redshift; and (ii) already at z≳6 a substantial amount of dust must have formed over timescales ≲10{sup 8} yr in strongly star-forming galaxies, making these sources well within the reach of ALMA surveys in (sub)millimeter bands.
The proliferation of the Machine-Learning-As-A-Service (MLaaS) market has brought to light a number of clients’ data privacy and security concerns. One promising solution is private inference (PI) techniques using cryptographic primitives. These techniques often come with high computation and communication overhead associated with the non-linear operator such as ReLU. Several approaches have been developed in reducing the number of ReLU operations, however, they either require a heuristic threshold selection or introduce significant accuracy drop. This work presents AutoReP, a gradient-based framework for non-linear operators reduction that aims to mitigate these concerns from a systematic perspective. AutoReP automates the process of discrete selection of ReLU and polynomial functions on neurons to accelerate PI applications. We also introduce distribution-aware polynomial approximation (DaPa) to accurately approximate ReLUs under given distribution, preserving model expressivity. Our experimental results demonstrate significant accuracy improvements of 6.12% (94.31%, 12.9K ReLU budget, CIFAR-10), 8.39% (74.92%, 12.9K ReLU budget, CIFAR-100), and 9.45% (63.69%, 55K ReLU budget, Tiny-ImageNet) over current state-of-the-art methods, e.g., SNL. Morever, AutoReP is applied to EfficientNet-B2 on ImageNet dataset, and achieved 75.55% accuracy with 176.1 × ReLU budget reduction.
With the development of the Internet of Things, the application of computer vision on mobile phones is becoming more and more extensive and people have higher and higher requirements for the timeliness of the recognition results returned and the processing capabilities of the mobile phone for image recognition. However, the processing capability and storage capability of the user terminal equipment cannot meet the needs of identifying and storing a large number of pictures, and the data transmission process will cause high energy consumption of the terminal equipment. At the same time, multisource deep transfer learning has outstanding performance in computer vision and image classification. However, due to the huge amount of calculation of the deep network model, it is impossible to use the existing excellent network model to realize image recognition and classification on the mobile terminal. In order to solve the abovementioned problems, we propose a multisource mobile transfer learning algorithm based on dynamic model compression, this algorithm considers the realization of multisource transfer learning computing in the case of multiple mobile device computing source domains, and the method also guarantees data privacy and security for each device (origin domain). Meanwhile, extensive experiments show that our method can achieve remarkable results in popular image classification datasets.
Integration of advanced communication technologies into nuclear facility operation has the potential for enhancing safety and accident -resiliency of the existing fleet of aging light water reactors, as well those of future advanced reactors. Conventional wired and wireless communication systems face implementation challenges at nuclear facilities due to the presence of thick reinforced concrete walls with steel liners throughout much of nuclear facility, especially the containment building. In addition, concerns related to security gaps in conventional communication networks have to be addressed when developing communication systems for nuclear facilities. To address communication technology gaps for nuclear energy, we are currently developing the acoustic communication (AC) hardware and network protocols for efficient and secure transfer of data, and will provide a preliminary experimental demonstration of the AC system prototype in a representative environment.
Home Energy Management Systems (HEMS) consist of software and hardware systems that provide communication, control, and management among different electronic devices, such as heating, ventilation, and air-conditioning (HVAC) systems. HEMS are a growing market sector in the era of smart grids and smart homes. HEMS can help both homeowners and utilities save money and energy by monitoring and controlling smart devices in a house. Based on the Department of Energy (DOE) Building Energy Data Book, 41% of the energy consumed in the United States is used by buildings (see Figure 1). This shows the importance of saving energy in the building sector. This report introduces two approaches that existing HEMS vendors can use to leverage VOLTTRON as their control and interoperability engine. The goal of this project is to make open-source, user-friendly HEMS available to the market so that everyone can benefit from them. The HEMS introduced as a result of this study should satisfy HEMS specifications such as device monitoring and control, seamless communication between devices, DR, intelligence, data management, and security.
The Los Alamos Quantum Random Number Generator (QRNG) is a hardware-based, high-performance Random Number Generator capable of generating 200 Mbit/s or more of true random numbers. Like flipping a coin, it is very much random and essential for information security like encrypting data on the internet, checking email, or purchasing something from an online vendor. The device harvests entropy from fluctuations in an optical source that arise from quantum mechanical properties of light. Qrypt, Inc., a company launched in 2017, began making strategic investments and developing partnerships to advance cutting-edge quantum hardware solutions. One of those key investments was licensing QRNG from Los Alamos and subsequently collaborating with advanced quantum materials and technology researcher Dr. Raymond Newell to create high-quality random keys at scale.
Microgrids require reliable communication systems for equipment control, power delivery optimization, and operational visibility. To maintain secure communications, Microgrid Operational Technology (OT) networks must be defensible and cyber-resilient. The communication network must be carefully architected with appropriate cyber-hardening technologies to provide security defenders the data, analytics, and response capabilities to quickly mitigate malicious and accidental cyberattacks. In this work, we outline several best practices and technologies that can support microgrid operations (e.g., intrusion detection and monitoring systems, response tools, etc.). Then we apply these recommendations to the New Jersey TRANSITGRID use case to demonstrate how they would be deployed in practice.
Securing critical mineral supply chains is essential for transitioning to a clean energy economy and for maintaining national security. Big-data analytics can serve as a cost-effective means of identifying new domestic critical mineral resources but only if data can be easily located and digested. Using ArcGIS Enterprise Sites, EDX ClaiMM was developed to increase the accessibility of critical minerals data, reducing time spent on data collection and integration. Hosted tools provide rapid visualization and exploration of key datasets, unlocking insights to support resource assessments.
A major barrier in taking expert systems from prototype to operational status involves instilling end user confidence in the operational system. The software of different life cycle models is examined and the advantages and disadvantages of each when applied to expert system development are explored. The Fault Isolation Expert System for Tracking and data relay satellite system Applications (FIESTA) is presented as a case study of development of an expert system. The end user confidence necessary for operational use of this system is accentuated by the fact that it will handle real-time data in a secure environment, allowing little tolerance for errors. How FIESTA is dealing with transition problems as it moves from an off-line standalone prototype to an on-line real-time system is discussed.
A major barrier in taking expert systems from prototype to operational status involves instilling end user confidence in the operational system. The software of different life cycle models is examined and the advantages and disadvantages of each when applied to expert system development are explored. The Fault Isolation Expert System for Tracking and data relay satellite system Applications (FIESTA) is presented as a case study of development of an expert system. The end user confidence necessary for operational use of this system is accentuated by the fact that it will handle real-time data in a secure environment, allowing little tolerance for errors. How FIESTA is dealing with transition problems as it moves from an off-line standalone prototype to an on-line real-time system is discussed.
The objectives and the background of the Mars Observer mission are briefly reviewed with emphasis on the remote science operations portion of the mission. In particular, the discussion covers observational planning and instrument sequences, data retrieval, instrument health monitoring, and science analysis. Attention is also given to workstation technology utilization, science team, project data base, common data formats, and security.
The Shuttle Imaging Radar-C (SIR-C) mission will operate from the payload bay of the space shuttle for 8 days, gathering Synthetic Aperture Radar (SAR) data over specific sites on the Earth. The short duration of the mission and the requirement for realtime planning offer challenges in mission planning and in the design of the Planning and Analysis Subsystem (PAS). The PAS generates shuttle ephemerides and mission planning data and provides an interactive real-time tool for quick mission replanning. It offers a multi-user and multiprocessing environment, and it is able to keep multiple versions of the mission timeline data while maintaining data integrity and security. Its flexible design allows one software to provide different menu options based on the user's operational function, and makes it easy to tailor the software for other Earth orbiting missions.
An investigation was undertaken to build the software foundation for the WHERE (Web-based Hyper-text Environment for Requirements Engineering) project. The TCM (Toolkit for Conceptual Modeling) was chosen as the foundation software for the WHERE project which aims to provide an environment for facilitating collaboration among geographically distributed people involved in the Requirements Engineering process. The TCM is a collection of diagram and table editors and has been implemented in the C++ programming language. The C++ implementation of the TCM was translated into Java in order to allow the editors to be used for building various functionality of the WHERE project; the WHERE project intends to use the Web as its communication back- bone. One of the limitations of the translated software (TcmJava), which militated against its use in the WHERE project, was persistent data management mechanisms which it inherited from the original TCM; it was designed to be used in standalone applications. Before TcmJava editors could be used as a part of the multi-user, geographically distributed applications of the WHERE project, a persistent storage mechanism must be built which would allow data communication over the Internet, using the capabilities of the Web. An approach involving features of Java, CORBA (Common Object Request Broker), the Web, a middle-ware (Java Relational Binding (JRB)), and a database server was used to build the persistent data management infrastructure for the WHERE project. The developed infrastructure allows a TcmJava editor to be downloaded and run from a network host by using a JDK 1.1 (Java Developer's Kit) compatible Web-browser. The aforementioned editor establishes connection with a server by using the ORB (Object Request Broker) software and stores/retrieves data in/from the server. The server consists of a CORBA object or objects depending upon whether the data is to be made persistent on a single server or multiple servers. The CORBA object providing the persistent data server is implemented using the Java progranu-ning language. It uses the JRB to store/retrieve data in/from a relational database server. The persistent data management system provides transaction and user management facilities which allow multi-user, distributed access to the stored data in a secure manner.