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In this work, we extend our recently developed super-resolution (SR) model for cosmological simulations to produce fully time-consistent evolving representations of the particle phase-space distribution. We employ a style-based constrained generative adversarial network (StyleGAN), where the changing cosmic time is an input style parameter to the network. The matter power spectrum and halo mass function agree well with results from high-resolution N-body simulations over the full trained redshift range (10 ≤ z ≤ 0). Furthermore, we assess the temporal consistency of our SR model by constructing halo merger trees. We examine progenitors, descendants, and mass growth along the tree branches. All statistical indicators demonstrate the ability of our SR model to generate satisfactory high-resolution simulations based on low-resolution inputs.

 

Modern web browsers rely on layout engines to convert HTML documents to layout trees that specify color, size, and position. However, existing layout engines are notoriously difficult to maintain because of the complexity of web standards. This is especially true for incremental layout engines, which are designed to improve performance by updating only the parts of the layout tree that need to be changed. In this paper, we propose Medea, a new framework for automatically generating incremental layout engines. Medea separates the specification of the layout engine from its incremental implementation, and guarantees correctness through layout engine synthesis. The synthesis is driven by a new iterative algorithm based on detecting conflicts that prevent optimality of the incremental algorithm. We evaluated Medea on a fragment of HTML layout that includes challenging features such as margin collapse, floating layout, and absolute positioning. Medea successfully synthesized an incremental layout engine for this fragment. The synthesized layout engine is both correct and efficient. In particular, we demonstrated that it avoids real-world bugs that have been reported in the layout engines of Chrome, Firefox, and Safari. The incremental layout engine synthesized by Medea is up to 1.82× faster than a naive incremental baseline. We also demonstrated that our conflict-driven algorithm produces engines that are 2.74× faster than a baseline without conflict analysis. 

Tracheobronchial tumors, while uncommon, are often malignant in adults. Surgical removal is the primary therapy for non-metastatic lung malignancies, but it is only possible in a small percentage of non-small-cell lung cancer patients and is limited by the number and location of tumors, as well as the patient’s overall health. This study proposes an alternative treatment: administering aerosolized chemotherapeutic particles via the pulmonary route using endotracheal catheters to target lung tumors. To improve delivery efficiency to the lesion, it is essential to understand local drug deposition and particle transport dynamics. This study uses an experimentally validated computational fluid particle dynamics (CFPD) model to simulate the transport and deposition of inhaled chemotherapeutic particles in a 3-dimensional tracheobronchial tree with 10 generations (G). Based on the particle release maps, targeted drug delivery strategies are proposed to enhance particle deposition at two lung tumor sites in G10. Results indicate that controlled drug release can improve particle delivery efficiencies at both targeted regions. The use of endotracheal catheters significantly affects particle delivery efficiencies in targeted tumors. The parametric analysis shows that using smaller catheters can deliver more than 74% of particles to targeted tumor sites, depending on the location of the tumor and the catheter diameter used, compared to less than 1% using conventional particle administration methods. Furthermore, the results indicate that particle release time has a significant impact on particle deposition under the same inhalation profile. This study serves as a first step in understanding the impact of catheter diameter on localized endotracheal injection for targeting tumors in small lung airways. 

The paper considers a Mixture Multilayer Stochastic Block Model (MMLSBM), where layers can be partitioned into groups of similar networks, and networks in each group are equipped with a distinct Stochastic Block Model. The goal is to partition the multilayer network into clusters of similar layers, and to identify communities in those layers. Jing et al. (2020) introduced the MMLSBM and developed a clustering methodology, TWIST, based on regularized tensor decomposition. The present paper proposes a different technique, an alternating minimization algorithm (ALMA), that aims at simultaneous recovery of the layer partition, together with estimation of the matrices of connection probabilities of the distinct layers. Compared to TWIST, ALMA achieves higher accuracy, both theoretically and numerically. 

We examine the dual [both black hole (BH) active] and offset (one BH active and in distinct galaxies) active galactic nucleus (AGN) population (comprising ∼ 2000 pairs at $0.5\, \text{kpc}\lesssim \Delta r\lt 30\, \text{kpc}$) at z = 2 ∼ 3 in the ASTRID simulation covering (360 cMpc)3. The dual (offset) AGN make up 3.0(0.5) per cent of all AGN at z = 2. The dual fraction is roughly constant while the offset fraction increases by a factor of 10 from z = 4 ∼ 2. Compared with the full AGN population, duals are characterized by low MBH/M* ratios, high specific star formation rates (sSFR) of $\sim 1\, \text{Gyr}^{-1}$, and high Eddington ratios (∼0.05, double that of single AGN). Dual AGNs are formed in major galaxy mergers (typically involving $M_\text{halo}\lt 10^{13}\, M_\odot$), with simular-mass BHs. At small separations (when host galaxies are in the late phase of the merger), duals become 2 ∼ 8 times brighter (albeit more obscured) than at larger separations. 80  per cent of the bright, close duals would merge within $\sim 500\, \text{Myr}$. Notably, the initially less-massive BHs in duals frequently become the brighter AGN during galaxy mergers. In offset AGN, the active BH is typically ≳ 10 times more massive than its non-active counterpart and than most BHs in duals. Offsets are predominantly formed in minor galaxy mergers with the active BH residing in the centre of massive haloes ($M_\text{ halo}\sim 10^{13-14}\, \mathrm{M}_\odot$). In these deep potentials, gas stripping is common and the secondary quickly deactivates. The stripping also leads to inefficient orbital decay amongst offsets, which stall at $\Delta r\sim 5\, \text{kpc}$ for a few hundred Myrs.

 

Abstract It is challenging to locate small-airway obstructions induced by chronic obstructive pulmonary disease (COPD) directly from visualization using available medical imaging techniques. Accordingly, this study proposes an innovative and noninvasive diagnostic method to detect obstruction locations using computational fluid dynamics (CFD) and convolutional neural network (CNN). Specifically, expiratory airflow velocity contours were obtained from CFD simulations in a subject-specific 3D tracheobronchial tree. One case representing normal airways and 990 cases associated with different obstruction sites were investigated using CFD. The expiratory airflow velocity contours at a selected cross section in the trachea were labeled and stored as the database for training and testing two CNN models, i.e., ResNet50 and YOLOv4. Gradient-weighted class activation mapping (Grad-CAM) and the Pearson correlation coefficient were employed and calculated to classify small-airway obstruction locations and pulmonary airflow pattern shifts and highlight the highly correlated regions in the contours for locating the obstruction sites. Results indicate that the airflow velocity pattern shifts are difficult to directly visualize based on the comparisons of CFD velocity contours. CNN results show strong relevance exists between the locations of the obstruction and the expiratory airflow velocity contours. The two CNN-based models are both capable of classifying the left lung, right lung, and both lungs obstructions well using the CFD simulated airflow contour images with total accuracy higher than 95.07%. The two automatic classification algorithms are highly transformative to clinical practice for early diagnosis of obstruction locations in the lung using the expiratory airflow velocity distributions, which could be imaged using hyperpolarized magnetic resonance imaging.