Modeling interactions between cells and the aqueous environment

When nanoparticles, heavy metals and ions generated from industry are released to the environment, they may react with cells of organisms and can be toxic to them. The primary purpose of this study is to develop a novel cell model that mimics several reveal cell properties including nanomechanical behavior, and to investigate the interactions between them and the aqueous environment. The novel cell model developed in this work has potential applications as a platform to investigate cytotoxicity. In this study, the cell membrane model consists of a hydrogel-supported lipid-bilayer. Hydrogels are cross-linked polymer networks that can absorb large amounts of water without dissolving or loosing their shape. A number of hydrogels are stimuli-sensitive. They can change their structures and properties in response to changes in environment, such as pH, temperature, and ionic strength. These polymer hydrogels have wide applications in various biological and clinical fields, including drug delivery, contact lenses, and artificial implants. Biocompatibility and hydrophilic properties of hydrogels are the basis of these applications. In this study, neutral PAAm hydrogel is used as support for the lipid bilayer. The used hydrogel is a (neutral) polyacrylamide (PAAm) hydrogel. The lipid bilayer used in this study is Eggphosphatidycholine (EggPC). A layer-by-layer method with two polyelectrolytes, poly(sodium 4-styrenesulfonate) (PSS) and poly(allylamine hydrochloride) (PAH), was used to graft the EggPC to the neutral PAAm hydrogel. An electrostatic attraction is the main driving force for the adsorption of the bilayer on the hydrogel-supported polyelectrolyte multilayer (PEM). The developed cell model has been fully characterized in this work by using different surface analytic techniques. On a silica substrate, lipid vesicle ruptures and fuses above a critical vesicle concentration to form a continuous lipid bilayer. QCM-D measurements and AFM imaging were performed to verify the formation of the bilayer on the silica substrate. The adsorption kinetics of the lipids on the hydrogel-supported PEM completely differs from that on the “hard” silica substrate. However, the change in dissipation supported the formation of a lipid bilayer. Further, the adsorbed mass on bovine serum albumin (BSA) verified that the adsorbed lipids on the PAAm hydrogel-PEM complex form a lipid bilayer, but the surface coverage is only partial. Thus, BSA adsorbs on the PEM through the defects of the lipid bilayer. The interactions between cells and the environment happen through the cell membrane, and very often the nanomechanical behavior determines such interactions, and also cell sensing and response. In this work, the nanomechanical properties of PAAm hydrogels, PAAm-supported PEM and lipid bilayers were studied using atomic force microscopy (AFM), including both nano-indentation and the response to shear. A significant difference in the elasticity (and viscoelasticity) between the behavior of the hydrogel-supported PEM and the silica-supported lipid bilayer was concluded from these studies, as well as very different mechanisms for the energy dissipation upon shear. The question that remains to be answered is the behavior of the cell model constituted of the hydrogel-supported PEM and the lipid bilayer, which is the outlook of this work

Online Statistical Inference for Stochastic Optimization via Kiefer-Wolfowitz Methods

This paper investigates the problem of online statistical inference of model parameters in stochastic optimization problems via the Kiefer-Wolfowitz algorithm with random search directions. We first present the asymptotic distribution for the Polyak-Ruppert-averaging type Kiefer-Wolfowitz (AKW) estimators, whose asymptotic covariance matrices depend on the function-value query complexity and the distribution of search directions. The distributional result reflects the trade-off between statistical efficiency and function query complexity. We further analyze the choices of random search directions to minimize the asymptotic covariance matrix, and conclude that the optimal search direction depends on the optimality criteria with respect to different summary statistics of the Fisher information matrix. Based on the asymptotic distribution result, we conduct online statistical inference by providing two construction procedures of valid confidence intervals. We provide numerical experiments verifying our theoretical results with the practical effectiveness of the procedures

Adversarial Training with Fast Gradient Projection Method against Synonym Substitution based Text Attacks

Adversarial training is the most empirically successful approach in improving the robustness of deep neural networks for image classification.For text classification, however, existing synonym substitution based adversarial attacks are effective but not efficient to be incorporated into practical text adversarial training. Gradient-based attacks, which are very efficient for images, are hard to be implemented for synonym substitution based text attacks due to the lexical, grammatical and semantic constraints and the discrete text input space. Thereby, we propose a fast text adversarial attack method called Fast Gradient Projection Method (FGPM) based on synonym substitution, which is about 20 times faster than existing text attack methods and could achieve similar attack performance. We then incorporate FGPM with adversarial training and propose a text defense method called Adversarial Training with FGPM enhanced by Logit pairing (ATFL). Experiments show that ATFL could significantly improve the model robustness and block the transferability of adversarial examples.Comment: Accepted by AAAI 2021, code is available at https://github.com/JHL-HUST/FGP

MMNet: Multi-Mask Network for Referring Image Segmentation

Referring image segmentation aims to segment an object referred to by natural language expression from an image. However, this task is challenging due to the distinct data properties between text and image, and the randomness introduced by diverse objects and unrestricted language expression. Most of previous work focus on improving cross-modal feature fusion while not fully addressing the inherent uncertainty caused by diverse objects and unrestricted language. To tackle these problems, we propose an end-to-end Multi-Mask Network for referring image segmentation(MMNet). we first combine picture and language and then employ an attention mechanism to generate multiple queries that represent different aspects of the language expression. We then utilize these queries to produce a series of corresponding segmentation masks, assigning a score to each mask that reflects its importance. The final result is obtained through the weighted sum of all masks, which greatly reduces the randomness of the language expression. Our proposed framework demonstrates superior performance compared to state-of-the-art approaches on the two most commonly used datasets, RefCOCO, RefCOCO+ and G-Ref, without the need for any post-processing. This further validates the efficacy of our proposed framework.Comment: 10 pages, 5 figure

EAVL: Explicitly Align Vision and Language for Referring Image Segmentation

Referring image segmentation aims to segment an object mentioned in natural language from an image. A main challenge is language-related localization, which means locating the object with the relevant language. Previous approaches mainly focus on the fusion of vision and language features without fully addressing language-related localization. In previous approaches, fused vision-language features are directly fed into a decoder and pass through a convolution with a fixed kernel to obtain the result, which follows a similar pattern as traditional image segmentation. This approach does not explicitly align language and vision features in the segmentation stage, resulting in a suboptimal language-related localization. Different from previous methods, we propose Explicitly Align the Vision and Language for Referring Image Segmentation (EAVL). Instead of using a fixed convolution kernel, we propose an Aligner which explicitly aligns the vision and language features in the segmentation stage. Specifically, a series of unfixed convolution kernels are generated based on the input l, and then are use to explicitly align the vision and language features. To achieve this, We generate multiple queries that represent different emphases of the language expression. These queries are transformed into a series of query-based convolution kernels. Then, we utilize these kernels to do convolutions in the segmentation stage and obtain a series of segmentation masks. The final result is obtained through the aggregation of all masks. Our method can not only fuse vision and language features effectively but also exploit their potential in the segmentation stage. And most importantly, we explicitly align language features of different emphases with the image features to achieve language-related localization. Our method surpasses previous state-of-the-art methods on RefCOCO, RefCOCO+, and G-Ref by large margins.Comment: 10 pages, 4 figures. arXiv admin note: text overlap with arXiv:2305.1496

AutoLog: A Log Sequence Synthesis Framework for Anomaly Detection

The rapid progress of modern computing systems has led to a growing interest in informative run-time logs. Various log-based anomaly detection techniques have been proposed to ensure software reliability. However, their implementation in the industry has been limited due to the lack of high-quality public log resources as training datasets. While some log datasets are available for anomaly detection, they suffer from limitations in (1) comprehensiveness of log events; (2) scalability over diverse systems; and (3) flexibility of log utility. To address these limitations, we propose AutoLog, the first automated log generation methodology for anomaly detection. AutoLog uses program analysis to generate run-time log sequences without actually running the system. AutoLog starts with probing comprehensive logging statements associated with the call graphs of an application. Then, it constructs execution graphs for each method after pruning the call graphs to find log-related execution paths in a scalable manner. Finally, AutoLog propagates the anomaly label to each acquired execution path based on human knowledge. It generates flexible log sequences by walking along the log execution paths with controllable parameters. Experiments on 50 popular Java projects show that AutoLog acquires significantly more (9x-58x) log events than existing log datasets from the same system, and generates log messages much faster (15x) with a single machine than existing passive data collection approaches. We hope AutoLog can facilitate the benchmarking and adoption of automated log analysis techniques.Comment: The paper has been accepted by ASE 2023 (Research Track

Delving into Commit-Issue Correlation to Enhance Commit Message Generation Models

Commit message generation (CMG) is a challenging task in automated software engineering that aims to generate natural language descriptions of code changes for commits. Previous methods all start from the modified code snippets, outputting commit messages through template-based, retrieval-based, or learning-based models. While these methods can summarize what is modified from the perspective of code, they struggle to provide reasons for the commit. The correlation between commits and issues that could be a critical factor for generating rational commit messages is still unexplored. In this work, we delve into the correlation between commits and issues from the perspective of dataset and methodology. We construct the first dataset anchored on combining correlated commits and issues. The dataset consists of an unlabeled commit-issue parallel part and a labeled part in which each example is provided with human-annotated rational information in the issue. Furthermore, we propose \tool (\underline{Ex}traction, \underline{Gro}unding, \underline{Fi}ne-tuning), a novel paradigm that can introduce the correlation between commits and issues into the training phase of models. To evaluate whether it is effective, we perform comprehensive experiments with various state-of-the-art CMG models. The results show that compared with the original models, the performance of \tool-enhanced models is significantly improved.Comment: ASE2023 accepted pape