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INTRODUCTION The interactive relationships between Alzheimer's disease (AD) and white matter hyperintensities (WMHs) in multiscale brain structural networks still need to be clarified. METHODS Based on subjects enrolled from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, regional WMHs, amyloid beta (Aβ) accumulation, and microstructural changes detected by diffusion weighted imaging (DWI) in multiscale brain networks were modeled by time‐evolving graphs; their interactive relationships were further investigated using Granger causality after constructing pseudo‐time subject sequences. RESULTS In up to 86% of the extracted pseudo‐time subject sequences, Aβ was determined to be the Granger cause of WMHs in the structural connectivity of the inferior longitudinal fasciculus (ILF). Meanwhile WMHs were significantly correlated with microstructural changes measured by reduced fractional anisotropy in the inferior fronto‐occipital fasciculus, ILF, and cingulum, which Granger causality pathways detected in 91%, 94%, and 93% of pseudo‐time subject sequences, respectively. DISCUSSION These findings provide novel insights for understanding the multiscale space‐time interactions between WMHs and AD pathologies. Highlights This study proposed time‐evolving graph modeling of heterogeneous disease markers (amyloid beta [Aβ], white matter hyperintensities [WMHs], and microstructural changes of white matter tracts) across the Alzheimer's disease (AD) continuum to investigate their complex interactions in multiscale brain structural networks. Regional accumulation of Aβ promoted WMH progression in subnetworks connected by the inferior longitudinal fasciculus (ILF). Regional WMHs were strongly associated with bundle‐specific microstructural changes in the ILF, inferior fronto‐occipital fasciculus, and cingulum. These results might provide novel insights for understanding the interactive relationship between cerebral small vessel disease and AD.

Senescence is controlled by time-evolving networks that describe the temporal transition of interactions among senescence regulators. Here, we present time-evolving networks for NAM/ATAF/CUC (NAC) transcription factors in Arabidopsis during leaf aging. The most evident characteristic of these time-dependent networks was a shift from positive to negative regulation among NACs at a presenescent stage. ANAC017, ANAC082, and ANAC090, referred to as a “NAC troika,” govern the positive-to-negative regulatory shift. Knockout of the NAC troika accelerated senescence and the induction of other NACs, whereas overexpression of the NAC troika had the opposite effects. Transcriptome and molecular analyses revealed shared suppression of senescence-promoting processes by the NAC troika, including salicylic acid (SA) and reactive oxygen species (ROS) responses, but with predominant regulation of SA and ROS responses by ANAC090 and ANAC017, respectively. Our time-evolving networks provide a unique regulatory module of presenescent repressors that direct the timely induction of senescence-promoting processes at the presenescent stage of leaf aging.

We generalize time-evolving matrix product operators method to nonequilibrium quantum transport problems. The nonequilibrium current is obtained via numerical differentiation of the generating functional which is represented as a tensor network. The approach is numerically exact and the non-Markovian effects are fully taken into account. In the transport process, a part of the heat that flows out from a bath flows into the system and other baths, and the rest is stored in the system-bath coupling part. We take the spin-boson model as a demonstration to show the details of this heat flowing and the establishment of a steady current between two baths.

Tensor-network-based methods are promising candidates to solve quantum impurity problems (QIP). They are free of sampling noises and the sign problem compared to state-of-the-art continuous-time quantum Monte Carlo methods. Recent progress made in tensor-network-based impurity solvers is to use the Feynman–Vernon influence functional to integrate out the bath analytically, retaining only the impurity dynamics and representing it compactly as a matrix product state. The recently proposed Grassmann time-evolving matrix product operator (GTEMPO) method is one of the representative methods in this direction. In this work, we systematically study the performance of GTEMPO in solving equilibrium QIPs at a finite temperature with a semicircular spectrum density of the bath. Our results show that its computational cost would generally increase as the temperature goes down and scale exponentially with the number of orbitals. In particular, the single-orbital Anderson impurity model can be efficiently solved with this method, for two orbitals we estimate that one could possibly reach inverse temperature β  ≈ 20 if high-performance computing techniques are utilized, while beyond that only very high-temperature regimes can be reached in the current formalism. Our work paves the way to apply GTEMPO as an imaginary-time impurity solver.

Community structure is one of the most relevant features encountered in numerous real-world applications of networked systems. Despite the tremendous effort of a large interdisciplinary community of scientists working on this subject over the past few decades to characterize, model, and analyze communities, more investigations are needed in order to better understand the impact of community structure and its dynamics on networked systems. Here, we first focus on generative models of communities in complex networks and their role in developing strong foundation for community detection algorithms. We discuss modularity and the use of modularity maximization as the basis for community detection. Then, we follow with an overview of the Stochastic Block Model and its different variants as well as inference of community structures from such models. Next, we focus on time evolving networks, where existing nodes and links can disappear, and in parallel new nodes and links may be introduced. The extraction of communities under such circumstances poses an interesting and non-trivial problem that has gained considerable interest over the last decade. We briefly discuss considerable advances made in this field recently. Finally, we focus on immunization strategies essential for targeting the influential spreaders of epidemics in modular networks. Their main goal is to select and immunize a small proportion of individuals from the whole network to control the diffusion process. Various strategies have emerged over the years suggesting different ways to immunize nodes in networks with overlapping and non-overlapping community structure. We first discuss stochastic strategies that require little or no information about the network topology at the expense of their performance. Then, we introduce deterministic strategies that have proven to be very efficient in controlling the epidemic outbreaks, but require complete knowledge of the network.

In complex networks, the “rich-get-richer” effect (nodes with high degree at one point in time gain more degree in their future) is commonly observed. In practice this is often studied on a static network snapshot, for example, a preferential attachment model assumed to explain the more highly connected nodes or a rich-club effect that analyses the most highly connected nodes. In this paper, we consider temporal measures of how success (measured here as node degree) propagates across time. By analogy with social mobility (a measure of people moving within a social hierarchy through their life) we define hierarchical mobility to measure how a node’s propensity to gain degree changes over time. We introduce an associated taxonomy of temporal correlation statistics including mobility , philanthropy and community . Mobility measures the extent to which a node’s degree gain in one time period predicts its degree gain in the next. Philanthropy and community measure similar properties related to node neighbourhood. We apply these statistics both to artificial models and to 26 real temporal networks. We find that most of our networks show a tendency for individual nodes and their neighbourhoods to remain in similar hierarchical positions over time, while most networks show low correlative effects between individuals and their neighbourhoods. Moreover, we show that the mobility taxonomy can discriminate between networks from different fields. We also generate artificial network models to gain intuition about the behaviour and expected range of the statistics. The artificial models show that the opposite of the “rich-get-richer” effect requires the existence of inequality of degree in a network. Overall, we show that measuring the hierarchical mobility of a temporal network is an invaluable resource for discovering its underlying structural dynamics.

Zebrafish are widely used in vertebrate studies, yet minimally invasive individual tracking and identification in the lab setting remain challenging due to complex and time-variable conditions. Advancements in machine learning, particularly neural networks, offer new possibilities for developing simple and robust identification protocols that adapt to changing conditions. We demonstrate a rolling window training technique suitable for use with open-source convolutional neural networks (CNN) and vision transformers (ViT) that shows promise in robustly identifying individual maturing zebrafish in groups over several weeks. The technique provides a high-fidelity method for monitoring the temporally evolving zebrafish classes, potentially significantly reducing the need for new training images in both CNN and ViT architectures. To understand the success of the CNN classifier and inform future real-time identification of zebrafish, we analyzed the impact of shape, pattern, and color by modifying the images of the training set and compared the test results with other prevalent machine learning models.

Densification events in time-evolving networks refer to instants in which the network density, that is, the number of edges, is substantially larger than in the remaining. These events can occur at a global level, involving the majority of the nodes in the network, or at a local level involving only a subset of nodes.While global densification events affect the overall structure of the network, the same does not hold in local densification events, which may remain undetectable by the existing detection methods. In order to address this issue, we propose WINdowed TENsor decomposition for Densification Event Detection (WINTENDED) for the detection and characterization of both global and local densification events. Our method combines a sliding window decomposition with statistical tools to capture the local dynamics of the network and automatically find the irregular behaviours. According to our experimental evaluation, WINTENDED is able to spot global densification events at least as accurately as its competitors, while also being able to find local densification events, on the contrary to its competitors.

The time-dependent electrophoresis of an infinitely cylindrical particle in an electrolyte solution, saturated in a charged porous medium after the sudden application of a transverse or tangential step electric field, is investigated semi-theoretically with an arbitrary double-layer thickness in an arbitrary direction relative to the cylinder. The time-dependent modified Brinkman equation with an electric force term, which governs the fluid flow field, is used to model the porous medium and is solved by using the Laplace transform technique. Explicit formulas, for the time-dependent electrophoretic velocity of the cylindrical particle in Laplace’s transform domain, have been derived for both axially and transversely when the uniform electric fields are imposed. They can also be linearly superimposed for an arbitrarily oriented relative to the electric field. Semi-analytical results for the electrophoretic velocities are presented as functions of the dimensionless elapsed time, the ratio of the particle radius to the Debye length, the particle-to-medium density ratio, and the permeability parameter of the porous medium. The results demonstrate, in general, that the growth of the electrophoretic velocities with the time scale are more slower for high permeability, and the effect of the relaxation time for unsteady electrophoresis is found to be negligible, regardless of the thickness of the double layer, the relative mass density or the permeability of the medium. The normalized transient electrophoretic velocities exhibit a consistent upward trend as the ratio of the particle radius to the Debye screening length increases. Conversely, they display a consistent downward trend as the particle-to-fluid density ratio increases, while all other parameters remain constant. The effect of the relaxation time for the transient electrophoresis is much more important for a cylindrical particle than for a spherical particle due to its smaller specific surface area.

Optical satellite networks (OSNs) play an important role in space applications since it has some merits such as high data rate and low power. Increasing space applications generate a large amount of data on satellite so that satellites have difficulty in downloading these data to ground station timely under the limited contact window. Thus, we investigate the problem of collaborative data downloading in this paper. The problem lies on how to energy-efficiently offload data by optical inter-satellite links (ISLs) to balance data load and downlink capacity of each satellite so as to improve the performance of data downloading in OSN. Firstly, we develop the Cost Time-evolving Graph (CTEG) to describe optical transmission cost and the time-varying topology of OSN. Secondly, the problem of collaborative data downloading is formulated as Multi-objective Mixed-integer Linear Programming (MOMILP) which is proven to be NP-hard. For reducing computational complexity, we divide the scheduling time into multiple stages and propose the Multi-stage Collaborative Scheduling Algorithm (MCSA) which operates on a slot-by-slot basis. Simulations are conducted in the joint platform and results demonstrate that, compared with CoDld and No-ISLs, the MCSA provides a relatively high data throughput meanwhile drastically reducing energy consumption generated by data offloading.