When Causal Intervention Meets Adversarial Examples and Image Masking for Deep Neural Networks

Discovering and exploiting the causality in deep neural networks (DNNs) are crucial challenges for understanding and reasoning causal effects (CE) on an explainable visual model. "Intervention" has been widely used for recognizing a causal relation ontologically. In this paper, we propose a causal inference framework for visual reasoning via do-calculus. To study the intervention effects on pixel-level features for causal reasoning, we introduce pixel-wise masking and adversarial perturbation. In our framework, CE is calculated using features in a latent space and perturbed prediction from a DNN-based model. We further provide the first look into the characteristics of discovered CE of adversarially perturbed images generated by gradient-based methods \footnote{~~https://github.com/jjaacckkyy63/Causal-Intervention-AE-wAdvImg}. Experimental results show that CE is a competitive and robust index for understanding DNNs when compared with conventional methods such as class-activation mappings (CAMs) on the Chest X-Ray-14 dataset for human-interpretable feature(s) (e.g., symptom) reasoning. Moreover, CE holds promises for detecting adversarial examples as it possesses distinct characteristics in the presence of adversarial perturbations.Comment: Noted our camera-ready version has changed the title. "When Causal Intervention Meets Adversarial Examples and Image Masking for Deep Neural Networks" as the v3 official paper title in IEEE Proceeding. Please use it in your formal reference. Accepted at IEEE ICIP 2019. Pytorch code has released on https://github.com/jjaacckkyy63/Causal-Intervention-AE-wAdvIm

Modeling the Cell Cycle: Why Do Certain Circuits Oscillate?

Computational modeling and the theory of nonlinear dynamical systems allow one to not simply describe the events of the cell cycle, but also to understand why these events occur, just as the theory of gravitation allows one to understand why cannonballs fly in parabolic arcs. The simplest examples of the eukaryotic cell cycle operate like autonomous oscillators. Here, we present the basic theory of oscillatory biochemical circuits in the context of the Xenopus embryonic cell cycle. We examine Boolean models, delay differential equation models, and especially ordinary differential equation (ODE) models. For ODE models, we explore what it takes to get oscillations out of two simple types of circuits (negative feedback loops and coupled positive and negative feedback loops). Finally, we review the procedures of linear stability analysis, which allow one to determine whether a given ODE model and a particular set of kinetic parameters will produce oscillations

A Web-Services-Based P2P Computing-Power Sharing Architecture

As demands of data processing and computing power are increasing, existing information system architectures become insufficient. Some organizations try to figure out how to keep their systems work without purchasing new hardware and software. Therefore, a Webservices-based model which shares the resource over the network like a P2P network will be proposed to meet this requirement in this paper. In addition, this paper also discusses some problems about security, motivation, flexibility, compatibility and workflow management for the traditional P2P power sharing models. Our new computing architecture - Computing Power Services (CPS) - will aim to address these problems. For the shortcomings about flexibility, compatibility and workflow management, CPS utilizes Web Services and Business Process Execution Language (BPEL) to overcome them. Because CPS is assumed to run in a reliable network where peers trust each other, the concerns about security and motivation will be negated. In essence, CPS is a lightweight Web-Services-based P2P power sharing environment and suitable for executing computing works in batch in a reliable networ

AutoML-GPT: Large Language Model for AutoML

With the emerging trend of GPT models, we have established a framework called AutoML-GPT that integrates a comprehensive set of tools and libraries. This framework grants users access to a wide range of data preprocessing techniques, feature engineering methods, and model selection algorithms. Through a conversational interface, users can specify their requirements, constraints, and evaluation metrics. Throughout the process, AutoML-GPT employs advanced techniques for hyperparameter optimization and model selection, ensuring that the resulting model achieves optimal performance. The system effectively manages the complexity of the machine learning pipeline, guiding users towards the best choices without requiring deep domain knowledge. Through our experimental results on diverse datasets, we have demonstrated that AutoML-GPT significantly reduces the time and effort required for machine learning tasks. Its ability to leverage the vast knowledge encoded in large language models enables it to provide valuable insights, identify potential pitfalls, and suggest effective solutions to common challenges faced during model training

Charmed Ωc\Omega_c weak decays into Ω\Omega in the light-front quark model

More than ten $\Omega_c^0$ weak decay modes have been measured with the branching fractions relative to that of $\Omega^0_c\to\Omega^-\pi^+$. In order to extract the absolute branching fractions, the study of $\Omega^0_c\to\Omega^-\pi^+$ is needed. In this work, we predict ${\cal B}_\pi\equiv {\cal B}(\Omega_c^0\to\Omega^-\pi^+)=(5.1\pm 0.7)\times 10^{-3}$ with the $\Omega_c^0\to\Omega^-$ transition form factors calculated in the light-front quark model. We also predict ${\cal B}_\rho\equiv {\cal B}(\Omega_c^0\to\Omega^-\rho^+)=(14.4\pm 0.4)\times 10^{-3}$ and ${\cal B}_e\equiv{\cal B}(\Omega_c^0\to\Omega^-e^+\nu_e)=(5.4\pm 0.2)\times 10^{-3}$. The previous values for ${\cal B}_\rho/{\cal B}_\pi$ have been found to deviate from the most recent observation. Nonetheless, our ${\cal B}_\rho/{\cal B}_\pi=2.8\pm 0.4$ is able to alleviate the deviation. Moreover, we obtain ${\cal B}_e/{\cal B}_\pi=1.1\pm 0.2$, which is consistent with the current data.Comment: 12 pages, 2 figure