Efficient high order semi-implicit time discretization and local discontinuous Galerkin methods for highly nonlinear PDEs

International audienceIn this paper, we develop a high order semi-implicit time discretization method for highly nonlinear PDEs, which consist of the surface diffusion and Willmore flow of graphs, the Cahn-Hilliard equation and the Allen-Cahn/Cahn-Hilliard system. These PDEs are high order in spatial derivatives, which motivates us to develop implicit or semi-implicit time marching methods to relax the severe time step restriction for stability of explicit methods. In addition, these PDEs are also highly nonlinear, fully implicit method will incredibly increase the difficulty of implementation. In particular, we can not well separate the stiff and non-stiff components for these problems, which leads to the traditional implicit-explicit methods nearly meaningless. In this paper, a high order semi-implicit time marching method and the local discontinuous Galerkin spatial method are coupled together to achieve high order accuracy in both space and time, and to enhance the efficiency of the proposed approaches, the resulting linear or nonlinear algebraic systems are solved by multigrid solver. Numerical simulation results in one and two dimensions are presented to illustrate that the combination of the local discontinuous Galerkin method for spatial approximation, semi-implicit temporal integration with the multigrid solver provides a practical and efficient approach when solving this family of problems

Boosted ab initio Cryo-EM 3D Reconstruction with ACE-EM

The central problem in cryo-electron microscopy (cryo-EM) is to recover the 3D structure from noisy 2D projection images which requires estimating the missing projection angles (poses). Recent methods attempted to solve the 3D reconstruction problem with the autoencoder architecture, which suffers from the latent vector space sampling problem and frequently produces suboptimal pose inferences and inferior 3D reconstructions. Here we present an improved autoencoder architecture called ACE (Asymmetric Complementary autoEncoder), based on which we designed the ACE-EM method for cryo-EM 3D reconstructions. Compared to previous methods, ACE-EM reached higher pose space coverage within the same training time and boosted the reconstruction performance regardless of the choice of decoders. With this method, the Nyquist resolution (highest possible resolution) was reached for 3D reconstructions of both simulated and experimental cryo-EM datasets. Furthermore, ACE-EM is the only amortized inference method that reached the Nyquist resolution

Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal Transformers

We propose to address quadrupedal locomotion tasks using Reinforcement Learning (RL) with a Transformer-based model that learns to combine proprioceptive information and high-dimensional depth sensor inputs. While learning-based locomotion has made great advances using RL, most methods still rely on domain randomization for training blind agents that generalize to challenging terrains. Our key insight is that proprioceptive states only offer contact measurements for immediate reaction, whereas an agent equipped with visual sensory observations can learn to proactively maneuver environments with obstacles and uneven terrain by anticipating changes in the environment many steps ahead. In this paper, we introduce LocoTransformer, an end-to-end RL method for quadrupedal locomotion that leverages a Transformer-based model for fusing proprioceptive states and visual observations. We evaluate our method in challenging simulated environments with different obstacles and uneven terrain. We show that our method obtains significant improvements over policies with only proprioceptive state inputs, and that Transformer-based models further improve generalization across environments. Our project page with videos is at https://RchalYang.github.io/LocoTransformer .Comment: Our project page with videos is at https://RchalYang.github.io/LocoTransforme

Incorporating Fine-grained Events in Stock Movement Prediction

Considering event structure information has proven helpful in text-based stock movement prediction. However, existing works mainly adopt the coarse-grained events, which loses the specific semantic information of diverse event types. In this work, we propose to incorporate the fine-grained events in stock movement prediction. Firstly, we propose a professional finance event dictionary built by domain experts and use it to extract fine-grained events automatically from finance news. Then we design a neural model to combine finance news with fine-grained event structure and stock trade data to predict the stock movement. Besides, in order to improve the generalizability of the proposed method, we design an advanced model that uses the extracted fine-grained events as the distant supervised label to train a multi-task framework of event extraction and stock prediction. The experimental results show that our method outperforms all the baselines and has good generalizability.Comment: Accepted by 2th ECONLP workshop in EMNLP201

Incorporating Pre-trained Model Prompting in Multimodal Stock Volume Movement Prediction

Multimodal stock trading volume movement prediction with stock-related news is one of the fundamental problems in the financial area. Existing multimodal works that train models from scratch face the problem of lacking universal knowledge when modeling financial news. In addition, the models ability may be limited by the lack of domain-related knowledge due to insufficient data in the datasets. To handle this issue, we propose the Prompt-based MUltimodal Stock volumE prediction model (ProMUSE) to process text and time series modalities. We use pre-trained language models for better comprehension of financial news and adopt prompt learning methods to leverage their capability in universal knowledge to model textual information. Besides, simply fusing two modalities can cause harm to the unimodal representations. Thus, we propose a novel cross-modality contrastive alignment while reserving the unimodal heads beside the fusion head to mitigate this problem. Extensive experiments demonstrate that our proposed ProMUSE outperforms existing baselines. Comprehensive analyses further validate the effectiveness of our architecture compared to potential variants and learning mechanisms.Comment: 9 pages, 3 figures, 7 tables. Accepted by 2023 KDD Workshop on Machine Learning in Financ

INCREASING FOOD ACCESS IN HISTORICALLY REDLINED NEIGHBORHOODS IN DURHAM COUNTY, NC THROUGH MOBILE MARKETS WITH NUTRITION EDUCATION

Neighborhood and Built Environment, one of the five social determinants of health, encompasses the living, working, and recreational space which influence health outcomes (US EPA, 2017). Food access is one top issue currently facing the residents of Durham County (Hicks & Mortiboy, 2021). Access is determined by availability (number of food sources), accessibility (transportation to a food source), and affordability (cost of food) (USDHHS, 2022). In several neighborhoods within Durham city limits, less than 1% of residents have access to a nearby grocery store (Data Works NC, 2023). Our proposal aims to alleviate food access concerns by providing direct access to fresh fruits and vegetables within select communities via a mobile market. By modifying the built environment of Durham County, this program will increase consumption of fresh fruits and vegetables and improve long term health outcomes. Keywords: Food Access, Built Environment, Durham County, North Carolina, Social Determinant of HealthMaster of Public Healt

Huobahuagen tablet improves renal function in diabetic kidney disease: a real-world retrospective cohort study

ObjectiveWe aimed to explore the value of Huobahuagen tablet (HBT) in improving decreased renal function for patients with diabetic kidney disease (DKD) over time.MethodsThis was a single-center, retrospective, real-world study on eligible 122 DKD patients who continued to use HBT + Huangkui capsule (HKC) therapy or HKC therapy without interruption or alteration in Jiangsu Province Hospital of Chinese Medicine from July 2016 to March 2022. The primary observation outcomes included estimated glomerular filtration rate (eGFR) at baseline and 1-, 3-, 6-, 9-, and 12-month follow-up visits and changes in eGFR from baseline (ΔeGFR). Propensity score (PS) and inverse probability treatment weighting (IPTW) were used to control for confounders.ResultseGFR was significantly higher in the HBT + HKC group than in the HKC alone group at the 6-, 9-, and 12-month follow-up visits (p = 0.0448, 0.0002, and 0.0037, respectively), indicating the superiority of HBT + HKC over HBT alone. Furthermore, the ΔeGFR of the HBT + HKC group was significantly higher than that of the HKC alone group at the 6- and 12-month follow-up visits (p = 0.0369 and 0.0267, respectively). In the DKD G4 patients, eGFR was higher in the HBT + HKC group at the 1-, 3-, 6-, 9-, and 12-month follow-up visits compared with baseline, with statistically significant differences at the 1-, 3-, and 6- month follow-up visits (p = 0.0256, 0.0069, and 0.0252, respectively). The fluctuations in ΔeGFR ranged from 2.54 ± 4.34 to 5.01 ± 5.55 ml/min/1.73 m2. Change in the urinary albumin/creatinine ratio from baseline did not exhibit a significant difference between the two groups at any of the follow-up visits (p > 0.05 for all). Adverse event incidence was low in both groups.ConclusionThe findings of this study based on real-world clinical practice indicate that HBT + HKC therapy exhibited better efficacy in improving and protecting renal function with a favorable safety profile than HKC therapy alone. However, further large-scale prospective randomized controlled trials are warranted to confirm these results

Correlator Convolutional Neural Networks: An Interpretable Architecture for Image-like Quantum Matter Data

Machine learning models are a powerful theoretical tool for analyzing data from quantum simulators, in which results of experiments are sets of snapshots of many-body states. Recently, they have been successfully applied to distinguish between snapshots that can not be identified using traditional one and two point correlation functions. Thus far, the complexity of these models has inhibited new physical insights from this approach. Here, using a novel set of nonlinearities we develop a network architecture that discovers features in the data which are directly interpretable in terms of physical observables. In particular, our network can be understood as uncovering high-order correlators which significantly differ between the data studied. We demonstrate this new architecture on sets of simulated snapshots produced by two candidate theories approximating the doped Fermi-Hubbard model, which is realized in state-of-the art quantum gas microscopy experiments. From the trained networks, we uncover that the key distinguishing features are fourth-order spin-charge correlators, providing a means to compare experimental data to theoretical predictions. Our approach lends itself well to the construction of simple, end-to-end interpretable architectures and is applicable to arbitrary lattice data, thus paving the way for new physical insights from machine learning studies of experimental as well as numerical data.Comment: 7 pages, 4 figures + 13 pages of supplemental materia