320 research outputs found
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 , where is the train
set size and 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
Converse Barrier Certificates for Finite-time Safety Verification of Continuous-time Perturbed Deterministic Systems
In this paper, we investigate the problem of verifying the finite-time safety
of continuous-time perturbed deterministic systems represented by ordinary
differential equations in the presence of measurable disturbances. Given a
finite time horizon, if the system is safe, it, starting from a compact initial
set, will remain within an open and bounded safe region throughout the
specified time horizon, regardless of the disturbances. The main contribution
of this work is to uncover that there exists a time-dependent barrier
certificate if and only if the system is safe. This barrier certificate
satisfies the following conditions: negativity over the initial set at the
initial time instant, non-negativity over the boundary of the safe set, and
non-increasing behavior along the system dynamics over the specified finite
time horizon. The existence problem is explored using a Hamilton-Jacobi
differential equation, which has a unique Lipschitz viscosity solution
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
samples to approach its asymptotic error while the corresponding multiclass
logistic regression requires samples, where is the feature
dimension. To establish it, we present a multiclass -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
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