Consecutive Snapshots Research Articles

Tracking Temporal Community Strength in Dynamic Networks

Community formation analysis of dynamic networks has been a hot topic in data mining which has attracted much attention. Recently, there are many studies which focus on discovering communities successively from consecutive snapshots by considering both the current and historical information. However, these methods cannot provide us with much historical or successive information related to the detected communities. Different from previous studies which focus on community detection in dynamic networks, we define a new problem of tracking the progression of the community strength—a novel measure that reflects the community robustness and coherence throughout the entire observation period. To achieve this goal, we propose a novel framework which formulates the problem as an optimization task. The proposed community strength analysis also provides foundation for a wide variety of related applications such as discovering how the strength of each detected community changes over the entire observation period. To demonstrate that the proposed method provides precise and meaningful evolutionary patterns of communities which are not directly obtainable from traditional methods, we perform extensive experimental studies on one synthetic and five real datasets: Social evolution, tweeting interaction, actor relationships, bibliography, and biological datasets. Experimental results show that the proposed approach is highly effective in discovering the progression of community strengths and detecting interesting communities.

SU‐E‐T‐778: Use of the 2D MatriXX Detector for Measuring Scanned Ion Beam Parameters

Purpose:The quality assurance (QA) procedure has to check the most relevant beam parameters to ensure the delivery of the correct dose to patients. Film dosimetry, which is commonly used for scanned ion beam QA, does not provide immediate results. The purpose of this work is to answer whether, for scanned ion beam therapy, film dosimetry can be replaced with the 2D MatriXX detector as a real‐time tool.Methods:MatriXX, equipped with 32×32 parallel plate ion‐chambers, is a commercial device intended for pre‐treatment verification of conventional radiation therapy.The MatriXX, placed at the isocenter, and GAFCHROMIC films, positioned on the MatriXX entrance, were exposed to 131.44 MeV proton and 221.45 MeV/u Carbon‐ion beams.The OmniPro‐I'mRT software, applied for the data taking of MatriXX, gives the possibility of acquiring consecutive snapshots. Using the NI LabVIEW, the data from snapshots were logged as text files for further analysis. Radiochromic films were scanned with EPSON scanner and analyzed using software programs developed in‐house for comparative purposes.Results:The field dose uniformity, flatness, beam position and beam width were investigated. The field flatness for the region covering 6×6 cm2 square field was found to be better than 2%. The relative standard deviations, expected to be constant over 2×2, 4×4 and 6×6 pixels from MatriXX measurement gives a uniformity of 1.5% in good agreement with the film results.The beam center position is determined with a resolution better than 200 µm for Carbon and less than 100 µm for proton beam.The FWHM determination for a beam wider than 10 mm is satisfactory, whilst for smaller beams the determination is uncertain.Conclusion:Precise beam position and fast 2D dose distribution can be determined in real‐time using MatriXX detector. The results show that MatriXX is quick and accurate enough to be used in charged‐particle therapy QA.

Reconstruction of sound quadratic properties from non-synchronous measurements with insufficient or without references: Proof of concept

In inverse acoustics, the reconstruction of (distant) sound fields from microphone array measurements is fundamentally limited by the size of the array and by the microphone density. One approach to simulate large arrays and/or high microphone density is to scan the object of interest by sequentially moving a small prototype array. This typically requires a sufficient number of high-quality fixed references in order to preserve the phase relationships between consecutive snapshots or that measurements are made in the near-field in order to allow the successive reconstruction of local “patches” of the source by deliberately neglecting waves not radiating through the array (patch holography). In the present work, a solution is introduced to reconstruct the quadratic properties (e.g. source power, quadratic flux, sound intensity) of the radiating object from non-synchronous (sequential) measurements when references are insufficient in quantity or in quality. Remarkably, it also operates without any reference at all in certain conditions. It relies on the assumptions that the sound field is (i) stationary and (ii) spatially correlated and boils down to the factorization of a structured covariance matrix. This is undertaken within a Bayesian probabilistic approach where the source field is encoded by unobservable latent variables which are iteratively reconstructed by using the Expected-Maximization (EM) algorithm. The method is shown to return virtually similar results as if all data were captured simultaneously—or sequentially with sufficient high-quality references. As a consequence, it bears an advantage in terms of cost and simplicity. In addition, it is ultimately able to synthesize statistically equivalent realizations of the source field, which may turn out useful for numerical simulation of sound propagation. A solution is also provided within the Bayesian framework for automatically setting the regularization parameter to its optimal value.

Using grouping strategy and pattern discovery for delta extraction in a limited collaborative environment

This work considers extracting delta in a distributed environment where the collaboration from highly autonomous operational database management systems is limited to granting read only access on a set of selected relational tables. Because of inherently huge volume of data in data warehouse system, it is critical to minimise communication costs as much as possible. Based on the observation that usually, two consecutive snapshots are not very different, a statistical-based group hash method is developed to minimise the volumes of data required to complete the data extraction. In addition, to relax the assumption that the changes to remote data are only caused by random events, we define a progression pattern to describe data changes with temporal regularities and also propose a method for progression pattern discovery.

Dynamic clustering in Sparse MANETs

In dynamic networks like sparse mobile ad hoc networks used during rescue operations, data and service availability and reachability is crucial. Such dynamic networks can be analysed to identify stable clusters as well as nodes with frequent cluster affiliation changes; these structural and behavioural properties can then be utilised for careful data and service placement and ferry selection. Factors that influence the outcome are the clustering algorithms used and how and when they are applied. In this paper, we identify cluster evolution based on similarities between clusters detected by community detection algorithms on consecutive network snapshots. The community detection algorithms are node agnostic in the sense that they do not elect cluster heads, and are non-intrusive, i.e., they extract topology information from the local routing tables without interfering with the routing algorithm nor creating network traffic. We have performed extensive experiments to evaluate how the choice of clustering algorithm affects the evolution and timeline of clusters and the identification of node-cluster affiliations. Additionally, we compare how well the algorithms perform on selecting nodes for data and service placement and ferrying.

Study on the stability of the topology interactive growth mechanism using graph spectra

In terms of spectral graph theory, the graph spectrums represent more accurate structural properties and robust characteristics of the network topologies. In this study, the authors study the stability characteristics of the normalised Laplacian spectrum associated with the interactive growth mechanism for the increasing of nodes in the generation of large-scale autonomous systems (AS) graphs. With consecutive snapshots of the AS-level Internet topologies, the authors obtain the stable models of the spectrum. In addition, the authors investigate the stability conditions of the spectrum based on the interactive growth mechanism and the comparisons with other metrics and study how to influence the spectrum by the inputs of the interactive growth mechanism.

Estimating Local Decision-Making Behavior in Complex Evolutionary Systems

Research in systems engineering and design is increasingly focused on complex sociotechnical systems whose structures are not directly controlled by the designers, but evolve endogenously as a result of decisions and behaviors of self-directed entities. Examples of such systems include smart electric grids, Internet, smart transportation networks, and open source product development communities. To influence the structure and performance of such systems, it is crucial to understand the local decisions that result in observed system structures. This paper presents three approaches to estimate the local behaviors and preferences in complex evolutionary systems, modeled as networks, from its structure at different time steps. The first approach is based on the generalized preferential attachment model of network evolution. In the second approach, statistical regression-based models are used to estimate the local decision-making behaviors from consecutive snapshots of the system structure. In the third approach, the entities are modeled as rational decision-making agents who make linking decisions based on the maximization of their payoffs. Within the decision-centric framework, the multinomial logit choice model is adopted to estimate the preferences of decision-making nodes. The approaches are illustrated and compared using an example of the autonomous system (AS) level Internet. The approaches are generally applicable to a variety of complex systems that can be modeled as networks. The insights gained are expected to direct researchers in choosing the most applicable estimation approach to get the node-level behaviors in the context of different scenarios.

Structural Basis for the Recognition of Mycolic Acid Precursors by KasA, a Condensing Enzyme and Drug Target from Mycobacterium Tuberculosis

The survival of Mycobacterium tuberculosis depends on mycolic acids, very long α-alkyl-β-hydroxy fatty acids comprising 60-90 carbon atoms. However, despite considerable efforts, little is known about how enzymes involved in mycolic acid biosynthesis recognize and bind their hydrophobic fatty acyl substrates. The condensing enzyme KasA is pivotal for the synthesis of very long (C38-42) fatty acids, the precursors of mycolic acids. To probe the mechanism of substrate and inhibitor recognition by KasA, we determined the structure of this protein in complex with a mycobacterial phospholipid and with several thiolactomycin derivatives that were designed as substrate analogs. Our structures provide consecutive snapshots along the reaction coordinate for the enzyme-catalyzed reaction and support an induced fit mechanism in which a wide cavity is established through the concerted opening of three gatekeeping residues and several α-helices. The stepwise characterization of the binding process provides mechanistic insights into the induced fit recognition in this system and serves as an excellent foundation for the development of high affinity KasA inhibitors.

HIGH-DYNAMIC DOA ESTIMATION BASED ON WEIGHTED L<sub>1</sub> MINIMIZATION

In high dynamic environment, due to the rapid relative movement between receiver and transmitter, the DOA (Direction of Arrival) of signals will change even between two consecutive snapshots. Thus, covariance-based DOA estimation algorithms are inefiective. Compressive sensing algorithms, as a kind of novel DOA estimation algorithms, are still efiective with only one snapshot. At the same time, it is noted that the DOA changing is limited by relative moving speed and distance between receiver and transmitter. In this paper, a DOA tracking algorithm based on weighted L1 minimization is proposed which utilizing the DOA changing scope between two consecutive snapshots as a prior to improve the tracking performance. Difierent from other multiple snapshots compressive sensing algorithms which assumed flxed DOA among multiple consecutive snapshots, the proposed algorithm takes into account the DOA changing among difierent snapshots. The simulation results demonstrate the advantages of the proposed algorithm.

Using snapshot query fidelity to adapt continuous query execution

This paper explores the fidelity of queries issued in pervasive computing networks. A query’s fidelity, or how well its results reflect the state of the environment, can be significantly impacted by dynamics that occur during its distributed execution. We focus on continuous queries that can be built out of sequences of consecutive snapshot queries and show how the fidelity of snapshots can be used to determine the fidelity of continuous queries. This simple notion of continuous query fidelity can be used to adapt query processing to impact quality and cost tradeoffs given the current state of the environment.

A Two-Stage Charge Transfer Active Pixel CMOS Image Sensor With Low-Noise Global Shuttering and a Dual-Shuttering Mode

A complementary metal-oxide-semiconductor (CMOS) image sensor with low-noise global shuttering and a dual-shuttering mode is presented. The developed two-stage charge transfer pixel enables noise canceling by means of true correlated double sampling. The implemented prototype demonstrates for the first time that a noise level of less than three electrons can be achieved in a global-shutter CMOS image sensor while attaining high shutter efficiency of 99.7%. In the dual-shuttering mode, both a pinned storage diode signal and a floating diffusion signal are used for desirable functions such as wide-dynamic-range imaging, motion detection, and dual consecutive snapshot imaging.

Empirical extraction of mechanisms underlying real world network generation

The generation mechanisms of real world networks have been described using multiple models. The mathematical features of these models are usually extrapolated from statistical properties of a snapshot of these networks. We here propose an alternative method based on direct measurement of a sequence of consecutive snapshots to uncover the dynamics underlying real world generation. We assume that the probability of adding a node or an edge depends only on local features surrounding the newly added node/edge, and directly measure the contribution of these features to the node/edge addition probability. These measurements are performed using newly defined N-node local structures. Each N-node local structure represents the configuration of edges surrounding a newly added edge. The N-node local structure measurements reproduce for some networks the now classical addition of edges between high degree node mechanisms. It also provides quantitative estimates of more complex mechanisms driving other networks’ evolution, such as the effect of common first and second neighbors. This new methodology reveals the relative importance of different generation mechanisms. We show, for example, that the main mechanism driving hyperlink addition between two websites is the existence of a third website linking to both the source and the target of the new hyperlink.

Estimating surface velocities from satellite data and numerical models: Implementation and testing of a new simple method

A simple method of fusing tracer observations and model outputs for computing surface velocities in the ocean is implemented and tested in the framework of the twin experiment approach. Synthetic data from realistic velocity outputs produced by the operational Mediterranean Forecasting System (MFS) are used. The method ( Piterbarg, 2009) allows to estimate a velocity field using two consecutive tracer snapshots. The focus is on testing realistic time intervals between snapshots and partial tracer observations. The considered configuration consists of a tracer patch released and advected by the current, and is motivated by the practical problem of estimating velocities and concentrations using satellite data in case of pollutant releases such as oil spills. An extensive set of experiments has been carried out, and the method performance has been quantified in terms of improvements in accuracy with respect to the model. The improvement ranges from values of approximately 80–90% for concentration and 50–60% for velocity in the case of almost perfect data, to values of 30–40% for realistic time intervals of the order of days and reduced tracer information, and values of 15–20% when only the boundary of the patch is observed. The results are found to be robust to flow variability and patch parameters.

Using shellbag information to reconstruct user activities

Built into Microsoft Windows is the ability for the operating system to track user window viewing preferences specific to Windows Explorer. This information, which is called “ShellBag” information, is stored in several locations within the Windows Registry in the Windows Operating System. This paper introduces a novel method to examine ShellBag information within Registry snapshots to reconstruct user activities. It compares different states of ShellBag information within consecutive Registry snapshots in order to detect ShellBag-related user actions. Nine detection rules are proposed on the basis of analyzing the causality between user actions and updated ShellBag information. This approach can be used to prove that certain interactions between the user and system must have, or must not have happened during a certain time period.

Detection of Pulsus Paradoxus by Pulse Oximetry in Pediatric Patients After Cardiac Surgery

The presence or absence of pulsus paradoxus (PP), defined as an inspiratory decrease greater than 10 mmHg in systolic blood pressure, can have significant diagnostic and therapeutic implications for many clinical conditions including acute asthma, pericardial tamponade, heart failure, hypovolemia, shock states, and the like. However, PP may be difficult to measure in children. Indwelling arterial catheters facilitate the measurement of PP, but this invasive technique generally is reserved for critically ill patients. This study aimed to assess the use of the pulse oximetry plethysmographic waveform (POPW) for the detection of PP in pediatric patients after cardiac surgery. The study enrolled 40 pediatric patients 18 years of age and younger who had invasive blood pressure monitoring with an intraarterial cannula. Systolic pressure variability (SPV) and changes in POPW amplitude (DeltaPOPW%), calculated using five consecutive snapshots from every patient's monitor, were compared using linear regression, Pearson product-moment correlation, the Spearman rank method, and receiver operating characteristic (ROC) curve analysis. A strong correlation existed between respiratory SPV and DeltaPOPW% for the detection of PP (r = 0.682; p < 0.0001). A respiratory variation in DeltaPOPW% exceeding 25.44% (about one-fourth the amplitude of the tallest POP waveform) allowed detection of PP with a sensitivity of 86.7% and a specificity of 88%. Pulse oximetry is a readily available and easily performed noninvasive means for detecting PP in children.

USPIO-enhanced dynamic MRI: evaluation of normal and transplanted rat kidneys.

To evaluate first-pass renal perfusion with ultrasmall superparamagnetic iron oxide (USPIO) particles by MRI, 40 normal rats (20 Dark Agouti (DA) rats and 20 Brown Norway (BN) rats) and 16 transplanted rats (12 allografts and four isografts) were studied on day 4 post-transplantation with different USPIO doses (3.0-18.1 mg Fe/kg/body weight). All animals underwent 128 consecutive snapshot fast low-angle shot (FLASH) coronal dynamic studies in 43 s. In the normal rats, a larger maximum signal decrease (MSD) in the cortex and the outer medulla is observed with an increasing dose of USPIO particles (P < 0.01). No significant differences were observed between the right and left kidneys at all doses studied. Higher MSD, time of occurrence of MSD (tMSD), and wash-in slope appear with higher doses of USPIO particles. The dynamic curves for DA rats show similar shapes when compared to those for BN rats. In the transplanted rats, allograft kidneys show lower MSD, longer tMSD, and lower wash-in slope compared to those in the normal kidneys. Isograft kidneys show perfusion patterns similar to those of normal kidneys in the cortex and the outer medulla. Histopathology indicates acute vascular rejection in all allografts and normal kidney architecture in all isografts. The results clearly show good agreement between the renal graft perfusion measurements and histopathological changes associated with rejection. This work also introduces a new signal analysis methodology for the automatic detection of transplanted organ rejection. This method compares the dynamics of the intrarenal signal intensities for native and transplanted kidneys. A quantitative measurement to detect significant differences between these signals was developed, and showed that this technique exhibits good performance in identifying renal rejection.

An application of fuzzy logic reasoning for GIS temporal modeling of dynamic processes

The analysis and modeling of dynamic processes require the consideration of both spatial and temporal attributes of data and their integration into a GIS database. However, current GIS raster databases have severe limitations related to the temporal component of data. Data are generally stored through a series of snapshot layers associated to particular instants in time. If the interval between two consecutive snapshots is too long the essential information about the change may remain undetected. In this study, spatio-temporal interpolation based on fuzzy logic theory was applied in order to model the missing information about change that happened between consecutive snapshots. Three different scenarios of rural-to-urban land-use transformation were conceived to simulate different dynamics of change using module FUZZY_TEMP integrated in the GRASS4.1 environment. The obtained results were validated through a comparison of the urban boundaries obtained from aerial photographs and those which were generated using the proposed methodology. The results confirm the potential of the approach to produce realistic simulations of the dynamics of the urbanization process.

A fuzzy set approach for modelling time in GIS

This paper describes an application of fuzzy set theory to perform temporal interpolation in a raster GIS database. Specific fuzzy membership functions have been derived to simulate spatial changes between consecutive snapshots registered in the database. The proposed approach is tested on a very dynamic rural-urban environment of Montreal Metropolitan area in Quebec, Canada, covering the period from 1956 to 1986 with a temporal resolution of ten years. A user friendly software package named FUZZY_TEMP was developed and integrated in the GRASS4.1 environment in order to perform the implementation of the developed concepts.

Femtosecond time-resolved optical polarigraphy: imaging of the propagation dynamics of intense light in a medium

A time-resolved imaging technique for visualizing ultrafast propagation dynamics of intense light pulses in a medium has been demonstrated. The method probes the instantaneous birefringence induced by a pulse in the medium. Through consecutive femtosecond snapshot images of intense femtosecond laser pulses propagating in air, ultrafast temporal changes in the two-dimensional spatial distribution of the optical pulse intensity were clearly seen.

Spatio-Temporal Interpolation and Fuzzy Logic for GIS Simulation of Rural-to-Urban Transition

When studying dynamic geographic phenomena such as rural-urban transformation in a raster GIS environment, information between two consecutive snapshot layers may not be available. This paper describes an application of fuzzy logic which enables the completion of unknown information in generated intermediate layers using annual temporal resolution and spatio-temporal interpolation. Change is modeled temporally by performing three possible scenarios with a different duration of rural-urban transition, and spatially by applying two standard GIS methods. This methodology is tested with data from the Montreal Metropolitan area in Quebec, Canada, which cover the 1956 to 1986 period and have a temporal resolution of 10 years. User-friendly modules were developed and incorporated in GRASS4.1 environment in order to simulate the spatio-temporal changes which occur in the study area.