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

Kappa-Opioid Receptors in the Caudal Nucleus Tractus Solitarius Mediate 100 Hz Electroacupuncture-Induced Sleep Activities in Rats

Previous results demonstrated that 10 Hz electroacupuncture (EA) of Anmian acupoints in rats during the dark period enhances slow wave sleep (SWS), which involves the induction of cholinergic activity in the caudal nucleus tractus solitarius (NTS) and subsequent activation of opioidergic neurons and μ-receptors. Studies have shown that different kinds of endogenous opiate peptides and receptors may mediate the consequences of EA with different frequencies. Herein, we further elucidated that high-frequency (100 Hz)-EA of Anmian enhanced SWS during the dark period but exhibited no direct effect on rapid eye movement (REM) sleep. High-frequency EA-induced SWS enhancement was dose-dependently blocked by microinjection of naloxone or κ-receptor antagonist (nor-binaltorphimine) into the caudal NTS, but was affected neither by μ- (naloxonazine) nor δ-receptor antagonists (natatrindole), suggesting the role of NTS κ-receptors in the high-frequency EA-induced SWS enhancement. Current and previous results depict the opioid mechanisms of EA-induced sleep

Do environmental regulations cause enterprises to exit from market? Quasi-natural experiments based on China’s Cleaner Production Standards

Taking the implementation of Cleaner Production Standards at the industry level in China as a quasi-natural experiment, the impact of these standards on enterprises’ exit behavior was empirically analyzed by using the Difference-in-Differences method. Results suggested that the implementation of Cleaner Production Standards reduced the probability of enterprises exiting the market. A parallel trend test, Propensity Score Matching (PSM), and the exclusion of other policy factors were then used to verify the robustness of this finding. The impact mechanism test showed that implementation of the standards reduced the probability of enterprises exiting the market through improving total factor productivity and promoting enterprise product innovation. The heterogeneity test revealed that, on the one hand, the implementation of Cleaner Production Standards can reduce the probability of R&D intensive industries and medium-sized enterprises exiting the market, and protect innovative and moderately sized enterprises. On the other hand, the implementation of Cleaner Production Standards can increase the probability of state-owned enterprises and small-scale enterprises exiting the market and optimize the allocation of resources among enterprises. This paper has important implications for China’s future approach to environmental policy formulation as well as the optimization of domestic enterprise structur

Profit Maximization by Forming Federations of Geo-Distributed MEC Platforms

This paper has been presented at: Seventh International Workshop on Cloud Technologies and Energy Efficiency in Mobile Communication Networks (CLEEN 2019). How cloudy and green will mobile network and services be? 15 April 2019 - Marrakech, MoroccoIn press / En prensaMulti-access edge computing (MEC) as an emerging technology which provides cloud service in the edge of multi-radio access networks aims to reduce the service latency experienced by end devices. When individual MEC systems do not have adequate resource capacity to fulfill service requests, forming MEC federations for resource sharing could provide economic incentive to MEC operators. To this end, we need to maximize social welfare in each federation, which involves efficient federation structure generations, federation profit maximization by resource provisioning configuration, and fair profit distribution among participants. We model the problem as a coalition game with difference from prior work in the assumption of latency and locality constraints and also in the consideration of various service policies/demand preferences. Simulation results show that the proposed approach always increases profits. If local requests are served with local resource with priority, federation improves profits without sacrificing request acceptance rates.This work was partially supported by the Ministry of Science and Technology, Taiwan, under grant numbers 106-2221-E-009-004 and by the H2020 collaborative Europe/Taiwan research project 5G-CORAL (grant number 761586)

Part2Word: Learning Joint Embedding of Point Clouds and Text by Matching Parts to Words

It is important to learn joint embedding for 3D shapes and text in different shape understanding tasks, such as shape-text matching, retrieval, and shape captioning. Current multi-view based methods learn a mapping from multiple rendered views to text. However, these methods can not analyze 3D shapes well due to the self-occlusion and limitation of learning manifolds. To resolve this issue, we propose a method to learn joint embedding of point clouds and text by matching parts from shapes to words from sentences in a common space. Specifically, we first learn segmentation prior to segment point clouds into parts. Then, we map parts and words into an optimized space, where the parts and words can be matched with each other. In the optimized space, we represent a part by aggregating features of all points within the part, while representing each word with its context information, where we train our network to minimize the triplet ranking loss. Moreover, we also introduce cross-modal attention to capture the relationship of part-word in this matching procedure, which enhances joint embedding learning. Our experimental results outperform the state-of-the-art in multi-modal retrieval under the widely used benchmark