LayoutPrompter: Awaken the Design Ability of Large Language Models

Conditional graphic layout generation, which automatically maps user constraints to high-quality layouts, has attracted widespread attention today. Although recent works have achieved promising performance, the lack of versatility and data efficiency hinders their practical applications. In this work, we propose LayoutPrompter, which leverages large language models (LLMs) to address the above problems through in-context learning. LayoutPrompter is made up of three key components, namely input-output serialization, dynamic exemplar selection and layout ranking. Specifically, the input-output serialization component meticulously designs the input and output formats for each layout generation task. Dynamic exemplar selection is responsible for selecting the most helpful prompting exemplars for a given input. And a layout ranker is used to pick the highest quality layout from multiple outputs of LLMs. We conduct experiments on all existing layout generation tasks using four public datasets. Despite the simplicity of our approach, experimental results show that LayoutPrompter can compete with or even outperform state-of-the-art approaches on these tasks without any model training or fine-tuning. This demonstrates the effectiveness of this versatile and training-free approach. In addition, the ablation studies show that LayoutPrompter is significantly superior to the training-based baseline in a low-data regime, further indicating the data efficiency of LayoutPrompter. Our project is available at https://github.com/microsoft/LayoutGeneration/tree/main/LayoutPrompter.Comment: NeurIPS 202

Role of Li-Ion Depletion on Electrode Surface: Underlying Mechanism for Electrodeposition Behavior of Lithium Metal Anode

The application of lithium metal as an anode material for next generation high energy-density batteries has to overcome the major bottleneck that is the seemingly unavoidable growth of Li dendrites caused by non-uniform electrodeposition on the electrode surface. This problem must be addressed by clarifying the detailed mechanism. In this work the mass-transfer of Li-ions is investigated, a key process controlling the electrochemical reaction. By a phase field modeling approach, the Li-ion concentration and the electric fields are visualized to reveal the role of three key experimental parameters, operating temperature, Li-salt concentration in electrolyte, and applied current density, on the microstructure of deposited Li. It is shown that a rapid depletion of Li-ions on electrode surface, induced by, e.g., low operating temperature, diluted electrolyte and a high applied current density, is the underlying driving force for non-uniform electrodeposition of Li. Thus, a viable route to realize a dendrite-free Li plating process would be to mitigate the depletion of Li-ions on the electrode surface. The methodology and results in this work may boost the practical applicability of Li anodes in Li metal batteries and other battery systems using metal anodes

The Efficacy of Adalimumab as an Initial Treatment in Patients with Behçet’s Retinal Vasculitis

Background: No study has evaluated the effectiveness of Adalimumab (ADA) as first-line in treatment-naïve patients with retinal vasculitis due to Behçet’s Uveitis (BU).Objective: To compare the efficacy of ADA plus conventional therapy and conventional therapy alone as initial treatments in naïve BU patients characterized by retinal vasculitis.Methods: Medical records of BU patients characterized by retinal vasculitis treated with conventional therapy (CT, refers to glucocorticoid and immunosuppressive agents) alone or ADA plus conventional therapy with at least 6 months of follow-up between February 2015 and June 2020 were analyzed. Only patients who were first diagnosed with BU without previous systemic treatment were reviewed. The retinal vasculitis score based on fluorescein angiography (FA), best-corrected visual acuity, glucocorticoid-sparing effect, the number of relapses and ocular complications were evaluated.Results: A total of 45 patients (87 eyes) were included. Twenty-four patients (55.33%) in the CT group were treated with conventional therapy and 21 patients (46.67%) in the ADA group were treated with ADA plus conventional therapy. The inflammatory parameters improved in both groups. FA scores showed significantly greater improvement in ADA group than CT group (p < 0.001). The median number of relapses was significantly lower, and the duration of remission was longer in ADA group than CT group (p < 0.001). At the last visit, a significantly better BCVA improvement (p = 0.024), better inflammation control (anterior chamber inflammation p = 0.017 and vitritis p < 0.001) and lower daily glucocorticoid dosage (p = 0.005) were identified in patients received ADA therapy. In CT group, 1 patient suffered hepatitis B and tuberculosis, 1 had growth retardation, 1 patient had with osteoporosis, then followed by other mild AEs (mostly respiratory upper tract infections); while in ADA group, 1 patient experienced a mild pneumonia (n = 1) while milder AEs were represented mostly by respiratory upper tract infections followed by gastrointestinal discomfort.Conclusion: ADA plus conventional therapy achieved superiority over conventional therapy as initial treatment in naïve BU patients with retinal vasculitis

Optimal Power Allocation and Relay Location for DF Energy Harvesting Relaying Sensor Networks

This paper considers a simultaneous wireless information and power transfer (SWIPT) based decode-and-forward (DF) relaying sensor network, where the “save-and-forward” strategy is utilized at the relay sensor node. We investigate a joint power splitting (PS) and relay location (RL) optimization scheme for delay-sensitive transmission mode using the instantaneous channel state information (CSI). In particular, two optimization problems are formulated to minimize the outage probability and maximize the average capacity, respectively. For the two optimization problems, the optimal solutions to the PS ratio and RL are obtained based on the instantaneous CSI. On the basis of optimal solutions, the analytical expressions for outage probability and average capacity are derived, and the corresponding achievable throughputs are obtained. Numerical results verify the correctness of theoretical derivations and validate the advantages of our proposed scheme

Optimal Power Allocation and Relay Location for DF Energy Harvesting Relaying Sensor Networks

This paper considers a simultaneous wireless information and power transfer (SWIPT) based decode-and-forward (DF) relaying sensor network, where the “save-and-forward” strategy is utilized at the relay sensor node. We investigate a joint power splitting (PS) and relay location (RL) optimization scheme for delay-sensitive transmission mode using the instantaneous channel state information (CSI). In particular, two optimization problems are formulated to minimize the outage probability and maximize the average capacity, respectively. For the two optimization problems, the optimal solutions to the PS ratio and RL are obtained based on the instantaneous CSI. On the basis of optimal solutions, the analytical expressions for outage probability and average capacity are derived, and the corresponding achievable throughputs are obtained. Numerical results verify the correctness of theoretical derivations and validate the advantages of our proposed scheme

Characterizing three-dimensional features of vortex surfaces in the flow past a finite plate

We extend the vortex-surface field (VSF), a Lagrangian-based structure identification method, to investigate vortex dynamics in flows past a plate simulated by the immersed boundary method. As an example, the VSF evolution characterizes the three-dimensional features of vortex surfaces in the flow past a finite plate at the Reynolds number of 300, aspect ratio of 2, and angle of attack of 30 degrees. The VSF isosurface displays that near-plate vortex surfaces first roll up from plate edges and then evolve into hairpinlike structures near the leading edge and semiring structures near plate tips and in the wake. We quantitatively distinguish two types of vortical structures by the vanishing streamwise vorticity on VSF isosurfaces and refer them to as the leading edge vortex (LEV) and the tip vortex (TIV). Based on circulations through cross sections of vortex surfaces, we demonstrate that the lift generated from the LEV is suppressed by the finite growth of TIVs. In the wake region, we quantify the geometry of helical vortex lines in TIVs and the contribution of the helical vorticity component to the streamwise vortical impulse. Published under license by AIP Publishing

Estimating forces from cross-sectional data in the wake of flows past a plate using theoretical and data-driven models

We report a comparative study of theoretical and data-driven models for estimating forces from velocity data in the wake of three-dimensional flows past a plate. The datasets with a range of angles of attack are calculated using the immersed boundary method. First, we develop a theoretical model to estimate forces on a flat plate from cross-sectional velocity data in the far wake. This algebraic model incorporates the local momentum deficit and pressure variation. Second, we develop several data-driven models based on the convolutional neural network (CNN) for force estimation by regarding the velocity field on a series of cross sections as images. In particular, we design three CNN architectures for integrating physical information or attention mechanism, and use different training datasets for interpolation and extrapolation tasks. The model performances indicate that the optimized CNN can identify important flow regions and learn empirical physical laws. The theoretical and CNN models are assessed by multiple criteria. In general, both models are accurate (with errors less than 10%), robust, and applicable to complex wake flows. The theoretical model is superior to the CNN model in terms of the completeness, cost, and interpretability, and the CNN model with the appropriate training data and optimized CNN architecture has better description and accuracy. Published under an exclusive license by AIP Publishing

Predicting the near-wall velocity of wall turbulence using a neural network for particle image velocimetry

Near-wall velocity prediction for wall-bounded turbulence is useful for constructing a wall model and estimating dissipation and wall shear stress. A convolutional neural network is developed to improve the near-wall velocity prediction and spatial resolution for wall-bounded turbulent velocity fields obtained using particle image velocimetry (PIV). To establish the relationship between the low-resolution and high-resolution fields, this machine learning model is trained on a synthetic PIV dataset generated based on velocity fields obtained from the direct numerical simulation of turbulent channel flows at Re-tau = 1000. Using a test dataset with a higher Reynolds number of Re-tau = 5200, the performance of this model is assessed in terms of instantaneous fields, error analysis, velocity statistics, and energy spectra. The influences of the interrogation window, image resolution, and particle concentration on the performance of this network are also considered. We further apply this network to practical PIV data from a turbulent boundary layer at Re-tau = 2200 to assess the network performance under real experimental conditions. The results indicate that the proposed machine-learning-based model can predict missing near-wall velocity fields and enhance the spatial resolution of PIV fields, but the accuracy for Reynolds shear stress prediction needs to be further improved. The presented approach shows the potential ability to predict the near-wall instantaneous velocity of high-Reynolds-number turbulence from low-Reynolds-number flow fields

Grid-dependence study for simulating propeller crashback using large-eddy simulation with immersed boundary method

Simulating the flow around a propeller using large-eddy simulation (LES) with the immersed boundary (IB) method is challenging and computationally expensive. In this work, we carry out the grid-dependence study of LES with IB for simulating a propeller in crashback mode using four sets of gradually refined grids. The simulation results show that the grid-resolution requirements for accurately predicting different flow quantities are different. Specifically, it is found that the side-force coefficient and the averaged streamwise velocity are less sensitive to grid resolutions as compared with the thrust force coefficient and the turbulence kinetic energy, respectively. Furthermore, it is found that the computed results in the near wake region and the region around the blade are more sensitive to grid resolutions as compared with the far wake region, where the predictions from the four different grids are similar to each other. This suggests that a coarse grid simulation is adequate if only the far wake region is of interest, while a fine grid simulation is required if one cares about the flow around the blade and the flow in the near wake region