Time-evolving Model Research Articles

Community-Aware Evolution Similarity for Link Prediction in Dynamic Social Networks

The link prediction problem is a time-evolving model in network science that has simultaneously abetted myriad applications and experienced extensive methodological improvement. Inferring the possibility of emerging links in dynamic social networks, also known as the dynamic link prediction task, is complex and challenging. In contrast to the link prediction in cross-sectional networks, dynamic link prediction methods need to cater to the actor-level temporal changes and associated evolutionary information regarding their micro- (i.e., link formation/deletion) and mesoscale (i.e., community formation) network structure. With the advent of abundant community detection algorithms, the research community has examined community-aware link prediction strategies in static networks. However, the same task in dynamic networks where, apart from the actors and links among them, their community pattern is also dynamic, is yet to be explored. Evolutionary community-aware information, including the associated link structure and temporal neighborhood changes, can effectively be mined to build dynamic similarity metrics for dynamic link prediction. This study aims to develop and integrate such dynamic features with machine learning algorithms for link prediction tasks in dynamic social networks. It also compares the performances of these features against well-known similarity metrics (i.e., ResourceAllocation) for static networks and a time series-based link prediction strategy in dynamic networks. These proposed features achieved high-performance scores, representing them as prospective candidates for both dynamic link prediction tasks and modeling the network growth.

Time-dependent photoionization spectroscopy of the Seyfert galaxy NGC 3783

We present an investigation into the spectroscopic properties of non-equilibrium photoionization processes operating in a time-evolving mode. Through a quantitative comparison between equilibrium and time-evolving models, we find that the time-evolving model exhibits a broader distribution of charge states, accompanied by a slight shift in the peak ionization state depending on the source variability and gas density. The time-evolving code tpho in SPEX was successfully employed to analyze the spectral properties of warm absorbers in the Seyfert galaxy NGC 3783. The incorporation of variability in the tpho model improves the fits of the time-integrated spectra, providing more accurate descriptions of the average charge states of several elements, in particular Fe, which is peaked around Fe XIX. The inferred densities and distances of the relevant X-ray absorber components are estimated to be approximately a few times 1011 m−3 and ≤1 pc, respectively. Furthermore, the updated fit suggests a potential scenario in which the observed absorbers are being expelled from the central active galactic nucleus at the escape velocities. This implies that these absorbers might not play a significant role in the active galactic nucleus feedback mechanism.

Evolutionary Features for Dynamic Link Prediction in Social Networks

One of the inherent characteristics of dynamic networks is the evolutionary nature of their constituents (i.e., actors and links). As a time-evolving model, the link prediction mechanism in dynamic networks can successfully capture the underlying growth mechanisms of social networks. Mining the temporal patterns of dynamic networks has led researchers to utilise dynamic information for dynamic link prediction. Despite several methodological improvements in dynamic link prediction, temporal variations of actor-level network structure and neighbourhood information have drawn little attention from the network science community. Evolutionary aspects of network positional changes and associated neighbourhoods, attributed to non-connected actor pairs, may suitably be used for predicting the possibility of their future associations. In this study, we attempted to build dynamic similarity metrics by considering temporal similarity and correlation between different actor-level evolutionary information of non-connected actor pairs. These metrics then worked as dynamic features in the supervised link prediction model, and performances were compared against static similarity metrics (e.g., AdamicAdar). Improved performance is achieved by the metrics considered in this study, representing them as prospective candidates for dynamic link prediction tasks and to help understand the underlying evolutionary mechanism.

On the Stability of Tidal Streams in Action Space

In the Gaia era it is increasingly apparent that traditional static, parameterized models are insufficient to describe the mass distribution of our complex, dynamically evolving Milky Way (MW). In this work, we compare different time-evolving and time-independent representations of the gravitational potentials of simulated MW-mass galaxies from the FIRE-2 suite of cosmological-baryonic simulations. Using these potentials, we calculate actions for star particles in tidal streams around three galaxies with varying merger histories at each snapshot from 7 Gyr ago to the present day. We determine the action-space coherence preserved by each model using the Kullback–Leibler divergence to gauge the degree of clustering in actions and the relative stability of the clusters over time. We find that all models produce a clustered action space for simulations with no significant mergers. However, a massive (mass ratio prior to infall more similar than 1:8) interacting galaxy not present in the model will result in mischaracterized orbits for stars most affected by the interaction. The locations of the action space clusters (i.e., the orbits of the stream stars) are only preserved by the time-evolving model, while the time-independent models can lose significant amounts of information as soon as 0.5–1 Gyr ago, even if the system does not undergo a significant merger. Our results imply that reverse-integration of stream orbits in the MW using a fixed potential is likely to give incorrect results if integrated longer than 0.5 Gyr into the past.

Dynamics retrieval from stochastically weighted incomplete data by low-pass spectral analysis.

Time-resolved serial femtosecond crystallography (TR-SFX) provides access to protein dynamics on sub-picosecond timescales, and with atomic resolution. Due to the nature of the experiment, these datasets are often highly incomplete and the measured diffracted intensities are affected by partiality. To tackle these issues, one established procedure is that of splitting the data into time bins, and averaging the multiple measurements of equivalent reflections within each bin. This binning and averaging often involve a loss of information. Here, we propose an alternative approach, which we call low-pass spectral analysis (LPSA). In this method, the data are projected onto the subspace defined by a set of trigonometric functions, with frequencies up to a certain cutoff. This approach attenuates undesirable high-frequency features and facilitates retrieving the underlying dynamics. A time-lagged embedding step can be included prior to subspace projection to improve the stability of the results with respect to the parameters involved. Subsequent modal decomposition allows to produce a low-rank description of the system's evolution. Using a synthetic time-evolving model with incomplete and partial observations, we analyze the LPSA results in terms of quality of the retrieved signal, as a function of the parameters involved. We compare the performance of LPSA to that of a range of other sophisticated data analysis techniques. We show that LPSA allows to achieve excellent dynamics reconstruction at modest computational cost. Finally, we demonstrate the superiority of dynamics retrieval by LPSA compared to time binning and merging, which is, to date, the most commonly used method to extract dynamical information from TR-SFX data.

Controls on Greenland moulin geometry and evolution from the Moulin Shape model

Abstract. Nearly all meltwater from glaciers and ice sheets is routed englacially through moulins. Therefore, the geometry and evolution of moulins has the potential to influence subglacial water pressure variations, ice motion, and the runoff hydrograph delivered to the ocean. We develop the Moulin Shape (MouSh) model, a time-evolving model of moulin geometry. MouSh models ice deformation around a moulin using both viscous and elastic rheologies and melting within the moulin through heat dissipation from turbulent water flow, both above and below the water line. We force MouSh with idealized and realistic surface melt inputs. Our results show that, under realistic surface melt inputs, variations in surface melt change the geometry of a moulin by approximately 10 % daily and over 100 % seasonally. These size variations cause observable differences in moulin water storage capacity and moulin water levels compared to a static, cylindrical moulin. Our results suggest that moulins are important storage reservoirs for meltwater, with storage capacity and water levels varying over multiple timescales. Implementing realistic moulin geometry within subglacial hydrologic models may therefore improve the representation of subglacial pressures, especially over seasonal periods or in regions where overburden pressures are high.

Cloudy-sky land surface temperature from VIIRS and MODIS satellite data using a surface energy balance-based method

Land surface temperature (LST) has been effectively retrieved from thermal infrared (TIR) satellite measurements under clear-sky conditions. However, TIR satellite data are often severely contaminated by clouds, which cause spatiotemporal discontinuities and low retrieval accuracy in the LST products. Several solutions have been proposed to fill the “gaps”; however, a majority of these possess constraints. For example, fusion methods with microwave data suffer from coarse spatial resolution and diverse land cover types while spatial-temporal interpolation methods neglect cloudy cooling effects. We developed a novel method to estimate cloudy-sky LST from polar-orbiting satellite data based on the surface energy balance (SEB) principle. First, the hypothetical clear-sky LST of missing or likely cloud-contaminated pixels was reconstructed by assimilating high-quality satellite retrievals into a time-evolving model built from reanalysis data using a Kalman filter data assimilation algorithm. Second, clear-sky LST was hypothetically corrected by accounting for cloud cooling based on SEB theory. The proposed method was applied to Visible Infrared Imaging Radiometer Suite (VIIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) data, and further validated using ground measurements of fourteen sites from SURFRAD, BSRN, and AmeriFlux in 2013. VIIRS LST recovered from cloud gaps exhibited a root mean square error (RMSE) of 3.54 K, a bias of −0.36 K, R2 of 0.94, and sample size (N) of 2411, comparable to the accuracy of clear-sky LST products and cloudy-sky LST estimation from MODIS (RMSE of 3.69 K, bias of −0.45 K, R2 of 0.93, and N of 2398). Thus, the proposed method performs well across different sensors, seasons, and land cover types. The abnormal retrieval values caused by cloud contamination were also corrected in the proposed method. The overall accuracy was better than the downscaled cloudy-sky LST retrieved from passive microwave (PMW) observations and former SEB-based cloudy-sky LST estimation methods. Validation using time-series measurements showed that the all-sky LST time series, including both clear- and cloudy-sky retrievals, can capture realistic variability without sudden abruptions or discontinuities. RMSE values for the all-sky LST varied from 2.54 to 4.15 K at the fourteen sites. Spatially continuous LST maps over the Contiguous United States were compared with corresponding maps from PMW data in the winter and summer of 2018, exhibiting similar spatial patterns but with additional spatial details. Moreover, sensitivity analysis suggested that the reconstruction of clear-sky LST dominantly impacts the accuracy of cloudy-sky LST estimation. The proposed method can be potentially implemented in similar satellite sensors for global real-time production.

Development and evaluation of a high-resolution reanalysis of the East Australian Current region using the Regional Ocean Modelling System (ROMS 3.4) and Incremental Strong-Constraint 4-Dimensional Variational (IS4D-Var) data assimilation

Abstract. As with other Western Boundary Currents globally, the East Australian Current (EAC) is highly variable making it a challenge to model and predict. For the EAC region, we combine a high-resolution state-of-the-art numerical ocean model with a variety of traditional and newly available observations using an advanced variational data assimilation scheme. The numerical model is configured using the Regional Ocean Modelling System (ROMS 3.4) and takes boundary forcing from the BlueLink ReANalysis (BRAN3). For the data assimilation, we use an Incremental Strong-Constraint 4-Dimensional Variational (IS4D-Var) scheme, which uses the model dynamics to perturb the initial conditions, atmospheric forcing, and boundary conditions, such that the modelled ocean state better fits and is in balance with the observations. This paper describes the data assimilative model configuration that achieves a significant reduction of the difference between the modelled solution and the observations to give a dynamically consistent “best estimate” of the ocean state over a 2-year period. The reanalysis is shown to represent both assimilated and non-assimilated observations well. It achieves mean spatially averaged root mean squared (rms) residuals with the observations of 7.6 cm for sea surface height (SSH) and 0.4 °C for sea surface temperature (SST) over the assimilation period. The time-mean rms residual for subsurface temperature measured by Argo floats is a maximum of 0.9 °C between water depths of 100 and 300 m and smaller throughout the rest of the water column. Velocities at several offshore and continental shelf moorings are well represented in the reanalysis with complex correlations between 0.8 and 1 for all observations in the upper 500 m. Surface radial velocities from a high-frequency radar array are assimilated and the reanalysis provides surface velocity estimates with complex correlations with observed velocities of 0.8–1 across the radar footprint. A comparison with independent (non-assimilated) shipboard conductivity temperature depth (CTD) cast observations shows a marked improvement in the representation of the subsurface ocean in the reanalysis, with the rms residual in potential density reduced to about half of the residual with the free-running model in the upper eddy-influenced part of the water column. This shows that information is successfully propagated from observed variables to unobserved regions as the assimilation system uses the model dynamics to adjust the model state estimate. This is the first study to generate a reanalysis of the region at such a high resolution, making use of an unprecedented observational data set and using an assimilation method that uses the time-evolving model physics to adjust the model in a dynamically consistent way. As such, the reanalysis potentially represents a marked improvement in our ability to capture important circulation dynamics in the EAC. The reanalysis is being used to study EAC dynamics, observation impact in state-estimation, and as forcing for a variety of downscaling studies.

Non-Interactive Key Establishment for Bundle Security Protocol of Space DTNs

To ensure the authenticity, integrity, and confidentiality of bundles, the in-transit Protocol Data Units of bundle protocol (BP) in space delay/disruption tolerant networks (DTNs), the Consultative Committee for Space Data Systems bundle security protocol (BSP) specification suggests four IPsec style security headers to provide four aspects of security services. However, this specification leaves key management as an open problem. Aiming to address the key establishment issue for BP, in this paper, we utilize a time-evolving topology model and two-channel cryptography to design efficient and noninteractive key exchange protocol. A time-evolving model is used to formally model the periodic and predetermined behavior patterns of space DTNs, and therefore, a node can schedule when and to whom it should send its public key. Meanwhile, the application of two-channel cryptography enables DTN nodes to exchange their public keys or revocation status information, with authentication assurance and in a noninteractive manner. The proposed scheme helps to establish a secure context to support for BSP, tolerating high delays, and unexpected loss of connectivity of space DTNs.

Prediction of time-evolving sand ripples in shelf seas

The ability to predict the presence and physical properties of small-scale sand ripples on the sea bed is important for determining the bed roughness felt by currents and waves, for sediment transport applications, and for acoustic applications. A fully time-evolving model is presented for predicting the height, length and orientation of sand ripples generated by currents, waves or both, which includes processes dealing with threshold of motion, ripple wash-out and biological degradation. We believe that this is currently the only predictor to feature all these properties, and is thus more widely applicable to shelf seas than other more sophisticated, but less comprehensive, methods. Tests against a large data-set of observations using an Acoustic Ripple Profiler at a site on the east coast of England, where ripples are generated by both tidal currents and waves, showed that the predictor captures most of the main features observed. A modified criterion for the wave/current dominance in the prediction equations improved some aspects of the agreement. The properties of current-generated ripples are predicted accurately, but the wavelength, and to a lesser extent the height, of wave-generated ripples show some under-prediction. Ripple orientations are modelled in an approximate way, but the main features of the observed orientations are captured. The bio-degradation feature of the predictor could not be tested with this data-set.

Dynamic Nonparametric Bayesian Models for Analysis of Music

The dynamic hierarchical Dirichlet process (dHDP) is developed to model complex sequential data, with a focus on audio signals from music. The music is represented in terms of a sequence of discrete observations, and the sequence is modeled using a hidden Markov model (HMM) with time-evolving parameters. The dHDP imposes the belief that observations that are temporally proximate are more likely to be drawn from HMMs with similar parameters, while also allowing for “innovation” associated with abrupt changes in the music texture. The sharing mechanisms of the time-evolving model are derived, and for inference a relatively simple Markov chain Monte Carlo sampler is developed. Segmentation of a given musical piece is constituted via the model inference. Detailed examples are presented on several pieces, with comparisons to other models. The dHDP results are also compared with a conventional music-theoretic analysis. All the supplemental materials used by this paper are available online.

Use of a Kalman filter for the retrieval of surface BRDF coefficients with a time-evolving model based on the ECOCLIMAP land cover classification

The goal of this study is to demonstrate the asset in using a Kalman filter to improve the spatial coherence and time consistency of surface Bidirectional Reflectance Distribution Function (BRDF) and albedo retrievals from moderate resolution sensor data sets. For this purpose, we use a simple surface model describing BRDF seasonal evolution for the land cover classes of the ECOCLIMAP database. The application of temporal composition windows used so far for BRDF retrieval is limited in regions characterized by a high frequency of cloud coverage, which induces a lot of gaps in the temporal series. Instead, the present method ensures a continuous production of surface BRDF parameters thanks to the Kalman filter recursive data processing. An application of the method is performed with SPOT/VEGETATION data over the western Africa equatorial region for the year 2003. Compared to presently available products from VEGETATION and MODIS instruments, this new approach allows to fill the gaps and improves the retrieved parameters time consistency. Another interesting possibility of the Kalman filter is the production of surface biophysical variables in quasi-real-time for applications that require a frequent update of the surface parameters.

Physical and biological modeling in the Gulf Stream region:: I. Data assimilation methodology

Physical and biological data are assimilated into a time-evolving, mesoscale-resolution three-dimensional (3-D) ocean model using optimal interpolation. Simulations are conducted in the Gulf Stream region during the BIOSYNOP/Anatomy of a Meander Experiment in September–October of 1988. Physical data assimilation only or biological data assimilation only resulted in misalignment of the physical and biological fronts, causing spurious cross-frontal fluxes of biological quantities. Assimilation of both physical and compatible biological fields was necessary for adequate equilibration of the simulated fields. The resulting combined 4-D fields substantially extend the value of the observations alone. A technique is presented for deriving the necessary, dynamically consistent 3-D physical and biological field estimates from data for initialization and assimilation into time-evolving model simulations.