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The problem of maximizing the adoption of a product through viral marketing in social networks has been studied heavily through postulated network models. We present a novel data-driven formulation of the problem. We use Graph Neural Networks (GNNs) to model the adoption of products by utilizing both topological and attribute information. The resulting Dynamic Viral Marketing (DVM) problem seeks to find the minimum budget and minimal set of dynamic topological and attribute changes in order to attain a specified adoption goal. We show that DVM is NP-Hard and is related to the existing influence maximization problem. Motivated by this connection, we develop the idea of Dynamic Gradient Influencing (DGI) that uses gradient ranking to find optimal perturbations and targets low-budget and high influence non-adopters in discrete steps. We use an efficient strategy for computing node budgets and develop the “Meta-Influence” heuristic for assessing a node’s downstream influence. We evaluate DGI against multiple baselines and demonstrate gains on average of 24% on budget and 37% on AUC on real world attributed networks. Our code is publicly available at https: //github.com/saurabhsharma1993/dynamic_viral_marketing. 

Microstructure refinement and optimized alloying can improve metallic alloy performance: stable nanocrystalline (NC) alloys with immiscible second phases, e.g., Cu-Ta, are stronger than unstable NC alloys and their coarse-grained (CG) counterparts, but higher melting point matrices are needed. Hypoeutectic, CG Ni-Y-Zr alloys were produced via arc-melting to explore their potential as high-performance materials. Microstructures were studied to determine phases present, local composition and length scales, while heat treatments allowed investigating microstructural stability. Alloys had a stable, hierarchical microstructure with ~250 nm ultrafine eutectic, ~10 µm dendritic arm spacing and ~1 mm grain size. Hardness and uniaxial compression tests revealed that mechanical properties of Ni-0.5Y-1.8Zr (in wt%) were comparable to Inconel 617 despite the small alloying additions, due to its hierarchical microstructure. Uniaxial compression at 600 °C showed that ternary alloys outperformed Ni-Zr and Ni-Y binary alloys in flow stress and hardening rates, which indicates that the Ni17Y2 phase was an effective reinforcement for the eutectic, which supplemented the matrix hardening due to increased solubility of Zr. Results suggest that ternary Ni-Y-Zr alloys hold significant promise for high temperature applications. 

Abstract EX Lupi, a low-mass young stellar object, went into an accretion-driven outburst in 2022 March. The outburst caused a sudden phase change of ∼112° ± 5° in periodically oscillating multiband lightcurves. Our high-resolution spectra obtained with the High Resolution Spectrograph (HRS) on board the Southern African Large Telescope also revealed a consistent phase change in the periodically varying radial velocities (RVs), along with an increase in the RV amplitude of various emission lines. The phase change and increase in RV amplitude morphologically translates to a change in the azimuthal and latitudinal location of the accretion hotspot over the stellar surface, which indicates a reconfiguration of the accretion funnel geometry. Our three-dimensional magnetohydrodynamic simulations reproduce the phase change for EX Lupi. To explain the observations, we explored the possibility of forward shifting of the dipolar accretion funnel as well as the possibility of the emergence of a new accretion funnel. During the outburst, we also found evidence of the hotspot’s morphology extending azimuthally asymmetrically with a leading hot edge and cold tail along the stellar rotation. Further, our high-cadence photometry showed that the accretion flow has clumps. We also detected possible clumpy accretion events in the HRS spectra that showed episodically highly blueshifted wings in the CaiiIR triplet and Balmer H lines. 

Abstract The Gaia Alert System issued an alert on 2020 August 28, on Gaia 20eae when its light curve showed a ∼4.25 magnitude outburst. We present multiwavelength photometric and spectroscopic follow-up observations of this source since 2020 August and identify it as the newest member of the FUor/EXor family of sources. We find that the present brightening of Gaia 20eae is not due to the dust-clearing event but due to an intrinsic change in the spectral energy distribution. The light curve of Gaia 20eae shows a transition stage during which most of its brightness (∼3.4 mag) has occurred on a short timescale of 34 days with a rise rate of 3 mag/month. Gaia 20eae has now started to decay at a rate of 0.3 mag/month. We have detected a strong P Cygni profile in H α , which indicates the presence of winds originating from regions close to the accretion. We find signatures of very strong and turbulent outflow and accretion in Gaia 20eae during this outburst phase. We have also detected a redshifted absorption component in all of the Ca ii IR triplet lines consistent with a signature of hot infalling gas in the magnetospheric accretion funnel. This enables us to constrain the viewing angle with respect to the accretion funnel. Our investigation of Gaia 20eae points toward magnetospheric accretion being the phenomenon for the current outburst. 

Brain age (BA), distinct from chronological age (CA), can be estimated from MRIs to evaluate neuroanatomic aging in cognitively normal (CN) individuals. BA, however, is a cross-sectional measure that summarizes cumulative neuroanatomic aging since birth. Thus, it conveys poorly recent or contemporaneous aging trends, which can be better quantified by the (temporal) pace P of brain aging. Many approaches to map P, however, rely on quantifying DNA methylation in whole-blood cells, which the blood–brain barrier separates from neural brain cells. We introduce a three-dimensional convolutional neural network (3D-CNN) to estimate P noninvasively from longitudinal MRI. Our longitudinal model (LM) is trained on MRIs from 2,055 CN adults, validated in 1,304 CN adults, and further applied to an independent cohort of 104 CN adults and 140 patients with Alzheimer’s disease (AD). In its test set, the LM computes P with a mean absolute error (MAE) of 0.16 y (7% mean error). This significantly outperforms the most accurate cross-sectional model, whose MAE of 1.85 y has 83% error. By synergizing the LM with an interpretable CNN saliency approach, we map anatomic variations in regional brain aging rates that differ according to sex, decade of life, and neurocognitive status. LM estimates of P are significantly associated with changes in cognitive functioning across domains. This underscores the LM’s ability to estimate P in a way that captures the relationship between neuroanatomic and neurocognitive aging. This research complements existing strategies for AD risk assessment that estimate individuals’ rates of adverse cognitive change with age.