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

The Impacts Of Presentation Modes And Product Involvements On “Line” Short Message Service (SMS) Advertising Effectiveness

In today’s ubiquitous commerce (UC) era, short message service (SMS) advertisement has played an important role in the world of marketing. Convenience and economical reasons influence SMS usage frequency along with social involvement to influence attitudes towards SMS advertising. SMS advertising creates numerous opportunities for the marketers in promoting their products effectively. Adopting the competition for attention theory as the theoretical framework, we developed hypotheses to investigate the influences of presentation mode and involvement on SMS advertising performance (recall of advertising information). An experiment was conducted to examine the effects of three types of information presentation modes (text-only, image-text, and emoji-text) in the contexts of two product types (high- versus low-involvement products) in the “LINE” SMS environment. Specifically, in this current study, we allocate participants to six experimental environments (text-only for high-involvement products, text-only for low-involvement products, image-text for high-involvement products, image-text for low-involvement products, emoji-text for high-involvement products and emoji-text for low-involvement products) randomly to collected empirical data to examine the proposed hypotheses. The research findings are expected to provide instrumental guidelines for the practitioners to better achieve the goals of ads in the “LINE” SMS environment. Also, the empirical results may provide insights into the research of advertising interface design of SMS and integrating efforts from cognitive science and vision research to understand users’ involvement of SMS advertising processes

Space Net Optimization

Most metaheuristic algorithms rely on a few searched solutions to guide later searches during the convergence process for a simple reason: the limited computing resource of a computer makes it impossible to retain all the searched solutions. This also reveals that each search of most metaheuristic algorithms is just like a ballpark guess. To help address this issue, we present a novel metaheuristic algorithm called space net optimization (SNO). It is equipped with a new mechanism called space net; thus, making it possible for a metaheuristic algorithm to use most information provided by all searched solutions to depict the landscape of the solution space. With the space net, a metaheuristic algorithm is kind of like having a ``vision'' on the solution space. Simulation results show that SNO outperforms all the other metaheuristic algorithms compared in this study for a set of well-known single objective bound constrained problems in most cases.Comment: 12 pages, 6 figure

Difference in the regulation of IL-8 expression induced by uropathogenic E. coli between two kinds of urinary tract epithelial cells

Bacterial adherence to epithelial cells is a key virulence trait of pathogenic bacteria. The type 1 fimbriae and the P-fimbriae of uropathogenic Escherichia coli (UPEC) have both been described to be important for the establishment of urinary tract infections (UTI). To explore the interactions between the host and bacterium responsible for the different environments of UPEC invasion, we examined the effect of pH and osmolarity on UPEC strain J96 fimbrial expression, and subsequent J96-induced interleukin-8 (IL-8) expression in different uroepithelial cells. The J96 strain grown in high pH with low osmolarity condition was favorable for the expression of type 1 fimbriae; whereas J96 grown in low pH with high osmolarity condition was beneficial for P fimbriae expression. Type 1 fimbriated J96 specifically invaded bladder 5637 epithelial cells and induced IL-8 expression. On the contrary, P fimbriated J96 invaded renal 786-O epithelial cells and induced IL-8 expression effectively. Type 1 fimbriated J96-induced IL-8 induction involved the p38, as well as ERK, JNK pathways, which leads to AP-1-mediated gene expression. P fimbriated J96-induced augmentation of IL-8 expression mainly involved p38-mediated AP-1 and NF-κB transcriptional activation. These results indicate that different expression of fimbriae in J96 trigger differential IL-8 gene regulation pathways in different uroepithelial cells

Interpretable Self-Attention Temporal Reasoning for Driving Behavior Understanding

Performing driving behaviors based on causal reasoning is essential to ensure driving safety. In this work, we investigated how state-of-the-art 3D Convolutional Neural Networks (CNNs) perform on classifying driving behaviors based on causal reasoning. We proposed a perturbation-based visual explanation method to inspect the models' performance visually. By examining the video attention saliency, we found that existing models could not precisely capture the causes (e.g., traffic light) of the specific action (e.g., stopping). Therefore, the Temporal Reasoning Block (TRB) was proposed and introduced to the models. With the TRB models, we achieved the accuracy of $\mathbf{86.3\%}$, which outperform the state-of-the-art 3D CNNs from previous works. The attention saliency also demonstrated that TRB helped models focus on the causes more precisely. With both numerical and visual evaluations, we concluded that our proposed TRB models were able to provide accurate driving behavior prediction by learning the causal reasoning of the behaviors.Comment: Submitted to IEEE ICASSP 2020; Pytorch code will be released soo

Decreased Risk of Osteoporosis Incident in Subjects Receiving Chinese Herbal Medicine for Sjögren Syndrome Treatment: A Retrospective Cohort Study with a Nested Case-Control Analysis

Sjögren syndrome (SS) is a long-lasting inflammatory autoimmune disease that may cause diverse manifestations, particularly osteoporosis. Though usage of Chinese herbal medicine (CHM) can safely manage autoimmune disease and treatment-related symptoms, the relation between CHM use and osteoporosis risk in SS persons is not yet recognized. With that in mind, this population-level nested case-control study aimed to compare the risk of osteoporosis with and without CHM use. Potential subjects aged 20–70 years, diagnosed with SS between 2001 and 2010, were retrieved from a national health claims database. Those diagnosed with osteoporosis after SS were identified and randomly matched to those without osteoporosis. We capitalize on the conditional logistic regression to estimate osteoporosis risk following CHM use. A total of 1240 osteoporosis cases were detected and randomly matched to 1240 controls at a ratio of 1:1. Those receiving conventional care plus CHM had a substantially lower chance of osteoporosis than those without CHM. Prolonged use of CHM, especially for one year or more, markedly dwindled sequent osteoporosis risk by 71%. Integrating CHM into standard care may favor the improvement of bone function, but further well-designed randomized controlled trials to investigate the possible mechanism are needed

Flood Damage Assessment in Taipei City Taiwan

In this study, we reviewed the literature on flood damage assessment and collected information for related research in Taiwan to analyze the relationships between direct flood damage, flood frequency, flood depth, and land-use. The procedure for flood damage assessment was then developed that includes the following steps: (a) Scenario simulation of inundation potential. (b) Establishment of the relationship between inundation depth and damage loss for varied land-use. (c) Risk analysis of inundation damage. Taipei City in north Taiwan was adopted as the case study to demonstrate the proposed algorithm. Flood events with return periods of 5, 10, 25, 50, 100 and 200 years were used for flood hazard analysis to cover possible flooding scenarios. The inundation hazard maps were first generated via hydraulic modelling. The regional flood damage was then estimated using a relationship between inundation depth and damage. The flood damage exceedance probability (EP) curve for Taipei City was constructed following the association of the loss with its probability of occurrence. The flood damage EP curve was further used to integrate the damage assessments for individual flood events for a full probability range presentation of the flood risk. The expected annual damage was calculated by integrating the area under the EP curve

Overweight worsens apoptosis, neuroinflammation and blood-brain barrier damage after hypoxic ischemia in neonatal brain through JNK hyperactivation

<p>Abstract</p> <p>Background</p> <p>Apoptosis, neuroinflammation and blood-brain barrier (BBB) damage affect the susceptibility of the developing brain to hypoxic-ischemic (HI) insults. c-Jun N-terminal kinase (JNK) is an important mediator of insulin resistance in obesity. We hypothesized that neonatal overweight aggravates HI brain damage through JNK hyperactivation-mediated upregulation of neuronal apoptosis, neuroinflammation and BBB leakage in rat pups.</p> <p>Methods</p> <p>Overweight (OF) pups were established by reducing the litter size to 6, and control (NF) pups by keeping the litter size at 12 from postnatal (P) day 1 before HI on P7. Immunohistochemistry and immunoblotting were used to determine the TUNEL-(+) cells and BBB damage, cleaved caspase-3 and poly (ADP-ribose) polymerase (PARP), and phospho-JNK and phospho-Bim_ELlevels. Immunofluorescence was performed to determine the cellular distribution of phospho-JNK.</p> <p>Results</p> <p>Compared with NF pups, OF pups had a significantly heavier body-weight and greater fat deposition on P7. Compared with the NF-HI group, the OF-HI group showed significant increases of TUNEL-(+) cells, cleaved levels of caspase-3 and PARP, and ED1-(+) activated microglia and BBB damage in the cortex 24 hours post-HI. Immunofluorescence of the OF-HI pups showed that activated-caspase 3 expression was found mainly in NeuN-(+) neurons and RECA1-(+) vascular endothelial cells 24 hours post-HI. The OF-HI group also had prolonged escape latency in the Morris water maze test and greater brain-volume loss compared with the NF-HI group when assessed at adulthood. Phospho-JNK and phospho-Bim_ELlevels were higher in OF-HI pups than in NF-HI pups immediately post-HI. JNK activation in OF-HI pups was mainly expressed in neurons, microglia and vascular endothelial cells. Inhibiting JNK activity by AS601245 caused more attenuation of cleaved caspase-3 and PARP, a greater reduction of microglial activation and BBB damage post-HI, and significantly reduced brain damage in OF-HI than in NF-HI pups.</p> <p>Conclusions</p> <p>Neonatal overweight increased HI-induced neuronal apoptosis, microglial activation and BBB damage, and aggravated HI brain damage in rat pups through JNK hyperactivation.</p