Single molecule analysis of integrin-ligand interactions

The precisely regulated lymphocytes trafficking plays very important roles in immune surveillance and host defense. The adhesive interaction between leukocyte integrin α4β7 and its endothelial ligand, mucosal addressin cell adhesion molecule-1 (MAdCAM-1), mediates the rolling and firm adhesion of leukocytes to the high endothelial venules of mucosal tissues. A key property of α4β7 is that it mediates rolling on resting leukocytes. Upon leukocyte activation, α4β7 is induced to adopt a high-affinity conformation and thus mediates firm adhesion. We used single-molecule force spectroscopy (SMFS) and single-cell force spectroscopy (SCFS) to determine the mechanical strength of the low- and high-affinity α4β7/MAdCAM-1 complexes. An atomic force microscope (AFM) was used to pull individual α4β7/MAdCAM-1 complexes to determine their mechanical strength and force-dependent dissociation kinetics under ionic conditions that produce low- and high-affinity complexes, which mediate leukocyte rolling and firm adhesion, respectively. Integrin α4β7 also binds to vascular cell adhesion molecule-1 (VCAM-1) expressed in other tissues. To regulate the adhesion of lymphocytes to target tissues, integrin α4β7 must be able to distinguish different ligands. Here we used SCFS to pull individual α4β7/MAdCAM-1 or α4β7/VCAM-1 complexes under different chemokine stimulations and demonstrated the chemokine CCL25 promotes α4β7-mediated lymphocyte adhesion to MAdCAM-1 but suppresses adhesion to VCAM-1, whereas the chemokine CXCL10 regulates adhesion in the opposite way at the single molecule level. In addition, Sin Nombre Hantavirus (SNVs) bind to the Plexin Semaphorin Integrin (PSI) domain of inactive, bent conformation β3 integrins. We used SMFS to pull individual integrin αIIbβ3 and P2Y2R complexes with or without SNVs binding and demonstrated that binding of SNV to the PSI domain induces an increase in the affinity of the integrin’s RGD binding site, and stimulates activation of several heterotrimeric G-proteins, Rho GTPases and infection.In conclusion, this work provided new insights into the kinetic mechanism of integrin-mediated leukocyte rolling and firm adhesion, but also a mechanism for lymphocyte homing through the unique ligand-specific regulation of integrin adhesion by different chemokines. Our findings are also fundamental to understanding integrin-GPCR transactivation, and Hantavirus pathogenesis

Enhancing the SST Turbulence Model with Symbolic Regression: A Generalizable and Interpretable Data-Driven Approach

Turbulence modeling within the RANS equations' framework is essential in engineering due to its high efficiency. Field inversion and machine learning (FIML) techniques have improved RANS models' predictive capabilities for separated flows. However, FIML-generated models often lack interpretability, limiting physical understanding and manual improvements based on prior knowledge. Additionally, these models may struggle with generalization in flow fields distinct from the training set. This study addresses these issues by employing symbolic regression (SR) to derive an analytical relationship between the correction factor of the baseline turbulence model and local flow variables, enhancing the baseline model's ability to predict separated flow across diverse test cases. The shear-stress-transport (SST) model undergoes field inversion on a curved backward-facing step (CBFS) case to obtain the corrective factor field beta, and SR is used to derive a symbolic map between local flow features and beata. The SR-derived analytical function is integrated into the original SST model, resulting in the SST-SR model. The SST-SR model's generalization capabilities are demonstrated by its successful predictions of separated flow on various test cases, including 2D-bump cases with varying heights, periodic hill case where separation is dominated by geometric features, and the three-dimensional Ahmed-body case. In these tests, the model accurately predicts flow fields, showing its effectiveness in cases completely different from the training set. The Ahmed-body case, in particular, highlights the model's ability to predict the three-dimensional massively separated flows. When applied to a turbulent boundary layer with Re_L=1.0E7, the SST-SR model predicts wall friction coefficient and log layer comparably to the original SST model, maintaining the attached boundary layer prediction performance.Comment: 37 pages, 46 figure

Toward Understanding Generative Data Augmentation

Generative data augmentation, which scales datasets by obtaining fake labeled examples from a trained conditional generative model, boosts classification performance in various learning tasks including (semi-)supervised learning, few-shot learning, and adversarially robust learning. However, little work has theoretically investigated the effect of generative data augmentation. To fill this gap, we establish a general stability bound in this not independently and identically distributed (non-i.i.d.) setting, where the learned distribution is dependent on the original train set and generally not the same as the true distribution. Our theoretical result includes the divergence between the learned distribution and the true distribution. It shows that generative data augmentation can enjoy a faster learning rate when the order of divergence term is $o(\max\left( \log(m)\beta_m, 1 / \sqrt{m})\right)$, where $m$ is the train set size and $\beta_m$ is the corresponding stability constant. We further specify the learning setup to the Gaussian mixture model and generative adversarial nets. We prove that in both cases, though generative data augmentation does not enjoy a faster learning rate, it can improve the learning guarantees at a constant level when the train set is small, which is significant when the awful overfitting occurs. Simulation results on the Gaussian mixture model and empirical results on generative adversarial nets support our theoretical conclusions. Our code is available at https://github.com/ML-GSAI/Understanding-GDA.Comment: 39 page

Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps

Texts appearing in daily scenes that can be recognized by OCR (Optical Character Recognition) tools contain significant information, such as street name, product brand and prices. Two tasks -- text-based visual question answering and text-based image captioning, with a text extension from existing vision-language applications, are catching on rapidly. To address these problems, many sophisticated multi-modality encoding frameworks (such as heterogeneous graph structure) are being used. In this paper, we argue that a simple attention mechanism can do the same or even better job without any bells and whistles. Under this mechanism, we simply split OCR token features into separate visual- and linguistic-attention branches, and send them to a popular Transformer decoder to generate answers or captions. Surprisingly, we find this simple baseline model is rather strong -- it consistently outperforms state-of-the-art (SOTA) models on two popular benchmarks, TextVQA and all three tasks of ST-VQA, although these SOTA models use far more complex encoding mechanisms. Transferring it to text-based image captioning, we also surpass the TextCaps Challenge 2020 winner. We wish this work to set the new baseline for this two OCR text related applications and to inspire new thinking of multi-modality encoder design. Code is available at https://github.com/ZephyrZhuQi/ssbaselin

DynaPipe: Optimizing Multi-task Training through Dynamic Pipelines

Multi-task model training has been adopted to enable a single deep neural network model (often a large language model) to handle multiple tasks (e.g., question answering and text summarization). Multi-task training commonly receives input sequences of highly different lengths due to the diverse contexts of different tasks. Padding (to the same sequence length) or packing (short examples into long sequences of the same length) is usually adopted to prepare input samples for model training, which is nonetheless not space or computation efficient. This paper proposes a dynamic micro-batching approach to tackle sequence length variation and enable efficient multi-task model training. We advocate pipeline-parallel training of the large model with variable-length micro-batches, each of which potentially comprises a different number of samples. We optimize micro-batch construction using a dynamic programming-based approach, and handle micro-batch execution time variation through dynamic pipeline and communication scheduling, enabling highly efficient pipeline training. Extensive evaluation on the FLANv2 dataset demonstrates up to 4.39x higher training throughput when training T5, and 3.25x when training GPT, as compared with packing-based baselines. DynaPipe's source code is publicly available at https://github.com/awslabs/optimizing-multitask-training-through-dynamic-pipelines.Comment: 18 pages, 18 figure

Revisiting Discriminative vs. Generative Classifiers: Theory and Implications

A large-scale deep model pre-trained on massive labeled or unlabeled data transfers well to downstream tasks. Linear evaluation freezes parameters in the pre-trained model and trains a linear classifier separately, which is efficient and attractive for transfer. However, little work has investigated the classifier in linear evaluation except for the default logistic regression. Inspired by the statistical efficiency of naive Bayes, the paper revisits the classical topic on discriminative vs. generative classifiers. Theoretically, the paper considers the surrogate loss instead of the zero-one loss in analyses and generalizes the classical results from binary cases to multiclass ones. We show that, under mild assumptions, multiclass naive Bayes requires $O(\log n)$ samples to approach its asymptotic error while the corresponding multiclass logistic regression requires $O(n)$ samples, where $n$ is the feature dimension. To establish it, we present a multiclass $\mathcal{H}$-consistency bound framework and an explicit bound for logistic loss, which are of independent interests. Simulation results on a mixture of Gaussian validate our theoretical findings. Experiments on various pre-trained deep vision models show that naive Bayes consistently converges faster as the number of data increases. Besides, naive Bayes shows promise in few-shot cases and we observe the "two regimes" phenomenon in pre-trained supervised models. Our code is available at https://github.com/ML-GSAI/Revisiting-Dis-vs-Gen-Classifiers.Comment: Accepted by ICML 2023, 58 page