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

Flow pattern in inner cores of double emulsion droplets

This paper was presented at the 4th Micro and Nano Flows Conference (MNF2014), which was held at University College, London, UK. The conference was organised by Brunel University and supported by the Italian Union of Thermofluiddynamics, IPEM, the Process Intensification Network, the Institution of Mechanical Engineers, the Heat Transfer Society, HEXAG - the Heat Exchange Action Group, and the Energy Institute, ASME Press, LCN London Centre for Nanotechnology, UCL University College London, UCL Engineering, the International NanoScience Community, www.nanopaprika.eu.The efficacy of applications of water-in-oil-in-water (w/o/w) double emulsions moving in microchannels is significantly impacted by the flow conditions in the inner aqueous cores. For example in the case of shear sensitive cells transported in the cores, high shear conditions may be deleterious. This study reports on the flow topology inside w/o/w cores determined by means of micro-particle image velocimetry (μPIV) and compares it to the flow in single water-in-oil (w/o) microdroplets with equal sizes moving in a rectangular microchannel. The multiphase flow system employed in the study had a viscosity ratio, λ, between oil and aqueous phase of the order of unity (λ = 0.8) and both single and compound droplets filled the channels. This configuration resulted in a weak recirculating flow inside the w/o single droplet: the measured flow field exhibited a uniform low velocity flow field in the central region surrounded by small regions of reversed flow near the channel walls. This flow topology was maintained in the inner cores of w/o/w double emulsions for intermediate capillary numbers (Ca) ranging from 10-3 to 10-2, and core morphologies varying from large plug to pancake cores. The core morphology affected the magnitude and distribution of the velocity in the droplets. The similarity in the flow pattern results from the fact that inner cores were located at the back of the outer droplet in such a way that inner and outer interfaces were in contact for half of core surface area and separated by a thin lubricating film

Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancement

Recent studies have highlighted adversarial examples as ubiquitous threats to the deep neural network (DNN) based speech recognition systems. In this work, we present a U-Net based attention model, U-Net$_{At}$, to enhance adversarial speech signals. Specifically, we evaluate the model performance by interpretable speech recognition metrics and discuss the model performance by the augmented adversarial training. Our experiments show that our proposed U-Net$_{At}$ improves the perceptual evaluation of speech quality (PESQ) from 1.13 to 2.78, speech transmission index (STI) from 0.65 to 0.75, short-term objective intelligibility (STOI) from 0.83 to 0.96 on the task of speech enhancement with adversarial speech examples. We conduct experiments on the automatic speech recognition (ASR) task with adversarial audio attacks. We find that (i) temporal features learned by the attention network are capable of enhancing the robustness of DNN based ASR models; (ii) the generalization power of DNN based ASR model could be enhanced by applying adversarial training with an additive adversarial data augmentation. The ASR metric on word-error-rates (WERs) shows that there is an absolute 2.22 $\%$ decrease under gradient-based perturbation, and an absolute 2.03 $\%$ decrease, under evolutionary-optimized perturbation, which suggests that our enhancement models with adversarial training can further secure a resilient ASR system.Comment: The first draft was finished in August 2019. Accepted to IEEE ICASSP 202

2DMatPedia: An open computational database of two-dimensional materials from top-down and bottom-up approaches

Two-dimensional (2D) materials have been a hot research topic in the last decade, due to novel fundamental physics in the reduced dimension and appealing applications. Systematic discovery of functional 2D materials has been the focus of many studies. Here, we present a large dataset of 2D materials, with more than 6,000 monolayer structures, obtained from both top-down and bottom-up discovery procedures. First, we screened all bulk materials in the database of Materials Project for layered structures by a topology-based algorithm, and theoretically exfoliate them into monolayers. Then, we generated new 2D materials by chemical substitution of elements in known 2D materials by others from the same group in the periodic table. The structural, electronic and energetic properties of these 2D materials are consistently calculated, to provide a starting point for further material screening, data mining, data analysis and artificial intelligence applications. We present the details of computational methodology, data record and technical validation of our publicly available data (http://www.2dmatpedia.org/)

Quantum optical coherence can survive photon losses: a continuous-variable quantum erasure correcting code

A fundamental requirement for enabling fault-tolerant quantum information processing is an efficient quantum error-correcting code (QECC) that robustly protects the involved fragile quantum states from their environment. Just as classical error-correcting codes are indispensible in today's information technologies, it is believed that QECC will play a similarly crucial role in tomorrow's quantum information systems. Here, we report on the first experimental demonstration of a quantum erasure-correcting code that overcomes the devastating effect of photon losses. Whereas {\it errors} translate, in an information theoretic language, the noise affecting a transmission line, {\it erasures} correspond to the in-line probabilistic loss of photons. Our quantum code protects a four-mode entangled mesoscopic state of light against erasures, and its associated encoding and decoding operations only require linear optics and Gaussian resources. Since in-line attenuation is generally the strongest limitation to quantum communication, much more than noise, such an erasure-correcting code provides a new tool for establishing quantum optical coherence over longer distances. We investigate two approaches for circumventing in-line losses using this code, and demonstrate that both approaches exhibit transmission fidelities beyond what is possible by classical means.Comment: 5 pages, 4 figure